diff --git a/README.md b/README.md index bb48554..c02654b 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ The final assignment will be through a Google Form where you will be answering a |---|---| | **MODIS EDA** | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NASAARSET/ARSET_ML_Fundamentals/blob/main/session2/1_MODIS_EDA_Session2.ipynb) | | **MODIS Train & Eval** | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NASAARSET/ARSET_ML_Fundamentals/blob/main/session2/2_MODIS_Train_Eval_Session2.ipynb) | -| **Assignment Session 2** | ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg) | +| **Assignment Session 2** | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NASAARSET/ARSET_ML_Fundamentals/blob/main/session2/Assignment-Session2.ipynb) | ## Session 3 Materials: @@ -29,7 +29,7 @@ The final assignment will be through a Google Form where you will be answering a | **MODIS Model Tuning** | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NASAARSET/ARSET_ML_Fundamentals/blob/main/session3/1-MODIS-Tuning-Session3.ipynb) | | **MODIS Explainability** | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NASAARSET/ARSET_ML_Fundamentals/blob/main/session3/2-MODIS-Explainability-Session3.ipynb) | | **MODIS AutoML** | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NASAARSET/ARSET_ML_Fundamentals/blob/main/session3/3_MODIS_AutoML_Session3.ipynb) | -| **Assignment Session 3** | ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg) | +| **Assignment Session 3** | Day before Session 3 | ## Additional Resources diff --git a/session2/Assignment-Session2.ipynb b/session2/Assignment-Session2.ipynb index 964d83f..e6d6ec1 100644 --- a/session2/Assignment-Session2.ipynb +++ b/session2/Assignment-Session2.ipynb @@ -1,33 +1,2978 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "8c4019c2", - "metadata": {}, - "source": [ - "# 4-ML-Algorithms-Assignment-Session2" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "okPSWKH_ZyZA" + }, + "source": [ + "# Session 2 - XGBoost Training\n", + "\n", + "In this notebook we will follow some of the same workflows we discussed in Session 2, this time using strictly GPUs. For this work, we will use data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument to test several data science techniques and machine learning algorithms.\n", + "\n", + "**Author**: Jordan A. Caraballo-Vega, Caleb S. Spradlin \n", + "**Release Date**: 2023.04.06 \n", + "**Last Modified**: 2023.04.06 \n", + "\n", + "## Prerequisites\n", + "\n", + "Before running all cells in this notebook, you will need to enable the GPU runtime from Google Colab.\n", + "\n", + "First, click Runtime in in the top toolbar:\n", + "\n", + "![image](https://miro.medium.com/v2/resize:fit:500/format:webp/1*eAg8XGh4S8GjNRPE-0RgbQ.png)\n", + "\n", + "Then, click Change runtime type:\n", + "\n", + "![image](https://miro.medium.com/v2/resize:fit:300/format:webp/1*gZHTwaB6M3Z-z6JyLyy4-g.png)\n", + "\n", + "select GPU for Hardware accelerator:\n", + "\n", + "![image](https://miro.medium.com/v2/resize:fit:400/format:webp/1*ZrrmH8Li3ulBnpr3y4qw3A.png)\n", + "\n", + "Once the GPU Runtime has been set, run the following Python code to check GPU type:\n", + "\n", + "## 1. Import Libraries\n", + "\n", + "In this section we import the Python libraries to use during the development of this notebook. The default Python kernel from Google Colab does not include all fo the packages we need, thus we proceed to install them via pip.\n" + ] + }, + { + "cell_type": "code", + "source": [ + "!wget https://raw.githubusercontent.com/NASAARSET/ARSET_ML_Fundamentals/main/src/folium_helper.py" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AwsD9WO1l3-4", + "outputId": "f23a2df1-5181-4891-8ff2-99aeb1240444" + }, + "execution_count": 111, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-04-27 12:34:41-- https://raw.githubusercontent.com/NASAARSET/ARSET_ML_Fundamentals/main/src/folium_helper.py\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 6189 (6.0K) [text/plain]\n", + "Saving to: ‘folium_helper.py.4’\n", + "\n", + "folium_helper.py.4 100%[===================>] 6.04K --.-KB/s in 0s \n", + "\n", + "2023-04-27 12:34:41 (46.3 MB/s) - ‘folium_helper.py.4’ saved [6189/6189]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install datasets rasterio pyproj rioxarray" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NlROnMlHfRXn", + "outputId": "fa1d3b5f-4811-447e-b6aa-f34c832f45b3" + }, + "execution_count": 112, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: datasets in /usr/local/lib/python3.9/dist-packages (2.11.0)\n", + "Requirement already satisfied: rasterio in /usr/local/lib/python3.9/dist-packages (1.3.6)\n", + "Requirement already satisfied: pyproj in /usr/local/lib/python3.9/dist-packages 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glob\n", + "import joblib\n", + "import datasets\n", + "import datetime\n", + "import numpy as np\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "from pprint import pprint\n", + "\n", + "from huggingface_hub import snapshot_download\n", + "\n", + "import xarray as xr\n", + "import xgboost as xgb\n", + "import rioxarray as rxr\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier as skRF\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score\n", + "from sklearn.metrics import classification_report, roc_curve, auc\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.model_selection import RandomizedSearchCV, KFold\n", + "from sklearn.inspection import permutation_importance\n", + "\n", + "# Visualization\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "import warnings\n", + "\n", + "# Geospatial related imports\n", + "from osgeo import gdalconst\n", + "from osgeo import gdal\n", + "from osgeo import gdal_array\n", + "\n", + "import folium\n", + "from folium import plugins\n", + "import folium_helper\n", + "\n", + "plt.style.use('fivethirtyeight')\n", + "warnings.filterwarnings('ignore')\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 2. Define General Variables\n", + "In this section we define general variables to work with through this notebook. A description of each variable is listed below as a comment next to the variable definition." + ], + "metadata": { + "id": "kKL3wMeWi88j" + } + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "id": "YJ0DBUX5ZyZD" + }, + "outputs": [], + "source": [ + "# directory where we will output figures\n", + "FIGURE_OUTPUT_DIR = 'output'\n", + "\n", + "# directory where we will output raster\n", + "RASTER_OUTPUT_DIR = 'output'\n", + "\n", + "# directory where we will output our models\n", + "MODEL_OUTPUT_DIR = 'models'\n", + "\n", + "# url of the dataset we will be using, this is a link to the Hugging Face repository\n", + "# of this tutorial\n", + "DATASET_URL = 'nasa-cisto-data-science-group/modis-lake-powell-toy-dataset'\n", + "\n", + "# ratio of the dataset split for testing\n", + "TEST_RATIO = 0.2\n", + "\n", + "# controls random seed for reproducibility\n", + "RANDOM_STATE = 42\n", + "\n", + "# column name for label, in our case this will be a categorical value\n", + "LABEL_NAME = 'water'\n", + "\n", + "# data type of the label, you would change this to something else if your\n", + "# problem was for example a regression problem of type np.float32\n", + "DATA_TYPE = np.int16\n", + "\n", + "# Columns that are offset, years, julian days, etc (always need to be dropped).\n", + "offsets_indexes = ['x_offset', 'y_offset', 'year', 'julian_day', 'tileID']\n", + "\n", + "# columns not needed for training\n", + "colsToDrop = ['x_offset', 'y_offset', 'year', 'julian_day']\n", + "colsToDropTraining = colsToDrop.copy()\n", + "colsToDropTraining.extend(offsets_indexes)\n", + "\n", + "# columns used as features during training\n", + "v_names = ['sur_refl_b01_1','sur_refl_b02_1','sur_refl_b03_1',\n", + " 'sur_refl_b04_1','sur_refl_b05_1','sur_refl_b06_1',\n", + " 'sur_refl_b07_1','ndvi','ndwi1','ndwi2']" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here we create an output directory to store any artifacts out of our models and visualizations." + ], + "metadata": { + "id": "9jtKmyg5j078" + } + }, + { + "cell_type": "code", + "source": [ + "os.makedirs(MODEL_OUTPUT_DIR, exist_ok=True)\n", + "os.makedirs(FIGURE_OUTPUT_DIR, exist_ok=True)" + ], + "metadata": { + "id": "kWXb-K5ugW8e" + }, + "execution_count": 115, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Output any columns we will need to drop for training\n", + "\n" + ], + "metadata": { + "id": "6zqgTYvckFAM" + } + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G_V4UwJiZyZD", + "outputId": "9d2f6e0d-9c11-42e3-daf6-4b6183977157" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['x_offset', 'y_offset', 'year', 'julian_day']" + ] + }, + "metadata": {}, + "execution_count": 116 + } + ], + "source": [ + "colsToDrop" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WAV1tPqsZyZE", + "outputId": "814c7055-ee09-4c22-b7f6-dc4321e6a8bd" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['x_offset',\n", + " 'y_offset',\n", + " 'year',\n", + " 'julian_day',\n", + " 'x_offset',\n", + " 'y_offset',\n", + " 'year',\n", + " 'julian_day',\n", + " 'tileID']" + ] + }, + "metadata": {}, + "execution_count": 117 + } + ], + "source": [ + "colsToDropTraining" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15X8TCdSZyZE" + }, + "source": [ + "## 3. Data Loading\n", + "\n", + "In this section we will go ahead and load our data to analyze. We have extracted a tabular dataset from MODIS GeoTIFF files for the purpose of performing EDA. Here we will:\n", + "\n", + "- Read in data to a Dataframe\n", + "- Drop unnecessary columns\n", + "- Split into Xs and Ys" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3QK6H5B9ZyZF", + "outputId": "a869afdd-4ac5-40c6-accc-6dcacbe446af" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/nasa-cisto-data-science-group___csv/nasa-cisto-data-science-group--modis-lake-powell-toy-dataset-0bd54b5baf4ed80c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n", + "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/nasa-cisto-data-science-group___csv/nasa-cisto-data-science-group--modis-lake-powell-toy-dataset-0bd54b5baf4ed80c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 431 ms, sys: 36 ms, total: 467 ms\n", + "Wall time: 2.06 s\n" + ] + } + ], + "source": [ + "%%time\n", + "train_dataset = pd.DataFrame(datasets.load_dataset(DATASET_URL, split='train'))\n", + "test_dataset = pd.DataFrame(datasets.load_dataset(DATASET_URL, split='test'))" + ] + }, + { + "cell_type": "markdown", + "source": [ + "After we download our dataset, we proceed to split it into training and test set. Note how the water column is set as the y feature and is dropped from the X features." + ], + "metadata": { + "id": "RrneXHDVkcPv" + } + }, + { + "cell_type": "code", + "source": [ + "X_train, y_train = train_dataset.drop(['water'], axis=1), train_dataset['water']\n", + "X_test, y_test = test_dataset.drop(['water'], axis=1), test_dataset['water']\n", + "X_train.shape, X_test.shape" + ], + "metadata": { + "id": "kULPzAP7kbiQ", + "outputId": "ca394d8c-db58-43a8-dda9-8f4080ef0cee", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 119, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((800, 10), (200, 10))" + ] + }, + "metadata": {}, + "execution_count": 119 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Note how we further split our training dataset into training and validation dataset to monitor model performance during XGBoost training." + ], + "metadata": { + "id": "mK70rGFBc3hi" + } + }, + { + "cell_type": "code", + "source": [ + "X_train, X_val, y_train, y_val = train_test_split(\n", + " X_train,\n", + " y_train,\n", + " random_state=RANDOM_STATE,\n", + " train_size=0.80,\n", + ")" + ], + "metadata": { + "id": "jn6cLnF8eSSf" + }, + "execution_count": 120, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Note how we now have a larger number of test samples, and smaller number of training samples. Which one to choose will depend on the problem you have in hand.\n", + "\n", + "## 4. Training Preparation\n", + "\n", + "Once we have our training and test data ready, we proceed to prepare for training our model. One technique often used to better validate the robustness of a machine learning model is K-fold cross validation.\n", + "\n", + "Cross-validation is a statistical method used to estimate the skill of machine learning models. When using k-fold, the dataset is split into ‘k’ number of subsets, k-1 subsets then are used to train the model and the last subset is kept as a validation set to test the model. Then the score of the model on each fold is averaged to evaluate the performance of the model.\n", + "\n", + "sckit-learn provides a useful feature to quickly setup these experiments. A K of 10 has been described by literature as the most effective value to decrease variance and nominally test performance. In this example, we will use a value of K = 5 for time purposes." + ], + "metadata": { + "id": "ob_eY2l5nFsT" + } + }, + { + "cell_type": "code", + "source": [ + "kf = KFold(n_splits=5)\n", + "kf.get_n_splits(X_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kgKalXdCersM", + "outputId": "cce1ec77-d2b3-4110-cccb-d13688211257" + }, + "execution_count": 121, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "5" + ] + }, + "metadata": {}, + "execution_count": 121 + } + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7sr0ED3jZyZF", + "outputId": "ef4f7ef8-c30f-421e-da18-f05771682ba4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "sur_refl_b01_1\n", + "sur_refl_b02_1\n", + "sur_refl_b03_1\n", + "sur_refl_b04_1\n", + "sur_refl_b05_1\n", + "sur_refl_b06_1\n", + "sur_refl_b07_1\n", + "ndvi\n", + "ndwi1\n", + "ndwi2\n" + ] + } + ], + "source": [ + "_ = [print(column) for column in X_train.columns]" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Like we did in the first exercise of this Session, we can look at some of the features of the data before proceeding to train." + ], + "metadata": { + "id": "jEcerzamopqX" + } + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "DB_kwYEVZyZF", + "outputId": "72ca2a1f-4721-411f-e9d8-121e6eaf7b71" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " count mean std min 25% 50% \\\n", + "sur_refl_b01_1 640.0 1226.790625 1326.056292 8.0 241.00 898.0 \n", + "sur_refl_b02_1 640.0 1600.375000 1583.392651 1.0 171.75 1436.5 \n", + "sur_refl_b03_1 640.0 757.912500 1049.446733 -100.0 279.00 548.0 \n", + "sur_refl_b04_1 640.0 1038.996875 1103.524548 6.0 410.50 817.5 \n", + "sur_refl_b05_1 640.0 1961.509375 1481.292677 -92.0 551.00 1802.5 \n", + "sur_refl_b06_1 640.0 1914.764062 1550.032855 51.0 504.00 1441.0 \n", + "sur_refl_b07_1 640.0 1568.620312 1406.276378 0.0 306.50 1119.5 \n", + "ndvi 640.0 129.134375 2519.341228 -9493.0 -1475.00 746.5 \n", + "ndwi1 640.0 -2207.950000 3437.366950 -9862.0 -4380.50 -1538.0 \n", + "ndwi2 640.0 -739.209375 3884.652229 -9692.0 -2540.00 -682.0 \n", + "\n", + " 75% max \n", + "sur_refl_b01_1 1878.50 9222.0 \n", + "sur_refl_b02_1 2667.75 8837.0 \n", + "sur_refl_b03_1 847.00 8909.0 \n", + "sur_refl_b04_1 1297.25 9162.0 \n", + "sur_refl_b05_1 3203.25 6382.0 \n", + "sur_refl_b06_1 3167.00 6610.0 \n", + "sur_refl_b07_1 2585.50 6128.0 \n", + "ndvi 1763.00 8587.0 \n", + "ndwi1 -689.00 8823.0 \n", + "ndwi2 951.00 10000.0 " + ], + "text/html": [ + "\n", + "
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\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 123 + } + ], + "source": [ + "X_train.describe().T" + ] + }, + { + "cell_type": "markdown", + "source": [ + "We can also use thresholding methods based on our physical knowledge to extract interesting features of outliers that we might want to visualize later when evaluating the model." + ], + "metadata": { + "id": "FxITjdO9ox6Q" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sGJblGC6ZyZF" + }, + "source": [ + "## 5. Model definition and training\n", + "\n", + "In this section we will define our model and train it using our dataset. The hyperparameters listed below are the default from the scikit-learn library, but these are usually very similar across other programming languages. Some of the most useful parameters to know about from the XGBoost are:\n", + "- n_estimators: number of trees in the forest\n", + "- max_depth: the maximum depth of a tree\n", + "- objective: it defines the loss function to be minimized. Most commonly used values are given below -\n", + " - reg:squarederror : regression with squared loss.\n", + " - reg:squaredlogerror: regression with squared log loss 1/2[log(pred+1)−log(label+1)]2. - All input labels are required to be greater than -1.\n", + " - reg:logistic : logistic regression\n", + " - binary:logistic : logistic regression for binary classification, output probability\n", + " - binary:logitraw: logistic regression for binary classification, output score before logistic transformation\n", + " - binary:hinge : hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.\n", + " - multi:softmax : set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes)\n", + " - multi:softprob : same as softmax, but output a vector of ndata nclass, which can be further reshaped to ndata nclass matrix. The result contains predicted probability of each data point belonging to each class." + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "id": "LJtegLSkZyZF" + }, + "outputs": [], + "source": [ + "hyperparameters = {'objective': 'binary:logistic',\n", + " 'n_estimators':100,\n", + " 'base_score': None,\n", + " 'booster': None,\n", + " 'colsample_bylevel': None,\n", + " 'colsample_bynode': None,\n", + " 'colsample_bytree': None,\n", + " 'gamma': None,\n", + " 'gpu_id': None,\n", + " 'interaction_constraints': None,\n", + " 'learning_rate': 0.003,\n", + " 'max_delta_step': None,\n", + " 'max_depth': None,\n", + " 'min_child_weight': None,\n", + " 'monotone_constraints': None,\n", + " 'n_jobs': -1,\n", + " 'num_parallel_tree': None,\n", + " 'random_state': None,\n", + " 'reg_alpha': None,\n", + " 'reg_lambda': None,\n", + " 'scale_pos_weight': None,\n", + " 'subsample': None,\n", + " #'tree_method': '',\n", + " 'validate_parameters': None,\n", + " 'verbosity': None\n", + "}" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here we define the classifier by including the hyperparameters we defined above." + ], + "metadata": { + "id": "2abwWm8ur1O9" + } + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "id": "apFQQjJRZyZF" + }, + "outputs": [], + "source": [ + "classifier = xgb.XGBClassifier(**hyperparameters)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "We can then define which metrics to use to monitor model performance during training." + ], + "metadata": { + "id": "gaIYY1peeU-q" + } + }, + { + "cell_type": "code", + "source": [ + "eval_set = [(X_train, y_train), (X_val, y_val)]\n", + "eval_metric = [\"error\",\"auc\"]" + ], + "metadata": { + "id": "-Icq0Xyvek_9" + }, + "execution_count": 126, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 5.1 k-Fold fitting\n", + "\n", + "Then we can proceed to train our model using the k-Fold approach. We perform K iterations, and by the end of the iterations we select the best performing model, and calculate the average score across all of them." + ], + "metadata": { + "id": "8QjprcSWr6W2" + } + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rxHbZ0nLZyZF", + "outputId": "1110db58-681d-4996-f390-d3e82647f91d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train [128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145\n", + " 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163\n", + " 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181\n", + " 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199\n", + " 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217\n", + " 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235\n", + " 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253\n", + " 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271\n", + " 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289\n", + " 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307\n", + " 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325\n", + " 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343\n", + " 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361\n", + " 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379\n", + " 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397\n", + " 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415\n", + " 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433\n", + " 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451\n", + " 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469\n", + " 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487\n", + " 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505\n", + " 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523\n", + " 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541\n", + " 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559\n", + " 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577\n", + " 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595\n", + " 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613\n", + " 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631\n", + " 632 633 634 635 636 637 638 639], Test [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n", + " 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n", + " 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n", + " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", + " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n", + " 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n", + " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n", + " 126 127]\n", + "Fitting model\n", + "[0]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99450\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "[1]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99441\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "[2]\tvalidation_0-error:0.01562\tvalidation_0-auc:0.99441\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "[3]\tvalidation_0-error:0.01562\tvalidation_0-auc:0.99441\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "[4]\tvalidation_0-error:0.01562\tvalidation_0-auc:0.99441\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "[5]\tvalidation_0-error:0.01562\tvalidation_0-auc:0.99441\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "[6]\tvalidation_0-error:0.01562\tvalidation_0-auc:0.99441\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "[7]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99441\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "[8]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99441\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "[9]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99441\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99898\n", + "Time to fit model: 0.0835561752319336s\n", + "Getting score\n", + "Training accuracy score: 0.984375\n", + "Predicting for test set\n", + " precision recall f1-score support\n", + "\n", + " 0 0.98 0.98 0.98 61\n", + " 1 0.99 0.99 0.99 67\n", + "\n", + " accuracy 0.98 128\n", + " macro avg 0.98 0.98 0.98 128\n", + "weighted avg 0.98 0.98 0.98 128\n", + "\n", + "Score: 0.984375\n", + "Train [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n", + " 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n", + " 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n", + " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", + " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n", + " 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n", + " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n", + " 126 127 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271\n", + " 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289\n", + " 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307\n", + " 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325\n", + " 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343\n", + " 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361\n", + " 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379\n", + " 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397\n", + " 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415\n", + " 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433\n", + " 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451\n", + " 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469\n", + " 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487\n", + " 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505\n", + " 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523\n", + " 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541\n", + " 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559\n", + " 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577\n", + " 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595\n", + " 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613\n", + " 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631\n", + " 632 633 634 635 636 637 638 639], Test [128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145\n", + " 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163\n", + " 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181\n", + " 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199\n", + " 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217\n", + " 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235\n", + " 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253\n", + " 254 255]\n", + "Fitting model\n", + "[0]\tvalidation_0-error:0.02031\tvalidation_0-auc:0.99442\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[1]\tvalidation_0-error:0.01875\tvalidation_0-auc:0.99447\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[2]\tvalidation_0-error:0.01875\tvalidation_0-auc:0.99443\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[3]\tvalidation_0-error:0.01875\tvalidation_0-auc:0.99443\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[4]\tvalidation_0-error:0.01875\tvalidation_0-auc:0.99443\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[5]\tvalidation_0-error:0.01875\tvalidation_0-auc:0.99443\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[6]\tvalidation_0-error:0.01875\tvalidation_0-auc:0.99443\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[7]\tvalidation_0-error:0.01875\tvalidation_0-auc:0.99443\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[8]\tvalidation_0-error:0.01875\tvalidation_0-auc:0.99443\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[9]\tvalidation_0-error:0.01875\tvalidation_0-auc:0.99443\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "Time to fit model: 0.07942652702331543s\n", + "Getting score\n", + "Predicting for test set\n", + " precision recall f1-score support\n", + "\n", + " 0 0.98 0.95 0.97 65\n", + " 1 0.95 0.98 0.97 63\n", + "\n", + " accuracy 0.97 128\n", + " macro avg 0.97 0.97 0.97 128\n", + "weighted avg 0.97 0.97 0.97 128\n", + "\n", + "Score: 0.96875\n", + "Train [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n", + " 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n", + " 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n", + " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", + " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n", + " 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n", + " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n", + " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n", + " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n", + " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n", + " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n", + " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n", + " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n", + " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n", + " 252 253 254 255 384 385 386 387 388 389 390 391 392 393 394 395 396 397\n", + " 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415\n", + " 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433\n", + " 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451\n", + " 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469\n", + " 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487\n", + " 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505\n", + " 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523\n", + " 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541\n", + " 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559\n", + " 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577\n", + " 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595\n", + " 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613\n", + " 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631\n", + " 632 633 634 635 636 637 638 639], Test [256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273\n", + " 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291\n", + " 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309\n", + " 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327\n", + " 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345\n", + " 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363\n", + " 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381\n", + " 382 383]\n", + "Fitting model\n", + "[0]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.98820\tvalidation_1-error:0.03750\tvalidation_1-auc:0.97942\n", + "[1]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.98969\tvalidation_1-error:0.03750\tvalidation_1-auc:0.97942\n", + "[2]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.98969\tvalidation_1-error:0.03750\tvalidation_1-auc:0.97942\n", + "[3]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.98969\tvalidation_1-error:0.03750\tvalidation_1-auc:0.97942\n", + "[4]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.98970\tvalidation_1-error:0.03750\tvalidation_1-auc:0.97942\n", + "[5]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99400\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "[6]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99406\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "[7]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99406\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "[8]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99407\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "[9]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99407\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "[10]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99407\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "[11]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99407\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "[12]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99407\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "[13]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99407\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "[14]\tvalidation_0-error:0.02344\tvalidation_0-auc:0.99407\tvalidation_1-error:0.03750\tvalidation_1-auc:0.98506\n", + "Time to fit model: 0.16530680656433105s\n", + "Getting score\n", + "Predicting for test set\n", + " precision recall f1-score support\n", + "\n", + " 0 0.93 0.95 0.94 66\n", + " 1 0.95 0.92 0.93 62\n", + "\n", + " accuracy 0.94 128\n", + " macro avg 0.94 0.94 0.94 128\n", + "weighted avg 0.94 0.94 0.94 128\n", + "\n", + "Score: 0.9375\n", + "Train [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n", + " 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n", + " 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n", + " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", + " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n", + " 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n", + " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n", + " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n", + " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n", + " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n", + " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n", + " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n", + " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n", + " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n", + " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n", + " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n", + " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n", + " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n", + " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n", + " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n", + " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n", + " 378 379 380 381 382 383 512 513 514 515 516 517 518 519 520 521 522 523\n", + " 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541\n", + " 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559\n", + " 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577\n", + " 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595\n", + " 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613\n", + " 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631\n", + " 632 633 634 635 636 637 638 639], Test [384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401\n", + " 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419\n", + " 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437\n", + " 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455\n", + " 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473\n", + " 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491\n", + " 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509\n", + " 510 511]\n", + "Fitting model\n", + "[0]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99598\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[1]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99602\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99233\n", + "[2]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99602\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99171\n", + "[3]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99602\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99171\n", + "[4]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99602\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99171\n", + "[5]\tvalidation_0-error:0.01719\tvalidation_0-auc:0.99602\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99108\n", + "[6]\tvalidation_0-error:0.02187\tvalidation_0-auc:0.99589\tvalidation_1-error:0.04375\tvalidation_1-auc:0.99108\n", + "[7]\tvalidation_0-error:0.02187\tvalidation_0-auc:0.99589\tvalidation_1-error:0.04375\tvalidation_1-auc:0.99108\n", + "[8]\tvalidation_0-error:0.02187\tvalidation_0-auc:0.99589\tvalidation_1-error:0.04375\tvalidation_1-auc:0.99108\n", + "[9]\tvalidation_0-error:0.02187\tvalidation_0-auc:0.99589\tvalidation_1-error:0.04375\tvalidation_1-auc:0.99108\n", + "[10]\tvalidation_0-error:0.02187\tvalidation_0-auc:0.99589\tvalidation_1-error:0.04375\tvalidation_1-auc:0.99108\n", + "Time to fit model: 0.3856949806213379s\n", + "Getting score\n", + "Predicting for test set\n", + " precision recall f1-score support\n", + "\n", + " 0 0.95 1.00 0.98 59\n", + " 1 1.00 0.96 0.98 69\n", + "\n", + " accuracy 0.98 128\n", + " macro avg 0.98 0.98 0.98 128\n", + "weighted avg 0.98 0.98 0.98 128\n", + "\n", + "Score: 0.9765625\n", + "Train [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n", + " 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n", + " 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n", + " 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", + " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n", + " 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n", + " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n", + " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n", + " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n", + " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n", + " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n", + " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n", + " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n", + " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n", + " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n", + " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n", + " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n", + " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n", + " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n", + " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n", + " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n", + " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n", + " 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n", + " 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431\n", + " 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449\n", + " 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467\n", + " 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485\n", + " 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n", + " 504 505 506 507 508 509 510 511], Test [512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529\n", + " 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547\n", + " 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565\n", + " 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583\n", + " 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601\n", + " 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619\n", + " 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637\n", + " 638 639]\n", + "Fitting model\n", + "[0]\tvalidation_0-error:0.02813\tvalidation_0-auc:0.99259\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99859\n", + "[1]\tvalidation_0-error:0.02500\tvalidation_0-auc:0.99279\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "[2]\tvalidation_0-error:0.02813\tvalidation_0-auc:0.99264\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "[3]\tvalidation_0-error:0.02813\tvalidation_0-auc:0.99268\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "[4]\tvalidation_0-error:0.02656\tvalidation_0-auc:0.99274\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "[5]\tvalidation_0-error:0.02813\tvalidation_0-auc:0.99264\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "[6]\tvalidation_0-error:0.02813\tvalidation_0-auc:0.99268\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "[7]\tvalidation_0-error:0.02813\tvalidation_0-auc:0.99268\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "[8]\tvalidation_0-error:0.02813\tvalidation_0-auc:0.99268\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "[9]\tvalidation_0-error:0.02813\tvalidation_0-auc:0.99268\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "[10]\tvalidation_0-error:0.02813\tvalidation_0-auc:0.99268\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99875\n", + "Time to fit model: 0.30870723724365234s\n", + "Getting score\n", + "Predicting for test set\n", + " precision recall f1-score support\n", + "\n", + " 0 0.94 0.97 0.96 67\n", + " 1 0.97 0.93 0.95 61\n", + "\n", + " accuracy 0.95 128\n", + " macro avg 0.95 0.95 0.95 128\n", + "weighted avg 0.95 0.95 0.95 128\n", + "\n", + "Score: 0.953125\n", + "CPU times: user 1.32 s, sys: 20.2 ms, total: 1.34 s\n", + "Wall time: 1.24 s\n" + ] + } + ], + "source": [ + "%%time\n", + "bestModel = None\n", + "bestModelScore = 0\n", + "scores = []\n", + "for trainIdx, testIdx in kf.split(X_train):\n", + " print(\"Train {}, Test {}\".format(trainIdx, testIdx))\n", + " X_train_valid, X_test_valid = X_train.iloc[trainIdx], X_train.iloc[testIdx]\n", + " y_train_valid, y_test_valid = y_train.iloc[trainIdx], y_train.iloc[testIdx]\n", + " print('Fitting model')\n", + " st = time.time()\n", + " classifier.fit(X_train_valid, y_train_valid, eval_set=eval_set, eval_metric=eval_metric, early_stopping_rounds=10)\n", + " et = time.time()\n", + " print('Time to fit model: {}s'.format(et-st))\n", + " print('Getting score')\n", + " score = classifier.score(X_test_valid, y_test_valid)\n", + " if score>=bestModelScore:\n", + " bestModelScore = score\n", + " print('Training accuracy score: {}'.format(score))\n", + " bestModel = classifier\n", + " print('Predicting for test set')\n", + " test_predictions = classifier.predict(X_test_valid)\n", + " print(classification_report(y_test_valid, test_predictions))\n", + " print('Score: {}'.format(score))\n", + " scores.append(score)\n", + " del test_predictions, score" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here we can calculate the average score, and the score of the best model." + ], + "metadata": { + "id": "upivFFq7qhdK" + } + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "88BkVOnoZyZF", + "outputId": "e031f580-8608-4730-9170-78bec356cdcf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Average accuracy score: 0.9640625\n", + "Best accuracy score: 0.984375\n" + ] + } + ], + "source": [ + "scoreAvg = np.asarray(scores).mean()\n", + "print('Average accuracy score: {}'.format(scoreAvg))\n", + "print('Best accuracy score: {}'.format(bestModelScore))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZBtSJVgNZyZG" + }, + "source": [ + "## 5.2 Regular fitting\n", + "\n", + "We can also train our model without doing any k-Fold cross-validation. We can simply define our classifier, and perform the fit operation directly on the training dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": { + "id": "bGuVRTzeZyZG" + }, + "outputs": [], + "source": [ + "classifier = xgb.XGBClassifier(**hyperparameters)" + ] + }, + { + "cell_type": "code", + "source": [ + "eval_set = [(X_train, y_train), (X_val, y_val)]\n", + "eval_metric = [\"error\",\"auc\"]" + ], + "metadata": { + "id": "_a5PwfzIeAeT" + }, + "execution_count": 130, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 500 + }, + "id": "Fwi1Zox8ZyZG", + "outputId": "5533860c-431e-46ec-da74-e83085e34ffa" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[0]\tvalidation_0-error:0.01406\tvalidation_0-auc:0.99624\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[1]\tvalidation_0-error:0.01406\tvalidation_0-auc:0.99626\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[2]\tvalidation_0-error:0.01250\tvalidation_0-auc:0.99624\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99233\n", + "[3]\tvalidation_0-error:0.01250\tvalidation_0-auc:0.99625\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99233\n", + "[4]\tvalidation_0-error:0.01250\tvalidation_0-auc:0.99622\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[5]\tvalidation_0-error:0.01250\tvalidation_0-auc:0.99625\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[6]\tvalidation_0-error:0.01250\tvalidation_0-auc:0.99623\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "[7]\tvalidation_0-error:0.01406\tvalidation_0-auc:0.99622\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99233\n", + "[8]\tvalidation_0-error:0.01406\tvalidation_0-auc:0.99624\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99233\n", + "[9]\tvalidation_0-error:0.01406\tvalidation_0-auc:0.99622\tvalidation_1-error:0.03125\tvalidation_1-auc:0.99233\n", + "[10]\tvalidation_0-error:0.01406\tvalidation_0-auc:0.99622\tvalidation_1-error:0.01875\tvalidation_1-auc:0.99233\n", + "CPU times: user 117 ms, sys: 1.45 ms, total: 119 ms\n", + "Wall time: 83.5 ms\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=0.003, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " n_estimators=100, n_jobs=-1, num_parallel_tree=None,\n", + " predictor=None, random_state=None, ...)" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 131 + } + ], + "source": [ + "%%time\n", + "classifier.fit(X_train, y_train, eval_set=eval_set, eval_metric=eval_metric, early_stopping_rounds=10)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Then we can simply compute the score from the model output." + ], + "metadata": { + "id": "rE-NO6RrqwCi" + } + }, + { + "cell_type": "code", + "source": [ + "score = classifier.score(X_test, y_test)\n", + "print('Average accuracy score: {}'.format(score))" + ], + "metadata": { + "id": "jdX36MwesVKF", + "outputId": "d7bb2321-bac9-4724-f5d7-6bf3e6a8c0c1", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 132, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Average accuracy score: 0.98\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tme4Xx6dZyZG" + }, + "source": [ + "## 6. Model testing and training/testing data validation\n", + "\n", + "Once we have trained our model we can proceed to use the best model and perform testing and validation using our dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WDgz_nKOZyZG" + }, + "source": [ + "### 6.1 Get model metrics\n", + "\n", + "We can calculate accuracy metrics from our model using the test dataset, but we can also output prediction probabilities to understand the drivers behind the model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "id": "f_ny3WDVZyZG" + }, + "outputs": [], + "source": [ + "classifier = bestModel" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iq9GFYSkZyZG", + "outputId": "39d5d4b0-746c-40d4-8a19-4ead0eeb2cc5" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.975" + ] + }, + "metadata": {}, + "execution_count": 134 + } + ], + "source": [ + "score = classifier.score(X_test, y_test)\n", + "score = round(score, 3)\n", + "score" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Note how easy it is to perform predictions once we have datasets in dataframe format." + ], + "metadata": { + "id": "9y3vrnUKq5k_" + } + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "id": "iM6eWZwxZyZG" + }, + "outputs": [], + "source": [ + "train_predictions = classifier.predict(X_train)\n", + "test_predictions = classifier.predict(X_test)\n", + "prediction_probs = classifier.predict_proba(X_test)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here we can take the prediction probabilities straight out of the model so we can visualize them. These are important since they tell use the confidence of our model when perform the classification." + ], + "metadata": { + "id": "CVFeuEnKtGOy" + } + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "id": "DqHrnLjIZyZG" + }, + "outputs": [], + "source": [ + "predictionProbabilityList = list()\n", + "for i, subarr in enumerate(prediction_probs):\n", + " predictionProbabilityList.append(subarr[1])\n", + "predictionProbabilityArray = np.asarray(predictionProbabilityList)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PFJUYgpUZyZG" + }, + "source": [ + "### 6.2 Show the distribution of the probability of the predicted values.\n", + "\n", + "These are the probability that each test data point is water p=1 vs land p=0. Usually a relatively flatter distribution on one of the sides shows us that the model is not as confident on predicting that side." + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 543 + }, + "id": "HVbiSWiCZyZG", + "outputId": "5ab81cad-323f-47e9-f35c-014520c72539" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribution of the probability of predicted values')" + ] + }, + "metadata": {}, + "execution_count": 137 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.displot(predictionProbabilityArray, bins=30)\n", + "plt.title('Distribution of the probability of predicted values')" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "id": "zTUHsxR2ZyZG" + }, + "outputs": [], + "source": [ + "test_predictions = test_predictions.astype(np.int32)\n", + "y_test_int = y_test.astype(np.int32)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 6.3 Additional Metrics \n", + "\n", + "We can compute additional metrics to understand model performance related to producer and user accuracy." + ], + "metadata": { + "id": "S07Ucjo2tUtL" + } + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CugjJEEoZyZG", + "outputId": "fe95cba1-2902-4140-cc40-f6748ac2ac9a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Performance\n", + "-------------------------------------------------------\n", + " precision recall f1-score support\n", + "\n", + " 0 0.96 0.99 0.98 105\n", + " 1 0.99 0.96 0.97 95\n", + "\n", + " accuracy 0.97 200\n", + " macro avg 0.98 0.97 0.97 200\n", + "weighted avg 0.98 0.97 0.97 200\n", + "\n", + "Test Recall\n", + "-------------------------------------------------------\n", + "0.9904761904761905\n", + "Confusion Matrix\n", + "-------------------------------------------------------\n", + "[[104 1]\n", + " [ 4 91]]\n" + ] + } + ], + "source": [ + "print('Test Performance')\n", + "print('-------------------------------------------------------')\n", + "print(classification_report(y_test, test_predictions))\n", + "cm = confusion_matrix(y_test_int, test_predictions)\n", + "recall = (cm[0][0] / (cm[0][0] + cm[0][1]))\n", + "print('Test Recall')\n", + "print('-------------------------------------------------------')\n", + "print(recall)\n", + "print('Confusion Matrix')\n", + "print('-------------------------------------------------------')\n", + "print(cm)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "We can also look at Receiver Operating Characteristic (ROC) plots to understand the performance of the model across all samples between True Positive and False Positive rates." + ], + "metadata": { + "id": "g-gGWZ9Mte8j" + } + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 + }, + "id": "0Gb2SaNkZyZG", + "outputId": "52504000-160b-4012-f641-d5ede396170f" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "clf = classifier\n", + "\n", + "probs = clf.predict_proba(X_test)\n", + "preds = probs[:, 1]\n", + "fpr, tpr, threshold = roc_curve(y_test, preds)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.plot(fpr, tpr, 'red', label = 'ROC AUC score = %0.2f' % roc_auc)\n", + "plt.legend(loc = 'lower right')\n", + "plt.plot([0, 1], [0, 1],'b--')\n", + "plt.xlim([0, 1])\n", + "plt.ylim([0, 1])\n", + "plt.ylabel('True Positive Rate')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GnhTiNhfZyZH" + }, + "source": [ + "### 6.4 Permutation Importance\n", + "\n", + "Another important metric to understand model performance is to evaluate permutation importance, particularly focused on the importance of each feature in the training and inference of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_gZvoZ12ZyZH", + "outputId": "241afeb6-bbc1-4643-9275-ba7d8fc6cb91" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 871 ms, sys: 9.74 ms, total: 881 ms\n", + "Wall time: 674 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "permutation_importance_results = permutation_importance(classifier,\n", + " X=X_test,\n", + " y=y_test,\n", + " n_repeats=10,\n", + " random_state=42)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Let's look at some of the variables of most importance during the training and inference of the model." + ], + "metadata": { + "id": "Eoj8QcdYr5pS" + } + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 737 + }, + "id": "Uz5F9XMAZyZH", + "outputId": "e6d8f628-52a9-421c-a3f6-0b81bad05a26" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "png_save_path = 'mw_{}_{}_rf_{}_permutation_importance.png'.format(\n", + " score,\n", + " hyperparameters['n_estimators'],\n", + " datetime.datetime.now().strftime('%Y_%m_%d_%H_%M'))\n", + "\n", + "png_save_path = os.path.join(FIGURE_OUTPUT_DIR, png_save_path)\n", + "\n", + "sorted_idx = permutation_importance_results.importances_mean.argsort()\n", + "plt.figure(figsize=(8, 8))\n", + "plt.barh(X_test.columns[sorted_idx], permutation_importance_results.importances_mean[sorted_idx])\n", + "plt.xlabel(\"Permutation Importance\")\n", + "plt.savefig(png_save_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WX1xjlAsZyZH" + }, + "source": [ + "Garbage collection" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "id": "OiKE9T-mZyZH" + }, + "outputs": [], + "source": [ + "del X_train, X_test, y_train, y_test, test_predictions, train_predictions, y_test_int" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OcshNYp7ZyZH" + }, + "source": [ + "## 7. Save the model for future use\n", + "\n", + "We can then save our model for future use so we can apply it or share it elsewhere. The only prerequisite would be for the input dataset to be in the same format as the input training data." + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-lRJ9A7jZyZH", + "outputId": "381d041e-c64c-4dc1-d143-e8b4977c8b97" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving model to: models/mw_0.975_100_cpu_2.0.0_tuned_2023_04_27_12_34.sav\n", + "XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=0.003, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " n_estimators=100, n_jobs=-1, num_parallel_tree=None,\n", + " predictor=None, random_state=None, ...)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['models/mw_0.975_100_cpu_2.0.0_tuned_2023_04_27_12_34.sav']" + ] + }, + "metadata": {}, + "execution_count": 144 + } + ], + "source": [ + "model_save_path = 'mw_{}_{}_{}_2.0.0_tuned_{}.sav'.format(score,\n", + " hyperparameters['n_estimators'],\n", + " 'cpu',\n", + " datetime.datetime.now().strftime('%Y_%m_%d_%H_%M'))\n", + "model_save_path = os.path.join(MODEL_OUTPUT_DIR, model_save_path)\n", + "print('Saving model to: {}'.format(model_save_path))\n", + "print(classifier)\n", + "joblib.dump(classifier, model_save_path, compress=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C2iuu5AOZyZH" + }, + "source": [ + "## 8. Raster Inference\n", + "\n", + "## 8.1 Data download\n", + "\n", + "Here we can proceed to perform inference using raster objects. The first step is to download some imagery to test with." + ] + }, + { + "cell_type": "code", + "source": [ + "powell_dataset = snapshot_download(repo_id=DATASET_URL, allow_patterns=\"*.tif\", repo_type='dataset')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "39e2451a32a245a18d73f5388fa57841", + "d570343ddfac4ccfaa33d47326f69d13", + "575282b580904030bd1f2bb8fef84597", + "d7e8be9e7511425f86263a1e4fd8e72e", + "df8d8995d7b1482faeca4dddf1051a41", + "f9db2efb2f484c008b43cbbde25a83b1", + "73bb57f0c9a64ef6ba1991150446e58c", + "efe1556c213447dc9f5b5bd5ed944a2d", + "46e29ab1b20b4e35a4a22988e7d21ef0", + "cba523983b0b4ccba7371829163b218a", + "b8b6139284544582b1b8f983ed470c75" + ] + }, + "id": "wiBgxXqPTlTV", + "outputId": "d96e15bc-b4d2-4de6-b8a6-15ff4eebc238" + }, + "execution_count": 145, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Fetching 11 files: 0%| | 0/11 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(array([0, 1, 3], dtype=int16), array([3661727, 2823187, 868386]))\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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RFC0onr+tuPzyyzn00EO58MIL56RmH/rQh3LMMcdw0kknkaYpr3jFK7jooot2uZ4rr7ySl770pbz+9a9f8P3vfve7FXF629vettP+H3HEEZx44om88IUv5Pzzz+ff/u3fOPHEE+ektC666KKKXB599NGcd955xHFcvf+oRz2Kgw8+mDe/+c17fBz6ccoppzA1NYUxhve+9708/elPn/P+/e9/f/7+7/+eM888s/IqGxoa4pBDDpmTujrwwAP3iCz1I8syXvKSl1TE6atf/SpHHXXUnDE85SlP4TGPeQzXXHMN733veznhhBN2GTX95S9/yWGHHcbXvva1Ocf0/ve/P/e73/14/OMfT5Zl/Pd///dO36MyQ/KEJzyBs88+e6d1H3PMMZx++uns2LFjj/ZxoHn6E+PII49cMD2wdu1ajj/+eAC2bdvGk5/85J3IBsChhx7K3/zN3wDsFPotsRBx6scznvGMahZ2wQUX7PT+Jz/5ySoE/u53v3sOcSrxd3/3dwuOr4SI8O53vxuA5z73ubv8bKPR4D/+4z8ADeX259Tn4yEPecgc4jQf+++/f0UEP/3pT88J45dot9vVl+nxj3/8/1l33Lsa3vWud82pUnvc4x7Hk5/85EWX2b59e/X3/Eq9+bj55purlPX8n1tuuWXRZc8880we9KAHVT8PfvCDefzjH88b3/hGtm/fzvOf/3y+853vcOSRRy64/Ec+8hEARkdHOeussxZMb7zgBS/gYQ97GAAXXnjhTmn727uOzZs3V38/+MEPXnR/ly9fvuj7dzTOOuusBTVtxx57LCeffDKgcoeF0v0lducB9q53vQvQasuFiCOAc453vvOd1Go1pqen+cpXvjLn/Y997GOAEsizzjprDnEq8YpXvOJ2kcvvfve7/PznPwf0vjufOPVjeHj4Trv/ff3rX6/Su6effvoc4lRixYoV1TMihMBHP/rRRdf5vve9b8HxloEHYEHD2fLa3d11u9hEayEMyNOfGE95ylN2+d6hhx66R5+7/vrrd7s9EWHz5s1ce+21c274e+21FwC/+c1vdlqmTHvc8573XFQPtJi49uqrr+aPf/wjoERrMRx88MHVhfuTn/xkl59bSpSojIbdeOONC4oIv/zlL1eVVYOU3V0HP//5z+eIrdevX7/bZWZmZqq/F5og9ONf//Vf5xCg/p9///d/v83jDiHwhS98gY997GMLioVvvfVWrr76akAjRos97J7znOdU6/z+979/h66jP32zq4nJnwOHHHII97vf/Xb5/jOe8Yzq7/K+thCe8pSn7DLiPTU1VaV9dncvGxsbq6QU/fcy7321joc+9KHsu+++Cy5vra0I321Bv5b2n/7pn27zem4vSosQgGc961m7/NyDHvSgKs3Wv8x8HHLIIbuMSgHc9773BRZ+JpbX7pe+9CXa7fai494TDMjTnxh3v/vdd/leGdpe6uf6b/7zcdFFF/EP//AP7LvvvtzznvfkyCOPnHPDL0PY80OV3W63Ij33uc99Ft2X8oJdCP3h3yc84QmLtqoYGxurxtGvMZiPpbgTH3/88VUUYX7qov+1fffdd6d05QB/ndixYwevfOUr57z28Y9/fLeVPMPDw9XfS6lUu6143/vex8TExJyfW265hUsvvZTTTz+dmZkZ3v/+9/OkJz1pp5t7f7pvodl7P/ojV/3L3RHr2G+//aqZ+/vf/37+5m/+hjPPPJPvfve7i96H7mwsRpxA71FllO3KK6/c5ecWu7f86le/qtzfTzvttN3ey8rq3f572XXXXVed292NeXfvL4YyurZ69epFnyF3Nn77298COonZVRFEifKau+mmm6qJ7XzM1zHNRzkhWOhaLCsmf/zjH3Of+9yHV7ziFXzlK1+ZE029LRiQpz8xduVwDMwJpS/lc/MrPkAjTaeffjr/8A//wEUXXbTLi7FEp9OZ839Z3g2watWqRZdd7P1SS7KnWGxmsJQQc5Ik1cztggsuqByjQW9gZarzaU97GsaY2zTGAf6y8OpXv5qtW7cC8MY3vpFarYaIcMYZZyxa+t8fpu9P4S2Ej3/843PIz2IpoKWg2Wxy2GGH8e///u9V2vqyyy7jP//zP+d8rl8/tLvvY3/lbP9yd8Q6QNNOf/u3fwvA7373O975znfypCc9if33359HPepRvO9979vt/eaOxurVqxd9v16vMzIyAuw8UezHYveWO+JetifnYKGigaWivI4Xc7z/U6Dc393tKyx+zZVY7HkIiz8TX/nKV/Kc5zwHYwxbt27lox/9KM9+9rO55z3vyVFHHcXrX//6ORWvS8VAMH4Xw9lnn12J4g477DBOPfVUjjzySNavX0+z2axEsS984Qv53Oc+d6eF3/v7dH3qU59a8ixosZvY7kpZSzzrWc/ive99L91ul/PPP58XvOAFgEadRARr7aJagAH+evCtb32rcvl+8pOfzEte8hKyLOPMM8+sHvD/7//9vwWXXb9+PStXrmT79u385je/IYSw5GvsjsQzn/lM3vCGNzA+Ps4555yzy+bFdwTZvz3rWLduHd/4xje49NJLueCCC/jBD37Ab3/7W/I856c//Sk//elPOeusszj33HN3qd+6o3FHTYAWO+/997K3ve1tSy7tbzabC77+f2nS9pewr1EU8e53v5vTTjuN888/n0svvZTLL7+cbrfL73//e97znvfwgQ98gHe84x27LYKas947ccwD/BnwqU99ClAB5Le+9a1dMvb+iEw/+lOHu5txLfZ+vwB3dHT0Dq2w2R0OOugg/vZv/5bLLruMs88+mxe84AWEECqvqaOPPnqXNgYD/PVgenq6agy8fPly3v72twPaz+5LX/oSV155Je9+97t5ylOewsEHH7zT8sYYHvzgB/PVr36VmZkZfvjDH96pnje7grWWAw88kJ/97Gfceuut7Nixo4qK9Yuvy+jartCfhuhf7o5YRz8e+tCHVsdpYmKCSy+9lHPPPZdvfvObbN68mWc+85lcccUV1Ov1Rbd1R2CxND+oDKGMhu2pILhE/72s0WjcpntZ/6Rwd+dgd/u0GMqx3t6U1O1Fee3sbl9hadfcHYF73OMeVT++Xq/HT3/6U7785S9z9tln0+v1eOlLX8p973vfRbVV/Rik7e5iKIWhj33sY3dJnERkl2mHer3OAQccALDb1MRijqz9F+CuqgLvTJQixV/+8pf85je/4dvf/nZV/dEvIh3grxevf/3r2bhxIwBvfvObqxROHMf813/9F8450jTljDPOWDCcD8wR537gAx+48we9C5Ql4/P/7vdx+9nPfrboOsoqK2DOA/6OWMeuMDY2xhOe8AQ++9nPVmLzTZs28b//+79zPndnRSAuv/zyRd//xS9+UZ372zqBO/TQQ6vx39Z72QEHHFDdj3c35t29vxhKnerWrVv5/e9/v8fL31HnqbzmNm3atJOp6nyU19yGDRuqFOudjVqtxkMe8hDe+c538r73vQ/QlN9Xv/rVJa9jQJ7uYihvvItph77+9a9z66237vL9Ukj9u9/9btHWJQu5hpc4/PDDK6Hg2Wef/ScXlT7pSU+qomhnn312JRRfvnx5ZQkxwF8vfvCDH/Df//3fADzykY/cqULpfve7Hy960YsArXoqS/Xn4zGPeUxVvXrhhRcuek3fWWi325Vpa6PRmBPpWL9+fRU1+9rXvjZHkzgfn/zkJwGNZJWWA3fUOpaC/gKM+RqyMgq11LZQS8VVV1216D2qv2jk6KOPvk3bWLVqFQ984AMB+MpXvrJba4qFEEVR1Zj60ksvXbADBMy2EbmteOxjH1v9/f73v3+Pl++PFt6ec/WIRzyi+nuhwp0S//u//1td+/3L/Cmx2HW7GAbk6S6Gu93tboCWrC4kvrvuuut2qkyaj1JcB/DSl750wUqkr3zlKwt6RJWw1lbtY2655Rae//znL0rout0uH/7wh/fY5XVXaDQalbXB5z73uTntOm5PI9cB/vzodDqcccYZiAjDw8OVB898vPa1r61M/v793/+9ilL1wxjDRz/60cqq4LTTTuO9733vnOjPQtiVsPW24K1vfWtVuPHIRz5yJ7PO0ql6YmKCl7/85QvqFP/7v/+7KsV/3OMet1Na+vau41e/+tVuI9H9rU3mmyuWouCtW7fe4aLyM844Y8HJ2SWXXFK13znqqKNuVxum8p7Zbrd55jOfuehD1nvP5z73uZ0iLmVHhjzPeclLXrLgNfaud71rTpXjnuLhD394VQX9iU98Yk77oflotVo7yTf6xduL9QXcHY4//vjKof6ss85akOBOTEzwz//8z4B+D0tt6h2J8fFxLrzwwkW1vYtdt4thoHm6i+Hkk0/mX//1X9m0aROPetSjeMlLXsIhhxxCt9vl+9//Ph/4wAdI05T73Oc+u7wZHnHEETz72c/mE5/4BFdccQWPeMQjOOOMMzj00EOZnp7ma1/7Gh/72Me4//3vX4VcFwr3Puc5z+G73/0uX/nKV7jooot4wAMewHOf+1yOOuooxsbGaLVa/OEPf+Cyyy7jggsuYHJycsFGnLcVz3zmM/nIRz4y5wYx8Hb668eb3/zmyk7jX//1X3fpmdNsNnn3u9/Nk5/8ZGZmZnj5y1/O5z73uZ0+d/DBB3POOefw7Gc/m6mpKV73utfx0Y9+lCc96Uk84AEPYPXq1ZXx4R/+8Ae+973vzZk47EoYXGK+wzjoZOHaa6/lc5/7HN/+9rcBJfwLiduf85zncP7553PZZZdx/vnnc/PNN3PKKadwwAEHsH37ds4///wqYjY2NrZg14Hbu45f//rXnHbaaRxxxBE85jGP4T73uQ/r1q0jhMDGjRs577zzqmNyxBFHcP/733/O8mXkJoTAy172Mk455ZQ5EbZy0renuO9978sVV1zBwx/+cF7ykpdw2GGH0Wq1+MY3vsGHP/xhQggkScI73/nO27T+Eo985CN58YtfzHvf+15+/vOfV/eyBz/4waxcuZJOp8MNN9zAT37yE772ta+xefNmfvSjH81pcfPYxz6WxzzmMXzzm9/kkksu4dGPfjSnnXYaBx54INu3b+czn/kM559/frVPtxUf/vCHOeaYY5ienuaf/umf+OpXvzqnPcuNN97ID37wA77whS/wqU99ao7O7/DDD6der9PtdnnTm95EHMds2LChEtSvX79+t5VvoKnz97znPZx44om0Wi2OP/54Tj31VB75yEfOac9SRuBOP/30JWuN9gRTU1M87WlPY5999uEJT3gCRx55JPvuuy9xHLN161a+/e1vVxHskZGRPeo4MSBPdzG86EUv4jvf+Q6XXHIJ1157Laeffvqc9xuNBh/84Ae56KKLFp1Jvv3tb2fTpk1cdNFFXHPNNbz4xS+e8/5+++3HRz/60WqWs5A41BjDxz72MdatW8dHPvIRNm7cuKih4NDQ0B3aIuPwww+fcyM64ogj5hiRDvDXh8svv7xKRzzwgQ9csH9YPx7xiEfwtKc9jU9/+tNcdNFFnH/++Qs2jX7EIx7BJZdcwr/8y79wySWXcP3111fux7vC2rVreclLXrLbMZx55plVv7BdYc2aNXzkIx9ZUJfjnOMzn/lM1dT3sssuW1B7s9dee/HZz352p550d9Q6QDVEi6XJDjnkEM4+++ydJlMPe9jDOOqoo/jpT3/Keeedx3nnnTfn/V0VsOwOj370oznuuON461vfyhlnnLHT+/V6nY9+9KO79axbCs4880xWrFjBW9/6VrZv38473/nOXZKyJEkWvCd+5CMf4cQTT+R///d/ufzyy3n+858/5/3DDz+cd73rXbc5xQgqjP7617/OM57xDG688Ua++c1v7tSIflcYGRnhhS98Ie95z3v45S9/uZNL/540Ej722GP58Ic/zOmnn06r1drl8frHf/zHO72R9MaNGxfVNI6NjfHJT36yMo9eCgbk6S6GOI75/Oc/z8c+9jE++9nP8rvf/Q4RYf369Rx99NG86EUv4qCDDlq0zxPol/+zn/0sn/70pznnnHO48soryfOcDRs28PjHP34nUrZQewTQXP/b3vY2nvvc5/LJT36SH/zgB5UZ2tDQEHvvvTeHHXYYxxxzDMcff/ySZjV7gn/4h3+oyNMg6vTXjSzLePGLX4z3niRJdtlmZD7e9KY3cfHFF7NlyxZe/epXc8wxxyxYeXX3u9+dL37xi1xxxRVccMEF/PCHP+TGG2+s/IGWLVvGvvvuyxFHHMGxxx7Lsccee5vJfq1WY/ny5RxyyCE86lGP4uSTT17UpmNsbIwLLriAL37xi3z+85/nF7/4BTt27GBoaIiDDjqI448/nuc///lzjD/vyHWccMIJrF27lu985ztcfvnlbNq0ia1bt5JlGStWrOCwww7jCU94AieffPKCbUestXzxi1/kPe95D9/85je5/vrrabVad4hVyqtf/Woe+MAH8qEPfYgrrriC8fFx1qxZwyMe8Qj++Z//ebftqvYEL3vZyzjxxBP5xCc+wXe/+12uu+46pqamqNfrrFu3jnvf+94cffTRPPGJT1yw5c/IyEjVWP2zn/0s11xzDcYY9t9/f57ylKdw6qmn3iGVcocffjg//elPOffcc7ngggv4zW9+w/j4OHEcs88++3D/+9+fJzzhCQs2w37DG97AgQceyGc+8xmuvvpqpqam5tg17AlOPPFE/vZv/5YPfvCDXHLJJdx0002kacqaNWt40IMexPOe97wqKnlnYN999+WSSy7h4osv5ic/+Qk33ngjW7ZsYWZmhpGREQ466CCOPfZYnve85+22RdN8mImJib8Mn/0B/upw2WWXVQLFL3/5y7drtnRn4YwzzuBTn/oUjUaDq6++eo4VwwADDPDXiZJovupVr+I1r3nNn3cwA/yfxEAwPsBtxhe+8AVAo0u3R4x5Z2FmZmZOR+0BcRpggAEGGOCOwIA8DbAgxsfHF9Uh9AvtHvOYx9xp3blvDz7+8Y9XlT3ztQUDDDDAAAMMcFsx0DwNsCCuvvpqTjrpJP7u7/6Oo48+mgMOOADnHDfddBMXXnghn//85/HeU6/X+f/+v//vzz1cQEuAb7zxRnq9Hj/60Y9461vfCsCDH/zgOzWvPsAAAwwwwP8tDMjTALvE9PQ055xzzi5NzkZGRvj4xz++247XfyrcfPPNO3UkbzabvOMd7/gzjWiAAQYYYIC7IgbkaYAFcfjhh/OBD3yAb3/72/z6179m27ZtTE5OMjw8zN3udjce+chHcsopp+y2q/mfC6tWreL+978/r33ta/+kffUGGGCAAQa462NQbTfAAAMMMMAAAwywBxgIxgcYYIABBhhggAH2AEtO2/3sih+R+5xeJ8dFMSDU6o5AyuZbt3LjDbfwy59dzTe+cTFCSqvV6VvaFD8CCNYYgpR9faR4T/+2NiKJ6/TSLiLZvHXY2XVYRxTFGAxZnhJCub6SDy7cRR3A2giDwQdfrW9XMMYWRm6CSQx2yGD3NpgahCkwDcHMOPJbAqYu2L0Ndsxh10I0aRmeanK32j1YNjLCb7u/Ziqaph1l2K6h2auzt13HpJtiys+wPFvGEE22+u2ktRSpCzjBtR15lGPEQNviagYEMuvxIsTOkgVP3gpEKyxYIeSiR9ZYpCfIrQZWCxIHjIOQCeL0qEZGj2PuMyQIImCdwRggGHwIkAF1kA5IKrjlFuuMfi61uI5BguBtgBjIIVg9txIF7LCekpBDaIm+Xx5XZyDR02AbFrwgOUgmiBEQo9svLhdTM3p6DdAFu8xgxGAyhwmQmxysgAcbLLGrYTBYLFma4r3HxAYbLDg9ViF4Qk8gBhPrWAXAyOzllxuSeoKzMXk3Q0zAGIutO6K6RbqQb/GYJhCBNzl2eSBkQugKEsDWDGYZek6mIVpmyTuB0NXtmIbB9HRzLrYkwzE+92Qdjw9CNGIIAhKkGJpBuiC5ICLYxGAiU1y7xd8p+GsMdibCYitjQhGQELBWX8vzDEHweU4Q/f7cVufnvzScsPwFmFoNt2ol+abN2EYdRLDLRtn2qAPI62A8DG32ZEMW6wWbCeIM8YynuzzCeiGZ8nRXRIQIEAixIZkJpEMWcXqpmgAuE4IzhERfsxnEbSFuBbIhvUelwwZx4FKq22DcFvKawRftF8XqJSjFLdJmum6fGOKWYHOht0zXF+bdzY2H5nZPe6WjNhUYua6FvWkLfstWTBRjkpjQbmOiGLv/PkgjgetuBu8JRR9KOzICB+yNH67hLv8dob/3pHVEe63Db96KZCnRPntDEpP/8fo7/gQaoxfsrv7fk/XAzsvOW59tNjFJjJ+aAQm73pZ1EJZgIGkMGItxDvF+dp3l8uX2d7df/eOf91kTJ0iWzv1c/2eNnR1ruV3rZsfS/5n+1xfaX2N0X3bTA3Knsd+W42gM8qD7sONeDWSRcI8JYHP0O9a0+BO2892H/MvSx7eHWDJ5iiJLngvGBupNy9R4m+nJLqnv0u329GbuDFmW4aL5fc5myREIQfwcUtKPEDwigZ3Jj2H2iWlwLmK/Dfuz6dZbyb0HZsmYwSLYBdZRbiPHuYRGvUkv7fYRrwVgDFEUYZfnehOsWRgW7LDBJpBvD1gBF0W4vQLN5TXyEY/PA6YBvcmM6/y19NIMyEldjg0GUzNILkzmU4y7aRpSIyJiKB5iW76DYAK+PF7DgjghnrZ6w64FggXfVXKRJwGJBVc3iBPljw6cWEIGNraw2uCnAmZMvxNuxCIIYUYIeEzswOtDGadjM7EhSKjOLQGiEUve8uDApg6mhXwyQOQwqyALHhOBZGBTQ3BBiVIPsEpAk3pM3vJYBz4NiBcQEBHEe0zNYCNDSEVJU9J3KhMlCngw1lRXcJAAmYABqQfweqkEF+iFDs5ExC4mbiTYXk4Q0XMQBLySIFPTsUgAaw3kIPXius0AL6TtlKgRsI1IH2oIeZYBDovFLbcYMQQbiFyEeI+tCQbBGofEAWqBbDoQ2VjJS6JkWHLRe16kJNOIHhfrLFEMknlCVwii5NIaUx1TBIwzep6k+L9ekipglRCSHMYdUs4ZjPYaE5HqJ8+zuTfeuxK8R9odCB6zz3raBy7H1yw+1huvEcibFl8zmI7ga5a45TFe71FRK5A39e5tAoTIKFmvG+KO0BsxuBR8HbLY4HrMIUUmCO01DuPBetHP1pT0mOK5kTUMSSsAFp/oayZn9jqPAWNIpvUBEfV02XId0md4Lk63H/WEsZ9uwm/ajE9TMBbJUn3QGoMdaoC1cO2NFWmq1pGmyK9/hxHZ+W4aPP7WzUoGrAMR0r3GiG6pzyVZdwREMFE0+7C+rc7kCxAwW69jkgQ/NTX7sSzHJPHOJGcejDUISjQWJBP9hEECUty3JFgQP7vOpe5P+bmS3BiDSRI9T/0u4CKYWg1J09n/kwhJi/3p3xdTjEWkeLyaXROZ4nXjHDgHe0KeFtvHxQioCO7nV7N6cn/Gjxgja5o5E4oSthhKXrf4Opx+j+8Cdx55WnLaLvc5Q8N1Vq4eJY4ifcgI5Fm505a064njBu12h9loU/lTQv9WgjQ36lT+zvK0eH8WkYuJo1p1X4+jhHq9CRicdcWuaHRr9mu+693zPiOEQKM+TJI0FmxsCyDFSbWJJRpT8iKZIeQCTT2DfirAciUmTiyu46BlCF4ITc+UbdGe7tHNc0zLkGxzxJMRUeqYWDZFGAlgAh3b4Qa7kV6jBw2o5RGmZ8g3CX5akDpEQxabGUwKefCkt3h6m3J8CISG4BuB3AQlADMO11OSJ6MBQRBvqoiM8QaTGUQE7zXqQIJGeyxgjT6H0+JMNQ1SE2ziiKKIMC50b8xIp1K64yneK2mRjgFvkFt0/UTo9ZILBEinM6xxGKzeSDJ93RgDqY4PCyY2UDygTGLA6UMLW3zWgVthcYlBUiF0AsEEJVq+iO4E0Ssi92Qhw1uPqWnEKU9zAh4S/RwCtm6wsVFi1jFYsUpKTHEdO8GTkWc9yME6h4m0k3rWzfEuRxLBWouzDtOOcN0YF0fYkSKil4FrWGorE4yzGgXMBWMNJlhszWAbBhMbPaZdQ80n1OoxrmYhLR72xSUvqcySp9wQxgXaSrqMUUIdjCcsy/AjGWKDLitS3Nv1GJWwd0XyZPXB5sfH9f+JKaK2p7vc6jG0s8TD9QSbF1GfXPCJJW4rcfKxwceGvGZwqcwhPiYoKYo6gvH6v1iIumCCEM8ETIDalIY1pTh/NofGDq8PBAfBGcSCywqSFs99UIgBl2rUKR3SN+wunmHt1Q6xBnppQQgSfegWMM5hih5sodPZaXnp9RY9rJLn1cPYb9lG9LOr94w4GbNksi63sUWIqdUW3IZJEqJ99sauXAFJPHc81pDe90CitWv09ZI0RNGcdYn3egzLiFL/duZHf4r1VASr/33rMHEyl2BYt8j6gkYPndPzGsV6z5q3f3PWFeYRx2I9VeSrJE3zSI6JIl2+D5Lnu702bhOMwS7QE1DyHLn6WlZeejO1ScH1dLJj5vExl2pGpb3W8Lih6+748fVhyeQp7Wm0qFZPkABR4jTCZIRut0uep2AE70tCVP7YOf8bHHPJFMyNPskCkSCDIIwtW0ESNzDGkntPt9sjcpGmTuacXEGfoIuxeUPuczrdFtZYhoZGSZKFmttafJ5BZpEEWCbINoGOpp00s6ikLd8hzMRd2t2U0AjIEPScJ+8KkTM0JSGJHM5FxDMRaZKRSg4zgs89YgK9NKVnM3Ln6dmMEAdYLQQnpHFOZzTFOUtsLW4M7HJwy4EJkLZgegbrlbhlkuNqBrGCb3vMcpApJQRjtWXYIYtJACMYb/RGnllMpofPAGT6wEc0thIZh6kJkgfyLQEbIqJ9IkwDxGokLHQ9oRdgnRAmdF22aXDDtrjPCtlMRiDHOMHWixShQVNeGUgLJQChIDJl1tcomZKghEtmhNDTK8tExVVmi0hOoC+qEshDThZSsiglWH3dB08gKEnMmV0uCNSUiiMocasbTK3Yhyxovyev4zU9JVYhBDKfaurSWZy1RM2YetxAEvDOa9pNoD3Twae53qtrBtuwSBSUvDqUdAZDsBBqQhQ7Td1aiuiUFM9BgzOOSBJsEuGGLa5p9Pq0gmSCqYGZsqxYvoKx5WNExUNASWFBvozBWFuks+9aiPZeP/uPdUi7g/F6o+1PBdhcEAs+MRpxMoZsxNEdc1ivaTeXCtZTpeh8zZA19BrNmgaxBpsr6TEe4nYgRIaZvTTdZ4IQ9aRIDUJw0Bst7l8C6Uj5XSyIWBm4KG+bFsQZXKZETCyEhGKZ2QeKLchX3A5It6eRjywvWLfCRBFhahq5YSNumfantEND+sBEiYdZYv8+ydI9jzhJ8WVYKmG/DcReer1ZQtC3vLnHAXQOXkcYn8AMNavxmCTBGENyxR+gObfXpqbcpDo+iGgUz8+LIBVprV2OvyQpJWkpCW3/+KyZO+5+UlOm3vO8IGTZnOVNFBFafVHE8jMLRH9MFGH7+xnOO8blNu409BNoEd1e+X/xngTBNpv4WzYz9qvtRF2pvg/l9R7iYlKRCf6QGda4oTtvzOwBeWq3O+zYPsnk+Azj26cRCRgn1Bs1kiRBxLJt6yRp2s9GLbPkCax1WGuJXA1jdvWFrJ6Sc4bofUan2yaEQORiRITNmzfR6bbopR1CyNiZLC1GngLep4TgyfKUtNfDWldEocrDYjSdYxxZz8OYwKggsRDagumAjEPoBg2rGyHbEvBbdSaf36wRjWSlJXExiSS4ZkytmRAlDtM1mA7UJSEyEXkaGLFN4l6EpODjQF4L+EZARoSA4LtCp5mRpxqpsGuKjNYyME5v1iYFMvDG0ws5ea6pNNMQiDXl081SojzCxlZTVKKpsCBFFAiDs/rwt9bi0Bm6yQyhJwQjBJcThnIlRisjjLWYWCMmOEGGBOJA+Xy2NavfBUdBTPQ0OxMp+RVmU1iZEghBkBQodEA4dHwxSi66QF5EouJC41OkpUq6bsRUUYXQ8eSdHD+dK1nIrV7LdU3FSqRjElNEobxVtVSs+2+d1ahZbAh1jycnRB5rLfVmAyOqOfNZDl6ImwnSE3Iy6raBySxMAdPF+IzB1izRsMNZC96Q9wRTB1e3EAV8y9Odyui1VPfmIlscA4PFENuYyMUaZQoBOwrSALqG0FIiKgGSeo3YJAyPDWFjQxxHmjo3pu9edReMOgF+3XKidWtVp7PfPtjREfKhCJvOfsbkSmxcJkTdQNRRIhXPeIwI6bBFrEaFalNeo0O9vohdXnz/glCf8NgcatOF9ik2RF2hsSOQNyx5vXjIBYi6gq8XEaaIgrxBNqTLua4SofLzAL1RS9bQ9EXUVRJmMx1D1NGUIEbHlw5bWDk2m64p0j0YgxkZwa5eCc4haYZbuwa7ZhUSBLdSmyfPSUVZp4QqmlV87OrvRTEvkrGktNUd0ER4jkbIe+q3TBM6HfytW2Y/0ushPuAnpwhbty+4vIQFxtJPdIwt5A5FRGlelKncfzs8jG02KxI2Zx9dX+SpeL2KAlmnZKgvOtU/Jp1cekw0r0nzfM2XFFH3EGbXX6QpFzyXxuBGR3Wf7igYOydKVkUz+8cZPJKmuFUrCH+8kdFrpjRNbWYnFeVkxgQ44Z6/uOPGtwssWfMUQmDrlgkmxiOynpDUIuKG0On06HR6ZFnGli3jfbPW/rRbOasqWKSEed20S8LUj34CpZ8REWq1Op1uG5+nZFlngeUWwvzU4PztaNolzVLiKCaOkip1GIInimJCOxBuEGRYHzCyBdjmsD2QJCA2INMgLcGutfhNBuPArYJoq8Ov9rTynNwEosQRRxF+JJBkMSv9ctp06EYpeZpDByLjyIc8BtXPkIMnIGLIJ4RaPVYBdOSxy4o0IgZrQWp6I5XMaHSmJdDQL5SJDeKF1ngLG2lqKThLcJnqekxx6opMqKsb1SsJ2HFL3g5E6xwSCQwZjBW889imIE2DCUYJy5BGgrwVxGgYq0rdSXHcvSkuCQEnepai4svQo0rPmSaQG52clcFMlGSZuBCgDxfLCIgRTGSqUgAjgDdgBcGolikRsltyzBr0C5prCtY2C92QF9VRFSlAUwdC32u2OFQmh0gjZHlXo68GSzAB5yzpTIobsrgoopf2MIlgxDE81qTVm0G8YCND5CJCM5AYR26NyhA6Ai2D3+Yxa0FiC12DS6ySTMB4Q57nmoItI2U5+I7gMqfjScDVIkbWDJNvyhlxIyRJQjtt634EqpuqNZa7XtwJTJqz7VF3o7FjXzqxIZlQQlCKrI1AfTKQ1ywhgnpL71++YXHdQGObL27OQmN7qHRQIVIyVeqekDKlZ3E9IWoHemW63xl6RVQp6ii7T4fRtFoxBop0rJjZiJj1GunKim1B8X4MPtWJQTITVJM1ZFUL1RXSESVfIYLO3VdRu+kWXdh7cA47tgx/wDrcdbeC95hGg7BhDfa6W3ArxjAjw8j2HXMPZPBIb256yu2zF/mNN+t7C5GKOSfCzJK424KlEKhdiMJNnGBXjOE3F0Rpy3bKMLBkc7MdpfDaDg8RZmaKFdiCeOpvE0VVJGp+VMhEmsaToDrhOeORvn33HjMyDK3WrEapJGhZvtM+qLxAx1CmCkv9UfX5vkhWFZGafzzmRbLKFFxFlINnnnJmdsgzrT0Sydt6bSct3bydmtVm9cHW64Q0q7YVul3McBMz08Jum2T0pia9sYju2Oy1mDUNvWWWZy7/X6C5+zHeDiyZPIkIIQ/ccus2IpewYtUw9WadUKTpRIQd49tR4Xbo+ymfdpYgQmQduS/yI4tvkVlSpVV4kYuV1FRVcksePfOJWP+6vc+q97M8xRhLHNfIc41Mea9RCoxBfEBmBEuEGSuKuqYhjGuYNb6Xwa60KtweBzNpoAZ5zRNiQdpg2oILAR95pGHZMrGDpB4RxgPTWYcQBRyGSBx58EqMcgOJKSrBhInpDuHGgNtgCIkU+iAhWDRSUwhggwQ9Uh0wERrxCUJoCXk3YJeDcUo0KFi7OL3B9yY8kqg+w0YG6hDagfz3HrtWiYa0Na1nhwzOGciFLHhsz2JmLC5x0DT4aRVLkhSXiGjETjKQMVHClRREKOjnTMNgOkokqupArxGXMrpEXfeNNkhP72s0QFLBFLoVbEEgEzB1UfKbgxnW/Q0ZmioMBYlzpogwmSqN5qeKUBZSECej4k8HBINrRmQzGS6K8D5HIsGbnObwELnLEZsSfIa0hahhyCk0Zm5WY2RE9zuua2SJDNJOgBV6eVpjyF1AUiGq6/iCEYwRvAnY3EAwhFCIv9G0orTAhRgz7GAasnqGcw7vy0mMqcaQ5TvfxO4KCDW91c3spb/FGhqbe8WXgmquFyJ9r702IpkO5A2N/Liex9ccIbG4jqe7MsZ4jSoZUVG2CUpobK43cRMgbzhCpNGhMtokRtOC8yGGap5Zwic6nrgl2FSjU1G32Faq1X6m0OvlQ5ZkyhN1Ap2VEa6ryxsP8WRaCa7t8BAYS5iYxP66RX6fuxNNLodbtyKX/1YnwMbAtu07jXF2sLP33/z6G/sO9G4eqqU4+fZiscqtUuDN3GoxyVIIQrTP3uS33Iofn8TE5fmfO6aSyPit2yjF18a5ggSUKTYLi001iqq1Kg21QAVb6HRwzQYm1lSh9B+ffvYyv1qOAC5R8ueckrA40hywhMWF9TulAHW27FauwG/bPvv+fJH8TmPYDQoBuqaKFzlfUBzLeccmzVQHmtQxQ03MslFa91hJc2oGvCcdcXrt9z3b84Zh8l6eg+KdJTh3NJZMnqyzZKmwYtUInZan280Ylho+F7rtjPEdU0X0w2JIEMr622I6VdgM5L6vLrciQPMjQztHokLImZ6ZRMSzQN3HEjCXjC0c6Sr+Ek+W9Yo0o0bdTDBVdClsBxnymDFDSMGuM8iUhRoa4Sg0PNYZXLBkK3JCqgTAtg1xIQp2PUdKjh8KpGmGLSJzxll8EiAWfKaaICVPqn0KHSASTBOkK5jcwDBIYQ9gMpRMhCINVUMJi9eok7WWUBesVcJnxSpBwOOMxccBnwRku1HB9nIheMENCSwvInVbAnalxSQWF1v9/hZkwI1aPV5ZUPsEZ7BjIBNAVnyJBHAGWyu+L6EgUKYgPnWq6j8bLNGw6txMbCp+Ll40HTkCfksxW2vopRZa+n3UijPUgiBR7ZyIQBfMiJIwUzNIog8kugZpgE+CUuzUYmtFZWgStAIwR9OSpaxOIE1S4tGYtJvpDcMGsjiF3BDVItJOBinYrsOOGTKf4mJbCdXJhLznVfvU1e35rl4DIRXVrmWCswYbHCFXWwjjNHWndgsqVA7lsfQQtoBLHA6HjIPDkeYpjVqDGdciLclSpX+6a8J2M0IEwzfndFZGdFZaajssNtOKNyzYVIhBIxGOWS1FYshchE8M1gv5sFOLgFGNKJXEqRSRG1HyBGVUT8lS1FaLgf67br8QfH61HFCJyNNRg03B9VRQHspngwBGRewuE2xmiVpeiV/dVOXbvhnhRHArltO9197Ubp5ErplA0hT786uRJEE6naISjKVFeG4LFnrwLvSQ3t32d/d+8EoWw9woVxgfxzQauNUr8Vu27hRxqlaf5bMVZV5TRnZkZM7YJM8WJIMmTqoKRDc6TGh1Cr1RnyVA3+8wMYldNqr3QB+Q4LH1uoqk8xzbbBK6vVlhN2hasBx7EUkEtAAlXeBY9lsilK/3RdLEe3wZZexLlc3ZL+cwSbJ4FGkh2N2cTxE9hguJ+hsNrXpctYKpQ1biawZ72Aaidk5v1Gr1af+qLDzgvr/H9en67iwseQsnPvlkRkab7LPfaoZH6uSZzlq7rYzWTIdeNysiShSVO45a3MSaYgYwh7yUU6z5my8jQlS/VRtVRq6KKAoG52LMHnt8zmWp1jqci3Eu0h8bVe+XQmNjLM4V2pBUU3FmFNzdgGmDrTmiFZb4Hha33mLWGExLMNupLAPybiD/fcBmhsRF1Bs1uitS0jhHbgU7oyko51T34r0nmECO15SdQBIi4k6kwm5BycAYuJUWM2wIbRUGS1uQHkqY6oKpm9ldjjTKFFzQCEczJrIRIqFKt0oMdsio/mNIoI0SHmMg02iLrVuiUa0yEyuEttoqaNl7McmyBhtZojUGYv0eBquWCiYujk1ciP5y/ZGeKJkpJ42pIF7Ib/Tk3eKL3NUf6aLkKhMlSaNKlEiB1GBHwSxTsbmJdTymuOyM6LGQTLTU3xVj6hRkNBTrCRB8IM9zpK6WAKamaT3pUZwLg6tZvMno5h2C5Lq9uiFkgTxJtdovCFEvZvXKVSSmhm8H8gm1HhCr5xOr1woBsm1aHWmtIRoz0DOEGX04GlFtE6Li+1BYSUihRTORKaJtepya9SZ2yJKFDPGCkwgXOZIkgeLU3oV5EwDpqiGGNuc0f/wHGjty1ToZFvBGUsGp60mlafKJJW9YQmT077olOBWXx51CRN4T6pNer53iORG3hWQ6UN8RqE0GalOF2DwFm0IyLXOqhUriVFYRVe9pNlB9plqB+qQnbql+KmkJtSkdh800SpUPO6K2VyIXlLiFWB+2EgK1m8YJf7heyYGxSK9HmJ6uHtbzYeIEt3z5wgf2jrhw5hOq+Q/a27gN8R43PDRneclzpNNBpmdABDc8pNV4C4xJvJ8Vm4vMWhn0pwX79GOV9sj3WRAYiz1ggx7rShNlC62S1WxGnhMmp0ACdlSF26HXq6r3QiH2n91msd1Cv6YEUeaQqJ2OX//yxlIWDVSi9nK987Vo845nP3GyzeYuz02llwrFMezTee16A3PPu23UNfq3Yxy27SBueYKDkFiiazZSmwo7VdzlTcM/rvve4tu5g7BHve1WrR8BK3TaPRW6RoZeNzA0UidNPZGtU4uHSTNV8XpfFkD3p+9M4cNUvtYfZZrPTk2VVig/p2TGMdwcpd1pkWZ7yIILAmWMIY4SnIuqGXcIgdxnhdeUVK8BGGMI06KGiPsFzEixO60iolHTB7BpommkvLzGCzIzrJGozHmmTAvfUWF5NKyC7HgiwkYWaQi+7gkNcMbivGBnVLCd1vOqKg2jREg65SxIIyIGJX4UqTfXNoQaSKJGjcaiGpjEYlqQdjMViZcanjhohCQ2eCeYFYKJrZJGhz64c7U8kFzTXKFHUW2mhEFSMA2IrMUTMDkEpCrNlhRNaxpDVdUHsykLW5Aj0WPLEITtATOqxFEmwTQMdpkSMxMZXBN8JpiiwEKKqrwywmSbGp0xmR43nFZKFhN3JVVFBEKKMlgpKulUuB7Um8WBtbZKYUpLhfwhBKKmxTYMNlPvF8kDIoGs0SWKHHEzYvKGGbK9u4WuSkX5QqC3KYdlgq0ZJXQBwoymhSXTseWTghhPPORwkSUPhf0EGm2SrBx3cU1MwejQKHVqdLb28MaTu5y6qeu3sIg2iah3VLe7p9+lvx7Uf7cJv3kLUquRNa1GZLoq6g6g0cpYr8So4wmxqSwKtIJOiVRetyQzAZcGfOIITqvtjEeNMq3BJ2ozkNcMcVGhZ7OAT2xRSm3I66qXMh6NDvejDKwX2qfyC+IT9ZEqq/3mz7xtqmOxmZDX1K/K9YS4oxV5bu91yI4J/Nbts1GIJczQxXvCTGvnN8o01J5iF5qkXQ9gsXTP4uk7Pz298+eNxTTq0O2pfmcP12uSZG6Zfn80ac72A35iArdyDLfPXviNt4CxuL3W4m/ZPKf6TbwndLq63mJdJk4w9Rph/j6U44gTsKbQOimJkk5XtVDF9hdMs1XEzmCSBtLtLRilWwhu5YoqQhW6s8fA1uuEvqpGO7aMMK3ptZKQ26KiMbTaC26njLZhLJJn2JFhpNuFA/ejs2GUdFmkth43TmOGh0imPb1ldk60trVP4G9rHVQfcudiyaGbL1/4eYIE0o5nxZoh6kOOPMtZsbZOa6bNju1TTLemCRIYGVqGMarXUfSzU6Oi3XkRJsX8dNrs/1EUUUsa1JI6Bku31y0Izm0Lz4kIadaj222TZRk+aFTLFdoqjUq5wgqhmFG0tJLMDTmMWK3Kq6MP/RqqsZksiEKOpuGGQKYBD9nmgN8cyMcDflKoL48Y7jWIWxE2WHziySSDFoRpIWwF0zWqZUl1/RjAi0YqRB/0koGNrEaCvEZEjKhwu+YSImMxkVE7ASBMQz7lSXsZxml1mAEkKg0Y1cMrGopU+F0HYw15LxAmQbIANQ9B8Dd5Qqo3UJc7TK7eQiEPeFvkvOOiFD9RfYix+tA2xT2HGKgrQZVUNOo0g0aDBOxKg0wJYYcaj5pRo+Jw9R1Vj6RIo0JEFFV0Bcka0e1KVJTtpxA6+tsWqZXSTBABrMEMo/qoxKgzOvq/WE2xeR/AGpLRBDuqUSBTWioAxgpx7Gg0G4w0lxGTQAJpLaM13qZ3Xa4asFvBTBv8Vp0+hXEVevuO4EYNssNgMosLFkkhGrMYTGUAanz5ENIonSlcr0mBDjRrDUZHR6FtqLl6kRrMmZqYIs89tXpdr2+KSNZdGGF8ApzDxBHDN7RxmdDeu8h9Gaoqubjl6S1zhfGkIWvYqiLO5hQGluBrOtsIsSEdtvSWWSVcdV2fWCp38LxhmdkrobM6Imvawq6g+PFzZ86g12N1Tc6D66ntAaLRsWRKKp+pEGkKMhs2hLj4e8iQ1w0hMbTvuUb9jPqNGZeiX1HvDrBOU1dlJdYCnkBLwnyB9Z2J/m0ZgxsbAyDMtLD1GvI3h1ZVhVVkpD/VtdD6+v2cSvJZbqNIj/VbGYSbbiHbewVu3Vq1NZicwq1ZtfiYATs6PNduoFi/iZPC3ynVsUBV2UfwhbbLz57f+dGhPvJaEqA50bJdwEQx+cH7Eu23QaORBx+IW7Ect3KFpkJXrqj2O0xOY/fdu4rqGWs0utnpzCFv/QhphuS5PpOAfPMWqNXYcd8VTO0Xkw4ZsiFDumYIadRmiyr6LqW1995C0975xAn2IPLknDA1OUVrukevmzM10WZkbA3N4YRaPUJCKCI2Xq0DJOhDzGgp+Fz0p+6KJ8Ec3dN8QqWv5D5TIiPQSzs4q6k2H0qR+u7Qvw3931hDlvcwXh9McZJUb1vrNKIQRdq+QwRbt1qZVUi37LIinRJUhBxuEewGjawEAmEbhAnBLS8e5HVDPuGxTYNNLa7tVN8igTCp1gRRzdJII7r1DO8EZ9QNXJpFy5WagVAYK4ZC7G0EfCEoLyPvKbRDj6iwI5C4EBhjEBM0EpOrf5e1GhFE9DhLDYLzlWYDq15BdkxIfxG0stXrYbcrNKXp4gjvvKYPM0NIVMQscRHF6xTRMSuzRC8YiGZnbsai3lKjEHaAzAjsBW5fW9kXmIYQZlA9VqIRMRzqbZQWJCfWCJtMCWa1EjUymGM8P1snoOtoFNG0oNow2gLLVbBuTfEexTF2gWAgasZ6baSB0BGsscT1mNBRIo7X9GiYCXRv6uG7QYXxV4HxQSNGRjAjGm2TKSGsE1xkkGnIMyGpOeJYo3rBqNFi3tMIn1qiCa5mCakQdug+2cSwormCKESkRr3BDGq6mUlG1ImxzpAkCXmeM/c7d9eDaTboPPBu9Ja5ysdp7A89skYdl1Kl4dIRR9wK+Lrql/KaIW6HyiIgaxq1BPBKWmwmKipPVRAuBpIZqVqohMioS3nQSKhL9fVkJijhcoZ4RvA1M+soPn8OiRpt1sc9NhO1L0gFsaawRVBSb0Ror9L9C5GOxWXgY0M6ZLUDUrswwlxIFLxARMjUarjlY/jt4yBZFQWR+fbOdyR2Ky7edapo0XYhIoTpacyhBzF94AigVZIrfj+7zqqCDnZJJuZsY/6zrZQ/eI9JEtzqVeS33Ir98W/wBdHyUzNEhadW/9gkTedok6Tbww41d4o8mThSDVazqCZLUx2Tc3PHX0addoqI9UXK5reGmR+p6nvP3m1fzVLknvwhh5INRwy3OuADkmWYZgO7fBnh+o0AdPdbTt0YzA0bK8JZjc061df1H8vgcaOjmnrs9ZQMek99wpM3I/VNC5CNOtrrVuokAqoK1BAbnr3f/y54zu4MLJk8TYy3mJlqMz3VJu8Jt94ygbWGvfYbxfuc7TvGMVga9RrdtI1Oh5VMzRKlMkU3m8LTIQj97VV2nnKp/iiJE60iKtk24FxJnpaCuSlAtUxQghdCYQvgvUadLNpeo4ieGBeTdntEtUgdqesByQU/oaXmAqqJ2qEPPWoB34DQEo1k1ACjEY94xEJqiDoObz2ewoqgJ9jU4oPgo6CeLzGEmkDRqy6IVGLl2UgHGplyGnmhKAIJXU0Z+kT09a46SsdE5JlXclQzjDBCnCR0fZe0l6qwPCuWEYNBq/jUesES7etIb9BKLjMEtgFkQnCBmtQxMfgQyCWFHuolZSyeFDFSHHNT3DAKQpSI6pmi2dmEXaEpDcmLWKUzVFncpgrwTQ1NxQVNE5pEhesmGMKwVOXf4lAtUK84dpFGBM3KIloVCaEV1IHdiVa5OghZ0HQsFlMvSFVekC0rYAKxiTBNo22HckvwQjxVo1avk+UZabdH+5YuYVpzlKahtgJmCK0CDIJJLLSE0DGkVwXssIGegdSQG9EqwUhgFLKeRyIlaraHVlu2DUwZZGvA7A31boPm2iEI0Ov2SDPt6WdxRC7WIgicXutRpIafd2FMHHsPxGg/uRLttTWinhIkcdBb5vCxIeoEonbA9gRbpOKiVk48lWHW1rCpEBKNOJkAQ5syuisjGts97VVOiVBtlnwF59Q6YNiqkNyCSzWllo8Z4mIyXk5UxBQEqvyNRsVCrOOs7cj1b2sqU0+bCvmQrdYR4jLqVawjVRJlhoag3a4eqHNSPDDnwWriBNtsIu3ObM+0EndE5GhXJOk2isWX4kAueY6bamNkhHTYUt/RJ5QuyvPL6ElVJbdQ5Vm5C/NbsswTdPvNWytLAZtYxBfVcJNTLFZ5aGs1Qqu1IFGU0mpi+Rj5LZuq4yFZOhstKyrdFjzGO1Xb9f9dyFSKPnnV2JtNOvuPEbU1DZdsnKA209ZIpgikGTQbyFAdjrgnbtsUoWbJ1o+S9NSlXaZm8BMThZN6URFYpH5NkmBHhmFsFNm4CRPFSJ7ht+/A5AfgelT9HjsrnEadCv1qOfnp7A1PGLoG9a2587Fk8tRp90jTnMmJGbZummZqvI21gM2ZnuqyfdsUQ41hmo06vbSNs0VDWWBuZKlPtNPX6HeWXEEV0ihCBNZGjC1bTqvdIstSrLU06kNKdiSQUC98mZZComYJlLUOa2wR0QKRQJp1cTZSOufzyrk8jiOiWkyURbhYvZckCPmo1yhTqxBrC7g1wFDRN80KdlS9eUSKCMG4EEWW4ALGG2xaaCoiffiLE8KKQF40tzVeXcKjGUcWe3U1TzRUK13VDeE1ImOtphalI1rJZg1hq0Y2bENbtfhGjvUGkzt8CDTqTWqNhM7WDj73RSl/QRqbhtALKpSOCq3YgQ6zXMh+GTA1iIwldAI2qFN6PBTTaCR0e4Y8yzAYXGKIRyLy6UDIZktSpU1V9GEj3U8DZfUsZlQvAwl6tZqMymYAXxCnmsHFSlpMbLBtbXlinKYqy8vKjhiN1Bg06lQ4QKumSV83NSnacvRdplFJlsBPKMkzheWCrRvykGPFEEUxNjgaUQOz0mJjQy/rkoYubl+II1c1dZYgZOOC29uQTwZkJuDWGugKYUqr5CwqFjcrwLcCoQs2AoLBdCwhF7KpACOC73lsOyKqO5rdJqPLRjEGEpfQGXJ0gyeIRgxjp02gDVCv16k36mzc2FdufhdEOmJ2mpOlQxqRDLHqhfK6fg9NgM7KSI0wDZWIfOpuDcRq1AiU5IQYWutijWbFhfFhLohR13FX6NDSYVv5NfnY0F1hq/H4whnZ18xs9V0ZBCp+B2fojRrithBWqFYqrxswar7ZG9M0os0hmtFUlRTLiCkqAhd4Tpf97aroQlnZJepIHaand0tKbLOprV2WSqj6U17Vl53bT8jKcvfdrCv/4/UM3byJ+OGHEc0U5fBRTW1ovKfs/acpskh1SIuktHbye7IOm8SEbnc262IsodfD1mqaaZgqvaP6iExZdQYQx9Dt7rwfhc+WHRpSl3znZrVKZfSsEIEvdN7c2DIwdrZNUYl55LBctiSGodUi+Z8rQAJeBLZsxY2M4G+dBmNxy0bxt9yK2exwq1fSPngtPrE0JrqErdvVy8pqZMsONfRceY9ZvgxpdwlTU0pae5kygvJ4AvUf/JbawfszfeAIvWV67Vf+26hnWtwOTD4yZX30pyFOsAfkKctysp6nFtextkUUO+LE0ss63HTDrWzbto08y7FGMCZi2dAo49NbisiTMJueKzVKs6TJqEqIKmQyBxp16vZScp/jg1ctUhSRpmkl7JYlffHKyFeRRgpee5sV47DWEEKOD3lR5WeKGbkg7UCyLEbWB3ICeTdon7EZkEnBDoOsMYgL+Ez1LHZYW5KoxUDQXmV5wMZaju5yh5/RKjk3qj3VosTRa6Tkw17F1JlqXMTrA920zSyxNhQpPLQybUojU6apuxTFTo01h3V/fVcIKRhjMS4gHUOj3qQRNejlXZYPL2dbuo1e1p2dAWcaaaka8jq9auyQVffrHeAPEOLljvS6lLiWsGzdKHnu6eUp8VBML+2puWRNoCezppW5Nia2xmDHjGp4vNHUlNPKPk3pUf2YgmcbQdulxGCDNhH2UwEXWaLM0mtneAm4UYsd1R566vNE1X7FNA1hSrDLjQrTE5CuqMbM6jVlm4Zo2BJ6osc5UuKWtzUSJAatCGyBGbIktQZxo04qPSa7U6S+h889LrZEPafeS4JqsLwQJsE5QxAViLsxjR6EzUU7mxXahy5eaaDrsJHBxQ5HRDqTkUTamiXPckLTE6KARMJ0e5panFCr1TWS6IoefyGHVDQlnQtJLNgltt/4a0d/GxYjVNU6nRUqAs+aFiNSNQeOJzPEJmo0WaTKrC8iPV4qi4JkytNZGWmUJ+hMGJSMBAdREfmMZjTFJ4URbMmT8rrB+oLIF9+7/qyYzdUE0xYTmtKWwNR0/aW2Sgwk04HaREY2HNFdbok6GrVKhywrfjNFKB6aC6a4RIqHVtCIU5kO2hWMwdZq2NERCGHprVkqstB3r7du9oAA8/2Q5m+3X8dURdFsmVpfwhCyHNfxTN69ycrfL8dv34EbHkLSVIXP5foWMG+cM5TCGVvKKrQikiI+zDG9tI0aodPRar/S6LJ//4xRN3Br1OE8TZUg5bkKwueRN+n1wHs1Op1jMRAWPQZ+YnKBnTBEa1aR37p59rWFyGL/ayLgHG7Fcq2GswY7OorfuhUzVcM31lPflmI2btanf+nUHnw1BiVLxbF2TsXhou1xrLWz15+1uC2TxOua5PWomrSUWYX6eE594xRH7bd1l/t9Z2DJ5ClNM23xkCQsWzaCJcI6w5Zbt3PjdZtodybxPqeTTmKModszWBOhhT8lQSlP6uzN2gDWxqr9qIjT3OmXptcCzcYQeVyjVqthjaPX6xURp/51l8v2X0Dzo15S6LFmq+mU5Nnqfd2msnlrHHmWQRtqtQbZlNey/HrQZrlTaDXbtPoA+VsCZtho+XxuMV001dcBlxuct9qzrWtwDrJcyNsBu9qQDeXkcdBGwLHgapCSq4N1oKrEqvQ6pe6lTHXVLcZC3gpkbY8dLYTZOVVrExurTiuMC2F9jidnrD7GtJmhYRoEAj3fwzrtkUfp4FD6KwG26kor+JmAawrJvhHOGqbSaULb44bUXygrG/A6JRqkaP+2gBI9B5IJvqhMlBa4fVCvqmmgqGy0NSWYSjhBxjW6loeglgLXC9F6i18FWbvQmy33uGnBrlSNGbnBmIL0GMGsAGpaIRckzAYmi0iXbRismNlUYh2imkZ9xBdXWa1wU08CPdtBbMB3At20o6m9goilkuPTUB42GAHT1eICt9pWBp0mVVIYOgKbVdvi1lq8ycl7EAeo1+okzRp58DTqTWSFEIKmnIlg+8btbJ7YTMu3yUKGFVcUAgje6/Gy1hYO+ntUdPtXiTl+SsXtwgQVVJvArNGlF6KWJ68bspEYcSrSLpfL64b6Dk/etNQmPZ0VEemI02VFiY6PC81RIfoOBVnqLi/FGfrMT2aEZNrja9pwOK8bXNFUOBs21Rjr41rxF7fBiM6yfWIL4mRIhwxRT20RAFrrEnyiWi0AceoyHpIIE2S2smqBVJT0tPLKRJGWpe+mmq3S2oyMaKTkdp2knQnFLtN6Bdmo0olFyf+SPKIKhLgQ70d6HPqtCMpo2pzITolSr+P9XBsBqw2CsVFFusq0V1niX47RRLH6FwGm2cQMNWjfcw3Na7aSX3cDJknIj7g7yR83KzGd1LGVET7JcyW65TGfv99LOQ7lZ0TIt2xb0jHrR3/0Ksy0ZisFk5jatpR0LCHaZy2m3cNcd+NOkTAJAl7JpRij6eRyaLWaFickMfR6zNxnLzorXZWWLokTFlprYwgjPGvtV/Z4H24PlnzXbM+0mZ7sktQimsMxtaahl8/wx9/fzE03bSJ2Mc440lxPZi2pk/sMH8qUXL/zePkaCKpjker9Erbvs9DptGg0hqklNWpJnU63Q71WJ/d50deuPw04H9L3W29gcRSr1iPPESDLeogEraBDW8jYZjE9LOwAfJ7R6wo2JOAF6Qg+BMKUxcwYwq3g8zLCAv5WwewFTDut/Iq1hYnNTWFkGNSkclhvsCEKSE9/mwikDV4KN2FvCJFUgTvJRZvENsGkqIaoZopKPbQqLdIHcOjpZ01TyQAuqAfUmKE7nrI1bGUim8Q2LG1a5DZXvmvRCFEklN5SPgQSHyGREK01pNNCuBnyPBDvbTHWkmUZ1hhcy2GGLENuiI5vk6ce6c6mwMJ44a8UiVorFJNcuxrdfmnx5dXoUZymSk1JfJaBTwNhXIomupDfEnCNYtkMwnZgUlSI3RTcsCMas6TBq+i/pg+osEOQulTpOyLRSsPCeymyVqtEQ9A+e5Pgd6D6qNhg1gpZmpPHOd1eFxc7oiRCUo/HV734LIaQFU9vq2lKnJIr4wqzy25B6jCY7Y5l60cxw9CamEHE4/OAJ5B1UmzdMdWeJnSEuOmIbER7qo24gA01ZnptsjSlYRtYa8l6hc2IcUVBhLBt+592xvbngOuhzXOLyFBJLPKGunf7oqdi3A3kTdU+5UOaWjMefKEnitv6gHe9gBhD3Am4ruC6nnzIkTU1FVWaZZZVd3Fb1PLAzUaY0hGDCcV9sJSqFL3zoo4QItVFhUgJkK8pOTOFg3leiNfF6P8h0gha2Si4+ceUfCiitU5v874ZEUnh9N8vCnfar4577IedbEGnC7UEZlpzicF8gS9KBvyWbVWF1G3GLkwy5+iP5mxYCpPKWQJQvr7U7dU2TSEHrCKsWY6dmlaNUUkog+pwCEXWpJ+MlK1L+tKPGin3ejLKXREpUpPFk75PT1Y2E7aNOmZYhd9R25PttRy3aTN2eIh0LMatX0GIHdHECGamAxOTC1oXuBXLkVZbXbnLscxHX7WhWTaCXzWqUbFuTrjmOk0xlhqKPUyhVtYNItDrEV99E/HaVbTutoyhn2zXKrrSPLQ4hrZeU+8x76s0p4miqkJP0hQ7NoqMDpMNqy2HK2xkSkNZ41ULdf3fRTyiMYOWbv9psGTytH3rFDPTbZrNBqtWj9Futbnp+lu5+qo/0u2kDDfGaHWmgQ4iwnR7AmscI81lzHSmkSpmXhIiX/2EOdEpmJ3+u+r/IJ5ur41I0Jmz6MxZ20yUfhP9KcESZt7/xavW0ag3mZmZxvu8+qwUDdSMsYTiBmuMEMcJMuJhSPDdFLnFYergJz2+myEZuOUOtwxYBm7M4K9HdSqxCoBtw+CCwbWsJirNLJGwicFkmrbr+IAXTdvRAhqoB1GGpmC6ApVIXYmRDUYJRBPClEbWbBP8SFDhtddISciE4A3OWKLhiDzO6Y1ru45urU0uWoWHB+mgERBNVeuMCaMtVDqiJfZAaAMNgYZqnpx12hMvzvFTMDI0TJ7m5FkH6wqPLwGzDGRKT71JNO+gvfFQXZcvUpA1tWIgovA1KiI0gLSFsA11YC98xfzWYr3FpRYy0fNQF2QvQVY6vYf5IjTfEcKwzNYsOIG6VkbaZmHzMKWWrL4l5NMCE8AohJvQ6NKUqBZKLHYU8lUePyXYyMEKh099VQlpSt2Tp9gPdQkviZU4tYyI1lkk9nSiLn4mV+dgIxAFuq5DY2WTWq1Bq9smb2X0pjxhNBAT06wN0ZtK6fZauDwmxFJ4mqU4F1fWBEtLd//1o4wCidOJSt4wVesVNb/USJCvWYwXmlty8qbVzxTNdW0uRB0IiX5GItUiRRJor42V+Dh9fkah+H57qrRaMiOko2Z2AmTUC6r0atKGvrotXyuq8zIhaxQREg/JVNAquyJN53pSpPM0AtVdpu7ncUuY2VBTMXyh46rdMqV6FWNUl1OvYRp1ZJ+1mDxAL4NOFz8+gfR62LpaOYRud5YwLHRss1S/r3cQTJJU6a5dpg379D2LphYXQplaSyLEwfTdRxm5tniv7KNWeBbZZnNWf7OQaLx06M5zJXrOVYTLxMmsl5N1+oXv/74FFV+n+64gRJb2GtXOrdi2D9MHr6C92hHiIU39rqzR2FzHltEZY6rjZBsN/I5xjWbZQmu7i++1GR0mjAzR3m+E5vVTbL/fCqJuYHjonvRW1vF1S31bSnLtJsKa5ZiNmzUtt7vqx/73azXwHn/VNQxPrCfMtJQUxfpT2iP0G25KbuYQZRNF2NUrEWcZP3JN9Tlf08lPfzNgBA49/AZq5k9HnGAPyFOvmxLHEc1mnVtvGSfN22zbMsXM9DTTrXE0hmQLqhLIfQ9rIobcKAuTmfKnJE0Ga9RFfC6JUlgbEUdaVt3rdTSGZF1BfOaTLlMJwMv3dJZdbktnlL1e2pf2Y6f1lDYLSthy3JASlnwyYFZ6LBFsiiBVkzLf9ioA7xn8DJhhg11pCv+d4vhkFu80dWNHBdMyxCEitzkhCuSZEKeOuOkINpDWPCGItj4JVBog0ynKq9EHccg1IkNQsuBW6EOC4qFhjFGNEPpZEXBWG/xmtZzWdJsostBUEmBjSzCiGj+jN19ysNZoNNEWPfesXkVhXJAxiHAE68mcwblAPhyY8dMIgo0tYgMhNdpjr4umqqxRY9E6yIyoQH4GrXiLioxhOyeq28o8k3rxUBqxSB4o8sN69voj0H2nNHSBGbChiHQlhd2DNdDTaBZGI3a2SNmV6w01NcNkCOwaPaehhWrNtgbYVlQCJqLaTQRWe3Um7zjwahOglSnMpu4wGFEGrVYEglml5DVkARmGNOtodKopJI0YW7O4EJOaHJdn5O2MTFLVvLUDw6PDSBd8x1NzNSIXkWehsKRQp2N1zQ9V2vqujtJJPOrOkidbBKyjTiDqGnwMtXYo+mVBMpmTDSVIpJGk0rAyxBqR9DVlQe3V0aznTHF7K8uok25AnCGZUv8oFbeqbiprWuoTns5KV9kUWC/UpkNVAVhGu/K6UXPNhgrP84JcJTMBX2qfnGpBQqwi8v52L1FP0AofsI0GAGZoiOmH3o3OCkvcFuo7PA3Atjv4Xk+bst4WE8zbCePc4qS+JA693mwabKGqt12mG/V7aLoZUQeiblDTzFafYWaxbL8R5E7EyVglBFEE3mvUx5QRMTu3QlHCbH+87qwA3RhDe01Cb9RW53v8fqvUxDWbNULtjYGvNRltrYLtO4roVV6N0TiHcbaIKu6CyYqQ33ATALWrHJLELBtrMLNPnfF7DRPiwvXeB6TVRq4eJ/Q1Kl4QpVFq3/H2W7ZWf+e3bJo9noX55ULrM0mi57Few9TrdO61Htf1uG6uOsHy+0XxXS4MZI0BXzc8b68f7HqMdxKWTJ6iyCEIzdGIdiewceMOOq0UF0EUGbppZ572CILkTLcnCj1Rf8oO5hIoKV6Z/VvF23rDj+MYay3e5/iiAq5Ra9BqTRfpvmqLzKbv+tWHUK8NIRLodtsFucuqVN1CkMKnqrwbhuAxHZX4qyBPtT4+LdzIDbi1QF2F20YMdr3BFu7Dpls00Q2oeDfTB6y4ACHgWxqdMrkhdTkhFaKaVTFx0T5ECi8n8mJWm+sD3C5DI0UpWgUWgyTMehs5g/XFjMRY6q5GTk7ay7DWUBupkfm0aN3hCCM50hX9STQyIik4W8yae0ZnsOWp7IHfLGRJhgwVWppRIUw7jYQlgZQU0ysIhCv2QzRq5mr6TZBUkNFAaOu+2KFin6x+eXymYnuZ1io6E4OUvcKWUmlv1P5A2SBF9SDa+84Wqc+6rtfEptBmoQL1IVT7VC8+GxuixBCMh4aFdpEq3V8wjbJyEBWaZzkWrVaUSEkkuaZ4tfIw19SfUQIlkUbBXN0R0N6FJjJ468lzj2sKefDknZxetw1WKxnjWp0wEcjGPeLUT6rphjEBuqGnBRYhYGyEtYbc/98hT6Dfh9qkkhhvNNLja4a8rk7icQ5501Y+ScYLjW05NtcGwHlDJyquIETGizrAF9EmcbNeTT7RB2G3cEAW6ypTTJ8o2WluyemsmiVOecNgglUxuIDrbybs9HryNYObUh8p04PumCPEuv2yT59W/hkiL1UZdzZkCHW93Zu912G6KThLOqwTSTFQ29aB3EMSazk5IFmfB9SeRB+WfFJ2Xm4OYdkVSqPK/pRdP/pSZDttMkl0eRGSmcDMuggecDca3+tqL7lGHV9ojHapwzKmIpaS51CYVEqeVTqnhcYsQbD1WhV1CWlG1BW6y4vVh1kS7lI9z6VrfdwKsPHWvgPlZ4mj9yq4Xup5CJ7Q9dheTjKjGon6zRn1n/weP9Oq2nUt1GB5zrHoP979f/dF6apIYlFtV/Wx6ztHod0G63DDQ0w8aAONbRnxjjbt/ZbpKt3ssZGSOBVDbK8XHl7fgs62/3RYMnmq1WJySdm2ZZyJ8RZTE9M466glTSK3kMW9Egztdxf6fqCMDs3/vPRVX4gI1kYkcYIxmsYAdRr3eY5IIIpjsqw/3Vdut+wWX24LemmXwrEIAO93H2eeP/vx40L+W8FvC5BaYhepMeNQjrRmS9xNHY2cbAZWFl+GILi61agOBqmBm7JgDJn3yEpRX5/y4e1BTFBfpI6mCKxTnyEzhPZhs4WouIamjMpoTCkx8yogl7xwDQ/gvMHVHYlL9DUntFttQi0QgKRlscOl5X0g9MAmSmCiYMknA/l0Tuh4pDN76CWD/EZBVuRIx2DWCFkvJ9rPIVGgZmv0OinSg6gRk3VTvdbrFL3ZtOmxyVSbJXX0i5UJUi/2qbha3TIlE8GqLs2OQliCbMeuBLvC6vG3og7+Af27uFRM6VCemELgDqYr4KyKrI0ac0pajHsD6hQ+bDBNQzRqsOMGVgR6rfLmoVIpEyupFi9YKXQSmRLoEIw2agako1GyvBO0WrMOxlpcQwlfIEBksEG9p4gMeS/TPnddg8s9xhniWqyWCL286juo1hvaTsYaQ+su3JJlPowvvJxqszde41VbFGJD3AqajnOG4LSizXUD0xuSQlSu+qNQ+DtFnUBeOJCHRKM9ITLYMhhulMzYtBSSa2NhH2sLlfaaiOA0DeETfUCUHlHqQq5j8rVy3foAyRtmtjmwzEaXfF/RZIghR4XkrqfLdPYaYviWdbTusRJf12l8Mh2ob+2RjcSYPGDaXfzkdBHFCLMPyF15Ei1krLgn5ySKqwo/QB+ou1tXmc4rUmGlJ1E1nlIvVZKYefdxyXLsUJOpw1dVIv7W2gjzkHvR+NkfMcNDKhgJfe1dimhX2W6kFKtXImljZ3sFFpNUrQwoc7ShSkeFTmd2MMHTuLVDa52WUBuv+rhyXGLV6LQ2GWj8ccfOTXkLjdJtbehsr7mRRr4PSTMmmugQej3c3fcnXHdTNeY5myuPdUVc/U7vl1G2+WMt28joP/PGWV5nY6PUJj3ZcEQ2NEZ3hasiTpXyxzDrfyYQHTzFcvenJU6wB+TJS87MdJttmyfYvnWKbjslz3OmpiZxVpv0CkpIDNqvTUSN+LzMJzglyhReP5EqUmZAo94kTbv4kOGcRp9Mocdrd2ZwLiJJGuR5VojGYWfReBFK9IuXnC4JuZDdUN51Ax6PGzW4g41GWG40hC2C3WBw+2tfOQKYVAdtdlhMZGFIMLHgIouPA6YpeKdRlSxVQaddBr4m6l3kQabRtFHhcSSZ3lzdKIRt4Fbrgx9vCLUA42BGtXJLrOgN1oM3gV6vx9p1a5iamib4AMVDnB2GrGhETBNMDWQcWK4pLD8j5Fs8YUfALbPkJsweaosei1HIOkLYhBK9YY9LI3o+Y9maUXpbM1JJcTWHzzwyKdripiBoxhVR58JSgFh/24aZnezlQKLHQrxgV6iX1aKwYPfR68oYJUYadVJX86LKF1ogQ5rWpIhM4YqKLDTlGdqBUDRUpwZmH73OBJDIISMWiQxuyOoxoLixmSJ1mQrGOiKxeAkYK0XrF9HMShkBc6hwPdN0bwiBzKbYpraisU2NVOWTAZmGOESY5YKZNGTdjCxNyV2ORZtNO6fEKctSvM9JkgUaot7F4WuAQH0iYFPB1w05YDPBBNU8+cQUfbOc6o2SIrKTa6pP+9NBVhhoBscsufH6fnBKWKKOVhL5RAmTTwy16YDrBU332VlyhRTrtugyNQiRVR+nEb0G1eZg/sRTo05Vj7yg6wsJ5MZQH1cReXfM0X7sATqLz6Gxw9Pc2IJf/g7nHGZsGbJyDLZsq8r1+20F+kvvK8x/gO5hBEq8J1q7Gr9tx85GnLtD6eZdPMzdslGo1aq0UT/BKsdmnMPtsxdh+7hq1IpD2ZgINK6fIMy0cI0Gss968pUN4l9dr15XQdNktlGf3dV+s0ej+2KsUd2T90goLFKMrSJ44v3cKkHA/u4G3MH3VqLt1Qy1irK4wn5BQDZu2vkYFwUAVYPfPTz+fmoKfnEVhr7g/ZZtc8ZnCg2T2ibM9QWb0+dvfjSuikAVZNYacMlO11BZ3YlzyA0bSUYbZCNJUWihhRA2Kwo+ymV8UWRl4eR7/HzJ+3tHYsnkaXx8gj/+7hbGt08xPdViZHSEmekZdZLOM0IfQYqiQp9UpMZm03UlUdqZ3MxCr+ZarYkxhizPADWVTKIaWa4mWnGckGeZ+hTVmkzNjPelB28PCiGisXMiYfNhlxmN9rQiws1eQ/OTQM9glwvE+sU0KepflKhRZXBa/SY24GupVtUh0ATpabUdiUaaBC0tF0fVDgaDtvEoq3ZCQbS2CNFa1ZzZYKGhomoMSKQPcJdEqolqBTZv20Kz2dAegV20WmalID2BLSCjYBMleq5lkUjwvZx8h9eo2vzimgB+WrAjFrdeCEGjQWbUEg/HRJ0Yl0cMjSXQUY2TGCWNOKoiCZNbJAqz67ezk1umwXdywg6I1hdNeGsGGTWqBduNblTaonqvOIARTMcQrYoIJlNLhBRYJUp4y+bBRQ+9nIBPRQmNoCSrCK+bCG3wbAzZZMCbgI1MZQ9gXRF39aGqIhQRbLDEJkKK9JlJBKzgYiArhPvBkDRrpOMZPvfYwlwxTAuhl5Nv1DE645ANvtBXCZIHbDFxCUbIMy0KsFYrIvMs+z+VsoPZmasJELUDM+sjGjs8SfFw8omG/kwQsmEVXifTgawZKcG2YHMpmgJbfL2vek5UTA6QNQqbAqORHV9TW4FsWAXqJmiD4bLVSiWCtSogt14q92TVRunDtBSWu+K55ovUfDKtfe7SkcKvqmgX0ysaEeeN4p4WinZODlZdMYlJc6YOWc6y8X2g0yWsHMOP1rC2L6XSf/x6vVk/pl1Fh5b64C4fwMEjrTZ22Qh+2/bdLze/GbFIZVLpJyY19TMyMtvwt0+PY5IEt3yMqfuuZ+SaBi4NGFG36s5KS3TAcuq3bMZv3ortdHDx+qqyzSax9l7r9TSyhNdjUDp6l0ROZtNoWmnXp30qRPp27Wr8LbdW/k2h09UqyU6Rqm1o4YxLdeLsiuvERFGlbxLv1YW8bMZrrNor9AnKFzsXC7qoF69XBp7Feoxzs2S6OOb6nlViVF4nIn3VdH2ECbSVjPdzmgcD2OFhjXJmuaZMh4forK6rwesy/cKWx6EsyMBodC4dNnRXGU5a9nNgaJf7emdhyeRp88072HzLdvI8J/c54+MTiAhZltPuTRJHESKWRq1Jvd7EWseOia2FJqn/JM6PMkHkYgQh+LxSP3V7HeIoqXRHeZ6R5znWGpKkRp5lRHGCc448zwuyQ9+2BHYiZrtCcZKsKyqSVG+Ve9mlJipMCkiGWaOl8flmsLkj6+bk10K0GkLTY1OnVW5T6pnkLESJxaYWMULuPL4ZcLnB97R/HXWdGdpM03u4YuZhNPVTWVI1QbYWHkZRoWlyBZGogdiA6amDObFBrIfYQg2ymZypGZ1RRcMRwYpW1Q0LMiOEjYLbL8KMGvz1Hr/DE1ravBaBfNvOx1YyyDYGzDDYEYj20ibBmWTE9ZiaqZPnOYmLSdMUl0QEqw7kONEvXFI4jAuYQhtlEo0w+e2BbGMRSdzucasM8UEO0zCagluMPAWtjLP7GGQ4ELYFoltqHHivA7h64zVa1dfQz5FD6AGtgjx1tFKRukCkUcRou8OPBSQpWsFkKBkRjTRKouSG2BIP1/FpRt4uOp+LIKlXfZpRIoQxMFZE3iTgcBRBL9X6idpHmFy1XyY1+C1ghiBMgycQdQQm1K8qzzyz1eOibWKyVFscFSm7mdbOJc93VfS3PpEIpjcUDUxjg+sWafFcCUhJVlxPxeD6QUhaUv1dEhElW4AxZE29wYMuW5polhGpxraAOEiHrFoOFLeoMloFZQRMI0d5fVZD1W/wGWK9Doyobqu5OSWvO2SZpbNcCWApss2K5tfaj0/30eaQL6sxvWGU0T90oN1BRoexM22yVU3iNavJb95UEQI7NKStM5xDpmcIvR7Su21pugrVA9jM+itVJ2uRB39fH74y5TenuqwgI2X/N0Rwq1bqW/utY/vBI+QNQ/eBK3QY5ePIwPTeEdmjD2Hkd5OE3/0R+eXVla5IghaWmCSBTJvXikQQimfETmmoIp0W1yqRdLR2NRMP2Q+bQ3zgKmpb28hVf0CylPqOlN5YXFSBanWlj41q1mK9HuSAvXEbN+PHJ4nWrmH6gfsxdPGVhFar2MbCzypTq+kzrc+HyzQaSKkt60vJShA9dhmVNcVOqUL6om7eq1g9inT9Bem2SaytaKxRJ/FmE7J01peqrHjs9rAb9kKadeymLeT33KB6wiAaTbUaKEhmAlFPXfs1Na6TlfYBOQdGjYWvlTsZe9Dbboo8z8jyDO8zsjxHxFR97HzIiGzM6MhyrI0YnxjXWa7KovvWVIq6oSQ3xhh9oPSl8AyF9xJqahhHMVmekeehshYQhHanTQhBSQ+hqtZTvdLSv+CRiyuiJlJ+IRZZoPyu5oHa2ojIGdIbigdiWx+IzlnoqKNwPFI03w1GReDdoJVYhUGkdQ7jtG8ZBiU7edFA1/RRwhmULJTHK9HdtAmqQXKobUCvWCZCPZ6MwVsNMTsccS0iS3PtlRbn+gCwSgr9zUG1QO0AmSHb7vE7ZElBPSlSjKTghsAkkKUZaZSSZRmu7hhxTZJaTDfrMmNnyLq5ksEcgtNtSlr0ratTde7Jd4Q5Y5ApNZhUsfXuqbJJIFrvNGKYW7wRrrt+Y1XGbiJ9uooIoS2UuXUx6Ha9Nn82EeQrvKYAM5RATakjuhjRKFoHTV0HyMmwzuFig4hX0byFgFc38+HSINZgOwmSqL7NFJPCPM2xNaMtWkTTjH4mYGILXYsV1U1lt3o9PEEPRp7n2m6nLF820Ot1iaIYY2+nL89fGaqoU3GRuC5gtNRf0yQqzvax3n+ijqbYop5Wx2EojC8NvqGmlraw9Mjrms4LxbLWK7lyPU3Pxbbox+W0cq7WLc7FqMX1NFWT13V9JmgkSglUMTY/G+Us3cel0EVqRMqSjrq5ruQZlS0D6ANYIjQ6JjC1X52oGEe+eQtmfALbqBNPDEPkcKPD+IlJ7MiIRiO2jy8trbYY8VnovV2JvXeHwitpp03UanDYPTDdHH5/HSaKyA/ah+n9GmBUrF9VRZbLlJPRogKydeAoQ/ZucNW1szoi8YgoEZDiB4rIyrw0ZlkFB4X4vSAUfu9VVTQla8bYvEFcr+l+eCFr2uoaKo1brRf1/IqNrmP7DiUecUw87VX4Xm661Cf1H2djdFIa9wnirJvrFdUfRQweScOsOehCKNJzpYZLgiBpVq2b4NVhvV5DOh1wTtuvJEn1vm3U1ZA1icnWj+Gmu5hGg2iySzYS0xuLiHqBrKHHKx3W9LX6pSmxFGe4191vxpk/z71s6SaZra5G54zgg6fVmcYHj0iGPlm03HpqehJrY9KsqxVccwjMXNJUvWZM8VCcnQYYazBiK9W/D5rasNZSq9UZHV5Gq93S9F2e0em2SZKkiBhlhODnfQfnpwv7UQrbdSpoChGM7I4tWLDGqCP2iIVhT7y3IbTQB3RqtKfcEBoVyYChgLRtlU7zSWFqFwf1OIpM5QRd3iwpND9+WqpohYl0faZIwYuA1AXyMnYHJkX1RA596AZL3SZILEgTfNerjqpVfNe64G8IhCJq2/118WXc0+xO0CiUzQ0Eg10GvaxHEkdY42i12iS2xv57reGPW65jcvO0PiAyICmIUKzXhHSAIXVDl3kTIElV6+T2UlE1C9Ut9A+rB3krxzYtzRUNOhM9PJkS2CIFKgLYYgw9qdKGkjD7OQu2bvR99DyI0TY7MmFgCIbXDRGSQLfb1bZFXnAmxrkIsoyQB23lE+fY4NQPKhHynvacyyUQD0dEUUzvlsLNPMrVOsFoVVa+2WvEZC8w04YwIbgiJQgU1aoGn/riei6OgwScveu7ii+ESuOyPScbdn3ESavctO0KIEW6JJoVe4dIU2qlxQFGH7zBmcJbKRRkRjfia4UmqtSwGMjrSrxK4pYOg3hTpfAQra7CMU9DZSrNR9SZFZgHp+OKegEjGjVTY01TNVLt3/e8YbCprnP4xrb6OzmnbUm8h99cg4wtI3Q0QhBmZpZGZqqNLPLZRRrh3mb0ey9Zhzn4bmw7bERTO/ceQ9xsz8J+wlQ1X6Y4vzX939ehEzmMH6Xx23kP5TkkqdD2eGZ725XC+b5IlIkcEjy20WB6vyGM14hkMhXIhxy10RHC9DQ29UUKWIpHlSlSxKKu8tMBk82K42VyimTHqNpJ7DQ2qSJzpl7o1IoU/YJteUrPrD7Xc8lzyHuzx7fvONvSxLJ0OS/325iiF6AvvL+U3EarVmolYJ9Tu4kjzH57gwjxHzaBMbTuu4G8qbIE66WoNoWspuPL64baVCBpaWBjZr3jjA3fXtp1cidg6b3t0pwodgQioign8z3SrEupZxpqNkEiWu0JhL7IyIKi8FmSZK02dNUmwsWsL4pRf6VyOS3jLjUbeZGCiKOIeq2OqTfU5LHbIQSPs9ECeo5df6lt8SCpfKAqp9XdHxfx2nndGovbYDEr0Oay40aFnWOoRqnUEKQQYg8ReAlVKs4Hvbua4nBpBrLoA1c2wrIgPTBGKk2QOLAxVbuQ0qXBUN6nhGC1zN44i8SCF0+aZ5qS90YjZZmmskI/QbmNkhi3XLcdCBptS5VI5gRGhpskaR1PoN3qMRQN016h0cNgBWaEUNNoJAElnCmEmbDTeMQL+Q1l2H43gypulsZoaqzTbBPuJvhEqstT2sXxXUYh2DY4HPWhhHa3QyhbyoimSfMpUfG+B2lrWH/Z2mG8C7SzDngVl5OhIvRpwdmIaCzGJY7UdMklh64QpypgyZpa9iwevA943yVPA7LDIMvUaR1vMOMWKwYbgd/iydsBIwIuJsvSijgZDFEUISGQl80+g+h2/w9BtSOzFgIqzg6YXAmFT2YjTqX3k82EzqqI4EzRC09bqdhMdU95w5JM5aQjCTZTrUpJtFxaCNATQ9QNZA2n9gdSRrGU+IhT0asJun2xSnritl6UpW1CaUegOzOre7K5kA3ZighIESGzXm1A+qNR5fu+aBiejtVo3DCBLx6mZWWa3zGBGx6CRn3hXmi3FRIWfnjfHvSl8exQk87ew1XvwN4ys/iy5SpKWxdRIrXyigkkUUPHypeo1PUU6buKAxYGmbp/s6L6cj/FK/GwoyP0lmk6LrOGvOZwmeAfsIHRyzyhm+GKyZhGLkPhnl1Y3QjkYw1sQdr8TAt+9bu5acwifSZ5XvxkkGrkSSxg+6JJZdozS1Vs7rS1jJKtTKNnRfot9AIEP9u2p7REABDt22eaDcLk9KwGqi/yJVmGGR7W51lBytMjDqS7Kmb019vpHL4B1/GExJDXDEkrVB5l/aTXiBZpgH4/O+sMf1ufQPUWf3osmTwNjTSYnmqRZZmmHQp38DiOCcHQ7rSQIIVOrBSI933bF4C1FmcjfNmTC50tRy5GpLjBS6ZptBAKjVWGNZZer6M6KJ8RgkaOsizVNGFhbLl4tKmEwVqHK0q4s1yV2UtyXg4Gt85i9zOYCXCxVYPKMWDUEG5CK+HqQmgCEnCF31KQQGhqRMh6qgbAAOTaYFjqaPVValQAXvC5slqm1AqFrCBTRlQkI8wKrr2GlpO4BrGhm3f1c0V0SLaAsQHpht0KrpcKaaNVcp3ippTruSa33Lp9K/uv25dhN4w3OS2ZwYlFutqkVYZRf6Ugqh0yYXadC20rA39TH7EqBd0B1RAWl6K1FrsKreIz4BoQgmj1Yl7MIAtNk1G/NkzHIEmg1engIkfYkeNiQzDah6/s+WdqxTkSyEJguDlMnuWknZSolRANOTJ6mJFAvjFFgra3kbhwMK8b1o6tJXRhR7pNhfQzgV6vo/0Sp8HmEW4yJqpHZK2U0NHmyc5HOB/To6cu/N3SRXQ2FS5Z4ThvDKGc1NwWT56/cthM02MmqJ+TEUjHTKE/kiICrO/7xEII1CbKm7pGkeKWVxJUtEXprIqpTXqNQMVFD7kYQCdKUTeoDUKn8GwSJTw+NhrVDLOOyXFbq+pcEfmKuoHeqJ2NQKMk0GZFRZ5VC4POitkISfF10WjLItyh9MsxEwvo3oo+b25s2cIVdrcDC/of3Q6UlWAAptmgsyqadXZ3zCGViw9MfwUHptXFZDFm3RrC9TdROoObSHtAVdEWCVXEpTw+ZU+7kjSVuiFZOVZZStB3LnujluyAdbjpHvFMIOrqvnRWJwxt7BBqDtfKkMgSbZupiG5/tK2MdM1pOoxGiKQQcRsxVaVg6BSO8blGrSTPsUW1mx+fBAnYWg2TJJjly2DbjioSaUZGdAIAKiTvdDQ1uZOFgpkdQ6sNK8YIww1smmEiR2tFjM2FifuuIh02RN2I2mQo+kda4plQRV3zojAjFKlqBC2OOGKcZfbPQ5xgD8hTvVFjYsckea5lzs5qiwtrDbV4mNyneB8Qv5BjN8wlUHpzD0Hds+d/rtsrHwCh0EOBLxqY1pKIXtqj0+0AOpuWEIrsn9OHQyWc29UXfm76MM9T5S1F2McaR1hivwG33CJ1UXsAK9gI1TkFsGuK1JMB1zVIXfAiuFCyatHy516hb8qKkFEGpm60TQfAjGj1VqTtQUhNtQtVAKGwFdGmo/qAxxUl923ouh4MFaQwLciCBbMCZFIIW7jDWiyEokLNOLRVzAqDF89Ub4I88myf2E7Hddlvr31JfEze8UiMNu1NpTKtJBGNOE1DmNj19iqeDHNkbqaB2gj0IOpYzHqN8uEgZFqBWEvrmO0R+VBK3uxBVKQHO4ZYYowYuu0eURazbGgZdsgw7afJJFMN2Q5N4ZEZrLPkQxm90GMoGkF606xav4bVy1ezZXozU9EOsmU5tuUwawTwWhSQwfaJHUS1iFrSoJO1CVbwWSBsVdG6mBzySM01pwN5T0+8t544ToiimDyXvojr7ORF5kwG/u+RJoBS7wJKMMQZTCZVBMf11JAwHbaV3kmcrZy8y154PrGVPiluB3UJ39LDNyN8Tfvk5Q1N++cNS17Xqr36hMd4JULxjPp3dZdZbPEwcLm2WUlmZsXj3TFLbVrwyawAPGmJErpI7wF53RC3dQIG0BsxlVHmLg+FaHQDQ9VwFtiJIPmJyblRhN1hofYl/Sgr425L5Kl/HP2kpNfDDg2R/s3BtNbFlV6svwVOtQqhSteV2rHyMwYK3RpMHLmW2nhO8+rNs/5JoFGmfuJUpiHnHZ+yGq60LXDDQ0wdPAYoUS5NV40UEaWhCJsHJDLkDVdFReNNE4Qt28BazPo16r00/7BYg9tnA2H7uKZYAZMk2NFRwvi4EqAkgSxTXZIEqgpEY4nWriJMz2CazSK1pucvdLuYPEdKbZSoT2CYmsIOD+HHJyuPq37Tz3mDA2uxK5YjxmB7ma6/MVQd83gmkNcced0QdXRy4WNDUkxmEI0GlxWj/RWzz7j7T5Z27dxJWDJ5khAYHhkmiGd6eoZmfYRe1iH4QNe3CN5rVGT2SlPtEAtVrJURIVu0TZn9MhlTpvz0c2XqTkRF46tWrmWmNUWn26bb7fQ9CzQss7SHhMz7W6pUIwh+iS0JTA01VewUYdwEmEYNHmsg06IBoY4hjATMDIRYME5TLiVJCFHQFhBeb6YSU0VciAQZNdXxIBFCS7AxUMxeEZBUIyFS9HcjFk0XdXWckhRl8iXRMKLRncziN91xxKlCABuXrWmAHJaNjDLjZmilM5DDdDpJt9vTiJcpJlF1o1V2NXS8XfDXA4uNr5889UG2gx1V922aQFOwTqsebdBqR9uNSBiCCDJRlX3UjIi310hISBoRK0dXsfe6vRkaGWbrjq0kk9u5dWozLhiWDS+j2Wgy3Z0haSa0/TSZ6VFzNZY1l5P2enR6XZaPrIC2YUdjC41aXc9ZJJgUmskw1jh6vYxWb4o07+FbnrCjIM893T0vKfnNGZLP7qyIEAoPpziqaSTWF0UHmNlijDk3+KVEZO9iMBB3VM+UDheOzTPFU7ZIdeWFGaVLix5zI1oVV01UnFF3cau957IhvemnKxLEqNZIbQxMFWUqt2sK8Xc6rO/lNUNtWv2gxCphsplUkalsSMfVG9GnjClOaTLt1czSWHz9/2fvv54s2bL0Tuy3hYsjQmVEpM68+paurmpZ3V3djQZADGZADh44RnJmzIAHgDSj8YXC+Mqn+Qtoxteh0YYcGknAbGYI46BtoKwBdKNFdWl1ZWoVOo5ytffmw9rux+NkRN681SVuo2pfyxsR57jc7r7352t96/skGtYMFKMnMoZWYxHefFELSkCCS7VoEsH5wOfjqoa/hFDmhSTki1p7XCt8o+B1JxPQ/PLbnLyeLqNzcbE23dUpwCs6lfewYoMWFF1lYzVWeGMZzIvzjzf4pSBm/Hupu7T04ws+oFND+ctvUmyKVlFbct+KmAL4TOOi+nuIHLagYf72LsPGgVaEw+Oz2kutGGWa4nbWUQdH3bERRUf1xjru6GRZMBKvj9ndFW6TNWImvCjEDHpl3jtXYLSqcEdnxS5XgZOyFpRGX9okTKYUb19lfjUhmXmGWcL09TG6CWQHYlw9ehpY7Bjpo1gE0eS6e0HIjxyVM9TDqJumYX5F85+sfRMYP399fkrtpcHTYDBgMS9jVRpUTcF4sE7tCrx3MRqV0PTEKPNsBMGzKFsmbz/iE28cvyRqEzlQCiKBGzp0ACyKBfsHT7BRo8IYSbeVVSUP1HO8KtXbNivfsfL5x59MlFWoDSVWI0msUFOSCgolUKnORgUfRHYgAz8Pkrbxkk5BybIqifE2FWIVXSCkEqEKFsJMFvVVgIjEaQUeW2AUffSo4vcbYVnGHyeCMJFJN5wE/KG7MCX2l22ttQqZgiRw0BxIhK1R5MmARb1gmAywKqFWFWqgpG8K0KlCJ0qMmN2LwaziuedePs/ATi1qatCf8hKSzxRJZcnNgMVJwWA7Y1adUjCPUSuFm3oG84zgYefWLq+//hrb25c4nhwxyG6xe2mHwZMRczfj8qVd1sbrPNh/yOFkn83hBgfHh5w0h6wnmwzTIZvjDQ5Oj0hVytWd6zRFw8iOOTo6ZLGYs76zziAf8eDxQ+p98Qz0x8B67w6Og7hvVlNuUsDhvCNNxMfOa+HgKSUcOuc81ZnKpJ8z4BRbvl/TDA310AifJI8vXO2j5EKX2jOVpNaUFa0mlGgwEaQiqp1sRV/JoOtAMnVUa4Z6qJepDR91n5SJBODlDF+uLcEacZI3pY/edJ6gRLDTW0npKRdwqSYoIaE3A9W9ic+uRN7my47oHvLDSqqh4Hzg03J9/rIpu/42VtJcH9nOfbB1JxvQ/MqnOHo7PxNRkvWQF9MeX6b1Jgw9cNlWLHYCo4FOnFJl6Zn9dwKR7d9tCrJPhF89V2B+NZEik3Jpl9Mq3NtShFmtFbCdTmrKzYR6qBk8bahv79CMLNkffmd5zFa4WMoKAlTfeU/0k2LVmUpTwtVdFrfWyP/ZN5+rSnT7+8tqunOuewt+QlM/D6pj5eF5TWUZen0dGgFXSmu4dZ1iJ6EeKZrcoPxI+HgOFleFeJ6eOrJjj7tsojBmfNEYiVRDPdLdC0Hr5/j0tz237U9fVbzfXho8nRxPKBYlRydHWKPROuB9wBpLFTu3eU7FO5AkCWWlO3uVPnBRKnR2Ea38gPcOrTVpkuK9EKlb8csQPPPFLBoEy40sz3f7VLAii7DaYtg+PgBndaE+fvMLEbw0aZC3w4UcTwhBytVDwJ0iVXReSZWbl6hKqEJU0A6RlxPfOoPCjXoyCbV8JpXsQiRWIwUphCIs1f8NAo6SZRe3BPIQjYAViEDjwEMR8PsSmfpJtWbqsacatQVYMfc1ymJVQqEKHsweMGDIWrrGqT+Wa12LN5wyIoCpnaG2nnBelbQCc0VBFnCPWEanFKgx2IFBDw3mRpxUjhQM5fPZ4QybJFze3WXP7ZM0hnk1h0qTHg1IkwGNLdjd2eX3vvJVlNG8c/ddXr15m5PJCbv3d3jvww9Y31zjsDyimC9QteK4nMBCkzSWUT7mtVuvsbGxgTGG0pXsnx7gC8X61U2uXr6GQbM2XOP99z/APfXk8wHD0TZPF09xddOlYrMsFwJ5U5+5xyWqJDmY1vdRK9M9V6LK3wrZ/ogVAP8etKBgdi3pgI/IAgSSU4dPNeWGQfuWbI1EQpvQAZTkJPJqWiKrWgpXukSRVwJutAudxEBbNdRGk8r1GCmJfnUmVr1JCkWAkXK+Q8y6iTypyE8ypRxXPdJRqdzR5Jom603+K63zSlttGopLKeOPSqP9OLhOLyNPcF67IA3YCi/Wv/Fpjt/IluNdO7X06K4K+u/qHYBquVC2EH/DFny1oIYA889fJ98/OKOPRAjP28H0S+VXvNt8UYqxc2ZkX23lpRHLlfG3HhNmc4ovvRojoBJN3Hh/jp5X+Dwh/Zffwzc1ejQSocmikGPoSyb0KuXUzWsEID2pOl7Tav8/Z7tirViqLArMzjZhYw3//p2PlWLV6+uEa9uosmHx2haDeyf4UUa5Hnl8iMq9qaUfXNQzDLqtVg3UI4kKtyn2FsimE08Z/auaXPFrX3zvZyZR0LaXBk+PHj5BKYU1BqXh5o3bHBzuUVUli3J+Jl3XNh2FK+U+be9giQRprcjSDK01dVPHtJ9MBDIhqCiF8Hw0qYl55xCIHA+pKvJnjIfPb1prkkQ8GsryL4kcfKC560kuG0JGp6VDjUR7BgJ8wiSgZpowCPgB4p/lApRE0155Q/I2Rsl0+xNZJiDq10hEJiCAiEEgeCXl6yDAyck2hQMVOg6QsojA40K4Ve5xFID8SbYgfoD2ivDCMJ7QBCpfMnNTbGbIdEae5pQ6Yz5ZyLiTgEZJheBAk38Gqic17unZzasc7G3hi7kH8bMh6FcVZqxJh0buoVyRLBJyO2S8PeCkOmF7Z535g4Jn9hlFvmDNrbOT7PLk0SHNvMZuG8w449d+5Zf53Kc/w9d/8G3ev3uXRbHg+pVrXN25zK0bN/jT73+NR3ce4ouGgRliTxvSQcorb93mlZuvsLm2wd379xnZEb72WGfYvrLF5e1dnk6e4YPj6f4ei+OCtcEauzd2mRxPmeQz5ospIXi2L+/w2muv8c1vfJvmzGDYT79J0YRyDqU12vsIoJyAJhXzFj+nUSdT0QERXcng7DLR0kKB8lLe3w437aQq/JNAsFqKNFzAxEm22NQdiVW5IIKAyCRsykA6FW5SPRSwY+q2+k44L0ms6EumDcUlG6v/Ik3BSOpCN6DqECMksn41lglYVwG3vtxna1nRb6vAqc/38Rb0lV383ee5NGdXuiBS9KNGpVZB0UXb8edEc7Sky/Sbr3B6OxPxSEVXZSzLL89bBQGQykvEB332MWjBcVttFyLNQIVAPdYMty/hHz46c6yrgOQML6qNrLUgyjvW/uQe40sbHH9+Syx7Crmeg0cz3MPHYAzFTiIRxmnD2r96B3dySuhsXqSKT+UZPqp/h6bGrK3h5/NoQ6NQ4xHl527RjAy68gzf2aPp9etFfDNlLXptDYJHkxPK6uMDp9EINR7C4wOat64zu2qp1i91LwougeG+w6WKcl38IHUdI0mnLnpEBrITmUNNBdVInYkY5oeOasPgMsU/vPqHL31sP6n28mIvymOsRTmL8zWuaVgUM5xrhGMhCwFKQBMBY6ykXEL7uYoRKEUICpskwuVwDdqI/xaR3Nq4+oxUAUgawmiD86Iobo1YBdR1veQELQ+Y52vtZdsKziGq/2it/sCT3vLo25pQi0Gj0hrWA/4kEE4DKlUiYhkQoBMr64IDarEyUVoJGNOS9iMgV8cjQCtKaYkHGp23mxgIK2iEAE0bQU6DaB81iOiiVkI8nokIpj/8sZz+R7cyoGogUwQbqEMlKUwj8ghPFk8ZrOckIYWyRCVi9qsSYkolYK9rytXx3YC9LWac7mkAF9DXFckbBpMqVFA473EqoDGEoaexBcfVgqZpmD0oMalhMjlFDRTGJ5iDDLeQipXhKGNOwzvvvc/jvT3+7Bt/Dj7w+bc/TV0LSXv/4ID9ZwckWcL1W69gq4Tm1HHr5k1GG0Ou7l5BBcXW5hYPnz5kPB6T5JaDkyO+/rVvUMxKyqIgZ4CuLOW85Ojxe9R1RZblhBAYDkfs7u4ymUwpy54Tc9eWM0EIXn5zHq8UOjiJekbuU1t88fMIoIJeRmGClhJw3YDLRWPJ1AE7bWhGpiuHhlg2HrXTXCZjUTJ1BCvAxieAFsPhoNUZsFKtiXBlOonK4mMZn9qqunJNEzSM60B66pYpoUa0pAZ1w3zHYgoBOk2uu6iJLUKMQPUA0mqK6iNaM9AUb1wmefT0QgHMTsPo3E79Ee+j1WjS6nZWwVSnmSSCjObmNQ6/tN2pTwvYkXPWtfzeehi2admQ0BXvtGrzqllG7NqIUxvh07VE/sJsRTzunHNepu/ica+ktprHT1A3d7sKSG8V+ZFD33uGNwZz/Sr1QLH1zgL1x9/utA1Dr6IulCWuV/VodnYIs1kHiFSaEq7t4NPozXjUEIZ5970eiX3JeYAoNA3u6Gj5weo5n9PstatgLSFL8GtDpq+OqYexqjQWWSgvnR80nYhstSa8pjZ1mcw9dtEwu5ITjJDDBwci12IXJhZuCA/MRQ2oo08pfiufIBPcz669PHgKwjGq6hKF4vD4EKVMBEqzlUUDRidRYTl0aTWlNTgvwFwpEpPGKjpR9y5D1VULnZUakJ9aLO8x2pCmKc5JlMrofpVdC5rUc+vLdgNlVbycFMHLtCZQvusYXNfiD3eERKEKifroywpVKJwLUoEWRHFdJjnEKw15c/FKuE3kCGepjJEsiwAqC6pRwqsKyJtuEgcOp9BB4/Gy9faUteo4QaEO+AMvfJqf8PypdEyptm9lOg5QBnwh5+MbT2igKAsa7YQPlWhsotBWwHIgUHxQiS1Mu20F9qph+GoKGup3RaE8fcWQrlkhyhNo5qAbzciMqGwFKlAWlXCKRvL2l+cDqqpkdrygeOywxpAOBmRZyt/4m7/L7uVtirrizVuvs7a5xqIp2B5e4s6D+9RVzavXXuWHd0rKoqKqay5tb/HKGzfY2NgkS1MG+ZCrt66w/WSLB/sPGTVD9t0B09kJzROo9gvKUBK8wjsftdMCi4UQ0YpFwXQyxxhNlqWUncfUxVGkFig5584UYCh18Tr/vjdvY3TBCyBK5j7ylkRHx6WiLm4L8Ylrct3pzPi4TD3UZBHktMa9QKedxCSCMyXVc52NxFxSg4yBEG1X2qowBcUlQ3biyQ4r6rHFDU1XaWTqZVVfmwJMJ0HMgjf02bTcORVmbVutMANocuHjbL16E/fuB+f224XRhxVPtx9re8HYbK/scvxr17pro7wASZD0qXZRZDRG4rqIUm952UcLQOP42Eo3BPmZH9S43Ije0YvI9H2ByNbTbuX47Y3r7H9mjC0DdbQeGb1/jNvfhxBw22toB/Z7dzvgBHR9rAe5kLJ72w2zGXp9DXd0jEpS9O42k1fHNJmOavIadXjSXT//EoDo47TmyVPMm6/htsdMbw06BXBvo5hsLddB+ag3ZmJKPBprmzpQrmm8UVRrScdDc6lisW0jaJLUpil9l9pDQfK5CWP9swVO8HHAE5IiC8Eznc+YLU5E4LITs4QW6ocgxO+qKlFKo7XGeY81FqMDxiR43+C8o2lcFMUEFUkrZ6UGemBIgTUGrS3eh86HLss0dV2LW7zvlcdc0EL48USd2tY89BT/oiG5rdG5hipAoUQqYCA6O7TqwRkiTFnJ70EDTTu50b1ZYpRoZoUYUTLylhTKID5wIBEckGosrfB1wKcx3ecQtfJobUIN4cBL6usnMN4914KCGAlRqQh1tppTygZChVQbGjpNJ2UVKolSBQrQgXraUH/oz1wunWjy2xadKJojTziNVXVjg/YS1auVJ9iAHiv0lhiy1nVD03hCGVCJQq8rfOUwjaE+cQxSS9M4Lu1s8YUvfg7dGC7v7LB2aczrb97mez/4IV/7ztfZ2dzl2pWrbF+9ga8D85MFpSu4dHWL61eusqhLPnznW2il+Ozbn4Icfrj/Q05mx5STiv2jJzS2pLnmCItAxoDpwUyMgwFQUgxhxGvx9OSUolhQ1/2J7OL7N4SA7shwS4bUj+2F4a9oU41EfVyqSCaOat10AKatdmsGkkZvcoWdC+fIW4VtPC4VfRm/pbqqLV1H0ckIxEwl1UCmCGgbJ/WBxmU66kuBC0s+B4j/XDMwlBt5l2LSUbogGCWE2ah0jhJApXxUHX/JEfyiSFQ1Vkw+v8ua1oQHj5eT7EfJDvwkQNNH7FOPR8y+dEsESjPTyTy0gCg/cd0kLoruIkraik920ameVlA7xZxJbyoothNcqhhduwzvfXjmONoIWJuWAziPRwSgkpTZl25IyjBRMaIFPHgCIaDX1jh9bcToUYU7Pj67cgRl5wEfP593VW6tya8uA0YFTI2AdWvP8rN+nC0E/If30Pct69NXmXxqg/RE0of1tUSqxnWsNI1AVblANTa4XAlPRYGaBxbbBlMJ5ylo4fS1EUWXyXJSISrP4t9/+9/9+M/nR2gvDZ6UUgSvqaqKsl6Ago31LaazE+q6T56Rp7SqC+qmJLEZWZrROE2SWAhaxC6dY7FYUDdiVgoXDe4hEsgzUBqjNcYkkqoLYoLqoqGwMQYfOVPtsVhjCcFHRH9eKu/H0FygftgQSkP2pkKNFGER03OzELOW0fD2NKCswjd0dh/duNZGq2s5bwxQRh5Gm65TPfG7yGvSVuGVl+WBTkQjUsAU4EuPexZ+bEKYH9VaAFyfOswzLeKUMQVpxwZXefxMDs7PAmUo0VajY8jXVR536qnea6T6bqSgAL2rSa5r/CDQ1A41UAw+kxDSQGIM3gW89lGGRVKFx4tT0tpIqtRHUHmqqJuGJEtIRoZgFTeuXufTn36b45MTTK7Zm+6zVY34xrtP8B6GawP+xl/7PXTQDPKc/b1jnt7f4/a1m9S+Zmtrkyu7u0zmMw4Pjrh+/Sp39+7ztQ+/xuKogAqOp0ckeYqeWlThSS+l2HnCcDRiNpl29+7W1hZ5PmA+nzOfzynOTdm9qP9jpJXls9DKFvw8Nt3QGeO6TFFu2m7SNAuPz1SnJeONDPa29LhMSqhNpSXKGU2D7SKQVBHoOIl6tFpQENOCLqCrQD02ECTKsUpYFvK6/GwyRTqVt/I2SiL6Nx5bQzCyfhUnF1OGmNJYbuu5846GwC3AO6/NdzWL39ll908tfOddmbQ/Chz9OKrwVjcZrT3Obdrg3r4t1yla6fjQizpFIVORixCU5K1EO2whL18t0d5GQV4TjZubXHWDcMsFC0b6bPr5XUaPn50BMGdAUstvOqeKUA+HuC+/TbFpWLtfMb+SsHavJPn+vc4M2U8mbP7rOwTncR+nP7vIlxLvuI0RtnDUa4bNP3sM0fT3XML4j9p656rinI0xqErU0XUTcPHa1EMhfqdTSVsrF5hdscvongFbSAS43NR4JJqbznxnL9Q+I96qGCEOTG+on7lEQdteGjyNx+s8eHSPWXGE1gqjEwg6ajkpkkQATT/qE4KncVVMs+V4LyrgTdPE7+TCllUhCtRqmdaCEKNWBqNtp0BujJEIk5MSbaWkQq/lP4kRcS89oUC1oZifcMqi2Zd9JDcNektsSUKpCGkgGAEK2ip8IUn60BraBkSWQIm6tT+V6JHwoFiqZjfI3xWiSB8CeIRHpunAU0cVywO60vhjASLhHDFhactr1k6wclx/+f7Sier8+IjpO1fHV0AfRIU8Q7zhlMdNpTzcLzzVd0VGQV0BqzXmdY25okV80wkINYlCXRUD3rpxBCWGzKbRBB1lDjyUtUNHTpjSCr2paCqP0zXNQ0UyG3KaTPnud77PMQf8YPpNyAN/OlHUusGqhDQzvL71KT5747Pcezbh4OAQheb27m12L+2wMdzg6vZVFkXB597+DH/+3b/g/Tt3OLx3AAlcGu+wu3aFJ4+f4CrH5a2rhGPF2uY6zbZj/9kBz549iQUTUBYSuZVnq+oqUgUIsTTejjpOvhex9Uv/iO5aPK+39vPTlBNLh7bUGZZmo81QdyAo36tYXE5JZiI7YAuJPjVRyLiteGtNdb2JqYjSU49k8rZzR3HJYk889dgsDYd7nCRd0YEkU0oKrtV78smSOxW0AINkIdc4GNVxferRRxObggFVBXnT/wj9p/1f2WJ38Qru/bvnk7Vftq+TdGlr8jFaKM+vYFHWYm5eZ3p5QDUSjS1TCi8sRJK+rqNTQ+sNh/SlSAEoTCHpT1Tkqtml8OiZfQW5xi2hOTuqzx5Xz5ZkSQxfPldtpEdlGYvf+xzllmHr28e4tZxLf3KIe//OcyCpebJSCXOmU17Qh/Ha6M0NiqtjggI794Q8haNjSTn+OAFun8ulFTiH3twgPNmDt7c5eS0jO/WdXhnIfV+tifCsdhKJSqaeal1u8NllgSAqAvw6CBdRyORy7NWazINpBfO3y5+5REHbXho8nU5OqJsFWhusFR851BplWTAcjLA2oa6PWaba2lerQFUXKK3x3lHX5TlyAkJqbfO9KqboQgh471DomKbTcRJx1I0opSZJik2SjvMU4kVrI0wKUbf+abXm0OOLQPIpjckl2kEiyuNBIbyjoyD6UFXMCwcEDLX+VUOE6F0jPnYeqd7zxEq8+Nx4iXC1qtkABOnLUAXIBYTUP3QiSXDBcyQcGR2BaJt6hbPpzx/tIbSbGn0JXAHKyETvHwXUNuhcC5A00XrGB6kuVOAeyQOodxTptibxCfa6wusgIpdKiXUN0RbHe+paqjgDkCQ2Sjxoaldh4tulDwEdlKTvCPiFQu2nVEXDk8dPUEPH5mcT1NAQTKCZB2rl0UnFYq54b/Eua+kGqjYsypInz54wLaaMH63zq5/7Er/+S18msQl/9LU/59vf/j6T+Yyr2zcxqeHp6WPMyLKzs8Nr9jXKacnaK2tUC8fe432KomB39zJHh4ccHx9DCIzGY7I0I88HHB8f0TQ1xhh5RuIlMkrO3zd17zqpF7Cifv5amwKrcknlJLNAfuhQQZTA66HwnUTtWbR3Wh5GOg3ikxYgnXm8VVRjLSalc0cz1NQDzeCgEdHKkaxYRWmCNpIhx0FnDqt8m7JrAQBiE6OEM+KiIaotJKUYlOrOo12+8/+6oAUNzfBikHWGF2Th9Jd2GW+OUD+4g59Mzp98W0uQi/b5IguWPvho/+7vo/07pvB0nhO+8BanN4dUa1r4ZJWkpgihK3v3kaJgFyEaNcvkayJ4aq1z2nSoRJzozJY7kr2XaEiTKzHwvbNP0480hYDO86V8wYpAZmeN8vptfKrY+P4EPVkQUkt48PjHB2Z62yk/d4vTVxLsPDB6XBPuPfrLcZxeAjCHshSfu+MTcE7uYSUVqPmJx5YCXKuxph4p6qEiOw2Y0kvaLm4+O/XdC4FLJfrUZCLx4VK5BsksdGn1L79x72cuUdC2lz6K0+kh1lrSJCPLMgaDEQpNkqQkJmc67Yc1WgAVy+pDwMUoU5Ik52y9VSNv15Y7ua0OalwtZsBVJd563glXOkkFZMVd1nXz3GQhLwgr5Sg/yebBTwPVdzzuqRel7IFUf5mFFl7PgJhSU7CIz5uKJ+JBDZUAKBMI0YstxK+DQiBvlLJXSW/wtPHtskIq3MqA+8ATXpjxkT6R6K9lOBiQpmmnhdWfjJf99/L9qGoIT4F9UKWSVGaKcI5SSccl2qCCQhmFGsl32WVL+kWLfUuTOIu9IpWaraWBShUajfFC5m2fdVcHNBbnHAM1ZJyNUVaqDdvz1EpI90pDOIF6WlIWM2pVkH4KKueZ3qtxiwjzPTRzjysDs/mM7935Huk4YXxpxNbWJVwVGNoBuc05PZ3wwXv3GdoRf+8/+V/w1V/9LdbHG+yMd9iy2xwc7PPk6WOmpzN2t3e4fPkK+88OePTwIYeH+0wnE0k/R+HLsiwYjcZMTk87XyvvPY1rOj/Hsio6T8ZlC72XlPCiG+DnorW6SyoI2VqFQLmpKdcN5ZqWwdkFii3TacyYKnSTsI7Zj3qgyfdrtBMF8npsoj9d6PR7TOFFoymTFFF26klmIaYpQseLKteVKIwrmfS3vzNFV4H8oMYWfimeqSWiopvQaVAFJWDnRcCpbatEceilDSP4alW4F5c0h58bU/z2p7E3rv+Inf08aXp5MGEZtWn/Xv0+kq9RCnX7BvPrA0zlu2ukm1itFXlNbfTJJQJqO56MkaiH8hIZ1E2MzBi5H7KJI1m0HbFMwaoQCwnacbnfb9YSWm5iey79w2/Vxe8+ZPQH3yJ843u4S2P0h4/O6kW9qGmDzl+OEG2vXmFxOSEoJCX4b7+DyjPM5z710tvoN5WkmLdex2xtXbyMtR2Z3c/nqFvX0Q6GzxymTWVHsJNGj7p0IhWlwYi6vk/kRcCUkuKrh5KWk1RrFKSNXZ+duGiYrfkH1//1xz6nn1R7eXsWPEmasLa2jvMNk9MJRVngg2c6P+mlCaRprbEmoW4k0qSUuLvXsaS+ZWS0WxdF8RAjIObMd0TpApD3aa0Nxqg4ISpQCt/V9/dvdtWt99NuoQyU7zvMvsdc1aihxlhN0C1DFYmyJAKAlJFIE7GklpIuzdUVD6bx7/4zmCqiC47weQA1DlAGmu953NHqkfVBkfRhmxJtr0OW5TRNIwUBvs8ho7fuy7V64mnmAbOmUNuaMFfoTeAUmgPQ2+DymHbUoBoBOnoo0hauCqhXwLU+hwAWUq2l8rD9TwVc6dHaUk1rOA3c/vRl9s0eYQJ16aLUhdiz4BQmBIo7jWhmAYyhGQZ0EUi2NMoGmjrQNB7nAzoT77iFm/K1O18DozB1wlfe/g2+cPtzXN25zPd+8B63b97gxtYVvvHOt/ng0R32j/b5wpufpWoqfBV48ugxn/3VT/P67df4i699i8OjQyaTU7RSTCan7S2NtQlXrlyjLEuquuyeMZlf1AteDvsxp59v0NS1sOS2VGMBUm31TjbxkXOksaW8HTcDvay+q7zwjHSsqtMq+t+JyriuIZl5ggY7c9TrUhUcNJRr8mxJSbZMAsWWkWhUX1vRweLqIHJtbBf5GO41HfAD4TtBL1JyTmuFPLu/V5ZtI00rQ2W3bnbiKbYMzW/cYvB4F/vO/c4wdrm8jgH+HzGq/6LIRozgmDdfY/rZbco1IwDWCX/GJypWREatrshla8+rGYioYksI9wmEVEW7Dx0BqTwjPhqpm4rOfHaxLX1cjRTVrW3Mzjrqex8IULAWX5YCMi7v0Dw6P5rkF4sOCIavffdj1egoY1Ab6/ACsKWyDKUUx7/7GmXU+gpGRWPiETx88vJgbfXY7z64MIXacqxoyu6lv76+EStOhfenXMBFWYZqLC8m2YnD5QJuR09qvFEsdi3lOp32VnsN2uErxAptbxV27tn7kuX38mN+1hIFbXtp8OS9YzqbMV9MCCFQlAuMTkgSGyvc+hGKaFBq2qiPpNQUogG1DAuupoWWf7dpPLEvaQcPH9+hhQjkIwlcKb1MY3RN946pv4+fRlu+VbnjgDv22EsGdUOBBb2mCFNEi6kFTp6Y4kMI4wpUFj2J+lepVQ1PQTmFNz6Wo0dOeSkmyv4D8AcXHFfsB6111OJaAleAxWJOmqY0jUOp0PHKfpTm6wBR+E89VKhLgFP42uN9FP1MggCYOaJdNQhoY7ADCDpQLhpMpklTIYQbI09b0DG9GETBPQkpeZYyW8wxlzWLMGdeTwmxKlFFjS/nAl55/D0vxP7YbKIYGovPoQhN1OISYIaCKGrPfn2Enh/TBM/GcIt/9/U/Y2e0ExXAPQfHR+wd7fODH7zLvXfvcXp0QnWtItSO2emU3/j1X+OLn/88h4fHPNl/CnjGa2vMpzOyPGexmLG5IYTxxXzB0dFBz/C3vbXkuEX3bPXa9J/F/t8/vy2deZpMNGPswndl1D5R1AMZ4F2msKWnGhu0E9Kqy6AZyN8u0gnqcVsd5GmGYtfikzbVJs9Qk6nOEd5UMZqRa9KThjRVLC5JRKQd9lymcJUcn/KS8rBzj/KtwXAPvPHywOm81pHW25ey+FlbFeUjaKzWNMWnh6Q332Lza09x9x9J5dZPkAbRpsT0W68x+cwl6qGIi4JE91yqIidMdRE1E4GTi+CnBU5BL/ltaLm2ugz4NFa9lYFUiRdhMvM0A4lUtVE4LBy/mZMfp6w/vUSoKrFBieDO7R+cBU7aiFxBXQlIw/Ox0nQxVRmcwz3be2H1YShL9Fuvd8DJLiB9eIwLAXdw+FL7OXe7q5V5SmxwWjCl0jQCtPZ7jV40mDLpwFEyW/a/cnIN6rGOgBem1xLGDyvSifR9PVKdlE1rX9MCKO0C9VB87pJfPfpESBS07aXBU57nDM2A2XzaadA43xBqjzGmE7hcgiAhsBptSZIkTtAtCxKIQCqEEMFQ/KrNGWuRy1ZKI+XWfXClqJs6RrCWKcLn00qKs0Txn8Ykcv6E1Rw6mhOP3dXotxEiNEqiSlYJedoFIXY6oIKQBiHmhSCAyiLfR4K43JxKuFImLjsD/xj8dLlvrU1HKu4fo6RTm+5z4dNIGW5VShrI/zhkHYIARPIATrSw9Iak6MIUkSuoITQBnUgqo8lcF01rKWwuiCWQJtB4j44hdeE7OXKdUpsKtQ02sRybAyjBGAVO44L4xgUXMFbRTPoDH5g1jfdQ1A2+FtBkMt3hfEmdBupFg8pEGuLw8IhC1fyb7/8Rr15+ldu7tzm4f0iiEwbJgCuXrvCpV95iZ2ubk/kpn/r0p7h2/SofPrrDO9/+gPe+/66cJ3J9RsMRN2/eZHI65fDokPl88kLg2r5gLKNRfZC1+oLy89u8UR0o0A2YQspcW2NdiSQZqfKJauNt1RARVKgApqD7XiJZMolnJ64zMq2HGpfBcH9JUAZIpo56bPBRvwalSE8aim1LduziG7sApaSU67jYsVE7SuwsYAmczo0+qbOfd7pG56TtdCNjSFutZ0q6Mv9qvPQTq0eavd+5Rjq9wsbXHtPcubfc0EdJGnzMFpzHfOYtZm9s4RIBnoSldhMIOdwWAqQknSMcJ0mdLtNBPhEFeZdHKx2j0Cp0SuzVRvQbrAVQedtGsVgaNtfC2yFEay2lhbvZNOLh1uMHKd1TH/9R+qR9djv5g14F3zk8pObyehe9zCYO/+E9Pk5T1qKHQ/yieIFIanImCtX/XVmLfvUWzCrSiWW6nsVorBYgvhC+0uKS6QA7CM/s9LUUU0pENpnTpbldZPXYUq5vPdCRtwd//80/+Vjn95NuL08Ynx5z/doNjIlkcQDEu66unudbgExq4+EYlESRvPcYk5JlUJYVxlhJqwV660euUwRUy4mj7f0Y1ZJkoFwP1Sc490FUYBmB+mlWGl0ANlyg2ZOqL2UVek1kDciCpPGSyMtpZLBEAXUERT5ItV0U1VQaQgPqVBO2RDrXPwny77jdofRl21/tZyD8JmN0l54DcM6t2H98dFMRAL/wvGmffw02wKYchjLgJ0L+Vh7UEOxAExpoCi9H2koLxMFLW2gaDxps0KigqCtHU3ma1OFCg6oNIXMU3oMTuYphOmZezyibSsDc7CzARIMbeGZF1Uk8hIB4ECIRwhBvp+ACqoR1v8HuzmXMSHPn2Yc8PHrAhw9u8tqVV9gabaGM4vXXXqNoCvZmh4To2fcX3/0mAzvgaO8YaxO2Ny/x9OEznJfU+GAwYD5bMJtOzrxYXNy3obsWRluxL/pF2u5My44bXJZ03mK6EZCUTmpMZajWDclC+Bm28pTrUmlXjRTBgppLhZzLoTCmSx+1+jXNQMdJXKKUtpA3ZkDuNx9EV8rJpCLpOZn8W+NgG8u920hUMGIqrF1MPa2m3lgBSm2qo3/JL8LOcb0WOKkgqccWdLQRLlNGgJVKOst/5Tqb1uDv3L/QWPalW7/MH9Bra7jPv87RW0Pswku0L1FoJ1EjAbGgImneVKE7V5F0CLTp2CaXvg8m+qQFiUi5XHfipaYKMSXkRFso6hLVPcuWlvMWBhktOfzM+0kbYVL6Y9mZnNvOI9G321/tuixjdi3r/naphpckUitjRDzZWkJVvZDg/yKNKJVloDXqdIa7NiaZeeoo8GpKyA+kmMKUgWbYq2yM3LKghUNo6oApgqSku5RdnAu9AOdyU/F3177FJ0GioG0vDZ6axnN8dIoy/UiToq4bsmxAUc7PcGOsSVBKU1YlShkpJfcVo+GYPMuo6yM2NtaZTCaUVUEXXkDuG2ushEY7fSZ6P5dv1CGusNSwuegGWoKvn+mE4iAcg/01JWKXc4kehbUosNDnWgaWkSeDpKpqkTtQJdiZpsFLWukU3D1PmEmkqa/p09rctCApz3OGwyFaKxaLkqJYnK0oObetpljls6WC9Yv71Kxr1KYijALaRN6RAjWGoAMqBZUq3EKiT8pIdI4M8eULUilnlOqibg1S2diUHpUqgvEkWcLVrWvszZ7QBIclQaWBMAk03uGjr6CfhzNioSrTQtRXxGhCfAGIEaHOdDgBHQwqgM8CT46f0JxWuMKjjKYcVlRVxc7GDjcvX2dtc4358ZyH+4+4t/8h88Mp17Zusnl1g7c/8zp7H+yz93SPxXxB8I7hMOP2KzeE8P5swHw+Y3nfvrh57/Bdld3LrfPz0oKRiGY6k2hS0JKqA9upF7tUR/84TbKIqt4N0XdyqRHlrUzIbYVea0XhMtGjsQsBTiGCKN0ICICoPJ6oSCqPk0u0rgixgkw3MpnUY91FPPGgo4Cmt3Q8EVim3zrrkcBStuQC8PSc513kDdVjLYAx6kwpL5E6lynsIipAv7lD+sE9EbxNc0LTCHB4GVmDlUhVp+2kDf4zr7K4mmMXvjNwFjJ+VJiuYwozXgNTC2fJlkJObq08gtHYeP1cpmNUykt0ycjvrSG0txKRTOYe3eguGokEm5ZWI9P5+efWnUufD3ZxtOhF7TwrHEkDPg9u9HAoETEE4I0eLM7XdLrgGIIPhKruTJa7CsqPlWbU+I0hfmdMtWnlJSGCIpdBM5QUnvI6RmTbfdElhLyVVJ3LFOnUU1zSHbAXWyOFT2D+RsWrnxCJgrZ9DHsWR1UXLCbTM/3rvcM5hzXpmYqfEMAH4bUkiRECuU1w3mGM6dTJz6Yklumkuq4IvZSbikbAZ1MR/WhKG2G6CBy14KkFYz+pieWifdOlGe2rBrOrcNNAqAKuiODGKxgLqCCPh6rlu6BCZ3IpOFNRP/Y0qoFJQB1rmCvyPMdoQ1M3OCemsGKGnEbAK+CqqmqM0ZRlEfW52mN/watqdy7LZc5GtS5oSioQ1ZEXHtdDhRqKnpXaUvhSeFXUCpqAq0WbCafQQ4k8+UJAjzJK5B58iCm4WPihwWUObxwPeSDmwk5hMqhqh04NrnCSGm2QPlegE43CoreQ2cmDapSAsUZAqRD1AziFKkDlAVVpJrMTyMCi8QQ8nsNZRVVWVE3FrJyR5xl7R/s8efKE2fyUfJRhR5p33n2Xg0eH+OPAld0rVPMal6SsrY95861XOT2d8hn9ad595z2quqIsFh8JUNvrFHr33C+aNJeJplN60uCNlYnZtZpMSWfY297e5VhSOtnESwVXtItooztdiifqMnlLB5B04xkcOMoN0XgKWqru7EIUzJWX7ZRjEyeP0IkA6lqOoRV01DVLaxnk2ATMxD97w53ieVDUb8+l8Fbeh4pNiX6hl8u2qb38yHdgZfj9JzTeRdFbh0pT8EGAUM9/7dzWG+/tlcuQZ1A3hM015rs5zUB1NiumkmvkUkkh+pEWKQe9BE7J3HdaXMEIwGr1geQcomWIkRejZBZBVNKCCrmOi0uWYGIRQdslOnKopjEM3WoNbm0QZnP05gZu/0D6wJho0BtBSGvX8jGiUef6zl2UThsOIEB2Ghg9Lkk+fNqZAPcNgJVN5PgSi84ykYqZzVGJFdPheMxKK6GyXZSGXbXj0QbqWkRkdzPKdU0yD0t9MgXFJS0RPSVFGS6V50Q1cm1cQgeqdBOtdeL7K0qiU42VF5DPvfnwEyNR0LaPZc+yKOYr+j8CQuq6YjhcwwfXCfkJiRyUshiTCEk/AiabJKRpgneN+N2dATySjBNPtPZzjVYm6jWtAqd+dIWVbZ0Xser//Gk1RZbmJKlFG5g/XOAH8kCGk4A3Hr2pcGsKNVUwAO1jNVX7ZqAgVKCmQBVoTp2E3Z+B0ZYsycg2c9IkwTnHPMzRxtA0TScsKpVzMJ02Uq7fRlV6x3m2nddPP0LfBWhmHrUv5+WP47O4AHNZE2zUoXIBNRTwEqoIkGaSOsNE8FnL78YqVKNwSgYqrFTkmUbRGIfxGl1r0iyjPGmYuimq0ag64GtPmIhFS3Y1IZxY1E6DCwFLwkCNsFrTDByT6QRdaVQOSZYwSIbM9ZSyKmWw9UhFYB5i1NBRhDmnpyfivXjqeXb0lLIo0YOAtzUPDu9SHzrqA0emBwwGA/S2xRjFYDDi5HhCXdUEFDu7u+zv7ZEkCdPp5KVSpGev5y+iT0AHjoIRLpNuJB1QjyzpxOEyTT3US3XvENNnaeRh6F5kR0mVnRDBQwRmHp9IX9cjLZGQhAi4ZL8ulwnBW0W13gIrUGUQErtRmNJTbiQ0ufB4kkI0plS9NBQGznrVGc6k8dpL/sIUXjj7kXZyTlUb8QHwEaREv7J6qMUC5dY2SVHCopDKK+dwVXWWSHxRUwplE8zOJbCW6tY2zchSj3VnhRMU8oKTgqkkWpQsPCra0wihW+HtUvZBrkuM6lWiidWS+JO574QaRVpCot8uF4DaGva2EgUtAGj70qWK0996lbV/WeGnM1Sa4k+n+NNJD6TIVNoHQME54RQV5ceP6qz0WT+9CeAub6FdYPNbQg73p5PlsvGagET2VCq8JT+fL733vInzK+g0wVd1B7TO1d4CzoiBaoW+sku9lrC4JCT7ahRfMNqiJiUgX9eSfm45aeKBB8qrzpjZpeAikG2Bu0h9BBY7mn9445MjUdC2lwZP1mZ472hcG12KD3EkqzrXMBpuUNclZVn0yqo9VV0KcTxPqcqSoqgAIxGBrky+15Ro8bQyl9AKaC4VlM8s3Ev5nQVK7XGeFzX5aU0qCoVmMMjZvbrJpZ1NDvaOufudh8IDC16uwjSgrgGlpKu8DagdhdVaqtGKgN8L+IdByOW1QhvNeDBma2uTjc01yrJiNp0znxcS5XNRyb12XdSpPXf/Ug/yeam6H7EFcJOAuaHRV5RwiOaivB48+MKjM0kt+raKDggLCEYq7HQq0gpN4yXtB5LSMxIiVx58FUTKQYHGcro/w5YGhWboR8zDjKCjH94ahKlBbzlc5sArrLdc3tpluD7goN6nOJ2j5gYzNcybOWFdwJmKQoXKIFy0E4O1oBKFaSypTsiTlCfzJ4zGI5pZg5t56tLRuBpVG3Y2d/jMpz6Nq+Fw/4hr16+SJAlFUbGxsY42Gtc0PHxwn/WNDZEsqISw2VatBu97V6Z/jdqI7c+vJUu/mToSiL3wX3Qd8AkxEiTAoB4p6qDOGM0mi9D54fXFLlt2QD2MlXqp7irdgoJyQ1IVKgjIahXLk6mnXNMdqDG1RJu8jdIGm7abwH0CLoIkn4jEgvKIvlPctmqe50OdeWxbuZOVoHJnk6GWBOl+lCuoWL2r5TuXKjbfnZM8OqJ8dYejv/G6CFImiuywZvDDp/i9feHADAfQNLjTKX2lcj0eo25epdkastjOCAaaXKoOh48K6o2EYtNIJV2sOlQ+YKOwaHfcDSjV4zVpqZYTCYOo4xR9Bgkx5VhIWrXJFclc0rj1UEVgKP3Tj9q1/aljmrTY0IxvXSF8512ap3vgHebyjnCGmgaShFCtpM1CQKUJLBb8pVoE8n1x0uLqkLU7Bc3WkOTePi6KYiqbnFEWD9EPU7z4fPy+OhPRCk2DThNUnuFnC1Sa4xdFj7geo/SDQeenh9KE8ZD8yZzZ1XW579tU8cqUrjwdPzCZiQJ5Pdadar8sJMvlxx5TeIpLRpThq8D8quL38meI+OEnp30MzlOJ6oDO8ikMQZFE1G1jpVZIxXJFKn8CdV2iEkVRFhCUvIVrg3NNpxK9bDLYN111XpsWCpwFSKvAiJXv+sepVn7/6U4mASiKgqIo8S5wfHTK9RtXyPOMu3ceUBQFfg9srnDPQlQDD+iniqoJ6OtCrA6HxC7QWGvYurTFxsYaaZpQVTXTyZyqrLBW/Py0TkVY9C9LZAR+HP0W6oA6AbUJzX1PKMS3kELScSoTrhM10Cgym1BFRW29Bn4RaFyIz/LS2iYsAmqMVAYrMF5hvKX2lfCkdMbOcBc9TThODjg42CcU8c19HPCbDWohti9ro3XGmyOOJkeoiUZvaCpXoZoMPdPUBxVhHGAUYbFXhAw4UGTrGWgx9b28uQtDz+JoRrUQa5XtbIdXX3mFB/cfcTo5hQTqqqEqGubzBXc+vEdiE5492eP2q7d48L13ODo6pGkajo4OCT25AumDsAKOhO+ktO7U9n8BnKS1AEXSOvJZNdLkRwJq+kKRtVbkx54Q1fyHzxoWO4nIBkTJgFbML2iFPW0oLklVXEswtsUyihEU1CNDNVYigxCjKsL5kHHJlMLnKdfj27hv97H0+OpK6GNry+lfpPnUTmSKJeCC5Trdul30Ro6r9e8D4nkFfCrClc3QMHxSo51ncisTE91fus74O5ry1R3qNYvLFOvfPgCj4cET1MY60y9d73SZghbgaguJui2uZGK6bJfHoBvxR8sPmqhCrTqA6q0iO3URQAWCbXlpdArtTS5k+7Z5K0DQG1hcEjuvDjDplf5o+09L5VeTKYrra+Tv5/j5HLO1RRjmHUfJT1a8r2K0yM8icPq4z+EZ0rgC79CjkUgUbKwzv2wZGMXw/gT35FkXHeo4U9p0aVRlYwmb0kuiuG9vKi9pPWvxUwFgZ4BTezhaSQSt3U9doWYL/PpWFz167hSCFFlIOlr6sr3usOzn0Opmx2m+Huku8toMFOGLE7bMJws4wcfReQoifiWtnUjjG5r3bG2tk2UZsykdn6mqliJdwmOSSEiXjcNj2nTMSjRJ9nBe5Gg1itSPMJ0XgeqOoLdMH0ydKZ3oLfvjbMLpqauah/cfMz2d8+Zbr7N75RLeBz784C5NU9PcObtfP4nHclehMORpRpIkZFlGnmdcvrqN0oqjg1PqqkFrSXcq76nrplNkf759FBDqv6b++PoiOIk+qSee5pEjfcOI2rhRwlHKJYoZarn2VVWLOnimcBOPrwOtfmoIgIXQCICgBq9aAVaFKwI6SJrU6oTK1Fx77RKfu/42/81/+f+VbbiAul2j8ug3aEWY8s7pXa7aq3z1936bf/HOv+C+v0d1XBC2QJUahgFqjQ0WlSgSl5BuZoyHY9RIUbiCvdke1aykqAoCjmQtgTTw9Nk+YRCwmxYOFJPJjOODU4rFgsFwgLWGg4ND7t69y9HxIXk2IE0zFsWc/r0fEHCklen5EC4rUJXSIsdQ/5ScoD/hrR7G1MK6icBALCTa6qxqTUtaKE7e3gqfptgylJsWn8hkLuuEZfouWSpcB6PwCHAKmkgcj+rJ8ZFqo0jQAi0poTcEslNHuW5wkRDetg70SFFX91n/50XtTHqvt7xyElUJmk5jpy9Q2AeYreZVMzSE13YoNwzr+wXm/Ydka29SXDJUY0Nx6QYdCbgOnHxxG1MF8o0B1aW0uwZi1htYu1OgXGDy2mCZLusBN9MC3agi7lOFrmLESYELumeWHK9B1O8KWgnQy4VT1p5HNpFqujbS0YK1VWDZ7z+XCghbbBsGwyGqaWBnC/ZWdJ76/W4MejwS8LS6zGoa7jxSdwjik9fyqepKUnHGEG5cxqcSXeOHH0ZdqbOptZbfRAgdkVxnmZD7fUCPhlDXBOfRo4GYCx8exePRZ4+rO96VtGzjcAN75j5r+87OA8OnNYvLUuGaTTz1cKn4nsw8KtpmtABWN1I1aeqAXSxfcv6j1797bh//rNvH4DxpQid1Te8n8Y04kKYZEz/DOYfCYHTWcZ8a15DYlKYJXRQy4MVyQ+slEm632f22Oon3gU/793nRpxe1fuRKr3z+k5A0CNjEUJY1zokf383bVxkMc5Ik6VJQy2NYNmMM6+vrZFkadUYUeZ6xsbmO91CXNQRJdznnWMwXVFVJ09NvOu94lvtaBag/2UhFfewwSqMHGjJRDNdWyTPfSHWbVE+Cu+/R24rGenzjRbKghhBJ26F9C4raLSqVSSo46SeDhRrSQcbp4phnd5/wnUffEtkHQF0CM1K4CgFlFnRtuL55ne2NbaaLOZtqi/vNAxg3WGVxpw41U+T5gFE+pvYNVhs2dtcp5gWLk4JBNuBwekitSnzlMLXBjAwEODjep0gXhDqQzAaoxR7FoqAoF6xvrmOtZXK6z+GxeGo1TSOVp2eu1/IarSr7g/hB6gEkNy3Nuz/Ry/lXpqVTJxyYMrDYkbyXLQLBRgK3p9NRUkiqx9RC4m51lrwV77DkuGD62hi7kJxYM9Sx9DosrT1WwMiZ1FocYlSUIBCwIZO98kG020CGph7wWU2/9avs4PwI1HnyBkA00ZVzTieSlnQZIpMSjzv0LFDSiZgql1tCrDYfPsGfnGIXDjCxmEUq82wpvxOgGWhmN3M5tqidZAuxoGnGCfVIC5iJ55MsZHJIJ9JJzUBRbpju+F0isgVtyg4iyVsJ0dwnAW+F35TMPbMrpuM0BSeFNx2pOb6ItRydbipoPyNGnXK5nski4G9eRt9pUFVNeBGBWWnYuYRWx7ijvqhkFJ3sv9RcNE4HH4ncMbrVKoYn0h/5XtXz2JNt6NFI/qxq4cv2COC+J3RJXeOLApVlhKrGL07pSPHBLwnirQzDGYAm/e0vrTG9mUrfzENMg0rfpTNPsFKkQYiWRbXqvO6UU5gyVngOl7+31aLrdwrs9+4y++pb/GeX/h1StfPJah8DPIWV33vgKf5XFAXWJBRFSdM4tLa99IHCe4kSqOAjEAsCvBRcDIL6k3r/Zu0fw3lgavWz/u+roOE8UPhxQMRFkRwBZ1prsjylWJQsFhVXrlxGKc3R4SnHR1NCO0qurq1ECsAYTVVWlGUV1dQ109NZJNvDYrHg6PCIsihfQtTyvKjSReDtxw+klFWoJKDXNKxJbNHUhlCL8GUog5RZT4ERMAa/8KK83oAOGlc7kRUwEgkKJqLxGhlsdCAkjlBrsnFGo2uKosCFBrRGDzVOOcy62L8QJytVaMyGYnO8wZ2Hdzl4ekSaZgwYMq8D3jjUQDMyY964+SaLxYKKCo9jPB4RAtzYvolvAsfNEQ/276KDIR/kpCrj5MmE4qDEpaJeXpzUzKu5DBYbmzS1Y+7mzBdTWpkv7x11l657yethIL1iWbz7i6hTv7WEY1MJ0Vi50PFkdK6wLKMfrf1HOpOS93QmFi3lpsXlQ7wRry1vFOmpSA4oo7pqvHY7neBiPyWkOGPq2wKuegCmXk7qZwjfK+873ZDZfux7X1/w7riajgJJq5hSNt6W/YuUQyzTj0BCtHqEVG/LAE2DuXKZSolAqMt0BCmiYWUbIQkLCb4t+Q8dIXyxk3SRO1sEkkXALGQucJmOZe4O7RREANvkUn3YGiQrJ/6U3rbaXbLv4V6DSzWLSyaqVkftqpROOLOteFQxnUuMdjXRjLmrbAaGeyLYq0LAjVNUWRKeLs63P+lXqx2d4I6Pz37WAq4XRZ3aZsy51YvmcIpdrJG+95imH8VSSironJf0nDEC0roKQFlOKdUBqVA3EtFq99GTXlDR47QPuvQg8qGcQ+8dk042RHB0rLtqVO3E6kjXTjS2lIi9iinzUoi1Bee6WaaoB0eO4cMFfP37uKZhet3w+fSjgiE/m/bS4OmsGGK/CaiRtFzJfLGgqoVrEgIYnVC7Ok4EPpbMi/+IigNJXZc8B/3PBWvtz35ntuu9TOTkvIuwClxaMPXjAg4BozVaKWbTBWvr67z+5m2SJOFbX/8BJ8cTwrl2B5H71ThOjifi8+YaAoGyLJhOMwiwKBYrZPAXt8RmUcLgIsmGVeD5Mu1FgOvsPnwRCE8gUQHttKTz24o5B3pdSeIpD7BAlNbrZWQoENAbmlCJHpQZGYlYaS+K9sFQ+1omMgOucjSlw3iDSxqp1NtS1I9AjaU6xFiFnwZGesyVtWtM5jNev/UaeMUoGXF8ekSySDlxhygP65tr3Lx2g7XBmFk14+nBM54+e8b6aJ3Pv/E5rLG8+/g9SKVCdbaYcjI/wdUNzjp8gRg614GmqTve0nwuVXpKKTY2LnF0uI/zLqavzwO9K9dPQbJjSHckslfc+QV4altQKhJQkTFnqEmnIkOQnYrpbHYq/CdbCGDyRhSOlSOqXbdaQsKRqkaiBxWsgASQ9JF2ApjSacAZSWEFqzrQ04pOppPQaUW1MgcteRnORopWK8CU74Ehs/L9OW21Cq8l+CZTIazXsVKqHglA6S/b6j1VI4lGjB4WkCacfOXWmX2I6GR7fIEkFr60qu0hFnUELWDR1IH8yIkIaQPJpKbeSIRfZeWatTpLwSytO+qh6qoW20iHnXtUoqhtrHTMJKonEcClblX7GHUpOi98uGBidWNP5qDVlyrXheCfzD2Lyylrb72CerQPrVVLv0XwEeoKd3gkAMTaJY8o+Od94/rpMei22VUvrsgH+P1DRo93hLgeAq1eizLiJKFSyQv7RSGCmMGjbEZwXmgzfdAX/Bm3HZ3nIkhclvKvf5jGSNTeB6ki3N6kGUi/KUkYkB9JZWM1ViQzidy2UcJ6qDr5CVOFDjyJl6IUXoy/f4h//05XtXj4qw2ZSvgktpc3Bm5Zhb0BXCvxRlNKs5hVpGl6pnouBE/jAiqCmxB1nbRWJElGVS0Y5KOIig113YkY9f61rR8Zav+1YCu+qnXAB14UKTs7+axut7/Pl0nhfTTQsIlFm1agMuPTn3uDqqo5PjqlrlfV2VePMUTe0nIZ5/wFXKbztnH2+EKInDR30bm9bORtFcRe1MfnrJko3J6M/slbkr5TKMJAPO5CJaKZ7S2gMonC0UBIQsTKQdKdJjAII+ZHCxhKJZ5KhHOnvEbPDSqH9cEOe4unhMZjjWFwW6EyOrIsBtRM81tf+g3+4I/+B8zYsL9/SLPVoDNF4efkNmNgRvjg2ZvucfPKTcZujXwwpDYNgyTHuYZskGEzy+baOoezAwq/EJmIQSDJNc3M4/aQKj0ghEBVlaRpRlPXJGmCd+4FnnXn/60zxeBNg841rvzxRw3/Krf82YLpqyNmlyPnYi4EbSFvK+qR7oxk7SKWSZdLBeu2mqtc11AJ2MiPBXyleFxCl5ryyNu0pCeQ9EkvkkEs53bxjdoWIXrhxfTeBdmglvcUTHzaPsYlPuNn130oP5qB6lS4UUvQ0ZWNx3UFwATsD++j8rwDHgCLHd2la4BYPbgcD5J5iOKiUlHlE1H2DlpRjw124ak2U5qhEPIF0Mj+Wu0nS+j6sbXzaE2ZAUIK2akTcv5Io+sY1WuFuP0yXWkL3/Wpt4rGiu6QqeVnUsl5iSCngC+XmJjq1TSfvomd7OK/9cPnyNVLMKTBh+cr1/rLxFSZMhJBEimBKDNgDGIHo4Vu1AKzoiR/7xlNX2w0giQ9GBGKUrhMaSKpuZ7u03ncqjNNa6jOphnbY/SLhZgWpykklsmnNmSVWjS5WsFR5SNSCGLPIpWTdGr27b2YzMW/sV1n47057p33u2My6+v8z371z/iktpcGT1qJEqEPnSkKoBgONkgTMZd13pGmGWVbvomUlocAeZbTuAbnGtIkEXKzUjjnSCIR+uRkztno02pkpP17NRLVe8LPFcsMK+uttj4QO287f7k2GOTkgxxQ3Lh5jeFwyNPH97A2AV5GC7oP9lY/f9Haz59r4z6e/cpF2wGNHWvsZUV134kB8AtBYK8ZUGugL0lEU88VulZ462l8IJSBMI9vgI2k50KQEL0yChrVFSWEBmpVQe4Z+REzMydVCaqRaOjtG68wW0x5On9C3TTiczcENdSgRfPJWM3IjnjljVf4zS//Jr/yhS/zvTs/5Os//CajrQHGap6+/xQfPLPpnFQb3rv/Lre3b/H6rTf4wQ9/yMHkgDCBjf/bNr/HF7j/v3nAyf4EVSl8JRpQysjEbQ24yp/pmtls2tke2WApq/7b6cvdg3ZTYdeNiI7+KJf53+M2vzmMnBxRLFZBJkaXcWaSV4GOkNzqHWknhPJ04skmnsWWjBUtL6YaS9Ve0EIO7z8uyVyiFy5RHS+kBewulW0Um60auSdUiipGRFdbiBkaaKvKIpCI0ZyLqp7ObGMFmLUWJZI/p+M8tWktbyU6ZWcOdmzn0+d3NqKIp6TEWnHJcl13VXpCtI6RraGK0Rw5TjH4tZjSYwsvYGigcUkElVEXqI3otWKd2YmYJQv5XKHLdvINvShSPEYbwZ+nk5/oiOKJIj9oGNw5Zv7mFt6IQKi3Ik+hqyV4a7lW0i8BczDBPGkIk+kyXQbdpK9sgh4Igc5NZ8+Dq96ycqCOgInFML7bXmgaiVqV9ZnlQ13R3L0v+8qyKJdQixTEeCw3yWIhY6a1qMGg4zitNpVlXSRMj0borU1CnuLev3Mu0ArOQVWhXrtFkytGT2qaoSGdBJTX8Z5sVemj5c1MrpkpPC7VBKtoBhrlAuWmiNHaRcA+Pjoj9Nl87jX+V5f+L3ySLFn67QWMt7NtbbjJIBv1xgUtPCbncN5HSxDNYiF8p26p1hjYe9I0Q2uFsYa6rkmSnLIqeo7xfZBwNsp1PpA57/MYRsCu/Gu31//XttXf+wDqpbvoghaYzxY8e3LAaDTitTdvR+8+hfcNw1HOaDwSEKF1Z6Hy/HH1z3f1+4+TZgvn/DtvmYua9E26Y0iuKerHDl+7lXVePNnrRImfXaHQzzRmKvY9zgmgaCvsdK4gIRLB4zWZiWCgQl7QQhmoqxqDpagKUIHUZKQ6JbMpj08eEqaKX7n9q1hrJZjlAr5yeOel4i7AK1df4VNvvsVpecy1q1fZ2tzkW+9/k3/ztX/Lt//i22zodZKQMs5G3Np6letrNyGAKxt2hzvUZc0rN17ht/kK4JnN56ghmEQTGi/cNQx4RUjAjjTatIxiuX7OiR7XfD4jBI85QxR/cVMW8msWFRTWaE7/rPzIdX6e2uKSpOFGTwRVulRAUZtyaJtp+betjtNAUkfaQbmpO+kAMTzVAhqCEKO7yE78qfyyyk82GvkeTjhPbQqrBVOt1lSrDXUmStRuIqacQm94bLlTIb5HvWxEqn3z7wO1Vm6hjYApJ6nI2fUU7QLje3NwHjUvhcOUqpiik34wMVrXesfZRTRirpbbd4mcY3rSRL0lLZykGIGwRVj61sVtifq0otg0Ii1hBPC1AE75aLw80sx3TVdFp4JY8ujIfUonXlTSn9QM/uIufpxRrpvuOIKKqcHWZ03RmQcHHZXft9cQ6wwx1jUb6yIH0JMLcNMZ7vSUs0Z4SApOqef/DmLv1DbxnxMhS51l6LW15y+gUktOlNL42Rz3dK+zy2l960JVQZJ0+1PWopJ0+Xsr8BnTkOHp/lLe4LxIldLw8An5sYtK+3Lts2NHduw6XbT2/jEL4cRV65Z6TW42l0rEs41cDvZq3KOny904x+PfGX3iLFn67aWRwbyYUVZzjDH0U2plXaIIAp6IXme9CaFpGrwD1zgWiwWNq6N4oyNNLMPBkBA88/mCs5PEeRN8/2/f+9lLZnfLKQRErf7TvX9mZZ3V/bXLrYKttl30Od3nSmmSJOHS9gYbm2Me3nvMdDLn6tUd3njrVbI8R6HRWgCE92KnMhgMWV9fWwFTy+1+NAB60YT7ovVW1105R6XIrlmy1xXVkwa3OJ+v9aL9u5mn3vdUew3ltMINHT7xneSADCYCjtQovm0rwIF3UnnXBA91QNWatWyNpLY03omVi/YM8yFVU1FPa7Y2N/lrv/e7DPIB1AFf+QhgAQOu8TRFw0l9yv/7D/8/fPjoA167fYvr45t8+Y0v87u/9zt85Td+g2beRFL3jFdu3+bNV9/gs596m6/80q+Dh9pV/Ou/92/5Q/6Iw//rIfee3eXgdE+Uxn3AuUZeNp4FkV64gKsWQiBNU4aD/sDxouupGFxLxBtw6s949v2iSRs/ajpzXruQwd2Uoavqaltf7K8ZiOaSdkAQgDDYq6JSMh3R20aFcDsX8N/KEbSEb28V6VREOVsCrct6kZEY5ZIDoNuGKQVkiZL5ygm1UacYCRIuydltqR6QWgViAuAE/PQ1riBWPTV0oMQWgexEJsXp7SFHf/tTNNtjxvcXon010JjSk858F60qN/S5Kcgmk6hD0FBs247rReBMmlQ3y2gPYZkeDCZGDFMhgLepT4m8qS4t1wKvZB46UKljGnLzh1PSP/oui19+haPPrkdV7CDEeSWRxHooYK4a6a5ary00QCmaW7uoNYmIuOOTpa9cy09qQdMK+FBaPf+30gJ+Wg5VazIcvNioaE04T2izTfshYKuVNJC0naibt5V9fjJB57ks4xyhrkQYM/4NEHzAHx2LjEH/GK09Q3DXowFqY516ZHADHYnggXLDUK8ZAU5KhEWVDzRj0ym56zqQTJvOxLm9X/P3n521olGa9Hf2P3GWLP328iKZriKxKcakpEnSSQ4M8yE+BHzddK8/iU1omoZWp8lai9YpZTWPKvUiazAvZhhjI2BopZrPm9T7I0u/rQKm1ehHfxJf3UYbWXIrn60u20/hnXcML25KKbSFtfUxR4cnPHn8jFdfv4m1litXtzk9PeXkeEL5SIQUjbFkWSopotmsF5XrH9fLtNVzuuj4XzwpK9N7Axsqkmsauwv1Y4frJp5V0HteRKwXdm513AbgTj110UhkNq7q53ItVSITjS8ktYlGQttGCV42gJHUm80SdFMRnKegwNaW8WCNdJTyP/+7/1P+q3/2/2Q+m0cLleilFwKqUdg6Zb47Z3Y64wc//CE7yQ7b2zuUuuTe6R020k2aZ47tfIdxtkbtKsqq4E/e+1PC/8GzxTa/9A+/yL3ZfbJByv+af8B/yX/F931DcJEYqaQ8tzV/xfT75fn+m8/n51yZc1K0SpHuaJJdEf0zmUJ/8qp6f+bN5brjVkg0QdJk8x17pgpNeBkRvMfqoWosI3w2Cajao5wAK6nYE7DRmv2aOnTgH+h+ttVE7eXu85eATvNJeQFWrYwB0EWqfHvP9ErtXSRC++gTBmeBVqfv1JuDOu2nSKQWTSQ6LaomUzFNJiu3dicyxAYGew3VpZThn3yA2nkjgj2FnUokoslVBBp9BfblPgZ7zXKSTdtz1NHPLkSvsyCGya2Sf0DkD5TqhC5RYOYikGmKgF00VOMMWwoBP596kkks00816WlNeveA5t5D1Ou3mdxMBLC1lYo+dEbCOkakgwFV0dm+NLli8tqIZOpR33wHs9tTGZc98SJj4L6dS2eovOon551EhoIn1A16cwO/d3DOXc2SKB7BW2curDShqs8qiYdAV3UXnPChnMdc2sIdHIJ3+Ai8+ibDZzz3lCYUJSQFpvKUayaqwtMBVNFQW/LeXKY6OySXKcrNpLsfANa/f0Jz/9GZ07LXrvC/f/ufn3/On5D2MartEtJkzCDPY3puQZIkjMbi/F5WYrmilMH7Ko4ZRsCD1vjQSFrKa+GYjEeUhwuKYo5W/QHsrI3I2Qmj/3s/etICHHrfry7TTw2upubcyrZW93Ne+uyFvdX95r1jc2uNuq452D8iSeY8fbLPaDRkMplSzCsOD4+j7UaIaZuzgogXgZCfbFPogWb0pUTukgR0At4EMeatPs5xrIZ+5Yefgpt7TKFhE0zQoueUICa8sYRY2UBoQCVSQRcqoAJier9UJSkpo7Uhk8UE7xw2tQzNiBN/xMPjBzRVg9UWb9tqmIDSivX1Dd6+/Gn2nuzxzt57jLfWSAYpaPCuoZiVJG5OeVLzqVufYvfSLo1z3Lx5jX/25/+Kf8J/z3/Of8rnP/M57v/xQz784C7/3f/uD7j78D7hIWAVNBH8BQincg4Kujfi81pVXVRIcLallzXZqwanPYN1K5Nf89O6R/7qtCYTLhFEkOLECLeNIK1GflT7YhyHhpZzU+6kUehRUnemDtQxkmIKGYPaZdtIhoAR1Zn+epbgqiWL99/C2xRXOvVdZKYZLCcbUxC99CSliFpGjlqSd+gFzNvj6aQPWgBZL73gJBIXeVhtn2gBTtlxg8tkg0ErFrsiM1D//ps0A83oSY1qAvWaWKuYMoi/XxNIFj6mPUMHDstN0V5q03ud9EAESKakUxNXSvrMdcA3EKKcgF1Ek+CFj8clqcX2Og6eViTfv0coK/Aevyg6knXIM1pxUDlG3XGnXNSLc4nqIpMigyDHVK0J32q0s0355hXs3j70CdlKicBlXxup/Q5QSSrq3+065/CmQl1JxCd43HnAqSeKqQcjfClZn9A0SyAWhUIFLLUIUaMHifjzTSagFf7kNC5vlhGs8x4ipSQq5RxhMumU4n0PRag6dM9TOvXL+84LEd9lqnuRsEVg408e0jx4+BzInPzqTf7Ho38MDPiktpcHTyR4H9AqoW7k6U7TnNFwhPeO2XweyeAZic3wVQkojNaMx0Oms0mspgukScJwOMC7TQ4ODiLfp51J+hGk80ATF3zWB0j9z/og6Tzg1d9e/yecv8/VqNRFyy6P8dnTQ5SSCNxiseDuhw/4zOff5vKVHbYubaCN4oeTSWdncz5YO29fq31wURRpdZnV9dXz3ylIX9WYbYmahDio+kaiJvaKpnroVlJE50UGL25uthzoujf9bGk3EhaI7Ur0udJKiY6VAUbL46yrGmcdptKoWrSgvPeYzFA+rvnn//Rfc6JPqItG0jBBFO211uig+fY73+R/+R//AybljHfvvM+7Dz7g5s4N1rc3yeucD568j6sdttBUJyWZzrgU1tnZvMSb/6c3eLd+n/JhRVKmKA13jz+kLioR9AxBdGqMQmkwl8EdxsjZc9Ifq/fzajsbRVSpIr2uhUOWtL5nisN/Nn9hv/88tnq4FK40EZe6VDF60jC7ZjE1NAP53vce8xaEqDpgC4k6edMDMgsfeR9IyX1M8ekmgp9sGbGwpShd9/WEWnPgfrqwPb620ksI1QBCdncZ0bpEJvlWRwraCE18VtuoTgumQF5GIjCsh5rBQUMzlJNsRUKDkj4wVUzHbFkBDWORZnBa9t1kitGzptPLqoditJvMYwRpsJSG8FqRlNJX7XF30g9KJlVdBzFCXjOoqOIetELFlwEVwCvVnUOroeVyqfSzCw9e/tYhkDw5we2fH7HR+0foeotmoEinMXqoVAd6k7l44blUonAC7kIECwIATn/9lhDeVzfeEqvbiJI6Oy6vess9F3kCxLcyPKe19FxTGj+fCykcRHC66fnZ9TSgQlkKQEKMgoNzncaTyqQkUThUF1Rih4CfTFDWotfWRH6iFVRVEh11LaDXrd6T6vwbm03dvYyYKjB6UNA8fHzOOAiPfkezoT+5wAk+BnjK0gEEz2JR4IWYIqRWa0hsgrUpVVVQ+iKm4GQiThJNmhlsqahraJzn5PQEVMBaSz4YkNiMomxvplVQc95EH3i+Gi6cs2y7XB/0KM7ypdp1zktB9ddrb+5VQnc/YuXhOQCjWMyLyAUTxfAH9x9x8/Z11tbGeA/D0YjRaMR0OjnnGPr9cBEYWT2O/mfnbacfXVsFjfIzu2EYvGqXd0jM9SsvXWB3NNltS3nHrezmIgB38aEEFzCZwTRR0qKBMEfI4tHny4wNoQlnwJqO+1LRH8zVQvqw1hAI3Nq8zd/+yv+IX/m1L/Nf/9N/xN43nxKCEyidKUyR0BSOTbXF1qUtmmNH4RaoSvOH3/tD3NwzUAO5NmrCvWd3KbZKfu3zv8LXf/BN7nz7HsVRwZc//0V++5e/wkIXqHuQ1DnKiFgnDloNKyLJVY8Vzd5qzfhqhHG1qeevqEciW0qRoEnWDNXiZeQ1fg5b7F7lIT9yNANNZRXDO8dU69tUa2qpodQCod4j5RK5dqoJDA4c5YYIRoJU5yULT5PqLkrlsjjpZ6p7CweWjvJxP23lH9ARbPtmvLaMGkQ1gKTCRGtK1lk1Be6DMGCpbRSWwAolJOqg6Xz9TBmBo1kuE7QSArtf7qMa6+XfChbbNpoLh25/fb5Rq9dkC0890vhYwdamDNshusmVRJY9nZhmUgWEa+Y7uYAQJSR0Lf0g3oSa7FSqHctNSROu3Znj3r974e3QPHnK1g+uMHl1KN5rERy2PDRbho5P1VhJ26XT0AE+bxTFpiKdKYZZhuuDmzjWq8QSStdFo87ej2oJGs6rxlMas7Eu17RpRFOq30LovO4wRjhRrRJ4t4mlOjmx8i40DT6aCOs8Fz5VVaGHQ9zR0XJ/L2jBOTC6E0/1iYD61rC6bdWaiqlphTdGxFOLeD2Nol5LyNs+6vWHyjJ+9TffeeExfBLax2BjKZIkBQV5OmQ0GGGtBS+AyDtRLkUpnG8INARKymrO0fEhdV3TNBXSm4Hj41OOjyYQNHk+ZjhYY1kZ91ETcH+ib39vkBlqlUjuzvmc3vfngTV/zvfqnH/942mjW+ctJ9EUEb1saFzDk8fPODo6xrmGwSBnc3OLPB+SJBm7uzuMx2s9zayLgNN5kbH+z3aZs8eiPuKy6xyGn7KYTKNLhXW6484rQAXx0Mt3bSydfhGwe4k2BkpwRx6ttESc1kBtgEoUOlW4hcctvIi0KTkGHy+RChAaoBGbF6MSsjTn2tuXWd9d4xvf/Tbf+873SWwiA4oCW6dsjDa4ffkWv/Xbv80HT+9QmJL/6Pf/FpubmyyYojLPL33xC2yvb7OhN3lt6zU2s03+/I/+gumTKX/zt3+fz7/9WW7evMGzkz0+fPwB7957h9PyhHk5Q2stHD8Nwcb+sQjv6cwt/jKAMzzXwyGALzymMdxafxV7MDr3Le4XTQbtNk06u2oo1yV6VF8ey8Td3ks+ilrGdyFTS5osmcvbc7UhUZHsxMe0hBCXm0zSEE0u27ULj8u1eLIlElXRMZKTTj3JLMSqMjrJg7a16T0QUNHnh3ijSKe+i1gR6PSMWtDSetm1E1mfDwV0j6qY3voOsHTRKbU8prb6rAWediHH3SJ5l8j2myhcaSKxu62QE3mC0AEnibL1RDPbSFlMN+pGoj7ZRLSzglkaxZo6kM58R6DPj5xoCc29lMUbAanJ1KO++/75oKTfz9/9EOVhftl2IqimkohJPZDjbVNSrc1ICxRtubxGrSSA2d0FpdDDIatimMom0NNAbMHM8mD6iFfUwf1kgjs+Xpr7rgAwvygkOtVWy7XAqV1O6eV40Fu3q7Yzss/gnCihx+9Wl3+uhYC/dZU6AmlTBXR1FjhBvAfjNdexIq/JBXjauSffE92odptdV7x+m//ttf/h4v1/QtpLR57SJEEhPJFAjEIoxXyxwJoEYxKUcgTvUTrIzYPH+YbZtCb0JlgfO8q5QOM8TdOQJBlKJTF1pViCHjgbRVlNQfVTGf1/q8s0vWX76/TbeRGv1ehMP2rTj4StttXPl39rrbj74X329w743BfeZnvnEk1Tk2UCTm/cvII2hg/fu8v9+/fx5w4Cq+fwooiTvN5prSV+oeSBl+32z0+WTa9a1BDRCtLgbUAFhVNiY2EahXeBau7ER+7c9pKTeIAwDwTrCXugxhq9E++xAjwBNVcCjgYKmgA+EGqFikUhoZEN6SAeW1VdoZXm//cv/ilf/cJvY0zK7/7a7/DHH/wR957eQaO4unON1GaY3KIUTKYz/uJbX+OLt3+Jb37nm9jcMi9nfHj3Q47LIyyGm7du8uDBQ/7uf/g/4a1X3mThCsbJkB/ceZc7j+9ycLLHjc3r3H98j+PiCJ96STG2hTdKIlF+GgjnFM8831av5UqfOqgfw9Vf2WLw365xlYw/4cnL9fvPWWu5Q21URiET48lreVfmv6x8k0ndllKBlcw8duGoxxZvoBoLaa0jcKtIKD502FwiVEEr7Lxhdi0RQcxMZA7Ex0smX1N4fCnHNd8Wg2e0AJH+ROSyZXm/pBH77G86fadWl6pN03XVd3oZ6dJxKGl5SC0p3Fvh97hUUoXaLVXP29amaJyNKbdEsfagphkZynUdifWQyTsx1UA0k6qxOjPEuESWIUhEqTsV31YjKhEdjdfDG+FELbZMZ2RsCtC1ROR0EyBGs1yuGDxe4OcfnbpWV3Yo11RXhdeCSEmFLpXiW38/5X3UtGo7JC5vNGo8gs01ODh8TkFcZAEU1L1xvOUnXdSCJ/iAWVuT5axakrhbYNOm41oSeiSZqyzDz+diKtwSyEGAVpZJkENrSdklPUkCpbp0Y2dGfA7p3ayvc/SZNeHJVfKi0UZIYfmMtc+UqUTryZRLwdFmpFnYIePDy/gP7tBxs4C939zhy1lbEfTJbR9DYRySxERrjxaImKjr1ECgm4y9dwQkPaK06U3+/QleyjOzdIBSFu9CNLeFqCWLTBbnVcO1362CmvMAVvv3eVGa86JI/eX765iVZdU5y73oGJbH753Hu4qjo4offv8DfvevX+bK1V02tzaYTecMhwNQMBoPANXZd1y0vRefqxyz1rpLHRJTQGK5czbNo1NF/ppF+7hPG0niBLTR2EotL8mFL3bPA7IXNfcoEE4dZqTFXNUCtQSZbK7FL2sqmwntIJfSkcdVjOxZYzHK4mxDmRaM1oY8Kh+RNRl5NWDIkCzkrA3XOD44RltNPs25tn6NG69cZ3x/DZV7GARyN+TNy58i2bCU+xVXx1dIlOXq1Wv8+Xtf4xvvfJvZfMa17av4LPDO/vcpy5LTxSnVSUVuchbZHCd1FDI2GfB7AXfgVy5RQG8qwhzCS8kz9e6zEJi6KSdMaaheuNYvmrTWCFaFZUVb+7kXji7JLHTl8QAuM6JntAiY0lNstno1qkuNaSfSB03eVhXZDpC4VFJX3kr4th6IMGxLSh7uO+a7ZsldijIELvKWOr87BU2+jDJ1x97Q2bRIdFjOsdWFaiM73i6J1rb1tLPSD9Wa7ibCVs8KopCmEUHRaqxihaGk/pqRITuq8TYlKCg3FOW66qJJthAkV41VdzytwGdrsdJGJlpj2GJTlsVLxC+Zi+VHm+IMSgyNay+cqiY3JAuPXQg3zPzg7supdSQy/enIZ5PKQDm+ak0uhC1Cl5LsomrxuLOoGcX1K/B0X/55J5QhbVA2Sgc0dQcMuijRqkzJitddaBoBkROhcugsOyMr0F33qPjtZ3PwAnZCa8tijAAkAKU7mYROddw5OM+fb2X7Z6vtFM3nX8NFYdRW12v8wYTy8pD5ru0kPLyJXo0aynWDqQOjhyX1umVxyeLSQLOzBh9w5oXg4PfKT6wlS7+9NHhaFAu8T8gyi9I6XpeSoiwpiiKSyJ+PhqQ2o6wXECBJU6qqwlqLc6C1oaobFBWJzTDaiOVGp8G0mmo7D5hclM66KLq0ukx/FHpR+ukikLS6zIuOQT231PHxKQ/uPeb6jSsMhzm2DWOqwHQ6j8bKLxnF+agWYvyv2+bz281vJ5iB6gZjrwOqAqUVPsjbCyiUV+hsFbwplFHosZK0m4HmMYTq7Pmz0gt+EvAT8ANPtq4JmxIS1xmS5YVlusvLsciH4o+oPCJBACyYk+qE2hcsThY8OXjCp699hkN3xI1r15m7GbNyxhvXX6cyFZNnM6qy4p/96T/nZHbMe3/0Dik54/GIggV3nzxlsVhgrOLk8JiT2QlHj08Yjgf8+qe/wng84o++9cccLPbxdcOVtWtcf/0av/75X+W//7d/wPeq7+GCAw++8ri5l2rBXtNbCvu6ornvCU9fEC4/r68zja/B/e2a3/mnX+X7vPuS6/+ctQgidBSfbHpcVOUj56dXsNu+VZdrmmTuaQai/ZOdSrRE/LgMyUJSdT5BbEZmjjSmlVwm5fH1yFJsxUq0KkStI4UKuiNLS5WagJjWH64Fdhca/baK4Jbn1cXj8NOarrYU0ZY035LRtQsd4V331MW7yBVCtidIH7URNwFzci6DSoDLYtt0cgetfEKxYZYWL2EJ+toqrJYs74wSOQR6+w/ynW7CMsrnwThIJ55yXXeRs1a3anB/IuKUL3NLnM6AncjZiVEUrcj3SsYflOAC89fWcZmKavSK/MDhci2aXgrcWHH62S2aX77Exntz9Hc/lCq2Fc84WEaGQt+IF+in2ZTuRaR6KTdf1WcAlkrTpfedc73vliAp1L3Ku+DEYs05dJ7jy3IJjLRZ8qOURiVWLFjgjGQBgL16hcNXBp3+l24C4x8c4t79kPzNVyk3d3GtxEa81qYMncJ7MzSdufTowQK7PxGXqiBGxGZrk7//pX/3UtfvZ91eGjytrw84OjlgMLyENZokFZVwYwNZrnELesriAnqU0mijSckIAdIko2lERTkEcFHWyeiEwcCwubnB0dGJRLIwLLlK54EV1VXpKaXjOueRcC9q56VEXia6ExnTz0WdVoHVeeDteRBR1xXf+IvvcnIy4dXXbqK15uR4wunJhOlk+lxk6PnjPQ+g9Y9ZjicEjxjrqAu2GbBrhvQVLanyGBykbmcdCHXUeyGIbtEwSgc42Y/SmvQthb4OZqHQ3zdMmqp3HKv9sPKnATWU5bQBXwf0zOBDE8eREEPOUlFng8VpJ/IGaHIrFkBDRgLcVEGiE/an+4zWc77+wdfI1ICyKjgY7aOnFtd4DiaH7Ax2ePTsAdoYjNLYJmFjuMG9vbsixWFLgoNqv2Y0GrAx2uJPvvfHbK5t4WlYTzcYj0ccT4/5+ve+wf37j/itL3+F2je89+Qdggo0Dxz+6Jyu3wZ/DP7Z6nW8ADQrAZA6V+RXU0gdE3vACedt/BcNIDsVEcvFto2WITF9HQFTq9XUifchViNBIcBHCbcpnUrUISkc+bGk65pc7tlyTSwndB3QpReD2kRj544kmqOK8GNMN9VS0VUPdVeV11aYoSIw6r2Anwui1PPfhf4QEEFI33y4tYrpnz+0qusCXpKZHJtyMsa0it19Qc0ml0lwejMnP2q49M0Z5ZUhsysWn5wFbrpaVv8FGw2IG9UBOp/G0TvEyHJFB0yDkfSfS6N8gQ+dZlfQS+sVUzrCDz986XsizBedplPQAiY3vrGHe+8OavsSJ7//RicAaYmgexjTryMBvMoHilaNHJaApmfYKwBGWPbBhzOgaOmB5wTgrA7NnQfeSmSqLNF5LhpQxydL/zrvQItqeKiWwp1CXi+XQKytCHyuU7zoVtXNko8Um9nZ5uCvvyrRvTYKVwfYP8Ssj5m/eQlv5LpVRnVehi6TZyRZBJJJiPIXFbpsaO7c7/orVBXVW9f5e5v/mE+qJUu/vTR4slaxuT6mbgrqBpJ0HWM0W5ubnE7ECX4+n3UyBiCpnrquMMZGM9qA0SnOOZQOeC+WHCEEGl8xHA4YFDmTySlLYLCaKltWtIXgCV5jE4NONFVd8lFg5fzvLorsqJWf7XKrulDngbAXAbez+yvLgnd+8D4He4e89sZtjo9PufPB3aj181HtxVEpraVsvyv/vyA6phNN/obBZAqvA81AHnYzl4dVeQUuCN8BhY/6K/3TDCFgTy1mK2BPNWUZ4mCwmlY9v6lEqAHBxLB9BnVRCz/IBqmqC6CCxliLdhpVa4pFgR5BwYI8zxiNRszDjNrXaBSFWzA7PqUsS0IGCsXJ0SnBweTJlLDpKI4WjOp15m7CXE1Js5QPn37A5a0r+GlgrNdZnBRs5FvcvHmDx08f88raqxxNjzk8OmJta8Tps1Nu3LjBG194nff/z/d49C/3uPyfXWVWznh89FBMjZ9/B8Bohc00jfYrqdB+n/U7GoIL+AJsMOgMpsz5f/HfvLB/f56bt4piy4gKtg/UQ4NySxf4eqg6WxCXSSqtDkKIrUbL6rZyXTzQWjJ4qzytnEykTa5JK4dLNS7Xop006ClUR/DRpjYgajgFmYjaz8+LJp3Ro2IZubmo9cv5g15ydVqlbKmCAhN5i93xRDJ6J5SpInG+CmdERL2FBiGwL7YtzXDE6M4UGFFcMgLW4pAdetw/HasMfSKftdIMoivVaj/F62akCs6UAkqDFtDikWOxRYjilor0/Wc0F5X0X9B0E8hOfTdsF69ukR+d0rx5nXqgGBz6zlImWXjR7uqqFEMHSoMBPa1iZL7XvOvI4H2BTOAsv8i75eerXKgWgLWk8hC5Ts73wJDvluvEN/ut3VebqsxGUqUX+8vP553+lC9KzPYl1DDHPXx85rhd9IMUH0GwM9m2O52SntSdhpcT29aOp9fep9WG7axv8iZg8mzJTwuBR18dcPsTbMnSbx9D5wnWxmscnRxGblJr/KtJUwFEeZ6h65jbVUgaLnisTXAN+KAJ7UQe77GAp64rRJk+Rqx0IJwhM/fBS0yGd38HvJd96KaKZPSX59u8uL0ognQRSPqodOH5+/G+YW9vn8PDIyDE/v1Rtnf2mJbR4T4QbD+Ua2WGivxTGi4HIYd7eRaVC3Ih24oTp6m8Q6HhKLD4Th3J2std10/Azi11E1gsVsvK+lG558/HHQXK9xrs21ZkCgyQQVgsgZoKCuMNTWigMQIwo8l0o2oSLNWiZmEKjDYMGGCCoVgsSHRKUI5iVmKShCRPUBuBsiwoXUlZVeihJjMpp8cnBBXImpxbt26xu7XDwcEhZsNwtDjmC5/9AnuTPS75S7zqXqWYLfi9v/U7vP3Z1/lH//1/y1/nd3mTN/m/j/8fpIsUMzX4abUMqMb+MGsKu65Q89ALnMYIYa/PtNIopfFelPutSXDe4SYKOzdY+wuJghe1LvUUS9zbwbz9PRghard/u8R04n50QIIORNVRr6nl7IgGkKIegS0U6UmDzxSLHUsyl0nXlG3ppUQ7yigToGvh8DQD3QGoLqu+zOI/F3nq5A7s2c/OKKa36/sQAYpUrbUaRj6J1YQLSUN5q/B66cHnooCnN/I8urR9gZHt6kZMk3UUCz3+zPqScB1VpTvidUzDdVWCsU/7Q5PLIJ20RsIBW9IdZ1By7MoLZ8zEKsNk7hl985GILb5kM+vrTP7a2zRD0XkyhScYzemthMX2G100sYpp25YHVkcPwxaItobHtpAU33h+nebDu5xJy12UPVAanSYCgqDjOZ0habdRqXY7fYPgpsYfHceUW7vNuG5feTwGGZRWksoDQg84STWfRa+Pl4KZ3hFOp2fAYHCe/FiMsU0UDU1PKtzJKcom1CMLIdoatdcbzvg4NrlEM6uRxiwsdjSCFjxpw/C3P9mWLP320uBpkA8IyjEajtFGLlBd15Sl7jpY5AhqjDaYJMHYFIuiaRoWRUmII1ZYIVY3TcF8XrFYKPJsSJoklJX4353fluv7EFDBUdWuF1VZRqc+ul2UFmz302/npejOP66L24uA3Spoumh/F/29sicjhrpKGYIK2Msau6VoDpUQrVOwWxp7VaEysUMJBDSKpBCbAqfAK9BWSKYJoqG0uO9oTv3KcUDtHe7Y4/G9o2rPpX+c5/RtEACVeoUaqNgfXtTMW86lQ+QMEkdQAVUqyEFZua/qusYlDhQ0tsarDJNomrphc7DJbDGjSRwmJMyrGWmW0qiaRT0nZJCqAWjFcG1Es6ixlWV2Oudh+ZDf/Mqv8xfvfpPioOROeYfT+pRHTx4z1EOyKwmH6oB/8m/e5bNvfJo5c/4L/gvuf+eeVAe6gF5TOB9g1usFDTpXlB+e1cs6e0UVPvjeICx/a2WoTz32sWZ8fZ09pufeB79oQpI2VRAtn559TT1eVrYl06UIYkuu9rHqq+VtlBtSwl4PVCcCqXxUzybqFxmFyzT5XgWkeLN0kUdJ5VE90qggSKclhbcVZh8VUYIlQAorwKn92UotLOULJHLiLZ0quS0CzqslITrazfihipMcHQfKxKgPIVZWKTpNp2Vka7mMbuRc+3pJOlJ9Qm84CGpJipcTIhoNCyDtRESVYvPdGfbpCX5tgFvLIQTs0Zzw4AnNZMLHafWX32CxrSWdmEgKsSXV12PVq5AVMdG2JB+IRsc+UhiWquTVmqa+soG+t0KyjmCmixj50AEiv5pc6OkxyTpngVf3XQRR/gzh29Nar3Scq8hhCnXTgTOVpviiRNTJB2ATAWInp4SmwWxuEGbzlW1DmM9Z+84+/pd2kIrIgE8NJgT0a7dYbMfImVlGppK570ycm1xSeS1vcLFjSd6+gZlM8EWBef02/8e3/+BjXcefZXtp8JQPMk5PT3HOUVU11lrKciGDOoH5YobzjsZVNE5R1gVaWUbDdYxJQFXdu/SSAL1MJXkn0galmpNnOWW5ChzgPHCiYOn/Ftr/rUY7XtQ+Kr123rZWwdl5wpkf1foRmIsA1ernL7NdeaDNWDP6nKU5kvSO2gyYy3JDp68YtFuqdXsdSNCoWhGKgErAz8BvgGoVgtPQvYG7IlAfnAdsAwF/TqXLRdcy/hb1SAKBUIM6VJh1hVcQphJVUgNQhcKkhsY1JCGh9jUhC2ij8I1oRGVJLpHRwqGNYrQ2IIBEpOYVZVGSZAkh9fi5xwTQaIyzOO9ABZxv8HUjCuV1wRff/irvPXiPf/X1f8WzvX3evPo2U3tKEhJGyYhf/pUv8cGdD/nv/uCf4IznvfGHlP/pnMfPHuKnXs7Ug9nWhMrji9BFn+yWJkwENF7cngfr4gep8NPA8LWcrStb3OHR+av/ojF8WjO7JrYirQhmZ1cCkeCsZLBvy98RHlMwywo0HWkktgzRoFbK7pWTiq1qLOmqrPQ0I4s3qvNrq9aMpL6SFrCorrpNBDMjkdwtAVAnoHnOMNVWDbYpr+eW0XF0aV1AIkG8HgmXS6UyHgQDPkSdIxc6+wwdRWm9UZ0wZ7utYBTpzIlievSSa9XCtQMXyfEoOr0f15OtU345ybYSCMrRkdTT04bsLz4g3L5Ks5aRfviM5tFj/HiMvztHRfCxOta0VWRm+5L4tbWtryOU55zeyKK/ofR9kwtAyqIdS9v3diHFApKKEhseKqEQBAXpxMm6URai2kwZDofPk9bbtFvLf2p/78vQtHymlou0IlHTqo13wKxX3i8H7c8Qx+WHQg9yGe+mU1puUdsnoW5QaSJ+dnG/7nT63L4B/KJAP3hMfnOTxeWEoBXpgyN8kjL53E53DXUl92My97FfY4o4lfun9TsEmN3I2XhvHdU0HP36Ff6D4RM+yZYs/fbS4Gk0HlCUUlXnvGOQDDouTZKmgMdaQ9PoLnXmg2M6m5AmOcGLojgEfFNzdiJtAUSMHHSmwhe1ZcQltK8vf6n03Itaf+JaBTCBJXB6mfTaiyJZLwJxF4Gr1X3FN7xUk76mcENPelke7LoO8iai5YGyiYxqwQXqAJTgq0D5oUOvK8ICrNcYZ7A24DadTNYE3NTjiouib+eBwfP6ZLl+CNEqJXIpmr2AvklUhwgoAzqLWk8N5CajrmuwAZ2oOIIqrE6wPqF2NT54XOU5eHiEGYvIZ1VX1K6mftaQXLUoq6inNXquRSk/sYxHY6azGXVdybaSBf/6+39I1VSM7JjdwWWKecHBowNeuXab/+Crf5Ovf/At6lnNl978Mu/ee49Td8Lp8RGucRij5YUwB48HswROZgjJrqJ69HxlznnXtf1/+2x431CWnoO7nnt/8vhFG/i5b9m371Fsv96lY7om8zv5sacaix9bm4oxpUgT1APVucKbUiwp7EIsWHQtOlCLbUN64kkBvFTaEZW1YakK3kkEqMgZKaTcPRjZlo+Tsq4jJ+hFQ0MEJhd9325H0kwiWlkPBAyagqX1TEyrWRftUiLZV/n2Z2+XQc4lmXmqke6qEttUljcSWWtyARoui1IOPbUab+nSOmdOxy1Ba/6DxzRHR3B0hCaq9GUZeC/pp35AJkZTdJZR/9IbJAczmq0h5hu9yIlzXSRFjYadvlQy80swq8ArAdA+iUbHmZZCgFg92IJcl0oKscl1V2HoUkWxbcjfugVf++7zF6RLvwWRMVhJwwHiidercNNrawJ6oJOsaavlZF3fEbvPpu5k2yEo/KIAH2Q5pYVY2upTxao9s7WBOzgU8OkD51rGRF5WsW2pRorxkwZ/5z761VuiRB+5bAI65SWh7ZdOEkQvbX+SudgdhavbaB/Y+zvlJ96Spd9eGjw1jWM4GHByckwWEbDSirIo8SGgtUGhxaaljjWtUZZabo/IS2liid2ZfxB6yt/hOV5T/2f7ez/605/I+9GcjwOoftTU3UXrvChFdd6yL5Pu64+k5wMSpTXZWxo1gBQtaTYTCBOFLwJ+7qkPHf6KJr2l4UihikA1D/j9gNeQ3dT4PKAXOppzgnsKfivg80CzF3r8nBeN7ued1/n9uRTsBLen4J4lfcNgRhpfeTQK7wMOTx1qiIM7LLFzo2saVVNWJUEHtNfUs5pqERhczpgfzVEDIeM658QPbqhoQs1oOMY5xyAdMj2ZS7lvBcYaZsWULMtIsxQbUoy1/J2/8x+yvbXNn/zFn/HkyRN+/9d+l6KsePOVN7h/+IB7R3cgaFxo0EG21Txz+N4Lqd1ROO+ozli19K/vKgjVGCORt3bZQMCNf6Hv9JFtc421O3N4dUi5vnxb77hPiZCn66HuDH11HdA2kDWSPpLKLOG5JHOJNLWk8WQRqDYsydThE00z1IzvTKm2cnyqSaeSMnOZaDstxSFlglFeohgo0wlinveIn4lI9W6PNgr1XPSpBWllQLlAGNPpJAUFtpD92KqXXmtkJ6p9CW6tahDgY8o4AUa9IxSdfx9B+lD4YlK9qB1d+TqKpbxAn0je8skaWPvmk7MVZIAejaR8/vI21Y0N0oM54bvvojc3UGtj/PqQ6Wtrotn0Z/dQVb3UF9QGe+0KnE5ERuDytlROTgLJrBGujkJ4YUgUSjcBlxuKS0bSjVoAQUvCtwuPaqRwwFvpM/F0U8xeGbP+7CbN/Qcr12J5cYSLtHKB26hT73OlFGZtDTedoTbWYTJFpcnSrqVdpxeB6vSg4v46yQKlAFlWD8TI0M/noge1KJYim31bl17ToxFc3paUs4fhHclEuUtjshPfRZR0I2m5cl2LHctM0t1LGYz4IqEUtmxQj/ahrvhbb3//uX1+kttLg6fZtMD7htFwRJIJUdcaizUWpRR5NqAoSlEabxrRlEAT0NHlXaGVIYQWVZ8FT0uCLJ3KuFaKNEsozhXyUis/z27vR4tEXQRMXgxYzkagPu6+Pu46L474JJcNZjuQjA1Wib2KKwLVB8JRamYehaLec5g6I7+hYRuUCrgkkK0ZTHw711kQxfhaYXYVzSLgjwLu0XmT/fIYzk7856U9PwKA+oB7EtCvKUgU2iNvS9bTFJ7GR9EFLeuGAFopmqmnyisaXZP4lMV+gV5T6GFMOYwglKDXASWROK3EdqihYWOwwWw2p95rSLYSGi2VfnpgoNCoIQzzHF1bfvj+OwzyEbo0/NpnfwUyxWu7r/D+0w85PTxFVZrGlOLHZxWUAX8SICqLm0yTX02pDhtCdVF/9vu0Pdez0Uo9UAyvZBTvvdiP6ue9nf7SLslUIiKm4owJLkRCefxpi0CTKhhrbDT+FYXp0JnwViNNfuy6CbTJFflBE0njWqIva5nct0o4Mk0uwMguPCYOg5IOigcRTARWnKmL6fOfOq5THzjFW6KVA1j1urMTj2rTdXpZ3eZTEdzUZgmAWgK6yBYIYPTJMpLQkubL9ShMGb36gl6qjreRKt+94LS5UUkBtgKgxHewEM9VOdh4f05zd1m+jlLowQD3xTcprmRUYy3X4lrCVngT/733CNH8d+30FcJ8gVudL7yjefQYPRxir13l6IvbXb/VYyE5t4R6s/CoZNnh6cSjwrLST/pNRducWHBTtVWAYAq5RxafuUry4OFZgNT/XUfUuCpb0Iv26NEIX5Zdms0fHKLSFHd8cnY7bbpOi/K9yjKoakJTP6/lFCNQLVlcD4f4RYEv5/J75EKtNmUtemuTky9dZrgnY43/4fvYK5c5vTHsUuBSOSo6aKNnDYttK/dN7zZIZ1HJPlGkD45x+/voL3yK/3z7v+bjz6M/u/byCuM+MJ8tyAcpeZYRvKd0VUyP1NGHTQb3xGTUTdMDQzLQl1UZI0yrQGeZiohrADAY5ly9fpm7Hz6g6VJ9z7+Ny33X6i8t1//o6NOLvv+oSNTqdx+VentRe5mU33nLnv3dji3ZW1oquBKNn8Dk6yXBQ/6qopmEOGB50nVNek0RTKDxHhLgKqh1eYZDE4RwHt9efCJRH/covCBl91HpuhdF6c6u60qPrz1GGYLWogsWjSeDE10nkZxSaCT9iAtUixJjpMrTzzxhALnKCcahc4W3QULHQRNQrKXrmNRQmYrFfEFDTWNLsmZMaQuubF7FJ55qVjOdTyimJdsbO9AE1tUGb33hTdI04Y///E/59vEP2Li8xq+9/cu8d7zON+99U94MfYAknEnNJbsGMzTwrE8UP68vlt8F6IEnWSbf1aj5J9vG4JPQvFGc3raMHzlGP9hj/6tXzxKVW+k2hOfUcjLqoRbtowB2Hig2pdpKhYCuPPWaqIILuNA0A0nnBAvlVkIycyLC2IKGyI3xMTUmNil0/JlkrkRp3NNpG3lY2sr0Ik+qXwFowLcgpAVesZKv1W7q7DR0tD/pcb9azlSwrSTBsopQN6qTU4AYxfJy7KamM/ltU4jeRlPgRM7T1AKqfBJ/j0DUFoEmCHi0ZZDIztd/2N3jOs9Z/P4XCFYx/s5TistXumVHjyrCDz88Q8x2j548Z43StRDwsxnhs69TrYsIsK7DUrkdkUDQtaSUvBWrFwJUIyMgsVVwD21/xeq/AMoF0hnYuV+aQrfk7XOa0gpUsvSja82D+1GnNJWUWyszc54EgWxM9tPatbT6Tu3Y05NKUFYk9IMP6DwTz9UonKm3NuHouANr/X0FH3C7m9QDicyOv/U4ZpHk/jOljPflhhEwGnQsvmBZURmTRfISokgLD3sH6OGQJ1+9xK9mLYnvr0Z7afCU5ZlIAiQiSpkkKd4Hcp93aYQQAq6qUEphbSrEWx8iYGp7r02AfxSogbqqOTk+ZevSOnvPDi9YbvVtfPm5tL6g5fP7eH7CX/3s46b/zmsvC6wuShH2f+8f99lIVHJNQQKJFZ6NnwbspiLZ0mgLvm6EY5grsksGPxLIKvpbogze6ICtQAVFbT1GKTSapDQ0BNzio/hoH6eddw2k+Zln9ic1+eVActMIuVUredFKFDTit+ejRo3ySlJ5aJrGEUKDbwJ6oXCXHFSI7UsA4yxKK0yjMUPDoigwqeb6xnXuHt8hW0uobckoGTNYH7CYllzaWqM6rdnY3uDhwWNeG7zC4/lD7nz9fT597XN86pW3uPbVayR5witXb/D0n++RkVEmpVQkFRL1QoNZ09hdIe02kxeJoD4PlP3KW+HioWf24Qo59RftuZadOOqhRYWAqhvW75Ycv5EBnJn4Tc8owRuFLT0uEU+61oql9TorN220FgEVvACnSgyAXS5AKmjD4FlFuZ10Zf9NLsRzU4ZOqqAaq+5YBgdi1UJA0iEjAS99s1/g+Uq7QJcaaav2CAKa6qGKPm1LHlMrddCeb8dPCm1aro0kLcU7XSr9klSeJtNd33WVdgiQ8Akd0GmtPFpQ0p53J9Y5kGNd+94Brm+mOxiQP5ljjiaEkwlwpSMiarTs5gABAABJREFUJ4dz4f302oXAqddcJkBCNyJP0Eb4tAu4VC9Td0Md05eQnToBUy1frGrHnEAylefRp9HsOILcasOif+sLpPcPaO49YDVFJ2mxnvAlgNIooyIY8mAtOk3wxQWEyNVqvJZwXp9N4wtQE/qMStPIG9OdvpLZ3EDlOaEoJMUXbV30aISfxdJg70CLpEW+VxCOYvTLmFgxueT36VqqUZMoONsVZkhdEMWGaKWhYf6bb2MXjuF//OSvhCVLv718tV0mxsBP956wvr4mPmLGYozDJgmLxQJQWGOpfEOapFg7YlHMqesmAqg26tS21TTP2QhM4zwnx6fs7G6TpsmKaOTLgJFV0PKidV4UFTlvey/a7ovSax+174uWP+/zZQTPDDTmiiIdadl7I9Gj0dsJWsHR10q8C6TXNM0eFPseg8cYhY0CpipqEPmoKO6rQKLEW84ZCCwHh5dr5wHT1e9W+2m5nDvxzCYVGZb0thXw040zikQllF6E6YKJb25aZhiDoa6dePN5T24zEXB1Cm2kDCnxGY1vsGNNKODAH1CwwCuJeNnMsH+4z9bGJZRS3Hz1BqfFCVvjDW7fvsU3vvktJtWC7/vv88uf/zL/+I/+EWVRkuYpJ/Nj7MBQFjK+hUJ0z9QIhp9P0KnFO4dbnAee+mB/+VOdKU6IwLc67779RVttg6/fJTu4wvT2kOrVnQ4Q+JUR0CcSYWrTeLaIWke0oCR0vm3tZ0JC17hEIlXjhxUuVm+5TOETTTKRCGOxaQgIkEln0RvMg24kMtNuU9eSQkwn8uLZ8Zx6t0Y/GgWcTe9FUGDLEPlIulsumS0FJtvqPlsEkQbIJLLmUoVjqYeFErDXpvyarI1IS2qrGsuC2sm62i3FOAl0lhwdSTxqI7lMxdL/gG/VpoGW9By+/l0aIPzmL+GtYuudgsntjPDmBqPv2fMjMS9opmiwi1gdmGuRHLAS3WsGClsQ1dFDDxgqqlE8v5quv3SMMEqF2XJsNJE/VlzOmN24wZYxz2s/tRylNjIVQm8IjGPC7hYqePGfWzXo1b2BOISl9UvT8P9n78+eJMmyND/sdxdVtc332CNyX2rLWrqne6ZngMEApJDAA4UQPnBAAUDhAx8owv8IfwefICQbM+gZYpbu6q2qqyorKzMjM2MP9/DFFl3uwodzVU3N3NzDIytrUNkTR8TDPczUdL2m99PvfOc7Znu764lH8EQMSoeV3njKmG4d/nQKGyrs1nvaqV98xs7jXdyjJ53+PxxsS4WqlmpS3dClnV2x7I/YsaUJ1AcL82tW7D92Df+3N//t1S/i70lcmSPzQXx7BsWAzGYYa8jzDK0N1mQUeQFEtJa7kfMO7wPWZCgVWbJOFz1pw/pEGkKkaTyL+YKd3e0Llt/EzizXcf79TT/t8mptufXX1y0J+u+tb4u19za9vynW92t9fcL8rS6rsCONNWCNIswi879zuOOABmECdzR2V5NfM5iJwuwpnIsEItFGtEqC2CiS/arxNHNPNfP4Rm6AebAUw7zXYPhlx9Hfx9VjuPi415YJ0HwRpVULCluYdBkiTnmU0WAgyzPIIiF61ECBjugbwqYBON8QgmwjeI8LjrooaWJDXTXMzuY8f/oM7z1YxdZwm1vj28QQefT0If7MUdc1eSz46L2PuP/kc/Ii5/sffp9Jvs1ffv5TSrcg2sDR4SF1LT0fNZBnRnyqCrC3NQwVxirK+43YFlx47deeVuVq0fLfXzdJ/B9jqEFByKWvVjAaM3cUJ6ETALdgqPUvaivi5jcMxUmgOJWqOlNLusdWARQUx45sJve04kReq3dsVzWmm0i1J7oaaUAbOxDhCtW1dwkWqh2DLzRuKMaMKLqeeH2WKBq6W5FaeybtGKekbWor+HQTO5dukJQg0Ingg4VqywhLNUz3XyuMjK3ks74QECf995SkYDzJtkCWs2Ukm4s4XUWW4nekEq89D9lCGLVmKKzU4Gj5YKxHI9QffUR45143wWdPTiiOHflnTxm88IweLq4GnNbuVebREcVxSGxh7IT7PoHL1qKhE/Qr8W8ydSSfBYozL9eGpYFo1MICSkWm6N90y4hHOP3xTXRRrO5X36agBUV9d3Cl4avH+KPj1WX6n+8fY2KXALFJiFFYpnT+1HDYVdnhPWE2Rw9kn5TpVdYpJUDs3HnUhMUC9/BRt+/23l3K2xNMKeC5tapoCxdCturhpfyy0MANUnpbw+K65r8c/+qCC/j7G1cGT03dUJUN4/GYwXBAllm0MiilqOua4XBEnhdYYxgUQ7TWTCZDhsNB0kNdFJtAy+rEO53OGA4LsuwiWu9ljNLLWKDLAMv6Ns4zZBdv76JtbVr3JjC2ed3ne9MprDXk0aKiws8iOldMvpcRTcTHALli6w9zBncto/ct/kVEL0CnijVMBJtMNbUiyzT5xKBHCmXT9xOFGZJuSC87l5uOrTV0WT/el7CDDtRcGuGqDKzRaKPwePLCkg3EnkChkrdNINiAva7RVicBq0INFKpQhCyABW0Vw2JACA52AwzShBIMPg9MmfLRdz7i1uguf/SHf0Rwkclwm1LNqZqGn/zoRwQfGF8bMZsvOJueUlZzbuzfJDcF6Ii1mtBEmCsoQO0p0JrmrCY+S+rjV4pI7B5G2t+v42Xhbu9x+vaArS8rij//NfEv/o7tXx6ja9ErDY5DN8k3k6WIu/UoUj52qSc30NQTmSSrPZt60qUeaGXsmJs2vdOW8qPoJoyW0QlWGs6223aFoh6rro9bV+LdB0gtkIKumW+fIZIdp7MDUD52zXn7241Gdccl+XxZpgUW7XFIfznRMbVu2i25rltfqOQ+3hpwtoJwFejSdp1XVEoBKk/HUAHo3R05hPEYtWiIf/nzXpUYZP/qZ7gHDxn/9QPmt4eYvb2XX/h10JFnLK4Z0TY1chwtgG3PUb2VAIeXc2fqSD4NcjxtyxroGgqLK72AgZZtUx6yUyfgNVOora2X72t/t5taQNAGv6XlQklwnnrdrTfxjVWFzrMlSLIWtNg6KK0gBGGr+mm+GGW966Bze4IejVbO5+yHd5jdzpjfyGgmqjOBveiWFqywex34zGXcn3zffWtasvTjymk7abNi0wWDGBXOe9HLmIwiHzAaOs6m09THznN6dsa9e7dYlGXSyqwLujduKf1eXgHvPWVZMZ6MOH5x3Fu2TY/FleWX/9+Udlv/7HpsYgJW92d1ufX3L2NW1v++LEX3MpZquQ6tFCpo6ieRYid93ENzEih2NOUTjy7AKrl5mX1pCyIi7Aht1aySxrvaQAgKG9KTZgmxhugjKodsoqlPXW//++f/smOP5DcN1JH6xTl7uw3LSwQfqO43qG0LGqzWNMpDA6WrpRVQZggugBYNnNKKGBUqp7M0IICppJmwwaCConJlahcUcVMHIzC5ppnXhDrwafMph2dH/OlfTsnHOYvHn1JMckII/OvfvGCv2Of54XNpTlxrmqmnHlSQBUIVsV7jy4A/Dthbmp1ih5IFzXGkqRznbu5XiQjfnO7sP45QlRejy7ZCam+H+sa4Ax6TL+ZEM6bcTX3o0qUJRirrWm8aVQZpGExbNRZRLhIHCl0HgpXURXHsiEbRjGUibrYM2ZmX9NUiaYgUQBqrSrQ8rbBcDC1T5VtGZyQJdJVpSrNsv9IbDq2gOVgod4RRESG0vB8N1MMElqL8NnVyOE/ryWYta7VcZ8sitE15W31QfyiK4Wb6TFrGpNYt2SJKanO8NAJtPZWasUW/cwuePAXnUD3NjX3zHixKETLHiHv0hOHTG8KkXOniK5QxAs7KitETuTatkL+vRWud6EE0UX4oD19tpeLocUXUitO3B10j55ApmrY6sYn4QmPLQLWfyfFXgfDmDXj+/Ot93y+KtXXp4VCa+rZO4jYjVBV6MoEQpH+dMYS6BqWJbVViv6WLVucYPWUt4f03ME+ORScVI3o0opnoDnDqVisYWzay9/kWuCsYHIWuwCBkUBvFH3z/s29NS5Z+XF3zNMiJUXN6ckpZLXDOY60VfyetqBtHlg0YFJ66qcmLnKqqePr0kDzPKEtpgOouNQOE/jdRKamoCiEymy44uLbL8YsT+oBJodNEsj4orwo+1uMiVqkPeNbfe9mX+Ot8Ya4KoBRmqKCJzD9z1C6SjRWmUFRfeCoccaAo7pr0pKhoTiT9U/6tY/A9i73eo58RMKW99EtqZpFYRvS2QjlZb7arqE/Xj2sdwG467og7liq6y8//+WNvDgP2kWewn0GjsVtA9LgqojIxuSxiwSIu0FGja423nthEVBaxxkplqAroE0NTe4q3Ckkvx0B0EBTkpzlbgx12bm2jhwrvIqe7J8zKM0osO7vbvL33DiUVnz7/hBeHx1S+ZKiHHOTXuHfjTQ6zZzw9e0ymDDoodKlQeWRybUS+nTM/mdKc+K6n1ev43Yc5OmViNWdvj6j/99/DFSJobf2JZvdGZDNPtWO7liHDowSUjErNhDWDI0dxGih3k3GiEe0MQL1rKY7FMNMNsi5F0ZZlj76a00xMB1psLd5AOvVr6xrpstRiaYfkB/q3JS02A+3f3evQTWQhl79jqsILqam2LcXtuf81a7VfbSqvbYmSLQLl0HSNeqOW86CbZHGQPK9QdPYErReS+EHJ+pqRaAxNHQhGd2xY18olua6fvjtid/5d1NEZlDXhe++A1YTSwS8/XV7Lg31ciMz/0XuM/sLinzy9+MIn4BRDhLohzBcMni04/u4E7ZJ438Wu/Yp2EbPwhFzjxlp0O612zcD03kAYtiFEo7oUXdQKXaVWJO0xAyoBqvLGiFHya5ILvGTUukq79u8LjqN9z+zuEBblOYF8rGthlawl+tB5RoWzsyQul8o6ZUxqCuyX2imFuJFvckcH4k9/geuZeur9vc4vzZYyHlohlPaJAmkrOHspZu0ii/S5qMBNFP/3O3928fX7PY4rwz1jDcNBwXwxYzafYYymaRryXNJpZ2dnnByforUmyzKssWSZZTY/IYSawWBAUQxQK5qdPnO0iY1ZVtKFELl15xpFsZqPPf8EfhELsmlbl8VlaSlY3tHWl3sZgHjZ+tvPrLNkF0e2p4mFp6kd8984mqNIdlvjXaCeeYo7GjuSlJZ30pcOj/Syq8V6IIRITD3k8MACQhNFpL0POofCKBGUNxel5S7a/3a5SKj88tH00uNfO+4A5W8cJz8tmX0uWiJrdDLMVRS6YKvYplADCAqbi/EdBmID9aJBIy7mPgbYiaioubV7G6MtsQHmUE8bTp6dsHhUsqW38K7B4fCZQwVh60bbY0pXkpkc7xzjfMwgG/LOO+/wk3/wI/IiEzbMKKonnvqJR+caPYSj8ojoxaV9c7xqGu91XCXCs0P0tCQaJSm3oaLcFfPKwXFgcU0zv267oRcNVDu6S8OI63YSGVeB0TMvomEvzMLgyCWfHy8gRJEmPDoxtXlyTHHcEFVqymtVpxGKhi4FJBM5HVOEkpRZ94Cz9lXrt5hpo03rrdwKtTQ/DlYc1cXskuTsLT8+U+TTSHHi8bmwZN05SbfQtipPrAdERN2917ZocUlgngBUNGI+GZKPlU/pPV8oihe1NObVcPgHe9RvX8d/9YhoNdFo+PV9cQdPfd/c+3cIhYC6C/WXSq2U6BM8/vSUsFjgB1b0Zsnc0lQxCfMF9CyuZyyuWZqhXlo2eDlXzUjhC1YaMLcO7a12TQT/keLYJwCrqLcNi3/8IdV/9YfEf/RROqFxFTgpLXqjTTKXnn0DoddipRd6awu1NUEf7KPHQ1Yq8WJAWYuyGRjTnTdlhPxQmaD1sFj1yFLWYq4doMerabXmreuSyl0kwFmLGWZxGrr07ooXWRBNYLlrRGSfxtTiZuSfFJsq6X//48rM03w+xxqH1oa8yAkxYq0hRE9mMhblnEW5YFttY4zBGMX2YIuytJTVAmNynBOutvV9WqboWhZJoY1NzXGXwMkYy5tv3ybPM2xmqarWwZze59nw/4uYkcvScf1QG5ZbB2QXrf+bissYKDln2R2N3oYmKkKpqJ8HIo7mKDC4Z7DJryRG8POI2VYM3rTU04AZqeTWnZ5AnfRt0oKTMEMwQWEaRQjgp5F4Djz1j38zaLqcrbvoWvSvozQzjkgRATOL3dLSLiYESlcyLibsTLZ5cfZC+t7FiGoAqwhOROdRATaCicymM1Qt/fxijPJErz3lzoJHPODoy0PG+QQdxaW9qhdsxQl/df8vmZ5OCSFQZANiFpn6Mz579DlfTb/i8OwZ2iRgehjxMxi+a4gLj60V3vm1KjlF/ztwlZTcVXjJ17GM8KP3MYfTTp/TphcW+4bRM5+YkYgKqVFvTMLoTDyBokn6ouSObMpI2BJmoRkLO2WqyPxmRj4N2AVdyxVbKrbuz4llhc802UyQzRIsCUDxGTIGM9X5NLXRtpVpUyDnvm7tE35ikSRVJwxa6wiOomO5QFyyQSZ37WLXh0+7SEggSHx6xHKAfJmmiz25XraIBLdsw2FLsFVIKVEBiOWOOHEL+50YsXbdT04wf/WU4rtvs7gzZvrGgG33XeZ3how/m3Yl9SBicn00w3/8G/L9vSWTsx6pdck6k2O2tqgnGaaRybye6GTuKNfFDTTVtu7cz22ZnNcVXT/CpcN67Hys1jVlUQvQbsGuinB2LwMNw4FmsrUljFC3rwCB6Nb2eY2dClWVGkqf//aH6QyVZ4S6p31q26y0rJPVECLB1aKBAjHOxKeGxWs2B99/n3pnSPa3ny63qQ3NxGJLKI49pgrYuabeNqkaNem84vLYdSNVmdWuXk77Bnh3xp759umd4JVMMhW1b9jangACaBSwKBfM5nOMtuRZxunZMePRBKMN3geKYkCIkcVihjE5eVZQ1X10G3t/Bbw/P9GG4Hny+DkxBkajAbMNXxiFDPC4kfZ81Wlmffn1Krv+cusA7aL1vAqj0L8rXn48ShhYTK7Y+mFBzCL+JBJOwC8i5ZcefUfBSJGhYYC0ZdERdKR5Esne1FLun76TUUXiKGJrSdXF5AnjieQTjX4zo5lH/CKidEQXGjIFDvzssnOwfi7aGeAyILzh6B2Ux57hlsag8dHT+IZnPGWsx+R5TrWoMMrQmJoQAsootBHhSJwEfMpc1E2NzTN8FXDGiVlohFAEEaSbjHy4Tz2vaGKDVZYqVgxHA4zLGW4NeD57iqo0cSfw+PkjFvWcIrNEF4k+onOwE4WqFTqH2VdePJ82nJN4RQH4a+D0ahEyg3tjT1gepZg8qMmPFhz+eIfZTSMaDCPu321Zf/fkrKW83xdSeZWfBZqJZvjc4UbCTrQ+N/k0LFmZhZhoqhCZ3xmydSQVw1GJniZkwjCYRpgtFYUsUEFegyVoEiDWE5CnW4+pWxDS05Yk+59WMN6yAy1rZJqQmuFKCw3twS5aRklSidGIF5WwYGHF6LP9urbMmDQF7u0XdIaTbVpMOwFyKtAZbmovYI6zmfgJ/fTvGP61YXLrJnE8ZGA1Zx9ssf3JpAMafjpDff6l3KhCRL95F//JZxvBxLnXtCEsSkZ/+4D447vduc/OvADGLFWMtQAoiduzhZyTFlxmc1mvL5J2J2m7ohExtM/1ChsYbAKlXpjraluzPRkvwVN/f9eZtL5pZrIa2Ohn1bJsVbXa4kWx7FcH6b1AW5kXm1q2qTKIq4JzZS362THmF5/ie6Jy8513md/MRK+Wi95WWrZE7CJQ7iXmLC7Z0JCBHygGR4FyT3e3/v/q/W9XS5Z+XF0w3hhGk5yymVNXjQx+Y7A2Y7EoGRQFEKibUpr7ak+sIc9zaeNixQtK601ARO4EWmtC6D9apQse4fTklJ3dLay1G5/OI6CVTmPmVbQkfcZofb/a9/uPgZeBpfV4GdO1adsvi/UbApiUj/eZoJ9sVxEzqB4qaZ47VDS1jGSTKbQGH4WBykbCkKAgZhALME4RYsTrSO6VOAIDMRM9Q7av2f+HOeUiEBTStDf538x/KqDq6sewDjAvY5+Wi5lcExvEWdwBFnzjOYtnTLIJo2JEWZVoZaSaDiibUppr6lTtET2YSvR7KsMMDKEI4nfVKIyylIuSWIILDdcOrlNTUZ6VNHUj4u9qgfaGOwd3mPk5w8GQ0i8IRNyjAE6hBxFjNXgFOhLml52f15zS7yKm9wp2fnmGrjMW3xsyv5mhYmRwkgwpAV/A4Dh2RpGqB55QMHghgm/lI83QiEvy1NMMU4l4bnqMBGijMMnSwBWasDXANIF6mEGEbOalgTCIBkglAGUhRNWlinxBV/rdWSok5scPWL3dtber3teoHivy2VJHFYy0NvKtu7lGXLfT9to+fm4g65HKvAT6WrdwkzJjfsl0taCjGStCLYyXNAFu01LLViYmscDaA22qKEaic7ivHqCyHP/WjzBVWE1RBS9aHcCnxsFXjuCJweMePGSYWp1gDGE6I7pG2sD87z7CDUznit2M5BxpFwlIVZ0byPn3eWpfkzRxphafLEkHCsOpvDinRyPMi89TdV5YXrR+D7+Vdi2tWWYSc8ewmXFa9rCrl4NWG0nFtX3vEjCSJsJpXa18RulOC7XiKu4c7tHjc5tzu0umyJTJ6DU1VnYDeZhQXnzM5HhlXJmFmC5XiYVsJor/Zv/f8grqod+ruDrzhCdGxIZAGZzzVGWFD0EcxiPUTp7ynWuIiKmfNobMWrKskKq70FrabqAdQ19ILL+1lk7SIcCTx885ONjtddUWUNJqqJRSa15i68zG+javAlw2AaBNy6wDoasCiFf93No6gsITIRfWrZmKI3IxNGx9lMl1sHQeHypP38cGAR0RqDWMo6RUkebA0Udi0NRGGgS7QUBHRPOUiQg1WrBRSRVfVBilsB/A8c/ri7oSXBIXXQsFuTBr+Vua+mmAUoAfeUTVYqWAg4iU8Z/WZxQxp8gLbLTMqzkhBLRX0iYlRNCIQ7mS6tHCFizUXChtL5OUzxzRRvJhhpkbZm7O6ekxN3dvUeYlg7xAeU2la16cHDMYFzShwWhNXETcs0BsQOUaMi0tqOpIqDYxkpvG5msQ9U2FG0qj2+zJKYObBW6olg7j6RLkZ5H8tKHcFbao9TIKmVgI2LL1ZLLks7ZfnAjAs5mIhZuhpLiJET9YegZpFzl9f4vx4yppowJ27infLGjHQPuU3mpoyIRt6qe62iqmNh2SiuWW3k8bnsekF92SUVFjtQKwlGdphRBb76eILiNuqAnZcntdI+AmsV1e/Nda5mvZkkMlcBmodoRp0A2dV1TLOgWrID9vQRObmsG/+zXAiuv4bxW9ySGUpZhP9iLM50z+518T/4sPpQ1POufeJLsFL4L4eqK6VJSsN7FwStKWcr1iOpeR/LhGu4D57DH19+7RTCyh9W+Ci/2qeqLyti/dRcezXJn8XxkFfkMqrgVkkNJ4tut9t75+s7dH89Hb5F88X7qkK4UfWooTL8agCnQdxEh02+AKuea6iV2hQNQwOPIoH3ETLdqnfc3iZuTHeQ0MLrtqv7dxZcjnguidhoMhg6LAezEN1EqTZ9KKxTUutW7J0EpTNXPKck5ZVXgXyGzO1az9lqhcK41O4KiuqjW9kwCjGEWzEkNY85S6yrY2gaJNDMhl6+sDoctA1lX25aJ92hSyP/WTQKhAZ5BPNPlASx/dkcKOhSoWnY8I74OLoo1wkZqA16HrlZXVWrqGe03bn9RocRrPlIIMYogsvMdoJaJtpbBW2K6A3tRX8pL9fxk4lX0dvGnIb1kGH1nyH1r8IOKD6J1CSM7FBhk6LlI1NTFGdNRkNpN7Rpa2EREHdh8JdURjsAMrVXfIOsyp+JgFAoenz1EZvJgdolCclicYDEU2QI81VhtuXrsBAbTVaKPxTwOhjMQAbuHxc4/yUL9w0ix443WXcyLd+jazoZvfex0vi9bMkWeH7PybL0XI3IKHlJYePakpD3J8IexLthDWAARAtSkuP5D2Ey3IaQXlrW6q9XsSAN76JKnkiaRpxeShbUCbwIipxbE6tE76kU4n1AdN55oDa5bNgV0qGw9pcm9BUW/ItK1otKPTOkICPpkIon2uUhNeVlJVkFJtsbd8nqr1ZiF5I9FphlrWSXsBFG0FYnvOTR3h+v7Ga+aPT1ab4P62cRWbAO+llU3qo6naTmIJRDcj1TtvS1+nFmDKeVFJ6yWAOhQG89lj/JOnmH/5lwz/3399roXKerpOGbNkn7TpRO8XHs/ae8raJVBSaslOJS8onWfCxCUAde6mrQ3cukZ1kOFu7wlLR9KM7Vhh4MZL+DC/brteh7IDco7yNCbcUFFey8SkNmn6hh8eM9HfTuAErwKenEvCb8twOGIwGICKlNUiAeDIoCgYDoYpfRaIEZqmYT6fEUIgz3KyrA+gzutd1EpdLoQobEi7jfl8wUUTbYhRHKLPxXrarL/di1JFm9iB9b9Ze30dCFyV6boIvMUNP+cX9c+TzskLU6eiFLT5tnFoJqCpqYP0jxQbcaxKVH0mV0R5RXTJrwRwabkYI/i4vGnriEkiSmXkO6qiwniFb/wVseP6Ob0kZecii88d7qHHoAiDiPOBauFo2qeoFji1E4WNzOsFC78gN7mAjqBS76jeshoyZXG66baulcYfOBQwGW0Ju9RU6KDwwbPwc2pd8d03P2B/uM/OZI/GO1zmaVxNdEGE9UEMTaOLUEbQEV+uI8uLxtmm86ISu/rtpLn/14yQQ3ljRGwc4cUxW5/NUMmqrBX1Hn9QsDgQka9phDWwVWB4GCQNk5H6mUlqr97WUvqfSvjdoGUkRDTbMj2mXup95jczsjNpFuzGhmyejDVJ2qSmx0AFOhNMWIIlFXs/fhVQichdjkscwpO5pVuCGmiBD53PlUkNhNv0S7/VSzA9c0tF0mfFrkdd61sFyxQepH1PIE0AVjLRjMt1oWD+9i723t3fxWV/5VD7u3Id/VLT1R1PLeeoNcOMJvU8zBRRCftm6khxJiyLuNlDeZDh3ruNuXmD1tDyXKyJxJdpvJBSbZt2VkCRHg5X/o9ShLLsWrEIiIodUxW9J5QlqihQNltqpHoAzOzv8uInB1JxmJlOXI5WZDOPqQP5qccPNOW1ZMvhpAhBHhi0OPIvAsVpINjUX9EmY1YP/4e3fv41r9LvR1z5LqxUZDafMZ1OgchoOGQyGZNlBq0V1hiUFhYixih977RU44XoKauF9LlzNVprjG455jZi+jeytDOIhOAJISR2CRbzMrXZ6IOf/n4qlGod5dYB0td9Yn/FVNpybza8d1WWq//e5fut9hF9TSOgyTURFyO+CHgdITEdKpOqMxo6psYUCpVuoE0diU5o+PYnIhRwTIDDRdFwFMGgAF/JjTk4YbSq46vm614CCvunAAhzAVBmphh4i7IKKohllF58Sm76inRzjopIwGTi92QKQ9DCUtFORhFiLcDSE6TqUIstRwyRyXCL2tfkpsAWFqU0u6NddDBUseSvv/wbnPcM9YDT+SnT+pT5YkGoIn6+Og6i10QCzVlfkKLO/Sg0SmmszZOxgkoPG6kSL/YbYb+Oq0ZUML1r0Tevy4Tz+SMOfjZNY0WWaVNkLaOkvLiJtwChOBZBeAsk2hSWz6XEvU2j2blPk4UIaaUFSZv6kwa0uonJhTyIbiQNiXYS6oCT6oGp3r52gEkv3yMgICmlz0jLuIHs54oTeVpHm75rRefBqA6QyYrlV5t6bIFVMKpzDIe2T57u9GJtU+K24bF4YrHURsXYVS8OH07xt/dR6y1M/lcIfyBO4Nkidi1WIGl2eoxb+yPXSpZrmyPLjxiA2oUYjZ6+O6T+3r0lCAFhdy6xJejcwztR93IeUNaK8efoJZVqMXY2BPKUq5bbTuLxc61jlCK8dYtmJOO13s065okb16h2xDOw3LdEA6PHNcULR3HicUPd+YS5kWZxzQqoSg8I3XdKwf9x5y8v3/ff87gyeIoEROstwm6lNYNB235FobVOPwKigNQBXibHED0+uK61SAuOzk+evYGzMqkIEyPj6mKwEbvVbQZXrxZfB3RddbvrKbqXTYgXMzNmX5O9I/nlUAWaJhB0JGSRoCC2d1oDagSxgmAirokYkk4sAzNGjM2Qe2qIER2VgBNBJpio8aeR2a8adCUmkvPK42Jk+rChefIqYv3Lzoccsz5Q2LsaZRWhjMzuN+haSSXgOLVcUQirFJS0LjHCjpFLa5oQAkEHbGHJrKSUlZbzECsw3pBpm74NilE+QkdNE2oqV2ImmqAd2sp7o9GYYlIwnc7wsUEbxZ7b4/r2TVmvjyupOaUUKotUJ03SO63mUVRvfIN8b5yr6RdFrPKzr+NrhYKwNUTdvQU+oH91n/GTJdj3+RJ06LZn26lPnkDiPq28AAkVl2Xsnb9SujDNxGAq6fVWnATxI/I9w8xiyVKIW7i4cJtFID8LZDP53TpYayegqP1qtMCmY0bC8vhacKOiMCWmkf1oAdm50InZ6hHzuhGw0IKq1t6hNbXss1e6EdYqWLVMN6ZzIUxdErwnTVYfvJk6CDh8+Bz1d5/KZP4fIPRggP7ou+eAh713l6OPlhWRXesbIzYSbZuaYEXP1fb+A+RcJQ8rAdVijDp8PGfn1zOyWeT0rQL+4HtLANPvb/eyaEuhe95VylpUZlGtZixG8W1qmaoYJOVXVbLNtC1llyk7+QmYgx54TSyVqUmmqMKW6fGY5uY2+VlY+nVlivmtHDcx+IGi2pJjC6n1j63kO9J6pkUt6evT9+FH+X+Y6/27iisLxvPcEiMpdacIIVLkGXm+w2w2oywrqSCIdCBKK42PPae3NEGEGLDaoFJ6j97EIYuqHshSafs5rnFJcM7aZ1ZBRdzwWv+d9clrNV7l9Q2g76Wxzjpctp6XT5nRRymaGEMoI8Env3UlYuqopemvUuJOoIwiDqT6o5lL9Z0aKin7DQoyCERMEOYp6ASgkvYoVjD7hSOUkdGb4EIgVpGwCJSfu1crdLxCxBMY/yQnXI+U9133xJ0rQ1TQRC92CsmnSVkgKNlXwOGSHk7Sv9qAxdDUyJ19DCGLuMyh08wZR56RHrKzu00daxblAuMMg6Em387Y07vUrsFet/jccXx8yv/zn/8/+NOP/wWfPv+YcBxFjJ+uYYzQ0FAfxQ3APvZAUltFKvxrf0naHCPQWfm+jitHNhPWJAwz9K/vo4oC//23Gf/mGN3scPaGXTH18wWEUqGNojgNIpzO04RQxk7rRAJRLUMVtUqgRkrgqz3TpXdMI+aTwcrEChAGGhVj96RuUnNdUwmwsEONzzU+B8+S6WldwYEV4XirNYI2fZdaoQRZqGOl0ue6Z9W1mUDauaieUJzO/6ofwjSldfvlvnT9+9I2dBKY67QvLVOHAmUNfnaBX9M3HCrLiT/8gGYrJ/u1F9NJY0Brzv7BXfGpWsSuwhDoiq1DOhfKge4xgzEx8z4Tpqb17dJNxA8z7L//BVufDMn/8F1Cbr7+43zr2QS0qTnqRnrXtSm5Nt3XB2gIYOp0Vq2+KTFcsamXTBTCavlxRnHqU8FAZDgawv4u0bb+X8KwZXNJ09UTTUj6NnmwENNTn5P0fyllq8EHxc5PnjPSGxoQf4viyuBpe2ubqqpQWtqhtBV1eZZhjCXLMhrX4L1Ha4NJDYIX5ZzQ0UEpHZe0SWI5oLr3zgOH1KcsgalIFD0VAqbquuk1yd0ERNZfV2uvbwIkF4GXTbFpHe1rF+3H+r5cBuiuAMiUpOK0U5LK8qBz0FHM/bACmFSI0vi3Vuid9CTrNS41yDRaQ5323STwpUErASJ4CJVi9nGFIjJ+W+OaQPVLT35T0xwFYnn5rq5E2+H7wuOV8xEbiGcwuGXJJwavhC0jQG4kD9Cm7JVuz3yUY1ZI5VsM2Ey+9DFEtIJiklOPannSVpCrHKcasjwjRmks/Kx8Ks7kmWJ3uM98MeP+88/Q2uCDx58FabC5E/kf/ux/4HR2BnPwj6A/NlSM1F+COzs/vjcdfwupNo/X34ZJ/Y83Dv70c+r3bhEygz3YJ05nmF/eR+3tMvrbB8xuvoXvZy9iKwpXnZlkMEgLC7dM37RNbkMSR4tfkKK28qTdMjLCNIkZI0iJt6R5Is1ESuPzM49yApx07XGTjOJ5TXWQE7XurANUoNPktPomoLMzaAFP2w6jOA6JXdKds3cXG4ZhbFmUXt+8VjjdrjdYOmZMytOX4Ega7qpuuHY6qLAEeW0VomwwrgKD32Go779HvZVT/O0XBKD5k+9T3D8k7IxZHJiuWbEv6I6/TX92wNOAqsUgs/Xh0k7SdK0Avz0+PzRkkzH++SH2T39Kpz86t2Nq8+v92HR+gicsFudfX1tXp39aB1hpOf/smbymDebmDbyPmDLQjDX1WFP/8C105YVxKwT8y0ODgKKQLcdJSG7y2UK0Tm4grX60V9QT+fx/+9ZfXH6s34K4OvNUFDTOEUKgqmqMMVJVpzXWWooixxjDbD4jBI8xCu81WVZQ1+VKCiIiFKTS7UBaBRZLNiq9EgN13Uj6A7ngUuFlaJo1zrwXArpgyfL0aZGLwFP/9c3M1nlAdtGgv4ytuozhunpiRmXyCf8gkt3URCMoHwMxgaiQBUylYKYIw4hapKfEXOFVxDeBQMCfRuxIkVsNWsnTlZZz6ByEo0j11KNsZP5FxOwHwjTgJyp5O73Cvl/YoqV9hI7dMvWpo7glqTabHptVDeRgo6EwOU55YTkD+FTKo72CWqGL1JIlChiMGlzTkBcZbuaJPlLnNeOtMc0Dz+xkjgoKV3kRymdQTp4QTyPmAFSjcfMAx4AFvQ9fHX9JHIL/VOEXYeU0RMCd9q/v5uNehdP9f1l553W8evijF9jruzQHI/ytPcxzQ3j6XEasc2TzQNR6ycAoEZnXE41dhKTVEBaq7cvWjOW9kCW/HxXJZiGxFjKxZjN532fCZLjk1t2k9eo6gaUm4geaMBZ2K5tq0c5MMoqjhizTuEHeeQgND5f+VMBqa5Zeaq3ty9b+3aXc0i2xTVO273c6Kn9+nbQVdD0w0Tpsx16T3ja9108HBpM8oQwEldq7qOSB5AP25vWNnkKvEit+SefeVJj9PY5+uEM2i5gP72KfTCgPMrKjIc3ugGwWcUOVTELZ/LydfrcO2p2VhFs2CbapKCRkiio3DK7vw/PDxNBdABA3ACc9Gl1sigldCk8Zs2SVkr9Tm9ILixKztwtKEWdzwnwuD5DjEWow6ACZP3oBMWLfuMPs+7eSS7hOLCkcv1cweu6pk5u+z3rgnQQYG2GZ5KFAGmorv/S3cmkc1jvwX2/9DTDZfFzfkrgyeMpshjUZUcvTtnNOzDCVxtqMwWAkE1JeEGNgsZgDc5qmIXamIi37lNJylz5prD6lixB8OTmXZcXW9pgYI677wixBj1KppFtxDowtwdRFrBdry160X5uWOc+eXBybGKqrhnxOt4ZkYy2VYzppn9LTJwZUkyTHSokWJw34oEWbowAsmB2FW0SYB7JCCzBIk7pG4bYc0UTRTNURcyB30frLbyhXpxTZdcPgnkFFmH/p8NNA9cyT3XQU2zaJsaDRAR/EdC0az2Q4YbqYEqJMbiGL4n+lvJgVtsqiRHgpp4h1gErRnDrIIj5zhOip7zsx31RCM+PkXFmb4Y4bob3HEQpQWxAd+DMwM0u2yAiqTHq/i8BzP223fkU3geuXAe7X8bLQuzuoswV6UhCsJtzaRX3+BTx6gnrzrtz8E4NpF+Im7gZpUqgDcaylDDuCL7QIvRvRMlkXcQMjOiHo2nm0l003ybW7l3YDWU921oj/0dBIaq9OTWitJZ96vFU04xw7D50gWbU6qSriRjIm2v5rrddTy4qtVIwl0CMvsDKcWiF3J0hvW6m0RV/t+ll+XgXE/qT1n0opO8xy2ZXPJVDXCu51qmhURU48XXPb/hoRvZfecDGsMC3t7zCdYRdRzvNORvYwkJ95qlsT6m3plScu7uKu3meeuspGuzyufCokQEzO9MrD5P4M89UzUIry+3dZXMuYfmePyeM9MfXcFOsNgtt02iUasLYBsDIGimLJHAHhj77H6dsDBsceswicHbT5XcXk8xnmrERNF8RBDsdnxNkMZTP0cMD0h7cFGKYWKz6Tak1TpdR00nq150U7YSBtGRPLpDqwXqR+gYt96WfXjr35W4437fBK1/T3Oa4Mnk6P58zLKahIkRc457qqOh980pQojNHEaBiNJtRNIwMvVeDRS8O1iQmJPtOzOlG0n5WfdkISFqmpG7LM9sDT8s4QY0p8dDeIdTapH68CYC4CU5uAUv9OtSll0wdu62m8y2K5jNJQfuahDAz+0GB2FKqJWCu0ewiSn3cu4qoGbQVkxVRZ52cRdBKLp+9uSP5HVotje3SgdcSONXZb4Z5L2qL6fP16vSoQ7B27geJdw9YbWSdQHR1ksIgsHjjmnzjs9yx6IJ5ORitCrmmiJ1MaoxXGalxw5LqQtizWizFlFPPMDIO1Gu8gHycX9VlAF8LQZd4SrivKsSOeIDex9kk9BJq6gtaepUzn6xjUEIzRaG+pyiqZva4fY/v3y65pXzrejotWUA7xtd7pa8Xsj9+W8vGJZvS4ZnanYPfkA3h6iHJeyu9TFa9pBDypxJ64sUmVVmJ82Ta79ZnClHQaEFNLr7TWusAngXE2a72AJJWeLYTF8q3O1+r0f/nOZnNZTzZTmDKgcukb1qW5SOCpAZf0OCrSSeL6omyfCXvcphqzReyq//rVet06erfYFtxEI2xtZ2tQR4JKab20bKv/ijqlKdv+el26T0TrRLoqvZYhm/7kLvWWZv9/+gz3+MlvdZ2ja6QKrShEE9R/r6oYPSw5/s6IxYGl+ePbEIUt1E3y5SqkMMANl61ouj5+CfQRBTiZSlhFOwuoEBl9fIj/5DNcAj/28RN2igLef5v6J+9g/+xslRlrfZx6ZphyLSSFeSHjBMTGQYio3RGx3+YleOzzKfqNAfPrVq4LKcXqIuXNIXqvwI33xZepvs7Onz8UVuqNW6gojJMrtBQ8nIolgRubBMgjdkFq9Os7Y8xyV3WpaVOLFKQea/xgCZpaPdyHHzwkUxfYL3yL4uppu8ziQsZ0NsU5h9EG5x15loFSTKdTFGIA1zQ1w+EQay3ge7ok6AAUalOi7dwrq2XZqxN0VdWMJ6Nzr5+PNmW3+Yl/bYuXvNdfZh0YbVr3y7Z3lf15STiZWl0Z8Kcau6ekZQok0XQkRNC25wE1jYQYMUOF2de44wAOTKExBphDrKHxEU0kG0rqTrmIMQp3DjC+hEHbmM9fHrMZK+ybGrutUVHhCZQzT/bCEPchv2ZYPGrwRxF7W8vTrhLWSZERc1iYBcEEAe8qEAmiAWuSBNtC7Tw6KrQTVk0NwN5VNE885S8j7o2SbNegthXM6Cwelsfa2+/2gbaRHzWS8Rj82uP52rGeB9nr43uVgbpqo+DXcXkMnyzwhWF6Z0i1PUB7ePHjffb+wosz/2jZn80NUmo4wuipw1SBZmI6R+w2XWerIEApSln7sg9aW9pPNyGHrHXWFhaqXdf8ViaTt1/2swNZTzuRtaFdXBGUS7m/fKfbFi7tftNN/ApdBew8oFLrjLYCqmWaWv8nAUp02p5W/xOTW3bLKLR960yzfM8NBTSZik6I3gIvEcQr6vGSDcvmccXFvZkowo09+G3AU+uuneeo0RDlA2i1AkJ06ZJBpwDYqBRuKNYRKqaKuh6oVF7Ar4ifl6BTu0gzFi+j4rDC/voh9LajrMXcvoX78ivUrz/D3/0hxbWDVXDYT+O9qv1IajXjnwnLZba3iXVNqCo4PmX8aIv5zeSgr8VKY+k7lsTdKf149E/usvvzY2bvTCQtl2wWVCR5kml8tnrfA5jdst13RjcwPAoEI6numKru2vRu5z6fKf75nT9/tWP9PY0rgyc0WNOvRRX9iNJaJmUFZbnA2oy6rpjPZzjvMVrKw30QR7r2qTyuT0aXxjo4kgkoxkDTOLLM0jQNbAAxq2Ny0wT+skF7GaP0sn1uP39+grx8ny5673y4KpLnwkuEJ4FwV0OqiGg5OmvFbiDUsesBaazqbrB20OqJlNDSEyVm3T0WOQbQtSK2zItRZLtaPI2ml++nnihx3Z6n1ihpu3qksCPN8AMr/eMeRKodDzcBH1GjiA2K2ZeOUEXmjyqyGwN0rjBoGu9RWSpgqGsCkq4zQWOClaIGDzqT8WkzjW4U0Ubq44AdKdyZp/rUE84iYWbwWYBK91LKl13r5Xvi67SevrzovGx6fZ2haoFU7H6/dhb/+mGen6LGQ1BDYVFSmooYUdO5VFhla+c3QL1lyCGlKAS8SKPbxD7luuvvVW1rhkcei/ieAd0XaHjoKfdM8jaSt7STqrOMVN4elxYJgyPXTeK6CUmYLI2JVQwob8hmoXMB76eUZOXyy2cy/nWmyE8cZuFAKRY3CklNJgG5z5fgAEOvV5laFUtDp/nyqdelz2WZbCbgKySfo1YHpdSqLqqtwopKKg2LE/ne1NfHZJfplq4YYT6H+Xzje/XBQK5jHRk9WODGlrM38y7V2gHQBPJ0E9n6fEHMND7Tcs22LLoOFEcBe1KhPv4cjMF//23skxPcZ/dBaUJyR1cfvMPZGxmoN8n/x6cvB0pXaNFgrh3gD49QNsNc2+f0T97CVIFs5ii3s65QoRm1aV5o07luII2KXbLZ0A2cfH8XgPGjmmbLStHDSFGPpRjANCRdYErNJdDU9kMcHjmakaba1StVq60uzJaReltRHcB/Of6Eb7veCV4BPD14+JDxaJRSO1o0HVETYyTPCgaDAY1zGKMYjoacnBzjfUPjGgbFGK0VzjVUdYnWRpyYzw0S1f27Cq5WwYe1WWeeWVf1ihZqGeupsFdJK71qCmrTdjal814lNXeVbQoY0SMIU0XzPGIPA2ZHSptjHXEhwkBScXqQdD9OHnhCFCoXBdqIG66Czp7AhGX6E4Rhbg9BGbFGUAOFWqS+S73DUiaJ9UMknAUoFDpXFO9ZtBEn8uZxwE+heuKJMxjctOjUd8vmst8swJ8EKXF1gebYM7guwzYLRjyovKKywodpp7FWUweP8goVFbnJCHhsMOAjFJH8uqL5KlB/CqFtZOxTyjK0gu+XgRW5tsvR8nVSl5uu60Vbe52y+7px9I9vd38v/XsUYWdE/PWnjB/cot4edqkqN1AdOzK/boWJ2THY5KpdvPBdSkd7aV/SDBPj1Ih2pvUHyk/l6b1t1xKNCK1DllIcdcQXKfWlVOcrFXKFroTRMo34RMXOekCe4tt0WmegmSarvi4lZMKuRKMoQkQ1gfzUUe/YDgQ2Iy0VZmlSDFboa1PKtt1A9Srp5LjEV20JqkKPnWhtC5ZC8dRMOMkIohIGLBjITxtmNwwnb+fc/OUN3IOHv7NxoL00NlYe6t2c4rBkcGw7QXPr65TNWgE4zO4OxAjy+QL95Ii8riFE4lu3KW+OGJZ3cPtjsk8f458fySloamJTo7Kc6Yc7HWNlrl1b0SdtjCtU4/nnh+jBgMX/9ofMr1uUh4GLvHh/kI6TZbsY1ZuB0rWJuj3/8o60VomU1zLqreV1lDQc2ErScCEHWi1dTEagZaSeGOottWxp07OpCFYaT0cFi7dqbpuXGHt+S+LKJpnzxZSyKvE+EEMkT46j3nuc9wyKAePRiOFwyNZkW4wEiwFaa6paSimNyShyeU1SvL1pRymM1humn/MDyXuXXMZJTYP7WqjLPruJBdo0WW363MviomX6R3SeWdi8npekwdbYqRDA3lOYiUIVCkwU2ihLIEf1PGGgy98bo6T5aKbQqZWSDgrrNSFCaCBUUmLvjwP1M487Tee9FhdtO1LkW/r87hrI9qRqD2Q9firf5PyaIRyDfyGao6AjeldhCmkurBSY1CMvIOk1kGOpn3sIKo0XQ64ztFHYqMnQqBnUXzjMmSHLM0bFCO00JmSEoBhOhpApfBkpv/L4cglIgg947zt93lWBULzSNbssNqX32lcu6nX3Ol4lxIsmMR8Gsqk4P8/vjrDvvEX+1RH5WWT82LH3s1N0I0/tIMLXdiJtRjLJVruGkCtMGVlcs106LBpS82CFTm7hfihO4wD5VNZlq9iZUbbVS61fUNsTTTeRaBV+qFEuWSe0YmIFpgoMjmV99NiFru1KT8QdjHS1V00gFKZzOW/1WtpJp4A22s/6Ip2HtI5mpDo2qk37ZfNkAJot7zPt5NruK0qWjckvKmTCYGVz2adrPz1l/NQTvgHheBdaHLjNwT5mdwf71huU+xkhk8m82jWoJjB4VlOPpadfH/hNvlwweCF6pMXNnNMPtoRhOnpBePcO5c2RsGkhYp9PCadn53rWRddQvGjQTSQ7a4h3r1+6y+ZgH/Od9zHX15bru4OD/P3dd/GFTtcQyt2ljqirotTyE/LltQlG2qgEo6SidEt1acl6rNA15KfCCCqf2MSWjUxcgPIwOPZErZhfN9QTJSnbBJz6hQmdZgz44QdfYf6etJe68lGczY45fPEM7x0oWCxKdGqxMptN8SEwKAadhcFoMMYaizGJCaglH6y1Ic8GZDaN1C69FsWB/NwktA48SJPbphTJpvTYprQbFyz32y6zCSj1P79pHd/ApGhAv6nIf6TRW2mVFmIBuoBYRVSDtCyJAkKkgacSPygjDI0J8n8dFLrU1IeB2S8d1aeO+a8d88fNMh0BFAeGyXuZ9Mpbi1hHmhfL1JceKQbvWvJ9jbGK4mavgfChpNRIbuYxpF5cTuFtwNzUECDMoT7yzD91Ys6pxZLAKsPAZ2insLuKbAIqBDIU49GAQV4IGNOBabUgVtAcetxp2OAT1r8um66NuuS932W8Bk+/TWx9WTM4TixrIxqdyYMGUwae/9M7xMyy96+/Ij8qUfcfMTzyMulYtVLO34qhu/L/TggtOiKfiybKDYXtaRvstm7b4ucUunSZpMSSfkgnFivZHdRbRlqeuIhuQtIpyXZNHTCVx87T+O27d6c0mwrL10OmKA8Mzbao1IePZuQvaooT+Y7ahbicd15M7W2zN9RNs9o6pr2VNaOlz0/sLd+1iDHLibvfbqZNlakQ4We/ZvSnPyecfXPgyd68TvOPvot/7y7lH71Pc3efajvp2TyMnjbws1+jXOjE660ovt5SHH8wEs3T1DN43rDz8xf4Z88hRnSqqItWUd3dgcMXxLo5vxMxUvz6Cb5QnL09YP7GhPhPfszGtiwIo6Sm86VreAplzArzZA72OfrhDos9aeTe6uU6J/cWPAfQNZJtMO0Yg2asO5sBYKXdTHEWukbYrZatGSfm0aVCAh9ZHBjcMH0/Uiq2n/qMvTHZWmb897f/l69xJX8/48rg6dq1A4yRp+DgA845FIo8E1FaWZYYa1MfusBgWJBlGQf71xmPtsis2NNqrQjBkWcF1rQOoy3wuUqaZLmsTkzVKqNz0efWJ712PeuTZ1z7ucr6+3HRdn5bduLi8POILjR6X54kGCCC6JhE4oX0sIsuSlVNI9VlJBG5CclQsxHmKRLBBrLbMPmeJW5DdeKpDyNKt5YRSI84FQnlBccUkIoQA8VbmslHlizXuONI81RYq+Edw+T9jHykCY240gYF3kqzY9Vo1Fh1IzW4yPxxRT33hBhxzss9xSoya7BKY69rsuuixfPBsTXYZnu4LROPjkQdcC/CZnr8pdG/hv3x0GeP1v9+2c9lcRXg/jpeFuW1rLup+/QUriIUhyW+gOM/vEEsK5ptuScVR3V3yss2Dd67WwoLpWnGBlOFrgpLetPJE7obKFyqbMvmMVXvyWSkQuyq7YjC2rTgyw3FCqHVqPhkrJnNZb127nFDQzO21DupXUc7TPp39HYSS2XiPlfiJZVJCq/17cimQRq9ToPsZ6thXmMP2vYkXXuYlBrK5lGqDnvVfj5npaFxm2rsAIpJGqsm4kbifh2+YZdx9+gx5l/+NfaLp1R7lpP3RiLMT7pus3AQA9W1ogN1XWPmBHTrbXGIL+4f4v/uY2JVYXZ3CPeEGRp9OSX/t7/EHx2jzOp0ag72QSnCi2OpUDQCpKdvDjF7Oxfv94OH51KX52wLDvbEvbuKhHbstN1V1PKnq6Jk+dsXvVZE6ZpISla+F4t93VUaKk8Hxn0uvmW2WgK1mB5GZAMIc5fYu9a6owWm9S7808GDK1y5b0e8gsO4AKDoE3sBTGdT8ibHGEsInqZuiEHcwwGstdJWxTVUqk7Vd1A3NY0rybKcEFxyIIeLJ4h2klllm5Yl4esTTJ8B6pZmdaK6bFublrkqgLqIWdq0rou2/2oTpVIQkwcM7QOKghjSwE2mdNppNFF63QEE0TYRwamA8hrdNtj1oL1Y88dSnkiVhvyGpj72UEL93HH8VyEZZMq+2x0l+rZFQBcKKrB7mtG+Rfv0lGIjw5vSpkCPtTQs9hFfRrKJkkIEr/DbYGppqlv1TntbCRQRn6ugAx2MTk9bUUWsNuTDDBM0MWYUeU7VlIQqEmebUrOvwu5sSsFuAlNfZ939T7Ye/K/1Tr9NRL280bdi19M3M7Jr6Z60rWi+c5d6x2K/+yaq8alvW6RRq9dOebpy9sFxwJSBMDHCsATpe6a0pEZUZGmcqeTpu/VOMrWkQkJiYEzSU7WWBK1beblj0GNNPg0Ux1JW32wpgtXUE7UCWjpNS58IV3TMULmrGfqI2yrQTcDOnIi+BxpdR6wSrU89UV0D7fbW2TJGplnqhkjHJGmdBErT621/uGDpnNDtQqoNuwl1yzCapVL930XD6+CJW2N86ie4+/Gc8kbB7Kbh5P0Rw50/oNw1mAaUj2zdLwXwpCrHkGsGj6aEp8+7VcayQv3ycwbVG6jHz6S1jDbo3R3C46U9gn9xAlH6wmWzSLkn+zB8dkVBfHtO1Pl7R9gedoC9PcfxAiqkBUodM9iPTq8GuscxNEPVgeYWYCkvBQ4gWifdJGd9TUpBy/Vrx2/UYJOly/C54/k/ght/T/RO8Arg6fD5Ee+89QFPnj4hxkCWyWNT0zS4xjMYFFR1hTEG7wNKKYrUaHA4GDGfL8TiwBi01gTvqENAaU1hcuqmJsZlmmc1+sxRfyJaX+78IGu15Oer+86DsfPxdSa8Tft+0Xt9oBTX/s/a6xeHGSkRg7dd2JN9QQuOnBJtg8qieDZFRDCegY/SIiIGRZxBXTvMSJHlWgTai0BzlCokA1SPl5N49AiD09vX0IDdUez+oBDvLwfFjkEriA34ENEWvNOoQURFRWik4a9JFXzoCFZhayP6kLnBaIVPTThjE5n9ooZ3YXgvI8whDiKOgHdJZ+IijALlfI4uImQGv3A479Ea9KCnaDx3Da4S7bW6CqjuA+hXHVNfH3i9jmWYOlK8aKj2MubXZAKImk4HpQIsbhZoF/Eji1k4xk8c5Z6RNhRDRTaLNGOZAKXdhOhN5jeyzlW67WEnDVCXDEwrLA9Jm5RVQcTf6Uk0KgUmdnobX0hl19JvStKBwgqFZFqousmznyJp0y8BZHJMDz7tcZZ7pqusM3VyNy80+YkT5iAomrHphnZn7BnbCVh17tKtv5MKyfcpLpcX9iF2ZfEg7FX7YCfLRLLjsvcA/fVDb22hRkPidCYsljboPKO5ud1ZMxx/Z9Ttm89gerfNJ0r1uBtbBo9n8KvPUDvbKKVwT1Yr5EJZYq4dQFWjtrdQxyfo77wn3kt9K4K2YtcYiuOGcrcQ13itWPzRu4w+3pLKvHMHYtDDgVQNApucydWiYXASaIZL8LwJHHVV1+vXsvd+t7t57/9rBqGhHaMJRDfjZVWdijIWlu12SGB0KdCf3rH8449++fdG7wSvAJ7u3H6Dp8+f0LiG4WCQtE+aGCLT+RnWGqnY8pJOyTJLnmfMZnOMsWitca4hy4YoJaLvqi4hSMPLzObEGHC+WfN2amMTo/PyiWu5qk3s1EXrvSwumsxexiZtYrQuA1ZXuZnIZ8yuFq8hm2hnJdVvks6KyYtFyd3UQlOn1yxgFMbLU2QYRpSF+ZeeGB3+JBCmcdmH9gqkWKgjTRXxh5Hxm5agI42POCJFlLJX1SjyHSC5mMcykl3TGKUTS6bwXm7IyoPRhmInYzGTXnRxjgi+H3mGdzLMRNKPrhG9lI9QP/ZwC8wQFvWcJgQwAVIVVPGhwc3EouDrx/oYetl43DQGLh9vm/3QXserRjb1NGObjAFZ6j1SGkk78adRERYHhvEjzfDBGbNbe10azievJpS0nihOAj5VKbWNbtuUhkoFLW2VkriTx85PySRAFmxiqtJ7UYPLZCO2jAyOvAibvZTX+0LhC4NJXk2kWo1OpwQQReDbVTyp5futsWU04gfnc9MxBovrGSo566+4iRvRzbS9+lQUQGST2SdKStF9tpxcpaJLUj0qptHeTuDp+cg0kXqi0afzb6SfeJjN0d4Tv/cOSuvk6zXoGhQr3wrVWfnqtkwgSlKkbqsgv3kdd//LC7flnx+ijk9En2kMfPnoYosF78mfzhiNZboVy4pIdntXXO43tWbZ31uCpw0Rf/Upg4MfUL09IJsnH6pMUnfnGKjelNV5eMXVMaNaC40euOqPpz44795vDYR746vVw7Xj3yXGz40V/5cb//bC4/k2xpVh4GQy4fTshEEx4ODgGltbWyglHeGVUizKsutcL7O0uI9nueX07ITMWkIQg0xrcvJsgFLJo8c3+NCklhbrodasCC5Kg63/XBTraZZNoGUToFIXLH+VWF/nReu7CExdfEym0JgtjQrifaSMgloRHZDJzVIp0TH5EHF1kC/IBKm4a5LeSYFVkOWa4S2D0pEw6wMnhRkYBrsWu7XcN23VKq3swB9HFo8cfhFEhK4UPgvSUoVItEIzm0KhLKhtxXDPkm+LjYGOYJzClw5Xe9iOFDcycThP284Glq3rA9F0Icxirg3oiI9ipqmNomocTe2FhQugM8TzqgCzvelcXzU2sVX9sfkyyHM1AH6+gOJ1fJ0Y/vyhuEFXkWv/+gmjZ17GvaZrdNuvFMuPa/j4c7bvV7iUwgiZpCFUADcST7RoFPmJpzgLnQ+UrE/0LUQ6P6W251vIFH4g+qYuRdfI5Nem9wSwiVA8m0razxcaW4q+ypRe/JHS7VY3y0ksts7fLH9LgUj6OwgD1AwT6xalCazy0muOGKUaMInCW3NPsUuQKkEV6cTCLVBqy9pV72ErZCRmCro0EPK73krl8k9eUrp/1QieMJ+jf/0l+nRBdS0X3dlAmLLiNDB65tm+X5NPI/k0MjwKXbk9CICe3Ruw+M5N9Hh86eaic5IWbGr86ek5sKMHA+lPFyJ8+YjhoxnFCxECFSdejDU3EQXB4778avN77babGvtCqtiDgWwalmMjXQPVAsM0fbb3+dVtpfVZ5LvQ62kY129rveiAVbvuNAZbT6fBoadoK0E1lAfwTwbf0HX+PYkrM0/loqTIBwyKAYOiYG93h6ou+eLLL5jPPa5p8EXB1tY2WivKStpUbE+2OTs9wych+fHJoVTk2WLlmhhtiUTCxn5330TqYhNt8jIq5aL3Nu3LRevftJ5XWe/51/v/y3eNsC4nHj/TmOsCDkybdw6AiQSniCZiiiQGJN3Ii4ipJMUgryqM0sSpg1abMdAUtwyDOwZjAWOZfe6Z33fYkULliup5WB5XiNTHnulXju0PMpqnnsEtjbeRoBOlGxUqaoIRAbekVoM4jPvI4ouG8rlDWyg+sKiZwr2QG7spNFsfFJhcE33A60CIkeAjNQFbaLRTuDoIjNce40UVlUWDNqKR0m8p4onHTb+unuiycbkJlK+/dhmAehUg9jpeFv7OAeWepjgNxMfP2Gocgzt7nL47TCBGJo69vztDf/kU9+5t1DtvUPziAfqtd/F5AlVnQcr1RwpXSDUdmGQjAPlp6LQezUheM8myIGQsAZTVRCXbbVuYtBqnTrBtFPW27froNSONCho7D2LU2PZiy1n2YWPJAHRg0AtYWZGWRgFEtkzAbJyaGY/EtiDGSH6a2m8EAXUitFcYL/5XbQPyVnvTgjaZuCOBZaVi+3s9tTQ4EsBjrh2A0i/3QLpChEWJGuZUW8LY5dPI6OEC85tHxNmM2DiKD9/h7MNdBs8qTOWZ3xliGkW5K8UBKljyD9+Gv/4l9o07l7JQ66GyHD76gOk7E/JjR/HzL2Fni9mbE7SLTL6qsD/9WLRSl4U2cgO/AETFX3zK9ZObLD64gR+arjK0S821WqV0fVaq69proFevB6YHjFhjp9rCzsTet7clndKzppZx0Yw05b6Rh4SkL2zeXXDNXA5Gv21xZfCktGJrssVkMsYYy2g85t4bd1EKPvnNb3Au0DQNIUQGgwExiqA8yzLyomA6nWKtpXEVjWskjdva6SuF1ialmwLOr9KfWhl8L+e7GfJcBIRk0ur311v9zGXpMrX29ya2YX0fWFtuU6yv67LtrTNQq9t1xwG+iphtCIcRs60xeaJngzhuE0Fp6QcnXwTV5cFjI61atNYEJ+JwfxoZ3BL3cFNoxh9aEXIHGQdRwfgdhT8OqAKGdw31cZC0fPuFyhV6KCXMxQ2DJ6IjqFLuBRGIWaCxXp5alMakY5vdr6meeUnpOlj8XSMgsP0SW8gOQOtINBGvIk0VxEVcazKvUzVPRNfyueiEIbVaYZQR1m0C9cDjZv3L8KqA6GWfWV/+VR8CrpArfR0vDdV4snlkcNigt7cgtQzZ/cUZRx9ty51QgSod4egYuz3h6B/eYP/f+R6YoWMxsmmU5UPsJqygRQtiq0C1JT5KIGBCBL1Lo8iWBTBN7ECUCokJCsJgRSPVetFomqGkvH2m8DvSZ2xwlAwKs+QClp7yoefrFlMVVe+1qOV+EHKFqRSDQ4cKkXrX4ooklLaKZmI6kNVWAkKyaYjLiTm2RphZD0ihln3x1JKN6hrrpq/B/Lol+5MfoZ6c4G5so05PL+3pdmkkjZPe3SE+es7w9phs7sj+8jf409OVkovwq98w3PkB9ukp8clz9LXv4kaSigxWkU095niKCx7/4NEr7Yb6wfuUN0cEq5jdyaj23yGYlgGL6Npg1o9xk2A+ePRodGH6LjY18XSK9tdxyYJAu17KVgOtHi49HMTeuIDlddANK817V7aTrp+t0ljNeoUBGlwG7X3NFbp7YGir/wD+sw8+ufoJ/JbEldN2b9y7x62bN4kEnh8+Yz6fMx6P+c53vsOd23dob/B1XTOflxgj8Pfs7ExatGQZw8GIQTES24IYuzRdjJHG1Yl1On/1fHDp1dj7dz0uByqrOqo2tehZisb7T/wvYwQuSrVt+n9/+ctSd+sC5k3pILW2FoWrAuUXnupLYVPCp6BmWpbRoJ1omkxQ2KAxQaebnsLE1n5R4U/BHQXKF45IYHDNsvMHOZM/kDSdUgqC6G90kDYtWmlMprEjTbazes6Ci1SPPNFHtAGTKGG8gJ8QIsEFskb2T2tJg/gy0hytMkF9UAbgFxF36lFZQCvIvSKPWvypWs8qryjQGKWwUUxAtUI8rtJ6mueO5jQhuZekZdWlQsd10HsRsL5s3JxfpjXIfB2/fagvH7Pzs2MA5h/dIexOwCj06YL9v5t2T9WLN7fQ779F+OwL8lng6B/eWAEdzUjRjKV6NT/zyT1bGsXaRUxgJTJ65joHbZv60dkydk/o2gk7Y+oElppItoid/ihqUv+61E4jNa5tU25upJjdtp0bdOzdYvotRgBpztovWU+TrPKIF1WhCbk0gw2ZWDMA2LmktPKpCNRNtbQx0F4sGWwZOpAox0LX2qUFWS3zFNdZDsAN4eijEac/uUkoDPEPvnOhB9JlocdjzP4u6t5tYtPgnz5j8Kd/g/6zv8Gfnp4fD0VBs5WJw3xdM3hedl8/00TcSNPc2QNtNmuZNlTAteutrg3RtaRB2xL/1gU+WEW5bzF3b69+zhhhrNYilBcDSVUUcOOAZmJBJSPUOi6tAqolYM7mUbzL1lJym4TkHaByPRZKk7zLZNl+RSWI/UFr/jpI9+92226o+G+u/bsLj+PbGle+M1dVw81bN3n/vfd4//13Cd7z4KuHbG9vcfPmbUbDEZPxiLqpWCzmokHJc0LweO/IsxylFMOhVDtkWb4yIQXvcc4l08I+WJCf881R1zjol8ZlwKW/zKb1XTaxXpaKaUHaVdJ2lzFikfV9WN9TdxLxL8TVu/61x/0qEEoRZqrYlqHKf4xVqAzxggpyg8smUOxqtIbyQUN55DEDJS7kgCdS5YEI+KjwUxi+ayhuGhbPA81xXD2EAM1JICRwYoICpfBeoUuLDYosGkzQ3SE2WSCaKCjnAgWp0opsoGlmQc6GTaxarsgKTW6FedNtywoHYQb1saT2lFEoqwingdkvPaG+GqsTO+qrD10v8/1+2XqvylR9E1La16GyDHcwxJ7KZKTKhuzZDOU8wWqyWWTns5p6yzB/d5foPcPHZde4to124qgnCjfQAipSSxcV6QwpW5bK50qa8obUZ87TNeANJmmGQmKlemm2VuMEiQVKLK4bSoq9syMgMUA9kNK+1n9mM4mBbTUwwdKZV9Y7Bt0E8qlPuiURf89vGDGV7A1lEbYnRi0uJ1dbxo4xa/uqtW1l2mbBK21kehEVLA40s1sF9skJ/j/7Mfon378QoGyKMJtJr7fZgub7b2Fv3RQGa6MMBNRoxPDPP0V9/AXm+jXKawN0E8lPA/mpmI/O7g7O+zG1+3RBKk0pYZfKA0szTgaWVUQ3UZpJl5FsEYjZatInOnfOnRylxFcvyzHb2yi7/IzKctR33qF8a1dYw2TM2lbftSL4dhwEq1gcmE7jJxtdtqHpi+q7fep5enW+Uelv1Oo2VKrczGaBZqw7PR1AeSPyj4oXG8/XtzmunLbzDZTzmp3dHbYmBU8eP+fsbM6dO3coFyXvvvseR0fPOZsG5osFW1tbbE0maK05PDqUFFIIhBBTo+CAVjql41pBs+9Bg/Wnedb+f9VUxmWMVP/9y76om8DUVZbdtNz6ZHhZanB9mc2pvHbJ2VlFPM6INhK/hMxq1AdKvjAk7QOAkiaz7d86ylq8i5SfOUIF1XOPvdN+S1pDvIhTEeqI2dFoEwkhko81zQudbAx6+6bEmsB4cESawmN9jtIKGpWsFCBDix4rKtiCnR/lVM8ciyeeuFg9W7oAcwOaMtIsAipXwiYVibsL4GeB+tQz/9QTkrFgcBG7pxl/N0MPFOVnHt+x4a+aTmsZ0D5LdP6qvXwsbF4vPVAWX4OnbyQWP3qD/NmC6bsTQqYw1TblQcbWr08ws4qdTwJRQWEVs1sZoxvXiX/1MfvNexz9sKss6ACSrUQ8HVJrFRA2ylQRO/X4oSGbC5BwI42pAtWW7RzJW9CkgqRL2p5xnVhXg0vArWW0xB9JgE1nR5D1JjmVgEl/yCRA1TdQbCdLnXqpNyPF7FaOLWWS95lKzXPlM20loTBHqmMptItd+Xq1pbGpQXK2iLgoAMp1VgvL7XfnsXe8IIDU70+wZxWnH26ze/wG7vMvrnyNVZ6z+OE9monBlNfh0eMLl+20VdoQZnOGB9vM3tmSc9mV3L96ujyUJflf/Yb87i1m7+ywuGZQXmFL0a3ZqWPw8Ew0Y1kOMZxjtlrLhfKHb9CkMeMzxehxTfa3n4qT+Qdvcfz9HQYvfNKjqQ7YqzW/LaJc/3UBeNskuP9/WF6bTSm8NlojVOVFwwpQbZluH6JdjrPwzoK9v0f+Tm1cGTw9ffaEvb1dYoTt7UlilAKPHz/h+PiE7Z1tDg6us1hUCSQFrM3wzqOVoQ41zjvquqZxFd47fGitSZeDNMsGON+sCcfXr+KmlNZl0WqevqmJ6LJJ8WXArn2/D5L6TNP659cBVX/Z1fRejFKiv/VORn0UiCdI1Z0Fn0WyRqrwlJbXWo2ESk0+g46YUWL5YiTOQY0Rc0slnkw6yhcjEvFabvg6KibvZDQvAqGS3VEWxu9Z8rHB2tak01D7BldDlkmKTQdZv1OgVERHjdlR5NuGbN9x+tf1isVJrKC8L8UH1SOHzjRmBMO3DTbXzL9yLB56cVNfuwzuKHDybyppWuxe/cZ4Pnrn/tx7faB71W0tx4Z88lUA3eu4LJqJYfiLE7bP5rgb4jY/fFKhnx3j3rhOdW3A6LNj1HYuLv3X9ohHx9QHw3OMaj4NmDqSTT1+ICLu1mV8cc0QrBUn5jpQbxsBI7VieORFRJuppGmSSc4N5Dq3Jf5Z6jvWNu1tGxVHLWyVShri0NOodExTZDWfEHpkSSpFbw+nLdtvU2puKJofFeTvlqkyjTQQb8GaK9o+cGIG2hpkrqYG5aGpZT268viWaFbL41VBRO9EmL4zYedf38e8McYfbMHnr3CRg+ynCqK1ukwvtPyM3FziLz9FvfljAX+nHhRs/eox/nS6srhKcpQLbQkAf3wCxyeMH+7h/zcfCnun5BwsrmfM7hwIA/Wd6+gmYKpA/ukT4tkU3rjN8Ud7Xfuf4tRjStHnTd8oGA4/EN8sLeOw3wolmyXwWyic7T2AWTm3ivOAKGxAAH2AayrOueu30VZj+qR1a60sWm+xluX6T9/9zYXn6tscVwZPi0VFVT3m5s2bXL9xwL1795iezThLjRwfP3pCkYvbuNFaLAmsJcaIMVL9FIIIk40WR3JrMpTSNG5JV4oXVE7dlD2d0qtOQv1ogUW7jqsYY77qdn7bibj9fM+YZSMD1V9erX1GXle1Ij7U2BGEANEvb1TexVT1oog6opVCu7bJZ8QMFfmeoXnhiDNovvKMP8gJqZWEaST1pjLpPSf61ih350xhx4q6krt4bKB6FBgcBCkGiAqtIFOaJpOUm1NBAJST/dSdmZ78LkYGnUG/M0HbEBog1IpQB9wsUj33KM26l9z5CPBSDG0juE3A5SJgexHIeRWA3y7ZT1G/Bk/fVKgQOf7H99j9s89xo33s3GGfT4lNg4oi9F68tYtObVH8VoGKAZ+nTvIpommZmMDiegYR8qkn5AkU1VLuns1E82IaaUAcckXxouH0zQIVYfgs+TelSScqYSdahqlNF3YVeFF0Ri7rp/pYTYG1qbv+sNkgH+qLhRUJOI0ELLXr1slwN2RgqyVDJCk71bUxaasFdTKwbVkxqbyCNkXYgSvfe61ZnlOdUp+uUJz9wzfJTxx+kqO1uTD1th6xqSkenVJeOyBkBvPOG/DzX13xs47hozmn708SW6fIbu2hnh8Sey3rLgNN6+GPjyVl55fHplPxQD1RVNtWUpohou69KX0GFeSnnsU1Ky7eCZCYMuAG0uswGoXyApRCIT5hpknaKpUqGDNJK9fbMhh0Q+fyDkt26dJzotYYq160bGIH3oykq9uwpYAqN/77qXeCVwBP167tU9cVz58f4ppAZnOUjhweHbIoF8xnC4zV7O7uMJlsoZSiaZrUPBi8l2+fUiq1aYmU9aJrH9ICpRA9zjXk2YCqXmzYk/5kdZV0yyYwtJr2Or/+V4mLln/ZpLppu62uRvWWuYiFal9ZnWybxpMXGWrbMz9pUDGlx7zcg4xVonVq2lQc4u4b5ItdXDO4aaA5EtdxpcBYIwjGQOoF3TWDjICJ4E3EN+1dWX43p56z38DWewqbnqSMXoqgdZA8Q50HMt/WMoP3HtNoymee0PCSWFLsq8BpE1vZf/0CkKxh8KHBfRVxp1cZG68OcC6D5/FrrvN1XB6tlmP2h28QlcJUgbA1wN19C59rqm1NM9Ls/80pW19pzFmFd47isGJ6ZzXl4AuodLI9MAqf6yXb4SLZHBYHluLUy+SXKq3M0FCcBtxQdf3qfLZMgzVj0VChlj3yWtG1dikFlkBT7N8eWjuACybGLpXX/+72lmu1gbIOYZW0Swxzj3Eod7WwUDFi50sxdGu+GVK6L1i1bExLC8bkde1iZ+vQ6cRc6puXKnkHx5Hs2Zzm+ugKTzqrEb94yHZuWdydkD29ugWJMobqYCAVcY2c58XtEeP5m6iPP30l0IRSKGMw1w5oJrpjEG1qv2MapLcgKZ2qhPGrdgyDIyfgKEbK/VZln1r5+Egz1isANZ+Jv9jWlxVuYLALT/5Xv8F9723poVemXnYFKynSlwGnly13Lv3aG2P9zyxutnqn/4jTdtdv7bCY1Xz51UMOjw7xLmAzw6Kc8vzFU4psxMgMOXpxKE1/reXsbIYxNjFQ0rJlNJwQY6SuK7TWlNUCrQwh+A5A+eDJVE5m8x4rtWny68cmduoy4HQVkHQZA3CV9NymdV203Hoq77L9aT+5DrDSU4ZWlE+D2AqwfEqNIOk3FKE9XyriieRei+lpLtVswUWqp47smqK4YQWkhZgsA4S5UkH2wWuExcoUvrePphDwRQj4UuGIZAMRYHkXUUrE3QDBCM0/f+Yo7ztoFH4eXnKZ1q/1ZXHRNVwNu6UY38zw+5HF557mcSBsTPF9PYCzaYTC5pH2up/dNxezW5KvakaWqKHcN2hXLNMdaW58/g+2k9nfhKJ6B05L8umwa+PSRsikmqx44XHjZXm2ChE7C8xv2g4MyTYkhad8C6boqtRCy44nbWHLLoVMyfqqQDPU4laeXLw3kZ6qdys4lyLTPaDUxyMpxdICHZ8nH7YQO7PFqKCZ6MRcyEQfEyAU1ii2t5LeeUir9yIZIIqonBixOvlFpcown6tumz4Xn6vpBzvSLuYq0S/z95762ohmYpi/u8fo6PqVvKNiU1McVcxuZ1TbIvQeHMv3T1mLyuVGFcokQr+gF5+5eQP3/h0RaG9nNEPVVVDK+EtgKI2LVnPUNeW9ZgmZWvHtaq9L9D1T0tRYubSG4ixQHmRS/fisxp+cov/8F+wevUn51i5nd7OVda3ozdaZyotOcY+tBAFzoQeQgZV0sRuk/X67/Hupd4JXAE8vjk4JPlLkOfPZgmcvnjAshoyGI9568w0ePHzIW299l+l0ymw+R6Go65osswyKAWVRUtU1i8UcHwJZZimKITGK6Nh7R+OqTopb1yV5PkQpRd1sKtdcB0ubQNUmQHUVcHWVtMmrMlTrbNnLgNL6Z85vXylDWKFbpI1sGRrsh4r5LwNZpQmDRLGGiI2a6EEZkjmlWBb4MjD/ssFXEZ+a5vp5ZPp3jvo4YMea7I4mNJJ6Rcdl38qkwYhNlPW2TyEK6qPAixe1sIta3rfbGj1WDG5bIgobNc0isHjumP+mSTT5Vc/vVdjHi87r+e2YXLygKGD0gSHuG84+afDzi1ioq2x7uY/rvGk7Fl5zTb/baFNUrZDW1K13kfy/OA1kM8/sViYp6tJz+pMbTO7PGT+sce8W5/QhzVjhc0tUUgquQiRmGgaiR6knwhIMTrz0umtSuX8MuELR6mBg1YOJuPRWkvRHAlMsJ68uDcNyMgU636XzJ4CuIq89H7CcFDsRuev3qxN7hWYsDzymEgZD+vfJOqMSIJTNg5zbuMpYqUa0kkDyuFIiMo/CvkSWmqioRQsGsPVnnzD7k/fIh8OX65ZiRA8G8J13iJlhfjOT1KcxZN+/R/ZoGzUvcV89uBD0oBTNVs7wUFJe9USjfKS6vUV484cEq7Bzjykd9sUcdTrDPXrSpRT1aIQ+2Of0j+92LUmiVmI/UUnqtatoCwk0pjSX8nRAtNWf2bJlpZbXu2XzVsahIoG9SFSaQW5QMYrr+a8+YXB4AOFt5jczAWW2+9ilwGkdLPVvdx0w73228/JKwL5tVvzP3vv15dfuWxxXtioYDApsZhiNhoQovkshBMbjMYt5RVM7PvvsPk3t2N7a6kDTcDhkOBQQlNmsY5eMtmilybICpRQhijamZVJCDDhXkdkCY9rRchX2YNNzvdrwGqzaCGwGKK82ifd/Nr2/6e+Xre+i2JTWkwhFYDFtZKBbxMvJCSNFqsagUSiX9EYRyhNP+dRTPwurTLmK+BeR+qGHBhobqJSnIYiJpZbGwr4O+EUke0uhWjapgVBFfBlxi4CfSSqs/MpTP/L4ZwHdVhc1omcyo68DI/pApv+z6Zytv766fOhdwoaIPdDs/jgn27noq3KVa9m/VnHl1fW/XsfvJnRqVDp67qV8PqXFtr5yqbGp4vTNDFNFdj+tKK/l5KceVTXkX71Y7fWWIqpUcRRF92SaSJZsCdqy9JDLJNmMFM1I4wsBUYNjL9V9J4F8Jl+C1uBQewEvrVA8pIILYWuW/j1dO5Z2aK4NxQ4YxeWk3C7XtwxoJ+SoRbitvByHaXppw2bpQYUWPQtKUlFt42Jbxq5FTdSq86TqSuGRz+i0fu1i10zYVJFsFkBBdlKjJmPs3F/qc7RyfQ/2WdyZcPSDrU635XNFM7E0N7YIB9viX3KRxUCeE3LN+BfPMGXALuSazG9kLA4M1a7m7I2c6ZtDZu/t4e8cYN57i1aNr4YDsIbiRdO13TGN6JJMKccqacrk8RWEicvmMVVlJlDUgtvEMHUasWx5zbsWKkpe8zlpnNDtTxv++SHFv/o5258tsIvkRZZ88y6tplsDViuNp9VyvKx8xqwu40aK//b6//LSa/dtjVcATwMUhv39Pe7du8N4PCGEwGg0ZjScMBpuYW2GNoYYwTmPTsLxPM8ZjycAGGtRSuGDxydTzCwrGBZjBsUIa3O6iSxIqq/IBr3n9n5cFdwolpar7U9/0uwDqHXTzKvG+sR8EZi6aGLfFBcfXws41/dBAXkw5E76vGkFGDGqVEG0SQGIUVJwoVH4CNmBTr3mFIMbBjtOmoxZxJ0F7ETRPA/wCPKgyL0WrVQj37L6mQi2B2NNTJnWCyvaFPI5g9gWaMj2DIO3MrItTZYZXr3SrL12LwOwLwGktaKuxY08i5qy9gQLdnAZIHvZfq2Du/Pvr+/1q46+13F55NMg1W5WsfPJnHosZrHFkznjB5XocNKE5EZGnMLPGqIxKOeZPKgvXLfPYXbTUm0ZSctA5wFFgGpHky3EULI1mpQS7yhP/2sCcJV8nkwy1wyp513bXy6bhU7UbWo6BiHqHoPVMksBujYd7YTZY59UMkJsK98Aql3N7Iah3DU0Q51YEdUBpe64U5VVSJ5WzVjRDKWyrO0XZ8vYuau7oZTUd/uiU8oqieKbsRaPqIEhDgv8QHNVzZN/8hS78NgysnW/YvTMgYLpLUu9l6PmFfbGNczNGxs/r9+QVBvPj/ADAbnTO5ZmpLrKN/GyEnBZ7xUot0TU/vAId/8rzExEmsIEyXWsdk3nzm7q5H1Vy1ho9VDiFdYK55ci+za6sRERXVFPW9SZYnbgdpV6DGWJOa1WgNk3+bzW9TTsjY1oYHEr8kfF9OIPfsvjymm773z/XT755X20tmxtb+F84PT0hC+++oLJaJvxaMKgKAghUlWNUKk6YoD5fIFrnDhSayNGiESssbjoJdlkpG2GpPECIfjEPjUUxRDnm176blO6DVannotSM30esl1ObfjMpvVeZcRdtG/r67lsuatsZZNxKAyuWc6eV1ijsdcMIY/Q722lkFFulvsT6kjzlceODPkWTN60+AgnH9e4k0CoI+WzAE+lBUr9SJFva+yOJlooTz2L+x67rfGLuLmg0bBsMhzB+8j8C0c20eht8Tk3KGKtMLciaqoJDfgqEJtXOU/ry266tpvWJ8s1p4Hp3zYMbhkGB5qsMMQqUp+sr++qcX7MbN6b9Vdf+zt9k1Fta/JZpDj2lNcG2CpSbSmOv7+VSqyTKHugcIVl/MRRXSsYPvKEyUgq6y6JZWNhJSXkSQuUTyP1tpgXFqeBkCZR7SJ2LqXmKlNdtVpUdGmvFlxk81RsE9ptLUXm0SDgCJaPwgkMxeSm3xeId9Fbpk1prkQCZKGgY6rqie4EyK0wuNOCtWyGaa0XRK8lbJpagr0qJrZEk09DZ5WAEoZu+35FeZBRb+8x+fgF/gpeS3owQL15F3tSsfvkDP/LTxgOh2R/+CGzuwNhoO7soKsJUSvstT1oHBwdE6cz1GRM9dY+w8dz1HDYgZ624tCWsestqJtIvW2wZaC+t499+pyQetQprWgmObsfz/EjSzMxYqi6rTsA63M5Hy3YECAZE9OjOpZQGu0K2o1+yUq1Hkpt9WJ3OVOLnNndAaP/5Edkj44Jn39JdA5lLdWdSWdH0Y6zq1TcnRsW7TNqT0PX7tv6utQ7M3b08NU28C2KK4On50+OyHLLbDrH+1yeqEoBSWVZUjelTKyzM6w1nE3PuLZ/g2ujA2azRfcNzazchEL0eC+lmd578mRNH4xf+SKX9QJQFPkIUKKLigAKrfQG9uVlAOoiIPWySXHTsptSQL8NZ3C1CX755+qxaCsCbVcFGdxnihwtFXONVNV5BcEmk0wNSotQ294yZEbjzgLzR558z7D3Uc78uWf2qwY/lTu0MkpaqBwH8muarXczZs8EFWVvaeov1v255OmaAEqDnWia00CsI00dOfllTXZdM3gzI1bIt3Gq2P1Rhsqlue+Ln1aEs697Xi/icjYxQPK7OYk0J4H5UAkjp3VyIv96+7B+VTeNmvbfJQ/1Or7pqMcCjAYnnvzUU21ZolbUW2pZip8mqnJPGpsWR5bFm2Oa8dVmGT8AdRbBK/n8iccNLW6kiNpIOqySyiqZPEXbFPVSDKydVFFFRSpLlwm2NUMkgq57mqdW59QDRChWXMbPNXtt/5MmwBXqMz3bdQxFAkWBpb1At902TaNlZSqCt3K/cYXC6GWVmSsUNvY/H6m3kkZHiQ+WcoFqR8t5eGeX4peKi1JtbYSqQn32pTh0b29jvv8hPH6G+Tc/Y++Nu5TvXKPezlBemimHwuBGhuGXGfFXU8K7dwiZRn/2EPfe3Q5g6CbZDDQikCdCceKZX5e2BnrLYt95A3t4jHv8BJQme1FSXx+S/8u/JQsRPRyQ/+hdZvekkq/tWRi1sH4k7y5btQyUpGrpQFQEpTqG0dSxcxFvAUt7neuxLDe/lcOtG2TfucbwwQxVNTRjQ34mdgd9zVRfL3eV6BcftP/vxlh760/M2H/+zt9fvRO8Anh6+PApo+GQ2XyGMSU7u1s8evKI5y+eoLVlPNzCzxr29w6IqkHbEVVVcvj8CFREa8N4NKEsF2JhbxSz2SxhgEDd1ORZQVEMQUHTNDSuxvmGqp4ToifPRMLfNCIsD7F1yu5/618lrjpJqQ1/98HUpvVten99XS/bn4vAmCKE88yE3VWEKqJDTNWNqbGnUx3rpF0kWiU3Qid3zqjAZlKBZ4caPVA0pxEz1owOLM2epz4MYkhZKNxMwJk/jVQPAjZotj+yNI2nOZT9zSaGwW2DGUL5JHkwOQiJRdIDxeieITqoHwVi2RB8ZOu9HA3MPmtwFWT7GnXlUfqqcTlY9QvwX7X9D/us4fqy56/by67ypvdj79/X8Q1HAgMxwvy6QTcwOAmdpqR1yHYD0e4UpzJJHn13SEy961yhlo1VXQ+8pKfxlsWZH5jUhkOMMIuTQLWt8QMwtSKbBZn8Qup7Nw+Ue0ZE5cmeIFgpVydCHKgOjOg0QXUToFoCpw4wwerkmhq4wvL9/vPfijs5LDUx7XpTRCW96PrntB/RivebbiSVh1otkW/7AKKk+rBtQkwUHdTgyYL53ZEcm4Vs5l4KnOTzsWtt4qcz+MUn2Ns3iS9OcJ9/QbY/wY3G0isQYb2iVtTXxuT6PertnOFf3gdgcVsOcHjoGX05RU0XuJs76MrhB3LS85EItIcPpijvqd+/TbY1llY/H39O8YlGvfcWflLAWYnysTM4NSp2QNMbOQ9hpFaAantuWxNK1UR8vnyvTee1GrXuOiNjshoI01VPNNX2NrYUgG6qkAoA1BI06VdnoC5ctgeq3EDxf9r/6dVX+i2MK09LxSBnPBlRDHIePHhIZnO2t7bwXpqzluWCwWDA6dkZIToW1SkEy/Z4H2M1qu3TozQheqzOsFmGcw6FYrY4I8bIcCAaquP6EK0M1oDWSyNNAVCRupFmTVefajYxDf3RetGaXjaqLgI4m4DUyz67DrguYp82rz+/ZlBTCF6+Sea6MFEBuYnjl5b6hOXZUwZ8BD+NqFoq4kIWaRaeQW4ZXTPUh4HQBEZvZ5gT6aUXKigPHXokruCzX4mIXBeKvT/M0UNFNJHshubsbx3uLBCaiMnF1mB8O8NMFL6KzB96fBUwAzj9rGaRGKzqyQWn7huJPiDSnD/vmxjJl1/Xq4yYTXvxGjj9DiOd2rbarhUqu6GYDGaL2OmeTB3RlTS8bbUqLfPUTRxmdd3DowSQCpmUbJXE3kYTMsXouaMZaapdDcpQHIvFgQjIIZ8Fyt3U3qJNzxkRHYtlgZgfNltG7ATqJNptRcZpAm0BXUdQp3RRy2TIi2my7QGojn1qwdhatD5Q9MXf66c4nZt+FSBx6U8ly8gxiRVDuh5BxM7maMpIK1QckJ84sl8+eCWzDrO9DZklLkrifIEeDojOYR48J7w1oRxadv7mkFhYFve2KA8sIRuRTR3+2SHqD75LnUD06E9/3qXj9P0c/yc/wI0tzURjSmHT1WcP8GdnGJuh3rgjIK6q0G+/wfM/PpB7cDWWXnbpwTUkBqtNdwlITn0QM9Wxh63Y3pYiqm/NNoOBrGWn5Oa+vP4993iV2J96S3XO8e04gh5j+Kp8wyXRX9eyn93fT5sCeAXwdHCwz2hU4L04hp+cnDLZ2sJYzWiSEyMcXNtlMV+gTUZZa6qmpqwXbBUTvPNUiwptNCF4yiAGmNZYvJGvyHR+AijGowlFMeL07AXGSFmHAuqmxJqMzBbECI1LvUC+lsy2n3q77HOXTZbrHMJVRmJ/wu7/jhveexnD0duyUoRTMNuIxulYkY01qgSXyeecCii3/PLKk43Cm0jz3ONnAVMpzASmv3FkOxo3CtTT0G1+9plj97sZ3IXFA2Ga8hsK30gqD2T7zUkgHxhp7ZLB1g8yZp80NM8igzsa+4bGVRGrNWoEo/ctfhE4+nlF8/Q/hN5nU1p305i4bIycZ6PUyuvLV/qvXsZhrS7xOr6pUH0Dv5Qe80XLAskEY+pIfibpaT80XcoMYN38b0VvkjyZitNAuacJVjRW4yeOctckAbBKbU5kQiOaJPxOTKyLqG1BLZ1vUr3UqAC4kSY/9fiBuEfHVD3blZ/3WSW13LeVPnpp0Cm1fL0fneFhO4knTVI7cffdx/vVVRvZCyUWDividdrPJcuC9LppwF/bwj49ZeQC87sjsrMrio2Vwr55jziQBn7q6ITFP3ib7LTGvpjjtgaUO5po4ORHB+z+q/uMHh3i37vN2dsjhl+dEYwhDDLxogL03m4HnmJTY3/6MebDt2kmE4ZPFuhf3YfMdqyX++w+yhjiH36PF++NgdSTMMQkoqezGlCerkVPW824BFQJXKZz16YM5HonYJ5a6Pginffe+T9nMYCMj3pL0Yyk32Lf1+ubjpaFbd6o/t76O7VxZfC0t79NWZZkmeWDD9/jyePn1M2nHOwfMJvOuH3nFv/9f/ffcXR0BMFweHjIv/xX/4L5tGK+mJNlGc57gqsxWpPbHOcanBPdlDWWyjfMF6eE4FBKEwHnaywZo+EWdbOgcRV5NsRoS0NbAXOVFNpFQOdVJqqXTaAte3ER4EqPbueWuSheBuzWFj2D+rGk7LRWhEODHSgyxLxSp6ceDQQt7JKpFWEBqlCMblr8YaR+7FEKmheB5kX7BU5H20ROfumwE0V94tFWk+8Z3FlIXkgC2txpJN9TMJTKNUaR0fcywlsRO1DooaJ+HnA+ErOIQeE0uHn4D6yV7s00y6Nce20T3Fm9NmrltVVAvL5U+966u9PXGZGv42oR7eqE0gqbo9aJlZH2KNnUcfp2IU7hswAjmfiCoWObNjEvwUB+GrADMY9stTKtn9Ri35AtpCy93pbKNFsCKHyyIyhOPT5V1gWryBdS4t6MxaDSDVIFbJ6AXhNxwyUIOTf8AmIF0gNKIWdF/wTLCQ/EBqFN10UNMfW/axm7bCH3g3qiMbUw2V3qbz1U8oSql8DLVhFvVVd2r30CAR7md4YsPhoLcHSwdbD/co8nwGxtEcsK/+VDzPUD4t3ruJFmcW1INhtQT3QHFoJVvPhnbzO5v6C8UQhQ/fIRejigHluUh/zUEfa34IHq7n1hNkP97a/Y/mIb/+IEHzxmd0cq24Lvlqv3CtE2JSuMJo0fSADZp/OtRITuM0XIlhV2bfECadPByj3b1LEDT/1quU7wvzb1KZbXpAXD0lpIXe0Z/2tGVICB/+LDj393G/k9iSuDJ63FYdqFhqpZcHBth1/8XcN3PviQTz+9z2JRMl8s2N3d58FXj3n06AmLuYi9m6aiqkoyUzAvzyjyAdpp0e0k2wLxfTIopZiXU7RKZSSA9w3eZ2xN9qiqBc47SQOyOjWdn9xeFi9bfn0a2zSh9v8fWU3/rIOf/kR9GWO2PnlfFKv740IgEsm2DPnY4J8r2IvobZk8VFSEZFWgW6GDRdifQgBMfg1G1wyLpx53FiDC/PNVp9/QBOoX6e86UB97fOoJBhE1gOJNjcpTM2EtreKMFx2HzsDHiN1XArK2Nd4FTn5aEU+vcNjfSKjez2XXuR+br0ufW4rp99XuT6vjRNb+CoD5dVw5+pO7XYhAOZsHyn2NqdKklSlO3y6Su7MYaLYTWkzO3tlUNCPrAKpvcjh52EilVWqm63OZHOuJVHC1Ym8VID9pmE2KlCJMZodtJduWFoF5LakbKftXnZdSW7m3ydCwZZn6eqgWwHQpPVhW6PW+Dq3f1IrgvK0WyyCaFsSx8nVYZ5+65r8hYhs6RkWMMmXfQlrG1BGdSu0HL8Rn6eif3mPv/1vjnzy99Nr601M4lRuHf/IUnjxl+/ldwv4WMTPUP9gWQJbYRiKcvT1M/lYKbt9AzRboJuAGlsHzAJ98wbreKnpPODmlNcb0xyedLYCylvAPf8DsVtZpjKQLg3hemTp24KWtlGvBYwtOTS2op7Vz6FLMectYieu7z5Jx6kU3mfSM3n9bJ8lGNOfB828TS2uN5Wu+UPyfr/37b24jv6dxZfBkjOXg2gHDYsD9L78AFOPJiEcPn3Dv3m3uf/El/79/9e/44P0PqGuHc55hMWa+mDIsxtS1lGlMhtsEgjhTI5VM0QSqekGIgeDl2+3jcqJSSrGoZsQYGQyGeC/2BlobfFi38L/o+f2ykbaJCdrESGwCW5tYij6QakP3lr1om/24ygjvbTsoQhkZ3cgwPwR8pPo3nvzUoPeRp+aQ7gcWYku/A9iIjhB17PpLDW8YuGFw08jiS3dpw93yiSdUceX/9UkgPzCM7hnyHUNmlbTjGXhiBKPEA8w999gBLB443OFFQPjrxlW5nPVtxY280vKabWKqLt/KJu5z+WkpfECD3VE0L16yu6/jlaOd2FuDQl8sU2I+E0Dlc2F4oqKzF9IuLZcuXDPZPGm1fky2jLiR6VJ5pgoEK6CoVho3oDM59LnqKpNatiIYsFVAe9HAKA8xl3W1WqJ8Grv2HNlc2Iso7UJXwE7LOGm3ZCjkTVadyNME2OqtlI+QjDmJS3NGjDQHb8XLHfPEckLuTk1cZUiascJ51YmgUcsJvW3RUu4btr9oyP+nv0HlGdV//UPCGzfgJeBpU7ivHsBXYHZ3GNz+zkqPuXJPC/BM56h8axfldmi2RHN29lZBsfMRoy/PUPcfCkgCiJHYa0yOUtg37uAfPsG8cYfFQUGeqpL7IMjUoRN56ybihrqzplAhacQSy9TZO2RyPTRt+k7AZwvsTd2yjmnXercoW6ZG06nKsXXS78fXsSnYFO24aPsjbn9RU+1ZfpwfApPffgO/x3Flk8yvvnzE6cmUydaE/d0Dvrz/AO8d12/sE6PixvXr/MVf/lt+9rO/4/jFC/Ii40c//gHvvv8Wx6eHnM4OOZu/ECdxZWjqhhgjTVOjtWFQDDG6vztLENK6kjeuJoRAng8IwWFthlKbPvMqoThvmtmuq/+7//c6g8SGZS7bn/a1dbey9X24yr6nv5RicqfALyLNryL1J4HgxR3cP0+bFKJPbmw+EpTk8JQSZgoDwSQgoOWpMHowo8uHiTsNK+AJIJSR8oHnxZ/XnH5SE+tIzAI2V1BDnEVwEHTEzQPTX/dB8Kt+qxXZjmH8Rkaxt57Mvwqk6YPaTT+s/f/VxtlFI2X5vsKMFcUbmubF6352v4voTy7BKrbv1wwOfdf2ohkl36G2kskuQZN2MDwMFCcRfYFXZjOSBsG+EDfxqBVuZLrPR0NikRBT2LmkdKq9jGweKE5D50xdJ4PaFmDpJlKcBnGITpqceqttySGpMF33WKXYYwKSMLtt5N2eA6Kk6FQPzLR97EISLrf7oBvk3hGTs3hab1t8YkthRNrl+nYG7delNcYM2VJQ3rFbCchtfVHRTAzuP/kI90cfiuB5t7jQ2PIqEeuG/LhO10eui67prjPA6RsZ03s55Z5JjI4AuRc/3GH2n34H++7bdM7dMaDHY/RoBEqD1qjMMv3+DXQTsYt0HU1KrU7lpuuGyWV+rJMIXCoo23Tq4Miz97NTtr6qxAy1jlKNWUohgxQ20Im+28bC69EZrKb0nwDwuAKq+9fzt432+k0eOq79+xdk//PfcvKu4cbfc70TvALzlFmDaxqODl9w7doB7777Lp9/9gWnZ6c8+OoR9968zY9++EOaqkEbePHolOnilNE4Y3tni6ouqauak+lhSrlpRoMJSmu0VgyKIXme8+L4EL9Shh+7ZIgPDWfTY4pixKAYUdUlWilQJn3mojTYZXER67T+96uus/3dnzo1rNSPvEqK8bLtKDEXrSP5xDL9ssTsa9RYKubiZ5rh0BBHEdUSYG2uPOXgdZRJPCK76SOoGZiRwk4U7uxr7qGH+WcOP4uM3jZUX4I7kmuqBxB0QI/1pczWy0LniuFNzfCa4flfbDq/V9rTS/53+bougsfnr+ry1dgbV+YA8u8o9EzB/Svu7ut4pWiftNvJe34zY+eTGaEYdlVu5Y5eScfphgR+lqm0bBHJZ6L56T/NR5PScs2STZHXtZhExuTfNA00I009EZYqapUqqEJK7Si8Vah5JCsDbiRPN1HFjq1o27W0TXTlAFmCoHSsKgqDpgKdxUK7TLTgzdLRWtio2FXDtRMvKb2Gps3Kd0aN7bbaarqlroiVVjBivSBgrdX29L8cbSrJDQ3ZqeP4g4LxI8/ghafaz/B//DbD//G4syO48jXPcsJigZ3WaDckWEUz7LGEi+X5bNvNaCdeXe1JjcoQfnyT8c4Y/flD6p+8w8ndHO0gP/XE4xr74hjdtE+mUiUZMoi1QjcBVUSpKEwNf4OV6soOVHoY/eaI+NUj3H/2A7FxGGq0kWvQpvnaBsqhp99rr7+pZb+jBVXLsv1qxrYvobQOEt3dqzBPnW5Kr76mHex8siD72Weo3R2ca4h/coJRV+ZlvrVxZfA0GAzY3d3h9u2bjMZDpmdTFvMFs+mCPC84en6C1orZbEGmhygMOMON/bvsb98k4GiqwMe//pjHh58TiRRhQG4LlNJYayiKEdYanjx7ktim88AmIk2DrbaindLSyqOqF70U3lVTPledXC9KuFwU59NAy5pSOL+uddC3iQF72R4Gpk8rbGawhcFXHhYRm1vCPFI/jhTvajwBR2rqayCoiA4KFyM6gLFpAlhEjBLjzXjF5uab9kruvorqSaA5DKigUFG8uVwZUVrhD5GGwn7Tubk87J4GD8YoqudADnagMWNFcxYJZxerzzePkk3nfXldfptkYuf+ZEFb0BOF3dHomwpVKHGDfx2/k2jBRHEcGBw5Ftcypm+OutRH22YkW6yaFAYLpGo8lVJ+IBNv1L1GqxEGxwFTBsp9k0TfqW2FWgqsVRAGyucaN1TJjVs+n58Fqh0jICl9P00lE6mpBdi02qo2zRZM0kDV4kPVpWPS/reNettY6TMXWdFLhUw+3yQ9jWmW7FBfmNwX37csUmv62KbigGVftrT9YJdtSOS8svJlmt0WwbadR9xIU48VxZl4YA1/8h34859xJd8nBDjJjiVAmgtQzOZtL75lY97O78gvHyi7qrgcyl1N/cNtBnfHNGNp9uwzCMZQbw/ZPb6J8qJBq7eWJpS+UOLflbRV2kWyBV2fQu0i468W4ixeN6iDfVSIFIcNw9pTXR/gUquYoFoROtI3MAGvTrpaBvxAy3XMwZR0rGo2lyrS2U3TVd69avS9wOxcbBPGDyqyp2eE33yOdw6OTzB7e/xfP/h3r7z+b2NcGTyNJyNu3LzO/sEexy9OefDwMR9//ClffvGAZ0eP8cGxvbXNMB8TgsPHwNlsxtGLI5SG+XzOoppiM01mc2pX4oNDqxHWZoxGA7RWGGs4enFI0zSsym6XX5oQPbWryNUAhcJ5h9aGEDxxA+B69VgHS1cdbOsT7kWf66eJXhZXB1EhRprasXtzm1m1gDcjsY6EZxF/pnBOQw46iP+SBrmBOQjpRhqaSHBimqkyha+lse+r7lO2bQhNlHYt7f658ykvFWH+WduM+BVBYwJ3ozcsMYNYRwHhbyoGtwrK53D6F69KmbXb7Rmo9Bk+Wobu5fu3yjsq9DZkNzVqAOq6Iss0IUKoIxqFUa9+U3sdVw+7iOz85VNOf3yD4bOGZsvgCo1OqaeWJWmBQzYP1FuaAF26LBrVTSQtUxQVDI4C2anDlJ5626B87FyjVUwl+61lgJd9cakJtk4GmNlUTBObifR4M37pRu0Gmmwa0JkY3kZFcqtu2YXkLVUoWk1TFxuezfogSuwS0r70tDhRAanaq+0Q0KXh4FzzWCz4NSVCq+9qNT+h1cesl8unffOFNG/2hWb3k4poFfWbBfVOzmBne6k/2hRKoWxG9J7ovQi7tcEPM0krOtnxVsjfgiSvlgBa9lXARjPWnZu3NzC7ZVZ8knyucBae//EB2seVJsittitY1Z2HDtAmoO4zuQDZV4fEuoYQ8YVm+Mkx7tPPGd+7y+yHd1BR47O24q7d19itG8QQs58uzRZB9serBMo1xYlYabSgNl559pdrNnnoGH12AofHhOMTYlWd8+Fy332Tf779/+Lvu94JXgE83bt3j3fffRtrLYfPX3B2esbh80Oy3PDP/vN/zLPnz/ngww85fjbj6Pkpw6JgPB7y8OFjTmeH7Ez2yLMh1/b2UAq+evwp8/IU5xu2J/sMigH7e7vUTcXO9h6LxYKqLmlcP4G+nLDqpkKlXnmNK4kxlcSfm9M2cQV98BI3LLMOnNbTa1cBZ/1lFcvJeFNKr13+PLi4erS8hsI3ASoFRcQfRbKBZvi+pjmNMAe9ozptE+kpTAdFOIPgIypD2KFTz/yBI7ooZpaz9V3bDG7t0HD9hwVBw+wLx9kXzZLzX4sQ42omc+1Or5K9wlKouTyv2iiYg1eRfMdgMkVYKNzU458F3OcvP49q7Rhit23Ve0/19nzdYuBiLkr1/tJDxegjjZ2Ik501iiYGMJFcCYg6/hflS/f3dXy9yM8ie/+f3xDPzvB/fJN614qeyMHkUYMbadEsJR2PLxChtwebLAeakXg4ZXMpQ9eNAJ96S/Q0w0+eETNL8+GwY3WKYy9skluCJ+0itqRL4wwPvZS1T2Riy6aBxYHBDQymiklILtqVFuhISkYYCFNJdZeKAqDa8vZW69SChNadnP8/e3/2a0l2pXliv7X3tuEMd/LZIzwmRgRnJslMZlYOlVVQCS0JBQENqV+60Wigu9BvetWDoD+g/wo9aHqQhEZL6GqUVFWq6hpTVZlZyZmMIGPyCPfw2e94zrFh7730sOycO/h19+vBCJIR9AWQ4fcMZnbs2DH77Fvf+r4hlNhsEYCBbV4yIkcnwZaeTrIcpj1yijoa0wGsxMPAit1Z6ayG0/hye5ZamZXDtTsEc7E2Vm5y0HHw+pR6N1H/h/eQrU1CXVsUyinlxmNkVKNtR57NcZMJ8sqLxMqz9mFLqi3wWd2RycVOV0AxD0xUaJTxP/0ROAdvvMzBGxu0a+4R0f0SfBiDL4fn0u5wejM0dqx0E7cSVS8ZQdcpiys1azeU/OAhmhL1nSvk9TF+a4v2jcuEJtFuWlSQS7LSMy0Zy9W+90e+H1htLwr9EIK9Ml99ckzjqTW+mxj9i5+RBu+rx9WdvzXh5fDF1zvBM4Gnq1y6dJ7FomF9fcrXvvEmP3/r58S58s7bH3LlxYtsrm/yxuuv8+Pvv839e9t0Xc/23kPabsaonDAdb9AsepyWiNSgc7q+YWfvHjknLlw4x/r6BovFgnvpPn2MrH51wMmLddctKIuasqjp+gXeeWI62WN63AX0NDblLADqrO3Ao9N1Rx9/Gjv1rCzXo6/fuFAT70bmb/c4Z5Ml7fsZxhBeEXJlFgLq1KYesxKiI00VSjFl1r7S7WeqFwKTNwXNsPfDzlzICyEt8vEz5xEgKgHa3UxxzjG+Fmh3It3uyfbZkz7n4T5yLwh+zZH3MjQmYndOGLsSF5S2iYSRwwNOoHhJ6D+A3Q8XxIdPN4w6+g3okf+XE9//ydcc/n0a+D4BnAph/KbHT4SIEmOmct50Aao2KZl+Vbb0eT2pNn++R7p3DwmBye0OdcLeyyWhUQ5eKFbAJjQ2xYRiYl8YpvOORGgAxX4ijRxhkUFsuo6U0dsfM757gYMXPLkH1IBNaDKus2gki8pQSsl0E0e74VbxK6OHEXVmuLls9xT7iX7qmV8OK+NFl45rVg7jWxRJxqy4oQ2lXmDIzFuxI3LY4jMwYyyX74bpveJRbdLSom6pkdLMsSvII0Bq+K/ooZcTbriNHATsJz2KRGF0t6M9X3D/e+uQoZwpiz96nWKvx3//7cd+x3k2g2VIb1HiLp5HP75D8e51SAn/va+DlhR7Pe15M9RcAkXf60oL5ZuMu3iBeOMm8rN3GNdfI74xgX7QMw0WAnAIUHN49HMU88z6jx+AE9LGiDSynbVstbouEacF+f4DY8pU4a9/hnz1deJXX6Y5XxwyiLqcyBz+XBq4Dtu/atEu9/sRgNSuH2rknlSPm8Cr9pTpX7z/VOCECIs/Pvid0DvBM4CnKy9cwHvPy6+8yGhUcfnF87Rdyz/9x/+c27fv8sF7H3Ljw1v88Z/9Pn/4p9/io+u3WN+csHV7jf39fUieuh7RLDpyHnjg4ZcZU8v27h1ufjziwvnzjGrTPjl3qDHxzpPycZJQUbrYMKqmVOXY9E/9SbYKngQ0jtdJoHNa6+40tuUk6DrttUefOwqiHtfee9wyH/dZDi/it2/skfpMMQn484Jbd2hW/DVQS7chZyV7G5GXZL5LqkBUYgsShMm1YDBBFU1Qng+MvuxhDM0HkcXHkdQe3Z4hmHie2fugY9QF0gHHoyxO/XzL958AIKXgrhgM9dnBOuQ9cAUUV5X2dibehsW7CfdNbyfiAKNXPO2OkFMk7+RHV7XagiczisdZqKOvPzs3KEDxquCuCFkgarapxgEsuUJwFdD/bpxwflMl12+BiMV1/Ivv4y9dJFz+kmmIjpwFfXs4+t9uutVUkumPrH2DQC4dKmLZYhjAmn3rKqOba4xuz5ldXhsMEJfeTQLOgFQcDTqlRqlSZnFuiHVxNvW3FOcuL7JxMoCzgc0SteusBjsOixZcn0mFWzEoy1aeS7oifbM30CZHlq0iqyDhJTu2Ot0cJcuFQ0H4Uc1UOs4anVbLNufqNQNLctq0l4pZBaRSKPeV8Z2OYntgZN96j9w8nZ11dW02KB98eOQAEPzugtFb18kHMyZvvkZ/acriQkmxyKvQ5fIggcLia1cYOSFvTOz6MzB69X4ijoyBtNO4HHppDd+RH/yqwiLDrbvgPZ4LhBtzulcv2nTij95Bv/oqszfGyJ9+g+Jf/RCNEXIiv3udsL5ONXqZ+eViNWxgVgWChoGFDHZTu2QAnyQAX8bhPGtJgo2f7JDu3Xvqa/3aGv/51/762VfyOa0zg6dMIknk+gc3WCwa7j+4z6WLF/n2t3+Pqnybu3cfcPfePf7yL/89X/nqm7z+5qvs7u5y/6HyvT/5Jh99eJO7tx5QBgNGVVnSR1lFrCiZ23c/4mC2z6geURQldVWZel/zY3SCdsHPOQ0O5hHvAtllsmZUj4q0j9bjWy3L5T4daD2OpTrJRJze2jr+3LOt6+SF/OQyFvOOIgTqywX6ZkarQSC+zLRbnuyGKZ5cC0SFHpw3d2QvpoMiK06FOFfChkfGgsuw9lrB+OXA7E5k/m6P9oofCRuvl/ja0e4lDj7oyd1ZYcaJ14lQbDrc+4K/IuRNqOsAhbUV/YZjVDqcZMIFA3s5QvQZF4X16YT1b8D9n+wSd57MQFkj7lDQIY9sz6Nu4E/a9qMNPze2jEEVA6zjIpATZBRp7cSXBeIpQc/P61Os85u4xQIpS2RtyuIbL9iFyJspozoTARcD29StuWMeSa4DSUoxW3ou2TecnV3IjJkSHnxn0y5Sw8Us1bLyMfKd/e7K/TxMTh2229QtW4CO8sBaPaHNNmqOrVMd9COh2ldYgqNorFGqxCb5BpuDJRORh5F2l3QFYvKgo1KVlceQpIECWl78B1CwtNs76la9bLstp+pOA07HJv4e89N5xKMIW24cCWRYf3cG3/85OT7jxIpz0B2fzHPjMdzfIe0OZpo//yXh/Zr1V64xf2MLgHInUv/wQ2hbZGuD/oVzdOfKoZ1r31ccWWtXnXkyLfVjS1C7FIu7XqnvNsz//CtMfvgxeusuadFQlgWkRJzNkJ+8w7n0Ou72A+IR/ygRgb6j/Hc/x3/3yxy8VBMH5/qj7bclW6pnuVzx+O9hVYNF1RIky2BBIDduPX3hQP7KK/xnm/9vYHKm13/e68zg6Z1fXOf2nTv8h3//U15++QXGk4rz58/zla98mT5mDmYLNroNylBz8+ZNBEfKkb/9539M3ydef+MV7t3d4Wc/fI/tnW2qMKXrH3KUhUk5snewjRPHtJgyGk2JKeG95+Bgj9PbZ0ofW0Io8M7Txp4ilGTNxNiT9XEXpccxOc8KnB633KOvPcuyT77m8e3GJzUiB/6HUHqKa56YMjSDxikJWik5KLTDm5YCRA8+OpgLLisSBnZqcLgVJ8gaJK8klPZmorzgqV8MFiy8r5TXPGXt8U5IjT4DcHp0X/ipgbTxtUB11RM14xH0BZv+M8pacOLwFVAoPgr9LWX/lz37qae4rODPsg12DMoRZvDkXl5KxI+28077JpbPq0A4J4y+5ggTT3QZJ0KhQhJoNNFFRaLgSsf+v3yud/osa/7mBdK3LpEqIVYDY7Q0Sdx0K1ftbsOz9sGcflowu1ra+Pfw+8jlcsrOWmT1w0i7GfCd4ruMqFLvJuYX/IqRaTYNWVS7NmGHyOrGJdbLbD1HPzW2KLRKuyb43oS+xTwP3j528YbDSS3ActN0AGBLMbsOztV6qH8yjyP73SyDeVVkZcdg9gpH+248MrW3PFUfm9ZbmnGeOK0dbcc9iyHjctlFq7i3r5Oymou3ZhODn8Gu4FikiwzMedej7TbiPYjDX7lEun0XffcDJm2Hjmv0+s1Va8pfOs/sWk1xYBOUomZaGhbZwp7DAKAG8bUc0WTG2tzr23MV4188IN78mOXdf3zvg8PP2nW4tidt77B0LQ+vvsz+d66QSsfGD+8/sl+OWT0s2bwz7tun1VEBv+9g/YOW4i/fIp0hIgfgzh+t8XoYfTob8zmos/s81cLDBw+paoO74/GYza0NUlKUxGgyIqZE13X89Me/5I//5I/49ne/RV2X/OKtD7h3Z5uiLHj5tcv0acHBdseD7RG37l/ncELOGJKua9ne6RjVI6aTKc45+r4jzpc91+MXrZQjfewoQmmmmWJRMjL8cKyeBlqO/v0sF/2TgOjo38+y7McxSY/flie9om8TzVuRTEIjhGsev8EwraErx/G8XEAw1sbPHalWohgTlZJSiEPWWJmVhiBUlzxU4J1QvxgITlAMGABQnrJRZylnTE3xmsMfOMJV8zup8PQxkwW6OuE6myTxm0L3IBNaQV2m+yiR5oqS0bs8xWbh0WPiSVN0cuL1x/4WO8dLAW4khJEjXBSKsTNheGfPNZLxUWwQqJdVq/F5fbYVFonZlbDKd1OBjY96VGDvlWI1Xt+uC+nNCdVeYnKrQxR2X1v25hgmmMxpvJi7Fdug3uH6w+R6FyHWrKa6jHnK1uoLw8VWTUvlOyW3QqqMfZLS2jQ5LI8RGYwXB5YjKJIG76ikK7+iNDATxWKwLUjGbC4ns5aC7jgMYSxBz5I1gcOpuJOZdavXLW0LTtgMnAaQHslcO/K6pwGqWAvzP/8KEpU0ckzf3oZbd0k7z+D1tJy+67tHQFe+d381kXesvbd866I1Z+5zfqWFAvuuXdRVa7Q8yPQjE2T71tqgax+1lD/+AM5tkt55n8faK6jCvYf4K5fQgxm6aJh97TLtmqGYvW+eH5jCIROwkGEa8jAb8OR+fNx+XTKGq8iek88nW4fvzL8qHPSU1+8TnwScjl5fRdj748XvjN4JngE8bd+f8Z3v/B5f/UrLfLbAOeHihXO88+51DmYz2qYdpu9K3n3nfe58/ICvf/3rfPePvsbG1hpb5zfY3dknpcSXv/YqD+7uU3xYcG/7Fn3qYXVfbzEeMUUUqKoR3jvqpmWxmNt01ikgJKVI8AUpRY6LbGyZ+si0GzwZUJ084J/292nLfByQ0hPPP74Fd/r7jr/7tE+hKdPc63Gi+NoRtgRXmjA5LQSt7eS9Ms1cCg4rc3SX4W42TMV6+VkJ2REXaqLyIScpiTmVq0JNQV3WzNOMFPTZcSiCf1VwLwmuEkYTjwwnj6yZzhn75NQsCtJYKWtH0XvijrK4kaCT1V7LzeG3/+jeO+0RPfbYcUuC048VKcBPhfJFR7HlkGA6poSiPTR9QlvQDKEUnFNaTaZVqR2xVfrZc1fxz7rml0vWr3f0655m05ih0U8/ZvZ7Lx4Ch/6w7dKtOeaX/Gr8++iU2NLnqNn0jB4kFuf9IcuSDo0vRc2SQJRh5H0ZAjtYXmTTKqHW5kmDzUBYDBN2QVYxHb5VdIiTWbWJkuIzq2DhHIb1ZTsuVxolf5jFt5ycWzIYq6m45WfMh69RGc4Pw/uOsRx65L1P+okcYZ+WLJdv7bNmf/qFfLktBy+YqCs0CjfvWIbds5Tqo0zVcMF/mnZKZzMDLkHoBkNNyUo/lmFSz44Ha6WZFimPha2f7aF/8zOLF9vefTxwGirdfwAPdwiXL9J+5zXixK0Yw+bccucbE+TS0jTz8LpysmW6+u5OefxknsjSbbzeSUx//gD98CYao2mvxLz/nryTDp930yn/62/84Mmv/4LVmcHTyy9f48VrF/no+h0uXrR4lbKsePeXH/D+u9e5eeMmfWopqoA4uHnrBjkKi/mcP/t7v09djdjd2eXGjRssFgs+/vgWt27eeyReJWsi5UTbLYipJ6Ue7z37+7uEUND33QCE7PXLgyimnpALRMxtO6W0atnpMRADj/+VPwv/edZlnPbY01mlx6/T3uP86RNackRxo5oR55HgVmJGh+CckDMgutQ8rjZJSwXVwSx3uLttHG6gisvKkRqlnxljokEp1oaWgFPm/YyuSczf75/48WQpuMQczKtzHk0GnMraIUAsFZ+ExieKKAS1z5aTtR4LEZJmXHBwAXzriB+r5SWqIiMTl2cg7j6Z8TsKmY4/s9zng7g+gFSCXxfCOYc7D1orJR6XB5+bLOallQ30qTcWVDLUpSc3R1qhWdH0LMfd8/oktfHTHfTt9yi/8QbTeYuOK3T/YPV1S2Ll7YNYAHCszFyx3B9yxNww7n8kCDWOZJVP5nrA2d/j+4lUCu26G9p6toyl8FiyXXizt+etPecHYGRHXjGzVmAqnd0PDgfp0i5Bki2rW3OrFpo6HRipIZojKzjBD+3GJcg6dNFmZRkAhwaXT6wjIvqjWqjTauUlNbS3lsCpPMiH4/RPKh3E2mdsHT21VJGiRLwD58wTqm2PvcSNx/Tf+tLqe1q25FaM4kgYPUjE2pjH4iAxvm7ATt+9vmLoyWe8KcqJePsOdV0hr5xn540KFEb3s2UjlkK75gmN+Y5JN2jxgmnxjA1jpV9b6rCW7dtlXiNiry8PMvXDnur6Q/ThNmnv4JFhrDOZkS6ZJ+fRN17mvzz3fwCet+0eqY9u3GB9c8Irr72Il4L9vRkf37pF1oz3ga1zWzx8+ICvfv11bn18h93dXe5t32Ly8Zgf/Ye3+P0/+iaXLl1gMhnzwXs3eXB3j6rap+87unh49fYukHMmqzk3+mAptoqSYjzRVjnOyvR9i6raeP4q7+Mo/fGkA+Ikw/OswObkMo7W40DV0557fD1ttF3E9mnOis4T7n3wlx3hnNh0xqAFkgZkGb9wpI+uYBN6jaB+GOkYdBTdwwwt1C85EnaClgytRjhQdn/cEg8eL4Cutjxb36roNJPUPKSK7Ei7pl8qvTDXSEqZFvCdEGsIve2nkOyMbEBQyU7JLlNeE7R35KmjnAi6CanPdPNoHlb55D7Tx/z7ENwKAiK4MRQvOcILzpivCjQNsTbJ2S+pNMNLPOZPVUHZObo2k5qMKyGps0gsEUpXID6z81dPGQF+Xr9yPfzuFhe2LxB/+PPDC+hrL9Gcs9GvJatiZoT2nnonszhveiSw30eqgOEiupyiK+bDTZpj5e0DUO4nujUTG4dWV7Eby1DfpV7oaHSLic/dStciA7Au9xI5eLMfcMPYemv/XQrbXdJBzySU+4lm8AcCjjmZA0eHnU89bS2F8svPvawlG3Xs1/K4uZxjC7RJxRyMXWs23FPfs1xXt+bQ3/8a4aN7xFu3n7Kip5e/dhVyJn544xGQ4Dc3mP35V2i2/MrCYQl01dt3O73ZUfzl20hZghPy7h4pRlxdn2ka8PQPq8T3r1NsTNh8B8p7M/SDG6bRunKRxZ9dItaWi7cE6xKh2s+oM1+nJWBatVWxyUhJMNrJTD6c4d7/GO168v4+zyTDl+HLOAqyjoDEB99d58vFJ9VqfD7rzODp/IV1pus1O7vblH7E/sEBf/mXf83de3eoqoK7dxvatudnP36Hc1vn2NjYYGfnIdu7D7l5o6Qelbz2xkuIczRNx7he58r5kg/bD4BtQKjLMSlF2t4uJqpKWZRG3Yb5KR5Ox2sJnKpyhHZzUhpsXVf1uD6SPuX5Z6nTWKXlcp8ErnjC8yeX/6TPpIg4E1J7a1eKKt2HPf7A47YCDBdv4nAyHe5ixFl7KTs1jU4EOodUioqxJxohnBd8Mhdyj+Xg5Xlmdr2n285PBE7+nFC/FHBrQlBHgdkkiDf/KfLgmRnA41bGftop3WAo6cWYs6wGbkIGcTamXVzxOK+E2qwBtHMUE8ciRtqPEvkRt/TT9r0t11WO8qppl2RDcIMjtE9QJNMypWxJ6wFj90Ll6FIiqOCbYK7nfcKJUhSeLia7bgXzfNKz3OE9r1+51j9syDu7+MGl2r/0Ag+/d3EVz+L6oSUysEpLb6blhNySaVLD0ivB8HK83/dKX7pVm8labn7V5ou1EBYW8rvMGVtd6ARSZcdvvwRbjZIZbnQcSBbKWaabutWk1xKoLUNidWgzhcWQjZYOc/ZW03dHxd8cPq6eVYaause3046Br2XT4CmnLTl5uuLxyz/6nqWXkjp48O0p0/M11Z17Z2d0HlPpxi2kLI4BJ6kq3OuvsHhpfYjS0ZXnVaps8tB3yuRWR/XOXeIRT6llfWLgdLTeeo/CueMi7YMZF0Yls9fWSKUzn69o3zMcAlL1xpguc+3KA2V6fUHx0X3S7TtojI84gj+xVmHITz9H3f/jSCFPoxG/WHVm8PTqay9z5dIFxvWI7puJddb4r/hP+RE/4YP/5jo//+kv+dEPf8ovf/k+s4MZmcx4PGFn/z7bP7vHfD6n63ouXD6HuIwvlEXTMFss+9iJtp8DsgJBzllUS8qRcT1GRFg0syPf5fEv1TnPqJ6g5EH/lDkJLB593+NUQ09jg54F8Cxf/7SD8GnLfBQ4GSSzxwWbPltqdXLOOG8tsPKNAFMDFPnIMtRjjBJYnEphYECS4BEozeNJFVKfKEuPH5iq3mfK5EgJFg8j848Owa04u8poGj63QLXuKMae6eVgk3xZcWIaJlUoxrZc9faZPKYfkgjBCX3I+IH+x5l5gPbWliiTo/eKriU0CbMu4kQI0cNDwddQbAhxoqQHx/f30RtwCYKbCuVLDn/ZIRUU2SEeYtKVniBj63eYCSYOYrYpvSI40lwJ3lFNhDT3rE+n7PX7ZCd0fUSApm3Nv+p5feYV/voXaNuSF40dlw+32fpnc+IbL9BcLFlsGUuzDO41PyaP6y1mZRkEvAxDNR2KHddx5BC1yTjXK6l2q5Dd0Ci+zcwvero1R72dcF1G1CER2g37/iXb+5YGnamQI61tY1/DPBO8mifUwBAvp+hQY0HVCe2GZetJOmQplstZAqSlMHzVejtx6jkpRl7+e8VCLWNWjrzm6OuO/v24fz/Rl0iGdQxX+ws/OMBtHzzbxf9xyz5FQO6vXubu3zoPGJsDliuo4giN+UBV25FiuyHfu//IMj+tegSAOU946QV4sEe1ab3W3dfq4Twk9OVgYzF8j7kQ1m501O/dJ9+6Q26aZ2OYjtZRpkmVZXq7hGC6qOUm1jX/s+/85JOu5XNbZwZPZRVA4MLFcxSUrLFFZMGIgot/a5NQelRhb8/y7DTbflfJzJt93n7npzRtw8svvUzOwmI/MZ/PaLvDzA/V5ZWRQduSaNMcJ56iKOn6bgUUTmvfLe/iy6Ii+ECM/Yle7qMszdPrca85yjCdxgb9KjqWo8s82U6Ek8u2Vx0Zfxme9s5BtvBdmTjCSw46hd4RvUWDIJgFgJ2zcR1oONQX4ZQ+Kt4JvnQkp4RoYAeHiQo9VOcCvAbN7UR50TO5Fgjesftex+LjyOSqZ/pqgWw48uBs7vPgfxTMS2p5R9xrJsa8YsKCs40rekcKoN58vzoydeFNXwRU6im8I4tSeMds0ZMlExdA5w2EXVJISt5Z7ihj1FztcJcVPxa8d5Qve/Nf6jLBmcrOqeAUwpCFlT1oyqYhWzF9FtshXqx1nAJrF6ZUZUHdVMTckctE20Vyp0jx3N/p11LLO66ldYn35Ic7yF/cY/rml8i/d4FmwyG1sUNLrYtFuKg5fgs0W4ctNYbjwAJ6HaE1l2oV+w2ZDsnacEtfpFQa6gkHiTj1lAd5MF0cfoDDNFsuoZgN619aCzhWIcDtujsEQIPZpUWrKKs2JDK0/ux1S4+gHA6dsZeWCivN1NF4lZPtuuGx5Q3EadN0R+vYc8vD/Mg6V+0+4XQAphyafe7ObXLtM6p8+y7nf7DG4tqE8fU95CNjanjtRZqrU6Y39snvfEDuujMxMZ9mpYsb5NLTjwOTt+4yHl/i4MWAiqwuE9WusvbuPv7BPqRM/OjGr77ixzB8K+A0aJ7klWv8Vxf+7zzGDfkLW2cGT//sn/5bYox47/k2X+f3+DqJxEd8jKrjwd1d/uJf/w1N03HthWvcun2L2WyGc57gS/rU8O71n3Pv3j1yzoRQMG/2UZaTdscra8aJH0KGIzH2XDh/gVt3bhHj8pe4BBj2/pR7mnaOkyllWVJVI+aLRwLZjtTTAM5ZfySPa8n9Kj+ykyzZ6dt6yDopThxV4SEIsU2kpHgZUsQ/VrgIBPOQ8wjJD3swQG5Bg+KH/AQR7EQ8MFVxlqiLgC+cjaM6HXxh1NpPhTB5qcSvJcZXPZTmaTR9tcAPF4b5LWWyZSxNTooGxeUBLPdqYuuhlx+csyE/UXwYPqUO37aahUKJI4gQnZrVQp9ZyrNyUqZVYa+7JswfJMqrHgV8raTrgu5jgvMLINdgvFHgZsOFL4JmNQrdKZIHGb6Htsuk4UKkvZCckHymriokgg+BECucg14i827BIjX0oUdJdIuIHlirb/Yvn2H0+nl94pr/vW8Sx45ilhjdOIAPbyN1hb9wFWYLluJaFPqJUO3poGWyVpxvMq7LqAuD6FpMzF3ISv+09IoCYwUsgT4jOeN6ZyHDg0dTGvnVv8s9y79TbzYD/URWLbhlmO3S1wkZbA/6YYLPH4KZpS2Ctbr1kJVa3lctWaSTRotD2+4oCXoaK3Q01+3oY0eXdVpEC3A41btsIS4nwk4AJ9/Z5OFSoL9c7vzNc9TXbzwi7v60Krct8uO3mXy0QXrwcAWQ/Ie3qPw1NDjka6+jP/jZZ7L+x29Ygh//kuLKJfyPd8h9z/jBNvz5V5hdMkJj6+dz/A9/SZ7PSUsx/KdVx+x+TtTw+MPvXeC7VeY5eHpM/ff/3T+h73vWN6fof5P4q//9D3iFayxoSL9IXLx8gf/0v/hf8sO/+TlVNSKmTLO4TtPvswQ5qpnd2d0jSz3tS1G8KymCWeznnHDeM1/MWV/fxDtv6d0xcjh1d/jePrYsWkcoSsb1hK5riYMVgohDxJGPIepTGvLH/n4SAHoaOHra+59WR9mtk8s5nKoTBDf8f0rg1NphMgWJgraZ/DCS3gIuilmHqyDnFZkIvhu0UcGRsX760vFW1EJsZRyoCk9GKbyd8WIywJP2hV6y6aHGDrK12hJKMXWMv12TOqXdhaBCH5MBErU2ostCKpSkShHtk6Sg+N4mA1HwapqQNDTMsijSQSqUkCx+hmx8pHPCyHukEA6anjgYWuVCKUpvjFsBum8RB5wHl5y5lG+ATpRi7lAgxsHcEiGpGuCMQt9EnDjKScAlD0np53b1rQpPS0NZBHrt6NqOpAkB+pxIg9nhp9KDeF5nqtlVi+9pNwLd2gb5WxuM7kW6jbAamZfE6qLdj4VYBySb0Nu3Gdcm6oewf600EFPIoXeOEyTllbg4Ds7ixYEOYbT2W80FpCyDMzi4VnF9xveOJIfTeLE2vVJYYDcwXtDaWnFhnokTQx7HgNAKHCnq3KG55ZBFB6zaO0cjaRiSB+zNPHoaPFEnHcGfKOc89kZW2qr8mCtPKgdXdDXnd5smFA5eCPg/+yb1e/fQ4JGmQw8OzDH8U2SCdHG8bZZ2duH7uxCCuZT/JiqrMaUHB4j3qCqj//GnTNbXoK7It++u2n0aLe3hTLUERif/6/zxdt1T6t4fQiWfIPvlc15nBk9f/9brLJqGt3/2Dv+P/9s/5E//N9/j63/vddb9mB/94C1u37rHxtaU7/2tb/HDv/kFfdtThJqmPxjaccs67ct4tOnex4WNeaM48aQUWSwW1NWYvu9oUh7adI+Cn65vmM33GdVj6mrEbN4PjEUeXv64A+I0luc0APRJfqyfBEiddlY67nG9/Ms5Z4BJrDPhJg73LSV0nvhORBul30m46PBXBVcrbk+ID5V8FcreIVFoimgXk860PipApRRYO0witJoI2ZnZZh7WV0C1ZiPVZWGTNG2f8V7okiIeRudtGa4X0iwj60rKih80T3UyoXsqM16HT+YwTyfJ1l4TYbYfGRf2eXM07dUYY5WSV3BCnzOphTyyi2KYQLottA/MNDSPQV4znUj+KWbq+YZAqfS7CpopvNBnLIXee0QzsYFWIq7wjIoSJ0AhBBdMp+UzXdvS5sT+IhsLIMak5WSA0e2b51Pwjudzdr+eWl3w1YCROgizSLnb8fDrk1U8CseAgRlQFvuRNPKkcaCfeLvwHxnB970F7i4NLwHqXTvX2HTdEAQ7V5otRyqE8f00aFWEFLxlzmVWYvGVlUEQ6p1kLFQ/OIaj9GPbgOwHYmloofne9Fkrc8sj7M3Sa2npU3U0dFZPBNs+reTIqfJJuXaHX8AwNu8eD5zkSNuy2lXW/9FPkOmE/T97jW7iWFwq0HCJ0dt30LZDNjfwL1wm/fyXvzqAUj30ODrt6Rif3WfqMeXW1sgHB2ffZicwX+C+/TX231izacrdSP1vfo4Tofm73yTME/4vf4Z2Z2Syl9NzS0HrUYbpKYJ8qaoVAyhVxR9875dnW+cXrM4Mnvb297l39wFN2/LRhzfZOrfBm2++xutvvMrVq5f5R//wn5Nz4j/6+3+bonLsH+ytBODHGZSTJdjPP61ek3KPd2GlYbL8O9jefUBVVjTdYrAiMHARvL32MM9OadoD+r5hVE8py4q2ax67Bc9WRwHW00DY0x47+/oEcN7hnAEN8aA9JgoXE4UPGm1A8C9aFAtTpfgDv5qos5OkARM3gnwfuAtsWQtLspBVbT1JCdETq0xyavojhKzDCXsQTodNQZPg/SF40wBVcIc6Q4XOZXOkz0pWCJ3gC4iiOKAXxXWZrksU6vDe4Z0jpUw/BC/FPYWbSvm63c2HbNqnxmVKnAEsBXEOX0KFYz/0tE0if5zQblDLLQSiQxegXTbAeDGj520/pqi2/UlJCfLEtGOhApcK+pxwXux7yImsafi3glNSm4hJCd4E8TFnfCnkVpEx9IuMq89yu/68Po1aJtCj5pEUK2FxuWLt3X023m/Zfa2yH9nQfQiLYex/OD25PtNPgnkqDUDLd8robk+7FXCDEWWsDfy4Xlds1tIvSJ1pCqv9vAJxrhi81HozYfQtg9mlraOfCHHkqLbjKmQ4ThySlOogr6wQVAZvp8GJfKktOkbOL3V6S7CzvDf7BIfh0+JWRK39vhqbH9pzrrN/u16JY3ms2/j6ezPyMNE2+Yc7TIdMuNy2h+aN98C/+SXCC1ctAuXXWFKUZ4qKsRcvkazit7bQly7Dj98+87q0bcmXz/Hg2xs2pQm06wX5732DdsPRjwXfFVz88JLZL5y2/pNAbXliXj73DOBT23b1Pn/1Mv/gyj8+83u/SHVm8PSDH/2Y7Xs7ODHX5x/+4McsFgv+zt/9U9748qv83f/pH/N//T/+t/zNX/2Uvo9mMxAquriAI6Grj/Qqlqpg+8M2yleIyCPGXWUo2do4T073STnR9Q2gQ1sOjgKb4AuKUJJSpCxG9LEf2nWfFPysNviMj522vLOdpQTMp8lBKBxu6vDJJr7EC/2OAQlfOfo2EoJb3SyYkaPgt4DgLNNuuC5IHgxE49C2csBV0A/Bb1relxyYh9HKNK839q93OrBNJpgOIli8nJhn1IHDrR9x5NbBi2oQNcaktLcVvwVFZb5IXZMpxaaKimj+SW2TiJW1ynAWs+NUKJ2ncwmtoXrFMZdEKY7CGwMVUyaJEnMmiCNHJRSOPmVcJ+QNRb7icHeFtKdQKzSKdsOxVwtxXa3FkQ2cNilDIVSjoS2XlKRQBkeohBx66EFV8V5I2f6dRAc2yi4MRSFkoGuzgSkR2lmmS8/F4r+uqnetBdQPppahUbo1x8NvbbD+/oLxPXMKX2qIUmXj3tVOpjlXInro6ZTDks6x2JfFpWLQPJnuKVWWg1btpZVuKQfL1PO9rjx5fK8rQOMiuG4pUB9afyNrC3YTQZJfTcr5wQqBQRe1rOyXxpiD9ikaI/ZYZcITNEqnleuHyb3H1DHmSgcmbKGW61dbS1KisrgY7MZnoauw2yUbtpyw87cerqbETpuOW33mD27gvvTy8VbTJyi3tkbe33/ia8KrL5Nu3j7cntNAyeOAynJ7D2bws3eemSnLP/w5Fx9eo3nzMotLBdVOop8O05nYDQFdf/pyn7SuT8LYiRCuvUh+8JC9717lb9e7QP3Ut33R6szgqV/fQ+47Yx98Qds1/PAHP6IIBe+9+yEvXLvMn/zZ93j/vY+4fv06sTPzzGVkymFAr2N5OyTiOLd2hf3FNl0/BxQRTxnqgbU6LAHarmF3bxdwOAHvA5ozWTOC4v3gMM6gA3IeN7hNj6ox88XB6uJ+PHrjWeokEDoLIHp6y845QZzFjlTnPSEE+lkiR0XnkDNIbeeI6pqDCnQG6aZNiunscPkyBd0ExQwkYRBsesidTYf55EwQ7kAuCv09c+yODNYEftBFKTATdGTLErXxfPF24+IHDQdJ6dtsX6/LhGwsWR8zXsQm67aVcB7rlZVAr0jncDrYACRjZSq8MV/uUNWW9hQ2hNo5NCgZJasSxZbvcNbey8bM5QJaSXZnWwmVevppxm056DO6EHQOPIR8X/EbglwU4t1MjgaGsg6TdKo4ZTh0M32fKWtHlxOFdza06KHvkonXAV3G06QhqmX4DpwK2kJ50bH3rz4b8evzerQ2/upj7v+da2bmui4mTI4mTN7+6ojRw7wyvSwPLH4jVtaKMxBirI9vbfKtmNv3vLhsbtDLKbhUCt1EGD3MxNra1zIIuIuFTcnpkCsZmsNzSayFWPljAnAV0CGHb+k9JBn6icO3ulovMGRWsmqjKceB1Wq6jUOm55h2KR2+93Glp1wtjvo+STZWrX7QoyLEiSdVQrthrUvJmdArm7+Y4eYdcWPE9pdrA7LTw42pdzPp9p0zfa/ad3DnHuHVl4jvX//k7bu0vPs8BfwAOE937Rz+5hGTzpP6oOVjp5Tf3CDt7J6drYJHLAHiRzcIH91gvSiRumJ8foviK5fYf6lgdLf7VAxEz1rLab47f+iYut894ATPAJ5GF4VuuyNv16SUSMnYpV+8/S537zzg4cOH3Lt7n4cPdtjc3OTjj29aZIovTMicO1JOQ3yKLbMqRkzqNfrY0scG1Yx3YdDvuIGjMihUVRMAmnZBzhHvzeRM1UbNy6KmrsbGvIhd+FLuV9vvXMD7QEzdsNRPetd/Glg6TZt08vnTl1QUgWLszfAxC3GR0EbgFaV81c6CuQGmBizC3NoGaZSRXSE9dOTZwAAtgWHNYZyDgHYKXpCgeHVDC2950lXcGPIccmvgTRWksRYcgr2+MQo+OSVLIpLNLDCKtdTGBqgCJrR23tzNJRt4yBF0zYJR25iJmMA8FRk6Y6kkDALcZNN0sYwUlbFqChSd4AR00HYtgXKO0FeZQhwhm8i8iI5WE/MykkdKWXr6h3loqwlMbdqPiSDnQKMiD0CHyBoiSClU3uN7R58zzgtV8JRBWKSEdodtkBQzMRnwFG8Ac2mg2SxMr+VHtk/c6FDs/7x+PZUf7rD+wQW2v1LjOwZRtX0H6qDZdLjeGKB+JIzvR/xoaTEghNaO3X5sv8lle26x5ShnZlRp7SlZEe2hMd+nfuKJIyF7Y5U0D8BFBN9a1km75g8jU4b4E8lHpvcChH0lzDLtpi0v1n4lAF8xWXFwL9cjsRxHawnmjrTSTgKmJSA6Lej3ZK1crgcWrNlw+CYw/fEt9MAUfbI2RfcPSNu7K6CRgTCZUF39Fs3m8QUXB/m49uhJE1+YqDtMJrjR6BPHuKze97j15ER5c5sY+0ceP5X1OrLNbjxGuxPvO0M9Vn/Vd2jsyQcHVLfuMH7hCjqq0PH404uxeeKGDdeZEHjxD3+97dLfpjp7MPD399FSiY0QczQRtyZ2dh8QY8+VqxdZzNuVTimEgpwzVTmi8CUx98zme8TUImITAylnQlFw8cJlqv0R93duImIX31WfeKi2nVMUNUUoSMnhvSfnhJLwg6XBfHGAiCPGjqIouXDuEl1v005ZM2VRkfMy8+4Ex3yKaP3p9ST26XG6J7tsVtMCHStyIPRtJDWZ4AqqrUD4ljE9WhsDpGpaGkFIk2yMTAC3AeH3hPQ3WGTEJYEHoLvgFkIemTM4ATTaRX2QDpHV/q2YHYBsOLom42vIcbAhmJmWKiczzXQTITih6zPJZ4rSrAL6mZCdMi0KFncTxdYAHJTVbW5qlThTmjaTvVkUuNqmBGEIGF4oXUy45CgnjgILzi0KB1Pbg9EpmpRC3GoSL4vpsXwSkiguGxhq+mQ5ZHOlmUeIxvxktX1LAE2KXBQkCe0iIVsQ9oZsumXHI9hdvnjbbx1mYZAl0w+xKyLmW5WywkyQCvzMkcbZAGFv+zs5BW+tvOf166v9//nXGd07ftcvSQmLZfSG5RL61kBHHBmiaNedtbPTYWBveWBxKal2ZjwbrF22FIuD6Zl8A4vzwVptHWjNYQ5ZNbTgWqXdNL+n1JlzuO9sQq+fHGGOZIjgWFrh6SBUd4ONhzPwshSeL18jR96/AknDjdVJMHQaI3XyuSWwKvcHi4VNT6xkta2TO4nxP/8J8ehF/MHDU7+TPJux8T++S//330AdTD/umV0tWFzw1EfbaCcFzY9snJDu3LVpuKcAraeV39w4dYJPQiCvj23abQjOPeYdJoKrqhXg0XQIpj4xoBF5PCAc1q1tS3z/Om5tDakqWL72V9wPMDBfyQTlEsIw0Xz4ufzlS/zXL/+bX2kdn+c6syGE7jnSPTtwNGXqYsKomjAZrVP4ird+/ktC8Kyvr/PGm6/x4rUXSLk3Aa/3bG1u4b2nLCY4sV+vdyVF6Th//jwba1sUviKmlpzS8L3L6n+KrtgucUJKEec8VVFTlTVrk02898TUAub+nJLd4i1bhlU1YjrZIPjiyLI55b+/2kH3uLJprIHRqRVfi4m+Wzs5hMJRfNnhC4sqyc7aU4oaCEimp1GATkkhIxsGmtwVQf5A8V81Ibhct+k55waWo1RytDYd5bANnemgJBprJJWSHii6LegMpAfZd7iFw2eHV0fCQoPr4Cmco1dzChfFLAAq23edJloi0ak5Ja8p7kuKK4XCe0Z1oHRmNOVKoSo9ReXwtSP5zOIgsrfb087S4EBu+4JWV3+rqpl9KuSZ0JGRoQWoHqrCwF2hjqJ2FKWxdrIBYc3hyiFyJUIRPLIl+DVHmDpC5Si9s+MOE357BivSYXrQF4KKkrslwIXUZWKXiY0ivVCIUE6FohLywhi4rsk0zXOfgl9nNVuO7TfrwZXbLAlMXG0I2Q0xLKk2hrUfRNpL49ZiPoT7Dlly3bpRQhbfYlqmbhByS7THcnGod0rl0GJTVsLxfizMLwa6iTC/aOabogxmlzxyGsoFLM6Z7rDeTSuQtGzXLS0XVhErSyPLoyaZw2eVMxLvovYZZYhycr0tr9pJFHv9MeDkelj799efCSyke/dYv96y9lFH+U/+Axf+/X2KmaJffvn4C5+i29EYyYsGf/4cbjIxICGChPDIjfjpH3RgIZvTW+kaI/lHbx2yQUe354jgWspy0Eg8ptwzeCHp2QOR8/4+aXv7+DZ90nLegNsRawYDUcu+r+2rxdeu8h+NP/zk6/mc15mZJ3kpUz1cI3eBvtmzO20XSDniJdDnjvv3H+C8I+XItZeucuPmh3S9teOuXLnMvfsli+YAJeOcp4tz7j98gJOCvu+HrDJjtMBMHw+n6iBrMtPN4Oi6DsX0V07MCW5UjVmbrtN1HX0f2d/fxTlvifZi33kRCkb1lKadr6b4TnzSM+6RkyzVowerIPjK4byYT1GCUHpyr3QPevSBUjrzjFEUdxHwg+eQmpYrp0HbgBij4pQ8xKm4KMjc4RByzMhDIb+mcA3kwwwR81EKBpIUY2R0mNhgYLMIBuBcENJFhV7xM4/rHX7hyLVCsBZUikqjPSl5xhTEAdRIhHaRKWtPctkm01RsIi3augoRpBJyp+SglOoI4knZpu2khIkEUqF02xkJSj9X9mcd5TgQJoIUw011tkk/zdDsJXPbLbFcP2+eTEEcIZm4WxAal0iF0pLwrVBWgUimO8hEybiFQGstw4wynnhSYrgBwEAiEOfmSZUTFkMDJqavFO8DvbfA4zxOjLK1ZGdtQpaGhArzv3h2Gv95ffI697MF218eHYKSgYlZAgrLLjOAY3lm0K0HJCnl/mEUi++s3WfBvmqAJYBvLWssB8vHK/fzsA4Tni8jXYBVqKsKxCMh9HEsxPEA5gbDy5MtNfX2OsR0T5KEolXCItNu+FVW3/JzweEyjhpiSj50MD+aZScKvhmy98SYOEn2mTOsdF37LwUgHItvWb/eoc84BCFFiW8icavCX7pI+vkvmbxT4l69RnpGEbj2Hen+Awt99g7KEr+1Sd7bfzoIGcDGE/PpngJIVu8VwU2n5NkcP52A9+hiQW7bUz+PVJV51D2DHuq0cnWN1JV5Uz2hnjopOICktL9/hF07ARaBO39UcclPfqVt/jzX2eNZ3lTSgx3k/S3KXFHWAZKjbVu8DxRFxbzZp5k3dM09NjbXmY7Xmc/nbGxsklIm+BIRYVJvsrVxkQc7tzmY7xBjj3fF0FKLpCSkfORsY0omVDNd37C+domyiAag1O76+75nbW0dsIt830dijibmFSiKEhkYL8E0QkdLcEcE5GdD7aavgjAxBiXnjHhBOsElh6+F8LKg+0K+J/Qx0seEc45iHNBakRZ8axOMxYsON4VMNqAUBBdAh7tio9pNs2PZcaDbit6D3GfcmsAVkH1BLytSYctJw82Q+VfiB2sPy7IdDCcLVmP+lJAlk0NGNJAnGe8GvU4pbLgKN7iGV+pRr+Rsy1q4SGAQpKPEzsJznQh1EZj5nlxDmQ2k9DEPyxKyM/IqZMf8fqa86qgnQuwS84875JIgI2sdlrUjqHkn0cL4oiOKQrQLWs5mt1AUSwZpGUOjTKrAYpFwCF6FIjiyQNpX8oESXhL6vUyaOorCDS06pcuZts3kYK1DHQKCY2/PEYFB1K89tF1CexOfyzDBFyb23T2Xiv96S/7ih1y6cY2Hf/oisRZ8o6t2nQ5tLD/Eq+Rgx+KSUTG/JQNC9U6yxweNj+8VjbacpfN4WJippnk1AWLO4am01l4McurUWjEzCwVb1sDyLNvrRwgLlUHXlAdH8rGgMjBXQ1tvCZKWrNRR6wAwq4AlgKz27GK59KNKpZCTUCyUjbf26M6P2L9WkgvoBmsF30CxyMRKVnl89S9uE+/de7bv5Wtf4uFXJ6QSqgeX4c5dpAjkD29+4uk5m4azf8czCs8/zXJVBa+8iLz1DhojUgT4ymv46x+fCmykLJ866XemKgr05avwFPB0KnByHjeqLfsxH7EwOPGaw7Fuwf/R9qPL+R2qM4OnEBzusqM5OMA1IzQrVVHjCHjniSkRpCI4m1C6+eHH7O3tURQl3nnu3r1NSolRvU7WxMULFxnXU+7c/5hZs8va6DxL0GJC76UOyWqpFoqx5eHOfZzzJi4fKFIRmB0c4LynLArGI2HRLIwtyRmXI6kbbvdQszHIcWVzIINB0eNhk22B9w4/cYR1C5tNdwW3hekI5p7cmC7GeUGzkN63i6YEpTjvkYng1sFfEhiBHED/Q4U9IZSOTCZOMimquTgIUB3ZjCxIP4CpEuTaICR/y5Ev51UjNlemd5I4NCiTAb1cWksrJ10ZOArLk6pSJoePQtdk2hyRNWN0iOCdQBRiD2UF1dgcx4MbgnMlDxEvHs0W2xJDNm+jQcjtk4N5JpVq0SbZmLHsbXtiViSBbBhDVlWeUEL7TiLuJdzrQhYhNUqxZi1Et2laoyKb63kkUWRjoNxwAWkkU5WOkIRQ2MXQJ9CFUHtP91BJs4isQd5TY+1aCMGsBVIyM89iMNPqugxziJrJneK8o5OMzM1Coqw9FEAHfZfJgwZFF0o1+d2KMfitKFXi9Y/YOLfO7tfWjHHxx/ljcxeXlUbJpsBMZ6TeWm/Npl8xTjkMwb9RadcNOPnOgE0qTcckgzi82XRUe9km7MSW2U/kGLPUTQ8dw3Ng5elzWtdpCdZcP0gThsm6lafSUpTOIXCSbF5QoVHqBz1hv+Pg1Qnj2x2SMt1GSRxCkcE+x/Y31q1dl5TxDWvVhZ0Grt8kz+a4Uc38f/INmnOevL1zuIFn1Nzoz97h4sE1Hv7xFcK9PbMnEDmcfvs8lnPInQfkpelm09L/wZeIr36F6b982wDUkf2TDw4+ldXKeITszk4fhTriNXXssSPGmHnR4MqC3KTTv7ujeqfNTf7LN/79p7Ldn9c6M3hyYqaJ9YuBolljfbzBbLdnnhMHi12cKwiuoEsLxmGdrumIqWdz4zxN0zJb7NO0c4pg7NOdu3eZTtaY1Gu03Zx5u4dzgZyOThgcBVCHvFCMPVXp6foGN4jPnTg7GCIsmkxZVhShIKaIF89oNCL2PV0/6LCcpwg1KRnD5ZwjZ12ZbFoJh7DNMT5f4F8StFfiLaW5m6AH9gXvPL52FC8LnAPZBJzSf5QpLnhkZGBJxdpw9IAoOhL8hiPPlFwqcaTQmo+S9mqWAc5e75IYozowLCrGJMk5Rf5EYIIhtyH9HQWS2FRZsqlEBBu9z4IXO1njTFOWBzalz2YDUIUAg9u4FKb1cV4oJ0LqlG6R6YM9JoNIu3COjoR6CIOvVOHMbymSSfuKeGN8Wh/pc0aiWUr4YX8nMmFDKL2j14yUsP7Vkr23O6RXmELsFdcoUTO+EMpgI+AdNr0Xs1KUQ3BxMId0F82t3OMIaqHCtVdip3TnomnFMFPOMMRrOMxrCrGWZQbTWgVFSmAxtFKG6UM3FWKTcQuMcfMw3Qw0baY9yKROaT7PF4bPecnb7+Nf/5b5PZ0UTcshWyMKYZFJpXlDFXMzsDQ7A1YO4P1EKA4GxmqYuFt6GC0n8GTQOcVabBn9oZP4MhD4qFYJZWVX8DjX7+V2Lw04c2G6rG5NTt53mgi8gMndxPQn99C6YvbGOvvXpqQK2g0b8/MNbLy3YP3H+/BgG1JCNtYtQFlk5bB99OKcZzPq+w3d+vj4dNgZNTcaI/G9D9i8c28lMtcY7Tf3Oa08n8NiceSBRPX/e5v851+FMFxyxSFebJ99SvEy6e4TWL+z+D9pfmLbUooSjeYlFb/6Mv+rtX/IapLnd7DODJ6Ss4mC4ECnDbGb8uJLl/nxj38CDjbW12nmPaNyjaY/IMaelHoWixmqUPgaLU3rlFKi7RaUoaIe1Wxxiflin0W7A4PVwKPRK0soYw7kXS/knCiKejDUjIh4vPOIeAsxdo4ilHjvGFUjGnXE1YVL8d5ThIqU+5UA+XBNx9frBFzn6N9SYptw4ggSrPU1cfjXBP+KwNSczjOG5eQlSFU2i4A0aJaGQFwZ7AXkAsg9Id+E4radvftXI+pt6k3TcIeyBFJ5+OaWVHwArbK5h2drGeblWdeboNslE427LCvdnybMfDNbxEnGXpvUJtxSa0CmCrYykcF2QE0w3ckRcay3AN9Ym3u4dmYqWVjUMC548zvKmBO8dwQxbybvrf3WzM2XqfCOunL03oBR4Rx+S0hfLWgkEcZC3LV94XH0beLAR4t56TDzzMo8pqrSk3rFJ1lp39QpdeEpe0eTkpkL1hmckguFDkYakE5IDfgS6lHAOxN6950SnKCFtUmWWXXjdU/XKdmbsF8yeHGIioUbB9ve+V8/1zv9pirP54xut/TjEak6Ak6OnGqqfWOW2g1vppNqLNHS/dsP4EeHNlk/tpsSydb6W5Zvs9kJDK0zdcv23/L5QUvkTUuUl+28Qyu81XuXBplHmapUMWTqMdgsqA1/6KNmlqLQjx0P/uSKieKPnOJ8Axvvzgk/+4C0s3vMxlgWjUV+POEC79+9RXHlNfTbX8Z1CWJGbtx6pty5PDsMKjpzxMhvc5343Hl/n9G/+CnpKKg6rX6VKbkTrNIzWzc8zfBzuHBIUXL3Dya8HH5DWX+/JXXmaTufSyodU2pJ3ppz6YULfPjRDVSVzY0txpMxMds8dulH5BypyhFtu6CPHaEIrK9tsTbZAJSHe7fpUwcYwOlSM3BLg3fTMQAzPI7l3AmOmMx+oO3mNN1sFc0SU2fLUQsCtlgWYb5YcDDbG+JMxIBTEaiqGu8Klqaaj+wg56jOB+qNwiap2ogXZ8aPTqhedNR/x1F8XdANJflMdsYKqYKfWMSIumXLTJFObHouKiwUmUDxNbHprQ8VHmQIlgEnHUgc7iYLzIHXzI7s2zvi58SQw5ZF8cOBrzIYcAaxWBZ3aA8gSVY4MfQOH+1uKOBYLCJRBtFrMP1QzJmYM5ogd7berEqXko3oI8gCYpMhQz6Afj9ZC8FZtIuouaZXhbfPgtDlZOzhSBiteYp6aLmJEDulTYl5m8zrxjlSY0BOMwMwcvTzRGqsRaiiLFIiRTui4r6FCNe1NzG5KL61VlwZHI1LFAhrVUHlPdXEGwithGrD2Vi5KK6wFmNROUbeM679ipx0TsgdaFY8BpbG64Hcwd4i0vdqIPEzmuR8Xmev4q2PTPgtEOZqvxs1KwLXqwXyNrqyH1haEKTKxNqSlMmdSJjroWZqOA6WrT9jm+x3K2kQXuelQFtItYEZdaZbUjc4nw/mm6ID+zUQAeqW4u3Dz6FiIClVNiXYrrvV51ouw0VYuxkZ3c+kQoij4+286ceJ8//obeQvfniqHkfb9qkX83TvHpN/+lPcvOf+dzfY+9oG2vU2efZJ61NiY36bKs/nq88lxcBbDJNtqzrlc0tRIsWz7Us3GiHlM4T1ntobNouC1Z8DS6axZ/ePGvyTpgp/B+oZNE8l2UVGrNN2PT/50Y/pdkCk4NzmOS5c2WJ/b07pKw4Wezjv2dzcYnv7PkriwoXzpJg4OJgxHW8ym+8xm+/inWfeHDBbHBWf6ZEm3fHKmgd9kr0OZKV76lNLXY5XLbycrZ3XdR0xdjTdAWUxogxTytLGtpz0dtJLA/vUHwYO13XJ+WsTdu7MiTMlaiQ4jxfTzbjLDvcdwY890SfzD1rmSQ2bqM4Akw44ReaCzMS8g5yi1cAsFeDPifkAMQi5a0VmYqCrxMCGDq2/ZjCIdIqPxhJJBYiu9E1grT8f7C6ZDFoYC+UQ8INR4CAcTTGTnU3IFbUzsfdCYQQFbtXOEDCDS1WqkSN3CQnWunIZtFXyxLaXDFoqAWcAy8Go9vQuU4pDC6C3zxGW2XReyL3iENqc0Jni1Jn+KEPfg6+tTai9IoXgowHahgyqjHxAHMybyKgKKMZs9XVGe6UsAm20ce/YZnSu+LFwbr1gtsi0fabyghRClTwpZuZNWpkKI+YZVY7MWqKbZ/q5GWW6IJQjoSyFNBZcB6VzhJHgfOAWR1vTz+vXXenhDsUs002X6mn7z1JAHhbYEEQBxcJ+zltvN8xeqOjWxYJ3vRgDFbHjnEO9UaqgPDBglUr7fblkYnA3xKbkaBN9vjWLgezNtymVQxtvuJal4ZrpOxNoI44khwzUkpmyF0EuTMQevemv1j5qmV0p6aeHU3jLduDaR5H6X/+MdIT1+aSVZzPkrXe44N6wv38Vs8YvIHA6WhIC2raHwOQpPlb6Cdr8sjZ9Nt3Y49Z9SvvUjcf8J9/8/jNv0xetngE8jWjiAeAoXEU99YSuYj5vuHP7HqNxhfeealqhKF1cGMvjPJPxBqO65uFDc2g9mO3RpxZapfAjdg7uPhYsPdLA57T2mqycw5t2RlkYm9SnnhAKRA1M1dWUPrbMFnv4sEVRFCA9oSgoioI+9mbSObBY47WS+d2evLwbHBBRRtHS4b5tACf7SM6Kf+hIIaG1QIsJvYO1ACQBjTEzsi+kcybulgJcZ5+j14S7aEGyuRM0ZCgFrdW+qWitNpdsaoyZmK+SWksxB7MI0EqJ+0A5CNeHmxuBgbESy+oKEJ1NnUkWCEf0EhlSpziHtfrKwQRSFVcIXbL3JVGK0iFBiJqJUQmVQ1usvTZX+ibbHXqGYuQgCX1O1mZbMmOt+SYJg+nlcCdfqTf2qMu0ksxtPCssIxOB3FuLrBnaiajtQ98LnWT6LrMgM60CMSlNkyizo0c58JE8tN3alPFzofBCuOjJMrQtK0fTmAC+9EIQZwwisOiVbpYZTx3NfrYgYVWag0Ga1tsARVnJALp/t+/WfisqJ8rtjtml0UocDaxOJ6m23xhqbbrsheZCST8xILwyjMx20QsLJVXDsTwMc8SRtfmKhbKUcfrlc7WBqFxALg0Y+V4HO4ND+4Dlsmyyz5iqsMi4aFqr0xzAU2kACqCYZfavVcfsEJagq5gp9b/66WNBjlSVsU7PUJoS+qO3zv6GXzGP7nNZYjqnQ+CUD7VEjyvNzwwo84OHPNFv6mgtv4clgBOx9+Z0+vfz2kv851v/J45PMv3u1ZnP5Itmj8uTFxlP1qiLKfW4YrJWkzXycPc+H398B/EJZMi0cyXNojUANYyy2xlnGSOijKt1Ft3+Izl2j9bh7dVp7TzzghpeKYKII6mNmcXY0fULck6URU1Z1MTU07bGhztxiDjEOdxKMyWU00D4mhCLhHOW0efFm2UCQl6DPDKRdwoZLZV0PqGjgTcbq4mt1TQJEsViSColrlveiCwE6YcstjLbic1DXyZSOZg/ltY6kxZzB0+sjELdCBiy16SxthyK+TidM9AjB4PNgxv2fwacWlyLYDqs4be5DNmVBKKCG7ZHBGKbDLAJJB2AX2XAB4GuT3Rdom+sTRdqC/r1lcONHV3M5MF7ScW23yOWo9cPIlyxVqJEoe8yLgmFmLElCiWO4I1x63Km04zWZp8glX2eMnhCdswWkdZnysqcyDVAKE2EXlaOtkh0C9veSQiMRp5QCl2yCJfCCT4J7UFi/3bEC0wKb59bIIhp2JyaHm4xT1CwajeiSju377BpEovObCzi51gI+0Uq38TD1thCcUMbejlosRJiB6GcWcyK+sGjKekQEGwapiVjhTOQA4OLeLDjIC/bf61SzDLlzFqBSxsCxNqEy0k7xJir0NjrioMhN6+Cbuky/gQ7pWU7cuPffXTM9fxo1Q/iE9kh7ePprZxliTxq+PisjNHvGnBa1tKQM5u85FRwdHTffgImTmNcWRK4yVO8mJbfw3I9S+D0mHr43S2+Vj6/CTwz85S7yMP2Hlv1OXwIrI/HbO/MGI+n7O7tcPvOHYpQEHygLEZU5ZhFtzfolzw3bt5AML+lcT0lxoqDxQ59fIIp2eHaj/xbVuzQ8m9QBIf3wYBT6vG+QDVTFjVdbEipY9EoZVFSVxNijDRNg/O2LMEChBHBB0/9hoMLieiiAXCUZW4eAnJVh+wotQm3ZZ+uNYAhCK4T8iQbmE8Dxe4UKQ1I6VRN0B0dOlJcZSP3qbITqYsOkgnBLVoEo+ZFUTdEqGSFYEaUbr4cv1YobD2+9XAb4iiRJ9YKkzQ4dg+/z9ybC7gm0zY5sX0RxCGVaYNSspFs54W+S4wmhWXQ9UoxdjSzRNckvDhrQ2ZwwaGtgcJi3cEcmlkiF8po6vHiiG0yawcRm6BzppnqYoIx5DbTRXMO73qlGnsKdcQ+UxXmMdWTqYOny8by4M1moJkl2iLhS2HsAo3LFMlRJ8/+zMC1CjQ5UlceMnhvWjY/BBMH9cgY2mxAqKxMMSbDjaLLQox2Eszl8LmdMUya1Z7zQtMlRIXtf32W4/15fdaVRoUNJyyUejvSnAt0hd1cuAipOARSqbDgXjhkjnLh7TfaY7/PJWM7sFKpMKYJkQF4DTc2XghNpl33q/ekQg79mQbzyqWRZayEai+jakxWrDkWontaSYZz339IvHUH373Ecebenh99eFwY/kj9rgKbz7qWsSrp0A7gkQw7MW2qPgEgP0vlp7VljzJNg2XPk+reH2UqeQY91Re0zgyezk+vcNDvs93tMbm3yWKnZ29vn2vXrqGq7Ow+YFTXTEfrxD4RUzd4KAnzxQEp9YzqqSXVe5gd7BxjjE6WMTyPIm7lNDE5q9cKQtJEjg3OBapqRFXVdJ2xYOI8hZgGJqWI4tCc6TWRcyalSFh3yFWlfdjS7Zo3lGoeYmXcKuJEFUiYX0CLjfSPZNA4KdHlAeAYyFolmzvQarAOcIJOrU2VBpAoYpoiiQPjpgoHBux0A2u7CdBiIbPJ4lVMDKH4wiGdoAtFKyUkb20uVVxt4K/3idxZmy/rMD0GK5ZHnBBcgN6TYw8p06XE+HxBmYMxX4WQJZFRypE30XRc3r0Mn9NjeqwMEuw9KCxiomtNWD6qjdHLTom9Taq52oCk9w6XBiG+2DSgLwTnHKPaQyXkZK3B7KzNlpJp5jLgRWjbjLYw1UAoHYs+MakDB9GsJrqYCc7hEqRCB70cBBE6zdTBvK/6nPG9wzvL4mtTJpfK2oVAO8/WtiwtW88PKfexNy+q4B0+PPmi97w++5KixH3pZba/VJu1xZqQQzCgNPzccsmh7khMt1QsMv3IgLlkKA8y7ZodMwwhCCtfpiPXnlRgYvBe8Z094RtFN4xd6qZCLiFH82BaWiWEdrAxcNBuuENWDI4ZZj7y+dRy5/K71/Hnz9kkIIetRlsAyOJES+7oBfQspWoagt/2+hQy3j6TOkPczGPr0/5M4p4MmJYsWE5IUfL733n301v357jODJ520zY5OeqPaua7cXBwdtz8+ENGk3XG1PRxga/XiBrZ23k4MEABBDa3zlOEgtnBnM3Nc+wd7JCj/fic+BOtN48TN5hlPlqPejEBZFLqKOt1SlcxGU8BsbT7GBFxZnDp7DbRO5u4s+Ul+r6jjQ197PC5JH4szN/qKVKJOOhzNMAiahNte2omi70Jh/OuooWxTrq0IshLW3BMrxRtn7m5kGt7LI/UXt8I1CDN0HYOrKIOJFssCSKmd0oDM1WIATeF7Aw8GHNkF+5QOmuPTYQi++FzpGHyzWhXp3ZBcAxBuB10i4QLQyaei7hCycn8jnK0dmSvCW1BvJiJ5tD28EHMx6k0p3VzLXc0+z1+7ChGDikhNA5Va/fFmKkqT116oioygkIcfTdM8rlMWHekPhm7lC2LriidMWLicCOh7zO9mM9T7hWpQXtBhiy/Lip1sGDiuUuUhZCyCb4DNjIeNaNdJs8HY8+ReUc5FbqDTFnbug76aLYLPYQRTMeBve3etGRuOB8NmYGhcNS1p+8+pVvJ5/WJyq+v0/3+G+x+qVoF9C6DdK0N51ds7Gg70Wx4JFnbTbJpk+LI2CcXTaMUBmsCC961GxOzIGCVa4dakHeYG/CJI7shsNfY+oq5nTuaDbea3ovVAHzycvmP/2xL1+9lsLC/epm97161KJdh+46Cr/7qFvLBYS6ZLM+FX7RD9DcInI4G637yhRy54TotKuVp733aa0WOA+anmWO+eIX/+uo/Ptv6v+B1ZvD06tpXuP6Dm+zcnDGdrhFjpusawssNXG2pXY/DEeND8kHL+vqIRRfRJrM13kCyo2t6EGGxaAihHLLljoIgXf0rP/YO6OSXe0RQbuiGItTkpOSc6PueppvhnDe9U+xwzg9ZjglwVFVFPRrjvKdp5lTB093uKbpqsANQQrJU6WXIsDYGNPS2kjcHwDQylsnPh0m5pSHmYCHgS4f0BpjABNl+LripR5yJtlOIloHWWDsgZGeGmkOGmi7BlQjemb1A0ozHkwdfKDdkyqlYmy54Y4TUK4U6Y+eGybeM4nGkbF5UMWZCacAGtWxB13mcgz4lulnCTcwtPLdKUdu2Ow9SCBqNGWLwNio0EL3piJq56UyqaUCGCaOycBChGQToSx2VQwgR+tLaiIIwmRbETskLJbXKwid87QiVidmL2lpq3dyOHe1s11d4yolDetsv8zbS7EaK4KhKb9OIDsrC21QhEPcyqTB2sG0zvSqLHGn3MqPoWdsoKCeOB/c6fCdEDtugWkDXZ3Kvpv0SMXbiOfH0G634jdfYfa1a6ZIkw9aPd3B3t9G2M7u/EJDg0dmctfNbpAvrtOcqRM0lHDBGVQSXdHAbl5ULdyqFVJkBpsrgAaVQzgyEpdIiUIqZicx9a483m45ioSuGaAWc0pCnJ7Jitk4rFRO6SxRGDxLx0gbNlm2v7xgkBoevz5VNDfvNTfJ8/lQvp89lHRVCw6/98z2RPXpSHQE9rqpsCrxtn51xOstrn3GfHHzzCn9S7wCjp730C19nBk8HszkXz11gTSMP7u4wOzhgFre5+HKJn0Dhkk1mScfFrReRSwW39QOYZK75C8zuttz+xT0kOkLhee21V/nww+vmvXSC/i1CjZLp+tMMxQ5B1vF/YzqenIh9x6LpBvNM8xCyZSneFahagOt4NKXtWvq+X+XfpZRoY8PaV0vamMhFhq8r3Y8yendYkQMmGT3wyJqgu2Z0mR8obHFouuqGY7Mx48jCG7uT1PQzUhhgkiH42zfmQxRzb3l0GDMe1YCNy0KUhFMzq3Qo4pQuJ0pnmp1EJiPUPpCWxpXRWl4+C0HM70AUuuGC3uVkoE7TwDbZHWjOCalhXI6ZtzNj6nqI8x6ZirUAB+fxlEz344PpqdQpGWPBSu/tt9bacnXwafLBWgXem2N5O0uUE0/wBhidCKF2ltYQlDJ5UquwpjR9IqGQs00dlnY0+04ovOV85TS0y2ph3kf8zOErs10Yn6twajorAdoukzFvqEnpoXQEB32f0ARNHy0wOCZYKJNxIGalmWXb54WSxWwgyg2bEJTGchelFHqn7P7T54l2v8kK9/fx3QjJJvQu5gq/+ID4OFflvT14H0rncXWF/p1vcHA1oAEW590QJMxK7hgrsy+QwdS2HzuQIwaWqiumKpUWg+Jbu8EKrSLJ2E4Y9FKwshcomkw/xLDkpbfUiVo6iauA++l7jK5+A9cp4796j9kfv87BC/YmyVBsL6Cq0K7Dn9si3r5DePEF0v0Hzzxl91tbww24eI+mZEzQJwU08OzvfxJoexIQWj7uPLnrV5/DjUbkxeI3CnLv/GFgwz0HTvAM4Om7X/k9/s2P/y3zu5G19THjtZJ6lHCjjrFfYz/voCnjQoGvHa1fIJJRn3gYbnLuxRe5VG+wf2vOq5svc/HcJW7dvoXMhbXpJrP5/hCVAm0/47Tb9MLXpBwHsDWoOU+8ziJbAmVhseClOLq+hWjPKXMKX0Nk5TidUiTlRFEUVFWFrjvcOFJ8V4gI7rwgXxaa+/GQQXC2fh1jzNBCoDY2RwYdhC6dgocU9eysBedrR1Y7aeaoSGuGniLZ4mlaA02SBiZLhaEjwNjV5CKRnQGwpJnKe2KyCbjSe9NXiRoIGfycfBBIQopK4U2IXnhHKxGnUJTeAFMy7U4fzUw0lI6GOdrDKNT0ydpscZHMEDIoIbjhaxDK2qONeSflYHYIXZ8oxCJqupxs2m2RCGL+RwDF1JMXmfl+T3HRglq8CCE76sI+UwgONxEOuh7dUNpbCXEwGhWm+Ro0jObqrXiBypu4PEqGc7Bo7bspXKAqHU6UhIHJZjcRKsdeH9Fo/lEqFkOTwnBhi9bGmzeJ2nt8bdOX4s1yQgrb5t4Z+hVn+qdQPKedftOV3nmf9Qtr7L4xplgo6z/beWIcxapyIs/nlP/kb7j4xqt0L26w/1JFMRsGGSYOyTrk1tmUnbX1zM9p6R6uTigOEql21Pd7+nVvU32N0k/coIk0n7ZUciTaxY6d0BjAioNu8ahw/KimSbKSZzPG/+gHuFFN2ttj+rM1Zleu2vMJpIuwtkbe2UX7HsShbWtTdl+w0hgHM0r3q7FQz2oK+RRd06oeZ9lw4rE8nx9v48Gvl1VznvXvPVvw8xe5zgyevv3Vb/Lmq6/xF3/x1/z0rZ8x3qrJRYWGiq5oobVMLy0j23KXmJWsEVcLB3EfDR8j6456zZPbju2DhzbSHgrKsiLGyHxxNFn65MFgAnLn/UordfJ50KEVl/GuJGkaPJvs9cuWWxcXeFcQY48bLt5t25BToqgKyvVAdj2pzibIFnDrgB9crWOCH3kkKG4suE0h/JEjlaYD0h7TKGXMQC+ZWDrPFVcNwbfBPqHD4fcC5WYFAfpFSwwRJRG7Qcg8gJPgC6blOrO8T9YOiZgOShyjUNK4Fs0W1JvV2K5MxjkjaHwhpJRxGEPVionhvZgEv++ztUyjxZmoh8p5s3EII+ZxwaJf2LTfPONLi16RQkizTOUC3plLuc/W/go4Y5sChLFNoFUhQBaimulkyEJwjn7dWogaDRRrgNluT5pkmygsE7lSusLy7tzIRO69S6QDNaNNb+vQKOSULTqltBBjXzhCFPzYsYiJ3QcdJKjXPJNpYB4j7SKRVKEUu5AFMwN1UfClQ7EsvP3dnu5cRs4LZfKwD2wNk4IHxlZFowKJmk2f9rx+4xV+8RHn9y7CR7dIB89oDpkT6ZfvEd7znH/1Jfa/denQU2kxZE0GO26W8ShLCwKXlLDI5EJwndKve1LpiJUQWqU8yCsfp6UFArB0dxk8nIyp8p2BNMlHgoXz4bW9vnVgpul9RxrG1XV7F0lXbXBD4e6fXsClCxSzTP0gUvyrH5LuP/jVdu5vc2lG+19N4L4c/f+V6yRYepbJxkfy6H4NoGlgyfz5c/yD1/7is1/f56TODJ6avqGsC772e69zEHZ4sHuf2EZEhaoIlHGLnWaHdqcnjjNuDL4YLuIpM9ddxDmCjNmTHeazO/jKca66wP7+PuOxGVj2px6gdnGPqbV/r7LvlnX4b+8LyrIGUSajCfO5hREHH6AzTygn5uWUNZlWwZldwaKd48oJct6ha9ZS017R2jLgXCWEwsN8yIvrBV1A/Sc2zp6ztbL6nJBCcK0cGgENNLyI2QporxRSME5Txt2E/H8xu4Xwn3l2F9vklFgUcztuvaIRejp2+22yZJxCcOY7ZPlzJY6enDKSrVWWs6Iu4xL0OeNUoYBWEyGakDs5E1DnZPs49cZg4QSNmfYg4SulWezigpg1QlR85Qil4EfW5vOVwyUxhqezlpsUFulSFZ556pEAwZvezHtnES4p2xg3lpM3qgNJTZ+VRWEqtCETkiO0Qhk9Mdg0Xdhw9HsZopmPViOHJKHtE4hSFJ6MMhp7vHPM24g6YSSBunLsJVjMImSYVIHJpcD9+y0H+z05Zsqxo0yO8dSGHnx0FBvCQdMTp5gJaS9080TcMz+uPLQB9WhGqrOJw+f1G6zhApAePIQHDz/5coZJKL11F37v0tCqU/qR4CIr40zziMJaeg4yZkfgk2mdsreYl9DYcRErY6ti7cxLSs1/qh/LIDIfQoW9GWGuPKFgJTz3vbmYy8ePsgMa40qUvnQ6zx76iWPjX3xI+oTtLKkqpCyRECzLLg/tsay/WbuDR3LZ9PTHfx3rPlm/jv3yrJ/zDG3E/isv8ven/09+l8OAj9aZwdP1mx8ymY5w6nn50kvE3HCvvYFEoekd02rMhXNXmc1mdMWC7Lsh7gP61mI/cErvZnS+J00hXjjg4c+bAQhlxqN19uO2sUUcB0dH/wp+KTZ/tFLq6fqWc5sXVjqmPOieRBxtvyDGlqqcmJ1CVIqiBpSUOrKraEYRF4De2C5tQKvM6PdLJGVmP1SavZ6UI4UrkDEQxTyBBi1C3M0WHFsIBJusqdqaoikp6pKYIqWU/OH/8D2+xx+www4HzNl+4yHbBzvcv/OAbX3A3YNbSDT4mIEcE0ErRDPRxZVtw6KfkftMyplQZxyOlPKQfWdAMEoe2oV2chfTyxsz0jvK4HGF0AZrWzk8bZOgH1p03lNvBGKnaDajy2Ayb4rKoQso8aQErnbkmBCBXpKJ0JMSJeE1UBWeFIU40M5OhX6WSSHjkofW2LrgIM0Utya0WHBwUCE/EMRBUXhSp2RJNLNk+inNFIWnKj1lcOYjlSG3Slk7c06vHBuTklEdmMee7d2OydgTNZOTTRSmbGyCx8wt+05NVzWy6Jp+P9MtErHNq+9Bkx2rosPU4eAsvvfvviA6ks9rfcoXzDybsfaT+9z/00t0pVAsBoFvBJLl4rneAM2xzXDWhiv3E+2GX03ouWii7vIg02cDNycjWFJtWijfKv3YUe7nlUDdDRYhxULJu/ucrLy/z8b/8GNQ5eB/8S2aTWc3jr2ip/kAndYOOnqBFcGvrZmreNOitOZNpLI6Nxxa8f0aAMtZ69ewHeL9r6St+rWX82cas7z7vTEv+t/tMOCjdWbwdOf+XcbtmG+/8U3G9Yj5fMa7O++Qg7U68LtM3RbiodlbECYOCsjJjAZdK2SfST6SfKKkYnKlYv8XDRqhaRrqasr6+hYpJlJOzBd7x7bBuYB3npyTGTkuNU8rsGU/+HmzBzvKeDRdgaZle061wjtPyj19bBGE4EuKIXhRCwu+1YcQxKNrFgfjeo9uZuIHCdcFSi80sSHFob029lAoOWKapbmxJ6F3EITz/gJXXrmCy57vvfSHTP93U8bU3ORjzrHJf8f/i0v/2yt8eOM6osKVzSv03YKdecGa26DwFfPFgvFkzLn/9jxXucIveZuHf/8erVtAD8llXDD/qZQzLRGXhRwdSfIQ6wJl9pRSo76lyy0Em0jzzpFQQu8NeGXLm3NO0JEiAxgc1YF+YXl2XWd3UTE5ttam9G1PUY2YVmMm1YiDbo8H3UNrhYnFreTBtyn5YboyDqZwSclzYD1T4IiNgZhCHOM+MPNKKo3xSzGRs1rA8NAeLUszzSx7T+0Do9JTlo6dg45Z11PiWAslXW/GpU6EceFpSew96Fi0jrVRQV16CxbulU4UV1jYbxL7fnOEtJ8tAHl52IlBfHECWQi16b/qkaMaefZ4Dp6+aKU3blHML9Ku2c2IhQIfMkIuDtFMQ6ssjh2SLHw4jpzFutTWjlOx90s2gBQreUQUrgL9yG6EfKf43rIpY33ITI1242OjPvJshn/jtUNH9CPs1ckS7wcH7IElOQGApCzJbbvKTzsGFjQduxb7jXUoSmOz9w9MvC0C3j/dwPGT1icEScdE4Z9QT/RbAZyeZZtPMGGPs1iY/eHidz4M+GidGTzd3PuQoim5cv4SOWe+/Pqb3G/u8/HudZoU6ZuePR4ydhuM6wmRhqxq+poeYqfIxNo1PgnJ92iRcIVdjOp6xNa5TdbWp8xnC+7dvU8IjoPZ/hHbAqWqRuYGTmY22xvcCTw6tOMKX9PFOW03x4nDuWG6bPA1KssaVbvweleQUk/STECo6xGuziAdoSlw55xFzjwE/YUnTZT8QbBgXVG8eHKfaN/r4cuD+7iHvJPxE4FS8H1gjXUYKb+49xZeAlplvsSXuMENdtmnoePBP3jA/Qf3cDngEfa7XT7eu0nbtmi9zcbGJhe2zjGdTplg+/dVXua1L73GT2/+hF0eoknx0dpii7InuUyRTWy9NL/UqEQytbeWlnildgXOH1qPptZad0nVHJeTI9SOPieq4My7qHRoMkF2HxPdfmIREuU00O41lD4QY8vd2UNzWVgXimysT1E6a2tF6LuMyuC75Rz9XkIXBlh6TUxcgauFGJS6LxBnk2+WtefofCZ3kFXxYfDyykJdBIIIqWPQdJlmKeuQSbeXWF8vyAHWKEwH1UbmZYJ1DIz1HumxLLE+0/eZtKuEkQUYF6NB2O4PJxTFgarl41W1J/VKTL8ld93P61MtTYliPxFrEzDmYEBIkjFJxWKYfBtYIdcr/dStWn3VbkKdw7cmApdo7y/3EpKFfuQOc/SMnF9pqXIpxNpbhEtvBp1xBK59NOrDnz9H++3XKPY7Hn5lcmicKZiH1avXcB/dgpTIzcAgnbx4nlimdt2pj59WaWeX1QqXrtpLYHLEgPE3WgP78kgI769RU/SZv+eM71vFxhwpN5nwH3/1h8++vi9wnRk83VvcZi1u8WBvm4PmgI3pGi9evcLNvQ+pizGlZHLRkvtoU3YyXHizQ4uMlMZg9EUmpyEEt8fufhCKUJJiZjweM51MEBG6tmVjY4ObH98k+IK19XUuX75E1/a0TUfTzFeBwEcF5UErkvYs2gPKomZtusF8MWPRzBjXa1RVhRum5ZL3pGTu4kVZUEyDaaCmQgyWMZeDoveUfNNiSyxyRqgnNfK1SHjRIduQ5opcEvx5R6oSl/IV1i6t86C5y3a/h2oma+LnH/yEn/8nP0VHSuUr9sMuL9Wv8rM7P8C3JXVdc58HRO3xI4emzPb2A+719yz1/T+2i3dwjiuzF/jOq9/m7s4dfrr7U3KrZG8BtRbcmyFZDpsPDkmeohBy6AnZoV1pxo7CKvR3vFbQx0yKEVeKiaVxK+f+FI2dkwZSrcRoAu4mNgSt2Rytk3NPESomYcysnVPMIUYzN42LRF0UK1NP7xxRM8WGt8Bgv9S1QVdlCwyeWSts05dUzjFLkYPUQWsRMb4zV3W8TTNqZtC1KV2r1Kmgdo5Fl8zTygv7s97iVpxQFA51gcWFCGPF7wjaKrmBZhaNLWutXZkXmXocCIXl9+Fsn4TSUQyTlCrW9gu1GIP2vL5wJd6jXij3M3F0OBCg3rxrY7YAbt9ZC67ddKRBYN5PBBc9rj9s9/ku00+c5ddlKGeZTtxhmO9cadeN3VLBBkwmQ7TLUMXBKayT8+y9UpIHdn31cA/n//oB+tEt3HQC3qO375yNOfkkF+7TwJiaD5MU5acnyP4kdRp4Ext++ayZpBXL96sIxz/N952yHfLqNf6Lc/9nftfDgI/WmcHTop3Rx54FM4qq5OF8hx/e/z6zap/QViQSqWnxoULriLZqk2SVtWNcst9J1XpSUHy2sXMpBB89IRT0fc/tj+9x4cI5Xn31JfrY89GHN7hy6SobWxusr69z/sIWzbzh+3/zo4FNOnLS0kxMHUVRU1KbDUFOxBhXgb95cBO31yvBB5x4nDdAQlBQh8+BmOxz6FRxW4rcMWFzl1tc6Si+I4SXAlKJuYNXSpor9MIkrrGIc3bSNiOZULiStm+IWCSKdxba20hDpEfXkml9ikwfW8blGGkcuIRGQYNCNsZLRREPqo5b9z/m9t07XLl0hYvuKrvuLl2IuCD4ZKaXSUyTU6pjsjmhXyg7zS6Ni9SuxPuSREdqFV9a/Iw6CJUZPIZKIEDhD/sITYoWvSIZ6U04XlSOPkX6ccQtCg6aGbHtSY21/zQd0bIFGEtBrpUmW+6bSzCpSpv4G77WdJCRIYS3mDiSt3YqKeK82QKkbtAYNWaumRLkIpNSJqH0mqlLPxzHPWMfmE4KFgsz5jQ/LsUFGHWetKv0Dy12JkUlpYz3JpAPXlZZfG2XqIJHexPMs/TjS8aEOSdGc5fPwdMXsXLT4ptEs1UOhpmshOLqhriWwT8g1nLMIbyY6cr7qR8bWLLcO6VbE1CbpkNtUpXBZVxO0UIt/xYFv7t4JLMu3b/P5O6r7L94eLoPDVz4Z9eJNz+2z7L/qE7q11aqBpyW2pvfFn1UHtqPn7Fm69BK4deoDXvGdT0PA360zgyeRIWFLvg3H/5bXpq+jM+OWX9A7Dr6mChlCj5zsH+ASKaceKOFxS66LpuAOTu19lJpMQSjumKxGMzMREg5srG5zsuvvMRoUvP1b3yFe3cfMF2bUBQFly6dZ3d3n1+8/R77B/tDtIuN2FflhJR6co4UoURVjdFKNhJflWP84C5ux46uWKes5pJduzFtM6f3DZ4CCohVSy4SIoGYE1pn5E3FXQ4myAbLvIpCqRXra2vsH+zRLhaEUDDeHNHPG/MOEnDlACiD4FtHcomH+w8oKQjqyRHuLG6TXIQSZGQ0d+kdIQ++RWRKnOX4+ciN+UdMyjVc8gTNlBpImhFnnkuh8KjDBP29RbRIJ+y9P6d6raeuStYma3Suhy7hxdtYviZC4QbzTZuIy1GJQwiwV4cbOcpBe9T5yGw24+sXvsWtvRukpJzzU0bFmN1+h4P9PZwKu31Dt1YY08SgwehZTeeBsuZLYj/YNZQOvDJrIkUxgGaBhBJ9hiyU6sHZhJ/ZFJgTeu0DRYBFjgQVJqOCXpWidowKTxuNiRIVOFByUqJYRl7wDnE2kVmWjjB47swObB/2KZu1gjhiyswbO7am42K1z2/893un/KKe1+e+NJNLt/J20qE1vnp68HyLI1m13mSZX+fsRix7ISwsCLibOlyvuM7y9fKRs7M4Y5lWmirh0NdpWKfrgXunTxKGWUI0mObxQNn6/gPix7c+g53yK9SzZsOIEF57hfThjc+UHZKytBvvs3iCPXVhJ0CLCBKKx+rUTn3Pp7XuZ1jHvT/geRjwiTozeHIuIC7Txjl3dj7GBU9OkZgt3HWhLWUW1sfnmPfbhMpRRL9yyvahIGtP6aDrzXcot0pe2IRcM18wGU/52tfeZH1jja1z62ZaWVdMphPKsuTChXNsbm7ww+//FHCUxQhVZdHugQhlUZFDQc55yL8TQrA23KJZkFJP281RTdTlFOf8SkguIrixkM8nyrJCOiHuJnRLSXlOiongHeGKw73i0Rdt76nD2KrG8WJ+Ba2Vve09+kWiWK/pq5bbi5toC0EHA8sOECXn4ZgdImDifiK6SFIzqATTG6nmwcZKLDNPhJQzfbIfXPIZtxC6PMc7N7AvedDgCHUobEQ5ZnJWkh88kQqHe6WkKk1L1GtDnim0QlFVhKrAO2HW79OnnrL2JJ9RgVrMq0kyxKykPWxCsYJaA2vjMfcXFcVG5OL0PPPtOdpEs68Y2LCYMnGRyQJVFeijnTyjWsuxmw+Tb0FhK5FixuPomoREwfeO5iBBhlCCVpB9xqtpnRazhIwNJKuY6Pvy1hg3WCO4KCxiohCHOiFrRjuoS08jGV+ZZxZ45k1Pn5RcOGos0qUInhgzvc80zcCqqeJE8MUQy/L8Zu0LW240op/a3VOqDsEMHE6ywuHjrofx3cjiQiAHA0sAoweRbmq5erkw7dPKI+7o+nooFhlJFhWjxfHll/uZvNQXnShJBsomdyLjt+4Qr3/0qK6lrj8dgPBJ61kBgir59t3PvK2mbYs6//QXPq6WwOSU/7rxGFJCT0xlSgi4zQ2z1vi06gkaNhNrnt42lKLkG7//wae3HV+QOjN4ShpxUfEPhP1qB60EvFK0Bb32dLpHkBJxnmoSoM302AXcAVE6iuBwauaJXc50bc+iaXBiLo7NfG5O2zh2d/e58sJFDvYPuHT5AtPphPX1Neq6oqpL6rpANa1Cf4Mv6GNHXY2JMVLXNTH2K42SeTkFKh9IMZJypE/d0M5z5BwJ05KFdIQcKKTAdQ5/zyHqkYegr7UUFwvkSkBHalN5C+juJqY65dVvvcrewT57cZ9qUtPXZnjpZ4GyKI1xU0/bLwzgBR0y9uy/hTeTSWhxmF6n0BIfHIu4QDJ0kog+4+PgQeV19VvMQ2SLfV9mSxBzxuXBtHKYnvOFkBwU2ZHXbEqsFNN+xaTEeaahZ1SWFBqoJpb4tnQtj03GI4g6vDOjypl0uOCoNBAl8uHd97m0fpG02/PRrY8oCcSo5nflhaoykW3pAr6GgCe5YZ8mc/3OUUgLRa5mfLKWHFHJnZJ603WF2pihOgR8YXYIQRxxnm1dBUhnIDInY66IkEQJWYZ9kelFkaD0jZoovjRheRetpUgUepSuT/hCqMeOrsnknAmFIzsbfMhqGqzZQc/6WoVz8rif1PP6nJdUlpMX60efc2loPQuEheXTVbuJ+sY+7cYmqFDO8iqFIJWDN5THLE5OIWFSDal2uN6eD83w+mDAanJz8QiQcHWNu3KJ7RcqLvzb26T3PiQe1bQcuZD/RoHTJ6w8n3/m65CiNGboGRkgV9fIaETa3j7+XlVkiMZ53LShxojOP8Uolsdt++rikR77Gn/5Iv/ghX/56WzHF6jODJ58cqgkeh/JDCP4ahehSTllPQWyzHGuJyRBk0ODnQg8guvt4hc107tMjInupl0AIdH0+zQ7C37207d4483XuX//IaNxzZe//DqvvvoSRVnQLBqKIvDitct8+StvcOfuPRbtAWAAZOn9VIZ6MGIMaM6kYTR2yUal3Nu0VFGtJgtCVcDlZD5Dmuk1kUqBnPHR0V1t8CNjGoKavqXTiP5SKYqS+bl93rrxNi9feJn1zSn39IDcmiXD5ugc+7LDbHtGXdWUqSKlRFe3g1AQtIO1sAFFomCD/bhjWh8Vi2fJlv9GCVoMJp2r36IiztzEyc4AjrfP6sXiQSSJsU6lnZVHqQDMhdymhs2XSEmkxoiuIIp3GZyZdGqEEJxprqxnRqeJLDZB50XQZCBuZ3+HvWbGPM3xwy1yL5G+yLjhwlD5QKhNz9XGSC4VrQcDzgNHR6YvM5K8MYGaSV021++5QqkUpacQDwJ9mQi9EJyBIlcIqU+EMIQhh8yii6TOTDZHRWA0KZi3idyYU3m3SPRtMmF+EJtwUqUohajgkmWL5SS44MhB2ZyU7Gx3Fucy5NlpFnIUmsVveIroeZ2tPkFrJL9xjX5yCI6Pmlam0tpp43uJ6b95F207eOVF3P6M0GywOCfkZjDCnDp8ZxFBkh8NAHa9LdMlm7YLC7P96EeHdgYuKv6XNx7RO+W2xTUtmz9+iH5855HPEC5fIm3v/Mp5dhICiPvNir4/o3pkAu+MlZsGloD0JMs3HpP6+Fi2x97/KdqbPMUA81id+C3Mv3GVv13fASaf3vZ8AerM4CmnDkFw3pFyxIkQc7JgWpnjQyCoksXufJwYIyGCXbjVwmNjzGhW+pmit8Jg/mjictXMRx+9x/379/jmt77F+sYa585vceXqZfwQu/HRRx/zwgtXeOXVl/iLf/vvlqEspNzbNFuO9LGlSmOKUCBO6GOPE8E5Z9l3zuOcJ2c72znn8BtQVA5phSAB3xc0446+iKjLhBc9+SFIznR3wL9owLB8qSL2kd717Kc9FrFhwZymXxC0QFtl5g7otEWCkGKkmJQk18MCfOfIolyqL3Nh8wJ3Zh/TxpaUFNUOWiEVtg91YPIkWd5dVkU6M2KUaM7mSo9EoQimMRM/jDdnhQ4ooFRPGyMpZMaxYC6Rrk2MRgVF6QmXnPktBQVnhpuVD/S9GdboMCHZHEQDMLXDedu/2SuxVfqQ6FNDoz2woI4l4zLQJaXE42rTfZGNhfTqKMDacpJsGtOb5YAE6HaiyZwUa/UFNfDlBbLagIgocTAbLIIjFhnx7tBOQM1eITghqtLESNG4QaBurVLBgpKdCAWBlDJ9l3HVIOAVIXWZwgt15Yki7C166jKwyJGi8DjxTNdHJDpu/5PneqfPRT0jcPLr62y/OVnFokg0gOM7kKzEsRDmyuh2cxh78rNfIm++xtovdoi/tzWAIRuUkD7Tlp5ioY8AqKVWCjXglCo5zL0bfMbWPmpJD7dP/Vzx9h24cxfx3mwIOIwHiXfufirsht0EDsDp15m39lnUSSD9tCm4TwC80/Yp39XJWq73hDnpZ71fT5p83v39kgv+OXA6WWcHT2pzUoUU5oaLkmKmroJNF+Vkd+bBWBJzasTYk2yCc+2NsRAnsIDURMR5nOpK9J1R5os9rl+/zv/3//Ov2NrY5Dvf+QZ1XdN3kbKsCCHw0ssvUpQVNizlcM4+SkwtmpRF3qOPJaN6asaaaoaGdTmiKErariX4QMoJXzgmr3gIEe8DrrvLdAJNsTWwmQ7aZPlkJTDOpBmUriBNe+KBtXbqULPb7fBg5z6+DiSNsAfzSU8Olm+mMbPoDoyuT0BvobIHbp/9+6ZX8F65XF+ma1v2R/vM04JwxJwsRzVjyTCAKpdRb1Eh1iY1lsbLMMXTWs6ajszEscmRRKZMnugzpTj2Q0ubImtaWQBvYaxLdBlytomfAnI23ZEyWFF4YxQ7EhVCEEcms9fPKMUzLgpEoCw8Xh0VjtQqfZfommjrEnPhns966CHMHEE9i0VPRvGl4DYc/W4ynRxKGLuVIagLtg+LRaCtIm0fbWhHhknoPAT3Jof0MD7nyars7XccHPS2P5MwuRgIGwWznX4IAE4G+sVsCoJziCi5h2Jo1VTOozVID9W8QALk3rFRbbHTf4Gzwn7X69J5+rGspt+W/ku6NMWMkCoh1eFQupQT3H0A3lFvr9NPHC5ZaHCq7Pft24wkoVjA/5+9Pwu27EzPM7Hn+4c17OlMOQBIzEAVi6yJLIlUk6IkipItuU1L1GhKLYWi1e7LvnDYd47oG1/Zboc7osPRHZY8tFpSK9wardZEDZRMiywWWSyyilWFKoyJTOR48ox7WGv9w+eLf5+TmUAmkAASBVQxPwSQibP3Xnvttc9a/7ve7/3eN1thmJWQYCh2BtEVHZ3E0q5TKcxU9d2rxPWiKs4VE8vlsrScUgLNd7f0HvZk29sXemMRZ78/magPoL36SMpYxNoS7nx8XKwTvLs3S/h+QNV9nmvqukxE38G0ia+QH7+3ju53ez2wnFUQklKMKMXgHDhnoRd0WeI1rCtj7iXaggIOIuussnIxSLboRHSRiDlSuzHeNThbc2I7IGI42D/gzYuX+c2vfoPvvPQKb711lZwT585t0TQVs9mUc2fPwtrfKaaerAkQvG8YjzZIObLqF6SUTqdgsirHiwOGYUk/FErVnyticVWHalkMuzFYaUq8i2byLdAKYk6YYJjoBLWFRXNiMEPxpQpD5KnqWXxX4XKNjF2xY8gWH2saNz497CKlBWeS0A0rUh/wzkFUgonYcQW1YNVQ2RpX2XIXejJen3UdflwofSMGZ22ZbhyKEWUORSMk6/BdkwSrlqq2ZK8sbTEJbY3H5sLCJFXMab5d+fZREKtFn5YKU9hsOsajiqpxRJs5Sh19ihgtoKL1Hi+mTLtVRWtlpUy1aRaa2uOcI2dlGQK1d9RYYkhrVnOtVYoloLeaWuotRzVzVE05FpoLmCSXSb2YlPlqYLEKpFgm5tQqocuEPtFslBDgqraMx77YLwhglXCYT32ujBdiyCXKRcEZU7YlijMlwzDmzGIRqJLBuTKN58eGjbMjhj7g9W5fnUf1A1R1VYDT+vbzZOLNDoqJJRBYBY6fqjFf+Ax2cwOAdHiE9gMm5LssCkwqgb+2L8xSbErAsO3WzJOup+8M+HnJxDNDAWqzi0NhlwCkxKOctHxOpriketvvYn6ng/RDqZNt5vT9CZw+QSVrvWQ6OkLs2sTzfu3VEyH6g9R9vvfc9+U7O9E/AWYy5i+++Bvve99/N9QDM08EoRoJrrIQChuBCk5qkgzkkAsYoDAfpUWVC5JVitFkLaSghJjodteLvimLp2rgBOFkLe2hzc0NXn35dd54/TKVrzh7Zoe2bRlCwHvHbLaBEUta08U5p+Jn5Bu2N8+w6hYMYUXOidq3Rfe0/jhZFXLETyz6YmKwoEbRymHyOUKWwhZVBjdY7BlHGgVkaahDSzAD2ZW7RPHFf2l1eMRC51hxeG9pbMOGbrGKPRvtlCd2nuDq/AqrVc/KLjkejkh1IDex0OlGGWRgs9ksFgRJqbUmuUTlK9IqoE4xahCrhQmi6J+SZpwrESs557VjuF9n7WU0FB+rTnqq2rKSQBsctTaIN2R6liGw0kCLp9dI6jMyGJwVYlqLxJOgA3iUZApb6DBsSstgI0EyNsPI+hKCLMXrKJGLIDYp/TxBrWxOp1hrGXLgYLVEycSQ0QCmMpgKvBMkGVwnpKhknzG+6K9iyGWE2GT6sI5QCcVqgJQxYhDVouVSQJRVjFTBMPQZhyGtQVolxU4jr4rrucdiRmWyz7t1iHHOKEWMT1pfrJwQYsaooVtmGmOITc/GZJPVozDgH8wylnDmbRlf6+k4NQU4parc4KQG5i9uMI0ZDg4LqFitSHWxajFDxp7k3a0SsS0O5M1BxnZ57ftkiiv4em3MTnC9wqA0B5nqy98u1zMoC6PeXvxO/l+H92kDcK/6JGXUfdLK3G6FPoy6Ha689iT8qIxL73zPO9t1623lFy7wp2b/BHiUaff2emDw1I5bkhtA15NQoSxg2Q9YEXKG2BVfoVQpVovuKQUlixK7vBYxg42WNoxwjZSFXuwpa1Q0TDCbzej7gclsAiL4qsJ7j6oyDJHj4wVhCKWlRmGrmmpEP6xYro6K/iYXoYCe/lKV7TfViGV3TDV21E8JWSLRC1IV8XrU4uFjeksMPVWuMIeWPibEGBYsoClu3DhKjl1UEhkW5fMOrme8qTx94SlqaeikYxlWPPP/epbznOe3/5NvUFUVN+dXMaGwKARlljepRhVZAj7XnNs4y6sH36ELK9QJ4tZmnqmM2otIufvVIhgPrO9YsuITjGUDrRJ1XfOpC5/m1euvoloAwe8AAGRHSURBVDlzEA4Jbknflc+HF3xtkbQWowsscqSuHGEowNJqYauiUZra0UvErnsHxkKDp85KrtY6EAurFBhRlYEDUwTqGMF46DXRGoPHMhlXdClgFHK2qAiRiEZh7BzGCSEETJAyGVeDrQwmCtmVu/2Yi3hd1t0IpQCnLGBGxVJVRZmvAiYanBTRu2ZIVnEtpHEm5owGxabiqo5VQizvWTC30q8So5HHrhepvk+MZg0hBXwvHA1Hp629R/WDVWY84uiJ+rRVd+dkXKoENXJqXSCp5NTJ0e2pKrOzzTAtzTwTlURhlYapw60y1TwzTA3Tg0C3U2QSkjl1Fk8VVIvyOz/58huk5fKdi/fbF9L3s7De2dJ7+5TY6aJ+R5vuzsfukYn2oeohg5KHUutjcpcr+sk+GoupfMn9ez/H4W26prvA0r1A68MEsutt3es9b31hyov+kav4verBTTLJGCm/yzGe+D7p+qwWfOUZ5kqMA34zk7RY2w+HijpFXBmXT3sQvulLq0/MqUeJFVdcxmMPIhwfHXHu7Hm2tjaovOPMmW1iTFhjCUNk1fWMxiPMepLMu4amHjMajbm1f4OuW6CUq1rKA6s+MWqmWOsQI7TjEfVzYNqE7RziLX7pYALLYUVKmWZrjJtXBOnILjJzG3Rtx3E3L2aRPbj1JJj2hmQC59sneGr7KW52t7gSLnKtv8KTjz/JxVfeYFLP+HF+jC/za4RmRTzuaFOLtzUGaMYtZ7fPcHC0R0iZWHfsdwOjNCYQWekSm9ap5dkWt3EtbUt1imhpcYHShDFnxmd5+vGnWMqcoR8Y+sj27AzPPvY0+/MDXrn+HXbTdZS10zZFu5Fj0RVV3pJiYQE9ll4j41RhBTBKI65okKqM1xN2prRorVtbLSRLbDI2mbJvDTSuaI6WDHQhMjEeI5a6UlQdxoGIp1sOpKQs8oDNJbcu9IkhRRhKq7gyFg2KsQbblOBVLKSkEIrGK1uFKdTBwdrEMq+U3hcXcWfWonIVXBDCWuMlNfhoqKzFVJnkFQlgR5YcMkeLgbp1WGtoZxU5K8M8gXFIG3j5/3Pj4Z6tj+pjL/fMU+z/5AXC5E5Tp6JHSmvmyfYKKsS2POZXmbx7h/6t78vPF5lho1h2qC2DIX6x1kxlJVWGbEv7rjpW+llhq4oEAjZ+6ZXbYvS32Q9IVX24CboT35+3LdDviBFZ65tKGLA+vAX9BDTdy1bh4671PtyzLZlTIYtk7UHxoPv7bh5M9wBOZjS6f6jygx6n+4GyO95z9/emMs38qN5RD+7zVCXSkEs7BIOajFSlDZZywqin9TPmYZ/oIm7dIHMbQpwL3gvpKDF829AfdmRNiPEkhSF1zMbb5JQRsbSjlo3NGarK5uaMuq44OjpiOh2x6pYoxbByNpswHW9yeLxHUxUg5V1NU40IccAad5p9p+t/nLMYL2w9ts3orOPIHjLkQE2NYOi7VfFZ2RC66YI6j2nDlHbcMD035uLqNXxrCvu2Uky21FWFEc9xOiD6yDMXnubZ+ml+6ZV99pd7XHnpClt+hwN/i7/1V/4mDBDejNjkSS4xcMR4NGEIA6tVx3S8SegHdrtrmGiZbE5ZdR3L5Yq0vph4J+RUNFe6putEimA8Z0XFkGxmd3WTveNbDHFF3s780IUfZn68QDM07QQ3HBBih8Ti2RRypjcRkwqrVvsSIOzVkrPSaaQ2loFEc2JBMCRynWnwxVtKMhrL/lgtU3Uq64lKo7jaEPuMzUKUYjI5rSuGYBhMpHbC1vgM3WoJGGIccF4YjSrczDAMiTQkVjHiK0s+CAwhQigXd2MNzKDqHNqtHchXQuoyXiwxJbJRslVyX8TilDWMvA4sjrG0ZIlgmwJKTV/ajnHt/Cp1sVmwIhgMwyrjbIVJnu3tM7zOI/D0A1HrRcaMxxz8xBP0s7dpS/S2YBxZC8X9Wgd1QiZU1e2xdQrAWp0pnk1+kYl12WZsBL9S6oNMaoqFQZniU4ZJ2b4dYPra/DZwenupouFDGEe+G9NzD8bp9GcPkyG617bu1PV8EkDUvUqktL9SKtONdV1MNj+skefbGMC86t7585N6v4DtjueL82v2MCF1zY9/4dUPt98/wPU+4lkM3vt1lEm5I0mSCo2cwPYwc2PSdGDFEQGFBcTDIo/u3zSkW+vcsNhhxNLFY0pUrDJf3EJMuQJNJuf59Gc+TeU93WpAxLC/f8S5c2dYrTp2b+7T9wMhBEDIOaGaGY8n5JzZmG2BwDAEFosjQBmPZ3jncZXBzRStchHATyu0H+gOVqgI6jL5fCr6p8NE7Aasb+EC7Ha3SpgnJfW88g0jGbOMx0QN1K5irCOiDlw/usJidUA2cN48QTWpeH3vUhFLd8rj//hpfoIf59X/1at03RKjhvPb57l+dI2jW4dM2glbo7Ms4hGr5YrD3WOkFmxtUIWYY2G8LGUhD4XSx5TmZMgDt/pb3OivYBBmdpODvSO+tvoaTTNCG9hdXkdNaXX1mkhdxtRC3yVG3pNUGaTkOxktgNH0QqSI0I1LxWcpW7Qvnl/ZZaLJjNShsg4m9rf9nxzFhykapRaDBmEgs+gHclCcqzBG2d2/wc50i1EzZggdi+WcbtHTtFA7Q8zgW0vlLcF4FqsBY4TlasC6EhtjKyE7kFj0Y5jivE4WYkwQBRMNwZWJyz4nLMV2QQfQtfdOH9ZGmaWrS06KE0vuMyEpSQJaK10XmUxGuJyJ/SOx7Pd7SV1jt7fQENAL51g8N6XbeueMzWm23NpV/E7TTBGoDiP6/JPwW99CfEX43HMMU3N602NDsXHJlRBHQmzLFF8xwizgqdsqAcHVsbL5rUPk2i3Su7W0HhTI3CMu5D0X35NIkZNF9s721X1eL859eABh1lYLH2Y7HzV7pSWn76SFqavuVPj9UFuQJ9v5MJ/lxDX9jn26k02zj5/nrz72Lz749n/A68GZJzkBTUWEm7WEpWYFryMq1zLaGrG/ukI+Bt8YqokhN8LqRmB+saMJLRao3Yg+DyAG1YQAMYcyqGcqVqsl08mEnJXj+YKrV29w7vxZnPN03cCt3X1uXN9lf/+Q5WqOUvatbirGkzFbmxuMp2PeunSFl156CRGDNQ5VCEMgnc00Zx2LW4H8luL7BqcVsR6KZmblGNwKNXoa+LvXXcdrTawDMgh1HPFY/QSRiBOHbzzLfAwm81tXf5P95a3CeofMUbVPvDZgEDbTGZ7cfJJ9DniF1ziztcPlKyvavzXhOV7g/P/2McbNmHZW8/976ZeZL6E2I7ZnDUdhnz4vCyuk5nTSR6W0yhgoruNjwCrDsieFQLKZ1Bzyxae+iBs7Dm8d0veB43hUwoiTQWzGyRogGWFIidpaiEKBiyW6xIQRR6sjHIKMhETxR5r4miQZn03xkZqnEizshRAS1hSPMJMMOYJVSxdSaQ0KLFPk3GyGGMv14wNm0w3ERvaPD5mMWrY2dhCB+bBP7CPLPhSdWcyIChuzmmFIiG3IMWFiacWZpGAMscqkOpOSYFaC7lMYo7Xbuca11ssrphYsxarBUNjVIkIHW6/NN2PGOFMWzAzBZprGEbuEtxXj0SOB5fd7ad8Tr17DNA3HL85Y7dx/OFmUchMTKSzmCUFiYH6hYufiXtGU72xx9GRDbAo48stMdkK2t/PvkNtMluuLF9SJWDzVgtk7Jl6/UcbI26YwEV1fgMXb22rv+SHfH2thZzN0GNCUT99HU8LubJPnizIVBuU5d4CcD2o0+Y7d/TDAaT36DyDWvH9d0vuo2/uZiw+mCHdl971fEPduz/2ggPBevycimLYlL5fMP/cYv6/e55FY/N714OBpAdllMAYnDjSUVlEyeKmIuePK3itEF8EIOUAwpX2jKGoTEopXVMyBlAYgF98nDEoRjgP0fc/vfP0lcs688KlnmM3GnD+/g/eO69du8sYbl3nl5Yu89dZVUooIgnOere0tNjc3cdayt3uArzxnz57l1t4uSkKsQetEXEaCG9Bzgt9rSyRMU9yzwTAMIJNi+qhdIiwDySSyWVINDRtmk43Nbc7tnGM+HHN13rFMc7rdFUPsCT4iFC+msZ8y9B25Sjg1uE3YNW9x608fsNfc4Mb1ERUtP8qP8iKfgv9CuPy/u8p3v/0K25OzzIcFC50TR5nJasqkH+NGloP+Fprz2nw0lzaiKLpVzqVohuKz1RkkZOa64Dfe+nXG44a6rzgzPs+G3eQwHOFGHhaJFCKmEWoslXGolBZcTmsPrgx1rui7isY1uFXFsp8TpcdursXVJ/+uHQBSLlYBKRWLLEVJnYIrhpMEoadHVbl0eAOlGF6uckflDY2fEW0k5IG6qdHOFvG61Hhf0cUFkiEwlDcWve1TBWgomjjXW1YaCC4hzlBNLMYbnJbfwLzQ4nAuiveGGE0JQlYhrXJpO/oi0DcexJXtWwyVcQw5suoiXgy9GfiVv/3bD+scfVQfc6kqrsu83dnlxJ7gNKA3F+2TKqfeTEBpwb1VQnjThTMM0/ICtyr2BHeCozvz8QCGseA6hSzYBPVRJm9N4LIUGwLNpyLt95ur+551jxZZOrqH6WtO6BAwbYkj0ekYubFLujNnb70NM52S5/MHX+zvZGs+pFmlWIsZt0jTgPdl+jGVycT76oc+bL2Xs/fDYMIeJgAUgw6FfbrxJceWfQSc7lcP3rZbecwEnHPlF06LX4+rPLEfiMfQ24RtFRcN8VBgUyEY8hWh7cvVJOSBkDqcOILqegIKjLhivIgyXxzwxsWBL33pR6nrip0z22xtbbBYLHj99Tf59S9/jbfeusZysWA0HrNcKtPpDF95jBiuXbvJcrFkNG7Y2NhkMpmy6Oakx1bESgmDsvodZfRMhZzNVOrJmgk5kY8SHCpVN0JNwrQUjVVJhaM2LVXdMq7HHIQ9lmnOfthFj1MZn5eMrdesRjaYSrGhuKcbhcOjvcK+5PJ+x24gs88/+bP/gN/5u7/DT/H7OfPMeT53/vNcvvkGi/9qicNw9U9d4anNp5hOJrx+67tor0iwUCUkCcYq1GXyTIUSgWKLpkgTNLVjcJF5XJJNZKnHjO0YrTKrbkmXA0OK+GBoncPk4sKtpTNKiBmtMweH+3z22c9R2YrDxSE7dofFYs7x/JC5PaKqLKPoqStHjAmPRXtL1ydcDb4VOhSJa4G7L9E/NpcYmWHIxKxM7BQNA4kl8x6axjAQGeqABOGx8XlMrRzMoeuXxAxDF9GsdH2kbR1N60rLsIMQE406qmTJbh1ns/bioYexr1m5gUU/4PLasNALzhvwEA4SxpV8PCPFdLN4boFRVxaxkDCVYTKaAo8MMr8va61ZsWfPkHb3EO9IX/wU88ffeak8YYpOsuYARBW7KmzRiYHl+PVDUt8jvuL4henp67K/bY55qpmiPHYniJq+2dOdrZh+4yYylIw1/dKPYC5eu7/u6UR7c8JEfZBF+n08Px8fl7ddrGBv/76aq5PnPfiGPwSL9vaHY4B6k3x0XOJk1gkTpPTuLbUPorN60OP9SdJuracsNZU26+jHdz/uPfpE1wODJz9q6XIsdK2WrLjQJ4aVcnb2JILjON0s3kqXgJuO5BUdIC+K0WDMkZgzVmrQjMGRUKzxNPWEmIYCYmLPM88+xf/sf/FH+MIXfoTHf+48Y0Zc//IuL3/nda5f2+XmjZvknLBOcNYzDAPXr94gbAdyzqxWHfPjOc24ptksU3zhZoJkaD/TYLaFtA8hZ2Q2FN8km5GNDN5ieouMBJ0OBInUfY0kBwhOHcEGdg9ucnCwR6wi3lbEOmBC8ZAaSEULFFa47KhTS0gdWC3tQFvEjyGl4kZslZu/cJ3/Pv1N5B8Z6qrl/NkddthhyowwDtwK19nbv85yWJF72Gw26GNHnztUIKaIDLlceB2wBNMZal/jxKGyImlk4XtCd5NRPWbIHX0YWC0DBMUkWIwD3iZGrioxPCmTNFOrY94OvHbzdT737Gf5Q5/7aS5evsyiX/Ltq99krGMWYYn6gGZwWvRZguAdDCGBUSpnWIaIU1tIKmNQVaq6orGew7jE1o6j+Yq0HGjbKb1mvIBqwiXHUg5ZLJakoCVTLkEKhYkzCMMyE0OJ5bHRYOoylShRSH3xGPPiSptFoM8RyYKJwvFej7rCRIqWgOBqVgxh8WWQxlbFSd9RpmqcwOzxFqJlY9Z+FOfqo/qIS3yFmYyJP/w0h8+2uNUzxFYIY3kHI3RSuiaNsiv6JITTsF/fKzu/co187UZph4xbQlsCgE0oLeVYCyZq0dPdg33yK0VS8XzqntsmVYbRv/s2XL0O0wniy03pnVoVceWyfleL63u0SGsY3jF2/67vfaK7eZhu5/fdOS1hu5S2ItauW5Dp3cXoH4QlOnneB9VpfRTarDvc3+85xXjHz8xkzC8899WH+/4/YPXA4ClWPUZLTz3FiK0cSIVZOagdg3YcXe1Qp1QXhP6KkheQ1xlsXVixSj0n5LeluJUrwriZkXJCVRAsVjw7Ozv8nt/zBc6c3WHGhEOO+e53X+fb33qZNy+9zrKbA0rlGuqqxTtPDJHr12+gWVmtVnSrJUtr2XiypXo8U9eWdCzoTYM/a6mfMoTjSPdyj9+0UDlCNyANqM3IqjiiYyCkQJl4n1Hlit3lTQ7cAUMVSSljbYZ63drJBmcsYsBXhrZqSp7f7nqaq1asKdlz0RbmSJNyFPbIuYi33WrO8soer/+JlzHOYBYGWWt8IkptGvAGX7ecdY/RNiMWqyWX99+k61coxbyy1opWx9TWkzth3hzhosFH6PJxGQZyIGOQbIrxZBIaU8yjJAqqWlzLneBHFsnC/mqff/tbv8yZyVneunWZ/aM9rLO4piLEwC2WWGvwxtA6T20s7sSkVMCLIEYRlGwyWikiBpcSU2vZX+0RRamrGhkZqqoho7RuxM5kB28dV/Z32U+HWImoVfzUFs1aALLQWMvxfIAktHbMYjjCYsoosVfwStQS/xJSMbpEwAwWY4pGQdYrmmsNMWecM7StR9bTjt5acoBKWpxLdBrZXz26Y/t+LHvuDDiLe/kK6VMvMkzug5jeVtnfZpLUAFoc/3f+7SXilWuQi6g6fvY5soN2L2OGEydxCji7oyN4IhZXA81ewnzlm7Rti65W2MfOkz7zLObN66QbN7EbM/ILT2KPVsiyg5TQ5Yq8XH58o/3vNnb/9voIcuPerbTrS8SJKvQlmN207SmYIqX7e1V9gP04mbx7/zt6B/i6XyTL/ep+LNrJNt/+2D0+V37hSX5u8s94pHe6fz0weDpKS1r1CEoURVRpmhFj3UJQum6JHxvMyJO7Hs0lgiPlWPQgqSNThLjrIZO1VqmiblqGYaAPXQnrFUO36jk8OmJza8ry6wu++9IbfOWXfpPLl95iNt0gpkBKseiSVOlWPTkXFuPgcI9ld4QRQ78vyDczdnA0n7bYcxmrFcO1SL4VkC3FbCj5fI/pDfXIM7Q9MljohESCLMWWoIKhDdzgGkfLfaKJGGtwncFWliABrGIWRRvTVg6NsGBJT8C3FlmBLosDe/G+WhtdDgaVIlK2tSBK8VhyxQrAJMEkA+s22mQyZWtji+v9FTpbMR1tcGZyhpAit27eJB1lckzkAM2ZhrZqWM0DQTskF5G+SyWE16hgaqGJNdYrfY6EnKmzK55JKE12kJWqMaz0iBuNZaYbXBuucazHTKsZvVsR3YAXi0meqJFeE5h1GxHIqVhUGC+nzJuKklXpc8mSG/oyg0mf6WTJsl+sXc0t3lissZyZnsGokHOgwqAB4rIYuNatxXpD6qC2FV7G7Mx26PZXMCjWg7clgy+ntTeNEVJK5EFxE8vGzpTD4yPEQON9mSpFsRQ/K7LgjMXYssD2Q4dnRNtY+lV4yKfpo/peVHzrCgB2a+uUBXrQulMgfjL9Gp/cwS6WZVT9sy9w8GKLZOhnBtuvI6uc3PVeouCXSjXPpLp4PKWf/Dyr8xWzf/ca8a0ruBjJj+1gHtuBK7vYK7dOW4x5tfpktYLuVw8AikzTYM6fJV689NC2qWG4ezJQTAGaxkIaHrJ+SD50RI2Y9Tj7Sb0dGN0LKD2Eib5bX5w9Msd8j3pg8GR7i2TLUhfkSQYyo6Giti3H/QGj6ZSmqVkcHRGOMyJK0MQ8zIllUOkkuQ5FMKQ1o1GhGeq6pm7Ocnx8yOMXzlM3NUeHC5bLjl//8tf5pX/9q7z68uucO3uOvu8wRpjP51RVg7OO1WrFarkgpljCfp1DKsU3HjqDGVm6lxW3K9SbCXdWGHxRMVcbQiCR2kRyBl2CLJTBDDBI0cc0mVxFDnSXGFKJZOlMERSPlMiqtIzWWVWSYEjplLmonAMHo3FDWqVi85CV4DMuG1xVevAqUHUWosHVnjD05eCZMiovtgQSI5EsAVkIvq7YzdfYe2sPu/ToUtma7PDEM0+hmnnuhec5DAcsLn0Na7ZZ6QF2rdeOkslGkV4wzpJTgAzVWouxkpJ9p5Ix0WCtpc6wWOyz3++iFVgPo/NTnqme5+j4iOPukBw7KmcwtkykdSYiUWi8QxwYUxhJ5y2aBRKYvGamFGRlkblQPw5WC+uTJGMd3JzvEnKH857ZaMxqKLmGo3ZEToY+rpDesjXaIqTA2e3H2O2vUWtLW7dMJ2NSCByGXY5iX4T2a/VvjoqplMXxAm8tVis0KMkrMWRMMlSNwWRhOpox5BW5SnRIMQvNwpv/fP8hnqKP6ntZpmmQ2QTetoa+XRx+r9KTYQkKG7U63zC9OmH5Iy8yf9JRHSlbv34NgOMvnGN5dj35FSneclpYp1QLuRPcMuMWkTD1VAeR4z/wPO3VJ+DyLfSbLyN1TY7xlJX4RGXJvZtO6E6Qc8fiL74qbO861Dj3PXrl+oO/5wfRdOkdYnRjeV/Glu9Rpq7Jd3h7vfMJ92ifrffrtB0bw+n+nHhG3bPuzLV70P1/F7D5yBzzvevBNU+LCisO1OAbYVJNODt6jKfPP8tXLn2Z+fIQlY6QeuJVR+gy82HBoBlOZ+rKim3EkHJGUYahY9SOaNuWje0Zk/mIP/u//DkODxe88vIbXL50lV/95a9y9cr1YtLY9Vjn2N7aQcSwXCyJa3Gi9x4kY61j87mW6pyDYLBTQW0mviHko4w8rrgNgyZH1kxKCV0JQSPWKVSGbHTdShJyzMix0G8VTZbYop8xTkh+nW+3HvZSC0Jp+4Qu4CqLi0X749Wj0XBufJ6Dfp9hdYhYQ0RhlZGsqJTpsM3JFqaFvYP1yWIUSYbWjBi1La1MioWABEZtS+o9R8sjGpnwxBPbPPf0C/yBn/ppnnziAs55vvyNr/Bbr36dVCWMkWIdgOBV6TViRFjGJSYKtS2C8RQVLOSRoiuorMNXLYfhEJuEkff4XOwqUt/xVnqdZCDWkdoYupQIOWIrg1eLEUtlG8TFQvaokIZijFqtsxAx4FqhiobxOcegERHwasiqrBaRSgxd6JCqJ61AeqEyNTg4PztP0AGDofYNmhJh6MkLw1azBVnQbDCtZ+gydd0yxA5rDM5VSF7ibFVo/aG0FLPRMi6gjtQpLrbkamDoAio1YhTvPcdxThP8fc+hR/XJrRNfHmlqFp99jPy2HN13A00nwOrUXqCD5GG1bYk/eYFhItSHil8kwvkN/NV9pl+7in7pCVZnimv46bZyMcScvrRHfuUiZjLGrEXW7ic+y+Kplo2XV2iMH9436WHVPRbhu3LS3l4n+iLVonVagwipfDF/HIZT1kmP5qTD9FDYlPesh/we+b1abXcajoq522/pJND55Diaos86HQQ4ObZvHwa4AwC+Z1zOffy9xFePzDEfoB4YPLnBkyXjG4+GgEsNuU689vorLC/N6d/KJBF0VZGWyiosiOgpaNL1DLsqqAjOtaQUUE10q466bXDO8tRTF5jPF3ztN75B3w1UdcX1qzfxzjOEgWEoVxprLYJFVRlCoVubesZkPEGbiJ0kEpHkIkrCNoI8KZgjy+q1iPqMP2dhBHbsi9+RJga/QCUhrS3skAFj12aPBowTNICsymeRLJhU/u5wxTBREpoMUhWPI7HChmzhK4sdW0gZiRkJQn1cUfmWiZ9y2O8xhB41ynHcJ+8rLAVxJTpEHJgxbEy2qExLkh6JypVrl1ld77Gh4lOf+wyf+fQP86n//Qv87F//g4xoUJQzbPGV//grfGv/t8kKMa21XBTNlZxoLiIl8kTKexoVHGvBrFc0D1RUa5F3gAAiQsqGnCH4hDgh+oTLxdivMSXCRDKsYkedbMlBXCdJr2IkSZn0EIGm8piJwdcGO4ehSwwhoSjjtkIydF2kimU/22bMuB4j1vLEuQvsHx1y2B+xSD2yNrR89vxzHBweknLi5uU97DhhfYuoBQwbow3myyW1F7ypC/PgyvCCVcEMQjMyDKtAzEpYKUf9Ps55RtWU1rfMu0Min7Acrkd1Wu9YTESwO9tw/gxHn9nEDiUCJYyLqFuUu7RID1pp3e3IFgZT2nKpgn7m4HGH+aER7W4iNiceB+sX5pJ1Vx1l0ndeK1Efx6XfrTFivvoSGzeeoPvSc9jlU/jvvEXa3f342nSnAEjfAaBOFvz7ApI73a2lWBjIqMWkhDx9AbyDg2PS4RGmqYvh5IeJm/k46v2Iy/UeOqQ7M+7WzJhpG8yZbdK1GwVMqd4dxXMnADtp9935PdxLzH+SZbgue+GxR+aYD1APDJ76tiOngASl6lvsuOGg3+fQ3MKcCTgj5NeE3JdRk9lkG+Msh4d7hDSggDUeVMk5lUgXKSG3Q+yK/0/KjCYt3/7my3z9679N24xZLOc462mbMdYaVCsUJSwGur4jp0Rbt6DKannMajlnNJkybltcHenDAtMIQx6KxkAEzkP4rpDmCXk+E5sB094GdxIov8z1uummQnIJI1Jcp6Pgg0WXoBNFsmCjZ7I141Z9Yx35UdypT9Txq3qOqVvCwtDPA90qoCvILtHHBckORI14b4lRyXN4YvYUu+0NVmFJQrAGjhdL+nyJcbXB4fKQ1Y0V9ijw1PZT/NQf/Gmeeeo5nn3yaV7gWRThiAVTJmww40/98Z9H/33m65e/QtYBchm792ILCxhzCeClOG1LLfhsYFmMM0NK5CyIOKy3xKygESvFL0mDlIw7pQiyLXhri+jcrK8RtcFgCf1AJSUqJ/cDi66jchbXGkLIhGXCZpChMHEmW8ZS4Y2ByuCCMCSwPtNULZVp8G3NIs6JJqA2YZxFg1BJy3Q2Y39xTLdYkWeB2nkkOdQ4yIaN0SZePV0q2jlU8dbRpYqkEYtlsVrgbAUDzOyMowFyzAz9wEqXTLc3OFocPPST9FE9nNIYi7HkmuEQ79DlCtndZ/atzP6P7hDbtwGad6kTS4F7sVJuVSboSqyKIYxuPylVMH/CnsSC3t4/C2EkxMZS/dTncV97GWkb0t5BeTxEOJzTfjtw8JNP4mfPMvqV4W4/pe9lneTfwd0LMiBVhRmNSPvv3sI24zHS1IVRGbWETz0BgP21b5UFPZeYEDNqTzVpH7rey+n7kxJG/HY2z0gxbr105TbjZOz9QeUagBXB+duc4O/c/voYa19yZZefOf/IHPMB6oHB01B3GATJmWQ79vrryERJfSLtQtiH+lPFCLG/rMS9SOVdMcxMuUSfmITFYdQUwbeWTJHpeIoYePLpxzlzdpu9WwdsbW9x9eoVco4YcVS+RsTSdSuatsY6A11i6BesVgcIUib2BFZ5zjEVcgViN9A+b6lecORbQnpDMNtQ/UiJNtGNSF4molNMI2XiypfJMzxrVmYdepu0CIQN6LqVnQdFJdOMWzQmvNYE7QtLlYomytuGMHQc9ZHGtGzPdmhcy+5yl3mzoE4VzlUMOSAIO7Mz7IzO8eKLL/DPvvFPQSCSMH2FVnAcl8zzCklFkL9Vb/PFz/4Yf/in/yCTdsozTz3F+cvnSsbb48qKFW9xDWsd3lXklAg5FUfwNXtXYXGa0KSEOoMIKWciGRcMNpU76C4PTMaeUTtirspi2eO0BP8GfyL0tiQXGeHWrJVAgBShHjniELFWinGpccXsUvJ60tJhksXVWgKovWXStvhmTO09fQxETSzpERIez5AHQh9IK6VqKowzZQw5JmrTUJmKK9dv0HU9bduyuTFjY2PC4mjJ4dGCsHQ0vmG0NeJgcUjMuRi5EqnHFVZaUs5UpuZ4fkwMmc4NWBweSzCRSTNDg3Lwy6uP5kx9VA+l7hQM65BRMdgLjxG3x2z/8iW0qVh+6gzzJ12xIVhHrqBrA0y53Z67E/icTMed/FyNlMy6Rk49oE6fq8DbmK2T1p8ZCuDqz1SEn/kRUm2YfPcQffl17IXHWfzwWZprSzZ/+Q10+BiBE9xmK07iWmIAMYh3hY0+OLjr6e9YvNeVj+bYnS3yxhj/O6+T54u7Wn5pfx97R3vvQ5XIe27DtM1HZ5r5IeodbdA1a2dns3ubl5487UQTt24F3pm/dwKgdBhObSNu/Jh/ZI75APXgJpm+SL2NhyARU5XJJllZ8iTiz5jiFC4ZZzOLYQWbieZxYdaOAQEnHL7coXuCEcgn9iYCTzx5nhSLiLrvBqaTGYvJkvniiJwzXb+ioeFzX/wMP/NHforf+uo3+a1f/waiwmJ5ULRFtcGfdzRPG9zMQg9x1zF/bUW4EfBtjfvhDIeGtAviDSY5dG03wGBAM8YbsmRYgq41L2oV6w2yEiRAmJQpMTHF8S5JQhsY25aj1VCuhp1BgrBihasESY7eBw5Xc+rQUI8aVlVH7TwTPyIPieQiwQxMpmMOjg5xvSWrYZDI4BOSLTmUabkcIB8qTz73ND/62S/y+LnHMT9jeIrHS2sNB2SmjBnR8s34EhdvXiQGxQXPqK1Y5gV9ztgkNOIZUiQuM762dBrxagiSiQIWS9OO0GBZLRYszJzBRkxyGKf0MWKCUDmBoUynhVgmM7sciRYmPVgc7ahCo8Eai/WFOjZB8PWIqInUmdIS9YJvKpz3JDVUVQ0hMhlB0lgWJmsJZExQyJkYEzp0eDydzWSB8zvnObO9xaJfkHPi1uImRixnH9tmsVwShoiMBCrFiWCzZ94vaBhzvJqDBY3Czs4OaUiEEHDeYLxlqSvqtkbD+xzRelQfb+kaxVzfxV65TlwuEecZL1aIPoWbB/zeEkJEYkIPjuh/7HkOXqzeuam3tfdSDdkaTIRc3QZh9aFi+8zyrL0LOJkBqnlm/NYK+9JF0tEcMx4h58/QP7NNvvB5TFRGv/IKeuE88ZlzmPmAWXVlWuzjqBPmSfU2KNKEDhmp63cwJ3cBp/VCXdp7As6h336VHCKm8pjRCFUlL5aItaTDo3dqdOCDicTfoz4wcPo4rCFyIs0X2DM7pL2DwjKFeJuZkrXp2El7Fcr3lfVuGwVVZD05rL/n/kDsUd2uBwZP7UbDcT4qoajZYCswS4PZVnCKQyFmIgmpoH3CoNpTR4szBoPh+OWe4TAg2aCY04EWY4WNjSkxRvb3DzEG2lHN9s4WYgxHR/u0bcOzzz/Nn/uFP4G1li/9uOXm9Zu8/J05s+enuLMKWxk7MWUwTRRbG2SSmZzx5GOl2gRtBTYVVUV7hdeF+pwjWy0MtBWsejRA7mOxKagTti5xHdpnnLeAlqNniq6r8ys67dBBscmgqUxwaV2y38SAbQx5lUiLxHRnyubmJnpctEwb0xnLozm9Dji7ia0dvXTQCt28BxWi9JALiLN9zVm3zZEe8Sf/5Z/gD//Ln2HMiBEjEhBLIxSDUOGZM+ff/fa/5c29VwhpYCI1KQ8oJehX54KZCK4yDItIcBmykqMiU6FpGto4omo82SSS9tSxxvem6LEStMkRTCI7xWExOPJgEQsj50ne4dUhqmjK5Fimg623+BNNlUYclmR98YOqIGgm54iqYmKi71OxiPC2tFIVUiwifgvU0oDPxFTuzJKJXD24yqSaoCT6bkUMmaZ1vHHtNYyzPHX2aa7evE6KCV9bKq3Y3ppx5fp1WreBbx2rZc88H9M2LVYs57fOIhaO5kd474n2YedjPKqPvFTvumvXMJBu7lL94s2yMN3xVPfMUyzPP9hAgEqZoINiiJk9oFDvBeLYnrYFTYDZxYHmW5dJt/bXrvql8vExHB/jXnsT0zZo35NihHUrTOsae+4sGuLHM2l3p44G7lqc33XKDDDjUXH4Vi1Tg7u30L7HNE3xWzKFsbMbM/Sp88h337h7m58kO4Y7tV/fo/e7U+ckRorTvMjpz0zTlON1EtycUgFLpyA3o/EOkbmmoqubTvlzn/ra9+ZzfJ/XA4OnbujRqiymwSaiydjGYOYGaZVgCyslCrKeQqsWrlxATBkBj0TSmjUpVRa80XhEVVUsFiuGPvD8i8+wsTnj+rVdcnqD2XTChScf59OfeYHd3T2sNdy6tYefeM58fpP5uVsEG0g+oVKm3wCCKx5DYgRvbclYy5kcFFMbzJFBnKA3HP5sZhiXVpbqgFWHVA7BlKOUIOeMyQIdiC+gRwpSKX5TdQFgmhWLkCmiaPHAIKz6FbXz+A0YdGDTbjGWCTrqmMd9ckrQWZZmxZvyJvvDLbq0ALem/oc1wFNQSXTa8cKLL/JHXv1D1FRssVnCR/FU1Nj1Pz0rfoVf55UrL6GpTLplk0k5g0CVLaYu+YLDEBEMJtVoijTSQLSkRceq6rAIfZqjMRa2R4BU9GKVc0yblqqq0FxaneoMnU0gliYZ+hDxGFQM6hNSG8IylAlGLdEsYgaGkBHJmFyEYxoarHVgLFUtZJVitZADmmKxCVBLSIoxGVKmdSMWi5567Iia6POKqhYen51n3E44mh9hnWOR59w4uIL3nmZkWS46XJuwTDE+Y5vMdDQh5cTh8oDKWlzt2F1e45kLT+OrTabjKUYMv8mjC8/3e913SkyE7OVU6yRlkPjd7Qvcel1d2xgcPVchCfxCqfcT429dI755mfhuC29O92RDzGQMqtgnHye+8ebHCyjuM7l1v8rzOaaukbYtC3kqbJVUFTIeldzOJ86SJhX+6gHpPcDYx1r3s2O432Pvp+7VqlS9rXO6U1R+hwbtTqB5YnegJ/v1dqB35/afu8Cf2fiXQPPh9vt3QT0weHqieYrLw0WMFFrWZwNWYFpabxhFgiHZTHIJqwZpC7hwScgKzUbFkemLySCsv8jE3t4eznvOnG2ZHy9p25YnLjSAcPPGLcaTljcvXsG5ijBE9vf30Wkk7wzMNsZ08ZBhdyAnJY1KdIxa0KqIlk0UnIBWmZSV3ChGMnI2IxOBq4KbO3QESfO6DReQXHrDUhnsymDmxbVbM0iXCVUgt+s5QikSABsFluX9Y8pUvghDK+sZVkJKSl93tDFycHxEWA24xrLqekZ5g7Yx7IUbnMnnaWzDPB6Wi3QUjDMkl0lRCXlgyPtsTrc44pjneBaPo2WCAoHAHvssWPCL/Ev+Gv8Nl26+DiJ4Z+kpAniTDDYAJrPUQJ5DpQ3GCpVOmNYTjLfkJpewXw9Dn5CQqbAFLHvBOUflHE4bCKCa0ZghZyrjyBkSibq2aCw+SagrES5mIEnCVgXMVqkiuhU5JVIQrANxmRAGTOPwxuKx5KwY6xhiRrOitpiu4rS0BtsRm7MdjHiuHb5VrCVEuLx4gw2d8NjWBV54/il+56VvcewOsaYMPIxGnsOwz+b2mA1ahjiwt7rGs2df5DC1HM73kCxFED92HMdDxm7MP/iv/snDPj8f1SeodL7ARL09JadrrdPbrqJ36p+g/P1Ocbnrla1/+Sr54JD4IRijdGsPjMVOxneLt9+tHqS19EHaT6ctIm57Jr2btuiEnep77MYMszElHx7D+TOod8jxEjNE5DDB7t4D78Yp4/Jx18MCsu+l8bpz0vG9JhsfQC+2//lNfsg/8nd6kHpg8PQb/4/f5sJf3cYFR0dHrDNVsBiELIVtSU4hFENNrZVkCvPSU8bZRfTUNFLElDs5hdVqwbe/+R1+9Pd8nu2dDUIIiDFcvXoDay1PPPk4y+WKGzducPXmZfrzx2zsTGnbEVfiJVZdV6wETlgu1uzTAtQrpjOkhRJHCa3XjPnckKoMLbiZJ/mEjAQbBCKQpJjXtQntE0kVGQtRhbwEX3uceFKIZJfJZGw26MFaZD4GdUokUatDo+BTRaoGaDPLdIhJFdONDW4c3CBJ4oVzT2Arx/7VXexEGBbD2iHrdm6bNIXJcpRcuEtvvsm/4pf4HJ9lxgRFuckt/mv+Ov+aX2TFgstcpP/jK+KguOxpKiFIxGYh7ys5mxIboxaXLZvjTbytcd4ymY4Ra+i1p7c9UQa6KjIOhjxXTDbUGw6kTBGuhgipHAPXeHxtqA3kLARj1xdZin5MBZJi12ZsBoONFnGWplXCUEIqq9ZhjYGUiSmjongjZQIw5BLk6izGO6yzaARDDZVjlZaELuLaTCTiY8O4mpKycrDc48b+FXK0NFIz5I7JZISaiuXRgv3FAWaoCWGJbSydX3Jm4wyaMyEmbG155a2XaeuW13Zffqgn5qP6BJaYU5ZJUolguffzyuMn/k2iYLvyc7fKbP7GNeL7tRi4b+RGJh0fP/i23ut5p1lzHyCQ987X6D1a2Pf4DFJVZfLROdJ/8Fm0j0jK8OpN9NLld27jPsDOjEbrib0R8knxwLqHf9NHUh8EqN3nON78PVDLI6+6B6kHBk8AGrVkvtWG4AayWXt0aAlUNYUXLN5OufDaWZUhJsJlWH6nxLYYcetprogxFlR5+ZXvEFPkU59+AV85RuMR1gm+toiDH/39n+X1vde41L8GdWA/7bLXQa/F5sAaSyACxdhSnWKCYJYG2TcwLpYCrMovTELL37MgGwJWMLnEJWRfPJgwQKIEyhpQKTodUQMuY63BqCPlRPJFYG5UYKMIydvsyFrcxZuJgwgBIaVMMCtMHeiwJOnJKlxevslj5gKT6ZRXli/BEqxaGm34/POf51vXvsXxcLgW2ZfjvO3OkYAj5mQMv85X+R/4e/wj/j755zqQIg4kFdYom8iQBAlCzuClZmO8gxVPVZeR4Z2tHTY2Nui0Y6Ur9ha36ELHEHoCHVkTAaV2jta3tL6l1xV9CFjjqKqWurEYb3B1hdViTUCORajowVaWCktOiohba+BKlpzxhipUrOiJIaFqwEl5rsmINSiJsD75feNwzlP7hpwzQQL9amAUMjvTbQ7zEb1CzIlOVyzn+9jsmMuE0Ae2pjt4N6FfZZZxwDnHJlscLQ/IaShO8F3HrcObDGGgajzz5QodEvNuTu1GLPL8YZ6Xj+ph14cQ84qvsOfOcPx7nySO1sG+J/Y7rrTgYit3TduJKQyU7WF0M9J++bvlsa4nvl9m5G0+PB9pPayA3ju2Ib66HR0Dtz+PGKSqIATM9haptVRv3CTeCzTdY7t31um2P0lTcvfyb/og9VFYJ9zjOIpzPP/Ftx7u+/wA1/sCT2IrUgiEqkRtBMlYZE1bCzYJoc3Ug8Ukw3I30F9JDFcS8TiDghGHrxpC6IGic4GSefbaa69irSXmyHMvPMUPf/5F9g4P8BNDbHqOl/tQJ7QvC6QGUCNYLG5SYbyltx3E0kpUQwFBnaAbpYUnFHGz5nJnaBDiKqAevHNoXdgUdUq2xZ4AtIDD3qBGC1CKoeT3SdE9Ggy2MpiqACuHIXdFuG6MgZhJPiJ7EF3GzwzBD6T5MSZB0kCMA4GeM/Vj3Dq+CV4JMVCPRtyK+wwy4LwnLBK5UtrU8NhjT1BR8Tt8k7/F/5t/y79h+T89LgAwKSSlMRUqiZQyqsX8UyLYpccbz3Q8Y3vzDNvbZzDWYqyhHbUs44JbV1+my0u0ydAr2iW8Cos24HBUoxprPdqtUAfj0Zjaj4pruxSfLWImuYhxhrwS/DorLkgghoARi7eWFBJRI5ozMUVWw4CzINHgs5AlMUjEaY2nLubELSRVEEMDTMctN472CKJc319xsLfHxvlNel0ycmNCP8ckSwwJbMWnnnyRrEoiM53OOB4OGfqeUT1BOscqz6mkJpLo5pntcc2lwzdOLSkCPbe6m0za2UM+NR/Vx112NkO2Nlj8yGP0W5ZYy2lor+TbzNLa//c0XsUvlOZWwM8D9qWL5MWK9GEE3e8GaB62zulhbO8dhpnh7pgRa9GY15YAy6LpunQZf/mtd2q/HpS9+SQJyE/qYU3fvf2zf0RTffbMDn/xwq8+9O3+oNb7Ak/JD+DWuh/NYBXbCzaa0qrKQqse1xuO3+w5+EZHDifxLOW2rMShlNs2Zx3GGJz1iElkk5Fpws6U3q+oJx7rlE6W3OxvkCaBuIzgwKeaaAckSdG7SEBixgdTfJhEyKJkr5gGss2ICtmtQZRKMaVSRSqDLCEOiZxyCS62Bfic+mBUWqb41i4EuVu3B01huYhlosZYgx8XVoXWkIZUbA/qXDLcKqFyZRrPWkF9oElTZDlwvDpmMBd5bPMJPBVD7MgJFnHBK8vvoElp7Yh2MuY4H2MqS71Z83d/7u/yN/N/xyovykUpGzRnKlNhJJNTJpGxlRDWvYamd7QyYTqa0TQtO9s7WO8w1pIl88b1i+wf7XIUjgpY7COxX6JDT2scJgm1c8QYCGFFnwLUmWQi3aojpx58Qq3QxYE6C4gjm4ziWPRzjAbCEPBVxSIlcrCIhDJtR8bWkSEnrHFYU4GxODFoDnQxFoCcA2CRec14symsgDpUelZhwSIdcePNt2hnLQfuFlt+i53JOWKjNEx48vwFun7gaHXEYbfHtcPL5Kg0rsZXDavQE3MkakJtZvfoJl1fGLlRO0KDKbYc+kgn8Emud40LuUe5p57k6McvANBPTRGGOzjxe0pV+VO0sNW2AxuU2cvHyDdfPdXdPBS+4JMIDN6t3r6/b2OhToHUegKMkymwe33Oh8XefBz1UX1vd273hMV7CO81/NAT/Ozo7wGTD72t3w31vsBTHHqSXav2DWiEaNahqkYxvYEBsirzK8MaOJlT4HRSOWdUM85WmEYZbVqaTzlsLTCZs++usdg94Hx+jBv9NZbTI1TgmIOiWVo5qmWLeCGYDlYluJcBxBoYKTqU1HLxSj67nvCT0roigYwKpMtWkUGRpnweSaBGUS3xJKy1NTlpCZD1oIMgFkwwRDL04L3B9hZGmdArsU/UlUPqoo2IrDVZm4IeU8KTG4EEKSZMdkiE5XzJ5XwJRckDa8uDjFFhVm9zZnuHo+URE6ZoNNzcu8F0MuXo5gGhCqgIVmrECEYVyUq3GnCVIXtwRrDGYGqHTzXPPvUCTdvinEcVlqsFXei4eeMaySZ8XZFD5Lg/pNOOnJUqWSxCItHLkhAyTgyV94RhYBiWmAx160kkNGaSONREksks+zm9ROosDH1iGBIiFeIDrlKyMeSUiilptgg1rR+TUqQzKzTmoocSRUxRf418gxVHTMqTmy/w+q3X8dNUAPZhTd1NkTphGLE1OcuV4S3iMGf3cI/a1miAqZtxvr7A3B7RdT1nxtusushyuFWYKq8sdElbT5iHQyRbNppt5vmIN//B1YdzRj6qj6TerwYmPbZFt7EGTeu4MChsdnac/ryaF5ap+eZl8sEhuevenin8u6LEufc8xicj8ycLfR4CpqkhZ8R+QoTen9S6T5jyO4KFP4SO7saPtjz+yBzzgeuBwdPjf3FG1uIgLbGwLVFyuTGg3IHNv9ujB0o9qpBhzWXfo3IuJ1n9pDL7bE1qi7u0iZbslFW14Nz4PKNpAy6xu7xGGhKDLXkGO2e2+PzOF7mxe4NXd79L8pG4TFjrEAP9cgVNiTMhA7WuJ8AUWRargTTNGFMML81csJ1FxkqoEzoIyWXk5OistV0nlL3JJc5EsmAzREnIerpPncLeSbsu4TDFWHSRSFaZdDXiIJhMN0RcZ+mHOWbwRf9VUXRYrhh9mmTQBDYa1CqXbr7Jk+1TNOMxZzfO8ubiEreGXbRV2tWY7XaL/XiL3cWcPCjNpsOKoBFstFSpJtnISEc4MUzHM+qmZdS2zBcLzmycYTEsCQTmacHe8U36eEwcBkTLNOJySLhUpg5rU8z+kigSM9aCqww9kW5YkMgYa4rWKcLxsi/pLSPDkCzL5YCtLOPG4a3HGgXNRE24yuC9w9ualglxiHjnuR6ul8DiqDiXCSaT60w2sFh1rLqA9UJaKmkwPLX9NE+dfYplt0ST4alzT+N3Gw4OD4nLzIUnzrDx5JSogau3trh6cJW+WzJyU3RD2FtdxxhLlUdEOzCzM+KgWPGcGZ1hdfAJ0lk8qg9c7rlnyNMW6SP9tDo957MD7iAWsyvXu9kbA9WvvUReLPgEyJM/1tJ8j3bbvUwyRRBXLqwa422DT5Ei+rYWQjgFVnc+Ls4X8PUwXMa/X9m8tzukrz2fgOISfr86+cx3Wii87Tgef6nDirnHix/VveqBwdPVv33Ehb+8QRtaSIbOzjHRkLpMOs50bya6bw+gghA4FQLcA0CJhfGnPbMfdpg6k1tFkyOGRG4iUQLNrKEdjdA9IXal7ZRzxqhl3+zytau/zqbfwY4cuU74toJeCKlHRDCVImrIlYLTwmz6tbOqFsE0AubAIAcGGa8nv5yQh8I2qWSyK4Lxonxa0/Sjot+SCixCHWroIY3K+L80AksIXUYdeIQmOoJkOg2FqXPgMcQqY4Il14FKLDZbenqstzRpRDKRoANYSxdXWCr8rMFUwjwcc7C/x2pYsSGbfPFTX+D89nm++dK3iHuR9mxTjme8xYbdZvAdlXXEINixslFNObt9hrNnzqFZuWX32Nrc5vWrr9MzcLO7yl6/SxxWxCHjxeDV0PcZ1LNaJPJUaX1DlkhUMEuL+Iyz0A9KTkofAzjFW1tYr+RwoSLEgHWGurXEmJFVQnMiaQIFW5cpviF2YA7ZmZ1n92gJ0ZAUjIEYysTk4eKIfpWwApWtyVGZ6JTslMVigX+sgtSxPduCBDuTMzQyoq0bnjz/BK523DzYJaly4/Aaq3RMDsrW7DEm9RbZKI/VjyNALwPzoxUhJ+zU8uzOC7zFjYd0Sj6qj6Tut2CuFx/72Dn2fvJx0trHycTCXOd7XCHtANvfnMPXv0v+fgur/ahK893H+F00WqcMlbGIKWDqZOpOVytMXRdzzNXdUUcPzQhUzLoL4T65YcP3na7Ud/wum8mY/HYH9jvrjueftq/ftn3TNPxPfvjbD/Uj/KDX+2rbjdOUppoQJXJ89ZjFNzviQSItFY1QxtNuAya5D/OkKPUFQdtCg4c+Y0YdYtcXrkq5vHeV7Y0dPv30Z7j62iUWcY5RQW1GV4Y+BS6bi8QcICrSG6SzaJVhXPZAUbCKGoUWTnxZpGadLyWYoTAoy2rA2mJd1TeZ1jtETIlA8br2dlq34E2ZvjMGnLOIF2JQxK3ZrapYCaQ+k63SHWUGm8u4vSiuEWLM+FRG7YOJNNnjG8MwwLA02OA5OzlPcIFrwxWGPCBiEIlcu3GD4HpijMzaKWe3zvIjT30WK5YXP/U8Fx6/wF958i/z9371H/Cbr/4aqobzZ88xqke8dv01+jrQLQNf+pHfx86ZLZ578mm6vqduK4w1bJ/f4lD3efNwRc5dmVys10aYweJaj2YhuAB9xRAUbTMpR7oQGVU1ViyNg5BjEc8HiGJobEMKwrBKJClC7zRklB5jUtGUWcEZhyZHWqWSsecX5LjLEAbSMpHIpEqodIx0xaDVtgbbe86PH2drsk3fR5b9iuzKFB3ec/PokC5FPvPMi1RVhRdHParxzrNc9nSrQBM3uLm6iRg4ay2oIR5GwtlAJTVDFyBlnpg8ydHRgmMOP/gZ+Kg+lrKzGekzzzBs1Qwze3t6bp28ktdRFSf+TG8v89pbpE/qwvtx1J3MyHsZZp4s3DmhWLTroOtOxeF5COX/77X9O7d7r58/SJ2AhiSn4PmeIOoOgHG/XL6PrN7DI+ukyrRiLMG++f5eT6ZpyH1/ClxP2qUnrVJ58nH+ws4/enj7/7ug3hd42lydJ1WR/kpg+auRfh4pFpEnVJ/c8eedVxy9609NEA4z9nxCjOAaS1gzMj5b/EoIdccbhxfZnm5iK4eowWRINkGEXpbgFXtsSX0quoSNhNiiwUpkMILG4hUkCAxSWKgTJ/QAeZwZZpHkMzY5IorzhXla36Dg1CC5gKisWnRFyNpPqLTo9OSxXDRfKSusDEESihL7TOMcXg2VmmJzEAVfK52LoJmhKxqekbSIg2azYiJjdlc3yOv3k85Aq2gyxJTYXd1CeljKnN4EXh5e5rOPfY5/8yv/hm+88VVCChgRbiwuU6UWaiUfKy/ufJr9/haXblzkuSefYzqd8PrNixx1h3zj0u9w8ca36brFqTNyChkNkHJmNHJ4MyWZTLe3YjVEqtajx5E4hjiFrJGkSr8qjuXTZhOq0kfNJjEy4zIB6NeINEPKAzlDLa6YXYpBgwEC/apnMLeKjs6CZItRR8wB7yrGGxOsekJM7A9zzlaPgzgwBhnBrX6PnckOMzvj6o3r/Kuv/n9pRxUvXniO6XJKU9UMdBws9/ncD32GH2++yC9+9V/z8tXvcK5+gq7uaUzNoltxrjmHC54kmbZtuHXz1gc59x7V97DsZ15EL76FDgNmY8byP3iBfmZPLQZOWvInQOkkWsUORRx+Z2UHMpvCrQc3b/xdU+8iFoe1NurOFt+di/2DisMfkkhaY8RubsDj59CLZUT/rpzAnEpGH+/SEvuYW4Da9+8O4tf7l/u+7OcaEObl8jYABY6/cI4fqxcUluFRPUi9L/B0+O2eIAOtHzEsIkUMLm8TSN4LNN1dxgt+5BhsQr0iknC56GI0ZzQou/kae3s3kCUEHdCusEgmCHbuaEdj5mGfTCoJ5ceQNzJmLEgtWDHktWjcSLErOPFeyWuGWToIdSpZdBTwY6KAV5AShSKmWDCYLESbMQGMW3tZ1UKKSuxKU09SYZ5yhKAJmYETQ1gpW36bXhfgihu2wyBWqY1BkmPe9/jsSnbezGKd542DV5Foi6DcQCUV0+0t2nHL1aOriAguO7KJ3Dy+TkbZW+7y0sXfKfudoY6O7OC4X+KGiCTYmGxz0B1wfOmI85+5wNXlW1y/eMBbty7z1Vd/lXl3QNJQTDkD5EqxrQELaVBUTWlfscA5z9RsML95DCnxQ5/5NNVGzW9/86uFgRt5RtUMo5Y8QEqgeSC7DlFouxHZxGJRUHmsWGqZMfFjVsOc1byDqkxOmmigtzRVRTKC9w6znhAMy8SR3cPTIMnw2vWL1G1NN/TIoMWvar7PVrPBhTNPcPGtt8g97O8fcfHqW/ixcmt/ny7OOXrzFtvNWT597od46ZLSpY7NdpN+WI9aeuXMbJsYE1EGrvziI2+UT3rtfWkH+7ltbFBMUFY75bw4GZJUd++rVb7HEKVbKfl9uF5/35fIg08rvguYONE6fWjN0sP0PMqKXr5W8vWOj08ZGqC0GEMs4e/3a/nadcDxu7A+d9VH4dn0LiVVhQ5D0YuF4Z1gdV27n7dsmEfA6f3UA4On2fQsr738Ok3dEOJAPhm5Oy3l3YHTmnVCMY2gm4WzUuXUiHLdayNUASOJkBMslKxFlF0NFquGtm5JsUcCsDRlsk7AWlsm5Uwu4MidmCTI6Z2kZMHFYlWQkmK8YFWKWHttJ+Iqcxq3Irqm7rUon6xIaR8qxIUSV0rqlTxRJEkRZzvFiCXmTL/ImGwZbYwKEJElVg1Ziz9RGhTvhMY7VpOIBsFpTa89MUTERtQrKYATS7Qdl+bXS7hylcg5kzkRtMupB42IErxiTcYYQ9CE2gHvPYdHB8w2Nnj8/HncyPDPvvJPee3G64Q00M2XJejXC9EmxEHyWtoaFfjo6FY9cYDWjHjxhU9zYfYk+zcPeeHZZ/ij/+XP8kv8W77+5Ndpn6ypJg2h64lDwmtFO6oJyVG7lhSUra0tvPX0sSdK5NatAxa6JOeAmMLszPtDpvUOeRCWw6pokKqMn3mSZoZ+4KjbBxVMlVnRoYNg8oKkCRctVoWwDNQXatq2YWNnxrNnnsQky7A7cPnKJXaXN8h5YBCld/CpF57jJ77w43zz4ku0zYi4SpzZ2mYVOqSGT59/gVvH++/vjHtUH0vJ+nIUWkOeCakQCne15ETv8RrhHXErrtPiT/S7pU50Sg/CstxDk3P6UC7DIB+2HmSy7903sAaDWUnHx9jpFNmYYiqPeI9ev1n0UOvA5dNdfvvnUn1w0LR+vak8ufvegaeTduS7thyNxf/Yo+vY+60HBk8bGzNElL7r6YYVJ4qmO7qvp387gVHOeqwz9Cdf4PrZKWRiTtTYwhpxAlA4jVdBy3RWBiQIDgMrIVfK4JdkSegCOCoambydoDGkVbEOwJefW1t8oE5M7bLNWIrPktaKF8M6HYRoM9YbrJTIGbHlwyhaqP3ivEBCSUlJS4iHxeZAFAjr51tIKcPS8cLWMxyZA0Thi+e/xMXFK8z7G3QhoE6xahiGcsS8MZjWYKNhHjuMUpKvE4gIsYnMdUHUiDlhutZC+OxK68vqbafjenBohOzW3lVkQugZjaZYNVy8cZGVduwd73I8PyAnJcaMFUFicVhPvojbBSALk9EY4w2TrQ0m7QaRyM5f2+KP88f4YX6IAw55mdf5mct/hJ+6/JP89v/md/jFX/+nZOlxWGz21G5M8oGkgaPhGCMGyYYhdMSup65aNAlkRx8SrdnCDzV9N7BRb9G6MaOtEdIKe4e36A97XGyKZ5h4fONLK1UzVbRon6mqhsEPHAxH1POGIfdc3n+LZ3ae5vGd84ybEdNbUy5fusITF57i+Wee4dKNK8TDADmzWByjWehTT8g9o3bMG/tvMptM3+cp96g+jgqtlPSmCCZmUmXuAkTwTiDlluWmRE0BW6ctPbMW3n4PGYSPrB6AKboreJb3aJeJ3P+xh3S8NH3I7ajetkwQIa86bD8gIqTrNzHbmzCU817D3a97R73Pz/RJHDCwkzF//vmvfdy78X1XD848zSY88+wFvvXNl+iHMgVx/1OogAFFEbHUdU3Xr8OdKIt8HRxtsgwmkbOehmaa3mKjJbdx7RBgyvvk4t6tJhPigDrITrE7BqNCrpXkEkYgL4oOSVhf8ExhZVAto/+h7IlJQj5hk0xp74mHlBWTKSdWLhoqUSGrkrOSE8SciU7J21r0UWugZWLJjRu6TF1ZDtweQTu26i1sI2zmMxwt96lp6cyCxSLSjBom/3BGYE78k5FkIy6VNiHxNmiLQ0RtLJl7aygqTooGyBSTTk1QLWzxoVLDkBNeDNYUu4NBEguZkzRjsLx1fHn93SiV2pKZJ5CNEkjkXtFGyQaa6EmaCBrolzeZD0ukcxgc22xziUscc8yn+TR/lj+Jw/M//p//OZs/u0OXV4QcWMQFOR7DCqwzxBAxxiCqEGHUTtje2MHVFbEPDDKwMz2LNx5nHY+dP087HtFpz6Vbl3B46qqlnrZYbzFqaKqa0A8YFVyyxDbiRp5xnIAKl25dRiRxLa9YLhZ88dnPgyh9v8Po2QkiwnfefBnXWKqRIw8D3dAzmUxIDJCUH3rqBf79S1/mjf3XPtCJ96i+t2VSAVDVXKkWmX5WkJMUD993iMIlFYYp1eubkcypXUH2gjT191ZA/FHVu0xonbJN93jstA20zsIT74ouSMyDs0sfVC/0MONjVNEwEK9dBzGId5AVGbVo3xfNU0oPJyvvE2qPoM9d4E/N/gWP9E7vrx4YPH3zW7/Nz/6RP8rm5iYHBwdlqmz92O1fiTv/lvHek3PCWnPHYwpZSAHyOnONO+7ofGupl2P65Yow6suFLRZ/IbMEKi1i7R6sFbJLpY0GkJVsFK0LcDPJQFRilXDGoANkUawXdAAGQapy4RQrOC0sVTQZyaYI7dZ77aOQcmnTRZeo6uILRSphx5oKsEpV+aS+8YRRYPdgF6vwFlc4/7fP8xjnWf75OSu7YBY3OfjFIz7Lj3CGsxxyzEvm2+zKFTQKyWSMFfAFuJlQworFFs1VRkuuYCgtSFkfA/XljspnU9pukkvbUfPaYTyTwpLaNQxpIGtGFLoQaZ1HVbHZccY8zlX/FlkyVg3d0cCQA7ayeNOAUaTOXPsPr/N3/T+g14HnH3ueP/1/+3nGjPgVvsKKFZ3pCTGCQKcrQh4wvcNiqdsWK0DIjHSCRIvpLBoUiZbHZhd44vzjnD97ntlkysbGjGbU8Gvf+g0Ojg+JKTLeGLEcVgxmoI0NT208wdHRHCPCzuNbXDm8ws3+FtNqggmKXUE/DPRmzo34Ji/fqPnsk59l3i2YzsZ88/VvMqmnLIZjXtm7TLcIqMlcX17huY3nmTQzjrsjxAnXrj8yx/x+qGzBL/W0NdccZrotc5tRvgdG6GcG1xfW2YYynIWuxeXvxrD8IJSYcid252SXKS7DGgMahmIx4NZDGZVHrV23uh6QjfkowYSx78+KYC1W1yGTDw7vzuL7oJExb3uNWIvZmJEPj763wcXvAVL3vrDJi/59yZ8fFe9TMP4bv/Y1QixC4pN6t0vIMBTPpWGIOOuLPkdj0TzNMp1VLOC0AIGMkk3mzBNnUJQb+1fpFj1oxgSDrQWJpgi2UxF0RxeLmWI25CGTrRSwkMqEG0ZRhNiV6TrNQh4JtjdlgT5hoVVKq08olgIW4hy0UqwR+pyLZcFUcVrYLiumGGgmSGnd/46CNxV97Iuc3ktpUy4TN9nliCN2zpzlyn7P5/+HzzNmwlf5Tc5wnvOc50Z9jaN8k1QlbLZYV+wbBhKtTBCUmAqotKKn+qusxUU9iVJulJUsGecEpxZi2UaFJaRMtplFnhfTzySkWAxPexPLZ0M4e+YcwxA4zPsQI1bLhNuonmDxBbBqyeUbhqIxe/3q6/yX/F+ZMWH5Hy7Zne+Su0SOkUFWJDfgnCHlgKmVnBI+OMI8YRrLbLTBE2cvsLG5QTd0PPfUc2xvbrPzn+0wYcIVrrLDNp/56z/EMq64sneV/eM9rDXYXLFRT3nysceYPTvjYH7EoD1n7A5yLMQ0gGRyB/SCxDHDSrjYvYUGR93WqA7sLW9y1O3TjGpyCpgQ2N4+w95in8PuEDO2fPPytzk6PuTWPzl4v+fco/o4yqwZo6yEkSFVd/bo3vn0EyNM1hPzyRcmykRo9iNp/j0wRv04J7neBgxOp+Tymm06OWbWFtH04+eQt67f3VJ7+/5/L8XSOd3dcnvQWjNR72AVRTCTCXk+L5ISK4VgW1sdmLYhHRyePvdkW1COnfnUcxATcvwxGOqetFvv2Kc769YXlVr893affgDqfYGnMBT2wFl3mk93d534O5W2XV6fKMZYrDjEJDRnbFV0RnUuOXTZlOgTFDRk3jx8FRHBiqOZVaz6FcYLrG0AWBq0N6jPOLGkmEk+EyQjWQghkU1pQxlrMLnEqxgVZAS+jK4RfYkvyXPIS5CG4vBdFSYnVhkxkEzGOoMRCKq4bEpUi5Z2YIq5sGgRsErSiPcOqUtzTUaWUdtw/c9cJcbE5u4WG1szLnMZi+dZnucv8R/x7/kVvlN9m1mcETUw0Yp4AGIUrQcG19P1A7W1azMIwR0Lq/2AnF2bc6qhLwp8FGWUTpgkU6YNk4ADjbkIyx0FgNUG74r5ZIyJqWzznf2XiuGoOmQF3nvGbkRbjRg0ElMg5AwmYJxBo5JTZPnH5uwvd8lHmWwyQVZEeuI8FiOtCVjj0ZgYDjtSjOxsneWzn/48z/+3z/NZfhiAN/6Pl0AUW1lmTDjPOY6Z802+y62bt5iHBbeWt1h1C2I3MPFjXCtcunGJSmsQOFweMx+OGLpufXOZwAmVsxgs3ntG9Yi+W/H6zZfYP7pJ1ow1lmE5IdqIinJwfECihBZfvXaJTjv0XW8dHtUnqdTczqPLTogj4cT37STUVw13+TqpQBjf/o6TF9wqY1cPweV6bRD5PWUgPkTdGasCFHCSCzCQ2RQOjstVXwQ9AU13jMafvOZ7UsZiJ+MCcB8CYBNfIdZgdrYwGzPSzd11i3JtsHruLFp7ZNVhJuPymtEIPTwiHR2hKZG+8xqmqYvZ8vf6O8/p/sfBWJ764iP2/IPUA4Ontt7Ee09VOQ4OD05VTQBGDFroGzj97+2bk5wTQZW89vAQAYvBGCH4hKjg1DCkMjlmYrmahRyIErBrlie5tXC7UaZuzKA93Xwgd4W5cvlE6yOYfYO05WKYjcJYb3s2KQSfCjsTFOqScee8Aa8lF86uJ+y0OIEbLfqnsi9C0lzE2pT2o6T1B1MQY6gqz2pYYUaCGRwHq0O2hy3Ob5xlo9ngRv8Ww1/umYYN/sLf+QUucIF/w7/ijasXYRoYGY9Vg44SKSr+2NFtDOCVYEvsi+zBwUsrnPGcP3ueW+ZmAacYspb4mZSLKF4tt3MJczkOJxI0MWXXo824VGGz5/zjj3NV32LVL5FDx0g38U7IGum0J2iPTyUp1Xoh2YFEQJKlrivwHV3fswxzUp8xA9S+BQcpDMhSWN0c2JyM+f0/9dP8oZ/+w2xvbPOl//aLrBg45pjuTGB/sc833vg2//5//WvM/y/zYimBcvzNY3rTsxpW9LHHG4tEy+GtI27t7XJ+6xzjUcvezVuEGAhdIEmkaWpsFDSWYOamrrBqSysapZKGqqlp6jEmO4Y8IsSBZCJkYTEcY/zaqiF88sSfj+o+tZ5iyVbwy1zAE+tJOsc60/Kd2qcTUbkt4Qlr4FV0nB/KnTon9F7um3e9+cfEOt2L8bpziu4OTZOmXIJ9VyvS0RFmPL77dScL9ruBmA/SBnu30kw6Ono421qXTKfowSGaMiJSrA2GgKnr4oQ+X2CeeRJu7CJNAyEUVu7k90TT3R5S3+u6z7G1Z3f4K0/96vd4Z34w6oHBk6oSQmC5mpNSvO0hLrIGToVh0rvMy/QUSKmmohPQAmSooK+KXimLIgJNsmXsnhPBd1ndRYoWyXAyOacsOS4+RE7w1qwNCYTUK8koNGDrwhplk0uUhxQBtlGBWNqFiqBN8X4yrDVRSNE3sGZgtdgliAqkkkunAjGn8p4nJ5QpXlFZM2ERsGMp4nQV2nHF2Y2zHC6OyEQO945xs4on/86THHPI3+Hv8NZfeINP1S+gVeTy9e+uW5mZXlLJ3JO1s1YCOYLujYFnnnyW//Qb/ylP/tJT/B/+k/8T3732LVwu4b8riUV/hSGbAvwUSGuhvBdDzJmUCygtcrTEbLLFsl8yj8eE0BcbB42MsgeTceIRYnEPlwrJhhADmjpq3yDWr7VZgdpaghbdmjUGkx3NvOazz32e428c8/P8PD/3yh/D/g3HwZcPeZNLfIoXGNOy+eMz/v1v/Qbf+fLLXNq7SPrDkcY3VE3F8vqc6BO96SDBKBYLBA2KqzyHB3N2b96kMSOaaoPgBqwXpqMpbT2iDz1DGjDWEkk4Ksa6yeaZs6gmFIhDou8DlR8xj4d4P6J1FVhY9AvEeQ55FGb6/VQ2KP3MIAnsUG56YiOYVMCRHSA1YNYtn+w5HWaxg5I91LurhxPr8QkVED/QNN36T/EOPTq+e4rsRC9152vus3iLrzDjlnT4kMDOR9HqNAJ9X/ySmhqkBBzbrVHR2R4elzbffhn3N6OADsP3BasYX3icnx39fWDyce/K9109MHjqhkOsNO/QB6ieqC2VnEtwsBFblv27tFHll1op02mk4sYdTDGptFZIVm9bFiQ5hV65DJRhFHIU8rBusdn1czJYW1ppYqHyUsCNZLIo0RXfJ9tZNAjZQC5mSFgtF9KcC9ASLdtMmnEqmCTlxigJyShJS16bmgLcVNdHMSqmNuvWI2DLPsVFxlbls187vErjWlbHCx7beIyj7pAKx2/yW3yHb9P3gTDruHF8qWTs5YwMhrMbZ7g6vwIWaizSCYvXerauneE/vvpX+Y/4BW6yS/N/r7F/3BCjIlJy+mpjy6QguYT3Ukw/uxyJUoTi+QQ8CYhXjtMRQqYWT44BKwbVQMbg2xEh9wzLAaJQVXXx2UpKTBaTDd4paiJOLSkolTP4actqvqQ/7PmJH/p9/MQ//n38AX6aL/FFEpGLXOLVN99g+DuBcDYw7xZ895df5d995Ve4dPMiIfUQlVxHSO40aHgympKJ1OqZtlM2p5uM6wneW45XS0KMjJoROWWaqsJaR8oR7yow5bgsw4qgsWQrDkrQQM7KEAO4TE4DPjtizKxYIiEjRrn2Dx85i3+/lCTKxGotaCFMiY2AFKBkopIqKW7i68vWCXACUFtE48NYSK0vxrufVPDzUdU9hNO56zFVLu63QF4s3vEcsfbutt8aeJm6LmDEWkzbPhxm5qP4TrLedhiva1h1mHNnyqLRD3B8fPfTP06G6X3W7hdGXLCjj3s3vi/rfWmeskZOKYq76m6mKd/DYv9kbs2IgbkhXxTspwubtLSBjVRhEKIUYGJSETJHl3F5LSjPWjQzIwG7bsPZ8phA0cf4An4Kc1VaXB2RZnCYBFQFwOVcgJpRJebCfBkxBJtICjkWpiYZRdftLpNKO9FGQ8ypgKg1WNIMeVVAmqmkiOKXMPFjBu2KGaN0TEcjslquH17n7Pgcv/kLv8n25hkuH1wqHlrdkr4r1gFDn+hVebyasWt2MTowSjXhhrDVTPnP+c/50/w8E0b8Df42r/EdGt/SLyPB9ozxGG+KHYMKWQoDZUQwg5w6qsdlxo4L+BxSINnIzVUHKngxJJdx1qLZYQIM2jMPgda2eD8i5oBVxbsGNRBjwHSeVA0YI+QOgltiW6FlwtNPP80f5Kf4vfwol7jEL/NlfpOvM/3alOASF69fZNHNWeUlB4eHuNZgFxbXeDLC6qgj+kBTt0xGM5AyiTipZpzbOcfjZ87jq4qr128wxAgK3juccYgI/bBiiD0xRZZDx6pf0McVfRxgEKbTCVFD0eMpxFQAp6gSwopEQu0HUaM+qo+r/EpJXm4DojtuAlNVxOS61j5BAVMgJRhYKZ5nVWnz9WcqJp95kfTtl7/3H+STVKpALll0J8zSu4HKdXvuJAzYbG2WgFAR9MR09MNk1r3X+7+PbdjNTdKaSSoC8vXPYyT3PVYVHbfkaze+Lxim+9X+j6Yy+PSo3ne9z/nEtUjmPiYFJz/1lcdZy2q1egfMqmaOjZ+uChWuig8G7yoklztCl4SUIZqMzYXxOZHnQHmNSGFKDGUSTTLEkJEoGAfJUbzcXJk+W+fPkqq8jvkoW0smkyihxlKXKJVyPShgSTNYEfpY9E0ZpcoWPWHIkmIwBcjZ9V1sLcVXqhOsWrBQJc+ggambMQ8L5odLks3I4iZnZ+e4eOM1silBwYuwZB57YsqkXxQ+xac5wxav/oxinMHaih954XPMRtv88Buf4To3+Bv8C/4b/muW//MVSTJmbBjhcdESQ7E7UFFS1FNT+BpHlyKJjKmKhkcCiAcMNN4hS8N4NOVYj3HZkk1HT0JzYjZuqO2sxNGkyKpfULuKauboTc+03ibmilW3wIwsvvIc9weoZrbPbfME5/h1vsJLvML+f3HI/m/tkw8yVFB5x5XDA1b5GDcyaG8J1pAGaPqawQxMZju02yOCCbjkscnxxLknmLVTjDosjuloTMqZ8WjMEIfiCh8TMUckRVIOJYJHDRIUZ5SlW7C3GHDOQhZcqqiqjIzBWofGlr7vyCkAD1dX8ag+ukqVUM2LPcH9SpTSbtaijZJEMYgtsyzEWhjdSoz/+ddR8wlacD4uFuwE6NxH1yR1Xa6lKWHqeh1Cm5GqLdYGw4B4T9o/uN0G/SCf487P/5A8oO7ZRlRFF8sSebJ3gF4b7m7fmhMjsPtrt6SuC4v1CWAtxVf8+Ode/bh34/u2PoC5w50w6Z2PQGnR5XyqXjp9XBDqJwxmvIZhWYgKthO0AlSJrFmgvJ7ZW4MUtESPWJG1sHM93RbB5nVL0CsRSkaeLa034wXvLRqBfg20JBM1F+2TAR2V7aVkSgtwnWWn61iGOltOIpCzKEESMRS1uK0ocSu5tPFIZX9sLjqsnAPGGEwydCwJi8SQAwSll56bh9cRUZz3iBOOwjFdjCSr/Bhf4k/x8/x3/A2e3XyO17/1Ooth4OLOJf7KX/qj/P2/9Y/5H/mH3OIGw58M1OpQYCQe6YTQKzpA8pnoit4rVgnvLdmsncSTIxHJgTUrxanGzDZFQF8FwdpiHrrMKwTBq+J1Qcw1xMyWP4MfeZIPdMOKve4GtZnSuinJB47yATEFxjtTvvzaV7jCNV7nVexf8jS/3fL8uec5s3GWm8ubHO7uc9zt46wh9crR4gAQHpML/MinPsOVG1cIVaLveo7nx4xMw7SaUbuKrY1NvHgEw2y8wWK1ZFSPWHU9B0f79DEQU8DaotMahoAGwZsGMULTCikFcoiQLTEWHVtVj0ATyWS0Fnxs3v+p86g+1kqVvEMQDmu2aZ1AgCs3TfWxEtpyQ6SmtPayhdGlxWkS/SemPq6F+M73Pc2zug1kZH2tJkEewl2hwOnw6D21VWItUlXv3gb7qOwP7rPN3HVwn+/fTsbvKlQX50oL86Hs4Icv+9g5/tL5f/Nx78b3bb1P8HRb3/T2uvOnw1Cs7tdyb6D8jo+e84w+a8sTFbCl7RYpFgOWYv6Y1pN7JoPkcgHLZu3yvdY4kUu7LFFaZdj1tIzqadinxWBYt92kOIOTizt46cqUfVMKACIWxiuSCzhzYHPxkBIt4EHlRIR+4rFUwKR1xXbBiJRpLoUssTBqtmyvC7FYMNSCJqHLPaYGly1H8ZgsELpF0fLsTZA/o/w/q7/G0fUFes1wdnyOP/fdP8/ZN87y/Fef55/+Z/+M65evYjK0exXV2Jbj0QmpU4LLpHHRNRkEcUIVPKlKiC0ZfaJCWn+B4qV8D2pwGCKBZZxTSYl/lmQwwMiOimFeVlwWnBuxPTlLs1Xz5v4raM6cmT5GW41RVfb//+3dv28cRRTA8e/M7O2e76edI78gGBMkoCASJV0ApaBIgyKaNFDxZ0GDKGmIhCjpoAiBgEBIAUFsmcDF9vl+7a/ZGYo5x3HsxLkYkBHv01yzp70rdvbt2zfvpRt4W6IimNoRP/d/4JfLPzLp5yzeeoqldo96rUG/ustoOmSwNcJmFV5ZyrHF+pxecpYTzRMMtya8/ukb1Ij4ju+58ebX1JYi2o0W3VaXVr1JzYTOp8YYplnO9mhMu9kBD6PpiEnmyIuUSEe0621Sm4WF3oArQmdypQx6AbzxtBttkmiB7ekWWZExsgPu7SgQ/wm1iaOK1Z5WBHvM2hSoKgRRRWu3s7i2kIwcjfUUbv7PX9U9zAOBlDImrI1VhapF6E4HNxyCs/jy8NdcO7PnfHZIYf5RAqcjZuyiM6fxZUm1tQ2u2h847RTVxzE+z/HWorsdTLsFVUV19yE1k/9SJjF96TSv1ftA89BjxX6PHTydOfUsf/R/3xc3hVzP/WZTpv3uEcooOq/GDK6njG9lPH21vZsOV46opnA52Nxj6mEHnwbUvV5sivWPQ1He8jsdKsIumZKKyGl0pWYjVACtKF3Y/VZphy4UWx+GC7DzboJGYazGm1D3pGdZq9I5ylldlvLgY1BFKIgfXwvFgvXL4clJe02kFK70mGT2TrEKH8oSzj8rYvfah74nCoaf7S4Ep95uMS3KMLS2mWCzFFVpGm6B4e0x+XbBXbtJYSxxnEBmuHL5Cu/duEqXLh/wET+t3qQxqlFZj8kitFfE9RrWK/pf7r0wFy8u4E24czjtMS7scixUSfFFWIDiSwajNJEKOxdjIhKnsZVDxwpyw1K8QLPRwFrQ1tBqdmnXu6wsr/Dt7W8oshStDKUrmG5O+fXzVQB6b7WJi5jS5FSqxA0M068ypqyxzhrn3j/HxmCTO4M7ZJtTJpvb1OoRPvKc7JzlleULnH9mhRfPv8ylTy4yIaXPBs8vv0C30+FEs0dvscf551bC67nSkmU5Z06e4s/BBl55olocGnxGNcapIja1MFfPh2yhdjAtxqSqQCeKdtLFtEyYdegtTgHaE+UxhT9m2QfxSGVTE6V+f+J89tS3Uwe1E1gpuDdkOxk5OtfXsavrTz7P7qgZkqN+/++6IR/WcgDC6JaqQscG1WqGAGipg2k1sL+thWPvayB50HBdb20414OjXg76H4/73w4Y7PsoutHY02X8/oHEKorAmJCFeuC3m8UuqtnETya4NNstNgeoKtxwHArlDzypQdcTXBaaQ/+TQdTGhYSelpEsT0oNBoPjkkUUQgghhDj2jlHVoxBCCCHE8SfBkxBCCCHEHCR4EkIIIYSYgwRPQgghhBBzkOBJCCGEEGIOEjwJIYQQQsxBgichhBBCiDlI8CSEEEIIMQcJnoQQQggh5vAXuxBzJiBGPiUAAAAASUVORK5CYII=\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(array([0, 1, 3], dtype=int16), array([6118323, 132092, 544585]))\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Define the folium map for visualization:" + ], + "metadata": { + "id": "rCMe9vPMdYOX" + } + }, + { + "cell_type": "code", + "source": [ + "m = folium.Map(tiles='https://mt1.google.com/vt/lyrs=s&x={x}&y={y}&z={z}', zoom_start = 10, attr='Google')" + ], + "metadata": { + "id": "pogcPdoXbhT2" + }, + "execution_count": 151, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Then iterate over each output prediction and add it as a layer to our map." + ], + "metadata": { + "id": "ug3ezyDCdbks" + } + }, + { + "cell_type": "code", + "source": [ + "for filename in predicted_filenames:\n", + " mask_3857 = folium_helper.reproject_to_3857(filename)\n", + " mask_d = folium_helper.get_bounds(mask_3857)\n", + " mask_b1 = folium_helper.open_and_get_band(mask_3857, 1)\n", + " folium_helper.cleanup(mask_3857)\n", + " print(\"Unique values in mask: \", np.unique(mask_b1))\n", + " mask_b1 = np.where(mask_b1 == 255, 0, mask_b1)\n", + " print(\"Unique values in mask after cleanup: \", np.unique(mask_b1))\n", + " zeros = np.zeros_like(mask_b1)\n", + " mask_rgb = np.dstack((mask_b1, zeros, zeros))\n", + "\n", + " m.add_child(\n", + " folium_helper.get_overlay(\n", + " mask_rgb, mask_d,\n", + " f'Water classification {Path(filename).stem}',\n", + " opacity=0.7\n", + " )\n", + " )" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "m8w0GS7AbDHD", + "outputId": "4316cb4a-2f99-48f7-a9d4-91d270e74315" + }, + "execution_count": 152, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Transform: | 916.18, 0.00,-13078554.72|\n", + "| 0.00,-916.18, 4865942.28|\n", + "| 0.00, 0.00, 1.00|\n", + "Width: 3051 Height: 1487\n", + "Unique values in mask: [ 0 1 255]\n", + "Unique values in mask after cleanup: [0 1]\n", + "Transform: | 769.16, 0.00,-0.00|\n", + "| 0.00,-769.16, 8399737.89|\n", + "| 0.00, 0.00, 1.00|\n", + "Width: 2895 Height: 2540\n", + "Unique values in mask: [ 0 1 255]\n", + "Unique values in mask after cleanup: [0 1]\n", + "Transform: | 447.54, 0.00,-6782206.40|\n", + "| 0.00,-447.54, 0.00|\n", + "| 0.00, 0.00, 1.00|\n", + "Width: 2718 Height: 2500\n", + "Unique values in mask: [ 0 1 255]\n", + "Unique values in mask after cleanup: [0 1]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "From there we can define a basemap and the region of interest for our map to initially start." + ], + "metadata": { + "id": "t6LvuquKwivs" + } + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "id": "KTxeRr_jZyZJ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 599 + }, + "outputId": "d1314013-dfd9-4d87-c7c4-eaebf9e07393" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ] + }, + "metadata": {}, + "execution_count": 153 + } + ], + "source": [ + "m.add_child(plugins.MousePosition())\n", + "m.add_child(folium.LayerControl())\n", + "m" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 10. Some Questions to Practice Before Final Assignment\n", + "\n", + "- What is the data type of our surface reflectance features?\n", + "- What are the basic steps in any Exploratory Data Analysis workflow?\n", + "- What are the most common data types used in machine learning?\n", + "- What are examples of visualizations to analyze data distributions? Why is this important?\n", + "- What are two techniques to handle imbalanced datasets?\n", + "- What steps are missing from Session 2 assignment to make predictions production ready?" + ], + "metadata": { + "id": "ZgOtW8XLwvk2" + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "ILAB Kernel", + "language": "python", + "name": "ilab-kernel" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "39e2451a32a245a18d73f5388fa57841": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + 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