{ "cells": [ { "cell_type": "markdown", "id": "fantastic-invitation", "metadata": {}, "source": [ "# Machine Learning with Python\n", "\n", "The aim of this lab is to implement a complete ML project in python. We will go through the steps of data exploration, cleaning and transformation followed by model training and selection.\n", "\n", "The lab consists of two parts. The first part is an interactive tutorial adapted from [Aurélien Géron's excellent ML book](https://github.com/ageron/handson-ml2) with some modifications to make it a classification tutorial instead of a regression one. We will learn to classify neighborhoods by median house value. The tutorial will contains conceptual questions as well as fill-in code that we'll give you a few minutes to write.\n", "\n", "In the second part of this lab, we expect you to pick any dataset of your own choosing and go through the steps mentioned above. You should try at least 3 different classes of models, and one of them should be an `xgboost` classifier or regressor. We recommend that you pick a dataset from [Kaggle](https://www.kaggle.com/). We will be very flexible in grading this lab; we just need to see that you've taken the right steps. You should submit your notebook to gradescope. Submission instructions will be given on Piazza." ] }, { "cell_type": "markdown", "id": "external-blend", "metadata": {}, "source": [ "## Setup\n", "Run the following code to install libraries and download required files." ] }, { "cell_type": "code", "execution_count": 2, "id": "european-guyana", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: sklearn in /usr/local/lib/python3.9/site-packages (0.0)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/site-packages (3.5.1)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.9/site-packages (1.22.2)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.9/site-packages (1.4.1)\n", "Requirement already satisfied: xgboost in /usr/local/lib/python3.9/site-packages (1.5.2)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/site-packages (from sklearn) (1.0.2)\n", "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/site-packages (from matplotlib) (1.3.2)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/site-packages (from matplotlib) (4.29.1)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/site-packages (from matplotlib) (0.11.0)\n", "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.9/site-packages (from matplotlib) (3.0.7)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/site-packages (from matplotlib) (21.3)\n", "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/site-packages (from matplotlib) (9.0.1)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/site-packages (from pandas) (2021.3)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.9/site-packages (from xgboost) (1.8.0)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (3.1.0)\n", "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (1.1.0)\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n", "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n", "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", " from pandas import MultiIndex, Int64Index\n" ] } ], "source": [ "import sys\n", "assert sys.version_info >= (3, 8) # You might later get errors if you have a lower version.\n", "\n", "# Install necessary libraries\n", "!pip install sklearn matplotlib numpy pandas xgboost\n", "\n", "# Do all necessary imports\n", "import sklearn\n", "import numpy as np\n", "import pandas as pd\n", "import os\n", "%matplotlib inline\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import xgboost\n", "mpl.rc('axes', labelsize=14)\n", "mpl.rc('xtick', labelsize=12)\n", "mpl.rc('ytick', labelsize=12)" ] }, { "cell_type": "code", "execution_count": 3, "id": "modified-depth", "metadata": {}, "outputs": [], "source": [ "# Download necessary files\n", "import tarfile\n", "import urllib.request\n", "\n", "def perform_downloads():\n", " # Download data file\n", " root_url = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n", " housing_url = f\"{root_url}datasets/housing/housing.tgz\"\n", " tgz_path = \"housing.tgz\"\n", " urllib.request.urlretrieve(housing_url, tgz_path)\n", " housing_tgz = tarfile.open(tgz_path)\n", " housing_tgz.extractall(path=\"./\")\n", " housing_tgz.close()\n", " \n", "\n", "def to_classification():\n", " # Original dataset was for a regression tasks.\n", " # Transform it to classification task.\n", " classes = ['LOW', 'MEDIUM', 'HIGH']\n", " housing = pd.read_csv('housing.csv')\n", " housing[\"house_value_class\"] = pd.cut(housing[\"median_house_value\"], bins=[0.0, 200000, 400000, np.inf], labels=classes)\n", " housing.drop('median_house_value', axis=1, inplace=True)\n", " housing.to_csv(\"housing_classification.csv\", index=False)\n", "\n", " \n", " \n", "perform_downloads()\n", "to_classification()" ] }, { "cell_type": "markdown", "id": "metallic-rally", "metadata": {}, "source": [ "## Preliminary\n", "Before we begin, we should split our data into a training and a testing set. All our learning should be done on the training set. The testing set is going to be used only once after training. \n", "**Q: Why is a good idea?**\n", "\n", "You could manually implement the splitting logic, but Scikit-Learn has builtin functions that do this for us. There are many splitting methodologies, but the simplest one that works well for us is as follows:" ] }, { "cell_type": "code", "execution_count": 4, "id": "young-candidate", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data size=20640; training size=16512; testing size=4128\n" ] } ], "source": [ "# Import splitting function\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Read data\n", "data = pd.read_csv(\"housing_classification.csv\")\n", "\n", "# Split data. 80% is used as training; 20% as testing. The data will be randomly split.\n", "train_set, test_set = train_test_split(data, test_size=0.2, random_state=37)\n", "\n", "print(f\"data size={len(data)}; training size={len(train_set)}; testing size={len(test_set)}\")" ] }, { "cell_type": "markdown", "id": "unnecessary-words", "metadata": {}, "source": [ "## Data Exploration" ] }, { "cell_type": "code", "execution_count": 5, "id": "trained-style", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximityhouse_value_class
9694-121.6336.6824.02591.0739.03243.0702.02.1766<1H OCEANLOW
18859-122.0941.3252.04019.0824.01728.0706.02.2462INLANDLOW
11074-117.8633.8034.01793.0480.01722.0441.02.8235<1H OCEANLOW
10858-117.8633.7136.0191.042.0208.037.03.3750<1H OCEANLOW
16462-121.2738.1111.03163.0794.02106.0762.02.4482INLANDLOW
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" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "9694 -121.63 36.68 24.0 2591.0 739.0 \n", "18859 -122.09 41.32 52.0 4019.0 824.0 \n", "11074 -117.86 33.80 34.0 1793.0 480.0 \n", "10858 -117.86 33.71 36.0 191.0 42.0 \n", "16462 -121.27 38.11 11.0 3163.0 794.0 \n", "\n", " population households median_income ocean_proximity house_value_class \n", "9694 3243.0 702.0 2.1766 <1H OCEAN LOW \n", "18859 1728.0 706.0 2.2462 INLAND LOW \n", "11074 1722.0 441.0 2.8235 <1H OCEAN LOW \n", "10858 208.0 37.0 3.3750 <1H OCEAN LOW \n", "16462 2106.0 762.0 2.4482 INLAND LOW " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read data, and take look at the attributes\n", "housing = train_set # Rename for convenience\n", "housing.head()" ] }, { "cell_type": "markdown", "id": "occupational-therapy", "metadata": {}, "source": [ "Each row contains housing data aggregated by *block group*, which is how the US officially reports housing data. Our goal is to predict the housing value class (low, medium or high) given information about a block. Let's take a look at the columns within the dataframe." ] }, { "cell_type": "code", "execution_count": 6, "id": "israeli-natural", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 16512 entries, 9694 to 1935\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 longitude 16512 non-null float64\n", " 1 latitude 16512 non-null float64\n", " 2 housing_median_age 16512 non-null float64\n", " 3 total_rooms 16512 non-null float64\n", " 4 total_bedrooms 16345 non-null float64\n", " 5 population 16512 non-null float64\n", " 6 households 16512 non-null float64\n", " 7 median_income 16512 non-null float64\n", " 8 ocean_proximity 16512 non-null object \n", " 9 house_value_class 16512 non-null object \n", "dtypes: float64(8), object(2)\n", "memory usage: 1.4+ MB\n" ] } ], "source": [ "# Info gives a quick summary\n", "housing.info()" ] }, { "cell_type": "markdown", "id": "promotional-worcester", "metadata": {}, "source": [ "First, note that the `total_bedrooms` columns contains missing values. \n", "**Q: What techniques can we use to handle them?**. We'll implement one of them in the *Data Preparation* section." ] }, { "cell_type": "markdown", "id": "dca12b05-6c9c-47d2-ba84-6272829991c7", "metadata": {}, "source": [ "---\n", "\n", "We also note that only the target class `house_value_class` and the feature `ocean_proximity` are categorical. In the following code, find the distinct values both columns:" ] }, { "cell_type": "code", "execution_count": 9, "id": "refined-youth", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LOW 9538\n", "MEDIUM 5567\n", "HIGH 1407\n", "Name: house_value_class, dtype: int64\n", "<1H OCEAN 7355\n", "INLAND 5196\n", "NEAR OCEAN 2139\n", "NEAR BAY 1818\n", "ISLAND 4\n", "Name: ocean_proximity, dtype: int64\n" ] } ], "source": [ "# TODO: Distinct values and count of house_value_class\n", "print(housing.house_value_class.value_counts())\n", "# TODO: Distinct values and count of ocean_proximity.\n", "print(housing.ocean_proximity.value_counts())" ] }, { "cell_type": "markdown", "id": "previous-geneva", "metadata": {}, "source": [ "**Q: What's the standard way of dealing with categorical features in ML algorithms?**. We'll implement this in the *Data Preparation* section." ] }, { "cell_type": "markdown", "id": "fe2b0e77-2b75-4043-8673-6dae0fc09ccd", "metadata": {}, "source": [ "----\n", "\n", "As for the numerical attributes, we can quickly summarize their distribution as follows:" ] }, { "cell_type": "code", "execution_count": 10, "id": "timely-china", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_income
count16512.00000016512.00000016512.00000016512.00000016345.00000016512.00000016512.00000016512.000000
mean-119.56852235.62685428.6277862644.478622539.3028451428.389777500.4216933.877276
std2.0089382.13758912.5891422187.314665422.0623831131.002342382.4234111.908253
min-124.35000032.5400001.0000008.0000001.0000003.0000001.0000000.499900
25%-121.80000033.93000018.0000001445.000000295.000000787.000000279.0000002.566350
50%-118.49000034.25000029.0000002128.000000435.0000001167.000000410.0000003.541700
75%-118.00000037.71000037.0000003160.000000648.0000001728.000000607.0000004.750000
max-114.31000041.95000052.00000037937.0000005471.00000035682.0000005189.00000015.000100
\n", "
" ], "text/plain": [ " longitude latitude housing_median_age total_rooms \\\n", "count 16512.000000 16512.000000 16512.000000 16512.000000 \n", "mean -119.568522 35.626854 28.627786 2644.478622 \n", "std 2.008938 2.137589 12.589142 2187.314665 \n", "min -124.350000 32.540000 1.000000 8.000000 \n", "25% -121.800000 33.930000 18.000000 1445.000000 \n", "50% -118.490000 34.250000 29.000000 2128.000000 \n", "75% -118.000000 37.710000 37.000000 3160.000000 \n", "max -114.310000 41.950000 52.000000 37937.000000 \n", "\n", " total_bedrooms population households median_income \n", "count 16345.000000 16512.000000 16512.000000 16512.000000 \n", "mean 539.302845 1428.389777 500.421693 3.877276 \n", "std 422.062383 1131.002342 382.423411 1.908253 \n", "min 1.000000 3.000000 1.000000 0.499900 \n", "25% 295.000000 787.000000 279.000000 2.566350 \n", "50% 435.000000 1167.000000 410.000000 3.541700 \n", "75% 648.000000 1728.000000 607.000000 4.750000 \n", "max 5471.000000 35682.000000 5189.000000 15.000100 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "housing.describe()" ] }, { "cell_type": "markdown", "id": "difficult-tribe", "metadata": {}, "source": [ "We see that some columns (e.g., `latitude` vs `population`) have very different ranges of values. \n", "**Q: What's the standard way to deal with such differences in numerical data distributions?**. (Also to be implemented in the data prep section).\n", "\n", "----\n", "\n", "While these numbers are informative, plots are usually a lot more intuitive. Using [pandas.DataFrame.hist](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html), show a histogram for all the numerical columns. \n", "**Q: Briefly describe what stands out most to you?** Hint: Look at the `housing_median_age` column." ] }, { "cell_type": "code", "execution_count": 11, "id": "creative-context", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ],\n", " [,\n", " , ]],\n", " dtype=object)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# TODO: Plot histograms (< 2min)\n", "# Hint: Set figure size to (15, 15), and the number of bins to 50 for good formatting.\n", "housing.hist(bins=50, figsize=(15, 15))" ] }, { "cell_type": "markdown", "id": "continued-increase", "metadata": {}, "source": [ "It's also possible to visualize multiple attributes together. Using [pandas.DataFrame.plot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html), we can plot the relationship between total number of rooms and population." ] }, { "cell_type": "code", "execution_count": 12, "id": "wooden-negotiation", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "housing.plot(kind=\"scatter\", x=\"population\", y=\"total_rooms\", alpha=0.2)" ] }, { "cell_type": "markdown", "id": "89d8daa1-a244-479b-b199-c94a0fb44b3e", "metadata": {}, "source": [ "Naturally, we see a fairly linear, positive trend. These two values strongly depend on one another. \n", "**Q: How can you reduce the number of features in the dataset using correlation?** \n", "Since we don't have too many features, we won't worry about this here, but you might have to in your project." ] }, { "cell_type": "markdown", "id": "c70fbc50-347d-4c23-9186-aa0206da5991", "metadata": {}, "source": [ "----" ] }, { "cell_type": "markdown", "id": "occupational-biology", "metadata": {}, "source": [ "More interesting visualizations are possible (there will be a lab about them). For now, take a look at the following: \n", "\n", "**Q: What pattern does it show?** Hint: [Look at this map.](https://www.google.com/maps/place/California/@36.4998513,-121.7064629,6.55z/data=!4m5!3m4!1s0x808fb9fe5f285e3d:0x8b5109a227086f55!8m2!3d36.778261!4d-119.4179324)" ] }, { "cell_type": "code", "execution_count": 13, "id": "offshore-answer", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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j5YOapsdPWcts20t5tneH1lrGlBHAVSuUSqVSeWupbdBXzMxbYlbWKeHFsPeWtkCXoSQ1oHCWIn2faJ2w2754a3JMmVWIGIR5Y3HGsN96PlmNJFWcEe7N2zqH9Qz227IAYkyZC6xUKpXK20kVa68YEWGv8+zdQhD268RI2WZNmgkpY4xhOWZ22xd73JgzD9cBkRLv1a/TuTATCCmTM9sqXtYSAWaQOpP1GCJC+wYGburmPRN5KYbNlUql8q5RxVrludhtHQ/XgahTkL0TFu2Lb7aOqcRSNbY81ia8/GyMfLIcUIHGGD5Zj9ydNRz3gU3z9aB1dL5+pN9kVJWHfZhmN+Fw1tSqX6VSqTyDemV7CegUXu7Mu1s5aKzh7rwh5szdrsUKtxIRZaVElm8qZ0qZWVPN9DnRGAMIISWO+4CzZqryKcdDwtvrLS1Urs9mvhJe/D0ec0muaJ0lpMx6TPiuirVKpVJ5GlWsvQSO+kCfMt4Id2fNOztb5YzgbtknrrGGHW9ZhrK9uN+U8PKDruGwjaxCpLXCXutZxwxZMaI4Y1AySfUJ37eQM8sxkhXmztTq2w05C5GzMSEUI+Sd5vnPn5EiwFOeWqFVWFcqlcozqVetW0ZVGXPGG0PMmaw81ez1Jo8bc5nzeZcrRyLCbuu3WaKbyqQ1hu+/s2CcKjwhJD7sB5w1CMLcG6wI9jFhnLLyYD0ilISFh0PkUOS5o8JeJWlq/yaU3ca9lnahqrIMidYaFFiH+EJizRvDQetYxcyOtyyuEVFWqVQqn3Zq/+GWERH2p4vZTuNuTVidDpH765FPViMhvftR50ZKFNWHy56PlkOxoBChtYbWGpYpcdA5DIJq5mSI7DZPtkA3gtlbgzNFzI1vyfk7GyPrmImpmAK/DkSE1hRfu5Ie8eLiauYdd2cNu61/Z6vOlUqlcpvUytpLoPOO23bkWKdEszE4TflW5sPeZFJWToaIswZVOOpH3pu2QlUVBVrnaGwRZEmV2SXtTRFBtFSIZJpte1vmCBPFT84aIea8fQ2vmv3O009t6a5WwiqVSuWV825f8d8h5t5OFSGlfceFGkBGyTDZO5QUg83Wp4iw4x1Dygwx8WA9MsZMSE+a8noj23M3xIQ3hvlbIjh2G4dIEaP7zeurQhkR5o1j3ri3RuhWKpXKu0StrL0l7PhicCqfEj8xJ0LnDENMKMrcPSoU5s6AWr5xsi7RU8A3T1d89/7ikfOz8bWbZ4tqWYp4W1pv3hjem7evraJWqVQqlTeDKtbeEkQE9ym6YIsIB61n9IpQKmQbQso87AOtFRpraaZlgazKKkZ2myd70O4t9vKqQq1SqVQ+3VSx9pLYDGRbgc7ZesGd2LjXX0c8lYWCJ8+bNcLCW6Jm5t4SckYpth/6duwOVCqVSqVybapYewn0U8j5ZqB9V3khu4N3iaMh0IfMYeefe1jdiLBoXDFVDSOz6XFi1joAX6lUKpV3jqogXgJ9SlgjOGPIqs/tTXU2RpYhYkU46Pxb3co75/YqjN4a7swazkIEhd3O0XwKli8qlUql8umiirVborT3igGuNUIfM84UCwr3HAJiTJnTMdJaQ1TluI/cnTcv4chfLQetIzUO9wJLEikrYTIebqzhjn37z0ulUqlUKldRxdotkFW39hGzxrLfemJWhphwRti7ZOD9aYSUOe4Dw+Qcb0VI+d0YxiqLEs9//3KuB6KCE7g7b6udRKVSqVTeaapYe0FiLuLh4TqUDp/A3FkaY5i1hvaGywUpK/fXIwIMOfOwH1l4x15b3yooYi0ptLa46r9NJreVSqVSqTwPVQG8IGNMZC0zZadjQnPmQR8wF5YLmilXsbmGx1fSYuzaOMsdEZIqd+fNa8mFfBOxIsycZZ0Sc2efyAKtvHpSLkkLdeO5UqlUXg5VrL0gIpA1Y4wwd8JRPzCkTOc8+53jk9VAYw0yhYcftO6pF7VNfmUfE6iy3/pbEWoxl9bqQde81aa6IsJ+59nTp5/HxxlT5ngIRUD74sb/OFmV4z4w5sx+69+KsPfXzdkYORsji8Ze6m9XqVQqlRenirUXpLWGrHDaR5Zj4LdO+7JowMhua1g0Dd+1N0NE6ENEW7fdh0ybuTZrtluMRoQ7s4YxZ8wUXH4bCDKJxlt5uKeSsrKOCSclJ/VlcBOhllU56keMlDD34zE+cs43hKz0KeOMYTmma4u1MZVsUm/MCy1OvI1s0iOGqOzWPY9KpVJ5KVSx9qKI4Ixwp/P81vGSqJCBBBwNmYbAb50UmwlvhffmLWYyej0eAkPKGMqg/OZCb40wM9ev6myCzZ82u2WNsNu+msrHpjqlwF3zpCh61aiW/+x03oUi4B7HTSkRKedLK2+PPmaxZDkaAvdXgZm37DSO9xefroWHvdbTxETnapu+UqlUXhZVrL0gRoSdxnHSB7KWKsOGBByHRBJYiGfMcH898t50Qd8MxxfhoDyPB1nKpWoUsjJzlr1ntFlfBZnyupJmLtFErxwjJWdziAkRQaavH8ca4e68IU8Zok9jHRLfOFnz0Wokq7JIjqzKnVlDc0nqwruKM1INnyuVSuUlU3/K3gI7jWPuLd889ZycBS7qk5whU6o1zhpizvQhMW8cB51nNSa8lec2vF3HRMhKYw2rGJl5+8JioY+JdUx01jB7jjbmfudZjhFn/AsdyzhZmIiUx3ze2T0R4WDmWYUSCj9z9sq5PSPCdTqZJ0PgZIxYKRvBCSXE/Klrg1YqlUrl5VPF2i0QUyoCzBoaYLjwvcaWi7k1hv22wcj53Jgzhr3uxdpHhlKTK3U5eeF8gFKpC1gjHMeIMwZ/wzamN4aD7sUGmFSVh/2IFUNGOe0jd17AFHhTAb0tjCmt1dYZkiqdsby/8/QWqKqyDIk+ZjpnWPiaGVupVCqVZ1PF2gugqvzdj4/4mw/7S79vKK21xhjm3tI5w05rb3XLcOYtIStjyuxNyQCqeisi4A3oYALTjNkrfs6sWmzzrjiPd7qWs1nkeIi8N+s46ByHs6eLyU0qhbeG0zHgrbk0qL5SqVQqlYtUsfacqCpfP15eKtQ8cLcrrTYxlvfnLYvWYUVu3d5gY2UBxZ7j49Ww3Sh9nkF3a4SD1rOKiUVjL62qZVWOh0DKsNdenscZUt4uT8yeo4IkUo7jeCiedfuvyBRYVTkZIuuYsAIHs8s97pw1fM/hDmPKJZXByDPP90b8WhGCFlFdqVQqlcqzqGLtOYlZebAar/z+D79/AKKcjYlF4xlTxr/keaaUSxszSWnRPW9PtPOWzl9d/VuGyBAz1hge9iOHredoCHTOstd6Qs48mBIdclbGrBx0V4tUVeUsRPqQ2Wnsdk6udZb3r6hCxpR5OASMwEF7e95xMSurmLaZrKdj5M4VLV0Rob1BlbSxhtaWRYfOvf4t2UqlUqm8HVSxdgNUlZj1PFbqCkHjKBuIu20DRPopI3T3JW/NNbYkKRhjXqrx7WYyTlEEIWQlZNCYiliLGZXiQYctCwv6FBPbpEXUemM4GeMTSw2bSl4fEzGWZYqjfiSpYozBibD/gjNyj76+CS2t7NvCiHDY+e3eb51Xq1Qqlcp1qGLtmmzaY6uYEGDuDJ/dnfOdsxX3LxTY5sDnDmasU2ZPinh6mlB51nPe5H4i8lzbmzdl7i05K0Eze63HGilxWhtj32luTlVJqs8UjkYEb4SY8qUVvdMhshwCn6wHjvpIzpmZ83SNoRNhiIkxlak2f41Ir6fhrWGncZyFSEoZbzwpP/s1XBeRF18CqVQqlcqniyrWrklStu0xgFXM3Js1/Pjn7vIbD5asxsi8sew2DdYa9i9U0Z5HPGxifJwpgu95rT1eBmaa0VoF5WyMHHT+kU3L1hp2GssypKnq5Z96DjYzdinrE9YXp2Pg/mrkk9Wa+30gpvL3MY9Y0/D+rGOdMnk9okDnDAft05/vWew0jsYK99eBISVORuXwFit3lUqlUqnchCrWrolwbpOxwRhht234wfcs3zhesU6ZUTPftzt/oXmkmDNnY6Sxhpg3gujNEgunIRaj2ZQnn7dzcSTTIsVNlyncY1WxmJWzIbKOkfurwPrCyQ8Jwmpk4QyzxuGn5xpiJnh9Ya85I2ZKOgDzlFrYbW3eviriZKLsjXkjDJQrlUql8myqWLsmJa7JcTJEoASsG5ESOdQHjtcDy5g4aD2nY+DurH3dh0zKypgzrTXPtRmasmLk8spgZy3rkDCmbDe+CKsxcjLGsizQNVuhq6p8uBz48HT9iFDbHp/CMmZO0sBJSMydLdFfz7DQuA7OCHdnDUn1UuFd8kYDY8pvVYh5yJkxKTEndi/k1L4oyxBRpaYZVCqVykug/mS9ATNn6WNmiIkhKZ0rwd8n/ciHq0BSGNKICOw2Dm/MI0InpMw6JrwROne1nYUz53NT7gXMXI/6QJ8yO95u7T2uImXldAxkYK9x9LF4gs3d5ffda0tqgxF5oXmurMrJVEUsiwZxK7aWY+BoPbC6wmQtAg+WI403hJA5FdifteSc4RKBlbISVbHCtdrK3hquOmv9NCfX2BL6PnPurUgvaK1ht7U4ebbVyE1ISV+5F16lUql8Wqhi7SmoKkPKCGV4fkiZIWY671iHYr8ws4aHfRFqRmAVlY+Wgf2uZ79r2J9aTSkr99cjIsIyZw476J6yDLDTuBd2uHdGMImniqnNazwdInHKKj0ZI+h5Ze4yRAR/odW4sd8IWSdz3qvFUFZFp/MllP+NquSsNBcCwR/0I5mnm/OuAc2ZRdNM70/idExPnNuYMw/WI3l6sP3WPbKMEbMSc94uOzzrvFsRlLIdvHkNbwPmCq+/Z5kAP4u9Z/wyUKlUKpXnp4q1p3AyRFYhwVQpc5O6yFrsOwSh9Zb9med4DIRYxIc1QkZZx8R8yupMqiilsjEC8Rp+qC86T7TXOnYa91SxdjpGlmNiHSMZ2PGenDMhFS+zg2ua0ZbZuhKUfjYmDq6I0RpT5qgvoqmxhoPOczhrWI4RMYbd6flizpyOicYYDPnKqo0CKcFZCKga5rYEyT/O2ZgAoXVmW83bVDdDytxfj9PjKXPnnjnP1VjDfusZk24rjG8jedpy7mNCBBb+xX9JqFQqlcrt8uasGL6B9CnROLMdpG+tYcdbci7mrZsh9i/uzvlg3jH3ZRRdc+bhOjDEtJUN3sjWEFVQOvfsU5+1VHue1+lertGiHKbw8SKKhLkztNYiAvPGXUtUQhGo3hQ33qdFKJVEAkPrLGPKrEPaZonud34retYhYRDECO0zTpWnmADHXIR1d+lyx/kLER6t1p2OEWuKwW1rLeuYiM845yLC3DsOOv/ETFtWJaTnf9+uyyY/9aPlQEjP14QsQq20c70xnI4lvaFSqVQqbw61svYUFt5xNkaAbTtzt/Xsto+2fBat54ff3+cbR2d8ZzlixLCOmVWINJNYkskQtcxMPXte6GLbbnbF3NhtMPeWj1cDJ0Pk/UXDXueJubRGk+qVxr+PY6QM5Gd9ets167k9h4hcWgWDIqZmXnjY67Z1eRkCZAtWhbk3fLDTYC9pwS68o4/jVkDvN+eVs6znVcxtRek5dVbKyoP1QNISaH9n9mI2Ik8jZqUPGWOkzELecAM5q9LHRGPPZyu9MaxDZn7h4xZz3vrYtda+VMPlSqVSqTxJFWtPYadxtK5Uy541kN5Yw7zxyKosGMSYELWcDpGMstsU81h/zQv3EDMZobHlQryr7qW02hZNySw96BI70wzXZhMSbtaKFRGe5Zix6x3HY5zmo4oQvQxvhGXI5T2wwnEfWKeioS7WkBQYEmTJGLEsg/Le4smD8NZwb95u59IuVsPm3nDcB9QaUi6v/3mXBcaciQqdswwxEbM+Mtt3mzgjdM4Qcr7yPD6NKYgDmOYTUyKpMrsg0Dd+f5vbnxDZaxzzC0svYaqQdt7WCK1KpVJ5CVSx9gwuC/G+ir3WY4yQc6J1FmMMpyFOG5PxRvYO3hp0jAxJaczTnL5uxjpEBHkkKeCyLNDbqgaFlMmq+Mk+ZN44nC1zY/4psVhGhL3GMSZlNAZjDH4IeCOcDIn+QuVLAWOKIa7msrxwGUWEPSlq5r5YWAwp0zlh7p7ff8yJIAJDTBjhpc6yiQiHL2BTIiLMG8txH7i/HlmHDAqf2Wm52zWErJwOsfzCsq1CKsdjxE/jAQDHQyTmTJ8y7y9ev2VNpVKpvGtUsXaLzJzh/VnLmDOdM1hjthuPXm5WcWis4d4Fn6/bEE86XWgFHrkAP42N2LpJYPmGPiYe9gFRcLZU6+SxqtZVOGPonGNMA30s1bD91rPXekQGTB8IkyizsLX+WMbIyRDprvn6Nsy8Y3YLnWZvDXe7hvgMMXobZFVSLqa8N60E6tQCDTGzHBLLMWGniuLxGPit054hJ9CylLHBTC38PiZ8U/6+dUIc2aZ7VCqVSuV2qWLtFjHG8NndjqO+GLwuGsfMWRSeq632NJ+v50FEuNPdrL1ZbEky78+7GwuPPuZp8aAsVkTVa7eBrRHeWzR0zrDTxKnS5kkozhqGmWcZE0NIKIq3jtYbdhuPUubcXlL38Znc9vt2GSFlHvbnViQ7jbuRH986JI7HUCqNtrSkO2e3Ii6pYjAMU5rGxTnNskRz/li7jWfu3FtjX1KpVCpvG1Ws3TIz72idRZ8xaP+yGFLGwJXD5jedKdprHXlKMrgprRXWIZNzWaq4adKBM4bDWcNO4wgp450pXmzW0KcSWn9v3vKZRcfHq4FliMjkFfeuC4fjISBSNox1MhNuL7Qmn8VZiDS2WI4ctp7VEFmOI95aZtayaBxjzERVxsc2W5PqI9U2eD2f9UqlUvm0UMXaS8CIcJMhM9VSCboq2um6hFQ2SEXg/Xl7K/NS3XO0PzfMvMOIIeVcZvie43jG6TWBoGNEVWms5f253W4oOmuYeccYlYzyNluExZT5cFWsOA660va9eN50Ek9jznS2vDcXt1qvy8Wbemv5zE6HFWHR2ClSDbwVhj4xxGJNnHL5nF5mV1KpVCqVl0cVazdk0yYCnhoZdZPHOxridiD9sGtubMGwwRqhMYJ5zoUEVWUZEjN3e/YMpQLz/Bf2kPJ2zm09lpSFmZfpscvGZcpFvOzPPAr0Ib7ULcyXyf31yHEfcNbwyWpEEPamTNqEorlsnPYhlUURZ0mqCIK7wWdx4R2nY8Qbg6LbZYXNIsjJEDkNEUE5nDnur0bm3rHfeuSGv1RkVcJUXX0bIrkqlUrlTaOKtRsypMzDoQzNH87khSpPAGNWhli2R2POnIbIHfvohl+abmMmY92rLpRGhLvzF9vGs8IbVZnabsXGko4gqjxcjxgjzKzFCFhjmFm7tZkwBh6sR+7Om2tlgL5JhJhLhUyV1lv6lHBRuL8acLaI8M5bOlvyPYcpeWC/8zcS2AtvMcB6s7wx89tfEmbe0VhLPFMOW7+NSwPBO3Mj6V1858atoDyc1apcpVKp3JQq1p4DmXpI1700DimzDpHGGGaXRPmUa3OxnHj8MTcu9TGXuKq9xrF4zmD3ZyEij+Rlvgk01nB31pQKmxE+Ouu3wi2mxBf3F2X5oHXknFkHHklH2H1W/MEbxqKxnITAzDq8CDkpx+tAAsaQ+NxOS1SYdZ6dxj13pqdMNirzK5w/rBFmzk5brcLMOUJWHMJud/3PSMh5mnGzWz+2KtYqlUrlZrxZV+a3gNYa7sxK9aq5RpstZeVoHRAD6xi2sUYbGiPMXYk4skbYfUyIJS1O9ZvK25Ayi2scZ8iZ1ZiemQ36NuAnW46jPhQbDyMsvKWxFm+Eo/XIhydLHo6JMWVm3k12FmVL8m3KudyfNaiUimuMmYwyJmW/dcSszLx7pE3+Mn3c9jvPUV9i0+aNZf+x+bnrYERQymc4Z8X7t+e9qFQqlTeFKtZuiIjQuhvM61CG3lsxZEqg++OPt9953FisLlZjYqc9j6OyUox5hylQfqe9Xts1ZWWdEnO12GfUADebfi9T1OgUZP+84mIZEtYYDmae4yEwpMz7i47lGPmNh2f81ulAmG77GRH25x1jKrNS1xHVbwpGhDuzFtXS+j4aInMPRgw73jx1nrHMHEZCUnYa99yzjxusEe7OG1T1qZ+NIRUz3cbKE7drrOGgdaxjZta6R9IRKpVKpXI9qlh7yTg5r5x5Yy6dcRtT5ngoyQJ9jGTKxh1sXOo9IZXZoutegDtneX8aFn8am5kiRbkze3kzXidDZBUTd2fNc7XBRNhugu43pdXZ2BIT9WB1LtQAjobInQUgsBwjD3MJKn+eytDrQkTovOODGyyxnA6Rb52uEYHj0fLde/NbEeBPe4zttq6Wz89l5sm3ZThcqVQqn1aqWHvJbCpnu+qunC2KOXM6RqyUylvWjDVFSM29o7HmuRIEriNMQs6EXIblQlbcSxwnehHZsNs4HqwG1jFP7c1yPlpnSNNjb2qWCixDwBnP2ZhYNJYhZlYm3cg49k3gumJLVTkZS5vdimEIOsVnXf25yaqcDOU+O/752sW6/X+P2oFUKpVK5faok76vCCNPtog2ZC3/CYJQ3OXPxsSYy+Zjvirs8hZorGHuLXNnXmpc0F7reG/ePvdwuVDWVFUUxExnqsykfWanoQE8MLNwd2a50zU01vJwGAm52H+8yHk86gN9SM99/1eBlSK6vCut+jE9/XjHlFlOn7WbeLRdpMxwNqWqVhcHKpVK5aXwdpUZ3hKyKiFl3DWzId20WCAoSQ2rkMmaabCE6fGep3238bdqzNVC0YhsW64vExF5ofinVYiAMPe+bNfGUiUzIvzwvQNmbslqjHTe0sfMEDNJEyFlPlkO7Heeuy/Qi2us4N7g2TcRYbf1nI6RcUzErJyNiZnLT02z2PFlAeVFllAeTzOoVCqVyu3yWn7Kisj3i0gvIn/2wt/9H0TkN0VkKSL/qYjceR3HdhucDIEHfeBhPz4S03MVrTXsTsJjSMrMW076yHKMxaD2OeeOViHx8XIoA+BvOSIX2myPndPGWX7bvT1+5DOHvLdo2e8aVimxjIn3Zi27rWeIxTYi5PxcFba5dzea5zsbI+sQb/w8L8JO47g7K3mqd+cNRp5caLmImVr0b1truFKpVD5tvK5fif8k8N9uvhCRvx/4vwN/EPgAWAH/3us5tBdn01K6bmtpM9d2b96w4y17refOvGHRePba57eeaK1hp7mZyHhTmTuHExhTWdSYX7JV6IzgTUk1kMlYVsmcDYGclY+WA/dXI/dXw2Ty+vLIU4TYq6axhoW3nA4BJzfPgq1UKpXKm8cr/5VaRP5J4Aj4K8DfN/317wf+oqr+pek2/2fg10VkV1VPX/Uxvij7rWdIxfzzJkLLGUNji9gAWLQvFmflreHgHblYWyPcnTUUH/2rB+8bKywax37nORsiD/pSnWy9AS0ed30sFTZrXp6NxF7ryVOOpzPySrdQh1iMbHeat2f7tVKpVCpX80qv5CKyB/wbwL/82Lf+fuBvbL5Q1d8ARuAHrnicPyIiXxWRr3788ccv63CfG2vkxm0zOLfpOOwa7s6bF46yetcQkSsXNWJWHvQjp2NEc8k4PR2L6M2aOWgcu62jj2nrXZdVr9Wmfh6yKvdXIw/WIw/X4aU9z+M8XI/0sZgHD6lkpFYqlUrl7eZVV9b+TeBPqeo3H7vg7gDHj932GNi97EFU9eeBnwf4yle+8k5djcwNTXffBWIuCwGb3MvnqQadDIExZc7GwG+d9lhTBBlqCBlOxsTdecOicVgRhpQ4GSKtNRx0/tYNgVNWkiqNNQyp5H2+zP0EVeXhOvDhsi8zeQIP+kiflLsv0T/veUm5WI2kXGxY6i8mlUqlcjWv7Ce4iPwO4H8B/Nwl3z4D9h77uz3grWuB3jaq+lKtO143MWfur0bOQuJkjDx4TquSTZXsaD0Sp7SEVcjEpOx6R+ctx0PxsrNGWIaEM0VIPW0If0NJFMisxkjMz17YcFOs2JgyC2dfeuRXVGVICdXMwz6wHONkpSH08c1bMDkZA2Mq5/2oDy99hrBSqVTeZl5lZe13A18Cvj5VMXYAKyI/DPznwI9ubigi3wu0wN99hcf3xpG1+KyFrOy+xAD318k6JhDZDsIPMRGy0t6wDLXTOL55vGZIikdQFUSUnaYsbFhjiDmRVLEIO95yPEa6S7ZtL4vGKm3VgGCQEC+tVqWshFxm1JwxHHaerO5W5sZizqgWEXhZFdBMPnQiBmcM3gBaXsubVVMrxKxb4Rwz6HYasVKpVCqP8yqv/j8P/CcXvv5XKOLtnwXeB/6qiPwk8Ncoc23/r7dxueB5yapPDM6PKU/ZlobTEJn7F1s4eBORSy7Qz/MK1zEx84bdzqEZDMqQSxi5tWZbrdsIp5l3dJdEOa1C5HSIpAydF/bbBmuEdUg0trRoN4LSmSKG+pg46QPfOu3JlFzOz+/O2L2leKtNpJNSkhwus9qwRthvHGdj4O7MT7momT3v6W4pj7MkHkTGlGitZbd9fiG66y1HQyRkXsieplKpVD4NvDKxpqoriiUHACJyBvSq+jHwsYj8UeDPAXeB/y/wz7yqY3vdrMbIyRjxxnA48xdC3Mv/jjHTuuttlm5mpa6zgaiqrCdX/uedFXtRZs6yDpEhJhTorME/R8uwj4nOWT6zmPGwHzloPTNnWKe83a7dax3uwmM/fj6HlLm/HhljKgP6y0Rn13zX/gJj5IJAOxd9J0PkaBj58GzgqB/prGFMmbm3NM7eiqv/mEoCg5PS0txpLr/dvHF89/6CszFiBBbeXVmJi1mJOdPeYGP5eAjb2cLTMaKqzBvHKpQK5U3mzjrveM9aMop7SrpHpVKpVF5jgoGq/rHHvv4F4Bdez9G8XpYh4aaLfMxKM7UAvS3mpjnrtQLcYy5iY9MuuzNrnirATsdSRcooO8lxOLtCBbxErBHuzNoSCQU3tjsBWIfIOiQexsDcW+7NGvZaT5rExEZcPWtubDkGlkNkHUsE0xAjIStHQ+T9Rcth15JV2W8d7VStW4bISR/45GygB85iwpHYcY57sxZuQay1zrAKZbNzv336P9lmin96Fg/7sczztZdX6gBCyijKmErVd4iJMSpDzqgqISX6nDEIQww0C/PUz5uqchYiKSu7jS/JCbX1WalUKs/k3RuCeguZe8vpGGmMPFL5gWmj8ZrX+zFmVIXWGYaYHhF+lzHEzDImmIbnd9vXY6Bb4o6er1WnqhwPkZ3GIRr5+KznO9Pfz5zl3rzlc3uzR0REzJmzMZIVZs4w8+WfwZgyy5j5ZDWwjOcD7+MqsBojYSfxA+/tn4u+aabwm0dr+gvHFIHvnK353juL53pNj+ON4d68Bbi16qcTIYleuaG6qfauY8IIeGOJMfFJH9hrilFzVMVSlhucXNbQfpRNBFZ5TemdnMGsVCqVl0H9afkGsGgcM2+favZ6HZw1KJEhZUR45hxQY4WcS3ZkYw1DzBgvDCnTXDPX9I1Ayubnw2HkaIzsTiJgFRO/dbbGGuHevMUZ4WyMfGfZ400RaUNfIqFm3iEID5Zrlo/lnydgHZUPVyN3VkMx5xWIk1XI+pJDGvPlGjurkrJib2iU+7TbbtISHhf6T+Og86QL98k6tc+nluSYM8YUAZYTiAFvLUhkGRML77jTNRx0DWPOePPsiqg1QmOKyKvJCpVKpXJ9qlh7Q7iNikljDXdnDTFlGvdssdV5x17rccaQNGMFTofIMiQ6a7gzv722qKq+lLkkEWHPWx6uAyEpM2sRBGNKe1kmyXQyRObe8KAPqELMpa038451zMw8hJgY0+XPE4AhJVZjZFTFi5BymSWzFEF3EW/h8dOfVXmwHolZr9Wmvi5DTAxJOeiuH1RfZuDKn1Mux5VUt75zc+8Y+pG5t3TW4kzZ2N1tHUk3xs9lznF2zaqoEdm2aOuMWqVSqVyfKtbeYlJWRMpFMKTMMkQMJW7pOlWx1hZ7iT5ldoyndZasCSOKu0UH11WILCdT2hcRJyHn7bLFxXbtvPF8ZqGknLnfj6RpW9FJqQYppYI0pIw3wjhZhZStxoyfBuP7nNlpDMN4uS9ZTMV8954ztN5xvC5LIbtOOLrQNm2Ae13LKindxftnJWaldfZaberr0jnLM0bZnkrImahKNx1X0iL8359arxeF1c4LVsSqSKtUKpWbU8XaW8rpGFiOCaHYUxwPAUHIKLEP166KzbxjdqEgM2+KgextXlIN8sLib+M5l1VYhsh78/aRC/9B50tLTzNHQ0SB1jk+v9Ox3xb7ij4k1iS8EVYhYMQgUhz0oWSzvr/TkY5XHF1SYVt4YciljTnGxDJl7i069jtPipGdxnM8RuaNY+YdY8pk1Ue2e42USpiVJ+cTN2yiqa4rbOQa82JPwxmDkbJR2xjZzrG9qLBahshHy57DqV1aqVQqleejirW3kKzKcixB8WnyvlKFxhl0Cg9/kbbjbVt4dN7eiteXAlcdmoiw23p+8N4+H56tGXNZHnhv0W2rjDNviZoxY/nzzuS1tvn+e/OWr59kPn+wYL7q+WidiJTZs7mDrvG8N28JqowhMrOWhbdI4xiiY6/xiA1YI+cGtheOsYTRtyVpAMj6ZKtUddOShLuz5pXMDToj3Ju1RC0B8LdV/VqOkdMxI4xXirWX1R6vVCqVd4kq1t5ChFKliblEUc1cyb/sY0K0CJF37QJoRLjTNfQp0dmrX5+zhs/tzUtSwWP+XTErfcyoEfYax9w/+vHvnOWDuWcZEnuHu+y0IydjJGfdbo0uGoc3ZSZuHYrQKIkHpXV8Z9aUnNJchvAfJ1PENcApkXvzR5MQFEjTwsCrdPV/GTYamxm6q7Y+V6FsnLbm5eSzViqVyrtCFWtvISLC4cyzHBPGsBUFfUwlCP4d3bTz1lzLb25jIPs4p0NERPAiU4j7eVVNVXnYj4QMRgwzb/nC3oyjIRBzok/QOYMAu61HgJDGrZnv3LltVcqKIAjrkGlMovOWkDIgxFTitdpp+3aMGdecv6YyhN8W8feGha/flMZaDjtBubyCdjoZQffbpI4q1iqVSuUyqlh7S3HGsN89ejF/vFL0aSJlZRWL4WrKypgzc+fYa925SCgjbdti1UXtoJTKW2sNI4o3hkXj2Gkd65hQLekKzp4bv96ZNcSsU6WztKfPhsjHq4F1iHTWsnDCg5iIScvzaqmujarElFHsE0KmzLJJ8X0Lkc6ee8E9i43v3JgSB13zWi0y1iFyPM0PLnzJaL3IzFpWMU1ZqlWoVSqVylV8eq/ubzAvY45HVYv/Gs+XEvAms6mKrWPik+XAMkQOuobQKjNvtxWbvdZx1Adizuw/lmtpRFh4yzKU4f/WnS8d7DaXCx4jQmOFmDIfLUeOh5H7q4GH60gCvJTt0YNZGbAXEcaUmDvDKuYybziWZIJHROXEUT8CwhAjzYUq4NPIWiqsMC0M3IJY2yQ1COUcXfezM6Ti1WZF6GN6Qqztto55U+w/XkfUWaVSqbwtVLH2BhFS5uP1QM5wd+avXU152uP1KYGWC2eYqjtzZ9m/gSfXm07WYlB70o+chciYlOMhYACze26e4S4kAVzGblv8xYw8uglZZtIuX7xQVT5aDRz1gU+WPfeH0hYFiArfWI70KXIyJnZbS8qKl5L/ujuJl3VMLBr7SOs2q05+cMUzb/OtOG2iusnI+HGMwKKxhKTMbynAfRVK/JZqqSBe93PZOcvQB8IUbv84V7WrK5VKpfIoVay9AZTtzshHZ2s+6QMOIcbIl+7sPvdjjinzYD0iIpPtxcD7iw4jwjom9vTJSs7bihEwpoScoyCiqCqdL35s/ZT12TlzZQ5myooRnqhelapdYEylGve4UMlaRHG5n6I8yWrMWAnMJn+4qPLEFujj5hunQyRpOaY7XfGnK6a6A1kFJXJn1jwxnygi7Da3K8Q3zyDcrOLbOYufm3di/q5SqVReJ1WsvQF8vOw57gO/ebRkLHqDPiXu7XQIwmxyir8JZyFuI41OhshyzHwjrThoHI215Glb8l1ApqH8MSa+ebrG4+is4zOLGUCpsonhdIx0jxnqApyNkbMxXpoqkLQIX5FiqjvzpbqVppguI2X7NmWldQ4/jITHjm+n8yy8RRVmzrLTWizCyRhRVRpT2oSb9znmsjG6jom99rz9mXLZEm2dYUyZmPIrWSaZeTtFT8mNlwDsNH9XqVQqleenirXXzIPVwDdOe8YxsUywsJApQewfLgfm3pFUn5j3eRaqpVqzGhPrEFmGyGoVOXWGLx7ucDxEDm/RLiGrvnC26YvgjPCF/QWf2ZkRpjgoNwmZxhr6mHFXzEatQ8RbQ0j5iVQBO7UVh5gxAh8v19xfBRRh0Ri+sDvjzqyltZa91mFRPlwGhun+npIU8dmdjkXjsaZsgooInbecDIF1zIRpdm2/8xz3AW8FYyyqsp1hdEbwpnw2RIpoexWICJ27nZZqpVKpVG5OFWuvkTFl7veBvcYxGsP9PhAUWntuBQE811zP3BmO+kDImfurMsslCv20NemtYV89t+GWsNlAnDtL615vFcVZ88SH+qD1BK9TgsCTxzf3bmsj8fhW4qat6CTy7bOe+6uRdSgzZvfXpQx6b9GWVISoHM5b1gmGEEAMhzPHe4uOe/MWM1X0NsbF3siFAPZSUQOIWqp2DaXFmqdZsVJB9MSsU8RY4nSqCC68e6uG9EPKJZECeG/eYGubtFKpVK6kirXXSEyZxghjLi7/X9xreTgWZ/wv7M34YNGiPJ+twcy74pK/DuViD5NdvhBTaUw97WE3Q/XXqZYVQWPf2LaqyHn77rIop0XjipEwV7/WPpWw9MYIn4S4jbQaQ+BknDNzlr3Oc9A15JxZRos3hv3p78wFMdLHxIM+cKfz7DaOh/2IouxPLv+7jdsa5y78o1ugIoK3xdbjaCit7j5mNMNe57eLCY8vSbxqYi5GwSmV+UFnDRlojJAU7q9GHg4jMRXD4f0aR1WpVCpXUsXaa8SYEijeeSHnzG5j+byYMjdl5tOtFLahRzdrRc292243/t2HZ5Aye23DwhkOJyuJPiZOhpHWWhZNydfMqhxNQ/XOyJS7+fTKx5s0QJ5ysZpAYdYU0bT5+4f9SFLl8DEPsquqUimX5Q/NCqoIyvR/GGCVoB9LLmsTMwtv2Wka7syE1jvaS4x8vbXseMVbizPCe48Fps99mSuEUg1UVc7GWFq5VthrPGkSnc4YRJQx69bXDErrd791r6RilTeRZyj7bRGM3zkbWA4lASJlaGxJ3Zh5x/s7HSdjKK/NCEO8JIi1UqlUKluqWHuNtNYUe4OUEYlAZL9zGBOATJEDw/RngIbnecs+s9PRWlMMSEVYtI7OlS3Jrx2tplsF9rvEzFpOxoAq7HUNIWdO+njtYPgXJauyCgnD88VmZVUe9CN58qBdrxL35i3WCCEXp3wrwipEGtugqsSs22WMxzkdA31UclbuzhtOBmE+RHRI9IAo3O8D71lDVkvIgnPFlX8cI+3syfPmjGytU0LKnE5Vut3WbQWkuzCYfzoEzkLxTBtTyQ5tbBHapYIFe43leIjT0oNw1I+cDIGdxrHfekTKDOPLyBqNk1BUhJnLqBZ/uT5mhmkR4iyU96Zx5ZhFwFmLNcXEuFKpVCpXU8Xaa0SkVK3GrKiWmalHizDKuWjL09fP9zx35i3700WxBI0rJ0OgsUKaNlCXQ+RbQ8/CG0QMu9PGaMz56U9wi5yNkVUonl5yA0+vDUl12swsVcghJmLOWGO3M2k5K507j+g6GiKNEe4+5sGmWkpoORdz14POc6drmDvD/3D/jDxmEuX8WlO2TGfWchICZ2Nitzm3R+lDImimtXYryDbCsohE4cF65L15iZoaYplpQ4SzkPBybsJ7tC5t0+UQWMXE3DvGZLFSvNdSzgwxM5+2VL992tNMywiX2Y9cxjrEsv3q7PZcXoUzZVliFRJh2lCde8t+64snnBG+dbLibKqgGYFF6/HWkLPS3pIfXKVSqbyrVLH2mhERWivARaFgmabFKPuEYfrz813UUi5tNAF22vKWxyks/KBrJh+yiLWG1hnWMTP3hjAFxe9e4U224bI5sBdB4bndHgznNhebw7lYMbvbebiGY/46RI7HWLZcDex3fmt5khW6xrNoy2EGLdFJAjzoR3ZbjxWLoDgj9CHxcAgYEc7GxN1ZacFu5ss2W6tJIeUyi5Y0czYM9DFxGpTWWA5nDTNvOOkH1pN3nDGlSX4yRPan97Z1hsQUnZUzQ87sutKePB4jnXt6xfKRebg+8N7cPLUiZ0Q47Bo6G0laMlw3n6u9rmHhLcdjxIaEBQ67Bm/L50xRhpB5qANDzGSU9+Yt3lYBV6lUKhuqWHtjcGzDI7diDYpY27xNz6dgVjGymqoazgjzxm1FjebMKkQ+XvaIwE7jeW/ests6nDFY4am2DcVwdySoctB4uheskuw0ZavR8PTnvQo7eaUdD4Gclf3W4W3xJbu/HtltHHNvebAeiBn2G8u9WfOIGAk5P9JSHFNmiImZs/QhERUaU8RGY9iKopQnU17AG7bbnUHzVBUz27agn2KY/OSxJpT3xpoSSZV1ZIhr0tS2HZLSR8txP/Cdk4H1dKwG8CHgLLSGbXbpe60nSzmW2XQeBbbzdk/7JG3qt1aEfIXR72VcrNjtt1Mua4lx54NFy5imlm3nOR0jWTMfnfU01qICgsEYpXOWO7Mq1iqVSmVDFWtvDMLFt2NTDUtatuVKNeTJe21mrkSuHvK3IsV4jXPnfGuKUPjGyYqjvrTtFBhi5M7MsvDdE4PxlzGmzJjKtt8yphcWa0bkypSB69JYsx3a32Cl+Js5I3yyHPhkPWIQzoLwpf3Fo0a4k8ja/J03xRAXSmvWmlKBNKKsY2a1HsgC3nQczlqO+5FVSCyc42FfkguyFr+7PiZyzpyMsQzcO0Njy3N1zpY2riqaEkPMjNmw3wqrYLg78/zGg5GL4/gZGKYdlFOTcCaw13oSwr3Oswq5zN1tsj3bZxssN6ZkgA6xtHKfZxtZRCahFoDEnZkwRIez5XOcsjKmwJAzUYXOGQ46y5AVL2/OskqlUqm8CVSx9gaSdbO1WC7iR0Ngn7IleJGNv9kmuPuqeaSZs9hZyZe8OC8VcybmEiiOlipNSDAzEW83s3JPxwAPVj2I8Lmd7pm3fxVsTGQ3hJRZhmIkiyp9TDhT5ruGmMj6qI2JkakeND1OzIo3xTR3jJnOCtkbPhojD5fjtsr1YFjhWJGBVuDuoiGifH5vxkHr+PrxmjFnjBj2O0dWLYsDxnAwKxXFISRiKtW0e/OG4zGxjIo1hji1NB/fnRSgdYIX4SxkuiECSswJEQMKrTfsNH7r3fc0RKSYMN/QiPlJNjOXFkgkDfSjTubDPfvdWEyfc8uscYSkLIxh0dYfS5VKpXKR+lPxDSTlUi1rt+0rO82RPXY7LQPyrSuzVKdjvFSsicgTZrUhKYowhszxeGGBIMODIbM/z1zHIP9sDJyMmc4J3z4b8NawaC7PHc1TFdAbeWkeYKsxchIiC2fZbf02I9WIIAIPY6R1hlUqcU6HndsaA4+ptDI7Z9ltHGdjscGwIsy95f70OCEpR33k/vq8HcnU7iv3ENYqHK2Kj9jcCTtts02iGFNGtRjbtq5UsFYhsdM4ViHROUvIDZ1XMpm8TrTecjomjJZ/tBvBtqnHCmUZobGlKmZE+Hg1stNYskLjLg9+f7ls3uPEKgQ+WRsernucMey30JrJpkYWwJMiu1KpVCqFKtbeQER4pLKTVWkuaXGaaSYpTosA/gaeWjqJiz49WqdRhTFBzIIzk7XEGJk5c6kQ7Lxn3hiO1oFZo3x7OfD5x7Y4Nxfgoz7Qp8ydzr+0+KKzEHFiWIbEYhJc1si2RWylVHa+tD+bsi5L9FPKxRIjK+y3ZXaus4aoSmMNIZXWqLcGBe6ve4btqdML/52zzMIuykerkT4qO41lzMWLbXNOVMvm5zIMzJ0pFTyj7PmG3cZy0MJeG+lTnqqYA/PGwJiL19s05tinTAY6VzzYrJTgddWyIWuumFILKfOwD1s/vdtNQSiLM1kjp33kk2Vpt8887LctYuYYOf8NpAq1SqVSuZwq1t5AnDGPVXa4dI7LiHA4a6aopBKLdF28MagKc+84DmErMxQIWZBp8/R0jISsjI9tEepkhLoMEcPkCBcT91c9KWecGIJm9lvPB4sOOw3UG7nagDZP5q9G5BFBcxmbc/P4eVl4x2ko0VdmEmHmsQQAFfDm8WSAUkFTFCPlWI6GQMzKXuNonEWQ0lIdA+ugF9qRG6H26PEqpVpnc2aVMrvGcfcS37WouVT1FPZax7yxODmvPiow9oHTdV8838h4C85OG6QRvBN2Wou3wvEw8tmdGZ/bnbGOibkIe93ln41lKK9iiOUYbl9EG/pgWKcydzl3ht224bv2Z1WcVSqVyjWpYu0NZacpxrWb7MirBE5jzaUC4FlYI+y3jnvzlmWIDEnRDIed4+6s2+4AzpxhHCNz+6h4UuCT1cA3TlYMKYGWY3HiGGLk/liqO8d9YO4MB7OWvdaxx+UtUiheZMsxbV9Xc0VwaZn1KmJt7h8dmF9M256b55h5y9kYty3lkDNenrSiMCLcnZdW5WY+bWOg26fMvHHcmXmO+gDA+4uGb5yN0703jzWlCiBTO7Q836F1eANjTuRc2pJ2agWLSMmGzcXmQ6TMnm1eZ9lEzYgIQQVjSuWzNUrKQsyw18Je6xmyEBXIpaJ2OPPsU6plVy0JdFYYomLNy0uhaJzhzrzhs0bw1kypC1WoVSqVynWpYu0N5jYvnhtj24uPOW8cn93tttUuBXYbT+sN7TTfNPPukYpaSJl1LAkDWRN9TKQMWaG1xXZhHTKQS+tODEfDSMglXmnuig1FzJm5d48YrharjHIsT8sZNSLcnbIkLxOxm2Ndh8g6JFahzKd1U/TTfnf5x95c8F9z00bkmM595rw1LLwlATveMcbiR4YKuUyNoWSKnCuPY6UYGodsmDszxS+VtnVjDZ0rBsQ7l1QSH65L5JdIxpqMNZG5VawoaGY9Cg7DmEsbNGZh5g1t40r+6HrkvUWLUCp85pKN4c47/LSh+bKC4J0p8WaVSqVSeT6qWHvHCSlzPASGmLFGSpvtwjxZ5xw7rS/xTAIHsydzQDciYjPXhQhjTFgmUWUUMpO1iDJzwtw3fHs5suMhpBHTZFaD4f5S6XPZbkRHvrA327YyvTVbcfGsysuzbEX6kDjq4yTOmiLWvOWg9deq6mw3Ih/DWYOOETGGLx4u2FkOjDnTOsPdznE6JH7zeE2fYW6F778zZ53KXNoYi0fb3FtOhsjCO4aY2Ws9O7MnN32jZhqXEA3MvUM6wSL0IbOOkJs8CTHD2VjegKSKE8uAMuaISGl59ykhlA3Tx9/flxFBdTWbhvuF5zw6gsUC/Itun1Yqlcq7SRVr7zAhl03Ih/2Is4Z951mG9IhYs0a4O2uJORcTXHM+k7YMCUWZO4edAt6VYgKbVREsd2cNZ2PCWtjzjj5l1jHTR5g7g0oGhHUoKQkP1sVZ31llFSIxJb7/zg7tdEzLUOKm9prrxSJdxSomnD3PwpxPGawvSmMNh61nFRO7jeezO7OSQRpT2fA0kd+x6BhjAoTP7c1K3JMtRrspZ061bOImLedit5latdZsfepEZIprGvHOY41llcq5GVUxBg59ma972IORzJgMUWVqsVIqgiIc9YGd1hFT2ca9zpbvy0GBnpJxO1VUT07gL/0l+IEfgB/8wW3rV3OetlvtKxaTlUql8uZRxdo7TJpCvufesRwTvU3sXbKEYI1gzaOD5cuQOB1KZSZm5bBrcEZoXfEmcwJ3d2YsWs9qTMy84WyInJyu6UNiMMr37e+w05V5qNWY6GMmpMxZCIyx2KV+sgqsY+THPnOIs3bKBRXWKTN74ULL7V7kN9u5nbdbUZVVub8unng5l6SDeePx1tK6kn05pMzpOKJquDfv2GkdXkrkV2sM9/uAEVgFuGfb7VZv5wyd84CwCkVorgNkUfpYWqz3ZmV7dcybbd3Syt1tzlvM3hpUlZm3j9h3xKyknLdJDa+Ghkf8++Zz+KEfQt9/nyEEvn225mjV851lojHwmb0Z33uwYHaD5ZlKpVJ516hi7S1gTHlrzfF4lWGzlblOpRp1sWrmp7gobw17Dey3nsU10wFKJqZgBTYFKRHhoPWkRrfzXY1tOOw2zyd842SNdwYrQuNLbJbmjFLmqVIOLNeJYttaBNt3loGvH6/4nsMd9htXNidfoKoGpar3sA+I2O3M1k7z9A3TpxFz5sE60FjDwYXNyqxawsidRaf3prMGY6Czlj4mZs6w08xZjoHdxrLfuslsN/PJauRkDFiKeJp7x363ETPFRS3mntN+gBw4jZn5pKvXSfmkVxoRrC1B9UNSGitboVZm44T35u0jrz1l5f56KDmn7lXNlAlP5Ns6B9///Rz1A1//5Ihvn0TWFFuSdTKMJ2taa/m+O1cvplQqlcq7ThVrbzirEDkeSgi7Fbgzax8RbEmVdUx4azgd45Mtznlb8iWfslF6GQvviDmQVbcB4VAEm7vicXbbhh98b4+PztbElMkKR+tIRnl/3jDEQOc9uzHxcPIFA0DhaIisQmTReDpfRMYQM419fgPdpEoYiwnubuNYXBGFNabM2RjxVtjxl4uCYlRckjJVz29jRDBGGKZQ8oV37Hee1Rh5sB5LuoQIO65U5GbT4+dc/M2sERoxfPOsSJQ+Jn743g6zxpFyJuTEeugZUuTBOpGycD8Y9lrFTx5rUYXDuadxjiYkhlS85ErOaGb3EpPiTSC9MyU39bbYxJ9tNlufyoMHsLdHEMPf++SUT1ZFqF20QQkJhpQ5C/FG1jSVSqXyLlHF2hvOKhQhZkUYYiLk/EjLchMGHlJmfokYMSKYKywwnsYmEP2mLJxlr2uwUtqCmWL/MXeOlc+0rsy5PVgPfLTsWY6Kd+U2F8XkcowcDZF7M/9cs2vHY6Rzjpgz9x4TuBfZBNEbIwxDxolc+nyNLW779jERYkS4My0wGIpVCMBpiDhrmIkwpETOym7jsQIP1iOrGFmOkYVz3F8PpFRm3o418Vunkc/sNgxxZMiC5pHGlgSLxigxGU4GgzOKZoOzxWNv3nhik3m4Dgwx0TxFpJZtV8NyLBXH63CdhIExZe6vRw5az/xpVdyU4Dd+A77v+3jYLjgdIqPC48bCd2fFXmZjSlypVCqfRqpYe8PxxrCOCYyZHOvPPbh0yrS8M2tICs+hyW4dpbjlOyPcXXTshcjpmDiaKlxl9s2w33ne35nRp0RKyp2Zp51mq0RKssCisdcKk79ImdNTWmtLxdGch9dffczTeZVyTp/87jSr9phh7MkQ6GMq25yPCZOSO5pRhYPOb6tC91cjIWdSUk6HRB8yfcwMUWksDBEerJWQV7TOkBREM7tNZuYNnROsKEOCiKFxpohS1XLuKIsJhzNPSsWMN2Vld6qO9jFhpXjYpamAdRYS1sgT2bMXCSlzNAQOuye3hS/ijGHHO5pnbTFYCz/+46gxnDw8Q4xM3n4WA3QC7y1aPtjtytzl69uKqFQqlddOFWtvOJuLbMjKQetorKGPieMhwGSuuhn+f1lkLZYd3z5dETN8fqfjvZ3u0ipL5yx352ZKAyiLCs00wD7GNPmdlVirw67ZVrxWY+Sj1YgROOgaWmcf8WC7DmGq6kAJtV/4JythG2LOHPWBuS8zZGdjZObOFwfOGShh5C1gJ0uNYiC8nETOckwXhFzZAt1vPWuJZEq1EUplKuRMzHlapFBWMWEFOlfeYy+lOnXSZ+7OlEVrEEqF0hvD3TmcDQabISZKa9Ua5s4y5oQhczhzGFEehIhFOAqB0zHSTFu8qspeW2xDOmdJWekvyZ69iBGhc89eRLDm6rSEJ29czmdjLHttgzNlO9gZ4XN7863H327rbhSlVqlUKu8aVay94RgR9h8baD/uA24jgFLmdAwcvMQB8eMh8O3lmuMhgcDfe7jCe3vJUHpGJE3JA5aUmap/03yX2cQ1JRTocuawa+hj4miI2+3JB+uR9+ZXty6vIk3VRqTEMM2eUpWLWYtpryltzKtbrY8ew3KyINkkNgTN7DYXhdoACKoN65SJWQlZOew2/m7Kx6sBtFh3rEMCFRqbmTllt1HGnLECswYaAyGft7O/OMt8slIe9hYnFmM8RgwzJyyahGKIKTLmCGpQEfqkSIqsIhy2nrOYYQgYU4Q/6Lbyl7Iik2AcU2bmSnXT3jDO7LrErBzOHFETe51jxztiVjpvibkYB1ehVqlUPu1UsfaWsfE6u+i0/zLneTZB46JlQcEag2rmbAyPiTWlCJXNsXiMuKkdmDCm5HQ2zjDmtF1S6EPkfh84GQLvmZZVSJOAKNWxm8yrNdYw9xZFmT2jKtdaw71Zcw0hUF5jVggpcToEpESkM28snb3cZHfMJa6qc3aaNVS8gX6MnPRxm0UqIowhopO/2MkAhzPB2PJet4YpLUJpbWLmG95fWJxJxCT0OROS8mA1kFRQzUDESKSxnuMePjoLGCM4VT5ZDqCZ/a4rWaKmiH7RzCfrgQerkeMhsBwinSt2IO8vGnZaf+XyxfOiqpwOgZ3G8b2Hu5N3X6kwno2R1ptL5zArlUrl00YVa28Zdopiurjtd9WW420hAjuNZYhlU69xlpTgw2VPM82fGdls8VlK27DkWR50nlVIxKy0TjgNpSrlrbDXeIaUMAgzZ+lTaZMeTikKJ2O8kVh7vAr59NckU+bq02+XFU6HwOkQsFNm6FkorbrD2eNCzVLapTLNDyZCygwh8knKfLhc89F6ZJiCQxXOw+Cjbv9SBth1wAyMOa+ulaQCWPjMwnu+M4IzStfA3Es5LgksGkvKFs0Q0sAYE2dRts+1YyHqQB9K5W23dRwPMlUMlYfrgVLwE2IO3J23nI3pyuWL50VEOJw129rl5heQxj7fckulUqm8q1Sx9pZRLnCekyESc2bh7Da7kq17meUR49Er0E0bjrLFeFnVRETYbxxJlf2utMc6WzYQi69XZhUiO02muKaNlI9VOSYj8sjw/TplvGFbcWqd4aBzGNPgBD5aDlgps1VPywd9XlSLPUYfE6uQWTTuEd+0i4Sc+XsfHfG104GYoTXw/XcXtNYQcua3Tnu+e3/xWLt2MqI1yswKD9aBj1c9nywDw9OP7Px5I4xGORvBTmJNgYgyhmLf0VhP6wyCEHPkdBhxRhETQTMhOk5C5mTMtFY5jufHuE6AJE76yN1FwzpkToaAM4a91tE6R8iRdYh0naNzhjjlv942r86Mt1KpVN5eqlh7C3HGXFJ5yBShBEW0dTzLwX8dyqyYTPd+fKNxQ+cd71tD7Er1zCA86ANKKiHjJIo4bDivFV0mFpXDzpFVeNiP9DFzp3vU4mG39Xy8HNhpHPvXiDDQyWcOvVpwXuSj5cD99bjdnL26Kpn58PiUr5/1rDOAEDL82sdLvvegY6dt6GMJtS/WF5s6WUbV8bBPPFgHHq4HPlwG4tNfxSNfDYCMwkfolO3JNknCWSU7CKmnDw6Rkk4xxIgYuNOVc9hY0KC0RuhlaqVOj2+AMZV2emfLlrEzmzavlG1db+i8597cE3OJtbrpwsfLJObMgz7gjVw777VSqVTeVqpYe+e4/kUrU0RAGXt/etnEGYMziVI9g4M206dE66D1GehQzdNFc9MSvUjJhSyzWm47U/a4NUfnDIezhr3WXavqErJy3EdUSjh8666+T1bleAjMvCVNTv8zZ3mwHok5s9f4aRs0AwOncc1Ol7FBOBkBhAgMceTOHBbesI6RnSayaf0CPFxHvn0WWY0Dp0MiXjB5fRK95FxNqwpZ+GSlNKY88qKdnkWFUaZYrjglXAAhKg/Wyo4fMWLRLOw0htMgtCYz5FL3WzghKBx2lv2utCEPO8dvHq3xAhnDbNZxd95sN3bdDU2VXzZjzFNclpIavdKouVKpVN4Fqlh7Z7hY2SoZkc9iM4wPJbHg2WxqM2nKxyyVL9Weo/Upn6xLDNUHC0vn7GTeuxFvF1ukgUUzvzT6yhnDQXf97b+y9ABK+d+nIcDMWc7GRJYSPD/kxJhKPubxGEprUUp1MCbDGIW5SwwBkkp5jCmDM2vGEjiPULKsAvzdB0uGmEiaOYkbMXbZ+1Gam5eRKJ5r61gEYieW1hsam4kZusbQWGW1hD6W1vLMCUMURJTOCctRWSXDfgd3ZoazUcjToogCd7tmmjcUQkrsth5DEb0zb5l5S+fMG1m1ap1lnsvc5stol1cqlcqbRBVr7xTns2LXoQR+38SOwVNE4cWmWgkXPxkCqpY+Zb52lHl/UYbRS2u157x6NE6PUwTM2RD4rbOelAJ35x33Fh1GyuPH3LMcMn22NNay1/gn7DycEe7N2+3reRoiwmd3Z5wNAWsNO96yDgnVYhyrunHpB1DuzS2r0NM45WSKdmotxBxZDiuyCs603JnNMEbJavgfPjniqB/oLIgm5haWaWP4OqnJEMspeAoXZa5D2O8M1liMgBXFiJLV897cTGa6mTEL3greOMY8Z9YKNgbGHDHAbptxpsVMqRcHrWc5zSwaI/zg3R3OQiRl3W7ivolCDYqf28u0q6lUKpU3iSrWPsWkrKRpkP96nmYboWaBNRurjqyZk9ESEwQtW4hmkVmPK1qb8VYu3K9l0zIck/DNkxVJB5xRPlpHOg977ZysA6dDAITWloDyh/3I3VnzhIC4SXvOGeVgVry8zkIkpmKpsQ7FXuT+OnA4c0UELhKK8skadlohFps5lkFRjew3gvfCOvQs2jmn/UifR7wo603BTMCgFGe5SeD6cibS5YfIrlEaD5pLxXDmy/N7EWbec9hlcjaso9AIWGk4HRPeZPbahkCDE8t7s4asnqP1GRmlMS1RHUlhr3FYEVpXbE4aWypoT42IugEXZwlbZ2/smVepVCqVc6pY+5QScubBekS1zK3d6ZobRDsV0ZZyIGuZOzN4Hg4jQsYb5ZNVovOR3QzOOEpk02auywDFV0wksHBKRkhRGWKENhHzCDrinRCzobGeMSaSPjmfpFNG1LOrQGUWLeTMg1VmSJazIbJOiUaEz+7NSAqrkNlrHd4G7s4tSS1JA8c9hCSEoKwCBBU+30DIAVgz5gHJyphg1CLsrloskOk/w7lo2wi4MRt2jHAwM3z3QUPCElOkdToJnzkgtC4zxMDCOO7Mytci3SNB8+C4t9ilD5FVLAa9O97SWsMn65HSzk23vjxwPETWISECLqZLRfbrZuNtB2zFaqVSqbyJVLH2KWU5JgShccWGYhkiB/bytlJWLTNYWRmTgilZjatQtvKccbSuYb+FmBLeJJZBGLJy0GWUQM6KNxbvDFkNWYXGWBoLMYOYTGMMrcvAkj6suL/OGBEOZhbwU+P00QtqysqDfkRVuTNrnxG7VebHhpCIGVaxzKtZ4HhMLIbAzG8WGwKrQegTZE14Ka1MYwRnlRRhCGCwiJTZs5yVnUb5eKXkZyxsGIEdDyHBclJraZp9O+gMdxaWz8xa5u0uRdL1bLZNNy1kbw1lmXVj1zIlR2ip5m2TI8Qybyx9HMk5sQppmikUYso4cSVGa0ovMCIcDwGDsNfevBW6sUfZRHcNV4jsV0nKyulYNp93GldiwkLidCxyerdxV25DVyqVyuum/nT6lFJqW4VSmLr8QqpTvFWfMn1IrELkzrxhOSTWUVmHjBIJCRbec5KUQROdUbJGPlxmQhKyKnMbmVk4GkGlpxPHB7ue4yGT8OzNWnYbQXXgtA8YgTHDg3XPTtMwd+6JJYKkSkwZREg544zlbIwsQ6Sz9jGxYQBPyMqQFC9CMBljDDZmLMJOY5k7w9E6sgzFskK15G6iuo1jEgPNJHhKxngZ6s+quCmt4Wl2HUHLeTivsRVh0wIH3Yz3Zh17s4ZzCxTDuTWLufD3LVlHxpgJ2bKOI2NSWlvsXTbtR1XlZBxZhrQVpHe6hqSKvyBchJJHux4TfUoMKTP39kZCRkS2yRUixSD4dW+Snoyh2JVMVdj9zhOnBQWBbYWtUqlU3kSqWPuUsmgcQz8yxIR9zLj2IklhSCXwezVGlmMiMWJRRMw06B4xoiRG7s2Eh31ps+askIWjNZxFwRpltwWnwv5MMTYSU+Tz+xcXFzxZe5DiZG9MGfzfbzOdS5QK07mHnDfCblum9RtrSFNUUWMN65iYeTtllTLdx09f96jAzBmWMbLTwuf3Gpz1jClzf62MyZA14STTWmHHG4asxFxyR+/OobHQOgMI1thiH+IzKQjxCRfZc9Go2w3R88WDux6+66Djc7szWucR2QiyzPmWb5j+XL6XMjzsy+aqMcJRP6IU4brfeez0nErZhFUV9tsykzemqQKYlD4kvDEk1SKsvWHMmZNh5GitfHF/TneD9IKDzrMMEdWydfy6xdrmnZBpExZg7h1jP5KB/fb2c08rlUrltqhi7VOKNcK9WUNSsHL1vNemq1gSBYqL/hASO97Sp8jcZcacMFnJOZMNtC7Tj4YhwukIy1jkRQPEJDgHCcGKYi0UARIoE147GJlzOAs8WJfh/3vzhtbB+XRXYJPZKY8JTaEc56ZScm7rcG6TMfMlIuvBakQ1sttkPlg0OBsBy5ASfVTurwU7CdKDRtltLZ/xJfqpdbDfNnTe4yaLEmuUOzPhfi+lDcnkZQfoJJs6k6fXLSSFs2DYc8JuC+/PHV867CbrkCIszwUdlOrbxny3/N3xEMhqaSchtdc6TsfImLlwv1LZOugarIl4IzzsA0PKU3xWpBWwtmSFtp0jICzHnpMxklJm0Xg+f0GsrUNkSEprL4+guvmm8ctlr3GcbCqH0+elsYb3pk3iOq9WqVTeZKpY+xQjIjzFQxYoYu2gdRyNEWcth51nZi1jzngVOhdxGaIE9tsSZeSAtSbORqGfYs+htDSHrHgVhlD2Se/OLz5bT7l3w7zpmPkEdNNM2KaatBF1m/H8TbOxVOeMCIezljEmvDVTG3ATMl+kk5FSGVv40rZUhdOxZ4yGmVeEMgwfszCqwRtlGZT3F8qd+Yy5nyPipueX6dgGGuvwtuOLuz1fz0oehVFl27AUYO5LC3SMYI3igVUUdhroGjdV08rCwnk1bfMIGxFXhGrMmTFZWncuilrnaKwlZKWPGUHKkL8plcZVTHyyKqLl7qylz0prDB+tejpr2Gs9ITk65xhCYjUWYXjSBz632yEiDClz1EesFdZBMWKm6uLVjCmTcr7VbNGb4IzhziVWH1WkVSqVt4Eq1ipPIQKBzsMHzpLV0K6F4yFijbA/g32vdFY5CSXDcreDPpR7Ho8KqdhPbIbi+wgtym5ThOCj13ilzGUJRaRBESb99P0iHFSVEI9JqhhpaJyfBF0HFO8190hbd2ObYclZ+c7ZGb95PHLUC6KJz+4qrSsLEPTKBzuehbcsvOVkTIhYhiSsU8Pp2NC65rFh+U0bVFk0HRnLnXlP5yN9VIZUjtwbRadToVLm4UQgqrKOwkG7qZxFYjacjQklsPBzGttyLtbKa1PdRIxtWq5lqcCIgJZt327a8txpHGPKnA2RhKIZvn3WoyirmOhD4k7XMCZlHTNzD3NveBigxeCtISk4gZRKUoU3hqxFhD0ri3YVyjJH554dCVapVCqVR6lirfIUNhUsEBmwYjmclYvz6ZhpvGBMh3NrdsSA5m181X4Ld2cwnEG40I5zgHWlBdhZJaqhvVAhU80c9yNnI8xc4nCWMKbMsm2OZYiBPvScjsoyDux7y25nmfmANQ3n5r0bNkP8ZSPw4XpgPSbGXFrA9wfwozJ3GWcGQlryXXs7OCOcjGXLc7exeOvwRra5oo8+fpkna52hsS2tMdxfj4w508fMkBKWzHdOBTHgLeRc7D8EmcLtOyCV7NR1iQATgYfrwN15izMeVS1bqRe88c7GwBAVpDRbdzvPKpaWsTMGoczxWSmiTUQ46cfJWqMIvdZY+pTwxtJZS1Jlp2v4gBLptdO6bUu8cQYJcVogKF8/i73WTTYxVahVKpXKTalirfIUNi2+TWUs40ziM7uZu7lFVfFGERnpzOS7lpTOQRhgrwVEOe7hJMg2BVMpUUrawpiExQVxNUThG8eR42GkT8KdduTLn2lw0xqoauRs6Pn6qXI8CMshM3eZ7z0c2WsHDub7GMmUvcqLIkJZhzX3VwNHq8RZVNIkkoYoBDGMWdhvIafMOiU+s9Px/iQwnCktU+EqwbFpRQZEhN2uGO9+vA54W8RV0o57CzjuR1ahPD/AXODzuzuIlDZdUkPSfso5tQy5hLgrmYfrkaxF/My9o3HCg9M4DfHbMoM2BkIqiwIpBxprShi8MThj6JwhZ8eD9TjVG4XDuUdVeG/RsmhKNW7ROHI+n+3aLAk4YzhoGx6shyesVK7CbFR8pVKpVG5MFWuVp9BwbhfRUeaoFHB4oxSBEsnqyWpxpsWaEUnKrlgaP3LQCp/YSDqdxt2lzGu5BrKAN1CqamUcfzkKHy8TZdRfOYuZ75xGPr/fIVLanQ/XhuWQibHc8yTCt5dlu7FxK3baBeWjXapLIY5866TnW2cDp4PiLbS2JBKEVDI18yRIswoHzpNyaQdeXF549kajByxZR4YYCTmx1ziiOgyWqCCdcNh5vnWyYh0UZ8qs2pgyH60GDluPNR4hsRxL69hKWQhZh4RSIqXOxsjcO+bO0bkwtUFTEZTZMPeGZUgYgbMxctA5YsqcDpE+lgD79+YNq7jxt7OTW5uWdqcRWiOsbRFZIecp67UQckbEoALrmNhtzoWxqnLUB5Iqd2bNa98ErVQqlbedKtYqT0EoFaoNShl+39hsOGK2HPcRRZl7z9yDsx5nI/MpXmneZvqUub8uw+45KzHCaQ/eJEJK7M/mGPFYBt5fKA8G2G/LjJuijFFpfakDBY2sI0Q937ZcBxgcnPaRnXYADKpw1Ec+Wa741qnQj8IoCc1CSNBIWSAYkzJzRcClbJm3DmcNqxBZ+JvNWIUE3z7LxcfNOERh0XgOL3iepZzZaT3LMXLUB06Gkb/7YEV7suLzOzN+8L19nLEc94EsmT1vcSI0RlhqMSaeT7No3hgWjStzcEAfEzNrCFnZa0q0lEipkq1C4u68zKXdmzfc6Twf9yNDyGSUHe+JKjxcj9ybN4xZcbacx5AzHedizZmNuJ3eyxywVib7FPjwbCCjzLxl8ZqWCl4EVWUdEpk3w3qkUql8unn7fopWXiMNxT5i4/vVM0ZHyL5EH4XE3M/ZZH9C+fPce77nYIWVzJDAGJgXfcFHS3jYG75vf+QLhx2NM+zPYEygWWkNdNagWGABDOy3kW9JxFzwEEtaZGSZjitB8adD5rfOAkMoDvYrMlYVa4SkZdsThFEhBYhZ+OxOh7fl4hwU8mRtch1US35pzMU892xIHHT+wlZqaWfGXIyIM+Xx15PiVIQPlwN3Zj1jzhzOSnD9yRB4sB5pnOGw8wgQUua4D+y2jjuzhtMxklJZWkCERPG5a6xl0Xh2fWmRno6R/abEf52MCScG3xiy6jZyaoiJrMWHbOhHhOLRdpHOWe7OhAfrkft94GwINM5w0DUcdg0zZ0oI/VsqcpYhcTrEbVXxsIbGVyqV10gVa5UbsBmk31hpQOMEE4QhWnaa9sL3PRubDRHlYD5j4XvGnHiwnqpiucy1nY6Zb68N7+0aZk3DQU40e5F1ALA41+HtPpslgw8WgU9WI984OT+uMSrr4Mg4UrJY27KO65LPqRadNkkThnVQlE3rEwxCY4sA2Wn8eWTVDXVGnsTdbmM5G4t1xswXmxOA1Rg5GSOqypDzFPGUSalIzJyVIHA6RJw13F+NOAs5C8krqzFhTWa/9ZyOperTOMPM2ZJGkEvUlTFCDOBEWUzpA5u5u84ahpRJQ0QpIeunQ2BM5fHstLhgBKwxvP8UHzIjUjzdUmadMquUMWLYbRyf3ZuhygVD4reLTbqBMUJMT48Oq1QqlZdNFWuV52Bj2Ko447g3d2TlsVzO8wzLUo0zeK9oWLHbFGPZddgkAIBM4/Yiyk7nmGlXYrDEYWV3akMp0JIlsGiUz84jywifjMVF7eN1qQp9Z7niu/eKx9gQTXl+ObeXSNvXcP5qHKXdtdOVfxIpK04M5gqtoZPTvzXns1pGpqzSMTBzlu/enyEi2zbgaYj47aJEpp98PKZCFwaYW5g3lqhKjJmc4N6smc6FMITECSW5ASlt0A3WCAtv+fbZgKIsJiPY1m1SHCavkClkPlNsOFYh0jjDakx0znB31m3F2eMirbzu8lqNQJ6MWXLO02ZpyXNtHs8Fe8tYeMeYSpX04A0y961UKp9OqlirPCcXB+95TNRENr5mRYJYNtYczkVsGDG2CIajobRE782U1i+BBpF2ap/p9DyZUqUDVc/JEElY2kY5GgLn1hzFZkNz5usnicMODjvhYe84aCxDUIbHXoUBdrzh/Z2W3abBG1N83HLm4LEQ8zGVwPukyvE6ELKy3zo+s1PEzTjlht6btYSU2Wubkm+aMsspMHxMpZ7XeU/IIw9WmZnAMmVU4SzD/3D/mLuLDmcsfUx883QNCJ2zxJTZyw4xwo53rMY42WqU49xpHDtNxBnhbEz0qWSefrDTTvYZSh8jIQtjLHHz3pbtThHZ5nleRsyZh30oQtaURIQP5h2frEZ2Goczhg922jJD95bja7pBpVJ5g3j7f6pW3kA2odib+k1JJYCMEc/ObE3MZ/gInQdroHMgkoh5Tc4lucCaZgpuH9lEz6/jwGo0eJMZVTlJm+eJZMrXZkg0tsxr3Z0pu23idDB8btdzMiaGmDEI3sHcefan4f9F48iqhJSZO7s1lIVSMXu4LpWWD5drhpS507Xc70f2u2YaQi+twSKAyjLFWYicjQkjpZ3WTPFMfYysQ6ZzmW/3kEil4qfCKsDJUc93LxrWWRmzcqcrmaUopa2aDXMHq5iJGsos2wVR8VtnA6LQOSlVuqwkVYaUcdNMnlAC5/10tzyFnG+Et063B2it4aSPZFW8NWWzdAwcdg3zxoLOcEauLWxU9Y0XQW/68VUqlU8PVaxVbonz7M1HlxAcFwPMwSHi8dbjbTi/t0KYvsyMhKwMo2XuLXNRWB6XbUfb4l3H0Ur5cDVMCZznWZkAR1GYRTCScMZwt4WFt7RuhlC2FWPOHA+BVSjGrp21pJxRKTmSs8e2QBUlaOZkiFs3/pXLxJzRKbDdG8OdWTM59Zc24HJMtNZsZ8Ziziy8ZTmGkrOaIJG3qaeb15GAozFwOG85GiJBM40pcV/OlrkyZw0OGGPaNpxXofy5s0Ify2PZqft5NpTzsVl2aJ2lDxGLbCt++1OVTlX5aDlwNsbyuuYNQypxVZvnWqiD7tFW7HXoQ+J4DHTWst/VFmOlUqk8iyrWKrdEolTANqJpE/q5aVFuyJwbyJ4T4/mfBWhMQskcn2Xk7/xturQu7UntWP7Qb8eLIFt99vgAuNKjmLFUofogfHHfcGcGpcJXxMh+61lNbU0jpeI1c2ZKTHgUK0JrLctxoHGWxhicwE4ZuNvSWLOd19pUqh6npBDA/dXIaixC8NFXD5BIWVDKUoAoHHSO3dZjRFmFUvGKeYqtmu7Vx0xrLe3CcjZG+pCYe4szhqT5CWFljGHmzXaubtNOXYfEwz4gqqxTwvUQVAlJaaxhTJlg8qUVspSVMecS+yVCmsyEjZTKWz+1fNcxsaeuVrAqlUrlGVSxVrklNvmbmxrRAMyuuO3kVTEGOF0WsWIsNB6alpQNUWGdhNOPjznLlsP5XfZSYP3xA9KDI/y9QxZWWMVEfEKsnSclRMq26f94FPBW2GmLE/86Jn7j/gkPhmIk21jBGliPEQN8sJhxb7fDoLQu0VjDjkssXGQVha5xHHQeZwx9DGSNdM7h7aMmuovGbtugOZdNzj5mvnG05H4ft0e7mdDbbNruWGG3LcJvt3V01nJv3gBKVMEZZYjlcQ+7Zit4rIExKd4Y9hpHZw3zaYastcVTzl/Y0MyqNMY84SM25ow38NEqMubE3Bpm3tN0hpRKbujZGPnm8aoc3yT2HMpHq2LSG7UY7o45M0Slc8KdWcvCl5b2puJYqVQqlafzSsWaiPxZ4H9OMcz6DvDvqOp/OH3vfw/8ceALwDeA/5Oq/qev8vgqNyPmzCokvClVKbYtybIper4NCuch7bH8lwJ860MQJd89BGvRlFmeDgx+Ro6Zj4YRo5HOWBThvm053TvkYUicfnyKc4o3ic4VR9h1kAvtRBgzxFEZJBJT5H980BO1x+L4aDUyaklQ0AQPH3tt949W+KMl+22pFEHxe5u3grNw3BfPssNWuDs3xGw4HUf228UjTv873uGNIWbFG09jha89POPBas2BV1ZhU48scq3kOMDdJrNoG+7MGtK08HA2RD5Zj4jAbus47Jqt4NlsaS68JaTAMGWDLhq7rabtNK7klU5LBEmVzplLNzdbWwScMyBTkLu3xe6kc4bTYeTBOmCkBMJ/sDNDUR6ue476iGAwUkx1Z9Nz9NGwiglj4OAd9y1TVZYhMebM3rR8UalUKs/Lq66s/V+B/6OqDiLyg8Avi8h/RxFufxb4fcB/DvxvgP+niHxJVT96xcdYuQaqyoN1iWhfThYWjZ1TKmobn7XH25/Fdw1GePAJnJ3CFz6PGFMeJ1vy/ft0uwtWpsWe9ORun55j7HrFmXE8aDruuwaDQozsz84brXOnHA/gRVgniCgLJ1OkVakinQ4Dx2EkcT5In41CfvyfQglMfziATJFPoypjhs4L3gburwRRYeFb9meOkCJJI/aC07+IPLKooKqswsiihbNwMWirCNs8hT6tYmJHIh+vepaxVKFWJtInZe4tgtDHROcsqsrJEFnHhBWmObCy4HCx7emM4d6spY+pLBZYobWGsxC3oe673tJ5R+cs7y9arCnpn5/ZmSECR31gCJGTPrAaRlaxiMSQAiJwOigqEFNmpzVlvi8rHywcC+9orGGIyu67rdVIqpyOESMyxX294y+4Uqm8VF6pWFPVv3nxy+m/75uO40hV/z/T9/4zEVlO36ti7Q1FmeaV8qYNKZQM0YsVNS58b/OWA8sRTldwdIy5s0d0M5rf/BryS/81+f4R6bNfYnz/izixyBe/xNDOOBLL/dkuMPl7mTJE3zlopNT0PpiDZrBjCYtvrTJzwjqUlAMsaCh5CF/YUfY7iBl+8ySwDEKRTlLEIKXidfHVDFkhwswru14JCfo00ATDzEnxP5vuUWwyElEVL0LrytLCvIGPVkrKwvnEmn3kHH0cDB8/UD47G5m1DTEnWrGTmbCiqpjpOZIq61hatSEXM9699vLB/c3W64bTMbAcEt5N8VxD5GASmIvGs3jMY+zevOV0CHy0GlgFpZ/+/mGvOFv884wBZ6EPRXqOKbP2hvfn3VYkvusYKZ+FmJXW2mffoVKpVJ7CK59ZE5F/D/hpykDTfwf8vykJ4b8uIv874D8D/reUEs2vXvEYfwT4IwBf/OIXX/5BV55AplmpZYjMnZ9Eyva7l9zDUN7yqfKmAsdLeD/BySkpLPH/wZ8jxcS4HDCfrLjL3+bkf/YPoR9/xIPf+WPcPx4mYVgMdzd2u5phMS+JCJ3NdEfHfPb4hG/5HWj2CMHgjCGKKbcxcNgVY1lvFKfwxQX8+hFs5unKYLxsq3aq5T8jZah/TNC4TMwlU3SnyaXiJWF6jIaToWxPGhGyKrtaWpGfXcz4u5+scEaJaWN08vgiRvn62+vEFyQyazzeWd7zYK0lZWUZI31KJaEAiKrkrDeKeFqHItSMlC2FrMV/rXNXC4yzMW4tjzdEwOdJ6k55pEOG1pbqXkzKmBNWhN1PgcmsEeHurClxZVc5K1cqlco1eeViTVV/VkT+eeB3Ab8bGFQ1icifBn6BUpoZgZ9S1eUVj/HzwM8DfOUrX6lZMK+Jsvl4k/aOZRsM3+7AqPDwDIY19mvfho/vY967h/ZnGLdkLgb/N/8GZz/8I7gPv8V874CTvgynW0qFaZbO+NzyPqN5H7c7473f/A26//qr6Le+zb1vfcT4v/pJhh/+IVbvvc+HeQcvghqYTdmkVsD4Mt+2EBhUiVMLUVWR6c9usr9oBJIBUIwoVgyahdaV220WLFLuWaeyQbqZKTubguG7ZsYHC8//7zRxHkW/4XHRVsRR1zjeW7R0zrIaI30urdFNu22/8/Qx4Zxl5h8XWhtpZZ54bHnsa+WJmzyBUt771pWYLwVaU+b/ZBInzlqEyKJxeDHstJbDtsFbg6IMMdO6VzPHFXOZL9xr/CsVTiJy7VzZSqVSeRqvZRtUVRPwl0XkDwD/rIj8LeDfoYi3vwb8T4C/ICL/a1X966/jGCv/f/b+NEiyNT/vw37vdpbMrL26735nxWAuZoABhtjJISGAEoOig7Rph23YlsO0wwrTS0j+Qsr0Iips2g6Jlh0hWVTQ+mA7JIdkUgwDgi2SNgkJogCSM8AA4GAZYDBz75279lJrLuecd/n7w3uytq7qruqu7tvd9/wiKro6Myvr5KmqzCf/y/NcM6GFgztgLKytwI1X4Z9agzs78B/9PPrrv0m6vQNvvo27cZNYV9jS4DfXSOOa1X/0y9z5yk9TUB9Z5Nap4eVJ4sbLN0hdR3SG7jvvUP7+t7Fvv4OsTah/7/eox47xP/4q6dXP8sHLnyFM1pkHxVoJhYaFF/YWiq0xvDZRzBvFLCkWUej6gIRJAVpbZj4wjzlaK2DwKfudGZVIYvs5uN5fTiJwXoVK8dnNLQ7bO9zu7i9YCmB7UvLiuGSlz/g85Dgg3ShFlFy9qqqLqmFdf0zVPcczKQx7bcBolSOtJM/E3Y+1wrLoIiulxelEQtgsHdpo9pqOorcasYXFakXpDC9OanSfObpMVtg2xRPcBh1U08DAwLPLR23dYclzaQXwSyLytf7yryql/hHwx4Ff/4iObeA6+dbvwe6d7C3xvW/A2gTsTai24Ht+iOJf/2sc/viPY3/3m8hrLyOjVQ6iQt5+j6ooWT28w+t+h/nkFYoICuHVUnB1wQeNIaqCooP1QtO8dJPJO+/g3n0P+a3fof3uh7jplFde/xajN76f/VdeJ5Yj9ItbfLCyyuLWIUWheGGt4oVZC2/eQSI0X/gsfrRKaQsQxX7r2W8DtmlpQvY3e3EEm7Vm4T1tBGcMY5dNa2trmIfUt0ETK8VxysCkcnz5lW3+YGfKt/abC0/bJ1Ydm6OKzVF59LWl0Rx2AZQmJsHofP9tl0PcT26I5q85Kb6Wlbx8X7XLArAJCa048mS7iJASKBgVmqKzlA5u1gWTIufDbtUFB12ksAot2UKEPorr7qKjspq1wjJyT85fzWrNRjVsYw4MDDy7PDGxppS6Cfw08AvkGbU/Dvxs/7EP/EtKqR8UkV9XSv0Q8BXg33pSxzfwmNk9gFENB1NYRFit4Ne+Dh9+CDdfQG/dZPIHf8Di1Zfx2zdJUbBvvkv57rus/Movob/vM2z/B3Pe+PQb/N6P/xSMalJRM5NIh4UE+10ifvp7eOHWHfzqBPfNb6OCp110VOMJ+vYuaytvsva1r6FvbKFCS9jaIgXB/P63qW7egP/SfxHKdVTbUn/9m9Q/9uU85AZsaMdKqblRFxwGg1HCZt0ifZxTaRNdBB8rCgurpaUwufJllaU6U7EaF471uuTVEPlg5gn3njXeOfBMwwynFNvj7BE37u+nCQmjFEoJb+/NsUZTWIPTebQvAbUxrJQWrSzHFTZF/tPPs2PVmWit81hunM5DRJGXNZzJge0bdcEiJJIIL63UrPjArMvzabuLDqMV63VORmhCpDSJkXvwU4+PiTbmxIfBj21gYODjzEOJNaXUD5MrYr8gIjOl1Jg8e3be680SAf488G+Tu1hvAf+iiPx8f59/GfibSqkXgNvA/1ZE/u7DHN/AU8gbX4Df+i1YuwnbN7KauH07rw4aA3/+f4D53a8zuf0h1Xtv0o0n6A++g9m5S/z8Z4ivv4quK9a//g/5wdkB3/ozP8t39hIvrlqU5I3Ow0Yw5QbqS3+IzdU10nfewd3dwb77AbMvf4laJ1TX5U2El27AL38V6z38oS/h9vfg3ffha1+Hn/gKUELTwJvvwxc/DyScAWdKaqdZP7G52XhPTBaFwqdjYaGUpXbHIiOkRBv6rM1eHDmteGFS04XEnTYepapmhAUK1XjePZyjlcIYRUw5fH5kNXfmDVMfmXcJYxTrBeynyNaohCR8MG24PVfcHCvGDoy2/XF7csXtchWnhc/LEsuqXYnBKEUbc4pBFo35tiuFY+wsTYjsd3kqzseU47F0/prRJXYMmhiZ+UhtzTMx+xVSrqKeNRgeGBgYeFSuJNZ6IfVzwI+Sn/G/B/g28K8DDfAvXPS1InIb+GP3uf7fBP7NqxzPwDPE+jr84T98+rIvfxlu3YJPfRK++Bn4lU/BwbvYyiHf+D34yg/D2++iNtZJrSf6AD/yQ9iv/SYbB7c4qLc5aPKywKyDmDQzU9CMb7LzE5/AVDfY+to/4ubt9/GzObP9Q1hfZbw+Qf3BW7BoIQi8/S6sjsG+DKEDFUFZKEs4OETE0sbEPHhSaii0pi4MTufqVGGFSIePMHa6byPmWCvIL+J3Zg0fzlqS5OzRSemOKkbOGlyMjG5/yHR1DcqqP0F5d2aeFAsf+XDWUDuLU7DbpD4LNOWAdyJtUHx73rBVF7TBcthFdmbZpPb2LLFVFXxyfUKxNJ472uc8z2rlmKXBa3EmcaByFmeFWUjcGJWnvkYrhVE5xsrHvARhlGLkzFFu6oOYOEtt7bUsBfiUiElyBNZjMKg9bDt2G09tLdujJzmLNzAw8HHgqpW1/yPwIbAFvH3i8r8B/BvXdVADHxNu3swfRGABf/QH4eBl+O672A/vEj75CiDoV19Gx0Dcn6O6Bn/jBikEXl+B9+fCIubqTWkiIcHCjGgPFcVnv592tELxnW+wefd9mrdvIbOWRAWrFiZjcBashekU9g/gx74EawUEA3GObN5gt1mw8B5nBK01ixiZzzvWS03lSrQaY1RJK0JhLEotg+wXJIGdObw3XfDdg46IMNHC990omcmIkGAWIlUKtId7TCerZ05SFlLTJjDtIpXJ+ZoBKLWi6wIhelIUOlPkhNbYcmfhMZLY7Wvdt+dQm479rmPblnk3VKn+OFtyhe1YYJ4limDV+Zmpy+raWYFSGEWhNYdtCyi6lKidpraXe9pRSmGvQfMsfGCvDUePbKPOyQ/XRZI8jzfz8Z7t2oGBgYHr4Kpi7WeAnxGR3TNPzH8ADIZnAw+JJguGBqqSsGgJpcO8+jJMZxAjdB365jry/m1mr75G2t5icwRBQeeF2w1EZWli9kMLgChD+MynmX3xdQo1Z1RZVBD49nexJPhHX4Pbuyhn8FpDjITv/R5kcwzvHVKqxOGNCfvNXQQhRENpLYWuEWPYa4UbxmO0xqc8jzV2kAtHLZDbf01s2Z93RCKlFjpRvH3Q8LktwzxYQLFxc5s4GnE47fqvXJ6XLKGyuawwj71tCbmJmaYHRGuRRQOTCcYVLBI0KZ3xQVPsd8IrCPtNRxsNIxdZKTXLPNL7YVQWJWdbfFHygsN5lSSlFBt1tss47Dy10azXJfacStm0C8x8wGnNWnm9FhvTLlcFtcomtdM2UI6uL1FAAWOX2+CbH1FVLUn+aQ8t2IGB55OrirWaPKV8lhvAxSttAwMPpB98N4YwWcF8/+dhdQTf/73wu38AzqHmDY1y3PnRH0asQ5HNbcUFCgu35g6joEl9loKFIIppsnR6lc2YzXP1Fz9PUFBOavQ/+BqUFpQmra5B06CcpnlhjeblbRqJFAqmAQ5DwDWeyfRDxvMAr3+SNpaMtGfiSipjzhUiWsW+YCUoBTEpupBtfZczYF2MrIxKgoJbh93RH9mmg5dWa751d8FJVzYDdMGDcaANTFaOzmL+TmdRWOXQqman8YSU2G0WfFKPqFwJ5Fk4n+I9AetKKcbOst+FU9eJCD4m1sqLn0a0UqxXjvUqD6mlfhnj5HnyMTHtAs5ouiTMusBqdb3GuXLis+sWU0optkflR2aA2/SVQ4C10vY5vQMDA88TV/2r/iVy+sBf6v8vKvd8/iLw967xuAY+ViwTMg2YRBxPUAe76MMFrE7gh38A5jn5oLUrdHOHE01IKRvVOssLBWzWws4cPpySbT8Emg4WFgrXv5vooDAQIujXXqP84Qbe/pB44wby+c9ittfAOKoRLAKYmJ34NfT5oIrDusZO72L29kjjF8gCQHCnYoVKwPf5m46tOrDrISYojLBVayprSaJYdZGQhCYYtuuS2phsxKs1us/mLG2Oz4Is1jySbVDk3kzT5a0ssF3nma/1qqC2msPG8+50gVGa0mp2G89LrgZgv+tYdJG1yjHpI6lCSjQh5RB7a1iE5TZpDsqaOEP9gE3SJTEJdxd5bm+9cqc3UCXf6zLI/jpZKSx7bd62VSjW6usXM+cZ4CaRo9SLx1ltO+wi1mgUcNAFKjtszw4MPG9c9VnrLwD/qVLqR8ivRv8H4AvAGvCH7/eFAwMXE8i/ih4A+8ImfmWEtQbjbH4FX8vh5HW0bKUs0LYnQpQEyQAChZBESBJhkV8oPdAF0GVeAnW2nyaLeVuz+NxnUZ9+DbG5upRfcX0vzhxJ54VVUs69tAqs1bkO6Bzm6EXxbC1LAyVawWbt0EpRuZb9RWSldLy+PqF2BU2Yk8h5mc4IUGKNZtZXmlLKvmovTEYwm+d80wSzpEDp3HMNvYpzFkgUCB0a0KyVlvW6pLKWu03HrA3UxmKsIqXEXhN4YZzQWuO0ojPqVFzVrIvsd4GtyrFWOcbJ0PUbrYXVp4b1fUrsNx4B1kpHcWYuLCHEPrYrpePzZbWidqYPoldMivuLv4XP83ubdXGpSlblDNv9Fq3V6glUv4R55/nW7pyFD7wwrvjE+vixCSijFT718Wvq/Jb0wMDAs82VxJqI/LZS6vvJFhwt2RL9bwD/ZxF5/zEc38Bzz4lw975io62lHE/oJ8/yVZ2HpqUsLDcRbLWKVg7dxZyCMJtDNWZjY5upVay4gBcweVGSKFmstTFHRvkIi+gZ73xAaRT6hRt4ZdF9tU4SGDxKO6YpV7Ws7luQAmb7JnptPUcmNQv4zndAW/jMZ/LCwgmMNmyN1tga3fvoV0rNXhNJKr/oRkk4pXhhXBKSEFMWRmulQasRMx/YXXiONjgV4JYtwxwrJQgFkQ7NnUWHaE3ZBWYhUjtDiNClRBaUQkKhyZYbkzNmtcs0g6XViNUaW5w/nH/Y5MxQjWKv6bhxwsgXcsrCepnNc096zimlWKscK5IzTh8kNrRSWHNsFXIZrNY8mXQrAVr22zn7TWDmE9NuxsaoZK18PJmoa6Vj2oVc6fwY5K4ODHwcuXI/QEQ+AP7lx3AsAx9LlhNYS/GQq1e5VmQhdXnJ4BvfBB8gJoq1CTpFuLEFrQfr4M4evLeD045P/MzP8GbluD3zKAHRcNDAKyt5jg1gtQSFo3npRXTqUCGQvCcWFm2zq2wZAx0RSRVTn491vYJxVaHUiI26j5f61rfg1gE0bQ4Q/dznzjzGQJ4mM5z9k6tszVatmflIF7OgWK0chz4i5IzNmIRXVmvmned3704fcC57iZsEbRIHnRBlwUrhqIxBoWhjIEjCqux7trPocEaxVrp7BtSd0axdcnPyZMrpRYG995unuuxwvDOa9TNzdU8XuVKqVP510Jeo5CURDttAFxO1M1cyAjY6i92BgYHnlweKNaXUH73snYnILz3a4Qx8PDlZGQLVNlhjCDj48A4cHII1MBqhv/0m+u/9Mmyvw8s34Ss/BtM5fOO3YecACQatHC//6f8Kijnf3jlgXAqrlTALuaW5WubvmAAfNbEoMAoq5dHzOcxbjDWoJmLfvsXLP/mH2F5RJFG96amhslXfTkuwP4eqzka/3dn9m0jeyVH955rTRrQKZypWtbC38HQx4WMkSjoSNjFFggjWGEbOIGJpZv6c/c2cTCCA1sJWJRglhBDZXyQ6qzHWsQgJaxQ+RTZHDqVyGoLT8WhW7WFYK/NsWJLEeukei5iad4GDLmBUtuB4HJ5pj4YCKlZLxyfWWvbbwGppWXlA3urMBxYh4ozmsPM4rSmvw7dkYGDgueAyz8z/CaddM0/3rE6/ib7cpPHAwCk0edE4t5CYzlEiuBtbyPu3Cb/zLVgbgVKYv/8r8O234Z/9mf7mfeXtu+/hP7hD2tom/NrXCX/0jzOqRrxx0zJddKyVedC7tnksLQpogcJoRCm8CME4koHKOOytBZBQPlHeXVC+usVx7chx3L518L3fB7/xm1DX8OlPn3lscuIxRi6qOTUh0qVEaQ1tiPiUMDEdtfqUQBMCtbOUzjBxlt/bW1w4jO90QiM0UVFYxWEHB1Eo2o7SaibWMRoZRBStz+kHlxnlamMixIQz+p6ZNGf0Pea4kKtGMx9ICcZ9uPtFxCQkBHvB7NXM51k+H4U2pAtbsh8tisJYXlk1vCT59+1BwjUm6YPuFaBIF9YmBwYGPo5cRqzdOPH5jwF/FfgrwK/0l/0EeTv0L1zvoQ18vFhmVZawsgptAx/eQb3+Eu5Xvgp/4xfyzd77ABYNbK7Bze2swNZW8D/6JegCemMN++3vcmv3He6MXyREYaMEZ2Clt9aKCSQljO2H3UUh0s9/FZbdxrBWRkZBQzWB3X149RVOBqAfba+ic3zWz/zMBY/LcHFV7ZhleNWS9TIfrCAU1nB73jDzgUJr1qqCVDgqq3h/2nLYRRbp9NeLQO0EF2EWs00I5Bqfk4SzOWh+5Awh5krYg/JB5z6w3wS0VqQusH5Jm4i5j0y7iFaK2Hg2L/A4a0Jkr19QqKxhvbS9v1yOcXJ9+sA8RETAqKfboiJXYS9325GztAtPmyJWZTPhgYGBgSUPfLYTkbvLz5VS/2vgXxCR/++Jm3xbKXUL+FeB//f1H+LAx4PIkVhrdrJ6mU5za/GPfwV+8Evwi/85/K138xbkV38D/pk/ksPhD6fw+c/A7V3SKy8hb3yO8M4+3W+/y3T9BnubG3Sx5FMbWajNQrbCWAF8ypYLid7M1in0OLJIlroDxQRUQa78PQy5LXZcnD7/1buyhiom2hAptKZymsM2MHGW3UXLd/bmBBFUSry6LlTGUBYFr647JCb2W8+Hs4YQ+3k/0dyaQlLQRn3q+/oII5vNZ7skrNfFpUTXzEec1RiliEmY+0R9iVEp6aWiUty3YjTzEaPzHN3MB6wiVxmFo/zRJELbh8nvdx5ndF7MSNnD7lk1hS2MZntU5KQIPeSLDgwMnOaqb02/D3jnnMvfBT7/6Icz8PFlKWYqOFjAWg1FmataaxvwhS+DXYG3PoCf+9vw9W/An/gK/PbvQlGj/uGv4n/tdwg//oeY/6k/ibcFr/69v407OGT2I19m/hM/xk5xg1FpszFthJAErfKWaBJw7Rz57d9H39rFrqzQfv6TVEpgveZ4QeBhuFikHd1CKTaq4ii2qQkxz6+ZxK15i1IJlQCt2W8iplIsQiKkxErpeGFS0V+Nj4m7845ZPP94NRBEKIxmtbzXyFdE6FLCx4RRisqaI4kVY8JYQxKhuGS6+thZYsp2HSv3MdC1SjELkYXP4jPGiE+wNSppQuCwC2yNSqII0y5SS26xLrrItMtxUht1cU979lnBaIUZ4qoGBgbO4api7beAf1kp9edEZAGglKqB/1V/3cDAQ7L0WdMwvglvfQvaObzyCrxwE5SDH/oB+Omfybf56q/DX/rX4AfegFdewE7nNKurmNt3CN95G9cJ1d276K011rsp5WKPxV3H6osT2uioXcIHwQfDnlfcLDzqW9/BlxVqdQX9D/4xBze2mb3yEvXYUcsdlLrBRW3MR0FE6GI6FZReWcPNcdm3RxW1LehUNs/1MSBYVkrLfuMpjCZK6meeYKMumXYRH+O5cSPWKuY+sdd6trXCmfw0MG09e41nHkJuFZMrVSNrKIzKIilEnA+sFJZJcbnIpmWKwYMYOcPdRcsiJGIUFBoh0oRIjLniJL3IHLu80OCMZqfpcEb31b74zIq1R0FE8L13nbsg/mtgYODZ5api7c8DvwC8q5T6zf6y7yeXHf7UdR7YwMeN5a9igM0tWJ+AMuRyUt5yZLwBX/wi3N2B1U346tfg7/3n8Kd+GhDKyRhvLJO7HzKbgt7ZxYxLYoyEgwVbr4FqWja6fRajFYItcBbWU6IgkHwgVSV2aw2+9AVqZ+gM7H24z7ResLpRULl1HlQluypRhP0usF66U9Wq5abjjVHBu4cNXUi5IqYNbUx4gdXSslUX7Cw6tsqC3a4jRmG7LujCgu7MyujEwnpVMO8C7x3MmS4jpEjsNJF5G0gqoUWxWZfMU2TaeV5fHVE6S2Xz9zZa3+PY/6gEEWpnWa80h61nHiOvrdT9koViZ95ya94ytoYXJiVFnxjhdG4ZK4Sxu77Mz6eFpa1HGyOVMayUp73wpA+SX4SEAerCsn7mNgMDA882VzXF/apS6tPAf5Pjtue/B/w/RGR23Qc38HFiuWXZm2poOLft+P1fgPkUfv878IUvwEsvwT/5Jrx0E3drB/s5g33rbTZ//feY37iJ2bpB871v4LbW0AeH1MkSRNF8uAub64xWS6qJsOgK0vYLNIcNKMt4a4P52gZNtOhCqCSwN9thY2wo3Qr3F2zLTdEHtz8hi7Kt6mI3/pcmFWNnOWw9bUqsV46dxhNToqoKDn2gS9LPdWnGhYEONusSmg7bJwckFCNnaEPgoIsoH+miUDvDzEdiCMxDrm8KMPdzamdw1iAIKQnzkKt7bYiMrKa6xhzKPKclCNmPbaVQjHsrkb2mw1nDtjP4lI4WJkSkbx0LKH10+fPE3EcWIVcMZz5ijWLUn3cRYafxfDBrCCERRVBK89pqzfYDQuUbH2hiwmrNyJlhTm5g4CnmYUxxZ8BffwzHMjDQs9y0PDbKBaBtYX8HvvD98PnfhjffhH/6n4Z2Af+/vw3tFHUww/7j32T1d75Fub2Nj1/CrVSo7Z9A3WnRv/EW9tYO9c2XMG98Brf2KRSKiGa2eRNXTtnba9l55XtwZY1ToCpNUInVSmg++BblXMP3fomLhVjH8cLE5cxK7xeBpLVmvS5Yr4+rRmtVQeMjH84aJOXh/dppRpi8AEAOTB9ZA1pRKJh5obaGvTaQBCZOIQrakEgpMQvLwK/MLIH4yAThu/sL7sxaWsk/mbUSlLS8vrGOVseiepklWpxj7XGWJMK0DQSEFWcptGKlcNmeQ+lTYe4h5SgloxWhF6b5PvLs4UpZ5Pk1H5gUliTLpYbj89rFdFSdcs9QqzQlQfdWJlorTiR19ZmyESS3qZuYKAzsNB21Mxf65jU+sNsErFE0IeBTYqN6/qqSAwPPC1cSa0qpP3u/60Xkbz3a4QwMLHHkRLOl5QXwta/C7h7cfBV+9mfht74Bi0OghP/WfwP+zt+F995Hj1cp/uhX0Ldvw+/9Pja0mO98G/v51+Gr30CXBZP/wksok1CzKaytUDtFxGJHG0zW4P0ZrApYKzgVSaJQWlFsr5D+yR+g02dBTy449oerUPiYsA+YN2pCZL/NpqnzLrDXhKPZss26YK0yzNpAaXLeZhMVPmaD3e2RRmtLSg1aEglNFxKblcPLvXuaihxiH9pE17ZHl0eE261isdNh1CEvrq72SxuJO4tsvWGV4sa4vK8InfvIPESMVuz28VSTwp4rMMZOs9+GLNq0OhKCuo/pamNCRKiMZr/pmPuE0rBeWCqXxdvuogOlWPjA9qh6ZipJtTPMQ6QNEa2gOpmbpbKILQwsvAJRjK1BowjpIhc+ckXNqKMYrjZEksgzc04GBj5uXLWy9jcvuHz5PD+Y4g5cEzkIPYu1Pqg8KlBljp26cQP+2B+F2U6+7WgEN2/C3/w5GNfwxTewZc3k3/v3mX3tV8FoePMtePs9+PIX0HsHOfi8D0FXwKRQpAji4KUx7Hc5KqhLjtL0Ge820X7yVWq9rJ6d9yvvyH9al3/hi0k47LLbvb2vWEuIwN6iozCaldIiAiHl4bRlVQkFpbG0MeeFvjiuqJ3JFSklrHSWAx8xwEphKLXCh8hhOP5ey2buvUsKOU1+ETXvzDzoBkS4s+jYaTylzjYU26PivudA+qcNdQkT2NrlZYIoeYB+KSqWW7SLEGlDYNYFDnyg0IrKWr57sGCjLlgtHAIYElZ3HP+Mnn6WZsNBslnwSQHstGbsDG0wlCbh+jlHaxSVufjp2GpNEwJW94a86ronMQcGBq6Tq86sneodKKUs8EPAvwb8z6/xuAYGODaR7dthP/zjsLMDW1v5/0rBZERulwb4wS/Cl34kLybs3IVv/jbqv/xnmGytwS//CiDQNAD4tQm89TZUn4W11WyQC4jW1ApKAyMHd+bZl227htC70cfVFbJQ85wv1i43q3YSo/PG5IMqG+PC9A7/Dp8SSoSdRcDHyHbfJi20YiZCaQ3W5EWAus+aVEqxXhYsdGBrXNKFxEZl83B/YXl7f84sJNTyzGvYD2ePIj++2H8u5IrfTtNhew+2JjzYg3/sbB9WL5eKp7Jan/uEZbSiNJrDDqzVFEljlObuvKWwOe1gESLrlaMJgZGrTrVur8LSWuVJcz9bj0lh0bqf75P8vmTsLMV9TI5H/ezfslq3Ud1/vm1gYOCj5ZHeWopIAL6qlPpLwF8DvnQtRzUwcB6jUf44Yun7v1QTlqN8ps0t+ImvwO0PYH8PDmfw2/8EVsfw/oe4775H+uzrxLaFnT3Y3kTpiFOxvw/DwiuaqKiNIAJNF6iIFOXjCc2+TAvKac1mleez9hrPe9M5bYzUzvLetGVrVFI5y6pINrHV2fz25AvxamkxOs87TWpH7WzfQjS8vFrT+UibcrxVksR3dmbs+JPS6/i+VgqD04qqcnw4awFBaUXpFFESMWQz2/NmxHRfFbuImPJjDJIYu3vboz6lPskgz2spoOhNdX3Mw/amLxlp3RsP9wJGRPpZLcnHbx8cnN74wF4XqI15qoLTZz5y2HlAMSkMK8WDj2157s+b7RsYGHj6uK4+wB7wmWu6r4GB+3AypjaRK1vLF6fIPUsJN16An/3n4PNvwF/538PvfAdu78IX3kZ/7tOws090LrvkanviewSUdhgt1A7aAEVKlLGlMA5c4qKcz8eNiOCjUFtNaSxSaipjWPiYHfBF8L3R75FoOSGwtFL3vKBHEfbbQBLBGc3WyOGjY6dp+dTmKvHu/qkKmwU+tVLyqbUJhc0D+wp4d9bglObGqGKvOf6CtUtGU53ksAsEkWzN0VuMOKOR3spiHo59Scb9lusiKpSovKlq8uO0Jm87niQLnIBRuQq5IjwwxL7pzecWMbIqT481RpfyRqdC0QWBK+wJDDNqAwPPBlddMPjy2YuAl4C/CHz9ug5qYOBeltNTyzmx4sTl51d9IIuVhMd936fR/9P/EWxvw9/6OfiFvw9vvov+6R9H/cDnkapEb27gnT26i9IGCmOIorBWKCSi7u4hH95GvfAS3KwBDb6D776PzBaEjW3cKy8fV/iu+yz0omrRRy6NbK7+NSGwWZeUxhxtCJbWEFJi2gU26/u/gh80ARQUxtCFyMznqKuJs0yJfHJjhZ1FS5cSI615bX3ESplD25cJCDcmNVvjCoBpl6tyS7Pa2SWjqU6S6Gep+nO5/Cl3MTELMfvD9RFU+62ni5DIgqoBXl2pmFxQBZ37QGE0WilsHzT/ILGWrw9UJ8yLnwZG1rDXZMOV9fskRAwMDDy7XPUv+2ucLm0s+YfAf/dajmhg4FwCx0JtWUFbVtVOtEHR/XWRwzZyd9EwdnMKrZh85UcwP/D98DN/BL79bfj9b8Hf/c9QMeUX35dfwL7yEkEpCIFyPOKlUb53ExP2d/8AXa+iUPDBLdjcgHYH3v8A9me0c2H/zg5b00Ps5x9P+lqULMSOQ9cLNmpFYc2RiFD9yFzs58EK+2CbipyAkG+nlOrtQBQrfcB7W1hujEsKoymN5qAN7DZ59aC2x21BrXLKQBI52m5NIliTLz9yn7uE2Flxlt2mow2R2uZ2Kyxjwo63ZnU/J1c7Q2E0CSFEue/MVj7OfDBJLldhslqz/hD2FjHl8/GgTd+HJaddLLdjnx4ROTAwcH1cVax96sz/E3BbRJprOp6BgQtYCo6zrceTW33LGbaWmKALC2oTsFqTpCPERFytmP2zfwr95ncYzQ9wv/dN+I3fhZ//Rfipn0AlcC/d6DdFIzYFmM2QD++S3r2Nmb6Vg93/2I/Ch+/BbAHzGaxPKLZK1vc9ZucDCK+DLbnuBWl1wgx2+cJfGnNkkgq50rVWWGY+UVlz3zzOJWNn2W8Dqt8KrE60DV3fflySRI5MWpVSLEJkRSwKWPjAfuN5b9pw2HbUzvLq6pj1qmCv9XQh5Xmp2h0lNFyEM5rtUYkIpypsRitSkqNTm0QwOrdhjVZoUaBzrul5tCFfN/MxCyhypujjoIuJ3YVHyDFZG9WDFykehkGkDQw831xVrH0C+OV+seCIfiv0J0Xkl67tyAYGTqHJv65Ls9mTL/RnX6hyyUQrwZlETInSCBDZmy+40yn0C6+w4dfYeH2b4qd+LEdZjWt470P4j38ZvMCNm6iihkWL2rmLfvEGjEcwb+HWXdgY5/8DWI1eqyknJXzHk9/HtFzFGPcyGK0YF4ZZt9zFBNVnZsZeRNk+fL0wmt1FwzfvznBas1WXrJbnJyXUzhBlmR5gjiwgzkORBWHOopQjwXPQBg5azzv7M3aagNEQJdLGSOuzWW5l8xbiQfvg1iz0IuTM4ZZGUzlD28+sCbBZOxA49IGUsheZnNMEmHeB/dajtUaRTYJHzuJjooupT3iIxH6GrfHZf2y5TXtVZj6gNVid79cnORUpNjAwMHAZrirWfpE8o3brzOVr/XWDz9rAY0JxuclpBZQYHShMQaLFKt/nWEq2vRBQFtRkgi8m5DzyMn9tsmBGsH8Iv/F78KUvwcaNHCT/wqv5W+zvwe1b8NLns09CsQIpEhUkreC1m2itMWhyE/VqnmsPYqXIrckuJA66HL5eW00XcwxTEmGSEm8fLHhnf04b8x/marHg01sr3BxX92xnTn1g6rP4KbTG3afopZRivSqYdfk927jIw/ZNPA5RF8DopfVKzv08alty2oX/qmT7EUvXx2Cd3DZ1VnN33tEmwS86tkflqarT1AcKm6OVrFZ0MaGIzPoZwJAETW43iwh7nSf1reT7+d9dhFGKNqXe4CRXCAcGBgauylXF2rLPdJYtYMgGHXhKMIBhXFp8nCPiSQJd6KfcHFgFtYbi6MWzAwooC3jlBfjsp2G+gO//IdjYgq9+NXu8jUawaGBllVzCKcAp/GKRo4/KikXhiPN9Rm7EuNBotZyvO4nnWMhdvfLmtCZqAckmqT4JSaAwii7B3blnZ94R4/F32+kSdn9OYQw3xuXpo4lZ9PiUBWASobpPXqTV6mhOLSRhp+lofQItjArLoY+klKgLjSFnWfq2O6qGrZeWJkQOGs+kMIzObKd2MTHzAasVE3fv5qVSitLee2wp7xdQakUbpZ9HO/11y1k1kfz/sJwnIz/+7bpgr/HsLDpWrEX1KQEPw6T3PvOSh/8f1PodGBgYOI9LiTWl1M/3nwrw7yql2hNXG+CLwC9f87ENDDwiCmcK9ps2t580WAurBmIQFkkxPtJQedaNVyZgXodpgje+ACsVMIUf/CzyO99GZh36C1+Agz34xX8EP/oGrK+SypJYjkkCFdCmjiYIYJkUGqX6zdEj0omPh6PoLSl8SqyVjiZEZj7HNy1bgEtzi+W7LBFh1gWMVqyW7miTc6Ww3J4t2GvzMH9IwigJ65fwEztoPT4JpdOEmPjM5oRJ4ehiOjLmdUaxVWcXfqPylu6tacPeosMYxcsrNePCUfbWHHtNl6t1PmWx94BNzeNzoqispg2JiTNHj2/JWunYWXSE/rQvW7F7TUdCWCvdUcYmIkzKfEwPi1bqVMbpwMDAwMNw2cra3f5fBewCixPXdcA/AP4v13hcAwPXgCImxyI4ChNIJFKMdEEIKHRSNF4zOllkMsAra8Co//BAS6OF+MZrdKmEu3Mm77yP+9QXYZYQ17CoN1ApV3EEKHT2vwpJ9XNKJzJOgVxNUzyK1aFW6pQ5qzM6tyTJQ/erpWHaeRZp2RzO1hyjwuCTcNB6Nusi24E0He8eNuw1gdJqNussWiqbB/eLE3YVMQkHXY58Xy3cUfVKoVEGJoVjXka01mhylQyOXfhFhA8XnsPW8/5hQyvw7sGCG5Xj9c0VNuuCkISskbLsjEmY9r5rtdEXzpAt46cuShoo+uimKHIUDA9wo/8lWH7NemmJktMgBgYGBj5qLvVKISJ/DkAp9SbwV0VkaHkOPBOkvuellCb6hCBYa6g1JLLLvgSNOvWXkMhdfcGHwEHj2esSPsJmGXF6we7GOtsFaFehtCbJcchUPx4H5Mvjsu92Cs2V3Evv8/iSHHudLduWG1VBinn+arcLKIFXJhWFMziTlwliPzjWxsSteUcbE7XTdH1ygFEaqzSo7OW1FIaHXaCLcvT5WmnZbTyJxHrva7ZaOvZbT+gH9ZfHJSLstYGdpuXNvQW+fxyzCLOZp5MDPrkxIcbEYRuxxlCZwN15i9Ga0hp2G0+URGnzD82eyApdcr9lgPOim87e/qoGvgMDAwOPk6tmg/4rj+tABgaun4BWGqgQ6VApEI3CKXAGmigohCgee8/cmJAS3J55DrvEYZtnnPabxHpRUm7WHCw6ivmCUgzllmGRIqovnsVksMYRRfXLDcsFasP5ywY5HJ2jVM4H08XEbtMhkh38V04YwBZGc2NSsTEqUKijDcS9NtD0w/RrvaVH6rc6jTZYDYREoTVGZwsP6bdMl679miy6IFfUnNHcHJenqlnLbdR8m+PHG5LQhkipzZFQO8lBE7g9a7k5rqgLy4ezhu+2PkdnGcNaaUgi3G06rAJrcvVu4iyi8izfRnX+xuupsy1C6MXq4/I/e57pYjpVmRwYGHi8PFCsKaV+E/hjIrKrlPon3CdjR0R+4DoPbmDg0TAYramspgkllesgRWZeSL1jmVIXSSNNiJH9Los2raGNEBLse6Ba55AZGzFRb73Mel3Rtp4oHq0cRjuiuCxmtOY4gaEADElaIPWB4obcbl1W4E56x13M3Ad0/4I585GRs6dePK1WWH16sWG9tITCoDl+oXVWM3GOmY80Mc+IrZaGlaLAxxzIflLQTEp7JErHJypQ6kT1DC72/sqZncf2xidJalkNzZFaPuQZNxS0wXMreFZKS0gQkCNheWs2Y1JYRs4SorBeuxPGwfdy0KdAQDb1XS3tqeMPfZt08C+7F5HcQi+tvlQO6cDAwKNzmcraf0g2jFp+/tEEIg4MXBkNZG8xkcAigmMPgLnPsmliElqd94KT1wWnXuN0RFSue5UWfMittGprhe7mJkVRYXTBSlmy6DwhgSiDUY5JAYJi7hMhJpRqmbeeINkOYmQjo0LQquiPV8jC7aIK3DFGKxYhIQqMupwthFIKd0aAOK15caWidoZFCNTWsF4VKLWMjRIUisPOI0noouBMTjc4K2a6mPphfVgr7s0DXYTAoou0MbFewN3u+DoNjIwmJdhdBAqrUTpbgBQitCkRAe+z1cYiJpIo1voZvFvTBq0Vn1qriQgblWPkLDFJFuUnZu6Wpr75mCKTwmJUFiI7C49PCaMVm5eo0n3cWM4FDqdlYODJ8UCxdrL1KSJ/+bEezcDAtaPRCtYrTUiJKBtMiBgVMdqh1VIY5UWCkxYb1lWMZ3e4haGsNEuniLqA9QImRW7prRa5aub0Cq6akCQP1GsVgYZpl1h46GLgD3ZaugijUtguFWZs0d4yKpbzaxe549zLxFkU2c1/VDycaauIZK8xpXo7j9OWHiuF4+6iRciGs7enLVESKcHLqzWvrtanBNt+69FKYxTsd4HKHh9XtuNIbNSOII7V0vFa8Ow2PrekBZzT3JyUWG3oYuBmXfDu4Yx5CFgEqxJ7DRxEEAwgLBYnPbqFt3ZnrNUl+wvPam3z3B0nK2jLWx6zvCwkwae8xdqESEgJowf7yLMMAnZg4Mly1SD3vw/8WRHZO3P5KvD/EpGfvsZjGxi4NpRSOAOOgiyIIsceZ8tGqIJeAIBFH3a89I1vcGfrNfZfvsG6EzZG+daTIlfZxkXEma7/Gg1M+tZmAgIilplvaL3i9syz3ypWy8CqExbRoOhy2LxU/Xwd/f08+MVQKfXA8PEHsX+iHbhe3lsJCymRBEqraXx/W5VXXm/PWtYry6Q4rrBlqZn93+7JlRDJE3laU5ArgZPCcGNlROPz/N1aaShsrnS2AQ6jZ+YDjY84k+hinh20Crycf552AuwftrwySsx95PX1EVYr5iHbmkwKy3pp2e9NfdfK4wUIo1U2sg0RzdLYd9maHWKdBgYGPhqu+kz/U5y/wlYBX3nkoxkYeKxY8qB/IguipX2GZym0sogLQAWjMfXnPsfroxWmY8ei9WidWCtAFPiUFxV8jLRBISyAiNUTCmP7UHVBxLGIQhv7769A+k3RlBRJCyKaPAi2jNV6MjQxUhpNEOm3QU9fr5U68mfT/XJBTDmYPabEB7OOVZ+OqlZr/RZokuxZptRxgPvSEDaknA8aUmLSB8WHFBCBW7OOkcstyL2mY+4Dsw6C9AFe6XLOdBGYes+41Ow3HWNniQh35i1G9eHn/UzbyYqkVorNusCn1M/8ZeG213qk32x9VIE8MDAwcFUua4r75RP//QGl1M6J/xvgTwDvXueBDQxcP5bjKtqyKrPM7vQci7YKKMAqyu97g3q2oFnss14ZJoWmsAGfIEbFbhTaLjEuFJVTaJU49A2ogvWqwGrYrITDrqGwBiEQk0YkMXaKwuaKXG4rlfce8mNmbA0zn7dDq3NEiDOalcIy7dMEXppUTENg1gWaCIeLjnnrKZ1BUbFaFWyfMK5rQmT/hNDZrAumbSCKsFY4nFbsLDp2mw6jFE1INLEDNFbD/iLQCGTPvLMN4vtXuRYJVIiUwdDFjiYkVivHXutZA0YX2HMYrU61Pg+7gFEarfMMX23N0AYcGBh4olz2LeLX6A3Qgb97zvUL4H9yXQc1MPB4WLY5z8Nx3ALNQq7xgf0u8Pb+gnmA0iiaqKidIiaNT4n9NrfIZJptQF5fg3FhMLrlsG1ZLWsK6/j0+oh5V/LCuOTWbIrGs1EDYhmXy4rfeX5sj5dJYamcISZwFwiQSWEZu3zeksC09bx1uKCMkUVIKNF0KfCh7litThfe91uP1dlYd9oFtkclm6N8m8ZH3tqbs995QhLG1iJKGJm81drFmGfJes86ObI2yf/PLJ+WOHM5rBbZ2LZLiZQSXUis1RarDU1IjC65yKgEvGSriuUG8cDAwMCT5LJi7VPkZ8lvAz8K3D5xXQfcEpGzW/gDA88Yxy/00y6wu+j47sGcgzbgI1QWmghFpxGyjUcTFD7lF3SU8Ps7gdfXPJujEoWiiy21lrwtWjlWKsdra2NCWgCL3lpjWfG7vFjLZrjyyFmTSimaLjDzy7k1R+XuFbTLVqFRMCkdk6bjdhf775/bhXn2Ti411yUivHe44N3DRW5KqywEHYrV2rJRW27NPePC0zWxd6k7+3Ql3CvWjqmdJcTArcOmr87B3E+5Ofa8tFoz9wGr9dFW6HkkEZISDpt8BC+tlKe2SuchEBNUVt/XKmRgYGDgUbhsgsFb/adDCvHAc88y2minaWl8YuwMUwnZZ02gTYqoICSFJkcXLOeouqS4NY9UVlgpDYsgVDaizoS5W131n53aSbz0Me4uOrok3BgVjyTYYhJmPttYJHLL7zyxdhKjFdujkp2FZ+EThQWUYqW09zyC5Qzbsg26TFrwMbHf+n7z0pJSH0nlDClvIfD62ohF5zno4lGW5/mcrbblhrKIsNuEI6EGMI9w0HQ4Y0iSv642CpSmNOqeBYuZz2Jse1wSUqIJwqTIIm5n0Wa/PgWLJrJRCtWQfDAwMPAYuPIzi1LKkqtrr3Nm2UBE/u/XdFwDA0+YpRrIA+U+JqZtICFoZRg7Q5OAFPFofIJEouo7p6n38dIKWp99vFZKi0iuGJkzYi0LjIrjZAPLVcRaNtpNj7ydqBR9lmg2q72oFXqW1dLxxvYKt2ZNn4Wq2R6V99iHVNbkcHZO+5zttQFB8uKAj5RW56QFrRg5Q+MTK4Xj1bURIcFh5zlo45Hh42mEs+1ta+hbqffe2otgtKI0uZX9waxlc1Sy8IJWmtLmy2MSGn98jrXKSxWQbUiC5IoagEKYhcTZzPaYlhXQZyslIYkgy8zXZ+i4BwaeV65q3fF54D/iuC269D9YmlQNYm3gGaQBDoEWmsThB8Jb08hdU5K0pvEdo8KwWjh8UnSLgCHXcYzqp80SFFYQUShZDlqpPoZ8OQd3luWCw9VZPasKHpJlGPy0izijWL2CI/24sLxux0etz/sN3S+TEAqjWYRIEuHV1TFGaw4bz8hp6I8lCdS9CKqs4cZKxYov2A6Bu7MFt7plqayPNjhDBZRac9gGKgP+hGBLQOMhxoRPib0mW4PUXZ5jsxrGyfD+rCWlLOoUirqwSEqnIr1Od1+Fs2YlIsJO0xGSsFbaCxcaPirmXWDmA87kTd6lKPV9jFn+OZxOdxgYGPhouOqzx/8J+FXgB4EP+n/XgL8G/C+u8bgGBp4MoYOv/2ewWcELm3itMdPbNL8/oxyt0L7yKp0roYuUNgsxa0GRUOTYJJWg62euRKC2xxFTViW0yjNpuY2WUEBpzVFL8KOmsuah563OC0U/iYic8nJzWuUZMaVwRvPq6ohulNgeFYSUaGO27Rj1x1M5y7bWxJQwqqKwmrg359AnluEHy7WRCJQKVkqDKMVBG1Api7fmxO0EuDtfsNN4DpqAAnZmLTcnFW0w7Cw6pj6i+hbnxFkKBaOqOGoRF0bjTK7CLu1J1s76nvSPP/vOPdTpfWz4mNjvAoXRNCGiVa6WAv38Ys6TXYTIuLBHhtADAwMfDVcVaz9CzgmdqTyEY0Xk15RSfwH4N4AhG3Tg2eLnfg6++9vwR76MNxrWVxm/sknl19nfmTN+89t0L7xIqkco5RgXmtpq2pjoQm4jGgulwMxn37SVkWKt0ohEKmtRqqSLwu6iQ/oKxbSLbNTuvsPtTxNnI5su/XWS7TuWYrAJkdoqDDn9AWC9sjijcUZjdZ5lm/nI2BkmzlIaDUYTkxASjKsCYyJN8HkpwWi01nQxf40Wza1Fx8lcg1qBMYYgkRRh1sHixC2aCH6/IcQsrqzRiMqbr433GAXFCUG79GNrfMwxZP3xn0QpxWZdEpNQmIdTOz5lcf+oiyRnWe7V6j7/dGn6CzkHdhGywFSXjDEbGBh4vFxVrClg3n9+G3gF+CbwDvDZazyugYEnw6//JtQdzBagdkESoZxQGZiKYNsWOThATQ+YrH6aVzdXMGrGe9PInbkhJiGm3PZUKF6clNwY1wQxGAWlKwHFQduhtT6qpoWUmHaBzfo8j+mni8POM+8iQrbDGF3BFHb5Oi8iR600qxVbo5KQBK2OhYiIsNd0oDSFVky7iFV56N/H1Ec/Qe00k8LidEVMkVkXMVoTo0GM5qA5LdQAFgK1JFK82FS3AT6ctYwMtBG85PkOByRZYE3eel0Kz7TMK025HX5WrC0f68NWUBc+sN/mR7JZF9cq7It+Zq/tq2pjd1wVXNq0hJQYuXvzXwcGBp48VxVr3wC+RLbw+MfAX1RKReC/D3zrmo9tYODx833fB//Xvw4/8YNwdxecJc4i3bTCNTNMNyeUN7mxd4fPlIpRrVBKUznLehnZbSEmTe00o36YPpJwOmdfLl/oljmTSuUWn9YKny7jxf/RElJi1h2Hnh92gfIKprC6TwuY+4BWitoaCqNRSt1TbRJyK3l5uVYq+6TFxN1FRxciC7+Mu0qslgWldrSpQSSb+r66WvOdHWG3jfd0HrsoR+YotYbunNPvJackRPLHcvpw2gZ8jCz6KmHeNPX9EL5ivw05cP4aBVUbU1/1yufgOu9bKcV65YjLZIoTguw6YswGBgaul6v+Rf4VYNx//r8EfgH4ReAO8F+7xuMaGHgy/Mk/Cf/P/wD+5n+M/YE3CLMF5u6cmzc/QbOzh9k7pHSRFzfWGW2u94ZqeXvxpVXDi5LwydIFh5ArP05bnFlWzISQPD56vIDVhoPWM3KW0QMsMp4Glt2xk7FRlyWkxO4iR08pFCuFYeQuHlZX5OpU18+BJREKo+l60aL6JQalVL4NEETYKF1u6ynokjAuHSUNzTnfw5JnCl8c19RNx+02stw/cP1mbFQ5ezRXzPLjbhPcXXRsj7PlynJ7tjTLKlT+/1mWW6WF0VeuUI2dpYvdqWredaKUwg5Vs4GBZ4IriTUR+TsnPv8D4A2l1CawK3LOM9XAwNPO+jr8hb8If/PfR/3a7+KqCnfzRSYvvET47IukT/8oVhvM5mvgHMdWGwIElDIUxlGYcX/ZckF6iSemjkkRmXuDF8HHxLjIvmNJ8uxWkjz3ZB+pepL641Nc1QrkIqxWlNbQhJC3Iq9QVTvoQt4AtYYo2c/tfhuRy2rP3EdCEkbOHFWT9ltPEyJdyJmdK6VDRIjAqMgCMC9w5IH4z2yP+fCwZREjIQjKgCELwJXSsT4qmFSWeuHZbzoOu4T0ewB139L0XUKpHD4vAkbU0aKAVmCV6oVkf67OCB/fVwQRKK1m44otb2c0N86xQxkYGPj48UCxppT6+cvcUf/O+08/+iENDDxhfvRHYW0N3nsXtMCnX0GtT3BrK6AMvRlEf+Nl0HokTzNJf93yvcrZjcCE1gatIiuloouWaIStuiAhvLs3Z7+NKITCal5eqVgtH3aObelCtrQKefRqjFKK9dLinekH3S8vHGJMmH4ezfQtzQdlNJy8bqlRtAJJghJwRiG9H1sElCRmXb58ESJWCV4U61XJ2FgWMfDuYZM3HJUwcpaXJhUJmHthvbSMreGg7biz8CgF66WlsI42NHjJYm1cKMrCsNcGnNGMnGWjKph1AS+JUWHumVmLfSWysJoupVNze5dlEGoDAwNwucra3cd+FAMDHyXG5Nm17/scuTq1NHiAfmfuxI2X3mjLeKjln1Bz4rqTf1YWp7ucgtDlqtRamZ38b81a3p+2x4axCj44bBg5e+3bf4/CefNll6FyhlkbsSbblhRGP7DWt/CRwzagFcx9YKsu6ELisPMsQkIrjVH5vtsoOK1pQgLJ26pGG2IUrILNlYp5CIjAIkTamNiuC4wxlNaggYMu8upqzafdCu8czLk1bTDWYI3m05tjuphoU8SIYlw4Cg17Tc47dVrhjOKDg+yltl0Hbk7qo8dSmDzD5lNira/+xSRHyQ2jftt1EGQDAwMP4oFiTUT+3JM4kIGBj5ZE3v1b1n4uModdJg94jqtsDcfi7ezUugUMlYPKKeY+cNDljzuLlpAS48ISEgSELsJh61kp3UMItvLEcX30Ym/iLApFGxMja5hcwlw1AV2MuV0cssFESIndxjMuLIjk2TDJ1TqrNc7kzcyFDxxGnz3cVG4rS4LVqmBF5eD49cLSplwtq51Fa8XI5eD4ymo+sTEmJeGgC8y7gLUGQ/65TbtAm3J7MkSYlIY7s4b9NmI0fPcgsVIV1P18mVaKrbo4VVE77Dw+ZZE57SKF1pRDpujAwMADGFZ+BgaAq813aaCE+Rx++zfyxPrnP5MN1y5MKsjD5vttOJrDSqmfMku5itd5IWphp1EcdIHaGrbq8tIzYkfH9ZSw3CqcnHNdEmHaBtqYbTdWSovTOg/zx4RSsFE7SmtISVFbzW7ToSVXJpMIWoQm5K3QWRfY7wKSEntzzy0N1ihuTuq8pJAEjaI1edi/jQkfIkYrdhYtoFj4SGEV7+8vuNt4Fr3Nh6avpWqonUEkceA9xkCXEiC0MRslzzp/JNZOnoclIQmmX5ZQnL+UMDAwMHCWQawNDADHFbMHTVWd4M034e5dCB5eeBm2t7lfRSulYyNSoLdOyOHgSfLGY2kU+03oN/UCXUy8slI/d62ywz7VwBlNlGwYvF4WzHw8J2NUsVYW+CBYA6t1wXf25iSEot/GbH2EEHl/kRdAFhGIwu7OnE+slmzUJaulo4uJ0iqcGPaS0MXIfhvYqBySWt7Z6/hwnghHph054L4FTILURZzuN0FJLA1CaqMJ/RLF1pnZtC4mogiF1oydYb8NhJRn8ZbbpAMDAwP3YxBrAwNHnJ81eSHr6/DWW1BWMJrwoNaj1TnovYu5VVpazee3V9mdd9xdtIydZepbaqsRDDElDttAW6ejmKPnAZEcdL/0W9MqxzbNfRZaZ4VpG3NKwHrtuL3wHOzOmPu8QWu0Z69pqIzhzuLe1PYE3Jq21M4yLiwiYI3B975lSbIA7GKiiYGdeSScmldc1tZ67zWBFKF2sNMmuhhJkpiUlhvjmsJkX7TliN/CB/aaAP326NaoYEtngeq0vkLVdGBg4OPMINYGBh6Wl1/OW6TWQvng9qPROaJo3mVRMSoMVmu2xiVK90P8qgEC005jjAYRwn1aZTHljUOjnq3NQaVO1zCXcffnPQbd39YYA+SA9aY/JSaB78Cre01wlyTygkHjI5u1o+wXHaZdXj7wUQgxMWsT/qiidj7LJ8y5T0Cki0KQhPUJi+C0ORXP1MTUJx/ktIDQe65dNBE5MDAwcB6DWBsYOJfso3bsWXYB4/HF152D1ZrV6nQFzhwJFIUzJdMu5Bd4BfRbh8A91g/TLjDrvcyc1qxX7pmo1CilWC0se232bhMRKmco9HHV8SSFMUwKCFHYGpV8t5vl+zl1G4UkYXHmyxVZFG+UBc7oU+sfAhiEjcpwa9ahTEHIDU9ObwMfU1kIMc+eZYEpFErjk+AFXqiKUz+jUmuaEIiS/djMMySoBwYGnh4GsTYwcC4dHHnbK67Ds+wiTO9Q38TEyJVoZfFJEJHexkPx3b05t+YNhdZ8amNEYS2HbaC0uZXYxpw1ulY9GzUbozVOQxcSdWFYKwxCzgM9K0qVUlTW0qnEpLCEEJnvNUTo80o1n14f8960ZRQ8sy6b5dYGVgrHxrhkXObz4qMcZZA6rXGFZq/pWC0M391bcCzOTouqvNObG6KV0YgSfOorayknHrTek1ICffy7UjuDUrl9Wtn7tz2bEOlitvR4mqxbBgYGPnoGsTYwcC7ZNuJ+LbHrZLW0SOOP8iDLPjR8oyr44GDBN3cP0ShEYB4Cn7+xijrR+nRa9ZuJTz9NiOw2HqMUzmpanzgkslY5xoXlsAun4pmkbwUrhEnh+OT6BIVm5iOlUby2VuOswZosjHxKbNUFpTEces+iyxubIUm2/6B30+t1UxTBp8Rdf/7xvlgp6qJk4gzj0mYrjxDZnXfc8i2BnHDw1v6CKIrPb6+ge7Gl+iD6B5FE2Gt8jtKKwvboYY2RBwYGnkcGsTYwcC5P1rNMK8VGXRxlSWYPsWzxcKdtsy9YYQkhMfNCTP2cV1+F8lGo7ZOrxrTheEkipsTcxz6kXh8JlYuYdfGoHajIDv+LEBkny7hPSpj60Mc/CQdtYOwML04qDrs8e7YxKnjFGSprjuKgSqsJSVgtHbU1+CSsF45KJ7qUmCxvrxQrznBr3uGTMHGG9w4vUGrA1rhmrSoxCrZHJW1MpIWnKhKV9zRtQpOrZ4ddoI1CfcUfhSJXWEMSymsMbB8YGHg+GMTawMC5XNGz7OAgLxqMRo/0XV3fHjxJbSwiHT4kgiScVke5mdMub1AWJnuVPQlCEnYWHQClFr5xZ8rCR1YLy6trI15dvb/ViE+RRUiElFMH1vp4rSSC1ZpxYaldFlup90ebFDnVoYsdRS/KjMreaG1MCDBxho3KolW2BtnvAlu1Y1RYzv5UBEWpNWOnSUlYLc5vcys4Mq7NCwKpz7QQZj4Q+hm7RA53d0Zx2bE06eOoliH1m1VBEKF4BuYOL4uIEOXZW4AZGHjaGMTawMCj8sEH8Gu/loPef/Inr7x08CA+sT4+Mn11SvE9GyOq3nh15AwieWPySb0Y5rgnjQi8uTvjoM2uZPudp1y03JxUlPeJpyqNYbcJjJ2liwmfEkadzh1dtoIxee5ryUphOWjDUXrBzHtKaxARZiEy6WOdSqsZk6tuHxzMmIfIRuXYGOU4qJASzmqs1gSV2BgV3Jg13G5Pt5I3HVibbVS6GPlgFgkRrBLG1iCVwy88PkJtNaXNxr4hpaPq4cJH5iGhNaz2olNE2G08XcxzeJMipyiYS1jHxCS0IVJa81QvlCQRdheeLiUqa1i/RILFwMDA+QxibWDgUVkssuNt14G/uJ32sJRW86UX1/BJMFqdGj5fZoo+SZTKs3Q+JkRJL7KyhxySqyn3Y6Ww7DWeqfcYFCKatdKhVd4Mzf5p5z+okbNHrc/Yf5skkqtyfYUK6IWM5u3dKd/cmdKmXCv90o2OT2yuURrN3Ht8Xxlbrxw/8OIGtw8WeKUgRYKAc4ZpG/AhzxLG3iNORFivHSulpTSGO/OGLiR+f2fB7+8sKBS8NC75xHrNIuXjCTFXJLdHJTEJXciCceYDk+LyT8V7jadNiSokNp/i2baY8ixg1VclU2F5iIjZgYEBBrE2MPDovPYaxAhVlX3XHgNaa8pHHGVa9LmkVmnWKneqkvUwGK3YLB1Nl6tjVik2K3cUp3UR1mg+sVaziLmlWJrjCtFev2SxVWerjfNYLh5YBWtlXkjQSt2zCZszRTvaPjkiAN/am/P6xiqVNTgT2G8Dq4WltobaGsy6Zt5FZt73AfE5M3RkNWjFtA0kEea9ZYpRiruLjrnPE45LGoHvTFven7a8uFbx6kqNM1m0xF50O6PwMTG+oAV7EVqDioJ+iqtqQP/GQtGESGU0T/nhDgw81TxRsaaU+neBnwHGwAfAvyoi/05/3Qj4q8B/lRzF9xsi8kef5PENDDwU1sJnP/tRH8V9CUnYb0OOd0rCQefZrB6tKqOV4pXVEdYaFl1gpXDcGJdHYuoiRAStNeNrsKeonb1w2zK3UvVRDgHk+TNY2oEYfMrzfvttQBBWC5eNhpVjUsLuomMeAoV1NF023q2tJSWIKRHJKQgX7eE2wLwLfDBtWC0dVuctV62yQfLJKmIX86KGVtzXvmO9dHhnj/z3nlaWjzGeqXoODAxcnSddWfvfAf89EWmVUp8H/hOl1NdF5FeBv94fzxvADvCDT/jYBgaeY3LPUJGFilyTy0dhDa+tXm6pQiRvdi5ixCrFelXcU91brxxzH5n5gImKsbPniD8he+Ddf1O3MJpPrI/Zbzt220RtFG/cWD0SDZO+pdr4yNRHFIoZWbQplTdMFz6AUviQKLRmHiJOaUbOsIiwVhU5/cC393nkisMuUFqD0+pInCmljtqCXUzcXXQYpRCyvclWXZ7bDlaqT7t4BlgK04EHE5KgFcP5GjiXJyrWROS3Tv63//iMUmoG/GngVRE56K//1Sd5bAMDzzNGKUbWMA8xb2A+YlXtYciD9pHS5G3Og8bfM3PVxpRbtVrRhIQPwkbtzlRlhGxarID6vt9zpSr4yddvElOeOTtpK6KUwilFp3K+p0ajtaIyhtm85aAJ/aZmno/bKvMSQIiJLuV0gnkX0FpxozbsLSLNme9fqZyusFUX1NYyumA2be7zY15W09oQaUO88PYDzxfTLuTkkj6SbhBsA2d54s8ESql/C/jvkJ9lvw78f4A/C7wF/CtKqX8OeB/4yyLyH15wH/888M8DvP7660/gqAcGHh/LiKUHzXqdR0hZ3MQkjJ1hdEFLUCnFauUYJ4v6iN69B8l2G0oprM7mtWfpYjra9LQ6i5aTwegZRZ6UuNxjUEphzflzYSEJsy4iolipTPZ5U4q1MrdDa1dgVPY/CwIKBUoxcpqJNSQFMQqH3lGYlp15npFzGtYKw0pV8sm1MWWR28/3+xmf3MuQ5cMc+FjQxZT9ElNelhnE2sBZnrj7ooj8D4EV4CvA3wJa4FXgi8A+8DLwPwb+b0qpNy64j78uIj8sIj9848aNJ3PgAwOPgTbk9tfdeZdbbldARNhddISYjXH3m3BkVnsRRn90banKGqIIXUy0MTGy9woo229cQt4mzG2hs7dairVHf68ZUiLSnxeO56oKkzNZl+IyiZBSQilYKR21c6A126OK9brAaYXSihcmFTcnJePCcnNS8+mNEZPK4rSmsubCcz92lrQ8NyFi+wiygY8HK0WeQVzprV0GBs7ykfxWiEgUkX9AFml/HliQl6n+NyLSich/Cvwi8M98FMc3MPCkiJJyRUVlU9WrfW3+cEZjlEJrdW616tER8p+n51Hitwqj2aoLRk6zXtp77Cry4oGiMLqvqCXWzwSjXzeF0ZRGU/bGt0ucVtTWcNh59uZ5Hm1UWIRsFRL7KiHk5YUs7gzK5OpcaQ03JiWj4t6sVumtRk7ijGZ7VDB2htXCslkNrbCPE85oNuviShYuAx8vPurfDAt8Bvj5c657MqGMAwMfIbU1xDL/qp80f70MRuUP3xuwppRw+nH8SS/FGuQ484cXEYXRF7YCl8kII2fZGJdHyxCPk+XG4llmPnLYBW7POxTCKEZGVjMpDHOf5+6WL6xZtCm2Ro7bs442CeuVZWQN+43vI600Vd+invrArIv32JNYrZkUQ1VlYGDgXp7YM4NS6qZS6r+ulJoopYxS6k8APwv8PeCXgLeB/5lSyiql/jDwTwF/50kd38DAR4FSipXCsVK4K1dSVJ8napUiJWGtdKeqQ9d4lGSR9mhCbUnoHfjPYrVipbSMnDmKYPooiEmYdoEuRpSA0xalNLuNZ6VwvDCu2KgKtIKDxjPtArXTGKW5MSp4bbXi0+sTmpgNdAH22zxXeMRQNBsYGLgCT7KyJuSW579NFolvAf+iiPw8gFLqzwD/DvAv9df9t0Xkd5/g8Q0MPHNYrc+tDF0viivlpD4AQc4tmy+F60fN0bGJkJSQJBFF3yOm25iY+thvcSpujEqE4yWKFHK4vVbZkHd5vyuFu8CSJLdIZ33e6dgeV+MGBgY+3jyxZwIRuQ38sftc/1vATzyp4xkYeB4JSfqw8ezFdZnqlIg80SrWeWH1TxMaQURoIrRd4G6XN1JHThFT4LW1CaY3t1UqV+JKo3tPtOPzmLNPu6P8z7PZp+fhk3DYBZzW7LWBm/dZShgYGPj4MLxtGxh4TuhiYnfhj+pWtTWs3ic8OyZhr/F4SdTm/reFbBA793mBYeye36pPFxMCbI8KfAzstzkn9NAL37w7A6X55PoErfIWn1Gcu7lpdPZXg8vP3i1vlkS4ikZrYw6ad0pTXXH28XHgYzpKZ3jUWLOBgYFBrA0MPDdMu4DWYLUBhEUIjJzBXeB2P+0CQeTImb+0+kK7iMYH9tpc8QHYawPr6vm0l5iHhDO5cnbo88xZYXLIfEywO/e8uircnXckyfmkF4mxq1YsndZslJYuCpV7cFUtJGGv6ThoPZW1KCIbyGMX0svIsi4mRs4wccfnYNYFDrtsQ6OAtco9l78nAwNPkkGsDQw8J6SlaSuh/0h9ja3ivIn2SPYxU0qdaydxkiYm7ImopEROGHgeX4SlP48i9OcHQsz5n4pst+JjPDKujde8uF45S3XO6F5I+QiWlaokwu6iZe4jPgkSImNn6JJQXesR3cthF+iiYLVm2sUjH7nlckZh9JE/3X7rc0br0M4dGHhoBrE2MPCcMHKa/cYjeKLkCpvViRzNdO+CwMRZvrs/Y+YTIsK089TWslZaVsqzEU9Z0C1ZCpnnkdpq9rtAaTQbVcG0DXT9daWG7ZFj6iOrhSGhGD2BtqOIsLPwaAXbo/yzjEmIksPsu+TxMdFpxXr5aE/rPiZmPmC1Pkp0OEtMeZFCq/z2IPWbrsvlkeXXLK970nORAwPPG4NYGxh4Thg5iyLQRqi0YWRNL6gieRfx9IvlzrzhsIvZo00S0zYRRTHtIjeTsFkfG9KOC0M7j0fzXAp5IiLlqlyHKKicoVlmczpDYQyFyT54K4VBa0MbEmulu3JEWBKhjTmntLzC1yqlWC3sqR+hVgoEokjOKY0BJMePBYGR1XQpi6fS3LvNeh4iwm6Tc1cXPmBUFoNnGbm8AOFT9vpbWsYYpQgxste0WK3ZX7TcbTy/qxSvrI74nq2VQbQNDDwEg1gbGHimWCYUnN48XFI7R+0S2RMNjkXa6dvuNx1v7TfEFOkkYpVQGUUThMpo7i4Ca5XGqtyPc1qzNSpoQspGHtY8VYPjR8sSKbdmV8vzrTEehI+JvabDx1w6rPrUhZXSorVmp+k4bANKa9arq4u1wzYw9xEUbNXFlb7+7OKA0QqnFbf7VqhB2B6PUUpx2AWmrSB95ctpzWZ9tlp6Pok+9ksJF3XGa5djkaIITuuj9vhBGzjoclv2w4MZd/3yDoTduzNijHzuxvrR7QcGBi7HU7xAPzAwcIyQY3Rn5HS2jvNDPjRZqEXyy64Ap33YpJ8jiinhRagM+AD7nWB1R+0SMXnm3Zzj5IKlw75lfMaG4iRJhCZEmhDvOwN33ex3PleYrGERYhZEVySkxLd2pnx7b86H85YYE6IU49Jlg1sfUSLULm+ARrl6tFdIkoWKcC3nJyCUVtPFxDQkdhcdWuXIroO+lVtag0/pUnFmSinWS4cgjKy572apM3lObSm8YhKmPuC0ojKGRbz3G7572HB30T30YxcRQrr/fOXAwPPIUFkbGHjqSWSR1pLF2PLP1nJcQVuiyOLsZAVu+Z5M+ss9WnkKE2k6aBF8AquFWiuCgEKzCMKKeJS6nFFtSDlYfhnErhRsVqcjlR4XMSZMv6lq+kSHq3J73nJn3lFaQxMSt+ctL6/UbFWORUi0MZJE975phtJcvQ28WloO20DpLo7dugpWKXbmngQYlYf916qE1rnN2sZcCbVaXXrGsLLmoRZHlAKDojCGg86fb3zcV+NiEvQFW8oX0fjAQReOROe4OL2FOjDwPDOItYGBp5oENOQKV+K4Yna/RIFlPNRZAuD7+SfDhwoUiSZkGWcUKKUREWpn0Fplr6xLvhbOupCPrH+hDymx3wa2R487YSG35Q67gO43EB/Ga2zR5ZwBRRZ8bUgsfASV5/e0KvqqTsIZ81BiyxnN5jWej8po1ivL3Ee6JBRWswiBwhhujArydGEWYI9D1MSUI7U0eaZvvbLstx6rC0SEd/YXNL240sBGZfPbhysei4+J3TZvmbp+e3nWRhRqCD8f+Fgw/JYPDDzVnGxnLj8C+U/3Ki94uaKWXzIVtXOslBarPbMmcLfVaFFEDGOrsBqcBq0uL3q63t5jidWaNsQnsgk4dgarIMj9w+Lvx8gZgsDUe7RSOCXcnrcUrWLeBW6MKlZKR0jZ1uNp2HA0RjNyjo26xKd0lH6wUdonYlq81wfVi2Rrk0lx/H1XSksUofGRLkSsMdwY16xX7sozawsfj7ZPIbdrndXMfbhwY/VJkPq2LCyrl0OVb+DxMIi1gYGnmiyu8r+OLLoMudV5lVbfybYoKCVUJtJ0kVElKJPYaRW/d6fjpYnhlZWyfxGM5PZrwYPEYWE0TUwUfSkupEShn4y/llLqkcXJ9qjkxcaz22SLDAQOWg9K9UHs+QxM+3m4ixIinqSIK42mcpo25C3dzbJgvXb3FQ1dzFYtxTV4nwXJBsIxcTqovj+2F8YVTudqbRRhe1Q+VFs8cm+ig4JLzeE9DpYZrrMunPorHFqzA4+LQawNDDzVaI5nzkz/UXIsvk4S+ssV91beTm+EWp0YOc3Mw16TTV83CrizUMx8zrw02vTfO3KRV9tJJoWlazrakMWMVrBynrvrU4o1hs9vrxJS4vas5a39eQ5pN5oYEzMf2Ws8lbMYrViESG0NWguavu3beA5DIiVhrbRsVAXWKPL5S2TBfX1Pu8uFgOCyRYfT98+DXfRJFJBboxuP+PNZLSz7XcCgGBenq7CF0dTOHv0+jN3FiykPojKavRCwJ3ReSHm54qMQRsut3uLE989ehZGUcmrDwMB1Moi1gYGnGgVHfvSRLNaWguxkhSKQBYE6cdvyxP9PLhnkr10rNbMO9lBoA4VWaA1Gg5DogvRO+przxeFpjFZs1wVdzO1ad0lvr6cJpRRaaYzWlC478msBYwxG5ZmzRQhooIvCXVoOG8+decfCexYhN5sLYKXQlMZgtDAqA2uFxVlLbceMi/tXv656zBdFip2ljXkb1SpFGyMij1YFqp09WkY4ez9ZSFpiL+KsfviFisoaqpBowtIXUPps1icvikJKzMNpoQb58ZZGswiRUTJH0WwDA9fBINYGBp56loLtZOXMcbpyFjlumS4XETpEEiEplHJYbclSIn+ds5btEey3QuMV+w2USlE6l+OWSP19CucvLJxzpEpR2qdboCURDttAFyMiOZRraTpb91Wz7NXmUJINg3MguWZv0dJFIYgwKQyLNvHO/gxRicMAqRfFLdB2ifyzSOiZUKrIC5PAjXFBkCxknnRVqLKatom0CJNrmvW6330olYXhdXyP9crRxUSXEqbPpf0o3gx0If8Nnve4lcqXtyHhikGsDVwfg1gbGHgmWAq0+7FcQOiASJKOvUaz8IEmataLms1RiVJL0WcYl6u8ttry3f0uV5V0wQujAlGavFC5FGruaCbpWTc0nfmQNxiV4m7TMiksI2fYawPWaJzWvalurjYGEZTkjUSlDStOcXvecmvWcmcRiAjj0+EC95CAhSjePQShpQmJkR0/cWFbWYMbaZLIU2VqfBnyGwFDeck3Do+LBPfMz50kz9INPnAD18sg1gYGngscuZ5zvD3aBc+sTfgkzDrYmXucWWO1Wt42G+iuVQarNZ1ERBQhRVbLAquzOMyzOIFZP1g/KewzbZfg+1ZgzjfN9iTHGZeAzsJgpXRMCksS4fa8w+pEl3IVLqTE7iKQz4gwC0sTXuG4wrlEHf3rgf3WU1vDfttxw5RPvLpmtMJcaZN44CROn87JPUsSwT1jQnjg6efZfcYdGBg4geZ48SCnHURR7DeRDxe5OadU4rsHd/neYtILMU0bWg5nU8xswVhr9MYKtgatjmfUstjLMzoA0y5QWv3MzuTUVrPXZHsOkeyr5mNCKe6pNqmlwW6fkenbjjYkYp+5CQlD4jgvYVndvHi5owuJRJ59uoqP3cDTQWE0WvVh9md+X2LKs3TlQ5gKDwzcj0GsDQw8Nyw3Rysg4nTkVpOoDUxKmPlEFxWzbs5atQZ0+GaPYrpPKB1BJdTeHLOxBcXSKiTbLqBOzyY9y12e2mVj1i4JY1eTJMurUWHObfEu46G0hvWqYNYFVkvL3iJvVeb1C9WLtwefGEveyqQqLp0q8LA8DV5wzxtKKTaqgp2mIwaFObKqEZClgfJwzgeul0GsDQw8dyyFlqFQC9bK7HE1dgofEyLLpYEZpTR0qyV1dVwl86HFYPv7KHEGjBLaEBByePizNu90lsrZox3bB6FVdsk/7DxWG7QCTV6k6EJ/G6Svrt3/vDhAW320VTrrAuPi+hcNcv5roAkR14fOP+uzhk8Tzmi265JFiDQhV6EnzlDb8wX/wMCjMoi1gYHnjuzFZnVkXBi6FDBAEKG0ipGzZLEW0QqcO/3iYizkrVFhmSO6WdP7ZVkqV37sKgfLhIR5SNg+amrsNPMg+KMNXDjb8lxeUiqonaYuHK0PdD5yABidg9dHZ2YAQ0osQsTHbF57VRGwCJGFj1TO0MWUq4GD99e1YrTq5zc/6iMZ+DgwiLWBgecSi9aWz21u8dbeDkKkMopXVyYUtiRvjIIuS2LoTlm2ZdmxDH0PgMFozai4nN/adbEc4n4a2njLhITK5fm2hQ+slSVdbNntIoLh7KzaqoFFzB3lwhmc0iBCELjbeHTn2V14FIrX3LENhY+Ju4t+O1cppl1k7gNbdXlpwSZyfN60yvYkAx9fkghdTJTXkFox8NEwiLWBgeeYcVnwxs2bCL5fGlhuKzrAoVxAUZAraSDi+sqa4ziHVLjX1+3xkkTYXXT4JKwW9p7K06Oy8IGDNiKSKLRhXJpLxy81MVI7y7iwlFajDxc0MRKjwhpFbTW1y+evaAMYRWUtldHMfURSQpH9x5IIO4uWldKyWecSzWEXjnzdIC89nK2OpeWGbhdQCgptGDlzFGBfWcPch6P0gLXySVbVIveaNg98lCx8ZKfxbI8K6mH54ZlkEGsDA885SgnqlGluIG+OrgER5zpickebiUkUIQlGdagjgQe5vbqcd+uHta4cKH852phtMgqjOfCB+hrDuhc+8O3dGYsQQXLu5GZpeXVtTH2FfFGrNVvjiknpuDNrmPnAyFmc0SjpI4cm8ObBnNIojFJ5i1QUTgvO5BknrRQzH9isC2RZATnzgmp1ThxY+t29N11w2Aa0UlgNYwtNCKxJwbjIxr5bo7L/OaqHnqNKIkzbgBdhbI/F4MCzRWkN65VQPKMb3AODWBsY+BjRp5MfiS8BxoCQJDHthPcPhbkXhIbN0vPyiqYul08TRf8RWFbiMtdftTG9MLvu1o2I8PbBnNvzliRCiII1Cqc1Pgr1JR5KZQyzLvaRR9modWtU8rKrGdm8abqM2hIRgiQ+mHX4fkawcuBFM3Ime3ZxemFDK47ue0kSMP0L7cwHpl1k7CxBhFuzhg9iAwrWK8/ntyZYk0Vg8Yi+IDMfmPcGwu8cLlgpHSvOPKDS+fgEnYiw8JGIMLJ2GOa/JFarjySaa+D6GMTawMBzTza/PfZgWz5p5xmrkAx3F8LcJ96bwsIrjILbs8jOQvjcFqyNCo5boecZvi4JHEdfPXzVrTCarbogSa6uXRddFA5bT0qC0hpnhUWIKOTIR+5BOJMTDg7acHRZ5Sxrpb1n8UIpxSurI7ZG1ZGxcEiBRRAWIVcP1yvLVl0e3X7kLIddOBKpSQQfI5O+leljymdVwd68w4ccwSQCU98wKQ2fXJtcy/nKvmGKJiR8TCDCfpeTHq7z53JZpj4wbSNKK9rQsVUXwwzWwMeCQawNDDz3KHJFbFlRWy4KLMWaRgQkQWGg8WANxARNFO42kdW6Q5iRxGGURanqxH0tyXmk+fst55Ye/inmcYgBQTBKg1LEvnqVK2GGJHJpX7JRH2AeehPU+4WUK6WonaF2hu2+zTn3AZ+EyhrG7nSFaOwMQjYihryFK8B+F6A3XE2Skxh8yqa+IuCMIsTErEuXehwhCYJg1fk5l/lYLG3sWIRA3bd4u5g+sjgl31dCjcpzfGfthwcGnlcGsTYw8LHgvIpY3mDUqg8fD8stwvyvSN+O0+BjYLfp6FLEKLgxGuHMWc+C5aboSTH4dFEYzXplWXSBWUgs+sdTGs1+G2ijXDpg/WHajEf5lvcZ8lYqt6zGztLFxO5CKK3Jgq0N3BgVrBaWJkWcBuMMXoQmJpyC1eLB833TLjDtAiEmpj4wcoaXJhXOnD4uIbfQ1gpHlyIHrac+k14hciwa3RmBHZPQxpgfd98afhQmhWVn0REQJsVHE+Q+MPBRMIi1gYGPJctqm6EwlpXSMO0aCu2zwWuC0sJGpbg5shx2hkUMdEETkxBlxmurZzdEDcdtUHgaax66b0sapXj3oMFqjdUmu85rTRMioTC4p0AE6OViwLLydVTNUmyPC/ZaT6HgsItoJYRoeGFc8vLK6L73G1LisAs4rbgzD+wsWqxW+CR8ZuO4fRpT3sjVWuWKntEopdk8YbArIuw2ni5moT4uzNFsVEx50zVIX2PV6pHd/QujuTEqj0TkwMDHhUGsDQx8bFm2KS1j53h1tWKlnHHQdGhgvYL12qK1Zq/pOGgUIpHGB+7OhbGds1aNTrTwTuaTLqOvnj4KY3h9fcJqWTAPkSZEOJETqp4ikWmVorKGJkSUQO2yOa7Rhm2tmFtD5SKaiklpqOyDn9L7qFPoA+mNzn5unU+nFhtyk3TZ4k1YrdmoTldTu5iOtldFcut2OfjfhpjbxFrThci0jZTWsPKINizDUsHAx5FBrA0MDDDtIlMvGFWyOdIUOnLQCd/dj6yWwsJrmgC7c8+s73be+u4BE7XPH/nEGqOyJD+dPL0i7SzrdcGqCG2I7PfLAquFfWDFRkRoQkQECmsea4VHKcV6afG9ZYY78b2s1qyWmtUreqjZPi4sJmHkNIugGFnD5rg4XSc9IRThIq82dXGzW8E0RFIMTH3AKsXeomP0hCKZYhLSA2byBgaeFQaxNjDwMSekxNRHSmNQyuKj4fd3FxgliCTmQWGUIcWOWQKFp7aC05CS4lff3+MnX1/F6Jpn7SlFK0XdLwtI///7ISLsNZ4mZmNb5QNbdXHfBYNHRV2DBcdJdB9EPg+B0tS8OKlxOs/SnRQ1SinWSsukMCjO92orjKKyuX2syDNly9tV1iD9EoOQFx4eh7Bd+MDcp2xP0W/k+pTYWXQkgZE1rPZziDEJB50nxJSNja/Rv29g4HHybD2zDgwMXDvLjbrli1aIeRO0cg6lDW3wlCoy9aAIbFSC1aAFlBU0AZ8iRufs0GcRpS7X/AxJaGOi6hcEuphoQmJSPP3VxOUWZ2k05pK+W0rlpIX7Xb9eOkIhOZXhlF+c4ua45KANGKWp7fUHyvuY2G8DVmtmXSBKYqMq6EIiJAghcmfeYhFCSpTGsFoVOGs47DxOa0r7oK3ZREz0qRKDsBv4aHg2n1kHBgauDdtXPLqYMEox9Z4QBa8jXQCDRpnUb01moda7SmCA7Rqc7lhGWJ0XZv68sNQtS2uM5Zzb084ybxRgrY/vyv5tidZn37LSmoeyS1FKXbiQsVY6Rs6euyl6lpDkyKakPLNxehFR8lxdEuGwC9xpEruNZ2Q0+61n1kV2521fEc4N+psjz/dsraBQRFnOV57PwoejFrlCsVG7j8Rf7knSxcRB6xk7c6VEj4+KLNg9Qv59e15/Pk//T2JgYOCxovq22LQL+JRwxvDqmmHaRTZ6fzAfBas7vnOwx0mLLafBKUhJMLpdXkreNH3+MCrPdx36QJI8Q1Y9S1mL/c+ui4l39ue8N23QStisC1bLgo3KXesL9GVbuE2I7DchV3lVthZZK+0Dj6UwGqsVu4uOmQ9IgpmAKRRzn4gxskjH1WMBdhvPfuuZFJbCXPyzExEOunCURhGTcNB6tkflubePSZiHgEj24XtWq3CNjzQx9SMCH/XRPJjdxh9Vxveajhuj8rlsbQ9ibWBgAKNVzrIkm7LOQ+KFcUVpDW2I7DaetVHFi51jkTwBqHQ2z0XBtBU27HLBIPCkg9+fFNknTXPQQY7pgrkPrJwzfC8izHxk2nmMUqzXxaWqRY8DZ/RR9mhpNHcXHbtN1xv6WqY+slJkcfKkqykiwmEb+i3X/DuTeqFU2vt7qWml2Kwcs87nSC6TEx/mPhIkUViD6nNsE7kSbLRirbCs1+UDW7Iix7/Fqn9TctFj2G26I5uSJrRsj8pn0gduXFiU5pl4EyKSo/JCFGZdYBEis9azXhds1OeL6meVQawNDAyconb21DvqwmicVsx9xBYOGk9pwao+1l0UXiC/FC7rF88v+22g6jcaRYSpj1TW3NPma0Piw1lDSkKXEm1MvLJSf2Tv+ssTx6dRRxXSnGLQp1jApVMcroskEBHKE0I256pylDLxoK8vjWGzzlvN48LQxYRFUTpNafIcJuTf0FfGFRuXEFJKKcZFrjBrpUgpXbARm48zJDkyO17allznYsiT4rLzjE8DSilKrbkzb2lDxMdAF+Fu4/leo1gpnp8K/yDWBgYG7otSio26oDSBLlbMlGfaCAsCK4VBSkft1jlOLSh4klW1kFKf76kYucfrap/fycuRMFu2X9I5t21jpAuCM4o2Ch/OOl6aVEch9U+KpdVIGwWr8zmqnWZSGO4sEpVS1FbjY0JQ7DUeaxS1NY91y3WJ7kX/KY+3Xkle5lwl6RcdqoLKZt+8yhoqYyi0xq0rDruIVfDipObGpLr078jEWaxSRMkVyIuqTVoptAKf0tFv/pP+OX9csf3iymELM/LvUQQaL6w8P1ptEGsDAwMPRivFpMzzTNujirn3RBEKY6msZlI8KLT9AQHvIYAxxxP8lySJsLPoEFQfe5TuMW69TpZt0DYmSpPTHIBzNyYLoxGE1Fcbx85gHkL8pD5PVPX3edWq18xHDruA1Yqpj9yegdFCFCiNoo2R1Am1tZiUaEKgspqZMdQnbC8eFzley7LXt0IVWYCvFO5Sosr2XyMsq8KWNkTGLsd6bUu2VnmYLVTVW7s8CN2/oTnsAgis1de79TpwMZUxzFTAqOz6ZxU4a1irni9583w9moGBgcdGiIk2RgotjMclkAO1z38hX0ZOLXNCTwa8Q55pI4u0b3wDPvgAigK+/GVYX7/8MaU8N1baZb5pfOxtvLXSsd962hDRfYTSeS/MlTXcHJccdoGtquDFSXXl77UUo6EXhYXRbFTu3McnIix8pEvHFTStFDMfsnAUofWJLiVUhJkPzLvAtEskQNFiFDgDtbW8tFL1GQbZT20e8kbwpLAXVtyaEJn7iFZ59umyM3q1sxitmfulOXFx6a0+0/urHbZ5QUHILd9xYZ/ozJjTms3H+EZh4Hyc0WyPS1Yqhw8RpTRjZ7DP2VboINYGBgYeSBcTb+4e0sSWLkRqqxiVYzbKkrVqaSy6/AhkcQZZlGmOjRMSp5qGb74J778PN2/CYgG/9mvwUz8Fl3yRz2Ixr+8nkYeqPJ1HTLlKl7NDT9/f0lT2QaJQKcVWXbBRFSj1YMPd8+hiwic5ar+1IYux8swslIhw0AYWPqKNogm5GrdRuTyfhjD3gZgiIQUKBfOuYa9TpBNVTi/QBuhCIMmCT6yP6WKHJleYupTF43J4XiRX6BR5bmu38VitCAnaRcf2JYb4lxRGU5gHix2fEtJv4i7P/8hZSmPwKW8xnrxu4Pkn/51qeAasRh6W5/eRDQwMXBu7i5aDtqPzkdttwiC8NJnhY2TkLIXVHPusRY6F20mj3OXljqMlhNkMRn3weF3DdAreQ3m5TS7TV7YWXURpGF/Tk/Ve4+lSriRdtNV3GTGglOK6Z8zPrWMKLEKksL1Y1bnK5VNipYjsND5vpeKRBLtdwhhhtYBpJ4QTXmNCltohJQ5bT11YjM7n2nCc+Zn6QPjYz5eFlH36rD4Wlj4ljH64rcLc1pa8ZECuoMx9ZNrlClpl8qzS8uewzEy9CjEJbYzUdkgyGHi6GcTawMDAA2l84NY00PRtzAi8M028pjvSUfUscizYmv4rLccibTmvRn+9h1fW4eAOzOe5sra+ntuhV8Bpjauut+URycPuST7azdbCaAqtaH1EVBYo7txKVT7OU3FRAAQqp1gVSxMa9haRJijmAaIoEKGwQIiEo59RvrdFEBYh9j57/Xc5Icz22+xBVqo8u7ffeqZt6JdRFAp1qWpiTNn2IgGblcNqzSJEDhp/Zq84VwsnRRZoWYw+2sblPAQO2oAd6Wdyc3Pg48Mg1gYGBh6Ij5HmyFo0E4FFF3FKyK3N5bq/Bur+8wDMOWp/egOth2/8OpQaXn0ZfuCz8N4OyBZ85jNXXjJ4HGxUjoWPFL0h6uVYbsMu274Pg5z4UGil2agLfMxTZYU5v71nlMLpvPjg+pB2o8H2RsWVTRQGuqDy5QZiyK1ZRQIthGSOvi9AJ7DXerqQKJ2l7EViaU0fIZVnFn1MHHQe3SdhzH2gDf3M3CUe8bJSh8r2MF303Jq1mD4KauLysH4SYa8JWJ1/Ll0ITDtFZXJl92FMaEc2z9WdL4AHBp4eBrE2MDDwQCq73OA8/aI2cgXG9G3MUy/Ny9t1ZBET4b334f134J1byM1NFuUG6a33qb74fdg3PgWscr/onyeJ0xpXXuVYEtCSxc6yunhVAuD7z5c1JYNW7si/6yKyvYrjsA10MVEYxUph0KoBNFYn1grL3SIx9+no7rUGSZzbqi0UQA5FrxXsNB0bZUEXE3ttYK10GEXeNFUarRWmPN7uTAKHPrDxgDk0ZzXaZ7+3JkamTRZjRitCFA6TZ6W0RIGRMxy2npBy9ucsCJqWSWmZOMdqebWlgodpnQ4MfBQMYm1gYOCBbI9LJvqQ6RlDse/ZXOHSAuv2HVgdQedJVUUsHconosT+iWh5589qtuijtEyXSxlnq3IJkYYuFUetyGxSDOqooqmBXAFcplAsv/Z4qUOoC7g5snwwD/iQcP23SmppkHv6vAsQYyIoKNoOV1iaGCHlubV9ERDTx24t26eKQmuM1mgR2pD6Kt/FP0+nNdujkpiEu4sOUbndeTDPbVAlwu25xiowRnPQdDil2RgVaK2QBPsLz7wLLLzlhXGF/oiSIq6D5azesgXvHtJ2ZOD5YhBrAwMDD6Ryjp98bYtv7Rzw4aFnZOEHX15nUlnojR/OF1gF9HE/vP4KvPM2fPENjG8p9g8JN17GlhXZW35p76F50sa6j44GSvK5uOrTqpDP0b3n0EfFrPP4FEkUgDDtIrXtGBf0FhqamCwh5QqW0Qqrl2sCRxatWJ24MXEYbfmAjqkP2dlfwUzOb90GASWw0yZqSbS+6/3jYC6RKIlx73afyAKjPGEYDNl+xDzgZ6mVIik5Mh2e+0gbhCYEEoIWaNPRbxKKyDwGKm2Zp4Qib8zuNh6N4uakeuDCQEzCtAuE/pjH7qNfMlj4wLQLxDO6v7KGlcI+16ItieCjXNjq/7gziLWBgYFLMakKfvDl7ROXeE4vEpzX7nLAGAiwMYaNm0AHYiilpNTL6s/JWa/IcZLjfVj8/9v782DLsv2uD/z81lp7OPOdMiszq7Kyxveq6umNGqwnIXhhWbRsSRFWt7Eh7KAB22ALGmxshQmPgB12SBF4wtBAu40aN9EIaISNAduSsEBCSA89nt5Y9erVXFk55x3PsIc1+I+1z82bWVk5VmbezFqfiFM3zzn7nLP2WZV3f/M3fRfwjW/A7i4cPw4f+1i3rDY2Kdz36Irmpmu+LssI2dWvtV3RvhZFaQI2WLTUaGkAS90qnNLMbCwDtKHEaINg6WeOfiadmNPEfWgwynF0KPSygje3AiELXJp5rL86mqdQEKK7wLKCThGwIZCJYlIYWheonWfeun2ZOczfP9/qgy68jfNUreuG2WoyJRitUFZwIdB6G6Ns9sp0vuX6ArDTKLa7tLGCOKhYKU7vLRjfxJA+hMB21dKGgBGJw2yhG+78YJg2lr2mJdf6qiHLIcTvuV00HzjT71Fg1sZmj41+8b7RNIkk1hKJxB0RiGJteWG+kXm74cqvmjK+VqQ7NEBVQbMLo/EHNBdEueCDJ+zuol59BVlUcPosHD0SO0jffBW++TXY2YON7rHPfQ6Gw7s6R99UcO4iyvs4C245ZmR/XXzAOd8u73+PRRs9KbUSRCoyHFosIg4h0DjHharFe01hBM2cxpUUJo7y2KmFSSEYtayFK4n70DLIPJPSsF0FPI6cQEvcwRZFruJWxAaCODrDdUItV0KmNVoCo0LT2EAvU5TaXLV9rfMUXeG/D4Gqdcy7UJ4ET+3D/nDdeesYFYaVImPWOIKP0a6mdtcItbD/M1zVZRxr7+bek7eW3cbdUKz5EDtal7WAIrED9W7EmvWBxkbxGc/71v+x0DrPtLEU+v3RPZHYWNJ0jRz30qHjQdIzGkFSs8cHkMRaIpG4A5axlDsRLAeO3d6BL34RXA2nHoeXXiBGghQ0DWxdwJma6WAQO0/3NjHPrFFmQv7UEDEqvt3XXofXT4Pz8A+3YG0DfvEX4Xu/F06ehJdeimGi/bq4m3Vs1lT1FvWZs7hZTXZ5j+G3v4l83/fBYLJ/TIz59G96/tYHQvBotRxncfD45Z+vdGL6bm6aEvDekplm/zBFiC0bAUoNKEcThEyDs57KChqPUsKs9kx6QhRrdXfeGqUM40IxbxvGRUutW2zoTNHjRA+0gA9RaBUqCjdLoLKegfcMMh0dHErDtHVsVg0QyJUm1wqjYJTH4cE7VUvtPEYrgg9cXDT0jKE0MeVlVExJ9gcFk8KwsIYLU4e/bhng8nsKVz3iQtfm0TouTBcMjWZQXP8St9RDSz9S56OH651ivY/1diEOaZ42cYTJrbowLFoXfWZvkP7LlFBbj+0cKh41jFIM84e31vBek8RaIpG4QwqudC9+UFTtJpw7Fz1B147DO+fhhU+B0tBa+PVfxe2eZT4Zoj7+FJPxmMWkx55TzAOsHFmlFxz85tfB1vCJU5Dl0Cvgq6/An/8Z+Na34Pnn4fu+F37oB8AcTLkWXP9XYIttd6jPXwBbUxwdUU36NK+9S3H+PXimxxULrWV0p+B6AtB7z4VZzW5Tkas4PmO1V9DL+oCi9R7fXXy1uiLWqq52KVPCIKuwXjAKVFe1FboUZdlF0bwNVE0LZy5S7u7RjEf0Tz2OU4rWZ120YplqtYBhUmY0DrRYdluDcwGRpRCIEbWFhcdHcY5ekcXKs1wZ+oVCIQxzwzA3VN3IEB+km82mWOvcC2rnY4qyi2JZAplWsaMzRIW4/B6cD4hEzV0aHaNV7TVdLVcJtauH+Sri/5GN97y7t+BJKennV3fmNs4zbS2t98xaS2k0mVKM8jvp4I1MG4sg3XDoKM5jSu/WomAL525qzbUUctb7/cHDidvjXlvR3UuSWEskEnfIUvDcBevr8OabcPEiHDsGqvuVNJ3CYk7z3EnMqA9NTevBocnF4TZ3qEcFmaswNsDTp+DZk/DWe3DpMpx6Aj5+Cn75l+HFF+Gd12Hv47Da48p4jZZYT3fwIh3Tu4sLm+ydu4zkOflIU5RCWBtCnhHFzsEIXUsInr0GFlZjRLNSGrRyXF5UXJjV+NBysRFEArt1w7NrHuf7bNdRfCkJrPdAq2h2P7MOrYRCe5SE7gINyrV4PD5EJwgtXV2ZBvvmu9ivvYlMJmTvnmUxa9AvvYDzhkz57nz9/vkrMWz0waiMoglUzhNCFFDeazItPDnOOTosqWx0K9BKWCkMStRVFlpCYFRE4/XWeUp9pYMxzoiLImnpchB8vGjOG0vrA9IdV1uHEWGnalECRivy1u+bl33QPwgUYCTelBL6RmOUsNtYetkVI/rWe7YWDUpF+ywtHq1g7S6N11242k5MCTh/rci8PiFEB9ZbExFyVz3HH1Ua59muWjyBcWboP8DaxDvl4VtxIpF4dDhyBH7Lb4kpz4MG7kUB4xI7r9C9DDcc4R0ggl20iPVkShH2KthY7a7WBtZXYHcP2gU8fhz+ycvw8jfh2D8F810YZwfq+HOigOnSrgAEWldT9Ur0ZESrDdMWFI6+MXB0tTvOAtEcfd46duuGeVPjg8boAiUlkzKw01isb3E+Fs+74NiuYNa0IDWZFpQYauupnaavIIQWLYFcCY1bRs8EjSUOxwBNwIUY1fQ+pjbzCxeZjlfIRwPUcIR69138Cy9wJfIXiL/yM5YiWyvFWk8zKoRp42P3ZdCUOmNUZhQ6flmDXNHPlvVd7xcVvcyw19h914dlhIn4LTFrLT6w71OKQOMDLniUaAKBYWGoXIjDdzvRpUJAa+Hq9sgraWTV3db7Joo1NFXw1C4wzOV9NZCL1oHIfj1ZmWlq63DhzlpDlhRadecf7b6s8/t+rjdDuuHCLgT0TQVbQD1UXdKHg+0qDm3WEgV83on5h4kk1hKJxINlNHr/Y/0+vPQduDe/jTMlpl+iAuAE0zaxi3R7F7oLP70yvq7M4dQJ+Oq34NylWKs2WYEnT8YK9KbujnVc5RYQQhSMmcYFsMawt3Ec5wO+dfS1Qa9o+Pbb0BvDiTVCodmuWi7MAvO2xSiPFs/cWjSKcRFN1J0IQQKKgEMolEOUR+PxwXcizKAlAzQihlJnzNqG1jW4QDeKA7zSKN+itUK84F0UPNZrzrY57e6c1mT02pY8K9Bdk0AUl5roLHFQRChEcnJtWespYhNCwfUiWDeK/AwyzVJTRUP2K2JNK0XbpUlbH2idw3pYLQ0iitYHhrkGortC4wNH+wXn53Wc/ybhmgq1bj1EoVYa2OgV9DPDXtMy1jnOx1ll67m5at0h3JuBMINM43y054LYYDD6gHq5D3r9bm3RNxB4sb6OZIt1m4QQ8EQhrES6v/FXu7E8DCSxlkgkDifjNcyLLzJtKmztUaLwIaPx0Hv3m9hFTf7MEXjuOfBNvBLv7EHZh3wIP/Z/i0INYHIUpnPYWIGqiQG1UefK0LTwpS/Dzg4MB1SfeJFZEEKIn4kxLN44w+zseYZrY9A7MD/P/OmnmLYaFxTO082HChCir6ZyntHWBXZaRVv2aMTjQqBXQCaGwhhc43C+ZZS3GLUgRuscvcxxTAut9QSJ9Vw++NiQsbNNyDKYrDBXBZcqw8U57J54gdy/QzZb0LMBXnqOY9ZjlGcZCbw+GfFSsLyA3f5FTEQ+sPsyOhpk7NYtC+fQovAhRtJWyyKO0PAeo4RxnrFTt4zLjABsLRqcVyCexTVqrRAoM8XRQcGozOh1ItERa99Wy4z+NWsqM828tfuNHtZHAWSU3FU9k3QDiYc+fp6SW01rdusyOs588/66XaQhxEHBk2vEZ+LmiAjjzLDbWALEFPlD+B0msZZIJA4phsKM2Ks1VcyBEoKivzZkNlknty1mVYFu2G9wKFfB9+BHX4Taxgjd6dOxJq53BMo12NqCVjqxlsG778RZbUeO4Dc32Tm7Rb0+ZGFjjVXZTDm+8w4yhua4Rq+v4qsFUm0zKse084Z6q6LWwqBnkMoz3NDIV99h7c23EO+5HBRbz3yc/MgqmdJcnjcUuqU0LVXrmNWBModCx7orB1gbg4DiwAqEzU30bI4PgjU5jQ3seGFzIWw1gi9z2qefA2fZVppephjUu8xrhRWhtZrSBAZ5iUjeBRVDN4/uzkTaLe2iEjItFEaTdWlVn4X9sSRrveyKTBShbxRbtWOtl5Nr4cIMRBzGRjN78TAoDGv9gpUyZ600ZDq+1ge6pgkhu04nZqEVoyJj1to4R04Jq2XOTtWy11gmudkXinu1pXbRyH6Y3ZpIutO6NyXCWi9nc9FQW0fWedKGELA+4EJgmGl6WWosuBP6uSE3Md1ubtJ1e1hJYi2RSBxaMpWx3h+hFzVNV5SuRSjKgkkxQMQDC2LqLo8/lcCQeIPYDfr881fedHWFK1EkBc7t1zZZk7HwYL0ibO3xpNpjbdWgv/NFfJ5DJrQOmlmFTCvk7Dbjb71BbqEXHKZUqCePUZ49C++9iz57kY1RiabAvP4K2WwN89IzZKrGzj3zesG2NYz7mmAKgo6jSHarWK+21cAgC4yqbcxXX4bZHFmZEE6dpFhZQU89gwy0CWxVUCGg4691UQE/X7B9eZvt7YrKZIxWBmyMe6zOZsibF2Le8siROFD4eunoDwElURA1rmK3tmRa0Tc6zlFzMaJ2sDi/MJpR6MSSDQwzwyg3NM7FmjeB1bLouk8t2/WMXLWEoLAhY5gPycwHd3YOc0PRdaMGH7g8rzk7rfAhcGEGRwcFkyJjYT2ZVkyb2Kl5qzVod4rpbLeq1jFrHW3w8d8fWtPP9C2PAUlcn2WX88NKEmuJROJQk2vFkUFJ60Oc/6U4kCrSXFFlt8o1F72TJ+HMGbh4EclzwuoK9vxZjnzjy0xODFF6DV+UqON9fACnwI5XYGsH8+pryOoaWb+Px8FsioighgXMp3BsBXRG02qU0fRCTbbYwWcj/N4eOljG75xjdHSC286wTxxHGUOYzulPd/FANRox2t2Gb78F3/Exap0jX38V/8Qeg6eeYZjB+TmUGZzZC9GrE8i2d+h/6yvUtiVvW8biKD5+irLJsWfPk53fhWc/SdjbgV/7VeTz3wfDWxdszgdmjUUpuY5V09KVITpUZFrxxLjPvG6ou9lmu7UDHEbJVZP5ReJIkNhR6SizgsY5zk8r5tYxayzv7VZkCp4Yafp54HKX2tQS2K6mrJZ9VgqNlxihOlhMXlnH1qJhr7ZsVQ2Va6na2NBRmozTuwv28pZRkWFd2LfRuhta52m9R6s4VPiDIjtKhH4euxWd9/GfEw+xz2niwyOJtUQicehZTnG/J/R6sSN1PseUJf15C7/2Gnp1COurkBfQ1FjvqYPBBsXZBQxP7zA2BaHMaUzG0FvyXOHOX6Y6ss7gpecIb57GupZ8mNOiMOtjwvZOTD8WOaZuCaMerI7R8wV+Noe33iV/5zxagRhFVvZQvoLCMB1M2DIjpDKU04Zy8yL+saMoySgF1ntdH4WD4lsvMzU9hsOSfqHQCspzZ+Dpz8FkgM3eYfbX/yrn/oXfQcg1vW/+Jke+49P0+wOWgtaHgA+CFo8PjsZ6HIpM6W5WGfg2oCTQzxRdArf7uRQZccZb1bQ03iI4GheY1pogBUpiyna49Bj1nkvzmkuzmtq3TErD7sJxedHuW08pgcrB6V3Lk6uB2kLPBKbOMWstlxdTjBJGRcZqkbExKPajd3uNJXQjMDywaAK1Bwho78h1xrxxIAqtYnTrbtJm0ybOzFtSGMUkN7jQ9ecqQYiz2Za1dMvXKIFxnmFD9E3Nuro8dYP1tC6OX8mUuuOOR9dNI35Ura2uh+98afUhTZMmsZZIJBLGwHiMAMe05kKWE6RlNm8ZSkAU7DYKGzR7DXiEWSu0NWStUBQBKxm+J2jrcIMBwTfYz3wCnKO/WOBXjtJmBl1VSFHQLzTFAOq2RlU19HPMq6+hNzdZHN3AKkU27lNUc9Tb56mLHtXqBqVzVI9t0BhB7exSygUKM2FWDFm0MdbY9zWaQDkqGOYOhyIfaHRt0P/kq3DmHE4pzn3sJd7btZiPPcWgmaP+xl+haPaQZ09RS0Fbe9zxY6gnj+GzHEWGC5o9F5so+lnGlc5aD1dZQMWfIShm9YLKxdlpy4hZpsH6hq2FwXrHaq+kUHBh1nB6b4b38QJ6etfuj17epwt0tU64OA30cmGzhnktiPb0jVC3ntbFNOr1EBFKLSw0tL6bsOcDuQloFf1Pi0xj3a3NS7se1kf3BudjG2qhhUVjaa3f/6a0EnKlmFtHpoTVMmPaWLQIl6uGS7MGT2BhPWWmWC8zVouc3nVmhTXOs7loCEQHivVuMPHtslU1uBA40i9uKAwfFZbfG0Tbq3Fx+Bo5klhLJBKJAxRa8/h3f47qi/+IcH4Ls32Oiy9+krM7hj0LWgtGQNaOMHzrXezQkg9LUKAWFZx4DF8UsBtih8DmNqosGamG2gfUpI+WgMZDnmPGfez2HDed03v1NcxjG6z7Cj8a4qsp4j2cPIHemkFdI3VDuTImf/0Neq+8itneYrUKuN/+2xmvH8esDMEpBiUE8YzsDL++jipy1HsVAWFRWXZ7Ey4ff4JJvUeRx87X6ru/k9n2WXqFwvR6VEEzGw2Yf+N19KKhGY5xJx9n4WLx+2ofJs0O4dKMZm0FBn3aLohklEZpw6KqsbYhDx4rmi2bgYpiotCO9dKx1wTemDfs1nEeW9tFneIgjJhKvR4BOF8psgqO9YWVXkHdtpRG4yQQZOkDGvYjs6PcsLVoEInz4VZKoWorFjYDUfQzw9F+EefA+YBSclN3geuxWzdcmNbs1C0rZY4ooW480vncrpYZpqvbW1gLIc5ai00FnktVi/Oe3GgyUdGCrPVcdg3TxvHMygBzTR1b6zyIUGpFbR02BPQd1Gn1jMZ3M/0+CuzVFt1FIufW0c81WRJriUQicbhRx0/Q/8IPwsUz8PWK2WidhQs4FOKhEagHK7Qnn+WxM69j2j3MuI9bGRCOH4/WTJMR7M6gbUApZDqnHPZB551PqYKmxmihMKAu7sLFXSjLOBS2LHClwc9bEIUa95G2pR0NGYjDvHsaefUN1GzK+PImRbNg9qM/jFt7BjJDpUC99m2CWPTuDN3LMEpRnTpJ/cIn4NvvkiGMcqG8dA67MkaGffKVx2F7C3/mAvrNM3irWHzun6LprdFeuMR0YlmQIQiXK3iy32deFCwuNbRzzyhX9DKw3lG1jkHuCGiMNrROsN6DV7QCeReAq9qGSwtPe11RdvOLZgtcmAeeWQkIhtYHBrlGiyLXwry15N38NSXCkX7BeuccoJXHqD4hZIhc+Xzrow9nptRtR6f26pa3tuZMW8u89YhEm7HWei7O61iTR2BcZgiQabVve7XoRJZ1ARtgqBSi4piRMggL56l94Oy04viovGrUR64V0ljqzlf2TkdUDB7CCf93g1ZgXcCFZTz4cAk1SGItkUgkrs9kBQYlXHyXvJqhizHBBbzEcRc2gHvmFM2zxxDmUJSw3qcw0QOUlQkUOZgMbANZQSAgrY3DeYMC76BpUR7wLTRVdHLQCs5eQD//FL4H1Bb11tsMzAXqF1/Cf/WbZF/7JvrcOYL3yKImf+1N/Pnz1M8/jd3cRJ48QW+lR3vmDOQ9QtmjeGadBoPs7iH9HqJKmmZBeWkTFQL94+uIEfYuzfBfehV58jiyvWD+3gW2PvYdVMdGQIYQ55NVHt6c5qxITeNzZCacmwvegZbAag9mVuN8HEtiPfQzoekyi7M2Gp/vNtB+oJHSrV04a+D0bs24n3Ni2GOjFzs6G+8pNYTg2apaWucZFRnDfUGydGa49lMF5z3FTZpAbReBWza+LKzj3Z0F07YldKnW7coxbxcxBRxiJC0QmFtPpoWT4/7+TLjLVUOpNYMisFe3BIFMFKNc0TpLtfAMc4MjsLVo2ejn+ym7TCvW+/kdi8yPKqM8IzQt1gUmhTmU31sSa4lEIvFBmByef4H1115ht53S0qdWBuugVDApApN+yXB4lH42JJq7L83ia7JeQZv3cB7aWYu6tIPRHr9bo9oGGfSQqoGdXZgvovfobAaDMWzuwa/+BtnHn8Hu7hL+wW9QvvUO5bCPqxvc2U3U5ctIXcNoiHz8aXovv0K+s4kdT1DPfQzVL3DHH8M98UQcpLvSo8ZQrx7BTit63/g2VTGk7peMtWfYV1R6yM6lBcWJ44SiT+gFrCmpnAEFuQ6sdxNSWg/zJtBimC8aWlH7kssAuYOmFhwwyuKkkMoGehnMLGQavIW5u5tNWn6isOcD1bRm3jSYjTE9oxkYixJPCC2xbl51E+zjsNnd2tI4zzDXVw32VQK5UTesXTrYPBCATAmV9SycpzAZi65LNScQJHQiVuhnsdvVOs9KkVMesOfKRNF4H0fgIYQDWWAb4gBdrYSeiYLNh5hSXmKUwtx+1vYjje7m7R1mklhLJBKJD0TBxgmG/YwndvYYaGHuo/lRoWGQa9Z7I8qszxU7J7hiFt8j04FMG8oVBa/8Ku4br+HXxmBbFILem8GgB888BUUfvv4ybL8OFy7BW+/C6j/G2BbGQ0KZY7/8TfTmNrpaEJRBemVsAX3tHXjxefTf/iry5Enk9GXc6oSdx09Rb7f0Tm2g8HjJONdm+KykfvolVi6eYWWxRTYYU3lh6gysrdI0DYimzXpMjx6Pp5UF1nKiWDUwKeJs4c2FIuiss4eKh9og1C7Q+jh3t/HgPGQG5jYKt6aJs4uvaLVr69OWlmDXzsjy3f3l8zFU1zOQBaic4ktndhjmsm+ovlIqTgwFrQtK3QMyGueZW0euFTu1Jdf6qhEiN6pbar1n2tiYeuwG2F6aN136NWC0ptQ5u3VDECEXwZlAX2lWCkNhNEqEo4PyqkjOpIxuD7UDlWscXacisZ5NqxgF9D50naG38v9x4mHnvoo1Efn/Aj8IDIBzwE+HEP77a475j4E/AfxQCOEX7uf6EolE4v1kSP8Yq70JvbahdQ5PIFOGMivQKuf9v0qFKN6uyaHtTNFfehn9nZ+ARQXDAWysRxWzV8Hzz8HlXXjtV6MhvTEwq+FXvwjOIYuKTAIoHWeiHdugnYzgxDGYzaOKKkpCpskXU06/8Cm2Vx9n5oTRXLhcGTIFO120azAZ4kZPcY6WlbUeWXA0Xrhw4hlkLpi2Yuvpx1mMx/tnhcCwgJXOccGGwKwRZtn7VcOihV4G3sNeEyNDdRdRI0CQZVTt2hltN1MgiijQlmINhiY2E7gApfHMvLDdhH0fyMXUs2hhtVczyjWZ7tKHIaYxRd6fCr0RthtvsYy8RcHmIWhWyoytRYvR0bVhqAWjNKulIUgc1RFCYFRk70u5LefOrZYZ89axtaiZtQ6lom1XoRVK0Q0XPnxdi4l7w/2OrP0XwL8aQqhF5AXgl0TkyyGELwGIyLPA7wDO3ud1JRKJxA1QiAzo5YMudnYl9XZbHDsGpg/vXICjq5D14PweDApQBUgOL3w8+pduPAbTXfjKK/CtN2BzE4yFTEX184nn4OgGHNuItXHDIbz4LO6V17F7DWo0YPfoMXa8wQ6HtI3uziTWdwWEpgnsUZCrgsUsYJ1iu4XKZfDUx+Ka2+6WxbO2ASaZZ5DFKA8W1grPXg0N+iqt5RCmbfy6em+/RWk0zRMnsR6MFhZtIHygZ+mS5Xe9jKZd+73H51Wn35SEGBELyzhdjMA5AtNWoXXg3Kxh3Fa0IXZjivccvc0xFXppCt51cM5ay3bVsFu1rA+iFdbcOka5YaNfMC4MRsWOz7ZrdChu4EogIgxywyA3eB97M0X4SIzSSLyf+yrWQgjfOHi3uz0LfKl77M8A/x7wZ+/nuhKJROL2uMML5qlT8KlPQZ5Hy4HJEI4fhW9+EzY24m/ENZ49pgAANp9JREFUrV3o9eHxk1BZ2Krg4x+PYu2110BCrGs7sg6ffCFG5z72FGQZrizZOvY083Nb7Kw/xoV8Ba9zMAdTjVefhyXQU9C4OKvMHqzzb2GZYqRVoIS9JuBNgxdFEwLOC6MssKICrbM0VkVB2WEAT2B4+QLh6BFU5wS1sIEMqJfvD0Qpuaz5iw4IAxWYlNBY2GzBh2W08qB4g0UDoyKQa5Ag3fkq/IHjah+7LM/sOS6oCqNAI1TOsrVoeHIyYFwYtqoWLcJKmX1g5CpTQmkUl+cNs8ayU7V4YmPFOztzjvRyjgx7jIuMQl8ZtGpEbrumLLkYJO57zZqI/Fng9xCLO74M/J3u8d8B1CGEv3OzsK6I/H7g9wM8+eST93K5iUQi8eGxsgLf//3w7rtQFPDMM3EWGyW8/ArYS4AG1YcmRPeEz3wmirvdXXj1VXjt2/Dm6zFn9+wpsiKHo+u05y8xnRzBr65SbxynyYasGsPCBubAVQLTAgYyCaz3YjG9t4GRga326mN1pujh8RLo5dF8YR5ymMXuxjzXOK9Y9VPaNqcVRTMP9Ec9vNa0PnZW1t/zneRaCAFKDYMM9mqo36ci/f7Png48MQKjoVCw0sAbu6FrFIh1bAqNwqEBZwUvCkRRamHm4GANnAMu1gGFp5AaVCf5PGStw3p4arVP4zwuBEoTR2os9evBqJaIsFJkOOu4MF3g8Qix09W5OCft1KR/VfNAInGn3HexFkL4CRH5fwCfB74A1CIyAv5z4Idu8T3+AvAXAL7ru77r7kzbEolE4n5y5Ei8HeTE43DsOFgLWRbF3CuvxHRnlsGP/Vi0xfr5n4+G68bEUFPTRqXRtNgnHqfd88hb7zE8eoR5MWRhPeNC410M0u1bAWQxZ1gaYdoABIY6Bu2uFmpRWPW0cLTvmLUCohCtsFmfDIdIg2ktJ9UUP1infvccVe2o6lX80SMYDUKg9QoPjPNYrLZoiTrqKrHmiXV+nlJBT8cpJnUnLocFDLPoJrFcZ4y/KSwB70GcsDYwHOtr3ptatmt3TVRRUEAVwLhYP2eFfR/Q7aplkBkCge3aUliPdR7V1ZItBdus6wKd9HK0VigfYmrZ2piuBLZry6CIocTKOmrnMUrRM/pQjodIHF4eSDdoCMEBvyIi/wrwbwKngP8xhPDWg1hPIpFIPHCUihE0gCefhBMnoK7jkFzdpf5+/MfhC1+AqoKf+R/gtTOx/fHN9+C5ZzFffR314nMMVgdk7LK1cpSWWOp2Zhrw+00AMXq11GaCMPNACPQV1P6Ku6cSeKzv6GWBhdMYFYWf04LODBhhrVig8xEbCJtHRpjz2+Ta0ijwOtaP1a2iLBylwDzE7tDWQSbRseAKrhsUG0eDuBAbGQoVPU8z5bn+pUvwISAqUKAZ9np870rGe7sLzk0rLi+u2FZprmjEgEKLJ1eKTAmNDQwyOistWLQWQa6UzhG7M6PHKKwUmkXTstuEfQupDOj144iQEAJ7jWPeOowWamtZWMf6AeH3oAkhsLAOApSZPjTruhVm3T4MH/FBvg/67AyxZu23AU+IyE90jx8B/qqI/FQI4ace2OoSiUTiQWFMvB1EBNbW4p9/4g/Bz/1VmM9g+xzmy79J9vRz6O98CTMZgtLMTcA2glKwUsJm1b2PpytOE3SX2tQqpiSDRMExNjDMY8E+SrPTCDbEwbbzNrBqYipzmAWkDmAM470dzGqfeuNxpjU04gh5j50ajI5pwh0b4iDZENd1PetNDRiiwXrTwrAfG11dgIVdjuwQ8B6xltCJ3EIHVgoYFAHnApnWPLM2op8ZDDPOL9r9ajhDN+ct+K6eLEbmJoMojP2+w4GwVub7JusQ06FrvRxoef3yNrULV/WmOuI0lbl1nJ/VBLgyw01BbR21dVfNdbsR0Xnh7gzlb8S0tUxrBxL9USdldk8+516Q36BJ41Hivp2liBwVkd8pIkMR0SLyfwF+F/CLxHEe3wF8prudAf4AseEgkUgkEteysgL/138Rjh6Fl7+N+cor9PoZYWePUNeoXk6pfbR+cnFw6tAAbUBdvoze2cZdvIRxlp6OGdjgoZTAxLSUJnZ75lpYWNhpYGsRh+A2Pgq83ECmAq5po2/p5ja90+9Qnj/HqAAdHIhGC4xyqIPQOlAqWmyFLlp15UIUOF4GjvQCJ4bw1BgmZXym8bBTQeU0UU56emfPsPLylb41F9+ChfVsLmouzSpcJz4+eWzCiWFGz8Ao12z0o/2WEIWbFsUw1xztl4wLQ+Mce3WDUYI5INSW5NpDaHhva4Hb24b5FNoYu7PAXt2wtWh5Z2fOpXkNvkZJg5IWJdHa6FZofbSo2q7fZ2f/oeF8QOvogbocSfKwkGlF9hEQbPczshaIKc8/R/y7+Tbwb4UQ/udrDxQRB2yFEKb3cX2JRCLxcDFZgZe/DecuI3WFKnJ6zRx3ukIyzdqqYL2iVIpcgSaw2JqR1Q3iGkaqpb6ssBuraAXHhp71XkAFT20bNpuMvhHmLfR0ICuF1gsjA6iA9SC5oSn6uGpGlmXopqW3t02mA8WTz2Ey2GsU25Wnniuq1mMCKO8hKDTRO7SvYa2EY0OYVtDr5rhVNqY/9xq4XGl8WF6YFfWRI7TdDDgA7xSNy2hrhZLAhXlL4wKrvYy3tmdsLloWDvZwlI1j3DMc6edkWlNbx0Y/x2gVDdZt7CJtbGDWuuuk2Ty7jSfYlqwweJXRHGil3bZgfc2kp5k3LbMmZ1TmhOAQYspx0ULdhRaHubnK53OJXP2fe8IgMzRVgyOwUtzfqJr1nsp6+g9Z+vV+c9/EWgjhIjHdeSvHPnVvV5NIJBKPAG+9Bb/w9wAHiwXmjbexR9fQ/RzeegeaY4zXjzAzBfNWkSnozXaZvP0GzaDPuAxkix1mT6wyNI4jg1g655xGa7hcCzuV4AgM8+hJn3X1Y84L0zqmQotSY/MxcxTDxRlEebKtHfovAWgaHxgWlkUDM6XBe7TvEodKEBS58qyXsfw/yxRaGcosUFtHZWGnBu+Xw3AjPs/xyzo/YqRPKUVjHUECIorNquGdnSk7jWNxoNOgApqFpXaelSJjUuYM8oyqtUxbRwhxFpr1nlnbIlyxeooI1nlMLrg6IBKIMzkcoDFi6RUwLlrqFs5NA5tVg/fgRSi0BcmYLhpahJ7SHBuXbPRzigPpb6MUR/vFPbUWz7TiSL+IZ3WfBdO8cew0lkwJhbmJEetHmAdds5ZIJBKJO+WLX4zq6t33YLqDfOWbmOkMu7Ee7ap+9AdRiwY1OoGXkrnXTFfWmeRvU+7twp7DffYFVkrIQ6BatDS7NWVoCNbS9B6jCYa+iY0GhQFCoLKC50oTgFGgXUuuoO2VFO++h1lbIYQcCJQmdoOuDzUmc1xeaDRC7gIVijzAel8ojMEGzUoR55IFD73cULWeceHQEuvlHFemrAnLZghBAuw0cUbavHU4V5MJXKo99jpfnwd2Go93NSfHfXKtmLbRK3RSZMyso3YOJYa9xjFrLeu9ohNshn6mwBQEHweIlKKoQqxaG+ZgrWc+d+hcY7SjcQqCZ1TE1tu3t1tmTrreBcu7s5q+gU8cmbAxKPeH5t6PiNODckIY5IbMqI9M7dmdksRaIpFIPIw4F2evffzjcPo0XN6G85eQuiX7mIcioz1/kZ2Nk5i9Pfqm5ZJagbLgzKc/Rz5fIHlOOelR7lgen52ntgF5+VXmj5/A7ewx7m/jXnqJXh4FUR4CSoGX2ATQum76RghQt6g8w+Y97KkT7Kwco5kFRByl9mig0JqjfUOuAoEe+BYlliJTZJKz0isw2rNb1cyblj0b2J5DQ2Cs4zDZTAvKx8+1AZQKGGIH69wJqwYgkCuh8YGpDdcVagepHLy1vQfAej+n1HrfbSDkGblWhBCYW0/VLsdxCIO84LHhgvd2BOdDZ74OHiF4j2kqbKHJg0IrT7+MY0gqF+fCiQjhQMzMA1ML37iww8c2Akf7Of384Sn2vxO0ilHFxI1JYi2RSCQeRkKInaEvvRQdDS5fhq++Ct/9yWgA/8IzhCyDeU2zOacZjNDrK5gANi+o85j2Ms7T277A5Jf+N7aPn6KpWvjqK5Qn1uDCHvrTT6OyHgHYDTBvAYmdo/18afMkZEVG8AHrPOHESej1yL7xDXSWUX3seYxAT6bMrUGJATSS9Si1opdpWhfIdMbFecXFuadxhhAUztcoCVyuPUY041xROc/CRmEkIhQ6gI2pWS1C7WFgNHut67xBb4xREIKndh7robhmkG0IgZ26pWod4qGfR09OrQwfWxuwUlac3rHM6jjKzvtAaC0Du2A8LMk0OF2gladBMWviYN9cs9+Ve5Cpg9PbMxrneXZVo5ODwW1zrzto7zdJrCUSicTDiDHwxBPRE/SZZ+JYj1/8RfjVL8PGKrzzHtn5TdRLm7TPvkj7xBqDPLACXKoUNQEhkHvLQLeoS5c48jN/hbY/xn/Hx5DZ06A0of4s1rcMJn3AxOiRBFQWhVrjwKDQAXzbEoZDbH+AUQovAT+dgrc4b8mMZcWAboXNtmDYaso336R9+jmKoxv4IGxXDudMHIHhHTMXU5xxLEYcPNvTgvdXImYCTPI4mmTWepQonApoPFqDd9wwupZpMFp14zmuPN7LNPPWsWg9lXXkRoOKkUQjMRVq9IATowEnRpaqbdhcOHbqBmcFu1VRBYNXPUodPUEFT+UFTaDIQNcOx3LI75UP32kcflqxWmas36Zv6UeVEOJYmHlrmVtP3+iHagzJjUhiLZFIJB5Wnn0Wfv3X4/iOP/gHo1H83/27sLcH57aRp6Yc+dwzyKmTLDJNnrWsDEpKc4Tt+ZStt99DnT7N6ltvM1B9/Kc+SV01hI01yguXUE8cRfkaqT3BanqZwrmAwbNwArMaP28J4x6N85iyhy80Nhisg/a55xjkQl/7LuVnUBLoqZrivbNs+gHriwWj6Q6Dk8eYtZbWB7RWNK2j8TFJKCLRMF1AVBwxob2naT2irtTN5RqUDrjW0QgURiMi5A4qZ5leY20lwJEcjoxKcp2xWmZXjYHIlGK9n7NoHS5EEdgzGn2VcFr+OVBmmuPGcHSY0bjAZi9nr61pPBgPPkDjAipAkUHVBlYLYeEcM6s5KNZ8gGljObsXh+Ot9gpMcj34QCrreG93zrx1VK2jMJojgyKJtUQikUg8YNbXozH8178eI2v/7D8Ln/88vPNOtLQajyn2Fjy+2COEBnnsJMgYyDk6XuPoJybRLeGzn4MzZ9Bf+g36O1tsPbaGuXyOvMiQnV0Y9RAEs5gxeucctmrgwhZWabJC0Ntb2O/+LG2vh1205EfXmTdCEI0xnlwLrVM0wZPjMAqG2uPefIP13/J9FBuPxfUTDdIb52P6sksVhhC7Ioe5QkJ0E9hPHob4UuviqA8lYHLo61gnFsSwPsoxumTRBAKW4By7reAJ9LOMnsk4Me5dd3RGphRZoRjlBttF1K6k1gJQdz9zoO5cCzxGcjIDQwKbC8fcRpP3EAJlJrhuZIcnkCnhYJ+rEIcUZ0oRRPAI89Yyvs9jNR4WfAi8vjVl2riYig+OtvEMi4wQwiORCk1iLZFIJB5mnngCNjbg/HmYz2NK9Id/GPr96C369tugWuT4MZAMKNiP4CgTZ7VNiMd/+cuUaI574N0zNMfX8ecvoIZP4lHo0+9gXnkN/fZZBt9+HR5bJ2ys4b76KsXulPr5j2GzEjeeoE0WxVM38jbTgYyAd+CVQjU1hXiU6Oh/SmxAKIxQuegnGofHKga5J9cGHwLOOgZ4Gg/exO7K0kSxVjvoZzDINQTFsVHGKIcgLc5nbPSE0hSICOemls1FINOKo4PyukLtICJCdt2L/r7NO1CyW9VUTmFUYLVXYp1mo29xwbPXFLxe14gPaAGtLYUIlVPsHmg1KFTcoUILpZZ9UXrYsT5QWYcPgULHDs/7IZTmrWOvbjESsAitjyns1nusD2Q6ibVEIpFIPGjKEk6dev/jSsHTT3d3llf7D7hwjcfwIz8Cf/NvwvYufO67yd56i92pw5/dgt6I3vmLyNdeJru0jd7ZAdvAz/8DTGsJb5+mPfkE7nf9i1gLgzi1g0Ac/lpoQVS0jHKbU9AZw2PHMDt7hMeOUTvPrLFEi8rYWemR+DoE5x2qajhy5h0GOFTWZ7A6gZURlfXMGoUFHIFcO0QC00axUmp6maZzOu3OP7Ba5vQzTaEVs8ZROc+4MLdZGyZAeeDPQuMVIrHA3ShFrsv9736Qg/cLzs1qPIFx3uPYIGOrdnipqGygdQ4tnWNCgNo55q1lpSxpnI8jSToxdJgM4Rvn2Vq0hM7Eft46SqOZFOaeCzZF54ahNEYH8qAwEiiNehg07i2RxFoikUh8JLiFC+axY/B7fy+cPQvSIj/yzzGZ9PG7m4Qv/jrN3/8i6uImam8X3j0Db56GrR3sqVOIzMgnO2z8xj+iffEkVb6KKWPKsFCCC0JVgdnZo6wsUq6QXTzP4qkR03mND6CVYkMc2bmzLFZWmeUlrfNYAgUw2b7ISk+jsj5GFaj5HnZY0riM3EBPYJgLIpCrQGkatutoKVVmnhhV1ECgMIbCGHbrliYEgvX0TIzs3c33ulpmzFpHadQB4Rd/isDjkz6PjUq8j2NIRIRJD44N+7y3O2fRxnluShRrpaFX5HgfmFaWphvUq0SYNlHEXZn79uBYdstqJVetpbLuDr/T26M0ikmZsbloMFqTa2GYmf0RLI8CSawlEolE4gpFAU89xRVbckFNRvBPn6CXrcO58/h/+A+Z+R7NxuNIUZJfuowUCr25hXntLbLLF3EI3kzwWggSx8LNWjh6+hzlliO0nt3RCov+INaFLYXNzg4rb7/B0DzL5MmncD42JxRbF2neeY/pUydZVYGhWC40junUUZuMTMDkikxDrjy5XtZ9GfYaKEzoyuIcUTw1QEzVzVuHETBKqLpOwlFurmo2sD5WlN0sXZppxcqB1/lOYB3EKPU+Z24BBnnGqMjZrdtuXEegZzTWey4uGjYOdIXqrrZv2tj3FdH7znT1fnWQ+hDHleTXjDxRItTOvW8UyoeNUopnV4cUes6s9XFMTCfgHpUu2iTWEolEInEdrh4lgc7he74PzpxBfd8PMFpbwyqN/fa30X/sj6HPn8atT+CxdeTSZXqDATaMaJ3GtZY2CMGCWh3DC08zvbRg8cpr5DvbSP/4lc85ehT5zGfIViZkSz/OxRzzld+k99prXAyK7RPHGdZ7DNqa2doT6EuXMbM9ePopFmKwPjBWUIhBK01tPY0DrTyC6YRQ9EEojeFIP6YulQiblaNxAS2OSSe6aufZWjQIsNrLb3nafus8l6uGSW7oZTe+3Ib9n2G/eWLpqe5CN3D3GuGRKaFyjglXxFrVWrZrixJYLfP7YnLeZWzfV8wf9kec3HsKo3l6dUjrPJfmTYyuPkKuCI/OmSQSiUTi3tLvw3PPxQ7S4RDT71F+6pNkf/TfRh17HLW2ClkOb7xNbmvKC+fxO3uI85R7O4zsHD3IqHfPMl0Zkn/mJWRj4+rPMAYeewyKZS2YBeMI/QyeOcWwV7LY3aOaL+g/+zQh10huMHlGnuvYn+kM02aZAosRwt3ac2nuuThvaJwlyqF4CYzz1aKoGOWGvhF88JyfVcwbi3OeEGK93TJqdSsoEQqtbmmobaYUSiD4KHIaF1OpANYFiusUyQfefxGfWY9WioCwsO59r7kXKBH6RlM7T+i+H9spzfvp96kk+oseHRSP3Gy6FFlLJBKJxJ0jAj/0Q/CFH0B/8ZdRX/8a9p03YTpHWct4NscNBrCoEKMIsymVGmLsDvKJz4K+2cXcQ5bhvvfz4Cx5MWZcVcydp18I5XSBPrJCOLqGBYwI/TwOuPVBoQj4EGi9ZpAV2OCYt4pcF1wvXlGaKPIuzhtyrdhtLEf6OYOgEWHfr/NW0EpYLfObH9gdu9bL4/gJAq33CFBbRz9TWK2w3qNF9lOrjfOM86sv44VWTJs4Avh+RpZGhQGJjQUQU8qrvfyB1NPdj2ji/SaJtUQikUjcPVkBp55GsGQrY3j1DTj1OG2eYaZTwulzyOUtfGMJG4+RHXuMMN+BUe8mb9xNIMsMZBnWei43cxDLfA6lEgqt6XWZQCEQ8DRO4YPgg0YLZErTOCGgyYzhRoklJbI/763QsVHgfsw4M0qxUipWygzfTeNXEh9vvefyrGGzaaicQyEcG5b0s6vF7jA3FDqmdW9WX/dhIt13NMyXLhePjtXTYSCJtUQikXhUCQGaBvJ8f+jsvUPg+FNx7Efeg2oBfUW2fRnefI+2yMAH2uPHYWsPNndiY+ZNOXiQ4txsCy1trEETUNozbYReptACCxu9Qwme3VooM896mVGYHgvr0RKjZzdCSYxytc5f41Zw/1Ai5AdSn5lSjEtD5SzWxXrC4H2sdbumVux6kaUQAi7E7+xeiiglckuNx4nbI4m1RCKReBTZ3ISf+zk4dwY+8R3wIz+6P3z2nqENfOIzsNgD3XkqbV4C9SW0d7jzl3DvnkUubOM/91noT7h56XT04ATwzlJbS2EELbGGrJCAUZ6dyhGQ6GRgoMiiQwFB0ct6iEQHBOcDjYsdppmSDxQuzsdxFJ6YWl0ps/saqboehdYU2rAQjzGKTGu2qxZC9Ewd5eZ9HZkQhdrFecNeY5kUho1+8QBWn7gbklhLJBKJR40Q4Nd+DRbbsD6E178J5z4HJ68zOPfDRlQnwgLgoVyHuodam6A+XeCmc8LzCvXCp4kDZW8ehgkhTsav2poQYFbDsAzoAJUFozxH+4atWrMAxqVQ6IxMFbh9SyiN9YqtRYMLQkAojWblOkNbQwhsVQ1KFJkSWu/ZqVrWH6DIab2ntZ6jg5xJaSAEyszgfGBz3nBhEU3fjw9776sTC8BW1QCwtWhZ7+UpRfmQkcRaIpFIPIooYNiH02ehzKC4tUL3Dw8BNPSH8OJL8M1vguSEyZjw7DOQj7mVgQSt81xa1GzNG1yIPpqzFhYuoBWMciFTmiIvWFeWPW0YFyVKDI2rKXSNoODyHou9OWZlnbLXwwVDZR1Npt/XaelDvC1tijKlqK370Hwmrzd77WbMGsessRwZFPSM5vKioXYto8IwKgyV9xh1/Yn9SoRJYdipWlZ6WRJqDyFJrCUSicSjhgh88lOQAZMJPPU0HD324NZz6hSsrUFdo3VOW1y/E/NaWus5M11wbrqgdtH0fJJ5Blk8RSOBYQbjQmhtHCS73jfMrUdCRaYbxjlQz2Cxhd9uYXtOePHjaLGA2R81cRAloEWwnQBqnCdXH47P5aK17NSWntHvG2Z7I2LjwJXUba4VW1VsNnisX3BsED1PP2hi/7Fhj6OD8pZE4rK5QYs8cHeERCSJtUQikXgUefwJWF8H76A/4IFXfY9GMBrRs46qarnRUPsQAnt1y+ntPS7WAY+N9lEq+oXOl80FSmjmgar1PDGCldIhIozyohstC9ZluFAhjSW3NdP+GLwAHq0c2XXq0ESE1V7GdtVS23jM7QirG9G4uLKFc4zDrftmGiUYdeWSPS5ix2htHYhc1yVg0VpEZL+h4laEmvOBzUWNC/H/mLXe/Rmsm7gxSawlEonEo0p5s7EY959cx+GvN0oFThvL+ffOs72zix+tkJWKXEProAkx/QmgAoxyEBwXF465BZGASMu8hb0GlLSs9oTByhH0wDIcDWklem+OsuwDI0dGKTb6xYeW+lwyzM3+dP27fd+VMr/h+gIgtzDD13UDbLWK9lAuxGG2bWdnNSrMA2+u+KiTxFoikUgk7hsiQj8zzBpLfp0RGt4Hqt0p9Wuv4Z54Em3ihapxV2KDIrGHwiioHVQtlAa8hzemHmcDey0YPEoJF2bCek/z2KjAi2FSBJRo4ObRsg+7vkuru5/ZZr3fN3S/0fr6N7G4Wr7X5UVDCLDey9EisTUkBCprWYRotzXMDcM8SYYHRZLKiUQikbiv9DON6mrCrsWFANUCMYqJaelnggoxQmQE8uhvTqFiKjSPBWbsWeH0FGaLwE7rccQe0IUPTK3n3MyzuWjxvqK1dfdpD98l0IfA5UXDXm0/lPdzPuw3VLgQyLWi30XVlAi9TJNrxaz9cD4vcWckmZxIJBKJ+4rqasI2Fw2t91fVjSkBlWesDRULt2ChBlil0EFRmhhBa1xM8bUOcgP9zpDg/BQalqboVwvBQRZwTrHbBLTKKLJutAiqEyzhKo/Qw4oSYZwbzIdUR5ZrxSg3BAKFVlTWsV01NC6Qa7AiOO/f55SQuL8ksZZIJBKJ+45RirVewXbVUFuHUdHPUytFsTJBHT9O8da3GeQwHaxTlB6jDdJF1BobaLwwKaI4m7XQ+OsLLQ8UOta7VTYjYAhBmDcVFxZxhlumAqURJkXOMD84h+zge14ReMvH72QMx93Su4X05q0iIlelN7cWLY3zKKWYNo7Hxzml0bfliZr48EliLZFIJBIPBNOZl9fWMWsdtV06Cyj80ceYjweMdEMRQhxuqxy5FkQCqz3HVpVFv88A8zZgAx9oq+UDFAI+CLkKLNoFF+YO6wt6JkbWFjYQcITQMC6XgqwgXiqXw3WjWPMhZ6eyLKyLs87ye+8dej8QJbgABkEpITvQTXovcD5gvSfrPFgT1yeJtUQikUg8MGJdlKE0GttZQbmg6IUhudIE5mzohkxqggbnYW4V01qTtTV23mLyjF4NfcmYo9ESGOQwUGA9LGxMr5bGM8wDuXZcmDtmTUBJzV4Tjd61hsK3tB6cL7quUw8Mu9UGQOG94+zegq3a4kNg3lj0SOg/ZAX4PgSmjYUAg9yglbDRy2icp7aO1dJQfohRvGsJIbBZNVgf6N/m3LmPGg/X/1mJRCKReCQRETItV830GhcZrctwYQtNiwuw1yoaKyx2Fwy2LzGsG8btAjtr6K8dZ/PICUwm9DNog6AIHAlQtY7h3i4bKxNs6LG18Hg8CnDeEnzD0VJhPcwbwzAL6P1auporETbLtFFsNy0amFvPer9gt7EURj9UQ2R3a8uitcwaj6kbnhz1mDWOUgvjvGBcmHt+PiEErjOXOHENSawlEolE4lAiIt14DwMLaJWgFPjWYhZzbOspAd56m77SDD/5NEcGNUEXXK4FE8AGYZxZNt75FsU75/B5TvP5H0Ao6RmPVoFAoDQO5wO5MdStwpEBmlif5olRtQzICKGhMI6m9YxzE63mlfAQ6TSgq7dTQhBPY+HLZy5zZmYJwDgXnlkdcXLSR0SoWsfMOgqtGGT6QxlpIiKs9Qqs9+SpJu6GJLGWSCQSiUOMgdbA5SmmUFgpsIuWSjL6TUN26RLFt15HvuuTNP0eeaFBBNUEZq1nYTUShOHWnFblTFyDm+3RG2W0IVC1msbBMFOEIAgaEYVGcfVoj2jBJMCwyAgCvgyM8gwfolh72Dw3R4Vhu2oZZoadRcW7M7vfQ7vVBN7enrHWy+nnhu062nntNS25VuT6wznX6MyQOk1vRhJriUQikTjEKDAFzBryX/kSR9cmzE8+QbCOws/Iv/Zl/OqYS9/1PUCJVIpeDpPcsr0DQzdHZi17k3WyxSU4dhQ/3mBoFFNr2a09LngqJxRa0TpDP9NkVxXVh1hftahBhKP9gpUyv2qVzof92WQPSyo0U4oj/YILs5rN2u0LNSHGESvnaZynTzeImADh4Ti3R40k1hKJRCJxiBGQPhw5DtOK7L0zTIqMoBX16+8y/8QnmH/6O9jJR9CCC5BbWJ3vsu4gLGrmTaBWJerIGmeOnaKatuRKcXykeGxoaJ2lsYHKG3p5YKXMD6Q0PSCIaPrZ9R1WG+fZqpr92quVIqN8AHPJ5q1Fi1DcRvdmCDENPDAaQ4uls6kiWk71ugHGq2XOvLXk3ZDcxP0libVEIpFIHHIUZBPYeDwagr51mkuf+gzbv+3jKK1odEZrA2UmCDBftKy++jrV+glya1nseczKkGruuXhmm7B+jJbA6ZllNfdMCkVhMtYHyyn+DuCA72YBxHlk1gc2Fw2N9+RKMS4yduoWLQqtBR8CO01LYe7e+/N2qV0cMFzcxmti3VgOIZAp4b3pgsbBpNA8vzbcH9sRU5/RizRx/0liLZFIJBKHnywHrWFjnfk3X8e7rzH+5IuE4ZD8nXMsVIZ66Tlmphd9M+c1Y73JLOtHo3IXqPdqwjiw2B9sC5uNY7dxjLJou3R0AK1XzJpAGzSZKpkUsXkghMB21eAD5EphfWBrUWNDoNBR1KiYL9yPTt1PVq9Jzd4qrfU01pMZzWceW2GlzJm1jsp5tquWlTLW5W1VDS4EJkV2T2evJd5PimUmEolE4nDjPfz8z8O59+Cv/U2yX/wl8tkM+dorqHdPIyePUhSG5tV3sC04ZZg+dpxse5PB5fNM+hpsyyIY7PoqENA4SuUZm4BWHucsmQpkYjm7V9F4T6Ycta3Zq1sgRt2sD2RadaNGFC5AoVScD+cDjXXktzDg1fnAbt1SW3cfvsAPpnGeC/OaN7amvH5pylfO73Bp3tA4j1GK2nl8gIV1VK3jzO6CL53d4s3NPayLHbSHjRACtfPU1uMfkUhgiqwlEolE4nCzuwvvvUd4+RvYv/9rLI4cY+voE+gja/iqiV5TzrJ7oaZ5GlZ6MH/qaWarRwhtw6xW7Cwci6eexWbZ/kAO66M5/MgISjyFgTLzbDegiANaM+1pXAvkCDHNukyPhhC7Q8dFxsI5GhvoGc2wuPmltfGendrSN/q2aszulBCiU8DC+s73M7YRtB7Obu9yuekOtIHfOLvNp48MaRGMFlwIGIHz84rNhSVXwulpRe0cK72StV5+aOrYQgjs1JaqE8GZUqz2sofeHSGJtUQikUgcboyBo0dpf+kyojV7H3+R7M23cUVOg2ZvLujNmvrZZ5k1kGkwuabqjdgKntoIvQmMtSEA1sE8elPhAozKQKni6yobu0Ktd2hlsa6gNPFCr5UwyA17jd3vmBx1puojreA2spCFVqyUZj99eq+ZtpZLsyaOHikzLi/in4XA2frqY1vgze0pz2ys0DOa7arhSL9Ai6LU3XdQt1Qu2nhZd3jmpNkQvV6XArhuHa3z90UQ30uSWEskEonE4WY4hM9/Hv/uaXjj20iZM1/boDY9Qq9EAKU0w0GGNcLlBezVAaMUcxQa6BdCZSEEODIIbC9gr4WBDqwXkBuoLLRKsVIEqhBonVAaz6i4IkSGuSHXat/A/VZFivWenSraU41yTZnduZ+o9QEl3Fa0SBBs8BRaoSXW4FXtctjv+5m2sLmoWetFI3cRYZArLi8CddXS04onhgX93NB7AJ2vS6rWUbk4rLeXmf06wf3op8D9rx788EliLZFIJBKHn+PHKX73v4T9zscpf+3rzNZWCKKw/SGDt9/BB0t49XXsJz6DCzB1grgoRTSB2oF3oE3887AApeDUmDiSQ0FhcgZ5Tm4sfRTxErk0cb/SMrAUa7PWMm0smRYGmbmheNruhJpSwnZtOaLvzJqqdp6tRYNWwnovv2XBNsg0pdHMW0euXTeMFgLCRGDnGs2mBHarBkXg+fURAJM849QYahc4Oe4xvsOGhg8L6z3bdYtSQmUtRimyzmFh1sY0aGk+vAG+D5Ik1hKJRCLxUCCrY7JPf4Zi2Ke/GzCjEdPG0d89TxNy5htHqR0sS/aX+iOoGEGbB8gVFBrqFo6UgTKP9Wv9rKTM+sQxHQ3x8ri8yDsOirUQAjtVS90V4c8bR+sCq2W2P65jOeJied8Hj1bqSuTnDvtFnY8F//jOU/MW30JEmBQZw1xTGoMWmDWOAEyOK75ydpfd7gvLgDbAZuWonaOfZTwx7mNDwCI8NiwYFQ/edH3Z22BEaAj7Q31Hyzl3D6mzxPVIYi2RSCQSDwkGyj69osD3K+a7WwyrOeqzH+dymLBTru5fsJcUKjDMITNwvAetizdPTH32NChRZHqpfHR3W0o9392/csF3Xbfhsg5KK01tXdcpKixay24TPTaHmWGQaUa5YbeOj/WMRt+hgOgZjZSgldqPzIUQ2Kwasm7u2wdx7XN5T+N84KLzfOrxVc7sLVi0jq2qpfFRIDgP56YLSqPQSrHWixHBwyCAMiX0jGbhHD2jyQ9EKjN1OGroPiySWEskEonEQ0IG2RpqxzF66y0G/YIdcuavv4d9YkAouUqsCYHVIkZgtitYWOiZ+DMToTSKTHsyrdDKEDsElunPlmUSNT5+a+Kk7bo8Mx2jaHuNjaIiM+RaEwhoeb/YaZzfT6kOM/OBYkgkvtf7Hke4G/0UiOndUiu2q5ZCQ98YWh9ofBxLYjR4Hx6IO8P1EBEmZcY4fPD39aiQxFoikUgkHiIK+J4vwNMvYOuG6uxlzM//L4y/fYZ3f/DHQPUwmWCJMosAwUcx0voouyZF7MbUSpEbg5KSmPwzQM0VyXd9oRYtnRS1dWilcD52QxolNC6+dllLpkRofaCALhJ2fVGxU7UgUDeOQuvbqrPadyG4A7QSVsqMRWsZ5QatFMOsYe58nCsXAqUIwyKLNl/Exo3DxKMu1CCJtUQikUg8bKgMHjtJsB5Zexx57nn48lfRXnCdGIqySKh8jLApoK/AoOjngkgZuz/d0kuzIEq6ZdoTrq1VW7Ks/5orR+s9pdEMumjYMjXpfEAkRqJuJSVntFBZjyDcbx/40uj929ai4cgg59y8prKeUiueGA+YFOaGYjNxb0liLZFIJBIPJZmOQ2qnKsceP4Hr5oVZB+hOdikgxHlqLYFJAUoyRBms97ROU5h+fAEH3QRuPPleSfQKvRajFCtlxl5j8T4wKQyFublYmxQZPePRIg8schVHXwhlZhgXOZrAsDD08+wjEb06zCSxlkgkEomHEtXVLO02lmp1FS7P4hM+3lymmNaetR64AEopetogUmAD9I3QuGVDAcR6tYOiLeNOXBmXUarbPZfD4LdZZjrWpPUe9EoSB0liLZFIJBIPLaXRnBz3OKfgnb32ffGwOmguzD39zFOI4EKGwpMpQauccNVlUIg1ast3OVy1WYmPLkmsJRKJROKhpjSaJ8c9vK14Y6tl95r5HQ7FXqs41s/JdQ+tBCWKxgYm5bXRrFSXlTh8JLGWSCQSiYcepTQnJisoPWdz0XJx0dDYmBHNBB7r56wNBmilCARqFygzdShSj4nEzUhiLZFIJBKPBGVmODYaMCwcjw0tlXMMjGKlV5ApzcI6autjc0Ch9z0vE4nDThJriUQikXhkWBb3+5DFhOYBMTbMDcMHa2eZSNwRSawlEolE4pHjVg3OE4mHgdTqkkgkEolEInGISWItkUgkEolE4hCTxFoikUgkEonEISaJtUQikUgkEolDTBJriUQikUgkEoeYJNYSiUQikUgkDjFJrCUSiUQikUgcYpJYSyQSiUQikTjEJLGWSCQSiUQicYhJYi2RSCQSiUTiEJPEWiKRSCQSicQhJom1RCKRSCQSiUOMhBAe9BruChG5CLz9oNdxi2wAlx70IhJXkfbk8JH25PCR9uTwkfbk8HGre3IqhHDkdt74oRdrDxMi8hshhO960OtIXCHtyeEj7cnhI+3J4SPtyeHjXu5JSoMmEolEIpFIHGKSWEskEolEIpE4xCSxdn/5Cw96AYn3kfbk8JH25PCR9uTwkfbk8HHP9iTVrCUSiUQikUgcYlJkLZFIJBKJROIQk8RaIpFIJBKJxCEmibVEIpFIJBKJQ0wSax8yIvKHROQ3RKQWkZ+55rnvFZGfF5FNEbkoIn9NRI5f5z1yEXlZRE7ft4U/wtzNnojIT4rI10VkT0TeFJGfvO8n8Ihyl/siIvJTInK5u/2UiMh9P4lHjJvsSS4if11E3hKRICJfuOb5QkT+nIic7/btb4nI4/dx+Y8kd7Mn3TGfE5F/ICLTbm/+yH1a+iPL3e7JgeNu+TqfxNqHzxngPwP+h+s8t0rsFnkKOAXsAX/xOsf9JHDxHq3vo8jd7IkAv7s77oeBPyQiv/NeLvYjxN3sy+8H/nng08CngB8D/sC9W+pHhhvtCcCvAP8KcO46z/0R4PPE/TgBbAF/+h6s8aPGHe+JiGwA/yvw54F14Dngf783y/xIcTd/T5bc1nXe3PLSErdECOFvAIjIdwFPXPPc3z14X0T+O+DvX/PY08RN/qPA/+ueLvYjwt3sSQjhpw88/S0R+Z+A7wf+yj1b8EeEu/y78n8H/lQI4XT3/J8C/nXgz93LNT/q3GRPGuC/7p5313n508D/FkI43x3zs8B/eS/X+1HgLvfkjxL35C9392vg5Xu22I8Id7knd3SdT5G1B8tvBb5xzWN/Gvj3gcX9X06C6+8JEFNvwA980POJe8q1+/IJ4CsH7n+leyzx4Ph/A98vIidEpA/8y8DfvclrEveW7wU2ReRXReRCl5p+8kEvKnH71/kk1h4QIvIp4D8mhkKXj/04oEMIP/fAFvYR5np7cg1/nPh35nqp68Q94gP2ZQjsHLi/AwxT3doD5dvAu8B7wC7wIvAnH+iKEk8Qo9B/BHgSeBP4/z3QFX3EudPrfBJrt4GI/FJXMHi926/cxvs8R/wX5x8JIfxy99gA+GngD9+b1T+a3Ms9ueb5P0SsXfuREEL94Z3Bo8l92JcpMD5wfwxMQ5ry/YF8WHtyA/4MUBBrowbA3yBF1m7IfdiTBfBzIYR/HEKogD8BfJ+ITD6E934kuZd7cjfX+VSzdhuEEL5wt+8hIqeAXwD+0xDC/3jgqeeJxdS/3AUHcmAiIueA7w0hvHW3n/0oco/3ZPn87wP+GPBblzVSiRtzH/blG8Tmgi929z9NSk/fkA9jT27CZ4D/IISwCSAifxr4kyKyEUK4dI8/+6HkPuzJV4GD/4BJ/5i5Cfd4T+74Op8iax8yImJEpAQ0oEWkFBHTPfc48PeA/y6EcG0h9NeBk8RfeJ8B/jXgfPfnd+/L4h9R7mJPEJF/GfjPgR8KIbxxP9f9qHM3+wL8JeCPisjjInIC+HeAn7lPS39kudGedM8X3fMAeff8MvX8j4HfLSITEcmAnwDOJKF2d9zlnvxF4MdF5DPdnvxHwK+EEHZI3DF3sSd3fp0PIaTbh3gj1jWFa25/vHvuP+nuTw/ePuB9vgCcftDn8yjc7mZPiDUe7TXP/7kHfU6Pwu0u90WI6YTN7vbTdF7H6XZv9qR7/q3rPP9U99w68JeBC8A2cXzB9zzoc3rYb3ezJ93z/yaxjnAL+FvAyQd9Tg/77W735MBxt3ydT0buiUQikUgkEoeYlAZNJBKJRCKROMQksZZIJBKJRCJxiEliLZFIJBKJROIQk8RaIpFIJBKJxCEmibVEIpFIJBKJQ0wSa4lEIpFIJBKHmCTWEonEoUREfkZE/pf7/Jm/R0Sm9/D9pyLye+7V+ycSiUeTJNYSiUTiCj8LPLO8IyJ/XES+/gDXk0gkEskbNJFIJJaEEBZE8+tEIpE4NKTIWiKROPR0Xnv/tYicF5FKRH5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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "colors = {'LOW': 'lightblue', 'MEDIUM': 'lightyellow', 'HIGH': 'red'}\n", "\n", "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.2, s=housing[\"population\"]/100, label=\"population\", figsize=(10,7), c=housing['house_value_class'].map(colors))" ] }, { "cell_type": "markdown", "id": "touched-smith", "metadata": {}, "source": [ "If interesting patterns exist in the dataset you choose, try showcasing them.\n", "\n", "---\n", "\n", "We are now done with data exploration. We know what the data looks like, and what preparation we need to do before moving on to training." ] }, { "cell_type": "markdown", "id": "packed-export", "metadata": {}, "source": [ "## Data Preparation" ] }, { "cell_type": "code", "execution_count": 14, "id": "restricted-remedy", "metadata": {}, "outputs": [], "source": [ "# First separate features from target variables.\n", "features = housing.drop(\"house_value_class\", axis=1)\n", "target = housing[\"house_value_class\"]" ] }, { "cell_type": "markdown", "id": "multiple-batman", "metadata": {}, "source": [ "We need the following transformation to make our data work with ML algorithms:\n", "1. One hot encoding of the categorical features. \n", "2. Median imputation on numerical features.\n", "3. Standardize numerical features.\n", "4. Encode class labels into ordinals (0, 1, 2 instead of LOW, MEDIUM, HIGH). This is what the ML libraries we'll use expect.\n", "\n", "`sklearn` has builtin functions for all of these. It also has convenience functions to put them all together.\n", "\n", "----\n", "\n", "One hot encoding with `sklearn` works as follows." ] }, { "cell_type": "code", "execution_count": 15, "id": "undefined-graph", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1., 0., 0., 0., 0.],\n", " [0., 1., 0., 0., 0.],\n", " [1., 0., 0., 0., 0.],\n", " ...,\n", " [0., 1., 0., 0., 0.],\n", " [0., 1., 0., 0., 0.],\n", " [0., 1., 0., 0., 0.]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# One hot encoding\n", "from sklearn.preprocessing import OneHotEncoder\n", "\n", "# Get columns to transform.\n", "categorical_col_names = ['ocean_proximity'] # stored as a separate variable for later.\n", "categorical = features[categorical_col_names]\n", "\n", "# Do one hot encoding.\n", "encoder = OneHotEncoder()\n", "categorical_one_hot = encoder.fit_transform(categorical)\n", "categorical_one_hot.toarray()" ] }, { "cell_type": "markdown", "id": "professional-cooking", "metadata": {}, "source": [ "As for imputation, `sklearn` has a [variety of implementations](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute) (corresponding to the techniques we described in class). We will stick to simple median imputation, but feel free to try something different in your custom project." ] }, { "cell_type": "code", "execution_count": 17, "id": "growing-penny", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Num missing before imputation: 167\n", "Num missing after imputation: 0\n" ] } ], "source": [ "from sklearn.impute import SimpleImputer\n", "\n", "# Get columns to transform.\n", "numerical_col_names = [c for c in features.columns if c != 'ocean_proximity'] # stored as a separate variable for later.\n", "numerical = features[numerical_col_names]\n", "\n", "# Print missing count\n", "print(\"Num missing before imputation:\", np.count_nonzero(np.isnan(numerical)))\n", "\n", "# Do transformation.\n", "imputer = SimpleImputer(strategy=\"median\")\n", "imputed_numerical = imputer.fit_transform(numerical)\n", "\n", "# Print missing count. Should be 0.\n", "print(\"Num missing after imputation:\", np.count_nonzero(np.isnan(imputed_numerical)))" ] }, { "cell_type": "markdown", "id": "economic-curve", "metadata": {}, "source": [ "Take a look at the documentation for [sklearn.preprocessing.StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) and use it to normalize numerical columns in `imputed_numerical`." ] }, { "cell_type": "code", "execution_count": 18, "id": "mineral-objective", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1.02618438e+00, 4.92694304e-01, -3.67612514e-01, ...,\n", " 1.60447517e+00, 5.27123615e-01, -8.91248289e-01],\n", " [-1.25516802e+00, 2.66342955e+00, 1.85659377e+00, ...,\n", " 2.64914832e-01, 5.37583542e-01, -8.54774030e-01],\n", " [ 8.50485909e-01, -8.54658609e-01, 4.26746875e-01, ...,\n", " 2.59609643e-01, -1.55386650e-01, -5.52236821e-01],\n", " ...,\n", " [-1.14067620e+00, 9.69881794e-01, -1.24140784e+00, ...,\n", " 2.61776635e+00, 2.32884611e+00, 1.47019796e+00],\n", " [ 8.90309151e-01, -4.89750528e-01, -1.79745941e+00, ...,\n", " 4.18633403e+00, 3.34868904e+00, 1.58497574e-03],\n", " [-7.07598442e-01, 1.42367774e+00, -1.63858754e+00, ...,\n", " 5.09837745e-01, 5.82038234e-01, 8.19635528e-01]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "\n", "# TODO: Do scaling (~2 mins)\n", "# Hint: Reuse numerical column names from above\n", "scaler = StandardScaler()\n", "scaled_numerical = scaler.fit_transform(imputed_numerical)\n", "scaled_numerical" ] }, { "cell_type": "markdown", "id": "rocky-arabic", "metadata": {}, "source": [ "`sklearn` has a simple API to chain all of these transformation together. Take a look at the documentation for [sklearn.pipeline.Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) and [sklearn.compose.ColumnTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html)." ] }, { "cell_type": "code", "execution_count": 20, "id": "developing-texture", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 \\\n", "0 1.0 0.0 0.0 0.0 0.0 -1.026184 0.492694 -0.367613 -0.024450 \n", "1 0.0 1.0 0.0 0.0 0.0 -1.255168 2.663430 1.856594 0.628425 \n", "2 1.0 0.0 0.0 0.0 0.0 0.850486 -0.854659 0.426747 -0.389292 \n", "3 1.0 0.0 0.0 0.0 0.0 0.850486 -0.896763 0.585619 -1.121719 \n", "4 0.0 1.0 0.0 0.0 0.0 -0.846980 1.161692 -1.400280 0.237066 \n", "... ... ... ... ... ... ... ... ... ... \n", "16507 0.0 1.0 0.0 0.0 0.0 1.104359 -0.779806 -0.288177 -0.113603 \n", "16508 1.0 0.0 0.0 0.0 0.0 0.671281 -0.695596 1.856594 -0.574456 \n", "16509 0.0 1.0 0.0 0.0 0.0 -1.140676 0.969882 -1.241408 3.189634 \n", "16510 0.0 1.0 0.0 0.0 0.0 0.890309 -0.489751 -1.797459 3.483153 \n", "16511 0.0 1.0 0.0 0.0 0.0 -0.707598 1.423678 -1.638588 1.037158 \n", "\n", " 9 10 11 12 \n", "0 0.477936 1.604475 0.527124 -0.891248 \n", "1 0.680298 0.264915 0.537584 -0.854774 \n", "2 -0.138673 0.259610 -0.155387 -0.552237 \n", "3 -1.181432 -1.079066 -1.211839 -0.263220 \n", "4 0.608876 0.599142 0.684023 -0.748915 \n", "... ... ... ... ... \n", "16507 -0.529112 -0.422107 -0.487489 2.286572 \n", "16508 -0.636245 -0.613094 -0.683613 -0.071730 \n", "16509 1.918277 2.617766 2.328846 1.470198 \n", "16510 3.156256 4.186334 3.348689 0.001585 \n", "16511 0.489840 0.509838 0.582038 0.819636 \n", "\n", "[16512 rows x 13 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import Pipeline\n", "\n", "\n", "# Categorical Pipeline\n", "categorical_pipeline = Pipeline([\n", " (\"one_hot_encoder\", OneHotEncoder()),\n", "])\n", "\n", "# TODO live: Numerical Pipeline\n", "numerical_pipeline = Pipeline([\n", " (\"imputer\", SimpleImputer(strategy=\"median\")),\n", " (\"scaler\", StandardScaler()),\n", "])\n", "\n", "\n", "# Full transformation\n", "feature_transformer = ColumnTransformer([\n", " # Categorical\n", " (\"categorical\", categorical_pipeline, categorical_col_names),\n", " # TODO: Numerical\n", " (\"numerical\", numerical_pipeline, numerical_col_names),\n", "])\n", "\n", "features_prepped = feature_transformer.fit_transform(features)\n", "pd.DataFrame(features_prepped)" ] }, { "cell_type": "markdown", "id": "acceptable-narrative", "metadata": {}, "source": [ "Finally, we need to encode label (LOW=i, MEDIUM=j, HIGH=k) to satisfy `sklearn`." ] }, { "cell_type": "code", "execution_count": 21, "id": "governmental-friendship", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 0])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "\n", "# Encode labels\n", "label_encoder = LabelEncoder()\n", "target_prepped = label_encoder.fit_transform(target)\n", "pd.Series(target_prepped).unique()" ] }, { "cell_type": "markdown", "id": "moving-hours", "metadata": {}, "source": [ "The data is now ready for ML models." ] }, { "cell_type": "markdown", "id": "static-university", "metadata": {}, "source": [ "## Training and Validation\n", "We'll now have to train and validate a few models to find the best performing one.\n", "We start with the simple kind of model: linear classifier. We will then give you a few minutes to try out gradient boosted trees.\n", "\n", "Training a model is simple with `sklearn`." ] }, { "cell_type": "code", "execution_count": 22, "id": "funded-mother", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SGDClassifier()" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import SGDClassifier\n", "\n", "# Train model\n", "lin_sgd = SGDClassifier(loss='hinge')\n", "lin_sgd.fit(features_prepped, target_prepped)" ] }, { "cell_type": "markdown", "id": "beneficial-whale", "metadata": {}, "source": [ "Performing predictions is equally easy." ] }, { "cell_type": "code", "execution_count": 23, "id": "cardiac-indication", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['MEDIUM', 'LOW', 'LOW', 'LOW', 'MEDIUM', 'LOW', 'LOW', 'MEDIUM',\n", " 'MEDIUM', 'MEDIUM'], dtype=object)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get some random input\n", "sample = housing.sample(n=10, random_state=1)\n", "\n", "# Tranform input\n", "sample_prepped = feature_transformer.transform(sample)\n", "# Make prediction\n", "prediction = lin_sgd.predict(sample_prepped)\n", "# Decode prediction\n", "label_encoder.inverse_transform(prediction)" ] }, { "cell_type": "markdown", "id": "contrary-enterprise", "metadata": {}, "source": [ "---\n", "Let's validate the efficacy of the linear model. `sklearn` provides builtin k-fold cross-validation. \n", "**Q: Briefly explain what k-fold cross-validation is and why it's used?**" ] }, { "cell_type": "code", "execution_count": 24, "id": "worldwide-garden", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 76.86% (1.24%)\n" ] } ], "source": [ "from sklearn.model_selection import cross_val_score, KFold\n", "\n", "# Create model\n", "lin_sgd = SGDClassifier(loss='hinge')\n", "\n", "# Do cross validation\n", "# The cv parameter is the number of cross-validation splits.\n", "kfold = KFold(n_splits=10, shuffle=True, random_state=37)\n", "scores = cross_val_score(lin_sgd, features_prepped, target_prepped, cv=kfold)\n", "print(\"Accuracy: %.2f%% (%.2f%%)\" % (scores.mean()*100, scores.std()*100))" ] }, { "cell_type": "markdown", "id": "unnecessary-specific", "metadata": {}, "source": [ "We get a validation accuracy of ~$76\\%$. This may or may not be ok depending on your application. Try `xgboost.XGBClassifier`." ] }, { "cell_type": "code", "execution_count": 25, "id": "traditional-latter", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 85.58% (0.73%)\n" ] } ], "source": [ "from xgboost import XGBClassifier\n", "\n", "# You'll need at least these flags.\n", "xgb_cls = XGBClassifier(eval_metric='mlogloss', use_label_encoder=False)\n", "\n", "# TODO: Do cross validation with xgboost. Can be somewhat slow.\n", "kfold = KFold(n_splits=10, shuffle=True, random_state=37)\n", "scores = cross_val_score(xgb_cls, features_prepped, target_prepped, cv=kfold)\n", "print(\"Accuracy: %.2f%% (%.2f%%)\" % (scores.mean()*100, scores.std()*100))" ] }, { "cell_type": "markdown", "id": "center-worth", "metadata": {}, "source": [ "You should have obtained an accuracy better than before. \n", "**Q: Why do think `xgboost` performs better than linear SGD?**\n", "\n", "---\n", "\n", "**Q: Given a model, how should we tune it to improve training performance?**" ] }, { "cell_type": "markdown", "id": "rental-indianapolis", "metadata": {}, "source": [ "## Testing\n", "We can now test our model to see how well it performs in the \"real-world\". \n", "**Q: Why can't we compare our models in the testing phase?**.\n", "\n", "Fill-in the following code." ] }, { "cell_type": "code", "execution_count": 26, "id": "legislative-coupon", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", "# Train\n", "xgb_cls = XGBClassifier(eval_metric='mlogloss', use_label_encoder=False)\n", "xgb_cls.fit(features_prepped, target_prepped)\n", "\n", "# TODO: Implementing testing code here.\n", "test_target = test_set['house_value_class']\n", "test_features = test_set.drop('house_value_class', axis=1)\n", "\n", "test_target_prepped = label_encoder.transform(test_target)\n", "test_features_prepped = feature_transformer.transform(test_features)\n", "\n", "test_predictions = xgb_cls.predict(test_features_prepped)\n", "accuracy = accuracy_score(test_target_prepped, test_predictions)" ] }, { "cell_type": "code", "execution_count": 27, "id": "c8003552-f715-4739-8fa3-949d2ab8b3c3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8604651162790697" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy" ] }, { "cell_type": "markdown", "id": "monetary-forge", "metadata": {}, "source": [ "Thanks to k-fold cross validation, you should see that your test error is fairly similar to your validation error. K-fold cross validation allows to predict performance on unseen data, as long its distribution is reasonably similar to the training data." ] }, { "cell_type": "markdown", "id": "a08a391a-4d49-47da-9395-03437a5aa11a", "metadata": {}, "source": [ "# Go your own Way\n", "Now pick any dataset of your choosing, and and roughly follow the steps above" ] }, { "cell_type": "code", "execution_count": null, "id": "bc0e0af3-4487-477f-8836-a18accf90735", "metadata": {}, "outputs": [], "source": [ "# ..." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.10" } }, "nbformat": 4, "nbformat_minor": 5 }