{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: Feature engineering\n", "------------------------------\n", "\n", "This example shows how to use automated feature generation to improve a model's performance.\n", "\n", "The data used is a variation on the [Australian weather dataset](https://www.kaggle.com/jsphyg/weather-dataset-rattle-package) from Kaggle. You can download it from [here](https://github.com/tvdboom/ATOM/blob/master/examples/datasets/weatherAUS.csv). The goal of this dataset is to predict whether or not it will rain tomorrow training a binary classifier on target `RainTomorrow`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import packages\n", "import pandas as pd\n", "from atom import ATOMClassifier" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pm...Humidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
0MelbourneAirport18.026.921.47.08.9SSE41.0WSSE...95.054.01019.51017.08.05.018.526.0Yes0
1Adelaide17.223.40.0NaNNaNS41.0SWSW...59.036.01015.71015.7NaNNaN17.721.9No0
2Cairns18.624.67.43.06.1SSE54.0SSESE...78.057.01018.71016.63.03.020.824.1Yes0
3Portland13.616.84.21.20.0ESE39.0ESEESE...76.074.01021.41020.57.08.015.616.0Yes1
4Walpole16.419.90.0NaNNaNSE44.0SESE...78.070.01019.41018.9NaNNaN17.418.1No0
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5 rows × 22 columns

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" ], "text/plain": [ " Location MinTemp MaxTemp Rainfall Evaporation Sunshine \\\n", "0 MelbourneAirport 18.0 26.9 21.4 7.0 8.9 \n", "1 Adelaide 17.2 23.4 0.0 NaN NaN \n", "2 Cairns 18.6 24.6 7.4 3.0 6.1 \n", "3 Portland 13.6 16.8 4.2 1.2 0.0 \n", "4 Walpole 16.4 19.9 0.0 NaN NaN \n", "\n", " WindGustDir WindGustSpeed WindDir9am WindDir3pm ... Humidity9am \\\n", "0 SSE 41.0 W SSE ... 95.0 \n", "1 S 41.0 S WSW ... 59.0 \n", "2 SSE 54.0 SSE SE ... 78.0 \n", "3 ESE 39.0 ESE ESE ... 76.0 \n", "4 SE 44.0 SE SE ... 78.0 \n", "\n", " Humidity3pm Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am \\\n", "0 54.0 1019.5 1017.0 8.0 5.0 18.5 \n", "1 36.0 1015.7 1015.7 NaN NaN 17.7 \n", "2 57.0 1018.7 1016.6 3.0 3.0 20.8 \n", "3 74.0 1021.4 1020.5 7.0 8.0 15.6 \n", "4 70.0 1019.4 1018.9 NaN NaN 17.4 \n", "\n", " Temp3pm RainToday RainTomorrow \n", "0 26.0 Yes 0 \n", "1 21.9 No 0 \n", "2 24.1 Yes 0 \n", "3 16.0 Yes 1 \n", "4 18.1 No 0 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load data\n", "X = pd.read_csv(\"./datasets/weatherAUS.csv\")\n", "\n", "# Let's have a look\n", "X.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the pipeline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Initialize atom and apply data cleaning\n", "atom = ATOMClassifier(X, n_rows=1e4, test_size=0.2, verbose=0)\n", "atom.impute(strat_num=\"knn\", strat_cat=\"remove\", max_nan_rows=0.8)\n", "atom.encode(max_onehot=10, infrequent_to_value=0.04)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training ========================= >>\n", "Models: LGB\n", "Metric: auc\n", "\n", "\n", "Results for LightGBM:\n", "Fit ---------------------------------------------\n", "Train evaluation --> auc: 0.9846\n", "Test evaluation --> auc: 0.8783\n", "Time elapsed: 1.012s\n", "-------------------------------------------------\n", "Time: 1.012s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 1.017s\n", "-------------------------------------\n", "LightGBM --> auc: 0.8783\n" ] } ], "source": [ "atom.verbose = 2 # Increase verbosity to see the output\n", "\n", "# Let's see how a LightGBM model performs\n", "atom.run('LGB', metric='auc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deep Feature Synthesis" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully created new branch: dfs.\n" ] } ], "source": [ "# Since we are going to compare different datasets,\n", "# we need to create separate branches\n", "atom.branch = \"dfs\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting FeatureGenerator...\n", "Generating new features...\n", " --> 50 new features were added.\n" ] } ], "source": [ "# Create 50 new features using dfs\n", "atom.feature_generation(\"dfs\", n_features=50, operators=[\"add\", \"sub\", \"log\"])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Location 0\n", "MinTemp 0\n", "MaxTemp 0\n", "Rainfall 0\n", "Evaporation 0\n", " ..\n", "WindDir3pm + WindGustSpeed 0\n", "WindDir9am + WindGustDir 0\n", "WindDir9am - WindGustDir 0\n", "WindGustDir + WindSpeed3pm 0\n", "RainTomorrow 0\n", "Length: 74, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The warnings warn us that some operators created missing values!\n", "# We can see the columns with missing values using the nans attribute\n", "atom.nans" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting Imputer...\n", "Imputing missing values...\n" ] } ], "source": [ "# Turn off warnings in the future\n", "atom.warnings = False\n", "\n", "# Impute the data again to get rid of the missing values\n", "atom.impute(strat_num=\"knn\", strat_cat=\"remove\", max_nan_rows=0.8)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting FeatureSelector...\n", "Performing feature selection ...\n", " --> Feature MinTemp was removed due to collinearity with another feature.\n", " --> Feature Location + MinTemp was removed due to collinearity with another feature.\n", " --> Feature MinTemp + RainToday_No was removed due to collinearity with another feature.\n", " --> Feature MinTemp - WindDir3pm was removed due to collinearity with another feature.\n", " --> Feature MinTemp - WindDir9am was removed due to collinearity with another feature.\n", " --> Feature MaxTemp was removed due to collinearity with another feature.\n", " --> Feature Temp3pm was removed due to collinearity with another feature.\n", " --> Feature Location + MaxTemp was removed due to collinearity with another feature.\n", " --> Feature Location + Temp3pm was removed due to collinearity with another feature.\n", " --> Feature MaxTemp - RainToday_Yes was removed due to collinearity with another feature.\n", " --> Feature MaxTemp - WindGustDir was removed due to collinearity with another feature.\n", " --> Feature Evaporation was removed due to collinearity with another feature.\n", " --> Feature Evaporation + WindDir3pm was removed due to collinearity with another feature.\n", " --> Feature Sunshine was removed due to collinearity with another feature.\n", " --> Feature WindGustDir was removed due to collinearity with another feature.\n", " --> Feature WindGustSpeed was removed due to collinearity with another feature.\n", " --> Feature WindSpeed3pm was removed due to collinearity with another feature.\n", " --> Feature Humidity9am was removed due to collinearity with another feature.\n", " --> Feature Humidity3pm was removed due to collinearity with another feature.\n", " --> Feature Humidity3pm + Sunshine was removed due to collinearity with another feature.\n", " --> Feature Humidity3pm + WindGustDir was removed due to collinearity with another feature.\n", " --> Feature Humidity3pm - Location was removed due to collinearity with another feature.\n", " --> Feature Humidity3pm - RainToday_No was removed due to collinearity with another feature.\n", " --> Feature Humidity3pm - RainToday_infrequent was removed due to collinearity with another feature.\n", " --> Feature Cloud9am - RainToday_infrequent was removed due to collinearity with another feature.\n", " --> Feature Temp9am was removed due to collinearity with another feature.\n", " --> Feature RainToday_Yes - WindDir3pm was removed due to collinearity with another feature.\n", " --> Feature MinTemp + Temp3pm was removed due to collinearity with another feature.\n", " --> rfecv selected 41 features from the dataset.\n", " --> Dropping feature Location (rank 5).\n", " --> Dropping feature RainToday_No (rank 2).\n", " --> Dropping feature RainToday_Yes (rank 3).\n", " --> Dropping feature Location - RainToday_Yes (rank 4).\n" ] } ], "source": [ "# 50 new features may be to much...\n", "# Let's check for multicollinearity and use rfecv to reduce the number\n", "atom.feature_selection(\n", " strategy=\"rfecv\",\n", " solver=\"LGB\",\n", " n_features=30,\n", " scoring=\"auc\",\n", " max_correlation=0.98,\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dropcorr_featurecorr_value
0MinTempLocation + MinTemp, MinTemp + RainToday_No, Mi...1.0, 0.9979, 1.0, 1.0, 1.0
1Location + MinTempMinTemp, MinTemp + RainToday_No, MinTemp + Win...1.0, 0.9979, 1.0, 1.0, 1.0
2MinTemp + RainToday_NoMinTemp, Location + MinTemp, MinTemp + WindGus...0.9979, 0.9979, 0.9978, 0.9979, 0.9979
3MinTemp - WindDir3pmMinTemp, Location + MinTemp, MinTemp + RainTod...1.0, 1.0, 0.9979, 0.9999, 1.0
4MinTemp - WindDir9amMinTemp, Location + MinTemp, MinTemp + RainTod...1.0, 1.0, 0.9979, 0.9999, 1.0
5MaxTempTemp3pm, Location + MaxTemp, Location + Temp3p...0.9834, 1.0, 0.9834, 0.9999, 0.9985, 1.0
6Temp3pmMaxTemp, Location + MaxTemp, Location + Temp3p...0.9834, 0.9834, 1.0, 0.9833, 0.9825, 0.9835
7Location + MaxTempMaxTemp, Temp3pm, Location + Temp3pm, MaxTemp ...1.0, 0.9834, 0.9834, 0.9999, 0.9985, 1.0
8Location + Temp3pmMaxTemp, Temp3pm, Location + MaxTemp, MaxTemp ...0.9834, 1.0, 0.9834, 0.9833, 0.9825, 0.9835
9MaxTemp - RainToday_YesMaxTemp, Temp3pm, Location + MaxTemp, Location...0.9985, 0.9825, 0.9985, 0.9825, 0.9984, 0.9984
10MaxTemp - WindGustDirMaxTemp, Temp3pm, Location + MaxTemp, Location...1.0, 0.9835, 1.0, 0.9835, 0.9999, 0.9984
11EvaporationEvaporation + RainToday_Yes, Evaporation + Win...0.9936, 0.9999
12Evaporation + WindDir3pmEvaporation, Evaporation + RainToday_Yes0.9999, 0.9935
13SunshineSunshine + WindDir3pm0.9999
14WindGustDirLocation + WindGustDir1.0
15WindGustSpeedWindDir3pm + WindGustSpeed1.0
16WindSpeed3pmWindGustDir + WindSpeed3pm1.0
17Humidity9amHumidity9am + WindGustDir1.0
18Humidity3pmEvaporation + Humidity3pm, Humidity3pm + Sunsh...0.9857, 0.9911, 1.0, 1.0, 0.9998, 1.0
19Humidity3pm + SunshineHumidity3pm, Evaporation + Humidity3pm, Humidi...0.9911, 0.9804, 0.9911, 0.9911, 0.9907, 0.9911
20Humidity3pm + WindGustDirHumidity3pm, Evaporation + Humidity3pm, Humidi...1.0, 0.9857, 0.9911, 1.0, 0.9998, 1.0
21Humidity3pm - LocationHumidity3pm, Evaporation + Humidity3pm, Humidi...1.0, 0.9857, 0.9911, 1.0, 0.9998, 1.0
22Humidity3pm - RainToday_NoHumidity3pm, Evaporation + Humidity3pm, Humidi...0.9998, 0.9855, 0.9907, 0.9998, 0.9998, 0.9998
23Humidity3pm - RainToday_infrequentHumidity3pm, Evaporation + Humidity3pm, Humidi...1.0, 0.9857, 0.9911, 1.0, 1.0, 0.9998
24Cloud9am - RainToday_infrequentCloud9am0.9992
25Temp9amTemp9am + WindGustDir1.0
26RainToday_Yes - WindDir3pmRainToday_Yes0.9944
27MinTemp + Temp3pmMaxTemp + MinTemp0.9949
\n", "
" ], "text/plain": [ " drop \\\n", "0 MinTemp \n", "1 Location + MinTemp \n", "2 MinTemp + RainToday_No \n", "3 MinTemp - WindDir3pm \n", "4 MinTemp - WindDir9am \n", "5 MaxTemp \n", "6 Temp3pm \n", "7 Location + MaxTemp \n", "8 Location + Temp3pm \n", "9 MaxTemp - RainToday_Yes \n", "10 MaxTemp - WindGustDir \n", "11 Evaporation \n", "12 Evaporation + WindDir3pm \n", "13 Sunshine \n", "14 WindGustDir \n", "15 WindGustSpeed \n", "16 WindSpeed3pm \n", "17 Humidity9am \n", "18 Humidity3pm \n", "19 Humidity3pm + Sunshine \n", "20 Humidity3pm + WindGustDir \n", "21 Humidity3pm - Location \n", "22 Humidity3pm - RainToday_No \n", "23 Humidity3pm - RainToday_infrequent \n", "24 Cloud9am - RainToday_infrequent \n", "25 Temp9am \n", "26 RainToday_Yes - WindDir3pm \n", "27 MinTemp + Temp3pm \n", "\n", " corr_feature \\\n", "0 Location + MinTemp, MinTemp + RainToday_No, Mi... \n", "1 MinTemp, MinTemp + RainToday_No, MinTemp + Win... \n", "2 MinTemp, Location + MinTemp, MinTemp + WindGus... \n", "3 MinTemp, Location + MinTemp, MinTemp + RainTod... \n", "4 MinTemp, Location + MinTemp, MinTemp + RainTod... \n", "5 Temp3pm, Location + MaxTemp, Location + Temp3p... \n", "6 MaxTemp, Location + MaxTemp, Location + Temp3p... \n", "7 MaxTemp, Temp3pm, Location + Temp3pm, MaxTemp ... \n", "8 MaxTemp, Temp3pm, Location + MaxTemp, MaxTemp ... \n", "9 MaxTemp, Temp3pm, Location + MaxTemp, Location... \n", "10 MaxTemp, Temp3pm, Location + MaxTemp, Location... \n", "11 Evaporation + RainToday_Yes, Evaporation + Win... \n", "12 Evaporation, Evaporation + RainToday_Yes \n", "13 Sunshine + WindDir3pm \n", "14 Location + WindGustDir \n", "15 WindDir3pm + WindGustSpeed \n", "16 WindGustDir + WindSpeed3pm \n", "17 Humidity9am + WindGustDir \n", "18 Evaporation + Humidity3pm, Humidity3pm + Sunsh... \n", "19 Humidity3pm, Evaporation + Humidity3pm, Humidi... \n", "20 Humidity3pm, Evaporation + Humidity3pm, Humidi... \n", "21 Humidity3pm, Evaporation + Humidity3pm, Humidi... \n", "22 Humidity3pm, Evaporation + Humidity3pm, Humidi... \n", "23 Humidity3pm, Evaporation + Humidity3pm, Humidi... \n", "24 Cloud9am \n", "25 Temp9am + WindGustDir \n", "26 RainToday_Yes \n", "27 MaxTemp + MinTemp \n", "\n", " corr_value \n", "0 1.0, 0.9979, 1.0, 1.0, 1.0 \n", "1 1.0, 0.9979, 1.0, 1.0, 1.0 \n", "2 0.9979, 0.9979, 0.9978, 0.9979, 0.9979 \n", "3 1.0, 1.0, 0.9979, 0.9999, 1.0 \n", "4 1.0, 1.0, 0.9979, 0.9999, 1.0 \n", "5 0.9834, 1.0, 0.9834, 0.9999, 0.9985, 1.0 \n", "6 0.9834, 0.9834, 1.0, 0.9833, 0.9825, 0.9835 \n", "7 1.0, 0.9834, 0.9834, 0.9999, 0.9985, 1.0 \n", "8 0.9834, 1.0, 0.9834, 0.9833, 0.9825, 0.9835 \n", "9 0.9985, 0.9825, 0.9985, 0.9825, 0.9984, 0.9984 \n", "10 1.0, 0.9835, 1.0, 0.9835, 0.9999, 0.9984 \n", "11 0.9936, 0.9999 \n", "12 0.9999, 0.9935 \n", "13 0.9999 \n", "14 1.0 \n", "15 1.0 \n", "16 1.0 \n", "17 1.0 \n", "18 0.9857, 0.9911, 1.0, 1.0, 0.9998, 1.0 \n", "19 0.9911, 0.9804, 0.9911, 0.9911, 0.9907, 0.9911 \n", "20 1.0, 0.9857, 0.9911, 1.0, 0.9998, 1.0 \n", "21 1.0, 0.9857, 0.9911, 1.0, 0.9998, 1.0 \n", "22 0.9998, 0.9855, 0.9907, 0.9998, 0.9998, 0.9998 \n", "23 1.0, 0.9857, 0.9911, 1.0, 1.0, 0.9998 \n", "24 0.9992 \n", "25 1.0 \n", "26 0.9944 \n", "27 0.9949 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The collinear attribute shows what features were removed due to multicollinearity\n", "atom.collinear_" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "hovertemplate": "(%{x}, %{y})auc", "legendgroup": "auc", "line": { "color": "rgb(0, 98, 98)", "dash": "solid", "width": 2 }, "marker": { "line": { "color": "rgba(255, 255, 255, 0.9)", "width": 1 }, "opacity": 1, "size": [ 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 12, 6, 6, 6, 6 ], "symbol": [ "circle", "circle", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# After applying rfecv, we can plot the score per number of features\n", "atom.plot_rfecv()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training ========================= >>\n", "Models: LGB_dfs\n", "Metric: auc\n", "\n", "\n", "Results for LightGBM:\n", "Fit ---------------------------------------------\n", "Train evaluation --> auc: 0.991\n", "Test evaluation --> auc: 0.8768\n", "Time elapsed: 1.673s\n", "-------------------------------------------------\n", "Time: 1.673s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 1.678s\n", "-------------------------------------\n", "LightGBM --> auc: 0.8768\n" ] } ], "source": [ "# Let's see how the model performs now\n", "# Add a tag to the model's acronym to not overwrite previous LGB\n", "atom.run(\"LGB_dfs\", errors=\"raise\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Genetic Feature Generation" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully created new branch: gfg.\n" ] } ], "source": [ "# Create another branch for the genetic features\n", "# Split form master to avoid the dfs features\n", "atom.branch = \"gfg_from_main\"" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting FeatureGenerator...\n", " | Population Average | Best Individual |\n", "---- ------------------------- ------------------------------------------ ----------\n", " Gen Length Fitness Length Fitness OOB Fitness Time Left\n", " 0 3.02 0.139961 3 0.490021 N/A 23.20s\n", " 1 3.18 0.357605 7 0.49915 N/A 22.79s\n", " 2 3.82 0.425184 10 0.517679 N/A 22.07s\n", " 3 4.54 0.4404 9 0.527987 N/A 20.03s\n", " 4 6.87 0.445283 11 0.538222 N/A 18.79s\n", " 5 9.75 0.455204 15 0.543529 N/A 17.89s\n", " 6 11.53 0.456228 19 0.546248 N/A 16.75s\n", " 7 11.94 0.456611 21 0.547784 N/A 17.84s\n", " 8 12.74 0.463229 23 0.54873 N/A 14.39s\n", " 9 12.66 0.473977 21 0.548153 N/A 13.51s\n", " 10 11.80 0.478966 19 0.546534 N/A 11.67s\n", " 11 11.74 0.482432 19 0.547183 N/A 10.21s\n", " 12 12.63 0.473585 19 0.548053 N/A 9.43s\n", " 13 13.58 0.460883 23 0.550949 N/A 7.55s\n", " 14 14.06 0.462297 21 0.550264 N/A 6.35s\n", " 15 14.81 0.461855 25 0.551663 N/A 5.34s\n", " 16 14.88 0.467768 23 0.550597 N/A 3.89s\n", " 17 14.85 0.477248 21 0.550264 N/A 2.55s\n", " 18 15.07 0.478368 16 0.558362 N/A 1.27s\n", " 19 14.70 0.477435 16 0.558362 N/A 0.00s\n", "Generating new features...\n", " --> 20 new features were added.\n" ] } ], "source": [ "# Create new features using Genetic Programming\n", "atom.feature_generation(strategy='gfg', n_features=20)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namedescriptionfitness
0x23add(add(sub(Cloud3pm, mul(RainToday_No, abs(Wi...0.542362
1x24add(add(sub(Cloud3pm, mul(RainToday_No, abs(Wi...0.542049
2x25add(add(sub(Cloud3pm, mul(RainToday_No, abs(Wi...0.540022
3x26add(add(sub(Cloud3pm, mul(RainToday_No, Sunshi...0.534542
4x27add(sub(sub(sub(Humidity3pm, Pressure3pm), mul...0.533542
5x28add(add(sub(sub(Cloud3pm, mul(RainToday_No, Su...0.533542
6x29add(sub(sub(sub(Humidity3pm, Pressure3pm), abs...0.533542
7x30add(sub(sub(Humidity3pm, Pressure3pm), mul(Rai...0.532984
8x31sub(Sunshine, add(add(sub(Cloud3pm, abs(WindSp...0.532205
9x32sub(Sunshine, add(add(sub(Cloud3pm, abs(WindSp...0.532200
10x33add(add(sub(Cloud3pm, abs(WindSpeed3pm)), Wind...0.532200
11x34add(sub(sub(sub(Humidity3pm, Sunshine), Pressu...0.532200
12x35add(sub(sub(sub(Humidity3pm, Pressure3pm), Sun...0.532200
13x36sub(Sunshine, add(add(sub(Cloud3pm, abs(WindSp...0.532200
14x37add(add(sub(Cloud3pm, abs(WindSpeed3pm)), Wind...0.532200
15x38sub(add(add(sub(Humidity3pm, Pressure3pm), Win...0.532200
16x39add(sub(sub(sub(sub(Humidity3pm, Pressure3pm),...0.531546
17x40add(add(sub(Cloud3pm, abs(WindSpeed3pm)), Wind...0.531546
18x41sub(Sunshine, add(add(sub(Cloud3pm, abs(abs(Wi...0.531200
19x42add(sub(sub(sub(Humidity3pm, Pressure3pm), Sun...0.531067
\n", "
" ], "text/plain": [ " name description fitness\n", "0 x23 add(add(sub(Cloud3pm, mul(RainToday_No, abs(Wi... 0.542362\n", "1 x24 add(add(sub(Cloud3pm, mul(RainToday_No, abs(Wi... 0.542049\n", "2 x25 add(add(sub(Cloud3pm, mul(RainToday_No, abs(Wi... 0.540022\n", "3 x26 add(add(sub(Cloud3pm, mul(RainToday_No, Sunshi... 0.534542\n", "4 x27 add(sub(sub(sub(Humidity3pm, Pressure3pm), mul... 0.533542\n", "5 x28 add(add(sub(sub(Cloud3pm, mul(RainToday_No, Su... 0.533542\n", "6 x29 add(sub(sub(sub(Humidity3pm, Pressure3pm), abs... 0.533542\n", "7 x30 add(sub(sub(Humidity3pm, Pressure3pm), mul(Rai... 0.532984\n", "8 x31 sub(Sunshine, add(add(sub(Cloud3pm, abs(WindSp... 0.532205\n", "9 x32 sub(Sunshine, add(add(sub(Cloud3pm, abs(WindSp... 0.532200\n", "10 x33 add(add(sub(Cloud3pm, abs(WindSpeed3pm)), Wind... 0.532200\n", "11 x34 add(sub(sub(sub(Humidity3pm, Sunshine), Pressu... 0.532200\n", "12 x35 add(sub(sub(sub(Humidity3pm, Pressure3pm), Sun... 0.532200\n", "13 x36 sub(Sunshine, add(add(sub(Cloud3pm, abs(WindSp... 0.532200\n", "14 x37 add(add(sub(Cloud3pm, abs(WindSpeed3pm)), Wind... 0.532200\n", "15 x38 sub(add(add(sub(Humidity3pm, Pressure3pm), Win... 0.532200\n", "16 x39 add(sub(sub(sub(sub(Humidity3pm, Pressure3pm),... 0.531546\n", "17 x40 add(add(sub(Cloud3pm, abs(WindSpeed3pm)), Wind... 0.531546\n", "18 x41 sub(Sunshine, add(add(sub(Cloud3pm, abs(abs(Wi... 0.531200\n", "19 x42 add(sub(sub(sub(Humidity3pm, Pressure3pm), Sun... 0.531067" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can see the feature's fitness and description through the genetic_features attribute\n", "atom.genetic_features_" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training ========================= >>\n", "Models: LGB_gfg\n", "Metric: auc\n", "\n", "\n", "Results for LightGBM:\n", "Fit ---------------------------------------------\n", "Train evaluation --> auc: 0.9884\n", "Test evaluation --> auc: 0.8791\n", "Time elapsed: 1.487s\n", "-------------------------------------------------\n", "Time: 1.487s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 1.493s\n", "-------------------------------------\n", "LightGBM --> auc: 0.8791\n" ] } ], "source": [ "# Fit the model again\n", "atom.run(\"LGB_gfg\", metric=\"auc\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize the whole pipeline\n", "atom.plot_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyze the results" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "hovertemplate": "(%{x}, %{y})LGB - test+train", "legendgroup": "LGB", "legendgrouptitle": { "font": { "size": 16 }, "text": "LGB" }, "line": { "color": "rgb(0, 98, 98)", "dash": "solid", "width": 2 }, "marker": { "color": "rgb(0, 98, 98)", "line": { "color": "rgba(255, 255, 255, 0.9)", "width": 1 }, "size": 8, "symbol": "circle" }, "mode": "lines", "name": "test+train", "showlegend": true, "type": "scatter", "x": [ 0, 0, 0, 0.00012923235978288964, 0.00012923235978288964, 0.0002584647195657793, 0.0002584647195657793, 0.0003876970793486689, 0.0003876970793486689, 0.0005169294391315585, 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flQ7cdVV6wYGHpEfsumq2h3j1igKCqDdGCAFBNMSYqj2kIFrtaF2MAIGeBQTRnkE9jgCBqgUE0arH63IECPQoIIj2iDmnRwmiguic3lq9PFYQ7YXRQwhUISCIVjHG+i8hiNY/40W+oSC6yNNxNgIEFklAEF2kaTgLAQKLLiCILvqEnI8AgUUREEQXZRJbPsc1d92RLrjx+nTw7nukp+/7iMU/cMETvvTLnx/vtsi/IXrN3Xekb920Jh20arf0i/seWFCnzFZ9BdHPXH152rjxgfQbhx1Z5uAddtl3zx3TrXdsSBsf+FmH1ZYQqF9AEK1/xlXcUBCtYoxhLyGIhh2dgxMgUFhAEC0MbjsCBEILCKKhx+fwBAgUFBBEC2LbqncBX5nbO+nMD+wriM688QALBNEB0G0ZSkAQDTWudg8riLY7+0W4uSC6CFNwBgIEIggIohGm5IwECCyKgCC6KJNwDgIEFl1AEF30CTnf1gQE0eHfH4Lo8DNwAgKLIiCILsokgpzjymvWpHee9qH0nlNflw599La/AmP0+pNOPiPdcNOt6XdPfk16yfHP3uJNf/+ss8d/9taTTkzfvuiydMZZZ6cPvvctafUeuyVBNMgbpNJjCqKVDta1CBDoXUAQ7Z3UAwkQqFhAEK14uK5GgECvAoJor5weVlhAEC0MvsJ2gujwM3ACAosiIIguyiQKnuO2dXem15/yB+l7l161adcTjjsmvfvtr0k777TDVk8ySxC9d/2G9K73fSQdc/TjtxpCJxsKogXfBLaaSUAQnYnLiwkQaFhAEG14+K5OgMDMAoLozGQWECDQqIAg2ujgK7n2JIhWcp3Q1/jsC3499PmnObyvzJ1GyWtaFhBEG5z+JIi+7aQT01OfdESahMv9991r/OnMvv5avs+2niuIbkvInw8lIIgOJW9fAgSiCQii0SbmvAQIDCkgiA6pb28CBCIJCKKRpuWsywUE0cV5TwiiizMLJyEwlIAgOpT8gPuuFCo/d94/pAsuvGTTp0RHf//bp39k0yk/duYp43i6/BOik3WrVu2S/vKcr41fP3rtYYccuNmnUA/Yb+901ulvG//55Ct0R//v177i+E0RVhAd8E1h660KCKLeIAQIEJhOQBCdzsmrCBAgMBIQRL0PCBAgMJ2AIDqdk1cRIEDAJ0S9BwhsXUAQbfAdsqVPiE6+2nb0idHPnvv19NITnjP+Ct1R9Dz7C+ePf89z7e13bvYbopNwOgmmS3/7c0Q7+mreySdRR3//d+f/czrskEeOf3908vuip536unFsFUQbfDMGubIgGmRQjkmAwOACgujgI3AAAgQCCQiigYblqAQIDCogiA7Kb3MCBAIJCKKBhuWogwgIooOwD7vpSr8hOgmaK51s6adCR3/+ztM+lN5z6uvGUXP5J0tHr33fBz6dTnvH68aPWh5Elz5/+W+MCqLDvi/svmUBQdS7gwABAtMJCKLTOXkVAQIERgKCqPcBAQIEphMQRKdz8ioCBAgIot4DBLYuIIg2+A5Z/gnRpSFywjH59OYNN906/kdLv/I2J4hOIui5X71gk/zvnvya9JLjn+0Tog2+F6NcWRCNMinnJEBgaAFBdOgJ2J8AgUgCgmikaTkrAQJDCgiiQ+rbmwCBSAKCaKRpOesQAoLoEOoD77k8iC7/++VfZdvXJ0QnMXT/ffca/26oT4gO/Eaw/dQCgujUVF5IgEDjAoJo428A1ydAYCYBQXQmLi8mQKBhAUG04eG7OgECMwkIojNxeXGDAoJog0NfHkBHBKPf/jz1tA+ls05/21hk6adAt/Zns3xl7vIAOjnHiS8+1idEG3wfRrqyIBppWs5KgMCQAoLokPr2JkAgmoAgGm1izkuAwFACguhQ8vYlQCCagCAabWLOW1pAEC0tvgD7rRRER8caxc0PfPyccRQ950vfTB/+1Hnj0z7xiEPG/zv63dDRXzlfmTuKq69683vHzxl9De8+q3dPJ/7bXxJEF+B94QhbFhBEvTv6ErjyJzenkz/zyb4e5zkzCPzpf3hNevhuu8+wwku7CAiiXdSsIUCgVQFBtNXJuzcBArMKCKKzink9AQKtCgiirU7evacVEESnlfK6QQXW3HrvoPvbvG0BQbTt+fd5+4uvuzYd/4en9/lIz5pS4IJT350OWr33lK/2sq4CgmhXOesIEGhRQBBtceruTIBAFwFBtIuaNQQItCggiLY4dXeeRUAQnUXLawcTEEQHo7dxSkkQ9TboS2ASRFfvskt6/pFP6OuxC/mcv7/80vSTu+5Mxx5+ZNp3t90GO+O537s43b3hviSIlhmBIFrG2S4ECNQhIIjWMUe3IEBg/gKC6PyN7UCAQB0Cgmgdc3SL+QkIovOz9eQeBQTRHjE9amYBQXRmMgu2IDAJovvttnt65dOfWbXT2d+5IF13+23pZU9+Wjpor+E+mfnn3zw/3bH+3vRPp7w7PWrAc1Q97CWXE0RbmbR7EiDQh4Ag2oeiZxAg0IKAINrClN2RAIE+BATRPhQ9o2YBQbTm6VZ0N0G0omEGvIogGnBoC3pkQbT8YATRsuaCaFlvuxEgEFtAEI09P6cnQKCcgCBaztpOBAjEFhBEY8/P6ecvIIjO39gOPQgIoj0gekRnAUG0M10zC9fdc0+65Ibr05677JKOPODALd67xSB64tFPS48c8Lc7P/zN89O69fcO8pW5a26/LV1z6y3pwNWr06P22qeJfx8E0SbG7JIECPQkIIj2BOkxBAhULyCIVj9iFyRAoCcBQbQnSI+pVkAQrXa0dV1MEK1rntFuI4hGm1j58379B5emf//nf5Ke/dgj0qde90ZBNKXkK3NT+sD5/yO957xz0uuf87z0X074tfJvzAF2FEQHQLclAQJhBQTRsKNzcAIECgsIooXBbUeAQFgBQTTs6By8kIAgWgjaNnkCgmien9V5AoJonl8LqwXRB09ZEBVEW/h33x0JECCQIyCI5uhZS4BASwKCaEvTdlcCBHIEBNEcPWtbEBBEW5hyBXcURCsYYuArCKKBh1fo6IKoILrSW80nRAv9C2gbAgQIBBUQRIMOzrEJECguIIgWJ7chAQJBBQTRoINz7GICgmgxahvlCAiiOXrW5goIormC9a//+g8uS//+z/84PWr13unfHf20LV74xjvWpU/+8/+X9ttt9/TKpz+zapjJJ0QX5TdEX/vMY9MeO+9c1PzbP7oyfeOKH6TXP+e49F9O+PWiew+1ma/MHUrevgQIRBQQRCNOzZkJEBhCQBAdQt2eBAhEFBBEI07NmUsKCKIlte3VWUAQ7UxnYQ8CgmgPiJU/YvIJ0Wmv2VIQfdmTn5YO2mvvaWl6f92ff/P8dMf6e3t/7iwP9Buis2h5LQECBNoREETbmbWbEiCQJyCI5vlZTYBAOwKCaDuzdtNuAoJoNzerCgsIooXBbbeZgCDqDbEtAZ8QfbCQT4im5BOi2/o3x58TIECgbQFBtO35uz0BAtMLCKLTW3klAQJtCwiibc/f7bctIIhu28grFkBAEF2AITR8BEG04eFPeXW/IbrlILoonxD9p1PenR5V+JOqfkN0yn+BvIwAAQKNCgiijQ7etQkQmFlAEJ2ZzAICBBoVEEQbHbxrTy0giE5N5YVDCgiiQ+rbWxD1HtiWgCAqiK70HhFEt/Vvjj8nQIBA2wKCaNvzd3sCBKYXEESnt/JKAgTaFhBE256/229bQBDdtpFXLICAILoAQ2j4CIJow8Of8uqCqCAqiP4fgYeklPbba6d049r1U/7b42UECBBoV0AQbXf2bk6AwGwCguhsXl5NgEC7AoJou7N38+kEBNHpnLxqYAFBdOABNL69INr4G2CK66+75550yQ3Xpz132SUdecCBW1xx8XXXpuP/8PS03267p1c+/ZlTPDnuSxbtN0QvOPXd6aDVexcFXXP7bemaW29JB65enR611z5F9x5qM0F0KHn7EiAQUUAQjTg1ZyZAYAgBQXQIdXsSIBBRQBCNODVnLikgiJbUtldnAUG0M52FPQgIoj0gesRYoMUg2vJviLb4thdEW5y6OxMg0FVAEO0qZx0BAq0JCKKtTdx9CRDoKiCIdpWzrhUBQbSVSQe/pyAafIDBjy+IBh/gAh1fEC0/jD//5vnpjvX3pn865d3pUXuV/YRo+dsOv6MgOvwMnIAAgTgCgmicWTkpAQLDCgiiw/rbnQCBOAKCaJxZOekwAoLoMO52nVFAEJ0RzMt7FRBEe+Vs+mGTILp6l13S8498QtUWf3/5peknd92Zjj38yLTvbrsNdtdzv3dxunvDfWmIr8wd7NIDbiyIDohvawIEwgkIouFG5sAECAwkIIgOBG9bAgTCCQii4UbmwIUFBNHC4LbrJiCIdnOzqh8BQbQfR0/516/MZVFeQBAtYy6IlnG2CwECdQgIonXM0S0IEJi/gCA6f2M7ECBQh4AgWscc3WJ+AoLo/Gw9uUcBQbRHTI+aWUAQnZnMgi0IXPmTm9PJn/kknwEE/vQ/vCY9fLfdB9i5rS0F0bbm7bYECOQJCKJ5flYTINCOgCDazqzdlACBPAFBNM/P6voFBNH6Z1zFDQXRKsYY9hKCaNjROTgBAoUFBNHC4LYjQCC0gCAaenwOT4BAQQFBtCC2rQgQCC0giIYen8MXEBBECyDbIl9AEM039ITuAoJodzsrCRBoS0AQbWvebkuAQJ6AIJrnZzUBAu0ICKLtzNpNCRDIExBE8/ysrl9AEK1/xlXcUBCtYoxhLyGIhh2dgxMgUFhAEC0MbjsCBEILCKKhx+fwBAgUFBBEC2LbigCB0AKCaOjxOXwBAUG0ALIt8gUE0XxDT+guIIh2t7OSAIG2BATRtubttgQI5AkIonl+VhMg0I6AINrOrN2UAIE8AUE0z8/q+gUE0fpnXMUNBdEqxhj2EoJo2NE5OAEChQUE0cLgtiNAILSAIBp6fA5PgEBBAUG0ILatCBAILSCIhh6fwxcQEEQLINsiX0AQzTf0hO4Cgmh3OysJEGhLQBBta95uS4BAnoAgmudnNQEC7QgIou3M2k0JEMgTEETz/KyuX0AQrX/GVdxQEK1ijGEvIYiGHZ2DEyBQWEAQLQxuOwIEQgsIoqHH5/AECBQUEEQLYtuKAIHQAoJo6PE5fAEBQbQAsi3yBQTRfENP6C4giHa3s5IAgbYEBNG25u22BAjkCQiieX5WEyDQjoAg2s6s3ZQAgTwBQTTPz+r6BQTR+mdcxQ0F0SrGGPYSgmjY0Tk4AQKFBQTRwuC2I0AgtIAgGnp8Dk+AQEEBQbQgtq0IEAgtIIiGHp/DFxAQRAsg2yJfQBDNN/SE7gKCaHc7KwkQaEtAEG1r3m5LgECegCCa52c1AQLtCAii7czaTQkQyBMQRPP8rK5fQBCtf8ZV3FAQrWKMYS8hiIYdnYMTIFBYQBAtDG47AgRCCwiiocfn8AQIFBQQRAti24oAgdACgmjo8Tl8AQFBtACyLfIFBNF8Q0/oLiCIdrezkgCBtgQE0bbm7bYECOQJCKJ5flYTINCOgCDazqzdlACBPAFBNM/P6voFBNH6Z1zFDQXRKsYY9hKCaNjROTgBAoUFBNHC4LYjQCC0gCAaenwOT4BAQQFBtCC2rQgQCC0giIYen8MXEBBECyDbIl9AEM039ITuAoJodzsrCRBoS0AQbWvebkuAQJ6AIJrnZzUBAu0ICKLtzNpNCRDIExBE8/ysrl9AEK1/xlXcUBCtYoxhLyGIhh2dgxMgUFhAEC0MbjsCBEILCKKhx+fwBAgUFBBEC2LbigCB0AKCaOjxOXwBAUG0ALIt8gUE0XxDT+guIIh2t7OSAIG2BATRtubttgQI5AkIonl+VhMg0I6AINrOrN2UAIE8AUE0z8/q+gUE0fpnXMUNBdEqxhj2EoJo2NE5OAEChQUE0cLgtiNAILSAIBp6fA5PgEBBAUG0ILatCBAILSCIhh6fwxcQEEQLINsiX0AQzTf0hO4Cgmh3OysJEGhLQBBta95TyT/eAAAgAElEQVRuS4BAnoAgmudnNQEC7QgIou3M2k0JEMgTEETz/KyuX0AQrX/GVdxQEK1ijGEvIYiGHZ2DEyBQWEAQLQxuOwIEQgsIoqHH5/AECBQUEEQLYtuKAIHQAoJo6PE5fAEBQbQAsi3yBQTRfENP6C4giHa3s5IAgbYEBNG25u22BAjkCQiieX5WEyDQjoAg2s6s3ZQAgTwBQTTPz+r6BQTR+mdcxQ0F0SrGGPYSgmjY0Tk4AQKFBQTRwuC2I0AgtIAgGnp8Dk+AQEEBQbQgtq0IEAgtIIiGHp/DFxAQRAsg2yJfQBDNN/SE7gKCaHc7KwkQaEtAEG1r3m5LgECegCCa52c1AQLtCAii7czaTQkQyBMQRPP8rK5fQBCtf8ZV3FAQrWKMYS8hiIYdnYMTIFBYQBAtDG47AgRCCwiiocfn8AQIFBQQRAti24oAgdACgmjo8Tl8AQFBtACyLfIFBNF8Q0/oLiCIdrezkgCBtgQE0bbm7bYECOQJCKJ5flYTINCOgCDazqzdlACBPAFBNM/P6voFBNH6Z1zFDQXRKsYY9hKCaNjROTgBAoUFBNHC4LYjQCC0gCAaenwOT4BAQQFBtCC2rQgQCC0giIYen8MXEBBECyDbIl9AEM039ITuAoJodzsrCRBoS0AQbWvebkuAQJ6AIJrnZzUBAu0ICKLtzNpNCRDIExBE8/ysrl9AEK1/xlXcUBCtYoxhLyGIhh2dgxMgUFhAEC0MbjsCBEILCKKhx+fwBAgUFBBEC2LbigCB0AKCaOjxOXwBAUG0ALIt8gUE0XxDT+guIIh2t7OSAIG2BATRtubttgQI5AkIonl+VhMg0I6AINrOrN2UAIE8AUE0z8/q+gUE0fpnXMUNBdEqxhj2EoJo2NE5OAEChQUE0cLgtiNAILSAIBp6fA5PgEBBAUG0ILatCBAILSCIhh6fwxcQEEQLINsiX0AQzTf0hO4Cgmh3OysJEGhLQBBta95uS4BAnoAgmudnNQEC7QgIou3M2k0JEMgTEETz/KyuX0AQrX/GVdxQEK1ijGEvIYiGHZ2DEyBQWEAQLQxuOwIEQgsIoqHH5/AECBQUEEQLYtuKAIHQAoJo6PE5fAEBQbQAsi3yBQTRfENP6C4giHa3s5IAgbYEBNG25u22BAjkCQiieX5WEyDQjoAg2s6s3ZQAgTwBQTTPz+r6BQTR+mdcxQ0F0SrGGPYSgmjY0Tk4AQKFBQTRwuC2I0AgtIAgGnp8Dk+AQEEBQbQgtq0IEAgtIIiGHp/DFxAQRAsg2yJfQBDNN/SE7gKCaHc7KwkQaEtAEG1r3m5LgECegCCa52c1AQLtCAii7czaTQkQyBMQRPP8rK5fQBCtf8ZV3FAQrWKMYS8hiIYdnYMTIFBYQBAtDG47AgRCCwiiocfn8AQIFBQQRAti24oAgdACgmjo8Tl8AQFBtACyLfIFBNF8Q0/oLiCIdrezkgCBtgQE0bbm7bYECOQJCKJ5flYTINCOgCDazqzdlACBPAFBNM/P6voFBNH6Z1zFDQXRKsYY9hKCaNjROTgBAoUFBNHC4LYjQCC0gCAaenwOT4BAQQFBtCC2rQgQCC0giIYen8MXEBBECyDbIl9AEM039ITuAoJodzsrCRBoS0AQbWvebkuAQJ6AIJrnZzUBAu0ICKLtzNpNCRDIExBE8/ysrl9AEK1/xlXcUBCtYoxhLyGIhh2dgxMgUFhAEC0MbjsCBEILCKKhx+fwBAgUFBBEC2LbigCB0AKCaOjxOXwBAUG0ALIt8gUE0XxDT+guIIh2t7OSAIG2BATRtubttgQI5AkIonl+VhMg0I6AINrOrN2UAIE8AUE0z8/q+gUE0fpnXMUNBdEqxhj2EoJo2NE5OAEChQUE0cLgtiNAILSAIBp6fA5PgEBBAUG0ILatCBAILSCIhh6fwxcQEEQLINsiX0AQzTf0hO4Cgmh3OysJEGhLQBBta95uS4BAnoAgmudnNQEC7QgIou3M2k0JEMgTEETz/KyuX0AQrX/GVdxQEK1ijGEvIYiGHZ2DEyBQWEAQLQxuOwIEQgsIoqHH5/AECBQUEEQLYtuKAIHQAoJo6PE5fAEBQbQAsi3yBQTRfENP6C4giHa3s5IAgbYEBNG25u22BAjkCQiieX5WEyDQjoAg2s6s3ZQAgTwBQTTPz+r6BQTR+mdcxQ0F0SrGGPYSgmjY0Tk4AQKFBQTRwuC2I0AgtIAgGnp8Dk+AQEEBQbQgtq0IEAgtIIiGHp/DFxAQRAsg2yJfQBDNN/SE7gKCaHc7KwkQaEtAEG1r3m5LgECegCCa52c1AQLtCAii7czaTQkQyBMQRPP8rK5fQBCtf8ZV3FAQrWKMYS8hiIYdnYMTIFBYQBAtDG47AgRCCwiiocfn8AQIFBQQRAti24oAgdACgmjo8Tl8AYGqgujnzvuHdMGFl6R3v/01aeeddtiM78pr1qR3nvah9J5TX5cOffQjCtD2u8W96zekd73vI+mYox+fXnL8s2d6eJe1377osnTGWWenD773LWn1HrvNtN88XiyIzkPVM6cVEESnlfI6AgRaFxBEW38HuD8BArMICKKzaHktAQItCwiiLU/f3QkQmEVAEJ1Fy2tbFJhbEB0FyJNOPiPdcNOtm1xf+4rj01tPOnFuzpGC6G3r7kyvP+UP0ttOOjE99UlHjE1WipC/f9bZ6eCD9k8veu4xvQXR0T6vevN7N5vDCccds1lIniaILp/x8mf0OWhBtE9Nz5pVQBCdVczrCRBoVUAQbXXy7k2AQBcBQbSLmjUECLQoIIi2OHV3JkCgi4Ag2kXNmpYE5hJEV4p9o08pfvDjf51e/fIXze0Th1sLoos41EnsnHzic/T3533tW+ms0982/hTrSo5d7rH8E6JbCq//fNFlM30idOR90CP23RR0R+cf/TWP6C2Idpm8NX0JCKJ9SXoOAQK1CwiitU/Y/QgQ6FNAEO1T07MIEKhZQBCtebruRoBAnwKCaJ+anlWjwFyC6LY+XbhS6FsaBydhc9WqXdJfnvO1sfvHzjxlU3hb/snEySdPt7Zu+Vfmjva765716a677knnfvWCdMB+e28KkaP9JhFx9Gejv3735NdM9VW1o30+8bmvpLe//uUP+tre5W+g0Xl/9OMbxwFxEoxX7bpz2mevPcZ7jZ71vg98Op32jtelnXbccbNPiG7LaGL8vUuv2rTt5A5bms/SoLn0NaMHjD7N+ivP+8X0sbO/NH7eJNouvdPSIL3mplvGX1H8K89/Rjrtjz4xftnSTwiP9rrx5rXp2jU3p9EZR/5n/s4b038/++9WnIcgWuN/fuLcSRCNMysnJUBgWAFBdFh/uxMgEEtAEI01L6clQGA4AUF0OHs7EyAQS0AQjTUvpy0vMJcgOgmWxz/36St+WnCaIPrbp39kUwRdGucmYfBlLz52HEhHIfGz5349vfSE56S//doFaUvr1t5+52a/Ibr805hLY95oDKPf69x/373G5x+d99Tf+1B6+xtevs3fH50liC4NnqPzjULqsb/4C+k7F18+3nd077/6wvnjr7KdnGnyG6Kj827LaHL+aT4hOnr+Uucrrr5+02+Ijv5sFEQf9Yh9V/x91tGfT/aY7Ln8PbB85iP/pZ9IXT6P5Z82FUTL/8fBjv8qIIh6NxAgQGA6AUF0OievIkCAwEhAEPU+IECAwHQCguh0Tl5FgAABQdR7gMDWBeYSREdbrvQbopNPeU4TRC+48JJN8W1pOJzEuRNffOyDPrG5/CtzlwfH0ScW33Pq68ZRc3lwWxofJ59unLx2tOfyr7fdEussQXRpaF172x3px2tuTr/0zKPSaX/4iXTqf35l+uin/3b8+6GjT4suj5qz3HXaILrUa6UguvT3Tpfef2Tz4U+dl5b+hujyT+RODEf/O4q9y/2X32f53wui/lM2pIAgOqS+vQkQiCQgiEaalrMSIDC0gCA69ATsT4BAFAFBNMqknJMAgaEFBNGhJ2D/RReYWxBdfvFR4PrAx88Zf9XqXnvuNv7E4dLAttJX5o4+GbnzTjts9tWxq/fY7UGxdRJaZ4mE2wqiJ518Rrrhpls3u8aWvjZ38mnNlYb9xCMfs9Xf5Zzce/TVuc96+s+nJxzxmPS+D346veT4Z42/PnbpJ2FHn1pd+gnRLUXj0adNJ1+1O/KaNohu6xOiWwqik3uv9JW5S6Py0q8IFkQX/T8NzrdUQBD1fiBAgMB0AoLodE5eRYAAgZGAIOp9QIAAgekEBNHpnLyKAAECgqj3AIGtCxQLoks/FXrYIQdmBdGlV1oa8f7+H/9X2loknOUTokuD4ixvolk+ITp67igSfv/yH6VVu+yUXv3yF6VRwBz9s1vWrkujT2mOPim6UtScJf5OG0S39Rui2wqiW/tE7uiuS58viM7yrvLaoQUE0aEnYH8CBKIICKJRJuWcBAgsgoAgughTcAYCBCIICKIRpuSMBAgsgoAgughTcIZFFphLEB1Fym9867ub/X7oSr8DOvm04+Trdd/wm786/nrYrcW+EeZ5X70gvfIlzx+7ziOIjp679DdER38/OuMVV1+XfvnYp211nrMG0cndn/yEx276iuDRnV715vdu9hW0s3xl7uR3Vrfku9RsFFtHfy3/Tc+lrxn9+fJP9I7+2Z/9xRfScc86etPvqo6ecePNa8f3WP61w8u/QlcQXeT/LDjbcgFB1HuCAAEC0wkIotM5eVVZgctvvCF98bv/s+ymFe925AEHpuOf+KSKb1juaoJoOWs7ESAQW0AQjT0/pydAoJyAIFrO2k4xBeYSRCefBv3epVdtUln+1bFLf2N09NuTq1btkp7wuIO3GUQnse/cr14wfvYB++09/hre0e+CzvKpya19Ze7oa3onAXKlfbY26lmD6MTqaU86YlNAnvyzpb+TOksQXf61wqOv4h399YLnPGXsOwmuS++x9Pc/R/98miC6/DnLf0N0+dcOT77aePR8QTTmfzBaPbUg2urk3ZsAgVkFBNFZxby+hMA5F1+Y3vCJj5bYqok9fu0Xjk5/8spXN3HXeV9SEJ23sOcTIFCLgCBayyTdgwCBeQsIovMW9vzoAnMJotFRnD9fYPknQnOfuObWe3MfYT2BzgKCaGc6CwkQaExAEG1s4EGuOwmie++6Kh2+7/5BTv3gY16z9pa0Zt3t6VGr904H7rm6+D1uuevO9MOf3JQE0f7oBdH+LD2JAIG6BQTRuufrdgQI9CcgiPZn6Ul1Cgiidc518FsJooOPwAF6FBBEe8T0KAIEqhYQRKseb9jLnXPRhekNn/xoetx++6cTnnhU2Ht844eXp29fc1X6N4cdnp528KHF73HpDdenv/2X7wqiPcoLoj1iehQBAlULCKJVj9flCBDoUUAQ7RHTo6oUEESrHGt9l/IJ0fpmGulGgmikaTkrAQJDCgiiQ+rbe0sCgmg/7w1BtB/HpU8RRPs39UQCBOoUEETrnKtbESDQv4Ag2r+pJ9YlIIjWNc9qbyOIVjvaEBcTREOMySEJEFgAAUF0AYbgCA8SEET7eVMIov04CqL9O3oiAQL1Cwii9c/YDQkQ6EdAEO3H0VPqFRBE651tVTcTRKsaZ7jLCKLhRubABAgMJCCIDgQ/p20vvu7adPwfnp5+/sCD0t++6bfmtMv8HyuI9mO8PIhuuP/+dMg73pwetv326Uenvb+fTRp7ik+INjZw1yVAoLOAINqZzkICBBoTEEQbG7jrziwgiM5MZsEQAoLoEOr2nAgIot4LBAgQmE5AEJ3OKcqrBNHFmtSi/YaoIJr//hBE8w09gQCBNgQE0Tbm7JYECOQLCKL5hp5Qt4AgWvd8q7mdIFrNKENeRBANOTaHJkBgAAFBdAD0OW4piM4Rt8OjBdEOaAu+RBBd8AE5HgECCyMgiC7MKByEAIEFFxBEF3xAjje4gCA6+AgcYBoBQXQaJa+Zl4AgOi9ZzyVAoDYBQbSuiU6C6GP22Ted/tJXhL3cN6+4PJ351S+lx+27fzrh548Ke49v/PCy9O1rrk7POuxx6akHP6b4PS678YZ03vcvSv/msMPTm497Ubr/gQfSyz/0R+NzXH/6Hxc/Tw0bCqI1TNEdCBAoISCIllC2BwECNQgIojVM0R3mKSCIzlPXs3sTEER7o/SgDgKCaAc0SwgQaFJAEK1r7JMgWsutHrff/umEJ0YOopenb19z1ThIPu3gQ4uPZfIboss39hui3UchiHa3s5IAgbYEBNG25u22BAh0FxBEu9tZ2YaAINrGnMPfUhANP8LQFxBEQ4/P4QkQKCggiBbELrDVJIjuusOO6YkHHlRgx/lscevdd6Yf3nyTT4hm8k4+IbrPrqvSYfvun36WUvrW1VeMn+oTot1wBdFublYRINCegCDa3szdmACBbgKCaDc3q9oREETbmXXomwqioccX/vCCaPgRugABAoUEBNFC0IW28RuihaCn3MZviE4JFehlgmigYTkqAQKDCgiig/LbnACBQAKCaKBhOeogAoLoIOw2nVVAEJ1VzOv7FBBE+9T0LAIEahYQROuariC6WPMURBdrHn2cRhDtQ9EzCBBoQUAQbWHK7kiAQB8Cgmgfip5Rs4AgWvN0K7qbIFrRMANeRRANODRHJkBgEAFBdBD2uW0qiM6NttODBdFObAu9SBBd6PE4HAECCyQgiC7QMByFAIGFFhBEF3o8DrcAAoLoAgzBEbYtIIhu28gr5icgiM7P1pMJEKhLQBCta5433bEu/cUF30z777FneuXTnxn2cudcfGF6wyc+6jdEMyd42Y1r0nnfvzj92i8cnf7kla8eP+2ML5+btt9++/Tm416Y+fQ2lwuibc7drQkQmF1AEJ3dzAoCBNoUEETbnLtbTy8giE5v5ZUDCgiiA+LbOgmi3gQECBCYTkAQnc7Jq8oKnHPRhekNn/xoetx++6cTnnhU2c173G3RPiHa49WafZQg2uzoXZwAgRkFBNEZwbycAIFmBQTRZkfv4lMKCKJTQnnZsAKC6LD+re8uiLb+DnB/AgSmFRBEp5XyupICgmg/2pfecH3623/57mafEO3nye0+RRBtd/ZuToDAbAKC6GxeXk2AQLsCgmi7s3fz6QQE0emcvGpgAUF04AE0vr0g2vgbwPUJEJhaQBCdmsoLCwoIov1gC6L9OC59iiDav6knEiBQp4AgWudc3YoAgf4FBNH+TT2xLgFBtK55VnsbQbTa0Ya4mCAaYkwOSYDAAggIogswBEd4kMAkiKLpR2Dpb4j288R2nyKItjt7NydAYDYBQXQ2L68mQKBdAUG03dm7+XQCguh0Tl41sIAgOvAAGt9eEG38DeD6BAhMLSCITk3lhQUFBNF+sQXR/jwF0f4sPYkAgboFBNG65+t2BAj0JyCI9mfpSXUKCKJ1zrW6Wwmi1Y001IUE0VDjclgCBAYUEEQHxLc1AQLhBATRcCNzYAIEBhIQRAeCty0BAuEEBNFwI3PgwgKCaGFw23UTEES7uVnVj4Ag2o+jpxAgUL+AIFr/jN2QAIH+BATR/iw9iQCBugUE0brn63YECPQnIIj2Z+lJdQoIonXOtbpbCaLVjTTUhQTRUONyWAIEBhQQRAfEtzUBAuEEBNFwI3NgAgQGEhBEB4K3LQEC4QQE0XAjc+DCAoJoYXDbdRMQRLu5WdWPgCDaj6OnECBQv4AgWv+M3ZAAgf4EBNH+LD2JAIG6BQTRuufrdgQI9CcgiPZn6Ul1Cgiidc61ulsJotWNNNSFBNFQ43JYAgQGFBBEB8S3NQEC4QQE0XAjc2ACBAYSEEQHgrctAQLhBATRcCNz4MICgmhhcNt1ExBEu7lZ1Y+AINqPo6cQIFC/gCBa/4zdkACB/gQE0f4sPYkAgboFBNG65+t2BAj0JyCI9mfpSXUKCKJ1zrW6Wwmi1Y001IUE0VDjclgCBAYUEEQHxLc1AQLhBATRcCNzYAIEBhIQRAeCty0BAuEEBNFwI3PgwgKCaGFw23UTEES7uVnVj4Ag2o+jpxAgUL+AIFr/jN2QAIH+BATR/iw9iQCBugUE0brn63YECPQnIIj2Z+lJdQoIonXOtbpbCaLVjTTUhQTRUONyWAIEBhQQRAfEtzUBAuEEBNFwI3NgAgQGEhBEB4K3LQEC4QQE0XAjc+DCAoJoYXDbdRMQRLu5WdWPgCDaj6OnECBQv4AgWv+M3ZAAgf4EBNH+LD2JAIG6BQTRuufrdgQI9CcgiPZn6Ul1Cgiidc61ulsJotWNNNSFBNFQ43JYAgQGFBBEB8S3NQEC4QQE0XAjc2ACBAYSEEQHgrctAQLhBATRcCNz4MICgmhhcNt1ExBEu7lZ1Y+AINqPo6cQIFC/gCBa/4zdkACB/gQE0f4sPYkAgboFBNG65+t2BAj0JyCI9mfpSXUKCKJ1zrW6Wwmi1Y001IUE0VDjclgCBAYUEEQHxLc1AQLhBATRcCNzYAIEBhIQRAeCty0BAuEEBNFwI3PgwgKCaGFw23UTEES7uVnVj4Ag2o+jpxAgUL+AIFr/jN2QAIH+BATR/iw9iQCBugUE0brn63YECPQnIIj2Z+lJdQoIonXOtbpbCaLVjTTUhQTRUONyWAIEBhQQRAfEtzUBAuEEBNFwI3NgAgQGEhBEB4K3LQEC4QQE0XAjc+DCAoJoYXDbdRMQRLu5WdWPgCDaj6OnECBQv4AgWv+M3ZAAgf4EBNH+LD2JAIG6BQTRuufrdgQI9CcgiPZn6Ul1Cgiidc61ulsJotWNNNSFBNFQ43JYAgQGFBBEB8S3NQEC4QQE0XAjc2ACBAYSEEQHgrctAQLhBATRcCNz4MICgmhhcNt1ExBEu7lZ1Y+AINqPo6cQIFC/gCBa/4zdkACB/gQE0f4sPYkAgboFBNG65+t2BAj0JyCI9mfpSXUKCKJ1zrW6Wwmi1Y001IUE0VDjclgCBAYUEEQHxLc1AQLhBATRcCNzYAIEBhIQRAeCty0BAuEEBNFwI3PgwgKCaGFw23UTEES7uVnVj4Ag2o+jpxAgUL+AIFr/jN2QAIH+BATR/iw9iQCBugUE0brn63YECPQnIIj2Z+lJdQoIonXOtbpbCaLVjTTUhQTRUONyWAIEBhQQRAfEtzUBAuEEBNFwI3NgAgQGEhBEB4K3LQEC4QQE0XAjc+DCAoJoYXDbdRMQRLu5WdWPgCDaj6OnECBQv4AgWv+M3ZAAgf4EBNH+LD2JAIG6BQTRuufrdgQI9CcgiPZn6Ul1Cgiidc61ulsJotWNNNSFBNFQ43JYAgQGFBBEB8S3NQEC4QQE0XAjc2ACBAYSEEQHgrctAQLhBATRcCNz4MICgmhhcNt1ExBEu7lZ1Y+AINqPo6cQIFC/gCBa/4zdkACB/gQE0f4sPYkAgboFBNG65+t2BAj0JyCI9mfpSXUKCKJ1zrW6Wwmi1Y001IUE0VDjclgCBAYUEEQHxLc1AQLhBATRcCNzYAIEBhIQRAeCty0BAuEEBNFwI3PgwgKCaGFw23UTEES7uVnVj4Ag2o+jpxAgUL+AIFr/jN2QAIH+BATR/iw9iQCBugUE0brn63YECPQnIIj2Z+lJdQoIonXOtbpbCaLVjTTUhQTRUONyWAIEBhQQRAfEtzUBAuEEBNFwI3NgAgQGEhBEB4K3LQEC4QQE0XAjc+DCAoJoYXDbdRMQRLu5WdWPgCDaj6OnECBQv4AgWv+M3ZAAgf4EBNH+LD2JAIG6BQTRuufrdgQI9CcgiPZn6Ul1Cgiidc61ulsJotWNNNSFBNFQ43JYAgQGFBBEB8S3NQEC4QQE0XAjc2ACBAYSEEQHgrctAQLhBATRcCNz4MICgmhhcNt1ExBEu7lZ1Y+AINqPo6f8q8Bd929Iv/m1cxeK5KBVu6czn3HcQp3JYeIJCKLxZubEBAgMJyCIDmdvZwIEYgkIorHm5bQECAwnIIgOZ2/nGAKCaIw5NX9KQbT5t8CgAILooPxVbi6IVjlWl0opCaLeBgQIEJheQBCd3sorCRBoW0AQbXv+bk+AwPQCguj0Vl7ZpoAg2ubcw91aEA03sqoOLIhWNc6FuMwkiD50u+3SSw4+vMiZLr99bbp47c3psXusTkftvd+mPW/fcF/68nVXJ58QLTKG6jcRRKsfsQsSINCjgCDaI6ZHESBQtYAgWvV4XY4AgR4FBNEeMT2qSgFBtMqx1ncpQbS+mUa6kSAaaVoxziqIxpiTU84uIIjObmYFAQLtCgii7c7ezQkQmE1AEJ3Ny6sJEGhXQBBtd/ZuPp2AIDqdk1cNLCCIDjyAxrcXRBt/A8zh+oLoHFA9ciEEBNGFGINDECAQREAQDTIoxyRAYHABQXTwETgAAQJBBATRIINyzMEEBNHB6G08i4AgOouW1/YtIIj2LVrP8/7yikvTdtttl172mMfNdKm77/9p+o9f+2Iq+ZW5P7hjbbrolpvT4buvTk/a51+/MnfdTzekv/vxVemRq3ZL73/G86a+x9r71qf/8eOr094775yed+DBU6/zwroFBNG65+t2BAj0KyCI9uvpaQQI1CsgiNY7WzcjQKBfAUG0X09Pq09AEK1vplXeSBCtcqxhLiWIhhlV0YP+9IEH0su/ck7a/iEPSWc//9dm2ruGT4j+cN1t6ZRvnZ8O233P9N+O+aWZ7u/F9QoIovXO1s0IEOhfQBDt39QTCRCoU0AQrXOubkWAQP8Cgmj/pp5Yl4AgWtc8q72NIFrtaENcTBANMabihxREBdHib7oAGwqiAYbkiAQILIyAILowo3AQAgQWXEAQXfABOR4BAgsjIIguzCgcZEEFBNEFHYxjbS4giHpHDCkgiA6pv7h7C6KC6OK+O4c7mSA6nL2dCRCIJyCIxpuZExMgMIyAIDqMu10JEIgnIIjGm5kTlxUQRMt6262jgCDaEc6yXgQE0V4Yq3zIS7/8+fG9Hr96n5nu98D//rrdy9atHeY3RPdYnZ60936KBXoAACAASURBVIN/Q3SH7bZPh+2xeup7rP/fv4N61Z3r0mG7r07/7Zhjp17nhXULCKJ1z9ftCBDoV0AQ7dfT0wgQqFdAEK13tm5GgEC/AoJov56eVp+AIFrfTKu8kSBa5VjDXEoQDTOqogedfEI0Z9OHbrddesnBh+c8Yuq1l9++Nl289ub02D1Wp6OWBNHbN9yXvnzd1VM/Z/kL/YZoZ7oqFwqiVY7VpQgQmJOAIDonWI8lQKA6AUG0upG6EAECcxIQROcE67HVCAii1Yyy7osIonXPd9FvJ4gu+oSGO9/kE6K/89RnzXSIe++/P532v/5poT4huu/Ou6Q3PuHoqe9x3d13pj+75CKfEJ1arI0XCqJtzNktCRDoR0AQ7cfRUwgQqF9AEK1/xm5IgEA/AoJoP46eUq+AIFrvbKu6mSBa1TjDXUYQDTeyIgf2G6J+Q7TIGy3YJoJosIE5LgECgwoIooPy25wAgUACgmigYTkqAQKDCgiig/LbPICAIBpgSI6YkiDqXTCkgCA6pP7i7i2ICqKL++4c7mSC6HD2diZAIJ6AIBpvZk5MgMAwAoLoMO52JUAgnoAgGm9mTlxWQBAt6223jgKCaEc4y3oREER7YazuIYKoIFrdm7qHCwmiPSB6BAECzQgIos2M2kUJEMgUEEQzAS0nQKAZAUG0mVG7aEcBQbQjnGVlBQTRst5221xAEPWO6Fvgrvs3pN/82rlFf0P08tvXpovX3pweu8fqdNTe+2260u0b7ktfvu7qdNCq3dOZzziu76t6XmMCgmhjA3ddAgSyBATRLD6LCRBoSEAQbWjYrkqAQJaAIJrFZ3EDAoJoA0Ou4YqCaA1TjHsHQTTu7Bb15ILook7GuXIFBNFcQesJEGhJQBBtadruSoBAjoAgmqNnLQECLQkIoi1N2127CAiiXdSsKS4giBYnt+ESAUHU26FvAUG0b1HPWxQBQXRRJuEcBAhEEBBEI0zJGQkQWAQBQXQRpuAMBAhEEBBEI0zJGYcUEESH1K9g798/6+zxLd560okPus2V16xJ7zztQ+k9p74uHfroR2TdVhDN4rM4U0AQzQS0/EECd9//0/Qfv/bF8T9//J77FBG6df296ab1d6e9dtwp7b/zqk173rfx/nTlnbenR67aLb3/Gc8rchab1CsgiNY7WzcjQKB/AUG0f1NPJECgTgFBtM65uhUBAv0LCKL9m3piXQKCaEXzHAXIk04+I91w062bbvXaVxy/Yqzs69qCaF+SnrPIAoLoIk8n5tmWBtFFuYEguiiTiH0OQTT2/JyeAIGyAoJoWW+7ESAQV0AQjTs7JydAoKyAIFrW227xBATReDNb8cS3rbszvf6UP0hvO+nE9NQnHTF+zb3rN6QPfvyv06tf/qK0eo/d5nLTrQXRPjf0CdE+NT1rVgFBdFYxr9+WwIYHNqbPX/WDbb2s6J/vseOO6YUHPabonjarT0AQrW+mbkSAwPwEBNH52XoyAQJ1CQiidc3TbQgQmJ+AIDo/W0+uQ0AQrWOO6dsXXZbOOOvs9MH3vmXF+LlSMB3FzIMP2j+95Phnp8+d9w/pggsvSatW7ZL+8pyvjVU+duYpm+Lq6M9/+/SPbNKa/NnoGXfdsz7dddc96dyvXpAO2G/vdNbpbxt/Re7yr8zd2mtHD56c8XuXXvWg/QXRSt6oQa8hiAYdnGMTIFBcQBAtTm5DAgQCCwiigYfn6AQIFBUQRIty24wAgcACgmjg4Tl6EQFBtAjz/DeZfF3u8c99+opfkTtNEB0Fz0noXBpY195+Z3rfBz6dTnvH68axdbTXFVdfl3752KelUeQ872vf2hRBJ2H13W9/TVpz0y2b/Ybo1l67/r77xp9wPfHFx44D7WiPpXsKovN/D9lhywKCqHcHAQIEphMQRKdz8ioCBAiMBARR7wMCBAhMJyCITufkVQQIEBBEvQcIbF1AEK3oHbLSb4hOAuc0QXT0CdFRyNx5px02C5JXXH19OvW0D22KnkvJln9l7iik/tUXzh8/Z6UgOlr71pNOHD9i6Wu/f9lVm33CdfR1v+9630fSy1587PhTqoJoRW/UgFcRRAMOzZEJEBhEQBAdhN2mBAgEFRBEgw7OsQkQKC4giBYntyEBAkEFBNGgg3PsYgKCaDHq8huNPq35gY+fMw6Ze+2524N+Y3Slr8xdKYiOPhW69Ctzn3jkYzZ9NW+fQfRVb37vg5AmQVcQLf/+seO/Cgii3g0ECBCYTkAQnc7JqwgQIDASEES9DwgQIDCdgCA6nZNXESBAQBD1HiCwdQFBtOJ3yNJPhR52yIFZQXQp09II2mcQnXyydPQJ1eV/CaIVv1EDXE0QDTAkRyRAYCEEBNGFGINDECAQREAQDTIoxyRAYHABQXTwETgAAQJBBATRIINyzMEEBNHB6PvdePT1s9/41nc3+/3Qpb8DutOOO46/gvaYox+/6Tc6Tzr5jPSG3/zV8d8v/e3Plb4yd3Ta0VfXjv6aRxBd/huio31G55/sK4j2+37xtNkEBNHZvLyaAIF2BQTRdmfv5gQIzC4giM5uZgUBAm0KCKJtzt2tCRCYXUAQnd3MirYEBNFK5j35NOj3Lr1q042WfrXt6B8u/Y3RE447Jq1atUt6wuMO3mYQXXv7nWkUT2+46dbxs0drJ1+t29cnREcRdvkdlp5fEK3kjRr0GoJo0ME5NgECxQUE0eLkNiRAILCAIBp4eI5OgEBRAUG0KLfNCBAILCCIBh6eoxcREESLMNskV0AQzRW0PkdAEM3Rs5YAgZYEBNGWpu2uBAjkCgiiuYLWEyDQioAg2sqk3ZMAgVwBQTRX0PraBQTR2idcyf0E0UoGGfQagmjQwTk2AQLFBQTR4uQ2JEAgsIAgGnh4jk6AQFEBQbQot80IEAgsIIgGHp6jFxEQRIsw2yRXQBDNFbQ+R0AQzdGzlgCBlgQE0Zam7a4ECOQKCKK5gtYTINCKgCDayqTdkwCBXAFBNFfQ+toFBNHaJ1zJ/QTRSgYZ9BqCaNDBOTYBAsUFBNHi5DYkQCCwgCAaeHiOToBAUQFBtCi3zQgQCCwgiAYenqMXERBEizDbJFdAEM0VtD5HQBDN0bOWAIGWBATRlqbtrgQI5AoIormC1hMg0IqAINrKpN2TAIFcAUE0V9D62gUE0donXMn9BNFKBhn0GoJo0ME5NgECxQUE0eLkNiRAILCAIBp4eI5OgEBRAUG0KLfNCBAILCCIBh6eoxcREESLMNskV0AQzRW0PkdAEM3Rs5YAgZYEBNGWpu2uBAjkCgiiuYLWEyDQioAg2sqk3ZMAgVwBQTRX0PraBQTR2idcyf0E0UoGGfQagmjQwTk2AQLFBQTR4uQ2JEAgsIAgGnh4jk6AQFEBQbQot80IEAgsIIgGHp6jFxEQRIsw2yRXQBDNFbQ+R0AQzdGzlgCBlgQE0Zam7a4ECOQKCKK5gtYTINCKgCDayqTdkwCBXAFBNFfQ+toFBNHaJ1zJ/QTRSgYZ9BqCaNDBOTYBAsUFBNHi5DYkQCCwgCAaeHiOToBAUQFBtCi3zQgQCCwgiAYenqMXERBEizDbJFdAEM0VtD5HQBDN0bOWAIGWBATRlqbtrgQI5AoIormC1hMg0IqAINrKpN2TAIFcAUE0V9D62gUE0donXMn9BNFKBhn0GoJo0ME5NgECxQUE0eLkNiRAILCAIBp4eI5OgEBRAUG0KLfNCBAILCCIBh6eoxcREESLMNskV0AQzRW0PkdAEM3Rs5YAgZYEBNGWpu2uBAjkCgiiuYLWEyDQioAg2sqk3ZMAgVwBQTRX0PraBQTR2idcyf0E0UoGGfQagmjQwTk2AQLFBQTR4uQ2JEAgsIAgGnh4jk6AQFEBQbQot80IEAgsIIgGHp6jFxEQRIsw2yRXQBDNFbQ+R0AQzdGzlgCBlgQE0Zam7a4ECOQKCKK5gtYTINCKgCDayqTdkwCBXAFBNFfQ+toFBNHaJ1zJ/QTRSgYZ9BqCaNDBOTYBAsUFBNHi5DYkQCCwgCAaeHiOToBAUQFBtCi3zQgQCCwgiAYenqMXERBEizDbJFdAEM0VtD5HQBDN0bOWAIGWBATRlqbtrgQI5AoIormC1hMg0IqAINrKpN2TAIFcAUE0V9D62gUE0donXMn9BNFKBhn0GoJo0ME5NgECxQUE0eLkNiRAILCAIBp4eI5OgEBRAUG0KLfNCBAILCCIBh6eoxcREESLMNskV0AQzRW0PkdAEM3Rs5YAgZYEBNGWpu2uBAjkCgiiuYLWEyDQioAg2sqk3ZMAgVwBQTRX0PraBQTR2idcyf0E0UoGGfQagmjQwTk2AQLFBQTR4uQ2JEAgsIAgGnh4jk6AQFEBQbQot80IEAgsIIgGHp6jFxEQRIsw2yRXQBDNFbQ+R0AQzdGzlgCBlgQE0Zam7a4ECOQKCKK5gtYTINCKgCDayqTdkwCBXAFBNFfQ+toFBNHaJ1zJ/QTRSgYZ9BqCaNDBOTYBAsUFBNHi5DYkQCCwgCAaeHiOToBAUQFBtCi3zQgQCCwgiAYenqMXERBEizDbJFdAEM0VtD5HQBDN0bOWAIGWBATRlqbtrgQI5AoIormC1hMg0IqAINrKpN2TAIFcAUE0V9D62gUE0donXMn9BNFKBhn0GoJo0ME5NgECxQUE0eLkNiRAILCAIBp4eI5OgEBRAUG0KLfNCBAILCCIBh6eoxcREESLMNskV0AQzRW0PkdAEM3Rs5YAgZYEBNGWpu2uBAjkCgiiuYLWEyDQioAg2sqk3ZMAgVwBQTRX0PraBQTR2idcyf0E0UoGGfQagmjQwTk2AQLFBQTR4uQ2JEAgsIAgGnh4jk6AQFEBQbQot80IEAgsIIgGHp6jFxEQRIsw2yRXQBDNFbQ+R0AQzdGzlgCBlgQE0Zam7a4ECOQKCKK5gtYTINCKgCDayqTdkwCBXAFBNFfQ+toFBNHaJ1zJ/QTRSgYZ9BqCaNDBOTYBAsUFBNHi5DYkQCCwgCAaeHiOToBAUQFBtCi3zQgQCCwgiAYenqMXERBEizDbJFdAEM0VtD5HQBDN0bOWAIGWBATRlqbtrgQI5AoIormC1hMg0IqAINrKpN2TAIFcAUE0V9D62gUE0donXMn9BNFKBhn0GoJo0ME5NgECxQUE0eLkNiRAILCAIBp4eI5OgEBRAUG0KLfNCBAILCCIBh6eoxcREESLMNskV0AQzRW0PkdAEM3Rs5YAgZYEBNGWpu2uBAjkCgiiuYLWEyDQioAg2sqk3ZMAgVwBQTRX0PraBQTR2idcyf0E0UoGGfQagmjQwTk2AQLFBQTR4uQ2JEAgsIAgGnh4jk6AQFEBQbQot80IEAgsIIgGHp6jFxEQRIsw2yRXQBDNFbQ+R0AQzdGzlgCBlgQE0Zam7a4ECOQKCKK5gtYTINCKgCDayqTdkwCBXAFBNFfQ+toFBNHaJ1zJ/QTRSgYZ9BqCaNDBOTYBAsUFBNHi5DYkQCCwgCAaeHiOToBAUQFBtCi3zQgQCCwgiAYenqMXERBEizDbJFdAEM0VtD5HQBDN0bOWAIGWBATRlqbtrgQI5AoIormC1hMg0IqAINrKpN2TAIFcAUE0V9D62gUE0donXMn9BNFKBhn0GoJo0ME5NgECxQUE0eLkNiRAILCAIBp4eI5OgEBRAUG0KLfNCBAILCCIBh6eoxcREESLMNskV0AQzRW0PkdAEM3Rs5YAgZYEBNGWpu2uBAjkCgiiuYLWEyDQioAg2sqk3ZMAgVwBQTRX0PraBQTR2idcyf0E0UoGGfQagmjQwTk2AQLFBQTR4uQ2JEAgsIAgGnh4jk6AQFEBQbQot80IEAgsIIgGHp6jFxEQRIsw2yRXQBDNFbQ+R0AQzdGzlgCBlgQE0Zam7a4ECOQKCKK5gtYTINCKgCDayqTdkwCBXAFBNFfQ+toFBNHaJ1zJ/QTRSgYZ9BqCaNDBOTYBAsUFBNHi5DYkQCCwgCAaeHiOToBAUQFBtCi3zQgQCCwgiAYenqMXERBEizDbJFdAEM0VtD5HQBDN0bOWAIGWBATRlqbtrgQI5AoIormC1hMg0IqAINrKpN2TAIFcAUE0V9D62gUE0donXMn9BNFKBhn0GoJo0ME5NgECxQUE0eLkNiRAILCAIBp4eI5OgEBRAUG0KLfNCBAILCCIBh6eoxcREESLMNskV0AQzRW0PkdAEM3Rs5YAgZYEBNGWpu2uBAjkCgiiuYLWEyDQioAg2sqk3ZMAgVwBQTRX0PraBQTR2idcyf0E0UoGGfQagmjQwTk2AQLFBQTR4uQ2JEAgsIAgGnh4jk6AQFEBQbQot80IEAgsIIgGHp6jFxEQRIsw2yRXQBDNFbQ+R0AQzdGzlgCBlgQE0Zam7a4ECOQKCKK5gtYTINCKgCDayqTdkwCBXAFBNFfQ+toFBNHaJ1zJ/QTRSgYZ9BqCaNDBOTYBAsUFBNHi5DYkQCCwgCAaeHiOToBAUQFBtCi3zQgQCCwgiAYenqMXERBEizDbJFdAEM0VtD5HQBDN0bOWAIGWBATRlqbtrgQI5AoIormC1hMg0IqAINrKpN2TAIFcAUE0V9D62gUE0donXMn9BNFKBhn0GoJo0ME5NgECxQUE0eLkNiRAILCAIBp4eI5OgEBRAUG0KLfNCBAILCCIBh6eoxcREESLMNskV0AQzRW0Pkdg3kH0WzetSV+89sqcI/ay9reedExa9bCH9fIsDyFAoE0BQbTNubs1AQLdBATRbm5WESDQnoAg2t7M3ZgAgW4Cgmg3N6vaERBE25l16JsKoqHHF/7w8w6i5/34yvThS787uNOHjz0+7bnDjoOfwwEIEIgrIIjGnZ2TEyBQXkAQLW9uRwIEYgoIojHn5tQECJQXEETLm9sxloAgGmtezZ5WEG129Atx8VJB9MBdV6XD99ir050vX7c2rbn7rvTYPVanR+6620zP+Mcbr0sbHnggCaIzsXkxAQIrCAii3hYECBCYXkAQnd7KKwkQaFtAEG17/m5PgMD0AoLo9FZe2aaAINrm3MPdWhANN7KqDjzvIPrFa65MH738u+mw3VenJ++zXye7C2+5MV15x+3pqH32S4/dffVMzzjnmivSfRvvTx969gvTXjvtPNNaLyZAgMBSAUHU+4EAAQLTCwii01t5JQECbQsIom3P3+0JEJheQBCd3sor2xQQRNuce7hbC6LhRlbVgQXRqsbpMgQIzFFAEJ0jrkcTIFCdgCBa3UhdiACBOQkIonOC9VgCBKoTEESrG6kL9SwgiPYM6nHzERBE5+PqqdMJ9BVEb15/T/rJvfekfXfaJT185102bT75DdHR190etXfXT4jelK6847Z09D77p0N333O6i/3fV/3NNVek9Rvv3+wrcy9btzZtfOCB9HOr95npWV5MgEDbAoJo2/N3ewIEZhMQRGfz8moCBNoVEETbnb2bEyAwm4AgOpuXV7cnIIi2N/OQNxZEQ46tmkP3FUQ/ecWl6bNXXZZefuiR6WWHHrHJZxG/Mvc155+X1m24L334OS9Ke+64UzWzdBECBOYrIIjO19fTCRCoS0AQrWuebkOAwPwEBNH52XoyAQJ1CQiidc3TbfoXEET7N/XEOQgIonNA9cipBQRRQXTqN4sXEmhcQBBt/A3g+gQIzCQgiM7E5cUECDQsIIg2PHxXJ0BgJgFBdCYuL25QQBBtcOgRryyIRpxaPWcWRAXRet7NbkJgvgKC6Hx9PZ0AgboEBNG65uk2BAjMT0AQnZ+tJxMgUJeAIFrXPN2mfwFBtH9TT5yDgCA6B1SPnFqgryD6qR9ekj5z9eXj3+Vc+tucP1i3Nl10682pj98QffI++6XDdl899d1GL/yba36Y1m/cmF786MPSzts/dLz2nB/9MN33wMb0kWOPT3vssONMz/NiAgTaFRBE2529mxMgMLuAIDq7mRUECLQpIIi2OXe3JkBgdgFBdHYzK9oSEETbmnfY2wqiYUdXxcH7CqKT3xDdEsooZI6CZpe/LrzlxnTlHbeno/bZLz12xiB6zjVXpPs23r/itn5DtMs0rCHQroAg2u7s3ZwAgdkFBNHZzawgQKBNAUG0zbm7NQECswsIorObWdGWgCDa1rzD3lYQDTu6Kg7eVxD1CdEq3g4uQYDAVgQEUW8PAgQITC8giE5v5ZUECLQtIIi2PX+3J0BgegFBdHorr2xTQBBtc+7hbi2IhhtZVQfuK4hOPiH68kOPTC879IhNRl+85sr00cu/O/6q2yE/IfqhZ78w7bXTzuNzveb889K6DfclnxCt6q3sMgTmLiCIzp3YBgQIVCQgiFY0TFchQGCuAoLoXHk9nACBigQE0YqG6SpzERBE58LqoX0LCKJ9i3reLAKC6E6zcHktAQINCzzkISntt+dO6cbb1jes4OoECBCYTkAQnc7JqwgQICCIeg8QIEBgOgFBdDonr2pXQBBtd/ahbi6IhhpXdYcVRAXR6t7ULkRgTgKC6JxgPZYAgSoFBNEqx+pSBAjMQUAQnQOqRxIgUKWAIFrlWF2qRwFBtEdMj5qfgCA6P1tP3rZAX0H05vX3pJ/ce0/ad6dd0sN33mXTxuf9+Mr04Uu/mx67x+p01N77bftAK7ziwltuSlfecVs6ep/906G77znTM/7mmivS+o33pw8fe3zac4cdx2svW7c2bXzggfRzq/eZ6VleTIBA2wKCaNvzd3sCBGYTEERn8/JqAgTaFRBE2529mxMgMJuAIDqbl1e3JyCItjfzkDcWREOOrZpD9xVEtwSyiL8hWs3wXIQAgaICgmhRbpsRIBBcQBANPkDHJ0CgmIAgWozaRgQIBBcQRIMP0PHnLiCIzp3YBn0ICKJ9KHpGVwFBtKucdQQItCYgiLY2cfclQCBHQBDN0bOWAIGWBATRlqbtrgQI5AgIojl61rYgIIhWOOXfP+vs8a3eetKJvd7utnV3ptef8gfpbSedmJ76pCN6ffa2HiaIbkvIn89TYN5BdPKVuQfuuiodvsdena5y+bq1ac3dd42/dveRu+420zP+8cbr0oYHHtjsK3NneoAXEyBA4P8KCKLeCgQIEJheQBCd3sorCRBoW0AQbXv+bk+AwPQCguj0Vl7ZpoAgGnTu967fkN71vo+kc796waYbvPYVx48jaKkgeuU1a9JJJ5+Rbrjp1vEZTjjumPTut78m7bzTDr2rCqK9k3rgDAKlgugMR5rLS5f+huhcNvBQAgSqFxBEqx+xCxIg0KOAINojpkcRIFC1gCBa9XhdjgCBHgUE0R4xPapKAUE04Fgnn9R82pOO2OxToH/2F19Ixz3r6HTOl745vtW8PyH6ufP+IR30iH03fVp0XiF2dBdBNOAbtaIjzzuIfuumNemL1145uNhvPemYtOphDxv8HA5AgEBcAUE07uycnACB8gKCaHlzOxIgEFNAEI05N6cmQKC8gCBa3tyOsQQE0VjzGp92FCIvuPCSLX4ac3mYXPpJzgP22zuddfrb0qGPfsT4Wctf++2LLkt/9YXzNz17+adAR2s+duYpK35l7tJzrbnplvTO0z6UfuX5z0in/dEnxntNPsE62ffGm9ema9fcnL536VVpdK4zf+eN6b+f/XfjT70uP6cgGvCNWtGR5x1EK6JyFQIEGhcQRBt/A7g+AQIzCQiiM3F5MQECDQsIog0P39UJEJhJQBCdicuLGxQQRIMNffJVuccc/fj0kuOfveLpl0bO5b/7OQqep572oU1RdGtBdP19941/M/TEFx873mtrvyE6Odf+++41/mTqJKQe/9ynj/9++drRvv980WXpg+99S1q9x27jMHve1761xXMJosHeqJUdVxCtbKCuQ4DA3AQE0bnRejABAhUKCKIVDtWVCBCYi4AgOhdWDyVAoEIBQbTCobpSrwKCaK+c83/YJDy+7MXHrvgpzdEJlkbOUQA946yzN4XH5UF1a0H0+5ddtdnaLQXR0TM+/KnzNvsN0VEQHX1C9D2nvm7FT6Mu33f5p16X/70gOv/3lh22LCCIencQIEBgOgFBdDonryJAgMBIQBD1PiBAgMB0AoLodE5eRYAAAUHUe4DA1gUE0WDvkFk/Ibr8K3AnwfTgg/Yff+pzW0F06dfnbu0ToqPnrvSVuUuD6OjPf/TjG8efGBVEg73xGj+uINr4G8D1CRCYWkAQnZrKCwkQICCIeg8QIEBgSgFBdEooLyNAoHkBQbT5twCAbQgIogHfIrP8hmiJT4hOCEefCn3fBz6dTnvH69La2+/0CdGA7y1HXllAEPXOIECAwHQCguh0Tl5FgACBkYBPiHofECBAYDoBQXQ6J68iQICAIOo9QGDrAoJowHfI5JOaT3vSEeNPW07++rO/+EI67llHp3O+9M3xP1r6252T3wFd/huiS+PqaM273veR8dp3v/01afIbom876cTx1/OO1r7qze9NHzvzlPHfT/Y79NGPGK8ZferzxpvXjteuuemWzYLo8q/Q9QnRgG+8ho8siDY8fFcnQGAmAUF0Ji4vJkCgcQFBtPE3gOsTIDC1gCA6NZUXEiDQuIAg2vgbwPW3KSCIbpNoMV8w+ercc796waYDvvYVx6/4dbSjGHnSyWekG266NR2w397prNPftul3PZc+Z/Rnrzrxhem7l1w5jpo777TDpgg62uSE445J1665OS0PpJMDjP58sm7pnpM/n4TUSTwd/e8k6PoN0cV8nznV/xFoNYgecuqb0oaNGxfybfDxV52Unvf4Jy7k2RyKQMsCgmjL03d3AgRmFRBEZxXzegIEWhUQRFudvHsTIDCrgCA6q5jXtyYgiLY28UL3Xf6J0Nxt19x6b+4jrCfQWUAQ7Uw3t4WC6NxoPZhAloAgmsVnMQECjQkIoo0N3HUJEOgsIIh2prOQAIHGBATRxgbuujMLCKIzk1kwjYAgOo2S10QRaD2I/ufn/nJ66HbbzTSur//g0nThtT9Kz37sAzlw7QAAIABJREFUEekpjz5kprVbe/HnL/pOuvqWnyRBtDdSDyLQq4Ag2iunhxEgULmAIFr5gF2PAIHeBATR3ig9iACBygUE0coH7HrZAoJoNqEHlBDwCdESyvbYkoAgKoj6t4MAgekEBNHpnLyKAAECIwFB1PuAAAEC0wkIotM5eRUBAgQEUe8BAlsXEES9Q0IICKIhxlTtIQVRQbTaN7eLEehZQBDtGdTjCBCoWkAQrXq8LkeAQI8CgmiPmB5FgEDVAoJo1eN1uR4EBNEeED1i/gKC6PyN7bBlgdqC6BlfPjc9dPvt05uOe+FWx37gyW8c//lbn/eimd8ek6/Mfc7hR6ajH3XwzOu3tGDTV+a++j+l5x35hAe97DvXXJW+fvml6SkHH5qec/gRve3rQQQITCcgiE7n5FUECBAYCQii3gcECBCYTkAQnc7JqwgQICCIeg8Q2LqAIOodEkJAEA0xpmoPWVMQXf/Tn6ZD3/mWtONDH5qu+r0ztzqzQ059U9qwcWOK9Buif/r1r6TfPfev00nPem76ry9+SbXvSRcjsKgCguiiTsa5CBBYRAFBdBGn4kwECCyigCC6iFNxJgIEFlFAEF3EqTjTIgkIoos0DWfZooAg6s0xpIAgGucrcwXRIf9NsTeBlARR7wICBAhMLyCITm/llQQItC0giLY9f7cnQGB6AUF0eiuvbFNAEG1z7uFuLYiGG1lVBxZEBdGq3tAuQ2COAoLoHHE9mgCB6gQE0epG6kIECMxJQBCdE6zHEiBQnYAgWt1IXahnAUG0Z1CPm4+AIDofV0+dTqCmIHrf/T9Nj3nHW8YXP+aQw7YKcMHVV4z/POc3RI89/Mj05Dn8hujj9j8grd551wedf83tt6Vrb7vVV+ZO99b2KgK9CwiivZN6IAECFQsIohUP19UIEOhVQBDtldPDCBCoWEAQrXi4rtaLgCDaC6OHzFtAEJ23sOdvTaCmIDr5DdFZJr6IvyG6rfP7DdFtCflzAvMREETn4+qpBAjUKSCI1jlXtyJAoH8BQbR/U08kQKBOAUG0zrm6VX8Cgmh/lp40RwFBdI64Hr1NgZqC6NJPiH7mpDdt9e7/7qz3j/98ET8heuoL/206+tGHPOj851x8Yfp/L/imT4hu813tBQTmIyCIzsfVUwkQqFNAEK1zrm5FgED/AoJo/6aeSIBAnQKCaJ1zdav+BATR/iw9aY4CgugccT16mwI1BdHJJ0R3fOhD01W/d+ZW737IqW9KGzZuTIv4CdGPv+qk9LzHP/FB5//Tr38l/e65fy2IbvNd7QUE5iMgiM7H1VMJEKhTQBCtc65uRYBA/wKCaP+mnkiAQJ0Cgmidc3Wr/gQE0f4sPWmOAoLoHHE9epsCgugvp4dut902nZa+4Os/uDRdeO2P0rMfe0R6ygqf5JzpYUte/PmLvpOuvuUnSRDtKmgdgfkKCKLz9fV0AgTqEhBE65qn2xAgMD8BQXR+tp5MgEBdAoJoXfN0m/4FBNH+TT1xDgKC6BxQPXJqAUFUEJ36zeKFBBoXEEQbfwO4PgECMwkIojNxeTEBAg0LCKIND9/VCRCYSUAQnYnLixsUEEQbHHrEKwuiEadWz5lrCqKzTCXiV+bOcj+vJUCgfwFBtH9TTyRAoF4BQbTe2boZAQL9Cgii/Xp6GgEC9QoIovXO1s36ERBE+3H0lDkLCKJzBvb4rQoIonE+IeqtTIDAsAKC6LD+didAIJaAIBprXk5LgMBwAoLocPZ2JkAgloAgGmteTlteQBAtb27HDgKCaAc0S3oTEEQF0d7eTB5EoHIBQbTyAbseAQK9CgiivXJ6GAECFQsIohUP19UIEOhVQBDtldPDKhQQRCscao1XEkRrnGqcO7UaRA88+Y3jIT394EPTdqPKMcNfP1p7S7ph3e3p0Xvtkx6xx54zrNz6Sy+9cU26/d570sdf/Z/S8458Qm/P9SACBPoREET7cfQUAgTaEBBE25izWxIgkC8giOYbegIBAm0ICKJtzNktuwsIot3trCwoIIgWxLbVgwRaD6KL+JYQRBdxKs5EICVB1LuAAAEC0wsIotNbeSUBAm0LCKJtz9/tCRCYXkAQnd7KK9sUEETbnHu4Wwui4UZW1YFbDaLv/+qX0v0bNy7kLH/9qKekxzx8v4U8m0MRaFlAEG15+u5OgMCsAoLorGJeT4BAqwKCaKuTd28CBGYVEERnFfP61gQE0dYmHvS+gmjQwVVy7FaDaCXjcw0CBAoKCKIFsW1FgEB4AUE0/AhdgACBQgKCaCFo2xAgEF5AEA0/QheYs4AgOmdgj+9HQBDtx9FTugkIot3crCJAoD0BQbS9mbsxAQLdBQTR7nZWEiDQloAg2ta83ZYAge4Cgmh3OyvbEBBE25hz+FsKouFHGPoCgmjo8Tk8AQIFBQTRgti2IkAgvIAgGn6ELkCAQCEBQbQQtG0IEAgvIIiGH6ELzFlAEJ0zsMf3IyCI9uPoKd0EBNFublYRINCegCDa3szdmACB7gKCaHc7KwkQaEtAEG1r3m5LgEB3AUG0u52VbQgIom3MOfwtBdHwIwx9AUE09PgcngCBggKCaEFsWxEgEF5AEA0/QhcgQKCQgCBaCNo2BAiEFxBEw4/QBeYsIIjOGdjj+xEQRPtx9JRuAoJoNzerCBBoT0AQbW/mbkyAQHcBQbS7nZUECLQlIIi2NW+3JUCgu4Ag2t3OyjYEBNE25hz+loJo+BGGvoAgGnp8Dk+AQEEBQbQgtq0IEAgvIIiGH6ELECBQSEAQLQRtGwIEwgsIouFH6AJzFhBE5wzs8f0ICKL9OHpKNwFBtJubVQQItCcgiLY3czcmQKC7gCDa3c5KAgTaEhBE25q32xIg0F1AEO1uZ2UbAoJoG3MOf0tBNPwIQ19AEA09PocnQKCggCBaENtWBAiEFxBEw4/QBQgQKCQgiBaCtg0BAuEFBNHwI3SBOQsIonMG9vh+BATRfhw9pZuAINrNzSoCBNoTEETbm7kbEyDQXUAQ7W5nJQECbQkIom3N220JEOguIIh2t7OyDQFBtI05h7+lIBp+hKEvIIiGHp/DEyBQUEAQLYhtKwIEwgsIouFH6AIECBQSEEQLQduGAIHwAoJo+BG6wJwFBNE5A3t8PwKCaD+OntJNQBDt5mYVAQLtCQii7c3cjQkQ6C4giHa3s5IAgbYEBNG25u22BAh0FxBEu9tZ2YaAINrGnMPfUhANP8LQFxBEQ4/P4QkQKCggiBbEthUBAuEFBNHwI3QBAgQKCQiihaBtQ4BAeAFBNPwIXWDOAoLonIE9vh8BQbQfR0/pJiCIdnOzigCB9gQE0fZm7sYECHQXEES721lJgEBbAoJoW/N2WwIEugsIot3trGxDQBBtY87hbymIhh9h6AsIoqHH5/AECBQUEEQLYtuKAIHwAoJo+BG6AAEChQQE0ULQtiFAILyAIBp+hC4wZwFBdM7AHt+PgCDaj6OndBMQRLu5WUWAQHsCgmh7M3djAgS6Cwii3e2sJECgLQFBtK15uy0BAt0FBNHudla2ISCItjHn8LcURMOPMPQFBNHQ43N4AgQKCgiiBbFtRYBAeAFBNPwIXYAAgUICgmghaNsQIBBeQBANP0IXmLOAIDpnYI/vR0AQ7cfRU7oJCKLd3KwiQKA9AUG0vZm7MQEC3QUE0e52VhIg0JaAINrWvN2WAIHuAoJodzsr2xAQRNuYc/hbCqLhRxj6AoJo6PE5PAECBQUE0YLYtiJAILyAIBp+hC5AgEAhAUG0ELRtCBAILyCIhh+hC8xZQBCdM7DH9yMgiPbj6CndBATRbm5WESDQnoAg2t7M3ZgAge4Cgmh3OysJEGhLQBBta95uS4BAdwFBtLudlW0ICKJtzDn8LQXR8CMMfQFBNPT4HJ4AgYICgmhBbFsRIBBeQBANP0IXIECgkIAgWgjaNgQIhBcQRMOP0AXmLCCIzhnY4/sREET7cfSUbgKCaDc3qwgQaE9AEG1v5m5MgEB3AUG0u52VBAi0JSCItjVvtyVAoLuAINrdzso2BATRNuYc/paCaPgRhr6AIBp6fA5PgEBBAUG0ILatCBAILyCIhh+hCxAgUEhAEC0EbRsCBMILCKLhR+gCcxYQROcM7PH9CAii/Th6SjcBQbSbm1UECLQnIIi2N3M3JkCgu4Ag2t3OSgIE2hIQRNuat9sSINBdQBDtbmdlGwKCaBtzDn9LQTT8CENfQBANPT6HJ0CgoIAgWhDbVgQIhBcQRMOP0AUIECgkIIgWgrYNAQLhBQTR8CN0gTkLCKJzBvb4fgQE0X4cPaWbgCDazc0qAgTaExBE25u5GxMg0F1AEO1uZyUBAm0JCKJtzdttCRDoLiCIdrezsg0BQbSNOYe/pSAafoShLyCIhh6fwxMgUFBAEC2IbSsCBMILCKLhR+gCBAgUEhBEC0HbhgCB8AKCaPgRusCcBQTROQN7fD8Cgmg/jp7STUAQ7eZmFQEC7QkIou3N3I0JEOguIIh2t7OSAIG2BATRtubttgQIdBcQRLvbWdmGgCDaxpzD31IQDT/C0BdYpCD6l1dcGtpy6MP//+zde9QlVX3g/Q0NfaEbuhsQmga5GxAFNUQFI6KAGCGODiYE4ryjwZcwmExGZWDROoY4jmkCgxp9oxImvJj1GpUsGZVAdAIIGDMIXlBUiJFLA3YDcpWWbvrGu+pkzsPpw3OpOs+uU7V3ffqfCF1n196fX+laM99V5+w8f0F4/V77Nr0N9yeQrYAgmu1oHYwAgRoEBNEaUC1JgECWAoJolmN1KAIEahAQRGtAtWRWAoJoVuPM9zCCaL6zTeFkbQmiG7ZsDqde85UUyFq7x/13XBIuPPJ1rd2fjRFIXUAQTX2C9k+AwDgFBNFxarsXAQIpCwiiKU/P3gkQGKeAIDpObfdKUUAQTXFqHdyzINrBobfoyG0Moocs2XXWQpue2Rx+8sRjYc4224SDFu8y6/XavMC6zRvD3U8+EQTRNk/J3nIQEERzmKIzECAwLgFBdFzS7kOAQOoCgmjqE7R/AgTGJSCIjkvafVIVEERTnVzH9i2IdmzgLTtuW4Lo05s3h9+99ith2222Cb+130GzVlq3eVO4ctVPw7w524U373PgrNdr8wKPPr0+XPOzewTRNg/J3rIQEESzGKNDECAwJgFBdEzQbkOAQPICgmjyI3QAAgTGJCCIjgnabZIVEESTHV23Ni6IdmvebTutINq2iVTfjyBa3cwnCIwiIIiOouYzBAh0VUAQ7erknZsAgaoCgmhVMdcTINBVAUG0q5N37rICgmhZKdc1KiCINsrf+ZsLouk/AoJo+jN0gjQEBNE05mSXBAi0Q0AQbccc7IIAgfYLCKLtn5EdEiDQDgFBtB1zsIv2Cgii7Z2NnQ0ICKIehyYFxhVE3/q//mfvmF88/t9OelxfmTv6UzCbIPr7N341PLJ+Xfj0a94Qnjd/h9E34ZMEOiAgiHZgyI5IgEA0AUE0GqWFCBDIXEAQzXzAjkeAQDQBQTQapYUyFRBEMx1sbscSRHObaFrnEUTTmtdkuxVE05+hE6QhIIimMSe7JECgHQKCaDvmYBcECLRfQBBt/4zskACBdggIou2Yg120V0AQbe9s7GxAQBD1ODQpIIg2qR/n3oJoHEerEJhJQBCdScjfEyBA4FkBQdTTQIAAgXICgmg5J1cRIEBAEPUMEJheQBD1hCQhIIgmMaZsNznuIPpfX37UpJYbt2wJH/rON3t/d/L+B8/ae/3mzeErq/4lzJszJ7x5nxfMer02L9APonvssCic+aKXVdrqRbd+KzyxcUO4+DW/EXadv6DSZ11MoGsCgmjXJu68BAjMRkAQnY2ezxIg0CUBQbRL03ZWAgRmIyCIzkbPZ7sgIIh2YcoZnFEQzWCICR9h3EF0Jqptt9km/NZ+B8102Yx/v27zpnDlqp+GeXO2C2/e58AZr0/5gn4Qnc0Z/IbobPR8tisCgmhXJu2cBAjEEBBEYyhagwCBLggIol2YsjMSIBBDQBCNoWiNnAUE0Zynm9HZBNGMhpngUcYdRA9ZuuukSs8880y4/fFHen/nDdFqD1I/iM6fs13Yf6cllT7848ce7l3vDdFKbC7uqIAg2tHBOzYBAiMJCKIjsfkQAQIdFBBEOzh0RyZAYCQBQXQkNh/qkIAg2qFhp3xUQTTl6aW/93EH0S8e/28nRXt68+bwu9d+JXhDtPoz5TdEq5v5BIFRBATRUdR8hgCBrgoIol2dvHMTIFBVQBCtKuZ6AgS6KiCIdnXyzl1WQBAtK+W6RgUE0Ub5O39zQTT9R0AQTX+GTpCGgCCaxpzskgCBdggIou2Yg10QINB+AUG0/TOyQwIE2iEgiLZjDnbRXgFBtL2zsbMBAUHU49CkgCDapH6cewuicRytQmAmAUF0JiF/T4AAgWcFBFFPAwECBMoJCKLlnFxFgAABQdQzQGB6AUHUE5KEgCCaxJiy3eS4gugXfnp7z/B3DnzhpJYbtmwOp17zld7f+Q3Rao/bbILolat+Gp7auDG8ad8XhB22267ajV1NoGMCgmjHBu64BAjMSkAQnRWfDxMg0CEBQbRDw3ZUAgRmJSCIzorPhzsgIIh2YMg5HFEQzWGK6Z5hXEF0JiG/ITqT0NR/P5sgOvpdfZJA9wQE0e7N3IkJEBhdQBAd3c4nCRDoloAg2q15Oy0BAqMLCKKj2/lkNwQE0W7MOflTCqLJjzDpAwiiSY+vt3lBNP0ZOkEaAoJoGnOySwIE2iEgiLZjDnZBgED7BQTR9s/IDgkQaIeAINqOOdhFewUE0Rpnc8utd4SLLr48fOr894Sli3esdKfZfLbSjSpcvG79hnDehZeGIw4/JJx0wmsqfHL2lwqisze0wugCgujodm35pCDalknYR+4CgmjuE3Y+AgRiCgiiMTWtRYBAzgKCaM7TdTYCBGIKCKIxNa2Vo4AgWmGqV1x9Y7jpOz8OHzz7tLBg/twwWSC8c9XqcOEnPx9Wvu/08NO7fxYtiD72xJPhzHM/Gm67/a6JHZ947BETe6lwjJEvney8hckHLri0t+ahL9x/pPhbZkOCaBkl19Ql0LYgWtc5u7Du/jsuCRce+bouHNUZCTQiIIg2wu6mBAgkKiCIJjo42yZAYOwCgujYyd2QAIFEBQTRRAdn22MTEEQrUA/GzuKNz+KfzzjnonDCMa8M7z3j5N5Kw9G0wvJbXTr4huj8efMmfTPzL/+/K8OxRx0eDthn+ai3qfS54SA6/BbrRy6+PDzw0KO1RFpBtNKoXBxZQBCNDNrgcoJog/hu3QkBQbQTY3ZIAgQiCQiikSAtQ4BA9gKCaPYjdkACBCIJCKKRIC2TrYAgWmG0xVuaK/70knD2u07pRcgiCH7vh//SexN0xR+9rfe1uEUU3Pf5y3pfKTsYDIvbFG94/uZxR4bLLv9qWPPgI2H4Dc/Bty2L6/tvXD76+JPh/SsvCR9ecfqU8bO479qn1oe1a58KV117U9hj913CxRecNXH98Buml33s3PDylx7cO32Vvyuu/9A5p4U3HnPEcyJtEYgH9zl8nsF79uPpvasf6r31Wuz3Y//1D8NfX/61SfcviFZ4UF0aXaAtQTT6wSxIgACByAKCaGRQyxEgkLWAIJr1eB2OAIGIAoJoRExLESCQtYAgmvV4HS6CgCBaEXEweBb/+ddeclC4/n9/P7ztpOPC8t137UXC337Ta3uxcbIguvfy3XpvUBZ/Bn+Ps7h2xcpLJiLmdJ8tvq53+E+xl6uv+9bE5wffVF3/9NO9GHvym17bC7WDb7oW60z1d/03U5fttnPvDdjBN0QnC6L9sHrWGSeHFx+8f/jiVTeEt554dO/rhYv9XH7l9RNfqVvs9+Zb79jqnwf3X/x98af/5q0gWvFBdXlUAUE0KqfFCBDIWEAQzXi4jkaAQHQBQTQ6qQUJEMhUQBDNdLCORYBAdAFBNDqpBTMTEEQrDrQIe/fc90A48+1vCZ/6zJfC753yxvD1b36vt8pLXnTgxO+HFm+LThY1i1jYfzNzOK4OBsDhr6Od7DdEizc1i8BZ/BkOiINvaz762C+2+i3Tftgswm3x56KLL58Ik4N/t/PSnbZ643P4K3OHvx54MIj2z9jnHX57dHi/w2sN/7MgWvFBdXlUAUE0KqfFCBDIWEAQzXi4jkaAQHQBQTQ6qQUJEMhUQBDNdLCORYBAdAFBNDqpBTMTEEQrDrT/duU7Tz0h/P3Xbw5nn3lK+OEdd4VvfOsH4ahXHhb+9srrJ35Ds2oQ7X/VbrGl4SA6vM3i79/x7vND/2tohwPj4Nf7FkG0uHb4T/HZ4s9Uf1cE0Qs/+fmw8n2n974OeDiI9v+5+Ire/p/Br+rt/8Zq8fXAxZ/BvxNEKz54Lm9UQBBtlN/NCRBISEAQTWhYtkqAQOMCgmjjI7ABAgQSERBEExmUbRIg0LiAINr4CGyg5QKCaMUB9UPjq19xaNhhwbzeG5rFv1v58c+GA/fbM+y68+KJtzarBtFiK/2viJ0piA7HyZneEB0MtYNHLu4z1d8Nv9U5fM9huuL6z15xTS8Sr37w4XDGOReFlStO770R6w3Rig+ay1slIIi2ahw2Q4BAiwUE0RYPx9YIEGidgCDaupHYEAECLRUQRFs6GNsiQKB1AoJo60ZiQy0TEERHGEgRH//qc1dPvJ05+KZk/43NYtkqQXQ4gA7+xuajjz85ERr7vx86U2AcDKT9r7Lt/4Zof2/F/y0i7uBviA7+XfE7oIO/c9p/4/Ndb3/zRPTt8w1/Xe7w/oZ/I9UboiM8eD7SmIAg2hi9GxMgkJiAIJrYwGyXAIFGBQTRRvndnACBhAQE0YSGZasECDQqIIg2yu/mCQgIoiMMqfh9y09+5svh4gvOCgfss7y3QvHvLr/y+onf4uyHxf7vcxb/XITHqX5DtPj7fmgt/nPxlbw333pHb7358+b1wuRUX007/Nnin0889oiJr+4t/nn4N0gPfeH+E3ud7u8Gv/a2+Erg4s/xR//axJuxxZluu/2urb4Ot086eJ5DD96v968/vOL0npkgOsKD5yONCcQMoude8fnwLw8+0NhZJrvx83bcKXz6353Wqj3ZDAECaQoIomnOza4JEGhGQBBtxt1dCRBIT0AQTW9mdkyAQDMCgmgz7u6ajoAgms6spt3pcGDM5FgTx1j9yLrcjuQ8CQnEDKInfuLCcOt9q1p1+j2XLA03v+9DrdqTzRAgkKaAIJrm3OyaAIFmBATRZtzdlQCB9AQE0fRmZscECDQjIIg24+6u6QgIounMShDNZFaOkZ5AHUH0uINfFHZeuHDWGE9v2hS+/P3vhnlztgtvfumvVlrvqQ0bwt/ddmsQRCuxuZgAgWkEBFGPBwECBMoLCKLlrVxJgEC3BQTRbs/f6QkQKC8giJa3cmU3BQTRbs49uVN7QzS5kWW14ZhB9ISPXxC+f/+94XdffmRYtnjJrJ2e2vB0+PSN14UF288NZx59bKX1nly/Llzyj9eHPRYvCd9+/3+r9FkXEyBAYDIBQdRzQYAAgfICgmh5K1cSINBtAUG02/N3egIEygsIouWtXNlNAUG0m3NP7tSCaHIjy2rDgmhW43QYAgRqFBBEa8S1NAEC2QkIotmN1IEIEKhJQBCtCdayBAhkJyCIZjdSB4osIIhGBrVcPQKCaD2uVi0nMFUQ/dbdPw1btjwTjjzgBeUWCiH0f0P0ba94Vdh9p8WlPzfVhes2bgyfuuGasMP2c8N/qPiG6Nqn14e//MbXK31l7urHHwurHnk4LF+yJOyzy/NmvX8LECCQl4Agmtc8nYYAgXoFBNF6fa1OgEA+AoJoPrN0EgIE6hUQROv1tXr6AoJo+jPsxAkE0U6MubWHnCqI7nPuH4VNW7aEe1b+edh+zpxS+0/9K3M/fcM14UNXfSmccdQx4Y/fdFKpM7uIAIHuCAii3Zm1kxIgMHsBQXT2hlYgQKAbAoJoN+bslAQIzF5AEJ29oRXyFhBE855vNqcTRLMZZZIHEUSfHZsgmuQjbNMExiYgiI6N2o0IEMhAQBDNYIiOQIDAWAQE0bEwuwkBAhkICKIZDNERahUQRGvltXgsAUE0lqR1RhEQRAXRUZ4bnyHQRQFBtItTd2YCBEYVEERHlfM5AgS6JiCIdm3izkuAwKgCguiocj7XFQFBtCuTTvycgmjiA0x8+1MF0T3P+cPeyd597G+EbYsKUOLPZ2/+p/DgL54IsX9DdMH224czjz6uxA6evaT/G6I7zpsXTj/qmFKf/faqu8KN//LPvjK3lJaLCHRPQBDt3sydmACB0QUE0dHtfJIAgW4JCKLdmrfTEiAwuoAgOrqdT3ZDQBDtxpyTP6UgmvwIkz7ATG+IjnK43335kWHZ4iWjfHSrzzy14enw6RuvCwu2nxvOPPrYSus9uX5duOQfr6/0mf7FfkN0JDYfIpC9gCCa/YgdkACBiAKCaERMSxEgkLWAIJr1eB2OAIGIAoJoRExLZSkgiGY51vwOJYjmN9OUTuQN0Wen5Q3RlJ5ceyUwfgFBdPzm7kiAQLoCgmi6s7NzAgTGKyCIjtfb3QgQSFdAEE13dnY+HgFBdDzO7jJLAUF0loA+PiuBmd4QvWfln4ft58wpdY8TPn5B+P7994Y2vSG6x+Il4dvv/2+l9v/pG64JH7rqS74yt5SWiwh0T0AQ7d7MnZgAgdEFBNHR7XySAIFuCQii3Zq30xIgMLqAIDq6nU92Q0AQ7cackz+lIJr8CJM+gCD67PgE0aQfZZsnULuAIFo7sRsQIJCRgCCa0TAdhQCBWgUE0Vp5LU6AQEYCgmhGw3SUWgQE0VpYLRpbQBCNLWq9KgKCqCBa5XlxLYEuCwiiXZ6+sxMgUFVAEK0q5noCBLoqIIh2dfLOTYBAVQFBtKqY67smIIh2beKJnlcQTXRwmWx7qiD6rbt/GrZseSaiNtabAAAgAElEQVQcecALSp/0xE9cGG69b1V42yteFXbfaXHpz0114bqNG8Onbrgm7LD93PAfjj620nprn14f/vIbXw97Llkabn7fh0p9dvXjj4VVjzwcli9ZEvbZ5XmlPuMiAgS6IyCIdmfWTkqAwOwFBNHZG1qBAIFuCAii3ZizUxIgMHsBQXT2hlbIW0AQzXu+2ZxOEM1mlEkeZKogOsphUv8N0VHO7DMECHRHQBDtzqydlACB2QsIorM3tAIBAt0QEES7MWenJEBg9gKC6OwNrZC3gCCa93yzOZ0gms0okzyIIJrk2GyaAIEGBATRBtDdkgCBZAUE0WRHZ+MECIxZQBAdM7jbESCQrIAgmuzobHxMAoLomKDdZnYCgujs/Hx6dgIxg2j/K3OPO/hFYeeFC2e3sRDC05s2hS9//7th3pztwptf+quV1ntqw4bwd7fdWukrcyvdwMUECHROQBDt3MgdmACBWQgIorPA81ECBDolIIh2atwOS4DALAQE0Vng+WgnBATRTow5/UMKounPMOUT1BFE2+RR5TdE27RveyFAoH0Cgmj7ZmJHBAi0V0AQbe9s7IwAgXYJCKLtmofdECDQXgFBtL2zsbN2CAii7ZiDXcwgIIh6RJoUiBlEz73i8+FfHnygyeM8597P23Gn8Ol/d1qr9mQzBAikKSCIpjk3uyZAoBkBQbQZd3clQCA9AUE0vZnZMQECzQgIos24u2s6AoJoOrPq9E4F0U6Pv/HDxwyijR/GBggQIFCjgCBaI66lCRDITkAQzW6kDkSAQE0CgmhNsJYlQCA7AUE0u5E6UGQBQTQyqOXqERBE63G1ajkBQbSck6sIECAgiHoGCBAgUF5AEC1v5UoCBLotIIh2e/5OT4BAeQFBtLyVK7spIIh2c+7JnVoQTW5kWW1YEM1qnA5DgECNAoJojbiWJkAgOwFBNLuROhABAjUJCKI1wVqWAIHsBATR7EbqQJEFBNHIoJarR0AQrcfVquUEBNFyTq4iQICAIOoZIECAQHkBQbS8lSsJEOi2gCDa7fk7PQEC5QUE0fJWruymgCDazbknd2pBNLmRZbVhQTSrcToMAQI1CgiiNeJamgCB7AQE0exG6kAECNQkIIjWBGtZAgSyExBEsxupA0UWEEQjg1quHgFBtB5Xq5YTEETLObmKAAECgqhngAABAuUFBNHyVq4kQKDbAoJot+fv9AQIlBcQRMtbubKbAoJoN+ee3KkF0eRGltWGBdGsxukwBAjUKCCI1ohraQIEshMQRLMbqQMRIFCTgCBaE6xlCRDITkAQzW6kDhRZQBCNDGq5egQE0XpcrVpOQBAt5+QqAgQICKKeAQIECJQXEETLW7mSAIFuCwii3Z6/0xMgUF5AEC1v5cpuCgii3Zx7cqcWRJMbWVYbFkSzGqfDECBQo4AgWiOupQkQyE5AEM1upA5EgEBNAoJoTbCWJUAgOwFBNLuROlBkAUE0Mqjl6hEQROtxtWo5AUG0nJOrCBAgIIh6BggQIFBeQBAtb+VKAgS6LSCIdnv+Tk+AQHkBQbS8lSu7KSCIdnPuyZ1aEE1uZFltWBDNapwOQ4BAjQKCaI24liZAIDsBQTS7kToQAQI1CQiiNcFalgCB7AQE0exG6kCRBQTRyKCWq0dAEK3H1arlBATRck6uIkCAgCDqGSBAgEB5AUG0vJUrCRDotoAg2u35Oz0BAuUFBNHyVq7spoAg2s25J3dqQTS5kWW1YUE0q3E6DAECNQoIojXiWpoAgewEBNHsRupABAjUJCCI1gRrWQIEshMQRLMbqQNFFhBEI4Narh4BQbQeV6uWExBEyzm5igABAoKoZ4AAAQLlBQTR8lauJECg2wKCaLfn7/QECJQXEETLW7mymwKCaDfnntypBdHkRpbVhgXRrMbpMAQI1CggiNaIa2kCBLITEESzG6kDESBQk4AgWhOsZQkQyE5AEM1upA4UWUAQjQxquXoEBNF6XK1aTkAQLefkKgIECAiingECBAiUFxBEy1u5kgCBbgsIot2ev9MTIFBeQBAtb+XKbgoIot2ce3KnFkSTG1lWGxZEsxqnwxAgUKOAIFojrqUJEMhOQBDNbqQORIBATQKCaE2wliVAIDsBQTS7kTpQZAFBNDKo5eoREETrcbVqOQFBtJyTqwgQICCIegYIECBQXmD5LguC/3dOeS9XEiDQXQFBtLuzd3ICBKoJCKLVvFzdPQFBtHszT/LE/j8KkhxbNpsWRLMZpYMQIFCzgCBaM7DlCRDISkAQzWqcDkOAQI0CgmiNuJYmQCArAUE0q3E6TA0CgmgNqJaMLyCIxje1YnkBQbS8lSsJEOi2gCDa7fk7PQEC1QQE0WperiZAoLsCgmh3Z+/kBAhUExBEq3m5unsCgmj3Zp7kiQXRJMeWzaYF0WxG6SAECNQsIIjWDGx5AgSyEhBEsxqnwxAgUKOAIFojrqUJEMhKQBDNapwOU4OAIFoDqiXjCwii8U2tWF5AEC1v5UoCBLotIIh2e/5OT4BANQFBtJqXqwkQ6K6AINrd2Ts5AQLVBATRal6u7p6AINq9mSd5YkE0ybFls2lBNJtROggBAjULCKI1A1ueAIGsBATRrMbpMAQI1CggiNaIa2kCBLISEESzGqfD1CAgiNaAasn4AoJofFMrlhcQRMtbuZIAgW4LCKLdnr/TEyBQTUAQreblagIEuisgiHZ39k5OgEA1AUG0mperuycgiHZv5kmeWBBNcmzZbFoQzWaUDkKAQM0CgmjNwJYnQCArAUE0q3E6DAECNQoIojXiWpoAgawEBNGsxukwNQgIojWgWjK+gCAa39SK5QUE0fJWriRAoNsCgmi35+/0BAhUExBEq3m5mgCB7goIot2dvZMTIFBNQBCt5uXq7gkIot2beZInFkSTHFs2mxZEsxmlgxAgULOAIFozsOUJEMhKQBDNapwOQ4BAjQKCaI24liZAICsBQTSrcTpMDQKCaA2olowvIIjGN7VieQFBtLyVKwkQ6LaAINrt+Ts9AQLVBATRal6uJkCguwKCaHdn7+QECFQTEESrebm6ewKCaPdmnuSJBdEkx5bNpmMF0R899vDIJnsu3DEsmTtv5M/7IAECBMYhIIiOQ9k9CBDIRUAQzWWSzkGAQN0CgmjdwtYnQCAXAUE0l0k6R10CgmhdstaNKiCIRuW0WEWBGEF0w5bN4dRrvlLxzs9e/gcvPjwcs3zvkT/vgwQIEBiHgCA6DmX3IEAgFwFBNJdJOgcBAnULCKJ1C1ufAIFcBATRXCbpHHUJCKJ1yVo3qoAgGpXTYhUFYgfR581f8JwdrN+8OTy5cUOYN2dO2Gn7uRN//4uNG8LTmzcHQbTi0FxOgEAjAoJoI+xuSoBAogKCaKKDs20CBMYuIIiOndwNCRBIVEAQTXRwtj02AUF0bNRuNBsBQXQ2ej47W4EYQXT9pk3hbdddGeZss014634HPWdL9zz5RLj552vC3ot2Ckfstnzi74t/V/zdmYe8LBy3176zPYrPEyBAoFYBQbRWXosTIJCZgCCa2UAdhwCB2gQE0dpoLUyAQGYCgmhmA3Wc6AKCaHRSC9YhIIjWoWrNsgKCaFkp1xEg0HUBQbTrT4DzEyBQRUAQraLlWgIEuiwgiHZ5+s5OgEAVAUG0ipZruyggiHZx6gmeWRBNcGgZbVkQzWiYjkKAQK0CgmitvBYnQCAzAUE0s4E6DgECtQkIorXRWpgAgcwEBNHMBuo40QUE0eikFqxDQBCtQ9WaZQVmCqJ/dutNYe3GjeHclx0RFm63/aTLbtiyOZx6zVem/MrcVWufCN96aE3YZ9FO4ZUDX5l7y0Nrwt1rnxjpN0S/vvrecN3PVoVj9twnvG753mWP6zoCBAiMLCCIjkzngwQIdFBAEO3g0B2ZAIGRBATRkdh8iACBDgoIoh0cuiNXEhBEK3G5uCkBQbQpefctBGYKoqddf3V4YsPT4a+OfmNYMm/+pGhN/IboF+68I1x+5+3h5ANeGH7ngIMNkwABArULCKK1E7sBAQIZCQiiGQ3TUQgQqFVAEK2V1+IECGQkIIhmNExHqUVAEK2F1aKxBQTR2KLWqyIgiFbRci0BAl0WEES7PH1nJ0CgqoAgWlXM9QQIdFVAEO3q5J2bAIGqAoJoVTHXd01AEO3axBM9ryCa6OAy2bYgmskgHYMAgdoFBNHaid2AAIGMBATRjIbpKAQI1CogiNbKa3ECBDISEEQzGqaj1CIgiNbCatHYAoJobFHrVREoG0TLrDlnm23CW/c76DmX3vPkE+Hmn68Jey/aKRwx8Buixb8r/m42f3xl7mz0fJYAgSoCgmgVLdcSINB1AUG060+A8xMgUFZAEC0r5ToCBLouIIh2/Qlw/pkEBNGZhPx9KwQE0VaMobObEEQ7O3oHJ0CgooAgWhHM5QQIdFpAEO30+B2eAIEKAoJoBSyXEiDQaQFBtNPjd/gSAoJoCSSXNC8giDY/gy7voGwQ/auj3xiWzJs/KdX6TZvC2667Moz6huiZh7wsHLfXvpXG8IU77wiX33l78IZoJTYXEyAwCwFBdBZ4PkqAQOcEBNHOjdyBCRAYUUAQHRHOxwgQ6JyAINq5kTtwRQFBtCKYy5sREESbcXfXfxUQRD0JBAgQKCcgiJZzchUBAgQKAUHUc0CAAIFyAoJoOSdXESBAQBD1DBCYXkAQ9YQkISCIJjGmbDcpiGY7WgcjQCCygCAaGdRyBAhkLSCIZj1ehyNAIKKAIBoR01IECGQtIIhmPV6HiyAgiEZAtET9AoJo/cbuMLXATEH0z269KazduDGc+7IjwsLttp90oQ1bNodTr/nKlF+Zu2rtE+FbD60J+yzaKbxyt+UTa9zy0Jpw99onwh+8+PBwzPK9K43p66vvDdf9bFU4Zs99wusqfrbSjVxMgACB/yMgiHoUCBAgUF5AEC1v5UoCBLotIIh2e/5OT4BAeQFBtLyVK7spIIh2c+7JnVoQTW5kWW14piBa5rBN/IZomX25hgABAjEFBNGYmtYiQCB3AUE09wk7HwECsQQE0ViS1iFAIHcBQTT3CTvfbAUE0dkK+vxYBATRsTC7yRQCgqhHgwABAuUEBNFyTq4iQIBAISCIeg4IECBQTkAQLefkKgIECAiingEC0wsIop6QJAQE0STGlO0mBdFsR+tgBAhEFhBEI4NajgCBrAUE0azH63AECEQUEEQjYlqKAIGsBQTRrMfrcBEEsg2iH7n48h7Pe884OQLTzEs89sST4cxzPxrOOuPk8PKXHjzzB0a44s5Vq8P7V14SPrzi9HDAPs/+xuBUSxXXn3HORWHNg4+ED51zWjjphNeMcNd2fEQQbcccurqLGEG0/xuiheHz5i94DuX6zZvDkxs3hHlz5oSdtp878fe/2LghPL1580i/IdrVeTk3AQLNCQiizdm7MwEC6QkIounNzI4JEGhGQBBtxt1dCRBIT0AQTW9mdjxegVYH0SuuvjF84IJLtxK57GPnlgqOZYPoZPfo3/DQF+4fPnX+e8LSxTvOOJW2BdF16zeE8y68NBxx+CHJhdDJZieIzvgIuqBGgdhBdJSt/sGLDw/HLN97lI/6DAECBMYmIIiOjdqNCBDIQEAQzWCIjkCAwFgEBNGxMLsJAQIZCAiiGQzREWoVaH0Qvek7Pw4fPPu0sGD+3HDLrXeEFSsvCRdfcFapNySryhVxdPB+VT4/jiCa8n6q7F0QraLl2nEIxAiixT5/9NjDI293z4U7hiVz5438eR8kQIDAOAQE0XEouwcBArkICKK5TNI5CBCoW0AQrVvY+gQI5CIgiOYySeeoSyCpIDocHQe/ErYAeuepJ0x8Re5gVOt/1exvvv5VYeUnPtuzHLy2jztZEB18g/TEY4+YiLPFZ4bvX/y7/husU+1tsq+9LULvRRdfPuPbqMOf7e930aIdwhe+fF3vGMX9D9xvz97X9952+129f7fH7rv0IvKXv/qPYe1T68PatU+Fq669aeJrdKc74/AbtP23Zot1h78iuDDf9/nLJt5ILc71jnef39vD4Nu2081j8DPF5/rmj/1yc13/HbAugRkFYgXRGW/kAgIECCQuIIgmPkDbJ0BgrAKC6Fi53YwAgYQFBNGEh2frBAiMVUAQHSu3myUokFQQHQ6HX7v+5nDgfnv13hbtB8iVK07vfaXucBAtfkvzhGNe2QumU73NORxEh+9XrPnAQ4/2ouj6p5/uBcGT3/TaXgAcXnOmvfXDYZWvtp0siBZfKdyPsIP7nSpYXn3dt7Z6w7Y48+VXXj8RYwej5vAbuWXW759r+LPFfe6574Gef39WU83DG6IJ/i9J5lsWRDMfsOMRIBBNQBCNRmkhAgQ6ICCIdmDIjkiAQBQBQTQKo0UIEOiAgCDagSE74qwEWh9EB39DdPgNzcGTD4fFyd4Q/fCK03vxtH/tb7/ptVv9HulwEB0Oc4NB8tHHfrHVW53TfWXu8N6KdS785OfDyvedHh59/MmJ/zzTb5VO9YZo/yuFB9edKogW/76IksWfyWJsETL/9srre9H3U5/5Uu+6/vVVguhkdoNnfv/KS8JU8xBEZ/XfaR+uQUAQrQHVkgQIZCkgiGY5VociQKAmAUG0JljLEiCQnYAgmt1IHYgAgZoEBNGaYC2bjUDrg2j/Nz1XP/hwGIxog0Gv+PrX/p8PnXNa743NWEF08Ctgi+i54k8vCWe/65RQBNF+OCx+33Q4iPZj42R7Gwyy961+aOLNyZmeqrqC6OAeiz30w3MRRIe/Arf/1b7FddN9ZW7h/1efu3qrI/W/NreIwILoTNP2920SEETbNA17IUCgzQKCaJunY28ECLRNQBBt20TshwCBtgoIom2djH0RINA2AUG0bROxn7YJJBNEi+g4+AZnAXnehZeGZbvt3HuDsU1viL744P2n3Vux997blp/+Qu956L8pOdPDUVcQHX5Ttr+P4Tc1q74hOhhTB882fI7hN3a9ITrTk+Dvxy0giI5b3P0IEEhVQBBNdXL2TYBAEwKCaBPq7kmAQIoCgmiKU7NnAgSaEBBEm1B3z5QEkgqi/XBWRNAz3/6WXnQ84vBDtvoNz/5vesZ4Q3T4dzAn+w3Rs844ufe1u8W173j3+b3f8+wH0an2Vjwg/TdK916+W+/raYvgO9Of2EG0uN/wb4gWxl+86obw1hOPDj+8466tvha4OP/Nt97R+73R+fPmbeXf/13Qd739zb15DNsV9/rsFf8QTjj2iN7XBE/3hujwVxcXn139yLqZePw9gdoEmgqif37tV8OmLVsqnWuXhYvCO171mkqfcTEBAgRiCQiisSStQ4BAFwQE0S5M2RkJEIghIIjGULQGAQJdEBBEuzBlZ5yNQFJBtDhoPyS+4qUHh6NeeVgvQhZ/9th9l7Dr0p3Cyf/mddG+MrdYt4hz/d8xHf4N034ELa4r/u7e1Q+F4UA62d6KfzfV75hON8w6gujwGYt/7n/tcPGfB7/69p2nnjARRIvfO+1H0DUPPtI7/6JFO4QXH7Rvz7/4M+hT/HPx+eJt3pneEO3P+Lbb75r4+t7Hfrl5Ns+5zxKYlUBTQfSA978nrN+4sdLeX7DbsnD9f/4vlT7jYgIECMQSEERjSVqHAIEuCAiiXZiyMxIgEENAEI2haA0CBLogIIh2YcrOOBuBVgfR2Rys7Z8touBnr7gmnH3mKaXeDm3DeQa/MrcIouP84w3RcWq717BA00H0FfsdEO7++UPh52ufDAc8b7ew246LnzOkdRs3hFvvWxUEUc8vAQJNCgiiTeq7NwECqQkIoqlNzH4JEGhKQBBtSt59CRBITUAQTW1i9jtuAUF03OL/537Fm5fDv7E5+Dbq8LaKN2AvvuCscMA+yxva8f/53dOLL+99Za4g2tgY3LgBgaaD6H885g3hujt+FH60+v5w/CGHhhfv+fznKDzyy7XhM/90YzjwebuHG87+QANKbkmAAIEQBFFPAQECBMoLCKLlrVxJgEC3BQTRbs/f6QkQKC8giJa3cmU3BQTRbs49uVN7QzS5kWW1YUE0q3E6DAECNQoIojXiWpoAgewEBNHsRupABAjUJCCI1gRrWQIEshMQRLMbqQNFFhBEI4Narh4BQbQeV6uWExBEyzm5igABAoKoZ4AAAQLlBQTR8lauJECg2wKCaLfn7/QECJQXEETLW7mymwKCaDfnntypBdHkRpbVhscdRG/4ye3hd//HX0wYxvzK3L/51jfD2V/8XDj1Fa8K//23fjerOTkMAQLNCwiizc/ADggQSEdAEE1nVnZKgECzAoJos/7uToBAOgKCaDqzstNmBATRZtzdtaKAIFoRzOVRBfpB9GePPBl+cP/qsNeSpWGXRTtGvcfgYoJobbQWJkCgZgFBtGZgyxMgkJWAIJrVOB2GAIEaBQTRGnEtTYBAVgKCaFbjdJgaBATRGlAtGV9AEI1vasXyAkUQ/e9fuyr8t7/7u7Bx8+beB48/5NDwP/796WHOttuWX6jklYJoSSiXESDQOgFBtHUjsSECBFosIIi2eDi2RoBAqwQE0VaNw2YIEGixgCDa4uHYWisEBNFWjMEmZhIQRGcS8vd1Cjy67vFw6Hn/5Tm3+OjJ/y6c/GtHRL/1jT+5PZy61VfmHh++fsePww9X3x/e8KLDwouW7/Wcez66dm247H/fGPZaunMo9jXVn2tv/1H49I3X+src6FOzIAEChYAg6jkgQIBAeQFBtLyVKwkQ6LaAINrt+Ts9AQLlBQTR8lau7KaAINrNuSd3akE0uZFlteF/uOP74R2XXvKcM/3fr35t+OC/+a3oZ53NG6JlN+M3RMtKuY4AgSoCgmgVLdcSINB1AUG060+A8xMgUFZAEC0r5ToCBLouIIh2/Qlw/pkEBNGZhPx9KwQE0VaMobObuGXVT8Jb/uLjzzn/e19/Qjjr9SdEd5nNG6IL5s4NL91r7yn39OAvfhHuevghb4hGn5oFCRAoBARRzwEBAgTKCwii5a1cSYBAtwUE0W7P3+kJECgvIIiWt3JlNwUE0W7OPblTC6LJjSyrDS+Yv2142Qc/EFY98sjEueZss0245r3vC7+y+x7RzzqbN0QPfN7u4YazPzDlnv7mW98MZ3/xc4Jo9KlZkAABQdQzQIAAgWoCgmg1L1cTINBdAUG0u7N3cgIEqgkIotW8XN09AUG0ezNP8sSCaJJjy2bTSxbNDY+vezJc9NV/CN+///6w15Kl4XdefmR4yTRvYs7m8ILobPR8lgCBJgW8IdqkvnsTIJCagCCa2sTslwCBpgQE0abk3ZcAgdQEBNHUJma/4xYQRMct7n4jCQiiI7H5UCSBIojuMG9OeHzthvDU05sjrTr1MoJo7cRuQIBATQKCaE2wliVAIEsBQTTLsToUAQI1CAiiNaBakgCBLAUE0SzH6lARBQTRiJiWqk9AEK3P1sozC4w7iN7z8M/DF793S/j4tV8Nm7ZsCf/xmDeE627/YfjRmp+FN7zosPCi5Xs9Z9OPrl0bLvvfN4YX7LYsXP+f/8uUh7rt/nvD/7r9h+HQPZ8fjj/k0JkP7woCBAhUEBBEK2C5lACBzgsIop1/BAAQIFBSQBAtCeUyAgQ6LyCIdv4RADCDgCDqEUlCQBBNYkzZbnLcQbQPecD73xPWb9z4r0H0jh+FH62+vxcxX7zn859j/cgv14bP/NONYabfEM12SA5GgEArBATRVozBJggQSERAEE1kULZJgEDjAoJo4yOwAQIEEhEQRBMZlG02JiCINkbvxlUEBNEqWq6NLSCIxha1HgECuQoIorlO1rkIEKhDQBCtQ9WaBAjkKCCI5jhVZyJAoA4BQbQOVWvmJCCI5jTNjM8iiGY83ASOJogmMCRbJECgFQKCaCvGYBMECCQiIIgmMijbJECgcQFBtPER2AABAokICKKJDMo2GxMQRBujd+MqAoJoFS3XxhZoOohWOY+vzK2i5VoCBGILCKKxRa1HgEDOAoJoztN1NgIEYgoIojE1rUWAQM4CgmjO03W2GAKCaAxFa9QuIIjWTuwG0wgIoh4PAgQIlBMQRMs5uYoAAQKFgCDqOSBAgEA5AUG0nJOrCBAgIIh6BghMLyCIekKSEBBEkxhTtptsKohmC+pgBAhkKyCIZjtaByNAoAYBQbQGVEsSIJClgCCa5VgdigCBGgQE0RpQLZmVgCCa1TjzPYwgmu9sUziZIJrClOyRAIE2CAiibZiCPRAgkIqAIJrKpOyTAIGmBQTRpifg/gQIpCIgiKYyKftsSkAQbUrefSsJCKKVuFwcWUAQjQxqOQIEshUQRLMdrYMRIFCDgCBaA6olCRDIUkAQzXKsDkWAQA0CgmgNqJbMSkAQzWqc+R5GEM13timcTBBNYUr2SIBAGwQE0TZMwR4IEEhFQBBNZVL2SYBA0wKCaNMTcH8CBFIREERTmZR9NiUgiDYl776VBATRSlwujiwgiEYGtRwBAtkKCKLZjtbBCBCoQUAQrQHVkgQIZCkgiGY5VociQKAGAUG0BlRLZiUgiGY1znwPI4jmO9sUTiaIpjAleyRAoA0CgmgbpmAPBAikIiCIpjIp+yRAoGkBQbTpCbg/AQKpCAiiqUzKPpsSEESbknffSgKCaCUuF0cWEEQjg1qOAIFsBQTRbEfrYAQI1CAgiNaAakkCBLIUEESzHKtDESBQg4AgWgOqJbMSEESzGme+hxFE851tCicTRFOYkj0SINAGAUG0DVOwBwIEUhEQRFOZlH0SINC0gCDa9ATcnwCBVAQE0VQmZZ9NCQiiTcm7byUBQbQSl4sjCwiikUEtR4BAtgKCaLajdTACBGoQEERrQLUkAQJZCgiiWY7VoQgQqEFAEK0B1ZJZCQiiWY0z38MIovnONoWTCaIpTMkeCRBog4Ag2oYp2AMBAqkICKKpTMo+CRBoWkAQbXoC7k+AQCoCgmgqk7LPpgQE0abk3SwkOBIAACAASURBVLeSgCBaicvFkQUE0cigliNAIFsBQTTb0ToYAQI1CAiiNaBakgCBLAUE0SzH6lAECNQgIIjWgGrJrAQE0azGme9hBNF8Z5vCyQTRFKZkjwQItEFAEG3DFOyBAIFUBATRVCZlnwQINC0giDY9AfcnQCAVAUE0lUnZZ1MCgmhT8u5bSUAQrcTl4sgCgmhkUMsRIJCtgCCa7WgdjACBGgQE0RpQLUmAQJYCgmiWY3UoAgRqEBBEa0C1ZFYCgmhW48z3MIJovrNN4WSCaApTskcCBNogIIi2YQr2QIBAKgKCaCqTsk8CBJoWEESbnoD7EyCQioAgmsqk7LMpAUG0KXn3rSQgiFbicnFkAUE0MqjlCBDIWmDnHeeGR5/ckPUZHY4AAQIxBPzvZQxFaxAg0AUB/3vZhSk7IwECMQSWLto+PPHUxrBlS4zVrEEgPwFBNL+ZZnkiQTTLsSZzKEE0mVHZKAECDQt4Q7ThAbg9AQJJCXhDNKlx2SwBAg0KeEO0QXy3JkAgKQFviCY1LpttQEAQbQDdLasLCKLVzXwinoAgGs/SSgQI5C0giOY9X6cjQCCugCAa19NqBAjkKyCI5jtbJyNAIK6AIBrX02r5CQii+c00yxMJolmONZlDCaLJjMpGCRBoWEAQbXgAbk+AQFICgmhS47JZAgQaFBBEG8R3awIEkhIQRJMal802ICCINoDultUFBNHqZj4RT0AQjWdpJQIE8hYQRPOer9MRIBBXQBCN62k1AgTyFRBE852tkxEgEFdAEI3rabX8BATR/Gaa5YkE0SzHmsyhBNFkRmWjBAg0LCCINjwAtydAICkBQTSpcdksAQINCgiiDeK7NQECSQkIokmNy2YbEBBEG0B3y+oCgmh1M5+IJyCIxrO0EgECeQsIonnP1+kIEIgrIIjG9bQaAQL5Cgii+c7WyQgQiCsgiMb1tFp+AoJofjPN8kSCaJZjTeZQgmgyo7JRAgQaFhBEGx6A2xMgkJSAIJrUuGyWAIEGBQTRBvHdmgCBpAQE0aTGZbMNCAiiDaC7ZXUBQbS6mU/EExBE41laiQCBvAUE0bzn63QECMQVEETjelqNAIF8BQTRfGfrZAQIxBUQRON6Wi0/AUE0v5lmeSJBNMuxJnMoQTSZUdkoAQINCwiiDQ/A7QkQSEpAEE1qXDZLgECDAoJog/huTYBAUgKCaFLjstkGBATRBtDdsrqAIFrdzCfiCQii8SytRIBA3gKCaN7zdToCBOIKCKJxPa1GgEC+AoJovrN1MgIE4goIonE9rZafgCCa30yzPJEgmuVYkzmUIJrMqGyUAIGGBQTRhgfg9gQIJCUgiCY1LpslQKBBAUG0QXy3JkAgKQFBNKlx2WwDAoJoA+huWV1AEK1u5hPxBATReJZWIkAgbwFBNO/5Oh0BAnEFBNG4nlYjQCBfAUE039k6GQECcQUE0bieVstPQBDNb6ZZnkgQzXKsyRxKEE1mVDZKgEDDAoJowwNwewIEkhIQRJMal80SINCggCDaIL5bEyCQlIAgmtS4bLYBAUG0AXS3rC4giFY384l4AoJoPEsrESCQt4Agmvd8nY4AgbgCgmhcT6sRIJCvgCCa72ydjACBuAKCaFxPq+UnIIjmN9MsTySIZjnWZA4liCYzKhslQKBhAUG04QG4PQECSQkIokmNy2YJEGhQQBBtEN+tCRBISkAQTWpcNtuAgCDaALpbVhcQRKub+UQ8AUE0nqWVCBDIW0AQzXu+TkeAQFwBQTSup9UIEMhXQBDNd7ZORoBAXAFBNK6n1fITEETzm2mWJxJEsxxrMocSRJMZlY0SINCwgCDa8ADcngCBpAQE0aTGZbMECDQoIIg2iO/WBAgkJSCIJjUum21AQBBtAN0tqwsIotXNfCKegCAaz9JKBAjkLSCI5j1fpyNAIK6AIBrX02oECOQrIIjmO1snI0AgroAgGtfTavkJCKL5zTTLEwmiWY41mUMJosmMykYJEGhYQBBteABuT4BAUgKCaFLjslkCBBoUEEQbxHdrAgSSEhBEkxqXzTYgIIg2gO6W1QUE0epmPhFPQBCNZ2klAgTyFhBE856v0xEgEFdAEI3raTUCBPIVEETzna2TESAQV0AQjetptfwEBNH8ZprliQTRLMeazKEE0WRGZaMECDQsIIg2PAC3J0AgKQFBNKlx2SwBAg0KCKIN4rs1AQJJCQiiSY3LZhsQEEQbQHfL6gKCaHUzn4gnIIjGs7QSAQJ5Cwiiec/X6QgQiCsgiMb1tBoBAvkKCKL5ztbJCBCIKyCIxvW0Wn4Cgmh+M83yRIJolmNN5lCCaDKjslECBBoWEEQbHoDbEyCQlIAgmtS4bJYAgQYFBNEG8d2aAIGkBATRpMZlsw0ICKINoLtldQFBtLqZT8QTEETjWVqJAIG8BQTRvOfrdAQIxBUQRON6Wo0AgXwFBNF8Z+tkBAjEFRBE43paLT8BQTS/mWZ5IkE0y7EmcyhBNJlR2SgBAg0LCKIND8DtCRBISkAQTWpcNkuAQIMCgmiD+G5NgEBSAoJoUuOy2QYEBNEG0N2yuoAgWt3MJ+IJCKLxLK1EgEDeAoJo3vN1OgIE4goIonE9rUaAQL4Cgmi+s3UyAgTiCgiicT2tlp+AIJrfTLM8kSCa5ViTOZQgmsyobJQAgYYFBNGGB+D2BAgkJSCIJjUumyVAoEEBQbRBfLcmQCApAUE0qXHZbAMCgmgD6G5ZXUAQrW7mE/EEBNF4llYiQCBvAUE07/k6HQECcQUE0bieViNAIF8BQTTf2ToZAQJxBQTRuJ5Wy09AEM1vplmeSBDNcqzJHEoQTWZUNkqAQMMCgmjDA3B7AgSSEhBEkxqXzRIg0KCAINogvlsTIJCUgCCa1LhstgEBQbQBdLesLiCIVjfziXgCgmg8SysRIJC3gCCa93ydjgCBuAKCaFxPqxEgkK+AIJrvbJ2MAIG4AoJoXE+r5ScgiOY30yxPJIhmOdZkDiWIJjMqGyVAoGEBQbThAbg9AQJJCQiiSY3LZgkQaFBAEG0Q360JEEhKQBBNalw224CAINoAultWFxBEq5v5RDwBQTSepZUIEMhbQBDNe75OR4BAXAFBNK6n1QgQyFdAEM13tk5GgEBcAUE0rqfV8hNoPIjecusd4aKLLw+fOv89YeniHfMTjnCiO1etDu9feUn48IrTwwH7LK+04rr1G8J5F14ajjj8kHDSCa+p9NlxXfyRiy/v3eq9Z5w85S0F0XFNw30mExBEPRcECBAoJyCIlnNyFQECBAoBQdRzQIAAgXICgmg5J1cRIEBAEPUMEJheoLYgWoTOd7z7/PChc07bKsRdcfWN4QMXXDrx78sG0f7nJjvOoS/cP6mg+tgTT4Yzz/1oOOuMk8PLX3pw70iTORShcN/nLwsvedGB0YPoZJ6Xfezcif2M8l+cYs2bvvPj8MGzTwsL5s/tLTHZfd556glbxU9BdBRtnxmngCA6Tm33IkAgZQFBNOXp2TsBAuMWEETHLe5+BAikKiCIpjo5+yZAYNwCgui4xd0vNYFag+hFn/5CWLJ4x7Dyfaf33v4sQuCKP70kPP7Ek+Hkf/O6kd9YnCy8pQbfj539tzaLf776um+Fiy84q/cW6GTRdJQzDr8h2v/nYq3BcFncb+XHPxtW/NHbRn5Td6ogOhhJp7r/TGfzhuhMQv6+TgFBtE5daxMgkJOAIJrTNJ2FAIG6BQTRuoWtT4BALgKCaC6TdA4CBOoWEETrFrZ+6gK1BtG/vfL6cNghB4QdFszrxc8imD217unwgx/fOfEVroNvRhaYxZuTv3nckeGyy78a1jz4SDjx2CO2CnfFNZOFt35AvO32u3oz6b/t2P+62UMO2i984cvX9f6ueEPxqFce1nuDtf/P/a9r7a+9aNEOE9eXfXOyuNdnr7gmnH3mKRNvSE71gBT3uee+B3pvShaR8FOf+VJYtHBB2HXnxT2rYq0LP/n5Xkx+9PEnt3pDdKY9DlsUe+i/qVvmjdzhr+jtR8zfftNre2+QFn9/xjkX9ebT93vzb7x6q3/Xf2v369/83nPeGh2OtINviPbv/Zuvf1VY+YnPhv466zZtl/p/1+w/YQFBNOHh2ToBAmMVEETHyu1mBAgkLiCIJj5A2ydAYGwCgujYqN2IAIHEBQTRxAdo+7UL1B5E3/P7vx0++pd/G4r/+7kvXRtOfcuxvX/u/6blZEF07+W79SJo8Wey378cDqL9AHjym147aUws4t273v7mib8r/vmEY17Zi5HD8a//Fa/9CFomIPanVCWIDgfPIqS+9siXhG9//597+yruWwTlwmH1gw8/J4gWXzs82R7nz5vXM1u2284TsXXQsMzX004XRF988P699ftxtIibX7zqhvDWE48Of3/dTZN+Ze7w1+gWXoMzLGJw8ac/j8H59G29IVr7/xa4wTQCgqjHgwABAuUEBNFyTq4iQIBAISCIeg4IECBQTkAQLefkKgIECAiingEC0wvUHkSLoFeEsq9df0t4w2tfHt54zBFbRc7Jgujgb2sOf7XscEwrfqtyOFoOvtG489KdtoqJw19FO/zPU8XWwT1NRVoliPa/Pvjsd50SHn3sF+G+1Q+F1/36yya+tvb//fzf934/tP+26PtXXhI+vOL03tfpDu9xurdJJ3sbs79u37KIq8Wf/tuYw2+kDnoeuN+evbd4+/F50KLMV+b2rx8MvsNBdPCs/esFUf9T1qSAINqkvnsTIJCSgCCa0rTslQCBpgUE0aYn4P4ECKQiIIimMin7JECgaQFBtOkJuH/bBcYSRIs3HPtf/9p/g3G6N0RHCaL9r78dBC/eoJxtEB3+utjhgfbfKJ1s0P3AWPx+6mR/+rG3+Orc4it8i7cvL/zU58NJJxwV/vryr028hTnZW6yDb10OB9G+dXHf6b6ednBPg1F5uiA62Vfm9t9UrRJEp3tDVBBt+/9sdG9/gmj3Zu7EBAiMJiCIjubmUwQIdFNAEO3m3J2aAIHqAoJodTOfIECgmwKCaDfn7tTlBcYSRIu3OPt/hgNdrDdE+18vO3iv4p7DMbEtb4gWeyui4A//+Z6waIf54fdOeWMoAmbx7x5+9Inw07t/Flb80dt6/65qEB0MitN5D4baKkF0qpA62e+FThZJy/yGaP9t2P69vCFa/r/UrowvIIjGN7UiAQJ5Cgiiec7VqQgQqEdAEK3H1aoECOQnIIjmN1MnIkCgHgFBtB5Xq+YjkEUQHf4N0WI8ReAr/sz2DdHJgt5U46/ylbnFGsX1xe9l/uqLX9D7rdD+1/8Wb7ueeOwRE/+uShAdfgO3f4/+b6j2Y2Rx//49+14XXXx5+NT57+kdr/ha3P6buoVlsafiTdDiK3Ovvvam8LaTXt+7bjCkFhG3v0Y/tg77TXb/wd81HT5r31oQzed/dFI8SVNB9OlNG8N3V93TONnBy5aHpQsXNr4PGyBAoP0Cgmj7Z2SHBAi0R0AQbc8s7IQAgXYLCKLtno/dESDQHgFBtD2zsJN2CmQRRAvafhS97fa7etJT/R5mmTdE+7+pObjOVF97OzjWqkG0v5dXvPTg8N4zTu4tNVncrRJE+2+UFqF1zYOP9L6Kt/hz/NG/1vs90v6f4a/6Hf56334ELa7/nTcfE9aufar3Fb7F1/qed+Gl4aprb+ottcfuu4SLLzir99um/dhZ/F1/veKt0UHP4jPvPPWEifMW/yyItvN/HOzqWYGmgui9jz0Sjlx5XuOjuOwdZ4TXH3Jo4/uwAQIE2i8giLZ/RnZIgEB7BATR9szCTggQaLeAINru+dgdAQLtERBE2zMLO2mnQG1BtJ3HnXlXVd4InXk1V8QS8IZoLEnrjCLQdBDdbpttwrLFS6bc+v2PP9b7u72WLB3leFN+5uG1a8P6TRuDIBqV1WIEshYQRLMer8MRIBBZQBCNDGo5AgSyFRBEsx2tgxEgEFlAEI0MarnsBATRoZEKou18xgXRds6lK7tqKoiueuTh8Ko/+5OweMGC8M5ff+2k3Fue2RI+du3XwjZhm/Ce434j6ki+/P3vhDt//lC49O2/H97won9929wfAgQITCcgiHo+CBAgUF5AEC1v5UoCBLotIIh2e/5OT4BAeQFBtLyVK7spIIh2c+7JnVoQTW5kWW1YEBVEs3qgHYZAjQKCaI24liZAIDsBQTS7kToQAQI1CQiiNcFalgCB7AQE0exG6kCRBQTRyKCWq0dAEK3H1arlBARRQbTck+IqAgQEUc8AAQIEygsIouWtXEmAQLcFBNFuz9/pCRAoLyCIlrdyZTcFBNFuzj25UwuiyY0sqw2PI4iueeLx8Id/c1nYc+nS8PFT3t7zu/exR8KRK88Li+cvCO989eRfmVtc95Fr/r53/XuPe2NU9y/f+p1w58MPRf8N0RVXfD785MEHwsqTTgm/svuyqHu2GAECzQoIos36uzsBAmkJCKJpzctuCRBoTkAQbc7enQkQSEtAEE1rXnY7fgFBdPzm7jiCgCA6ApqPRBMYRxC98+cPhtdc+KGw/667hW+c88e9vef6G6Jv+sSF4bv3rQpf+YOzwuH77BdtThYiQKB5AUG0+RnYAQEC6QgIounMyk4JEGhWQBBt1t/dCRBIR0AQTWdWdtqMgCDajLu7VhQQRCuCuTyqgCAa9ytzBdGoj6fFCLRKQBBt1ThshgCBlgsIoi0fkO0RINAaAUG0NaOwEQIEWi4giLZ8QLbXuIAg2vgIbKCMgCBaRsk1dQkIooJoXc+WdQnkJiCI5jZR5yFAoE4BQbROXWsTIJCTgCCa0zSdhQCBOgUE0Tp1rZ2DgCCawxQ7cAZBtANDbvERxxlEJ2NYvGBBeOevT/4bolue2RI+du3XwjZhm/Ce434jquKXv/+dcOfPH4q65uBivjK3NloLE2hMQBBtjN6NCRBIUEAQTXBotkyAQCMCgmgj7G5KgECCAoJogkOz5bEKCKJj5XazUQUE0VHlfC6GgCAaQ/G5awii9bhalUCTAoJok/ruTYBAagKCaGoTs18CBJoSEESbkndfAgRSExBEU5uY/Y5bQBAdt7j7jSQgiI7E5kORBMYZRPffdbfwjXP+uLfzVY88HF71Z38Smn5D9NK3+8rcSI+SZQhkLyCIZj9iByRAIKKAIBoR01IECGQtIIhmPV6HI0AgooAgGhHTUlkKCKJZjjW/Qwmi+c00pRMJooJoSs+rvRJoUkAQbVLfvQkQSE1AEE1tYvZLgEBTAoJoU/LuS4BAagKCaGoTs99xCwii4xZ3v5EEBNGR2HwokoAgKohGepQsQyB7AUE0+xE7IAECEQUE0YiYliJAIGsBQTTr8TocAQIRBQTRiJiWylJAEM1yrPkdShDNb6YpnWgcQXTNE4+HP/yby8KeS5eGj5/y9h7PvY89Eo5ceV5YPH9BeOerXzsl2Ueu+fve3733uDdGZf3yrd8Jdz78ULjsHWeE1x9yaLS1V1zx+fCTBx8IK086JfzK7suirWshAgSaFxBEm5+BHRAgkI6AIJrOrOyUAIFmBQTRZv3dnQCBdAQE0XRmZafNCAiizbi7a0UBQbQimMujCowjiE624Vx/QzTqcCxGgECrBATRVo3DZggQaLmAINryAdkeAQKtERBEWzMKGyFAoOUCgmjLB2R7jQsIoo2PwAbKCAiiZZRcU5eAIBr3K3PrmpN1CRBoXkAQbX4GdkCAQDoCgmg6s7JTAgSaFRBEm/V3dwIE0hEQRNOZlZ02IyCINuPurhUFBNGKYC6PKiCICqJRHyiLEchYQBDNeLiORoBAdAFBNDqpBQkQyFRAEM10sI5FgEB0AUE0OqkFMxMQRDMbaK7HEURznWwa52oqiPZ/Q3S7bbYJyxYvmRLr/scf6/3dXkuWRgV9eO3asH7Txui/IRp1kxYjQKBVAoJoq8ZhMwQItFxAEG35gGyPAIHWCAiirRmFjRAg0HIBQbTlA7K9xgUE0cZHYANlBATRMkquqUug6SBa17nKrnvZO84Irz/k0LKXu44AgQ4LCKIdHr6jEyBQWUAQrUzmAwQIdFRAEO3o4B2bAIHKAoJoZTIf6JiAINqxgad6XEE01cnlse+mgujTmzaG7666p3HEg5ctD0sXLmx8HzZAgED7BQTR9s/IDgkQaI+AINqeWdgJAQLtFhBE2z0fuyNAoD0Cgmh7ZmEn7RQQRNs5F7saEhBEPRJNCjQVRJs8s3sTIEBgFAFBdBQ1nyFAoKsCgmhXJ+/cBAhUFRBEq4q5ngCBrgoIol2dvHOXFRBEy0q5rlEBQbRR/s7fXBDt/CMAgACBkgKCaEkolxEgQCCEIIh6DAgQIFBOQBAt5+QqAgQICKKeAQLTCwiinpAkBATRJMaU7SYF0WxH62AECEQWEEQjg1qOAIGsBQTRrMfrcAQIRBQQRCNiWooAgawFBNGsx+twEQQE0QiIlqhfQBCt39gdphYQRD0dBAgQKCcgiJZzchUBAgQKAUHUc0CAAIFyAoJoOSdXESBAQBD1DBCYXkAQ9YQkISCIJjGmbDcpiGY7WgcjQCCygCAaGdRyBAhkLSCIZj1ehyNAIKKAIBoR01IECGQtIIhmPV6HiyAgiEZAtET9AoJo/cbuMLWAIOrpIECAQDkBQbSck6sIECBQCAiingMCBAiUExBEyzm5igABAoKoZ4DA9AKCqCckCQFBNIkxZbtJQTTb0ToYAQKRBQTRyKCWI0AgawFBNOvxOhwBAhEFBNGImJYiQCBrAUE06/E6XAQBQTQCoiXqFxBE6zd2h6kFBFFPBwECBMoJCKLlnFxFgACBQkAQ9RwQIECgnIAgWs7JVQQIEBBEPQMEphcQRD0hSQgIokmMKdtNCqLZjtbBCBCILCCIRga1HAECWQsIolmP1+EIEIgoIIhGxLQUAQJZCwiiWY/X4SIICKIREC1Rv4AgWr+xO0wtIIh6OggQIFBOQBAt5+QqAgQIFAKCqOeAAAEC5QQE0XJOriJAgIAg6hkgML2AIOoJSUJAEE1iTNluUhDNdrQORoBAZAFBNDKo5QgQyFpAEM16vA5HgEBEAUE0IqalCBDIWkAQzXq8DhdBQBCNgGiJ+gUE0fqN3WFqAUHU00GAAIFyAoJoOSdXESBAoBAQRD0HBAgQKCcgiJZzchUBAgQEUc8AgekFBFFPSBICgmgSY8p2k4JotqN1MAIEIgsIopFBLUeAQNYCgmjW43U4AgQiCgiiETEtRYBA1gKCaNbjdbgIAoJoBERL1C8giNZv7A5TCwiing4CBAiUExBEyzm5igABAoWAIOo5IECAQDkBQbSck6sIECAgiHoGCEwvIIh6QpIQEESTGFO2mxREsx2tgxEgEFlAEI0MajkCBLIWEESzHq/DESAQUUAQjYhpKQIEshYQRLMer8NFEBBEIyBaon4BQbR+Y3eYWkAQ9XQQIECgnIAgWs7JVQQIECgEBFHPAQECBMoJCKLlnFxFgAABQdQzQGB6AUHUE5KEgCCaxJiy3aQgmu1oHYwAgcgCgmhkUMsRIJC1gCCa9XgdjgCBiAKCaERMSxEgkLWAIJr1eB0ugoAgGgHREvULCKL1G7vD1AKCqKeDAAEC5QQE0XJOriJAgEAhIIh6DggQIFBOQBAt5+QqAgQICKKeAQLTCwiinpAkBATRJMaU7SYF0WxH62AECEQWEEQjg1qOAIGsBQTRrMfrcAQIRBQQRCNiWooAgawFBNGsx+twEQQE0QiIlqhfQBCt39gdphYQRD0dBAgQKCcgiJZzchUBAgQKAUHUc0CAAIFyAoJoOSdXESBAQBD1DBCYXkAQ9YQkISCIJjGmbDcpiGY7WgcjQCCygCAaGdRyBAhkLSCIZj1ehyNAIKKAIBoR01IECGQtIIhmPV6HiyAgiEZAtET9AoJo/cbuMLWAIOrpIECAQDkBQbSck6sIECBQCAiingMCBAiUExBEyzm5igABAoKoZ4DA9AKCqCckCQFBNIkxZbtJQTTb0ToYAQKRBQTRyKCWI0AgawFBNOvxOhwBAhEFBNGImJYiQCBrAUE06/E6XAQBQTQCoiXqFxBE6zd2h6kFBFFPBwECBMoJCKLlnFxFgACBQkAQ9RwQIECgnIAgWs7JVQQIEBBEPQMEphcQRD0hSQgIokmMKdtNCqLZjtbBCBCILCCIRga1HAECWQsIolmP1+EIEIgoIIhGxLQUAQJZCwiiWY/X4SIICKIREC1Rv4AgWr+xO0wtIIh6OggQIFBOQBAt5+QqAgQIFAKCqOeAAAEC5QQE0XJOriJAgIAg6hkgML2AIOoJSUJAEE1iTNluUhDNdrQORoBAZAFBNDKo5QgQyFpAEM16vA5HgEBEAUE0IqalCBDIWkAQzXq8DhdBQBCNgGiJ+gUE0fqN3WFqAUHU00GAAIFyAoJoOSdXESBAoBAQRD0HBAgQKCcgiJZzchUBAgQEUc8AgekFBFFPSBICgmgSY8p2k0UQvfPJx8InvvvdsHlLvcfcZd688O7DXlHvTaxOgACBmgQE0ZpgLUuAQJYCgmiWY3UoAgRqEBBEa0C1JAECWQoIolmO1aEiCgiiETEtVZ+AIFqfrZVnFiiC6HcffiCcfeMNM188yyt2X7AwfPKo42e5io8TIECgGQFBtBl3dyVAIE0BQTTNudk1AQLjFxBEx2/ujgQIpCkgiKY5N7sen4AgOj5rd5qFgCA6CzwfnbXAYBDdZd78cNguu02seeOa+8LmZ54JRy17fthu221Gvte6TZvCTQ+tDoLoyIQ+SIBACwQE0RYMwRYIEEhGQBBNZlQ2SoBAwwKCaMMDcHsCBJIREESTGZWNNiQgiDYE77bVBATRal6ujiswGET32GFROGrZXhM3uOLun4RNz2wJb9n3BWHutnNGvvHaDRvC1fffFZbtsDD8xau9IToypA8SINCogCDaKL+bEyCQmIAgmtjAbJcAgcYEBNHG6N2YAIHEA22o+gAAIABJREFUBATRxAZmu2MXEETHTu6GowgIoqOo+UwsAUE0lqR1CBDIXUAQzX3CzkeAQEwBQTSmprUIEMhZQBDNebrORoBATAFBNKamtXIUEERznGqGZxJEMxzqmI50/y/Xhic2rA97LdwxLJ47b6S7bh1EF/a+Hrf/54p7fhI2bdkSTtrvoLBdUQJG/LN244Zw9X13Pecrc1et/UUo/m6fRYvDou23H3F1HyNAgMB4BATR8Ti7CwECeQgIonnM0SkIEKhfQBCt39gdCBDIQ0AQzWOOTlGfgCBan62VIwoIohExO7bUR35wS/jmA/eHdx/68nDUHs9+1W0VhibfEP3gt78ZfvDoQ+GPD//18JKB3y6tsn/XEiBAYFwCgui4pN2HAIEcBATRHKboDAQIjENAEB2HsnsQIJCDgCCawxSdoU4BQbROXWtHExBEo1F2biFBtHMjd2ACBBoUEEQbxHdrAgSSExBEkxuZDRMg0JCAINoQvNsSIJCcgCCa3MhseMwCguiYwd1uNAFBdDQ3nwpBEPUUECBAYHwCguj4rN2JAIH0BQTR9GfoBAQIjEdAEB2Ps7sQIJC+gCCa/gydoF4BQbReX6tHEhBEI0F2cJmP/uCW8I8P3B9eufvysM/CnUYSmD93Tlizbm34h1WrwvIdFoVXL3v2q3f7vyH6b/f9lbD9ttuOtH7xof5viC7cbvtw4t4HTKxz/er7wkPrfxnOO/zV4bBdnjfy+j5IgACBcQgIouNQdg8CBHIREERzmaRzECBQt4AgWrew9QkQyEVAEM1lks5Rl4AgWpesdaMKCKJROTu1WP8N0ViH3mOHReGowSB690/Cpme2hLfs+4Iwd9s5I99m7YYN4er775ry835DdGRaHyRAYIwCgugYsd2KAIHkBQTR5EfoAAQIjElAEB0TtNsQIJC8gCCa/AgdoGYBQbRmYMvHERBE4zh2cRVviHZx6s5MgEBTAoJoU/LuS4BAigKCaIpTs2cCBJoQEESbUHdPAgRSFBBEU5yaPY9TQBAdp7Z7jSwgiI5M1/kP+g3Rzj8CAAgQGKOAIDpGbLciQCB5AUE0+RE6AAECYxIQRMcE7TYECCQvIIgmP0IHqFlAEK0Z2PJxBATROI5dXEUQ7eLUnZkAgaYEBNGm5N2XAIEUBQTRFKdmzwQINCEgiDah7p4ECKQoIIimODV7HqeAIDpObfcaWUAQHZmu8x8URDv/CAAgQGCMAoLoGLHdigCB5AUE0eRH6AAECIxJQBAdE7TbECCQvIAgmvwIHaBmAUG0ZmDLxxEQROM4dnGV+3+5NjyxYX3Ya+GOYfHceSMRLFk0N3z34QfC2TfeEPbYYWE4atnzJ9a54p6fhE1btoST9jsobFeUgBH/rN24IVx9311h9wULwyePOn5ilVVrfxGKv9tn0eKwaPvtR1zdxwgQIDAeAUF0PM7uQoBAHgKCaB5zdAoCBOoXEETrN3YHAgTyEBBE85ijU9QnIIjWZ2vliAKCaERMS1UW2DqILgpHLdtrYo0r7v5J2PTMlvCWfV8Q5m47p/La/Q+s3bAhXH3/XWHZDgvDX7z62SA68oI+SIAAgQYEBNEG0N2SAIFkBQTRZEdn4wQIjFlAEB0zuNsRIJCsgCCa7OhsfEwCguiYoN1mdgKC6Oz8fHp2AoLo7Px8mgCB7ggIot2ZtZMSIDB7AUF09oZWIECgGwKCaDfm7JQECMxeQBCdvaEV8hYQRDOc7y233hEuuvjy8Knz3xOWLt4x2gnXrd8Qzrvw0nDE4YeEk054TbR1yywkiJZRck1dAoNBdJd588Nhu+w2casb19wXNj/zTO9rdLfbdvSvzF23aVO46aHVz/nK3LrOZF0CBAjUISCI1qFqTQIEchUQRHOdrHMRIBBbQBCNLWo9AgRyFRBEc52sc8USEERjSTawzkcuvjz81eeunrjzicceET549mnhh3fcNbYgesXVN4YPXHBpbw+HvnD/6BG2fzhBtIEHzC0nBAaDaN0sw78hWvf9rE+AAIGYAoJoTE1rESCQu4AgmvuEnY8AgVgCgmgsSesQIJC7gCCa+4Sdb7YCguhsBRv4fP9NzeLWRQBdMH9ubxdFnHz+8n99c20cb4gOv4laBNoHHnp0qz3F4hFEY0laZxSBIoje+eRj4RPf/W7YvGWUFcp/Zpd588K7D3tF+Q+4kgABAi0SEERbNAxbIUCg9QKCaOtHZIMECLREQBBtySBsgwCB1gsIoq0fkQ02LCCINjyAUW4/01fiTvb3g2+TvvPUE8J7zzi5d+siot70nR9PRMw7V60OF37y82Hl+07vfd3uY088Gc4896Phttvvmtjqh845LbzxmCOe8/W5xWffv/KS8OEVp4cD9lneW7v/9mjx4cs+dm54+UsP7q3Tj6f3rn6ot/Yeu+8SPvZf/zD89eVfC1dde1Pvny++4KzeOsUfQXSUJ8VnYgkUQXSHeXPC42s3hKee3hxrWesQIEAgOwFBNLuROhABAjUKCKI14lqaAIGsBATRrMbpMAQI1CggiNaIa+ksBATRBMdYxMTiTz9qDh9hOIgORs/i2uJ3QJfttnPv89MF0fnz5m117eBviE4WRPvx9KwzTg4vPnj/8MWrbghvPfHo3husxX0uv/L6ia/ULc5w8613bPXPV1/3rYkIOnxGQTTBBzWjLQuiGQ3TUQgQqFVAEK2V1+IECGQmIIhmNlDHIUCgNgFBtDZaCxMgkJmAIJrZQB0nuoAgGp20/gWLWLjv85eFk054zaQ3Gwyi/ah5xOGHTFw/+Pdf/+b3pnxD9NHHn9zqjc/BIFrcezimDgbR/pug/Q0Ovz06HDyH1xr+Z0G0/ufKHaYWEEQ9HQQIECgnIIiWc3IVAQIECgFB1HNAgACBcgKCaDknVxEgQEAQ9QwQmF5AEE3wCanyhmg/iP72m1478XW1g1+LO1MQHfz63OEg2v/n4itu+38Gv+q2uM8Z51wU1jz4SO+vB/9OEE3wwevwlgXRDg/f0QkQqCQgiFbicjEBAh0XEEQ7/gA4PgECpQUE0dJULiRAoOMCgmjHHwDHn1FAEJ2RqH0XVPkN0TrfEB2WKQLoZ6+4Jpx95ilh9YMP92LoyhWn90KsN0Tb9xzZUXkBQbS8lSsJEOi2gCDa7fk7PQEC1QQE0WperiZAoLsCgmh3Z+/kBAhUExBEq3m5unsCgmiCM++/mVls/YNnn9b7jc7iT/E1s89fvlvvP1908eUTv885+Pudw78LOhxXB3/bczim9t/4fNfb3/ycr+sd/rrc4QBa3GfFykum/I1QX5mb4IPYoS0Loh0atqMSIDArAUF0Vnw+TIBAxwQE0Y4N3HEJEBhZQBAdmc4HCRDomIAg2rGBO25lAUG0Mll7PlDEy7/63NUTGzrx2CN6gfSHd9y1VRAtLhi89p2nnhDee8bJE58b/LsV//Ft4R9vvi2sfN/pYeniHXtvdva/9vaoVx7W+8zxR/9aL4j2I+htt9+11dfh9hceXPfQg/fr/esPrzg9HLDP8t5+ij/9fQii7Xmu7OS5AqME0R+tvj8c/7Hzx8p523nnh50XLhrrPd2MAAECgwKCqOeBAAEC5QUE0fJWriRAoNsCgmi35+/0BAiUFxBEy1u5spsCgmg3557cqVc/si65PdtwPgKCaD6zdBICBOoVEETr9bU6AQJ5CQiiec3TaQgQqE9AEK3P1soECOQlIIjmNU+niS8giMY3tWINAoJoDaiWLC0wmyC666Idw78/4tWl7zXVhZd/+6Zw/+OPhd/+1VeE5++8y1aXffL6a8L6TRuDN0RnzWwBAgRmKSCIzhLQxwkQ6JSAINqpcTssAQKzEBBEZ4HnowQIdEpAEO3UuB12BAFBdAQ0Hxm/gCA6fnN3fFZAEPU0ECBAoJyAIFrOyVUECBAoBARRzwEBAgTKCQii5ZxcRYAAAUHUM0BgegFB1BOShIAgmsSYst2kIJrtaB2MAIHIAoJoZFDLESCQtYAgmvV4HY4AgYgCgmhETEsRIJC1gCCa9XgdLoKAIBoB0RL1Cwii9Ru7w9QCZYLoHQ+sDlf94HvhhXvsGU449KXh9jU/C8d9dGV43qIdw/8V8StzTz78FWGvpVN8Ze6f/FnYeYeF4c+v/WrYtHlzOOv4E42VAAECYxUQRMfK7WYECCQuIIgmPkDbJ0BgbAKC6Nio3YgAgcQFBNHEB2j7tQsIorUTu0EMAUE0hqI1RhUoE0T/5/duCX/4uc+Et7zk8PAXb/u98KPV94fjP3Z+aOI3RPdb8Z/Chs2bw11/+rEwb7vtRj22zxEgQKCygCBamcwHCBDosIAg2uHhOzoBApUEBNFKXC4mQKDDAoJoh4fv6KUEBNFSTC5qWkAQbXoC3b6/INrt+Ts9AQLlBQTR8lauJECAgCDqGSBAgEA5AUG0nJOrCBAgIIh6BghMLyCIekKSEBBEkxhTtpsURLMdrYMRIBBZQBCNDGo5AgSyFhBEsx6vwxEgEFFAEI2IaSkCBLIWEESzHq/DRRAQRCMgWqJ+AUG0fmN3mFqgTBD90q3fDn/wN5eFXRcuCgfutiys27ghfP/+e8f6G6K/tvd+Ybs5c8JNd/+0d5ifXfD/GCsBAgTGKiCIjpXbzQgQSFxAEE18gLZPgMDYBATRsVG7EQECiQsIookP0PZrFxBEayd2gxgCgmgMRWuMKlAmiPZ/Q3T4HuP8DdHhe/sN0VEn7nMECIwqIIiOKudzBAh0UWCPneeHNY+u7+LRnZkAAQKVBATRSlwuJkCgwwKCaIeH7+ilBATRUkwualpAEG16At2+f5kg2n9D9NUH/kp497FvDKsefTic9befHesbope+/ffDTvMXhN+6+M97A/OGaLefW6cn0ISAINqEunsSIJCqgCCa6uTsmwCBcQsIouMWdz8CBFIVEERTnZx9j0tAEB2XtPvMSkAQnRWfD89SoEwQ7b8h+paXHB7+4m2/F360+v5w/MfOD+N8Q/S2884POy9cFPZb8Z/Chs2bgzdEZzl4HydAoLKAIFqZzAcIEOiwgCDa4eE7OgEClQQE0UpcLiZAoMMCgmiHh+/opQQE0VJMLmpaQBBtegLdvr8g2u35Oz0BAuUFBNHyVq4kQICAIOoZIECAQDkBQbSck6sIECAgiHoGCEwvIIh6QpIQEESTGFO2mxREsx2tgxEgEFlAEI0MajkCBLIWEESzHq/DESAQUUAQjYhpKQIEshYQRLMer8NFEBBEIyBaon4BQbR+Y3eYWqBMEB3+dJNfmWuWBAgQaEpAEG1K3n0JEEhRQBBNcWr2TIBAEwKCaBPq7kmAQIoCgmiKU7PncQoIouPUdq+RBQTRkel8MIKAIBoB0RIECHRCQBDtxJgdkgCBSAKCaCRIyxAgkL2AIJr9iB2QAIFIAoJoJEjLZCsgiGY72rwOJojmNc/UTiOIpjYx+yVAoCkBQbQpefclQCBFAUE0xanZMwECTQgIok2ouycBAikKCKIpTs2exykgiI5T271GFhBER6bzwQgCowTR29f8LBz30ZVhh7lzw0v23HvWu/jRmp+FX6xfFw7ZY3lYPH+Hrda7ZdVdYdOWLeG2P/mzsPMOC2d9LwsQIEBgVAFBdFQ5nyNAoIsCgmgXp+7MBAiMIiCIjqLmMwQIdFFAEO3i1J25ioAgWkXLtY0JCKKN0btxCGE2QXScgILoOLXdiwCByQQEUc8FAQIEygsIouWtXEmAQLcFBNFuz9/pCRAoLyCIlrdyZTcFBNFuzj25UwuiyY0sqw2PEkR/vvbJ8Nf/dONYHd71uteHBdvPHes93YwAAQKDAoKo54EAAQLlBQTR8lauJECg2wKCaLfn7/QECJQXEETLW7mymwKCaDfnntypBdHkRpbVhkcJolkBOAwBAgRKCgiiJaFcRoAAgRCCIOoxIECAQDkBQbSck6sIECAgiHoGCEwvIIh6QpIQEESTGFO2mxREsx2tgxEgEFlAEI0MajkCBLIWEESzHq/DESAQUUAQjYhpKQIEshYQRLMer8NFEBBEIyBaon4BQbR+Y3eYWkAQ9XQQIECgnIAgWs7JVQQIECgEBFHPAQECBMoJCKLlnFxFgAABQdQzQGB6AUHUE5KEgCCaxJiy3aQgmu1oHYwAgcgCgmhkUMsRIJC1gCCa9XgdjgCBiAKCaERMSxEgkLWAIJr1eB0ugoAgGgHREvULCKL1G7vD1AKCqKeDAAEC5QQE0XJOriJAgEAhIIh6DggQIFBOQBAt5+QqAgQICKKeAQLTCwiinpAkBATRJMaU7SYF0WxH62AECEQWEEQjg1qOAIGsBQTRrMfrcAQIRBQQRCNiWooAgawFBNGsx+twEQQE0QiIlqhfQBCt39gdphYQRD0dBAgQKCcgiJZzchUBAgQKAUHUc0CAAIFyAoJoOSdXESBAQBD1DBCYXkAQ9YQkISCIJjGmbDcpiGY7WgcjQCCygCAaGdRyBAhkLSCIZj1ehyNAIKKAIBoR01IECGQtIIhmPV6HiyAgiEZAtET9AoJo/cbuMLWAIOrpIECAQDkBQbSck6sIECBQCAiingMCBAiUExBEyzm5igABAoKoZ4DA9AKCqCckCQFBNIkxZbtJQTTb0ToYAQKRBQTRyKCWI0AgawFBNOvxOhwBAhEFBNGImJYiQCBrAUE06/E6XAQBQTQCoiXqFxBE6zd2h6kFBFFPBwECBMoJCKLlnFxFgACBQkAQ9RwQIECgnIAgWs7JVQQIEBBEPQMEphcQRD0hSQgIokmMKdtNCqLZjtbBCBCILCCIRga1HAECWQsIolmP1+EIEIgoIIhGxLQUAQJZCwiiWY/X4SIICKIREC1Rv4AgWr+xO0wtIIh6OggQIFBOQBAt5+QqAgQIFAKCqOeAAAEC5QQE0XJOriJAgIAg6hkgML2AIOoJSUJAEE1iTNluUhDNdrQORoBAZAFBNDKo5QgQyFpAEM16vA5HgEBEAUE0IqalCBDIWkAQzXq8DhdBQBCNgGiJ+gUE0fqN3WFqAUHU00GAAIFyAoJoOSdXESBAoBAQRD0HBAgQKCcgiJZzchUBAgQEUc8AgekFBFFPSBICgmgSY8p2k4JotqN1MAIEIgsIopFBLUeAQNYCgmjW43U4AgQiCgiiETEtRYBA1gKCaNbjdbgIAoJoBERL1C8giNZv7A5TCwiing4CBAiUExBEyzm5igABAoWAIOo5IECAQDkBQbSck6sIECAgiHoGCEwvIIh6QpIQEESTGFO2mxREsx2tgxEgEFlAEI0MajkCBLIWEESzHq/DESAQUUAQjYhpKQIEshYQRLMer8NFEBBEIyBaon4BQbR+Y3eYWkAQ9XQQIECgnIAgWs7JVQQIECgEBFHPAQECBMoJCKLlnFxFgAABQdQzQGB6AUHUE5KEgCCaxJiy3aQgmu1oHYwAgcgCgmhkUMsRIJC1gCCa9XgdjgCBiAKCaERMSxEgkLWAIJr1eB0ugoAgGgHREvULCKL1G7vD1AKCqKeDAAEC5QQE0XJOriJAgEAhIIh6DggQIFBOQBAt5+QqAgQICKKeAQLTCwiinpAkBATRJMaU7SYF0WxH62AECEQWEEQjg1qOAIGsBQTRrMfrcAQIRBQQRCNiWooAgawFBNGsx+twEQQE0QiIlqhfQBCt39gdphYQRD0dBAgQKCcgiJZzchUBAgQKAUHUc0CAAIFyAoJoOSdXESBAQBD1DBCYXkAQ9YQkISCIJjGmbDcpiGY7WgcjQCCygCAaGdRyBAhkLSCIZj1ehyNAIKKAIBoR01IECGQtIIhmPV6HiyAgiEZAtET9AoJo/cbuMLWAIOrpIECAQDkBQbSck6sIECBQCAiingMCBAiUExBEyzm5igABAoKoZ4DA9AKCqCckCQFBNIkxZbtJQTTb0ToYAQKRBQTRyKCWI0AgawFBNOvxOhwBAhEFBNGImJYiQCBrAUE06/E6XAQBQTQCoiXqFxBE6zd2h6kFBFFPBwECBMoJCKLlnFxFgACBQkAQ9RwQIECgnIAgWs7JVQQIEBBEPQMEphcQRD0hSQgIokmMKdtNCqLZjtbBCBCILCCIRga1HAECWQsIolmP1+EIEIgoIIhGxLQUAQJZCwiiWY/X4SIICKIREC1Rv4AgWr+xO0wtMI4g+p2fPxB++ovHK4/hpbvsFg5asnPlz/kAAQIE6hAQROtQtSYBArkKCKK5Tta5CBCILSCIxha1HgECuQoIorlO1rliCQiisSStU6uAIForr8VnEBhHEL3k9u+Hr953V+VZvOOgQ8Ob9jmw8ud8gAABAnUICKJ1qFqTAIFcBQTRXCfrXAQIxBYQRGOLWo8AgVwFBNFcJ+tcsQQE0ViS1qlVQBCtldfiLQqiy3ZYFHaZt2CrHa3duCGsWvtEWLj99mHfRUt6f7fmqbXh0afXBUHU40uAQJsEBNE2TcNeCBBou4Ag2vYJ2R8BAm0REETbMgn7IECg7QKCaNsnZH9NCwiiTU/A/UsJCKKlmFxUk8A43hD9y9tvDV+77+7wsl13Dy9YvPVX4K556pfhG2vuDbsvWBiOXr5375Tfe/jB8C9PPCqI1jRzyxIgMJqAIDqam08RINBNAUG0m3N3agIEqgsIotXNfIIAgW4KCKLdnLtTlxcQRMtbubJBAUG0QXy3DoKoh4AAAQLlBATRck6uIkCAQCEgiHoOCBAgUE5AEC3n5CoCBAgIop4BAtMLCKKekCQEBNEkxpTtJgXRbEfrYAQIRBYQRCODWo4AgawFBNGsx+twBAhEFBBEI2JaigCBrAUE0azH63ARBATRCIiWqF9AEK3f2B2mFogVRO947JHw/ltuDAct2Tn86SuO3uqGsb8y93eu+XLYtGVL+Nxxbw5zt93WeAkQIDAWAUF0LMxuQoBAJgKCaCaDdAwCBGoXEERrJ3YDAgQyERBEMxmkY9QmIIjWRmvhmAKCaExNa1UVEESrirmeAIGuCgiiXZ28cxMgMIqAIDqKms8QINBFAUG0i1N3ZgIERhEQREdR85kuCQiiXZp2wmcVRBMeXgZbF0QzGKIjECAwFgFBdCzMbkKAQCYCgmgmg3QMAgRqFxBEayd2AwIEMhEQRDMZpGPUJiCI1kZr4ZgCgmhMTWtVFYgVRP/58UfD+26+Iey9aKfwzhcettU2rlp1Z7j5oTXhV3ddFg5cvHSrv1vz1C/DN9bcG3ZfsDD8/+zde/hdVX3g/8U1CRohARMIKjcxgMVRGYS2XkA6rUWpM14YZaY/FEsZ8fJwKQyRAW8PhgFBHtviICPFaRktOnQcKupYWoqdKYKOjFouKqCi4WJIgkGScDG/Z5/2fOd8T76XffbtnLXWK/8oyd5rr/X6HPIE3pxzXrXieb1f+9bah8L3H10XXvPc/cOv7rlimyN96Bv/Kzy9dWv48994fdjRR+aOOnLXEyBQUUAQrQjnNgIEshQQRLMcu0MTIFBBQBCtgOYWAgSyFBBEsxy7Q48gIIiOgOXS8QkIouOz9+QQmgqi/e8Qncv0JXssDwfuunTaJXMF0fnm4ztE5xPy6wQINCkgiDapaS0CBFIXEERTn7DzESDQlIAg2pSkdQgQSF1AEE19ws5XV0AQrSvo/k4EBNFOmD1kFoGmgmj/HaKLdtgx7L/rbtOj5y8eC+u2bB75HaJ77vKMsPvCRdvs/B/Wre39nHeIelkTINClgCDapbZnESAQu4AgGvsE7Z8Aga4EBNGupD2HAIHYBQTR2Cdo/20LCKJtC1u/EQFBtBFGi1QUaCqI9t8hunK3peEjL3vVtN188s7bw1fuvy+M+g7Rt608NBy3z/O3Odm//qsvhKd++cvgHaIVh+42AgQqCQiildjcRIBApgKCaKaDd2wCBEYWEERHJnMDAQKZCgiimQ7esUsLCKKlqVw4TgFBdJz6ni2Ieg0QIECgnIAgWs7JVQQIECgEBFGvAwIECJQTEETLObmKAAECgqjXAIG5BQRRr5AoBATRKMaU7CYF0WRH62AECDQsIIg2DGo5AgSSFhBEkx6vwxEg0KCAINogpqUIEEhaQBBNerwO14CAINoAoiXaFxBE2zf2hNkFmgqiazdvCjf+9Edhj4ULwzF77zvtgVfe+X/Dl++/N7x0j+Xh+bsunfZrDzz+i/C1B34cli96RnjViuf1fu1bax8K3390XZjtI3M/f+9d4emtIbxp/5Vhh6JQ+EGAAIEOBATRDpA9ggCBZAQE0WRG6SAECLQsIIi2DGx5AgSSERBEkxmlg7QkIIi2BGvZZgUE0WY9rTaaQFNBdK6nNv0doqOd0NUECBBoRkAQbcbRKgQI5CEgiOYxZ6ckQKC+gCBa39AKBAjkISCI5jFnp6wuIIhWt3NnhwKCaIfYHrWNgCDqRUGAAIFyAoJoOSdXESBAoBAQRL0OCBAgUE5AEC3n5CoCBAgIol4DBOYWEEQzfYXcdvtd4ZIrrg2fuPD0sGTXxROvIIhO/IiS3qAgmvR4HY4AgQYFBNEGMS1FgEDyAoJo8iN2QAIEGhIQRBuCtAwBAskLCKLJj9gBawoIojUBJ+32InS+7bQLw4fPPim84dhXTm3vuhtuDudddNXUz5cNov37ZjrnoQfv31lQFUQn7ZWW1366DKKjys72HaKjruN6AgQINCEgiDahaA0CBHIREERzmbRzEiBQV0AQrSvofgIEchEQRHOZtHNWFRBEq8pN6H290Pmf/jzstuvisPp9J/fe/bn+0Y1h1UeuDBse3RiO/52jp4XSUY5RxNFbvnlH+OBZJ4VFC3ce5dba1wqitQktUENAEK2B51YCBLISEESzGrfDEiBQU0AQrQnodgIEshEQRLMZtYMSIFBTQBCtCej25AUE0cRGXATRz11/U3jRIQeEXRYt6MXPImQ+vmlL+PYd94QjDzuk93OD7xAtCN55zsfC637jV8PV1345PPDQI+G1xxy5TficKYgWsbW49zt33tv+kvjXAAAgAElEQVSTvPqyc8LhLz4o3POjNeHc1VeGQ1buF/78C3/d+7V3vPXY8IojXtR7B2v/r8845fje/++v/cxn7jJ1fX+t4tcF0cReqJEdp4sgGhmJ7RIgQGBGAUHUC4MAAQLlBQTR8lauJEAgbwFBNO/5Oz0BAuUFBNHyVq7MU0AQTWzu/SB6+u+/OXzsk58Lxf9+5r/fGN76L4/p/fVcQfR5K5b1Imjx4/0XXzV1bZ9oOIj2Y+jxxx3Vi6xFBL348s/23pm6bsPGcMrZl4RTT3z91K8Vf33sq48IRQTtB9MLVp0cDthnRS+IFh/p24+gwx/pK4gm9kKN7DiCaGQDs10CBMYmIIiOjd6DCRCIUEAQjXBotkyAwFgEBNGxsHsoAQIRCgiiEQ7NljsVEEQ75W7/Yf0gWoTNL/31LeErN90Wfuuow8Nvv/rIaZFzpneInnnK8b13dxY/Lr3i2rDvc/fc5ntIBz8ydzhabtr8RO8Zbz7uqLB0ybN67xDtB89+PO0/Y/ivZ4ut/esF0fZfO54wu4Ag6tVBgACBcgKCaDknVxEgQKAQEES9DggQIFBOQBAt5+QqAgQICKJeAwTmFhBEE3uFDAbRNQ+tnXrH5sIFC1oJov2Pvx1kLN7lWTeIDsbVItIKoom9UCM7jiAa2cBslwCBsQkIomOj92ACBCIUEEQjHJotEyAwFgFBdCzsHkqAQIQCgmiEQ7PlTgUE0U6523/YYBBdtHDnqQf2A+NcH5lb5R2ixfeVFu9GHXxW8dDhj8T1DtH2Z+8J7QkIou3ZWpkAgbQEBNG05uk0BAi0KyCItutrdQIE0hEQRNOZpZMQINCugCDarq/V4xcQROOf4bQTdBlEh79DtNhI8fziR913iA5/hK53iCb2Qo3sOIJoZAOzXQIExiYgiI6N3oMJEIhQQBCNcGi2TIDAWAQE0bGweygBAhEKCKIRDs2WOxUQRDvlbv9hXQbR4jT9KPqdO+/tHe7Qg/cPn7jw9LBuw8aRv0P0vIuumgLqr7Nk18W9nxNE23/teMLsAoKoVwcBAgTKCQii5ZxcRYAAgUJAEPU6IECAQDkBQbSck6sIECAgiHoNEJhbQBD1CpkIgeF3hA5vShCdiDFluwlBNNvROzgBAiMKCKIjgrmcAIGsBQTRrMfv8AQIjCAgiI6A5VICBLIWEESzHr/DlxAQREsguaR9AUG0fWNPqC4giFa3cycBAnkJCKJ5zdtpCRCoJyCI1vNzNwEC+QgIovnM2kkJEKgnIIjW83N3+gKCaPozTuKE3iGaxBijPYQgGu3obJwAgY4FBNGOwT2OAIGoBQTRqMdn8wQIdCggiHaI7VEECEQtIIhGPT6b70BAEO0A2SPqCwii9Q2tUF1AEK1u504CBPISEETzmrfTEiBQT0AQrefnbgIE8hEQRPOZtZMSIFBPQBCt5+fu9AUE0fRnnMQJBdEkxhjtIQTRaEdn4wQIdCwgiHYM7nEECEQtIIhGPT6bJ0CgQwFBtENsjyJAIGoBQTTq8dl8BwKCaAfIHlFfQBCtb2iF6gKCaHU7dxIgkJeAIJrXvJ2WAIF6AoJoPT93EyCQj4Agms+snZQAgXoCgmg9P3enLyCIpj/jJE4oiCYxxmgPIYhGOzobJ0CgYwFBtGNwjyNAIGoBQTTq8dk8AQIdCgiiHWJ7FAECUQsIolGPz+Y7EBBEO0D2iPoCgmh9QytUFxBEq9u5kwCBvAQE0bzm7bQECNQTEETr+bmbAIF8BATRfGbtpAQI1BMQROv5uTt9AUE0/RkncUJBNIkxRnsIQTTa0dk4AQIdCwiiHYN7HAECUQsIolGPz+YJEOhQQBDtENujCBCIWkAQjXp8Nt+BgCDaAbJH1BcQROsbWqG6gCBa3c6dBAjkJSCI5jVvpyVAoJ6AIFrPz90ECOQjIIjmM2snJUCgnoAgWs/P3ekLCKLpzziJEwqiSYwx2kMIotGOzsYJEOhYQBDtGNzjCBCIWkAQjXp8Nk+AQIcCgmiH2B5FgEDUAoJo1OOz+Q4EBNEOkD2ivoAgWt/QCtUFBNHqdu4kQCAvAUE0r3k7LQEC9QQE0Xp+7iZAIB8BQTSfWTspAQL1BATRen7uTl9AEE1/xkmcUBBNYozRHkIQjXZ0Nk6AQMcCgmjH4B5HgEDUAoJo1OOzeQIEOhQQRDvE9igCBKIWEESjHp/NdyAgiHaA7BH1BQTR+oZWqC4giFa3cycBAnkJCKJ5zdtpCRCoJyCI1vNzNwEC+QgIovnM2kkJEKgnIIjW83N3+gKCaPozTuKEgmgSY4z2EIJotKOzcQIEOhYQRDsG9zgCBKIWEESjHp/NEyDQoYAg2iG2RxEgELWAIBr1+Gy+AwFBtANkj6gvIIjWN7RCdQFBtLqdOwkQyEtAEM1r3k5LgEA9AUG0np+7CRDIR0AQzWfWTkqAQD0BQbSen7vTFxBE059xEicURJMYY7SHEESjHZ2NEyDQsYAg2jG4xxEgELWAIBr1+GyeAIEOBQTRDrE9igCBqAUE0ajHZ/MdCAiiHSB7RH0BQbS+oRWqCwii1e3cSYBAXgKCaF7zdloCBOoJCKL1/NxNgEA+AoJoPrN2UgIE6gkIovX83J2+gCCa/oyTOKEgmsQYoz2EIBrt6GycAIGOBQTRjsE9jgCBqAUE0ajHZ/MECHQoIIh2iO1RBAhELSCIRj0+m+9AQBDtANkj6gsIovUNrVBdQBCtbudOAgTyEhBE85q30xIgUE9AEK3n524CBPIREETzmbWTEiBQT0AQrefn7vQFBNH0Z5zECQXRJMYY7SEE0WhHZ+MECHQsIIh2DO5xBAhELSCIRj0+mydAoEMBQbRDbI8iQCBqAUE06vHZfAcCgmgHyB5RX0AQrW9oheoCgmh1O3cSIJCXgCCa17ydlgCBegKCaD0/dxMgkI+AIJrPrJ2UAIF6AoJoPT93py8giKY/4yROKIgmMcZoDyGIRjs6GydAoGMBQbRjcI8jQCBqAUE06vHZPAECHQoIoh1iexQBAlELCKJRj8/mOxAQRDtA9oj6AoJofUMrVBcQRKvbuZMAgbwEBNG85u20BAjUExBE6/m5mwCBfAQE0Xxm7aQECNQTEETr+bk7fQFBNP0ZJ3FCQTSJMUZ7CEE02tHZOAECHQsIoh2DexwBAlELCKJRj8/mCRDoUEAQ7RDbowgQiFpAEI16fDbfgYAg2gGyR9QXEETrG1qhuoAgWt3OnQQI5CUgiOY1b6clQKCegCBaz8/dBAjkIyCI5jNrJyVAoJ6AIFrPz93pCwii6c84iRMKokmMMdpDCKLRjs7GCRDoWEAQ7Rjc4wgQiFpAEI16fDZPgECHAoJoh9geRYBA1AKCaNTjs/kOBATRDpA9or6AIFrf0ArVBQTR6nbuJEAgLwFBNK95Oy0BAvUEBNF6fu4mQCAfAUE0n1k7KQEC9QQE0Xp+7k5fQBBNf8ZJnFAQTWKM0R5CEI12dDZOgEDHAoJox+AeR4BA1AKCaNTjs3kCBDoUEEQ7xPYoAgSiFhBEox6fzXcgIIh2gOwR9QUE0fqGVqguIIhWt3MnAQJ5CQiiec3baQkQqCcgiNbzczcBAvkICKL5zNpJCRCoJyCI1vNzd/oCgmj6M07ihIJoEmOM9hCCaLSjs3ECBDoWEEQ7Bvc4AgSiFhBEox6fzRMg0KGAINohtkcRIBC1gCAa9fhsvgMBQbQDZI+oLyCI1je0QnUBQbS6nTsJEMhLQBDNa95OS4BAPQFBtJ6fuwkQyEdAEM1n1k5KgEA9AUG0np+70xcQRNOfcRInFESTGGO0hxBEox2djRMg0LGAINoxuMcRIBC1gCAa9fhsngCBDgUE0Q6xPYoAgagFBNGox2fzHQgIoh0ge0R9AUG0vqEVqgsIotXt3EmAQF4Cgmhe83ZaAgTqCQii9fzcTYBAPgKCaD6zdlICBOoJCKL1/NydvoAgmv6MkzihIJrEGKM9hCAa7ehsnACBjgUE0Y7BPY4AgagFBNGox2fzBAh0KCCIdojtUQQIRC0giEY9PpvvQEAQ7QDZI+oLCKL1Da1QXUAQrW7nTgIE8hIQRPOat9MSIFBPQBCt5+duAgTyERBE85m1kxIgUE9AEK3n5+70BQTR9GecxAkF0STGGO0hBNFoR2fjBAh0LCCIdgzucQQIRC0giEY9PpsnQKBDAUG0Q2yPIkAgagFBNOrx2XwHAoJoB8geUV9AEK1vaIXqAoJodTt3EiCQl4Agmte8nZYAgXoCgmg9P3cTIJCPgCCaz6ydlACBegKCaD0/d6cvIIimP+MkTiiIJjHGaA8hiEY7OhsnQKBjAUG0Y3CPI0AgagFBNOrx2TwBAh0KCKIdYnsUAQJRCwiiUY/P5jsQEEQ7QPaI+gKCaH1DK1QXEESr27mTAIG8BATRvObttAQI1BMQROv5uZsAgXwEBNF8Zu2kBAjUExBE6/m5O30BQTT9GSdxQkE0iTFGewhBNNrR2TgBAh0LCKIdg3scAQJRCwiiUY/P5gkQ6FBAEO0Q26MIEIhaQBCNenw234GAINoBskfUFxBE6xtaobqAIFrdzp0ECOQlIIjmNW+nJUCgnoAgWs/P3QQI5CMgiOYzayclQKCegCBaz8/d6QsIounPOIkTCqJJjDHaQwii0Y7OxgkQ6FhAEO0Y3OMIEIhaQBCNenw2T4BAhwKCaIfYHkWAQNQCgmjU47P5DgQE0Q6QPaK+gCBa39AK1QUE0ep27iRAIC8BQTSveTstAQL1BATRen7uJkAgHwFBNJ9ZOykBAvUEBNF6fu5OX0AQTX/GSZxQEE1ijNEeQhCNdnQ2ToBAxwKCaMfgHkeAQNQCgmjU47N5AgQ6FBBEO8T2KAIEohYQRKMen813ICCIdoDsEfUFBNH6hlaoLiCIVrdzJwECeQkIonnN22kJEKgnIIjW83M3AQL5CAii+czaSQkQqCcgiNbzc3f6AoJo+jNO4oSCaBJjjPYQgmi0o7NxAgQ6FhBEOwb3OAIEohYQRKMen80TINChgCDaIbZHESAQtYAgGvX4bL4DAUG0A2SPqC8giNY3tEJ1AUG0up07CRDIS0AQzWveTkuAQD0BQbSen7sJEMhHQBDNZ9ZOSoBAPQFBtJ6fu9MXEETTn3ESJxREkxhjtIeYLYg+/tRT4b6NG6I41x6LFoXlC58RxV5tkgCBeAUE0XhnZ+cECHQvIIh2b+6JBAjEKSCIxjk3uyZAoHsBQbR7c0+MS0AQjWte2e5WEM129BNx8NmC6N0b1oX33fq3E7HH+Tbxxv1eEE448IXzXebXCRAgUEtAEK3F52YCBDITEEQzG7jjEiBQWUAQrUznRgIEMhMQRDMbuOOOLCCIjkzmhnEICKLjUPfMvsB8QXTH7bYLSxYsbARs/ZbN4amtW8NuOy8IO22/fe01f/HUk6F4J6sgWpvSAgQIlBAQREsguYQAAQL/JCCIeikQIECgnIAgWs7JVQQIEBBEvQYIzC0giHqFRCEgiEYxpmQ3OVsQvWv9I+Hc224Ouy9cFI5ZsU8j579xzY/CI5s3haNX7BOevXBR7TXvWL82fHf92vDG/VaGEw48pPZ6FiBAgMBcAoKo1wcBAgTKCwii5a1cSYBA3gKCaN7zd3oCBMoLCKLlrVyZp4Agmufcozu1IBrdyJLasCCa1DgdhgCBFgUE0RZxLU2AQHICgmhyI3UgAgRaEhBEW4K1LAECyQkIosmN1IEaFhBEGwa1XDsCgmg7rlYtJyCIlnNyFQECBARRrwECBAiUFxBEy1u5kgCBvAUE0bzn7/QECJQXEETLW7kyTwFBNM+5R3dqQTS6kSW14SKIXnfv3eHm+38SXr/vyvCS3Zf1znf3hnXhfbf+bdhjwcLw6r33beTMN/70h+GRLZvDq1fsE/Zo4CNz79zwSPjOup/V/g7RT9317fDDjY+Gkw56Udhv8a6NnNUiBAikJyCIpjdTJyJAoD0BQbQ9WysTIJCWgCCa1jydhgCB9gQE0fZsrZyGgCCaxhyTP4UgmvyIJ/qARRC99Fu3hS/ed2941wtfGl699z9+X2hO3yF6/je+Fv5h3drwwcNfHn5lybMnel42R4DA+AQE0fHZezIBAvEJCKLxzcyOCRAYj4AgOh53TyVAID4BQTS+mdlxtwKCaLfenlZRQBCtCOe2RgQE0RAE0UZeShYhkLyAIJr8iB2QAIEGBQTRBjEtRYBA0gKCaNLjdTgCBBoUEEQbxLRUkgKCaJJjTe9Qgmh6M43pRIKoIBrT69VeCYxTQBAdp75nEyAQm4AgGtvE7JcAgXEJCKLjkvdcAgRiExBEY5uY/XYtIIh2Le55lQQE0UpsbmpIYDCIzrTk7gsXhWNW/OPH6Nb9ceOaH4VHNm8KR6/YJzy7ge8QvWP92vDd9Wvrbmvqfh+Z2xilhQgkKSCIJjlWhyJAoCUBQbQlWMsSIJCcgCCa3EgdiACBlgQE0ZZgLZuMgCCazCjTPoggmvZ8J/10guj/m5AgOumvVvsjMF4BQXS8/p5OgEBcAoJoXPOyWwIExicgiI7P3pMJEIhLQBCNa152272AINq9uSdWEBBEK6C5pTEBH5nrI3MbezFZiEDiAoJo4gN2PAIEGhUQRBvltBgBAgkLCKIJD9fRCBBoVEAQbZTTYgkKCKIJDjXFIwmiKU41njMJooJoPK9WOyUwXoHtQgjLly4MD67bPN6NeDoBAgQiEBBEIxiSLRIgMBECguhEjMEmCBCIQEAQjWBItjhWAUF0rPweXlZAEC0r5bo2BARRQbSN15U1CaQoIIimOFVnIkCgLQFBtC1Z6xIgkJqAIJraRJ2HAIG2BATRtmStm4qAIJrKJBM/hyCa+IAn/HhFEL3u3rvDzff/JLx+35XhJbsv6+347g3rwvtu/duwx4KF4dV779vIKW786Q/DI1s2h1ev2CfssXBR7TXv3PBI+M66n4U37veCcMKBL6y83qfu+nb44cZHw0kHvSjst3jXyuu4kQCBtAUE0bTn63QECDQrIIg262k1AgTSFRBE052tkxEg0KyAINqsp9XSExBE05tpkicSRJMcazSHKoLoLgt2CBseeyI8vuXpqX3ftf6RcO5tN4fdFy4Kx6zYp5Hz3LjmR+GRzZvC0Sv2Cc9uIIjesX5t+O76teGN+60MJxx4SCN7tAgBAgRmExBEvTYIECBQXkAQLW/lSgIE8hYQRPOev9MTIFBeQBAtb+XKPAUE0TznHt2pBdHoRpbUhgXRpMbpMAQItCggiLaIa2kCBJITEESTG6kDESDQkoAg2hKsZQkQSE5AEE1upA7UsIAg2jCo5doREETbcbVqOQFBtJyTqwgQICCIeg0QIECgvIAgWt7KlQQI5C0giOY9f6cnQKC8gCBa3sqVeQoIomOe+6VXXNvbwRmnHN/JTtY/ujG885yPhTNPOT4c/uKDOnlmEw8RRJtQtEZVgdmCaP87RHfcbruwZMHCqstPu2/9ls3hqa1bw247Lwg7bb997TV/8dST4fGnnqr9HaK1N2IBAgSyEBBEsxizQxIg0JCAINoQpGUIEEheQBBNfsQOSIBAQwKCaEOQlklWQBCtOdpNm58I77/4qvDFG2+ZWunQg/cPn7jw9LBk18Xzrl42iBbXfeozN8y43muPOTJ88KyTwqKFO8/7vLaDaN/jyMMOCW849pVT+6n7XEF03tG6oEWB+YJoi49ubOk37veCcMKBL2xsPQsRIEBgJgFB1OuCAAEC5QUE0fJWriRAIG8BQTTv+Ts9AQLlBQTR8lauzFNAEK0595kCYBEvH3x4XelIOeoWykbUmdatGybL7LV4xqqPXBnOOvUt4YB9VvRuKfa873P3nBZJy6zVv0YQHUXLtU0LzBZEi3de3rdxQ9OPa2W9PRYtCssXPqOVtS1KgACBvoAg6rVAgACB8gKCaHkrVxIgkLeAIJr3/J2eAIHyAoJoeStX5ikgiNac+0xB9Lbb7wqXXHHt1LtE7/nRmnDK2ZeEBx56pPe0d7z12KmPyB2Mm8V1566+MrzuX/xaWP2H12xzbX+rMwXRwXeQDq5f3DP8/OLnrr7snN5H5s62t/5eLlh18lTUHD7XXHTX3XBzuOWbd/Si8HfvuneaR7HO2067sHf78LtpB8+x1/LdwxUXndl7viBa84Xq9loCswXRWou6mQABAgkKCKIJDtWRCBBoTUAQbY3WwgQIJCYgiCY2UMchQKA1AUG0NVoLJyIgiNYc5GzvEC2W7X8v6FduujU8f7/n9MJeP0CuXnVyL0gOB9EinB776iN69872bs7hIDoYH4vnFh/hu+eypdPWOP64o3rvzhxec7699d/VOdtH4c7G17/+RYccEL59xz3hzccd1TtvEUNXrb5yKnQWe//h/Q/29lr82ueuv2nqnbXFXxc/ivsE0ZovVLfXEhBEa/G5mQCBjAQE0YyG7agECNQWEERrE1qAAIFMBATRTAbtmAQI1BYQRGsTWiBxAUG05oBn+g7RD5990qwfDTscFmd6h2j/XZn9a/sxsb/VwXvme4fqD+776bR3Z871kbnDaxXx9uLLPxtWv+/ksG7Dxqn/X+a7UYu99t8JOts7YotrBp/xN//rW+Ha62+a8ftXBdGaL1S31xIQRGvxuZkAgYwEBNGMhu2oBAjUFhBEaxNagACBTAQE0UwG7ZgECNQWEERrE1ogcQFBtOaAhyPi4Ls1Fy3cubf6XNG0qSA6GE0HI2MRRAffdTkcROfa22CQvX/Nw1Pv5CxLNlPQHfxI3P46gx+bO/jrrz3myKl3iwqiZdVd14aAINqGqjUJEEhRQBBNcarORIBAWwKCaFuy1iVAIDUBQTS1iToPAQJtCQiibclaNxUBQbTmJIeD6Gx/3f8I20l6h+ivHLT/tI/XnfXdpv/pz3tKg98nWoZttiDa/xjeudYY3osgWkbcNW0JCKJtyVqXAIHUBATR1CbqPAQItCkgiLapa20CBFISEERTmqazECDQpoAg2qautVMQEERrTnGmiDj4PaH96HjkYYdM+w7P/nd61n2HaLH94l2p/Y+aXbhgwYzfIXrmKcdPfYfn2067MFx92Tlhvr0Va/ffUfq8Fcum3q1ZlmymIDr8HaLFWtdc99Vw7DFHhlu/defUd60KomWVXdeFgCDahbJnECCQgoAgmsIUnYEAga4EBNGupD2HAIHYBQTR2Cdo/wQIdCUgiHYl7TmxCgiiNSc3UxAtlux/f2YRHosfRYQsfuy1fPewx5JnheN/5+heIG0iiBbrDn7U7OB3dg7upfj/xcfQ/njNw2E4kM60t+LnZvse0zJss93bt+mv0d/vbD9fXOcdomXEXdOWgCDalqx1CRBITUAQTW2izkOAQJsCgmibutYmQCAlAUE0pWk6CwECbQoIom3qWjsFAUE0hSm2eIbi3a7XXPdX4ax3viX0vxO1xcfNurQgOg51z+wLCKJeCwQIECgnIIiWc3IVAQIECgFB1OuAAAEC5QQE0XJOriJAgIAg6jVAYG4BQdQrZE6B4p2nw9/5Ofhu1OGbDz14//CJC08PS3Zd3KisINoop8VGFBBERwRzOQEC2QoIotmO3sEJEKggIIhWQHMLAQJZCgiiWY7doQkQqCAgiFZAc0tWAoJoVuOO97CCaLyzS2HngmgKU3QGAgS6EBBEu1D2DAIEUhEQRFOZpHMQINC2gCDatrD1CRBIRUAQTWWSztGWgCDalqx1GxUQRBvltNiIAoLoiGAuJ0AgWwFBNNvROzgBAhUEBNEKaG4hQCBLAUE0y7E7NAECFQQE0QpobslKQBDNatzxHlYQjXd2KexcEE1his5AgEAXAoJoF8qeQYBAKgKCaCqTdA4CBNoWEETbFrY+AQKpCAiiqUzSOdoSEETbkrVuowKCaKOcFhtRQBAdEczlBAhkKyCIZjt6BydAoIKAIFoBzS0ECGQpIIhmOXaHJkCggoAgWgHNLVkJCKJZjTvewwqi8c4uhZ0LoilM0RkIEOhCQBDtQtkzCBBIRUAQTWWSzkGAQNsCgmjbwtYnQCAVAUE0lUk6R1sCgmhbstZtVEAQbZTTYiMKCKIjgrmcAIFsBQTRbEfv4AQIVBAQRCuguYUAgSwFBNEsx+7QBAhUEBBEK6C5JSsBQTSrccd7WEE03tmlsHNBNIUpOgMBAl0ICKJdKHsGAQKpCAiiqUzSOQgQaFtAEG1b2PoECKQiIIimMknnaEtAEG1L1rqNCgiijXJabEQBQXREMJcTIJCtgCCa7egdnACBCgKCaAU0txAgkKWAIJrl2B2aAIEKAoJoBTS3ZCUgiGY17ngPK4jGO7sUdi6IpjBFZyBAoAsBQbQLZc8gQCAVAUE0lUk6BwECbQsIom0LW58AgVQEBNFUJukcbQkIom3JWrdRAUG0UU6LjSggiI4I5nICBLIVEESzHb2DEyBQQUAQrYDmFgIEshQQRLMcu0MTIFBBQBCtgOaWrAQE0azGHe9hBdF4Z5fCzgXRFKboDAQIdCEgiHah7BkECKQiIIimMknnIECgbQFBtG1h6xMgkIqAIJrKJJ2jLQFBtC1Z6zYqIIg2ymmxEQUE0RHBXE6AQLYCgmi2o3dwAgQqCAiiFdDcQoBAlgKCaJZjd2gCBCoICKIV0NySlYAgmtW44z2sIBrv7FLYuSCawhSdgQCBLgQE0S6UPYMAgVQEBNFUJukcBAi0LSCIti1sfQIEUhEQRFOZpHO0JSCItiVr3UYFBNFGOS02ooAgOiKYywkQyFZAEM129A5OgEAFAUG0AppbCBDIUkAQzXLsDk2AQAUBQbQCmluyEhBEsxp3vIcVROOdXQo7F0RTmKIzECDQhYAg2oWyZxAgkIqAIJrKJJ2DAIG2BQTRtoWtT4BAKgKCaCqTdI62BATRtmSt26iAINoop8VGFBBERwRzOQEC2QoIotmO3sEJEKggIIhWQHMLAQJZCgiiWY7doQkQqB/ZT54AACAASURBVCAgiFZAc0tWAoJoVuOO97CCaLyzS2HngmgKU3QGAgS6EBBEu1D2DAIEUhEQRFOZpHMQINC2gCDatrD1CRBIRUAQTWWSztGWgCDalqx1GxUQRBvltNiIAoLoiGAuJ0AgWwFBNNvROzgBAhUEBNEKaG4hQCBLAUE0y7E7NAECFQQE0QpobslKQBDNatzxHlYQjXd2KexcEE1his5AgEAXAoJoF8qeQYBAKgKCaCqTdA4CBNoWEETbFrY+AQKpCAiiqUzSOdoSEETbkrVuowKCaKOcFhtRQBAdEczlBAhkKyCIZjt6BydAoIKAIFoBzS0ECGQpIIhmOXaHJkCggoAgWgHNLVkJCKJZjTvewwqi8c4uhZ0LoilM0RkIEOhCQBDtQtkzCBBIRUAQTWWSzkGAQNsCgmjbwtYnQCAVAUE0lUk6R1sCgmhbstZtVEAQbZTTYiMKCKIjgrmcAIFsBQTRbEfv4AQIVBAQRCuguYUAgSwFBNEsx+7QBAhUEBBEK6C5JSsBQTSrccd7WEE03tmlsHNBNIUpOgMBAl0ICKJdKHsGAQKpCAiiqUzSOQgQaFtAEG1b2PoECKQiIIimMknnaEtAEG1L1rqNCgiijXJabEQBQXREMJcTIJCtgCCa7egdnACBCgKCaAU0txAgkKWAIJrl2B2aAIEKAoJoBTS3ZCUgiGY17ngPK4jGO7sUdi6IpjBFZyBAoAsBQbQLZc8gQCAVAUE0lUk6BwECbQsIom0LW58AgVQEBNFUJukcbQkIom3JWrdRAUG0UU6LjSggiI4I5nICBLIVEESzHb2DEyBQQUAQrYDmFgIEshQQRLMcu0MTIFBBQBCtgOaWrAQE0azGHe9hBdF4Z5fCzgXRFKboDAQIdCEgiHah7BkECKQiIIimMknnIECgbQFBtG1h6xMgkIqAIJrKJJ2jLQFBtC1Z6zYqIIg2ymmxEQUE0RHBXE6AQLYCgmi2o3dwAgQqCAiiFdDcQoBAlgKCaJZjd2gCBCoICKIV0NySlYAgmtW44z2sIBrv7FLYuSCawhSdgQCBLgQE0S6UPYMAgVQEBNFUJukcBAi0LSCIti1sfQIEUhEQRFOZpHO0JSCItiVr3UYFBNFGOS02ooAgOiKYywkQyFZAEM129A5OgEAFAUG0AppbCBDIUkAQzXLsDk2AQAUBQbQCmluyEhBEsxp3vIcVROOdXQo7F0RTmKIzECDQhYAg2oWyZxAgkIqAIJrKJJ2DAIG2BQTRtoWtT4BAKgKCaCqTdI62BATRtmSt26iAINoop8VGFBBERwRzOQEC2QoIotmO3sEJEKggIIhWQHMLAQJZCgiiWY7doQkQqCAgiFZAc0tWAoJoVuOO97CCaLyzS2HngmgKU3QGAgS6EBBEu1D2DAIEUhEQRFOZpHMQINC2gCDatrD1CRBIRUAQTWWSztGWgCDalqx1GxUQRBvltNiIAoLoiGAuJ0AgWwFBNNvROzgBAhUEBNEKaG4hQCBLAUE0y7E7NAECFQQE0QpobslKQBDNatzxHlYQjXd2KexcEE1his5AgEAXAoJoF8qeQYBAKgKCaCqTdA4CBNoWEETbFrY+AQKpCAiiqUzSOdoSEETbkrVuowKCaKOcFhtRQBAdEczlBAhkKyCIZjt6BydAoIKAIFoBzS0ECGQpIIhmOXaHJkCggoAgWgHNLVkJCKJZjTvewwqi8c4uhZ0LoilM0RkIEOhCQBDtQtkzCBBIRUAQTWWSzkGAQNsCgmjbwtYnQCAVAUE0lUk6R1sCgmhbstZtVEAQbZTTYiMKCKIjgrmcAIFsBQTRbEfv4AQIVBAQRCuguYUAgSwFBNEsx+7QBAhUEBBEK6C5JSsBQTSrccd7WEE03tmlsHNBNIUpOgMBAl0ICKJdKHsGAQKpCAiiqUzSOQgQaFtAEG1b2PoECKQiIIimMknnaEtAEG1L1rqNCgiijXJabEQBQXREMJcTIJCtgCCa7egdnACBCgKCaAU0txAgkKWAIJrl2B2aAIEKAoJoBTS3ZCUgiGY17ngPK4jGO7sUdi6IpjBFZyBAoAsBQbQLZc8gQCAVAUE0lUk6BwECbQsIom0LW58AgVQEBNFUJukcbQkIom3JWrdRAUG0UU6LjSggiI4I5nICBLIVEESzHb2DEyBQQUAQrYDmFgIEshQQRLMcu0MTIFBBQBCtgOaWrAQE0azGHe9hBdF4Z5fCzgXRFKboDAQIdCEgiHah7BkECKQiIIimMknnIECgbQFBtG1h6xMgkIqAIJrKJJ2jLQFBtC1Z6zYqIIg2ymmxEQUE0RHBXE6AQLYCgmi2o3dwAgQqCAiiFdDcQoBAlgKCaJZjd2gCBCoICKIV0NySlYAgmtW44z2sIBrv7FLYuSCawhSdgQCBLgQE0S6UPYMAgVQEBNFUJukcBAi0LSCIti1sfQIEUhEQRFOZpHO0JSCItiVr3UYFBNFGOS02ooAgOiKYywkQyFZAEM129A5OgEAFAUG0AppbCBDIUkAQzXLsDk2AQAUBQbQCmluyEhBEsxp3vIcVROOdXQo7F0RTmKIzECDQhYAg2oWyZxAgkIqAIJrKJJ2DAIG2BQTRtoWtT4BAKgKCaCqTdI62BATRtmSt26iAINoop8VGFBBERwRzOQEC2QoIotmO3sEJEKggIIhWQHMLAQJZCgiiWY7doQkQqCAgiFZAc0tWAoJoVuOO97CCaLyzS2HngmgKU3QGAgS6EBBEu1D2DAIEUhEQRFOZpHMQINC2gCDatrD1CRBIRUAQTWWSztGWgCDalqx1GxUQRBvltNiIAoLoiGAuJ0AgWwFBNNvROzgBAhUEBNEKaG4hQCBLAUE0y7E7NAECFQQE0QpobslKQBDNatzxHlYQjXd2KexcEE1his5AgEAXAoJoF8qeQYBAKgKCaCqTdA4CBNoWEETbFrY+AQKpCAiiqUzSOdoSEETbkrVuowKCaKOcFhtRQBAdEczlBAhkKyCIZjt6BydAoIKAIFoBzS0ECGQpIIhmOXaHJkCggoAgWgHNLVkJCKJZjTvewwqi8c4uhZ0LoilM0RkIEOhCQBDtQtkzCBBIRUAQTWWSzkGAQNsCgmjbwtYnQCAVAUE0lUk6R1sCgmhbstZtVEAQbZTTYiMKCKIjgrmcAIFsBQTRbEfv4AQIVBAQRCuguYUAgSwFBNEsx+7QBAhUEBBEK6C5JSsBQTSrccd7WEE03tmlsHNBNIUpOgMBAl0ICKJdKHsGAQKpCAiiqUzSOQgQaFtAEG1b2PoECKQiIIimMknnaEtAEG1L1rqNCgiijXJabEQBQXREMJcTIJCtgCCa7egdnACBCgKCaAU0txAgkKWAIJrl2B2aAIEKAoJoBTS3ZCUgiGY17ngPK4jGO7sUdi6IpjBFZyBAoAsBQbQLZc8gQCAVAUE0lUk6BwECbQsIom0LW58AgVQEBNFUJukcbQkIom3JWrdRAUG0UU6LjSggiI4I5nICBLIVEESzHb2DEyBQQUAQrYDmFgIEshQQRLMcu0MTIFBBQBCtgOaWrAQE0azGHe9hBdF4Z5fCzgXRFKboDAQIdCEgiHah7BkECKQiIIimMknnIECgbQFBtG1h6xMgkIqAIJrKJJ2jLQFBtC1Z6zYqIIg2ymmxEQUE0RHBXE6AQLYCgmi2o3dwAgQqCAiiFdDcQoBAlgKCaJZjd2gCBCoICKIV0NySlYAgmtW44z2sIBrv7FLYuSCawhSdgQCBLgQE0S6UPYMAgVQEBNFUJukcBAi0LSCIti1sfQIEUhEQRFOZpHO0JSCItiVr3UYFBNFGOS02ooAgOiKYywkQyFZAEM129A5OgEAFAUG0AppbCBDIUkAQzXLsDk2AQAUBQbQCmluyEhBEsxp3vIcVROOdXQo7F0RTmKIzECDQhYAg2oWyZxAgkIqAIJrKJJ2DAIG2BQTRtoWtT4BAKgKCaCqTdI62BATRtmSt26iAINoop8VGFBBERwRzOQEC2QoIotmO3sEJEKggIIhWQHMLAQJZCgiiWY7doQkQqCAgiFZAc0tWAqWC6G233xUuueLa8IkLTw9Ldl3cGNCmzU+E9198VTjysEPCG459ZWPrTspCddzq3NvW+cc5L0G0ralat4yAIFpGyTUECBAIQRD1KiBAgEB5AUG0vJUrCRDIW0AQzXv+Tk+AQHkBQbS8lSvzFJgWRC+94trwqc/cMCXx2mOODB8866Tw3bvu7SyIXnfDzeG8i67q7eHQg/dvPMLWGXOxt1u+eUfPZNHCncNMgfCeH60JF1/+2bD6fSeHH9z308puw0F0/aMbwzvP+Vj4zp33bjOfYi9d/JjpvF3NSxDtYsKeMZuAIOq1QYAAgXICgmg5J1cRIECgEBBEvQ4IECBQTkAQLefkKgIECAiiXgME5hboBdF+6Cou7ce+4v8Xseu5K5b1VujiHaLDEbAItA8+vG7ansY50MHYWbxTtvjrU86+JBz76iPCGacc39vacDStut9Bi4ULFsz4TtpP/tn14ZhXHBYO2GdF1ceMdN9wEO1yXoLoSKNyccMCgmjDoJYjQCBZAUE02dE6GAECLQgIoi2gWpIAgSQFBNEkx+pQBAi0ICCItoBqyaQEekF0vo9nne/dildfdk44/MUH9WCKiFn86AfC4t7PXX/TVNSc6Z2OHz77pPDbrz5ym+hXBMdzV18ZLlh1ci/6Db4bsXjG8HOLePrjNQ/33kW51/Ldw2Ufenf4L9d+JXzxxlt6f33FRWfWiofF3ld95Mpw1qlv6a1TnO1b3/1+752gq977b3ofJ1ycf9/n7tn7COBBt2K/xTs8X/cbvxquvvbL4YGHHgn9d+D23+E5fL7+O2TXbdg4zWGmV2Dx3Mce3xwee+zxGc877D5oN8qvFc8ex7wE0aR+34nuMIJodCOzYQIExiQgiI4J3mMJEIhSQBCNcmw2TYDAGAQE0TGgeyQBAlEKCKJRjs2mOxToBdHhiDn8/Lnerdh/l+TqVSf3ouhcQbRYt/jO0D2XLe0F08F3HM4URPuh7sxTjg+/ctD+4b998W/DG1/7qt7H1Rbx8Nrrb5r6SN3iubfefte0v77hr78+FUHnO2NZ88HgWfz/f/7PVoab/v7/hn/zht8IK5bv0Tvfm487qmcxUxB93oplvTjct+h/f2px7arVV07td657Z/qI3GIvg+cdfKfq5i1bejH2+OOO6oXawXe6FvuY7df670ydhHkJomVfoa5rQ0AQbUPVmgQIpCggiKY4VWciQKAtAUG0LVnrEiCQmoAgmtpEnYcAgbYEBNG2ZK2bisBUEO2/q3Gmgw3GuZnerTgYG+cKomseWjvtnY7DH8E6/HGzg0G0/w7U/v6G3z06/NzhtZr6KNtinR/e/2B454n/Mnzi0/89vP0tvx3+5n99q7etf/bC5099f2jxbtGZomYRdwffTdt3n8lt8GOKZ3tnbRE4ix/D9w/6rFv/82kfedx3L8Jt8WPwOYO/tnTJsyZmXoJoKr/lxHkOQTTOudk1AQLdCwii3Zt7IgEC8QoIovHOzs4JEOhWQBDt1tvTCBCIV0AQjXd2dt6NwMjvEC2C6MWXfzasft/JvY+ILX70I2Hxrs/5gujgvcNBtP/XxUfc9n8MftRt/92oxcfNFj8Gf61OEB1+7vBH2Q6Oov/uyne89djwpb+5NZz1zreE7951b/ja178dXnHEi6Z9PPCoQXQwSpf5GOO3nXbh1McGD59/8ON9iyBaXDv8o/jY3OLHbL9WBNFJmZcg2s1vCJ4ys4Ag6pVBgACBcgKCaDknVxEgQKD3z7NLF4YH1m2GQYAAAQLzCAiiXiIECBAoJyCIlnNyVb4CI3+HaJvvEB0eQxEfr7nur3rRsXh36SlnXxL6H807rneI9kPjy192aNhl0YLeR9AWP7f649eE5++3d9hj6a69nyt+jBpEi3sGv3t18J2bwzbDMXm+d4gOfo/r4FrD3/E6+GvDxsPP7HJegmi+v0lNwskF0UmYgj0QIBCDgCAaw5TskQCBSREQRCdlEvZBgMCkCwiikz4h+yNAYFIEBNFJmYR9TKpAL4j2Q1exyeL7LfvfUVm88/O5K5b19t6Pc8PfKzn8HaKDH01b3Fd8p2Z/3f5f9783s3/vqSe+fioi9qGGPy53OM4Nf+dmnXeIjjqc4lmf+swNU+/OHHyHafGuy/5H4o4SRIffETr4nahFhO6H4f5s5gvCgx59y/53iBbnLZ5X/Cgi7uB3iA7+WvG9rcX8JmFeguior1LXjyrw9/d8f9Zbnrlox7Bgp+3DY5ueClue/OWoS7dy/W677BIO3mvvVta2KAECBKoKCKJV5dxHgECOAoJojlN3ZgIEqggIolXU3EOAQI4CgmiOU3fmUQR6QbR/Qz/09f+6/9GxxUfCzvV9loMRcDAOFh9p+7bjXxO+fcc9U6F18GNvi4+YLX785qv++dQ7LYs495077532cbgz7e/Qg/br/fQFq04OB+yzYpuP6m3rO0SLZxZrX/7pL4QrLjqz9+z+z117/U3hExeePvVRwqME0WKNQf/iI3lvvf2u3nr9CD3bRwkP31v89fDH/g5/B+mhB+8/tde5fm1S5iWIjvK3tWtHFVizYX04/CPnjXrbWK8/6sCDwjUnv3use/BwAgQIDAsIol4TBAgQKC8giJa3ciUBAnkLCKJ5z9/pCRAoLyCIlrdyZZ4C04JongRpnHr4HbJpnOr/nUIQTW2ik3WewSD6nN2WbLO57bfbLmy3XQi/3BrCwH9DMtIhNj3xRHjk8V+ERTvtFHZ/xjNHunfw4s1PPhnW/uKxIIhWJnQjAQItCgiiLeJamgCB5AQE0eRG6kAECLQkIIi2BGtZAgSSExBEkxupAzUsIIg2DDqu5QTRccl7bgoCP1m/Lhyx+vyweOHCcPLLj97mSAt33iHstOP2YfMTT4cnn6r2kbnfe/CB8JffvT0cuGx5OO5FL63Mdt/ah8Nf3P7NcNQLDg7X/N67Kq/jRgIECLQhIIi2oWpNAgRSFRBEU52scxEg0LSAINq0qPUIEEhVQBBNdbLO1ZSAINqUpHVaFfAO0VZ5s19cEM3+JQCAAIGGBATRhiAtQ4BAFgKCaBZjdkgCBBoQEEQbQLQEAQJZCAiiWYzZIWsICKI18NzanYAg2p11jk8SRHOcujMTINCGgCDahqo1CRBIVUAQTXWyzkWAQNMCgmjTotYjQCBVAUE01ck6V1MCgmhTktZpVUAQbZW39uL/sOYn4fwvfD68cMVzwode/6ba63W9QP87RJ+1cFH4vZcftc3jm/jI3O8//GC4/tvfCi9Yvmd43aEvqXzEH679Wbju9m+08h2i7/nM1WHNhg3hj054W9hr190q79GNBAjkKyCI5jt7JydAYHQBQXR0M3cQIJCngCCa59ydmgCB0QUE0dHN3JGXgCCa17yjPa0gOtmj+9/3fC+8+YqPh1/d//nh8//utMne7Ay78w7Rf0R5+UUfDPet/Vn42h+cF/Zftjy6OdowAQLjFxBExz8DOyBAIB4BQTSeWdkpAQLjFRBEx+vv6QQIxCMgiMYzKzsdj4AgOh53Tx1RQBAdEazjywXR+cG/9+AD4S+/e3s4cNnycNyLXjr/DbNccd/ah8Nf3P7NcNQLDg7X/N67Kq8z0405BdEbvnN7uPymr4a7Hnog7Lf7s8Pbf+2V4YQjfr1RT4sRyFFAEM1x6s5MgEBVAUG0qpz7CBDITUAQzW3izkuAQFUBQbSqnPtyERBEc5l05OcURCd7gILo/PMRROc36uqK+9c9En79P34gPL1167RHXv/uPwgvfd6+XW3DcwgkKSCIJjlWhyJAoCUBQbQlWMsSIJCcgCCa3EgdiACBlgQE0ZZgLZuMgCCazCjTPoggOtnz7QfRyd7l/LtbvHBhOPnlR29zYRPfIdp0EJ3/NNWvSP0jc6/9xi3h9Gv/bBugVb/9O+HdR/9mdTh3EiAQBFEvAgIECJQXEETLW7mSAIG8BQTRvOfv9AQIlBcQRMtbuTJPAUE0z7lHd2pBdLJHJojOPx9BdH6jrq4QRLuS9pwcBQTRHKfuzAQIVBUQRKvKuY8AgdwEBNHcJu68BAhUFRBEq8q5LxcBQTSXSUd+TkF0sgfoI3Pnn0/TQdR3iM5vPtsVPjK3up07CcwnIIjOJ+TXCRAg8P8EBFGvBgIECJQTEETLObmKAAECgqjXAIG5BQRRr5AoBATRyR6TIDr/fATR+Y26vOKG79weLr/pq+Guhx4I++3+7PD2X3tlOOGIX+9yC55FIEkBQTTJsToUAQItCQiiLcFalgCB5AQE0eRG6kAECLQkIIi2BGvZZAQE0WRGmfZBBNHJnq8gOv98BNH5jVxBgED8AoJo/DN0AgIEuhMQRLuz9iQCBOIWEETjnp/dEyDQnYAg2p21J8UpIIjGObfsdi2ITvbI/2HNT8L5X/h8eOGK54QPvf5Nk73ZGXa3ZsP6cPhHzgvPWrgo/N7Lj9rmioU77xB22nH7sPmJp8OTT/2y0vm+//CD4fpvfyu8YPme4XWHvqTSGsVNP1z7s3Dd7d8IRx14ULjm5HdXXmemG9/zmavDmg0bwh+d8Law1667Nbq2xQgQyENAEM1jzk5JgEAzAoJoM45WIUAgfQFBNP0ZOyEBAs0ICKLNOFolXQFBNN3ZJnUyQTSpcU7cYX6yfl04YvX5YfHCheHklx+9zf6aCKIxvEN04gZjQwQIRCcgiEY3MhsmQGCMAoLoGPE9mgCBqAQE0ajGZbMECIxRQBAdI75HRyEgiEYxJpsURL0G2hQQRNvUtTYBAjkJCKI5TdtZCRCoKyCI1hV0PwECuQgIorlM2jkJEKgrIIjWFXR/6gKCaOoTTuR8gmgig5zQYwiiEzoY2yJAIDoBQTS6kdkwAQJjFBBEx4jv0QQIRCUgiEY1LpslQGCMAoLoGPE9OgoBQTSKMdmkIOo10KZA/ztEi2c8Z7cl2zxq++22C9ttF8Ivt4awdevWSlvZ9MQT4ZHHfxEW7bRT2P0Zz6y0RnHT5iefDGt/8Vgr3yFaeVNuJECAwD8JCKJeCgQIECgvIIiWt3IlAQJ5Cwiiec/f6QkQKC8giJa3cmWeAoJonnOP7tSCaHQji2rDg0E0lo0fdeBB4ZqT3x3Ldu2TAIFMBATRTAbtmAQINCIgiDbCaBECBDIQEEQzGLIjEiDQiIAg2gijRRIWEEQTHm5KRxNEU5rmZJ7l7+/5/qwbe+aiHcOCnbYPj216Kmx58pcTcYDddtklHLzX3hOxF5sgQIBAX0AQ9VogQIBAeQFBtLyVKwkQyFtAEM17/k5PgEB5AUG0vJUr8xQQRPOce3SnFkSjG1lSG97tmTuHXRbsEDY89kR4fMvTSZ3NYQgQINCkgCDapKa1CBBIXUAQTX3CzkeAQFMCgmhTktYhQCB1AUE09Qk7X10BQbSuoPs7ERBEO2H2kFkEBFEvDQIECJQTEETLObmKAAEChYAg6nVAgACBcgKCaDknVxEgQEAQ9RogMLeAIOoVEoWAIBrFmJLdpCCa7GgdjACBhgUE0YZBLUeAQNICgmjS43U4AgQaFBBEG8S0FAECSQsIokmP1+EaEBBEG0C0RPsCgmj7xp4wu4Ag6tVBgACBcgKCaDknVxEgQKAQEES9DggQIFBOQBAt5+QqAgQICKJeAwTmFhBEvUKiEBBEoxhTspsURJMdrYMRINCwgCDaMKjlCBBIWkAQTXq8DkeAQIMCgmiDmJYiQCBpAUE06fE6XAMCgmgDiJZoX0AQbd/YE2YXEES9OggQIFBOQBAt5+QqAgQIFAKCqNcBAQIEygkIouWcXEWAAAFB1GuAwNwCgqhXSBQCgmgUY0p2k4JosqN1MAIEGhYQRBsGtRwBAkkLCKJJj9fhCBBoUEAQbRDTUgQIJC0giCY9XodrQEAQbQDREu0LCKLtG3vC7AKCqFcHAQIEygkIouWcXEWAAIFCQBD1OiBAgEA5AUG0nJOrCBAgIIh6DRCYW0AQ9QqJQkAQjWJMyW5SEE12tA5GgEDDAoJow6CWI0AgaQFBNOnxOhwBAg0KCKINYlqKAIGkBQTRpMfrcA0ICKINIFqifQFBtH1jT5hdQBD16iBAgEA5AUG0nJOrCBAgUAgIol4HBAgQKCcgiJZzchUBAgQEUa8BAnMLCKJeIVEICKJRjCnZTQqiyY7WwQgQaFhAEG0Y1HIECCQtIIgmPV6HI0CgQQFBtEFMSxEgkLSAIJr0eB2uAQFBtAFES7QvIIi2b+wJswsIol4dBAgQKCcgiJZzchUBAgQKAUHU64AAAQLlBATRck6uIkCAgCDqNUBgbgFB1CskCgFBNIoxJbtJQTTZ0ToYAQINCwiiDYNajgCBpAUE0aTH63AECDQoIIg2iGkpAgSSFhBEkx6vwzUgIIg2gGiJ9gUE0faNPWF2AUHUq4MAAQLlBATRck6uIkCAQCEgiHodECBAoJyAIFrOyVUECBAQRGd/DSxetKMXSOQCGzc9VfsEgmhtQgt0ISCIdqHsGbMJCKJeGwQIECgnIIiWc3IVAQIEBFGvAQIECJQXEETLW7mSAIG8BQRRQTTlvwME0ZSn62zTBARRL4hxCgii49T3bAIEYhIQRGOalr0SIDBuAe8QHfcEPJ8AgVgEBNFYJmWfBAiMW0AQFUTH/Rps8/mCaJu61p4oAUF0osaR3WYE0exG7sAECFQUEEQrwrmNAIEsBQTRLMfu0AQIVBAQRCuguYUAgSwFBFFBNOUXviCa8nSdbZqAIOoFMU4BQXSc+p5NgEBMAoJoTNOyVwIExi0giI57Ap5PgEAsAoJoLJOyTwIExi0giAqi434Ntvl8QbRNXWtPlIAgOlHjyG4zgmh2I3dgAgQqCgiiFeHcRoBAlgKCaJZjd2gCBCoICKIV0NxCgECWAoKoIJryC18QTXm6zjZNQBD1ghingCA61vP0JgAAIABJREFUTn3PJkAgJgFBNKZp2SsBAuMWEETHPQHPJ0AgFgFBNJZJ2ScBAuMWEEQF0XG/Btt8viDapq61J0pAEJ2ocWS3GUE0u5E7MAECFQUE0YpwbiNAIEsBQTTLsTs0AQIVBATRCmhuIUAgSwFBVBBN+YUviKY8XWebJiCIekGMU0AQHae+ZxMgEJOAIBrTtOyVAIFxCwii456A5xMgEIuAIBrLpOyTAIFxCwiigui4X4NtPl8QbVPX2hMlIIhO1Diy24wgmt3IHZgAgYoCgmhFOLcRIJClgCCa5dgdmgCBCgKCaAU0txAgkKWAINpsEL31vnvDE089NfJr6eUHvmDke9wwv4AgOr+RKxIREEQTGWSkxxBEIx2cbRMg0LmAINo5uQcSIBCxgCAa8fBsnQCBTgUE0U65PYwAgYgFBNFmg+gLzj07PPjzn4/8ivj+BReF5c961sj3xX7D9f/z78JffOnmcOkH3hN223Vx7zgbHt0YzvjAH4Y77r6v99eXX/gH4aUvWlnpqIJoJTY3xSggiMY4tXT2LIimM0snIUCgXQFBtF1fqxMgkJaAIJrWPJ2GAIH2BATR9mytTIBAWgKC6OzzXLxox5GHvfI//PvwwKOPhr13WxJ22K74Nx5z//jx+nW9C6oG0SIofuq//mX42IfeG/Z73oo5HzYcGvsX/+6bXhPeddIbp+6978drwunnfzw8+PAj26x37mknhuN+8+XzHav0rw8H0U2bnwgXfvzTYdkeS6ftqfSCQxcKolXl3BedgCAa3ciS2rAgmtQ4HYYAgRYFBNEWcS1NgEByAoJociN1IAIEWhIQRFuCtSwBAskJCKKzj7RKEO2/Q/Q9r/6N8IwFC+Z9vfzRX/9VeGzLltaDaBEeL7js02E4aPYj6br1P5+Kqv0g+o4TXjctfv6fb98dTj3no9usMe8h57hgOIj2n33+GW+v/K7QwccJonWm496oBATRqMaV3GYF0eRG6kAECLQkIIi2BGtZAgSSFBBEkxyrQxEg0IKAINoCqiUJEEhSQBBNP4j2Q+ZcHz1749duC/vvs3fvXaazBdF+PD3s0JWNvHuzkJ8piH740j8J553x9nnf8Vrmb0hBtIySa5IQEESTGGO0hxBEox2djRMg0LGAINoxuMcRIBC1gCAa9fhsngCBDgUE0Q6xPYoAgagFBNG0g2j/I2iLU57z3hPDooU7z/t6bTOI/vFV/y386ee/PG0Ph6zcr/cdousf3bjNR/X+1lEv6+27+FF8lO5Xbrp16t49l+0+70cFC6LzjtsFqQgIoqlMMs5zCKJxzs2uCRDoXkAQ7d7cEwkQiFdAEI13dnZOgEC3AoJot96eRoBAvAKCaNpBdLa4OdcrdrZ7Rvm+0uH1ZwuzZT4yd657997z2XN+tK4gGu/vTXY+ooAgOiKYyxsVEEQb5bQYAQIJCwiiCQ/X0QgQaFxAEG2c1IIECCQqIIgmOljHIkCgcQFBNI8gOsp3cvaD6IMPP7INTv8dm2XeaTp4c/GxvR+69E+2eUdnmSBa53tFBdHGf8uw4KQKCKKTOpk89iWI5jFnpyRAoL6AIFrf0AoECOQjIIjmM2snJUCgnoAgWs/P3QQI5CMgiOYZRIsQecFln546/ODHz871rtLiI2+/evNt835U7bBqcd/Da9dt87G9ZYJo/7tL163/+cjPFUTz+b0s+5MKotm/BMYKIIiOld/DCRCISEAQjWhYtkqAwNgFBNGxj8AGCBCIREAQjWRQtkmAwNgFBNE8gug7TnhdOO43Xz7jYYc/CneuINqPk4cdujK866Q3ln791gmixUNmetfq777pNfPuQRAtPSIXxi4giMY+wbj3L4jGPT+7J0CgOwFBtDtrTyJAIH4BQTT+GToBAQLdCAii3Th7CgEC8QsIomkH0dm+f3Pw1DEE0eEp9d/heu5pJ84aeot7BNH4f49ygpICgmhJKJe1IiCItsJqUQIEEhQQRBMcqiMRINCagCDaGq2FCRBITEAQTWygjkOAQGsCgmjaQbQ4XfH9naee89EwWzysEkT/1W+/cs4QOVPA/Isv3Rwu/cB7wm67Lp765TIfmTvThPqhd9keS+d8l6gg2tpvHRaeNAFBdNImktd+BNG85u20BAhUFxBEq9u5kwCB/AQE0fxm7sQECFQTEESrubmLAIH8BATRZoPoyv/w78MDjz4a3vCSw8LCnXaa9wX1F9/6Ztj05JPh+xdcFJY/61nzXj9TaPzUf/3Leb9bs/+Oypk+Znb4e0Fn+8jcfoS8/4GfbRM259v4TB+12w+1h6zcb2q9/rPPP+Pt4aUvWtlbtvi5z13/N+E973hzWLRw56mfO/38j4fB62bagyA632T8ejICgmgyo4zyIIJolGOzaQIExiAgiI4B3SMJEIhWQBCNdnQ2ToBAxwKCaMfgHkeAQLQCgujso1u8aMeR5/qCc88OD/785yPfVyeIXnDZp2d83mBoLC7oR8k77r5v2vXDkXSm7+vs3/BbR70snPPeE6fC5CgHHX5+8dznPWd5GHzn6ExBtB9iv3LTrdMed/mFfzAVTWfbhyA6yoRcG7WAIBr1+KLfvCAa/QgdgACBjgQE0Y6gPYYAgSQEBNEkxugQBAh0ICCIdoDsEQQIJCEgiM4+xipB9N/+5yvCusceG/m1cc3v/7uwZJdnjHyfG+YWEES9QrIREESzGfVEHlQQncix2BQBAhMoIIhO4FBsiQCBiRUQRCd2NDZGgMCECQiiEzYQ2yFAYGIFBNFmg+jEDrqFjRUftfunn//yvCvP9DG9897U0AWCaEOQlpl8AUF08meU8g4F0ZSn62wECDQpIIg2qWktAgRSFxBEU5+w8xEg0JSAINqUpHUIEEhdQBAVRFN+jQuiKU/X2aYJCKJeEOMUEETHqe/ZBAjEJCCIxjQteyVAYNwCgui4J+D5BAjEIiCIxjIp+yRAYNwCgqggOu7XYJvPF0Tb1LX2RAkIohM1juw2I4hmN3IHJkCgooAgWhHObQQIZCkgiGY5docmQKCCgCBaAc0tBAhkKSCICqIpv/AF0ZSn62zTBARRL4hxCgii49T3bAIEYhIQRGOalr0SIDBuAUF03BPwfAIEYhEQRGOZlH0SIDBuAUFUEB33a7DN5wuibepae6IEBNGJGkd2mxFEsxu5AxMgUFFAEK0I5zYCBLIUEESzHLtDEyBQQUAQrYDmFgIEshQQRAXRlF/4gmjK03W2aQKCqBfEOAUE0XHqezYBAjEJCKIxTcteCRAYt4AgOu4JeD4BArEICKKxTMo+CRAYt4AgKoiO+zXY5vMF0TZ1rT1RAoLoRI0ju80IotmN3IEJEKgoIIhWhHMbAQJZCgiiWY7doQkQqCAgiFZAcwsBAlkKCKKCaMovfEE05ek62zQBQdQLYpwCgug49T2bAIGYBATRmKZlrwQIjFtAEB33BDyfAIFYBATRWCZlnwQIjFtAEBVEx/0abPP5WQbRS6+4tmd6xinHN2q7/tGN4Z3nfCycecrx4fAXH9To2harLyCI1je0QnUBQbS6nTsJEMhLQBDNa95OS4BAPQFBtJ6fuwkQyEdAEM1n1k5KgEA9AUFUEK33Cprsu5MNops2PxHef/FV4Ys33jI1gXe89dheBO0qiN7zozXhlLMvCQ889EhvD6895sjwwbNOCosW7jz2V8Xw3oY3dPVl53QSdbs0EkTH/rLLegOCaNbjd3gCBEYQEERHwHIpAQLZCwii2b8EABAgUFJAEC0J5TICBLIXEEQF0ZT/JkgyiPbfqfmyFx807V2gn/yz68MxrzgsfOHLf9ebadvvEL3uhpvDc1csmwqLbYXYui/QIkqeu/rKcMGqk8MB+6you9xI93dpJIiONBoXNywgiDYMajkCBJIVEESTHa2DESDQgoAg2gKqJQkQSFJAEE1yrA5FgEALAoKoINrCy2pilkwyiBaR7ZZv3jHruzGHw+TguxT3Wr57uOKiM3thcKaPwC3u3fe5e4Y3HPvK3hBneqflbO+uHNzXmofW9iLk6/7Fr4XVf3hNb63+O1iL/18858GH14Ufr3k4fOfOe0Oxr8s+9O7wX679Su9dr4P7rPtqmimIDr/D9sNnn9Q782Bs/tRnbug9unjn6/93/G+F087/o967YQffCdtfe7ZzDu+9TSNBtO4rxf11BATROnruJUAgJwFBNKdpOysBAnUFBNG6gu4nQCAXAUE0l0k7JwECdQUE0dkFFy/asS6v+8cskFwQ7Ye8Iw87ZCpaDhsPBtHh6Hnb7XeFVauv7EXRpbst3uY7QQeDaP/e4487alosnOk7RPv72nPZ0t47U/sh9dhXH9H76+F9FM+59fa7wicuPD0s2XVxL5De8Ndfn4q1Tb7bdDiIDu+12Nuqj1wZzjr1LVMm/Xff9vf9vBXLegF685Yt08zmO+fgbNo2EkTH/LtN5o8XRDN/ATg+AQKlBQTR0lQuJECAQBBEvQgIECBQTkAQLefkKgIECAiigmjKfxckG0TffNxRs34H5mBMLALoJVdcOxUeB4Pq0b/+kjmD6PC9M72jtHjxFM8r3k050zsnBz+mdnBfw8Fz+F2v870LdpQX7XAQnekdo/0QPGwyHKCH/3q2tYr9DX5kcRdGgugorwrXNi0giDYtaj0CBFIVEERTnaxzESDQhoAg2oaqNQkQSFFAEE1xqs5EgEAbAoKoINrG62pS1kw2iJZ9h2gRNT93/U3TPl53tvjXj5v9j8wdvne2INof9kwfBzsYRItf/+H9D/ZCYZ0gOvxxt4MhdqYX3kxB9JSzL+l9/O3gj+Jjc5sIooPnHN5Pm0aC6KT8tpPnPgTRPOfu1AQIjC4giI5u5g4CBPIVEETznb2TEyAwmoAgOpqXqwkQyFdAEBVEU371JxdEi2HN9+7Jrt8h2n8BFeHx4ss/G1a/7+SwbsPG3neITuo7RPv7LD6ud/DHcPRt6h2iXRgJoin/Vjb5ZxNEJ39GdkiAwGQICKKTMQe7IEAgDgFBNI452SUBAuMXEETHPwM7IEAgDgFBVBCN45VabZdJBtF+tOt/z2Wf5pN/dn045hWHhS98+e96PzX43Z397wEd/A7RFcv3CO+/+KrQf7dp//swTz3x9TN+Z2hx79tOuzBcfdk5vY/r7T/vgH1W9J5XhNgHH17XezfqmofWTguiw+/SrPMO0VFfCvN9h2ixXnHND+77SXjZSw6e9jHCowbR4Wd1aSSIjvrKcH2TAoJok5rWIkAgZQFBNOXpOhsBAk0LCKJNi1qPAIFUBQTRVCfrXAQINC0giM4uunjRjk1zW69jgSSDaGE4/LGxxc+9463HzvhxtP3QWXxE7F7Ldw9XXHRm6EfMwV8rPnr2mc/cJfzKyn17QbT40Y+gxf8vfv3Hax4OZ55yfC+IDv5a/9eLGLpo4c69wDj8sbT9kFpcO84gOpNf32XpbotHDqJznbNLI0G0499dPG6aQJ0g+uimx8Mh7z+7lOj+eywLXzv7/FLXuogAAQKTKCCITuJU7IkAgUkVEEQndTL2RYDApAkIopM2EfshQGBSBQTR2ScjiE7qq7b8vpINouUJxnPl8Dslx7OL9p9a55x17p3pZIJo+/P2hNkFBFGvDgIECJQTEETLObmKAAEChYAg6nVAgACBcgKCaDknVxEgQEAQbTaIfv6e74XHn3xy5BfWmw9cGRbt4B2pI8PNc4Mg2rRoyfWajn0lH9v5ZXXOWefemQ4qiHY+fg8cEGgiiC7YccfwrqN/c5rr/77ne+GWe38QDt5r73DnAz8NBzx7ebj5rPPYEyBAIFoBQTTa0dk4AQJjEBBEx4DukQQIRCkgiEY5NpsmQGAMAoLo7OhV3iH6li//j7Buy5aRJ/nZ1/xOWLpgwcj3pXjDhkc3hjM+8Ifhjrvv6x3v8gv/ILz0RSsrHVUQrcTmphgFBNEYp5bOngXRdGbpJAQItCsgiLbra3UCBNISEETTmqfTECDQnoAg2p6tlQkQSEtAEG0niB60ZGnYafsd5n2x3Ln+kfDUL38ZqgbR6//n34VP/de/DB/70HvDfs9bMefzhkNj/+LffdNrwrtOeuPUvff9eE04/fyPhwcffmSb9c497cRw3G++fN5zVb2g+GrMCz/+6bBsj6XT9lR1PUG0qpz7ohMQRKMbWVIbFkSTGqfDECDQooAg2iKupQkQSE5AEE1upA5EgEBLAoJoS7CWJUAgOQFBdPaR1nmH6BufX3wE7vxB9PM/uDtsfvrp1oNoEU4vuOzTYTho9iPpuvU/n4qq/SD6jhNeNy1+/p9v3x1OPeej26zR5N8U/Weff8bbK78rdHA/gmiT07HWRAsIohM9nuQ3VyWIbty8KXzya38Ttm7dGj72V18KZT8y998e8eth45bN4eRXHB2etXBR8rYOSIBAWgKCaFrzdBoCBNoVEETb9bU6AQLpCAii6czSSQgQaFdAEE0/iPZD5lwfPXvj124L+++zd+9dprMF0X48PezQlY28e3Mm+eLZH770T8J5Z7x93ne8lvk7QxAto+SaJAQE0STGGO0hqgTRNY9uCIdf8B/C4gULe4GzbBDd9MSWUNx766oPhb2XLI3WzMYJEMhTQBDNc+5OTYBANQFBtJqbuwgQyE9AEM1v5k5MgEA1AUE07SDa/wja4pTnvPfEsGjhzvO+UNoMon981X8Lf/r5L0/t4VcP+5Xw6GO/CJd+4D1h/aMbt/mo3t866mVT+x7+KN9DXrBvWLdhYxh+J+vgAQXRecftglQEBNFUJjnZ53jq6afD57/59fC337+rt9FXHXhQeNNhR4Q9dl0UdlmwQ9jw2BPh8S1PlzqEIFqKyUUECCQmIIgmNlDHIUCgVQFBtFVeixMgkJCAIJrQMB2FAIFWBQTR2XlT+Mjc2eLmXC+q2e4Z5ftKh9fvh9n7H/hZL37utuvi3iVFIP3md+6e+rnZPjJ3po/rLXM2QbTV3z4sPkkCgugkTSPdvXzkhi+EP77pq9MO+K6j/kW46Pg3C6Lpjt3JCBBoUEAQbRDTUgQIJC8giCY/YgckQKAhAUG0IUjLECCQvIAgmkcQHeU7OYffiTkoNPiOzVH+5iiC5ocu/ZOp7ynt31tE1r/40s1zBtF+TF22x9JpH9Xb/wjff/Xbr5z2XaeD+xJER5mSa6MWEESjHl80mz9i9fnhJ+vXTdvvsxc/K/zwP360chDdYbvtwtNbt878kbk/+F645b4fhEP22jvc8cBPw8Kddup95+iWp54Kt73vw2HFbkuisbNRAgQIFAKCqNcBAQIEygsIouWtXEmAQN4Cgmje83d6AgTKCwiieQbRIkRecNmnpw6/57Ldp2LlXO+8LN7R+dWbb9smbM73iivue3jtum0+trdMEJ3tXaOC6Hzqfj0rAUE0q3GP7bD7v++0Xowc/vHTj14WVixZPNpH5m5YHw7/yHlTS5X5DtHB5wqiY3sZeDABAjUEBNEaeG4lQCA7AUE0u5E7MAECFQUE0YpwbiNAIDsBQTSPIDrX92wOfxTuXEG0HyEPO3TltHdrzvc3jiA6n5BfJ1BTQBCtCej2UgJv+k+Xhb+/9wfTrn3pc/cJX1t1buV3iC7aeeew6YknSr1DdMWuu4Unnn46rH1so3eIlpqYiwgQmDQBQXTSJmI/BAhMsoAgOsnTsTcCBCZJQBCdpGnYCwECkywgiKYdRPsfN1uc8pz3nhgWLdx5mwMLonP/Hbrd1uLzGf0gMOECguiEDyiR7f3fn/w4/P6f/uepj819zpKl4fIT3h6OeeHKykF08YKFYeOWzTMH0Xu+F2659wfh4L32Dnc+8NNwwLOXh01PbAlrHt0Qbl31obD3kqWJyDoGAQK5CAiiuUzaOQkQaEJAEG1C0RoECOQgIIjmMGVnJECgCQFBNO0gWpyu+P7OU8/5aDj3tBNn/K7NKkF0ru/tnEl0+KNx+9eU+cjc2T4a10fmNvE7gDWSERBEkxnlxB/kl1u3hnt/9lBvn/s/e3nYfrvtwm7P3FkQnfjJ2SABApMgIIhOwhTsgQCBWAQE0VgmZZ8ECIxbQBAd9wQ8nwCBWAQE0XaC6EFLloadtt9h3pfBnesfCU/98pfhs6/5nbB0wYJ5rx++YDhmzrZA/ztDf/dNr9nmo26Hvxd0to/M7b/b9P4HfhYu/cB7wm67Li6935k+arf/nKVLnjW13mzfFzrTOYt9/+nnvzxr6C02t3HTtl91V3rT/3Shd4iOKub6sQgIomNh99B/EhBEvRQIECBQTkAQLefkKgIECBQCgqjXAQECBMoJCKLlnFxFgAABQXT218DiRTuO/AJ5y5f/R1i3ZcvI99UJohdc9ukZn3fIyv2mhct+lLzj7vumXT8cSftR8sGHH9lm3d866mWzfvTufIcefn6x1gtX7h++fNPX5w2ixdr9qNt/zgWrfj9cc91Xw1zvVhVE55uKX09GQBBNZpRRHqRKEO0f9NFNj4dD3n926Y/Mvfms86I0smkCBAgUAoKo1wEBAgTKCwii5a1cSYBA3gKCaN7zd3oCBMoLCKKzW1UJop+/53vh8SefLD+Af7ryzQeuDIt2GD3AjvygCbuhiJzfuP3OSpG1H1jf/fY3hpe+aOWMJxNEJ2zgttOegCDanq2V5xcQROc3cgUBAgQKAUHU64AAAQLlBQTR8lauJEAgbwFBNO/5Oz0BAuUFBNHZraoE0fLy8V/Z/8ja+U5SvAP1pBOOC3/4qc+FNx93dNjveSt6t8z28bgzrVd8D+rff+O70z7ut3j+w2vXzRlTBdH5puPXkxEQRJMZZZQHEUSjHJtNEyAwBgFBdAzoHkmAQLQCgmi0o7NxAgQ6FhBEOwb3OAIEohUQRAXRLl68/e8f/cpNt0573OUX/sGs7+4cvLAIoqee89Fp95b5+F5BtIvpesZECAiiEzGGbDfRRBDdYfvtw8v2PWCa4f3rHwk/Wb8u7PHMxWHtYxvDAc9eHnxkbrYvMwcnkISAIJrEGB2CAIGOBATRjqA9hgCB6AUE0ehH6AAECHQkIIgKoh291MbyGEF0LOweOg4BQXQc6p7ZF2giiJbRFETLKLmGAIFJFhBEJ3k69kaAwKQJCKKTNhH7IUBgUgUE0UmdjH0RIDBpAoKoIDppr8km9yOINqlprYkWEEQnejzJb65OEN385JPhj2/6aimjpbs8I7z9119V6loXESBAYBIFBNFJnIo9ESAwqQKC6KROxr4IEJg0AUF00iZiPwQITKqAICqITuprs4l9CaJNKFojCgFBNIoxJbvJOkE0WRQHI0CAwAwCgqiXBQECBMoLCKLlrVxJgEDeAoJo3vN3egIEygsIooJo+VdLfFcKovHNzI4rCgiiFeHc1oiAINoIo0UIEMhAQBDNYMiOSIBAYwKCaGOUFiJAIHEBQTTxATseAQKNCQiigmhjL6YJXEgQncCh2FI7AoJoO65WLScgiJZzchUBAgQEUa8BAgQIlBcQRMtbuZIAgbwFBNG85+/0BAiUFxBEBdHyr5b4rhRE45uZHVcUEEQrwrmtEQFBtBFGixAgkIGAIJrBkB2RAIHGBATRxigtRIBA4gKCaOIDdjwCBBoTEEQF0cZeTBO4kCA6gUOxpXYEBNF2XK1aTkAQLefkKgIECAiiXgMECBAoLyCIlrdyJQECeQsIonnP3+kJECgvIIgKouVfLfFdKYjGNzM7riggiFaEc1sjAoJoI4wWIUAgAwFBNIMhOyIBAo0JCKKNUVqIAIHEBQTRxAfseAQINCYgiDZGaaFEBbbbunXr1kTP5lgJCQiiCQ0zwqMIohEOzZYJEBiLgCA6FnYPJUAgUgFBNNLB2TYBAp0LCKKdk3sgAQKRCgiikQ7OtjsTEEQ7o/agOgKCaB0999YVEETrCrqfAIFcBATRXCbtnAQINCEgiDahaA0CBHIQEERzmLIzEiDQhIAg2oSiNVIWEERTnm5CZxNEExpmhEcRRCMcmi0TIDAWAUF0LOweSoBApAKCaKSDs20CBDoXEEQ7J/dAAgQiFRBEIx2cbXcmIIh2Ru1BdQQE0Tp67q0rIIjWFXQ/AQK5CAiiuUzaOQkQaEJAEG1C0RoECOQgIIjmMGVnJECgCQFBtAlFa6QsIIimPN2EziaIJjTMCI8iiEY4NFsmQGAsAoLoWNg9lACBSAUE0UgHZ9sECHQuIIh2Tu6BBAhEKiCIRjo42+5MQBDtjNqD6ggIonX03FtXQBCtK+h+AgRyERBEc5m0cxIg0ISAINqEojUIEMhBQBDNYcrOSIBAEwKCaBOK1khZQBBNeboJnU0QTWiYER5FEI1waLZMgMBYBATRsbB7KAECkQoIopEOzrYJEOhcQBDtnNwDCRCIVEAQjXRwtt2ZgCDaGbUH1REQROvoubeugCBaV9D9BAjkIiCI5jJp5yRAoAkBQbQJRWsQIJCDgCCaw5SdkQCBJgQE0SYUrZGygCCa8nSdjQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDmAoJo5i8AxydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQsoAgmvJ0nY0AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA5gKCaOYvAMcnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLKAIJrydJ2NAIHaArfdfld422kX9tY59OD9wycuPD0s2XVx7XUtQIAAgVgE1j+6MbzznI+F79x5b2/LV192Tjj8xQfNuv25rh/+tWKRvZbvHq646MxwwD4rYiGxTwIECJQSGPX3z/6il15xbdj3uXuGNxz7ylLPcREBAgRiE9i0+Ynw/ouvCl+88Zbe1j989kmlfs+77oabww/vfzCcccrxU0f258vYpm+/BAjUERjlz5fDv9eW+ef5OntzL4EYBATRGKZkjwQIjEXgnh+tCeeuvjJcsOrk3r+oL/7h65Zv3hE+eNZJYdGpSXZoAAALrUlEQVTCnceyJw8lQIBAlwL9f4A68rBDev+Savj3xeG9zHd9/x/ezjzl+Dmjapdn9CwCBAi0ITDf74czPbP4s+Z5F101UhxoY+/WJECAQNsCxX/4UfwowmaZPx8O/ofK73jrsTMGUX++bHtq1idAYNwCo/75svj99U8++6XwzhP/Ze/fYxa/l65afaX/IHncg/T8sQoIomPl93ACBCZZYPi/Pp0vBEzyWeyNAAECVQSK3/cuvvyzYfX7Tu69O374H8CG15zv+jL/wqvKPt1DgACBSROY7/fDufbrHaKTNk37IUCgSYHiz4OrPnJlOOvUt0x9QshgIJ3rWXO9Q1QQbXJK1iJAYBIF6vz5sjiPfx6fxKnaU9cCgmjX4p5HgEA0AsP/UOYPDtGMzkYJEGhIoPgvSC+54tppHxc+17+wmu/64Y/38XG5DQ3KMgQITJzAfL8fCqITNzIbIkCgI4GZ/kPjsp/GVOYjc/35sqNBegwBAp0L1PnzZbFZb/TofGQeOIECgugEDsWWCBCYDIHh/zpfEJ2MudgFAQLdCRT/wPW562+a9lHh8wXRUa4v/qXWtdff5PuZuxupJxEg0JHAqL9/Dm7LO0Q7GpLHECAwFoHhdzgVm6gTRIcP4c+XYxmrhxIg0IFAnT9fzvdpTx1s3yMITISAIDoRY7AJAgQmUcA7RCdxKvZEgECXAqP+F6ijXj/TR6Z1eT7PIkCAQFsCo/5+OLgPQbStqViXAIFJEGj6HaLDZ/Lny0mYsj0QINCGQNU/X/Zj6J7Llk77DuY29mhNApMuIIhO+oTsjwCBsQn4DtGx0XswAQITIjDqd5SMer1/YTUhg7YNAgQaFxj190NBtPERWJAAgQkVaPo7RAXRCR20bREg0LhAlT9fiqGNj8GCkQsIopEP0PYJEGhPYPi/XC37MT7t7cjKBAgQ6FZg+GN1hn9f7H+U+PHHHRXecOwrw3zXF/9Fa/Hj8Bcf1Ptfv692O09PI0CgO4H5fj8sfj895exLwupVJ0/9ntjfnXeIdjcnTyJAYDwCg5/GNPzVNMN/vhzc4UzfIerPl+OZoacSINC9wHx/vpzvn8+737EnEpg8AUF08mZiRwQITJBA8Q9Xbzvtwt6ODj14f99zN0GzsRUCBLoR6P9D1XfuvLf3wKsvO2fqX97P9C+s5rq+HwAeeOgRv692Mz5PIUBgjAJlfj8cDKLFv+g/76Krpna81/LdwxUXnRkO2GfFGE/h0QQIEGheoP8v9b944y29xT989km9/7iu+DHTny8H/7m8v5v+n0n9+bL5+ViRAIHJFRjln8+Hf3/sn+odbz3WR+dO7ojtrGUBQbRlYMsTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDA+AUF0fPaeTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAywKCaMvAlidAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYHwCguj47D2ZAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGWBQTRloEtT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDA+AQE0fHZezIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAi0LCKItA1ueAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHxCQii47P3ZAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWhYQRFsGtjwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAuMTEETHZ+/JBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0LCCItgxseQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExicgiI7P3pMJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhZQBBtGdjyBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiMT0AQHZ+9JxMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0LKAINoysOUJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBifgCA6PntPJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgZQFBtGVgyxMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMD4BQXR89p5MgAABAgQIECBAgAABApkKXHrFteFTn7lh2ulfe8yR4YNnnRQWLdw5UxXHJkCAAAECBAgQIECAQDsCgmg7rlYlQIAAAQIECBAgQIAAAQLbCGza/ER4/8VXhR+veTh84sLTw5JdF/euWf/oxvDOcz4WnrdimSjqdUOAAAECBAgQIECAAIGGBQTRhkEtR4AAAQIECBAgQIAAAQIEZhO47oabw+Wf/kK44qIzwwH7rNjmsq/cdGt45ZEv9i5RLyECBAgQIECAAAECBAg0KCCINohpKQIECBAgQIAAAQIECBAgMJfAfEF0+N7+O0q/eOMtU7906MH7T3t36fDH73747JPCG4595dT19/xoTTjl7EvC6lUnh699/du9j+rda/nu06LsfGuYKgECBAgQIECAAAECBGIWEERjnp69EyBAgAABAgQIECBAgEBUArfdfld422kXhne89dhwxinHz7n32T5G95N/dn045hWHhRXL9+h9/G7xo//do/34eeqJr5+Kov2fe+ChR8LVl50TDn/xQVPP7QfX+daICtlmCRAgQIAAAQIECBAgMCQgiHpJECBAgAABAgQIECBAgACBDgWG341ZPHqmQFq8m/Ta62+a9m7QwW0WcXXV6iu3+fjd4Z8ffIfoYAwt1prtGcXP3/LNO3yfaYevC48iQIAAAQIECBAgQKA9AUG0PVsrEyBAgAABAgQIECBAgACBWQWK6HjeRf/4Ds/+j34Y7b9zc89lS2d9J2kRVh98eN020XI4gM4WROd6RhFVL7ni2lljrLESIECAAAECBAgQIEAgJgFBNKZp2SsBAgQIECBAgAABAgQIJCtQBM4b/vrrvXd89j8Ot0oQ7X/U7vHHHdX72Nz5gujg95MO4g5/z2iy8A5GgAABAgQIECBAgEDyAoLo/9/eHbJEEIRhAJ4kGkQsBoPBZLBZrpi2adB/cHBNuzaD2PwBBkFFUDQJImjwhykmmcVZbk9WDk/wZnguzt3OzTwz7WW/r/gjtkECBAgQIECAAAECBAgQyEEg9ReNfT7X11br/qC/CUT/4g3RHLyskQABAgQIECBAgAABAuMKCETHlfI7AgQIECBAgAABAgQIECAwocDtw2vYqnphcWH+20yxhO7Z9WPTE/Q/e4hOuE2PEyBAgAABAgQIECBAYKoEBKJTdRwWQ4AAAQIECBAgQIAAAQIlC8SyuBd3zyH1Ck17TW+HDo+n0rcry0utPqHnN0+h2txoyurGOY4PBmFudqYpj7vf36nL5cZPV8nc+F3Xf8Txq/uXsNffref1IUCAAAECBAgQIECAQM4CAtGcT8/aCRAgQIAAAQIECBAgQCA7gfjm59HpZWvdXf06394/6tK5w30+t6teKyBNIWua8ORw0IShceynQDQ9MzpHHB+dJztoCyZAgAABAgQIECBAgMCXgEDUVSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoFgBgWixR2tjBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgIRN0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSKFRCIFnu0NkaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgEDUHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoFgBgWixR2tjBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgIRN0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSKFRCIFnu0NkaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwCd+BVlp1uu5MAAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We can check the feature importance with other plots as well\n", "atom.plot_permutation_importance(models=[\"LGB_dfs\", \"LGB_gfg\"], show=12)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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