diff --git a/ab_test_guest_journey_monitoring.ipynb b/ab_test_guest_journey_monitoring.ipynb new file mode 100644 index 0000000..0dc983b --- /dev/null +++ b/ab_test_guest_journey_monitoring.ipynb @@ -0,0 +1,564 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A/B test monitoring\n", + "\n", + "## Initial setup\n", + "This first section just ensures that the connection to DWH works correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This script connects to a Data Warehouse (DWH) using PostgreSQL. \n", + "# This should be common for all Notebooks, but you might need to adjust the path to the `dwh_utils` module.\n", + "\n", + "import sys\n", + "import os\n", + "sys.path.append(os.path.abspath(\"./utils\")) # Adjust path if needed\n", + "\n", + "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n", + "\n", + "# --- Connect to DWH ---\n", + "creds = read_credentials()\n", + "dwh_pg_engine = create_postgres_engine(creds)\n", + "\n", + "# --- Test Query ---\n", + "test_connection()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from statsmodels.stats.proportion import proportions_ztest\n", + "from scipy import stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A/B test configuration\n", + "In this section we configure the parameters for the A/B test. Likely you do NOT need to change anything else than this, unless of course you want to create new metrics and so on.\n", + "\n", + "The parameters to be specified are:\n", + "* **ab_test_name**: this should be the name of the feature flag corresponding to the A/B test. If you don't know the name, ask Guest Squad\n", + "* **var_A** and **var_B**: these correspond to the name of the variants. At this moment, we can only handle univariant testing (though updating the code to include multivariant testing should not be extremely difficult). In general, choose var_A to be the Control group." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A/B test name to measure\n", + "ab_test_name = 'YourTripQuestionaire'\n", + "\n", + "# Define the variations in which we want to run the tests\n", + "var_A = 'DontShowYourTripQuestions' # Ideally, this should be the control group\n", + "var_B = 'ShowYourTripQuestions' # Ideally, this should be the study group\n", + "\n", + "variations = [var_A, var_B]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Extraction\n", + "In this section we extract the data from the Guest Journey monitoring within DWH by configuring which A/B test we want to measure. Here we already handle the basic aggregations that will be needed in the future, directly in SQL." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Query to extract data\n", + "data_extraction_query = \"\"\"\n", + "select \n", + "\tgj.ab_test_name,\n", + "\tgj.variation,\n", + "\tmax(gj.first_appearance_date_utc) as last_update,\n", + " \n", + " -- SIMPLE COUNTS --\n", + "\tcount(gj.id_verification_request) as guest_journeys_count,\n", + "\tcount(gj.verification_started_date_utc) as guest_journey_started_count,\n", + "\tcount(gj.verification_completed_date_utc) as guest_journey_completed_count,\n", + "\tcount(gj.experience_rating) as guest_journey_with_responses_count,\n", + "\tcount(gj.last_payment_paid_date_utc) as guest_journey_with_payment_count,\n", + "\tcount(gj.guest_revenue_without_taxes_in_gbp) as guest_revenue_count,\n", + "\tcount(gj.deposit_fees_without_taxes_in_gbp) as deposit_count,\n", + "\tcount(gj.waiver_fees_without_taxes_in_gbp) as waiver_count,\n", + "\tcount(gj.check_in_cover_fees_without_taxes_in_gbp) as check_in_cover_count,\n", + " \n", + " -- SIMPLE SUMS --\n", + "\tsum(gj.guest_revenue_without_taxes_in_gbp) as guest_revenue_sum,\n", + "\tsum(gj.deposit_fees_without_taxes_in_gbp) as deposit_sum,\n", + "\tsum(gj.waiver_fees_without_taxes_in_gbp) as waiver_sum,\n", + "\tsum(gj.check_in_cover_fees_without_taxes_in_gbp) as check_in_cover_sum,\n", + " \n", + " -- AVGs/SDVs PER GUEST JOURNEY (ANY GJ APPEARING IN THE A/B TEST) --\n", + " -- NOTE THE COALESCE HERE. THIS IS IMPORTANT FOR THE T-TEST COMPUTATION --\n", + " avg(coalesce(gj.guest_revenue_without_taxes_in_gbp,0)) as guest_revenue_avg_per_guest_journey,\n", + " stddev(coalesce(gj.guest_revenue_without_taxes_in_gbp,0)) as guest_revenue_sdv_per_guest_journey,\n", + " avg(coalesce(gj.deposit_fees_without_taxes_in_gbp,0)) as deposit_avg_per_guest_journey,\n", + " stddev(coalesce(gj.deposit_fees_without_taxes_in_gbp,0)) as deposit_sdv_per_guest_journey,\n", + " avg(coalesce(gj.waiver_fees_without_taxes_in_gbp,0)) as waiver_avg_per_guest_journey,\n", + " stddev(coalesce(gj.waiver_fees_without_taxes_in_gbp,0)) as waiver_sdv_per_guest_journey,\n", + " avg(coalesce(gj.check_in_cover_fees_without_taxes_in_gbp,0)) as check_in_cover_avg_per_guest_journey,\n", + " stddev(coalesce(gj.check_in_cover_fees_without_taxes_in_gbp,0)) as check_in_cover_sdv_per_guest_journey,\n", + " \n", + " -- AVGs/SDVs PER GUEST JOURNEY WITH CSAT RESPONSE --\n", + " -- NOTE THAT THERE'S NO COALESCE HERE. THIS IS IMPORTANT FOR THE T-TEST COMPUTATION --\n", + " avg(gj.experience_rating) as csat_avg_per_guest_journey_with_response,\n", + " stddev(gj.experience_rating) as csat_sdv_per_guest_journey_with_response\n", + " \n", + "from\n", + "\tintermediate.int_core__ab_test_monitoring_guest_journey gj\n", + "where\n", + "\tgj.ab_test_name = '{}'\n", + "group by\n", + "\t1,2\n", + "\"\"\".format(ab_test_name)\n", + "\n", + "# Retrieve Data from Query\n", + "df = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n", + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check A/B test Allocation to Variation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure Seaborn styling\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "# Calculate the total guest_journeys_count per variation\n", + "grouped_data = df.groupby('variation')['guest_journeys_count'].sum()\n", + "\n", + "# Find the total count and other metadata\n", + "total_count = grouped_data.sum()\n", + "ab_test_name = df['ab_test_name'].iloc[0] # Assuming all rows are for the same A/B test\n", + "last_update = df['last_update'].max()\n", + "\n", + "# Create a pie chart using Seaborn styling\n", + "plt.figure(figsize=(8, 6))\n", + "colors = sns.color_palette(\"pastel\") # Seaborn pastel colors\n", + "\n", + "# Pie chart with labels inside each sector\n", + "plt.pie(\n", + " grouped_data, \n", + " labels=[f\"{var}\\n{count} ({count/total_count:.1%})\" for var, count in grouped_data.items()],\n", + " autopct=None, \n", + " colors=colors, \n", + " startangle=90,\n", + " wedgeprops={'edgecolor': 'none'}, # Remove edges around sectors\n", + " pctdistance=0.70, # Places the labels closer to the center (inside)\n", + " labeldistance=0.2 # Ensure labels are positioned inside the sectors\n", + ")\n", + "\n", + "# Add title\n", + "plt.title(\"Guest Journey - Variation Allocation\", fontsize=16)\n", + "\n", + "# Add total count to the bottom-left\n", + "plt.text(-1.4, -1.3, f\"Total Count: {total_count}\", fontsize=10, ha='left', color='black')\n", + "\n", + "# Add A/B test name and last update to the bottom-right\n", + "plt.text(1.2, -1.3, f\"A/B Test: {ab_test_name}\", fontsize=8, ha='right', color='gray')\n", + "plt.text(1.2, -1.4, f\"Last Update: {last_update}\", fontsize=8, ha='right', color='gray')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Statistical Analysis\n", + "In this section we compute the metrics needed for monitoring as well as check if there's any statistical difference between the different variations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Z-test for Proportion Metrics (Rates)\n", + "This section defines the functions used to compute Z-test Proportion analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generalized function to calculate Z-test for any metric\n", + "def calculate_z_test(df, metric_name, variation_a, variation_b, success_counts, total_counts):\n", + "\n", + " # Aggregate the success counts (numerator) and total counts (denominator) for each variation\n", + " success_a = df[df['variation'] == variation_a][success_counts].sum()\n", + " success_b = df[df['variation'] == variation_b][success_counts].sum()\n", + "\n", + " total_a = df[df['variation'] == variation_a][total_counts].sum()\n", + " total_b = df[df['variation'] == variation_b][total_counts].sum()\n", + "\n", + " # Calculate conversion rates for each variation\n", + " value_A = success_a / total_a if total_a != 0 else 0\n", + " value_B = success_b / total_b if total_b != 0 else 0\n", + "\n", + " # Absolute difference (B - A)\n", + " abs_diff = value_B - value_A\n", + "\n", + " # Relative difference (B - A) / A\n", + " rel_diff = (value_B - value_A) / value_A if value_A != 0 else 0\n", + "\n", + " # Perform the z-test for proportions\n", + " count = [success_a, success_b] # Success counts for A and B\n", + " nobs = [total_a, total_b] # Total counts for A and B\n", + " \n", + " # Calculate z-stat and p-value\n", + " z_stat, p_value = proportions_ztest(count, nobs)\n", + " \n", + " # Flag for significance at 95% level (p-value < 0.05)\n", + " is_significant = p_value < 0.05\n", + "\n", + " # Return the result as a dictionary\n", + " return {\n", + " 'metric': metric_name,\n", + " 'variation_A_name': variation_a,\n", + " 'variation_B_name': variation_b,\n", + " 'variation_A_value': value_A,\n", + " 'variation_B_value': value_B,\n", + " 'absolute_difference': abs_diff,\n", + " 'relative_difference': rel_diff,\n", + " 'statistic': z_stat,\n", + " 'p_value': p_value,\n", + " 'is_significant_95': is_significant\n", + " }\n", + "\n", + "# Function to run Z-tests for multiple metrics and aggregate results into a DataFrame\n", + "def run_z_tests(df, z_stat_metric_definition, variations):\n", + " results = []\n", + " \n", + " # Loop over all metrics in z_stat_metric_definition\n", + " for metric_name, metric_definition in z_stat_metric_definition.items():\n", + " success_counts = metric_definition['success_counts']\n", + " total_counts = metric_definition['total_counts']\n", + " \n", + " # Run the Z-test for each metric\n", + " result = calculate_z_test(df, metric_name, variation_a=variations[0], variation_b=variations[1], \n", + " success_counts=success_counts, total_counts=total_counts)\n", + " results.append(result)\n", + " \n", + " # Create a DataFrame from the results\n", + " results_df = pd.DataFrame(results)\n", + " \n", + " return results_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### T-test for non-proportion metrics\n", + "This section defines the functions used to compute T-tests for metrics outside of the proportion scope, mostly Revenue-related metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Generalized function to calculate T-test for revenue-related metrics\n", + "def calculate_t_test(df, metric_name, variation_a, variation_b, metric_avg_column, metric_sdv_column, total_counts):\n", + " # Aggregate the avgs and standard deviations for each variation\n", + " mean_a = df[df['variation'] == variation_a][metric_avg_column].mean() # Assuming the avg is calculated for each group\n", + " mean_b = df[df['variation'] == variation_b][metric_avg_column].mean() # Assuming the avg is calculated for each group\n", + " \n", + " sdv_a = df[df['variation'] == variation_a][metric_sdv_column].mean() # Assuming the stddev is calculated for each group\n", + " sdv_b = df[df['variation'] == variation_b][metric_sdv_column].mean() # Assuming the stddev is calculated for each group\n", + " \n", + " total_a = df[df['variation'] == variation_a][total_counts].sum()\n", + " total_b = df[df['variation'] == variation_b][total_counts].sum()\n", + "\n", + " # Absolute difference (B - A)\n", + " abs_diff = mean_b - mean_a\n", + "\n", + " # Relative difference (B - A) / A\n", + " rel_diff = (mean_b - mean_a) / mean_a if mean_a != 0 else 0\n", + "\n", + " # Calculate the T-statistic and p-value using the formula for two-sample T-test\n", + " se_a = sdv_a / (total_a ** 0.5) if total_a != 0 else 0\n", + " se_b = sdv_b / (total_b ** 0.5) if total_b != 0 else 0\n", + "\n", + " # Standard error of the difference between the means\n", + " se_diff = (se_a ** 2 + se_b ** 2) ** 0.5\n", + " \n", + " # T-statistic formula\n", + " if se_diff != 0:\n", + " t_stat = (mean_a - mean_b) / se_diff\n", + " else:\n", + " t_stat = 0\n", + " \n", + " # Degrees of freedom (for independent samples)\n", + " df_degrees = min(total_a - 1, total_b - 1) # Using the smaller of the two sample sizes minus 1\n", + " \n", + " # P-value from the T-distribution\n", + " p_value = stats.t.sf(abs(t_stat), df_degrees) * 2 # Two-tailed test\n", + " \n", + " # Flag for significance at 95% level (p-value < 0.05)\n", + " is_significant = p_value < 0.05\n", + "\n", + " # Return the result as a dictionary\n", + " return {\n", + " 'metric': metric_name,\n", + " 'variation_A_name': variation_a,\n", + " 'variation_B_name': variation_b,\n", + " 'variation_A_value': mean_a,\n", + " 'variation_B_value': mean_b,\n", + " 'absolute_difference': abs_diff,\n", + " 'relative_difference': rel_diff,\n", + " 'statistic': t_stat,\n", + " 'p_value': p_value,\n", + " 'is_significant_95': is_significant\n", + " }\n", + "\n", + "# Function to run T-tests for multiple revenue metrics and aggregate results into a DataFrame\n", + "def run_t_tests(df, t_stat_metric_definition, variations):\n", + " results = []\n", + " \n", + " # Loop over all metrics in t_stat_metric_definition\n", + " for metric_name, metric_definition in t_stat_metric_definition.items():\n", + " metric_avg_column = metric_definition['metric_avg_column']\n", + " metric_sdv_column = metric_definition['metric_sdv_column']\n", + " total_counts = metric_definition['total_counts']\n", + " \n", + " # Run the T-test for each metric\n", + " result = calculate_t_test(df, metric_name, variation_a=variations[0], variation_b=variations[1], \n", + " metric_avg_column=metric_avg_column, metric_sdv_column=metric_sdv_column, \n", + " total_counts=total_counts)\n", + " results.append(result)\n", + " \n", + " # Create a DataFrame from the results\n", + " results_df = pd.DataFrame(results)\n", + " \n", + " return results_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specify the metric definition for Z-stat and T-stat tests" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the Z-test metric definitions (with both success_counts and total_counts)\n", + "z_stat_metric_definition = {\n", + " 'conversion_rate': {\n", + " 'success_counts': 'guest_journey_completed_count',\n", + " 'total_counts': 'guest_journeys_count'\n", + " },\n", + " 'payment_rate': {\n", + " 'success_counts': 'guest_journey_with_payment_count',\n", + " 'total_counts': 'guest_journeys_count'\n", + " },\n", + " 'waiver_payment_rate': {\n", + " 'success_counts': 'waiver_count',\n", + " 'total_counts': 'guest_journeys_count'\n", + " },\n", + " 'deposit_payment_rate': {\n", + " 'success_counts': 'deposit_count',\n", + " 'total_counts': 'guest_journeys_count'\n", + " },\n", + " 'CIH_payment_rate': {\n", + " 'success_counts': 'check_in_cover_count',\n", + " 'total_counts': 'guest_journeys_count'\n", + " }\n", + "}\n", + "\n", + "# Define the T-test metric definitions (with both metric_avg_column and metric_sdv_column)\n", + "t_stat_metric_definition = {\n", + " 'avg_guest_revenue_per_gj': {\n", + " 'metric_avg_column': 'guest_revenue_avg_per_guest_journey',\n", + " 'metric_sdv_column': 'guest_revenue_sdv_per_guest_journey',\n", + " 'total_counts': 'guest_journeys_count'\n", + " },\n", + " 'avg_waiver_revenue_per_gj': {\n", + " 'metric_avg_column': 'waiver_avg_per_guest_journey',\n", + " 'metric_sdv_column': 'waiver_sdv_per_guest_journey',\n", + " 'total_counts': 'guest_journeys_count'\n", + " },\n", + " 'avg_deposit_revenue_per_gj': {\n", + " 'metric_avg_column': 'deposit_avg_per_guest_journey',\n", + " 'metric_sdv_column': 'deposit_sdv_per_guest_journey',\n", + " 'total_counts': 'guest_journeys_count'\n", + " },\n", + " 'avg_CIH_revenue_per_gj': {\n", + " 'metric_avg_column': 'check_in_cover_avg_per_guest_journey',\n", + " 'metric_sdv_column': 'check_in_cover_sdv_per_guest_journey',\n", + " 'total_counts': 'guest_journeys_count'\n", + " },\n", + " 'avg_csat_per_gj_with_response': {\n", + " 'metric_avg_column': 'csat_avg_per_guest_journey_with_response',\n", + " 'metric_sdv_column': 'csat_sdv_per_guest_journey_with_response',\n", + " 'total_counts': 'guest_journey_with_responses_count'\n", + " }\n", + "\n", + "}\n", + "\n", + "# Define the metrics that will be the main ones for this A/B test:\n", + "main_metrics = ['avg_guest_revenue_per_gj', 'conversion_rate', 'payment_rate']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the computation of the metrics and statistical significance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Call the function to calculate the Z-test for each metric and aggregate the results\n", + "z_test_results_df = run_z_tests(df, z_stat_metric_definition=z_stat_metric_definition, variations=variations)\n", + "\n", + "# Call the function to calculate the T-test for each metric and aggregate the results\n", + "t_test_results_df = run_t_tests(df, t_stat_metric_definition=t_stat_metric_definition, variations=variations)\n", + "\n", + "# Add a new column to identify whether it's from Z-test or T-test\n", + "z_test_results_df['test_type'] = 'Z-test'\n", + "t_test_results_df['test_type'] = 'T-test'\n", + "\n", + "# Combine the dataframes after adding the 'test_type' column\n", + "combined_results_df = pd.concat([z_test_results_df, t_test_results_df], ignore_index=True)\n", + "\n", + "# Print the main aggregated DataFrame\n", + "print(combined_results_df[['metric','relative_difference','p_value']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('\\n{} results (last updated at {})\\n'.format(ab_test_name, last_update))\n", + "\n", + "# Get main volume indicators per variation\n", + "grouped_data = df.groupby('variation')[[\"guest_journeys_count\",\"guest_revenue_sum\"]].sum()\n", + "\n", + "# Find the totals over any variation\n", + "total_count = grouped_data.sum()\n", + "\n", + "# Print overall indicators for volumes\n", + "print('Total Guest Journeys affected by this A/B test: {} - Total Guest Revenue: {} GBP.'.format(int(total_count.loc[\"guest_journeys_count\"]), \n", + " int(total_count.loc[\"guest_revenue_sum\"])))\n", + "for var in variations:\n", + " print(' Variation {}: Guest Journeys {} ({}%) - Guest Revenue: {} GBP ({}%).'.format(\n", + " var, \n", + " int(grouped_data.loc[var,'guest_journeys_count']), \n", + " round(100*(grouped_data.loc[var,'guest_journeys_count']/total_count.loc[\"guest_journeys_count\"]),1),\n", + " int(grouped_data.loc[var,'guest_revenue_sum']),\n", + " round(100*(grouped_data.loc[var,'guest_revenue_sum']/total_count.loc[\"guest_revenue_sum\"]),1)\n", + " ))\n", + "\n", + "# Split results whether the metrics are main metrics or not\n", + "main_metrics_rows = combined_results_df[combined_results_df['metric'].isin(main_metrics)]\n", + "other_metrics_rows = combined_results_df[~combined_results_df['metric'].isin(main_metrics)]\n", + "\n", + "def print_metrics(df, header=None):\n", + " if header:\n", + " print(f'\\n{header}\\n')\n", + "\n", + " for row in df.iterrows():\n", + " metric = row[1]['metric'].upper().replace('_', ' ')\n", + " if row[1]['test_type'] == 'Z-test':\n", + " value_a = str(round(100 * row[1]['variation_A_value'], 1)) + '%'\n", + " value_b = str(round(100 * row[1]['variation_B_value'], 1)) + '%'\n", + " abs_diff = str(round(100 * row[1]['absolute_difference'], 1)) + '%'\n", + " else:\n", + " value_a = str(round(row[1]['variation_A_value'], 2))\n", + " value_b = str(round(row[1]['variation_B_value'], 2))\n", + " abs_diff = str(round(row[1]['absolute_difference'], 2))\n", + " rel_diff = str(round(100 * row[1]['relative_difference'], 1)) + '%'\n", + " stat_sign = row[1]['is_significant_95']\n", + "\n", + " if stat_sign:\n", + " print(f\"{metric} - SIGNIFICANT RESULT: {value_b} vs. {value_a} ({abs_diff} ppts.| {rel_diff}).\")\n", + " else:\n", + " print(f\"{metric} (not significant): {value_b} vs. {value_a} ({abs_diff} ppts.| {rel_diff}).\")\n", + "\n", + "# Print main metrics\n", + "print_metrics(main_metrics_rows, header=\"Main Metrics - Comparing {} vs. {}.\".format(var_B, var_A))\n", + "\n", + "# Print other metrics\n", + "print_metrics(other_metrics_rows, header=\"Other Metrics\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ab_testing/ab_test_guest_journey_monitoring.ipynb b/ab_testing/ab_test_guest_journey_monitoring.ipynb deleted file mode 100644 index e1ccf77..0000000 --- a/ab_testing/ab_test_guest_journey_monitoring.ipynb +++ /dev/null @@ -1,679 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A/B test monitoring\n", - "\n", - "## Initial setup\n", - "This first section just ensures that the connection to DWH works correctly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "šŸ”Œ Testing connection using credentials at: /home/joaquin/.superhog-dwh/credentials.yml\n", - "āœ… Connection successful.\n" - ] - } - ], - "source": [ - "# This script connects to a Data Warehouse (DWH) using PostgreSQL. \n", - "# This should be common for all Notebooks, but you might need to adjust the path to the `dwh_utils` module.\n", - "\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.abspath(\"../utils\")) # Adjust path if needed\n", - "\n", - "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n", - "\n", - "# --- Connect to DWH ---\n", - "creds = read_credentials()\n", - "dwh_pg_engine = create_postgres_engine(creds)\n", - "\n", - "# --- Test Query ---\n", - "test_connection()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from statsmodels.stats.proportion import proportions_ztest\n", - "from scipy import stats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## A/B test configuration\n", - "In this section we configure the parameters for the A/B test. Likely you do NOT need to change anything else than this, unless of course you want to create new metrics and so on.\n", - "\n", - "The parameters to be specified are:\n", - "* **ab_test_name**: this should be the name of the feature flag corresponding to the A/B test. If you don't know the name, ask Guest Squad\n", - "* **var_A** and **var_B**: these correspond to the name of the variants. At this moment, we can only handle univariant testing (though updating the code to include multivariant testing should not be extremely difficult). In general, choose var_A to be the Control group." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# A/B test name to measure\n", - "ab_test_name = 'YourTripQuestionaire'\n", - "\n", - "# Define the variations in which we want to run the tests\n", - "var_A = 'DontShowYourTripQuestions' # Ideally, this should be the control group\n", - "var_B = 'ShowYourTripQuestions' # Ideally, this should be the study group\n", - "\n", - "variations = [var_A, var_B]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Extraction\n", - "In this section we extract the data from the Guest Journey monitoring within DWH by configuring which A/B test we want to measure. Here we already handle the basic aggregations that will be needed in the future, directly in SQL." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ab_test_name variation last_update \\\n", - "0 YourTripQuestionaire DontShowYourTripQuestions 2025-07-01 \n", - "1 YourTripQuestionaire ShowYourTripQuestions 2025-07-01 \n", - "\n", - " guest_journeys_count guest_journey_started_count \\\n", - "0 24169 24137 \n", - "1 2726 2724 \n", - "\n", - " guest_journey_completed_count guest_journey_with_responses_count \\\n", - "0 18454 6760 \n", - "1 2153 786 \n", - "\n", - " guest_journey_with_payment_count guest_revenue_count deposit_count ... \\\n", - "0 10159 10159 1916 ... \n", - "1 1190 1190 223 ... \n", - "\n", - " guest_revenue_avg_per_guest_journey guest_revenue_sdv_per_guest_journey \\\n", - "0 10.207814 15.711112 \n", - "1 10.247566 14.609434 \n", - "\n", - " deposit_avg_per_guest_journey deposit_sdv_per_guest_journey \\\n", - "0 0.53246 2.020852 \n", - "1 0.53625 2.000870 \n", - "\n", - " waiver_avg_per_guest_journey waiver_sdv_per_guest_journey \\\n", - "0 9.524052 15.745335 \n", - "1 9.518755 14.646680 \n", - "\n", - " check_in_cover_avg_per_guest_journey check_in_cover_sdv_per_guest_journey \\\n", - "0 0.151303 1.127958 \n", - "1 0.192561 1.268302 \n", - "\n", - " csat_avg_per_guest_journey_with_response \\\n", - "0 3.757249 \n", - "1 3.764631 \n", - "\n", - " csat_sdv_per_guest_journey_with_response \n", - "0 1.051046 \n", - "1 1.041469 \n", - "\n", - "[2 rows x 26 columns]\n" - ] - } - ], - "source": [ - "# Query to extract data\n", - "data_extraction_query = \"\"\"\n", - "select \n", - "\tgj.ab_test_name,\n", - "\tgj.variation,\n", - "\tmax(gj.first_appearance_date_utc) as last_update,\n", - " \n", - " -- SIMPLE COUNTS --\n", - "\tcount(gj.id_verification_request) as guest_journeys_count,\n", - "\tcount(gj.verification_started_date_utc) as guest_journey_started_count,\n", - "\tcount(gj.verification_completed_date_utc) as guest_journey_completed_count,\n", - "\tcount(gj.experience_rating) as guest_journey_with_responses_count,\n", - "\tcount(gj.last_payment_paid_date_utc) as guest_journey_with_payment_count,\n", - "\tcount(gj.guest_revenue_without_taxes_in_gbp) as guest_revenue_count,\n", - "\tcount(gj.deposit_fees_without_taxes_in_gbp) as deposit_count,\n", - "\tcount(gj.waiver_fees_without_taxes_in_gbp) as waiver_count,\n", - "\tcount(gj.check_in_cover_fees_without_taxes_in_gbp) as check_in_cover_count,\n", - " \n", - " -- SIMPLE SUMS --\n", - "\tsum(gj.guest_revenue_without_taxes_in_gbp) as guest_revenue_sum,\n", - "\tsum(gj.deposit_fees_without_taxes_in_gbp) as deposit_sum,\n", - "\tsum(gj.waiver_fees_without_taxes_in_gbp) as waiver_sum,\n", - "\tsum(gj.check_in_cover_fees_without_taxes_in_gbp) as check_in_cover_sum,\n", - " \n", - " -- AVGs/SDVs PER GUEST JOURNEY (ANY GJ APPEARING IN THE A/B TEST) --\n", - " -- NOTE THE COALESCE HERE. THIS IS IMPORTANT FOR THE T-TEST COMPUTATION --\n", - " avg(coalesce(gj.guest_revenue_without_taxes_in_gbp,0)) as guest_revenue_avg_per_guest_journey,\n", - " stddev(coalesce(gj.guest_revenue_without_taxes_in_gbp,0)) as guest_revenue_sdv_per_guest_journey,\n", - " avg(coalesce(gj.deposit_fees_without_taxes_in_gbp,0)) as deposit_avg_per_guest_journey,\n", - " stddev(coalesce(gj.deposit_fees_without_taxes_in_gbp,0)) as deposit_sdv_per_guest_journey,\n", - " avg(coalesce(gj.waiver_fees_without_taxes_in_gbp,0)) as waiver_avg_per_guest_journey,\n", - " stddev(coalesce(gj.waiver_fees_without_taxes_in_gbp,0)) as waiver_sdv_per_guest_journey,\n", - " avg(coalesce(gj.check_in_cover_fees_without_taxes_in_gbp,0)) as check_in_cover_avg_per_guest_journey,\n", - " stddev(coalesce(gj.check_in_cover_fees_without_taxes_in_gbp,0)) as check_in_cover_sdv_per_guest_journey,\n", - " \n", - " -- AVGs/SDVs PER GUEST JOURNEY WITH CSAT RESPONSE --\n", - " -- NOTE THAT THERE'S NO COALESCE HERE. THIS IS IMPORTANT FOR THE T-TEST COMPUTATION --\n", - " avg(gj.experience_rating) as csat_avg_per_guest_journey_with_response,\n", - " stddev(gj.experience_rating) as csat_sdv_per_guest_journey_with_response\n", - " \n", - "from\n", - "\treporting.core__ab_test_monitoring_guest_journey gj\n", - "where\n", - "\tgj.ab_test_name = '{}'\n", - "group by\n", - "\t1,2\n", - "\"\"\".format(ab_test_name)\n", - "\n", - "# Retrieve Data from Query\n", - "df = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n", - "print(df.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check A/B test Allocation to Variation" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Ensure Seaborn styling\n", - "sns.set_theme(style=\"whitegrid\")\n", - "\n", - "# Calculate the total guest_journeys_count per variation\n", - "grouped_data = df.groupby('variation')['guest_journeys_count'].sum()\n", - "\n", - "# Find the total count and other metadata\n", - "total_count = grouped_data.sum()\n", - "ab_test_name = df['ab_test_name'].iloc[0] # Assuming all rows are for the same A/B test\n", - "last_update = df['last_update'].max()\n", - "\n", - "# Create a pie chart using Seaborn styling\n", - "plt.figure(figsize=(8, 6))\n", - "colors = sns.color_palette(\"pastel\") # Seaborn pastel colors\n", - "\n", - "# Pie chart with labels inside each sector\n", - "plt.pie(\n", - " grouped_data, \n", - " labels=[f\"{var}\\n{count} ({count/total_count:.1%})\" for var, count in grouped_data.items()],\n", - " autopct=None, \n", - " colors=colors, \n", - " startangle=90,\n", - " wedgeprops={'edgecolor': 'none'}, # Remove edges around sectors\n", - " pctdistance=0.70, # Places the labels closer to the center (inside)\n", - " labeldistance=0.2 # Ensure labels are positioned inside the sectors\n", - ")\n", - "\n", - "# Add title\n", - "plt.title(\"Guest Journey - Variation Allocation\", fontsize=16)\n", - "\n", - "# Add total count to the bottom-left\n", - "plt.text(-1.4, -1.3, f\"Total Count: {total_count}\", fontsize=10, ha='left', color='black')\n", - "\n", - "# Add A/B test name and last update to the bottom-right\n", - "plt.text(1.2, -1.3, f\"A/B Test: {ab_test_name}\", fontsize=8, ha='right', color='gray')\n", - "plt.text(1.2, -1.4, f\"Last Update: {last_update}\", fontsize=8, ha='right', color='gray')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Statistical Analysis\n", - "In this section we compute the metrics needed for monitoring as well as check if there's any statistical difference between the different variations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Z-test for Proportion Metrics (Rates)\n", - "This section defines the functions used to compute Z-test Proportion analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Generalized function to calculate Z-test for any metric\n", - "def calculate_z_test(df, metric_name, variation_a, variation_b, success_counts, total_counts):\n", - "\n", - " # Aggregate the success counts (numerator) and total counts (denominator) for each variation\n", - " success_a = df[df['variation'] == variation_a][success_counts].sum()\n", - " success_b = df[df['variation'] == variation_b][success_counts].sum()\n", - "\n", - " total_a = df[df['variation'] == variation_a][total_counts].sum()\n", - " total_b = df[df['variation'] == variation_b][total_counts].sum()\n", - "\n", - " # Calculate conversion rates for each variation\n", - " value_A = success_a / total_a if total_a != 0 else 0\n", - " value_B = success_b / total_b if total_b != 0 else 0\n", - "\n", - " # Absolute difference (B - A)\n", - " abs_diff = value_B - value_A\n", - "\n", - " # Relative difference (B - A) / A\n", - " rel_diff = (value_B - value_A) / value_A if value_A != 0 else 0\n", - "\n", - " # Perform the z-test for proportions\n", - " count = [success_a, success_b] # Success counts for A and B\n", - " nobs = [total_a, total_b] # Total counts for A and B\n", - " \n", - " # Calculate z-stat and p-value\n", - " z_stat, p_value = proportions_ztest(count, nobs)\n", - " \n", - " # Flag for significance at 95% level (p-value < 0.05)\n", - " is_significant = p_value < 0.05\n", - "\n", - " # Return the result as a dictionary\n", - " return {\n", - " 'metric': metric_name,\n", - " 'variation_A_name': variation_a,\n", - " 'variation_B_name': variation_b,\n", - " 'variation_A_value': value_A,\n", - " 'variation_B_value': value_B,\n", - " 'absolute_difference': abs_diff,\n", - " 'relative_difference': rel_diff,\n", - " 'statistic': z_stat,\n", - " 'p_value': p_value,\n", - " 'is_significant_95': is_significant\n", - " }\n", - "\n", - "# Function to run Z-tests for multiple metrics and aggregate results into a DataFrame\n", - "def run_z_tests(df, z_stat_metric_definition, variations):\n", - " results = []\n", - " \n", - " # Loop over all metrics in z_stat_metric_definition\n", - " for metric_name, metric_definition in z_stat_metric_definition.items():\n", - " success_counts = metric_definition['success_counts']\n", - " total_counts = metric_definition['total_counts']\n", - " \n", - " # Run the Z-test for each metric\n", - " result = calculate_z_test(df, metric_name, variation_a=variations[0], variation_b=variations[1], \n", - " success_counts=success_counts, total_counts=total_counts)\n", - " results.append(result)\n", - " \n", - " # Create a DataFrame from the results\n", - " results_df = pd.DataFrame(results)\n", - " \n", - " return results_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### T-test for non-proportion metrics\n", - "This section defines the functions used to compute T-tests for metrics outside of the proportion scope, mostly Revenue-related metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Generalized function to calculate T-test for revenue-related metrics\n", - "def calculate_t_test(df, metric_name, variation_a, variation_b, metric_avg_column, metric_sdv_column, total_counts):\n", - " # Aggregate the avgs and standard deviations for each variation\n", - " mean_a = df[df['variation'] == variation_a][metric_avg_column].mean() # Assuming the avg is calculated for each group\n", - " mean_b = df[df['variation'] == variation_b][metric_avg_column].mean() # Assuming the avg is calculated for each group\n", - " \n", - " sdv_a = df[df['variation'] == variation_a][metric_sdv_column].mean() # Assuming the stddev is calculated for each group\n", - " sdv_b = df[df['variation'] == variation_b][metric_sdv_column].mean() # Assuming the stddev is calculated for each group\n", - " \n", - " total_a = df[df['variation'] == variation_a][total_counts].sum()\n", - " total_b = df[df['variation'] == variation_b][total_counts].sum()\n", - "\n", - " # Absolute difference (B - A)\n", - " abs_diff = mean_b - mean_a\n", - "\n", - " # Relative difference (B - A) / A\n", - " rel_diff = (mean_b - mean_a) / mean_a if mean_a != 0 else 0\n", - "\n", - " # Calculate the T-statistic and p-value using the formula for two-sample T-test\n", - " se_a = sdv_a / (total_a ** 0.5) if total_a != 0 else 0\n", - " se_b = sdv_b / (total_b ** 0.5) if total_b != 0 else 0\n", - "\n", - " # Standard error of the difference between the means\n", - " se_diff = (se_a ** 2 + se_b ** 2) ** 0.5\n", - " \n", - " # T-statistic formula\n", - " if se_diff != 0:\n", - " t_stat = (mean_a - mean_b) / se_diff\n", - " else:\n", - " t_stat = 0\n", - " \n", - " # Degrees of freedom (for independent samples)\n", - " df_degrees = min(total_a - 1, total_b - 1) # Using the smaller of the two sample sizes minus 1\n", - " \n", - " # P-value from the T-distribution\n", - " p_value = stats.t.sf(abs(t_stat), df_degrees) * 2 # Two-tailed test\n", - " \n", - " # Flag for significance at 95% level (p-value < 0.05)\n", - " is_significant = p_value < 0.05\n", - "\n", - " # Return the result as a dictionary\n", - " return {\n", - " 'metric': metric_name,\n", - " 'variation_A_name': variation_a,\n", - " 'variation_B_name': variation_b,\n", - " 'variation_A_value': mean_a,\n", - " 'variation_B_value': mean_b,\n", - " 'absolute_difference': abs_diff,\n", - " 'relative_difference': rel_diff,\n", - " 'statistic': t_stat,\n", - " 'p_value': p_value,\n", - " 'is_significant_95': is_significant\n", - " }\n", - "\n", - "# Function to run T-tests for multiple revenue metrics and aggregate results into a DataFrame\n", - "def run_t_tests(df, t_stat_metric_definition, variations):\n", - " results = []\n", - " \n", - " # Loop over all metrics in t_stat_metric_definition\n", - " for metric_name, metric_definition in t_stat_metric_definition.items():\n", - " metric_avg_column = metric_definition['metric_avg_column']\n", - " metric_sdv_column = metric_definition['metric_sdv_column']\n", - " total_counts = metric_definition['total_counts']\n", - " \n", - " # Run the T-test for each metric\n", - " result = calculate_t_test(df, metric_name, variation_a=variations[0], variation_b=variations[1], \n", - " metric_avg_column=metric_avg_column, metric_sdv_column=metric_sdv_column, \n", - " total_counts=total_counts)\n", - " results.append(result)\n", - " \n", - " # Create a DataFrame from the results\n", - " results_df = pd.DataFrame(results)\n", - " \n", - " return results_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Specify the metric definition for Z-stat and T-stat tests" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the Z-test metric definitions (with both success_counts and total_counts)\n", - "z_stat_metric_definition = {\n", - " 'conversion_rate': {\n", - " 'success_counts': 'guest_journey_completed_count',\n", - " 'total_counts': 'guest_journeys_count'\n", - " },\n", - " 'payment_rate': {\n", - " 'success_counts': 'guest_journey_with_payment_count',\n", - " 'total_counts': 'guest_journeys_count'\n", - " },\n", - " 'waiver_payment_rate': {\n", - " 'success_counts': 'waiver_count',\n", - " 'total_counts': 'guest_journeys_count'\n", - " },\n", - " 'deposit_payment_rate': {\n", - " 'success_counts': 'deposit_count',\n", - " 'total_counts': 'guest_journeys_count'\n", - " },\n", - " 'CIH_payment_rate': {\n", - " 'success_counts': 'check_in_cover_count',\n", - " 'total_counts': 'guest_journeys_count'\n", - " }\n", - "}\n", - "\n", - "# Define the T-test metric definitions (with both metric_avg_column and metric_sdv_column)\n", - "t_stat_metric_definition = {\n", - " 'avg_guest_revenue_per_gj': {\n", - " 'metric_avg_column': 'guest_revenue_avg_per_guest_journey',\n", - " 'metric_sdv_column': 'guest_revenue_sdv_per_guest_journey',\n", - " 'total_counts': 'guest_journeys_count'\n", - " },\n", - " 'avg_waiver_revenue_per_gj': {\n", - " 'metric_avg_column': 'waiver_avg_per_guest_journey',\n", - " 'metric_sdv_column': 'waiver_sdv_per_guest_journey',\n", - " 'total_counts': 'guest_journeys_count'\n", - " },\n", - " 'avg_deposit_revenue_per_gj': {\n", - " 'metric_avg_column': 'deposit_avg_per_guest_journey',\n", - " 'metric_sdv_column': 'deposit_sdv_per_guest_journey',\n", - " 'total_counts': 'guest_journeys_count'\n", - " },\n", - " 'avg_CIH_revenue_per_gj': {\n", - " 'metric_avg_column': 'check_in_cover_avg_per_guest_journey',\n", - " 'metric_sdv_column': 'check_in_cover_sdv_per_guest_journey',\n", - " 'total_counts': 'guest_journeys_count'\n", - " },\n", - " 'avg_csat_per_gj_with_response': {\n", - " 'metric_avg_column': 'csat_avg_per_guest_journey_with_response',\n", - " 'metric_sdv_column': 'csat_sdv_per_guest_journey_with_response',\n", - " 'total_counts': 'guest_journey_with_responses_count'\n", - " }\n", - "\n", - "}\n", - "\n", - "# Define the metrics that will be the main ones for this A/B test:\n", - "main_metrics = ['avg_guest_revenue_per_gj', 'conversion_rate', 'payment_rate']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the computation of the metrics and statistical significance" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " metric relative_difference p_value\n", - "0 conversion_rate 0.034395 0.002133\n", - "1 payment_rate 0.038553 0.104369\n", - "2 waiver_payment_rate 0.033479 0.235023\n", - "3 deposit_payment_rate 0.031911 0.643535\n", - "4 CIH_payment_rate 0.272450 0.072652\n", - "5 avg_guest_revenue_per_gj 0.003894 0.893715\n", - "6 avg_waiver_revenue_per_gj -0.000556 0.985831\n", - "7 avg_deposit_revenue_per_gj 0.007118 0.925383\n", - "8 avg_CIH_revenue_per_gj 0.272689 0.103764\n", - "9 avg_csat_per_gj_with_response 0.001965 0.850990\n" - ] - } - ], - "source": [ - "# Call the function to calculate the Z-test for each metric and aggregate the results\n", - "z_test_results_df = run_z_tests(df, z_stat_metric_definition=z_stat_metric_definition, variations=variations)\n", - "\n", - "# Call the function to calculate the T-test for each metric and aggregate the results\n", - "t_test_results_df = run_t_tests(df, t_stat_metric_definition=t_stat_metric_definition, variations=variations)\n", - "\n", - "# Add a new column to identify whether it's from Z-test or T-test\n", - "z_test_results_df['test_type'] = 'Z-test'\n", - "t_test_results_df['test_type'] = 'T-test'\n", - "\n", - "# Combine the dataframes after adding the 'test_type' column\n", - "combined_results_df = pd.concat([z_test_results_df, t_test_results_df], ignore_index=True)\n", - "\n", - "# Print the main aggregated DataFrame\n", - "print(combined_results_df[['metric','relative_difference','p_value']])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "YourTripQuestionaire results (last updated at 2025-07-01)\n", - "\n", - "Total Guest Journeys affected by this A/B test: 26895 - Total Guest Revenue: 274647 GBP.\n", - " Variation DontShowYourTripQuestions: Guest Journeys 24169 (89.9%) - Guest Revenue: 246712 GBP (89.8%).\n", - " Variation ShowYourTripQuestions: Guest Journeys 2726 (10.1%) - Guest Revenue: 27934 GBP (10.2%).\n", - "\n", - "Main Metrics - Comparing ShowYourTripQuestions vs. DontShowYourTripQuestions.\n", - "\n", - "CONVERSION RATE - SIGNIFICANT RESULT: 79.0% vs. 76.4% (2.6% ppts.| 3.4%).\n", - "PAYMENT RATE (not significant): 43.7% vs. 42.0% (1.6% ppts.| 3.9%).\n", - "AVG GUEST REVENUE PER GJ (not significant): 10.25 vs. 10.21 (0.04 ppts.| 0.4%).\n", - "\n", - "Other Metrics\n", - "\n", - "WAIVER PAYMENT RATE (not significant): 35.1% vs. 34.0% (1.1% ppts.| 3.3%).\n", - "DEPOSIT PAYMENT RATE (not significant): 8.2% vs. 7.9% (0.3% ppts.| 3.2%).\n", - "CIH PAYMENT RATE (not significant): 2.3% vs. 1.8% (0.5% ppts.| 27.2%).\n", - "AVG WAIVER REVENUE PER GJ (not significant): 9.52 vs. 9.52 (-0.01 ppts.| -0.1%).\n", - "AVG DEPOSIT REVENUE PER GJ (not significant): 0.54 vs. 0.53 (0.0 ppts.| 0.7%).\n", - "AVG CIH REVENUE PER GJ (not significant): 0.19 vs. 0.15 (0.04 ppts.| 27.3%).\n", - "AVG CSAT PER GJ WITH RESPONSE (not significant): 3.76 vs. 3.76 (0.01 ppts.| 0.2%).\n" - ] - } - ], - "source": [ - "print('\\n{} results (last updated at {})\\n'.format(ab_test_name, last_update))\n", - "\n", - "# Get main volume indicators per variation\n", - "grouped_data = df.groupby('variation')[[\"guest_journeys_count\",\"guest_revenue_sum\"]].sum()\n", - "\n", - "# Find the totals over any variation\n", - "total_count = grouped_data.sum()\n", - "\n", - "# Print overall indicators for volumes\n", - "print('Total Guest Journeys affected by this A/B test: {} - Total Guest Revenue: {} GBP.'.format(int(total_count.loc[\"guest_journeys_count\"]), \n", - " int(total_count.loc[\"guest_revenue_sum\"])))\n", - "for var in variations:\n", - " print(' Variation {}: Guest Journeys {} ({}%) - Guest Revenue: {} GBP ({}%).'.format(\n", - " var, \n", - " int(grouped_data.loc[var,'guest_journeys_count']), \n", - " round(100*(grouped_data.loc[var,'guest_journeys_count']/total_count.loc[\"guest_journeys_count\"]),1),\n", - " int(grouped_data.loc[var,'guest_revenue_sum']),\n", - " round(100*(grouped_data.loc[var,'guest_revenue_sum']/total_count.loc[\"guest_revenue_sum\"]),1)\n", - " ))\n", - "\n", - "# Split results whether the metrics are main metrics or not\n", - "main_metrics_rows = combined_results_df[combined_results_df['metric'].isin(main_metrics)]\n", - "other_metrics_rows = combined_results_df[~combined_results_df['metric'].isin(main_metrics)]\n", - "\n", - "def print_metrics(df, header=None):\n", - " if header:\n", - " print(f'\\n{header}\\n')\n", - "\n", - " for row in df.iterrows():\n", - " metric = row[1]['metric'].upper().replace('_', ' ')\n", - " if row[1]['test_type'] == 'Z-test':\n", - " value_a = str(round(100 * row[1]['variation_A_value'], 1)) + '%'\n", - " value_b = str(round(100 * row[1]['variation_B_value'], 1)) + '%'\n", - " abs_diff = str(round(100 * row[1]['absolute_difference'], 1)) + '%'\n", - " else:\n", - " value_a = str(round(row[1]['variation_A_value'], 2))\n", - " value_b = str(round(row[1]['variation_B_value'], 2))\n", - " abs_diff = str(round(row[1]['absolute_difference'], 2))\n", - " rel_diff = str(round(100 * row[1]['relative_difference'], 1)) + '%'\n", - " stat_sign = row[1]['is_significant_95']\n", - "\n", - " if stat_sign:\n", - " print(f\"{metric} - SIGNIFICANT RESULT: {value_b} vs. {value_a} ({abs_diff} ppts.| {rel_diff}).\")\n", - " else:\n", - " print(f\"{metric} (not significant): {value_b} vs. {value_a} ({abs_diff} ppts.| {rel_diff}).\")\n", - "\n", - "# Print main metrics\n", - "print_metrics(main_metrics_rows, header=\"Main Metrics - Comparing {} vs. {}.\".format(var_B, var_A))\n", - "\n", - "# Print other metrics\n", - "print_metrics(other_metrics_rows, header=\"Other Metrics\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data_driven_risk_assessment/experiments/001_basic_booking_attributes.ipynb b/data_driven_risk_assessment/experiments/001_basic_booking_attributes.ipynb deleted file mode 100644 index 8b85060..0000000 --- a/data_driven_risk_assessment/experiments/001_basic_booking_attributes.ipynb +++ /dev/null @@ -1,1438 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "84dcd475", - "metadata": {}, - "source": [ - "# DDRA - 001 - Basic Booking Attributes\n", - "\n", - "## General Idea\n", - "The idea is to start with a very simple model with basic Booking attributes. This should serve as a first understanding of what can bring value in the data-driven risk assessment of new dash protected bookings.\n", - "\n", - "## Initial setup\n", - "This first section just ensures that the connection to DWH works correctly." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "12368ce1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "šŸ”Œ Testing connection using credentials at: /home/uri/.superhog-dwh/credentials.yml\n", - "āœ… Connection successful.\n" - ] - } - ], - "source": [ - "# This script connects to a Data Warehouse (DWH) using PostgreSQL. \n", - "# This should be common for all Notebooks, but you might need to adjust the path to the `dwh_utils` module.\n", - "\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.abspath(\"../../utils\")) # Adjust path if needed\n", - "\n", - "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n", - "\n", - "# --- Connect to DWH ---\n", - "creds = read_credentials()\n", - "dwh_pg_engine = create_postgres_engine(creds)\n", - "\n", - "# --- Test Query ---\n", - "test_connection()" - ] - }, - { - "cell_type": "markdown", - "id": "c86f94f1", - "metadata": {}, - "source": [ - "## Data Extraction\n", - "In this section we extract the data for our first attempt on Basic Booking Attributes modelling.\n", - "\n", - "This SQL query retrieves a clean and relevant subset of booking data for our model. It includes:\n", - "- A **unique booking ID**\n", - "- Key **numeric features** such as number of services, time between booking creation and check-in, and number of nights\n", - "- Several **categorical (boolean) features** related to service usage\n", - "- A **target variable** (`has_resolution_incident`) indicating whether a resolution incident occurred\n", - "\n", - "Filters applied being:\n", - "1. Bookings from **\"New Dash\" users** with a valid deal ID\n", - "2. Only **protected bookings**, i.e., those with Protection or Deposit Management services\n", - "3. Bookings flagged for **risk categorisation** (excluding incomplete/rejected ones)\n", - "4. Bookings that are **already completed**\n", - "\n", - "The result is converted into a pandas DataFrame for further processing and modeling.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3e3ed391", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialise all imports needed for the Notebook\n", - "from sklearn.model_selection import (\n", - " train_test_split, \n", - " GridSearchCV\n", - ")\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import StandardScaler\n", - "import pandas as pd\n", - "import numpy as np\n", - "from datetime import date\n", - "from sklearn.metrics import (\n", - " roc_auc_score, \n", - " average_precision_score,\n", - " classification_report,\n", - " roc_curve, \n", - " auc,\n", - " precision_recall_curve\n", - ")\n", - "import matplotlib.pyplot as plt\n", - "import shap" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "db5e3098", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " id_booking number_of_applied_services \\\n", - "0 919656 3 \n", - "1 926634 3 \n", - "2 931082 2 \n", - "3 931086 2 \n", - "4 931096 2 \n", - "\n", - " number_of_applied_upgraded_services number_of_applied_billable_services \\\n", - "0 2 2 \n", - "1 2 2 \n", - "2 1 1 \n", - "3 1 1 \n", - "4 1 1 \n", - "\n", - " booking_days_to_check_in booking_number_of_nights \\\n", - "0 87 4 \n", - "1 109 3 \n", - "2 50 7 \n", - "3 15 3 \n", - "4 8 5 \n", - "\n", - " has_verification_request has_billable_services \\\n", - "0 False True \n", - "1 False True \n", - "2 False True \n", - "3 False True \n", - "4 False True \n", - "\n", - " has_upgraded_screening_service_business_type \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_deposit_management_service_business_type \\\n", - "0 True \n", - "1 True \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_protection_service_business_type has_resolution_incident \n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "Total Bookings: 16,193\n" - ] - } - ], - "source": [ - "# Query to extract data\n", - "data_extraction_query = \"\"\"\n", - "select \n", - " -- Unique ID --\n", - " ibs.id_booking,\n", - " -- Numeric Features --\n", - " ibs.number_of_applied_services,\n", - " ibs.number_of_applied_upgraded_services,\n", - " ibs.number_of_applied_billable_services,\n", - " ibs.booking_check_in_date_utc - booking_created_date_utc as booking_days_to_check_in,\n", - " ibs.booking_number_of_nights,\n", - " -- Categorical (Boolean) Features --\n", - " ibs.has_verification_request,\n", - " ibs.has_billable_services,\n", - " ibs.has_upgraded_screening_service_business_type,\n", - " ibs.has_deposit_management_service_business_type,\n", - " ibs.has_protection_service_business_type,\n", - " -- Target (Boolean) --\n", - " ibs.has_resolution_incident\n", - "from intermediate.int_booking_summary ibs\n", - "where \n", - " -- 1. Bookings from New Dash users with Id Deal\n", - " ibs.is_user_in_new_dash = True and \n", - " ibs.is_missing_id_deal = False and\n", - " -- 2. Protected Bookings with a Protection or a Deposit Management service\n", - " (ibs.has_protection_service_business_type or \n", - " ibs.has_deposit_management_service_business_type) and\n", - " -- 3. Bookings with flagging categorisation (this excludes Cancelled/Incomplete/Rejected bookings)\n", - " ibs.is_booking_flagged_as_risk is not null and \n", - " -- 4. Booking is completed\n", - " ibs.is_booking_past_completion_date = True \n", - "\"\"\"\n", - "\n", - "# Retrieve Data from Query\n", - "df_extraction = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n", - "print(df_extraction.head())\n", - "print(f\"Total Bookings: {len(df_extraction):,}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Processing\n", - "Processing in this notebook is quite straight-forward: we just drop id booking, split the features and target and apply a scaling to numeric features.\n", - "Afterwards, we split the dataset between train and test and display their sizes and target distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training set size: 11335 rows\n", - "Test set size: 4858 rows\n", - "\n", - "Training target distribution:\n", - "has_resolution_incident\n", - "False 0.988619\n", - "True 0.011381\n", - "Name: proportion, dtype: float64\n", - "\n", - "Test target distribution:\n", - "has_resolution_incident\n", - "False 0.988473\n", - "True 0.011527\n", - "Name: proportion, dtype: float64\n" - ] - } - ], - "source": [ - "# Drop ID column\n", - "df = df_extraction.copy().drop(columns=['id_booking'])\n", - "\n", - "# Separate features and target\n", - "X = df.drop(columns=['has_resolution_incident'])\n", - "y = df['has_resolution_incident']\n", - "\n", - "# Scale numeric features\n", - "numeric_features = ['number_of_applied_services', \n", - " 'booking_number_of_nights', \n", - " 'number_of_applied_upgraded_services',\n", - " 'number_of_applied_billable_services',\n", - " 'booking_days_to_check_in']\n", - "X[numeric_features] = X[numeric_features].astype(float)\n", - "\n", - "# Split the data\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.3, random_state=123)\n", - "\n", - "print(f\"Training set size: {X_train.shape[0]} rows\")\n", - "print(f\"Test set size: {X_test.shape[0]} rows\")\n", - "\n", - "print(\"\\nTraining target distribution:\")\n", - "print(y_train.value_counts(normalize=True))\n", - "\n", - "print(\"\\nTest target distribution:\")\n", - "print(y_test.value_counts(normalize=True))" - ] - }, - { - "cell_type": "markdown", - "id": "d36c9276", - "metadata": {}, - "source": [ - "## Classification Model with Random Forest\n", - "\n", - "We define a machine learning pipeline that includes:\n", - "- **Scaling numeric features** with `StandardScaler`\n", - "- **Training a Random Forest classifier** with balanced class weights to handle the imbalanced dataset\n", - "\n", - "We then use `GridSearchCV` to perform a **grid search with cross-validation** over a range of key hyperparameters (e.g., number of trees, max depth, etc.). \n", - "The model is evaluated using **Average Precision**, which is better suited for imbalanced classification tasks.\n", - "\n", - "The best combination of parameters is selected, and the resulting model is used to make predictions on the test set.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "943ef7d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; 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total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.3s\n", - "Best hyperparameters: {'model__max_depth': 10, 'model__max_features': 'sqrt', 'model__min_samples_leaf': 2, 'model__min_samples_split': 2, 'model__n_estimators': 100}\n" - ] - } - ], - "source": [ - "\n", - "# Define pipeline (scaling numeric features only)\n", - "pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', RandomForestClassifier(class_weight='balanced', # We have an imbalanced dataset\n", - " random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid\n", - "param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "grid_search = GridSearchCV(\n", - " estimator=pipeline,\n", - " param_grid=param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=5, # 5-fold cross-validation\n", - " n_jobs=-1, # Use all available cores\n", - " verbose=2 # Verbose output for progress tracking\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "grid_search.fit(X_train, y_train)\n", - "\n", - "# Best model\n", - "best_pipeline = grid_search.best_estimator_\n", - "print(\"Best hyperparameters:\", grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba = best_pipeline.predict_proba(X_test)[:, 1]\n", - "y_pred = best_pipeline.predict(X_test)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean_fit_timestd_fit_timemean_score_timestd_score_timeparam_model__max_depthparam_model__max_featuresparam_model__min_samples_leafparam_model__min_samples_splitparam_model__n_estimatorsparamssplit0_test_scoresplit1_test_scoresplit2_test_scoresplit3_test_scoresplit4_test_scoremean_test_scorestd_test_scorerank_test_score
421.1916640.0608650.0602390.00391310log222100{'model__max_depth': 10, 'model__max_features'...0.0354310.0239020.0194520.0225380.0263370.0255320.0054261
301.2953140.2959650.0717690.01918510sqrt22100{'model__max_depth': 10, 'model__max_features'...0.0354310.0239020.0194520.0225380.0263370.0255320.0054261
312.3181250.1018940.1052940.00927310sqrt22200{'model__max_depth': 10, 'model__max_features'...0.0376340.0214050.0188780.0223860.0256250.0251860.0065893
432.5130330.1613500.1202590.02084110log222200{'model__max_depth': 10, 'model__max_features'...0.0376340.0214050.0188780.0223860.0256250.0251860.0065893
443.8620080.3697370.1707430.02973410log222300{'model__max_depth': 10, 'model__max_features'...0.0345150.0215610.0190280.0236100.0247280.0246880.0052835
.........................................................
144.7050511.0095300.2632260.106331Nonelog212300{'model__max_depth': None, 'model__max_feature...0.0287400.0150510.0152440.0180430.0129870.0180130.00559967
132.7781920.1753400.1217700.012860Nonelog212200{'model__max_depth': None, 'model__max_feature...0.0305430.0134190.0145270.0164480.0128570.0175590.00660769
13.2948910.4855180.1340530.017547Nonesqrt12200{'model__max_depth': None, 'model__max_feature...0.0305430.0134190.0145270.0164480.0128570.0175590.00660769
01.3166590.1086680.0640570.006920Nonesqrt12100{'model__max_depth': None, 'model__max_feature...0.0263170.0144950.0138190.0148430.0126230.0164190.00500771
121.4976230.3851280.0838250.028476Nonelog212100{'model__max_depth': None, 'model__max_feature...0.0263170.0144950.0138190.0148430.0126230.0164190.00500771
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" - ], - "text/plain": [ - " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", - "42 1.191664 0.060865 0.060239 0.003913 \n", - "30 1.295314 0.295965 0.071769 0.019185 \n", - "31 2.318125 0.101894 0.105294 0.009273 \n", - "43 2.513033 0.161350 0.120259 0.020841 \n", - "44 3.862008 0.369737 0.170743 0.029734 \n", - ".. ... ... ... ... \n", - "14 4.705051 1.009530 0.263226 0.106331 \n", - "13 2.778192 0.175340 0.121770 0.012860 \n", - "1 3.294891 0.485518 0.134053 0.017547 \n", - "0 1.316659 0.108668 0.064057 0.006920 \n", - "12 1.497623 0.385128 0.083825 0.028476 \n", - "\n", - " param_model__max_depth param_model__max_features \\\n", - "42 10 log2 \n", - "30 10 sqrt \n", - "31 10 sqrt \n", - "43 10 log2 \n", - "44 10 log2 \n", - ".. ... ... \n", - "14 None log2 \n", - "13 None log2 \n", - "1 None sqrt \n", - "0 None sqrt \n", - "12 None log2 \n", - "\n", - " param_model__min_samples_leaf param_model__min_samples_split \\\n", - "42 2 2 \n", - "30 2 2 \n", - "31 2 2 \n", - "43 2 2 \n", - "44 2 2 \n", - ".. ... ... \n", - "14 1 2 \n", - "13 1 2 \n", - "1 1 2 \n", - "0 1 2 \n", - "12 1 2 \n", - "\n", - " param_model__n_estimators \\\n", - "42 100 \n", - "30 100 \n", - "31 200 \n", - "43 200 \n", - "44 300 \n", - ".. ... \n", - "14 300 \n", - "13 200 \n", - "1 200 \n", - "0 100 \n", - "12 100 \n", - "\n", - " params split0_test_score \\\n", - "42 {'model__max_depth': 10, 'model__max_features'... 0.035431 \n", - "30 {'model__max_depth': 10, 'model__max_features'... 0.035431 \n", - "31 {'model__max_depth': 10, 'model__max_features'... 0.037634 \n", - "43 {'model__max_depth': 10, 'model__max_features'... 0.037634 \n", - "44 {'model__max_depth': 10, 'model__max_features'... 0.034515 \n", - ".. ... ... \n", - "14 {'model__max_depth': None, 'model__max_feature... 0.028740 \n", - "13 {'model__max_depth': None, 'model__max_feature... 0.030543 \n", - "1 {'model__max_depth': None, 'model__max_feature... 0.030543 \n", - "0 {'model__max_depth': None, 'model__max_feature... 0.026317 \n", - "12 {'model__max_depth': None, 'model__max_feature... 0.026317 \n", - "\n", - " split1_test_score split2_test_score split3_test_score \\\n", - "42 0.023902 0.019452 0.022538 \n", - "30 0.023902 0.019452 0.022538 \n", - "31 0.021405 0.018878 0.022386 \n", - "43 0.021405 0.018878 0.022386 \n", - "44 0.021561 0.019028 0.023610 \n", - ".. ... ... ... \n", - "14 0.015051 0.015244 0.018043 \n", - "13 0.013419 0.014527 0.016448 \n", - "1 0.013419 0.014527 0.016448 \n", - "0 0.014495 0.013819 0.014843 \n", - "12 0.014495 0.013819 0.014843 \n", - "\n", - " split4_test_score mean_test_score std_test_score rank_test_score \n", - "42 0.026337 0.025532 0.005426 1 \n", - "30 0.026337 0.025532 0.005426 1 \n", - "31 0.025625 0.025186 0.006589 3 \n", - "43 0.025625 0.025186 0.006589 3 \n", - "44 0.024728 0.024688 0.005283 5 \n", - ".. ... ... ... ... \n", - "14 0.012987 0.018013 0.005599 67 \n", - "13 0.012857 0.017559 0.006607 69 \n", - "1 0.012857 0.017559 0.006607 69 \n", - "0 0.012623 0.016419 0.005007 71 \n", - "12 0.012623 0.016419 0.005007 71 \n", - "\n", - "[72 rows x 18 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Retrieve cv results\n", - "pd.DataFrame(grid_search.cv_results_).sort_values(by='mean_test_score', ascending=False)" - ] - }, - { - "cell_type": "markdown", - "id": "fc2fcc89", - "metadata": {}, - "source": [ - "## Evaluation\n", - "This section aims to evaluate how good the new model is vs. the actual Resolution Incidents.\n", - "\n", - "We start by computing and displaying the classification report, ROC Curve, PR Curve and the respective Area Under the Curve (AUC)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "30786f7c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " False 0.99 0.92 0.95 4802\n", - " True 0.02 0.16 0.04 56\n", - "\n", - " accuracy 0.91 4858\n", - " macro avg 0.51 0.54 0.49 4858\n", - "weighted avg 0.98 0.91 0.94 4858\n", - "\n" - ] - } - ], - "source": [ - "# Print classification report\n", - "print(classification_report(y_test, y_pred))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Classification Report\n", - "\n", - "The **Classification Report** provides key metrics to evaluate how well the model performed on each class.\n", - "\n", - "It includes the following metrics for each class (0 and 1):\n", - "* Metric: Meaning\n", - "* Precision: Out of all predicted positives, how many were actually positive?\n", - "* Recall: Out of all actual positives, how many did we correctly identify?\n", - "* F1-score: Harmonic mean of precision and recall (balances both)\n", - "* Support: Number of true samples of that class in the test data\n", - "\n", - "Interpretation:\n", - "* Class 0 = No incident\n", - "* Class 1 = Has resolution incident (rare, but important!)\n", - "\n", - "A few explanatory cases:\n", - "* A high recall for class 1 means we're catching most incidents.\n", - "* A high precision for class 1 means when we predict an incident, we're often correct.\n", - "* The F1-score gives a single balanced measure (good for imbalanced data).\n", - "\n", - "Special note for imbalanced data:\n", - "Since class 1 (or just True) is rare (1% in our case), metrics for that class are more critical.\n", - "We want to maximize recall to catch as many real incidents as possible ā€” without letting precision drop too low (to avoid too many false alarms)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4b4da914", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC Curve\n", - "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')\n", - "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC Curve')\n", - "plt.legend(loc='lower right')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the ROC Curve\n", - "\n", - "The **Receiver Operating Characteristic (ROC) curve** shows how well the model distinguishes between the positive and negative classes across all decision thresholds.\n", - "\n", - "A quick reminder of the definitions:\n", - "* True Positive Rate (TPR) = Recall\n", - "* False Positive Rate (FPR) = Proportion of negatives wrongly classified as positives\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is False Positive Rate\n", - "* The y-axis is True Positive Rate\n", - "\n", - "The curve shows how TPR and FPR change as the threshold varies\n", - "\n", - "It's important to note that:\n", - "* A model with no skill will produce a diagonal line (AUC = 0.5)\n", - "* A model with perfect discrimination will hug the top-left corner (AUC = 1.0)\n", - "\n", - "The Area Under the Curve (ROC AUC) gives a single performance score:\n", - "* Closer to 1 means better at ranking positive cases higher than negative ones\n", - "\n", - "**Important!**\n", - "\n", - "While useful, the ROC curve can sometimes overestimate performance when the dataset is imbalanced, because it includes negatives (which dominate in our case, around 99%!). Thatā€™s why we also MUST check the Precision-Recall curve." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6790d41d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# PR Curve\n", - "precision, recall, _ = precision_recall_curve(y_test, y_pred_proba)\n", - "pr_auc = average_precision_score(y_test, y_pred_proba)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(recall, precision, color='green', lw=2, label=f'PR curve (AUC = {pr_auc:.4f})')\n", - "plt.xlabel('Recall')\n", - "plt.ylabel('Precision')\n", - "plt.title('Precision-Recall Curve')\n", - "plt.legend(loc='lower left')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Precision-Recall (PR) Curve\n", - "\n", - "The **Precision-Recall (PR) curve** helps evaluate model performance, especially on imbalanced datasets like ours (where positive cases are rare).\n", - "\n", - "A quick reminder of the definitions:\n", - "* Precision = How many of the predicted positives are actually positive\n", - "* Recall = How many of the actual positives the model correctly identifies\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is Recall \n", - "* The y-axis is Precision \n", - "\n", - "The curve shows the trade-off between them at different model thresholds\n", - "\n", - "In imbalanced datasets, accuracy can be misleading ā€” the PR curve focuses only on the positive class, making it much more meaningful:\n", - "* A higher curve means better performance\n", - "* The area under the curve (PR AUC) summarizes this: closer to 1 is better" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Importance\n", - "Understanding what drives the prediction is useful for future experiments and business knowledge. Here we track both the native feature importances of the trees, as well as a more heavy SHAP values analysis.\n", - "\n", - "Important! Be aware that SHAP analysis might take quite a bit of time." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d66ffe2c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## BUILT-IN\n", - "\n", - "# Get feature importances from the model\n", - "importances = best_pipeline.named_steps['model'].feature_importances_\n", - "features = X.columns\n", - "\n", - "# Create a Series and sort\n", - "feat_series = pd.Series(importances, index=features).sort_values(ascending=True) # ascending=True for horizontal plot\n", - "\n", - "# Plot Feature Importances\n", - "plt.figure(figsize=(8, 5))\n", - "feat_series.plot(kind='barh', color='skyblue')\n", - "plt.title('Feature Importances')\n", - "plt.xlabel('Importance')\n", - "plt.grid(axis='x')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Feature Importance Plot\n", - "The **feature importance plot** shows how much each feature contributes to the modelā€™s overall decision-making.\n", - "\n", - "For tree-based models like Random Forest, importance is based on how often and how effectively a feature is used to split the data across all trees.\n", - "A higher score means the feature plays a bigger role in improving prediction accuracy.\n", - "\n", - "In the graph you will see that:\n", - "* Features are ranked from most to least important.\n", - "* The values are relative and model-specific ā€” not directly interpretable as weights or probabilities.\n", - "\n", - "This helps us identify which features the model relies on most when making predictions.\n", - "\n", - "**Important!**\n", - "Unlike SHAP values, native importance doesn't show how a feature affects predictions ā€” only how useful it is to the model overall. For deeper interpretability (e.g., direction and context), SHAP is better (but it takes more time to run)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e2197cea", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ExactExplainer explainer: 4859it [09:15, 8.73it/s] \n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## SHAP VALUES\n", - "\n", - "# SHAP requires that all features passed to Explainer be numeric (floats/ints)\n", - "X_test_shap = X_test.copy()\n", - "X_test_shap = X_test_shap.astype(float)\n", - "\n", - "# Function that returns the probability of the positive class\n", - "def model_predict(data):\n", - " return best_pipeline.predict_proba(data)[:, 1]\n", - "\n", - "# Ensure input to SHAP is numeric\n", - "X_test_shap = X_test.astype(float)\n", - "\n", - "# Create SHAP explainer\n", - "explainer = shap.Explainer(model_predict, X_test_shap)\n", - "\n", - "# Compute SHAP values\n", - "shap_values = explainer(X_test_shap)\n", - "\n", - "# Plot summary\n", - "shap.summary_plot(shap_values.values, X_test_shap)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the SHAP Summary Plot\n", - "\n", - "Each point on a row represents a SHAP value for a single prediction (row = feature).\n", - "The x-axis shows how much the feature contributed to increasing or decreasing the prediction.\n", - "* Right (positive SHAP value): pushes prediction toward the positive class (i.e., higher chance of incident).\n", - "* Left (negative SHAP value): pushes prediction toward the negative class (i.e., lower chance of incident).\n", - "\n", - "Color shows the actual feature value for that point:\n", - "* Red = high value\n", - "* Blue = low value\n", - "\n", - "In other words:\n", - "* The position tells you impact.\n", - "* The color tells you feature value.\n", - "* The density (thickness) of dots shows how often a value occurs." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/data_driven_risk_assessment/experiments/002_contactless_full_attributes.ipynb b/data_driven_risk_assessment/experiments/002_contactless_full_attributes.ipynb deleted file mode 100644 index e494402..0000000 --- a/data_driven_risk_assessment/experiments/002_contactless_full_attributes.ipynb +++ /dev/null @@ -1,2319 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "84dcd475", - "metadata": {}, - "source": [ - "# DDRA - Contactless (Full)\n", - "\n", - "## General Idea\n", - "The idea is to play only with numeric features (floats, integers or booleans) that are CONTACTLESS.\n", - "\n", - "This considers the FULL set of features.\n", - "\n", - "A more readable EDA is available in Notion here: [EDA Uri: Contactless](https://www.notion.so/truvi/EDA-Uri-Contactless-2170446ff9c980909624d45a6c124ec2)\n", - "\n", - "## Initial setup\n", - "This first section just ensures that the connection to DWH works correctly." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "12368ce1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "šŸ”Œ Testing connection using credentials at: /home/uri/.superhog-dwh/credentials.yml\n", - "āœ… Connection successful.\n" - ] - } - ], - "source": [ - "# This script connects to a Data Warehouse (DWH) using PostgreSQL. \n", - "# This should be common for all Notebooks, but you might need to adjust the path to the `dwh_utils` module.\n", - "\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.abspath(\"../../utils\")) # Adjust path if needed\n", - "\n", - "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n", - "\n", - "# --- Connect to DWH ---\n", - "creds = read_credentials()\n", - "dwh_pg_engine = create_postgres_engine(creds)\n", - "\n", - "# --- Test Query ---\n", - "test_connection()" - ] - }, - { - "cell_type": "markdown", - "id": "c86f94f1", - "metadata": {}, - "source": [ - "## Data Extraction\n", - "In this section we extract the data.\n", - "\n", - "This SQL query retrieves a clean and relevant subset of booking data for our model. It includes:\n", - "- A **unique booking ID**\n", - "- Key **numeric features** such as number of services, time between booking creation and check-in, number of nights, etc.\n", - "- Several **categorical (boolean) features** related to service usage\n", - "- A **target variable** (`has_resolution_incident`) indicating whether a resolution incident occurred\n", - "\n", - "Filters applied being:\n", - "1. Bookings from **\"New Dash\" users** with a valid deal ID\n", - "2. Only **protected bookings**, i.e., those with Protection or Deposit Management services\n", - "3. Bookings flagged for **risk categorisation** (excluding incomplete/rejected ones)\n", - "4. Bookings that are **already completed**\n", - "\n", - "The result is converted into a pandas DataFrame for further processing and modeling.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "3e3ed391", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialise all imports needed for the Notebook\n", - "from sklearn.model_selection import (\n", - " train_test_split, \n", - " GridSearchCV\n", - ")\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import StandardScaler\n", - "import pandas as pd\n", - "import numpy as np\n", - "from datetime import date\n", - "from sklearn.metrics import (\n", - " roc_auc_score, \n", - " average_precision_score,\n", - " classification_report,\n", - " roc_curve, \n", - " auc,\n", - " precision_recall_curve,\n", - " precision_score,\n", - " recall_score,\n", - " fbeta_score,\n", - " confusion_matrix\n", - ")\n", - "import matplotlib.pyplot as plt\n", - "import shap\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "db5e3098", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Bookings: 21,384\n" - ] - } - ], - "source": [ - "# Query to extract data\n", - "data_extraction_query = \"\"\"\n", - "WITH \n", - "service_information AS (\n", - "\tSELECT\n", - "\t\tid_booking,\n", - "\t\tcount(DISTINCT CASE WHEN service_business_type = 'SCREENING' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_screening_services,\n", - "\t\tcount(DISTINCT CASE WHEN service_business_type = 'DEPOSIT_MANAGEMENT' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_deposit_management_services,\n", - "\t\tcount(DISTINCT CASE WHEN service_business_type = 'PROTECTION' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_protection_services,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'WAIVER PRO' THEN id_booking ELSE NULL END)>0 AS has_waiver_pro,\n", - "\t\tcount(DISTINCT CASE WHEN service_name IN ('BASIC DAMAGE DEPOSIT','BASIC DAMAGE DEPOSIT OR BASIC WAIVER','BASIC DAMAGE DEPOSIT OR WAIVER PLUS','BASIC WAIVER','WAIVER PLUS') THEN id_booking ELSE NULL END)>0 AS has_guest_facing_waiver_or_deposit,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'GUEST AGREEMENT' THEN id_booking ELSE NULL END)>0 AS has_guest_agreement,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'BASIC PROTECTION' THEN id_booking ELSE NULL END)>0 AS has_basic_protection,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'PROTECTION PLUS' THEN id_booking ELSE NULL END)>0 AS has_protection_plus,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'PROTECTION PRO' THEN id_booking ELSE NULL END)>0 AS has_protection_pro,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'ID VERIFICATION' THEN id_booking ELSE NULL END)>0 AS has_id_verification,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'SCREENING PLUS' THEN id_booking ELSE NULL END)>0 AS has_screening_plus,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'SEX OFFENDER CHECK' THEN id_booking ELSE NULL END)>0 AS has_sex_offender_check\n", - "\tFROM\n", - "\t\tintermediate.int_core__booking_service_detail\n", - "\tGROUP BY\n", - "\t\t1\n", - "),\n", - "listing_information AS (\n", - "SELECT \n", - "\tica.id_accommodation,\n", - "\t-- Defaults to 0 if null\n", - "\tCOALESCE(ica.number_of_bedrooms, 0) AS listing_number_of_bedrooms,\n", - "\t-- Defaults to 0 if null\n", - "\tCOALESCE(ica.number_of_bathrooms, 0) AS listing_number_of_bathrooms\n", - "\tFROM intermediate.int_core__accommodation ica \n", - "),\n", - "raw_bookings_checked_in_prior_to_TCR AS (\n", - "\tSELECT\n", - "\t\tb.id_booking,\n", - "\t\t-- Using group by on check-in date to remove booking duplicates\n", - "\t\tb2.booking_check_in_date_utc,\n", - "\t\t-- Using min as a conservative approach to reduce outliers\n", - "\t\tmin(b2.booking_number_of_nights) AS min_booking_number_of_nights\n", - "\tFROM\n", - "\t\tintermediate.int_booking_summary b\n", - "\t-- Note that by joining with BS we're only considering New Dash bookings\n", - "\tLEFT JOIN intermediate.int_booking_summary b2\n", - " ON\n", - "\t\tb2.id_accommodation = b.id_accommodation\n", - "\t\t-- Exclusion based on actual booking creation!\n", - "\t\tAND b2.booking_check_in_date_utc >= b.booking_created_date_utc - INTERVAL '30 days'\n", - "\t\tAND b2.booking_check_in_date_utc < b.booking_created_date_utc\n", - "\t\t-- Note that since is based on TCR we can remove Cancelled\n", - "\t\tAND b2.booking_status NOT IN ('CANCELLED')\n", - "\tGROUP BY\n", - "\t\tb.id_booking,\n", - "\t\tb2.booking_check_in_date_utc\n", - "),\n", - "bookings_checked_in_prior_to_TCR AS (\n", - "\tSELECT\n", - "\t\tid_booking,\n", - "\t\tLEAST(\n", - "\t\t\tcount(booking_check_in_date_utc),\n", - "\t\t\t30\n", - "\t\t) AS listing_check_ins_prior_to_TCR_in_30_days,\n", - "\t\t-- Capping\n", - "\t\tLEAST(\n", - "\t\t\tGREATEST(\n", - "\t\t\t\tsum(min_booking_number_of_nights),\n", - "\t\t\t\t0\n", - "\t\t\t),\n", - "\t\t\t30\n", - "\t\t) AS listing_occupancy_prior_to_TCR_in_30_days\n", - "\tFROM\n", - "\t\traw_bookings_checked_in_prior_to_TCR\n", - "\tGROUP BY\n", - "\t\t1\n", - "),\n", - "raw_known_bookings_checking_in_prior_to_TCI AS (\n", - "\tSELECT\n", - "\t\tb.id_booking,\n", - "\t\tb.booking_check_in_date_utc,\n", - "\t\t-- Using group by on check-in date to remove booking duplicates\n", - "\t\tb2.booking_check_in_date_utc AS other_bookings_check_in_date_utc,\n", - "\t\t-- Using min as a conservative approach to reduce outliers\n", - "\t\tmin(b2.booking_number_of_nights) AS min_booking_number_of_nights\n", - "\tFROM\n", - "\t\tintermediate.int_booking_summary b\n", - "\t-- Note that by joining with BS we're only considering New Dash bookings\n", - "\tLEFT JOIN intermediate.int_booking_summary b2\n", - " ON\n", - "\t\tb2.id_accommodation = b.id_accommodation\n", - "\t\t-- Exclusion based on check-in\n", - "\t\tAND b2.booking_check_in_date_utc >= b.booking_check_in_date_utc - INTERVAL '30 days'\n", - "\t\tAND b2.booking_check_in_date_utc < b.booking_check_in_date_utc\n", - "\t\t-- that are known!\n", - "\t\tAND b2.booking_created_date_utc < b.booking_created_date_utc\n", - "\t\t-- Note that since is based on TCI we cannot remove Cancelled\n", - "\tGROUP BY\n", - "\t\tb.id_booking,\n", - "\t\tb.booking_check_in_date_utc,\n", - "\t\tb2.booking_check_in_date_utc\n", - "),\n", - "known_bookings_checking_in_prior_to_TCI AS (\n", - "\tSELECT\n", - "\t\tid_booking,\n", - "\t\tLEAST(\n", - "\t\t\tcount(other_bookings_check_in_date_utc),\n", - "\t\t\t30\n", - "\t\t) AS listing_known_check_ins_prior_to_TCI_in_30_days,\n", - "\t\t-- Capping\n", - "\t\tLEAST(\n", - "\t\t\tGREATEST(\n", - "\t\t\t\tsum(min_booking_number_of_nights),\n", - "\t\t\t\t0\n", - "\t\t\t),\n", - "\t\t\t30\n", - "\t\t) AS listing_known_occupancy_prior_to_TCI_in_30_days,\n", - "\t\tCOALESCE(\n", - "\t\t\tbooking_check_in_date_utc - max(other_bookings_check_in_date_utc),\n", - "\t\t\t30\n", - "\t\t) AS lead_time_between_prior_known_check_in_to_TCI_30_days\n", - "\tFROM\n", - "\t\traw_known_bookings_checking_in_prior_to_TCI\n", - "\tGROUP BY\n", - "\t\tid_booking, \n", - "\t\tbooking_check_in_date_utc\n", - "),\n", - "incidents_prior_to_TCP AS (\n", - "\tSELECT\n", - "\t\tb.id_booking,\n", - "\t\t-- Using distinct count on check-in date to remove booking duplicates\n", - "\t\tCOUNT(DISTINCT b2.booking_check_in_date_utc) AS listing_incidents_prior_to_TCP_in_30_days\n", - "\tFROM\n", - "\t\tintermediate.int_booking_summary b\n", - "\tLEFT JOIN intermediate.int_booking_summary b2\n", - " ON\n", - "\t\tb2.id_accommodation = b.id_accommodation\n", - "\t\t-- Filter on Check Out date\n", - "\t\tAND b2.booking_completed_date_utc >= b.booking_created_date_utc - INTERVAL '30 days'\n", - "\t\tAND b2.booking_completed_date_utc < b.booking_created_date_utc\n", - "\t\tAND b2.has_resolution_incident = TRUE\n", - "\tGROUP BY\n", - "\t\tb.id_booking\n", - ")\n", - "SELECT\n", - "\t-- UNIQUE BOOKING ID --\n", - "\tbooking_summary.id_booking,\n", - "\t\n", - "\t-- CONTEXTUAL SERVICE INFORMATION --\n", - "\t-- We're not including number_of_applied_services as it 1-correlates with upgraded services\n", - "\tbooking_summary.number_of_applied_upgraded_services,\n", - "\tbooking_summary.number_of_applied_billable_services,\n", - "\tservice_information.number_of_applied_screening_services,\n", - "\tservice_information.number_of_applied_deposit_management_services,\n", - "\tservice_information.number_of_applied_protection_services,\n", - "\tservice_information.has_waiver_pro,\n", - "\tservice_information.has_guest_facing_waiver_or_deposit,\n", - "\tservice_information.has_guest_agreement,\n", - "\tservice_information.has_basic_protection,\n", - "\tservice_information.has_protection_plus,\n", - "\tservice_information.has_protection_pro,\n", - "\tservice_information.has_id_verification,\n", - "\tservice_information.has_screening_plus,\n", - "\tservice_information.has_sex_offender_check,\n", - "\tNOT booking_summary.has_verification_request AS is_contactless_booking,\n", - "\t\n", - "\t-- CONTEXTUAL LISTING INFORMATION --\n", - "\tlisting_information.listing_number_of_bedrooms,\n", - "\tlisting_information.listing_number_of_bathrooms,\n", - "\t\n", - "\t-- CONTEXTUAL TIMELINE OF OUR BOOKING\n", - "\t-- Defaults to 0 if booking_created_date_utc > booking_check_in_date_utc\n", - "\tGREATEST(booking_summary.booking_check_in_date_utc - booking_summary.booking_created_date_utc, 0) AS booking_lead_time,\n", - "\tbooking_summary.booking_check_out_date_utc - booking_summary.booking_check_in_date_utc AS booking_duration,\n", - "\t\n", - "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: PRIOR TO TCR\n", - "\tbookings_checked_in_prior_to_TCR.listing_check_ins_prior_to_TCR_in_30_days,\n", - "\tbookings_checked_in_prior_to_TCR.listing_occupancy_prior_to_TCR_in_30_days,\n", - "\t\n", - "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: PRIOR TO TCI (KNOWN)\n", - "\tknown_bookings_checking_in_prior_to_TCI.listing_known_check_ins_prior_to_TCI_in_30_days,\n", - "\tknown_bookings_checking_in_prior_to_TCI.listing_known_occupancy_prior_to_TCI_in_30_days,\n", - "\tknown_bookings_checking_in_prior_to_TCI.lead_time_between_prior_known_check_in_to_TCI_30_days,\n", - "\t\n", - "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: INCIDENTAL BOOKINGS\n", - "\tincidents_prior_to_TCP.listing_incidents_prior_to_TCP_in_30_days,\n", - "\t\n", - "\t-- TARGET (BOOLEAN) --\n", - "\tbooking_summary.has_resolution_incident\n", - "\n", - "FROM\n", - "\tintermediate.int_booking_summary booking_summary\n", - "LEFT JOIN service_information \n", - "\tON\n", - "\tbooking_summary.id_booking = service_information.id_booking\n", - "LEFT JOIN listing_information \n", - "\tON booking_summary.id_accommodation = listing_information.id_accommodation\n", - "LEFT JOIN bookings_checked_in_prior_to_TCR\n", - "\tON booking_summary.id_booking = bookings_checked_in_prior_to_TCR.id_booking\n", - "LEFT JOIN known_bookings_checking_in_prior_to_TCI\n", - "\tON booking_summary.id_booking = known_bookings_checking_in_prior_to_TCI.id_booking\n", - "LEFT JOIN incidents_prior_to_TCP\n", - "\tON booking_summary.id_booking = incidents_prior_to_TCP.id_booking\n", - "WHERE\n", - "\t-- 1. Bookings from New Dash users with Id Deal\n", - "\tbooking_summary.is_user_in_new_dash = TRUE\n", - "\tAND \n", - " booking_summary.is_missing_id_deal = FALSE\n", - "\tAND\n", - "\t-- 2. Protected Bookings with a Protection or a Deposit Management service\n", - " (\n", - "\t\tbooking_summary.has_protection_service_business_type\n", - "\t\t\tOR \n", - " booking_summary.has_deposit_management_service_business_type\n", - "\t)\n", - "\tAND\n", - "\t-- 3. Bookings with flagging categorisation (this excludes Cancelled/Incomplete/Rejected bookings)\n", - "\tbooking_summary.is_booking_flagged_as_risk IS NOT NULL\n", - "\tAND\n", - "\t-- 4. Booking is completed\n", - "\tbooking_summary.is_booking_past_completion_date = TRUE\n", - "\n", - "\n", - "\"\"\"\n", - "\n", - "# Retrieve Data from Query\n", - "df_extraction = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n", - "print(f\"Total Bookings: {len(df_extraction):,}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessing\n", - "Preprocessing in this notebook is quite straight-forward: we just drop id booking and split the features and target." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop ID column\n", - "df = df_extraction.copy().drop(columns=['id_booking'])\n", - "\n", - "# Separate features and target\n", - "target_col = 'has_resolution_incident'\n", - "X = df.drop(columns=[target_col])\n", - "y = df[target_col]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploratory Data Analysis\n", - "In this section we focus on explore the different features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### EDA - Dataset Overview" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape: (21384, 25)\n", - "has_resolution_incident\n", - "False 98.8\n", - "True 1.2\n", - "Name: proportion, dtype: float64\n" - ] - } - ], - "source": [ - "# Shape and types\n", - "print(f\"Shape: {X.shape}\")\n", - "\n", - "# Target distribution\n", - "print(round(100*df[target_col].value_counts(normalize=True),2))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countmeanstdmin5%25%50%75%95%99%max
number_of_applied_upgraded_services21384.02.6642821.5320381.01.01.02.04.05.06.07.0
number_of_applied_billable_services21384.01.8427800.9461840.01.01.02.02.04.04.05.0
number_of_applied_screening_services21384.02.0079030.9856491.01.01.02.03.04.04.04.0
number_of_applied_deposit_management_services21384.00.6206510.4858140.00.00.01.01.01.01.02.0
number_of_applied_protection_services21384.00.7271320.4454440.00.00.01.01.01.01.01.0
listing_number_of_bedrooms21384.02.0494761.7554990.00.01.02.03.05.08.015.0
listing_number_of_bathrooms21384.01.5908161.3125730.00.01.01.02.04.06.017.0
booking_lead_time21384.018.15142224.3495790.00.02.09.025.069.0113.0220.0
booking_duration21384.04.1750844.8510550.01.02.03.05.010.028.0116.0
listing_check_ins_prior_to_tcr_in_30_days21384.02.4811072.8044360.00.00.02.04.08.011.025.0
listing_occupancy_prior_to_tcr_in_30_days21384.08.7808179.2608550.00.00.06.016.027.030.030.0
listing_known_check_ins_prior_to_tci_in_30_days21384.02.6611492.9377770.00.00.02.04.08.012.026.0
listing_known_occupancy_prior_to_tci_in_30_days21384.09.4709139.7155110.00.00.06.017.030.030.030.0
lead_time_between_prior_known_check_in_to_tci_30_days21384.015.28731811.4246571.02.05.011.030.030.030.030.0
listing_incidents_prior_to_tcp_in_30_days21384.00.0134680.1304930.00.00.00.00.00.01.03.0
\n", - "
" - ], - "text/plain": [ - " count mean \\\n", - "number_of_applied_upgraded_services 21384.0 2.664282 \n", - "number_of_applied_billable_services 21384.0 1.842780 \n", - "number_of_applied_screening_services 21384.0 2.007903 \n", - "number_of_applied_deposit_management_services 21384.0 0.620651 \n", - "number_of_applied_protection_services 21384.0 0.727132 \n", - "listing_number_of_bedrooms 21384.0 2.049476 \n", - "listing_number_of_bathrooms 21384.0 1.590816 \n", - "booking_lead_time 21384.0 18.151422 \n", - "booking_duration 21384.0 4.175084 \n", - "listing_check_ins_prior_to_tcr_in_30_days 21384.0 2.481107 \n", - "listing_occupancy_prior_to_tcr_in_30_days 21384.0 8.780817 \n", - "listing_known_check_ins_prior_to_tci_in_30_days 21384.0 2.661149 \n", - "listing_known_occupancy_prior_to_tci_in_30_days 21384.0 9.470913 \n", - "lead_time_between_prior_known_check_in_to_tci_3... 21384.0 15.287318 \n", - "listing_incidents_prior_to_tcp_in_30_days 21384.0 0.013468 \n", - "\n", - " std min 5% 25% \\\n", - "number_of_applied_upgraded_services 1.532038 1.0 1.0 1.0 \n", - "number_of_applied_billable_services 0.946184 0.0 1.0 1.0 \n", - "number_of_applied_screening_services 0.985649 1.0 1.0 1.0 \n", - "number_of_applied_deposit_management_services 0.485814 0.0 0.0 0.0 \n", - "number_of_applied_protection_services 0.445444 0.0 0.0 0.0 \n", - "listing_number_of_bedrooms 1.755499 0.0 0.0 1.0 \n", - "listing_number_of_bathrooms 1.312573 0.0 0.0 1.0 \n", - "booking_lead_time 24.349579 0.0 0.0 2.0 \n", - "booking_duration 4.851055 0.0 1.0 2.0 \n", - "listing_check_ins_prior_to_tcr_in_30_days 2.804436 0.0 0.0 0.0 \n", - "listing_occupancy_prior_to_tcr_in_30_days 9.260855 0.0 0.0 0.0 \n", - "listing_known_check_ins_prior_to_tci_in_30_days 2.937777 0.0 0.0 0.0 \n", - "listing_known_occupancy_prior_to_tci_in_30_days 9.715511 0.0 0.0 0.0 \n", - "lead_time_between_prior_known_check_in_to_tci_3... 11.424657 1.0 2.0 5.0 \n", - "listing_incidents_prior_to_tcp_in_30_days 0.130493 0.0 0.0 0.0 \n", - "\n", - " 50% 75% 95% 99% \\\n", - "number_of_applied_upgraded_services 2.0 4.0 5.0 6.0 \n", - "number_of_applied_billable_services 2.0 2.0 4.0 4.0 \n", - "number_of_applied_screening_services 2.0 3.0 4.0 4.0 \n", - "number_of_applied_deposit_management_services 1.0 1.0 1.0 1.0 \n", - "number_of_applied_protection_services 1.0 1.0 1.0 1.0 \n", - "listing_number_of_bedrooms 2.0 3.0 5.0 8.0 \n", - "listing_number_of_bathrooms 1.0 2.0 4.0 6.0 \n", - "booking_lead_time 9.0 25.0 69.0 113.0 \n", - "booking_duration 3.0 5.0 10.0 28.0 \n", - "listing_check_ins_prior_to_tcr_in_30_days 2.0 4.0 8.0 11.0 \n", - "listing_occupancy_prior_to_tcr_in_30_days 6.0 16.0 27.0 30.0 \n", - "listing_known_check_ins_prior_to_tci_in_30_days 2.0 4.0 8.0 12.0 \n", - "listing_known_occupancy_prior_to_tci_in_30_days 6.0 17.0 30.0 30.0 \n", - "lead_time_between_prior_known_check_in_to_tci_3... 11.0 30.0 30.0 30.0 \n", - "listing_incidents_prior_to_tcp_in_30_days 0.0 0.0 0.0 1.0 \n", - "\n", - " max \n", - "number_of_applied_upgraded_services 7.0 \n", - "number_of_applied_billable_services 5.0 \n", - "number_of_applied_screening_services 4.0 \n", - "number_of_applied_deposit_management_services 2.0 \n", - "number_of_applied_protection_services 1.0 \n", - "listing_number_of_bedrooms 15.0 \n", - "listing_number_of_bathrooms 17.0 \n", - "booking_lead_time 220.0 \n", - "booking_duration 116.0 \n", - "listing_check_ins_prior_to_tcr_in_30_days 25.0 \n", - "listing_occupancy_prior_to_tcr_in_30_days 30.0 \n", - "listing_known_check_ins_prior_to_tci_in_30_days 26.0 \n", - "listing_known_occupancy_prior_to_tci_in_30_days 30.0 \n", - "lead_time_between_prior_known_check_in_to_tci_3... 30.0 \n", - "listing_incidents_prior_to_tcp_in_30_days 3.0 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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countuniquetopfreqfreq/count
has_waiver_pro213842False190820.892349
has_guest_facing_waiver_or_deposit213842True109700.513
has_guest_agreement213842False147870.691498
has_basic_protection213842False118940.55621
has_protection_plus213842False200830.93916
has_protection_pro213842False166260.777497
has_id_verification213842False124380.58165
has_screening_plus213842False110010.51445
has_sex_offender_check213842False191580.895903
is_contactless_booking213842False131850.616582
has_resolution_incident213842False211270.987982
\n", - "
" - ], - "text/plain": [ - " count unique top freq freq/count\n", - "has_waiver_pro 21384 2 False 19082 0.892349\n", - "has_guest_facing_waiver_or_deposit 21384 2 True 10970 0.513\n", - "has_guest_agreement 21384 2 False 14787 0.691498\n", - "has_basic_protection 21384 2 False 11894 0.55621\n", - "has_protection_plus 21384 2 False 20083 0.93916\n", - "has_protection_pro 21384 2 False 16626 0.777497\n", - "has_id_verification 21384 2 False 12438 0.58165\n", - "has_screening_plus 21384 2 False 11001 0.51445\n", - "has_sex_offender_check 21384 2 False 19158 0.895903\n", - "is_contactless_booking 21384 2 False 13185 0.616582\n", - "has_resolution_incident 21384 2 False 21127 0.987982" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Summary statistics for numerical features\n", - "display(df.describe(include= ['number'], percentiles=[.05,.25,.5,.75,.95,.99]).T)\n", - "# Summary statistics for boolean features\n", - "summary = df.describe(include= ['bool']).T\n", - "summary['freq/count'] = summary['freq']/summary['count']\n", - "display(summary)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Correlation heatmap\n", - "plt.figure(figsize=(17, 13))\n", - "cmap = sns.diverging_palette(220, 20, as_cmap=True)\n", - "sns.heatmap(df.corr(), annot=True, cmap=cmap, fmt=\".2f\", linewidths=.5,)\n", - "plt.title(\"Correlation Matrix\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Processing for modelling\n", - "Afterwards, we split the dataset between train and test and display their sizes and target distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training set size: 14968 rows\n", - "Test set size: 6416 rows\n", - "\n", - "Training target distribution:\n", - "has_resolution_incident\n", - "False 0.98744\n", - "True 0.01256\n", - "Name: proportion, dtype: float64\n", - "\n", - "Test target distribution:\n", - "has_resolution_incident\n", - "False 0.989246\n", - "True 0.010754\n", - "Name: proportion, dtype: float64\n" - ] - } - ], - "source": [ - "# Split the data\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)\n", - "\n", - "print(f\"Training set size: {X_train.shape[0]} rows\")\n", - "print(f\"Test set size: {X_test.shape[0]} rows\")\n", - "\n", - "print(\"\\nTraining target distribution:\")\n", - "print(y_train.value_counts(normalize=True))\n", - "\n", - "print(\"\\nTest target distribution:\")\n", - "print(y_test.value_counts(normalize=True))" - ] - }, - { - "cell_type": "markdown", - "id": "d36c9276", - "metadata": {}, - "source": [ - "## Classification Model with Random Forest\n", - "\n", - "We define a machine learning pipeline that includes:\n", - "- **Scaling numeric features** with `StandardScaler`\n", - "- **Training a Random Forest classifier** with balanced class weights to handle the imbalanced dataset\n", - "\n", - "We then use `GridSearchCV` to perform a **grid search with cross-validation** over a range of key hyperparameters (e.g., number of trees, max depth, etc.). \n", - "The model is evaluated using **Average Precision**, which is better suited for imbalanced classification tasks.\n", - "\n", - "The best combination of parameters is selected, and the resulting model is used to make predictions on the test set.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "943ef7d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 4 folds for each of 72 candidates, totalling 288 fits\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n", - "\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.2s\n", - "Best hyperparameters: {'model__max_depth': 10, 'model__max_features': 'sqrt', 'model__min_samples_leaf': 2, 'model__min_samples_split': 5, 'model__n_estimators': 300}\n" - ] - } - ], - "source": [ - "\n", - "# Define pipeline (scaling numeric features only)\n", - "pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', RandomForestClassifier(class_weight='balanced', # We have an imbalanced dataset\n", - " random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid\n", - "param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "grid_search = GridSearchCV(\n", - " estimator=pipeline,\n", - " param_grid=param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=4, # 4-fold cross-validation\n", - " n_jobs=-1, # Use all available cores\n", - " verbose=2, # Verbose output for progress tracking,\n", - " refit=True # Refit the best model on the entire training set - it's already true by default\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "grid_search.fit(X_train, y_train)\n", - "\n", - "# Best model\n", - "best_pipeline = grid_search.best_estimator_\n", - "print(\"Best hyperparameters:\", grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba = best_pipeline.predict_proba(X_test)[:, 1]\n", - "y_pred = best_pipeline.predict(X_test)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean_fit_timestd_fit_timemean_score_timestd_score_timeparam_model__max_depthparam_model__max_featuresparam_model__min_samples_leafparam_model__min_samples_splitparam_model__n_estimatorsparamssplit0_test_scoresplit1_test_scoresplit2_test_scoresplit3_test_scoremean_test_scorestd_test_scorerank_test_score
355.4923630.0741030.1939780.01656010sqrt25300{'model__max_depth': 10, 'model__max_features'...0.0412620.0212220.0289580.0587790.0375550.0141851
223.0784270.0370900.1290330.003915Nonelog225200{'model__max_depth': None, 'model__max_feature...0.0468990.0237210.0290790.0492300.0372320.0110282
541.7259340.0303680.0658140.00226820sqrt22100{'model__max_depth': 20, 'model__max_features'...0.0464550.0210840.0303970.0509860.0372300.0120593
234.7548960.2847600.1971590.010598Nonelog225300{'model__max_depth': None, 'model__max_feature...0.0452810.0246240.0288840.0494240.0370530.0105114
643.1501470.1233930.1332040.01087520log215200{'model__max_depth': 20, 'model__max_features'...0.0487860.0215360.0319820.0458610.0370410.0109745
......................................................
13.6551330.0529940.1410720.002776Nonesqrt12200{'model__max_depth': None, 'model__max_feature...0.0446980.0194240.0263360.0417510.0330520.01051368
493.4994030.0441260.1467130.00331220sqrt12200{'model__max_depth': 20, 'model__max_features'...0.0434880.0195350.0261280.0416670.0327050.01016569
482.0299980.0850490.1182260.01963220sqrt12100{'model__max_depth': 20, 'model__max_features'...0.0406830.0183700.0265020.0385850.0310350.00909770
122.1020990.0299900.0927190.007638Nonelog212100{'model__max_depth': None, 'model__max_feature...0.0352290.0205180.0249700.0399500.0301670.00776971
01.9836770.2770250.0917030.020498Nonesqrt12100{'model__max_depth': None, 'model__max_feature...0.0371040.0166520.0236310.0345120.0279750.00826472
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" - ], - "text/plain": [ - " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", - "35 5.492363 0.074103 0.193978 0.016560 \n", - "22 3.078427 0.037090 0.129033 0.003915 \n", - "54 1.725934 0.030368 0.065814 0.002268 \n", - "23 4.754896 0.284760 0.197159 0.010598 \n", - "64 3.150147 0.123393 0.133204 0.010875 \n", - ".. ... ... ... ... \n", - "1 3.655133 0.052994 0.141072 0.002776 \n", - "49 3.499403 0.044126 0.146713 0.003312 \n", - "48 2.029998 0.085049 0.118226 0.019632 \n", - "12 2.102099 0.029990 0.092719 0.007638 \n", - "0 1.983677 0.277025 0.091703 0.020498 \n", - "\n", - " param_model__max_depth param_model__max_features \\\n", - "35 10 sqrt \n", - "22 None log2 \n", - "54 20 sqrt \n", - "23 None log2 \n", - "64 20 log2 \n", - ".. ... ... \n", - "1 None sqrt \n", - "49 20 sqrt \n", - "48 20 sqrt \n", - "12 None log2 \n", - "0 None sqrt \n", - "\n", - " param_model__min_samples_leaf param_model__min_samples_split \\\n", - "35 2 5 \n", - "22 2 5 \n", - "54 2 2 \n", - "23 2 5 \n", - "64 1 5 \n", - ".. ... ... \n", - "1 1 2 \n", - "49 1 2 \n", - "48 1 2 \n", - "12 1 2 \n", - "0 1 2 \n", - "\n", - " param_model__n_estimators \\\n", - "35 300 \n", - "22 200 \n", - "54 100 \n", - "23 300 \n", - "64 200 \n", - ".. ... \n", - "1 200 \n", - "49 200 \n", - "48 100 \n", - "12 100 \n", - "0 100 \n", - "\n", - " params split0_test_score \\\n", - "35 {'model__max_depth': 10, 'model__max_features'... 0.041262 \n", - "22 {'model__max_depth': None, 'model__max_feature... 0.046899 \n", - "54 {'model__max_depth': 20, 'model__max_features'... 0.046455 \n", - "23 {'model__max_depth': None, 'model__max_feature... 0.045281 \n", - "64 {'model__max_depth': 20, 'model__max_features'... 0.048786 \n", - ".. ... ... \n", - "1 {'model__max_depth': None, 'model__max_feature... 0.044698 \n", - "49 {'model__max_depth': 20, 'model__max_features'... 0.043488 \n", - "48 {'model__max_depth': 20, 'model__max_features'... 0.040683 \n", - "12 {'model__max_depth': None, 'model__max_feature... 0.035229 \n", - "0 {'model__max_depth': None, 'model__max_feature... 0.037104 \n", - "\n", - " split1_test_score split2_test_score split3_test_score mean_test_score \\\n", - "35 0.021222 0.028958 0.058779 0.037555 \n", - "22 0.023721 0.029079 0.049230 0.037232 \n", - "54 0.021084 0.030397 0.050986 0.037230 \n", - "23 0.024624 0.028884 0.049424 0.037053 \n", - "64 0.021536 0.031982 0.045861 0.037041 \n", - ".. ... ... ... ... \n", - "1 0.019424 0.026336 0.041751 0.033052 \n", - "49 0.019535 0.026128 0.041667 0.032705 \n", - "48 0.018370 0.026502 0.038585 0.031035 \n", - "12 0.020518 0.024970 0.039950 0.030167 \n", - "0 0.016652 0.023631 0.034512 0.027975 \n", - "\n", - " std_test_score rank_test_score \n", - "35 0.014185 1 \n", - "22 0.011028 2 \n", - "54 0.012059 3 \n", - "23 0.010511 4 \n", - "64 0.010974 5 \n", - ".. ... ... \n", - "1 0.010513 68 \n", - "49 0.010165 69 \n", - "48 0.009097 70 \n", - "12 0.007769 71 \n", - "0 0.008264 72 \n", - "\n", - "[72 rows x 17 columns]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Retrieve cv results\n", - "pd.DataFrame(grid_search.cv_results_).sort_values(by='mean_test_score', ascending=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We apply a threshold selector to find a proper value for F2 optimisation, rather than defaulting to 0.5." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the best threshold for F2 score\n", - "\n", - "def find_best_threshold(y_true, y_proba, beta=2.0):\n", - " thresholds = np.linspace(0, 1, 200)\n", - " f2_scores = []\n", - "\n", - " for t in thresholds:\n", - " preds = (y_proba >= t).astype(int)\n", - " score = fbeta_score(y_true, preds, beta=beta)\n", - " f2_scores.append(score)\n", - "\n", - " best_index = np.argmax(f2_scores)\n", - " return thresholds[best_index], f2_scores[best_index]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best threshold: 38.2% ā€” F2 score: 15.31%\n" - ] - } - ], - "source": [ - "# Predict probabilities\n", - "y_pred_proba = best_pipeline.predict_proba(X_test)[:, 1]\n", - "\n", - "# Find best threshold for F2\n", - "best_thresh, best_f2 = find_best_threshold(y_test, y_pred_proba, beta=2.0)\n", - "print(f\"Best threshold: {100*best_thresh:.1f}% ā€” F2 score: {100*best_f2:.2f}%\")\n", - "\n", - "# Use that threshold for final classification\n", - "y_pred_opt = (y_pred_proba >= best_thresh).astype(int)" - ] - }, - { - "cell_type": "markdown", - "id": "fc2fcc89", - "metadata": {}, - "source": [ - "## Evaluation\n", - "This section aims to evaluate how good the new model is vs. the actual Resolution Incidents.\n", - "\n", - "We start by computing and displaying the classification report, ROC Curve, PR Curve and the respective Area Under the Curve (AUC)." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "30786f7c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " No Incident 0.99 0.89 0.94 6347\n", - " Incident 0.04 0.43 0.08 69\n", - "\n", - " accuracy 0.89 6416\n", - " macro avg 0.52 0.66 0.51 6416\n", - "weighted avg 0.98 0.89 0.93 6416\n", - "\n" - ] - } - ], - "source": [ - "# Print classification report\n", - "print(classification_report(y_test, y_pred_opt, target_names=['No Incident', 'Incident']))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Classification Report\n", - "\n", - "The **Classification Report** provides key metrics to evaluate how well the model performed on each class.\n", - "\n", - "It includes the following metrics for each class (0 and 1):\n", - "* Precision: Out of all predicted positives, how many were actually positive?\n", - "* Recall: Out of all actual positives, how many did we correctly identify?\n", - "* F1-score: Harmonic mean of precision and recall (balances both)\n", - "* Support: Number of true samples of that class in the test data\n", - "\n", - "Interpretation:\n", - "* Class 0 = No incident\n", - "* Class 1 = Has resolution incident (rare, but important!)\n", - "\n", - "A few explanatory cases:\n", - "* A high recall for class 1 means we're catching most incidents.\n", - "* A high precision for class 1 means when we predict an incident, we're often correct.\n", - "* The F1-score gives a single balanced measure (good for imbalanced data).\n", - "\n", - "Special note for imbalanced data:\n", - "Since class 1 (or just True) is rare (1% in our case), metrics for that class are more critical.\n", - "We want to maximize recall to catch as many real incidents as possible ā€” without letting precision drop too low (to avoid too many false alarms)." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "4b4da914", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC Curve\n", - "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')\n", - "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC Curve')\n", - "plt.legend(loc='lower right')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the ROC Curve\n", - "\n", - "The **Receiver Operating Characteristic (ROC) curve** shows how well the model distinguishes between the positive and negative classes across all decision thresholds.\n", - "\n", - "A quick reminder of the definitions:\n", - "* True Positive Rate (TPR) = Recall\n", - "* False Positive Rate (FPR) = Proportion of negatives wrongly classified as positives\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is False Positive Rate\n", - "* The y-axis is True Positive Rate\n", - "\n", - "The curve shows how TPR and FPR change as the threshold varies\n", - "\n", - "It's important to note that:\n", - "* A model with no skill will produce a diagonal line (AUC = 0.5)\n", - "* A model with perfect discrimination will hug the top-left corner (AUC = 1.0)\n", - "\n", - "The Area Under the Curve (ROC AUC) gives a single performance score:\n", - "* Closer to 1 means better at ranking positive cases higher than negative ones\n", - "\n", - "**Important!**\n", - "\n", - "While useful, the ROC curve can sometimes overestimate performance when the dataset is imbalanced, because it includes negatives (which dominate in our case, around 99%!). Thatā€™s why we also MUST check the Precision-Recall curve." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "6790d41d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# PR Curve\n", - "precision, recall, _ = precision_recall_curve(y_test, y_pred_proba)\n", - "pr_auc = average_precision_score(y_test, y_pred_proba)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(recall, precision, color='green', lw=2, label=f'PR curve (AUC = {pr_auc:.4f})')\n", - "plt.xlabel('Recall')\n", - "plt.ylabel('Precision')\n", - "plt.title('Precision-Recall Curve')\n", - "plt.legend(loc='lower left')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Precision-Recall (PR) Curve\n", - "\n", - "The **Precision-Recall (PR) curve** helps evaluate model performance, especially on imbalanced datasets like ours (where positive cases are rare).\n", - "\n", - "A quick reminder of the definitions:\n", - "* Precision = How many of the predicted positives are actually positive\n", - "* Recall = How many of the actual positives the model correctly identifies\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is Recall \n", - "* The y-axis is Precision \n", - "\n", - "The curve shows the trade-off between them at different model thresholds\n", - "\n", - "In imbalanced datasets, accuracy can be misleading ā€” the PR curve focuses only on the positive class, making it much more meaningful:\n", - "* A higher curve means better performance\n", - "* The area under the curve (PR AUC) summarizes this: closer to 1 is better" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n", - "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_opt).ravel()\n", - "\n", - "# Total predictions\n", - "total = tp + tn + fp + fn\n", - "\n", - "# Compute all requested metrics\n", - "recall = recall_score(y_test, y_pred_opt)\n", - "precision = precision_score(y_test, y_pred_opt)\n", - "f1 = fbeta_score(y_test, y_pred_opt, beta=1)\n", - "f2 = fbeta_score(y_test, y_pred_opt, beta=2)\n", - "f3 = fbeta_score(y_test, y_pred_opt, beta=3)\n", - "fpr = fp / (fp + tn) if (fp + tn) != 0 else 0\n", - "\n", - "# Scores relative to total\n", - "tp_score = tp / total\n", - "tn_score = tn / total\n", - "fp_score = fp / total\n", - "fn_score = fn / total\n", - "\n", - "# Create DataFrame\n", - "summary_df = pd.DataFrame([{\n", - " \"flagging_analysis_type\": \"RISK_VS_CLAIM\",\n", - " \"count_total\": total,\n", - " \"count_true_positive\": tp,\n", - " \"count_true_negative\": tn,\n", - " \"count_false_positive\": fp,\n", - " \"count_false_negative\": fn,\n", - " \"true_positive_score\": tp_score,\n", - " \"true_negative_score\": tn_score,\n", - " \"false_positive_score\": fp_score,\n", - " \"false_negative_score\": fn_score,\n", - " \"recall_score\": recall,\n", - " \"precision_score\": precision,\n", - " \"false_positive_rate_score\": fpr,\n", - " \"f1_score\": f1,\n", - " \"f2_score\": f2,\n", - " \"f3_score\": f3,\n", - " \"roc_auc_score\": roc_auc,\n", - " \"pr_auc_score\": pr_auc\n", - "}])" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_confusion_matrix_from_df(df, flagging_analysis_type, name_of_the_experiment=\"\"):\n", - "\n", - " # Subset - just retrieve one row depending on the flagging_analysis_type\n", - " row = df[df['flagging_analysis_type'] == flagging_analysis_type].iloc[0]\n", - "\n", - " # Define custom x-axis labels and wording\n", - " if flagging_analysis_type == 'RISK_VS_CLAIM':\n", - " x_labels = ['With Submitted Claim', 'Without Submitted Claim']\n", - " outcome_label = \"submitted claim\"\n", - " elif flagging_analysis_type == 'RISK_VS_SUBMITTED_PAYOUT':\n", - " x_labels = ['With Submitted Payout', 'Without Submitted Payout']\n", - " outcome_label = \"submitted payout\"\n", - " else:\n", - " x_labels = ['Actual Positive', 'Actual Negative'] \n", - " outcome_label = \"outcome\"\n", - "\n", - " # Confusion matrix structure\n", - " cm = np.array([\n", - " [row['count_true_positive'], row['count_false_positive']],\n", - " [row['count_false_negative'], row['count_true_negative']]\n", - " ])\n", - "\n", - " # Create annotations for the confusion matrix\n", - " labels = [['True Positives', 'False Positives'], ['False Negatives', 'True Negatives']]\n", - " counts = [[f\"{v:,}\" for v in [row['count_true_positive'], row['count_false_positive']]],\n", - " [f\"{v:,}\" for v in [row['count_false_negative'], row['count_true_negative']]]]\n", - " percentages = [[f\"{round(100*v,2):,}\" for v in [row['true_positive_score'], row['false_positive_score']]],\n", - " [f\"{round(100*v,2):,}\" for v in [row['false_negative_score'], row['true_negative_score']]]]\n", - " annot = [[f\"{labels[i][j]}\\n{counts[i][j]} ({percentages[i][j]}%)\" for j in range(2)] for i in range(2)]\n", - "\n", - " # Scores formatted as percentages\n", - " recall = row['recall_score'] * 100\n", - " precision = row['precision_score'] * 100\n", - " f1 = row['f1_score'] * 100\n", - " f2 = row['f2_score'] * 100\n", - " f3 = row['f3_score'] * 100\n", - " roc_auc = row['roc_auc_score'] * 100\n", - " pr_auc = row['pr_auc_score'] * 100\n", - "\n", - " # Set up figure and axes manually for precise control\n", - " fig = plt.figure(figsize=(9, 8))\n", - " grid = fig.add_gridspec(nrows=3, height_ratios=[1, 15, 2])\n", - "\n", - " \n", - " ax_main_title = fig.add_subplot(grid[0])\n", - " ax_main_title.axis('off')\n", - " ax_main_title.set_title(f\"{name_of_the_experiment} - Flagged as Risk vs. {outcome_label.title()}\", fontsize=14, weight='bold')\n", - "\n", - " # Heatmap\n", - " ax_heatmap = fig.add_subplot(grid[1])\n", - " ax_heatmap.set_title(f\"Confusion Matrix ā€“ Risk vs. {outcome_label.title()}\", fontsize=12, weight='bold', ha='center', va='center', wrap=False)\n", - "\n", - " cmap = sns.light_palette(\"#A73A52\", as_cmap=True)\n", - "\n", - " sns.heatmap(cm, annot=annot, fmt='', cmap=cmap, cbar=False,\n", - " xticklabels=x_labels,\n", - " yticklabels=['Flagged as Risk', 'Flagged as No Risk'],\n", - " ax=ax_heatmap,\n", - " linewidths=1.0,\n", - " annot_kws={'fontsize': 10, 'linespacing': 1.2})\n", - " ax_heatmap.set_xlabel(\"Resolution Outcome (Actual)\", fontsize=11, labelpad=10)\n", - " ax_heatmap.set_ylabel(\"Flagging (Prediction)\", fontsize=11, labelpad=10)\n", - " \n", - " # Make borders visible\n", - " for _, spine in ax_heatmap.spines.items():\n", - " spine.set_visible(True)\n", - "\n", - " # Footer with metrics and date\n", - " ax_footer = fig.add_subplot(grid[2])\n", - " ax_footer.axis('off')\n", - " metrics_text = f\"Total Booking Count: {row['count_total']} | Recall: {recall:.2f}% | Precision: {precision:.2f}% | F1 Score: {f1:.2f}% | F2 Score: {f2:.2f}% | ROC AUC: {roc_auc:.2f}% | PR AUC: {pr_auc:.2f}%\"\n", - " date_text = f\"Generated on {date.today().strftime('%B %d, %Y')}\"\n", - " ax_footer.text(0.5, 0.7, metrics_text, ha='center', fontsize=9)\n", - " ax_footer.text(0.5, 0.1, date_text, ha='center', fontsize=8, color='gray')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot confusion matrix for claim scenario\n", - "plot_confusion_matrix_from_df(summary_df, 'RISK_VS_CLAIM', 'Contactless')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Importance\n", - "Understanding what drives the prediction is useful for future experiments and business knowledge. Here we track both the native feature importances of the trees, as well as a more heavy SHAP values analysis.\n", - "\n", - "Important! Be aware that SHAP analysis might take quite a bit of time." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d66ffe2c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## BUILT-IN\n", - "\n", - "# Get feature importances from the model\n", - "importances = best_pipeline.named_steps['model'].feature_importances_\n", - "features = X.columns\n", - "\n", - "# Create a Series and sort\n", - "feat_series = pd.Series(importances, index=features).sort_values(ascending=True) # ascending=True for horizontal plot\n", - "\n", - "# Plot Feature Importances\n", - "plt.figure(figsize=(8, 8))\n", - "feat_series.plot(kind='barh', color='skyblue')\n", - "plt.title('Feature Importances')\n", - "plt.xlabel('Importance')\n", - "plt.grid(axis='x')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Feature Importance Plot\n", - "The **feature importance plot** shows how much each feature contributes to the modelā€™s overall decision-making.\n", - "\n", - "For tree-based models like Random Forest, importance is based on how often and how effectively a feature is used to split the data across all trees.\n", - "A higher score means the feature plays a bigger role in improving prediction accuracy.\n", - "\n", - "In the graph you will see that:\n", - "* Features are ranked from most to least important.\n", - "* The values are relative and model-specific ā€” not directly interpretable as weights or probabilities.\n", - "\n", - "This helps us identify which features the model relies on most when making predictions.\n", - "\n", - "**Important!**\n", - "Unlike SHAP values, native importance doesn't show how a feature affects predictions ā€” only how useful it is to the model overall. For deeper interpretability (e.g., direction and context), SHAP is better (but it takes more time to run)." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "e2197cea", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PermutationExplainer explainer: 6417it [45:34, 2.34it/s] \n" - ] - } - ], - "source": [ - "## SHAP VALUES\n", - "\n", - "# SHAP requires that all features passed to Explainer be numeric (floats/ints)\n", - "X_test_shap = X_test.copy()\n", - "X_test_shap = X_test_shap.astype(float)\n", - "\n", - "# Function that returns the probability of the positive class\n", - "def model_predict(data):\n", - " return best_pipeline.predict_proba(data)[:, 1]\n", - "\n", - "# Ensure input to SHAP is numeric\n", - "X_test_shap = X_test.astype(float)\n", - "\n", - "# Create SHAP explainer\n", - "explainer = shap.Explainer(model_predict, X_test_shap)\n", - "\n", - "# Compute SHAP values\n", - "shap_values = explainer(X_test_shap)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "9cae1a51", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_881/3711913411.py:2: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", - " shap.summary_plot(shap_values.values, X_test_shap)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot summary\n", - "shap.summary_plot(shap_values.values, X_test_shap)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the SHAP Summary Plot\n", - "\n", - "Each point on a row represents a SHAP value for a single prediction (row = feature).\n", - "The x-axis shows how much the feature contributed to increasing or decreasing the prediction.\n", - "* Right (positive SHAP value): pushes prediction toward the positive class (i.e., higher chance of incident).\n", - "* Left (negative SHAP value): pushes prediction toward the negative class (i.e., lower chance of incident).\n", - "\n", - "Color shows the actual feature value for that point:\n", - "* Red = high value\n", - "* Blue = low value\n", - "\n", - "In other words:\n", - "* The position tells you impact.\n", - "* The color tells you feature value.\n", - "* The density (thickness) of dots shows how often a value occurs." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show the individual prediction for the highest predicted instance\n", - "highest_pred_index = np.argmax(shap_values.values[:, 0]) \n", - "\n", - "# Use waterfall plot for a single instance\n", - "shap.plots.waterfall(shap_values[highest_pred_index], max_display=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show the individual prediction for the lowest predicted instance\n", - "lowest_pred_index = np.argmin(shap_values.values[:, 0]) \n", - "\n", - "# Use waterfall plot for a single instance\n", - "shap.plots.waterfall(shap_values[lowest_pred_index], max_display=20)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/data_driven_risk_assessment/experiments/003_contactless_reduced_attributes.ipynb b/data_driven_risk_assessment/experiments/003_contactless_reduced_attributes.ipynb deleted file mode 100644 index 7e3cf04..0000000 --- a/data_driven_risk_assessment/experiments/003_contactless_reduced_attributes.ipynb +++ /dev/null @@ -1,2170 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "84dcd475", - "metadata": {}, - "source": [ - "# DDRA - Contactless (Reduced)\n", - "\n", - "## General Idea\n", - "The idea is to play only with numeric features (floats, integers or booleans) that are CONTACTLESS.\n", - "\n", - "This considers a subset of the features. This is mostly a copy from 002_contactless_full_attributes that just selects the most relevant attributes.\n", - "\n", - "## Initial setup\n", - "This first section just ensures that the connection to DWH works correctly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12368ce1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "šŸ”Œ Testing connection using credentials at: /home/uri/.superhog-dwh/credentials.yml\n", - "āœ… Connection successful.\n" - ] - } - ], - "source": [ - "# This script connects to a Data Warehouse (DWH) using PostgreSQL. \n", - "# This should be common for all Notebooks, but you might need to adjust the path to the `dwh_utils` module.\n", - "\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.abspath(\"../../utils\")) # Adjust path if needed\n", - "\n", - "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n", - "\n", - "# --- Connect to DWH ---\n", - "creds = read_credentials()\n", - "dwh_pg_engine = create_postgres_engine(creds)\n", - "\n", - "# --- Test Query ---\n", - "test_connection()" - ] - }, - { - "cell_type": "markdown", - "id": "c86f94f1", - "metadata": {}, - "source": [ - "## Data Extraction\n", - "In this section we extract the data.\n", - "\n", - "This SQL query retrieves a clean and relevant subset of booking data for our model. It includes:\n", - "- A **unique booking ID**\n", - "- Key **numeric features** such as number of services, time between booking creation and check-in, number of nights, etc.\n", - "- Several **categorical (boolean) features** related to service usage\n", - "- A **target variable** (`has_resolution_incident`) indicating whether a resolution incident occurred\n", - "\n", - "Filters applied being:\n", - "1. Bookings from **\"New Dash\" users** with a valid deal ID\n", - "2. Only **protected bookings**, i.e., those with Protection or Deposit Management services\n", - "3. Bookings flagged for **risk categorisation** (excluding incomplete/rejected ones)\n", - "4. Bookings that are **already completed**\n", - "\n", - "The result is converted into a pandas DataFrame for further processing and modeling.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e3ed391", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialise all imports needed for the Notebook\n", - "from sklearn.model_selection import (\n", - " train_test_split, \n", - " GridSearchCV\n", - ")\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import StandardScaler\n", - "import pandas as pd\n", - "import numpy as np\n", - "from datetime import date\n", - "from sklearn.metrics import (\n", - " roc_auc_score, \n", - " average_precision_score,\n", - " classification_report,\n", - " roc_curve, \n", - " auc,\n", - " precision_recall_curve,\n", - " precision_score,\n", - " recall_score,\n", - " fbeta_score,\n", - " confusion_matrix\n", - ")\n", - "import matplotlib.pyplot as plt\n", - "import shap\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db5e3098", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Bookings: 21,384\n" - ] - } - ], - "source": [ - "# Query to extract data\n", - "data_extraction_query = \"\"\"\n", - "WITH \n", - "service_information AS (\n", - "\tSELECT\n", - "\t\tid_booking,\n", - "\t\tcount(DISTINCT CASE WHEN service_business_type = 'SCREENING' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_screening_services,\n", - "\t\tcount(DISTINCT CASE WHEN service_business_type = 'DEPOSIT_MANAGEMENT' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_deposit_management_services,\n", - "\t\tcount(DISTINCT CASE WHEN service_business_type = 'PROTECTION' THEN id_booking_service_detail ELSE NULL END) AS number_of_applied_protection_services,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'WAIVER PRO' THEN id_booking ELSE NULL END)>0 AS has_waiver_pro,\n", - "\t\tcount(DISTINCT CASE WHEN service_name IN ('BASIC DAMAGE DEPOSIT','BASIC DAMAGE DEPOSIT OR BASIC WAIVER','BASIC DAMAGE DEPOSIT OR WAIVER PLUS','BASIC WAIVER','WAIVER PLUS') THEN id_booking ELSE NULL END)>0 AS has_guest_facing_waiver_or_deposit,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'GUEST AGREEMENT' THEN id_booking ELSE NULL END)>0 AS has_guest_agreement,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'BASIC PROTECTION' THEN id_booking ELSE NULL END)>0 AS has_basic_protection,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'PROTECTION PLUS' THEN id_booking ELSE NULL END)>0 AS has_protection_plus,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'PROTECTION PRO' THEN id_booking ELSE NULL END)>0 AS has_protection_pro,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'ID VERIFICATION' THEN id_booking ELSE NULL END)>0 AS has_id_verification,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'SCREENING PLUS' THEN id_booking ELSE NULL END)>0 AS has_screening_plus,\n", - "\t\tcount(DISTINCT CASE WHEN service_name = 'SEX OFFENDER CHECK' THEN id_booking ELSE NULL END)>0 AS has_sex_offender_check\n", - "\tFROM\n", - "\t\tintermediate.int_core__booking_service_detail\n", - "\tGROUP BY\n", - "\t\t1\n", - "),\n", - "listing_information AS (\n", - "SELECT \n", - "\tica.id_accommodation,\n", - "\t-- Defaults to 0 if null\n", - "\tCOALESCE(ica.number_of_bedrooms, 0) AS listing_number_of_bedrooms,\n", - "\t-- Defaults to 0 if null\n", - "\tCOALESCE(ica.number_of_bathrooms, 0) AS listing_number_of_bathrooms\n", - "\tFROM intermediate.int_core__accommodation ica \n", - "),\n", - "raw_bookings_checked_in_prior_to_TCR AS (\n", - "\tSELECT\n", - "\t\tb.id_booking,\n", - "\t\t-- Using group by on check-in date to remove booking duplicates\n", - "\t\tb2.booking_check_in_date_utc,\n", - "\t\t-- Using min as a conservative approach to reduce outliers\n", - "\t\tmin(b2.booking_number_of_nights) AS min_booking_number_of_nights\n", - "\tFROM\n", - "\t\tintermediate.int_booking_summary b\n", - "\t-- Note that by joining with BS we're only considering New Dash bookings\n", - "\tLEFT JOIN intermediate.int_booking_summary b2\n", - " ON\n", - "\t\tb2.id_accommodation = b.id_accommodation\n", - "\t\t-- Exclusion based on actual booking creation!\n", - "\t\tAND b2.booking_check_in_date_utc >= b.booking_created_date_utc - INTERVAL '30 days'\n", - "\t\tAND b2.booking_check_in_date_utc < b.booking_created_date_utc\n", - "\t\t-- Note that since is based on TCR we can remove Cancelled\n", - "\t\tAND b2.booking_status NOT IN ('CANCELLED')\n", - "\tGROUP BY\n", - "\t\tb.id_booking,\n", - "\t\tb2.booking_check_in_date_utc\n", - "),\n", - "bookings_checked_in_prior_to_TCR AS (\n", - "\tSELECT\n", - "\t\tid_booking,\n", - "\t\tLEAST(\n", - "\t\t\tcount(booking_check_in_date_utc),\n", - "\t\t\t30\n", - "\t\t) AS listing_check_ins_prior_to_TCR_in_30_days,\n", - "\t\t-- Capping\n", - "\t\tLEAST(\n", - "\t\t\tGREATEST(\n", - "\t\t\t\tsum(min_booking_number_of_nights),\n", - "\t\t\t\t0\n", - "\t\t\t),\n", - "\t\t\t30\n", - "\t\t) AS listing_occupancy_prior_to_TCR_in_30_days\n", - "\tFROM\n", - "\t\traw_bookings_checked_in_prior_to_TCR\n", - "\tGROUP BY\n", - "\t\t1\n", - "),\n", - "raw_known_bookings_checking_in_prior_to_TCI AS (\n", - "\tSELECT\n", - "\t\tb.id_booking,\n", - "\t\tb.booking_check_in_date_utc,\n", - "\t\t-- Using group by on check-in date to remove booking duplicates\n", - "\t\tb2.booking_check_in_date_utc AS other_bookings_check_in_date_utc,\n", - "\t\t-- Using min as a conservative approach to reduce outliers\n", - "\t\tmin(b2.booking_number_of_nights) AS min_booking_number_of_nights\n", - "\tFROM\n", - "\t\tintermediate.int_booking_summary b\n", - "\t-- Note that by joining with BS we're only considering New Dash bookings\n", - "\tLEFT JOIN intermediate.int_booking_summary b2\n", - " ON\n", - "\t\tb2.id_accommodation = b.id_accommodation\n", - "\t\t-- Exclusion based on check-in\n", - "\t\tAND b2.booking_check_in_date_utc >= b.booking_check_in_date_utc - INTERVAL '30 days'\n", - "\t\tAND b2.booking_check_in_date_utc < b.booking_check_in_date_utc\n", - "\t\t-- that are known!\n", - "\t\tAND b2.booking_created_date_utc < b.booking_created_date_utc\n", - "\t\t-- Note that since is based on TCI we cannot remove Cancelled\n", - "\tGROUP BY\n", - "\t\tb.id_booking,\n", - "\t\tb.booking_check_in_date_utc,\n", - "\t\tb2.booking_check_in_date_utc\n", - "),\n", - "known_bookings_checking_in_prior_to_TCI AS (\n", - "\tSELECT\n", - "\t\tid_booking,\n", - "\t\tLEAST(\n", - "\t\t\tcount(other_bookings_check_in_date_utc),\n", - "\t\t\t30\n", - "\t\t) AS listing_known_check_ins_prior_to_TCI_in_30_days,\n", - "\t\t-- Capping\n", - "\t\tLEAST(\n", - "\t\t\tGREATEST(\n", - "\t\t\t\tsum(min_booking_number_of_nights),\n", - "\t\t\t\t0\n", - "\t\t\t),\n", - "\t\t\t30\n", - "\t\t) AS listing_known_occupancy_prior_to_TCI_in_30_days,\n", - "\t\tCOALESCE(\n", - "\t\t\tbooking_check_in_date_utc - max(other_bookings_check_in_date_utc),\n", - "\t\t\t30\n", - "\t\t) AS lead_time_between_prior_known_check_in_to_TCI_30_days\n", - "\tFROM\n", - "\t\traw_known_bookings_checking_in_prior_to_TCI\n", - "\tGROUP BY\n", - "\t\tid_booking, \n", - "\t\tbooking_check_in_date_utc\n", - "),\n", - "incidents_prior_to_TCP AS (\n", - "\tSELECT\n", - "\t\tb.id_booking,\n", - "\t\t-- Using distinct count on check-in date to remove booking duplicates\n", - "\t\tCOUNT(DISTINCT b2.booking_check_in_date_utc) AS listing_incidents_prior_to_TCP_in_30_days\n", - "\tFROM\n", - "\t\tintermediate.int_booking_summary b\n", - "\tLEFT JOIN intermediate.int_booking_summary b2\n", - " ON\n", - "\t\tb2.id_accommodation = b.id_accommodation\n", - "\t\t-- Filter on Check Out date\n", - "\t\tAND b2.booking_completed_date_utc >= b.booking_created_date_utc - INTERVAL '30 days'\n", - "\t\tAND b2.booking_completed_date_utc < b.booking_created_date_utc\n", - "\t\tAND b2.has_resolution_incident = TRUE\n", - "\tGROUP BY\n", - "\t\tb.id_booking\n", - ")\n", - "SELECT\n", - "\t-- UNIQUE BOOKING ID --\n", - "\tbooking_summary.id_booking,\n", - "\t\n", - "\t-- CONTEXTUAL SERVICE INFORMATION --\n", - "\t-- We're not including number_of_applied_services as it 1-correlates with upgraded services\n", - "\tbooking_summary.number_of_applied_upgraded_services,\n", - " --Removed! booking_summary.number_of_applied_billable_services,\n", - "\tservice_information.number_of_applied_screening_services,\n", - "\tservice_information.number_of_applied_deposit_management_services,\n", - "\tservice_information.number_of_applied_protection_services,\n", - "\t--Removed! service_information.has_waiver_pro,\n", - "\t--Removed! service_information.has_guest_facing_waiver_or_deposit,\n", - "\t--Removed! service_information.has_guest_agreement,\n", - "\t--Removed! service_information.has_basic_protection,\n", - "\t--Removed! service_information.has_protection_plus,\n", - "\t--Removed! service_information.has_protection_pro,\n", - "\t--Removed! service_information.has_id_verification,\n", - "\t--Removed! service_information.has_screening_plus,\n", - "\t--Removed! service_information.has_sex_offender_check,\n", - "\tNOT booking_summary.has_verification_request AS is_contactless_booking,\n", - "\t\n", - "\t-- CONTEXTUAL LISTING INFORMATION --\n", - "\tlisting_information.listing_number_of_bedrooms,\n", - "\tlisting_information.listing_number_of_bathrooms,\n", - "\t\n", - "\t-- CONTEXTUAL TIMELINE OF OUR BOOKING\n", - "\t-- Defaults to 0 if booking_created_date_utc > booking_check_in_date_utc\n", - "\tGREATEST(booking_summary.booking_check_in_date_utc - booking_summary.booking_created_date_utc, 0) AS booking_lead_time,\n", - "\tbooking_summary.booking_check_out_date_utc - booking_summary.booking_check_in_date_utc AS booking_duration,\n", - "\t\n", - "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: PRIOR TO TCR\n", - "\t-- Removed! bookings_checked_in_prior_to_TCR.listing_check_ins_prior_to_TCR_in_30_days,\n", - "\tbookings_checked_in_prior_to_TCR.listing_occupancy_prior_to_TCR_in_30_days,\n", - "\t\n", - "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: PRIOR TO TCI (KNOWN)\n", - "\t-- Removed! known_bookings_checking_in_prior_to_TCI.listing_known_check_ins_prior_to_TCI_in_30_days,\n", - "\tknown_bookings_checking_in_prior_to_TCI.listing_known_occupancy_prior_to_TCI_in_30_days,\n", - "\tknown_bookings_checking_in_prior_to_TCI.lead_time_between_prior_known_check_in_to_TCI_30_days,\n", - "\t\n", - "\t-- SAME-LISTING, OTHER BOOKING INTERACTIONS: INCIDENTAL BOOKINGS\n", - "\t-- Removed! incidents_prior_to_TCP.listing_incidents_prior_to_TCP_in_30_days,\n", - "\t\n", - "\t-- TARGET (BOOLEAN) --\n", - "\tbooking_summary.has_resolution_incident\n", - "\n", - "FROM\n", - "\tintermediate.int_booking_summary booking_summary\n", - "LEFT JOIN service_information \n", - "\tON\n", - "\tbooking_summary.id_booking = service_information.id_booking\n", - "LEFT JOIN listing_information \n", - "\tON booking_summary.id_accommodation = listing_information.id_accommodation\n", - "LEFT JOIN bookings_checked_in_prior_to_TCR\n", - "\tON booking_summary.id_booking = bookings_checked_in_prior_to_TCR.id_booking\n", - "LEFT JOIN known_bookings_checking_in_prior_to_TCI\n", - "\tON booking_summary.id_booking = known_bookings_checking_in_prior_to_TCI.id_booking\n", - "LEFT JOIN incidents_prior_to_TCP\n", - "\tON booking_summary.id_booking = incidents_prior_to_TCP.id_booking\n", - "WHERE\n", - "\t-- 1. Bookings from New Dash users with Id Deal\n", - "\tbooking_summary.is_user_in_new_dash = TRUE\n", - "\tAND \n", - " booking_summary.is_missing_id_deal = FALSE\n", - "\tAND\n", - "\t-- 2. Protected Bookings with a Protection or a Deposit Management service\n", - " (\n", - "\t\tbooking_summary.has_protection_service_business_type\n", - "\t\t\tOR \n", - " booking_summary.has_deposit_management_service_business_type\n", - "\t)\n", - "\tAND\n", - "\t-- 3. Bookings with flagging categorisation (this excludes Cancelled/Incomplete/Rejected bookings)\n", - "\tbooking_summary.is_booking_flagged_as_risk IS NOT NULL\n", - "\tAND\n", - "\t-- 4. Booking is completed\n", - "\tbooking_summary.is_booking_past_completion_date = TRUE\n", - "\n", - "\n", - "\"\"\"\n", - "\n", - "# Retrieve Data from Query\n", - "df_extraction = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n", - "print(f\"Total Bookings: {len(df_extraction):,}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessing\n", - "Preprocessing in this notebook is quite straight-forward: we just drop id booking and split the features and target." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop ID column\n", - "df = df_extraction.copy().drop(columns=['id_booking'])\n", - "\n", - "# Separate features and target\n", - "target_col = 'has_resolution_incident'\n", - "X = df.drop(columns=[target_col])\n", - "y = df[target_col]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploratory Data Analysis\n", - "In this section we focus on explore the different features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### EDA - Dataset Overview" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape: (21384, 12)\n", - "has_resolution_incident\n", - "False 98.8\n", - "True 1.2\n", - "Name: proportion, dtype: float64\n" - ] - } - ], - "source": [ - "# Shape and types\n", - "print(f\"Shape: {X.shape}\")\n", - "\n", - "# Target distribution\n", - "print(round(100*df[target_col].value_counts(normalize=True),2))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countmeanstdmin5%25%50%75%95%99%max
number_of_applied_upgraded_services21384.02.6642821.5320381.01.01.02.04.05.06.07.0
number_of_applied_screening_services21384.02.0079030.9856491.01.01.02.03.04.04.04.0
number_of_applied_deposit_management_services21384.00.6206510.4858140.00.00.01.01.01.01.02.0
number_of_applied_protection_services21384.00.7271320.4454440.00.00.01.01.01.01.01.0
listing_number_of_bedrooms21384.02.0494761.7554990.00.01.02.03.05.08.015.0
listing_number_of_bathrooms21384.01.5908161.3125730.00.01.01.02.04.06.017.0
booking_lead_time21384.018.15142224.3495790.00.02.09.025.069.0113.0220.0
booking_duration21384.04.1750844.8510550.01.02.03.05.010.028.0116.0
listing_occupancy_prior_to_tcr_in_30_days21384.08.7808179.2608550.00.00.06.016.027.030.030.0
listing_known_occupancy_prior_to_tci_in_30_days21384.09.4709139.7155110.00.00.06.017.030.030.030.0
lead_time_between_prior_known_check_in_to_tci_30_days21384.015.28731811.4246571.02.05.011.030.030.030.030.0
\n", - "
" - ], - "text/plain": [ - " count mean \\\n", - "number_of_applied_upgraded_services 21384.0 2.664282 \n", - "number_of_applied_screening_services 21384.0 2.007903 \n", - "number_of_applied_deposit_management_services 21384.0 0.620651 \n", - "number_of_applied_protection_services 21384.0 0.727132 \n", - "listing_number_of_bedrooms 21384.0 2.049476 \n", - "listing_number_of_bathrooms 21384.0 1.590816 \n", - "booking_lead_time 21384.0 18.151422 \n", - "booking_duration 21384.0 4.175084 \n", - "listing_occupancy_prior_to_tcr_in_30_days 21384.0 8.780817 \n", - "listing_known_occupancy_prior_to_tci_in_30_days 21384.0 9.470913 \n", - "lead_time_between_prior_known_check_in_to_tci_3... 21384.0 15.287318 \n", - "\n", - " std min 5% 25% \\\n", - "number_of_applied_upgraded_services 1.532038 1.0 1.0 1.0 \n", - "number_of_applied_screening_services 0.985649 1.0 1.0 1.0 \n", - "number_of_applied_deposit_management_services 0.485814 0.0 0.0 0.0 \n", - "number_of_applied_protection_services 0.445444 0.0 0.0 0.0 \n", - "listing_number_of_bedrooms 1.755499 0.0 0.0 1.0 \n", - "listing_number_of_bathrooms 1.312573 0.0 0.0 1.0 \n", - "booking_lead_time 24.349579 0.0 0.0 2.0 \n", - "booking_duration 4.851055 0.0 1.0 2.0 \n", - "listing_occupancy_prior_to_tcr_in_30_days 9.260855 0.0 0.0 0.0 \n", - "listing_known_occupancy_prior_to_tci_in_30_days 9.715511 0.0 0.0 0.0 \n", - "lead_time_between_prior_known_check_in_to_tci_3... 11.424657 1.0 2.0 5.0 \n", - "\n", - " 50% 75% 95% 99% \\\n", - "number_of_applied_upgraded_services 2.0 4.0 5.0 6.0 \n", - "number_of_applied_screening_services 2.0 3.0 4.0 4.0 \n", - "number_of_applied_deposit_management_services 1.0 1.0 1.0 1.0 \n", - "number_of_applied_protection_services 1.0 1.0 1.0 1.0 \n", - "listing_number_of_bedrooms 2.0 3.0 5.0 8.0 \n", - "listing_number_of_bathrooms 1.0 2.0 4.0 6.0 \n", - "booking_lead_time 9.0 25.0 69.0 113.0 \n", - "booking_duration 3.0 5.0 10.0 28.0 \n", - "listing_occupancy_prior_to_tcr_in_30_days 6.0 16.0 27.0 30.0 \n", - "listing_known_occupancy_prior_to_tci_in_30_days 6.0 17.0 30.0 30.0 \n", - "lead_time_between_prior_known_check_in_to_tci_3... 11.0 30.0 30.0 30.0 \n", - "\n", - " max \n", - "number_of_applied_upgraded_services 7.0 \n", - "number_of_applied_screening_services 4.0 \n", - "number_of_applied_deposit_management_services 2.0 \n", - "number_of_applied_protection_services 1.0 \n", - "listing_number_of_bedrooms 15.0 \n", - "listing_number_of_bathrooms 17.0 \n", - "booking_lead_time 220.0 \n", - "booking_duration 116.0 \n", - "listing_occupancy_prior_to_tcr_in_30_days 30.0 \n", - "listing_known_occupancy_prior_to_tci_in_30_days 30.0 \n", - "lead_time_between_prior_known_check_in_to_tci_3... 30.0 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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countuniquetopfreqfreq/count
is_contactless_booking213842False131850.616582
has_resolution_incident213842False211270.987982
\n", - "
" - ], - "text/plain": [ - " count unique top freq freq/count\n", - "is_contactless_booking 21384 2 False 13185 0.616582\n", - "has_resolution_incident 21384 2 False 21127 0.987982" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Summary statistics for numerical features\n", - "display(df.describe(include= ['number'], percentiles=[.05,.25,.5,.75,.95,.99]).T)\n", - "# Summary statistics for boolean features\n", - "summary = df.describe(include= ['bool']).T\n", - "summary['freq/count'] = summary['freq']/summary['count']\n", - "display(summary)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Correlation heatmap\n", - "plt.figure(figsize=(10, 8))\n", - "cmap = sns.diverging_palette(220, 20, as_cmap=True)\n", - "sns.heatmap(df.corr(), annot=True, cmap=cmap, fmt=\".2f\", linewidths=.5,)\n", - "plt.title(\"Correlation Matrix\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Processing for modelling\n", - "Afterwards, we split the dataset between train and test and display their sizes and target distribution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training set size: 14968 rows\n", - "Test set size: 6416 rows\n", - "\n", - "Training target distribution:\n", - "has_resolution_incident\n", - "False 0.98744\n", - "True 0.01256\n", - "Name: proportion, dtype: float64\n", - "\n", - "Test target distribution:\n", - "has_resolution_incident\n", - "False 0.989246\n", - "True 0.010754\n", - "Name: proportion, dtype: float64\n" - ] - } - ], - "source": [ - "# Split the data\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)\n", - "\n", - "print(f\"Training set size: {X_train.shape[0]} rows\")\n", - "print(f\"Test set size: {X_test.shape[0]} rows\")\n", - "\n", - "print(\"\\nTraining target distribution:\")\n", - "print(y_train.value_counts(normalize=True))\n", - "\n", - "print(\"\\nTest target distribution:\")\n", - "print(y_test.value_counts(normalize=True))" - ] - }, - { - "cell_type": "markdown", - "id": "d36c9276", - "metadata": {}, - "source": [ - "## Classification Model with Random Forest\n", - "\n", - "We define a machine learning pipeline that includes:\n", - "- **Scaling numeric features** with `StandardScaler`\n", - "- **Training a Random Forest classifier** with balanced class weights to handle the imbalanced dataset\n", - "\n", - "We then use `GridSearchCV` to perform a **grid search with cross-validation** over a range of key hyperparameters (e.g., number of trees, max depth, etc.). \n", - "The model is evaluated using **Average Precision**, which is better suited for imbalanced classification tasks.\n", - "\n", - "The best combination of parameters is selected, and the resulting model is used to make predictions on the test set.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "943ef7d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 4 folds for each of 72 candidates, totalling 288 fits\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; 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total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 7.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 11.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 11.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 7.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n", - "Best hyperparameters: {'model__max_depth': None, 'model__max_features': 'sqrt', 'model__min_samples_leaf': 1, 'model__min_samples_split': 5, 'model__n_estimators': 300}\n" - ] - } - ], - "source": [ - "\n", - "# Define pipeline (scaling numeric features only)\n", - "pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', RandomForestClassifier(class_weight='balanced', # We have an imbalanced dataset\n", - " random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid\n", - "param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "grid_search = GridSearchCV(\n", - " estimator=pipeline,\n", - " param_grid=param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=4, # 4-fold cross-validation\n", - " n_jobs=-1, # Use all available cores\n", - " verbose=2, # Verbose output for progress tracking,\n", - " refit=True # Refit the best model on the entire training set - it's already true by default\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "grid_search.fit(X_train, y_train)\n", - "\n", - "# Best model\n", - "best_pipeline = grid_search.best_estimator_\n", - "print(\"Best hyperparameters:\", grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba = best_pipeline.predict_proba(X_test)[:, 1]\n", - "y_pred = best_pipeline.predict(X_test)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean_fit_timestd_fit_timemean_score_timestd_score_timeparam_model__max_depthparam_model__max_featuresparam_model__min_samples_leafparam_model__min_samples_splitparam_model__n_estimatorsparamssplit0_test_scoresplit1_test_scoresplit2_test_scoresplit3_test_scoremean_test_scorestd_test_scorerank_test_score
55.8005670.3675330.3097580.016611Nonesqrt15300{'model__max_depth': None, 'model__max_feature...0.0327950.0204150.0315540.0525390.0343260.0115681
175.7482600.1568030.5194340.307019Nonelog215300{'model__max_depth': None, 'model__max_feature...0.0327950.0204150.0315540.0525390.0343260.0115681
294.7845000.0830960.1764120.00631710sqrt15300{'model__max_depth': 10, 'model__max_features'...0.0322330.0185020.0278460.0584320.0342530.0148153
414.5217590.0736400.2065600.00952510log215300{'model__max_depth': 10, 'model__max_features'...0.0322330.0185020.0278460.0584320.0342530.0148153
163.8281320.5503180.1468530.016658Nonelog215200{'model__max_depth': None, 'model__max_feature...0.0332270.0204720.0306660.0514370.0339510.0111665
......................................................
13.7455280.2950010.1595670.023629Nonesqrt12200{'model__max_depth': None, 'model__max_feature...0.0297980.0178250.0300800.0397800.0293710.00778467
613.4908000.0960020.1639260.00897120log212200{'model__max_depth': 20, 'model__max_features'...0.0312500.0170320.0280300.0405880.0292250.00841669
493.5690890.0912600.1510840.00309820sqrt12200{'model__max_depth': 20, 'model__max_features'...0.0312500.0170320.0280300.0405880.0292250.00841669
02.3327130.9329920.1056830.037904Nonesqrt12100{'model__max_depth': None, 'model__max_feature...0.0301120.0173680.0289270.0390040.0288530.00769071
122.1386000.4266320.1016660.020386Nonelog212100{'model__max_depth': None, 'model__max_feature...0.0301120.0173680.0289270.0390040.0288530.00769071
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\n", - "
" - ], - "text/plain": [ - " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", - "5 5.800567 0.367533 0.309758 0.016611 \n", - "17 5.748260 0.156803 0.519434 0.307019 \n", - "29 4.784500 0.083096 0.176412 0.006317 \n", - "41 4.521759 0.073640 0.206560 0.009525 \n", - "16 3.828132 0.550318 0.146853 0.016658 \n", - ".. ... ... ... ... \n", - "1 3.745528 0.295001 0.159567 0.023629 \n", - "61 3.490800 0.096002 0.163926 0.008971 \n", - "49 3.569089 0.091260 0.151084 0.003098 \n", - "0 2.332713 0.932992 0.105683 0.037904 \n", - "12 2.138600 0.426632 0.101666 0.020386 \n", - "\n", - " param_model__max_depth param_model__max_features \\\n", - "5 None sqrt \n", - "17 None log2 \n", - "29 10 sqrt \n", - "41 10 log2 \n", - "16 None log2 \n", - ".. ... ... \n", - "1 None sqrt \n", - "61 20 log2 \n", - "49 20 sqrt \n", - "0 None sqrt \n", - "12 None log2 \n", - "\n", - " param_model__min_samples_leaf param_model__min_samples_split \\\n", - "5 1 5 \n", - "17 1 5 \n", - "29 1 5 \n", - "41 1 5 \n", - "16 1 5 \n", - ".. ... ... \n", - "1 1 2 \n", - "61 1 2 \n", - "49 1 2 \n", - "0 1 2 \n", - "12 1 2 \n", - "\n", - " param_model__n_estimators \\\n", - "5 300 \n", - "17 300 \n", - "29 300 \n", - "41 300 \n", - "16 200 \n", - ".. ... \n", - "1 200 \n", - "61 200 \n", - "49 200 \n", - "0 100 \n", - "12 100 \n", - "\n", - " params split0_test_score \\\n", - "5 {'model__max_depth': None, 'model__max_feature... 0.032795 \n", - "17 {'model__max_depth': None, 'model__max_feature... 0.032795 \n", - "29 {'model__max_depth': 10, 'model__max_features'... 0.032233 \n", - "41 {'model__max_depth': 10, 'model__max_features'... 0.032233 \n", - "16 {'model__max_depth': None, 'model__max_feature... 0.033227 \n", - ".. ... ... \n", - "1 {'model__max_depth': None, 'model__max_feature... 0.029798 \n", - "61 {'model__max_depth': 20, 'model__max_features'... 0.031250 \n", - "49 {'model__max_depth': 20, 'model__max_features'... 0.031250 \n", - "0 {'model__max_depth': None, 'model__max_feature... 0.030112 \n", - "12 {'model__max_depth': None, 'model__max_feature... 0.030112 \n", - "\n", - " split1_test_score split2_test_score split3_test_score mean_test_score \\\n", - "5 0.020415 0.031554 0.052539 0.034326 \n", - "17 0.020415 0.031554 0.052539 0.034326 \n", - "29 0.018502 0.027846 0.058432 0.034253 \n", - "41 0.018502 0.027846 0.058432 0.034253 \n", - "16 0.020472 0.030666 0.051437 0.033951 \n", - ".. ... ... ... ... \n", - "1 0.017825 0.030080 0.039780 0.029371 \n", - "61 0.017032 0.028030 0.040588 0.029225 \n", - "49 0.017032 0.028030 0.040588 0.029225 \n", - "0 0.017368 0.028927 0.039004 0.028853 \n", - "12 0.017368 0.028927 0.039004 0.028853 \n", - "\n", - " std_test_score rank_test_score \n", - "5 0.011568 1 \n", - "17 0.011568 1 \n", - "29 0.014815 3 \n", - "41 0.014815 3 \n", - "16 0.011166 5 \n", - ".. ... ... \n", - "1 0.007784 67 \n", - "61 0.008416 69 \n", - "49 0.008416 69 \n", - "0 0.007690 71 \n", - "12 0.007690 71 \n", - "\n", - "[72 rows x 17 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Retrieve cv results\n", - "pd.DataFrame(grid_search.cv_results_).sort_values(by='mean_test_score', ascending=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We apply a threshold selector to find a proper value for F2 optimisation, rather than defaulting to 0.5." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Find the best threshold for F2 score\n", - "\n", - "def find_best_threshold(y_true, y_proba, beta=2.0):\n", - " thresholds = np.linspace(0, 1, 200)\n", - " f2_scores = []\n", - "\n", - " for t in thresholds:\n", - " preds = (y_proba >= t).astype(int)\n", - " score = fbeta_score(y_true, preds, beta=beta)\n", - " f2_scores.append(score)\n", - "\n", - " best_index = np.argmax(f2_scores)\n", - " return thresholds[best_index], f2_scores[best_index]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best threshold: 5.0% ā€” F2 score: 13.95%\n" - ] - } - ], - "source": [ - "# Predict probabilities\n", - "y_pred_proba = best_pipeline.predict_proba(X_test)[:, 1]\n", - "\n", - "# Find best threshold for F2\n", - "best_thresh, best_f2 = find_best_threshold(y_test, y_pred_proba, beta=2.0)\n", - "print(f\"Best threshold: {100*best_thresh:.1f}% ā€” F2 score: {100*best_f2:.2f}%\")\n", - "\n", - "# Use that threshold for final classification\n", - "y_pred_opt = (y_pred_proba >= best_thresh).astype(int)" - ] - }, - { - "cell_type": "markdown", - "id": "fc2fcc89", - "metadata": {}, - "source": [ - "## Evaluation\n", - "This section aims to evaluate how good the new model is vs. the actual Resolution Incidents.\n", - "\n", - "We start by computing and displaying the classification report, ROC Curve, PR Curve and the respective Area Under the Curve (AUC)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30786f7c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " No Incident 0.99 0.88 0.93 6347\n", - " Incident 0.04 0.43 0.07 69\n", - "\n", - " accuracy 0.87 6416\n", - " macro avg 0.52 0.66 0.50 6416\n", - "weighted avg 0.98 0.87 0.92 6416\n", - "\n" - ] - } - ], - "source": [ - "# Print classification report\n", - "print(classification_report(y_test, y_pred_opt, target_names=['No Incident', 'Incident']))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Classification Report\n", - "\n", - "The **Classification Report** provides key metrics to evaluate how well the model performed on each class.\n", - "\n", - "It includes the following metrics for each class (0 and 1):\n", - "* Precision: Out of all predicted positives, how many were actually positive?\n", - "* Recall: Out of all actual positives, how many did we correctly identify?\n", - "* F1-score: Harmonic mean of precision and recall (balances both)\n", - "* Support: Number of true samples of that class in the test data\n", - "\n", - "Interpretation:\n", - "* Class 0 = No incident\n", - "* Class 1 = Has resolution incident (rare, but important!)\n", - "\n", - "A few explanatory cases:\n", - "* A high recall for class 1 means we're catching most incidents.\n", - "* A high precision for class 1 means when we predict an incident, we're often correct.\n", - "* The F1-score gives a single balanced measure (good for imbalanced data).\n", - "\n", - "Special note for imbalanced data:\n", - "Since class 1 (or just True) is rare (1% in our case), metrics for that class are more critical.\n", - "We want to maximize recall to catch as many real incidents as possible ā€” without letting precision drop too low (to avoid too many false alarms)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b4da914", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC Curve\n", - "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')\n", - "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC Curve')\n", - "plt.legend(loc='lower right')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the ROC Curve\n", - "\n", - "The **Receiver Operating Characteristic (ROC) curve** shows how well the model distinguishes between the positive and negative classes across all decision thresholds.\n", - "\n", - "A quick reminder of the definitions:\n", - "* True Positive Rate (TPR) = Recall\n", - "* False Positive Rate (FPR) = Proportion of negatives wrongly classified as positives\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is False Positive Rate\n", - "* The y-axis is True Positive Rate\n", - "\n", - "The curve shows how TPR and FPR change as the threshold varies\n", - "\n", - "It's important to note that:\n", - "* A model with no skill will produce a diagonal line (AUC = 0.5)\n", - "* A model with perfect discrimination will hug the top-left corner (AUC = 1.0)\n", - "\n", - "The Area Under the Curve (ROC AUC) gives a single performance score:\n", - "* Closer to 1 means better at ranking positive cases higher than negative ones\n", - "\n", - "**Important!**\n", - "\n", - "While useful, the ROC curve can sometimes overestimate performance when the dataset is imbalanced, because it includes negatives (which dominate in our case, around 99%!). Thatā€™s why we also MUST check the Precision-Recall curve." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6790d41d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# PR Curve\n", - "precision, recall, _ = precision_recall_curve(y_test, y_pred_proba)\n", - "pr_auc = average_precision_score(y_test, y_pred_proba)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(recall, precision, color='green', lw=2, label=f'PR curve (AUC = {pr_auc:.4f})')\n", - "plt.xlabel('Recall')\n", - "plt.ylabel('Precision')\n", - "plt.title('Precision-Recall Curve')\n", - "plt.legend(loc='lower left')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Precision-Recall (PR) Curve\n", - "\n", - "The **Precision-Recall (PR) curve** helps evaluate model performance, especially on imbalanced datasets like ours (where positive cases are rare).\n", - "\n", - "A quick reminder of the definitions:\n", - "* Precision = How many of the predicted positives are actually positive\n", - "* Recall = How many of the actual positives the model correctly identifies\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is Recall \n", - "* The y-axis is Precision \n", - "\n", - "The curve shows the trade-off between them at different model thresholds\n", - "\n", - "In imbalanced datasets, accuracy can be misleading ā€” the PR curve focuses only on the positive class, making it much more meaningful:\n", - "* A higher curve means better performance\n", - "* The area under the curve (PR AUC) summarizes this: closer to 1 is better" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n", - "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_opt).ravel()\n", - "\n", - "# Total predictions\n", - "total = tp + tn + fp + fn\n", - "\n", - "# Compute all requested metrics\n", - "recall = recall_score(y_test, y_pred_opt)\n", - "precision = precision_score(y_test, y_pred_opt)\n", - "f1 = fbeta_score(y_test, y_pred_opt, beta=1)\n", - "f2 = fbeta_score(y_test, y_pred_opt, beta=2)\n", - "f3 = fbeta_score(y_test, y_pred_opt, beta=3)\n", - "fpr = fp / (fp + tn) if (fp + tn) != 0 else 0\n", - "\n", - "# Scores relative to total\n", - "tp_score = tp / total\n", - "tn_score = tn / total\n", - "fp_score = fp / total\n", - "fn_score = fn / total\n", - "\n", - "# Create DataFrame\n", - "summary_df = pd.DataFrame([{\n", - " \"flagging_analysis_type\": \"RISK_VS_CLAIM\",\n", - " \"count_total\": total,\n", - " \"count_true_positive\": tp,\n", - " \"count_true_negative\": tn,\n", - " \"count_false_positive\": fp,\n", - " \"count_false_negative\": fn,\n", - " \"true_positive_score\": tp_score,\n", - " \"true_negative_score\": tn_score,\n", - " \"false_positive_score\": fp_score,\n", - " \"false_negative_score\": fn_score,\n", - " \"recall_score\": recall,\n", - " \"precision_score\": precision,\n", - " \"false_positive_rate_score\": fpr,\n", - " \"f1_score\": f1,\n", - " \"f2_score\": f2,\n", - " \"f3_score\": f3,\n", - " \"roc_auc_score\": roc_auc,\n", - " \"pr_auc_score\": pr_auc\n", - "}])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_confusion_matrix_from_df(df, flagging_analysis_type, name_of_the_experiment=\"\"):\n", - "\n", - " # Subset - just retrieve one row depending on the flagging_analysis_type\n", - " row = df[df['flagging_analysis_type'] == flagging_analysis_type].iloc[0]\n", - "\n", - " # Define custom x-axis labels and wording\n", - " if flagging_analysis_type == 'RISK_VS_CLAIM':\n", - " x_labels = ['With Submitted Claim', 'Without Submitted Claim']\n", - " outcome_label = \"submitted claim\"\n", - " elif flagging_analysis_type == 'RISK_VS_SUBMITTED_PAYOUT':\n", - " x_labels = ['With Submitted Payout', 'Without Submitted Payout']\n", - " outcome_label = \"submitted payout\"\n", - " else:\n", - " x_labels = ['Actual Positive', 'Actual Negative'] \n", - " outcome_label = \"outcome\"\n", - "\n", - " # Confusion matrix structure\n", - " cm = np.array([\n", - " [row['count_true_positive'], row['count_false_positive']],\n", - " [row['count_false_negative'], row['count_true_negative']]\n", - " ])\n", - "\n", - " # Create annotations for the confusion matrix\n", - " labels = [['True Positives', 'False Positives'], ['False Negatives', 'True Negatives']]\n", - " counts = [[f\"{v:,}\" for v in [row['count_true_positive'], row['count_false_positive']]],\n", - " [f\"{v:,}\" for v in [row['count_false_negative'], row['count_true_negative']]]]\n", - " percentages = [[f\"{round(100*v,2):,}\" for v in [row['true_positive_score'], row['false_positive_score']]],\n", - " [f\"{round(100*v,2):,}\" for v in [row['false_negative_score'], row['true_negative_score']]]]\n", - " annot = [[f\"{labels[i][j]}\\n{counts[i][j]} ({percentages[i][j]}%)\" for j in range(2)] for i in range(2)]\n", - "\n", - " # Scores formatted as percentages\n", - " recall = row['recall_score'] * 100\n", - " precision = row['precision_score'] * 100\n", - " f1 = row['f1_score'] * 100\n", - " f2 = row['f2_score'] * 100\n", - " f3 = row['f3_score'] * 100\n", - " roc_auc = row['roc_auc_score'] * 100\n", - " pr_auc = row['pr_auc_score'] * 100\n", - "\n", - " # Set up figure and axes manually for precise control\n", - " fig = plt.figure(figsize=(9, 8))\n", - " grid = fig.add_gridspec(nrows=3, height_ratios=[1, 15, 2])\n", - "\n", - " \n", - " ax_main_title = fig.add_subplot(grid[0])\n", - " ax_main_title.axis('off')\n", - " ax_main_title.set_title(f\"{name_of_the_experiment} - Flagged as Risk vs. {outcome_label.title()}\", fontsize=14, weight='bold')\n", - "\n", - " # Heatmap\n", - " ax_heatmap = fig.add_subplot(grid[1])\n", - " ax_heatmap.set_title(f\"Confusion Matrix ā€“ Risk vs. {outcome_label.title()}\", fontsize=12, weight='bold', ha='center', va='center', wrap=False)\n", - "\n", - " cmap = sns.light_palette(\"#A73A52\", as_cmap=True)\n", - "\n", - " sns.heatmap(cm, annot=annot, fmt='', cmap=cmap, cbar=False,\n", - " xticklabels=x_labels,\n", - " yticklabels=['Flagged as Risk', 'Flagged as No Risk'],\n", - " ax=ax_heatmap,\n", - " linewidths=1.0,\n", - " annot_kws={'fontsize': 10, 'linespacing': 1.2})\n", - " ax_heatmap.set_xlabel(\"Resolution Outcome (Actual)\", fontsize=11, labelpad=10)\n", - " ax_heatmap.set_ylabel(\"Flagging (Prediction)\", fontsize=11, labelpad=10)\n", - " \n", - " # Make borders visible\n", - " for _, spine in ax_heatmap.spines.items():\n", - " spine.set_visible(True)\n", - "\n", - " # Footer with metrics and date\n", - " ax_footer = fig.add_subplot(grid[2])\n", - " ax_footer.axis('off')\n", - " metrics_text = f\"Total Booking Count: {row['count_total']} | Recall: {recall:.2f}% | Precision: {precision:.2f}% | F1 Score: {f1:.2f}% | F2 Score: {f2:.2f}% | ROC AUC: {roc_auc:.2f}% | PR AUC: {pr_auc:.2f}%\"\n", - " date_text = f\"Generated on {date.today().strftime('%B %d, %Y')}\"\n", - " ax_footer.text(0.5, 0.7, metrics_text, ha='center', fontsize=9)\n", - " ax_footer.text(0.5, 0.1, date_text, ha='center', fontsize=8, color='gray')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot confusion matrix for claim scenario\n", - "plot_confusion_matrix_from_df(summary_df, 'RISK_VS_CLAIM', 'Contactless')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Importance\n", - "Understanding what drives the prediction is useful for future experiments and business knowledge. Here we track both the native feature importances of the trees, as well as a more heavy SHAP values analysis.\n", - "\n", - "Important! Be aware that SHAP analysis might take quite a bit of time." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d66ffe2c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## BUILT-IN\n", - "\n", - "# Get feature importances from the model\n", - "importances = best_pipeline.named_steps['model'].feature_importances_\n", - "features = X.columns\n", - "\n", - "# Create a Series and sort\n", - "feat_series = pd.Series(importances, index=features).sort_values(ascending=True) # ascending=True for horizontal plot\n", - "\n", - "# Plot Feature Importances\n", - "plt.figure(figsize=(8, 8))\n", - "feat_series.plot(kind='barh', color='skyblue')\n", - "plt.title('Feature Importances')\n", - "plt.xlabel('Importance')\n", - "plt.grid(axis='x')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Feature Importance Plot\n", - "The **feature importance plot** shows how much each feature contributes to the modelā€™s overall decision-making.\n", - "\n", - "For tree-based models like Random Forest, importance is based on how often and how effectively a feature is used to split the data across all trees.\n", - "A higher score means the feature plays a bigger role in improving prediction accuracy.\n", - "\n", - "In the graph you will see that:\n", - "* Features are ranked from most to least important.\n", - "* The values are relative and model-specific ā€” not directly interpretable as weights or probabilities.\n", - "\n", - "This helps us identify which features the model relies on most when making predictions.\n", - "\n", - "**Important!**\n", - "Unlike SHAP values, native importance doesn't show how a feature affects predictions ā€” only how useful it is to the model overall. For deeper interpretability (e.g., direction and context), SHAP is better (but it takes more time to run)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e2197cea", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PermutationExplainer explainer: 6417it [1:26:06, 1.24it/s] \n" - ] - } - ], - "source": [ - "## SHAP VALUES\n", - "\n", - "# SHAP requires that all features passed to Explainer be numeric (floats/ints)\n", - "X_test_shap = X_test.copy()\n", - "X_test_shap = X_test_shap.astype(float)\n", - "\n", - "# Function that returns the probability of the positive class\n", - "def model_predict(data):\n", - " return best_pipeline.predict_proba(data)[:, 1]\n", - "\n", - "# Ensure input to SHAP is numeric\n", - "X_test_shap = X_test.astype(float)\n", - "\n", - "# Create SHAP explainer\n", - "explainer = shap.Explainer(model_predict, X_test_shap)\n", - "\n", - "# Compute SHAP values\n", - "shap_values = explainer(X_test_shap)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9cae1a51", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_795/3711913411.py:2: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", - " shap.summary_plot(shap_values.values, X_test_shap)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot summary\n", - "shap.summary_plot(shap_values.values, X_test_shap)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the SHAP Summary Plot\n", - "\n", - "Each point on a row represents a SHAP value for a single prediction (row = feature).\n", - "The x-axis shows how much the feature contributed to increasing or decreasing the prediction.\n", - "* Right (positive SHAP value): pushes prediction toward the positive class (i.e., higher chance of incident).\n", - "* Left (negative SHAP value): pushes prediction toward the negative class (i.e., lower chance of incident).\n", - "\n", - "Color shows the actual feature value for that point:\n", - "* Red = high value\n", - "* Blue = low value\n", - "\n", - "In other words:\n", - "* The position tells you impact.\n", - "* The color tells you feature value.\n", - "* The density (thickness) of dots shows how often a value occurs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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TkwMLCwt4eHjA29sbMplM1yFSKfv000+xfv169O/fH02bNoWZmRmMjIx0HRYRERERUcV17Gsg7Bkwcqnm7QN8gZY1gQGLgK0BuWWbzwIhS4GvBgNDfyr4+HP6A6ZGQKMZwOOY3LLAUODwl8B77YAVh3LLrj3MfeTlapP7WHkYyMou2nnp6wELdwAdfPD06VPY2dkVbX8qUUyOEf3nzp07uHnzJqpUqYJq1apBT08P4eHhuHjxIu7fv48+ffpAKuVL5k12+PBhuLi4YO3atW9UUmzMmDEYNmzYG3VO9GaqUqUKkpOT+V5LRERE2hvgC0TG5/bsUohJzE2QDWsNGEiBzAISV/2bA7svvkiMAcCRYODOU2BQixfJMU2GtAL09IB1J4sed44cOHgVuPm46PtSieOwSqL/eHp6YujQoWjfvj3q1q2L2rVro2PHjmjQoAHi4uJw+/ZtXYdIAJ4/f47s7CJ+K6OlmJgYmJqavnFJJKlUCjMzMyYctJSZmclhoiUgJycHCQkJRdpHIpHA1NQUhoaGpRMUERERvXkaeACX7yvn71IKDM3tEVajgPm8nK0Bh0rAxXvq2wJDC5+3bGjr3GGVJ4u52qS+HvDDv6hVq1bx9qcSw+QY0X/s7OxgYGCgVl61alUAQHx8/OsOCQDQt29fSCQS3Lp1C4MGDYKlpSVkMhm8vLywZcsWlboFzW2kaW4oxXxFgYGBaNSoEYyMjGBhYYH+/fsjMTER6enpGDlyJKytrSGTyVCrVi0cPnxYY5zZ2dn48MMPYWdnB5lMBjc3NyxYsAByuVyt7unTp9G6dWuYm5tDJpPByckJ77//vlpCQhHz5cuX0a5dO5iZmaFSpUp49uyZ1tcvOzsbs2bNQuXKlSGTyWBmZobmzZtj//79atctIiICISEhkEgkRZ736MSJE+jRowecnZ1haGgIIyMjeHl5YcmSJWrXQPGcBgUFoUePHjA3N4ehoSG8vb2xc+dOlbp552D6888/4eHhAZlMBhsbG4wYMUKrJE5+90VGRgamTZsGNzc3yGQymJiYoEmTJti1a5faMeLi4tCvXz9lrHXq1MH27du1vj4vy2+urOPHj0MikWDWrFlq8f/2229a32PffvstXFxcIJPJ4OjoiI8//hhLly5Vuw6KOdp2796NESNGwMbGBkZGRli/fj0A4M8//0TLli1ha2sLmUwGc3NzNGvWDHv37tV4XosXL4arqytkMhkcHBzw4YcfIisrS2PdhIQEjB49Gk5OTspjt27dGgEBAWp1Hzx4gA4dOsDExATGxsZo0qQJTp8+XfBFLsDhw4fRrFkzVKpUCTKZDFZWVmjUqBE2b95crBjzPkdTp06Fo6MjDA0N8d1336FOnTqwsLBAVFSUWhy7d++GRCLB+PHjARQ859jq1atRr149ZfLMyckJffv2RUzMi295hRBYsGABqlWrpnwd1q1bF6tXr1Y73i+//AIvLy/l8ezt7dGuXTvcuHGjWNeUiIiIdMTJCojQ8FlNUeZsXfC+eeu+vL+NeW7PM01qVwbquQMbiv83GXLkwD/HEX6lmMk1KjHsRkBUiOTkZACAsbGxVvUzMjIgXv7WIh8ymQz6+vpa1e3Tpw/Mzc0xYcIEJCYm4u+//8bIkSPRoEEDVKtWTatjaBIfH49OnTqhbdu26NGjB06ePIlt27ZBCIGYmBjExcXh/fffx/Pnz/H3339j2LBhuH37NipVqqRynCVLliA1NRWDBw+GTCaDv78/5s6di8TERHz33XfKev7+/hgyZAjs7e3x7rvvwt7eHhcuXMCqVatw7do1nDx5UiVJmZ6ejs6dO6NWrVr46KOPEBcXBxMTE63Pr2fPnjhw4AB8fHzg5+eH2NhYbNiwAX369MG6devQv39/tGvXDosWLcK8efNgYmKCadOmAQDq1aundTubN29GSEgIOnfuDA8PD8THx2Pr1q2YNm0a4uPjMW/ePLV9+vXrB1NTU3zwwQeIjY3F+vXrMXjwYGzZsgW9evVSqXvu3Dns3LkT/fv3R+XKlXHw4EH8888/ePToEQ4dOlTkXmFZWVlo2bIlgoKC0L59ewwfPhzJycnYtGkT+vfvjzVr1mDw4MHKum+99RZu3bqFdu3aoUWLFrhx4waGDRsGCwuLQloqOdreY59++im++eYbuLu7Y9KkScjOzsbatWsLjHXy5MmQSqUYMWIEDA0NUaNGDQDAsmXLYGhoiAEDBsDFxQUPHjzAli1b0K9fP+zevRsdO3ZUHmPevHn4/PPP4ebmhokTJyI7Oxvr16/X2O7z589Rv359REZGolu3bvDx8UF0dDTWr1+Pzp07Y9++fXjrrbcA5CaofH19ER0djR49eqBevXoICAhA7969kZWVBUtLyyJdx1u3bqFnz56wsLCAn58fXFxc8OzZM5w/fx7nz5/HoEGDihyjwuLFi5GZmYn+/fvDxsYG3t7esLW1xYwZM7BmzRpMnz5dpf4ff/wBPT09jB49usCYJ02ahN9++w3Ozs7w8/ODq6sr7t+/j4MHDyI8PBy2trYAgLfffht79uxBixYtMHDgQGRnZ2PHjh0YM2YMYmJiMGPGDGW7U6ZMQc2aNTFx4kSYmZnhyZMnOHbsGO7du4c6deoU6ZoSERFRCZHq5648mZdMChjKchNVecUl5/YWMzYAMjSMLEnPzP3XWL0DhJJiW4aGLzPTs17U0TQsc2jr3H+LM6QyL7mA8V/HgeYNXu049GoEEeUrJydH7NixQ/z5558iPj5eq33WrVsnli9frtXj9u3bhR6vT58+AoDo0qWLyM7OVpb/888/AoD46KOPlGW///67ACD++OMPteM0bNhQ2NjYiMTERGWZg4ODACC+/vprlbpeXl5CIpGIhg0bivT0dGX5ggULBADx888/q7VpbW0t7t27pyyPj48X9vb2wsTERDx48EAIIURaWpqwsrISNWrUULueX3/9tVrsDRs2FADEoEGDRE5OTqHX6mW7d+8WAETz5s1VzuPChQtCT09PVK9eXaXcwcFB1KhRo8jtCCFEQkKCkMvlKmUpKSnC1dVVODk5iZSUFGW54jlt3LixSvsBAQFCIpGIunXriqysLCGEEA8ePBAAhEQiERs3blTWlcvlol27dgKA+N///qcs//jjjwUAcfjwYWWZpvti3rx5as+lEEI8f/5c2NnZCS8vL5GRkSGEEOLHH38UAMQ777yj8jwojvHyfaWN/K71sWPHBADxySefqMWvzT0WGxsrDAwMhIuLi4iKilLWffr0qTAxMVG7Dorr5eXlpfEcNL3ug4ODhVQqFZ06dVKpZ2hoKJycnMTTp0+V5WFhYcLIyEit3ffee0/IZDKxbds2lWPfv39fGBsbi9atWyvvp4kTJwoAYubMmSp1R44cKQAU+Z5VvI7XrFlTYL2ixKh4jhwcHMSTJ09U6sfGxgqpVCqaNm2qcv8kJiYKQ0NDUb9+feV7m+J+f+edd5T1zpw5IwCIWrVqiZiYGJVjy+Vy5TE3bNig9p4ohBBZWVnCy8tL2NjYiISEBCGEEB06dBBGRkYqz1VxxcbGqryOk5KSVO6ljIwMtbjDw8ML/DkiIkLl/YRtsA22wTbYBtsoN20kJgqBvsV/tPlUaK3K2Nx9klKFWHlI/Vjd5uXW6/xV/u01mp5bZ9hP6tu+/+9vIIOBmvd9ECVEcNirnS/6CqHfXyS1mVV+n/Ny0kZhOKySqAABAQGIiopC48aN1XpK5ad9+/bo3r27Vo/KlStrHcuUKVNUepl1794dAHD//v0indPLKlWqhA8//FClzNfXF0IIjBo1SmXun65duwIAQkJC1I7Tq1cveHq+GJNfqVIljBkzBqmpqcrhd3v37kV8fDz69u2L5ORkPHnyRPno3bs3AODQIfUJL+fOnQs9vaK/XSmGxk2fPl3lPBo3boy2bdsiNDQUwcHBRT6uJpaWlsolmJOSkvD06VPExcWhVatWiIiI0Pg8TZ48WSWu5s2bo1mzZrh58yZCQ0NV6tapUwcDBgxQ/iyRSPD1118DQLGGN65duxaOjo7o0aOHyvOQmJiIVq1aITQ0FA8f5q7G4+/vDz09PXz66acqz8OMGTOK1IvvVWlzj+3evVvZc8ne3l5Z19nZGX369Mn32O+99x7Mzc3VyhWveyEE4uLi8OTJE1hZWcHNzQ137txBZmamst2MjAz0798fzs4v5rWoUqWK8t5WEEJg27ZtqFGjBho3bqxy/WUyGXx8fBAUFITnz58rj21hYaHW6+qbb77R4qqps7KyUh43v+HiRY1RoU+fPnBxcVEps7a2Rrt27XDlyhXcvPliyMCaNWuQkZGBAQMGFNiDdsWKFQCA2bNnw8bGRmWbRCJR3pOrVq2CkZER3nvvPZV4IyMj0aNHD8TGxuLs2bMAcl+vmZmZWLduXb7DXrVlbW2t8jo2MzNTuZcMDAzU4nZycirwZ0dHR5Ul3dkG22AbbINtsI3y1MYrCQoDOn6p+ggKAw5cUS+PTMjdJyL+xfBIleD/KwuPy789xXDK/PaPTdLca6xlTcDd/tV7jQFAjhzGnwwo1895eWijMBxWSZSPCxcu4MaNG6hZsyYaNNC+i6ujo2OpxPPyMB/Fm0FRJ7x+ma2trVpSwNo6d1x+9erVVcoVH6rj4tR/wWiaRFJx3RSJoatXrwIAvv/+e3z//fca44mNjVX52dzcvEhJxLwU8xc1atRIbVudOnVw9OhR3LlzB02aNCnW8fOKjIzEBx98gKNHj6olDACozIukUL9+fbWy2rVr49y5cwgJCVG5plWqVFFLIHh7ewMAnj59WuR4w8LCkJGRUeCQ3PDwcFSvXh2PHz+GpaWl2vNgaGgIFxcXjfdDadDmHlMkbmvWrKlWt6ChcrVr19ZYHhAQgGnTpuHy5cvIyMhQ2WZra4vMzEwYGBgoF+zQ1K6Pjw82bdqk/Dk6OhqJiYm4ceMG3NzcNLYrkUiQlpaGSpUqITw8HFWqVNH4B0Bx/gAdOXIkVq9ejU2bNmHbtm3w8vJC69atMWrUKOVrpagxKiiGo75s3LhxOHToEFatWoUff/wRAPDXX3/BxMQEI0aMKDDe0NBQSCQS+Pr6FlovPT1d4+tKITw8HEBuYvHs2bOYOXMmvv76a3h7e6Njx44YP368SnKTiIiIXrOElNyVIvOKT85NYr1crnA1DGhVC5BIVCflb1YdSEkHQsLzby88Dnj2HGhcVX1b0+rA1Qea9xvaGpDLgfWnCjydQkkkQE0XXHPUQ/1XOxK9IibHiDS4ePEirly5gho1aqBVq1ZF2jctLU3rOccMDAy0nisqv3ratpWTk6OxXE9PL99eWfn15tC2zfz2GzduHNq0aaOxzssfTItyjXRFCIFWrVohLCwM/fr1g6+vL2xsbCCVSrFmzRrs27cv3+uvK0IIuLi4YOHChSrf0uSlSL6VhvzaVPTEet1MTU3Vyh4+fIj27dvDyMgIo0ePhre3N8zNzaGnp4c5c+YgKSmpWK8FxT516tTB3Llz862nSFKXNENDQ5w/fx7Hjh3D1q1bERAQgD///BMrVqzAvHnz8MknnxQ7Rk3XEQB69+4NS0tL7N69G99//z0iIyNx+fJldOzYEa6urlrFnd89oyCEgLm5OX755Zd8V7ts0aIFgNwkZlhYGLZu3Yq9e/fi/PnzmDdvHn799Vds374dbdu21SomIiIiKgP8A4CBLYB+zYGt/y0aZGOeW7bromrPL0+H3H/v51koaGsAMKId4GoDPPnvi/r23oCXC/Cj+kJVkOrnHvv0LeCx+hfgRSIEMLNPbpKMdKpsf+Ik0oGLFy/i8uXLqFGjBtq0aVPoB7KXbd++XTmJf2HatGmjccW+4lIMI3u59xUAPHnypMTa0eTWrVtqZVeuXAEA5VA4Rc8dQ0PDIq0EWVyenp44e/YsLl26BHd3d5VtiuFdJXH9g4ODcffuXbzzzjvYsGGDyrY1a9bku9/Vq1fh4+OjFpeenp5aD5yHDx8iJydHJWF57do1AFAbxqaNypUrIzo6Gl26dFHrkaSpbkBAAB4/fqzS+yojIwNPnz7VerGKvCwtLZGcnIz09HQYGRkpy/MOu3uZNveYorejoidXXkVdhXDdunVIT0/Hjz/+qFxNUWHSpEkq7w2K+0hTuy8P3bWzs4OZmRlSUlIwYMAAyGSyAuNwdnbGs2fPEBsbCzs7O2V5RESE1u81mrRr1w7t2rUDkLsaZr169fDzzz8rVwQtSoyFkUql6NevH1avXo0TJ07gyJEjEELg3XffLXTfGjVq4MyZMwgICFCuHqyJh4cHHj9+jGbNmmm1HLqhoSH8/Pzg5+cHANi6dSsGDBiAb7/9lskxIiKi8sQ/AAi4A6yeBNR2BWKSgA+6Avp6wBcbVese+Sr3X488f9st2Jqb7Dr2NfDzHsDMCJjRGwgOA1YfVW+vS33A1gJY94q9xgDA1hwY0gqO8eqf3+j14pxjRHlcunQJly9fRvXq1YuVGANKb84xbSgSFydOnFDp0fLXX39pTJiVpF27dqnMq5WQkICVK1fCxMREOddTr169UKlSJaxdu1ZjEiE1NRXPnj0rsZjeeecdAMAPP/ygMiTuypUrOHbsGKpXr16kFSnzo0hYyeVylfIrV65onENN4ddff1WJS7FaYK1atdSGtN64cQP+/v7Kn4UQ+PzzzwFAbU4rbQwbNgyJiYmYNWuWWtxAbrJEoX///pDL5Zg/f75K3UWLFiE1NbXIbQO5yazIyEhcunRJWZaRkYGlS5fmu48291jPnj0hk8mwdetWlXspPDwcO3bsKFKMiuf15d5hP/30k9pQ0p49e8LAwABbt25VDt0DcpOaO3fuVKmrp6eHvn37IiwsLN/hxWFhYcr/9+jRA4mJiVi8eLFKnYJ6dBUkPDxc7Tl3d3eHlZUVUlJSkJGRUeQYtTFx4kQAue9HGzZsgIODA/r161fofmPGjAEAfPvttxqHkSvOZcyYMRBCYNq0acjOVp8b5PHjx8oenJq+LFD0KktMTNTuhIiIiKhskMuB7vOBTWeAD3sAi4YDMYlA+y8KHlKp8CQWaPMZcC8S+G5Ybk+uvZeBTl/lv0plZhaw5eyrxa0nAab2Agxlr/xFJL069hwj+s+NGzdw6dIlmJmZwcXFBXfv3lXZbmxsrNXwn9Kac0wbXl5eaNasGQ4dOoSePXuiSZMmuH79Og4fPgwnJ6dSHbJWqVIltGzZEv3794dMJoO/vz+ePXuG6dOnw8PDA0DukKvVq1dj0KBBaNKkCXr16gUvLy8kJiYiJCQER48exZIlSzBu3LgSialHjx7o0qULDhw4gGbNmqFr166IjY3Fhg0bIJPJ8O2338LAoIClnbVUq1YteHh4YNu2bRg4cCC8vb1x9+5d+Pv7o0qVKrh3757G/eLi4tCkSRN0794dMTExWL9+PQwMDPDNN9+oDSWtUqUKRo4cid27d8PNzQ0HDhzApUuX0KpVKwwdOrTIMc+dOxcHDhzAypUrcenSJbRu3RrW1tZ49OgRzpw5AyGEMoE5ceJE/PHHH9i4cSOioqLQsmVLXL9+HQcPHoSjo2OxJjSfOXMm9u3bh4EDB8LPz085AXxBz4c295iNjQ2mTZuG7777Ds2aNUPfvn2RlZWFTZs2wcnJCffu3dM66d2vXz988cUXmDNnDoKDg2Fvb4/AwECcOnVK7bytrKwwe/ZsfPXVV/D19UW/fv2QnZ2NDRs2wMnJSSXZCOQmRgMCAvD5558r708TExM8fPgQJ06cQJUqVXDixAkAwPz58+Hv74/Fixfj1q1bqFevHgICAnDlyhWNiwgU5tNPP8WBAwfQpk0beHp6Qk9PDwcPHsSjR4/Qt29f5RxiRYlRG40aNULVqlWxY8cOpKenY8SIEfkOw8yrRYsWGD9+PP744w/UrVsXPXr0gIuLC8LCwnDgwAHs27cPPj4+GDZsGLZv345t27ahdu3a6NSpExwcHPDkyRNcvnwZDx8+xP3792Fubo4OHTpAJpOhWbNmqFKlChITE+Hv7w+JRIJBgwYV+ZoSERFRKWr3eeF1ElKA95flPgriMV5z+c3HQNd52sXj96N29QpjIAXGdwGQ+yVeYaM5qHQxOUb0n+joaABAcnIyjh8/rrbdyclJ67lxdGnbtm0YNmwYjh49iiNHjqBWrVrYunUrZs6cqVx9sDR8/PHHuH37NjZs2IDnz5/D0dER8+bNw5w5c1Tq9enTBwEBAZg7dy7279+PzZs3w9TUFI6OjvDz80OnTp1KNK7du3dj7ty5WL9+PX744QcYGBigbt26+Oqrr5Srb74qfX19HDp0CO+//z4OHDiAf//9F5UrV8b8+fPx5MkT5QTkL9u2bRtmz56N3377DZmZmahevTrmz5+vsSdY8+bN0a5dO3z77bd48uQJzM3NMWzYMCxdurRY3zTJZDKcOnUK33zzDdatW4fff/8dQG6Sp1atWsqhZkDuvG9nz57F6NGjceTIEZw5cwbVqlXDmjVr8M033xTrvmrbti1++eUXfPfdd/jpp59gZWWF/v37o0+fPujWrZvGfbS9x7799luYmZnht99+w6+//gobGxsMGzYMtra2mDt3rtYrbFavXh07duzA9OnT8ffff0MikaBWrVrw9/fH3Llz1c77yy+/hImJCX7++WcsXboU1tbW8PPzQ/Xq1dVWhLW0tERQUBBmz56NnTt34vz589DT04ONjQ18fHyUvaWA3OckICAAo0ePxqFDh3Do0CHUrVsXO3fuVFnBVFv9+/dHWFgYDh8+jISEBMhkMjg7O2Pu3Ln47LPPihWjtt599118+eWXkEgkGDVqlNb7/f7776hXrx5+/fVX/PPPP5DL5bCxsYGvr6/KPIVbt27Fr7/+iuXLl+Ovv/5CdnY2KlWqhGrVqmHGjBnKIcBjxozBxo0bsXXrViQnJ8PMzAxVq1bFV199heHDhxf5vIiIiIiKRF8PGNUBsC76F51UOiSiuDNrExFRsfTt2xc7duzA06dPC10ZLywsDB4eHhrnM6so/vjjD0yYMAF//PHHK/UqHD58ONasWYOzZ88WuvIhERERERWDEIBef11HUfZJAIT8BlRzApC7qFtx5vGlksM5x4iI6I2SlpamNufU06dPsXXrVjg7O5fIPHNERERERMUi1QPebqJMjAG5Cy2RbnFYJRGVO2FhYRonkc/LxsYGlpaWr9RObGwsnj9/XmAdAwODcjHc9nUoK9fr6NGjGD9+PDp37gw3Nzc8evQI/v7+SEtLw7x587QeVlneREVFISUlpcA6JiYmOp0XkYiIiKjCy5YDM/qoFHFBIN1jcoyIyh3F5OsF+eSTT/Ddd9+9UjtjxowpdIXDGjVq4M6dO6/UzpuirFyv6tWro0qVKti2bRuSk5Mhk8lQtWpVTJkypVjzZJUX3bt3x+XLlwus06pVK5w8efI1RUREREREKvT0gAYeQIuaKsWGhoY6CogUOOcYEZU7/v7+ha6QWL9+fdSqVeuV2rl06RJCQkIKrGNpaYnu3bu/UjtvCl4v3Tpx4gTCwwtertzZ2Rlt2rR5TRERERFRhcM5xwq3eTowsIVKkVwuh54eZ73SJSbHiIiIiIiIiOjVMTlWMFcb4MEfgFRfpfjq1auoX7++bmIiAJyQn4iIiIiIiIiodEkATO+tlhijsoHJMSIiIiIiIiKi0mRqBIzqoHGTvb39aw6GXsbkGBERERERERFRadHXAz7oCpgba9xsbKy5nF4fJseIiIiIiIiIiErT5PwXpXr48OFrDIQ0keo6ACIiIiIiIiJ6Q3wxCEl7A2FubqbrSMoO35qAq62uo6ACcLVKIiIiIiIiIioxCQkJqFSpkq7DKDdSUlJgamqq6zAqNA6rJCIiIiIiIqISk5KSousQypXo6Ghdh1DhMTlGRERERERERCWGyZ6iSUhI0HUIFR6TY0REREREREREOiKTyXQdQoXHOceIiIiIiIiIqMQIISCRSHQdBpHW2HOMiIiIiIiIiErMrVu3dB1CuRIUFKTrECo8JseIiIiIiIiIqMRkZmbqOoRyhQP6dE+q6wCIiIiIiIiI3mhX7gOTVwLyNyAJIgHw3btAq9r5VrGwsHh98bwBbG1tdR1ChcfkGBEREREREVFpWnMC4uwdSN6EHkJ6EuAbf2D/5/lWcXR0fI0BlX/m5ua6DqHC47BKIiIiIiIiolIm9N+QCerlAjhwFbj1JN8qISEhry+eN8CDBw90HUKFx+QYEREREREREWlPqgf8sFPXURCVGCbHiIiIiIiIiEqZRPKG9BwDgGw58M9xICpB42Y3N7fXGk55V7VqVV2HUOExOUZERERERERERSMXwLL9GjdlZGS85mDKt/j4eF2HUOExOUZERERERERUysSbMBl/Xjly4Jc9QJp6IiwqKkoHAZVfcXFxug6hwmNyjIiIiIiIiIiK7nlK7vBKeiX6+vq6DqHCY3KMiIiIiIiIqJTpvUlzjikIAAt3AHK5SrG3t7dOwimveL10j8kxIiIiIiIiolImf9OGVSrcjwJ2X1QpCg0N1VEw5dO1a9d0HUKFx+QYERERERERERWPvl5u77E80tPTdRNLOZWTk6PrECo8JseIiIiIiIiISpmkpIdVWpoAy8cDz1YDyeuBo18BDTy137+mC7DvMyBpHRD7P+CfDwFbC9U6VewAsU3zY3DL3Do5cuDMbeDiXeVu5ubmJXCCFYe1tbWuQ6jwpLoOgIiIiIiIiIiKQCIB9nwK1KsCLNoJxCQCH3QFjn8NNJoB3I0oeH8XG+DkfOB5KjBnHWBmDEx/G/CuAjT9BMjKVq2//iSw97JqWUDIi/9L9YDFO4GN03IP7+JSAidZcVhZWek6hAqPPceItDR27FhIJBJcv35dWfbtt99CIpHA39+/RNuys7PjpIykU9evX4dEIsHYsWN1HUqF4u3tDTs7O12HoTN87yMiojeZKMqcY8e+BlZPyn/7AF+gZU3gvaXA15uBZfuBtp/n9uL6anDhx5/THzA1Atp/Afy6F/h2KzDoB6C+B/BeO/X6lx8A606qPh5Fv9ieLQe2nFWW3b59W/tzJdy7d0/XIVR4TI5RmSSEQHBwMDZt2oRVq1Zh3bp1CAgIQFZWlq5DKzFjx47Fn3/+qeswiMosf39/jB07FlFRUaXe1rFjxzB27FiV5Peb6uHDh+jWrRuqVKkCMzMzyGQyODo6okePHrhy5YrGfe7evYvOnTvDwsICBgYGqFatGpYuXfqaIyciIiKlAb5AZDyw7dyLsphEYPNZoHdTwKCQQWL9m+dOov845kXZkWDgzlNgUAvN+5gYArICjiuRAD/t1v4ciMoQJseoTAoICMC5c+dgZWWFFi1awNPTE9evX8eBAweK9o1LKZs+fTqSkpLQt2/fIu+7YsUKbNu2TeO2hw8fIjAw8FXDIyq22rVrIykpSacJkIMHD2LFihWIjo4uvPIrOnfuHFasWKHzbzkDAwPx8OHDUm3j2bNnCAsLQ8uWLTF58mR8/vnn6NSpE06dOoUWLVogICBApX54eDhatGiBkydPYtCgQZg7dy6MjY0xefJkLFiwoFRjJSIiepOU6JxjDTyAy/eBlz8bBYbm9gir4Zz/vs7WgEMl4KKG3kqBoZrnLftiEJCyAUjfCAQuBDrVU6+TIweWHwSep8DV1bVIp1PReXh46DqECo9zjlGZExcXh+vXr8Pd3R2dO3dWlpubm+Ps2bO4d+8eqlWrpsMIX5DJZJDJZCV+XBMTkxI/JpE2YmNjYWNjAz09PZiZmZXa8SuKjIwMZGVlaX0tjY2NSzkioEmTJrh165Za+YEDB9C1a1csXLgQ27dvV5bPnDkT0dHRWLlyJUaPHg0AmDNnDmrXro0FCxZgwoQJnCeDiIjodXOyAk7eVC+PiM/919kauP4o/33z1n15fxvz3J5nmdmAXAAHrgDbzwNP4wBPB+Djt4F9nwJvfwfsvaS6f1omsPIwcoY1L/65VUBJSUmwtLTUdRgVGnuOUZmjGG/98rwzNWvWhFQqRWhoqC7C0kjTnGPJyckYO3YsnJ2dYWhoCBMTE7i5uWHo0KEAXszlBOR+GJVIJMqHgqZ5dxRlgYGBaNq0KYyMjGBiYoJWrVrh/v37arGdPHkSDRs2hKGhIczMzNClSxc8efIEEokEXbt2Ldb5zps3D56enjAwMICJiQkaNmyInTt3aqy7ZcsWNG3aVDlsy97eHj169MCTJ09U6i1btgze3t4wMTGBoaEhnJ2dMXDgQKSlpQEoeF43TfMzKa7TkSNHUK9ePRgZGSnP/+UeObGxsRg1ahRq1KgBc3Nz5fCy4cOHIzExUaWuv78/JBIJvv32WyxYsACVK1eGTCaDra0tPvroI43X4OjRo2jdujUsLS0hk8lgY2OD9u3b4+rVq0hLS4OFhQVq1aqlcd+pU6dCIpGoJCkKkzfG2bNnw8nJCTKZDE5OTpg9e3a+1y8oKAitWrWCmZkZbG1tAeQ/51hmZiamTJkCV1dXyGQymJmZoWXLljh9+rRKvbz7//LLL6hatSoMDAyUr4PCdO3aFStWrFDGqXiN5I3n+vXr6NSpk/L6Ojg44L333lN77gozduxYzJkzBwAwcOBAZVt5XydpaWn46KOP4OHhoXxdV6tWTbmf4jgSiQRnz57FO++8A2traxgbG2P//v1ax6LpnlaU3b17F+3bt4epqSkMDQ3RqFEjXLx4sUjnWpCaNWsCAJ4/f65SvnfvXjg4OCgTY0DuFwPvv/8+UlJSsGbNmiK3dfv2bbRp0wYmJiYwNjZGs2bNcPXqVY11f/nlFzRv3hy2traQyWQwNzdX9mTLy93dHba2thqXQl+6dCkkEgkWLlwIIHe59JkzZ6JKlSowMjKCsbExnJ2d0aNHD2RkZBT5fIiIiLSR7wgYqX5uQirvQyYFDGXq5YrPDMYGQEa2+rHSM19sz49iW4aGKWvSs1TrPI4Bus7L7RG2+yLwyx6gwTQgOhH4YYSmkwR++BcRj56ob6N8xcTEFF6JShV7jlGZEx0dDYlEAnt7e5VyqVQKGxsbrYZYCSGK9AHH0NCwxLo5Dx48GHv37kXnzp3h6+uL7OxshISE4Ny53PkAXF1dsWjRIsyYMQO1a9fGyJEjtT52TEwMOnfujLZt26Jbt24ICgrCv//+iwEDBuDy5Rerx1y+fBldu3aFXC7HO++8AxcXFxw6dAht27Yt9nm9++67WLt2LapXr44pU6YgKSkJmzdvRr9+/bBq1Sq89957yrpffvklvv76a1hZWWHQoEFwd3fHw4cPcfToUYSGhiq7WY8cORJ///03XF1d8e6778LZ2Rl3797FoUOHkJiYWOxeNNHR0Xj77bfRtm1bvP3227h8+TL27duHt956Czdu3ICFRe4S1ffu3cO2bdvQvn179O3bFzKZDKdOncLatWtx48YNXLp0Se3Yf//9N+Lj4zFgwABUqlQJW7duxc8//ww3Nzd8/PHHynp//fUXxo0bB0NDQ/Tp0wfVq1dHREQETpw4gcDAQNSvXx89e/bEhg0bcOHCBTRp0kSlnc2bN8PJyalYQ3ZXrlyJ+Ph4DBo0CObm5tixYwe+++47xMXFYfny5Sp109PT0b59e/j4+ODjjz8udH6vzp0748SJE2jQoAHeffddREZGYtOmTejYsSP27t2L9u3bq9Q/dOgQ1q9fj4EDB8LDwwOVKlXS6hwmT56M5ORknDlzBjNmzFC+HzRt2hQAcOPGDbRo0QJpaWno168fatSogZMnT+J///sfLl68iMuXL8PAoIA/CvMYNmwYIiMjsWvXLrz33nuoU6cOACgTl2lpaWjWrBmuXbuGhg0bom/fvjAyMsL169exZ88etaGFQ4cOhYGBAUaPHg2JRIIqVapoFUdBMjIy8NZbb6Fu3bqYNm0a7t+/j02bNqF3794ICwsrVg/WjIwMREdHIyMjA9euXcMXX3wBACpJwXv37iE+Ph4dOnRQ279jx44AgPPnzxep3aioKLRu3RqxsbHo06cPateujVOnTqFjx47IzMxUq798+XJYWlpi0KBBcHJywt27d7F161Z07twZZ8+eRcOGDQEAfn5++Pbbb7FhwwYMGzZM5RirV6+GsbExJkyYAACYNGkS/vjjDzRr1gzDhw+Hvr4+7t+/j6NHjyI1NRWGhoZFOiciIqJX0rImcHye5vIhrVTL3McBD6Nze2gZavg4b/Tf3z9p6r9TlRTbDDX8/WAkK3z/+GRg9VFgdv/cVS+fxqpuj4iH5alQoEmj/I9BKkp0yC0VjyAqYzZv3iz++ecfjdsOHTokli9fLrKzsws8RmJioli+fLnWj8TExELjev/99wUAce3aNWXZggULBACxZcsWZZmpqalo3LhxoccDILp06aJxm62trahbt65aGQDx888/q5T37dtXABCBgYHKstatWwsAYufOnSp127RpU2C7+QkMDBQSiUTUqlVLpKamKstDQ0OFsbGxsLW1FZmZmUIIIW7fvi2kUqlwcXERkZGRasdSPHf79u0TAISPj49ISkpSqZOTkyNycnKEEJqvsULdunWFra2tSpniOs2YMUOlfPr06QKAmDx5srIsNTVVpKenqx131KhRAoDYt2+fsmzLli0CgKhUqZJ49uyZsvz58+fC3Nxc1KhRQ63M3NxchISE5HsNLly4IACIoUOHqmzfuXOnACCmTJmitm9BFDEaGhqK27dvq5xntWrVhJ6enkp53bp1BQAxcuRItWNdu3ZNABDvv/++smzdunUCgGjTpo3y+RFCiOPHjws9PT1Ru3Zttf319PREQEBAkc5DQdNrTqFDhw4CgFi9erVKuZ+fnwAg5s2bV6S2CrrPpkyZIgCIESNGqG3L+16kiLdu3boiIyOjSO0raLqnFc/Ty/fDRx99JACINWvWFKutVatWCQDKh6WlpZg2bZpKnUOHDgkAYtiwYWr7P3/+XAAQ7dq1K1K7I0aMEADEN998o1I+cOBA5fXLKz4+Xu0YAQEBQiqVij59+ijLIiMjhYGBgWjdurVK3Vu3bgmJRKJS18PDQ7i4uBQp7vzExsaqvI8kJSWp/E7JyMgQMTExKvuEh4cX+HNERISQy+Vsg22wDbbBNt6gNpLH/irksgFCoK/6o9IwITp8ofq4+kCI/ZfVyw0H5e4T8lSIPRfVjzVqaW6Ddadobgt9hXAenVtnxv/Ut/1zTIiYxPz3VTwmLM89hvdHatvkev1EZujTMv18sI2K10ZhmByjMmf9+vVi7dq1GrcdPXpULF++XGNCI6+srCzx+PFjrR9ZWVmFxqVtcszOzk7Y2NiI06dPF3i84iTHrKys1Or+9ttvAoBYtWqVEEKIzMxMYWhoKKpXr65WV5GQKmpyTJEc+PPPP9W2DRo0SAAQBw8eFEK8SEK9nMR7meKD8N69ewusV5zkmLGxsUoSTwghUlJShLGxscbrIkTuG2xERIR4/Pix8Pf3FwDErFmzlNsViaf33ntPbd+mTZsKMzMz5c+///67ACA+/PDDAs9NcQ6WlpYqyZSuXbsKPT09cf/+/UL3z0sR49tvv622bcmSJQKA+Oyzz1TaBqAxiakpOdanTx8BQJw4cUKtfosWLQQA8fDhQ5X9fX19i3QOeeWXHMvOzhaGhobCw8NDbZ+nT58KiUQimjZtWqS2CrrPqlSpIkxNTdWSuPnFu3z58iK1nVd+yTGJRKLWviJxNXv27GK19fjxY7F+/XqxcuVK8eGHHwp3d3cxatQolXtx+/btAoAYPXq02v7Z2dnFeo5dXFyEpaWlMqGucPfuXY3JMYWcnBwRHR2tfN92c3MTnp6eKnW6dOkipFKpePLkibJs3LhxAoA4dOiQsszb21sYGxuLHTt2FCl2IiKiYpv6l8iR9i886aR4HLsmxOoj+W/ffEaIiDghJP1Uy5cfECI5TQiDgQUfPypBiE2n1ctvPxHicFDh8S3673eo4yjVcml/IQYtEnfu3NHt9S5nrl+/rusQKjzOOUZljlQqhVwu17hNMZeMVFrwiGCpVApXV1etH4Udryjmz5+PlJQUvPXWW3BwcECPHj3w559/apwHp6icnJzUyhTDzRTDTR8/foyMjAy4u7ur1W3UqHhdm8PCwgBAbegfANStWxcAlBN83717FwDQokU+S0D/58GDB5BIJGjVqlWB9YrDwcFBbUimiYkJHBwcEBkZqVL+2WefKecdcnJyQuXKlTFgwAAAQHy8+iSlVatWVSuzsrJCcnKy8mfFioeKIYAFGTlyJJ4/f46///4bQO48aMeOHUPz5s2LvWqNYu6ovBRDzxRz+imYm5vDwcFBq+M+fvwYEokEzZo1U9vm5eUFALh5U3ViWE9PDasdvaJHjx4hIyND43Ph7OyMSpUq4enTpyXWXkREBFxdXbWeVP/l+QJLgpWVlVr7jo6OAHLvmeJwdXXFkCFDMHr0aPz88884dOgQNm/erLz/ASjb1DRMPSUlBQBgZGRUpHajoqLg4uKiNhS0atWqGhcjOXr0KJo1awYTExPY2dmhcuXKqFy5Mh49eoSkpCSVuhMnTkR2djZ+/vlnAIBcLse2bdvg7u6uHAYK5M5lKJPJ0KdPH1hbW6Njx45YsmSJcq5DIiKiMs8/AHC0AvrlmfjexhwY2ALYdTF3Mn0FT4fcR15bA4CejQHXPAsltfcGvFyALWdflNlaqLftbA2Mag8EhQGRL/29nC0HpvdGampqsU+tIsrK0jD/G71WnHOMyhxTU1MkJCQgJycH+vr6KttSUlJgZGSkVv4yuVyO9PR0rds0MjKCnl7J5IrHjh2Lnj17Yv369Th+/DgCAwOxd+9e/Pzzz7h48eIrrUZXUIz5JRTLusKue0HbXzXhOH36dPzwww9o2LAhxowZg8qVK8PQ0BCPHj3CrFmzNF7Twu69oho/fjy++OILrFq1CmPHjsWyZcuQkZGBMWPGlGg7+SntuZUq4sqr5ubmJX7Mgl4HIr/JfYuoWrVqaNy4Mfbs2YPU1FSYmJgoE7QRERFq9RWJcE1J+5Jy69YtdO/eHSYmJhg3bhzq1KkDMzMzSCQSfPLJJ2rJrF69eqFy5crYuHEjFi5ciE2bNiE6OlptYYkePXogLCwMGzZswJEjRxAYGIgjR45gyZIlCAwMhLOzc6mdExERVVwlOq+UfwAQcAdYPQmo7QrEJAEfdAX09YAvNqrWPfJV7r8e41+ULdiam0g79jXw8x7AzAiY0RsIDsudT0xh4XCgqgNw5BoQHge42wPjOgOmRsCUVart6OsBzaoDTarDtAwtolYeaDsvL5UeJseozLGzs8OTJ0/w7NkzlQ9d2dnZiI2N1eqDWEpKCjZs2KB1m0OGDCnRD7TOzs6YPn06pk+fDrlcjhEjRmDt2rVYtWoVJk2aVGLtaKJI8Ch6e+WlaYJ5bSg+IF+4cAH169dX2Xbjxg0ALyYvr169OgDg7NmzaNy4cYHHvHjxIk6ePFng6pmK1RM1LcQQGRmpMVkVFRWFtLQ0lURkamoqoqKilIsBAMDWrVthZ2eHwMBAleMUZ/W9vGrXrg0g93oVtjqjiYkJevTogS1btuDu3btYu3YtrKys1CYULwpFz7W8FAs2aOptpS03NzdcunQJ58+fR+vWrVW2hYSEAHhx7iUhvz8g3dzcYGRkpNYLDsi9JxISEpT3obYKSj45OzvjyZMnyoTRmyw9PR1yuRwxMTFwc3ND1apVYWVlhevXr6vVPXz4MABo7ElYEAcHBzx9+hRZWVkqvcfu3bun9i3z33//jYyMDKxZswYDBw5U2TZhwgSNCxEMHToU3333Hfbv348VK1ZAJpNh8uTJavWsrKzwwQcf4IMPPgCQ24t0/vz5WLRoEX788ccinRMREdFrJ5cD3ecDi0YAH/bIXV3ywl3gvV+BkPDC938SC7T5DFjyHvDdsNyeZnsuAdP+Vu11dvAqML4LMLEbYGUKJKQAJ28C8/2BK/dVj5kjB2bmLiZVEgsSVSQvr1ZOrx+HVVKZo/jwfu3aNZXy27dvIzs7G9WqVSv0GMbGxujevbvWj1fpzZVXVlaW2mp/enp6yuGMeZfoNTQ0REJCQom0m5dMJkPTpk0RGhqKf//9V2Xbd999V6xj+vn5QSKR4KefflIZXnXv3j3s2rULtra2ypUwR48eDalUikWLFmlckljRG2vUqFEAgFmzZmkcyqSo5+PjAwA4ePCgyvYff/xR47BHIHd1QcXKewpffPEF0tLS0KVLF2WZIiGWt4dYZmYmFi5cqPG42nrnnXdgbm6O1atXa0zgvNwj7aOPPoJcLseECRMQEhKCPn36FGv1QYUDBw7gzp07yp/T0tKwbNky6Onpwc/Pr9jHVQy3+/LLL1XO4cyZMwgICEDt2rXh5uZW7OO/TDGk7+XXlL6+Plq2bIkHDx6oJTJnzJgBIQR69epVpLYUyXFNSdg+ffogJSVFY4KlPPbYfPDggcbygIAAXLlyBQ4ODirPY7du3RAVFYVVq158O5yVlYUVK1bAxMSk0ATwyzp06IDnz59j0aJFKuWzZ89Wq6t4jb7cO+7rr7/G8+fPNR5/ypQpkMlkWLBgAU6dOoXWrVurDR1+8kR9eXlfX18AQFxcnPYnQ0REVARF6u3d7nNg5NKC6ySkAO8vA+zeA8z8cve5pP63JzzGq/YaU7j5GOg6L3df6+HAuz8Dz176/brxNND2M8BhJGAwCLAfCfRfqJ4YAwAPe6BX7pfjL0+1QQULZU87nWPPMSpzrK2tUadOHdy4cQMHDx6Em5sb4uPjcf36dTg5OWmVHFPMOfa6xcXFoXLlymjRogV8fHzg4OCA+/fvY/PmzTA1NVXpDVSrVi1cuXIFU6ZMgbu7OyQSCT766KMSieOHH35A69atMWjQIAwePBiVK1fGgQMHlMmkonbpbtKkCYYOHYq1a9fCx8cHvXv3RlJSEjZv3oz09HT89ttvymSOl5cXZs+ejfnz56NmzZro3bs33N3d8eTJExw6dAirVq1Cu3bt0LVrV7z77rtYs2YNvLy80KNHDzg5OeH+/fs4cOAArl69CgcHBzRp0gT169fHzp070adPH9SvXx9Xr17F8ePH4ejoiOzsbLV4HRwc8Ntvv+HGjRto1KgRLl26hH379sHZ2Rnz5r1YJrt79+749ddf0axZM/Tq1QvPnz/Hzp07X3nopIWFBX744QdMmDAB9erVQ9++fVG9enU8e/YMx48fx4cffqgyzKt58+aoXbs2Dh8+DIlEgilTprxS+y4uLvD19cXgwYNhbm6O7du34+7duxg9erTG+ci05efnhz///BPHjh1DkyZN0KVLF0RGRmLTpk2QyWT49ddfXynul7Vp0wZLlizB9OnTMWjQIBgbG6NJkyZo2bIlfv75Z/j6+mLUqFHYs2cPqlevjtOnT+P48eOoU6cOZs6cWaS22rVrB4lEgsWLFyM2NhZmZmbw8vJCt27dsGDBAhw8eBB//fUXrl69inbt2sHY2Bg3btzA/fv3cfXq1RI979L2ySefICAgAG3atIGHhweEELh+/Tr279+PnJwcLF68WKX+woULcfDgQUycOBHnzp2Dq6srtm7dirt372LevHmwsbHJpyXNvv/+e+zZswefffYZLl26hDp16uDkyZO4fv26Wg/eQYMG4YcffsD48eNx6tQpWFtb4+zZswgICICDg4PGodWOjo5o06aNsmfb+PHqHwZq166NWrVqoVGjRnBxcUF4eDg2bdoEqVSqTNwTERFREUgAzOgDlNBUNUSvnU6XAyDKR05OjggKChIbN24UK1asEGvWrBFnz55VW93sddJmtcrU1FQxfPhwUa1aNWFqaiqkUqmwtbUVXbt2FZcuXVI53qVLl0SDBg2EkZGRACDyvhzzW61S0ypuihUKFyxYoFJ+9OhRUa9ePWFgYCBMTU1Fp06dRFBQkAAg+vTpU6xr8NVXXwl3d3chlUqFkZGRqF+/vti+fbvGumvXrhX169cXxsbGQiqVCjs7O9GjRw+VVeSEyF1FsWbNmsLQ0FAYGBgIZ2dnMXDgQJXVJu/duydatWoljIyMhKGhoWjUqJEICAjId7XKunXrisOHDwsfHx9hYGAgTExMRMeOHdVWf8zMzBSTJk0SDg4OQiqVChsbGzF06FAREBCgtlJjftdZiNwV8jS9ne7bt0/4+voKMzMz5fE7dOggrl69qlb3+++/FwCEj4+Pxuupjbwxzpo1Szg6OgqpVCocHR3FzJkz1eprun4KmlarFCJ3Vc8PP/xQODs7C6lUKkxNTYWvr684deqUVvsX1ZQpU4SdnZ3Q09NTO15QUJDo2LGjMDc3F/r6+sLOzk6MGDFCPH/+vFhtfffdd8LFxUXo6+urreqalJQkPvjgA+Hq6iqkUqkwNjYWVatWFZ9++qmyTn6raxZFfqtVanqeinuN169fL9566y1ha2srDAwMlK/Prl27ijNnzmjcJyQkRHTs2FF5L3t4eBS6Im1Bbt68Kd566y1hZGQkjIyMRNOmTcWVK1c0vs9t27ZN1KpVSxgaGgpjY2PRpEkTcfr06QLvX8Uqm46OjiInJ0dt++TJk0Xt2rWFubm5kEqlwtraWrRq1UocPny42OdERERUoKl/CblsgParVZa3h+VQIVLSlacbFRWlw4td/sTFxek6hApPIkQJzeRLRGXe4cOH0alTJ0yePBm//PKLrsMpFXZ2dnB0dFQbllvW/fLLL5gyZQqWLFmCqVOnFusY/v7+GDhwIBYsWKBxiBpRRbF//35069YNkyZNKvEejURERMXy8WqIpXshyXr1FezLHD09YE4/YN6L6Tuio6M5j1YRhIeHc0EgHWOfR6I3VFJSksrPcrkc8+fPBwC8/fbbugiJCvDnn3/C3Nwc48aN03UoROXekiVLoK+vX+xEMxERUWl4Y/ul6ElyJ+zP4+nTpzoKpnx69uyZrkOo8DjnGNEbysvLC02bNoW3tzeSk5Nx8OBB3Lx5E23btkXHjh0BAI8ePdI4Z1deVlZWsLKyeh0hVzgPHz7Etm3bcPLkSdy4cQOTJ09WWw0xKysLjx8/LvRY2qziWhYkJSVpnPT+ZZ6enq/cVlpaGiIiIgqtV7ly5VdaAKEw4eHhSE9PL7COubn5K327WpT7pKQWIMmrLFzrhIQE/P3337h27RoOHz6MXr16lch9RERERAWQ6gHvtgEc+XmByjcmx4jeUO3bt8exY8eUk2zb29tjwoQJ+PHHH5V1GjVqpHFFybzef/99/Pnnn6UdboV04cIFfPzxxzAxMUHv3r3VVu8DgDt37sDb27vQY23ZsqU0QixxS5cuxZw5cwqtVxLfrO7ZswcDBw4stN61a9dQt27dV24vP126dMH169cLrbN///5it1GU+0Sx6mhJKgvX+smTJ5g6dSoMDQ3RqlUrldU1iYiIygK9Ii6KVS5ky4Fp6qNSatWqpYNgyi8fHx9dh1Dhcc4xogrs33//RUpKSoF1ateujXr16r2miOhlycnJ2LVrV6H12rVrB0dHx9cQ0au5d+8eAgMDC603ZMiQV24rMjISx44dK7Rer169YGZm9srt5efw4cOF9pZzd3eHr69vsdvQ9X1SVq41ERFRmfXxash/3QO9bLmuIyk5+npAe2/g4Bdqm+7evYtq1arpIKjy6datW0wo6hiTY0RERERERESl6U1MjgHA4S+BDuq9nq5evYr69eu/9nDKK14v3eOwSiIiIiIiIqJSJnmThlVKJEBt19yeYxoYGRm95oDKNwsLC12HUOFxtUoiIiIiIiIi0p4QwCd9c5NkGnBIZdGUl8W13mRMjhERERERERGVNvkbNKORvSUwuGW+mwtbjIhU3blzR9chVHhMjhERERERERGVJlcbSHLeoPnGPu4FGMh0HQVRieGE/ERERERERESlSQgkHbkMc9M3ZNVmnyqAaf7zikVGRpaLldTLitjYWNjY2Og6jAqNE/ITERERERERlSaJBIm1HGHu4qLrSF4LJsaKJisrS9chVHgcVklERERERERUyqKjo3UdApVRkZGRug6hwmNyjIiIiIiIiIiIKizOOUZERERERERUyoQQkEgkug6DyqDs7GxIpZz1SpfYc4yIiIiIiIiolN26dUvXIVAZdffuXV2HUOExOUZERERERERUyjIzM3UdApVR6enpug6hwmO/PSIiIiIiIipbsrKBEzeA8jQLkKMV4F0l380WFhavMRgqT8zMzHQdQoXHOceIiIiIiIiobPl0PfCNv66jKBobc+DJCsDIQOPm1NRUmJiYvOagqDzIyMiAoaGhrsOo0DiskoiIiIiIiMqWqAQIaTn7uBqbBKw7me/mkJCQ1xgMlSecj073ytm7DREREREREVUE5W6Mk0QCLNxRDgMnIibHiIiIiIiIiF6VEEBIOLD/isbNbm5urzkgKi9cXFx0HUKFx+QYERERERERlTkSiUTXIRSdvh6wcLvGTRkZGa85GCovOBW87jE5RkRERERERGVQOUwY5MiB4zeAqw/UNkVFRekgICoPwsPDdR1ChcfkGBEREREREZU55bYzjVQP+GGnrqMgoiJgcoyIiIiIiIjKnHI4qDJXthzYcBp4GqtS7O3traOAqKyrXbu2rkOo8JgcIyIiIiIiojKnXM45lteve1V+DA0N1VEgVNY9fPhQ1yFUeEyOERERERERUZkjL7fjKpE799iyfUBymrIoPT1dhwFRWZaSkqLrECo8JseIiIiIiIiISlpyOvDXUeWP5ubmOgyGyjITExNdh1DhSXUdABEREREREdHLSmRUpaUJsHA40LcZYGIIBIYC0/4HXLmv3f41XYAfRwFv1QQys4E9l4CP/wZiEl/UqWIHhC3XvP/EP4GcHEBfHy4uLq98OvRm8vDw0HUIFV6F7DnWq1cvSCQShIWF6SyG8PBweHl5legbZFJSElq0aFEuxubfuXMHtra2aNGiha5DoQKUhXvq4sWLkEgkGDp0qM5iKI82btwIiUSCL7/8Uiftl/R7nK7Pp6wqC7/PShJf70REpOoV/waVSIA9nwJ+rYCl+4CZ/wD2lsDxr4FqToXv72IDnJwPVHME5qwDFv8L9GgEHPoCkGnoZ7L+JDDsJ9XH7kvA9vMAgNu3b7/a+dAb68aNG7oOocIrUs+xhIQEXL58GTExMUhJSYFcLoeZmRnc3NxQr149jV0BExIScP78eUREREAul8PW1haNGjVi1ryYkpKSsHjxYshkMnz66ae6DqdMOnLkCPz9/TFu3DjUr19f1+EQUTmRkZGBX3/9FREREfjhhx9Kvb3ly5fj1q1b+PLLL1GpUqVSby+vf//9F0uWLEFISAgSEhKQk5MDOzs7dOnSBfPnz4eTk+oHBiEEdu3ahXnz5uHmzZvIyclBzZo1sXDhQnTu3Pm1xk5ERBWHEKLg9Nixr4GwZ8DIpZq3D/AFWtYEBiwCtgbklm0+C4QsBb4aDAz9qeAA5vQHTI2ARjOAxzG5ZYGhwOEvgffaASsOqda//ABYd1K1TE8P+G470N+34LaISKeK1HMsJSUFqampcHd3R9OmTdGiRQu4urri1q1b2LZtG9LS0lTqJyYmYufOnXj27Bnq1auHZs2aISsrC3v37sWTJ09K9EQqiqSkJGzcuBG///672jYzMzMcOXKkwk/md/78efzxxx+4evWqrkMp93hPUVkxcOBAJCUlleqXApmZmdi2bRuWLFlSam3ktXv3bvz8889ISEgo9jG2bNmClJQUVKlSpUj73b59G4mJiejWrRumT5+OmTNnwsfHB+vWrYOPjw+ePXum1o6fnx/u3r2LsWPHYtKkSYiLi0P37t1x6NChfFohIiLSsQG+QGQ8sO3ci7KYxNwEWe+mgEEhfUX6Nwd2X3yRGAOAI8HAnafAoHxGwJgYqvYqk8uBS/eAs7fh6upa/HOhN9rLX0zS61eknmMuLi4ae3w5OTnh8OHDuHPnjkpPncDAQGRmZqJv376wtbUFANSoUQNbtmzBmTNnMGjQoHIxBLC8kEgkMDY21nUY9AbIyMhAVlYWzMzMSuWeev78OUxNTSGVctpD0o6+vj7MzMx0HUaZY2RkVKz9Zs6ciZkzZ6qUyeVyzJgxA0uWLMHq1avxySefAADi4uKwcOFCpKWl4fTp08rf84MGDUK3bt0wbtw43Lt3j7/PiYioxL3y75YGHsDl+8DLq14GhgLjOgM1nIHrjzTv62wNOFQCLt5T3xYYCnRvpF7+xSBg8Yj/EmL3gbnrgENBgFQPWLQDOctHvdr50BtLX19f1yFUeCUy55jiA0tmZqayLCsrCw8fPoSTk5MyMQYAMpkMNWvWxPPnzxEdHV0SzZeYmzdvYsCAAXBwcIBMJoOFhQW6d++O+/dVJ2u8ffs2hg4dimrVqsHc3BwymQzOzs6YNGkSUlNT1Y4bFBSEjh07wtzcHEZGRmjUqFGxejXduXMHPj4+CAkJQXh4OCQSifJx/PhxjfND5S27ePEi2rVrB3NzcxgbG6Njx46IjIxEcnIyPvroIzg7O0Mmk8HNzQ3+/v5q7aempuLLL7+El5cXjIyMYGhoiNq1a2P9+vVFPhcFIQTWrl2LOnXqwMjICGZmZujXr59arwUAePLkCUaOHAkXFxfIZDKYmpqidevWuHTpkrLOuHHjMHfuXADAyJEjldenRYsWCAgIgIGBAfz8/FTOqXXr1pBIJJg4caKyPD4+Ho0bN4aZmRmys7OV5dreI9rGCwALFiyARCLB8uXLMWPGDGV9e3t7fPXVV0W+pnnnetq3bx8aN24MExMTmJiYoEOHDmqxKtr//fffMXnyZLi4uMDMzAyLFy/Od84xuVyOzZs3o2nTpjAzM4NMJkOVKlXw5ZdfIicnR1kv7/6BgYHo0KEDLCwsYGVl9Uq9R+VyOVauXAlLS0t4enri0aNHKm1du3YNXbt2hYWFBQwMDODj44OAgAC149y7dw8DBw6Evb09ZDIZrKys0Lt3bzx8+FBZ51XvG8WcUGfOnMHAgQNhbW0NmUyGatWq4d9//y3W+WdmZmLZsmVo2LCh8vo7Ojpi4MCBiImJUambk5OD77//Hp6enpDJZLC2tsbEiRNV7msg97W4b98+tGnTBpUqVYJUKoWDgwMmTJigsefg+fPn0atXL+W1s7S0RJMmTbB///4CYz9//jyqVq0KU1NTHDx4UOtz1jTnmKLss88+0+ocCxIeHo7GjRsr75O8769///03AO3v+8Io7tXdu3cDyJ2AVdFW3vO7c+cO3nnnHeV7s7m5Oby9vfHPP/8o65TknGN6enqoXLkygNyEmML169cRFBSEFi1aqHwB1rBhQ7Ru3RoPHjzAhQsXitSWEAL+/v6oV68eDA0NYWZmhrffflulXYWEhARMmzYN9erVg5WVFaRSKaysrPD222+rnPelS5dgbm6OJk2aqB0jJycHgwcPhkQiwcmTucNdwsLCMHToUDg5OcHQ0BCmpqZwd3fHBx98UKRzISKi0iQKr1IQJysgIl69XFHmbF3wvnnrvry/jfmLnmdyARy4Asz4H9BrATB1de7cZvs+zU2iZcuBfy8g9jznlSLNOLJO94rVbSM7O1v5UMwpBkD5RzWQ+4d1Tk4OHBwc1Pa3t7cHAERHRyv/n5+srCytP3To6+tDJpNpexoqAgMD0adPH8THx6Nv376oUaMG7t+/jx07diiTWYphKwEBAdi7dy86duyI6tWrIycnB8ePH8eyZctw/fp1HD9+XHnckJAQ9OnTB2FhYejRowd8fHwQGBiIQYMGwcTEpEjxOjk54ZNPPsGSJUuQlpamHF5kYGCAWrVqFbr/oEGDUKVKFUyZMgVBQUHYu3cvunXrhlq1auHkyZMYPHgwsrKysGnTJrzzzjsIDQ1VrpqRnJyMoUOH4t9//0Xz5s3Rr18/ZGRkYN++fRg6dCiio6MxZcqUIlzxXGFhYRg/fjw6deqE3r1749y5c9ixYweuXbuGoKAg5Tx29+7dQ48ePXDv3j307NkT3t7eiIyMxPbt29GyZUucPn0ajRs3Vsayfft2DB48GI0bNwYAeHl5wd3dHZ6enirPz9OnT3H79m1IJBIcPnxYpTwkJAS+vr7K3k1FuUe0jTevxYsXIzk5GQMHDoRMJsP27dvx5ZdfomrVqhg2bFiRr21ycjLeffdd1K9fH1OnTkVwcDD279+Ppk2b4vr163B0dFSp/9133yEtLQ2DBg2CpaUlmjVrlu+xf//9d3z88ceQyWTw8/ODhYUF9u/fj6+++grXr1/XmFwdMGAAXFxc8OGHHyIzM7PYvYCys7Px/fffY968eahTpw4OHToEa2trJCUlKev07dsXTk5OmDx5Mh4+fIitW7eie/fuePTokXIJ7bt376Jr1664d+8eOnTogGbNmiE4OBh79+7F6dOnERQUBFdX11e+bxSGDx8Oc3NzjB07FnFxcdi8eTP69++P0NBQuLu7a33+GRkZmDx5MlauXAkbGxv4+fnByckJ9+/fR0BAAJ48eaLyhcTGjRsRHR2Nd955B1ZWVti9ezeWLVsGR0dHfPbZZ8p6y5cvx9SpU2FhYQE/Pz/Y2dkhMDAQK1euRGBgIM6dO6d8v9qxYwdGjhyJxMRE9OjRA/Xr10d8fDwCAwNx+PBhdO3aVWPsu3fvxpgxY5CdnY3Tp0+jQYMGWp93QbQ9x4JUqlQJ06ZNw6JFi3D37l0sWrQIQO7vldatWwMo3n2viZGRET766COkpaXh6tWrmDNnDqyscv8AV8zfde7cOfTv3x/h4eFo37493nvvPaSlpeHy5cvYtWsXhg8fXtTLpFFCQgKSkpLw/PlznDhxQjmktGfPnso6Fy5cQHZ2Nlq1aqWyr1QqRf369bFjxw5cuHABTZs21brdrVu3YsSIEcjJycHQoUNhY2ODffv2Yfz48Wp1w8PDsXbtWtSvXx8dOnSAubk5Ll26hAMHDqBJkya4ffs2bGxsUKdOHbRo0QIHDx7EnTt34OXlpTzGgwcPcPLkSXh4eKB169ZISEjAu+++i9OnT6Nnz55o3Lgx0tLScPv2bZw4caKol5GIiEqJEHmm5Jfq5648mZdMChjKchNVecUl5+5sbABkaPiyLP2/Th3GBvk3rtiWkaVh/6wXdTKzc4dddp2nWmfNCeDmz8API4C9lwA9PditOwe83T7/NolId0QxXLt2TSxfvlz5WL9+vQgJCVGpc+/ePbF8+XJx48YNtf3j4uLE8uXLxfnz5wtt69ixYyptFfQ4duyYVvH37NlTABAPHjwQQgiRkpIiOnToIAwMDMTVq1dV6m7dulUYGRkJPz8/ZVlycrLIzMxUqZeSkiJ69eolACjPKycnR4waNUoAEHPmzFHWzcjIEMOGDRMAhLOzs1YxKzx9+lTUqFFD436JiYnC19dX5H1a85aNGTNGWZ6amio6deokAAhHR0fx/Plz5bbff/9dABAzZsxQlv36668CgJg2bZpKm8+ePRP16tUTpqamIjExUevzuH37trCxsREAxKJFi5Tlea/NN998I4QQIisrS/j5+QkAYu/evSrHOXPmjLCyshK+vr7Ksm+++UYAEKtXr1apm5OTI/r27SsAKO/XjRs3CgCiVatWAoCIjIwUQgjxxx9/qMRWlHukuPHa2tqKhIQEZXlAQIAwMzMTDRs21Pq6CvHiHgEgPvjgA2V5dna2mDlzpgAgRo0apda+hYWFiIqKUjmWpnvq8ePHomrVqkJPT0/lWkRHR4tGjRoJAOLw4cNq+w8cOLBI56Fw4cIFAUD4+fmJtLQ0MWXKFKGvry86d+4sUlNTNcY6evRoZblcLhdTp04VAMQff/whhMi9F0aPHi0AiI8++kjlGn366acCgBg0aJCybnHvGyFevN907NhRyOVyZflPP/2k9jrTxtatW4VUKhWenp4iPj5eZZtcLhc5OTlCCCE2bNggAAgjIyPle50QQty9e1c4OzsLW1tbZdn9+/eFq6ur8PDwUHkd5+TkKO+Zv/76SwghRFRUlKhbt64AIPbs2aPWfnZ2thBC9b1KLpeLVatWCQsLC+Hu7q4Sj7YU5/PFF1+olWlzjtrQdL8rFOW+19bLv4sUkpKSRNu2bQUAsXTpUrX9FNe4oGNoS/F+q3g4ODgon2uFjz/+WAAQy5YtU9t/5cqVAoD45JNPtG4zPj5eNGnSRO2aPXv2TNSvX1/5elfIzMwUycnJKseQy+Vi3rx5AoD47rvvlOV///23ACCmTp2qUn/hwoUCgPj222+FELl/x5iYmIgOHTpoHXd+YmNjRXp6uvLnpKQklddRRkaGiImJUdknPDy8wJ8jIiJU3i/YBttgG2yjQrYx5jeRo99fCPTNfbT5VGitytjcfZJShVh56MUxFI9u83Lrdf5KfZvi0Wh6bp1hP6lv+35b7jaDgfnvj75CLPDPrecyRsj1+okc3xe/L8vd88E2SrWNtLS0N+I8ynIbhSlWciwpKUk8fvxYPHjwQFy4cEH4+/uL4OBglTp37twRy5cvF7du3VLb//nz52L58uXizJkzhbYVFxcnHj9+rNUjLi5Oq/hf/jARHBwsTE1NRZs2bdSOGRQUJGrVqiXs7Ow0His1NVWEh4eLx48fi++//14AEL/88osQ4sWHSH19fZXkkxBCnDt3ThgZGb3W5NjLCczp06cLAGL27Nkq5UFBQcLIyEj06tVLCJF743Xq1ElIJBIRFBSkdo3GjRsnAIgDBw5ofR6K5JiZmZnIyMhQ2aa4NvXr1xdCCBEWFibc3NyEp6enWtt3794VLVu2FPr6+spESX7JMSGEWLx4sUqSZNy4cUJfX19s2rRJSCQSsX79eiGXy8XQoUMFAHH58mUhRNHukeLGO2vWLJVYnz17Jry9vUWlSpW0vq5CqCbHIiIiVLaFhoYKBwcHYWNjoyxTtD9p0iS1Y2m6p/79918BQPTo0UOt/rJlywQAMW7cOLX9r1y5UqTzUFAkxwYOHCiGDBkiJBKJGDFihEqC4OW2Xr7X9+zZo/KBWfHa1NPTU3vfePDggahcubIwNTVVJpqKe98I8eL95uDBgyrtBAUFCUNDQ+XrTBtyuVwMGTJEABAbN24ssK4icfRyUjItLU2ZeElKShJCvEjqffrpp2r37KFDh4ShoaHo37+/EEKIffv2CYlEItq3b19g+3nfq7799lvla/rlX2LaKig5ps05aqOg5FhR7ntt5ZfYunLlijAxMRFVq1Yt9jG0dfHiRbF+/XqxdOlS4efnJzw9PVWSTUIIMWHCBAFArFq1Sm3/tWvX5vv+kZ9Lly4JQ0ND4ePjo7ZN8VrLmxzLKyMjQ0RGRorHjx+LI0eOCGNjY9GzZ0/ldsV9Z2NjI7KysoQQuc9rixYthL6+vjKRfefOHWFjYyMcHR2L/d5ERESlbMxvQkgHvEg0VRomRIcvVB9XHwix/7J6ueGg3H1Cngqx56J60mrUf18+1Z2Sf2LL+b8vW2f8T33bP8eEiEksODGGvkJMWJ57DO+PhEBf8fRn/9d4Aak8uXv3rq5DqPCKNazSzMxMOSTK3d0dHh4e2L59O7Kzs5XDZBRDiuRyudr+imGS2kzGbWVlpRxuUlrCwsKQkpKCEydOqAwNzUtP78X0bImJifj000+xfft2PH36FOKlCR7j4+OV9aKjo2FnZwcLCwuVOg4ODrCzsyvSPDWvytPTU+VnxXWtWrWqSrli7hXF3EUJCQmIiIiAEAL16tXL9/hRUVFFjsnNzQ0GBqrdmRXXRjHvU3x8PCIiIpCVlZXv8wMAMTExBW4HgDZt2sDIyAj79+/HkCFDEBQUBB8fHzRp0gQuLi44ePAgOnXqhOvXr8PS0lI5v05R7pHixlutWjWV7Yo52Iq7kp25ubna0EkrKys4OTnh6tWrSElJgampqXKbNkNzgdzhiABU5h5SqFevHiQSibJOXjVq1ChC9Op27dqF9PR0jBgxQjkHVH5evtcVw7cVc9klJSUhJiYG9vb2au8vimv0+PFjZZ3i3jcFxaSYZ+nlOcIKkp6ejvDwcADQekjiy9ddcV8BQGxsLMzMzHD79m0AwPz58zF//nyNx1G8vm/fvg0hhMZ5nTSJjo7Gp59+iurVq+PMmTPKodIlSZtzfFXFve+L4/Hjx0hNTdXYVklr1KgRGjXKnVB44sSJWLFiBT744ANkZmYqh6QqFuXIyMhQ2z89PR0AVN5LCvPo0SNkZGRofM/x9vZWK8vKysLy5cvx22+/4d69e8jKUh3eovidCwDOzs7o3Lkzli5dit27d6NPnz64fPkyLl26hC5duiineqhatSomTJiAxYsXo0GDBnB0dETz5s0xePBgDBo0SOV3PhER6Y5ciBeTZCek5K4UmVd8cu78Xy+XK1wNA1rVAiQS1Un5m1UHUtKBkPD8Gw+PA549BxpXVd/WtDpw9UHhJ+D53xRD0YlAZRs8a+kB58L3ogoo7xQxpBslslScjY0NbG1tcfPmTeUHNsUfypomclaUafPHdGZmptaTKkulUrVEizYUCbwWLVpg0qRJGusoPtBlZWVhwoQJWL9+PZo3b44PPvhAOYF6YGAgfvrpJ40JwbIgvxUwtFkZQwgBiUSCf/75R2N9fX19tfloSlr16tXznaDeyMgIdnZ2hR7Dzc0N1apVw8mTJ5XzRo0dOxbOzs7w8vLC4cOH8eTJE4SGhqJ9+/bKieiLco8UN15dr1BSGkmLkjx+nTp18OjRI2zbtg0TJ04sMDmT37V8OZGtreLeN9rEVNoK+hJCcT0U/44dOxZt27bVWLco86LlZWFhAVdXVwQHB+Pvv/8ulcnOtTlH0k7Xrl1RuXJlLFu2TJkcc3bO/TP+8ePHavUVk8cW9sXEq1i2bBk+/vhjuLq6YubMmfDw8ICxsTFiYmIwZ84ctd+5w4cPx+rVq/H777+jd+/eWLt2LTIyMlTuPX19fcybNw/Dhg3Dli1bcOrUKZw6dQo7duzAkiVLcPr06WL9PUFERGWMfwAwsAXQrzmw9b/FmWzMc8t2XcydL0xBkci6n+cL/60BwIh2gKsN8CQ2t6y9N+DlAvy460U9WwsgJlG1bWdrYFR7ICgMiIoHfhoNU4uX5kYj+k9xVyCnklMiyTEgd5LsvN8qW1tbQ19fX2NvIkXvDW2SGWfPnkVISIhWMdSoUSPfD3YFqVKlCkxMTJCamoohQ4YUWPfp06c4deoUXFxccObMGZVvl2/duqVS18LCAnZ2drh16xYSExNVeo9FRUUhOjoa1tYFrJBSRlhYWMDe3h43btxAo0aNtO5hpI1Hjx4hMzNT5UOI4trUrFkTAGBpaQl7e3vEx8dj8ODBhX6jX9CSz7a2tqhTpw6uX7+ODRs2ICEhAZ07d4ahoSGaNm2KI0eOYMuWLUhNTUWXLl2U+xXlHilqvKUlKSkJkZGRKr3HFL3abGxsitTTIy9FDzdNK64GBwdDCKHWC64keHl5YcGCBRg+fDjatWuHgwcPokWLFsU6lrm5uTKhn5CQgEqVKim3Ka6RmZmZcmL74t43Jc3IyEiZqLh69eor98ZTUDxf5ubmhd7fNWvWVK5+qw1DQ0Ps3LkT/fr1w6RJk5CZmYmPPvroVUN+7V7nfe/m5gYTExMEBQWVyPGKIjs7G1lZWSo9Vhs3bgypVIpTp06p1VVcj5cXGClI5cqVYWhoqPY7EwCuXbum8nNGRgZ27doFuVyO48ePKxeJAYDjx49rXCHa29sbvr6+OHLkCC5evIgjR47AwcFB40IRXl5eysVtkpKS8M4772Dv3r3YuXMnBg4cqPU5ERFR6Sjgz3rt+AcAAXeA1ZOA2q5ATBLwQVdAXw/4YqNq3SP/fantkWdxmAVbcxNpx74Gft4DmBkBM3oDwWHA6qMv6i0cDlR1AI5cy+1x5m4PjOsMmBoBU1bl7jeqPaoY6PbLcCq7qlevrusQKrwifWrX9EcokLuSVHx8vMrKkzKZDG5uboiIiEBsbKyyPCsrC7dv34alpaVWybF69eqhe/fuWj0KGvJXkGrVqqFZs2a4evUqNm3apLY9MzMTERERAHITL4pH3h4JYWFhavva2tqiSZMmyMnJwffff69yvKVLlyqHoxSFTCaDoaEhEhMTX1uPCCMjI/Tu3RsAMG3aNLWhoEIIhIWFFevYycnJ+OWXX5Q/5702AwYMAAC4urqiVatWiImJUbmOCtnZ2So9GvIOp3qZnp6esofbqlWrYGBggJYtWwIAOnToAD09PeWQvQ4dOij3K8o9UtR4S9O8eS9WzcnJycGKFSsQFRWFt99+u9jHbNCgAapWrYr9+/erfJCNjY3FX3/9BQCl9qGyc+fO2LBhAywsLNCpUye1D+vasrW1RbNmzSCXy1WukVwux+rVq/H48WN0795dmdgs7n1T0iQSCQYMGACpVIo5c+YgMVH1G0qRO49kkY/brVs3ODs7448//sD9+/fVticmJiIuLg4A0LBhQ9SpUwdHjhzBgQMH1Opq6jlbpUoV7Ny5E40aNcLUqVOVK0GWNYr3VwDK81Uojfte0Zvy5baqVq2Kpk2b4u7du1i+fLnafq/aOzkrK0vje5BcLsfGjRvx9OlTlWSXt7c3fHx8cPbsWZWE3eXLl3Hy5Em4u7sXaaXKqlWrwsfHB8HBwThy5IiyPDo6GmvXrlWpK5FIlK/DvPd2amoqfvjhB433u5GREd555x3k5ORg1qxZePDgAUaOHKnSezMlJUVtyLqZmZkywfnyc0JERLryitkxuRzoPh/YdAb4sAewaHhuD6/2XxQ8pFLhSSzQ5jPgXiTw3TBgZh9g72Wg01eqvc4OXs1d2mZiN2DZWGBsJ+DkTcB3NnD6FvBBN8DMGDdv3ny186E31stfENLrV6SeY6dPn0ZqaiqcnZ1hZmaGnJwcxMTE4N69e5DJZGjevLlK/aZNmyI8PBx79+6Ft7c3ZDIZbt++jZSUFHTt2rXAHj4Kr2POMTMzMyxcuBD9+vXDkCFDsGrVKuUcLA8ePMDp06fRpk0brFu3Do6OjmjWrBm2bNmCli1bokuXLnj27Bn27NmjNgRDT08PM2fOxJEjR7BgwQIEBQWhXr16OH/+PM6dO6dVcvBlVlZWqFatGq5duwY/Pz80btwYxsbGGDBggHJemNIwevRonDx5Etu2bUP16tXRvXt32NnZ4cmTJ7hy5QqCgoLU5oHRhqOjI7788kucOXMGtWrVwrlz53D8+HF4enoqe5fIZDJ88803uHbtGubMmYNdu3ahZcuWMDIyQlhYGAICAmBvb4+zZ88CyJ1Dx8jICD/++CNSUlJgYWEBLy8vdOvWDQDQqlUrmJqaIjw8HL6+vsourDVq1ECVKlXw4MED2NnZqfSQK8o9UtR4S4uFhQU2bdqEkJAQNG3aFMHBwdi/fz+sra3xzTffFPu4rq6umDp1Kj7++GP4+vrCz88PlpaW2L9/P65fv47+/fuXaoKoXbt22LhxI/z8/NClSxfs2bMH7dq1K9Ix9PT0MGvWLBw/fhxLlizB9evX0bhxY1y7dg379u2DtbU1Fi9erLJPce6b0tCjRw+MGDECf/31F6pXr44+ffrA2dkZYWFhOH36NLZs2VLkuaqqVq2Kr776ClOmTEGdOnXQp08feHl5ITExESEhIThz5gx+/PFHvPfee7C3t8fXX3+N9957Dz169EDPnj1Rr149PH/+HOfPn0fLli3Vrh2Qe9/s3LkT/fv3x8yZM5GdnY3Zs2eX0FUpGUZGRqhTpw6OHz+OoUOHokOHDjA0NETPnj3h4eFR4vd948aNsXnzZnzwwQfo27cvDA0N0bFjR9StWxcLFixAv379MH78ePj7+6NZs2bIyMjApUuXYGtri82bNxf7PJ8/f47evXsjPT0dvr6+cHNzQ1JSEgIDA3H27FmYmJjg119/Vda3trbGzJkzMWrUKLRt2xajRo2CRCLB5s2bkZCQgHXr1mn1+1yhUqVKmD59uvIeGjp0KKytrbFv3z61L+EMDAzQuXNnHD58GO3atcOQIUOQk5ODI0eO4OnTp/nOJ9e1a1dUq1YNR48ehUQiwfvvv6+y/fTp0+jVqxdat24Nb29vWFtb4+bNm9i1axcsLCzQq1evIlxRIiIqLUKIgtNj7T4v/CAJKcD7y3IfBcnbYyyvm4+BrvM0b1PYeDr3oYm+HjC5e+FxEpFuFWX2/rt374p9+/aJtWvXipUrV4qVK1eKjRs3itOnT+e7IlhcXJzYv3+/WL16tVi5cqXYsWOHePz4cTHWDig5+a3ude/ePTF8+HDh6uoqZDKZMDIyEq6urqJfv37i9OnTynpRUVFixIgRwt7eXkilUmFnZycmTJigXEI+72pqQghx+fJl0b59e2FqaioMDQ1Fw4YNxe7du/NddbIwt2/fFu3btxfm5uZCIpEIAOLYsWOFrlb5svxWdVSsJOnr66tSnpaWJhYvXix8fHyEiYmJkEqlwsbGRjRv3lz88MMPRT4Hxb5r1qwRtWvXFgYGBsLU1FT07t1buaJYXlFRUWLixInCw8NDGBgYCAMDA+Hk5CS6du0qdu3apayXlZUlvvvuO1GlShUhlUoFAJVziY2NFQ0aNBAAxJdffqksz8zMFN27d9e4+p2CtvdIUeLN73ko6LkrSN5VAvfu3SsaNWokjIyMhJGRkWjbtq0IDQ1VqV/Q6p75xZCTkyM2btwoGjdurLwXKleuLL744guVVSSLew55KVarfHn1urNnz4rKlSsLQ0NDceDAgQLbyu8YoaGhYsCAAcLW1lbo6+sLCwsL0atXLxEWFqZ2jOLeN/m93+T3OtNGenq6+Omnn0TdunWFsbGxkEqlwsHBQQwaNEi5GqSm1R0Li+no0aOiS5cuwsbGRujr6wszMzNRrVo1MW7cOPHo0SOVuqdOnRJdunQR1tbWQl9fX1haWoomTZooV63Nb2XdyMhI0bJly3xjy09Bq1UW5RwLExERIXr37i2srKyU76+K14a29722EhISxHvvvSdsbW2Fnp6e2rlcv35d9OvXT9jb2yufD29vb7FmzZpXOs+0tDTx1VdfiYYNGwpra2shlUqFTCYTrq6uYtiwYRrv/5ycHLF9+3bRqFEjYWxsLAwMDISPj4/Yt29fkc9biNyVVzdu3Cjq1q2rfO/v1auXOHDggNprNS0tTcybN0+4u7sLAwMDYW5uLrp16yaOHj2a72tILpeLDz74QAAQb731ltr2Bw8eiHfeeUd4enoKU1NT5e/yfv36iTt37hTrnIiIqISN+U3k6PcvfEXIsvyQ9hdi2I/KU4qKitLd9aQyLTw8XNchVHgSIThbMdGbJDw8HO3atUNycjKePn2q63CIiHRi6tSp+Omnn7B+/fpC59IjIqIy6P1lEH8fgyQ7p/C6ZdnVH4B6uXNmRkdHF2v0EL354uLiysV85G8yrlVOREREb5SnT59iz549sLKyQr9+/XQdDhERFVs57sehrwe0q6tMjAHgF9eUr0ePHuk6hAqvxFarpOKTy+WIiopCWlpagfXMzc3L9DcNOTk5iIiIQGZmZoH1Xsc8cm+aotwj5UF2djaePn2qtrjDy+zt7fOdU+hNUlGvR1pamnIhi/zo6enB2dlZbU7Honj27BmSk5MLrGNsbAwnJ6dit6EQFxenNtH7y2QyGSpXrvzKbb0sMTERMTExBdaRSqVwcXFRmZy+pOjy3BWCgoJw6NAhHDhwAKGhoZg/f75ykQUiIip/hHjlKfl1J0eeO4E/EZULTI6VASkpKejfvz8CAgIKrOfr61vqk7i/iqioKHTo0AEhISEF1vPz88O6deteU1RvhqLcI/7+/q8pquK7d+8eWrZsqXFF0by++OILfPnll68nKB2qqNdj586dWg13O3bsGNq2bVvsdkaPHo3du3cXWMfZ2blEvs2dPXs2/vzzz0LrlcaMBkuXLsXcuXMLrffgwQO4u7uXePu6PHeFPXv2YO7cuTA1NcXw4cMxc+bMUmuLiIgoXxIJUMMJ6NJApbi0F26i8qtGjRq6DqHC45xjZUB2djZOnjyJqKioAuu5u7vD19f3NUVVdBkZGThy5AieP39eYL06derAx8fnNUX1ZnhT7hGF1NRUHDhwAOnp6QXWa9asGTw9PV9TVLpTUa9HREQEjh8/XmAdmUyGDh06vFJv0zNnzhTaVd3e3r5EVlkNCgoqdJl2ExMT9O7d+5XbelloaCguXrxYYB0jIyN069ZNudpqSdLluRMR0Rvo/WXA38eA8jrn2KqJwCjVvy3u3r2LatWq6SggKsvu37//Rv2dXx4xOUZERERERERly/vLIFYfhSRHrutIis7GHHi6EjCUqRRfvXoV9evX101MVKbx3tA9TshPREREREREZU657MWhJwE+6qmWGANQKj236c3wKvPrUslgcoyIiIiIiIjKHEl5nI1fpg9M6KpxE4dUUn44H53uMTlGREREREREZVA5y45J9YCRHXKHVWpw/fr11xwQlRdBQUG6DqHCY3KMiIiIiIiIyp6ccjYZf44c+LiXrqMgomKQ6joAIiIiIiIiIhUTuiDp5gNYmGvuhVUmeVcBqjvnu9nR0fE1BkPliZ2dna5DqPC4WiURERERERGVOQkJCahUqZKuwyAqdbzXdY/DKomIiIiIiKjMSUlJ0XUIRK9FWFiYrkOo8JgcIyIiIiIiojInOjpa1yEQUQXB5BgRERERERERkY5Uq1ZN1yFUeEyOERERERERUZlTr149XYdA9FrExMToOoQKj8kxIiIiIiIiKnNu3bql6xCIXouEhARdh1DhMTlGREREREREZU5mZqauQyB6LaRSqa5DqPD4DBAREREREb0pTtwApv8NCF0HooW3agI/jc53s4WFxWsMhkh36tatq+sQKjyJEKI8vG0SERERERFRYUb+CvG/Y5CUh095Eglwbxng4aBxc2pqKkxMTF5zUESvX1BQEOfY0zEOqyQiIiIiInqT6JWTj3l6EuCn3fluDgkJeY3BEOkO+yzpXjl51yQiIiIiIqI3So4cWHEISEjRdSREOmVjY6PrECo8JseIiIiIiIjeJBJdB1AEGVnAnwc1bnJzc3vNwRDpBufX0z0mx4iIiIiIiN4k5WmEllwAS/4FMrPUNmVkZOggIKLX78GDB7oOocJjcoyIiIiIiIh0J+o5sPmsenFUlA6CIaKKiMkxIiIiIiIi0h09CfD9doCTklMF5enpqesQKjwmx4iIiIiIiN4gkvI05xiQO7Ty+iPg2HWVYm9vbx0FRPR6JSQk6DqECo/JMSIiIiIiojdIueyApa+X23ssj9DQUB0FQ/R6xcXF6TqECo/JMSIiIiIiItKtHDlw8Cpw87GyKD09XXfxEL1GenpMzeganwEiIiIiIiLSPake8MO/yh/Nzc1Lr62EFGDs74Dde4DpEKDd58Dle9rvf+sJ0PVrwMwPsB4OvPszEP284H3WnQAk/XL3IcrDx8dH1yFUeEyOERERERERke5ly4F/jgNRCQAAFxeX0mlHLgd6zAfWnwImdQMWDgeePQfafg6Ehhe+/5MYoPWnwN1IYMFQYPrbwJ5LQKevgMwszfskpwEz1wCmRiV7LvRGuHbtmq5DqPCYHCMionKhb9++kEgkCA/X4o/WUhIWFgaJRIIhQ4YUWrcsxKut48ePQyKRYNasWboOpUDlJU4iInoFcgH8tg8AcPv27eIdo+1nwHu/5r/dPwA4ewf4exLwxWBgYjfg+Ne58559sanw4y/YCqSkA0e/Aj7sAcwZAGyeBgSFAX8f07zPfH/A3Bjo07RYp0RvtpycHF2HUOFJdR0AERGVLQkJCbh79y6ePHmCxMRE5OTkwMLCAh4eHvD29oZMJtN1iPQGCwwMxMqVKzF69Gg0a9ZM1+EQEdHrJpcDv+wBZvUrvTb8AwCHSkC/5i/K7CyBQS2AtSeBjCzAsIC/d7aeA3o2BtzsXpR1rAfUcAY2nwXGdlatHxoO/LgL2P5J7nail1hZWek6hAqPPceIiEjFnTt3cO3aNVhYWKBhw4Zo1qwZLC0tcfHiRezcuRPZ2dm6DrFc2LhxI5KSkuDk5KTrUMqVy5cvY8WKFbh69arattatWyMpKQnz5s17/YEREdHrk5gK/HMcrq6upXP8Kw+Ahp7Ay5OgN60OpGYAIQX0+n4amzsEs3FV9W1NqwNX7quXf/QX0K4u0L3Rq8VNbywbGxtdh1DhsecYERGp8PT0RIMGDWBgYKAsq127Ni5cuIArV67g9u3bqFu3rg4jLB8MDQ1haGio6zB0KjY2tkT/2NPT04OZmVmJHY+IiMooAWDRDuT0LqVJyiPigda11cud/uu9Ex4HeFfJf9+8dV/ePy5ZtefZnovAwSAgaMmrx01vrLt376J+/fq6DqNCY88xIiJSYWdnp5IYU6haNfcb0vj4+Ncdkoro6Gj06NED5ubmMDQ0hLe3N3bu3KlWLzs7G7NmzULlypUhk8lgZmaG5s2bY//+/a9UV5Nt27bB2NgYXl5euHcvd6UrTXOOKcpu3bqFQYMGwdLSEjKZDF5eXtiyZYvGc+3duzfMzc1hZGSEunXr4t9//0WjRo1ga2uLpKQkbS+b0tq1a1G1alXIZDLY2Njg3XffRXJyslq9adOmQSKR4MiRI2rbHB0d4eXlpVImkUjQunVr/Pvvv6hbty6MjIzQokULAMDTp08xbNgweHp6wtTUFDKZDC4uLhg/fjxSUlJU2pwwYQIAYPz48ZBIJMrjAvnPOZaSkoKxY8fC0dERMpkMlpaWaN++PS5cuKBSL+/+P/30E9zc3CCTyWBra4vx48cjKyufSZSJiOj1ux+FlE0nC6+XlQ3EJKo+srJzE1Qvl8vlufukZQKGGvqJGBm82J4fxTZNwy6NZKp1MrOAqauB8Z2B2pULPxci0hn2HCMiIq0oEijGxsZa1c/IyIAQQqu6MpkM+vr6WtXt168fTE1N8cEHHyA2Nhbr16/H4MGDsWXLFvTq1UtZr2fPnjhw4AB8fHzg5+eH2NhYbNiwAX369MG6devQv3//YtV92dKlS/HRRx+hYcOG2LNnD+zs7PKtq9CnTx+Ym5tjwoQJSExMxN9//42RI0eiQYMGqFatGoDc69eyZUuEhoaiQ4cO8PX1xa1bt+Dn51fspe3XrVuH4cOHw8bGBmPGjIGxsTG2bNmCM2fOFOt4L7t//z4GDx6MHj16YODAgcpk09WrV3HgwAG0a9cO1apVg1wux9GjR7F8+XLcu3cP+/fvh76+PgYNGoRHjx7B398f77zzDho1yh1+4unpmW+bWVlZaNGiBYKDg+Hr64vRo0fjwYMH8Pf3R8eOHXHw4EG1ucu2bduG+Ph49OvXD7a2tti2bRuWL18OOzs7DtkkIior9CSw3XQB+GhwwfXO3Abafa5efvYOsPG0atmDPwB3e8DYAMjQME1E+n9JLWP1LwmVFNsyNHyhkp6lWufHXUBMEvDVOwWfA1V47u7uug6hwmNyjIiICiWXy3HlyhVIJBJl8qYwW7du1dgjSZM2bdqo9UTKj7W1NU6fPq0csjhmzBi0aNECc+bMQbdu3SCVSrFnzx4cOHAAzZs3x/Hjx5V1x48fj2bNmmH27Nno2bMnDA0Ni1T3ZbNmzcL333+Prl27YuvWrTAxMdHqHDw8PLBnzx5lQtDX1xfDhw/Hb7/9hh9//BEA8MsvvyA0NBTvvvsu/ve//0EikQAAFi1ahJkzZxZ5uGJOTg6mTp0KExMTHDt2TDk09vPPP9f62hfm6dOnWLp0KSZOnKhS3q5dO4SHh6st5vD2229j9+7dCAwMhK+vL5o1a4YOHTrA398fbdu2xbhx4wptc9myZQgODkafPn3g7++vvKb9+/fHgAEDMG3aNJw8eRJ6eeaVefbsGS5evKi8l+fOnQsnJyesX78en3/+eZEWnYiLi4Opqany/khOToYQQpnAzMzMRFJSksrzFRERoTIX3cs/R0ZGwsHBQfmcsw22wTbYRlHb+O+w5Z5px4aFX6uqdtDbORMmJrlf3mVlZUMy43+QutgCM3oDAGJj42BjYw04VgIAZNubQxrxoie8so3/yp6b6sMoI0Pz8/HfcMqUu09hmifWiIgIOEXEA9ZmgKEMkXfuw3G+P/BBVyAxDdH3HsHW1haS5DRACCRcDYGxTSUYVrZXbwNl875iG6XXRnJyMkxMTMr9eZTlNgrD5BgRERUqICAAUVFRaNKkCSpVqqTVPu3bt9d68n5ra2utY5k8ebJKoqp58+Zo1qwZAgMDERoailq1amH9+vUAgOnTp6vUbdy4Mdq2bYujR48iODgYTZo0KVJdBSEEhg0bpuyJtWrVKkil2v9KnTJlikpPue7duwPI7XmlsGPHDujp6WH27NnKPxQU+3711Vdat6Vw8eJF5TDNvHPGVapUCWPGjMGCBQuKfMyXubq6YsyYMWrleZOG6enpiI+PR05ODnr16oVdu3bh9OnT8PX1LVabW7ZsgUQiwZdffqlyTfv374/q1avj0qVLiI6OhoODg3Jbp06dVJK8JiYmqF+/Pi5duoSkpKQi3Y8v1315TjQDAwO1RObLf6i9/LOjoyPbYBtsg228UhtCAOU+P6avh7DuNVG1sGtV2QGo/OI9XgYA3+/ITWJ1rAcAePnrJGmjasCpW7nDLPX0Xjwf50MBE0NYNqmlMmxS5flwsQHsLGB6K0LlmE5OTkBgKFDfAwDgaGgGJKcDC3cAC3fg5X7llRrMAno3BXbMUm8DZfO+Yhul10ZMTAxcXV3L/XmU5TYKw+QYEREV6MKFC7hx4wZq1qyJBg0aaL3fy7/kSoqmyUpr166Nc+fOISQkBLVq1UJYWBgkEolyWF5ederUwdGjR3Hnzh00adKkSHUVdu7cifT0dIwYMQJ//fWXSq8kbdSpU0flZ8Uv94SEBGXZkydPYGlpCRcXF5W6BgYGcHZ2RlxcXJHavH37NgBo7PlXlOe1IE5OThp72GVnZ2PmzJnYuHEjIiMj1YbbFvVc8nr8+DEqVaoEDw8PtW01a9ZEaGgoHj58qJIcq1JFfZJla2trpKSkID09vdixEBFRCZHqAe+1R5JxKU2RPcAX8A8Atp0DBuTOj4mYRGDLWaBXY9X5xO5F5v5bNc/fNf19gf8dAx7HAJVtc8uOBOeucjn1vyke7C2B7Z+ot/3LHiAgBNgwVfOk/kSkE0yOERFRvi5evIgrV66gRo0aaNWqVZH2TUtL03rOMQMDgyL1vNK1evXq4datW9i9ezfOnj2Lt956q0j753eu2l6v10FSwJicnJwcjeVGRkYay8eMGYP//e9/aNmyJSZNmgRnZ2cYGhri2rVr+Pbbb1/7eZene42IqELKlgMf94KpflrpHH+AL9C8BjByKXDzCWBrDizbD/yfvfuOb6p6Hzj+SVe6B7R0UvaeMlv2HmXvKUMRUREVFP06QBAXiP5URBkiyN57j7JH2XtvympLKauL9vz+iAkNSUtbWkLp83698tKce+49z725ScmTM5KSTecHazhc999LE56Ufd5Bl0irPww+aKHrITZmqW6Fy74NdHUctdDWeM5LAJaEQdg589tEriUrVVqe/OtQCCGEWfv27ePAgQMUL16cunXrppksMWfx4sXZMufYoUOHKF/eeGn3EydOYGVlRfHixQHdBO47d+5k//79JhOcnjhxAsDQXkbq6hUqVIi//vqLevXq0bp1axYuXEj9+vXTFX96BQQEsHv3bsLDw3F1dTWUJyYmcv369VQTUanRn8O5c+dMth08eNCkzNNT90t4ZGSkUXlcXBx37tzJ0NDDhQsXUrx4cTZv3myUmLpx40Yae6VPYGAgO3bs4NKlSyb3xenTp7G3tzfbU0wIIcRLytoKmlaEkgEUSEhj1cjnasMaVn0Jn0zT9eSKTYCqRWHq+1DC/9n75/eELd/A4Knw2Qyws4EWlWFsH/OrWArxDMePHzcZWSBeLEmOCSGEMLF//34OHDhAsWLFMpUYg+ybc+z333+nS5cuhuF7e/bsYc+ePZQuXZpixYoB0LVrV2bMmMHYsWONJtM/ePAgoaGhFCtWjAoVKmS4bkoVK1Zk27Zt1K1bl3bt2jF//nwaN26c7vN4ljZt2rBz506+//57own5/+///o+HDx9mODlWtWpVvLy82LBhA8eOHTPMOxYTE8PkyZNN6pcrVw6AtWvX0qXLk5XCRo4cSXJycobatrKyQill1EPswYMHhsUHUnJzcwMgKioqXcfu2LEj27dvZ8SIEcyfP98wxHXx4sWcOXOGmjVrpmsFUSGEEC+JpGT4pC2g+5EqUz1qNqdj5WEPZ5j8nu6RlpQ9xlIqEwhrzayS+SxT39c9hEhBv8K3sBxJjgkhhDBy/Phx9u/fj7OzM/7+/iY9jRwcHAgICHjmcbJrzrE7d+5QtWpVQkJCiIyMZNasWdjZ2fHtt98aeiW1aNGCpk2bsnbtWqpXr06zZs2Iiopi9uzZ2Nra8v3332NnZ5fhuk8rV64c27dvp27dunTs2JE5c+bQvHnzLDnPDz74gAkTJjB9+nSuX79OcHAwJ0+eZM2aNfj4+GT4H1HW1tb89NNP9OnTh/r169OpUyccHByYN28eTk5OJvWbNWtGYGAg06dP59GjR5QoUYI9e/Zw8OBBo55s6RESEsKcOXOoV68ejRs3JjIykgULFphMrgpQs2ZNNBoNEydOJDExETc3NwoWLEjbtm3NHvvdd99lypQpLFq0iLp161KnTh0uX77MvHnzcHV15aeffsrwnHBCCCEsxEoDZQOhrvSgEbmL/sdBYTmSHBNCCGEkIiIC0PXs2bx5s8l2X1/fdCXHssuiRYv43//+xx9//EFCQgLFihVj1KhRtGnTxqjeihUr+OKLL5g1axZjx47Fzs6OsmXLMmLECJo1a5bpuk8rXbo0O3bsoHbt2nTp0oWZM2fSqlWr5z5PrVbLzp076devH6GhoWzbto1ixYoxe/Zshg4dSkxMTIbnzurVqxdKKUaMGMGkSZNwdXUlJCSELl26mMRsbW3NqlWr6Nu3L4sXL8ba2ppKlSqxZs2aDCcA//77bxwcHFi+fDlhYWHkzZuXNm3a0LZtW8NKnXqBgYH8+uuvjB49mm+++YakpCRq166danLM1taWnTt38uGHH7Js2TJ27dqFo6MjtWrV4scffzRaSEEIIcRLLlnBp+3gv97Sfn5+Fg5IiBcj5cJBwjI06mWa/VcIIYQQaUpKSsLd3Z2CBQty+PBh6RUlhBDCWN/fUdO3oEnK2BD4l4KPO1yZCLa6H38iIiJkaLzIFQ4dOiST8luY/ItaCCGEeEndv3/fpGzs2LE8ePCAoKAgSYwJIYR4dWg0MKS1ITEGEB4ebsGAhBC5iQyrFEIIIV5S3bt3JyoqiqCgIJycnAgLC2P9+vXky5ePL774AtBNXB8TE5Pmcezs7Cw6FFYIIYR4Jgc7eCvrFrYRIicJDAy0dAi5niTHhBBCiJdU06ZN+f3335kwYQJxcXG4ubnRtGlTfvrpJwoWLAhAv379WLJkSZrHKV68OKdPn87+gIUQQrwUMrHItGVZW0H/xuBmvEBMqVKlLBSQEC9WXFycpUPI9WTOMSGEECIH279/P2fOnEmzjpubm8nE90IIIV5ROXHOMSsNXPgTCuQzKj537hxFixa1UFBCvDgy55jlSc8xIYQQIgerXLkylStXtnQYQgghRObYWEH7IJPEGOhWzhZCiBdBZvIVQgghhBBCCGEZj5Ph4zZmN9nb27/gYISwjHLlylk6hFxPkmNCCCGEEEIIIV48ayuoWRKqFjO7WYZUitziWVNkiOwnyTEhhBBCCCGEeJXklGmlk5JhaNtUNx87duzFxSKEBcXHx1s6hFxPkmNCCCGEEEII8aoI9EKTnEOSY4W9oWUVS0chhMW5uLhYOoRcT1arFEIIIYQQQohXRVIS97ccwcXF2dKRPFs+N7MT8evdvHkTHx+fFxiQEJYRFxcnc+xZmCTHhBBCCCGEEOIVEh4ejr+/v6XDEEKk06FDh6hYsaKlw8jVZFilEEIIIYQQQrxCIiIiLB2CEELkKJIcE0IIIYQQQgghhLCQgIAAS4eQ60lyTAghhBBCCCFeIRUqVLB0CEKIDHj8+LGlQ8j1JDkmhBBCCCGEEK+QkydPWjoEIUQG3Lx509Ih5HqSHBNCCCGEEEKIV0hCQoKlQxBCiBzFxtIBCCGEEEIIIcRL4WEcbD1h6SieLdATygSmutnV1fUFBiOEeF5ly5a1dAi5niTHhBBCCCGEEALgvUkwLdTSUTxboCdc+BOsrc1u9vHxecEBCSGex/nz5ylRooSlw8jVZFilEEIIIYQQQgBExFg6gvS5EglLwlLdfObMmRcYjBDiecXGxlo6hFxPkmNCCCGEEEIIkZNYaeDHxZaOQgiRRZycnCwdQq4nyTEhhBBCCCGEyEmSFew9BztPmd0cGJj6fGRCiJePvGctT5JjQgghhBBCCJHT2FjBmCVmN8XHx7/YWIQQz+XkyZOWDiHXk+SYEEIIIYQQQuQ0j5NhaRicv2my6datWxYISAghci5JjgkhhBBCCCFETmRlBb8st3QUQojn5OfnZ+kQcj1JjgkhhBBCCCFETpSUDH9vgDv3jYrLlStnoYCEECJnkuSYEEIIIYQQQuRUCY9hwjqjorNnz1ooGCFEZly/ft3SIeR6khwTQgghhBBCiJwqWemGViYkGori4uIsGJAQQuQ8khwTQgghhBBCiJws4h7M3m546uLiYsFghBAZVapUKUuHkOtJckwIIYQQQgghcjKNBn5cDEoB4O/vn31t3X0I/f8Erz7g1A3qD4MD59O//8lr0GwkOHeHPL3g9V8hIibtfWZuAU173T5CvIKuXLli6RByPUmOiXTp378/Go2GY8eOGcq+//57NBoNCxYsyNK2vLy8XrpJRDUaDc2aNbN0GEJYxLFjx9BoNPTv39/SoeQYmf18zO3XesGCBWg0Gr7//ntLhyKEEDmLUrqk04bDAJw6dSp72klOhhajYNY2GNgcRveC2zFQbxicTcecSdcioc6XcO4mfNcDPm4NK/dD4xFGw0KNPIiFodPByT5rz0WIl8jDhw8tHUKuJ8mxl5BSiiNHjjB37lz+/vtvZs6cya5du0hMTOUPRg7Uv39/Jk6caOkwhBA5xIIFC+jfvz+3bt3K9rZCQ0Pp37+/0Y8Br5p//vmHoKAgvLy80Gq1ODk5UbhwYYYOHcqDBw/M7jN16lRKliyJVqvF2dmZOnXqcOTIkRccuRBCiFRZW8HoJc93jHpfQZ/fU9++YBfsPA1TB8LwLvBec9g8Utf28LnPPv53C+FhHGwaAYNawOcdYd4QOHwJpoaa32fUAnBxgLbVMnVKQuQEDg4Olg4h15Pk2Eto165d7N69Gw8PD2rUqEHhwoU5duwYa9euRf3XVfpl8PHHH3P//n3atWuX4X0nTZrEokWLzG67fPkyYWFhzxueECKLlC5dmvv37zNu3DiLxbBu3TomTZpEREREtre1e/duJk2a9Fy/umf28/FFXeuDBw9ibW1Nx44d+fLLLxk0aBD58+dnzJgx1K5dm+TkZKP6f/75J2+88Qbx8fEMHTqUvn37cvjwYerWrcu5c+eyNVYhhBDplJQMG47AscsEBARkTxsLdoG3O7QPelLm5Qada8DSMIh/xo/5C3dDyyoQ6PWkrFEFKO4H83aa1j97XbfYwM99wMY6K85AiJdSkSJFLB1Crmdj6QCEsTt37nDs2DEKFixIkyZNDOUuLi7s3LmT8+fPU7RoUQtG+IStrS22trZZflxHR8csP6YQIuOioqLImzcvVlZWODs7Z9vxX0WZ/XzMrmv9tN9++81sefv27Vm8eDHr1q0zDCWPj4/niy++IE+ePBw4cAAPDw9D3YYNG/LRRx+xfPnybI9ZCCFEOthYwdhlJP3QOXuOf/AiVCoMVk/1sahWDCauhzPXoVwB8/uGR+mGYFYxkwSoVgxW7Tct/3AK1C8LIZXNJ8+EeEUcO3aMihUrWjqMXE16jr1kzp/XTWb59JxbJUuWxMbGhrNnz1oiLLPMzanz4MED+vfvj5+fH1qtFkdHRwIDA+nRowfwZD4dgLVr16LRaAwPPXNzjunLwsLCqFatGvb29jg6OlK7dm0uXLhgEtvWrVupVKmSYfhP06ZNuXbtWpbOHbZx40bc3d0JCAjg5MmTADRr1gyNRkNERARt27bF1dUVW1tbSpUqxerVq02OcffuXXr37o23tze2tra4u7vTpEkTjh8/bqjz6NEj7OzsaNq0qdG+rVu3RqPR0Lmz8T9+6tWrh4ODAwkJCcCT+eL27t1Lnz59yJMnD7a2thQsWJCpU6dm+vy/+eYbChcujJ2dHY6OjlSqVImlS5earTt//nyqVauGs7Mztra25MuXjxYtWnDt2jWjeuPHj6dcuXI4Ojqi1Wrx8/OjU6dOxMbGAmnP41SuXDm8vLyMyvT3zcaNG6lQoQL29vaG++Hy5ctGdaOionjjjTcoXrw4Li4u2Nra4uPjQ69evbh3755R3ZTzIn333Xfkz58fW1tbPD09+fDDD81eg02bNlGnTh3c3NywtbUlb968NGjQgEOHDhEbG4urq2uqq9R89NFHaDQaFi9ebHa7OSlj/N///oevry+2trb4+vryv//9L9Xrd/jwYWrXro2zszOenp5A6vNgJSQk8MEHHxAQEICtrS3Ozs7UrFmT7du3G9VLuf9vv/1GkSJFsLOzM3wuPEuzZs2YNGmSIU79Z0bKeI4dO0bjxo0N19fb25s+ffqYvHbP0r9/fz7//HMAOnXqZGgr5edGbGwsH374IYUKFTJ8zhUtWtSwH2TtnGMpy6ZMmULRokUNnxc9evQwvNezQmBgIACRkZGGskWLFhEdHU2nTp0MiTGA+vXrG95f8fHxGW5r4sSJFCpUyPB+ePPNN82eS3rfm5s2bUKj0dC3b1+z7VWrVg0HBwfu3r0L6Objad68OZ6entja2uLq6krJkiX58ccfM3wuQgjx0nicDDO2EHHkTPYc/0Y0+HqYluvLrt9Je9+UdZ/e/84D455nK/fBusPws/nPdSGEyErSc+wlExERgUajIV++fEblNjY25M2bN11DipRSGfqiotVqjZJTz6NLly6sWrWKJk2aEBwczOPHjzlz5gy7d+8GICAggDFjxvDJJ59QunTpVL/EmBMZGUmTJk2oV68ezZs35/DhwyxbtoyOHTty4MABQ70DBw7QrFkzkpOT6dq1K/7+/qxfv5569eplyTkCzJgxg379+lGoUCE2btyIn5+f0fZatWrh7u7OwIEDiYqKYvr06XTu3JlLly4ZesrEx8dTo0YNTp48Se3atalbty5nzpxh0aJF1KxZkz179lCiRAkcHR0pVaoU+/cb/5oWFhaGRqMxXFuA5ORkDh48SLly5bCzszOq37NnT2xsbOjXrx8JCQlMmzaNfv36UaVKFcqWLZuh83/99deZMWMGxYoV44MPPuD+/fvMmzeP9u3b8/fff9OnTx9D3a+//pqRI0fi4eFB586dKViwIJcvX2bTpk2cPXvW0O2/b9++TJ06lYCAAF5//XX8/Pw4d+4c69ev5969e5kehx8REUHr1q2pV68erVu35sCBA6xevZpatWpx/PhxXF1dAV1ietGiRTRo0IB27dpha2vLtm3bmDFjBsePHze5/qCbgyk6OpqOHTvi7u7OwoUL+fXXXwkMDGTw4MGGelOmTOHtt99Gq9XStm1bihUrxo0bN9iyZQthYWFUrFiRli1bMnv2bPbu3UvVqlWN2pk3bx6+vr6ZGsI8efJkoqOj6dy5My4uLixZsoQffviBO3fuMGHCBKO6cXFxNGjQgPLlyzN48OBnzu/VpEkTtmzZwmuvvcbrr7/OzZs3mTt3Lo0aNWLVqlU0aNDAqP769euZNWsWnTp1olChQri7u6frHN5//30ePHjAjh07+OSTTwyfj9Wq6eYeOX78ODVq1CA2Npb27dtTvHhxtm7dyrRp09i3bx8HDhwweT+kpmfPnty8eZPly5fTp08fypQpAzxZXjs2Npbq1atz9OhRKlWqRLt27bC3t+fYsWOsXLmS7777Ll3tZEZoaChz5syhc+fO+Pn5sXLlSmbNmoWHh0emh2FGRUURGxtLVFQUa9euZfLkyTg7O9O4cWNDnZ07db/U16lTx2T/SpUqceTIEfbv30+NGjXS3e5ff/3Fu+++i6enJ2+//TY2NjbMnz+f0FDT+WbS+95s0KABRYoUYenSpSQmJhr13Dt79iz79+8nJCQEd3d3EhISaNiwIVFRUXTq1IkSJUpw9+5djh07xtatW/n000/TfS5CCPHSUeA5dy80rp12vcTHEPPItCw+ESKf+nEpj7Out1hsAmjNfIW0/+/vbGwaP9jot2nN9Ky2t31SR2urm5z/o39gQBMonT/t8xDiFeDj42PpEIQSL5V58+apf//91+y29evXqwkTJqjHjx+neYx79+6pCRMmpPtx7969Z8b11ltvKUAdPXrUUPbdd98pQM2fP99Q5uTkpKpUqfLM4wGqadOmZrd5enqqsmXLmpQB6tdffzUqb9eunQJUWFiYoaxOnToKUEuXLjWqW7du3TTbTW+8P/74o7K2tlbBwcEm165p06YKUG3btjUq//333xWghg8fbigbMWKEAlSPHj2M6k6ZMkUBqlGjRoayd999VwFq//79SimlTpw4YagDqAsXLiillNq2bZsC1ODBgw376l+7atWqqaSkJEP5mjVrFKB69eqVoWsRFhamNBqNKlWqlHr06JGh/OzZs8rBwUF5enqqhIQEpZRSp06dUjY2Nsrf31/dvHnT5Fj6e3n16tUKUOXLl1f37983qpOUlGSI29w9p1e2bFnl6elpVKa/bz755BOj8o8//lgB6v333zeUPXr0SMXFxZkc94033lCAWr16taFs/vz5ClDu7u7q9u3bhvKYmBjl4uKiihcvblLm4uKizpw5k+o12Lt3r9n7YenSpQpQH3zwgcm+adHHqNVq1alTp4zOs2jRosrKysqovGzZsgpQffv2NTnW0aNHFaDeeustQ9nMmTMVoOrWrWt0X23evFlZWVmp0qVLm+xvZWWldu3alaHz0DP3GaTXsGFDBah//vnHqLx79+4KUN98802G2krrPvvggw8UoHr37m2yLeVnc1rHSIu5a60vs7OzMzr/pKQklT9/fuXm5pahNlKqVauWAgyP4sWLq/Xr1xvV6dChgwLMvnZffvmlAtSMGTPS3WZCQoLKmzevcnZ2VlevXjWU3759W+XNm1cB6rvvvjOUZ+S9+fXXX5u9F/Sfofq6W7ZsydT7ypyoqCij+O7fv2/0tyE+Pl5FRkYa7XP9+vU0n9+4cUMlJydLG9KGtGGJNkK+UYp2Ofth3UEltRz1zGv1aFVY+o958ZZSSqkkxy5KvTHO9PVYuU8p2ql787ek/nrsPaurM3656fX/ZJqunbgE3fMfFirl8bpSUfeetNH7N6WcuuXM+0rakDae0UZERMQrcR4vcxvPIsMqXzKPHz/G6ukx/P+xtrY21EmLg4MDISEh6X5k5coYjo6OXLx4kR07dmTZMfU8PDwYNGiQUVmjRo0AOHr0KACJiYns2bOHYsWK0bp1a6O6n3322XPHMGjQID777DNatGjBli1bcHFxMVvvq6++Mnqu7/Fz5syTLu7Lly9Ho9Hw008/GdXt27cvBQsWZPv27SQlJQEQEhICwLJlywBYsmQJVlZWjBkzBo1Gw5IlSwBYuXIlAC1atDCJ6YMPPjC6t5o2bYpWqzU7LDUtM2fORCnFRx99ZHTvFC1alFatWhEZGcnmzZsBXa+lx48fM3ToULy9vU2Opb+np0yZAsAPP/xgMt+SlZVVqu+J9HBwcGDEiBFGZSNGjMDBwYE1a9YY1dNqtYBuuODNmze5du2a4dpv2bLF5Nht27Y1GsqpHxp5/fqTpcxnzZrF/fv36du3L8WKFTM5hv4a6HvwrVixwmho2Z9//omVlRUffPBBZk6fpk2bUqJECaPzfPfdd0lOTmbmzJkm9b///vt0HXf+/PkAjBw50uj1qVu3LkFBQZw4cYIrV64Y7VO9enWCgoLISklJSWzfvp1ChQoZ9VgEDO+PrJwPa8mSJTg5OZntqaV/LbNL7dq1jXp5WllZUa1aNWJiYoiOjs7UMb///ntmzZrFzz//TMuWLQFMegzqhzWb+1thb28PZGz58Y0bNxIVFUXr1q2NJoz28vKiW7duJvUz8t4cNGgQWq2WyZMnG8qSk5NZsGABgYGBhuGx+h6827dvNxlinVF58uQxxAfg7Oxs9LfBzs7OZG49X1/fNJ/7+PgY9eiWNqQNaePFtpHjJSUT3qnSM6+VQ1ApWD/c+FG+ADSpaFru4w6AlV/eJ8MjSfF6/FfmUqJA6q/Hf8MpXe4bT9rv6+ur2z+PM2ht8XV01a1Q+VYjuBeLT5wVmssR8CAWlCLPvcdoY2LNt8HLe19JG9JGWm1cu3btlTiPl7mNZ5Hk2EvGxsbGZJUwPX2ixMYm7dGwNjY2BAQEpPvxrONlxKhRo3j48CG1atXC29ubFi1aMHHiREPsz8Pcza0fXqUfbnr16lXi4+MpWLCgSd3KlSs/V/tbtmzh999/p0WLFixdujTNybafnjPN398fwOgL7PXr1/Hw8DDbhbZIkSLExcUZ5uRq3Lgx9vb2hqRTaGgoRYsWpWLFigQGBrJx40ZDjE5OTmaHkJYuXdqkzMXFhZiYmLRP/CmXLl0CMBn6Bxi+uOvnYNOvYves4VYXL15Eo9FQu/Yzuv9ngre3t8mXekdHR7y9vbl586ZR+VdffUWBAgWwt7fH19eX/Pnz07FjRwCzyQdzq8p4eHjw4MEDw3P9iof6IYBp6du3LzExMYa54KKioggNDSUoKIhChQo9c39zSpYsaVJWqVIl4Mkch3ouLi5mk5jmXL16FY1GQ/Xq1U226ZNxJ06cMCovXLhwuo6dEVeuXCE+Pt7sa+Hn54e7uzvh4eFZ1t6NGzcICAh4IZPmP61AAdMJjvPkyQNglJDNiFq1atGtWzfDpPrdunXj9ddfNyTi4UlSTJ8kSykuLg4AJyendLepf0+Ym2Pv6c9OvfS+Nz08PGjcuDG7d+82JGcXLlzI7du36dq1q6FemTJl6Nu3LwcOHKBQoUIUKVKEnj17snbt2nSfhxBCvJSsNFAukKhy6Rii5eGsWyky5cPDWZfEerpcP2yyYkE4cAGe/r6y5yw4anWrTqbGPy94ucK+86bbws5Cxf/+rRP9EB7EweglUGjAk8fC3fAoXvf//f9Kz9UQQoh0k+TYS8bJyYm4uDizyaSHDx9ib2//zN4JycnJPHr0KN2P1JJxmdG/f3/Onz/PmDFjqFq1Knv37uXtt9+mfPnyZr9YZURavYey8hxSU6JECby9vQkNDWX9+vVp1k0tcaaUylTbdnZ2lCtXjgMHDpCcnMy+ffsIDg4GdL1xwsLCSEpK4vDhw1SsWNHstUotCZrZmLLDs3qIpbX9eROwH3/8MaNGjcLT05MRI0bwzz//MGvWLH744QfA/D2W1T2FBgwYgLOzM3///TegW6AgPj6efv36ZWk7qUn560x2kJVon09a91tWvY/1C0r8/vvvhjL9DxMXL140qa9P4Gc2eZseGX1vDho0iKSkJH799VcAJkyYgI2NjUnvyylTpnDw4EE+/fRTAgICWLJkCc2aNeP111/PtnMRQohsl6zg03Y4ZdePOB2D4dZdWPRkzlsi78H8ndCqivF8Yudv6h4pdQiGFfvg6pOFX9h4RLfKZaf/fkzN5waLPzV91C+rS9It/hT+1z57zk8ICzH3g7Z4sWRC/peMl5cX165d4/bt20Y9pR4/fkxUVFS6ugY+fPiQ2bNnp7vNbt26pTo8MDP8/Pz4+OOP+fjjj0lOTqZ3797MmDGDv//+m4EDB2ZZO+bkz58frVZr6N2UkrkJ1TPCx8eH+fPnU7duXdq0acOCBQsMw3oyw9/fn3379nHr1i2T3joXLlzAwcHBaMhR7dq12bt3L+PHjyc6OprmzZsDumFz8+bN46+//uLRo0dZuvCAOfovwXv37jVZbli/yqa+R4h+GOHOnTupUqVKmsfct28fW7duTXM1Uf3qieYWprh586bZ5MGtW7eIjY016j326NEjbt26ZXR9Fy5ciJeXF2FhYUbHmT59eqrxpIe+x97evXufuTqjo6MjLVq0YP78+Zw7d44ZM2bg4eFBz549M92+vpdOSvoFLMz1tkqvwMBA9u/fz549e0wmatcPHzbXWzGzUls0JDAwEHt7e5NecKC7J+7evWt2OGta0krC+vn5ce3aNR49evRKJvvi4uJQShn1KK1Rowbjxo1j69atJsMeDxw4gIODQ4Z65ur/8afvYZqSfoh8Shl9bzZu3JhChQoxd+5chg4dytatW6lVq5bJwikAFSpUoEKFCoButeWgoCBmzJjBqFGjzPbUE0KIl56PO3SuSQGVTT8cdwyGoOLQdxycuAaeLjB+DSQlw4iuxnUbDtf991KKBYA+76BLpNUfBh+00PUQG7MUyhWAvv8t5OOohbamPdNZEgZh58xvEyKHCw8Pf65/m4vnJz3HXjL6N8TTXxBOnTrF48ePKVq06DOPYak5xxITE03mqrGysjJ8aYqMfPILkVar5e7du1nSbkq2trZUq1aNs2fPGg0LAgy9DJ5HsWLF2L59O3ny5KFDhw4mbWREy5YtUUrxySefGJVPmzaNixcvUrNmTaMvgvp5xH744QdsbGwMz1u3bo2VlZXh/PTzBmWX7t27o9Fo+L//+z+jVVHPnz/P8uXL8fT0NCTo3nzzTWxsbBgzZozR66+n7/HxxhtvALp54cz1MNTXK1++PADr1q0z2v7LL7+kOudSbGwsw4cPNyobPnw4sbGxNG3a1FCmv9Ype6EkJCQwevRos8dNr65du+Li4sI///xjNoHzdK+XDz/8kOTkZN555x3OnDlD27Zt0xzC+yxr167l9OnThuexsbGMHz8eKysrunfvnunj6oe0ff3110bnsGPHDnbt2kXp0qUJDAzM9PGfph/G+PRnjLW1NTVr1uTixYsmyZJPPvkEpRStWrXKUFv6HwvMJWHbtm3Lw4cPef/99022vYgerFkltbkG9e+VlMmu9u3b4+7uzvz5843eZ1u2bOHo0aPUr18/Q70OGzZsSJ48eVi2bJmh5xno/kaY+2EnM+/Nnj17Eh4eTs+ePUlMTKR///5G2yMiIkxWdXZ2djb8Db59+3a6z0cIIV4aGg0Mbg22NiZTG2QZa2tY9SV0qQm/rYRP/gVPV9g0Akr4P3v//J6w5Rso4gOfzdANnQyppJvXzNwqlkLkEvfv37d0CLme9Bx7yeTJk4cyZcpw/Phx1q1bR2BgINHR0Rw7dgxfX990Jcf0c469aHfu3CF//vzUqFGD8uXL4+3tzYULF5g3bx5OTk5GvV9KlSrFwYMH+eCDDyhYsCAajcYwnOd5jR07ljp16tC5c2e6dOlC/vz5Wbt2reFLXWo9UNKrcOHC7Nixw9DGzJkz6dChQ4aP8+mnnzJnzhymT5/O1atXqVWrFmfPnmXhwoW4ubkZDWsCqFevHk5OToSHh1OuXDlDssDT05OiRYty5swZ3N3ds3zC86dVrVqVHj16MGPGDMqXL0+bNm24f/8+8+bNIy4ujj/++MOQzClRogT/+9//GDVqFCVLlqRNmzYULFiQa9eusX79ev7++2/q169vGMo0ffp0SpQoQYsWLfD19eXChQusXbuWQ4cO4e3tTdWqValYsSJLly6lbdu2VKxYkUOHDrF582Z8fHzMLlbh7e3NH3/8wfHjx6lcuTL79+9n9erV+Pn58c033xjqhYSE8Pvvv1O9enVatWpFTEwMS5cufe6hk66urowdO5Z33nmHChUq0K5dO4oVK8bt27fZvHkzgwYNMvriHhQUROnSpdmwYQMajSbTE/Hr+fv7ExwcTJcuXXBxcWHx4sWcO3eON99887m6b3fv3p2JEycSGhpK1apVadq0KTdv3mTu3LnY2tqa3L/Pq27duvz88898/PHHdO7cGQcHB6pWrUrNmjX59ddfCQ4O5o033mDlypWGJPbmzZspU6YMQ4cOzVBb9evXNyyWERUVhbOzMyVKlKB58+Z89913rFu3jilTpnDo0CHq16+Pg4MDx48f58KFCxw6dChLzzu7VKhQgXLlylG+fHkCAgKIiIhgy5YtHD58mMDAQEaNGmWoq9Vq+eabbxg0aBCVKlWiZ8+exMTEMG3aNFxdXfm///u/DLVta2vLyJEjef/996lUqRJdunTBxsaGefPm4erqSlRUlFH9zLw3Bw0axOjRo9mwYQNeXl506dLFaPvixYv5+OOPadCgAcWLF8fFxYX9+/ezYsUKihcvbnZORSGEeOnZ28JbjZ/vGJu/eXYdD2eY/J7ukZaUPcZSKhMIa4dlPLap7+seQryCsnt6E5EOGVrbUrwQSUlJ6vDhw2rOnDlq0qRJavr06Wrnzp0qISHBYjG99dZbClBHjx41lH333XcKUPPnz1dKKfXo0SPVq1cvVbRoUeXk5KRsbGyUp6enatasmdq/f7/R8fbv369ee+01ZW9vrwCV8lb09PRUZcuWNapvrkwppebPn68A9d133xmVb9q0SVWoUEHZ2dkpJycn1bhxY3X48GEFqLZt22b4/AHVtGlTo7Jr166pggULKjs7OzVnzhyllFJNmzZVqb2tzB0jOjpa9erVS3l5eSlra2vl4uKiGjVqpI4dO2b2GDVr1lSAGjBggFF5nz59FKAaNmxoso+5104vteuaHiNGjFAFCxZUNjY2yt7eXlWsWFEtXrzYbN0ZM2aoihUrKgcHB2VjY6O8vLxUixYt1LVr14zq/fzzz6pkyZJKq9UqOzs75efnpzp16qQePXpkqHP+/HlVu3ZtZW9vr7RarapcubLatWuXKlu2rPL09DR7fhs2bFDly5dXdnZ2ytHRUTVq1EhduHDBqG5CQoIaOHCg8vb2VjY2Nipv3ryqR48eateuXQpQb731lqFuavedUqnfA6tXr1bBwcHK2dnZcPyGDRuqQ4cOmdT98ccfFaDKly9v9nqmR8oYP/vsM+Xj46NsbGyUj4+PGjp0qEl9c9dP7+jRoybXQCndEsqDBg1Sfn5+ysbGRjk5Oang4GC1bdu2dO2fUR988IHy8vJSVlZWJsc7fPiwatSokXJxcVHW1tbKy8tL9e7dW8XExGSqrR9++EH5+/sra2trk/fu/fv31bvvvqsCAgKUjY2NcnBwUEWKFFFffvmloc7Tn4/pZe5apXX90np/p2XAgAGqdOnSytXVVVlZWSl7e3tVpEgRNXDgQBUdHW12n7///lsVL17c8D6qVauWOnjwYIbaTWn8+PGGz5A8efKoN954Q82cOdPkvZWR92ZKISEhZj8vldLdL61bt1Z+fn7K3t7e8Hnz5ptvqtu3b2f6nIQQr4CQb5SiXc57WHdQ6oO/Dadx69YtC15EIURGPX782NIh5HoapV6i2biFyEYbNmygcePGvP/++/z222+WDke8AF5eXvj4+Jidx+hl9ttvv/HBBx/w888/89FHH2XqGAsWLKBTp0589913/O9//8viCIV4+bVr145ly5Zx4sQJwwqqQgjxTC1GwaoDlo4i46w0cP5PKPhkJXcvLy8LByWESK9Dhw6ZzOcsXiyZc0y8kp4es52cnGwYJtS6dWtLhCREuk2cOBEXFxfefvttS4ciRI4UERHBmjVrqFKliiTGhBCvPhsr3SqQ/yXGQDe5txBCiPSTOcfEK6lEiRJUq1aNcuXK8eDBA9atW8eJEyeoV68ejRo1AuDKlStm56hKycPDAw8PjxcRssXJ9bCsy5cvs2jRIrZu3crx48d5//33TVZDTExM5OrVq888VnpWtX0Z3L9/3+yk908rXLjwc7cVGxvLjRs3nlkvf/78z7UAgjkZed2eZ4GU6OjoVBem0LOxscnShRJSioiIeOZksvb29mZXjcwqO3bsYMeOHcyaNYv4+HiGDcvEnDZCCJHTPE6GT9pYOgohxHPIly/fsyuJbCXJMfFKatCgAaGhoaxZs4akpCTy5cvHO++8wy+//GKoU7lyZbMrKKb01ltvMXHixOwO96Ug18Oy9u7dy+DBg3F0dKRNmzaMGTPGpM7p06cpV67cM481f/787Agxy40bN47PP//8mfWyYvT/ypUr6dSp0zPrHT16lLJlyz53eyll5HXTrwKaGZ9++imTJk1Ks46np2e6EpKZ8frrr7N27do065QtWzZbhzlPmzaNSZMm4eHhwVdffWVY1VcIIV5Z1lZQvRhULWZUXKpUKQsFJITIDHt7e0uHkOvJnGMi11q2bBkPHz5Ms07p0qWpUKHCC4rIsuR6vPwePHjA8uXLn1mvfv36+Pj4vICIns/58+cJCwt7Zr1u3bo9d1s3b94kNDT0mfVatWplWAk2q7yo1+3w4cOcOHEizTpOTk7ZNrR8165dXLp0Kc06Xl5eht67QgjxUsqJc44t+QzaVDMqOnfuXLpWuRdCvBxkzjHLk+SYEEIIIYQQQkDOS44VygfnxoOV8VTS8kVbiJxF3rOWJxPyCyGEEEIIIUROowE+aWuSGAMZoiVETlO8eHFLh5DrSXJMCCGEEEIIIXIaV0foXd/sJhlSKUTOcuvWLUuHkOtJckwIIYQQQgghchIrK3g/BBy1ZjcfO3bsBQckhHgeMTExlg4h15PkmBBCCCGEEELkJFYaeK+5paMQQmQRW1tbS4eQ69lYOgAhhBBCCCGEeCl83IbE5KSX/4tqUAnw8Uh1c05YtVoI8USZMmUsHUKuJ6tVCiGEEEIIIcR/wsPD8ff3t3QYQohcRFartDwZVimEEEIIIYQQ/4mIiLB0CEIIIV4wSY4JIYQQQgghhBBCWIinp6elQ8j1ZFilEEIIIYQQQvxHKYVGo7F0GEKIXOTu3bu4u7tbOoxcTXqOCSGEEEIIIcR/Tp48aekQhBC5zKVLlywdQq4nyTEhhBBCCCGE+E9CQoKlQxBCCPGCSXJMCCGEEEIIIf7j6upq6RCEELlM0aJFLR1Crmdj6QCEEEIIIYQQr4irkdD9Z7gfZ+lIUteuOgzvkupmHx+fFxiMEEJAVFQUzs7Olg4jV5PkmBBCCCGEECJrrDsE209ZOoq0nQ6H90Mgj4vZzWfOnKFixYovNiYhRK4WHR1NgQIFLB1GribDKoUQQgghhBC5R8JjmLDO0lEIIYSBtbW1pUPI9SQ5JoQQQgghhMg9khX8shwSEs1uDgwMfMEBCSFyu3Llylk6hFxPkmNCCCGEEEKI3CXiHszebnZTfHz8Cw5GCJHbHTlyxNIh5HqSHBNCCCGEEELkLhoN/LgYlDLZdOvWLQsEJITIzZKTky0dQq4nyTEhhBBCCCFE7qIUnLwGGw5bOhIhhCBPnjyWDiHXk+SYEEIIIYQQIvextoLRS0yKZe4fIcSL5u7ubukQcj1JjgkhhBBCCCFyn6Rk2HAEjl02Kj579qyFAhJC5FYXLlywdAi5niTHhBBCCCGEELmTjRWMXWZUFBcXZ6FghBBCWIokx4QQQgghhBC50+NkmLEVbtwxFLm4uFgwICFEblSoUCFLh5DrSXJMCCGEEEIIkXspBeNWG576+/tnX1t3H0L/P8GrDzh1g/rD4MD59O9/8ho0GwnO3SFPL3j9V4iISXufmVtA0163jxDipXTv3j1Lh5DrSXJMvFR++eUXNBoN//77r6VDeW4PHz6ka9eueHp6YmVlhaenp6VDyrCBAwei0WjYs2ePoSw7X6OKFStm6XVatWoVGo2G4cOHZ9kxX6ScHr8QmdGyZUs0Gg3379+3dChCiNwiKRnGrYKHuuGUp06dyp52kpOhxSiYtQ0GNofRveB2DNQbBmevP3v/a5FQ50s4dxO+6wEft4aV+6HxCEhINL/Pg1gYOh2c7LP2XIQQWSoqKsrSIeR6NpYO4FV28OBBIiMjiYyM5P79+zg7O9O9u/xik1sMHTqUuXPn0qVLFypVqoSzs7OlQxKvgNOnTzN27Fg6duxIkyZNLB2OsJClS5eycuVKvvrqK/Lnz2/pcIQQIue7HwtTQ+G95pk/Rr2voGA+mPq++e0LdsHO0zD/Y+hYQ1fWuQYUHwjD58Ksj9I+/ncLdQm8/WMg0EtXVq2YLjk2NRT6m/l3wagF4OIA9cvCkrDMn5sQIltpNBpLh5DrSXIsG+3duxetVounpycJCQmWDke8YJs3byYgIIDZs2e/Uh9277//Pm+99Rb29vILZHZr2rQp9+/fR6vVGsrOnz/PpEmT8PX1leRYLrZ+/XomTZrEm2+++colxxYuXEhiYiJOTk6WDkUIkduMWQIDmhAQEJA9x1+wC7zdoX3QkzIvN12CbMZWiE8ErW3q+y/cDS2rPEmMATSqAMX9YN5O0+TY2evwy3JY/KluuxDipVWhQgVLh5DrybDKbNS1a1d69+5NixYtcHR0tHQ4wozo6GiSk5Oz5dhRUVG4uLi8UokxABsbG5ydnbGxkdx6drO2tsbZ2Rlb2zT+oSzESywzQwS0Wi3Ozs6v3GenEOIlp4DLEbBsL0lJSdnTxsGLUKkwWD31FaxaMXgUD2fSGFoZHqUbglmliOm2asXg4AXT8g+n6HqMhVR+vriFENnu2LFjlg4h15PkWDZydXW1dAgm9HO5XL58mQ4dOuDm5oatrS0lS5Zk6dKlRnXTmlvK3NxQ3t7elC5dmp07d/Laa69hb2+Pq6srnTt3JjY2ltjYWHr16kWePHmwtbWldOnSbN261WycSUlJvPvuu3h5eWFra0uBAgUYM2aM2bqbN2+mVq1auLi4YGtri5+fHwMGDCA2NtZszCdOnKBu3bo4OzuTJ08eHj58mO7r9/jxY4YMGUL+/PmxtbXF2dmZ4OBgQkNDTa7brVu3OHnyJBqNBo1GQ58+fdLdzsaNG2nevDl+fn5otVrs7e0pWbIkv//+u0ld/Wt65swZmjVrhrOzM1qtlvLly7NmzRqjupcuXTLEMm7cOAoWLIitrS158+blzTffNLlm5qR2X8TFxfHhhx8aro2joyPVqlVj3bp1JseIiIigTZs2uLi4oNVqKVu2LKtWrUr39TFn6tSpFC5c2HA+ffr0SfV80htryjm/hg0bhp+fH7a2tvj6+vLJJ5+YTawuX76cSpUq4ejoiJ2dHYUKFWL48OEmdffu3Uu9evUM7wd3d3fKlSvHxIkTzbYPumvfokULAEaOHGm4t0qXLp3u65TyHvjrr78oVKgQdnZ2eHt7M2LECADOnj1reI/Y29vToEEDbt68aXScAwcO0L59e/Lnz4+DgwNarZbChQszbNgwk3PVz123detW3nzzTfLmzWt4X0+aNMkkxhEjRlCpUiXy5MmDjY0N7u7uNGjQgIMHD5rUTUpK4v333zd8VgQGBvLTTz+ZnS8P4PLly7Rv3x5PT09sbGzw8PCgRYsWnD9vPBmyfv/Q0FC6d++Oh4eH4X21Y8cOACZPnkyRIkWws7PDy8uLL7/80uw1X7BgAZUrV8bR0dFw3p9//rnJddJ/hh44cIDq1avj4OCAo6MjtWrV4vTp04Z6LVu25I8//gAgKCgoU58xsbGxvPPOO4bPGEdHRwIDA+natatJXBmNf9u2bVSpUgVHR0dKlCjBvHnz0Gg0fPSR+eFCpUuXxsXFxTARbWpzjl25coUuXbrg7e2Nra0trq6uVKxYkalTpxrVO3LkCE2bNsXd3R0bGxu8vLzo1q0bkZGRRvXOnj1Ly5YtDfeOq6srJUuW5Jtvvkn3dRRCvGKsrWD0Em7cuJE9x78RDb4epuX6sut3TLel3Ddl3af3v/NA1/NMb+U+WHcYfu6b+XiFEC/M48ePLR1CriddP15ycXFx6a5rZ2eH1dO/RKWiSZMmuLm58d577xEdHc20adPo2bMnp0+fxs/PL7PhEhUVRfPmzWnYsCGtW7cmNDSU+fPno9FouHr1Kg8ePKB///5ER0czdepUunTpwsWLF02G6I0aNYrY2Fi6du2KtbU1CxYsYOjQoTx8+JCvv/7aUG/WrFn07t0bb29vevXqhZeXF3v27GHixIkcPXqUbdu2GV2TuLg46tSpQ5kyZfjoo4+IjIzMUK+cZs2asXHjRipUqECPHj24ffs2c+fOpXnz5ixatIiQkBCaNGnCmDFjGDlyJM7OzgwePBiAypXT/6vdnDlzOH/+PE2bNqVgwYLcuXOHhQsXMmjQIO7fv8/nn39usk/z5s1xdnZm4MCB3L59mzlz5tC2bVtWrFhBo0aNjOpu3bqVefPm0alTJ/z9/VmzZg1Tpkzh8uXLrF+/PsM9NhITEwkKCuL48eM0atSIPn36cO/ePebOnUvLli1ZsGABrVu3NtStVasWZ86coWHDhgQHB3PkyBE6deqU6aXTp06dyhtvvIGnpyf9+/fHzs6O+fPnm02+ZiRWvRkzZnDnzh06deqEu7s7S5Ys4aeffiIyMpJ//vnHqF7v3r1xc3OjZ8+euLu7s3TpUkaOHMm5c+eYOXMmoEsONm7cGKUUnTp1olChQkRGRnLw4EF27NhB//79zZ5nkyZN6NevH5MnTyYkJIT69esDuqRERm3bto3FixfTpUsXvLy8mD17Nl9//TVarZaxY8fy2muvMXjwYA4ePMiKFSvo3bs3a9euNey/Zs0a9u7dS926dSlcuDCPHj1ixYoVfPPNN9y8edMoyaf31ltvYWVlxRtvvMHjx4+ZNm0a77zzDhUrVqRq1aqGen/++SelSpWiZ8+eeHp6cvz4cZYsWUL9+vU5ePCg0VLXXbt2ZcGCBZQtW5Y333yTqKgoRowYYfaanDt3jmrVqpGYmEjbtm0pVqwY58+fZ968edSsWZN9+/aZDKXRDyF+++23uXPnDtOmTaNNmzZ8/vnnjBw5ks6dOxuu37fffkuZMmXo1q2bYf8ffviBzz//nGLFitG/f39cXFzYvHkz33//PWfPnmX+/PlG7d25c4cGDRpQp04dhg4dytGjR1myZAmdO3fm4MGDWFlZMXDgQO7du8e2bdv48MMPDauqZeQzplu3bixdupSGDRtSs2ZNkpKSOHfuHGFhYSQlJRk+NzMaf2RkJM2aNaNevXq0bNmSmJgYOnToYHjfjB071ugz+fjx45w8eZI2bdqk+YPS6dOnCQ4OJiYmhsaNG1OlShUePXrEvn37WLNmjSExuG3bNpo2bYqjoyMdO3YkMDCQo0ePsmDBAvbv38/BgwdxcnLi8ePH1K9fn8jISDp06ECpUqWIiYnh2LFjbN++Pd3XUQjxiklKht1ncDx6DSpWTLtu4mOIeWRaFp8IkU+tOpfHWddbLDYBtGa+ftnb6f4bm8Y0LPpt5oZd2ts+qaO11U3O/9E/MKAJlH61ht4L8apyd3e3dAhCiRdi3rx5aubMmRneb8KECel+hIeHP/N4LVq0UIBq0aKFSk5ONpRPmjRJAerTTz81lP38888KUNOmTTM5ToUKFVTevHmNyvLly6cANXr0aKPyokWLKo1Go6pWraoeP35sKB82bJgC1IQJE0zazJs3r7p27ZqhPCoqSnl6eipHR0d1+/ZtpZRSsbGxys3NTZUsWVI9fPjQqM0vvvjCJPYKFSooQPXo0eOZ18mcRYsWKUDVrFlTJSYmGsp37dqlrKysVIkSJVRSUpLR9ShVqlSm2oqOjjYpe/DggfL391e+vr5G7ehf0+rVqxvFtXXrVqXRaFSFChUMZRcvXlSA0mg0avny5Yby5ORkVadOHQWoefPmGcrfe+89Bajdu3cbyszdF/rrPXHiRJPz8PT0VKVKlTLcbz/88IMCVK9evYzqfvXVV4bXPiMeP36sPD09lbOzszp37pyhXH/PAGrYsGGZinXlypUKUFqtVh0+fNhQNy4uThUvXlxZWVmpAwcOGOLw8vJSDg4O6ujRo0Z1S5curTQajdq6datSSqnp06crQP34449pnpu+/ZTxmyvLCP09YGdnp/bt22cov3HjhrK1tVUajUYNHDjQaJ+6desqa2trdfXqVUPZ3bt3jT5DlFIqKSlJVahQQdnb26u7d+8ayvX3UaVKlYzu0S1btihAde3a1eg45u7/xYsXK0ANGDDAULZ//34FqAoVKqj4+HhD+Z49e5RGozG5d2vVqqVcXFzUkSNHjI69efNmZWVlpXr37m0Sc+XKlY1i1n9u2dvbq4MHD5pcv1q1ahnKrly5omxtbVWdOnWM3rNKKdW9e3el0WjUzp07DWX6z9Cn74vOnTsrQIWGhprEl/L8MsLZ2VlVrFgxzTqZjf/zzz83OVafPn0UoNavX29UPmDAAAWoZcuWGcr0n2n37t0zlAUFBZl93yqljP6uFCpUSPn6+qrr168b1fnnn3+M3jf79u0zuZ+EEFlk8nqlaJdzH9YdVNLr//fs8ww9mv5jXryl28epm1JvjDM91sp9unprDqTe3t6zujr/hppu+2Sabltcgu75DwuV8nhdqagnn6Oq92+69oUQL6X79+9bOoRcT4ZVvuRCQkLS/cibN2+6j/vpp58a9Q5q164dgMnQooxyd3c3GTpTrVo1lFIMGDAAa2tro3MDjIYL6XXo0MHQGwIgT5489O7dm0ePHrFo0SIAFi1aRExMDJ07d+bOnTtcu3bN8OjUqROAybBCwDB0LKNmz54NwBdffGE031ZQUJBh2JO5c8mMlL8c3Lt3j/DwcKKjowkODubGjRtcuXLFZJ8hQ4YYxVW7dm2qVKnC0aNHuXr1qlHd8uXL07JlS8NzjUZj6JG3YMGCDMc7d+5cfH19adasmdHr8ODBA2rUqMHp06cNw/KWLFmClZWVyevwxRdf4ODgkOG2d+zYYeitUqTIk3k49PfM88Sq16BBA8qXL294rtVq+eijj0hOTjbcFzt27CAiIoLmzZtTtmxZo7qfffYZSinmzJkDYHivrl+/nlu3bmX4nLNCUFCQUU8jHx8fAgIC0Gg0fPXVV0Z1a9WqRVJSEidOnDCUubm5GT5DHj58SHh4ONevX6dBgwbExcWxe/dukzbfffddo3u0Tp062Nvbc+nSJaN6+vs/KSmJiIgIrl27Zhiml3Jopf56vvPOO9jZ2RnKq1WrRvXq1Y2OeffuXXbs2EFwcDAeHh5Gr32RIkXw8/MzDJdM6Z133jGKWf+5Vb16dSqm6FWgv37Xrl0zlE2ZMoXExET69u3L9evXTT6jlFIsW7bM5Nz1vU31mjZtCuh6WWUVR0dHrl69yubNm1Otk5n4nZyc+OKLL0yONXDgQEA3FFVPKcWiRYsICAgwXFdzoqKi2LNnDxUrVuStt94y2a7/u3LgwAEuXrxIs2bNSEpKMoq3YcOGaLVawxB4Dw/dsKQ9e/Zw4YKZeXoy4M6dO8THxxueP3jwwGhIaEJCgsnca08P2Xr6+c2bN1FKSRvSRo5vI0dKSuZ6IddnX6vCnrB+OKwfTuKqL7i38CMoXwCaVIT1w4ma875hOz7uumvl62EYHml0rf4rS/B0Nm4j5euR19moLqS4/jeiIY8zN+5EQsxD3QqVbzUi4vwV1MVbcOk2PIhFqWTiT1+D23fNt5FD7itpQ9p4Fds4d+7cK3EeL3MbzyLDKl9y2bVaTsov7/Dky3p0dLS56umWL18+k4na9V9CSpQoYVSuH/Z0547p/AplypQxKdOv4HH27FkADh8+DOjmXho5cqTZeJ6eY8bFxYUCBQo88zzMuXLlChqNhmrVqplsK1WqFFu3buXkyZOUKlUqU8dPKTw8nPfee4/Q0FDDPDwpRUREULBgQaOyKlWqmNQrUaIEe/fu5fTp00Yr2hUuXNikbqVKlQBMEmnpceXKFRISEggMDEy1Tnh4OL6+vly7dg03NzeTulqtFj8/P+7evZuhtk+ePAmY3l9gftWXjMSqV7x4cZM6+ut98eJFAE6dOgVg9vXX3zOXL18GdENgQ0JCWLVqFf7+/hQtWpSaNWvSp08fateunWpcWcnc+bu6uuLm5mYyn6D+8+H27duGsnv37vHee++xevVqs5OuP/3eAyhZsqTZNp++xxcvXszw4cM5deoUiYmJRttS/lHUJzXKlStnctyiRYsaJeiOHDmCUop169alurrj0+dtLmb955a5Y7i6uholx/TJrL59U5/vJeU1BfOfoT4+PoD5a5pZo0ePZsCAAdSvX598+fJRuXJlWrduzZtvvmkYap7Z+M0tQFO5cmWKFi3KunXriIuLw97enjVr1nD79m3eeecdox9Onnb8+HGUUs/8bN2/fz8A//zzj9Fw55T0f28KFy5M//79mTRpEkWLFqVgwYIEBwfTvXt3w7x+6ZUnTx6j587OzkbP7ezsTH68Svn5Yu65/jWXNqSNHNmGJodPKu3hTFRIOQJS/Ihs9lo5O0Og7pra/vfgt3W6BFijCjz9k7Wvry9ULAjbTkJysvHrsecsOGqxK1vQuI0U7Ar5gJcr7HvyQ7bh+oedhYqFdM8v3YYHcTB6CV6jlxgdQwNoSw6CNtVgyWc5676SNqQNaUPaeM42nkWSYy+5R48ePbvSf7RabZpfMFJKbaXBlNnatOadSm0Vn7TaT0+bGaHfb+DAgdSoUcNsnae/wNrZ2b30qywqpahTpw5XrlyhU6dOBAUFkSdPHqytrZk6dSrr1q3LthU2M0spRUBAAKNHj061TrFixV5gRKl7WWJduXIlu3fvZv78+ezcuZOZM2cydepUPvroI3766adsbz+196qVlVWqcxemvO9CQkLYuXOnYe4zfVJn5cqVzJw50+xnRHo+A7Zs2ULHjh3x9vbmww8/pFixYjg5OaHRaHj77bef+/Oifv36ZnsfASZzH6YVc3o+a/Vtjho1ymxCGkyTb2kdNyvf971796ZZs2bMnDmT0NBQ9uzZw+rVq/n111/Zs2cPrq6umYrf3DXU6969OyNHjmTWrFm88cYbTJgwASsrK955550sOSd9vJ06dTL0hn5ayn9oTZgwgUGDBjF79my2b9/O0qVLmTVrFp07d2bu3LlZEpMQIoex0sAHLXDM45Y9x+8YDAt2waLd0PG/f7tG3oP5O6FVFeP5xM7/14u9SIovjB2CYVooXI2E/P/9oLPxiG6Vy49a6Z7nc4PFn5q2/dtK2HUGZn9kflJ/IYRFPd3xQbx4L3eWQDBjxox0123ZsuVzTab/tHz58gHmeytcv349w5O2Z4S54UP6nmL6xIV+hT4HBwejCbCzS4ECBdizZw9hYWE0b97caFtavYYyat++fVy4cIFevXoxbdo0o21TpkxJc7+UE5WDbsiqlZWVSa8qc8OIDhw4AJjvEfMs/v7+3Llzh/bt26PVatOsGxAQQFhYGFeuXDH6IxAfH8/169fN9jpJi/6amxvSqr9nMhur3pkzZ0zK9u3bB2C45vo49D3ZUtq7dy+ASa/FoKAggoKCAN37rGLFiowbN46RI0emeh2y832XXnfv3mXnzp3Uq1ePFStWGG3buHHjcx178uTJJCcns2jRIsO1Ad3QzR49ehjV1V/7o0ePmiTIz507Z/S8TJkyaDQa4uPjX8jnBTzpcejp6ZnlbaZ38ZW0eHt7M3jwYAYPHoxSin79+jFlyhQmTJjAJ598kuXxv/vuu3z77bdMnz6dbt26sW7dOsqXL2+2519K+tdO/zmbGv1xNBpNuuMtU6YMo0aNAnQ/RgUHBzNv3jyGDRtmtgezEOIVZ2MN7zajgFvGp3lIl47BEFQc+o6DE9fA0wXGr9EtBDCiq3HdhrqVqrk04UnZ5x10ibT6w+CDFroeYmOWQrkC0LeBro6jFtoaTy0AwJIwCDtnfpsQwuIePnwok/JbmMw59pLLrjnH0kM/x9KmTZuMyv/880+zQyGz0sKFCwkPDzc8168S5+joaOgR0LFjR9zc3JgyZYrJnEWg+4CJiIjIspi6dtX9o+X777836hWzd+9etm3bRokSJcwO7csofU+Vp3vI7N27N835gcaOHWu0BPD27dvZt28fZcuWNUl4HTlyxCipoZQyzDnWsWPHDMfctWtX7t27ZzJXkp5+6CFA69atSU5OZvjw4UZ1vv32W2JjYzPcds2aNcmbNy9r1qwxmjNPvwrr88Sqt2nTJo4cOWJ4Hh8fzy+//IKVlZXhvqhRowZeXl6sWbPGKEGWkJDAjz/+iEajMdS9efOmyXLNnp6e+Pv7k5CQQExMTKrn6+am+zU7u9+DadH3bnr6Hr1y5Qrz5s3LkmM/3UtKn8BJSZ8A+fPPP0lIeLLCV1hYGHv27DGq6+npSVBQELt372bhwoUm7SqlzM7l9zz69euHra0to0ePNjtkPSoqymiYaEbou5Y/PawxPZKSkkzm1Us5ZFw/TDar4/f29iY4OJhdu3bxyy+/EBsbS5cuXZ65X968ealevToHDx40+wOB/l4JCgqiYMGCLFu2jLCwMJN6iYmJhr8rUVFRJp83jo6Ohh9fsvJvhxAih7Cxgj4NwMvNaI7NLGVtDau+hC41dT25PvkXPF1h0wgo4f/s/fN7wpZvdL3JPpsBo5dASCXdvGbmVrEUQuQY8m8Py5OeY9nozJkzPHjwAIC4uDiSkpIMvXOcnZ3NzmP0tOyacyw9ypYtS+XKlVm1ahWtWrWicuXKHDlyhI0bN+Lj42MyF1BWcnNzo2rVqnTs2BFra2vmz59PZGQkX375paFHm7OzMxMnTqRnz56UK1eO1q1bU7x4cWJiYjhz5gyhoaH8+eef9OrVK0tiateuHQ0bNmTjxo1UrVqVZs2acfv2bWbPno2trS0///xzlvTmKF++PAUKFGD27NkkJCRQunRpzp49y4IFCyhQoECqiyZERERQpUoVQkJCuHXrFrNnz8bOzs7sEL2CBQvSuXNnOnXqREBAAKtXr+bgwYPUr18/U8mxkSNHsmHDBsaPH8/u3bupU6cO7u7uXLlyhR07dmBtbW3oDThkyBCmTJnCv//+S3h4OMHBwRw5coQNGzbg7e1tkjR6Fmtra3744Qf69+9PcHAwnTp1QqvVMm/ePFxcXEx6PmYkVj1/f3/q1q1L586dcXd3Z/HixZw9e5bXX3/dMFebtbU1P//8M71796ZmzZp07twZNzc3li1bxqlTp+jevbthPrHx48czfvx46tevT7FixdBqtWzfvp2wsDBq1qyZ5vj41157DQcHB+bNm4e7uzt58+bF29v7hfWGAt3cfVWrVmXLli20bNmS6tWrc/nyZebPn0++fPkynfAB3dC7f//9l44dO9KtWzfs7e3ZvHkzZ86cwcXFxahupUqVaN++PYsWLaJy5cq0aNGCqKgo5syZQ+HChTl//rxRT7vp06cTFBREly5daNy4MZUqVUIpxcWLF9m0aRPNmzdn6tSpmY79aYUKFeL777839MJq1aoVhQoVIiIighMnTrBt2za2bt1qsnhAetSvX5/vv/+eL7/8kmPHjuHg4MBrr71G3bp1n7nv/fv3CQwMJDg4mPLly+Pj48PFixeZO3cujo6O9OzZM9vif/PNN9m+fTujRo3CwcGBfv36pWu/f/75h+DgYN566y3mz59PlSpViIuLY9++fXh7ezNnzhw0Gg0zZ86kSZMm1KlThxYtWlC2bFliY2M5f/48mzZtYtCgQYwYMYKVK1cycOBA6tevT/HixXF1deXgwYMsXbqUokWLUqtWrXSfkxDiFfE4GQa3er5jbP7m2XU8nGHye7pHWlL2GEupTCCsHZbx2Ka+r3sIIYQwS5Jj2ej06dMmKyToh2L5+vqmKzlmaYsXL+b1119nw4YNrF+/njJlyrB8+XIGDRpkNPF0Vvvyyy8JCwtj1qxZxMTE4Ovry/fff89nn31mVK9z584UKFCAr776itWrVzN37lycnJzw9fWlZ8+e1KtXL0vjWrNmDZ9++ilz585lzJgxaLVaypcvz7fffkuDBg2ypA1ra2vWrl3L22+/zapVq1i8eDH58+dn9OjRnD59mj/++MPsfqtXr2bQoEH89ttvJCYmUrx4cX788UcaN25sUrdOnTpUqVKFn376ifDwcFxdXenTpw/jxo3L1LA9W1tbdu3axddff83cuXMZP348oFuMoXTp0kYJSjs7O3bs2MGbb75JaGgo27Zto1ixYsybN48vvvgiU/dVv3790Gg0jBo1iokTJ+Lq6kqrVq1o1aqVSbIvI7Hq9ezZk+TkZCZPnkxkZCR58+Zl8ODBjBkzxqSeq6srX3/9NdOmTePx48cEBAQwbNgwo55yTZo0ISwsjG3btrFs2TKsrKzIly8fH3zwAd98k/Y/rB0cHJg8eTJfffUVP/zwA48fP6ZUqVIvNDkGsHTpUvr378+2bdtYu3Ytvr6+fPDBB3h4eKTaKy89mjRpwuTJk/n2228ZN24cdnZ2VKpUiY0bN5q9l+fNm8egQYOYO3cuY8eOxdfXl+HDh3Ps2DHOnz+Pk5OToW6RIkU4evQoQ4cOZcOGDWzYsAFbW1s8PT2pXbs2b775ZqbjTs2QIUMoXbo03377LQsWLODRo0e4uLgQEBDAu+++m+nepo0bN+bjjz/m33//5auvviIpKYnevXunKzmmT4Bt376d/fv3Ex8fj7u7OzVr1mTEiBFGC7Zkdfzdu3fn/fff58GDBzRp0sTsIgjmlCxZkv379zNkyBC2bdvG+vXrcXJyonDhwrzxxhuGejVq1GD//v189tlnbN26laVLl2Jvb0++fPlo2bKl4fOgWrVqNGjQgH379hnmcfT09KRXr1589913L/3clEKILGZtBc1eM/TeysppSoQQIj3MLSQmXiyNyvHrLQuRu7Vs2ZKVK1dy7949k541T7t06RKFChWid+/eWdpD5lW1atUqWrRowbBhwxgxYoSlwxEZULNmTfbt20dkZOQz3xdCCCGy0N8boN94S0eRcZu/gbq6uQYjIiLw8vKycEBCiNzkxIkThjm1hWXInGNCCCFyLHNDOPfs2cPu3bspW7asJMaEEEKkzUoDFQpCnSdfSlPOeyuEEC9CyvlzhWXIuAGR6128eNFkou+neXp64urq+lztREZGcu/evTTraLVa/P3TMSFrLiDXK2Ne1H38svn999+ZNm0aDRs2xNvbm9OnT7No0SKsra2fOUT1VRMXF8f169efWS8gIAA7O7sXEJEQQuQAyQo+bQcvwWrQQojc61X7N3pOJMkxkesVLlz4mXWyYlhdnz59WLlyZZp1SpUqlX0rJOUwcr0y5kXdxy+boKAgFi5cyMyZM3n48CH29vZUqFCB4cOHExISYunwXqhNmzbRokWLZ9bbvXt3phYBEEKIV5KvB3QMNioqVaqUhYIRQuRWPj4+lg4h15M5x0SuN2/ePJKSktKsU7ly5edeQCEsLCzVlSb18uTJQ9OmTZ+rnVeFXK+MeVH3sXh5RUREsGHDhmfWCwkJwc3N7QVEJITIlXLSnGMaDYzpBUPaGBWfO3eOokWLWigoIURudOjQISpWrGjpMHI1SY4JIYQQQgghskZOSo45auH6ZHBzMiqWL6lCiBdNPncsTybkF0IIIYQQQuQu1lYwoKlJYgzA3t7eAgEJIXKzwMBAS4eQ60lyTAghhBBCCJG7KAUfmJ+nUYZUCiFetPj4eEuHkOtJckwIIYQQQgiRe9hYQecaEOhldvOxY8decEBCiNzu1q1blg4h15PkmBBCCCGEECL3eJwMH7e1dBRCCCFeIpIcE0IIIYQQQmSNQE9LR/BstUpC5SKpbvbx8XmBwQghBJQrV87SIeR6slqlEEIIIYQQIsvc23cKVxutpcNInY87+HhYOgohhDA4deoUJUuWtHQYuZqNpQMQQgghhBBCvDru+7rg6u9v6TCEECLHiIuLs3QIuZ4MqxRCCCGEEEJkmYiICEuHIIQQOYqLi4ulQ8j1JDkmhBBCCCGEEEIIYSH+0tvW4iQ5JoQQQgghhMgyFSpUsHQIQgiRo5w6dcrSIeR6khwTQgghhBBCZJmTJ09aOgQhhBAiQyQ5JoQQQgghhMgyCQkJlg5BCCFylICAAEuHkOvJapVCCCGEEEK8bI5dhou3LR2FeS4OUK9sqptdXV1fYDBCCJHzJSUlWTqEXE+SY0IIIYQQQrxM7tyHKkMhPtHSkZhnpYFz46GQt9nNPj4+LzggIYTI2W7cuIG3t/nPVPFiyLBKIYQQQgghXiaxCS9vYgxAo4Fflqe6+cyZMy8wGCGEEOL5SXJMCCGEEEIIkX5JyTBpA0Q/sHQkQgjxSihTpoylQ8j1JDkmhBBCCCGEyJiERJi4zuymwMDAFxyMEELkbBcvXrR0CLmeJMeEEEIIIYQQGZOs4OfluiTZU+Lj4y0QkBBC5FyPHj2ydAi5niTHhBBCCCGEEBl3OwbmbDcpvnXrlgWCEUKInMvJycnSIeR6khwTQgghhBBCZJyVBn5cDEpZOhIhhMjRChQoYOkQcj1JjgkhhBBCCCEyLlnBiWuw8YhRcbly5SwUkBBC5EwnTpywdAi5niTHhBBCCCGEEJljbQWjlxgVnT171jKxCCGEEJkkyTEhhBBCCCFE5iQlw/rDcPyKoSguLs6CAQkhRM7j5+dn6RByPUmOCSGEEEIIITLP2grGLjM8dXFxsWAwQgiR82g0GkuHkOtJckwIIYQQQgiReUnJMH0L3IwGwN/fP/vauvsQ+v8JXn3AqRvUHwYHzqd//5PXoNlIcO4OeXrB679CREza+8zcApr2un2EECIbhIeHWzqEXE+SY0IIkcL169dp1aoVGo2GS5cuAXD//n369OmDRqNh8+bNWdbW5s2b8fT0pF69ell2zFfdvn37KFGixCuxok9cXByTJk2iTJkyODk5odFo+Prrr9O176+//pqh+llh+fLlaDQa+vTp88LaFELkIErBH6sBOHXqVPa0kZwMLUbBrG0wsDmM7gW3Y6DeMDh7/dn7X4uEOl/CuZvwXQ/4uDWs3A+NR0BCovl9HsTC0OngZJ+15yKEEOKlYmPpAIQQudPdu3c5d+4c165d4969eyQlJeHq6kqhQoUoV64ctra2lg4xS5w+fZr/+7//o2nTprRt29bS4YiXyPbt2/niiy+wsrJiyJAhkigVQuRsScnw+yr4X4fMH6PeV1AwH0x93/z2Bbtg52mY/zF0rKEr61wDig+E4XNh1kdpH/+7hfAwDvaPgUAvXVm1Yrrk2NRQ6N/EdJ9RC8DFAeqXhSVhmT83IYRIQ6lSpSwdQq4nyTEhhEWcPn2aEydOUKBAAYoWLYqVlRXXr19n3759XLhwgbZt22Jj83J8RDk7OzNhwgTGjx+PvX3Gfjk+c+YMf/31F7GxsSbJsTp16nD58uVXJhEoMubgwYNEREQwduxYBg8ebOlwhBDi+d17BFM3EdC5avYcf8Eu8HaH9kFPyrzcdAmyGVshPhG0afxNXbgbWlZ5khgDaFQBivvBvJ2mybGz1+GX5bD4U912IYTIJlevXqVo0aKWDiNXk2GVQgiLKFy4MD169KBBgwaULVuW0qVL06hRI1577TXu3LmTfUMyMkGj0aDVanF0dMTKKus+Nq2srHBycsLOzi7LjimyVkJCAvfv38+WY9+6dQuAPHnyZMvxLSU7r5kQIgcYs5Sk1IYoPq+DF6FSYXj6b3G1YvAoHs6kMbQyPEo3BLNKEdNt1YrBwQum5R9O0fUYC6n8fHELIcQzPHjwwNIh5HqSHBNCWISXl5fZpFCRIrp/tEZHR7/okFKV2pxjR48epUuXLvj6+qLVanFycqJw4cJ89JFuWMevv/5K69atAZg2bRoajcbwAPNzjunL6taty4wZM6hYsSL29vY4OTnRqlUrbt68aRRbXFwcf//9N2XLlkWr1eLq6kqXLl2YOXNmpuaH0s8r1bNnT37//XdKlixpOG6vXr2M/nCnNf+XuTmxhgwZgkaj4ZdffuHtt9/Gx8cHOzs7ihUrxooVK4iPj+eff/6hfPnyODg44OrqyjvvvENioumXLKUU8+bNo1KlSjg6OuLo6EizZs24cMH0y83Vq1d57733KFiwIFqtFnt7e6pVq0ZoaKjZmIcOHcpnn31GwYIFcXZ25v33UxneY0ZiYiJLliyhdu3auLq6Ymtri5+fHx9//DGPHj0yum5jx44FoG/fvob7Qj/PXXo9evSITz75hPz582Nra4u3tzdffPEFjx8/NqqXlJTEunXraNKkCXnz5sXGxoa8efPSq1cvbt++bVQ3Pj6eadOmUa5cObRaLS4uLnTo0IG7d++atP+sa3bjxg0GDRpEgQIFsLOzw8nJiZo1a7J9+3aTY6W3rv4e7dGjB6NHj6Zo0aJotVq8vLz47rvvUEqxZcsW6tevj6urK1qtlsaNG3P9+pMvzUoptm/fTsuWLcmXLx+2tra4uLhQokQJRo8enaHXQAiRggIu3ebR3C3Zc/wb0eDrYVquL7t+J+19U9Z9ev87D3Q9z/RW7oN1h+HnvpmPVwgh0imjo1NE1ns5xiwJIcR/9MkXBweHdNWPj49HKZWuura2tlhbW2c6tpTOnDnDO++8w44dO2jdujWvvfYajx494sSJE4akS+PGjRk4cCDjxo2jSpUqdOnSBQAPDzP/MH/K2bNnGTRoEI0bN6Z58+bs3r2blStX0rNnTzZs2ADoEh6TJ0/mk08+QaPR0KtXL5ycnAgNDWXv3r3PdX7bt29n2bJldOjQgTZt2hAaGsr06dPRarVMmjTpuY79+++/k5iYSLdu3bh//z5Lly6lXbt2DB48mAkTJtCiRQuaNWvGhg0b+Ouvv/D39+fLL780OkZMTAzvvfcer732GoMGDeLQoUNs3LiR4OBgDh8+jI+PDwAnT56kT58+7Nu3j0aNGtGjRw8iIiJYs2YNjRs3ZtGiRYYEpt6cOXO4e/cu3bp1w8fHh9KlS6frvJKTk5k6dSpDhgwhLi6Orl27ki9fPrZs2cLYsWPZvXs3mzdvplChQvzvf/9j3rx5rF69mi5dulClShXc3Nzw8vJ6dkMpzJ07l6ioKLp3746DgwPr16/nu+++49q1a0ybNs0Q17Rp0xgyZAhKKTp27Iifnx+HDh1i/vz5bN++nYMHD+Lm5kZycjL//PMPQ4YMITk5mZ49e+Li4sKGDRsYNmxYqnGYu2aXL1/m7bffZu3atZQpU4bOnTtz7do11qxZQ7169Vi2bBkhISEAGaqrt337dlasWEHnzp1xcXFh5cqVfPHFF9y6dYvly5dTqFAhBg4cyIEDB1i/fj09e/Zk06ZNgG5Ia79+/Th37hxdunShePHixMTEcOzYMTZv3szQoUMz9DoIIVKwtiLf1J3wUde06yU+hphHpmXxiRB5z7g8j7Out1hsAmjNfH2x/+/HttiE1NvTbzM37NLe9kkdra1ucv6P/oEBTaB0/rTPQwghsoAMqXwJKCGEeEkkJSWpJUuWqIkTJ6ro6Oh07TNz5kw1YcKEdD1OnTr1zOOFh4erli1bKkBdvHhRKaXUvXv3VO/evRWgQkNDlVJKrV27Vjk5OalmzZqlebxly5YpQPXu3dtkW2hoqMqbN6+qW7euSRmg5s6dayg/f/68qlu3rgIM53Hs2DFVuXJlBajt27cb6qYsN9dueuLVaDRq9+7dhvJDhw6psmXLKhsbG3X//n2llFJ79+5VxYsXV4GBgSbH+b//+z8FqOHDhxvKBg8erADl4+Oj7t69q5RS6vHjx2rUqFGGNhcuXGiov3PnThUYGKi8vb0NZfo2ATVo0CBD+f3799V7772nAPX2228rpZR6+PChevfddxWgJkyYYBTfli1bVIECBVRgYKBKTk42itnGxkadOHEiQ9dNKaVOnDihqlWrpgC1cuVKQ/nly5dV48aNFaAmT55scj3++eefDLelj9XKykrt37/fUH7o0CFVsWJFBahdu3YppZQ6fPiwqlChgsqbN6+6evWqoW58fLwaOXKksrKyMrxOJ06cUFWrVlWA2rRpk6Hu4cOHDcdNeU+lds2SkpLUmDFjlEajUcHBwSo+Pl4ppXu9p0yZohwcHFT+/PnV48ePM1RXqdTv0RUrVihXV1el0WjUwIEDDeUXL15UDRs2NHrvTJs2TQHqnXfeyfC1f1pUVJSKi4szPL9//766d++e4Xl8fLyKjIw02uf69etpPr9x44bhvpQ2cmcbd46eU4p2OfaR7NlLqceP075WoUfTf8yLt3T7OHVTD7uNNnk9Epbs0tVbcyD112PvWV2df0NNrv/9d/7QbYtLUEopde+LaSrZ43Wlou4Z2njc8xelnLpl22v+qty70oa0IW1kro2DBw++EufxMrfxLNJzTAjx0ti1axe3bt2iatWquLu7p2ufBg0amAwhS01Wzu1kZ2eHvb09R44c4ejRo5QrVy7Ljg3g6+tL586dDc/9/PwoWbIkW7Zs4ezZs5QoUYKLFy9y7NgxqlatSs2aNQ11S5YsSUhICPv37890+0FBQVSvXt2k/WPHjnHp0iXKli2b6WO/8cYbuLm5AWBtbU2VKlWwt7cnT548tG/f3lDPx8eHokWLsmnTJh48eICzs7PRcf73v/8Z/t/Z2ZnOnTuzYMECFi9ezF9//cWFCxfYtWsXzs7ONGvWjGvXrhnqu7u7U65cOVasWMHZs2cpXry4YVtISEimVgy6cOEChw8fpmLFika9nAIDA+nevTsbNmxg8eLFvPnmmxk+dmoaNWpEpUqVDM/Lli1LSEgIhw4dYvHixQQFBXH48GGOHDlC7969AYyuQ/ny5fHy8mLt2rV8/fXXXLhwgSNHjlC5cmXq169vqFemTBnDcc15+ppFRkaya9culFJ89tlnhiHU1tbW1K9fnxo1arBx40YOHjxIYGBguutWqVLF0MbT92j+/PkpVKgQhw8f5oMPPjCU+/r6UqxYMTZu3Gh47zg7O2NjY8P27du5dOkSBQsWzMTV13n6c+Xp+9TOzo68efMalfn6+qb5XN/zUdrIvW14eLiTY1lpiOoZjKe1ddrXqkJBWD/ceN8hU8HHAz5pY1zu4677r68Hjnfj4b/pCeC/1yPyvyH/fnlSfz3i/iu4EW1y/Z3vJ+p6p2ltIeYhLr+ugXebwb1YuBdLHtD1KlMKLt3G2dEO8rmbtpHCy3hfSRvShrQhbeTmNp5FkmNCiJfC3r17OX78OCVLluS1115L935Pf5C+KK+99hq9e/dm/PjxlC9fHl9fX2rUqEHXrl1p3779c0/cr597TU8/7xhAVFQUAOHh4cTHx1OyZEmjutbW1s/dNTs97WdW4cKFjZ6nnK/t6XJHR0dDmyn/KLq5uZn5MumBr68vhw4d4uHDh9y9e5ebN2/y4MEDs/Oi6d26dcsoOVaiRIlMnZf+9TCXOAwMDMTNzY3z589n6tipeXrIp7W1tSF+/fxrp06dQinF1KlTmTp1qtnj6K/ztWvXiI+PN0kOpjyuOU9vu3//PpGRkYAusZaSh4cHfn5+hhg9PDzSXTdlcuzpezTl/VKoUCGz5fp7t2bNmvTs2ZMZM2ZQqFAhChQoQJ06dejZsydNmjy1Wp0QImNsrbnRpjyez6rn4axbKfLpMl8P03K9igVh20lITjaelH/PWXDU6ladTI1/XvByhX1mPofDzkLF/z43oh/CgzgYvUT3eFqhAdCmGiz5LI2TE0KIjLHUdxrxhCTHhBAWt2/fPg4ePEjx4sWpXbt2hvaNjY1N95xjdnZ22Nhkzceem5sbo0ePpnfv3ixcuJCdO3eyefNmFi5cSI0aNQgNDX2uVSjTmhstvef7PNLTvibFL/dPS6s3X2rHzupzVkqhlCJPnjyMGzfObB1HR0eTZJY+kfKqad++PR07djS7TZ+AyixLXLO07pfUtunvI29vbyZOnMjbb7/N4sWL2bVrF0uXLmX69Ol07NiR+fPnZ0vMQrzyrK3gjUbY+2ds/sR06xgMC3bBot3QsYauLPIezN8JraoYzyd2/r8FbIqk+MLZIRimhcLVSMj/X/pu4xHdKpcftdI9z+cGiz81bfu3lbDrDMz+yPyk/kII8Rxsbc3MhyheKEmOCSEsat++fRw4cIDixYtTt27dNBMu5ixevDjdSx/XrVs3072CzLG2tqZ8+fKUL18e0E0q3r9/f9atW8fSpUvp1KlTlrVljp+fH1qtllOnThmVJycnc+7cuWxtG570+Eq5CiDoEmNXr17N1rZjYmK4efOm0a9s0dHR3LhxAy8vL5ycnHBzcyNfvnwcO3aMVq1amXTHzmr+/v7Y29tz7Ngxk21Xr14lJibGaPhrVjhx4oTR86SkJE6fPg086aFXtGhRNBoNNjY2dOvWLc3j+fv7o9VqOXnyZKrHTQ8XFxc8PXVfPI8fP27Uyys6OtpwzxQuXDhDdbOSra0tQUFBBAUFAXDkyBH69evHggUL2Lt3L1WrVs3S9oTIFZKTYXArCgTmfXbdzOgYDEHFoe84OHENPF1g/BpISoYRTy0A0PC/IZuXJjwp+7yDLpFWfxh80ELXQ2zMUihXAPo20NVx1ELb6phYEgZh58xvE0KI53T16lWTYYLixXq+cT9CCPEc9u/fz4EDByhWrFimEmOgm3MsJCQkXY/8+bNuxSn9sL2UvLy8CAwMBODOHd1y8ra2tmi1WsPzrFS4cGHKlCnD3r172bFjh6H85MmTrFq1Ksvbe5qbmxve3t48ePCAsLAwQ/nhw4cNK2pmp++//97w/w8ePGDevHncunWLdu3aAbrrU6NGDZKTk/n0U9NeAAkJCSaJvedRuHBhypcvz6FDh1izZo2h/MqVK8yaNQullCG2rLJhwwYOHDhgeH7s2DHDa9+2bVsAKlWqRNmyZVm0aBG7d+82OcbDhw+JiIgwOof9+/cbVl0FXdIqI/eUp6cnwcHBaDQafvzxRxITEwFd4nbz5s3s2rWL/Pnz89prr2Wobla5c+cOjx4Zr5Ln7e1NQECAYbsQIoOsraBVVSjqa5K4z7o2rGHVl9Clpq4n1yf/gqcrbBoBJfyfvX9+T9jyja432WczdMMmQyrp5j4zt4qlEEKIXEN6jgkhLOL48ePs378fZ2dn/P39TXo6OTg4GL6opsVS4/MXLlzIW2+9RZ06dShfvjyurq4cOXKEtWvX4ubmRqtWuuEZ+fLlo3Dhwqxbt45PPvkEPz8/8ubNS69evZ47hpIlS9KrVy9OnDhB48aN6dGjB87OzoYJ7CHtoY/Py9/fn5CQENavX0/Lli15/fXXiYuLY+PGjeleJCGzXF1dmTVrFqdOneK1117j0KFDbNy4ES8vL77++mtAN3Hnhx9+yJEjRxg/fjzbt2+nYcOGODk5cfnyZfbv38+jR4+4ePFilsRUokQJ+vXrx8mTJ2ndujXdunXDy8uLrVu3snfvXmrWrGmYFD+rBAQEUKdOHXr06IG9vT3r16/n5MmT9OjRg+DgYADKlSvHkCFDGDx4MLVr16ZVq1aUKVOG2NhYzpw5Q1hYGAMGDODrr7+mRIkS9OnTh2PHjhESEmK4pzZs2MDDhw/THZeVlRWdOnViw4YNrF27lsqVKxsWRVizZg3x8fH89ddfhuGPGambFf744w9++OEHGjRoQKlSpbC3t2f//v1s2LCBAgUKZHh4txACXe+tT9o+3zE2f/PsOh7OMPk93SMtKXuMpVQmENYOy3hsU9/XPYQQIhtk5egWkTmSHBNCWIS+p8qDBw/YvHmzyXZfX990JccspUKFCrRu3ZoDBw6wa9cu4uPjyZMnDy1btuSbb74xzOFUvnx5vvjiC3744QfGjRtHXJxuuaysSI5ZW1vTv39/nJyc+OWXX/j333+xs7MjJCSEevXq8e677+Lg4PDc7aTGysqK119/nTt37vD333/z22+/4erqSu/evfHz8+OTTz7Jtrbd3Nz46aef+OGHH/j1118BqF+/PuPHjzdamaZEiRIsXLiQn3/+maVLl/Lnn3+SlJSEh4cHZcuWzdKVI62srOjTpw9eXl6MHTuWxYsX8+jRI/Lly8eQIUMYOXJkls15p9elSxeUUsyZM4ebN2/i4eHBZ599xsiRI43iev311ylcuDBjx45l586dLF++HDs7O7y9vWnRooVhZVRra2veeOMNHBwcGDt2LNOnT8fOzo4mTZrQtm3bDN23BQoUYMqUKXz//fcsW7aMX375BVtbWypWrMiPP/5olIDKSN2sULt2bfbu3cvhw4cNyVwvLy/69OnD119//crOOydEtrGygtcKQU3dAjHPO4+hEELkNjdu3MjyKSRExmjUi5jZWQghxAuTnJzMyJEjGTFiBN9//z2ffSYragkhRI4SHgUBb1k6ioyZ9zF00k2SHxERgZdXNk3KL4QQr6BDhw5RsWJFS4eRq8mcY0IIkYM9fvyY2NhYo7ITJ06wYsUKABo3bmyJsIQQQuQmAXmh3ZOJ6sPDwy0YjBBC5DxardbSIeR6MqxSCCFysK1bt9K7d2+Cg4MpUaIEt2/fZtOmTZw7d45OnTpRuXJlEhISuHHjBklJSakex8rKCk9Pz2xf0TGnSU5OJjo6mpiYmDTrabVa/P3TMRl0GuLi4p65QICNjQ1eXl7ZOlxWCCEyRKOBj9uATdbNCyiEELmNzDlmeZIcE0KIHCwgIIAqVaqwfft2li1bRnJyMr6+vgwbNowvv/wSgJ07d9KxY0eioqLSPNbw4cMNk9kLnYcPHzJkyBCmTZv2zLrPO0vB+vXrad269TPr/fPPP/Tp0+e52hJCiCzjpIU3GhoVlSpVykLBCCFEznTkyBEZVmlhkhwTQogcrHjx4ixevDjNOhUqVGDSpEmGxQDMsbW1pVKlSlkdXo7n4ODA+++/T9OmTdOs5+bm9txtBQUFMWvWrDTr2NvbExQU9NxtCSFElrC2gnebgYtxb9arV69StGhRCwUlhBBCZJxMyC+EEEIIIcTLJKdMyG9tBZf+ggBPo2KZWFoIITLm+vXrstKvhUnPMSGEEEIIIUTG2FhBl5omiTHQ9XIVQgiRfjKfrOXJapVCCCGEEEKIjHmcDB+3NbtJhlQKIUTGXL582dIh5HqSHBNCCCGEEEKkn7UV1C0DFQuZ3Xzs2LEXHJAQQgjxfCQ5JoQQQgghxEvopZ0YOCkZhra1dBRCCPHKKFasmKVDyPVkzjEhhBBCCCFeJr4e8GVHEg9dwM7W1tLRmHJ3gmavpbrZx8fnBQYjhBA5X0REBE5OTpYOI1eT1SqFEEIIIYR4CYWHh+Pv72/pMIQQQmQzWeXX8mRYpRBCCCGEEC+hiIgIS4cghBDiBbB9GXsJ5zKSHBNCCCGEEEIIIYSwkDJlylg6hFxPkmNCCCGEEEK8hCpUqGDpEIQQQrwAhw8ftnQIuZ4kx4QQQgghhHgJnTx50tIhCCGEeAFkKnjLk+SYEEIIIYQQL6GEhARLhyCEEOIF8PT0tHQIuZ4kx4QQQgghhHgJubq6WjoEIYQQL4CLi4ulQ8j1bCwdgBBCCCGEENkm+gF0HAM371o6EmPWGpj5EZQrkGoVHx+fFxiQEEIIS7l48SIVK1a0dBi5miTHhBBCCCHEq+vABdh01NJRmLK2gp+WwLQPUq1y5swZ+bIkhBBCvAAyrFIIIYQQQogXLSkZZm2D63csHYkQQggLK1KkiKVDyPUkOSaEEEIIIYQlKGDcqlQ3BwYGvrhYhBBCWEx0dLSlQ8j1JDkmhBBCCCGEJSQlwx+r4WGc2c3x8fEvOCAhhBCWcOeO9CK2NEmOCSGEEEIIYSn3Y+GfTWY33bp16wUHI4QQwhKsra0tHUKuJ8kxIYQQQgghLEUBY5ZAUpKlIxFCCGEh5cqVs3QIuZ4kx4QQQgghhLCkK5GwdK9JsXxZEkKI3OHo0ZdwVeVcRpJjQgghhBBCWJKVBn5cbFJ89uxZCwQjhBDiRUuS3sMWJ8kxIYQQQgghLClZQdhZ2H3aqDguzvxE/UIIIV4tefLksXQIuZ4kx4QQQgghhLA0Gyvd3GMpuLi4WCYWIYQQL5SHh4elQ8j1JDkmhBBCCCGEpT1OhsVhcOGmocjf3z972rr7EPr/CV59wKkb1B8GB86nf/+T16DZSHDuDnl6weu/QkRM2vvM3AKa9rp9hBBCGDl/PgOfwSJbSHJMCJFpv/zyCxqNhn///dfSoTy3hw8f0rVrVzw9PbGyssLT09PSIWXYwIED0Wg07Nmzx1CWna9RxYoVc+R1etFatmyJRqPh/v37lg4lR9BoNDRs2NDSYQhhGVYa+GWF4empU6eyvo3kZGgxCmZtg4HNYXQvuB0D9YbB2evP3v9aJNT5Es7dhO96wMetYeV+aDwCEhLN7/MgFoZOByf7rD0XIYQQIovYWDoAIV4WBw8eJDIyksjISO7fv4+zszPdu8uvm7nF0KFDmTt3Ll26dKFSpUo4OztbOiQhhBC5TVIyTN4AI7uCRyb/DtX7Cgrmg6nvm9++YBfsPA3zP4aONXRlnWtA8YEwfC7M+ijt43+3EB7Gwf4xEOilK6tWTJccmxoK/ZuY7jNqAbg4QP2ysCQsc+clhBCvsEKFClk6hFxPkmNC/Gfv3r1otVo8PT1JSEiwdDjiBdu8eTMBAQHMnj0bjUZj6XCyzPvvv89bb72Fvb38Wm8pCxcuJDExEScnJ0uHkiM8ePAAa2trS4chhOUkJMKEdfBZewICArL++At2gbc7tA96UublpkuQzdgK8YmgtU19/4W7oWWVJ4kxgEYVoLgfzNtpmhw7ex1+WQ6LP9VtF0IIYeL+/fu4ublZOoxcTYZVCvGfrl270rt3b1q0aIGjo6OlwxFmREdHk5ycnC3HjoqKwsXF5ZVKjAHY2Njg7OyMjU3u+S0kPj6eBw8eWDoMA61Wi7Oz8yt3bz2LUoo7d+5keD8nJydJ5orcLVnpkkkJiSQlJWX98Q9ehEqFweqprwHVisGjeDiTxtDK8CjdEMwqRUy3VSsGBy+Yln84RddjLKTy88UthBCvsMjISEuHkOtJckyI/7i6ulo6BBP6uYouX75Mhw4dcHNzw9bWlpIlS7J06VKjumnNLWVubihvb29Kly7Nzp07ee2117C3t8fV1ZXOnTsTGxtLbGwsvXr1Ik+ePNja2lK6dGm2bt1qNs6kpCTeffddvLy8sLW1pUCBAowZM8Zs3c2bN1OrVi1cXFywtbXFz8+PAQMGEBsbazbmEydOULduXZydncmTJw8PHz5M9/V7/PgxQ4YMIX/+/Nja2uLs7ExwcDChoaEm1+3WrVucPHkSjUaDRqOhT58+6W5n48aNNG/eHD8/P7RaLfb29pQsWZLff//dpK7+NT1z5gzNmjXD2dkZrVZL+fLlWbNmjVHdS5cuGWIZN24cBQsWxNbWlrx58/Lmm2+aXDNzUrsv4uLi+PDDDw3XxtHRkWrVqrFu3TqTY0RERNCmTRtcXFzQarWULVuWVatWpfv6PG3v3r3Uq1fPcG+5u7tTrlw5Jk6caFQvPj6ejz/+mEKFCqHVanF0dKRIkSIMHTrUUEc/z9rGjRvp2bMnefPmxcHBgUWLFmX4PJVSjBw5kiJFihhex3LlyjFz5kyjeilfl+nTp1OsWDHDeXTt2tXkdTE351hG3tsAt27domXLljg7OxviWrt2babnfUvva5De67dq1So0Gg3Dhw/n66+/Jn/+/NjZ2TF48GAaNWqEjY0NZ8+eNYnj6NGjaDQa2rVrZyhLbc6xJUuWUL16dZydnbG1tSVfvnw0b96cixcvGtUbP348ZcqUwcHBAa1WS7Fixfjll19MjjdjxgzKlSuHi4sLdnZ2eHp6UrNmTXbulJ4t4iVwOwbmbOfGjRtZf+wb0eBrZlU0fdn1NJLaN6KN6z69/50Hup5neiv3wbrD8HPfzMcrhBC5QG77EfVllHu6EgjxAsTFxaW7rp2dHVZP/2qbiiZNmuDm5sZ7771HdHQ006ZNo2fPnpw+fRo/P7/MhktUVBTNmzenYcOGtG7dmtDQUObPn49Go+Hq1as8ePCA/v37Ex0dzdSpU+nSpQsXL1406dUxatQoYmNj6dq1K9bW1ixYsIChQ4fy8OFDvv76a0O9WbNm0bt3b7y9venVqxdeXl7s2bOHiRMncvToUbZt22Z0TeLi4qhTpw5lypTho48+IjIyElvbNIZ6PKVZs2Zs3LiRChUq0KNHD27fvs3cuXNp3rw5ixYtIiQkhCZNmjBmzBhGjhyJs7MzgwcPBqBy5fT/wj1nzhzOnz9P06ZNKViwIHfu3GHhwoUMGjSI+/fv8/nnn5vs07x5c5ydnRk4cCC3b99mzpw5tG3blhUrVtCoUSOjulu3bmXevHl06tQJf39/1qxZw5QpU7h8+TLr16/P8B/TxMREgoKCOH78OI0aNaJPnz7cu3ePuXPn0rJlSxYsWEDr1q0NdWvVqsWZM2do2LAhwcHBHDlyhE6dOuHi4pKhdkGXaGvcuDFKKTp16kShQoWIjIzk4MGD7Nixg/79+wOQkJBA9erVOXz4MBUrVqRt27Y4Ojpy/PhxVq1axejRo42O269fP2xtbenbty82NjYUKVIkQ+cJEBISwtq1a6lVqxZdunQhMTGRRYsW0atXL6Kjoxk4cKBRmzt27GDRokV07NiRbt26sXr1aubOnYuLiwuTJk1K1/VIz3s7Pj6eGjVqcOHCBRo3bkz16tU5fvw4HTp0yNbXIKPXD+Dff//l3r17dOjQAX9/f/Lnz4+zszMbN27kr7/+YuzYsUb1x40bB8Cbb76ZZszfffcdX375JR4eHoaYr1y5wsaNGzl58qRhCu56nwAAjQZJREFUno4BAwYwYcIEKlWqxMCBA7GxsWHVqlUMHjyY8PBwfvrpJwBWrlxJr169yJ8/P2+99RZ58uTh+vXrbNu2jSNHjlCjRo0MX1chspSVBn5cDDOekVRKfAwxj0zL4hMh8p5xeR5nXW+x2ATQmvkKYG+n+29sGlNL6LeZG3Zpb/ukjtZWNzz0o39gQBMonT/t8xBCiFyuQoUKlg5BKCGEiXnz5qmZM2dmeL8JEyak+xEeHv7M47Vo0UIBqkWLFio5OdlQPmnSJAWoTz/91FD2888/K0BNmzbN5DgVKlRQefPmNSrLly+fAtTo0aONyosWLao0Go2qWrWqevz4saF82LBhClATJkwwaTNv3rzq2rVrhvKoqCjl6empHB0d1e3bt5VSSsXGxio3NzdVsmRJ9fDhQ6M2v/jiC5PYK1SooADVo0ePZ14ncxYtWqQAVbNmTZWYmGgo37Vrl7KyslIlSpRQSUlJRtejVKlSmWorOjrapOzBgwfK399f+fr6GrWjf02rV69uFNfWrVuVRqNRFSpUMJRdvHhRAUqj0ajly5cbypOTk1WdOnUUoObNm2cof++99xSgdu/ebSgzd1/or/fEiRNNzsPT01OVKlXKcL/98MMPClC9evUyqvvVV18ZXvuMmD59ugLUjz/+mGa9oUOHKkB169bN6N5XShndl/pzLlWqlIqNjTWql5HznDp1qgLUZ599ZlQ3MTFRFStWTHl6eqq4uDil1JPXxc7OTu3du9dQNzk5WRUoUEC5urqqhIQEQ7n+Nb93755JWXre2998840CVL9+/Yxi05dn12uQkeu3cuVKBShHR0d1+vRpo/qPHz9W7u7uqnDhwkbvheTkZOXl5aUCAgKMXlNANWjQwPD84sWLysbGRvn5+Rl9zqQ8vlJKbd++3XDPPK127drK3t7esP/rr7+uAHXs2LE0r4F4RWw4rBTtcuQjcesz7tHQo+k/3sVbun2cuin1xjjTY63cp6u35kDq7e09q6vzb6jptk+m6bbF/ff598NCpTxeVyrqyWef6v2brn0hhBBG5N8klifDKoXIQiEhIel+5M2bN93H/fTTT416B+mHIJ0/f/654nV3d+ejj4xXpapWrRpKKQYMGGA0KXZISAgAp0+fNjmOvpeIXp48eejduzePHj0yDG9btGgRMTExdO7cmTt37nDt2jXDo1OnTgAmwwoBRowYkalzmz17NgBffPGF0XxbQUFB1KpVi9OnT5s9l8xwd3c3/P+9e/cIDw8nOjqa4OBgbty4wZUrV0z2GTJkiFFctWvXpkqVKhw9epSrV68a1S1fvjwtW7Y0PNdoNIYeeQsWLMhwvHPnzsXX15dmzZoZvQ4PHjygRo0anD59mps3bwK6oWxWVlYmr8MXX3yBg4NDhtvW3/fr16/n1q1bqdabP38+jo6OjBs3zqRnnLnJ2t9++22THo0ZOc+pU6dib2/PG2+8YVT35s2bNG3alMjISPbs2WN0/KCgIKpUqWJ4rtFoCA4O5t69e+keCpWe9/aKFSuwsrJi2LBhRvsOHTo0U/Mjpvc1yMj102vSpAnFixc3KrO2tqZNmzZcuHCBLVu2GMpXrlxpGLKb1gT8kydP5vHjxwwaNMjocybl8QEmTpyIRqNhwIABRvFeu3aN1q1bExcXx+rVq4En79l///03Qz1+zblz5w7x8fGG5w8ePDAaQpuQkEBUVJTRPk/fH08/v3nzJkopaSOL2nj06KmeVTmEcrbnorNuns1Ur1WFgrB+OKwfTtSc93X/X74ANKn45Pl/j5vE666VrwfciDa5VnEX/3s/++UxbiOFW1b/DZn8b3il0etxI5pkDyeUnQ3EPIRRC4jtWYv4iBi4dBsu3ebx3Qcolax7fvtujr6vpA1pQ9qQNrKyjcTExFfiPF7mNp5FhlUKkYWyZVUpoGzZskbP9V9uo6Ojn+u4+fLlM5mo3cNDN49IiRIljMq9vb0BzE6wXaZMGZMyfddg/TxDhw8fBmDkyJGMHDnSbDxPT0Tp4uJCgQIFnnke5ly5cgWNRkO1atVMtpUqVYqtW7dy8uRJSpUqlanjpxQeHs57771HaGgo9+7dM9keERFBwYIFjcpSJlX0SpQowd69ezl9+jT58z8ZglK4cGGTupUqVQIwSaSlx5UrV0hISCAwMDDVOuHh4fj6+nLt2jXc3NxM6mq1Wvz8/Lh7926G2m7evDkhISGsWrUKf39/ihYtSs2aNenTpw+1a9c2aj8wMJA8efKk67jm7sGMnOf58+eJi4szSeykdO3aNaPn5o6rf2/evHkzzXb10vPeDg8Px83NzSQxZGdnh6+vb7a9Bhm5fnpFixY1W2/gwIFMmzaNSZMmUb9+fUCXzLKysmLAgAFpxnvmzBkAgoODn1lPKUXdunVTraP/R9KXX35pGJ7722+/UaZMGRo0aMA777yT4aXUn75HnZ2djZ7b2dmZ/BiS8pqZe+7j4yNtZGUbOXGRHWsrNANDeKjRTcif5rVqpPt7a7hyHs7g60HeLvWN9jFcqYoFYdtJ8ri7G03Kb3/4CjhqdatOPt3Gf7wrlQIvV9inS+AbvR5hZ7F6rTBoNBD9EB7E4fD7Gvj9yQ9fhn9xFBoAbapht+SznHtfSRvShrQhbWRhG+7u7q/EebzMbTyLJMeEyEIZ+XVaq9Wm2VsipdRWGkyZXU9r3qnUVrtKq/30tJkR+v0GDhyY6nw+KRNCoPvQe9lXWVRKUadOHa5cuUKnTp0ICgoiT548WFtbM3XqVNatW5dtK2xmllKKgIAAk3m7UipWrFi2tb9y5Up2797N/Pnz2blzJzNnzmTq1Kl89NFHhjmhMsrc3FsZPU8XFxf++uuvVN9LderUMXqe1vsnve+TrH6fpVd6XoPM3Cep9WSrUqUKRYoUYe3atcTHx5OUlMSGDRsoX768SYIws5RSaDQaJk2alGoc+rkE8+XLx+nTp1m2bBkrVqxg9+7d/PTTT/zxxx/8+++/dOjQIUtiEiLTNMD7ITg9jHpm1QzrGAwLdsGi3dDxv7/Hkfdg/k5oVcV4PrHz//UmK5LiS0uHYJgWClcjIf9/C4JsPKJb5fKjVrrn+dxg8aembf+2EnadgdkfmZ/UXwghcikvLy9Lh5DrvdzfOoXIYWbMmJHuui1btnyuyfSfli9fPsD8MsDXr1/P1hVQjh8/blKm7ymm//JcunRpABwcHOjWrVu2xaJXoEAB9uzZQ1hYGM2bNzfadurUKYAs6TW2b98+Lly4QK9evZg2bZrRtilTpqS539M9VE6fPo2VlZVJr70LFy6Y7H/gwAHANKGYHv7+/ty5c4f27duj1WrTrBsQEEBYWBhXrlwx6v0WHx/P9evXMzWsD3RDEoOCggDdPVuxYkXGjRvHyJEjcXR0JCAggOvXrxMdHW3ozZhRGTnPwMBArl27Ru3atTN1TbOTn58f+/btIzw83Ci2xMREbty4kanhrfDs1yAj1y89unfvzjfffMPs2bN5+PChYRGPZylZsiQAu3fvNklQplS4cGH27NlDwYIFza52+TRra2vatWtnGMq6e/duatWqxbfffivJMWFZNlbQrTb45aFAgvOz62dUx2AIKg59x8GJa+DpAuPXQFIyjHjqPdlwuO6/lyY8Kfu8gy6RVn8YfNACHsTBmKVQrgD0baCr46iFttVN214SBmHnzG8TQohc7OzZs1SsWNHSYeRqMueYEFkou+YcS4/y5csDsGnTJqPyP//80+xQyKy0cOFCwsPDDc/v3LnDtGnTcHR0NHzx7NixI25ubkyZMoVLly6ZHOPhw4dERERkWUz6L93ff/+9Uc+5vXv3sm3bNkqUKGGShMoMfc+fp3v67N27l82bN6e639ixY3n8+LHh+fbt29m3bx9ly5Y1Sc4cOXKEFStWGJ4rpQxzjnXs2DHDMXft2pV79+4ZVuZ82sWLFw3/37p1a5KTkxk+fLhRnW+//ZbY2NgMt33z5k2j8wbw9PTE39+fhIQEYmJiAOjUqROPHj1i0KBBJtc2vT3xMnKeffv2RSnFwIEDzR4/Zd0XrWXLliQnJ5sMR/7xxx8zNZdSel+DjFy/9Hj33XextrZm+vTpTJs2DQcHB/r16/fM/d544w1sbGz47bffTOY4gyf3w9tvvw3A559/bjRHhd7Vq1cNnwVPD5EFeO2117CzszM7NFqIF+pxMgzRrQR74sSJrD++tTWs+hK61NT15PrkX/B0hU0joITpvH4m8nvClm90vck+mwGjl0BIJd3cZuZWsRRCCCFyAOk5JsR/zpw5w4MHDwCIi4sjKSnJ0DvH2dk5zbmI9LJrzrH0KFu2LJUrV2bVqlW0atWKypUrc+TIETZu3IiPjw+JiYnZ1rabmxtVq1alY8eOWFtbM3/+fCIjI/nyyy8NPdqcnZ2ZOHEiPXv2pFy5crRu3ZrixYsTExPDmTNnCA0N5c8//6RXr15ZElO7du1o2LAhGzdupGrVqjRr1ozbt28ze/ZsbG1t+fnnn7Gyev7fB8qXL0+BAgWYPXs2CQkJlC5dmrNnz7JgwQIKFCiQ6qIJERERVKlShZCQEG7dusXs2bOxs7MzO6ywYMGCdO7cmU6dOhEQEMDq1as5ePAg9evXz1RybOTIkWzYsIHx48cbeuO4u7tz5coVduzYgbW1taE34JAhQ5gyZQr//vsv4eHhBAcHc+TIETZs2IC3t7dJkuVZxo8fz/jx46lfvz7FihVDq9Wyfft2wsLCqFmzpmFugJEjR7Jq1SpmzJjB8ePHqV+/Po6Ojpw4cYKzZ89y5MiRLD3PN998k6VLl7Js2TJKly5No0aNyJcvH9euXWP//v1cuXIlS5O3GfHJJ58wZcoUJk+ezOXLl6levTrHjx9n3bp1mXpvZ+Q1SO/1Sw8fHx+CgoLYsWMHiYmJNGnSJF0/EhQsWJDhw4czbNgwypYtS+vWrSlUqBDh4eGsX7+e33//nZCQEOrWrcvAgQMZN24cRYsWJSQkhICAAG7cuMHhw4fZs2cP0dHRuLi40KNHD0NPwYIFCxIXF8eyZcuIjY2lffv2GbqeQmQpayuoUxoqZGzuOyObv3l2HQ9nmPye7pGWlD3GUioTCGuHmd+Wlqnv6x5CCCGMZHaeZZF1JDkmxH9Onz5tsqLFvn37AN1kfulJjlna4sWLef3119mwYQPr16+nTJkyLF++nEGDBpntKZFVvvzyS8LCwpg1axYxMTH4+vry/fff89lnnxnV69y5MwUKFOCrr75i9erVzJ07FycnJ3x9fenZsyf16tXL0rjWrFnDp59+yty5cxkzZgxarZby5cvz7bff0qBBgyxpw9ramrVr1/L222+zatUqFi9eTP78+Rk9ejSnT5/mjz/+MLvf6tWrGTRoEL/99huJiYn/3959hzV1vXEA/wbCDkOWCiqICzfuvSe496qK2lpbtcO2tj+tOGptqx3WWmfdq45qtXVXce+JVAUHiqgooIjskfP7IyUSEyBA4Ar5fp6Hx+bcc+957z2BhpczULVqVXz33Xfo1KmTVt3WrVujYcOG+P777/Hw4UPY2dnB399f506O+jAzM8Pp06cxY8YMbN68GYsWLQKg2oyhRo0aGglKc3NznDx5EmPGjEFgYCCOHz+OKlWqYMuWLZg6dWqe31edO3fGuXPncPz4cezatQsmJiZwdXXFhx9+iK++evULnbm5Oc6ePYvPP/8cO3bswIIFC2BmZgY3Nze9E4J5uU8A2LVrF+bPn4/ffvsNK1asQHp6OhwcHFC1alWt93JRsrS0xMmTJ/H222/j2LFjOHr0KKpWrYrt27djwoQJ6qS+vvTtg7w+P32MHj0aJ0+eBACMHDlS7/O+/PJLVK1aFd9++y1+//13pKWloVSpUmjUqJF6yjYA/PLLL2jSpAnmz5+PDRs2ICUlBXZ2dvD09MQXX3yhnoI6fPhwrFixArt27cLLly9hZWWF8uXL4+eff8bEifzFnSSUoQQm91a/NOTyC0RE9OZKSkrK91IiZBgyUdir/hIRkVr37t2xe/duxMXF6VxEPqt79+6hYsWKGDlyJFavXl00AVKxkZGRAXt7e1SuXBlXrlyROhyiN9ehIKDjDKmjyJ1MBlQtC9z4RfXfUI0y5iLNREQl35UrV7jmmMS45hgREdEb7uXLl1plc+bMQUJCAlq2bClBRERkcEIAk/uoE2MANNbzJCIiosLDaZVEVKyEhYVpLdD+OmdnZ9jZ2RWonejo6FwX5rawsIC7ux6LFxsBPq/CNXDgQLx8+RJNmjSBlZUVzp07p173berUqQDYB0TFnpMtMCz7HVmJiKjkytxcjaTD5BgRFSteXl651gkICMDMmTML1I6/vz92796dY53q1asXzk5ixRCfV+Hq3LkzFi9ejCVLliA5ORn29vbw9fXF/Pnz1Qvosw+IijETGfBRd63dHqtXry5RQEREVJRCQkL4M19iXHOMiIqVLVu2ICMjI8c6DRo0KPAGCufOnct2p8lMjo6O6NKlS4HaKSn4vKTHPiDKRnFYc8xCDjxcoRo9lsXt27dRuXJliYIiIqKiwjXHpMfkGBERERGVXG96ckxuArzdCVj8rtYh/rJERGQc7t69q9cMGSo8XJCfiIiIiEgqGUpgUg+dhywtLYs4GCIikkLmMhkkHSbHiIiIiIikIDcBujcEqrjpPMwplURExiEkJETqEIwek2NERERERFJIVwKT+2R7ODg4uAiDISIiMl7crZKIiIiIqKiZyACfikALb6kjISIiiZUvX17qEIweR44RERERUcnl5gil/A38yKsUwOd9AJks2yplypQpwoCIiEgqaWlpUodg9LhbJRERERGVaC+C78IeplKHoc27HCB/A+MiIqIixd2JpcdplURERERUosWXsoC9u7vUYRAREdEb6g0cY05EREREZDhRUVFSh0BERJStWrVqSR2C0WNyjIiIiIiIiIhIIrdv35Y6BKPH5BgRERERlWh169aVOgQiIqJsJScnSx2C0WNyjIiIiIhKtBs3bkgdAhERUbYUCoXUIRg9JseIiIiIqERLTU2VOgQiIqJslS9fXuoQjB53qyQiIiIi/QgBHLgCJKRIHYkmUxOgZyNAJtN52M7OrogDIiIi0t+NGzfg4+MjdRhGjckxIiIiItLPjrNAv7lSR6Fb4Cygre7dvsqUKVPEwRAREVFxwmmVRERERKSfZy+ljkA3UxNg3p/ZHg4NDS26WIiIiPLI3d1d6hCMHpNjRERERFS8ZSiBPZeAGxFSR0JERJRnQgipQzB6TI4RERERUfFnagL8uEvnoQoVKhRxMERERPp79OiR1CEYPSbHiIiIiKj4y1ACawKBp7Fah1JS3rANBIiIiOiNwuQYEREREZUMGUpg0T6t4idPnkgQDBERkX5q1KghdQhGj8kxIiIiIioZlAL4eTeQxJFiRERUfNy/f1/qEIwek2NEREREVHLEJgDrjmoU1a5dW6JgiIiIcpeQkCB1CEaPyTEiIiIiKjlkAObuAJRKddGtW7eki4eIiCgX1tbWUodg9JgcIyIiIqKSQwC48wTYc0ldlJycLF08REREuahYsaLUIRg9JseIiIiIqGQxNVGNHvuPra2thMEQERHl7N9//5U6BKPH5BgRERERlSwZSuD4DeDSHQCAu7t74bUVmwCMXQy4+AM2Q4B2Aep29XIjAug6C1AMBRxHAMN/BqJe5HzOhqOArK/qHCIiIiowJseIiKjIhYSEoHnz5pDJZJLG8fPPP0Mmk2HGjBk51ntT4s3qTYnpyJEjcHZ2Rtu2bQ12zQsXLqBatWrw8PAw2DXJCMlNgHk7AQA3b94snDaUSqDbbGDjcWCCLzB3BPD0BdA2ALj1KPfzI6KB1l8CtyOBOcOAT3sCuy8CnWYCqWm6z4lPAiavA2wsDXsvREQkmbJly0odgtGTSx0AEREVntjYWNy+fRsRERGIi4tDRkYG7OzsULFiRdSuXRtmZmZSh0hG6t69e1iwYAHq1KkDf39/qcOhkihdCWw5qUpY5VfbaYCnK7B6ou7j204Dp0KArZ8C/ZurygY2B6pOAKZvBjZ+nPP15/wBJCQDF+cBFVxUZY2rqJJjqwOBsZ21z5m9DbC1AtrVAv48l/97IyKiN4apqanUIRg9jhwjIirBQkJCcO3aNdjZ2aF+/fpo0qQJ7O3tceHCBezcuRPp6elSh1gsVKlSBYcOHUJiYqLUoZQY9+7dw9q1a7F69WqtY/Xr18fly5cREhJS9IFRySKTAT//jXLlyhXO9bedBko7AH2bvipzsVclyHaeA1KyGf2V6Y8zQPeGrxJjANCxLlDVDdhySrv+rUfAT38BP/oDcv4iRURUUkREREgdgtFjcoyIqATz8vLCsGHD0L59e9SqVQs1atRAx44dUa9ePTx79qzwphqVMCYmJrCysoKVlZXUoRgFExMTWFtbw9KS08aogDKUwJL9UMbGF871L4cB9b0Ak9c+UjeuAiSmAKE5TK18GKOagtmwkvaxxlWAy3e1yz9aqRox5tegYHETERGRBibHiIhKMBcXF5ibm2uVV6qk+mXs+fPnRR2SloMHD6JVq1ZQKBSwtLRE8+bNcenSJa16t2/fxsiRI+Hm5gYzMzPY2dmhS5cuuHbtWoHqvi4uLg5TpkyBlZUVmjVrhufPn+tc3ytr2YkTJ9CxY0fY2dnB3Nwc9evXx9mzZzWuq1QqcfjwYbRt21Z9r82aNcOGDRsMur5WRkYGDhw4gM6dO8PJyQlyuRxOTk4YMWIEnj59qq4nhMD+/fvRu3dveHh4wMbGBubm5vD09MTs2bORkZGhcd309HTs3LkTTZo0gbW1NaytrdGpUyc8fPgwzzH+9ddfaNeuHWJiYnD06FHIZDL1171793SuOZZZVqFCBfzxxx9o2LAhrK2toVAoMHToUMTHx+POnTsYOnSo+n1fo0YNnDx5Uqv9Bw8eYPz48fD09ISFhQUsLS3RuHFjBAYG5vleqBhITEXa4r2Fc+3Hz4GypbTLM8sePcv53Kx1Xz//WbzmyLPdF4ADV4EfR+U/XiIieiN5e3tLHYLR45pjRERGKD5eNYpC35FQKSkpEELoVdfMzCxP6ya8++67cHV1xfjx4xEaGop9+/ahVatWOHv2LGrVqgVAtb31yJEjcfHiRTRt2hTDhw/HrVu3sG/fPjRp0gQnT55EvXr18lz3ddHR0ZgyZQpWrlwJPz8/bNmyBZaWlhpJJV1Gjx6NsmXLYsKECbh16xb+/vtv+Pr64v79+7C1tQUABAYGYtSoUXjw4AF69eoFb29vnDt3DuPGjTPYKCmlUok1a9bgk08+gRAC/fv3h5ubG65cuYKtW7fixIkTuHz5Muzt7dVJtLNnz6Jjx47w8PBAbGwsjhw5gmnTpiE8PBzLli1TX3vXrl0YO3Ysnj9/jkGDBsHNzQ3Hjh3Dhx9+iJSUlDzFWa9ePXz88cdYvnw5XF1d8d577wEA7O3t4eLigujo6GzPffHiBcaPH4+mTZuiU6dOOHHiBDZt2oQXL14gJSUFjx49gr+/Px4/foydO3eie/fuCA8PV/fDjRs34O/vjwsXLqBjx44YNmwYoqKisG/fPnTq1Anbt29Hz5498/H06Y0lBFzWnQa+G5vzVMS0dOBFonZZShoQHadZ7qhQjRZLSgUsdHyctvzvjxJJqdm3l3nMQsfaj5Zmr+pYmKkW5/94FTCuM1CjfPbXJCKiYunhw4fqP16TRAQRERmVjIwM8eeff4ply5aJ58+f63XOhg0bxNKlS/X6unnzZq7Xu3nzpmjWrJkAIHr16iWUSqUQQoiUlBQxe/ZsIZPJROfOnYUQQiQnJ4tJkyYJAKJ///4adb/55hthYmIimjdvnue6Qggxf/58AUBMnz5dPHjwQPTr108AEGPHjhUZGRk649VVNm7cOHV5fHy8ePfddwUAsWTJEiGEEDExMWLIkCECgPjqq6/UdWNiYsTQoUMFAFGhQgW9+iKnmK5evSrq1q0rnJycxIMHD9TlKSkpYtasWcLExERMnz5dCCGEUqkUcXFx6meU6caNG6JJkybCxMREPHr0SAghxIMHD0TXrl0FALFy5Up13bCwMNG+fXsBQLRp0yZP8QcGBgonJyed550/f15UrVpV45lklgEQ3333nbr82rVrwsfHR8hkMlGvXj2RmpoqhBAiMTFRTJgwQaMfEhISxPvvvy8AiKVLl2q0efToUeHh4SEqVKig9UxyEhMTI5KTk9WvX758KeLi4tSvU1JSRHR0tMY5mc81u9ePHz/WiOGNamP5ASHQp9h9Kc0HCBETl/OzCrym9/WSb/73/WUzRKQO/0nrWb3YFKiqu+9S9v1x/paqztpArf5InLhEdSxZ9X5OnrlJKB3eEiImTt1G0uC5QtgMKfw+z+GabINtsA22wTYM08bly5dLxH28yW3khiPHiIiMzOnTp/HkyRM0atQIDg4Oep3Tvn17vRfvd3R0zFM8X3zxhXq6orm5Ofz8/LBx40YcOnQI8fHxePLkCS5evAgAmDZtmkbdbt26YfPmzTh16hSioqIQFxend10Xl1cLYEdHR2PEiBEIDAzEzJkzERAQkKd7mDRpkvq/bWxs0LhxYyxduhS3bt0CAISHhyMoKAimpqb46KOPNJ7VoEGDsGPHjjy1l52rV68iKCgII0eOBKC5uGudOnXg4uKC/fv3Y8aMGZDJZOrRVEqlEgkJCXj58iUsLCxQo0YNnD17FhcuXECPHj1w7949XL16Fa6urhgx4tXOf56enhgwYAAOHz5skPj1YWpqig8++ED9ukyZMqhSpQquXLmC999/X70Dq5WVFRo0UK3LlNkPd+/exenTp6FQKNC1a1eN5+Pg4IDatWvj77//xq1bt1C1alW94nn9/a5QKDRem5ubw8nJSaPs9e3aX39dpkyZYtFGsSE3wYve9eHgaAtzIPtnVdcTODhd89xPVgNlSgGf9dIotvBw/e/kUjCLjofZf99LgOpZmSf89/PSzVGzjaxtKmNULx4/1+oPq+fJqtFpFmbAiwRYzNsFvN8ViEsC4pJgDgBpAhACuPcUsDYvMe8rtsE22AbbMMY2LC0tS8R9vMlt5IbJMSIiI3L+/Hn8+++/8Pb2znZqoS6v/w/LkKpXr67xulSpUihbtiyuX7+O+/fvIy0tDdHR0TAxMcm27pUrVxAWFgZzc3O962ZNjq1YsQLJycmYPXs2pk6dmud78PLy0nidee2YGNUvvy9fvkR0dDTKli2r9T93Z2dnODs757lNXW7evAkhBFavXq1zF0gAsLa2Vv93SEgIZs6ciUOHDiEqKkpr6mzmmnRxcXGIiopC06ZNtabMenl5aVyzsJUpU0ZjCqqFhYW6/YoVK2rUzfzQlNkPsbGxiIyMRHx8fI5rvD158kTv5BgVA+lKPB7cEA651SulUO0U+XpZ2VLa5Zl8PIHjNwClUnNR/rO3AGsL1a6T2XF3AlzsgAt3tI+duwX4/Pd+fp4AxCcDc/9Ufb2u4jigV2Pgzy9yuDkiInqTValSReoQjB6TY0RERuLChQu4fPkyqlatilatWuXp3KSkJL3XHDM3N4dcXrz+9+Lj44Pg4GAsWbIEQ4YM0Up25Sa7Ndb0fWaG1rdvX/Tv31/nMTc31S/rYWFheO+99xAYGAhfX1906NABrq6uMDU1xR9//IHt27dDqVQWZdh6yWk9u9z6QQgBIQQcHR2xcOFCnXWtra3Va91RCWBqAnTxgbyWYTa80NK/GbDtNLD9DNC/uaosOg7Yegro0VBzPbE7kap/K2X5Y0O/ZsCaQOBBNFD+vyT5oSDVLpcf91C9drUHdnyu3faC3cDpUGDTx7oX9SciomLj2rVr8PHxkToMo1a8fnshIqJ8uXDhAi5duoSqVauiTZs2Grsu6mPHjh3qRfxz06ZNG1SrVk3va9+4cQNNmzZVv37+/DkeP34MU1NTeHh44OnTp3B2doZSqcSNGzdQp04ddd3Y2Fg8fvwYgGrU0MuXL/Wum1XHjh0xdOhQTJ06FS1btsTRo0cN+hc8W1tbODk5ISQkBPHx8Rqjx2JiYhAdHa0xki2/KleuDJlMBrlcjiFDhuRY99atWzh79ixatWqFPXv2qMtjY2Oxe/dujbp2dnZwcXFBaGgoMjIyNJJQd+/eRWLia4uYv6Hs7e3h6uqK4OBg9OjRQ2sUH5VAGUrgs94G2w1WS/9mQNOqwKiFwPUIwNkWWLRP1e7MwZp1O/w3ZfPe0ldlU/qpEmntAoAPu6lGiM3bCdT2AEa1V9WxtgB6N9Fu+89zwLnbuo8RERFRnpjkXoWIiIqzixcv4tKlS6hSpUq+EmOAas0xPz8/vb7Kl8/bTmrffvutemRPamoq9uzZgxs3bqB9+/ZQKBRwd3dXrx319ddfa9Tdu3cvgoKC0KJFC7i4uOSpblampqYYN24cvv32WyQmJqJly5a4efNmnp9TdipUqIA6deogIyMD8+fPV5c/e/YMv//+O5KSkgzSTv369VGrVi1s374dZ86c0TqekJCAqKgoAIBMJoOJiYnG6DYhBE6ePIlDhw5pnOfh4YG6devi6dOnWLt2rbr83r172Lp1a75iNTMzg4WFhXrKY1Hw8vJC8+bNoVQq8fnn2iNxUlNT8ejRoyKLhwqZiQyo4wG0qYnr168XThumpsCeL4FBLVQjuT5bCzjbAYdnAtXccz+/vDNw9CvVaLIv1qumTfrVV619pmsXSyIiKpFKly4tdQhGjyPHiIhKsH///RcXL15UJ5lu376tcdzKygrlypXL9TqFueZYUFAQmjdvjjZt2iAkJAT79u2DlZUVvv/+ewCqNaVGjx6No0ePYsuWLYiIiECrVq0QGhqK/fv3w8LCAr/88kue677OzMwM77zzDuRyOb744gu0atUKR44cQc2aNQt8j46OjhgzZgyOHz+OadOm4cKFC/D29sa5c+dw/vx5uLq65itp+bratWvjk08+waRJk9CqVSv06NEDNWvWRFJSEkJDQ3Hu3DmMGzcOM2bMQMWKFVG/fn0cO3YMXbt2RZMmTXDnzh38888/Gmt6AUD58uUxevRonDt3Dm+//TYOHjwId3d3HD16FHfv3s3XCCxXV1d4eXnh5MmTeP/991G5cmU4ODhg0KBBBX4O2VEoFPjoo48QFBSERYsW4cSJE+jQoQNsbGxw//59XLx4EYmJiQgLCyu0GKgIKQXweR+gIN9bR77KvU4pBfDbeNVXTrKOGMuqZgVgf942AQEArJ6o+iIiomLPwsJC6hCMHpNjREQlWOYoofj4eBw5ckTreNmyZfVKjhWmpUuXYubMmVi4cCHS0tJQv359LFy4UGNKZM2aNbFp0ybMnj0bBw8exLlz52BpaYkWLVrghx9+QO3atfNV93VmZmYYPXo0TE1N8fnnn6N169YIDAw0yAeW9u3bY/Xq1Zg1axYOHjyIffv2oV69evj1118xbdo0WFlZFbgNExMTDB8+HF5eXvjhhx9w6tQp/PXXXzA3N0fp0qXRrVs3DBw4EIBqCuZPP/2EGTNm4OTJkzh8+DBcXV0xfvx4PHv2TGOEGwD06dMHcrkc33zzDXbs2AGZTIYWLVrg559/xocffpjnWCtXroz//e9/mDFjBtasWaNe1659+/YFfg45qVatGv744w/8+OOP2LlzJxYvXoyMjAyUKlUKtWrVwpgxYwq1fSpCZRyAAap1wDLX2iMiInoThYeH53nHdzIsmZBqtWAiIiLC7t27MXDgQLRs2RL79++XOhyinP12EHhnsdRR5E4mA+YOBz7tDUD1hwJDrOtHRERUGK5cucIF+SXGNceIiIiKQHp6utbaYjExMdiwYQMSExPRuXNniSIjKoGszIF3OqlfPnz4UMJgiIiIcla1alWpQzB6nFZJRERUBEJDQ/H+++9DLpejQYMGyMjIwMmTJ3H27FlUrFgRY8eORXp6OqKionJdoN/BweGNHHqflpaGJ0+eIDU1Nds6MpkMjo6OsLe3L8LIyKiYmgDvdgbsbaSOhIiISC+RkZHw8vKSOgyjxuQYERFREXB2dkaDBg3w559/4vTp00hJSYGDgwPeeustzJs3D7a2tggJCcGoUaNw+vTpHK/Vpk0bnWvISe3q1asYNmwYQkNDc6w3cuRIrF69umiCIuMjBPBRd42i6tWrSxQMERFR7uLi4qQOwehxzTEiIqI3RGJiIs6cOYMnT57kWK9q1apo0KBBEUWlv7i4OJw4cQIvXrzIto6pqSlq1aqFGjVqFGFkZDBv+ppjchOgXzPg9080im/fvo3KlStLFBQREVHOrl+/zs9GEuPIMSIiojeEtbV1oe/WWJjs7Ozg5+cndRhkzNKVwKe9tIrj4+MlCIaIiEg/HOEsPS7IT0RERETFn6kJ0MIbaKg9QszS0lKCgIiIiPRz9epVqUMwekyOEREREVHxl6EEJvfWeYhTKomIiCgnTI4RERERUfEmA+BVGujeUOfh4ODgoo2HiIgoD1xcXKQOwehxzTEiIiIiyhuZTOoIXiOAz3oDJvy7LxERFT82NjZSh2D0mBwjIiIiIv30awacvAkkpEgdiSZTE2BE22wPlylTpuhiISIiyqN79+7Bx8dH6jCMmkwIIaQOgoiIiIiIiIjIGF25coXJMYkxOUZEREREREREJJH4+HgoFAqpwzBqXJiBiIiIiIiIiEgi0dHRUodg9JgcIyIiIiIiIiKSSGxsrNQhGD0mx4iIiIiIiIiIJCKXc69EqXHNMSIiIiIiIiIiMlocOUZEREREREREJJGrV69KHYLR49g9IiIiouLuTiTQfy6QkFLwaznbAUe/Asz4MZGIiKgocEKf9Piph4iIiKi4OxQEXLlnmGvdegxsPQUMbW2Y6xEREVGOnJycpA7B6HFaJRERERG9YiIDvtsB8K/YRERERcLOzk7qEIwek2NERERE9IpSAEH3gaP/Sh0JERGRUQgLC5M6BKPH5BgRERERaTI1Aeb9KXUUREREREWCyTEiIiIi0pShBPZcAm5GSB0JERFRiefl5SV1CEaPyTEiIiIi0iY3AX78S+ooiIiISrzY2FipQzB6TI4RERERkbZ0JbAmEIh6IXUkREREJdqzZ8+kDsHoMTlGRERERLqlZwCL9kkdBRERUYlmYsLUjNTYA0RERESkm1IAP+8GklKkjoSIiKjEqlOnjtQhGD0mx4iIiIgoe8/jgfXHpI6CiIioxLp27ZrUIRg9JseIiIiIKHsyAHN3AEpl4bcVmwCMXQy4+AM2Q4B2AcClO/qffyMC6DoLUAwFHEcAw3/WXjPtZgQweS3gMwmwHQqUHQ10mw1cuG3QWyEiItJXRkaG1CEYPSbHiMgoTZgwATKZDGfPnlWX/fTTT5DJZFi7dq1B2ypdujRq1Khh0GuWdD4+PnB2dpY6DIM4ceIE6tWrBxsbG8hkMvj7++t13r179/JU31B0fW+QkRMAbkcCey8VbjtKpSpJtfE4MMEXmDsCePoCaBsA3HqU+/kR0UDrL1WxzhkGfNoT2H0R6DQTSE17Ve+3f4DlB4GGlYAf/IFJPYGQR0DTL4B/rhba7REREWWnVKlSUodg9ORSB0BEJd/ly5cRHR2N6OhovHz5EgqFAkOHDpU6LIN67733UKtWLYwfP17qUOgNkp6ejgEDBiA1NRUffPABnJyc0KBBA6nDws6dO7F7925MmzYN5cuXlzocKg5MTYC5fwLdGub/Gm2nAZ6uwOqJuo9vOw2cCgG2fgr0b64qG9gcqDoBmL4Z2Phxztef8weQkAxcnAdUcFGVNa6iSo6tDgTGdlaVDWkFzBgEKKxenTu6A1B9IjBjM9Cxbv7vkYiIKB+cnJykDsHoceQYERW68+fP49GjR7Czs4OFhYXU4WRr4sSJePnyZb4Sd0uWLMH27dt1HgsLC8PFixcLGh4VQ7du3UJkZCSGDh2Kb775Bp9++inatWsndVg4ePAgli9fjkeP9BiNQwQAGUrg2HXg8t3Ca2PbaaC0A9C36asyF3tVgmznOSAlLdtTAQB/nAG6N3yVGANUia6qbsCWU6/KGlTSTIwBgJMt0KqGalomERFREbt9m1P7pcbkGBEVusGDB2PkyJHo1q0brK2tpQ4nW3K5HAqFAnK5YQfVWltbw8rKKveKJJmYmJhCue79+/cBAI6OjoVy/TdVYT1PkpjcBJi3s/CufzkMqO8FvL6dfeMqQGIKEJpDMvdhjGoKZsNK2scaV9EvqRf5HHC2y1vMREREVCIwOUZEhc7Ornj8sqFrzbGkpCS89957cHNzg4WFBaytrVGhQgUMHjwYSqVSvS4UABw+fBgymUz99fLlSwC61xzLLLt06RKaNGkCKysrWFtbo2XLlggJCdGK7fTp02jQoAEsLCygUCjQqVMnhIWFQSaToUOHDnm+18zzDhw4gNq1a6uv6+vri6dPn2rUzW79L11rYu3ZswcymQzTp0/HzJkz4e7uDjMzM1SoUAFLliwBAJw9exaNGjWClZUVFAoF+vbti4SEBJ1xXr9+HS1atFAnGJs1a4YLFy5o1RNCYNasWahUqRIsLCxgaWmJ2rVrY8OGDdnGvHjxYlSqVAnm5uYYOHBgnp5fSEgIunTpAnt7e5iZmaF06dIYPnw4nj17pvHcfH19AQCzZs1Svy/ys5bXwoUL4enpCTMzMzg5OWHMmDFISkrSqHPp0iX07dsX5cuXh5WVFSwsLODl5YWAgAAosyyk3r17d/z6668AgKZNm6rjen1ts5SUFIwZMwZOTk4wMzODh4cHli9frlFHn+f57bffqsutra3h4+ODrVu36rxPfetmvn937NgBb29vWFhYwNHREe+//z6USiWioqLQo0cP9WjVRo0a4fr16xrXePr0KYYOHYrSpUvDzMwMCoUCXl5emDBhQu4dYqzSlcCWk6q1vQrD4+dAWR1rrmSWPXqmfSzruVnrvn7+s/icR54dvw6cDgUGtdA/XiIiIgPx9PSUOgSjxzXHiKhYSE5O1ruuubk5TF4feZBPQ4YMwc6dO9GhQwe0aNECGRkZuH37Ns6dO4eMjAy4uLhg3rx5+Oyzz+Dt7Y0xY8aoz7W0tMzx2s+ePUP79u3RunVrTJ48GdeuXcOff/6JgQMH4vLly+p7CA4ORocOHaBUKjFw4EBUqFAB//zzT76SYlnduXMHffv2Rffu3dGvXz+cOHEC+/btw4gRI7Bv374CXXvDhg14+fIlBg4cCGtra6xevRrjx4+HUqnEF198gY4dO8LPzw+BgYHYsWMHPvjgA6xYsULjGikpKWjTpg2qVauGSZMm4c6dO9i2bRs6duyI06dPo3r16uq6fn5+2L9/P1q2bIlBgwYhLS0N27dvx4gRI/D8+XOthMeRI0ewbds29OvXD5UqVYJCodD73m7duoXGjRsjMTERvXv3hre3N06cOIH169fj4sWLuHDhAqytrTFlyhQcPHgQv/32G/z8/NTTKb28vPL0LI8dO4YtW7ZgwIABcHd3x759+7By5Urcv38fBw8eVCdn9+3bh/Pnz6NNmzbw8vJCYmIi/v77b3z11VeIjIzEsmXLAKgW3I+Li8Px48fx0Ucfwd3dHQC01kJ75513YGJigtGjRyM9PR1r1qzBe++9Bx8fHzRq1Eiv5zlmzBisXLkSlStXxgcffICEhARs2bIFgwcPRkxMDMaNG6e+Rl7qAqrpB8OHD0efPn0wcOBA7Ny5E4sXL4alpSV27doFV1dXfPDBBwgLC8Pvv/+OQYMGISgoSP28fH19ceXKFfTs2RM+Pj5ITk7GzZs3cfr06Tz1j1H6eTcwb2TOddLSgReJ2mUpaUB0nGa5o0I1WiwpFbDQ8dHU0lz1b1Jq9u1lHrMw03G+2as6uo4/jQWG/gRUdAUm986+DSIiokISHx8PBwcHqcMwboKIqAht2bJFbNiwIc/nLV26VO+vhw8f5nq98ePHCwDizJkz6rIff/xRABBr1qxRlykUCuHj45Pr9QCI9u3b6zzm6uoqqlevrlUGQHz33Xca5QMHDhQARGBgoLqsffv2AoDYtGmTRt0OHTrk2G5u8cpkMrFt2zaN8qZNmwoTExPx4MEDdVndunWFk5OT1jXCwsIEADFy5Eh12e7duwUA4eDgoHGNs2fPqtv84YcfNK5TuXJlYW9vL1JTUzXaBCB69+4tlEqlunzVqlUCgPD19VWXrV69WgAQX3zxhcZ109LSRJUqVYSzs7NITk7WiNnExEQcPnxYn0elpUuXLgKAWLRokUa5v7+/ACCmTp2q9TwCAgLy3E5mrDKZTPz111/qcqVSKVq3bi0AiC1btqjLY2NjNZ6VEEJkZGSIunXrCktLSxEbG6su1/X+f/1Y/fr1RVpamrr86NGjAoAYPHiwVoy6nmdQUJCQyWSiWrVq4uXLlxrnWFtbCycnJ5GQkJDnukK8ev/+8ccf6rKUlBTh4OAgZDKZ6Nmzp8azGD58uMb3VWxsrAAgunXrpnX/xdbS/UKgT9F82Q0VIj0953gCr+l/vbAnqnNshggxeqH2tXZfUNXbdyn79s7fUtVZG6h97LM1qmPJqdrH4pOEaPSZEPbDhLh2L+d7IiIiKiSXL1+WOgSjx2mVRFQs+Pn56f1lyN1erK2t8eDBAxw5csRg18zk4OCASZMmaZR16dIFAPDvv/8CADIyMnDy5El4eXlh0KBBGnUDAgIK1H7lypXRr18/jbJ27dpBqVRqTUHLKz8/P5QrV079unHjxrCysoK9vT0+/PBDjbqNGzfGixcv8PjxY63rZE5HzOTv749y5crh5MmTSEtTTZFavXo1LC0tMXr0aERERKi/IiMj0aVLF0RHR2tNZWzQoEG+FsZXKpU4evQoPDw88O6772ocmzdvHmQyGfbu3Zvn6+akTp066N69u/q1TCbDjBkzAADbtm1Tl9vb26ufVUJCAh4+fIhHjx6hffv2SE5OxpkzZ/LU7vvvv6+x/l7r1q1haWmJe/fuadXV9TzXr18PIQTGjx+vMTLP09MTffv2RUxMDA4cOJDnupkqV66Mvn37ql+bm5ujVq1aEELg008/1XjfZI6yzPy+srKygpmZGf79919cuXIlT8/ldc+ePUNKSor6dXx8vHpKNQCkpqZqrcH2+nv99deRkZEQQuSpjfh43VOTDU5uArSpCZia5nwfdT3xbPMHEAcCgIPTgYPTkV6zHJQd66hfJ+2ajMSdk4EyDgAAUcYBqeFPtK+ZOWXSzTH7Z/XfdMrEOw+1nlVa+FPV6DQLM83+SE0D+s6FCLoH7PwfUMtD+z6Qv/4oij5nG2yDbbANtsE22Ib+beSG0yqJqFjImmgpSnPnzsW4cePQrl07uLq6okGDBujZsyfGjBkDMzMd03PywNXVVWvx/zJlygAAoqNVa/pERUUhJSUFFSpU0PhlHwDq1q1boPYzp9Nl5eKi2uXt9XXH8qpixYpaZba2tnBwcICpqalGealSql9qIyMjUaFCBXW5tbU1qlWrpnUdLy8vHDt2DJGRkShfvjzu3LmD5ORkVK1aNdt4IiI0d6DTFZ8+oqKikJycjIoVK2pN3XV2doajo/Yv7wWlaxpm/fr1AQAPHjxQl8XFxWH8+PHYu3evzgXxM99T+vL29tYqs7OzQ1xcnFa5rud59+5djVizqlOnDgAgNDQ0z3Uz6Xr/2tvbAwBq1aqlUZ75vs58Bubm5pg+fTpmzJiBevXqwd3dHY0bN0a/fv0wdOhQre+1nLy+2cLrU3TNzc21EvZly5bN8XXmz4G8tGGusNE75gJJVwKf9QaQy32UUsBxYFuN43Jne8DdSbWDJIDXtymR1asI8+M3AKVSvSh/2bJlgbN/AtYWQFU3lH1tWqT6Wbk7AS52sL7+GMiyK7JCoQAu3wN8VO9RdX8olcCIBcChIMi2fKpK+Om6D+SvP4qiz9kG22AbbINtlJw2fHx8AKDY38eb3EZumBwjomIhMTEx90r/sbCw0ErA5NfIkSPRtWtXbNiwAYGBgTh79iz27t2Ln3/+GWfPni3QZgM5xZh1AfXCktO6bFnbzy5RkJqa/fo/2d1bTvec9a9FeWVra4slS5ZkG2vr1q01XpfE3UP9/Pxw6tQp9fpmmcnX3bt3Y8OGDcjIyMjT9bLbtVVXP0nxPHN6/2YXe9b39dSpUzF06FBs3LgRR48eVa9/t2TJEhw5csRgP0NKFBMZUNcTaFk916r50r8ZsO00sP0M0L+5qiw6Dth6CujRUHO9sDuRqn8rZfkw3a8ZsCYQeBANlP9vE5FDQapdLj/uodnWxN+AzSeBpeOAvk0L536IiIj09O+//6JmzZq5V6RCw+QYERUL69ev17tu9+7d4ebmZrC2S5cujUmTJmHSpEkQQuDtt9/GypUrsXTpUnz22WcGa0cXFxcXWFhYIDw8HEIIjeTP1atXC7XtTA4ODrh58yaSk5M1NhkIDg4u1HYTExMREhKC2rVra5TfvXsXtra26r8wVahQAREREWjVqhXKly9fqDG5uLjAysoKYWFhUCqVGgmamJgYPHv2DPXq1TNom5mjqrK6dOkSAKjvNzY2FqdOnULbtm3x999/a9Q9dOiQ1vmG2rAiJ5UqVQKgirVFC80dAK9duwYA6tF+ealrSBUrVsTUqVMxdepUpKeno1u3bjhw4AD++usv9O7d2+DtFXtKAXzeB8jDyLo86d8MaFoVGLUQuB4BONsCi/YBGUpg5mDNuh2mq/69t/RV2ZR+qkRauwDgw25AfDIwbydQ2wMY1f5Vvfl/qa7brJpqRNr6o5rX7tMEsMl5QxUiIiJDylwuhKTDNceIqFiQYs2xjIwMREZGapTJZDI0btwYADSmrllaWuLFixcGaTcrU1NTNG/eHHfv3sXmzZs1js2aNcvg7elSuXJlJCcnY8+ePeoypVKJ7777rtDbDggI0BiptGbNGkRERKBFixbqaa2jRo2CEAITJkzQOeIuLCzMYPGYmJigdevWuH//PpYvX65xbPLkyRBCwNfX12DtAUBQUJBGwksIoV5zrH///gBejch7fVRXeHg4tmzZonXNzKHpBZ0+m5Nhw4ZBJpPh119/RULCq/Ww7t+/j+3bt8PJyUm9xl5e6hpCfHw8nj9/rlEml8vVUzgL87kUa+6OqtFZhcXUFNjzJTCoBbBgN/DZWsDZDjg8E6imPY1WS3ln4OhXqtFkX6wH5v4J+NVXrXGWddTZlXuqf0+HAMN/1v6K0p46TEREVJgyl4Yg6XDkGBEVutDQUMTHxwMAkpOTkZGRoR75olAo9BoRIsWaYy9fvkSFChXQrFkz1KlTB2XKlEFYWBg2b94Ma2trvPXWW+q6NWvWRFBQECZMmIBKlSpBJpNh/PjxBV6XDADmz5+PJk2awN/fH7t374aHhwcOHjyoXj8pL+sj5cenn36KVatWYcyYMThy5AisrKzw999/Iz09vVDbVSgUOHHiBFq3bo02bdrgzp072LZtG+zs7PD999+r640ZMwY7d+7Erl27UKNGDXTs2BGurq6IiIjAxYsXER4ejqioKIPF9csvv6Bhw4aYMGECDh06hGrVquHUqVM4fPgwqlevjilTphisLUC1KP3AgQMxYMAAlCtXDnv37sXly5fRrl07dXLM1tYWjRo1wtGjR9G9e3c0adIE9+/fx9atW+Hq6qqxgCmg2njhm2++wZdffong4GBYWVmhXr16aNOmjcHirl27NkaNGoWVK1eiXr166NWrFxISErBlyxYkJSXh+++/V0/HzEtdQ7h+/Tratm2Lli1bolatWnB0dMTNmzexdetWuLi4oE+fPgZrq8SQyYBPegHyAkw3PfJV7nVKKYDfxqu+cpJ1xFhWNSsA+3PZrGT1RNUXERHRG6J06dJSh2D0mBwjokIXEhKitUj5hQsXAKgWSiyM6VKGkJkAO3HiBC5evIiUlBQ4ODigRYsWmDlzpsai36tWrYK/vz9WrFiB5ORkADDIov2AakHygwcPYuLEidiyZQvMzMzQtGlTLF++HHXr1tWY6lgYqlWrhrVr12Lq1KlYvHgxFAoFfH198b///U890qYwWFhY4OjRo3jnnXfwww8/QAiBBg0a4Oeff9Zak2HXrl2YP38+fvvtN6xYsQLp6elwcHBA1apV8cUXXxg0ripVquDMmTP46KOPsH//fmzfvh2Ojo5466238PPPP8Pa2tqg7bVu3RoNGzbE999/j4cPH8LOzg7+/v5YuHChRmJ0586dGDt2LI4fP479+/ejbNmy+PDDD1GqVCmtXVE7deqETz/9FGvXrsW0adOQkZGBkSNHGjQ5BgArVqxA5cqVsXz5csyfPx9yuRzVqlXDokWLMHDgwHzXLShPT0/06tULZ86cwYkTJ5Ceng5HR0f06NEDX3/9tXoBf8rC2hwY00HqKIiIiEqk0NBQ9aL8JA2ZKMgKyEREJJlTp06hRYsWGDt2LJYuzWYUBREZh2UHgHeXFM61TU2Aj7oD3/sXzvWJiIiM3JUrV5gckxjXHCMiKgZenxYnhEBAgGrqkJ+fnxQhEZEx+bC71BEQERGVWBUqVJA6BKPHaZVERMVA9erV0aBBA9SuXRuJiYk4ePAggoOD0bJlS/To0QMA8ODBg1x3unFwcICjo2NRhFysxMfH67UIu6enZ4F3egwLC9NaOP91zs7OsLOzK1A7RAYhNwH6N1ctdk9ERESFInNZFpIOk2NERMVAp06dcPDgQezfvx8ZGRlwdXXF2LFj8eOPP6qTNQ0bNsw1wTNy5EisXr26CCIuXpYvX661LpcucXFxsLW1LVBbXl5eudYJCAjAzJkzC9QOkUGkK4HPekkdBRERUYn29OlTuLm5SR2GUWNyjIioGFi1alWudVasWKE1/fJ13t7ehgqpROnTpw/KlCmTaz1D7Ji4efNmZGRk5FinQYMGBW6HqMBMTYDm1YD6laSOhIiIiKhQcUF+IiIiouKusBbk/2sK0L2h4a9LREREahkZGTA1NZU6DKPGBfmJiIiISJMMQKXSgF99qSMhIiIq8UJDQ6UOwegxOUZEREREmgSAyX2AAm5AQURERLlLSUmROgSjx088RERERKSplAIY3kbqKIiIiIxCQTd8ooJjcoyIiIiIXjGRAR92A6wspI6EiIjIKLi7u0sdgtFjcoyIiIiouPMqrVonzBDMTIH3uxroYkRERJSbmzdvSh2C0ZNLHQARERERFVDHusDdJUByasGvZWkOuNgX/DpERERExYRMCCGkDoKIiIiIiIiIyBhFR0fD2dlZ6jCMGqdVEhERERERERFJJD09XeoQjB6TY0REREREREREEomMjJQ6BKPH5BgRERERERERERktrjlGRERERERERCSR9PR0yOXcL1FKHDlGRERERERERCSRO3fuSB2C0WNqkoiIiEhqCcnA7os517G3BrrUK5p4iIiIqMgkJSVJHYLRY3KMiIiISGqfrwN+3ZtzHRMZcHsRULF00cRERERERcLGxkbqEIwep1USERERSS02QZX8yolMBvz0V9HEQ0REREWmQoUKUodg9JgcIyIiInoTyHJJjmUogeX/AM/jiyYeIiIiKhI3btyQOgSjx+QYERERUXGRmgYsOyB1FEREREQlCpNjRERERMWFUgA//qVKkhEREVGJ4ObmJnUIRo/JMSIiIqLi5OkL4PcTUkdBREREVGIwOUZERERUnJjIgO92AEJIHQkREREZwKNHj6QOwegxOUZERERUnCgFcD0COBQkdSREREREJQKTY0RERETFjakJMPdPqaMgIiIiA6hevbrUIRg9JseIiIiIipsMJXDwKvBvuNSREBERUQGFh/P/51JjcoyIiIioODI1AX7YJXUUREREVEAJCQlSh2D0mBwjIiIiKo4ylMC6o0Dk88JvKzYBGLsYcPEHbIYA7QKAS3f0P/9GBNB1FqAYCjiOAIb/DES90K739Tag5xyg9ChA1heY8bvBboGIiOhNZWVlJXUIRo/JMSLS208//QSZTIa1a9dKHUqBJSQkYPDgwXB2doaJiQmcnZ2lDinPJkyYAJlMhrNnz6rLCrOPfHx8iuVzehPIZDJ06NBB6jCKBb7P8kgpgF/3FnIbSqDbbGDjcWCCLzB3BPD0BdA2ALilx+5aEdFA6y+B25HAnGHApz2B3ReBTjOB1DTNul9uBM7fBupVLJx7ISIiegNVqlRJ6hCMnlzqAIikcvnyZURHRyM6OhovX76EQqHA0KFDpQ6LisjkyZOxefNmDBo0CPXr14dCoZA6JDKg2NhYTJ48Ge3bt8fgwYNLXHtEakol8Mse4H/9AGuL/F2j7TTA0xVYPVH38W2ngVMhwNZPgf7NVWUDmwNVJwDTNwMbP875+nP+ABKSgYvzgAouqrLGVVTJsdWBwNjOr+qGLVHFEh2nGqVGRERkBIKDg+Hj4yN1GEaNyTEyWufPn4eFhQWcnZ2RmpoqdThUxI4cOYJy5cph06ZNkMlkUodjMBMnTsQ777wDS0tLqUORVGxsLJYvX47U1NQiS47l1F58fDxMTU0LPY6S4MyZM8jIyJA6jOIlLhFYEwi817Vwrr/tNFDaAejb9FWZi70qQbb+GJCSBliYZX/+H2eA7g1fJcYAoGNdoKobsOWUZnLM09Xg4RMRERHlhtMqyWgNHjwYI0eORLdu3WBtbS11OKTD8+fPoVQqC+XaMTExsLW1LVGJMQCQy+VQKBSQy0vO3z6EEHj27JnUYRSIjY2NUSYsY2Ji8nyOpaUlbGxsCiGaEkwAmPenahRZYbgcBtT3Akxe+9jYuAqQmAKE5jC18mGMagpmQx3TRRpXAS7fNWysRERExVCZMmWkDsHoMTlGRsvOzk7qELR0794dMpkM9+/fR79+/WBvbw8zMzN4e3tj586dGnVzWltK15o9pUuXRo0aNXDq1CnUq1cPlpaWsLOzw8CBA5GUlISkpCSMGDECjo6OMDMzQ40aNXDs2DGdcWZkZOD999+Hi4sLzMzM4OHhgXnz5umse+TIEbRs2RK2trYwMzODm5sbxo0bh6SkJJ0xX79+HW3atIFCoYCjo2Oedm5JT0/HJ598gvLly8PMzAwKhQLNmjVDYGCg1nN78uQJbty4AZlMBplMBn9/f73bOXToEHx9feHm5gYLCwtYWlrC29sbv/zyi1bdzD4NDQ1F165doVAoYGFhgTp16mDfvn0ade/du6eOZeHChfD09ISZmRmcnJwwZswYrWemS3bvi+TkZHz00UfqZ2NtbY3GjRvjwIEDWteIiopCr169YGtrCwsLC9SqVQt79uzR+/m8LmvftmjRAtbW1rCyskKzZs1w4cIFjbp79uyBTCbD9OnTMWPGDJQvXx7m5uaYNGkSAP36eM+ePahYUbVe0Zo1a9R9/Pr3xKJFi1CzZk1YWVnBwsICVapUwU8//aTzHv788080adIECoUCZmZmcHV1ha+vL8LCwvRqL7s1x7799ltUqlQJ5ubmsLa2ho+PD7Zu3apVL/P8AwcOoHbt2rCwsIBCoYCvry+ePn2qTzdoePr0KYYOHYrSpUurn6OXlxcmTJigVVff55QZ4/bt21GzZk1YWlqiVatW+PrrryGTyXR+fyiVSjg7O8PDw0OdCM9uzbFr166hW7ducHJyglwuR6lSpdC0aVOt96a+P3POnz+Ptm3bqn/mOTg4oHbt2li2bFmenuUbI+wp8NeF3Ovlx+PnQNlS2uWZZY9ySF4/fq5Z9/Xzn8WrRp4REREZsZL0h+3iij1AVADJycl61zU3N4fJ6391z0bnzp1hb2+P8ePH4/nz51izZg3eeusthISEwM3NLb/hIiYmBr6+vujQoQN69uyJwMBAbN26FTKZDA8ePEB8fDzGjh2L58+fY/Xq1Rg0aBDCwsK0RrzMnj0bSUlJGDx4MExNTbFt2zZMnjwZCQkJmDFjhrrexo0bMXLkSJQuXRojRoyAi4sLzp49i2XLluHatWs4fvy4xjNJTk5G69atUbNmTXz88ceIjo6GmVkOU3Ve07VrVxw6dAh169bFsGHD8PTpU2zevBm+vr7Yvn07/Pz80LlzZ8ybNw+zZs2CQqFQJ10aNGigdzu///477ty5gy5dusDT0xPPnj3DH3/8gQ8++AAvX77ElClTtM7x9fWFQqHAhAkT8PTpU/z+++/o3bs3/v77b3Ts2FGj7rFjx7BlyxYMGDAA7u7u2LdvH1auXIn79+/j4MGDeR7tlpaWhqZNm+Lff/9Fx44d4e/vj7i4OGzevBndu3fHtm3b0LNnT3Xdli1bIjQ0FB06dECzZs0QFBSEAQMGwNbWNk/tZpWSkoI2bdqgWrVqmDRpEu7cuYNt27ahY8eOOH36NKpXr65Rf+3atYiLi0O/fv3g7u6O8uXLA9Cvj+vUqYMpU6Zgzpw5aNq0Kfr16wcAGiNEx40bh6VLl6J+/fqYMGEC5HI59uzZg0mTJuHhw4f4/vvv1XXnzJmDL7/8EqVKlcKAAQNQsWJFhIeH49ChQ7hx44Ze7ekyZswYrFy5EpUrV8YHH3yAhIQEbNmyBYMHD0ZMTAzGjRunUf/OnTvo27cvunfvjn79+uHEiRPYt28fRowYoZVozY2vry+uXLmCnj17wsfHB8nJybh58yZOnz6tUS8vzwkAQkNDMWzYMPTo0QODBg1CRkYGxo0bh5kzZ2Ljxo2YOFFzTatdu3YhJiYGQ4cOzfHn49GjR+Hr64v09HR0794dtWvXxvPnz3Hq1CkcOnQIfn5+APT/mRMVFYVOnTpBCKHu0+joaFy+fBknT57E2LFj8/Q83wimJsB3O4BejXOul5YOvEjULktJU63zlZWjQjVaLCkVsNDxkdHSXPVvUg5LE2Qe0zXt0tLsVZ2cpmUSERGVcBEREdyQSGqCiMSWLVvEhg0b8nze0qVL9f56+PBhrtfr1q2bACC6desmlEqlunz58uUCgPj888/VZT/++KMAINasWaN1nbp16wonJyeNMldXVwFAzJ07V6O8cuXKQiaTiUaNGon09HR1eUBAgAAgli5dqtWmk5OTiIiIUJfHxMQIZ2dnYW1tLZ4+fSqEECIpKUnY29sLb29vkZCQoNHm1KlTtWKvW7euACCGDRuW63PSZfv27QKAaNGihUhLS1OXnz59WpiYmIhq1aqJjIwMjedRvXr1fLX1/PlzrbL4+Hjh7u4uypYtq9FOZp82adJEI65jx44JmUwm6tatqy4LCwsTAIRMJhN//fWXulypVIrWrVsLAGLLli3q8vHjxwsA4syZM+oyXe+LzOe9bNkyrftwdnYW1atXV7/fvv32WwFAjBgxQqPutGnT1H2fV5l927t3b4339apVqwQA4evrqy7bvXu3ACCsra1FSEiIxnXy0seZz3LkyJFa8Zw4cUIAEEOGDNE61qpVK2Fpaal+f4eFhQm5XC7c3Nw03vOZMr9ncmpPCCEAiPbt26tfBwUFCZlMJqpVqyZevnypLg8LCxPW1tbCyclJ4/sm832xbds2jes2bdpUmJiYiAcPHuhsV5fY2Fj1z5mc5OU5ZcYIQKxYsUKrfrt27YRcLhd3797VKPf19RUmJibi+vXr6rLXf34plUrh4eEh5HK52L9/v9a1M/sgLz9z1q1bJwCI7777LsdnoI+YmBiRnJysfv3y5UsRFxenfp2SkiKio6M1znn06JH262E/CWHaTwj0ydeXEn2EKDtaiP++B3S2IYQQgdf0vubLa3dU59gMEen+P2vdR8y6A6q6+y7pbPPx48dCeS5UVWdtoNazSvlwuepYcqr2s4p6IQT6iLhJy3N8do8fP9b4uWKw/mAbbINtsA22wTaKsI3Lly+XiPt4k9vIDZNjRCL/ybEHDx7o/ZX1mzs7mYmUY8eOaZRHR0cLAKJ///7qsvwkxxwcHDSSCkIIMXToUJ2/0J45c0YAEJMmTdJqc+zYsVptfvLJJwKAWLJkiRBCiA0bNggAIiAgQOtZXLlyReuX7swEyu3bt3N7TDoNGDBAABB79uzROpaZWMr6C3hBkmNZvXjxQkRERIgHDx6I/v37CwAiLCxMfTyzT7MmtTI1atRImJiYiPDwcCHEqwRL1oRZpsOHDwsAYuDAgeoyfZNjlStXFmXLlhXh4eFafdGzZ09hYmKi/p9HZrIl6z0IIURycrKwsrIqUHIsKChI61i5cuWEnZ2dSE1V/XKcmRzr3bu3Vt289HFOyaoRI0YImUwmjh49qvU85s2bJwCI5ctVv5BnJlW+/fbbHO8xr8mxyZMnCwBiwYIFWnXfeustAUDs2LFD4/wqVapo1f3f//4nAOhMGmUnJSVFmJmZCU9PT3H58uVs6+XlOWXGWL58eY3kcKaNGzcKAGLy5Mnqsri4OGFhYSHq16+vUff1n1+nT58WAISfn1+O95WXnzl79uwRAETHjh1FZGRkjtctMgVMjglZXyG+/zP3dp69FOLgFc2vOh8J0XmmdnlSiuqcyu8L4fuV9rV+O6hqO+he9u1FRKvqfLdd+9hb84VwHK77vP+SY2L6ptzviYiIqJhLSkqSOgSjx2mVRAVQrly5QrlurVq1NF47OTkBUC1QXxCurq5a89lLlVKtA1OtWjWN8tKlSwOAzoXQa9asqVVWt25dAMCtW7cAAFevXgUAzJo1C7NmzdIZT3R0tMZrW1tbeHh45HofuoSHh0Mmk6FxY+0pRdWrV8exY8dw48YNrel7+fHw4UOMHz8egYGBiIuL0zoeFRUFT09PjbKGDRtq1atWrRrOnz+PkJAQ9bRBAPDy8tKqW79+fQDAgwcP8hxveHg4UlNTUaFChWzrPHz4EGXLlkVERATs7e216lpYWMDNzQ2xsbF5bh9QTTF8/T0GqO712LFjiIyM1HgGlStX1nkfhujj0NBQCCHQpk2bbOs8fvxYXRcAmjVrluM18+ruXdUi5Jn9mlWdOnU02s7k7u6uVdfFRbX7X17WHTM3N1ev6VavXj24u7ujcePG6NevH4YOHaqetpuX55SpfPnyOqdHDhgwAO+99x527NiBb7/9FjKZDCtWrEBKSkquu4leu3YNwKvnkp28/Mzx9fWFn58f9uzZA3d3d1SuXBktWrSAv78/WrVqlWM7bywrc+DtjrnXK6VQ7RT5elnZUtrlmXw8geM3VAv+Z+3fs7cAawvVrpPZcXcCXOyAC3e0j527BfhUzD1mIiKiEu7hw4eoVEnH5jVUZJgcIyqAxMTE3Cv9x8LCAqampnrVzW5BRiGE+r9zWncqIyNDZ3lO7evTZl5knjdhwgQ0b95cZ52syRBA9Uv7m74YpRACrVu3Rnh4OAYMGICmTZvC0dERpqamWL16NQ4cOFBoO2zmlxAC5cqVw9y5c7OtU6VKlSKMKHeFuYOsEAIymQzLly/Ptp28rEFXVHJakyuv77mpU6di6NCh2LhxI44ePYrAwEDs2LEDS5YswZEjR2Bqapqv55TdjpxyuRw9evTA+vXrceLECbRq1Qrr1q2DtbU13n777TzFnp28/szZvXs3zpw5g61bt+LUqVPYsGEDVq9ejY8//lhrLbU3nqkJ8G5nwL6Qdvns3wzYdhrYfgbo/9+zjY4Dtp4CejTUXC/sTqTq30pZdt3q1wxYEwg8iAbK/7eeyqEg1S6XH/conJiJiIiKkZcvX0odgtF7s38LJXrDrV+/Xu+63bt3L9Bi+q9zdXUFoD36CgAePXqU50Xb8+Lff//VKssctZGZZKlRowYAwMrKCkOGDCm0WDJ5eHjg7NmzOHfuHHx9fTWO3bx5EwAMMmrswoULuHv3LkaMGIE1a9ZoHFu5cmWO52XuaJgpJCQEJiYmWiOqMkcVZXXp0iUA2glFfbi7u+PZs2fo27cvLCwscqxbrlw5nDt3DuHh4Rqj31JSUvDo0aN8J60SExMREhKC2rVra5TfvXsXtra2em1fnZc+zun97+XlhbNnz8LT01PnDpJZeXt7AwDOnDmD1q1bZ1svr99vmX8ZvHTpElq0aKFxLHOkVNWqVfN0zbyqWLEipk6diqlTpyI9PR3dunXDgQMH8Ndff6F37955ek76GD9+PNavX4/ly5ejXLlyuHz5Mnx9fdWjV7OTOWIs87lkJz8/c5o2bYqmTZsCUP0s9fHxwcKFCzFr1qxCTdAanBDAR90L7/r9mwFNqwKjFgLXIwBnW2DRPiBDCcx8beRfh+mqf+8tfVU2pZ8qkdYuAPiwGxCfDMzbCdT2AEa11zx/3RHgfhSQmKJ6few6MPu/HVyHtwE8XAvjDomIiCSV22d0Knz6bZ1HRDr5+fnp/ZU5NdJQMn9hPHz4sEb54sWLdU6FNKQ//vgDDx8+VL9+9uwZ1qxZA2tra/Tp0wcA0L9/f9jb22PlypW4d++e1jUSEhIQFRVlsJgyp2Z98803GiPnzp8/j+PHj6NatWo6p/XlVebIttdH1J0/fx5HjhzJ9rwffvgB6enp6tcnTpzAhQsXUKtWLa2EV1BQEP7++2/1ayGEehfQ/v375znmwYMHIy4uTr0z5+vCwsLU/92zZ08olUpMnz5do87XX3+NpKSkPLedVUBAgMZzW7NmDSIiItCiRQu9diXNSx8rFAoAuqciv/vuuwCAKVOmICUlRev4gwcP1NcfPXo05HI5FixYgMjISK26mSO2cmpPl2HDhkEmk+HXX39FQkKCuvz+/fvYvn07nJyc0KVLF72ulVfx8fFaccrlcvXPlMwpmnl5Tvpo2rQpKlasiL1792LBggUQQsDf3z/X8xo3bgwPDw/s378fhw4d0jqe2Qd5+ZkTGRmp8f0IAM7OznB3d0dqaipevHih931JTm4CDGgOVHApvDZMTYE9XwKDWgALdgOfrQWc7YDDM4Fq2tN9tZR3Bo5+pRpN9sV6YO6fgF994OB07V0qVxwCpm0Cvtmueh0YrHo9bRMQpv/0YSIiouKksP8oSrnjyDEyWqGhoYiPjwcAJCcnIyMjQz06R6FQ6PUDqrDWHNNHrVq10KBBA+zZswc9evRAgwYNEBQUhEOHDqFMmTJIS0srtLbt7e3RqFEj9O/fH6ampti6dSuio6Px5Zdfqke0KRQKLFu2DG+99RZq166Nnj17omrVqnjx4gVCQ0MRGBiIxYsXY8SIEQaJqU+fPujQoQMOHTqERo0aoWvXrnj69Ck2bdoEMzMz/PjjjzlOS9NXnTp14OHhgU2bNiE1NRU1atTArVu3sG3bNnh4eODOHR3r6kC1DlnDhg3h5+eHJ0+eYNOmTTA3N9c5fcvT0xMDBw7EgAEDUK5cOezduxeXL19Gu3bt8pUcmzVrFv755x8sWrRIPQLKwcEB4eHhOHnyJExNTdWjAT/55BOsXLkSa9euxcOHD9GsWTMEBQXhn3/+QenSpbUSCvpSKBQ4ceIEWrdujTZt2uDOnTvYtm0b7Ozs9J7Clpc+dnJyQtmyZREYGIjJkyfDzc0NVlZWePfdd9GmTRtMmDABCxcuROXKleHn54dy5crh8ePHuHr1Ks6ePYvnz5/D1tYWnp6emD59OgICAlCrVi307NkTFStWxMOHD3Hw4EH88ssv6uR3du3pUrt2bYwaNQorV65EvXr10KtXLyQkJGDLli1ISkrC999/Dysrq3w969xcv34dbdu2RcuWLVGrVi04Ojri5s2b2Lp1K1xcXNQJ7rw8J30NGTIEc+bMwdKlS1GmTBl1WzmRyWRYtWqV+g8N3bt3R+3atfHixQucOnUKLVu2xA8//JCnnzmLFi3CokWL0K5dO1SpUgUWFhY4ceIEzp07hxYtWqBs2bL5fr5FLl0JfNqrYNc48lXudUopgN/Gq75yknXEWFY1KwD7AwwTCxERUQlz7do1+Pj4SB2GcZNiFwCiN8GuXbvE0qVLdX7t2rVLkpgydzbMuk1tJry2250QQoSHh4s2bdoIS0tL9a5vR48ezXa3Sl27M+ra8VAI3bvvZe6EuHLlSjFu3Djh5OQk5HK5KF++vPjmm2903tOZM2dEp06dRKlSpYSpqamws7MT1apVE2PHjhX3799X19MVc16lpaWJSZMmCXd3dyGXy4WNjY1o2rSpOHTokFbdguxWefPmTdGmTRtha2srzM3NRaVKlcSCBQt0PsvMPg0JCRFdunQRNjY2wtzcXNSqVUvs3r1b47pZn/kvv/wiPDw8hFwuF46OjsLf31/Ex8dr1Nd3t8rMZzN16lRRuXJlYW5uLszNzUXp0qVFu3btxKpVqzTqPnnyRHTv3l0da82aNcXff/+d7z7KPO/ff/8VzZs3V79fmzRpovW+y9ytMiAgQOe18tLHhw4dEtWrVxfm5uYCgFbs69atEw0aNBA2Njbq51y/fn0xdepUrV1dN2/eLOrVqyesrKyEXC4XLi4uws/PT2NXz5za0/X9K4QQc+bMERUrVhRyuVxYWlqKunXris2bN2vVy+78nHatzc6TJ0/E4MGDhaenp7CyshJmZmaidOnSol+/fuLmzZta9fV9TtnFmNXDhw+FqampACDefvttnXWye59dunRJdOrUSdjb2wtTU1Ph4OAgmjZtKvbu3atRT5+fOcePHxddunQRpUuXFubm5sLS0lJUqFBBfPjhhzp//haJ/OxWadpPiBb/kyZeIiIiMpicdhCnoiETIp+rbRMRUa66d++O3bt3Iy4uLtcRNvfu3UPFihUxcuRIrF69umgCLAI+Pj6IiIjQuT4eEf3nrfnA7ydU63jlxc4vgJ7aO7gSERFR8fHo0SODrk9NecdplURERETFjQxAxdJA94ZSR0JEREQFlN2O31R0mBwjojdaWFiY1uL3r3N2doadnV2B2omOjkZcXFyOdSwsLODursfi00aAz+vNEx8fr15MPyeenp4GWX+P3gCTewPsSyIiomIvPDwcjo6OUodh1JgcI6I3mpeXV651AgICMHPmzAK14+/vj927d+dYp3r16rh+/XqB2ikp+LzePMuXL892R9Ks9JniS8WAvQ0woq3UURARERGVCEyOEdEbbfPmzcjIyMixToMGDQrcTkBAAIYNG5Zjnfz8Nefvv//Wu66np2euo+TeFHl5XleuXCmCiKhPnz4oU6ZMrvUKaxdMKkKmJsAH3QArC6kjISIiIgOoWrWq1CEYPS7IT0RERCS1vCzIb2YKRCwHXB0KOyoiIiIqAmFhYahYsaLUYRg1LlRBREREVFzITVTTKZkYIyIiKjFevHghdQhGj8kxIiIiouIiXQl80kvqKIiIiMiAzMzMpA7B6HHNMSIiIqLiwNQE6FQXqF5O6kiIiIjIgGrWrCl1CEaPI8eIiIiI3gRKJWBiks2XTLUe2We9pY6SiIiIDIwbWEmPI8eIiIiIpDa5N5DbHklOtkC7WkUSDhEREZEx4W6VREREREREREQSiYiIQLlyXDZBSpxWSUREREREREQkEYVCIXUIRo/JMSIiIiIiIiIiidy7d0/qEIwek2NERERERERERGS0uOYYEREREREREZFE4uPjObVSYhw5RkREREREREQkkZiYGKlDMHpMjhERERERERERSeT58+dSh2D05FIHQERERFRifLsdWHZQ97EuPsDid4s0HCIiInrzmZqaSh2C0eOaY0RERESGUm0CEPpI9zETGXB3MeDhWrQxEREREVGOOK2SiIiIqCjIZMD8v6WOgoiIiN4wQUFBUodg9JgcIyIiIioKGUrVlMsXCVJHQkRERG8QpVIpdQhGj8kxIiIioqKSlAosz2ZNMiIiIjJKjo6OUodg9JgcIyIiIioqQgA/7ALS0qWOhIiIiN4QDg4OUodg9JgcIyIiIipKkbHA1lNSR0FERERviLt370odgtFjcoyIiIioKJnIgO92qEaREREREZHkmBwjIiIiKkpKAQTdB47+K3UkRERE9AaoWLGi1CEYPSbHiIiIiIqaqQkw70+poyAiIqI3QFxcnNQhGD0mx4iIiIiKWoYS2HMJuBkhdSREREQksZiYGKlDMHpMjhERERFJQW4C/PhX0bQVmwCMXQy4+AM2Q4B2AcClO/qffyMC6DoLUAwFHEcAw38Gol5o1/t6G9BzDlB6FCDrC8z43WC3QEREVFLJZDKpQzB6TI4REZVwY8eOhUwmQ3BwsNShlHhdu3YtVh9u3oR4g4ODIZPJMHbs2FzrvgnxGlS6ElgTCDyNLdx2lEqg22xg43Fggi8wdwTw9AXQNgC49Sj38yOigdZfArcjgTnDgE97ArsvAp1mAqlpmnW/3Aicvw3U49opRERE+qpbt67UIRg9udQBEBGVJEIIXLt2DTdu3EB8fDwsLS3h5eWFhg0bwszMTOrwSqRvvvkGz58/x9y5c0tke1TCpWcAi/YBMwbn/xptpwGersDqibqPbzsNnAoBtn4K9G+uKhvYHKg6AZi+Gdj4cc7Xn/MHkJAMXJwHVHBRlTWuokqOrQ4ExnZ+VTdsiSqW6DjVKDUiIiLKVXBwMGrVqiV1GEaNI8eIiAzo9OnTOHPmDEqVKoXmzZvDy8sLwcHB2L9/P4QQksS0cOFCvHz5EjVq1JCk/cK2ceNGrFq1qsS2R6/s3LkTL1++lDoMw1IK4OfdQFJK4bWx7TRQ2gHo2/RVmYu9KkG28xyQkpbtqQCAP84A3Ru+SowBQMe6QFU3YMspzbqergYLm4iIyFikp6dLHYLRY3KMiMhAnj17huDgYHh6eqJz586oXr06mjVrhmbNmuHRo0e4cycP6/sYkLm5ORQKBUxM+COfijcLCwsoFAqpwzC82ARg3dHCu/7lMKC+F/D6z4DGVYDEFCA0h6mVD2NUUzAbVtI+1rgKcPmuYWMlIiIyQg4ODlKHYPT4mxIRkYFkJr9q166tUe7t7Q25XI5bt25JEZbONccePXqEQYMGoXTp0jAzM4NCoUClSpXw8ce5TK/KxtatW9G4cWMoFAqYmZnB1dUV3bp1Q0TEq534UlNT8eGHH6JcuXLqNlu0aIETJ05oXCvrGlQrV65E5cqVYWZmBgcHBwwbNgypqanqui4uLggODkZ0dDRkMpn6a9u2bQCAffv2oWvXrihbtiwsLCxgaWmJ6tWrY8mSJTrv4+7duxgwYABcXV1hZmYGOzs71KtXDxs3btSrvezcuXMHvXv3hrOzM+RyOUqVKoUePXrg/v37GvX07Ze5c+eiSpUqsLa2hoWFBVxdXdGxY0eEh4fn0lO6hYeHo3PnzlAoFLCwsEDdunVx6NAhrXr69mFe6+qyfv16WFpaombNmoiMjASge82xzLKoqCj07t0bdnZ2MDMzQ/Xq1bF3716t6z58+BC+vr5QKBSwtLRE3bp1cfjwYdSuXRsuLi5a9YuEDMDcHaq1wQrD4+dA2VLa5Zllj57lfG7Wuq+f/yw+95FnRERElCNnZ2epQzB6XHOMiMhAoqKiIJPJ4OqqOa1ILpfDyckJUVFRuV5DCIGUFP2nV1lYWORrgXJfX18EBwejT58+qFOnDpKSknDjxg2cPHkyz9eaMWMGZs2ahVKlSmHgwIHw9PTE/fv3cfjwYdy6dQvlypUDAHTu3BlHjx5FvXr1MHz4cERGRmLz5s3o2LEj9uzZg/bt22tcNzAwEL///jsGDhwINzc37N69Gxs3bkSpUqWwcOFCAMDXX3+NmTNn4uXLlwgICFCf26BBAwDApk2bEBYWBl9fX3h6eiI6Ohp//PEH3nvvPSQmJmLSpEnqc4KDg9G6dWu8ePECnTt3RsOGDREfH4/z589j7969GDp0aK7t6XLjxg20aNEC6enp6N27NypXroxbt25h27ZtaNq0KYKCgtRJGX36Ze7cufj8889Ro0YNfPjhh7CyskJ4eDiOHDmCiIgIVKhQIc992K5dO9jZ2WHixInqfunWrRsOHTqEFi1aqOvlpQ/z2t9Zfffdd5gyZQqaNWuGffv26TVarGXLlnBwcMCECRMQExODdevWYeDAgbh37x6cnJwAAImJiWjZsiXu3buHrl27olGjRggKCkLPnj1ha2ub5+dmMALAnSfAnkuq6Ys5SUsHXiRql6Wkqdb5yspRoRotlpQKWOj4yGdprvo3KVX7WKbMYxY61ky0NHtVR9dxIiIi0svt27fh4+MjdRhGjckxIiIDSUhIgKWlJUxNTbWO2djY4MmTJ8jIyNB5PFN8fDw2bdqkd5tDhgzJ8y/1UVFRCAoKQu/evXMd8ZSbkJAQfP3113Bzc8PFixdRunRpjeMZGRkAVOt0HT16FG3atMHhw4fVUzz9/f3Rvn17TJw4Ef/++6/GueHh4bh48aJ6cdIZM2bA09MT69evVyfHxo4di19++QWpqan49NNPteL7+eeftYapz549G97e3vjxxx81kmOjRo3C8+fPsW7dOrz11ls67yO39nQZPXo00tPTcf78eVSrVk1dPmLECHTt2hVTp07FsmXL9O6XXbt2wdLSEpcvX4a5ubleMeSmbNmyOHbsmLpfhg0bhs6dO+ODDz7AxYsXAeStD/PT35kmTpyIhQsXqp9DTt8vWdWoUQM7duxQv65duzYmTpyIX375BTNmzACg2kzh3r17eP/99/Hrr7+q606bNg2zZ8+W9q+2pibAsgO5J8dO3gTaBWiXnwoBfn9tVF7m4vhW5kCKjrVMkv9LfFnl8D7KPKZrdFhyWu7nExERERUDnFZJRGQg6enp2a7rlfkLfm6LbVpZWcHPz0/vLysrqzzHqVAoIJfLERQUpDHVMj9+++03pKenY/LkyVqJMeDVfW/duhUAMGvWLI1n1KZNGzRt2hTXr1/XmhLYqlUrjV17TExM0LhxY7x48QLPnz/XK76sibG4uDg8fPgQcXFxaNy4MR4+fIjo6GgAqumMFy9eRIMGDbQSY1nvI6+ioqJw9uxZtGrVCjY2NoiIiFB/Va9eHWXKlMGxY8cA6N8vtra2SE1NxerVq6E00DS8KVOmaPRLx44dUa9ePVy5ckX9rPPSh/npb6VSiX79+mHhwoV47733sGPHjjw992nTpmm87tOnDwAgNDRUXbZ3716YmJhg9uzZWvefn+8lQLXWYNbRnvnuE6VStcg9gMjISI0NPDTaqOuJpF2TkbhzMnBwOnBwOpS1KyC1XQ31axycjpjfJwJlHFTnlC2F5HuPNZqLjIyEyJxO6eaodR/x8fGqzQ/+m06Z/iAaMTExGtdIvPtQNTrtv1Fjjx9rtgEAWfchybaN/6Smpmq18fo1X3+d47NiG2yDbbANtsE2ikkbnp6eJeI+3uQ2ciWIiMggtmzZItauXavz2MGDB8XSpUtFenp6EUclxDvvvCMAiGvXrqnLAgIChFwuFwBEuXLlRL9+/cTvv/+e52v37t1bABDnz5/PsV6DBg2ETCYTycnJWsdGjRolAIi9e/cKIYS4du2aACBGjx6d7b0EBwery2rVqiWcnZ11tnvv3j3RvXt3YWdnJ6CavKbxdf36dSGEEHv37hUAxMiRI3O955za69Kli8j6v9bM6+b05erqqq6vT79cvHhRuLq6CgBCoVCIFi1aiFmzZono6OhcY88u3qdPn2odGzhwoAAgTp48KYTIWx/mp78tLS0FADF+/Phc49VVlpqaqlUfgOjatav6tZubm3ByctJ5bU9Pz2z7NU+qjhcCffL+ZTtEiJeJ+WuzzZdCjFyQ/fH+c4UoPUqIjAzN8ncWCWE9WIhk7WenwWWkEAPmaZdXHS9E+wDd50S9UN3X9E05X5uIiIhERESE1CEYPU6rJCIyEBsbG8TGxuqcOpnTlMuslEolkpOT9W7T0tIyX7tQzpw5EyNGjMDGjRtx7Ngx/PPPP/jjjz+wZMkSBAYG5vl6hSGnZyWyDkfJhlKpRNu2bREREYFBgwahcePGcHR0hKmpKVasWIFDhw6pp0sWlsw4O3bsiNGjR+usY2Njo/5vffqlfv36CAsLw5YtW7B//36cP38eAQEB+Omnn3D48OFiu15FgwYNEBQUhM2bN+Odd95B3bp183S+mZnuNa/0ea9IztQEeN8XUORv9Fqu+jcDtp0Gtp8B+jdXlUXHAVtPAT0aaq4Xdke1+QEqlXlV1q8ZsCYQeBANlP9v6umhINUulx/3KJyYiYiIjEhUVBTc3d2lDsOoMTlGRGQgLi4uiIiIwNOnT1G2bFl1eXp6OmJiYjTKspOQkFDoa45lqlSpknoqWlpaGrp06YLAwEDs378fXbp00esaVapUAQCcOnUKDRtmv1ZShQoVcPHiRZw9exatW7fWOJY57a1GjRr5uY1sNyQ4fvw47t27h7fffhvLly/XOLZs2TKN13Xq1IFMJst2HSx92tMl87ppaWkYMmSIXufo0y/W1tbw9/eHv78/AGD16tUYNWoUZs2ahe3bt+sdX6bz58/Dz89Po+z27dswMTFB9erVAeStD/PT3zVq1MDcuXPh6+uL9u3b4+DBg6hfv36e7yUnZcqUUU8VLVXq1e6LKSkpePLkiUaisshN9Mu9Tn71bwY0rQqMWghcjwCcbYFF+4AMJTBzsGbdDtNV/95b+qpsSj9VIq1dAPBhNyA+GZi3E6jtAYx6bWOFdUeA+1FA4n9TIY5dB2arptlieBvAQ3PDEiIiIqI3AdccIyIykEqVKgEArl27plF+8+ZNpKeno3LlyrleoyjWHIuLi0NcnOaudmZmZur1vfTZVTPTmDFjIJfLMW/ePPX6XVllrr/Uv39/AKpF9bOuyXTy5EmcPn0aNWrUyNcui4DqmSUkJGit9SSXq/7+8/rIoZMnT+LECc2Fy93c3NCwYUNcuHBBZ3Iy67Wza08Xd3d3NGrUCMePH8eePXt0Xjdz7S19+yUiIkLrOpkJqNjY2Fxj0mXOnDka93Po0CFcvnwZPj4+6iRSXvowv/3dvHlzHDx4EEqlEh06dMC5c+fydT/Z8fX1hVKpxJdffqlRPnv2bCQlJRm0Lb3JTYAhLQF3p8Jrw9QU2PMlMKgFsGA38NlawNkOODwTqKbHX6nLOwNHv1KNJvtiPTD3T8Cvvmp9s9d3qVxxCJi2CfjmvyRtYLDq9bRNQNhTg98aERFRSZDXEfNkeBw5RkRkII6OjqhZsyb+/fdfHDhwABUqVMDz588RHByMsmXL6pUck8vlKFeuXKHGefHiRXTr1g2tW7dGjRo14OjoiOvXr+OPP/6Ai4sLevbsqfe1qlWrhv/973/qHSB79eoFT09PRERE4ODBg1ixYgXatWuHoUOHYtmyZQgMDESjRo3QpUsXREZGYvPmzTAzM8Mvv/yS7/tp2LAhzp07h/79+6NFixYwNTVFnz590LhxY5QvXx5r1qxBYmIivL29ERISgu3bt8PDwwN37tzRuM5vv/2G1q1b46233sL69evRoEEDJCYm4sKFCyhfvjzWrVuXY3seHh4641uzZg1atmyJnj17okuXLqhXrx4yMjJw9+5dBAYGonfv3li2bJne/dK6dWvY2tqicePG6vfYtm3bIJPJMGLEiHw9w8ePH6Nhw4bo2rUrHj9+jM2bN8Pc3Bw//fSTuk5e+rAg/d24cWMcOnQInTp1QufOnbFnzx40b948X/f1uilTpmDdunVYtGgR7t69i0aNGiEoKAj//PMPypQpU+jTbHVKVwKf9irYNY58lXudUgrgt/Gqr5xkHTGWVc0KwH4du2TmJxYiIiLScOPGjXzPoiADkXbJMyKikiUjI0NcvXpV/P7772L58uVi3bp14tSpUzoXCy8qry/IHxERIQYOHCg8PT2FlZWVMDMzE6VLlxYDBgwQt2/fzlcb69evFz4+PsLKykrI5XLh4uIiunXrprG4aEpKivjggw+Em5ubkMvlwsbGRjRr1kwcP35c41qZC7S/8847ud6LEEK8ePFC+Pn5CTs7OyGTyQQAsXXrViGEEMHBwaJVq1bC1tZWmJubi8qVK4vFixfrvI4QQoSGhoqePXsKJycnYWpqKuzs7ES9evU0FsXPqT1dC8YLIcSDBw/E0KFDRZkyZYRcLhdWVlaifPnyYsCAAeLUqVNCCP37ZebMmcLHx0fY29sLU1NTYW9vLxo2bCi2bNmid39lyoz3/v37omPHjsLGxkaYm5uLOnXqiIMHD2rV17cP81I3u/6+cuWKcHZ2Fra2tuLo0aPZPt/snrkQqgX5u3TpolF2//590aVLF417PXz4sKhUqZJwd3fP/aHlJi8L8pv2E6LdtIK3SURERMXa5cuXpQ7B6MmEKA4r1RIREREVjrS0NDg4OMDb2xsXL14s2MWqTVAtVK+vvV8CXQ27thoREREVL3fv3oWXl5fUYRg1rjlGRERERuPly5daZTNnzkRiYiLatm1bdIHIZEA1N6BLvaJrk4iIiN5IZcqUyb0SFSquOUZERFrCw8ORnp6eY51SpUpp7PhHb4bnz5/j+fPnOdaRy+X53gChuOvTpw9SUlLQpEkTWFpa4syZMzh8+DDKlCmDKVOmFF0gQgCT+6iSZERERGTUQkND4ePjI3UYRo3JMSIi0tKgQQOdu09m9c4772DZsmVFFBHp6/PPP8fy5ctzrOPs7JynXUlLks6dO+O3337D4sWLkZycDHt7e3Tr1g0LFiyAk1Mh7hj5OidbYFjromuPiIiIiLLFNceIiEjLrl27kJCQkGOdGjVqcNvpN9DVq1dx/fr1HOvY2NjkaVdSygN91hwzkQEzBwNfDiiamIiIiOiN9uzZMzg6OkodhlFjcoyIiIjIUPRJjlnIgYcrVKPHiIiIyOg9fvwYZcuWlToMo8YF+YmIiIiKitwEGNWBiTEiIiJSe/LkidQhGD0mx4iIiIiKSoYSmNRD6iiIiIiIKAsuyE9ERERUFExNgG4NgCpuUkdCREREb5DatWtLHYLR48gxIiIioqKQoQQ+6y11FERERPSGuXXrltQhGD0mx4iIiIgMxds9+2P1vYAW3kUXCxERERULycnJUodg9DitkoiIiMhQtnyK+ycvwcPDQ/uYgw0gkxV9TERERPRGs7XlRj1SkwkhhNRBEBEREZUUycnJsLS0lDoMIiIiKib42UF6nFZJREREZEA3b96UOgQiIiIqRvjZQXpMjhERERERERERkdFicoyIiIjIgMqVKyd1CERERFSM8LOD9JgcIyIiIjKgjIwMqUMgIiKiYoSfHaTH5BgRERGRAT1+/FjqEIiIiKgY4WcH6TE5RkRERERERERERksmhBBSB0FERERUUqSlpcHMzEzqMIiIiKiY4GcH6XHkGBEREZEBhYWFSR0CERERFSP87CA9JseIiIiIDCgxMVHqEIiIiKgY4WcH6TE5RkRERGRANjY2UodARERExQg/O0iPa44RERERGVBqairMzc2lDoOIiIiKCX52kB5HjhEREREZ0PXr16UOgYiIiIoRfnaQHpNjRERERERERERktJgcIyIiIjIgNzc3qUMgIiKiYoSfHaTH5BgRERGRAclkMqlDICIiomKEnx2kx+QYERERkQE9fPhQ6hCIiIioGOFnB+kxOUZEREREREREREZLJoQQUgdBREREVFKkpKTAwsJC6jCIiIiomOBnB+lx5BgRERGRAT148EDqEIiIiKgY4WcH6TE5RkRERGRA8fHxUodARERExQg/O0iPyTEiIiIiA7K0tJQ6BCIiIipG+NlBelxzjIiIiMiA0tPTIZfLpQ6DiIiIigl+dpAeR44RERERGVBwcLDUIRAREVExws8O0mNqkoiIiCgfMjIyEBoaqlV+9+5d7jhFREREeuNnh8JXtWpVmJqaZnucyTEiIiKifAgNDUWNGjWkDoOIiIiIcnH9+nVUr1492+Ncc4yIiIgoH3SNHIuMjET79u1x+PBhlClTRqLIjEN8fDwaN26Mc+fOQaFQSB0O5RH7r/hi3xVv7L/ii31XMLmNHGNyjIiIiMhAIiIiUL58eTx48ADlypWTOpwSLS4uDvb29njx4gXs7OykDofyiP1XfLHvijf2X/HFvitcXJCfiIiIiIiIiIiMFpNjRERERERERERktJgcIyIiIjIQOzs7tGnThtMdioCFhQWmT5/O3b2KKfZf8cW+K97Yf8UX+65wcc0xIiIiIiIiIiIyWhw5RkRERERERERERovJMSIiIiIiIiIiMlpMjhERERERERERkdFicoyIiIiIiIiIiIwWk2NERERE9EZQKpX46aef4O3tDUtLS5QvXx6ffPIJEhIS9Dr/m2++wYABA+Dl5QWZTAZPT8/CDZg0FKT/QkNDERAQgKZNm8LFxQW2trbw8fHB119/rXf/U/4VpO9CQkIwbNgwVK9eHfb29rC2toa3tzcmTZqEx48fF0H0VNCfnVklJiaqf4ZOmDChEKKlrAradzKZTOeXQqEo5MhLHrnUARARERERAcDHH3+MBQsWoE+fPvjkk09w48YNLFiwAJcvX8Y///wDE5Oc/647ZcoUODo6on79+oiNjS2aoEmtIP23cuVK/Prrr+jZsyeGDRsGMzMzBAYG4ssvv8SWLVtw5swZWFlZFeHdGJeC9F1ERAQeP36MPn36oFy5cpDL5bh27RqWLVuG33//HVeuXIGrq2sR3o3xKejPzqwCAgIQFRVViNFSVobou1atWmHs2LEaZWZmZoUVcskliIiIiIgkFhwcLGQymejbt69G+YIFCwQAsWHDhlyvcefOHfV/16xZU3h4eBg6TMpGQfvv/PnzIjY2Vqt86tSpAoD45ZdfDBovvWKI7z1dtmzZIgCI7777zhBhUjYM2X8XL14Upqam4ocffhAAxPjx4w0dLmVhiL4DIEaOHFlIERoXTqskIiIiIslt2rQJQgh89NFHGuXvvPMOrK2tsX79+lyv4eXlVUjRUW4K2n8NGzaEvb29VvmgQYMAAMHBwQaLlTQZ4ntPFw8PDwDA8+fPCxoi5cBQ/ZeRkYF33nkHXbt2Rd++fQshUnqdIb/3UlNTER8fb+AIjQuTY0REREQkufPnz8PExASNGzfWKLe0tISPjw/Onz8vUWSkj8Lqv4iICABA6dKlCxwj6WaovktOTkZ0dDQiIiJw4MABvPvuuwAAPz8/g8dMrxiq/3766SfcvHkTCxcuLIwwSQdD9d22bdtgbW0NW1tbuLq6YuLEiXjx4kVhhFyiMTlGRERERJJ79OgRnJ2dYWFhoXXM3d0d0dHRSE1NlSAy0kdh9F9GRga++uoryOVyDB061FCh0msM1Xe//fYbXFxcUL58eXTp0gWxsbFYv349WrVqVRhh038M0X9hYWGYPn06AgICuJFJETJE3zVu3BgzZszAtm3bsGbNGrRv3x4LFy5Eq1atOJIsj7ggPxERERFJLjExUecvCIDqr+iZdczNzYsyLNJTYfTfRx99hNOnT2POnDmoVq2aQeIkbYbqu969e8Pb2xvx8fG4fPkydu3ahejoaIPHS5oM0X/jxo2Dl5cXJk2aVCgxkm6G6LuzZ89qvB4xYgTq1KmDqVOn4ueff8bUqVMNF3AJx5FjRERERCQ5a2trpKSk6DyWnJysrkNvJkP337Rp07Bw4UKMHTsW//vf/wwSI+lmqL4rV64cOnbsiN69e2PmzJlYs2YNJk+ejG+++cag8ZKmgvbf+vXrcfDgQSxevJg7HBaxwvr/3meffQZzc3Ps3r27QPEZGybHiIiIiEhybm5uiI6O1vmLwsOHD+Hs7MxRY28wQ/bfjBkzMHv2bIwaNQpLliwxdKj0msL63qtTpw7q1auHRYsWGSJMykZB+i8lJQWTJk2Cn58fypQpg9u3b+P27du4f/8+AODFixe4ffs2YmNjC/MWjFZhfe+ZmZmpr036Y3KMiIiIiCTXqFEjKJVKnDt3TqM8OTkZV65cQcOGDSWKjPRhqP6bMWMGZs6ciZEjR+K3336DTCYrjHApi8L83ktKSsKzZ88KGiLloCD9l5SUhKioKOzevRtVqlRRf7Vt2xaAalRZlSpV8NtvvxXmLRitwvreS05ORkREBDcyySMmx4iIiIhIcoMGDYJMJsP8+fM1ypcvX47ExEQMGzZMXXbnzh3cvHmziCOknBii/2bNmoWZM2di+PDhWLlyJUxM+KtKUSho30VGRuq8bmBgIIKDg9G0aVODx0yvFKT/bGxssHXrVq2vzNF+Xbt2xdatW9GzZ88iuRdjU9DvvZiYGJ3XnTZtGtLT09GjRw+Dx1ySyYQQQuogiIiIiIgmTpyIhQsXok+fPvDz88ONGzewYMECtGjRAocPH1YnSzw9PXH//n28/jF23bp16ulAv/zyC1JTU/HJJ58AADw8PDB8+PCivSEjU5D++/XXXzFhwgRUqFABX331lVZirHTp0ujUqVOR3o8xKUjf9enTB48fP0b79u3h4eGB5ORkXLx4Eb///jusra1x5MgR+Pj4SHRnxqGgPztfd+/ePVSsWBHjx4/HwoULi+IWjFZB+u7jjz/GmTNn0K5dO1SoUAHx8fHYs2cPAgMD0aRJEwQGBsLKykqqWyt+BBERERHRGyA9PV18//33omrVqsLc3Fy4ubmJjz/+WLx8+VKjnoeHh9D1MbZNmzYCgM6vNm3aFNFdGK+C9N/IkSOz7Tv2X+ErSN9t3rxZdOvWTZQrV05YWFgIS0tLUa1aNTFhwgRx//79orwNo1XQn52vCwsLEwDE+PHjCytk+k9B+u7PP/8UnTt3Fm5ubsLCwkJYW1uLunXriq+//lokJSUV5W2UCBw5RkRERERERERERosT+YmIiIiIiIiIyGgxOUZEREREREREREaLyTEiIiIiIiIiIjJaTI4REREREREREZHRYnKMiIiIiIiIiIiMFpNjRERERERERERktJgcIyIiIiIiIiIio8XkGBERERERERERGS251AEQERERvUn8/f2xZs0aAEDNmjURHByscVypVGLOnDlYtWoVwsPDUaFCBdy5cwdz587FypUrcf36dZiY5P3vj0uWLMGcOXNw69YtWFhYaBybP38+Pv74Y/XrqKgoODs75+Pu8icjIwMXLlzArVu3kJKSAkdHRzRq1AjlypUz2PlpaWm4evUqnj59iqioKKSkpKBNmzaoVq2azmu+ePEC58+fx5MnT5CcnAyFQoHKlSujbt26kMv5EZeIiIj0x5FjREREZDT2798PmUyW7dfatWsBAM7Ozli3bh2+/fZbrWssWrQIAQEB6Nu3L1auXImlS5ciLi4O3333HT7//HOtxNjMmTNhYmKCGzduaF1r9OjRMDU1xe7du+Hv74/U1FQsXbpUq17Xrl2xbt069OnTx0BPIm+OHDmCoKAgVK5cGc2bN4eJiQn27t2LyMhIg52fnJyMS5cuITY2Fo6OjjleLz4+Hjt27MDTp09Rs2ZNNG/eHKVLl8bFixdx6NChAt0rERERGR/+WY2IiIiMxtWrVwEACxYsQKlSpbSOd+nSBYcPH4aNjQ3eeustnddYtWoVOnXqhHnz5qnL5s+fj/T0dAwZMkSr/nvvvYdvv/0W8+fP10h8/fLLL1i1ahVmz56Nbt26AQBGjhyJH3/8ERMnToRMJlPX9fb2hre3N27fvo0dO3bk7+bz6enTp7hz5w6aNGmCunXrAgCqVKmCbdu24ezZs+jVq5dBzre2tsZbb70Fa2trREVF5Xift27dQmpqKnr27KlOpFWvXh1CCPXotNdH3xERERFlh8kxIiIiMhpBQUGwt7fHhAkTNJJP+kpOTsbVq1cxc+ZMjfJVq1ahZ8+esLS01DrH1dUVw4YNw7p16/D111/D2dkZR48exaRJk9CvXz9MnTpVXXfgwIGYO3cuAgMD0b59+7zfYCG4e/cuZDIZqlevri6Ty+WoVq0azp8/j/j4eCgUigKfb2pqCmtra71iSk1NBQCt+tbW1pDJZPma1kpERETGi58ciIiIyGhcvXoV9erVy1dibMyYMbCyskJGRga+/PJLyGQyNGvWDGFhYQgKCkLHjh2zPffjjz9GUlISlixZggcPHmDgwIHw9vbG6tWrNeo1aNAAjo6O2LlzZ57j00WpVCI5OVmvLyGEzmvExMTA3t4e5ubmGuWurq7q4zkp6Pm6uLm5AQCOHj2K6OhoxMfH486dO7h+/Tpq1qwJMzOzPF+TiIiIjBdHjhEREZFRSE1NRUhICFq2bIno6Git4/b29jkmVYYNGwYzMzMsXboUP//8MxwdHeHh4YFTp04BAOrXr5/tuTVr1kTnzp3x66+/4s8//0RaWhr+/PNPnSOu6tevj5MnT+bjDrVFRkbi77//1qvukCFDYGtrq1WemJioc0RXZllCQkKO1y3o+bqUL18eDRs2xOXLl3H//n11eb169dCoUaM8X4+IiIiMG5NjREREZBSuX7+OtLQ0LFmyBEuWLNE6HhISgqpVq2Z7fvv27XHo0CHY2NhgwoQJ6ql706ZNAwBUrFgxx/YnTZqErl274unTp9izZw8qVaqks56XlxfWrVun723lyMnJCX5+fnrVtbKy0lmenp4OU1NTrfLMsoyMjByvW9Dzs2Nra4uyZcuiYsWKsLS0RHh4OC5fvgwrKyvUqlUrX9ckIiIi48TkGBERERmFoKAgAMDq1avh7u6udbxKlSp6XaNmzZoaa1rFxMRALpfnuO4WAPVulV5eXujSpUu29UqVKoWkpKRsR1zlhYWFBcqVK1ega8jlcp0JrMwyXYkvQ56vy+3bt3Hs2DEMGjRI/dwrVqwIIQTOnTuHypUr61z/jYiIiEgXJseIiIjIKFy9ehVyuRxDhgzRWv8qL9fIKbGVnX/++QeffvopqlSpglu3buHAgQPo3LmzzrqZa3/lZ12012VkZCAlJUWvupaWljoXsre2ttY59TExMREAYGNjk+N1C3q+LtevX4ezs7NWQtLDwwOhoaGIjo4ucFKQiIiIjAeTY0RERGQUgoKCULFixXwnxmJjY/HgwQPUrl1bo9zJyQnp6el4+fKlzjW77t69i0GDBqFevXr4559/ULVqVfz000/ZJseeP38Oa2vrbKc55sWTJ08KvOaYk5MTHj16hNTUVI1n9/TpU/XxnBT0fF2SkpJgYWGhVa5UKgEg280FiIiIiHRhcoyIiIiMQlBQEJo2bVqg8wGgTp06GuXe3t4AgLCwMK1j8fHx6NWrF8zMzLBjxw7Y29vj/fffx8yZM3Hjxg1Ur15dq52wsDCd5flhiDXHvLy8EBQUhBs3bqBu3boAVCPSQkJC4Orqqh69lZ6ejvj4eFhaWmpMadT3/Lywt7dHREQEYmNj4eDgoC6/c+cOZDIZHB0d83xNIiIiMl5MjhEREVGJFxkZiadPn6oTWflx9epVANrJsWbNmgEALly4oHFMCIHhw4cjJCQEgYGB6ml+77//Pr799lvMnz8fS5cu1Wrn0qVLGDZsWL7jzMoQa465urrCy8sL586dQ1JSEuzt7REaGoqXL1+iTZs26npPnz7F33//jfr166Nhw4Z5Ph8AgoODkZqaqp5yef/+ffWUzFq1aqlHntWtWxcPHjzAX3/9hZo1a8LCwgLh4eF48OABvL298zVVk4iIiIwXk2NERERU4mUmtqKiorB+/Xqt43Xr1tWaLvm6oKAguLu7a41K8vLyQq1atfDPP/9g9OjR6vIZM2bgzz//xNKlS9GiRQt1uYuLC9566y2sW7cOc+bM0ZhWePHiRTx79gy9evXK130WlrZt20KhUODWrVtITU2Fo6MjunbtirJlyxr0/KCgIMTHx6tf37t3D/fu3QOg2jAhMzlWtmxZ9OrVCxcvXsS///6LlJQU2NraolGjRurRaURERET6kgkuykBEREQl3Lx58zB58uRsj69duxbDhw8HAPj7++PIkSPqpEymJk2awMnJCXv27NE6/6effkJAQACePn0KKysr7NixA/369cO7776LxYsXa9W/fv06atWqha+++gpTp05Vl3/xxRfYtGkT7t27p3NB/hkzZmDmzJmIioqCs7OzvrdPRERERDlgcoyIiIgoC39/fxw+fBiXLl2CXC7XWNMqOy9evICXlxfmzp2LMWPG5KvdlJQUeHp64osvvsCHH36ocSw5ORnx8fGYO3cu5s2bx+QYERERkQFp79dNREREZOQePHgAFxcXtGzZUq/69vb2mDx5MubNm6feMTGvVq1aBTMzM4wbN07r2JIlS+Di4oJ58+bl69pERERElD2OHCMiIiLK4vr163j06BEAQKFQFGiHS0N58OABQkJC1K/btGkDMzMzCSMiIiIiKjmYHCMiIiIiIiIiIqPFaZVERERERERERGS0mBwjIiIiIiIiIiKjxeQYEREREREREREZLSbHiIiIiIiIiIjIaDE5RkRERERERERERovJMSIiIiIiIiIiMlpMjhERERERERERkdFicoyIiIiIiIiIiIwWk2NERERERERERGS0/g8nTZn7DYIOmwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show the individual prediction for the highest predicted instance\n", - "highest_pred_index = np.argmax(shap_values.values[:, 0]) \n", - "\n", - "# Use waterfall plot for a single instance\n", - "shap.plots.waterfall(shap_values[highest_pred_index], max_display=20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show the individual prediction for the lowest predicted instance\n", - "lowest_pred_index = np.argmin(shap_values.values[:, 0]) \n", - "\n", - "# Use waterfall plot for a single instance\n", - "shap.plots.waterfall(shap_values[lowest_pred_index], max_display=20)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/data_driven_risk_assessment/experiments/ddra_joaquin.ipynb b/data_driven_risk_assessment/experiments/ddra_joaquin.ipynb deleted file mode 100644 index a97e8e4..0000000 --- a/data_driven_risk_assessment/experiments/ddra_joaquin.ipynb +++ /dev/null @@ -1,7082 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "84dcd475", - "metadata": {}, - "source": [ - "# DDRA Joaquin\n", - "\n", - "## General Idea\n", - "The idea is to start with a very simple model with basic Booking attributes. This should serve as a first understanding of what can bring value in the data-driven risk assessment of new dash protected bookings.\n", - "\n", - "## Initial setup\n", - "This first section just ensures that the connection to DWH works correctly." - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "12368ce1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "šŸ”Œ Testing connection using credentials at: /home/joaquin/.superhog-dwh/credentials.yml\n", - "āœ… Connection successful.\n" - ] - } - ], - "source": [ - "# This script connects to a Data Warehouse (DWH) using PostgreSQL. \n", - "# This should be common for all Notebooks, but you might need to adjust the path to the `dwh_utils` module.\n", - "\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.abspath(\"../../utils\")) # Adjust path if needed\n", - "\n", - "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n", - "\n", - "# --- Connect to DWH ---\n", - "creds = read_credentials()\n", - "dwh_pg_engine = create_postgres_engine(creds)\n", - "\n", - "# --- Test Query ---\n", - "test_connection()" - ] - }, - { - "cell_type": "markdown", - "id": "c86f94f1", - "metadata": {}, - "source": [ - "## Data Extraction\n", - "In this section we extract the data for our first attempt on Basic Booking Attributes modelling.\n", - "\n", - "This SQL query retrieves a clean and relevant subset of booking data for our model. It includes:\n", - "- A **unique booking ID**\n", - "- Key **numeric features** such as number of services, time between booking creation and check-in, and number of nights\n", - "- Several **categorical (boolean) features** related to service usage\n", - "- A **target variable** (`has_resolution_incident`) indicating whether a resolution incident occurred\n", - "\n", - "Filters applied being:\n", - "1. Bookings from **\"New Dash\" users** with a valid deal ID\n", - "2. Only **protected bookings**, i.e., those with Protection or Deposit Management services\n", - "3. Bookings flagged for **risk categorisation** (excluding incomplete/rejected ones)\n", - "4. Bookings that are **already completed**\n", - "\n", - "The result is converted into a pandas DataFrame for further processing and modeling.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "3e3ed391", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialise all imports needed for the Notebook\n", - "from sklearn.model_selection import (\n", - " train_test_split, \n", - " GridSearchCV\n", - ")\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.feature_selection import RFE\n", - "from sklearn.linear_model import LogisticRegression\n", - "from imblearn.over_sampling import SMOTE\n", - "from sklearn.feature_selection import SelectKBest, f_classif\n", - "import pandas as pd\n", - "import numpy as np\n", - "from datetime import date\n", - "from sklearn.metrics import (\n", - " roc_auc_score, \n", - " average_precision_score,\n", - " classification_report,\n", - " roc_curve, \n", - " auc,\n", - " precision_recall_curve,\n", - " precision_score,\n", - " recall_score,\n", - " fbeta_score,\n", - " confusion_matrix\n", - ")\n", - "import matplotlib.pyplot as plt\n", - "import shap\n", - "import math" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "db5e3098", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " id_booking days_from_booking_creation_to_check_in number_of_nights \\\n", - "0 919656 26.0 4.0 \n", - "1 926634 17.0 3.0 \n", - "2 931082 20.0 7.0 \n", - "3 931086 15.0 3.0 \n", - "4 931096 8.0 5.0 \n", - "\n", - " host_town host_country host_postcode host_age host_months_with_truvi \\\n", - "0 Madison CT United States 06443 125.0 8.0 \n", - "1 Madison CT United States 06443 125.0 8.0 \n", - "2 London United Kingdom N16 6DD 125.0 8.0 \n", - "3 London United Kingdom N16 6DD 125.0 8.0 \n", - "4 London United Kingdom N16 6DD 125.0 8.0 \n", - "\n", - " host_account_type host_active_pms_list \\\n", - "0 Host Hostaway \n", - "1 Host Hostaway \n", - "2 PMC - Property Management Company Hostify \n", - "3 PMC - Property Management Company Hostify \n", - "4 PMC - Property Management Company Hostify \n", - "\n", - " number_of_listings_of_host number_of_previous_incidents_of_host \\\n", - "0 2 0 \n", - "1 2 0 \n", - "2 467 0 \n", - "3 467 0 \n", - "4 467 0 \n", - "\n", - " number_of_previous_payouts_of_host guest_town guest_country guest_postcode \\\n", - "0 0 NaN NaN NaN \n", - "1 0 NaN NaN NaN \n", - "2 0 NaN NaN NaN \n", - "3 0 NaN NaN NaN \n", - "4 0 NaN NaN NaN \n", - "\n", - " guest_age number_of_previous_bookings_of_guest \\\n", - "0 NaN 1032 \n", - "1 NaN 1900 \n", - "2 NaN 610 \n", - "3 NaN 136 \n", - "4 NaN 73 \n", - "\n", - " number_of_previous_incidents_of_guest \\\n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "\n", - " has_guest_previously_booked_same_listing \\\n", - "0 True \n", - "1 True \n", - "2 True \n", - "3 True \n", - "4 False \n", - "\n", - " listing_address listing_town \\\n", - "0 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "1 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "2 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "3 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "4 Aird House, 15 Wellesley Ct, Rockingham Street Greater London \n", - "\n", - " listing_country listing_postcode listing_number_of_bedrooms \\\n", - "0 United States 05464 2.0 \n", - "1 United States 05464 2.0 \n", - "2 United Kingdom BH1 3EE 12.0 \n", - "3 United Kingdom BH1 3EE 12.0 \n", - "4 United Kingdom SE1 6PD 2.0 \n", - "\n", - " listing_number_of_bathrooms \\\n", - "0 2.0 \n", - "1 2.0 \n", - "2 12.0 \n", - "3 12.0 \n", - "4 1.0 \n", - "\n", - " listing_description \\\n", - "0 Mountain Life Retreat at Smuggler's Notch Resort \n", - "1 Mountain Life Retreat at Smuggler's Notch Resort \n", - "2 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "3 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "4 Your London Home: 2BR Flat with Modern Amenities \n", - "\n", - " previous_bookings_in_listing_count \\\n", - "0 3 \n", - "1 5 \n", - "2 5 \n", - "3 2 \n", - "4 0 \n", - "\n", - " number_of_previous_incidents_in_listing \\\n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "\n", - " number_of_previous_payouts_in_listing days_to_start_verification \\\n", - "0 0 0.0 \n", - "1 0 0.0 \n", - "2 0 0.0 \n", - "3 0 0.0 \n", - "4 0 0.0 \n", - "\n", - " days_to_complete_verification screening_status government_id_status \\\n", - "0 0.0 NaN NaN \n", - "1 0.0 NaN NaN \n", - "2 0.0 NaN NaN \n", - "3 0.0 NaN NaN \n", - "4 0.0 NaN NaN \n", - "\n", - " contract_status selfie_confidence_score_status payment_validation_status \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " first_name_status date_of_birth_status last_name_status \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " autohost_partner_status criminal_record_status guest_csat_score \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " guest_csat_comments guest_has_email guest_has_phone_number \\\n", - "0 NaN False False \n", - "1 NaN False False \n", - "2 NaN False False \n", - "3 NaN False False \n", - "4 NaN False False \n", - "\n", - " is_guest_from_listing_town is_guest_from_listing_country \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " is_guest_from_listing_postcode is_host_from_listing_town \\\n", - "0 NaN False \n", - "1 NaN False \n", - "2 NaN False \n", - "3 NaN False \n", - "4 NaN False \n", - "\n", - " is_host_from_listing_country is_host_from_listing_postcode \\\n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "\n", - " has_completed_verification number_of_applied_services \\\n", - "0 False 3 \n", - "1 False 3 \n", - "2 False 2 \n", - "3 False 2 \n", - "4 False 2 \n", - "\n", - " number_of_applied_upgraded_services number_of_applied_billable_services \\\n", - "0 2 2 \n", - "1 2 2 \n", - "2 1 1 \n", - "3 1 1 \n", - "4 1 1 \n", - "\n", - " booking_days_to_check_in booking_number_of_nights \\\n", - "0 87 4 \n", - "1 109 3 \n", - "2 50 7 \n", - "3 15 3 \n", - "4 8 5 \n", - "\n", - " has_verification_request has_billable_services \\\n", - "0 False True \n", - "1 False True \n", - "2 False True \n", - "3 False True \n", - "4 False True \n", - "\n", - " has_upgraded_screening_service_business_type \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_deposit_management_service_business_type \\\n", - "0 True \n", - "1 True \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_protection_service_business_type has_resolution_incident \n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "Total Bookings: 21,307\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_51877/805553034.py:455: DtypeWarning: Columns (50) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df_extraction = pd.read_csv(\"/home/joaquin/data-jupyter-notebooks/data_driven_risk_assessment/experiments/data.csv\")\n" - ] - } - ], - "source": [ - "# Query to extract data\n", - "data_extraction_query = \"\"\"\n", - "with\n", - " int_core__verification_requests as (\n", - " select *\n", - " from intermediate.int_core__verification_requests\n", - " where created_date_utc >= '2024-10-21'\n", - " ),\n", - " int_core__bookings as (\n", - " select *\n", - " from intermediate.int_core__bookings\n", - " where created_date_utc >= '2024-10-21'\n", - " ),\n", - " stg_core__verification as (\n", - " select *\n", - " from staging.stg_core__verification\n", - " where created_date_utc >= '2024-10-21'\n", - " ),\n", - " int_core__guest_journey_payments as (\n", - " select *\n", - " from intermediate.int_core__guest_journey_payments\n", - " where payment_due_date_utc >= '2024-10-21'\n", - " ),\n", - " filtered_bookings as (\n", - " select *\n", - " from intermediate.int_booking_summary\n", - " where\n", - " is_user_in_new_dash = true\n", - " and is_missing_id_deal = false\n", - " and (\n", - " has_protection_service_business_type\n", - " or has_deposit_management_service_business_type\n", - " )\n", - " and is_booking_flagged_as_risk is not null\n", - " and is_booking_past_completion_date = true\n", - " and booking_created_date_utc < '2025-06-25'\n", - " ),\n", - " previous_booking_counts as (\n", - " select\n", - " id_booking,\n", - " id_accommodation,\n", - " id_user_guest,\n", - " booking_check_in_date_utc,\n", - " booking_check_out_date_utc,\n", - " count(*) over (\n", - " partition by id_accommodation\n", - " order by booking_check_in_date_utc\n", - " rows between unbounded preceding and 1 preceding\n", - " ) as previous_bookings_in_listing_count,\n", - " count(*) over (\n", - " partition by id_user_guest\n", - " order by booking_check_in_date_utc\n", - " rows between unbounded preceding and 1 preceding\n", - " ) as previous_guest_bookings_count\n", - " from filtered_bookings\n", - " ),\n", - " listing_info as (\n", - " select\n", - " id_accommodation,\n", - " address_line_1 as listing_address,\n", - " town as listing_town,\n", - " country_name as listing_country,\n", - " postcode as listing_postcode,\n", - " number_of_bedrooms,\n", - " number_of_bathrooms,\n", - " friendly_name as listing_description,\n", - " id_user_host\n", - " from intermediate.int_core__accommodation\n", - " ),\n", - " host_info as (\n", - " select\n", - " scu.id_user as id_user_host,\n", - " icuh.account_type,\n", - " icuh.active_pms_list,\n", - " scc.country_name,\n", - " scu.billing_town,\n", - " scu.billing_postcode,\n", - " scu.id_billing_country,\n", - " extract(year from age(current_date, scu.date_of_birth)) as host_age,\n", - " extract(\n", - " month from age(current_date, scu.joined_date_utc)\n", - " ) as host_months_with_truvi\n", - " from staging.stg_core__user scu\n", - " left join\n", - " staging.stg_core__country scc on scu.id_billing_country = scc.id_country\n", - " left join\n", - " intermediate.int_core__user_host icuh on icuh.id_user_host = scu.id_user\n", - " ),\n", - " guest_info as (\n", - " select\n", - " scu.id_user as id_user_guest,\n", - " scc.country_name,\n", - " scu.billing_town,\n", - " scu.billing_postcode,\n", - " scu.id_billing_country,\n", - " extract(year from age(current_date, scu.date_of_birth)) as guest_age,\n", - " scu.email,\n", - " scu.phone_number\n", - " from staging.stg_core__user scu\n", - " left join\n", - " staging.stg_core__country scc on scu.id_billing_country = scc.id_country\n", - " ),\n", - " host_listing_counts as (\n", - " select id_user_host, count(*) as number_of_listings_of_host\n", - " from intermediate.int_core__accommodation\n", - " where is_active = true\n", - " group by id_user_host\n", - " ),\n", - " listing_incident_counts as (\n", - " select\n", - " i.created_date_utc::date as date_day,\n", - " i.id_accommodation,\n", - " count(*) over (\n", - " partition by i.id_accommodation\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_incidents_in_listing,\n", - " count(i.calculated_payout_amount_in_txn_currency) over (\n", - " partition by i.id_accommodation\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_payouts_in_listing\n", - " from intermediate.int_resolutions__incidents i\n", - " where\n", - " i.id_accommodation is not null\n", - " and i.created_date_utc::date between '2024-10-21' and current_date\n", - " order by i.id_accommodation, date_day\n", - " ),\n", - " guest_incident_counts as (\n", - " select\n", - " i.created_date_utc::date as date_day,\n", - " i.id_user_guest,\n", - " count(*) over (\n", - " partition by i.id_user_guest\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_incidents_of_guest\n", - " from intermediate.int_resolutions__incidents i\n", - " where\n", - " i.id_user_guest is not null\n", - " and i.created_date_utc::date between '2024-10-21' and current_date\n", - " order by i.id_user_guest, date_day\n", - " ),\n", - " host_incident_counts as (\n", - " select\n", - " i.created_date_utc::date as date_day,\n", - " i.id_user_host,\n", - " count(*) over (\n", - " partition by i.id_user_host\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_incidents_of_host,\n", - " count(i.calculated_payout_amount_in_txn_currency) over (\n", - " partition by i.id_user_host\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_payouts_of_host\n", - " from intermediate.int_resolutions__incidents i\n", - " where\n", - " i.id_user_host is not null\n", - " and i.created_date_utc::date between '2024-10-21' and current_date\n", - " order by i.id_user_host, date_day\n", - " ),\n", - " verification_requests as (\n", - " select\n", - " icvr.id_verification_request,\n", - " extract(\n", - " day\n", - " from\n", - " age(\n", - " icvr.verification_estimated_started_date_utc,\n", - " icb.created_date_utc\n", - " )\n", - " ) as days_to_start_verification,\n", - " extract(\n", - " day\n", - " from\n", - " age(\n", - " icvr.verification_estimated_completed_date_utc,\n", - " icvr.verification_estimated_started_date_utc\n", - " )\n", - " ) as days_to_complete_verification,\n", - " -- CSAT Results\n", - " gsr.experience_rating as guest_csat_score,\n", - " gsr.guest_comments as guest_csat_comments,\n", - " -- GUEST_PRODUCT fields\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'GUEST_PRODUCT' then product_name\n", - " end\n", - " ) as guest_product_name,\n", - " max(\n", - " case when guest_journey_product_type = 'GUEST_PRODUCT' then currency end\n", - " ) as guest_currency,\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'GUEST_PRODUCT'\n", - " then total_amount_in_txn_currency\n", - " end\n", - " ) as guest_total_amount,\n", - " -- VERIFICATION_PRODUCT fields\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'VERIFICATION_PRODUCT'\n", - " then product_name\n", - " end\n", - " ) as verification_product_name,\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'VERIFICATION_PRODUCT'\n", - " then currency\n", - " end\n", - " ) as verification_currency,\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'VERIFICATION_PRODUCT'\n", - " then total_amount_in_txn_currency\n", - " end\n", - " ) as verification_total_amount,\n", - " -- Verification Results\n", - " max(\n", - " case when scv.verification = 'Screening' then id_verification_status end\n", - " ) as screening_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'GovernmentId' then id_verification_status\n", - " end\n", - " ) as government_id_status,\n", - " max(\n", - " case when scv.verification = 'Contract' then id_verification_status end\n", - " ) as contract_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'SelfieConfidenceScore'\n", - " then id_verification_status\n", - " end\n", - " ) as selfie_confidence_score_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'PaymentValidation'\n", - " then id_verification_status\n", - " end\n", - " ) as payment_validation_status,\n", - " max(\n", - " case when scv.verification = 'FirstName' then id_verification_status end\n", - " ) as first_name_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'DateOfBirth' then id_verification_status\n", - " end\n", - " ) as date_of_birth_status,\n", - " max(\n", - " case when scv.verification = 'LastName' then id_verification_status end\n", - " ) as last_name_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'AutohostPartner'\n", - " then id_verification_status\n", - " end\n", - " ) as autohost_partner_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'CriminalRecord' then id_verification_status\n", - " end\n", - " ) as criminal_record_status\n", - " from int_core__verification_requests icvr\n", - " left join\n", - " int_core__bookings icb\n", - " on icb.id_verification_request = icvr.id_verification_request\n", - " left join\n", - " stg_core__verification scv\n", - " on scv.id_verification_request = icvr.id_verification_request\n", - " left join\n", - " int_core__guest_journey_payments gjp\n", - " on gjp.id_verification_request = icb.id_verification_request\n", - " left join\n", - " intermediate.int_core__guest_satisfaction_responses gsr\n", - " on gsr.id_verification_request = icvr.id_verification_request\n", - " and scv.verification in (\n", - " 'Screening',\n", - " 'GovernmentId',\n", - " 'Contract',\n", - " 'SelfieConfidenceScore',\n", - " 'PaymentValidation',\n", - " 'FirstName',\n", - " 'DateOfBirth',\n", - " 'LastName',\n", - " 'AutohostPartner',\n", - " 'CriminalRecord'\n", - " )\n", - " group by 1, 2, 3, 4, 5\n", - " )\n", - "select\n", - " fb.id_booking,\n", - " extract(day from age(fb.booking_check_in_date_utc, fb.booking_created_date_utc)) as days_from_booking_creation_to_check_in,\n", - " extract(day from age(fb.booking_check_out_date_utc, fb.booking_check_in_date_utc)) as number_of_nights,\n", - " -- Host Info\n", - " hi.billing_town as host_town,\n", - " hi.country_name as host_country,\n", - " hi.billing_postcode as host_postcode,\n", - " hi.host_age,\n", - " hi.host_months_with_truvi,\n", - " hi.account_type as host_account_type,\n", - " hi.active_pms_list as host_active_pms_list,\n", - " coalesce(hlc.number_of_listings_of_host, 0) as number_of_listings_of_host,\n", - " coalesce(\n", - " hic.number_of_previous_incidents_of_host, 0\n", - " ) as number_of_previous_incidents_of_host,\n", - " coalesce(\n", - " hic.number_of_previous_payouts_of_host, 0\n", - " ) as number_of_previous_payouts_of_host,\n", - " -- Guest Info\n", - " gi.billing_town as guest_town,\n", - " gi.country_name as guest_country,\n", - " gi.billing_postcode as guest_postcode,\n", - " gi.guest_age,\n", - " coalesce(\n", - " pbc.previous_guest_bookings_count, 0\n", - " ) as number_of_previous_bookings_of_guest,\n", - " coalesce(\n", - " gic.number_of_previous_incidents_of_guest, 0\n", - " ) as number_of_previous_incidents_of_guest,\n", - " case\n", - " when pbc.previous_bookings_in_listing_count > 0 then true else false\n", - " end as has_guest_previously_booked_same_listing,\n", - " -- Listing Info\n", - " li.listing_address,\n", - " li.listing_town,\n", - " li.listing_country,\n", - " li.listing_postcode,\n", - " li.number_of_bedrooms as listing_number_of_bedrooms,\n", - " li.number_of_bathrooms as listing_number_of_bathrooms,\n", - " li.listing_description,\n", - " coalesce(pbc.previous_bookings_in_listing_count, 0) as previous_bookings_in_listing_count,\n", - " coalesce(lic.number_of_previous_incidents_in_listing, 0) as number_of_previous_incidents_in_listing,\n", - " coalesce(lic.number_of_previous_payouts_in_listing, 0) as number_of_previous_payouts_in_listing,\n", - " -- Verification Info\n", - " case\n", - " when fb.id_verification_request is null then 0\n", - " else vr.days_to_start_verification\n", - " end as days_to_start_verification,\n", - " case \n", - " when vr.id_verification_request is null then 0\n", - " else vr.days_to_complete_verification\n", - " end as days_to_complete_verification,\n", - " vr.screening_status,\n", - " vr.government_id_status,\n", - " vr.contract_status,\n", - " vr.selfie_confidence_score_status,\n", - " vr.payment_validation_status,\n", - " vr.first_name_status,\n", - " vr.date_of_birth_status,\n", - " vr.last_name_status,\n", - " vr.autohost_partner_status,\n", - " vr.criminal_record_status,\n", - " vr.guest_csat_score,\n", - " vr.guest_csat_comments,\n", - " -- Boolean features\n", - " gi.email is not null as guest_has_email,\n", - " gi.phone_number is not null as guest_has_phone_number,\n", - " case \n", - " when gi.billing_town is null or li.listing_town is null then null \n", - " when gi.billing_town = li.listing_town \n", - " then true else false \n", - " end as is_guest_from_listing_town,\n", - " case \n", - " when gi.country_name is null or li.listing_country is null then null\n", - " when gi.country_name = li.listing_country \n", - " then true else false \n", - " end as is_guest_from_listing_country,\n", - " case \n", - " when gi.billing_postcode is null or li.listing_postcode is null then null\n", - " when gi.billing_postcode = li.listing_postcode \n", - " then true else false \n", - " end as is_guest_from_listing_postcode,\n", - " case \n", - " when hi.billing_town is null or li.listing_town is null then null\n", - " when hi.billing_town = li.listing_town \n", - " then true else false \n", - " end as is_host_from_listing_town,\n", - " case \n", - " when hi.country_name is null or li.listing_country is null then null\n", - " when hi.country_name = li.listing_country \n", - " then true else false \n", - " end as is_host_from_listing_country,\n", - " case \n", - " when hi.billing_postcode is null or li.listing_postcode is null then null\n", - " when hi.billing_postcode = li.listing_postcode \n", - " then true else false \n", - " end as is_host_from_listing_postcode,\n", - " case\n", - " when vr.days_to_complete_verification is null then false\n", - " else true\n", - " end as has_completed_verification,\n", - " -- Numeric features\n", - " fb.number_of_applied_services,\n", - " fb.number_of_applied_upgraded_services,\n", - " fb.number_of_applied_billable_services,\n", - " fb.booking_check_in_date_utc\n", - " - fb.booking_created_date_utc as booking_days_to_check_in,\n", - " fb.booking_number_of_nights,\n", - " -- Categorical features\n", - " fb.has_verification_request,\n", - " fb.has_billable_services,\n", - " fb.has_upgraded_screening_service_business_type,\n", - " fb.has_deposit_management_service_business_type,\n", - " fb.has_protection_service_business_type,\n", - " -- Target\n", - " fb.has_resolution_incident\n", - "from filtered_bookings fb\n", - "left join previous_booking_counts pbc on fb.id_booking = pbc.id_booking\n", - "left join listing_info li on li.id_accommodation = fb.id_accommodation\n", - "left join host_info hi on hi.id_user_host = fb.id_user_host\n", - "left join guest_info gi on gi.id_user_guest = fb.id_user_guest\n", - "left join host_listing_counts hlc on li.id_user_host = hlc.id_user_host\n", - "left join\n", - " lateral(\n", - " select *\n", - " from listing_incident_counts lic\n", - " where\n", - " lic.id_accommodation = fb.id_accommodation\n", - " and lic.date_day <= fb.booking_check_in_date_utc\n", - " order by lic.date_day desc\n", - " limit 1\n", - " ) lic\n", - " on true\n", - "left join\n", - " lateral(\n", - " select *\n", - " from guest_incident_counts gic\n", - " where\n", - " gic.id_user_guest = fb.id_user_guest\n", - " and gic.date_day <= fb.booking_check_in_date_utc\n", - " order by gic.date_day desc\n", - " limit 1\n", - " ) gic\n", - " on true\n", - "left join\n", - " lateral(\n", - " select *\n", - " from host_incident_counts hic\n", - " where\n", - " hic.id_user_host = fb.id_user_host\n", - " and hic.date_day <= fb.booking_check_in_date_utc\n", - " order by hic.date_day desc\n", - " limit 1\n", - " ) hic\n", - " on true\n", - "left join\n", - " verification_requests vr on vr.id_verification_request = fb.id_verification_request\n", - "\"\"\"\n", - "\n", - "# Retrieve Data from Query\n", - "# df_extraction = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n", - "df_extraction = pd.read_csv(\"/home/joaquin/data-jupyter-notebooks/data_driven_risk_assessment/experiments/data.csv\")\n", - "print(df_extraction.head())\n", - "print(f\"Total Bookings: {len(df_extraction):,}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "b56a8530", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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id_bookingdays_from_booking_creation_to_check_innumber_of_nightshost_townhost_countryhost_postcodehost_agehost_months_with_truvihost_account_typehost_active_pms_listnumber_of_listings_of_hostnumber_of_previous_incidents_of_hostnumber_of_previous_payouts_of_hostguest_townguest_countryguest_postcodeguest_agenumber_of_previous_bookings_of_guestnumber_of_previous_incidents_of_guesthas_guest_previously_booked_same_listinglisting_addresslisting_townlisting_countrylisting_postcodelisting_number_of_bedroomslisting_number_of_bathroomslisting_descriptionprevious_bookings_in_listing_countnumber_of_previous_incidents_in_listingnumber_of_previous_payouts_in_listingdays_to_start_verificationdays_to_complete_verificationscreening_statusgovernment_id_statuscontract_statusselfie_confidence_score_statuspayment_validation_statusfirst_name_statusdate_of_birth_statuslast_name_statusautohost_partner_statuscriminal_record_statusguest_csat_scoreguest_csat_commentsguest_has_emailguest_has_phone_numberis_guest_from_listing_townis_guest_from_listing_countryis_guest_from_listing_postcodeis_host_from_listing_townis_host_from_listing_countryis_host_from_listing_postcodehas_completed_verificationnumber_of_applied_servicesnumber_of_applied_upgraded_servicesnumber_of_applied_billable_servicesbooking_days_to_check_inbooking_number_of_nightshas_verification_requesthas_billable_serviceshas_upgraded_screening_service_business_typehas_deposit_management_service_business_typehas_protection_service_business_typehas_resolution_incident
091965626.04.0Madison CTUnited States06443125.08.0HostHostaway200NaNNaNNaNNaN10320True389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States054642.02.0Mountain Life Retreat at Smuggler's Notch Resort3000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse322874FalseTrueFalseTrueTrueFalse
192663417.03.0Madison CTUnited States06443125.08.0HostHostaway200NaNNaNNaNNaN19000True389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States054642.02.0Mountain Life Retreat at Smuggler's Notch Resort5000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse3221093FalseTrueFalseTrueTrueFalse
293108220.07.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify46700NaNNaNNaNNaN6100TrueTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EE12.012.0Mansion by the Sea, 12BR/12BA, Perfect for Events5000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse211507FalseTrueFalseFalseTrueFalse
393108615.03.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify46700NaNNaNNaNNaN1360TrueTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EE12.012.0Mansion by the Sea, 12BR/12BA, Perfect for Events2000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse211153FalseTrueFalseFalseTrueFalse
49310968.05.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify46700NaNNaNNaNNaN730FalseAird House, 15 Wellesley Ct, Rockingham StreetGreater LondonUnited KingdomSE1 6PD2.01.0Your London Home: 2BR Flat with Modern Amenities0000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse21185FalseTrueFalseFalseTrueFalse
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" - ], - "text/plain": [ - " id_booking days_from_booking_creation_to_check_in number_of_nights \\\n", - "0 919656 26.0 4.0 \n", - "1 926634 17.0 3.0 \n", - "2 931082 20.0 7.0 \n", - "3 931086 15.0 3.0 \n", - "4 931096 8.0 5.0 \n", - "\n", - " host_town host_country host_postcode host_age host_months_with_truvi \\\n", - "0 Madison CT United States 06443 125.0 8.0 \n", - "1 Madison CT United States 06443 125.0 8.0 \n", - "2 London United Kingdom N16 6DD 125.0 8.0 \n", - "3 London United Kingdom N16 6DD 125.0 8.0 \n", - "4 London United Kingdom N16 6DD 125.0 8.0 \n", - "\n", - " host_account_type host_active_pms_list \\\n", - "0 Host Hostaway \n", - "1 Host Hostaway \n", - "2 PMC - Property Management Company Hostify \n", - "3 PMC - Property Management Company Hostify \n", - "4 PMC - Property Management Company Hostify \n", - "\n", - " number_of_listings_of_host number_of_previous_incidents_of_host \\\n", - "0 2 0 \n", - "1 2 0 \n", - "2 467 0 \n", - "3 467 0 \n", - "4 467 0 \n", - "\n", - " number_of_previous_payouts_of_host guest_town guest_country guest_postcode \\\n", - "0 0 NaN NaN NaN \n", - "1 0 NaN NaN NaN \n", - "2 0 NaN NaN NaN \n", - "3 0 NaN NaN NaN \n", - "4 0 NaN NaN NaN \n", - "\n", - " guest_age number_of_previous_bookings_of_guest \\\n", - "0 NaN 1032 \n", - "1 NaN 1900 \n", - "2 NaN 610 \n", - "3 NaN 136 \n", - "4 NaN 73 \n", - "\n", - " number_of_previous_incidents_of_guest \\\n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "\n", - " has_guest_previously_booked_same_listing \\\n", - "0 True \n", - "1 True \n", - "2 True \n", - "3 True \n", - "4 False \n", - "\n", - " listing_address listing_town \\\n", - "0 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "1 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "2 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "3 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "4 Aird House, 15 Wellesley Ct, Rockingham Street Greater London \n", - "\n", - " listing_country listing_postcode listing_number_of_bedrooms \\\n", - "0 United States 05464 2.0 \n", - "1 United States 05464 2.0 \n", - "2 United Kingdom BH1 3EE 12.0 \n", - "3 United Kingdom BH1 3EE 12.0 \n", - "4 United Kingdom SE1 6PD 2.0 \n", - "\n", - " listing_number_of_bathrooms \\\n", - "0 2.0 \n", - "1 2.0 \n", - "2 12.0 \n", - "3 12.0 \n", - "4 1.0 \n", - "\n", - " listing_description \\\n", - "0 Mountain Life Retreat at Smuggler's Notch Resort \n", - "1 Mountain Life Retreat at Smuggler's Notch Resort \n", - "2 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "3 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "4 Your London Home: 2BR Flat with Modern Amenities \n", - "\n", - " previous_bookings_in_listing_count \\\n", - "0 3 \n", - "1 5 \n", - "2 5 \n", - "3 2 \n", - "4 0 \n", - "\n", - " number_of_previous_incidents_in_listing \\\n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "\n", - " number_of_previous_payouts_in_listing days_to_start_verification \\\n", - "0 0 0.0 \n", - "1 0 0.0 \n", - "2 0 0.0 \n", - "3 0 0.0 \n", - "4 0 0.0 \n", - "\n", - " days_to_complete_verification screening_status government_id_status \\\n", - "0 0.0 NaN NaN \n", - "1 0.0 NaN NaN \n", - "2 0.0 NaN NaN \n", - "3 0.0 NaN NaN \n", - "4 0.0 NaN NaN \n", - "\n", - " contract_status selfie_confidence_score_status payment_validation_status \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " first_name_status date_of_birth_status last_name_status \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " autohost_partner_status criminal_record_status guest_csat_score \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " guest_csat_comments guest_has_email guest_has_phone_number \\\n", - "0 NaN False False \n", - "1 NaN False False \n", - "2 NaN False False \n", - "3 NaN False False \n", - "4 NaN False False \n", - "\n", - " is_guest_from_listing_town is_guest_from_listing_country \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " is_guest_from_listing_postcode is_host_from_listing_town \\\n", - "0 NaN False \n", - "1 NaN False \n", - "2 NaN False \n", - "3 NaN False \n", - "4 NaN False \n", - "\n", - " is_host_from_listing_country is_host_from_listing_postcode \\\n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "\n", - " has_completed_verification number_of_applied_services \\\n", - "0 False 3 \n", - "1 False 3 \n", - "2 False 2 \n", - "3 False 2 \n", - "4 False 2 \n", - "\n", - " number_of_applied_upgraded_services number_of_applied_billable_services \\\n", - "0 2 2 \n", - "1 2 2 \n", - "2 1 1 \n", - "3 1 1 \n", - "4 1 1 \n", - "\n", - " booking_days_to_check_in booking_number_of_nights \\\n", - "0 87 4 \n", - "1 109 3 \n", - "2 50 7 \n", - "3 15 3 \n", - "4 8 5 \n", - "\n", - " has_verification_request has_billable_services \\\n", - "0 False True \n", - "1 False True \n", - "2 False True \n", - "3 False True \n", - "4 False True \n", - "\n", - " has_upgraded_screening_service_business_type \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_deposit_management_service_business_type \\\n", - "0 True \n", - "1 True \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_protection_service_business_type has_resolution_incident \n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False " - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_extraction.head()" - ] - }, - { - "cell_type": "markdown", - "id": "e9a9da26", - "metadata": {}, - "source": [ - "## Exploratory Data Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "f4545e95", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset size: 21,307 rows and 63 columns\n" - ] - } - ], - "source": [ - "# Copy dataset to make changes and drop id_booking column\n", - "df = df_extraction.copy().drop(columns=['id_booking'])\n", - "\n", - "# Check size of the dataset\n", - "print(f\"Dataset size: {df.shape[0]:,} rows and {df.shape[1]:,} columns\")" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "de574969", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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days_from_booking_creation_to_check_innumber_of_nightshost_townhost_countryhost_postcodehost_agehost_months_with_truvihost_account_typehost_active_pms_listnumber_of_listings_of_hostnumber_of_previous_incidents_of_hostnumber_of_previous_payouts_of_hostguest_townguest_countryguest_postcodeguest_agenumber_of_previous_bookings_of_guestnumber_of_previous_incidents_of_guesthas_guest_previously_booked_same_listinglisting_addresslisting_townlisting_countrylisting_postcodelisting_number_of_bedroomslisting_number_of_bathroomslisting_descriptionprevious_bookings_in_listing_countnumber_of_previous_incidents_in_listingnumber_of_previous_payouts_in_listingdays_to_start_verificationdays_to_complete_verificationscreening_statusgovernment_id_statuscontract_statusselfie_confidence_score_statuspayment_validation_statusfirst_name_statusdate_of_birth_statuslast_name_statusautohost_partner_statuscriminal_record_statusguest_csat_scoreguest_csat_commentsguest_has_emailguest_has_phone_numberis_guest_from_listing_townis_guest_from_listing_countryis_guest_from_listing_postcodeis_host_from_listing_townis_host_from_listing_countryis_host_from_listing_postcodehas_completed_verificationnumber_of_applied_servicesnumber_of_applied_upgraded_servicesnumber_of_applied_billable_servicesbooking_days_to_check_inbooking_number_of_nightshas_verification_requesthas_billable_serviceshas_upgraded_screening_service_business_typehas_deposit_management_service_business_typehas_protection_service_business_typehas_resolution_incident
026.04.0Madison CTUnited States06443125.08.0HostHostaway200NaNNaNNaNNaN10320True389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States054642.02.0Mountain Life Retreat at Smuggler's Notch Resort3000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse322874FalseTrueFalseTrueTrueFalse
117.03.0Madison CTUnited States06443125.08.0HostHostaway200NaNNaNNaNNaN19000True389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States054642.02.0Mountain Life Retreat at Smuggler's Notch Resort5000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse3221093FalseTrueFalseTrueTrueFalse
220.07.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify46700NaNNaNNaNNaN6100TrueTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EE12.012.0Mansion by the Sea, 12BR/12BA, Perfect for Events5000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse211507FalseTrueFalseFalseTrueFalse
315.03.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify46700NaNNaNNaNNaN1360TrueTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EE12.012.0Mansion by the Sea, 12BR/12BA, Perfect for Events2000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse211153FalseTrueFalseFalseTrueFalse
48.05.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify46700NaNNaNNaNNaN730FalseAird House, 15 Wellesley Ct, Rockingham StreetGreater LondonUnited KingdomSE1 6PD2.01.0Your London Home: 2BR Flat with Modern Amenities0000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse21185FalseTrueFalseFalseTrueFalse
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" - ], - "text/plain": [ - " days_from_booking_creation_to_check_in number_of_nights host_town \\\n", - "0 26.0 4.0 Madison CT \n", - "1 17.0 3.0 Madison CT \n", - "2 20.0 7.0 London \n", - "3 15.0 3.0 London \n", - "4 8.0 5.0 London \n", - "\n", - " host_country host_postcode host_age host_months_with_truvi \\\n", - "0 United States 06443 125.0 8.0 \n", - "1 United States 06443 125.0 8.0 \n", - "2 United Kingdom N16 6DD 125.0 8.0 \n", - "3 United Kingdom N16 6DD 125.0 8.0 \n", - "4 United Kingdom N16 6DD 125.0 8.0 \n", - "\n", - " host_account_type host_active_pms_list \\\n", - "0 Host Hostaway \n", - "1 Host Hostaway \n", - "2 PMC - Property Management Company Hostify \n", - "3 PMC - Property Management Company Hostify \n", - "4 PMC - Property Management Company Hostify \n", - "\n", - " number_of_listings_of_host number_of_previous_incidents_of_host \\\n", - "0 2 0 \n", - "1 2 0 \n", - "2 467 0 \n", - "3 467 0 \n", - "4 467 0 \n", - "\n", - " number_of_previous_payouts_of_host guest_town guest_country guest_postcode \\\n", - "0 0 NaN NaN NaN \n", - "1 0 NaN NaN NaN \n", - "2 0 NaN NaN NaN \n", - "3 0 NaN NaN NaN \n", - "4 0 NaN NaN NaN \n", - "\n", - " guest_age number_of_previous_bookings_of_guest \\\n", - "0 NaN 1032 \n", - "1 NaN 1900 \n", - "2 NaN 610 \n", - "3 NaN 136 \n", - "4 NaN 73 \n", - "\n", - " number_of_previous_incidents_of_guest \\\n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "\n", - " has_guest_previously_booked_same_listing \\\n", - "0 True \n", - "1 True \n", - "2 True \n", - "3 True \n", - "4 False \n", - "\n", - " listing_address listing_town \\\n", - "0 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "1 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "2 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "3 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "4 Aird House, 15 Wellesley Ct, Rockingham Street Greater London \n", - "\n", - " listing_country listing_postcode listing_number_of_bedrooms \\\n", - "0 United States 05464 2.0 \n", - "1 United States 05464 2.0 \n", - "2 United Kingdom BH1 3EE 12.0 \n", - "3 United Kingdom BH1 3EE 12.0 \n", - "4 United Kingdom SE1 6PD 2.0 \n", - "\n", - " listing_number_of_bathrooms \\\n", - "0 2.0 \n", - "1 2.0 \n", - "2 12.0 \n", - "3 12.0 \n", - "4 1.0 \n", - "\n", - " listing_description \\\n", - "0 Mountain Life Retreat at Smuggler's Notch Resort \n", - "1 Mountain Life Retreat at Smuggler's Notch Resort \n", - "2 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "3 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "4 Your London Home: 2BR Flat with Modern Amenities \n", - "\n", - " previous_bookings_in_listing_count \\\n", - "0 3 \n", - "1 5 \n", - "2 5 \n", - "3 2 \n", - "4 0 \n", - "\n", - " number_of_previous_incidents_in_listing \\\n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "\n", - " number_of_previous_payouts_in_listing days_to_start_verification \\\n", - "0 0 0.0 \n", - "1 0 0.0 \n", - "2 0 0.0 \n", - "3 0 0.0 \n", - "4 0 0.0 \n", - "\n", - " days_to_complete_verification screening_status government_id_status \\\n", - "0 0.0 NaN NaN \n", - "1 0.0 NaN NaN \n", - "2 0.0 NaN NaN \n", - "3 0.0 NaN NaN \n", - "4 0.0 NaN NaN \n", - "\n", - " contract_status selfie_confidence_score_status payment_validation_status \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " first_name_status date_of_birth_status last_name_status \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " autohost_partner_status criminal_record_status guest_csat_score \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " guest_csat_comments guest_has_email guest_has_phone_number \\\n", - "0 NaN False False \n", - "1 NaN False False \n", - "2 NaN False False \n", - "3 NaN False False \n", - "4 NaN False False \n", - "\n", - " is_guest_from_listing_town is_guest_from_listing_country \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " is_guest_from_listing_postcode is_host_from_listing_town \\\n", - "0 NaN False \n", - "1 NaN False \n", - "2 NaN False \n", - "3 NaN False \n", - "4 NaN False \n", - "\n", - " is_host_from_listing_country is_host_from_listing_postcode \\\n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "\n", - " has_completed_verification number_of_applied_services \\\n", - "0 False 3 \n", - "1 False 3 \n", - "2 False 2 \n", - "3 False 2 \n", - "4 False 2 \n", - "\n", - " number_of_applied_upgraded_services number_of_applied_billable_services \\\n", - "0 2 2 \n", - "1 2 2 \n", - "2 1 1 \n", - "3 1 1 \n", - "4 1 1 \n", - "\n", - " booking_days_to_check_in booking_number_of_nights \\\n", - "0 87 4 \n", - "1 109 3 \n", - "2 50 7 \n", - "3 15 3 \n", - "4 8 5 \n", - "\n", - " has_verification_request has_billable_services \\\n", - "0 False True \n", - "1 False True \n", - "2 False True \n", - "3 False True \n", - "4 False True \n", - "\n", - " has_upgraded_screening_service_business_type \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_deposit_management_service_business_type \\\n", - "0 True \n", - "1 True \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_protection_service_business_type has_resolution_incident \n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False " - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Remove columns limit to display all columns and rows\n", - "pd.set_option('display.max_columns', None)\n", - "pd.set_option('display.max_rows', None)\n", - "\n", - "# Preview of the dataset\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "de4c6753", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21307 entries, 0 to 21306\n", - "Data columns (total 63 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 days_from_booking_creation_to_check_in 21307 non-null float64\n", - " 1 number_of_nights 21307 non-null float64\n", - " 2 host_town 21281 non-null object \n", - " 3 host_country 21300 non-null object \n", - " 4 host_postcode 15800 non-null object \n", - " 5 host_age 21307 non-null float64\n", - " 6 host_months_with_truvi 21307 non-null float64\n", - " 7 host_account_type 17831 non-null object \n", - " 8 host_active_pms_list 20363 non-null object \n", - " 9 number_of_listings_of_host 21307 non-null int64 \n", - " 10 number_of_previous_incidents_of_host 21307 non-null int64 \n", - " 11 number_of_previous_payouts_of_host 21307 non-null int64 \n", - " 12 guest_town 11676 non-null object \n", - " 13 guest_country 11677 non-null object \n", - " 14 guest_postcode 11676 non-null object \n", - " 15 guest_age 11677 non-null float64\n", - " 16 number_of_previous_bookings_of_guest 21307 non-null int64 \n", - " 17 number_of_previous_incidents_of_guest 21307 non-null int64 \n", - " 18 has_guest_previously_booked_same_listing 21307 non-null bool \n", - " 19 listing_address 21307 non-null object \n", - " 20 listing_town 21307 non-null object \n", - " 21 listing_country 21307 non-null object \n", - " 22 listing_postcode 21307 non-null object \n", - " 23 listing_number_of_bedrooms 21185 non-null float64\n", - " 24 listing_number_of_bathrooms 21185 non-null float64\n", - " 25 listing_description 21294 non-null object \n", - " 26 previous_bookings_in_listing_count 21307 non-null int64 \n", - " 27 number_of_previous_incidents_in_listing 21307 non-null int64 \n", - " 28 number_of_previous_payouts_in_listing 21307 non-null int64 \n", - " 29 days_to_start_verification 20084 non-null float64\n", - " 30 days_to_complete_verification 18500 non-null float64\n", - " 31 screening_status 9332 non-null float64\n", - " 32 government_id_status 8082 non-null float64\n", - " 33 contract_status 5856 non-null float64\n", - " 34 selfie_confidence_score_status 6622 non-null float64\n", - " 35 payment_validation_status 8047 non-null float64\n", - " 36 first_name_status 4810 non-null float64\n", - " 37 date_of_birth_status 4810 non-null float64\n", - " 38 last_name_status 4810 non-null float64\n", - " 39 autohost_partner_status 0 non-null float64\n", - " 40 criminal_record_status 2075 non-null float64\n", - " 41 guest_csat_score 3221 non-null float64\n", - " 42 guest_csat_comments 454 non-null object \n", - " 43 guest_has_email 21307 non-null bool \n", - " 44 guest_has_phone_number 21307 non-null bool \n", - " 45 is_guest_from_listing_town 11677 non-null object \n", - " 46 is_guest_from_listing_country 11677 non-null object \n", - " 47 is_guest_from_listing_postcode 11677 non-null object \n", - " 48 is_host_from_listing_town 21307 non-null bool \n", - " 49 is_host_from_listing_country 21300 non-null object \n", - " 50 is_host_from_listing_postcode 18102 non-null object \n", - " 51 has_completed_verification 21307 non-null bool \n", - " 52 number_of_applied_services 21307 non-null int64 \n", - " 53 number_of_applied_upgraded_services 21307 non-null int64 \n", - " 54 number_of_applied_billable_services 21307 non-null int64 \n", - " 55 booking_days_to_check_in 21307 non-null int64 \n", - " 56 booking_number_of_nights 21307 non-null int64 \n", - " 57 has_verification_request 21307 non-null bool \n", - " 58 has_billable_services 21307 non-null bool \n", - " 59 has_upgraded_screening_service_business_type 21307 non-null bool \n", - " 60 has_deposit_management_service_business_type 21307 non-null bool \n", - " 61 has_protection_service_business_type 21307 non-null bool \n", - " 62 has_resolution_incident 21307 non-null bool \n", - "dtypes: bool(11), float64(20), int64(13), object(19)\n", - "memory usage: 8.7+ MB\n" - ] - } - ], - "source": [ - "# View summary of dataset\n", - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "9c79c06a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing Values (%):\n", - "autohost_partner_status 100.000000\n", - "guest_csat_comments 97.869245\n", - "criminal_record_status 90.261416\n", - "guest_csat_score 84.882902\n", - "date_of_birth_status 77.425259\n", - "last_name_status 77.425259\n", - "first_name_status 77.425259\n", - "contract_status 72.516075\n", - "selfie_confidence_score_status 68.921012\n", - "payment_validation_status 62.233069\n", - "government_id_status 62.068804\n", - "screening_status 56.202187\n", - "guest_postcode 45.201108\n", - "guest_town 45.201108\n", - "guest_country 45.196414\n", - "is_guest_from_listing_country 45.196414\n", - "is_guest_from_listing_postcode 45.196414\n", - "guest_age 45.196414\n", - "is_guest_from_listing_town 45.196414\n", - "host_postcode 25.845966\n", - "host_account_type 16.313887\n", - "is_host_from_listing_postcode 15.042005\n", - "days_to_complete_verification 13.174074\n", - "days_to_start_verification 5.739898\n", - "host_active_pms_list 4.430469\n", - "listing_number_of_bedrooms 0.572582\n", - "listing_number_of_bathrooms 0.572582\n", - "host_town 0.122026\n", - "listing_description 0.061013\n", - "host_country 0.032853\n", - "is_host_from_listing_country 0.032853\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "# View percentage of missing values\n", - "missing_values = df.isnull().mean() * 100\n", - "missing_values = missing_values[missing_values > 0].sort_values(ascending=False)\n", - "print(\"Missing Values (%):\")\n", - "print(missing_values)" - ] - }, - { - "cell_type": "markdown", - "id": "1837c541", - "metadata": {}, - "source": [ - "Despite the small amount of data with on CSAT, I want to check if there might be any interesting correlation with the incidents." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "6e89712c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "guest_csat_score\n", - "1.0 0.010695\n", - "2.0 0.013761\n", - "3.0 0.018293\n", - "4.0 0.013105\n", - "5.0 0.022619\n", - "Name: has_resolution_incident, dtype: float64" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.groupby('guest_csat_score')['has_resolution_incident'].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "ce9ed8a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation: 0.02\n" - ] - } - ], - "source": [ - "correlation = df['guest_csat_score'].corr(df['has_resolution_incident'])\n", - "print(f\"Correlation: {correlation:.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "8ac447bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dropping columns with more than 50% missing values: ['autohost_partner_status', 'guest_csat_comments', 'criminal_record_status', 'guest_csat_score', 'date_of_birth_status', 'last_name_status', 'first_name_status', 'contract_status', 'selfie_confidence_score_status', 'payment_validation_status', 'government_id_status', 'screening_status']\n" - ] - } - ], - "source": [ - "# Remove columns with more than 50% missing values\n", - "threshold = 50\n", - "columns_to_drop = missing_values[missing_values > threshold].index\n", - "print(f\"Dropping columns with more than {threshold}% missing values: {columns_to_drop.tolist()}\")\n", - "df.drop(columns=columns_to_drop, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "20bd5c86", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 18 categorical variables\n", - "\n", - "The categorical variables are: ['host_town', 'host_country', 'host_postcode', 'host_account_type', 'host_active_pms_list', 'guest_town', 'guest_country', 'guest_postcode', 'listing_address', 'listing_town', 'listing_country', 'listing_postcode', 'listing_description', 'is_guest_from_listing_town', 'is_guest_from_listing_country', 'is_guest_from_listing_postcode', 'is_host_from_listing_country', 'is_host_from_listing_postcode']\n" - ] - } - ], - "source": [ - "# Find categorical variables\n", - "categorical = df.select_dtypes(include=['object']).columns.tolist()\n", - "print(f'There are {len(categorical)} categorical variables\\n')\n", - "print('The categorical variables are:', categorical)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "67ddd437", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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host_townhost_countryhost_postcodehost_account_typehost_active_pms_listguest_townguest_countryguest_postcodelisting_addresslisting_townlisting_countrylisting_postcodelisting_descriptionis_guest_from_listing_townis_guest_from_listing_countryis_guest_from_listing_postcodeis_host_from_listing_countryis_host_from_listing_postcode
0Madison CTUnited States06443HostHostawayNaNNaNNaN389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States05464Mountain Life Retreat at Smuggler's Notch ResortNaNNaNNaNTrueFalse
1Madison CTUnited States06443HostHostawayNaNNaNNaN389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States05464Mountain Life Retreat at Smuggler's Notch ResortNaNNaNNaNTrueFalse
2LondonUnited KingdomN16 6DDPMC - Property Management CompanyHostifyNaNNaNNaNTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EEMansion by the Sea, 12BR/12BA, Perfect for EventsNaNNaNNaNTrueFalse
3LondonUnited KingdomN16 6DDPMC - Property Management CompanyHostifyNaNNaNNaNTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EEMansion by the Sea, 12BR/12BA, Perfect for EventsNaNNaNNaNTrueFalse
4LondonUnited KingdomN16 6DDPMC - Property Management CompanyHostifyNaNNaNNaNAird House, 15 Wellesley Ct, Rockingham StreetGreater LondonUnited KingdomSE1 6PDYour London Home: 2BR Flat with Modern AmenitiesNaNNaNNaNTrueFalse
\n", - "
" - ], - "text/plain": [ - " host_town host_country host_postcode \\\n", - "0 Madison CT United States 06443 \n", - "1 Madison CT United States 06443 \n", - "2 London United Kingdom N16 6DD \n", - "3 London United Kingdom N16 6DD \n", - "4 London United Kingdom N16 6DD \n", - "\n", - " host_account_type host_active_pms_list guest_town \\\n", - "0 Host Hostaway NaN \n", - "1 Host Hostaway NaN \n", - "2 PMC - Property Management Company Hostify NaN \n", - "3 PMC - Property Management Company Hostify NaN \n", - "4 PMC - Property Management Company Hostify NaN \n", - "\n", - " guest_country guest_postcode \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " listing_address listing_town \\\n", - "0 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "1 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "2 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "3 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "4 Aird House, 15 Wellesley Ct, Rockingham Street Greater London \n", - "\n", - " listing_country listing_postcode \\\n", - "0 United States 05464 \n", - "1 United States 05464 \n", - "2 United Kingdom BH1 3EE \n", - "3 United Kingdom BH1 3EE \n", - "4 United Kingdom SE1 6PD \n", - "\n", - " listing_description \\\n", - "0 Mountain Life Retreat at Smuggler's Notch Resort \n", - "1 Mountain Life Retreat at Smuggler's Notch Resort \n", - "2 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "3 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "4 Your London Home: 2BR Flat with Modern Amenities \n", - "\n", - " is_guest_from_listing_town is_guest_from_listing_country \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " is_guest_from_listing_postcode is_host_from_listing_country \\\n", - "0 NaN True \n", - "1 NaN True \n", - "2 NaN True \n", - "3 NaN True \n", - "4 NaN True \n", - "\n", - " is_host_from_listing_postcode \n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False " - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# view the categorical variables\n", - "df[categorical].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "841347ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "host_town 26\n", - "host_country 7\n", - "host_postcode 5507\n", - "host_account_type 3476\n", - "host_active_pms_list 944\n", - "guest_town 9631\n", - "guest_country 9630\n", - "guest_postcode 9631\n", - "listing_address 0\n", - "listing_town 0\n", - "listing_country 0\n", - "listing_postcode 0\n", - "listing_description 13\n", - "is_guest_from_listing_town 9630\n", - "is_guest_from_listing_country 9630\n", - "is_guest_from_listing_postcode 9630\n", - "is_host_from_listing_country 7\n", - "is_host_from_listing_postcode 3205\n", - "dtype: int64" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check missing values in categorical variables\n", - "df[categorical].isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "a58cd17e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_51877/2855830200.py:2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_guest_from_listing_town'] = df['is_guest_from_listing_town'].fillna(False)\n", - "/tmp/ipykernel_51877/2855830200.py:3: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_guest_from_listing_country'] = df['is_guest_from_listing_country'].fillna(False)\n", - "/tmp/ipykernel_51877/2855830200.py:4: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_guest_from_listing_postcode'] = df['is_guest_from_listing_postcode'].fillna(False)\n", - "/tmp/ipykernel_51877/2855830200.py:6: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_host_from_listing_country'] = df['is_host_from_listing_country'].fillna(False)\n", - "/tmp/ipykernel_51877/2855830200.py:7: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_host_from_listing_postcode'] = df['is_host_from_listing_postcode'].fillna(False)\n" - ] - } - ], - "source": [ - "# For all missing values in listing location with both host and guest, we will fill with False\n", - "df['is_guest_from_listing_town'] = df['is_guest_from_listing_town'].fillna(False)\n", - "df['is_guest_from_listing_country'] = df['is_guest_from_listing_country'].fillna(False)\n", - "df['is_guest_from_listing_postcode'] = df['is_guest_from_listing_postcode'].fillna(False)\n", - "df['is_host_from_listing_town'] = df['is_host_from_listing_town'].fillna(False)\n", - "df['is_host_from_listing_country'] = df['is_host_from_listing_country'].fillna(False)\n", - "df['is_host_from_listing_postcode'] = df['is_host_from_listing_postcode'].fillna(False)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "e5aefb50", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "host_town 26\n", - "host_country 7\n", - "host_postcode 5507\n", - "host_account_type 3476\n", - "host_active_pms_list 944\n", - "guest_town 9631\n", - "guest_country 9630\n", - "guest_postcode 9631\n", - "listing_address 0\n", - "listing_town 0\n", - "listing_country 0\n", - "listing_postcode 0\n", - "listing_description 13\n", - "is_guest_from_listing_town 0\n", - "is_guest_from_listing_country 0\n", - "is_guest_from_listing_postcode 0\n", - "is_host_from_listing_country 0\n", - "is_host_from_listing_postcode 0\n", - "dtype: int64" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Checking again missing values in categorical variables\n", - "df[categorical].isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "292eaad2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique values in 'host_account_type':\n", - "host_account_type\n", - "PMC - Property Management Company 12719\n", - "Host 5112\n", - "Name: count, dtype: int64 \n", - "\n", - "Unique values in 'host_active_pms_list':\n", - "host_active_pms_list\n", - "Hostify 6468\n", - "Hostaway 3675\n", - "Guesty 3108\n", - "Hospitable 2739\n", - "Hostfully 1905\n", - "Lodgify 1341\n", - "OwnerRez 649\n", - "Avantio 248\n", - "TrackHs 142\n", - "Uplisting 61\n", - "Hospitable Connect 15\n", - "Smoobu 12\n", - "Name: count, dtype: int64 \n", - "\n", - "Unique values in 'host_country':\n", - "host_country\n", - "United States 10962\n", - "United Kingdom 6707\n", - "Canada 2007\n", - "Australia 305\n", - "Mexico 273\n", - "New Zealand 154\n", - "Sweden 122\n", - "Norway 117\n", - "Bulgaria 117\n", - "Portugal 87\n", - "South Africa 78\n", - "Costa Rica 75\n", - "Puerto Rico 50\n", - "Belgium 50\n", - "Italy 35\n", - "Barbados 34\n", - "Spain 31\n", - "France 26\n", - "Jamaica 20\n", - "Egypt 19\n", - "Switzerland 10\n", - "Isle of Man 8\n", - "Bahamas 3\n", - "Guernsey 3\n", - "United Arab Emirates 2\n", - "Colombia 2\n", - "Germany 1\n", - "Greece 1\n", - "Hungary 1\n", - "Name: count, dtype: int64 \n", - "\n", - "Unique values in 'guest_country':\n", - "guest_country\n", - "United States 7409\n", - "Canada 1458\n", - "United Kingdom 1175\n", - "Australia 287\n", - "Colombia 151\n", - "Mexico 134\n", - "Germany 100\n", - "Ireland 77\n", - "New Zealand 70\n", - "France 56\n", - "Spain 53\n", - "Costa Rica 43\n", - "Netherlands 37\n", - "Brazil 36\n", - "Switzerland 34\n", - "Puerto Rico 31\n", - "Italy 29\n", - "Argentina 23\n", - "Singapore 23\n", - "China 21\n", - "Belgium 20\n", - "Ecuador 20\n", - "India 20\n", - "United Arab Emirates 20\n", - "Panama 19\n", - "Poland 17\n", - "Dominican Republic 15\n", - "Israel 14\n", - "Saudi Arabia 13\n", - "South Africa 12\n", - "Romania 11\n", - "Malaysia 11\n", - "El Salvador 10\n", - "Chile 9\n", - "Norway 9\n", - "Japan 9\n", - "Portugal 9\n", - "Sweden 8\n", - "Hong Kong 8\n", - "Austria 8\n", - "South Korea 8\n", - "United States Minor Outlying Islands 8\n", - "Finland 8\n", - "Philippines 7\n", - "Czech Republic 7\n", - "Guatemala 7\n", - "Hungary 6\n", - "Venezuela 6\n", - "Denmark 6\n", - "Honduras 6\n", - "Jamaica 5\n", - "Thailand 5\n", - "Peru 5\n", - "Taiwan 5\n", - "Russian Federation 5\n", - "French Polynesia 4\n", - "Turkey 4\n", - "Kazakhstan 4\n", - "Curacao 4\n", - "Martinique 3\n", - "Cayman Islands 3\n", - "Saint Pierre and Miquelon 3\n", - "Slovenia 3\n", - "Estonia 3\n", - "Iceland 3\n", - "Georgia 3\n", - "Indonesia 2\n", - "Qatar 2\n", - "Greece 2\n", - "Egypt 2\n", - "Latvia 2\n", - "Pakistan 2\n", - "Barbados 2\n", - "Bolivia 2\n", - "Aruba 2\n", - "Malta 2\n", - "Suriname 1\n", - "Lebanon 1\n", - "Nauru 1\n", - "Fiji 1\n", - "Cook Islands 1\n", - "Bahamas 1\n", - "Albania 1\n", - "Uruguay 1\n", - "Jersey 1\n", - "Croatia 1\n", - "Bulgaria 1\n", - "Belize 1\n", - "Nicaragua 1\n", - "DR Congo 1\n", - "Kuwait 1\n", - "Niger 1\n", - "Cyprus 1\n", - "Name: count, dtype: int64 \n", - "\n", - "Unique values in 'listing_country':\n", - "listing_country\n", - "United States 10067\n", - "United Kingdom 6574\n", - "Canada 1870\n", - "Colombia 599\n", - "Australia 305\n", - "Mexico 303\n", - "Ireland 168\n", - "New Zealand 153\n", - "Virgin Islands, U.s. 130\n", - "Bahamas 130\n", - "Norway 125\n", - "Sweden 122\n", - "Bulgaria 117\n", - "Costa Rica 108\n", - "Portugal 87\n", - "South Africa 83\n", - "Puerto Rico 50\n", - "Belgium 48\n", - "France 46\n", - "Italy 44\n", - "Spain 36\n", - "Barbados 34\n", - "Morocco 25\n", - "Jamaica 20\n", - "Egypt 19\n", - "Saint Lucia 10\n", - "Germany 10\n", - "Sint Maarten 9\n", - "Isle of Man 8\n", - "United Arab Emirates 2\n", - "Lithuania 2\n", - "Antigua and Barbuda 1\n", - "Greece 1\n", - "Hungary 1\n", - "Name: count, dtype: int64 \n", - "\n" - ] - } - ], - "source": [ - "# Check unique values in host_account_type, host_active_pms_list, host_country and guest_country with their counts\n", - "print(\"Unique values in 'host_account_type':\")\n", - "print(df['host_account_type'].value_counts(), \"\\n\")\n", - "print(\"Unique values in 'host_active_pms_list':\")\n", - "print(df['host_active_pms_list'].value_counts(), \"\\n\")\n", - "print(\"Unique values in 'host_country':\")\n", - "print(df['host_country'].value_counts(), \"\\n\")\n", - "print(\"Unique values in 'guest_country':\")\n", - "print(df['guest_country'].value_counts(), \"\\n\")\n", - "print(\"Unique values in 'listing_country':\")\n", - "print(df['listing_country'].value_counts(), \"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "7289f9fd", - "metadata": {}, - "outputs": [], - "source": [ - "# Due to the many unique values in host_country, guest_country and listing_country, we will only keep the top 10 most frequent values and set the rest to 'Other'\n", - "top_host_countries = df['host_country'].value_counts().nlargest(10).index\n", - "top_guest_countries = df['guest_country'].value_counts().nlargest(10).index\n", - "top_listing_countries = df['listing_country'].value_counts().nlargest(10).index\n", - "\n", - "df['host_country'] = df['host_country'].where(df['host_country'].isin(top_host_countries), 'Other')\n", - "df['guest_country'] = df['guest_country'].where(df['guest_country'].isin(top_guest_countries), 'Other')\n", - "df['listing_country'] = df['listing_country'].where(df['listing_country'].isin(top_listing_countries), 'Other')" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "7348866c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New columns created from one-hot encoding: ['host_account_type_Host', 'host_account_type_PMC - Property Management Company', 'host_active_pms_list_Avantio', 'host_active_pms_list_Guesty', 'host_active_pms_list_Hospitable', 'host_active_pms_list_Hospitable Connect', 'host_active_pms_list_Hostaway', 'host_active_pms_list_Hostfully', 'host_active_pms_list_Hostify', 'host_active_pms_list_Lodgify', 'host_active_pms_list_OwnerRez', 'host_active_pms_list_Smoobu', 'host_active_pms_list_TrackHs', 'host_active_pms_list_Uplisting', 'host_country_Australia', 'host_country_Bulgaria', 'host_country_Canada', 'host_country_Mexico', 'host_country_New Zealand', 'host_country_Norway', 'host_country_Other', 'host_country_Portugal', 'host_country_Sweden', 'host_country_United Kingdom', 'host_country_United States', 'guest_country_Australia', 'guest_country_Canada', 'guest_country_Colombia', 'guest_country_France', 'guest_country_Germany', 'guest_country_Ireland', 'guest_country_Mexico', 'guest_country_New Zealand', 'guest_country_Other', 'guest_country_United Kingdom', 'guest_country_United States', 'listing_country_Australia', 'listing_country_Bahamas', 'listing_country_Canada', 'listing_country_Colombia', 'listing_country_Ireland', 'listing_country_Mexico', 'listing_country_New Zealand', 'listing_country_Other', 'listing_country_United Kingdom', 'listing_country_United States', 'listing_country_Virgin Islands, U.s.']\n" - ] - } - ], - "source": [ - "# Lets one hot encode host_account_type, host_active_pms_list, host_country, guest_country and listing_country\n", - "df = pd.get_dummies(df, columns=['host_account_type', 'host_active_pms_list', 'host_country', 'guest_country', 'listing_country'], drop_first=False)\n", - "# Check the new columns created\n", - "new_columns = df.columns[df.columns.str.startswith(('host_account_type_', 'host_active_pms_list_', 'host_country', 'guest_country', 'listing_country'))]\n", - "print(f\"New columns created from one-hot encoding: {new_columns.tolist()}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "id": "b443ccf4", - "metadata": {}, - "outputs": [], - "source": [ - "# Drop the original categorical columns and the ones we are not going to use like postcodes and towns\n", - "df.drop(columns=['host_postcode', 'guest_postcode', 'listing_postcode', 'listing_town', 'host_town', 'guest_town', 'listing_description', 'listing_address'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "a31ae1fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 22 numerical variables\n", - "\n", - "The numerical variables are : ['days_from_booking_creation_to_check_in', 'number_of_nights', 'host_age', 'host_months_with_truvi', 'number_of_listings_of_host', 'number_of_previous_incidents_of_host', 'number_of_previous_payouts_of_host', 'guest_age', 'number_of_previous_bookings_of_guest', 'number_of_previous_incidents_of_guest', 'listing_number_of_bedrooms', 'listing_number_of_bathrooms', 'previous_bookings_in_listing_count', 'number_of_previous_incidents_in_listing', 'number_of_previous_payouts_in_listing', 'days_to_start_verification', 'days_to_complete_verification', 'number_of_applied_services', 'number_of_applied_upgraded_services', 'number_of_applied_billable_services', 'booking_days_to_check_in', 'booking_number_of_nights']\n" - ] - } - ], - "source": [ - "# Find numerical variables\n", - "numerical = df.select_dtypes(include=[np.number]).columns.tolist()\n", - "print('There are {} numerical variables\\n'.format(len(numerical)))\n", - "print('The numerical variables are :', numerical)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "cf795d45", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Summary statistics of numerical variables:\n" - ] - }, - { - "data": { - "text/html": [ - "
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days_from_booking_creation_to_check_innumber_of_nightshost_agehost_months_with_truvinumber_of_listings_of_hostnumber_of_previous_incidents_of_hostnumber_of_previous_payouts_of_hostguest_agenumber_of_previous_bookings_of_guestnumber_of_previous_incidents_of_guestlisting_number_of_bedroomslisting_number_of_bathroomsprevious_bookings_in_listing_countnumber_of_previous_incidents_in_listingnumber_of_previous_payouts_in_listingdays_to_start_verificationdays_to_complete_verificationnumber_of_applied_servicesnumber_of_applied_upgraded_servicesnumber_of_applied_billable_servicesbooking_days_to_check_inbooking_number_of_nights
count21307.00000021307.00000021307.00000021307.00000021307.00000021307.00000021307.00000011677.00000021307.00000021307.021185.00000021185.00000021307.00000021307.00000021307.00000020084.00000018500.00000021307.00000021307.00000021307.00000021307.00000021307.000000
mean8.7400383.87680196.5330175.482142152.8758152.7184960.75130242.3178902175.9998120.02.0529621.6018416.2150940.1233870.0435070.9967640.7131353.7215942.7216881.86520917.5922474.144507
std8.3892423.33561543.6163412.714314179.0288295.5828572.95705313.2125093038.8374960.01.7452811.2977396.7278960.5374640.2709943.4233032.7684741.5536121.5536290.94985723.5729014.799364
min-20.0000000.00000019.0000000.0000000.0000000.0000000.00000018.0000000.0000000.00.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000001.0000000.000000-48.0000000.000000
25%1.0000002.00000039.0000004.0000009.0000000.0000000.00000032.0000000.0000000.01.0000001.0000001.0000000.0000000.0000000.0000000.0000002.0000001.0000001.0000002.0000002.000000
50%6.0000003.000000125.0000005.00000072.0000001.0000000.00000041.0000000.0000000.02.0000001.0000004.0000000.0000000.0000000.0000000.0000004.0000003.0000002.0000008.0000003.000000
75%15.0000004.000000125.0000008.000000247.0000003.0000001.00000051.0000004302.5000000.03.0000002.0000009.0000000.0000000.0000000.0000000.0000005.0000004.0000003.00000024.0000005.000000
max30.00000030.000000125.00000011.000000467.00000085.00000062.00000089.0000009629.0000000.015.00000017.00000041.0000009.0000006.00000030.00000030.0000008.0000007.0000005.000000218.000000116.000000
\n", - "
" - ], - "text/plain": [ - " days_from_booking_creation_to_check_in number_of_nights host_age \\\n", - "count 21307.000000 21307.000000 21307.000000 \n", - "mean 8.740038 3.876801 96.533017 \n", - "std 8.389242 3.335615 43.616341 \n", - "min -20.000000 0.000000 19.000000 \n", - "25% 1.000000 2.000000 39.000000 \n", - "50% 6.000000 3.000000 125.000000 \n", - "75% 15.000000 4.000000 125.000000 \n", - "max 30.000000 30.000000 125.000000 \n", - "\n", - " host_months_with_truvi number_of_listings_of_host \\\n", - "count 21307.000000 21307.000000 \n", - "mean 5.482142 152.875815 \n", - "std 2.714314 179.028829 \n", - "min 0.000000 0.000000 \n", - "25% 4.000000 9.000000 \n", - "50% 5.000000 72.000000 \n", - "75% 8.000000 247.000000 \n", - "max 11.000000 467.000000 \n", - "\n", - " number_of_previous_incidents_of_host \\\n", - "count 21307.000000 \n", - "mean 2.718496 \n", - "std 5.582857 \n", - "min 0.000000 \n", - "25% 0.000000 \n", - "50% 1.000000 \n", - "75% 3.000000 \n", - "max 85.000000 \n", - "\n", - " number_of_previous_payouts_of_host guest_age \\\n", - "count 21307.000000 11677.000000 \n", - "mean 0.751302 42.317890 \n", - "std 2.957053 13.212509 \n", - "min 0.000000 18.000000 \n", - "25% 0.000000 32.000000 \n", - "50% 0.000000 41.000000 \n", - "75% 1.000000 51.000000 \n", - "max 62.000000 89.000000 \n", - "\n", - " number_of_previous_bookings_of_guest \\\n", - "count 21307.000000 \n", - "mean 2175.999812 \n", - "std 3038.837496 \n", - "min 0.000000 \n", - "25% 0.000000 \n", - "50% 0.000000 \n", - "75% 4302.500000 \n", - "max 9629.000000 \n", - "\n", - " number_of_previous_incidents_of_guest listing_number_of_bedrooms \\\n", - "count 21307.0 21185.000000 \n", - "mean 0.0 2.052962 \n", - "std 0.0 1.745281 \n", - "min 0.0 0.000000 \n", - "25% 0.0 1.000000 \n", - "50% 0.0 2.000000 \n", - "75% 0.0 3.000000 \n", - "max 0.0 15.000000 \n", - "\n", - " listing_number_of_bathrooms previous_bookings_in_listing_count \\\n", - "count 21185.000000 21307.000000 \n", - "mean 1.601841 6.215094 \n", - "std 1.297739 6.727896 \n", - "min 0.000000 0.000000 \n", - "25% 1.000000 1.000000 \n", - "50% 1.000000 4.000000 \n", - "75% 2.000000 9.000000 \n", - "max 17.000000 41.000000 \n", - "\n", - " number_of_previous_incidents_in_listing \\\n", - "count 21307.000000 \n", - "mean 0.123387 \n", - "std 0.537464 \n", - "min 0.000000 \n", - "25% 0.000000 \n", - "50% 0.000000 \n", - "75% 0.000000 \n", - "max 9.000000 \n", - "\n", - " number_of_previous_payouts_in_listing days_to_start_verification \\\n", - "count 21307.000000 20084.000000 \n", - "mean 0.043507 0.996764 \n", - "std 0.270994 3.423303 \n", - "min 0.000000 0.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 0.000000 0.000000 \n", - "75% 0.000000 0.000000 \n", - "max 6.000000 30.000000 \n", - "\n", - " days_to_complete_verification number_of_applied_services \\\n", - "count 18500.000000 21307.000000 \n", - "mean 0.713135 3.721594 \n", - "std 2.768474 1.553612 \n", - "min 0.000000 2.000000 \n", - "25% 0.000000 2.000000 \n", - "50% 0.000000 4.000000 \n", - "75% 0.000000 5.000000 \n", - "max 30.000000 8.000000 \n", - "\n", - " number_of_applied_upgraded_services \\\n", - "count 21307.000000 \n", - "mean 2.721688 \n", - "std 1.553629 \n", - "min 1.000000 \n", - "25% 1.000000 \n", - "50% 3.000000 \n", - "75% 4.000000 \n", - "max 7.000000 \n", - "\n", - " number_of_applied_billable_services booking_days_to_check_in \\\n", - "count 21307.000000 21307.000000 \n", - "mean 1.865209 17.592247 \n", - "std 0.949857 23.572901 \n", - "min 0.000000 -48.000000 \n", - "25% 1.000000 2.000000 \n", - "50% 2.000000 8.000000 \n", - "75% 3.000000 24.000000 \n", - "max 5.000000 218.000000 \n", - "\n", - " booking_number_of_nights \n", - "count 21307.000000 \n", - "mean 4.144507 \n", - "std 4.799364 \n", - "min 0.000000 \n", - "25% 2.000000 \n", - "50% 3.000000 \n", - "75% 5.000000 \n", - "max 116.000000 " - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# View summary statistics of numerical variables\n", - "print(\"\\nSummary statistics of numerical variables:\")\n", - "df[numerical].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "2cf714c9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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777+vy5cvW2s3AACgiJijAQAofcy/AABUblZLtH/33Xdq0aKFxo8fLx8fHz3xxBP69ttvrbU7AABQSMzRAACUPuZfAAAqN6sl2lu1aqWFCxfq5MmTeuedd3Tq1Cl16NBBzZs317x58/Tnn39aa9cAACAfzNEAAJQ+5l8AACo3q98M1dbWVn369NHq1av1yiuv6Oeff9YzzzyjunXravDgwTp16pS1uwAAAPLAHA0AQOlj/gUAoHKyeqJ97969evLJJ1W7dm3NmzdPzzzzjH755RfFxsbq5MmTeuCBB6zdBQAAkAfmaAAASh/zLwAAlZOttRqeN2+eli9friNHjqhHjx5699131aNHD1Wp8ndu39/fX9HR0apfv761ugAAAPLAHA0AQOlj/gUAoHKzWqL9jTfe0LBhw/TYY4+pdu3aecZ4eXnp7bfftlYXAABAHpijAQAofcy/AABUblZLtB89erTAGHt7ew0ZMsRaXQAAAHlgjgYAoPQx/wIAULlZbY325cuXa/Xq1bnKV69erRUrVlhrtwAAoADM0QAAlD7mXwAAKjerJdpnz56tmjVr5ir38vLSyy+/XOh23njjDbVo0UKurq5ydXVVcHCwvvzyS7P+8uXLioyMlKenp1xcXBQeHq7k5GSLNhITExUWFqZq1arJy8tLEydO1JUrVyxitm7dqtatW8vBwUENGzZUdHR00QYMAEAFUVJzNAAAKDzmXwAAKjerJdoTExPl7++fq9zPz0+JiYmFbqdOnTr697//rfj4eO3du1ddunTRAw88oIMHD0qSxo0bpy+++EKrV6/Wtm3bdPLkSfXp08fc/urVqwoLC1NmZqZ27typFStWKDo6WtOmTTNjjh07prCwMN1zzz1KSEjQ2LFjNXz4cG3YsKEYrwAAAOVTSc3RAACg8Jh/AQCo3KyWaPfy8tIPP/yQq/z777+Xp6dnodvp1auXevTooUaNGun222/XSy+9JBcXF+3atUupqal6++23NW/ePHXp0kVBQUFavny5du7cqV27dkmSNm7cqEOHDun9999Xq1at1L17d82aNUtLly5VZmamJCkqKkr+/v6aO3eumjZtqtGjR+uhhx7S/PnzS+bFAACgHCmpORoAABQe8y8AAJWb1W6G2q9fPz311FOqXr26OnXqJEnatm2bnn76afXt2/em2rx69apWr16tCxcuKDg4WPHx8crKylJISIgZ06RJE9WrV09xcXFq166d4uLiFBgYKG9vbzMmNDRUo0aN0sGDB3XHHXcoLi7Ooo2cmLFjx95UPwEAKM+sMUejYjl8+HChY2vWrKl69epZsTcAcGtg/gUAoHKzWqJ91qxZ+u2339S1a1fZ2v69m+zsbA0ePLjI68/t379fwcHBunz5slxcXPTJJ58oICBACQkJsre3l7u7u0W8t7e3kpKSJElJSUkWSfac+py6/GLS0tJ06dIlOTk55epTRkaGMjIyzOdpaWlFGhMAAGWlJOdoVCxX089JNjYaOHBgobdxdKqmIz8eJtkOAMXE/AsAQOVmtUS7vb29PvzwQ82aNUvff/+9nJycFBgYKD8/vyK31bhxYyUkJCg1NVUfffSRhgwZom3btlmh14U3e/ZszZgxo0z7AADAzSjJORoVS3ZGumQY8uw5QXaedQuMzzpzQmfWzFVKSgqJdgAoJuZfAAAqN6sl2nPcfvvtuv3224vVhr29vRo2bChJCgoK0p49e7Rw4UI9+uijyszM1Pnz5y2uak9OTpaPj48kycfHR99++61Fe8nJyWZdzn9zyq6NcXV1zfNqdkmaMmWKxo8fbz5PS0tT3boFn7ACAFBelMQcjYrJzrOuHHwalnU3AOCWxPwLAEDlZLVE+9WrVxUdHa3Nmzfr9OnTys7OtqjfsmXLTbednZ2tjIwMBQUFyc7OTps3b1Z4eLgk6ciRI0pMTFRwcLAkKTg4WC+99JJOnz4tLy8vSVJsbKxcXV0VEBBgxqxbt85iH7GxsWYbeXFwcJCDg8NNjwEAgLJizTkaAADkjfkXAIDKzWqJ9qefflrR0dEKCwtT8+bNZWNjc1PtTJkyRd27d1e9evX0119/aeXKldq6das2bNggNzc3RUREaPz48fLw8JCrq6vGjBmj4OBgtWvXTpLUrVs3BQQEaNCgQZozZ46SkpI0depURUZGmonykSNHasmSJZo0aZKGDRumLVu2aNWqVVq7dm2JvR4AAJQXJTVHAwCAwmP+BQCgcrNaov2DDz7QqlWr1KNHj2K1c/r0aQ0ePFinTp2Sm5ubWrRooQ0bNujee++VJM2fP19VqlRReHi4MjIyFBoaqtdff93cvmrVqlqzZo1GjRql4OBgOTs7a8iQIZo5c6YZ4+/vr7Vr12rcuHFauHCh6tSpo7feekuhoaHF6jsAAOVRSc3RAACg8Jh/AQCo3Kx6M9ScddWL4+2338633tHRUUuXLtXSpUtvGOPn55draZjrde7cWfv27bupPgIAUJGU1BwNAAAKj/kXAIDKrYq1Gp4wYYIWLlwowzCstQsAAHATmKMBACh9zL8AAFRuVruifceOHfrqq6/05ZdfqlmzZrKzs7Oo//jjj621awAAkA/maAAASh/zLwAAlZvVrmh3d3fXgw8+qLvvvls1a9aUm5ubxQMAAJSNkpij33jjDbVo0UKurq5ydXVVcHCwvvzyS7P+8uXLioyMlKenp1xcXBQeHq7k5GSLNhITExUWFqZq1arJy8tLEydO1JUrVyxitm7dqtatW8vBwUENGzZUdHR0sccPAEBZ4BwZAIDKzWpXtC9fvtxaTQMAgGIoiTm6Tp06+ve//61GjRrJMAytWLFCDzzwgPbt26dmzZpp3LhxWrt2rVavXi03NzeNHj1affr00TfffCNJunr1qsLCwuTj46OdO3fq1KlTGjx4sOzs7PTyyy9Lko4dO6awsDCNHDlSMTEx2rx5s4YPH67atWtzw3IAQIXDOTIAAJWb1a5ol6QrV65o06ZNevPNN/XXX39Jkk6ePKn09HRr7hYAABSguHN0r1691KNHDzVq1Ei33367XnrpJbm4uGjXrl1KTU3V22+/rXnz5qlLly4KCgrS8uXLtXPnTu3atUuStHHjRh06dEjvv/++WrVqpe7du2vWrFlaunSpMjMzJUlRUVHy9/fX3Llz1bRpU40ePVoPPfSQ5s+fb50XBQAAKyuNc+SrV6/q+eefl7+/v5ycnNSgQQPNmjXLYm14wzA0bdo01a5dW05OTgoJCdHRo0ct2jl79qwGDBggV1dXubu7KyIiIlc/f/jhB3Xs2FGOjo6qW7eu5syZU2LjAACgorFaov348eMKDAzUAw88oMjISP3555+SpFdeeUXPPPOMtXYLAAAKUNJz9NWrV/XBBx/owoULCg4OVnx8vLKyshQSEmLGNGnSRPXq1VNcXJwkKS4uToGBgfL29jZjQkNDlZaWpoMHD5ox17aRE5PTBgAAFUlpnSO/8soreuONN7RkyRIdPnxYr7zyiubMmaPFixebMXPmzNGiRYsUFRWl3bt3y9nZWaGhobp8+bIZM2DAAB08eFCxsbFas2aNtm/frhEjRpj1aWlp6tatm/z8/BQfH69XX31V06dP17Jly0psLAAAVCRWWzrm6aefVps2bfT999/L09PTLH/wwQf1+OOPW2u3AIrh8OHDhY6tWbOm6tWrZ8XeALCWkpqj9+/fr+DgYF2+fFkuLi765JNPFBAQoISEBNnb28vd3d0i3tvbW0lJSZKkpKQkiyR7Tn1OXX4xaWlpunTpkpycnPLsV0ZGhjIyMsznaWlphR4TAADWUlrnyDt37tQDDzygsLAwSVL9+vX13//+V99++62kv69mX7BggaZOnaoHHnhAkvTuu+/K29tbn376qfr27avDhw9r/fr12rNnj9q0aSNJWrx4sXr06KHXXntNvr6+iomJUWZmpt555x3Z29urWbNmSkhI0Lx58ywS8gAA3Cqslmj/+uuvtXPnTtnb21uU169fX3/88Ye1dgvgJlxNPyfZ2GjgwIGF3sbRqZqO/HiYZDtQAZXUHN24cWMlJCQoNTVVH330kYYMGaJt27aVdHeLbPbs2ZoxY0ZZdwMAAAuldY581113admyZfrpp590++236/vvv9eOHTs0b948SX/fAyUpKcniV2Nubm5q27at4uLi1LdvX8XFxcnd3d1MsktSSEiIqlSpot27d+vBBx9UXFycOnXqZDGe0NBQvfLKKzp37pxq1KiRq2/8MRwAUJlZLdGenZ2tq1ev5ir//fffVb16dWvtFsBNyM5IlwxDnj0nyM6zboHxWWdO6MyauUpJSSHRDlRAJTVH29vbq2HDhpKkoKAg7dmzRwsXLtSjjz6qzMxMnT9/3uKq9uTkZPn4+EiSfHx8zCvrrq3Pqcv5b07ZtTGurq43vJpdkqZMmaLx48ebz9PS0lS3bsHfbQAAWFNpnSP/61//Ulpampo0aaKqVavq6tWreumllzRgwABJ//fLsbx+NXbtr8q8vLws6m1tbeXh4WER4+/vn6uNnLq8Eu38MRwAUJlZbY32bt26acGCBeZzGxsbpaen64UXXlCPHj2stVsAxWDnWVcOPg0LfBQmGQ+g/LLWHJ2dna2MjAwFBQXJzs5OmzdvNuuOHDmixMREBQcHS5KCg4O1f/9+nT592oyJjY2Vq6urAgICzJhr28iJyWnjRhwcHOTq6mrxAACgrJXWOfKqVasUExOjlStX6rvvvtOKFSv02muvacWKFSW2j5s1ZcoUpaammo8TJ06UdZcAACgxVruife7cuQoNDVVAQIAuX76s/v376+jRo6pZs6b++9//Wmu3AACgACUxR0+ZMkXdu3dXvXr19Ndff2nlypXaunWrNmzYIDc3N0VERGj8+PHy8PCQq6urxowZo+DgYLVr107S38mGgIAADRo0SHPmzFFSUpKmTp2qyMhIOTg4SJJGjhypJUuWaNKkSRo2bJi2bNmiVatWae3atVZ7bQAAsJbSOkeeOHGi/vWvf6lv376SpMDAQB0/flyzZ8/WkCFDzF+OJScnq3bt2uZ2ycnJatWqlaS/f1V27R/DJenKlSs6e/Zsgb88y6nLi4ODgznPAwBQ2Vgt0V6nTh19//33+uCDD/TDDz8oPT1dERERGjBgQL4/9wYAANZVEnP06dOnNXjwYJ06dUpubm5q0aKFNmzYoHvvvVeSNH/+fFWpUkXh4eHKyMhQaGioXn/9dXP7qlWras2aNRo1apSCg4Pl7OysIUOGaObMmWaMv7+/1q5dq3HjxmnhwoWqU6eO3nrrLYWGhpbsCwIAQCkorXPkixcvqkoVyx+vV61aVdnZ2ZL+nl99fHy0efNmM7Gelpam3bt3a9SoUZL+/lXZ+fPnFR8fr6CgIEnSli1blJ2drbZt25oxzz33nLKysmRnZyfp71+eNW7cOM9lYwAAqOyslmiX/l7DrSg3VwQAAKWjuHP022+/nW+9o6Ojli5dqqVLl94wxs/PT+vWrcu3nc6dO2vfvn031UcAAMqb0jhH7tWrl1566SXVq1dPzZo10759+zRv3jwNGzZM0t9L1owdO1YvvviiGjVqJH9/fz3//PPy9fVV7969JUlNmzbVfffdp8cff1xRUVHKysrS6NGj1bdvX/n6+kqS+vfvrxkzZigiIkKTJ0/WgQMHtHDhQs2fP9+q4wMAoLyyWqL93Xffzbd+8ODB1to1AADIB3M0AAClr7Tm38WLF+v555/Xk08+qdOnT8vX11dPPPGEpk2bZsZMmjRJFy5c0IgRI3T+/Hl16NBB69evl6OjoxkTExOj0aNHq2vXruav1BYtWmTWu7m5aePGjYqMjFRQUJBq1qypadOmacSIESUyDgAAKhqrJdqffvppi+dZWVm6ePGi7O3tVa1aNU7iAQAoI8zRAACUvtKaf6tXr64FCxZY3Hj1ejY2Npo5c6bFkm3X8/Dw0MqVK/PdV4sWLfT111/fbFcBAKhUqhQccnPOnTtn8UhPT9eRI0fUoUMHboYKAEAZYo4GAKD0Mf8CAFC5WS3RnpdGjRrp3//+d66/5AMAgLLFHA0AQOlj/gUAoPIo1US79PfNX06ePFnauwUAAAVgjgYAoPQx/wIAUDlYbY32zz//3OK5YRg6deqUlixZovbt21trtwAAoADM0QAAlD7mXwAAKjerJdp79+5t8dzGxka1atVSly5dNHfuXGvtFgAAFIA5GgCA0sf8CwBA5Wa1RHt2dra1mgYAAMXAHA0AQOlj/gUAoHIr9TXaAQAAAAAAAACoTKx2Rfv48eMLHTtv3jxrdQMAAFyHORoAgNLH/AsAQOVmtUT7vn37tG/fPmVlZalx48aSpJ9++klVq1ZV69atzTgbGxtrdQEAAOSBORoAgNLH/AsAQOVmtUR7r169VL16da1YsUI1atSQJJ07d05Dhw5Vx44dNWHCBGvtGgAA5IM5GgCA0sf8CwBA5Wa1Ndrnzp2r2bNnmwcQklSjRg29+OKL3FEdAIAyxBwNAEDpY/4FAKBys1qiPS0tTX/++Weu8j///FN//fWXtXYLAAAKwBwNAEDpY/4FAKBys1qi/cEHH9TQoUP18ccf6/fff9fvv/+u//3vf4qIiFCfPn2stVsAAFAA5mgAAEof8y8AAJWb1dZoj4qK0jPPPKP+/fsrKyvr753Z2ioiIkKvvvqqtXYLAAAKwBwNAEDpY/4FAKBys1qivVq1anr99df16quv6pdffpEkNWjQQM7OztbaJQAAKATmaAAASh/zLwAAlZvVlo7JcerUKZ06dUqNGjWSs7OzDMOw9i4BAEAhMEcDAFD6mH8BAKicrJZoP3PmjLp27arbb79dPXr00KlTpyRJERERmjBhgrV2CwAACsAcDQBA6WP+BQCgcrNaon3cuHGys7NTYmKiqlWrZpY/+uijWr9+vbV2CwAACsAcDQBA6WP+BQCgcrPaGu0bN27Uhg0bVKdOHYvyRo0a6fjx49baLQAAKABzNAAApY/5FwCAys1qV7RfuHDB4q/0Oc6ePSsHBwdr7RYAABSAORoAgNLH/AsAQOVmtUR7x44d9e6775rPbWxslJ2drTlz5uiee+6x1m4BAEABmKMBACh9zL8AAFRuVls6Zs6cOeratav27t2rzMxMTZo0SQcPHtTZs2f1zTffWGu3AACgAMzRAACUPuZfAAAqN6td0d68eXP99NNP6tChgx544AFduHBBffr00b59+9SgQQNr7RYAABSAORoAgNLH/AsAQOVmlSvas7KydN999ykqKkrPPfecNXYBAABuAnM0AAClj/kXAIDKzypXtNvZ2emHH36wRtMAAKAYmKMBACh9zL8AAFR+Vls6ZuDAgXr77bet1TwAALhJzNEAAJQ+5l8AACo3q90M9cqVK3rnnXe0adMmBQUFydnZ2aJ+3rx51to1AADIB3M0AAClj/kXAIDKrcQT7b/++qvq16+vAwcOqHXr1pKkn376ySLGxsampHcLAAAKwBwNAEDpY/4FAODWUOKJ9kaNGunUqVP66quvJEmPPvqoFi1aJG9v75LeFQAAKALmaAAASh/zLwAAt4YSX6PdMAyL519++aUuXLhQ0rsBAABFxBwNAEDpK4v5948//tDAgQPl6ekpJycnBQYGau/evRZ9mjZtmmrXri0nJyeFhITo6NGjFm2cPXtWAwYMkKurq9zd3RUREaH09HSLmB9++EEdO3aUo6Oj6tatqzlz5lh1XAAAlGdWuxlqjusPKgAAQPnAHA0AQOmz9vx77tw5tW/fXnZ2dvryyy916NAhzZ07VzVq1DBj5syZo0WLFikqKkq7d++Ws7OzQkNDdfnyZTNmwIABOnjwoGJjY7VmzRpt375dI0aMMOvT0tLUrVs3+fn5KT4+Xq+++qqmT5+uZcuWWXV8AACUVyW+dIyNjU2u9eVYbw4AgLLHHA0AQOkr7fn3lVdeUd26dbV8+XKzzN/f3/x/wzC0YMECTZ06VQ888IAk6d1335W3t7c+/fRT9e3bV4cPH9b69eu1Z88etWnTRpK0ePFi9ejRQ6+99pp8fX0VExOjzMxMvfPOO7K3t1ezZs2UkJCgefPmWSTkAQC4VZR4ot0wDD322GNycHCQJF2+fFkjR47MdUf1jz/+uKR3DQAA8sEcDQBA6Svt+ffzzz9XaGioHn74YW3btk233XabnnzyST3++OOSpGPHjikpKUkhISHmNm5ubmrbtq3i4uLUt29fxcXFyd3d3UyyS1JISIiqVKmi3bt368EHH1RcXJw6deoke3t7MyY0NFSvvPKKzp07Z3EFPQAAt4ISXzpmyJAh8vLykpubm9zc3DRw4ED5+vqaz3MehTV79mzdeeedql69ury8vNS7d28dOXLEIuby5cuKjIyUp6enXFxcFB4eruTkZIuYxMREhYWFqVq1avLy8tLEiRN15coVi5itW7eqdevWcnBwUMOGDRUdHX3TrwMAAOVNSc/RAACgYKU9//76669644031KhRI23YsEGjRo3SU089pRUrVkiSkpKSJCnXzVi9vb3NuqSkJHl5eVnU29raysPDwyImrzau3cf1MjIylJaWZvEAAKCyKPEr2q/9eVpJ2LZtmyIjI3XnnXfqypUrevbZZ9WtWzcdOnTIvAJg3LhxWrt2rVavXi03NzeNHj1affr00TfffCNJunr1qsLCwuTj46OdO3fq1KlTGjx4sOzs7PTyyy9L+vuv+mFhYRo5cqRiYmK0efNmDR8+XLVr11ZoaGiJjgkAgLJQ0nM0AAAoWGnPv9nZ2WrTpo15rnvHHXfowIEDioqK0pAhQ0q1L9ebPXu2ZsyYUaZ9AADAWko80V7S1q9fb/E8OjpaXl5eio+PV6dOnZSamqq3335bK1euVJcuXST9fSDTtGlT7dq1S+3atdPGjRt16NAhbdq0Sd7e3mrVqpVmzZqlyZMna/r06bK3t1dUVJT8/f01d+5cSVLTpk21Y8cOzZ8/n0Q7AAC4aYmJiUpJSSlU7OHDh63cGwBAZVe7dm0FBARYlDVt2lT/+9//JEk+Pj6SpOTkZNWuXduMSU5OVqtWrcyY06dPW7Rx5coVnT171tzex8cn1y/Jc57nxFxvypQpGj9+vPk8LS1NdevWLeoQAQAol8p9ov16qampkiQPDw9JUnx8vLKysizWl2vSpInq1aunuLg4tWvXTnFxcQoMDLT4WVtoaKhGjRqlgwcP6o477lBcXJxFGzkxY8eOtf6gAABApZSYmKjGTZrq8qWLZd0VAMAton379rmWW/3pp5/k5+cn6e8bo/r4+Gjz5s1mYj0tLU27d+/WqFGjJEnBwcE6f/684uPjFRQUJEnasmWLsrOz1bZtWzPmueeeU1ZWluzs7CRJsbGxaty48Q3XZ3dwcDDXqgcAoLKpUIn27OxsjR07Vu3bt1fz5s0l/b32m729vdzd3S1ir19frqC1424Uk5aWpkuXLsnJycmiLiMjQxkZGeZz1pYDAADXS0lJ0eVLF+XZc4LsPAu+Yu/Sr3uV+vX7pdAzAEBlNW7cON111116+eWX9cgjj+jbb7/VsmXLtGzZMkmSjY2Nxo4dqxdffFGNGjWSv7+/nn/+efn6+qp3796S/r4C/r777tPjjz+uqKgoZWVlafTo0erbt698fX0lSf3799eMGTMUERGhyZMn68CBA1q4cKHmz59fVkMHAKBMVahEe2RkpA4cOKAdO3aUdVdYWw4AABSanWddOfg0LDAu68yJUugNAKAyu/POO/XJJ59oypQpmjlzpvz9/bVgwQINGDDAjJk0aZIuXLigESNG6Pz58+rQoYPWr18vR0dHMyYmJkajR49W165dVaVKFYWHh2vRokVmvZubmzZu3KjIyEgFBQWpZs2amjZtmkaMGFGq4wUAoLyoMIn20aNHa82aNdq+fbvq1Kljlvv4+CgzM1Pnz5+3uKo9OTnZYu24b7/91qK969eOu9H6cq6urrmuZpdYWw4AAAAAUD717NlTPXv2vGG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XoEGDlJaWpvDwcEnSrFmzNGTIEBUvXlwvv/yyeSwys3TpUvXp00ePPvqoJk+erLi4OM2ePVt79+7VkSNHbEZgpKamKiQkRPXq1dPbb7+trVu3avr06apQoYIGDhx4+ycEuA3yqi3y6v2dV69cuaJGjRrpxIkT6tu3r+rWrasLFy5o3bp1+u2331S6dOksPTe3Hovk5GQNGTJEFy9e1NSpU9W1a1c1b95cO3fu1JgxY/TTTz9p7ty5evHFF7V48WKbx586dUrdunXTgAED1Lt3by1ZskRdunTRpk2b9MQTT2S6L9u3b1fr1q0VGBio1157Tfb29lqyZImaN2+uPXv26LHHHlOnTp30448/6uOPP9bMmTNVunRpSf97Db3xxht69dVX1bVrV/Xr109//PGH5s6dq8aNG6fLQ5l55JFH9PDDD2vVqlXq3bu3TdvKlStVsmRJhYSESJLi4uJUv3592dnZafDgwSpTpoy+/PJLhYWFKTExMd1FAidOnCiLxaIXX3xRSUlJGY6enTVrlpYtW6bPP/9cCxcuVPHixTP9rJKamqq2bdtq27Zt6t69u4YNG6a//vpLkZGR+u6778zPCln5/PThhx+an8/69+8vSbf9rJGd1/ClS5fUqlUrderUSV27dtWaNWs0ZswY1ahRQ61bt77DM4J/E3K4rcKew3PTp9vZs2eP1q1bZ35nmTx5stq2bauXXnpJCxYs0KBBg3Tp0iVNnTpVffv21fbt220ef+nSJbVp00Zdu3ZVjx49tGrVKg0cOFAWi0V9+/aV9M+o9CeffFJfffWV+vfvr4CAAB07dkwzZ87Ujz/+mO56Frd+98zqRW2z8v0pu98Fb/bLL79o7dq16tKli/z8/BQXF6d33nlHTZo00ffffy9vb2+b+Ky+1rNyDLMqq7n7/fff1wsvvKDHH39cw4cP1y+//KInn3xS7u7u8vHxyfL24uPj1bJlS5UpU0b/+c9/5ObmpjNnzuizzz6ziXvhhRfM52fo0KE6ffq05s2bpyNHjqTLcydPnlSPHj30wgsv6Pnnn1eVKlXUrVs3jR8/XrGxsfLy8jJjv/rqK507d07du3fPtI/nzp3TY489psuXL6t///7y9/fX77//rjVr1ujvv/+WxWLJct0hMjJSPXr0UIsWLTRlyhRJ0okTJ7R37950nwHvW/lc6Eceu9Ov9oZhGK6urkadOnXM+7f+ymcdiXvzyKRb3W6uN+svoBERERm2ZfSr/YMPPmgkJiaay1etWmVIMmbPnm0uy8qv9nfq262/Xq9du9aQZEyaNMkm7qmnnjLs7OxsRudIMiwWi82yb7/9Nt3o4ozMmjXLkGR89NFH5rLk5GQjKCjIKF68uM2+W3+hvJPo6GhDkjFo0CCb5U8//XS6Xyj//vvvdI+PiooyJBnLli0zl73zzjuGJOPEiRM2/SxdurTNsXd1dTXCw8Pv2MfbyWwu2fj4eMNisRgtW7a0+WV03rx5hiRj8eLFWd5GrVq17ngsbzdH+63HLTk52ahevbrRvHlzm+WSDHt7e+P48eM2y3MzR7v1uTh27JjN8qpVq9psf+LEiUaxYsWMH3/80SbuP//5j1GkSBHzl3nrL/wuLi5GfHy8TWxGv/5n9rfk1vfQ9u3bDUnG0KFD0+3DzSNfMnoNhoSEGA8//LDNsszmaL91BF5ycrLh4eFhVK9e3WbevQ0bNhiSjHHjxtn0Wfpnfs6b1alTxwgMDEy3LeBm5FXyKnn1f8aNG2dIMj777LN0bda/+Vl9bqy5p0yZMsbly5fN2LFjxxqSjFq1atnMH9qjRw/DYrHYjOyzjsa+eQR7QkKCUbZsWZv35K05JC0tzahUqZIREhKSLlf5+fkZTzzxhLkssznaz5w5YxQpUsR44403bJYfO3bMcHBwSLf8dsaOHWsULVrUZtRaUlKS4ebmZvTt29dcFhYWZpQtW9a4cOGCzeO7d+9uuLq6mq9L6/4+/PDD6V6rGY3ut/7NuvVv1K1/CxYvXpzpWRq3y/mZfX7KbI72W0e0Z+c1bP17efP7MCkpyfDy8rIZzYd/B3L4vyeHZ6dP2R3R7ujoaJMDrLndy8vLpq/W/HVzrPX5nz59urksKSnJqF27tuHh4WEkJycbhvHPCGt7e3tjz549NtuPiIgwJBl79+616VNG3z3vJDvfn7Ly3snI9evX041uPn36tOHo6GjzXSw7r/WsHsOsjGjPau62HqvatWvbjOhetGiRISnD76uZ+fzzz+94LPfs2WNIMpYvX26z3Hpm283LrZ99Nm3aZBN78uTJDN9/gwYNMooXL26Tm2/9XNurVy/D3t4+wz5a83tW6w7Dhg0zXFxc/tVnjtvfuRSP+03x4sVve4V16y94//3vf9ONdM0qR0dH9enTJ8vxvXr1UokSJcz7Tz31lMqWLasvvvgiR9vPqi+++EJFihTR0KFDbZaPGjVKhmHoyy+/tFkeHBxs82tuzZo15eLiol9++eWO2/Hy8lKPHj3MZUWLFtXQoUN15coV7dq1K0d9l5Su77eOZpJk82twSkqK/vzzT1WsWFFubm765ptvzLauXbvKyclJy5cvN5dt3rxZFy5c0DPPPGMuc3Nz04EDB3Tu3Lls9/tOtm7dquTkZA0fPlz29v/7E/X888/LxcVFGzduzPK63NzcdPz4cZ06dSpHfbn1V/SEhAQ1atTI5phZNWnSJE/nD+zUqZMcHBy0cuVKc9l3332n77//Xt26dTOXrV69Wo0aNVLJkiV14cIF8xYcHKzU1FTt3r3bZr2dO3e+40iQ7Pj0009lZ2eXbuS9JNnZ2Zn/v/lYJiQk6MKFC2rSpIl++eUXJSQkZHu7hw4dUnx8vAYNGmQzx1xoaKj8/f0zfJ0MGDDA5n6jRo3u+N4FsoK8+j/k1fs7r3766aeqVauWOnbsmK7N+jc/u89Nly5d5Orqat6vV6+eJOmZZ56xmVe1Xr16Sk5O1u+//27zeG9vb5v+uLi4qFevXjpy5IhiY2Mz3I/o6GidOnVKTz/9tP78808zd169elUtWrTQ7t277/he/eyzz5SWlqauXbva5F8vLy9VqlRJO3bsuO3jb9atWzelpKTYjG7bsmWLLl++bOZ8wzD06aefql27djIMw2abISEhSkhISPf5pHfv3tka/Xonn376qUqXLq0hQ4aka8ss59/p81NWZPc1XLx4cZv3l8Vi0WOPPUbOR4bI4f9TmHN4bvt0Oy1atLAZMW7NU507d7Z5nqzLb92Wg4ODXnjhBfO+xWLRCy+8oPj4eB0+fFjSP9/pAgIC5O/vb/P3vXnz5pKULqfk5LtnTr4/ZZejo6P5dzo1NVV//vmnihcvripVqmSYA7L6Ws/KMcyKrOZu67EaMGCAzdlgzz33nM1nlqyw/g3ZsGGDUlJSMoxZvXq1XF1d9cQTT9j0KzAwUMWLF0/3/Pv5+Zlnu1lVrlxZtWvXtqkfpKamas2aNWrXrl2mnwfS0tK0du1atWvXzuZaFlbW/J7VuoObm5uuXr2qyMjIrB2g+xCF9n+hK1eu2Pwxu1W3bt3UoEED9evXT56enurevbtWrVqVrQ8WDz74YLYu7lKpUiWb+3Z2dqpYsaLOnDmT5XXkxK+//ipvb+90x8N6qvWvv/5qs7x8+fLp1lGyZEldunTpjtupVKmSzZeD220nq323t7dPdxpXlSpV0sVeu3ZN48aNk4+PjxwdHVW6dGmVKVNGly9ftilyurm5qV27dlqxYoW5bPny5XrwwQfNJC9JU6dO1XfffScfHx899thjGj9+fJ59ebEei1v3w2Kx6OGHH87WsZowYYIuX76sypUrq0aNGho9erSOHj2a5cdv2LBB9evXl5OTk9zd3VWmTBktXLgww8Kwn59fltebFaVLl1aLFi20atUqc9nKlSvl4OBgTkMj/XPK/KZNm1SmTBmbW3BwsKT/XcjmbvXz559/lre3t9zd3W8bt3fvXgUHB6tYsWJyc3NTmTJl9H//93+SlKNCe2avE0ny9/dP9zpxcnJK9wNDVt67QFaQV/+HvHp/59Wff/5Z1atXv+P2svPc3PoasH6BvfW0bOvyW18bFStWtCnySv982ZSU6evd+gN879690+XP9957T0lJSXfMTadOnZJhGKpUqVK6dZw4cSJd/r2dWrVqyd/f3+bL8cqVK1W6dGnzdfLHH3/o8uXLWrRoUbrtWQt49yLnV6lS5Y4XlsvO56esyO5ruFy5culeE+R8ZIYc/j+FOYfntk/ZWWd285S3t7eKFStms+zWPHXq1CkdP3483d93a1xe/H3P7vennEhLS9PMmTNVqVIlm89HR48ezTAHZPW1npVjmBVZzd3WY3Fr/4oWLaqHH344y9uT/vlRpHPnznr99ddVunRptW/fXkuWLFFSUpJNvxISEuTh4ZGuX1euXMny89+tWzft3bvXHJSwc+dOxcfH2wzUu9Uff/yhxMTEO36+y2rdYdCgQapcubJat26tcuXKqW/fvtq0adOdD9R9hDna/2V+++03JSQkqGLFipnGODs7a/fu3dqxY4c2btyoTZs2aeXKlWrevLm2bNmiIkWK3HE7eTl6xurWD8xWqampWepTXshsO4Zh3JPt59SQIUO0ZMkSDR8+XEFBQXJ1dZWdnZ26d++e7kNir169tHr1au3bt081atTQunXrNGjQIJsPQl27dlWjRo30+eefa8uWLZo2bZqmTJmizz77rEDNfdm4cWP9/PPP+u9//6stW7bovffe08yZMxUREaF+/frd9rF79uzRk08+qcaNG2vBggUqW7asihYtqiVLltgUTKzuxmu+e/fu6tOnj6Kjo1W7dm2tWrVKLVq0MOeIlf75MPPEE0/opZdeynAd1g8gd7Ofd/Lzzz+rRYsW8vf314wZM+Tj4yOLxaIvvvhCM2fOzPHooOy4V38j8O9DXs0d8uo/CktevRsyew3czdeG9TmaNm2aateunWFM8eLF77gOOzs7ffnllxn29U6Pv1W3bt30xhtv6MKFCypRooTWrVunHj16mEVta5+feeaZdHO5W906t3p+5Pzsfn66Gwrr3xXce+Tw3CmI77Ws9Ol2xy4768zL/U9LS1ONGjU0Y8aMDNtvLernx9/3rHjzzTf16quvqm/fvpo4caLc3d1lb2+v4cOH35PvfHeS17k7K+zs7LRmzRrt379f69ev1+bNm9W3b19Nnz5d+/fvV/HixZWWliYPDw+bMyBvduuAscye/27dumns2LFavXq1hg8frlWrVsnV1VWtWrXK9X5kte7g4eGh6Ohobd68WV9++aW+/PJLLVmyRL169dIHH3yQ634UBhTa/2WsF3y89TSTW9nb26tFixZq0aKFZsyYoTfffFMvv/yyduzYoeDg4EyTU07dOrWHYRj66aefbL4wlCxZUpcvX0732F9//dXmV8Xs9M3X11dbt27VX3/9ZfPL/Q8//GC25wVfX18dPXpUaWlpNl+sc7MdX19fpaWlmaOLrE6ePJkuds2aNerdu7emT59uLrt+/XqGx7NVq1YqU6aMli9frnr16unvv//Ws88+my6ubNmyGjRokAYNGqT4+HjVrVtXb7zxRpYLApk9T9ZjcfLkSZvnNTk5WadPnzZ/Mc0qd3d39enTR3369NGVK1fUuHFjjR8/3iy0Z9aPTz/9VE5OTtq8ebMcHR3N5UuWLMnytnP7PunQoYNeeOEFc4Tbjz/+qLFjx9rEVKhQQVeuXMn2cckrFSpU0ObNm3Xx4sVMR7WvX79eSUlJWrdunc2IkIxOq8/qMbv5dXLzqFDrsrx67wJ3Ql61RV69nC72fsqrFSpU0HfffXfbmLvx3NzOTz/9JMMwbPb/xx9/lKRMLw5nPWvBxcXljvuf2XGtUKGCDMOQn59fuh+1c6Jbt256/fXX9emnn8rT01OJiYk2Fy4rU6aMSpQoodTU1HzN+QcOHFBKSkqmF9DNzuennOT8vPhsCFiRw20V5hyeHbc7dnfDuXPndPXqVZsR2bfmqQoVKujbb79VixYt8vz1ZHUvvj+tWbNGzZo10/vvv2+z/PLlyzaDxayy8lqXsnYMsyKrudt6LE6dOmVzrFJSUnT69GnVqlUry9u0ql+/vurXr6833nhDK1asUM+ePfXJJ5+oX79+qlChgrZu3aoGDRrk6kcUPz8/PfbYY1q5cqUGDx6szz77TB06dLDJx7cqU6aMXFxc7vj5Ljt1B4vFonbt2qldu3ZKS0vToEGD9M477+jVV1+97Q+b9wumjvkX2b59uyZOnCg/Pz/17Nkz07iLFy+mW2Yd7WM9vcX6By6jBJUTy5Yts5kbb82aNTp//rzNl8sKFSpo//79Sk5ONpdt2LBBZ8+etVlXdvrWpk0bpaamat68eTbLZ86cKTs7uzwbRdamTRvFxsbanBJ848YNzZ07V8WLF1eTJk2yvU5r3+bMmWOz3Hq155sVKVIk3S/rc+fOzfBXewcHB/NK3kuXLlWNGjVsEl1qamq60748PDzk7e1tc/rTnWT2PAUHB8tisWjOnDk2fX7//feVkJCg0NDQLG/jzz//tLlfvHhxVaxY0aafmfWjSJEisrOzszlGZ86cSXfF99t54IEHMlx3Vrm5uSkkJESrVq3SJ598IovFog4dOtjEdO3aVVFRUdq8eXO6x1++fFk3btzI0bazqnPnzjIMQ6+//nq6NuvzZx0tcPPzmZCQkOGX7mLFimXpeD3yyCPy8PBQRESEzfP55Zdf6sSJE9l6nQA5RV5Nj7x6f+fVzp0769tvv9Xnn3+ers267rvx3NzOuXPnbPqTmJioZcuWqXbt2vLy8srwMYGBgapQoYLefvttXblyJV37H3/8Yf4/s+PaqVMnFSlSRK+//nq614JhGOk+g9xJQECAatSooZUrV2rlypUqW7asGjdubLYXKVJEnTt31qeffprhl+Gb+3y3dO7cWRcuXEj3/pZsc35WPz9lNefn5WsYsCKHp1eYc3h2VKhQQQkJCTZTip4/fz7D3JYXbty4oXfeece8n5ycrHfeeUdlypRRYGCgpH++0/3+++9699130z3+2rVrunr1aq77cS++P2X0+Wj16tXprq9ilZXXupS1Y5gVWc3djzzyiMqUKaOIiAib99jSpUuz/T6/dOlSum3d+jeka9euSk1N1cSJE9M9/saNG9naZrdu3bR//34tXrxYFy5cuO20MdI/PyR26NBB69ev16FDh9K1W/ue1brDrZ9/7O3tzc+92flcW5gxov0+9eWXX+qHH37QjRs3FBcXp+3btysyMlK+vr5at26dzcUvbjVhwgTt3r1boaGh8vX1VXx8vBYsWKBy5cqpYcOGkv5JTm5uboqIiFCJEiVUrFgx1atXL8dzQbq7u6thw4bq06eP4uLiNGvWLFWsWFHPP/+8GdOvXz+tWbNGrVq1UteuXfXzzz/ro48+SjeXanb61q5dOzVr1kwvv/yyzpw5o1q1amnLli3673//q+HDh6dbd071799f77zzjp577jkdPnxYDz30kNasWaO9e/dq1qxZt50XMDO1a9dWjx49tGDBAiUkJOjxxx/Xtm3b9NNPP6WLbdu2rT788EO5urqqatWqioqK0tatW1WqVKkM192rVy/NmTNHO3bs0JQpU2za/vrrL5UrV05PPfWUatWqpeLFi2vr1q06ePCgzci+rPS/SJEimjJlihISEuTo6KjmzZvLw8NDY8eO1euvv65WrVrpySef1MmTJ7VgwQI9+uijNhe3upOqVauqadOmCgwMlLu7uw4dOqQ1a9Zo8ODBZow1MQ8dOlQhISEqUqSIunfvrtDQUM2YMUOtWrXS008/rfj4eM2fP18VK1bM8jzvzs7Oqlq1qlauXKnKlSvL3d1d1atXv+P8Zzfr1q2bnnnmGS1YsEAhISHmxVSsRo8erXXr1qlt27Z67rnnFBgYqKtXr+rYsWNas2aNzpw5k+HogbzSrFkzPfvss5ozZ45OnTqlVq1aKS0tTXv27FGzZs00ePBgtWzZ0vxV+4UXXtCVK1f07rvvysPDQ+fPn7dZX2BgoBYuXKhJkyapYsWK8vDwSDfiQvpnfrwpU6aoT58+atKkiXr06KG4uDjNnj1bDz30kEaMGHHX9hn/TuRV8urN/q15dfTo0VqzZo26dOmivn37KjAwUBcvXtS6desUERGhWrVq3ZXn5nYqV66ssLAwHTx4UJ6enlq8eLHi4uJuewaavb293nvvPbVu3VrVqlVTnz599OCDD+r333/Xjh075OLiovXr10v63+eEl19+Wd27d1fRokXVrl07VahQQZMmTdLYsWN15swZdejQQSVKlNDp06f1+eefq3///nrxxReztS/dunXTuHHj5OTkpLCwsHTzF7/11lvasWOH6tWrp+eff15Vq1bVxYsX9c0332jr1q0ZFgTzUq9evbRs2TKNHDlSX3/9tRo1aqSrV69q69atGjRokNq3b5+tz0+BgYHaunWrZsyYIW9vb/n5+ZkXE7xZmTJl8uw1jH8ncvj9n8Ozo3v37hozZow6duyooUOH6u+//9bChQtVuXLlHF+0+Xa8vb01ZcoUnTlzRpUrV9bKlSsVHR2tRYsWmWcHPfvss1q1apUGDBigHTt2qEGDBkpNTdUPP/ygVatWafPmzRleqDI77sX3p7Zt22rChAnq06ePHn/8cR07dkzLly/PdF7zrLzWpawdw6zIau4uWrSoJk2apBdeeEHNmzdXt27ddPr0aS1ZsiTbc7R/8MEHWrBggTp27KgKFSror7/+0rvvvisXFxe1adNG0j/zuL/wwguaPHmyoqOj1bJlSxUtWlSnTp3S6tWrNXv2bD311FNZ2l7Xrl314osv6sUXX5S7u3uWRqC/+eab2rJli5o0aaL+/fsrICBA58+f1+rVq/XVV1/Jzc0ty3WHfv366eLFi2revLnKlSunX3/9VXPnzlXt2rXN6zDc9wzcV5YsWWJIMm8Wi8Xw8vIynnjiCWP27NlGYmJiuse89tprxs0vhW3bthnt27c3vL29DYvFYnh7exs9evQwfvzxR5vH/fe//zWqVq1qODg4GJKMJUuWGIZhGE2aNDGqVauWYf+aNGliNGnSxLy/Y8cOQ5Lx8ccfG2PHjjU8PDwMZ2dnIzQ01Pj111/TPX769OnGgw8+aDg6OhoNGjQwDh06lG6dt+tb7969DV9fX5vYv/76yxgxYoTh7e1tFC1a1KhUqZIxbdo0Iy0tzSZOkhEeHp6uT76+vkbv3r0z3N+bxcXFGX369DFKly5tWCwWo0aNGma/bl1faGjoHddnGIZx7do1Y+jQoUapUqWMYsWKGe3atTPOnj1rSDJee+01M+7SpUvmtosXL26EhIQYP/zww237Xq1aNcPe3t747bffbJYnJSUZo0ePNmrVqmWUKFHCKFasmFGrVi1jwYIFWerzzd59913j4YcfNooUKWJIMnbs2GG2zZs3z/D39zeKFi1qeHp6GgMHDjQuXbqUrfVPmjTJeOyxxww3NzfD2dnZ8Pf3N9544w0jOTnZjLlx44YxZMgQo0yZMoadnZ3Ne+H99983KlWqZDg6Ohr+/v7GkiVL0r1fDCPz14ZhGMa+ffuMwMBAw2KxpHtesiIxMdFwdnY2JBkfffRRhjF//fWXMXbsWKNixYqGxWIxSpcubTz++OPG22+/be7r6dOnDUnGtGnT0j3e2nbz69H6t+TgwYM2sRm9h27cuGFMmzbN8Pf3NywWi1GmTBmjdevWxuHDh82YdevWGTVr1jScnJyMhx56yJgyZYqxePFiQ5Jx+vRpMy42NtYIDQ01SpQoYUgy39vWvxU3v0YMwzBWrlxp1KlTx3B0dDTc3d2Nnj17pnvN9u7d2yhWrFi6/c7ouQRuRV69fd/Iq/+uvGoYhvHnn38agwcPNh588EHDYrEY5cqVM3r37m1cuHDBjMnKc5NZXrK+hlevXm2zPKO8ZH1uN2/ebNSsWdPM17c+NrMccuTIEaNTp05GqVKlDEdHR8PX19f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6OsrR0fGO+wAAQGGUo0L7sGHDtHTpUoWGhqp69eqys7PL634BAIAcIEcDAFB45UUeL1GihKpXr26zrFixYipVqpS5PCwsTCNHjpS7u7tcXFw0ZMgQBQUFqX79+pKkli1bqmrVqnr22Wc1depUxcbG6pVXXlF4eLhZKB8wYIDmzZunl156SX379tX27du1atUqbdy4MZdHAQCAwilHhfZPPvlEq1atUps2bfK6PwAAIBfI0QAAFF73Ko/PnDlT9vb26ty5s5KSkhQSEqIFCxaY7UWKFNGGDRs0cOBABQUFqVixYurdu7cmTJhgxvj5+Wnjxo0aMWKEZs+erXLlyum9995TSEjIXe07AAAFVY4vhlqxIheeAACgoCFHAwBQeN2tPL5z506b+05OTpo/f77mz5+f6WN8fX3TTQ1zq6ZNm+rIkSN50UUAAAo9+5w8aNSoUZo9e7YMw8jr/gAAgFwgRwMAUHiRxwEAKLxyNKL9q6++0o4dO/Tll1+qWrVqKlq0qE37Z599liedAwAA2UOOBgCg8CKPAwBQeOWo0O7m5qaOHTvmdV8AAEAukaMBACi8yOMAABReOSq0L1myJK/7AQAA8gA5GgCAwos8DgBA4ZWjOdol6caNG9q6daveeecd/fXXX5Kkc+fO6cqVK3nWOQAAkH3kaAAACi/yOAAAhVOORrT/+uuvatWqlWJiYpSUlKQnnnhCJUqU0JQpU5SUlKSIiIi87icAAMgCcjQAAIUXeRwAgMIrRyPahw0bpkceeUSXLl2Ss7Ozubxjx47atm1bnnUOAABkDzkaAIDCizwOAEDhlaMR7Xv27NG+fftksVhslj/00EP6/fff86RjAAAg+8jRAAAUXuRxAAAKrxwV2tPS0pSamppu+W+//aYSJUrkulMAACBnyNEAABRe5HFkR0xMjC5cuJDl+NKlS6t8+fJ3sUcA8O+Wo0J7y5YtNWvWLC1atEiSZGdnpytXrui1115TmzZt8rSDAAAg68jRAAAUXuRxZFVMTIyq+Afo+rW/s/wYJ+cHdPKHExTbAeAuydEc7dOnT9fevXtVtWpVXb9+XU8//bR5KtuUKVPyuo8AACCL7lWOTk1N1auvvio/Pz85OzurQoUKmjhxogzDMGMMw9C4ceNUtmxZOTs7Kzg4WKdOnbJZz8WLF9WzZ0+5uLjIzc1NYWFhunLlik3M0aNH1ahRIzk5OcnHx0dTp07Ns/0AAKAg4bs2surChQu6fu1vlWo7Sl69Z93xVqrtKF2/9ne2RsADALInRyPay5Urp2+//VaffPKJjh49qitXrigsLEw9e/a0uWALAAC4t+5Vjp4yZYoWLlyoDz74QNWqVdOhQ4fUp08fubq6aujQoZKkqVOnas6cOfrggw/k5+enV199VSEhIfr+++/l5OQkSerZs6fOnz+vyMhIpaSkqE+fPurfv79WrFghSUpMTFTLli0VHBysiIgIHTt2TH379pWbm5v69++fZ/sDAEBBwHdtZFfRUj5y9KqY390AACiHhXZJcnBw0DPPPJOXfQEAAHngXuToffv2qX379goNDZX0z0XaPv74Y3399deS/hnNPmvWLL3yyitq3769JGnZsmXy9PTU2rVr1b17d504cUKbNm3SwYMH9cgjj0iS5s6dqzZt2ujtt9+Wt7e3li9fruTkZC1evFgWi0XVqlVTdHS0ZsyYQaEdAHBf4rs2AACFU44K7cuWLbtte69evXLUmVulpqZq/Pjx+uijjxQbGytvb28999xzeuWVV2RnZyfpny/yr732mt59911dvnxZDRo00MKFC1WpUiVzPRcvXtSQIUO0fv162dvbq3Pnzpo9e7aKFy9uxhw9elTh4eE6ePCgypQpoyFDhuill17Kk/0AAOBeuVc5+vHHH9eiRYv0448/qnLlyvr222/11VdfacaMGZKk06dPKzY2VsHBweZjXF1dVa9ePUVFRal79+6KioqSm5ubWWSXpODgYNnb2+vAgQPq2LGjoqKi1LhxY1ksFjMmJCREU6ZM0aVLl1SyZMk82R8AAAqCe5XHAQBA3stRoX3YsGE291NSUvT333/LYrHogQceyLPkz2npAABkz73K0f/5z3+UmJgof39/FSlSRKmpqXrjjTfUs2dPSVJsbKwkydPT0+Zxnp6eZltsbKw8PDxs2h0cHOTu7m4T4+fnl24d1raMCu1JSUlKSkoy7ycmJuZmVwEAuGfuVR4HAAB5L0eF9kuXLqVbdurUKQ0cOFCjR4/OdaesOC0dAIDsuVc5etWqVVq+fLlWrFhh5s3hw4fL29tbvXv3zrPt5MTkyZP1+uuv52sfAADIiXuVxwEAQN6zz6sVVapUSW+99Va6X+Bz4/HHH9e2bdv0448/SpJ5Wnrr1q0l3fm0dEl3PC3dGpPRaeknT57M8IMOAACFyd3I0aNHj9Z//vMfde/eXTVq1NCzzz6rESNGaPLkyZIkLy8vSVJcXJzN4+Li4sw2Ly8vxcfH27TfuHFDFy9etInJaB03b+NWY8eOVUJCgnk7e/ZsLvcWAID8czfyOAAAyHt5VmiX/jnd+9y5c3m2PusXeH9/fxUtWlR16tTR8OHD78pp6Rmt4+Zt3CopKUmJiYk2NwAACqq8ztF///237O1tP0YUKVJEaWlpkiQ/Pz95eXlp27ZtZntiYqIOHDigoKAgSVJQUJAuX76sw4cPmzHbt29XWlqa6tWrZ8bs3r1bKSkpZkxkZKSqVKmS6fzsjo6OcnFxsbkBAFCY5XUeBwAAeS9HU8esW7fO5r5hGDp//rzmzZunBg0a5EnHJE5LBwAgu+5Vjm7Xrp3eeOMNlS9fXtWqVdORI0c0Y8YM9e3bV5JkZ2en4cOHa9KkSapUqZJ5HRVvb2916NBBkhQQEKBWrVrp+eefV0REhFJSUjR48GB1795d3t7ekqSnn35ar7/+usLCwjRmzBh99913mj17tmbOnJln+wIAQEFxr/I4AADIezkqtFu/IFvZ2dmpTJkyat68uaZPn54X/ZJke1q6JNWoUUO//vqrJk+erN69e9ucll62bFnzcXFxcapdu7aku3ta+siRI837iYmJ8vHxycXeAgCQe/cqR8+dO1evvvqqBg0apPj4eHl7e+uFF17QuHHjzJiXXnpJV69eVf/+/XX58mU1bNhQmzZtMi9WLknLly/X4MGD1aJFC9nb26tz586aM2eO2e7q6qotW7YoPDxcgYGBKl26tMaNG8c1VAAA96V7lccBAEDey1Gh3Xpa+N2WndPSrYV162npAwcOlGR7WnpgYKCkjE9Lf/nll5WSkqKiRYtKytpp6Y6Ojnm+zwAA5Ma9ytElSpTQrFmzNGvWrExj7OzsNGHCBE2YMCHTGHd3d61YseK226pZs6b27NmT064CAFBo3Ks8DgAA8l6eztGe16ynpW/cuFFnzpzR559/rhkzZqhjx46SbE9LX7dunY4dO6ZevXplelr6119/rb1792Z4WrrFYlFYWJiOHz+ulStXavbs2TYj1gEAAAAAAAAAyEiORrRnpwA9Y8aMnGxCEqelAwCQXfcqRwMAgLxHHgcAoPDKUaH9yJEjOnLkiFJSUlSlShVJ0o8//qgiRYqobt26ZpydnV2uOsdp6QAAZM+9ytEAACDvkccBACi8clRob9eunUqUKKEPPvjAnMP80qVL6tOnjxo1aqRRo0blaScBAEDWkKMBACi8yOMAABReOZqjffr06Zo8ebLNhUJLliypSZMmcSV0AADyETkaAIDCizwOAEDhlaNCe2Jiov744490y//44w/99ddfue4UAADIGXI0AACFF3kcAIDCK0eF9o4dO6pPnz767LPP9Ntvv+m3337Tp59+qrCwMHXq1Cmv+wgAALKIHA0AQOFFHgcAoPDK0RztERERevHFF/X0008rJSXlnxU5OCgsLEzTpk3L0w4CAICsI0cDAFB4kccB/NvFxMTowoULWY4vXbq0ypcvfxd7BGRdjgrtDzzwgBYsWKBp06bp559/liRVqFBBxYoVy9POAQCA7CFHAwBQeJHHAfybxcTEqIp/gK5f+zvLj3FyfkAnfzhBsR0FQo4K7Vbnz5/X+fPn1bhxYzk7O8swDNnZ2eVV3wAAQA6RowEAKLzI4wD+jS5cuKDr1/5WqbajVLSUzx3jU/48qz83TNeFCxcotKNAyFGh/c8//1TXrl21Y8cO2dnZ6dSpU3r44YcVFhamkiVLcjV0AADyCTkaAIDCizwOAFLRUj5y9KqY390Asi1HF0MdMWKEihYtqpiYGD3wwAPm8m7dumnTpk151jkAAJA95GgAAAov8jgAAIVXjgrtW7Zs0ZQpU1SuXDmb5ZUqVdKvv/6aJx0DAADZR44GAKDwyos8vnDhQtWsWVMuLi5ycXFRUFCQvvzyS7P9+vXrCg8PV6lSpVS8eHF17txZcXFxNuuIiYlRaGioHnjgAXl4eGj06NG6ceOGTczOnTtVt25dOTo6qmLFilq6dGnOdhoAgPtEjgrtV69etfl13erixYtydHTMdacAAEDOkKMBACi88iKPlytXTm+99ZYOHz6sQ4cOqXnz5mrfvr2OHz8u6Z9R8+vXr9fq1au1a9cunTt3Tp06dTIfn5qaqtDQUCUnJ2vfvn364IMPtHTpUo0bN86MOX36tEJDQ9WsWTNFR0dr+PDh6tevnzZv3pzLIwAAQOGVo0J7o0aNtGzZMvO+nZ2d0tLSNHXqVDVr1izPOgcAALKHHA0AQOGVF3m8Xbt2atOmjSpVqqTKlSvrjTfeUPHixbV//34lJCTo/fff14wZM9S8eXMFBgZqyZIl2rdvn/bv3y/pn1H133//vT766CPVrl1brVu31sSJEzV//nwlJydLkiIiIuTn56fp06crICBAgwcP1lNPPaWZM2fm/UEBAKCQyNHFUKdOnaoWLVro0KFDSk5O1ksvvaTjx4/r4sWL2rt3b173EQAAZBE5GgCAwiuv83hqaqpWr16tq1evKigoSIcPH1ZKSoq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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Select numeric columns\n", - "numerical = df.select_dtypes(include='number').columns\n", - "n_cols = 3\n", - "n_rows = math.ceil(len(numerical) / n_cols)\n", - "\n", - "# Create subplots\n", - "fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, 4 * n_rows))\n", - "axes = axes.flatten()\n", - "\n", - "# Plot each numeric column\n", - "for i, col in enumerate(numerical):\n", - " axes[i].hist(df[col].dropna(), bins=30, edgecolor='black')\n", - " axes[i].set_title(f'Distribution of {col}')\n", - " axes[i].set_xlabel(col)\n", - " axes[i].set_ylabel('Frequency')\n", - "\n", - "# Hide any unused subplots\n", - "for j in range(i + 1, len(axes)):\n", - " fig.delaxes(axes[j])\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "311da64d", - "metadata": {}, - "outputs": [], - "source": [ - "# We see that there are some outliers in host_age with ages above 100, we will remove those\n", - "df['host_age'] = df['host_age'].where(df['host_age'] <= 100, np.nan)\n", - "\n", - "# We drop number_of_previous_incidents_of_guest as it has only 0 values\n", - "df.drop(columns=['number_of_previous_incidents_of_guest'], inplace=True)\n", - "numerical = df.select_dtypes(include='number').columns" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "692854bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing Values (%):\n", - "host_age 69.826817\n", - "guest_age 45.196414\n", - "days_to_complete_verification 13.174074\n", - "days_to_start_verification 5.739898\n", - "listing_number_of_bathrooms 0.572582\n", - "listing_number_of_bedrooms 0.572582\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "# Check missing values for the remaining columns\n", - "missing_values = df.isnull().mean() * 100\n", - "missing_values = missing_values[missing_values > 0].sort_values(ascending=False)\n", - "print(\"Missing Values (%):\")\n", - "print(missing_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "9f333fd5", - "metadata": {}, - "outputs": [], - "source": [ - "# We will fill the remaining missing values with the median for numerical columns\n", - "for col in numerical:\n", - " df[col] = df[col].fillna(df[col].median())" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "ccd46ddc", - "metadata": {}, - "outputs": [], - "source": [ - "# Convert all boolean columns to int\n", - "bool_columns = df.select_dtypes(include='bool').columns\n", - "for col in bool_columns:\n", - " df[col] = df[col].astype(int)" - ] - }, - { - "cell_type": "markdown", - "id": "2c84ebe5", - "metadata": {}, - "source": [ - "### Feature Relevance Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "74a582c8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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uXbh48SIyMzM1ffOdO3cOQMl+ee2118ot16NHjwqv4z59+qB+/fq4f/8+Tp48ifbt29d4X1WmcePGMDY2xq+//ooFCxZgxIgRcHJykrS9pddF6XXCGGOMvQy4gJAxxtgLydLSEgCQnp5OXvbWrVsASn4EmpiYVBjj4uKiFfu4yvrWkxKTkpICABWOuvy4u3fvVltAmJOTg7feegvbt2+vMu7Ro0fVrk+K0n1S1Y/tqvaftbV1pX0rmpiY4OHDh8jPz69x/lxcXNCpUyccPnwYycnJ2LBhA2QymaTBOP766y8MHToUqamplcY8yX6Uct5UZdKkSfjll1+wd+9eCIKAzZs3o169euR0MjMzAaDS878mnnTbqkI9Z65fvw4ApBGh6+LY379/X7OMnZ2dpPhGjRrh5s2bAKq+xpycnJ5KAeHcuXOxYMECFBUVVRpT2X6pKv+Ojo64f/++Zltruq8qY2xsjKioKIwaNQoff/wxPv74Y1hbW6Nt27bo1atXlYXHpddFRYMtMcYYY/9VXEDIGGPshdSyZUt88803OHnyJFQqVbkBQuqaUqmscUxpzb5BgwbB0NCwyjSk1PCaPXs2tm/fjsaNG2PRokVo1aoVLCwsNINftG/fHkePHoUoitWm9TTIZLI6X8fo0aNx6NAhTJ06FX///Td69OhRbaFDbm4uBgwYgDt37mDUqFF499134erqChMTE8jlcly6dAkeHh5PtB+lnDdVuXz5smbUXlEUcfz4cbRt25acjpmZGYDaKzQGnmzbHh8h+HF1fc7U1bEvu10V1VZ+nEKhIOe9Lm3btg1hYWEwMjLCqlWr0KVLF9jY2ECpVEIQBHz44YcIDw9/omuidNm62FcDBw5Et27dsHPnThw5cgRxcXHYvn07tm/fjk8++QT79u1Ds2bNyi1XWoBek8J3xhhj7EXFBYSMMcZeSH369MG0adOQkZGBnTt34vXXX5e8bGmtk9IaKxXVoiqt5VdVDZWasrOzw+XLlzFz5kz4+fk9cXrff/89AGDLli0VNvm9fPnyE6+jrNJ9UrqPKlKX+0+KQYMGaUaDBkoKDKtz+PBh3LlzBy1atKhwlNTa3o9U+fn5GDJkCLKyshAYGIitW7dixowZaN++Pfk8atCgAYCSa+BpKC2sLm2q+7jSGn+1xd7eHgBw8eJFSfF1dewtLCygVCqRl5eHpUuXSh7VufS6uXbtWqUxVc2rLaX3lgULFmDs2LHl5le3X65evVrpvNL829raAqj5vqqOqakp3nrrLbz11lsASka3njRpEn766SdMnDgRhw4dKrdM6XXRsGHDWskDY4wx9iKo+1f4jDHGWB1wcXHB8OHDAQAhISF48OBBlfHp6ema/sNsbW01TWCjo6PLxYqiqJneuXPn2sv0/7z66qsA/v3x/aRKt93BwaHcvN9++w337t2rcLnSQpvS/sSk6tSpE2QyGRITE3Hq1Kly89PS0jR9wNXF/pPCwMAAwcHBqF+/PpycnDBgwIBqlyndj6WFS4/79ttvK122tPkrdV9SvP/++0hMTETnzp3x9ddfIyIiAoWFhRgyZAgyMjJIabVo0QIAcP78+TrIaXmlBV6VFdiV9stZW0r7K/zuu++Qk5NTbXxdHXu5XI7u3bsDoF3vnTp1giAIOHnyZIX77NSpU0+leXFV95b09HTs27evyuX37t1bYTcQv/76K+7fvw9jY2O0bNkSQM33FZWdnR3mzp0LAEhMTKww5uzZswCgyRtjjDH2MuACQsYYYy+slStXwtXVFVevXkWHDh3wxx9/lIspLCzEhg0b4OvriwsXLmimT58+HQDw6aefahVyiaKI+fPnIzExEWZmZnjnnXdqPd8zZsyAmZkZli1bpinkedzVq1erLJQoy9PTE0DJ/igrKSkJ48ePr3S50po7pQMNSGVvb4/BgwdDFEWMGzdOqxZaTk4Oxo4di/z8fLRv377cACVP0+eff4579+4hJSVFUnPE0v144MCBcgVn69atw5YtWypdtqb7UqpNmzZh3bp1aNiwITZt2gSZTIb33nsPgwYNwtWrVyXVkCyrtOC2tLlyXWvdujVMTExw/vx5fPPNN1rzfvjhB6xYsaJW19evXz/4+vri9u3bGDx4cLmakvn5+ZoBMYC6PfahoaHQ09PDjBkzEBMTU2Fz6rNnz2Lbtm2a/+3t7fH6669DrVbj3Xff1WoK/vDhQ0yYMOGpdBlQul/WrVundZ/KzMzEyJEjNU1xK5OXl4d3330XeXl5mmm3b99GSEgIAGD8+PFaA43UZF9VJiEhAVu2bNFad6nSmsUVFXwCwJ9//gkA6NKlS7XrYYwxxv4zns3gyYwxxljtuHPnjhgQECACEAGITk5OYv/+/cXhw4eLXbp0EY2MjEQAoomJiXjs2DHNcmq1WnzrrbdEAKKOjo7YtWtXcfjw4aKHh4cIQFQqleKvv/5abn0ODg4iAPHq1auV5qk0L1U5dOiQaGFhIQIQGzRoIHbp0kUMDAwU+/TpI7q4uIgAxDZt2mgtExoaKgIQQ0NDtab/+OOPoiAIIgCxWbNm4rBhw8QuXbqIurq6YpcuXcT27duLAMSDBw9qLXfq1ClRJpOJMplM7Natmzhq1ChxzJgx4k8//VTt9t67d09s3ry5CEA0NTUVBwwYIA4aNEi0tLTUHIfHl7l69aoIQHRwcKh0v0jZv4975ZVXRADiN998I3mZ0mN048YNren9+/cXAYh6enpijx49xGHDhomNGzcWBUEQP/roo0rzP336dBGAaGFhIQ4ZMkQcM2aMOGbMGPHevXuiKIpiVFSUCEAcOXKkpHyVdfHiRdHIyEiUyWTigQMHtOZlZGSIzs7OIgBx+fLlkrdfFEXR29tbBCCeP3++2tiDBw9Wel5Xdl4+LjIyUpNGu3btxEGDBolNmjQRBUEQ58yZU+G+fZJz5tq1a5rr2cDAQOzRo4c4fPhwsVOnTqKpqWm5NOvq2IuiKH7//feigYGBCEC0tbUVe/ToIQYGBoqvvvqqaGtrKwIQhw4dqpVuWlqa5l5gbm4uvvHGG+Lrr78umpmZiS4uLmK/fv1EAGJUVFSV+72s0vNQT09PbNOmTaWfd999VxRFUUxJSRHNzMxEAGKjRo3EgQMHiv369RNNTU1Fa2trcfTo0RUe+9JzIigoSDQ3NxetrKzEwYMHi3379hUNDQ0150Bubm65PNZkX40cObLcvti+fbvmXu7v7y8OGzZMHDRokOac0NPTE3fv3l1u/enp6aKOjo5oY2MjFhUVSd63jDHG2IuOCwgZY4z9J+zevVsMCgoSXV1dRSMjI1FXV1e0srISu3fvLi5fvly8f/9+hctt2rRJDAgIEM3MzERdXV3Rzs5ODA4OFi9evFhhfG0VEIpiSeHmnDlzxBYtWojGxsainp6eaGtrK7Zv314MDQ0VT58+rRVfVUHM4cOHxa5du4oWFhaigYGB2LRpU3HBggViQUGBpgDt8QJCUSz5Ee3v7y8aGxtrChnLpl/V9ubk5Ijh4eGij4+PaGBgIOrr64uenp7ihx9+KD548KBc/ItQQFhYWCh+9tlnYrNmzUQDAwPR3Nxc7NGjh7h3794q85+Xlyd+8MEHoqurq6inp6dJv3Q7alpAmJubKzZr1qzKAri///5bVCgUop6ennj8+HHJ+2DdunUiAPGDDz6oNrY2CghFURRjYmLEFi1aiPr6+qKJiYnYpUsXcd++fZXu2yc9Z7KyssTFixeLrVq1Eo2NjUWFQiE6ODiI/fr1Ezdv3qwVW1fHvuy2TJ06VWzatKloaGgo6uvriw4ODmJAQIC4aNEi8cqVK+XSvnfvnjhp0iTR1tZWc38YP368ePfu3QoLxapTeh5W93nllVe08h0YGCja29tr9t/48ePFf/75p9JjX3Z6SkqKOHz4cLFhw4ainp6e6OrqKn7yySdiTk5Opfmk7quK9kVaWpq4aNEi8bXXXhOdnJxEAwMD0cTERPTy8hLfe++9Su/xy5YtEwGIc+fOlbxfGWOMsf8CQRSfkyENGWOMMcbYU5ObmwtHR0fo6Ojg2rVrmj4pGXtZiaKI5s2b48qVK0hJSYGVldWzzhJjjDH21HAfhIwxxhhjLyEDAwMsWLAAaWlpWLdu3bPODmPP3NatW3HmzBnMnDmTCwcZY4y9dLgGIWOMMcbYS0qtVqN169a4efMmkpOTYWho+KyzxNgzoVKp0KRJE+Tl5eHixYtQKpXPOkuMMcbYU8UFhIwxxhhjjDHGGGOMvcS4iTFjjDHGGGOMMcYYYy8xLiBkjDHGGGOMMcYYY+wlxgWEjDHGGGOMMcYYY4y9xOq0gDAgIABTpkypy1VIkpubi4EDB8LExASCICAjI+NZZ0kjOjoaZmZmdb6esLAw+Pj4PPN8MMYYY4wxxhhjjLHni86zzsDTEBMTgyNHjuDPP/+EhYUFTE1Nn3WWnjtDhw7Fa6+9Rlpml64HKd77/HZSfI7MhBRvWnRPcmyWrjkp7Vw1bSQ7Y3kWKV5RnEuKVwtyybG6qgJS2nm6xqR4HXUhKV5PlS85NlPXgpQ2VYPsFFK8Sq5Hir+pcJUcKwNtvChVHVcAVwjSj6sIgZS2UqCd79Rz7JFQjxSfUUg75yn0ZMWk+KZ/LiPFp3cKlBz7T3FDUtrGOrTjlFlkRIrXl9OOa32Z9Hu8SqB9vckVaSP3GgrZpHiKhyra+WsgzyPF64H2TFAT7zWFokJ6XgRaXgSBdp+k5IVKJUp/DgOAHuGeCtD3e47KgBRvKJd+fQvE55MuaNtaDF1SvAwqQqyalDZVIWjnGHVfZhVLv68q5dK/XwH0c5L6rC8Wpd+HZULdHielmEOKzxVozzPKeUY9B+QC7XvEgyLabxzKeVOgpn0PptxnakIB2jkvE6XfO/IF2j1VDtpxKgJtX3q6NCLFM1ZTL0UBYXJyMjw9PdG0adNKYwoLC6GnR7tQ/0uUSiWUSlohGGOMMcYYY4wxxhh78dVaFZScnBwEBQXByMgI1tbWiIiI0Jr/zTffwM/PD8bGxrCyssKIESOQnp4OABBFEa6urli6dKnWMomJiRAEAVeuXIEoiggLC4O9vT0UCgVsbGwwefLkavMVEBCAiIgIHD58GIIgICAgAADg6OiITz/9FEFBQTAxMcHYsWMBAD/++COaNGkChUIBR0fHctvh6OiI+fPna7bVwcEBO3fuxN27d9G/f38YGRnB29sbf//9N2n/7dixA25ubtDX10fPnj1x48YNrflr1qyBi4sL9PT04OHhgW+++UZrfmpqqmb9JiYmGDJkCO7cuVPp+pKTk+Hs7IyJEydCFEVuYswYY4wxxhhjjDH2kqq1AsIZM2bg0KFD+Omnn7B3717Exsbi5MmTmvlFRUX49NNPcerUKezYsQPXrl1DcHAwAEAQBIwePRpRUVFaaUZFRaFTp05wdXXFjz/+iMjISKxduxaXL1/Gjh070KxZs2rztW3bNrzzzjto164d0tLSsG3bNs28pUuXonnz5khISMCcOXMQHx+PIUOGYNiwYThz5gzCwsIwZ84cREdHa6UZGRkJf39/JCQkoHfv3njrrbcQFBSEN998EydPnoSLiwuCgoIgitKqkOfm5mLBggX4+uuvERcXh4yMDAwbNkwzf/v27Xj//fcREhKCs2fPYty4cRg1ahQOHjwIAFCr1ejfvz8ePHiAQ4cOYd++fUhJScHQoUMrXN/p06fRoUMHjBgxAqtWrYIg0JoMMMYYY4wxxhhjjLH/jlppYpydnY3/+7//w7fffouuXbsCKOn3z9bWVhMzevRozd/Ozs5YsWIFWrVqhezsbBgZGSE4OBiffPIJjh8/jtatW6OoqAibNm3S1CpMTU2FlZUVunXrBl1dXdjb26N169bV5s3c3BwGBgbQ09ODlZWV1rwuXbogJCRE839gYCC6du2KOXPmAADc3d1x/vx5fPbZZ5rCTAB47bXXMG7cOADAJ598gjVr1qBVq1YYPHgwAGDmzJlo164d7ty5U26dFSkqKsKqVavQpk0bzb7z9PTU7IulS5ciODgYEyZMAABMmzYNf/31F5YuXYrOnTvjwIEDOHPmDK5evQo7OzsAwNdff40mTZrgxIkTaNWqlWZdf/75J/r06YOPPvpIa9sZY4wxxhhjjDHG2MupVmoQJicno7CwUFPABZQUzHl4/DuIRXx8PPr27Qt7e3sYGxvjlVdeAVBS8AcANjY26N27NzZs2AAA+Pnnn1FQUKApdBs8eDDy8vLg7OyMd955B9u3b0dxMa0z0Mf5+flp/X/hwgX4+/trTfP398fly5ehUv3bqam3t7fm74YNSzp9L1ubsXRaaRPq6ujo6GgV4jVu3BhmZma4cOFClfkqO9/Ozk5TOAgAXl5eWmkAJfu6e/fu+OSTT8iFgwUFBXj06JHWp0is2w6FGWOMMcYYY4wxxljdq9thMP8nJycHPXv2hImJCTZu3IgTJ05g+/aSEW0LC/8dQevtt9/G5s2bkZeXh6ioKAwdOhQGBiUjCNnZ2SEpKQmrV6+GUqnEhAkT0KlTJxQVFdU4X4aGtFELS+nq/jvaWmnz3IqmqdXPVwGapaUlWrduje+++w6PHj0iLRseHg5TU1Otz/fqB3WUU8YYY4wxxhhjjDH2tNRKAaGLiwt0dXVx7NgxzbSHDx/i0qVLAICLFy/i/v37WLRoETp27IjGjRtXWLvutddeg6GhIdasWYM9e/ZoNUsGSkba7du3L1asWIHY2FgcPXoUZ86cqY1NAAB4enoiLi5Oa1pcXBzc3d0hl8trbT2PKy4u1hrUJCkpCRkZGfD09KwyX15eXpr5N27c0BrY5Pz588jIyNDEACX775dfftEMhJKVlSU5j7Nnz0ZmZqbWZ4jMvEbbyxhjjDHGGGOMMcaeH7XSB6GRkRHGjBmDGTNmoH79+mjQoAE++ugjyGQl5Y/29vbQ09PDypUrMX78eJw9exaffvppuXTkcjmCg4Mxe/ZsuLm5oV27dpp50dHRUKlUaNOmDQwMDPDtt99CqVTCwcGhNjYBABASEoJWrVrh008/xdChQ3H06FGsWrUKq1evrrV1VERXVxeTJk3CihUroKOjg4kTJ6Jt27aaPhZnzJiBIUOGwNfXF926dcPPP/+Mbdu2Yf/+/QCAbt26oVmzZggMDMTy5ctRXFyMCRMm4JVXXinXjNrQ0BC7du3Cq6++ildffRV79uyBkZFRtXlUKBRQKBTa+RaeSgVUxhhjjDHGGGOMMVaHaq2E57PPPkPHjh3Rt29fdOvWDR06dEDLli0BlDRtjY6Oxg8//AAvLy8sWrRIM/jI48aMGYPCwkKMGjVKa7qZmRm++uor+Pv7w9vbG/v378fPP/+M+vXr19YmoEWLFvj++++xefNmNG3aFJ988gnmzZunNUBJXTAwMMDMmTMxYsQI+Pv7w8jICFu2bNHMHzBgAD7//HMsXboUTZo0wdq1axEVFYWAgAAAJU2af/rpJ9SrVw+dOnVCt27d4OzsrJVGWUZGRti9ezdEUUTv3r2Rk5NTp9vHGGOMMcYYY4wxxp5fgiiK4rPORFlHjhxB165dcePGDc1gH+z5tEvXo/qgMrzPbyfF58hMSPGmRfckx2bp0ppH56qVpHhjufTm2wCgKM4lxasF6U3edVUFpLTzdI1J8TrqwuqDytBT5UuOzdS1IKVN1SA7hRSvkuuR4m8qXCXHykC7FavquAtZhSD9uIoQSGkrBdr5Tj3HHgn1SPEZhbRznkJPRhtMq+mfy0jx6Z0CJcf+U0x7phrr0I5TZlH1tdHL0pfTjmt9mfR7vEqgNZDIFWl9EhsK2aR4iocq2vlrIM8jxeuB9kxQE+81haKi+qDSvAi0vAgC7T5JyQuVSqR1PaNHuKcC9P2eozIgxRvKpV/fAvH5pAvathZDt/qgMmRQVR+kia3b/sALQTvHqPsyq1j6fVUpl/79CqCfk9RnfbEo/T4sE+r2OClFWmWIXIH2PKOcZ9RzQC7Qvkc8KKL9xqGcNwVq2vdgyn2mJhSgnfMyUfq9I1+g3VPloB2nItD2padLI1I8YzX13LQRLSgowM2bNxEWFobBgwdLLhwMCAjAlClT6jZzNXTx4kW0bdsW+vr68PHxeeL0oqOjYWZmRlomODgYAwYMeOJ1M8YYY4wxxhhjjLH/puemBmF0dDTGjBkDHx8f7Ny5E40aSSsl9/X1xdmzZ8v1j1cqO7vu3vJXZ+jQodi/fz/y8vIgCIJmdONSH374IT788EPJ6eXl5SErKwsNGjSQvExwcDAyMjKwY8eOSmMcHR0xZcoUckHrjcvnSfGnvV4nxbte3E+Kp9QglKtpo18/VFiR4qlv+wTiZUipUZUp0N4kUt9sUt/OU99AU9T1G2i1SHuncr+AVguWwkr/Pin+dp4lKb6RsvxAUpVRgVaThnrOFIq0t6yZhbQ3/9RaSRQ6xHPSRnaj+qAyVEs/lhx7dfJ3pLQt9TNI8XfzzUjx1G8fjXWTJMfegBMpbXvQahPn69BqHOYJ0uOp9xnqfa9ITautJRek17gAAPPiO5Jjs3VptSWLiV1nywk1zQDavYa6X6jPVup5QK1xqC9Ir3lK3Y/5oLW6oNY4pNyzZSLt+tAvov1uuCk4kuKNdWjpk2rhEZ+t1HOYmn6RKP1eQ72PUa8nizzas/WGwo0Un6+Sfu8w06W1MqLWfswUafdVPZn06y9fTasxayij1SCk1sITBdpvCqOCh5JjU2UupLSp17YOaL9FXV1o32sYq6laGaSkNgQHB9eorz9jY2MEBgbi44+l/0iqjkqlgiAImkFWaio5ORlBQUF47733Kpxvbk6sAq5UQqmkfelijDHGGGOMMcYYY6wqtdbEOCAgAJMnT8YHH3wAc3NzWFlZISwsDABw7do1CIKAxMRETXxGRgYEQUBsbCwAIDY2FoIg4LfffoOvry+USiW6dOmC9PR07N69G56enjAxMcGIESOQm/vv2wiZTAYjIyMsX74cLVu2RNu2bRETEwMXFxe4upb0/1VQUIDp06ejUaNGMDQ0RJs2bTTrBf5turtz5054eXlBoVAgNTW1yu1Vq9WYN28ebG1toVAo4OPjgz179mjmC4KA+Ph4LF++HG5ubvj222/h6uqq9SlbQFi6j7Zt24bOnTvDwMAAzZs3x9GjR8vls6z58+ejQYMGMDY2xttvv41Zs2ZV2Jx56dKlsLa2Rv369fHee++hqKhIc9yuX7+OqVOnVljLkTHGGGOMMcYYY4z9t9VqH4QxMTEwNDTEsWPHsGTJEsybNw/79u0jpREWFoZVq1bhzz//xI0bNzBkyBAsX74cmzZtwq5du7B3716sXLmy3Hp1dHRw/PhxfP7551i2bBnWr1+vmT9x4kQcPXoUmzdvxunTpzF48GD06tULly9f1sTk5uZi8eLFWL9+Pc6dO1dtM97PP/8cERERWLp0KU6fPo2ePXuiX79+mjTT0tLQpEkThISEIC0tDdOnT5e0/R999BGmT5+OxMREuLu7Y/jw4Sgurri69caNG7FgwQIsXrwY8fHxsLe3x5o1a8rFHTx4EMnJyTh48CBiYmIQHR2N6OhoAMC2bdtga2uLefPmIS0tDWlpaZLyyRhjjDHGGGOMMcb+G2q1ibG3tzdCQ0MBAG5ubli1ahUOHDgANzfp/TjMnz8f/v7+AIAxY8Zg9uzZSE5OhrOzMwBg0KBBOHjwIGbOnKlZxs7ODpGRkRAEAR4eHjhz5gwiIyPxzjvvIDU1FVFRUUhNTYWNjQ0AYPr06dizZw+ioqKwcOFCAEBRURFWr16N5s2bS8rn0qVLMXPmTAwbNgwAsHjxYhw8eBDLly/HF198ASsrK+jo6MDIyAhWVtL7r5s+fTp69+4NAJg7dy6aNGmCK1euoHHjxuViV65ciTFjxmDUqFEAgE8++QR79+4t1+9ivXr1sGrVKsjlcjRu3Bi9e/fGgQMH8M4778Dc3BxyuRzGxsakfDLGGGOMMcYYY4yx/4ZarUHo7e2t9b+1tTXS06V3dP94Gg0bNoSBgYGmcLB02uNptm3bVqtpbLt27XD58mWoVCqcOXMGKpUK7u7uMDIy0nwOHTqE5ORkzTJ6enrl8l+ZR48e4fbt25qCzFL+/v64cOECaXsfVzYP1tbWAFDpPkxKSkLr1q21pj3+PwA0adIEcvm/gwjU5LgUFBTg0aNHWp+CQlqn0owxxhhjjDHGGGPs+VOrNQh1dbVHqxIEAWq1WjPYR9kBk0v7wKsqDUEQKk1TquzsbMjlcsTHx2sVkgGAkdG/I10qlcrnov+9x7cfAGl7q0uzNF1qmuHh4Zg7d67WtCkTJ2Da5IoHYGGMMcYYY4wxxhhjL4ZarUFYGUtLSwDQ6t+u7IAlT+rYsWNa///1119wc3ODXC6Hr68vVCoV0tPTyw0SUtMmtSYmJrCxsUFcXJzW9Li4OHh5edV4O6g8PDxw4sQJrWmP/y+Fnp4eVCpVlTGzZ89GZmam1ue98e+Q18UYY4wxxhhjjDHGni+1WoOwMkqlEm3btsWiRYvg5OSE9PR0fPzxx7WWfmpqKqZNm4Zx48bh5MmTWLlyJSIiIgAA7u7uCAwMRFBQECIiIuDr64u7d+/iwIED8Pb21vT3RzVjxgyEhobCxcUFPj4+iIqKQmJiIjZu3Fhr21WdSZMm4Z133oGfnx/at2+PLVu24PTp01pNsqVwdHTE4cOHMWzYMCgUClhYWJSLUSgUUCgUWtMy9fSeKP+MMcYYY4wxxhhj7Nl7KgWEALBhwwaMGTMGLVu2hIeHB5YsWYIePXrUStpBQUHIy8tD69atIZfL8f7772Ps2LGa+VFRUZg/fz5CQkJw69YtWFhYoG3btujTp0+N1zl58mRkZmYiJCQE6enp8PLyws6dO0kDsjypwMBApKSkYPr06cjPz8eQIUMQHByM48ePk9KZN28exo0bBxcXFxQUFGg1BWeMMcYYY4wxxhhj/22CyKVB/yndu3eHlZUVvvnmmzpf143L50nxp71eJ8W7XtxPijctuic5Vq6uuA/MyjxU0Jqjy4ViUrxAvAx11NIHiMkUzGl5AS0vMtD6sxRRd319yoQn66+zOmqR1ivD/QKTOsoJYKV/nxR/O8+SFN9IKX0gIxXk1QeVQT1nCkVabeXMQqPqg8oQhLp7DOoQz0kb2Q1SvGqp9Nr4Vyd/R0rbUj+DFH8334wUT/320Vg3SXLsDTiR0rZHCik+X8eQFJ8nSI+n3meo970itW71QWXIhaq7IHmcefEdybHZuvVIaRcT32vLQcs75V5D3S/UZyv1PFATew3SF/Ikx1L3Yz6UpHhd0Aa9o9yzZSLt+tAvyibF3xQcSfHGOrT0i0Xp5zz12Uo9h6npF4nS7zXU+xj1erLIoz1bbyhoFT7yVdLvHWa6WaS0lWIOKT5TpN1X9WTSr798taL6oDIMZbmkeDlov59E4vgBRgUPJcemylxIaVOvbR3Qfou6utC+1zBWU0+tBuGLKiAgAD4+Pli+fPmzzko5ubm5+PLLL9GzZ0/I5XJ899132L9/P/bt2/ess8YYY4wxxhhjjDHGXhBcg7ASpSMc5+XlQSaTafW/t3v3bnTs2JGU3sKFC7Fw4UKtaUVFRSgsLESvXr2we/duch7z8vLQt29fJCQkID8/Hx4eHvj444/xxhtvkNOqiYvJN0nx1Ld9Vxp3I8VTahzezi3fz2KVaetfJcWrBFrZe4FAe9tO2ZePio1JaZvqPCLFF4i0t4lZRQaSY630pNdiA4Bi0GrGUGtcUM9hSk1SUaS9BVUTa+1R806hJ+aT4gsFfVL8vULa2/AGCum1iQFASawxQiEQa6881GlAiqfUutBDASlt6pt5uZr25l8u0uJvqRpJjnUSr5DSztel1TrNgikpXodwL6jrGoTUWkANMy+R4hMV7SXHOiho3yNEgbZvqM8EyvOMckwBoNHDs6T4dHN3UryimFZTJ1cuvYY7tZY4lZ5Auzflq6V/Z6I++woJtd5qwjH7DCk+01j6fS8HtO97ugKtBhMVpcVIXbYuAQCVSGzpQLyvFqql1yB0yjxJSpt6L9BT0b6TZQj1JccqiNcqFfVeQ619nK2W/qyXE88Bak1rag1eDxc7UjxjNcU1CCtROspyYGAgPD09tQZVadRI+sO61Pjx4zFkyBCtaT/++CMWLFiA9evX1yiPSqUS+/fTmuEyxhhjjDHGGGOMMVYW7VXsS8TV1RWurq5QKpUwMTHBunXr0Lp1a3To0AGLFy/WxKWmpqJ///4wMjKCiYkJhgwZgjt3/u1/59SpU+jcuTMcHBzQokULDB06FBkZGbh58yZmzZqFrKws2NraQhAEhIWFVZuvb775Bn5+fjA2NoaVlRVGjBiB9HTtWlalg6Xo6+ujc+fOiImJgSAIyMjI0MT88ccf6NixI5RKJezs7DB58mTk5ND6uGCMMcYYY4wxxhhjLz4uIJQgJiYGhoaGOHbsGJYsWYJ58+Zh3759UKvV6N+/Px48eIBDhw5h3759SElJwdChQzXLBgYGwtbWFidOnEB8fDxmzZoFXV1dtG/fHsuXL4eJiQnS0tKQlpaG6dOnV5uXoqIifPrppzh16hR27NiBa9euITg4WDP/6tWrGDRoEAYMGIBTp05h3Lhx+Oijj7TSSE5ORq9evTBw4ECcPn0aW7ZswR9//IGJEyfW2j5jjDHGGGOMMcYYYy8GbmIsgbe3N0JDQwEAbm5uWLVqFQ4cOAAAOHPmDK5evQo7u5J+Ab7++ms0adIEJ06cQKtWrZCamooZM2agcePGmuVLmZqaQhAEWFlJHyF39OjRmr+dnZ2xYsUKtGrVCtnZ2TAyMsLatWvh4eGBzz77DADg4eGBs2fPYsGCBZrlwsPDERgYiClTpmjytGLFCrzyyitYs2YN9PVp/YMxxhhjjDHGGGOMsRcX1yCUwNvbW+t/a2trpKen48KFC7Czs9MUDgKAl5cXzMzMcOHCBQDAtGnT8Pbbb6Nbt25YtGgRkpOTnygv8fHx6Nu3L+zt7WFsbIxXXnkFQElTZwBISkpCq1attJZp3bq11v+nTp1CdHQ0jIyMNJ+ePXtCrVbj6tWKB+MoKCjAo0ePtD6FBXXbUS1jjDHGGGOMMcYYq3tcQCiBrq72iGaCIECtljayUVhYGM6dO4fevXvj999/h5eXF7Zv316jfOTk5KBnz54wMTHBxo0bceLECU1ahYXSR3HKzs7GuHHjkJiYqPmcOnUKly9fhouLS4XLhIeHw9TUVOuz7ssvarQdjDHGGGOMMcYYY+z5wU2Mn4Cnpydu3LiBGzduaGoRnj9/HhkZGfDy8tLEubu7w93dHVOnTsXw4cMRFRWF119/HXp6elCppA9xfvHiRdy/fx+LFi3SrO/vv//WivHw8MCvv/6qNe3EiRNa/7do0QLnz5+Hq6ur5HXPnj0b06ZN05p27eZdycszxhhjjDHGGGOMsecT1yB8At26dUOzZs0QGBiIkydP4vjx4wgKCsIrr7wCPz8/5OXlYeLEiYiNjcX169cRFxeHEydOwNPTEwDg6OiI7OxsHDhwAPfu3UNubm6V67O3t4eenh5WrlyJlJQU7Ny5E59++qlWzLhx43Dx4kXMnDkTly5dwvfff4/o6GgAJTUfAWDmzJn4888/MXHiRCQmJuLy5cv46aefqhykRKFQwMTEROujp1A8wd5jjDHGGGOMMcYYY88DLiB8AoIg4KeffkK9evXQqVMndOvWDc7OztiyZQsAQC6X4/79+wgKCoK7uzuGDBmCV199FXPnzgUAtG/fHuPHj8fQoUNhaWmJJUuWVLk+S0tLREdH44cffoCXlxcWLVqEpUuXasU4OTlh69at2LZtG7y9vbFmzRrNKMaK/xXoeXt749ChQ7h06RI6duwIX19ffPLJJ7CxsantXcQYY4wxxhhjjDHGnnOCKIris84Eq1sLFizAl19+iRs3btRquheTb5LiBdBOtSuNu5HiXS/ulxx7O9eClrZ+xYO3VEYl0FrvFwhKUjxlXz4qNialbarziBRfINJqkmYVGUiOtdJLJ6VdDN3qg8pQE9+RUM9huVAsOVYUBVLaashJ8dS8U+iJ+aT4QoE2Uvq9wnqk+AaKe6R4ZVE2KZ5CEKX1V1vqoU4DUrxMkJ6+HmgDS4kC7ZyUq6Wf7wAgF2nxt1SNJMc6iVdIaefrGpHis2BKitch3AvUIu2+RDkHAEAGWnzDzEuk+ERFe8mxDgra9whRoO0b6jOB8jyjHFMAaPTwLCk+3dydFK8orroVyuNy5SaSY1XE5w2VnkC7N+WrpX9noj77CkXaOUPlmH2GFJ9pLP2+lwPa9z1doYgUTyVC+jOEElsTKpF2DlPvq4VqPcmxTpknSWlT7wV6Ktp3sgyhvuRYBfFapaLea3QhvQ9+AMhWS3/Wy4nngBzSuw0rSZ8W7+FiV30QY7Wg1moQBgQEYMqUKbWV3H9OcHAwBgwY8FTWtXr1apw4cQIpKSn45ptv8Nlnn2HkyJEAgOjoaJiZmT2VfDDGGGOMMcYYY4yx598LMUhJdHQ0pkyZgoyMjGedlWpdu3YNTk5OSEhIgI+PD2nZI0eO4NVXX610fna2tFouly9fxvz58/HgwQPY29sjJCQEs2fPBgAMHToUr732miY2LCwMO3bsQGJiIimvAGBaRKulQ32LS6kRCNBqHHbdOomU9q1mr1UfVEa9nNuk+LsGDqR4q4wLkmNlZrQ3j5kqWs0YfRntbaKrWnreHwmWpLRlxLd3eSpazU018Q330WTp+SdW1kJrp/uk+LNp0t8QA0AbW+k1jqm1qRoWpJLis3RotbuOpDqT4uuyHr2FCa2WUUuj83WUE2DzxWak+AFNU0jxPye5keKpgi1/rT7of5LrtSal7XY3jhSfY0lLn/L8S82m1XC3NXpAis9T0WrwFpo0JcW3ub5VcuwFe9qz1URGq+FOrThNqcWUp6btx2tmvqR4UxXtuB594FV9UBlt6idJjs0A7flhJMsixeuoaLWAsgg15QxltJqVzlf3kuKPWg0lxcuMaNdTykPp+96lHu17eR02LAAAFIvSf2YqiN8li4g1PV2TdpDi91m9TYq3NMyRHEu9F+QX0Vrq3Myk1SS1NZV+vRYKtP1eX0Y7Jx+JZqR4Q9CeCY2TD0iO/dt+OCnthsTWK3VdM5uxmnohCghfFn5+fjUqqHtcZGQkIiMjK5ynVCqhVNIKRRhjjDHGGGOMMcbYf1etDlKiVqvxwQcfwNzcHFZWVggLC9PMS01NRf/+/WFkZAQTExMMGTIEd+7c0cw/deoUOnfuDGNjY5iYmKBly5b4+++/ERsbi1GjRiEzMxOCIEAQBK10K+Po6Ij58+cjKCgIRkZGcHBwwM6dO3H37l1NPry9vfH3339rLffjjz+iSZMmUCgUcHR0RERERLl0Fy5ciNGjR8PY2Bj29vZYt26dZr6TkxMAwNfXF4IgICAgQGv5pUuXwtraGvXr18d7772HoqJ/+/+IiorCq6++iqZNm8Lf3x+zZs2Cq6ur5lORX375BWZmZlCpSmpOJSYmQhAEzJo1SxPz9ttv48033wSg3cQ4Ojoac+fOxalTpzT7tnTEY8YYY4wxxhhjjDH2cqjVAsKYmBgYGhri2LFjWLJkCebNm4d9+/ZBrVajf//+ePDgAQ4dOoR9+/YhJSUFQ4f+Wy0/MDAQtra2OHHiBOLj4zFr1izo6uqiffv2WL58OUxMTJCWloa0tDRMnz5dUn4iIyPh7++PhIQE9O7dG2+99RaCgoLw5ptv4uTJk3BxcUFQUBBKx2mJj4/HkCFDMGzYMJw5cwZhYWGYM2dOuUKziIgI+Pn5ISEhARMmTMC7776LpKSSphrHjx8HAOzfvx9paWnYtm2bZrmDBw8iOTkZBw8eRExMDKKjozVp//3335g8eTLmzZuHpKQk7NmzB506dap2Gzt27IisrCwkJCQAAA4dOgQLCwvExsZqYg4dOlSuoBIoaW4cEhKCJk2aaPZt2WPCGGOMMcYYY4wxxv77arWJsbe3N0JDQwEAbm5uWLVqFQ4cKGnrf+bMGVy9ehV2diUj8Hz99ddo0qQJTpw4gVatWiE1NRUzZsxA48aNNcuXMjU1hSAIsLKyIuXntddew7hx4wAAn3zyCdasWYNWrVph8ODBAICZM2eiXbt2uHPnDqysrLBs2TJ07doVc+bMAQC4u7vj/Pnz+OyzzxAcHKyV7oQJEzRpREZG4uDBg/Dw8IClZUmfY/Xr1y+X33r16mHVqlWQy+Vo3LgxevfujQMHDuCdd95BamoqDA0N0adPHxgbG8PBwQG+vtX3UWFqagofHx/ExsbCz88PsbGxmDp1KubOnYvs7GxkZmbiypUreOWVV8otq1QqYWRkBB0dHfK+ZYwxxhhjjDHGGGP/DbVag9Db21vrf2tra6Snp+PChQuws7PTFA4CgJeXF8zMzHDhQsmABdOmTcPbb7+Nbt26YdGiRUhOTq7V/DRs2BAA0KxZs3LT0tPTAQAXLlyAv7+/Vhr+/v64fPmypgnv4+mWFlyWplGVJk2aQC7/t0PS0v0DAN27d4eDgwOcnZ3x1ltvYePGjcjNldax8iuvvILY2FiIoogjR47gjTfegKenJ/744w8cOnQINjY2WgWuNVFQUIBHjx5pfQoKaZ1KM8YYY4wxxhhjjLHnT60WEOrqao9sJAgC1Gq1pGXDwsJw7tw59O7dG7///ju8vLywffv2WsuP8L8hQiuaJjWPFaVbmo6UNKpaztjYGCdPnsR3330Ha2trfPLJJ2jevLmkkZsDAgLwxx9/4NSpU9DV1UXjxo0REBCA2NhYHDp0qMLag1Th4eEwNTXV+qxct+GJ02WMMcYYY4wxxhhjz1atFhBWxtPTEzdu3MCNGzc0086fP4+MjAx4eXlpprm7u2Pq1KnYu3cv3njjDURFRQEA9PT0tGrw1WU+4+LitKbFxcXB3d1dq+ZfVfT09ACgRvnV0dFBt27dsGTJEpw+fRrXrl3D77//Xu1ypf0QRkZGagoDSwsIY2NjK+x/sGx+peR19uzZyMzM1PpMGjta8rYxxhhjjDHGGGOMsefTUykg7NatG5o1a4bAwECcPHkSx48fR1BQEF555RX4+fkhLy8PEydORGxsLK5fv464uDicOHECnp6eAEpGDs7OzsaBAwdw7949yU1vqUJCQnDgwAF8+umnuHTpEmJiYrBq1SrJg6IAQIMGDaBUKrFnzx7cuXMHmZmZkpb75ZdfsGLFCiQmJuL69ev4+uuvoVar4eHhUe2y9erVg7e3NzZu3KgpDOzUqRNOnjyJS5cuVVmD0NHREVevXkViYiLu3buHgoKCCuMUCgVMTEy0Por/FYYyxhhjjDHGGGOMsRfXUykgFAQBP/30E+rVq4dOnTqhW7ducHZ2xpYtWwAAcrkc9+/fR1BQENzd3TFkyBC8+uqrmDt3LgCgffv2GD9+PIYOHQpLS0ssWbKkTvLZokULfP/999i8eTOaNm2KTz75BPPmzdMaoKQ6Ojo6WLFiBdauXQsbGxv0799f0nJmZmbYtm0bunTpAk9PT3z55Zf47rvv0KRJE0nLv/LKK1CpVJoCQnNzc3h5ecHKyqrKQsaBAweiV69e6Ny5MywtLfHdd99JWh9jjDHGGGOMMcYY+2+otVGMY2Njy03bsWOH5m97e3v89NNPFS6rp6dXbcHUmjVrsGbNGsn5uXbtWrlppYN5DBgwAEBJ7TlRFLViBg4ciIEDB5LSTUxM1Pr/7bffxttvv601LTo6utxyy5cv1/xtYWGB/Px85ObmonHjxuXSfDwPTk5OSEhI0Ixg/Pnnn+Phw4cwMzOrNF8AEBwcrFXgqVAosHXrVgQEBMDHx4dUGMoYY4wxxhhjjDHGXny1VkDInkxoaCgMDQ2RlJQEIyMj0rLt27dHWloaTE1Nq42NjY1F586dyxUmbtu2rdwgKtXJ0jUnxdcr+IcUfzvXghTfdeskybEHBq0kpe17thMpXiWnNb/OVStJ8fLCPMmx5jk3SWlfUTcixVsakMJhfPWk5Ngk5yBS2nqyYlJ8sUirRC1ArD6ojLv3pedHLhdoeXEm5uUhLb6okfT7wb386u89ZSkNaNd2Zg7tJLtzjzbwlFpN2zcU9x/SzjFnb0tSvJn6nuTYW7do3XPIm9CupwcPafH37km/jwEAGkrrCxgADl2knWOuN/4ixev0bEGKv5ZtJTm2SEU7Z2Sgne9pWcakeEGgfSfJmLZccmx2zDBS2rl6+qT4enqPSPEUekIRKT5PRct7ppz2HauhMe16uic2kBwrE2jn2CO1CSm+WE37OWKkkyM5VibQ+gS/t/NXUvxfHd8ixff0kX4fA4AGRtLv24ZCNintAtDOSTXxOxPlvFGJtP1Cpb57hxRv7pRPir+QJv17UDMb2vWkkBWS4hsY0e4FNsKN6oP+564g/VkGAPfVtGexnHi9/qOyIcXLfvlNcuzhjqNIab/awpAUz9jz6oUsIDxy5AheffXVSudnZ9MekE9CpVJBEATIZE/WWjs5ORm9e/eGg4OD1vTU1FStgVyAf0ddbteuHZKSkmBvbw8rK9oN+3Hm5rQvoowxxhhjjDHGGGPsv+Gp9EH4uICAAEyePBkffPABzM3NYWVlhbCwMAAlzWcFQdBqHpuRkQFBEDTNmHNzc5GTk4OVK1fC3t4excXFaNasGQ4cOIDPP/8cnp6eMDExwYgRI8oNaFJcXIyJEyfC1NQUFhYWmDNnjlYz44KCAkyfPh2NGjWCoaEh2rRpo9V8Ojo6GmZmZti5cye8vLygUCiQmppa5faq1WrMmzcPtra2UCgU8PHxwZ49ezTzBUFAfHw85s2bB0EQNPsCAGxsbJCYmKj1+fXXkrebW7ZsgY2NDWJjYyEIAjIyMgAA169fR9++fVGvXj0YGhqiSZMm+PXXX3Ht2jV07twZQMnAJoIgaJoUBwQEYMqUKdUcOcYYY4wxxhhjjDH2X/PMahDGxMRg2rRpOHbsGI4ePYrg4GD4+/vDzc2t2mUVCgUAYN26dfjqq69gYGCAIUOGYObMmVAoFNi0aROys7Px+uuvY+XKlZg5c6bWeseMGYPjx4/j77//xtixY2Fvb4933nkHADBx4kScP38emzdvho2NDbZv345evXrhzJkzmrzl5uZi8eLFWL9+PerXr48GDapupvH5558jIiICa9euha+vLzZs2IB+/frh3LlzcHNzQ1paGrp164ZevXph+vTpWk2MdXR04OrqqpWejk7JYbO3t9f8XdZ7772HwsJCHD58GIaGhjh//jyMjIxgZ2eHH3/8EQMHDkRSUhJMTEygVNKatjLGGGOMMcYYY4yx/5ZnVkDo7e2N0NBQAICbmxtWrVqFAwcOSCogLDV//nz4+/sDAMaMGYPZs2cjOTkZzs7OAIBBgwbh4MGDWgWEdnZ2iIyMhCAI8PDwwJkzZxAZGYl33nkHqampiIqKQmpqKmxsSvo0mD59Ovbs2YOoqCgsXLgQAFBUVITVq1ejefPmkvK5dOlSzJw5E8OGlfS1s3jxYhw8eBDLly/HF198ASsrK+jo6MDIyOiJmwoDJc2SBw4ciGbNmgGAZn8A/zYlbtCggVYfhIwxxhhjjDHGGGPs5fRMCwjLsra2Rnp6eo3TaNiwIQwMDLQKwxo2bIjjx49rLdO2bVsIwr+DAbRr1w4RERFQqVQ4c+YMVCoV3N3dtZYpKChA/fr1Nf/r6emVy39lHj16hNu3b2sKMkv5+/vj1KlTktKgmjx5Mt59913s3bsX3bp1w8CBAyXntzIFBQUoKCjQmlZYUAC9/9XmZIwxxhhjjDHGGGMvpmfSByGAciPmCoIAtVqtGeyjbL+ARUUVjxRXNg1BECpNU6rs7GzI5XLEx8dr9fl34cIFfP7555o4pVKpVcj4vHn77beRkpKCt956C2fOnIGfnx9WrqSN2vu48PBwmJqaan3Wfrm6lnLMGGOMMcYYY4wxxp6VZ1ZAWBlLS0sAQFpammZa2QFLntSxY8e0/v/rr7/g5uYGuVwOX19fqFQqpKenw9XVVetT06a/JiYmsLGxQVxcnNb0uLi4cqMT1yY7OzuMHz8e27ZtQ0hICL766isAJbUfgZLRlylmz56NzMxMrc+48RNqPd+MMcYYY4wxxhhj7Ol6Zk2MK6NUKtG2bVssWrQITk5OSE9Px8cff1xr6aempmLatGkYN24cTp48iZUrVyIiIgIA4O7ujsDAQAQFBSEiIgK+vr64e/cuDhw4AG9vb/Tu3btG65wxYwZCQ0Ph4uICHx8fREVFITExERs3bqy17SprypQpePXVV+Hu7o6HDx/i4MGD8PT0BAA4ODhAEAT88ssveO2116BUKrUGRamMQqHQDA5TSk+RURfZZ4wxxhhjjDHGGGNP0XNXQAgAGzZswJgxY9CyZUt4eHhgyZIl6NGjR62kHRQUhLy8PLRu3RpyuRzvv/8+xo4dq5kfFRWF+fPnIyQkBLdu3YKFhQXatm2LPn361HidkydPRmZmJkJCQpCeng4vLy/s3LmTNCALhUqlwnvvvYebN2/CxMQEvXr1QmRkJACgUaNGmDt3LmbNmoVRo0YhKCgI0dHRdZIPxhhjjDHGGGOMMfb8eyYFhLGxseWm7dixQ/O3p6cn/vzzT635ZfskDAgI0PofAIKDgxEcHKw1LSwsDGFhYRWud82aNRXmTVdXF3PnzsXcuXMrnF/Reqojk8kQGhqqGbW5IpRm1I6OjlXuj+r6G5wzZw7mzJmjNa2iY8IYY4wxxhhjjDHG/vueuz4IqQICAjBlypRnnY0KXbx4EW3btoW+vj58fHyedXaqJAiCViEtY4wxxhhjjDHGGHs5COLjVfFeMAEBAfDx8cHy5cufWR4q68MvPz8fzZs3x7Zt22BkZIT69etLSm/hwoVYuHBhhfM6duyI3bt31zivlfnnn39Qr169cv0MViXx8l3SOgxkeaR4pTqbFF8o15eedhEt7YSmg0nxvhNbkOJvj6eNCG0pT5ccqxJoFYUfqU1I8SJoI3qbyjIlxza4e46UtiBKH7UcAG42aEmKp9IRKh6BvSKiSNuPashJ8dTjRImXgzbo0e18S1K8Qi59PwJAfd0MUrxSlUWKp8iV066nHLUBKV4py5cca6h+REq7WKZHy0sRbT8Wy3RJ8efz3CXH6slp56Rf9j5SfEr9tqR4haxAcuydfHNS2pb6GaR4lUi7d6hF2rtkx0cJkmNTTGjPSpui66T4BwraAHPFovTnpQy0540g0L5uU49TdrGSFG+oI/3eoUt4lgG0/QjQn0+UfU9NW0coJsXXK7xDis/Qa0CKzyquvp/wUnbqlDrNC3VfUs5hfeJvhCKR9nyqa3qQfo8vgPTfKwD9XpBLvBc0vb9fcmxyww6ktKnfbfVkhaT47GJDUryxjvTff7qg5aUYtO80AmjPBDcXB1I8YzX1XPZB+KypVCoIggCZTNqX4sqaB7/++uvo2bMnHBxoF/T48eMxZMiQCucplf/e9EVRhEqlgo7Okx/Gmo7SzBhjjDHGGGOMMcZebLXWxDggIACTJ0/GBx98AHNzc1hZWWn6/7t27RoEQdAqSMvIyIAgCJq+72JjYyEIAn777Tf4+vpCqVSiS5cuSE9Px+7du+Hp6QkTExOMGDECubm5WusuLi7GxIkTYWpqCgsLC8yZM0erT76CggJMnz4djRo1gqGhIdq0aaPV5150dDTMzMywc+dOeHl5QaFQIDU1tcrtVavVmDdvHmxtbdGkSRMMGjQIV65cgaurK1xdXeHm5oazZ88iPDwcgiBo9YVYkdJ9tHnzZvTp0wdNmzbFgAEDcOvWLU2aN2/ehK2tLXbv3o2WLVtCoVDgjz/+gFqtRnh4OJycnKBUKtG8eXNs3bpVk09bW9tyfS4mJCRAJpPh+vWSt/HcxJgxxhhjjDHGGGPs5VSrfRDGxMTA0NAQx44dw5IlSzBv3jzs20drthMWFoZVq1bhzz//xI0bNzBkyBAsX74cmzZtwq5du7B3795yg3DExMRAR0cHx48fx+eff45ly5Zh/fr1mvkTJ07E0aNHsXnzZpw+fRqDBw9Gr169cPnyZU1Mbm4uFi9ejPXr1+PcuXNo0KDqaveff/45IiIisHTpUpw+fRo9e/ZEv379NGmmpaWhSZMmCAkJQVpaGqZPny5p+2fMmIGQkBAkJCSgXbt26Nu3L+7fv68VM2vWLCxatAgXLlyAt7c3wsPD8fXXX+PLL7/EuXPnMHXqVLz55ps4dOgQZDIZhg8fjk2bNmmlsXHjRvj7+5NrNzLGGGOMMcYYY4yx/5ZaLSD09vZGaGgo3NzcEBQUBD8/Pxw4cICUxvz58+Hv7w9fX1+MGTMGhw4dwpo1a+Dr64uOHTti0KBBOHjwoNYydnZ2iIyMhIeHBwIDAzFp0iRERkYCAFJTUxEVFYUffvgBHTt2hIuLC6ZPn44OHTogKipKk0ZRURFWr16N9u3bw8PDAwYGVfcDtXTpUsycORPDhg2Dh4cHFi9erNUXopWVFXR0dGBkZAQrK6tK+yl83MSJEzFw4EB4enpizZo1MDU1xf/93/9pxcybNw/du3eHi4sLDA0NsXDhQmzYsAE9e/aEs7MzgoOD8eabb2Lt2rUAgMDAQMTFxWlqRarVamzevBmBgYGS8sQYY4wxxhhjjDHG/rtqtQ9Cb29vrf+tra2Rni59MIXH02jYsCEMDAzg7OysNe348eNay7Rt2xaC8G8nqO3atUNERARUKhXOnDkDlUoFd3ftzs0LCgq0Bg3R09Mrl//KPHr0CLdv34a/v7/WdH9/f5w6dUpSGpVp166d5m8dHR34+fnhwoULWjF+fn6av69cuYLc3Fx0795dK6awsBC+vr4AAB8fH3h6emLTpk2YNWsWDh06hPT0dAweLH3gjYKCAhQUaHfAW1hYAD096YOaMMYYY4wxxhhjjLHnT60WEOrqao/eIwgC1Gq1ZrCPsv0CFhVVPCJa2TQEQag0Tamys7Mhl8sRHx8PuVx7FKiytfqUSqVWIePzzNDw3xGbsrNLRmPatWsXGjVqpBVXdkTiwMBATQHhpk2b0KtXL8mjKgNAeHg45s6dqzVt3MTpGD/5g5psAmOMMcYYY4wxxhh7TtRqE+PKWFpaAijpl69UZSP/1sSxY8e0/v/rr7/g5uYGuVwOX19fqFQqpKenawb7KP3UdOReExMT2NjYIC4uTmt6XFwcvLy8arwdpXkvVVxcjPj4eHh6elYaX3ZQlce3z87OThM3YsQInD17FvHx8di6dSu5efHs2bORmZmp9Rk9/n36BjLGGGOMMcYYY4yx50qt1iCsjFKpRNu2bbFo0SI4OTkhPT0dH3/8ca2ln5qaimnTpmHcuHE4efIkVq5ciYiICACAu7s7AgMDERQUhIiICPj6+uLu3bs4cOAAvL290bt37xqtc8aMGQgNDYWLiwt8fHwQFRWFxMREbNy48Ym25YsvvoCbmxs8PT0RGRmJhw8fYvTo0ZXGGxsbY/r06Zg6dSrUajU6dOiAzMxMxMXFwcTEBCNHjgQAODo6on379hgzZgxUKhX69etHypdCodCqkQgAenoFlUQzxhhjjDHGGGOMsRfFUykgBIANGzZgzJgxaNmyJTw8PLBkyRL06NGjVtIOCgpCXl4eWrduDblcjvfffx9jx47VzI+KisL8+fMREhKCW7duwcLCAm3btkWfPn1qvM7JkycjMzMTISEhSE9Ph5eXF3bu3Ak3N7cn2pZFixZh0aJFSExMhKurK3bu3AkLC4sql/n0009haWmJ8PBwpKSkwMzMDC1atMCHH36oFRcYGIgJEyYgKCgISqXyifLJGGOMMcYYY4wxxv4bBLFsx4Dsmbl27RqcnJyQkJAAHx+fZ50dSRIv3yXFG8jySPFKdTYpvlCuLz3tIlraCU2lD+gCAL4TW5Dib49fTYq3lEsf/Ecl0N4DPFKbkOJF0PruNJVlSo5tcPccKW1BlN4/KQDcbNCSFE+lI1Tc12pFRJG2H9WQVx9UNn3icaLEy6EipX0735IUr5BL348AUF83gxSvVGWR4ily5bTrKUdtQIpXyvIlxxqqH5HSLpbp0fJSRNuPxTLd6oPKOJ/nXn3Q/+jJaeekX/Y+UnxK/bakeIVMeo37O/nmpLQt9TNI8SqRdu9Qi7TeaBwfJUiOTTGhPSttiq6T4h8oaF3JFIvSn5cy0J43gkD7uk09TtnFtBe/hjrS7x26hGcZQNuPAP35RNn31LR1hGJSfL3CO6T4DL0GpPisYqPqg/7HTp1Sp3mh7kvKOaxP/I1QJNKeT3VND9Lv8QWQ/nsFoN8Lcon3gqb390uOTW7YgZQ29butnqyQFJ9dbFh9UBnGOtJ//+mClpdi0L7TCKA9E9xcHEjxjNXUU+mDsC4FBARgypQpzzobFbp48SLatm0LfX39577QTxAE7Nix41lngzHGGGOMMcYYY4w9ZU+tifGLpuwIx4/bvXs3OnbsWG0aoaGhMDQ0RFJSEr766qtK0+zYsSPWrFlT47zWhrS0NNSrV4+0jLG87mrdAPSab/VybktPW05780itEZiw6iQp3vE96W/yAcAw/4Hk2AwlrQYF9U2lTKDVojAszJAeTBxZXP5Aes1KACi2rNtaDplFtNpjFCa6tOsvV0V7o2wgl/42X0181+SouEGKfyjSalQ9UhmT4u+qaOlTKECreWMkzyHFU2rS/KO2IaVtLpd+nwGA2zJ7Ujz1XmNvJP36pp7veSa0+6RcoNVQLFArqg/6HyNd2vOAWguBGk+tUZVj2JAUT6HMptXWArEGIeV6otYIpO53ag1FPRntOFGOK6V2FEB/JpBrEBK+d1BrMNUvkP5dEgBu6jqT4g2RS4qXy6Tfa+7LrUlpU8+xujxO1BqBOsRnq0Ex7TtThrzq7p0elwPpNT2pNXKpx0mHeC9IaegvOZbaYkSQ0e571HuNHrGlg3m+9Ov7H4UjKW3qvqGeB4w9LVxAWAGVSoWTJ09CJqv4C06jRo0kpZOcnIzevXvDwcEB06ZNQ3BwcIVxSqUSjRo1ArW1tyiKUKlU0NF58sNY0xGdGWOMMcYYY4wxxtiLrdaaGAcEBGDy5Mn44IMPYG5uDisrK4SFhQEo6V9PEAQkJiZq4jMyMiAIAmJjYwEAsbGxEAQBv/32G3x9faFUKtGlSxekp6dj9+7d8PT0hImJCUaMGIHcXO23b8XFxZg4cSJMTU1hYWGBOXPmaBW2FRQUYPr06WjUqBEMDQ3Rpk0bzXoBIDo6GmZmZti5cye8vLygUCigp6cHV1fXCj9KpRJqtRrz5s2Dra0tFAoFfHx8sGfPHk2agiAgPj4e8+bNgyAIWLFiRaXpNWrUSLOPNm/ejPbt20NfXx9NmzbFoUOHNGmW7qPdu3ejZcuWUCgU+OOPP6BWqxEeHg4nJycolUo0b94cW7duBQCo1WrY2tqWq6GYkJAAmUyG69eva/LLTYwZY4wxxhhjjDHGXj612gdhTEwMDA0NcezYMSxZsgTz5s3Dvn20jr/DwsKwatUq/Pnnn7hx4waGDBmC5cuXY9OmTdi1axf27t2LlStXlluvjo4Ojh8/js8//xzLli3D+vXrNfMnTpyIo0ePYvPmzTh9+jQGDx6MXr164fLly5qY3NxcLF68GOvXr8e5c+fQoEHVHfd+/vnniIiIwNKlS3H69Gn07NkT/fr106SZlpaGJk2aICQkBGlpaZg+fbqk7Z8xYwZCQkKQkJCAdu3aoW/fvrh//75WzKxZs7Bo0SJcuHAB3t7eCA8Px9dff40vv/wS586dw9SpU/Hmm2/i0KFDkMlkGD58ODZt2qSVxsaNG+Hv7w8HB+7wlDHGGGOMMcYYY+xlVqsFhN7e3ggNDYWbmxuCgoLg5+eHAwcOkNKYP38+/P394evrizFjxuDQoUNYs2YNfH190bFjRwwaNAgHDx7UWsbOzg6RkZHw8PBAYGAgJk2ahMjISABAamoqoqKi8MMPP6Bjx45wcXHB9OnT0aFDB0RFRWnSKCoqwurVq9G+fXt4eHjAwKDqkSSXLl2KmTNnYtiwYfDw8MDixYvh4+OD5cuXAyhpsqujowMjIyNYWVlV2adhWRMnTsTAgQPh6emJNWvWwNTUFP/3f/+nFTNv3jx0794dLi4uMDQ0xMKFC7Fhwwb07NkTzs7OCA4Oxptvvom1a9cCAAIDAxEXF4fU1FQAJbUKN2/ejMDAQEl5YowxxhhjjDHGGGP/XbXaB6G3t7fW/9bW1khPpw0aUDaNhg0bwsDAAM7OzlrTjh8/rrVM27ZtIZQZzKBdu3aIiIiASqXCmTNnoFKp4O7urrVMQUEB6tevr/lfT0+vXP4r8+jRI9y+fRv+/tqduvr7++PUqVOS0qhMu3btNH/r6OjAz88PFy5c0Irx8/PT/H3lyhXk5uaie/fuWjGFhYXw9fUFAPj4+MDT0xObNm3CrFmzcOjQIaSnp2Pw4MGS81VQUICCgoJy0xQK6R2vM8YYY4wxxhhjjLHnT60WEOrq6mr9LwgC1Gq1ZrCPsv0CFhVVPHJP2TQEQag0Tamys7Mhl8sRHx8PuVx7xMSytfqUSqVWIePzzNDQUPN3dnY2AGDXrl3lBk8pW3gXGBioKSDctGkTevXqpVVAWp3w8HDMnTtXa9qkyZPx/vvv12QTGGOMMcYYY4wxxthzolabGFfG0tISQEm/fKXKDljypI4dO6b1/19//QU3NzfI5XL4+vpCpVIhPT293OAgNR2518TEBDY2NoiLi9OaHhcXBy8vrxpvR2neSxUXFyM+Ph6enp6VxpcOqpKamlpu++zs7DRxI0aMwNmzZxEfH4+tW7eSmxfPnj0bmZmZWp/x48fTN5AxxhhjjDHGGGOMPVdqtQZhZZRKJdq2bYtFixbByckJ6enp+Pjjj2st/dTUVEybNg3jxo3DyZMnsXLlSkRERAAA3N3dERgYiKCgIERERMDX1xd3797FgQMH4O3tjd69e9donTNmzEBoaChcXFzg4+ODqKgoJCYmYuPGjU+0LV988QXc3Nzg6emJyMhIPHz4EKNHj6403tjYGNOnT8fUqVOhVqvRoUMHZGZmIi4uDiYmJhg5ciQAwNHREe3bt8eYMWOgUqnQr18/Ur4UCkW55sSKe/foG8gYY4wxxhhjjDHGnitPpYAQADZs2IAxY8agZcuW8PDwwJIlS9CjR49aSTsoKAh5eXlo3bo15HI53n//fYwdO1YzPyoqCvPnz0dISAhu3boFCwsLtG3bFn369KnxOidPnozMzEyEhIQgPT0dXl5e2LlzJ9zc3J5oWxYtWoRFixYhMTERrq6u2LlzJywsLKpc5tNPP4WlpSXCw8ORkpICMzMztGjRAh9++KFWXGBgICZMmICgoCAolconyidjjDHGGGOMMcYY+28QxLIdA7Jn5tq1a3ByckJCQgJ8fHyedXYkSU5JqdP05epiUrxJnvQBcVRyPVLaRV9FkuITVp0kxTteiCXFN8y/Jjk2Q0lrSp+pMiXFywTpfYICgE3xdcmxho9uk9LWuZ9WfVAZye59SfEiaP2U5hbXXUG8iW4WKT5XRcuLgTxPcqwA2mNEX8wlxT8UzUnxKlFefVAZBSrd6oNqSCGvuL/dyhjJc0jxMki//jKKade2ue4DUnwmMX3qcTLWyZYcSz3f7YqTSfHpenbVB5VB2dZi4n6hXKvUvAD067tesfRn8R15o+qDynB5eLz6oDJu1W9OileL0nveEQTafqHuR0peACBfTRs0TinPlxyrgPRYAMgXadcf9dlK+d4hirS0LQtvkuJv6jpXH1SGoYz2/MtRG0iOVQiFpLSp39+ox4mCen3ogPZsNSimfWfKkFddOeNxKki/r+oKtLzX9b1ATyY9P5TvHAD9Pkm912SrjUnx1oVXJcf+o3AkpS2HihRPPQ/cXBxI8YzV1FPpg5AxxhhjjDHGGGOMMfZ84hqElSg7wvHjdu/ejY4dO5LSW7hwIRYuXFhuemFhIfT19XH69OkXrgbhzUtnSfGFctob5SLQavlR5KppeVGpaTUuKG/mAeCaZwAp3u78EcmxOgKtJiY1nvrWN1/Ur7O0qfEKgXacVMReGZRq6TWeRIH2vuae2pIUby6n1QbLE6XXWpALtLemBmram/wiGe1tuFykncO5QuX3+6fNtPg+KT5XR/rbcx2R9rY6C7QagYaC9PMdANTEd5TFkF7Tk1rLoUik1SLVl9Fq7clE6fnJEWnnI/X6U4q0Wqpqgfb8Sy+SXvPGjFgTmlq7pADSnzcA7byhnr/Uc5KaPrVmKOVZLwftnkq5VoG6rYWeL0h/lj0N1H1ZQPjOVEi8jxnJiPeCOqxXQr0HK4QCUnyWinZfrSd/SIrXUUuvvZkno+Wlru8FOcXSrxFT3UektKmo14dI3DeUfakLWo1c6u9W6jnf1LVmg6syRvXU+iB80VQ1ynKjRrQmMQAwfvx4DBkypNz0FStW4MCBA3B0dASX1TLGGGOMMcYYY4yxp+2lb2KclZWFwMBAGBoawtraGpGRkQgICMCqVavg6uoKNzc3nD17Fq6urpqPtbU1oqOjNWncuHEDQ4YMgZmZGczNzdG/f39cu3ZNMz82Nha9evVC8+bN4efnh5EjR0JXVxd//PEHVq5cifPnz0MQBAiCoJVuZZYtW4ZmzZrB0NAQdnZ2mDBhArKztWtufPXVV7Czs4OBgQFef/11LFu2DGZmZloxP/30E1q0aAF9fX04Oztj7ty5KC6mvblhjDHGGGOMMcYYYy+2l76AcNq0aYiLi8POnTuxb98+HDlyBCdPSh9goqioCD179oSxsTGOHDmCuLg4GBkZoVevXigsLERxcTEGDBiAV155BadPn8bRo0cxduxYCIKAoUOHIiQkBE2aNEFaWhrS0tIwdOjQatcpk8mwYsUKnDt3DjExMfj999/xwQcfaObHxcVh/PjxeP/995GYmIju3btjwYIFWmkcOXIEQUFBeP/993H+/HmsXbsW0dHR5eIYY4wxxhhjjDHG2H/bS93EOCsrCzExMdi0aRO6du0KAIiKioKNjY3kNLZs2QK1Wo3169dDEARNGmZmZoiNjYWfnx8yMzPRp08fuLi4AAA8PT01yxsZGUFHRwdWVtL7FZgyZYrmb0dHR8yfPx/jx4/H6tWrAQArV67Eq6++iunTpwMA3N3d8eeff+KXX37RLDd37lzMmjULI0eOBAA4Ozvj008/xQcffIDQ0FDJeWGMMcYYY4wxxhhjL7aXuoAwJSUFRUVFaN26tWaaqakpPDw8JKdx6tQpXLlyBcbG2h3F5+fnIzk5GT169EBwcDB69uyJ7t27o1u3bhgyZAisra1rnO/9+/cjPDwcFy9exKNHj1BcXIz8/Hzk5ubCwMAASUlJeP3117WWad26tVYB4alTpxAXF6dVY1ClUmmlU1ZBQQEKCrQ7BC4oLIRCr+4GEmGMMcYYY4wxxhhjde+lb2JcHUEQyg0eUlT070iQ2dnZaNmyJRITE7U+ly5dwogRIwCU1Cg8evQo2rdvjy1btsDd3R1//fVXjfJz7do19OnTB97e3vjxxx8RHx+PL774AkDJiMhSZWdnY+7cuVp5PnPmDC5fvgx9/fIjpoWHh8PU1FTr88Xa9TXaBsYYY4wxxhhjjDH2/HipaxA6OztDV1cXJ06cgL29PQAgMzMTly5dQqdOnQAAlpaWSEtL0yxz+fJl5Obmav5v0aIFtmzZggYNGsDExKTSdfn6+sLX1xezZ89Gu3btsGnTJrRt2xZ6enpQqVSS8xwfHw+1Wo2IiAjIZCXlu99//71WjIeHB06cOKE17fH/W7RogaSkJLi6ukpa7+zZszFt2jStaXdTr0jON2OMMcYYY4wxxhh7Pr3UBYTGxsYYOXIkZsyYAXNzczRo0AChoaGQyWSa/gS7dOmCVatWoV27dlCpVJg5cyZ0dXU1aQQGBuKzzz5D//79MW/ePNja2uL69evYtm0bPvjgAxQVFWHdunXo168fbGxskJSUhMuXLyMoKAhASR+CV69eRWJiImxtbWFsbAyFQlFpnl1dXVFUVISVK1eib9++iIuLw5dffqkVM2nSJHTq1AnLli1D37598fvvv2P37t2abQKATz75BH369IG9vT0GDRoEmUyGU6dO4ezZs5g/f3659SoUinL5esTNixljjDHGGGOMMcZeeC99E+Nly5ahXbt26NOnD7p16wZ/f394enpqmtlGRETAzs4OHTt2xIgRIzB9+nSt/vkMDAxw+PBh2Nvb44033oCnpyfGjBmD/Px8mJiYwMDAABcvXsTAgQPh7u6OsWPH4r333sO4ceMAAAMHDkSvXr3QuXNnWFpa4rvvvqsyv82bN8eyZcuwePFiNG3aFBs3bkR4eLhWjL+/P7788kssW7YMzZs3x549ezB16lStpsM9e/bEL7/8gr1796JVq1Zo27YtIiMj4eDgUFu7ljHGGGOMMcYYY4y9AATx8Q72XnI5OTlo1KgRIiIiMGbMmGednVrzzjvv4OLFizhy5EitpXnz0llSfKFcSYovQt3VUMxV0/KiUstJ8Up5Pin+mmcAKd7uvPTjqCMUk9Kmxgug3ULyxfJ9XNZW2tR4hUA7TipipWulOltyrCjQ3tfcU1uS4s3lD0jxeaJB9UH/Ixekd5MAAAbqLFJ8kazyWtUVkYu0czhXMCLF1yXT4vuk+Fwd4+qD/kdHLKo+qIwsmJLiDQXp5zsAqInvKIuhW33Q/8igJqVdJEpPGwD0ZXmkeJkoPT85Iu18pF5/SjGHFK8WaM+/9CILybFmurR7gQK0e3YBpD9vANp5Qz1/qeckNX2VSDtOlGe9HLR7KuVaBejPbn0xt/qg/8kXpD/LngbqviwgfGcqJN7HjGTEe0Ed1iuh3oMVQkH1QWVkqWj31Xryh6R4HbX0fuDzZLS81PW9IKdY+jViqvuIlDYV9foQifuGsi91If2YAvTfrdRzvqmrFSmesZp64WsQBgQEYMqUKTVePiEhAd999x2Sk5Nx8uRJBAYGAgD69+//xHm7ePEi2rZtC319ffj4+DxxehRLly7VjLC8cuVKxMTEYOTIkZXGC4KAHTt2PL0MMsYYY4wxxhhjjLHnwkvdB2GppUuXIikpCXp6emjZsiWOHDkCCwvpb8ArExoaCkNDQyQlJcHISNrboo0bN2qaHz/OwcEB586dk5TO8ePHsWTJEmRlZcHZ2RkrVqzA22+/XWl8Wloa6tWrJyntUtRaBZS3awBQLKO9WbHKuCA5Vl5Iq/1xz9KTFG+YT6utpSLUCASAG14dJcd6n99OSvuuuiEpXl9Ge4vrcuug5NhrjTqR0qbWfryd34AUr1LT3qmsWCu9touugna+zxhLy8virfVJ8RMHSj+u+qBdT48E2r2mQEV7K/v5t7Sacr5+tHOeor6ZUH1QGb3q3yTFy3Wkv/lfvd+OlPbo7rTajBuO0NJ3daQd1/71DkmOTdb3puUlN4EUn2VEe5N/R21Niqcw0qHVAspG5QOqVUQgNjTxzD1RfdD/pJu6kdLOFQ1J8dRnggrSv9dQa70Vi7Sv3PoC7b76UE27rxrIpddoLCTUKAeAeuq7pHjq98lcmfSa02ZFtLwYZt8hxf+l25kUb66kXa8P8qSf85YGtNpdBSKtdr5apH3vEATp14iKmLZIrB1ssewdUnzyextJ8YdPSb++B7elnZPGYgYp/paqESneXDdTcmy+mrbfdWW072PUe411fgopXpEnvWZonG43UtrOxv+Q4qm1yhl7Wl76AkJfX1/Ex8drTVOpVFCr1ZpRgmsqOTkZvXv3JvXr169fP7Rp06bCeWUHRwEAURShUqmgo1P+MD4+snF1rKy42jJjjDHGGGOMMcbYy6jWmhgHBARg8uTJ+OCDD2Bubg4rKyuEhYUBAK5duwZBEJCYmKiJz8jIgCAIiI2NBQDExsZCEAT89ttv8PX1hVKpRJcuXZCeno7du3fD09MTJiYmGDFiBHJztfseKS4uxsSJE2FqagoLCwvMmTMHZbtWLCgowPTp09GoUSMYGhqiTZs2mvUCQHR0NMzMzLBz5054eXlBoVAgNTW1yu1Vq9WaUYsVCgV8fHywZ88ezXxBEBAfH4958+ZBEATNvqhM6T7atWsXgoKC0LRpUwwYMAC3bt2Cq6srXF1dcfXqVQiCgN27d6Nly5ZQKBT4448/oFarER4eDicnJyiVSjRv3hxbt27V5NPW1hZr1qzRWl9CQgJkMhmuX7+uyS83MWaMMcYYY4wxxhh7+dRqH4QxMTEwNDTEsWPHsGTJEsybNw/79u0jpREWFoZVq1bhzz//xI0bNzBkyBAsX74cmzZtwq5du7B3716sXLmy3Hp1dHRw/PhxfP7551i2bBnWr1+vmT9x4kQcPXoUmzdvxunTpzF48GD06tULly9f1sTk5uZi8eLFWL9+Pc6dO4cGDapuevj5558jIiICS5cuxenTp9GzZ0/069dPk2ZaWhqaNGmCkJAQpKWlYfr06ZK2f8aMGQgJCUFCQgLatWuHvn374v597WZes2bNwqJFi3DhwgV4e3sjPDwcX3/9Nb788kucO3cOU6dOxZtvvolDhw5BJpNh+PDh2LRpk1YaGzduhL+/P49azBhjjDHGGGOMMfaSq9UCQm9vb4SGhsLNzQ1BQUHw8/PDgQMHSGnMnz8f/v7+8PX1xZgxY3Do0CGsWbMGvr6+6NixIwYNGoSDB7X7MLOzs0NkZCQ8PDwQGBiISZMmITIyEgCQmpqKqKgo/PDDD+jYsSNcXFwwffp0dOjQAVFRUZo0ioqKsHr1arRv3x4eHh4wMKi6D4SlS5di5syZGDZsGDw8PLB48WL4+Phg+fLlAEqa7Oro6MDIyAhWVlaS+yCcOHEiBg4cCE9PT6xZswampqb4v//7P62YefPmoXv37nBxcYGhoSEWLlyIDRs2oGfPnnB2dkZwcDDefPNNrF27FgAQGBiIuLg4Ta1ItVqNzZs3awZkkaKgoACPHj3S+hQU0voUZIwxxhhjjDHGGGPPn1ovICzL2toa6enpNU6jYcOGMDAwgLOzs9a0x9Ns27YtBOHfjuDbtWuHy5cvQ6VS4cyZM1CpVHB3d4eRkZHmc+jQISQnJ2uW0dPTK5f/yjx69Ai3b9+Gv7+/1nR/f39cuCB9oIyKtGvXTvO3jo4O/Pz8yqXp5+en+fvKlSvIzc1F9+7dtbbv66+/1myfj48PPD09NbUIDx06hPT0dAwePFhyvsLDw2Fqaqr1Wf3luifZVMYYY4wxxhhjjDH2HKjVQUoeH0RDEAStwT7K9gtYVFTxqEZl0xAEodI0pcrOzoZcLkd8fDzkcu1R0srW6lMqlVqFjM8zQ8N/RzTLzs4GAOzatQuNGmmPWqVQ/Ds6WWBgIDZt2oRZs2Zh06ZN6NWrF+rXlz6q6ezZszFt2jStaXduXK1J9hljjDHGGGOMMcbYc6RWaxBWxtLSEkBJv3ylyg5Y8qSOHTum9f9ff/0FNzc3yOVy+Pr6QqVSIT09XTPYR+mnpiP3mpiYwMbGBnFxcVrT4+Li4OXlVePtKM17qeLiYsTHx8PT07PS+LKDqjy+fXZ2dpq4ESNG4OzZs4iPj8fWrVtJzYuBksJGExMTrY9CT4++gYwxxhhjjDHGGGPsuVKrNQgro1Qq0bZtWyxatAhOTk5IT0/Hxx9/XGvpp6amYtq0aRg3bhxOnjyJlStXIiIiAgDg7u6OwMBABAUFISIiAr6+vrh79y4OHDgAb29v9O7du0brnDFjBkJDQ+Hi4gIfHx9ERUUhMTERGzdufKJt+eKLL+Dm5gZPT09ERkbi4cOHGD16dKXxxsbGmD59OqZOnQq1Wo0OHTogMzMTcXFxMDExwciRIwEAjo6OaN++PcaMGQOVSoV+/fo9UT4ZY4wxxhhjjDHG2H/DUykgBIANGzZgzJgxaNmyJTw8PLBkyRL06NGjVtIOCgpCXl4eWrduDblcjvfffx9jx47VzI+KisL8+fMREhKCW7duwcLCAm3btkWfPn1qvM7JkycjMzMTISEhSE9Ph5eXF3bu3Ak3N7cn2pZFixZh0aJFSExMhKurK3bu3AkLC4sql/n0009haWmJ8PBwpKSkwMzMDC1atMCHH36oFRcYGIgJEyYgKCgISqXyifLJGGOMMcYYY4wxxv4baq2AMDY2tty0jIwMODo6AgA8PT3x559/as0v2ydhQECA1v8AEBwcjODgYK1pYWFhCAsLq3C9a9asqTBvurq6mDt3LubOnVvh/IrWUx2ZTIbQ0FCEhoZWGlOTZtSenp7lmkyXqmgfASX9Mr7//vt4//33q0z73XffxbvvvlvhvKioKAQHByMjI4OcZ8YYY4wxxhhjjDH24hLEikqcaklAQAB8fHywfPnyulrFc00QBGzfvh0DBgyoNvbatWtwcnJCQkICfHx86jxvj8vLy0NWVhYaNGggeZm0i4mkddzTsSbFF6hofRya6WRIjjXPuUlKO8PQhhSvJnbvWSgqqg8qw1jMkBx72ut1Uto25+OqDypDIRSS4p0TvpMce6slLe9UBWp9UryeUECKz1IbS46VQ/rgSwCgK6t4oKfKFKl1qw8qQ0colhwrgjbAk1xQkeIF0B5ThSLt3pFTTDsPKOQCLe8Oqiuk+Bs6zpJjC1W0d4I2umnVB5WRrmpIiqceV3OdB5Jjc0XD6oPKkBGvv+xiWvrGutmSYxXIJ6VdCNrzI0dlQIqncslJlBybZuJBSlslyqsPKoN6r1GL0p/d1PseNV5XoN3jqecw5T5JeR4AgA5oeU8vrLqlzOMMdKRfI5RjCgB2xcmk+Fu6TqR4PeJxpXzvEIjPmyLis5KKcs5TnwfUeH0xlxSfDRNSfJEo/fkqF2jXqh7xezZ13+hCevpFoJ0zBcTfN9RtvVdQjxTvjvOSY2/ouJDSNpE/IsUXiLTvnk1cab+jGauppzJIyYvIyMio0s+RI0fI6S1cuLDS9F599dUa57OwkHYjrYxSqSQVDjLGGGOMMcYYY4yx/4Y6LyBUq9X44IMPYG5uDisrK63mwcuWLUOzZs1gaGgIOzs7TJgwAdnZ/75lv379Ovr27Yt69erB0NAQTZo0wa+//ippvaX9Aerr66Nz586IiYmBIAiaJrRhYWHlauotX75c0yQ6MTERiYmJ+PDDD9GwYUMUFRWhYcOGmDFjBvz8/ACUFM5NnDgR1tbW0NfXh4ODA8LDwwFAk87rr78OQRDw5ZdfatJ8/LN+/Xo4OjoiNDQUwcHBWLt2Lezs7GBgYIAhQ4YgMzNTk8fg4GAMGDAACxYsgI2NDTw8St6+37hxA0OGDIGZmRnMzc3Rv39/XLt2DQCwd+9e6Ovrl2s+/P7776NLly4AgOjoaJiZmUnat4wxxhhjjDHGGGPsv6POCwhjYmJgaGiIY8eOYcmSJZg3bx727dtXsnKZDCtWrMC5c+cQExOD33//HR988IFm2ffeew8FBQU4fPgwzpw5g8WLF8PIyKjadV69ehWDBg3CgAEDcOrUKYwbNw4fffQRKd+urq44duwYVq1ahc8++wwXL17EZ599hhUrVuD7778HAKxYsQI7d+7E999/j6SkJGzcuFFTMHjixAkAJX37paWlIT4+Hq6urhV+GjVqpFnvlStX8P333+Pnn3/Gnj17kJCQgAkTJmjl7cCBA0hKSsK+ffvwyy+/oKioCD179oSxsTGOHDmCuLg4GBkZoVevXigsLETXrl1hZmaGH3/8UZOGSqXCli1bEBgYSNovjDHGGGOMMcYYY+y/pc5HMfb29tYM5OHm5oZVq1bhwIED6N69O6ZMmaKJc3R0xPz58zF+/HisXr0aAJCamoqBAweiWbNmAABnZ2l9La1duxYeHh747LPPAAAeHh44e/YsFixYQMp7aGgoIiIi8MYbbwAAnJyccP78eaxduxYjR45Eamoq3Nzc0KFDBwiCAAcHB82ylpaWAAAzMzNYWVlJXmd+fj6+/vprTaHhypUr0bt3b0RERGjSMTQ0xPr166GnV9IPxLfffgu1Wo3169dDEEr6+4iKioKZmRliY2PRo0cPDBs2DJs2bcKYMWMAlBQyZmRkYODAgaR9whhjjDHGGGOMMcb+W55KAWFZ1tbWSE9PBwDs378f4eHhuHjxIh49eoTi4mLk5+cjNzcXBgYGmDx5Mt59913s3bsX3bp1w8CBA8ulV5GkpCS0atVKa1rr1q1J+c7JyUFycjLGjBmDd955RzO9uLgYpqamAEqa+3bv3h0eHh7o1asX+vTpgx49epDW8zh7e3utGoXt2rWDWq1GUlKSpoCwWbNmmsJBADh16hSuXLkCY2PtARHy8/ORnFzS2XJgYCDatm2L27dvw8bGBhs3bkTv3r0lNysuKChAQYF2R8kFhYVQ6NVtB8eMMcYYY4wxxhhjrG7VeRNjXV3tkTMFQYBarca1a9fQp08feHt748cff0R8fDy++OILAP8OvPH2228jJSUFb731Fs6cOQM/Pz+sXLmyVvIlk8nw+ADORUX/jixW2hfiV199pdVf4NmzZ/HXX38BAFq0aIGrV6/i008/RV5eHoYMGYJBgwbVSv6qYmioPXpidnY2WrZsWa5vw0uXLmHEiBEAgFatWsHFxQWbN29GXl4etm/fTmpeHB4eDlNTU63PynUbanW7GGOMMcYYY4wxxtjTV+c1CCsTHx8PtVqNiIgIyGQl5ZSlffuVZWdnh/Hjx2P8+PGYPXs2vvrqK0yaNKnKtD08PMoNZlLaJ2ApS0tL/PPPPxBFUdMsNzExUTO/YcOGsLGxQUpKSpUFaSYmJhg6dCiGDh2KQYMGoVevXnjw4AHMzc2hq6sLlUpVZV4fl5qaqqnlBwB//fUXZDKZZjCSirRo0QJbtmxBgwYNYGJiUmlcYGAgNm7cCFtbW8hkMvTu3VtyvmbPno1p06ZpTXtw7aLk5RljjDHGGGOMMcbY86nOaxBWxtXVFUVFRVi5ciVSUlLwzTff4Msvv9SKmTJlCn777TdcvXoVJ0+exMGDB+Hp6Vlt2uPGjcPFixcxc+ZMXLp0Cd9//z2io6MBQFMYGBAQgLt372LJkiVITk7GF198gd27d2ulM3fuXISHh2PFihW4dOkSzpw5g6ioKCxbtgxAySjM3333HS5evIhLly7hhx9+gJWVlabZrqOjIw4cOIB//vkHDx8+lLRf9PX1MXLkSJw6dQpHjhzB5MmTMWTIkCr7MQwMDISFhQX69++PI0eO4OrVq4iNjcXkyZNx8+ZNrbiTJ09iwYIFGDRoEBQKhaQ8AYBCoYCJiYnWh5sXM8YYY4wxxhhjjL34nlkBYfPmzbFs2TIsXrwYTZs2xcaNGxEeHq4Vo1Kp8N5778HT0xO9evWCu7u7ZgCTqjg5OWHr1q3Ytm0bvL29sWbNGs0oxqWFYp6enli9ejW++OILNG/eHMePH8f06dO10nn77bexfv16REVFoVmzZnjllVcQHR0NJycnAICxsTGWLFkCPz8/tGrVCteuXcOvv/6qqREZERGBffv2wc7ODr6+vpL2i6urK9544w289tpr6NGjB7y9vavdZgMDAxw+fBj29vZ444034OnpiTFjxiA/P1+rRqGrqytat26N06dP8+jFjDHGGGOMMcYYYwwAIIiPd8T3H7VgwQJ8+eWXuHHjxrPOSqXCwsKwY8cOrabOz7O0i4mk+Hs61qT4AhWthqKZTobkWPOcm9UHlZFhaEOKVxPL3gtF6bU5AcBYzJAce9rrdVLaNufjSPEKoZAU75zwneTYWy1peacqUOuT4vWEguqDyshSG1cf9D9yqElp68qKqg8qo0itW31QGTpCseRYEQIpbblA63pBAO0xVSjS7h05xbTzgEIu0PLuoLpCir+h4yw5tlBF61XERjeNFJ+uakiKpx5Xc50HkmNzRcPqg8qQEa+/7GJa+sa62ZJjFcgnpV0I2vMjR2VAiqdyyUmUHJtmUnn3KRVRiXJSPPVeoxalP7up9z1qvK5Au8dTz2HKfZLyPAAAHdDynl5oQYo30JF+jVCOKQDYFSeT4m/pOpHi9YjHlfK9QyA+b4qIz0oqyjlPfR5Q4/XFXFJ8NirvsqkiRaL056tcoF2resTv2dR9owvp6ReBds4UEH/fULf1XkE9Urw7zkuOvaHjQkrbRP6IFF8g0r57NnGl/Y5mrKaeSg3CgIAATJkyBUBJs9vly5fXOK3o6GhJI++uXr0aJ06c0DRf/uyzzzBy5Mgar/dZedL9xRhjjDHGGGOMMcZYVZ76ICUnTpwoNwpvZRwdHTFlyhRN4SIADB06FAcPHoSRkVGFy7z55pv48ssvcfnyZcyfPx8PHjyAvb09QkJCMHv27NrYhBpr0qQJrl+/XuG8tWvXPuXcPLk8Xem1owBAIFZWNdWhvYnJVJlKjr2ibkRK20qdSYqn1nIwlkuvXQIAd9XSa+pQawTe9vInxbsPob1hi/+o/GBElXHLpw2EU6hDexsnl9FqRahBO672BZckx1Jrl2QoK++XtCINCiq+91TmvtJWcqycWLtEJtLenlP3jXkhreabXj7tXkNBfZOfaUy7N1Fq8DrlnyOlfUvXnRTvmn+KFF9MvF4f6VpKjrXKu0pK+7zgTYq306edY4YFGZJj9fPuk9K+W8+NFG8tSq+JCQCKItrz6S+hg+TYh//QajZbGNFqlzgY3SHFFxO+FlNr+CnFHFJ8jkj7jmUgZpHiRZn0+6octHt8nkirpWquR/uORbmv6hCP06n85qT4ZsJZUjz1u7NcLX3fZ8ukfw9+GnQI5w31OU91Nd+eFG+tvEeKtyi6LTk2X7fi37CVuVvcgBRvA1pruYdy6c9WSgsmAOSqSDLQan3X06N9f0vI8ZEc6ysmkNK+AVdSvIWQTooHuAYhezqeegGhpaX0m1BFlEolPvvsM8yZM6fC+aV97kVGRiIyMvKJ1lXbfv31VxQVVfxFpWHDhjA2NkZYWFid56OwsBB6PMAIY4wxxhhjjDHGGMMzGKSkbJNZURQRFhYGe3t7KBQK2NjYYPLkyQBKmiVfv34dU6dOhSAImtGHo6Oj4e7uDldXV7i6uuLbb7/FoEGDcPToUXTr1g1ubm4YNmwYsrL+fZOalZWFwMBAGBoawtraGpGRkVrNnqXkeeHChRg9ejSMjY1hb2+PdevWaebHxsZCEARkZGRopiUmJkIQBFy7dk2T7+bNm+PixYvo3bs3vL29MWvWLNjY2CAuLg7NmjVDvXr1MHnyZKhU2m9PsrKyMHz4cBgaGqJRo0b44osvtOZnZGTg7bffhqWlJUxMTNClSxecOvVvTY6wsDD4+Phg/fr1cHJygr5+3fW3xRhjjDHGGGOMMcZeLM9sFGMA+PHHHxEZGYm1a9fi8uXL2LFjB5o1awYA2LZtG2xtbTFv3jykpaUhLa3y5jzJycnYsWMHfvnlF/zyyy84dOgQFi1apJk/bdo0xMXFYefOndi3bx+OHDmCkydPkvIaEREBPz8/JCQkYMKECXj33XeRlJRESiM3NxcrVqzA5s2bsWfPHsTGxuL111/Hr7/+il9//RXffPMN1q5di61bt2ot99lnn6F58+ZISEjArFmz8P7772Pfvn2a+YMHD0Z6ejp2796N+Ph4tGjRAl27dsWDB/82I7py5Qp+/PFHbNu27YUZBIUxxhhjjDHGGGOM1b2n3sS4rNTUVFhZWaFbt27Q1dWFvb09WrduDQAwNzeHXC6HsbExrKyq7mtLrVYjOjoaxsYl/Xq89dZbOHDgABYsWICsrCzExMRg06ZN6Nq1KwAgKioKNja0UWlfe+01TJgwAQAwc+ZMREZG4uDBg/DwkD4CX1FREdasWQMXl5I+2wYNGoRvvvkGd+7cgZGREby8vNC5c2ccPHgQQ4cO1Szn7++PWbNmAQDc3d0RFxeHyMhIdO/eHX/88QeOHz+O9PR0KBQlI0UtXboUO3bswNatWzF27FgAJc2Kv/766ydu4s0YY4wxxhhjjDHG/lueaQ3CwYMHIy8vD87OznjnnXewfft2FBfTOkIGSpoAlxYOAoC1tTXS00s6/kxJSUFRUZGm4BEATE1NSQV7AODt/W8H5oIgwMrKSrMOqQwMDDSFg0BJv4OOjo5aA640bNiwXLrt2rUr9/+FCxcAAKdOnUJ2djbq168PIyMjzefq1atITk7WLOPg4PBEhYMFBQV49OiR1qegoKDG6THGGGOMMcYYY4yx58MzLSC0s7NDUlISVq9eDaVSiQkTJqBTp06VDuRRGV1d7VHwBEGAWk0bJfNJ1iGTlexGscwovRVtQ0VpPGnes7OzYW1tjcTERK1PUlISZsyYoYmTOnJ0ZcLDw2Fqaqr1+fLLL58oTcYYY4wxxhhjjDH27D3TJsZAyajEffv2Rd++ffHee++hcePGOHPmDFq0aAE9Pb1yA3ZQOTs7Q1dXFydOnIC9fckQ95mZmbh06RI6depUG5ugqZmXlpaGevXqAUCt9vP3119/lfvf09MTANCiRQv8888/0NHRgaOjY62t83GzZ8/GtGnTtKbdunmzztbHGGOMMcYYY4wxxp6OZ1pAGB0dDZVKhTZt2sDAwADffvstlEolHBwcAJQ0HT58+DCGDRsGhUIBCwsL8jqMjY0xcuRIzJgxA+bm5mjQoAFCQ0Mhk8k0IyM/KVdXV9jZ2SEsLAwLFizApUuXEBERUStpA0BcXByWLFmCAQMGYN++ffjhhx+wa9cuAEC3bt3Qrl07DBgwAEuWLIG7uztu376NXbt24fXXX4efn1+t5EGhUGj6OCx177H/GWOMMcYYY4wxxtiL55k2MTYzM8NXX30Ff39/eHt7Y//+/fj5559Rv359AMC8efNw7do1uLi4PFH/ecuWLUO7du3Qp08fdOvWDf7+/vD09IS+vn6tbIeuri6+++47XLx4Ed7e3li8eDHmz59fK2kDQEhICP7++2/4+vpi/vz5WLZsGXr27AmgpEnyr7/+ik6dOmHUqFFwd3fHsGHDcP36dTRs2LDW8sAYY4wxxhhjjDHG/psEsWzHeS+JnJwcNGrUCBERERgzZsyzzs4LK6XMIChS5Iq0fhAVAm0QlEcq4+qD/udurlH1QWVYGWaS4lWinBRvLM8mxWerpO9LQaBd4re9/Enx7kNcqg8qI/2j7yXHuuEiKe1CHVqhf7FMjxSvFmjH1TTvjuRYEbQazRnKqkd3f1y9vDRS/H2lreRYuUAbXEom0vqIpe4b44L7pHi9/EekeAoBtOsv07gRLV4wlxxrk3uZlPYtA3dSvG0O7XotJl6vj5TSXxSa5kq/9gDgvOBdfVAZdvq068mwIENyrH4e7fy9W8+NFK8soj1vFMT4E8XSWy48zNGtPqgMC6NCUryDEe08KBClt4zQFWj9ZSvFHFJ8DqR/pwEAI5H2PSVPJv17kBy0e3yeaECKp+5Lyn1Vh5j21WzaPbiZ7llSfJ4u7bjKRen7PltmSkq7rukQzhvqc57qdh6toom18h4p3qxQ+sCV+bq03yB3VQ1I8Ta4QYp/KJe+b4zFDFLa+TLabz8ZaF2LFRLu2QBwM0d6a0RfnQRS2jd0XUnxFgJtsFN7N09SPGM19VRqEAYEBGDKlCkASpoNL1++vMZpRUdHw8zMjLRMQkICvvvuOyQnJ+PkyZMIDAwEAPTv37/G+XhannR/McYYY4wxxhhjjDFWlafeB+GJEyckj6jr6OiIKVOmaAoXAWDo0KF47bXXyOtdunQpkpKSoKenh5YtW+LIkSOwsLDAkSNH8Oqrr1a6XHY27c35iyA1NRVeXl6Vzj9//rxmQJeq6Khpb/JlgpIUT3mTDwD6Muk1Di1pL7fJbzZlAq2GFLWWEWVbqag1Ai99T6tJaheWKzm2ALQDpRJotzTqfheJ/ZbmKsykp008x4pBq3mTr0ertUBKW027tk3VtBpSBTq08yBXj1aLQny2vW1okYm0t+eUe02evhkxNzS5Sum1GQH69aooln7vKNKhnZNyNe1eoBZo54xaRqh9TEybSiWj7XfqvclCKb2m3INsM1LaKpGWF/KzG7RnN0UB8TsQNS/U+xilZp1I3O/UZys1nrJvqDWM9HVotSWL5bSWCIUCrea0UpT+O4R6zqjr+NmXR/huoJTlkdKm5t1IN58Un0v8XmNEOA+o9wLqfYz6XVVPkP577naxDSltSznt+x75XkNsJWWsJ/08KJLV7XGitmJi7Gl56gWET9KXIFAy6rFSSbtgfX19ER8fX+E8Pz+/Wh1x+EVgYWFR5Tbb2NBu/owxxhhjjDHGGGPsxfXUq02UbTIriiLCwsJgb28PhUIBGxsbTJ48GUBJs+Tr169j6tSpEARBM+Lw402Mw8LC4OPjg2+++QaOjo4wNTXFsGHDkJWVpYnJyspCYGAgDA0NYW1tjcjISE2zZ6VSCVdX10o/pXleuHAhRo8eDWNjY9jb22PdunWa9GNjYyEIAjIyMjTTEhMTIQgCrl27ppXvX375BR4eHjAwMMCgQYOQm5uLmJgYODo6ol69epg8eTJUKu0aJFlZWRg+fDgMDQ3RqFEjfPHFF1rzMzIy8Pbbb8PS0hImJibo0qULTp06VW4frV+/Hk5OTjAyMqpym3V0nung1owxxhhjjDHGGGPsKXqm7ap+/PFHREZGYu3atbh8+TJ27NiBZv/P3p3HRVX1fwD/3JlhBpgZQBRZFEFWRxRFcEHSUDHcsVQwScRILTM1l5SeR0WsNHMtc3myQHvcWtSsrDQLI0RFENwQEResSMwFZB+Y+f3Bz4lRkPtVEJ/8vl+veb2YO98599zLzL13zv2eczp2BADs3LkTrVu3RmxsLPLy8pCXV/eA4Dk5Odi9eze++eYbfPPNNzh48CCWLFlieH3GjBlISkrCnj17sH//fiQmJiItLY1U1+XLl8PPzw/Hjx/H5MmT8corryArK4tURklJCd5//31s374d33//PRISEvDss89i79692Lt3Lz799FNs2LABX3zxhdH73nvvPXTq1AnHjx/H3LlzMW3aNOzfv9/w+qhRo5Cfn4/vvvsOqamp6NKlC/r164cbN24YYs6fP48vv/wSO3fufOIyJhljjDHGGGOMMcZY3Zo0VSw3Nxd2dnYICgqCiYkJ2rRpg27dugEArK2tIZVKoVarYWd3/9k6dTod4uPjoVZXj7E1duxYHDhwAG+//TZu376NTZs2YevWrejXrx8AIC4ujtyNdtCgQZg8eTIAYM6cOVi5ciV+/vlneHp6ii5Dq9Vi3bp1cHWtHuNt5MiR+PTTT3H16lWoVCq0b98effr0wc8//4ywsDDD+wICAjB37lwAgIeHB5KSkrBy5Ur0798fv/76K44ePYr8/HwoFNXjrCxbtgy7d+/GF198gYkTJwIAKioqsHnz5ofu4s0YY4wxxhhjjDHG/lmaNINw1KhRKC0thYuLCyZMmIBdu3ahspI2ODBQ3QX4TuMgANjb2yM/v3rq8AsXLkCr1RoaHgHA0tKS1LAHAN7e3oa/BUGAnZ2dYR1imZubGxoHAcDW1hbOzs5QqVRGy+4u19/f/57nmZmZAICMjAwUFRWhefPmUKlUhsfFixeRk/P35BFOTk4P1ThYXl6OwsJCo0d5BW2SEsYYY4wxxhhjjDH2+GnSBkJHR0dkZWVh7dq1MDMzw+TJk9G7d29oteJnVgMAExPj2TwFQYBO17Az0d1vHRJJ9W7U6/+eSam2baitjIete1FREezt7ZGenm70yMrKwuzZsw1xYmeOrsvixYthaWlp9Fi7/j/1v5ExxhhjjDHGGGOMPdaafDYKMzMzDB06FEOHDsWrr76Kdu3a4eTJk+jSpQvkcvk9E3ZQubi4wMTEBCkpKWjTpg0AoKCgAOfOnUPv3r0bYhMMmXl5eXlo1qwZADToOH+HDx++57lGowEAdOnSBX/++SdkMhmcnZ0bbJ13i46OxowZM4yWXb1ysdHWxxhjjDHGGGOMMcYejSZtIIyPj0dVVRW6d+8Oc3Nz/Pe//4WZmRmcnJwAVHcd/uWXXzB69GgoFAq0aNGCvA61Wo1x48Zh9uzZsLa2RsuWLbFgwQJIJBLDzMgPy83NDY6OjoiJicHbb7+Nc+fOYfny5Q1SNgAkJSVh6dKlGD58OPbv34/PP/8c3377LQAgKCgI/v7+GD58OJYuXQoPDw/88ccf+Pbbb/Hss8/Cz8+vQeqgUCgMYxzecUsub5CyGWOMMcYYY4wxxljTadIuxlZWVvjoo48QEBAAb29v/Pjjj/j666/RvHlzAEBsbCwuXboEV1fXhxo/b8WKFfD398eQIUMQFBSEgIAAaDQamJqaNsh2mJiYYNu2bTh79iy8vb3x7rvv4q233mqQsgFg5syZOHbsGHx8fPDWW29hxYoVCA4OBlDdJXnv3r3o3bs3xo8fDw8PD4wePRqXL1+Gra1tg9WBMcYYY4wxxhhjjP0zCfqaA+c9IYqLi9GqVSssX74cUVFRTV2d/1m52Zmk+CLBkhSvI7ZfCxD/US7TKeoPqkEiNOyYlndTSYpJ8Vq9Sf1BD8hkwUuk+HOf5dQfVIPjmUTRsc3wF6nsKoGWFE35zABApYS23+VVZaJj9aBlNJdI1PUH1aCuukmKL5aK/75SP4+Wuuuk+HKZOSlepqONY2tWcZsU35i0MtqxqUDSXHQsdb/fkLQkxVvraBN3Ub+vEr34IUekOtqEZxd0bqR4O1PatiorCkTHmpfQjnvXrFzrD6qBclwCALNy8XUHgEsm7UTHnrtmRSrbWkX7brdVXyXFV+rFfyalAm0IHAlo1xHUayAzPe06gnI+0+tp56cyvRkp3kSg/V8p+7KS2FnqerkVKb6t7AIpnnJuBQAzXZHoWOp1AfUzRqXVif+MmUlKSWVT6367UlV/UA1SCe373VKfJzqW+hkoqLQgxTsIV0jxJVLx5V/XWpHKtpHTrjuoxxotaL3ZblWI/444SS6Rys4TWpPim0lp1+UurrRzPWMPqkkzCBtDZGQkhg8fbrTs+PHj2LZtG3JycpCWlobw8HAAQEhISKPWJT4+HlZWVo26DgCIiYlB586dm7wejDHGGGOMMcYYY+x/T5NPUtLQVq9ejdqSIpctW4asrCzI5XL4+voiMTERLVq0QGJiIgYOHFhneUVF4u/cPa7CwsIwaNAgw/Pc3Fy0b9++zvgzZ84YJnS5H2oWgl5mRYq/raVlDbnpxGc0qi+mkcr+Q/MMKV5ZcYsUn29Cu+vk+vvPomMl+b+Tyk7912ekeMeYElL8lfa9RMeant1PKlsq0LKGiqtod5Tl+gpS/IWSVqJjqUOitlLSsozOlruT4u3NboiOpd7Jr5DSskvKdbThIHJL7UnxpdrGOxXKpLQs1W7lv5Lir6vEDyVx9Fbdx/3adG5Om4gqtZBWvkxCy6jyM80QHXte8CSV3VpOO05e09KyK28KzUTHWliKzwoFgCq9lBR/tZI2ZItO4kSK98vZIjpW7j6cVHYlcVupxyYtIYOQWnaVnhYvJ2bVlQu042qlTvy2UjPczQVaNuNfWtoY46bSctGxOmLdffP3kOJ/thhJim9jQcuokkjE/1+pvSKo8VQmEvGf4SoQv9vE75NbqfjzBwDkKmnns3Pl4rPQm5nSfluaSWm/t0oEWsYhZd9bm9Ayyst0tOMS1e1K2m/FTvl7Rcf+YvkcqWwX9Z+k+BK9khTP2KPy2DQQVlRUQN4Ak15YWt6btu3j44PU1NRa4/38/Bp0xuHHkZmZGczM/j5AOzg43HebHRwcHkGtGGOMMcYYY4wxxtjjoNG6GAcGBmLKlCmYMmUKLC0t0aJFC8ybN8+Q3efs7IxFixYhIiICFhYWmDhxIgDg119/Ra9evWBmZgZHR0dMnToVxcXVdyLffPNNdO/e/Z51derUCbGxsQDu7WJcXl6OqVOnomXLljA1NcVTTz2FlJQUw+s7duyAn58f3NzcDI9Tp07B3d0dbm7Vd4MyMjLQp08fqNVqWFhYwNfXF8eOHRO9L3bv3g13d3eYmpoiODgYV64Yjw2xbt06uLq6Qi6Xw9PTE59++qnR67m5uQgJCYFKpYKFhQVCQ0Nx9Wrd4+rk5OTAxcUFU6ZMgV6vv6eL8VtvvYWRI0ciOTkZQUFB8PX1xb///W/Y2trCzc0NMtlj027MGGOMMcYYY4wxxhpZo45BuGnTJshkMhw9ehSrV6/GihUrsHHjRsPry5YtQ6dOnXD8+HHMmzcPOTk5GDBgAEaMGIETJ05gx44d+PXXXzFlyhQAQHh4OI4ePYqcnL8nRTh9+jROnDiBMWPG1FqHN954A19++SU2bdqEtLQ0uLm5ITg4GDduiO8+Fx4ejtatWyMlJQWpqamYO3cuTEzEDbxbUlKCt99+G5s3b0ZSUhJu3bqF0aNHG17ftWsXpk2bhpkzZ+LUqVOYNGkSxo8fj59/ru5CqtPpEBISghs3buDgwYPYv38/Lly4gLCwsFrXd+LECTz11FMYM2YM1qxZA6GOfos5OTnYvXs3vvnmG3zzzTc4ePAglixZInqfMMYYY4wxxhhjjLF/hkZNFXN0dMTKlSshCAI8PT1x8uRJrFy5EhMmTAAA9O3bFzNnzjTEv/TSSwgPD8f06dMBAO7u7nj//ffx9NNPY926dfDy8kKnTp2wdetWzJs3DwCwZcsWdO/e3ZDtV1NxcTHWrVuH+Ph4wziDH330Efbv34+PP/4Ys2fPFrUdubm5mD17Ntq1a2eol1harRZr1qwxZD5u2rQJGo0GR48eRbdu3bBs2TJERkZi8uTJAIAZM2bg8OHDWLZsGfr06YMDBw7g5MmTuHjxIhwdHQEAmzdvhpeXF1JSUtC1a1fDug4dOoQhQ4bgX//6l9F+rY1Op0N8fDzU6urZnMaOHYsDBw7g7bffFr1tjDHGGGOMMcYYY+x/X6NmEPbo0cMog83f3x/Z2dmoqqqeOt7Pz88oPiMjA/Hx8VCpVIZHcHAwdDodLl6sHiw9PDwcW7duBQDo9Xps27bNMCvx3XJycqDVahEQEGBYZmJigm7duiEzU/yEFjNmzMBLL72EoKAgLFmyxCiDsT4ymcyoEa9du3awsrIyrD8zM9OofgAQEBBg9Lqjo6OhcRAA2rdvb1QGUN2I2b9/f8yfP7/exkGguov3ncZBALC3t0d+fn6d8eXl5SgsLDR6lFfQJmxgjDHGGGOMMcYYY4+fRm0grI9SaTx7T1FRESZNmoT09HTDIyMjA9nZ2XB1dQUAPP/888jKykJaWhoOHTqEK1eu1NndVgyJRHLPrMdarfGsWzExMTh9+jQGDx6Mn376Ce3bt8euXbseeJ2NwcbGBt26dcO2bdtQWFhYb/zdXaQFQYBOV/eMkosXL4alpaXR44P/fPLQ9WaMMcYYY4wxxhhjTatRGwiPHDli9Pzw4cNwd3eHVFr7dOpdunTBmTNnjCYMufO4M8Nx69at8fTTT2PLli3YsmUL+vfvj5YtW9Za3p2JP5KSkgzLtFotUlJS0L599fT1NjY2uH37tmEiFAC1zvDr4eGB119/Hfv27cNzzz2HuLg4UfugsrLSaEKTrKws3Lp1CxqNBgCg0WiM6gcASUlJhvppNBpcuXLFaGKTM2fO4NatW4YYoHqm4m+++cYwEcrt27dF1U+s6OhoFBQUGD1em/hig66DMcYYY4wxxhhjjD16jdpAmJubixkzZiArKwvbtm3DBx98gGnTptUZP2fOHBw6dAhTpkxBeno6srOz8dVXXxkmKbkjPDwc27dvx+eff15n92KgOkPxlVdewezZs/H999/jzJkzmDBhAkpKShAVFQUA6N69O8zNzfHmm28iJycHW7duRXx8vKGM0tJSTJkyBQkJCbh8+TKSkpKQkpJiaOCrj4mJCV577TUcOXIEqampiIyMRI8ePdCtWzcAwOzZsxEfH49169YhOzsbK1aswM6dOzFr1iwAQFBQEDp27Ijw8HCkpaXh6NGjiIiIwNNPP31PF22lUolvv/0WMpkMAwcORFFRkag6iqFQKGBhYWH0UPx/oy1jjDHGGGOMMcYY+9/VqA2EERERKC0tRbdu3fDqq69i2rRpmDhxYp3x3t7eOHjwIM6dO4devXrBx8cH8+fPh4ODg1HcyJEjcf36dZSUlGD48OH3rcOSJUswYsQIjB07Fl26dMH58+fxww8/oFmzZgAAa2tr/Pe//8XevXvRsWNHbNu2DTExMYb3S6VSXL9+HREREfDw8EBoaCgGDhyIhQsXitoH5ubmmDNnDsaMGYOAgACoVCrs2LHD8Prw4cOxevVqLFu2DF5eXtiwYQPi4uIQGBgIoLrr71dffYVmzZqhd+/eCAoKgouLi1EZNalUKnz33XfQ6/UYPHiwUWYkY4wxxhhjjDHGGGN3E/R3D8DXQAIDA9G5c2esWrWqMYp/5Ot5EGfPnkVkZCTS09PRrl27WrsuPy4EQcCuXbvqbXCt6c+zx0nruC6zI8UXapX1B9XgphM/8Yz6Yhqp7D80z5DilRW3SPH5Jq1J8a6//yw6VpL/O6nsVK9JpHi1SQkp/kr7XqJj3c/uJ5UtFSpJ8cVVKlK8XEKbmOdKkY3o2BrzOYnSSvkXKf6PkuakeHuzG6Jjq1D7sBF1UQq07OYyvRkp/mppM1J8qVZGiqeQSWmn2G66X0nxuaoOomNzbtI+A52bXyTFn7zZlhQvk9Q97m1t/EwzRMeeFzxJZbcy+YMUf62q9qFNGoKFjDZECPX7d7PCghSv09PuJftd2iI69pz7cFLZlXratppJy0jx5TrxPSOkAu3zW0Xcj3JBW39QDTLi+a9SL/64pwftBGUm0K4L/tK2IMWbSstFx+qIdff8/UdS/M8WI0nxbSyuk+JNhVLRsTrisUDXyEPRUz43AmjnSupxyaHoHCk+V9m+/qAarpep6w/6f81MaddAMqGKFK8QxH8/ANo5RALacU+rN6k/6CHcrjQnxXtf/U507C+Wz5HKdlH/SYqv0NN64nVwo/2OZuxBNd6vIoYFCxZAqVQiKysLKhWtIeJRy8vLM2RVilVgQrugI577YSeve1bl2hQK4htjslwiSGV3vnaAFE9t7RGatyLFX2rVW3SswpH2A8m97Cwpvhy0k7MpodEvu11/UtmacHdSvHzeelo8aBdd7czFz3hO/QFWAVNSvIfpBVJ8iSD+Ylch0D5jVYQfpg/C1fQSKV4ho/2YpZDoaRf2VxW0z7CUUL5fsyxS2WWg3aTxtThDijeppH2frsodRcd2KDxMKvsUepDirUxojXiU77eZjvbjsVhCa/CzkYtv/AcAGWgNVeku4ieOay65RSpbpb1Jir8ttSbFUxrlJMQGQrlAbAAhNt6U6WjnBIWE9v2jKCeen1QyWm8XgbgvKU7bDyDF+1WJv3EBALcE2s2FUr34aywTYqMytbGHivIZptaF+hm4YN6RFK+U0K4LPOXiG4duS2i/t6jHglId7aaqiUT854ZyYwGg37igUsnEN6ADQKa9+ISPLpUnSGUXgnYT1rqK9jsX4AZC9mhwA2EtqqqqIAgCJJL7H5AHDhyIxMTEWl+7M6bh4MGD4eTk1BjVhF6vR1VVFWSyh/832tnxQYcxxhhjjDHGGGPsSdRgueWBgYGYOnUq3njjDVhbW+Ps2bOwsrICAFy6dAmCIBh1sb116xYEQUBCQgIAICEhAYIg4IcffoCPjw/MzMzQt29f5Ofn47vvvoNGo4GFhQXGjBmDkhLjuzqVlZWYMmUKLC0t0aJFC8ybNw81e06Xl5dj1qxZaNWqFZRKJbp3725YLwDEx8fDysoKe/bsQfv27aFQKJCbm3vf7dXpdPDy8oJKpUJFRQUcHR3x/vvvIz09Henp6fjXv/6F1NRUxMbGQhAEo3ENa3NnH23fvh09e/aEqakpOnTogIMHDxpi7uyj7777Dr6+vlAoFPj111+h0+mwePFitG3bFmZmZujUqRO++OILQz1bt26NdevWGa3v+PHjkEgkuHz5MoDqLsa7d+++bx0ZY4wxxhhjjDHG2D9Pgw4+sWnTJiiVShw5cgRLly5FbGws9u+njSEWExODNWvW4NChQ7hy5QpCQ0OxatUqbN26Fd9++y327duHDz744J71ymQyHD16FKtXr8aKFSuwceNGw+tTpkxBcnIytm/fjhMnTmDUqFEYMGAAsrOzDTElJSV49913sXHjRpw+fRotW96/C8Dq1avx0UcfYdWqVTh58iSGDRuGl19+GXq9Hm5ubsjLy4OXlxdmzpyJvLw8w6zE9Zk9ezZmzpyJ48ePw9/fH0OHDsX168bjlcydOxdLlixBZmYmvL29sXjxYmzevBnr16/H6dOn8frrr+OFF17AwYMHIZFI8Pzzz2Pr1q1GZWzZsgUBAQGNlt3IGGOMMcYYY4wxxv43NGgDobe3NxYsWAB3d3dERETAz88PBw7Qxm576623EBAQAB8fH0RFReHgwYNYt24dfHx80KtXL4wcORI//2w8QYOjoyNWrlwJT09PhIeH47XXXsPKlSsBALm5uYiLi8Pnn3+OXr16wdXVFbNmzcJTTz2FuLg4QxlarRZr165Fz5494enpCXPz+4/5sWzZMsyZMwejR4+Gp6cn3n33XaPJUuzs7CCTyaBSqWBnZyd6DMIpU6ZgxIgR0Gg0WLduHSwtLfHxxx8bxcTGxqJ///5wdXWFUqnEO++8g08++QTBwcFwcXFBZGQkXnjhBWzYsAEAEB4ejqSkJENWpE6nw/bt2xEeHi6qTowxxhhjjDHGGGPsn6tBxyD09vY2em5vb4/8fNoAnDXLsLW1hbm5OVxcXIyWHT161Og9PXr0gFBjUgh/f38sX74cVVVVOHnyJKqqquDh4WH0nvLycjRv/vdgonK5/J7616WwsBB//PEHAgICjJYHBAQgI4M2WPHd/P39DX/LZDL4+fkhM9N4dl4/Pz/D3+fPn0dJSQn69zeezKGiogI+Pj4AgM6dO0Oj0WDr1q2YO3cuDh48iPz8fIwaNUp0vcrLy1FebjygdUV5OeQKhegyGGOMMcYYY4wxxtjjp0EbCE1MjKcyFwQBOp3OMNlHzXEBtdraZ0yqWYYgCHWWKVZRURGkUilSU1MhlRpP414zq8/MzMyokfFxplT+PbNkUVH1bIfffvstWrUynglXUaPxLjw83NBAuHXrVgwYMMCogbQ+ixcvxsKFC42WvfradLw2bcaDbAJjjDHGGGOMMcYYe0w0aBfjutjY2AAA8vLyDMtqTljysI4cOWL0/PDhw3B3d4dUKoWPjw+qqqqQn58PNzc3o8eDztxrYWEBBwcHJCUlGS1PSkpC+/btH3g77tT9jsrKSqSmpkKj0dQZX3NSlbu3z9HR0RA3ZswYnDp1Cqmpqfjiiy/I3Yujo6NRUFBg9Jj08qv0DWSMMcYYY4wxxhhjj5UGzSCsi5mZGXr06IElS5agbdu2yM/Px7///e8GKz83NxczZszApEmTkJaWhg8++ADLly8HAHh4eCA8PBwRERFYvnw5fHx8cO3aNRw4cADe3t4YPHjwA61z9uzZWLBgAVxdXdG5c2fExcUhPT0dW7Zseaht+fDDD+Hu7g6NRoOVK1fi5s2bePHFF+uMV6vVmDVrFl5//XXodDo89dRTKCgoQFJSEiwsLDBu3DgAgLOzM3r27ImoqChUVVVh2LBhpHopFAqjjEQAkCsK6BvIGGOMMcYYY4wxxh4rj6SBEAA++eQTREVFwdfXF56enli6dCmeeeaZBik7IiICpaWl6NatG6RSKaZNm4aJEycaXo+Li8Nbb72FmTNn4vfff0eLFi3Qo0cPDBky5IHXOXXqVBQUFGDmzJnIz89H+/btsWfPHri7uz/UtixZsgRLlixBeno63NzcsGfPHrRo0eK+71m0aBFsbGywePFiXLhwAVZWVujSpQvefPNNo7jw8HBMnjwZERERMDMze6h6MsYYY4wxxhhjjLF/BkFfc2BA1mQuXbqEtm3b4vjx4+jcuXNTV0eUrJwrpPhKPa092lwoJsXrBPE95vPKbElldy6kzcYN4niWl5p3JcVLIH4cToWkjFS2uuwGKb7c5P4zft+tVCJuRm8AyG7Xv/6gGjThtAb6innrSfFylNcfVIOJTny8HrTPTIXElBSv0JWS4kskatGxMqH2MWXrotNL6w+qQas3qT+oBpWelt2sqCwhxVNI9FWk+BsKe1I85bhqob9JKrtMoqw/qAbzqkJSvEkl7ft0Ve5Yf9D/cyo8QSr7lFkPUryVyW1SPOX7Tf0/FUssSPFS0D6TMtC+31e1NqJjm5vcIpWt0tL2zW0Ta1I85fskEcSfhwFAAO1yW0ccBUirox0nFRLa94+CWne9nnb+E4TG++lSXEm7pmlTlUOKv6VoSYov14ufDNCEeC6mXEs+CMrngFoX6jVTcRXt/6qU0q4L1JXij023Zc1IZTf2scBEIv5zQ/2uSgXa+YaKen1YrpOLjnWsvEAqu1Ahfmx/ADCvpF1HOHiKm0yVsYf1SMYgbEyBgYGYPn16U1ejVmfPnkWPHj1gamr62Df6CYKA3bt3N3U1GGOMMcYYY4wxxtgj9si6GP+vqTnD8d2+++479OrVq94yFixYAKVSiaysLHz00Ud1ltmrVy+sW7fugevaEPLy8tCsGe2OFhX1bnslaHeFJISsCLmkklS2oKfVXXojn1Z+c9rdcJlAqz9FhYyWmVYl0A4jUkLdqRmBmVuySfGu80jh9Lu4EvF3/qn0xLpUSsTfNQXo2S4UUtA+v1risYC6rY15JpTpaBkdVJTjaqVA3C9E1M+7IKV9xiiZbxVy8ZnKAKCQ0v5P1AwmnV7891Un0DJsGxslOx8AZJTPJPHLJ9NVkOKpx+zGzExrbNS6m0D8vtQTe0VUELLeHgTl/ETNNJMRrw8rBNo10/8y6veJgnrNQY03If5fqZ+bSgntOqUxUX9vUc5P1LKpPUyoqBmElIxGrYx2HKP+bmXsccUNhLWoqqpCWloaJJLaD5itWrUSVU5OTg4GDx4MJycnzJgxA5GRkbXGmZmZoVWrVqD29tbr9aiqqoJM9vD/xged0ZkxxhhjjDHGGGOM/W9rsFtBgYGBmDp1Kt544w1YW1vDzs4OMTExAKrH1xMEAenp6Yb4W7duQRAEJCQkAAASEhIgCAJ++OEH+Pj4wMzMDH379kV+fj6+++47aDQaWFhYYMyYMSgpMR4XorKyElOmTIGlpSVatGiBefPmGTW2lZeXY9asWWjVqhWUSiW6d+9uWC8AxMfHw8rKCnv27EH79u2hUCggl8vh5uZW68PMzAw6nQ6xsbFo3bo1FAoFOnfujO+//95QpiAISE1NRWxsLARBwPvvv19nea1atTLso+3bt6Nnz54wNTVFhw4dcPDgQUOZd/bRd999B19fXygUCvz666/Q6XRYvHgx2rZtCzMzM3Tq1AlffPEFAECn06F169b3ZCgeP34cEokEly9fNtSXuxgzxhhjjDHGGGOMPXkaNFd806ZNUCqVOHLkCJYuXYrY2Fjs37+fVEZMTAzWrFmDQ4cO4cqVKwgNDcWqVauwdetWfPvtt9i3bx8++OCDe9Yrk8lw9OhRrF69GitWrMDGjRsNr0+ZMgXJycnYvn07Tpw4gVGjRmHAgAHIzv67O2JJSQneffddbNy4EadPn0bLlvcfRHj16tVYvnw5li1bhhMnTiA4OBjDhg0zlJmXlwcvLy/MnDkTeXl5mDVrlqjtnz17NmbOnInjx4/D398fQ4cOxfXr141i5s6diyVLliAzMxPe3t5YvHgxNm/ejPXr1+P06dN4/fXX8cILL+DgwYOQSCR4/vnnsXXrVqMytmzZgoCAADg5OYmqF2OMMcYYY4wxxhj7Z2rQBkJvb28sWLAA7u7uiIiIgJ+fHw4coM3++tZbbyEgIAA+Pj6IiorCwYMHsW7dOvj4+KBXr14YOXIkfv75Z6P3ODo6YuXKlfD09ER4eDhee+01rFy5EgCQm5uLuLg4fP755+jVqxdcXV0xa9YsPPXUU4iLizOUodVqsXbtWvTs2ROenp4wN7//bFfLli3DnDlzMHr0aHh6euLdd99F586dsWrVKgDVXXZlMhlUKhXs7OzuO6ZhTVOmTMGIESOg0Wiwbt06WFpa4uOPPzaKiY2NRf/+/eHq6gqlUol33nkHn3zyCYKDg+Hi4oLIyEi88MIL2LBhAwAgPDwcSUlJyM3NBVCdVbh9+3aEh4eLqhNjjDHGGGOMMcYY++dq0DEIvb2Np9+2t7dHfj5tsoaaZdja2sLc3BwuLi5Gy44ePWr0nh49ekCoMYCyv78/li9fjqqqKpw8eRJVVVXw8PAwek95eTmaN/97OnK5XH5P/etSWFiIP/74AwEBAUbLAwICkJGRIaqMuvj7+xv+lslk8PPzQ2ZmplGMn5+f4e/z58+jpKQE/fv3N4qpqKiAj48PAKBz587QaDTYunUr5s6di4MHDyI/Px+jRo0SXa/y8nKUl5cbr6O8HHJF4w5EzRhjjDHGGGOMMcYaV4NmEJqYGM/eIwgCdDqdYbKPmuMCarW1z2pUswxBEOosU6yioiJIpVKkpqYiPT3d8MjMzMTq1asNcWZmZkaNjI8zpVJp+LuoqAgA8O233xpt35kzZwzjEALVWYR3uhlv3boVAwYMMGogrc/ixYthaWlp9Niw/sMG2iLGGGOMMcYYY4yx/z2//PILhg4dCgcHB9HzOyQkJKBLly5QKBRwc3NDfHz8PTEffvghnJ2dYWpqiu7du9+TLNfQGm+++hpsbGwAVI/Ld0fNCUse1pEjR4yeHz58GO7u7pBKpfDx8UFVVRXy8/PvmRzkQWfutbCwgIODA5KSkoyWJyUloX379g+8HXfqfkdlZSVSU1Oh0WjqjL8zqUpubu492+fo6GiIGzNmDE6dOoXU1FR88cUX5O7F0dHRKCgoMHpMevlV+gYyxhhjjDHGGGOM/UMUFxejU6dO+PBDcUlUFy9exODBg9GnTx+kp6dj+vTpeOmll/DDDz8YYnbs2IEZM2ZgwYIFSEtLQ6dOnRAcHEzupUvRoF2M62JmZoYePXpgyZIlaNu2LfLz8/Hvf/+7wcrPzc3FjBkzMGnSJKSlpeGDDz7A8uXLAQAeHh4IDw9HREQEli9fDh8fH1y7dg0HDhyAt7c3Bg8e/EDrnD17NhYsWABXV1d07twZcXFxSE9Px5YtWx5qWz788EO4u7tDo9Fg5cqVuHnzJl588cU649VqNWbNmoXXX38dOp0OTz31FAoKCpCUlAQLCwuMGzcOAODs7IyePXsiKioKVVVVGDZsGKleCoUCiru6E8sVBfQNZIwxxhhjjDHGGPuHGDhwIAYOHCg6fv369Wjbtq2h3Uqj0eDXX3/FypUrERwcDABYsWIFJkyYgPHjxxve8+233+KTTz7B3LlzG34j8IgaCAHgk08+QVRUFHx9feHp6YmlS5fimWeeaZCyIyIiUFpaim7dukEqlWLatGmYOHGi4fW4uDi89dZbmDlzJn7//Xe0aNECPXr0wJAhQx54nVOnTkVBQQFmzpyJ/Px8tG/fHnv27IG7u/tDbcuSJUuwZMkSpKenw83NDXv27EGLFi3u+55FixbBxsYGixcvxoULF2BlZYUuXbrgzTffNIoLDw/H5MmTERERATMzs4eqJ2OMMcYYY4wxxhijSU5ORlBQkNGy4OBgTJ8+HUD1nBKpqamIjo42vC6RSBAUFITk5ORGq5egrzkwIGsyly5dQtu2bXH8+HF07ty5qasjSlbOFVK8jtijXYZKUrxEqBIde71C/PiLAOB18+f6g2qQ3qSl/eZ4DCXFmwi1j+FZa10E2n5UVJaQ4islclJ8lUT8fQkhdiqp7Mwt2aR417O0WdZlEL/fAUAC8eOlUlUR7+9Q666F+P8r9TMmEE87ZXraDQ1ToZQUL9NVkOJpZdP2+21ZM1I85bhqCtp+qYRJ/UE1mOjL6w+qQV5VRoq/Jb3/zbKaWpZdJpV9We5JijeX0vZllV4qOlYNWnZ+qaCsP6gGKcSfKwHauRUAbmrFf4aVMtr5pkX576T46woHUrwe4seiFkA7jlHjqddMlXraOUEpFImO1RPH6K7Q0yaw0+tp5UsE8edWyv8UAMp1tGuaFvqrpPgSqQUpvpywLynXhgD9GoX6maSgHpeoyvSmpHjqvjTX3RYdWypRkcqm7nfK+QagHZso3z0AkAu06wKqUp05Kb6SsG9s9H+Syr4tpV2/WVTeIMU7eIqbTJU1nNomaa2tV2VtBEHArl27MHz48DpjPDw8MH78eKMGwL1792Lw4MEoKSnBzZs30apVKxw6dMhoIts33ngDBw8evGeYvYbyyDII6xIYGIjOnTtj1apVTV0VxhhjjDHGGGOMMdbEvjWh3cBtSCn/eh4LFy40WrZgwQLExMQ0TYUekSZvIHxcODs7Y/r06YaUTpWq7rs73333HXr16kUq/5133sE777xjeF5aWgqJRAKFQoFevXph3bp1D1TvO+6u/6PQsugCKf5PlRspnnrHrLRKfJZRpZ5W9m8tfUnxlTa0r5ZCoGXS/FHWUnRsC+JYkVIJMRuMmBVRXCX+zql83npS2a7zSOHIadePFO9z6nNSvKpYfGaBTqDd8b1qQRvOwEwr/u42AGhNxGfZavW0jAsd8ftHvWNtfZuW3ayV0+5AU5TLaGXb3s4hxeepxX8Omt+ilX3Fkna3unkpbb9XSmkZHeWEbOVCM/HHSAAw0dGOe9RsF5Ve/HH4tmBFKpuaBUTNqJIS9w3l+009f1AzAqnlmwjis4mpxz3q/4maBUT9TFKusaj7kZoRSM58I2S1UrMZy6qI/1fi+YmacS8nfF8Velpms0RP+8xUSWhZ5RUQv++lxF5DVNfLaJmbjuZ59QfVQOl9U0685iAfC4hZ3+Z68dnExXo1qWzq+UYQqMds2rGjpFL8b0W9hFZ36rYWyqxJ8bSzH2sI0dHRmDFjhtEyMdmDYtnZ2eHqVePfilevXoWFhQXMzMwglUohlUprjXnQyXbF4AbCOtxvluVWrVqRy3v55ZcRGhpqeB4eHg6NRoN///vfMDMzQ6tWrfAgvb0rKiogl9MuZhhjjDHGGGOMMcYeV4IJreG1IYntTvyg/P39sXfvXqNl+/fvN3Qnlsvl8PX1xYEDBwxdlXU6HQ4cOIApU6Y0Wr0abzCJWhQXFyMiIgIqlQr29vaGGVvu+PTTT+Hn5we1Wg07OzuMGTPGMIWzXq+Hm5sbli1bZvSe9PR0CIKA8+fPQ6/XIyYmBm3atIFCoYCDgwOmTq1/DLPAwEBcvnwZr7/+OgRBgCAIcHNzg5ubGzIyMhASEgIvLy8EBQXhq6++Ej3Bx9q1a+Hu7g5TU1NoNBrMnTsXbm5ueOutt3D06FFs2rQJ7u7uaN26NS5duoSqqipERUWhbdu2MDMzg6enJ1avXm1UZmRkJIYPH463334bDg4O8PT0rLX+91NYWAgzMzN89913Rst37doFtVqNkhLamECMMcYYY4wxxhhjT6KioiKkp6cbEs0uXryI9PR05ObmAqjOSIyIiDDEv/zyy7hw4QLeeOMNnD17FmvXrsVnn32G119/3RAzY8YMfPTRR9i0aRMyMzPxyiuvoLi42DCrcWN4pBmEs2fPxsGDB/HVV1+hZcuWePPNN5GWlmaYlEOr1WLRokXw9PREfn4+ZsyYgcjISOzduxeCIODFF19EXFwcZs2aZSgzLi4OvXv3hpubG7744gusXLkS27dvh5eXF/78809kZGTUW6+dO3eiU6dOmDhxIiZMmGBYnpqaitDQUMTExCAsLAyHDh3C5MmT0bx5c0RGRt63zGPHjmHq1Kn49NNP0bNnT9y4cQOJiYkAgNWrV+PcuXPo0KEDYmNjAQA2NjbQ6XRo3bo1Pv/8czRv3hyHDh3CxIkTYW9vb5R9eODAAVhYWGD//v0AAHt7+1rrXxcLCwsMGTIEW7duNZqKe8uWLRg+fDjMzRuvux1jjDHGGGOMMcbYP8WxY8fQp08fw/M73ZPHjRuH+Ph45OXlGRoLAaBt27b49ttv8frrr2P16tVo3bo1Nm7ciODgYENMWFgYrl27hvnz5+PPP/9E586d8f3338PW1rbRtuORNRAWFRXh448/xn//+1/061c97temTZvQunVrQ8yLL75o+NvFxQXvv/8+unbtiqKiIqhUKkRGRmL+/Pk4evQounXrBq1Wi61btxqyCnNzc2FnZ4egoCCYmJigTZs26NatW711s7a2hlQqNWQu3rFixQr069cP8+ZVD2zm4eGBM2fO4L333qu3gTA3NxdKpRJDhgyBWq2Gk5MTfHx8AACWlpaQy+UwNzc3Wp9UKjUaCLNt27ZITk7GZ599ZtRAqFQqsXHjRqOuxbXV/37Cw8MxduxYlJSUwNzcHIWFhfj222+xa9cuUe9njDHGGGOMMcYYawwSWdN1MaYKDAy875Bx8fHxtb7n+PHj9y13ypQpjdql+G6PrItxTk4OKioq0L17d8Mya2treHr+PTNNamoqhg4dijZt2kCtVuPpp58GAENLq4ODAwYPHoxPPvkEAPD111+jvLwco0aNAgCMGjUKpaWlcHFxwYQJE7Br1y5UVj74oLeZmZkICAgwWhYQEIDs7GxUVd1/ANj+/fvDyckJLi4uGDt2LLZs2SKq6+6HH34IX19f2NjYQKVS4T//+Y9RSzMAdOzY8aHHHRw0aBBMTEywZ88eAMCXX34JCwsLBAUF1RpfXl6OwsJCo0d5hfjBvBljjDHGGGOMMcbY4+mRjkF4P8XFxQgODoaFhQW2bNmClJQUQzZbRY2GqJdeegnbt29HaWkp4uLiEBYWZugS6+joiKysLKxduxZmZmaYPHkyevfuDa2WNsNRQ1Cr1UhLS8O2bdtgb2+P+fPno1OnTrh161ad79m+fTtmzZqFqKgo7Nu3D+np6Rg/frzR9gPVGYQPSy6XY+TIkdi6dSsAYOvWrQgLC4NMVntS6eLFi2FpaWn0WPnxloeuB2OMMcYYY4wxxlhNgomkyR5Pqke25a6urjAxMcGRI0cMy27evIlz584BAM6ePYvr169jyZIl6NWrF9q1a2eYoKSmQYMGQalUYt26dfj++++NuiUDgJmZGYYOHYr3338fCQkJSE5OxsmTJ+utn1wuvycrUKPRICkpyWhZUlISPDw8IJXWP+W8TCZDUFAQli5dihMnTuDSpUv46aef6lxfUlISevbsicmTJ8PHxwdubm7Iycmpdz11lVef8PBwfP/99zh9+jR++uknhIeH1xkbHR2NgoICo8frUXXHM8YYY4wxxhhjjLH/DY9sDEKVSoWoqCjMnj0bzZs3R8uWLfGvf/0LEkl1G2WbNm0gl8vxwQcf4OWXX8apU6ewaNGie8qRSqWIjIxEdHQ03N3dDdNAA9X9uquqqtC9e3eYm5vjv//9L8zMzODk5FRv/ZydnfHLL79g9OjRUCgUaNGiBWbOnImuXbti0aJFCAsLQ3JyMtasWYO1a9fWW94333yDCxcuoHfv3mjWrBn27t0LnU5n6FLt7OyMI0eO4NKlS1CpVLC2toa7uzs2b96MH374AW3btsWnn36KlJQUtG3b9oHqX5/evXvDzs4O4eHhaNu2rVH377vVNs131UN2c2aMMcYYY4wxxhhjTe+R5k6+99576NWrF4YOHYqgoCA89dRT8PX1BVA9i298fDw+//xztG/fHkuWLDFMPnK3qKgoVFRU3DO9s5WVFT766CMEBATA29sbP/74I77++ms0b9683rrFxsbi0qVLcHV1hY2NDQCgS5cu+Oyzz7B9+3Z06NAB8+fPR2xsbL0TlNypy86dO9G3b19oNBqsX78e27Ztg5eXFwBg1qxZkEqlaN++PWxsbJCbm4tJkybhueeeQ1hYGLp3747r169j8uTJ9a6rrvrXRxAEPP/888jIyLhv9iBjjDHGGGOMMcbYoyKRCU32eFIJ+vtNtfKYSkxMRL9+/XDlypVGneKZ3d/NjIOk+D9VbqR4CXSk+HKdov6g/1dUaUYqu7n8Fim+Uk9LzlUIZaT4P8paio5toSgglW2OIlK8ANohpEDfTHSsXNK4E+HktOtHivc59TkpXlV8VXSsTqh/2IKarlq4k+IttX+R4gtN6r+xcoeOeK9Jp6fFSwTascDu9nlSvFZuToqnKJfRylaV0P5PeWrxn4NWt06Ryr5i6U2Kty8VNyTGHZVSU1L87ybOomMtJbTjXpFOTYo3ldCO2WY68cfV24IVqWzq94N6bpXraduaXyn+uszC5DapbCrq+UkmiB+zWqun9aKQgjYMjFZvQoqnkgvlomMFgbYfy3W077YJYb8DgEQQvy8r9OKvDQGgQKsixbcRLpPiS01o5VcSPgcKfSmpbIme9pmsktA+kxUQv+9N0LjXe7mlDqR4R/M8Uryq/Kbo2EJ5/b28aqIeC6jfV3O9+PNTMWjnSspxBqDXnfL9AIACrYXoWAfJFVLZt6Xif98A9PNTO9fWpPh/iv22HZps3f2v0q6b/ykaNYMwMDAQ06dPb7DyysvL8dtvvyEmJgajRo3ixsFaxMfHw8rKqqmrwRhjjDHGGGOMMfZABBOhyR5Pqkc2BmFD2LZtG6KiotC5c2ds3rxZ9Pvs7e1x48YNmJjUfpehqIiWMQVUZzEOHDiwztcfpMyGNHDgQCQmJtb62ptvvok333zT8FwQBOzatQvDhw8nraNKSrt7Ts0akhLuEAOADuK/yNS7NlR6Ql0AoIr4VazSid+X1Lt3OtAy2fQCbVvlevF3ieWg1p32GaNmBB7vMIoU3+vw+6JjqXdrqFlA0ira3XldI87eRf0/mYKWFSGtKCbFlytod8QbU2NmM1bJaFk9MqGSFK8XiJmhOlrWUKVO/HFSkFCzEGjHPapyifisdep3m3o+o2bqVElo5ydKRmNjn4up5VcRs/8bEzWThpyhCPHXcDI97VhArXsV8bqD8m+lZ5HSvn864nFPr6ddM1GyWstB6x0jJR7jqf9Xyq6nXgdTr7MrCdfNACAQO9hJdLR92ZhkoNWF0oOF+n2iHGcA+rGminjulkrEHyepGbPk/f5oR3pjTLTH50pIhMjISFHj/91NLpdj1qxZ94xZ+DD8/PyQnp7eYOU1tI0bN6K0tPYf1dbW1o+4NowxxhhjjDHGGGPscdVgTdfFxcWIiIiASqWCvb09li9fbvT6p59+Cj8/P6jVatjZ2WHMmDHIz88HAOj1eri5ud0zKUl6ejoEQcD58+eh1+sRExODNm3aQKFQwMHBAVOnTq23XoGBgcjNzcU777wDd3d3uLu7w83NDW5ubsjIyEBISAgUCgWcnZ3vqfP9SCQSfPTRR+jTpw+8vLwwYMAAHDx40FD2wYMH0a1bNygUCtjb22Pu3LmorPz7zkJgYCBee+01TJ8+Hc2aNYOtrS0++ugjFBcXY/z48VCr1XBzc8N3331neE9CQgIEQcC3334Lb29vmJqaokePHjh16t7+8a1atTLU5fTp0wgNDUWHDh3wzDPP4IMPPjDUxdnZGQDw7LPPQhAEw3PGGGOMMcYYY4yxpsCTlDx6DdZAOHv2bBw8eBBfffUV9u3bh4SEBKSlpRle12q1WLRoETIyMrB7925cunTJkA0oCAJefPFFxMXFGZUZFxeH3r17w83NDV9++SVWrlyJDRs2IDs7G7t370bHjh3rrdfOnTvRunVrxMbGIi8vD3l51YPOpqamIjQ0FKNHj8bJkycRExODefPmIT4+XtT2RkREYNu2bXj//feRmZmJDRs2QKWqHnj4999/x6BBg9C1a1dkZGRg3bp1+Pjjj/HWW28ZlbFp0ya0aNECR48exWuvvYZXXnkFo0aNQs+ePZGWloZnnnkGY8eORUlJyT37evny5UhJSYGNjQ2GDh0Krbb2LgiJiYmIiIjAtGnTcObMGWzYsAHx8fF4++23AQApKSmGfZ2Xl2d4zhhjjDHGGGOMMcaeDA3SxbioqAgff/wx/vvf/6Jfv+pZQTdt2oTWrf+ebefFF180/O3i4oL3338fXbt2RVFREVQqFSIjIzF//nwcPXoU3bp1g1arxdatWw1Zhbm5ubCzs0NQUBBMTEzQpk0bdOvWrd66WVtbQyqVGjIX71ixYgX69euHefPmAQA8PDxw5swZvPfee/V2Yz537hw+++wz7N+/H0FBQYZtumPt2rVwdHTEmjVrIAgC2rVrhz/++ANz5szB/PnzIZFUt8t26tQJ//73vwEA0dHRWLJkCVq0aIEJEyYAAObPn49169bhxIkT6NGjh6H8BQsWoH///kb7edeuXQgNDb2nrgsXLsTcuXMxbtw4Qz0XLVqEN954AwsWLICNjQ0AwMrKymj/MMYYY4wxxhhjjDWFJ3mykKbSIBmEOTk5qKioQPfu3Q3LrK2t4enpaXiempqKoUOHok2bNlCr1Xj66acBVDf8AYCDgwMGDx6MTz75BADw9ddfo7y8HKNGVU8KMGrUKJSWlsLFxQUTJkzArl27jLrsUmVmZiIgIMBoWUBAALKzs1FVdf8BTNPT0yGVSg3bUFvZ/v7+EGpM3hAQEICioiL89ttvhmXe3t6Gv6VSKZo3b26UFXlnluY7XbHv8Pf3N/x9Zz9nZmbWWpeMjAzExsZCpVIZHhMmTEBeXt49mYn3U15ejsLCQqNHeQVtoHPGGGOMMcYYY4wx9vh5JNPnFBcXIzg4GBYWFtiyZQtSUlKwa9cuAEBFjUaml156Cdu3b0dpaSni4uIQFhYGc/PqGR0dHR2RlZWFtWvXwszMDJMnT0bv3r3r7FrbmMzMaLOE1eXuWZUFQTBadqeBUaejzahWU1FRERYuXIj09HTD4+TJk8jOzoapqfgZLRcvXgxLS0ujx+qN4meSZowxxhhjjDHGGGOPpwbpYuzq6goTExMcOXIEbdq0AQDcvHkT586dw9NPP42zZ8/i+vXrWLJkCRwdHQEAx44du6ecQYMGQalUYt26dfj+++/xyy+/GL1uZmaGoUOHYujQoXj11VfRrl07nDx5El26dLlv/eRy+T1ZgRqNBklJSUbLkpKS4OHhAan0/lOmd+zYETqdDgcPHjR0Mb677C+//BJ6vd7QyJeUlAS1Wm3U7fpBHT58+J79rNFoao3t0qULsrKy4ObmVmd5JiYm9WZNRkdHY8aMGUbLbp8/Tqw5Y4wxxhhjjDHG2P09yZOFNJUGaSBUqVSIiorC7Nmz0bx5c7Rs2RL/+te/DGPttWnTBnK5HB988AFefvllnDp1CosWLbqnHKlUisjISERHR8Pd3d2oK218fDyqqqrQvXt3mJub47///S/MzMzg5ORUb/2cnZ3xyy+/YPTo0VAoFGjRogVmzpyJrl27YtGiRQgLC0NycjLWrFmDtWvXiipv3LhxePHFF/H++++jU6dOuHz5MvLz8xEaGorJkydj1apVeO211zBlyhRkZWVhwYIFmDFjhmGfPIzY2Fg0b94ctra2+Ne//oUWLVpg+PDhtcbOnz8fQ4YMQZs2bTBy5EhIJBJkZGTg1KlThklTnJ2dceDAAQQEBEChUKBZs2b3lKNQKKBQKIyWVcjlD70tjDHGGGOMMcYYY6xpNVgX4/feew+9evXC0KFDERQUhKeeegq+vr4AABsbG8THx+Pzzz9H+/btsWTJEsPkI3eLiopCRUUFxo8fb7TcysoKH330EQICAuDt7Y0ff/wRX3/9NZo3b15v3WJjY3Hp0iW4uroaJuXo0qULPvvsM2zfvh0dOnTA/PnzERsbW+8EJXesW7cOI0eOxOTJk9GuXTtMmDABxcXFAIBWrVph7969OHr0KDp16oSXX34ZUVFRhglJHtaSJUswbdo0+Pr64s8//8TXX38NeR2NdcHBwfjmm2+wb98+dO3aFT169MDKlSuNGlaXL1+O/fv3w9HRET4+Pg1SR8YYY4wxxhhjjLEHIUiFJns8qQS9Xq9v6krUlJiYiH79+uHKlSuGSTpYtYSEBPTp0wc3b96ElZVVU1cHf51KJsVfNXUmxZsItPElS3Tix4YsrVTUH1SDtbyAFK/Vm9QfVAN1W/8oaSE61tH8T1LZEtDGvNQLtAOoVi8+81SOclLZOuI9D/PK26T44x1GkeJ7HX5ffLBAq/tVSw9SvFUZ7XNw3bQVKZ6iUk9LXjcXiknxNtfPkuJL1I03g7tWSjvWKCrFTx4FAH8pxP+f7IrOk8rOV7mQ4luUXiHF60E7dlw0qX0ojVrrIvuLVPbNqnsz5+9HJaV9JiW4/zAeNVWCdv4QQLuMIx9Xicemm1rx+1ItKyKVTUXdNxTU842U8BkAgEpiJx9q+VW4/3A6NclAmxSQWnfq/4myrZTtBICiSiUp3k74nRRfJqWVLwji900l8dpTSvy/UuoC0OpDvvYknj9yi2m/KV2Uv9UfVIO6TPw555Yp7ZqjsX9TyCA+vgwNMw5/XajHGuq+KdWJH3+/heQaqewKQXzZAP0c4unqSIr/p/ilQ9MlL/U+9WQOp/ZIJikRo7y8HL/99htiYmIwatSoR9o4KAgCdu/e/cjW97hwdnbGqlWrDM+f1P3AGGOMMcYYY4wx9iRrkDEIG8K2bdsQFRWFzp07Y/Nm8bPjJiYmYuDAgYbnFRUVqKqqMsw0XFREvzt9d5l3e5AyH0cpKSlQKml3Mmv6TVH3xCe1KSw3J8W3MrtKik/OsREde+067Q5VuD8tG6VAa0GKt5PeJMW/v6FMdOz8abTMmDbl50jxJQorUvyFEvEZT+3Mc0hlayW0bC1VMe0zRsoIBJDYY6roWLk17S6od8ImUvyfc+aS4tWrNoiOLRVoxxHq3W1lxS1S/MnJMaR4t2AvUjyFtWv94+TWlPnUNFJ8RYX403jFnHmksk3WbCfFX387lhRv17UdKT6nx0rRse5maaSyD5e4kuK9bStI8faluaJjqyS0YwE1G8WkSvz5A6BndyWfbys6to8nLZvRUn+DFF8iUZPiTSD+/9rYGYSFVbS6t9Fmk+JvmDqIjpXraZ+ZQp346zEAUEho3ycZ4RxSUqUilX0w04oU/6rZTlL8TSdfUry6QHxm9k1r2nGsUkIbS1yio32GKRmKEj2tbJ1AywyN+ddhUvzeGNpnvuyLLaJjZS/NJ5VdqKMdC+zKaNfON1Xir8tVVbQeVWUS2vUh9VjzFyFjHQASz4g/HkxV7SKVfdZlGCleLaX1YnpSSZ7grr5N5bFpIIyMjBQ9/l9Nfn5+SE9PNzx///33sX//fnz99dcPXJe7y3xcBAYGoiF7hN8Zj5ExxhhjjDHGGGOMPbkavIvx7du3ER4eDqVSCXt7e6xcuRKBgYGYPn06gNq7sVpZWSE+Pt7w/MqVKwgNDYWVlRWsra0REhKCS5cuGV5PSEhAt27dDOsYN24cTExM8Ouvv+KDDz7A2bNn4e7uDnd3d6Ny7+evv/7Cs88+C3Nzc3h7e+PMmTNwc3ODm5sb2rZti8WLF6N///7o2LEjPD09sXr1aqP316yTlZUVAgICcPnyZVHr/uqrr9ClSxeYmprCxcUFCxcuRGXl33fdBEHAhg0bMGTIEJibm0Oj0SA5ORnnz59HYGAglEolevbsiZycv+8Y5eTkICQkBLa2tlCpVOjatSt+/PFHo/Xe3cWYMcYYY4wxxhhjrKkJEqHJHk+qBm8gnDFjBpKSkrBnzx7s378fiYmJSEsT39VHq9UiODgYarUaiYmJSEpKgkqlwoABA1BRUYHKykoMHz4cTz/9NE6cOIHk5GRMnDgRgiAgLCwMM2fOhJeXF/Ly8pCXl4ewsDBR6124cCFCQ0Nx4sQJDBo0COHh4bhxo7ori06nQ+vWrfH555/jzJkzmD9/Pt5880189tlnAHDfOtUnMTERERERmDZtGs6cOYMNGzYgPj4eb7/9tlHcokWLEBERgfT0dLRr1w5jxozBpEmTEB0djWPHjkGv12PKlCmG+KKiIgwaNAgHDhzA8ePHMWDAAAwdOhS5ueK7OTHGGGOMMcYYY4yxf74G7WJ8+/ZtbNq0CVu3bkW/fv0AAHFxcXBwED/OyY4dO6DT6bBx40ZDA1tcXBysrKyQkJAAPz8/FBQUYMiQIXB1rR5rQ6P5e2ZDlUoFmUwGOzvamDyRkZF4/vnnAQDvvPMO3n//fRw9ehQDBgyAiYkJFi5caIht27YtkpOT8dlnnyE0NBSFhYX3rdP9LFy4EHPnzsW4ceMAAC4uLli0aBHeeOMNLFiwwBA3fvx4hIaGAgDmzJkDf39/zJs3D8HBwQCAadOmYfz48Yb4Tp06oVOnTobnixYtwq5du7Bnzx6jhkSxysvLUV5uPFZQRUU55HLamG+MMcYYY4wxxhhj7PHSoBmEFy5cgFarRbdu3QzLLC0t4enpKbqMjIwMnD9/Hmq1GiqVCiqVCtbW1igrK0NOTg6sra0RGRmJ4OBgDB06FKtXr0ZeXt5D193b29vwt1KphIWFBfLz8w3LPvzwQ/j6+sLGxgYqlQr/+c9/DNl4D1OnjIwMxMbGGrZVpVJhwoQJyMvLQ0lJSa31uzPDc8eOHY2WlZWVobCwEEB1BuGsWbOg0WhgZWUFlUqFzMzMB84gXLx4MSwtLY0en6xfXf8bGWOMMcYYY4wxxggEqaTJHk+qR77lgiDcM9GGVvv3bGRFRUXw9fVFenq60ePcuXMYM2YMgOqMwuTkZPTs2RM7duyAh4cHDh+mzU51NxMT41kDBUGATqcDAGzfvh2zZs1CVFQU9u3bh/T0dIwfPx4VFX/PuvagdSoqKsLChQuNtvXkyZPIzs6GqalprfW7k1lZ27I7dZ41axZ27dqFd955B4mJiUhPT0fHjh2N6kwRHR2NgoICo8eLL9Nm22SMMcYYY4wxxhhjj58G7WLs4uICExMTpKSkoE2bNgCAgoICnDt3Dr179wZQPXNuzey67Oxso0y5Ll26YMeOHWjZsiUsLCzqXJePjw98fHwQHR0Nf39/bN26FT169IBcLkdVVVVDbhaSkpLQs2dPTJ482bCs5oQg9dXpfrp06YKsrCy4ubk1eJ0jIyPx7LPPAqhuiKw50QuVQqGAQmHcnVguL68jmjHGGGOMMcYYY+zBSKRP7mQhTaVBMwjVajXGjRuH2bNn4+eff8bp06cRFRUFiURiyHDr27cv1qxZg+PHj+PYsWN4+eWXjTLhwsPD0aJFC4SEhCAxMREXL15EQkICpk6dit9++w0XL15EdHQ0kpOTcfnyZezbtw/Z2dmGMf+cnZ1x8eJFpKen46+//rpn3LwH4e7ujmPHjuGHH37AuXPnMG/ePKSkpBher69O9zN//nxs3rwZCxcuxOnTp5GZmYnt27fj3//+90PXeefOnUhPT0dGRgbGjBljyC5kjDHGGGOMMcYYY+yOBu9ivGLFCvj7+2PIkCEICgpCQEAANBqNobvs8uXL4ejoiF69emHMmDGYNWsWzM3NDe83NzfHL7/8gjZt2uC5556DRqNBVFQUysrKYGFhAXNzc5w9exYjRoyAh4cHJk6ciFdffRWTJk0CAIwYMQIDBgxAnz59YGNjg23btj30Nk2aNAnPPfccwsLC0L17d1y/ft0om7C+Ot1PcHAwvvnmG+zbtw9du3ZFjx49sHLlSjg5OT1UnVesWIFmzZqhZ8+eGDp0KIKDg9GlS5eHKpMxxhhjjDHGGGOM/fM0aBdjoDqLcMuWLYbnxcXFWLhwISZOnAgAcHBwwA8//GD0nlu3bhk9t7Ozw6ZNm2ot38LCArt27apz/QqFAl988UWdrwuCgF27dmH48OGGZXePiXh3nRQKBeLi4hAXF2cUs3jxYgDVE4Tcr071CQ4ONsxGHBkZiVu3bmHChAl11s/Z2fmeZYGBgUbLnJ2d8dNPPxnFvPrqq0bP7+5yXNt+YIwxxhhjjDHGGHuUBAl3MX7UGryB8Pjx4zh79iy6deuGgoICxMbGAgBCQkIaelWIiYnB7t27kZ6e3uBls/pJ0LgNino97YAgEMKlxPEMqHWh0gu0ZF4ThUn9Qf9PClrXcj2I+4YYT/k/Ucum0glSUjw15VpuLf7/VHFDW39QDdTPjExBO9xT983jRK6kbatE2ojbSixbIB5XKd8R6mdAEGh1IZdP3DcSSn2I3w+qxj42PU6o2yp5jC7mdcSjNjX+fxllWxv9XKwn7vdGrM7jNnEl9Vz/v4q6ndRrFKmJ+OsxAIBAm9RRQi2/MVEutAEIjZggQj2mNvZvEOrvP8aeRI1y1lm2bBk6deqEoKAgFBcXIzExES1atGiMVdVry5YtUKlUhgcAjB49GiqVCl5eXo2+fi8vL6P113zUzLRkjDHGGGOMMcYYY9WTlDTV40n1UA2Et2/fRnh4OJRKJezt7bFy5Uq8/vrr6NWrF4qKinDz5k28+uqr6Nixo+E9VlZWiI+PNzy/cuUKQkNDYWVlBWtra4SEhBh1fU1ISEC3bt2gVCphZWWFgIAAXL58GfHx8Vi4cCEyMjIgCAIEQTAq945hw4YhPT3d8ACqJwbp2bMnLly4AHd3d+zZs8cQX1VVhaioKLRt2xZmZmbw9PTE6tWrjcqsq0612bt3r2HdL774IhwdHTFnzhyo1WpMmDABoaGhKCgouOd9y5Ytg729PZo3b45XX30VWu3f2UU3b95EREQEmjVrBnNzcwwcOBDZ2dmG1+Pj42FlZYUffvgBGo0GKpUKAwYMMJo9GgA2btxoGB+yXbt2WLt2ba3bwBhjjDHGGGOMMcb+uR6qgXDGjBlISkrCnj17sH//fiQmJiItLU30+7VaLYKDg6FWq5GYmIikpCRDY1ZFRQUqKysxfPhwPP300zhx4gSSk5MxceJECIKAsLAwzJw5E15eXsjLy0NeXh7CwsLuWYdarYabm5vhAQDr1q3D+PHjcfLkSQwaNAjh4eG4ceMGAECn06F169b4/PPPcebMGcyfPx9vvvkmPvvsMwC4b51q4+TkZFi3tbU1rly5goSEBHz33Xf4/vvvcfz4caMJTwDg559/Rk5ODn7++Wds2rQJ8fHxRo2fkZGROHbsGPbs2YPk5GTo9XoMGjTIqBGxpKQEy5Ytw6effopffvkFubm5mDVrluH1LVu2YP78+Xj77beRmZmJd955B/Pmzatz7EfGGGOMMcYYY4wx9s/0wGMQ3r59G5s2bcLWrVvRr18/AEBcXBwcHBxEl7Fjxw7odDps3LjR0MAWFxcHKysrJCQkwM/PDwUFBRgyZAhcXV0BABqNxvB+lUoFmUwGOzs7Ut0jIyPx/PPPAwDeeecdvP/++zh69CgGDBgAExMTLFy40BDbtm1bJCcn47PPPkNoaCgKCwvvW6f6lJWVYfPmzWjVqhUA4IMPPsDgwYOxfPlyw3Y0a9YMa9asgVQqRbt27TB48GAcOHAAEyZMQHZ2Nvbs2YOkpCT07NkTQHVjn6OjI3bv3o1Ro0YBqG58Xb9+vaGOU6ZMMYwHCQALFizA8uXL8dxzzxm288yZM9iwYQPGjRtH2p+MMcYYY4wxxhhjDUV4grv6NpUHbiC8cOECtFotunXrZlhmaWkJT09P0WVkZGTg/PnzUKvVRsvLysqQk5ODZ555BpGRkQgODkb//v0RFBSE0NBQ2NvbP2i1AQDe3t6Gv5VKJSwsLJCfn29Y9uGHH+KTTz5Bbm4uSktLUVFRgc6dOwMArK2tH6pObdq0MTQOAoC/vz90Oh2ysrIMDYReXl6Q1hi83d7eHidPngQAZGZmQiaToXv37obXmzdvDk9PT2RmZhqWmZubGxoH75RxZxuLi4uRk5ODqKgoo9mSKysrYWlpWWu9y8vLUV5ebrSsoqIccrlC1HYzxhhjjDHGGGOMscdTo06NJQgC9HfNjFSzG2xRURF8fX2NxghMT0/HuXPnMGbMGADVGYXJycno2bMnduzYAQ8PDxw+fPih6mVy10xTgiBAp6ue6XX79u2YNWsWoqKisG/fPqSnp2P8+PGoqPh7NqvGqJPY+j1MGXf+F0VFRQCAjz76yGi/nzp1qs7tWLx4MSwtLY0eH69/n1QnxhhjjDHGGGOMsfoIEkmTPZ5UD7zlLi4uMDExQUpKimFZQUEBzp07Z3huY2NjNDFGdnY2SkpKDM+7dOmC7OxstGzZ0micQDc3N6NMNh8fH0RHR+PQoUPo0KEDtm7dCgCQy+Woqqp60E2o1Z2uu5MnT4aPjw/c3NyQk5NzT1xddapPbm4u/vjjD8Pzw4cPQyKRiM681Gg0qKysxJEjRwzLrl+/jqysLLRv315UGba2tnBwcMCFCxfu2e9t27at9T3R0dEoKCgwekS9PFXU+hhjjDHGGGOMMcbY4+uBGwjVajXGjRuH2bNn4+eff8bp06cRFRUFiURiGE+wb9++WLNmDY4fP45jx47h5ZdfNspsCw8PR4sWLRASEoLExERcvHgRCQkJmDp1Kn777TdcvHgR0dHRSE5OxuXLl7Fv3z5kZ2cbxvxzdnbGxYsXkZ6ejr/++uueLrAPwt3dHceOHcMPP/yAc+fOYd68eUaNoPXVqT6mpqYYN24cMjIykJiYiKlTpyI0NFT0OIru7u4ICQnBhAkT8OuvvyIjIwMvvPACWrVqhZCQENHbuXDhQixevBjvv/8+zp07h5MnTyIuLg4rVqyoNV6hUMDCwsLowd2LGWOMMcYYY4wxxv73PVTu5IoVK+Dv748hQ4YgKCgIAQEB0Gg0MDU1BQAsX74cjo6O6NWrF8aMGYNZs2bB3Nzc8H5zc3P88ssvaNOmDZ577jloNBpERUWhrKwMFhYWMDc3x9mzZzFixAh4eHhg4sSJePXVVzFp0iQAwIgRIzBgwAD06dMHNjY22LZt28NsDgBg0qRJeO655xAWFobu3bvj+vXrRrMM11en+ri5ueG5557DoEGD8Mwzz8Db2xtr164l1TEuLg6+vr4YMmQI/P39odfrsXfv3nu6Fd/PSy+9hI0bNyIuLg4dO3bE008/jfj4+DozCBljjDHGGGOMMcYeBUEiNNnjSfXAk5QA1VmEW7ZsMTwvLi7GwoULMXHiRACAg4MDfvjhB6P33Lp1y+i5nZ0dNm3aVGv5FhYW2LVrV53rVygU+OKLL0h1vntMxLvrpFAoEBcXh7i4OKOYxYsXA6junnu/Oonxyiuv4JVXXqn1tfj4+HuWrVq1yuh5s2bNsHnz5jrLj4yMRGRkpNGy4cOH37PtY8aMMYz1yBhjjDHGGGOMMcaeTA+VQXj8+HFs27YNOTk5SEtLQ3h4OADct6trYGAgpk+f/jCr/Z9VVFSEnJwcKJVKWFlZNXV1jMTExBhmamaMMcYYY4wxxhhrKhKp0GSPJ5Wgry2lTqTjx4/jpZdeQlZWFuRyOXx9fbFixQp07NixzvfcuHEDJiYmUKvVD7raOm3ZsqXOrr5OTk44ffp0g60rJiYGu3fvRnp6umGZl5cXLl++XGv8hg0bsHbtWqSlpeHEiROwtLREy5YtG6w+D6u27anP8ey/SOtQSkrqD3oIRVVK0bGCQPvYU+uuI7a9F1eZ1x9Us3y9+PJVsmJS2SaoqD+ohkqI79oOAHqIP+BS66In7nfq/0kC2mziFhXivyN6gVaX1I6jSfHd0uvOOq6NecHvomPzbb1JZeuI21quMyXFW1deJcULetr/laJKQvt+5EvsSfHmklLRsWrtDVLZRSbNSPEq7U1SfKVUToovkljWH/T/ZKgklV2mp33GZAKtfAHizznmutuksksktOspKWiTu1GO2QBQRvi+KiS0saOpdaGilN/Y+5Ear66iff8KpdaiY830tOuIMoF2TUPdVsr3iapMRxtfm3IMBujbalWRLzr2ptyWVPbjhLpfLKpo57NLlbQhlOxMxe93AJDqxJ8TygUzUtnUa1XLyuuk+FvSFqJjpQLtuEetO/VYUwza+U+rF995UiUpopUN2jWNAmWkeBdXV1L8P0X6M72abN2d9yU22bqb0kN1Mfbx8UFqairpPdbW4i9IqIYNG4bu3bvX+hplfL4HtXfvXmi12lpfu9M1OTQ0FO7u7nWWodVqH0ldGWOMMcYYY4wxxhgDHrKL8YOo2cV47dq1cHd3h6mpKWxtbTFy5EhRZdy+fRvh4eFQKpWwt7fHypUrERgYiHnz5sHNzQ1ubm5wd3fHqVOnDM+dnJxgZWVlNMbflStXEBoaCisrK1hbWyMkJASXLl0yvJ6QkIBu3boZugQHBATg8uXLiI+Px8KFC5GRkQFBECAIAuLj4+Hk5GRY392Pjh074ssvv8TmzZshCIJhjEBBELBu3ToMGzYMSqUSb7/9NgBg3bp1cHV1hVwuh6enJz799FOjfSAIAjZs2IAhQ4bA3NwcGo0GycnJOH/+PAIDA6FUKtGzZ0/k5OQ88P+KMcYYY4wxxhhj7FHjSUoevUfeQHjHsWPHMHXqVMTGxiIrKwvff/89evfuLeq9M2bMQFJSEvbs2YP9+/cjMTERaWlppPVrtVoEBwdDrVYjMTERSUlJUKlUGDBgACoqKlBZWYnhw4fj6aefxokTJ5CcnIyJEydCEASEhYVh5syZ8PLyQl5eHvLy8hAWFnbf9aWkpGDAgAEIDQ1FXl4eVq9ebXgtJiYGzz77LE6ePIkXX3wRu3btwrRp0zBz5kycOnUKkyZNwvjx4/Hzzz8blblo0SJEREQgPT0d7dq1w5gxYzBp0iRER0fj2LFj0Ov1mDJlCmm/MMYYY4wxxhhjjLEny0N1MX4Yubm5UCqVGDJkCNRqNZycnODj41Pv+27fvo1NmzZh69at6NevHwAgLi4ODg4OpPXv2LEDOp0OGzduhCAIhnKsrKyQkJAAPz8/FBQUYMiQIXD9/z7/Go3G8H6VSgWZTAY7OztR67OxsYFCoYCZmdk97xkzZgzGjx9veP78888jMjISkydPBlDdIHr48GEsW7YMffr0McSNHz8eoaGhAIA5c+bA398f8+bNQ3BwMABg2rRpRuUyxhhjjDHGGGOMPe4ESZPlsz2xmmyP9+/fH05OTnBxccHYsWOxZcsWlJTUPxHEhQsXoNVq0a1bN8MyS0tLeHp6ktafkZGB8+fPQ61WQ6VSQaVSwdraGmVlZcjJyYG1tTUiIyMRHByMoUOHYvXq1cjLyyNvpxh+fn5GzzMzMxEQEGC0LCAgAJmZmUbLvL3/nhjA1rZ6YOKaE8TY2tqirKwMhYWFD13H8vJyFBYWGj0qKmiDizPGGGOMMcYYY4yxx0+TNRCq1WqkpaVh27ZtsLe3x/z589GpUyfcunWrQcoXBAF3T9BccwKRoqIi+Pr6Ij093ehx7tw5jBkzBkB1RmFycjJ69uyJHTt2wMPDA4cPH26Q+tWkVIqffbemmpOZ3MmCrG2ZTvfwM3QuXrwYlpaWRo9P1q+u/42MMcYYY4wxxhhjBDwG4aPXpDmbMpkMQUFBWLp0KU6cOIFLly7hp59+uu97XFxcYGJigpSUFMOygoICnDt3zijOxsbGKOMvOzvbKEOxS5cuyM7ORsuWLe+ZUMTS0tIQ5+Pjg+joaBw6dAgdOnTA1q1bAQByuRxVVbSp3sXSaDRISkoyWpaUlIT27ds3yvrEiI6ORkFBgdHjxZenNVl9GGOMMcYYY4wxxljDaLIxCL/55htcuHABvXv3RrNmzbB3717odLp6uwqr1WqMGzcOs2fPhrW1NVq2bIkFCxZAIpEYMuYAoG/fvlizZg38/f1RVVWFOXPmGGXXhYeH47333kNISAhiY2PRunVrXL58GTt37sQbb7wBrVaL//znPxg2bBgcHByQlZWF7OxsREREAACcnZ1x8eJFpKeno3Xr1lCr1VAoFA2yb2bPno3Q0FD4+PggKCgIX3/9NXbu3Ikff/yxQcp/EAqF4p7tk8srmqg2jDHGGGOMMcYYY6yhNFkGoZWVFXbu3Im+fftCo9Fg/fr12LZtG7y8vOp974oVK+Dv748hQ4YgKCgIAQEB0Gg0MDU1NcQsX74cjo6O6NWrF8aMGYNZs2bB3Nzc8Lq5uTl++eUXtGnTBs899xw0Gg2ioqJQVlYGCwsLmJub4+zZsxgxYgQ8PDwwceJEvPrqq5g0aRIAYMSIERgwYAD69OkDGxsbbNu2rcH2zfDhw7F69WosW7YMXl5e2LBhA+Li4hAYGNhg62CMMcYYY4wxxhh7HEmkQpM9nlSC/u6B+v4HFRcXo1WrVli+fDmioqKaujpPjOPZf5HilZL6J6F5GEVV4sdyFATax55adx2x7b24yrz+oJrl68WXr5IVk8o2AS0ztBIm9QfVoIf4Ay61Lnrifqf+nySgjedpUSH+O6IXaHVJ7TiaFN8tfTMp3rzgd9Gx+bbe9QfVoCNua7nOtP6gGqwrr5LiBf3Dj9NalyoJ7fuRL7EnxZtLSkXHqrU3SGUXmTQjxau0N0nxlVI5Kb5IYll/0P+ToZJUdpme9hmTCbTyBYg/55jrbpPKLpGoSfFS0IZHoRyzAaCM8H1VSGiTnVHrQkUpv7H3IzVeXUX7/hVKrUXHmulp1xFlAu2ahrqtlO8TVZmO1huIcgwG6NtqVZEvOvam3JZU9uOEul8sqmjns0uVbUnxdqbi9zsASHXizwnlghmpbOq1qmXldVL8LWkL0bFSgXbco9adeqwpBu38p9WL7zypkhTRygbtmkaBMlK8i6srKf6f4nRI3yZbt9dX9x/67p/qkWcQBgYGYvr06Q9VxvHjx7Ft2zbk5OQgLS0N4eHhAICQkJAGqGHj+fPPP9G/f38olUpYWVk1dXWMxMTEoHPnzk1dDcYYY4wxxhhjjD3heJKSR++Rj0G4c+dOo7EA75abm3vfyTjOnDkDAFi2bBmysrIgl8vh6+uLxMREtGgh/g7Iw4qJicHu3buRnp4OANiyZYuh+/HdnJyccPr0aaxcuRJ5eXlIT083mgjlUfDy8sLly5drfW3Dhg2PpA5/lNqQ4luZ0e7encprLjr22k3a3echHWl3iEuqaHcHraW0O6HvfiF+Wyc/R8tgalle++ekLmVy2t27s+XuomM9TC+Qyq6U0O7emWlpmTrSKlpG459z5oqOlSloh2NqRuDRzhGk+A6Ze0TH/lVJO/a21WaS4nXELLziZbGk+ObebqR4CpOWtOPeH74vk+KvacVnASk3zCaVLUxdQov/eBkp3qZLR1J8ZvvXRcf6V9DG7P389iBSvHcrWraWS/kpUjxFuZKWrSUhZoCoymnb+vMVX9GxvVz+IJVtVUXruVAoE//9AGjZK1US2jFbQsxUvqWzIsVbV9KyMcsFwvmSmE5AzcKr1NH2pUIi/lxcRaz8r+fEX18BwATQrqH/9KRlw5jf/lN0bHkz2rGgQKB9P6oIvVcAwEwiPkOKmhVaLqVt6/z5Z0jxn79J+z6Vxa0VHaufsohUNvVY0FJLy3wrFVqLjpVLtKSyqZ8Z6rEmv8SKFH/6ivgM95eKadfZZzpFkuIVUloGIWOPyiNvILS2vv/JyMHBwdDoVtfrbdq0QWpqagPX7OEMGzYM3bt3r/W1Ow2iOTk58PX1hbt73Q0kWq32vg2oD2rv3r3Qams/qNva2mL58uUNvk7GGGOMMcYYY4wx9vhr0i7Ga9euhbu7O0xNTWFra4uRI0dCJpPBzc2tzodMJsPt27cRHh4OpVIJe3t7rFy58p6uy4IgYPfu3UbrtrKyQnx8vOH5lStXEBoaCisrK1hbWyMkJASXLl0yvJ6QkIBu3boZugQHBATg8uXLiI+Px8KFC5GRkQFBECAIAr788ss66+zk5ARnZ2d8+eWX2Lx5MwRBQGRkpKGe69atw7Bhw6BUKvH2228DANatWwdXV1fI5XJ4enri008/NdoWQRCwYcMGDBkyBObm5tBoNEhOTsb58+cRGBgIpVKJnj17IicnB0B1FmNd9VOraRlgjDHGGGOMMcYYY41FkEia7PGkarItP3bsGKZOnYrY2FhkZWXh+++/R+/evUW9d8aMGUhKSsKePXuwf/9+JCYmIi0tjbR+rVaL4OBgqNVqJCYmIikpCSqVCgMGDEBFRQUqKysxfPhwPP300zhx4gSSk5MxceJECIKAsLAwzJw5E15eXsjLy0NeXh7CwsLuu76UlBQMGDAAoaGhyMvLw+rVqw2vxcTE4Nlnn8XJkyfx4osvYteuXZg2bRpmzpyJU6dOYdKkSRg/fjx+/vlnozIXLVqEiIgIpKeno127dhgzZgwmTZqE6OhoHDt2DHq9HlOmTCHtF8YYY4wxxhhjjDH2ZHnkXYzvyM3NhVKpxJAhQ6BWq+Hk5AQfH59633f79m1s2rQJW7duRb9+/QAAcXFxcHBwIK1/x44d0Ol02LhxIwRBMJRjZWWFhIQE+Pn5oaCgAEOGDIHr/88apNFoDO9XqVSQyWSws7MTtT4bGxsoFAqYmZnd854xY8Zg/PjxhufPP/88IiMjMXnyZADVDaKHDx/GsmXL0KdPH0Pc+PHjERoaCgCYM2cO/P39MW/ePAQHBwMApk2bZlQuY4wxxhhjjDHG2OPuSZ4spKk0WQZh//794eTkBBcXF4wdOxZbtmxBSUlJve+7cOECtFotunXrZlhmaWkJT09P0vozMjJw/vx5qNVqqFQqqFQqWFtbo6ysDDk5ObC2tkZkZCSCg4MxdOhQrF69Gnl5eeTtFMPPz8/oeWZmJgICAoyWBQQEIDPTeGB/b29vw9+2trYAgI4dOxotKysrQ2Fh4UPXsby8HIWFhUaPigraAL6MMcYYY4wxxhhj7PHTZA2EarUaaWlp2LZtG+zt7TF//nx06tQJt27dapDyBUGAXm88I1bNSTqKiorg6+uL9PR0o8e5c+cwZswYANUZhcnJyejZsyd27NgBDw8PHD58uEHqV5NSqXyg99WczOROFmRty3Q62sx5tVm8eDEsLS2NHp+sX13/GxljjDHGGGOMMcbYY61JR1+UyWQICgrC0qVLceLECVy6dAk//fTTfd/j4uICExMTpKSkGJYVFBTg3LlzRnE2NjZGGX/Z2dlGGYpdunRBdnY2WrZsec+kHZaWloY4Hx8fREdH49ChQ+jQoQO2bt0KAJDL5aiqqnqo7a+LRqNBUlKS0bKkpCS0b9++UdYnRnR0NAoKCoweL748rcnqwxhjjDHGGGOMsX8mQSI02eNJ1WRjEH7zzTe4cOECevfujWbNmmHv3r3Q6XT1dhVWq9UYN24cZs+eDWtra7Rs2RILFiyARCIxZMwBQN++fbFmzRr4+/ujqqoKc+bMMcquCw8Px3vvvYeQkBDExsaidevWuHz5Mnbu3Ik33ngDWq0W//nPfzBs2DA4ODggKysL2dnZiIiIAAA4Ozvj4sWLSE9PR+vWraFWq6FQKBpk38yePRuhoaHw8fFBUFAQvv76a+zcuRM//vhjg5T/IBQKxT3bJ5dXNFFtGGOMMcYYY4wxxlhDabIMQisrK+zcuRN9+/aFRqPB+vXrsW3bNnh5edX73hUrVsDf3x9DhgxBUFAQAgICoNFoYGpqaohZvnw5HB0d0atXL4wZMwazZs2Cubm54XVzc3P88ssvaNOmDZ577jloNBpERUWhrKwMFhYWMDc3x9mzZzFixAh4eHhg4sSJePXVVzFp0iQAwIgRIzBgwAD06dMHNjY22LZtW4Ptm+HDh2P16tVYtmwZvLy8sGHDBsTFxSEwMLDB1sEYY4wxxhhjjDH2OOIMwkfvkWcQJiQk1Po3hVqtxpYtWwzPi4uLsXDhQkycONGwzMHBAT/88IPR++4e39DOzg6bNm2qdR0WFhbYtWtXnXVQKBT44osvSPXevXv3PcvuHifxjldeeQWvvPJKnWXd/T5nZ+d7lgUGBtZZ/t1iYmIQExMjKpYxxhhjjDHGGGOM/XM88gzCwMBATJ8+/aHKOH78OLZt24acnBykpaUhPDwcABASEtIANWw8f/75J/r37w+lUgkrK6umro6RmJgYdO7cuamrwRhjjDHGGGOMMcYeMUEvNsWsgdy4cQMmJiZQq9W1vp6bm3vfyTjOnDmD69ev46WXXkJWVhbkcjl8fX2xYsUKdOzYsbGqfY+YmBjs3r0b6enpAIAtW7YYuh/fzcnJCadPn8acOXPw7bffYteuXbC0tETLli0fWX29vLxw+fLlWl/bsGEDsrOzjbZHjDPn/yDVwUTQ1h/0EHSE9m6t3qT+oBpkQiW1OiQ6Pa2tnrKtEtBmsW7sba3Ui09clgq0iYAENO7hjLLfAcBUX1J/0J2yBSmpbNurGaT4v1rWP3xDTac0w0THepzdRyqbirrf5SinlS803r0ygXiKLdWb1x9Ug1wQPxasDLRjsBZyUjy1/CpiJ4YqiP+OSEE7dlDKfhCU4zD1OEb9flDLlxD3ZbnetP6g/0c9xlPPZ3rQughR4qmfMSlo59YK0Ma1pl5HUM711M9AJWjXWNT/K/UzT0G5RgEAM4n48zxAPyeUQ/z3ifrdpsZTv08Ujf19KtLV/ruzLnIJbZx1BcpEx1L3I/VYQC2/Ma+dqd9t6rGGeh1B+f1H/W5r9bRrJuq+8XBtQ4r/p8gOH9Rk63bfsrfJ1t2UHnkXY2tr6/u+7uDgcN9GKgcHB7Rp0wapqakNXLOHM2zYMHTv3r3W1+5MjpKTkwNfX1+4u7vXWY5WqzWaTKWh7N27F1pt7T/ebG1tsXz58gZfJ2OMMcYYY4wxxhh7/DVpF+O1a9fC3d0dpqamsLW1xciRIyGTyeDm5lbnQyaT4fbt2wgPD4dSqYS9vT1Wrlx5T9dlQRDuGfPPysoK8fHxhudXrlxBaGgorKysYG1tjZCQEFy6dMnwekJCArp162boEhwQEIDLly8jPj4eCxcuREZGBgRBgCAI+PLLL+uss5OTE5ydnfHll19i8+bNEAQBkZGRhnquW7cOw4YNg1KpxNtvvw0AWLduHVxdXSGXy+Hp6YlPP/3UaFsEQcCGDRswZMgQmJubQ6PRIDk5GefPn0dgYCCUSiV69uyJnJwcANVZjHXVr65sTsYYY4wxxhhjjLFHTSIVmuzxpGqyWYyPHTuGqVOnIjY2FllZWfj+++/Ru3dvUe+dMWMGkpKSsGfPHuzfvx+JiYlIS0sjrV+r1SI4OBhqtRqJiYlISkqCSqXCgAEDUFFRgcrKSgwfPhxPP/00Tpw4geTkZEycOBGCICAsLAwzZ86El5cX8vLykJeXh7CwsPuuLyUlBQMGDEBoaCjy8vKwevVqw2sxMTF49tlncfLkSbz44ovYtWsXpk2bhpkzZ+LUqVOYNGkSxo8fj59//tmozEWLFiEiIgLp6elo164dxowZg0mTJiE6OhrHjh2DXq/HlClTSPuFMcYYY4wxxhhjjD1ZHnkX4ztyc3OhVCoxZMgQqNVqODk5wcfHp9733b59G5s2bcLWrVvRr18/AEBcXBwcHBxI69+xYwd0Oh02btwIQRAM5VhZWSEhIQF+fn4oKCjAkCFD4OrqCgDQaDSG96tUKshkMtjZ2Ylan42NDRQKBczMzO55z5gxYzB+/HjD8+effx6RkZGYPHkygOoG0cOHD2PZsmXo06ePIW78+PEIDQ0FAMyZMwf+/v6YN28egoODAQDTpk0zKpcxxhhjjDHGGGOMsbs1WQZh//794eTkBBcXF4wdOxZbtmxBSUn9g4FeuHABWq0W3bp1MyyztLSEp6cnaf0ZGRk4f/481Go1VCoVVCoVrK2tUVZWhpycHFhbWyMyMhLBwcEYOnQoVq9ejby8PPJ2iuHn52f0PDMzEwEBAUbLAgICkJmZabTM29vb8LetrS0AGE3UYmtri7KyMhQWFj50HcvLy1FYWGj0qCinTQTAGGOMMcYYY4wxVh9BIjTZ40nVZA2EarUaaWlp2LZtG+zt7TF//nx06tQJt27dapDyBUHA3RM015yko6ioCL6+vkhPTzd6nDt3DmPGjAFQnVGYnJyMnj17YseOHfDw8MDhw4cbpH41KZXKB3pfzclM7mRB1rZMp6PNklSbxYsXw9LS0ujx0YY1D10uY4wxxhhjjDHG2P+yDz/8EM7OzjA1NUX37t1x9OjROmMDAwMN81nUfAwePNgQExkZec/rAwYMaNRtaLIGQgCQyWQICgrC0qVLceLECVy6dAk//fTTfd/j4uICExMTpKSkGJYVFBTg3LlzRnE2NjZGGX/Z2dlGGYpdunRBdnY2WrZsec+kHZaWloY4Hx8fREdH49ChQ+jQoQO2bt0KAJDL5aiqok3FLpZGo0FSUpLRsqSkJLRv375R1idGdHQ0CgoKjB4TJvH4howxxhhjjDHGGGtYgkTSZA+qHTt2YMaMGViwYAHS0tLQqVMnBAcHIz8/v9b4nTt3GuazyMvLw6lTpyCVSjFq1CijuAEDBhjFbdu27YH2pVhNNgbhN998gwsXLqB3795o1qwZ9u7dC51OV29XYbVajXHjxmH27NmwtrZGy5YtsWDBAkgkEkPGHAD07dsXa9asgb+/P6qqqjBnzhyj7Lrw8HC89957CAkJQWxsLFq3bo3Lly9j586deOONN6DVavGf//wHw4YNg4ODA7KyspCdnY2IiAgAgLOzMy5evIj09HS0bt0aarUaCoWiQfbN7NmzERoaCh8fHwQFBeHrr7/Gzp078eOPPzZI+Q9CoVDcs31yRVET1YYxxhhjjDHGGGOs6a1YsQITJkwwzAGxfv16fPvtt/jkk08wd+7ce+Ktra2Nnm/fvh3m5ub3NBAqFArR8140hCbLILSyssLOnTvRt29faDQarF+/Htu2bYOXl1e9712xYgX8/f0xZMgQBAUFISAgABqNBqampoaY5cuXw9HREb169cKYMWMwa9YsmJubG143NzfHL7/8gjZt2uC5556DRqNBVFQUysrKYGFhAXNzc5w9exYjRoyAh4cHJk6ciFdffRWTJk0CAIwYMQIDBgxAnz59YGNj06AtucOHD8fq1auxbNkyeHl5YcOGDYiLi0NgYGCDrYMxxhhjjDHGGGOMGattDobyOuZgqKioQGpqKoKCggzLJBIJgoKCkJycLGp9H3/8MUaPHn3P8HMJCQlo2bIlPD098corr+D69esPvlEiCPq7B+prAoGBgejcuTNWrVr1QO8vLi6GpaUl+vTpg/379zds5R5CSUkJxo4di/379+P27du4efMmrKysmrpaAID4+HhMnz7dMOZjTEwMdu/ejfT0dNFlnDn/B2mdJoK2/qCHoCO0d2v1JvUH1SATKqnVIdHpaW31lG2VgDYGZWNva6VefOKyVKB14xfQuIczyn4HAFN9/RMvGcoWpKSyba9mkOL/aln/zZeaTmmGiY71OLuPVDYVdb/LQZtASSc03r0ygXiKLdWb1x9Ug1yoEB0rA+0YrIWcFE8tv4rYiaEK4r8jUtCOHZSyHwTlOEw9jlG/H9TyJcR9Wa43rT/o/1GP8dTzmR60QcYp8dTPmBS0c2sFaL1SqNcRlHM99TNQCdo1FvX/Sv3MU1CuUQDATCL+PA/QzwnlEP99on63qfHU7xNFY3+finRqUrxcIv7cCgAKlImOpe5H6rGAWn5jXjtTv9vUYw31OoLy+4/63dbqaddM1H3j4dqGFP9PcemlkCZbd3xrHyxcuNBo2YIFCxATE3NP7B9//IFWrVrh0KFD8Pf3Nyx/4403cPDgQRw5cuS+6zp69Ci6d++OI0eOGE3GeyersG3btsjJycGbb74JlUqF5ORkSKWNc+3aZF2Ma9q5c6dR99/6HD9+HGfPnkW3bt1QUFCA2NhYANXjEzaWuxvUxNi0aRMSExNx6NAhtGjRwmhsw8fNrFmz8Nprr5HeQz0BUX+AmemLSfG3IX7//lVG+1+0Mqt97IC6UC9eqT+STFEqOpba4CAlNhCW6cxI8ZR9oxDEX3A9COrJnKpUeLAJiMTIt/WuP6iGvypbkOIpjX7n2j1DKtvxTCIpvnVpFin+T3NXUjz1hzUFtcGdeqFOOa5WED/vWuIPZZlAK99Sf4MUfxPiP8MqyW1S2SZ6WqMy9f9UKRF/jVNJvIFFRa27XEc7Dl8pE98Npo0yr/6gGsp14htLAPq5lXIs0AvEH/nE75/T9WOk+AvW3UnxlGs452tppLIv23SrP6gGle4WKV5RKf6H+zV5a1LZFvqbpPgrZY6k+JYKWuaHVif+eEA935gK4q8lAfpn3qL0mujYawrafrSquEqKJ7axwUxLO4dcQVvRsUoZsVGZer4hnrsp3H8/QIrPad2HFN/2Gm0y0HIV7dr2L3PxjWzXKpqTyraQ0X63Us9P7NGLjo7GjBkzjJY11JByd/v444/RsWNHo8ZBABg9erTh744dO8Lb2xuurq5ISEhAv379GqUuj0UDYc3+17m5ufedjOPMmTMAgGXLliErKwtyuRy+vr7w8fGBmRmtoaIhbdmyxdD9+I7y8nLodDqEhYXh9OnTtb6voqICcnnjNlIAgJeXFy5fvmx4rtVqUVFRAZVKhQ0bNiA8PBwqlarR68EYY4wxxhhjjDF2P4Kk8TKX61PbHAx1adGiBaRSKa5eNb55cfXq1XrHDywuLsb27dsNSW/34+LighYtWuD8+fON1kDYpLMY3xEYGIjp06cDAL766iu0aNECWq0WpqameOqpp5Cenm54ODg4wMfHB6mpqSgqKsKNGzewf/9+KJVK6HQ6vPHGG7C2toadnd096Z+5ubkICQmBSqWChYUFQkNDjf6JGRkZ6NOnD9RqNSwsLODr64tjx44hISEB4wflnLIAAQAASURBVMePR0FBgWF66bvLHjZsmFE9vby8UFlZCZ1OhzNnzhjGD3R2dsaiRYsQEREBCwsLTJw4EQDw5ZdfwsvLCwqFAs7Ozli+fLlR+c7OznjrrbcQEREBlUoFJycn7NmzB9euXTNsk7e3N44dq/2O8969e43qFxsbC7VajfT0dAwbNgwxMTHo3LnzA/8PGWOMMcYYY4wxxp4kd5LWDhz4O8tWp9PhwIEDRl2Oa/P555+jvLwcL7zwQr3r+e2333D9+nXY29s/dJ3r8lg0EN5x7NgxvP7661i8eDHOnTuH/fv3Y9CgQXBzczM8ZLK6kx43bdoEpVKJI0eOYOnSpYiNjTWMSajT6RASEoIbN27g4MGD2L9/Py5cuICwsDDD+8PDw9G6dWukpKQgNTUVc+fOhYmJCXr27IlVq1bBwsLCML30rFmzjNatVquN6vndd99hwoQJ8Pf3R15eHnbu3GmIXbZsGTp16oTjx49j3rx5SE1NRWhoKEaPHo2TJ08iJiYG8+bNQ3x8vNE6Vq5ciYCAABw/fhyDBw/G2LFjERERgRdeeAFpaWlwdXVFREQEahtW0snJyah+tra2kEgkcHNzg1pNG5eDMcYYY4wxxhhjjAEzZszARx99hE2bNiEzMxOvvPIKiouLDbMaR0REIDo6+p73ffzxxxg+fDiaNzfu1l5UVITZs2fj8OHDuHTpEg4cOICQkBC4ubkhODi40bbjsehifEdubi6USiWGDBkCtVoNJycn+Pj4iH6/t7c3FixYAABwd3fHmjVrcODAAfTv3x8HDhzAyZMncfHiRTg6Vo9zsXnzZnh5eSElJQVdu3ZFbm4uZs+ejXbt2hnKuMPS0hKCIIieYtra2hrm5uaQy+X3vKdv376YOXOm4Xl4eDj69euHefPmAQA8PDxw5swZvPfee4iMjDTEDRo0yNCNef78+Vi3bh26du1qmAp7zpw58Pf3F5XKyhhjjDHGGGOMMfY4EiSPVT7bfYWFheHatWuYP38+/vzzT3Tu3Bnff/89bG1tAVS3dUnu2p6srCz8+uuv2Lfv3rHfpVIpTpw4gU2bNuHWrVtwcHDAM888g0WLFjXaWIjAY9ZA2L9/fzg5OcHFxQUDBgzAgAED8Oyzz8LcXNwkC97exoP229vbIz+/enKJzMxMODo6GhoHAaB9+/awsrJCZmYmunbtihkzZuCll17Cp59+iqCgIIwaNQqurrTB7sXw8/Mzep6ZmYmQEOMZegICArBq1SpUVVUZZqipuX13PmgdO3a8Z1l+fn6DNxCWl5ffM613RXk55I344WSMMcYYY4wxxhh73E2ZMgVTpkyp9bWEhIR7lnl6etba+xMAzMzM8MMPPzRk9UR5rJpk1Wo10tLSsG3bNtjb22P+/Pno1KmT6JmD754JWRAE6HTipxCPiYnB6dOnMXjwYPz0009o3749du3aRdkEUZTKB5vZtOb2Cf8/k1htyyjbLNbixYthaWlp9PhowwcNvh7GGGOMMcYYY4w92QSJ0GSPJ9Vj1UAIADKZDEFBQVi6dClOnDiBS5cu4aeffnrocjUaDa5cuYIrV64Ylp05cwa3bt0ymjXZw8MDr7/+Ovbt24fnnnsOcXFxAKoHnqyqapzpyDUaDZKSkoyWJSUlwcPDw5A92NSio6NRUFBg9Jgw6bWmrhZjjDHGGGOMMcYYe0iPVRfjb775BhcuXEDv3r3RrFkz7N27FzqdDp6eng9ddlBQEDp27Ijw8HCsWrUKlZWVmDx5Mp5++mn4+fmhtLQUs2fPxsiRI9G2bVv89ttvSElJwYgRIwBUzyJcVFSEAwcOoFOnTjA3Nxfd9bk+M2fORNeuXbFo0SKEhYUhOTkZa9aswdq1axuk/IZQ2zTfckVxE9WGMcYYY4wxxhhjjDWUxyqD0MrKCjt37kTfvn2h0Wiwfv16bNu2DV5eXg9dtiAI+Oqrr9CsWTP07t0bQUFBcHFxwY4dOwBUDwJ5/fp1REREwMPDA6GhoRg4cCAWLlwIAOjZsydefvllhIWFwcbGBkuXLn3oOt3RpUsXfPbZZ9i+fTs6dOiA+fPnIzY21miCEsYYY4wxxhhjjLEngSCRNNnjSSXo6xoVkbF6nD6fR4qXCrQu2mZ6WobibViKjv2rTHwsALQyyyfF6xq57d0EFaJjS/W0TFdTSSkpvkxnRoqn7BtzSeNmqWr18kYtX4/GG7/CFLT/01+VLUjxVrIC0bHn2j1DKtvxTCIpvnVpFin+T3Pa5FKN+X2VCZWkeJ2eVheJIH7M2So9bcgKrZ7WyUBGPMZb6m+Q4m9C/GdYJblNKluip9VdAO3SqVJiUn/QnVi9+NgHIQFtnGKFroQUn1PmLDq2jZJ2HVGuMyXFU687KN8RQaB9Bqjfbafrx0jxF6y7k+Ip+8b52hFS2ZdtupHiLXS0Y4GiUvxn8pq8NalslV78uQ8Armgd6w+qoaXiOim+lHCNRT3fmAq06wi9QLumsSi9Jjr2moK2H20qfiPF31Y0J8WbaWnnkCtoKzpWKaMdU8nnG+K5m8L99wOk+JzWfUjxba8dJsWXq2jXtn+ZtxEde0NL+61oIaP9ZjERtKR4d1cnUvw/xW9TRjXZuluv+bzJ1t2UHoum0cDAQEyfPr3Jy2hoJSUlGDFiBCwsLCAIgujJVh6F+Ph4WFlZGZ7HxMSgc+fOTVYfxhhjjDHGGGOMMQCAIDTd4wn1WIxBuHPnzntmIK4pNzfXaCKRu505c6YxqmUkPj4e06dPNzTyJSYmYuDAgXXGFxUVYdOmTUhMTMShQ4fQokULWFrS7kQ8jIEDByIxsfYsnTfffBMODg5Gy2bNmoXXXqNNOmIm0O6AVYKWFVEh0DIFbMtzRceamdPuOP1WakeKd1ZcqT+oBmr2SqHQTHQsNYNCoqdll1jqaHfDK6Ti74ZXEe+CStG42VrUTDPq3UEKnUCrS1ttJin+psyh/qD/R80IvNK+FyneIvNrUjw1K+JWlRUpnkImpX0mG/MzU6ZX1B9UgwkxG6VcR8vIvS7YkOLb3s4QHVuoFv/5BYA/dbR4M2kZKd5aKz4LnZqlUyylXVtQMwJ1Ai3zVCUvFx1LPbe2NvuTFF9QScwAkRaKjtWBtl/kEvH7BQDyWniT4k1AO3bIBfH1+aOlT6PWpURqQYovlaobrS6yKlq8VNI4ExjeYSHcEh179JobqezOLWlZeJU62jXZRWk70bGSKtq1500F7dhRRsw+LpPRese0qbwgOrZYQjsuHc0Tn50IAE+1pF3vLfyspejYgJ7Pk8rujj9I8XktO5HiC6tox442pedEx9qX0doXvi4LJsV3tRf/u5WxR+mxaCC0tra+7+sODg5IT0+/7+uPmp+f333rBAA5OTnQaDTo0KFDnTEVFRWQyxu+m+PGjRtRWlr7j2Rra2vs2bPHaJlKpYJKpWrwejDGGGOMMcYYY4yxx9tj18V47dq1cHd3h6mpKWxtbTFy5EjIZDK4ubnV+ZDJqts5dTod3njjDVhbW8POzg4xMTFG68nNzUVISAhUKhUsLCwQGhqKq1evGl7PyMhAnz59oFarYWFhAV9fXxw7dgwJCQkYP348CgoKIAgCBEHAu+++e986BQYGYvny5fjll18gCAICAwMBVM+GvGjRIkRERMDCwgITJ04EAHz55Zfw8vKCQqGAs7Mzli9fblR3Z2dnvPXWW4iIiIBKpYKTkxP27NmDa9euGbbJ29sbx45Vj1nTqlWrOutWW4MsdzFmjDHGGGOMMcbY40CQCE32eFI9Fg2Edxw7dgxTp05FbGwssrKy8P3336N3796i379p0yYolUocOXIES5cuRWxsLPbv3w+guvEwJCQEN27cwMGDB7F//35cuHABYWFhhveHh4ejdevWSElJQWpqKubOnQsTExP07NkTq1atgoWFBfLy8pCXl4dZs2bdty47d+7EhAkT4O/vj7y8POzcudPw2rJly9CpUyccP34c8+bNQ2pqKkJDQzF69GicPHkSMTExmDdvHuLj443KXLlyJQICAnD8+HEMHjwYY8eORUREBF544QWkpaXB1dUVERER4HlnGGOMMcYYY4wxxphYj0UX4ztyc3OhVCoxZMgQqNVqODk5wcdH/Lgn3t7eWLBgAQDA3d0da9aswYEDB9C/f38cOHAAJ0+exMWLF+HoWD1T1ubNm+Hl5YWUlBR07doVubm5mD17Ntq1a2co4w5LS0sIggA7O3FjXlhbW8Pc3Bxyufye9/Tt2xczZ840PA8PD0e/fv0wb948AICHhwfOnDmD9957D5GRkYa4QYMGYdKkSQCA+fPnY926dejatStGjaqe3WfOnDnw9/fH1atXRdeTMcYYY4wxxhhj7HEiSB6rfLYnwmO1x/v37w8nJye4uLhg7Nix2LJlC0pKxA+m7e1tPKCzvb098vOrBwbPzMyEo6OjoXEQANq3bw8rKytkZlYP5jpjxgy89NJLCAoKwpIlS5CTk9MAW3UvPz8/o+eZmZkICAgwWhYQEIDs7GxUVf09+HHN7bO1tQUAdOzY8Z5ld7a5IZWXl6OwsNDoUV5OG3CbMcYYY4wxxhhjjD1+HqsGQrVajbS0NGzbtg329vaYP38+OnXqZJg5uD53z4QsCAJ0OvGzYsXExOD06dMYPHgwfvrpJ7Rv3x67du2ibIIoSqXygd5Xc/uE/5/lsLZllG0Wa/HixbC0tDR6rF+/vsHXwxhjjDHGGGOMMcYerceqgRAAZDIZgoKCsHTpUpw4cQKXLl3CTz/99NDlajQaXLlyBVeuXDEsO3PmDG7duoX27dsblnl4eOD111/Hvn378NxzzyEuLg4AIJfLjbL5GpJGo0FSUpLRsqSkJHh4eEAqlTbKOqmio6NRUFBg9Hj55ZebulqMMcYYY4wxxhj7h+FJSh69x2oMwm+++QYXLlxA79690axZM+zduxc6nQ6enp4PXXZQUBA6duyI8PBwrFq1CpWVlZg8eTKefvpp+Pn5obS0FLNnz8bIkSPRtm1b/Pbbb0hJScGIESMAVM8iXFRUhAMHDqBTp04wNzeHubn5Q9cLAGbOnImuXbti0aJFCAsLQ3JyMtasWYO1a9c2SPkNQaFQQKFQGC37667njDHGGGOMMcYYY+x/z2OVQWhlZYWdO3eib9++0Gg0WL9+PbZt2wYvL6+HLlsQBHz11Vdo1qwZevfujaCgILi4uGDHjh0AAKlUiuvXryMiIgIeHh4IDQ3FwIEDsXDhQgBAz5498fLLLyMsLAw2NjZYunTpQ9fpji5duuCzzz7D9u3b0aFDB8yfPx+xsbFGE5QwxhhjjDHGGGOMPQkEiaTJHk8qQa/X65u6Eux/0wXiJC6VMKk/qAYBtI+mVflV0bGFpi1IZf9WSpsV2llxpf6gGiR6Wvf1QqGZ6FipQCtbgTJSvEkVLb5CaiY6toqY5CxFJSm+TC++LgCgI95TMRG0pHgKGbFsyxLx3w8AuGnmIDq2VEfbj1fa9yLFe2V+TYrXCbShGW5VWZHiKcylpaR4KRpnKAsAKNbRst5NBNr3qVwnJ8VLBNp4uW1vZ4iOLVSL//wCwJ86WryZlHbcs64SP3mYXqB1aymWWpLiTXXFpHjq9+laVUvRsSWVtM9Ma7M/SfEFlbR9YyEtFB2rA22/SInfJ52eVn4l8XwpF8RPNEetC5UetM889fqQQllZQIrP1TuR4pub3CLFm+jF/5+OXnMjld255W+k+Eo97TNWVCl+zHXq+cBCdpsUX6YzJcXLiN9X60rx11jFctpx6WheW1L8Uy0zSfELPxN/zA7oSfv91N3xD1I85bgEAIVVFqT4NhXnRMcqymjHgq/LgknxXe1zSfHurrRjzT/Fn7NfaLJ127333yZbd1N6LJpGAwMDMX369CYvo6GVlJRgxIgRsLCwgCAIoidbeRTi4+NhZWVleB4TE4POnTs3WX0YY4wxxhhjjDHGWNN4LMYg3Llz5z0zENeUm5trNJHI3c6cOdMY1TISHx+P6dOnGxr5EhMTMXDgwDrji4qKsGnTJiQmJuLQoUNo0aIFLC1pd4wexsCBA5GYmFjra2+++SYcHIyzJWbNmoXXXnuNtA6ZroIUXyLQZm++raVlu9yWqUTHFhTTyjYzoW3rTb01Kd5CIj5rAQDKq8RnXSilJaSyqXfyy2W0fVlOvItLoSVmqVLvWJuClg2mrLhFiqe4bmJPitdJaPuGki3ZujSLVLYFMSPwtGYoKd735HZSvETWeFl7sirasYOarXVbsBIda1v1O6nsEjntznyzqmukeJNKWhZesVJ8loNWQhsj968i2vnJ2YKWhWf1V7bo2Iu2PUllU7NOb4CWAWIp0LIoCivEH+Nbmf9FKluipx2zqee/Cr34z40g0LLYKnW0S25qBpNWRzzGC+KP8dRzpV5Pu46w1F0nxRdKxV9jUeuSVeFOiu+sO0qKz5PRyi8l9HToYEPLsKVe75Gz/ExoWX4UVcQMXupvCrUJ7dhhViL+M3xDZksqu5NtHin+FpqT4qeFiT+HKARa1ptWTzsuaUHLKreS3iLFn9R3Fh3bRZJUf1ANne1o/ydq1veT6kmeLKSpPBafTGvr+5/oHRwckJ6eft/XHzU/P7/71gkAcnJyoNFo0KFDhzpjKioqIJfTDoZibNy4EaWltTdmWFtbY8+ePUbLVCoVVCrxDWyMMcYYY4wxxhhj7J/hsetivHbtWri7u8PU1BS2trYYOXIkZDIZ3Nzc6nzIZNXtnDqdDm+88Qasra1hZ2eHmJgYo/Xk5uYiJCQEKpUKFhYWCA0NxdWrf48ZkZGRgT59+kCtVsPCwgK+vr44duwYEhISMH78eBQUFEAQBAiCgHffffe+dQoMDMTy5cvxyy+/QBAEBAYGAqieDXnRokWIiIiAhYUFJk6cCAD48ssv4eXlBYVCAWdnZyxfvtyo7s7OznjrrbcQEREBlUoFJycn7NmzB9euXTNsk7e3N44dOwYAaNWqVZ11q61BlrsYM8YYY4wxxhhj7HEgSIQmezypHosGwjuOHTuGqVOnIjY2FllZWfj+++/Ru3dv0e/ftGkTlEoljhw5gqVLlyI2Nhb79+8HUN14GBISghs3buDgwYPYv38/Lly4gLCwMMP7w8PD0bp1a6SkpCA1NRVz586FiYkJevbsiVWrVsHCwgJ5eXnIy8vDrFmz7luXnTt3YsKECfD390deXh527txpeG3ZsmXo1KkTjh8/jnnz5iE1NRWhoaEYPXo0Tp48iZiYGMybNw/x8fFGZa5cuRIBAQE4fvw4Bg8ejLFjxyIiIgIvvPAC0tLS4OrqioiICPC8M4wxxhhjjDHGGGNMrMeii/Edubm5UCqVGDJkCNRqNZycnODj4yP6/d7e3liwYAEAwN3dHWvWrMGBAwfQv39/HDhwACdPnsTFixfh6OgIANi8eTO8vLyQkpKCrl27Ijc3F7Nnz0a7du0MZdxhaWkJQRBgZyduNltra2uYm5tDLpff856+ffti5syZhufh4eHo168f5s2bBwDw8PDAmTNn8N577yEyMtIQN2jQIEyaNAkAMH/+fKxbtw5du3bFqFGjAABz5syBv78/rl69KrqejDHGGGOMMcYYY+zJ9lhlEPbv3x9OTk5wcXHB2LFjsWXLFpSUiB8k1tvb2+i5vb098vPzAQCZmf/H3r3HxZT/fwB/nZma6V5KKimhJpVu5FJtZGXltm7rsqLNdUNLSNYuSnxFK7dcl5/KZRffr8vaL+uykSUk2dIqaZEs0bpUUqaamd8ffc02Kp0PJbvez8fjPJiZ13zO55yZOXP6zOd8PlmwsLBQNg4CgL29PQwMDJCVVTUd/KxZszBx4kT4+Phg2bJluHHjRgNsVU1ubm4qt7OysuDp6alyn6enJ3JyciCT/TVwbPXtMzGpGuDW0dGxxn0vtrkhSaVSFBcXqyzScrbB9wkhhBBCCCGEEELqJRA03fKeeqe2XFdXF5cvX8b3338PMzMzLFy4EM7OzsqZg+vz8kzIHMdBLuc/41Z4eDiuXr2K/v374+TJk7C3t8eBAwdYNoEXbW222RJfqL59HMfVeR/LNvMVGRkJfX19lWXDpm8bfD2EEEIIIYQQQggh5O16pxoIAUBNTQ0+Pj6IiorClStXkJubi5MnT75xuXZ2drhz5w7u3LmjvC8zMxOFhYWwt7dX3ieRSDBz5kwcP34cQ4cORWxsLABAJBKp9OZrSHZ2dkhKUp1KPSkpCRKJBEKhsFHWyWrevHkoKipSWaYGTm7qahFCCCGEEEIIIeQf5sUEsU2xvK/eqTEI//vf/+LmzZvo3r07mjVrhiNHjkAul8PW1vaNy/bx8YGjoyP8/PywevVqVFZWYurUqejRowfc3NxQVlaGOXPm4JNPPkGbNm3wxx9/ICUlBcOGDQNQNYtwSUkJEhIS4OzsDC0tLWhpab1xvQBg9uzZ6Ny5MxYvXoyRI0fi/PnzWLduHTZs2NAg5TcEsVgMsViscl+hSNREtSGEEEIIIYQQQgghDeWd6kFoYGCA/fv348MPP4SdnR02bdqE77//Hg4ODm9cNsdx+OGHH9CsWTN0794dPj4+aNu2Lfbs2QMAEAqFePToEfz9/SGRSDBixAj07dsXixYtAgB4eHggMDAQI0eOhLGxMaKiot64Ti907NgRe/fuxe7du9GhQwcsXLgQERERKhOUEEIIIYQQQgghhBDSGDiFQqFo6kqQv6e8nCymfDHXjCn/tIKth6ZYWME7WyRlK1tTnW1CFg0hW15PUMyUL5QZ8M5qC/lP9AMA6mCru4KxC7ZUrsGUb0wCjm28ThGkTHnt8kKmPItH6mZM+ebl95jyT8QmvLPGz3KZyi7W4l82AFy1G8iU75Sxmyn/XO31xoXlQ03O9nmSc2zDSjzlDHhnDSsfMJVdKtJjymtWlDDl1WXPmfIVQv7HDtbXNLvYkilvpcc2GVjr+xd4Z2+ZeDCVLeTYhj95LhfXH6pGX1DElL9Z2op31lzrIVPZYrC9Z8rBtq0yBf/PH8exnT4rFGzflWpcJVP+OeN3K8v7hvW7knVb9eWPmPLFQsNGq8uDMrZzVRf5RaZ8vrYNU75Swf9iL9b3DOuxQ4F355I71ro8luoz5XXV2c6dWz+9wjt7V8++/tAbkKHxhqcSc2znwRUK9fpD1bAea1i/E26XtuSd7ViRVH+oGtbXlfU7xK6dOVP+n+LhwglNtu7mEf/XZOtuSq/dg9Db2xvBwcENWJW/v6bcJ3FxcTAwMGiSdRNCCCGEEEIIIYSQv693agzCuuTl5alMJPKyzMxMWFqy/fr/ps6cOYO+ffuq3FdWVoakpCRs3boVJSX196SIi4tDcHAw71maWfTt2xdnzpyp9bGvvvoKX3311Ruvg7VHYFG5DlPeVIOth8aZvLa8sw8esv1CNdSVrS7FMl2mvFDB9qvvmp38e0uG+LONFWlYns+ULxWx/SqbV8a/51s7jVymsisFjNv69E79oWqE5c+Y8hlTw3lnRdpsh+NWm9nGKH22IoIpL/p6Je/sfa12TGVrcGVMedYegamOo5jyHyz9iCnPQtyKf28qALjmNp4p/7yS/3tec9l8prLLF2xmyotj5jLltRwlTPljrot5Zz8U/cJU9tPnbZjycl2231dL9fn3Wmhewdbbt1jUnCnfQs52jGftgXX9Lv+ebKbWbD1d1OWMPQiFbD0IWY5Ncsbf2NXA/3sbAB7LjJjyrSpvMeVZeomLFWzH7MdytrrLBcZMeTFDb/4SBVtv4mt3NZny3cR/MOUNxWw9s7We8T//zDdg68GkxrG9J4VytnNVlh7xAkXjTAT5wpIotl6qe6az9biX/bSfd1ZrJNvfrKyfJ8vy60z5h5oWvLMiBdsxmPUqI9Zjzf1KtitSMv/g//l2F7F9F1fqOjHldbinTPn3FSd4d3ouvy/+Fg2ELVu2RFpa2isff9vc3Nxq1MnPzw92dnaYP5/tj7DGsHXrVpSV1X6QNTTkf2kGIYQQQgghhBBCCPlne6NJSuRyOUJDQ2FoaAhTU1OEh4crH1u5ciUcHR2hra0NCwsLTJ06VaVX3e3btzFw4EA0a9YM2tracHBwwJEjR2pdj5qaGqytrZWLVCpFcHAwOnbsCFdXV/Ts2RM3btxQ1ikiIgKtWrWCWCyGi4sLjh49qiwrNzcXHMdh79698PLygqamJjp37ozr168jJSUFbm5u0NHRQd++ffHnn38qnxcQEIDBgwdj0aJFMDY2homJCVasWAFLS0tlvTQ1NWFgYABra2sAgFQqRUhICMzNzaGtrY2uXbsiMTERAJCYmIhx48ahqKhIOZX2i/33que9EBcXB0tLS2hpaWHIkCF49Ej1lzFzc3OVffZikcvlMDIywrVr11Tyq1atQrt2bL2ACCGEEEIIIYQQQsjf3xs1EMbHx0NbWxvJycmIiopCREQETpw4UVWwQIC1a9fi6tWriI+Px8mTJxEaGqp87rRp0yCVSvHLL78gIyMDy5cvh45O/Zeg3r17F927d4dYLMbJkyeRmpqK8ePHo7Kyqtv7mjVrEB0djRUrVuDKlSvo06cPPv74Y+Tk5KiUExYWhvnz5+Py5ctQU1PD6NGjERoaijVr1uDMmTP4/fffsXDhQpXnJCQkICsrC4mJifj++++xf/9+5SzHtQkKCsL58+exe/duXLlyBcOHD4evry9ycnLg4eGB1atXQ09PD/n5+cjPz0dISEi9zwOA5ORkTJgwAUFBQUhLS0PPnj2xZMkSHq8YIJFI4Obmhl27dqncv2vXLowePZpXGYQQQgghhBBCCCGNRiBouuU99UaXGDs5OSEsLAwAYGNjg3Xr1iEhIQG9e/dWmazDysoKS5YsQWBgIDZsqBo3Ky8vD8OGDYOjoyMAoG1bfuPHrV+/Hvr6+ti9ezfU1atmRpJI/hrHaMWKFZg7dy5Gjaoag2r58uU4deoUVq9ejfXr1ytzISEh6NOnDwBgxowZ+PTTT5GQkABPT08AwIQJExAXF6eybpFIhG3btkFLSwsODg6IiIjAnDlzsHjxYgheehPl5eUhNjYWeXl5ykugQ0JCcPToUcTGxmLp0qXQ19cHx3EwNTVlet6aNWvg6+urbHCVSCQ4d+6cSk/JV/Hz88O6deuweHHVeE7Xr19Hamoqdu7cyev5hBBCCCGEEEIIIeSf440bCKszMzNDQUHVYLo///wzIiMjce3aNRQXF6OyshLPnz9HaWkptLS0MH36dEyZMgXHjx+Hj48Phg0bVqO82qSlpcHLy0vZOFhdcXEx7t27p2zke8HT0xPp6el11t3EpGqA0xeNlS/ue7EtLzg7O0NLS0t5293dHSUlJbhz5w5at26tks3IyIBMJlNpvASqLh82Mqp7sFk+z8vKysKQIUNUHnd3d+fdQDhq1CiEhITgwoUL6NatG3bt2oWOHTuiffv2dT5HKpVCKlUdFLpcKoVIzDYAOCGEEEIIIYQQQsir0CQlb98b9Z18uZGO4zjI5XLk5uZiwIABcHJywr59+5CamqrsvVdeXg4AmDhxIm7evImxY8ciIyMDbm5uiImJqXedmppss4vxqTv3vxmWXr5PLmeb6ba6kpISCIVCpKamIi0tTblkZWVhzZo1Df48Fqampvjwww/x3XffAQC+++47+Pn5vfI5kZGR0NfXV1m2bq7/9SKEEEIIIYQQQggh77ZGubg6NTUVcrkc0dHR6NatGyQSCe7dqzlVuIWFBQIDA7F//37Mnj0bW7ZsqbdsJycnnDlzBhUVFTUe09PTQ8uWLZGUlKRyf1JSEuzt7V9/g/4nPT1dZWbgCxcuQEdHBxYWNaeHd3V1hUwmQ0FBQY2JQl5cUiwSiSCTyZifZ2dnh+TkZJXnXbhwgWlb/Pz8sGfPHpw/fx43b95UXpJdl3nz5qGoqEhlmfj5F0zrJIQQQgghhBBCCCHvnkZpILS2tkZFRQViYmJw8+ZN7NixA5s2bVLJBAcH49ixY7h16xYuX76MU6dOwc7Ort6yg4KCUFxcjFGjRuHSpUvIycnBjh07kJ2dDQCYM2cOli9fjj179iA7Oxtffvkl0tLSMGPGjDfervLyckyYMAGZmZk4cuQIwsLCEBQUVGP8QaBqXEA/Pz/4+/tj//79uHXrFi5evIjIyEgcPnwYQNXYjCUlJUhISMDDhw9RWlrK63nTp0/H0aNHsWLFCuTk5GDdunW8Ly9+YejQoXj69CmmTJmCnj17Ksc7rItYLIaenp7KQpcXE0IIIYQQQgghpKFxnKDJlvdVo2y5s7MzVq5cieXLl6NDhw7YtWsXIiMjVTIymQzTpk2DnZ0dfH19IZFIlBOYvIqRkRFOnjyJkpIS9OjRA506dcKWLVuUlwdPnz4ds2bNwuzZs+Ho6IijR4/i0KFDsLGxeePt6tWrF2xsbNC9e3eMHDkSH3/8McLDw+vMx8bGwt/fH7Nnz4atrS0GDx6MlJQUWFpaAgA8PDwQGBiIkSNHwtjYGFFRUbye161bN2zZsgVr1qyBs7Mzjh8/jvnz5zNti66uLgYOHIj09PR6Ly8mhBBCCCGEEEIIIf9crz1JSWJiYo37Dh48qPz/zJkzMXPmTJXHx44dq/w/n/EG6+Lk5IRjx47V+phAIEBYWJhyduWXWVlZQaFQqNzn7e1d476AgAAEBATUeP6iRYuwaNGiWst+eZ+oq6u/Mg8AGzduxMaNG5mfN378eIwfP17lvtmzZ9eZr82ePXuwZ88epucQQgghhBBCCCGENCqapOSt+0f0nfT29kZwcHBTV6NW165dQ7du3aChoQEXF5emro7Sy/vMysoKq1evbrL6EEIIIYQQQgghhJCm8do9CBtLYGAgdu7cWetjY8aMqTGW4bsuLCwM2trayM7Oho6OzltZp4ODA27fvl3rY5s3b671kuKUlBRoa2szraewXJcpL+DYZoXWrChhyr/UCfSV5HKGMABN2VOm/J8yQ6Z8qZDtveHqZsI7+6yymKls0XO2vILxd4ayCv6HHbFaKVPZrEe0CpEWU14qZnvPW/dx4J0VCIVMZcsUbJ8nIydrpvyfDGNvyBVs74FCmQFTXqAmqz9UzQdLP2LKn/3qOFOehVFHfaa8dBvb5E+awpoTdtVFwXjcMyy/z5R/8qCIKa9l+pAp396Qf3244kqmsjnGH6ilchFTvlDTlHe25b1LTGU/acn/+wAACoXNmfKmZTeZ8rra/N9nCrDteA5s72HW8ksV/M+DRFw5U9kVjOMZiQT8P9sAoFuQw5QvtGjBlGfBut/VOLbPa4mc/+ukULDVJTevrP5QNZw52/lhqboeU96gMIN3VlPPkqns5wK2834F44FSDv7nNcJG7iVk7WLFlP9Dj+042bbtZd7ZhxzbGO6snyed/Gy28s35v06FYrbvGwHYzlVZCTm274Tbtxk+3+3Y6s4x1oX180TI2/LONRBGREQgJCSk1sf09Ni+VN+ETCYDx3HKCUji4uJeq5wbN26gf//+aN26dQPW7tWOHDlS6yzPAGBiUvuB3djYuDGrRAghhBBCCCGEEMILV8tksKRxNege9/b2xvTp0xEaGgpDQ0OYmpoqJ/HIzc0Fx3FIS0tT5gsLC8FxnHLsvsTERJiYmODGjRsYPnw4HB0dMXnyZOjp6SEnJwc9evSAnp4eRo8ejdJS1Z5FlZWVCAoKgr6+Ppo3b44FCxaojCsolUoREhICc3NzaGtro2vXripjBsbFxcHAwACHDh2Cvb09xGIx8vLyXrm9crkcERERaNWqFcRiMVxcXFRmE+Y4DqmpqYiIiADHca+c0OSFuXPnQiKRQEtLC23btsWCBQtUGvvCw8Ph4uKCzZs3w8LCAlpaWhgxYgSKiv7quREWFoaQkBDs2rUL7u7u6NixI1asWAFLS0vo6tbeA4ouMSaEEEIIIYQQQgh5PzV4D8L4+HjMmjULycnJOH/+PAICAuDp6ck0i3B4eDjWrVunbPwaMWIExGIxvvvuO5SUlGDIkCGIiYnB3LlzVdY7YcIEXLx4EZcuXcLkyZNhaWmJSZMmAQCCgoKQmZmJ3bt3o2XLljhw4AB8fX2RkZGhrFtpaSmWL1+OrVu3wsjICC1avPryizVr1iA6OhqbN2+Gq6srtm3bho8//hhXr16FjY0N8vPz4ePjA19fX4SEhPC6xFhXVxdxcXFo2bIlMjIyMGnSJOjq6iI0NFSZ+f3337F37178+OOPKC4uxoQJEzB16lTs2rVLmUlISICGhgYSExORm5uLcePGwcjICP/61794vw6EEEIIIYQQQgghbxtHk5S8dQ3eQOjk5KScQdjGxgbr1q1DQkICUwPhkiVL4OnpCQCYMGEC5s2bhxs3bqBt27YAgE8++QSnTp1SaSC0sLDAqlWrwHEcbG1tkZGRgVWrVmHSpEnIy8tDbGws8vLy0LJlSwBASEgIjh49itjYWCxduhQAUFFRgQ0bNsDZ2ZlXPVesWIG5c+di1KhRAIDly5fj1KlTWL16NdavXw9TU1OoqalBR0cHpqb8xh+aP3++8v9WVlYICQnB7t27VRoInz9/ju3bt8Pc3BxA1YzQ/fv3R3R0tHI9IpEI27Ztg5aWFhwcHBAREYE5c+Zg8eLFysumWUilUkilUpX7yssrIRKxjaNBCCGEEEIIIYQQQt4tDX5Rt5OTk8ptMzMzFBQUvHYZJiYmysttq9/3cpndunUDV22wT3d3d+Tk5EAmkyEjIwMymQwSiQQ6OjrK5fTp07hx44byOSKRqEb961JcXIx79+4pGzJf8PT0RFZWFtP2Vrdnzx54enrC1NQUOjo6mD9/fo1LnS0tLZWNgy+2VS6XIzv7r0FpnZ2doaWlpZIpKSnBnTt3XqtekZGR0NfXV1l2blnxWmURQgghhBBCCCGEkHdHg/cgVFdXV7nNcRzkcrmy11r1cQHrmkijehkcx9VZJl8lJSUQCoVITU2F8KWZQqtf9qupqanSyPi2nT9/Hn5+fli0aBH69OkDfX197N69G9HR0U1WpxfmzZuHWbNmqdx36SbbrHOEEEIIIYQQQggh9eJokpK37a3NYvxiltz8/Hy4uroCgMqEJW8qOTlZ5faFCxdgY2MDoVAIV1dXyGQyFBQUwMvLq0HWp6enh5YtWyIpKQk9evRQ3p+UlIQuXbq8Vpnnzp1D69at8fXXXyvvu337do1cXl4e7t27p7xc+sKFCxAIBLC1tVVm0tPTUVZWBk1NTWVGR0cHFhYWr1U3sVgMsVj1cmKR6NlrlUUIIYQQQgghhBBC3h1vrYFQU1MT3bp1w7Jly9CmTRsUFBSojLf3pvLy8jBr1ix8/vnnuHz5MmJiYpQ97yQSCfz8/ODv74/o6Gi4urrizz//REJCApycnNC/f//XWuecOXMQFhaGdu3awcXFBbGxsUhLS1OZLISFjY0N8vLysHv3bnTu3BmHDx/GgQMHauQ0NDTw2WefYcWKFSguLsb06dMxYsQIlXEOy8vLMWHCBMyfPx+5ubkICwtDUFDQa40/SAghhBBCCCGEEPK20CQlb99bayAEgG3btmHChAno1KkTbG1tERUVhY8++qhByvb390dZWRm6dOkCoVCIGTNmYPLkycrHY2NjsWTJEsyePRt3795F8+bN0a1bNwwYMOC11zl9+nQUFRVh9uzZKCgogL29PQ4dOsQ0IUt1H3/8MWbOnImgoCBIpVL0798fCxYsQHh4uErO2toaQ4cORb9+/fD48WMMGDAAGzZsUMn06tULNjY26N69O6RSKT799NMa5RBCCCGEEEIIIYQQ0qANhImJiTXuO3jwoPL/dnZ2OHfunMrj1cck9Pb2VrkNAAEBAQgICFC5Lzw8vEZjl7q6OlavXo2NGzfWWjd1dXUsWrQIixYtqvXx2tZTH4FAgLCwMOWszbXZvXs3AgICsGzZMrRv377ey6qjoqIQFRWlcl9wcHCN3JQpUzBlypRXlvWq7SWEEEIIIYQQQgghBHjLPQjfR2FhYdDW1kZ2drbKhCjvmpSUFGhrazd1NQghhBBCCCGEEPK+o+HR3jra43WQyWTQ1taGjo5OrcuZM2d4lXPjxg188MEHaN26NTZv3lxneX379m3kLXo1Y2NjaGlpNWkdCCGEEEIIIYQQQsjb16ANhN7e3pg+fTpCQ0NhaGgIU1NT5aXAubm54DhO5RLbwsJCcBynvDQ5MTERHMfh2LFjcHV1haamJj788EMUFBTgp59+gp2dHfT09DB69GiUlpaqrLuyshJBQUHQ19dH8+bNsWDBApXLlaVSKUJCQmBubg5tbW107dpV5ZLouLg4GBgY4NChQ7C3t4dYLMbhw4eRlpZW6+Lm5ga5XI6IiAi0atUKYrEYLi4uOHr0qLJMjuOQmpqKiIgIcByHoqKiOsvbunUrAGDu3LmQSCTQ0tJC27ZtsWDBAlRUVNTY15s3b4aFhQW0tLQwYsQIFBUV1cgsWrQIxsbG0NPTQ2BgIMrLy+t87aysrLB69eo6HyeEEEIIIYQQQgh5GziOa7LlfdXglxjHx8dj1qxZSE5Oxvnz5xEQEABPT0+miTvCw8Oxbt06ZePXiBEjIBaL8d1336GkpARDhgxBTEwM5s6dq7LeCRMm4OLFi7h06RImT54MS0tLTJo0CQAQFBSEzMxM7N69Gy1btsSBAwfg6+uLjIwMZd1KS0uxfPlybN26FUZGRsoGuLqsWrUK0dHR2Lx5M1xdXbFt2zZ8/PHHuHr1KmxsbJCfnw8fHx/4+voiJCRE2VvwVXR1dREXF4eWLVsiIyMDkyZNgq6uLkJDQ5WZ33//HXv37sWPP/6I4uJiTJgwAVOnTlWZPTkhIQEaGhpITExEbm4uxo0bByMjI/zrX//i/ToQQgghhBBCCCGEkH++Br/E2MnJCWFhYbCxsYG/vz/c3NyQkJDAVMaSJUvg6ekJV1dXTJgwAadPn8bGjRvh6uoKLy8vfPLJJzh16pTKcywsLLBq1SrY2trCz88PX3zxBVatWgUAyMvLQ2xsLP7973/Dy8sL7dq1Q0hICD744APExsYqy6ioqMCGDRvg4eEBW1vbei+5XbFiBebOnYtRo0bB1tYWy5cvh4uLi7InnqmpKdTU1KCjowNTU1NeYxDOnz8fHh4esLKywsCBAxESEoK9e/eqZJ4/f47t27fDxcUF3bt3R0xMDHbv3o379+8rMyKRCNu2bYODgwP69++PiIgIrF27FnK5vN46EEIIIYQQQgghhJD3R4P3IHRyclK5bWZmhoKCgtcuw8TERHm5bfX7Ll68qPKcbt26qXQFdXd3R3R0NGQyGTIyMiCTySCRSFSeI5VKYWRkpLwtEolq1L8uxcXFuHfvHjw9PVXu9/T0RHp6Oq8yarNnzx6sXbsWN27cQElJCSorK6Gnp6eSsbS0hLm5ufK2u7s75HI5srOzYWpqCgBwdnZWaeB0d3dHSUkJ7ty5g9atWzPXSyqVQiqVqtxXXl4JkUjMXBYhhBBCCCGEEEJInWiSkreuwRsI1dXVVW5zHAe5XA7B/17c6uMC1ja23stlcBxXZ5l8lZSUQCgUIjU1FUKhUOWx6r36NDU1m/R68/Pnz8PPzw+LFi1Cnz59oK+vj927dyM6OrrJ6vRCZGQkFi1apHLfuKnzMH7a101UI0IIIYQQQgghhBDSEBq8gbAuxsbGAID8/Hy4uroCgMqEJW8qOTlZ5faFCxdgY2MDoVAIV1dXyGQyFBQUwMvLq0HWp6enh5YtWyIpKQk9evRQ3p+UlIQuXbq8Vpnnzp1D69at8fXXfzW63b59u0YuLy8P9+7dQ8uWLQFUbatAIICtra0yk56ejrKyMmhqaiozOjo6sLCweK26zZs3D7NmzVK579LNytcqixBCCCGEEEIIIaQunOD9nSykqby1BkJNTU1069YNy5YtQ5s2bVBQUID58+c3WPl5eXmYNWsWPv/8c1y+fBkxMTHKnncSiQR+fn7w9/dHdHQ0XF1d8eeffyIhIQFOTk7o37//a61zzpw5CAsLQ7t27eDi4oLY2FikpaWpTBbCwsbGBnl5edi9ezc6d+6Mw4cP48CBAzVyGhoa+Oyzz7BixQoUFxdj+vTpGDFihPLyYgAoLy/HhAkTMH/+fOTm5iIsLAxBQUHKnpysxGIxxGLVy4lFomevVRYhhBBCCCGEEEIIeXe8tQZCANi2bRsmTJiATp06wdbWFlFRUfjoo48apGx/f3+UlZWhS5cuEAqFmDFjBiZPnqx8PDY2FkuWLMHs2bNx9+5dNG/eHN26dcOAAQNee53Tp09HUVERZs+ejYKCAtjb2+PQoUNMMzZX9/HHH2PmzJkICgqCVCpF//79sWDBAoSHh6vkrK2tMXToUPTr1w+PHz/GgAEDsGHDBpVMr169YGNjg+7du0MqleLTTz+tUQ4hhBBCCCGEEEIIIQ3aQJiYmFjjvoMHDyr/b2dnh3Pnzqk8Xn1MQm9vb5XbABAQEICAgACV+8LDw1Uau6qvd+PGjbXWTV1dHYsWLaoxjt6r1lMfgUCAsLAwhIWF1ZlhvYw6KioKUVFRKvcFBwfXyE2ZMgVTpkx5ZVmv2t6XX6vc3FyWahJCCCGEEEIIIYQ0Do4mKXnb/hF73Nvbu9ZGtHfBtWvX0K1bN2hoaMDFxaWpq6P08j6zsrLC6tWrm6w+hBBCCCGEEEIIIaRpvNVLjP9uqs9w/LKffvqJ14QnYWFh0NbWRnZ2NrZs2VJnmV5eXvjpp59eu65vKiUlBdra2kzPEQkad5ISTsF/pmoAaK7Hvz6PnrC1jZcK9ZjyYtQ+Q3dDMTLgP2CrkFPUH6qGA1uelZqQf/kChYytbDnbfpeqaTHlWRm2a80//NIM6/UpFKjXH6pGvYUxU55T8H+d1Di2Y4GakDEvK2fKi1u1YsobddRnyrN4dLmIKW8kZHsPCzj+x8kWndozla0ofciU5yQtmfJadmz1MXuWwzur/pSt7s8Zf6GWKdg+r2J5Ge+sXKTBVLZcwVb3SgXbqZ9AxvZ5VSj4fz8JwPY9L6rkvx8BQCE0ZMqXy/kfV0VCtuOSEGz7UcSxDcwu06z7nLU2LO8DTQVb3YUc23c363uyrFJcf+h/tNSeM5WtpcX4p5GQLc96jlWpbcA7KxWwndOw7nc1xrkC5Cz9UFjrwnie3bIl2984JZWM58L/mxSSD9ZjAcv3PADItXSZ8kVitvNDFpWMTQ0aisYd315bh6E+crbjGOv3mYDx79z3Fk1S8tZRA2EdZDIZLl++XOekHubm5rzKuXHjBvr374/WrVtj1qxZdV7GrMnzi+Xly6trExcXx6us6l7MMk0IIYQQQgghhBBC3i8Neomxt7c3pk+fjtDQUBgaGsLU1FTZmJWbmwuO41TG5CssLATHccrx8BITE8FxHI4dOwZXV1doamriww8/REFBAX766SfY2dlBT08Po0ePRmlpqcq6KysrERQUBH19fTRv3hwLFixQGc9QKpUiJCQE5ubm0NbWRteuXVXG4YuLi4OBgQEOHToEe3t7iMViiEQiWFtb17poampCLpcjIiICrVq1glgshouLC44ePaosk+M4pKamIiIiAhzHYe3atXWW96LBce7cuZBIJNDS0kLbtm2xYMECVFT89StZeHg4XFxcsHnzZlhYWEBLSwsjRoxAUdFfvVQCAgIwePBgLFq0CMbGxtDT00NgYCDKy+v+tZsuMSaEEEIIIYQQQgh5PzV4D8L4+HjMmjULycnJOH/+PAICAuDp6ck0s294eDjWrVunbPwaMWIExGIxvvvuO5SUlGDIkCGIiYnB3LlzVdY7YcIEXLx4EZcuXcLkyZNhaWmJSZMmAQCCgoKQmZmJ3bt3o2XLljhw4AB8fX2RkZGhrFtpaSmWL1+OrVu3wsjICC1atHhlPdesWYPo6Ghs3rwZrq6u2LZtGz7++GNcvXoVNjY2yM/Ph4+PD3x9fRESEvLKS5Zf0NXVRVxcHFq2bImMjAxMmjQJurq6CA0NVWZ+//137N27Fz/++COKi4sxYcIETJ06Fbt27VJmEhISoKGhgcTEROTm5mLcuHEwMjLCv/71L96vAyGEEEIIIYQQQsjbxtEkJW9dg+9xJycnhIWFwcbGBv7+/nBzc0NCQgJTGUuWLIGnpydcXV0xYcIEnD59Ghs3boSrqyu8vLzwySef4NSpUyrPsbCwwKpVq2Braws/Pz988cUXWLVqFQAgLy8PsbGx+Pe//w0vLy+0a9cOISEh+OCDDxAbG6sso6KiAhs2bICHhwdsbW2hpfXqsTxWrFiBuXPnYtSoUbC1tcXy5cvh4uKi7IlnamoKNTU16OjowNTUlFcD4fz58+Hh4QErKysMHDgQISEh2Lt3r0rm+fPn2L59O1xcXNC9e3fExMRg9+7duH//vjIjEomwbds2ODg4oH///oiIiMDatWshl9N4B4QQQgghhBBCCCHkLw3eg9DJyUnltpmZGQoKCl67DBMTE+XlttXvu3jxospzunXrBq7agM7u7u6Ijo6GTCZDRkYGZDIZJBKJynOkUimMjIyUt0UiUY3616W4uBj37t2Dp6enyv2enp5IT0/nVUZt9uzZg7Vr1+LGjRsoKSlBZWUl9PRUJ8iwtLRUGQPR3d0dcrkc2dnZMDU1BQA4OzurNHC6u7ujpKQEd+7cQevWDBMn/I9UKoVUKlW5r7xcCpGI/0DRhBBCCCGEEEIIIfWiSUreugZvIFRXV50BjuM4yOVy5WQf1ccFrD62Xl1lcBxXZ5l8lZSUQCgUIjU1FcKXZgqt3qtPU1NTpZHxbTt//jz8/PywaNEi9OnTB/r6+ti9ezeio6ObrE4vREZGYtGiRSr3TZw2F5OC5jVRjQghhBBCCCGEEEJIQ3hrF3W/mCU3Pz9feV/1CUveVHJyssrtCxcuwMbGBkKhEK6urpDJZCgoKKgxOciLHnes9PT00LJlSyQlJancn5SUBHt7+9cq89y5c2jdujW+/vpruLm5wcbGBrdv366Ry8vLw71795S3L1y4AIFAAFtbW+V96enpKCsrU8no6OjAwsLiteo2b948FBUVqSyfTZ71WmURQgghhBBCCCGE/FOsX78eVlZW0NDQQNeuXWtc9VpdXFwcOI5TWTQ0NFQyCoUCCxcuhJmZGTQ1NeHj44OcnJxG3YYG70FYF01NTXTr1g3Lli1DmzZtUFBQgPnz5zdY+Xl5eZg1axY+//xzXL58GTExMcqedxKJBH5+fvD390d0dDRcXV3x559/IiEhAU5OTujfv/9rrXPOnDkICwtDu3bt4OLigtjYWKSlpalMFsLCxsYGeXl52L17Nzp37ozDhw/jwIEDNXIaGhr47LPPsGLFChQXF2P69OkYMWKESmNneXk5JkyYgPnz5yM3NxdhYWEICgpS9uRkJRaLIRarXk4sEinqSBNCCCGEEEIIIYS8Hu412y6awp49ezBr1ixs2rQJXbt2xerVq9GnTx9kZ2fXOfmtnp4esrOzlbdfvpo1KioKa9euRXx8PNq0aYMFCxagT58+yMzMrNGY2FDeWgMhAGzbtg0TJkxAp06dYGtri6ioKHz00UcNUra/vz/KysrQpUsXCIVCzJgxA5MnT1Y+HhsbiyVLlmD27Nm4e/cumjdvjm7dumHAgAGvvc7p06ejqKgIs2fPRkFBAezt7XHo0CGmGZur+/jjjzFz5kwEBQVBKpWif//+WLBgAcLDw1Vy1tbWGDp0KPr164fHjx9jwIAB2LBhg0qmV69esLGxQffu3SGVSvHpp5/WKIcQQgghhBBCCCGEvL6VK1di0qRJGDduHABg06ZNOHz4MLZt24Yvv/yy1udwHFfnFa0KhQKrV6/G/PnzMWjQIADA9u3bYWJigoMHD2LUqFGNsh0N2kCYmJhY476DBw8q/29nZ4dz586pPF59TEJvb2+V2wAQEBCAgIAAlfvCw8NVGruqr3fjxo211k1dXR2LFi2qMY7eq9ZTH4FAgLCwMISFhdWZYb2MOioqClFRUSr3BQcH18hNmTIFU6ZMeWVZr9rel1+r3NxclmoSQgghhBBCCCGENI4mnB+itklaa7uqEqi6ejM1NRXz5v01P4NAIICPjw/Onz9f5zpKSkrQunVryOVydOzYEUuXLoWDgwMA4NatW7h//z58fHyUeX19fXTt2hXnz59vtAbCBuuz6e3tXWtDVkOysrLC6tWrm7QO9UlMTATHcSgsLGzSehBCCCGEEEIIIYQQNpGRkdDX11dZIiMja80+fPgQMpkMJiYmKvebmJjg/v37tT7H1tYW27Ztww8//ICdO3dCLpfDw8MDf/zxBwAon8dSZkN4q5cYN7b9+/fXmPH4TVSf4fhlP/30E7y8vJjKW7p0KZYuXVrrY15eXvjpp5+YyqtPXFwcgoODG62xssO5lUz5xx8MZ8o/EdR+rX5dOulk8s62dTJmKvuZXIspryN8xpTXrXzClPc1+oN3ViET1h+qpkjXnCkvUMiY8l2kZ3lnH4hf73J9vkye3mDKV4jY3gdZH8zgneXANqangOM/kzsA3OsUyJTXVDznnWWtuzpX+wz2dZFzbO/ha27jmfLSbV8w5VkYCdm29ZFjF6a8Q9aPvLO/fVR3b/fa6KmzHccejf6AKa+jzv89BgDG3APe2ce6bBNydZA9ZKuLgu3ETCrkf+zIbubJVLYWV1Z/qBptQQlT/oFuO6a8o/afvLMGjPu9WKM5U57x0AQNgbT+0P9UKNjONxWMvSAUYMvnGLoz5TU4/tv6jNNjKruZ4jFTXsppMuWNRfzrrl1ZxFT2p+5sn6c8sA2VxPq6ZjXrzjurpWCru5hjOwbLwfZdLFfw74fC+vmQM/ZxGeuSxZQvFhgy5W92+IR3Vgi282YT+b36Q9Vcbd6LKa+h4P95kjG8pgAgFpQz5Z8J2I41JsivP1SNnzv/MdvuwKf+UDVisH2eyhRsf1OQt2/evHmYNUt1Utbaeg++Lnd3d7i7//Xd7eHhATs7O2zevBmLFy9usPWw+kc1EBoash3M6/Oqy4PNzdkaUQAgMDAQI0aMqPUxTU1+J0cvX15dm7i4OJV/CSGEEEIIIYQQQv42mnCSkrouJ65N8+bNIRQK8eCB6o/ZDx48qHOMwZepq6vD1dUVv//+OwAon/fgwQOYmZmplOni4sKrzNfRoHu8srISQUFB0NfXR/PmzbFgwQLlmIJPnjyBv78/mjVrBi0tLfTt27fGFM379u2Dg4MDxGIxrKyslLMQ12Xr1q0wMDBAQkICgJqXGFtZWWHp0qUYP348dHV1YWlpiW+//ValjHPnzsHFxQUaGhpwc3PDwYMHwXEc0tLSYG1tXefyokHvyJEjkEgk0NTURM+ePWuM5ffo0SN8+umnMDc3R6tWrTBkyBCkpKQoyzl37hy6du2K5s1VfxUfPHgwxo4dCwBIT09Hz549oaurCz09PXTq1AmXLl165b5JTEzEuHHjUFRUpJw2+0XDIp/XghBCCCGEEEIIIYTUTSQSoVOnTsp2KQCQy+VISEhQ6SX4KjKZDBkZGcrGwDZt2sDU1FSlzOLiYiQnJ/Mu83U0aANhfHw81NTUcPHiRaxZswYrV67E1q1bAVRNAnLp0iUcOnQI58+fh0KhQL9+/VBRUXX5VWpqKkaMGIFRo0YhIyMD4eHhWLBgQZ294KKiovDll1/i+PHj6NWr7q7U0dHRcHNzw6+//oqpU6diypQpyqmki4uLMXDgQDg6OuLy5ctYvHgx5s6dy3t779y5g6FDh2LgwIFIS0vDxIkTa8xQ8/z5c3Tq1AmHDx/Gb7/9hsmTJ2Ps2LG4ePEiAGD48OGQyWQ4dOiQ8jkFBQU4fPgwxo+vukzOz88PrVq1QkpKClJTU/Hll1/Weym1h4cHVq9eDT09PeTn5yM/Px8hISEA6n8tCCGEEEIIIYQQQpoMxzXdwmjWrFnYsmUL4uPjkZWVhSlTpuDZs2fKWY39/f1VJjGJiIjA8ePHcfPmTVy+fBljxozB7du3MXHixP9tOofg4GAsWbIEhw4dQkZGBvz9/dGyZUsMHjy4QXZvbRr0EmMLCwusWrUKHMfB1tYWGRkZWLVqFby9vXHo0CEkJSXBw8MDALBr1y5YWFjg4MGDGD58OFauXIlevXphwYIFAACJRILMzEx88803NWYXnjt3Lnbs2IHTp08rZ3mpS79+/TB16lTl81atWoVTp07B1tYW3333HTiOw5YtW6ChoQF7e3vcvXsXkyZN4rW9GzduRLt27ZQ9HV9s8/Lly5UZc3NzZcMcAHzxxRc4duwY9u7diy5dukBTUxOjR49GbGwshg+vGqNv586dsLS0hLe3NwAgLy8Pc+bMQfv27QEANjb1j8smEomgr69fY+rsnJycel8LQgghhBBCCCGEEFK/kSNH4s8//8TChQtx//59uLi44OjRo8pJRvLy8iCodsn0kydPMGnSJNy/fx/NmjVDp06dcO7cOdjb2yszoaGhePbsGSZPnozCwkJ88MEHOHr0KDQ0+I+nyapBGwi7desGrlprq7u7O6Kjo5GZmQk1NTV07dpV+ZiRkRFsbW2RlVU1aGxWVhYGDRqkUp6npydWr14NmUwGobBqYNzo6Gg8e/YMly5dQtu2beutk5OTk/L/LxrLCgoKAADZ2dlwcnJS2cFduvAfID4rK0tlm15sc3UymQxLly7F3r17cffuXZSXl0MqlUJL66+BSSdNmoTOnTvj7t27MDc3R1xcHAICApT7ctasWZg4cSJ27NgBHx8fDB8+HO3asQ0aXr3O9b0Wtaltmm9ZRSXE6v+oYSwJIYQQQgghhBBCmAQFBSEoKKjWxxITE1Vur1q1CqtWrXpleRzHISIiAhEREQ1VxXo13aiPr8nLywsymQx79+7llX/5UlyO4yCXs83++Sa++eYbrFmzBnPnzsWpU6eQlpaGPn36oLz8r1mdXF1d4ezsjO3btyM1NRVXr15V6TUZHh6Oq1evon///jh58iTs7e1x4MCBt7YNQO3TfK84wn82WkIIIYQQQgghhBA+OIGgyZb3VYNueXJyssrtCxcuwMbGBvb29qisrFR5/NGjR8jOzlZ2obSzs0NSUpLK85OSkiCRSJS9B4GqHn4//fQTli5dihUrVrxRfV9cEly9Z1xKSgrv59vZ2SnHEnzhwoULKreTkpIwaNAgjBkzBs7Ozmjbti2uX79eo6yJEyciLi4OsbGx8PHxgYWFhcrjEokEM2fOxPHjxzF06FDExsbWWz+RSASZTFajzvW9FrWZN28eioqKVJaQfh/UWwdCCCGEEEIIIYQQ8m5r0AbCvLw8zJo1C9nZ2fj+++8RExODGTNmwMbGBoMGDcKkSZNw9uxZpKenY8yYMTA3N1deVjx79mwkJCRg8eLFuH79OuLj47Fu3TqV8fte8PDwwJEjR7Bo0SKsXr36tes7evRoyOVyTJ48GVlZWTh27Jiy0ZHjMTBlYGAgcnJyMGfOHGRnZ+O7776rMamKjY0NTpw4gXPnziErKwuff/55jemvX9Tljz/+wJYtW5STkwBAWVkZgoKCkJiYiNu3byMpKQkpKSmws7Ort35WVlYoKSlBQkICHj58iNLSUl6vRW3EYjH09PRUFrq8mBBCCCGEEEIIIQ2OEzTd8p5q0C339/dHWVkZunTpgmnTpmHGjBmYPHkyACA2NhadOnXCgAED4O7uDoVCgSNHjigvAe7YsSP27t2L3bt3o0OHDli4cCEiIiJqTFDywgcffIDDhw9j/vz5iImJea366unp4ccff0RaWhpcXFzw9ddfY+HChQDAa+BHS0tL7Nu3DwcPHoSzszM2bdqEpUuXqmTmz5+Pjh07ok+fPvD29oapqWmts87o6+tj2LBh0NHRUXlcKBTi0aNH8Pf3h0QiwYgRI9C3b18sWrSo3vp5eHggMDAQI0eOhLGxMaKiogDU/1oQQgghhBBCCCGEkPcHp1AoFE1diXfJrl27MG7cOBQVFUFTU/OtrrtXr15wcHDA2rVr3+p6X1fptjCm/OMP2GZILhYYMuUNZA95Z58IjZnKLlewNZ5qCcqY8rqVT5jy6pX8y1dwwvpD1UjVteoPVSNQyOoPVaP97E/e2Qd69c/Y/SZMnt5gyleI2PZNnpo17ywHtkOxgGMbS7VSztbjV1P4nHeWte7qXAVTXiwvZco/RnOmfFG5NlOehVjItq2PHPlPlAUADlk/8s7mV5jWH6pGT/0ZU/6RVI8pr6PO/z0GAMZczd73dSkXss3uVixjq7ux4j5TXqrG/9hRWGnAVLaWkO37RsRJ6w9VU8n4/Vcm57/vW8jz2cpW12HKlyvETHmFov6rR5RZ8M8CgBpXyVYXxvJZ9jsAaAj4vw9Yj/GaCrZjh5RjO99mqY92ZRFT2SVqBkx5Vqyva3GlLu8s67FAzLEdg+VgO5+sVPA/7+A4tvcY63tSR1bIlGf9G4SFkGM7b2b9G+EuLJnyGkL+xwKZgq1vkVhQXn+oGtbXVUv+lClfLuB/nFQw9qPiwHZezvr9ZG/dkin/T1G2c2n9oUaiOearJlt3U2J653t7eyM4OLiRqlLFysrqlZcNN3Qdtm/fjrNnz+LWrVs4ePAg5s6dixEjRrzVxsEnT57gwIEDSExMxLRp05T3JyYmguM4FBYWvrW6EEIIIYQQQgghhDQpAdd0y3vqbzeI3P79+xv0Utj79+9j4cKFuH//PszMzDB8+HD861//AlA1xuDOnTtrfd6YMWOwadOmBqmDq6srnjx5guXLl8PW1pb38/r27YszZ87U+thXX32Fr75q3Fbvgu5+THkuKpQpL5i7jinPwkDOv7chADwVNmPKCxh/RSpV4/8LMQAIGXqjPJax/Qoq5th+7WPtyfZIx4R3VsjYO5G1Lvm6jdtDsbyc/yGWtVdBcxHbL8p/VrC9D/TVinlnZYy9Clg95QyY8s8rRUx5TcZefixY35MsPQIB4KrdQN5Zwyv8J+F6HWoCtm19LmN7nTgh//KLKvWZyjZV3GXKN2ZPNk0BW68e1h4XUsaeZqw9ewzB//uVtbeWTMF2rFEDW6+9cvB/T7J+tlmP8aznEeqMPRRZevnJBGx/LpSC7fPB2quVpacn6/mVpryEKf+ngv85DQBoC9l6xDdmb34Z45+BrOWzHDuYr6Jg/HyIKtmOq5WM46yXy/n/baqjxtbD9rka21UOYjnbeTxb72a2/aIBtl6tcsZx3yoFbOcRGjL++74AZkxlN3ZvfkLelr9dA6GhYcN2+Q4NDUVoaO0NVxEREbVOkgJUjV/YUHJzc1/reVu3bkVZWe0Ho1ftp/LycohEbAdUQgghhBBCCCGEkLeBe48nC2kqzHu8srISQUFB0NfXR/PmzbFgwQK8GMbwyZMn8Pf3R7NmzaClpYW+ffsiJydH5fn79u2Dg4MDxGIxrKysEB0d/cr1bd26FQYGBkhISABQ8xJjKysrLF26FOPHj4euri4sLS3x7bffqpRx7tw5uLi4QENDA25ubjh48CA4jkNaWtor192iRQv88ccfsLGxwe3btzFq1Cg4OTnB398fT5781XsnICCgxsQjwcHB8Pb2Vt729vbGF198geDgYDRr1gwmJibYsmULnj17hnHjxkFXVxfW1tb46aefatQjKSkJTk5O0NDQQLdu3fDbb78BAMzNzWFtbY379+9j3LhxcHR0RM+ePbF27VqIxX/1VrCyssLixYvh7+8PPT095cQxhBBCCCGEEEIIIYQwNxDGx8dDTU0NFy9exJo1a7By5Ups3boVQFVD2aVLl3Do0CGcP38eCoUC/fr1Q0VF1eVbqampGDFiBEaNGoWMjAyEh4djwYIFiIuLq3VdUVFR+PLLL3H8+HH06tWrzjpFR0fDzc0Nv/76K6ZOnYopU6YgOzsbAFBcXIyBAwfC0dERly9fxuLFizF37lzWzcbXX3+N6OhoXLp0CWpqahg/fjxzGfHx8WjevDkuXryIL774AlOmTMHw4cPh4eGBy5cv46OPPsLYsWNRWqp6+cGcOXMQHR2NlJQUGBsbY+DAgcp9euPGDfj6+mLYsGG4cuUK9uzZg7NnzyIoKEiljBUrVsDZ2Rm//vorFixYwFx3QgghhBBCCCGEEPLPxHyJsYWFBVatWgWO42Bra4uMjAysWrUK3t7eOHToEJKSkuDh4QGgakZgCwsLHDx4EMOHD8fKlSvRq1cvZQOVRCJBZmYmvvnmGwQEBKisZ+7cudixYwdOnz4NBweHV9apX79+mDp1qvJ5q1atwqlTp2Bra4vvvvsOHMdhy5Yt0NDQgL29Pe7evYtJkyYxbfe//vUv9OjRAwDw5Zdfon///nj+/Dk0NPiP5ePs7Iz58+cDAObNm4dly5ahefPmyrosXLgQGzduxJUrV9CtWzfl88LCwtC7d28AVY2MrVq1woEDBzBixAhERkbCz89P2avSxsYGa9euRY8ePbBx40Zl/T788EPMnj2baZsJIYQQQgghhBBC3rr3eLKQpsLcg7Bbt27guL9eKHd3d+Tk5CAzMxNqamro2rWr8jEjIyPY2toiKysLAJCVlQVPT0+V8jw9PZGTkwOZ7K/JCKKjo7FlyxacPXu23sZBAHByclL+n+M4mJqaoqCgAACQnZ2tvDz3hS5dujButeo6zMyqBi19sY7XKUMoFMLIyAiOjo7K+0xMTGot193dXfl/Q0NDlX2anp6OuLg46OjoKJc+ffpALpfj1q1byue5ubkx1fVlUqkUxcXFKotUyjYILiGEEEIIIYQQQgh597yToz56eXlBJpNh7969vPIvz2rMcRzkcrbZrVjW8aKB9MU6BAKBchzGF15cAlxfPV9VLh8lJSX4/PPPkZaWplzS09ORk5ODdu3aKXPa2mwzYL0sMjIS+vr6KsvGzZvfqExCCCGEEEIIIYSQGjhB0y3vKeZLjJOTk1VuX7hwATY2NrC3t0dlZSWSk5OVlxg/evQI2dnZsLe3BwDY2dkhKSlJ5flJSUmQSCQQCoXK+7p06YKgoCD4+vpCTU2tzpmE+bC1tcXOnTshlUqVE3ekpKS8dnm1MTY2Vk4c8kJaWlqNBsHXdeHCBVhaWgKomgjm+vXrsLOzAwB07NgRmZmZsLa2bpB11WXevHmYNWuWyn35d/IadZ2EEEIIIYQQQgghpPExN43m5eVh1qxZyM7Oxvfff4+YmBjMmDEDNjY2GDRoECZNmoSzZ88iPT0dY8aMgbm5OQYNGgQAmD17NhISErB48WJcv34d8fHxWLduXa0NgB4eHjhy5AgWLVqE1atXv/YGjh49GnK5HJMnT0ZWVhaOHTuGFStWAIDKpdJv4sMPP8SlS5ewfft25OTkICwsrEaD4ZuIiIhAQkICfvvtNwQEBKB58+bKWZPnzp2Lc+fOISgoCGlpacjJycEPP/xQY5KSNyUWi6Gnp6eyiMWiBl0HIYQQQgghhBBCCHn7mBsI/f39UVZWhi5dumDatGmYMWMGJk+eDACIjY1Fp06dMGDAALi7u0OhUODIkSPKnnQdO3bE3r17sXv3bnTo0AELFy5EREREjQlKXvjggw9w+PBhzJ8/HzExMa+1gXp6evjxxx+RlpYGFxcXfP3111i4cCEAME0w8ip9+vTBggULEBoais6dO+Pp06fw9/dvkLIBYNmyZZgxYwY6deqE+/fv48cff4RIVNU45+TkhNOnT+P69evw8vKCq6srFi5ciJYtWzbY+gkhhBBCCCGEEELeGo5ruuU9xSleHjyviXh7e8PFxeWNegvytWvXLowbNw5FRUXQ1NRstPW8vE2lpaUYO3YsTpw4gadPn+LJkydwcXFBcHCwchbixpCYmIiePXviyZMnMDAwaLByc3+/zpTnokKZ8mVz1zHl9Sof884qGD/0T4XNmPJCyOoPVcOBbcxMlvIfywyZyhYL2CafETDWvVLBf2QDIce2HwVcw449+qYKy3V5ZxVge082Fz1hyv8pZXsftBA/4p2VQVh/qBrWz0e5gq238tNKLaZ8Y44ywvqebC78kyl/1W4g76zhFbbhNfTUnzHliyp0mPKsLIW3eWcLYMZUtqniLlO+TJ1tW8sVYt5ZhYLtWMD6HpMr2N7xHMd2mqgtL+adfS5gGx+Z9VijhkqmPMuxhnW/c2Dbj6zfrazHSR3wf51kArYRiVje7wAg4qRMedbPCAuxvIwp/6fChCmvLSxlylco+A9XxPqeYT3HYn0Ps3xeG/vzYSBlm1jyvrolU75czv910lFj+24Vge3zUSLnf+4JAOqCmuPm14XlHB4AdLinTHk547hvHGMzhkjG//PNeh6hJWQ7dqiB/34HAOt2bZjy/xTP/x3dZOvWGD67ydbdlJjHIPw72r59O9q2bQtzc3Okp6dj7ty5GDFiRKM2DgLA/v37VcYhjI+Px5kzZ3Du3Dk0b94c+vr6SElJeeMJRKqrraHVw8MD+fn50NfXb7D1AMD9SraToufTv2fKW+IeU373Ncf6Q/9z9y7bCVpwb/5/mALAfTlbD05jIduJy4afLXhnh/Rg+wJq8/wqU75Mw4Apf7HQnnfWrVk2U9mVHNsfSEaFN5jyMjW2XsflcxfwzqqJ2Q7HustXMuW1N89hypdP/xf/LOMfps8Z/3g0kbE13mgum8+UV8gb73eyFp3aM+V/+yiMKc/S6PfYqTNT2aKr55nyGPshU7xdL7Z9s7/nDt5ZP+NjTGX/t+wjpryLaT5T3rTsJlOexUMttj9kdeVsPy4I5GyNCDuuOvHO9nVmaxBvJmfLlwnZ/lBmajRjPIVmbQB5WNmcKW9VyfZ9+UzM/4dPdRlbA8VjOdsPUgZqbA25LH9YP6o0Yir74Hlzpvx83fVM+UeOPkx53ZL7vLOFuq2YymY9ZxIo2I4FCobGHtay5RzbjwWBmwyY8tvGZzLlhad/5J19OGAKU9msx4J20gymfKUa/7+Hn6uz/c2qYPwJVihn+5ulQG7KlN9/lv8PyAuNtzKVndl+FFNeV8j2nn9vCd7fyUKaynuxx+/fv48xY8bAzs4OM2fOxPDhw/Htt98CAAIDA6Gjo1PrEhgY+FrrKy+v6oFlaGgIXd2/Tk5v3LgBOzs7dOjQAaampuA4DsbGxtDSYuvtwkokEinXBwBLly6tc5v79u3bqHUhhBBCCCGEEEIIIe+Wd6qBUC6XIzQ0FIaGhjA1NUV4eLjysZUrV8LR0RHa2tqwsLDA1KlTUVJSonz89u3bGDhwIJo1awZtbW04ODjgyJEjAIDQ0FDk5ubi+fPnuHXrFlatWgUtLS3I5XIcOnQIc+bMQVpamnLZtWsXSktLMWnSJABAYWEhJk6cCGNjY+jp6eHDDz9Eenq6ct3h4eFwcXHB1q1b0aZNG+XYht7e3spLh729vREdHY1ffvkFHMfB29sbAGBlZaXS26+wsBCff/45TExMoKGhgQ4dOuC///0vgKpZoT/99FOYm5tDS0sLjo6O+P77v3rlBQQE4PTp01izZg04jgPHccjNzUViYiI4jkNhYSGAqkbRZcuWwczMDOXl5TAwMMAXX3yBtLQ0bN3K9msJIYQQQgghhBBCCPl7e6caCOPj46GtrY3k5GRERUUhIiICJ06cAAAIBAKsXbsWV69eRXx8PE6ePInQ0L/GtJs2bRqkUil++eUXZGRkYPny5dDRefUYQQKBAH5+fvj5559hbW2tXM6cOQNPT0906tQJADB8+HAUFBTgp59+QmpqKjp27IhevXrh8eO/xrz7/fffsW/fPuzfvx9paWk11rV//35MmjQJ7u7uyM/Px/79+2tk5HI5+vbti6SkJOzcuROZmZlYtmwZhMKqbvTPnz9Hp06dcPjwYfz222+YPHkyxo4di4sXLwIA1qxZA3d3d0yaNAn5+fnIz8+HhUXNS1Fv3bqFGTNmwN/fH7/99huWLFmCNWvW4OzZszA3Z7u0ghBCCCGEEEIIIaRBcYKmW95T79QYhE5OTggLqxp/ycbGBuvWrUNCQgJ69+6tMomHlZUVlixZgsDAQGzYsAEAkJeXh2HDhsHRsWocurZt2/Jap5+fH6Kjo5GXlwdLS0vI5XLs3r0b8+dXjWF19uxZXLx4EQUFBRCLq8bNWrFiBQ4ePIj//Oc/yhmcy8vLsX37dhgbG9e6HkNDQ2hpaSkv963Nzz//jIsXLyIrKwsSiaTGdpibmyMkJER5+4svvsCxY8ewd+9edOnSBfr6+hCJRNDS0qpzHUBVb8xevXphwYKq8dEkEgkyMzPxzTff1DmjNCGEEEIIIYQQQgj5Z3qnmkadnFQHtjYzM0NBQdXkDT///DN69eoFc3Nz6OrqYuzYsXj06BFKS6smm5g+fTqWLFkCT09PhIWF4cqVK7zW6eLiAjs7O3z33XcAgNOnT6OgoADDhw8HAKSnp6OkpARGRkYqY/XdunULN278NcFB69at62wc5CstLQ2tWrVSNg6+TCaTYfHixXB0dIShoSF0dHRw7Ngx5OXlMa0nKysLnp6eKvd5enoiJycHMlntA6ZKpVIUFxerLOXlbINWE0IIIYQQQgghhNRLwDXd8p56pxoIq8/4CwAcx0EulyM3NxcDBgyAk5MT9u3bh9TUVKxfXzVr2IsJQSZOnIibN29i7NixyMjIgJubG2JiYnit18/PT9lA+N1338HX1xdGRlWznpWUlMDMzExljMK0tDRkZ2djzpy/ZgVtiJmI65tV+ZtvvsGaNWswd+5cnDp1CmlpaejTp49yHzSmyMhI6Ovrqyzbv2WbRZUQQgghhBBCCCGEvHveqQbCuqSmpkIulyM6OhrdunWDRCLBvXv3auQsLCwQGBiI/fv3Y/bs2diyZQuv8kePHo3ffvsNqamp+M9//gM/Pz/lYx07dsT9+/ehpqamMk6htbU1mjdnm3a+Pk5OTvjjjz9w/fr1Wh9PSkrCoEGDMGbMGDg7O6Nt27Y1siKRqM5egC/Y2dkhKSmpRtkSiUQ53uHL5s2bh6KiIpXFf/Ishq0jhBBCCCGEEEIIIe+iv0UDobW1NSoqKhATE4ObN29ix44d2LRpk0omODgYx44dw61bt3D58mWcOnUKdnZ2vMq3srKCh4cHJkyYAJlMho8//lj5mI+PD9zd3TF48GAcP34cubm5OHfuHL7++mtcunSpQbezR48e6N69O4YNG4YTJ07g1q1b+Omnn3D06FEAVeMynjhxAufOnUNWVhY+//xzPHjwoMa2JCcnIzc3Fw8fPoRcLq+xntmzZyMhIQGLFy/G9evXER8fj3Xr1qmMb/gysVgMPT09lUUkEjfo9hNCCCGEEEIIIYTQJCVv399iy52dnbFy5UosX74cHTp0wK5duxAZGamSkclkmDZtGuzs7ODr6wuJRKKcwIQPPz8/pKenY8iQISqX+nIchyNHjqB79+4YN24cJBIJRo0ahdu3b8PExKTBtvGFffv2oXPnzvj0009hb2+P0NBQZY/A+fPno2PHjujTpw+8vb1hamqKwYMHqzw/JCQEQqEQ9vb2MDY2rnV8wo4dO2Lv3r3YvXs3OnTogIULFyIiIoImKCGEEEIIIYQQQgh5D70zsxgnJibWuO/gwYPK/8+cORMzZ85UeXzs2LHK//Mdb7AuU6ZMwZQpU2p9TFdXF2vXrsXatWtrfTw8PBzh4eE17n95m1avXl0jk5ubq3Lb0NAQ27Ztq3U9hoaGKvukNhKJBOfPn1e5z8rKCgqFQuW+YcOGYdiwYa8sixBCCCGEEEIIIeSt497fyUKaSpP1IPT29kZwcPBbX29iYiI4jkNhYWGdmbi4OBgYGChvh4eHw8XFRXk7ICCgRs+9V8nNzQXHcUhLS3ujehFCCCGEEEIIIYQQ0tDemR6EjSUwMBA7d+5U3n5xua65uTnGjh1bYyxDABg5ciT69ev31ur4d6WrVtqoeQXjLwaDO9zknRU6VDKVXcmJmPKGwsdM+adyfab8+N6PeGc1Fc+Yyr6rLmHKs3IxusU7+xxvPjv4q9zRd2LKq3Fs7xv1dbt5ZzlOUX+omhKw1YWbvowpL0ftExbVpkLB9lWizrgfS0V6TPnyBZuZ8obl95nyLBSlD5nyeupsn1cWoqvn6w9Vc9/BnSnfOX0XU/6OWlum/Kiy07yzt7VdmMruZPAHU/7iXQumvEer57yzBVK2CdAMUcSUlwq1mPIKNbbv4k+drvHOPuPYPtvPhWzfCXLG38GlnGb9odfEge0Yr69WzJR/qsb2vpEx/AnwHBpMZbPWPa+Ubcie4+f578sJvf5kKvtzz3ym/F3hIKa8XMH/uxUAyvR1+JfN8L0NAM/lbK+rkHv1JIgvK5er885qCcqYyq5kPO9YNqOCKZ8PG6a8cMAM3tnGPhY8UTdnyrO8JxVg+z5g3VbWY82tJ2x/P03pzv/76Q8BW1uADtjO31i/nwh5W/7xDYQREREqk28kJydjzJgxOHPmDFq1alXrczQ1NVXGISSEEEIIIYQQQgghb4mAGlLftibd43K5HKGhoTA0NISpqanKOH4rV66Eo6MjtLW1YWFhgalTp6KkpET5+O3btzFw4EA0a9YM2tracHBwwJEjR2qso0WLFrC2tlYu5uZVv6rk5+fDx8cHGhoa6NatG3777Tflc16+xLg+R48exQcffAADAwMYGRlhwIABuHHjRo3ctWvX4OHhAQ0NDXTo0AGnT7+6N8TZs2fh5eUFTU1NWFhYYPr06Xj2jN+vExs2bICNjQ00NDRgYmKCTz75RPmYXC5HZGQk2rRpA01NTTg7O+M///kP7+0lhBBCCCGEEEIIIf8cTdpAGB8fD21tbSQnJyMqKgoRERE4ceJEVcUEAqxduxZXr15FfHw8Tp48idDQUOVzp02bBqlUil9++QUZGRlYvnw5dHT4d8OfM2cOoqOjkZKSAmNjYwwcOBAVFWzdz1949uwZZs2ahUuXLiEhIQECgQBDhgyBXC6vsc7Zs2fj119/hbu7OwYOHIhHj2q/VPTGjRvw9fXFsGHDcOXKFezZswdnz55FUFBQvfW5dOkSpk+fjoiICGRnZ+Po0aPo3r278vHIyEhs374dmzZtwtWrVzFz5kyMGTOm3gZLQgghhBBCCCGEkEbHcU23vKea9BJjJycnhIWFAQBsbGywbt06JCQkoHfv3ioTmFhZWWHJkiUIDAzEhg0bAAB5eXkYNmwYHB0dAQBt27KNZRQWFobevXsDqGqobNWqFQ4cOIARI0Ywb8fLswFv27YNxsbGyMzMRIcOHZT3BwUFKbMbN27E0aNH8X//938qDZ8vREZGws/PT7kfbGxssHbtWvTo0QMbN26EhkbdYzTk5eVBW1sbAwYMgK6uLlq3bg1XV1cAgFQqxdKlS/Hzzz/D3b1qfKm2bdvi7Nmz2Lx5M3r06MG8/YQQQgghhBBCCCHk76vJGwirMzMzQ0FBAQDg559/RmRkJK5du4bi4mJUVlbi+fPnKC0thZaWFqZPn44pU6bg+PHj8PHxwbBhw2qU9yovGscAwNDQELa2tsjKynqt7cjJycHChQuRnJyMhw8fKnsO5uXlqTQQVl+nmpoa3Nzc6lxneno6rly5gl27/hr0XaFQQC6X49atW7Czs6uzPr1790br1q3Rtm1b+Pr6wtfXF0OGDIGWlhZ+//13lJaWKhtHXygvL1c2ItZGKpVCKpWqPkcqhUgsrvM5hBBCCCGEEEIIIeTd16SXGKurq85uxXEc5HI5cnNzMWDAADg5OWHfvn1ITU3F+vXrAVQ1ZAHAxIkTcfPmTYwdOxYZGRlwc3NDTEzMW98GABg4cCAeP36MLVu2IDk5GcnJySp1fR0lJSX4/PPPkZaWplzS09ORk5ODdu3avfK5urq6uHz5Mr7//nuYmZlh4cKFcHZ2RmFhoXIcx8OHD6uUnZmZ+cpxCCMjI6Gvr6+ybNncNPubEEIIIYQQQggh/2CcoOmW99Q7OYtxamoq5HI5oqOjIfjfzDV79+6tkbOwsEBgYCACAwMxb948bNmyBV988QWvdVy4cAGWlpYAgCdPnuD69euv7JVXl0ePHiE7OxtbtmyBl5cXgKrJRepa54uxACsrK5GamlrnmIIdO3ZEZmYmrK2tmesEVPVQ9PHxgY+PD8LCwmBgYICTJ0+id+/eEIvFyMvLY7qceN68eZg1a5bKfTfuPH6tuhFCCCGEEEIIIYSQd8c72UBobW2NiooKxMTEYODAgUhKSsKmTZtUMsHBwejbty8kEgmePHmCU6dOMTXwRUREwMjICCYmJvj666/RvHlzDB48mLmuzZo1g5GREb799luYmZkhLy8PX375Za3Z9evXw8bGBnZ2dli1ahWePHmC8ePH15qdO3cuunXrhqCgIEycOBHa2trIzMzEiRMnsG7dulfW6b///S9u3ryJ7t27o1mzZjhy5AjkcjlsbW2hq6uLkJAQzJw5E3K5HB988AGKioqQlJQEPT09fPbZZ7WWKRaLIX7pcmKRmN+MyoQQQgghhBBCCCG8Cd7fnnxN5Z3c487Ozli5ciWWL1+ODh06YNeuXYiMjFTJyGQyTJs2DXZ2dvD19YVEIlFOYMLHsmXLMGPGDHTq1An379/Hjz/+CJFIxFxXgUCA3bt3IzU1FR06dMDMmTPxzTff1LnOZcuWwdnZGWfPnsWhQ4fQvHnzWrNOTk44ffo0rl+/Di8vL7i6umLhwoVo2bJlvXUyMDDA/v378eGHH8LOzg6bNm3C999/DwcHBwDA4sWLsWDBAkRGRir33+HDh9GmTRvm7SeEEEIIIYQQQgghf2+cQqFQNHUlyN/T1d/zG7V8DUEZU55jeCsLFZVMZcs5IWOere29VK7NlFfnKnhnNRVsPT2LYcCUZ6XDPeWdrYR6/aE3UK5g+1FAjWN738gU/N83HMd2KFYDW104yJnycvCve6lck6lsdcb9yHosKFewTZ5kWH6fKc9Cs/QhU/6OPv/Jtlg9l7Ptl/sO7vWHqumcvqv+UDV31Noy5duWZfDO/qHdnqlsEcc2ZvDFuxZMeY9WN3lnC6S1/3BYF0NREVNeHWzbquA4prxI9px39plAj6lsNfD/7gMAGeOFMhwa75SYtWwF2PZ7Y+6b5woNprLFnLT+UDV3Slsw5Y+f578vJ/T6k6lsTXkJU75cyLZv5AznBQDbdzfL9zYAPJez1V3IyZjy5Qr+53BajN/zlQq2zzbLeTMAyBj3pRD8901jHwuEjOdYLO9J1rqwbivrsebaQ2OmfDeja7yzUgHbuS3r5491X9q2Yzvv+Kd4fnRrk61bw3dik627Kb12D0Jvb28EBwc3YFX+2eLi4mBgYPDG5XAch4MHD75xOYQQQgghhBBCCCHvJI5ruuU99U6OQfgmAgMDsXPnzlofGzNmTI2xDP+OMjMzMWbMmDoffzFTcWMrqtBhypfL2N5uVhr8e5oBwI/ZNryzj5+w/br2udtvTPl7AkumvIGwkCm/7Qz/X5EGuZcylW39PJ0pX6ppyJRPLbbnne2kl8lUdoWArYeUUdkdpryCsWfoo39F8M6qidk+H5pfLmHKc/+3gilfGriId1aNY+uJKZWz5ZvJ2HqAiGPmMuWfPGDrgcWCk9Q/LER1j0Z/wJRXEzD0DB37IVPZrD0CU5z9mPIeEb2Y8ns68h9GZHTzY0xlH37+EVPezuQJU96oKJd3VkeDrewisPWgEMvYvhNYeucDwKFc/r1gPds9YCpbV862b0qFbD0UxXL++0YmYOvhzinYenE/lLG9rm0qspjyz8TNeGfVGXsEPqxkq7uJZiFTfqQ3/33/VMZ2rrovzZwpP1N7C1P+kY0nU1736T3e2UI9tnNPTsD22RYw9iDUVPD/PLFe2SPj2M6Zvt7M9nldP4Z/r28AEJ07wjv70GccU9msxwLrMrbz+Ap1/j3lnot0mcpm7SXHeqxpoctWn+2ptryzs412MJWdZTWAKa8rYPs7l5C35R/XQBgREYGQkJBaH9PTYztRfFl5eflrjVPY0Nq1a4e0tLSmrgYhhBBCCCGEEEJIw2PsnEHe3BvtcblcjtDQUBgaGsLU1BTh4eHKx1auXAlHR0doa2vDwsICU6dOVenZdvv2bQwcOBDNmjWDtrY2HBwccORI/b++1Hap7sGDB8H9rxtoixYtsHPnTnzyySdISEhAz5494eTkhK+++kplFt7KykpMnz4dBgYGMDIywty5c/HZZ5+pzGTs7e2NoKAgBAcHo3nz5ujTpw+vbXtRT0tLS2hpaWHIkCF49OhRjW354Ycf0LFjR2hoaKBt27ZYtGgRKiv/+gUtJycH3bt3h4aGBuzt7XHixAkAVTMKW1tb17kAVY2ZQUFBMDMzg4aGBlq3bq0y0UthYSE+//xzmJiYQENDAx06dMB///vfevc/IYQQQgghhBBCCPlneaMGwvj4eGhrayM5ORlRUVGIiIhQNmIJBAKsXbsWV69eRXx8PE6ePInQ0FDlc6dNmwapVIpffvkFGRkZWL58OXR02C4DeJXff/8de/fuxY8//oijR4/i119/xdSpU5WPL1++HLt27UJsbCySkpJQXFxc69h+8fHxEIlESEpKUl6eXN+2JScnY8KECQgKCkJaWhp69uyJJUtULwc8c+YM/P39MWPGDGRmZmLz5s2Ii4vDv/71LwBVja9Dhw6FSCRCcnIyNm3ahLlz+V82t3btWhw6dAh79+5FdnY2du3aBSsrK2XZffv2RVJSEnbu3InMzEwsW7YMQiHb4KqEEEIIIYQQQggh5O/vjS4xdnJyQlhYGADAxsYG69atQ0JCAnr37q0ygYmVlRWWLFmCwMBAbNhQNYZQXl4ehg0bBkdHRwBA27ZssxnW5/nz59i+fTvMzavGEomJiUH//v0RHR0NU1NTxMTEYN68eRgyZAgAYN26dbX2YLSxsUFUVJTKffVt25o1a+Dr66tsNJRIJDh37hyOHj2qfN6iRYvw5Zdf4rPPPgNQtf2LFy9GaGgowsLC8PPPP+PatWs4duwYWrasGsNq6dKl6Nu3L6/tz8vLg42NDT744ANwHIfWrVsrH/v5559x8eJFZGVlQSKRKNdPCCGEEEIIIYQQ0uTe48lCmsobNxBWZ2ZmhoKCAgBVjVCRkZG4du0aiouLUVlZiefPn6O0tBRaWlqYPn06pkyZguPHj8PHxwfDhg2rUd6bsLS0VDYOAoC7uzvkcjmys7OhqamJBw8eoEuXLsrHhUIhOnXqBLlcdRDpTp061Si7vm3LyspSNjxWX3/1BsL09HQkJSUpewwCgEwmU5aTlZUFCwsLZePgizL4CggIQO/evWFrawtfX18MGDAAH31UNQh7WloaWrVqpWwc5EMqlUIqVR04tmpMRrZJIQghhBBCCCGEEELIu+WNLjFWV1edEYrjOMjlcuTm5mLAgAFwcnLCvn37kJqaivXr1wOoalQCgIkTJ+LmzZsYO3YsMjIy4ObmhpiYmPorLBBA8dKMehUVFW+yGa+kra2tcpvPtvFRUlKCRYsWIS0tTblkZGQgJycHGhoab1zvjh074tatW1i8eDHKysowYsQIfPLJJwAATU3+s1W9EBkZCX19fZVlx7fRb1xPQgghhBBCCCGEEBUCQdMt76lG2fLU1FTI5XJER0ejW7dukEgkuHfvXo2chYUFAgMDsX//fsyePRtbtmypt2xjY2M8ffoUz549U95X24y+eXl5Kuu8cOECBAIBbG1toa+vDxMTE6SkpCgfl8lkuHz5coNsm52dHZKTk1Xuu3Dhgsrtjh07Ijs7u9ZJRgQCAezs7HDnzh3k5+fXWUZ99PT0MHLkSGzZsgV79uzBvn378PjxYzg5OeGPP/7A9evXeZc1b948FBUVqSxjJ89mqg8hhBBCCCGEEEIIefe80SXGdbG2tkZFRQViYmIwcOBAlQk+XggODkbfvn0hkUjw5MkTnDp1CnZ2dvWW3bVrV2hpaeGrr77C9OnTkZycjLi4uBo5DQ0NfPbZZ1ixYgWKi4sxffp0jBgxAqampgCAL774ApGRkbC2tkb79u0RExODJ0+eKGdDfpNtmz59Ojw9PbFixQoMGjQIx44dU7m8GAAWLlyIAQMGwNLSEp988gkEAgHS09Px22+/YcmSJfDx8YFEIsFnn32Gb775BsXFxfj666/r3T8vrFy5EmZmZnB1dYVAIMC///1vmJqawsDAAD169ED37t0xbNgwrFy5EtbW1rh27Ro4joOvr2+t5YnFYpVZoAFAJHrKuz6EEEIIIYQQQggh5N3UKD0InZ2dsXLlSixfvhwdOnTArl27EBkZqZKRyWSYNm0a7Ozs4OvrC4lEopzk41UMDQ2xc+dOHDlyBI6Ojvj+++8RHh5eI2dtbY2hQ4eiX79++Oijj+Dk5KRS/ty5c/Hpp5/C398f7u7u0NHRQZ8+feq9vJfPtnXr1g1btmzBmjVr4OzsjOPHj2P+/PkqmT59+uC///0vjh8/js6dO6Nbt25YtWqVcjIRgUCAAwcOoKysDF26dMHEiRNVxiusj66uLqKiouDm5obOnTsjNzcXR44cgeB/3WX37duHzp0749NPP4W9vT1CQ0Mhk8l4l08IIYQQQgghhBDSGBQc12TL++q1exAmJibWuO/gwYPK/8+cORMzZ85UeXzs2LHK//MZb7AugwcPxuDBg1XumzRpUo3clClTMGXKlFrLUFNTQ0xMjLIecrkcdnZ2GDFihDJT2zYC9W8bAIwfPx7jx49XuW/2bNVLcvv06YM+ffrUug6gavbjM2fOqNz38viLdZk0aVKt++QFQ0NDbNu2jVdZhBBCCCGEEEIIIeSf67V7EHp7eyM4OLgBq/L6EhMTwXEcCgsLeT/n9u3b2LJlC65fv46MjAxMmTIFt27dwujRoxukTnFxcTAwMGiQsgghhBBCCCGEEELeG5yg6Zb3VKOMQfgmAgMDsXPnzlofGzNmTI3x/l6XQCBAXFwcQkJCoFAo0KFDB/z888+8xkHkY+TIkejXr5/ydnh4OA4ePFjrhCqvY+nSpVi6dGmtj3l5eeGnn35qkPW8ioaQ/6zNACCtZHu7CRWVTHkWDx+WMeUrBer1h6qRKYRMeTljW721lYh3lsOz+kPVVKqxzaIt49heVzWBnHdWvVLKVDYn5NfD9oVKIdu2CuRsM6abdm7PO8sJ2d4zhUL+7wEAMO7oyJR/yvD1oK94zFT2I86YKa9e+Zwpr+UoYcubPmTKM5Vtx/89AAA66mzb+lzG/33QrhdbXe6otWXKe0T0YsqfW5jAlC/ezf/Y8dzCiKlsbQX/sgFAS8j2HXJD24V31qYklanspyJDprz+41tM+ec6bJ/Xoqf896UQbEObKNC4l/woGP4gYK1LJSeuP1RNc+GfTHlhKduxQ6qpxTsrUrCV3Uz9CVNeXFnKlK9g+O5+WNmcqexnpYzD7TRjO48oF2oy5eUM558s718AEILtPJvjeRXT62A9z2ati6SDGVMeuM2UFtp24J2tYDwWsB5rhIznTOUinUarS7mA7fOhrmD727K1+A+m/PHS1ryzClO210muYPv8qSnY/qYg5G155xoIIyIiEBISUutjenp6vMoIDw+vdVzC6iwsLJCUlMRaPd40NTWhqcl2EsAiMDBQ5XLol9dNCCGEEEIIIYQQQggfb9R3Ui6XIzQ0FIaGhjA1NVVplFu5ciUcHR2hra0NCwsLTJ06FSUlJcrHb9++jYEDB6JZs2bQ1taGg4MDjhw5ghYtWsDa2rrWpUWLFgCAI0eOQCKRQFNTEz179kRubm6Nup09exZeXl7Q1NSEhYUFpk+fjmfP/upJZWVlhcWLF+PTTz+FtrY2zM3NsX79epUy8vLyMGjQIOjo6EBPTw8jRozAgwcPlI+np6ejZ8+e0NXVhZ6eHjp16oRLly4BUL3EOC4uDosWLUJ6ejo4jgPHcbXOvPwyjuOwefNmDBgwAFpaWrCzs8P58+fx+++/Y+jQoXB2doa/vz84jlPuI47jMHXqVJiYmEBHRwedO3fGzz//rFKulZUVli5divHjx0NXVxeWlpb49ttv660PIYQQQgghhBBCSKOjS4zfujfa8vj4eGhrayM5ORlRUVGIiIjAiRMnqgoWCLB27VpcvXoV8fHxOHnyJEJDQ5XPnTZtGqRSKX755RdkZGRg+fLl0NGpv4vznTt3MHToUAwcOBBpaWmYOHEivvzyS5XMjRs34Ovri2HDhuHKlSvYs2cPzp49i6CgIJXcN998A2dnZ/z666/48ssvMWPGDGX95XI5Bg0ahMePH+P06dM4ceIEbt68iZEjRyqf7+fnh1atWiElJQWpqan48ssvoa5es4v8yJEjMXv2bDg4OCA/Px/5+fkq5bzK4sWL4e/vj7S0NLRv3x6jR4/G559/jnnz5uHSpUtQKBQq21VSUoJ+/fohISEBv/76K3x9fTFw4EDk5eWplBsdHQ03Nzf8+uuvmDp1KqZMmYLs7GxedSKEEEIIIYQQQggh/xxvdImxk5MTwsLCAAA2NjZYt24dEhIS0Lt3b5UJTKysrLBkyRIEBgZiw4YNAKp65w0bNgyOjlXjYrVty2+so40bN6Jdu3aIjo4GANja2iobGF+IjIyEn5+fsg42NjZYu3YtevTogY0bN0JDo2o8BE9PT2XjokQiQVJSElatWoXevXsjISEBGRkZuHXrFiwsLAAA27dvh4ODA1JSUtC5c2fk5eVhzpw5aN++vXI9tdHU1ISOjg7U1NRgamrKaztfGDdunPJS4rlz58Ld3R0LFixQzn48Y8YMjBs3Tpl3dnaGs7Oz8vbixYtx4MABHDp0SKUhsV+/fpg6daqy3FWrVuHUqVOwtbVlqh8hhBBCCCGEEEJIQ1JwjTv2MKnpjXoQOjk5qdw2MzNDQUEBAODnn39Gr169YG5uDl1dXYwdOxaPHj1CaWnVQMTTp0/HkiVL4OnpibCwMFy5coXXOrOystC1a1eV+9zd3VVup6enIy4uDjo6OsqlT58+kMvluHXrVp3Pc3d3R1ZWlnI9FhYWysZBALC3t4eBgYEyM2vWLEycOBE+Pj5YtmwZbty4wWsbWFTfxyYmJgCgbFR9cd/z589RXFwMoKoHYUhICOzs7GBgYAAdHR1kZWXV6EFYvVyO42Bqaqp87WojlUpRXFysspSXs00gQQghhBBCCCGEEELePW/UQPjy5bQcx0EulyM3NxcDBgyAk5MT9u3bh9TUVOX4fuXlVbMTTZw4ETdv3sTYsWORkZEBNzc3xMTEvEl1lEpKSvD5558jLS1NuaSnpyMnJwft2rVrkHUAVZOhXL16Ff3798fJkydhb2+PAwcONFj5gOo+5v7Xgl7bfXJ51ayBISEhOHDgAJYuXYozZ84gLS0Njo6Oyv1eW7kvynlRRm0iIyOhr6+vssRuXv1G20YIIYQQQgghhBBCml6jzGKcmpoKuVyO6OhoCARVbZB79+6tkbOwsEBgYCACAwMxb948bNmyBV988cUry7azs8OhQ4dU7rtw4YLK7Y4dOyIzMxPW1tavLOvl5124cAF2dnbK9dy5cwd37txR9iLMzMxEYWEh7O3tlc+RSCSQSCSYOXMmPv30U8TGxmLIkCE11iUSiSCTyV5Zn4aQlJSEgIAAZR1KSkpqncSF1bx58zBr1iyV+zLzSupIE0IIIYQQQgghhLym93iykKbSKHvc2toaFRUViImJwc2bN7Fjxw5s2rRJJRMcHIxjx47h1q1buHz5Mk6dOqVsnHuVwMBA5OTkYM6cOcjOzsZ3331XY0bguXPn4ty5cwgKCkJaWhpycnLwww8/1JikJCkpCVFRUbh+/TrWr1+Pf//735gxYwYAwMfHB46OjvDz88Ply5dx8eJF+Pv7o0ePHnBzc0NZWRmCgoKQmJiI27dvIykpCSkpKXVug5WVFW7duoW0tDQ8fPgQUmnjXJ5rY2OD/fv3K3tNjh49+pU9A/kSi8XQ09NTWUQicQPUmBBCCCGEEEIIIYQ0pUZpIHR2dsbKlSuxfPlydOjQAbt27UJkZKRKRiaTYdq0abCzs4Ovry8kEolyApNXsbS0xL59+3Dw4EE4Oztj06ZNWLp0qUrGyckJp0+fxvXr1+Hl5QVXV1csXLgQLVu2VMnNnj0bly5dgqurK5YsWYKVK1cqJ//gOA4//PADmjVrhu7du8PHxwdt27bFnj17AABCoRCPHj2Cv78/JBIJRowYgb59+2LRokW11nvYsGHw9fVFz549YWxsjO+//573/mSxcuVKNGvWDB4eHhg4cCD69OmDjh07Nsq6CCGEEEIIIYQQQhocxzXd8p567UuMExMTa9x38OBB5f9nzpyJmTNnqjw+duxY5f/fZLzBAQMGYMCAASr3VZ/JFwA6d+6M48ePv7IcPT29Wi99fsHS0hI//PBDrY+JRKJXNvIFBAQgICBAeVssFuM///nPK+vzMoVCoXLbysqqxn3e3t4q91lZWeHkyZMqmWnTpqncru2S47S0NKa6EUIIIYQQQgghhJB/hrdyUbe3tzeCg4Pfxqr+kRITE8FxHAoLC5u6KoQQQgghhBBCCCHkH4ZTvNwlrRF4e3vDxcUFq1evrjcbGBiInTt31vrYmDFjaoxl+LqsrKwQHBz82g2XcXFxCA4Ofu1Gu127duHzzz+vcX9ZWRkMDAzw6NEj5X3l5eV4/PgxTExMlLMWvwtu/57NlBdVlrGVjzZMeYfis/zDAiFT2efVP2TKW+oUMOVZmZTc4J19otOKqWwF43tMXFnKlBdVPuedfSCyYCpbCLaJgKQKEVO+Us7W6frGI33eWQHHdih2bJHPlL9TYsyUZ3kPl8vZ9mPrp1eY8s+0WzDlLxR1YMq3N7zPlGdh9iyHKV+ixfY6cQr+Y8z+eJNtv4wyO82U33O/B1O+uIRtfFyHUfWPU/xCy8wkprKFYKuLuqCCKa9f+aj+0P8IFGzHsUdqpkz528XNmfLt9Nk+H/oVD3lnH6uZMJUtZ/xdW8D4uj4p1+Od1VArZypbxPie0eGeMuU1KtgmjmN532hxz5jKfiJrxpRnJZPzP4czVv+TqWzNCrb9fkvRjq18NbYxyA3B//P0RyXb+Z6OGtt5uYBj+zxJGc4N1LlKprI1BPzPJQHAvOAyU/6QfBBT3rp5Ee+shpDtPcB6LGA9L2c5Z2qhw/aeMRCx1Z31WFMk53+eDQCtyvn//XRfbMVUNuvrJIUGU759O7bP9z9FadK+Jlu3luewJlt3U2qUWYzfREREBEJCQmp9TE+P/4lbfeqa2Vcmk4HjOOXsy43l448/RteuXWvc7+fnBwcHB5X7RCIRTE3Z/gAghBBCCCGEEEIIIYSPtzZvtFwuR2hoKAwNDWFqaorw8HDlYytXroSjoyO0tbXRqVMnrFy5EqamprC2toa1tTXU1dUxc+ZM2NraQltbGw4ODjhy5Ei963xxae7hw4fh5OQEDQ0NdOvWDb/99psyExcXBwMDAxw6dAj29vYQi8XIy8vDkydP4O/vj2bNmkFLSwt9+/ZFTk6Ostxx48ahqKgIHMeB4zjl9kilUoSEhMDc3Bza2tro2rVrjfEak5KSMHDgQDg5OaFz586YNm0ajIyMsGTJEly8eBGxsbHKcnNzc2u9xHjfvn1wcHCAWCyGlZUVoqOjVdZhZWWFpUuXYvz48dDV1YWlpSW+/fZbtheNEEIIIYQQQggh5C1TcFyTLe+rt9ZAGB8fD21tbSQnJyMqKgoRERE4ceJEVSUEAqxduxZXr15FfHw8Tp48idDQUOVzp02bBqlUil9++QUZGRlYvnw5dHR0eK97zpw5iI6ORkpKCoyNjTFw4EBUVPx1qUdpaSmWL1+OrVu34urVq2jRogUCAgJw6dIlHDp0COfPn4dCoUC/fv1QUVEBDw8PrF69Gnp6esjPz0d+fr6y12NQUBDOnz+P3bt348qVKxg+fDh8fX2VjYtpaWno1asX7O3tcf78eZw9exYDBw6ETCbDmjVr4O7ujkmTJinLtbCoeYllamoqRowYgVGjRiEjIwPh4eFYsGAB4uLiVHLR0dFwc3PDr7/+iqlTp2LKlCnIzma7LJgQQgghhBBCCCGE/LO9tUuMnZycEBYWBgCwsbHBunXrkJCQgN69e6uMA2hlZYUlS5YgMDAQGzZsAADk5eVh2LBhcHR0BAC0bduWad1hYWHo3bs3gKqGylatWuHAgQMYMWIEAKCiogIbNmyAs7MzACAnJweHDh1CUlISPDw8AFSNGWhhYYGDBw9i+PDh0NfXB8dxKpf+5uXlITY2Fnl5eWjZsiUAICQkBEePHkVsbCyWLl2KqKgouLm5KbcNgMolxSKRCFpaWq+8pHjlypXo1asXFixYAACQSCTIzMzEN998ozJzcr9+/TB16lQAwNy5c7Fq1SqcOnUKtra2TPsPqOoZKZVKX7qvHGIx29hjhBBCCCGEEEIIIeTd8tZ6EDo5OancNjMzQ0FB1SD4P//8M3r16gVzc3Po6upi7NixePToEUpLqwZZnT59OpYsWQJPT0+EhYXhyhW2Ae7d3d2V/zc0NIStrS2ysrKU94lEIpX6ZWVlQU1NTWWMQCMjoxrPe1lGRgZkMhkkEgl0dHSUy+nTp3HjRtWgqC96EL6JrKwseHp6qtzn6emJnJwcyGR/DW5efZteNGa+2OesIiMjoa+vr7Js2Lz59TaAEEIIIYQQQgghpC6coOmW99Rb23J1dXWV2xzHQS6XIzc3FwMGDICTkxP27duH1NRUrF+/HkDV7L0AMHHiRNy8eRNjx45FRkYG3NzcEBMT02B109TUbJDZgUtKSiAUCpGamoq0tDTlkpWVhTVr1ijX9bbUtc9fx7x581BUVKSyTK1lFmZCCCGEEEIIIYSQ98n69ethZWUFDQ0NdO3aFRcvXqwzu2XLFnh5eaFZs2Zo1qwZfHx8auQDAgKUc1O8WHx9fRt1G5q8aTQ1NRVyuRzR0dHo1q0bJBIJ7t27VyNnYWGBwMBA7N+/H7Nnz8aWLVt4r+PChQvK/z958gTXr1+HnZ1dnXk7OztUVlYiOTlZed+jR4+QnZ0Ne3t7AFW9Dqv31gMAV1dXyGQyFBQUKCdYebG8uGTYyckJCQkJda67tnJrq19SUpLKfUlJSZBIJBAKha987usSi8XQ09NTWejyYkIIIYQQQgghhDQ0BSdosoXVnj17MGvWLISFheHy5ctwdnZGnz596ryCMzExEZ9++ilOnTqF8+fPw8LCAh999BHu3r2rkvP19VXOT5Gfn4/vv//+tfYlX03eQGhtbY2KigrExMTg5s2b2LFjBzZt2qSSCQ4OxrFjx3Dr1i1cvnwZp06demUD38siIiKQkJCA3377DQEBAWjevDkGDx5cZ97GxgaDBg3CpEmTcPbsWaSnp2PMmDEwNzfHoEGDAFSNlVhSUoKEhAQ8fPgQpaWlkEgk8PPzg7+/P/bv349bt27h4sWLiIyMxOHDhwFU9cRLSUnB1KlTceXKFVy7dg0bN27Ew4cPleUmJycjNzcXDx8+rLXH3+zZs5GQkIDFixfj+vXriI+Px7p165QTpRBCCCGEEEIIIYSQxrdy5UpMmjQJ48aNg729PTZt2gQtLS1s27at1vyuXbswdepUuLi4oH379ti6dSvkcnmNzmRisRimpqbKpVmzZo26HU3eQOjs7IyVK1di+fLl6NChA3bt2oXIyEiVjEwmw7Rp02BnZwdfX19IJBKVST7qs2zZMsyYMQOdOnXC/fv38eOPP0IkenXvt9jYWHTq1AkDBgyAu7s7FAoFjhw5orxs18PDA4GBgRg5ciSMjY0RFRWlfJ6/vz9mz54NW1tbDB48GCkpKbC0tARQNaHI8ePHkZ6eji5dusDd3R0//PAD1NSq5osJCQmBUCiEvb09jI2NkZeXV6NuHTt2xN69e7F792506NABCxcuREREhMoEJYQQQgghhBBCCCGk8ZSXlyM1NRU+Pj7K+wQCAXx8fHD+/HleZZSWlqKiogKGhoYq9ycmJqJFixawtbXFlClT8OjRowat+8s4hUKhaIyCvb294eLigtWrVzdG8bwkJiaiZ8+eePLkCQwMDJqsHv9Ut3/PZsqLKsvYykcbprxD8Vn+YQHbpdjn1T9kylvqvN5kMHyZlNzgnX2i04qpbAXjeJziylKmvKjyOe/sA5EFU9lCvPry/JdJFWyXyVfK2SZ+v/FIn3dWwLEdih1b5DPl75QYM+VZ3sPlcrb92Pop20RTz7RbMOUvFHVgyrc3vM+UZ2H2LIcpX6LF9jpxCv7jyv54k22/jDI7zZTfc78HU764hG1MXIdR/K8caJmZVH+oGiHY6qIuqGDK61fyP5kTKNiOY4/UTJnyt4ubM+Xb6bN9PvQrHvLOPlYzYSpbzvi7toDxdX1Srsc7q6FWzlS2iPE9o8M9ZcprVJQw5VneN1rcM6ayn8gat3eDTM7/HM5Y/U+msjUr2Pb7LUU7tvLVpEx5Q/D/PP1RyXa+p6PGdl4u4Ng+T1KGcwN1rpKpbA0B/3NJADAvuMyUPyQfxJS3bl7EO6shZHsPsB4LWM/LWc6ZWuiwvWcMRGx1Zz3WFMn5n2cDQKty/n8/3RdbMZXN+jpJocGUb9+O7fP9T1GS/GOTrVvd5SNIpaqfV7FYDLFYXCN77949mJub49y5cyoT5IaGhuL06dMqQ9fVZerUqTh27BiuXr0KDY2q98fu3buhpaWFNm3a4MaNG/jqq6+go6OD8+fPN9rQcmx/7b4j4uLiEBwcjMLCwqauylvHcRwOHDjwykukqwsPD8fBgweRlpbW4HWRcWxvnzuMDX5tFL8z5W8068I7e/oa2x9Izm3YTkRKZWyT0egI2b4Qb2g41R/6Hw0F24mIadktpnyFGtu2/s7Z8s52KL5Qf6iacpEOU75Yk63hiROwNeLZaDKckDKOdfEn4+fJvfxnpvx9OPDO6gjYToqKdVsy5SsENb+IX+VD0S9Mea6Y7Q8TFupP+f9xBwCPddkaxYsq+Z8c+xkfYyr7trYLU350c7byn1sYMeXvMjT63bP3ZCp7/0KGH5gAfDGK8ccFgXr9of8RKNiOBax/UNnps+V1y9gaWA4+cK8/9D+DTOs/YX4TJRps7zGZiP/JtohjbCAE23dxiUKXKZ+vYGtsNeT4N2g8V7B9z1tU3mTK/ykyZ8orBPx/yGTdj8mP2Rr8Bj+t/dKxutyx9qk/VM39Cv7fl7rqbA1DaoyNchzYzoEEAv4NikKO7YcR1h8LftHoz5R31H3AlG/3xyne2fsWnZnKfqZgO7e9L2c7t7XQL+adFQsYj3uMx0nWY42WgK3B8lQx/33/8eNvmcq+1X4gU15Hzv8YTJpGZGQkFi1apHJfWFgYwsPDG3xdy5Ytw+7du5GYmKhsHASAUaNGKf/v6OgIJycntGvXDomJiejVq1eD1wN4By4xfhOBgYHQ0dGpdQkMDGzq6r2zli5dWud+69u3b1NXjxBCCCGEEEIIIe+xppykZN68eSgqKlJZ5s2bV2s9mzdvDqFQiAcPVH9cePDggXKy2rqsWLECy5Ytw/Hjx+Hk9OpOQG3btkXz5s3x++9sHalYNGoDoVwuR2hoKAwNDWFqaqrS2pqXl4dBgwZBR0cHenp6GDFihMoOTU9PR8+ePaGrqws9PT106tQJly5dQmJiIsaNG4eioiJs3rwZz549w/jx45GWlqayREREwNvbGwqFAgYGBtixYwfc3Nygq6sLU1NTjB49usaMMlevXsWAAQOgp6cHXV1deHl54caNv7oib9u2DQ4ODhCLxTAzM0NQUBDv7QkICKjR6y84OBje3t7K297e3pg+fXqd+8zKygoAMGTIEHAcp7xdl7i4OCxatAjp6enKabHj4uJw9epVdO7cWWV/paSkQCwWN1pLNCGEEEIIIYQQQsi7TiwWQ09PT2Wp7fJiABCJROjUqZPKBCMvJhypfsnxy6KiorB48WIcPXoUbm5u9dbpjz/+wKNHj2BmZsa+QTw1agNhfHw8tLW1kZycjKioKERERODEiROQy+UYNGgQHj9+jNOnT+PEiRO4efMmRo4cqXyun58fWrVqhZSUFKSmpuLLL7+Euro6PDw8sHr1aujp6Smnel66dCmsra1VlhYtVLtXV1RUYPHixUhPT8fBgweRm5urMqnH3bt30b17d4jFYpw8eRKpqakYP348Kiurut9v3LgR06ZNw+TJk5GRkYFDhw7B2toaAHhtz5vuMwBISUkBUDURSn5+vvJ2XUaOHInZs2fDwcFBua9GjhyJadOm4cyZM9DW1lbur2vXrkEqlVLPS0IIIYQQQgghhBCeZs2ahS1btiA+Ph5ZWVmYMmUKnj17hnHjxgEA/P39VXogLl++HAsWLMC2bdtgZWWF+/fv4/79+ygpqRpTuKSkBHPmzMGFCxeQm5uLhIQEDBo0CNbW1ujTp0+jbUejjkHo5OSEsLAwAICNjQ3WrVunbFXNyMjArVu3YGFRNd7S9u3b4eDggJSUFHTu3Bl5eXmYM2cO2rdvr3z+C/r6+uA4rt7umtWNHz9e+f+2bdti7dq16Ny5M0pKSqCjo4P169dDX18fu3fvVs5ULJFIlM9ZsmQJZs+ejRkzZijv69y5ahyDhISEerfnTfdZ7969YWxcNXi9gYEBr23X1NSEjo4O1NTUVPIeHh6wtbXFjh07EBoaCqCq0XH48OHQ0WEb54IQQgghhBBCCCGkQTFOntmURo4ciT///BMLFy7E/fv34eLigqNHj8LEpGqM4Ly8PAgEf/XP27hxI8rLy/HJJ5+olPNinEOhUIgrV64gPj4ehYWFaNmyJT766CMsXry4zp6MDaHRGwirMzMzQ0FBAbKysmBhYaFsTAMAe3t7GBgYICsrC507d8asWbMwceJE7NixAz4+Phg+fDjatWMbOLi61NRUhIeHIz09HU+ePIFcXjVwbl5eHuzt7ZGWlgYvLy9l42B1BQUFuHfvXp2X3/LZHr7q2mcNbeLEifj2228RGhqKBw8e4KeffsLJkyfrzEul0hqz+Eil0kZ9cxJCCCGEEEIIIYS864KCglSGoasuMTFR5XZubu4ry9LU1MSxY2wTADaERr3E+OXGNo7jlA1z9QkPD8fVq1fRv39/nDx5Evb29jhw4MBr1ePZs2fo06cP9PT0sGvXLqSkpCjLKi+vml1JU7PuWZNe9RhfAoEACoXq7F8VFRU1cm+yz1j4+/vj5s2bOH/+PHbu3Ik2bdrAy8urznxkZCT09fVVlk2bNjV4vQghhBBCCCGEEPKe4wRNt7ynmmTL7ezscOfOHdy5c0d5X2ZmJgoLC2Fvb6+8TyKRYObMmTh+/DiGDh2K2NhYAFWDQMpkMt7ru3btGh49eoRly5bBy8sL7du3r9Erz8nJCWfOnKm10U5XVxdWVlYqg06ybo+xsTHy8/NVnpeWlsZ7G15QV1dn2va69pWRkREGDx6M2NhYxMXFKa+Nr0tts/jQeIWEEEIIIYQQQgghf39N0kDo4+MDR0dH+Pn54fLly7h48SL8/f3Ro0cPuLm5oaysDEFBQUhMTMTt27eRlJSElJQU2NnZAaiazbekpAQJCQl4+PAhSktLX7k+S0tLiEQixMTE4ObNmzh06BAWL16skgkKCkJxcTFGjRqFS5cuIScnBzt27EB2djaAqh6N0dHRWLt2LXJycnD58mXExMTw2h4A+PDDD3Hp0iVs374dOTk5CAsLw2+//ca87140VN6/fx9Pnjzhlb916xbS0tLw8OFDlcuEJ06cqBxE87PPPntlOSyz+BBCCCGEEEIIIYSQv48maSDkOA4//PADmjVrhu7du8PHxwdt27bFnj17AABCoRCPHj2Cv78/JBIJRowYgb59+2LRokUAqibZCAwMxMiRI2FsbIyoqKhXrs/Y2BhxcXH497//DXt7eyxbtgwrVqxQyRgZGeHkyZMoKSlBjx490KlTJ2zZskV5ye9nn32G1atXY8OGDXBwcMCAAQOQk5PDa3sAoE+fPliwYAFCQ0PRuXNnPH36FP7+/sz7Ljo6GidOnICFhQVcXV3rzQ8bNgy+vr7o2bMnjI2N8f333ysf8/HxgZmZGfr06YOWLVsy14UQQgghhBBCCCGkoSk4rsmW9xWneHlgPPLeKCkpgbm5OWJjYzF06FDm59+8cYMp/7CiOVO+teJ3pny+miXv7OlrbHVxbvOcKa8nenWv1pfpCJ8x5YsrdXlnNYTS+kPVmD6/xZSvUGMbo/OWgv9kQx3KLjCVXS5im4W7WLMFU54D2+GyWXEeQ+Fsv9f8qduGKW/6+CpT/r6hA++sEJVMZavL2d6TFQK23sqGT+/UH6qGk7PVn4X604dM+XyLLkz5okp93lnrp5eYyr6t78KUb12UxpR/rmnElL+rbsU7e8/ek6ns/QvPMuW/GMUUhyH4vw8ECv7DiABAhVCDrTKMdMv+ZMoffODOOzvINJm1OkxKNNjeYyUK/t+tIq6cqWwR2I57LHUBgKcVWkx5Q1ER76xcwfb91Lz8HlP+T5E5U14B/n+4sWQB4GoB23nB4KfbmPJ3rH2Y8k8qDHhnNdXYzlXVOLbvPtZzIJb3jZBjO+6xvq63ik2Y8ha6j5jy7f44xTt734L/5JUA8EzBdm7LeixQE/Af614sYDvuaQjY3pOVCrb5U1nfN+kF/I81Hz/+lqnsW+0HMuV15PyPwQBgYWNff+gfqDj17U/S8YJepz5Ntu6m1Gg9CL29vREcHNxYxZM3IJfLUVBQgMWLF8PAwAAff/xxU1eJEEIIIYQQQgghpApNUvLWsTXTvyPi4uIQHByMwsJCAMCZM2fQt2/fOvMlJSVvqWaNj+M4HDhwAIMHDwYAODg44Pbt27VmN2/ejJycHBw8eFBlQpS8vDy0adMGrVq1QlxcHNTUXu9tUKrQZspb4iZT/rk62y9mNn8m8c5a32HrmQZjD6Z4mZ4pU75AYMGUty79lXf2nm57prIzOSemvFDO9otyK9Fd3tnf0I2pbLGw5iRDr6LO2HOsUiFkyl8o5d9bklUHnYL6Q9X8+2k/pryH4R+8s+oKtp4x9+VsQxo8LGE71jx9zta7sjGvInjOeILRQcbW49BUwf/z9N+yj5jK7mTA/z0AAIefs5WvreDfawEArPT59+hg7RE4NOIDprzxyP8w5XPKrXln84vYegQ6mdxnyrN6KtZjyo/K/pJ39nzzRUxlG2qy9bbXYDw26Sse885KObZeOuVg6wmth0KmfAFDb2IAEIsYevYwHiPzhGzffcZg+z5j6T3G2uvt45zNTPk404VM+V6o/Zy9LoVS/ldpmKixHQvKwXaskTP2KxEz9B6TsfYcY7xyoefDXUz5x1odmfIH1Ph3K+8Etu9WXbD1NLtXbsiUd1VP4519LmD726ycY3uPqYOthyLHeB4x8PelvLNbjb5mKrtrJdvrVClke8+z/aVIyOv7WzYQvszNze21ZgT+Jzhy5EitMy8DgImJCaKjo2vcb2VlBbqynBBCCCGEEEIIIYQAjTxJiVwuR2hoKAwNDWFqaorw8HDlY3l5eRg0aBB0dHSgp6eHESNG4MGDB8rH09PT0bNnT+jq6kJPTw+dOnXCpUuXkJiYiHHjxqGoqAgcx4HjOCxfvhzW1tZ1LgCwY8cOuLm5QVdXF6amphg9ejQKClR/sbx69SoGDBgAPT096OrqwsvLCzeqjbO3bds2ODg4QCwWw8zMDEFBQby3JyAgQNnr74Xg4GB4e3srb3t7e2P69Ol17jMrKysAwJAhQ8BxHKysrNC6des6t3vfvn1YtGgR0tPTlfsqLi4O48ePx4ABA1TqUlFRgRYtWuD//u//eL22hBBCCCGEEEIIIY1BAa7JlvdVozYQxsfHQ1tbG8nJyYiKikJERAROnDgBuVyOQYMG4fHjxzh9+jROnDiBmzdvYuTIkcrn+vn5oVWrVkhJSUFqaiq+/PJLqKurw8PDA6tXr4aenh7y8/ORn5+PkJCQeutSUVGBxYsXIz09HQcPHkRubi4CAgKUj9+9exfdu3eHWCzGyZMnkZqaivHjx6OysqoL+8aNGzFt2jRMnjwZGRkZOHTokLLxkc/2vOk+A4CUlBQAQGxsLPLz85W36zJy5EjMnj0bDg4Oyn01cuRITJw4EUePHkV+fr4y+9///helpaWvVWdCCCGEEEIIIYQQ8vfVqJcYOzk5ISwsDABgY2ODdevWISEhAQCQkZGBW7duwcKi6or67du3w8HBASkpKejcuTPy8vIwZ84ctG/fXvn8F/T19cFxHExN+Y/zNn78eOX/27Zti7Vr16Jz584oKSmBjo4O1q9fD319fezevRvq6uoAAIlEonzOkiVLMHv2bMyYMUN5X+fOVbNQJSQk1Ls9b7rPevfuDWNjYwCAgYEBr23X1NSEjo4O1NTUVPIeHh6wtbXFjh07EBoaCqCq0XH48OHQ0WEbW4IQQgghhBBCCCGkISne48lCmkqj7nEnJ9XJDszMzFBQUICsrCxYWFgoG9MAwN7eHgYGBsjKygIAzJo1CxMnToSPjw+WLVumcqnv60hNTcXAgQNhaWkJXV1d9OjRA0DVpcEAkJaWBi8vL2XjYHUFBQW4d+8eevXqVWvZfLaHr7r2WUObOHEiYmNjAQAPHjzATz/9pNKI+jKpVIri4mKVpVzKNvg3IYQQQgghhBBCCHn3NGoD4cuNbRzHQS7nN9tQeHg4rl69iv79++PkyZOwt7fHgQMHXqsez549Q58+faCnp4ddu3YhJSVFWVZ5edVsSZqadc8S9qrH+BIIBDUmBqltcpE32Wcs/P39cfPmTZw/fx47d+5EmzZt4OXlVWc+MjIS+vr6KsvWzTENXi9CCCGEEEIIIYQQ8nY1SZ9NOzs73LlzB3fu3FHel5mZicLCQtjb2yvvk0gkmDlzJo4fP46hQ4cqe7yJRCLIZDLe67t27RoePXqEZcuWwcvLC+3bt6/RK8/JyQlnzpyptdFOV1cXVlZWysujX2d7jI2NVcb8A/BaMy+rq6szbXtd+8rIyAiDBw9GbGws4uLiMG7cuFeWM2/ePBQVFaksEz//grn+hBBCCCGEEEIIIa/ECZpueU81yZb7+PjA0dERfn5+uHz5Mi5evAh/f3/06NEDbm5uKCsrQ1BQEBITE3H79m0kJSUhJSUFdnZ2AKpm8y0pKUFCQgIePnyI0tLSV67P0tISIpEIMTExuHnzJg4dOoTFixerZIKCglBcXIxRo0bh0qVLyMnJwY4dO5CdnQ2gqkdjdHQ0/p+9f4+K6toXvO/vKu5KFQiygR1u2ygIHDlshHMeMYCJnESjicaOBVrjMXhJ4s6hbeOQvDFJb4n0iGyGOmxM4rZ9346XgfGWJ2h6EBtbHgzqjgIRkhjFGxcTLwQNkBIEoer9g5NKSkFrKohJfp8x1lCqfjXnXJdaa9Vc85Kfn8+ZM2f44osvWLdunUPrA/DEE09QUVHBli1bOHPmDMuXL+frr79W3nY/VVRevnyZH374waH42tpaqqqqaGpqouMX3YIXLFjA5s2bOXnyJC+88MId03Fzc8NgMNgtrm5uyuUXQgghhBBCCCGEEA+XQakg1DSNPXv2MGzYMJKTk0lNTWXEiBHs2LEDACcnJ65evcqcOXMIDw/HaDQyefJk3n77baBnko2FCxeSlpaGn58feXl5d8zPz8+PTZs2sWvXLqKiosjNzWXVqlV2Mb6+vpSUlGA2m0lJSWHs2LFs3LjR1uX3hRdeYO3atbz//vtER0czdepUzpw549D6ADz11FP81//6X3nttddISEjgxx9/ZM6cOcrbbvXq1ezfv5/g4GD+/Oc/3zX+P/2n/8SkSZN4/PHH8fPz48MPP7S9l5qaSmBgIE899RR//OMflcsihBBCCCGEEEII0d+smjZoy+/VgM1iXFpaettrhYWFtv+HhISwZ8+eXj/r6upqV5HVm/Xr17N+/XqHyzNr1ixmzZpl99qtYwLGxMTwv//3/+4zjZdffpmXX3651/futD4/efvtt22VnL252zYDeOaZZ3jmmWfumM8vubm5sXv37l7fu379Oj/88APz5893OD0hhBBCCCGEEEII8dvSry0IJ0yYwOLFi/szyYdOXV0dmqbd0/iBDwuLxUJjYyM5OTl4e3vz7LPPDnaRhBBCCCGEEEIIIcQg0ay3NqO7DxMmTCA2Npa1a9f2V5I2mzZtYvHixTQ3N9/2XllZGZMnT+7zs2az+Z7yzMjIoLm52a4VX3d3N99//z3Dhw/H2XnAGmA6LDo6mvr6+l7f27BhAyaT6bbX6+rq+NOf/kRQUBCbNm1i4sSJ95R37bmzSvGaVW02ZjMGpXgdjqfvrHUppX3D4q4U76Q5PpEMgLuuXSnetfuGw7GXuh9RStvP5XuleIviIK4/3BzmcOwQJ7XtomlqpzMn1PaTqptWl7sH/Qcrak3ZVY+x611DlOI9na87HOthdTwW4Ee8lOJV19ViVTsmOyyuSvEquq1OSvGB2rdK8R3Oju/XDsXzWOVFtaEnIv3vPi7uLw3k91t1n/pZLyvFV0U/rxQfcvIzpXgVrlqnUnyHRW38YJ2mdu3utDh+3nPTqZVdQ/Ecr3juUD0PD2TaquvaZVW7J1XZNqplUaW6bf74/+Q6HHujqVkp7e/mr1WKV+Xh5Pj9G4DV6vi2Uf2uutJx96BflkWxy51Lt+Ppt+n0SmmrUl3XbsVOdjesjl9fVX+DDPS5QOX3k+oxNtDnvbCvC5Xizypcu9u71e6Z9M5q9Q2q2ybyUbXfc78V174sG7S8fWKSBi3vwTT4NVz9ID4+/oG16HNyciIgIOCB5OWIoqKiXmdeBvD39+/19bCwsNu6VwshhBBCCCGEEEKI36d+n6TEYrHw2muv4ePjQ0BAANnZ2bb3GhoamDZtGp6enhgMBoxGI1euXLG9X11dzeOPP45er8dgMDB27FgqKiooLS1l7ty5tLS0oGkamqbZpevh4cHIkSNvW/7xj3+Qnp6OXq8nICCA2bNn09jYaFfeEydOMHXqVAwGA3q9nqSkJM6dO0d2djabN29mz549tjxLS0vtuhhbLBaCgoJuGwvx+PHj6HQ6W8u+5uZmFixYgJ+fHwaDgSeeeILq6mqHtmd2djaxsbFs2LCB4OBghgwZgtFopKWlBYDQ0FD+23/7byxdupSdO3cyfvx44uPj2bZtGx4eHmRlZeHj40NQUBAffPCBLd3Ozk4yMzMJDAzE3d2d0NBQVq5c6VCZhBBCCCGEEEIIIQaMpg3e8jvV7xWEmzdvZujQoRw9epS8vDxWrFjB/v37sVgsTJs2jWvXrnHw4EH279/P+fPnSUtLs33WZDIRFBREeXk5lZWVvP7667i4uJCYmMjatWsxGAxcunSJS5cusXTp0ruW5ebNm+Tk5FBdXU1hYSF1dXVkZGTY3v/uu+9ITk7Gzc2NkpISKisrmTdvHl1dXSxduhSj0cikSZNseSYmJtqlr9PpmDVrFtu2bbN7vaCggPHjxxMaGgrAzJkzaWxs5NNPP6WyspK4uDgmTpzItWvXHNqmZ8+eZefOnXzyySfs27eP48eP88orr9jFlJSUcPHiRT777DPWrFnD8uXLmTp1KsOGDePo0aMsXLiQl19+mW+/7em+lp+fz969e9m5cyc1NTUUFBQQFhbmUHmEEEIIIYQQQgghxG9Hv3cxjomJYfny5QCMGjWKd999lwMHDgDw1VdfUVtbS3BwMABbtmwhOjqa8vJyEhISaGhoICsri9GjR9s+/xMvLy80TVPq3jtv3jzb/0eMGEF+fj4JCQmYzWY8PT1577338PLyYvv27bi49IyZEx4ebvuMh4cHHR0dd8zTZDKxevVqGhoaCAkJwWKxsH37dt566y0ADh06xLFjx2hsbMTNrWfcn1WrVlFYWMju3bt56aWX7roeN27cYMuWLTzySM/YA+vWrWPKlCmsXr3aVjYfHx/y8/PR6XRERESQl5dHW1sbb7zxBgDLli0jNzeXQ4cOkZ6eTkNDA6NGjeKxxx5D0zRbZaYQQgghhBBCCCGE+H3p9xaEMTExdn8HBgbS2NjIyZMnCQ4OtlUOAkRFReHt7c3JkycBWLJkCQsWLCA1NZXc3FzOnTt3X2WprKzkmWeeISQkBL1eT0pKCtDT1RmgqqqKpKQkW+XgvYiNjSUyMtLWivDgwYM0NjYyc+ZMoKfbtNlsxtfXF09PT9tSW1vr8PqFhITYKgcBxo0bh8VioaamxvZadHQ0Ot3Pu9Pf358xY8bY/nZycsLX19fWxTojI4OqqioiIiJYtGgRxcXFdyxDR0cHra2tdktHh9qAv0IIIYQQQgghhBB3Y9V0g7b8XvX7mt9a2aZpGhaLYzMeZWdnc+LECaZMmUJJSQlRUVF8/PHH91SO69ev89RTT2EwGCgoKKC8vNyWVmdnz6x5Hh4e95T2rUwmk62CcNu2bUyaNAlfX1+gZwblwMBAqqqq7JaamhqysrL6JX/ofbvfaV/ExcVRW1tLTk4O7e3tGI1Gnn++75mdVq5ciZeXl92y/u8b+q38QgghhBBCCCGEEGJwPLCq0cjISC5cuMCFCxdsr33zzTc0NzcTFRVley08PJxXX32V4uJiZsyYYZtYw9XVle7ubofzO3XqFFevXiU3N5ekpCRGjx592wQlMTExlJWV9TkLsKN5zp49m6+//prKykp2796NyWSyvRcXF8fly5dxdna+bRKV4cOHO7QuDQ0NXLx40fb3559/butKfD8MBgNpaWls3LiRHTt28NFHH/U5LuKyZctoaWmxW/6y8OX7yl8IIYQQQgghhBDiVla0QVt+rx5YBWFqaipjxozBZDLxxRdfcOzYMebMmUNKSgrx8fG0t7eTmZlJaWkp9fX1HD58mPLyciIjIwEICwvDbDZz4MABmpqaaGtru2N+ISEhuLq6sm7dOs6fP8/evXvJycmxi8nMzKS1tZX09HQqKio4c+YMW7dutXXdDQsL48svv6SmpoampqY+KxLDwsJITExk/vz5dHd38+yzz9qt97hx45g+fTrFxcXU1dVx5MgR3nzzTSoqKhzadu7u7rzwwgtUV1dTVlbGokWLMBqNSuMx3mrNmjV8+OGHnDp1itOnT7Nr1y4CAgLw9vbuNd7NzQ2DwWC3/DSmohBCCCGEEEIIIYT49XpgFYSaprFnzx6GDRtGcnIyqampjBgxgh07dgA9Y+RdvXqVOXPmEB4ejtFoZPLkybz99tsAJCYmsnDhQtLS0vDz8yMvL++O+fn5+bFp0yZ27dpFVFQUubm5rFq1yi7G19eXkpISzGYzKSkpjB07lo0bN9q65r744otEREQQHx+Pn58fhw8f7jM/k8lEdXU1zz33nF3XZU3TKCoqIjk5mblz5xIeHk56ejr19fX4+/s7tO1GjhzJjBkzePrpp3nyySeJiYnh/fffd+izfdHr9eTl5REfH09CQgJ1dXUUFRXZjWMohBBCCCGEEEIIIX77NKvVah3sQoi+ZWdnU1hYSFVV1WAX5Ta1584qxWtWx8ai/IkZg1K8DsfTd9a6lNK+YXFXinfSHO8OD+Cua1eKd+2+4XDspe5H7h70C34u3yvFWxQHcf3h5jCHY4c4qW0XTVM7nTmhtp9U3bQ6PgGSalN21WPsetcQpXhP5+sOx3pYHY8F+BEvpXjVdbVY1Y7JDourUryKbquTUnyg9q1SfIez4/u1Q/E8Vnnxj0rxkf4/KMUP5PdbdZ/6WS8rxVdF9z1mb29CTn6mFK/CVetUiu+wqLX+12lq1+5Oi+PnPTedWtk1FM/xiueOgexSpJq26rp2WZ2V4lW2jWpZVKlumz/+P7kOx95oalZK+7v5a5XiVXk4OX7/BmC1Or5tVL+rrqhNNmjV1PaTS7fj6bfp9Eppq1Jd127Uvk83rI5fX1V/gwz0uUDl95PqMTbQ572wrwuV4s8qXLvbu9XumfTOZqV41W0T+aja77nfiu9PHB20vP2i/3XQ8h5M/dpcbMKECSxevLg/k3zo1NXVoWnaQ1lhJ4QQQgghhBBCCCGEKrVHDINo06ZNLF68mObmZgDKysqYPHlyn/Fms1otfm8yMjJobm6msLDQ9lpwcDCXLl1yeIKRu4mOjqa+vr7X9zZs+G3NEtyuDVWKd2bgnrDVmdXGbwz1vKIUr9xCQ7F15RVLoMOxehe178LQjmaleItOrYXUD5rjLQhVn66pthzztLYoxXfo1GY+D2xvUIpX0TgkTCl+RMfXSvFNzqEOx3bpHG8xBOBzs/HuQb/g3XRGKb7NS63lW7PHvY/nejduFrVWch1Oai09O62On2sC2s8rpZ0YpNbSxbelTin+3NBYpfhAq+OtK1WPyTOdI5XiVVsENkQmOxzrWXVcKe0/uKu13GxsV2vBO8RFrZXf5R8dv9YHGX5USttZp3ZfoNpKVaXljUqrGwAvy1Wl+Gad2n2maqtZLyfHr383rGrXPtVWQENROw6+fe4NpXgVN26qbcdRlm+U4n90UtuvrVbHe9M4K/aKUO11odqQtENzvAWWags/lWsfQMMNtfsCbze1e+dHm8sdjm31ClZKW/VcoNKKG9SOAxfF32YeujvPGXCrdovaPVDtPz2nFP9jp+PpR9ysVkr7kpPafYRqC/ffLcWWy+L+/WoqCG8VHx8/KK34nJyc7mtykFsVFRX1OfmJv78/er2e7OzsfstPCCGEEEIIIYQQQohf6vcZKSwWC6+99ho+Pj4EBATYVW41NDQwbdo0PD09MRgMGI1Grlz5uWVWdXU1jz/+OHq9HoPBwNixY6moqKC0tJS5c+fS0tKCpmlomsbf/vY3Ro4c2ecCsHXrVuLj49Hr9QQEBDB79mwaG+1brpw4cYKpU6diMBjQ6/UkJSVx7tw5srOz2bx5M3v27LHlWVpaatfF2GKxEBQUxPr16+3SPH78ODqdztYysLm5mQULFuDn54fBYOCJJ56gurrnqURoaGif6/BT5WBsbCwbNmwgODiYIUOGYDQaaWn5+clvRkYG06dP55133sHf3x9vb29WrFhBV1cXWVlZ+Pj4EBQUxAcffGD7TGdnJ5mZmQQGBuLu7k5oaCgrV67sn4NACCGEEEIIIYQQQvxq9HsF4ebNmxk6dChHjx4lLy+PFStWsH//fiwWC9OmTePatWscPHiQ/fv3c/78edLS0myfNZlMBAUFUV5eTmVlJa+//jouLi4kJiaydu1aDAYDly5d4tKlSyxduvSuZbl58yY5OTlUV1dTWFhIXV0dGRkZtve/++47kpOTcXNzo6SkhMrKSubNm0dXVxdLly7FaDQyadIkW56JiYl26et0OmbNmsW2bdvsXi8oKGD8+PGEhvZ00Zs5cyaNjY18+umnVFZWEhcXx8SJE7l27ZpD2/Ts2bPs3LmTTz75hH379nH8+HFeeeUVu5iSkhIuXrzIZ599xpo1a1i+fDlTp05l2LBhHD16lIULF/Lyyy/z7bc9XbTy8/PZu3cvO3fupKamhoKCAsLCwhwqjxBCCCGEEEIIIcRAsaIbtOX3qt+7GMfExLB8+XIARo0axbvvvsuBAwcA+Oqrr6itrSU4uGfshS1bthAdHU15eTkJCQk0NDSQlZXF6NGjbZ//iZeXF5qmKXXvnTdvnu3/I0aMID8/n4SEBMxmM56enrz33nt4eXmxfft2XFx6xmsIDw+3fcbDw4OOjo475mkymVi9ejUNDQ2EhIRgsVjYvn07b731FgCHDh3i2LFjNDY24ubWM17GqlWrKCwsZPfu3bz00kt3XY8bN26wZcsWHnmkZ/aidevWMWXKFFavXm0rm4+PD/n5+eh0OiIiIsjLy6OtrY033ugZp2XZsmXk5uZy6NAh0tPTaWhoYNSoUTz22GNommarzBRCCCGEEEIIIYQQvy/9XjUaExNj93dgYCCNjY2cPHmS4OBgW+UgQFRUFN7e3pw8eRKAJUuWsGDBAlJTU8nNzeXcuXP3VZbKykqeeeYZQkJC0Ov1pKSkAD1dnQGqqqpISkqyVQ7ei9jYWCIjI22tCA8ePEhjYyMzZ84EerpNm81mfH198fT0tC21tbUOr19ISIitchBg3LhxWCwWampqbK9FR0ej0/28O/39/RkzZoztbycnJ3x9fW1drDMyMqiqqiIiIoJFixZRXFx8xzJ0dHTQ2tpqt3R0qA0oLIQQQgghhBBCCHE3Vk0btOX3qt8rCG+tbNM0DYvFsZnesrOzOXHiBFOmTKGkpISoqCg+/vjjeyrH9evXeeqppzAYDBQUFFBeXm5Lq7OzZ0Y+Dw+1Wdn6YjKZbBWE27ZtY9KkSfj6+gI9sykHBgZSVVVlt9TU1JCVldUv+UPv2/1O+yIuLo7a2lpycnJob2/HaDTy/PPP95n+ypUr8fLyslvW//23NcuyEEIIIYQQQgghxO/RA+tcHRkZyYULF7hw4YLttW+++Ybm5maioqJsr4WHh/Pqq69SXFzMjBkzbBNruLq60t3t+HTgp06d4urVq+Tm5pKUlMTo0aNvm6AkJiaGsrKyPmcRdjTP2bNn8/XXX1NZWcnu3bsxmUy29+Li4rh8+TLOzs63TUIyfLhj09Y3NDRw8eJF29+ff/65rSvx/TAYDKSlpbFx40Z27NjBRx991Oe4iMuWLaOlpcVu+cvCl+8rfyGEEEIIIYQQQggx+B5YBWFqaipjxozBZDLxxRdfcOzYMebMmUNKSgrx8fG0t7eTmZlJaWkp9fX1HD58mPLyciIjIwEICwvDbDZz4MABmpqaaGtru2N+ISEhuLq6sm7dOs6fP8/evXvJycmxi8nMzKS1tZX09HQqKio4c+YMW7dutXXdDQsL48svv6SmpoampqY+KxLDwsJITExk/vz5dHd38+yzz9qt97hx45g+fTrFxcXU1dVx5MgR3nzzTSoqKhzadu7u7rzwwgtUV1dTVlbGokWLMBqNSuMx3mrNmjV8+OGHnDp1itOnT7Nr1y4CAgLw9vbuNd7NzQ2DwWC3/DSmohBCCCGEEEIIIUR/sWq6QVt+rx7Ymmuaxp49exg2bBjJycmkpqYyYsQIduzYAfSMkXf16lXmzJlDeHg4RqORyZMn8/bbbwOQmJjIwoULSUtLw8/Pj7y8vDvm5+fnx6ZNm9i1axdRUVHk5uayatUquxhfX19KSkowm82kpKQwduxYNm7caOua++KLLxIREUF8fDx+fn4cPny4z/xMJhPV1dU899xzdl2XNU2jqKiI5ORk5s6dS3h4OOnp6dTX1+Pv7+/Qths5ciQzZszg6aef5sknnyQmJob333/foc/2Ra/Xk5eXR3x8PAkJCdTV1VFUVGQ3jqEQQgghhBBCCCGE+O3TrFartb8SmzBhArGxsaxdu7a/knzo1NXV8ac//Ynjx48TGxs74PllZ2dTWFhIVVXVgOelqvbcWaX4G6iN+aihdmiqxNebHauc/Umo5xWl+A6LWutKva5VKf5ql6/DsUOd79za9la+HZeU4i06J6X4b7Uwh2M9nG4opW1FbUBZL2vvXer70qFTO4Z92r5TilfROCRMKf4P12uV4puGOj6zuZPWpZS2x02zUrx30xml+DavPyrFN3vce2vsu3GztCvFdzq5q8VbHT/X+LepHQM/DFXbjr4tdUrx54bGKsUHWr91OLZLpzb52PmOMKV4X3e1c3ZDZLLDsZ5Vx5XS/oP7D0rx3113bHiTnwxx6VSKv/zjUIdjgww/KqXtrFM71wxxUvv+dVmdHY7V4dj42j/xslxVim/Wqe2nG4r3HV5OLY6nbR3Y+7ehqB0H19ErxatouempFD/K8o1S/I/uavu11WJwONZZc3woJgAXrfeeUf2l2+r4/aGrpjbxocq1D6DxxjCleG83tfuUR5vLHY5t9Qq+e9AvqJ4LOiyuSvGa5vj31UX1fk+n9huk3TJEKd5J8Zhv7nT83BFxs1op7UtDRirFq5Z99KNBSvG/FZdOVQ1a3oGjYwct78Hk+J3QINu0aROLFy+mubn5geWZkZFBc3MzhYWFtteCg4O5dOmSw+MH/pb90K12sXXTqf3IUL3BbDA7vk9udqu1lLxyw0cp3tNFrWLrulXthlSFG2plcW9X+xGDYhNsg5fjlZseFrUbNIumVln5o+atFK/6g7BbsZJCxRCL2g8qVSrfvy6r2nqqzgxW65+oFD/85sW7B/3CHy86NtzDvbC4qlX41QwbrxTvoVP7fqto7FC7znkqVlSNMlcqxbcNcbw8OqvaeelSi9p+Uq0gVKn0M8f+WSlt/1MHlOKdNLXzmOq1WKfwY1Plh+m9xKtSXVcV13R/UIoP/vGEUrz7KccrKADOJGQ4HKtakaRSMQTQhto9kMp+Un1w2GhWq6DwHqZW2WNRPDeN/uIDh2PPjf2/1cqi2JFM9R5I5fvaYVU7BzsrVlSpnjtU99O3Pv/scKzKgwhQ3+6RR9crxZ/6vxYqxatoszj+wAjUzzXOqMV/f93x8vgOc/xBOYDq5UO17EI8KL+aCsJblZWVMXny5D7fN5vVKhYc5eTkdF9j/90qOjqa+vr6Xt/bsEFmCRZCCCGEEEIIIYQQA6vfB5yzWCy89tpr+Pj4EBAQQHZ2tu29hoYGpk2bhqenJwaDAaPRyJUrP3fdrK6u5vHHH0ev12MwGBg7diwVFRWUlpYyd+5cWlpa0DQNTdPYt28fVVVVfS4AW7duJT4+Hr1eT0BAALNnz75tJuMTJ04wdepUDAYDer2epKQkzp07R3Z2Nps3b2bPnj22PEtLS6mrq0PTNKqqqrBYLAQFBbF+vf2TmuPHj6PT6WwVf83NzSxYsAA/Pz8MBgNPPPEE1dU9zZaLior6XIdfTnayYcMGgoODGTJkCEajkZaWn7uGZGRkMH36dN555x38/f3x9vZmxYoVdHV1kZWVhY+PD0FBQbYZoQE6OzvJzMwkMDAQd3d3QkNDWbly5f3tfCGEEEIIIYQQQoj7JJOUPHj93oJw8+bNLFmyhKNHj/KPf/yDjIwMxo8fz8SJE22VgwcPHqSrq4t///d/Jy0tjdLSUqBnoo8///nPrF+/HicnJ6qqqnBxcSExMZG1a9fy17/+1TbDsKenJ56ed+6ScPPmTXJycoiIiKCxsZElS5aQkZFBUVERAN999x3JyclMmDCBkpISDAYDhw8fpquri6VLl3Ly5ElaW1ttFWs+Pj5cvPhz1zWdTsesWbPYtm0bf/nLX2yvFxQUMH78eEJDe5omz5w5Ew8PDz799FO8vLzYsGEDEydO5PTp07aYOzl79iw7d+7kk08+obW1lfnz5/PKK69QUFBgiykpKSEoKIjPPvuMw4cPM3/+fI4cOUJycjJHjx5lx44dvPzyy/zbv/0bQUFB5Ofns3fvXnbu3ElISAgXLlzgwoULDuxhIYQQQgghhBBCCPFb0u8VhDExMSxfvhyAUaNG8e6773LgQM/4OF999RW1tbUEB/eM17Flyxaio6MpLy8nISGBhoYGsrKyGD16tO3zP/Hy8kLTNKXuvfPmzbP9f8SIEeTn55OQkIDZbMbT05P33nsPLy8vtm/fbpu5ODw83PYZDw8POjo67pinyWRi9erVNDQ0EBISgsViYfv27bz11lsAHDp0iGPHjtHY2IibW8+AuqtWraKwsJDdu3fz0ksv3XU9bty4wZYtW3jkkUcAWLduHVOmTGH16tW2svn4+JCfn49OpyMiIoK8vDza2tp44403AFi2bBm5ubkcOnSI9PR0GhoaGDVqFI899hiapjlUUSmEEEIIIYQQQggx0FTHLRf3r9/bTsbExNj9HRgYSGNjIydPniQ4ONhWOQgQFRWFt7c3J0+eBGDJkiUsWLCA1NRUcnNzOXfu3H2VpbKykmeeeYaQkBD0ej0pKSlAT1dngKqqKpKSkmyVg/ciNjaWyMhItm3bBsDBgwdpbGxk5syZQE+3abPZjK+vr63Vo6enJ7W1tQ6vX0hIiK1yEGDcuHFYLBZba0roGctQp/t5d/r7+zNmzBjb305OTvj6+tq6WGdkZFBVVUVERASLFi2iuLj4jmXo6OigtbXVbunsVJtxTAghhBBCCCGEEEI8fPq9gvDWyjZN07BYHJt9KTs7mxMnTjBlyhRKSkqIiori448/vqdyXL9+naeeegqDwUBBQQHl5eW2tDo7e2bT9fDwuKe0b2UymWwVhNu2bWPSpEn4+vbM1Go2mwkMDLxtfMGamhqysrL6JX/ofbvfaV/ExcVRW1tLTk4O7e3tGI1Gnn/++T7TX7lyJV5eXnbLBxvW9lv5hRBCCCGEEEIIIcTgeGCjL0ZGRt42zt0333xDc3MzUVFRttfCw8N59dVXKS4uZsaMGbbx/1xdXenu7nY4v1OnTnH16lVyc3NJSkpi9OjRt01QEhMTQ1lZGTdv9j7NuKN5zp49m6+//prKykp2796NyWSyvRcXF8fly5dxdnZm5MiRdsvw4cMdWpeGhga7sQ8///xzW1fi+2EwGEhLS2Pjxo3s2LGDjz76iGvXrvUau2zZMlpaWuyWuS8vvq/8hRBCCCGEEEIIIW5lRRu05ffqgVUQpqamMmbMGEwmE1988QXHjh1jzpw5pKSkEB8fT3t7O5mZmZSWllJfX8/hw4cpLy8nMjISgLCwMMxmMwcOHKCpqYm2trY75hcSEoKrqyvr1q3j/Pnz7N27l5ycHLuYzMxMWltbSU9Pp6KigjNnzrB161Zb192wsDC+/PJLampqaGpq6rMiMSwsjMTERObPn093d7fd7MOpqamMGzeO6dOnU1xcTF1dHUeOHOHNN9+koqLCoW3n7u7OCy+8QHV1NWVlZSxatAij0ag0HuOt1qxZw4cffsipU6c4ffo0u3btIiAgAG9v717j3dzcMBgMdourq9s95y+EEEIIIYQQQgghHg4PrIJQ0zT27NnDsGHDSE5OJjU1lREjRrBjxw6gZ4y8q1evMmfOHMLDwzEajUyePJm3334bgMTERBYuXEhaWhp+fn7k5eXdMT8/Pz82bdrErl27iIqKIjc3l1WrVtnF+Pr6UlJSgtlsJiUlhbFjx7Jx40Zb19wXX3yRiIgI4uPj8fPz4/Dhw33mZzKZqK6u5rnnnrPruqxpGkVFRSQnJzN37lzCw8NJT0+nvr4ef39/h7bdyJEjmTFjBk8//TRPPvkkMTExvP/++w59ti96vZ68vDzi4+NJSEigrq6OoqIiu3EMhRBCCCGEEEIIIR40q6YbtOX3SrNardbBLoToW3Z2NoWFhVRVVQ12UW7zxemrSvFuuk6leA21Q7PB7FiXbYCb3Wpfei93tQlZPF1uKMW76npvndqXG92Ot970ce6923hfhjefVYpH8QR62cvxrvEeFrNS2hbNSSn+OnqleJ3m2HiqP/G98Z1SvIp2V4NSvGd7k1L81aEhDsdaFJ81eXY3K8U36xz/bgMMv3nx7kG/oG86rxSvwuLqrhRfM2y8UryHzvFzTUC72nrWOMfcPegXRnBGKd6jXe3c1DbE8eOgS6c2+VjZ99FK8f/k33j3oF+41uH4ucYc+2eltB89dUAp/lKbr1K8u7Pa9emKeajDsY8YflRK21nXpRSv8v0A6LY6fg1RvUfpRu36FPzjCaV491PlSvFnEjIcjnXR1I4Ble0I6ttShWo3sTM//EEpPnzYFaV41evlyMotDseeG/t/K6Wtek+jQy1eZV0tVrXt4qypnQsu31A77+ld2pXiPZwcP9d0WZ2V0lbd7iP+8f9Tij/1fy10ONZFcburfv9UzzXOqMV/84Pj97ajh6ndw9+0qt13uGlq16eRj/5JKf634sKZbwYt7+BRUXcP+g3q16rRCRMmsHjx4v5M8qFTV1eHpmkPZYWdEEIIIYQQQgghhBCq+rUF4YQJE4iNjWXt2rX9laTNpk2bWLx4Mc3NzQCUlZUxefLkPuPNZrWWR73JyMigubmZwsJC22vd3d18//33DB8+HGdntSdAvYmOjqa+vr7X9zZs2MCZM2ce2haEp859qxSv+hRJ9YmyyhM51adxmqZWFtWyD/S2UeGiqbX0VNVpdbz140Cu571QLY/qcaZCtTWKE45P8gTqrRxUqG5H1bKorqvqtlSh2ipiIM81qsfjQJ+XdIr7qcPqeGvMIdp1pbTbrI63egP11isqx7Dqdjw3eqJS/IhTJUrxA3k9G+hz/MN0DRno79PDdB+h6mEqu+r1xgW1e6Zu1H47PEzfp18z1f2qui1VWrJ1odbSbKDPBSoetvOSavoq8a6aWu+xLsUWhKoiHg0e0PQfVg1nTg5a3iGjIgct78F0/zVcgyQ+Pn5QKs2cnJzua3KQWxUVFfU5+Ym/vz96vZ7s7Ox+y08IIYQQQgghhBBCiF/q9yYiFouF1157DR8fHwICAuwqtxoaGpg2bRqenp4YDAaMRiNXrvw8dkd1dTWPP/44er0eg8HA2LFjqaiooLS0lLlz59LS0oKmaWiaxt/+9jdGjhzZ5wKwdetW4uPj0ev1BAQEMHv2bBob7ccNOnHiBFOnTsVgMKDX60lKSuLcuXNkZ2ezefNm9uzZY8uztLTUrouxxWIhKCiI9evX26V5/PhxdDqdrWVgc3MzCxYswM/PD4PBwBNPPEF1dTUAoaGhfa7DT5WDsbGxbNiwgeDgYIYMGYLRaKSlpcWWX0ZGBtOnT+edd97B398fb29vVqxYQVdXF1lZWfj4+BAUFMQHH3xg+0xnZyeZmZkEBgbi7u5OaGgoK1eu7J+DQAghhBBCCCGEEOIeySQlD16/r/nmzZsZOnQoR48eJS8vjxUrVrB//34sFgvTpk3j2rVrHDx4kP3793P+/HnS0tJsnzWZTAQFBVFeXk5lZSWvv/46Li4uJCYmsnbtWgwGA5cuXeLSpUssXbr0rmW5efMmOTk5VFdXU1hYSF1dHRkZGbb3v/vuO5KTk3Fzc6OkpITKykrmzZtHV1cXS5cuxWg0MmnSJFueiYmJdunrdDpmzZrFtm3b7F4vKChg/PjxhIaGAjBz5kwaGxv59NNPqaysJC4ujokTJ3LtmmMDtJ89e5adO3fyySefsG/fPo4fP84rr7xiF1NSUsLFixf57LPPWLNmDcuXL2fq1KkMGzaMo0ePsnDhQl5++WW+/banW3B+fj579+5l586d1NTUUFBQQFhYmEPlEUIIIYQQQgghhBC/Hf3exTgmJobly5cDMGrUKN59910OHOiZYe+rr76itraW4OCePvRbtmwhOjqa8vJyEhISaGhoICsri9GjR9s+/xMvLy80TVPq3jtv3jzb/0eMGEF+fj4JCQmYzWY8PT1577338PLyYvv27bi49IwbEB4ebvuMh4cHHR0dd8zTZDKxevVqGhoaCAkJwWKxsH37dt566y0ADh06xLFjx2hsbMTNrWfstVWrVlFYWMju3bt56aWX7roeN27cYMuWLTzyyCMArFu3jilTprB69Wpb2Xx8fMjPz0en0xEREUFeXh5tbW288cYbACxbtozc3FwOHTpEeno6DQ0NjBo1isceewxN02yVmUIIIYQQQgghhBDi96XfWxDGxMTY/R0YGEhjYyMnT54kODjYVjkIEBUVhbe3NydP9gw+uWTJEhYsWEBqaiq5ubmcO3fuvspSWVnJM888Q0hICHq9npSUFKCnqzNAVVUVSUlJtsrBexEbG0tkZKStFeHBgwdpbGxk5syZQE+3abPZjK+vL56enraltrbW4fULCQmxVQ4CjBs3DovFQk1Nje216OhodLqfd6e/vz9jxoyx/e3k5ISvr6+ti3VGRgZVVVVERESwaNEiiouL71iGjo4OWltb7ZbODrXBW4UQQgghhBBCCCHuxoo2aMvvVb9XEN5a2aZpGhaLYzMnZmdnc+LECaZMmUJJSQlRUVF8/PHH91SO69ev89RTT2EwGCgoKKC8vNyWVmdnz4xjHh4e95T2rUwmk62CcNu2bUyaNAlfX1+gZzblwMBAqqqq7JaamhqysrL6JX/ofbvfaV/ExcVRW1tLTk4O7e3tGI1Gnn/++T7TX7lyJV5eXnbL//j7e/1WfiGEEEIIIYQQQggxOB7YLMaRkZFcuHCBCxcu2FoRfvPNNzQ3NxMVFWWLCw8PJzw8nFdffZVZs2bxwQcf8Nxzz+Hq6kp3d7fD+Z06dYqrV6+Sm5try6+iosIuJiYmhs2bN3Pz5s1eWxE6mufs2bN56623qKysZPfu3fz973+3vRcXF8fly5dxdna+5zH+GhoauHjxIn/84x8B+Pzzz21die+HwWAgLS2NtLQ0nn/+eSZNmsS1a9fw8fG5LXbZsmUsWbLE7rW6b7+/r/yFEEIIIYQQQgghbvV7nixksDywLZ6amsqYMWMwmUx88cUXHDt2jDlz5pCSkkJ8fDzt7e1kZmZSWlpKfX09hw8fpry8nMjISADCwsIwm80cOHCApqYm2tra7phfSEgIrq6urFu3jvPnz7N3715ycnLsYjIzM2ltbSU9PZ2KigrOnDnD1q1bbV13w8LC+PLLL6mpqaGpqYmbN2/2mldYWBiJiYnMnz+f7u5unn32Wbv1HjduHNOnT6e4uJi6ujqOHDnCm2++eVuFZV/c3d154YUXqK6upqysjEWLFmE0GpXGY7zVmjVr+PDDDzl16hSnT59m165dBAQE4O3t3Wu8m5sbBoPBbnH9jzEVhRBCCCGEEEIIIcSv1wOrINQ0jT179jBs2DCSk5NJTU1lxIgR7NixA+gZI+/q1avMmTOH8PBwjEYjkydP5u233wYgMTGRhQsXkpaWhp+fH3l5eXfMz8/Pj02bNrFr1y6ioqLIzc1l1apVdjG+vr6UlJRgNptJSUlh7NixbNy40daa8MUXXyQiIoL4+Hj8/Pw4fPhwn/mZTCaqq6t57rnn7Loua5pGUVERycnJzJ07l/DwcNLT06mvr8ff39+hbTdy5EhmzJjB008/zZNPPklMTAzvv/++Q5/ti16vJy8vj/j4eBISEqirq6OoqMhuHEMhhBBCCCGEEEKIB03GIHzwNKvVah3sQoi+ZWdnU1hYSFVV1WAX5Tanzn2rFK/6RdNQOzS7rI73mNfh2LiYtrJoamVRLftAbxsVLlrngKUN0Gl1vOXpQK7nvVAtj+pxpqIbJ6V4JxwfogHAMoDPj1S3o2pZVNdVdVuqsFjVyj6Q5xrV43Ggz0s6xf3UYXV3OHaIdl0p7TbrUKV4Z61LKV7lGFbdjudGT1SKH3GqRCl+IK9nA32Of5iuIQP9fXqY7iNUPUxlV73euKB2z9StOLrTw/R9+jVT3a+q29KZ3nuZ9aYLtckxB/pcoOJhOy+ppq8S76qpTcbZZb33SU8dEfFo8N2DfoPO3+ektfdjxKOPDlreg6lffwFOmDCBxYsX92eSD526ujo0TXsoK+yEEEIIIYQQQgghhFD1wCYpuV+bNm1i8eLFNDc3A1BWVsbkyZP7jDebzfedZ0ZGBs3NzRQWFtpeCw4O5tKlSwwfPvy+0weIjo6mvr6+1/c2bNjQL3kMFFfUnqyYLXqleHfdDaX49m7HW5dc+lGtLCO81SZkUX0CNpQfleLNGByO7bC4KqUdaL2mFN+tUzuNXOnyczjWz1WtLKpUnzyqthRw6VY7hlW0O6kdwzpNrbWW1TpwrRbcLHceQ/ZW11A73/7Bckkpvtmpf87nvVFp2QwwVKd27eqwOH7e01t+UEvbaYhSvFu32n71ularFP+Za9/X/FtFeqm1IFTVYVEbg7ex3cvhWCdNraWnaovA86OfUIr3qv5CKf7bZsePm9Bhatc+Vye1lptDnNqV4rutjrcmHuieCKrnDg+d2vfP0O74fY3Oqnb9UNXi4diQOz9ptzp+jKle57sUjgGAbk0t3l1TOyZV1lX1mPTQ1I4Z1daPXQrxqvdXbha17djEH5TiR137h1J8re+/OByr2gJ9oM8FLV2OX59cdY63lATw7+j9N25fVM81ZndfpfgfrLdPxNl3YZSSHtBWp79nVu3329V3sPxqKghvFR8fPyit+JycnO5rcpBbFRUV9Tn5ib+/P3q9nuzs7H7LTwghhBBCCCGEEEKIX+r3QaYsFguvvfYaPj4+BAQE2FVuNTQ0MG3aNDw9PTEYDBiNRq5cuWJ7v7q6mscffxy9Xo/BYGDs2LFUVFRQWlrK3LlzaWlpQdM0NE3jb3/7GyNHjuxzAdi6dSvx8fHo9XoCAgKYPXs2jY2NduU9ceIEU6dOxWAwoNfrSUpK4ty5c2RnZ7N582b27Nljy7O0tNSui7HFYiEoKIj169fbpXn8+HF0Op2tZWBzczMLFizAz88Pg8HAE088QXV1NQChoaF9rsNPlYOxsbFs2LCB4OBghgwZgtFopKWlxZZfRkYG06dP55133sHf3x9vb29WrFhBV1cXWVlZ+Pj4EBQUxAcffGD7TGdnJ5mZmQQGBuLu7k5oaCgrV67sn4NACCGEEEIIIYQQ4h5ZrdqgLb9X/V5BuHnzZoYOHcrRo0fJy8tjxYoV7N+/H4vFwrRp07h27RoHDx5k//79nD9/nrS0NNtnTSYTQUFBlJeXU1lZyeuvv46LiwuJiYmsXbsWg8HApUuXuHTpEkuXLr1rWW7evElOTg7V1dUUFhZSV1dHRkaG7f3vvvuO5ORk3NzcKCkpobKyknnz5tHV1cXSpUsxGo1MmjTJlmdiYqJd+jqdjlmzZrFt2za71wsKChg/fjyhoaEAzJw5k8bGRj799FMqKyuJi4tj4sSJXLvmWPfJs2fPsnPnTj755BP27dvH8ePHeeWVV+xiSkpKuHjxIp999hlr1qxh+fLlTJ06lWHDhnH06FEWLlzIyy+/zLff9kwskp+fz969e9m5cyc1NTUUFBQQFhbmUHmEEEIIIYQQQgghxG9Hv3cxjomJYfny5QCMGjWKd999lwMHDgDw1VdfUVtbS3Bwzyw8W7ZsITo6mvLychISEmhoaCArK4vRo0fbPv8TLy8vNE1T6t47b9482/9HjBhBfn4+CQkJmM1mPD09ee+99/Dy8mL79u24uPTMPBQeHm77jIeHBx0dHXfM02QysXr1ahoaGggJCcFisbB9+3beeustAA4dOsSxY8dobGzEza1nzKJVq1ZRWFjI7t27eemll+66Hjdu3GDLli088sgjAKxbt44pU6awevVqW9l8fHzIz89Hp9MRERFBXl4ebW1tvPHGGwAsW7aM3NxcDh06RHp6Og0NDYwaNYrHHnsMTdNslZlCCCGEEEIIIYQQ4vel31sQxsTE2P0dGBhIY2MjJ0+eJDg42FY5CBAVFYW3tzcnT54EYMmSJSxYsIDU1FRyc3M5d5/TWldWVvLMM88QEhKCXq8nJSUF6OnqDFBVVUVSUpKtcvBexMbGEhkZaWtFePDgQRobG5k5cybQ023abDbj6+uLp6enbamtrXV4/UJCQmyVgwDjxo3DYrFQU1Njey06Ohqd7ufd6e/vz5gxY2x/Ozk54evra+tinZGRQVVVFRERESxatIji4uI7lqGjo4PW1la7paNDbZISIYQQQgghhBBCiLuxohu05feq39f81so2TdOwWBybVSs7O5sTJ04wZcoUSkpKiIqK4uOPP76ncly/fp2nnnoKg8FAQUEB5eXltrQ6O3tmyvLw8LintG9lMplsFYTbtm1j0qRJ+Pr2zKpkNpsJDAykqqrKbqmpqSErK6tf8ofet/ud9kVcXBy1tbXk5OTQ3t6O0Wjk+eef7zP9lStX4uXlZbf8/e9/77fyCyGEEEIIIYQQQojB8cCqRiMjI7lw4QIXLlywvfbNN9/Q3NxMVFSU7bXw8HBeffVViouLmTFjhm1iDVdXV7q7HZ/6/NSpU1y9epXc3FySkpIYPXr0bROUxMTEUFZW1ucswo7mOXv2bL7++msqKyvZvXs3JpPJ9l5cXByXL1/G2dn5tklIhg8f7tC6NDQ0cPHiRdvfn3/+ua0r8f0wGAykpaWxceNGduzYwUcffdTnuIjLli2jpaXFblm4cOF95S+EEEIIIYQQQghxKyvaoC334r333iMsLAx3d3f+9V//lWPHjt0xfteuXYwePRp3d3fGjBlDUVGR/fpbrfz1r38lMDAQDw8PUlNTOXPmzD2VzVEPrIIwNTWVMWPGYDKZ+OKLLzh27Bhz5swhJSWF+Ph42tvbyczMpLS0lPr6eg4fPkx5eTmRkZEAhIWFYTabOXDgAE1NTbS1td0xv5CQEFxdXVm3bh3nz59n79695OTk2MVkZmbS2tpKeno6FRUVnDlzhq1bt9q67oaFhfHll19SU1NDU1NTnxWJYWFhJCYmMn/+fLq7u3n22Wft1nvcuHFMnz6d4uJi6urqOHLkCG+++SYVFRUObTt3d3deeOEFqqurKSsrY9GiRRiNRqXxGG+1Zs0aPvzwQ06dOsXp06fZtWsXAQEBeHt79xrv5uaGwWCwW34aU1EIIYQQQgghhBDi92jHjh0sWbKE5cuX88UXX/DP//zPPPXUU7c1UvvJkSNHmDVrFvPnz+f48eNMnz6d6dOn8/XXX9ti8vLyyM/P5+9//ztHjx5l6NChPPXUU9y4cWPA1uOBVRBqmsaePXsYNmwYycnJpKamMmLECHbs2AH0jJF39epV5syZQ3h4OEajkcmTJ/P2228DkJiYyMKFC0lLS8PPz4+8vLw75ufn58emTZvYtWsXUVFR5ObmsmrVKrsYX19fSkpKMJvNpKSkMHbsWDZu3Gjrmvviiy8SERFBfHw8fn5+HD58uM/8TCYT1dXVPPfcc3ZdlzVNo6ioiOTkZObOnUt4eDjp6enU19fj7+/v0LYbOXIkM2bM4Omnn+bJJ58kJiaG999/36HP9kWv15OXl0d8fDwJCQnU1dVRVFRkN46hEEIIIYQQQgghhOjbmjVrePHFF5k7dy5RUVH8/e9/Z8iQIfzP//k/e43/7//9vzNp0iSysrKIjIwkJyeHuLg43n33XaCn9eDatWt56623mDZtGjExMWzZsoWLFy9SWFg4YOuhWa1W64ClLu5bdnY2hYWFVFVVDXZRbnNecRIZs0WvFO+uU6sZb+kyOBx76Ue1sozw/l4pXkPtazWUH5XizTi+rh0WV6W0A63fKsV369QmQ7/QFXz3oP/g59p7l/f+otp83IVOtfjugXu60+6kdgzrNMeHaADotjq+X1WPdzfLnVuA3+oajg3H8JM/WC4pxTc7qaWvokthOwIMdTIrxXdY3B2O9bJcVUvbaYhSvFu32n71ularFP+Z62SHYyO96pXSbrMOVYrvtjopxTe2ezkc66Q5Nm7zTwKGqJ0nz49+Qineq/oLpfhvmx0/bkKHqV37XJ26lOKHOLUrxavsVx1q+0nT1M6TqseYh07t+2dod/y+RmdVu36oavFw7IH5T9qtjh9jqtf5LsXt7qx4bXXX1I5JlXVVPSY9NLVjphu161mXQrzq/ZWbRW07NvEHpfhR1/6hFF/r+y8OxzprauexgT4XtHQ5fn1y1fXem64v/h1q12LVc43Z3Vcp/gerj8Oxbjq1Y1L1XtgZtW058tE/KcX/VtScu3D3oAESFvSH2yZldXNz67UXZWdnJ0OGDGH37t1Mnz7d9voLL7xAc3Mze/bsue0zISEhLFmyhMWLF9teW758OYWFhVRXV3P+/HkeffRRjh8/TmxsrC0mJSWF2NhY/vt//+/3vY696dfmYhMmTLBbwd+iuro6NE17KCvshBBCCCGEEEIIIcS9622S1pUrV/Ya29TURHd39209RP39/bl8+XKvn7l8+fId43/6VyXN/qD2KGgQbdq0icWLF9Pc3AxAWVkZkyf33ZrAbFZrhdGbjIwMmpub7ZpwBgcHc+nSJYcnGLmb6Oho6ut7f7qyYcOGfsljoFgU65edFJ+y+recVorvNPyTw7Ga5qmUtsWqtq6qTwctmtrTQW0AG/663VT77qg+nbfoQh2OVX26ZtEUj0mL4lNcxdaSKk8TVbejarxnxw9K8T+6Ov6U1dWi1lJS9Xj30lqU4q1WtW0T0H5eKV6FrlvtGLuif1QpXqVVks6idg62OqttR9Xz0g1PP6X4Rz0cvxnSK7SOAvjRzfFW2QA6xVZ+Q1wcb4mg2gpBNV61RWDLP8cpxXuXf+VwrGqLQBfFa6vqtlFpgaXaInDYzd7HIOrLNWfFVnUWtRa/Xe5/dDxWsSW06nZ3Qu3cpJK+alma2ocpxcdb1FqaXR+i1uLpppPjvUBU71U7URtLXHVbqlyLVctyU6fWO+bmTRel+AbfsUrxfl0X7x50j1TPBTcsHncP+gWVVrCqx8BV90eU4lVbS6p/vx2/1v9L12dKaX9riFaKv8c5MH537nWykP6wbNkylixZYvfa72EOhl9NBeGt4uPjB6UVn5OT031NDnKroqKiPic/8ff3R6/Xk52d3W/5CSGEEEIIIYQQQoje9dWduDfDhw/HycmJK1eu2L1+5cqVPuuOAgIC7hj/079XrlwhMDDQLuaXXY77W7/PSGGxWHjttdfw8fEhICDArnKroaGBadOm4enpicFgwGg02m2U6upqHn/8cfR6PQaDgbFjx1JRUUFpaSlz586lpaUFTdPQNI2//e1vjBw5ss8FYOvWrcTHx6PX6wkICGD27Nm3zSJz4sQJpk6disFgQK/Xk5SUxLlz58jOzmbz5s3s2bPHlmdpaaldF2OLxUJQUBDr16+3S/P48ePodDpby8Dm5mYWLFiAn58fBoOBJ554gurqagBCQ0P7XIefKgdjY2PZsGEDwcHBDBkyBKPRSEvLzy1qMjIymD59Ou+88w7+/v54e3uzYsUKurq6yMrKwsfHh6CgID744APbZzo7O8nMzCQwMBB3d3dCQ0P7bDIrhBBCCCGEEEIIIey5uroyduxYDhw4YHvNYrFw4MABxo0b1+tnxo0bZxcPsH//flv8n/70JwICAuxiWltbOXr0aJ9p9od+ryDcvHkzQ4cO5ejRo+Tl5bFixQr279+PxWJh2rRpXLt2jYMHD7J//37Onz9PWlqa7bMmk4mgoCDKy8uprKzk9ddfx8XFhcTERNauXYvBYODSpUtcunSJpUuX3rUsN2/eJCcnh+rqagoLC6mrqyMjI8P2/nfffUdycjJubm6UlJRQWVnJvHnz6OrqYunSpRiNRiZNmmTLMzEx0S59nU7HrFmz2LZtm93rBQUFjB8/ntDQnq6UM2fOpLGxkU8//ZTKykri4uKYOHEi1645NrD42bNn2blzJ5988gn79u3j+PHjvPLKK3YxJSUlXLx4kc8++4w1a9awfPlypk6dyrBhwzh69CgLFy7k5Zdf5ttveyagyM/PZ+/evezcuZOamhoKCgoICwtzqDxCCCGEEEIIIYQQA8WKNmiLqiVLlrBx40Y2b97MyZMn+ctf/sL169eZO3cuAHPmzGHZsmW2+P/yX/4L+/btY/Xq1Zw6dYrs7GwqKirIzMwEQNM0Fi9ezH/7b/+NvXv38tVXXzFnzhz++Mc/2k2E0t/6vYtxTEwMy5cvB2DUqFG8++67tlrPr776itraWoKDe2Yx3bJlC9HR0ZSXl5OQkEBDQwNZWVmMHj3a9vmfeHl5oWmaUvfeefPm2f4/YsQI8vPzSUhIwGw24+npyXvvvYeXlxfbt2/HxaVnbIrw8HDbZzw8POjo6LhjniaTidWrV9PQ0EBISAgWi4Xt27fz1ltvAXDo0CGOHTtGY2OjrYnqqlWrKCwsZPfu3bz00kt3XY8bN26wZcsWHnmkZxyHdevWMWXKFFavXm0rm4+PD/n5+eh0OiIiIsjLy6OtrY033ngD6OlDn5uby6FDh0hPT6ehoYFRo0bx2GOPoWmarTJTCCGEEEIIIYQQQjgmLS2N77//nr/+9a9cvnyZ2NhY9u3bZ5tkpKGhAZ3u5/Z5iYmJbNu2jbfeeos33niDUaNGUVhYyD/908/zKrz22mtcv36dl156iebmZh577DH27duHu7v7gK1Hv7cgjImJsfs7MDCQxsZGTp48SXBwsK1yECAqKgpvb29OnjwJ9NS6LliwgNTUVHJzczl37tx9laWyspJnnnmGkJAQ9Ho9KSkpQM/OAaiqqiIpKclWOXgvYmNjiYyMtLUiPHjwII2NjcycORPo6TZtNpvx9fXF09PTttTW1jq8fiEhIbbKQehpjmqxWKipqbG9Fh0dbXfA+fv7M2bMGNvfTk5O+Pr62rpYZ2RkUFVVRUREBIsWLaK4uPiOZejo6KC1tdVuuXXabyGEEEIIIYQQQoj7ZbVqg7bci8zMTOrr6+no6ODo0aP867/+q+290tJSNm3aZBc/c+ZMampq6Ojo4Ouvv+bpp5+2e1/TNFasWMHly5e5ceMG/+f//B+7Bm0Dod8rCG+tbNM0DYvFsVnhsrOzOXHiBFOmTKGkpISoqCg+/vjjeyrH9evXeeqppzAYDBQUFFBeXm5Lq7OzZzZBDw+1WZ76YjKZbBWE27ZtY9KkSfj69sxSZjabCQwMpKqqym6pqakhKyurX/KH3rf7nfZFXFwctbW15OTk0N7ejtFo5Pnnn+8z/d6m+d7w9/V9xgshhBBCCCGEEEKIX4d+ryDsS2RkJBcuXODChQu217755huam5uJioqyvRYeHs6rr75KcXExM2bMsE2s4erqSne349Ownzp1iqtXr5Kbm0tSUhKjR4++bYKSmJgYysrK+pxF2NE8Z8+ezddff01lZSW7d+/GZDLZ3ouLi+Py5cs4OzvfNgnJ8OHDHVqXhoYGLl68aPv7888/t3Ulvh8Gg4G0tDQ2btzIjh07+Oijj/ocF3HZsmW0tLTYLS8v/Mt95S+EEEIIIYQQQgghBt8DqyBMTU1lzJgxmEwmvvjiC44dO8acOXNISUkhPj6e9vZ2MjMzKS0tpb6+nsOHD1NeXk5kZCQAYWFhmM1mDhw4QFNTE21tbXfMLyQkBFdXV9atW8f58+fZu3cvOTk5djGZmZm0traSnp5ORUUFZ86cYevWrbauu2FhYXz55ZfU1NTQ1NTUZ0ViWFgYiYmJzJ8/n+7ubp599lm79R43bhzTp0+nuLiYuro6jhw5wptvvklFRYVD287d3Z0XXniB6upqysrKWLRoEUajUWk8xlutWbOGDz/8kFOnTnH69Gl27dpFQEAA3t7evca7ublhMBjsFken/RZCCCGEEEIIIYRw1K9pkpLfigdWQahpGnv27GHYsGEkJyeTmprKiBEj2LFjB9AzRt7Vq1eZM2cO4eHhGI1GJk+ezNtvvw30DOK4cOFC0tLS8PPzIy8v7475+fn5sWnTJnbt2kVUVBS5ubmsWrXKLsbX15eSkhLMZjMpKSmMHTuWjRs32rrmvvjii0RERBAfH4+fnx+HDx/uMz+TyUR1dTXPPfecXddlTdMoKioiOTmZuXPnEh4eTnp6OvX19bYBK+9m5MiRzJgxg6effponn3ySmJgY3n//fYc+2xe9Xk9eXh7x8fEkJCRQV1dHUVGR3TiGQgghhBBCCCGEEOK3T7NardbBLoToW3Z2NoWFhVRVVQ12UW5z9lytUnyHVW22naDWE0rxFwz/dPeg/1Db4lj37p+EGnrvet0XZ61LKd5da1eKb7cOcTj2hkWtpWdY5ymleNUnLKd0MXcP+g+PuF1SStuiqVVwO1nU9lO3Tm3id/eu6w7Hqm7HNieDUrxX5/dK8T+6+jgc62q5oZS2RXNSircq7lfXbrXvk0dHi1K8Cl232jF2Rf+oUrxF4Tmf9021Y6DNVe0Y87hpVop3ven49wPgqkeQw7G+7d8qpX3RTW276zTHxlb+SevNoQ7Haqjdlg1zbVWKb+oYphTf8s9xSvFO5V85HOs3RO2YcVG8trrqOpXiLVbHv0+aprifbjbePegXrjk79hD5JyrnAgAXrfdeMb3psqpd+1SPYSfN8SGEALqtatcQFRfbfJXi4y3/UIq/PkQt/VYnx6/FKscvqG931f06kPtJ9fv3401PpXhPZ7Xrk0/XFaV4FarnAtX7SZX9pHrMDPR3W/WYVPl+/0vXZ0ppf2uIVop31dQm+xz56J+U4n8rvj57edDy/qeR995b89esX5uLTZgwgcWLF/dnkg+duro6NE17KCvshBBCCCGEEEIIIYRQpfZIcBBt2rSJxYsX09zcDEBZWRmTJ0/uM95sVnsq3ZuMjAyam5spLCy0vRYcHMylS5ccnmDkbqKjo6mvr+/1vQ0bNvRLHgOl06rWMk316VqVW6JS/L/W73Y4tnnJWqW0w/7nSqX460PVnvbVo/ZUKLKt3OFY1dZXn2uPKcUP91B7yhp/rsDh2KoRaUppOyu26lF92q7aaugfZx3frzqd2hPfpJFqrVH+3wtjleIfG+F4680LN9SesHm6qj01be1Ua318+ju1eP3QgWtIb7Wq7dcxQ9Va+fnQ5HDs1hOOt94FmBWj1pp4b51a+i0/qn2fXvinaodjC6+MU0o7veZ1pfjTqVlK8Zd/dLwFoU6xZYy3z49K8d82O94CHcBboUUgQHfCGIdjW7+oUkrb3dnxVm8ALq5q8Sot5XSoHb/f6UKV4l2saq0lL173Vor/wxDHjxvVa59OsVXP9W61Y9LdyfFriOo5+F9KlinFF/3LaqX48CFqPVJa2x3fNqOd1c7ZA90yTSVepUUrwE2ri1J8zDeblOKPRSxQiq+57vi2DPFW67Wgei643OatFJ/Yvs/h2LO+atfWDourUrzqucZ8U+3c8S//5//jcOzOf16rlrZe7b68U/EYFuJB+dVUEN4qPj5+UFrxOTk53dfkILcqKirqc/ITf39/9Ho92dnZ/ZafEEIIIYQQQgghxMPs9zxZyGDp9xkpLBYLr732Gj4+PgQEBNhVbjU0NDBt2jQ8PT0xGAwYjUauXPm5VVl1dTWPP/44er0eg8HA2LFjqaiooLS0lLlz59LS0oKmaWiaxt/+9jdGjhzZ5wKwdetW4uPj0ev1BAQEMHv2bBob7Wv3T5w4wdSpUzEYDOj1epKSkjh37hzZ2dls3ryZPXv22PIsLS2162JssVgICgpi/fr1dmkeP34cnU5naxnY3NzMggUL8PPzw2Aw8MQTT1Bd3dMSIjQ0tM91+KlyMDY2lg0bNhAcHMyQIUMwGo20tPz89CkjI4Pp06fzzjvv4O/vj7e3NytWrKCrq4usrCx8fHwICgrigw8+sH2ms7OTzMxMAgMDcXd3JzQ0lJUr1VrJCSGEEEIIIYQQQohfv36vINy8eTNDhw7l6NGj5OXlsWLFCvbv34/FYmHatGlcu3aNgwcPsn//fs6fP09a2s9dCE0mE0FBQZSXl1NZWcnrr7+Oi4sLiYmJrF27FoPBwKVLl7h06RJLly69a1lu3rxJTk4O1dXVFBYWUldXR0ZGhu397777juTkZNzc3CgpKaGyspJ58+bR1dXF0qVLMRqNTJo0yZZnYqJ9l1edTsesWbPYtm2b3esFBQWMHz+e0NCe7iQzZ86ksbGRTz/9lMrKSuLi4pg4cSLXrjnWzeDs2bPs3LmTTz75hH379nH8+HFeeeUVu5iSkhIuXrzIZ599xpo1a1i+fDlTp05l2LBhHD16lIULF/Lyyy/z7bc9A7fn5+ezd+9edu7cSU1NDQUFBYSFhTlUHiGEEEIIIYQQQoiBYrVqg7b8XvV7F+OYmBiWL18OwKhRo3j33Xc5cOAAAF999RW1tbUEBwcDsGXLFqKjoykvLychIYGGhgaysrIYPXq07fM/8fLyQtM0pe698+bNs/1/xIgR5Ofnk5CQgNlsxtPTk/feew8vLy+2b9+Oi0vPOADh4eG2z3h4eNDR0XHHPE0mE6tXr6ahoYGQkBAsFgvbt2/nrbfeAuDQoUMcO3aMxsZG3Nx6xuxbtWoVhYWF7N69m5deeumu63Hjxg22bNnCI488AsC6deuYMmUKq1evtpXNx8eH/Px8dDodERER5OXl0dbWxhtvvAHAsmXLyM3N5dChQ6Snp9PQ0MCoUaN47LHH0DTNVpnZl46ODjo67Md86ezowNVNbRxCIYQQQgghhBBCCPFw6fcWhDEx9oOUBwYG0tjYyMmTJwkODrZVDgJERUXh7e3NyZMnAViyZAkLFiwgNTWV3Nxczp07d19lqays5JlnniEkJAS9Xk9KSgrQ09UZoKqqiqSkJFvl4L2IjY0lMjLS1orw4MGDNDY2MnPmTKCn27TZbMbX1xdPT0/bUltb6/D6hYSE2CoHAcaNG4fFYqGmpsb2WnR0NDrdz7vT39+fMWN+HijcyckJX19fWxfrjIwMqqqqiIiIYNGiRRQXF9+xDCtXrsTLy8tu2bjhXYfKL4QQQgghhBBCCCEeXv1eQXhrZZumaVgsjs1IlJ2dzYkTJ5gyZQolJSVERUXx8ccf31M5rl+/zlNPPYXBYKCgoIDy8nJbWp2dnUBPC8H+YDKZbBWE27ZtY9KkSfj6+gI9sykHBgZSVVVlt9TU1JCVpTYL4p30tt3vtC/i4uKora0lJyeH9vZ2jEYjzz//fJ/pL1u2jJaWFrvlxZcz+638QgghhBBCCCGEEAAWtEFbfq/6vYKwL5GRkVy4cIELFy7YXvvmm29obm4mKirK9lp4eDivvvoqxcXFzJgxwzaxhqurK93d3Q7nd+rUKa5evUpubi5JSUmMHj36tglKYmJiKCsr63MWYUfznD17Nl9//TWVlZXs3r0bk8lkey8uLo7Lly/j7Ox82yQkw4cPd2hdGhoauHjxou3vzz//3NaV+H4YDAbS0tLYuHEjO3bs4KOPPupzXEQ3NzcMBoPdIt2LhRBCCCGEEEIIIX79HlgFYWpqKmPGjMFkMvHFF19w7Ngx5syZQ0pKCvHx8bS3t5OZmUlpaSn19fUcPnyY8vJyIiMjAQgLC8NsNnPgwAGamppoa2u7Y34hISG4urqybt06zp8/z969e8nJybGLyczMpLW1lfT0dCoqKjhz5gxbt261dd0NCwvjyy+/pKamhqampj4rEsPCwkhMTGT+/Pl0d3fz7LPP2q33uHHjmD59OsXFxdTV1XHkyBHefPNNKioqHNp27u7uvPDCC1RXV1NWVsaiRYswGo1K4zHeas2aNXz44YecOnWK06dPs2vXLgICAvD29r7nNIUQQgghhBBCCCHulxVt0JbfqwdWQahpGnv27GHYsGEkJyeTmprKiBEj2LFjB9AzRt7Vq1eZM2cO4eHhGI1GJk+ezNtvvw1AYmIiCxcuJC0tDT8/P/Ly8u6Yn5+fH5s2bWLXrl1ERUWRm5vLqlWr7GJ8fX0pKSnBbDaTkpLC2LFj2bhxo61r7osvvkhERATx8fH4+flx+PDhPvMzmUxUV1fz3HPP2XVd1jSNoqIikpOTmTt3LuHh4aSnp1NfX4+/v79D227kyJHMmDGDp59+mieffJKYmBjef/99hz7bF71eT15eHvHx8SQkJFBXV0dRUZHdOIZCCCGEEEIIIYQQ4rev32cxvlVhYaHt/yEhIezZs6fXOFdXVz788MM7prV+/XrWr1/vcN6zZs1i1qxZdq9ZrVa7v2NiYvjf//t/9/p5Pz+/2ybvqKur6zX2L3/5C3/5y196fU+v15Ofn09+fr6DJVdLf9OmTbe9Vlpaettrvyz7iy++yIsvvnjP5RFCCCGEEEIIIYQQvw2a9dYas/swYcIEYmNjWbt2bX8labNp0yYWL15Mc3Nzv6fdl4yMDJqbm+0qObu7u/n+++8ZPnw4zs4DXr9KdnY2hYWFVFVVDXheqs6eq1WKtyo2WHWxdijF/2D1cTj2ynWDUtoBQ1uU4lW569TW1QnHx+PsUnwOUHX5j0rxqmeQf/L/3uFYD127Utqq66qhVnjV+JvWe58h/W5ctN6HPOhLt9VJKV6nOTa5VE9ZOpXS/rZdbXgEf/fex0bti+q6DmQ3Ah2Ob0cA7+4mpXizs7fDsR1WtXFjVY8x1eNd5Tymyq/jwt2DfqGqK1Yp/o9D1Y7J611DHI7VNLXzjN7ZrBT/Q6fa9c/VqUspvrXD8Qng2uJildIOOfmZUrzqMWyxOn6forqfVI/3Lqva9cxZU9tPKt9X1WufKg/tzsMG3eoGjh9jqmW/3u34dxXgD9pltfR1at+/GxbHz9uuise7kzZw52BVFsXfCKrXVhfU7lOsmtp9QafC9VX1XBD07RGl+FOB/6YU76Jz/NyhWnbV+yvV76urpvb7qbXb8e/fMKcflNL2azqpFH9x+D8rxYc/GqIU/1vxxemrg5Z3XLjvoOU9mAa+hmuAlJWVMXny5D7fN5vVbpgd5eTkdF9j/90qOjqa+vr6Xt/bsGFDv+UjhBBCCCGEEEIIIURv+n3AOYvFwmuvvYaPjw8BAQFkZ2fb3mtoaGDatGl4enpiMBgwGo1cuXLF9n51dTWPP/44er0eg8HA2LFjqaiooLS0lLlz59LS0oKmaWiaxr59+6iqqupzAdi6dSvx8fHo9XoCAgKYPXv2bTMZnzhxgqlTp2IwGNDr9SQlJXHu3Dmys7PZvHkze/bsseVZWlpKXV0dmqZRVVWFxWIhKCjotm7Px48fR6fT2Sr+mpubWbBgAX5+fhgMBp544gmqq6sBKCoq6nMdfjnZyYYNGwgODmbIkCEYjUZaWn5u0ZaRkcH06dN555138Pf3x9vbmxUrVtDV1UVWVhY+Pj4EBQXZZoQG6OzsJDMzk8DAQNzd3QkNDWXlypX3t/OFEEIIIYQQQggh7pNMUvLg9XsLws2bN7NkyRKOHj3KP/7xDzIyMhg/fjwTJ060VQ4ePHiQrq4u/v3f/520tDTbeHkmk4k///nPrF+/HicnJ6qqqnBxcSExMZG1a9fy17/+1TbDsKenJ56enncsy82bN8nJySEiIoLGxkaWLFlCRkYGRUVFAHz33XckJyczYcIESkpKMBgMHD58mK6uLpYuXcrJkydpbW21Vaz5+Phw8eJFW/o6nY5Zs2axbds2u/EBCwoKGD9+PKGhoQDMnDkTDw8PPv30U7y8vNiwYQMTJ07k9OnTtpg7OXv2LDt37uSTTz6htbWV+fPn88orr1BQUGCLKSkpISgoiM8++4zDhw8zf/58jhw5QnJyMkePHmXHjh28/PLL/Nu//RtBQUHk5+ezd+9edu7cSUhICBcuXODCBbUuWUIIIYQQQgghhBDi16/fKwhjYmJYvnw5AKNGjeLdd9/lwIEDAHz11VfU1tYSHBwMwJYtW4iOjqa8vJyEhAQaGhrIyspi9OjRts//xMvLC03TlLr3zps3z/b/ESNGkJ+fT0JCAmazGU9PT9577z28vLzYvn27bebi8PBw22c8PDzo6Oi4Y54mk4nVq1fT0NBASEgIFouF7du389ZbbwFw6NAhjh07RmNjI25uPeNTrFq1isLCQnbv3s1LL7101/W4ceMGW7Zs4ZFHHgFg3bp1TJkyhdWrV9vK5uPjQ35+PjqdjoiICPLy8mhra+ONN94AYNmyZeTm5nLo0CHS09NpaGhg1KhRPPbYY2ia5lBFpRBCCCGEEEIIIYT47en3LsYxMTF2fwcGBtLY2MjJkycJDg62VQ4CREVF4e3tzcmTPYN6LlmyhAULFpCamkpubi7nzp27r7JUVlbyzDPPEBISgl6vJyUlBejp6gxQVVVFUlKSrXLwXsTGxhIZGcm2bdsAOHjwII2NjcycORPo6TZtNpvx9fW1tXr09PSktrbW4fULCQmxVQ4CjBs3DovFYmtNCT1jGep0P+9Of39/xowZY/vbyckJX19fWxfrjIwMqqqqiIiIYNGiRbfN1nyrjo4OWltb7ZaODrWBYYUQQgghhBBCCCHuxmrVBm35ver3CsJbK9s0TcNicWymqezsbE6cOMGUKVMoKSkhKiqKjz/++J7Kcf36dZ566ikMBgMFBQWUl5fb0urs7JnJysPD8VnQ7sRkMtkqCLdt28akSZPw9e2Z9cZsNhMYGHjb+II1NTVkZWX1S/7Q+3a/076Ii4ujtraWnJwc2tvbMRqNPP/8832mv3LlSry8vOyWDX9f32e8EEIIIYQQQgghhPh16PcKwr5ERkbeNs7dN998Q3NzM1FRUbbXwsPDefXVVykuLmbGjBm28f9cXV3p7nZ8avVTp05x9epVcnNzSUpKYvTo0bdNUBITE0NZWRk3b97sNQ1H85w9ezZff/01lZWV7N69G5PJZHsvLi6Oy5cv4+zszMiRI+2W4cOHO7QuDQ0NdmMffv7557auxPfDYDCQlpbGxo0b2bFjBx999BHXrl3rNXbZsmW0tLTYLS8v/EuvsUIIIYQQQgghhBD3SiYpefAeWAVhamoqY8aMwWQy8cUXX3Ds2DHmzJlDSkoK8fHxtLe3k5mZSWlpKfX19Rw+fJjy8nIiIyMBCAsLw2w2c+DAAZqammhra7tjfiEhIbi6urJu3TrOnz/P3r17ycnJsYvJzMyktbWV9PR0KioqOHPmDFu3brV13Q0LC+PLL7+kpqaGpqamPisSw8LCSExMZP78+XR3d9vNPpyamsq4ceOYPn06xcXF1NXVceTIEd58800qKioc2nbu7u688MILVFdXU1ZWxqJFizAajUrjMd5qzZo1fPjhh5w6dYrTp0+za9cuAgIC8Pb27jXezc0Ng8Fgt/w0pqIQQgghhBBCCCGE+PV6YBWEmqaxZ88ehg0bRnJyMqmpqYwYMYIdO3YAPWPkXb16lTlz5hAeHo7RaGTy5Mm8/fbbACQmJrJw4ULS0tLw8/MjLy/vjvn5+fmxadMmdu3aRVRUFLm5uaxatcouxtfXl5KSEsxmMykpKYwdO5aNGzfauua++OKLREREEB8fj5+fH4cPH+4zP5PJRHV1Nc8995xd12VN0ygqKiI5OZm5c+cSHh5Oeno69fX1+Pv7O7TtRo4cyYwZM3j66ad58skniYmJ4f3333fos33R6/Xk5eURHx9PQkICdXV1FBUV2Y1jKIQQQgghhBBCCCF++zSr1Wod7EKIvmVnZ1NYWEhVVdVgF+U2Z8/VKsVbFeujXaxqk6D8YPVxOPbKdYNS2gFDW5TiVbnr1NbVCce723cpTlZedfmPSvGqZ5B/8v/e4VgPXbtS2qrrqqFWeNX4m9Z7nwDpbly03ls096Xb6qQUr9McGzu2pyydSml/267W+tnfvfehD/qiuq4D2Y1Ah+PbEcC7u0kp3uzs7XBsh1Wt1bfqMaZ6vKucx1T5dVy4e9AvVHXFKsX/cajaMXm9a4jDsZqmdp7RO5uV4n/oVLv+uTp1KcW3djg+vnNbXKxS2iEnP1OKVz2GLVbH71NU95Pq8d5lVbueOWtq+0nl+6p67VPlod25V9CtbuD4MaZa9uvdjn9XAf6gXVZLX6f2/bthcfy87ap4vDtpA3cOVmVR/I2gem11Qe0+xaqp3Rd0KlxfVc8FQd8eUYo/FfhvSvEuOsfPHaplV72/Uv2+umpqv59aux3//g1z+kEpbb+mk0rxF4f/s1J8+KMhSvG/FcdODexv8Dv5l9Feg5b3YOrX5mITJkxg8eLF/ZnkQ6eurg5N0x7KCjshhBBCCCGEEEIIIVSpPZ4cRJs2bWLx4sU0NzcDUFZWxuTJk/uMN5vVnqj3JiMjg+bmZgoLC22vBQcHc+nSJYcnGLmb6Oho6uvre31vw4YN/ZLHQFF9en5T+Wm42pNQg67V4dg2V3eltP94s/d91BcP8xWl+CZftQln2qxDHY5VfXo33FPtKWu34jTwXQqtuzxvqj29c7aolf2qm1prSVVeVrVWRiqua2qtEAZ14bHaAAEAAElEQVS0ZZpF7fsU5KHW4kJnVWwpYLmhFD+QrWNcu9Rawba6q11bVFpLDrM43noX4IaT4+cZAL1F7fuqem5q1vXPdbc3Ph7XleJVjxlnhRYaqtdWVaotAl0UW6a5Ozt+7R6u2CKwITJZKf7RUweU4lW2veoxoNoiUPU4aLM43qoO1FqbDfQxqXJPA2ot3FXPMzctai3Qh95Uu87/6OGtFO+ksK6q+0l12wx0azAVqmX5oXuYUrybTu1+0r/D8d8J37sFK6VdH5SkFO9sUWvlp9IaU/lcoBiumn6HVe3+s9Pi+HnYu/07pbQvDI9TindG7dr6e6X2C0D0h19NBeGt4uPjB6UVn5OT031NDnKroqKiPic/8ff3R6/Xk52d3W/5CSGEEEIIIYQQQgjxS/0+I4XFYuG1117Dx8eHgIAAu8qthoYGpk2bhqenJwaDAaPRyJUrP7e0qq6u5vHHH0ev12MwGBg7diwVFRWUlpYyd+5cWlpa0DQNTdP429/+xsiRI/tcALZu3Up8fDx6vZ6AgABmz55NY2OjXXlPnDjB1KlTMRgM6PV6kpKSOHfuHNnZ2WzevJk9e/bY8iwtLbXrYmyxWAgKCmL9+vV2aR4/fhydTmdrGdjc3MyCBQvw8/PDYDDwxBNPUF1dDUBoaGif6/BT5WBsbCwbNmwgODiYIUOGYDQaaWn5uT9+RkYG06dP55133sHf3x9vb29WrFhBV1cXWVlZ+Pj4EBQUxAcffGD7TGdnJ5mZmQQGBuLu7k5oaCgrV67sn4NACCGEEEIIIYQQQvxq9HsF4ebNmxk6dChHjx4lLy+PFStWsH//fiwWC9OmTePatWscPHiQ/fv3c/78edLS0myfNZlMBAUFUV5eTmVlJa+//jouLi4kJiaydu1aDAYDly5d4tKlSyxduvSuZbl58yY5OTlUV1dTWFhIXV0dGRkZtve/++47kpOTcXNzo6SkhMrKSubNm0dXVxdLly7FaDQyadIkW56JiYl26et0OmbNmsW2bdvsXi8oKGD8+PGEhoYCMHPmTBobG/n000+prKwkLi6OiRMncu2aY90Szp49y86dO/nkk0/Yt28fx48f55VXXrGLKSkp4eLFi3z22WesWbOG5cuXM3XqVIYNG8bRo0dZuHAhL7/8Mt9++y0A+fn57N27l507d1JTU0NBQQFhYWEOlUcIIYQQQgghhBBioFit2qAtv1f93sU4JiaG5cuXAzBq1CjeffddDhzoGQPmq6++ora2luDgnrEXtmzZQnR0NOXl5SQkJNDQ0EBWVhajR4+2ff4nXl5eaJqm1L133rx5tv+PGDGC/Px8EhISMJvNeHp68t577+Hl5cX27dtxcemZzS08PNz2GQ8PDzo6Ou6Yp8lkYvXq1TQ0NBASEoLFYmH79u289dZbABw6dIhjx47R2NiIm1vPDFerVq2isLCQ3bt389JLL911PW7cuMGWLVt45JFHAFi3bh1Tpkxh9erVtrL5+PiQn5+PTqcjIiKCvLw82traeOONNwBYtmwZubm5HDp0iPT0dBoaGhg1ahSPPfYYmqbZKjOFEEIIIYQQQgghxO9Lv7cgjImJsfs7MDCQxsZGTp48SXBwsK1yECAqKgpvb29OnuyZFnzJkiUsWLCA1NRUcnNzOXfu3H2VpbKykmeeeYaQkBD0ej0pKSlAT1dngKqqKpKSkmyVg/ciNjaWyMhIWyvCgwcP0tjYyMyZM4GebtNmsxlfX188PT1tS21trcPrFxISYqscBBg3bhwWi4Wamhrba9HR0eh0P+9Of39/xowZY/vbyckJX19fWxfrjIwMqqqqiIiIYNGiRRQXF9+xDB0dHbS2ttotHR1qU8sLIYQQQgghhBBC3I0VbdCW36t+ryC8tbJN0zQsFsfmn8nOzubEiRNMmTKFkpISoqKi+Pjjj++pHNevX+epp57CYDBQUFBAeXm5La3Ozp6ZqTw81GZ864vJZLJVEG7bto1Jkybh6+sL9MymHBgYSFVVld1SU1NDVlZWv+QPvW/3O+2LuLg4amtrycnJob29HaPRyPPPP99n+itXrsTLy8tu+fvf/95v5RdCCCGEEEIIIYQQg6PfKwj7EhkZyYULF7hw4YLttW+++Ybm5maioqJsr4WHh/Pqq69SXFzMjBkzbBNruLq60t3t+LTtp06d4urVq+Tm5pKUlMTo0aNvm6AkJiaGsrKyPmcRdjTP2bNn8/XXX1NZWcnu3bsxmUy29+Li4rh8+TLOzs63TUIyfPhwh9aloaGBixcv2v7+/PPPbV2J74fBYCAtLY2NGzeyY8cOPvrooz7HRVy2bBktLS12y8KFC+8rfyGEEEIIIYQQQggx+B5YBWFqaipjxozBZDLxxRdfcOzYMebMmUNKSgrx8fG0t7eTmZlJaWkp9fX1HD58mPLyciIjIwEICwvDbDZz4MABmpqaaGtru2N+ISEhuLq6sm7dOs6fP8/evXvJycmxi8nMzKS1tZX09HQqKio4c+YMW7dutXXdDQsL48svv6SmpoampqY+KxLDwsJITExk/vz5dHd38+yzz9qt97hx45g+fTrFxcXU1dVx5MgR3nzzTSoqKhzadu7u7rzwwgtUV1dTVlbGokWLMBqNSuMx3mrNmjV8+OGHnDp1itOnT7Nr1y4CAgLw9vbuNd7NzQ2DwWC3/DSmohBCCCGEEEIIIUR/kUlKHrwHVkGoaRp79uxh2LBhJCcnk5qayogRI9ixYwfQM0be1atXmTNnDuHh4RiNRiZPnszbb78NQGJiIgsXLiQtLQ0/Pz/y8vLumJ+fnx+bNm1i165dREVFkZuby6pVq+xifH19KSkpwWw2k5KSwtixY9m4caOta+6LL75IREQE8fHx+Pn5cfjw4T7zM5lMVFdX89xzz9l1XdY0jaKiIpKTk5k7dy7h4eGkp6dTX1+Pv7+/Q9tu5MiRzJgxg6effponn3ySmJgY3n//fYc+2xe9Xk9eXh7x8fEkJCRQV1dHUVGR3TiGQgghhBBCCCGEEOK3T7NardbBLoToW3Z2NoWFhVRVVQ12UW5z7vx5pfhOq1qLQ3faleItmuOVm5dv/EEp7VGcUor3MF9Rim/yVesu3mYd6nCs6iCrl64PU4rvVnzC8ochPzocG2i5cPegX3C2dCrFX3X7o1K8htrpcojF8XVVdV1nUIo3dPU+fEBfzM7eDsd2WZ2V0nbT3VCK11kdG8f2Jy7daumr7lcVrl1q57FWd8eGnviJynlV3/2DUto3nBw/zwB4dJuV4lXPTc06x7eNf0e9UtoNLqOU4j0Uj+EbFsf3k6apHY+qZbnePUQp3kXrUku/y93hWE8Xte9HQ2SyUvyjpw4oxasck6rnDYtV7QGs6nHQYXFVinfVeu8V0x9lUaW6bXSa2jVBRctNT6X48JtfKcVf9hihFN+Nk8OxzorfVdVjWPWcrZL+QKYN6uc9N53a/aTKNed7t+C7B/2C6vfvpkVt8k0nzfEhvFS/e6qtsFTXVfXc8WOX48dBREeVUtqXPUcqxTuj9n0d9WioUvxvxaFvrg9a3o9Fqd0H/1b0a3OxCRMmsHjx4v5M8qFTV1eHpmkPZYWdEEIIIYQQQgghhBCq1Jp9DKJNmzaxePFimpubASgrK2Py5Ml9xpvNai0ZepORkUFzczOFhYW214KDg7l06ZLDE4zcTXR0NPX1vT912rBhQ7/kMVBUWwQ64fgTKoAu1J6AqTxMHObaqpT0NU1xvEe3ex8f0hEqT4ktis8BQj3VWj+qPvVVKc+PTj4DljaoP4FWjW/T6R2OHeiytzqrbUuV9FWePgO0dHkpxQ91uvOYs7fqdFI7N6kew0ppKx7Dqo0ZVZ5Atzs5fjyC+jHZ5qTWqlWVSktSs7uvUtru1g6leNVjfoiTWks5FcotmxXLopq+i6tCyzTFtFVbBJ4bPVEpfuSp/6MUr0L1mFHlrlM7hgey5bSq4R3fKsVfc1dr/a/Cy0Xt98NlZ7UWgarHgQ7Hz3sDvU+9b36vFN/i4vhvpYEuu95Jbb+qXv+a3IMcjlX9PaTKVbH1owrl/TTAw7h5dV9Vite5OH5vcMlZrWeB6n5VPcaEeFB+NRWEt4qPjx+UVnxOTk73NTnIrYqKivqc/MTf3x+9Xk92dna/5SeEEEIIIYQQQgjxMLM8PM+yfjf6veraYrHw2muv4ePjQ0BAgF3lVkNDA9OmTcPT0xODwYDRaOTKlZ9bK1VXV/P444+j1+sxGAyMHTuWiooKSktLmTt3Li0tLWiahqZp/O1vf2PkyJF9LgBbt24lPj4evV5PQEAAs2fPprGx0a68J06cYOrUqRgMBvR6PUlJSZw7d47s7Gw2b97Mnj17bHmWlpbadTG2WCwEBQWxfv16uzSPHz+OTqeztQxsbm5mwYIF+Pn5YTAYeOKJJ6iurgYgNDS0z3X4qXIwNjaWDRs2EBwczJAhQzAajbS0tNjyy8jIYPr06bzzzjv4+/vj7e3NihUr6OrqIisrCx8fH4KCgvjggw9sn+ns7CQzM5PAwEDc3d0JDQ1l5cqV/XMQCCGEEEIIIYQQQohfjX6vINy8eTNDhw7l6NGj5OXlsWLFCvbv34/FYmHatGlcu3aNgwcPsn//fs6fP09aWprtsyaTiaCgIMrLy6msrOT111/HxcWFxMRE1q5di8Fg4NKlS1y6dImlS5fetSw3b94kJyeH6upqCgsLqaurIyMjw/b+d999R3JyMm5ubpSUlFBZWcm8efPo6upi6dKlGI1GJk2aZMszMTHRLn2dTsesWbPYtm2b3esFBQWMHz+e0NCewURnzpxJY2Mjn376KZWVlcTFxTFx4kSuXXNs0oCzZ8+yc+dOPvnkE/bt28fx48d55ZVX7GJKSkq4ePEin332GWvWrGH58uVMnTqVYcOGcfToURYuXMjLL7/Mt9/2dOPIz89n79697Ny5k5qaGgoKCggLC3OoPEIIIYQQQgghhBADxYo2aMvvVb93MY6JiWH58uUAjBo1infffZcDB3rGjPnqq6+ora0lOLhn9qYtW7YQHR1NeXk5CQkJNDQ0kJWVxejRo22f/4mXlxeapil17503b57t/yNGjCA/P5+EhATMZjOenp689957eHl5sX37dlxcesa7Cw8Pt33Gw8ODjo6OO+ZpMplYvXo1DQ0NhISEYLFY2L59O2+99RYAhw4d4tixYzQ2NuLm1jMu1qpVqygsLGT37t289NJLd12PGzdusGXLFh555BEA1q1bx5QpU1i9erWtbD4+PuTn56PT6YiIiCAvL4+2tjbeeOMNAJYtW0Zubi6HDh0iPT2dhoYGRo0axWOPPYamabbKTCGEEEIIIYQQQgjx+9LvLQhjYmLs/g4MDKSxsZGTJ08SHBxsqxwEiIqKwtvbm5MnTwKwZMkSFixYQGpqKrm5uZw7d+6+ylJZWckzzzxDSEgIer2elJQUoKerM0BVVRVJSUm2ysF7ERsbS2RkpK0V4cGDB2lsbGTmzJlAT7dps9mMr68vnp6etqW2ttbh9QsJCbFVDgKMGzcOi8VCTU2N7bXo6Gh0up93p7+/P2PGjLH97eTkhK+vr62LdUZGBlVVVURERLBo0SKKi4vvWIaOjg5aW1vtls4OtQGxhRBCCCGEEEIIIcTDp98rCG+tbNM0DYvFsRm4srOzOXHiBFOmTKGkpISoqCg+/vjjeyrH9evXeeqppzAYDBQUFFBeXm5Lq7OzZ3YnDw+Pe0r7ViaTyVZBuG3bNiZNmoSvb88sSWazmcDAQKqqquyWmpoasrKy+iV/6H2732lfxMXFUVtbS05ODu3t7RiNRp5//vk+01+5ciVeXl52y//4+7v9Vn4hhBBCCCGEEEIIAKtVG7Tl9+qBza8dGRnJhQsXuHDhgu21b775hubmZqKiomyvhYeH8+qrr1JcXMyMGTNsE2u4urrS3e349OGnTp3i6tWr5ObmkpSUxOjRo2+boCQmJoaysrI+ZxF2NM/Zs2fz9ddfU1lZye7duzGZTLb34uLiuHz5Ms7OzrdNQjJ8+HCH1qWhoYGLFy/a/v78889tXYnvh8FgIC0tjY0bN7Jjxw4++uijPsdFXLZsGS0tLXbLSwsz7yt/IYQQQgghhBBCCDH4HlgFYWpqKmPGjMFkMvHFF19w7Ngx5syZQ0pKCvHx8bS3t5OZmUlpaSn19fUcPnyY8vJyIiMjAQgLC8NsNnPgwAGamppoa2u7Y34hISG4urqybt06zp8/z969e8nJybGLyczMpLW1lfT0dCoqKjhz5gxbt261dd0NCwvjyy+/pKamhqampj4rEsPCwkhMTGT+/Pl0d3fz7LPP2q33uHHjmD59OsXFxdTV1XHkyBHefPNNKioqHNp27u7uvPDCC1RXV1NWVsaiRYswGo1K4zHeas2aNXz44YecOnWK06dPs2vXLgICAvD29u413s3NDYPBYLe4/seYikIIIYQQQgghhBD9xWodvOX36oFVEGqaxp49exg2bBjJycmkpqYyYsQIduzYAfSMkXf16lXmzJlDeHg4RqORyZMn8/bbbwOQmJjIwoULSUtLw8/Pj7y8vDvm5+fnx6ZNm9i1axdRUVHk5uayatUquxhfX19KSkowm82kpKQwduxYNm7caOua++KLLxIREUF8fDx+fn4cPny4z/xMJhPV1dU899xzdl2XNU2jqKiI5ORk5s6dS3h4OOnp6dTX1+Pv7+/Qths5ciQzZszg6aef5sknnyQmJob333/foc/2Ra/Xk5eXR3x8PAkJCdTV1VFUVGQ3jqEQQgghhBBCCCGE+O3TrNbfc/3owy87O5vCwkKqqqoGuyi3OXnuO6V4JxzvIg6gMXCH5k2r2sQ0Os2xcTQfFJVtY1F8DqC6n1SngVcpj2pZVNdV9RhTjVfZNgNddlUDmf717iFK8UOd7txi/H6pHsMPS9oAznQNWNqqx6SOgT1Pqpy3PTS1Y6bNOlQp3llT2+4DeRyoflcH+lyjsq4DfR47N3qiUvzIU/9ngEoy8FSPsYHe9ip8bly8e9AvXHP/4wCVRP37oTpOlZM2cPdYA71PvW42KcW3uDg2lNKDMNDnSZX9NNDXyofpejPQ9F0/KMW3OPs6HDvQ321VEY8G3z3oN+j//ap90PJ+fEz/zFfxa9OvzcUmTJjA4sWL+zPJh05dXR2apj2UFXZCCCGEEEIIIYQQv3YWtEFbfq+cB7sAjtq0aROLFy+mubkZgLKyMiZPntxnvNlsvu88MzIyaG5uprCw0PZacHAwly5dcniCkbuJjo6mvr6+1/c2bNjQL3k8LDqtrgOa/kA+MbNYB7YljWprlG6cHI5VLXuX4mlBdV1vWh1P31XrfdzPvmia2pNNF61TKb5boewALjievurT6puofZ88rNeV4m9ojrfyUz3GDE6tSvGdVrXxTt01taeNqq3HVHRa1Foru+s61NJXOA48UdvuHZrak1M3i1qrPaumdtw0djr+5L/b1fFzJICXtfcJuvrSpumV4rsUzh2qLTSUrx9WtW2jeo5XWVfVsque41VbBJ4dnepw7KOnDiilPdBU9+tAt3ZR0eQepBQ/oD1MFM/Zw1BrVXddMyjFq1xfB7q3S7OL34CmP5BUf4OonpuGWhy/vrbp1K4fqlTvyVTPqw+TZie13+NdFsevTz7WxrsH/YLZyVspXoiH1a+mgvBW8fHxg9KKz8nJ6b4mB7lVUVFRn5Of+Pv7o9fryc7O7rf8hBBCCCGEEEIIIR5mql29xf3r9xkpLBYLr732Gj4+PgQEBNhVbjU0NDBt2jQ8PT0xGAwYjUauXLlie7+6uprHH38cvV6PwWBg7NixVFRUUFpayty5c2lpaUHTNDRN429/+xsjR47scwHYunUr8fHx6PV6AgICmD17No2N9k8DTpw4wdSpUzEYDOj1epKSkjh37hzZ2dls3ryZPXv22PIsLS2162JssVgICgpi/fr1dmkeP34cnU5naxnY3NzMggUL8PPzw2Aw8MQTT1BdXQ1AaGhon+vwU+VgbGwsGzZsIDg4mCFDhmA0GmlpabHll5GRwfTp03nnnXfw9/fH29ubFStW0NXVRVZWFj4+PgQFBfHBBx/YPtPZ2UlmZiaBgYG4u7sTGhrKypUr++cgEEIIIYQQQgghhBC/Gv1eQbh582aGDh3K0aNHycvLY8WKFezfvx+LxcK0adO4du0aBw8eZP/+/Zw/f560tDTbZ00mE0FBQZSXl1NZWcnrr7+Oi4sLiYmJrF27FoPBwKVLl7h06RJLly69a1lu3rxJTk4O1dXVFBYWUldXR0ZGhu397777juTkZNzc3CgpKaGyspJ58+bR1dXF0qVLMRqNTJo0yZZnYmKiXfo6nY5Zs2axbds2u9cLCgoYP348oaGhAMycOZPGxkY+/fRTKisriYuLY+LEiVy75liXprNnz7Jz504++eQT9u3bx/Hjx3nllVfsYkpKSrh48SKfffYZa9asYfny5UydOpVhw4Zx9OhRFi5cyMsvv8y3334LQH5+Pnv37mXnzp3U1NRQUFBAWFiYQ+URQgghhBBCCCGEEL8d/d7FOCYmhuXLlwMwatQo3n33XQ4c6Bmn5auvvqK2tpbg4J5ZeLZs2UJ0dDTl5eUkJCTQ0NBAVlYWo0ePtn3+J15eXmiaptS9d968ebb/jxgxgvz8fBISEjCbzXh6evLee+/h5eXF9u3bcXHpGXMkPDzc9hkPDw86OjrumKfJZGL16tU0NDQQEhKCxWJh+/btvPXWWwAcOnSIY8eO0djYiJtbzzhaq1atorCwkN27d/PSSy/ddT1u3LjBli1beOSRRwBYt24dU6ZMYfXq1bay+fj4kJ+fj06nIyIigry8PNra2njjjTcAWLZsGbm5uRw6dIj09HQaGhoYNWoUjz32GJqm2SozhRBCCCGEEEIIIQaT9dc7ROavVr+3IIyJibH7OzAwkMbGRk6ePElwcLCtchAgKioKb29vTp48CcCSJUtYsGABqamp5Obmcu7cufsqS2VlJc888wwhISHo9XpSUlKAnq7OAFVVVSQlJdkqB+9FbGwskZGRtlaEBw8epLGxkZkzZwI93abNZjO+vr54enraltraWofXLyQkxFY5CDBu3DgsFgs1NTW216Kjo9Hpft6d/v7+jBkzxva3k5MTvr6+ti7WGRkZVFVVERERwaJFiyguLr5jGTo6OmhtbbVbOjvUBtMXQgghhBBCCCGEEA+ffq8gvLWyTdM0LBbHZtXKzs7mxIkTTJkyhZKSEqKiovj444/vqRzXr1/nqaeewmAwUFBQQHl5uS2tzs6emUU9PNRmaOyLyWSyVRBu27aNSZMm4evbM9ui2WwmMDCQqqoqu6WmpoasrKx+yR963+532hdxcXHU1taSk5NDe3s7RqOR559/vs/0V65ciZeXl93yP/7+br+VXwghhBBCCCGEEALAijZoy+9Vv1cQ9iUyMpILFy5w4cIF22vffPMNzc3NREVF2V4LDw/n1Vdfpbi4mBkzZtgm1nB1daW7u9vh/E6dOsXVq1fJzc0lKSmJ0aNH3zZBSUxMDGVlZX3OIuxonrNnz+brr7+msrKS3bt3YzKZbO/FxcVx+fJlnJ2db5uEZPhwx6Zmb2ho4OLFi7a/P//8c1tX4vthMBhIS0tj48aN7Nixg48++qjPcRGXLVtGS0uL3fLSwsz7yl8IIYQQQgghhBBCDL4HVkGYmprKmDFjMJlMfPHFFxw7dow5c+aQkpJCfHw87e3tZGZmUlpaSn19PYcPH6a8vJzIyEgAwsLCMJvNHDhwgKamJtra2u6YX0hICK6urqxbt47z58+zd+9ecnJy7GIyMzNpbW0lPT2diooKzpw5w9atW21dd8PCwvjyyy+pqamhqampz4rEsLAwEhMTmT9/Pt3d3Tz77LN26z1u3DimT59OcXExdXV1HDlyhDfffJOKigqHtp27uzsvvPAC1dXVlJWVsWjRIoxGo9J4jLdas2YNH374IadOneL06dPs2rWLgIAAvL29e413c3PDYDDYLa7/MaaiEEIIIYQQQgghhPj1emAVhJqmsWfPHoYNG0ZycjKpqamMGDGCHTt2AD1j5F29epU5c+YQHh6O0Whk8uTJvP322wAkJiaycOFC0tLS8PPzIy8v7475+fn5sWnTJnbt2kVUVBS5ubmsWrXKLsbX15eSkhLMZjMpKSmMHTuWjRs32rrmvvjii0RERBAfH4+fnx+HDx/uMz+TyUR1dTXPPfecXddlTdMoKioiOTmZuXPnEh4eTnp6OvX19fj7+zu07UaOHMmMGTN4+umnefLJJ4mJieH999936LN90ev15OXlER8fT0JCAnV1dRQVFdmNYyiEEEIIIYQQQgjxoFmsg7f8XmlWa//NDTNhwgRiY2NZu3ZtfyX50Kmrq+NPf/oTx48fJzY2dsDzy87OprCwkKqqqgHPS9XJc98pxXdbnQaoJD0GcqwADbWviQ7Hxt38ibPWpRTfjePb0mJVq/S1KD43UF3Xm1bHJ0931XpvtdsXTVPbT66a2kQ73QplB3DG8fKrbvebuCrFe1ivK8Xf0IY4HKt6jKlu906rWmtld61dKb7NOlQpXkWnRW0SLHed2rZROe950qqUdoemNk6vu0XtGLNqasfNt52P3D3oPxhc1criZe19eI2+tOn0SvFdCucO1euN6vXjplXtmFQ9x6usq2rZVc/xqmU/OzrV4dhHTx1QSnugqd5jOWmOD9sz0FTv31S/Iyo6LWrX1mE0KcVf1xmU4lWurzpN7Xj/PVE976mem4ZaHL++ql4/VKnek6mcVwfyu3cvVNdV5frkY228e9AvmJ28leJVjX40aEDTf1jtq+octLwnxapdD34r1H7tDqJNmzaxePFimpubH1ieGRkZNDc3U1hYaHstODiYS5cuOTx+4G/ZQN+Mql6E2i3uDscqVzwplkX1R4xq5ZBKeVRvvF0Ut40qlXUd6Jvdm9aBPfGr7lcVTqh9n7p1aqd7J6vj6Vs1tWPMolDBDQP/fXLVBu7mw9VJLW3lyhuF70j3AF/yu3VqZVc9N7k7O74tVfdph0KF+L1QragaSKplGchKOdW0B/rHqUql37nREwcs7Xvxa64cepgqHQxas1J8K8OU4lWv3arfEaW0Fbf7wzRo/0A/uB92U61y6AeXPzgcO9DH+8N2Xh1IqutqoNnh2FbNRynth+k6/1titT48553fi19NBeGtysrKmDx5cp/vm83mAcnXycnpvsb+u1V0dDT19fW9vrdhw4Z+y0cIIYQQQgghhBBCiN70e/MWi8XCa6+9ho+PDwEBAWRnZ9vea2hoYNq0aXh6emIwGDAajVy5csX2fnV1NY8//jh6vR6DwcDYsWOpqKigtLSUuXPn0tLSgqZpaJrGvn37qKqq6nMB2Lp1K/Hx8ej1egICApg9e/ZtMxmfOHGCqVOnYjAY0Ov1JCUlce7cObKzs9m8eTN79uyx5VlaWkpdXR2aplFVVYXFYiEoKIj169fbpXn8+HF0Op2t4q+5uZkFCxbg5+eHwWDgiSeeoLq6GoCioqI+1+GXk51s2LCB4OBghgwZgtFopKWlxfZeRkYG06dP55133sHf3x9vb29WrFhBV1cXWVlZ+Pj4EBQUZJsRGqCzs5PMzEwCAwNxd3cnNDSUlStX3t/OF0IIIYQQQgghhBC/Ov3egnDz5s0sWbKEo0eP8o9//IOMjAzGjx/PxIkTbZWDBw8epKuri3//938nLS2N0tJSoGeijz//+c+sX78eJycnqqqqcHFxITExkbVr1/LXv/7VNsOwp6cnnp6edyzLzZs3ycnJISIigsbGRpYsWUJGRgZFRUUAfPfddyQnJzNhwgRKSkowGAwcPnyYrq4uli5dysmTJ2ltbbVVrPn4+HDx4kVb+jqdjlmzZrFt2zb+8pe/2F4vKChg/PjxhIaGAjBz5kw8PDz49NNP8fLyYsOGDUycOJHTp0/bYu7k7Nmz7Ny5k08++YTW1lbmz5/PK6+8QkFBgS2mpKSEoKAgPvvsMw4fPsz8+fM5cuQIycnJHD16lB07dvDyyy/zb//2bwQFBZGfn8/evXvZuXMnISEhXLhwgQsXLjiwh4UQQgghhBBCCCEGTv/NliEc1e8VhDExMSxfvhyAUaNG8e6773LgQM+4K1999RW1tbUEBwcDsGXLFqKjoykvLychIYGGhgaysrIYPXq07fM/8fLyQtM0pe698+bNs/1/xIgR5Ofnk5CQgNlsxtPTk/feew8vLy+2b99um7k4PDzc9hkPDw86OjrumKfJZGL16tU0NDQQEhKCxWJh+/btvPXWWwAcOnSIY8eO0djYiJtbz0D7q1atorCwkN27d/PSSy/ddT1u3LjBli1beOSRngHa161bx5QpU1i9erWtbD4+PuTn56PT6YiIiCAvL4+2tjbeeOMNAJYtW0Zubi6HDh0iPT2dhoYGRo0axWOPPYamaQ5VVAohhBBCCCGEEEKI355+72IcExNj93dgYCCNjY2cPHmS4OBgW+UgQFRUFN7e3pw8eRKAJUuWsGDBAlJTU8nNzeXcuXP3VZbKykqeeeYZQkJC0Ov1pKSkAD1dnQGqqqpISkqyVQ7ei9jYWCIjI9m2bRsABw8epLGxkZkzZwI93abNZjO+vr62Vo+enp7U1tY6vH4hISG2ykGAcePGYbFYbK0poWcsQ53u593p7+/PmDFjbH87OTnh6+tr62KdkZFBVVUVERERLFq0iOLi4juWoaOjg9bWVruls0Nttk0hhBBCCCGEEEKIu7GgDdrye9XvFYS3VrZpmobF4tisPtnZ2Zw4cYIpU6ZQUlJCVFQUH3/88T2V4/r16zz11FMYDAYKCgooLy+3pdXZ2TO7oYeHxz2lfSuTyWSrINy2bRuTJk3C19cX6JksJTAw8LbxBWtqasjKyuqX/KH37X6nfREXF0dtbS05OTm0t7djNBp5/vnn+0x/5cqVeHl52S3/3w3r+q38QgghhBBCCCGEEGJw9HsFYV8iIyNvG+fum2++obm5maioKNtr4eHhvPrqqxQXFzNjxgzb+H+urq50d3c7nN+pU6e4evUqubm5JCUlMXr06NsmKImJiaGsrIybN2/2moajec6ePZuvv/6ayspKdu/ejclksr0XFxfH5cuXcXZ2ZuTIkXbL8OHDHVqXhoYGu7EPP//8c1tX4vthMBhIS0tj48aN7Nixg48++ohr1671Grts2TJaWlrslgUv/+f7yl8IIYQQQgghhBBCDL4HVkGYmprKmDFjMJlMfPHFFxw7dow5c+aQkpJCfHw87e3tZGZmUlpaSn19PYcPH6a8vJzIyEgAwsLCMJvNHDhwgKamJtra2u6YX0hICK6urqxbt47z58+zd+9ecnJy7GIyMzNpbW0lPT2diooKzpw5w9atW21dd8PCwvjyyy+pqamhqampz4rEsLAwEhMTmT9/Pt3d3XazD6empjJu3DimT59OcXExdXV1HDlyhDfffJOKigqHtp27uzsvvPAC1dXVlJWVsWjRIoxGo9J4jLdas2YNH374IadOneL06dPs2rWLgIAAvL29e413c3PDYDDYLa7/MaaiEEIIIYQQQgghRH+xWgdvGUjXrl3DZDJhMBjw9vZm/vz5mM3mO8b/5//8n4mIiMDDw4OQkBAWLVpES0uLXZymabct27dvVyrbA6sg1DSNPXv2MGzYMJKTk0lNTWXEiBHs2LED6Bkj7+rVq8yZM4fw8HCMRiOTJ0/m7bffBiAxMZGFCxeSlpaGn58feXl5d8zPz8+PTZs2sWvXLqKiosjNzWXVqlV2Mb6+vpSUlGA2m0lJSWHs2LFs3LjR1jX3xRdfJCIigvj4ePz8/Dh8+HCf+ZlMJqqrq3nuuefsui5rmkZRURHJycnMnTuX8PBw0tPTqa+vx9/f36FtN3LkSGbMmMHTTz/Nk08+SUxMDO+//75Dn+2LXq8nLy+P+Ph4EhISqKuro6ioyG4cQyGEEEIIIYQQQgjRP0wmEydOnGD//v38r//1v/jss8/uOHntxYsXuXjxIqtWreLrr79m06ZN7Nu3j/nz598W+8EHH/D/Z+/ew6q47sX/v4cNbOWyuXgDgYgICBgvrZdUPRFReryeeEmCtfSAIqbWYH/EGMU2UTE5mtgmxxgbtY0F1MZoj4l6rLGhphBLqKAW9UTURFFQiXdABLnt/fuDrztuuc0yoCZ+Xs8zz6N7PrNmzezZM8Oaz6xVXFxsnSZOnKhUN81ikcGjH2ZLlixh+/bt5OXlPeiqNPB/X32tFG/Q9L8iDqChdmhWmtvpjnXUGs8Gba26aJpivGL5KmotaoOV22u1bVSTejUW/YMCOSh+Tw8be9qu/mYMSvEGxe/VbNFffi1qx5gBtXNBneK2OlCtFK+6L9uSyu8DwE7T18cvgJFbSmXXolYX1f1uUewA+nqdh+5YF8NNpbLt0L8fQf2YNFva7uGb6rW1TuG3DerXM5Xy2/q+QDVe5Zg8FTJSqewex/cqxatS/T215X3Hd1k7S/NvKN3tJq5K8arHvMr3+jD9Ptqaat1V74U71Kr9jXPdobPu2Lb+7T1K5wLVbW1v0X9vUIGLUtkq92P3IqSHb5uW/7DaeUDtnNmanhrQNn8f5OfnExYWRm5uLgMGDABgz549jB07lnPnztG1a1dd5fz5z3/mZz/7GTdv3sTevv4cp2kaH330kXKj4J1a/Y51+PDhJCYmtnaxQgghhBBCCCGEEEK0qaqqKsrKymymqqqqb11udnY27u7u1sZBqO+Wzs7Ojv379+sup7S0FJPJZG0cvO3555+nY8eODBo0iD/+8Y+o5gOqPU55wFJTU0lMTKSkpIR9+/YxZsyYJmObe4f7fsvIyCAiIoLr16836OOvV69enD17ttHl1q1bd8/rzM7O5t/+7d8YPXo0f/nLX+65nOY4aqpZOmrt0T7X/08p/oz7D3THVtbpzzYEcLZXe6Ks+jRONXtF5UloWz5dA6jS1EYDr1PIpHFUzFxRpbrfValmyqmoUzx921nUtlXlGK62OCqV7WindnGtNStmwSpmbtZobZjdhVrmpkVT+70+TNklmuIxVqup9WPraKf/e3VE7RirRq0uqudVN/NV3bHX7PRnooD6eUw1I9Cj5lLLQXc4b9dNd6yrQe1eTTULSDVbS4VqRmBbZxyqZoaq7JtHKePpqlnfAIK3VdWpZVr3SJ2tFH9lZvNdKt2pLbMTHzZtnf14zV5fN1C3VdTqvxe++PiPlMpu63OByjVE9fqhSvV7Vc3OV/l915jV9qO7ww2l+O9y5uajYvny5dbu7m5bvHgxS5Ys+Vblfv3113TubHuvZ29vj6enJ19/rS97+cqVK7z66qsNXkteunQpI0aMwMnJiU8++YTZs2dTXl7OL3/5S931+041EN5pwIABD+Vrt6p2795tHfykuroaR8dv/tDu0qULrq6u93QQrl+/njlz5rB+/XouXLigO1VVCCGEEEIIIYQQ4kEyP8B21IULFzJ37lybz4zNDNKalJTEG2+80WyZ+fn537peZWVljBs3jrCwsAbtRK+88or13z/4wQ+4efMmv/nNb5QaCNskbcJsNjN//nw8PT3x8vKyqXhhYSETJkzAxcUFk8lEVFQUFy9etM4/fPgwERERuLq6YjKZ6N+/PwcOHCAjI4Pp06dTWlqKpmk4OTmxadMmAgMDG51uq6qqYsGCBfj5+WE0GgkMDGT9+vXW+ZmZmQwaNAij0Yi3tzdJSUnU1n6T8eHv78/KlStttq9fv34226RpGu+99x6TJk3CycmJoKAgdu7cCcCZM2eIiIgAwMPDA03TmDZtGlD/OvZvfvMbVq9ezY9+9COef/55li1bRmJiIoGBgbi61vdrUlNTQ+fOnW3q3Zzy8nK2bNnCL37xC8aNG0dqaqrN/NTU1AaZjNu3b0dTzFwRQgghhBBCCCGE+D4xGo2YTCabqbkGwhdffJH8/Pxmp4CAALy8vLh0yfbtjNraWq5du4aXl1ezdbpx4wajR4/G1dWVjz76yDq4blOeeOIJzp07p/RqdJtkEKalpTF37lz2799PdnY206ZNY+jQoYwcOdLaOJiZmUltbS3PP/88U6ZMISMjA6gf0eUHP/gBa9aswWAwkJeXh4ODA0OGDGHlypUsWrSIEydOAODi0nLnoTExMWRnZ7Nq1Sr69u1LQUEBV65cAeD8+fOMHTuWadOmsWHDBo4fP87MmTNp166dctZecnIyK1as4De/+Q3vvPMO0dHRnD17Fj8/P7Zt28bTTz/NiRMnMJlMNqMcp6Wl8Ytf/MI6QvLVq1cZNmwYxcXFeHt7A7Br1y4qKiqYMmWKrrps3bqVkJAQevbsyc9+9jMSExNZuHChNAAKIYQQQgghhBDiofddGk63U6dOdOrUqcW4wYMHU1JSwsGDB+nfvz8An376KWazmSeeeKLJ5crKyhg1ahRGo5GdO3fSrl3LXabl5eXh4eHRbMPm3dqkgbBPnz4sXrwYgKCgIFavXs3evfX9Jxw9epSCggL8/PwA2LBhA7169SI3N5eBAwdSWFjISy+9REhIiHX529zc3NA0rcWW1dtOnjzJ1q1bSU9PJzIyEoCAgADr/HfffRc/Pz9Wr16NpmmEhIRw4cIFFixYwKJFi7Cz059gOW3aNKZOnQrAsmXLWLVqFTk5OYwePRpPT08AOnfu3CBzLygoiBUrbPsX6dmzJxs3bmT+/PlA/VDVzz77rK4GUah/vfhnP/sZAKNHj6a0tJTMzEyGDx+ue3uEEEIIIYQQQgghROsIDQ1l9OjRzJw5k7Vr11JTU0NCQgI/+clPrN3CnT9/npEjR7JhwwYGDRpEWVkZ//7v/05FRQWbNm2yDpoC9Q2TBoOB//3f/+XixYv86Ec/ol27dqSnp7Ns2TLmzZunVL82ecW4T58+Nv/39vbm0qVL5Ofn4+fnZ20cBAgLC8Pd3d36PvbcuXOJj48nMjKS119/nVOnTt1zPfLy8jAYDISHhzc6Pz8/n8GDB9tk1g0dOpTy8nLOnTuntK47t9nZ2RmTydQgdbQxt1uN7xQfH09KSgoAFy9e5OOPPyYuLk5XPU6cOEFOTo61sdLe3p4pU6bofj25KY2N4lPdCqP4CCGEEEIIIYQQQjwK/vSnPxESEsLIkSMZO3Ys//Zv/8bvf/976/yamhpOnDhBRUX9QKmHDh1i//79HD16lMDAQLy9va1TUVERAA4ODvzud79j8ODB9OvXj3Xr1vHWW29ZE/f0apMMwrvfhdY0DbNZ3whJS5Ys4ac//Sl/+ctf+Pjjj1m8eDEffPABkyZNUq7Hna/y3is7O7sGQ0PfHlTkTve6zc7Ozg0+i4mJISkpiezsbD7//HO6d+/Ok08+qau+69evp7a21mZQEovFgtFoZPXq1bi5uenepjs1NopPwpxE5vx/L+iqlxBCCCGEEEIIIYQe3+XR1pvj6enJ+++/3+R8f39/m/aa4cOHN2i/udvo0aMZPXr0t65bm2QQNiU0NJSioiJrKyfAsWPHKCkpISwszPpZcHAwL7zwAp988gmTJ0+2ZtM5OjpSV1ene329e/fGbDaTmZnZZH2ys7NtdnZWVhaurq74+voC9SmbxcXF1vllZWUUFBTorsPtegO6696hQwcmTpxISkoKqampTJ8+XddytbW1bNiwgTfffJO8vDzrdPjwYbp27crmzZut23Tjxg1u3rxpXbalEaEXLlxIaWmpzfTzWbN11UsIIYQQQgghhBBCPLzuawNhZGQkvXv3Jjo6mkOHDpGTk0NMTAzh4eEMGDCAyspKEhISyMjI4OzZs2RlZZGbm0toaChQ35JaXl7O3r17uXLlijXlsin+/v7ExsYSFxfH9u3bKSgoICMjg61btwIwe/ZsioqKmDNnDsePH2fHjh0sXryYuXPnWvsfHDFiBBs3bmTfvn0cPXqU2NhYDAaD0nZ369YNTdPYtWsXly9fpry8vMVl4uPjSUtLIz8/n9jYWF3r2bVrF9evX2fGjBk8/vjjNtPTTz9tfc34iSeewMnJiV/96lecOnWK999/v8FIx3drbBQfR4XOLoUQQgghhBBCCCH0MFse3PSouq8NhJqmsWPHDjw8PBg2bBiRkZEEBASwZcsWAAwGA1evXiUmJobg4GCioqIYM2aM9dXWIUOGMGvWLKZMmUKnTp0aDO7RmDVr1vDMM88we/ZsQkJCmDlzpjVzzsfHh927d5OTk0Pfvn2ZNWsWM2bM4OWXX7Yuv3DhQsLDwxk/fjzjxo1j4sSJ9OjRQ2m7fXx8SE5OJikpiS5dupCQkNDiMpGRkXh7ezNq1Cib14Wbs379eiIjI3Fzc2sw7+mnn+bAgQMcOXIET09PNm3axO7du+nduzebN29WHrVZCCGEEEIIIYQQQnw/aJaWXmYWD0R5eTk+Pj6kpKQwefLkB12dRp08VagUb1Zsj/a5/n9K8Wfcf6A7tqrOUalsZ/vms1XvpqH2s7JDXx+dt9Va9HcfWodaxquJEqX4Kk2tr88Ks/749na3lMpWpbrfVdnTfN+e30Y1ahm8jqgNKqTS50elxUmp7HZ2lUrxVeZ2SvFOtJylfacare2yoQ3UKsXXoHZuUvmejKj9nmpxaDnoDg4WtWNMdb/fsug/Dly0G0plq/6eVK9nprprumOv2XVWKttBUzvPqPbn41HT8oBrdzpv1013rKtB7beqcu0DMGj6u6WBtu3r6FTISKX4Hsf3KsW35b5RvadRpbrf27I+N+vUrmdVdWrnyR6pat3zXJnZcjLEbarH+3eZ6v1btUXt2qpafkWd/nvbi4//SKnstj4XqGyrprXtuUD1t11nUfsb55ZZ/7W+xqxWtruD2n2H6rb27OHXctD30P/sb9u/1ZrzzBP3NZfuodHqWz18+HASExNbu9hHhtls5tKlS7z66qu4u7vz1FNPPegqCSGEEEIIIYQQQtw3FsuDmx5VbTKKcVtJTU0lMTGRkpISAPbt28eYMWOajNfT19/9kJGRQUREBNevX8fd3b3Z2MLCQrp3746vry+pqanY29vbzLtzMJe7HTt2jEWLFpGWlmb9zNPTk4EDB7JixQr69OnzrbflTqoZFGaLWvwlz2CleDeFDI1Sg6dS2apPqJQz0xSTFtpp+jOwVDNjblpcleJVt9VRIdtF9RhTpfq9qj45LatT25cqnA1qWa0lZneleJXszW5XDyiVXdxR7Vxkr6ll4V2r66AU72jXdpmejpraj1s1k0bl93eltqNS2W72ZUrxV+o6KcV3NFxWirdXOHeUK57HVDOnKzVnpfgSO/373u/GF0plXzQFKcWrZpdcs++iFO9g0f97Va1LW2evtCXVLCDVjMPu+X9Xim/rrMDvKgfF641mUNuP159brlb+o/xXajNU7w/dLPr/RgAot2vYXVNzVO4jVM8FqlTvy1XOqw/beaPH2XSl+JOP6R/h1c7Qtplr39fRecV333eqgfBuAwYMaHH03e+S6urqBkNa36lr167Nbu/tvgpHjx5tHfn566+/5uWXX2b8+PEUFqq9EiyEEEIIIYQQQghxv8kzkvuvTdJzzGYz8+fPx9PTEy8vL5sBMAoLC5kwYQIuLi6YTCaioqK4ePGidf7hw4eJiIjA1dUVk8lE//79OXDgABkZGUyfPp3S0lI0TUPTNN544w0CAwObnACqqqpYsGABfn5+GI1GAgMDraP5AmRmZjJo0CCMRiPe3t4kJSVRW/vN00N/f39Wrlxps339+vWz2SZN03jvvfeYNGkSTk5OBAUFsXPnTgDOnDlDREQEAB4eHmiaxrRp04D617ETEhJITEykY8eOjBo1iri4OMaPH2+zvpqaGjp37kxaWlqz23s729BoNOLl5YWXlxf9+vUjKSmJoqIiLl+uz9bIyMhA0zRrJiZAXl4emqZx5swZ/V+0EEIIIYQQQgghhPjOa5MGwrS0NJydndm/fz8rVqxg6dKlpKenYzabmTBhAteuXSMzM5P09HROnz7NlClTrMtGR0fj6+tLbm4uBw8eJCkpCQcHB4YMGcLKlSsxmUwUFxdTXFzMvHnzWqxLTEwMmzdvZtWqVeTn57Nu3TpcXFwAOH/+PGPHjmXgwIEcPnyYNWvWsH79el577TXlbU5OTiYqKoojR44wduxYoqOjuXbtGn5+fmzbtg2AEydOUFxczNtvv22zrxwdHcnKymLt2rXEx8ezZ88eiouLrTG7du2ioqLCZj/pVV5ezqZNmwgMDKRDB7XX7oQQQgghhBBCCCHE91+bvGLcp08fFi9eDEBQUBCrV69m7976/haOHj1KQUEBfn71I/Fs2LCBXr16kZuby8CBAyksLOSll14iJCTEuvxtbm5uaJqGl5eXrnqcPHmSrVu3kp6eTmRkJAABAQHW+e+++y5+fn6sXr0aTdMICQnhwoULLFiwgEWLFmFnp7/9dNq0aUydOhWAZcuWsWrVKnJychg9ejSenvX93XXu3LlBH4RBQUGsWGE7QlnPnj3ZuHEj8+fPByAlJYVnn33W2rDZkl27dlljb968ibe3N7t27VLaHiGEEEIIIYQQQogHwWyRvhrvtzZpMbp7MAxvb28uXbpEfn4+fn5+1sZBgLCwMNzd3cnPzwdg7ty5xMfHExkZyeuvv86pU6fuuR55eXkYDAbCw8MbnZ+fn8/gwYPR7uhEfujQoZSXl3Pu3Dmldd25zc7OzphMJi5dutTicv3792/wWXx8vLUPwYsXL/Lxxx8TFxenuy4RERHk5eWRl5dHTk4Oo0aNYsyYMZw9e1Z3GXerqqqirKzMZqquqrrn8oQQQgghhBBCCCHEw6FNGggdHBxs/q9pGmazvpGAlixZwhdffMG4ceP49NNPCQsL46OPPrqnerRv3/6elruTnZ1dg0FDamoajlR1r9vs7NxwJMSYmBhOnz5NdnY2mzZtonv37jz55JO66+zs7Gztl3DgwIG899573Lx5kz/84Q/WbQJstquxbbrT8uXLcXNzs5l+v/Z3uuskhBBCCCGEEEIIoYfF8uCmR9V9fec0NDSUoqIiioqKrJ8dO3aMkpISwsLCrJ8FBwfzwgsv8MknnzB58mRrNp2joyN1dXW619e7d2/MZjOZmZlN1ic7O9umoSwrKwtXV1d8fX0B6NSpk01/gGVlZRQUFOiuw+16A7rr3qFDByZOnEhKSgqpqalMnz5daX130zQNOzs7KisrgfptAmy2q6XRoBcuXEhpaanN9Nys579VvYQQQgghhBBCCCHEg3dfGwgjIyPp3bs30dHRHDp0iJycHGJiYggPD2fAgAFUVlaSkJBARkYGZ8+eJSsri9zcXEJDQ4H6EYXLy8vZu3cvV65coaKiotn1+fv7ExsbS1xcHNu3b6egoICMjAy2bt0KwOzZsykqKmLOnDkcP36cHTt2sHjxYubOnWvNshsxYgQbN25k3759HD16lNjYWAwGg9J2d+vWDU3T2LVrF5cvX6a8vLzFZeLj40lLSyM/P5/Y2Fil9VVVVfH111/z9ddfk5+fz5w5cygvL+c//uM/AAgMDMTPz48lS5bw5Zdf8pe//IU333yz2TKNRiMmk8lmcjQaleolhBBCCCGEEEIIIR4+97WBUNM0duzYgYeHB8OGDSMyMpKAgAC2bNkCgMFg4OrVq8TExBAcHExUVBRjxowhOTkZgCFDhjBr1iymTJlCp06dGgzu0Zg1a9bwzDPPMHv2bEJCQpg5cyY3b94EwMfHh927d5OTk0Pfvn2ZNWsWM2bM4OWXX7Yuv3DhQsLDwxk/fjzjxo1j4sSJ9OjRQ2m7fXx8SE5OJikpiS5dupCQkNDiMpGRkXh7ezNq1Ci6du2qtL49e/bg7e2Nt7c3TzzxBLm5ufz5z39m+PDhQP3r0Js3b+b48eP06dOHN954455GbhZCCCGEEEIIIYRobfKK8f2nWe7uYE88FMrLy/Hx8SElJYXJkyc/6Oo06vgptYFczBa19mij3S2leIc6/YOmlGqeSmVrqP1M7NDX56Y1XlOLd0T/tlajlump+j2p1l2lfE1r29OTRXFkLNX6VNR9+35Qm+JsaD6D+m6V5nZK8e0Vfn++V/6lVHZxxz4tB93Bgtr3VF7XsG/X5jjaNd8H67fhqFUrxZsVn9upnGtKa01KZbvZlynFl9W6KsV3NFxWirdo+vdNuUWtLiZKlOIrNbVjrM6i/80Dnxv5SmVfNAUpxddYHFoOuoPq9azWYq871l6rVSpb9RysWnfVc01bOhUyUim+e/7fleJVr91tSXW/q96Tqag2OyrF1yr8tgGcDJVK8Sr3TA/Td/qwcTarXc/K7dyU4mvM+s+rjnZq9wWqVO/jVc6rbfnbuxf+Zz5Vij/52GjdsXWK92Mq9833IqSHb5uW/7B6/x8P7pj76b89PPcE91OrZxAOHz6cxMTE1i72kWE2m7l06RKvvvoq7u7uPPXUUw+6SkIIIYQQQgghhBD3jdny4KZHlf7HvA+B1NRUEhMTKSkpAWDfvn2MGTOmyXg9ff3dDxkZGURERHD9+nXc3d2bjS0sLKR79+74+vqSmpqKvb29zbw7B3O527Fjx3jsscf4+uuv+a//+i/+8pe/cP78eTp37ky/fv1ITExk5Ei1J9LNuVnnpBRvVHxiZqxVy5DKvtb0vrlbF1e1J7iOBrUsB0c7tXjVfXPd7KE71tWg9jtwstxQircoPmeo0vRn1d1SzHpTzS4xoH/Qo3uJf6zmS6V4FdWo/f48a/VnnQJccdTftcFpzyeUynZALWNP5ck8gG+t2kBSrpfa7nuqa++iFP+l52CleAeFDCz/2hNKZd+w76gU371GLfPNUKH2tL2yvf7M72JLF6WyL9WqZYt0aV+iFF+lkJXU7niuUtmWQcFK8e3tVLOP1c41F266644NcP1aqewKs1pWdjs7tfOeSqanaraWStmgnhFYEBqhFN8tv/EB/BqjmjWkGl+H2r5RvRarOF+u//4K4MoNtetT/64XlOLbL5ulO7biV+uUyjZqbZvxVIv+fdPWb6+cremmFO/soPZ3wuUK/Rn6XZ2vKZXd7fohpfj9kxYoxXf++17dsQZN7bfX1tnBx/zGKcVfLNf/PZVWqjWT9Olc3HKQEN8B36kGwrsNGDCgxdF3v0uqq6vx9/enqbe+u3bt2uz2du3alTNnzjB06FDc3d35zW9+Q+/evampqeGvf/0rzz//PMePH2+j2gshhBBCCCGEEEKI76I2GaTEbDYzf/58PD098fLyYsmSJdZ5hYWFTJgwARcXF0wmE1FRUVy8eNE6//Dhw0RERODq6orJZKJ///4cOHCAjIwMpk+fTmlpKZqmoWkab7zxBoGBgU1OUD+i74IFC/Dz88NoNBIYGMj69eut68vMzGTQoEEYjUa8vb1JSkqitvabjAx/f39Wrlxps339+vWz2SZN03jvvfeYNGkSTk5OBAUFsXPnTgDOnDlDRET9U10PDw80TWPatGlA/evYCQkJJCYm0rFjR0aNGkVcXBzjx4+3WV9NTQ2dO3cmLS2t2e21t7dn9uzZaJpGTk4OTz/9NMHBwfTq1Yu5c+fyz3/+01rmW2+9Re/evXF2dsbPz4/Zs2c/NBmXQgghhBBCCCGEeHRZLNoDmx5VbdJAmJaWhrOzM/v372fFihUsXbqU9PR0zGYzEyZM4Nq1a2RmZpKens7p06eZMmWKddno6Gh8fX3Jzc3l4MGDJCUl4eDgwJAhQ1i5ciUmk4ni4mKKi4uZN29ei3WJiYlh8+bNrFq1ivz8fNatW4eLS/0rX+fPn2fs2LEMHDiQw4cPs2bNGtavX39PI/omJycTFRXFkSNHGDt2LNHR0Vy7dg0/Pz+2bdsGwIkTJyguLubtt9+22VeOjo5kZWWxdu1a4uPj2bNnD8XF36Qp79q1i4qKCpv91Jhr166xZ88enn/+eZydG3agfufrzXZ2dqxatYovvviCtLQ0Pv30U+bPn6+83UIIIYQQQgghhBDiu61NXjHu06cPixcvBiAoKIjVq1ezd299/wZHjx6loKAAPz8/ADZs2ECvXr3Izc1l4MCBFBYW8tJLLxESEmJd/jY3Nzc0TcPLy0tXPU6ePMnWrVtJT08nMjISgICAAOv8d999Fz8/P1avXo2maYSEhHDhwgUWLFjAokWLsLPT3346bdo0pk6dCsCyZctYtWoVOTk5jB49Gk/P+n6TOnfu3KAPwqCgIFasWGHzWc+ePdm4caO1wS4lJYVnn33W2rDZlK+++gqLxWLdd825cyAZf39/XnvtNWbNmsW7777b4rJCCCGEEEIIIYQQbaWJntdEG2qTDMI+ffrY/N/b25tLly6Rn5+Pn5+ftXEQICwsDHd3d/Lz6zs3nzt3LvHx8URGRvL6669z6tSpe65HXl4eBoOB8PDwRufn5+czePBgNO2bFNKhQ4dSXl7OuXPnlNZ15zY7OztjMpm4dOlSi8v179+/wWfx8fGkpKQAcPHiRT7++GPi4uJaLKupvgsb87e//Y2RI0fi4+ODq6sr//mf/8nVq1epqGi88/KqqirKyspspupqtc6/hRBCCCGEEEIIIcTDp00aCB0cbEet0jQNs1nfaFNLlizhiy++YNy4cXz66aeEhYXx0Ucf3VM92rdXG+2uMXZ2dg0a3mpqGo7Aea/b3NirwDExMZw+fZrs7Gw2bdpE9+7defLJJ1ssKygoCE3TWhyI5MyZM4wfP54+ffqwbds2Dh48yO9+9zugfqCUxixfvhw3NzebKWXdyhbrJIQQQgghhBBCCCEebm3SQNiU0NBQioqKKCoqsn527NgxSkpKCAsLs34WHBzMCy+8wCeffMLkyZOt2XSOjo7U1ekfXr13796YzWYyMzObrE92drZNA2BWVhaurq74+voC0KlTJ5v+AMvKyigoKNBdh9v1BnTXvUOHDkycOJGUlBRSU1OZPn26ruU8PT0ZNWoUv/vd77h582aD+SUlJQAcPHgQs9nMm2++yY9+9COCg4O5cOFCs2UvXLiQ0tJSm2n6zxN11UsIIYQQQgghhBBCL7PlwU2PqvvaQBgZGUnv3r2Jjo7m0KFD5OTkEBMTQ3h4OAMGDKCyspKEhAQyMjI4e/YsWVlZ5ObmEhoaCtT3lVdeXs7evXu5cuVKk6/D3ubv709sbCxxcXFs376dgoICMjIy2Lp1KwCzZ8+mqKiIOXPmcPz4cXbs2MHixYuZO3eutf/BESNGsHHjRvbt28fRo0eJjY3FYDAobXe3bt3QNI1du3Zx+fJlXaMFx8fHk5aWRn5+PrGxsbrX9bvf/Y66ujoGDRrEtm3b+PLLL8nPz2fVqlUMHjwYgMDAQGpqanjnnXc4ffo0GzduZO3atc2WazQaMZlMNpOjo1F3vYQQQgghhBBCCCHEw+m+NhBqmsaOHTvw8PBg2LBhREZGEhAQwJYtWwAwGAxcvXqVmJgYgoODiYqKYsyYMSQnJwMwZMgQZs2axZQpU+jUqVODwT0as2bNGp555hlmz55NSEgIM2fOtGbX+fj4sHv3bnJycujbty+zZs1ixowZvPzyy9blFy5cSHh4OOPHj2fcuHFMnDiRHj16KG23j48PycnJJCUl0aVLFxISElpcJjIyEm9vb0aNGkXXrl11rysgIIBDhw4RERHBiy++yOOPP86Pf/xj9u7dy5o1awDo27cvb731Fm+88QaPP/44f/rTn1i+fLnSNgkhhBBCCCGEEEK0BYvlwU2PKs2iMrKFuG/Ky8vx8fEhJSWFyZMnP+jqNOrgyWtK8Ua7xvs3bIq7+YpSfPa1sJaD/p8urpVKZTsaatXi7dTiVffNLbP+7E1XQ8sZq3dqZ274enpzLJrac4YqTX/foLfM7ZTK1jS105kB/V0WANhrat+rx63iloPuUbWDk1K8Q63aoEJXHPU/mKi12KvVxa5hP67NqTE7tBx0B6+aQqV410tfKsWrqGvf/Ojzd/vSc7BSvIPCMdml6qxS2TfadVSKd6u8qBRvqLmlFF/Z3lN3bIFF7UFeZa3aMdalfYlSfJXZUXdsr4N/UCr7zKBopXgHTe16U2lWO9ecK9f/PQW4fq1UdoVZrW/pdnZq5706i/43ROw0fX1r30vZAHaolV8QGqEU3y2/8e53GqOhdm1Vja9Dbd+oXrtVnL3RSSn+yg21c0f/rs136XO39stm6Y6t+NU6pbKNmto5WFUt+veN2aJ2L6n6+7tS5aEU7+yg9nfC5QqT7tiuzmp/P3W7fkgpfv+kBUrxnf++V3esQVP77VnQWg66g+q5Q+XaCnDxpv7vqbRS7d62T+e2u+cHCAvUf1/+fZLy9we37ulql9XvjVbJIBw+fDiJiYmtUdQjz2w2c+nSJV599VXc3d156qmnHnSVhBBCCCGEEEIIIcT3mFrT+AOQmppKYmKidYCNO+3bt48xY8Y0uayevv7aSkZGBhEREVy/fh13d3fdyxUWFtK9e3d8fX1JTU3F3t7eZt6dg7ncVlVVRW1tLdHR0WzatMlm3vPPP8+7775LbGwsqamp97o5jXI2NN8H5N3sUcsaqjDof8oD8ESHE7pjr1g6K5XtoJgRqJppppop4GTQH19tUXu6ZrFTe9pnr6l9r7Vm/acdo2L2hwNqmTFmxWckNajty2vt9D/tU62LqipNre5Omv6n56pPiB01te/VrJilet3YRSm+xE/tfKBCNbuyneK+aW/Rn/F706iWQVGneIugWn5Ve7XMtCqL/sxpT61UqWyjo1omzS3UMtncDPrr8+XAaUplOyqe90yVl5XiaxXOYwCdnW7ojq2xqGVfOSpeb1SzUVSzY9qybNW6q2QEApwNDdcd2+O4/gyje9HWGYoqujiXKcW7t1M7hlXfdKj8VfN9hN/JQfH3YVbM3GzL/e5/MVspvtDrCaX4Tka1rD3VjF9fZ/1vPakeA+c9+yjFe2WkK8Xbof9vFtXMzQsjxyrFd927WyleNUtc5ffdob3aPZDqvhH6yLuu999D30DYnAEDBpCXl/egq/GtVFdXW0c5hvqBVZp667tr166Nbu/8+fP55z//ya5du6isrKR9+/o/XG7dusX777/PY4891iZ1F0IIIYQQQgghhBDffa2WsmI2m5k/fz6enp54eXmxZMkS67zCwkImTJiAi4sLJpOJqKgoLl78pq+iw4cPExERgaurKyaTif79+3PgwAEyMjKYPn06paWlaJqGpmk25bZv357AwMAGk5+fH3/4wx/w8/PDaDQSGBjI+vXrrctlZmYyaNAgjEYj3t7eJCUlUVv7zdMTf39/Vq5cabN9/fr1s1m3pmm89957TJo0CScnJ4KCgti5cycAZ86cISKi/qV1Dw8PNE1j2rRpQP3r2AkJCSQmJtKxY0dGjRpFXFwc48ePt1lfTU0NnTt3tqm3vb19o9trMpkYNGgQfn5+fPjhh9b4Dz/8kMcee4wf/OAHDb6r5cuX0717d9q3b0/fvn35n//5n2a+XSGEEEIIIYQQQoj7w2x5cNOjqtUaCNPS0nB2dmb//v2sWLGCpUuXkp6ejtlsZsKECVy7do3MzEzS09M5ffo0U6ZMsS4bHR2Nr68vubm5HDx4kKSkJBwcHBgyZAgrV67EZDJRXFxMcXEx8+bNa7EuMTExbN68mVWrVpGfn8+6detwcanvKP78+fOMHTuWgQMHcvjwYdasWcP69et57bXXlLc5OTmZqKgojhw5wtixY4mOjubatWv4+fmxbds2AE6cOEFxcTFvv/22zb5ydHQkKyuLtWvXEh8fz549eygu/qZz0127dlFRUWGzn1oSFxdHSkqK9f9//OMfmT59eoO45cuXs2HDBtauXcsXX3zBCy+8wM9+9jMyM9VeTxFCCCGEEEIIIYQQ332t9opxnz59WLx4MQBBQUGsXr2avXvr+yw5evQoBQUF+Pn5AbBhwwZ69epFbm4uAwcOpLCwkJdeeomQkBDr8re5ubmhaRpeXl666nHy5Em2bt1Keno6kZGRAAQEBFjnv/vuu/j5+bF69Wo0TSMkJIQLFy6wYMECFi1ahJ2d/jbTadOmMXXqVACWLVvGqlWryMnJYfTo0Xh61o/i17lz5wZ9EAYFBbFixQqbz3r27MnGjRuZP38+ACkpKTz77LPWhk09fvazn7Fw4ULOnq0fqTIrK4sPPviAjIwMa0xVVRXLli3jb3/7G4MH14+WGRAQwD/+8Q/WrVtHeHjjfdJUVVVRVWXbz0N1VRWORv19QgkhhBBCCCGEEEKIh0+rZRD26WPbgaq3tzeXLl0iPz8fPz8/a+MgQFhYGO7u7uTn5wMwd+5c4uPjiYyM5PXXX+fUqVP3XI+8vDwMBkOTDV35+fkMHjwYTfumU/2hQ4dSXl7OuXPnlNZ15zY7OztjMpm4dOlSi8v179+/wWfx8fHW7L+LFy/y8ccfExcXp1SfTp06MW7cOFJTU0lJSWHcuHF07NjRJuarr76ioqKCH//4x7i4uFinDRs2NLvfly9fjpubm830+7W/U6qfEEIIIYQQQgghREsslgc3PapaLYPQwcF2FC9N0zCb9Y3ms2TJEn7605/yl7/8hY8//pjFixfzwQcfMGnSJOV63B6g49uws7NrMFBITU3DkcHudZudnZ0bfBYTE0NSUhLZ2dl8/vnndO/enSeffFKx5vWvGSckJADwu981bMC7PbLzX/7yF3x8fGzmGZvJBly4cCFz5861+ezMObWREIUQQgghhBBCCCHEw6fNRzEODQ2lqKiIoqIiaxbhsWPHKCkpISwszBoXHBxMcHAwL7zwAlOnTiUlJYVJkybh6OhIXV2d7vX17t0bs9lMZmam9RXju+uzbds2LBaLNYswKysLV1dXfH19gfpMvDv7AywrK6OgoEBpu2+PTKy37h06dGDixImkpKSQnZ3daN+BeowePZrq6mo0TWPUqFEN5oeFhWE0GiksLGwyy7IxRqOxQQOio1H/UPFCCCGEEEIIIYQQeujMNxOtqNVeMW5KZGQkvXv3Jjo6mkOHDpGTk0NMTAzh4eEMGDCAyspKEhISyMjI4OzZs2RlZZGbm0toaChQP6JweXk5e/fu5cqVK1RUVDS7Pn9/f2JjY4mLi2P79u0UFBSQkZHB1q1bAZg9ezZFRUXMmTOH48ePs2PHDhYvXszcuXOt/Q+OGDGCjRs3sm/fPo4ePUpsbCwGg0Fpu7t164amaezatYvLly9bM/eaEx8fT1paGvn5+cTGxiqt7zaDwUB+fj7Hjh1rtM6urq7MmzePF154gbS0NE6dOsWhQ4d45513SEtLu6d1CiGEEEIIIYQQQojvrjZvINQ0jR07duDh4cGwYcOIjIwkICCALVu2APUNWlevXiUmJobg4GCioqIYM2YMycnJAAwZMoRZs2YxZcoUOnXq1GBwj8asWbOGZ555htmzZxMSEsLMmTO5efMmAD4+PuzevZucnBz69u3LrFmzmDFjBi+//LJ1+YULFxIeHs748eMZN24cEydOpEePHkrb7ePjQ3JyMklJSXTp0sX62m9zIiMj8fb2ZtSoUXTt2lVpfXcymUyYTKYm57/66qu88sorLF++nNDQUEaPHs1f/vIXunfvfs/rFEIIIYQQQgghhBDfTZrl7s72voXhw4fTr18/Vq5c2VpFPlLKy8vx8fEhJSWFyZMnP+jqtOj4KbVBXexp2I9jc8yoZW22M9/UHXvF0lmpbAe7WqV4e00t3g61/GmzQtt+nUVtPzpoat+TvWJ8lbmd7liDpr97AQAHqpXiVfYjQA2OSvEq36tqXVRVmdXq7mRXqTu2xuLQctAd2imUDXDLrNa3rOrvT6PteiKutaj15KFpanVpb9F/3lNVo6mNUm80N5/hf7cqOye1eIv++qgeA0ZuKcXfQu2YVDk3VVrU9oujpnbe87x1QSn+Wju1B5Y36/TX32inVnfV36rqNcSC1nLQPVItW3VbVc81Z0P1dzPT4/hepbJVtfW+UVFRp/bbrqpTu/65O95QirdY9O8b1fOeKtX9XqdwH//Y1/uVyi70ekIpXrnuivfOKlSv8219LlC5V7XT1P5euTByrFJ81727leLNFrV751tm/fcRtWa1/WhyUPttqwrp4dum5T+s1v71wa17VsPe2h4Jbd4HYWtJTU0lMTGRkpKSB10VJRkZGURERHD9+nXc3d0bjTGbzVy5coU333wTd3d3nnrqqXta17Rp00hLS2P58uUkJSVZP9++fTuTJk1qMPDKt6V8o47ajbpqY0wJHXTHql7gVBvNHKlSildtHKpW+ANSte4G1G4wVW5eoY3/ANPa9o8Me4vavnG06G90aMv9Aijni9sp/F79Lx9SKvtC5x+o1UXx92q0qDVAtqX2isfMTa3p7O/G1Nnpv4w71Kmdl26hvzEfwEFTK1/l9wFgUPjj95ZFccAyxZ+f6rlDpT6q52xVdha1a7HyH5sKv1fV/aj6h/XDpC0bte6lfJVGv1MhI9us7IeN8v2hndrvybXuulJ8mZ2n7ljVe0nVh9OqVI5J1QY/v7+tUYo/M7LlN7nupHocqDRUPUz7Hdr2vKra4Kd6rgk4/qlSvMr3alD8batS/VtRiPulzV8xbm379u3DxcWlyem7qLCwkC5duvD+++/zxz/+EXt7e5t5zW1vYWGhTVnt2rXjjTfe4Pp1tRsQIYQQQgghhBBCiIeBxfLgpkdVqzcQms1m5s+fj6enJ15eXixZssQ6r7CwkAkTJuDi4oLJZCIqKoqLFy9a5x8+fJiIiAhcXV0xmUz079+fAwcOkJGRwfTp0yktLWXYsGHcvHmTuLg48vLyGkx3qqqqYsGCBfj5+WE0GgkMDGT9+vXW+ZmZmQwaNAij0Yi3tzdJSUnU1n6ToeDv79/gdel+/frZbJOmabz33ntMmjQJJycngoKC2LlzJwBnzpwhIiICAA8PDzRNY9q0aUD969gJCQkkJiYyYMAAhg8fzo9//GP++7//22Z9nTp1wmg08utf/7rR7b27r8LIyEi8vLxYvnx5s9/TP/7xD5588knat2+Pn58fv/zlL639NAohhBBCCCGEEEKIR0erNxCmpaXh7OzM/v37WbFiBUuXLiU9PR2z2cyECRO4du0amZmZpKenc/r0aaZMmWJdNjo6Gl9fX3Jzczl48CBJSUk4ODgwZMgQVq5ciclkori4mOLiYpYtW0ZgYGCD6U4xMTFs3ryZVatWkZ+fz7p166xZhufPn2fs2LEMHDiQw4cPs2bNGtavX89rr72mvM3JyclERUVx5MgRxo4dS3R0NNeuXcPPz49t27YBcOLECYqLi3n77bdt9pWjoyNZWVmsXbuW+Ph49uzZQ3FxsTVmz549VFVVMWfOnEa3985sQ6gf9GXZsmW88847nDvXeB+Bp06dYvTo0Tz99NMcOXKELVu28I9//EPXQCpCCCGEEEIIIYQQ4vul1fsg7NOnD4sXLwYgKCiI1atXs3dvfV8kR48epaCgAD8/PwA2bNhAr169yM3NZeDAgRQWFvLSSy8REhJiXf42Nzc3NE3Dy8tLVz1OnjzJ1q1bSU9PJzIyEoCAgADr/HfffRc/Pz9Wr16NpmmEhIRw4cIFFixYwKJFi7Cz0992Om3aNKZOnQrAsmXLWLVqFTk5OYwePRpPz/q+Qzp37tygD8KgoKAGozL37NmTjRs3Mn/+fABSUlJ49tlnlV6fnjRpEv369WPx4sU2GZO3LV++nOjoaBITE631WLVqFeHh4axZs4Z27dT6nRJCCCGEEEIIIYRoLeZH+FXfB6XVMwj79Olj839vb28uXbpEfn4+fn5+1sZBgLCwMNzd3cnPzwdg7ty5xMfHExkZyeuvv86pU6fuuR55eXkYDAbCwxsfpS0/P5/Bgwej3TGowdChQykvL28y864pd26zs7MzJpOJS5cutbhc//79G3wWHx9PSkoKABcvXuTjjz8mLi5OqT4Ab7zxBmlpadZ9e6fDhw+Tmppq05fhqFGjMJvNFBQUNFpeVVUVZWVlNlN1lXSuKoQQQgghhBBCCPFd1+oNhA4ODjb/1zQNs1nfiEFLlizhiy++YNy4cXz66aeEhYXx0Ucf3VM92rdXHMGwEXZ2dg1G/q2paTi64L1us7Ozc4PPYmJiOH36NNnZ2WzatInu3bvz5JNPKtYchg0bxqhRo1i4cGGDeeXl5fz85z+36cvw8OHDfPnll/To0aPR8pYvX46bm5vNtG7t75TrJYQQQgghhBBCCNEci8XywKZHVau/YtyU0NBQioqKKCoqsmYRHjt2jJKSEsLCwqxxwcHBBAcH88ILLzB16lRSUlKYNGkSjo6O1NXpH268d+/emM1mMjMzra8Y312fbdu2YbFYrFmEWVlZuLq64uvrC9QPEHJnf4BlZWVNZtg1xdHREUB33Tt06MDEiRNJSUkhOzub6dOnK63vTq+//jr9+vWjZ8+eNp//8Ic/5NixYw36bGzOwoULmTt3rs1nZ8+1nCUphBBCCCGEEEIIIR5urZ5B2JTIyEh69+5NdHQ0hw4dIicnh5iYGMLDwxkwYACVlZUkJCSQkZHB2bNnycrKIjc3l9DQUKB+ROHy8nL27t3LlStXqKioaHZ9/v7+xMbGEhcXx/bt2ykoKCAjI4OtW7cCMHv2bIqKipgzZw7Hjx9nx44dLF68mLlz51r7HxwxYgQbN25k3759HD16lNjYWAwGg9J2d+vWDU3T2LVrF5cvX6a8vLzFZeLj462vB8fGxiqt70639/eqVatsPl+wYAGff/45CQkJ5OXl8eWXX7Jjx45mBykxGo2YTCabydFovOe6CSGEEEIIIYQQQoiHw31rINQ0jR07duDh4cGwYcOIjIwkICCALVu2APWj7169epWYmBiCg4OJiopizJgxJCcnAzBkyBBmzZrFlClT6NSpU4PBPRqzZs0annnmGWbPnk1ISAgzZ87k5s2bAPj4+LB7925ycnLo27cvs2bNYsaMGbz88svW5RcuXEh4eDjjx49n3LhxTJw4sclXcJvi4+NDcnIySUlJdOnSRddIwZGRkXh7ezNq1Ci6du2qtL67LV26tMHrzn369CEzM5OTJ0/y5JNP8oMf/IBFixZ963UJIYQQQgghhBBCfFsWy4ObHlWa5VF+wfohVV5ejo+PDykpKUyePPlBV6dJJ04VKcU7ojaoyS3U+pGsMTu0HPT/mNFaDrpDOzu1uqtuq1mxrb7S4qQ71kFr2G9mc+xRi1dVZdE/Sra9VqtUtr3itqoyW9QyiB0tt3THWhSPSVWVWsM+T5vjQLXuWO9Lh5XKvtD5B0rxtYq9YbS33FSKb0sGi9oxfNPOpBTvoOn/nhzq1M5LpZqnUrwrpUrxGmq3H3Wa/uPglkXt+tFea/6thLtVof88BlBr0V931fOe6n7sWKl27f66XXel+BqFbXVUPGdrmtq22qGvD+zb2vo83JbqFK9PBk1/tz2nQkYqld3j+F6leNX9rnrMq7hlVns7ptasdn3y0s4rxZfZ6T8P22lqx7vq70N1v6vc26oeA35/W6MUf2Zkywkad1Ldl2aL/m1V+e3dC9Vzgcq2tuVvD9TPNQHHP1WKrzI76o5V3Y/OBrX7CNW/FQMUk5S+L975y4Nrqpoz7rt7T/BttGofhMOHD6dfv36sXLmyNYt9ZJjNZq5cucKbb76Ju7s7Tz311IOukhBCCCGEEEIIIcR9pXOsW9GK7tsgJd9WamoqiYmJlJSUALBv3z7GjBnTZLyevv7uh4yMDCIiIrh+/Tru7u7NxhYWFtK9e3d8fX1JTU3F3t7eZt6dg7nc7dixYyxatIi0tDSWL19OUlKSdd727duZNGlSq4/Go5JhBOoZgapc7G7oji0zq2XpqGR/gHpGoOqTSg/zZd2xtwxqmWMq2Ymg/jTRSdOf3aWapVNtUXvyb7EoZi0oZq+UmTspxatQfVKpmhWh2enf1rOdBimV7aCYpar6PV0zd1Arvw2zhlQzBTws15TiK3DRHXvNrJYR6GZfphR/pVbtePdwuK4UX1LrrjvWr/a0UtmFBrUn8y72almqKudJ1awF1YxDVarneLs2zjARjWvLzB7VjMC2zgJqS6rHr+r9W7nBXSm+LX9OqveqD5OiyF8oxZvMatebcs1NKV7lvN3WGYRtneXXllTPNf5n1OJPPjZad6xF8betqgb92YxC3E/fmQbCuw0YMIC8vLwHXY1WU11djb+/f5ONeF27dm12e2/3H9iuXTveeOMNfv7zn+Ph4dEWVRVCCCGEEEIIIYQQ3yOt/ujIbDYzf/58PD098fLyYsmSJdZ5hYWFTJgwARcXF0wmE1FRUVy8eNE6//Dhw0RERODq6orJZKJ///4cOHCAjIwMpk+fTmlpKZqmoWkab7zxBoGBgU1OAFVVVSxYsAA/Pz+MRiOBgYGsX7/eur7MzEwGDRqE0WjE29ubpKQkamu/eRLv7+/f4HXpfv362WyTpmm89957TJo0CScnJ4KCgti5cycAZ86cISIiAgAPDw80TWPatGlA/evYCQkJJCYm0rFjR0aNGkVcXBzjx4+3WV9NTQ2dO3cmLS2t2e29nW0YGRmJl5cXy5cvb/Z72rZtG7169cJoNOLv78+bb77ZbLwQQgghhBBCCCHE/SCDlNx/rd5AmJaWhrOzM/v372fFihUsXbqU9PR0zGYzEyZM4Nq1a2RmZpKens7p06eZMmWKddno6Gh8fX3Jzc3l4MGDJCUl4eDgwJAhQ1i5ciUmk4ni4mKKi4uZN29ei3WJiYlh8+bNrFq1ivz8fNatW4eLS/3rWOfPn2fs2LEMHDiQw4cPs2bNGtavX89rr72mvM3JyclERUVx5MgRxo4dS3R0NNeuXcPPz49t27YBcOLECYqLi3n77bdt9pWjoyNZWVmsXbuW+Ph49uzZQ3FxsTVm165dVFRU2Oyn5hgMBpYtW8Y777zDuXPnGo05ePAgUVFR/OQnP+Ho0aMsWbKEV155hdTUVOVtF0IIIYQQQgghhBDfba3+inGfPn1YvHgxAEFBQaxevZq9e+v7Bzh69CgFBQX4+fkBsGHDBnr16kVubi4DBw6ksLCQl156iZCQEOvyt7m5uaFpGl5eXrrqcfLkSbZu3Up6ejqRkZEABAQEWOe/++67+Pn5sXr1ajRNIyQkhAsXLrBgwQIWLVqEnZ3+ttNp06YxdepUAJYtW8aqVavIyclh9OjReHrW9/nUuXPnBn0QBgUFsWLFCpvPevbsycaNG5k/fz4AKSkpPPvss9aGTT0mTZpEv379WLx4sU3G5G1vvfUWI0eO5JVXXgEgODiYY8eO8Zvf/Maa4SiEEEIIIYQQQgjxIJgf4Uy+B6XVMwj79Olj839vb28uXbpEfn4+fn5+1sZBgLCwMNzd3cnPzwdg7ty5xMfHExkZyeuvv86pU6fuuR55eXkYDAbCw8MbnZ+fn8/gwYPRtG86px86dCjl5eVNZt415c5tdnZ2xmQycenSpRaX69+/f4PP4uPjSUlJAeDixYt8/PHHxMXFKdUH4I033iAtLc26b++Un5/P0KFDbT4bOnQoX375JXV1jXecW1VVRVlZmc1UVaU2PLsQQgghhBBCCCGEePi0egOhg4ODzf81TcOsc3zqJUuW8MUXXzBu3Dg+/fRTwsLC+Oijj+6pHu3bf/sRc+3s7BoMGlJT03D0zXvdZmfnhqPLxsTEcPr0abKzs9m0aRPdu3fnySefVKw5DBs2jFGjRrFw4ULlZRuzfPly3NzcbKa1a9e2StlCCCGEEEIIIYQQ4sG5b+Pbh4aGUlRURFFRkfWzY8eOUVJSQlhYmPWz4OBgXnjhBT755BMmT55szaZzdHRsMrutMb1798ZsNpOZmdlkfbKzs20aALOysnB1dcXX1xeATp062fQHWFZWRkFBge463K43oLvuHTp0YOLEiaSkpJCamsr06dOV1nen119/nf/93/8lOzvb5vPQ0FCysrJsPsvKyiI4OBiDwdBoWQsXLqS0tNRmmjVr1j3XTQghhBBCCCGEEKIxMkjJ/XffGggjIyPp3bs30dHRHDp0iJycHGJiYggPD2fAgAFUVlaSkJBARkYGZ8+eJSsri9zcXEJDQ4H6EYXLy8vZu3cvV65coaKiotn1+fv7ExsbS1xcHNu3b6egoICMjAy2bt0KwOzZsykqKmLOnDkcP36cHTt2sHjxYubOnWvtf3DEiBFs3LiRffv2cfToUWJjY5tsQGtKt27d0DSNXbt2cfnyZcrLy1tcJj4+3vp6cGxsrNL67nR7f69atcrm8xdffJG9e/fy6quvcvLkSdLS0li9enWzA78YjUZMJpPNZDQa77luQgghhBBCCCGEEOLhcN8aCDVNY8eOHXh4eDBs2DAiIyMJCAhgy5YtQP3ou1evXiUmJobg4GCioqIYM2YMycnJAAwZMoRZs2YxZcoUOnXq1GBwj8asWbOGZ555htmzZxMSEsLMmTO5efMmAD4+PuzevZucnBz69u3LrFmzmDFjBi+//LJ1+YULFxIeHs748eMZN24cEydOpEePHkrb7ePjQ3JyMklJSXTp0oWEhIQWl4mMjMTb25tRo0bRtWtXpfXdbenSpQ1ed/7hD3/I1q1b+eCDD3j88cdZtGgRS5culQFKhBBCCCGEEEII8cBZzJYHNj2qNMvdneyJB668vBwfHx9SUlKYPHnyg65Ok04rDiJTRbs2qkk9I7d0x5aZTUpl22v6X28HsENfv5vWeE0t3qWuRHfsLUPDvi6bU2lxUorXUDuFOGk3dce29TFjsWgtB91B09S2taLu2/eF2hRnQ/NZ1HerNKvty/Z2+n9PZovasyYHrWFfrs2psTi0HHSHW2a17GYLaseBCoPiucNDu6YUX6HpH+G+vFbtXOBmX6YUX1brqhTv4XBdKb6k1l13rF/taaWyCw1qD/9c7PWfxwDqLGpvH6iw12qV4jtWFrUcdIeL7fyV4mst9rpjVeuueg5WvRa35bmgrameh1XvO1ScChmpFB9w/FOleNX7DhXVZkel+FrF37aToVIpXuV7bcvv9LvO2ax2PSu3c1OKrzHrv09xtKtWKluV6rlA5bzalr+9e+F/Ru3ccfKx0bpj6xTzqFTum0F9X/bs4ddy0PfQbz98cOe1eZPvWy7dQ6XNtnr48OEkJia2VfHfS2azmUuXLvHqq6/i7u7OU0899aCrJIQQQgghhBBCCCG+5/Q/5n2IpKamMmfOnAYjDN9JT19/91tGRgYRERFcv34dd3f3BvMLCwvp3r07vr6+pKamYm9vbzPvzsFc7nbs2DEee+wxm8+KiopYvHgxe/bs4cqVK3h7ezNx4kQWLVpEhw4drHH+/v4kJiYqN+jWopbV44DaEzPVTAH7Ov3l15rVDn2DoW2zEFSfIpk1/U+sL1V3VCrb07FUKV617ldq9NdHNUtHlWomWx1qmQJGhafEqk98VY8x1WPezk7/Me9iLlEqu8KglsHrZr6qFG+266QUr5rFpEIlmwqgSlPLOnXUqnTHuturbWdhRRel+C7tS5TijbVqWbDgrjvysqOPUsmduKQUX4H+zE0AZ260Wdmq54LS9mrfqwG1LNibdfqz0F0Vs1QrLGpZsB2rzinFX2nnqztW9drX1tmJqtentswEUs0IPB0yQim+x/G9umNV93uVYgZhZa3avXB7g1qWUVseNypv3oD6Mab6d4KKts5kU70nU6H6napmQqt+TwaLwjn+IUuyLm9iMNKmVEXrT76prlO7f2tn1H8/Burf66PqEX7T94H5TjYQQn2fhQcOHHjQ1WhV/v7+WCwWqqurraMf39a1a1fy8vKaXPbuvgpPnz7N4MGDCQ4OZvPmzXTv3p0vvviCl156iY8//ph//vOfeHp6tsVmCCGEEEIIIYQQQojvkDZ9sdpsNjN//nw8PT3x8vJiyZIl1nmFhYVMmDABFxcXTCYTUVFRXLx40Tr/8OHDRERE4Orqislkon///hw4cICMjAymT59OaWkpQUFBBAUFsWnTJgIDA22mu1VVVbFgwQL8/PwwGo0EBgayfv166/zMzEwGDRqE0WjE29ubpKQkamu/ybbw9/dn5cqVNmX269fPZps0TeO9995j0qRJODk5ERQUxM6dOwE4c+YMERERAHh4eKBpmnVQkOHDh5OQkEBiYiIdO3Zk1KhRxMXFMX78eGvZ9vb2dOvWjSFDhpCZmdlge+/MNgR4/vnncXR05JNPPiE8PJzHHnuMMWPG8Le//Y3z58/z61//2rrus2fP8sILL6BpGpr2kD0aEkIIIYQQQgghxCPFYnlw06OqTRsI09LScHZ2Zv/+/axYsYKlS5eSnp6O2WxmwoQJXLt2jczMTNLT0zl9+jRTpkyxLhsdHY2vry+5ubkcPHiQpKQkHBwcGDJkCCtXrsRkMlFcXExxcTHz5s1rsS4xMTFs3ryZVatWkZ+fz7p163BxqX+N5/z584wdO5aBAwdy+PBh1qxZw/r163nttdeUtzk5OZmoqCiOHDnC2LFjiY6O5tq1a/j5+bFt2zYATpw4QXFxMW+//bbNvnJ0dCQrK4u1a9cSHx/Pnj17KC4utsbs2rWLiooKm/3UmGvXrvHXv/6V2bNn07697etqXl5eREdHs2XLFiwWCx9++CG+vr4sXbrUuj+FEEIIIYQQQgghxKOjTV8x7tOnD4sXLwYgKCiI1atXs3dvfb8hR48epaCgAD+/+hF5NmzYQK9evcjNzWXgwIEUFhby0ksvERISYl3+Njc3NzRNw8vLS1c9Tp48ydatW0lPTycyMhKAgIAA6/x3330XPz8/Vq9ejaZphISEcOHCBRYsWMCiRYuws9Pfjjpt2jSmTp0KwLJly1i1ahU5OTmMHj3a+kpv586dG/RBGBQUxIoVK2w+69mzJxs3bmT+/PkApKSk8Oyzz1obNpvy5ZdfYrFYCA0NbXR+aGgo169f5/Lly3Tu3BmDwYCrq6vu/SmEEEIIIYQQQgghvj/aNIOwT58+Nv/39vbm0qVL5Ofn4+fnZ20cBAgLC8Pd3Z38/HwA5s6dS3x8PJGRkbz++uucOnXqnuuRl5eHwWAgPDy80fn5+fkMHjzY5vXaoUOHUl5ezrlzah1c37nNzs7OmEwmLl1qufPz/v37N/gsPj6elJQUAC5evMjHH39MXFyc7ro0N4iLqqqqKsrKymym6iq1zliFEEIIIYQQQgghWmI2Wx7Y9Khq0wZCBwfb0as0TcNs1jdiz5IlS/jiiy8YN24cn376KWFhYXz00Uf3VI+7X7O9F3Z2dg0a3GpqGo6Aeq/b7OzccGS+mJgYTp8+TXZ2Nps2baJ79+48+eSTLZYVGBiIpmnWxta75efn4+HhQadO+kf6XL58OW5ubjbTurXv6l5eCCGEEEIIIYQQQjyc2rSBsCmhoaEUFRVRVFRk/ezYsWOUlJQQFhZm/Sw4OJgXXniBTz75hMmTJ1uz6RwdHamr0z8ke+/evTGbzWQ2MRR6aGgo2dnZNg2AWVlZuLq64uvrC0CnTp1s+ucrKyujoKBAdx1u1xvQXfcOHTowceJEUlJSSE1NZfr06bqX+/GPf8y7775LZWWlzbyvv/6aP/3pT0yZMsWaMalnfy5cuJDS0lKb6eezZuuqjxBCCCGEEEIIIYRe39dBSq5du0Z0dDQmkwl3d3dmzJhBeXl5s8sMHz7cOqjs7WnWrFk2MYWFhYwbNw4nJyc6d+7MSy+9ZDPwrh4PpIEwMjKS3r17Ex0dzaFDh8jJySEmJobw8HAGDBhAZWUlCQkJZGRkcPbsWbKyssjNzbX2qefv7095eTl79+7lypUrVFRUNLs+f39/YmNjiYuLY/v27RQUFJCRkcHWrVsBmD17NkVFRcyZM4fjx4+zY8cOFi9ezNy5c639D44YMYKNGzeyb98+jh49SmxsLAaDQWm7u3XrhqZp7Nq1i8uXL7d4EED9a8ZpaWnk5+cTGxure12rV6+mqqqKUaNG8dlnn1FUVMSePXv48Y9/jI+PD//1X/9ls38+++wzzp8/z5UrVxotz2g0YjKZbCZHo1F3fYQQQgghhBBCCCEeZdHR0XzxxRekp6eza9cuPvvsM5577rkWl5s5c6Z1YNni4mKbMSzq6uoYN24c1dXVfP7556SlpZGamsqiRYuU6vZAGgg1TWPHjh14eHgwbNgwIiMjCQgIYMuWLQAYDAauXr1KTEwMwcHBREVFMWbMGJKTkwEYMmQIs2bNYsqUKXTq1KnB4B6NWbNmDc888wyzZ88mJCSEmTNncvPmTQB8fHzYvXs3OTk59O3bl1mzZjFjxgxefvll6/ILFy4kPDyc8ePHM27cOCZOnEiPHj2UttvHx4fk5GSSkpLo0qULCQkJLS4TGRmJt7c3o0aNomvXrrrXFRQUxIEDBwgICCAqKooePXrw3HPPERERQXZ2tnXAFIClS5dy5swZevToofTasRBCCCGEEEIIIYRoWX5+Pnv27OG9997jiSee4N/+7d945513+OCDD7hw4UKzyzo5OeHl5WWdTCaTdd4nn3zCsWPH2LRpE/369WPMmDG8+uqr/O53v6O6ulp3/TRLa45kIVpdeXk5Pj4+pKSkMHny5AddHRsnTxUqxduh/7VwAE1TOzQd6vQPmnLJrDZis9Gg/0cFYIe+vjZvs9fUUn/bm1vOPr3tXK2vUtmejqVK8Rpq31NpjanloP/Hxf6mUtmqHLSG/Yg2pw61rOFai/6B4s0Wtec1DnZqdS+vbdjPaXPc7Mt0xzrV6Y8FqDDoPwYAnOvUjsnrdmoPOlR/fypUjgEAR03tXGNQqLvZonb8FlZ0UYrv0r5EKd7d3HjGelMuov8hWTs7tUG0XFA8hjUXpfj2Fv3nsgrUyrbT1K43jqjtmxocleLLal11x3a0VzsGKixq57HOVWr3KVfa6b9eql77LGgtB30LqueatjzvqTodMkIpvsfxvbpjVfe76rWystah5aA7dGpXohSvcm9g0NTus43cUopXvgdCbd+oUP39OZlvKMWXaR5K8XUK11fV+zfVvymqLWrnbIPC32eqf5u1tU5paplRBdFv6o6trlM7p3oa1e5VVfY7QHCPx5Tivy/+6wO1/dSafv0TtXOeXn/84x958cUXuX79uvWz2tpa2rVrx5///GcmTZrU6HLDhw/niy++wGKx4OXlxX/8x3/wyiuv4OTkBMCiRYvYuXMneXl51mUKCgoICAjg0KFD/OAHP9BVP7UjX8Hw4cPp168fK1eubKtVfK+ZzWauXLnCm2++ibu7O0899dSDrpIQQgghhBBCCCHE91pVVRVVVbYPVY1GI8Zv2c3a119/TefOnW0+s7e3x9PTk6+//rrJ5X7605/SrVs3unbtypEjR1iwYAEnTpzgww8/tJbbpYvtw/3b/2+u3Lu1WQNhW0pNTSUxMZGSkhIA9u3bx5gxY5qM19PX3/2QkZFBREQE169fx93dvdnYwsJCunfvjq+vL6mpqdjb29vMu3Mwl7sdO3aMRYsWkZaWBtSPrPzYY48RExPDr371K5uyvo22zgi8ZVYbffoG+rMWVDPTVJ4MgnpGRztL8/1o3q3CTv+2OtmrPSFWfSqr+mSznUF/9orqMaNcd8Wn7YrFY6+SoaiYXKL6ZN5op5aZZlbogcJYq3b8Vhr0H78AZQbPloPuYFTMkCo3q2WMqKisVbuJ6OSoVneLRf+BY49a1sIn2WoH/JThasdkjaGdUnxdjf7zsMVO7QelmmXU9cPXleLPTfqV7ljV85hqfKXFqU3LVznH30LtOq96bb3WTn/WKahv68NSNqhno7R1fVSoZAQCnAoZ2WZlqxy/APZ2apmYqvtdNStQqWyzYiabYl1ca67pjr3uqJaxrnrOvmmn9uaCE2p/J9xUyPxu6+xj5XOBwr32w3TeAHD68WileJXft2qmp+q+UbnPfpSZH+DLrsuXL7d2cXfb4sWLWbJkSaPxSUlJvPHGG82WmZ+ff8/1ubOPwt69e+Pt7c3IkSM5deqUctd3zflONhDebcCAATaplN911dXV+Pv709Tb3127dm12e2/3VTh69GhSUlKoqqpi9+7dPP/88zg4OLBw4cK2qLYQQgghhBBCCCHEd9rChQuZO3euzWfNZQ+++OKLTJs2rdkyAwIC8PLy4tKlSzaf19bWcu3aNby89HeD9sQTTwDw1Vdf0aNHD7y8vMjJybGJuXjxIoBSuW3adG02m5k/fz6enp54eXnZtLYWFhYyYcIEXFxcMJlMREVFWTcA4PDhw0RERODq6orJZKJ///4cOHCAjIwMpk+fTmlpqXV45zfeeIPAwMAmJ6hPEV2wYAF+fn4YjUYCAwNZv369dX2ZmZkMGjQIo9GIt7c3SUlJNkNC+/v7N3hdul+/fjbbpGka7733HpMmTcLJyYmgoCB27twJwJkzZ4iIiADAw8MDTdOsB9Dw4cNJSEggMTGRjh07MmrUKOLi4hg/frzN+mpqaujcuTNpaWnNbu/tDEGj0YiXlxfdunXjF7/4BZGRkdb6XL9+nZiYGDw8PHBycmLMmDF8+eWX9/AtCyGEEEIIIYQQQnw/GI1GTCaTzdRcA2GnTp0ICQlpdnJ0dGTw4MGUlJRw8OBB67KffvopZrPZ2uinx+2EMW9vbwAGDx7M0aNHbRof09PTMZlMzb59erc2bSBMS0vD2dmZ/fv3s2LFCpYuXUp6ejpms5kJEyZw7do1MjMzSU9P5/Tp00yZMsW6bHR0NL6+vuTm5nLw4EGSkpJwcHBgyJAhrFy5EpPJZB3eed68eS3WJSYmhs2bN7Nq1Sry8/NZt24dLi716eDnz59n7NixDBw4kMOHD7NmzRrWr1/Pa6+9przNycnJREVFceTIEcaOHUt0dDTXrl3Dz8+Pbdu2AXDixAmKi4t5++23bfaVo6MjWVlZrF27lvj4ePbs2UNxcbE1ZteuXVRUVNjsJxXt27e3jmAzbdo0Dhw4wM6dO8nOzsZisTB27FhqatTSqYUQQgghhBBCCCFak8X84Ka2EhoayujRo5k5cyY5OTlkZWWRkJDAT37yE+uboOfPnyckJMSaEXjq1CleffVVDh48yJkzZ9i5cycxMTEMGzaMPn36APDv//7vhIWF8Z//+Z8cPnyYv/71r7z88ss8//zzSv0mtukrxn369GHx4sUABAUFsXr1avbure8L5OjRoxQUFODn5wfAhg0b6NWrF7m5uQwcOJDCwkJeeuklQkJCrMvf5ubmhqZpulMlT548ydatW0lPTycyMhKoT++87d1338XPz4/Vq1ejaRohISFcuHCBBQsWsGjRIuzs9LejTps2jalTpwKwbNkyVq1aRU5ODqNHj8bTs74frc6dOzfogzAoKIgVK1bYfNazZ082btzI/PnzAUhJSeHZZ5+1NmzqZbFY2Lt3L3/961+ZM2cOX375JTt37iQrK4shQ4YA8Kc//Qk/Pz+2b9/Os88+q1S+EEIIIYQQQgghhGjen/70JxISEhg5ciR2dnY8/fTTrFq1yjq/pqaGEydOUFFR38+7o6Mjf/vb31i5ciU3b97Ez8+Pp59+mpdfftm6jMFgYNeuXfziF79g8ODBODs7Exsby9KlS5Xq1uYNhHfy9vbm0qVL5Ofn4+fnZ20cBAgLC8Pd3Z38/HwGDhzI3LlziY+PZ+PGjURGRvLss8/ec+eLeXl5GAwGwsPDG52fn5/P4MGD0bRvOoEdOnQo5eXlnDt3jsce0z+s+J3b7OzsjMlkavCOeWP69+/f4LP4+Hh+//vfM3/+fC5evMjHH3/Mp59+qrsuu3btwsXFhZqaGsxmMz/96U9ZsmQJe/fuxd7e3iaFtUOHDvTs2bPJjjMbG8WnqqrqW4/iI4QQQgghhBBCCHGnpsZk+K7z9PTk/fffb3L+3eNR+Pn5kZmZ2WK53bp1Y/fu3d+qbm36irGDg+2IhpqmYTbry9dcsmQJX3zxBePGjePTTz8lLCyMjz766J7q0b692ih5jbGzs2twgDb2Ou69brOzc8NRNGNiYjh9+jTZ2dls2rSJ7t278+STT+quc0REBHl5eXz55ZdUVlZaX/m+F8uXL8fNzc1mWrd2zT2VJYQQQgghhBBCCCEeHg9kfO3Q0FCKioooKiqyfnbs2DFKSkpsOlAMDg7mhRde4JNPPmHy5MmkpKQA9SmWdXX6h3Dv3bs3ZrO5yVbX0NBQaz98t2VlZeHq6oqvry9Q3+nknf0BlpWVUVBQoLsOt+sN6K57hw4dmDhxIikpKaSmpjJ9+nSl9Tk7OxMYGMhjjz1mHbgE6re3traW/fv3Wz+7evUqJ06caLIDy4ULF1JaWmoz/XzWL5TqI4QQQgghhBBCCCEePg+kgTAyMpLevXsTHR3NoUOHyMnJISYmhvDwcAYMGEBlZSUJCQlkZGRw9uxZsrKyyM3NJTQ0FKhPuSwvL2fv3r1cuXLF+m52U/z9/YmNjSUuLo7t27dTUFBARkYGW7duBWD27NkUFRUxZ84cjh8/zo4dO1i8eDFz58619j84YsQINm7cyL59+zh69CixsbEYDAal7e7WrRuaprFr1y4uX75MeXl5i8vEx8eTlpZGfn4+sbGxSutrSlBQEBMmTGDmzJn84x//4PDhw/zsZz/Dx8eHCRMmNLqM6ig+QgghhBBCCCGEEPfCbH5w06PqgTQQaprGjh078PDwYNiwYURGRhIQEMCWLVuA+g4Wr169SkxMDMHBwURFRTFmzBiSk5MBGDJkCLNmzWLKlCl06tSpweAejVmzZg3PPPMMs2fPJiQkhJkzZ3Lz5k0AfHx82L17Nzk5OfTt25dZs2YxY8YMm04fFy5cSHh4OOPHj2fcuHFMnDhRuU9EHx8fkpOTSUpKokuXLiQkJLS4TGRkJN7e3owaNco6qk1rSElJoX///owfP57BgwdjsVjYvXt3g1ekhRBCCCGEEEIIIcT3m2ZppZ4fhw8fTr9+/Vi5cmVrFCf+n/Lycnx8fEhJSWHy5MkPujo2vjql9oq1pqkdarfMan1H1lj0j7nTzq6q5aA71FnUskXtNLXHDk6WlrNJ71Sp6e9L8pZZLdOzvd0tpXg71La1UuF7dbBr2M9nczTUjjEHrVop3qx4HGiK+0ZFLWqN+dVmR6V4Rzv9+6ZD1QWlskuMXZTiaxXH07KnVim+3HxvfbPqUVmr9vvr5HhNKV7lGDOgv2sOgLV7fZTipwxvPpv/bh6G60rxl2s66Y51sq9UKtuFMqV4j4/eUYo/N+lXumNVz2MGTe17rVW4VoJ6ferQf56019R+q6pUr08WtJaDHlJmi9ozf9X7lIfJqZCRumN7HN+rVHaNRe3aWqt4X6B6j9WWnMw3lOItmtox1q5G/73tdUe1+4K2ZkTte7ppcdEd29bnPdVzgcrfZ6rXg7b22IVspfhT3sN0x9Yp7kfVvy1VhfTwbdPyH1aL0tT+VmtNS2PV/nb6vmjTUYxbS2pqKomJiZSUlDzoqijJyMggIiKC69ev4+7urrSs2WzmypUrvPnmm7i7u/PUU081G3/58mUWLVrEX/7yFy5evIiHhwd9+/Zl0aJFDB069FtsRdNUb7w1xbZo1YuQs53+P07tlP+gclKKt1jU/si4pamV715zWXfsBTv9o3AD2GtqjXLVFrUGEHMb/gGm+sedat1Vj8mKOv03jKqcDGoNIHVtmDB+2VHtpsUBtWNM9fdUblFr8FMtX4WTvdofGc61pUrxFfauumOv1nZQKnvGSP3nGYAbisf7ldqOSvGdHPTXp9yif7+A+m/71pUSpXgVbd1IpVq+6r5R+T2pPjj8Ljfgiaapfq8qjX4qjYkAfsf2KcW35fUD1Bp7VBt9K+zUzpOqKh3b7uGbKmez2kOgG5q7UrxSo5ycxlqN3a2bSvEqv1fVhlYhvi++Ew2ELdm3bx9jxoxpcr6evv4epOrqausAJrcVFhbSvXt3fH19SU1NtRlkpLCwsMFgIpWV9Y0Fjo6O/P3vf8fe3p69e/dy9erVtt8AIYQQQgghhBBCCPGd1apN42azmfnz5+Pp6YmXlxdLliyxzissLGTChAm4uLhgMpmIiori4sWL1vmHDx8mIiICV1dXTCYT/fv358CBA2RkZDB9+nRKS0vRNA1N02zKBRgwYAB5eXk20/79+/npT3+Kq6srRqORwMBA1q9fb10mMzOTQYMGYTQa8fb2Jikpidrab1K+/f39G7wu3a9fP5t1a5rGe++9x6RJk3ByciIoKIidO3cCcObMGSIiIgDw8PBA0zSmTZsG1L+OnZCQQGJiIh07dmTUqFHExcUxfvx4m/VXV1dTVVXFmTNnbOrRtWtXm2397LPPMJvNbNiwgaNHjzJo0CAGDRrEwoULrZmH8+bNsyl/5cqVaJrGnj17rJ8FBgby3nvvNfHtCiGEEEIIIYQQQrQ9s+XBTY+qVm0gTEtLw9nZmf3797NixQqWLl1Keno6ZrOZCRMmcO3aNTIzM0lPT+f06dNMmTLFumx0dDS+vr7k5uZy8OBBkpKScHBwYMiQIaxcuRKTyURxcTHFxcXMmzfPZr3t27cnMDDQZlq6dCl79uzh3XffJT8/n3Xr1uHiUv/60/nz5xk7diwDBw7k8OHDrFmzhvXr1/Paa68pb3NycjJRUVEcOXKEsWPHEh0dzbVr1/Dz82Pbtm0AnDhxguLiYt5++22bfeXo6EhWVhZr164lPj6ePXv2UFxcbI3ZtWsXFRUVNvsJwN7e3mZb+/Tpg4uLCwcOHMDPz88m2/C28PBw/vGPf1BXV/9qbWZmJh07diQjI8O6T06dOsXw4cOV94EQQgghhBBCCCGE+O5q1VeM+/Tpw+LFiwEICgpi9erV7N1b30/I0aNHKSgowM/PD4ANGzbQq1cvcnNzGThwIIWFhbz00kuEhIRYl7/Nzc0NTdPw8vLSVY+TJ0+ydetW0tPTiYyMBCAgIMA6/91338XPz4/Vq1ejaRohISFcuHCBBQsWsGjRIuzs9LebTps2jalTpwKwbNkyVq1aRU5ODqNHj8bT0xOAzp07N+iDMCgoqMHoyz179mTjxo3Mnz8fqB9p+Nlnn7U2bDbF3t6e1NRUZs6cydq1a/nhD39IeHg4P/nJT+jTpw8ATz75JDdu3OBf//oX/fv357PPPuOll15i+/btQH1/iT4+PgQGBurediGEEEIIIYQQQojWZnmUU/kekFbNILzdGHWbt7c3ly5dIj8/Hz8/P2vjIEBYWBju7u7k5+cDMHfuXOLj44mMjOT111/n1KlT91yPvLw8DAYD4eHhjc7Pz89n8ODBaNo3HZUOHTqU8vJyzp07p7SuO7fZ2dkZk8nEpUuXWlyuf//+DT6Lj48nJSUFgIsXL/Lxxx8TFxenqx5PP/00Fy5cYOfOnYwePZqMjAx++MMfkpqaCoC7uzt9+/YlIyODo0eP4ujoyHPPPce//vUvysvLyczMbHJ/AVRVVVFWVmYzVVW17WhNQgghhBBCCCGEEKLttWoDoYODg83/NU3DbNY3otaSJUv44osvGDduHJ9++ilhYWF89NFH91SP9u3b39Nyd7Kzs8Ny16i7NTUNR9681212dm44sldMTAynT58mOzubTZs20b17d5588knddW7Xrh0//vGPeeWVV/j888+ZNm2aNaMT6vs+zMjIsDYGenp6Ehoayj/+8Y8WGwiXL1+Om5ubzbR27VrddRNCCCGEEEIIIYQQD6f7Mn53aGgoRUVFFBUVWT87duwYJSUlNqPxBgcH88ILL/DJJ58wefJkazado6Ojte88PXr37o3ZbCYzM7PJ+mRnZ9s0AGZlZeHq6oqvry8AnTp1sukPsKysjIKCAt11uF1vQHfdO3TowMSJE0lJSSE1NZXp06crre9uYWFh3Lz5zfDvt/sh3Lt3r7WvweHDh7N582ZOnjzZbP+DCxcupLS01GaaNWvWt6qfEEIIIYQQQgghxN0slgc3ParuSwNhZGQkvXv3Jjo6mkOHDpGTk0NMTAzh4eEMGDCAyspKEhISyMjI4OzZs2RlZZGbm0toaChQP6JveXk5e/fu5cqVK1RUVDS7Pn9/f2JjY4mLi2P79u0UFBSQkZHB1q1bAZg9ezZFRUXMmTOH48ePs2PHDhYvXszcuXOt/Q+OGDGCjRs3sm/fPo4ePUpsbCwGg0Fpu7t164amaezatYvLly9TXl7e4jLx8fGkpaWRn59PbGysrvVcvXqVESNGsGnTJo4cOUJBQQF//vOfWbFiBRMmTLDGDRs2jBs3brBr1y6bBsI//elPeHt7Exwc3OQ6jEYjJpPJZjIajbrqJ4QQQgghhBBCCCEeXvelgVDTNHbs2IGHhwfDhg0jMjKSgIAAtmzZAoDBYODq1avExMQQHBxMVFQUY8aMITk5GYAhQ4Ywa9YspkyZQqdOnRoM7tGYNWvW8MwzzzB79mxCQkKYOXOmNZvOx8eH3bt3k5OTQ9++fZk1axYzZszg5Zdfti6/cOFCwsPDGT9+POPGjWPixIn06NFDabt9fHxITk4mKSmJLl26kJCQ0OIykZGReHt7M2rUKLp27aprPS4uLjzxxBP893//N8OGDePxxx/nlVdeYebMmaxevdoa5+HhQe/evenUqZN1MJhhw4ZhNpubfb1YCCGEEEIIIYQQ4n4xmy0PbHpUaZa7O9oTD1R5eTk+Pj6kpKQwefLkB12dZp1WHEhGQ+1Qq7Q4KcU7aA37iGyKnab/lXWASrNaXezQ1/fmbQbF+rjVXNEde8HuMbWy7UuV4qstapmkVWZH3bFGu2qlstua6jFcUfft+0NtipOhUim+0txOKb693S3dsWaL2rMmld8qQI3FoeWgO1RZ9B9jABaL1nLQPbLT1M4Fnc3FLQfdocLeVXdsSa27Utkm+xtK8TfqXJTiVY+bjvb6z3vlFv37BcBEiVJ8uz+8phR/ZWbLDzbvlb1WqxSv+ntSvZ7VWux1xzoqnuMtqP1WVeuuWv7DRPX3pHpuakuq+13lWnwqZKRS2X7H9inF11rU3i5SvXarfK8P03f6sHE2lynF39DcleLb8rynSvVcoGn6f0+q98Ftzf/035TiT/qP1R3b1r9tVSE9fNu0/IdV0h/0/z3S2l6fqfa30/dFq2YQDh8+nMTExNYs8pFhNpu5dOkSr776Ku7u7jz11FMPukpCCCGEEEIIIYQQ4hGg/3HHA5aamkpiYiIlJSXs27ePMWPGNBmrp6+/+yUjI4OIiAiuX7+Ou7t7k3GFhYV0794dX19fUlNTsbe3t5l352Audzt27BiPPfYYw4cPp1+/fqxcudJm/p377kFqV6P2vZQa3JTiAwo+0R17ZedupbJrEt9Siu9QdUEp/prRWyneufyi7lg/J7VsrcO3+irFt7NXy17pf2mn7tgvvEcrlW1vp1aXW3VqmWb2ik/nM/PddccaFB/XDA/Vn00F8I+THZTiI3rqP8ZMlutKZdvXqR2TJ6qDlOKPn1fL3DxT2HZPfZ2c1C6zUwer1aW9Wf95dXu2j1LZPx+qls24LU+t/JsVapnTCT/Sn9G4/5palyBPfblOKf74jJVK8bdq9J9rLpWrZaz39PxaKV41K+JKpYdS/KBPF+qO/XLsyy0H3aHGrFZ3Nwe1+44as/7sSpNWolT2VXNHpXgHxczQ8+Vq31MXZ/0ZVaqZaXaKWUYqbxYAtDNU6Y5VzQgsCntSKf7Pv/5MKf7Fn6ll8F6pNOmO9Windry3s9O/H0E909PJor8+pRa141f1zZtOeR8rxXfo4qcU/86lZ3THRvYpUSpb9Vxw4aa7Urx7O/33HQHmL5XKLrIPUIpX/V5zuj6rFF/Rf4Du2P9dlK1U9s8nqJ3HhD7ysuv9951pILzTgAEDyMvLe9DVaFVdu3Zt8gfQtWvXZrdXb1+FQgghhBBCCCGEEELcrdUHKTGbzcyfPx9PT0+8vLxYsmSJdV5hYSETJkzAxcUFk8lEVFQUFy9+k6Vy+PBhIiIicHV1xWQy0b9/fw4cOEBGRgbTp0+ntLQUTdNwcnJi06ZNBAYGNjrdVlVVxYIFC/Dz88NoNBIYGMj69eut8zMzMxk0aBBGoxFvb2+SkpKorf3mKY2/v3+DbLx+/frZbJOmabz33ntMmjQJJycngoKC2LmzPkPqzJkzREREAPUDhGiaxrRp04D617ETEhJITEykY8eOjBo1iri4OMaPH2+zvpqaGrp27UpmZmaT23tntqEe06ZNY+LEifz2t7/F29ubDh068Pzzz1NTo5bVI4QQQgghhBBCCNHaLOYHNz2qWr2BMC0tDWdnZ/bv38+KFStYunQp6enpmM1mJkyYwLVr18jMzCQ9PZ3Tp08zZcoU67LR0dH4+vqSm5vLwYMHSUpKwsHBgSFDhrBy5UpMJhPFxcUUFxczb968FusSExPD5s2bWbVqFfn5+axbtw4Xl/pO1M+fP8/YsWMZOHAghw8fZs2aNaxfv57XXlPreBwgOTmZqKgojhw5wtixY4mOjubatWv4+fmxbds2AE6cOEFxcTFvv/22zb5ydHQkKyuLtWvXEh8fz549eygu/ua1rl27dlFRUWGzn1rD3//+d06dOsXf//530tLSSE1NJTU1tVXXIYQQQgghhBBCCCEefq3+inGfPn1YvHgxAEFBQaxevZq9e/cCcPToUQoKCvDzq+/XYcOGDfTq1Yvc3FwGDhxIYWEhL730EiEhIdblb3Nzc0PTNLy8vHTV4+TJk2zdupX09HQiIyMBCAj4ph+Ed999Fz8/P1avXo2maYSEhHDhwgUWLFjAokWLsLPT33Y6bdo0pk6dCsCyZctYtWoVOTk5jB49Gk9PTwA6d+7coA/CoKAgVqywHdmwZ8+ebNy4kfnz5wOQkpLCs88+a23YbC0eHh6sXr0ag8FASEgI48aNY+/evcycObNV1yOEEEIIIYQQQgghHm6tnkHYp08fm/97e3tz6dIl8vPz8fPzszYOAoSFheHu7k5+fj4Ac+fOJT4+nsjISF5//XVOnTp1z/XIy8vDYDAQHh7e6Pz8/HwGDx6Mpn3T4e7QoUMpLy/n3LlzSuu6c5udnZ0xmUxcunSpxeX69+/f4LP4+HhSUlIAuHjxIh9//DFxcXFK9dGjV69eGAzfdPZ9+3tqSlVVFWVlZTZTVZVa58ZCCCGEEEIIIYQQLTFbLA9selS1egOhg4Pt6FyapmE263uJe8mSJXzxxReMGzeOTz/9lLCwMD766KN7qkf79mqjWDbGzs6uwcAhjfXTd6/b7Ozs3OCzmJgYTp8+TXZ2Nps2baJ79+48+aS+EdVMJhOlpaUNPi8pKcHNzXZEYNU6L1++HDc3N5tp7dq1uuolhBBCCCGEEEIIIR5erd5A2JTQ0FCKioooKiqyfnbs2DFKSkoICwuzfhYcHMwLL7zAJ598wuTJk63ZdI6OjtTV6R/6vHfv3pjNZjIzM5usT3Z2tk0DYFZWFq6urvj6+gLQqVMnm/4Ay8rKKCgo0F2H2/UGdNe9Q4cOTJw4kZSUFFJTU5k+fbrudfXs2ZNDhw41+PzQoUMEBwfrLqcxCxcupLS01GaaNWvWtypTCCGEEEIIIYQQ4m4Wi+WBTY+q+9ZAGBkZSe/evYmOjubQoUPk5OQQExNDeHg4AwYMoLKykoSEBDIyMjh79ixZWVnk5uYSGhoK1I8oXF5ezt69e7ly5QoVFRXNrs/f35/Y2Fji4uLYvn07BQUFZGRksHXrVgBmz55NUVERc+bM4fjx4+zYsYPFixczd+5ca/+DI0aMYOPGjezbt4+jR48SGxtr81quHt26dUPTNHbt2sXly5cpLy9vcZn4+HjS0tLIz88nNjZW97p+8YtfcPLkSX75y19y5MgRTpw4wVtvvcXmzZt58cUXlep9N6PRiMlkspmMRuO3KlMIIYQQQgghhBBCPHj3rYFQ0zR27NiBh4cHw4YNIzIykoCAALZs2QKAwWDg6tWrxMTEEBwcTFRUFGPGjCE5ORmAIUOGMGvWLKZMmUKnTp0aDO7RmDVr1vDMM88we/ZsQkJCmDlzJjdv3gTAx8eH3bt3k5OTQ9++fZk1axYzZszg5Zdfti6/cOFCwsPDGT9+POPGjWPixIn06NFDabt9fHxITk4mKSmJLl26kJCQ0OIykZGReHt7M2rUKLp27ap7XQEBAXz22WccP36cyMhInnjiCbZu3cqf//xnRo8erVRvIYQQQgghhBBCCPFoaNVRjDMyMhp8tn37duu/H3vsMXbs2NHoso6OjmzevLnZ8tesWcOaNWt016ddu3a89dZbvPXWW43ODw8PJycnp8nlTSYTH3zwgc1nd2f0NZZ+WlJSYvP/V155hVdeecXms8b21W03b97k+vXrzJgxo8mYpgwcOJBPPvmk2ZjU1NQGn61cuVJ5XUIIIYQQQgghhBCtzWx+dF/1fVBaNYNw+PDhJCYmtmaRjxSz2cylS5d49dVXcXd356mnnnrQVRJCCCGEEEIIIYQQ33OtmkHYllJTU0lMTLRm5+3bt48xY8Y0Ga+nr7/7ISMjg4iICK5fv467u3uzsYWFhXTv3h1fX19SU1Oxt7e3mXfnYC53O3bsGDExMfTr169BNuDd+661VKPWB+FVrYNSvAMNR4xuTrbXFN2x/3zyP5XKjqvOU4o/5xCgFO9EpVL8Px0idMd2cihTKru39n9K8bUGR6X4v5ue0R07oO6wUtnVWjuleDtN3wjrt5k1tWcqz7f/UCleRaHdj5XiZ7JOKf683b/rji265adUtsFO/4BTAP3MTWd6N+ZHxnNK8ZrPDaV4JQa1y2wh+vc7wGVLF92xL7v+Tqns84YJSvEvOP9BKR4Ptd/rvyyTdcdOvPFHpbJTvRYpxQ/iqlJ8kPmY7lh3D7XfkwPVSvF1mlpfygPM2Urxuwe9qTt2qJavVLZzzTWl+K/t1a7FHlzRHVuGh1LZVXUOSvGaQS1r4soNtfLd2+mPd1A8Z6teWytr1epub1erO9Zi0ZTK/vOvP1OKf/a/hinFV/5E7XpWXq1/3wS2u6xUdplF7RjWUDsmr1s8dcc62andB98yq10/NndU64vdy1Xtb5D4T5/THXui138rlY3aKZtr5Wq/p0EG/cfkacdeSmWr/v5U42vMajvnfxbs0x07/U8jlcr2mLBRKb5OU22G6awY//3wCI8V8sB8ZxoI7zZgwADy8vIedDVaTXV1Nf7+/k2OmNO1a9dmt1elr0IhhBBCCCGEEEIIIW5r9UFKzGYz8+fPx9PTEy8vL5YsWWKdV1hYyIQJE3BxccFkMhEVFcXFixet8w8fPkxERASurq6YTCb69+/PgQMHyMjIYPr06ZSWlqJpGpqm8cYbbxAYGNjkBFBVVcWCBQvw8/PDaDQSGBjI+vXrrevLzMxk0KBBGI1GvL29SUpKorb2m6eS/v7+DbLx+vXrZ7NNmqbx3nvvMWnSJJycnAgKCmLnzp0AnDlzhoiI+kwvDw8PNE1j2rRpQP3r2AkJCSQmJtKxY0dGjRpFXFwc48ePt1lfTU0NnTt3Ji0trdntvTPbsCXTpk1j4sSJ/Pa3v8Xb25sOHTrw/PPPU1Oj9rRMCCGEEEIIIYQQQnz3tXoDYVpaGs7Ozuzfv58VK1awdOlS0tPTMZvNTJgwgWvXrpGZmUl6ejqnT59mypRvXguNjo7G19eX3NxcDh48SFJSEg4ODgwZMoSVK1diMpkoLi6muLiYefPmtViXmJgYNm/ezKpVq8jPz2fdunW4uLgAcP78ecaOHcvAgQM5fPgwa9asYf369bz22mvK25ycnExUVBRHjhxh7NixREdHc+3aNfz8/Ni2bRsAJ06coLi4mLfffttmXzk6OpKVlcXatWuJj49nz549FBcXW2N27dpFRUWFzX5qDX//+985deoUf//730lLSyM1NbXRwUuEEEIIIYQQQggh7ieL2fLApkdVq79i3KdPHxYvXgxAUFAQq1evZu/evQAcPXqUgoIC/Pzq+9fZsGEDvXr1Ijc3l4EDB1JYWMhLL71ESEiIdfnb3Nzc0DQNLy8vXfU4efIkW7duJT09ncjISAACAr7pi+bdd9/Fz8+P1atXo2kaISEhXLhwgQULFrBo0SLs7PS3nU6bNo2pU6cCsGzZMlatWkVOTg6jR4/G07O+/43OnTs36IMwKCiIFStW2HzWs2dPNm7cyPz58wFISUnh2WeftTZsthYPDw9Wr16NwWAgJCSEcePGsXfvXmbOnNmq6xFCCCGEEEIIIYQQD7dWzyDs06ePzf+9vb25dOkS+fn5+Pn5WRsHAcLCwnB3dyc/v76T6rlz5xIfH09kZCSvv/46p06duud65OXlYTAYCA8Pb3R+fn4+gwcPRtO+6Qx16NChlJeXc+6cWgf3d26zs7MzJpOJS5cutbhc//79G3wWHx9PSkoKABcvXuTjjz8mLi5OqT569OrVC4Phm45db39PTamqqqKsrMxmqq6qavV6CSGEEEIIIYQQ4tFmtlge2PSoavUGQgcH25GTNE3DbNY3mtmSJUv44osvGDduHJ9++ilhYWF89NFH91SP9u3b39Nyd7Kzs2swaEhj/fTd6zY7Ozs3+CwmJobTp0+TnZ3Npk2b6N69O08++aSu+ppMJkpLSxt8XlJSgpub27eq8/Lly3Fzc7OZfr9WbURMIYQQQgghhBBCCPHwafUGwqaEhoZSVFREUVGR9bNjx45RUlJCWFiY9bPg4GBeeOEFPvnkEyZPnmzNpnN0dKSurk73+nr37o3ZbCYzM7PJ+mRnZ9s0AGZlZeHq6oqvry8AnTp1sukPsKysjIKCAt11uF1vQHfdO3TowMSJE0lJSSE1NZXp06frXlfPnj05dOhQg88PHTpEcHCw7nIas3DhQkpLS22m52Y9/63KFEIIIYQQQgghhBAP3n1rIIyMjKR3795ER0dz6NAhcnJyiImJITw8nAEDBlBZWUlCQgIZGRmcPXuWrKwscnNzCQ0NBepHFC4vL2fv3r1cuXKFioqKZtfn7+9PbGwscXFxbN++nYKCAjIyMti6dSsAs2fPpqioiDlz5nD8+HF27NjB4sWLmTt3rrX/wREjRrBx40b27dvH0aNHiY2NtXktV49u3bqhaRq7du3i8uXLlJeXt7hMfHw8aWlp5OfnExsbq3tdv/jFLzh58iS//OUvOXLkCCdOnOCtt95i8+bNvPjii0r1vpvRaMRkMtlMjkbjtypTCCGEEEIIIYQQ4m4ySMn9d98aCDVNY8eOHXh4eDBs2DAiIyMJCAhgy5YtABgMBq5evUpMTAzBwcFERUUxZswYkpOTARgyZAizZs1iypQpdOrUqcHgHo1Zs2YNzzzzDLNnzyYkJISZM2dy8+ZNAHx8fNi9ezc5OTn07duXWbNmMWPGDF5++WXr8gsXLiQ8PJzx48czbtw4Jk6cSI8ePZS228fHh+TkZJKSkujSpQsJCQktLhMZGYm3tzejRo2ia9euutcVEBDAZ599xvHjx4mMjOSJJ55g69at/PnPf2b06NFK9RZCCCGEEEIIIYQQj4ZWHcU4IyOjwWfbt2+3/vuxxx5jx44djS7r6OjI5s2bmy1/zZo1rFmzRnd92rVrx1tvvcVbb73V6Pzw8HBycnKaXN5kMvHBBx/YfHZ3Rt/dfRRCfZ9/d3rllVd45ZVXbD5rbF/ddvPmTa5fv86MGTOajGnKwIED+eSTT5qNSU1NbfDZypUrldclhBBCCCGEEEII0doe5Uy+B6VVGwjFt2M2m7ly5Qpvvvkm7u7uPPXUUw+6SkIIIYQQQgghhBDie06zNJYC9x2wb98+xowZ0+Dz6upq6urqlAY0eRikpqbyy1/+khs3buDr60tqaiojR460zi8sLLQZzOVux44d47HHHlNa5+bNm/nZz37GrFmz+N3v1EckPnGqqOWgO1jQlOK9yk4qxRe6PK47ttai1peks6H5Pi/vpqH2s7JD7XgtrXNrOej/aW93S6lsJ1ruJ/NO1Vo7tXiLo+5YR61aqWxVBq1WKd5iUTuGnWsajireWsocOijFGy2VSvFmTf9vpK6NnzXVWBxaDrqDZ+1FpfgKB5NSvArVc0E1bde3q0tdiVJ8hUFtvzjVlSnFVxvaK8WXmvWf95zt1M7ZqmoVj3kj+s/DFRZnpbIdtBqleANq5732dWrXhIvo7xrFyaB4XrKo9Yxj0NSurar3KSo8fr9QKf76c8uV4lX3jabpPze51l1XKrvc4K4Ur7rfVc+rKlSvN5W1avdAl3sPUor3z8/QHat6vD9KSmpcleJd7dWuIbfM+q/dvuv/P6Wy2/pcoEL1eqN6rVQ915TZeSrF11r016fKrP/vFQBXe7VrpR1mpfjgHmp/539fPP/bkge27t/Nc39g636QvrMZhAMGDCAvL6/B56tWrSI9Pf3+V6gV2NnZNfrKMkDXrl3Jy8ujurraOjLy3fNVrV+/nvnz57Nu3TrefPNN2rVTu8kRQgghhBBCCCGEaG3yhvH91+qPGG7cuEF0dDTOzs54e3vz3//93wwfPpzExESgfrCSO/slBHB3d7fpF6+oqIioqCjc3d3x9PRkwoQJnDlzxjo/IyOD8PBw+vbty4ABA4iNjcXBwYF//OMfvPPOOxw/fhxN09A0rdH+9u5WUlLCz3/+c7p06UK7du14/PHH2bVrl3X+tm3b6NWrF0ajEX9/f958802b5VvapjNnzqBpGh9++CERERE4OTnRt29fsrOzrdszffp0SktLrfVesmQJUD8a86uvvkpcXBw//OEPWbFiBc899xwrV64kMDDQOl2/fh1HR0f27t3b4vYCFBQU8Pnnn5OUlERwcDAffvihruWEEEIIIYQQQgghxPdLqzcQzp07l6ysLHbu3El6ejr79u3j0KFDupevqalh1KhRuLq6sm/fPrKysnBxcWH06NFUV1dTW1vLxIkTCQ8P58iRI2RnZ/Pcc8+haRpTpkzhxRdfpFevXhQXF1NcXMyUKVOaXZ/ZbGbMmDFkZWWxadMmjh07xuuvv47BUP963cGDB4mKiuInP/kJR48eZcmSJbzyyiu6Gh7v9utf/5p58+aRl5dHcHAwU6dOpba2liFDhrBy5UpMJpO13vPmzbMu99vf/pa+ffvyr3/9i1deeYX4+Hjef/99qqqqrDGbNm3Cx8eHESNG6KpLSkoK48aNw83NjZ/97GesX79eeXuEEEIIIYQQQgghWpvFbHlg06OqVV8xvnHjBmlpabz//vvW/vNSUlKUXn/dsmULZrOZ9957D03TrGW4u7uTkZHBgAEDKC0tZfz48fTo0QOA0NBQ6/IuLi7Y29vj5eWla31/+9vfyMnJIT8/n+DgYAACAgKs89966y1GjhxpHYU4ODiYY8eO8Zvf/IZp06bp3i6AefPmMW7cOACSk5Pp1asXX331FSEhIbi5uaFpWqP1HjFiBC+++KL1/z4+PiQkJLBjxw6ioqKA+j4Mp02bZt1nzTGbzaSmpvLOO+8A8JOf/IQXX3yRgoICunfvrrRNQgghhBBCCCGEEOK7rVUzCE+fPk1NTQ2DBn3TAa+bmxs9e/bUXcbhw4f56quvcHV1xcXFBRcXFzw9Pbl16xanTp3C09OTadOmMWrUKP7jP/6Dt99+m+Li4nuuc15eHr6+vtbGwbvl5+czdOhQm8+GDh3Kl19+qTwQSp8+faz/9vb2BuDSpUstLjdgwACb/7dr147//M//5I9//CMAhw4d4v/+7/90N1imp6dz8+ZNxo4dC0DHjh358Y9/bC2vMVVVVZSVldlM1XdkMAohhBBCCCGEEEKI76a2G+aoCZqmNRiIo6bmmxGRysvL6d+/P3l5eTbTyZMn+elPfwrUZxRmZ2czZMgQtmzZQnBwMP/85z/vqT7t26uNotiYlrbpNgeHb0ZGu53pZza3PIKRs3PDEQ3j4+NJT0/n3LlzpKSkMGLECLp166arvuvXr+fatWu0b98ee3t77O3t2b17N2lpaU3WZ/ny5bi5udlM69aqj3wshBBCCCGEEEII0RyLxfLApkdVqzYQBgQE4ODgQG5urvWz0tJSTp48af1/p06dbDL+vvzySyoqvhlK/oc//CFffvklnTt3thmEIzAwEDc3N2vcD37wAxYuXMjnn3/O448/zvvvvw+Ao6OjUmZfnz59OHfunE0d7xQaGkpWVpbNZ1lZWQQHB1v7KWxpm/RQrXfv3r0ZMGAAf/jDH3j//feJi4vTtdzVq1fZsWMHH3zwgU0D7L/+9S+uX7/OJ5980uhyCxcupLS01Gb6+azndddXCCGEEEIIIYQQQjycWrUPQldXV2JjY3nppZfw9PSkc+fOLF68GDs7O2vG3IgRI1i9ejWDBw+mrq6OBQsW2GTWRUdH85vf/IYJEyawdOlSfH19OXv2LB9++CHz58+npqaG3//+9zz11FN07dqVEydO8OWXXxITEwPUj/pbUFBgfXXY1dUVo9HYZJ3Dw8MZNmwYTz/9NG+99RaBgYHWUZBHjx7Niy++yMCBA3n11VeZMmUK2dnZrF69mnfffddaRkvbpIe/vz/l5eXs3buXvn374uTkhJOTU7PLxMfHk5CQgLOzM5MmTdK1no0bN9KhQweioqIa9Fc4duxY1q9fz+jRoxssZzQaG+xHR2OprnUKIYQQQgghhBBC6GV+hAcLeVBa/RXjt956i8GDBzN+/HgiIyMZOnQooaGhtGvXDoA333wTPz8/nnzySX76058yb948m4YwJycnPvvsMx577DEmT55MaGgoM2bM4NatW5hMJpycnDh+/DhPP/00wcHBPPfcczz//PP8/Oc/B+Dpp59m9OjRRERE0KlTJzZv3txinbdt28bAgQOZOnUqYWFhzJ8/35rN98Mf/pCtW7fywQcf8Pjjj7No0SKWLl1q099fS9ukx5AhQ5g1axZTpkyhU6dOrFixosVlpk6dir29PVOnTrXu35b88Y9/ZNKkSY0OZvL000+zc+dOrly5olR3IYQQQgghhBBCCPHdpVna+AXrmzdv4uPjw5tvvsmMGTPaclWPnDNnztCjRw9yc3P54Q9/eN/Xf+JUkVK8hZZHWL6TV1njr303pdDlcd2xtRaDUtnOBrVXxjXUflZ2qA14U1rn1nLQ/9Pe7pZS2U6UK8VXa/oap63xFkfdsY5atVLZqgxarVK8xaJ2DDvXtF2WbZlDB6V4o6VSKd6s6f+N1LVuMnoDNRa1jGzP2otK8RUOJqV4Farngmqaznj/tlzqSpTiKwxq+8Wprkwpvtqg1gdwqVn/ec/ZTu2crapW8Zg3ov88XGFp2O9wcxy0hn0eN8eA2nmvfZ3aNeEiXXXHOhkUz0sWtefaBk3t2qp6n6LC4/cLleKvP7dcKV5132ia/nOTa911pbLLDe5K8ar7XfW8qkL1elNZq3YPdLn3oJaD7uCfn6E7VvV4f5SU1Lgqxbvaq11Dbpn1X7t91/9/SmW39blAher1RvVaqXquKbPzVIqvteivT5VZ/98rAK72atdKO1oeh+BOwT0eU4r/vpi57OoDW/cffqX2t9b3RaufQf71r3+xefNmTp06xaFDh4iOjgZgwoQJrb2qRi1ZsoR+/frdl3W1ptTUVNzd3XXF1tTU8PXXX/Pyyy/zox/96J4aB7/66iumT5+Or68vRqOR7t27M3XqVA4cOKBclhBCCCGEEEIIIURrkUFK7r82Sfv47W9/y4kTJ3B0dKR///7s27ePjh07tsWqWvSnP/3J+vrx3bp168YXX3xxn2v07VRXV/P5558TERFBcHAw//M//2Mzf9++fYwZM6bJ5cvLyzlw4AAjR47k8ccfZ926dYSEhHDjxg127NjBiy++SGZmpq663Kh1Uap7O0OVUnypq49S/Onr+lv5O7uoPRlUzlqwU3uK66j4RO5apf4Mk+4uallsBrNidolF8YmZnf6soUqL2qv6qhwVsxbsFb8n11L9WbYWTe0Ys3NVO8acbnytFH+uQz/dsTVmtYwLk1aiFF9pUcs0c7p5SSneveSoUryKWmd3pfh8j2FK8e0N+jPTXMvVjoFKN7VzvOuNC0rxZju148bg4q079usa/VlsACVVaseYr7NaVxxlFv3ZmCGHUpTKPjPgp0rxqufVGoNaFkVZpf7y7YxqGRQGTS1eNUND5VqvkoEHcGVmy13H2JSv+MdJ+2WzlOIrf7VWd6xqlo5qgp9qBqFKppzq/duVSrXM6fJqtfNYgEJGIMCZ0OG6Y7vn/12pbNVjWPUtCpXy27JsgOIyteuZ0aPt7svbtfG5wP4VfQNX3lb9aqru2DrU3sBS/V5VzzWq9bl6S38maWWNWjOJs0nxb8vWz9MSolW0+pEZGBhISEgIFosFo9HI2LFjmTNnDomJiQBomsb27dttlnF3dyc1NdX6/6KiIqKionB3d8fT05MJEyZw5swZ6/yMjAwGDRqEs7Mz7u7uDB06lLNnz5KamkpycjKHDx9G0zQ0TePGjRs2o/XeOe3evRuAkpISfv7zn9OlSxfatWvH448/zq5du6zr27ZtG7169cJoNOLv78+bb75pU/+WtunMmTNomsaHH35IREQETk5O9O3bl+zsbOv2TJ8+ndLSUmu9lyxZAtQPXvLqq68SExODyWTiueeeY+nSpTz//POcOHGC3r17A3D58mUcHR2b3d68vDwsFgvTpk0jKCiIffv2MW7cOHr06EG/fv1YvHgxO3bsuIdvXQghhBBCCCGEEKJ1WMyWBzY9qlo9g3Du3LlkZWWxc+dOunTpwqJFizh06JDu135ramoYNWoUgwcPZt++fdjb2/Paa68xevRojhw5gp2dHRMnTmTmzJls3ryZ6upqcnJy0DSNKVOm8H//93/s2bOHv/3tbwC4ubnRvn3TmQFms5kxY8Zw48YNNm3aRI8ePTh27BgGQ/0TiYMHDxIVFcWSJUuYMmUKn3/+ObNnz6ZDhw42A5Xo8etf/5rf/va3BAUF8etf/5qpU6fy1VdfMWTIEFauXMmiRYs4ceIEAC4u3zzp+u1vf8uiRYtYvHgxAPv37ychIYE333zTOrLwpk2b8PHxYcyYMY0OQHLbv/71L7744gvef/997Owatg/rfc1ZCCGEEEIIIYQQQnw/tGoD4Y0bN0hLS+P9999n5MiRAKSkpNC1q/5XfbZs2YLZbOa9996zNnSlpKTg7u5ORkYGAwYMoLS0lPHjx9OjRw8AQkNDrcu7uLhgb2+Pl5eXrvX97W9/Iycnh/z8fIKDgwEICAiwzn/rrbcYOXIkr7zyCgDBwcEcO3aM3/zmN8oNhPPmzWPcuHEAJCcn06tXL7766itCQkJwc3ND07RG6z1ixAhefPFF6/99fHxISEhgx44dREVFAfV9GE6bNq3ZxkGAL7/8EoCQkBClugshhBBCCCGEEEKI76dWfcX49OnT1NTUMGjQ/8/e3YdVUe6L/38Pz88QioqoECCK4jNRyjEF+W61TC12EPJVSfDhmLvjVnrgVxpqpadErePOOmXAN8P0ZJl50hN6RNlIWxMwH1BQwaJNqZUIKrBgrd8fbFcuBZnbIGn3eV3XXBdrrc/cc8+sWTPDPZ+5759H6HJ3d6dPnz66yzh8+DCnTp3C1dUVFxcXXFxc8PT0pLa2ltOnT+Pp6UlCQgJjx47loYce4rXXXqOysvK261xUVESPHj3MjYM3Ki4uJjw83OK98PBwSktLaWxU6wNs4MCB5r+9vZv6Ujp3rvW+skJDQy1eOzg4MHXqVN59910ACgoKOHr0qK4Gy9vtcLOuro5Lly5ZTPX1an0KCiGEEEIIIYQQQrRGHjH+9f3qvWNqmnZTI5XB8HNHsDU1NQwbNuymvvNKSkqYMqWpI+709HTy8/MZMWIEmzZtIigoiC+++OK26nOrx4/1am2drrG1/bkj42uZfkZj6x1oOzvf3PFtUlIS2dnZVFRUkJ6eTmRkJL6+vq2Wda0h9MSJE63GXm/58uW4u7tbTBlvrVYqQwghhBBCCCGEEEJ0PG3aQOjv74+trS0HDx40v1dVVUVJSYn5tZeXl0XGX2lpKVeu/Dzqz9ChQyktLaVLly4EBgZaTO7u7ua4IUOGkJKSwv79+wkJCSErKwsAOzs7pcy+gQMHUlFRYVHH6wUHB5OXl2fxXl5eHkFBQeZ+CltbJz1U6z1gwABCQ0N5++23ycrKYsYMfSNWDR48mH79+pGWltZs4+TFixebnS8lJYWqqiqLKWH2n3XXVwghhBBCCCGEEEIPo8l0x6bfqzZtIHR1dWX69Ok89dRT7Nmzh2PHjpGYmIiVlZU5Yy4yMpK1a9dSWFjIl19+yZw5cywy6+Lj4+ncuTOTJk0iNzeXsrIycnJyePLJJ6moqKCsrIyUlBTy8/M5e/Ysn3/+OaWlpeZ+CP38/CgrK6OoqIgLFy5QV3frx2BHjRrF/fffT3R0NNnZ2ZSVlbFjxw527twJwMKFC9m9ezfLli2jpKSEzMxM1q5dS3JysrmM1tZJDz8/P2pqati9ezcXLlzQ1cCYlJTEihUrMJlMPPzww7qWo2ka6enplJSUMHLkSD777DPOnDnDV199xUsvvcSkSZOanc/e3h43NzeLyc7OXmkdhRBCCCGEEEIIIUTH0+aPGK9atYrhw4czYcIEoqKiCA8PJzg4GAcHBwDS0tLo2bMnI0eOZMqUKSQnJ+Pk5GSe38nJiX379tGrVy8eeeQRgoODSUxMpLa2Fjc3N5ycnDhx4gTR0dEEBQUxa9YsnnjiCWbPng1AdHQ048aNIyIiAi8vLzZu3Nhqnbds2cI999xDXFwc/fr14+mnnzZn8w0dOpTNmzfzwQcfEBISwuLFi1m6dKlFf3+trZMeI0aMYM6cOcTGxuLl5cUrr7zS6jxxcXHY2NgQFxdn3r56hIWF8eWXXxIYGMjMmTMJDg5m4sSJHDt2jDVr1ijVWwghhBBCCCGEEEL8trXpKMbQlEX4/vvvm19fvnyZJUuWMGvWLAC6d+/O//zP/1jMc+Njrd26dSMzM7PZ8t3c3Pj4449bXL69vT0ffvihUp09PT3NA340Jzo6mujo6BY/b22d/Pz8buqj0MPD46b31q1bx7p16yzeKy8vb3G5Fy5coLa2lsTExBZjWhIUFNTiNhZCCCGEEEIIIYS4U37Pg4XcKW3eQFhYWMiJEycICwujqqqKpUuXArT46KpQZzAY+OGHH3j++ee57777GDp06J2ukhBCCCGEEEIIIYT4jdJMN6ax/UKFhYUkJSVx8uRJ7OzsGDZsGKtWrWLAgAFtuZgWpaamsnXrVoqKigB4//33zY8f38jX15djx479KvVqTUZGBvPnz29xkJDr5eTkEBERQVBQEB9++KHFts3NzWX8+PEtzltTUwPAqVOneOmll8jOzub8+fN0796d++67j4ULFxIaGqqrzsdOVbYedB2N9r0D0Ii17lhnrUapbOf6i0rxP9h6K8XXmeyU4lW2pat1tVLZ9Sa1viWtaH0k7uup1N3YzgOtO5jUBhOq09RGPXcyqm17FZet3JTiVetyxcpVd6zRpPY9HT3fTSk+xOs7pXhrTf+ATwCORrXjgYo6K7XuJurb8VjgYqpSKrvWylkp3sF4WSnepKntN98auuuOdbVV+227cVEp/qqmtm0aTPrvx6qeK220hnary+3o1KD/9/qjTVelsjVNbduonp9MaErxHUmjSf81EICtZtAd297n4o6k1qh2DdSJ80rxqudulfNrWXCEUtkBJ3Yrxf+WXW5UOxc7WtUqxdeb9Pc972B16/7xfynVY0FAwfutB/1D+bDHlMpWPZ+pHoNVrz9Vzn+Nisc91X1GVd+AHu1afkc1bZFae0Nb+n/L1P6f/2fR5leJQ4YM4dChQ21d7G2bOHEi9957b7OfqQ4k0hHU19czevTomx5PviY0NNTcONqSL7/8kjFjxhASEsJbb71F3759qa6u5pNPPmHhwoXs3bu3HWouhBBCCCGEEEIIITqiNr8lWF1dTXx8PM7Oznh7e7N69WpGjx7N/PnzgaZRdLdu3Woxj4eHBxkZGebX33zzDTExMXh4eODp6cmkSZMs+uLLyckhLCwMZ2dnPDw8CA8P5+zZs2RkZLBkyRIOHz6MpmlomsaWLVsIDAxsdvL19QWa+gucPXs2Xbt2xcHBgZCQELZv325e3pYtW+jfvz/29vb4+fmRlpZmUf/W1qm8vBxN0/joo4+IiIjAycmJQYMGkZ+fb16fxx9/nKqqKnO9U1NTgab+C5ctW8a0adNwc3Nj1qxZREZGMm/ePIvlnT9/Hjs7O/bv39/i+gYGBmIymUhISKB3797k5uby4IMPEhAQwODBg3nhhRf45JNPFL5tIYQQQgghhBBCCPFb1+YNhAsWLCAvL49t27aRnZ1Nbm4uBQUFuuc3GAyMHTsWV1dXcnNzycvLw8XFhXHjxlFfX09DQwOTJ09m1KhRfPXVV+Tn5zNr1iw0TSM2NpaFCxfSv39/KisrqaysJDY29pbLMxqNjB8/nry8PDZs2MDx48dZsWIF1tZN6dmHDh0iJiaGxx57jCNHjpCamsqiRYssGjT1eu6550hOTqaoqIigoCDi4uJoaGhgxIgRrFmzBjc3N3O9k5OTzfOtXLmSQYMGUVhYyKJFi0hKSiIrK4u6up9T1Dds2ICPjw+RkZG3rENRURHHjh1j4cKFWFnd/PV7eHgor5cQQgghhBBCCCFEWzEaTXds+r1q00eMq6uryczMJCsrizFjxgCQnp5O9+76+w3atGkTRqORd955B03TzGV4eHiQk5NDaGgoVVVVTJgwgYCAAACCg4PN87u4uGBjY0O3bvr6uNq1axcHDhyguLiYoKAgAPz9/c2fr1q1ijFjxrBo0SKgafTf48eP8+qrr5KQkKB7vQCSk5N58MEHAViyZAn9+/fn1KlT9O3bF3d3dzRNa7bekZGRLFy40Pzax8eHefPm8cknnxATEwM09WGYkJBg3mYtKS0tBaBv375KdRdCCCGEEEIIIYQQ/5zaNIPwzJkzGAwGwsLCzO+5u7vTp08f3WUcPnyYU6dO4erqiouLCy4uLnh6elJbW8vp06fx9PQkISGBsWPH8tBDD/Haa69RWXn7nVcWFRXRo0cPc+PgjYqLiwkPD7d4Lzw8nNLSUhob1TrCHzhwoPlvb++mTi/PnTvX6nw3Dhri4ODA1KlTeffddwEoKCjg6NGjuhosb3dMmrq6Oi5dumQx1de1bye7QgghhBBCCCGE+P0xGU13bPq9+tWHJdM07aZGKoPh55HUampqGDZsGEVFRRZTSUkJU6ZMAZoyCvPz8xkxYgSbNm0iKCiIL7744rbq4+ioNippc1pbp2uuHxTlWqaf0dj6CHvOzjePlpiUlER2djYVFRWkp6cTGRlp7lPxVq41hJ44caLV2OstX74cd3d3i+ntt/5DqQwhhBBCCCGEEEII0fG0aQOhv78/tra2HDx40PxeVVUVJSUl5tdeXl4WGX+lpaVcuXLF/Hro0KGUlpbSpUuXmwbYcHd3N8cNGTKElJQU9u/fT0hICFlZWQDY2dkpZfYNHDiQiooKizpeLzg4mLy8PIv38vLyCAoKMvdT2No66aFa7wEDBhAaGsrbb79NVlYWM2bM0DXf4MGD6devH2lpac02Tl68eLHZ+VJSUqiqqrKYZs7+k+76CiGEEEIIIYQQQoiOqU0bCF1dXZk+fTpPPfUUe/bs4dixYyQmJmJlZWXOmIuMjGTt2rUUFhby5ZdfMmfOHIvMuvj4eDp37sykSZPIzc2lrKyMnJwcnnzySSoqKigrKyMlJYX8/HzOnj3L559/TmlpqbkfQj8/P8rKyigqKuLChQsWA3k0Z9SoUdx///1ER0eTnZ1NWVkZO3bsYOfOnQAsXLiQ3bt3s2zZMkpKSsjMzGTt2rUWg4i0tk56+Pn5UVNTw+7du7lw4YKuBsakpCRWrFiByWTi4Ycf1rUcTdNIT0+npKSEkSNH8tlnn3HmzBm++uorXnrpJSZNmtTsfPb29ri5uVlMdvb2SusohBBCCCGEEEII0RqTyXTHpvb0448/Eh8fj5ubGx4eHiQmJlJTU9NifHl5OZqmNTv913/9lzmuuc8/+OADpbq1+SPGq1atYvjw4UyYMIGoqCjCw8MJDg7GwcEBgLS0NHr27MnIkSOZMmUKycnJODk5med3cnJi37599OrVi0ceeYTg4GASExOpra3Fzc0NJycnTpw4QXR0NEFBQcyaNYsnnniC2bNnAxAdHc24ceOIiIjAy8uLjRs3tlrnLVu2cM899xAXF0e/fv14+umnzdl8Q4cOZfPmzXzwwQeEhISwePFili5datHfX2vrpMeIESOYM2cOsbGxeHl58corr7Q6T1xcHDY2NsTFxZm3rx5hYWF8+eWXBAYGMnPmTIKDg5k4cSLHjh1jzZo1SvUWQgghhBBCCCGEEK2Lj4/n2LFjZGdns337dvbt28esWbNajO/ZsyeVlZUW05IlS3BxcWH8+PEWsenp6RZxkydPVqqbZmrn5tHLly/j4+NDWloaiYmJ7bmo353y8nICAgI4ePAgQ4cO/dWXf+yU2uAwGu3bEt+Ite5YZ63lFvpm4+svKsX/YOutFF9nslOKV9mWrtbVSmXXm9QyQ61ovR/N66nU3djO3aQ6mNS6AqjT1PosdTKqbXsVl63clOJV63LFylV3rNGk9j0dPa9vlPlrQry+U4q31tQGkHI0qh0PVNRZqd0sqm/HY4GLqUqp7Fqrm/u/vRUH42WleJOmtt98a+iuO9bVVu237cZFpfirmtq2aTDZ6I5VPVfaaA3tVpfb0alB/+/1R5uuSmVrmtq2UT0/mdCU4juSRpP+ayAAW+3mvrJb0t7n4o6k1qh2DdSJ80rxqudulfNrWXCEUtkBJ3Yrxf+WXW5UOxc7WtUqxdeb9D855mDVvoM8qh4LAgre1x1bPuwxpbJVz2eqx2DV60+V81+j4nFPdZ9R1TegR7uW31HFPf31HVv2xld6tUu5xcXF9OvXj4MHD5oHo925cycPPPAAFRUVdO+u73p3yJAhDB06lPXr15vf0zSNjz/+WLlR8HptfsYvLCxk48aNnD59moKCAuLj4wFafHRVqDMYDHz33Xc8//zz3HfffXekcVAIIYQQQgghhBBC6JOfn4+Hh4e5cRAgKioKKysr/va3v+kq49ChQxQVFTWbgPfEE0/QuXNnwsLCePfdd5Ufl26X28grV67k5MmT2NnZMWzYMHJzc+ncuXN7LKpZqampbN26laKiIt5//33z48c38vX15dixY79avVqTkZHB/PnzWxwo5Jq8vDwiIiIICgriww8/tPgsNzf3pjTT6117tn306NHs3bv3ps8NBgM2Nvp2CzutXlfcNap3oFXv/KvcpKpD/yPZAAa7LkrxqnV30dQyb+oUsvwMihlJqlS/V5W7icr7gCIrk1qmmbVqpo5V+257FVWap1K8Hfp/3w7aVaWyB3epUIpXvaNso5AZA+qZcipUs7XsNbU70I0Kp/EGTW1/rDWqHSc1K7ULEGvUfk8uNvr3M9WsunrFc4IdahkgKplv7Z2t5aipZVfWo5ZRpZIVqJrt25Ey/No7M0aV6rHDqPDUheq5WHUftket7tZG/cd4lWx4UM/uumS6SyneGrV9XuXYoZoReLrvGKV41fJVfiOqvw/V35+t4jlBNVvZRvF7bU+qxwLVrEAV7Z3FrbqudZr+c71qdqL451NXV3fTeBb29vbY/8JxGL777ju6dLFsW7CxscHT05PvvtP3FMb69esJDg5mxIgRFu8vXbqUyMhInJyc+Pzzz5k7dy41NTU8+eSTuuvX5g2EQ4YM4dChQ21d7G2bOHEi9957b7OfqQ4k0lGMGDGixZbg0NBQioqKdJUzc+ZMli5davGe3sZBIYQQQgghhBBCiPZgNLZvF2W3snz5cpYsWWLx3gsvvEBqamqz8c8++yz//u//fssyi4uLf3G9rl69SlZWFosWLbrps+vfGzJkCJcvX+bVV19VaiBsl6bx6upq4uPjcXZ2xtvbm9WrVzN69Gjmz58PND0bvXXrVot5PDw8yMjIML/+5ptviImJwcPDA09PTyZNmkR5ebn585ycHMLCwnB2dsbDw4Pw8HDOnj1LRkYGS5Ys4fDhw2iahpubG3/9618JDAy8afL19TWXd/HiRWbPnk3Xrl1xcHAgJCSE7du3mz/fsmUL/fv3x97eHj8/P9LS0izq39o6XRt55qOPPiIiIgInJycGDRpEfn6+eX0ef/xxqqqqzCPOXNv5/Pz8WLZsGdOmTcPNzY1Zs2YRGRnJvHnzLJZ3/vx53N3dOXv2bLPrGxgYaBHv5OREt27dLCYhhBBCCCGEEEKI36uUlBSqqqosppSUlBbjFy5cSHFx8S0nf39/unXrxrlz5yzmbWho4Mcff9TVHvPhhx9y5coVpk2b1mrsvffeS0VFxU2ZkLfSLuliCxYsIC8vj23bttG1a1cWL15MQUEBgwcP1jW/wWBg7NixDB8+nNzcXGxsbHjxxRcZN24cX331FVZWVkyePJmZM2eyceNG6uvrOXDgAJqmERsby9GjR9m5cye7du0CwN3d/ZbLMxqNjB8/nurqajZs2EBAQADHjx/H2rrp8YtDhw4RExNDamoqsbGx7N+/n7lz59KpUyeL0Yz1eO6551i5ciW9e/fmueeeIy4ujlOnTjFixAjWrFnD4sWLOXnyJAAuLi7m+VauXMnixYt54YUXAPjb3/7GvHnzSEtLM6e5btiwAR8fHyIjI5XqJIQQQgghhBBCCNFRtPN4urek+jixl5cXXl5ercYNHz6cixcvcujQIYYNGwbA//7v/2I0Glt88vV669evZ+LEibqWVVRUxF133aW0Hm3eQFhdXU1mZiZZWVmMGdPUt0V6erru0VgANm3ahNFo5J133kHTNHMZHh4e5OTkEBoaSlVVFRMmTCAgIACA4OBg8/wuLi7Y2NjozojbtWsXBw4coLi4mKCgIAD8/f3Nn69atYoxY8aYUzaDgoI4fvw4r776qnIDYXJyMg8++CAAS5YsoX///pw6dYq+ffvi7u6OpmnN1jsyMpKFCxeaX/v4+DBv3jw++eQTYmJigKY+DBMSEszbrDVvvPEG77zzjvn17Nmzb8qMFEIIIYQQQgghhBC/THBwMOPGjWPmzJm8+eabGAwG5s2bx2OPPWZuM/v2228ZM2YM/+///T/CwsLM8546dYp9+/bx2Wef3VTup59+yvfff899992Hg4MD2dnZvPzyyyQnJyvVr80bCM+cOYPBYLBYEXd3d/r06aO7jMOHD3Pq1ClcXS07Fq6treX06dP84Q9/ICEhgbFjx/J//s//ISoqipiYGLy9vW+rzkVFRfTo0cPcOHij4uLim0ZhDg8PZ82aNTQ2NpozDfUYOHCg+e9r9T137hx9+/a95XzXj3ID4ODgwNSpU3n33XeJiYmhoKCAo0ePsm3bNt11iY+P57nnnjO/9vDwaDG2uU466+vqsPuFnXQKIYQQQgghhBBCXM90B/sgbE/vv/8+8+bNY8yYMVhZWREdHc3rr79u/txgMHDy5EmuXLEcWO7dd9+lR48e/OEPf7ipTFtbW/7yl7/w5z//GZPJRGBgIKtWrWLmzJlKdbsjI1JomnZTuqjB8POIZDU1NQwbNoz333//pnmvpVKmp6fz5JNPsnPnTjZt2sTzzz9PdnY29913n3J9HB0dlee5UWvrdM31A6Ncy/QzGlsf4cnZ+eZRNpOSkhg8eDAVFRWkp6cTGRlp0a9ia9zd3W/ql7AlzXXSOe9P/8aT//Zn3csTQgghhBBCCCGE+L3y9PQkKyurxc/9/Pyafbz65Zdf5uWXX252nnHjxjFu3LhfXLc2H6TE398fW1tbDh48aH6vqqqKkpIS82svLy8qKyvNr0tLSy1aR4cOHUppaSldunS5aZCN6/sTHDJkCCkpKezfv5+QkBDzRrazs6OxUf9w8wMHDqSiosKijtcLDg4mLy/P4r28vDyCgoLM2YOtrZMeqvUeMGAAoaGhvP3222RlZTFjxgyl5alorpPOOXPmttvyhBBCCCGEEEIIIcSvo80bCF1dXZk+fTpPPfUUe/bs4dixYyQmJmJlZWXOmIuMjGTt2rUUFhby5ZdfMmfOHIvMuvj4eDp37sykSZPIzc2lrKyMnJwcnnzySSoqKigrKyMlJYX8/HzOnj3L559/TmlpqbkfQj8/P8rKyigqKuLChQutjtoyatQo7r//fqKjo8nOzqasrIwdO3awc+dOoGlEmt27d7Ns2TJKSkrIzMxk7dq1Fs9zt7ZOevj5+VFTU8Pu3bu5cOGCrgbGpKQkVqxYgclk4uGHH1Zangp7e3vc3NwsJnm8WAghhBBCCCGEEG3NZDTdsen3qs0bCKFpUI/hw4czYcIEoqKiCA8PJzg4GAcHBwDS0tLo2bMnI0eOZMqUKSQnJ+Pk5GSe38nJiX379tGrVy8eeeQRgoODSUxMpLa2Fjc3N5ycnDhx4gTR0dEEBQUxa9YsnnjiCWbPng1AdHQ048aNIyIiAi8vLzZu3Nhqnbds2cI999xDXFwc/fr14+mnnzZn8w0dOpTNmzfzwQcfEBISwuLFi1m6dKnFACWtrZMeI0aMYM6cOcTGxuLl5cUrr7zS6jxxcXHY2NgQFxdn3r5CCCGEEEIIIYQQQuilmX6FsaMvX76Mj48PaWlpJCYmtvfiflfKy8sJCAjg4MGDDB069Fdddunps0rxRsX2aCta75vxegaT/oxNTVPb7VXrYkLfSNLXWKP/0XKAOpPCUOVag1LZqnVXpaF/26tud1UOxstK8fVWao3wqt+rijrU6qLy+wCw0+p1x9qiPxagAbW6qO6TNtrN/b/eSqOp/brjbVAs2067dcb7jRoVuhJW/Z5qjK6tB13H0eqqUrw1ascmlfrYWqntAzaKdVGtuwE73bHtfa5U3Q/qUXtawGjSX39rTe0YqXosUDnfqJbfnmXfDhvU9nkj+gfYU6W6D9tTqxRvbdS/rles1I5jqhpNatuxPfd51X3ydN8xSvEBJ3YrxavUp71/23VGteOY6jlEZT9QvS5XpXosUL0mU6F6Hay6H6iez1SunVXOZQBWWvv+z9I3oEe7lt9RPfLkqTu27I9e1zdWwz+bdvmvqLCwkBMnThAWFkZVVRVLly4FuGkkYHH7DAYDP/zwA88//zz33Xffr944KIQQQgghhBBCCCH+ObRb2sTKlSs5efIkdnZ2DBs2jNzcXDp37txeiwMgNTWVrVu3UlRUZPH++++/b378+Ea+vr4cO3asXevVmoyMDObPn8/Fixd1z5OXl0dERARBQUF8+OGHFp/l5uYyfvz4FuctKiri5ZdfZteuXXz//fd07tyZvn37MmPGDGJjY7Gx0bdbqN7lae9sMBWqd4VUMw5Vt43q3Xal+rdv0kKHorodG63U7poqZ54a2y+DUPV7bVTc502a/gWoxAI0GNVOPap3Za2NilmzivVXYaNYtGpWj1JGrkltf1TNdLFSjNcUH2BQ2Q9Us0uUs/ZU9xmF6rT3uVIl6xQ6VqacatkehvNK8RdtvdqtLh2N6vfanhoVj3uqxxoV7Z3JpspkUsggVLxGUc0IbO+MQxWq31Oj4jFeNaeuI12XK1+rmjrO/2fK525N8dpW4fckxO9VuzQQDhkyhEOHDrVH0bdl4sSJ3Hvvvc1+pjqQyJ1UX1+PnV3TY0qjR49uduhrgNDQ0JsaSa85fPgwQ4cOpX///vzlL3+hb9++AHz55Zf85S9/ISQkhEGDBrVL/YUQQgghhBBCCCFa83seLOROadNBSqqrq4mPj8fZ2Rlvb29Wr17N6NGjmT9/PgCaprF161aLeTw8PMjIyDC//uabb4iJicHDwwNPT08mTZpEeXm5+fOcnBzCwsJwdnbGw8OD8PBwzp49S0ZGBkuWLOHw4cNomoamaeZyXV1dCQwMbHZyd3dn9uzZdO3aFQcHB0JCQti+fbt5eVu2bKF///7Y29vj5+dHWlqaRf1bW6fy8nI0TeOjjz4iIiICJycnBg0aRH5+vnl9Hn/8caqqqsz1Tk1NBZpGNV62bBnTpk3Dzc2NWbNmERkZybx58yyWd/78eezs7Ni9u+lOnaOjY7PrGhAQwKJFiwgKCiIvL4+HHnqI3r1707t3b+Li4vjrX//KwIEDdX7bQgghhBBCCCGEEOKfQZs2EC5YsIC8vDy2bdtGdnY2ubm5FBQU6J7fYDAwduxYXF1dyc3NJS8vDxcXF8aNG0d9fT0NDQ1MnjyZUaNG8dVXX5Gfn8+sWbPQNI3Y2FgWLlxI//79qayspLKyktjY2Fsuz2g0Mn78ePLy8tiwYQPHjx9nxYoVWFs3Pepw6NAhYmJieOyxxzhy5AipqaksWrTIokFTr+eee47k5GSKiooICgoiLi6OhoYGRowYwZo1a3BzczPXOzk52TzfypUrGTRoEIWFhSxatIikpCSysrKoq/u5I/sNGzbg4+NDZGTkLetQVFREcXExycnJWFk1/9Vr7fionRBCCCGEEEIIIURrTEbTHZt+r9rsEePq6moyMzPJyspizJimPirS09Pp3r277jI2bdqE0WjknXfeMTdUpaen4+HhQU5ODqGhoVRVVTFhwgQCAgIACA4ONs/v4uKCjY0N3bp107W8Xbt2ceDAAYqLiwkKCgLA39/f/PmqVasYM2YMixYtAiAoKIjjx4/z6quvkpCQoHu9AJKTk3nwwQcBWLJkCf379+fUqVP07dsXd3d3NE1rtt6RkZEsXLjQ/NrHx4d58+bxySefEBMTAzT1YZiQkNBq415JSQkAffr0Mb937tw5i3V+5ZVXmDt3rtK6CSGEEEIIIYQQQojfrjbLIDxz5gwGg4GwsDDze+7u7haNUa05fPgwp06dwtXVFRcXF1xcXPD09KS2tpbTp0/j6elJQkICY8eO5aGHHuK1116jsrLytutcVFREjx49zI2DNyouLiY8PNzivfDwcEpLS2lsVOsg+fpHd729vYGmxrnWhIaGWrx2cHBg6tSpvPvuuwAUFBRw9OhR5QbLazp16kRRURFFRUV4eHhQX9/8cPF1dXVcunTJYqq/LotRCCGEEEIIIYQQQvw2tekjxq3RNO2mgTUMBoP575qaGoYNG2ZusLo2lZSUMGXKFKApozA/P58RI0awadMmgoKC+OKLL26rPo6Ojre/Mv/Q2jpdc/1gKNcy/YzG1keNcnZ2vum9pKQksrOzqaioID09ncjISHx9fVstq3fv3gCcPHnS/J61tbW5j8JbjV68fPly3N3dLaa33nyj1WUKIYQQQgghhBBCqDCZTHds+r1qswZCf39/bG1tOXjwoPm9qqoq82OtAF5eXhYZf6WlpVy5csX8eujQoZSWltKlS5dmBxO5ZsiQIaSkpLB//35CQkLIysoCwM7OTimzb+DAgVRUVFjU8XrBwcHk5eVZvJeXl0dQUJC5n8LW1kkP1XoPGDCA0NBQ3n77bbKyspgxY4au+YYMGULfvn1ZuXKlrsbJ66WkpFBVVWUxzZ4jjyILIYQQQgghhBBC/Na1WQOhq6sr06dP56mnnmLPnj0cO3aMxMRErKyszBlzkZGRrF27lsLCQr788kvmzJljkVkXHx9P586dmTRpErm5uZSVlZGTk8OTTz5JRUUFZWVlpKSkkJ+fz9mzZ/n8888pLS0190Po5+dHWVkZRUVFXLhwwWIgj+aMGjWK+++/n+joaLKzsykrK2PHjh3s3LkTgIULF7J7926WLVtGSUkJmZmZrF271mIQkdbWSQ8/Pz9qamrYvXs3Fy5c0NXAmJSUxIoVKzCZTDz88MO6lqNpGunp6Zw8eZLw8HC2bdtGaWkpx48f58033+T8+fPmhs8b2dvb4+bmZjHZ2dsrracQQgghhBBCCCFEa4xG4x2bfq/a9BHjVatWMXz4cCZMmEBUVBTh4eEEBwfj4OAAQFpaGj179mTkyJFMmTKF5ORknJyczPM7OTmxb98+evXqxSOPPEJwcDCJiYnU1tbi5uaGk5MTJ06cIDo6mqCgIGbNmsUTTzzB7NmzAYiOjmbcuHFERETg5eXFxo0bW63zli1buOeee4iLi6Nfv348/fTT5my+oUOHsnnzZj744ANCQkJYvHgxS5cutejvr7V10mPEiBHMmTOH2NhYvLy8eOWVV1qdJy4uDhsbG+Li4szbV4/77ruPQ4cO0adPH5544gn69evHiBEj2LhxI6tXr+Zf//VfleouhBBCCCGEEEIIIX7bNFM7PmB9+fJlfHx8SEtLIzExsb0W87tUXl5OQEAABw8eZOjQoXekDiWnv1aK12jfZ/kNJv2ZmyZuPeLzjaw1tUFpVMu3Qu0uRZ1Rf/amnVXzA8+0RLXuqlT2A9XtYlS852FPrVr5mlr5Nsab+yNtK1e1m/snvZVahX0GwMFK/yBEtpraPlZn1H9TA8BKU9sPHExq3Tw0aGpZ3yqMNJ+VfSfYmdT298u4KsU7WF1VircyqX2vl00uumNttAalslXPTzaa2m+7QeH81N7a+7jaYGq5P+MbqX5PqnW5y9D6YHDXu2jrpRTfkdigtk+a2rEb8kbF455q3W1N+s85qudK1Wsgk0nxek/xfGY06f+eVMtWdbrvGKX4gBO726km6q4qXneoXAMBGIz6j/Gq1+WqVK/JGhWO2ara+38/K8X/z+pN+q+F2/u3rapvQI92Lb+jemh28R1b9qdvBd+xZd9JbXpEKCws5MSJE4SFhVFVVcXSpUsBmDRpUlsu5nfNYDDwww8/8Pzzz3PffffdscZBIYQQQgghhBBCiPZgMv5+Bwu5U9r8lsHKlSs5efIkdnZ2DBs2jNzcXDp37tzWi9Hl/fffZ/bs2dTX19PY2GgxarGvry/Hjh27I/XSKyMjg/nz53Px4kXze3l5eURERBAUFMSHH35oEZ+bm8v48eNbLK+mpqbFz0aPHs3gwYNZs2aN7vqpZAncDtXMApX6qN7laTSp3Q1v77tImqb/YKl6N9wGte1+1ag2Gritlf5MAdVsEVX1KPajqXiOslbclu3J0Uote0xlv3G7el6p7DLrvkrxbrbVSvFGTe332p5Zfqr7sEq2CCgeCxQzYOsVMiIAHBUzN1XVGe10x1pZqR2D7RV/H7aNatkldZr+7BWV7xTUMwIbFC/9VLMo2jMLXTUbpcr2zlx/3gkNtF+WantnAanW3dXwo+7Yq3ZqGYROppavlZvzk8lTKd5BUzt2qB4PlMpW/F5VMwJVMg7bO9uwvffh9vyeVBlM+s+Vqny/yVWKP9tzpFK86vdkhVoGocr5sk4h2xDATjFzU4iOqk1beIYMGcKhQ4fasshfZOLEidx77728/vrrZGdn8+mnn5o/Ux1IpKMYPXr0TcNu19fXY2dnR2hoKEVFRXemYkIIIYQQQgghhBBtwKTYHY345do3PecfqquriY+Px9nZGW9vb1avXs3o0aOZP38+0DS67tatWy3m8fDwICMjw/z6m2++ISYmBg8PDzw9PZk0aRLl5eXmz3NycggLC8PZ2RkPDw/Cw8P58ccf+etf/8p//Md/cOLECXr37k3v3r3561//iq+v7y3rfPHiRWbPnk3Xrl1xcHAgJCSE7du3mz/fsmUL/fv3x97eHj8/P9LS0izmb22dysvL0TSNjz76iIiICJycnBg0aBD5+fnm9Xn88cepqqpC0zQ0TSM1NRVoGvV42bJlTJs2DTc3N2bNmkVkZCRPPfUUgYGB5snd3Z1+/fpx9uxZAgMDb/0lCSGEEEIIIYQQQojfpV+lgXDBggXk5eWxbds2srOzyc3NpaCgQPf8BoOBsWPH4urqSm5uLnl5ebi4uDBu3Djq6+tpaGhg8uTJjBo1iq+++or8/HxmzZqFpmnExsaycOFC+vfvT2VlJZWVlcTGxt5yeUajkfHjx5OXl8eGDRs4fvw4K1aswNq66TG0Q4cOERMTw2OPPcaRI0dITU1l0aJFFg2aej333HMkJydTVFREUFAQcXFxNDQ0MGLECNasWYObm5u53snJyeb5Vq5cyaBBgygsLGTRokUkJSWRlZVFXd3Pjy5s2LABHx8fIiMjleslhBBCCCGEEEIIIX4f2rcTOZqyBzMzM8nKymLMmKa+KNLT0+nevbvuMjZt2oTRaOSdd95B0zRzGR4eHuTk5BAaGkpVVRUTJkwgICAAgODgn0edcXFxwcbGhm7duula3q5duzhw4ADFxcUEBQUB4O/vb/581apVjBkzhkWLFgEQFBTE8ePHefXVV0lISNC9XgDJyck8+OCDACxZsoT+/ftz6tQp+vbti7u7O5qmNVvvyMhIFi5caH7t4+PDvHnz+OSTT4iJiQGa+jBMSEgwbzMhhBBCCCGEEEKIjk4GKfn1tXsG4ZkzZzAYDISFhZnfc3d3p0+fPrrLOHz4MKdOncLV1RUXFxdcXFzw9PSktraW06dP4+npSUJCAmPHjuWhhx7itddeo7Ky8rbrXFRURI8ePcyNgzcqLi4mPDzc4r3w8HBKS0tpbFTrLHXgwIHmv729vQE4d+5cq/OFhoZavHZwcGDq1Km8++67ABQUFHD06FHlBsuW1NXVcenSJYupvk6to2UhhBBCCCGEEEII0fH8Ko8Yt0bTtJsG3jAYfh7ptKamhmHDhlFUVGQxlZSUMGXKFKApozA/P58RI0awadMmgoKC+OKLL26rPtePdny7Wluna64fLOVapp/R2HpnnM7ON4/KlpSURHZ2NhUVFaSnpxMZGdlqX4t6LV++HHd3d4vp7bfWtknZQgghhBBCCCGEENeYjKY7Nv1etXsDob+/P7a2thw8eND8XlVVFSUlJebXXl5eFhl/paWlXLlyxfx66NChlJaW0qVLF4tBOK4NxHHNkCFDSElJYf/+/YSEhJCVlQWAnZ2dUmbfwIEDqaiosKjj9YKDg8nLy7N4Ly8vj6CgIHM/ha2tkx6q9R4wYAChoaG8/fbbZGVlMWPGDKXl3UpKSgpVVVUW08zZ89qsfCGEEEIIIYQQQghxZ7R7A6GrqyvTp0/nqaeeYs+ePRw7dozExESsrKzMGXORkZGsXbuWwsJCvvzyS+bMmWORWRcfH0/nzp2ZNGkSubm5lJWVkZOTw5NPPklFRQVlZWWkpKSQn5/P2bNn+fzzzyktLTX3Q+jn50dZWRlFRUVcuHDBYiCP5owaNYr777+f6OhosrOzKSsrY8eOHezcuROAhQsXsnv3bpYtW0ZJSQmZmZmsXbvWYhCR1tZJDz8/P2pqati9ezcXLlzQ1cCYlJTEihUrMJlMPPzww0rLuxV7e3vc3NwsJjt7+zYrXwghhBBCCCGEEELcGb/KI8arVq1i+PDhTJgwgaioKMLDwwkODsbBwQGAtLQ0evbsyciRI5kyZQrJyck4OTmZ53dycmLfvn306tWLRx55hODgYBITE6mtrcXNzQ0nJydOnDhBdHQ0QUFBzJo1iyeeeILZs2cDEB0dzbhx44iIiMDLy4uNGze2WuctW7Zwzz33EBcXR79+/Xj66afN2XxDhw5l8+bNfPDBB4SEhLB48WKWLl1q0d9fa+ukx4gRI5gzZw6xsbF4eXnxyiuvtDpPXFwcNjY2xMXFmbevEEIIIYQQQgghxG+F0WS8Y9PvlWa6saO8X8Hly5fx8fEhLS2NxMTEX3vx/9TKy8sJCAjg4MGDDB06tF2XdfzU39u1fButQSm+1qi/QdRKU/vRa6j9TFTLt0Itvt5kpztWdTvaoBZ/1ajWZ6et1c19cbZEdburau/y7U1X263sWk3thoPquprQP/p5p9pvlcous+6rFO9mW60Ub4faAEoNqGV3qzAq3oczmtTiNU3/9+qA2v540eihFO9uVaUUr7pP/tjoqTvW0apWqWx7xXjbRrV9rEZzbz3oH1S+U1A/f6jukyaT/mOBavm2mv7zAagdl6B9j3u/Jx1tO95V/73u2J/suiqV7Wi6rBT/k0n/cQnAwUrt2KGyLVW/p/b+Xk/3HaM7NuDEbqWyVdUa1Z56sreqV4pvMNnojlU97nUkvt/kKsWf7TlSKV51n7RR3JYNJv3Xe3WK+4yd4j6jqm9Aj3Ytv6MaO73oji37fzIH37Fl30n6j2a/QGFhISdOnCAsLIyqqiqWLl0KwKRJk9qk/NTUVLZu3UpRUVGblPdrycjIYP78+Vy8ePEXl2UwGPjhhx94/vnnue+++25qHBw9ejR79+5l+fLlPPvssxafPfjgg3z22We88MILpKam6l6maiOYKtULEXuFi65Gk7Vi2Wr/PBoUGvCa4tUaKBoVGhFsFf/ZVN3ujlZqjQ6N6N/2qv/4ql5YWCs2hjYqHjKtTPr7EDVpiv+0t/M/ytbor/t5+55KZVs1tu+xQ2W7A1hbteM/swr/NACYtPb7XlW3i5Pib9vapPZ7arBSO+7ZKtzssNbU1rVR8XsyWKkd41UaretMatn/qudiW9T+ialH7Z8klX9+VRsrVbX3TaCORPXmgt/3+bpjv+52r2p12pVqo5+KKtNdSvGqx8n2bKBXvbnQ3g25Ko1+Ko2JqmWD+u9D9caI2nW5UtHKVNe1k6Gy9aB/UG3w61XxV6X4r3v8i1J8vUnt/GQw6r/uaO//c4U+v+fBQu6UX20U45UrVzJo0CCioqK4fPkyubm5dO7c+ddavIX3338fFxeXZqf+/fvfkTrdSn196xfzeXl5eHt7c/DgQd58802Lz3Jzc8nNzUXTNP6//+//s1hfJycndu/ejbe3d3tVXwghhBBCCCGEEEJ0YG3SQFhdXU18fDzOzs54e3uzevVqRo8ezfz584GmPvsWLVpETU0NP/74I9nZ2YwcOZKMjAxzGd988w0xMTF4eHjg6enJpEmTKC8vN3+ek5NDWFgYzs7OeHh4EB4eztmzZ8nIyGDJkiUcPnwYTdPQNM2i3OZMnDiRffv2MWHCBBwcHDAYDHTv3p1Vq1bx2WefAU19EPbv3x97e3v8/PxIS0uzKEPTNLZu3WrxnoeHh3nZ5eXlaJrGRx99REREBE5OTgwaNIj8/Hzz+jz++ONUVVWZ630te8/Pz49ly5Yxbdo03NzcmDVrFpGRkcybZzlq8Pnz57Gzs2P37t2MHj0ak8nEyZMnGTBggEVcaGgooaGhxMXF4eHhwfr16ykqKqKoqIh//dd/5Q9/+ANdunS55TYTQgghhBBCCCGEEP+c2qSBcMGCBeTl5bFt2zays7PJzc2loKBA9/wGg4GxY8fi6upKbm4ueXl5uLi4MG7cOOrr62loaGDy5MmMGjWKr776ivz8fGbNmoWmacTGxrJw4UL69+9PZWUllZWVxMbG3nJ5zs7OPPHEExw9epSNGzdSXFzMqlWr6NmzJ76+vhw6dIiYmBgee+wxjhw5QmpqKosWLWq14bE5zz33HMnJyRQVFREUFERcXBwNDQ2MGDGCNWvW4ObmZq739aMgX8u4LCwsZNGiRSQlJZGVlWUxAvOGDRvw8fEhMjLylnVwdHTE0dERLy8vpk6dSnZ2NoGBgQQGBvLpp58yY8YM5fUSQgghhBBCCCGEaA8mo/GOTb9Xv7gPwurqajIzM8nKymLMmKb+I9LT0+nevbvuMjZt2oTRaOSdd95B+0f/S+np6Xh4eJCTk0NoaChVVVVMmDCBgIAAAIKDg83zu7i4YGNjQ7du3XQtb9euXRw4cIDi4mKCgoIA8Pf3N3++atUqxowZw6JFiwAICgri+PHjvPrqqxYjFeuRnJzMgw8+CMCSJUvo378/p06dom/fvri7u6NpWrP1joyMZOHChebXPj4+zJs3j08++YSYmBigqQ/DhIQE8zbTY8aMGYwcOZLXXnuNQ4cOmberSt+DQgghhBBCCCGEEOKfxy/OIDxz5gwGg4GwsDDze+7u7vTp00d3GYcPH+bUqVO4urqa+8bz9PSktraW06dP4+npSUJCAmPHjuWhhx7itddeo7JSf4eqNyoqKqJHjx7mxsEbFRcXEx4ebvFeeHg4paWlNDaqdXw+cOBA89/X+vk7d+5cq/OFhoZavHZwcGDq1Km8++67ABQUFHD06FHlBstBgwbRu3dvPvzwQ959912mTp2KjU3r7cR1dXVcunTJYqqvUxuJTQghhBBCCCGEEKI1JqPpjk2/V7/KICWapmEyWW5kg+HnUe5qamoYNmyYuV+8a1NJSQlTpkwBmjIK8/PzGTFiBJs2bSIoKIgvvvjiturj6Oh4+yvzD62t0zW2tj+PlnQt08+oI2XV2dn5pveSkpLIzs6moqKC9PR0IiMj8fX1Va06M2bM4C9/+Qsffvih7seLly9fjru7u8X0n2/+RXnZQgghhBBCCCGEEKJj+cUNhP7+/tja2nLw4EHze1VVVZSUlJhfe3l5WWT8lZaWcuXKFfProUOHUlpaSpcuXcx9412b3N3dzXFDhgwhJSWF/fv3ExISQlZWFgB2dnZKmX0DBw6koqLCoo7XCw4OJi8vz+K9vLw8goKCsLa21rVOeqjWe8CAAYSGhvL222+TlZV1230HTpkyhSNHjhASEkK/fv10zZOSkkJVVZXFNGvOE7e1fCGEEEIIIYQQQgjRcfziPghdXV2ZPn06Tz31FJ6ennTp0oUXXngBKysrc8ZcZGQka9euZfjw4TQ2NvLMM89YZNbFx8fz6quvMmnSJJYuXUqPHj04e/YsH330EU8//TQGg4H//M//ZOLEiXTv3p2TJ09SWlrKtGnTgKZRf8vKysyPDru6umJvb99inUeNGsX9999PdHQ0q1atIjAwkBMnTqBpGuPGjWPhwoXcc889LFu2jNjYWPLz81m7di1vvPGGuYzW1kkPPz8/ampq2L17N4MGDcLJyQknJ6dbzpOUlMS8efNwdnbm4YcfVlreNXfddReVlZVK9bW3t79pm9rZX7qt5QshhBBCCCGEEEK0xGT6/Q4Wcqe0ySPGq1atYvjw4UyYMIGoqCjCw8MJDg7GwcEBgLS0NHr27MnIkSOZMmUKycnJFg1hTk5O7Nu3j169evHII48QHBxMYmIitbW1uLm54eTkxIkTJ4iOjiYoKIhZs2bxxBNPMHv2bACio6MZN24cEREReHl5sXHjxlbrvGXLFu655x7i4uLo168fTz/9tDmbb+jQoWzevJkPPviAkJAQFi9ezNKlSy36+2ttnfQYMWIEc+bMITY2Fi8vL1555ZVW54mLi8PGxoa4uDjz9r0dHh4ezT7GLIQQQgghhBBCCCF+XzTTjR3ptYHLly/j4+NDWloaiYmJbV3871p5eTkBAQEcPHiQoUOH3tG6nDhd0a7la7Rf56CNJmuleHurWqV4g8lOKb7BpJbM26BQfwcrtcFkrFC7U6P6PTWiv+7tXRdrGpTiGxWTru2N+rsdMGlq92uuaC5K8arbUiXeYFLLnr7SqNYPrIvNZaV4B5Nadw+NVr84mb7lshV/2yq/D1Db5x1NatuxVlO76aW63Rus1Paby43693l7xeOe6rHDqHh/1Yab+yhuSZ1J7eafjaZ2HFM97tWbWn4io9nyNf1dp6huR1Wqxz0TWjvVpP0ZTWrb0u/7fN2xX3e7V7U6v1n1RrXrNwfF60PVfV7le7XSfruZNqf7jlGKDzixWyle9bpD9dq5XuE6SLVsVarHgk4G/QN//mTXVansXhV/VYr/use/KMWrHrMNRv3fk+rvSfVcrKpvQI92Lb+jioj52x1b9p7Nv59z3/Xa5L+iwsJCTpw4QVhYGFVVVSxduhSASZMmtUXxgqYBUH744Qeef/557rvvvjveOCiEEEIIIYQQQggh/jm0WdrEypUrOXnyJHZ2dgwbNozc3Fw6d+7cVsXrkpqaytatW3nqqafMjx/fyNfXl2PHjv2q9WpNRkYG8+fP5+LFiy3G5OXlERERQVBQEB9++KHFZ7m5uYwfP77FeWtqasx/jx49mr1797Jx40Yee+wx8/tr1qxhzZo1lJeX3/Z6tKY9MwJBPYtJrWy1O8oq2SKglnEBYEJ/hkl7b/f2vBuuaWp1b+91Vb1TadT0Z4OpxAK4Nf6oFF9nrZYNpsKj/nul+J/suynFq2bVdSSqxwLV35NKhpTqPqaa2dyoqcVrig8wqGTqqG5H1aw61fJVsvDaOwvB3nhVKd5gpXb+UzkX22pqv4+OlOGner5p7yxV1WwXlazAnrvWKZX9TdS/KsWrbpv23A9Ur8dqjWoZv3ZW9UrxqtdBSmV3oO2umhGomnHoc1wtk011u1srnItVM5vb+1igkhVojdrvQzUjsL2PNR0pw91WUzsWCPFraZMGwiFDhnDo0KG2KKpNTJw4kXvvbf7CR3UgkY5i9OjR1NXVYWd384V6aGgoRUVFustycHDg+eefJzo6+je7PYQQQgghhBBCCPHPyWT87Xad8FvVpk3j1dXVxMfH4+zsjLe3N6tXr2b06NHMnz8fAE3T2Lp1q8U8Hh4eZGRkmF9/8803xMTE4OHhgaenJ5MmTbLIasvJySEsLAxnZ2c8PDwIDw/n7NmzZGRksGTJEg4fPoybmxu9e/fmr3/9K4GBgRaTr6+vxfIvXrzI7Nmz6dq1Kw4ODoSEhLB9+3bz51u2bKF///7Y29vj5+dHWlqaxfytrVN5eTmapvHRRx8RERGBk5MTgwYNIj8/37w+jz/+OFVVVWiahqZppKamAk2jHC9btoxp06bh5ubGrFmziIyMZN68eRbLq6mpoV+/fpw9e/am9Q0MDLzpe4qLi+PixYu8/fbbzX2NQgghhBBCCCGEEOJ3pE0bCBcsWEBeXh7btm0jOzub3NxcCgoKdM9vMBgYO3Ysrq6u5ObmkpeXh4uLC+PGjaO+vp6GhgYmT57MqFGj+Oqrr8jPz2fWrFlomkZsbCwLFy6kf//+VFZWUllZSWxs7C2XZzQaGT9+PHl5eWzYsIHjx4+zYsUKrK2bHsM6dOgQMTExPPbYYxw5coTU1FQWLVpk0aCp13PPPUdycjJFRUUEBQURFxdHQ0MDI0aMYM2aNbi5uZnrnZycbJ5v5cqVDBo0iMLCQhYtWkRSUhJZWVnU1f3cwe2GDRvw8fEhMjJSV13c3Nx47rnnWLp0KZcvq3VaL4QQQgghhBBCCNGeTEbTHZt+r9qsD8Lq6moyMzPJyspizJimfiHS09Pp3r277jI2bdqE0WjknXfeQdM0cxkeHh7k5OQQGhpKVVUVEyZMICAgAIDg4GDz/C4uLtjY2NCtm77+rXbt2sWBAwcoLi4mKCgIAH9/f/Pnq1atYsyYMSxatAiAoKAgjh8/zquvvkpCQoLu9QJITk7mwQcfBGDJkiX079+fU6dO0bdvX9zd3dE0rdl6R0ZGsnDhQvNrHx8f5s2bxyeffEJMTAzQ1IdhQkKCeZvpMXfuXF577TVWrVplXj8hhBBCCCGEEEII8fvTZhmEZ86cwWAwEBYWZn7P3d2dPn366C7j8OHDnDp1CldXV1xcXHBxccHT05Pa2lpOnz6Np6cnCQkJjB07loceeojXXnuNykr9Q7PfqKioiB49epgbB29UXFxMeHi4xXvh4eGUlpbS2KjWSevAgQPNf3t7ewNw7ty5VucLDQ21eO3g4MDUqVN59913ASgoKODo0aPKDZb29vYsXbqUlStXcuHChVbj6+rquHTpksVUf10WoxBCCCGEEEIIIYT4bWrf4XluoGkaphtGLTQYfh7BrqamhmHDhlFUVGQxlZSUMGXKFKApozA/P58RI0awadMmgoKC+OKLL26rPo6Ojre/Mv/Q2jpdc/1gINcy/Yw6Ot10dna+6b2kpCSys7OpqKggPT2dyMjIm/pW1OP//t//i6+vLy+++GKrscuXL8fd3d1i+s83/6K8TCGEEEIIIYQQQohbMZmMd2z6vWqzBkJ/f39sbW05ePCg+b2qqipKSkrMr728vCwy/kpLS7ly5Yr59dChQyktLaVLly43DbTh7u5ujhsyZAgpKSns37+fkJAQsrKyALCzs1PK7Bs4cCAVFRUWdbxecHAweXl5Fu/l5eURFBRk7qewtXXSQ7XeAwYMIDQ0lLfffpusrCxmzJihtLxrrKysWL58OevWrbMYCKY5KSkpVFVVWUyz5jxxW8sVQgghhBBCCCGEEB1HmzUQurq6Mn36dJ566in27NnDsWPHSExMxMrKypwxFxkZydq1ayksLOTLL79kzpw5Fpl18fHxdO7cmUmTJpGbm0tZWRk5OTk8+eSTVFRUUFZWRkpKCvn5+Zw9e5bPP/+c0tJScz+Efn5+lJWVUVRUxIULFywG8mjOqFGjuP/++4mOjiY7O5uysjJ27NjBzp07AVi4cCG7d+9m2bJllJSUkJmZydq1ay0GEWltnfTw8/OjpqaG3bt3c+HCBV0NjElJSaxYsQKTycTDDz+stLzrPfjgg9x777289dZbt4yzt7fHzc3NYrKzt7/t5QohhBBCCCGEEEI0RwYp+fW16SPGq1atYvjw4UyYMIGoqCjCw8MJDg7GwcEBgLS0NHr27MnIkSOZMmUKycnJODk5med3cnJi37599OrVi0ceeYTg4GASExOpra3Fzc0NJycnTpw4QXR0NEFBQcyaNYsnnniC2bNnAxAdHc24ceOIiIjAy8uLjRs3tlrnLVu2cM899xAXF0e/fv14+umnzdl8Q4cOZfPmzXzwwQeEhISwePFili5datHfX2vrpMeIESOYM2cOsbGxeHl58corr7Q6T1xcHDY2NsTFxZm37+3693//d2pra39RGUIIIYQQQgghhBDit0kz3diBXhu6fPkyPj4+pKWlkZiY2F6L+V0qLy8nICCAgwcPMnTo0DtShxOnK5TiNdq3Jd5gUsvcVGGjNajFc3M/lLdiUmyrrzXpbxS20+qVylb9nkzoHz0boNFkrTvWWlMbDMgKtf4irFH7XhtQ28fsTPob3o2a/u0CYN+o1pVBnbXajQsVrnU/KMX/ZK9vpPlrGlHbNo6my2rlW9koxavQFE+x9ahlZqvs87aoHQuumG7uA/dWHDW1fVKVyu/PqHhMVT1mqx4LjCb99VE97qlyMlYrxddYubcedB2VY7ytpnquVDvftOf5TLVs1XjVfbg99dy1Tin+m6h/VYpv7+sOFSr77+3E21mpHYc70j7Znttd1em+Y5TifY7/VSneXvF7ajDpv46w19QGeexIxwJr1M5Pqtdv7X2sUfm9qm531fOZreL/ZwH+/krx/yxGTsq9Y8vO/WTkHVv2ndSm/xUVFhZy4sQJwsLCqKqqYunSpQBMmjSpLRfzu2YwGPjhhx94/vnnue++++5Y46AQQgghhBBCCCFEezDpGNRVtK02T5tYuXIlJ0+exM7OjmHDhpGbm0vnzp3bejHNSk1NZevWrRQVFQHw/vvvmx8/vpGvry/Hjh37VerVmoyMDObPn8/Fixdbjc3LyyMiIoKgoCA+/PBDi89yc3MZP358i/PW1NQAcOzYMZYsWcKePXu4dOkSvr6+PPbYYzz77LNKj0erZum41KplGX3v4KcUH3hyq+5Y4/nvlcr+eqRaBqxTg1qGxneaj1J851UzdceakpcrlV1W20sp3sVW7fH0wKuHdceecRqgVLatlVpG4A+1bkrxDUa1u4mpz+kfYd1ase/Sl18cqBS/ePFxpfjVy/z0Byt2R1prVOsWodqglv344itqx5rAwX5K8Sq6d1fLwps6uFgp3q5B/+9vzpseSmWv+De1u+HPvaW2DweFeCvFP937v3XH7nN4UKnsiAvvK8X/3V/trvLXtd11x2qaWlZPF4eflOIv0EUp3mBQ+14HHs/QHfv3QROUyv6p8S6leFfrGqX4epOd7ljVjHXV7Ct3049K8WcNvkrxXvb6yy8fM0+pbDej2j6p6rKV/nO3s/GSUtleRTuU4jd2XqgUH+xdpRRfeclFd2wvD7WybRWfjmlUzKhSyVBUybIG9YzAb/v9i1K86W9q/yNu/vi87tinp+s/zgB4GC8oxZfWBSjF93T6TndsjVH//gjgYKWWLal6rOl6tVwp3uWY/my09zo9rVT2H/xOKsXXmNT+BxHi19KmDYRDhgzh0KFDbVnkLzJx4kTuvffeZj9THUikI6ivr2f06NG09FR4aGiouXG0JV988QVRUVFERUXx3//933Tt2pUDBw6YB2TZs2cPdnZqJy4hhBBCCCGEEEKItvLXT0fd6Sr87rRppwbV1dXEx8fj7OyMt7c3q1evZvTo0cyfPx8ATdPYunWrxTweHh5kZGSYX3/zzTfExMTg4eGBp6cnkyZNory83Px5Tk4OYWFhODs74+HhQXh4OGfPniUjI4MlS5Zw+PBhNE1D0zS2bNlCYGBgs5Ovb9Od1osXLzJ79my6du2Kg4MDISEhbN++3by8LVu20L9/f+zt7fHz8yMtLc2i/q2tU3l5OZqm8dFHHxEREYGTkxODBg0iPz/fvD6PP/44VVVV5nqnpqYCTaMbL1u2jGnTpuHm5sasWbOIjIxk3jzLuyvnz5/Hzs6O/fv3t7i+gYGBmEwmEhMTCQ4O5qOPPiIsLAxfX18effRRPv30U/Lz81m9erXCNy6EEEIIIYQQQgghfuvatIFwwYIF5OXlsW3bNrKzs8nNzaWgoED3/AaDgbFjx+Lq6kpubi55eXm4uLgwbtw46uvraWhoYPLkyYwaNYqvvvqK/Px8Zs2ahaZpxMbGsnDhQvr3709lZSWVlZXExsbecnlGo5Hx48eTl5fHhg0bOH78OCtWrMDauqkD00OHDhETE8Njjz3GkSNHSE1NZdGiRRYNmno999xzJCcnU1RURFBQEHFxcTQ0NDBixAjWrFmDm5ubud7Jycnm+VauXMmgQYMoLCxk0aJFJCUlkZWVRV3dzynbGzZswMfHh8jIyFvWoaioiOPHj7NgwQKsrCy/+kGDBhEVFaVr5GchhBBCCCGEEEII8c+jzR4xrq6uJjMzk6ysLMaMaRpZKj09ne7d9fe7s2nTJoxGI++88w6appnL8PDwICcnh9DQUKqqqpgwYQIBAU39KwQHB5vnd3FxwcbGhm7d9I2QuWvXLg4cOEBxcTFBQUEA+F83QtCqVasYM2YMixYtAiAoKIjjx4/z6quvkpCQoHu9AJKTk3nwwaY+kZYsWUL//v05deoUffv2xd3dHU3Tmq13ZGQkCxf+3L+Jj48P8+bN45NPPiEmJgZo6sMwISHBvM1aUlJSAlhus+sFBwfz17+q9ekhhBBCCCGEEEIIIX7b2iyD8MyZMxgMBsLCwszvubu706dPH91lHD58mFOnTuHq6oqLiwsuLi54enpSW1vL6dOn8fT0JCEhgbFjx/LQQw/x2muvUVlZedt1LioqokePHubGwRsVFxcTHh5u8V54eDilpaU0NqoN8z5w4M8DCnh7N3XMfu7cuVbnCw0NtXjt4ODA1KlTeffddwEoKCjg6NGjSg2WLfVheCt1dXVcunTJYqqrUxueXQghhBBCCCGEEEJ0PG36iHFrNE27qXHKYPh5lMSamhqGDRtGUVGRxVRSUsKUKVOApozC/Px8RowYwaZNmwgKCuKLL/SPFHo9R0fH21+Zf2htna65flCUa5l+Rh3Ddjs73zwCZlJSEtnZ2VRUVJCenk5kZKS5T8VbudYQWlzc/CiZ12dS3mj58uW4u7tbTG+89VaryxRClSBJpwABAABJREFUCCGEEEIIIYQQHVubNRD6+/tja2vLwYMHze9VVVWZH2sF8PLyssj4Ky0t5cqVK+bXQ4cOpbS0lC5dutw0wIa7u7s5bsiQIaSkpLB//35CQkLIysoCwM7OTimzb+DAgVRUVFjU8XrBwcHk5eVZvJeXl0dQUJC5n8LW1kkP1XoPGDCA0NBQ3n77bbKyspgxY4au+QYPHkzfvn1ZvXr1TY2Thw8fZteuXcTFxTU7b0pKClVVVRbT3NmzdddZCCGEEEIIIYQQQnRMbdZA6OrqyvTp03nqqafYs2cPx44dIzExESsrK3PGXGRkJGvXrqWwsJAvv/ySOXPmWGTWxcfH07lzZyZNmkRubi5lZWXk5OTw5JNPUlFRQVlZGSkpKeTn53P27Fk+//xzSktLzX3q+fn5UVZWRlFRERcuXLAYyKM5o0aN4v777yc6Oprs7GzKysrYsWMHO3fuBGDhwoXs3r2bZcuWUVJSQmZmJmvXrrUYRKS1ddLDz8+Pmpoadu/ezYULF3Q1MCYlJbFixQpMJhMPP/ywruVomsb69es5fvw40dHRHDhwgK+//pr/+q//4qGHHmL48OHmEadvZG9vj5ubm8Vkb2+nsppCCCGEEEIIIYQQogNq00eMV61axfDhw5kwYQJRUVGEh4cTHByMg4MDAGlpafTs2ZORI0cyZcoUkpOTcXJyMs/v5OTEvn376NWrF4888gjBwcEkJiZSW1uLm5sbTk5OnDhxgujoaIKCgpg1axZPPPEEs/+RyRYdHc24ceOIiIjAy8tL14i8W7Zs4Z577iEuLo5+/frx9NNPm7P5hg4dyubNm/nggw8ICQlh8eLFLF261KK/v9bWSY8RI0YwZ84cYmNj8fLy4pVXXml1nri4OGxsbIiLizNvX73L+uKLL7C2tmb8+PEEBgaSkpLC9OnTyc7Oxt7eXqnuQgghhBBCCCGEEOK3rc1GMYamLML333/f/Pry5cssWbKEWbNmAdC9e3f+53/+x2KeixcvWrzu1q0bmZmZzZbv5ubGxx9/3OLy7e3t+fDDD5Xq7OnpaR7woznR0dFER0e3+Hlr6+Tn53dTH4UeHh43vbdu3TrWrVtn8V55eXmLy71w4QK1tbUkJia2GNOSAQMGKG8nIYQQQgghhBBCCPHPqU0bCAsLCzlx4gRhYWFUVVWxdOlSACZNmtSWi/ldMxgM/PDDDzz//PPcd999DB069E5XSQghhBBCCCGEEEL8hmmmG1PZfoHCwkKSkpI4efIkdnZ2DBs2jFWrVjFgwIC2WsQtpaamsnXrVoqKigB4//33zY8f38jX15djx479KvVqTUZGBvPnz78pm7I5OTk5REREEBQUxIcffmixbXNzcxk/fnyL89bU1ODn58f8+fNv6mvwxm2nR/Hpb3XHAtQZ1fosdLC6dR+SNyq92E13rKdjrVLZnewvKsUbTWpP7ztqagPblF/x0R3bxfGiUtk2WoNS/BWj2mjg1rQ+evc19or7gAlNKd5Wq1eK1xQPl50vnlIoXG2fOe8RoBR/V43a7/Wc8926Y90NF5TK/smmi1J8g0ntXpZf9WGl+Aq3/krxKmoabh6J/lZcbC4rxatsm16XjyuVXenaWym+6+UzSvGq/qf6X3THDuj6vVLZno3nlOIv27i3HnR9vFF/1yOq5w8n66tK8T0uFCrFf91pmFL8hTr928bHUe17uqqwHUH9fKZyDrnLoLbP/GjTVSle09TON7VGtS5i7BXOf6p1UaW8z2v6j5NXTWr7jN/f/6oUv9dVXz/g1/RyVTtf1pv092uu8p1C+3+vKkwmtes31bqfrlL8/d2rdl3gVFCkO7ar409KZatS2WcAbDT9A2V2v1KqVPZ3TmrXqqrfq+p+43v+gO7Y/U4t/0/dHB/nH5TirTT9/w8B9A3ooRQvxO1q0wzCIUOGcOjQobYs8heZOHEi9957b7OfqQ4k0hHU19czevTomx5PviY0NFSpgU8IIYQQQgghhBBCiDYdpKS6upr4+HicnZ3x9vZm9erVjB492pytpmkaW7dutZjHw8ODjIwM8+tvvvmGmJgYPDw88PT0ZNKkSRZ98eXk5BAWFoazszMeHh6Eh4dz9uxZMjIyWLJkCYcPH0bTNDRNY8uWLQQGBjY7+fr6Ak39Bc6ePZuuXbvi4OBASEgI27dvNy9vy5Yt9O/fH3t7e/z8/EhLS7Oof2vrVF5ejqZpfPTRR0RERODk5MSgQYPIz883r8/jjz9OVVWVud6pqalAU/+Fy5YtY9q0abi5uTFr1iwiIyOZN2+exfLOnz+PnZ0d+/fvb3F9AwMDFb5JIYQQQgghhBBCCPF70aYNhAsWLCAvL49t27aRnZ1Nbm4uBQUFuuc3GAyMHTsWV1dXcnNzycvLw8XFhXHjxlFfX09DQwOTJ09m1KhRfPXVV+Tn5zNr1iw0TSM2NpaFCxfSv39/KisrqaysJDY29pbLMxqNjB8/nry8PDZs2MDx48dZsWIF1tbWABw6dIiYmBgee+wxjhw5QmpqKosWLbJo0NTrueeeIzk5maKiIoKCgoiLi6OhoYERI0awZs0a3NzczPVOTk42z7dy5UoGDRpEYWEhixYtIikpiaysLOrqfn70csOGDfj4+BAZGalcLyGEEEIIIYQQQgjx+9ZmjxhXV1eTmZlJVlYWY8aMASA9PZ3u3bvrLmPTpk0YjUbeeecdNE0zl+Hh4UFOTg6hoaFUVVUxYcIEAgKa+jQIDg42z+/i4oKNjQ3duunri27Xrl0cOHCA4uJigoKCAPD39zd/vmrVKsaMGcOiRYsACAoK4vjx47z66qskJCToXi+A5ORkHnzwQQCWLFlC//79OXXqFH379sXd3R1N05qtd2RkJAsXLjS/9vHxYd68eXzyySfExMQATX0YJiQkmLeZEEIIIYQQQgghhBB6tVkG4ZkzZzAYDISFhZnfc3d3p0+fPrrLOHz4MKdOncLV1RUXFxdcXFzw9PSktraW06dP4+npSUJCAmPHjuWhhx7itddeo7Ky8rbrXFRURI8ePcyNgzcqLi4mPDzc4r3w8HBKS0tpbNTfoSvAwIEDzX97e3sDcO5c6x1ch4aGWrx2cHBg6tSpvPvuuwAUFBRw9OhR5QZLVXV1dVy6dMliqq9TG0BCCCGEEEIIIYQQQnQ8bfqIcWs0TbtpgA2DwWD+u6amhmHDhlFUVGQxlZSUMGXKFKApozA/P58RI0awadMmgoKC+OKLL26rPo6OaqOvNqe1dbrm+kFRrmX6GY2tj17k7HzzCJhJSUlkZ2dTUVFBeno6kZGR5j4VW+Pm5kZVVdVN71+8eBF395ZHHly+fDnu7u4W03++uVbXMoUQQgghhBBCCCFEx9VmDYT+/v7Y2tpy8OBB83tVVVWUlJSYX3t5eVlk/JWWlnLlyhXz66FDh1JaWkqXLl1uGmDj+sarIUOGkJKSwv79+wkJCSErKwsAOzs7pcy+gQMHUlFRYVHH6wUHB5OXl2fxXl5eHkFBQeZ+CltbJz1U6z1gwABCQ0N5++23ycrKYsaMGbrn7dOnT7MjTRcUFLSYSQmQkpJCVVWVxTRrzrwW44UQQgghhBBCCCHEb0ObNRC6uroyffp0nnrqKfbs2cOxY8dITEzEysrKnDEXGRnJ2rVrKSws5Msvv2TOnDkWmXXx8fF07tyZSZMmkZubS1lZGTk5OTz55JNUVFRQVlZGSkoK+fn5nD17ls8//5zS0lJzP4R+fn6UlZVRVFTEhQsXLAbyaM6oUaO4//77iY6OJjs7m7KyMnbs2MHOnTsBWLhwIbt372bZsmWUlJSQmZnJ2rVrLQYRaW2d9PDz86Ompobdu3dz4cIFXQ2MSUlJrFixApPJxMMPP6x7WX/+85/57//+b1566SWKi4s5evQozz33HPn5+fzbv/1bi/PZ29vj5uZmMdnZ2+terhBCCCGEEEIIIYTomNr0EeNVq1YxfPhwJkyYQFRUFOHh4QQHB+Pg4ABAWloaPXv2ZOTIkUyZMoXk5GScnJzM8zs5ObFv3z569erFI488QnBwMImJidTW1uLm5oaTkxMnTpwgOjqaoKAgZs2axRNPPMHs2bMBiI6OZty4cURERODl5cXGjRtbrfOWLVu45557iIuLo1+/fjz99NPmbL6hQ4eyefNmPvjgA0JCQli8eDFLly616O+vtXXSY8SIEcyZM4fY2Fi8vLx45ZVXWp0nLi4OGxsb4uLizNtX77J27NjBjh07CA8PZ/To0ezfv5/du3cTEhKiVG8hhBBCCCGEEEII8dunmW7sQK8NXb58GR8fH9LS0khMTGyvxfwulZeXExAQwMGDBxk6dOgdqUPx6W+V4uuMdkrxDlZqg6CUXtQ3ejWAp2OtUtmd7C8qxRtNam3vjpraY+nlV3x0x3ZxvKhUto3WoBR/xajWl6c1rfe9eY294j5gQm0kb1utXileUzxcdr54SqFwtX3mvEeAUvxdNWq/13POd+uOdTdcUCr7J5suSvENJhuleL/qw0rxFW79leJV1DTc3I/srbjYXFaKV9k2vS4fVyq70rW3UnzXy2eU4lX9T/W/6I4d0PV7pbI9G1sfNOx6l21a7rO32Xij/huHqucPJ+urSvE9LhQqxX/daZhS/IU6/dvGx1Hte7qqsB1B/Xymcg65y6C2z/xo01UpXtPUzje1RrWnOuwVzn+qdVGlvM9r+o+TV01q+4zf3/+qFL/XVf9TPAC9XNXOl/Um/U8lqXyn0P7fqwqTSe36TbXup6sUf3/3ql0XOBUU6Y7t6viTUtmqVPYZABtNfzdX3a+UKpX9nZPatarq96q63/ieP6A7dr/TeKWyfZx/UIq30vT/PwTQN6CHUrwQt6tNMwgLCwvZuHEjp0+fpqCggPj4eAAmTZrUlotpUWpqKoMHD/5VltWWMjIy8PDw0BVrMBj47rvveP7557nvvvuUGwf9/PxYs2bNTe//VredEEIIIYQQQgghhPhl1NIydFi5ciUnT57Ezs6OYcOGkZubS+fOndt6Mbq8//775sePb+Tr68uxY8d+5Rr9MvX19ezfv5+IiAiCgoL48MMPLT7Pzc1l/PiW73bU1NS0aX2sFDLBAGob1TIIrVC7i+TlrP+OcnGlWvbHqF5qWQ6XcVGKtzGq3fXdd1j/T3fSvWo/886GvyvFu1irfa8ldYG6Y/vYfadUdoOV2l1T+wa1zE0ro1o2Su2H7+svW7HvUutpz6jVJf0NpXj7ect0x36D/mxDgF4NaplmjlfU7so27vhIKd7fv0ApXomjWobtmZA/KsXXG/XvN9Z7P1Uq23pCy/3SNsdu/2dq5fdR69YisHuV7tiAij1KZX9s85hS/IBuatljARcPth70DxWeg5TKtsGgFF/WKUwp3qtB7Zxw8rL+TB0vB7Wst651Z5XiLzioZVw4Gy/pjv3JVi0T+kqD2rHAzkrtez1/xU0pvoez/kw21Qy/RpO1UrwqlWss1br/xzm1Y3DS/85Sii+f0no3Qtf78ar+LHQvJ/37L4AN+jPHQH1bqmSDNSqWrfI0CsDmj88rxT+ukBEIcGXoYP2xR79QKtvGSu3aU/VY0MlR//+HXzv2VSpbNatVdR+7ZFB7SmNd5UTdsVP/Nl2p7PMzX1WKd0TtfxAhfi1tmkEYGBhI3759MZlM2Nvb88ADD/CnP/2J+fPnA6BpGlu3brWYx8PDg4yMDPPrb775hpiYGDw8PPD09GTSpEmUl5ebP8/JySEsLAxnZ2c8PDwIDw/n7NmzZGRksGTJEg4fPoymaWiaRnV1NUVFRc1On33W9I/MxYsXmT17Nl27dsXBwYGQkBC2b99uXt6WLVvo378/9vb2+Pn5kZaWZlH/1tapvLwcTdP46KOPiIiIwMnJiUGDBpGfn29en8cff5yqqipzvVNTU4GmbL9ly5Yxbdo03NzcmDVrFkuXLuWJJ57g5MmTDBgwAIDz589jZ2d3y/UtKipS/0KFEEIIIYQQQgghxD+9Ns0gXLBgAXl5eWzbto2uXbuyePFiCgoKdD+6ajAYGDt2LMOHDyc3NxcbGxtefPFFxo0bx1dffYWVlRWTJ09m5syZbNy4kfr6eg4cOICmacTGxnL06FF27tzJrl27AHB3d8fxFtkbRqOR8ePHU11dzYYNGwgICOD48eNYWzfd+Tx06BAxMTGkpqYSGxvL/v37mTt3Lp06dbIYqESP5557jpUrV9K7d2+ee+454uLiOHXqFCNGjGDNmjUsXryYkydPAuDi8vOd0ZUrV7J48WJeeOEFAP72t78xb9480tLSsP/HKMIbNmzAx8eH8ePHm0eMFkIIIYQQQgghhBBCjzZrIKyuriYzM5OsrCzGjBkDQHp6Ot27d9ddxqZNmzAajbzzzjvmhq709HQ8PDzIyckhNDSUqqoqJkyYQEBAU6enwcHB5vldXFywsbGhWzd9g1Xs2rWLAwcOUFxcTFBQEAD+/v7mz1etWsWYMWNYtGgRAEFBQRw/fpxXX31VuYEwOTmZBx98EIAlS5bQv39/Tp06Rd++fXF3d0fTtGbrHRkZycKFC82vfXx8mDdvHp988gkxMTFAUx+GCQkJ0jgohBBCCCGEEEIIIZS12SPGZ86cwWAwEBb2c9827u7u9OnTR3cZhw8f5tSpU7i6uuLi4oKLiwuenp7U1tZy+vRpPD09SUhIYOzYsTz00EO89tprVFZW3nadi4qK6NGjh7lx8EbFxcWEh4dbvBceHk5paSmNjWr9dgwcOND8t7e3NwDnzrXeh1FoaKjFawcHB6ZOncq7774LQEFBAUePHlVusFRVV1fHpUuXLKb6OrURZoUQQgghhBBCCCFEx9OmfRC2RtM0TCbLDmsNhp87Ya6pqWHYsGE39Z1XUlLClClTgKaMwvz8fEaMGMGmTZsICgriiy/UOnu95laPH+vV2jpdY3vdAATXMv2MxtY72HV2vrnz1aSkJLKzs6moqCA9PZ3IyEh8fX111dfNzY2qqps7er948SLu7i0P3LF8+XLc3d0tprfe/IuuZQohhBBCCCGEEEKIjqvNGgj9/f2xtbXl4MGfR+qrqqqipKTE/NrLy8si46+0tJQrV34ewWfo0KGUlpbSpUsXAgMDLabrG6+GDBlCSkoK+/fvJyQkhKysLADs7OyUMvsGDhxIRUWFRR2vFxwcTF5ensV7eXl5BAUFmfspbG2d9FCt94ABAwgNDeXtt98mKyuLGTNm6J63T58+HDp06Kb3CwoKWsykBEhJSaGqqspimj3nCd3LFUIIIYQQQgghhBAdU5s1ELq6ujJ9+nSeeuop9uzZw7Fjx0hMTMTKysqcMRcZGcnatWspLCzkyy+/ZM6cORaZdfHx8XTu3JlJkyaRm5tLWVkZOTk5PPnkk1RUVFBWVkZKSgr5+fmcPXuWzz//nNLSUnM/hH5+fpSVlVFUVMSFCxeoa+UR2FGjRnH//fcTHR1NdnY2ZWVl7Nixg507dwKwcOFCdu/ezbJlyygpKSEzM5O1a9eSnJxsLqO1ddLDz8+Pmpoadu/ezYULF3Q1MCYlJbFixQpMJhMPP/yw7mX9+c9/5r//+7956aWXKC4u5ujRozz33HPk5+fzb//2by3OZ29vj5ubm8Vk949BUoQQQgghhBBCCCHEb1ebPmK8atUqhg8fzoQJE4iKiiI8PJzg4GAcHBwASEtLo2fPnowcOZIpU6aQnJyMk5OTeX4nJyf27dtHr169eOSRRwgODiYxMZHa2lrc3NxwcnLixIkTREdHExQUxKxZs3jiiSeYPXs2ANHR0YwbN46IiAi8vLzYuHFjq3XesmUL99xzD3FxcfTr14+nn37anM03dOhQNm/ezAcffEBISAiLFy9m6dKlFv39tbZOeowYMYI5c+YQGxuLl5cXr7zySqvzxMXFYWNjQ1xcnHn76l3Wjh072LFjB+Hh4YwePZr9+/eze/duQkJClOothBBCCCGEEEIIIX772mwUY2jKInz//ffNry9fvsySJUuYNWsWAN27d+d//ud/LOa5ePGixetu3bqRmZnZbPlubm58/PHHLS7f3t6eDz/8UKnOnp6e5gE/mhMdHU10dHSLn7e2Tn5+fjf1Uejh4XHTe+vWrWPdunUW75WXl7e43AsXLlBbW0tiYmKLMS35wx/+wB/+8Afl+YQQQgghhBBCCCHEP582zSAsLCxk48aNnD59moKCAuLj4wGYNGnSLecbPXo08+fPB5oa1NasWXPbdcjIyMDDw+O25+/oDAYD3333Hc8//zz33XcfQ4cOvdNVEkIIIYQQQgghhBC/YZrpxlS2X6CwsJCkpCROnjyJnZ0dw4YNY9WqVQwYMOCW840ePZrBgwezZs0azp8/j7Ozs67HdP38/Jg/f765cRHg6tWrVFdX06VLF95//33z48c38vX15dixY0rr1xHk5OQQERFBUFAQH374oXnbXnvfycnJ3OfjjWpqatq0LiWnv1aKNyq2R/v8dFQpvtxjiO7YukY7pbKdbdQGntFQ+1lZ0fqI1tdrMOlP/m3EWqlsNy4qxddpaqOBXzHqj3e0qlUqW5Xqdldlw80jmreVetT6ALXj1n2y3shE88eR5lw1qXWr4GB1VSm+zqi/GwUAJ9SOdQat/fpTtaZBKd6A2rFJ5XuyR+331IBaf7q2JrV9THW715r07wcuWrVS2aq/J9XzmVvjj7pjf7TqolS2raZ2nFHZZwDuMpxTiv/Wyld3rKu12m9V5dwHYK3pHwAO1LeNitN9xyjFB5zYrRTfnttG9ZpGlep2b8/6XG5UO5/VNaodJwMy5irFX5jZerdD16ju779lqtdv9Sa1c6tq+Vca9V/bfh9yn1LZ7X0sUFlXTWvfY4Hqb7vRpPY/Tq1R/7neYFQr28NW7bpDdV37BPRUihfidrXpI8ZDhgxpdoRcFV5eXr9ofkdHRxwdmw7SEydO5N577202TnUgkY5i9OjRNz2efL3c3Fzc3Nxa/Ly+vh47O7WTpBBCCCGEEEIIIYT459Wmjxi3hesfMTaZTKSmptKrVy/s7e3p3r07Tz75JNDUUHb27Fn+/Oc/o2maOWvu+keMXV1d2bBhA3/84x/Jz88nKiqKYcOG8fzzz+Pp6WleZnV1NfHx8Tg7O+Pt7c3q1astHntuTV1dHc888ww9e/bE3t6ewMBA1q9fb/587969hIWFYW9vj7e3N88++ywNDT9nlTT3WPXgwYNJTU01v9Y0jXfeeYeHH34YJycnevfuzbZt24CmvgojIiIAGDZsGL179+bFF18kMDCQpKQk1qxZw9q1a+ncuTNjx45lxowZTJgwwWJ5BoOBLl26WNRbCCGEEEIIIYQQQvzz63ANhNfbsmULq1ev5q233qK0tJStW7eaH6n96KOP6NGjB0uXLqWyspLKysoWyzl9+jRbt25l+/btbN++nb1797JixQrz5wsWLCAvL49t27aRnZ1Nbm4uBQUFuus5bdo0Nm7cyOuvv05xcTFvvfUWLi4uAHz77bc88MAD3HPPPRw+fJh169axfv16XnzxReXtsWTJEmJiYvjqq6944IEHiI+P58cff6Rnz55s2bIFgJMnT1JZWclrr71mni8zMxM7Ozvy8vJ48803SUpKYufOnRbbbPv27Vy5coXY2FjlegkhhBBCCCGEEEKI3642fcS4rX399dd069aNqKgobG1t6dWrF2FhYUDT6MPW1ta4urrSrVu3W5ZjNBrJyMjA1dUVgKlTp7J7925eeuklqquryczMJCsrizFjmvqHSU9Pp3v37rrqWFJSwubNm8nOziYqKgoAf39/8+dvvPEGPXv2ZO3atWiaRt++ffn73//OM888w+LFi7Gy0t9Gm5CQQFxcHAAvv/wyr7/+OgcOHGDcuHHmjMguXbrcNEhL7969eeUVyz5M+vTpw3vvvcfTTz9tXudHH33U3LAphBBCCCGEEEIIIX4fOnQG4aOPPsrVq1fx9/dn5syZfPzxxxaP5url5+dnbhwE8Pb25ty5pk63z5w5g8FgMDc8Ari7u9OnTx9dZRcVFWFtbc2oUaOa/by4uJjhw4dbDBwSHh5OTU0NFRUVSusxcOBA89/Ozs64ubmZ1+NWhg0bdtN7SUlJpKenA/D999+zY8cOZsyY0WIZdXV1XLp0yWKqr1PrkF4IIYQQQgghhBBCdDwduoGwZ8+enDx5kjfeeANHR0fmzp3L/fffj8GgNmLfjQOSaJqG0dg2o5deGxDll7Cysrpp4JHm1vF218PZ2fmm96ZNm8aZM2fIz89nw4YN3H333YwcObLFMpYvX467u7vF9Nabb7S6bCGEEEIIIYQQQgjRsXXoBkJoaoB76KGHeP3118nJySE/P58jR44AYGdnR2Nj4y8q39/fH1tbWw4ePGh+r6qqipKSEl3zDxgwAKPRyN69e5v9PDg4mPz8fIsGwLy8PFxdXenRowfQNHLz9f0BXrp0ibKyMqX1uDYysd7t0alTJyZPnkx6ejoZGRk8/vjjt4xPSUmhqqrKYpo9Z65SHYUQQgghhBBCCCFEx9OhGwgzMjJYv349R48e5cyZM2zYsAFHR0d8fX2BpkeH9+3bx7fffsuFCxduaxmurq5Mnz6dp556ij179nDs2DESExOxsrKyeCy4JX5+fkyfPp0ZM2awdetWysrKyMnJYfPmzQDMnTuXb775hj/96U+cOHGCTz75hBdeeIEFCxaY+x+MjIzkvffeIzc3lyNHjjB9+nSsra2V1sPX1xdN09i+fTvnz5+npqam1XmSkpLIzMykuLiY6dOn3zLW3t4eNzc3i8nO3l6pjkIIIYQQQgghhBCi4+nQDYQeHh68/fbbhIeHM3DgQHbt2sWnn35Kp06dAFi6dCnl5eUEBATg5eV128tZtWoVw4cPZ8KECURFRREeHk5wcDAODg665l+3bh1//OMfmTt3Ln379mXmzJlcvnwZAB8fHz777DMOHDjAoEGDmDNnDomJiTz//PPm+VNSUhg1ahQTJkzgwQcfZPLkyQQEBCitg4+PD0uWLOHZZ5+la9euzJs3r9V5oqKi8Pb2ZuzYsboHZRFCCCGEEEIIIYQQ/1w0042d3wkuX76Mj48PaWlpJCYm3unqtJuamhp8fHxIT0/nkUceUZ6/5PTXSvFGxfZon5+OKsWXewzRHVvXaKdUtrPNFaV4DbWflRVqfWI2mPQPQN6IWjaqGxeV4us0tX44rxj1xzta1SqVrUp1u6uyQa2/VBX1qGXw2qE2qJCJ1jOor7lqclIq28HqqlJ8nVHfzZprnGg9g/p6Bq39sqGtURtYy4DasUnle7JH7ffUgG3rQdexNantY6rbvdakfz9w0aqVylb9Pamez9waf9Qd+6NVF6WybTW144zKPgNwl6H1wdCu962Vr+5YV2u136rKuQ/AWlPrhkZ126g43XeMUnzAid1K8e25bVSvaVSpbvf2rM/lRrXzWV2j2nEyIEOte54LM1/RHau6v/+WqV6/1ZvUzq2q5V9p1H9t+33IfUplt/exQGVdNa19jwWqv+1Gk9r/OLVG/ed6g1GtbA9btesO1XXtE9BTKV6I29UhMghHjx7N/PnzgaZHdtesWXPbZWVkZODh4aE0T2FhIRs3buT06dMUFBQQHx8PwKRJk267Hh2Z0Wjk3LlzLFu2DA8PDyZOnHinqySEEEIIIYQQQggh7hC1Wwy/goMHDzY76m5z/Pz8mD9/vrlxESA2NpYHHnhAebkrV67k5MmT2NnZMWzYMHJzc+ncuTO5ubmMHz++xfn09PX3a8jJySEiIoKffvqp1QbSr7/+mrvvvpsePXqQkZGBjc3t7QbWmlpmjL1RLcvhnGeQUnytQf9dIXureqWyVe9QKWemKSYtuJou6o69YuWqVPb5BrXsFdU7/47W+rOYVLN0VKl+r6ouGdW2vQona7UsvItGD6V4lexN1bugByrvVoof1LWy9aDr/GjspBTfnllDVprasaCr8e9K8bU2+s6XABcaOiuV7W5zSSn+QqNaVx+q272zjf6+hi+bXJTKdqVKKf6qpn+7A1y00r/tVc8fqr8/1eySH226KsXbmvRfG/So2K9U9tkeI5XiOxLVLCBVvj8VKMV/6zmwnWqiTnWfb89jtq3ita3iQxr8NGu5UrwmD3o1S/X60MOo1lf9JStPpXgbK/37jeqxQDX7+O7iPUrxKlmB7Z1NrEr1Gkvl961ZdazMaSF+LR2ugfCX9CUITaMeOzqqPfI4ZMgQDh061OxnoaGhFBUV/aI6dST19fX4+fkhT5YLIYQQQgghhBBCCOggjxhf7/pHjE0mE6mpqfTq1Qt7e3u6d+/Ok08+CTQ9lnz27Fn+/Oc/o2maecThGx8xTk1NZfDgwbz33nv4+fnh7u7OY489RnX1z/0EVFdXEx8fj7OzM97e3qxevdr82LOjoyOBgYEtTgB1dXU888wz9OzZE3t7ewIDA1m/fr25/L179xIWFoa9vT3e3t48++yzNDT8fAejuceqBw8eTGpqqvm1pmm88847PPzwwzg5OdG7d2+2bdsGQHl5OREREQDcddddaJpGQkKCeTvNmzeP+fPn07lzZ8aOHcuMGTOYMGGCxfIMBgNdunSxqLcQQgghhBBCCCGE+OfX4RoIr7dlyxZWr17NW2+9RWlpKVu3bmXAgAEAfPTRR/To0YOlS5dSWVlJZWXLj6CdPn2arVu3sn37drZv387evXtZsWKF+fMFCxaQl5fHtm3byM7OJjc3l4IC/Y9qTJs2jY0bN/L6669TXFzMW2+9hYtL0+NN3377LQ888AD33HMPhw8fZt26daxfv54XX3xReXssWbKEmJgYvvrqKx544AHi4+P58ccf6dmzJ1u2bAHg5MmTVFZW8tprr5nny8zMxM7Ojry8PN58802SkpLYuXOnxTbbvn07V65cITY2VrleQgghhBBCCCGEEOK3q8M9Yny9r7/+mm7duhEVFYWtrS29evUiLCwMAE9PT6ytrXF1daVbt263LMdoNJKRkYGra1N/YFOnTmX37t289NJLVFdXk5mZSVZWFmPGNPXxkJ6eTvfu3XXVsaSkhM2bN5OdnU1UVBQA/v7+5s/feOMNevbsydq1a9E0jb59+/L3v/+dZ555hsWLF2Nlpb+NNiEhgbi4OABefvllXn/9dQ4cOMC4cePw9GzqK6NLly439UHYu3dvXnnFchS0Pn368N577/H000+b1/nRRx81N2wKIYQQQgghhBBCiN+HDp1B+Oijj3L16lX8/f2ZOXMmH3/8scWjuXr5+fmZGwcBvL29OXfuHABnzpzBYDCYGx4B3N3d6dOnj66yi4qKsLa2ZtSoUc1+XlxczPDhw82PQAOEh4dTU1NDRUWF0noMHPhzh9LOzs64ubmZ1+NWhg0bdtN7SUlJpKenA/D999+zY8cOZsyY0WIZdXV1XLp0yWKqq6tTqr8QQgghhBBCCCGE6Hg6dANhz549OXnyJG+88QaOjo7MnTuX+++/H4NBbTRcW1tbi9eapmE0Ko4y2wLVAVGaY2VlddOgIc2t4+2uR3OjQk+bNo0zZ86Qn5/Phg0buPvuuxk5suXRAZcvX467u7vF9Oabb7a6bCGEEEIIIYQQQgjRsXXoBkJoaoB76KGHeP3118nJySE/P58jR44AYGdnR2Nj4y8q39/fH1tbWw4ePGh+r6qqipKSEl3zDxgwAKPRyN69e5v9PDg4mPz8fIsGwLy8PFxdXenRowfQNHLz9f0BXrp0ibKyMqX1sLOzA9C9PTp16sTkyZNJT08nIyODxx9//JbxKSkpVFVVWUxz5sxRqqMQQgghhBBCCCGE6Hg6dANhRkYG69ev5+jRo5w5c4YNGzbg6OiIr68v0PTo8L59+/j222+5cOHCbS3D1dWV6dOn89RTT7Fnzx6OHTtGYmIiVlZWFo8Ft8TPz4/p06czY8YMtm7dSllZGTk5OWzevBmAuXPn8s033/CnP/2JEydO8Mknn/DCCy+wYMECc/+DkZGRvPfee+Tm5nLkyBGmT5+OtbW10nr4+vqiaRrbt2/n/Pnz1NTUtDpPUlISmZmZFBcXM3369FvG2tvb4+bmZjHZ29sr1VEIIYQQQgghhBBCdDwduoHQw8ODt99+m/DwcAYOHMiuXbv49NNP6dSpEwBLly6lvLycgIAAvLy8bns5q1atYvjw4UyYMIGoqCjCw8MJDg7GwcFB1/zr1q3jj3/8I3PnzqVv377MnDmTy5cvA+Dj48Nnn33GgQMHGDRoEHPmzCExMZHnn3/ePH9KSgqjRo1iwoQJPPjgg0yePJmAgACldfDx8WHJkiU8++yzdO3alXnz5rU6T1RUFN7e3owdO1b3oCxCCCGEEEIIIYQQ4p+LZrqx8zvB5cuX8fHxIS0tjcTExDtdnXZTU1ODj48P6enpPPLII8rznz5zRinexqjWd2SDlW3rQdf50eCpO9beql6pbCtNrc9KKxTjFct3Nl7SHXvFyrX1oOtUN6iNZG2i9Uzb6zla1+qOtdZ+WRcCrTGZ1Oqu6qpR302G2+FkfVUpvtaolvHraKX/ezKa1O41Hf7eWyl+UNfK1oOuo7quqvuwCtXfdlfj35Xia21u7mO2JT80dFIq291G/3EG4FKD2rFGdbt3ttH/pMBlk9pxzJUqpfirmv7tDtBoUnsqQIWNpjZ4m8Gkdm5VPZ81mGx0xwb+/X+Vyj7bo+W+kpujWvf2PBa0N58fv1KK/9ZzYOtBvxINtX9F2vN7qjfaKcUbFPZ3AGfrK0rxKudX1fPN74lL40Wl+EtW+v+nAKhXOK46WKkN8ni67xil+LuL9yjFa5r+35/qb7W9qR4LDEb931OD4nlb9bpcVd+AHu1avhDXdIgMwtGjRzN//nyg6ZHdNWvW3HZZGRkZeHh4KM1TWFjIxo0bOX36NAUFBcTHxwMwadKk265HR2Y0Gjl37hzLli3Dw8ODiRMn3ukqCSGEEEIIIYQQQog7RO2216/g4MGDzY662xw/Pz/mz59vblwEiI2N5YEHHlBe7sqVKzl58iR2dnYMGzaM3NxcOnfuTG5uLuPHj29xPj19/f0acnJyiIiI4Keffmq1gfTrr7/m7rvvpkePHmRkZGBjc3u7gUrGHqhn7d3VqNavZEWV/uyVLi5qd3lsrdQy2Wys1DI6VLfNt40+umM9rdQyY7rzjVK8SUdfnde7ornpjr1qVBslXPXuuWqGog1q32u32tP6gxW3Y62d/u0I0MWgdqw65+irO1YlYwjgX7oUK8VfRC3zrVe9vkGmrnGpPKkUr8LopJZVd6yzWqaAvVH/sSOg7ohS2T/Z6j/OAARePawUb92gP0sVoNpVf1cY3xm7KJX993q181l35x+V4usVshaC/7ZOqeyzIxKU4h2t1DKYahWPw99d8dAd2+D9f5TKtjGqHbPtFM+tKtlaKlk3oJ5Fqpr9+LeHn1GK75aTrTtWNWtINb4RtW1jTfs9XfD3yx5K8T/WqGXkDu6mdtyzWTRDd6xhWbpS2faaWl1U93mDSX82puqTCKrXe6V1al033eWgds10/or+azJv55+UylbNCCwLjlCK73k8V3es6nWzaoaf6rFDJXMT4PvL+r+nS1fVrm1DvNQyQ4XoqDpcA+Ev6UsQmkY9dnRUu5gdMmQIhw4davaz0NBQioqKflGdOpL6+nr8/PyQJ8uFEEIIIYQQQgghBHSQR4yvd/0jxiaTidTUVHr16oW9vT3du3fnySefBJoeSz579ix//vOf0TTNPOLwjY8Yp6amMnjwYN577z38/Pxwd3fnscceo7q62hxTXV1NfHw8zs7OeHt7s3r1avNjz46OjgQGBrY4AdTV1fHMM8/Qs2dP7O3tCQwMZP369eby9+7dS1hYGPb29nh7e/Pss8/S0PBzJlJzj1UPHjyY1NRU82tN03jnnXd4+OGHcXJyonfv3mzbtg2A8vJyIiKa7hbdddddaJpGQkKCeTvNmzeP+fPn07lzZ8aOHcuMGTOYMGGCxfIMBgNdunSxqLcQQgghhBBCCCGE+OfX4RoIr7dlyxZWr17NW2+9RWlpKVu3bmXAgAEAfPTRR/To0YOlS5dSWVlJZWXLndifPn2arVu3sn37drZv387evXtZsWKF+fMFCxaQl5fHtm3byM7OJjc3l4KCAt31nDZtGhs3buT111+nuLiYt956CxeXpg7Sv/32Wx544AHuueceDh8+zLp161i/fj0vvvii8vZYsmQJMTExfPXVVzzwwAPEx8fz448/0rNnT7Zs2QLAyZMnqays5LXXXjPPl5mZiZ2dHXl5ebz55pskJSWxc+dOi222fft2rly5QmxsrHK9hBBCCCGEEEIIIcRvV4d7xPh6X3/9Nd26dSMqKgpbW1t69epFWFgYAJ6enlhbW+Pq6kq3bt1uWY7RaCQjIwNX16a+oKZOncru3bt56aWXqK6uJjMzk6ysLMaMaer7KT09ne7d9fV1VFJSwubNm8nOziYqKgoAf39/8+dvvPEGPXv2ZO3atWiaRt++ffn73//OM888w+LFi7Gy0t9Gm5CQQFxcHAAvv/wyr7/+OgcOHGDcuHF4ejb1n9SlS5eb+iDs3bs3r7zyisV7ffr04b333uPpp582r/Ojjz5qbtgUQgghhBBCCCGEEL8PHTqD8NFHH+Xq1av4+/szc+ZMPv74Y4tHc/Xy8/MzNw4CeHt7c+7cOQDOnDmDwWAwNzwCuLu706dPH11lFxUVYW1tzahRo5r9vLi4mOHDh5sfgQYIDw+npqaGiooKpfUYOHCg+W9nZ2fc3NzM63Erw4YNu+m9pKQk0tObOjP+/vvv2bFjBzNmtNwRcl1dHZcuXbKY6uulM1YhhBBCCCGEEEKI37oO3UDYs2dPTp48yRtvvIGjoyNz587l/vvvx2AwKJVja2s5wpGmaRiNaqNftUR1QJTmWFlZ3TRoSHPreLvr0dyo0NOmTePMmTPk5+ezYcMG7r77bkaOHNliGcuXL8fd3d1iynhrdavLFkIIIYQQQgghhBAdW4duIISmBriHHnqI119/nZycHPLz8zly5AgAdnZ2NDaqDbd+I39/f2xtbTl48KD5vaqqKkpKSnTNP2DAAIxGI3v37m328+DgYPLz8y0aAPPy8nB1daVHjx5A08jN1/cHeOnSJcrKypTWw87ODkD39ujUqROTJ08mPT2djIwMHn/88VvGp6SkUFVVZTElzP6zUh2FEEIIIYQQQgghRMfToRsIMzIyWL9+PUePHuXMmTNs2LABR0dHfH19gaZHh/ft28e3337LhQsXbmsZrq6uTJ8+naeeeoo9e/Zw7NgxEhMTsbKysngsuCV+fn5Mnz6dGTNmsHXrVsrKysjJyWHz5s0AzJ07l2+++YY//elPnDhxgk8++YQXXniBBQsWmPsfjIyM5L333iM3N5cjR44wffp0rK2tldbD19cXTdPYvn0758+fp6amptV5kpKSyMzMpLi4mOnTp98y1t7eHjc3N4vJzs5eqY5CCCGEEEIIIYQQouPp0A2EHh4evP3224SHhzNw4EB27drFp59+SqdOnQBYunQp5eXlBAQE4OXlddvLWbVqFcOHD2fChAlERUURHh5OcHAwDg4OuuZft24df/zjH5k7dy59+/Zl5syZXL58GQAfHx8+++wzDhw4wKBBg5gzZw6JiYk8//zz5vlTUlIYNWoUEyZM4MEHH2Ty5MkEBAQorYOPjw9Llizh2WefpWvXrsybN6/VeaKiovD29mbs2LG6B2URQgghhBBCCCGEEP9cNNONnd8JLl++jI+PD2lpaSQmJt7p6rSbmpoafHx8SE9P55FHHlGe/+DJi0rx9lb1SvF3oZYV+uVP+gaWAejiclWpbFsrtUfZbazUBtNR3TbVDU66Yz1tq5TKdmm4qBRv0pFpe70r1m66Y68a1fr4tNLU+ha11hS/V9S+106Xv9YfrLgda+30b0cAe0PrWcXXO+foqzvWYLRtPeg6qr/ti1onpXjv2jNK8S6VJ5XiVRidXFsPus6xzmOU4lWOHT2uqq3nTy4+SvGdLins74B1Q61SfLWr/htZZ41+amXX67sheE135x+V4usVfiPBf1unVPbZEQlK8baa2vmmVvE4/O1l/b/Xzo7VSmXbKB6z7RTPrUaT/vvmmqZ2+dxoUnsyxAq189nfR49Viu+Wk607VkNtXVXjG1HbNtb8su6FbuXrms5K8T/WqJ3/BnerbD3oOjaLWh5A8EaGZelKZdtrasdg1X3eYLLTHavy2wP1673KK2rXEXc5qF0znb+i/5rM2/knpbJVjwVlwRFK8T2P5+qOVb1uNqF2bat67Kg3qf3+vr+s/3u6dNVGqewQr++U4lX1C5RkHvHr6BAZhKNHj2b+/PlA0yO7a9asue2yMjIy8PDwUJqnsLCQjRs3cvr0aQoKCoiPjwdg0qRJt12PjsxoNHLu3DmWLVuGh4cHEydOvNNVEkIIIYQQQgghhBB3iFrT+K/g4MGDzY662xw/Pz/mz59vblwEiI2N5YEHHlBe7sqVKzl58iR2dnYMGzaM3NxcOnfuTG5uLuPHj29xPj19/f0acnJyiIiI4Keffmq1gfTrr7/m7rvvpkePHmRkZGBjc3u7gaO12p1HG00t++qiSe1uXw93/ZkI3bVvlMq+67y+QWuuOdM1XCn+skJGIKhlBdqilkHxk7Xa4/p2itkoKpkCtlZqI5ar3oF2Mqn9fo2aWpaDSgaWppjMXWtSy+q5qvVQinfmilK8iiWbuyjF/1us2h3rC449leJNPmrfq4oqe7Xfk4OpTile5bjaYKO2zxgVM54Mtmrl19u5KMV/URWiO7an+yWlsofYFinFn0ft96SSeXPivjlKZTuidi6uanBXilfN2htxdafu2DLne5XKVs2kUaXyPalmuqjWXTVbq8ue3UrxVgoZ8ap1UWVtUtvH2rM+Hg5qT5iEWR9Qiv8Of6X4+mUZumMDCzYolV0+7DGleCtT+/3+OhnUMit/suuqFN/TSS27S/Uaq5Oj/uvJ9j4WqGQEAnzTb6TuWP8T/6tUtsmkmEGouK62ik/2qPy+HW3VshNV6666bYT4tXS4BsJf0pcgNI167OiodlAfMmQIhw4davaz0NBQioqKflGdOpL6+nr8/PyQJ8uFEEIIIYQQQgghBHSQR4yvd/0jxiaTidTUVHr16oW9vT3du3fnySefBJoeSz579ix//vOf0TTNPOLwjY8Yp6amMnjwYN577z38/Pxwd3fnscceo7r652yz6upq4uPjcXZ2xtvbm9WrV5sfe3Z0dCQwMLDFCaCuro5nnnmGnj17Ym9vT2BgIOvXrzeXv3fvXsLCwrC3t8fb25tnn32Whoaf73g091j14MGDSU1NNb/WNI133nmHhx9+GCcnJ3r37s22bdsAKC8vJyKiqb+Ju+66C03TSEhIMG+nefPmMX/+fDp37szYsWOZMWMGEyZMsFiewWCgS5cuFvUWQgghhBBCCCGEEP/8OlwD4fW2bNnC6tWreeuttygtLWXr1q0MGDAAgI8++ogePXqwdOlSKisrqaxsOTX99OnTbN26le3bt7N9+3b27t3LihUrzJ8vWLCAvLw8tm3bRnZ2Nrm5uRQUFOiu57Rp09i4cSOvv/46xcXFvPXWW7i4ND069e233/LAAw9wzz33cPjwYdatW8f69et58cUXlbfHkiVLiImJ4auvvuKBBx4gPj6eH3/8kZ49e7JlyxYATp48SWVlJa+99pp5vszMTOzs7MjLy+PNN98kKSmJnTt3Wmyz7du3c+XKFWJjY5XrJYQQQgghhBBCCCF+uzrcI8bX+/rrr+nWrRtRUVHY2trSq1cvwsLCAPD09MTa2hpXV1e6det2y3KMRiMZGRm4ujaNJjl16lR2797NSy+9RHV1NZmZmWRlZTFmTNPokenp6XTvrm+koJKSEjZv3kx2djZRUVEA+Pv/3L/IG2+8Qc+ePVm7di2aptG3b1/+/ve/88wzz7B48WKsrPS30SYkJBAXFwfAyy+/zOuvv86BAwcYN24cnp6eAHTp0uWmPgh79+7NK6+8YvFenz59eO+993j66afN6/zoo4+aGzaFEEIIIYQQQgghxO9Dh84gfPTRR7l69Sr+/v7MnDmTjz/+2OLRXL38/PzMjYMA3t7enDt3DoAzZ85gMBjMDY8A7u7u9OnTR1fZRUVFWFtbM2rUqGY/Ly4uZvjw4eZHoAHCw8OpqamhoqJCaT0GDhxo/tvZ2Rk3NzfzetzKsGHDbnovKSmJ9PR0AL7//nt27NjBjBkzWiyjrq6OS5cuWUz1dWqd6QshhBBCCCGEEEKIjqdDNxD27NmTkydP8sYbb+Do6MjcuXO5//77MRjURja1vWEUIk3TMBrbZiQu1QFRmmNlZXXToCHNrePtrkdzo0JPmzaNM2fOkJ+fz4YNG7j77rsZObLlUayWL1+Ou7u7xfTOW//R6rKFEEIIIYQQQgghRMfWoRsIoakB7qGHHuL1118nJyeH/Px8jhw5AoCdnR2NjY2/qHx/f39sbW05ePCg+b2qqipKSkp0zT9gwACMRiN79+5t9vPg4GDy8/MtGgDz8vJwdXWlR48eQNPIzdf3B3jp0iXKysqU1sPOzg5A9/bo1KkTkydPJj09nYyMDB5//PFbxqekpFBVVWUxJc3+k1IdhRBCCCGEEEIIIUTH06EbCDMyMli/fj1Hjx7lzJkzbNiwAUdHR3x9fYGmR4f37dvHt99+y4ULF25rGa6urkyfPp2nnnqKPXv2cOzYMRITE7GysrJ4LLglfn5+TJ8+nRkzZrB161bKysrIyclh8+bNAMydO5dvvvmGP/3pT5w4cYJPPvmEF154gQULFpj7H4yMjOS9994jNzeXI0eOMH36dKytrZXWw9fXF03T2L59O+fPn6empqbVeZKSksjMzKS4uJjp06ffMtbe3h43NzeLyc7eXqmOQgghhBBCCCGEEKLj6dANhB4eHrz99tuEh4czcOBAdu3axaeffkqnTp0AWLp0KeXl5QQEBODl5XXby1m1ahXDhw9nwoQJREVFER4eTnBwMA4ODrrmX7duHX/84x+ZO3cuffv2ZebMmVy+fBkAHx8fPvvsMw4cOMD/z96dx0VV9X8A/8wMMOyLIopsw+Iui2mmD7ljuODSYlqooGiZqaG5PqUCrpWCUbnkAoip+aSiklZqgshDjyaCpojKIi645MIuIHN+f/DjPgwzwD1jY1PP9/16zesFdz73zLmXO3cuZ849x9vbG9OmTUNISAg+/vhjYf1FixahX79+CAgIwPDhwzF69Gi4u7tzbYODgwPCw8OxcOFCtG7dGjNmzGh2HT8/P9jb28Pf31/0pCyEEEIIIYQQQggh5O9Fr2cxHj16NEaPHt3o87169UJmZmaTZYSFhSEsLExlWWhoKEJDQ4XfLSws8M033wi/l5WVITw8HO+8846oehobGyMyMhKRkZEan+/Xrx9Onz7d6PqWlpbYvXu3yrKGPfoajlEIAI8fP1b5ffHixVi8eLHKsqSkpEZft6ysDI8ePUJISEijGUIIIYQQQgghhBDy96Z3DYRnzpzROKmGJgqFQq2xb+zYsRg2bBjXa547dw6XL19Gz549UVRUhIiICADAqFGjuMr5MyUlJWHAgAF49OgRrK2tm8wqlUr8/vvvWLt2LaytrTFy5EitXrNSacSVfyrhu23aVFrBla+SGDYf+n/3JW24yn7Y2o4rLwPf2JhWhsVc+SdKcb1bAXD3E7Zgj7nyt5/y9T5tYVgkOvuU8Z2ipBK+yYfKmEXzoXokUG+ob4p5jfht5VUt5Xv/GUn5Jnfi0e7Wca687z/e4srLJQVceSP2hCv/WN6aK69LNYy3Y7/498gTQ3GfrXUYmh9mQ6V8I773E2/5dubiPxPk0iqusp9IzbnyvOcaQzzlyvPgPS/xngt4y7/Wsrf4uoCzLhK+uvDWXZd0XXeZhO+6g/cY1im+U4FO/65uyqtc+VyjLlx5Kwnf9V4NxF8753cfx1W2rrncSBGdve7U+ESJmvBeZ5cq+c7xzhWXufIFJh1FZ/XtXOB2+WfR2dyOA3VWNsC/rVLwnceMZeKvDQylfPuRt+68xwEhz4veNRA+y63CQO2kJtrMLLxmzRpkZ2fDyMgI3bt3R0pKCmxtbZGSkoKhQ4c2up6Ysf70SVVVFW7fvg1XV1c4OjoiNjYWBgZ6dxgQQgghhBBCCCGEkOdE78YgVCgUWLduHYDa22rDwsLg7OwMuVyOtm3bYtasWQCA/v374/r165g9ezYkEokwoUhsbKxKD7qwsDD4+PggPj4eCoUCVlZWGDduHEpKSoSMh4cHOnbsCMYY5HI5hg0bhpkzZyI0NBQ9evRARkZGow8AqKysxIIFC+Dk5AS5XA4PDw9s3bpVKD85ORk9e/aEXC6Hvb09Fi5ciKdP/9uboP421/Hx8VG5NVoikWDLli149dVXYWpqinbt2uHgwYMAgPz8fAwYMAAAYGNjA4lEguDgYGE/zZgxA6GhobC1tYW/vz8iIiIwfPhw3LhxA4MGDQIAVFdXw87OTqXehBBCCCGEEEIIIeTvT6+7ju3duxdRUVHYvXs3unTpgjt37ghjDu7btw/e3t545513MHXq1CbLycnJQUJCAhITE/Ho0SO8+eabWL16NVasWAEAmDNnDlJTU3Hw4EG0bt0aS5YsQXp6Onx8fGBiYgIPD48my584cSLS0tIQHR0Nb29v5OXlCbMq37p1C8OGDUNwcDC2b9+Oy5cvY+rUqTA2NlYbG7E54eHh+PTTT/HZZ5/hiy++QGBgIK5fvw4nJyfs3bsXr7/+OrKzs2FpaanSizIuLg7vvfceUlNTAQAPHjxA3759UVhYCHt7ewBAYmIiysvLMXbsWK46EUIIIYQQQgghhJC/Nr1uICwoKECbNm3g5+cHQ0NDODs7o2fPngCAFi1aQCaTwcLCAm3aND2enFKpRGxsLCwsasdHmjBhAo4fP44VK1agpKQEcXFx2Llzp9CbLiYmRvSsvleuXMGePXtw9OhR+Pn5AQDc3NyE59evXw8nJyd8+eWXkEgk6NixI27fvo0FCxZgyZIlkErFd+IMDg7GW2/Vjt21cuVKREdH4/Tp0xgyZAhatGgBALCzs1Mbg7Bdu3b49NNPVZZ16NAB8fHxmD9/vrDNY8aMgbk53xgdhBBCCCGEEEIIIeSvTe9uMa5vzJgxqKiogJubG6ZOnYr9+/er3JorlkKhEBoHAcDe3h737t0DAOTm5qK6ulpoeAQAKysrdOjQQVTZGRkZkMlk6Nevn8bns7Ky0Lt3b+EWaADw9fVFaWkpbt68ybUdXl5ews9mZmawtLQUtqMp3bt3V1s2ZcoUxMTEAADu3r2LI0eOYPLkyY2WUVlZieLiYpVHVVUlV/0JIYQQQgghhBBCiP7R6wZCJycnZGdnY/369TAxMcH06dPRt29fVFfzzXpnaKg6u61EIoFS+cfM3qbNhCgNSaVSMKY6k5GmbdR2OzTNCj1x4kTk5uYiLS0NO3bsgKurK/r0aXwGsVWrVsHKykrlEbNpXbOvTQghhBBCCCGEEEL0m143EAK1DXAjRoxAdHQ0kpKSkJaWhgsXLgAAjIyMUFPDNwV5Q25ubjA0NMSZM2eEZUVFRbhy5Yqo9T09PaFUKpGcnKzx+U6dOiEtLU2lATA1NRUWFhZwdHQEUDtzc2FhofB8cXEx8vLyuLbDyMgIAETvj5YtW2L06NGIiYlBbGwsJk2a1GR+0aJFKCoqUnlMejeUq46EEEIIIYQQQgghRP/odQNhbGwstm7dit9++w25ubnYsWMHTExM4OLiAqD21uGTJ0/i1q1bwqQgvCwsLBAUFIR58+bhxIkTuHjxIkJCQiCVSlVuC26MQqFAUFAQJk+ejISEBOTl5SEpKQl79uwBAEyfPh03btzAzJkzcfnyZRw4cABLly7FnDlzhPEHBw4ciPj4eKSkpODChQsICgqCTCbj2g4XFxdIJBIkJibi/v37KC0tbXadKVOmIC4uDllZWQgKCmoyK5fLYWlpqfIwMpJz1ZEQQgghhBBCCCGE6B+9biC0trbG5s2b4evrCy8vLxw7dgyHDh1Cy5YtAQARERHIz8+Hu7s7WrVqpfXrREZGonfv3ggICICfnx98fX3RqVMnGBsbi1p/w4YNeOONNzB9+nR07NgRU6dORVlZGQDAwcEBhw8fxunTp+Ht7Y1p06YhJCQEH3/8sbD+okWL0K9fPwQEBGD48OEYPXo03N3dubbBwcEB4eHhWLhwIVq3bo0ZM2Y0u46fnx/s7e3h7+8velIWQgghhBBCCCGEEPL3ImENB78jKCsrg4ODA9auXYuQkJA/uzo6U1paCgcHB8TExOC1117jXv/slYdceZmE73ZwU2kFV76kRvwMzLx1kUn4xqyUgbd8vnylUnzvTWPpE66yjZVlXPnbNXyNyy0Mi0RnnzK+idYlEr7TGWPN9xJ+lvLNleK3lVe51KL5UD1VzIgrbygRP9ary40UrrJ3s7e48r7OBVx5I8Z3zFdL9Kc3dJnSlCvPc26yYnzn7CdS9fFrm2KibL7nen0MfO+/gmpn0VlLQ77zmA347kIok1py5XnPZTx43qsAUM0Mmw/VIwHfea+aY1uNOOvOew6Wgu+zm/eY1GXZvPu9hvHdecJ73aFLut43PFpU3uHKF8j4vtC3MijmyteA7++qS7zvJ6cbqaKz150aHwddE97rbN7PVueKy1z5ApOOorNyaRVX2bo+F0g5riNyOw7kKtvt8s9ced5tVTK+vk5VHJ9/vPvRTFbOlefV0d1Rp+UTUkcvehD2798foaGhAGpv2V23bp3WZcXGxsLa2pprnXPnzmHXrl3IyclBeno6AgMDAQCjRo3Suh76TKlU4t69e1i2bBmsra0xcuTIP7tKhBBCCCGEEEIIIeRPoruvtLV05swZjbPuaqJQKBAaGio0LgLA2LFjMWzYMO7XXbNmDbKzs2FkZITu3bsjJSUFtra2SElJwdChQxtdT8xYf89DUlISBgwYgEePHjXbQFpQUABXV1c4OjoiNjYWBgbaHQa835TwftvH+41yS6n4HiAPlLZcZUs5v9GSSHXbMddQKr7XRSXj7B3F+bVBK9kDrvwTpfiZvw0kT7nKNuDsjcJ7jFWDrxceTw8sJeeO583XcH7LKuf4RjnHcQBX2S/hNleet8cTEzF+bH28vSJ4POX8mOXtWWAM8T2tGecxw/tNPu/7qUoqbhiPOtZGJaKzRhK+/Vgl4asLb+9jE6n4z8tyJV/PTd793rryOlf+gbEDV75SKf48yfte5e44prsOgXqH9zi4PUj8tXLb44d5q/OXdcPAjSvPey7g/UzgKZ+3V6guP/sAvl6BzjdPcZVd4PgyV95YWsmVv2PK1zNUzvmZo0u85wKeY4y3RyBvj0P3y8e58rzbytMrsFrJ2XtXfzr7EvJM9K6B8FnGEgRqZz02MRHfAAEA3bp1w9mzZzU+16NHD2RkZDxTnfRJVVUVFAoF6M5yQgghhBBCCCGEEALoyS3G9dW/xZgxhrCwMDg7O0Mul6Nt27aYNWsWgNrbkq9fv47Zs2dDIpEIMw43vMU4LCwMPj4+iI+Ph0KhgJWVFcaNG4eSkv/2RCgpKUFgYCDMzMxgb2+PqKgo4bZnExMTeHh4NPoAgMrKSixYsABOTk6Qy+Xw8PDA1q1bhfKTk5PRs2dPyOVy2NvbY+HChXj69L89ozTdVu3j44OwsDDhd4lEgi1btuDVV1+Fqakp2rVrh4MHDwIA8vPzMWBAbQ8eGxsbSCQSBAcHC/tpxowZCA0Nha2tLfz9/TF58mQEBASovF51dTXs7OxU6k0IIYQQQgghhBBC/v70roGwvr179yIqKgqbNm3C1atXkZCQAE9PTwDAvn374OjoiIiICBQWFqKwsLDRcnJycpCQkIDExEQkJiYiOTkZq1evFp6fM2cOUlNTcfDgQRw9ehQpKSlIT08XXc+JEydi165diI6ORlZWFjZt2gRz89oJM27duoVhw4bhxRdfRGZmJjZs2ICtW7di+fLl3PsjPDwcb775Js6fP49hw4YhMDAQDx8+hJOTE/bu3QsAyM7ORmFhIT7//HNhvbi4OBgZGSE1NRUbN27ElClT8MMPP6jss8TERJSXl2Ps2LHc9SKEEEIIIYQQQgghf116d4txfQUFBWjTpg38/PxgaGgIZ2dn9OzZEwDQokULyGQyWFhYoE2bNk2Wo1QqERsbCwuL2lk/J0yYgOPHj2PFihUoKSlBXFwcdu7ciUGDBgEAYmJi0LatuJlZr1y5gj179uDo0aPw8/MDALi5/Xf8kvXr18PJyQlffvklJBIJOnbsiNu3b2PBggVYsmQJpFLxbbTBwcF4663a2T9XrlyJ6OhonD59GkOGDEGLFi0AAHZ2dmpjELZr1w6ffvqpyrIOHTogPj4e8+fPF7Z5zJgxQsMmIYQQQgghhBBCCPnfoNc9CMeMGYOKigq4ublh6tSp2L9/v8qtuWIpFAqhcRAA7O3tce/ePQBAbm4uqqurhYZHALCyskKHDh1ElZ2RkQGZTIZ+/fppfD4rKwu9e/cWboEGAF9fX5SWluLmzZtc2+Hl5SX8bGZmBktLS2E7mtK9e3e1ZVOmTEFMTAwA4O7duzhy5AgmT57caBmVlZUoLi5WeVRV8g34SwghhBBCCCGEEEL0j143EDo5OSE7Oxvr16+HiYkJpk+fjr59+6K6mm+mUkND1RkwJRIJlMo/ZuYu3glRNJFKpWqThmjaRm23Q9Os0BMnTkRubi7S0tKwY8cOuLq6ok+fxmcbW7VqFaysrFQeX2/8qtnXJoQQQgghhBBCCCH6Ta8bCIHaBrgRI0YgOjoaSUlJSEtLw4ULFwAARkZGqKmpeaby3dzcYGhoiDNnzgjLioqKcOXKFVHre3p6QqlUIjk5WePznTp1QlpamkoDYGpqKiwsLODo6Aigdubm+uMBFhcXIy8vj2s7jIyMAED0/mjZsiVGjx6NmJgYxMbGYtKkSU3mFy1ahKKiIpXHO9Pe56ojIYQQQgghhBBCCNE/et1AGBsbi61bt+K3335Dbm4uduzYARMTE7i4uACovXX45MmTuHXrFn7//XetXsPCwgJBQUGYN28eTpw4gYsXLyIkJARSqVTltuDGKBQKBAUFYfLkyUhISEBeXh6SkpKwZ88eAMD06dNx48YNzJw5E5cvX8aBAwewdOlSzJkzRxh/cODAgYiPj0dKSgouXLiAoKAgyGQyru1wcXGBRCJBYmIi7t+/j9LS0mbXmTJlCuLi4pCVlYWgoKAms3K5HJaWlioPI7mcq46EEEIIIYQQQgghRP/odQOhtbU1Nm/eDF9fX3h5eeHYsWM4dOgQWrZsCQCIiIhAfn4+3N3d0apVK61fJzIyEr1790ZAQAD8/Pzg6+uLTp06wdjYWNT6GzZswBtvvIHp06ejY8eOmDp1KsrKygAADg4OOHz4ME6fPg1vb29MmzYNISEh+Pjjj4X1Fy1ahH79+iEgIADDhw/H6NGj4e7uzrUNDg4OCA8Px8KFC9G6dWvMmDGj2XX8/Pxgb28Pf39/0ZOyEEIIIYQQQgghhJC/FwlrOPgdQVlZGRwcHLB27VqEhIT82dXRmdLSUjg4OCAmJgavvfYa9/qXc/gmWZGB73ZwhuZ7cNZnxJ6Izj5Q2nKVbSjhmxzHUMo3TqYUfGNiKjna9p8yvsnK5RK+yWdknPvmiVL8uJ0GnGUbSPj2O2N8x1g1jLjyBhBff56/qTb5SiVf3U2lFaKzVYyvbN7jnfdcwHvc8NaHx1Pwvf94GUP830nK+M7BVRJxX5LVkSvL+cqX8pX/hIk/dxhJqrjK5j2PVTG+HvRGHOfVcqX6uMFNMeQ877V8cosr/8DYgStfXiP+7ySX8v2dJOC7ZJVJdHvdocuyebeV97P+nt8rorNtjx/mKpuXrvcNj7IaU668kvF9FpsblHHlea5TeI93XX72AUANxN8N5XzzFFfZBY4v81aHC+/fVSLR3TGp63MBz3HAu525HQdy5d0vH+fK1zC+O+6eKMV/dlcr+cq2NizhyvPq6O6o0/IJqaMXPQj79++P0NBQALW37K5bt07rsmJjY2Ftbc21zrlz57Br1y7k5OQgPT0dgYGBAIBRo0ZpXQ99plQqce/ePSxbtgzW1tYYOXLkn10lQgghhBBCCCGEEPIn0W3XBi2cOXNG46y7migUCoSGhgqNiwAwduxYDBs2jPt116xZg+zsbBgZGaF79+5ISUmBra0tUlJSMHTo0EbXEzPW3/OQlJSEAQMG4NGjR802kBYUFMDV1RWOjo6IjY2FgcHzOQxkHL2pAKASfL1Lipm1+LpwfstqxNnLwQh8vfB4e4NVMfHfcPP2pJHy9vTk7IWnT3i/CTVgfMcwT69WXfZcAcD9dRDPceB6/xeusgvtvLnyvD035Ux8rzpdM2Z8vUXKpJZceaVE/B9WpuTrafaE8xxsyNn72JBxnpsk4ns58PQ2BABD6LYnW4VS/Dmbt0cgb114e5Ly9tDg+Tvx1l2XvXR0TZe93rQpn6dXYE7HQVxl8/YC0ifcvU45r4Esah5x5YulLcTXhfM6Qtc9N3nyvD0CnY5t4MrnD2p+6KX6eM5jAF+PQ95jjJcuz6u8ZfOeC3jPNa5ZJ7jyXPtex92oeP9XJOR50bsGwmcZSxConfXYxITvH4Nu3brh7NmzGp/r0aMHMjIynqlO+qSqqgoKhQJ0ZzkhhBBCCCGEEEIIAfTkFuP66t9izBhDWFgYnJ2dIZfL0bZtW8yaNQtA7W3J169fx+zZsyGRSIQZhxveYhwWFgYfHx/Ex8dDoVDAysoK48aNQ0nJf8cJKCkpQWBgIMzMzGBvb4+oqCjhtmcTExN4eHg0+gCAyspKLFiwAE5OTpDL5fDw8MDWrVuF8pOTk9GzZ0/I5XLY29tj4cKFePr0vz2RNN1W7ePjg7CwMOF3iUSCLVu24NVXX4WpqSnatWuHgwcPAgDy8/MxYMAAAICNjQ0kEgmCg4OF/TRjxgyEhobC1tYW/v7+mDx5MgICAlRer7q6GnZ2dir1JoQQQgghhBBCCCF/f3rXQFjf3r17ERUVhU2bNuHq1atISEiAp6cnAGDfvn1wdHREREQECgsLUVhY2Gg5OTk5SEhIQGJiIhITE5GcnIzVq1cLz8+ZMwepqak4ePAgjh49ipSUFKSnp4uu58SJE7Fr1y5ER0cjKysLmzZtgrm5OQDg1q1bGDZsGF588UVkZmZiw4YN2Lp1K5YvX869P8LDw/Hmm2/i/PnzGDZsGAIDA/Hw4UM4OTlh7969AIDs7GwUFhbi888/F9aLi4uDkZERUlNTsXHjRkyZMgU//PCDyj5LTExEeXk5xo4dy10vQgghhBBCCCGEEPLXpXe3GNdXUFCANm3awM/PD4aGhnB2dkbPnj0BAC1atIBMJoOFhQXatGnTZDlKpRKxsbGwsLAAAEyYMAHHjx/HihUrUFJSgri4OOzcuRODBtWOexATE4O2bduKquOVK1ewZ88eHD16FH5+fgAANzc34fn169fDyckJX375JSQSCTp27Ijbt29jwYIFWLJkCaRS8W20wcHBeOuttwAAK1euRHR0NE6fPo0hQ4agRYvaMUrs7OzUxiBs164dPv30U5VlHTp0QHx8PObPny9s85gxY4SGTUIIIYQQQgghhBDyv0GvexCOGTMGFRUVcHNzw9SpU7F//36VW3PFUigUQuMgANjb2+PevXsAgNzcXFRXVwsNjwBgZWWFDh06iCo7IyMDMpkM/fr10/h8VlYWevfuLdwCDQC+vr4oLS3FzZs3ubbDy8tL+NnMzAyWlpbCdjSle/fuasumTJmCmJgYAMDdu3dx5MgRTJ48udEyKisrUVxcrPKoqqTBVQkhhBBCCCGEEEL+6vS6gdDJyQnZ2dlYv349TExMMH36dPTt2xfV1Xwz/BkaGqr8LpFIoFTyzU7VGN4JUTSRSqVqk4Zo2kZtt0PTrNATJ05Ebm4u0tLSsGPHDri6uqJPnz6NlrFq1SpYWVmpPL7e+FWzr00IIYQQQgghhBBC9JteNxACtQ1wI0aMQHR0NJKSkpCWloYLFy4AAIyMjFBT82xTxbu5ucHQ0BBnzpwRlhUVFeHKlSui1vf09IRSqURycrLG5zt16oS0tDSVBsDU1FRYWFjA0dERQO3MzfXHAywuLkZeXh7XdhgZGQGA6P3RsmVLjB49GjExMYiNjcWkSZOazC9atAhFRUUqj3emvc9VR0IIIYQQQgghhBCif/S6gTA2NhZbt27Fb7/9htzcXOzYsQMmJiZwcXEBUHvr8MmTJ3Hr1i38/vvvWr2GhYUFgoKCMG/ePJw4cQIXL15ESEgIpFKpym3BjVEoFAgKCsLkyZORkJCAvLw8JCUlYc+ePQCA6dOn48aNG5g5cyYuX76MAwcOYOnSpZgzZ44w/uDAgQMRHx+PlJQUXLhwAUFBQZDJZFzb4eLiAolEgsTERNy/fx+lpaXNrjNlyhTExcUhKysLQUFBTWblcjksLS1VHkZyOVcdCSGEEEIIIYQQQoj+0esGQmtra2zevBm+vr7w8vLCsWPHcOjQIbRs2RIAEBERgfz8fLi7u6NVq1Zav05kZCR69+6NgIAA+Pn5wdfXF506dYKxsbGo9Tds2IA33ngD06dPR8eOHTF16lSUlZUBABwcHHD48GGcPn0a3t7emDZtGkJCQvDxxx8L6y9atAj9+vVDQEAAhg8fjtGjR8Pd3Z1rGxwcHBAeHo6FCxeidevWmDFjRrPr+Pn5wd7eHv7+/qInZSGEEEIIIYQQQgghfy8S1nDwO4KysjI4ODhg7dq1CAkJ+bOrozOlpaVwcHBATEwMXnvtNe71L+fwTbJiBL5JTSohroG2TrXSsPnQ/1Oi+d6h9RlL+erOu61Kzrb6CmYqvi6SKq6yZeCfCIhHJRP/dzWQ8NXFQMI3PikvJePr2WvEnojOMs5jkleFRH0s0qYYQvxxY38vk6vsQjtvrnw1jLjyJqyMK69LMsZ3DJdJLbnyhhzvb8MavvNSkaQFV94CRVx5CfguP2okBqKzTxjf+MAmknKuPO/n01Mmvu685z3e/WhbcYMrf8fYlStfzbGtRpznbImEb1ul4BtvWtfnYV2q4fx8kknED9GT03EQV9nul49z5Xn3O+8xz+OJku/umKdK8cc7ALSR3OLKF0vFn4elEr7jnff9wbvfea5teY8Bp2MbuPL5g5rvOFEf775UMvHbyvPe0wbvuYBnW3X53gP4zzWuWSe48lVM/P+KvPvRTMZ3HcH7v6IbZ+chQrSlFz0I+/fvj9DQUAC1t+yuW7dO67JiY2NhbW3Ntc65c+ewa9cu5OTkID09HYGBgQCAUaNGaV0PfaZUKnHv3j0sW7YM1tbWGDly5J9dJUIIIYQQQgghhBDyJ+H72us5OHPmjMZZdzVRKBQIDQ0VGhcBYOzYsRg2bBj3665ZswbZ2dkwMjJC9+7dkZKSAltbW6SkpGDo0KGNridmrL/nISkpCQMGDMCjR4+abSAtKCiAq6srHB0dERsbCwMD7Q4DOcT3jgIApYSvPdqQ8fV8M0Ox6OydGr5bqnm/ITaScvZ4klZw5e2f5IrOZks9ucq2MRK/HwH+Hh0lT8X3fjQ34Nsv1RzfDAKAIWfvFd5vE3+vthGd5f323MqQ79xzr9yaK9/WVPy4rpXmtlxlF9fw9ZKzlj3myt952porL+M8hnWpNQqbD9XzlONcc0/ZhqvsvEdWXHk7CwuuvIucrxd6kVJ8fUw5z6kSxtdbRHExgSuf1/VV0VkD8J2XnoLvvFdq3JIrz9tjpLRa/Dm+jfweV9k8PdABwKrmAVf+sUz8uYz3s4+nhxEAuF8/ypW/5DScK89zZ4Tb5Z+5ylbk8/UgLG1kor/GmA4eIjorfcLXo/x02zFc+Wol33WBqQlfz+wacJTP+VEml+j2Ol4K8T3lqhhfz80bfu9x5VtX5HPl7xm7cOWLq8XfpWFpyHdM8vZm5OklBwCGHHcO6bpXNm+PwLxOA7jyFhnporPl1Xz70dSC77qjCjSWP9FPetdA+CxjCQK1sx6bmPDdWtStWzecPXtW43M9evRARkbGM9VJn1RVVUGhUIDuLCeEEEIIIYQQQgghgJ7cYlxf/VuMGWMICwuDs7Mz5HI52rZti1mzZgGovS35+vXrmD17NiQSiTDjcMNbjMPCwuDj44P4+HgoFApYWVlh3LhxKCkpETIlJSUIDAyEmZkZ7O3tERUVJdz2bGJiAg8Pj0YfAFBZWYkFCxbAyckJcrkcHh4e2Lp1q1B+cnIyevbsCblcDnt7eyxcuBBPn/732xpNt1X7+PggLCxM+F0ikWDLli149dVXYWpqinbt2uHgwYMAgPz8fAwYUPsNio2NDSQSCYKDg4X9NGPGDISGhsLW1hb+/v6YPHkyAgICVF6vuroadnZ2KvUmhBBCCCGEEEIIIX9/etdAWN/evXsRFRWFTZs24erVq0hISICnZ+3tkvv27YOjoyMiIiJQWFiIwsLGb8vKyclBQkICEhMTkZiYiOTkZKxevVp4fs6cOUhNTcXBgwdx9OhRpKSkID1dfBfkiRMnYteuXYiOjkZWVhY2bdoEc3NzAMCtW7cwbNgwvPjii8jMzMSGDRuwdetWLF++nHt/hIeH480338T58+cxbNgwBAYG4uHDh3BycsLevXsBANnZ2SgsLMTnn38urBcXFwcjIyOkpqZi48aNmDJlCn744QeVfZaYmIjy8nKMHTuWu16EEEIIIYQQQggh5K9L724xrq+goABt2rSBn58fDA0N4ezsjJ49ewIAWrRoAZlMBgsLC7Rp0/S4SkqlErGxsbD4//GRJkyYgOPHj2PFihUoKSlBXFwcdu7ciUGDamdOiomJQdu24saou3LlCvbs2YOjR4/Cz88PAODm5iY8v379ejg5OeHLL7+ERCJBx44dcfv2bSxYsABLliyBVCq+jTY4OBhvvfUWAGDlypWIjo7G6dOnMWTIELRoUTumiZ2dndoYhO3atcOnn36qsqxDhw6Ij4/H/PnzhW0eM2aM0LBJCCGEEEIIIYQQQv436HUPwjFjxqCiogJubm6YOnUq9u/fr3JrrlgKhUJoHAQAe3t73LtXOzB2bm4uqqurhYZHALCyskKHDh1ElZ2RkQGZTIZ+/fppfD4rKwu9e/cWboEGAF9fX5SWluLmTb4B2r28vISfzczMYGlpKWxHU7p37662bMqUKYiJiQEA3L17F0eOHMHkyZMbLaOyshLFxcUqj8pKvunZCSGEEEIIIYQQQoj+0esGQicnJ2RnZ2P9+vUwMTHB9OnT0bdvX1RX883wZ2ioOguRRCKBUsk3C1NjeCdE0UQqlapNGqJpG7XdDk2zQk+cOBG5ublIS0vDjh074Orqij59+jRaxqpVq2BlZaXy2LBxU7OvTQghhBBCCCGEEEL0m143EAK1DXAjRoxAdHQ0kpKSkJaWhgsXLgAAjIyMUFNT80zlu7m5wdDQEGfOnBGWFRUV4cqVK6LW9/T0hFKpRHJyssbnO3XqhLS0NJUGwNTUVFhYWMDR0RFA7czN9ccDLC4uRl5eHtd2GBkZAYDo/dGyZUuMHj0aMTExiI2NxaRJk5rML1q0CEVFRSqP96a9y1VHQgghhBBCCCGEEKJ/9LqBMDY2Flu3bsVvv/2G3Nxc7NixAyYmJnBxcQFQe+vwyZMncevWLfz+++9avYaFhQWCgoIwb948nDhxAhcvXkRISAikUqnKbcGNUSgUCAoKwuTJk5GQkIC8vDwkJSVhz549AIDp06fjxo0bmDlzJi5fvowDBw5g6dKlmDNnjjD+4MCBAxEfH4+UlBRcuHABQUFBkMlkXNvh4uICiUSCxMRE3L9/H6Wlpc2uM2XKFMTFxSErKwtBQUFNZuVyOSwtLVUecrmcq46EEEIIIYQQQgghRP/odQOhtbU1Nm/eDF9fX3h5eeHYsWM4dOgQWrZsCQCIiIhAfn4+3N3d0apVK61fJzIyEr1790ZAQAD8/Pzg6+uLTp06wdjYWNT6GzZswBtvvIHp06ejY8eOmDp1KsrKygAADg4OOHz4ME6fPg1vb29MmzYNISEh+Pjjj4X1Fy1ahH79+iEgIADDhw/H6NGj4e7uzrUNDg4OCA8Px8KFC9G6dWvMmDGj2XX8/Pxgb28Pf39/0ZOyEEIIIYQQQgghhJC/FwlrOPgdQVlZGRwcHLB27VqEhIT82dXRmdLSUjg4OCAmJgavvfYa9/p5Ode48koJX3s0Y8334KzPSPlEdPZODV+DqFTCN2alkZRvnEwTaQVX3qbituhsttSTr2yjYq68RMJ3CimqFj9TtrkB337hZSjh+zvVML6evTzbysB3vFsZNt9LuL57FdZc+bam4ntltyq/zlV2vrwTV95a9pgrf6/alisv4zyGdam1tLD5UD1PpUaisw+ftuAqO++RFVfezkL8ORgAXOR8E3U9YuLrb8p5TpUzvnybiz9x5fO6vio6a4gqrrKfwrD5UD0mrIwrXwaL5kP1PKwSf9y0kTc/0Vp9lUzcF7d1bGruc+Ufy8SfO3g/+5SM7xrI/fpRrvwlp+FceWOp+InmeLfVNf84V760kWF6GmM6eIjorPQJ3/F+uu0Yrny1ku+6wN7kIVe+BuLLl4Fv2CUTSTlXnvc6nkcV47srSQK+Y9K24gZX/p6xC1ee53rP0pDvmOT9H+SJkm9fGkrETwAqBV9deK9tef+ueZ0GcOUtMtJFZ8ur+T5bXSz4Pm94t7WjuyNXnhBt6UUPwv79+yM0NBRA7S2769at07qs2NhYWFtbc61z7tw57Nq1Czk5OUhPT0dgYCAAYNSoUVrXQ58plUrcu3cPy5Ytg7W1NUaOHPlnV4kQQgghhBBCCCGE/EkM/uwKNHTmzBmNs+5qolAoEBoaKjQuAsDYsWMxbNgw7tdds2YNsrOzYWRkhO7duyMlJQW2trZISUnB0KFDG11PzFh/z0NSUhIGDBiAR48eNdtAWlBQAFdXVzg6OiI2NhYGBtodBlLG902laVURV77QwJkr3zFH/DfW0sQfucoumx3JlW/xRHwPPwB4aMzXo1Fe8Uh0tr3ZJa6yz5X5cOUtjPh6DXnfOyw6m2X/ClfZMgnfMVn+lG8WcpmUr/yUS+K/UZbJ+L5lHdSFb79fvMHX86ZVe/HfnP5uyvdeda4QNwlUnQvMhyt/6Sbf3/X6dd31VDUz5zu/Bvbm+zsZ14jvibDvlClX2e/1vcyV3362A1f+p3K+HhqhL2WIzp4ofpGr7BHXVnLlr/WewpUvqRK/7++XibsGqtPO5g5XnqcnJgD8XmHJle95bIHo7LWAxVxlVyn53k9Sw5Zc+acc5VviMVfZD5R8PZuvOIvvJQcAd0v5/k6tzcTfLcDbg4m37pWBfF9UG8vE937kvRulvHsPrvx3C1K48rPe5DuGHzwR34O3pXEJV9mVEr7PG959ydPbrFrJ11uL93rP/CLf38nE+S5XfkOh+GN4QFfxPfYAvh5+AHC3jO9cYG0s/hrIWMbXw533zhvevytPj0AAKPF5QXT2yCenucp+ZwTfMUyIvtK7BsJnGUsQqJ312MSE75/Dbt264ezZsxqf69GjBzIyMp6pTvqkqqoKCoUCdGc5IYQQQgghhBBCCAH05Bbj+urfYswYQ1hYGJydnSGXy9G2bVvMmjULQO1tydevX8fs2bMhkUiEGYcb3mIcFhYGHx8fxMfHQ6FQwMrKCuPGjUNJyX+/ZSspKUFgYCDMzMxgb2+PqKgo4bZnExMTeHh4NPoAgMrKSixYsABOTk6Qy+Xw8PDA1q1bhfKTk5PRs2dPyOVy2NvbY+HChXj69L/fBmm6rdrHxwdhYWHC7xKJBFu2bMGrr74KU1NTtGvXDgcPHgQA5OfnY8CA2jEYbGxsIJFIEBwcLOynGTNmIDQ0FLa2tvD398fkyZMREBCg8nrV1dWws7NTqTchhBBCCCGEEEII+fvTuwbC+vbu3YuoqChs2rQJV69eRUJCAjw9aydc2LdvHxwdHREREYHCwkIUFjY+sHtOTg4SEhKQmJiIxMREJCcnY/Xq1cLzc+bMQWpqKg4ePIijR48iJSUF6eniuyxPnDgRu3btQnR0NLKysrBp0yaYm9feWnjr1i0MGzYML774IjIzM7FhwwZs3boVy5cv594f4eHhePPNN3H+/HkMGzYMgYGBePjwIZycnLB3714AQHZ2NgoLC/H5558L68XFxcHIyAipqanYuHEjpkyZgh9++EFlnyUmJqK8vBxjx47lrhchhBBCCCGEEEII+evSu1uM6ysoKECbNm3g5+cHQ0NDODs7o2fPngCAFi1aQCaTwcLCAm3atGmyHKVSidjYWFhY1I7jMWHCBBw/fhwrVqxASUkJ4uLisHPnTgwaNAgAEBMTg7ZtxY0Jd+XKFezZswdHjx6Fn58fAMDNzU14fv369XBycsKXX34JiUSCjh074vbt21iwYAGWLFkCqVR8G21wcDDeeustAMDKlSsRHR2N06dPY8iQIWjRonZMITs7O7UxCNu1a4dPP/1UZVmHDh0QHx+P+fPnC9s8ZswYoWGzocrKSlRWVjZYVgW5XPwMmoQQQgghhBBCCCFE/+h1D8IxY8agoqICbm5umDp1Kvbv369ya65YCoVCaBwEAHt7e9y7dw8AkJubi+rqaqHhEQCsrKzQoYO4wdYzMjIgk8nQr18/jc9nZWWhd+/ewi3QAODr64vS0lLcvHmTazu8vLyEn83MzGBpaSlsR1O6d++utmzKlCmIiYkBANy9exdHjhzB5MmTGy1j1apVsLKyUnms37SJq/6EEEIIIYQQQgghRP/odQOhk5MTsrOzsX79epiYmGD69Ono27cvqqurucoxNFSdVUgikUCp5JuRrTG8E6JoIpVK1SYN0bSN2m6HplmhJ06ciNzcXKSlpWHHjh1wdXVFnz59Gi1j0aJFKCoqUnlMf/fdZl+bEEIIIYQQQgghhOg3vW4gBGob4EaMGIHo6GgkJSUhLS0NFy5cAAAYGRmhpoZvOvSG3NzcYGhoiDNnzgjLioqKcOXKFVHre3p6QqlUIjk5WePznTp1QlpamkoDYGpqKiwsLODo6Aigdubm+uMBFhcXIy8vj2s7jIxqb/UVuz9atmyJ0aNHIyYmBrGxsZg0aVKTeblcDktLS5UH3V5MCCGEEEIIIYQQ8ten1w2EsbGx2Lp1K3777Tfk5uZix44dMDExgYuLC4DaW4dPnjyJW7du4ffff9fqNSwsLBAUFIR58+bhxIkTuHjxIkJCQiCVSlVuC26MQqFAUFAQJk+ejISEBOTl5SEpKQl79uwBAEyfPh03btzAzJkzcfnyZRw4cABLly7FnDlzhPEHBw4ciPj4eKSkpODChQsICgqCTCbj2g4XFxdIJBIkJibi/v37KC0tbXadKVOmIC4uDllZWQgKCuJ6PUIIIYQQQgghhBDy96DXDYTW1tbYvHkzfH194eXlhWPHjuHQoUNo2bIlACAiIgL5+flwd3dHq1attH6dyMhI9O7dGwEBAfDz84Ovry86deoEY2NjUetv2LABb7zxBqZPn46OHTti6tSpKCsrAwA4ODjg8OHDOH36NLy9vTFt2jSEhITg448/FtZftGgR+vXrh4CAAAwfPhyjR4+Gu7s71zY4ODggPDwcCxcuROvWrTFjxoxm1/Hz84O9vT38/f1FT8pCCCGEEEIIIYQQQv5e9HoW49GjR2P06NGNPt+rVy9kZmY2WUZYWBjCwsJUloWGhiI0NFT43cLCAt98843we1lZGcLDw/HOO++IqqexsTEiIyMRGRmp8fl+/frh9OnTja5vaWmJ3bt3qyxr2KOv4RiFAPD48WOV3xcvXozFixerLEtKSmr0dcvKyvDo0SOEhIQ0miGEEEIIIYQQQgghf28Spqnl6Tnr378/fHx8sG7dOty/fx9mZmYwNTVtdj2FQqHW2FdRUYGSkhLY2dmJfv1z587h8uXL6NmzJ4qKihAREYGkpCRcu3YNtra22mzSc5eUlIQBAwbg0aNHsLa2bjKrVCrx+++/Y+3atdi9ezdycnJgYMDfVpydc4MrX/zUovlQPcaySq58UZW56OzJC+J6h9YZ1yOfK1/GxNcFAMwkzd8SXt+VEifRWTvTEq6ynaqvceWrDfgm6jlf0VF09gWj85x1kXPlGZofRqC+Gqlh86F6Wuec4srzuOkxkCvvmL6PK3+r+6uis/erWnKV3aU0jSuvlPKdn+SPbnPl8QdNWqW5bL5xcm94+HHlS2rEn2s6X97dfKiem12GceUdczWPxdsYZsj3fr3mKP6Yb5e9n6vsLRK+Sbdeci/iyrtXXhCd/d3MhatsmeQpV76K8e13h6JLXPk9jwaLzg5S8H3eWJfe4soXmrXjylsqH4rOFktbcJVd/FR9wrimyGV8E/Jl3+c7D7u1KBadlUn5zmNSCd85tbya75rMzLBCdFbJ+G6W2nqQb4ift78ZxJU32n2cK3+/TPxxY2/Bd16SS6q48ryeMvGf3bzHDO/12y/5rbnyilZ8/4N47ZwiOps36Uuuso2kfOf4aw9suPKtLZ+Izpobis8CQLWS7/1kyHmuuV/O9/9W4s/izx1DF/TkKtv+4r+58i2lfMOjuXh04MoToi2960H4LLcKA7WTmmgzs/CaNWuQnZ0NIyMjdO/eHSkpKbC1tUVKSgqGDh3a6HpixvrTJ1VVVbh9+zZcXV3h6OiI2NhYrRoHCSGEEEIIIYQQQsjfg96NQahQKLBu3ToAtbfVhoWFwdnZGXK5HG3btsWsWbMA1PY6vH79OmbPng2JRCJMKBIbG6vSgy4sLAw+Pj6Ij4+HQqGAlZUVxo0bh5KS//aq8vDwQMeOHcEYg1wux7BhwzBz5kyEhoaiR48eyMjIaPQBAJWVlViwYAGcnJwgl8vh4eGBrVu3CuUnJyejZ8+ekMvlsLe3x8KFC/H06X+/Daq/zXV8fHxUbo2WSCTYsmULXn31VZiamqJdu3Y4ePAgACA/Px8DBgwAANjY2EAikSA4OFjYTzNmzEBoaChsbW3h7++PiIgIDB8+HDdu3MCgQbXffFZXV8POzk6l3oQQQgghhBBCCCHk70+vu47t3bsXUVFR2L17N7p06YI7d+4IYw7u27cP3t7eeOeddzB16tQmy8nJyUFCQgISExPx6NEjvPnmm1i9ejVWrFgBAJgzZw5SU1Nx8OBBtG7dGkuWLEF6ejp8fHxgYmICDw+PJsufOHEi0tLSEB0dDW9vb+Tl5QmzKt+6dQvDhg1DcHAwtm/fjsuXL2Pq1KkwNjZWGxuxOeHh4fj000/x2Wef4YsvvkBgYCCuX78OJycn7N27F6+//jqys7NhaWmp0osyLi4O7733HlJTUwEADx48QN++fVFYWAh7e3sAQGJiIsrLyzF27FiuOhFCCCGEEEIIIYSQvza9biAsKChAmzZt4OfnB0NDQzg7O6Nnz9rxAFq0aAGZTAYLCwu0adOmyXKUSiViY2NhYVE7Bt6ECRNw/PhxrFixAiUlJYiLi8POnTuF3nQxMTGiZ/W9cuUK9uzZg6NHj8LPr3bsKDc3N+H59evXw8nJCV9++SUkEgk6duyI27dvY8GCBViyZAmkUvGdOIODg/HWW28BAFauXIno6GicPn0aQ4YMQYsWtWPh2NnZqY1B2K5dO3z66acqyzp06ID4+HjMnz9f2OYxY8bA3JxvLAdCCCGEEEIIIYQQ8temd7cY1zdmzBhUVFTAzc0NU6dOxf79+1VuzRVLoVAIjYMAYG9vj3v37gEAcnNzUV1dLTQ8AoCVlRU6dBA3EGhGRgZkMhn69eun8fmsrCz07t1buAUaAHx9fVFaWoqbN29ybYeXl5fws5mZGSwtLYXtaEr37t3Vlk2ZMgUxMTEAgLt37+LIkSOYPHlyo2VUVlaiuLhY5VFVyTeALyGEEEIIIYQQQgjRP3rdQOjk5ITs7GysX78eJiYmmD59Ovr27Yvqar6Z3QwNVWcelUgkUP5Bs1ZqMyFKQ1KpFA0nk9a0jdpuh5mZ+gxoEydORG5uLtLS0rBjxw64urqiT58+jZaxatUqWFlZqTw2bfyq2dcmhBBCCCGEEEIIIfpNrxsIgdoGuBEjRiA6OhpJSUlIS0vDhQsXAABGRkaoqeGbDr0hNzc3GBoa4syZM8KyoqIiXLlyRdT6np6eUCqVSE5O1vh8p06dkJaWptIAmJqaCgsLCzg6OgKonbm5sLBQeL64uBh5eXlc22FkZAQAovdHy5YtMXr0aMTExCA2NhaTJk1qMr9o0SIUFRWpPN6d9j5XHQkhhBBCCCGEEEKI/tHrBsLY2Fhs3boVv/32G3Jzc7Fjxw6YmJjAxcUFQO2twydPnsStW7eESUF4WVhYICgoCPPmzcOJEydw8eJFhISEQCqVqtwW3BiFQoGgoCBMnjwZCQkJyMvLQ1JSEvbs2QMAmD59Om7cuIGZM2fi8uXLOHDgAJYuXYo5c+YI4w8OHDgQ8fHxSElJwYULFxAUFASZTMa1HS4uLpBIJEhMTMT9+/dRWlra7DpTpkxBXFwcsrKyEBQU1GRWLpfD0tJS5WEkl3PVkRBCCCGEEEIIIYToH71uILS2tsbmzZvh6+sLLy8vHDt2DIcOHULLli0BABEREcjPz4e7uztatWql9etERkaid+/eCAgIgJ+fH3x9fdGpUycYGxuLWn/Dhg144403MH36dHTs2BFTp05FWVkZAMDBwQGHDx/G6dOn4e3tjWnTpiEkJAQff/yxsP6iRYvQr18/BAQEYPjw4Rg9ejTc3d25tsHBwQHh4eFYuHAhWrdujRkzZjS7jp+fH+zt7eHv7y96UhZCCCGEEEIIIYQQ8veiF7MYJyUlCT/n5+cLP48ePRqjR49udL1evXohMzNTZVlwcDCCg4OF38PCwhAWFqaSCQ0NRWhoqPC7hYUFvvnmG+H3srIyhIeH45133hFVf2NjY0RGRiIyMlLj8/369cPp06cbXd/S0hK7d+9WWdawR1/DMQoB4PHjxyq/L168GIsXL1ZZVn/fNlRWVoZHjx4hJCSk0QwhhBBCCCGEEEII+XvTix6E/fv3FxrsFAoF1q1bp3VZsbGxsLa25lrn3Llz2LVrF3JycpCeno7AwEAAwKhRo7Suhz5TKpW4d+8eli1bBmtra4wcOfLPrhIhhBBCCCGEEEII+ZNImKauac9Z//794ePjg3Xr1uH+/fswMzODqalps+spFAq13oAVFRUoKSmBnZ2d6Nc/d+4cpkyZguzsbBgZGaF79+6IjIyEp6cnUlJSMHTo0EbXFTPW3/OQlJSEAQMG4NGjR802kObn58PV1RWOjo6IjY3FoEGDtHrNazl8E6k8hWHzoXqUjK/92lAifnbrUqX6zM5NMZOWc+WfMr7OuTIJ32Q7Eoh/20rBV3ZxjSVXnqH5sTrrs5CJf8/w1p33GOOtuwGecuXLlM2fx7Qll1Zx5StqxA2ZUMdUViE6y/tezbhjz5X3aVPYfKge3vefRKK7j0Epmp9pvj655AlXnueYL68x4SrbXFbGlec93nmPmxayh6KzT8C3raVP+T4TLAz4PvtrGN+4wjwMJHznJd7zHu/fief95/bwP1xl37B9gSvP+/7j3Tf6hPfvJJXw7Rtd4t3vPNdAvKqURlx5G/CNgV4m5bvG4vm76tPfVN+YMM7PM1hw5ZUcfW54z9m8eM8FPNdAunzvaYP33FGtFH/N9JTzc7uwyz+48h6Xj3HlO7g7ceUJ0ZZe3GJc37OMJQjUznpsYsL3j0G3bt1w9uxZjc/16NEDGRkZz1QnfVJVVQWFQqHxlmVCCCGEEEIIIYQQ8r9HL24xrq/+LcaMMYSFhcHZ2RlyuRxt27bFrFmzANT2Orx+/Tpmz54NiUQizDjc8BbjsLAw+Pj4ID4+HgqFAlZWVhg3bhxKSkqETElJCQIDA2FmZgZ7e3tERUUJtz2bmJjAw8Oj0QcAVFZWYsGCBXBycoJcLoeHhwe2bt0qlJ+cnIyePXtCLpfD3t4eCxcuxNOn//32SNNt1T4+PipjJ0okEmzZsgWvvvoqTE1N0a5dOxw8eBBAbY/AAQMGAABsbGwgkUiEcRj79++PGTNmIDQ0FLa2tvD398fkyZMREBCg8nrV1dWws7NTqTchhBBCCCGEEEII+fvTuwbC+vbu3YuoqChs2rQJV69eRUJCAjw9PQEA+/btg6OjIyIiIlBYWIjCwsZvQcvJyUFCQgISExORmJiI5ORkrF69Wnh+zpw5SE1NxcGDB3H06FGkpKQgPT1ddD0nTpyIXbt2ITo6GllZWdi0aRPMzc0BALdu3cKwYcPw4osvIjMzExs2bMDWrVuxfPly7v0RHh6ON998E+fPn8ewYcMQGBiIhw8fwsnJCXv37gUAZGdno7CwEJ9//rmwXlxcHIyMjJCamoqNGzdiypQp+OGHH1T2WWJiIsrLyzF27FjuehFCCCGEEEIIIYSQvy69u8W4voKCArRp0wZ+fn4wNDSEs7MzevbsCQBo0aIFZDIZLCws0KZNmybLUSqViI2NhYVF7XgSEyZMwPHjx7FixQqUlJQgLi4OO3fuFMbii4mJQdu2bUXV8cqVK9izZw+OHj0KPz8/AICbm5vw/Pr16+Hk5IQvv/wSEokEHTt2xO3bt7FgwQIsWbIEUqn4Ntrg4GC89dZbAICVK1ciOjoap0+fxpAhQ9CiRQsAgJ2dndoYhO3atcOnn36qsqxDhw6Ij4/H/PnzhW0eM2aM0LBJCCGEEEIIIYQQQv436HUPwjFjxqCiogJubm6YOnUq9u/fr3JrrlgKhUJoHAQAe3t73Lt3DwCQm5uL6upqoeERAKysrNChQwdRZWdkZEAmk6Ffv34an8/KykLv3r2FW6ABwNfXF6Wlpbh58ybXdnh5eQk/m5mZwdLSUtiOpnTv3l1t2ZQpUxATEwMAuHv3Lo4cOYLJkyc3WkZlZSWKi4tVHpWVlVz1J4QQQgghhBBCCCH6R68bCJ2cnJCdnY3169fDxMQE06dPR9++fVFdLX62WgAwNFSdsUgikUCp/GNm+uKdEEUTqVSqNmmIpm3UdjvMzNRnZ5w4cSJyc3ORlpaGHTt2wNXVFX369Gm0jFWrVsHKykrlsWnjhmZfmxBCCCGEEEIIIYToN71uIARqG+BGjBiB6OhoJCUlIS0tDRcuXAAAGBkZoaam5pnKd3Nzg6GhIc6cOSMsKyoqwpUrV0St7+npCaVSieTkZI3Pd+rUCWlpaSoNgKmpqbCwsICjoyOA2pmb648HWFxcjLy8PK7tMDIyAgDR+6Nly5YYPXo0YmJiEBsbi0mTJjWZX7RoEYqKilQe7057j6uOhBBCCCGEEEIIIUT/6HUDYWxsLLZu3YrffvsNubm52LFjB0xMTODi4gKg9tbhkydP4tatW/j999+1eg0LCwsEBQVh3rx5OHHiBC5evIiQkBBIpVKV24Ibo1AoEBQUhMmTJyMhIQF5eXlISkrCnj17AADTp0/HjRs3MHPmTFy+fBkHDhzA0qVLMWfOHGH8wYEDByI+Ph4pKSm4cOECgoKCIJPJuLbDxcUFEokEiYmJuH//PkpLS5tdZ8qUKYiLi0NWVhaCgoKazMrlclhaWqo85HI5Vx0JIYQQQgghhBBCiP7R6wZCa2trbN68Gb6+vvDy8sKxY8dw6NAhtGzZEgAQERGB/Px8uLu7o1WrVlq/TmRkJHr37o2AgAD4+fnB19cXnTp1grGxsaj1N2zYgDfeeAPTp09Hx44dMXXqVJSVlQEAHBwccPjwYZw+fRre3t6YNm0aQkJC8PHHHwvrL1q0CP369UNAQACGDx+O0aNHw93dnWsbHBwcEB4ejoULF6J169aYMWNGs+v4+fnB3t4e/v7+oidlIYQQQgghhBBCCCF/LxLWcPA7grKyMjg4OGDt2rUICQn5s6ujM6WlpXBwcEBMTAxee+017vWv5fDdBv0Uhs2H6lEyvvZrQ4n4sSlLlerjMjbFTFrOlX/K+CYIl0n4bpWXQPzbVgq+sotrLLnyDM33tK3PQtZ879Y6vHXnPcZ4624AvkmSypSmXHkecmkVV76iRtwXHnVMZRWis7zv1Yw79lx5nzaFzYfq4X3/SSS6+xiUgm+8W7nkCVee55gvr+EbM9dcVsaV5z3eeY+bFrKHorNPwLetpU/5PhMsDMSfxwCghvHdFcDDQMJ3XuI97/H+nXjef24P/8NV9g3bF7jyvO8/3n2jT3j/TlLJHzMW9x+Bd7/zXAPxqlIaceVtwHcHU5mU7xqL5++qT39TfWPCOD/PYNF8qB4lR58b3nM2L95zAc81kC7fe9rgPXdUK8VfMz3l/Nwu7PIPrrzH5WNc+Q7uTlx5QrT13HsQ9u/fH6Ghoc/7ZZt07tw57Nq1Czk5OUhPT0dgYCAAYNSoUc9cdnBwMEaPHv3M5YSFhcHHx+eZywEApVKJe/fuYdmyZbC2tsbIkSP/kHIJIYQQQgghhBBCyF8PXzeLv7E1a9YgOzsbRkZG6N69O1JSUmBra4uUlBQMHTq00fXEjPXHSyKRYP/+/SoNi3PnzsXMmTP/kPILCgrg6uoKR0dHxMbGwsBAu8OgGnzfshqCr8dTjYSvXjXQXQ8N3m/MeHozAkAl4xvPkadXBOPsVWcruceVfyrlOw7KmfieOi1q+OrCq9igBVee5xtiALCQlXDleUg4O3/LZbrrmcbbA/ZF+wLOuvCdC8wlfPudiRhvVltSxtejo4Lx9cIzklSKzlrI+P5O3Me7lG+/GzC+8+QTiN835soirrKfyviOMd5eCzzvEQPw7ZcazvcHb/m8neqqmPhzx21bb66yeXtx8x7DusR7HaFPvRmNIP48A/BfH/L29NSnvyvvtepfeVv1iaGE73+KUsbXc5P3usYE4u80qgLfNb+uzwWMiS9fl3dcAPznGt59yaOllK93sBlnj8BrHf248h2qs7nyhGjrf7aBkDGGmpoaGBgYoFu3bjh79qzGXI8ePZCRkfF8K6eBubk5zM3N/5CyFAoF6M5yQgghhBBCCCGEEAL8SZOUKJVKzJ8/Hy1atECbNm0QFhYmPBcZGQlPT0+YmZnByckJ06dPV+mld/36dYwYMQI2NjYwMzNDly5dcPjw4WZfMykpCRKJBEeOHEH37t0hl8tx6tQpKJVKrFq1Cq6urjAxMYG3tze+++47Yb0nT55g6dKl6N27Nzw9PTF06FCkpKTAw8MDHh4euHDhAgYOHAgTExO0bNkS77zzTpO9ChUKBdatW6eyzMfHR9gHCoUCAPDqq69CIpEIvze8xVipVCIiIgKOjo6Qy+Xw8fHBDz/8IDyfn58PiUSCffv2YcCAATA1NYW3tzfS0tKa3VeEEEIIIYQQQggh5H/Hn9JAGBcXBzMzM/znP//Bp59+ioiICBw9erS2QlIpoqOjcfHiRcTFxeHnn3/G/PnzhXXff/99VFZW4uTJk7hw4QI++eQTrp51CxcuxOrVq5GVlQUvLy+sWrUK27dvx8aNG3Hx4kXMnj0b48ePR3JyMgBg8eLFuHTpEo4cOYKsrCxs2LABtra2AGonM/H394eNjQ3OnDmDf/3rXzh27JioGYQbc+bMGQBATEwMCgsLhd8b+vzzz7F27VqsWbMG58+fh7+/P0aOHImrV6+q5D766CPMnTsXGRkZaN++Pd566y08farbwXEJIYQQQgghhBBCyF/Hn3KLsZeXF5YuXQoAaNeuHb788kscP34cgwcPVpnARKFQYPny5Zg2bRrWr18PoHb8vNdffx2enp4AADc3N67XjoiIwODBgwEAlZWVWLlyJY4dO4bevXsL5Z06dQqbNm1Cv379UFBQgG7duqFHjx5Cners3LkTT548wfbt22FmVjuu2pdffokRI0bgk08+QevWrbn3TatWrQAA1tbWaNOmTaO5NWvWYMGCBRg3bhwA4JNPPsGJEyewbt06fPXVV0Ju7ty5GD58OAAgPDwcXbp0wbVr19CxY0fuuhFCCCGEEEIIIYSQv58/rYGwPnt7e9y7VzsZwbFjx7Bq1SpcvnwZxcXFePr0KZ48eYLy8nKYmppi1qxZeO+99/DTTz/Bz88Pr7/+ulp5Talr6AOAa9euoby8XGgwrFNVVYVu3boBAN577z28/vrrSE9PxyuvvILRo0fjH/+oncY8KysL3t7eQuMgAPj6+kKpVCI7O1urBkIxiouLcfv2bfj6+qos9/X1RWZmpsqy+vvG3t4eAHDv3j3uBsLKykpUVqoOHFtVWQkjue4GhyWEEEIIIYQQQgghuven3GJsaKg6w51EIoFSqUR+fj4CAgLg5eWFvXv34uzZs0JvuKqq2tmqpkyZgtzcXEyYMAEXLlxAjx498MUXX4h+7fqNeXVjBX7//ffIyMgQHpcuXRLGIRw6dCiuX7+O2bNn4/bt2xg0aBDmzp2r9bZLpVK1CUKqqzlnFORQf19L/n+mTqWSbwY1AFi1ahWsrKxUHl9v/PIPqychhBBCCCGEEEII+XP8KQ2EjTl79iyUSiXWrl2LXr16oX379rh9+7ZazsnJCdOmTcO+ffvw4YcfYvPmzVq9XufOnSGXy1FQUCBMOlL3cHJyEnKtWrVCUFAQduzYgXXr1uHrr78GAHTq1AmZmZkoKysTsqmpqZBKpejQoYPG12zVqhUKCwuF34uLi5GXl6eSMTQ0RE1NTaP1trS0RNu2bZGamqqyPDU1FZ07dxa/AzgsWrQIRUVFKo93pmk/1iIhhBBCCCGEEEII0Q9/yi3GjfHw8EB1dTW++OILjBgxAqmpqdi4caNKJjQ0FEOHDkX79u3x6NEjnDhxAp06ddLq9SwsLDB37lzMnj0bSqUSL7/8MoqKipCamgpLS0sEBQVhyZIl6N69O7p06YLKykokJiYKrxcYGIilS5ciKCgIYWFhuH//PmbOnIkJEyY0envxwIEDERsbixEjRsDa2hpLliyBTCZTySgUChw/fhy+vr6Qy+WwsbFRK2fevHlYunQp3N3d4ePjg5iYGGRkZOCbb77Ral80Ry6XQ97gdmIjeYlOXosQQgghhBBCCCGEPD961UDo7e2NyMhIfPLJJ1i0aBH69u2LVatWYeLEiUKmpqYG77//Pm7evAlLS0sMGTIEUVFRWr/msmXL0KpVK6xatQq5ubmwtrbGCy+8gH/+858AACMjIyxatAj5+fkwMTFBnz59sHv3bgCAqakpfvzxR3zwwQd48cUXYWpqitdffx2RkZGNvt6iRYuQl5eHgIAAWFlZYdmyZWo9CNeuXYs5c+Zg8+bNcHBwQH5+vlo5s2bNQlFRET788EPcu3cPnTt3xsGDB9GuXTut9wUhhBBCCCGEEEII+d8jYQ0HxCNEpKycW1x5Q1Rx5Ws4268ZJKKzFUpjrrLNpWXNh55BJeOb7EUK8eNI8uwXALBgj7nyT6VGXPlyZtZ86P+1qLnHVTavYoMWXHkJ+E6XBhLdjS8q0fGp+ykMmw/9P979wusp57lAjidceSbhe4/wkDK+MV8rmClX3khS2Xzo/zHOUUWUnHkZnnLlDRjf++OJRPy+MVMWc5X9WNKSK28g4dtWnveIAfj2C+9nJe/fiVep0lx01lRawVU277mG9xjm/bzkwVt33rooGd+2SiXiz01GEH+eAYBq8F0X6PrvyqNKyVd3S8ljvvIlfNefT5n49zfP3/SvzlDC9z/FE6UJV14maXyoJ014rjuqwHfNr+tzAQ9dH2O85xrefcnz/raSPOIqm+caBQCudfTjyg+vzubKE6ItvRqDkBBCCCGEEEIIIYQQ8pyxv4l3332XmZmZaXy8++67f3b19M6QIUMa3V8rVqzQutwnT56wpUuXsidPnvzl8vpUF968PtVF13l9qgtvXp/qouu8PtWFN69PddF1Xp/qwpvXp7roOq9PdeHN61NddJ3Xp7rw5vWpLrrO61NdePP6VBdd5/WpLrx5faqLrvP6VBfevD7VRZs8Ibrwt2kgvHv3Lrt69arGx927d//s6umdmzdvNrq/Hjx4oHW5RUVFDAArKir6y+X1qS68eX2qi67z+lQX3rw+1UXXeX2qC29en+qi67w+1YU3r0910XVen+rCm9enuug6r0914c3rU110ndenuvDm9akuus7rU1148/pUF13n9akuvHl9qos2eUJ0Qa8mKXkWdnZ2sLOz+7Or8Zfh4ODwZ1eBEEIIIYQQQgghhOgBGoOQEEIIIYQQQgghhJD/YdRASAghhBBCCCGEEELI/zBqICR/KLlcjqVLl0IuFzftvD7l9akuvHl9qouu8/pUF968PtVF13l9qgtvXp/qouu8PtWFN69PddF1Xp/qwpvXp7roOq9PdeHN61NddJ3Xp7rw5vWpLrrO61NdePP6VBdd5/WpLrx5faqLNnlCdEHCGGN/diUIIYQQQgghhBBCCCF/DupBSAghhBBCCCGEEELI/zBqICSEEEIIIYQQQggh5H8YNRASQgghhBBCCCGEEPI/jBoICSGEEEIIIYQQQgj5H0YNhITooSdPnvzZVRDt5s2bUCqVOss/D3+l/U0IIYSQv6eHDx8+t9fSt2sxQgghfz6axZj8ZVRVVSEvLw/u7u4wMDBoNLd9+3aMHTtWbYr4qqoq7N69GxMnTtR1VbWiVCqxYsUKbNy4EXfv3sWVK1fg5uaGxYsXQ6FQICQk5M+uokaWlpbIyMiAm5ubTvLPg6WlJV577TUEBgZi0KBBkEob/+7Ezc0NZ86cQcuWLVWWP378GC+88AJyc3Nx8OBB0a89cuRItWXx8fHYuHEj8vLykJaWBhcXF6xbtw6urq4YNWqU+A0jpIHhw4djy5YtsLe3F5bVXQZIJJI/q1o6UbetP/zwA8aOHQtTU9Nm14mJiRGdbczjx49hbW2tF3l9qouu8/pUF23yfzVir8nq/F3PM3+Un376CVu2bMGhQ4dQUVEBADh79izmzp2LAwcOwNLSUiVfVFSE0aNHY926dfD29tYqL5PJUFhYCDs7OwDAvHnzsGjRIrRo0UJjHZVKJS5evAhPT08AwMaNG1FVVSU8L5PJ8N5770EqlWLOnDmitjsyMhIAEBERISq/ZMkSUTlNnj59iqioKOzatQtXrlwBALRv3x5vv/02PvjgAxgaGgIAunXrpvE4tbKyQvv27REaGopOnToJy3nz9fF85vB+PulTXp/qouv8H3Edocnq1asxbdq0v/XnCtEP1EBItNKiRQtcuXIFtra2sLGxafKCr+G3oQ0vSOo8ePAAdnZ2qKmpUVleXl6OmTNnIi4uDgCEhrOZM2fCwcEBCxcu5C5/zpw5WLZsGczMzJq9iOnfvz+GDh0KQ0PDZht+Ro4ciYMHD3Ll60RERCAuLg4RERGYOnUqfvvtN7i5ueHbb7/FunXrkJaWBgBal68rFhYWyMzMFN3gV5dv3bo1Vq9ejePHj+PevXtq32TXNbSJ3VYAWu+X/fv3Y+fOnfj+++9hZWWFsWPHYvz48ejRo4faulKpFHfu3FE7vu7evQtnZ2dUVlY22cBYn0QiUTveN2zYgCVLliA0NBQrVqwQjoPY2FjExcXhxIkTosomgIeHB8aPH4+3334b7du315iJjo7GO++8A2NjY0RHRzdZ3qxZs7jyALjKbqimpgYJCQnIysoCAHTp0gUjR46ETCZr9nUbU//9un37dnz22We4evUqgNp/lObNm4cJEyaorZeSkoJNmzYhJycH3333HRwcHBAfHw9XV1e8/PLLKtnDhw9DJpPB399fZfmPP/4IpVKJoUOHqpV/9epVnDhxQuO5QNt/COu2tXfv3qioqMCYMWMQEhKCf/zjH42u07p1a9FZAPjkk0+gUCgwduxYAMCbb76JvXv3ok2bNjh8+LDwj/jzyOtTXWhbm87zKCgoaPJ5Z2fnRp978uSJSgMOALWGI4Dv/c17TcZznuG9PgT4zpPbtm3DgAED4OrqqvacpnJjY2MbvUb5+eefnykPANevX8e2bdsQFxeHR48eYejQoXj99dcxZswYAMDbb7+NTp06YfHixRrruHLlSly6dAk7duzQKt/weqa5L3B37tyJjRs34uTJkwBqz7HW1tZCA/Hvv/+OdevWISQkBAMGDNBYRn0SiUTYL1KpFG3btoWdnR0a+9dUIpEgPT0dAODq6tpsY7NEIkFOTg4AoKKiAoMHD0ZaWhr8/PyEBrusrCwcO3YMvr6++Omnn2BsbIzw8HCN5T1+/Bjp6en45Zdf8PPPP8PX1xcAuPP18Xzm8H4+6VNen+qi6zxv2WLpYwcL8jfFCNFCbGwse/LkCWOMsZiYGBYbG9vooyGJRMLu3r2rtvzWrVvM2NhYbfmsWbNY9+7dWUpKCjMzM2M5OTmMMcYSEhKYj4+PxvLv3buntjwjI4PZ2Ngwxhjr378/e/TokfBzU4/69ZVIJI0+pFKp2vaJyddxd3dnx44dY4wxZm5uLmxnVlYWs7a21rj/xJRvY2PD7t+/zxhjzNramtnY2DT60Eb9uvLkx40bx+zt7dn8+fNZVFQUW7duncqDd1u13e/1FRcXs23btrHBgwczmUzG2rVrx8LDwxljjB04cIAdOHCASSQStn37duH3AwcOsH379rH333+ftW/fXqt9WF+nTp3Y/v37VfYVY4xduHCBtWzZkjHGmI+PD+vWrZuox/PId+vWjT18+FD0umKzvGU3FBkZyXr06MGkUinr0aMHW7duHSssLFTJKBQK9vvvvws/N/ZwdXXlzvOWXd/Vq1dZ+/btmampqbB9pqamrEOHDuzatWt8B1U9dcfU2rVrmampKZs/f75wHM+bN4+ZmpqyyMhIlXW+++47ZmJiwqZMmcLkcrlwTH7xxRds6NChaq/h6enJvv/+e7XlR44cYV5eXmrLv/76ayaTyVjr1q2Zt7c38/HxER6a/q6821pdXc327dvHRo4cyQwNDVmHDh3Y6tWr1Y4FxhhXlrHav2tqaipjjLGffvqJWVtbsx9//JGFhISwwYMHP9e8PtWFtrXxfN3nUGOPhnjzZWVl7P3332etWrUSled9f/Nck/GcZ+q2lef6kPc86eHhwaRSKXNycmLjx49nmzdvZlevXlXLMcbY+++/z8zMzNibb77JPvjgAxYaGqry0DZfWVnJdu3axQYNGsSMjY1ZQEAAk8lk7Pz582plurm5sczMTI31Y4yx8+fPq3x+8OYb7u/mruf8/PzY7t27G81v2LCB9e/fv9H1mzJs2DBmbGzMRo0axQ4cOMBqamqazDe8Zqz/CA0NZSYmJirH+5IlS5izs7PG/ZORkcGcnZ3Z0qVLRdX1n//8Jxs4cKDobWsqr8vPJ33K61Nd9G1bxeL9f4sQbVEPQqJTFRUVMDExAQChB83s2bOxbNkymJubC7mamhqcPHkS+fn5OHfunEoZLi4u+Pbbb9GrVy+V3i/Xrl3DCy+8gOLiYgD/7eKfmZmJLl26qNzyUlNTg7y8PAwZMgR79uzR9WZrxcTEBJcvX4aLi4vKdl66dAk9e/ZEaWmpVuXGxcVh3LhxkMvliI2NbfIb16CgIO7yte1B+MILL+D777/X+I2qPrh06RICAwNx/vx51NTUCL0CJRKJ2rfbhoaGUCgUWLt2LQICAp7pdRs7Dq5evQovLy9UVFQ0+m21JkuXLn0u+Xnz5sHU1FTUumKzvGUvXbpU4/IrV67gm2++wa5du5CXl4cBAwZg/PjxejvcAAAMGzYMjDF88803wu1eDx48wPjx4yGVSvH9999rVW7dMTVo0CCEh4er7YO4uDiEhYUhLy9PWNatWzfMnj0bEydOVDkmz507h6FDh+LOnTsqZZiYmCArKwsKhUJleX5+Prp06YKysjKV5S4uLpg+fToWLFig1TY1t631z013797Fjh07EBcXh8uXL2PIkCEICQnBiBEj1Hr+ismamJjgypUrcHJywgcffIAnT55g06ZNuHLlCl566SU8evRIpUxd5vWpLrStjecPHDigsm51dTXOnTuHuLg4hIeHqw0nkpmZqTEfGRmJFStW4LXXXlN5/v3338eJEyewbNkyTJgwAV999RVu3bqFTZs2YfXq1QgMDFTJ876/xV6TAbW9vMScZ7S9PtTmPHnr1i0kJSXh5MmTSE5OxtWrV2Fvb4/+/fsLPesAwNbWFtu3b8ewYcPUytBETH7mzJnYtWsX2rVrh/Hjx2PcuHFo2bIlDA0NkZmZic6dO6vkjY2NkZWV1WiPx7y8PHTu3Fm4JZk337AHYXPXc05OTkhKSoK7u7vGfFZWFnx9fbUaR9HS0hI//vgjkpKSEBsbi+LiYkycOBGTJ09Ghw4dRJXx8OFDLFu2DBs2bMBLL72ETz75BL169QIAdOjQAStXrsTrr7+ucd1//etf+Oijj4Rbj5ty8eJFDBgwAPfu3RNVL7H5P/rzSV/z+lQXfdvWpvD+v0WI1v7U5knytzBz5kyNy0tLS1W+SazrLSORSJiTk5NKD5r27duzV155hf3yyy9q5ZiYmAjfmNT/9iQjI4NZWloKubCwMBYWFsYkEgmbO3eu8HtYWBhbuXIl27lzJ6usrFQrf9KkSay4uFhj/SdNmqSyrKCgQMQe0S7/wgsvsPj4eMaY6naGh4ezl19++ZnL1xVtexAqFAp26dIl0evxbKu2+6WiooJ9++23bNSoUUwulzNnZ2e2YMEClYxCoRB6ZDbm888/ZxUVFcLPTT0a6tSpE0tISGCMqe7b6OjoZ+pNtXPnTlZaWqo3+T9LWloa8/HxabInqT4wNTXV2KMkIyODmZmZaV1u3TEll8s19py5cuUKk8vlKstMTExYXl6eyvqMMaGchlq3bs2OHz+utvzo0aOsVatWasstLCx08q14Y+emX375hb3zzjtMLpczhULBrKysmEKhYCdOnODO2tvbCz3H2rdvz/bs2cMYY+zy5cvMwsJCrTxd5vWpLrStTec1+eabb9jIkSNFZRljLDExkfXr109tuZOTk3B8WlhYCO/z7du3a+wRyPv+FntNxhgTfZ7R9vrwWc6TZWVl7IcffmBBQUHMwMCAyWQyleft7e1ZdnZ2k2Xw5mUyGfvnP/+pds1pYGDALl68qJZ3dHRkR44cabS8w4cPM0dHR63zEomEvfvuu2z27Nls9uzZzMjIiE2ePFn4ve5RRy6Xq/TMvHfvnkpPv6tXrzIjI6NGX78pDc/XycnJLDg4mFlYWLB//OMfrLy8vNF1y8vL2fLly5m1tTXz9vbW2INdLpc3eW1YUFCg8XjXJCsrS7ij44/O/5GfT/qc16e66Nu2NoZ6EJLnhRoIyTNzc3NjS5YsUVlWWlrKXn75ZY0NW/379xduGRSjT58+LDo6mjFWe3LMzc1ljDE2Y8YM5u/vr5aPjY0VGmfEkEqlGm9puX//vtoFo1QqZX379mVff/21qG3gySckJDArKyu2evVqZmpqyj777DM2ZcoUZmRkxH766adnLp8xxgYNGsRiYmJYUVFRs1mxeP+5r8vHx8ezN954g5WVlYlaj2dbeffLDz/8wCZOnMgsLS1ZixYt2DvvvMOSk5ObXa+x4+xZbi3dvHkzc3BwYLt372ZmZmZs165dbPny5cLP2tL276Sr/PP2n//8h33wwQesTZs2zNTUlI0dO5YxxtT+EWrqwZvnLbs+GxsbocGhvlOnTmk9HABj/73A7NKlC1uxYoXa88uWLWNdu3ZVWebq6sqOHj2qsj5jjMXFxbFOnTqplfHOO+8wT09PlX8kr169yry8vFhISIhafvLkyWzDhg1ab1Nj6tf1zp077LPPPmOdO3dmxsbGbNy4ccI2lZaWsvnz5zNnZ2fu7Pvvv89cXFyYn58fa9myJSspKWGMMbZr1y6NDfq6zOtTXWhbm85rkpOTw9X4f/XqVWZqaqq23MzMjF2/fp0xxpiDgwP7z3/+wxhjLDc3V2P5vO9vnmsynvMMY/zXh7znyR9//JEtWrSI9e7dmxkbG7Nu3bqx0NBQlpCQoPa6a9asYdOnT2dKpVJUXcTkd+7cyfz8/IRbkQ8dOsSePn3aaANhcHBwo18QK5VK5uvry4KDg7XO9+vXr9lhdgYMGCDknZ2dNTa+1Tl48KBwbuTVsPGjvLycxcXFsZ49ezITExON161Pnz5lGzZsYG3atGEKhYJt37690f3fqlUr9uuvvzb6+qdPn2a2trai6rpixQrWp08fUVkxeV19PulbXp/qom/bKgY1EJLnhRoIyTO7du0as7e3Z1FRUYyx2nHcevfuzfr06fOH9ChKSUlh5ubmbNq0aczY2Jh98MEHbPDgwczMzKzJD/vmFBUVscePHzOJRMKuXbvGioqKhMfDhw9ZXFwcs7e3V1knPT2dzZ07lzk6OjK5XM5GjRrF/vWvfwnjMTbEmz958iTz8/NjrVq1YiYmJszX15f9+OOPjW4Db/mzZs1ibdq0YSYmJuyNN95gCQkJrKqqSuQe00zbHoQ+Pj7MwsKCmZubs65duzY7nhzPtvLuFxMTEzZmzBhR+6OmpoZFRESwtm3bMplMJmz7xx9/zLZs2SJ6PzRlx44dzMPDQxg30cHB4ZnL1vbvJIa1tTUDwKysrJoc49LGxqbZcTAbjonJm68vOzubLVmyhLVr144ZGBiwV155hcXFxQn/vDPW/BikDf9J4snzll3fhAkTWJcuXdgvv/zClEolUyqVLC0tjXXt2pUFBQWJ/js2VPd3/e6775hMJmP+/v4sIiKCRUREMH9/f2ZgYMD27dunss7KlStZ586d2S+//MIsLCxYSkoK27FjB2vVqpXQUFDf48ePWa9evZiBgYHQGG5gYMAGDBggjP3asHxbW1sWFBTE1qxZ02wvW95tDQgIYIaGhqxLly4sKiqKPXjwQC179+5dJpFIuLKMMVZVVcU+++wzNmvWLJaeni5kIiMj2ebNm9XW1WVen+pC29p0vqHy8nL2wQcfaBzHtv61Sd11S1ZWFhs7dizz9vZWy3t6erKkpCTGWO2Xgh9++CFjrLY3u4ODg1qe9/3Nc03Gc56p7/79+8321GeM/zwpkUiYnZ0d++STTzSei+obPXo0s7KyYq6uriwgIIC9+uqrKo9nyefm5gpj4tna2jKpVMr+9a9/qZV57do1ZmVlxXr27Mm+/fZblpGRwTIyMtju3bvZiy++yKysrFR6aPLmeU2aNIn94x//0PicUqlkvXv3VrvzRqy68/W///1vNmXKFGZpacl69OjBvvrqK41/q2+//Za1a9eOtWrViq1bt07jHUL1vfnmm+y1115r9PnXXnuNjRkzhjHW+J0fERERbNSoUczAwEBo7NEmX58uP5/0Ka9PddG3bRWLGgjJ80JjEJI/xPnz5zFgwAAsXboUu3btglwux/fffw8zMzO1rDYzveXk5GD16tXIzMxEaWkpXnjhBSxYsACenp4AtJtVWSqVNpmTSCQIDw/HRx99pPYcYwxJSUnYuXMn9u7dC6VSiddeew3btm3TWBZvnhdP+UqlEseOHcPOnTuxf/9+yGQyvPHGGwgMDES/fv0A1I5xZGJigoyMDHTt2rXJ175x4wZatWoFc3Nz0fm2bdti+fLlTeYaG0+OZ1vFZktKSmBhYdFkfQBg9erVKC4uxrffftvsbNN/hPLycpSWlqrN6KgNbceKFJOPi4vDu+++i2XLlv0hda0TFBQkzJQpNl+fVCrFiy++iLfffhvjxo1D69at/7C66drjx48RFBSEQ4cOwdDQEADw9OlTjBw5EjExMbC2thay1dXVePfdd7F48eJmZ+dctWoV3nvvPVhbW+Ps2bOIiooSZv/s1KkTPvzwQ3Tr1k1lHcYYVq5ciVWrVqG8vBwAIJfLMXfuXCxbtkzj6zDGcPToUWRmZsLExAReXl7o27evxmxTdZZIJMjNzW1ymxpTt60ffvghpkyZgt69ezeaZYyhoKAAERERorMuLi5a1Yv8b2t4jcIYQ0lJCUxNTbFjxw6MHDlSJa/pWoUxBicnJ+zevVvtWI2KioJMJsOsWbNw7NgxjBgxAowxVFdXIzIyEh988IFaWbzv7+auyeoTe555/PgxPvroI3z77bfCmI02NjYYN24cli9frnLOq79OY+fJ2NhYWFlZqeTXrVuHkydP4uTJk5DL5ejXrx/69++P/v37q810P2nSJI3bXicmJuaZ8kDtvv/pp5+wdetWHDx4ELa2tnjttddUZr3/9ddfERwcjEuXLgnHAWMMnTt3RkxMDF588UWVMnnzPMzNzSGRSNC5c2fMnTtX2GfZ2dlYs2YNsrOzcfbsWXh4eHCXLZfL4ezsjKKiIgQGBmLSpEnw8vJqNC+VSmFiYoK33npL48zcdSIjIwHUjiv90ksvoUuXLpgzZw46duwIxhiysrIQFRWFS5cu4ZdffkGXLl0a/UyytLREhw4dMHv2bJX3HW++vpCQEJ19PvGUrev8X7nuut5WsYYNG4atW7fC3t5e9DqEaIMaCMkfJi0tDYMHD8ZLL72ExMREYXKShmbMmIHY2FgMHz4c9vb2ahe+UVFR3K+tzUQcycnJYIxh4MCB2Lt3rzDANQAYGRnBxcUFbdu2bfa109PTERISIkxm8UfnefGU/+TJExw6dAgrVqzAhQsXVPJubm7Yv38/vL29Rb0ub/6PwLOtf8R+t7S0hI2NDbZt24ZBgwapNKJdvnwZvXv3Vhu8HgBu3ryJgwcPoqCgAFVVVSrP1V286pouGwi1ya9evRrTpk3T+E/fs2Tr5+/fv4927do1m9+1axdGjhyp8oXGjRs3ANQOyi4GT54ne+3aNZV/rBv7x8vKygoZGRnNNhA+i6qqKly7dg2lpaXo3LmzykQCzXn8+LHov58YV69exYkTJzR+ybRkyRKV37dv346xY8dCLperLK+qqsLu3btVJlDgyda5ffs2Tp06pbEus2bNeq55faoLbavmfMMvPaRSKVq1aoWXXnoJNjY2auUmJydrzHt4eKhMxtaY69evC402TTW4PMv7+1k9fPgQvXv3xq1btxAYGIhOnToBqG3U2blzJ5ycnPDvf/9b4/4BxJ8n67tw4QKSk5Px888/IzExEXZ2drh58+Yft1GcHj58iO3btyMmJkZtYhoAyMjIwNWrV8EYQ/v27eHj49Nkebx5MSwsLLB9+3Z89NFHuHz5skoDZMeOHRETE4OXXnpJq7IlEgkcHBwwevRoGBkZNZqru2bq379/k9f6dWXW73Twyy+/ICQkBFlZWWp137p1a5MNOrpQXV2NIUOGYOPGjc1eq/Bk9S2vT3XRdZ637DpKpRLXrl3T+PnR2JerhOiMTvsnkr8tHx8ftVtCu3Xrxlq0aME6duzY5K2iLVu2bHIMk4Ya3l5T9yguLm72lgIx8vPzRY8xU+fGjRvsk08+Yd7e3kwmk7GXX365yTG0xOQbu5WyRYsWrG3btqxv375s27Ztf0h9GGOssLCQRUVFse7duzOJRMJeeukllee3bNnChg0bprFrvCa8eW3xbKs2+6Up5ubmTC6Xs/z8fOH3uu7+Fy9e1Di+07Fjx5ipqSnr2rUrMzAwYD4+Psza2ppZWVlpvLX0zp07bPz48cze3p7JZDImlUpVHs9Sd13dYqxNnmfMwuc1fmJ1dTX7+OOPmaWlpbC/LS0t2UcffaTx1nOePG/Z4eHhGsfnLC8vZ+Hh4WrLJ06cyCIjI5vczvpjODV2Xq17NFfO/v37G51kaPXq1Wz37t3C72PGjGFSqZS1bduWZWRkNFm2GF9//TWTyWSsdevWzNvbm/n4+AgPTZ85jY0z+/vvv6u9p3iyjDEWExPDjIyMmLm5OXNxcWl2jFFd5vWpLrStTecbc+HCBdHZv5MPPviAde3ald25c0ftucLCQubp6clCQ0OFZTU1NWz16tXsH//4B+vRowdbsGBBk5NY1KdUKtnZs2fZ2rVrWUBAALO2tmYymYz5+Pj8YdvzR9G3sYPrf86fO3eOffvtt+zbb79Vua1eW1KplL300kvcw3Foo37dz50798zlPct+t7W1ZVeuXBG1Hk9W3/L6VBdd53nLTktLY66urkwqlQpDC9U99H1CPfL3RA2ERCv1Zwhu7tEQ78xwdSfIxh7Ozs5syZIlwkxqEyZMYNu2bVMZIL8pR44cYSkpKcLvX375JfP29mZvvfWW2qDVGzduZH379mUymYx16dKFrVy5Umgs0oQnHxkZyVq2bMnGjx/PoqOjWXR0NBs/fjyztbVlK1asYFOmTGFyuZx9/fXXWtenqKiIbdu2jfn5+TEDAwPWvn17Fh4ernFf+fj4CA1i7du3b3aMQDF5GxsbYVyh5saWe5Z9ybtfxDI3N2ddunThmm36xRdfFCbxqcuXlJSwkSNHsvXr16vlhwwZwjp37szWr1/P9u/fzxISElQez1J3fWog5Mk/r7pMmzaN2dnZsY0bN7LMzEyWmZnJNm7cyNq0acOmTZumth5Pnrds3oaqZcuWMWtra/b666+zlStXahzHr36ZjZ1XNV2Qjhkzhn3xxReMsdoGyvbt2zNDQ0NmYGDAvvvuO7W6KBQKYeKAn376iVlbW7Mff/yRhYSEsMGDB2v8G9y4cYN99dVXbMGCBc1O4OLs7MxWr16tsRxNJBIJu3fvntryjIwMtXMNT5ax2llDly9frjKTZ1N0mdenuug6r0910SZfX3FxMdu0aRN78cUXNb636ya8Yqx2ptXFixezuXPnqk2kdfz4cdapUyeNDfyPHz9mnTt3ZidPnhSWTZo0SdSjTnPXYlKpVJjYjSfLGGMuLi7shx9+aHQfHTlyhLm4uAi/R0REMKlUyl555RU2atQoZmxsLGrsu4CAAGZjY8NkMhl74YUX2Jw5c9iBAwdUxrhr7NpEoVCwV155RW3SON48D336HNYmb2FhwXbs2CEqm5KS0ug40X8EXTaePst+DA0NZQsWLBC1Hk9W3/L6VBdd53nL9vb2ZmPGjGGXLl1ijx49Yo8fP1Z5EPK8NX9vAiEaNDY+nBgffvghPv/8c3z55ZfN3h4AALGxsfjoo48QHByMnj17AgBOnz6NuLg4fPzxx7h//z7WrFkDuVyOf/7znzAyMsKqVasQEhICBwcHYYyZfv36aezuPW/ePHzyyScAam85mTNnDj788EOcOHECc+bMURk3Zvny5XjrrbcQHR0t6lZanvypU6ewfPlyTJs2TWX5pk2b8NNPP2Hv3r3w8vJCdHQ0pk6dqlV9WrduDRsbG4wdOxarVq1Cjx49Gs2OHj262fJ481FRUcJYf1FRUaL+/nV4tpV3v/CYOXMmZsyYgVu3bkGpVGLfvn3Izs7G9u3bkZiYqJbPysrCrl27AAAGBgaoqKiAubk5IiIiMGrUKLz33nsq+VOnTiElJeUPuR2oPhcXF2GMJn3I66OdO3di9+7dGDp0qLDMy8sLTk5OeOutt7Bhwwat87xlM8Y0vj8yMzNVhkOos3XrVmFcwbNnz6o8J5FIMGvWLPz888/CuidOnBCzSwAAJ0+eFMZi3b9/P5RKJR4/foy4uDgsX74cr7/+ukr+zp07wu3TiYmJePPNN/HKK69AoVBovP3s+PHjGDlypHCrfteuXZGfnw/GGF544QW1/KNHjzBmzJhm692tWzdIJBJIJBIMGjRI5XbMmpoa5OXlYciQIdzZ+srLyzFu3DhIpdJm66PrvD7VRdd5faqLNnmg9n21detW7N27F23btsVrr72Gr776Snj+woULGDFiBG7cuIF27dph9+7dGDJkCMrKyiCVShEVFYXvvvtO+Oxdt24dpk6dqnE8NisrK7z77ruIjIxEnz59ANReW7m4uKBbt25gIkYb2r9/f6PPpaWlITo6Wrg1jicLAIWFhejSpUuj63Tt2hV37twRft++fTvWr1+Pd999FwBw7NgxDB8+HFu2bGnyb9CxY0e8++676NOnj9r4hHXWrVuncfnjx49x9uxZBAQE4LvvvsOIESO0yv8vYYxh8uTJWLJkCSZNmoSgoKBGh9Z4+eWXucq2tLRERkaG6GFNxBzjz5LX1tOnT7Ft2zYcO3YM3bt3Vxu7vf4wNDxZfcvrU130bVuvXr2K7777TquxOwnRBWogJH+Ys2fPCmPAdOnSRWUA6tdee00l+/PPP+PIkSPo0qWLWqPCvn37VH6Pi4vD2rVr8eabbwrLRowYAU9PT2zatAnHjx+Hs7MzVqxYgX/+85/YsmULAODWrVs4efIkkpOTsXbtWrz77ruwt7dXG2MmLy8PnTt3BgDs3bsXI0aMwMqVK5Geno5hw4apZAsKCrgatXjyP/74o9BQWd+gQYPw4YcfAqgdoHbhwoVa1+fgwYMYNGiQqH9ieBuBxeTrTyIRHBzcaK6iokJtGc+28u4XHoMHD0bnzp0REREBMzMzLFmyBC+88AIOHTqEwYMHq+XNzMyEcQft7e2Rk5Mj/CP0+++/q+WdnJy0vjAtLS1VG7uk7p/F33777bnn/2rkcjkUCoXacldXV41jIvHkxWbrJjCQSCRo3769ynFcU1OD0tJStS8RgNrzWHPqJiGqe10nJyeNkx/UjZFYp6ioSGhY/OGHH/D666/D1NQUw4cPx7x589Rex8bGBjdu3ICTkxN++OEHYUIixpjG8T8XLVqEuXPnIjw8HBYWFti7dy/s7OwQGBiosVFuzJgx+OmnnzTuh/rqGk4yMjLg7++vMqaakZERFAqF0LjJk60vJCQE//rXv1TOy03RZV6f6qLrvD7VhSd/584dxMbGYuvWrSguLsabb76JyspKJCQkCNchdebPnw9PT0988803iI+PR0BAAIYPH47NmzcDqP2yavXq1cKxm5mZqfEaos4rr7yCNWvWCL+/99572LVrF/Ly8jBp0iSMHz9e45cPdUaNGqW2LDs7GwsXLsShQ4cQGBiIiIgI7iwA2NraIj8/H46OjhpfOy8vT6VuBQUFKtdnfn5+kEgkuH37dqNlAMBnn33W6HN1Gk521ZCPjw9WrVolNPjx5v/KtLmuSk1NRUpKCuLi4hAeHo6BAwciJCSk2bEGm/O8GvB07bfffhO+CLty5cofltW3vD7VRdd53rJfeuklXLt2jRoIid6gSUrIM7t37x7GjRuHpKQkYRD6x48fY8CAAdi9ezdatWrV7Oxu9TWc6c3ExATnz59X6/139epVeHt7o7y8HHl5eejSpYswAx9Q+43+qVOncOLECSQlJSE9PR2dO3fGuXPnVMpp0aIFTp06hc6dO+Pll1/GxIkT8c477yA/Px+dO3dWKRMAUlJSsGnTJuTk5OC7776Dg4MD4uPj4erqqvEbULF5Z2dnzJ49G7Nnz1ZZPyoqClFRUSgoKMD58+fxyiuvqHyTzlufp0+fIikpCTk5OXj77bdhYWGB27dvw9LSUm1Q8sePH+O7775DTk4O5s2bhxYtWiA9PR2tW7eGg4ODWtk8+VmzZqnM1FenrKwMAQEBGns48Wwr734Rg3ciDqC24WH48OGYOnUq5s6diwMHDiA4OBj79u2DjY0Njh07ppL/6aefsHbtWmzatEljg1JDeXl5mDFjBpKSkvDkyRNheV0PtIYNMrrOi8WzL5/XBCs7duzA5cuXERMTI0xSUVlZiZCQELRr106tETwiIkJ0Xmw2Li5O6HWxbt06lV4udQ1VTQ2kXlVVhby8PLi7uzc5gYFMJkNhYaHarNMPHjyAnZ2dyt+1ffv2WL58OYYPHw5XV1fs3r0bAwcORGZmJgYNGqTW0D1jxgwkJiaiXbt2OHfuHPLz82Fubo7du3fj008/RXp6utr+z8jIgLu7O2xsbHDq1Cl06dIFmZmZGDVqFPLz81Xyq1atQmRkJIYPHw5PT0+1L5kaTiARFxeHsWPHwtjYuNH9AdROVlNSUoKJEyeKyo4cORLGxsYICAhARUWFxro07ClQU1Ojs7wuy9a3vD7VRWx+xIgROHnyJIYPHy40fstkMhgaGiIzM1OtgdDW1hY///wzvLy8UFpaCktLS5w5cwbdu3cHAFy+fBm9evXC48ePAQDGxsb47bffGv0n89q1a/D09FT5Aq6yshL79u3Dtm3b8O9//xvDhw9HSEgIXnnllSYbg27fvo2lS5ciLi4O/v7+WLVqFbp27ap1dvLkycjJycHRo0fVGo0qKyvh7+8PNzc3bNu2DUDt+evOnTto1aqVkLOwsMD58+c1TtSUlpaGBw8eICAgQFi2fft2LF26FGVlZRg9ejS++OILtcmJNLly5Qp69eqFhw8fNpvVJl+fvk0u9qz59PR0xMTECHdVvP322wgJCdHqTg992lZd14X8ve3fvx8ff/wx5s2bp/Hzo6nJpQjRBepBSJ7ZzJkzUVJSgosXL6rMPBcUFIRZs2Zh165dao1+PJycnLB161asXr1aZfnWrVuFWxUePHggzG73z3/+E0lJSTh37hw6deqEfv36YeHChejbt6/GGfBefvllzJkzB76+vjh9+jS+/fZbALUXdQ2/id67dy8mTJiAwMBAnDt3DpWVlQBqe9isXLkShw8f1jq/ePFivPfeezhx4oRwK/WZM2dw+PBhbNy4EQBw9OhRlV5AvPW5fv06hgwZgoKCAlRWVmLw4MGwsLDAJ598gsrKSuF1AOD8+fPw8/ODlZUV8vPzMXXqVLRo0QL79u1DQUEBtm/frlI2b/7777+HjY0NwsPDhWVlZWUaewzxbivvfhGrT58+jc7O3ZjIyEiUlpYCAMLDw1FaWopvv/0W7dq10ziD8dixY1FeXg53d3eYmpqqXSg0/Cdj/PjxYIxh27ZtaN26dbPf8Os6/1d27tw5HD9+HI6OjsI/LJmZmaiqqsKgQYNUekLv27ePK88Y4yrb1dUVvr6+zc5SWjdjs5GREWbOnCnMkHrlyhW4ublh5syZcHBwUOvZ1NgtzKWlpWqNY6GhoQgMDIS5uTlcXFzQv39/ALW3SHp6eqqVERUVBYVCgRs3buDTTz8VvngoLCzE9OnT1fK8vWy//vprmJubIzk5WW2G17rbqetrrodPnXfffRcZGRnNNg7WZV966SXs3LkTP/74Izp06CC8fv26NLRq1Sqd5XVZtr7l9akuYvNHjhzBrFmz8N5774ma3fLhw4do06YNAMDc3BxmZmYq1zA2NjYoKSkRfndwcGiygfD8+fOwt7dXWSaXy/HWW2/hrbfewvXr1xEbG4vp06fj6dOnuHjxotqXhnWfoV988QV8fHxw/Phx4ZblhniyERER6NGjB9q1a4f3338fHTt2BGMMWVlZWL9+PSorKxEfHy/kGWMIDg5WadB78uQJpk2bpnI7X90dKREREejfv7/QQHjhwgWEhIQgODgYnTp1wmeffYa2bdsiLCxMY/3qq6ys5Or5xpuvj/fzVtv8iRMnMGDAgGbzR44c0fjlsFgvvPAC2rRpg5YtW2L16tXYtm0b1q9fj969e2Pjxo1N3mauz57l7zR58mR8/vnnwvA7dcrKyjBz5kyhUZw3q295faqLvm1r3d0JkydPFpZJJJJn/iKeEK09v+EOyd+VpaUlO336tNry//znP8zKyuqZyz9w4AAzMjJiXl5eLCQkhIWEhDBvb28ml8vZoUOHGGOMrV+/XhjMXiKRMDs7O7Zq1SpRk6Fcv36dDR8+nHl5ebEtW7YIy0NDQ9nMmTNVsj4+PiwuLo4xpjrIcHp6OmvdurVa2bz5U6dOsXHjxgkTe4wbN04Y7F8T3vJHjRrFxo8fzyorK1XyJ06cYB4eHirZQYMGsXnz5qmVnZqaqjJYuLb5a9euMXt7exYVFcUYqx2ovXfv3qxPnz6stLT0mbaVd780NyFEcxOqNDW5Cq/Y2NgmHw2ZmZmxy5cviy5f13mxhg4dym7fvv2HZ7XJd+nShRUUFLDg4GDRD8YYV563bLHqBlKfNWsW6969O0tJSWFmZmbCMZ+QkKAyO2fdxB9SqZS9++67KpOBzJo1i7300kvsH//4h9rr/Prrr2zfvn2spKREWJaYmMhOnTrFVd/6hg0bxm7fvs1GjRolTL704YcfMg8PD7Z8+XL2wgsvsEGDBmldPi9tJs6xtrZmMTExol9Dl3l9qouu8/pUF7H5tLQ0NmXKFGZhYcF69uzJvvjiC3b//n1mYGDALl68qJZvOGGOubk5y83NFX6/c+eOyqQmM2bMYF27dmUVFRVqZZWXl7OuXbuqXdPUV1BQwMLDw5mrqytzcHBQea8zxtgnn3zCWrRowTp37tzshFk82Tq5ublsyJAhKjN5SqVS5u/vz65evaqS5T2XtmnThp05c0b4/Z///Cfz9fUVft+zZw/r1KmTqHp+8MEHzN/fX1RWm3x9z2vSESMjI+bm5saWLVvGCgoKtKmqRnWfT1VVVexf//oXGzp0KDMwMGC9evVimzdvZqWlpSwvL48FBgaK3v8N685bF13kn+Xv1Nj15/3791Um8uHN6lten+qi6zxv2fn5+U0+CHneqAcheWZKpVLj5ASGhoZq45UB/x0MviGJRAJjY2N4eHggODhY+DZz5MiRyM7OxqZNm5CdnQ0AGDp0KBISEoRbMOtP9HDu3DkkJycjKSkJa9euhZGRkTBRSf/+/dG+fXuV13V2dtY4uURUVJTasuzsbPTt21dtuZWVlXCbz7PkfX194evrq7a8Mbzlp6Sk4N///rfat9kKhQK3bt1SWXbmzBls2rRJrQwHBweVW5y1zbu7u+OHH37AgAEDIJVKsWvXLsjlcnz//fdqA/rybivvfmGNjLRQ981/Y4OQ82pqDL86Yns71XnxxRdx48YNoefKn52vc+/ePdy7d09te+tulajfi5Mnq02+OXXjJ/L2dH6WntF/lLpjNyEhAd9++y169eqlcn7t0qULcnJyhN/rhlhgjOHChQsq5wIjIyN4e3tj7ty5aq/TvXt34dbGOsOHD1f5nXfQ+JMnT6KiooK7l219ddv/Z/VslcvlXOdsXeb1qS66zutTXcTme/XqhV69emHdunX49ttvsW3bNsyZMwdKpRJHjx6Fk5OTWo+T+r3kGvaQq+sZX+fjjz/Gvn370L59e8yYMUM4Z1++fBlfffUVampqhMmG6tS/xfjUqVMICAjAl19+iSFDhqiNVbxw4UKYmJjAw8MDcXFxQm/lhvbt28eVrePq6oojR47g0aNHuHr1KgDAw8ND47iIy5YtQ9u2bUVPCvPo0SOV25GTk5NVJoyq+5wDgDlz5mgso6ioCOnp6bhy5QpOnjwpLOfNA/w99nSdv3XrFuLj43UyTmBYWBgOHz4MxhgmTJiATz/9VOUWczMzM6xZswZt27blKpv3nN/YdV5T+bS0NFGfZ9rsdwsLCxQVFYExhpKSEpWe6zU1NTh8+LAwBEhxcTEYY6Ky+pbXp7ro27bWcXFxUVtGyJ+JGgjJMxs4cCA++OAD7Nq1S/iAv3XrFmbPno1Bgwap5YcMGYINGzbA09NT5Vba8+fPIzg4GJcuXYKfnx/27dsnDHStUCiwatUqUfXx9vaGt7e3cJtZZmYmoqKi8P7770OpVKKmpgbFxcVCw0xxcXGT5dVvwGnTpg2uXbumNjbcqVOnNF5E8ObrPHnyRLjlTlM9tC2/bvsbunnzpto/JnK5XOO+uXLlisqFtrZ5oLZBJzExEYMHD8ZLL72ExMTERm/h5dlWsdm6MRAlEgm2bNmicjtVTU0NTp48iY4dO3I32tWnzRh+NTU1SEhIUJn0Z+TIkZDJZGrZLVu2YNq0abh16xa6du3a7Nglus6fPXsWQUFByMrKUmm80bS9PFmx+bpJPsRobEyoe/fuCV9GdOjQQeMFnbZ53rLFun//vsayysrKVPZH3diekyZNwueff67xvKIt3n/A6tR/T5qZmakMddCY7du347PPPhMaEtq3b4958+ZhwoQJWtVBWx988AG++OILjeOpPu+8PtVF13l9qgtv3szMDJMnT8bkyZORnZ0tDKGycOFCDB48GAcPHgSg/mXR+PHj1cqaOHGi8HPr1q3x73//G++99x4WLVqkco709/fHV199hdatWwv56dOnY/fu3XBycsLkyZOxa9cu2NraNlrviRMnij638mQbsrGxEa4NG9O5c2euLyOqqqqQlpYGFxcXVFVVIT09XWV4k5KSEuGzreE41XUsLS0xePBgYQiIOrx5oPY62NHRUfTMvrrOy+VyYQzsunECp0+fjunTp2scJ5CnIWzx4sX44osv8NprrzU6xqOtra3GcaebUnd867LxdNCgQVwzMPPsd6lUqjIhWUMSiUQ4Rq2trUVn9S2vT3XRt22tLycnB+vWrROu+zt37owPPvgA7u7uGvOE6BJNUkKe2Y0bNzBy5EhcvHhR+DC8ceMGunbtioMHD6qN4zd16lQ4Oztj8eLFKsuXL1+O69evY/PmzVi6dCm+//57/Prrr8Lz5eXlKCgoUGs4a9hAwRjDuXPnkJSUhKSkJJw6dQrFxcXw8vJCv379EBUVpTJAf92HdEOaGilWrVqFHTt2YNu2bRg8eDAOHz6M69evY/bs2Vi8eDFmzpypUgZPvry8HPPnz8eePXvw4MEDtfpoakjirc/YsWNhZWWFr7/+WhjQu1WrVhg1ahScnZ1VekRNmTIFDx48wJ49e9CiRQucP38eMpkMo0ePRt++fdV61YnJN9Z79Pr167Czs1NpHGw4kQHPtorN1l20X79+HY6OjioNcHUTQkREROCll15S+1vs379f5YN81KhRGseL8/X1BWMMH3zwgcYx/OqPKQnUDiQ/bNgw3Lp1S+gBkp2dDScnJ3z//fdqFwu//PIL3n77bZWJHJpqZNN13tvbG+7u7liwYIHG7a3/TSlPVmy+fk+VBw8eYPny5fD39xcm9khLS8OPP/6IxYsXq00IVFxcjPfffx+7d+8Wtksmk2Hs2LH46quvVCYM4c3zli1W3WDnwcHBGDNmDGbOnKkyWP/MmTNx9epV/PDDD1qVr01dnmWgdjG9bCMjI7F48WLMmDFD6LV16tQpfPXVV1i+fLna31UX9a/Lfvjhh/j555/RsmVLdOnSRa0BvX4PKQB49dVXdZbXZdn6ltenumiTb6impgaHDh3Ctm3bhAbCmzdvcvWSq59/9OgRrl27BsYY2rVrp3H8ZalUCmdn50Y/l8XWXUx9/sgswH+uMTQ0hKenJz7//HMkJCQgLi4Ot2/fFnrHffPNN1i3bh3OnDkjqrxnqfvNmzdhZGSEb775BnFxcbh48WKzPfZ+//13oYefLvIN3b59G19//TVWr14NAwMDPHnyRGWcQLlcLqohrLq6Gu+++y4WL16scfIYTcQ24J06dQovvvgiLC0tRdWljti6A7rd78nJyWCMYeDAgdi7d69KT1kjIyO4uLgInS54svqW16e66Nu21vnxxx8xcuRI+Pj4CNc0qampyMzMxKFDhzB48GC1dQjRJWogJH8IxhiOHTuGy5cvAwA6deoEPz8/jVkrKyucPXtWbSDta9euoXv37igqKsLly5fx4osvoqSkBPfv38ekSZNw5MgRjeU1bKCwsbFBaWkpvL29hVuL+/TpI8ywDNSexOsmAGg4yH1D9RtwGGNYuXIlVq1aJcxuLJfLMXfuXCxbtkzjfhGbf//993HixAksW7YMEyZMwFdffYVbt25h06ZNWL16NQIDA5+pfKC24XbIkCFgjOHq1avo0aMHrl69CltbW5w8eVKlB1JRURHeeOMN/PrrrygpKUHbtm1x584d9O7dG4cPH1a7DVhMvrFvzjRpOGMsz7by7pcBAwZg//79KsdIYy5evIiRI0fizp07QgNeXS/JQ4cOqc3OaG5ujrNnz4q+RXfYsGFgjOGbb74RLi4ePHiA8ePHQyqV4vvvv1fJd+7cGZ06dcL8+fNFNbLpOm9hYYFz5841OlC+tllt8q+//joGDBiAGTNmqCz/8ssvcezYMSQkJKgsHzt2LM6dO4cvvvhCpUHxgw8+gI+PD3bv3q11nrdsser+Ub59+zaGDh2K8ePHIzY2Fu+++y4uXbqEf//730hOTla7PRgAfv31V+zZs0fjFy/aNApo20AokUi4etm6uroiPDxcpfcUUDtbcVhYGPLy8rjrzlv/uqym80l9DW9DnzRpks7yuixb3/L6VBdt8mLw3rLPmzc0NMSoUaPU7h5oSNuhFHjqw1t33nONubk5OnfujF9//RXm5uaIi4vDq6++Kjw/aNAg9OrVCytWrBBV3rPUvWGed2ZfXeWrq6tx4MABbNu2DUePHkWPHj0QEhKCt956C/fv38fHH3+M9PR0XLp0iashzMrKChkZGaIbCHka8IDn13iqq/1+/fp1ODk5iWpg5snqW16f6qLrPG/Z3bp1g7+/v9pknAsXLsRPP/2k1mGCEF2jBkLy3LVu3RqfffaZ2j9327dvx7x583D37l1cunQJ/fr1w/379xEYGIjr169j3bp16N+/P/bv34+7d+9i+fLlWLt2rdo4WN9//z369OnT7K1zvN/61tTUIDU1FV5eXjA1NcW1a9dQWlqKzp07q830p03e2dkZ27dvR//+/WFpaYn09HR4eHggPj4eu3btUhtXjbf8Ok+fPsW3336LzMxMlJaW4oUXXkBgYGCjt/aeOnUK58+fF7KNNfxqmxeDZ1t590t1dTU6duyIxMREYRbupvTu3RutWrVCXFyc0Cvj0aNHCA4Oxv379/Hvf/9bJT9gwAB89NFHoveDmZkZfvnlF7XZYTMzM+Hr6yuM1VY/n5mZKbrRTNf50aNHY8KECcKsbH9UVpu8ubk5MjIyNH4Z4ePjo3Ff/vjjj8LtQnVSUlIwZMgQlJWVaZ3nLVus+v8o5+TkYPXq1Srv7QULFmicaXj37t2YOHEi/P398dNPP+GVV17BlStXcPfuXbz66qtaNQpo20A4YcIErl62xsbGGmdrvXr1Kjw9PVUaGXl07doVR44cafYfUt4sIdr4I3rk/pF5Xfbye17b2rJlS5ibm6sN1/Hw4UOYm5sLDUQ82/pH1L25HnsN/dH5mTNnYteuXcI4gVOmTFH7svPOnTto27atWg/v5hrCgoKC4OPjI7pn97P0ftR1Y6uu/k6PHz/G6dOnNY6t3PB/JZ6svuX1qS76tK3Gxsa4cOGC2uz2V65cgZeXl9bXNIRoTdvZTQipLykpiQUEBDB3d3fm7u7ORowYwU6ePKkxu2zZMmZiYsJmzZrF4uPjWXx8PJs1axYzNTVly5cvZ4wxFhkZyfz8/BhjtbPP/ec//2GM1c4qVjcz8YEDB1RmoeORmZnJTE1N2ffff88yMzObfNQnl8tVZhFsDk/ezMyMXb9+nTHGmIODg7DNubm5zMzM7JnLr6qqYm5ubuzSpUui8s/Lr7/+KhwH6enpjeZ4tpX379S2bVvR+8XY2Jj99ttvassvXLjAjI2N1ZZfu3aN+fn5sdjYWPbrr782eXwxxpiNjY3GmatPnTqlcZbkgIAA9t1334mq+/PI379/nw0bNoyFhYWx7777jh04cEDloW1Wm7yzszNbs2aN2vI1a9YwZ2dnteVOTk7s/PnzasszMzOZg4PDM+V5yxaLd8bmOp6enuzLL79kjP13RkWlUsmmTp3KlixZolVdeGeJrHtd3pmyu3TpwlasWKG2fNmyZaxr165qy11dXdnvv/+utvzRo0fM1dVV62x9d+/eZSdPnmQnT57UOHvh88zrU110ndenumiTb8rzmsFWLG3f37qoiz5tq7Z14Z3ZV5f5gQMHsp07d7InT540Wu/q6mqWlJSk8blbt26xpUuXMrlczszMzJhMJmMvv/wy++2339iyZcuYtbU1e/3119nKlSvZ559/rvJoytmzZ9mMGTNYy5YtWcuWLdnMmTNZRkZGk+s0VRdt8rr+Ox08eJBZWFgwiUTCrKysmLW1tfBoeM3Hk9W3vD7VRd+21dHRke3Zs0dt+bfffsucnJzUlhOia9RASJ5ZfHw8MzAwYG+++abwgT9mzBhmaGjIvvnmG43r7Nixg/Xq1YvZ2NgwGxsb1qtXL5VseXk5q6ioYIzVXqjl5eUxxmr/4T916hRjrLbhzMTERKs6SyQSBoBJJJImH1KpVGW97t27s2PHjol+HZ68p6encPE1aNAg9uGHHzLGGPv8888bbUDgrQ9PQxhjjB07dowNHz6cubm5MTc3NzZ8+HB29OjRPyR/9+5dNmDAACaRSITjQCKRsIEDB7J79+6p5Xm2lXe/rFixggUFBbHq6upms15eXuz48eNqy48fP66xcSItLY25urqqHVeaji/GGJswYQLr0qUL++WXX5hSqWRKpZKlpaX9H3vnHRfF1f3/zy69LEVBo0hVQEEUFCvGHns3FrBgj53YsQP2AmKLJUaKiT3W2Asi9g52EEGwxS5BUSnn9we/nS/D7MLehSH7PM9+Xq95KXffc/fcu+3OmXPPoZo1a5K/v7+A37BhA9na2tLcuXNVcpqJzR84cIDMzc1V+jyxsOrwERERpKOjQ506daJ58+bRvHnzqFOnTqSrq0sREREKx9q6dWt68eIF1/bixQtq06YNrV+/vkQ8a9/Xr1/nORT37dtHXbt2penTp9PXr18FPBFRTk4O7dq1i0JCQigkJIR2796t9D1tbGzMfa+WK1eOe6579+7Rd999p/Cc4sR6obxw4UJ6//49NW/evMjvlcLavXs36ejoUNu2bbmxtm3blnR1dWnPnj0CXiKRKHTWvHz5kvT19dVmiYg+fvxI/fv3J11dXe69qKurS/369aMPHz6UKa9JtmjHWjSvijTNaSYmL7azUtOcmwMGDKDy5ctTuXLlKCAggG7fvi3gXrx4QRKJhIiIc5KJwX/79o0GDx7MdFOVSHVHmIODg9KjqJsucqni8BPLiSfmvMvl7OxMAQEB9OnTp2LngoXVNF6TbBGbZ+07ODiYLCwsaPHixdwNpkWLFpGFhQWFhISo1IdWWpWmtA5CrUqs6tWrU1hYmKA9NDSUqlevXuL+vb296ejRo0RE1LlzZxowYAA9ffqUpk6dSk5OTmr1mZqaSsbGxnT27FlKTU0t8iioI0eOkKenJx08eJCeP39OHz9+5B2FxcKHhYVxd1NPnDhBhoaGZGBgQFKplMLDwxWOg9UeFkfY2rVrSVdXl/r27cs5fn19fUlPT4+LPCoJ37t3b/L29uY5LO/evUve3t7Ut2/fEo2VdV66detGMpmMKlWqRG3atKHu3bvzjoI6dOgQubu7065duyg9PZ3S09Np165d5OHhQYcOHRI8T40aNahHjx506dIlSklJKfL9RZQfqdSlSxeSSCSkr69P+vr6JJVKqVu3bgovNFkc3GXB29vb05gxY+jly5eCx0rCqsMTEV26dIn8/PzIy8uLvLy8yM/Pjy5duqSQ9fT0JFNTU9LT0+OiofX09MjU1JQ7X36w8qx9e3t7c5GbycnJZGhoSL6+vlStWjUKCAgQ2H7nzh1ycnIiY2Njrh8TExNycHBQeJFiY2PDOQU9PDxo69atRER04cIFMjMzUzg/X79+pQcPHij8/ti/fz8tWrRIoRO5OKcya5QtUX7kcb9+/ahOnTpUp04d6tevnyACWf58EomEoqOjeTbs2bOHxowZQy4uLsxsQfXu3ZucnZ3p6NGj3Of+6NGj5OrqSn369ClTXpNs0Y61aF4VaZIDT2xek2xh5dXpu1GjRkwRe6wRfqy8mZkZk4NQHUcYi1gcfmI68cSed6L8m3Wqvn9YWE3jNckWsXnWvvPy8igsLIxsbGy49bWNjQ2Fh4dTXl6eyv1opVVpSesg1KrE0tfXp6SkJEF7UlISGRgYlLj/LVu2cNE+165dIysrK5JKpWRoaEjbt29Xu1/WRV2HDh0EzhH5oYpzRRW+oFJTU+nPP/9UeoGsTv8sjjAbGxtavXq1oI81a9ZQ5cqVBe2svJmZGV25ckXQfvnyZTI3Ny/RWFnnZdCgQUUexfWt6G/58xgbGyv8fBSnxMREOnDgAB04cECt8/8tmZqa0qNHj0qdVYdnVVBQkMoHK8/at5mZGTfWxYsXU5s2bYgof6t5lSpVBLY3bNiQOnfuTO/evePa3r17R126dKFGjRoJeF9fXwoNDSUiopCQELK2tqZhw4aRvb294Lvg06dPNGTIENLR0SEdHR3ue3Ps2LG0aNEiIhI6kgt+Lgp/DguLNcpWVSmzRe58d3FxoYMHDzKzBWVsbExxcXGC9rNnz5KxsXGZ8ppki9i8JtmiDq+KxI6SEzOqjrV/Obt582aVom7S0tIoJyeHmVdVLGNVZx5//PFHpvQwLBF+6kQEDhw4UOGNfmVicYQFBwcrfI0+f/5MwcHBgnZWh59YTryTJ0+KPu9ERN27d6cdO3aUOqtpvCbZIjbP2ndBZWRkUEZGhlrnaqVVaUn3386BqNV/vmxtbXHq1ClBwviTJ09yCdzLlSuHxMREWFlZwdLSUpCEvqDevXvH+7t///7c/+vWrYsnT57gwYMHsLOzg5WVVSmOpGidPXsWf/zxh8IS9coUExOj9vPZ29sLKsQCgIeHBw4fPgxbW1vm/i0sLFQu8PDhwwe0a9dO0N6mTRtMmzatxHxeXh709PQE7Xp6eoKEvgDbXLLOC0tBBta+W7ZsyVTkQy5nZ2dBwuL/BPXo0QMxMTGoWrVqqbLq8ACQnJyMiIgIPH78GOHh4ahQoQKOHDkCOzs7QVLxwpWzixMrzyIi4j4HJ0+eRKdOnQDkf9++efNGwN+6dQvXrl3jCucA+RXdFyxYgHr16gn4NWvWcImvZ86cCT09PVy4cAE9e/bErFmzeOz06dMRHx+PM2fO8D7jrVu3RlBQEAIDA3mf2ZMnT2LatGlYuHAhr2LzrFmzsHDhQoEtQ4YMgZeXF7Zt26awSAkAZGRkcIWnMjIylMxavuSc3CZHR0dcvXq1yN8LFragypcvD3Nzc0G7ubk577UoC16TbBGb1yRb1OFVETHWEBSbZxVL/3I2MDAQAQEB6NWrF4YOHYrGjRsr5OVrS1ZeDKkzj0ePHsXSpUtVYvX09PDnn39i9uzZovBA/nojJCQE58+fR926dWFiYsJ7fPz48dz/s7OzYW9vj4YNG8LAwEBpn7q6umjWrBlatmyJkSNHwtjYmPf458+fERwcjDlz5vDa7927h9WrV6NHjx5K+7eyskJMTAyzLSx8q1at0KNHD1HnHQA6duyIKVOm4N69e/Dw8BCsi7t06aIWq2m8JtmiaWMtqOKqymulVZnoX3ROavVfol9++YX09fVp5MiRFB0dTdHR0fTTTz+RgYEBl1srMjKSu1sXGRlZ5KGuNO3uuVgqKzt8fX1p6dKlgvZly5Yp3DLFynfp0oWaNm1Kz54949qePn1KzZo1o27dupXQes0Raw6/nJwc2rRpE/n6+lKrVq2oRYsWvEORWIoEic3Pnz+frKysyN/fn5YvX15kQnIWVh3+zJkzZGRkRK1btyZ9fX3uc7No0SLq2bOn0vFqglq0aEEDBw6k6Oho0tPT46JIz5w5Q/b29gKeNTcmi+zs7OjixYtExP/+SUpKIplMJuDd3d2VRlMpSjuhSpStVCrlcgMWjkhkicwubYmZt5KV1yRbtGNVzosdJSdWVJ06UX6bNm1isiU7O5v27NlDXbp0IT09PXJ1daXFixfz5rWgWHlVJZPJaMmSJaLN+4ABA5gi9lgj/Fh51jyBLFuSJRKJwrzSp06dIisrK16bOlF4rNujWXix552ILY2LpqWU+V+xXYyxenl5cTs+PD09BalmCqed0UqrspSESORbiFr9T2jv3r0IDQ3F/fv3AQA1atTAlClT0LVr1zKzQSaTIT4+Hk5OTqLylpaW+O2337ixurm5YfDgwShXrpzC896/f8/Es9qtTv+vXr3Cw4cPAQCurq6oUKECAGDVqlUck5GRgeXLl8PHx4eLArp06RLOnz+PSZMmYdasWcx8QaWnp6NLly64e/cud3c/PT0dNWvWxIEDB1ClShWB3SxjZZ2X3bt3Y+fOnUhLS8O3b994j924cYP395cvX5CQkIBXr14Joh0L3xmUSqUKnw8AJBIJcnNzeW1jx45FZGQkOnbsiEqVKgmiqVasWMH7+/fff8fgwYPRo0cP+Pj4AADOnz+PvXv3IjIyEn5+fmXKOzo6Fjnex48fq8Wqwzdq1Ai9evXCxIkTeZ+bK1euoEePHnj69CmPz83NxYoVK5S+DwpHN7PwrH3Hx8ejX79+SE9Px8SJE7loxXHjxuHt27fYunUrL5Lu3LlzmDp1KoKCgtCwYUMA+Z+/kJAQLF68GB06dOD1f/jwYejo6KBt27a89uPHjyM3Nxft27fn2oyNjXHnzh04OTnx5jE+Ph5NmzbFx48feX0YGRnh6tWrqFmzJq89ISEBDRo0QFZWFq+9c+fOGDRoUJHRzbGxsfDx8YGuri5iY2OVcgDQrFkzQdupU6dw6tQphZ/ZzZs3q816eXnh0aNH+Pr1K+zs7AAAaWlpMDAwEEQA37hxQ1SeiDTGFu1YlfPPnj1DVlZWsVFvclWsWFFUXlXJP/uNGjVSuf+S2PL333/j999/R1RUFB48eIB27dph6NCh6Ny5s8LfVVa+uLEaGBjg27dvosz7/PnzERoailatWhUbsVcWPKv8/f3h6emJCRMmKGXku4U+fvwIMzMz3lomNzcXmZmZGDlyJNauXcs7z9zcHLdu3Sry957VFnV5TZt3rf57FBwcjClTpsDY2BhBQUFF7qwTc7eKVlopktZBqNW/ory8PDx69EjhBVjTpk3V6lOZwy8jIwOnT5+Gq6sratSowbWnp6ejcuXK0NHRUbn/X3/9FT/99BPMzc3h7e0NALh+/To+fPiAgwcPCmw/e/YsOnfurDLPOk7W/jMyMjBmzBhs376dc0zp6OigT58+WLt2LTw9PVWyQe6MUXUBp8h5A+Rf5J08eRIPHjwAkO9Ybt26tcI+WMbKOi+rVq3CzJkzMWjQIGzcuBGDBw9GcnIyrl69ijFjxmDBggUce/ToUQwcOFDhNk9FDj9WWVlZITo6WuDQUaYaNWpgxIgRgsVuWFgYfv31V85BWla8JsnU1BS3b9+Go6Mj73OTmpqK6tWrc1ts5ZozZw42bdrEObRnzpyJ1NRU7Nu3D3PmzBEs7ll41r6V6cuXL9DR0YGenh6kUilvUSn/OZe3Ffy78PuyVq1aCh2HR48exbRp0xAfH8+1NW3aFL169cK4ceMgk8mQkJAAR0dHjBs3DklJSTh69Civj6ZNm8LQ0BBbtmxBxYoVAeRfvA8cOBBfvnwROPg2btyI+fPnY8iQISptx0lLS4Otra1gQU1ESE9P5xw0cgUHByMkJATe3t4Kne579+5Vi5Xzqmru3Lmi8iwS2xbtWJVr5syZOHjwICIjI3HkyBE4OTlh8ODB8Pf3x3fffSfgc3JyROUjIiLQp08fwRbQwpKvmYhI5f5ZbSmsy5cvY/PmzYiKikKlSpXw/v17WFpaIiIiAs2bN2fmWcZaoUIFHD58WJR5F/vmGCsfEhKCyZMnC+YlKysLy5YtE2wDVsURFhUVBSLCkCFDEB4eztuCr6+vDwcHB+5mckGxOvzEdOKJPe9aaaWVVpoorYNQq1LTtWvXeNFadevWVchdunQJfn5+ePLkiSB3S0kcLHIHQGBgIJo2bYqxY8ciKysLtWvXRmpqKogI27dvVzkHn6L+K1eujGbNmmHdunWcYzE3NxejR4/GhQsXcPv2bd45Hh4eaNSokco8yzidnJyY++/Tpw9u3ryJ1atX83KDBQQEwNPTE9u3b2e2p6zEMlbWealevTrmzp0LX19f3vzOmTMH7969w5o1azjW2dkZbdq0wZw5czjnh6r68uULDA0Ni2QqV66MM2fOwMXFRaU+DQwMcPfuXUGOw0ePHqFmzZoCJ5jYfEEVdlgVJRZWVb5KlSrYuXMnGjduzHtd9+7di8mTJyM5OZnHV61aFatWrULHjh0hk8lw69Ytru3SpUvYunWr2jxr3/7+/hg6dGiRNxGKi6QrqMJRdUZGRrh//z4cHBx47ampqXB3d8enT5+4tnPnzqF9+/bo378/IiMj8dNPP+HevXu4cOECYmNjBd/1jx49Qvfu3ZGYmMiLDnZ2dsa+ffsE7yXWKFsdHR28ePGCi3yW6+3bt6hQoYKAr1SpEpYuXYoBAwYofR51WBZt27YNXbp0EVyM/hu8JtkiNq9JtijixY6SU4Uvqyg/Vdm///4bW7Zs4XLHduvWDUOHDkXr1q3x6dMnhISEYPv27Xjy5Akzr+5Y/81oxrIQ63cqiyMsNjYWjRs3VphzWpFYHX7/6U68kJCQIh8v6JxlYTWN1yRbxOZZ+3ZycsLVq1dRvnx5XvuHDx9Qp04drWNZq7JXGW9p1uq/UOnp6dSkSROSSCRkaWlJlpaWJJFIyMfHh9LT0wV87dq1qVevXnTv3j16//49ffjwgXeoK3lurIoVK9KtW7eIiOiPP/6gatWq0adPn+iXX34hT0/PEvVvYGBADx48EDz24MEDMjQ0FLQbGhoy8araIc8Bxtq/GJUWSyKW3HYsY2WdFyMjI0pNTSUiImtra+79k5iYSOXKleOxMpmMqZJuTk4OhYSEUOXKlXkVYGfNmkWbNm0S8MuXL6fRo0dTXl6eSv1XrVpVYU6sdevWUbVq1cqcJyKKioqimjVrkoGBARkYGJCHhwdFR0eXmGXlJ02aRE2aNKEXL16QTCajpKQkOnfuHDk5OXHVggvK2NiYnjx5QkRE3333HV2/fp2IiJKTk8nMzKxEPGvfXbt2JT09PapWrRotWLCAnj59qnRO1FHFihUV5iw8ceIEWVtbC9ofPXpEw4YNo3r16lGNGjWoX79+lJCQoLT/vLw8OnbsGJcf8vjx4yq/p4uTspxWqampCr/HypUrp/JnloVlkSZVpNUkW8TmNckWZfylS5doxIgRZGBgQA4ODmRubk4ODg4UExOjsI/S5kuax4/FnuLYTp06kZ6eHrm7u9OKFSvo7du3gj7+/vtvroItK1+SsZbmvLNW9hWbZ8kTqI5yc3Pp4cOHFBcXR7GxsbyjsFjzIYopseedKD//XMHD3d2djI2NyczMTJB/joXVNF6TbNG0sUokEi7HckG9fPmS9PT0BO1aaSW2tA5CrUqstm3bUoMGDXgOmQcPHlCjRo2obdu2Al6VhPTqSL7wNjQ0pLS0NCIiGjBgAE2bNo2IiJ48eUImJiZExE8Oq+wHvbAWLlxI9evXp7179woe27t3LzVo0EDQ3rhxYyZeFRV0ELL2b2trq/CiPj4+nmxsbHhteXl5tHPnTho1ahT17NmTunfvzjsKi5XfsmUL6erqUu/evTknQq9evUhPT4/++OMPAc8yVtZ5cXR0pBs3bhARUd26dTmH2LFjx8jS0pLHDh48WKFjT5mCg4PJycmJfv/9dzIyMuJeu+3bt1PDhg2JiARzZW5uTo6OjtSpU6di51GVIkFlyYeGhpKxsTFNnTqVK8QyZcoUMjY2FiTvZmHV4b9+/UrDhg0jXV1dkkgkpKenR1KplPr3768wOb+LiwtdunSJiIh8fHxo0aJF3GulyGnGwrP2TUT06tUrCg0NpVq1apGuri61a9eOdu7cSd++fVPIZ2Vl0eXLl+ngwYPFFsMZMWIEeXh48JxhSUlJVKtWLRo6dKjC/tVRVlZWqTkGy5cvT8OGDSOpVEo//fQTTZgwgTvGjx9PDRo0oMaNGwvOmzp1KoWEhKj0HCwsi1iLS4nJa5ItYvOaZEtB/uXLl7Rs2TJyc3MjQ0ND6tu3L504cYKIiDIzM2nq1KlkZ2fHnSc2X/C85cuXk4eHB+np6VHnzp1p3759lJubK+BU7Z+FHTJkCF24cKHIOczLy+Nu6LHyrGMVa94LFl0qqDdv3igskCAWb2FhQZaWliSVSrn/yw8zMzOSSqU0evRoQT8sjrCLFy+So6MjV0iquGIQrBLTiSf266RMHz9+pO7duxd5s1QdVtN4TbJFbF4RK1+fSSQSio6O5q3Z9uzZQ2PGjCEXFxeVbNFKq9KU1kGoVYllaGjIOVcK6tq1a2RkZCRob9GiBR05cqTU7ZAvvJ2dnWnHjh2UmZlJ1tbWXJTMrVu3qHz58pzN8uhGZT/oirR9+3ays7OjZcuWUVxcHMXFxdGyZcvIwcGBtm/fTvHx8dzBykdFRXGVngvq69evFBUVxf39xx9/UGZmplr2sFRaHD9+PBkYGFC7du3I39+fBg0axDsKi5WvXr26UieQokqnLGNlnZehQ4dyEWVr1qzhKt9aWFjQkCFDeHZ8+vSJOnTooHIl3apVq9LJkyeJiH8xef/+fbKwsCAiEsxVUYci7dmzh3x8fKhcuXJUrlw58vHxoX379ilkxeYdHBx471e5IiMjycHBQW1WHV6utLQ0OnToEO3YsYMSExOVctOmTaMFCxYQUf77TVdXl6pVq0b6+vrcjQZ1eda+C+v69es0duxYMjQ0JCsrK/r55595Yzly5AhZW1urXJHvw4cP1LBhQ9LV1eWiM3R1dalFixb0/v17+vjxo8pHYeXm5jJFzaoqHR0datCgAUkkEmrcuDE1b96cO9q0aUMjRoxQ+PqOHz+eLCwsqGnTpjR27FieY3HChAlqsyzSJEeVJtkiNq9Jtsj5li1biholx8oXVmlG+bGw3759o5YtWxb5HV1QrDzrWMWcd9aIPbH4yMhIioiIIIlEQitXrqTIyEju2Lp1q1LnK4sjjHXXEKvDT0wnntivU1FKSEgge3v7Umc1jdckW8TmC7MF12iF1236+vrk4uJCBw8eVNkWrbQqLWkdhFqVWM7OznT58mVB++XLl6lq1aqC9j179pCbmxtFRETQtWvXeM4aucOmoFRdLMTFxdGXL19o7dq1pKurSxYWFlS7dm3ubvCqVauoefPmRETUsGFDat26NQUFBZFEIqEpU6ZQcHCwwqOgiipdX/BLXr7IYOHVufPIao+npyeZmpqSnp4et61XT0+PTE1NycvLi3dYWlrSoUOHFD6vIrHy+vr6CiNJk5KSyMDAoERjZZ2X3Nxcys7O5p5r27ZtNG7cOFq1ahV9/fqVZ8emTZtIV1eXTE1Nyd7evtjtL4aGhlz0QsGLybt373IRreoqOzubgoODFW7l/zd4IiIDAwOFr2tiYqLgdWVh1eHV2e5TUBcvXqTQ0FA6cOBAsSwrz8I+f/6cFi9eTK6urmRiYkIDBw6kVq1aka6uLudkr1atGo0ePZpevnypkq1E/7cNeOnSpbR69Wredq+C30vKDmXOR1WiZtWR/PMzaNAghY5JZSroSCx8tGjRQm1WHds1gdckW8TmNckWOd+rVy9Ro+TUiaoTK8qP1RYrKysmhx8rT6T6WMWYdwsLCzI3N1c5Yo81wk/diMAzZ84ojUxXJBZHGOuuIda1sBhOvLKa96IUFxfH3UQuTVbTeE2yRWxeGevg4ECvX79W+Tm10kps6f7bORC1+s/XsmXLMG7cOKxdu5arGHvt2jUEBARg+fLlAl5eJGTIkCFcm0QiAREpTEgfHByMkSNHCqqrff78GcHBwVyy1yZNmgAARo8ejfr16yM9PR0//PADlxDayckJ8+fPBwBERkZi7ty5+OuvvyCRSHDkyBHo6go/DhKJhJdMNiUlhWluWHhHR0eFBReePn3Kq/6mbv8A0K1bN5XZyMhIQUXoomRubs7E29ra4tSpU4KCBSdPnuQKGxQU61hZ9PTpU95z9u3bF3379lVYFXXmzJkIDg5GYGCgSsnG3dzcEBcXB3t7e1777t274eXlpfS8V69e4eHDhwAAV1dXQfJwANDV1cXSpUsxcODAYu0oCx4AqlWrhp07d2LGjBm89h07dsDZ2VltVh1e1e8OZWrYsCEaNmwoaO/YsSM2bdqESpUqqc0Xx1pZWeHAgQOIiIjA8ePHUatWLfz888/w8/ODmZkZgPyKukOGDMGECRPw999/Y+LEiUyFcyQSCdq0aYM2bdoIHouJiVG5n8KKjo7Gxo0b0apVK4wcOZJrr127NlexvCQKDw9HTk6OoP3du3fQ1dXl5kculrGUZNxaaVWciAhPnz6FlZVVkZxEIoG9vT2ys7ORmpoqGg8AnTt3xrFjx+Di4oLhw4dj4MCBKFeuHMeamJhg0qRJWLZsGVP/lStXZralf//++O2337B48eIiz5GLlVd1rEuXLhVl3sPDw7nKvsHBwcVW9hWbl6tZs2bIy8tDYmIiXr16hby8PN7j8mJZlpaWkEgkkEgkcHFx4a1Zc3NzkZmZyfvOB4AGDRrg0aNHgrWeMsmvBQorPj6e91qx2sLCl9W8A8CqVasE43/x4gW2bNmC9u3bq81qGq9JtmjaWMW8vtFKK3WkdRBqpZbkP7Ryffr0CQ0aNOCcbDk5OdDV1cWQIUMETinWL0JVFwsF5e3tzTkr5erYsSP3f1dXV65ir1QqxalTpxQ6YAqrsJNHmeQX+arwXl5eePz4MSQSCVq1asVzVObm5iIlJQXt2rUrFXvmzp2rEr9t2zZMnz4dwcHB2Lx5M4yMjIo9JygoiImfNGkSxo8fj1u3bnGVBM+fP4/IyEisXLlSwLOOtbDzpijW0dFRYQW/d+/ewdHRkee0/vbtG/r06aNyJcI5c+bA398fz549Q15eHvbs2YOHDx8iOjoaf/31l4DPyMjAmDFjsH37du55dXR00KdPH6xdu1bgLG7VqhViY2MF1WiVSWw+ODgYffr0wdmzZ+Hj4wMg/3U9deoUdu7cqTarDq/Od4cqOnv2LLKyskTh5WylSpWQl5cHX19fXLlyBZ6engK2RYsWsLCwAAD8+OOPOHPmDKpWraq071WrVmHEiBEwNDQULGALq3ClSBY9e/ZM4cVgXl4esrOz1e5Xrr59+6Jz584YPXo0r33nzp04cOAADh8+XOLn0EorMSSRSLgbP6pIT08PCQkJovEAUKFCBcTGxip1YACAtbU1UlJSmPpXx5acnBxs3rwZJ0+eVFjBNiwsrES8qmNNTU0VrCGLkqpj9ff3B5B/Q1iVyr5i83JdunQJfn5+ePLkCYiI91jBG/fqOMLGjRuHSZMm4eXLl/Dw8BDYVKtWLQDsDj8xnXhlNe8AsGLFCt7fUqkU1tbW8Pf3x/Tp09VmNY3XJFs0baysVY+10kpsaR2EWqml8PBwtc9V1dGjp6cHU1NTpjuV8sciIyNx6tQphXdCT58+zfu78OOlIRaHQLdu3bBw4UIQEdq2bQtTU1PuMfmiRR51WRb2AMBPP/2ES5cuYc+ePahQoQIcHBwEC54bN27w/u7duze2bdumMj9q1Ch89913CA0N5Zw7NWrUwI4dO9C1a1eW4fGkjjNGmSMpMzMThoaGvDZ/f3/s2LFDEMWmTF27dsXBgwcREhICExMTzJkzB3Xq1MHBgwfxww8/CPjhw4fj5s2b+Ouvv7jF6sWLFxEQEICffvqJc2zL1b59ewQGBuL27dsKL5C6dOlSpnzPnj1x5coVhIWFYd++fQDyX9crV64IIiZZWBZenSgHTdOKFSvQq1cvwfuvoCwsLLgbLmvWrEGvXr0QFxen8CJs/PjxWLFiBfr16wdDQ0PBAragJBKJwEH4/v17/Pbbb7h//z6A/MjYwYMHK3S0qhs1q6ouX74suPAHgObNm2PmzJmC9hYtWij8fMtV8DeBhWWRvb0900WjmLwm2SI2r0m2yPmGDRuKGiXHwqsTccjSP6vtd+7cQZ06dQAAiYmJpcqzjlXMeVc1Yq+s+JEjR8Lb2xuHDh1CpUqVlH4HquMIU3XXEKvDryyceGLPO8AWNCHmLiaxeU2yRWyete+9e/fy/s7OzkZKSgp0dXVRtWpVrYNQqzKXhArfKtJKK5G0ePFijBw5kot4KU6GhoYICQlBYGAgwsPDVQ7ZHzt2LCIjI9GxY0eFCx1FF8bJyckIDw/nXfwGBAQUGY1TlGQyGeLj41XeciuTyTBnzhyMHz8eBgYGaj1nadvTtGlTXLlyBT/++CMqVqwomMfC0Yi9e/dGTEyMyrxYYhmrvr4+BgwYgMjISAwfPpy3FTU3NxeXL1+Gjo4Ozp8/z7WPHz8e0dHRqF27NmrVqiVYZCpyXKiibdu2oUuXLqhQoQKOHTvGbZmXKy4uDu3atcOnT5947UVFMirasi8mn52djZ9++gmzZ8+Go6Oj0vNYWVY+KiqKu9Bg+e5QVep8nlTlWfuW67fffsPIkSNhaGiI8uXL8z5/EokEjx8/xsePH5WmKyhKZ8+eRefOnWFubs5F1Vy/fh0fPnzAwYMHBRc9+/fv5+6Uh4SEIDg4mBc1q8gxrorkc+Ph4YFLly7Bw8OD9/jt27fRoEEDfP78mdc+YcIE3t/Z2dm4desW7ty5A39/f17EMgsr14cPH7B7924kJydjypQpKFeuHG7cuIGKFSvCxsamTHlNskU7VsX8uHHjEB0dDWdnZ5Wi3sTmra2tceHCBYVpGhSJpX9WW8QWy1jFnHdVI/bKijcxMUF8fLzK24CB/Jvrjx49KtYR9uTJkyL7KXwjKTY2ljkKT1VbWHmx572wnj59CgCoUqVKkRwrq2m8JtkiNs/at1wZGRkYNGgQunfvjgEDBjCdq5VWJZaYCQ610qqgZDKZWsm/z5w5wysgUZzKly/PVCzj6NGjpK+vT/Xr1+cqVdavX58MDAzo+PHjKvdTUOokLo+Li+MVg7h8+TIFBATQhg0b1LKhpPYYGRlRXFycyucYGxsz8XJdvXqVoqOjKTo6mq5du8Z8fmGxjFWdqqhiFTGQfz5sbW0pISFB8Hh8fDzZ2Nio3X9ZyczMjB4/flzqrDo863eHqiqrogpXr16lKVOmUJ8+fah79+68o7AqVqxICxYs4IoyKVLB5O/yasWqqGbNmjR8+HDKycnh2nJycmjEiBFUs2ZNheecPXuWWrduTdbW1mRkZEQ+Pj507NgxhSxrBffmzZvT2LFjBfzo0aOpSZMmKo2JiGju3Lk0adKkErHx8fFkbW1N1apVI11dXe61mzlzJg0YMKBMeU2yRTtW5XxRvyHyQmplyf/8888qVVJXp39WWwYPHkwZGRmC9szMTBo8eHCJeZaxijnvrJV9xeZbtGhBR44cUWleiPKLbDk6OiosCqesqB6LcnNz6eHDhxQXF0exsbG8o6S2sPBiz7t8rMHBwVwxE6lUSubm5hQSEiL4PWdhNY3XJFs0bazKxFpRWSutSktaB6FWZSZ1L6qvX7/Oc5js27ePunbtStOnTxdUmCUiqlSpEj18+FDl5/H09FS4YJw2bRp5eXmp3I8i21l4b29vio6OJiKiFy9ekEwmo0aNGpGVlZVKFVdL2x4nJyeFVaWVydXVlYlPT0+nJk2akEQi4aq9SSQS8vHxYaqaW1jqOGNYq6KKIbktGzZsoNatW9OLFy+4x168eEFt2rSh9evXE1F+xWh5xTNlF0gFJTZfUAMHDuQq65Ymqw5PRPTo0SOaOXMm9e3bl3OOHT58mO7cucPUT0GVhYNw27ZtpKenR506dSJ9fX3q1KkTubi4kLm5OQ0aNEhwnqWlJT169KjIvs3MzOjevXtEpLySoyIZGhrSgwcPBO0PHjwgQ0NDlfooSqxVK8+dO0eGhob0/fffU1BQEAUFBdH3339PhoaGdPbsWZWfNykpiSwtLUvEtmrViqZMmUJE/Nf5/PnzChf2YvKaZIt2rEXzmqSxY8eSmZkZ1a1bl0aMGMHdKJUfZSll3wWvX78mHR2dEvOaMlbWyr5i83v27CE3NzeKiIiga9euUXx8PO8oLBZHWFRUVJFHYbE6/MR04ok970REgYGBZG1tTb/88gs332vXriVra2uaMWOG2qym8Zpki6aNVZlYKyprpVVpSesg1KrMpO5Ftbe3N+3evZuIiJKTk8nAwIB8fX2pWrVqFBAQIDhv+fLlNHr0aMrLy1PpeQwMDARRYkREDx8+JAMDA5XtVWQ7C29mZsZdhK9cuZIaN25MRETHjh0jR0dHtewoiT2bNm2itm3bUkpKikrn/PXXX0x827ZtqUGDBjzHw4MHD6hRo0bUtm1blW0tLHWjtQrq48ePtHfvXrp//36R56enp5fImanIFk9PTzI1NSU9PT2qWrUqVa1alfT09MjU1JS8vLzIy8uLpFIpubm5EVH+BVJxjh4TExNunGLwBTVv3jyysLCgnj170sKFC2nlypW8Q11WHf7MmTNkZGRErVu3Jn19fW5MixYtop49e6o8psIqCwehh4cHrVmzhteWl5dHw4cPpzlz5gjO+/nnn2nBggVF9t2jRw+qWLEiNW/enHPGt2jRQuFRUI0bN6a9e/cK+tu7dy81aNBApXEVJWXOylu3bil14N28eZN8fX3Jzc2N6tatS4MHD1b4PV6UoqOjqVKlSiVizczMOMdswdc5NTVV4e+HmLwm2aIdq3Je7Cg5Vl7MKD9V2Y8fP9KHDx9IIpHQo0eP6OPHj9zx7t07ioqK4n3+WHl1xirmvLNG7InNF3bEyZ1xypxyLI4wCwsL3mFiYkISiYQMDAwUfr+zOvzEdOKJPe9E+UEN+/fvF7Tv27ePKleurDarabwm2SI2z9p34bVseHg4TZs2jSpXrky+vr4CXiutxJa2SIlWGq/ExESugueuXbvQrFkzbN26FefPn0ffvn0RHh6OHj168M45ffo0jhw5And3d0Eekz179vD+tra2xq1btwQ5aW7duqVSZePSUk5ODpd/8OTJk1zxh+rVq+PFixdlZodcEydOxJcvX1C1alUYGxsL5vHdu3e8v/v374/Pnz+rzMfGxuLChQtwdXXl2lxdXbF69Wp8//33pTyaotW7d280bdoUY8eORVZWFry9vZGamgoiwvbt23lFYvLy8jB//nyEhoYiMzMTQH5utEmTJmHmzJkqVzdWpsJVvxXp48ePePPmDQYPHgwiwvjx45VWjt68eTMaNWqEbt26oW7duqLwBfXbb7/BwsIC169fx/Xr13mPFS5+wcKqwwcGBmL+/PmYOHEiZDIZ196yZUusWbMGAFCnTh2cOnUKlpaWCAkJweTJk3m5KAurTp06mDBhAsqVK1csL+97xowZ2LRpE2bNmlVk3wAwY8YMlCtXDsnJyVzldX19fXz69AkSiQQTJkxAy5YtERwczDsvNzcXS5cuxbFjx5Tmxvz9998RFRWF5ORkxMbGwt3dXak9BStyjh8/HgEBAXj06BEaNmwIID/X0tq1a7mE/IUr2xcl+XeBl5cXV0yGtYK7p6cntm7dqtLzFf59ICK8ePEC165dw+zZs9VmAcDAwAAZGRmC9sTERFhbW5cpr0m2iM1rki2sfFRUFBYvXsz7TgKArKwsREdHC75TxeZjYmIEdhcllv5VZS0sLHiFpQpLIpHwvvNYeXXGKua8q1rZt6x41qIKDRo0wKNHj1TKWfj+/XtBW1JSEkaNGoUpU6YofGz37t0q50NksYWVF3vegfzfw+rVqwvaq1evLlg3s7CaxmuSLWLzrH2zVj3WSiuxpXUQaqXxIiIuifDJkyfRqVMnAICtrS3evHkDAILE+927d1e5/+HDh2PEiBF4/PgxGjduDAA4f/48lixZgokTJ4rqQJBrxowZ+PPPP7F+/Xp07NgRJ06cwLx58wAAz58/R/ny5Xn9s9gj719RtVFlsre3x5AhQ3jPW5xYK1vb2toiOztb0J6bm4vKlSsDYBtrSZwxZ8+e5aqf7t27F0SEDx8+ICoqCvPnz+c5CGfOnMlVKvTx8QEAnDt3DkFBQfjy5QsWLFjANA+FpUoxl5EjR2L48OH48OEDJBIJPn78iC9fvijlf//9d6xYsQLJycmi8BkZGTAzMwNQ/IUGC6sOX1C3b99W6ESqUKEC991x//59fPr0CZaWlggODsbIkSOLfN/cv38fBw4cgIWFRbG8vO/p06dDR0cHP//8c7HvSfli0NLSEv/88w8AwMbGBnfu3IGHhwc+fPggKMIhH6u8QvCdO3d4j8kdd0ZGRlz15mvXrmHJkiVKi0ZJpVKu0qRcU6dOFXB+fn7o06cP7/P/9u1bzJ8/H23btuVV4j527BjPySZ3ht+6dYu5gntycjIiIiLw+PFjhIeHo0KFCjhy5Ajs7Ozg7u7OYwv/PkilUri6uiIkJARt2rRRmwXyq3iHhIRwldglEgnS0tIwbdo0hbaLyWuSLdqxCvmMjAxQ/s4d/PPPP7wK5bm5uTh8+DDvpqTYvFxDhgzBypUrBY6tT58+Ydy4cZxji6V/VltiYmJARGjZsiX+/PNP3npFX18f9vb23LpAHZ5lrGUx76pW9i0rvnChkOKkjiOsoJydnbF48WL0798fDx484D3G6vAT04kn9rwDQO3atbFmzRqsWrWK175mzRrUrl1bbVbTeE2yRdPGyrq21Uor0VUWYYpaaeXl5cVtWwwODqZPnz4Ve87ChQvp/fv31KJFCxo4cCBFR0eTnp4etzXgzJkzpZLbJy8vj8LCwsjGxobbWmFjY0Ph4eGUl5dHhoaG3DZSZfluCoqVlysmJoYsLCxIKpXytqNMnz6dV5RA3f6JiBwdHenNmzeC9vfv35d4GzOr9u3bR/Xr16erV69ybVevXqWGDRty2xlZxlqSeTE0NKS0tDQiIhowYACXk/LJkydkYmLCY1m3Dqgq1m2r8qImDg4OCl9TZRKDZyl+wVooQ93CGkRENjY2dP78eSLiz++ePXvIycmJiIgaNmxIrVu3pqCgIJJIJDRlyhQKDg5WeLDyrH0XlK+vL4WGhhIRUUhICFlbW9OwYcPI3t5eYZGS0lZqaqrKR2H16NGDVq9eLWhfvXo1de3aVdAeGRlJWVlZKtsm1tZxdfThwwdq3bo1WVhYkI6ODtna2pKenh41bdqUMjMzy5TXJFu0YxXy8u2ayg4dHR2aP38+16fYvFyq5vFj6V9dW1JTU5mS+LPyqoy1LOad9TtVbJ41TyDrlmRFunnzJslkMkE7az5EVltYeLHnnSj/98zExIRq1KhBQ4YMoSFDhlCNGjXI1NRUkFOXhdU0XpNs0bSxaqWVpklCVKgOu1ZaiSAjIyM0bNgQW7duRZUqVfDixQuVt+8mJCSgX79+SEtLw8SJE7kIq3HjxuHt27eCCKGWLVtiz549gsiYjIwMdOvWDadPn1b6XPKInYJ3lxs1agRTU1PY2dkhIiICkydP5kW6FNScOXM4vkmTJggODi6WL6jc3FxkZGTA0tKSa0tNTYWxsTE3XyXpXyqV4uXLl4K5//vvv2FnZ4evX79ybWlpaQr7lMvOzo73typ84W2Inz59Qk5ODre1UP5/ExMTvHv3jmmsR44cUXteXFxcMH/+fHTs2BGOjo7Yvn07WrZsifj4eLRq1YqLNgMAQ0NDJCQkCLY1PXz4EJ6ensjKyipyHpSpZs2aOHLkCGxtbVXiZTIZ4uPj4eTkpBLv4eGBw4cPq9w/C29ubo4KFSrg9OnTsLe3x99//61wG56cvXTpEmrUqAGpVFokqw5fUJMnT8bly5exa9cuuLi44MaNG/j7778xcOBADBw4EHPnzsXDhw8xd+5cJCcn48aNGw3F7F4AAIgxSURBVHBzc+NtdZVLIpHgxo0bTPy2bduY+i6od+/e4cuXL6hcuTLy8vKwdOlSXLhwAc7Ozpg1axbvO0JVTZw4EfPmzYOJiQkmTpxYJBsWFsbcv1ympqa4deuWIALk0aNH8PT05Lbmq6tGjRqhV69e3NZx+efgypUr6NGjB54+farwvOvXr+P+/fsAAHd3dy7isqQskB9FnJCQgMzMTNSpUwetW7f+13hNskVsXpNsKY6PjY1linoTm5dHvllaWiIpKYn3vZqbm4uDBw8iMDAQz58/Z+6f1ZaC+vDhA65cuYJXr15xu0fkGjhwoFo8y1jFnndNVOHfk+zsbHz+/Bn6+vowNjYWbIt88uRJkf0VjEg8cOAA7zH6/2kb1qxZA1tbWxw5coT3uKI0LUVF4bHYog5fFnr27Bl++eUXLpqyRo0aGD16tML3DQurabwm2fJvj7VwOpOiVDg1llZaiS2tg1CrUtOrV68ULtBq1apVIqeWMn358gU6OjqC7QHKnGCvXr2CjY2Nwm2tRUnuENi1axcAlKoDobBTICcnB2fOnEFycjL8/Pwgk8nw/PlzmJmZcfOlTv/yBVq3bt0QFRXF20aXm5uLU6dO4cSJE3j48CHXLt9iqEyFF2mq8FFRUUofLyx/f/8yc8b88ssvCAgIgKmpKezt7XHjxg1IpVKsXr0ae/bs4eUtatCgARo0aCDYOjBu3DhcvXoVly5d4rWnp6dDIpGgSpUqAIArV65g69atcHNzw4gRI1Sej8JidRCKyffs2RN79+5F/fr1ceXKFTRu3Bj6+voKWUtLS5w/fx41atRAbGxskezp06fRs2dPJr6gvn37hjFjxiAyMhK5ubnQ1dVFbm4u/Pz8EBkZCR0dHR6v7LtDmVh41r5Z1aJFiyI/f6dPn0aLFi2wd+9eWFhYoHnz5kp5iUSi8EbKvXv3kJaWhm/fvvHa5flS5bK3t8f48eMxadIkXntoaChWrVqFJ0+eoFy5ckhMTISVlVWx+QsLX5yampri9u3bcHR05L1PU1NTUb16dcF2+FevXqFv3744c+YMd+Pow4cPaNGiBbZv385zFrCwQP7nW1Wnu9i8JtkiNq9JtrDyT548ga2trcr5asXii/vNlufxk6ffUMceVtsPHjyIfv36ITMzE2ZmZjz7JBKJ4LtAVV6dsYr5OkVHRxf5eGFHqNi8IhXME9i2bdtieWUqPB8SiQTW1tZo2bIlQkNDUalSJd7jmuTA+zfmXav/DQ0ePFhlNiIiQkRLtNJKKK2DUKsS6/r16/D398f9+/e5fFWF7/aVxGlW8HnkER1ubm6oU6cO73F5Un1PT0+cPn2adxc3NzcXR48exYYNG5CamqrWOGUyGT59+iSaA+HJkydo164d0tLS8PXrVyQmJsLJyQkBAQH4+vUr1q9fr3b/8gVa4ZxiAKCnpwcHBweEhoZy+R0BID4+nsdlZ2fj5s2bCAsLw4IFCwR3v1h5VbV48WKMHDkS5cqVE9UZc/36daSlpeGHH37gnLGHDh2ChYUFl2sQyI8W6NixI+zs7Hj51dLT03H48GFBgZXvv/8eI0aMwIABA/Dy5Uu4urrC3d0dSUlJGDdunMoO8cLSJAdhVlYWLC0tMWDAAPz2228YPny40lx7Cxcu5AplhIaGFsmuWLECWVlZTLwipaWl4c6dO8jMzISXl5egIJGmSFGRA2WS52WUa8KECby/s7OzcevWLdy5cwf+/v5YuXKl2nY9fvwY3bt3x+3bt3nfIfIL7sI3CyIjIzFs2DC0b98eDRo0AABcvnwZR48exa+//opBgwYhKioKffv2hYGBASIjI4u8ePf39+f9XaVKFezcuRONGzfmvU/37t2LyZMnIzk5mcf36dMHjx8/RnR0NGrUqAEg39np7++PatWqYdu2bWqxAKCjo4MmTZqgf//++PHHH4uN7BST1yRbxOY1yRZ1eDGi5Fj5soryY2FdXFzQoUMHLFy4sNh8rSy8umMV63VijdgTm1ema9euKcwTqEmOMDGdeGUx7xERETA1NUWvXr147bt27cLnz595v38srKbxmmSLpo1VK600TmW4nVmr/1LVqlWLunfvTpcuXaKUlJRic25IJBKm/HB///03NW/enCQSCVlaWpKlpSVJJBJq2bIlvXr1itevPOeLohwjxsbG9Ntvv6k9TtYccazq2rUr9e/fn75+/cp7rpiYGKpWrVqpPIeDgwO9fv26RH389ddf1KxZM9H4wpLn2tMEyW15+vQpzZgxg3r06EE9evSgmTNn0rNnzxSeY2FhQQ8ePCAiopUrV1Ljxo2JiOjYsWMlyvvI+n4sK7558+Yq5wlkYdXh1dGjR49o7Nix1KpVK2rVqhWNGzeOHj16VCp8cWxxuawKHqpq7ty5NGnSJF7bt2/fSEdHh27fvq1SH506daKuXbvS69evydTUlO7du0dxcXFUv359pfl0Ll26RH5+fuTl5UVeXl7k5+dHly5dUtnuojRp0iRq0qQJvXjxgmQyGSUlJdG5c+fIycmJgoKCBLyZmRlduXJF0H758mUyNzdXmyUiunHjBk2ePJmqVKlCBgYG1LVrV9q1axd9+fJFoe1i8ppki3asyvkDBw6QTCYjiURC5ubmZGFhwR2WlpZlzrPm8WPpn9UWY2Njpt8dVp5lrGLPe2ElJiZSq1at6OjRoyrZJzZPpDxPYMGxWVhYkImJCUkkEjIwMChyrHl5eZSXl1fkc7LmQ2S1RV3b5SrteXd2dqbTp08L2s+cOUMuLi5qs5rGa5ItYvOsfcv16tUriouLo7i4ON71rVZalbW0DkKtSixTU1OucIgY6t27N3l7e9O9e/e4trt375K3tzf17duXa0tNTaWUlBSSSCR09epVnpPy+fPnlJOTUyI75A4QsRwI5cqV4xxJBZ0zKSkpZGRkVOL+lYnV4ZKUlETGxsai8YVVcC5K0xlTUltUlYmJCaWkpBARUefOnWnx4sVElF8AxdDQsMxs0TSexfHLwk6YMIH09PRoyJAhNGHChCKPwjp69Cjp6+tT/fr1OaZ+/fpkYGBAx48fLxGvCnvmzBnuiIyMpO+++44CAwNp//79tH//fgoMDKRKlSpRZGSkSnNBlP/5U3TR4+joSLdu3VKpj/Lly3PJ4c3MzLjvqVOnTpGnp6fKtijT9evXKSEhgft737591LVrV5o+fTp9/fpVwH/9+pWGDRtGurq6JJFISE9Pj6RSKfXv31/h97ypqSndvHlT0H7jxg3BxS8LW1B5eXl0+vRpGjZsGFlaWpK5uTmv2FRZ8ppki3asQt7Z2ZkCAgJUKtZWFjxR/jrg2LFjtGXLlmIdMiz9s9rSvXt32rFjh8p2s/JEqo+1LOa9sK5evUqurq5lzst/Y+THvn37aN26deTu7k7t2rVTqe+iHGFRUVFUs2ZNMjAwIAMDA/Lw8KDo6GiF/ZTUgVecLaXBl+brZGBgwK0RCyolJUWwRmRhNY3XJFvE5ln7zszMpMGDB5OOjg4X1KKrq0tDhgwp0feJVlqpK62DUKsSq2vXrrR7926mc1icN6wRHWLJ1NSUIiIiRHMgWFhY0N27d7nnkjtE4uLiqEKFCgptYnVoLF68mLZv3879/eOPP5JEIqHKlSsLnAUfP37kHR8+fKD79+9Tnz59qHbt2oK+WXlVJZ+L0nbGqGvLkiVLaOfOnYLHdu7cqdBxU79+fZo2bRqdPXuWDA0NuXm+ePEi2djYqG2Lu7s7V3lZVds1yUHIwrOwzZs3Jx0dHWrQoAE1b95c6dGiRQvBuZ6enlwF64KaNm0aeXl5lYhn7btly5a0detWQfsff/zBFJEbHR1NlSpVErRv2rSJOnToQG/fvi22DwsLC3r8+DERETk5OXF3xh89elTkzQsioqysLMF3Q2F5e3tzvyHJyclkYGBAvr6+VK1aNQoICFDad1paGh06dIh27NhBiYmJSrkuXbpQ06ZNeVG+T58+pWbNmlG3bt3UZpXp+vXr5OnpqXKkp5i8JtkiNq9JthTFix0lx8qLGeXHasumTZvIzs6O5s6dS7t37xY4rkrKs4xV7HlXJGURe2Lziqr6VqxYkXx9fen58+cq96/IERYaGkrGxsY0depU7nWZMmUKGRsbU1hYmEr9qhP9KKaztTRfJ1tbW4Xv1X379gnWiCyspvGaZIvYPGvfI0aMICcnJzp8+DC3Tjp06BBVrVqVRo4cKeC10kpsaR2EWpVYr1+/pg4dOlBQUJBKCzRW5406ER2JiYm0YcMGmjdvHgUHB/MOdSWTycjNzU00B0Lv3r1p+PDhRJQ/5sePH9M///xDLVu2pEGDBim0idXp4ODgQOfPnyciouPHj5OFhQUdO3aMhg4dSj/88AOPVbTdUSKRkJ2dHV24cEHQNyuvquTOITGdMSy2ODg4MG0diImJIQsLC5JKpbxIkunTp1P37t0FfFpaGqWnp3N/X758mQICAmjDhg1q2y23XVMcfqy82LbIZWBgoNDR9PDhQzIwMCgRz9q3kZGRUl6RU6579+68o1u3btSgQQPS0dFRuO3W09OTTE1NycDAgFxcXLitwPKjoJo0aUJ79+4lIiJfX19q164dnTt3jgYOHEju7u6Cvj99+kRjxowha2trlbZHm5mZcTeIFi9eTG3atCEionPnzlGVKlUEfHBwsMK76p8/f1b4HZ+Wlkaenp6kp6dHTk5O5OTkRHp6euTl5cX7rLGyBZWenk5Lliyh2rVrk46ODjVp0oTWrVv3r/CaZIt2rEJe7Cg5Vl7MKD9WWxSlhynotCopzzJWMeedNWJPbL60pMgR5uDgoDASNTIykhwcHFTum9XhVxpOvLKY96lTp5K9vT2dPn2acnJyKCcnh06dOkX29vaC9CAsrKbxmmSLpo21fPnyFBMTI2g/ffo0WVlZCdq10kpsaR2EWpVYBw4cIHNzc5UXaKzOG9aIjo0bN5KOjg5VrFiRateuTZ6entxRuP+8vDx68uQJZWVlFTtOU1NT0tfXF82BkJ6eTm5ublSjRg3S1dWlhg0bUvny5cnV1VVpzkZWp4OhoSEXdTZ+/HgaMWIEx1tYWPDYgtsdz5w5Q2fPnqX79+9Tdna2QltYeVUld/aI6YxhsUVfX59p6wARUU5ODr17907AK3pdmzRpwm29efHiBZmZmVGjRo3IyspKofMjKipKYZ6rr1+/8hblf/zxB2VmZorOq6qydhCmpaUVG3FZpUoVhdGhO3bsIFtb2xLxrH27uLjQlClTBO1TpkxR6IgeNGgQ7xgyZAhNmzaNjh07JmCJiIKCgoo8Curo0aP0559/ElH+lmVXV1eSSCRkZWVFp06dEvQ9evRoqlGjBu3evZuMjIxo8+bNNG/ePKpSpQr9/vvvAl4mk3Gf19atW1N4eDgRKd+GL5VKFX523rx5ozS6Ky8vj44fP06rVq2iVatW0YkTJxRyrOz69eupadOmpKOjQ+7u7rRw4UKFuXfLgtckW7RjVc6LHSXHyosZ5cdqi9hiGauY884asSc2X1Cq5AlkcYQZGBgoTEOUmJjItB5T5vAT04lXFvP+9etX6t27N5cuQ09Pj3R0dGjw4MGCFBssrKbxmmSLpo3VyMiIl0ZLrjt37pQoRZNWWqkrrYNQqxLL3t6exowZQy9fvlSJZ3XeKIro0NXVVRrRYWdnx+V5K065ubmkp6dX5Pa0ghLTgUBElJ2dTVu2bKEpU6bQqFGj6Ndff6XPnz+Xmj2VKlXiIghdXFy4cx88eMB0p7UsJXf2iOmMUVUymYwqVarEtHWAVaxFTVidJWLzqqosHITZ2dk0a9YsMjMz46LXzMzMaObMmfTt2zfBecHBwWRhYUGLFy+ms2fP0tmzZ2nRokVkYWFBISEhJeJZ+z506BAZGhpSzZo1aejQoTR06FDy8PAgQ0NDOnTokMpzIZbevn2r9CLS1taWuxsuLyJClL/duX379gK+RYsWNHDgQIqOjiY9PT2OP3PmDNnb2wt4iUSiMIH3qVOnyvxue5UqVWjKlCkq53MUk9ckW8TmNckWVl7sKDlWXswoP1ZbxBbLWMWed00TS55AFkeYu7s7LViwQNDHvHnzqGbNmoJ2VodfWTpPxVRiYiLt3LmTDh48WOTNCFZW03hNskVsXlW2ZcuW1KtXL16wyufPn6lXr17UqlWrYm3SSqvSlu6/XUVZq/98vX37FhMmTEDFihVV4q2trXHr1i04Ozvz2m/duoUKFSoIeFtbW9y4cQOnTp3C/fv3AQA1atRA69atFfb//v17QWl5ZZJKpXB2dsbbt28F9ijS8OHDMWLECDx+/BiNGzcGAJw/fx5LlizBxIkTS8zr6uqif//+KtmuTv89evSAn58fN+b27dsDAG7evIlq1aoJ+KSkJMTExODVq1fIy8vjPTZnzpwS86ro+++/h5GREdNYWedFVREROnfujPHjx0Mmk6Fp06YAgNjYWAQEBKBv374Kz9u9ezd27tyJtLQ0fPv2jffYjRs3eH9nZ2fDwMAAAHDy5El06dIFAFC9enW8ePFCoU0SiUTQ/vTpU5ibm5c5r6oU9VkabEF+3Lhx2LNnD5YuXYpGjRoBAC5evIigoCC8ffsW69at4503e/ZsyGQyhIaGYvr06QCAypUrIygoCOPHjxc8DwvP2neHDh2QlJSEdevWcd97nTt3xsiRI2Fra6t07N++fVP4+bOzsxOwHz58wO7du5GcnIwpU6agXLlyuHHjBipWrAgbGxulzwEA5cqVU/rYu3fv4OTkBAAwMzPDu3fvAABNmjTBqFGjBHx4eDj69euHffv2YebMmdx30e7du7nPLwBYWlpCIpFAIpHAxcWF977Izc1FZmYmRo4cKeh//PjxqFatmmCe16xZg0ePHiE8PFwtFgDS0tKY3p9i8ppki9i8JtnCyhf+bP7bfMeOHTFlyhTcu3cPHh4e0NPT4z0u/w1Sp39WW0JCQop8vPA6gpVnGavY8y4XEQFQ/XdODD4sLAyzZ8/G2LFj4ePjAwA4d+4cRo4ciTdv3mDChAk8nmWswcHB6NOnD86ePcv1ff78eZw6dQo7d+4U8N26deP9LZFIYG1tjZYtWyI0NFTA/7e8Ts7OzkVeh5iZmeHWrVtwcnJiYln7FpvXJFs0ZawrV65E27ZtUaVKFdSuXRsAEB8fD0NDQxw7dkzp+VppJZYkJP8G00orNeXv74/vv/8ew4YNU4kPCQnBihUrEBgYqNB5M3v2bME5p06dwqlTpxRe+G7evJn399ChQ1GvXj2FF4qKdPDgQSxduhTr1q1DzZo1i2SJCOHh4QgNDcXz588B5F/kT5kyBePHjxcsBFj46OjoIp974MCBJbYnOzsbK1euRHp6OgYNGgQvLy8AwIoVKyCTyXiv4a+//opRo0bBysoK3333Ha8viUQicGyx8jdu3ICenh48PDwAAPv370dERATc3NwQFBQEfX19tcfKOi+Fn0duc2GdO3cOtWvXxrBhw7Br1y7o6ubfY8nLy8PAgQOxfv16gd2rVq3CzJkzMWjQIGzcuBGDBw9GcnIyrl69ijFjxmDBggU8vkGDBmjRogU6duyINm3a4NKlS6hduzYuXbqEH3/8EU+fPgUAeHl5QSKRID4+Hu7u7pwtQL6zJCUlBe3ateMW4GLzrJLJZIiPj+cWXaXFFuS9vLywfft2zhEu1+HDh+Hr64uPHz8q7eOff/7h+iqs8+fPw9vbm3PksvKsfRel0aNHIyQkBO/evcPQoUNx4cIF3uNyB29ubi6vPSEhAa1bt4a5uTlSU1Px8OFDODk5YdasWUhLS0NmZqZKzw8Ae/bs4f1dq1YtrF69Gs2aNUPr1q3h6emJ5cuXY9WqVVi6dCn3Hi5OX758gY6ODncRHxUVBSLCkCFDEB4eznNQ6+vrw8HBgXMEF5SNjQ0OHDiAunXr8tpv3LiBLl268OxRhU1ISEDNmjUhlUqRkJBQ5Bhq1aolKg9AY2zRjrVoXlMllUqVPqbou0NMydckcmVnZyMlJQW6urqoWrWqYB3BymvSWKOjo7Fs2TIkJSUBAFxcXDBlyhQMGDCgzHlHR0cEBwcL1plRUVEICgpCSkqK0nGo4gi7fv06VqxYwbvJP2nSJMHrV1KJ4cQT+3VSVWWxZtIEXpNsEZsvyH7+/Bl//PEHHjx4ACD/M9KvXz8YGRmp9LxaaVWa0joItSqxFixYgPDwcHTs2FHhHdnCkRiszpvg4GCEhITA29sblSpVEjy+d+9e3t+LFi1CWFiYyvZYWlri8+fPyMnJgb6+vuDLWB79Ulil7UDo1KkTry07OxufP3+Gvr4+jI2Nldqhrj3Fyd7eHqNHj8a0adNE4evVq4fAwED07NkTjx8/hru7O7p3746rV6+iY8eOgkidghLDGcO6qEtKSsKtW7dgZGQEDw8P2NvbK+SqV6+OuXPnwtfXl7cYmDNnDt69e4c1a9bw+DNnzqB79+7IyMiAv78/5wCfMWMGHjx4wDljgoODuX8nTZoEU1NTrg+5s6Rnz56cw1JsXq6QkBBMnjwZxsbGvPasrCwsW7aMi+g4d+4cjh8/jsDAQJXYevXqYcmSJSr3Xa9ePdja2iI2NhY1atTg8ffv30fTpk3x+vVrqKPCd5RLk1e37wEDBkBXVxeBgYEKvyfld6Xlat26NerUqYOlS5fy3pcXLlyAn58fWrRoodLzA0BERATv7xUrVkBHRwfjx4/HyZMn0blzZxARsrOzERYWhoCAAIX9XL9+nbuAdHNzQ506dRRysbGxaNy4seC7XZkMDQ1x584dQZT0o0ePULNmTXz58oWJlUqlePnyJSpUqACpVAqJRIKCSyn533KHg5g8EWmMLdqxFs3LJXaUHCvPKpb+S8OWjIwMDBo0CN27d1fJycLKK5OY864sYm/t2rWYP3++IGJPbF7Z915SUhI8PDx435FyieUIKyhVHX5iOfHEnncW/a842TTJFrF51r610qrMVKoblrX6n5SDg4PSQ1HOtILKyMigjIwMhY+dO3eOvnz5Qt99953SPCilYU9kZGSRhzqSyWRMOdOU8YmJidSqVSs6evSoWnYo6j86Opp8fHyoUqVKXE6MFStW0L59+1SySZXnUEWslUvFsEPOhoaGkrGxMU2dOpXLezNlyhQyNjamsLCwEtlhZGTEzbO1tTWXoyoxMZHKlSun8FyWoiaRkZEKi4gok9g8S85CsfMhBgcHk6+vL8/+L1++UL9+/RRW9lVVmlix2djYmO7fv6/yeQU/fwWfMzU1tUSFfBQpNTWV/vzzT4qPj1f4+N9//03NmzcniURClpaWZGlpSRKJhFq2bKkw1yBR/mdk9+7dNG/ePJo3bx7t2bOHcnJyFLLu7u60evVqQfuqVauoRo0azGxqaiqXfzE1NbXIQ2xek2zRjrVoXq6ChdM8PT3J3d2djI2NyczMTGGhNrF5VrH0X1q2JCQkKMxHWlq8Iok576yVfcXmWfMEsqyZDh06pHANe/ToUTp8+LCgnYgtHyLr+o2FF3veWVTWhd3+LV6TbBGbl7ORkZH0119/ce1Tpkwhc3NzatSokUp5EbXSqrSldRBqpbGSO1jKlSvHXcj+p6g0f1CuXr1Krq6upWLPL7/8QlZWVjR//nwyMjLinjMiIoKaN2/OO2fIkCG0bt06lZ+DlWetXKqq1PlxFmNRJ+/b0dGRbty4QUREdevWpfXr1xNRftERS0tLtfouqLS0NF6xnsuXL1NAQABt2LDhX+ElDAUkWFh1+G7dupFMJiMrKytq1aoVtWrViqysrMjMzIy6d+/OO1ikiYtjb29viouLU/k8a2tr7n1Z8DmPHz9eIgf9t2/fqGXLlioXfiIi6t27N3l7e/Oq+N29e5e8vb2pb9++Aj4pKYmcnZ3J2NiYvLy8yMvLi4yNjcnV1VXhb8Vvv/1GRkZGNGfOHK7K+uzZs8nY2Jg2btyoNktEFBsbq7BSe3Z2NsXGxpYpr0m2iM1rki3q8IX18eNH6t69u8o3Q0uTDw4OLvIobXtYbSciiouLIwsLixLzJR1rac07a2Vfsfndu3eTjo4OtW3blkJCQigkJITatm1Lurq6tGfPHgHPsmby8PBQWFjryJEjVKtWLUE7q8NPTCee2PPOov8VJ5sm2SI2L2ddXFzo1KlTRER04cIFMjIyog0bNlDnzp2Z16daaVUa0joItSpV5eXlKa1sySr5F+fUqVMVVvksTXsePXpEM2fOpL59+3IRSocPH6Y7d+6o9byl+YNy8+bNElcYlvdfo0YN2rt3r+A5b9++TeXLl+eds3DhQrKysiJ/f39avnw5rVy5kncUFivPWrmUdawsrBiLOnnfQ4cO5aLV1qxZQ0ZGRtS6dWuysLCgIUOGKDx3165d1KtXL2rQoAHnAJEfhdWkSRPu4uPFixckk8moUaNGZGVlpfCCRyzewsKCLC0tSSqVcv+XH/IqwqNHj2Zm1eHlGjRokMoHizRxcXzq1Clq1KgRxcTE0Js3b+jjx4+8o7CGDh1K3bp1o2/fvpGpqSk9fvyYnjx5Ql5eXhQQEECenp6C956yo7CsrKyYHIRmZmZ05coVQfvly5fJ3Nxc0N6+fXtq164dvX37lmt78+YNtWvXjjp06KDwOX755ReysbHhKlc6OjoqvEhkZTWpirgm2SI2r0m2qMMrkthRcsr4fyPKTxlbeN0QHh5O06ZNo8qVK5Ovr2+J+dIYa2nMO2vEntg8EdG1a9eoX79+VKdOHapTpw7169ePu4lUWCxrJkNDQ0pJSRGwKSkpZGxsLGhndfiJ6cQri3lXVersjhGjb7F5TbJFbL7gTqMnT54QEdHUqVNpwIABRER0584dhTe/tdJKbGmrGGtVKirtXCQTJ07E169fsWDBAshkMmzcuBEnT55ErVq1BHmnwsLCSmRPbGws2rdvDx8fH5w9exYLFixAhQoVEB8fj99++w27d+9WawysOnnyJL777jvubyLCixcvsGbNGi6XSUmVkpKiMCm0gYEBPn36xGvbuHEjTE1NERsbi9jYWN5jEolEkMuRlVe1cmlZqFq1ati5cydmzJjBa9+xY4dK1a2L0saNG7nCOmPGjEH58uVx4cIFdOnSBT/99JOAL1jUZP/+/YKiJoV1584d1K9fHwCwc+dOeHh44Pz58zh+/DhGjhwpyJMkFh8eHs4VkAgODi6ygAQLqw4vV+HceP/Nkld1b9WqFa+dlBQpCQ0NxY8//ogKFSogKysLzZo1w8uXL9GoUSMsWLAAy5cvV9uW/v3747fffsPixYtV4vPy8hTmE9TT01NYcTI2NhaXLl3iVVIuX748Fi9erPS7ctSoURg1ahRev34NIyMjXk7NkrDy+S2st2/fwsTEpEx5TbJFbF6TbFGHV6SPHz8WWThJLP7mzZuCtoJ5/MSwRxm7YsUK3t9SqRTW1tbw9/fnqr+XhC+NsZbGvLNW9hWbB4C6devi999/V2lMLGsmc3NzPH78GA4ODrz2R48eKfxsvHjxQuEasHHjxnjx4kWJbGHly2LeVRUxlAxgYTWN1yRbxOblrKmpKd6+fQs7OzscP34cEydOBJCfGzQrK4vp+bXSqjSkdRBqVWIpS8o7cuRIvHnzRq2kvDdv3kReXh7u3r0LIyMjeHp6Ash3VhSUogU5qz2BgYGYP38+Jk6cyCtk0bJlS0EBCTFVuOqyRCKBtbU1WrZsidDQ0FJ5DkdHR9y6dUtQUOPo0aOCQg5FVa1TJFa+Vq1auH37tqB92bJl0NHRYeqrpBJzUSeVSnmVE/v27Yu+ffsq5X/55Rds3LgRvr6+iIyMxNSpU3lFTQorOzubK8hy8uRJdOnSBUB+cRRFi2mxeH9/fwD57zEfHx9e1ePCYmHV4cWWqhUS1eFZ+5YrJiaGiTc3N8eJEydw7tw5JCQkIDMzE3Xq1OEcjXPnzlXLDgDIycnB5s2bcfLkSdStW1dwEVj4pk7Lli0REBCAbdu2oXLlygCAZ8+eYcKECQKHJ5B/Q0NefKigMjMzBUVzgPzvppycHDg7O8Pa2pprT0pKgp6eHu/CVVW2R48eAPJfr0GDBvEKQOXm5iIhIYF3kSsmr0m2aMdaNA/k3wQqKPnNwC1btggqrpcFr0hmZmYIDg5G586dBTdWWfpntUXsdYciKRurmPPes2dPXL58GStWrMC+ffsA5FctvXLlisKbuGLzhw8fho6ODtq2bctrP3bsGPLy8gT2s6yZunbtip9//hl79+5F1apVAeQ7BydNmsStJwqK1eEnphNP7Hln0ZEjR2BjY1PqrKbxmmSL2Lyc/eGHHzBs2DB4eXkhMTERHTp0AADcvXtX4FjXSquykNZBqFWJtXr1aqxbtw4DBw7k2rp06QJ3d3cEBQWp5SCMiYmBTCbD1q1bmas7sdpz+/ZtbN26VdBPhQoV8ObNG2bbAfUcCI8ePRKtkpXcnokTJ2LMmDH48uULiAhXrlzBtm3bsGjRImzatEmtvku7oquhoaFadsiljjNGjEVdQTs+fPiAK1eu4NWrV4KIqILvUwBIS0vjLiiNjIw4R8iAAQPQsGFDgdPa3d0d69evR8eOHXHixAnMmzcPAPD8+XOUL19eYJfYvEwmw/379+Hh4QEA2L9/PyIiIuDm5oagoCCeA4eFVYd/+/Yt5syZg5iYGIVzX9DhSkRIT09HhQoVin0PUn56DqSlpanE5+Xl4fnz56hcubJKfbOof//+MDMzQ7NmzVTiR48ejZCQEGRlZcHW1hZNmjRBkyZNmJ6zON25c4erQJyYmMh7TNHnc82aNejSpQscHBxga2sLAEhPT0fNmjUVRrR06tQJI0aMwG+//cZFt16+fBkjR45UeME5aNAgDBkyRHBxefnyZWzatAlnzpxhZuVRrEQEmUwGIyMjjtXX10fDhg0xfPhwrk1MXpNs0Y61aB4QP0qOlVem0ojyK4ktT58+BQBUqVJFJXtZ+YJSNFax550lYk9sPjAwUGHENxEhMDCwRA7OpUuXol27dqhevTr32jx9+hTff/+9wkh1Voef2E48sV8nebRYYUkkEhgaGqJatWro2rUrmjRpwsSy9i02r0m2aNpY165di1mzZiE9PR1//vknt7a+fv06fH19FfallVaiqlQ3LGv1PymxkvKy5n1Q1x4bGxs6f/48EfHzf+3Zs4ecnJw4Li8vj548eUJZWVnF2mBqakqPHj1i4ocMGUITJkwQHBMnTqQZM2bQ5s2beTm3WO2Rj+v333+natWqcfm1bGxsaNOmTcX2oUrfLLxEIiGpVKr0KCiWsZqYmFBcXBzzvJS25H0fOHCAZDIZSSQSMjc3JwsLC+5QVKSEtahJTEwMWVhYkFQqpcGDB3Pt06dPV5jcWGze29ubdu/eTUTE5Xf09fWlatWqUUBAgNqsOnz79u3J2dmZFi9eTBEREUVWKM/NzSU9PT2Vc+ex8Kx9ExHFx8crPBISEigxMZGpsnRByb9XpVIpNW3alDZu3CiomE1EZGlpSa9fvyYiEuR9LHyUhvLy8uj48eO0atUqWrVqFZ04cUIp+/79e+rSpQtJJBLS19cnfX19kkql1K1bN/rw4YPCMSv6TUhKShLkOGRhiYiCgoLo06dPxQ+wDHhNskVsXpNsUYfXJLHm8RNTubm5FBwczOWWlUqlZG5uTiEhIZSbm1tiXlPGylrZV2yeNU8gq/Ly8ujYsWO0dOlSWr16dbGFe1jyIYopseediKh58+ZkZmZGJiYm3HhNTU3J3NycGjRowP3+3r17l4ll7VtsXpNs0bSxaqWVpknrINSqxGJJyquuU0sse4iIJk2aRE2aNOEKMCQlJdG5c+fIycmJKy5BJK4DgYj9x0odp0NBffr0SWFSdVap6yDct28f79i1axfNmDFDocNSbGeMugnmiyqCExcXR1++fCFnZ2cKCAhQ+eJRnaImOTk5AidPSkqK0tdXTN7MzIyrJLt48WJq06YNERGdO3dOUB2XhVWHNzU1pVu3bikckyK5ubnRxYsXReFZ+y7OgW5gYEADBw5U6bu0oOSfvxs3btDkyZOpSpUqZGBgQF27dqVdu3ZxjsfIyEje/4s6ylLy35DPnz9TUlISHThwgA4cOKDQqSeXmZmZwovLa9eukampqdosEdHjx48VftckJiYqvOAWk9ckW8TmNckWdXi50tPTeVXii5MYvIODA+9wcnKiBg0a0PTp0ykjI6PU7FGFDQwMJGtra/rll1+4myJr164la2trmjFjRol5dcda2vPOWtlXbL5ixYpcFdWCOnHiBFlbWwva1XGEFaeaNWtSWloa83liOvHEnnciohUrVlCPHj14xcQ+fPhAP/74I4WHh9OnT5+oa9eu1KZNGyaWtW+xeU2yRdPGSkR09uxZ6tevHzVq1IiePn1KRETR0dEUFxen8H2jlVZiSusg1KrE2r17N+no6FDbtm0pJCSEQkJCqG3btqSrq0t79uzhsSV1apW2PUREX79+pWHDhpGuri5JJBLS09MjqVRK/fv3p5ycHB4rpgNBnR8UVnvEUGlXdP3jjz+oS5cugnaxnTGKnF3Pnj0jQ0NDQXtUVBTVrFmTDAwMyMDAgDw8PLhKv4VlbGzMND+5ubmUnZ3N/b1t2zYaN24crVq1ir5+/arwnOzsbDpx4gStX7+eu8h59uwZ/fPPP2XOy2Qy7vPdunVrCg8PJyKiJ0+eCOaShVWH9/b2ZnofHDhwgJo0aUK3b98udZ6173379pGrqytt2rSJEhISKCEhgTZt2kQ1atSg7du30++//05VqlShSZMmqdSfXIU/f3l5eXT69GkaNmwYWVpakrm5OS9SVB1169aNunfvLjh69OhBfn5+NGfOHHrw4AHvnJMnT1LHjh3JycmJnJycqGPHjgqjCNX5DenUqRP16tWL932ek5NDPXv2pHbt2qnNEhE1bdpUoZN0y5Yt1KxZszLlNckWsXlNsoWVFztKjpVnFUv/rLZUqlSJ9u/fL2jft28fVa5cucQ8i8Scd9aIPbH5ESNGkIeHB3cDjig/arpWrVo0dOhQAa+OI6w4yX+bWB1+YjrxxJ53IqLKlSsrjCS7c+cO9x6+fv06lS9fnoll7VtsXpNs0bSx7t69m4yMjGjYsGFkYGDArdFWr15N7du3F/SjlVZiS+sg1KpUdP36dZW3A5SFU0ud7QlPnjyhQ4cO0Y4dO5RefIrpQGD9QVHHHgcHB3J0dFR6qKPSdhAmJyeTiYmJoF0MZ4x8m5FUKqUFCxbwth6FhYVRt27dyNPTk3dOaGgoGRsb09SpU2n//v20f/9+mjJlChkbG1NYWJjgObp37047duwo1mZ1lZqaStWrVydjY2PS0dHh5nb8+PH0008/lTnfokULGjhwIEVHR5Oenh4X2XXmzBmyt7dXm1WHv3LlCrVs2ZLOnDlDb968oY8fP/KOwrKwsOC2qxoaGha7lZaFZ+27Xr16Si+S6tWrR0REe/fu5aVBUEVFff6uX79Onp6eCqNmc3JyaNeuXdxNl927d/Mc2QXl7+9P5ubmZG9vTz169KAePXqQg4MDWVhYUO/evcnV1ZUMDAzo3LlzRES0du1a0tXVpb59+3KfP19fX9LT06M1a9YI+mf9Dbl79y6VL1+eqlatSoMGDaJBgwZR1apVydraWvAdwcISsW9JFpPXJFvE5jXJFlZe7Cg5Vr6gSjvKj9UWAwMDevjwoaD9wYMHCm8CsfIsYxVz3lkj9sTmP3z4QA0bNiRdXV0uulJXV5datGhB79+/F/BibEmW/zaxOvzEdOKJPe9E+WlxYmJiBO0xMTFc1HpycjLJZDImlrVvsXlNskXTxurp6UlRUVFExF+j3bhxgypWrCjoRyutxJbWQahVifTt2zcaPHgwPX78WOVzWJ1aZamitowSietAYP1BUcee8PBw3rFs2TLy8/OjcuXK0aJFi1SdJp5Yc0UWxX/+/JkCAgLIxcVF8JgYzhj5QlgikZCtrS1v65GLiwu1adOGLl26xOvbwcGB+yEvqMjISHJwcCAi4hyH+/fvp02bNpGdnR3NnTuXdu/ezXtMUeQDUX6OtWPHjtGWLVsoKiqKdxRW165dqX///vT161fewiImJoaqVatW5nx8fDzVrFmTzMzMeFv0x44dK8jxxMKqwycmJpK3t7dge658+25hsW6lZeFZ+zY0NKT79+8L2u/fv89d+KakpJCRkZGAKUqFHYTp6em0ZMkSql27Nuno6FCTJk1o3bp1vHPu3LlDTk5OZGxsTF5eXuTl5UUmJibk4OCg8Ht82rRpNGrUKF7UTG5uLo0dO5amT59OeXl5NGLECPLx8SGi/Dywq1evFvSzZs0ahVFA6vyGPHv2jKZPn04dOnSgnj17UnBwMC+nq7os65ZkMXlNskVsXpNsYeXFjpJj5cWM8mO1pX79+jRu3DhB+9ixY6lBgwYl5lnGKua8s0bsic0TseUJVMcRVpzkv02sDj8xnXhlMe9+fn7k6OhIe/bs4ZzW8hzo/fv3J6L8nSR169ZlYln7FpvXJFs0baxGRkbce77gGk2ea1srrcpaWgehViWWmZkZk4OQ1amlrv7++2+6ffu2IMm/Im3atInc3d25hPfu7u7066+/CjgxHQisPyjq2KNMa9asoUGDBqnMF5S6EYSFCx9YWFiQjo4OyWQyhYtsMZ0xzZs3V1ioQZFUKYIjLwBT3KHIScVa1KRcuXLcds2Cr4Uy55HYvDJlZWXRt2/fSp0tiq9Xrx41atSItm/fTjExMXTmzBneocny9PQkf39/3rbyb9++kb+/PxfVeu7cOc4prarkr+H69eupadOmpKOjQ+7u7rRw4UJKTU1VeE7Dhg2pc+fOvM/Iu3fvqEuXLtSoUSMBb2VlpTCq5+HDh1wEdEJCAhdZZWJiovQzpSiauKx+Q1QR65ZkMXlNskU7VuW82FFyrLyYUX6stpw5c4ZMTEyoRo0aNGTIEBoyZAjVqFGDTE1N6ezZsyXmWcYq5ryzRuyJzasqeZ5AdRxhxUn+28Tq8BPTiVcW8/7PP//QsGHDuN80qVRK+vr6NHz4cMrMzCQiops3b9LNmzeZWNa+xeY1yRZNG6ujoyOXUqXgOjsqKopq1Kih8H2jlVZiSkJE9G9XUtbqP1v+/v7w9PTEhAkTVOKjoqKK7a8kun79Ovz9/XH//n0UfntLJBLk5uby2ubMmYOwsDCMGzcOjRo1AgBcvHgRa9aswYQJExASElIie1RVZmYmJkyYgOjoaOTk5AAAdHV14e/vjxUrVsDExAS3bt0CAHh6epbqcz9+/Bienp7IyMhgPvfcuXOoV68eDAwMmPjt27fz2qVSKaytrdGgQQNYWloy21EWMjMzw3fffYdBgwZhxowZvMfmz5+PHTt24Pbt2yV6DhcXF3To0AELFy6EsbFxsbylpSXOnz8PNzc3yGQyxMfHw8nJCefOnUPPnj3x999/lykv1/Xr13H//n0AgJubG+rUqaN0DCwsC29sbIybN2/C1dW1yP4KKjk5GREREUhOTsbKlStRoUIFHDlyBHZ2dnB3dy8Rz8JeuHABXbp0gVQqRa1atQAAt2/fRm5uLv766y80bNgQW7ZswcuXLzFlyhSVxzdq1CjMmzcPXl5e8PX1Rb9+/VC7du0izzEyMsK1a9cENt65cwf16tVDVlYWr93S0hJRUVHo0qULr/3AgQPw9/fH+/fvkZSUhPr16+P9+/fw8/ODl5eXYBzLly/HtWvXBN8V6v6GfP78GWlpafj27RuvXT6/6rD37t1D06ZNYWFhge+//x4AEBcXh4yMDJw+fRo1a9YsM16TbNGOVTnfoEEDNGjQAKtWreL1MW7cOFy9ehWXLl3itYvNV65cGevXrxd8Xvfv34/Ro0fj2bNnavfPagsAPHv2DL/88gsePHgAAKhRowZGjx6NypUrC1hWnmWsYs87EeHEiROIj4+HkZERatWqhaZNmyocY1nwqki+BihfvjzatWuHa9euoUqVKgCAp0+f4vvvv8eePXtgYWGhdt9LlizBxYsXsXfvXlStWhUA8OjRI/Ts2RP16tXDpk2beOd9/PiRyRZWvqzmPTMzE48fPwYAODk5wdTUtFRYTeM1yRaxeVXZRYsW4ffff8fmzZvxww8/4PDhw3jy5AkmTJiA2bNnY9y4cUXapJVWpS2tg1CrEmv+/PkIDQ1Fq1atULduXZiYmPAeHz9+fJnaU7t2bVStWhXTpk1DxYoVIZFIeI/b29vz/ra2tsaqVavg6+vLa9+2bRvGjRuHN2/e8NrFdCAA7D9WrP0r0tKlS/HLL78gNTWVa5s4caJCViKRwNDQENWqVUPXrl1Rrlw5tXh1JJYzRlXJZDIsWbIE48ePR+vWreHj4wMAOH/+PE6dOoWdO3eie/fuavXt4eGBw4cPo3r16rh9+zacnJxUOq9Pnz4wNzfHxo0bIZPJkJCQAGtra3Tt2hV2dnaIiIgoU/7Vq1fo06cPYmNjuYX2hw8f0KJFC2zfvh3W1tZqserwTZs2xZw5c9C6dWuV5jI2Nhbt27eHj48Pzp49i/v378PJyQmLFy/GtWvXsHv3brV51r4B4J9//sEff/yBxMREAICrqyv8/Pwgk8kEbEJCgsIxyT9/dnZ2PCc+EQm+GxVp9OjROHv2LFatWoWWLVvyHjt9+jQCAgIETvHx48dj27ZtmDFjBurVqwcAuHr1KhYuXAg/Pz+sXLkSmzZtQmRkJM6dO4f58+dj+fLl8PHx4W7SXLp0CefPn8ekSZNgZmbG65tVr1+/xuDBg3HkyBGFjxe8acTCyvX8+XOsWbOGd0E4duxYpd91YvKaZIt2rIr52NhYdOzYEXZ2drybkunp6Th8+DDnYCwr3tDQEAkJCXBxceG1P3z4EJ6enoIbACz9s9oitljGKva8qyL5usDW1lYj+II3CUvbAVkS5+O/7TwVe961+u8WEWHhwoVYtGgRPn/+DAAwMDDA5MmTMW/evH/ZOq3+J/UvRC1q9V+mgnnbCh/KCl88evSIZs6cSX379uWqxx4+fJju3LlTYntMTU0VbldTJnNzc4VFSR4+fChILn7mzBkyMjKi1q1bk76+PhcGvmjRIurZs6egD1aeVaz9e3p6cjnEvLy8yNPTk7777jvS0dGhDRs28NjmzZuTmZkZmZiYcMVeTE1NydzcnBo0aMBteZUXVmHlifJz7S1fvpyGDh1KQ4cOpbCwMPrw4UOJxyrWvMtD/9UpgqNq36xFTdLT08nNzY1q1KhBurq61LBhQypfvjy5uroqrMwsNt+7d2/y9vame/fucW13794lb29v6tu3r9qsOvzOnTvJzc2NIiIi6Nq1a8WmG2jYsCGFhoYSEX+bx+XLl8nGxqZEPGvfrJJvWVd2GBgY0MCBAykrK0ul/uSFXExNTbkUDLt27eLSH+zatUtpMvmcnByaP38+fffdd9x2+u+++44WLFjAbcF88uQJVyCgqN8QZb8nLEVT/Pz8yMfHh65evUomJiZ0/Phx2rJlC7m6utJff/2lNquVVurq6dOnNGPGDK6Iz8yZM+nZs2f/Cs+ax4+1fxZ28+bNtHPnTkH7zp07FaYHYeVZxyr261ScSrsIXFnzNWvWpLCwMPry5Yvgsa9fv/LyKv/xxx/c9kuWfIgstqSlpYnCl2QeMzMzadasWdSoUSOqWrVqkcUDWVhN4zXJFk0aa05ODsXGxtL79+/p69evdPfuXbp8+TL9888/Ahu00qqspI0g1EotZWRk8KI6WKROJA2LunXrhgEDBqBnz54q8ePGjYOenh7CwsJ47ZMnT0ZWVhbWrl3LtTVq1Ai9evXCxIkTeXdSr1y5gh49euDp06e8Plh5VrH2HxwczPtbvq23efPmqF69Ou+x8PBwxMXFISIignutP378iGHDhqFJkyYYPnw4/Pz8kJWVhWPHjjHz165dQ9u2bWFkZIT69esDyI8yysrKwvHjxwXbRlnGKta8F+yrNHXgwAH06dMHK1euhI6ODkJCQjB48GB4eHhAT0+PxxbeGgUAOTk52L59OxISEpCZmYk6deqgX79+MDIyUvh8YvLm5uY4efIkFzkm15UrV9CmTRt8+PBBLVYdXiqVCuyTSCRc9FzhaDBTU1Pcvn0bjo6OvNc6NTUV1atXx5cvX9TmWfsGgKSkJMTExODVq1fIy8vjPTZnzhze3/v378e0adMwZcoU7vN05coVhIaGYu7cucjJyUFgYCD69OmD5cuXC56rsKRSKSQSCfLy8iCVSrl0DfKow4J/K4qqk0uetkDd3wtFunv3Lrp06YKXL19y28cTExNhbW2NgwcPCrZ/VqpUCfv370f9+vVhZmaGa9euwcXFBQcOHMDSpUtx7tw5tdiCYtm+LDavSbaIzWuSLerwmiBNivJzcXHBhg0b0KJFC4GNI0aMwMOHD0vEa9JYVRHrmkMT+c+fP+PFixeoUKEC77G3b9+iQoUKRf5+FKWSRD+WNl+Svn19fREbG4sBAwagUqVKgsj+gIAA7v8srKbxmmSLpo3V0NAQ9+/fh6OjI7TSSiP0r7ontfqPlVQq5aKHWJMeix1J8/r1a+rQoQMFBQUprRo7YcIE7hg3bhzJZDJyd3fnItnklVLHjh3L69vExIQryFK4YIOiSlOsPKvE7L9y5cq8aD+57ty5w1Xku379Old0gJVv0qQJDRo0iBf1k52dTf7+/vT9998L+mEZq1jzYmpqynvvF9SbN28UFh1RRRKJhACoVdRE02RqasolXi6oGzdu8Cpws7Lq8KmpqUUehWVjY0Pnz5/nnkv+vpEXCyoJz9r3xo0bSUdHhypWrEi1a9cmT09P7vDy8hLw9erVo6NHjwrajx49SvXq1SMior179yp8LkWSF3IxMjKirVu3Cgq8qFLs5e+//6azZ8/S2bNn6dWrV8U+59evX+nBgwdKIwHlYi2aIpPJuAqBdnZ2dO7cOSIievz4saDQDgtLRPTq1Svq2LGj0sjNsuQ1yRbtWJXzYkfJsfJE4kX5sdpiYGCgtIKtssIgLDyR6mMti3kvTpoWEagOL5FIFH7/37p1q0RFpTRprCXp29zcnPudKU4srKbxmmSL2Dxr33Xr1qWTJ0+qzGulldjSOgi1UktmZmbcNj9lP/7KJLbT7MCBA2Rubl6kg6V58+YqHS1atOD1LaYDQR2x9i/fNlj4yMjI4FVLJcp/nWJiYgR9xMTEkKmpKRERJScnc44ZVt7Q0JDu378v4O/evavwQlxMZ4yqklcXVuQgfPbsmdILElXEusCUKyoqqsijrPkuXbpQ06ZNeRdcT58+pWbNmlG3bt3UZtXhWTVp0iRq0qQJvXjxgmQyGSUlJdG5c+fIycmJgoKCSsSz9m1nZ0eLFy9W2XZln6f79+9z70vWytNEbO/LUaNG0evXrykjI4P69+9POjo63Hevrq4u9evXT2EKgU+fPtGQIUNIR0eHdHR0uOcbO3YsLVq0SMAbGhoqTEdx+/ZthZ9Bb29vznnauXNnGjBgAD19+pSmTp0q+D5gYYnYtySLyWuSLdqxKuednZ3p9OnTgj7OnDlDLi4ugnaxeVax9M9qi62tLXcjt6D27dun8AYyK88iTZh3TXKCsfKenp6cg9zDw4OX3qZWrVokk8moV69eKj93SWwRmy9J3w4ODry0KUWJhdU0XpNsEZtn7fvIkSPk6elJBw8epOfPnwuu0bTSqqyldRBqpZZ69OhBFStWpObNm5NEIiEfHx9q0aKFwqOwxHaa2dvb05gxY+jly5cl7quwxHQglIU9xeUps7Ozozlz5lBubi75+fmRo6Mj7dmzh8s7Jn+N+vfvT0RE27Zto7p16xIRMfMVKlSgY8eOCWw8evQoVahQoURjFWPeV65cSfr6+iSVSmnBggW0cuVK7ggLC6Nu3bqRp6enWn0TqZfbJy0tjSwsLHiHiYkJSSQSMjAwUHh3Xmw+LS2NPD09SU9Pj5ycnMjJyYn09PTIy8uLyzmnDqsOT5Sf73Ts2LHUqlUratWqFY0bN44ePXqkkP369SsNGzaMdHV1SSKRkJ6eHkmlUurfvz+XO09dnrVvmUzG9H7w9PQkf39/nqP/27dv5O/vz70vz507Rw4ODir3ScT2vpTb3Lt3b3J2dqajR49yC9yjR4+Sq6sr9enTR3De+PHjqW7duhQXF0cmJibc8+3bt0/hZ6pWrVp06tQpQfupU6eoZs2agvYtW7ZQREQEERFdu3aNrKysSCqVkqGhIW3fvl1tlojou+++o8uXL3Pjf/jwIRER7d+/n3x8fMqU1yRbtGNVzosdJcfKixnlx2rL1KlTyd7enk6fPk05OTmUk5NDp06dInt7e5o0aVKJeZaxlkU0Y3EqKydYVFQUc57A4hQUFET6+vokkUho8uTJFBQUxB0LFy6krVu3Cm5Ms+i/xUG4ZcsW+vHHH+nTp0/FnsfCahqvSbaIzbP2XTiQRX78p+wc0uq/T1oHoVZq6fPnz7Ru3TqaPHkySSQSGjFiBP38888Kj8IS22lmamqq1AFQUonpQCgLe6KioqhKlSo0a9YsOnDgAB04cIBmzZpFtra2tGHDBpo/fz5ZWFjQggUL6J9//qFhw4ZxTjGpVEr6+vo0fPhwboF48+ZNbssnKz9u3DiqUqUKbd++ndLS0igtLY22bdtGVapUoYCAgBKNtaTznpOTQzdv3uRtYXRwcKBKlSqRRCIhW1tbXvEEFxcXatOmDV26dEmVl02hSnPxmpiYSK1atVK45bQs+Ly8PDp+/DitWrWKVq1aRSdOnFDaFwvLyh89epT09fWpfv36XEqB+vXrk4GBAR0/flzpeU+ePKFDhw7Rjh07FBYwKgmvKjtkyBBat25dsc8t1/nz56l8+fJkbW3NOUMrVKhA5cuXp4sXLxIRUXR0NC1dulTlPonUu0gyNjamuLg4weNnz54lY2NjQbudnR1nY8HnS0pKUrh1/NChQ0UWTSnuzvunT5/o+vXr9Pr162LHVBzLuiVZTF6TbNGOVTkvdpQcKy9mlB+rLV+/fqXevXtzv9t6enqko6NDgwcPVuhMYuVZxqoJ0Yxl5QQTI3WKvP+lS5cqdD6WVP8tDkJPT0+SyWRkampKNWvW5EVaFk4nwsJqGq9JtmjaWItK4VJUGhettBJLuv92DkSt/jNlZGSEkSNHAgCuXbuGJUuWwMLCQqVzFy5ciDFjxsDW1ha5ublwc3NDbm4u/Pz8MGvWrBLb1qNHD8TExKBq1aoq8V++fMHq1auVFgO4ceMG9399fX38+uuvmD17Nu7cuYPMzEx4eXnB2dlZYd+sPKtY+4+KikJoaCh69+7NtXXu3BkeHh7YsGEDTp06BTs7OyxYsAAzZszAr7/+ihUrVuDx48cAACcnJ5iamnLnenp6cv83NTVl4pcvXw6JRIKBAwciJycHAKCnp4dRo0Zh8eLFJRor67z8/PPP8PDwwNChQ5Gbm4tmzZrhwoULMDY2xl9//YXmzZsjJSUFANCiRQvs2bMHlpaWCvsqrOjoaPTp0wcGBga89m/fvmH79u0YOHAgAGDDhg2oWLGiSn0WJ2dnZyxevBj9+/fHgwcPypyXSCT44Ycf8MMPPxTbFwvLygcGBmLChAmC91NgYCCmTZumtA87Ozsu8Xnh5NIl5VVlq1WrhtmzZ+PSpUsKi9WMHz+e93fjxo2RkpKCP/74A4mJiQCAXr16wc/PDzKZDAAwYMCAYsdSWP3792cuMFK+fHmYm5sL2s3NzRV+bl6/fi1IYA8Anz59UjhHnTp1AgD07t1bUDSlc+fO3N/KCqgYGxsLiiAB+YVUbt26xUs0Xxzr6uqKhw8fwsHBAbVr18aGDRvg4OCA9evXo1KlSoLzxOQ1yRbtWJXzvr6+GD9+PGQyGZo2bQogv3hGQEAA+vbtK+hbbD4tLU1hcnx7e3ukpaWVqH9WW/T19bFjxw7Mnz8ft27dgpGRETw8PGBvby9g1eFZxirmvLOuC8Tm5d+XhfX06VOF3+UsatSoEV6/fo0qVaoAyC+etXXrVri5uWHEiBEl6lts7dmzB+PGjRNt3oH8woqqioXVNF6TbBGbZ+27WbNmKnGjR49GSEgIrKysmPrXSitm/csOSq3+h1R4yxxrlI6qmj9/PllZWZG/vz8tX76ctxV05cqVAt7Pz4+srKxo5MiRNHfuXN42iKIiGvPy8igvL09lu1h5VqnSv6GhocK5TkxM5KIclEVIiKVPnz5RQkICJSQkqByOzzKXqrA2NjZ09epVIsov5FC5cmV6+PAhzZo1ixo3bqzS8xSW/P0u5p35ou5Y37x5U2H0VVnwJ0+epOnTp9PQoUNp8ODBvKMkLCtvYGCg8P3+8OFDpflON23aRO7u7qSvr0/6+vrk7u5Ov/76q9J5YOFZ2IIRqoUPR0dHpfaoqiNHjvCi/NasWUO1a9cmX19fXuQsi+TvyQ0bNlDr1q3pxYsX3GMvXrygNm3a0Pr16wXnff/997Rq1SquD3mO2rFjx1Lbtm0FfHF329W9865OxAjrlmQxeU2yRTtW5bzYUXKsvJhRfqy2qCrWFAxynmWsYs4767pALF5e9Io1TyDLluRGjRpRdHQ0ERG3c6hRo0ZkZWVFwcHBgj5Ulbrbo1l4sV8nrbRiEev3nlZaqSutg1CrMpOiiy8xnGasF9ZmZmZM1abEdCCoI5b+nZ2dadq0aYL2adOmcdtrrl69SpUrV6bMzEyaNWsWNWrUiKpWrUqOjo68o7BYebHHysIaGBhwOeyGDx/ObXF+/Pgxk9OsoOTvd7Er+BWu0r1v3z5at24dubu7U7t27QTnic0HBQWRVCql+vXrU9euXalbt268Q11WHb5KlSoK803t2LGDbG1tBe2zZ88mExMTCgwM5MYbGBhIpqamNHv27BLxrH2ro8TERNqwYQPNmzePgoODeUdh1axZkw4dOkRERAkJCWRgYEDTp0+nhg0b0qBBg9R6fvl70tPTk0xNTUlPT4+qVq1KVatWJT09PTI1NVW41SYuLo5MTU1p5MiRZGhoSAEBAfTDDz+QiYkJXbt2Te35kBdNYbW/JCzL9mWxeU2yRWxek2xRlU9MTKSdO3fSwYMHFVZVLyueNY+fOvaw2l6c1N3Sqc5YxZh31nWBWLz8RjhrnkAWR5iFhQU9ePCAiPJzOctvuh47dkzh2pDV4SemE0/s10krrVikbjFDrbRilXaLsVb/in777TesWLECSUlJAPK3LP78888YNmxYifuWbwVVVTY2NtwWvOI0Z84chIWFYdy4cWjUqBEA4OLFi5gwYQLS0tIQEhJSIp5VrP0vX74cvXr1wpEjR1CvXj0A+VvEHzx4gN27dwMArl69ij59+mDYsGGIjY3FgAEDUKlSpWK3TqrC9+jRA5GRkTAzM0OPHj2K7G/Pnj1qj5V1XipWrIh79+6hUqVKOHr0KNatWwcA+Pz5M3R0dIq0U5k+f/6MLl26QCKRoFWrVtDV/b+v29zcXKSkpKBdu3Zq9V1QhbcySCQSWFtbo2XLlggNDS1zfv369YiMjFRpOysLqw4/fPhwjBgxAo8fP0bjxo0BAOfPn8eSJUswceJEAb9u3Tr8+uuv8PX15dq6dOmCWrVqYdy4cYL3DQvP2jerfv31V4waNQpWVlb47rvveJ8/iUSCOXPm8PiUlBS4ubkBAP7880906tQJCxcuxI0bN9ChQ4cS2cK6vaZJkya4desWFi9eDA8PDxw/fhx16tTBxYsX4eHhobYdv//+OyZPnlym23FYti+LzWuSLdqxCnlnZ+ci040U7lssft68eUhNTeX9TuXl5WHgwIFYuHCh0vNZ7GG1XSypM9bSnHcvLy8kJCSovC7w8vKCRCIRjZ87dy4AwMHBAX379hVsjVUmYtiSnJ2dzfV78uRJdOnSBQBQvXp1vHjxQtDH4MGD0a5dO0HaiX/++QeDBw/mtun6+fkx26IqL/a8lytXDomJibCysoKlpWWx62tV2Xfv3jH1LTb/n2y72GPVSitNltZBqFWZS2ynmaqSL+pCQ0Mxbdo0rF+/XmneGrnEdCCoI9b+u3TpgocPH2LDhg14+PAhAKB9+/bYt28fHBwcAACjRo0CAFhYWODQoUPw8fFRyZYjR44Uy5ubm3M/mqx5bcR0xgwePBi9e/fmHJutW7cGAFy+fBnVq1dnslMuXV1dtG7dGvfu3UPbtm15uRj19fXh4OCAnj17qtV3QRXOmflv89++feOccaXJqsPPnj0bMpkMoaGhmD59OgCgcuXKCAoKEuTwA/IvZLy9vQXtdevW5fJkqsurwk6cOBHz5s2DiYmJQgdmQYWFhfH+nj9/PhYsWIBp06YVeZ5c+vr6+Pz5M4D8izb5RVe5cuWQkZGhUh+FJc9XKL/oZFHVqlXx66+/qvW8ykT/Py+hJojVFjF5TbJFbF6TbGHly8oW1jx+YthTVp9VMcbKYnu3bt1w7949ZGdnq7QukN9suXXrlii8XC1btlQpTyCrIwwA3N3dsX79enTs2BEnTpzAvHnzAADPnz9H+fLlBbao6vAT04kn9ryvWLGCC0xYsWJFsY4nFpa1b7F5TbJFk8aqlVaaLK2DUKsyl9hOM1UlX9R5e3vjy5cvcHJygrGxsaAYQME7PWI6ENSROv07ODhg0aJFRfY7evRomJubo1y5cirbYmlpWSwfERGh8P+qqLSdMQUVFBSEmjVrIj09Hb169eLuduvo6CAwMJDJTrn09fUxfvx4eHl5Md2Z/zeKmpSmhg0bhq1bt2L27NmlyqrDSyQSTJgwARMmTMA///wDAEVGCw8YMADr1q0TON82btyIfv36lYhXhb158yays7O5/7Po/fv36NWrl8p8kyZNMHHiRPj4+ODKlSvYsWMHgPw74PKLRLmOHj0KU1NTNGnSBACwdu1a/Prrr3Bzc8PatWu5wiPyyFtVxOKEZC2Soq5YFvTaxb9W/23SlCi/stC/Nda5c+di+fLlmD9/PsaPH1/suoA1wk/diEA/Pz+MGDECAwYMwMuXL9G6dWvUrFkTf/zxB16+fMlFoKvjgFyyZAm6d++OZcuWwd/fH7Vr1wYAHDhwAPXr1+c4VoefmE48sefd39+f+/+gQYOKZAtKFZa1b7F5TbJFk8aqlVaaLK2DUKsyk/yCSmynGat8fX3x7NkzLFy4EBUrVizywk9MB4I6Eqv/33//HUFBQZgzZw6ioqJgbGxc7Dnz5s1j4ufPn49+/foprCioSKXtjCmox48f48cffxS0F1wcsEr+PlL1zrxcrNtrlEWaSSQSGBoaolq1aujatSvnvBWDv3r1KgwNDQHkRxxu3LgRJ0+eRK1atQQO94Iqjg0LC+M9vyp8QaWkpCAnJwfOzs48x2BSUhL09PTg4ODA618ikWDTpk04fvw4GjZsCCA/ijQtLY2bdxaete+YmBiOL/h/VdSrVy8cP36cqy5fnNasWYPRo0dj9+7dWLduHWxsbADkRwIXjgCZMmUKlixZAgC4ffs2Jk2ahIkTJyImJgYTJ04UOPulUmmR36O5ubmwsLBQ2cmmqBKxGNLEiCettNIUadJ7ntVBz8qLPVbWyr6s6whW/s6dO5yzbufOnfDw8MD58+e53xS5g1AdB2Tz5s3x5s0bZGRk8KrYjxgxgrdWZHX4lYXzVOx5B/JvRL948UKw5nv79i0qVKjA+/1jYTWN1yRbNG2sWmmladI6CLUqM8kXXGI7zVh14cIFXLx4kburWVhiOhDUkdj9A/mv1W+//Yb09HRUrFgRDg4OAmfMjRs3eH+HhoYiOTlZZX7Xrl2YO3cuGjRogP79+6N3796CXGFiOmMKqlq1amjWrBmGDh2KH3/8kXN2lUTy97uqd+YLnseST+fmzZu4ceMGcnNz4erqCiA/CkxHRwfVq1fHL7/8gkmTJuHcuXNwc3MThc/MzISXlxdMTEwAAJ6engDyLzoKSiKRCC68imLl42PhC2rQoEEYMmSIIFLk8uXL2LRpE86cOSPov27dugCA5ORkAICVlRWsrKxw9+5dhfYUxRcea3F9F1RMTAxatGghaAfyI/jGjBnDa6tWrRpmz56NS5cuwcPDQ/D5K7yl2s7ODn/99Zeg7xUrVgjaWPMV7t27l/d3dnY2bt68iaioKAQHB3Pjkys1NRWBgYEYNGgQL+1EVFRUsdHOqigkJASTJ08W3LjIysrCsmXLuM/gkSNHEBkZicDAQJVYuVNVK620KluJvf1abE2YMAHjx49XeV3Auo5g5VnzBLI6wogI169fR3JyMvz8/CCTyaCvr8/7nlU3+lFMJ57Y8y6fG0X6+vUr9PX11WY1jdckW8TmWftWVfI0LlppJba0DkKtSl25ubm4ffs27O3tubuFEydORIcOHbBy5UrRnFrqqnr16sjKylL6uJgOBHUkdv9y/fDDD7y7vcWJtTBBfHw87t69iz/++APLly/Hzz//jB9++AH9+vVDt27dYGxsXGbOmBs3biAiIgITJ07E2LFj0adPHwwdOpS3/UWZFL3fgf9zIKh6Z16d3D4AuGi/iIgIbuHw8eNHDBs2DE2aNMHw4cPh5+eHCRMm4NixY6LxWVlZOHbsWLHzJdfTp09RuXJlSKXSIrmYmBiV2cJ937x5U2FOzIYNG2Ls2LFc/yxi5dVVjx49cPLkSe59LNfKlSsxe/ZsgYNw48aNMDU1RWxsLGJjY3mPSSQShTkX5fry5Qu+ffvGayu4CGXNV9i1a1dB248//gh3d3fs2LEDQ4cORbNmzbjHQkJCEBYWJkg74eHhgY0bN5YokhcAgoODMXLkSIHT7/PnzwgODuY+g02aNEGzZs0wduxYlVgWiR3xJOb26P9kXpNsYeU1yRZ1pM5YWZz5NjY2zLwYUmfeExMTVVoXyKXqOkJdnjVPIIsj7MmTJ2jXrh3S0tLw9etX/PDDD5DJZFiyZAm+fv2K9evX8/pmdfiJ6cQTc95XrVoF4P9uaBeMmMzNzcXZs2e5HNgsrKbxmmSLpo1VLjHSuGilVYlU6nWRtfqfU0BAAG3atImIiHJycsjHx4ckEgmZmJhQTEwMERE1b95cpaNFixZlZrdMJqPk5GQ6duwYNW7cmGJiYujNmzf08eNH3vG/KFNTU0pOTi7T5zx37hyNHj2arK2tSSaTlelzy5WdnU1//vknde7cmfT09Mjd3Z1CQ0Pp1atXHKPK+72gTExMKCUlhYiIOnfuTIsXLyYioidPnpChoSHHBQUFUVBQEEkkEpo8eTL3d1BQEC1cuJC2bt1KX79+FfRfuXJlunv3rqD9zp07VLlyZSIiun79OpUvX75MeFUl//yVNluQNzMzoxs3bggev3btGpmamqrc37+hX3/9laytren+/ftc2/Lly8nMzIzOnj1b4v4zMzNpzJgxZG1tTVKpVHAUVOfOnalt27YUEhJCenp69PTpUyIiOnbsGDk7O6v8nMnJyWRiYiJoNzIyosTEREH7w4cPycjIiHFk/6eRI0fS69evSSKR8D7Dcp06dYqsrKx4bSwsi1i/U8XkNckWsXlNsoWV1yRbxOblrFQqpb///lvw+Js3bwTfS0TEzKsqsV8nY2NjldYFcqm6jlCXj4mJIQsLC5JKpTR48GCuffr06dS9e3cBb2FhQQ8ePCAiopUrV1Ljxo2JKP83wdHRkcd27dqV+vfvT1+/fuXNVUxMDFWrVk3Qd5MmTSg6OpqIiF68eEEymYwaNWpEVlZWFBwcXCJbWHkx593BwYEcHBxIIpGQra0t97eDgwO5uLhQmzZt6NKlS8yspvGaZIumjVWumjVr0qFDh4iIKCEhgQwMDGj69OnUsGFDGjRokIDXSiuxpXUQalVi2djY0NWrV4mIaO/evVS5cmV6+PAhzZo1i/vh1UTJFyoSiYQkEongAlne9r+of8NBePPmTZo0aRLZ2NgoXHiVpb58+UJhYWFkYGBAEomEDAwMaMCAAfT8+XPm93v9+vVp2rRpdPbsWTI0NKRbt24REdHFixfJxsZGwEdGRtKXL19UtlWZYzImJoZzgiUnJ3NOV7F5VVUWF8qdOnWiXr16UU5ODvdYTk4O9ezZk9q1ayc4Lysri5YuXUrt27enunXrkpeXF+8oCc/aNxHRkiVLyMbGhlJSUmjx4sVkZmZG586dU3keitLo0aOpRo0atHv3bjIyMqLNmzfTvHnzqEqVKvT777/z2CdPnlDHjh2pVq1anHOciOjnn3+mcePGqfR8nz9/poCAAHJxcRE85uLiQlOmTBG0T5kyRSF/5MgRiouL4/5es2YN1a5dm3x9fendu3dcu4WFBVlaWpJUKuX+Lz/MzMxIKpXS6NGjmdmCCg4Opk+fPikcb8GL2bi4OPry5Yuo/OzZszXGFu1Y/3NsV1XyGy9ijpXVQS+WQ18mk9HPP/8s2rzXq1ePaV3Auo5g5YnyfxsLfn8SEaWkpCh0wLI4wsqVK8c55Ar+lqekpCi8AcTq8BPTiVcW8968eXPBvCsTC6tpvCbZIjbP2nfB9+TcuXOpZ8+eRJR/871ixYoq96OVVqUlrYNQqxLLwMCA0tPTiYho+PDhFBAQQEREjx8//lciwVgXx2fOnCnyKCgxHQjqqLT7t7S0pNevX5OpqSmZmZnxLpILHwV5IhJcVCviC+vx48c0f/58cnNzIx0dHWrZsiVt2rSJPnz4UKKxqjsvV69epVGjRpGlpSVVqVKFZs6cSY8fP6azZ89Sq1atqF69eszvd9Y782lpaVz/RESXL1+mgIAA2rBhg0Kb/fz8yNHRkfbs2UPp6emUnp5Oe/bsIScnJ+rfvz8REW3bto3q1q1bJryqKgsH4d27d6l8+fJUtWpVGjRoEA0aNIiqVq1K1tbWdPv2bcF5fn5+ZGVlRSNHjqS5c+fyojiDgoJKxLP2LdfUqVOpfPnyZGFhQRcvXuQ9NmHCBMrMzOT+X9RRWLa2tpzjVyaTUVJSEhERRUdHU/v27Yue4GJU+LvAwsKCdHR0SCaT0f79+wX8oUOHyNDQkGrWrElDhw6loUOHkoeHBxkaGnJ31QtK1bvtkZGRFBERQRKJhFauXEmRkZHcsXXrVrpw4YJabEGJHfHEwmuSLWLzmmTLf7rtqkqdKD9VWVYHvboOfZaxijnvrOsCsXmi/B0UJ06coPXr11NGRgYRET179oz++ecfAcviCLOwsOB2IRT8LY+Li6MKFSoI+mZ1+InpxCuLeS+snJwcunnzpkoOJhZW03hNskVsvjjW0tKS+4z4+Phw631lTnSttBJb2hyEWpVYFStWxL1791CpUiUcPXqUy5Hw+fNn6OjoCPgvX75g9erViImJwatXr5CXl8d7vHAxC1ax5JsCwMuFVZyGDh2K48eP48cff0T9+vWLzT3DyrOqtPtfsWIFZDIZ+vfvDzc3t2KT4cp5+f9Znr9hw4a4evUqatWqhcGDB8PX17fIHEEsY2Wdl7CwMERERODhw4fo0KEDoqOj0aFDBy7nnaOjIyIjI+Hg4AAbGxum97uqFfzkYs2ns2HDBkyYMAF9+/blqoDr6urC39+fKzhRvXp1bNq0qUx4TZKbmxsSEhKwZs0axMfHw8jICAMHDsTYsWO5Ks0F9ddff+Hw4cMK8xYqEguvCivPX1NQNjY2MDY2RtOmTXHlyhVcuXIFQH7RkZs3byI7OxuAMDdpcXr37h2cnJwA5OcbfPfuHYD878VRo0YpPa+4fIUAEB4ezvtbKpXC2toaDRo0UJjXtEOHDkhKSsK6detw//59AEDnzp0xcuRI2NraCnhVi6bIcxc6OjrCx8eHl9ezsFjYgiIlRYXi4+MVvsfE5DXJFrF5TbLlP9121jx+Yow1PDwcRIQhQ4YgODiYV5BLXsFWXsBIHV6dsTZt2lS0eWddF4jNs+YJXLJkCbp3745ly5bB39+fK/J34MABQe7mNm3aIDw8HBs3bgSQn58tMzMTc+fOVVjkijUfIostrLzY8w4AP//8Mzw8PDB06FDk5uaiadOmuHjxIoyNjfHXX3+hefPmarGaxmuSLZo21iZNmmDixInw8fHBlStXsGPHDgD5BQHluTi10qpMVbb+SK3+GzV37lwyNzen6tWrk52dHbdl5bfffqOGDRsKeHUjaVQV65aT2NjYIo+CYt3iV5pbAkujf1W35pWFZsyYoTC3nTKxjJV1XqpVq0YLFy6k58+fK2W+fv1KkZGRzO93IrY786zba+T6559/KD4+nuLj4xX2m56eTrm5uWXGFydNysU1atQoev36NdWoUYPi4+NVPo+FV4UtmK+mqKOo94Gq8vDw4CKkW7VqRZMmTSKi/Pdc4SgKlnyFLJLPOyvPerf9+vXrlJCQwP29b98+6tq1K02fPl2Q11NVVuyIJzG3R/8n85pky3+67XKJEeWnri1nzpyh7OxsQbsysfKqjLWs5p1lXSA2z5onkEj1Lcnp6enk5uZGNWrUIF1dXWrYsCGVL1+eXF1dFb4W6kThsWyPZuXFfp0qV66sctoaFlbTeE2yRdPGWhppXLTSqjQlIVJSi1srrRi0e/dupKeno1evXtzdjqioKFhYWAgqWpqbmzNF6agqS0tLSCQSfPz4EWZmZrw7ubm5ucjMzMTIkSOxdu1a3nmKKqMWPlcuNzc3bN++HbVq1VLJJlaeVaz9e3h4YMmSJejQoQNu376NevXqYeLEiYiJiUH16tURERHBsTo6Onjx4gUqVKjA6+Pt27eoUKECb17U4eX69u0bUlJSULVq1SKjdljGKva8s7zfC9+ZT0xMhJOTEwICAhTemTc1NcWdO3fg4OCALl26wMfHB9OmTUNaWhpcXV2LrLhdlMzMzHDr1i0uauw/iS8rWx4+fIhVq1Zh/fr1sLe3L/a8I0eOqMyzsOooJiYGLVq0UPjY2rVrBVWPV6xYAR0dHYwfPx4nT55E586dQUTIzs5GWFgYAgICOHbMmDGIiYnBvHnzMGDAAKxduxbPnj3Dhg0bsHjxYvTr108tm9V9nX7++Wd8+/YNPj4+mDdvHlJSUmBjY4Pjx49j7NixSExM5J1Xr149BAYGomfPnnj8+DHc3NzQo0cPXL16FR07duRFPKrKRkVFcRFM4eHhxUYwiclrki3asf7n2C6XVCrF33//DWtra1776dOn0adPH7x+/Vr0scp148YN6OnpwcPDAwCwf/9+REREwM3NDUFBQdDX1y8Rr8pYy2LeWdcFYvPly5fHhQsX4OrqCplMhvj4eDg5OSE1NRVubm5cFfuCysnJwZkzZ5CcnAw/Pz/IZDI8f/4cZmZmvOqtcnb79u1ISEhAZmYm6tSpg379+sHIyEjQL5C/7i4chZeamgpjY2PBGpPVFhZe7HkHAENDQzx69AhVqlThIg3Dw8ORkpKC2rVrIyMjQy1W03hNskXTxqqVVhqnsvRGavXfKdZiFqxROqpK3RxSHz584B2vX7+m48ePU4MGDejkyZM89vDhw9SuXTtKTU1VySZWnlWs/bMkwpVIJArvpj579kxhHhhW/vPnzzRkyBDS0dEhHR0d7n00duxYWrRokYBnGau68/7p0ye6f/8+FyknPwqK9f3OemdenSTXqkiTKlay8mVly6tXr6h58+YklUrJ1NS02DyaLDxr3wWVl5dHeXl5RTIWFhZ07do1QXt4eLhKuWBTU1Ppzz//VPjdLFa+QnVfJ9a77WZmZvTo0SMiIlq8eDG1adOGiPIrp1epUkVtlkj8iCcWXpNsEZvXJFtYeU2xpSyi/Fht9/b2pt27dxNR/m+tgYEB+fr6UrVq1bh8v+rw6oxVzNeJdV0gNs+aJzA1NZWqV69OxsbGvPXb+PHj6aefflJpDooSSxQeqy0svNjzTkRkZ2dHx44do5ycHLK1taW//vqLiIju3LlDFhYWarOaxmuSLZo21oLKysqijx8/8g6ttCpraXMQalViVatWDc2aNcPQoUPx448/wtDQsEg+NDQU06ZNK/VIGnVzSBW82yvXDz/8AH19fUycOBHXr1/n2r29vfHlyxc4OTnB2NgYenp6vPPkebzU5VnF2r++vj53J/jkyZMYOHAgAKBcuXLcHS15HjSJRIJNmzbx7qbm5ubi7NmzqF69OtfGyssVGBiI+Ph4nDlzBu3atePaW7dujaCgIAQGBqo9VtZ5ef36NQYNGoSjR48K7JSPQy7W93tcXBwuXLggiGRwcHDAs2fPBDxrPh1NV0ZGBk6fPg1XV1fUqFGDa7937x4qV66sNqsOX5x8fX3x7NkzLFy4EBUrViw2dyULz9o3AERHR2PZsmVISkoCALi4uGDKlCkYMGCAgF22bBnat2/P+7yFhoYiJCQEhw4dUtj/qVOncOrUKYW5YDdv3sz9X918hWLJzs4Of/31l6BdnhOzsIiIG9/JkyfRqVMnAICtrS3evHmjNgsAMpkM9+/fVzmCSUxek2zRjlXzbVc3j5+YY01MTISnpycAYNeuXWjWrBm2bt2K8+fPo2/fvoL8pqry6oxVzNeJdV0gNs+aJzAgIADe3t6Ij4/n5QXs3r07hg8fzmOjo6MF5xeUfB0qF2s+RBZbWHmx5x0ABg8ejN69e6NSpUqQSCRo3bo1AODy5cuCtTMLq2m8JtmiaWP99OkTpk2bhp07d+Lt27eCx5XtwNJKK9H07/ontfpv0M2bN2n8+PFkbW1N5ubmNGLECLp8+bJSviSRNKqIJd9UUbp//z6ZmJjw2lq1akXOzs60ePFiioiI4EUpRkZGCvpg5VnF2n/nzp2pbdu2FBISQnp6evT06VMiys9v5+zsTET/lwdNIpGQra0tL/eZi4sLtWnThi5dusT1ycrLZWdnx1VlLXinNSkpSWHEE8tYWefFz8+PfHx86OrVq2RiYkLHjx+nLVu2kKurK3fnTy7W9zvrnXki9nw6qqisovB69epFq1evJqL8KFFnZ2fS09MjXV1dLtJDLhZWHZ7VdiMjIy5iUxWx8Kx9h4aGkrGxMU2dOpX2799P+/fvpylTppCxsTGFhYUpPGfJkiVkY2NDKSkptHjx4iJzcQYFBZFUKqX69etT165dqVu3bryjoFjyFbKoNN6Tqtxtb9GiBQ0cOJCio6NJT0+Pi4A8c+YM2dvbq80SiRfxpA6vSbZox/qfY7uYUX6stshkMkpMTCQiotatW1N4eDgRKa9gy8qzjFXMeWddF4jNs+YJLFeuHJcruWD/ivLAWlhY8A4TExOSSCRkYGCgcM3PGoXHYos6tos573Lt2rWLwsLCKD09nWuLjIykffv2lYjVNF6TbNGksY4ePZpq1KhBu3fvJiMjI9q8eTPNmzePqlSpQr///rtCW7TSSkxpHYRalZqys7Ppzz//pM6dO5Oenh65u7tTaGiooGCI2E4z1kVd4e2kt27doiNHjlCzZs3Ix8eHx4rpQFBHrP2zbM1r3rw5U+ESVv7/tXfuUVGW2x//DqCR3FVWSolIoKaJqGhmpXiJPHqATp6lKaaZZnoSCVO7Ha2jqxOpgJGmWRaXErtKqZV5QVKPCQKi4PEWKWKdvBSGokuw/fuDxfwY3hl898w8OMT+rDVryTPfeWY/m9fhmf0+e+9bb73VuHmqv5E6cOAAeXp6mtWrCsZ06NDBGOTz8PCgo0ePEhHRF198obkG6tB7vY8dO5aefPJJIqpdZ2lpKVVWVtKwYcPo8ccftzg3p8i1HpoqQHjbbbcZff/hhx9SUFAQXb58md566y0KDQ01eQ1Ha42ea3ufPn2MQWs9cPTcuQMCAigtLU0znpqaSgEBARZfN3/+fGrXrh15e3s3+n4dOnSg9PR0XbYkJSXRG2+8QUREW7duJVdXV7rlllvIycnJ+IXcGqy9xrhNU4qKiujuu+8mT09Pk0ZYs2bNovHjx1utJeKnJKvUO5ItstbmYzv3pqrKtXID9Fw9Z60q/c7dF6jWE9XuOzIyMmjevHk0c+ZMeuedd6iqqsqs1tpAWB3Hjh2j4cOH0zfffKN5jhvwUxnEawq/C4KqMi6CYC2SYizYDRcXFzzyyCMYPXo03nrrLbzwwguYO3cuXnzxRYwdOxavv/46OnbsiP/85z/Yu3evMXXS3nBTVEJDQ2EwGEAN+vUMHDjQJM0OALp3785qEsHVc+HOz0nNy87ONvn5+vXrOHToEDp37mxSONpafVhYGDZv3ozY2FgA/98Y5t133zWb1sRZK9cvly9fNha+9vHxwblz59C1a1f06tULBQUFZl+j93pPTEzEQw89hB49euDq1auYMGECjh8/jvbt2yMzM1MzLze9Ri96Ulrtob948SLatm0LAPjmm28wZswYtGnTBqNHj8a8efNMXsPRWqPnkpCQgGeffRavvvoqevXqpUlN9/T0tFrPnfvnn3/GoEGDNDYOGjQIP//8M4D/T++vz+233442bdpg8ODByM3NRW5uLgBg9uzZJrpr166Znd8c8fHxxn+PGDECR44cQX5+PoKCgpQ1AmqM+fPnIzs7G6tWrTLbNKUhISEhOHTokGZ86dKlcHZ2tloL8FOSVeodyRZZa/Ox/amnnsLzzz+PXr16obS0FOPGjcMjjzyCTz75BFVVVZo9k8q1Ll++HDExMcjKysJLL72EoKAgALWNwcx9XnH1nLWq9Dt3X6BaD9TuaSZOnGj2uYZwU5IbEhwcjISEBEycOBFHjhwxee6PP/4wm1JZXl4ODw8Pm23h6FX5PSUlBdOnT4erq6vZv+MN0audPXs2a27V+uZsu+q11sfRyrgIgpwgFOxGXl4ezZw5k3x8fOiOO+6gl156iUpLS+m7776j4cOHU//+/YmIf5KGCzfl5OTJkyaPsrIyunLlitm5t2zZQoMGDaLs7Gw6f/78DVPbuHoutsx/o9S8uLg44ynDmpoaGjRoEBkMBnJzczPe6bJFv2vXLnJ3d6cZM2aQq6srxcXF0YMPPkhubm5mmy1w1sr1S1hYmPFOdmRkJD322GNUXl5O8+fPp8DAQLP+03u9E/HuzFtT5FoPTXWCMDg4mD766CO6dOkS+fr60vbt24mo9mRou3btTF7D0Vqj18uMGTPo3LlzZDAYyGAwaE6k1Y01hKPnzt2zZ0969dVXNeOLFy+mu+++m4jIJJ2/sUeXLl0088yfP58WLVqk20fbtm2jF154gaZOnUpTpkwxeVhLnd+5emvvtu/fv58yMjIoIyOD8vPzG30vvVrVJ55Upkc3Z70j2dLcbVd5yo9riyWuXLlC165ds1nPWatqv3P2Bar1aWlpjT4awk1JNkdhYaHZcjLcU3hcW7h6FX4PCAig8+fPG//d2N9ujpY7t2q9I9niaGutj6oyLoJgLRIgFGwmMTGR7r77bmrVqhVFR0fTxo0b6fr16yaa06dPk7OzMxGpD5pZsyHV++VXZQDBGrjzc1Lz/Pz8KC8vj4iINmzYQH5+fnT06FH65z//SYMGDdLMzdUT1aaAT5s2jfr370933XUXxcTEmKT/WLtWrl8yMjLo/fffJ6LawED79u2NNXLWr19vouVe71y46TUNuXjxIm3YsIEOHz5sMl5WVkY1NTXK9StXriQXFxfy9vamkJAQo29SUlIoPDzc5DUcrTX6r7/+mnbt2mX8ecWKFdS7d28aP3682XT4nTt3NvqwRc+d+9NPPyVnZ2djzdBFixbRQw89RC4uLvT5559r9HqIj483PuLi4sjb25sGDx5Ms2bNMnkuPj7e5HWceoVEfL9z9W5ubnTq1CkiIrr99tuN5QFKS0s1dWOJiH755RcKDw8ng8FgrHVrMBho2LBhmpIAHC0RPyVZpd6RbJG1Nh/buTdVVa61Dk4wn6PnrFW13x0Jbp1AIv2Bs7oaunWPrKwsWrVqFfXs2ZNGjhyp0VsTfFQdbBUElagq4yII1mIgapBXKQhMgoOD8cQTT+Dxxx9Hx44dzWquXbuGzMxMTJ48GU5OTgC0KYxEBIPBYHO3poMHDyImJgZlZWWYM2cOXn75ZQBAbGwsLly4gHXr1pno//Wvf2HRokUICwszdpyqz4YNG4z/zsnJafS9hwwZYvIzV8+FO//TTz+N7OxsLF682GxqXkxMjFHr6uqKEydO4I477sD06dPRpk0bLF++HD/++CN69+5t7Hpsjb66uhpPPfUUFixYgC5duth9rbb6vaqqCkeOHIG/vz/at29v8hz3eud28PPx8cGePXvQo0cPeHh4oKioCIGBgdi9ezfGjBmDX375xUQ/duxYDB48GLNmzcKVK1fQu3dvnDx5EkSE9evXY8yYMU2qB4D8/HyUlZUhIiICbm5uAIDNmzfDx8dHk/bF0XL1vXr1wuuvv45Ro0bh0KFD6N+/P+bMmYPs7Gx0794d77//vsXfiyOQn5+P5ORk/Pe//wUA3HXXXXj22WfRp0+fRl9X92e94WfZ0KFDdb2vwWDAjh07jD937NgRS5YsMds92Rxcv3P1ISEhePPNNzFkyBCMGDECoaGhWLZsGVJSUrBkyRKUl5eb6MeNG4fS0lKkp6cbu10fPnwYkydPRlBQkEnqF0fbGFevXoWzs7Mmlfxm6B3JFtV6R7KFq79ZtgwbNgydOnXCiBEjMHXqVBw+fBhBQUHIycnB5MmTcfLkSbvbY0l79uxZjBs3Djk5OfD29gYAVFRUYOjQoVi/fj18fX1t0ttjrfbwO3dfoFpvjuPHj2PmzJmYN28eHnrooRvqLVG356/DYDDA19cXw4YNQ2Jiotl9VE1NDdavX4+DBw/i0qVL6Nu3L2JiYnDrrbdabYc1qPL7nDlzdL2/ufJHjWkTExNZc6vWN2fbVa+1MU6dOnVTy7gIggQIhSZHddDMEpY2ddwvv80Zf39/pKenIzw8HJ6enigoKEBQUBAyMjKQmZmJr776yqjt3Lkz3nnnHQwfPhxdunTBqlWrMHr0aJSUlOD+++/Hb7/9ZjI3V+/l5YUDBw7oDhDaG71/+AEgKSnJ6vdpWH+xuroaVVVVaN26Ndq0aWOsNVLHuHHj4OXlhTVr1sDDwwMHDx6Er68voqOj4e/vrwmWdOjQAVu2bEHv3r2xbt06vPzyyygqKkJaWhrWrFmDwsJC5fo5c+Zg8eLFcHNz0+VXvdqkpCTW3A1/T+7u7iguLkZAQABeeeUVFBcX49NPP0VBQQFGjRqF//3vfyb67777rtH5Bw8ebLWeO7c1pKenY+nSpTh+/DgAoGvXrpg3b57Nn23t2rVDbm4u7rzzTl16rt+5+uTkZDg7O2P27NnYtm0bIiMjQUSorq5GUlIS4uLiTPReXl7Ytm0b+vfvbzKem5uLiIgIVFRUWKWtT35+vjGQ26NHD/Tt27dRH6nUO5ItqvWOZEtztZ17U9Uae/RquQF6rt6atarwO3dfoFpvif3795utE2iPAKS9UBk8VeV3zs06TuBpx44d7BuBKvXN2XbVa23I9u3bsX37dpw9e9ZYy7SOhvXwBUE10qREsBtVVVUoKyvDtWvXTMYb3v1QFQBsiN5NHadYv8oAgjVw5+cUwp0yZQrGjh1rPFU5YsQIAMC+ffvQvXt3zXtx9Q8//DCysrJMGiDYa616tA0DYQUFBaipqUG3bt0A1Da7cXZ2Rr9+/czOofd6bxgYBUzvzDeEWxRbdaMPPfrCwkJUV1cb/22Juk2UXi137oa0bt0aVVVVAGqLxtdt/Nu2bas5AQsA4eHhjc7b8HQzR8+du24sKyvL+DnWs2dPREVFmW2WkZSUhAULFmDWrFm47777AAC7d+/GjBkzcP78ed3/z8wxbdo0rFu3DgsWLNCl5/qdq+c2Tfnjjz/MnvZp1aqVZiPO0QLqTzxx9I5ki6y1+djObcyjcq3ffPMNtm3bZgz2AbX7t5UrVyIiIkJjC1fPWatKv3P3Bar1lnBxccFPP/2kGW94E6ZhIKx+kM3SjT2DwQBXV1cEBQUhOjrauM/gBvw4tnD1qvzesKmfPeHOrVqvcm5H01vLjTLZBKHJafKkZuFPx9mzZ2nUqFFma9qZq/eWk5PT6MNWuDWkOMX662rb1X80tlaungt3fm4h3E8++YSSkpLo9OnTxrHU1FTKysoyaw9Hv3jxYvL29qYxY8bQv//9b3rjjTdMHrasleuXxMREioyMNKl39uuvv1J0dDQtW7bMRMu93i2Rl5dH3bp1M/scpz6O6kYfqhqDNAWRkZHGGn6tWrWi8vJyIqqtgxocHKzRV1RUmDzOnTtH3377Ld1zzz20bds2m/TcuY8fP05du3alNm3aUJ8+fahPnz7Upk0b6tatm7HIfn0CAgLMFpNPTU2lgIAA3T6rw9p6hUR8v3P1RLymKVFRUTR48GA6c+aMcay8vJyGDBmiqaHI0RLVFtQPCwszqclZUlJCYWFh9Oijjzap3pFskbU2H9vr0FvHT+Va3d3dqbCwUDNeUFBgtqEFV1+HnrU2ld/r09i+QKWeWyfQHMeOHaPhw4cbG77VER4eTp6enuTm5kZ9+/alvn37kru7O3l5edE999xD3t7e5OPjQyUlJURkXT1EvbbYS6/69yS0LDp06EDp6ek32wxBMCIBQsFmJkyYQPfddx/l5eWRm5sbffvtt5SRkUHdunWjTZs2afSqg2bcTdrs2bN1f/lVGUCwBu78jlQIl9vlS2Uwxs/Pj4qLizXjhw4doo4dO5qMca93S1jq4MdFdaMPrt6ROHXqFI0ePZpCQkKMHbaJiJ555hmKjY3VPc/OnTupb9++SvSWtH/5y19o5MiRdOHCBePY+fPnaeTIkTRq1CiN/pZbbjE2ZKrPsWPH6JZbbtFtex3h4eG6HkOHDtW8lut3rp7bNKWsrIxCQ0OpVatWFBgYSIGBgeTi4kJ9+vQxuZnB1RLVdkXNzc3VjO/bt4+8vLyaVO9ItqjWO5Itzd127k1VlWvlBui5es5aVfvdHNx9gb305vbkt912G40fP55++ukn3fObC4QlJyfTI488YtKEsKKigv7+97/T8uXL6fLlyxQdHW3sKG0ObgDPki320qv+PQkti7Zt25q98SsINwtJMRZsZseOHfjiiy8QFhYGJycndO7cGQ8++CA8PT3x2muvYfTo0Sb6hkfwq6urUVhYiAULFuDVV1+12R5uysnBgwcRGhoKACguLjZ5ruExby8vL83rH3zwQbRu3Rpz5sxBfn6+TXou3PlvlJqXkpKC6dOnw9XVFSkpKY2+9+zZs9n6+vz444/Gf5OFpgr14ayV65fff/8d586d07zm3LlzqKysNBnjXu9ffvmlyc9EhJ9//hkrVqwwpoLWh5te849//AP33HOPsXFHXUHwwMBAs/+fVOsdCX9/f2zatEkznpyczJrntttuw9GjR5XoLWlzcnLw/fffG9OugNpagAkJCWavm6CgIHz88cd48cUXTcY/+ugjBAcH67a9DltSa7h+5+pXr16N1NRU3bUVO3XqhIKCAmzfvt2k4UtdGQRrtQA/JVml3pFsUa13JFuau+2xsbG4dOkSSkpKNHX8Zs+erSltoXKtK1asQFRUFAICAtCpUycAQFlZGXr16oUPPvjAZj1nrSr9zt0XqNabW481mEtJXrp0KbZu3QpPT0/jmJeXF1555RVEREQgLi4OCxcuNLs/ryM4OBgJCQlm6yFybOHqVftdEAB+GRdBUI00KRFsxtPTEwcPHkRAQAA6d+6MdevW4b777sOPP/6Inj17GmtL3YicnBy7BM08PDywa9cuY9CvjsLCQgwZMsRsTStbOXLkCMLCwnDp0iUlenva01gh3OzsbOzfvx/t2rVrtHmIwWBAaWkpunTpwtI3ZO3atUhOTjY2VQgODsYzzzyDadOm6V0qy5eWtJMmTcKuXbuQmJiIAQMGAKitnThv3jw88MADSEtLM2q51zu3g5+eItfcxh2q9c2Bq1evaupF1v/CAtTeLKhP3eY+ISEBNTU12L17t9V67txt27bFpk2bNPVR9+zZg8jISE1x9M8++wzjxo3DiBEjjF9E9uzZg+3bt+Pjjz/G3/72N7N+UY0ev3P13KYpAK8AOEcbHR2NiooKZGZmws/PDwBw5swZxMTEwMfHBxs2bGgyvSPZImttPrZzG/OoXCtQ+9moN0DP1XPWqtLv3H2Baj23TmBjgbBOnTrh66+/Nj7n7u6OTZs2aerw7ty5E5GRkaisrERpaSlCQ0Mb3Z8fOHAAgwcP1mg4tnD1qv0utFzq/5/7448/kJaWhpCQEISEhGhuNDSXfbbw50FOEAo2061bNxw9ehQBAQHo3bs33n77bQQEBGD16tWsP4bcUzqWGDZsGOLi4jSbtPj4eAwfPtymuRv7kt8wIGmNXrU9NyqEW/9UX/1/W4Krr8/ChQuRlJSE2NhY3HvvvQCAvXv3Ij4+HmVlZVi0aJGJnrNWrl9Wr16NuXPnYsKECcaGGC4uLpg6dSqWLl1qouVe79w783qKXHMbd6jWOyqXL1/Gc889h48//hgXLlzQPN+wMUhoaKjZbnQDBw4020WOo+fO/de//hXTp0/H2rVrTYLWM2bMQFRUlEY/ZswY7Nu3D8nJycjKygJQ+0U5NzcXffr00ehVwvU7V8+9284pAM4tFq76xBNH70i2yFqbj+0qT/lxbQFqT+nv2LHDGKAvLCw0dhc291nJ0XPWqtLv3H2Ban1hYSEKCgpw/fp1TaO27t2746233sKzzz6L3bt3o0ePHnj44YdNXt8wEFaf6OhoPPHEE0hMTDQGZvPy8jB37lzjPLm5uejatSsA/ik8ji1cvWq/Cy2XhntrvZlsgtAUyAlCwWY++OAD1NTU4PHHH0d+fj5GjhyJCxcuoHXr1khLS8O4ceNM9NyTNFxOnz6NqKgolJSUaDZpX375Je644w6r53Zycmr0S37Dbr1cvWp7OnbsiCVLllhMzbvRabE6DAYDEhMT2fr6+Pr6IiUlBePHjzcZz8zMRGxsLM6fP28yzlmrtX6/fPkyfvjhBwDAnXfeCTc3N42Ge71z78xbYv/+/az0GgF4+umnkZ2djcWLF+Oxxx7DypUrcebMGbz99ttISEhATEyMif7UqVMmPzs5OcHX1xeurq5m5+fouXNXVFRg8uTJ2Lhxo/ELbXV1NaKjo5Gammo2jd5R4Ppdj96Wu+03+tyzVluHyhNPXL0j2SJrbR62qz7lx9HeKEDf0BaunrtWVX7n7gtU65cvX45du3bh/fffN57YvnjxIqZNm4b7778fTz75JCZMmIArV65gy5YtFtdvjkuXLiE+Ph7p6emoqakBUHsDdvLkyUhOToabmxsOHDgAoDZI4kin8FT7XRAEwRGRAKFgd6qqqnDkyBH4+/ujffv2mudVB80A/qZOLyoDCE1hz41S84YOHarrfQ0GA3bs2MHW18fb2xt5eXma+mjHjh3DgAEDNGlNKoMxtnCj633o0KGN3pk/evQoDAaD8c68JSyl1wiW8ff3R3p6OsLDw+Hp6YmCggIEBQUhIyMDmZmZ+OqrrzSv4aSXcvXcuQHgxIkTOHz4MIDaWqpBQUEW13v9+nVkZWUZP/d69uyJqKgoODs7W3yNCrh+16O35bOGk5KsOn1Ztd6RbJG1Ng/brbmpqmqt3AA9V89dqyq/c/cFqvW33347tm7dqtmDlJSUICIiAmfOnEFBQQEiIiJw/vx5qwJhly5dMpaaCQwMhLu7u4XfEg+VQTzVfhcEQXBEJMVYsAq9J8cA7WmOhqmoKoI33BQVvXTu3Jm1YeTqVdtzo9Q8bmMCWxoZPPbYY1i1apXm+lizZo3mhBHAW6u9/W7L9V630bzRnfn4+Hhs2bJFilzbkV9//RWBgYEAauvY1dXtu//++zFz5kyNnpteqjJ1FeDV6Dxx4gRGjx6N8vJy4xeT1157DZ06dcLmzZtZAS9b4fpdj96WzxpOSrLK9GXVekeyRbXekWxp7rZzG/OoXOu1a9c0dVftqeesVaXfufsC1fqLFy/i7NmzmqDVuXPnjDclvb29jfVhuSnJQG0twpCQkMZ/QeAH/Li2cPSq/S4IguCIyAlCwSoanuYoKChATU2N5o9tv379NKc5AOtO0uiFm3Kicm6Vtuid31EL4cbGxiI9PR2dOnXCwIEDAdTWWCsrK8OkSZNMbEtKSmL50t5+t+V6596Zd6T0muZOSEgI3nzzTQwZMgQjRoxAaGgoli1bhpSUFCxZsgTl5eUmepWnV7hzW6rRuWLFCsTHx2tqdI4aNQpEhA8//ND4xenChQuYOHEinJycsHnzZl3vaw+4fufq9cD53KuPyvRl1XpHskW13pFs4eodyZY6VJ3y49ry3HPPwd3dXXeAnqsH9K9Vpd+5+wLV+piYGOzdu9dsncBBgwYhIyMD69evx7Jly7B//36lKcncU3hcWzj64uJipX4XBEFwROQEoWAV9U9zJCUlwcPDA2lpacYOrL/99humTJmCBx54QPNaa07ScFi9ejVSU1NZm2NVc6u0Re/8jloIt7i4GH379gUAY92/9u3bo3379ia21dnF8aW9/W7L9c69My9Fru3HlClTUFRUhCFDhuD5559HZGQkVqxYgerqarPBcJWnV7hzr1q1Cu+8845Jjc6oqCiEhIQgNjZWEyDMycnB999/b5LS1a5dOyQkJDT5yVOu37l6PXA+9xreJ+V8Rqo+8aTyGmvOekeyhat3JFsAtaf89GgbBvPXrFmDbdu2WQzQc/X14axVpd+5+wLV+rfffhvx8fF49NFHzdYJBIDu3bvj3XffBQAsXboUW7duNekw7+XlhVdeeQURERGIi4vDwoULERERocsf9eGewuPawtFfvXpVqd8FQRAcEhIEG/Hz86Pi4mLN+KFDh6hjx46a8Q4dOlB6eroye9q2bUsnTpxwiLlV2tIU8zsSnLWq9Av3ep8wYQJ16dKFPv/8czp9+jSdPn2aPv/8cwoMDKSJEycSEVFmZib169ePiIji4+PNPubMmUMvvvgivffee3ThwgUla/uzc/LkSfrss8+oqKjI7PPz58+nRYsW6Z6Po+fO7eXlRceOHdOMHz16lLy8vDTjPj4+tGfPHs347t27ycfHR/f7quBGfrdVfzNRec1w9Y5ki2q9I9nC1TuSLUT8PZm91xoeHq7rMXToUKv01q5Vpd+5+wLV+joqKyupqKiIioqKqLKy0qL9bm5ulJ2drRnPzs4md3d3IiL64YcfyMPDQ5c/6uPn50clJSWa8eLiYvLz8yMiovz8fGrXrp1VtnD0TeV3QRAER0JSjAWb8fDwwMaNGxEeHm4ynp2djaioKFRWVpqMW1MEnoM1KSeq5lZpS1PM70hw1qrSL9zrndvBT4pc2xdO6lxcXBzS09N1p5dy9Ny5Y2Nj0apVK8343LlzceXKFaxcudJkfNKkSSgoKMDatWsxYMAAALUp+08++ST69euH1NRUc+5RhuomDDcTbtkGlfr63GxbZK3Nx/b66NmTNVXKvmputNam8jt3X6Baz4WbkszB3d0dmzZt0uyxdu7cicjISFRWVqK0tBShoaH4/fff2bZw9Dt37nQovwuCIDQFEiAUbGbSpEnYtWsXEhMTTb6Yzps3Dw888ADS0tJM9CqCN01VZ09lAKEp7GnOqAzGcOBe73Xo7eCnsrZPS4Nbi7KxTrnmuuNy9Hq09T/HampqkJqaCn9/f7M1Ot98802TOSoqKjB58mRs3LjReL1XV1cjOjoaqamp8PLysvj+9sbRarXaG9Xd3jl6vVu4prBF1mpZ72i210fPnkzlWpuSG621Kf0O8Dv7qtbrRWUgjBvwa4rgqaP4XRAEoSmQAKFgM1VVVZg7dy7ee+89VFdXA6j9Yzt16lQsXboUbm5uJnoVwRtbN2n2eB9bAwhNYU9zxt7BGGvhXu9cpMi1/bCmWP/NxB6fYydOnMDhw4cBAD169EBQUJDd7NNLUzRVEATBehy1eZkKWtJamxoVgTBrg48SxBMEQbAPEiAU7Mbly5eNzSbuvPNOi4GSlhTUEv686L3euXDTawTLqC5n4GisXbsWycnJOH78OAAgODgYzzzzDKZNm9akdnD93tJ+T4Jws2mqm6qOQEta658JCeAJgiDcHCRAKAiC4ECorO3T0mhJNToXLlyIpKQkxMbG4t577wUA7N27FytWrEB8fLym67FKHK1WqyAIgiAIgiAIN0YChIIgCA6EFLm2jZaaTubr64uUlBSMHz/eZDwzMxOxsbHK09GbsqmCIAiCIAiCIAj2RwKEgiAIDoik11hHS00n8/b2Rl5eHoKDg03Gjx07hgEDBqCiokLp+zd1cX9BEARBEARBEOyLBAgFQRAEoZkTGxuLVq1aaU7bzZ07F1euXMHKlStvkmWCIAiCIAiCIDQHXG62AYIgCIIg8KmfpmswGPDuu+/i22+/xcCBAwEA+/btQ1lZGSZNmnSzTBQEQRAEQRAEoZkgJwgFQRAEoRkiabqCIAiCIAiCINgLCRAKgiAIgiAIgiAIgiAIQgvG6WYbIAiCIAiCIAiCIAiCIAjCzUMChIIgCIIgCIIgCIIgCILQgpEAoSAIgiAIgiAIgiAIgiC0YCRAKAiCIAiCIAiCIAiCIAgtGAkQCoIgCIIgCIIgCIIgCEILRgKEgiAIgiAIgiAIgiAIgtCCkQChIAiCIAiCIAiCIAiCILRgJEAoCIIgCIIgCIIgCIIgCC2Y/wNcBdZwh8YTGwAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Check correlation matrix\n", - "import seaborn as sns\n", - "\n", - "# 1. Move 'has_resolution_incident' to the end\n", - "target_col = 'has_resolution_incident'\n", - "if target_col in df.columns:\n", - " columns = [col for col in df.columns if col != target_col] + [target_col]\n", - " df = df[columns]\n", - "\n", - "# 2. Create short column names (truncate to, say, 15 chars)\n", - "short_columns = [col[:15] for col in df.columns]\n", - "\n", - "# 3. Compute correlation matrix\n", - "correlation_matrix = df.corr()\n", - "\n", - "# 4. Plot with Seaborn\n", - "plt.figure(figsize=(14, 12))\n", - "sns.heatmap(\n", - " correlation_matrix,\n", - " xticklabels=short_columns,\n", - " yticklabels=short_columns,\n", - " cmap='coolwarm',\n", - " annot=False,\n", - " fmt=\".2f\",\n", - " square=True,\n", - " cbar_kws={'shrink': 0.6}\n", - ")\n", - "plt.title('Correlation Matrix (Truncated Labels)', fontsize=16)\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "a6f7988d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "number_of_previous_incidents_in_listing 0.101702\n", - "number_of_previous_payouts_in_listing 0.096180\n", - "host_account_type_Host 0.073745\n", - "number_of_listings_of_host 0.070200\n", - "listing_number_of_bedrooms 0.065542\n", - "listing_country_United States 0.062555\n", - "host_active_pms_list_Hostify 0.060898\n", - "host_country_United States 0.055897\n", - "has_deposit_management_service_business_type 0.055543\n", - "host_country_United Kingdom 0.049846\n", - "listing_country_United Kingdom 0.048641\n", - "guest_country_United States 0.047742\n", - "number_of_applied_billable_services 0.045234\n", - "host_account_type_PMC - Property Management Company 0.044632\n", - "has_completed_verification 0.040583\n", - "guest_age 0.039814\n", - "is_guest_from_listing_country 0.038440\n", - "listing_number_of_bathrooms 0.038292\n", - "listing_country_New Zealand 0.036971\n", - "is_guest_from_listing_town 0.036880\n", - "host_country_New Zealand 0.036791\n", - "previous_bookings_in_listing_count 0.035117\n", - "guest_has_email 0.033928\n", - "number_of_applied_services 0.032459\n", - "number_of_applied_upgraded_services 0.032452\n", - "guest_country_New Zealand 0.031652\n", - "guest_country_Other 0.031166\n", - "guest_has_phone_number 0.030379\n", - "booking_number_of_nights 0.026738\n", - "has_guest_previously_booked_same_listing 0.026621\n", - "host_active_pms_list_Hospitable 0.025430\n", - "host_active_pms_list_Hostfully 0.025058\n", - "number_of_previous_bookings_of_guest 0.024027\n", - "number_of_nights 0.023304\n", - "guest_country_Canada 0.022773\n", - "host_active_pms_list_Hostaway 0.021299\n", - "booking_days_to_check_in 0.020963\n", - "host_country_Canada 0.020417\n", - "has_upgraded_screening_service_business_type 0.020254\n", - "has_verification_request 0.019356\n", - "listing_country_Colombia 0.018607\n", - "listing_country_Canada 0.018591\n", - "is_host_from_listing_country 0.018029\n", - "number_of_previous_incidents_of_host 0.017803\n", - "number_of_previous_payouts_of_host 0.017717\n", - "days_from_booking_creation_to_check_in 0.016637\n", - "host_active_pms_list_OwnerRez 0.015977\n", - "is_host_from_listing_town 0.015359\n", - "is_host_from_listing_postcode 0.014238\n", - "host_active_pms_list_Avantio 0.011872\n", - "host_active_pms_list_Lodgify 0.010976\n", - "guest_country_Australia 0.009813\n", - "listing_country_Ireland 0.009753\n", - "listing_country_Mexico 0.009473\n", - "guest_country_Colombia 0.009243\n", - "is_guest_from_listing_postcode 0.009204\n", - "host_active_pms_list_TrackHs 0.008961\n", - "has_protection_service_business_type 0.008933\n", - "guest_country_Mexico 0.008703\n", - "host_country_Mexico 0.008603\n", - "listing_country_Bahamas 0.008572\n", - "host_country_Sweden 0.008302\n", - "host_country_Bulgaria 0.008129\n", - "guest_country_Germany 0.007512\n", - "guest_country_United Kingdom 0.007411\n", - "host_months_with_truvi 0.007277\n", - "host_active_pms_list_Guesty 0.007083\n", - "host_country_Portugal 0.007005\n", - "guest_country_Ireland 0.006589\n", - "host_active_pms_list_Uplisting 0.005862\n", - "guest_country_France 0.005616\n", - "host_country_Other 0.004820\n", - "has_billable_services 0.004251\n", - "listing_country_Other 0.003930\n", - "days_to_start_verification 0.003879\n", - "listing_country_Virgin Islands, U.s. 0.002997\n", - "host_age 0.002981\n", - "host_active_pms_list_Hospitable Connect 0.002904\n", - "host_active_pms_list_Smoobu 0.002597\n", - "host_country_Norway 0.002255\n", - "host_country_Australia 0.001435\n", - "listing_country_Australia 0.001435\n", - "days_to_complete_verification 0.001179\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "# Compute correlation with the target variable\n", - "correlation_with_target = df.corrwith(df['has_resolution_incident'])\n", - "\n", - "# Drop the target itself (its correlation with itself is always 1)\n", - "correlation_with_target = correlation_with_target.drop(labels='has_resolution_incident')\n", - "\n", - "# Sort by absolute correlation, descending\n", - "correlation_sorted = correlation_with_target.abs().sort_values(ascending=False)\n", - "\n", - "# Print the sorted correlations (you can keep the original signs too if preferred)\n", - "print(correlation_sorted)" - ] - }, - { - "cell_type": "markdown", - "id": "2caec836", - "metadata": {}, - "source": [ - "### Upsampling Unbalanced Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "e6d091fb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Class distribution in training set BEFORE SMOTE:\n", - "has_resolution_incident\n", - "0 16843\n", - "1 202\n", - "Name: count, dtype: int64\n", - "\n", - "Class distribution in training set AFTER SMOTE:\n", - "has_resolution_incident\n", - "0 16843\n", - "1 16843\n", - "Name: count, dtype: int64\n" - ] - } - ], - "source": [ - "X = df.drop(columns=['has_resolution_incident'])\n", - "y = df['has_resolution_incident']\n", - "\n", - "# 1. Split data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123, stratify=y)\n", - "\n", - "print(\"Class distribution in training set BEFORE SMOTE:\")\n", - "print(y_train.value_counts())\n", - "\n", - "# 2. Apply SMOTE only to the training data\n", - "smote = SMOTE(sampling_strategy='auto', random_state=123)\n", - "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n", - "\n", - "print(\"\\nClass distribution in training set AFTER SMOTE:\")\n", - "print(y_train_resampled.value_counts())\n", - "\n", - "# Now use X_train_resampled, y_train_resampled for model training\n", - "# and X_test, y_test for final evaluation." - ] - }, - { - "cell_type": "markdown", - "id": "ab8f7646", - "metadata": {}, - "source": [ - "### Feature Selection\n", - "\n", - "Since we have many columns, weā€™ll apply feature selection techniques like KBest, RFE (Recursive Feature Elimination), and Lasso (L1 regularization), to reduce the number of fields used in our predictive model. This helps:\n", - "- Avoid overfitting\n", - "- Potentially improve model performance (simpler models often generalize better)\n", - "- Reduce training time\n", - "\n", - "We'll also experiment with different numbers of features to determine which combination produces the model best suited to our objectives." - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "0246eb6c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected Features:\n", - "Index(['number_of_listings_of_host', 'guest_age',\n", - " 'has_guest_previously_booked_same_listing',\n", - " 'listing_number_of_bedrooms', 'listing_number_of_bathrooms',\n", - " 'previous_bookings_in_listing_count',\n", - " 'number_of_previous_incidents_in_listing',\n", - " 'number_of_previous_payouts_in_listing', 'guest_has_email',\n", - " 'guest_has_phone_number', 'is_guest_from_listing_country',\n", - " 'is_host_from_listing_postcode', 'has_completed_verification',\n", - " 'booking_days_to_check_in',\n", - " 'has_deposit_management_service_business_type',\n", - " 'has_protection_service_business_type', 'host_account_type_Host',\n", - " 'host_account_type_PMC - Property Management Company',\n", - " 'host_active_pms_list_Guesty', 'host_active_pms_list_Hostify',\n", - " 'host_country_Canada', 'host_country_United Kingdom',\n", - " 'guest_country_Canada', 'guest_country_Other',\n", - " 'guest_country_United Kingdom', 'guest_country_United States',\n", - " 'listing_country_Canada', 'listing_country_Other',\n", - " 'listing_country_United Kingdom', 'listing_country_United States'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "selector = SelectKBest(score_func=f_classif, k=30)\n", - "X_new = selector.fit_transform(X_train_resampled, y_train_resampled)\n", - "selected_features_kbest = X_train_resampled.columns[selector.get_support()]\n", - "\n", - "print(\"Selected Features:\")\n", - "print(selected_features_kbest)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "736a8d68", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected Features using RFE:\n", - "Index(['guest_has_email', 'is_guest_from_listing_postcode',\n", - " 'is_host_from_listing_postcode', 'has_verification_request',\n", - " 'host_active_pms_list_Avantio', 'host_active_pms_list_Guesty',\n", - " 'host_active_pms_list_Hospitable Connect',\n", - " 'host_active_pms_list_Hostify', 'host_active_pms_list_TrackHs',\n", - " 'host_active_pms_list_Uplisting', 'host_country_Canada',\n", - " 'host_country_United Kingdom', 'guest_country_Australia',\n", - " 'guest_country_Canada', 'guest_country_Colombia',\n", - " 'guest_country_France', 'guest_country_Germany',\n", - " 'guest_country_Ireland', 'guest_country_Mexico',\n", - " 'guest_country_New Zealand', 'guest_country_Other',\n", - " 'guest_country_United Kingdom', 'guest_country_United States',\n", - " 'listing_country_Australia', 'listing_country_Colombia',\n", - " 'listing_country_Ireland', 'listing_country_Mexico',\n", - " 'listing_country_Other', 'listing_country_United States',\n", - " 'listing_country_Virgin Islands, U.s.'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "# Recursive Feature Elimination (RFE) with Logistic Regression\n", - "model = LogisticRegression(max_iter=1000)\n", - "rfe = RFE(model, n_features_to_select=30)\n", - "rfe.fit(X_train_resampled, y_train_resampled)\n", - "selected_features_rfe = X_train_resampled.columns[rfe.support_]\n", - "\n", - "print(\"Selected Features using RFE:\")\n", - "print(selected_features_rfe)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "484786aa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected Features using Lasso Regression:\n", - "Index(['days_from_booking_creation_to_check_in', 'number_of_nights',\n", - " 'host_age', 'host_months_with_truvi', 'number_of_listings_of_host',\n", - " 'number_of_previous_incidents_of_host',\n", - " 'number_of_previous_payouts_of_host', 'guest_age',\n", - " 'number_of_previous_bookings_of_guest',\n", - " 'has_guest_previously_booked_same_listing',\n", - " 'listing_number_of_bedrooms', 'listing_number_of_bathrooms',\n", - " 'previous_bookings_in_listing_count',\n", - " 'number_of_previous_incidents_in_listing',\n", - " 'number_of_previous_payouts_in_listing', 'days_to_start_verification',\n", - " 'days_to_complete_verification', 'guest_has_email',\n", - " 'guest_has_phone_number', 'is_guest_from_listing_town',\n", - " 'is_guest_from_listing_country', 'is_guest_from_listing_postcode',\n", - " 'is_host_from_listing_town', 'is_host_from_listing_country',\n", - " 'is_host_from_listing_postcode', 'has_completed_verification',\n", - " 'number_of_applied_services', 'number_of_applied_upgraded_services',\n", - " 'number_of_applied_billable_services', 'booking_days_to_check_in',\n", - " 'booking_number_of_nights', 'has_verification_request',\n", - " 'has_billable_services', 'has_upgraded_screening_service_business_type',\n", - " 'has_deposit_management_service_business_type',\n", - " 'has_protection_service_business_type', 'host_account_type_Host',\n", - " 'host_account_type_PMC - Property Management Company',\n", - " 'host_active_pms_list_Avantio', 'host_active_pms_list_Guesty',\n", - " 'host_active_pms_list_Hospitable',\n", - " 'host_active_pms_list_Hospitable Connect',\n", - " 'host_active_pms_list_Hostaway', 'host_active_pms_list_Hostfully',\n", - " 'host_active_pms_list_Hostify', 'host_active_pms_list_Lodgify',\n", - " 'host_active_pms_list_OwnerRez', 'host_active_pms_list_Smoobu',\n", - " 'host_active_pms_list_TrackHs', 'host_active_pms_list_Uplisting',\n", - " 'host_country_Australia', 'host_country_Bulgaria',\n", - " 'host_country_Canada', 'host_country_New Zealand',\n", - " 'host_country_Norway', 'host_country_Other',\n", - " 'host_country_United Kingdom', 'host_country_United States',\n", - " 'guest_country_Australia', 'guest_country_Canada',\n", - " 'guest_country_Colombia', 'guest_country_France',\n", - " 'guest_country_Germany', 'guest_country_Ireland',\n", - " 'guest_country_Mexico', 'guest_country_New Zealand',\n", - " 'guest_country_Other', 'guest_country_United Kingdom',\n", - " 'guest_country_United States', 'listing_country_Australia',\n", - " 'listing_country_Bahamas', 'listing_country_Canada',\n", - " 'listing_country_Colombia', 'listing_country_Ireland',\n", - " 'listing_country_Mexico', 'listing_country_New Zealand',\n", - " 'listing_country_Other', 'listing_country_United Kingdom',\n", - " 'listing_country_United States',\n", - " 'listing_country_Virgin Islands, U.s.'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "# Lasso Regression for feature selection\n", - "from sklearn.linear_model import LogisticRegression\n", - "model = LogisticRegression(penalty='l1', solver='liblinear')\n", - "model.fit(X_train_resampled, y_train_resampled)\n", - "\n", - "# Check which features have non-zero coefficients\n", - "selected_features_lasso = X_train_resampled.columns[model.coef_[0] != 0]\n", - "print(\"Selected Features using Lasso Regression:\")\n", - "print(selected_features_lasso)" - ] - }, - { - "cell_type": "markdown", - "id": "04010a1e", - "metadata": {}, - "source": [ - "## Processing\n", - "Processing in this notebook is quite straight-forward: we just drop id booking, split the features and target and apply a scaling to numeric features.\n", - "Afterwards, we split the dataset between train and test and display their sizes and target distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "f735b111", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training set size: 33686 rows\n", - "Test set size: 4262 rows\n", - "\n", - "Training target distribution:\n", - "has_resolution_incident\n", - "0 0.5\n", - "1 0.5\n", - "Name: proportion, dtype: float64\n", - "\n", - "Test target distribution:\n", - "has_resolution_incident\n", - "0 0.988268\n", - "1 0.011732\n", - "Name: proportion, dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_51877/1488269494.py:8: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " X_train_kbest[selected_features_kbest] = X_train_kbest[selected_features_kbest].astype(float)\n" - ] - } - ], - "source": [ - "# Separate features and target\n", - "X_train_kbest = X_train_resampled[selected_features_kbest] # Use the features selected by SelectKBest\n", - "y_train_kbest = y_train_resampled\n", - "X_test_kbest = X_test[selected_features_kbest]\n", - "y_test_kbest = y_test\n", - "\n", - "# Scale numeric features\n", - "X_train_kbest[selected_features_kbest] = X_train_kbest[selected_features_kbest].astype(float)\n", - "\n", - "print(f\"Training set size: {X_train_kbest.shape[0]} rows\")\n", - "print(f\"Test set size: {X_test_kbest.shape[0]} rows\")\n", - "\n", - "print(\"\\nTraining target distribution:\")\n", - "print(y_train_kbest.value_counts(normalize=True))\n", - "\n", - "print(\"\\nTest target distribution:\")\n", - "print(y_test_kbest.value_counts(normalize=True))" - ] - }, - { - "cell_type": "markdown", - "id": "897eb678", - "metadata": {}, - "source": [ - "### Using RFE Features" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "301a8fb2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training set size: 33686 rows\n", - "Test set size: 4262 rows\n", - "\n", - "Training target distribution:\n", - "has_resolution_incident\n", - "0 0.5\n", - "1 0.5\n", - "Name: proportion, dtype: float64\n", - "\n", - "Test target distribution:\n", - "has_resolution_incident\n", - "0 0.988268\n", - "1 0.011732\n", - "Name: proportion, dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_51877/2037518775.py:8: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " X_train_rfe[selected_features_rfe] = X_train_rfe[selected_features_rfe].astype(float)\n" - ] - } - ], - "source": [ - "# Separate features and target\n", - "X_train_rfe = X_train_resampled[selected_features_rfe] # Use the features selected by RFE\n", - "y_train_rfe = y_train_resampled\n", - "X_test_rfe = X_test[selected_features_rfe]\n", - "y_test_rfe = y_test\n", - "\n", - "# Scale numeric features\n", - "X_train_rfe[selected_features_rfe] = X_train_rfe[selected_features_rfe].astype(float)\n", - "\n", - "print(f\"Training set size: {X_train_rfe.shape[0]} rows\")\n", - "print(f\"Test set size: {X_test_rfe.shape[0]} rows\")\n", - "\n", - "print(\"\\nTraining target distribution:\")\n", - "print(y_train_rfe.value_counts(normalize=True))\n", - "\n", - "print(\"\\nTest target distribution:\")\n", - "print(y_test_rfe.value_counts(normalize=True))" - ] - }, - { - "cell_type": "markdown", - "id": "2bbc1524", - "metadata": {}, - "source": [ - "### Using Lasso Features" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "f4b9c01a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training set size: 33686 rows\n", - "Test set size: 4262 rows\n", - "\n", - "Training target distribution:\n", - "has_resolution_incident\n", - "0 0.5\n", - "1 0.5\n", - "Name: proportion, dtype: float64\n", - "\n", - "Test target distribution:\n", - "has_resolution_incident\n", - "0 0.988268\n", - "1 0.011732\n", - "Name: proportion, dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_51877/3979584456.py:8: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " X_train_lasso[selected_features_lasso] = X_train_lasso[selected_features_lasso].astype(float)\n" - ] - } - ], - "source": [ - "# Separate features and target\n", - "X_train_lasso = X_train_resampled[selected_features_lasso] # Use the features selected by lasso\n", - "y_train_lasso = y_train_resampled\n", - "X_test_lasso = X_test[selected_features_lasso]\n", - "y_test_lasso = y_test\n", - "\n", - "# Scale numeric features\n", - "X_train_lasso[selected_features_lasso] = X_train_lasso[selected_features_lasso].astype(float)\n", - "\n", - "print(f\"Training set size: {X_train_lasso.shape[0]} rows\")\n", - "print(f\"Test set size: {X_test_lasso.shape[0]} rows\")\n", - "\n", - "print(\"\\nTraining target distribution:\")\n", - "print(y_train_lasso.value_counts(normalize=True))\n", - "\n", - "print(\"\\nTest target distribution:\")\n", - "print(y_test_lasso.value_counts(normalize=True))" - ] - }, - { - "cell_type": "markdown", - "id": "d36c9276", - "metadata": {}, - "source": [ - "## Classification Model with Random Forest\n", - "\n", - "We define a machine learning pipeline that includes:\n", - "- **Scaling numeric features** with `StandardScaler`\n", - "- **Training a Random Forest classifier** with balanced class weights to handle the imbalanced dataset\n", - "\n", - "We then use `GridSearchCV` to perform a **grid search with cross-validation** over a range of key hyperparameters (e.g., number of trees, max depth, etc.). \n", - "The model is evaluated using **Average Precision**, which is better suited for imbalanced classification tasks.\n", - "\n", - "The best combination of parameters is selected, and the resulting model is used to make predictions on the test set.\n" - ] - }, - { - "cell_type": "markdown", - "id": "fe3351be", - "metadata": {}, - "source": [ - "### Model 1 with Kbest Features" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "943ef7d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 10.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 14.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 14.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 15.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 15.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 18.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 21.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 22.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 23.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 28.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 28.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 18.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 21.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 22.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 22.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 23.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 24.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 12.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 15.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 22.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 25.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 25.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 25.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 25.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 16.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 16.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 21.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 21.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 22.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 13.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 25.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 13.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 13.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 13.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 26.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 17.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 20.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 20.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 12.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 12.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 20.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 24.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 20.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 19.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 19.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 23.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 12.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 23.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 18.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 19.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 19.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 22.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 12.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 21.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 12.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 18.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 19.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 18.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 19.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 10.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 18.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 11.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 16.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 16.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 15.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 16.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 10.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 10.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 10.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 11.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 19.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 15.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 16.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 10.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 16.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 10.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 19.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 19.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 15.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 16.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 16.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 16.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 18.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 10.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 10.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 12.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 13.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 17.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 16.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 9.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 8.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 16.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 16.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 9.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 20.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 11.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 10.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 13.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 13.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 14.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 9.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 9.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 14.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 9.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 17.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 11.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 11.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 15.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 13.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 8.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 16.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 15.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 9.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 15.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 9.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 10.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 9.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 13.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 14.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 9.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 14.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 9.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 9.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 9.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 17.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 17.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 13.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 9.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 15.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 13.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 14.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 14.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 14.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 14.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 14.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 14.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 17.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 20.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 21.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 21.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 26.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 26.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 22.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 23.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 23.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 24.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.7s[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.4s\n", - "\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 27.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 19.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 20.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 21.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 20.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 21.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 12.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 13.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 13.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 13.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 20.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 13.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 21.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 13.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 25.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 25.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 26.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 19.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 18.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 20.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 19.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 23.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 12.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 12.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 16.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 17.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 18.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 12.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 20.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 19.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 12.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 23.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 18.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 17.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 18.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 19.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 21.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 13.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 13.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 13.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 12.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 12.0s\n", - "Best hyperparameters: {'model__max_depth': None, 'model__max_features': 'sqrt', 'model__min_samples_leaf': 1, 'model__min_samples_split': 5, 'model__n_estimators': 300}\n" - ] - } - ], - "source": [ - "# Define pipeline (scaling numeric features only)\n", - "pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', RandomForestClassifier(class_weight='balanced', # We have an imbalanced dataset\n", - " random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid\n", - "param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "grid_search = GridSearchCV(\n", - " estimator=pipeline,\n", - " param_grid=param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=5, # 5-fold cross-validation\n", - " n_jobs=-1, # Use all available cores\n", - " verbose=2 # Verbose output for progress tracking\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "grid_search.fit(X_train_kbest, y_train_kbest)\n", - "\n", - "# Best model\n", - "best_pipeline_kbest = grid_search.best_estimator_\n", - "print(\"Best hyperparameters:\", grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba_kbest = best_pipeline_kbest.predict_proba(X_test_kbest)[:, 1]\n", - "y_pred_kbest = best_pipeline_kbest.predict(X_test_kbest)\n" - ] - }, - { - "cell_type": "markdown", - "id": "672444f7", - "metadata": {}, - "source": [ - "### Model 2 with RFE Features" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "id": "49cb625c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 7.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 7.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 7.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 9.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 11.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 11.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 13.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 9.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 14.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 10.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 10.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 10.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 11.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 11.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 8.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 8.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 9.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 9.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 9.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 10.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 10.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 7.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 13.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 9.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 9.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 10.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 7.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 8.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 12.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 7.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 12.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 8.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 12.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 11.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 6.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 8.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 10.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 10.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 10.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 12.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 11.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 8.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 9.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 9.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 9.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 10.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 11.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 9.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 8.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 8.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 9.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 11.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 8.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 8.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 8.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 8.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 9.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 9.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 9.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 6.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 8.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 8.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 9.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 9.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 6.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 6.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 8.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 9.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 7.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 7.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 7.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 7.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 7.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 8.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 9.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 12.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 6.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 10.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 6.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 9.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 9.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 9.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 8.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 11.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 11.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 8.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 8.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 8.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 10.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 11.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 8.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 8.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 11.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 8.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 8.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 8.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 10.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 9.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "Best hyperparameters: {'model__max_depth': None, 'model__max_features': 'log2', 'model__min_samples_leaf': 1, 'model__min_samples_split': 5, 'model__n_estimators': 200}\n" - ] - } - ], - "source": [ - "# Define pipeline (scaling numeric features only)\n", - "pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', RandomForestClassifier(class_weight='balanced', # We have an imbalanced dataset\n", - " random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid\n", - "param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "grid_search = GridSearchCV(\n", - " estimator=pipeline,\n", - " param_grid=param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=5, # 5-fold cross-validation\n", - " n_jobs=-1, # Use all available cores\n", - " verbose=2 # Verbose output for progress tracking\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "grid_search.fit(X_train_rfe, y_train_rfe)\n", - "\n", - "# Best model\n", - "best_pipeline_rfe = grid_search.best_estimator_\n", - "print(\"Best hyperparameters:\", grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba_rfe = best_pipeline_rfe.predict_proba(X_test_rfe)[:, 1]\n", - "y_pred_rfe = best_pipeline_rfe.predict(X_test_rfe)\n" - ] - }, - { - "cell_type": "markdown", - "id": "b763f4cd", - "metadata": {}, - "source": [ - "### Model 3 with Lasso Features" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "47c6ab43", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 9.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 9.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 10.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 10.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 17.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 17.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 9.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 18.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 11.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 11.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 23.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 16.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 27.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 18.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 9.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 19.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 11.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 11.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 21.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 29.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 17.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 15.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 27.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 29.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 29.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 20.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 30.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 18.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 11.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 9.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 9.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 19.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 10.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 10.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 26.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 27.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 44.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 18.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 31.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 19.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 18.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 19.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 20.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 9.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 35.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 9.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 37.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 28.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 30.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 31.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 28.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 15.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 31.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 15.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 15.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 10.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 22.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 22.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 27.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 32.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 11.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 20.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 24.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 23.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 25.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 15.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 15.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 25.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 33.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 19.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 22.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 22.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 23.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 28.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 16.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 28.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 21.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 11.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 21.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 22.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 21.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 13.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 25.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 17.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 17.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 20.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 19.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 20.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 11.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 11.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 11.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 12.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 18.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 17.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 19.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 17.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 12.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 12.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 12.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 20.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 18.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 17.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 18.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 19.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 21.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 12.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 13.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 17.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 9.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 20.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 10.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 9.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 18.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 9.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 21.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 11.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 21.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 15.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 15.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 16.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 16.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 17.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 10.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 10.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 10.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 10.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 11.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 14.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 14.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 16.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 14.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 14.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 9.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 9.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 10.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 12.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 14.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 14.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 15.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 9.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 15.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 9.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 10.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 9.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 17.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 10.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 9.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 9.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 14.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 14.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 16.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 16.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 15.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 17.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 10.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 10.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 19.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 17.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 24.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 24.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 16.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 16.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 16.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 26.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 28.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 17.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 9.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 24.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 24.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 24.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 15.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 28.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 27.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 16.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 16.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 26.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 26.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 23.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 26.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 26.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 18.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 16.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 16.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 19.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 19.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 23.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 24.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 24.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 24.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 14.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 28.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 14.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 10.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 17.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 19.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 21.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 21.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 10.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 31.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 33.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 22.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 23.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 22.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 23.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 25.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 28.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 12.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 21.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 21.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 13.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 23.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 29.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 27.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 18.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 18.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 18.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 19.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 15.8s\n", - "Best hyperparameters: {'model__max_depth': None, 'model__max_features': 'log2', 'model__min_samples_leaf': 1, 'model__min_samples_split': 2, 'model__n_estimators': 300}\n" - ] - } - ], - "source": [ - "# Define pipeline (scaling numeric features only)\n", - "pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', RandomForestClassifier(class_weight='balanced', # We have an imbalanced dataset\n", - " random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid\n", - "param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "grid_search = GridSearchCV(\n", - " estimator=pipeline,\n", - " param_grid=param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=5, # 5-fold cross-validation\n", - " n_jobs=-1, # Use all available cores\n", - " verbose=2 # Verbose output for progress tracking\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "grid_search.fit(X_train_lasso, y_train_lasso)\n", - "\n", - "# Best model\n", - "best_pipeline_lasso = grid_search.best_estimator_\n", - "print(\"Best hyperparameters:\", grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba_lasso = best_pipeline_lasso.predict_proba(X_test_lasso)[:, 1]\n", - "y_pred_lasso = best_pipeline_lasso.predict(X_test_lasso)\n" - ] - }, - { - "cell_type": "markdown", - "id": "fc2fcc89", - "metadata": {}, - "source": [ - "## Evaluation\n", - "This section aims to evaluate how good the new model is vs. the actual Resolution Incidents.\n", - "\n", - "We start by computing and displaying the classification report, ROC Curve, PR Curve and the respective Area Under the Curve (AUC)." - ] - }, - { - "cell_type": "markdown", - "id": "76099daf", - "metadata": {}, - "source": [ - "### Model 1 evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "78887f46", - "metadata": {}, - "outputs": [], - "source": [ - "# Actual and predicted\n", - "y_true_kbest = y_test_kbest\n", - "\n", - "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n", - "tn, fp, fn, tp = confusion_matrix(y_true_kbest, y_pred_kbest).ravel()\n", - "\n", - "# Total predictions\n", - "total = tp + tn + fp + fn\n", - "\n", - "# Compute all requested metrics\n", - "recall_kbest = recall_score(y_true_kbest, y_pred_kbest)\n", - "precision_kbest = precision_score(y_true_kbest, y_pred_kbest)\n", - "f1_kbest = fbeta_score(y_true_kbest, y_pred_kbest, beta=1)\n", - "f2_kbest = fbeta_score(y_true_kbest, y_pred_kbest, beta=2)\n", - "fpr_kbest = fp / (fp + tn) if (fp + tn) != 0 else 0\n", - "\n", - "# Scores relative to total\n", - "tp_score_kbest = tp / total\n", - "tn_score_kbest = tn / total\n", - "fp_score_kbest = fp / total\n", - "fn_score_kbest = fn / total\n", - "\n", - "# Create DataFrame\n", - "summary_df_kbest = pd.DataFrame([{\n", - " \"title\": \"Kbest\",\n", - " \"flagging_analysis_type\": \"RISK_VS_CLAIM using KBest Features from all features\",\n", - " \"count_total\": total,\n", - " \"count_true_positive\": tp,\n", - " \"count_true_negative\": tn,\n", - " \"count_false_positive\": fp,\n", - " \"count_false_negative\": fn,\n", - " \"true_positive_score\": tp_score_kbest,\n", - " \"true_negative_score\": tn_score_kbest,\n", - " \"false_positive_score\": fp_score_kbest,\n", - " \"false_negative_score\": fn_score_kbest,\n", - " \"recall_score\": recall_kbest,\n", - " \"precision_score\": precision_kbest,\n", - " \"false_positive_rate_score\": fpr_kbest,\n", - " \"f1_score\": f1_kbest,\n", - " \"f2_score\": f2_kbest\n", - "}])" - ] - }, - { - "cell_type": "markdown", - "id": "ea079e83", - "metadata": {}, - "source": [ - "### Model 2 evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "03c83137", - "metadata": {}, - "outputs": [], - "source": [ - "# Actual and predicted\n", - "y_true_rfe = y_test_rfe\n", - "\n", - "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n", - "tn, fp, fn, tp = confusion_matrix(y_true_rfe, y_pred_rfe).ravel()\n", - "\n", - "# Total predictions\n", - "total = tp + tn + fp + fn\n", - "\n", - "# Compute all requested metrics\n", - "recall_rfe = recall_score(y_true_rfe, y_pred_rfe)\n", - "precision_rfe = precision_score(y_true_rfe, y_pred_rfe)\n", - "f1_rfe = fbeta_score(y_true_rfe, y_pred_rfe, beta=1)\n", - "f2_rfe = fbeta_score(y_true_rfe, y_pred_rfe, beta=2)\n", - "fpr_rfe = fp / (fp + tn) if (fp + tn) != 0 else 0\n", - "\n", - "# Scores relative to total\n", - "tp_score_rfe = tp / total\n", - "tn_score_rfe = tn / total\n", - "fp_score_rfe = fp / total\n", - "fn_score_rfe = fn / total\n", - "\n", - "# Create DataFrame\n", - "summary_df_rfe = pd.DataFrame([{\n", - " \"title\": \"RFE\",\n", - " \"flagging_analysis_type\": \"RISK_VS_CLAIM using RFE Features from all features\",\n", - " \"count_total\": total,\n", - " \"count_true_positive\": tp,\n", - " \"count_true_negative\": tn,\n", - " \"count_false_positive\": fp,\n", - " \"count_false_negative\": fn,\n", - " \"true_positive_score\": tp_score_rfe,\n", - " \"true_negative_score\": tn_score_rfe,\n", - " \"false_positive_score\": fp_score_rfe,\n", - " \"false_negative_score\": fn_score_rfe,\n", - " \"recall_score\": recall_rfe,\n", - " \"precision_score\": precision_rfe,\n", - " \"false_positive_rate_score\": fpr_rfe,\n", - " \"f1_score\": f1_rfe,\n", - " \"f2_score\": f2_rfe\n", - "}])" - ] - }, - { - "cell_type": "markdown", - "id": "8c2f75c9", - "metadata": {}, - "source": [ - "### Model 3 evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "7d34f389", - "metadata": {}, - "outputs": [], - "source": [ - "# Actual and predicted\n", - "y_true_lasso = y_test_lasso\n", - "\n", - "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n", - "tn, fp, fn, tp = confusion_matrix(y_true_lasso, y_pred_lasso).ravel()\n", - "\n", - "# Total predictions\n", - "total = tp + tn + fp + fn\n", - "\n", - "# Compute all requested metrics\n", - "recall_lasso = recall_score(y_true_lasso, y_pred_lasso)\n", - "precision_lasso = precision_score(y_true_lasso, y_pred_lasso)\n", - "f1_lasso = fbeta_score(y_true_lasso, y_pred_lasso, beta=1)\n", - "f2_lasso = fbeta_score(y_true_lasso, y_pred_lasso, beta=2)\n", - "fpr_lasso = fp / (fp + tn) if (fp + tn) != 0 else 0\n", - "\n", - "# Scores relative to total\n", - "tp_score_lasso = tp / total\n", - "tn_score_lasso = tn / total\n", - "fp_score_lasso = fp / total\n", - "fn_score_lasso = fn / total\n", - "\n", - "# Create DataFrame\n", - "summary_df_lasso = pd.DataFrame([{\n", - " \"title\": \"Lasso\",\n", - " \"flagging_analysis_type\": \"RISK_VS_CLAIM using Lasso Features from all features\",\n", - " \"count_total\": total,\n", - " \"count_true_positive\": tp,\n", - " \"count_true_negative\": tn,\n", - " \"count_false_positive\": fp,\n", - " \"count_false_negative\": fn,\n", - " \"true_positive_score\": tp_score_lasso,\n", - " \"true_negative_score\": tn_score_lasso,\n", - " \"false_positive_score\": fp_score_lasso,\n", - " \"false_negative_score\": fn_score_lasso,\n", - " \"recall_score\": recall_lasso,\n", - " \"precision_score\": precision_lasso,\n", - " \"false_positive_rate_score\": fpr_lasso,\n", - " \"f1_score\": f1_lasso,\n", - " \"f2_score\": f2_lasso\n", - "}])" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "09609773", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_confusion_matrix_from_df(df, flagging_analysis_type):\n", - "\n", - " # Subset - just retrieve one row depending on the flagging_analysis_type\n", - " row = df[df['flagging_analysis_type'] == flagging_analysis_type].iloc[0]\n", - "\n", - " # Define custom x-axis labels and wording\n", - " if flagging_analysis_type == 'RISK_VS_CLAIM':\n", - " x_labels = ['With Submitted Claim', 'Without Submitted Claim']\n", - " outcome_label = \"submitted claim\"\n", - " elif flagging_analysis_type == 'RISK_VS_SUBMITTED_PAYOUT':\n", - " x_labels = ['With Submitted Payout', 'Without Submitted Payout']\n", - " outcome_label = \"submitted payout\"\n", - " else:\n", - " x_labels = ['Actual Positive', 'Actual Negative'] \n", - " outcome_label = \"outcome\"\n", - "\n", - " # Confusion matrix structure\n", - " cm = np.array([\n", - " [row['count_true_positive'], row['count_false_positive']],\n", - " [row['count_false_negative'], row['count_true_negative']]\n", - " ])\n", - "\n", - " # Create annotations for the confusion matrix\n", - " labels = [['True Positives', 'False Positives'], ['False Negatives', 'True Negatives']]\n", - " counts = [[f\"{v:,}\" for v in [row['count_true_positive'], row['count_false_positive']]],\n", - " [f\"{v:,}\" for v in [row['count_false_negative'], row['count_true_negative']]]]\n", - " percentages = [[f\"{round(100*v,2):,}\" for v in [row['true_positive_score'], row['false_positive_score']]],\n", - " [f\"{round(100*v,2):,}\" for v in [row['false_negative_score'], row['true_negative_score']]]]\n", - " annot = [[f\"{labels[i][j]}\\n{counts[i][j]} ({percentages[i][j]}%)\" for j in range(2)] for i in range(2)]\n", - "\n", - " # Scores formatted as percentages\n", - " recall = row['recall_score'] * 100\n", - " precision = row['precision_score'] * 100\n", - " f1 = row['f1_score'] * 100\n", - " f2 = row['f2_score'] * 100\n", - "\n", - " # Set up figure and axes manually for precise control\n", - " fig = plt.figure(figsize=(9, 8))\n", - " grid = fig.add_gridspec(nrows=4, height_ratios=[2, 2, 15, 2])\n", - "\n", - " \n", - " ax_main_title = fig.add_subplot(grid[0])\n", - " ax_main_title.axis('off')\n", - " ax_main_title.set_title(f\"Random Predictor - Flagged as Risk vs. {outcome_label.title()}\", fontsize=14, weight='bold')\n", - " \n", - " # Business explanation text\n", - " ax_text = fig.add_subplot(grid[1])\n", - " ax_text.axis('off')\n", - " business_text = (\n", - " f\"Flagging performance analysis:\\n\\n\"\n", - " f\"- Of all the bookings we flagged as at Risk, {precision:.2f}% actually turned into a {outcome_label}.\\n\"\n", - " f\"- Of all the bookings that resulted in a {outcome_label}, we correctly flagged {recall:.2f}% of them.\\n\"\n", - " f\"- The pure balance between these two is summarized by a score of {f1:.2f}%.\\n\"\n", - " f\"- If we prioritise better probability of detection of a {outcome_label}, the balanced score is {f2:.2f}%.\\n\"\n", - " )\n", - " ax_text.text(0.0, 0.0, business_text, fontsize=10.5, ha='left', va='bottom', wrap=False, linespacing=1.5)\n", - "\n", - " # Heatmap\n", - " ax_heatmap = fig.add_subplot(grid[2])\n", - " ax_heatmap.set_title(f\"Confusion Matrix ā€“ Risk vs. {outcome_label.title()}\", fontsize=12, weight='bold', ha='center', va='center', wrap=False)\n", - "\n", - " cmap = sns.light_palette(\"#315584\", as_cmap=True)\n", - "\n", - " sns.heatmap(cm, annot=annot, fmt='', cmap=cmap, cbar=False,\n", - " xticklabels=x_labels,\n", - " yticklabels=['Flagged as Risk', 'Flagged as No Risk'],\n", - " ax=ax_heatmap,\n", - " linewidths=1.0,\n", - " annot_kws={'fontsize': 10, 'linespacing': 1.2})\n", - " ax_heatmap.set_xlabel(\"Resolution Outcome (Actual)\", fontsize=11, labelpad=10)\n", - " ax_heatmap.set_ylabel(\"Flagging (Prediction)\", fontsize=11, labelpad=10)\n", - " \n", - " # Make borders visible\n", - " for _, spine in ax_heatmap.spines.items():\n", - " spine.set_visible(True)\n", - "\n", - " # Footer with metrics and date\n", - " ax_footer = fig.add_subplot(grid[3])\n", - " ax_footer.axis('off')\n", - " metrics_text = f\"Total Booking Count: {row['count_total']} | Recall: {recall:.2f}% | Precision: {precision:.2f}% | F1 Score: {f1:.2f}% | F2 Score: {f2:.2f}%\"\n", - " date_text = f\"Generated on {date.today().strftime('%B %d, %Y')}\"\n", - " ax_footer.text(0.5, 0.7, metrics_text, ha='center', fontsize=9)\n", - " ax_footer.text(0.5, 0.1, date_text, ha='center', fontsize=8, color='gray')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "7cc4a1d2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot confusion matrix for claim scenario\n", - "plot_confusion_matrix_from_df(summary_df_kbest, 'RISK_VS_CLAIM using KBest Features from all features')\n", - "plot_confusion_matrix_from_df(summary_df_rfe, 'RISK_VS_CLAIM using RFE Features from all features')\n", - "plot_confusion_matrix_from_df(summary_df_lasso, 'RISK_VS_CLAIM using Lasso Features from all features')" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "30786f7c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Print a table to summarize the results\n", - "summary_table = pd.concat([summary_df_kbest, summary_df_rfe, summary_df_lasso], ignore_index=True)\n", - "summary_table = summary_table[['title', 'count_true_positive', 'count_true_negative',\n", - " 'count_false_positive', 'count_false_negative', 'true_positive_score', 'true_negative_score',\n", - " 'false_positive_score', 'false_negative_score', 'recall_score', 'precision_score',\n", - " 'false_positive_rate_score', 'f1_score', 'f2_score']]\n", - "\n", - "# Rename them\n", - "summary_table.columns = ['Model', 'TP', 'TN', 'FP', 'FN',\n", - " 'TP Rate', 'TN Rate', 'FP Rate', 'FN Rate',\n", - " 'Recall', 'Precision', 'FPR', 'F1', 'F2']\n", - " \n", - "# summary_table.to_csv('flagging_analysis_summary.csv', index=False)\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Set up figure and axis\n", - "fig, ax = plt.subplots(figsize=(16, 4)) # Adjust width/height as needed\n", - "ax.axis('off') # Hide axes\n", - "\n", - "# Create table from DataFrame\n", - "table = ax.table(cellText=summary_table.round(3).values,\n", - " colLabels=summary_table.columns,\n", - " loc='center',\n", - " cellLoc='center')\n", - "\n", - "table.auto_set_font_size(False)\n", - "table.set_fontsize(10)\n", - "table.scale(1.2, 1.5) # Adjust cell size\n", - "\n", - "# Save as image\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Classification Report\n", - "\n", - "The **Classification Report** provides key metrics to evaluate how well the model performed on each class.\n", - "\n", - "It includes the following metrics for each class (0 and 1):\n", - "* Metric: Meaning\n", - "* Precision: Out of all predicted positives, how many were actually positive?\n", - "* Recall: Out of all actual positives, how many did we correctly identify?\n", - "* F1-score: Harmonic mean of precision and recall (balances both)\n", - "* Support: Number of true samples of that class in the test data\n", - "\n", - "Interpretation:\n", - "* Class 0 = No incident\n", - "* Class 1 = Has resolution incident (rare, but important!)\n", - "\n", - "A few explanatory cases:\n", - "* A high recall for class 1 means we're catching most incidents.\n", - "* A high precision for class 1 means when we predict an incident, we're often correct.\n", - "* The F1-score gives a single balanced measure (good for imbalanced data).\n", - "\n", - "Special note for imbalanced data:\n", - "Since class 1 (or just True) is rare (1% in our case), metrics for that class are more critical.\n", - "We want to maximize recall to catch as many real incidents as possible ā€” without letting precision drop too low (to avoid too many false alarms)." - ] - }, - { - "cell_type": "markdown", - "id": "c366cfe7", - "metadata": {}, - "source": [ - "### Results Summary\n", - "\n", - "- Model 1 (KBest) offers a low recall (12%) and precision (20.7%), but keeps the false positives very low, indicating a conservative model.\n", - "- Model 2 (RFE) achieves high recall (62%) but at the cost of extremely low precision (2.9%) and high false positives, meaning it's flagging many non-incident bookings incorrectly.\n", - "- Model 3 (Lasso) provides the best balance between recall and precision, resulting in the highest F1 (21.5%) and F2 (16.3%), with low false positive rate (0.2%)." - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "4b4da914", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC Curve\n", - "fpr, tpr, _ = roc_curve(y_test_lasso, y_pred_proba_lasso)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')\n", - "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC Curve')\n", - "plt.legend(loc='lower right')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the ROC Curve\n", - "\n", - "The **Receiver Operating Characteristic (ROC) curve** shows how well the model distinguishes between the positive and negative classes across all decision thresholds.\n", - "\n", - "A quick reminder of the definitions:\n", - "* True Positive Rate (TPR) = Recall\n", - "* False Positive Rate (FPR) = Proportion of negatives wrongly classified as positives\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is False Positive Rate\n", - "* The y-axis is True Positive Rate\n", - "\n", - "The curve shows how TPR and FPR change as the threshold varies\n", - "\n", - "It's important to note that:\n", - "* A model with no skill will produce a diagonal line (AUC = 0.5)\n", - "* A model with perfect discrimination will hug the top-left corner (AUC = 1.0)\n", - "\n", - "The Area Under the Curve (ROC AUC) gives a single performance score:\n", - "* Closer to 1 means better at ranking positive cases higher than negative ones\n", - "\n", - "**Important!**\n", - "\n", - "While useful, the ROC curve can sometimes overestimate performance when the dataset is imbalanced, because it includes negatives (which dominate in our case, around 99%!). Thatā€™s why we also MUST check the Precision-Recall curve." - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "6790d41d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# PR Curve\n", - "precision, recall, _ = precision_recall_curve(y_test_lasso, y_pred_proba_lasso)\n", - "pr_auc = average_precision_score(y_test_lasso, y_pred_proba_lasso)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(recall, precision, color='green', lw=2, label=f'PR curve (AUC = {pr_auc:.4f})')\n", - "plt.xlabel('Recall')\n", - "plt.ylabel('Precision')\n", - "plt.title('Precision-Recall Curve')\n", - "plt.legend(loc='lower left')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Precision-Recall (PR) Curve\n", - "\n", - "The **Precision-Recall (PR) curve** helps evaluate model performance, especially on imbalanced datasets like ours (where positive cases are rare).\n", - "\n", - "A quick reminder of the definitions:\n", - "* Precision = How many of the predicted positives are actually positive\n", - "* Recall = How many of the actual positives the model correctly identifies\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is Recall \n", - "* The y-axis is Precision \n", - "\n", - "The curve shows the trade-off between them at different model thresholds\n", - "\n", - "In imbalanced datasets, accuracy can be misleading ā€” the PR curve focuses only on the positive class, making it much more meaningful:\n", - "* A higher curve means better performance\n", - "* The area under the curve (PR AUC) summarizes this: closer to 1 is better" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature Importance\n", - "Understanding what drives the prediction is useful for future experiments and business knowledge. Here we track both the native feature importances of the trees, as well as a more heavy SHAP values analysis.\n", - "\n", - "Important! Be aware that SHAP analysis might take quite a bit of time." - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "d66ffe2c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## BUILT-IN\n", - "\n", - "# Get feature importances from the model\n", - "importances = best_pipeline_lasso.named_steps['model'].feature_importances_\n", - "\n", - "# Create a Series and sort\n", - "feat_series = pd.Series(importances, index=selected_features_lasso).sort_values(ascending=True) # ascending=True for horizontal plot\n", - "\n", - "# Plot Feature Importances\n", - "plt.figure(figsize=(8, 5))\n", - "feat_series.plot(kind='barh', color='skyblue')\n", - "plt.title('Feature Importances')\n", - "plt.xlabel('Importance')\n", - "plt.grid(axis='x')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the Feature Importance Plot\n", - "The **feature importance plot** shows how much each feature contributes to the modelā€™s overall decision-making.\n", - "\n", - "For tree-based models like Random Forest, importance is based on how often and how effectively a feature is used to split the data across all trees.\n", - "A higher score means the feature plays a bigger role in improving prediction accuracy.\n", - "\n", - "In the graph you will see that:\n", - "* Features are ranked from most to least important.\n", - "* The values are relative and model-specific ā€” not directly interpretable as weights or probabilities.\n", - "\n", - "This helps us identify which features the model relies on most when making predictions.\n", - "\n", - "**Important!**\n", - "Unlike SHAP values, native importance doesn't show how a feature affects predictions ā€” only how useful it is to the model overall. For deeper interpretability (e.g., direction and context), SHAP is better (but it takes more time to run)." - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "e2197cea", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PermutationExplainer explainer: 4263it [22:09, 3.21it/s] \n", - "/tmp/ipykernel_51877/2010823018.py:21: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", - " shap.summary_plot(shap_values.values, X_test_shap)\n" - ] - }, - { - "data": { - "image/png": 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MGNLT09m2bRsjR47kq6++olOnTgCcPn2aiRMn0qhRIwICAqhevToJCQmcOHGC69ev06BBA0aMGIFarebs2bN89tln2m20bt3a4LgvX77MoUOH6Nq1Ky4uLuTm5vLzzz+zZMkS4uLidJou5eTkMG7cOE6fPk2HDh149dVXMTc356+//uLQoUPaZCclJYW33nqLmJgYunfvTv/+/cnLy+PixYscPXpUm+ycP3+eoKAgTE1NGTBgAI6Ojvzwww8sXryYy5cvM3v27JJ8FFpbtmwhMTERX19fbGxs2L17N4sXL6ZOnTr07t0bgM8++4x58+Zhb2+v04SpRo0aBm3j/fffZ8mSJSQlJTFx4kTtdHd3d/z9/Tly5Ah79+6lX79+OstFRESgVCrx9fXVmX7kyBHi4uK0x+PIkSOsXLmSW7duMX36dG25W7duERAQQE5ODn369MHFxYXr16+zZcsWTp06RXh4uN7FbEmo1Wpu3LgBgL29vc48U1NTXn31VXbs2MHEiROxsLAA8hPFpk2b0rRpU731HT16lKysLLy8vLTlK1p2dja3bt3CxMRE55idP3+egwcP4ufnp3PTYtOmTcyZMwc3NzfefvttAHbs2MGkSZP48MMP9S7Cbt++zejRo+nWrRvdu3fnwoULREZG8ueffxIWFqa98MvNzWX8+PH89ttveHp6MnDgQFJTU9m2bRtvvfUWK1eupHnz5jrr/u6770hOTsbPzw9HR0fq1KlD06ZNS/WcLIqvry/z58/n9u3b1K5dG8j//B0cHLS/ZQ+LiooiOTkZT09P6tSpw+3bt4mIiGDMmDEsX76cZ599Vqd8RkYGgYGBtGrVirFjxxIXF8f69et5//332bBhg/bC9M6dO0RERNCtWzd69+6NiYkJZ86cISwsjIsXL7Jkyb+dkrOzsxkzZgyXLl3Cx8eHFi1acPnyZcaOHYutra1ezEePHmXy5Mm4uroyZMgQbG1tOXfunDYRnjNnjl7Mo0ePpm3btowbN47z588TGRlJVlYW9vb2/P777wwcOJDc3FzWrl3LxIkTiYqK0qk5/PTTT9m7dy/du3fHx8eHnJwcdu/ezdixY/niiy946aWXdLb5448/snnzZvr164evry/R0dGEh4djY2Oj/W0r6veqMCX5zsbHxzN8+HBSU1Pp378/9evX5/Tp04SGhvLrr7+ydOlSvZs3M2bMwMrKioCAAJKSkli7di3jx49n1KhRLFq0iP79+2Nra0tERAT//e9/adiwoV6CeOHCBQ4cOICfnx9eXl6cOnWK9evXc+XKFYKDg1Eq8+9kG/N/T6O4//FmZmZ4e3uzdu1aYmNjcXNz01k+MjKS+vXrS6JT3q4nQJuJoCrPLOdfCqD6/Wx6LTtLXoNImNKvyPIvbSjfAQUe3lq2Kv+lkZkJiZlw+jaE/qFiUFMF631K1jw5I0fNC+vyOJeQ//7zE3ks6a5k7LPStK4ilCjZyc7OJiwsTHvXq3v37vTp04eNGzdqf5BjY2MJDw/nmWeeYfny5dqyfn5+DBgwgDlz5tCxY0dMTEyIjo5GpVIRHBysc/dXc3EF0KFDB/bs2cPZs2d1mh4Zo23btkRERKBQ/Jtdv/7663zyySdEREQwcuRIatasCeR35D99+jQBAQGMHTtWZz0q1b/fjuDgYGJiYgq88Huw3Ny5c8nJySE0NJQmTZoAMGjQIKZNm8aePXvw9fXl+eefL9F+QX5CsHnzZu0FbJ8+ffD29mbDhg3aZMfT05Nly5bh4OBQomPYtWtX1q1bR1ZWlt7yL7zwAnXq1CEiIkLnwjIzM5O9e/fSoUMHvQ69ly9fJiwsTHsXf9CgQUyePJmoqCj69u1Lq1atAPjiiy/Izc3l22+/1VlHjx49CAgI4Ntvvy1RP67MzEySkpJQq9UkJCSwYcMGLl26RKtWrahfv75e+T59+vDdd99x6NAhevfuzT///MPx48cLrZm5cuUKAE899ZTRsZWW9PR0kpKStH12Vq9ezb1793jllVd07jjHxMQQHBxM+/bttdPu37/PokWLcHFx4ZtvvtGeW/379+eNN95gwYIF9OzZExsbG+0yN27cYOLEiTp9JBo2bMj8+fNZv349b775JgAbNmzg9OnTLF68mI4dO2rL9u/fn0GDBrFgwQK9pkKac/zhGqLSPCeL8uqrr7Jo0SJ27NjBiBEjyMzMZN++ffj5+RXaHPDjjz/GyspKZ1q/fv0YOHAgoaGheslOUlISQ4cO1amRrVGjBosWLeLEiRPaY+Xs7MzOnTt1tjtw4ECWLVvGqlWrdJpbRkREcOnSJUaPHs1bb72lLd+4cWPmzJmDk5OTdlpWVhazZs2iZcuWLFu2TLv+fv360aRJE+bPn8+pU6d0akaSkpIYNmwYw4YN005LSUnh+++/p1mzZoSGhmrX4+7uzvvvv8+ePXu0n8mhQ4fYvXu33m/o4MGDCQgI4KuvvqJLly46v9sxMTFs3LhRWyPdr18/Bg0axIYNG7TJTlG/V4UpyXc2ODiYe/fusWDBAm3SO2DAABYuXEh4eDg7duzAz89PZxlHR0fmzZun3Sd7e3vmzp3LF198wYYNG6hbN7+z9yuvvIKXl5fO/1aNv/76i7lz52prWAcMGMDcuXNZv349+/fv195oM+b/noYh/+P9/f1Zu3YtERERvPPOO9plf/nlF2JjYxk/frzBx1CUkk++q7BE52HKzzbBxD5gVvBvY0q2mtzKEWqhNlxU80lHNS1qGl8js/GiWpvoaMz4ScXoNgqUCqnhKW8lSjEHDBigU71fu3Zt6tevz/Xr17XToqOjUavVDBs2TKdsrVq1tM0LLl68CKC9iDp48CC5uWU3VrqlpaX2Bz8nJ4fk5GSSkpLo2LEjKpWK8+fPa8vu2bMHW1tbnYRLQ3PHTKVSsW/fPtzd3fUSnQfLJSYm8ttvv9GlSxdtogP5I2Rp/jEfOnTokfbNx8dH5069paUlrVq14tq1a4+0XkOZmJjg6+vL+fPn+euvv7TTv//+e9LS0ujTp4/eMu3bt9dprqRQKLQXTJrjkZqaytGjR+nSpQsWFhYkJSVpX/Xq1cPFxYXjx4+XKOaQkBB69OhBz549ee2114iMjKRLly7MnTu3wPKNGzemefPmREZGAvk1HJoan4KkpeUPL1ORfV8+++wzevTowSuvvMLw4cP58ccf8fb25uOPP9Yp99RTT+kkOgDHjx8nIyODwYMH65xb1atXZ/DgwaSnp+sde2trawYMGKAzbcCAAVhbW+uc47t378bNzY2nn35a5zPNzc2lffv2/Prrr2RmZuqsx8vLy6gmiyU5J4tib29Ply5d2LFjB5B/jqamphZZO/RgoqNJPE1MTGjZsiV//PGHXnmlUsngwYN1prVr1w5A57tsZmamTSByc3O5f/8+SUlJ2hsmv//+u7bsDz/8gImJCa+99prOev38/PRqRI8fP87du3fx8fEhNTVV57N58cUXtWUeZGJiotOsF/L7iqnVavr166eTkGmSuwf/V+zatQtra2u6du2qs73U1FQ6d+7MzZs39X7HunbtqtP0VqFQ4OHhwd27d0lPT6ekjP3OqlQqjhw5QtOmTfVq9958802USiWHDx/WW27QoEE6yYcmgejSpYs20YH8RLdBgwY6x0ujQYMGek1JNTcTHtymMf/3NAz5H9+gQQPatm3Lrl27dP5vR0REYGJiUqlGfExMTNQZQCY1NZWUlBTt++zsbO7evauzjKYJZGHvb926hVr979V6ZdhG3t3SecBnaVCkZ0N6fqwF7ceZm9kVFZpR4tP+jduYzyO+gNHl7mbA33G3dNf/0GdeFDUKg19CV4lqdpydnfWm2dnZcevWvx/izZs3AWjUqJFeWc20uLg4mjdvzsCBA4mOjubzzz9n8eLFPPPMM7zwwgv06tXL4CZWhsjNzeWbb75h165dXL9+Xeekhfw72RrXrl2jadOmRTZlSEpK4v79+zp3pguiORYNGzbUm+fu7o5SqSQuLs6YXdFT2GeSnJz8SOs1Rp8+fVi9ejURERHa2g5NM5+Hm6EAek0f4N9jpDkesbGxqFQqIiIiiIiIKHC7Be27Ifz9/enRowcKhQIrKyvq16+PnZ1dkcv4+Pjw5ZdfEh8fz44dO3jppZewtbUlKSlJr6zmgulRLr40yz+8Djs7uwL7EzxM0z9OqVRSrVo13NzcCryQK6gmS/MZFHTePvw5aTg7O+vFZW5ujrOzs07Zq1evkpWVpTN4wsOSkpJ0LvwKirE4xp6TxfHx8eHdd9/ll19+ITIykhYtWhR4fDRu3LhBcHAwx44d07koAXQudjVq1aql95ujOScf/i5v2rSJLVu2EBMTo1OLDOhsKy4ujpo1a+oN5mBmZka9evV0yl69ehVAp6nwwx6+YKtZs6ZezJrmcQ/3BdRMf3BfYmNjSUtL45VXXil0m4mJiTp9lgr7vdOsu6QDVxj7nb137x7p6ekFngN2dnbUrFmzwN92FxcXnfeFHS/I7yP04P9WjYKa49WsWRMbGxudbRrzf0/DkP/xAH379uXjjz/m6NGjdO3albS0NL7//ns6d+6s0+exoj18k+ThJN/c3Fwv3gdrPAt6/+BvU2XZhskUf9hROg/5fFSqzk+jtMv/PhW0H80s1EBeBURmuFpW0OmBfjbGfB59Giv4+CjkPfB182yowN2l6M9clI0SJTuaGouHPfwjaih7e3vCwsI4e/Ysx48f5+zZs8ybN4+QkBAWLlxoVL+cosyfP58NGzbQs2dPRowYQY0aNTA1NeXChQssXry4xPGXtoIuggDy8gr/YagMw97WrVuXjh07smvXLiZMmEB8fDxnzpxh6NChhTbzMdSrr75a6J3CkvaHqV+/vl5tRnF69+7NggULmD17NtevX+eDDz4otKwmqb948SIvv/xyiWIECA8PZ+XKlTrTiupk/XAMhuzjwx3ay0Pjxo157733Cp3/8I2OksRY2udkx44dqV27NitWrODUqVNMnTq10LLp6ekEBgaSkZHBa6+9RuPGjbG2tkahUPDNN98UOKBBYb+toPv7unbtWhYsWECHDh0YPHgwNWvWxMzMjDt37jBjxgy95MdQmm288847hTblqlWrlsExG/K/Qq1WU6NGjSL7LT5808zQ42Ss0vrOFqew+Ev7fyuU7P+eoXF069YNOzs7IiIi6Nq1K/v37ycjI0Ov2Z4oJ52awxfDYNpayKuYB2yqgbsuNtitfafIpkN1rBX0coO9seUTl7E86sDSHiZYmpasluRpRwXfeSv58AcVf98Hn0YKlvWQ/joVpcyGntbcGbpy5YreXayYmBidMpB/se7h4aG9gLt8+TJDhgxh1apVLFy4ECg8CTDUrl27aNu2Lf/7n+7wiIU1EYiNjSU7O1vvuRoa9vb22Nracvny5SK3q7lbp9nvB2lqLh48Fra2tly4cEGv7KPW/sCjH8Pilvf39+fo0aPaB8AChTYXio2N1Zv28Lnh4uKCQqHQNm+qaDY2NtphiOvUqVNkTJ06dcLCwoJdu3YxYsSIQs+j4nh5eem11y+PfkCa721MTIxefzJNDcDDd4Dj4uLIycnRqd3Jzs4mLi5OpybP1dWVe/fu0a5duyIvXA1RmudkcUxMTPDy8iI0NBQLCwud0RYfduLECe7cucOnn36q19Rt2bJlJdq+xq5du6hXrx6LFi3SOX4//fSTXtl69epx4sQJ0tPTdWo8cnNzuXnzpk6fK03tmZWVVbl931xdXbl27RqtWrUq9aHEjf29M/Y7W6NGDaytrQv8bb9//z4JCQll9l3VfAcflJCQQEpKis730pj/e8YyNzfHy8uLDRs2aAfNqF27drGtHUQZmuyX/zp9GTz0H4JclnKDevDNM0pUpkoCnIpvlRPlb8JnP+YxuwwfB2RrBl1cIOQVJfVslFxPUbH9MrjbqWlTW4mLjYLkLDX/pEGTGo9+jfSgAU2VDGgqCU5lUGafgqZDaXh4uE573oSEBKKionByctKOYFVQEyA3NzcsLS11qtg17d9L2jRLqVTq3ZnKyMhg3bp1emV79+7N/fv3WbVqld48zTqUSiW9evUiJiaG7du3F1rOwcGB1q1bc+TIEZ2+A2q1mtDQUACdu4gNGjQgLS1Np929SqUqME5jWVlZFdhswVDVqlXj/v37hd5p7NSpE7Vq1WLr1q3s2LGDZ555psDmapDf9v/BpE6tVhMWFgagbYtub2/Piy++yMGDBzl37pzeOtRqNffu3Svx/pTEm2++SWBgIB988EGRF+oODg4MHTqUmzdvMmvWLHJycvTKpKam8tVXXxW5PRcXF9q3b6/zKmgUrdLWvn17rKys2LBhg7YvA+T3a9iwYQPVqlWjQ4cOOsukpaWxaZPuw+U2bdpEWlqaTv8CLy8v7t69y7ffflvgth9uKlWU0jwnDdGvXz8CAwOZNm1akaMAampbH47r2LFjOt/tkjAxMUGhUOisW9Nc6WGdO3cmLy+P7777Tmf6tm3bSE1N1ZnWsWNHHBwc+Oabbwr8nc3MzNQ5F0qDl5cXKpVKZwS5BxlzLjysuHPjYcZ+Z5VKJZ07d+bixYt6ieY333yDSqXS61dTWv7++2+9/kBr1qwB0Gmiacz/vZLw9/cnLy+PRYsWce7cOby9vStFS4Mn3nNNoHur8tteyreolwSiMjX8stLMRMGsLqZ0dy39cP4IUKCeZEryO6ZE9TOlnk1+XK42Ssa3VeLdyAQXm/zExs5CwVMOilJNdETlUmY1O25ubgwdOpSwsDACAwPp2bOndujp9PR0Zs2apf1BnD17Nrdv36Z9+/Y4OTmRlZXF/v37SUtLw8vLS7vOVq1asXHjRj7//HM6deqkfW6PoX02unfvztatW5k2bRrPP/88d+/eJSoqqsB+Gq+99ho//PADq1at4vz587Rv3x4LCwtiYmL4+++/Wbp0KQCjR4/m5MmTzJ49m+PHj/PMM88A+c0gcnNzmTVrFgCTJk0iKCiIwMBA7VDLR48e5eeff6Z37946d841o9xMnjyZwYMHY2ZmxoEDB4psxmaoVq1aERERwbJly3B3d0ehUNClSxe9EaMK07JlS3744Qe++OILWrdujVKppF27dtq2q5pO4Zok8eGR7B7UpEkTRo0axYABA6hZsybR0dGcOHECT09PnaaLU6dO5e233yYwMBAvLy+aNm2KSqUiLi6OI0eO4OnpWaLR2EqqSZMmOgNNFCUoKIiEhAS2b9/Or7/+yiuvvKId/vXixYscOHAAMzOzcn3WjqFsbGyYMGECc+bM4c0339Q2I9yxYwfXr1/nww8/1LvYd3FxYeXKlVy5coWnn36aP//8k8jISNzc3HQ63r/22mscP36chQsXcvLkSdq1a4e1tTW3bt3i5MmTmJubExISYlCcpXlOGqJu3boGnW9t2rTB0dGRBQsWEB8fT+3atbl06RK7du2icePGOjc+jNW9e3eWLFnChAkTePnll0lLS2Pv3r0FNs3z8/Nj69atLFu2jBs3bmiHnv7+++9xdXXV+V2xsrJi5syZTJo0STuss6urKykpKcTGxnLo0CG+/PJLg5pQGqpHjx74+PiwceNGLly4QOfOnbG3t+f27dv89ttv3Lhxo9D+esUp7twoiLHf2bFjx3L8+HEmTZpE//79cXV15cyZM+zfv5+2bduWWUf9xo0b88knn+Dn50f9+vU5deoUBw4coG3btjr9n4z5v1cS7u7utGnTht27d6NQKIwazl2Usa1TwG5I2W/Hsw1Ut4ICbg4YYmp7BQeul143gq2+Cpo7SsIt/lVmyQ7AhAkTcHV1ZdOmTSxZskT7QL3Zs2frDLnq6elJVFQUO3fu5N69e1hbW9OwYUPmzJlD9+7dteV69erFxYsX2bdvHwcOHEClUjF9+nSDk52JEydibW3N/v37iY6Opk6dOvj7+9O8eXPGjBmjU9bMzIwlS5awdu1a9u7dy9KlSzE3N6d+/fo6DzG1tbUlNDSU1atXc+jQIQ4dOoS1tTXu7u46IxQ1b96c1atXExISwubNm7UPFR0/fjxDhuj+GDk7OzN37lyWLl3K8uXLsbOzw9PTE19fX/r372/UZ/CwMWPGkJyczKZNm0hJSUGtVhMZGWlwsvPGG28QFxfHgQMH2LJlCyqViuXLl+tcPPj5+REaGoqVlVWRHdC7dOmifajo33//jYODA2+//bbeCHh169Zl7dq1rFmzhujoaHbv3o25uTl16tShc+fOek+0r0yUSiUff/wxPXv2ZOvWrezatYvExETtuTRgwAC90csqE00i+mC/oaeeekpnyNsH1a5dm88//5wFCxawd+9ezMzM6N27N++++67OOWZqasqCBQvYvHkzu3bt0iY2tWrVokWLFkZdIJbmOVmabGxsWLJkCYsWLWLDhg3k5eXRrFkzFi5cSERExCMlO0OHDkWtVhMREcFXX32Fo6MjPXv2xNfXV+98Mjc3Z9myZSxcuJDo6Gj2799Py5YtWbp0KbNnz9Yb9a5jx46sWbOGNWvWsHv3bu7du4etrS0uLi688cYbBif6xpg+fToeHh5s27aNb775hpycHBwdHWnWrNkjJaeGnBsPM/Y76+TkxDfffMPy5cvZvXs3KSkp1KlTh4CAgAIfkFxamjVrxnvvvcfSpUvZunUr1tbWDBw4kLFjx+rUOBvzf6+k/P39+eWXX/Dw8NBrti4qkG016NsetpZsxFKDLdAftdYYPdxMcK2Wy/VHG8uH/k/Bwm4m1KteVWtoqup+lT2FurL0yhdVRkJCAl5eXvj6+hb4wLoHn4JenjUyomz5+Pjg5OSk93ycyqC4c/JJlJeXR48ePWjZsiWLFy+u6HCEETw8PPD29mbGjBkVHQoA+/fvZ9q0acyePVv7XDdRSajVoCz64Z6PLGcTmJponyUIEBAQYNCIoRq301TUWVbyQRWsFJD+fpnev69wasWw4gv9P4U6rAwjefxIzylR6jZv3kxeXl6Bzx4SoiI86efkw7U3AFu2bCElJaVSDPwhHm+bNm3C3t6ebt26VXQo4mFl3Q9l1Ctg+uhNxmpbP9rlaKin1HqIwj32aXBmZqZeJ9uCPPyEaPGv1NTUAi+GHmRmZlZsG++9e/dy69YtwsPD6dixI08//XRphlkkzcPyilOjRg3pPPsEqchzsjL5z3/+Q1ZWFq1bt8bc3Jxz586xZ88eXF1d8ff3r+jwxGMoMTGREydO8Msvv3DmzBnGjRtX4hEnRRlTkD8mdFlYNqrUVmVtAmlGdk22M4eveyno31T+r4vCPfbJzv79+5k5c2ax5U6dOlUO0Tye5s6dq30qfGHatm1bbPOkjz76CAsLC9q0acMnn3xSmiEW69dff2XUqOJ/dCMjIwt8cJ+omirynKxM2rdvz6ZNm1i1ahXp6ek4Ojri5+fHqFGjCnzIrBDFiYmJ4eOPP8bGxoZ+/frp9T0VlciLT8PRP0t/vXXtS3V1q19VMGiHcVlZ0oTH/jLWYGoj+uxIPZeux77PTkJCAleuXCm2nDTVKFxMTAx37twpsoytrW2lvit+//59/vyz+B/zNm3alPghpEIIIcRjJy0Tqr9e+us99QU811j79lH67ABk5KipttC4qh31pCcn2VEphhtcVqleU4aRPH4e+7OkZs2a0kTtETVs2JCGDRtWdBiPxNbWVhJaIYQQ4mHWloaV6/s8bDXwCZ9utXQSndJgZabAuTrEFd8zAYAe9Ut186IKkwEKhBBCCCGqMksDaln8Dbxh6OoIez59tHgK8b9OhpVrZAf7Bjxp/XQURrzEgyTZEUIIIYSoyr4wYNjiJs6gNOCycNMkaGrY8w2N9UYLE6oVk8Ps7Qd/BZqiKOuR5kSVIcmOEEIIIURVNt6r+DJP1YNjn4NFMbVAzcuu/ZhSoeD+O8pCE57hzeEV98e+B4YoZ5LsCCGEEEJUdUsCC583+zWoUR3aNYbMDfBlITVBXZqDjVXZxPf/TJRK/hlrQr8mutNfcIJvPJ/kREeasZXUk3zWCCGEEEI8Gca+Cp2fhq8i4cotiL8HdtVg0VvQqblu2Ul+4NsO3lsNJ/4CK3P4sB+M6l0uoVY3V7C5T/4l6tUkNbWq5U8ToiQk2RFCCCGEeBK0doM1Ewwr+5Qz7Kz455O520uSIx6NNGMTQgghhBBCVElSsyOEEEIIIUQlppa+OCUmNTtCCCGEEEKIKkmSHSGEEEIIIUSVJMmOEEIIIYQoNyq1mnuZ6ooO4zEjQ0+XlPTZEUIIIYQQ5eK/P+Xy0U//vu9QF34eIpejouxIzY4QQgghhChzB2J1Ex2AY7fg+TW5FROQeCJIsiOEEEIIIcpcj80FTz95B7ZclIRHlA1JdoQQQgghRJnKyVMVOb9/VDkF8phSG/ESuiTZEUIIIYQQZerc7aKTHYDlZ6V2R5Q+SXaEEEIIIUSZGryj+DKjD5R9HOLJI8NfCCGEEEKIMnU5uaIjeNzJkNIlJTU7QgghhBCiUjgVn1fRIYgqRpIdIYQQQghRZq4lG57AvLpJutiL0iXN2IQQQgghRJnput7wBCYhuwwDeYyppRlbiUnNjhBCCCFEVaVSQV7FNg27mlKhmxdPOEl2hBBlKioqCg8PD06dOlXRoVR6cqxKl4+PD0FBQTrTgoKC8PHxqaCIKrdTp07h4eFBVJTuA0+SkpL49NNP6d27Nx4eHnrHVFRS24+Boi+Y9AfTAfl/WwyAfxIrOrJiTT0sQ1CL0iPJjhCiyjp8+DAhISEVHYaOU6dOERISQkqK3Op8nFy8eJGQkBBu3rxZouVv3ryJh4cHc+bMKbSMj48PAwcOLGmIBinJfsyfP5/9+/fTr18/PvvsM0aMGFGGEYpHkngf2k7MT2z8v9Cfn50Hdd/On+8zu/zjM9CcU3DuTvHP5RHCENJnRwhRZR0+fJgdO3YwcuTIig5F6/Tp06xcuRIfHx9sbGx05nl6evLKK69gZmZWQdFVfcHBwajVxneAvnTpEitXruS5556jXr16ZRBZ+ShqP9q2bcuPP/6IqanupcHx48fp0KEDgYGB5RmqMMa+XyBoKfydYPgyO86Asi+YKqFtIxj4AlhbgI0VDHgRzErnEnHcnpLV0rReo+LUG2qeczIplTgef9Jnp6Qk2RFCiErCxMQEExP5x16WJJEsnFKpxMLCQm/63bt3sbOzq4CIhI6lu+HSTWjrDv/ZAvcz4FbSo61TDeSo4Pjl/JfGGwt1yzlUhyZOENAdGtSEl1uBRcHfpXuqakRcgefqqqllpSL495KH5/GtGsjFwgQa20LwKwqszZTcSlPzsqsCpQIOXVfjaKWgvZMkA6JgkuwIIcqFWq0mPDyczZs3c/v2bZycnBgxYgTe3t465bZv386mTZuIjY3F1NSUli1bEhgYSJs2bXTKHT16lLCwMK5cuUJmZib29vY0b96ccePG0aBBA4KCgjhz5gwAHh4e2uWmT59ucJ+NO3fusHbtWk6ePEl8fDxZWVk4Ozvj5eXF0KFD9RKTnJwc1q1bx969e/n7778xNTWlfv36eHt7M2jQIGbMmMGOHfmPEff19dUuFxgYyMiRI4mKimLmzJksX74cDw8PfvzxR9555x0mTZrE4MGD9eILCAjg+vXr7NmzR3s3/tq1a6xcuZITJ06QnJxMrVq16NGjB0FBQVhZWRm03xohISGsXLmSDRs2sHXrVr7//ntSU1Np3LgxY8eO5fnnn9cp7+Hhgbe3N15eXixdupRLly5hZ2fHwIEDefPNN7l//z4LFizghx9+ID09nXbt2vHRRx9Rq1Yt7TqSk5P5+uuvOXLkCHfu3MHKygonJydeeeUVhg0bZlT8BQkKCiI+Pl6nX8qVK1dYsWIFv/32G0lJSdja2uLm5sbQoUPp1KmT9jgAjBo1Sruct7c3M2bMeOSYinPmzBm+/vpr/vjjD3Jzc3Fzc2PAgAH4+fnplHvU/Th16hSjRo3SfkceLL9jxw7tufvee+8xf/58PvroI/z9/fXiHThwINnZ2Wzbtg2FQi5AH1lqBriPgoQKbPqamKqbEDk7wP4Z8LSLTrHo7Gasz+yAaocCKL1BEbLy4I970HWDWrteO3NQKCApK79MzwYKovyVWJjKOSd0SbIjhCgXwcHBZGVl0bdvX8zNzdm8eTMzZszAxcVFm8gsWrSIsLAwWrRowZgxY0hPT2fbtm2MHDmSr776ik6dOgH5TcEmTpxIo0aNCAgIoHr16iQkJHDixAmuX79OgwYNGDFiBGq1mrNnz/LZZ59p42jdurXBMV++fJlDhw7RtWtXXFxcyM3N5eeff2bJkiXExcXx0Ucfacvm5OQwbtw4Tp8+TYcOHXj11VcxNzfnr7/+4tChQwwaNIi+ffuSlpbGoUOHmDhxIvb29gA0adKkwO136NABR0dHdu7cqZfsXLt2jXPnzjF48GBtovPnn38yatQobGxs6Nu3L7Vr1+bSpUusX7+eX3/9lRUrVug1UTLE9OnTUSqVDBs2jPT0dLZu3cr48eNZtGgR7du31yl78eJFfvjhB/z9/fHy8mL//v0sWbIECwsLduzYQb169QgKCuL69ets2LCB6dOns3TpUu3yU6dO5cyZM/Tr148mTZqQlZXF1atXOX36dKkkOw9LSkpi9OjRAPTr14+6deuSlJTEn3/+ye+//06nTp3o1q0bCQkJbNu2jYCAANzd3QFwcXEpatUFys7OJikpqcB5KpV+H4UjR44wefJkHB0dGTJkCNWqVWPfvn3Mnj2buLg4xo4dW2b70a1bN1xdXfn000959tlntYlNixYtCAsLIzIyUi/ZOXfuHDExMYwZM0YSndLyQVjFJjoFiUuEqeEQMU076V4mbMp8HlU5dQdPfmiI6v1/qwk7ryawddU872To6ZKTZEcIUS6ys7MJCwvTNiPq3r07ffr0YePGjbRp04bY2FjCw8N55plnWL58ubacn58fAwYMYM6cOXTs2BETExOio6NRqVQEBwfj4OCg3cbbb7+t/btDhw7s2bOHs2fP4unpWaKY27ZtS0REhM5F2+uvv84nn3xCREQEI0eOpGbNmgCsW7eO06dPExAQoL0A1dBcxLZu3ZrGjRtrE6ji+n6YmJjg6elJeHg4MTExNGzYUDtv586dADo1Y5999hk1a9YkLCwMa2tr7fTnn3+eyZMns3v37hKNRGZiYsLXX3+t/Ux8fX3p378/X375JZs3b9Yp+9dffxEaGkrLli0B6NOnD97e3sybN4+BAwcyefJknfLr1q0jNjYWNzc3UlNTOXnyJP379+eDDz4wOs6S+PXXX0lMTOR///sfPXv2LLBMkyZNaN26Ndu2baN9+/Y6NYXGioiIICIiotD5D37GeXl5fPHFF1hZWbFmzRptDdjAgQMZOXIka9aswcfHh/r165fJfjRp0oQmTZrw6aef4uzsrPM98vX1JTQ0VO+8jIiIwMTEpFKNeJeYmIi1tbW2iV5qaipqtVrbZy47O5uUlBQcHR21y8THx+Pk5FTo+1u3blGnTh3tb0OZbuNMTKkdi1L1y1Xg37gv3YOcCr6s/OV2fn+8x/UzF2VDRmMTQpSLAQMG6PSXqF27NvXr1+f69esAREdHo1arGTZsmE65WrVq4ePjQ3x8PBcvXgSgevXqABw8eJDc3LIbotTS0lL7jy0nJ4fk5GSSkpLo2LEjKpWK8+fPa8vu2bMHW1tbnYRLQ6ks+U+tl5cX8G9yA/lNAnfv3k2jRo1o1qwZkJ9kXL58md69e5OTk0NSUpL21aZNG6ysrDh27FiJYnj99dd1PpM6derQu3dvYmNjuXr1qk7ZVq1aaRMdyO8j06JFC9RqtV7t1LPPPgugPQcsLCwwNzfn999/L/GoZ8bSnEs//fQTqampZb69l156ieDg4AJfD14UQX5N3a1bt/D19dVp6mdmZsawYcNQqVRER0dXyH74+fmhUCh0EreMjAz279/PCy+8oBNvRXNwcNDpi1S9enWdwUHMzc31jv3DF6APv69bt67OTZAy3UbvZ4vcvwrTsSnwb9wtHMGSin0iaMd6+Z/J4/qZi7IhNTtCiHLh7OysN83Ozo5bt24BaC9uGzVqpFdOMy0uLo7mzZszcOBAoqOj+fzzz1m8eDHPPPMML7zwAr169aJGjRqlFnNubi7ffPMNu3bt4vr163qjeN2/f1/797Vr12jatGmBHbwfRePGjWnWrBl79uxh7NixKJVKzpw5w82bN5kwYYK2nCbpCAkJKXS47cTEkj1fQ9Pc6UGau/lxcXE68wv6nG1tbQH0arI0FwbJyclA/kX8xIkT+eqrr/D19aVhw4Z4eHjQtWtXvf5BpeW5557Dy8uLqKgodu/eTfPmzWnfvj09e/bUqbEoLbVr19Zr+qdhbm6u817znSgojge/E1D+++Hs7Mzzzz/Prl27GD9+PKampuzfv5+0tDT69OlT6tt7on3UH6JOQWWq4WndAL4crjOpujkMszpKWEYnMjHHUgmZZTB6tIL8cRXq24BSCbHJ+dOGtVDwWjNp6iX0SbIjhCgXhdVulGQYYHt7e8LCwjh79izHjx/n7NmzzJs3j5CQEBYuXGhUv5yizJ8/nw0bNtCzZ09GjBhBjRo1MDU15cKFCyxevLhEsZeEl5cXX331FSdPnqR9+/bs3LlT28RNQxPLkCFD6NixY4Hr0SQdZamo0eQKm/fgcezfvz9du3bl6NGjnD59mgMHDrBx40Z69uzJ//73v1KPF2DmzJkMHTqUn376ibNnz7J27VpWr17NxIkTGTRoUJlssyyU9374+/szdepUoqOj6d69OxERETg6Omr71olSYmYKp+fCn9fh7zvQ1BkmfQP30+H8Dbh5r+y2rQDq14QWrtCwLgztCpZm0NqtwOLPmcXS0vQGHp5DaV7bFEsTFZYLHu13UgF0cQLvp2DMM0oSsxTcSYdnaufP+/UOOFhCfduqnuhU9f0rO5LsCCEqBU2NwJUrV/Q6TMfExOiUgfwLZw8PD22/g8uXLzNkyBBWrVrFwoX5w6Y+agfpXbt20bZtW72LbE2zqwc1aNCA2NhYsrOz9e7QP6gkMfXu3ZuFCxeyc+dOnnnmGQ4cOED79u21/YUA6tevD+QnlYXVHJTU1atXeeqpp3SmFfSZlJaaNWvi5+eHn58feXl5fPrpp+zdu5chQ4bQokWLUt8e5NegNW7cmGHDhpGSksLw4cNZsmQJAwcORKFQVEhne82x1RzrBxV2/MtzP7p27YqDgwMRERE0atSIX3/9leHDh5doEAxhgKdd818AW6bozkvLgJC98H6Ycevs+jQ0qgfDuoKLI1S3hOR0aOyUP9RZCVgocvGoC2ZmCsCEPf659N5WolWhnqR/LlUzB5cHHlHWpnbJ1i2eHNJnRwhRKXTp0gWFQkF4eLhOP5yEhASioqJwcnKiadP8NuIFjWbl5uaGpaWlTtMyzVDLmmZSxlIqlXq1NxkZGaxbt06vbO/evbl//z6rVq3Sm/fgOqpVqwboNoErTo0aNXjhhRc4dOgQe/bsIS0tTduXR6Np06Y0atSILVu2cOPGDb115Obmlvg4rFu3jpycHO37f/75h71799KgQYMCm7iVVGZmJpmZmTrTTExMtKPVGXPMDJWcnKw3CpqNjQ3Ozs5kZmaSlZU/rq3mXCqLGArTrFkz6tatS1RUFAkJ/z4sMjc3l/DwcBQKBS+99BJQMfthamqKt7c3x44d0w5RLU3YKoi1FUz0A/VWWKLfb7BAUdPg0H/g67HQpUV+zU1te2hSr8SJTkF6NSpZ8pv1rtRkiNIht1+EEJWC5nkgYWFhBAYG0rNnT+3Q0+np6cyaNUvbDGr27Nncvn2b9u3b4+TkRFZWlra/wINJQKtWrdi4cSOff/45nTp10j63x9DaiO7du7N161amTZvG888/z927d4mKiirwAYuvvfYaP/zwA6tWreL8+fO0b98eCwsLYmJi+Pvvv7XDK2s67y9atEg7PHWjRo1o3LhxkbF4e3tz5MgR5s+fT/Xq1enatavOfIVCwWeffcbo0aN57bXXtH1eMjMzuXHjBgcPHmTcuHElGiUrLy+Pt99+m169epGens6WLVvIysrSG1ntUf39998EBQXx8ssv06hRI2xsbIiNjWXz5s04OztrBzQoTTt37mTdunW8/PLLuLi4YGpqypkzZ/j555/p2bMnlpaWQP5wy0qlktWrV3P//n2srKxwdnbWGYyhtJmYmPDBBx8wefJkhg8fjr+/P9WqVWP//v2cO3eOgIAAbY1eRe2Hv78/4eHh7N27l7Zt22rjERVorGf+KzMbXN+GhAcGrGjlCt/PzE9qypGmn42hbgSCuak8YPlBMvR0yUmyI4SoNCZMmICrqyubNm1iyZIl2pG8Zs+erXOh6+npSVRUFDt37uTevXtYW1vTsGFD5syZQ/fu3bXlevXqxcWLF9m3bx8HDhxApVIxffp0g5OdiRMnYm1tzf79+4mOjqZOnTr4+/vTvHlzxowZo1PWzMyMJUuWsHbtWvbu3cvSpUsxNzenfv36OglGmzZtGD9+PFu3bmX27Nnk5eURGBhYbLLTuXNn7OzsSE5Oxs/Pr8CBEJo2bcq3335LaGgoR44cYcuWLVhbW+Pk5ISPjw/t2rUzaL8fNnPmTLZs2cKaNWtISUmhcePGTJ8+nQ4dOpRofYWpU6cOvr6+nD59msOHD5OTk0OtWrXw9/dn+PDh2gv20vTcc89pnw2UkJCAiYkJ9erV491332XgwIHacnXr1uXTTz9lzZo1fP755+Tm5uLt7V2myQ7k13guXbqUVatWER4eTk5ODm5ubnz88cc6DxWtqP1wdXXFw8ODkydPSq1OZWNpDneMbNZWRmpawp3M4stpONvJ5akoPQp1efWwFUII8VgJCQlh5cqVREZGFvtMIPHkmjBhAufOnWP37t1lkpCKx0NOTg6hoaEABAQE6AxXH7Q3l5XnDF9XQX11nnRZitEGl7VQLyvDSB4/0mdHCCGEECVy/fp1jh07xquvviqJjijUl10Nb5JWQ/IcUcrklBJCPFEyMzMNeujigyOdVSWP+/7fu3ePvLy8IstUq1ZNOxBEWcrLy+PeveKH/bWzs9O5y10V/P7771y9epX169djZmbGkCFDKjokUYnZWRje3+TKSOmbIkqXJDtCiCfK/v37mTlzZrHlTp06VQ7RlL/Hff+HDRtGfHx8kWUCAwMZOXJkmcfyzz//4OvrW2y55cuXa4dIryo2b97Mzp07cXZ2ZtasWdLMUZSaGlYyMIEoXdJnRwjxRElISODKlSvFlivtZ9VUFo/7/v/yyy/aYZQL4+zsrPesprKQlZXFL7/8Umy5p59+ulwe6CpERSqqzw5A869z+TOp6HUogTzpr1Mg6bNTcnJGCSGeKDVr1qy0TbTKw+O+/23atKnoELQsLCwqbVIoRGVzeriSagtVRZb5fXg5BfMYkqGnS04GKBBCCCGEEGXKyqzoS04F8HQtuQcvSp8kO0IIIYQQokKlTpCaC1E2JNkRQgghhBBlLr6QcUOmPAfVzGVggqIpjHiJB0myI4QQQgghylxdG1N2+oPpA9fj816Gz1+W5mui7MjZJYQQQgghyoVnI1Ny3q/oKMSTRJIdIYQQQgghKjEZja3kpBmbEEIIIYQQokqSZEcIIYQQQghRJUmyI4QQQgghhKiSpM+OEEIIIYQQlZr02SkpSXaEEEIIIUS5SkjPpdHXcD87//2Ap2Cjr1yWitInzdiEEEIIIUS5UavV1Fr6b6IDsOkS9N2eW3FBiSpLUmghhBBCCFFuPj+WV+D0bX+VcyCPERl6uuSkZkcIIYQQQpSbD38sfN6nR6R2R5QuSXaEEEIIIUS5SMtWFTl/1olyCkQ8MSTZEUIIIYQQ5aLO0qKTHYCMnOLLCGEoSXaEEEIIIUS5SDOgldormyTZEaVHkh0hhBBCCFFp/HGnoiMQVYkkO0IIIYQQotIwlYHHRCmSoaeFEEIIIUSZe3qlYSOt3ckuvsyTRoaeLjmp2RFCCCGEeFJkZcOOk3AuFu4kl+umLxixuXvpBT+LRwhjSbIjhDBYVFQUHh4enDp1qqJDEVWMj48PQUFB5brNU6dO4eHhQVRUVKmvOygoCB8fn1Jfr7FCQkLw8PDg5s2bFR2KqAxaTQDLweDzP2g9EWoHgKJv/susL/xxrcw2feRv456f02yluowiEU8aSXaEEI+Nw4cPExISUtFhlJt169aVyYW4sWbMmFHkBbMmCS7tWCvL/sO/ScP58+f15q1du5Z27doxYsQIUlJSKiA6IQzQdAz8fqPw+blAy3dh95ky2fxLm4wrfzsH3twhDxj9l8KIl3iQJDtCiMfG4cOHWblyZUWHUW6+++67SnOxX9a2bNlCcHCwzrTHYf+XLVvGggUL6NChA0uXLsXGxgaA4OBgtmzZUsHRiSdeWiYELc2vubl0y7BlPGdDwCLIK71mZAeNrNXRWHMBPjoiCY94NDJAgRBCVBFpaWlYW1tXdBglYm5uXtEhGEWtVvPll1+yceNGevbsyWeffYaZmZl2/oN/C1HqbtyFGwmQlQM1beDM1fwrui8i4dJNSH/EHv7fHM5/WZvDWE+oZw8ZOfBCM7Cxgvq1oJo5pGZCLTudRVNV5pz9BxQmuRy9DlOOQM4jhPLfE/DfE7mYAE87wNAW8Gxt8KijwNZSSWyyGqfqCv5OVqNUQlOHgu/j38tUY2UKljLU2xNHkh0hhNHUajXh4eFs3ryZ27dv4+TkxIgRI/D29tYru337djZt2kRsbCympqa0bNmSwMBA2rRpo1Pu6NGjhIWFceXKFTIzM7G3t6d58+aMGzeOBg0aEBQUxJkz+c0rPDw8tMtNnz7dqL4RqamprFmzhkOHDnHz5k2srKxwc3Nj4MCB9OrVS1vu8uXLhISEcPbsWTIyMnB2dsbb25shQ4ZgYmKiLRcUFER8fLxeDcTNmzfx9fUlMDCQkSNHAvl9REaNGsX06dNRq9WsXbuW69ev4+joyIABAxg+fLh2ec0+xsfH6+xvZGQk9erVw8fHBycnJyZOnMiSJUs4d+4cdnZ2vPfee0yePJmPPvoIf39/vf0fOHAg2dnZbNu2DYWi7P7pR0VFMXPmTJYtW8aFCxeKPVc0+7NixQqD9h/g/PnzrF69mrNnz5Keno6TkxNeXl4MHz4cU1Pdf2+HDx9mxYoVxMbGUqNGDby9vXn22WdLtG+5ubnMnDmT3bt34+/vz7Rp01AqdS+wCjovNNNWr17N/Pnz+fnnn8nOzubZZ59l8uTJNGjQQGcdN2/eZP78+Zw4cQKA5557jvfff59Ro0bpHCsAlUrFmjVr2LZtGwkJCbi4uBAQEFDoPhh6fs+YMYMdO3bw/fffs2DBAn744QdycnJo164d06ZNo2bNmmzdupV169Zx8+ZNnJycGD9+PF27di3RsRXFUKmg/RQ4daV8tpeWDV9sL3ieAlADzZxhzQTimrjxn1QfrqlqwfrSDyUP+D0RpvygmaL+/6m6nKxVnBqipJ5N/nfydpqaIbtU7P9bjbUZvO+hYOaLJnrLiapLkh0hhNGCg4PJysqib9++mJubs3nzZmbMmIGLi4tOErNo0SLCwsJo0aIFY8aMIT09nW3btjFy5Ei++uorOnXqBMDp06eZOHEijRo1IiAggOrVq5OQkMCJEye4fv06DRo0YMSIEajVas6ePctnn32m3Ubr1q0NjjslJYW33nqLmJgYunfvTv/+/cnLy+PixYscPXpUm+ycP3+eoKAgTE1NGTBgAI6Ojvzwww8sXryYy5cvM3v27Ec6flu2bCExMRFfX19sbGzYvXs3ixcvpk6dOvTu3RuAzz77jHnz5mFvb8+IESO0y9aoUUP79z///MPo0aPp0aMH3bp1Iz09nc6dO+Po6EhkZKResnPu3DliYmIYM2ZMmSY6DzL0XHlYcft/9OhRJk+ejKurK0OGDMHW1pZz584REhLCpUuXmDNnjnaZQ4cO8cEHH1CvXj3efvttTExMiIqK4ujRo0bvT3Z2Nh988AFHjhxh2LBhTJgwwajlMzIyCAwMpFWrVowdO5a4uDjWr1/P+++/z4YNG7SJRlJSEoGBgdy9e5d+/frh7u7O2bNnGTVqFBkZGXrrnT9/Pt999x1t27bl9ddfJzExkTlz5uDs7KxXtiTn94QJE6hduzajRo3i+vXrbNiwgcmTJ/Pyyy+zbds2+vTpg7m5ORs2bGDKlCls3bq1wG2LRzR5TfklOsXRjB9wIQ4GfMn4LxfnJzoVLD4N+mxXcXJofrLzzqH8RAcgLQc++1lNu7oqvBs9Xj05ZOjpkpNkRwhhtOzsbMLCwrRNdbp3706fPn3YuHGj9gI2NjaW8PBwnnnmGZYvX64t6+fnx4ABA5gzZw4dO3bExMSE6OhoVCoVwcHBODg4aLfz9ttva//u0KEDe/bs4ezZs3h6epYo7uDgYGJiYvjwww/p27evzjyVSqX9e+7cueTk5BAaGkqTJk0AGDRoENOmTWPPnj34+vry/PPPlygGgFu3brF582aqV68OQJ8+ffD29mbDhg3aZMfT05Nly5bh4OBQ6P7GxcXx8ccf4+fnpzPd19eX0NBQYmJiaNiwoXZ6REQEJiYm5TpKmCHnSkGK2v+srCxmzZpFy5YtWbZsmbYWp1+/fjRp0oT58+drR1rLy8tj7ty52NrasmbNGuzt7bVlBw8ebPT+TJ8+nbi4OMaNG8ebb75p9PJJSUkMHTpUpxavRo0aLFq0iBMnTtCxY0cA1qxZwz///MOsWbN49dVXAejfvz8LFy4kPDxcZ52xsbGsX7+edu3asWTJEm3C1K1bN4YOHaoXQ0nO7xYtWjBlyhSdaevWreP27dts2LBBey63a9eO1157jW3btjFu3Dijj48oRhkNHvDIriVw7eQNqNOg+LLl4Nc7//69N1Z/VLc9V9V4NyrHgESFerzSWiFEpTBgwACdPgm1a9emfv36XL9+XTstOjoatVrNsGHDdMrWqlULHx8f4uPjuXjxIoD2QungwYPk5pZNZ1SVSsW+fftwd3fXS3QAbTOkxMREfvvtN7p06aK9EARQKBTaGoZDhw49Uiw+Pj7afQawtLSkVatWXLtm3LCvdnZ2BSYufn5+KBQKIiIitNMyMjLYv38/L7zwArVqld/dV0POFWMdP36cu3fv4uPjQ2pqKklJSdrXiy++qC0D8Oeff/LPP//g6+urTXQg/5zr16+f0dtOSEjAxMREr8mZoZRKpV6S1a5dOwCdz/+HH36gZs2aOk0rgQKTF8137Y033tBpgtasWTPat2+vU7ak5/drr72m817TBNDLy0vnXG7SpAnW1tZGn8tlKTExkaysLO371NRUnVHzsrOzuXv3rs4y8fHxRb6/desWavW/F9HltY28RnUL3smKZmmOeX3Hio5Cy9FSpf08Gtnpz29gk1cpP3NRNiTZEUIYraDmKXZ2diQn//vEOM0wxY0a6d8+00yLi4sD8vuRNG3alM8//5zu3bszYcIE1q9fz71790ot5qSkJO7fv89TTz1VZDlN3A/WiGi4u7ujVCq1cZeUIcfP0PU8eHH74PTnn3+eXbt2aZPH/fv3k5aWRp8+fUoWtAEKahpXWvv6oKtXrwL5Td169Oih8+rfvz+A9iJD81kVlJy4u7sbve1PP/2UmjVrMm3aNA4ePGj08rVq1cLCwkJnmp1d/tXYw98fV1dXvb5ADg4O2hHfNDT76Obmpre9h/expOf3w5+jJgZN/6kH2draPtLnW9ocHBx0jnn16tV1jqG5uTmOjroX6k5OTkW+r1u3rs75Xl7bMFk6Eqwq4WAenw7gw1esURTQh6a8KYClPU21n8f/uiixfKAdU6uaMLKNWaX8zIuiRmHwS+iSZmxCCKM9fAGm8eBdL2PY29sTFhbG2bNnOX78OGfPnmXevHmEhISwcOFCo/rllLfC+r7kFTFsa0EJSklYWloWOs/f35+pU6cSHR1N9+7diYiIwNHRUdtPyhiaf+iZmZkFztf0IXn4Ih5K/1x5cNl33nmn0OS1rGqvXF1dCQkJYeTIkUybNo3//Oc/9OjRw+DlCzse8GjHpKwVds4WNr0y78tjzbUm3PkGPt8KP12E1Ay4lQT/JEGeCnJVxayghKz+v3bWwix/9LdurcClJlhbwsst4dmGeOfk8J/qmzmW3ZAsJw/+uKvgRmrph2IK2FvC807g6Q7RNxQ0qaHmfjaAgkkeChrY/fs969FAyV9vKYj4S01NK+jTWIGFjMj2RJFkRwhRJjR3gq9cuYKLi4vOvJiYGJ0ykH/R5OHhoR156/LlywwZMoRVq1axcOFCoPDEwhD29vbY2tpy+fLlIstp7lRrYnxQbGwsKpVKJ25bW1suXLigV/ZRa3/g0fa3a9euODg4EBERQaNGjfj1118LHKXMEJpjcvXq1QJrBDQ1LQXd5X8Uhe1//fr1AbCystJrpvUwzWf1999/683TxG0sFxcXbcLz0UcfARiV8BjCycmJ69evo1KpdBKkxMREvQeXavYxNjZW77v28D4ae36LSsjaEma9Xny5f5Jg0Q7479aSbefAdOj2jFGLOCrT8LI8R4CfB2Zm//7W+G7JJapkXzfsTOHuBBNMlAX/HoxtW/w6nG0UjHlWEpwnlTRjE0KUiS5duqBQKAgPD9fph5OQkEBUVBROTk40bdoUyG9i9jA3NzcsLS25f/++dpqVlRVAiZrIKJVKevXqRUxMDNu3b9ebr7kT7eDgQOvWrTly5Ah//fWXzvzQ0FAAXn75Ze30Bg0akJaWxu+//66dplKpWLdundExPszKykpn/41hamqKt7c3x44d0z6ItaRN2F566SUUCgUbN24kJ0f3iRl37txh37591K1bl2bNmpVo/YUpbP87duyIg4MD33zzTYHnQmZmJmlpaQA8/fTT1KlTh8jISJ3zLDU19ZEe+qlJeGrVqsVHH33E/v37S7yugnTp0oWEhAT27t2rM/3hwQng38/n22+/1alRvHDhgnbYag1jz2/xGKtjD/8ZAuqtEDnN8OVcHPKXMTLRKUpE35LVZje0gaR3TQtNdIQwhNTsCCHKhJubG0OHDiUsLIzAwEB69uypHXo6PT2dWbNmaZvAzJ49m9u3b9O+fXucnJzIysrS9jHx8vLSrrNVq1Zs3LiRzz//nE6dOmmf22PonejRo0dz8uRJZs+ezfHjx3nmmfx/5hcvXiQ3N5dZs2YBMGnSJIKCgggMDNQOzXv06FF+/vlnevfurTNSlb+/P2vXrmXy5MkMHjwYMzMzDhw4UGQzNkO1atWKiIgIli1bhru7OwqFgi5dumiTvuL4+/sTHh7O3r17adu2rbZGxFhubm68+eabhIaG8sYbb/DKK6/g4ODAjRs3iIiIIC0tjZkzZ5Za8zyNovZ/5syZTJo0iX79+uHr64urqyspKSnExsZy6NAhvvzySzw8PDAxMeG9995j2rRpDB8+HD8/P0xMTIiMjMTOzo5btwx8qnwBHqzh+fjjj1GpVHoDCpTU8OHD2bNnDzNnzuSPP/7Azc2Ns2fP8ttvv2Fvb69T6+Xm5saAAQPYuHEjo0ePplu3biQmJrJx40aaNGmiHQhEw5jzW1QRPu0gbR1YF1Mj1LsN7P601DevUCiY+CzMO2vccldGymWqeHRyFgkhysyECRNwdXVl06ZNLFmyBDMzM1q0aMHs2bN1Hujo6elJVFQUO3fu5N69e1hbW9OwYUPmzJlD9+7dteV69erFxYsX2bdvHwcOHEClUjF9+nSDkx1bW1tCQ0NZvXo1hw4d4tChQ1hbW+Pu7s6gQYO05Zo3b87q1asJCQlh8+bN2ocujh8/niFDhuis09nZmblz57J06VKWL1+OnZ0dnp6e+Pr6ajvLl9SYMWNITk5m06ZNpKSkoFariYyMNDjZcXV1xcPDg5MnTz7ywARjx46lSZMmbNmyhbVr15KRkYG9vT0eHh4MHz6c5s2bP9L6C1LU/nfs2JE1a9awZs0adu/ezb1797C1tcXFxYU33nhDZ6SxHj16oFQq+frrr1mxYgUODg7ah4o+6vDIzs7OrFixgpEjR/Lpp5+iVqu1w4c/Cnt7e77++msWLFhAZGQkCoWC5557juXLlzNs2DC9/lGTJk3C0dGRbdu2sXDhQlxdXZkyZQrXrl3TS3aMOb9FFVLNEjI3wPvfwPZjoFRCaiZ0bw0LAsC5Zplu/qvupsw7a/homzv1B80UokQUaulFKIQQVdaECRM4d+4cu3fvLnJAA/F4SEpKokePHvTt25cPP/ywosMRQkvz7CaAgIAAnSHnNRRzDU921JPkfvyDUhQTDS5ro55XhpE8fqTPjhBCVFHXr1/n2LFjvPrqq5LoPIYKGv1uzZo1AMUOzCBEZTSjg2Hl7PXzpCeeDD1dcpI2CyEea5mZmaSmFj++ac2aZdtEozL5/fffuXr1KuvXr8fMzKzApknp6emkp6cXuR4TExNq1KhRVmGKYrzzzjs4OTnRrFkzVCoVJ0+e5IcffqB169Z07dq1osMTwmjTO5ky41jxtTtlNIC2eEJJsiOEeKzt37+fmTNnFlvu1KlT5RBN5bB582Z27tyJs7Mzs2bNKnBI6PDwcO0obYVxcnIiKiqqrMIUxejcuTM7d+7k0KFDZGVlUadOHYYMGUJgYGCpDwYhRGViLxXRohRJnx0hxGMtISGBK1euFFtOmv3ounHjRrHPArKwsKBNmzblE5AQ4rFmSJ8dAPv5uSQXM1jl7r4KejeUhP5B9xWTDC5rq55bhpE8fqRmRwjxWKtZs+YT1USttLi4uOg9gFIIIcpa3FgTqi8qOtuRREeUJhmgQAghhBBClAtr86I70Pco2ePAhCiUJDtCCCGEEKLcvNGs8HkR/lKrI0qXJDtCCCGEEKLcLO5R8OVng+pQzUyGTi6IDD1dcpLsCCGEEEKIclPDUknwy7rTLJVwfoRclorSJwMUCCGEEEKIcjXmOVPGPAexySosTaBudUl0RNmQZEcIIYQQQlQINztJcgwhzdNKTs4wIYQQQgghRJUkyY4QQgghhBCiSpJmbEIIIYQQQlRq0oytpKRmRwghhBBCCFElSbIjhBBCCCHK3FcncrGcl4vVvFxWnM2t6HDEE0KSHSGEEEIIUaa6fpfLpCOQpYJMFYw8AH22SsIjyp702RFCCCGEEGUqOk5/WmQMqNRqlArpj1IcdUUH8BiTmh0hhBBCCFEhuq7Pq+gQRBUnyY4QQgghhCgzSZmqQuf9UECNjxClSZqxCSGEEEKIMtNxXeHJjjCMWoaeLjGp2RFCCCGEEGXmQmLR82OTZKACUXYk2RFCCCGEEBUmNbuiIxBVmSQ7QgghhBCiTGTkFN+EbX9MOQQinliS7AghhBBCiDLx593ik52JR8shkMeewoiXeJAkO0IIIYQQokxcLqa/jhBlTZIdIZ4gp06dwsPDg6ioKO20mzdv4uHhQUhISKlua8aMGXh4eJTqOkXVVFbnYHFCQkLw8PDg5s2bpb5uDw8PZsyYUerrNVZQUBA+Pj4VHYZ4XPxzD3z/C56zIDGlVFb52i7DykVelkEKRNmQZEcIUWJRUVGsW7euosOoklJSUggJCeHUqVMVHQo+Pj5FXjBrEtvSTBoq0/5DftLQuXNnvek5OTlMnToVDw8P5syZg1otzzkXj5nbSdB7Jij6Qt23IOoU7D4LjsPBpC/8Ff9Iqzf0G9En4pE2U+WpURj8ErrkOTtCPOGcnJz48ccfMTExMXrZqKgo4uPjef311/Xmffzxx0ybNq00QnwipaSksHLlSoAqX0NW0Dn4OOx/ZmYmkydP5ueff+att95i9OjR2nkl/U4JUW5uJIBbEOQVUUYFNBkLrerDbwuM3kTMvaJWru/nv3Pp2EAuTUXpkjNKiCecQqHAwsKi1NdramqKqan8xJSntLQ0rK2tKzoMo5XVOViWUlJSeOeddzh37hwTJ07US/gft/0RVVxuLvyTBNPXw5afISnDuOXPXQO71+HeWlAW3SgoTwUbL8JnP+dyKdm4zbywCd54KpeFPRVYmSqpZia1FOLRyZWIEE+4mzdv4uvrS2BgICNHjtRO37FjBxs3buTatWvk5ubi6OhIq1ateP/996lRowY+Pj7Ex+c3b3jwzvvy5cu1/RV27Nih0wxJM+3w4cMsXryYgwcPkpaWRrNmzZg4cSItW7bUiS0pKYmFCxdy5MgRsrOzadGiBe+++y7z5s0jPj5ep++Roa5fv87q1as5fvw4iYmJ2Nvb07x5cwIDA3n66ae15Q4fPkxYWBiXLl1CoVDQpEkThg0bRteuXXXW5+Hhgbe3t17/jKioKGbOnKk9HpDfR2TlypVs3ryZnTt3snPnTu7du4ebmxtjx46lU6dOQH7fqlGjRgGwcuVKbQ2Hk5MTUVFROp+Zu7s7YWFhXL16lZ49e1K9enXWr1/P1q1bqV+/vk5MCQkJeHl54enpyfTp040+dsYw5rN++Bwsbv819u3bx4YNG7h8+TJ5eXk0btyYoUOH0qNHD51YVCoVa9asYdu2bSQkJODi4kJAQECJ9+3u3buMHz+eK1eu8OmnnxbYxK+g80IzrW/fvixZsoTz589jYWFB165def/996lWrZrOOk6fPs2SJUu4dOkS1atXp2fPnvj7+zNo0CC97+v9+/dZtGgRhw4dIisri+bNm/Pee+8Vug+Gnt8+Pj44OTkxadIkFixYwLlz57C0tMTT05Px48eTl5fHsmXL2Lt3L8nJybRo0YIPP/wQd3f3kh1cUXKJKTBtLWz6CZLSDG8/Zqj7mWDSX3eaiYKMfi8y1f9NtifZcC31TUABe0qepHx7Cb69pKagKidLE3jnOfjsRRPMTZ6sREiap5WcJDtCCD07d+5kxowZPPvss4waNQoLCwv++ecffvzxRxITE6lRowbvv/8+S5YsISkpiYkTJ2qXNeQiZ9y4cdSoUYO3336b5ORkvv32W9555x0iIyO1NRPZ2dmMGTOGS5cu4ePjQ4sWLbh8+TJjx47F1ta2RPt1/vx5Ro8eTW5uLn369KFRo0bcv3+fM2fO8Ouvv2qTnU2bNjFnzhzc3Nx4++23gfzkb9KkSXz44Yf07du3RNvXmDFjBqampgwZMoScnBy+++47Jk2axNatW6lXrx7u7u5MnDiRefPm8fLLL/Pyyy8D6F0MR0dHs2HDBvr160e/fv2wtramYcOGrF+/nsjISMaNG6dTfseOHeTl5eHn5/dI8RvDkM/6YYbs/9KlS1m9ejUvvPACo0aNQqlUcujQIaZOncoHH3zAwIEDtWXnz5/Pd999R9u2bXn99ddJTExkzpw5ODs7G70/8fHxjB07llu3bvH5559rYzPUpUuXeO+99/Dx8aFXr16cPn2aiIgIlEolH330kbbcL7/8wrhx47C1tWX48OHY2Niwf/9+fv31V7115ubmMm7cOM6fP4+npyetWrXi0qVLjBkzBjs7O73yxp7ft2/fZuzYsfTs2ZNu3bpx/Phxvv32W0xMTIiJiSErK4vhw4eTnJxMeHg477//Pps3b0ZZTA2AKGWD58F+/fOjTOWpsdp4lIHH77Bo3CzKetjjzDyYcwKy8lTMf1maiQrDSLIjhNBz+PBhrK2tWbZsmU5TNM3ddoCuXbuybt06srKy8PT0NGr9zZo1Y+rUqdr3DRs2ZOrUqezZs4d+/foBEBERwaVLlxg9ejRvvfWWtmzjxo2ZM2cOTk5ORm1TrVYzY8YMcnJyWLNmDU2aNNHOCwgIQKXKfxaE5g65i4sL33zzDdWrVwegf//+vPHGGyxYsICePXtiY2Nj1PYfZG9vz/z581Eo8i8MPDw8GD58OFu3bmXcuHE4OjrStWtX5s2bR+PGjQs9vleuXGH9+vV6CWbr1q3ZsWMHo0eP1uk3EhkZibu7O88880yJYzeWIZ/1w4rb/wsXLrB69WoCAgIYO3asdvrgwYN5//33CQ4OxsvLC2tra2JjY1m/fj3t2rVjyZIl2uPRrVs3hg4datS+5OTk8Pbbb5OSksKCBQt4/vnnjVoe4PLly4SGhmprtvr160daWhqRkZG899572oRu3rx5KBQKVq1ahYuLCwADBgwgKChIb52RkZGcP39er7bH3d2defPm6XxXSnJ+37hxg88//1xbY9a/f3+GDBlCeHg4nTt3ZunSpdpz2c7Ojrlz53L8+HE6duxo9PERJRSfWP6JzgOa/3O9XLe35g818427zyCeYHLbRQihp3r16mRmZnL06NEyGV3q4f4NmmZe16//+w/zhx9+wMTEhNdee02nrJ+fn/YCzRgXL14kJiYGHx8fnURHQ3MX+vjx42RkZDB48GCd7VSvXp3BgweTnp7O8ePHjd7+gwYPHqy9OARo0aIF1apV49q1a0atp1OnTgXWpPn7+5OQkMCPP/6onXbmzBmuXbtGnz59Sh54CRjyWRtr9+7dKBQKvLy8SEpK0nl16dKFtLQ0zp07B+TXfqnVat544w2dxK9Zs2a0b9/eqO2qVCoSExOpVq0aderUKVHsrVq10muu2a5dO/Ly8rSj2d29e5fz58/z0ksvaRMdyO8H9/D3AfJvTpiYmPDGG2/oTO/fv79e7VlJzu/atWvrNQ1s06YNarWaQYMG6ZzLbdq0ATD6XC5LiYmJZGVlad+npqaSkvLvsMrZ2dncvXtXZxlNE93C3t+6dUvnt7HCt2Fhhtq04mo6rHKyoBxHIrRU6jZxq3SfRwm3IcqG1OwIIfQEBARw5swZJk2ahJ2dHW3btuXFF1+kZ8+epdIB/uHmQ/b29gAkJ//bmzUuLo6aNWvqNd0yMzOjXr16Ov9UDKG5uG7atGmR5eLi4oD8GoiHaaZpypTUgxewGnZ2djr7b4iH++Ro9OzZk6+++oqIiAi6dOkC5NeUmZmZ4eXlZXzABnrwolfDkM/aWFevXkWtVtO/f/9Cy2guMjSflZubm14Zd3d3jh07ZvB2LSwsmD17NlOnTmXkyJEsX768wPUWpaCmc5qmZppjokl6GjRooFe2oGma78rDNwHMzc1xdnbW+a6U5PyuV6+eXllNzc/D+6NpYvoon29pc3Bw0Hlf0HFydHTUmfZwzfHD7+vWrVu5tuFggyKgG6zcT0XY3LojFPD9LytT2pvpvK90n0cJtyHKhiQ7Qgg99evXZ9OmTZw4cYKTJ09y5swZZs+ere1gX9DFujEKG5K3Kj2jJC+v8CFXC+vLYOz+W1paFjr91VdfZevWrdy9excLCwsOHDhAly5dqFGjhlHbgPyL/KIuXjMyMrTlHlZWn7VCoWDRokWFHstGjRo90voL89JLLzFnzhymTJnCyJEjCQkJMSrhKWo46sp6/hfV96a0zmVRCpYFQbvGsPFHOPc3JKdDrgpyjRv+2WBKBbjXJu9dX1Ke786AW2qOXErkHzRJQOklP4r/31xzR/jsRSV+TaRhkjCcJDtCiAKZm5vTqVMn7QhhR48e5d133+Xbb79lypQpQMF38ktLvXr1OHHiBOnp6Tq1O7m5udy8edPoPjOaWpBLly4VWU6TyMXExOj1ybh69Sqgeze7sBqZR639edRj27dvXzZt2sSOHTu0zRJL2oStXr16xMbGkpSUpK2ZedDVq1extrYucF5JFbX/rq6u/PTTT9StW7fYATE0n1VsbKxekq75PI3VpUsXvvjiCz744ANtDU9pjj6mudv7999/680raJqzszPHjx8nNTVV5+5ydnY2cXFxOgN6GHt+i8eIiQkE9sx/FSY5DZJSocNUuGVk7duQzhCuP8KfCTAaeDsnh9Bb+U8GDQgI4F62kjrLVMZtA/h9OLSoJZenovRIaiyE0JOUlKQ3rVmzZoBu85Rq1apx//79MrmL27lzZ/Ly8vjuu+90pm/bto3U1FSj1/fUU0/RsGFDIiMjuXLlit58zT60b98eKysrNmzYQFpamnZ+WloaGzZsoFq1anTo0EE7vX79+pw7d47MzEzttPv37xMZGWl0jA+ysrLSrqskmjRpQosWLYiMjCQiIoK6devqxG0MzXDE3377rd68n3/+mZiYGDp37lyqo28Vtf+aAQuCg4MLrEF7sJ38Sy+9hEKh4Ntvv9Upe+HCBU6cOFHi+Dp37syXX35JSkoKo0aNIiYmpsTreljNmjVp3rw50dHR3LhxQzs9NzdX7/sA+fuYl5en9/ls3rxZ5xwG489vUcXYWUODOhAfCuqt8MkAw5Yb2bPARKcota2V5L1vXD+ivPdNJNEphBqFwS+hS84oIYSesWPHYmNjw7PPPkudOnVISUkhKioKhUKhMzJWy5Yt+eGHH/jiiy9o3bo1SqWSdu3a6bVlLgk/Pz+2bt3KsmXLuHHjhnbo6e+//x5XV9cim4kVRKFQMH36dMaMGcPw4cO1Q0+npKRw5swZOnbsyODBg7GxsWHChAnMmTOHN998E29vbyB/aN7r16/z4Ycf6tw9HzhwIJ988gmjRo3C09OTlJQUtm/fjpOTk17nVGPY29vj6urKvn37cHFxwcHBASsrK20fHEP4+/sze/ZsAAIDA0ucjPj6+rJnzx5CQ0O5ePEi7dq1w8LCgosXL7Jjxw4cHR31hrl+VEXtf4sWLQgKCmLFihW8/vrr9OjRg1q1apGQkMCff/7Jjz/+qO2L4+bmxoABA9i4cSOjR4+mW7duJCYmsnHjRpo0acLFixdLHGOnTp20NTyjRo1i2bJlpdZ87p133mHs2LG89dZb9O/fn+rVq7N//35yc3MB3ZovX19ftm3bxsqVK4mLi6N169ZcvHiR77//HhcXF53virHnt6jiPnst/5WSDi5v5z9L50H928P6Sfm1RiWgNKKG2tnKuPJCGEqSHSGEnv79+7N//362bt1KcnIydnZ2NG3alA8++EDnAaJvvPEGcXFxHDhwgC1btqBSqVi+fHmpJDvm5uYsW7aMhQsXEh0dzf79+2nZsiVLly5l9uzZOjUphmrRogVr1qxh1apVfP/992zZsgV7e3tatGihHUUK8of4rVmzJuHh4doHWj711FPMnTtX76GLr776Knfu3GHjxo3Mnz8fZ2dn3n77bZRKJb///vujHAJmzZrFvHnzCA4OJjMzEycnJ6OSnV69ejF//nwyMjLw9fUtcRympqYsWbKEdevWsW/fPkJCQsjLy6N27dr07duXt956i5o1a5Z4/YUpav+DgoJo3rw569ev57vvviMjIwMHBwcaNWrEpEmTdNYzadIkHB0d2bZtGwsXLsTV1ZUpU6Zw7dq1R0p2ID/h+fLLL5k8ebI24WncuPEjrRPgueeeY/HixQQHBxMaGoqNjQ09e/akd+/evPnmmzr9o8zMzAgODtZ+Vw4ePEjz5s0JDg5mwYIFeiM+GXN+iyeETTVIXpf/99V/wNIcnIzv31eQWuZwJ7v4cj8NkURHlA2FWnoRCiEeI3l5efTo0YOWLVuyePHiig6nUsvOzqZ37940b96cJUuWVHQ4ohQcOHCAKVOm8J///IdevXpVdDhCaOXk5BAaGgrk99kxM8sfMe18Qi4tvil+efUkuf9elNuKTwwuW1s9qwwjefxInx0hRKVVUO3Nli1bSElJMfoZKU+i3bt3c//+ffz9/Ss6FGEktVqt8wwPyO+z8+2332JiYsJzzz1XQZEJYZzqZsWXEaIsSRothKi0/vOf/5CVlUXr1q0xNzfn3Llz7NmzB1dXV+0FfGpqarFN2szMzLTPMnkSHDlyhPj4eFasWEHDhg0LbJqUnJxMTk5OkeuxtLSU/hsVJDs7Gx8fH3r37k2DBg1ITk5m//79XL58meHDh5dJs0EhyoKLrQlQdB/L6XLvSpQhSXaEEJVW+/bt2bRpE6tWrSI9PR1HR0f8/PwYNWqU9uGmc+fOZceOHUWup23btqxYsaI8Qq4UvvzyS+7cucPTTz/Nxx9/XOCzXSZPnsyZM2eKXI+3tzczZswooyhFUUxNTXnxxReJjo4mISEByH+g6JQpUxgwwMARtISoBAwZdGBc23IIRDyxpM+OEOKxFhMTw507d4osY2try9NPP11OET0e/vzzz2KHta5VqxYNGzYsp4iEEI+zwvrsACjm5ha5bOa7SixMpWdFUW4rPjW4bG31Z2UYyeNHanaEEI+1hg0bygV5CUjyJ4QoL+3rwPF/Cp8viY4oS3J2CSGEEEKIMnP0jcKf01NDBjAQZUxqdoQQQgghRJkxVRbeb2ervzxfxxDS56TkpGZHCCGEEEJUiK71C6/1EaI0SLIjhBBCCCHK1Hov/Wmh8lxcUQ6kGZsQQgghhChTg542ZWAzNRMO5JGeA1+9rMTeUu65i7InyY4QQgghhChzCoWCxT3k0rMk1EjfppKSlFoIIYQQQghRJUmyI4QQQgghhKiSpC5RCCGEEEKISkyasZWc1OwIIYQQQgghqiRJdoQQQgghRLm4dDePVzbl8npULkmZ8qhMUfakGZsQQgghhChztgtyScn99/13F/OI8FPg21geLFo8acZWUlKzI4QQQgghylS7MN1ER8Nvu9TuiLIlyY4QQgghhChTp24XPF1SHVHWJNkRQgghhBBCVEnSZ0cIIYQQQohKTIaeLjmp2RFCCCGEEBXm3f0FdOYRopRIsiOEEEIIIcqMWl10z5xlv5ZTIOKJJM3YhBBCCCFEmfnsaF6R87PLKY7HmQzkUHJSsyOEEEIIIQyTkgHZObDvF4i9ZdAic8+UbUhCFEVqdoQQQgghRNH6fg7bThQ8L2EN2FoWumh6TvGrz1WpMVVKJ3xR+qRmRwhR4aKiovDw8ODUqVMVHYqoYnx8fAgKCirXbZ46dQoPDw+ioqLKdbtClIl7KfDal4UnOgC1hxe5CpUBm8mQMQpEGZFkRwjxxDt8+DAhISEVHUa5WbduXaW4EJ8xYwYeHh7cvHmzwPmaJLi0Y60s+69x5swZ3nvvPXx8fOjYsSM9e/Zk6NChfPnll9y4cUNb7ubNm4SEhHDx4sVH2l5KSgohISFyc0EULTMLFH3BYTis/7nosirg9OVH2lxWrvRKKYoahcEvoUuSHSHEE+/w4cOsXLmyosMoN999912lutgvS1u2bCE4OFhnWmXa/82bNxMUFERMTAze3t5MmTKF119/HXd3d/bu3cuFCxe0ZW/evMnKlSu5dOnSI20zJSWFlStXcvr06UcNX1RFXrPzkxyr14xazLTjxwVOv5liSL0OHL9pWDkhjCV9doQQQhQpLS0Na2vrig6jRMzNzSs6hELl5uYSHBxM3bp1+fbbb6levbrO/JycHNLT0ysoOlGlqdWQkQW37sGMDbDhR8guesQ0QyhzVahMde+j9/zOsCTGeztMfT6XoFbgXkMuT0XpkbNJCFFpqNVqwsPD2bx5M7dv38bJyYkRI0bg7e2tV3b79u1s2rSJ2NhYTE1NadmyJYGBgbRp00an3NGjRwkLC+PKlStkZmZib29P8+bNGTduHA0aNCAoKIgzZ/KHCvLw8NAuN336dHx8fAyOPTU1lTVr1nDo0CFu3ryJlZUVbm5uDBw4kF69emnLXb58mZCQEM6ePUtGRgbOzs54e3szZMgQTExMtOWCgoKIj4/Xq4G4efMmvr6+BAYGMnLkSCC/j8ioUaOYPn06arWatWvXcv36dRwdHRkwYADDh//bnl6zj/Hx8Tr7GxkZSb169fDx8cHJyYmJEyeyZMkSzp07h52dHe+99x6TJ0/mo48+wt/fX2//Bw4cSHZ2Ntu2bUOhKLtmFFFRUcycOZNly5Zx4cKFYs8Vzf6sWLHCoP0HOH/+PKtXr+bs2bOkp6fj5OSEl5cXw4cPx9RU99/m4cOHWbFiBbGxsdSoUQNvb2+effZZg/YlKSmJlJQU2rVrp5foAJiZmWFnZ6ez3wAzZ87U/t22bVtWrFiBSqUiNDSUY8eOce3aNZKTk3F0dKRTp06MHj0ae3t74N9zBWDlypXaGk0nJyedc23fvn1s2LCBy5cvk5eXR+PGjRk6dCg9evTQibG475coB6evwKrvAQXUtYdNP0H8PTBVQlIaZJVPZxgF8NbYfWSag3rurwz3H8m3tZqSh0mxy2p8fiL/BbnadQKYKKCGJXSrr8C/iYKjcWpyVRDQUsnzTk9Ks60nZT9LnyQ7QohKIzg4mKysLPr27Yu5uTmbN29mxowZuLi46CQxixYtIiwsjBYtWjBmzBjS09PZtm0bI0eO5KuvvqJTp04AnD59mokTJ9KoUSMCAgKoXr06CQkJnDhxguvXr9OgQQNGjBiBWq3m7NmzfPbZZ9pttG7d2uC4U1JSeOutt4iJiaF79+7079+fvLw8Ll68yNGjR7XJzvnz5wkKCsLU1JQBAwbg6OjIDz/8wOLFi7l8+TKzZ89+pOO3ZcsWEhMT8fX1xcbGht27d7N48WLq1KlD7969Afjss8+YN28e9vb2jBgxQrtsjRo1tH//888/jB49mh49etCtWzfS09Pp3Lkzjo6OREZG6iU7586dIyYmhjFjxpRpovMgQ8+VhxW3/0ePHmXy5Mm4uroyZMgQbG1tOXfuHCEhIVy6dIk5c+Zolzl06BAffPAB9erV4+2338bExISoqCiOHj1q0D44OjpSrVo1zp49S2xsLG5uboWWffbZZwkICCA0NBR/f39tQuXg4ADk1wKFh4fTrVs3XnrpJSwtLTl//jwRERH88ssvrF27FjMzM9zd3Zk4cSLz5s3j5Zdf5uWXXwagWrVq2m0tXbqU1atX88ILLzBq1CiUSiWHDh1i6tSpfPDBBwwcOBAw7PslytiRP6DHTMipHL37FYBVNrzYczQ/1Wr2yOvT9OLJVcOdDNhwUc2Gi//27Vn5Wx57+ivp0UB6ZYjCSbIjhKg0srOzCQsLw8zMDIDu3bvTp08fNm7cqL2AjY2NJTw8nGeeeYbly5dry/r5+TFgwADmzJlDx44dMTExITo6GpVKRXBwsPaiEODtt9/W/t2hQwf27NnD2bNn8fT0LFHcwcHBxMTE8OGHH9K3b1+deSrVv0045s6dS05ODqGhoTRp0gSAQYMGMW3aNPbs2YOvry/PP/98iWIAuHXrFps3b9bWEvTp0wdvb282bNigTXY8PT1ZtmwZDg4Ohe5vXFwcH3/8MX5+fjrTfX19CQ0NJSYmhoYNG2qnR0REYGJiYlRN2KMy5FwpSFH7n5WVxaxZs2jZsiXLli3T1uL069ePJk2aMH/+fO1Ia3l5ecydOxdbW1vWrFmjrTnp168fgwcPNmgfFAoFQUFBLFiwgEGDBtG0aVNat25NixYtaNeuHTVr1tSWdXFxoX379oSGhtK6dWu92M3NzdmzZw+WlrrD/7Zu3ZrZs2dz+PBhevbsiaOjI127dmXevHk0btxYbz0XLlxg9erVBAQEMHbsWO30wYMH8/777xMcHIyXlxfW1tYGfb9EGZsfVWkSnQfVSr1fLtvJU8O8U2p6SF4tiiCpsBCi0hgwYID24hWgdu3a1K9fn+vXr2unRUdHo1arGTZsmE7ZWrVq4ePjQ3x8vHa0Ks1F/8GDB8nNLZsLApVKxb59+3B3d9dLdACUyvyf2cTERH777Te6dOmiTXQg/4JXU8Nw6NChR4rFx8dHpzmUpaUlrVq14tq1a0atx87OrsDExc/PD4VCQUREhHZaRkYG+/fv54UXXqBWrVolD95Ihpwrxjp+/Dh3797Fx8eH1NRUkpKStK8XX3xRWwbgzz//5J9//sHX11eb6ED+OdevXz+DtzlkyBDmzZtH+/btuXr1KuvXr+eTTz7By8uLzz77jMzMTIPWo1AotIlOXl4eKSkpJCUl0a5dOwB+//13g9aze/duFAoFXl5eOvuflJREly5dSEtL49y5c9p9hbL9fpWGxMREsrKytO9TU1NJSUnRvs/Ozubu3bs6y8THxxf5/tatW6jV/9YwVNQ2cu+lUhk9lVDwCItlITlLXWk+j0fdRlFkNLaSk5odIUSl4ezsrDfNzs6OW7f+fUq3ZpjiRo0a6ZXVTIuLi6N58+YMHDiQ6OhoPv/8cxYvXswzzzzDCy+8QK9evXSabT2KpKQk7t+/T8eOHYssp4n7wRoRDXd3d5RKJXFxcY8US2HHLzk52ej1PNh/6MHpzz//PLt27WL8+PGYmpqyf/9+0tLS6NOnT4njLk5BTeMMOVeMdfXqVQCd5owP01y8aD6rgppqubu7G7XdLl260KVLF/Ly8rh69SonTpxg/fr1REZGYmJiwkcffWTQevbv38/atWu5ePGiXvJx/75hd9qvXr2KWq2mf//+hZbRHIPy+H6VhgdrnQC9/lHm5uY4OjrqTHNyciryfd26dSvHNoa/DNHnqWzO13Ypt20Naa6sPJ/HI25DlA1JdoQQlYamFuRhD95NM4a9vT1hYWGcPXuW48ePc/bsWebNm0dISAgLFy40ql9OeSus70teXuEjJhWUoJTEw02hHuTv78/UqVOJjo6me/fuREREaDvCG8vCwgKg0NqLjIwMnXIPKu1z5cFl33nnHZ566qkCy5Rl7ZWJiQmNGzfWNi/z8/Nj586dTJ06tdjP9uDBg0ybNo0WLVowadIk6tSpg7m5OSqVivHjxxt1XBQKBYsWLSr0GGtuKjzO368qI6A7pGbC8n2gUoG1JZyLhZy8fzu8lCPNJkO3LONVx084XacBlFI/PgVQxxq6uij4/W7+AAWBrZWMbiONlETRJNkRQjxWNHf0r1y5gouL7t3DmJgYnTKQfwHp4eGhHXnr8uXLDBkyhFWrVrFw4UKg8MTCEPb29tja2nL5ctEP1NOM9KWJ8UGxsbGoVCqduG1tbXWesaLxqLU/8Gj727VrVxwcHIiIiKBRo0b8+uuvBY5SZgjNMbl69WqBNV6amhZNudJS2P7Xr18fACsrK9q3b1/kOjSf1d9//603TxP3o7C3t8fFxYULFy6QlJSEo6NjkZ/brl27sLCwICQkRCdZjY2N1Stb1HpcXV356aefqFu3rkE1VIZ8v0QZG++V/zJUbi58uR1O/AVH/4C7aaWWGF1p4cDBCc8TEBDAqf9vZmo7LxcDH7WDCfDG07CilxILU0liROmQM0kI8Vjp0qULCoWC8PBwnaY6CQkJREVF4eTkRNOmTYH8JmYPc3Nzw9LSUqdZj5WVFYDRzb0gv4ahV69exMTEsH37dr35mjvqDg4OtG7dmiNHjvDXX3/pzA8NDQXQjowF+c2j0tLSdPpaqFQq1q1bZ3SMD7OysjK4WdPDTE1N8fb25tixY9phi0vahO2ll15CoVCwceNGcnJydObduXOHffv2UbduXZo1e/RRnR5U2P537NgRBwcHvvnmmwLPhczMTNLS0gB4+umnqVOnDpGRkTrnWWpqKlu2bDEojszMzEIf7Hnt2jWuXr2Kvb29tkmYZsS0gmLT1MI8OCCGWq1m1apVemU153tBx0AzYEFwcHCBtYgP9kEw9PslKhlTU5jWH7ZNhTvhoNoK6q2QGAaHC2/CWRw1cHCC/gArN8YYdnPlvy9A7iRT1niZSqJTALURL6FLanaEEI8VNzc3hg4dSlhYGIGBgfTs2VM79HR6ejqzZs3SNvmZPXs2t2/fpn379jg5OZGVlaXtY+Ll9e+d0FatWrFx40Y+//xzOnXqpH1uT0H9QgoyevRoTp48yezZszl+/DjPPPMMgLbvxKxZswCYNGkSQUFBBAYGaoeePnr0KD///DO9e/fWGYnN39+ftWvXMnnyZAYPHoyZmRkHDhwoshmboVq1akVERATLli3D3d0dhUJBly5dtBfBxfH39yc8PJy9e/fStm1bbY2Isdzc3HjzzTcJDQ3ljTfe4JVXXsHBwYEbN24QERFBWloaM2fOLLXmeRpF7f/MmTOZNGkS/fr1w9fXF1dXV1JSUoiNjeXQoUN8+eWXeHh4YGJiwnvvvce0adMYPnw4fn5+mJiYEBkZaXDfoczMTEaOHEmjRo144YUXcHV1Ra1WExsby65du8jKyuKDDz7QJjLu7u5YW1uzefNmLC0tsbGxwcHBgXbt2tG9e3cOHjzIqFGj8PLyIjc3l+jo6AKbCNrb2+Pq6sq+fftwcXHBwcEBKysrunTpQosWLQgKCmLFihW8/vrr9OjRg1q1apGQkMCff/7Jjz/+yLFjxwDDv1/iMVGjOrzUMj/xycyCJTth8lqDF88b3avA6baWJmiem1OU/qV7T0MILUl2hBCPnQkTJuDq6sqmTZtYsmQJZmZmtGjRgtmzZ+s80NHT05OoqCh27tzJvXv3sLa2pmHDhsyZM4fu3btry/Xq1YuLFy+yb98+Dhw4gEqlYvr06QYnO7a2toSGhrJ69WoOHTrEoUOHsLa2xt3dnUGDBmnLNW/enNWrVxMSEsLmzZu1DxUdP348Q4YM0Vmns7Mzc+fOZenSpSxfvhw7Ozs8PT3x9fUtsvO4IcaMGUNycjKbNm0iJSUFtVpNZGSkwcmOq6srHh4enDx58pEHJhg7dixNmjRhy5YtrF27loyMDOzt7fHw8GD48OE0b978kdZfkKL2v2PHjqxZs4Y1a9awe/du7t27h62tLS4uLrzxxhs6I+n16NEDpVLJ119/zYoVK3BwcNA+VHTcuHHFxlG9enU+/fRTjh07RnR0NHfv3iUrK4saNWrQtm1bBg0apPPgU0tLS/7zn/+wbNky5s2bR3Z2Nm3btqVdu3b06tWL9PR01q1bx8KFC7GxsaFLly6MGzdO51zXmDVrFvPmzSM4OJjMzEycnJzo0qULkP9A2+bNm7N+/Xq+++47MjIycHBwoFGjRkyaNEm7DkO/X+IxZGkBk/rmv77YCh+tg9yi26KpF46A/6+lLgnn6lKbI8qGQv0ovTmFEEI8kSZMmMC5c+fYvXt3kQMaCCGqALUaHIfBvbSC56esJcfCTNskNyAgQGdoeMXc4mt21JPk/ntR/lb81+CyDdQflmEkjx85s4QQQhjl+vXrHDt2jP79+0uiI8STQKGAxHC4ngCRJ2Dzz3DtNnzgDyPzH1jMQ/3uHmSlgAy5tS4qiCQ7QghRgMzMTFJTi39g34NPua/qfv/9d+2DL83MzPSa3gGkp6eTnp5e5HpMTEwq1XNYhBAGcq0JYz3zX0bwawLfXSqjmIQohiQ7QghRgP379zNz5sxiy506daocoqkcNm/ezM6dO3F2dmbWrFkFDgkdHh6uHaWtME5OTkRFRZVVmEKISmbVqyZ8d6nwwVVK50k8VZtajlKJSbIjhBAF6NixI8HBwRUdRqUyY8YMZsyYUWQZLy8v2rRpU2SZgh4SKoSouqzMir5QH17645AIoSXJjhBCFKBmzZpPVBO10uLi4qL3sFchhChKSK/SHV5eiAfJOH9CCCGEEKLCmJtIEy1RdqRmRwghhBBCiEpNEsKSkpodIYQQQghRpt5qUdERiCeVJDtCCCGEEKJMff2qKeYFVE5Meq78YxFPFkl2hBBCCCFEmct635QVPaC6GThawG5/+PJl6VFhCLURL6FLzjAhhBBCCFEuAtuYEtimoqMQTxKp2RFCCCGEEEJUSZLsCCGEEEIIIaokacYmhBBCCCFEJaaWoadLTGp2hBBCCCGEEFWSJDtCCCGEEOKRpanMyVNLDYSoXKQZmxBCCCGEKLGP92bw3++zUav9wQQyT6t4r0NFR1W1SDO2kpOaHSGEEEIIUSI7z+fwn/3ZqDUPeMmDiZszOfJ3XoXGJYSGJDtCCCGEEEJLfe5vcmq8iVrRlzyzgeTMiSi07Gvr0gE1qNVY5ubQ5u49fK7fpN+ie2TlyiMuRcWTZEcIIYQQQgCg/iUGdev3UCbdJ1Nhzt+m9cj4aAt5O0/plf32TBYpmWpQKECpINPUjF8ca/BrrRr0uPEPL85KJDtbVQF7UfWoURj8Erok2RFCCCGEEKgyc8h99gOU5F8gWqhzsFGlcNbRnXTfr8i5fR+Ae8m5zAxPJmBLNhYWJlioVDhnZlM3OwfUaq5Vq8b6tk34zawaT32YSKbU8IgKJAMUCCGEEEII4qbswBUVakABKFBTK/sebROyCX+qF+qXNrPbszsXcqz4u5YtudZK7LJy6JKUgoUqP6FJMlESbVudnORsnrmfhm1OLi98msiZ/zrqbOtmihpTJdS2lpoIUbYk2RFCCCGEEGQePqdNdB5ko0pjzIVIrtrW5PPUrtyoYw0m+Y2DWib8m+gA2OepaJSZzQVTE87WtMUv9jY28amY/q86eSoF1UzVWFooSczML1/NFA6/bkK7etLYSJQNObOEEEIIIZ5wu44mYBkTW2SPD/f7CSw+spWnUjIwVakwzVNhk5UDgEqR/wKwzcsDpYI8pZKf6tqTbmaKVbYKlArSVQptogOQngsdw/PIzpOmbkVRG/ESuqRmRwghhBDiCZabnk2n7hOwzc5ERdF3wt0TE2h9J4meV+Oom5JGnlLJb/Vqsr1Fg/z599LIS8kGi/xLzHhrS25ZW6BWATl5YKqAXFX+oAYmClAoyFND29XZ/B5oUeb7Kp48UrMjhBBCCPGkupHAxTqfcKBGD/JQFnphmGZSjd21X+EP87b4RZ+h0dV4LtlYkwc0upNE81uJKNVqrjhWJ7aubX4yk54F2bn5tQ2mCjBTglKR/3eOCjJzQZU/bPUfdxUo5mQzLTq3/PZdPBGkZkcIIYQQ4gn1j2cwv9q1pXHaX5hQ+DDRp+2f5Z65A5Dfp6dO4n3+aurGt40bgJUZKjMTrO9nYWMK9ywtIC0bclR0SUjmrq0lf9Syy09yALLU+bU6psp/OwipAYWCz0+oWXwmh9T3zMp0vx8/MpBDSUnNjhBCGOnmzZt4eHgQEhJS0aGUik2bNtGvXz86duyIh4cHN2/eNGi5qKgoPDw8OHXq3+dvnDp1Cg8PD6Kioko1xqCgIHx8fEp1nZXRxYsXGT16NC+//LJR51hVOydF+bi14gTHb7ugVOdhlpeNqpAL6r9q1OIvWze96U/fTkSlUKDKyoW0bNKyVSSlqyAjJz+RsTDlSJO6/FHHXttkjSwV5Knza3gUD20vTwVZKtKy1Si+yOKXW3mlv9PiiSM1O0II8QQ7deoUc+bM4aWXXmL48OGYmppSo0aNColl3bp12NjYPBFJTUFyc3P54IMPyM3NZdSoUdjY2NCkSZOKDssgFy9e5PDhw/j4+FCvXr2KDkcYIGXhfjKn7aQ1CmKs3fnTrgXm6hxapF7QlkkzqcYp+7b8WqsxZrkqLHN0k4/r9jb5f6gBtVr7Jxm5YG5Kk9Q0Xrp0FevsHM7VdeBwo3qoihqIQE1+IpSnBnMlz4apsDVXEdRGwScdldhayD16YTxJdoQQ4gl2/PhxAD799FPs7OweeX1t27blxx9/xNTU+H8v3333HU5OTgUmO8HBwajVVXucobi4OOLi4nj33XcZNGhQRYdjlEuXLrFy5Uqee+45SXYqsZy0HHLT88j+/jy27y7j/1MV3DL+5geHF1E+MJaXGjhc8yWSzeyomZo/fJpKAcr/L3Khpj3R7s6Fbss2I4uhf1zB5P/fd79yEzOVir2u/39+qNT57YserN15YAhrclVgYsL9HJh7Qs3cE7mYKKFFDQULeyhoVdsER6snp2mXWpqxlZgkO0IIUcnl5uaSl5eHhUXpj1SUkJAAUCqJDoBSqSyTOM3Mqn77/bt37wKl91mIJ1xaJkScgNw81Fbm/Dbqe/4xqYkKJd0SDuldOrdJ/pU4y38T1btmDpipsmmcepk7FrVINrP/P/buOzyK6mvg+Hd30wtJSEIICRCalABSQlEBUXpvoYgUI11QkaL8bATFV0Gkt4D0Ih1JqAaki5QEEelVQkIL6T1b3j/WLFk2gfRQzud59iE7e2fmTtllztx7z3CkvDuny5Qi3tKCCy6OoFRiptWiVpq2uFSJjTcEOulKJfGWFmhStZCq1kdSKgXolI/G7Wj5L0Mb+pYdxX/vdTpDTmWNFv5+CG+t1aBCjU2aGiuNlge2Vv8tQ0cVB0hGSXTqo9iprD3UcobDt3QkpUPVkjCqvoq3vRTs+RfuJ4GDhY6G7gouPgQzpQ57CzgbqePPCAVJah0j6yrpViXrlqW4VB3brupQKqBLZQV2FhKYPEsk2BFCPJOCgoKYNGkSCxYs4OLFi2zatIn79+/j7u7O+++/T8eOHQH9WIXOnTszZMgQhg0bZrSMgIAAFi9eTGBgoOFus7+/P9u3b2fv3r3MnDmTw4cPk56eToMGDfjf//6Hi4sLW7ZsYe3atURERODu7s6HH35I8+bNs6zn7t27Wb58Obdu3cLJyYnOnTszaNAgk5aNyMhIFi9ezJEjR3j48CGOjo40bdqUESNGULJkSZM6r1+/nm3btrF3714iIyOZP38+Pj4+Od5/Bw4cYOXKlVy+fBmFQkGVKlUYMGCAYTsy9luGjGXXq1ePRYsW5Xg9jzt16hTDhw9n4sSJhhYarVbLunXrCAwMJCIiAoVCgbOzM3Xq1OHzzz/HzMzMsP47d+4YbWfGsRs6dCh37twxGguUMW3p0qXMmDGDY8eOkZaWRt26dRk/fjzly5c3qltERAQzZszgxIkTANSvX5+xY8cyfPhw3N3djbb7yJEjrFy5kmvXrpGSkoKjoyM1atRg1KhRJst9moiICBYsWMDx48eJj4+nVKlStG7dmkGDBmFlZWXYltDQUAAmTZrEpEmTjLY/Nw4fPszixYu5evUq9vb2tG/fnpEjR5qck6Ghofz888+cO3cOtVqNl5cXPXv2pGvXrkblrl27xqJFi/j777+JiYmhRIkSeHl50b9/f5o0aWI4ZwGGDx9umK9jx474+/vnqu6igIRFwhuf6/8F/i5Rk3MlagKg0Gkx05mOhbHXJFAt8fKj9+p4Wj/YZ3h/yNWHRQ1bo8vUEuOSmMykA8fZ4l2ZfV4ej1pplAoctPp13La34x83Fx5YmHPRzvrRg2A0OkAL5irjFh3QB0Iq9IGOQvHf2HyFIXMb5io0QLyZingtVIiO44aDHSiVXInVgUIHmQKwy9FwOVoHCv20kEgdfjs0KCwUj1pMMoKqzBQY8gLsu6Xls4Y6fmimMipyJVpH07Ua7iXp33vYwdG+Kso7SMDzrJBgRwjxTJs3bx6pqal0794dCwsLNm3ahL+/P56entSpUyfPy/3oo48oVaoUw4cPJywsjPXr1zN+/Hjeeusttm7dSpcuXbCwsGD9+vV89tlnbNmyBQ8P4y4bhw4dIjw8nJ49e+Ls7MyhQ4dYvHgxd+/eZeLEiYZyd+/exc/Pj/T0dLp06YKnpydhYWFs3ryZU6dOsWrVKuzs7IyW/dVXX2Fpacm7776LQqHAxcUlx9u2ceNGpkyZgpeXF4MHDwZg+/btjBs3js8//5zu3bvj5OTEN998w9atWzl9+jTffPMNgFHgVVCWLl3KwoULadq0KT169ECpVBIREcGhQ4dIS0vDzMyMb775hunTp+Po6Mj7779vmPdp44eSk5MZMmQItWrVYuTIkYSHh7Nu3TrGjh3L+vXrUan0FyYxMTEMGTKEhw8f0qNHDypUqMDp06cZPnw4ycnJRssMCQlhzJgxVKpUCT8/P+zs7IiMjOTEiROEhYXlKti5c+cOAwcOJCEhAV9fX8qVK0dISAjLli3jzJkzzJ8/HzMzM95//31effVVli1bRrdu3ahbt26Otv9xR48eZdOmTfTo0YPOnTtz8OBBVq1ahb29vdF+PXToEOPHj8fZ2Zl+/fphY2PDb7/9xuTJkwkPD2fkyJGG/TZixAgAevToQenSpYmJieHChQv8888/NGnShLfffpvIyEi2bt2Kn58fFSpUAMDT0zNXdRcFaOpWQ6ADcNW2suFvlU5j1F0tO5a6dKP3jR/+hUNqEjFWtoZpkbbWxFua0+ufKzS5Gc781+uQamFOnEJFqEtJGtx9yFk3F7RKJQ8tsrjk1OgMWdgMDN1Vs0hgoPwvZXVGvKFQgOK/cMVMqU9+YKEANaYpuDIvS6kA88e6hmW0ImU3DzAzRMcPzYyLTD6mNQQ6AOEJ8MNxLQtaGwdFovhIsCOEeKalpaWxcuVKQzemFi1a0KVLFzZs2JCvYMfb25vPPvvMaNratWu5f/8+69evNwQfDRo04J133mHr1q2MGjXKqPyVK1dYuXIl1apVA6B3796MHz+eoKAgunfvTq1atQCYOnUqarWaNWvW4ObmZpi/ZcuW+Pn5sWbNGpNWKTs7O8OFcG7ExcUxe/ZsPD09Wb58uWE7fH19effdd5k5cyatWrUy3PE/ceIEp0+fpn379rlaT27s37+fChUqMGPGDKPpH374oeHv9u3bs2DBAkqWLJmrusTExNC/f38GDhxomObk5MTs2bM5ceIEr732GgArVqzg3r17fPvtt7Rr1w7Q75NZs2axatUqo2UePHgQrVbLvHnzjIK/jMAxN+bNm0d0dDQzZ86kSZMmAPTs2dOw3u3bt9O1a1caN26MmZkZy5Yto3bt2nk+HtevX2fDhg2G1qAePXrQu3dv1q9fbwh2NBoNU6dOxdramhUrVuDq6gpAr169GDZsGCtWrKBTp06UK1eOM2fOEBUVxffff0+rVq2yXGeVKlWoXbs2W7dupVGjRrlqgSwKUVFR2NraGrpXJiQkoNPpsLfXj1hJS0sjPj4eZ2dnwzx37tzB3d092/d3797Fzc0NxX8Xw8/cOi7fye9uM2GhVVMu7qFRsGOblo5dmj4oUlqY82Z8MpDMQ3MzDrs6sLp6Rcon6z83e7z1Bp6cTTm7XARZtL4oMrcA5WdoX+b5s6hbaqYGsYzjcTnKNKi5HJ23Y/4kMmYn7ySthRDimdazZ0+j8RqlSpWiXLlyhIWF5Wu577zzjtH7jDvpHTp0MGplqVKlCra2tty6dctkGY0aNTIEOgAKhYIBAwYA+gt80F+QHDlyhGbNmmFpaUlMTIzhVaZMGTw9PQ1JAjLr27dvngb5Hz9+nOTkZPr06WO0HXZ2dvTp04ekpKQs11eY7OzsuH//Pn/99VeBL1upVNKnTx+jaQ0aNAAwOmaHDx/GxcWFNm3aGJXt379/lvUF+P3331Gr8/6AQ61Wy6FDh6hataoh0Mnw3nvvoVQqOXDgQJ6Xn5XmzZsbdXtTKBT4+Pjw8OFDkpL0t58vXLjA3bt36dy5syHQAf24qAEDBqDVajl48CDwaF/88ccfJCQkFGhdi0rJkiWNxpHZ2dkZAgQACwsLowtSwOQC9PH3pUuXNgQhz+Q6WtU2WlaVxKuGv9VKc552VqdgbhIzaBQKou0fnS/odHimJBPqWZq/y3twroqX4SPndDUVE1O4V8Ia7X+b4J6WbhrwmGVzGWquBJXStKVFqzNt7dGBc1Kq0dgeVDkIDLIKinSP/f3Y+qs4Pvo743i08jJdVysvRZ6OuSgc0rIjhHimPd51DPQDuO/evVugy824aMhqfESJEiWIjY01me7l5WUyrWLFioA+sxbAzZs30Wq1bNu2jW3btuWoLgDlypV78gZkI2O9GfV4Ut2KysiRIxk3bhyDBw/G1dWV+vXr06RJE1q0aJHvxAOurq4mCREyBvhnPmYRERF4e3ujfGwgdcmSJY0uGEHfwnHw4EF++OEH5syZw6uvvsrrr79OmzZtctWtLDo6mqSkpCyPhYODAy4uLgV+LLL7voB+f9jY2Bieo5RVvSpVqgQ8Okfq169Phw4dCAoKYteuXdSoUYNGjRrRqlWrLOcXz4iPO8I/YbD6IGi0VI87h6UmlTBrTxLM7EhV2WCmScp2divSTabdcihNw6RUKqRp+MfJlih7Ky4523CpfCnMEtPofCfK6A56tIMValsLbpS0pcLDRKy0OmrFJ3HF1ooEs/9aQ7IKSswVj6b/l3QA0Aceap2+m1oGnY6KD2II9XTVd4lTPZr+yH/NNZm7y+l0qLQ63O0V3E58NI+dhf5ZqAqFPtZKz/SMVU872NvLNDj7XyMll6K0bLqsb3t5p7qCT+pLK8yzRIIdIcQz7fGL0wwZaYgVj9/ly0Sjyf6BdBljOXI6Pb9pj9u1a2dIqvC4rLKXZQxcfxHUrl2bX3/9lWPHjnHq1ClCQkLYvXs3S5Ys4eeff85X9rHszg/I+zFzdHRk5cqVnD59muPHj3P69GmmT59OQEAAs2bNonbt2k9fSDEpjP0xadIk+vfvzx9//MHp06dZvXo1S5cuZcyYMc9diuyXhrkZLP8QZr4PGi3mOh3uS44St/IGPIjjrmUpKiXdzNUiI+1KogDMVAqiHG0efaBQoLa14L65itL/PYdHrVDwwFb/u3bPwZooGwts09QkmpuRrlI8aqHJnG0NwEyhH0/zOCX6bG3mCkjXARoUSnCwBAdvB5pZ6BfRooIS36pK1FoFNio4GqHl2B3oW01BuRJKzkXqSNcq8HEDe0t91rS7iTosVTqS0hWUsYOoFH2sZWWm/9vOXN99zdUm6/9rrM0VrO+sIipZh0IBTlaFE+i82In3C5cEO0KI51qJEiUA/ViVxxV2C8bNmzdNpl2/fh14dIfd09MThUKBWq2mUaNGhVqfjPVl1KNhw4ZGn924ccOobkXJxsaGFi1a0KJFC+BREoVt27YZuv49KXDNL3d3d8LCwtBqtUYBQVRUFPHx8SblVSoVPj4+hvEnV65coV+/fixZsoRZs2blaJ1OTk7Y2toazonM4uLiiIyM5JVXXsnjFuVdxvHPql6Pn78ZKleuTOXKlRkwYADx8fEMHDiQuXPn0qtXLxQKRaEeO5EPjo/G15T4rB0NMoYparUk2vthmxTPbUt3XNMiTRISPK5kdDwKnY74rBINKBTEWZrjkq4h2kxFtLVxi226uYoYVaZAXKXINDZG8Wh8TFankRLQ6fCwUzCgloL6pZV0qazALKug6DEVnJT08370vmwJ0zKlbfUp15z+u7/kbP3oszJ2puWzU/IleubP80bG7Aghnmu2trY4Oztz8uRJozvXt2/fLvDxEI87fvw4Fy8+etq4Tqdj5cqVAIYUz46Ojrzxxhv8/vvvnD171mQZOp2O6OjoAqtTo0aNsLa2Zv369SQmJhqmJyYmsn79emxsbGjcuHGBrS8nYmJiTKZljHXKHKRaW1tnGbQWhGbNmhEZGcmePXuMpj+enACyrq+XlxdWVla5qp9SqaRp06ZcunSJP/74w+iz5cuXo9Vqs01pXpiqVatG6dKlCQoKMjxnCfTPc1q1ahUKhYI333wT0Hd902q1RvPb29vj4eFBSkoKqampgP7YQdY3HcQzSKnE9kEAySpLnNOiOOnowy0r4+x5alRcty5HjJk9aQozyife5psdc6gfflOfECAzjZa/7Wz41dmRgw72/G1hBfFpRp+j1ur7ham1/6WUxii1s74cj1p6/ksz7eetIPZjc26PNOf/mpnR4xVljgIdITJIy44Q4rnXq1cvFixYwEcffcSbb75JZGQkmzdvplKlSpw/f77Q1lulShWGDx9Oz549cXFx4eDBg5w4cYL27dsbdXWaMGECgwcPZsiQIXTo0IGqVaui1WoJDw/n0KFDtG/f3iQbW17Z29vz0UcfMWXKFN577z1D17nt27cTFhbG559/bpLmurD5+vpSq1YtvL29cXV1NaQpNjc3p3Xr1oZytWrVYtu2bSxYsIAKFSqgUCho1qyZ4UI6PwYOHMju3buZNGkS586dw8vLi9OnT/P333/j6Oho1DIxefJk7t+/T6NGjXB3dyc1NZXg4GASExPp0KFDrtY7cuRIjh8/zrhx4/D19aVs2bKEhoYSHBxMvXr1su3aWJhUKhWffvop48ePZ+DAgXTr1g0bGxuCg4M5e/Ysfn5+hjFjO3bsYO3atbz11lt4enpiZmZGaGgox44do1WrVobulhnjoZYuXUpcXBzW1tZ4eHhQs2bNIt8+kUM2VlimrOGq42c0jj6GDhVaHt0FN0NDueTbmPEo2K0WeYMpO+ZwvtT/8UcZV9QqpT6QSUj777k5mSSr9X3BzBT6sTYZdOjLWmS6367NGFPz3+foKGMHt0eYS6uhyDcJdoQQz72M55js3LmTkJAQKlSowFdffcWFCxcKNdhp1qwZ5cuXZ/ny5fz777+ULFmSwYMHm6QoLl26NKtXr2bFihUcPHiQXbt2YWFhgZubG02bNs02pW9eZQRfq1atMjzs8ZVXXmHatGnF0pLQr18/jh49yvr160lISKBkyZLUrFkTPz8/o25cH3zwAbGxsWzcuJH4+Hh0Oh2BgYEFEuw4Ojry888/M3PmTAIDA1EoFNSvX5+FCxcyYMAAo3FT7du3JygoiB07dhAdHY2trS0VK1ZkypQphm54OeXu7s7y5ctZuHAhu3btIj4+Hjc3N/z8/LJ8+GxRadasGfPnz2fJkiWsWrWK9PR0vLy8+PLLL40eKlq/fn0uXbrE4cOHiYyMRKVSUaZMGUaPHk2vXr0M5UqXLs3XX3/NihUr+OGHH1Cr1XTs2FGCnWec0kxJpQ9qc2dmAp7p90w+zxzoZCiRmkjlB1ew1MAFKytuW1hkv4J0LWQ1DjIj01lGIPNYyui3yinY21slgU4mkno67xS6/I66FUIIIZ5TMTExtGzZku7du/P5558Xd3WEKBZam94ok588ZieztwZ9xdVSFSj/IJ6jDlkMhMngZKUfn6PO4lLTQmmcRlqjpXZpBWfey1+GxhfVBcWMpxf6T3XdJ4VYk+ePjNkRQgjxUkhJSTGZtmLFCoAiSR4hxLNKGbEUjVnWmSgfd9bNkwMVa/BQZYZ7ajoeaWlgZwE2jwUp1mZgodJnV3u8UUKJcaCj07GkrQQ6onBINzYhhHgOxMbGkp7+5DuvVlZW+R6Pk5KSkqOHR7q4uORrPcXh448/xt3dnWrVqqHVajl58iSHDx+mdu3auereV5T7KHMCgezY2dm9UKnKRTFwtEWVsIY4+8GUSDc9txPMLIi2sWN7tXqMb9cPgOR0HZtdS4KthT6gUSrA3kI/HkengxL/dQ3NHNQYkhIYBzoO5lrer2Oagl88It3Y8k6CHSGEeA6MHz+e0NDQJ5bp2LEj/v7++VpPcHAwkyZNemq5U6dO5Ws9xaFp06bs2LGD/fv3k5qaipubG/369WPIkCHZPl8pK0W5j9q2bfvUMhMnTqRTp075Xpd4yVlaYO1kBvdNP7JTp/GXkysnPCvhkphCYjKg0aFTKMAsUychhUKfkCBVDVHJ+tYetRbS1EztYs3Juwq6VlbQtiK8u03D3w90tK2kZH6bJ4z7ESKfZMyOEEI8By5cuPDUtL6urq75fqp9ZGQk165de2q5l7nbV1Huo+PHjz+1TKVKlZ7LljbxDPpsJbqpv2bbhnC/agV0+7+ly3dRhCksSVUqiXayQft4KujkdH2WNqUSlAr+GG7Fa+Xk/np+nFfMzHHZGrrRhVaP55EEO0IIIYQQAhKSeeD2ES5JD00CntSaFbA8+5PhfWSilldmphCtU2KUtC1dow90dIBCQYsqKvb65T+j4svuXC6CHW8JdoxIggIhhBBCCAF21pSMmE2MuYPR5DRzSyxPTzWa5mKr5NKo/7qfWSrBXKnPsGZvoR+vk65lUH0JdETxk2BHCCGEEEIAoHKwxiE2gIhu7Yn29CK+d0ss7iyCLLK1uTqY8YomTT8uR/VfkgKdDhLSqOSs4OceEuiI4ifd2IQQQgghRJ5cuplG/SWpJJqb6buvpWpoZZvGb1/JOLKCJN3Y8k5adoQQQgghRJ5U9bLg5mhrxtrH0y72Fl/YHWLHBIenzyhyRYcixy9hTIIdIYQQQgiRZy7OZnw/yomuNQ5TrlREcVdHCCMS7AghhBBCCCFeSJL0XAghhBBCiGeYdE/LO2nZEUIIIYQQQryQJNgRQgghhBBCvJAk2BFCCCGEEEK8kGTMjhBCCCGEKDR9t6v55aL+b5UCTvWFOu5yCZob8lDMvJOWHSGEEEIIUShG/vYo0AHQ6KDumuKrj3j5SLAjhBBCCCEKxfy/s57+wzF10VZEvLQk2BFCCCGEEAUuKlmb7WfzTxdhRV4AOhQ5fgljEuwIIYQQQogCdy8x+2AnLKkIKyJeahLsCCGEEEKIArfzWnHXQAjJxiaEEEIIIQrBhMPFXYMXh3RPyztp2RFCCCGEEAVOUhCIZ4EEO0IIIYQQosjtuS7hkCh8EuwIIYQQQogit/bi08sIkV8S7AghhBBCiCdbuR+c+0O5IXDswlOLa3W6p5ZZf74gKvZy0OXiJYxJsCOEKBCnTp3Cx8eHoKAgw7SIiAh8fHwICAgo0HX5+/vj4+NToMsUL6bCOgefJiAgAB8fHyIiIop0vUIUCitfGDgHohIh7CG8/gUouj96vTPNZBal4ukD6lMLo65CPEaCHSHEMykoKIi1a9cWdzVeSPHx8QQEBHDq1KnirgqdOnWiU6dO2X6eEdgWZNDwLG0/gEajYceOHQwaNIg2bdrw+uuv0759e4YNG8bChQtJS0szlD116hQBAQHEx8fna52XLl0iICBAgjGRvf1/Q+n39MFMavbPywFg3R+YWfTGKvZR+HLm/lPmEaKISLAjhCg07u7uHD16lEGDBuV63qCgIH755ZcsP/vyyy85evRofqv30oqPj2fx4sWEhIQUd1UKXVbn4LO2/V9++SUTJ04E4N133+XTTz+lS5cu2NjYsHLlSpKSHj19MSQkhMWLF+c72Ll8+TKLFy+WYEcYi08Ex3f1Ac7b/nAvLsezKoB3P91veP/m2pwFO79JkoIc0aHI8UsYk+fsCCEKjUKhwNLSssCXa2ZmhpmZ/HwVpcTERGxtbYu7GrlWWOdgQblw4QLBwcG89dZb/Pjjjyafx8TEYGdnVww1E889tQbOh0FpR9j7N6w9BEkpYGcN92LhrxuQpinQVSoBv2G7SavdnNg0L1A+/Z56my2QkaTatwr0eAVS1KBFQbfKCpys5b68yB+5WhBCFJqIiAg6d+7MkCFDGDZsmGH69u3b2bBhA7du3UKtVuPs7EytWrUYO3YsTk5OdOrUiTt37gAYjc1ZuHAhPj4++Pv7s337dqNuSBnTDhw4wJw5c/j9999JTEykWrVqjBkzhpo1axrVLSYmhlmzZnHo0CHS0tLw9vZm9OjRTJ8+nTt37hiNPcqpsLAwli5dyvHjx4mKisLR0ZEaNWowZMgQqlevbih34MABVq5cyeXLl1EoFFSpUoUBAwbQvHlzo+X5+PjQsWNH/P39jaYHBQUxadIkw/4A/RiRxYsXs2nTJnbs2MGOHTuIjo7Gy8uLkSNH0qRJE0DfDWr48OEALF68mMWLFwP6FpCgoCCjY1ahQgVWrlzJjRs3aNWqFXZ2dqxbt44tW7ZQrlw5ozpFRkbSoUMH2rdvb2ilKCy5OdaPn4NP2/4Mv/32G+vXr+fKlStoNBoqV65M//79admypVFdtFotK1asYOvWrURGRuLp6Ymfn1+Ot+XWrVsA2Y5Bc3R0NNlugM6dOxumZ2zbgwcPWL16NSdPnuTOnTukpqbi4eFBhw4d6N+/PyqVCnh0rgCGfQEYnWtpaWmsXr2a3bt3c/v2bSwsLKhbty7Dhg2jWrVqRtu/bt06AgMDiYiIQKFQ4OzsTJ06dfj888/lpkRxOX4ZfH+E2w+LdLUKwBzQvjYBpqzL9fybruhfejoG7dHxRSMdk5uqCrCW4mUjv0JCiCK1Y8cO/P39qVu3LsOHD8fS0pJ79+5x9OhRoqKicHJyYuzYscydO5eYmBjGjBljmLdChQpPXf6oUaNwcnJi8ODBxMbGsmbNGj7++GMCAwMNLRNpaWl88MEHXL58mU6dOuHt7c2VK1cYOXIkJUqUyNN2nT9/nhEjRqBWq+nSpQuVKlUiLi6O0NBQzpw5Ywh2Nm7cyJQpU/Dy8mLw4MGAPvgbN24cn3/+Od27d8/T+jP4+/tjZmZGv379SE9P55dffmHcuHFs2bKFMmXKUKFCBcaMGcP06dN56623eOuttwCwsbExWs7BgwdZv349PXr0oEePHtja2lKxYkXDhe2oUaOMym/fvh2NRkPXrl3zVf/cyMmxflxOtn/+/PksXbqU119/neHDh6NUKtm/fz8TJkzg008/pVevXoayM2bM4JdffqFevXr07duXqKgopkyZgoeHR462wdPTE4B9+/bRrl27J55/3bt3JzExkf379zNmzBhDIFSlShUArly5wv79+2nevDmenp6o1WqOHTvG3LlzCQ8P54svvgDg7bffJjIykq1bt+Ln52f4XmXURa1W8+GHH/L333/Tvn17evXqRUJCAlu3bmXQoEEsXryYGjVqALB06VIWLlxI06ZN6dGjB0qlkoiICMNNBAl2ioFOp08mUMSBTmYbX309R606OfHdcR0DvHW8UvJl7571sm9/3smvkBCiSB04cABbW1sWLFhgdCGU+Q5z8+bNWbt2LampqbRv3z5Xy69WrRoTJkwwvK9YsSITJkxg9+7d9OjRA4Bt27Zx+fJlRowYYTSWo3LlykyZMgV3d/dcrVOn0+Hv7096ejorVqwwXHwC+Pn5odXq+67HxcUxe/ZsPD09Wb58uaF7kq+vL++++y4zZ86kVatW2Nvb52r9mTk6OjJjxgwU/2VC8vHxYeDAgWzZsoVRo0bh7OxM8+bNmT59OpUrV852/167do1169aZBJi1a9dm+/btjBgxwtBSABAYGEiFChV49dVX81z33MrJsX7c07b/4sWLLF26FD8/P0aOHGmY3qdPH8aOHcu8efPo0KEDtra23Lx5k3Xr1tGgQQPmzp1r2B9vv/02/fv3z9E2eHt707RpUw4fPkz79u2pXbs2NWvWpGbNmjRs2BArKytD2dq1a1O5cmVDQFOmTBmjZdWrV49t27YZjj1A3759+eqrr9i2bRvDhg3DxcWFKlWqULt2bbZu3UqjRo1MWpXWr19PSEgIc+bM4bXXXjNM9/X1pXfv3sycOZNFixYBsH//fipUqMCMGTOMlvHhhx/maPtFIXgQC5fCi7UKN51LFejyjoRLsCPyTjpCCiGKlJ2dHSkpKRw5cgRdDp7DkFt9+/Y1ep9xIRcWFmaYdvjwYVQqFe+8845R2a5du+ZpfMSlS5e4fv06nTp1Mgp0Mij/u8N5/PhxkpOT6dOnj9F67Ozs6NOnD0lJSRw/fjzX68+sT58+Rhe73t7e2NjYGLpL5VSTJk2ybEnr1q0bkZGRRgkiQkNDuXXrFl26dMl7xfMgJ8c6t3bt2oVCoaBDhw7ExMQYvZo1a0ZiYiJnz54F9K1fOp2Od9991yjwq1atGo0aNcrxOn/88UfGjx9PpUqVCAkJYenSpYwZM4Y2bdqwevXqHC/HysrKcOzT09OJjY0lJiaG1157Da1Wy/nzOXuoya5du/Dy8qJ69epG269Wq2nUqBFnzpwhJSUF0J+79+/f56+//spxPYtaVFQUqamPsoQlJCQYJXhIS0vj4UPjVpCMbrTZvb97967R79cztQ5neyhTkuK0rH7zAl1eGcWT990zfTxysQ5ROKRlRwhRpPz8/AgNDWXcuHE4ODhQr1493njjDVq1alUgA+Af7z6U0dUnNjbWMC08PBwXFxeTrlvm5uaUKVMm15muMi6uq1at+sRy4eH6u60VK1Y0+SxjWkaZvMroipSZg4OD0fbnxONjcjK0atWKn376iW3bttGsWTNA31Jmbm5Ohw4dcl/hHFJk8cyOnBzr3Lpx4wY6nQ5fX99sy2RcwGQcKy8vL5MyFSpU4M8//8zROs3MzOjduze9e/cmJSWFixcvcvToUdavX8/MmTNxcXGhbdu2T12OWq1m+fLl7Ny5k7CwMJObCXFxOcusdePGDVJTU03GJ2UWExND6dKlGTlyJOPGjWPw4MG4urpSv359mjRpQosWLTA3N8/R+gpbyZLGF/6P39CwsLDA2dnZaNrjrbuPvy9duvSzvY55Q6DPdEhNpzh0OXeKGaU6P71gDnSrDG1ruhpNe+6ORw7XIQqHBDtCiCJVrlw5Nm7cyIkTJzh58iShoaFMnjzZMGg6q4v13Mh8hz2zwmhFKi4aTfYZlJTZ9JPP7fZn7j71+PR27dqxZcsWHj58iKWlJfv27aNZs2Y4OTnlah0AlpaWTwxOkpOTDeUeV1jHWqFQMHv27Gz3ZaVKlfK1/CexsrKiTp061KlTh/r16zNq1CgCAwNzFOzMmDGD9evX06pVK95//32cnJwwMzPj4sWLzJkzJ1f7pXLlynzyySfZfp5xrGvXrs2vv/7KsWPHOHXqFCEhIezevZslS5bw888/4+DgkON1igLUtRGELYKD56CsC+wIgYA9kJQGdlaQroaHCYWyag0woa0dcxJSUZvnLhOipx009YAkNVibwcf1lTQuI52QAEkpnQ8S7AghipyFhQVNmjQxZAg7cuQIo0ePZs2aNXz22WdA1nfyC0qZMmU4ceIESUlJRq07arWaiIiIXI+ZyWgFuXz58hPLZQRy169fp2HDhkaf3bhxAzBurciuRSa/rT/53bfdu3dn48aNbN++3dAtMa9d2MqUKcPNmzeJiYkxyjyW4caNG9ja2mb5WV49afvLli3LH3/8QenSpZ+aECPjWN28edMkSM84nvlRq1YtAO7fv2+Y9qS679y5k3r16vH9998bTc+qW9/T9kF0dDQNGjTINuDLzMbGhhYtWtCiRQvgURKObdu2MWDAgKfOLwqJqwP4vq7/u9Er8M07Ty4PsDMEOnyX51XqgKXzW/Pe4LfQzcrZ78zkN+CL1+RyVBQeCZeFEEUqJibGZFpGKtvMF/Y2NjbExcUVSotM06ZN0Wg0Jg8t3bp1KwkJub/b+corr1CxYkUCAwO5du2ayecZ29CoUSOsra1Zv349iYmJhs8TExNZv349NjY2NG7c2DC9XLlynD171jA+AvRdkQIDA3Ndx8ysra0Ny8qLKlWq4O3tTWBgINu2baN06dJG9c6NjHTba9asMfns2LFjXL9+naZNm+boojunnrT9GQkL5s2bl2ULWuY++G+++SYKhYI1a9YYlb148SInTpzIUV1u3bqV7RijAwcOAMZZCDOC86zqrlQqTb4vycnJrF271qTsk/ZBhw4dePjwYZbHBIz3wZO+z3k9v0Qxal8fdFv0r41jczWrDtj6aUN0Kv139VCfnMykY0IjSSstCpeE0kKIIjVy5Ejs7e2pW7cubm5uxMfHExQUhEKhMMqMVbNmTQ4fPszUqVOpXbs2SqWSBg0amPSTzouuXbuyZcsWFixYwO3btw2pp/fu3UvZsmWf2E0sKwqFgokTJ/LBBx8wcOBAQ+rp+Ph4QkNDee211+jTpw/29vZ89NFHTJkyhffee4+OHTsC+rTNYWFhfP7550b9vnv16sVXX33F8OHDad++PfHx8fz666+4u7ubDHzNDUdHR8qWLctvv/2Gp6cnJUuWxNra2jAGJye6devG5MmTAf1zXvIajHTu3Jndu3ezbNkyLl26RIMGDbC0tOTSpUts374dZ2dnkzTX+fWk7ff29mbo0KEsWrSIvn370rJlS1xdXYmMjOTChQscPXrUMBbHy8uLnj17smHDBkaMGMHbb79NVFQUGzZsoEqVKly6dOmpdbl8+TKff/459erVo379+pQqVYrk5GTOnTtHcHAwtra2DBkyxFA+4xlCs2fPpl27dlhYWFCpUiUqV65MixYt2LJlC//73/9o2LAhDx8+JCgoKMuuZN7e3iiVSpYuXUpcXBzW1tZ4eHhQs2ZN3nnnHY4fP86sWbM4efIkDRo0wNbWlrt373Ly5EksLCwICAgA9BnaatWqhbe3t2E/bd26FXNzc1q3bl0Qh0sUF983QPcGJKaAXd8nl7U0Qx27isgVKwyTGpZRoe/U9gQKBSqldM/KiRenI3bRk2BHCFGkfH19CQ4OZsuWLcTGxuLg4EDVqlX59NNPjVLgvvvuu4SHh7Nv3z42b96MVqtl4cKFBRLsWFhYsGDBAmbNmsXBgwcJDg6mZs2azJ8/n8mTJxu1pOSUt7c3K1asYMmSJezdu5fNmzfj6OiIt7c3derUMZTr2bMnLi4urFq1yvBgx1deeYVp06aZPFS0Xbt2PHjwgA0bNjBjxgw8PDwYPHgwSqWSf/75Jz+7gG+//Zbp06czb948UlJScHd3z1Ww06ZNG2bMmEFycrLRAy5zy8zMjLlz57J27Vp+++03AgIC0Gg0lCpViu7duzNo0CBcXFzyvPzsPGn7hw4dSo0aNVi3bh2//PILycnJlCxZkkqVKjFu3Dij5YwbNw5nZ2e2bt3KrFmzKFu2LJ999hm3bt3KUbBTr149PvroI06cOEFgYCBRUVHodDrc3Nzo1KkTAwYMoGzZsobyderU4cMPP2TLli1MnjwZjUbDkCFDqFy5MmPGjMHW1pbg4GAOHjyIm5sb3bp1o0aNGnzwwQdG6y1dujRff/01K1as4IcffkCtVtOxY0dq1qyJmZkZM2fOZNOmTezcudMQ2Li6uuLt7W0I0gH69etnSKaQkJBAyZIlqVmzJn5+frzyyit5Pj7iGWJrpW/pGTQH1hwBW0vY8QU0fiwhS7pxMgSzHAQxVhLniCKg0L1Io3aFECIfNBoNLVu2pGbNmsyZM6e4q/NMS0tLo23bttSoUYO5c+cWd3WEEMUsPT2dZcuWAfqsm+bm5iimqZ84j5c93Bgm991z4pRiQY7L+uhGFGJNnj8yZkcI8VLKqvVm8+bNxMfH5+oZKS+rXbt2ERcXR7du3Yq7KkKI51Tdgn32qBBZknBaCPFS+u6770hNTaV27dpYWFhw9uxZdu/eTdmyZQ0X8AkJCU/t0mZubv5Spdc9dOgQd+7cYdGiRVSsWNGk6x3oE02kpz/5+R5WVlZ5eoCrEOLFsbSdJCfIKUk9nXcS7AghXkqNGjVi48aNLFmyhKSkJJydnenatSvDhw83PNx02rRpbN++/YnLqVevHosWLSqKKj8TfvzxRx48eED16tX58ssvs3zWzfjx4wkNDX3icjp27Ii/v38h1VII8SxQ8OSB9Y4yaEcUAQl2hBAvpY4dOxoNtM7KgAEDaNeu3RPLlChRoiCr9cwLCgp6aplPPvnkqWmHXV1dn/i5EOL5N6IWzD9b3LUQLzsJdoQQIhsVK1akYsWKxV2N50716tWLuwpCiGfA569JsFNQJJtY3kmCAiGEEEIIUeBcbbK/zHS1LMKKiJeaBDtCCCGEEKLAWZhlf5np36QIKyJeahLsCCGEEEKIQlEnm2cCf1BXRlLkhhZFjl/CmAQ7QgghhBCiUJx+z4xXMwU8CuDCe8VVG/EykrBaCCGEEEIUmr/e019u6nQ6FAppeRBFS1p2hBBCCCFEoZNARxQHadkRQgghhBDiGaaTsTh5Ji07QgghhBBCiBeSBDtCCCGEEEKIF5J0YxNCCCGEEOIZpivuCjzHpGVHCCGEEELkzv4zoOiufym7Q/BfxV0jIbIkwY4QQgghhMi5kGvw9qRH73Vg1uF7LGNTi69OQmRDgh0hhBBCCJFzPuNNJimAXl8dKPKqCPE0MmZHCCGEEELkjFab7UeWqTKypLBI6um8k5YdIYQQQgiRM32mZfuRXI6LZ5EEO0IIIYQQImc2/vnkz9WaoqmHEDkkwY4QQgghhMg3BdB49T/FXY0Xkg5Fjl/CmAQ7QgghhBCiQDhEJhV3FYQwIsGOEEIIIYR4ui1P6cIGhHm7FkFFhMg5ycYmhBBCCCGersfUpxZRW6qKoCIvH8lzl3fSsiOEEEIIIQpEqqVcWopni5yRokAFBQXh4+PDqVOnirsq4jkQEBCAj48PERERxV0VA39/f3x8fAp1HZ06dWLo0KGFuo6nKervqo+PD/7+/kWyrudRVuddYX0/5FiIPHnC83Uye3vlhUKuiBC5I8GOEM+o+Ph4AgICJHAUQuSK/HaIQqHyzVExGR8hnjVyTgrxjIqPj2fx4sUAhd7SUFwGDRrEe++9h4WFRXFXRYhnTl6/H0/77Th69CgqlYyrELkwbH6Rr/JqlJrwBAVvlpNzFZCU0vkgwY4QIltqtRqNRoOlpWWhLN/MzAwzM/kZEiIrhfX9KKzvs3jBaLWQlAI9psBvZ3M3b5oazM2zXqxOR9BVDb+ch7034WH6kxakA9QmU+0VUMIazJTQ1gs+aaB/lqmFGagUkKyGKk4KktRgZ65AqVSgABQKBWqtFjOldGx6mchVhigUOp2OVatWsWnTJu7fv4+7uzvvv/8+HTt2NJT57bff2LVrF5cvXyYqKgobGxvq1KnD8OHDqVKlitHyzpw5w5IlS7h06RLx8fE4ODhQpUoVhgwZQq1atXJVt4iICGbMmMGJEycAqF+/PmPHjmX48OG4u7uzaNEiQ7nOnTszZMgQhg0bZrSMgIAAFi9eTGBgIGXKlDFMj4yMZPHixRw5coSHDx/i6OhI06ZNGTFiBCVLljSUi42N5eeff+bQoUM8ePAAa2tr3N3dad26NQMGDODUqVMMHz4cgMWLFxvu0rq7uxMUFJTjbfXx8aFjx460a9eOBQsWcOXKFezs7GjVqhUffPABNjY2Jtu0fv16tm3bxt69e4mMjGT+/Pn4+PiQlpbG6tWr2b17N7dv38bCwoK6desybNgwqlWrBsCNGzfo2bMnffv2ZcyYMSb1+fzzz/n999/ZtWsXTk5O2e7HiIgIFixYwPHjx4mPj6dUqVK0bt2aQYMGYWVlZSjn7+/P9u3bs+yuk7HtmccmbN++nQ0bNnDr1i3UajXOzs7UqlWLsWPH4uTklOU+nDZtGuvWrWPLli2UK1fO6LPIyEg6dOhA+/btmThxYs4OSiYXL15k5syZnDt3DnNzc5o2bcrHH39sdK4AxMTEEBAQwKFDh3j48CHOzs40a9aMYcOG4ejomOeyWVmyZAkLFiygV69ejBs3DqVSmePzGuDatWvMnDmT06dPY2Fhweuvv57luZBTR44cYeXKlVy7do2UlBQcHR2pUaMGo0aNonz58gDcvHmTdevWERoayt27d9FoNFSoUAFfX1+6du1qtLyMc27Dhg1s3bqV3377jYSEBGrXrs1nn32Gl5cXv//+O0uWLOHmzZuULFkSPz8/unfvblK348ePs3LlSs6dO0daWhrlypXD19cXX9+cdfd5mqy+HwXx25HVdyNjWvfu3Zk7dy7nz5/H0tKS5s2bM3bsWKPfCoCQkBDmzp3L5cuXDb8p3bp1o3fv3ln+ZornQMg1+HgJ/HkJNPnL/fXe+0fpcvE0HS+EkGRhyYSeg1he8/X8LtYgXgfx/z3OJ+Af/cuU7rF/M9PSowps6mJ6Gfz3Ay2dt2r5Nw7MlTColoL5LZUoFNKy8rySYEcUinnz5pGamkr37t2xsLBg06ZN+Pv74+npSZ06dQDYsGEDDg4OdOvWDRcXF27fvs3WrVsZNGgQq1evNlxY3rx5k5EjR+Ls7EyfPn0oWbIkUVFR/PXXX1y+fDlXwU5MTAxDhgzh4cOH9OjRgwoVKnD69GmGDx9OcnJyvrb57t27+Pn5kZ6eTpcuXfD09CQsLIzNmzdz6tQpVq1ahZ2dHQATJkwgNDSUHj16UKVKFVJTU7lx4wYhISEMGDCAChUqMGbMGKZPn85bb73FW2+9BWBywZETFy9eZN++fXTt2pUOHTpw6tQp1q1bx7Vr15g3bx7Kx+5wffXVV1haWvLuu++iUChwcXFBrVbz4Ycf8vfff9O+fXt69epFQkKC4XgtXryYGjVqUKFCBWrUqMGePXv4+OOPjbrKJCQkcPDgQV5//fVsAwuAO3fuMHDgQBISEvD19aVcuXKEhISwbNkyzpw5w/z58/N0t3vHjh34+/tTt25dhg8fjqWlJffu3ePo0aNERUVlW6euXbuybt06AgMDGTVqlNFn27dvR6PRmFxQ58T9+/cZMWIEb7/9Ni1atODixYsEBgZy4cIFVq5caQjqEhISeP/99wkLC6Nz585Uq1aNS5cusWnTJk6ePMmKFSuwtbXNddnHaTQapk6dyubNmxk1ahTvvfcekLvzOjw8nCFDhpCWlkavXr1wc3Pj8OHDfPjhh7neP6C/oB4zZgyVKlXCz88POzs7IiMjOXHiBGFhYYZg59SpU4SGhtKkSRPKlClDSkoKe/fuZfLkyURHR+Pn52eybH9/f6ytrfHz8yMmJobVq1fz4YcfMnz4cGbPno2vry8lSpRg27Zt/N///R8VK1Y0/HYBbNmyhe+//55atWrx/vvvY21tzfHjx/nhhx8IDw/n448/ztM2P01h/nZcvnyZTz75hE6dOtGmTRtCQkLYtm0bSqWSL774wlDur7/+YtSoUZQoUYKBAwdib29PcHAwZ86cKZRtFkUgORXaT4b7sflelAJYvHkxNhp9k41NehqLVszi2JiynC9dNt/LLyibr8Cqc1r6ez/6P1Ct1dF8nZboVP37dC0sPKPD20XLqLrF251OurHlnQQ7olCkpaWxcuVKzP9rxm7RogVdunRhw4YNhguGOXPmYG1tbTRfhw4d6Nu3L2vXrmXChAkA/Pnnn6SkpPDdd99Rs2bNfNVrxYoV3Lt3j2+//ZZ27doB4Ovry6xZs1i1alW+lj116lTUajVr1qzBzc3NML1ly5b4+fmxZs0ahg0bRkJCAidPnsTX15dPP/00y2U5OzvTvHlzpk+fTuXKlWnfvn2e63X16lWmTZtG8+bNAejZs6ehtSI4OJg2bdoYlbezszMJKNasWUNISAhz5szhtddeM0z39fWld+/ezJw509Ai1rFjR6ZOncqxY8do0qSJoezevXtJTU01at3Lyrx584iOjmbmzJmG+Xv27Gk4Rtu3b89TcHHgwAFsbW1ZsGCB0bZl3AXPTuXKlalduzbbt29nxIgRRgFcYGAgFSpU4NVXX811fW7fvs2YMWPo27evYVrFihWZMWMG69atMwQbK1as4NatW3z22Wf07NnTUPaVV15h6tSprFy5khEjRuS6bGYpKSl8+eWXHDlyBH9/f6NjlNPzGmD+/PnExcWxcOFCw1iRXr16MX78eC5dupTrfXTw4EG0Wi3z5s0zakEaPHiwUbkOHTqYtKb07duX4cOHs3z5cvr3728SIDs7OzN9+nTD3VpHR0emTZvG1KlTWb9+PaVLlwagdevWdOjQwei3KzIykmnTptG6dWu+++47wzIzvltr1qyhR48eeHp65nqbn6SwfzuuXLnCsmXLDL+zPXr0IDExkcDAQD755BNDwJSx35YsWWLYxp49exZ7hkGRD4cvFEigk8FaY9w3TanT0fXcyWcq2AGYHWoc7Jy4gyHQyWzZWR2j6hZhxUSBkk6LolD07NnTEOgAlCpVinLlyhEWFmaYlhHo6HQ6EhISiImJwcnJifLly/PPP4/apDPuGh88eJDU1Cx+hXLh8OHDuLi4mFzg9+/fP1/LTUhI4MiRIzRr1gxLS0tiYmIMrzJlyuDp6cnx48cBfX95CwsL/vnnnyJJuVy+fHlDoJMh40L6wIEDJuX79u1rcmG4a9cuvLy8qF69utG2qdVqGjVqxJkzZ0hJSQGgTZs2mJubs2PHDqNl7Ny5EwcHB5o2bZptXbVaLYcOHaJq1apGgVJGnZVKZZZ1zgk7OztSUlI4cuQIOl3u+lJ069aNyMhIjh49apgWGhrKrVu36NKlS57qY2traxSQgP57Y2try/79+w3TDhw4gJOTE926dTMq2717d5ycnPJcNkNcXBwjR47kxIkTzJgxwyjQyc15rdVqOXz4MDVq1DAaFK9QKBgwYEAe9tCj7/7vv/+OWm3abz9D5psmqampxMTEEBcXR+PGjUlMTOTmzZsm8/Tu3duoW0pGINOsWTNDoAMYfpMy/3bt3buXtLQ0unTpYrRPYmJiaNq0KVqt1tBNtiAV9m9HrVq1TG4oNWjQAI1GY1jfw4cPOX/+PG+++aZRMGdmZsY777xT4HUqCFFRUUb/dyQkJBAfH294n5aWxsOHD43muXPnzhPf37171+h35LlfRykHClJWv7D37UoU6DoKQln7R3/fuXMH12waQEvbFs3xEIVDWnZEofDw8DCZ5uDgwN27dw3vL168yMKFCwkJCTHpQpZ5/tatW7Nz506WLVvG2rVrqVWrFo0bN6ZNmza4u7vnql4RERF4e3ubdN0qWbIk9vb22cz1dDdv3kSr1bJt2za2bduWZZmMbTI3N2fMmDH89NNPdO7cmYoVK+Lj40Pz5s1p2LBhnuuQnQoVKphMc3Fxwd7envDwcJPPHh+XAvqxOKmpqbRs2TLb9cTExFC6dGkcHBxo0qQJhw4dIiEhATs7OyIiIjh9+jS+vr5GQfDjoqOjSUpKomLFiiafOTg44OLikmWdc8LPz4/Q0FDGjRuHg4MD9erV44033qBVq1bZdu3K0KpVK3766Se2bdtGs2bNANi2bRvm5uZ06NAhT/Xx8PAw2RcWFhZ4eHgYbWNERATVq1c3CUDNzMwoV64cFy9ezFPZDJMmTSIpKYnFixcbddOC3J3XUVFRJCUlGbqWZZbV8cyJXr16cfDgQX744QfmzJnDq6++yuuvv06bNm2Muh0mJSWxaNEigoODuXfvnsly4uLiTKY93upSooT+Qizz2LEM9vb2Rr9dGcHTBx98kG3do6KinrxxeVDYvx3Z/W6DfqwQYAh6sjrOWU17Fjw+riwjiM5gYWGBs7Oz0bTH/295/H3mgPiFWEedCtClIWwrmCD9L/fy1Lvzr+F9mIMzv9Rp8oQ5ip4CmN3i0bVAxr5p66Vg981HQY1KAd81VVG6VOEfjycpoOFOLyUJdkSheDyYyJBxV+Tu3bsMHToUW1tbBg0ahJeXF1ZWVigUCn766Sej4MfCwoL58+fzzz//8OeffxIaGmoYuDt58mRDn/SC9qTBiBqNJsvp7dq1y7abVuYMSL6+vjRv3pwjR44QEhLCvn372LBhA61ateL777/PX8XzKXMCgMwqV67MJ598ku18mS8+O3TowP79+9m7dy9du3Zl586d6HS6PAcG2cnuGGXVClCuXDk2btzIiRMnOHnyJKGhoUyePNlwLj2py5GVlRXt2rVjy5YtPHz4EEtLS/bt20ezZs2eOP7oedCqVSuCgoL4+eefmTZtWpbHP6fndUFzdHRk5cqVnD59muPHj3P69GmmT59OQEAAs2bNonbt2gB88cUXHDlyhG7dulGvXj0cHBxQKpUcPXqUtWvXos3iYYjZ/UY97bcr89+TJk3CxcUly/JZBQ4FoTB/O56Ujjq3raHiObRxHKw7ou/SdvIqnPtXP2gll3TA2VZv8Id5S5r/dYp7Vbw4+V43WiRZcfYB3DC991AklIClCpRKqOYEW7sq8bQ3/b5v765kTqiWdRd1lLGDH5opeaWkjJd5nkmwI4rF/v37SUpKYvr06SbPgYiNjc3yuRI1a9Y0dLG4e/cu7777LgsWLMhVsOPu7k5YWBhardbooiYqKsqo+Rke3enN6q7w460Lnp6e+pSW/3XrygkXFxe6du1K165d0Wg0fP311+zZs4d+/frh7e1dYJlfbty4YTItMjKS+Pj4HF+QlS1blujoaBo0aJDtxWBmTZo0wdHRkR07dhiCHS8vr6eOuXJycsLW1pbr16+bfBYXF0dkZCSvvPKKYVrGMYqNjTXcgQbT45PBwsKCJk2aGLrIHTlyhNGjR7NmzRo+++yzJ9ate/fubNy4ke3btxu6xOW1C1tGHdPT041ad9LS0ggPD8fLy8swzcPDg3///Re1Wm3UYqNWq7l165bRMcxN2Qxt27alQYMGfP3113zyySfMmDHDEPDk5rx2cnLCxsaGf//91+SzrI5nTqlUKnx8fAy/E1euXKFfv34sWbKEWbNmER8fz5EjR2jfvj2ff/650byF0ZUM9N8H0AdjOf2+F6Si+u3ISsad6KyOc1bTxHPE3Az6N9e/MtPpQNkjV4vq+3OX/37bOlATaPGU8nfj1Yw/ADtuQHSa6ecqoHtl+KAuVHJSUrZE4Y3CUCkVjPZRMfrFfLzdS0nG7IhikXHB/Pjdwq1bt5r0cY2JiTGZ383NDScnJ0PXipxq1qwZkZGR7Nmzx2h6VskJbG1tcXZ25uTJk0b1vH37tsm4EUdHR9544w1+//13zp41fR6BTqcjOjoa0A8GzxjfkkGlUhnSbWcEVxnjELIKtnLj33//NanvihUrAHjzzTdztIwOHTrw8OFD1qxZk+Xnjx8zMzMz2rZty19//cXu3bu5devWUxMTgP68aNq0KZcuXeKPP/4w+mz58uVotVqj8UcZXe4ev6hdvXq1ybKzOo8yUmbn5DyqUqUK3t7eBAYGsm3bNkqXLk3jxo2fOl92EhMT2bhxo9G0jRs3kpiYaLSNb775JtHR0fz6669GZX/99Veio6ONgv3clM2sTZs2fPfdd5w+fZqPPvqIpCR9TtfcnNcqlYomTZpw/vx5o1TgOp2OlStXPnV/ZCWrY5bRCpzxvcjutyQyMtJkPxSUVq1aYWFhQUBAgMl3GfR999PSsrhiy6ei/u3IiouLCzVq1ODgwYPcvn3bMF2tVvPLL78U+PrEM0ChgDjT39SCVNrejFWdzIj6yAzdONOXepwZG7qa0by8WaEGOuLFJC07oli88cYbzJkzh6+//ppevXphb2/PmTNn+OOPP/D09DTqJrZkyRL+/PNPmjRpgoeHBzqdjsOHD3Pz5s1cD3weOHAgu3fvZtKkSZw7dw4vLy9Onz7N33//jaOjo8kd0V69erFgwQI++ugj3nzzTSIjI9m8eTOVKlXi/PnzRmUnTJjA4MGDGTJkCB06dKBq1apotVrCw8M5dOgQ7du3Z9iwYfz7778MHTqUt956i0qVKmFvb8/NmzfZtGkTHh4e1K2rT/ni6OhI2bJl+e233/D09KRkyZJYW1sbxozkVOXKlfnqq6/o2rUr5cqV49SpU+zbt4969erRunXrHC3jnXfe4fjx48yaNYuTJ0/SoEEDbG1tuXv3LidPnjRc+GXWsWNH1q1bx/fff49SqTRkv3uakSNHcvz4ccaNG4evry9ly5YlNDSU4OBg6tWrZxQ0tWnThvnz5/Pdd99x8+ZNSpQowbFjx7K8SB45ciT29vbUrVsXNzc34uPjCQoKQqFQ5DhjVbdu3Zg8eTIAQ4YMyVErV3Y8PT1ZvHgx165do3r16ly4cIHAwEC8vLzo06ePodzAgQPZt28fU6dO5dKlS1StWpVLly6xbds2ypcvb/QdyE3Zx7Vs2RIzMzP+97//MWrUKGbPno2dnV2Oz2vQj2H5448/GD16NL1796ZUqVIcPnzYEBDl1uTJk7l//z6NGjXC3d2d1NRUgoODSUxMNHSJtLW1pXHjxuzatQtLS0u8vb25c+cOW7ZswcPDI9c3RHLCzc2NCRMmMHnyZHr27En79u1xd3cnOjqaq1evcuDAATZu3Jjl+J/8KOrfjux8/PHHjBw5kkGDBuHr64udnR3BwcGG7qPyPJIXkL0N/K8rfP9rcdfkpSWpp/NOgh1RLDw9PZk9ezbz5s1j2bJlKJVKXn31VQICApg6dapRhpKMIGPv3r1ERUVhaWlJ2bJl+fLLL3PdjcjR0ZGff/6ZmTNnEhgYiEKhoH79+ixcuJABAwaYjD/IeN7Lzp07CQkJoUKFCnz11VdcuHDBJNgpXbo0q1evZsWKFRw8eJBdu3ZhYWGBm5sbTZs2pVWrVoD+Qqlz586EhIRw4MAB0tPTcXV1pVu3bgwcONBozMS3337L9OnTmTdvHikpKbi7u+f6gqVatWp88sknzJ8/ny1btmBra0uvXr0YOXJkji/WzczMmDlzJps2bWLnzp2GwMbV1RVvb+8sW22qVatGpUqVuHbtGg0bNjRKW/wk7u7uLF++nIULF7Jr1y7i4+Nxc3PDz8+PQYMGGXXPsrOzY9asWUyfPp1ly5ZhbW3N22+/zbfffmvSiuHr60twcDBbtmwxdHurWrUqn376qUlXyuy0adOGGTNmkJycTOfOnXM0T3ZKlSrFDz/8wMyZM9mzZw/m5ua0bduW0aNHG2UXs7OzY8mSJYYHhQYGBuLs7EyPHj0YNmyYUXKF3JTNSvPmzfnxxx/59NNPGTVqFHPnzs3xeQ367/XPP//MjBkzWL9+veGhot98802OA+vM2rdvT1BQEDt27CA6OhpbW1sqVqzIlClTaNHiUceYb7/9ljlz5nD48GF27NhB2bJl+eCDDzAzM2PSpEm5Xm9OdO7cmXLlyrF69Wq2bNlCfHw8jo6OlC9fnhEjRpgMTC4IRf3bkZ369eszZ84cw++3vb09rVq1om3btrz33nuFOo5LFKP/G5CjYOdBKQue75GM4kWj0MmoQyGIiYmhZcuWdO/e3aTf//Msqyeli7xLS0ujbdu21KhRg7lz5xZ3dYR4puzbt4/PPvuM7777ziS9v3hBKLo/tciRThVptPn7J2beFLm3X7Esx2Xf0pk+SPllJh0fxUsnqz72GWNYimOwsXh+7Nq1i7i4OJPn2AjxMtHpdCbPPMt48KxKpaJ+/frFVDNR6BpWemqROHe7p5YRuafLxUsYk25s4rmXnp6eo375Tk5OqFQqPv74Y9zd3alWrRparZaTJ09y+PBhateubfLwzWdRdHR0tqmvM9jY2Biedi7y79ChQ9y5c4dFixZRsWLFLM+T2NhY0tPTTWfOxMrKyuRZDC+jl+UcTkhIyPLmSmbm5uZGmQSfB2lpaXTq1Im2bdtSvnx5YmNjCQ4O5sqVKwwcODDbdNziBbB/Mtg++eGx6bbSjVE8WyTYEc+9M2fOMHz48KeWCwwMpEyZMjRt2pQdO3awf/9+UlNTcXNzo1+/fgwZMuSJz5l4VgwYMOCpT10eMmSIYdC4yL8ff/yRBw8eUL16db788sssz5Px48cTGhr6xOVIl0K9l+UcnjZtGtu3b39imXr16rFo0aIiqlHBMDMz44033uDgwYNERkYC+geKfvbZZ/Ts2bOYaycKlc3TAxnds//fqHjJyJgd8dyLi4vjwoULTy1Xp06dF2Lg7F9//WXSheRxHh4eT3xIpih4Fy5ceGqqX1dXVypWrFhENXp2vSzn8PXr13nw4METy5QoUYLq1asXUY2EKABPGbez4YvGdJv4iYzZKWC/52LMztsyZseItOyI516JEiVeqrE2derUKe4qiCzIBWvOvSzncMWKFSW4FS8eK3NIybrLrg6Id7cv2vq8JCT1dN5JggIhhBBCCJEzv/tn+5EO0JhLPzbxbJFgRwghhBBC5Mxr0ootni8S7AghhBBCiJwb9HaWk29Xeb4yCz5PdChy/BLGJNgRQgghhBA5t3gkmBl3V9MqYPe414qpQkJkT4IdIYQQQgiRcwoFpG+EPV9B2zrw29doUtcXd62EyJJkYxNCCCGEELnXuq7+BfCUhyqL/NEWdwWeY9KyI4QQQgghhHghSbAjhBBCCCGEeCFJsCOEEEIIIYR4IcmYHSGEEEIIIZ5hOqWklM4rCXaEEEIIIUSeRMRqWBoUw8EzKUSltKGu83X8irtSQmQiwY4QQgghhMi1HquSuHkkihSlinSlGaWS1YSkvkLLr6I4+INbcVdPCECCHSGEEEIIkUv7/tUScegBTe4+pFRyKikqJSGuzjinpHLOwpGbt1Px8rQs7mq+MHTSiy3PJEGBEEIIIYTIldUh6TS4G0Wp5FQArDRaXr/7gGSVirLxiZw6m1zMNRRCT4IdIYQQQgiRK1bHwnFLTjGapgCsdFrSFZCQri6eignxGAl2hBBCCCFErlhejkKtMO1bddPVnhvujpxeG1EMtXpx6ZSKHL+EMQl2hBBCCCFEjv3zdzy3rWxR6XRG0+/ZWnHO3RmHlHQOOLmQGJNeTDUU4hEJdoQQQgghRI6NXxqNc2oaj7chWKepKZmUQpS5BVoU/HkivljqJ0RmEuwIIYQQQogcS4zXYZdmOiZHqYOG1+/hGpNAspkZ/1xKKobaCWFMgh0hhBBCCJFjOq2GRHPTp5fYqdW8HvGA2tFxAERfiC3qqr2wdMqcv4Qxec6OEEIIIYTIkSNHYqj5MAZrrS7bMlWjYwlxLUkJV0tOdwvm4a+PkhWYl7HmjUvdMLMzL4rqCiEtO0KIwnHq1Cl8fHwICgoCICIiAh8fHwICAgp0Pf7+/vj4+BToMl90AQEB+Pj4EBHx/GdLiomJ4euvv6Zt27b4+PgwdOjQHM/bqVOnXJUvCEOHDqVTp05Fuk4hCsrDeA2jVibgnJxGyZS0bMupgOpRsZSeG0rkr8a/M+kRSRwtvYa7w3eTdiGykGsshLTsCCGeA0FBQcTHx9O3b9/irop4xsyYMYPg4GDef/99PDw8KFmyZHFXSYgXVpuJkXS4Gc4VR3teiUvASqMFQK1QoECHKlNjT/3IKOyS1CZJDFSosU+MJj3gCjcDTnIPF0pzm8on/FA0qFx0G/Oc0akkpXReSbAjhCgS7u7uHD16FJVKlet5g4KCuHPnTpbBzpdffsn//ve/gqiieA4dP36cxo0bM2TIkOKuihAvnGsxWqyUWr47pmX/zoe0uH6HM6VK4p6QzB/l3Wh24y7HSrty3cEehU5H1eg4Gt6PRIH+AaOJjlbY3H2UftqN29TgL1Ro0KIkEVdKE0Y4lQhtGEgJIik1qh4lfuqKwkIuUUXBkDNJCFEkFAoFlpaWBb5cMzMzzMzkp+xZlpiYiK2tbaEs++HDhzg4OBTKsotTYe4zIVLVOnToWH5Wy5TjEJYAOjJeOhQaLUqNDo25CvuEVJxjU6j2MIFDnu4kmZlx0cmRVHMVt7xt0Wn+W6hCwXlnRxzS0qgWo09QoEt/1NSjIp3q/wU6AEq02HGPh1TBmQeUJY50bFHODSZ67m9EUQoHkjBXpGOmTMf8NQ+UYzuic3bE7NXSKB/EgHtJsCn4/1fEi0WuEIQQRSIiIoLOnTszZMgQhg0bZpi+fft2NmzYwK1bt1Cr1Tg7O1OrVi3Gjh2Lk5MTnTp14s6dOwBGY3MWLlyIj48P/v7+bN++nVOnThk+y5h24MAB5syZw++//05iYiLVqlVjzJgx1KxZ06huMTExzJo1i0OHDpGWloa3tzejR49m+vTp3LlzxzDuKKeGDh3KnTt3WLp0KTNmzODYsWOkpaVRt25dxo8fT/ny5Q1lAwICWLx4MYGBgZQpU8ZoOZ06dcLd3Z1FixYZpvn4+NCxY0c6dOjA/PnzuXz5Mg4ODvTq1Yv33nuPuLg4Zs6cyeHDh0lKSqJBgwZ88cUXuLq6mtQzOTmZH3/8kb1795KQkEDlypUZOXIkDRs2NCl7/PhxVq5cyblz50hLS6NcuXL4+vri6+ubZZ3HjBnD3LlzOXv2LA4ODgQGBuZ4/yUnJ7NkyRKCg4O5f/8+JUqUoFGjRowYMQJ3d3ej/Qb6c2j79u0ATJw4MddjYi5evMjMmTM5d+4c5ubmNG3alI8//tikS1xaWhqrV69m9+7d3L59GwsLC+rWrcuwYcOoVq2aUdm4uDhmz57N/v37SU1NpUaNGnzyySdZrv9p+yw0NJSff/6Zc+fOoVar8fLyomfPnnTt2tVkWTktm3GOBgQEMH36dE6dOoVCoeDNN9/k008/xcrKiuXLl/Prr78SGRlJhQoVGD9+PHXq1DEsQ6vVsm7dOgIDA4mIiEChUODs7EydOnX4/PPP5SZEAVjyt5rR+yGhSJ/NqUCnUqFRgUNMMi6xKVSNjkWHgqRMx9QyXUO6UoEZxokKwuxsqRYTx3knByJqq+i6/wIKwI54zP4LdMKoQDjlUaCjJLE4kEYSVtgTgRUxKAAHoomhPGk6R9I0oDySQIkj3xOLJ1ps/usep8OcROy5hxI1KZQggVLYcQcFOlIpgTUxqEgDS3MSzd3RJaZjTRTmJKNAB442KD5sB1/1hEXB8OsJ8HSG8V2huqfRtqn/uUdcv01orkehrOyMw7pemL3iYrIHtfGpJE09TPofYZjXc8fms6YoXeTmRXGRXyIhRLHZsWMH/v7+1K1bl+HDh2Npacm9e/c4evQoUVFRODk5MXbsWObOnUtMTAxjxowxzFuhQoWnLn/UqFE4OTkxePBgYmNjWbNmDR9//DGBgYGGu+ZpaWl88MEHXL58mU6dOuHt7c2VK1cYOXIkJUqUyPO2JScnM2TIEGrVqsXIkSMJDw9n3bp1jB07lvXr1+epO1+GS5cucfjwYbp160aHDh0IDg5m7ty5WFpasn37dsqUKcPQoUMJCwtj/fr1TJw4kfnz55ssZ+LEiSiVSgYMGEBSUhJbtmzhww8/ZPbs2TRq1MhQbsuWLXz//ffUqlWL999/H2tra44fP84PP/xAeHg4H3/8sdFy7927x4gRI2jZsiVvv/02SUk5f9aGWq1m1KhRnDlzhhYtWtCvXz9u3brF5s2bDQGXm5sbb7/9NmXLluXrr7+mbt26dOvWDYDatWvnal/ev3+fESNG8Pbbb9OiRQsuXrxIYGAgFy5cYOXKlVhZWRnq9eGHH/L333/Tvn17evXqRUJCAlu3bmXQoEEsXryYGjVqGG3D+fPnad++PbVq1eLy5ct88MEH2bZCZbfPDh06xPjx43F2dqZfv37Y2Njw22+/MXnyZMLDwxk5cqRhGbkpC/pzdMSIEdSrV89Q38DAQFJTU3F0dOSff/6hV69eqNVqVq9ezZgxYwgKCjJ8d5YuXcrChQtp2rQpPXr0QKlUEhERYbhpIMFO/swN1fLh78Vbh0RbC2xT1HhHxXCwjFuO5omxNGdrhbLEWFnSLiWVf90d8LoTSyL2aFBxFw8u8eh7Go8j5blDKe5gTYxhugoN9twlBv1vvRYLknBHh22mcUAK0rED7qJEgw3RgIIEPFGRSkmu6wMagNR0zFJjsCYGJRrDEohOgG82wo4QCLn+aPrW43B+lr71CNAlpxPVYCGk6J8vpDl9h6i683GN/QKFmfHveWyXNaTvvwFA+u/XSfvtKk6nP0ChzHteMK1SxuzklfwSCSGKzYEDB7C1tWXBggVGF0bDhw83/N28eXPWrl1Lamoq7du3z9Xyq1WrxoQJEwzvK1asyIQJE9i9ezc9evQAYNu2bVy+fJkRI0YwaNAgQ9nKlSszZcoUQ0tCbsXExNC/f38GDhxomObk5MTs2bM5ceIEr732Wp6WC3D16lWWLVtmaKHq0qULHTt2ZPr06fTq1Yvx48cblV+7di03b97Ey8vLaLpKpeLnn3/G3FyfArZz5874+vry448/smnTJgAiIyOZNm0arVu35rvvvjPM27NnT6ZNm8aaNWvo0aMHnp6P7oCGh4fz5ZdfZtny8DRBQUGcOXOG/v37GwVRjRo1YvTo0cydO5dvv/2WKlWqUKVKFb7++ms8PDxyfW5kuH37NmPGjDEaD1axYkVmzJjBunXreO+99wBYv349ISEhzJkzx+jY+fr60rt3b2bOnGlogQsMDOT8+fMmrZgVKlRg+vTpWZ5TWe0zjUbD1KlTsba2ZsWKFYbWuV69ejFs2DBWrFhBp06dKFeuXK7KZoiJiWHAgAEMGDDAMC0+Pp69e/dSrVo1li1bZvheVqhQgbFjxxp9d/bv30+FChWYMWOG0bZ8+OGHOT8AIlvTT2mLuwqozVVEuNmhPKejVHIKD62tjD631GlJUygMiQnUKgW3XOwpGZdK2fhESien8Psbr9B/y0nQmnOJWiRiZ7KeaOwpwzWT6eYY3yhRY51FLRWkUgIz9JndrIglAXesiH0U6PzHmmiUZLNfMwc6ADGJsOYQjOsKQMrKvwyBjkFSOilr/8Z6QN1Hdbxw3xDoGKb9fY/0I7ewaOaV9bpFoZLU00KIYmNnZ0dKSgpHjhxBp8v+mQ159XhCg4xucGFhYYZphw8fRqVS8c477xiV7dq1K3Z2pv8p55RSqaRPnz5G0xo0aADArVu38rxcgFq1ahl1xTM3N8fb2xudTmeyzrp19f8JZ97mDH379jUEOgBubm60bduWmzdvcuOG/j/rvXv3kpaWRpcuXYiJiTF6NW3aFK1Wy4kTJ4yW6+DgkOf0yvv370epVOLn52c0vUmTJrzyyiscOnQIrbbgLgJtbW3p2bOn0bSePXtia2vL/v37DdN27dqFl5cX1atXN9oHarWaRo0acebMGVJSUgB9EK9SqXj33XeNluvr65vtOJys9tmFCxe4e/cunTt3NuqGaG5uzoABA9BqtRw8eDDXZTOoVCp69+5tNK1OnTrodDp69OhhdAMiq/PIzs6O+/fv89dff2W5Tc+KqKgoUlNTDe8TEhKIj483vE9LS+Phw4dG82R0nc3u/d27d41+swpjHanpGp4JCgV/lXGmTmQUrkkphsklUtOIKGlLmIcDD5yseVDShjD3EmgtVLQIi+Dt2/rteWBjxX1H/W9pBOWJw8lkFUp0WQYyj09TknV/PkWmlhoteWs1z/J/IM2j35rY+Lgnzm845pps/i/TaJ96zEXhkJYdIUSx8fPzIzQ0lHHjxuHg4EC9evV44403aNWqVYEMzvbw8DB67+joCEBs7KOneoeHh+Pi4oKNjY1RWXNzc8qUKWN0wZIbrq6uJgkZMrowZV5/Xjy+XYChy93j437s7e2zXWdWXQErVqwI6PdLhQoVuHnzJgAffPBBtvWJiooyqV9eu+lFRETg6uqaZRfCSpUqcfnyZWJiYgosxbSHh4dRwAdgYWGBh4cH4eHhhmk3btwgNTWVli1bZrusmJgYSpcubTinHg+WM5ab1TmV1T7LeA5SxjHJrFKlSgCGOuambAYXFxeTczS78yhjeubzaOTIkYwbN47Bgwfj6upK/fr1adKkCS1atDDZp8Xp8XMlq+Pi7OxsNO3x1rfH35cuXbrQ1/FJQzPGHyz4m0B5sb9iGdKUSl6PuE+klQVXHEpw39YGHaBRKYkr8ajFR6nWUiYxGSVww96OKKUZpWMSDZ/rHrvPrkCHMzGkUoIUSmCFPqjQoiKejH2ixo4HmJFCDF7wWEc2Kx6dl0m4AjpScMKaKKOWnCScsSYaFY+10ACKWuXh7L+PJthZQd9mhrduHzfnwddHITlTwGVjjlWfWvriGce8pj3mr5cj/Y9HN7VU1Vwwb+aFu8p423PTc0AnzRN5JsGOEKLYlCtXjo0bN3LixAlOnjxJaGgokydPNgw+z9w1Ki+yu+AujFakxymf0Dc78/oViuz7YWs0Wd/ZfVIgUdDbnDHfpEmTcHExHYgLpsFXxjiXF03lypWzTTIA+m6KeVUc++xJ52h2n2U+j2rXrs2vv/7KsWPHOHXqFCEhIezevZslS5bw888/v5BZ8orSuAYq0tRqvv0TUoq5kSfNQsX+qp4cL1sK5+hkzDRaYu0sSLH+L6jV6VBpdag0WkrEpbKnbBnSVEpUqRqG7P4LlTbr3x8FWmyJx4lI0rAjDncScUaJlnRsACVmJODEv2gwR4EOe8KIpxRKFCjQAlqScUCJlhQcSccWC2IwJ5kYyj5KUGCuQm3hSGyS/X8JChL1Y3dKWKP8oA183Qtm74BtJ8DDGf7XHco++s1TmKtw+usD4vtuRHP1IaoqzpRY0zPLFNkOQe+S+O0B0o+FYV7XHZuvmqNQSbRSXCTYEUIUKwsLC5o0aUKTJk0AOHLkCKNHj2bNmjV89tlnwJMDgvwqU6YMJ06cICkpyah1R61WExERYWgZKSwZd8zj4uKM7qanpqYSGRmZ74DvSW7cuMErr7xiNO36dX2/9YwApmzZsoC+VSxz0oLC4uHhwbFjx4iPjzfZ99evX8fW1tbQQlcQwsPDSU9PN2qJSEtLIzw83GiMU9myZYmOjqZBgwZPDBIytuH48eMkJCQY3eHPWG5OE19kHIOMY5LZ48cpN2ULko2NDS1atKBFixYAbNy4kSlTprBt2zajsUAibz5/zYzP8z68L1txqVqS1LDsby0Tj0I6YA6POolptfDf765ZuhbzdDVpZiq0QIKNOZXiE7hnZ4cm4wJeocBCo+Hdk5f5082V+7bWpKkU9Ay9QKW70dnWQ4cSK9Jw5BbRlCYdZzSYoSEFFQmYkYQtMaRhQbpneSymdcfC1R6XeuVRxKegKGvcWvZ4R7jH+wdYPG3HfNpN/8qG+SsulDw14mlLQVnSBvsZeRtHKAqehJlCiGITExNjMi0jhW/m7jI2NjbExcUVSotM06ZN0Wg0/PLLL0bTt27dSkJCQoGv73EZaaiPHz9uNH3t2rUFOjYlK2vXriU9/VGXjHv37rFnzx7Kly9v6OLWqlUrLCwsCAgIMIxJySwhIYG0tLQCq1Pz5s3RarUsX77caPrRo0e5dOkSzZo1e2qwkRuJiYls3LjRaNrGjRtJTEykefPmhmkdOnTg4cOHrFmzJsvlZB6P8eabb6LRaEzKbtq0icTExMdnzVa1atUoXbo0QUFBREZGGqar1WpWrVplSBWd27IF5Unf37i4J49vEMWrhKWS0rZK/veaGWnjzNCNe/SvbpwZuk8t0I0354KfksMDVXxaLQ0LtRqH+FTKP4zn1TsPSbJ4dIPALjENp6hkTruWpFp0LOh0tLwVQdvQ6xjfqjL+DVegxYsLaFGQTAk0pGPWw4tS/36Ks24WDrrFmOk2Yqlbh13YFCx6N0L1dg2UjrYmgc6LTqdU5PgljEnLjhCi2IwcORJ7e3vq1q2Lm5sb8fHxBAUFoVAojLJr1axZk8OHDzN16lRq166NUqmkQYMGBTJuo2vXrmzZsoUFCxZw+/ZtQ+rpvXv3UrZs2Wy7khWUhg0bUr58eQICAoiNjaVMmTKcOXOGs2fPFmgLRlY0Gg2DBw+mTZs2JCUlsXnzZlJTU42yubm5uTFhwgQmT55Mz549ad++Pe7u7kRHR3P16lUOHDjAxo0bTcZ45FWnTp3Yvn07K1asICIignr16hEWFsamTZtwdnY2SZ+cX56enixevJhr165RvXp1Lly4QGBgIF5eXkbJHt555x2OHz/OrFmzOHnyJA0aNMDW1pa7d+9y8uRJQ0AI+qx2W7duZfHixYSHh1O7dm0uXbrE3r178fT0zPE5pVKp+PTTTxk/fjwDBw6kW7du2NjYEBwczNmzZ/Hz8zNkV8tN2YLi6+tLrVq18Pb2xtXVlcjISLZu3Yq5uTmtW7cu0HWJ4lHNRd8ttnFvJ/yBOl8/pGx4Eg4paSh0OnQKBXaJabhF6oP4GEtLTrta0Pz2HbwSkrjvZINbdOaMagrsiUIJ2JCAB/pEKHcUlXD95xMsauQsvbUQuSHBjhCi2Pj6+hIcHMyWLVuIjY3FwcGBqlWr8umnnxo9QPTdd98lPDycffv2sXnzZrRaLQsXLiyQYMfCwoIFCxYwa9YsDh48SHBwMDVr1mT+/PlMnjw5y9aMgqRSqZg+fTrTpk1j/fr1mJub07hxYxYtWmSUCrswTJo0ic2bN7NixQri4+OpXLkyEydOpHHjxkblOnfuTLly5Vi9ejVbtmwhPj4eR0dHypcvz4gRI0wGXueHmZkZc+fONTxUdP/+/djb29OiRQs++OADk4Hh+VWqVCl++OEHZs6cyZ49ezA3N6dt27aMHj0aa+tHnWLMzMyYOXMmmzZtYufOnYbAxtXVFW9vbzp27Ggoa25uzrx58wzn1O+//06NGjWYN28eM2fOzFUGpmbNmjF//nyWLFnCqlWrSE9Px8vLK8vU3rkpWxD69evH0aNHWb9+PQkJCZQsWZKaNWvi5+dn0j1SvBj++saZVv2jKZmcRoOwB5woV4oS8anGhRQKrjo6UCYpGaeYx5+xpSXF2grH8kpKftaOEn2roLRQ4VhUGyBeSgpdUYzUFUKI54xGo6Fly5bUrFmTOXPmFHd1hBDimbDkSApnfjiPW0oqtxxsOeHqQvxj2ffKxSXQIvwud93t8D10Hs2teMxJxZMbaLrVpfKWd7NZusjONqe1OS7bJbrv0ws9Y8LDwzl06BD37983PLtNo9EYboTm50Hc0rIjhHjppaSkmGTD2rx5M/Hx8UUyKF8IIZ4Xg5pY4e3uSv2oWKrGxqPVwn7PRymUFTodNaJj0QHVKlvz5qre2S9M5JjuBR2Ko9PpGDt2LHPnzkWtVqNQKKhVqxaenp4kJCTg5eXFN998w+jRo/O8Dgl2hBAvve+++47U1FRq166NhYUFZ8+eZffu3ZQtW5Zu3fSZeRISEp7apc3c3FzS7WZBo9EQHZ19RqYMDg4O+X4+S2xsrFHShaxYWVnl64GxQrzs6kbFUClOP07HKz6RlmERXHYsgUIH1aJjKZ2UzD8uDnjZmj4oVIjMfvzxR2bNmsVnn31GixYtaNWqleEzBwcHunfvzubNmyXYEUKI/GjUqBEbN25kyZIlJCUl4ezsTNeuXRk+fLjh4abTpk1j+/btT1xOvXr1WLRoUVFU+bly7949Onfu/NRyCxcuNBqrlRfjx48nNDT0iWU6duyIv79/vtYjxMusuioN/hsFkahSUTYhibIJxuNzbrqUoOz91KxmF8Jg8eLFDBgwgP/7v/8zymqZoXbt2uzatStf65BgRwjx0uvYsaPRAPOsDBgwgHbt2j2xTE6fn/KycXZ2Zt68eU8tVxCD2j/55JOnpj12dXXN93qEeJm9+3klVo27gEapYHd5D7rcCMPssSHgZlodnZrYZLMEkVsvakrpsLAwXn/99Ww/t7W1zXcqewl2hBAiBypWrEjFihWLuxrPJUtLyyIb+1S9evUiWY8QLzOvOo5c9XQkJRniLC3429mJepFRhs+vOdrhGZ1As87yfRRPVqpUKcLCwrL9PCQkJN9p8+WhokIIIYQQIleuVSnFn6X1raRnXEuyo7wHoS4l2edRmkOlS9GiXf4fDSBefN27d2fhwoVcv37dME2h0Ldi/fbbbyxfvpyePXvmax0S7AghhBBCiFxxtjdDpX3Ude2+jTVnXEtys4QdNmoNnmUti7F24nkxadIk3N3dqVOnDgMGDEChUDBlyhSaNGlCu3btqF27Np9//nm+1iHBjhBCCCGEyJX/62qD2izry8jGd+7zRkP7Iq7Ri02ryPnreeLg4MCff/7Jp59+Snh4OFZWVhw8eJCYmBgmTpzI4cOHsbHJ39gveaioEEIIIYTItdErY9l+MhVQoANU6HgrLIIS3o78+J1XMdfuxbLZ9Zccl+3x4J1CrMnzR1p2hBBCCCFErs0c4MDZaS4MrQstI+/RLvxf3Gvd5P/8PYq7akIYSDY2IYQQQgiRJ9aWSj5935X0/o4sW7asuKvzwnpRU0+///77Ty2jUChYsmRJntchwY4QQgghhBCiyP3++++G7GsZNBoNd+7cQaPR4Orqani4d15JsCOEEEIIIYQocjdv3sxyenp6OgEBAcycOZPg4OB8rUPG7AghhBBCCPEM0yly/noRmJubM2rUKFq3bs2oUaPytSwJdoQQQgghhBDPnFdffZVDhw7laxkS7AghhBBCiALVZr0a82lq3t+pLu6qiOdYcHBwvp+zI2N2hBBCCCFEgUhJV2Ix69H7Zedh2Xk1yR8rsDJXFV/FxDPpm2++yXJ6TEwMhw4dIjQ0lAkTJuRrHRLsCCGEEEKIAvFxcn/AdOCI3Swd6nFFX58XhU7xggzGeYy/v3+W052cnKhUqRILFy5kyJAh+VqHBDtCCCGEEKKAZD1CQlPEtRDPB61WW+jrkDE7QgghhBBCiBeStOwIIYQQQgjxDNO+IL3Ybt26laf5ypUrl+d1SrAjhBBCCCGEKHReXl4o8jD+SKPJe0dICXaEEEIIIUS+TYprR1bJCYTIsHTp0jwFO/khwY4QQgghhMi3CNyKuwovLJ3yxQgi33vvvSJfpyQoEEIIIYQQBeDFuCAXLxZp2RFCCCGEEEIUm6NHjxIaGkpsbKxJOmqFQsFXX32V52VLsCOEEEIIIYQoclFRUXTo0IETJ06g0+lQKBTodDoAw9/5DXakG5sQotAEBQXh4+PDqVOnirsqzzzZVwWrU6dODB061Gja0KFD6dSpUzHVSAiRcRErck+nyPnreTJ+/Hj+/vtv1q5dy/Xr19HpdOzZs4fLly8zfPhw6tSpQ0RERL7WIcGOEOKFdODAAQICAoq7GkZOnTpFQEAA8fHxxV0VkQuXLl0iICAg3//hajQadu7cyUcffUSbNm1o3LgxzZo145133mHq1KmcO3eugGpcMOR8FQWtQkDe0weLF9POnTsZNmwYvXv3xt7eHgClUknlypWZN28eXl5ejB49Ol/rkGBHCPFCOnDgAIsXLy7uahgJCQlh8eLFWV48tm/fnqNHj1KvXr1iqNnLYd68eWzevDnX812+fJnFixfnK9iJiYlh6NChfP3110RHR9OjRw/+97//8eGHH1KnTh0OHjzIwIED+fvvv/O8joL2pPNViLz4N6G4ayCeNTExMXh7ewNgZ2cHQELCoxOldevW7NmzJ1/rkDE7QgjxDFCpVKhUquKuxgvN3Ny8WNar0+n47LPPOHPmDOPHj6d3794mZcaOHUtQUBAWFhbFUEMBQGwirDkEkfHQozF4l4P9Z2H/P1DNA3q+DuZmcPIKBJ6E+7FQ0g7qVIC1hyH0OjjbQ+3yYGcFV+/CpXCoVBp+eg/+uAT3Y8DCHPb8BWduQHwyZNWzS6kA7TPY5UsBuDlCqhoSUqCiG+z+CrxK5XwR09Q4mIP/GxCTqsDTXsE71RTYWjxn/a+KmK6In01TVMqUKcPdu3cBsLS0pFSpUpw5c4YuXboAEB4enu/n8kiwI4QodDqdjlWrVrFp0ybu37+Pu7s777//Ph07djQq9+uvv7Jx40Zu3ryJmZkZNWvWZMiQIdSpU8eo3JEjR1i5ciXXrl0jJSUFR0dHatSowahRoyhfvjxDhw4lNDQUAB8fH8N8EydOzPGYjQcPHrB69WpOnjzJnTt3SE1NxcPDgw4dOtC/f3+TwCQ9PZ21a9eyZ88e/v33X8zMzChXrhwdO3akd+/e+Pv7s337dgA6d+5smG/IkCEMGzaMoKAgJk2axMKFC/Hx8eHo0aN8/PHHjBs3jj59+pjUz8/Pj7CwMHbv3o2Zmf6n/NatWyxevJgTJ04QGxuLq6srLVu2ZOjQoVhbW+douzMEBASwePFi1q9fz5YtW9i7dy8JCQlUrlyZkSNH0rBhQ6PyPj4+dOzYkQ4dOjB//nwuX76Mg4MDvXr14r333iMuLo6ZM2dy+PBhkpKSaNCgAV988QWurq6GZcTGxvLzzz9z6NAhHjx4gLW1Ne7u7rRu3ZoBAwbkqv5ZGTp0KHfu3CEoKMgw7dq1ayxatIi///6bmJgYSpQogZeXF/3796dJkyaG/QAwfPhww3wdO3bE398/R+s9fPgwISEhtG3bNstAB8DMzIxu3boZTctYd2BgIGXKlDH6rFOnTri7u7No0SKj6cePH2flypWcO3eOtLQ0ypUrh6+vL76+vkblzpw5w5IlS7h06RLx8fE4ODhQpUoVhgwZQq1atZ54vtrZ2TFjxgzmzp1L48aNjZablpZGu3btqFKlCgsXLszR/nkmPIyHhp/C9Xv6999sAN/XYP3RR2WW/g69XodhT9iu2w/hzE3jabciod643NXnWQx0QB+Y3Y159P5SOFQYDjcWsDWxZI4XE5sOnxzIWKCOWaHwZ1+VBDwvoWbNmhEcHMwXX3wBQO/evZk6dSoqlQqtVsvMmTNp06ZNvtYhwY4QotDNmzeP1NRUunfvjoWFBZs2bcLf3x9PT09DIDN79mxWrlyJt7c3H3zwAUlJSWzdupVhw4bx008/0aRJE0DftWbMmDFUqlQJPz8/7OzsiIyM5MSJE4SFhVG+fHnef/99dDodp0+f5ptvvjHUo3bt2jmu85UrV9i/fz/NmzfH09MTtVrNsWPHmDt3LuHh4YYfZtAHOqNGjSIkJITGjRvTrl07LCwsuHr1Kvv376d37950796dxMRE9u/fz5gxY3B0dASgSpUqWa6/cePGODs7s2PHDpNg59atW5w9e5Y+ffoYAp0LFy4wfPhw7O3t6d69O6VKleLy5cusW7eOM2fOsGjRIkPZ3Jg4cSJKpZIBAwaQlJTEli1b+PDDD5k9ezaNGjUyKnvp0iUOHz5Mt27d6NChA8HBwcydOxdLS0u2b99OmTJlGDp0KGFhYaxfv56JEycyf/58w/wTJkwgNDSUHj16UKVKFVJTU7lx4wYhISEFEuw8LiYmhhEjRgDQo0cPSpcuTUxMDBcuXOCff/6hSZMmvP3220RGRrJ161b8/PyoUKECAJ6enjlez759+wDo2rVrgW9DZlu2bOH777+nVq1avP/++1hbW3P8+HF++OEHwsPD+fjjjwG4efMmI0eOxNnZmT59+lCyZEmioqL466+/uHz5MrVq1Xri+erq6sq8efMIDAw0CXb2799PbGxsoW9rgfs5+FGgA6DRwoY/jMvs+xtCrxVtvZ4XfaYz7J3J5PU5O/9Ewi8XdQyuLcHOy2bMmDEEBweTmpqKpaUl/v7+nDt3zpB9rVmzZsyZMydf65BgRwhR6NLS0li5cqWhG1GLFi3o0qULGzZsoE6dOty8eZNVq1bx6quvsnDhQkO5rl270rNnT6ZMmcJrr72GSqXi4MGDaLVa5s2bR8mSj+4kDh482PB348aN2b17N6dPn6Z9+/Z5qnO9evXYtm2bUfN53759+eqrr9i2bRvDhg3DxcUFgLVr1xISEoKfnx8jR440Wk7G8wJq165N5cqVDQHU43fqH6dSqWjfvj2rVq3i+vXrVKxY0fDZjh07AIxaxr755htcXFxYuXIltra2hukNGzZk/Pjx7Nq1K0+ZyFQqFT///LPhmHTu3BlfX19+/PFHNm3aZFT26tWrLFu2jJo1awLQpUsXOnbsyPTp0+nVqxfjx483Kr927Vpu3ryJl5cXCQkJnDx5El9fXz799NNc1zMvzpw5Q1RUFN9//z2tWrXKskyVKlWoXbs2W7dupVGjRkYthTl17Zr+AvmVV14x+SwmJsbovaWlZa5b4QAiIyOZNm0arVu35rvvvjNM79mzJ9OmTWPNmjX06NEDT09P/vzzT1JSUvjuu+8Mx+pxTztf33rrLUNg4+DgYJi+bds2SpQowVtvvZXrbSgsUVFR2NraYmlpCejHA+h0OsNg6LS0NDTXIjDZ61llDotJLNzKPq9uPyQmPX+LCP9vmEZaWhrx8fE4OzsbPrtz5w7u7u7Zvr979y5ubm6G3+ucHPNncR0vo1q1alGrVi3DeycnJ/bu3UtMTAwqlcqwP/NDEhQIIQpdz549jcZLlCpVinLlyhEWFgbAwYMH0el0DBgwwKicq6srnTp14s6dO1y6dAl4NIDx999/R61WF1qdraysDP+ppaenExsbS0xMDK+99hparZbz588byu7evZsSJUoYBVwZlMq8/8x26NABeBTcgL5L4K5du6hUqRLVqlUD9EHGlStXaNu2Lenp6cTExBhederUwdramj///DNPdejbt6/RMXFzc6Nt27bcvHmTGzduGJWtVauW0cWzubk53t7e6HQ6k9apunXrAhjOAUtLSywsLPjnn3/ynfUspzLOpT/++MNoQGxBS0zUXyBnDkIBkpKSaNmypdFr9uzZeVrH3r17SUtLo0uXLkbHPyYmhqZNm6LVajlx4gTwaLsPHjxIampqntbXrVs30tLS2LVrl2FaREQEJ0+epG3btoYLwGdByZIljepjZ2dndAFlYWGBde9mpjM6GR8vLM2hedbB4UtvZHs88nHIFUCnSvrfWwsLC6MAATAJCB5/X7p0aaMbUzk55s/iOp5Eq8j563mS+f/SzBwdHQsk0AFp2RFCFAEPDw+TaQ4ODoZBiRkXt5UqVTIplzEtPDycGjVq0KtXLw4ePMgPP/zAnDlzePXVV3n99ddp06YNTk5OBVZntVrN8uXL2blzJ2FhYSbPh4iLizP8fevWLapWrVrgF3iVK1emWrVq7N69m5EjR6JUKgkNDSUiIoKPPvrIUC4j6AgICMg23XZUVFSe6pDRbSuzjFam8PBwo8+zOs4lSpQAMGkZyPhPLDY2FtAHRmPGjOGnn36ic+fOVKxYER8fH5o3b24yPqig1K9fnw4dOhAUFMSuXbuoUaMGjRo1olWrVkYtafmVEeQkJiYa9gfoA7x58+YB+uOTn4fm3bx5E4APPvgg2zIZ50Dr1q3ZuXMny5YtY+3atdSqVYvGjRvTpk2bHF98+fj4UK5cOQIDAw2BbFBQEDqd7vnrwgbQojbMGQyTN0FUgn68zvguMGY5HPgHqnrADD+oWwGGLICdIaBUgloDriXgQdyTl/9KGYhO0C/bXAUp+WwGeZY0qAz/687F5HTs5+nIaVc2FfoRO2XsYfIbSuq5PWdX6aJA1KxZk5o1a9KnTx969epF5cqVC3wdEuwIIQpddq0beXnAnKOjIytXruT06dMcP36c06dPM336dAICApg1a1auxuU8yYwZM1i/fj2tWrXi/fffx8nJCTMzMy5evMicOXOK7OF4HTp04KeffuLkyZM0atSIHTt2GLq4ZcioS79+/XjttdeyXE7mi+zC8qRsctl9lnk/+vr60rx5c44cOUJISAj79u1jw4YNtGrViu+//77A6wswadIk+vfvzx9//MHp06dZvXo1S5cuZcyYMdkmE8itSpUqcfHiRS5fvmzUDU6lUhnGPWXVmvWkDEQajfHzSjL246RJkwzdKx+XEYxaWFgwf/58/vnnH/78809CQ0MNyRAmT56c4y5o3bp1Y9asWVy4cIGqVasSFBREjRo1suyu91wY1V7/0mr1gQzA/m+M3wMEff5oWsa/Op1xOZ1O/7dGA5nP/czzgT5YSkvTZ4C7Fw1m5lC3on7+B7FwLxZ0GvDcOV8AAQAASURBVPBw1mc/0+ogPR0UKjBTQuR/ZWKT4do9fXY4a3NITIU70fqECVbm8HZNWLIfrtwGextQKSE5DZJT9RnkdDr9PHejoWQJeOc1qOipzxbnVgLa1gNXB4iOB2cHff1T08DayrBpljm8orw7HBytlFiaKdHqdChf0CxjImcWLFjAhg0b+Prrr/nqq6+oU6eOIfApX758gaxDgh0hRLHLuAi7du2aycDv69evG5UB/UWij4+P4cLxypUr9OvXjyVLljBr1izgyReKObFz507q1atncpGd0e0qs/Lly3Pz5k3S0tKemDo4L3Vq27Yts2bNYseOHbz66qvs27ePRo0aGV3QlitXDtAHlY8nDcivGzdumFy8ZnVMCoqLiwtdu3ala9euaDQavv76a/bs2UO/fv0Mz2IoaJUrV6Zy5coMGDCA+Ph4Bg4cyNy5c+nVqxcKhSLf51KLFi3YsWMHv/76a67G/GQEqHFxcUYtY6mpqURGRhp9V8qWLQvobwbk9BzIuKMK+vEI7777LgsWLDAEO0/b7k6dOjF//ny2bdvGm2++yd27d3nvvfdyvH3PrMdvzmR1syZjWsa/CoVxUJOx7x4P8h+fz0KpT0VtZwtepY3Lli6pf2VwxVSlJ4/9M/JuAYyjcv5vfJZSaRTo5Iab3aNLTwl0cu5FTT09bNgwhg0bxr1799i4cSMbNmxgwoQJTJgwgYYNG9KnTx969uz51HGuTyJjdoQQxa5Zs2YoFApWrVplNA4nMjKSoKAg3N3dqVq1KmA6oBvAy8sLKysro65lGYO8M7pJ5ZZSqTRpvUlOTmbt2rUmZdu2bUtcXBxLliwx+SzzMmxsbADjLnBP4+TkxOuvv87+/fvZvXs3iYmJhrE8GapWrUqlSpXYvHkzt2/fNlmGWq3O835Yu3Yt6emPutzcu3ePPXv2UL58+Sy7uOVVSkoKKSkpRtNUKpUhW11u9llOxcbGGhJIZLC3t8fDw4OUlBTDeJaMcymvdWjatCn16tVj9+7drF+/PssyWbUUZtzVPH78uNH0tWvXmtS7VatWWFhYEBAQYLIfQT94Oi0tDcj6O+Tm5oaTk5PRefK089XR0ZHmzZuze/duNmzYgJWVFW3bts2yrBAAJ94t7hqIZ5WbmxujRo3i0KFD3Lp1i59++gmFQsHYsWPz3cIjLTtCiGKX8VyTlStXMmTIEFq1amVIPZ2UlMS3335r6AY1efJk7t+/T6NGjXB3dyc1NZXg4GCTIKBWrVps2LCBH374gSZNmhie25PT1ogWLVqwZcsW/ve//9GwYUMePnxIUFCQUeapDO+88w6HDx9myZIlnD9/nkaNGmFpacn169f5999/DemVM+6iz54925CeulKlSk/to9yxY0cOHTrEjBkzsLOzo3nz5kafKxQKvvnmG0aMGME777xjGPOSkpLC7du3+f333xk1alSesrFpNBoGDx5MmzZtSEpKYvPmzaSmpppkVsuvf//9l6FDh/LWW29RqVIl7O3tuXnzJps2bcLDw8OQ0KAg7dixg7Vr1/LWW2/h6emJmZkZoaGhHDt2jFatWmFlpb9z7e3tjVKpZOnSpcTFxWFtbY2Hh0e2mcwep1AomDp1KmPHjuXHH39k+/btNG3aFDc3N8Mx2rt3L2A8tqlhw4aUL1+egIAAYmNjKVOmDGfOnOHs2bOGVNAZ3NzcmDBhApMnT6Znz560b98ed3d3oqOjuXr1KgcOHGDjxo2UKVOGJUuW8Oeff9KkSRM8PDzQ6XQcPnyYmzdvGqX4zsn52q1bN4KDgzl8+DAdO3Y0JD8QIisN3OWyUzydu7s73t7eVK9enX/++ceQ5CWv5KwTQjwTPvroI8qWLcvGjRuZO3euIZPX5MmTjS5027dvT1BQEDt27CA6OhpbW1sqVqzIlClTaNGihaFcmzZtuHTpEr/99hv79u1Dq9UyceLEHAc7Y8aMwdbWluDgYA4ePIibmxvdunWjRo0aJoPAzc3NmTt3LqtXr2bPnj3Mnz8fCwsLypUrZxRg1KlThw8//JAtW7YwefJkNBoNQ4YMeWqw07RpUxwcHAzPL8kqEULVqlVZs2YNy5Yt49ChQ2zevBlbW1vc3d3p1KkTDRo0yNF2P27SpEls3ryZFStWEB8fT+XKlZk4caLJ81Xyy83Njc6dOxMSEsKBAwdIT0/H1dWVbt26MXDgQEPgUZDq169veDZQZGQkKpWKMmXKMHr0aHr16mUoV7p0ab7++mtWrFjBDz/8gFqtpmPHjjkOdkDfChIQEMCePXvYs2cPmzZtIjY2FktLS8qUKUPz5s3p1KkTNWrUMMyjUqmYPn0606ZNY/369Zibm9O4cWMWLVrEoEGDTNbRuXNnypUrx+rVq9myZQvx8fE4OjpSvnx5RowYYcgM9eabbxIZGcnevXuJiorC0tKSsmXL8uWXXxqeWg45O18bNGhA2bJlCQsLM5pXCFGwdC9mLzYDnU7HgQMHWL9+PVu3biUyMhInJyf69OmT7/GTCl1RjbIVQgjx3MgYsB4YGJivvtLixderVy80Gg2bN28u7qqIYpSeno7FLHhSNjbdOLnHnlcrKmx6eqH/DLzhW4g1KViHDx9mw4YNbNq0ifv371OiRAm6du1K7969admyZZ4ehv04OeuEEEIIkScnT57k+vXrjB49urirIoR4Dr355pvY2dnRqVMnevfuTdu2bZ+Y6CcvJNgRQrw0UlJScvTwyOxS9z7vnvftj46ONkm5/DgbGxvDwPrCpNFoiI6Ofmo5BwcHo4eyvihOnjzJ7du3Wb58OU5OTs/ns3VEIcj5c3aEANi4cSMdOnQolK7KGSTYEUK8NIKDg5k0adJTy506daoIalP0nvftHzBgAHfu3HlimSFDhjBs2LBCr8u9e/fo3LnzU8stXLgwV+mmnxeLFy/mzJkzVKhQAX9/f0lMIABoqTzNXm394q7GC0n7gqae7tGjR6GvQ8bsCCFeGpGRkVy7du2p5Qr6WTXPiud9+//66y9DOujseHh4mDyrqTCkpqby119/PbVc9erVi+SBrkIUt/T0dJYtW8awOD+ya92RMTt5t6xizsfE+V0v/ADieSJnnRDipeHi4vLMdtEqCs/79tepU6e4q2BgaWn5zAaFQgghHpFgRwghhBBCiGfYi556ujApi7sCQgghhBBCCFEYJNgRQgghhBAFwo176LOyGfu4rmlZIYqCBDtCCCGEEKJAfFNiJx62xtO6VICZLWTkRH7oFIocv543cXFx/PDDD7Rp04a6dety4sQJAKKiopg+fTpXr17N1/LlzBNCCCGEEAXmxmAwN5dLTPF0t2/f5s033yQsLIwqVapw8eJFw/PgSpYsSUBAAP/++y+zZs3K8zrkTBRCCCGEEEIUufHjxxMfH89ff/1FqVKlKFWqlNHnXbt2Zfv27flah3RjE0IIIYQQQhS53377jY8++ogaNWqgyKILXsWKFQkLC8vXOqRlRwghhBBCiGfY8zgWJyeSk5NxdXXN9vP4+Ph8r0NadoQQQgghhBBFrkaNGhw6dCjbz3/99Vfq1s1fKj8JdoQQQgghRKFQp2vRak1TUQsBMHr0aNatW8eUKVOIjY0FQKvVcvXqVfr378+xY8f45JNP8rUO6cYmhBBCCCEK1P6NdwheFo5Kp3/qjndjO96ZWK24q/Xc0r2Yvdjo168f//77L19++SVffPEFAG3btkWn06FUKvm///s/unbtmq91SLAjhBBCCCEKTNjVJH5fGm64yFQAF4/Ecnr7Hep2dC/Oqoln0BdffEH//v3ZvHkzV69eRavVUqlSJbp3707FihXzvXwJdoQQQgghRIG4eKsip766TenHputUSkLHnqRux87FUi/x7ElKSqJp06YMGTKE4cOH57u7WnZkzI4QQgghhMi3pFRLrkS8gnlWY3QUCqrEXSTtblLRV0w8k2xsbLhx40aWKacLkgQ7QgghhBAi346d80GnVHLH3pYbjiW46uzIDScHYqwsAbjvWpKwib8Xcy2fTzqlIsev50nbtm3Zs2dPoa5Dgh0hhBBCCJFvqclWoNORZm5OtK0NcVZWRNtYc93ZiUgba5RaBTdC7hd3NcUz5KuvvuLy5cv079+fI0eOEB4eTlRUlMkrP2TMjhBCCCGEyDc1KsimS9I9O1uS1Vbo4p6vlgdRuLy9vQE4f/48a9euzbacRqPJ8zok2BFCCCGEEPmi1erATKV/o9PhlJyCdbqaBEsL4qwsUWo0pKRbYpkSX7wVfU7pCnlcS3H5+uuvC33MjgQ7QgghhBAiX/4ISTb8XSkqBoeUVP2bhETu2dpgHxOPTqEkycymmGoonkX+/v6Fvg4JdoQQQgghRLbS1Bo8Z6TwIAFI02daq14Kjo+wwd5KP/z73/A0UCiwSUt7FOj8p1RiEp5hd1AAGktz0s6EY/GqRxFvhXhZSbAjhBBCCCGy9OGOROYe1cFj2aQv3IcSk5JY20fFO69as2NPEqDCQqM1WYYCUGgVmKVrsE9L4bjvdppeGVYk9X9RPG9Z1nLqm2++eWoZhULBV199led1SLAjhBCiWERERNC5c2eGDBnCsGHP/4XPxo0bWbduHREREaSnpxMYGEiZMmUKfD0+Pj507Ngxz90//P392b59O6dOnSrYiokXSmSiGtcZaZCoAzMFWKr0AU+KBjI9R6fvOg2+NXUk65SggDhLCzQKBSrdozI2iSlUu/oAgLj/Z+++w6MouwYO/3bTSK8QQkICBKRIE0MVEKVDEnoREaSDFBXBFytFfBU/lF5CpBiKdIQQigihS0kogvQSCCVACOk9u98feXfJsimbTjn3de2lO/PMzJmyYc48ZazNiFUZlfTuiOdUbn/HFAoFarW60MmODD0thBBCFFJISAgzZsygUqVKfPHFF0ybNg17e/vSDqvQ7t27h5+fH5cvXy7tUEQJSEpTk65S0+y3FMrOTIMEFZgqwcYUyhiDuTHYmsIztQymXyeg/F9yo1IqOV3BiQdWZVADdlHx1Dt1M7N2B7BISqNMShqh1eaQkZRe4vsoni8qlUrvk56ezvXr1/n000/x8vLi4cPCDVcuNTtCCCFEIR0/fhzIHFnI1ta2WLd15MgRjIxK5sn4vXv38Pf3p0KFClSvXr1EtilKVlh0BjtvqBjzlxqVZlQstRLMlZm1OQoyh5NWqyFdDUYKKGMEibqJSqJSSRlAoVJhnpzMksY16BByjS93ndEpZ5Ku4kyVqow47c9flRW0+rEeZh++XSL7Kl4MSqWSypUrM3PmTN5//33Gjh2b67DUeZFkRwghxCshPT2djIwMzMzMinzdkZGRAMWe6ADFEr94PsWkqDl6V001ewVV7RWERKiJTVXTwlWBiZGCC5FqwuPUVDBX888DNQ9TFdxLULP5KsSlqFGkq4jJUKBSKzFWQhkjNdGJKtQqNaSpwEiZmcQYKcAs6y2hOjPB0dTgpGZAXNrT2SaZTday9uPZ72BNu6g4bJJTsEtOp2x8MmerOKMms6h2UdJQm6tZ1Hwwj62cOL4mnTe/96fltWMYE0+8aRlSrCzB2hwL4wyMrS0wa+COWZcGqBLTSfv7FkbuthiPeAeszIvv4D9vXtKhp/PSsmVL/vOf/xRqHZLsCCHESywwMJCpU6eyaNEiLl26xMaNG3n48CEuLi4MHjwYb29vIPf+M35+fvj7++v0QdH0+/jrr7+YPXs2hw4dIi0tjYYNG/LFF1/g5OTE5s2bWbNmDffu3cPFxYWxY8fSqlWrbOPctWsXK1as4Pbt29jb2+Pr68uQIUMwNtb9ZyoyMhJ/f38OHz7M48ePsbOzo0WLFowaNQoHBwe9mNetW8fWrVv566+/iIyMZOHChXh5eRl8/Pbv309AQABXrlxBoVBQrVo1BgwYoN0PzXHT0Ky7QYMGLFmyxKBtaGLduHEjQUFBBAUF8eTJEypVqsTo0aNp3ry5Tvns+uxkZGSwfPly/vjjD6KionB3d2fw4MHcvHlT79xpxMfHM2/ePPbt20dCQgI1atRg/Pjx1K5dG3h67QBMnTpV+/+afVOpVKxdu5Zt27Zx7949FAoFjo6O1K9fny+//FLv3In8Cbquou92FfFpmcmCqxXcic+c524N9csp2Hb9f9mGpn+M5n5Y/b9khac1gGkqSFIpwOR/00z/N+iApuYmq6zf055JdDQrM1FA2tNsxz49AyUQb16GhDJmNAmLxDw5jtjyChwi0snACFNSqcwtzK7Hs+WNd7BJjqX5jWM4JTwm2sKa8onxlE2Ng6g4iMqyvTOXYNmfKABNqq/6ehWK0BkoalXMx1EVL5qQkBCUysL1upG/REII8QpYsGABKSkpdO/eHVNTUzZu3MiUKVNwc3Ojfv36BV7vuHHjKFeuHCNHjiQ8PJx169YxceJE3nnnHbZs2UKXLl0wNTVl3bp1/Oc//2Hz5s24uuoOOXvw4EHu3r1Lr169cHR05ODBg/j7+xMREcHkyZO15SIiIhg0aBBpaWl06dIFNzc3wsPD2bRpEyEhIaxcuRIrKyuddX/zzTeYmZnx/vvvo1AocHJyMnjfNmzYoO2HM3ToUAC2b9/OhAkT+PLLL+nevTv29vZMmzaNLVu2cPr0ae3IQlkTL0NNmTIFY2Nj+vfvT1paGr///jsTJkxg8+bNeQ508NNPP7Fp0ya8vLzo378/0dHRzJgxI9flxowZg729PUOHDiUmJobVq1fz8ccfs23bNiwtLXnjjTcYNGgQy5cvp1u3brzxxhs6+7Zs2TIWL15MixYt6NGjB0qlknv37nHw4EFSU1Ml2SmEDJWaEXsyEx3IzEk0iQ7A7Ti4HZelWiW3ZCUnCoVulUtOEnPoV5OmOzybbXqGdr1qhQKnuHgmbt3FfStbbrnbYKJKo1b0feziH2IdeZ/O55Op8egatilPXzL6zIBv+iFn+X9lcjLpI5ZifGiKATshnlcBAQHZTo+OjubgwYNs3rxZ+/e3oOQvkRBCvAJSU1MJCAjAxMQEgNatW9OlSxfWr19fqGTn9ddf12tisGbNGh4+fMi6deu0yUfDhg1577332LJlC2PGjNEpf/XqVQICAqhRowYAffr0YeLEiQQGBtK9e3fq1KkDZN7Qp6ens3r1apydnbXLt2nThkGDBrF69Wq9WikrKysWLlyY7xvv2NhY5s6di5ubGytWrNDuR8+ePXn//feZPXs2bdu2xdramk6dOnHixAlOnz5Np06d8rWdrOzs7Jg1a5b2beJeXl4MHDiQzZs36x2zrK5fv86mTZto2rQpc+bM0T4FbdOmDf369ctxuRo1ajBp0iTt9ypVqjBp0iR27dpFjx49cHNzo3Hjxixfvpy6devq7VtwcDCVK1dm1qxZOtPHjh2b730vTlFRUVhaWmqb/8XHx6NWq7G2tgYyfxtxcXE4Ojpql7l//z4uLi45fo+IiMDZ2Vl7rop6Gw8S4W6W5KZUqfJKQTJVSk7V+f7I1obddepgE5OISUY6no8jKZNozH1FJcpm3KLxndOFDk1x5S5Q/OejpLaRm5d16OkPP/wwx3lOTk5MmjSJb7/9tlDbkNHYhBDiFdCrVy9togNQrlw53N3dCQ8PL9R633vvPZ3vmqf/nTt31qllqVatGpaWlty+fVtvHY0bN9YmOpA53OiAAQOAzBtqyLyROHz4MC1btsTMzIzo6Gjtp0KFCri5uWkHCciqX79+BaphOH78OElJSfTt21dnP6ysrOjbty+JiYnZbq8w+vbtq72RgsxE0sLCIttjltWhQ4e0y2dt7lG1alWaNGmS43LPJkKaJniGXhNWVlY8fPiQM2fOGFS+tDg4OOj0c7KystLekAKYmprq3JACejegz34vX768zrkq6m2Ut4RKNnnuWvHSNI0zMeBWUa3GLJuk6LpzWQBqPbyPc0Jc5nDUahMeUYU0Ct/3TN20JlD856OktvEqunnzpt4nLCyMmJgYHj58yH//+1/KlClTqG1IzY4QQrwCnm06Bpmd6SMiIop0vZp/7LNrPmVjY0NMTIze9EqVKulNq1KlCgB372Y+uQ0LC0OlUrF161a2bt1qUCwA7u7uue9ADjTb1cSRW2xFxc3NTW+ara1ttscsq3v37gHg4eGhN8/Dw4OjR49mu9yzx8vOzg4gz+1pjB49mgkTJjB06FDKli3Lm2++SfPmzWndurVOYi3yT6lQsKyDkl6BKh4ngbESqtjClSeZ82vYQ0MXBasvqjMrXlTq7Pve5EeaKnMgAhMlpKogJQNsTMDCBNRpmdMgs68OiszyGgoFKrL2EMoUb14GpUqFU2LCs3tIIvbYovv3R4USJaqnXY/QbbqWkWV+qrsbpite/PdzveoUCgVly5bF3Dz7wSaSkpJ49OhRgf+WgyQ7QgjxSsipg6f6f09vFbncJGVkZOQ4L6chkHOarlYb1iQmJx07dtQOqvCs7EYpK+wTwZKU1zkqaoU9R3Xr1uWPP/7g77//JiQkhNDQUHbt2sXSpUv59ddfS2RkupfZO+5K7oxQcOoBVLYFFysFV6LUxKbCm86Zv9npzdXcjQc3CxX/PlQTnaEgOV3NpisQkaCGDDV3EkCpUGJvDvZlIDwWbsWrMxOkVFVmJpWmeto3J131NGlSKjNHZLM2zazpUashJg1UKr1409FPdqKtLUlVKklTGmGq0v07YkQa6RiRRhlMSSINJQ8tHbBUp6L0cCStlgcm1coCJlhUc8CsZnmUqRmk3kvGyNMR00aVivqQi1JQuXJlVq5cmWOT223bttGvX79c/x3KiyQ7QgghsLHJbDMTGxurN6+oazCeFRYWpjftxo0bwNPaBzc3NxQKBenp6TRu3LhY49FsTxNHo0aNdObdvHlTJ7bSpqlFu3Xrll7t0K1btwq17tySYAALCwtat25N69atgaeDOmzdulXbFFEUXBljBc2yXGavOeieD3cbBe42AEZUtHs6/cM6hm8jLlnF2QgVPVan8zCezHfpWP3vBaJZ61YUisx5OfThye5KSTc2Jty5HGFRUbz25OmLIY1JwphkErDBJnYeCmsLjIC8nt1nHY3tVaN+SYeezuvhSlpaWqFHY5M+O0IIIbC0tMTR0ZGTJ0/q/ONz584d9u/fX6zbPn78OJcuXdJ+V6vV2hF6NEM829nZ8dZbb7Fv3z7OnTuntw61Ws2TJ0+KLKbGjRtjbm7OunXrSEh42gQnISGBdevWYWFhkWt/mJLUokULANauXYsqyxP3a9eucezYsUKt28LCAsi+aVt0dLTeNE3fq+ySZvF8si6jpHklYx58ZYX6BytU/7XkXec0SEzLrO1J+98TdbU6s2lbDkxzmH6mhgdzundgZcsmRNuYojROwViZyKOKVbBVL0VhbVH0OyWea7Gxsdy+fVvbH/Hx48fa71k///zzD2vXri103yap2RFCCAFA7969WbRoEePGjePtt98mMjKSTZs24enpyYULF4ptu9WqVWPkyJH06tULJycnDhw4wIkTJ+jUqRN169bVlps0aRJDhw5l2LBhdO7cmerVq6NSqbh79y4HDx6kU6dOeqOxFZS1tTXjxo1jxowZfPjhh9qmc9u3byc8PJwvv/xSb5jr0uLp6Um3bt3YsmULH330Ea1atSI6OpoNGzZQvXp1Ll68mGcNTU4qV66MpaUlGzdupEyZMlhbW+Pg4EDDhg3p2bMnderU4fXXX6ds2bJERkayZcsWTExMaNeuXRHvpSgpCoWCvUOtiEtR0/7XBP6+owJFWq7jQqv+a4nPsOhs55mkZ1A2MYknto5satYKq/gEfI//TY3bk7ItL15+s2bN0g7Tr1Ao+OSTT/jkk0+yLatWq5k+fXqhtifJjhBCCAAGDhxIfHw8O3bsIDQ0lMqVK/PNN99w8eLFYk12WrZsiYeHBytWrODWrVs4ODgwdOhQvXcrlC9fnlWrVvHbb79x4MABdu7ciampKc7OzrRo0YK2bdsWaVya5GvlypX4+/sD8NprrzFz5swcX45aWiZNmkTZsmXZunUrc+bMwcPDg0mTJvHvv/9y8eLFbPszGaJMmTJ8//33LFq0iF9++YXU1FQaNGhAw4YN6d+/P0eOHGHdunXEx8fj4OBA7dq1GTRoEK+99loR76EoadZmCo6OtmLbvyl0WZUGpgpI1c94tn5gikKhQEH2+ZBKoaBcfIK2KVGa0oj7tcpSI5uyImdqxcvTGKtdu3ZYWVmhVqv5/PPPee+992jQoIFOGYVCgaWlJW+++Wa+XgSdHYW6uHo+CiGEEKJUffrpp5w8eZIDBw7kOCCBEHm5F5fOu8tTuB6pRp2ROSDbwDeULO7xtAnaxP/e4eKN7Jc3TUuj9sPHKNMzsI+OYUBgC+zLZz/6lsjegvo7DS47+kzHYoykaE2dOpUePXpQu3btYtuG1OwIIYQQL7jk5GS9keeuXr3K0aNHadasmSQ6olAqWBtzaVzut4xe9cy5eD0x2+GvU42NSVUqscxIpcLdSEl0hNbkyZOLfRuS7AghhHhlxMTEkJaWlmuZMmXKFLo/TnJyMvHx8XmWc3JyKtR2NLZv386OHTt46623sLe3JywsjC1btmBsbFxk/ZiEyE2X1las3JyYOZDBswmPQoEasIyNJ9kop6EMRG7UypdzNDaNI0eOcOrUKWJiYnQGWoHMJm3ffPNNgdctyY4QQohXxsSJEzl16lSuZby9vZkyZUqhtrNnzx6mTp2aZ7mQkJBCbUejRo0a7N+/n3Xr1hETE4OlpSVeXl4MHz5cO0KaEMXJyCjnPiVG6RlUuPcQhUqFeeUX591XovhFRUXRuXNnTpw4gVqtRqFQ6Lz/TTNNkh0hhBDCAJ9++mmewyKXLVu20Ntp2rQpCxYsKPR6DFW7dm3mz59fYtsTIntqUCgza3cAFAqM09JpcfoiVolJxNlYonS0LN0QxXNl4sSJ/PPPP6xZs4bGjRtTpUoVdu/eTeXKlZk1axZ///03O3ca3l8pO5LsCCGEeGXUrFmzRLbj5ORUZE3UhHhRmJsmkpRmhXl6Ok3OXAIF2MUnaecbp2VQ7q3CP0wQL48dO3YwYsQI+vTpw+PHjwFQKpVUrVqVBQsW0L17dz755BN+//33Am/j5RnHTgghhBBClJomNU6iUKmwTk4l0dEWy5TM/nFqINbOGvuYJBqNrFa6Qb6g1AqFwZ8XSXR0NK+//jqAtq9k1v6O7dq1Y/fu3YXahiQ7QgghhBCi0KzNk3irxhEskpJIMzPlnocL99zLc6eyKwk2VtikJmFqZVLaYYrnSIUKFYiIiADAzMyMcuXKcfbsWe38u3fvFvilyBrSjE0IIYQQQhQJB7s4Ri6uwrLuoUTbW5NmZgpqNZWv3qPZ4ialHZ54zrRs2ZI9e/bw1VdfAdCnTx9++uknjIyMUKlUzJ49m/bt2xdqG5LsCCGEEEKIImNZ1pxukzw4O+YY8eYWlI2KoeoHlXDsWrW0Q3txvVit0ww2fvx49uzZQ0pKCmZmZkyZMoV///1XO/pay5YtmTdvXqG2IcmOEEIIIYQoUpW7VqFy1yqlHYZ4ztWpU4c6depov9vb2/PXX38RHR2NkZER1tbWhd6GJDtCCCGEEEKI54adnV2RrUsGKBBCCCGEEOI59rKOxgZw+/ZtRo4cSfXq1XFwcODgwYMAREZGMm7cOE6fPl2o9UvNjhBCCCGEEKLEXbhwgRYtWqBSqWjcuDHXrl0jPT0dyHxf2eHDh0lISGDp0qUF3oYkO0IIIYQQQogS9/nnn2NnZ8exY8dQKBSUK1dOZ37nzp1Zt25dobYhzdiEEEIIIYQQJe7gwYOMGjWKsmXLZvs+HXd3d+7evVuobUjNjhBCCCGEMExyKlQcBpFxmd/fqQ37ppVuTK8AtfLF64tjCJVKhYWFRY7zHz16hJmZWaG2ITU7QgghhBDCMOZ9nyY6AMHnwaxn6cUjXmgNGjQgKCgo23np6emsXbuWJk0K9zJaSXaEEEIIIUTevL/LfnqqCsIelmws4qXwxRdfsGvXLkaNGsX58+cBePDgAX/99Rft2rXj4sWLTJo0qVDbkGZsQgghhBAib0E5DwGs7P0zjKpRgsG8Wl7EIaUN0bFjR1asWMHHH3/MkiVLAOjfvz9qtRobGxsCAgJo2bJlobYhyY4QQgghhCicW48ASXZE/n3wwQd0796dP//8k2vXrqFSqfD09KR9+/ZYW1sXev2S7AghhBBCiMJJSy/tCMQL4ssvv6Rv377UrVtXO83S0pJu3boVy/akz44QQgghhMjd3cjc58enlEwc4oX3448/avvnADx+/BgjIyP27dtXLNuTmh0hhBBCCJG7Wh/nOluenhevl7XPjoZarS62dcu1KYQQQgghchebVNoRCFEgkuwIIYQQQoicJSXnWUQBVDwdke28Lw+mo5iZ+TH9OZ2zD6R/jyg50oxNCCGEEELkzGmQQcUabrhA+Bvldaa9sSKdM1m6+6Spof5KUE8oygBffi9bM7awsDBOnToFQExMDABXr17Fzs4u2/INGjQo8LakZkcIIDAwEC8vL0JCQko7FJGDKVOm4OXlVdphvBCep+s5JCQELy8vAgMDSy0GHx8fhg8fXmrbF+KFplZDomGDDzg+TtX57r5IN9HJqv06qd15lX3zzTc0bNiQhg0b0qZNGwA++ugj7TTNx8vLi4YNGxZqW1KzI4TIUWBgIHFxcfTr1++l3J4QL7qQkBBCQ0Pp169fkbyPQgg9Vu/lq7hZVGbfnvux6YTHqyGHGok/wwsdmXhBLV++vES3J8mOECJHgYGB3L9/v0STnZy29/XXX/PFF1+USBwvuk6dOtGuXTtMTExKO5TnwqZNm1C8ZE1ANEJDQ/H398fHx0eSHVG0vl8PX6/N1yIK4IMvDpDx2UdUWJQBRka5l5+Zzrh68N+3jbA0fTl/o0XlZWrGNnDgwBLdniQ7QrwiEhISsLS0LO0wCszY2Bhj41fvT1ZycnK+993IyAijPG4yXiWmpqalHYIQz5eEZEjPgO0hsO8cJKfA2Vtw4Q4UYgRgNRBlbkmrT69ApeoGLTP3LMw9mwGAqQLaeYCbDXjYQFsPMDeB27FgbKSgflmwNlViZvzy3PiL4vfq3TkIkQu1Ws3KlSvZuHEjDx8+xMXFhcGDB+Pt7a0t8+eff7Jz506uXLlCVFQUFhYW1K9fn5EjR1KtWjWd9Z09e5alS5dy+fJl4uLisLW1pVq1agwbNow6deoYHFdgYCBTp05lwYIFnDlzhsDAQB4/foyHhweDBg2iffv2OuV9fHxwcXFh/PjxzJ8/n3PnzmFra8u2bdsAOHXqFL/++iv//vsv6enpVKpUiV69etG1a1edddy/fx9Ap6/M4sWLtd9v376Nv78/J06cICYmhrJly9KmTRuGDx+Oubm5TkyRkZEsX76cw4cP8/DhQ6ysrKhWrRoDBgygSZMmeW5vypQpbN++Xa8fytWrV/Hz8+P06dMkJSXh6uqKt7c3/fv317nh1yy/f/9+5s2bx759+0hISKBGjRqMHz+e2rVrG3w+NLZv38769eu5ffs26enpODo6UqdOHT777DPs7e215Qw9TpoY9+zZw9y5czly5AhPnjxh1apVDBo0iLfeeov/+7//04tj/vz5rFixgtWrV1O9enXt9ZL1XAGkpaWxZs0adu/eza1btzA2Nsbd3R1vb2/69OmjLRcfH8+yZcvYt28fDx48wNLSkkaNGvHRRx/h5uaW7+OksXbtWtavX09ERATly5end+/e9O3bV6eM5tpdsmSJzvSQkBBGjhzJ5MmT8fHxASAlJYUVK1awe/duHjx4gImJCc7OzjRr1oyPP/4413Vqpn355ZfMmjWL06dPo1AoaNy4MZ9//jlOTk462zf0mBga0+HDhwkICOD69eskJydjZ2dHrVq1GDNmDB4eHgYdT831AuDr66udPmzYMKysrJg1axbz58+nSZMmOsulpqbSsWNHqlWrxuLFi3WOx/jx45k9ezb//vsvJiYmtGjRgo8//hgHBwe9daxatYpdu3Zx584dTE1NeeONNxgxYgQ1atQwKH5RCuKSYMh82Hgssy9OEVMATkkJDDtzlE88XsuxCVtOUtWwPezp9y8OZ52bGa+xIoNPvRTMaKl8aWtsRdGSZEeILBYsWEBKSgrdu3fH1NSUjRs3MmXKFNzc3Khfvz4A69evx9bWlm7duuHk5MSdO3fYsmULQ4YMYdWqVbi7uwOZI42MHj0aR0dH+vbti4ODA1FRUZw5c4YrV67kK9nRmDdvHklJSfTs2RPITIK++uorUlNTtTeAGg8ePGDUqFG0adOGd999l8TERAAOHjzIxIkTcXR0pH///lhYWPDnn38yffp07t69y+jRowH47LPPmD9/PtHR0YwfP1673sqVKwNw8eJFRo4cibW1Nd27d6dcuXJcuXKFtWvXcvbsWZYsWaKtjbh37x5DhgwhKiqKTp06UatWLZKSkjh37hwnTpygSZMmeW4vOxcuXGD48OEYGxvTq1cvHB0dOXToEPPmzePq1atMnz5db5kxY8Zgb2/P0KFDiYmJYfXq1Xz88cds27YtXzVfQUFBTJkyhTfeeIORI0diZmbGgwcPOHLkCFFRUdpkJz/HSUNz3QwZMoSkpCScnZ1p2bIlBw4cICYmBltbW21ZlUrFzp07qVatGtWr5/wkNS0tjTFjxhAaGkqTJk3o2LEjpqamXLt2jeDgYG2yEx8fz+DBg4mIiMDX15cqVaoQGRnJxo0b+fDDD1m5ciUuLi4GHyeNdevW8fjxY7p3746FhQW7d+9m5syZxMbGFnjwgBkzZrBt2zY6d+7M+++/T0ZGBuHh4Zw8edKg5R89esSIESNo1aoV48aN4+rVq2zevJmEhAQWLFigLZefY2JITKGhoYwfPx5PT08GDRqElZUVkZGRnDhxgvDwcIOTne7du5OQkEBwcDDjx4/XjmJUrVo1ypYty4IFC9i2bZteshMcHExMTIzOww2Ahw8fMmrUKN59911at27NpUuX2LZtGxcvXiQgIIAyZcoAkJ6eztixY/nnn3/o1KkTvXv3Jj4+Xvt30N/fn1q1ahm0D6KEfbUaNvxd7JuxT4zPd6JjqHQ1/N9JNa87qhlYW5IdkTdJdoTIIjU1lYCAAG1fh9atW9OlSxfWr1+vTXbmzZunV2vRuXNn+vXrx5o1a5g0aRIAx44dIzk5me+//75AtQbZiY6OZu3atVhZWQHQs2dP+vbty6xZs2jbtq32ZgTg7t27fP311zo3NBkZGfz000+Ym5vz22+/UbZsWQB69+7NiBEj+O233/Dx8cHd3Z1WrVqxZs0aUlJS6NSpk14s06ZNw8nJiYCAAJ0koVGjRkycOJGdO3dqE7Aff/yRR48eMW/ePJo2baqzHpVKBZDn9rIzc+ZM0tLSWL58ubZWrU+fPnzxxRfs2rULX19fGjVqpLNMjRo1tOcIoEqVKkyaNIldu3bRo0cPg7YLsH//fiwtLVm0aJFOsjJy5Eidcvk5Thqenp589913OtO8vb3566+/+PPPP+nVq5d2ekhICA8ePOC993LvRLxmzRpCQ0MZNGiQNqHV0JwDyKxJu3v3LsuXL+e1117TTvfx8aFv3774+fkxZcqUXLeVndu3b7NhwwacnZ2BzGtuyJAhLF26lC5dumin58f+/ftp1qwZU6dOzfeyAOHh4fzwww+0bdtWO02pVLJhwwbCwsKoVKkSkL9jYkhMBw4cQKVSsWDBAp0ak6FDh+Yr/rp161K1alWCg4Np1aoVFSpU0Jn/zjvvaBObrAny1q1bsbGx4Z133tEpf+fOHcaPH6/TZ65KlSrMmjWLtWvX8uGHHwKZiWtoaKje77lnz5706dOH2bNn69XMiefEztMlspnL5SrkXaiQdtxUM7Bo/ml9IbxMfXZKmgw9LUQWvXr10unUXa5cOdzd3QkPfzpsjCbRUavVxMfHEx0djb29PR4eHpw/f15bTpOQHDhwgJQUw4btzEvPnj2169Vso0ePHsTGxhIaGqpT1tbWVu8m+uLFi9qn05pEB8DExIQBAwagUqk4cOBAnnFcu3aNq1ev0qFDB9LS0oiOjtZ+6tevj7m5OceOHQMyx8//+++/adasmV6iA5k3lwURFRXFP//8Q8uWLXWaDyoUCgYPHgxkPsF+1rODH2iaeWU9x4awsrIiOTmZw4cPo86hOUh+jlNW/fv315vWpEkTHB0dCQoK0pkeFBSEkZERHTt2zDXeXbt2YWNjk+0NteYcqNVqdu7cyRtvvEG5cuV04jU3N6d27drZxmuIDh066CQ0JiYm9OvXj4yMDA4dOlSgdVpZWXHjxg2uXbtWoOXLli2rk+iA/vWQ32NiSEya3/C+fftITy++4Xe7detGamoqO3fu1E67d+8eJ0+epEOHDpiZmemUt7S01EmkIfNvoqWlpc5vaefOnVSqVImaNWvqHI/09HQaN27M2bNnSU7O+yWUJSUqKkrnb3B8fDxxcXHa76mpqTx+/FhnGU2T2py+R0RE6PzuX5RtpFSwpSQkmRR/P7nKWXblRT0fz34XxUNqdoTIwtXVVW+ara0tERFP3wp96dIlFi9eTGhoKElJSTku365dO3bs2MHy5ctZs2YNderUoUmTJrRv375AzYAA7ZPmrDTNvO7evasXy7Od1O/duwdkPq19lqenZ7bryc7NmzcB8PPzw8/PL9syUVFRQOZNo1qtzrWJVUHkti+VK1dGqVRmuy/PnmNN0x/NS80MNWjQIE6dOsWECROwtbWlQYMGvPXWW7Rt21Zbg5Of45RVds2YjI2N6dChA6tXr+bWrVt4eHiQlJREcHCwNhHKze3bt6levbreDW5WT548ISYmhmPHjmnfe/Csgian2TVH1Jw7Q6657IwfP57JkyfTt29fXF1d8fLyokWLFrRs2dKgOHP6vcPT6yG/x8SQmHr37s2BAwf48ccfmTdvHvXq1aNZs2a0b99ep69XYXl5eeHu7s62bdu0faMCAwNRq9V6Tdgg83g8O4Kfqakprq6uOufo5s2bpKSk5Hg8ILMWunz58jnOL0nP9jfK+sAIMvfx2d/Ps3+jn/3+7L69KNsw+3EgtJli8HtzCiLVyIhNdRoX2/oB3K1hXIOnv7sX9XwU9F5A5I8kO0JkkdMNkuZpTkREBMOHD8fS0pIhQ4ZQqVIlypQpg0Kh4Oeff9ZJfkxNTVm4cCHnz5/n2LFjnDp1Cj8/P/z9/Zk+fbpeE5KilrVJW1HTHI/+/ftnW1sDYGNjU2zbL4ycRinLqXYmJ+7u7mzYsIETJ05w8uRJTp06xfTp07Xn2M3NrcDHKadz17lzZ1avXk1QUBAfffQR+/btIzExUWcAjcLQxNuoUaMSHxpUI6cOxxkZGXrTWrVqxbZt2zhy5AinTp3ixIkTbN26lTfeeIOFCxfmOfR2bgmR5ljk95gYEpOdnR0BAQGcPn2a48ePc/r0aX755Rf8/PyYM2cOdevWzXM7hurWrRtz5szh4sWL2sEratWqpdMcryCqVq3Kp59+muP8okzaRBFqWh2uLoDVByEoBA5dAFXeixkqVWlEmF1ZnGMec9uhXKHWZQTYmIFaBWkqMDOGWo4wsLaCPjWUWL9iQ1VLM7aCk2RHiHwIDg4mMTGRX375RWeUK8h8EpzdELe1a9fW9tmJiIjg/fffZ9GiRQVKdsLCwvSmaWoPsntK/SxNmRs3bujN00zLup6cbjw1gzAolUoaN879CV7FihVRKBRcvnw5z/jyM7KOpn9CdvsSFhaGSqUy6JgUhqmpKc2bN6d58+ZA5ghbn3zyCatXr+Y///lPvo6TIV577TVee+01du7cyahRowgKCsLa2pqWLVvmuayHhwdhYWGkpqbmOBSzvb091tbWJCQkFEm8WWmu06yyu+ZsbGyIjY3VK5tT7Y+trS2dOnWiU6dOqNVq5s2bR0BAAAcOHMi15sFQBTkmhsRkZGSEl5eX9u/I1atX6d+/P0uXLmXOnDkGx5fXb8bHx4eFCxeydetW3n77bSIiIrR9b5519+5d0tLSdJLE1NRU7t69q1OrXLFiRZ48eULDhg0LXNMnSlEFB5jYNfOTk0cxUGUkxOevBshUlUHVxxHs/ak6NgtUYOD18U1jmNZCbklF8ZC/UkLkQ9a+DVlt2bJFr21udHS03vLOzs7Y29vnu8mUxsaNG4mPj9d+j4+PZ9OmTVhbW/Pmm2/muXyNGjUoX748gYGBREZGaqenp6ezcuVKFAoFb7/9tna6hYUFsbGxevtbvXp1PD092bRpE3fu3NHbTnp6unYfbW1tadasGUePHuX48eN6ZbOuO6ftZcfBwYG6dety8OBBnf4RarVa+3bm4qw9y+78aobc1ex7fo6ToTp37sz9+/fZtWsXISEhtG3bNtemaRodOnQgNjaWpUuX6s3THG+lUkmHDh34999/+euvv7JdT3bN7gyxa9cuHjx4oP2uGQbbyMhImyxCZiIdFhbGw4cPtdNSU1PZsGGDzvoyMjJ02sdD5o2/prlkQX9jz8rPMTE0puyuHU0tcXaJXm4sLCwAclzOzs6OVq1asWvXLtavX0+ZMmXo0KFDtmUTEhL0jvOGDRtISEigVatW2mmdO3fm8ePHrF69Otv1PPu3ULyAytpC3O+QsTHfi15pUI4ypgqals/7FvP+CFBPMJZERxQrubqEyIe33nqLefPm8e2339K7d2+sra05e/YsR48exc3NTaepzdKlSzl27BjNmzfH1dUVtVrNoUOHCAsLY8CAAQXavp2dHQMHDtQOPBAYGEhERARff/21Qc3WjIyM+Pzzz5k4cSIDBw6kW7duWFhYsGfPHs6dO8egQYO0tRGQWSt16NAhfvrpJ+rWrYtSqaRhw4Y4ODgwbdo0Ro0axXvvvacdjjc5OZk7d+6wb98+xowZo43z888/Z/DgwYwbNw5vb29q1qxJcnIy//77Ly4uLowbNy7P7WVnwoQJDB8+nGHDhmmHnj58+DB///03HTp00BuJrSiNHj0aa2tr3njjDZydnYmLiyMwMBCFQqEdTU6hUOTrOBmiY8eOzJ07lx9//BGVSmVwE7b33nuPQ4cOsXTpUi5cuEDjxo0xMzPjxo0b3Lp1i4ULF2r36+zZs3zxxRfs3buXOnXqYGJiwv379zly5Ag1a9Ys0Ghs7u7ufPjhh/To0QMLCwt27drFhQsXGDp0qE5b+N69e/Pnn3/y0Ucf0aNHD9LS0tixY4fe9Z2YmEiHDh1o2bIl1atXx97ennv37rFx40ZsbGwMqu0ylKHHxNCYpk+fzsOHD2ncuDEuLi6kpKSwZ88eEhIS6Ny5c75i09Qaz507VzucuKenJ1WrVtWW6datG3v27OHQoUN4e3vr9S3QcHNzw9/fn+vXr1OzZk0uXrzItm3bqFSpks77kN577z2OHz/OnDlzOHnyJA0bNsTS0pKIiAhOnjyJqalpjn3UxAtGqYQnAWBv2L9ZauDIoHp4Aof6G2H8i37zUw0joLy13IYaSq2UZmwFJVeZEPng5ubG3LlzWbBgAcuXL0epVFKvXj38/Pz46aefdEZWefvtt4mMjOSvv/4iKioKMzMzKlasyNdff02XLl0KtP2xY8dy5swZNmzYQFRUFO7u7kyfPj3HJ7XZadmyJQsXLmTp0qWsXLmStLQ0KlWqpDdMNcD777/P3bt32bt3L5s2bUKlUrF48WIcHByoXr06q1evZvny5Rw8eJBNmzZhaWmJi4sLPj4+NGzYULseV1dXVq5cya+//sqRI0cICgrCxsaGatWq0a1bN4O2l51atWqxbNky/Pz82Lhxo/alomPHjs12RLOi1LNnT/bs2cPmzZu1Q/tWr16dzz//XKeJY36OkyEcHBxo1qwZhw4dwt3d3eD+HSYmJsyfP59Vq1axe/duFi5ciKmpKe7u7jrJlpWVFcuWLWPVqlXs2bOHgwcPYmRkRLly5ahfv362HdsN0adPHxISEli3bp32paKfffaZ3pDZ9evXZ8qUKSxbtow5c+ZQrlw5evToQa1atRg1apS2XJkyZXjvvfc4ceIEJ06cIDExEScnJ1q2bMmgQYN0RhssLEOPiaExderUicDAQIKCgnjy5AmWlpZUqVKFGTNm0Lp163zFVr9+fcaOHcvmzZuZPn06GRkZDBs2TCfZadiwIRUrViQ8PDzXvz3lypXjxx9/ZPbs2ezevRsTExM6dOjAJ598ojPcvrGxMbNnz2bjxo3s2LFDm9iULVuW119/vcj6kInnhF32yXF20oAM08x+kUZKBRc/VFJzRfadgq4OKYrghMibQp3fXrlCiBIXGBjI1KlTWbx4sV5fISGEyEvv3r3JyMhg06ZN2c738fHBxcVF3o8jsrf/PLzzbZ7FbtRx5K8xDRk0aJBO3y/jmelkrePxcoaTH8jz9vz46e2DBpf9/EDR1Wy/DORKE0IIIV5iJ0+e5MaNG3zyySelHYp4UbXK++2dauBk9xrZzkufYMz+Wxn8cU3NqHoKqjtlPyqmEMVBkh0hSklaWppBnahlCNeSlXXghpxYWVkV69Dez7uMjAyePHmSZzlbW9s8h38W+pKTk3UGIsmJk5NTrvNPnjzJnTt3WLFiBfb29gVugiiEoWIqWOc4r5WHEa30XyEmDCRDTxecJDtClJKzZ88ycuTIPMtt27atBKIRGob0f5o8eXK+BhV42Tx48ABfX988y0mzy4LZs2cPU6dOzbNcSEhIrvP9/f05e/YslStXZsqUKTkOTCCEQRYMhdG/5ji7CF/XI0SRkj47QpSS2NhYLl68mGe5+vXrGzS0sCga2Q2P/SxPT888n6q/zFJSUjhz5kye5WrWrPncvlz2eRYZGcn169fzLFfU70ISIk+K7jnOyrAwYemszAE2nu2zIwpvRqtDBpf9z/4WxRjJi0dqdoQoJTY2NnKz8hySc5I3MzMzOU7FyMnJ6ZVOpsULSppZFStpxlZw8lJRIYQQQghROJavbj9G8XyTZEcIIYQQQuTNOOfaBfWAViUXhxD5IMmOEEIIIYTIW/zvOc5Sf9OrBAMRwnCS7AghhBBCiLyZmcLiEfrTj/8I5qYlH88rRK1QGPwRumSAAiGEEEIIYZgR7TM/j+PAwhTM/zdaaFpa6cYlRA4k2RFCCCGEEPnjmPMLRIV4nkiyI4QQQgghxHNMmqcVnPTZEUIIIYQQQryUJNkRQgghhBBCvJSkGZsQQgghhBDPMWnGVnCS7AghhBBCiCKTmKri4zFnsbz6iDQbC8b9WIfaNWRAA1E6pBmbEEIIIYQoEhlqBX0/+Jd/E8zZWqsul81sONxjO6fPRJd2aOIVJcmOEEIIIYQoEkfPVOSukzNPbG1wj4/noYM9c5o2Y/X4U6UdmnhFSTM2IYQQQghRJEJTX6NCUgIdwm7zwMaKZBNTIk2MuW5hVdqhvdCkz07BSbIjhBBCCCGKhKlazeRD60kyN6blidOoFQp2Vm/KD426cO9JOhXs5dZTlCxpxiaEEEIIIYrEwH8OEmNpTLurJ0g2NifcpiJtroYy9PwBZm+KKu3wxCtI0mshhBBCCFFoKhU4J0dRPv4J617vwG2711AAaqBC7HUSjz2AoeVKO8wXklpasRWY1OwIIYQQQohCUyhge80G/FPWg1v/S3QAFEC4bVXSEzJKMzzxipKaHSGEEEIIUWhHztZif6WqhFu68N6F69SPOIdVagLXHKpww6EyiqiY0g5RvIIk2RFCCCGEEIW2w7I6iUamxJkl0//sOmxS4wGo++Bf/qrSkqP1WpZyhOJVJM3YhBBCCCFEoSUrjHCOTqDHv39rEx2NhndPYa40LaXIXnxqhcLgj9AlyY4QQo+Pjw/Dhw8v7TDyzcvLiylTppRqDH5+fnh5eXHv3r08yz4P8RbElClT8PLyKvbtZHd8iuPaDAwMxMvLi5CQkCJdrxCvkmuhj6l97wmxpqaYoOLb1r0Y1H0ky9/szJbanTnh3gDPmEhUKnVphypeMZLsCCGEeOmFhITg5+dHXFxcaYfyUlqzZg2BgYGlHYYoRf+ciSbOyJhx/xyg2+WjDDu5l3duXOe2Y23OuNXlsOdbqI3K8MmXN0o7VPGKkT47Qgg9mzZtQiFV4cXuyJEjGBkZlXYY+fb111/zxRdflMq2C3pthoaG4u/vj4+PD9bW1jrzOnXqRLt27TAxMSmqMF85v//+Oy4uLvj4+JR2KKKUrA5V0OvCSSaHrNFOG3BmD8GeSeyv1gIApcKIh5cTSivEF5o0Tys4SXaEeAElJCRgaWlZbOs3NZV21SXBzMystEMoEGNjY4yNS+efj+K4No2MjF7IpFOIvKjUajJUkJqh5vQDFZsvweyzme+9MZQJKmqYprHpfTOqOWb/u09NSOVhWgb9/j2mN88xMZJ4MzMylEqMVCos1LDJ7wI9RtQq4F4JkT+S7AhRCgIDA5k6dSoLFizgzJkzBAYG8vjxYzw8PBg0aBDt27fXlvXx8cHFxYXx48czf/58zp07h62tLdu2bQPg9u3b+Pv7c+LECWJiYihbtixt2rRh+PDhmJubAzB37lwCAgL4/fffqVatmk4s8fHxtG/fniZNmvDzzz/rbHPJkiU6Zffv309AQABXrlxBoVBQrVo1BgwYQKtWrXTKeXl54e3trdffQrPfixcv1vb5iImJ4ddff+XgwYM8evQIc3NzXFxcaNeuHQMGDCjQ8T1+/DiLFi3i6tWrWFlZ0bZtWz766CMsLCx0yt27d49FixZx/Phx4uLiKFeuHO3atWPIkCGUKVOmwGWflZGRwYwZM9iyZQtjxoxh4MCBOR4nzbTu3bszf/58Lly4gJmZGa1ateKzzz7T24fQ0FDmz5/PlStXtPvarVs3+vTpw7BhwxgxYgQAKpWKtWvXsm3bNu7du4dCocDR0ZH69evz5Zdf5it5mTJlCtu3b9fp46KZtn//fubNm8e+fftISEigRo0ajB8/ntq1axu8/txkd22ePXuWpUuXcvnyZeLi4rC1taVatWoMGzaMOnXqaGMD8PX11S6nOT7ZXZeaaYsWLeLSpUts3LiRhw8f4uLiwuDBg/H29taJKyMjg+XLl/PHH38QFRWFu7s7gwcP5ubNm/j7+7Nt2zYqVKiQr32Nj4/nt99+Izg4mHv37mFubk6lSpXo3bu3zt+Iq1ev4ufnx+nTp0lKSsLV1RVvb2/69++vk8QNHz6c+/fv6zU3u3fvHr6+vjrXS0hICCNHjmTy5Mmo1WpWrVpFeHg4jo6O9OrVS3sNA9pjdv/+fZ2+XAXZZ5G7w3fUTD+m4m68mq5VFXzTVMn6y2p+OqHi4mNIL4buMGkoOZdqxmvL1Jnf1Gqm/PEXVZ4kE2tugWliKjfsTahWzgiPpHCdZdOVRqx9oxtGKhVKtZo0IyPKpqeyMdiUgJ3/kIKawzXdSTEyoWx8IlO3HKBCbDzJRiYkWJVhT/NqXHO2ZMytE7zfoyK816Lod1C89CTZEaIUzZs3j6SkJHr27Alk3mB99dVXpKam6jQHefDgAaNGjaJNmza8++67JCYmAnDx4kVGjhyJtbU13bt3p1y5cly5coW1a9dy9uxZlixZgrGxMd7e3gQEBBAUFMQnn3yiE8OePXtISUnRu3l71oYNG5gxYwaVKlVi6NChAGzfvp0JEybw5Zdf0r179wIdg0mTJnHq1Cl69OhBtWrVSElJ4ebNm4SGhhYo2bl06RJ79+6la9eudO7cmZCQENauXcv169dZsGABSmVmV8X79+8zcOBA4uPj6dmzJ+7u7oSGhrJ8+XLOnj3LwoULtQlAfso+Kzk5ma+++oojR44wdepUOnXqlOc+XLlyhU8//RQfHx/at29PaGgoW7duRalU8tVXX2nLnTlzhjFjxmBjY8PAgQOxtrZmz549nD17Vm+dy5YtY/HixbRo0YIePXqgVCq5d+8eBw8eJDU1tchqasaMGYO9vT1Dhw4lJiaG1atX8/HHH7Nt27ZiqY0MCwtj9OjRODo60rdvXxwcHIiKiuLMmTNcuXKFOnXq0L17dxISEggODmb8+PHY2dkB6CX+2VmwYAEpKSl0794dU1NTNm7cyJQpU3Bzc6N+/fracj/99BObNm3Cy8uL/v37Ex0dzYwZMwp8sx8XF8eQIUO4ceMGrVu3pmfPnmRkZHD58mUOHz6sTXYuXLjA8OHDMTY2plevXjg6OnLo0CHmzZvH1atXmT59eoG2r7Fp0yaioqLw9fXF2tqanTt3Mm/ePJydnenQoQMA06ZN45dffsHOzo7Bgwdrl7W3ty/UtoWua0/UtN2YQXJ65vfzkWpORmSwO6yEAvhfMyqfkEvYpRtz39EBgASLMjyxtWbS0RWYkYYatC8TPelWD2MVmKjSAFApFMSbmVE5No6dnh7cNjEiwSTzYdF9G2s+6t+Rwz+uwkSVBI9jqXn7EcOGdqb/Gz2x/G4mXS3NwLdRCe3w80UlzdgKTJIdIUpRdHQ0a9euxcrKCoCePXvSt29fZs2aRdu2bbU1Bnfv3uXrr7+ma9euOstPmzYNJycnAgICdG4kGzVqxMSJE9m5cyc+Pj5UqVKFWrVqsWvXLsaOHavztDcoKAhbW1uaN2+eY5yxsbHMnTsXNzc3VqxYoRPv+++/z+zZs2nbtq1eX4i8xMfHc/LkSXr27Mnnn3+er2Vzcu3aNWbOnKmtberVqxczZ85k7dq17NmzR3uTuGDBAp48ecLs2bO1+96rVy/mzJnDypUr2b59u/Z456dsVjExMXz66adcu3aN2bNn06RJE4P24erVqyxfvlxbG9KjRw8SEhLYtm0bn376qbZ255dffkGhULB06VLc3Ny0cWU3WllwcDCVK1dm1qxZOtPHjh1rUEyGqlGjBpMmTdJ+r1KlCpMmTWLXrl306NGjSLcFcOzYMZKTk/n+++9zrD2qW7cuVatWJTg4mFatWuUrAUlNTSUgIEDbn6d169Z06dKF9evXa5Od69evs2nTJpo2bcqcOXO0CXWbNm3o169fgfZrwYIF3LhxI9sHCSqVSvv/M2fOJC0tjeXLl2uTtz59+vDFF1+wa9cufH19adSo4DeHERERbNy4Ufub79KlC97e3qxbt06b7HTq1IlFixbh4OBgUDIvCmbtJbU20dH461bJx9H+wk0SbG10pnlGx1Ar8i5J2JOCNWbEYkwiV8q9RtZbdKVajVl6OsqMdBxS0vjHzkpnPelGRgTWr0r3U1cAMFKr6Xz6Gmc9yrPijbfpuiL4lU12RMHJaGxClKKePXtqbyIArKys6NGjB7GxsYSGhmqn29ra6nX8vXbtGlevXqVDhw6kpaURHR2t/dSvXx9zc3OOHXvafrpz585ERkZy/Phx7bS7d+9y9uxZ2rdvn2vn7OPHj5OUlETfvn314u3bty+JiYk66zWUmZkZpqamnD9/3qChmg3h4eGh16zuww8/BDKb4UHmzeLBgwepXr26XpL34YcfolQqC1Q2q/v37zNkyBDu3r3LkiVLDE50AOrUqaN3496wYUMyMjK0x+nx48dcuHCBt99+W5voQGZ/mvfee09vnVZWVjx8+JAzZ84YHEdBPHtzr2nWFB4enl3xQtNcjwcOHCAlJaXI19+rVy+d30a5cuVwd3fX2Z9Dhw4B0LdvX22iA1C1atV8nXcNlUrFn3/+SeXKlbOtMdVsIyoqin/++YeWLVvq1FIpFAptDUtwcHC+t5+Vj4+Pzm++TJky1KlTh9u3bxdqvSUpKipK59qIj4/XGZUvNTWVx48f6yxz//79XL9HRESgVj9tM1YS21CnJurtm1JR8sM4J5lk079NrSIFaxIoTzqWKMnAhFSSTPSb+FomJ3LN3p5UpRKV/pqwSk7V+Z76v+2VSU+DMpl99p6H81Ec2xDFQ5IdIUpRpUqV9KZVrlwZyExENFxdXfU6UN+8eRPIfK9LmzZtdD5t27YlKSmJqKgobXlNQhMUFKSdFhQUhFqtpnPnzrnGqYmlSpUqevM007LGaygTExPGjx/P9evX8fX1pXfv3vz000+cOHEi3+vS0By/rJycnLC2ttbG+OTJExITE7PdH1tbW5ycnApUNqvx48dz//59fv31V2rUqJGvfXB1dc12W5BZWwRokx4PDw+9stlNGz16NGZmZgwdOpSOHTvy9ddfs2vXLtLS0vIVW16ejV3TZEwTd1Fr164djRo1Yvny5bz77ruMHDmSFStWFNlNRE7nIuv+5Pdc5CU6OprY2Fhee+21XMtptpvdtVm5cmWUSmWBfpdZGbL/zzsHBwedwUCsrKx0aqFNTU1xdHTUWcbFxSXX7+XLl9cZFbAktjHcyxInc919+6CWEqMSbt20umkdrBKSdKY9MTPlrsXTa8WUzBeKVovUH2b6gaUllx1tuWFljtsT3aHgbZJTaHMhTPs9ycSYTQ1rYJyRztiQPTCmI/B8nI/i2IYoHtKMTYgXQHYd4DVPmPr370/Tpk2zXc7G5mlTAzs7O9566y3279+vHc1tx44dVK5cmddff714An9GRkaG3rSePXvSqlUrDh8+TGhoKHv37mX9+vW0bduWH374oUTiKg7t27dn8+bNLF26lG+//VbniX9echsZLOuTxfyoW7cuf/zxB3///TchISGEhoaya9culi5dyq+//qpNpgorp9gLGndeTE1NWbhwIefPn+fYsWOcOnUKPz8//P39mT59Ou+8806h1p/TeSuu/SlOOQ3Znd3vUkNGqXt+OFsqOP6+EXNOqbgXD12rKni/lpKhddXMDs3gQDg8TDRgpLWs164h/UDU6sxy/1vugqsjtxzUNAy7S5SlDUbxGbS4H0ai0hrb/yU5CSYWWKUl0PL6EWLK2HDOJXPktRSMCCnnSFp6Bg2vhJGhSMM6w57YMhZUe/iEifuOcdvDijhjCzIqWBPS3JO30x4w4v4Bmvz+ITTKu5/dy0qN9NkpKEl2hChFYWFhetM0NTbZPVHNyt3dHci8GWvcuLFB2/P29mb//v389ddfeHh4cOfOHcaMGZPncppmUjdu3NBr/59dvDk9+c3pKbOTkxNdu3ala9euZGRk8O2337J792769++f70RME09WkZGRxMXFaWO0t7fH0tKSGzf0nzrGxsYSGRmpfaqen7JZffjhh7i5uTF37lwyMjKYMmVKkd44ap4I3rql32g/u2kAFhYWtG7dmtatWwNPB53YunVrgUe+e17Url1b2/QvIiKC999/n0WLFmmTneJ8b5SmD9CtW7d0mhRqpuWXnZ0dNjY2XL161aDtZndthoWFoVKpdH6XNjY2XLp0Sa9sYWt/oHiPr3iqip2COe/q/h1pWkFB0wqG3c5FJmYw/SgcvJ7B41uJhNtZos4hobc0AjdrmNFCgYs1NKqgac5pAl/q9806aPcT5VGTZGKK31s9mXRgJSaqDLqf207du6c5bNuSUE93OoScZ+Tudyhf1u2ZNVQAdP/ePx3uoq5B+ydEdqQZmxClaOPGjcTHx2u/x8fHs2nTJqytrXnzzTdzXbZ69ep4enqyadMm7ty5ozc/PT1dL+Fo3rw5dnZ2BAUFERQUhFKpNKhDcePGjTE3N2fdunUkJDx9IVxCQgLr1q3DwsJCp2+Cu7s7586dIzk5WTstNjZWO1y2RnJysk4ZyHySrOl/EBsbm2dsz7p165ZeH5rffvsNgLfffhvITBBbtGjB5cuXOXr0qE7ZFStWoFKptP1+8lP2WQMGDGD8+PHs3LmTr7/+mvT09GzLFYSTkxO1atXiwIEDOuc/PT2d33//Xa98dHS03jRN87qCHOfnRXb75ezsjL29vc71rxnUoTj2tUWLzOFw165dqzN4wLVr13T6zRlKqVTSvn17bty4wR9//KE3X1Or5ODgQN26dTl48CDXrl3Tmb98+XIAnZotDw8PEhISOH/+vHaaSqVizZqnL4EsKHNz8xf6OnpVOFkYMbuNMadGmHHrv/aoPjdFPcE420/8p8ZcGmpMl+rGNDIgmTreoQnHKzgT4WjMvtcaMqTXl/g38uWnt/szrutnhJdzovnNGzyxsaB82dyH6xeiKEnNjhClyM7OjoEDB2oHHwgMDCQiIoKvv/46z3e3KBQKpk2bxqhRo3jvvffw9fWlSpUqJCcnc+fOHfbt28eYMWN0BjYwNjamffv2rF+/nkuXLtGoUSPKlSuXZ5zW1taMGzeOGTNm8OGHH2qHqd6+fTvh4eF8+eWXOp2Ye/fuzTfffMPIkSPp1KkTcXFx/PHHH7i4uOh02Lx16xbDhw/nnXfewdPTE2tra8LCwti4cSOurq688cYb+TqekNkp/JtvvqFr1664u7sTEhLC3r17adCgAe3atdOWGz16NMePH2fChAn07NmTihUrcurUKfbs2UODBg10huLOT9ln9evXDxMTE3766SfS09P54YcfimyY548//pjRo0czZMgQ7WAXe/bs0SZVWZ+29+zZkzp16vD6669TtmxZIiMj2bJlCyYmJjrH5UWzdOlSjh07RvPmzXF1dUWtVnPo0CHCwsJ0aqs0tT5z586lY8eOmJqa4unpSdWqVQsdg6enJ926dWPLli189NFHtGrViujoaDZs2ED16tW5ePFivms+Ro0axcmTJ5k+fTrHjx+nXr16AFy+fJn09HS+++47ACZMmMDw4cMZNmyYdujpw4cP8/fff9OhQwedmthu3bqxatUqJk6cSN++fTExMWHv3r25NmMzVJ06ddi6dSuLFi2icuXKKBQKWrZsqX3Xl3j5NerowuHzybS4cx7jjHTC7cqztn55ABRqNRWSYrFNTqbBx/Iy0YJQS+1pgUmyI0QpGjt2LGfOnGHDhg3aFxFOnz5dO6RrXqpXr87q1atZvnw5Bw8eZNOmTVhaWuLi4oKPjw8NGzbUW0YzbGxiYmKeAxNk1atXL5ycnFi5ciX+/v4AvPbaazrDPGt07NiRR48esX79embNmoWrqytDhw5FqVTqPFV2dnbG19eX0NBQ9u/fT1paGmXLlqVbt24MHDgwz4QvOzVq1ODTTz9l4cKFbN68GUtLS3r37s3o0aN1+l+4uLiwYsUKFi9ezM6dO4mLi8PZ2ZlBgwYxZMgQnYQkP2VzOnbGxsb897//5fPPP2fGjBm5jn5nqDfffJN58+axYMECli9fjrW1NW3btqVDhw58+OGHOp1n+/fvz5EjR1i3bh3x8fE4ODhQu3ZtBg0alGdH+OfZ22+/TWRkJH/99RdRUVGYmZlRsWJFvv76a7p06aItV79+fcaOHcvmzZuZPn06GRkZDBs2rEiSHch8X1TZsmXZunUrc+bMwcPDg0mTJvHvv/9y8eJFnXNhCBsbG5YvX86yZcsIDg4mODgYS0tLKleuTJ8+fbTlatWqxbJly/Dz82Pjxo3al4qOHTuW/v3766zT1dWVmTNnsnDhQhYvXoytrS2dOnXC19dX+66vgvroo4+IiYlhw4YNxMXFoVar2bZtmyQ7r5AmPSuyc2E4MRnOvHXmBgffqJrZRE6t5u3LV3n3/L9sbtKYz7qVLe1QxStGoX4Re1kK8YLL7o3tQhSVvXv38p///Ifvv/9e+14hUTo+/fRTTp48yYEDB6Szv3ippaWl0afnv3Q7fZNKDx9iZZJEWFkn3CMf4xwXhwpY9m4rpuzN+Z1uImffdArNu9D/fLcj92bwrxqp2RFCiBeUWq0mNTVVp9YgPT2d1atXY2RklGe/L1F0kpOT9Woir169ytGjR2nWrJkkOuKVkIaCU/XcMbmSQfMrlyibpU+qAkik6PotvmqkGVvBSbIjhHiuPXnyJM8+BRYWFtoO6K+S1NRUfHx86NChAx4eHsTExLBnzx6uXr3KwIEDcXJyMnhd8fHxeoNFPMvExKTQQ1RHRkbmWcbKyqpATRhL0/bt29mxYwdvvfUW9vb2hIWFsWXLFoyNjRkxYgSQmRBlHZAkJ/k5b0I8Tyo1COdxiCthTg40CDPFIvXpC0Kvl3fmfJXypRideFVJsiOEeK4NGDAgzxdEDhs2THtD+SoxNjbmrbfe4sCBA9okwsPDg//85z/06tUrX+uaOXMm27dvz7VMgwYNWLJkSYHjBQzqjzZ58mSdgTVeBDVq1GD//v2sW7eOmJgYLC0t8fLyYvjw4dpR7/bs2cPUqVPzXFdISEhxhytEsajlfJ/4eyacrlaRWIvm1Lt5E7cn0Vwu70y4jROOGVKzI0qe9NkRQjzXzpw5Q0pKSq5lXF1d9d5vIvLnxo0bPHr0KNcyNjY21KxZs1DbOX78eJ5lPD09X8rajcjISK5fv55nOUPfmyXE8yQtLY1ly5Zzb9VrnPVwZWtlV+1LS13jE/G4/5i3bZP572J5Z05BfN35lMFlpwc1KMZIXjxSsyOEeK7Vr1+/tEN4JVSpUoUqVaoU+3Ze5Rt5JyenlzKJEyKrWEtL9ro5axMdgLtWFthZJVK25qvX3LioSJ+dgpOXigohhBBCiEJTKOCehRnxpvpD6ycrFQweJH12RMmTZEcIIYQQQhSJCAsT3GOeGYhDraZGbAS2NtKgSJQ8SXaEEEIIIUSR6FDuFLEKBbZJmaM7GmWoKJOShqsqoZQje7GpFYZ/hC5JdoQQQgghRJFwrJFA08cRWKekYZmYTMWYWHpcP8v3S+VloqJ0SLIjhBBCCCGKzNZl1ZnfTk3rpEh8asCy9S1wspIX64rSIY0nhRBCCCFEkeri7UwXb+fSDkMISXaEEEIIIYR4nqlk6OkCk2ZsQgghhBBCiJeSJDtCCCGEEEKIl5I0YxNCCCGEEOI5ppZmbAUmyY4QQgghhCgSKjU0WQOnHqVjroTrw8DFWm43RemRZmxCCCGEEKLQ1GoYFTeIU48yayGSVFDBD7puSi/lyMSrTFJtIYQQQghRaFPivAH95lZbb4JarUYhTbEKTJqxFZzU7AghhBBCiEKLwCnHeRciM0owEiGekmRHCCGEEEIUK5XkOqKUSLIjhBBCCCGKVUJqaUcgXlXSZ0cIIYQQQhQBdY5zHieVYBgvIZX02SkwqdkRQgghhBBFIOcbchfrEgxDiCwk2RFCCCGEEMXK3KS0IxCvKkl2hBBCCCFEsar1W2lH8GJTKwz/CF2S7AghhBBCiCKQc58dgJT03OcLURwk2RHiFRUYGIiXlxchISGlHcpzT45V0fLx8WH48OE604YPH46Pj08pRSSEKBq5Vys8SJDxp0XJk2RHCFHi9u/fj5+fX2mHoSMkJAQ/Pz/i4uJKOxSRD5cvX8bPz4979+4VaPl79+7h5eXFjBkzcizj4+ND7969CxqiQQq7H0KUtoQ0yCvZuRldEpEIoUuSHSFEidu/fz/+/v6lHYaO0NBQ/P39s012OnXqxJEjR2jQoEEpRPZqWLBgAZs2bcr3cleuXMHf3/+FTxJelv0Qr67+2yGvZKfVhhIJ5aWkRmHwR+iSZEcIIfJgZGSEmZkZSqX8ySwuJiYmmJqalnYYQogC2nXbsHLBN9KLNxAhniEvFRXiFadWq1m5ciUbN27k4cOHuLi4MHjwYLy9vXXK/fHHH2zYsIGwsDCMjY2pXbs2w4YNo379+jrlDh8+TEBAANevXyc5ORk7Oztq1arFmDFj8PDwYPjw4Zw6dQoALy8v7XKTJ082uM/Go0ePWLVqFSdPnuT+/fukpKTg6upK586d+eCDDzAyMtIpn5aWxpo1a9i9eze3bt3C2NgYd3d3vL296dOnD1OmTGH79u0A+Pr6apcbNmwYI0aMIDAwkKlTp7J48WK8vLw4cuQIH3/8MRMmTKBv37568Q0aNIjw8HB27dqFsXHmn9nbt2/j7+/PiRMniImJoWzZsrRp04bhw4djbm5u0H5r+Pn54e/vz7p169i8eTN//fUX8fHxVK1aldGjR9OoUSOd8l5eXnh7e9O5c2cWLlzIlStXsLW1pXfv3nz44YfExsYye/ZsDh06RGJiIg0bNuSrr76ibNmy2nXExMTw66+/cvDgQR49eoS5uTkuLi60a9eOAQMG5Cv+7AwfPpz79+8TGBionXb9+nWWLFnCP//8Q3R0NDY2NlSqVIkPPviA5s2ba48DwMiRI7XLeXt7M2XKlELHlJdTp07x66+/8u+//5Kenk6lSpXo1asXXbt21SlX2P1ISEjgt99+4/jx49y5c4fExEScnZ1p3bo1w4YNo0yZMgCkpqbyzjvv0KZNG6ZOnapdz/fff8+WLVvo27cvEyZM0E7/4osvOHr0KHv37sXY2Njg31VwcDATJ07kq6++olu3bnrHpXfv3qSmprJlyxYUz+uLEMMjYZQfXL4L79SBeUPBLMvYyAf/hbWHwdYSRrSDSuXg6CVYfTBzfkoaHL8K8UlgbgqJqZCUChZmmd9T0yBdBZZmcDsSklIABahV6NV+qNWZ/fqNlKBUQIYKVC9eR/6fmrTlsx7D8iz37mYA/YRHCdQrC20qKRheV0lV++f02hEvHEl2hHjFLViwgJSUFLp3746pqSkbN25kypQpuLm5aROZuXPnEhAQwOuvv85HH31EYmIiW7ZsYcSIEfz88880b94cyGwKNn78eDw9PRk0aBBWVlZERkZy4sQJwsPD8fDwYPDgwajVak6fPs20adO0cdStW9fgmK9evUpwcDCtWrXCzc2N9PR0/v77b+bPn8/du3f56quvtGXT0tIYM2YMoaGhNGnShI4dO2Jqasq1a9cIDg6mT58+dO/enYSEBIKDgxk/fjx2dnYAVKtWLdvtN2nSBEdHR4KCgvSSndu3b3Pu3Dn69u2rTXQuXrzIyJEjsba2pnv37pQrV44rV66wdu1azp49y5IlS7Rl82Py5MkolUoGDBhAYmIimzdvZuzYscydO5fGjRvrlL18+TKHDh2iW7dudO7cmT179jB//nzMzMzYvn07FSpUYPjw4YSHh7Nu3TomT57MwoULtctPmjSJU6dO0aNHD6pVq0ZKSgo3b94kNDS0SJKdZ0VHRzNq1CgAevToQfny5YmOjubixYucP3+e5s2b8+677xIZGcmWLVsYNGgQlStXBsDNzS3f20tNTSU6OjrbeSqVSm/awYMHmThxIo6OjvTv3x8LCwv+/PNPpk+fzt27dxk9enSR7cejR4/YunUr7777Lh06dMDIyIhTp04REBDA5cuXmT9/PgCmpqbUrVtXbyCNkydPolQqOXnypHaaWq0mNDSU+vXra689Q39XLVq0wNHRkW3btuklO+fOnePGjRt89NFHz2+iE58E1cdkJicA1yLg4AW4NC/z+9rD0G9WZhIC4Pcn/Pd9+GjJ02kFon7mv8/IUMEL3H9fYVS4W0oVcPoRnH6kZvHZDE68b0QNx+f0GioFquf19/QCkGRHiFdcamoqAQEBmJhkPtVs3bo1Xbp0Yf369dSvX5+wsDBWrlxJvXr1WLx4sbZc165d6dWrFzNmzKBp06YYGRlx4MABVCoVCxYswMHBQbuNoUOHav+/SZMm7Nq1i9OnT9OpU6cCxdygQQO2bt2qczPVr18/vvnmG7Zu3cqIESNwcnICYM2aNYSGhjJo0CDtDaiG5ia2bt26VK1aVXujV6FChVy3b2RkRKdOnVi5ciU3btygSpUq2nlBQUEAOjVj06ZNw8nJiYCAACwtLbXTGzVqxMSJE9m5c2eBRiIzMjLi119/1Z4TX19fevbsyf/93/+xceNGnbLXrl1j+fLl1K5dG4AuXbrg7e3NL7/8Qu/evZk4caJO+TVr1hAWFkalSpWIj4/n5MmT9OzZk88//zzfcRbE2bNniYqK4ocffqBt27bZlqlWrRp169Zly5YtNG7cWKemML+2bt3K1q1bc5yf9RxnZGTw008/YW5uzm+//aatAevduzcjRozgt99+w8fHB3d39yLZD1dXV4KCgnQS4t69e7No0SKWLl3K+fPntefVy8uLkydPcvv2bdzd3YmIiODOnTt07NiRnTt38vjxYxwdHbl+/TpRUVE0bNhQu05Df1fGxsb4+vqyfPlyvet/69atGBkZPd8j6/3f1qeJjsblu3D+NtR2hx836yY1T+Jh2vpCJjovvzktOxfZuuJSYf5pFfPbGOVdWIg8SAN0IV5xvXr10t4sA5QrVw53d3fCw8MBOHDgAGq1mgEDBuiUK1u2LD4+Pty/f5/Lly8DYGVlBcC+fftITy++dtllypTR3pClpaURExNDdHQ0TZs2RaVSceHCBW3ZXbt2YWNjo5NwaRSmD07nzpn/sGuSG8h8Wr5z5048PT2pUaMGkJlkXL16lQ4dOpCWlkZ0dLT2U79+fczNzTl27FiBYujXr5/OOXF2dqZDhw6EhYVx8+ZNnbJ16tTR3hBDZh+Z119/HbVarVc79cYbbwBorwEzMzNMTU05f/58iXWg11xLR48eJT4+vti39/bbb7NgwYJsP46OjjplL168SEREBL6+vjpN/UxMTBgwYAAqlYoDBw4U2X6YmJhoE5309HRiY2OJjo7WNlc8f/68tqwmedHU4pw8eRIjIyNGjBiBQqHQTtfU/mRNrPLzu+ratSsKhUInQUxKSmLPnj00a9ZM57iUtqioKFJSUrTfU+9FZl/w8f8GJ4lO0Julik8qjtBeKmmFrNl5VvTTU8b9+/d15j37PSIiAnWWZPTZcx4fH68z+ExqaiqPHz/OdZ2lsQ1RPKRmR4hXnKurq940W1tbIiIiALQ3t56ennrlNNPu3r1LrVq16N27NwcOHODHH39k3rx51KtXj2bNmtG+fXvs7e2LLOb09HRWrFjBjh07CA8P1/kHCCA2Nlb7/7dv36Z69eqYmZkV2fYBqlatSo0aNdi1axejR49GqVRy6tQp7t27x7hx47TlNEmHn59fjsNtR0VFFSgGTXOnrDRP2e/evaszP7vzbGNjA6BXk2VtbQ1k9tOBzJvt8ePH8/PPP+Pr60uVKlXw8vKiVatWev2Disqbb75J586dCQwMZOfOndSqVYvGjRvTtm1bnZqEolKuXDm9pn8azw6coPlNZBdH1t8EFN1+bNiwgU2bNnHjxg29ZnVZb7Bq1aqFpaUlISEh9OjRg5MnT1KzZk3c3NyoWrUqISEhdOjQgZMnT2Jra0v16tW1y+bnd+Xq6kqjRo3YsWMHY8eOxdjYmD179pCQkECXLl0M3q+SkLWWGcB0Ynf4dZ9uIasy0KJm5v+/1yKzdicLZfcm8Nv+Yozyxdfh0mmWNW5dZOt7r8bTGkYXFxedec9+L1++vM73Z8+55qGDhqmpqd5DjOdhG7lRSzO2ApNkR4hXXE61G8/e6BjCzs6OgIAATp8+zfHjxzl9+jS//PILfn5+zJkzJ1/9cnIza9Ys1q1bR9u2bRk8eDD29vYYGxtz6dIl5s2bV6DYC6Jz5878/PPPnDx5ksaNGxMUFKRt4qahiaV///40bdo02/Voko7i9OygDYbMy3oce/bsSatWrTh8+DChoaHs3buX9evX07ZtW3744Ycijxdg6tSpfPDBBxw9epTTp0+zatUqli1bxvjx4+nTp0+xbLM4FHY/Vq1axezZs2nSpAl9+/bFyckJExMTHj16xJQpU3SSH2NjY+rXr09ISAhqtZqQkBBtLaSXlxcHDx5EpVJx6tQpvLy8dJqs5fd31a1bNyZNmsSBAwdo3bo1W7duxdHRUduH77n1WgVYMQY+XQ4xieDuBH9MAs3fwmn/q+lcfRDsLGFSN+j9FlR0goD9mV1uUlLhcTyoVKBQPB1kQHM4NYdKQY5ddF42PwT+xrJG72YejwJSAtUdYEJDJZ09pfGRKBqS7AghcqWpEbh+/bpex+8bN27olIHMG2cvLy9t85irV6/Sv39/li5dypw5cwAK3XF5x44dNGjQQO8mW9PsKisPDw/CwsJITU3NdWjjgsTUoUMH5syZQ1BQEPXq1WPv3r00btxY218IwN3dHchMKnOqOSiomzdv8tprr+lMy+6cFBUnJye6du1K165dycjI4Ntvv2X37t3079+f119/vci3B5k1aFWrVmXAgAHExcUxcOBA5s+fT+/evVEoFKXSCV5zbDXHOqucjn9h9mPHjh1UqFCBuXPn6jycOHr0aLblGzZsyJEjR9i7dy8PHz7UNm1r1KgRv//+O/v27SMuLk6nv45mO4b+rgBatWqFg4MDW7duxdPTk7NnzzJw4MACDbZR4ga+m/nJjokx/NA/85PVd/0yPy+S1LTM/YHMxOxhdOZn7k5o5AnWFpCRAbcfwZNESEwGFzsIuQlxydC6FvRvBelq+OMkpCTD7tNw9wmkp4ONBfynO7zfiiq/pIHKsN/jyg7Qs4aCMsbSJ0cUvxfgL5IQojS1bNmSefPmsXLlSt566y3tjUxkZCSBgYG4uLhom8JER0drRzLTqFSpEmXKlNFpAqMZajkmJgZbW9t8x6RUKvWeMiclJbFmzRq9sh06dGDu3LksXbpUOyqWhlqt1t5kWlhYAJlNdfIaoEDD3t6eZs2aERwcTIMGDUhISNA+RdeoXr06np6ebNq0ie7du+sljOnp6SQkJBToOKxZs4Z3331X22/nwYMH7N69Gw8Pj2ybuBVUcnIygHaIY8hMaqtVq8bu3bt1zm1RiYmJwdraWufm3traGldXV8LDw0lJSaFMmTLaa6k4YshJjRo1KF++PIGBgQwYMECb3Kanp7Ny5UoUCgVvv/12ke2HkZERCoVC55rXNDnLjuZBg5+fH6amptSrVw/I7ItlZGTEkiVLAPSSnfz8riCzFsnb25tVq1Zph85+3pqwvfJMswynbWQELo6Zn6Vj8r+uz/43LP+XvbOd3dAZ9hvQBUU9QW49RcmSK04IkSvN+0ACAgIYNmwYbdu21Q49nZiYyHfffadtBjV9+nQePnxI48aNcXFxISUlRduOP2sSUKdOHdavX8+PP/5I8+bNte/tMbQ2onXr1mzevJkvvviCRo0a8fjxYwIDA7NNGN577z0OHTrE0qVLuXDhAo0bN8bMzIwbN25w69Yt7fDKms77c+fO1Q5P7enpSdWqVXONxdvbm4MHDzJr1iysrKxo1aqVznyFQsG0adMYNWoU7733nrbPS3JyMnfu3GHfvn2MGTOmQKNXZWRkMHToUNq3b09iYiKbNm0iJSVFb2S1wrp16xbDhw/nnXfewdPTE2tra8LCwti4cSOurq7aAQ2KUlBQEGvWrOGdd97Bzc0NY2NjTp06xd9//03btm21idfrr7+OUqlk2bJlxMbGYm5ujqurq85gDEXNyMiIzz//nIkTJzJw4EC6deuGhYUFe/bs4dy5cwwaNEhbo1cU+9G6dWvmz5/PuHHjeOedd0hISGD37t051qBUr14dW1tbbt68yZtvvqntr2ZlZUXNmjU5f/48Tk5Oeglxfn5XGt26dWPlypXs3r2bBg0aaPdbvHo2dwWHRVnb8um7NKjEwnnpSJ+dgpNkRwiRp3HjxlGxYkU2bNjA/PnztSN5TZ8+XedGt1OnTgQGBhIUFMSTJ0+wtLSkSpUqzJgxg9atn3Zcbd++PZcvX+bPP/9k7969qFQqJk+ebHCyM378eCwtLdmzZw8HDhzA2dmZbt26UatWLT766COdsiYmJsyfP59Vq1axe/duFi5ciKmpKe7u7joJRv369Rk7diybN29m+vTpZGRkMGzYsDyTnRYtWmBra0tMTAxdu3bNdiCE6tWrs3r1apYvX87BgwfZtGkTlpaWuLi44OPjo/eE3VBTp05l06ZN/Pbbb8TFxVG1alUmT55MkyZNCrS+nDg7O+Pr60toaCj79+8nLS2NsmXL0q1bNwYOHKhT41NU3nzzTe27gSIjIzEyMqJChQp88skn9O799Mly+fLl+fbbb/ntt9/48ccfSU9Px9vbu1iTHcis8Vy4cCFLly5l5cqVpKWlUalSJb7++mudl4oWxX588MEHqNVqtm7dys8//4yjoyNt27bF19eXXr166cWmUCho0KABwcHBetdWw4YNOX/+fLbDdOfnd6VRsWJF7XDXUqvzarMyBd2OS/rszOSGXZQ8hbqkevIKIYQoEn5+fvj7+7Nt2zaDm9wJUVzGjRvHuXPn2LlzZ7EkvuLFkJaWhukcFZBzP5z4cUosTWXggYIY1+uiwWXnbqhZjJG8eOSKE0IIIUSBhIeHc+zYMTp27CiJjiC3Wh1AEp1CUCkM/whd0oxNCPFcSE5ONuili1lHOnuZvOj7/+TJEzIyMnItY2FhoR0IojhlZGTw5MmTPMvZ2trqvJRVGO78+fPcvHmTtWvXYmJiQv/+/fNeSLwCcm4sFD6kBMMQIgtJdoQQz4U9e/YwderUPMtp3vz+snnR93/AgAF5vg182LBhjBgxothjefDgAb6+vnmWW7x4cbZ9V0TeNm7cSFBQEK6urnz33XfSnFLkKa20AxCvLOmzI4R4LkRGRnL9+vU8yxX1u2qeFy/6/p85c4aUlJRcy7i6uuoNvV0cUlJSOHPmTJ7latasWSIvdBXiVZBXn52HI6GslTxjL6jRfS4ZXHbBuhrFGMmLR646IcRzwcnJ6bltolUSXvT9r1+/fmmHoGVmZvbcJoVCvKpuxUFZq9KOQryKpKeYEEIIIYQoAjk3FkqTdmyilEiyI4QQQgghikDOQ4GZyVggopRIsiOEEEIIIQrNkzs5zqtTVsZELgwVCoM/QpckO0IIIYQQotA+t9lLTk3ZTIxzftmoEMVJkh0hhBBCCFEk5lquIGvCY66EjM8k0RGlR0ZjE0IIIYQQRcLMSE3qx2BiIreYRUmtkOZpBSU1O0IIIYQQQoiXkiQ7QgghhBBCiJeSJDtCCCGEEEKIl5I0qBRCCCGEEOI5ppIuOwUmNTtCCCGEECL/rtyD6/dLOwohciU1O0IIIYQQwnDf/g7fbXj63VgJl+eVXjxC5EJqdoQQQgghhGH2nNZNdADSVRhXHQ2q7F8oKgpPpVAY/BG6JNkRQgghhBCGafddtpMVami87kIJByNE3iTZEUIIIYQQhVZnf3hphyCEHkl2hBBCCCFEoeXWgOpGtIrl5zJ4mJBRYvG8TNQKhcEfoUsGKBBCCCGEEHk7fDHX2QrA+kGCzjSVWo3rwgwikp5Oc7VM584ouQUVJUNqdoQQQgghRN5afJVnkdf/uqnz/b9HdRMdgLsJUNkvvSgjEyJHkuwIIYQQQojcZRjW/MwkIVXn+zd/Z18uLA5S01WFjUqIPEmyI4QQQgghcmfgqNIqI8P7jHyyV5IdQ6kUhn+ELkl2hBBCCCFE7hpNMKiYx+kHBq9y0bmCBiOE4STZEUIIIYQQuTt9y6BilmlP//9eXN41N1ejpO+OKF6S7AghXkmBgYF4eXkREhJS2qE8V7y8vJgyZYrONJVKhZ+fH126dKFx48Z4eXkBMGXKFO3/lxY/Pz+8vLy4d+9eqcYhxEstOTXvMtk4eCfvZKfusgKt+pWjRmHwR+iScf+EEELkavv27fj7++Pr60uDBg1QKkv2Odn+/fu5fPkyI0aMKNHtCsOFhIQQGhpKv379sLa2Lu1wRFHr+3OBFhu5O+8yyWQOVGBqLM/fRfGQK0sIIYTWkSNH+Prrr3WmHT9+HCsrK7755hu8vb3p1KkTAF9//TVHjhwp9pj279+Pv79/tvOGDBnCkSNHcHFxKfY4RM5CQ0Px9/cnLi6utEMRxWHryQItFmNgCzWz2SqCb6WjVhs4CoIQ+SA1O0II8YpLTk7G2NgYY2NjzMzM9OY/fvwYa2trFM+8mVuzTGl6HmIwREJCApaWlqUdhhC5u/kAev4El+9BQkqBVqEAFN9t4MYnvfO13LsbAHSHty6jBGcL8CoPl59AcgaMrg8fv2mk9/fo3CM1JyLUeDkrqFeuYE25ktPVbL+uJkMN3lUUWJo+P03CVIrnJ5YXzfP/L4QQQhQjtVrNypUr2bhxIw8fPsTFxYXBgwfj7e2tLfPnn3+yc+dOrly5QlRUFBYWFtSvX5+RI0dSrVo1nfWdPXuWpUuXcvnyZeLi4rC1taVatWoMGzaMOnXqGBTTkSNH+Pjjj5kwYQJ9+/bVmz9o0CDCw8PZtWuX9kb/9u3b+Pv7c+LECWJiYihbtixt2rRh+PDhmJuba5edMmUK27dvZ8+ePcydO5cjR47w5MkTtm7dSoUKFfDy8sLb25spU6YQEhLCyJEjtctq+udo5mvW9Wy/p8jISJYvX87hw4d5+PAhVlZWVKtWjQEDBtCkSRMAzp8/z8aNG/nnn3948OABRkZGVK1alQ8++IB33nlHu67hw4dz6tQpne0DTJ48GR8fH/z8/PD392fbtm1UqFBBO//evXssWrSI48ePExcXR7ly5WjXrh1DhgyhTJky2nKa5Tdu3EhQUBBBQUE8efKESpUqMXr0aJo3b27QOctKcww7deqEn58fV65coWbNmixZsgSACxcusGzZMk6fPk1iYiIuLi507tyZgQMH6iVu+/fvZ8mSJYSFhWFvb4+3tzdvvPEGY8aM0R6DrPvx7HEA8PHxwcXFRbt9jePHjxMQEMC///5Lamoq7u7u9OzZk549e+qUy+ua1lwHAL6+vtrlhg0bJk0PXyRHL8Jbeb801BDG322kZcY7YF+2UOtJVsGteLh17em0T/fDr+cyOD/o6W9lypEMpv79tFboi8YK/tvCKF/buhunpvnvGYTFZn53sYSDfY2oai9JxotOkh0hxCttwYIFpKSk0L17d0xNTdm4cSNTpkzBzc2N+vXrA7B+/XpsbW3p1q0bTk5O3Llzhy1btjBkyBBWrVqFu7s7AGFhYYwePRpHR0f69u2Lg4MDUVFRnDlzhitXrhic7DRp0gRHR0eCgoL0kp3bt29z7tw5+vbtq70xvnjxIiNHjsTa2pru3btTrlw5rly5wtq1azl79ixLlizRu4nWxDlkyBCSkpKwsLDQi6Ny5cpMmzaNZcuWER0dzfjx4wFwc3PLMfZ79+4xZMgQoqKi6NSpE7Vq1SIpKYlz585x4sQJbbKzf/9+wsLCaNOmDS4uLsTExLB9+3YmTpzI9OnT6dChAwCDBw9GrVZz+vRppk2bpt1O3bp1c4zh/v37DBw4kPj4eHr27Im7uzuhoaEsX76cs2fPsnDhQr3jMWXKFIyNjenfvz9paWn8/vvvTJgwgc2bN+slD4a4cOEC+/bto2vXrjqJ8+HDh5k4cSIVK1akf//+2NjYcO7cOW1SNGPGDG3Z4OBgPv/8cypUqMDQoUMxMjIiMDCQw4cP5zueZ23evJkffviBOnXqMHjwYMzNzTl+/Dg//vgjd+/e5eOPPwYMu6a7d+9OQkICwcHBjB8/Hjs7OwC9BwHiOdf9pyJdnWtMFHcLmezk5N/HcOC2irfdldyNUzP9mG7ztxkn1Iyoq8bD1vBE5aeTKm2iA3A/Ab77W8VvnfKXNInnjyQ7QohXWmpqKgEBAZiYmADQunVrunTpwvr167XJzrx583RqRwA6d+5Mv379WLNmDZMmTQLg2LFjJCcn8/3331O7du0Cx2RkZESnTp1YuXIlN27coEqVKtp5QUFBADo30NOmTcPJyYmAgACdplKNGjVi4sSJ7Ny5U1sDoOHp6cl3332XaxyOjo506tSJP/74g5SUFG1fndz8+OOPPHr0iHnz5tG0aVOdeSrV05GZhgwZwpgxY3Tm9+3bl379+rF06VJtstOkSRN27drF6dOnDdo+ZCawT548Yfbs2dqamV69ejFnzhxWrlzJ9u3b6dq1q84ydnZ2zJo1S9s0xsvLi4EDB7J582a9OA1x48YNFixYQOPGjbXTUlJS+O6776hduzaLFi3SJlw9evSgWrVqzJo1i5CQELy8vMjIyGDmzJnY2Njw22+/aROIHj16ZFvblx+RkZHMnDmTdu3a8f3332un9+rVi5kzZ7J69Wp69OiBm5ubQdd03bp1qVq1KsHBwbRq1apAyWFxioqKwtLSUttEMz4+HrVarR1IITU1lbi4OBwdHbXL3L9/X6cf2LPfIyIicHZ21l4vL8U2niTk46jmrd79W5yoVL1I15nViQh42x2uRUPGM119VGo4/zAVD1vDj9WZeymAqc56LkY9XXFJnA9RPGSAAiHEK61Xr17aRAegXLlyuLu7Ex4erp2mSXTUajXx8fFER0djb2+Ph4cH58+f15azsrIC4MCBA6SkFKy9u0bnzp2Bp8mNZvs7d+7E09OTGjVqAHDt2jWuXr1Khw4dSEtLIzo6WvupX78+5ubmHDt2TG/9/fv3L1R82YmJieHvv/+mWbNmeokOoDOKW9bkMTk5mejoaJKTk2nYsCE3b94kPj6+QDGoVCoOHjxI9erV9ZqgffjhhyiVSvbv36+3XN++fXX6ALz++utYWFhw+/btAsXx2muv6SQ6kNls7PHjx/j4+GivI83nrbfe0paBzNq6Bw8e4Ovrq010IPMa69GjR4Fi0vjrr79ITU2lS5cuOjFER0fTokULVCoVJ06c0G4PiuaaLi0ODg46fdGsrKx0RowzNTXVuSEF9G5An/1evnx5nevlpdhGgyoUFTXwt/trRba+7Ax4PfO/bzqDjW6OgpUJtHB/OtGQY9WpWhme9a770+NfEucjNyqFwuCP0CU1O0KIV5qrq6veNFtbWyIiIrTfL126xOLFiwkNDSUpKSnH5du1a8eOHTtYvnw5a9asoU6dOjRp0oT27dvn++ld1apVqVGjBrt27WL06NEolUpOnTrFvXv3GDdunLbczZs3gcw+G35+ftmuKyoqSm+ah4dHvuIxRHh4OGq1murV836aGxUVxaJFizhw4EC28cXHx2tvtPPjyZMnJCYm6tSGadja2uLk5MTdu3f15mXXNM/W1paYmJh8xwBomzZmpTlXWZvjPevx48cA2hizO0+VK1cuUEwaYWFhAHz00Uc5ltGck6K8psVzbs9kqDAE4pILvSo18Ngy/79fQ333FjhbZj48sTJV8Lu3kiG7VUQkZA5osKSdEhuz/N30f/KmgjOPFKy/rEatBh9PBV83kTqBl4EkO0KIV1pO74zRDIEaERHB8OHDsbS0ZMiQIVSqVIkyZcqgUCj4+eefdZIfU1NTFi5cyPnz5zl27BinTp3SdhyfPn26Tsd7Q3Tu3Jmff/6ZkydP0rhxY4KCgrRN3J6Ns3///tnWpgDY2NjoTcvaSb+kqdVqxowZw82bN+nbty+1atXCysoKpVJJYGAgu3bt0mnyVhLyug7yK7vjq1nXxx9/zGuvZf/Uu2zZgvVxeHZkqqwyMnRHuNLEMXXqVJycnLJdRpPEF/U1LZ5jVuYQuwZOXYer9+HCbThyCfaez3vZLNRAxumfuFe/PIqZBo49nUUFc2jiAlZloI4juNtCdQclqFVEJkNLNyUmRrq/105VlNweriA8Dipag4lR/ms3zIwV/O5txNx31ajU4GwpNSQvC0l2hBAiF8HBwSQmJvLLL7/ojAYGmc22TE1N9ZapXbu2tn9DREQE77//PosWLcr3jWGHDh2YM2cOQUFB1KtXj71799K4cWOdG1RNDYJSqdRrNlXSKlasiEKh4PLly7mWu3r1KleuXMl2tK4//vhDr3xuN/LPsre3x9LSkhs3bujNi42NJTIyMsdEo7hpzpW5uXme50qTbNy6dUtvnqaGKCtNQhsbG6vTZyYlJYXIyEidmquKFSsCmf2UDL1m8rqm83OOxHOugWfmJytF9/yt4/X81RzbGcOTT/K6Jc29lsXESEEVu3xtNltlLZ7Pa1n1fIb1QpD6OSGEyIXmif+zT/i3bNmibXKkER0drbe8s7Mz9vb2BWoOZW9vT7NmzQgODmbXrl0kJCRo+/JoVK9eHU9PTzZt2sSdO3f01pGenl7gplj5ZWtrS7NmzTh69Ki270lWmmOY0zG9du1atv1pNP17DNkPpVJJixYtuHz5MkePHtWZt2LFClQqFa1atTJkd4pc06ZNcXBwYMWKFdnuS3JyMgkJmZ3Ea9asibOzM9u2bdO5ruLj49m0aZPesprmbs8e9zVr1ujVkrVt2xZTU1P8/PxITtZvshQfH09qaipg+DWtGc0vNjZWr7x4Cfw8qECLtTJwrIq8Ex0hCk6uLiGEyMVbb73FvHnz+Pbbb+nduzfW1tacPXuWo0eP4ubmptNEaOnSpRw7dozmzZvj6uqKWq3m0KFDhIWFMWDAgAJt39vbm4MHDzJr1iysrKz0btQVCgXTpk1j1KhRvPfee/j6+lKlShWSk5O5c+cO+/btY8yYMXqjsRWXzz//nMGDBzNu3Di8vb2pWbMmycnJ/Pvvv7i4uDBu3DgqV65MlSpVCAgIIDk5GQ8PD27fvs3mzZupWrUqFy9e1FlnnTp1WL9+PT/++CPNmzfH2NiY2rVrZ9vfCjKH1T5+/DgTJkygZ8+eVKxYkVOnTrFnzx4aNGigM5JdSTI3N2fq1KlMmDCBHj164OvrS8WKFYmLiyMsLIzg4GD+7//+Dy8vL4yMjPj000/54osvGDhwIF27dsXIyIht27bp9SmDzJH3PDw88PPzIyYmhgoVKnD27FnOnTunM8ABZCYrkyZNYvr06fTq1YtOnTrh4uLCkydPtAnnhg0bqFChgsHXtKbWZ+7cuXTs2BFTU1M8PT2pWrVqsR9XUQLG+8Bny/O92HfNocX63Mv0LZ2KVvEKkWRHCCFy4ebmxty5c1mwYAHLly9HqVRSr149/Pz8+Omnn7h//7627Ntvv01kZCR//fUXUVFRmJmZUbFiRb7++mu6dOlSoO23aNFC21G+a9euOqP/aFSvXp3Vq1ezfPlyDh48yKZNm7C0tMTFxQUfHx8aNmxY4P3PL1dXV1auXMmvv/7KkSNHCAoKwsbGhmrVqtGtWzcgc2jtOXPmMHv2bLZv305SUhKenp5MmTKFK1eu6CU77du35/Lly/z555/s3bsXlUrF5MmTc0x2XFxcWLFiBYsXL2bnzp3ExcXh7OzMoEGDGDJkiN47dkpS06ZN+e233/jtt9/YuXMnT548wcbGBjc3N95//32dd9O0adMGpVLJr7/+ypIlS3BwcNB5qWhWRkZG/PLLL8ycOZN169ZhYmJCkyZNWLJkCUOGDNGLw9fXF3d3d1atWsXmzZuJi4vDzs4ODw8PRo0apR1FytBrun79+owdO5bNmzczffp0MjIyGDZsmCQ7LxMTI0jLyLNY1vra5u7GQO79dpZ2lEZGongp1AXtfSmEEEKIEhcSEsLIkSOZPHlyidXYCcHNCKiS8wh+GlF2xlg/XK0d0j+vQQrUE+S5uyH6DgwzuOza3yoVWxwvIkmnhRBCCCFE7iqXN6jYjYaGv1R2SMHfvSyEwSSdFkKIEpKWlmZQJ3t7e3uMjIxKICJhqCdPnugN4fwsCwsLbUd9IV5Vxql5N3XT+LWD3IaK4idXmRBClJCzZ88ycuTIPMtt27ZNZ/hgUfoGDBig0z8rO9kNpS3ES0WBbqecbNyuW47Xs3xf2wn67tAvt71rEcb1ClDL8O4FJsmOEEKUkNdee40FCxbkWU7TOVw8P7777jtSUlJyLZPTgAlFzcvLi5CQkBLZlhA6YlaBTf8cZ6uBB7XK6UzrU8uY4PB0/M49nTbnHehcVW5BRcmQK00IIUqIjY1Nqb/4UxRM/fr1SzsEIUqfde7NNNWAWqlfA7G4vTGL2xdTTELkQZIdIYQQQghRaEmmpR3By0slrdgKTEZjE0IIIYQQhaIG1v/YurTDEEKPJDtCCCGEEMIwp3/OdrKqqjNpliYlHIwQeZNkRwghhBBCGKZ+ZbgwJ3NkNo329VBdmFtqIQmRG+mzI4QQQgghDFezIqg2605LSyudWF4RKhl6usCkZkcIIYQQQgjxUpJkRwghhBBCCPFSkmZsQgghhBBCPMdUSDO2gpKaHSGEEEIIIcRLSZIdIYQQQgghxEtJmrEJIYQQQogiE5sCFeenk5SR+f01O7g8VG45RemQmh0hhBBCCFFknBajTXQArkTDa/7ppRbPyyBDYfhH6JJkRwghhBBCFImvYrtkO/1qTAkHIsT/SLIjhBBCCCGKRCQOICOHieeIJDtCCCGEEKLYrTwvTdkKSqVQGPwRuiTZEUIIIYQQhXYiyT3X+Z/sK6FAhMhCkh0hhBBCCFFoAWlvkVsTtqjUkotFCA0ZB1AIIYQQQhRaGmalHcJLSyWt0wpManaEEEIIIUQRyPuOXKVWl0AcQjwlyY4QQgghhCgRO69l5F1IiCIkyY4QQgghhCgR/YJKOwLxqpFkRwjxygoMDMTLy4uQkJDSDkUIIV5oK86DIc3YYmX06QJRoTD4I3RJsiOEEKVg//79+Pn5lXYYL7Q1a9YQGBhY4OUDAwNZs2ZNEUZU8kJCQvDy8mLlypU5lvHy8uKTTz4p9jj8/PyIi4sr1u2I59fWa6UdgRDZk2RHCCFKwf79+/H39y/tMF5ov//+e6GTnd9//70II3p1hYaG4u/vL8nOy+BmBPy8BdyHgUVvUHTP/uM+DG7c1y5244nhm0hNl347ouTI0NNCCCGEEC+6mAS4/wReqwDJqTBrO9SqCKZGcO0+HLsKdpaQmJyZ0Jy+lfn/qgJuL/wxeI7Wfv3i9YYMGDgBFHk3ozKbrWZB63Sq2Clo7a7ExEiaXuUlw4DjKrInyY4Q4pWnVqtZuXIlGzdu5OHDh7i4uDB48GC8vb11yv3xxx9s2LCBsLAwjI2NqV27NsOGDaN+/fo65Q4fPkxAQADXr18nOTkZOzs7atWqxZgxY/Dw8GD48OGcOnUKyGxipDF58mR8fHwMivnRo0esWrWKkydPcv/+fVJSUnB1daVz58588MEHGBkZ6ZRPS0tjzZo17N69m1u3bmFsbIy7uzve3t706dNHWy4+Pp7ffvuN4OBg7t27h7m5OZUqVaJ37960b99eW+7q1av4+flx+vRpkpKScHV1xdvbm/79++tse/jw4dy/f1+vBubevXv4+voybNgwRowYAWQ2hRo5ciSTJ09GrVazatUqwsPDcXR0pFevXgwcOFC7vOa43b9/X+cYbtu2jQoVKuR5/Hx8fLh//77OugAWL17MmjVrOH78OLt378bKykpnuX///ZeBAwcyYsQIhg0bprMfHh4erFixgtu3b2Nvb4+vry9DhgzB2Fj3n9rIyEj8/f05fPgwjx8/xs7OjhYtWjBq1CgcHBzyjL2o7N+/n4CAAK5cuYJCoaBatWoMGDCAVq1a6ZQ7e/YsS5cu5fLly8TFxWFra0u1atUYNmwYderUYcqUKWzfvh0AX19f7XJZz60oZj9sgu82QFIq2JhDbFKJhxBr62BQoqMxei+AGmNFBoHdlXSoLI2NRPGQZEcI8cpbsGABKSkpdO/eHVNTUzZu3MiUKVNwc3PTJjJz584lICCA119/nY8++ojExES2bNnCiBEj+Pnnn2nevDmQ2Zxn/PjxeHp6MmjQIKysrIiMjOTEiROEh4fj4eHB4MGDUavVnD59mmnTpmnjqFu3rsExX716leDgYFq1aoWbmxvp6en8/fffzJ8/n7t37/LVV19py6alpTFmzBhCQ0Np0qQJHTt2xNTUlGvXrhEcHKxNduLi4hgyZAg3btygdevW9OzZk4yMDC5fvszhw4e1yc6FCxcYPnw4xsbG9OrVC0dHRw4dOsS8efO4evUq06dPL9T52LRpE1FRUfj6+mJtbc3OnTuZN28ezs7OdOjQAYBp06bxyy+/YGdnx+DBg7XL2tvbG7SNzz77jPnz5xMdHc348eO10ytXrky3bt04ePAgu3fvpkePHjrLbd26FaVSqXNTD3Dw4EHu3r2rPR4HDx7E39+fiIgIJk+erC0XERHBoEGDSEtLo0uXLri5uREeHs6mTZsICQlh5cqVegmWIZKTk4mOjja4/IYNG5gxYwaVKlVi6NChAGzfvp0JEybw5Zdf0r17dwDCwsIYPXo0jo6O9O3bFwcHB6Kiojhz5gxXrlyhTp06dO/enYSEBIKDgxk/fjx2dnYAVKtWLd/7IQog5Bp8ufrp91JIdADOO7sVaLl0NfTcpuLJGIXU8IhiIcmOEOKVl5qaSkBAACYmJgC0bt2aLl26sH79eurXr09YWBgrV66kXr16LF68WFuua9eu9OrVixkzZtC0aVOMjIw4cOAAKpWKBQsW6Dyl19xQAjRp0oRdu3Zx+vRpOnXqVKCYGzRowNatW1FkeZLar18/vvnmG7Zu3cqIESNwcnICMjvyh4aGMmjQIEaPHq2zHpXqaRuWBQsWcOPGDZ2b3ezKzZw5k7S0NJYvX669oe3Tpw9ffPEFu3btwtfXl0aNGhVovyAzIdi4caP2pr9Lly54e3uzbt06bbLTqVMnFi1ahIODQ4GOYatWrVizZg0pKSl6yzdr1gxnZ2e2bt2qk+wkJyeze/dumjRpgrOzs84yV69eJSAggBo1agCZx2PixIkEBgbSvXt36tSpA8BPP/1Eeno6q1ev1llHmzZtGDRoEKtXry5QbYifn5/BA17ExsYyd+5c3NzcWLFihfY49+zZk/fff5/Zs2fTtm1brK2tOXbsGMnJyXz//ffUrl072/XVrVuXqlWrapNvQ2rWRBE68G9pRwBAzYd3C7xsQhpceQKvOxVhQC8ZleSBBSZ1hkKIV16vXr20CQxAuXLlcHd3Jzw8HIADBw6gVqsZMGCATrmyZctqm0NdvnwZQHvjuG/fPtLTi2+M1TJlymgTnbS0NGJiYoiOjqZp06aoVCouXLigLbtr1y5sbGx0Ei4NpTLznwGVSsWff/5J5cqV9RKdrOWioqL4559/aNmypc6Te4VCoa1hCQ4OLtS++fj46NRulClThjp16nD79u1CrddQRkZG+Pr6cuHCBa5dezrE1F9//UVCQgJdunTRW6Zx48baRAcyj8eAAQOAp8cjPj6ew4cP07JlS8zMzIiOjtZ+KlSogJubG8ePHy9QzN26dWPBggXZfp51/PhxkpKS6Nu3r85xtrKyom/fviQmJmrj0Mw/cOAAKSkpBYqttEVFRenEHh8frzOQQmpqKo8fP9ZZRtPEMafvERERqNXq52Mbr1fkedDo9jWMMwr2N89YocbdJvP/X/jzUYhtiOIhNTtCiFeeq6ur3jRbW1siIiKAzP4lAJ6ennrlNNPu3r1LrVq16N27NwcOHODHH39k3rx51KtXj2bNmtG+fXuDm1gZIj09nRUrVrBjxw7Cw8N1/hGGzKf3Grdv36Z69eqYmZnluL7o6GhiY2Np2rRprtvVHIsqVarozatcuTJKpZK7dwv+hBdyPh8xMTGFWm9+dOnShWXLlrF161Y+++wzILM/kIODA2+//bZe+UqVKulN0xwjzfEICwtDpVKxdetWtm7dmu12s9t3Q7i7u9O4cWODymriye4cPhtzu3bt2LFjB8uXL2fNmjXUqVOHJk2a0L59e1xcXAoUa0l7th/Us80ETU1NcXR01Jn27L49+718+fLPzzbavwE9m8LGvylNTcKv8fe8r+gw9EseW9nma9kfWiixNs18ePPCn49CbEMUD0l2hBCvPE2txbOeTSAMYWdnR0BAAKdPn+b48eOcPn2aX375BT8/P+bMmZOvfjm5mTVrFuvWraNt27YMHjwYe3t7jI2NuXTpEvPmzStQ7MVBkUOH5YyMnIeefXZwhdJQvnx5mjZtyo4dOxg3bhz379/n1KlTfPDBB3oDDuRXx44d9Qa/0MgtIS0NpqamLFy4kPPnz3Ps2DFOnTqFn58f/v7+TJ8+nXfeeae0QxQKBWyYCMcuw40H8G6dzKZt/90ETjZQ1gYuhMODGFBlZI7UlpAKRfwnItbEjME9Rxic6FS1gcYVYHIzJdUcpKGRKD6S7AghRB40T9uvX7+Om5tuJ9wbN27olIHMm3UvLy/tKF9Xr16lf//+LF26lDlz5gA5JwGG2rFjBw0aNOCHH37Qma5pepeVh4cHYWFhpKamYmpqmu367OzssLGx4erVq7luV9MfQ7PfWWlqLrIeCxsbGy5duqRXtrC1P1D4Y5jX8t26dePw4cPs379f20wxuyZskLnvz3r22nBzc0OhUJCenm5wLUxx0FzDN27c0OtbdfPmTUC/hql27draPjsRERG8//77LFq0SJvsFPZciCLQpHrmB6BP88xPfkXFwc9b4dc/4WF83uUHvg2/DAYHayrNTuNJumHXQdRosDeXW9D8yEB+YwUlqbQQQuShZcuWKBQKVq5cqdMPJzIyksDAQFxcXKhePfMmI7sRsSpVqkSZMmV0mpaZm5sDFLhpllKp1Ku9SUpKYs2aNXplO3ToQGxsLEuXLtWbp1mHUqmkffv23Lhxgz/++CPHcg4ODtStW5eDBw/q9GdRq9UsX74cQOdpv4eHBwkJCZw/f147TaVSZRtnfpmbm+sc0/yysLAgNjY2x1qw5s2bU7ZsWTZv3sz27dupV69ets3VILMfTNakTq1WExAQAKAdytnOzo633nqLffv2ce7cOb11qNVqnjzJx5sZC6hx48aYm5uzbt06EhIStNMTEhJYt24dFhYWNGnSBMj+enZ2dsbe3l7n2rWwsAAo1PkQzwEHa/i+PzwIAPXmvD8rPs5cBrAuY/hmJNERJUmuNiGEyEOlSpX44IMPCAgIYNiwYbRt21Y79HRiYiLfffedtunV9OnTefjwIY0bN8bFxYWUlBT27NlDQkICnTt31q6zTp06rF+/nh9//JHmzZtr39tjaJ+N1q1bs3nzZr744gsaNWrE48ePCQwMxNZWvwnJe++9x6FDh1i6dCkXLlygcePGmJmZcePGDW7dusXChQsBGDVqFCdPnmT69OkcP36cevXqAXD58mXS09P57rvvAJgwYQLDhw9n2LBh2qGWDx/+//buOzqK6m3g+Hd303sgkISE3kEQSCihBBACSO8iHaSroIiKWADl/SGKVEFBpUvvoUmRUKUXRXoJBAglpPdkd94/4q4su0k2IRCIz+ecPbp37szcOzMb5plb5iB//PEHrVq1Mmot6NSpE8uWLePDDz+kR48eWFtbs2fPniy7sVmqWrVqbNq0iR9++IHSpUujUqkIDAw0BJLZeeWVVzhw4ADffPMN1atXR61WU7t2bUNffP1EBfog8cmZ7B5Xvnx5hg0bRrdu3fDw8GDfvn0cO3aM1q1bG3VdHDt2LIMGDWLw4MG0adOGihUrotPpuHPnDvv376d169bP/N00zs7OjBw5kilTptC/f39Dl7otW7YQFhbGuHHjDOMPfvnlF44cOULDhg3x8fFBURQOHDhAaGioYQIGwNDqM2vWLMPU5mXLlqVcuXLPtC7ixdGmFPxwLttsQjx3EuwIIYQFRo4cSfHixVmzZg3ff/891tbWVK1alUmTJlGzZk1DvtatWxMcHMzWrVuJiorC0dGRMmXKMGXKFJo1a2bI17JlSy5dusTOnTvZs2cPOp2O8ePHWxzsjB49GkdHR3bt2sW+ffvw9PSkU6dOVKlShREjRhjltba25vvvv2fZsmX89ttvzJ07FxsbG0qUKGH0ElMXFxcWLlzIggUL2Lt3L3v37sXR0ZHSpUsbvXi0SpUqLFiwgHnz5rF27VrDS0XfffddevfubbRvHx8fpk6dyty5c/nxxx9xdXWldevWtG/fnq5du+boHDxpxIgRxMTEsGbNGuLi4lAUhc2bN1sc7PTq1Ys7d+6wZ88e1q1bh06n48cffzQaeNyxY0cWLlyIvb09zZs3z3RbgYGBhpeK3rx5k0KFCjFo0CCTGfC8vLxYtmwZixcvZt++fWzfvh0bGxs8PT1p1KgRQUFBuTsYOaQPypYuXcpPP/0EQIUKFZg6darRS0UbN25MREQEu3fvJjIyEltbW4oXL85nn31m1KWvRo0avPvuu6xfv55Jkyah1WoZPHiwBDv/ITNegx/+UrJ9sWj+j8h7OWmlF1uuqZQXZRSrEEII8YKJiIigTZs2tG/f3uhFrXp3796lffv2DB48+Jm3yAjxIktLS8NmBtkGOyd6gZ+3PGvPqUbDLJ+m+sCPMsvb42TMjhBCCJGJtWvXotVqzb57SAiRcxLoiOdNrjghhHhBJCcnEx+f/QxIHh7ymvHMxMfHk5ycnGUea2trs2ObHvfbb79x7949li5dSkBAAJUrV87LYmZJ/5LY7Li7u78Q03QLYaDSIR3VxItGgh0hhHhB7Nq1i4kTJ2ab78SJE8+hNC+nqVOnsmXLlizz1KpVi/nz52eZ59NPP8XW1pYaNWrw+eef52URs3X27FmGDRuWbb7NmzcbpgIX4kVgQyqpWDZmTuSMTqZ3zzUZsyOEEC+IiIgIrl27lm2+/HxHy4vu+vXrPHz4MMs8Li4uz7WlJqdiY2O5cOFCtvlq1Kjxwr2EVPx3paWlMezHvSxIaQpZvBNGGSPP2XOjwfB7Fuc99IPXMyzJy0euOCGEeEF4eHhIF7WnVKZMGcqUKZPfxXgqLi4uEtCKl1Jd21AWpGS+/DO5rEU+kGBHCCGEEEI8c+MbyHie3NJKN7Zck9nYhBBCCCFEnlCTlukyK7XcsIvnT4IdIYQQQgiRJ+Y4/wqYDge3kThH5BMJdoQQQgghRJ5QqxTO9XkiDXjwtkQ7TyM9Bx9hTMbsCCGEEEKIPFOhUMasa9ejdTjbQBEHebYu8o8EO0IIIYQQIs+VcZMgR+Q/uQqFEEIIIYQQBZK07AghhBBCCPECk6mnc09adoQQQgghhBAFkgQ7QgghhBBCiAJJurEJIYQQQogc02kVTkw/z98/XcImJQ0POysiBmjzu1gFUrr0Yss1adkRQgghhBA5tqX7Lv5YFIpWC8lW1kRo3bGb50L8o5T8LpoQBhLsCCGEEEKIHEmJTeXvWwpOySnoGx00ikKylQ17PzyZr2UT4nES7AghhBBCiBy58Vc0ibb2JukancL5UOnKltfSUVn8EcYk2BFCCCGEEDnyx6gQkm2sTdITba1xjYzJhxIJYZ4EO0IIIYQQwmJKqpbSl+8zNCSEovGxhvR0tZpUKyvsUxR+/+BgPpZQiH/JbGxCCCGEEMJilz2/olJcMim4UP3+XWKjHpFiZY1G0eKQmsqfnj6cCInjtSfWu/hIy74w6FhOhaeTPG8Xz4cEO0IIIYQQwjIHz1Mi+jrxeBqSXFJTIPXfGdhq373F0XIlSU3TYWOdEdQUmZNORFLG8mG7FXpU0LGivdyGWipNhuLkmoTVQgghhBDCIvF952NPPLbEm1mqA+CBoxO1wu4wedJNAHoF/xvo6K28DLdjZSID8exJsCOEEEIIIbKn0xF9J6MFx5kH2BNlWGRDPHeLqbniVZQiCfFodSruHHsIwPJL5jf3yiLlmRdZCAl2hBBCCCFEtpLbfIdX6iMAVCgUVl0nvHgal8rZ4GAbRpI9zGzdkj9LlsAxLZXiETE8StCh0unMbi8mFeJSzC8TxtJUKos/wpgEO0KI/7y7d+/i7+/PvHnz8rsoeWLNmjV06dKFgIAA/P39uXv3bn4XKUdOnDiBv78/wcHBhrRneY7mzZv3Uh6nrAwZMoR27drldzFEQXLsMrE77mBFOgDpKjW/VazP9426MatxD4Z0/ZxDPn7Ypqaxu1oVYm3t0OgUmnwbhaJk3oLjMlvH+ivpz6sW4j9IRoYJIUQBcuLECaZMmULjxo3p168fVlZWuLu753exhBAvs7M3SKj7f+jwRAFUgJWio/XFQ9S8c5FPg97FMTENXaotLY6cJcyrMFq1hn2VS3DO0QWyaW3osgniRio42UirhMh7EuwIIUQBcvToUQC++OILXF1d87k0ecfb25tDhw6h0WjyuygvhTlz5mT5NF0Ii92PRFvjAxwAR+J48qqaX68LD9wLgTvc8PGkZPhDal26wd7KJdn5atlsAx29IrO0JI2R21KR9+SqEkKI5yw9PR2tVoutrW2ebzsiIgKgQAU6ACqV6pkcr5dBQkICjo6OOVrH2tr0zfZCGKRr4UE0uDiAkz0oCkQnQEIyfLseQs5DQjLaO5Gok9NRk9Gaw2P/BbjiUZzjJV4x2vRNLw8qhd4htKhbRqCjKBYFPMlAsbnphLyhoqSrGlsraeV5XFp+F+AlJsGOEOK5CA4OZuLEifzwww9cvHiRtWvX8uDBA7y9vRk4cCBt27YFMsZmtG/fnsGDBzN06FCjbcybN4+ffvqJzZs3U6xYMQAmTJjAli1b2L17NzNmzODAgQOkpaVRu3ZtPvnkEzw8PFi/fj3Lly/n7t27eHt78+6779KkSROz5dyxYweLFi3i1q1buLu70759e9566y2srIz/XEZERPDTTz9x8OBBHj16hJubG40aNWL48OEUKlTIpMyrVq1i06ZN7N69m4iICObOnYu/v7/Fxy8kJIQlS5Zw+fJlVCoV5cuXp2/fvoZ66I+bnn7btWrVYv78+Rbt4+HDhyxbtozjx48THh5OSkoKPj4+tGnThj59+hi1qujP55w5czhz5gzBwcE8evSIkiVLMmDAAFq2bGm07Xbt2uHt7c3o0aOZMWMGf//9N9bW1jRq1IhRo0YZHTNzsroudu7cyapVq7hy5QparZZy5crRp08fmjdvbpRPp9OxePFiNmzYQEREBL6+vgwYMMCiY2NOTEwMP//8M/v37+fhw4fY29vj7e1NixYt6Nu3b67K6O/vT9u2bWndujXz5s3j8uXLVK5cmRYtWvD111/z3Xff0bhxY5N6tW3bFjc3N5YvXw5kjNkJDw83GvcEEBYWxoIFCzh69CiRkZG4ublRpUoVBg8eTOXKlQ35zp8/z4IFCzh9+jSJiYl4e3vTpk0bQ9dIvWvXrjF//nz+/PNPoqOjcXFxoVSpUvTp04eGDRvm+tgKCzyIhlEL4LczUMYTvu4NzV/NWLb/b/hoCVy6C5V9IDoR7kdD+9pw7T4cOG/RLrJrR422czZNVKlItbYitKi74bulwhOh4kIF0GJvBc5W4GQLVQqrOPswo01peA01Y+uoUMlAfGEhCXaEEM/VnDlzSElJoXPnztjY2LB27VomTJiAr68vNWrUyPV2R44cSdGiRRk2bBhhYWGsWrWKDz/8kKZNm7JhwwY6dOiAjY0Nq1at4uOPP2b9+vX4+PgYbWP//v3cuXOHbt26UbhwYfbv389PP/3EvXv3GD9+vCHfvXv3GDBgAGlpaXTo0AFfX1/CwsJYt24dJ06cYOnSpTg5ORlt+/PPP8fW1pZevXqhUqnw8PCwuG5r1qxhypQplCpVikGDBgGwZcsWxowZw7hx4+jcuTPu7u58+eWXbNiwgdOnT/Pll18CZBtEPO7KlSvs3buXJk2a4OvrS3p6On/88Qfff/89d+7c4dNPPzVZZ/bs2SQlJdG1a1cgIwj69NNPSU1NNRkg/+DBA4YPH85rr71Gs2bNuHjxIps3b+bChQssWbIEOzs7i8uqN3fuXBYsWED9+vUZNmwYarWavXv3MnbsWD766CO6d+9uyDt9+nRWrFhBrVq16NmzJ5GRkUyZMsXkOrDU2LFjOXXqFF26dKF8+fKkpKRw48YNTp48aRTs5KSMkBFo/P7773Ts2NHwEKBZs2ZMmzaNrVu3mgQ7x44d48GDB/Tq1SvL8p4/f57hw4eTnp5Ohw4dKFu2LLGxsZw6dYqzZ88agp2DBw/y4YcfUrx4cXr37o2Liwt//fWXIfiaMmUKANHR0QwfPhyALl264OXlRXR0NBcuXODcuXMS7DxrvWfCrrMZ/38yHtpNhivfg40VtP6/jFYagD8u/7vOor15WoTyD25hl5JC8mOtro6JyYS5O5NuY2Nxq445SekZnwfJcD3m385z4w7oKGynZsirEuwIy0iwI4R4rlJTU1myZImhm02zZs3o0KEDq1evfqpgp2rVqnz88cdGacuXL+fBgwesWrXKEHzUrl2bN998kw0bNvDOO+8Y5b9y5QpLliyhUqVKALzxxht8+OGHBAcH07lzZ6pVqwbAN998Q3p6Or/++iuenv++Rbx58+YMGDCAX3/91aT1wcnJiblz55q0EGUnNjaWWbNm4evry6JFiwz16Nq1K7169WLGjBkEBQXh7OxM69atOXbsGKdPn6Z169Y52g9ktAJt2rTJ6Ilpz549+fzzz9m0aRNDhw41CdKio6NZuXKlUbl69OjB9OnTCQoKMgpgbt++zejRo+nZs6chrUyZMkyfPp2VK1fSv3//HJX34sWLLFiwgAEDBvD2228b0nv06MEHH3zAnDlzaNOmDY6OjoSGhrJy5Upq167N999/b2ileu211+jTp0+O9gsQHx/P8ePH6dq1Kx999FGelFHv+vXrzJkzh7p16xptq1GjRhw4cIDY2FhcXFwM6Vu3bkWj0fD6669nWg5FUZgwYQJpaWksXryY8uXLG5YNGDAA3T9TA6ekpPDVV1/xyiuv8MMPPxiuV31AN336dMNseWfPniUyMpLJkycTFBRk4ZETeSIq/t9ARy85FTYfB3ubfwOdZ8wlNZ52Jw6z5xV/YpwcKBQTzytXb3GgVmX870Tz0MGGY8Xdcx3wZGb1JYUhr+bpJl94idKSlWsy9bQQ4rnq1q2b0XiCokWLUqJECcLCwp5qu2+++abR95o1awLQpk0bo1aW8uXL4+joyK1bt0y2UbduXUOgAxnjRPRP6PfuzXgiGh8fz8GDBwkMDMTW1pbo6GjDp1ixYvj6+homCXhcz549cxzoQMaEA0lJSfTo0cOoHk5OTvTo0YPExESz+8sNOzs7Q6CTlpZGTEwM0dHRBAQEoNPpOH/etOtL165dTcrVpUsXYmNjOXnypFFeR0dHunXrZpTWrVs3HB0dDcc3J7Zv345KpaJNmzZG5yE6OprAwEASEhL466+/ANi3bx+KotCrVy+j7niVKlUyCSosYWtri42NDefOnctyyuqclFGvQoUKZsvUtm1bUlNT2blzpyEtMTGRkJAQ6tevn2Ur3qVLl7h+/Trt2rUzCnT01OqM24GjR4/y6NEj2rVrR3x8vFF5GzRoYMgDGM774cOHiY+Pz3Tf+S0yMpKUlBTD9/j4eOLi4gzfU1NTefTokdE64eHhWX6/d++e0QQQz30f9jYojqZj2OJsVODhYpL+rKiB9tf30ODsRdofOEmDPy9SKDWRarfC0Gi1FE5MxS4t79+j40CS0fd8Px95tA/xbEjLjhDiuTLXZcjV1ZV79+7l6XadnTP6kuvH9jzOxcWFmJgYk/RSpUqZpJUpUwaAO3fuABAaGopOp2PTpk1s2rTJorIAlChRIusKZEK/X305sirb00pPT2fRokVs27aNsLAwk9m8YmNjTdYxd8xKly5ttlw+Pj4mA+dtbGzw8fHJVR1u3LiBoiiGLnTm6G829NvPrLxHjhzJ0b6tra0ZPXo03333He3bt6dMmTL4+/vTpEkT6tSpk6sy6mV2rQQEBFCoUCG2bdtm2N7vv/9OUlISbdq0ybK8+ocJFStWzDLfjRs3AAzdILMqr5+fH23atCE4OJjt27dTpUoV6tatS1BQkNnrNb88GQQ+2cXUxsaGwoULG6V5e3tn+d3Lyyt/92Fng+rDjjBh1b9p1Uri3KcZaNTgVxZOXiMvpWONlZlh8q6pcdilp2KfpqXW3VtY63S8dukS4a6uDO/flWSbvJ1B0d4KPm9kPGFHvp+PPNqHeDYk2BFCPFf6J8hP0t9YZzXoVKvVZrossymJM0t/2ml5X3/9dcN4iieZmzUsN+NRnrfp06ezatUqgoKCGDhwIO7u7lhZWXHx4kVmz579Qk5lrFKpmDVrVqbXVdmyZZ/Zvrt27UqTJk04ePAgJ0+eZM+ePaxevZqgoCAmT56c6zJmdq1YWVnRsmVLVqxYQVhYGMWLF2fr1q24uLgQGBiYJ3XSn+NRo0ZRoUIFs3mKFCli+P+JEyfSp08fDh8+zOnTp1m2bBkLFixg9OjRvPHGG3lSJpGJ8W9kBDU7TkM5LxjYDGz/eZgQ8mXG+JyLd6BOObgXDbcfQYc6UKoIdPgaLt8FKzXY20JMAmjN/771qfeohAvhuBBhtFyNgktqHCUiE7DW/duK4x0TQ9O/L3DGNwdjtxQFVOBkpaJrJShsp8LBChr7qth9K6Mk/V9RU7HQf69LV9J/r8p5RoIdIcQLRT8WwVwrQl61YGQmNDTUJO369evAv601vr6+qFQq0tPTc9X9Kad8fX0N5Xi8xQD+fQqf2wH2T9q2bRu1atUyulEHsuxiaO6YZVauO3fukJaWZtS6k5qayp07d8y2uGSnePHiHD58GC8vL0NrUmb0ZQkNDTUc0yfLmxseHh507NiRjh07otVq+eKLL/jtt9/o3bs3VatWzVEZLdG2bVtWrFjB1q1b6dixIydPnqRTp07Y2NhkuZ6+tejy5csW5bO3t7f4+i5XrhzlypWjb9++xMXF0a9fP77//nu6d+8uM2Y9a239Mz5PcrKHd7IYt3duZvbbTkwBGyvSrbthhQottsTijTMRRtNPa7Ei2saFSmlRJpuoeO9h9vsxULgzTEUxZ/O3ps1K5WBTQjxGxuwIIV4ojo6OFC5cmOPHjxu1JNy+fZuQkJBnuu+jR49y8eJFw3dFUViyZAmAYYpnNzc3GjRowO+//24y1kK/TlSU6T/6uVW3bl3s7e1ZtWoVCQkJhvSEhARWrVqFg4MD9erVy5N9qdVqk9abpKQkw3TG5qxdu9ZovEZ8fDzr1q3D2dkZPz8/o7wJCQmsWbPGKG3NmjUkJCRkOhV4VvSTMMyZM8dsq9/j3cMaN26MSqXi119/Ncp78eJFjh07luN9Jycnk5xsPAhco9EYxsPog/WclNESFStWpHz58mzfvp1t27YZpp3OToUKFShTpgybN2/m2jXT7k36867vKrdo0SKzXT2Tk5MN12FMTIxhYgM9Z2dnfHx8SE5ONhrPIF5CDrZgpcE6fQ3pag3WxKPFliiKo/sn3NGi4RGlKGbmWgFwTrLwGlAUxtfLPNAR4mnIVSWEeOF0796dH374gZEjR9K4cWMiIiJYt24dZcuWNTtIPq+UL1+eYcOG0a1bNzw8PNi3bx/Hjh2jdevWVK9e3ZBv7NixDBo0iMGDB9OmTRsqVqyITqfjzp077N+/n9atW5vMxpZbzs7OjBw5kilTptC/f3/Dje2WLVsICwtj3LhxJn3Fc6tZs2asX7+eTz75hDp16vDo0SOCg4OzfEGpm5sb/fr1M0wzHRwczL179/jss89MumP5+vry008/ce3aNSpXrsyFCxfYvHkzpUqVokePHjkub9WqVRkyZAjz58+nZ8+eNG/enCJFihAREcGFCxc4dOiQYSxOqVKl6NatG6tXrzZMfx0ZGcnq1aspX748ly5dytG+b968yZAhQ2jatClly5bF2dmZ0NBQ1q5di4+Pj2GCjJyU0VJt2rRhxowZLF68mBIlShhmCcyKSqVi/PjxjBgxgn79+hmmno6Li+PUqVMEBATQo0cP7O3tmThxImPGjKFLly60b9+e4sWLExcXR2hoKHv37uXbb7/F39+frVu3snz5cpo2bYqvry9WVlacOnWKP/74w2QmPvES02iw1q7G3aofD7VliMOTBApjRQpp2HPTpRAVIiNQ8++DEoWMl496JCRjn5JCUjYvBHa1VjGhodySimdDriwhxAunX79+xMfHs23bNk6ePEnp0qX5/PPPuXDhwjMNdgIDAylZsiSLFi3i5s2bFCpUiEGDBhnebaPn5eXFsmXLWLx4Mfv27WP79u3Y2Njg6elJo0aN8nwaXn3wtXTpUn766Scg40n91KlTc9UikpnRo0fj6OjIrl272LdvH56ennTq1IkqVaowYsQIs+u8++67nDlzhjVr1hAZGUmJEiWYNGkSrVq1MslbtGhRvv76a2bMmMFvv/2GtbU1rVq14r333sPe3j5XZR4yZAhVqlRh5cqVrFixgqSkJAoVKkTZsmUZM2aMUd4xY8ZQuHBhNmzYwMyZMylevDgff/wxt27dynGw4+npSfv27Tl58iQhISGkpaVRpEgROnXqRL9+/Yxu9HNSRku8/vrrzJ49m4SEBJOXl2alatWqLF68mF9++YXdu3ezbt063NzcqFq1qtG07wEBASxevJjFixezfft2oqKicHFxwdfXl169ehlar/z8/Lh06RIHDhwgIiICjUZDsWLFeO+990zeHSRefvbpi3FSDWVvGX+KP4rFPc6GOFs7VGAU6EBGsHOpmBeb/Kuzpw3U3535dgtbQ8QouR3NTirSJTS3VMqLOOJUCCHECy04OJiJEyfy448/4u9vZszAE9q1a4e3tzfz589/DqUTQjwLujOh3PBbxIEqJXnk4E3NK7ew0aZTPDbaKN8jR0e+6NEF25Rk1i0th2pqeqbbVMZIoGMJ1XuRFudVZlj+Mun/ArnChBBCCCFEttQ1SmGrxOB37TbptuGkpTkT6lYIn9hoo0HgNwplTLms/qc1opQLhJrOOUOL3M3IL0SOSLAjhBD5ICYmhrQ003dWPM7Ozu6px+MkJydb9MJHDw+Pp9pPQSHHS4is3Z3aF5ePdvLQwZ5S0XGEO7tyqlgJSkZHYqXTsr9yRW4U9sAxKYlOXTJaGA69qcJnnmlHok2d8/YdPAWa9GLLNQl2hBAiH3z44YecOnUqyzxt27ZlwoQJT7WfXbt2MXHixGzznThx4qn2U1DI8RIia9VGvMrVj3dx36EIKWonykRHoFVUXPMsyqa6tYh0ccY74hFd9x+hzc+9ACjmrCGku5b2GxTi0sDTAY73VmFnJXfw4tmTMTtCCJEPLly4YPZdQo8rUqTIU7+JPiIiwuxUw096Hu8MehnI8RIie/em7+H417d44OzK77WqEO9oj/axFzjXuHqdJBsrvttZPx9LWbCo3s/BmJ3pMmbncRLsCCGEEEKIHFlWYikqlTVXfLw5UaWsId0qXUvn/YfxW9mSqnXkpjuvSLCTe9KNTQghhBBC5EjKK0UI2PUXqRorbNPSuOXpgXV6OqXvPiDa3kkCnbymki5/uSXBjhBCCCGEyJHe8+uxukE0ikpF0ahYikY91i3XSW4vxYtDnX0WIYQQQggh/mXr64ZzegrKky0OikKdr2rkS5mEMEeCHSGEEEIIkWPNdrShUGIc6Id/KwoppXVUauWdvwUT4jHSziiEEEIIIXLMuZoHne72I/KPcCKuxrA3fj9W8uoc8YKRlh0hhBBCCJFrhQK8Kd2jLCoJdMQLSFp2hBBCCCGEeJHJbGy5Ji07QgghhBBCiAJJgh0hhBBCCCFEgSTBjhBCCCGEEKJAkjE7QgghhBAiVy6cimHTr/eoWCQdnAHr/C5RASVDdnJNgh0hhBBCCJFj4yfcJPJEKGkaW25fUqh304a4lmn5XSwhjEiwI4QQQgghciQuUcvJa0lE+VSgaHQ80Y52/OVdgk4HTuV30YQwIsGOEEIIIYTIkUu30nGPTmHw1t+xT00j3s6GA6+U4WiJshSbepnun1TN7yIWMNKPLbdkggIhhBBCCJEjsUfvMOy3oxSPisEjIZESj6IJOn2FeCsbDp5MNeQ7d19HtdmJFJmcyLcHUrPYohDPhgQ7QgghhBAiR/7+5gL2aemG72rALTGRivceccPNDYCDoVqqzUni3AOFiASFj3amUWJqQv4UWPxnSbAjhBBCCCFypHBcnEmatVbHAzcn4v6Zo6DRz8mgAzSqjF5YCoRFw9WIdJN1RTZUOfgIIxLsCCGEEEIIi/3Y/gBFtHdN0m95uHHZ2wM1sPtqGlirwcUabDVgpwHrjDvxuj8kP+cSi/8ymaBACCGEEEJYJPxyHPcSrFCKF+OaowM1r9/FWqvjcrEi/BJUB4AkO1v6b0wFW+uMlh1rDSgKKECalsiUfK2C+I+RYEcIIYQQQlhk+fATRDsXpmiEK7eKOrMysBY26VpiHewMeYrGJ3Ak1g0cAKt/OhGpVGBnBekKpOtI0ypYa6TPlXj2pBubEEIIIYTIVmqKDqfYGFCrefXSbWId7dFp1EaBjkarpUxEFOiUjLE6T/qnK1vFudKVLUdkzE6uSbAjhHhhBQcH4+/vz4kTJ/KtDO3atWPIkCHZ5nsRyvqyOXHiBP7+/gQHB+d3UZ6aTqdj3rx5dOjQgbp16+Lv7/9M9nP37l38/f2ZN29errcxZMgQ2rVrl4elEv8V6/53mateJUGl4veAV7B2sqNmRBTlYuJQKQqQ0Wst0TYNrOD1v0+x7ef/sWveV/Q4fTBjI9qMfGmXH+RTLcR/jXRjE0IIIZ7Sli1b+Omnn2jfvj21atVCrS4YzxIvXbpESEgI7dq1o1ixYvldHJHPzv4RjbNWR6SNDY8KuQAZDQkeySnEWVtx38EeRa3mbLEytLz8J1tWTEVNRnDT/OpfWKWls6xCAADhzq7citFRwrVg/FbEi0uCHSGEyAOtW7emRYsWWFtb53dRRD44evQoTk5OfP7556hUz64fibe3N4cOHUKj0TyzfTzu8uXL/PTTT/j5+Umw8x935NdQKl4NJ87dkRgnJ7RWxtege3Iy9x3sM8bmqFSMOLHXEOjoDTmy2xDs+CTGUvJ7G5RPHZ5bHV5u0j8ttyScFkKIPKDRaLC1tS0wT/QLouTkZNLTn837PR49eoSzs/MzDXQAVCoVtra2WFnJs0rx/Oi0WmI/3I7K1orbxYqi1Zj+nYu1tTX6bmXm5lz32O8j3toO9+QkJvyeQnK6YpJXiLwi/yoLIV54Wq2WefPm0bZtWwICAujRowe//fabSb6QkBAGDhxIw4YNadSoEQMHDiQkJMTsNnOS90l37tyhc+fOtGrVisuXLwPmx+zo044fP87SpUvp0KEDAQEBdO7cmS1btpit588//0zbtm2pX78+PXr0YOfOncybNw9/f3/u3jV9r0VW9OuFhoYyZ84cWrduTUBAAG+++SYHDx40ypvV+JkJEyaYjEHRj/u4e/cuY8aMoUmTJjRt2pQJEyaQmJiITqdjwYIFtG/fnvr169OrVy/OnDmTaVlXrlxJ586dqV+/Pp07d2blypVm8926dYvPP/+cli1bUq9ePdq1a8fMmTNJSkoyW+aoqCgmTpxIixYtaNSoEQ8e5GycwMaNG+nVqxcNGjSgcePGvP3220b10B+3EydOEB4ejr+/P/7+/kyYMMHifejLGh8fz+TJkwkKCqJ+/foMHDiQc+fOGeXNbMxOcnIy06ZNo2XLljRo0ID+/ftz7Ngxs+dO7+HDh4wbN46mTZvSoEED3nnnHW7evGlYPm/ePCZOnAjAsGHDTOqWkpLCvHnz6Ny5Mw0aNKBJkya88cYbzJw50+K6ixeL7uBVUt9cwPmqP7LLfSF/WM9jv/3P/OX8HVFWHpypUoZUG+uM1pvHWKclct/ezihtY5WGaJ/IN9evueH/42zt8I2KIGFqMJ2HHKX2uxcoO/Y29l8n4jkpnp+6HeLu50dJj858nuplZ9JpvyyZt9ancO6+DoDT9xX6b9fScaOWNZd0T3tIRAEgj4aEEC+82bNnk5SURNeuXYGMIOLTTz8lNTXVMNB6zZo1TJkyhVKlSjFo0CAgYxzFmDFjGDduHJ07dzZsLyd5n3Tx4kVGjRqFs7MzCxcuxNvbO9vyz5kzh5SUFDp37oyNjQ1r165lwoQJ+Pr6UqNGDUO+b775hnXr1uHv70/v3r2Jjo5mypQpT919aMKECVhZWdG7d2/S0tJYsWIFY8aMYf369U+17aSkJIYPH06tWrV45513OH/+PJs3byYlJQU3NzfOnTtH9+7dSU9PZ9myZYwePZrg4GAcHR2NtrNq1SoePXpE586dcXBw4LfffmPq1KnExsYaTQ5x4cIFhg0bhrOzM507d6Zo0aJcvnyZlStXcvbsWebPn2/S4vH2229TuHBh3nrrLZKSknBwsLzLzKxZs1iyZAlVq1ZlxIgRJCYmsmHDBoYOHcp3331Hw4YNKV26NF9++SULFiwgOjqa0aNHA+Dr65vj4/nOO+/g7u7OoEGDiImJ4ddff2XUqFFs3rzZ5Jg96eOPP+bQoUM0adKEOnXqcPfuXT788MNMz29SUhKDBw+mWrVqvP3229y5c4eVK1fywQcfsGrVKjQaDa+99hoRERFs2LCBAQMGULp0aaO6TZkyhc2bN9OmTRt69eqFVqslLCyM48eP57juIv/pDl4ltckMQq29uKcqRuF/HiDYpmuJs3blYVFXs+tZp6eiSYlCqy5hSEtTq1j/Sg22Vvqe/idCqHfzCr/UaMymiv8G3mlOdvzlUoZz7j4ozrag/jcwSraCYbVrs3nySsruuk2lI11M9jv1YBof7kg1fF99Lp21vezoFKyQ9E8D7qarCj8kwbAa8mz/v0yCHSHECy86OpqVK1fi5OQEQNeuXenRowfTp08nKCiI1NRUZs2aha+vL4sWLTLK16tXL2bMmEFQUBDOzs7ExsZanPdJR44c4aOPPqJcuXJMmzYNNzc3i8qfmprKkiVLDON5mjVrRocOHVi9erUh2Ll27Rrr1q0jICCAmTNnGrrDNW/enJ49ez7N4cPNzY3p06cbulj5+/vTr18/1q9fzzvvvJPr7UZHR9O3b1/69u1rSIuLi2P37t1UqlSJhQsXGoKP0qVL88EHH7Bjxw66dDG+cbl16xZr1qzB09MTgO7du/PWW2/xyy+/0KFDB0P6l19+iYeHB0uWLDG6+a9Tpw4ffvgh27dvN5llrGzZsnz11Vc5rltoaChLly7l1Vdf5ccffzScu44dO9KtWzemTJlCQEAAhQsXpnXr1mzcuJGUlBRat26d433pVapUibFjxxq+lylThrFjx5o9Zo87ePAghw4domPHjnz22WeGdH9/f9577z2z60RHR9OnTx/69etnSHN3d2fWrFkcO3aMgIAAypcvT/Xq1dmwYYPZGeZCQkKoX7++ofVHvNzS5+4HrY4brj4UiTKeFlpRqXFOTTK7XpqVDfcLlzZ816rgnI8rqdYawJ7/teoGSemQov13JTuN4f07ilqdscze+JZUp1azLLAaH27+g4Sj93Gs62m0fNYfaUbf41Phi71pJKUbb2fWKV3BCHZkyE6uFYCzL4Qo6Lp27WoISgCcnJzo0qULsbGxnDx5kqNHj5KUlESPHj1M8vXo0YPExESOHj0KkKO8j9u2bRvvvfce/v7+zJ071+JAB6Bbt25GExcULVqUEiVKEBYWZkg7cOAAAD169DAa91OuXDnq1atn8b7M6dGjh9FYkqpVq+Lg4MCtW7eearsajYY33njDKK1GjRooikKXLl2MWllq1qwJYFRnvVatWhkCGgBra2t69uyJVqs1HJerV69y5coVWrVqRVpaGtHR0YZPjRo1sLe358iRIybb7t27d67qtm/fPhRFoW/fvkbnrkiRIrRr147w8HAuXbqUq21n5smgVh9cmDtmj9Mfo169ehml61uezFGr1fTo0cMorXbt2gAWXxdOTk5cv36dq1evWpQ/v0RGRpKS8m9XqPj4eOLi4gzfU1NTefTokdE64eHhWX6/d+8eivLvOJMCsY+0jGBEhQKK8Rga+9Q01LqMLmEuMQlUuBRGhUu3cY5NBMA1JRWn1IzgI9zZ7p9A5zG2GtAAzlbgapPxclE9BcN01E/ST2cdEW7c/TQ8PFxfXCPpZtJSdS/P+RDPhgQ7QogXXqlSpUzS9Ddxd+7c4c6dO0DGk/An6dP0eXKSV+/ixYuMHz+eOnXq8O2332JnZ2eyblZ8fHxM0lxdXYmJiTF814/HKVmypElec2k5Ya5L1ZP7zw0PDw9snxiU7OKSMR3tk92n9Onm9mnuhvzJc3Hjxg0gYxxJ8+bNjT5BQUEkJSURGRlpsp3cHjv9+ShbtqzJMn3ak9fJ03ryOtEH1Nmdp7t376JWqylevLjJsszqX6RIEZNz5+rqatH+9EaPHk1cXBw9evSgQ4cOfPXVV4SEhKDTvVjjJAoVKmRUVycnJ6OWWxsbGwoXLmy0zpPdU5/87uXlZfQAoSDsQ/NWfQDKxd8i3s742lDUKpJtrHGPjKPGmWt434vC+14kNc5cwyUmAQ1QOSoGG60WK8wELipAC8Q90cKj02W8fNTKtNlCo9XRf+9Z7Kq4U6L9Kyb1eMvPuAXHRgMfNbTWNxgZDK6mfmnOh3g2pBubEEJko3jx4lhZWXHixAn++OMPGjZsmKP1M5uhTVGezwxEluw/q1nEtFozj0uz2K6l+8wJ/Xq9e/cmICDAbB59QPW4nAam+Smz6aSfxXWS1bmzdH9NmjRh8+bNHDp0iFOnTnHs2DE2bdpEzZo1mTt3rkzD/pLRtKoK64dQYnYIaTfiuBvlilNcCjq1ilQrDelpanxuPzTqTaVWFHzuRBDr6ogGKJScgtv9FG4WdjSexCDtsQA4WQsaFVhrABVeCdFUePiA+y7uaNVqHjq54qCFSfuPU7FHKbwn1EalNv379GUza9zsVaw5l46Hg4qPG1kTWFpDYScdU48rRKco9Kys5t2aBaX/V0Gpx/MnwY4Q4oUXGhpqkqZ/0u/j42OYiev69evUqVMn03zwbyuHJXn1HB0dmTZtGiNHjuTDDz9k8uTJNGnS5Okq9QR9S8jNmzdNWmIenyHrWcnqqX5et2A8SX/cH3f9+nXg33NRokTG4Ge1Wk3dunWfaXke3++1a9dMzseTZctv3t7e6HQ6wsLCTFrJnvbayW4qbVdXV1q3bk3r1q1RFIXZs2ezZMkS9u3bR/PmzbNcV7x4NJ1qoOlUg8pA5ccXJKVwu/oWVGaG7Wi0/wYyhZNTcU5P572QnWx+5VWuFy4KaQqGGQP00nQZwY5OoXdbTz5v4IWL3ZPBftbXj1qtYkxDa8Y0NA6qm5dU0/zpGsNFASPd2IQQL7y1a9cSHx9v+B4fH8+6detwdnbGz8+PunXrYm9vz6pVq0hISDDkS0hIYNWqVTg4OBjGveQk7+OcnJz4/vvveeWVVxg7dix79uzJ0zo2atQIyJiC+fFuQFevXjU7FiWvFStWDI1Gw7Fjx4zSz549y19//fVM971jxw7u379v+J6Wlsby5cvRaDSGVrSKFStStmxZ1q1bx+3bt022kZ6e/tTd8h4XGBiISqVi6dKlRu/miYiIIDg4GG9vbypWrJhn+3sagYGBACxfvtwo/eDBg2YDyZywt7cHIDY21ihdq9UajU+AjMBIf0zy8lyIF4C9Ld59y1MkLtZk0X1PdyBj6I1zejqa9BSmbfuFnfMnQUyqaaCjpyio07V828zKTKAjRN6Rlh0hxAvPzc2Nfv36GWbaCg4O5t69e3z22WfY2dlhZ2fHyJEjmTJlCv3796dt27ZAxnTSYWFhjBs3zjAZgbOzs8V5n+Tg4MCsWbN4//33GTduHF999RUtWrTIkzqWLVuWTp06sWHDBkaMGEGTJk2Ijo5mzZo1VKxYkQsXLjzTF1Y6ODjQrl07Nm7cyLhx4/Dz8yMsLIzg4GDKly9veJ/Qs1CiRAn69+9Ply5dcHBwYMeOHZw/f55Bgwbh5eUFZNxIf/nllwwfPpw333yT9u3bU6ZMGZKTk7l9+za///4777zzjslsbLlVqlQp+vTpw5IlSxg8eDBBQUGGqacTExP56quvMu129rw1aNCAgIAANmzYQHR0tGHq6fXr11O+fHmuXLmS621XrVoVtVrNggULiI2Nxd7eHh8fH0qWLEmrVq0IDAykYsWKuLu7c/fuXdauXYuLi4shABMFx2ufV2fytvuUDY1Aq7Ii3UrDPS93HhbJaBVWARptOgOOrSHO1oGhrQaYvI/HIE0HShoPx0hXR4tJL7Zck2BHCPHCe/fddzlz5gxr1qwhMjKSEiVKMGnSJFq1amXI061bNzw8PFi6dCk//fQTABUqVGDq1KkmXc5ykvdJ9vb2zJgxgzFjxvD555+j1Wp5/fXX86SeY8eOpUiRImzatImZM2dSsmRJxo4dy99//82FCxdMBpTntdGjR6MoCiEhIezbt4/KlSszbdo0NmzY8EyDnTfeeMPQsnbv3j28vLz44IMPePPNN43yVaxYkV9//ZWFCxeyf/9+1q1bh6OjI97e3rRr184wm1heGTlyJMWLF2fNmjV8//33WFtbU7VqVSZNmmSYXe5FoFKp+Oabb5g7dy6//fYbhw8fply5ckydOpU1a9Y81ax7Xl5efPHFFyxevJivv/6a9PR02rZty6effsqbb77JsWPHOHbsGImJiXh4eBAYGMiAAQMoUqRIHtZQvCiKNfEhYW0Cp6uXo1h4FEUfROEak8if5YsR4eKI/61LXHUtxoct+rOnuA9YqzNmdkt/YhyYAipFRyFHCXbEs6dSntcIWSGEELny/vvvc/z4cfbt2/fCtCaIl8Mbb7xBeno669aty++iiAJAURS2F17K3+VKUO56OKk2GiI8nUmztUZBIeDmWTa/UpMDJStwsFghcLbJaMVJfKIrmxpe0Ubx1//l/OW7/1WqsXHZZ/qH8rXpe+L+y2TMjhBCvCCSk5NN0q5cucLhw4epXbu2BDoiU+aunYMHD3Lt2rXnMqGD+G9QqVTc83KnWPgjFOChtytpthmtMypUHCpVk0MlK6JTa0CtyujGZqPJeImovhuWGtBB8IjCme1GiDwl3diEEOIFsWXLFrZt20aDBg1wd3cnNDSUDRs2YGVlxdChQ4GMm9rHJ2vIjIeHx7Mu7kspKioq06m09RwcHHBwcHiq/SQmJpKYmJhlHo1Gg7u7+1PtR+/nn3/m0qVL+Pn54eTkxOXLl9m8eTOurq7069cvT/YhBEDLdYHsb7aTdGsN6U+8PFSjKHjGJ3DHxZEKhRQupygZAY+dVcZHp4O4NEChlI99/lTgZSVjdnJNgh0hhHhBVKpUiZCQEFatWkVMTAyOjo74+/szZMgQKlWqBMCuXbuYOHFitts6ceLEsy7uS6lv377ZvrV88ODBhuAytx4fD5YZb29vgoODn2o/ejVq1ODs2bMsXbqU+Ph4XF1dee211xg+fDienp55sg8hAHwquxLp44LnzaiMF4I+8Q6ceFsbvOMi+bKdJ82XazNadlSqjLE7KTpQ4HXftHwqvfgvkjE7QgjxEomIiODatWvZ5pOuS+adOXOGlJSULPP4+PiYvFsnp27fvp3t+4lsbW2pUaPGU+1HiPyypPg6Ytwd0Dr8O8lAmJsLh8qUpPSjcFasqoHqs3+m99eoMgKjf+44lUmO+VDil5vqkxyM2ZksY3YeJy07QgjxEvHw8JAuak/heQUXvr6+Tx0wCfEiu+Ljwf5XylL/QigPPFyIdLIn3NkJq/R0ChfJmL7/l05WvLUhHbT/PldvVVGGi+eO9GPLLQl2hBBCCCFEjtg2LcZru65SPTSclGsa7hZ2xS0+iZBXy+JRIePGfKCfLa96aXhjQzoJKTCjlRVvVJVbT/F8yRUnhBBCCCFypFUTV84v/RMA23Qtpe9HAlAm/BEVq9oZ8vn5WHH1HbndFPlH2hKFEEIIIUSO1HzNg3h70xcdu6XE0OqdqvlQIiHMk2BHCCGEEELkiMZajVXDYqRp/r2VTLSzgopJOBW1y2JNkSuqHHyEEWlXFEIIIYQQOTZkYS3+3u3LgRlXsHG2IqnmeRzcZFpp8WKRYEcIIYQQQuRK1eZFqdq8KGlpaSxceDa/iyOECQl2hBBCCCGEeJGppH9absmYHSGEEEIIIUSBJMGOEEIIIYQQokCSYEcIIYQQQghRIEmwI4QQQgghck6rhSV74bfT+V0SITIlExQIIYQQQoicWfw79P/e8NUKsJrWjHRH6/wrkxBmSMuOEEIIIYTImccCHch4l2Xvj3/Pn7IIkQVp2RFCCCGEEJa7F2U22TpNec4F+Q+RmadzTVp2hBBCCCGE5f66mekih4iE51gQIbInwY4QQgghhLDcwNlmk1VAg8Vnn29ZhMiGBDtCCCGEEMJyt813YwMocTnWJC1dp5AkXdyekioHH/E4GbMjhBBCCCHyhBrQ/fP/6y6l0zXYePm9YWo8neRZu3h+5GoTQgghhBB5KjFNMQl0ALx+1JkmCvEMSbAjhBBCCCEsc+JK9nluP6LYD9pMF/92PT0PC/QfIb3Yck2CHSGEEEIIYZmmX2SfJzaBmNTMF4/Zm3fFESI7EuwIIYQQQgjLxKdkmyWpsHuWy89lPr+BEHlOgp0XWHBwMP7+/pw4cSK/iyIAf39/JkyYkOfbnTdvHv7+/ty9ezfPt12QyO8hZ4YMGUK7du3yuxgATJgwAX9//3zb/4kTJ/D39yc42MwAAiFEnvNbrcnvIghhIMGOEEII8RJbvny5BHLi+dh1OtssKuBaQvYDR1LSZaKCHJExO7kmU08LIV4KrVu3pkWLFlhbW+d3UV4Kc+bMQVHkvRYAtWrV4tChQ1hZFcx/8lasWIG3t/cL05InCrB3f7Iom3dCHHftHbLMYzdDhzJGnrmLZ69g/uUXAkhISMDR0TG/i/HSUhSFpKQkHByy/gfredFoNGg0/82uEbm5liUo/JdarcbW1ja/iyHEyykpBSasgmmbIN2yByglY6O46+GZbT7VVONZ2TztwMYKHKwhMQ2cbGCMPwysnv3t6tFwhV2hCuXdoXN5FdYaaeIQGSTYeQkoisLSpUtZu3YtDx48wNvbm4EDB9K2bVtDnp07d7J9+3YuX75MZGQkDg4O1KhRg2HDhlG+fHmj7Z09e5ZffvmFS5cuERcXh6urK+XLl2fw4MFUq1bN4nLNmzePn376ic2bN1OsWDGjZe3atcPb25v58+cb0vz9/Wnbti2vv/46P/zwA1euXMHJyYmgoCBGjBhhclN95coVZsyYwdmzZ7G1taVhw4a8//77NG/enLZt2xrGz9y9e5f27dszePBgSpcuzZIlS7hx4wZBQUFMmDCB0NBQVq5cyalTp7h37x5arZbSpUvTtWtXOnbsaFKva9euMWPGDE6fPo2NjQ3169dn9OjRmR6HnTt3smrVKq5cuYJWq6VcuXL06dOH5s2bG+XT6XQsXryYDRs2EBERga+vLwMGDLD4eD8pJiaGn3/+mf379/Pw4UPs7e3x9vamRYsW9O3b1yjvnj17WLVqFZcvXyYtLQ1PT08CAgJ47733sLa25sSJEwwbNozx48eTlJTEmjVruH37Nv3792fo0KE5qifA0aNHWbJkCX///TepqamUKFGCrl270rVrV6N8+utk3LhxTJ8+ndOnT6NSqahbty4fffQRHh4ehrzBwcFMnDiRH3/80TD+Q5/2ww8/cPHixSx/IwBarZaFCxeyceNGIiMjKVGiBAMHDuTGjRuZXst5dQ4sPX7630nr1q2ZN28ely9fpnLlyrRo0YKvv/6a7777jsaNGxuto9PpaNu2LW5ubixfvhzIGLMTHh5u0r0pLCyMBQsWcPToUSIjI3Fzc6NKlSoMHjyYypUrG/KdP3+eBQsWcPr0aRITE/H29qZNmzb069cv1y0kUVFRTJ8+nUOHDpGSkkK1atUYNWoUlSpVMuR5/Fp8sqViwoQJbNmyxWjc1rVr15g/fz5//vkn0dHRuLi4UKpUKfr06UPDhg0z3ebjaYqisGzZMsLCwihcuDDdunWjX79+JuW39JhYUqaUlBQWLVrEb7/9xv3797G2tsbT05P69eszatQoi4+p/rcQHh5uNC5q8+bNfPDBB8TGxhIcHIxabfwEfffu3YwdO5YJEybQtm1bo+ORkJDA6tWruXfvHl5eXnTv3p0ePXqY7PvWrVv89NNPHDt2jJiYGIoUKULz5s0ZMmQI9vb2FtdBvMASU6DiO3D7kcWrKECYe+Fc7e5+smnaWzvh0F0tv7TK/GHX9BM6Rof82y2uSXEVu7up0agLUsBTkOryfEmw8xKYM2cOKSkpdO7cGRsbG9auXcuECRPw9fWlRo0aAKxevRpXV1c6deqEh4cHt2/fZsOGDbz11lssW7aMEiVKABAaGsrbb79N4cKF6dGjB4UKFSIyMpIzZ85w+fLlHAU7uXHx4kX27NlDx44dadOmDSdOnGDlypVcu3aNOXPmGP5BvnXrFoMGDUJRFHr06EGRIkU4dOgQ7777bqbb3rdvH6tWraJLly506dLF8CT8xIkTnDp1ioYNG1KsWDGSk5PZvXs3kyZNIioqyijguHPnDoMHDyY1NZXu3bvj6enJgQMHMt3v3LlzWbBgAfXr12fYsGGo1Wr27t3L2LFj+eijj+jevbsh7/Tp01mxYgW1atWiZ8+eREZGMmXKFHx8fHJ1LMeOHcupU6fo0qUL5cuXJyUlhRs3bnDy5EmjG+05c+awcOFCypQpQ8+ePQ3Xx++//86wYcOMWgBWrFhBTEwMHTt2pHDhwnh6eua4nuvXr2fy5MlUq1aNgQMHYm9vz9GjR/n666+5c+eOyY3cw4cPGTp0KE2aNGHkyJFcuXKF9evXk5CQwJw5cyw6Fpb8RgC++eYb1q1bh7+/P7179yY6OpopU6bkKMB5nKXnICfHDzJuqn///Xc6duxoCNiaNWvGtGnT2Lp1q0mwc+zYMR48eECvXr2yLO/58+cZPnw46enpdOjQgbJlyxIbG8upU6c4e/asIdg5ePAgH374IcWLF6d37964uLjw119/GYKvKVOm5Op4vfvuu7i4uDB48GAePXrE6tWrGTJkCAsWLKBcuXI53l50dDTDhw8HoEuXLnh5eREdHc2FCxc4d+6cIbDIyrp164iMjKR9+/Y4Ozuzfft2Zs+ejaenJ61atTLks/SYWFqmKVOmsHnzZtq0aUOvXr3QarWEhYVx/PjxHB2DL7/8kmnTpuHm5sbAgQMN6e7u7nTs2JFvv/2Wo0ePEhAQYLTepk2bcHJyMgm2V61axaNHj+jcuTMODg789ttvTJ06ldjYWIYMGWLId+HCBYYNG4azszOdO3emaNGiXL58mZUrV3L27Fnmz59fYLsN/qesOJCjQAdgaa1AbrsXydNiLPpbYX4LxWzwkpyuMOGw8fifkDCFHTcU2pSVAEEAinhhbd68WfHz81PefPNNJTU11ZB+//59pV69esonn3xiSEtMTDRZ//r160q9evWUyZMnG9JWrFih+Pn5KX/99ddTl+/HH39U/Pz8lDt37pgsa9u2rTJ48GCjND8/P8XPz0/Zu3evUfq3336r+Pn5KTt27DCkffzxx4qfn59y+vRpo7xjx45V/Pz8lPHjxxvS7ty5o/j5+Sl16tRRrl+/blIWc8dGq9UqgwcPVgIDA5W0tDRD+rhx4xQ/Pz/l+PHjhjSdTqd88MEHJvu9cOGC4ufnp3z//fcm2x89erQSGBioxMfHK4qiKDdu3FD8/f2VYcOGKenp6Ubb8Pf3z/Q4ZiYuLk7x8/MzOrfm/PXXX4qfn58ydOhQJTk52WiZTqdTdDqdoiiKcvz4ccXPz09p2rSp8ujRI6N8Oannw4cPlYCAAGXcuHEmeb/99luldu3aSlhYmCGtbdu2ip+fn7Jz506jvF9//bXi5+en3Lhxw5Cm/z08fm5y8hu5evWq4ufnp7zzzjuKVqs1pF+5ckWpXbv2MzsHOTl+ivLv7+TIkSMm+T/66CMlICBAiYmJMUr/7LPPlDp16hidu8GDBytt27Y1fNfpdEq3bt2UgIAA5fLlyybb1h+T5ORkpUWLFsqgQYOMfhuKoijLli0zOQeWGD9+vOLn56eMGTPGcM0piqKcP39e8ff3V9555x1Dmv5a3Lx5c6bb0QsJCTF7/TzJ3Db1aS1btlTi4uIM6UlJSUqzZs2U/v37G9JyckwsLVPTpk2Vd999N8s8ljL391ZRFCU2NlapX7++8vHHHxulh4eHK7Vr1za6dvXHo1GjRsq9e/cM6ampqUqfPn2UOnXqGKX36NFD6dy5s9G1qyiK8vvvv2d6/vLLo0ePjP7+xcXFKbGxsYbvKSkpSkREhNE6d+/ezfJ7eHi40bVcUPeRMnaxotDJ4k+sTQ9FPSVF4du0PP6kKompOrP1uBevM7vOrOMpz/VY5cU+ssLniRZ/hDEZGfYS6Natm9HT96JFi1KiRAnCwsIMafouA4qiEB8fT3R0NO7u7pQsWZJz584Z8jk5OQEZrSApKdnPlZ/XSpYsSZMmTYzS+vfvD0BISAiQ0dXo0KFDVK1a1eipPJDlk+uGDRtSunRpk/THu1OkpKQQHR1NbGws9erVIyEhgdDQUCCjK9CBAweoUqWKUXcQlUpl0iUJYPv27ahUKtq0aUN0dLTRJzAwkISEBP766y8g43grikKvXr2Mxp1UqlSJunXrZlqnzNja2mJjY8O5c+eynLJ6x44dALzzzjsmYxZUKhUqlfFTrzZt2lCoUKFc13P37t2kpqbSoUMHk7yNGjVCp9Nx7Ngxo+0XKVKEoKAgozT98X/8Gs+KJb+RAwcOANCjRw+jLj3lypWjXr16Fu3ncZaeg5wcP70KFSqYvS7atm1LamoqO3fuNKQlJiYSEhJC/fr1Tc7d4y5dusT169dp166dSddWwHBMjh49yqNHj2jXrp3hb4n+06BBA0Oe3Ojbt6/RNVe5cmXq1q3LsWPHSExMzPH29H/PDh8+THx8fK7K1K5dO8N2AOzs7KhWrRq3bt0ypOXkmFhaJicnJ65fv87Vq1dzVW5LODs7ExQUxL59+4iOjjakBwcHo9Pp6NChg8k6rVq1MrToQsbYr549e6LVag2/oatXr3LlyhVatWpFWlqa0fGoUaMG9vb2HDly5JnVK6cKFSpk9PfPyckJZ2dnw3cbGxsKFzbuduXt7Z3ldy8vL6NruaDuw6azcYtgdpxSU3BMNdMX7Sn5OKmwt/63nI/Xw9NRhf8Tw4Os1NCu/L//Jrws50M8G9LG/BIw183J1dWVe/fuGb5fvHiRH3/8kZMnT5KUlJTp+i1atGDbtm0sXLiQ5cuXU61aNerVq0fLli2fy4/OXDDi4eGBs7Mzd+7cATL69SclJVGyZEmTvKVKlcp02/quek9KTExk/vz57Nq1i/v375ssj42NBSAyMpLExESz+y1TpoxJ2o0bN1AUxWQcyuMePcpo/tfXzVz5S5cuneMbA2tra0aPHs13331H+/btKVOmDP7+/jRp0oQ6deoY8t26dQuVSmX25tYcc8cwJ/XUB44jRozING9kZKTR98yub8gYE2MJS34j+oDE3PktWbIkhw8ftmhfepaeg5wcP73MruWAgAAKFSrEtm3bDNv7/fffSUpKok2bNlmWVx/4VaxYMct8N27cADK6R1laXkuZ+/3rr//w8HDKli2bo+35+fnRpk0bgoOD2b59O1WqVKFu3boEBQWZ/c2ak9m18/i1l5NjYmmZRo8ezfjx4+nRowc+Pj74+/vTqFEjAgMDTcbXPI1OnTqxZcsWtm3bRs+ePVEUheDgYCpUqGA0RkvP3DnSl1v/d0x/PObNm8e8efPM7vfJ37l4SdUuD9MGwOiFFmVXAd+v+4l+PUeCKm+6kBW1h/09sv5NrGynYeAOLftvQwlnmNpETSnXAtaFrYBV53mSYOclkNk/fMo/08reu3ePIUOG4OjoyFtvvUWpUqWws7NDpVLx3XffGQU/NjY2zJ07l3PnznHkyBFOnTplmGhg0qRJNG3a1OJyPdkq8DitVmvxdvKKnZ2d2fRPP/2UgwcP0qlTJ2rVqoWrqytqtZpDhw6xfPlydLrcz/WvUqmYNWtWpucopzdvOdG1a1eaNGnCwYMHOXnyJHv27GH16tUEBQUxefJkozJmda4el9kxtLSe+mty4sSJRpMLPO7Jm8usbuwUC6dOzu438qzk5Bzk5DrJ7DxYWVnRsmVLVqxYQVhYGMWLF2fr1q24uLgQGBiYJ3XSH7NRo0ZRoUIFs3mKFMnb/viPy+nflYkTJ9KnTx8OHz7M6dOnWbZsGQsWLGD06NG88cYb2e7Pkhn+cnpMLClTkyZN2Lx5M4cOHeLUqVMcO3aMTZs2UbNmTebOnZtns+m9+uqrlC1blk2bNtGzZ0+OHTvG3bt3+eijj3K9Tf3x6N27t8lYID0XF5dcb1+8YN5vB++1hdgEcDPt5fCkvmcOMbJtX2Jc3bPN66SCt6pDr1egrJsKJxs1iqKQpgO1CqzUKmwsmFWtrJuKfT2sSE5XsNVk/XdE/PdIsFMA7N27l8TERKZNm2bylvKYmBhsbGxM1nnllVd45ZVXgIxgqVevXvzwww85Cnb0/5jFxsYaDfBOSUkxzDb2JP0TwcdFREQQFxdnuAl2d3fH3t6emzdvmuTVtxxYKi4ujoMHD9K6dWvGjRtntOzJ7lTu7u44ODiY3e/169dN0ooXL87hw4fx8vIy+zT0cfq6hYaGmhwXc8fEUh4eHnTs2JGOHTui1Wr54osv+O233+jduzdVq1Y1tFhcvnzZcL5zKif1LF68OABubm656p73LOmv0Zs3b5qcA3Pn3FLZnYOcHD9LtG3blhUrVrB161Y6duzIyZMn6dSpk9nf+eP0rUWXL1+2KJ+9vX2en8MbN26YTIJy48YNNBqNoWU5q1Y9fcvCk8qVK0e5cuXo27cvcXFx9OvXj++//57u3bvnyU1Pbo6JJWVydXWldevWtG7dGkVRmD17NkuWLGHfvn1mZznMTHZ17NSpE1OnTuXcuXNs2rQJW1tbXn/9dbN5zf090v/90/8d0x8PtVr9wv3OxTOiUoGrE/xfD/h0ZbbZ7dLTyK5dftSrMCPI3G2oitxOFG9nJUGOMCVjdgoA/dPiJ59ib9iwwaS7yeP9tvU8PT1xd3e3uMuQnr470JP997NqLbl586ZhbI7e4sWLAQwzTGk0GurXr8/ff//NmTNnjPL++uuvOSpjZscmIiKCjRs3GqVpNBoaNmzI+fPnjaa2VRSFJUuWmGy7devWQMZMYOaeOD9+7Bs3boxKpeLXX381ynvx4kWToMsSycnJJCcb94vWaDSG7mr6rnktW7YEMmYDS0tLM9mOJS0fOalnUFAQNjY2zJs3z6R8APHx8aSmpma7z2ehUaNGAKxcudLo+rx69WquxhdYeg5ycvwsUbFiRcqXL8/27dvZtm2bYdrp7FSoUIEyZcqwefNmrl27ZrJcfy3ou8otWrTI7N+E5ORkEhISclRmvSVLlhhdc/rrv3bt2oap54sVK4ZGozH5XZw9e9ZkbFNMTIzJ3xpnZ2d8fHxITk7Os3GJOTkmlpRJq9USFxdnlEelUhm6GOb0b7G9vb3hejOndevW2NrasnTpUkJCQnjttdeMxhY8bseOHUbdfdPS0li+fLnh7yNkXINly5Zl3bp13L5922Qb6enpOa6DeEm81z7bLApwv1D2U09PafrffG+aeP6kZacAaNCgAbNnz+aLL76ge/fuODs7c/bsWQ4fPoyvr6/RDdYvv/zCkSNHaNiwIT4+PiiKwoEDBwgNDTU7CD8rderUoWTJksybN4+YmBiKFStmuCFxc3Mzu065cuX4/PPP6dixIyVKlODEiRPs2bOHWrVq0aJFC0O+4cOHc+TIEUaOHEn37t0pWrQoBw8eNARrlj6tdXR0pF69emzfvh1bW1uqVq1KeHg469evx8fHx+Qf5BEjRnD48GHee+893njjDYoWLcqBAweIiooy2XbVqlUZMmQI8+fPp2fPnjRv3pwiRYoQERHBhQsXOHTokOEmulSpUnTr1o3Vq1czfPhwXnvtNSIjI1m9ejXly5fn0qVLFtVH7+bNmwwZMoSmTZtStmxZnJ2dCQ0NZe3atfj4+FCzZk0gowWvX79+LF68mF69etGiRQsKFy7M3bt32bNnD4sXL870pic39fT09GTs2LFMmjSJbt260bp1a7y9vYmKiuLq1auEhISwZs2aXE/1/DTKli1Lp06d2LBhAyNGjKBJkyZER0ezZs0aKlasyIULF3LUCmDpOcjJ8bNUmzZtmDFjBosXL6ZEiRIWTRmvUqkYP348I0aMoF+/foapp+Pi4jh16hQBAQH06NEDe3t7Jk6cyJgxY+jSpQvt27enePHixMXFERoayt69e/n2229NWpEtER4ezjvvvENgYCARERGsXr0aW1tbo+nIHRwcaNeuHRs3bmTcuHH4+fkRFhZGcHAw5cuXN2qZ2rp1K8uXL6dp06b4+vpiZWXFqVOn+OOPPwgKCsq0O2BO5eSYWFKmuLg4WrVqRWBgIBUrVsTd3Z27d++ydu3aXHVJrFatGps2beKHH36gdOnSqFQqAgMDDZOzuLi48Nprr7F9+3YAsxMT6JUoUYL+/fvTpUsXHBwc2LFjB+fPn2fQoEF4eXkBGdfSl19+yfDhw3nzzTcNY9aSk5MN09q/8847Ju9JEgWAg2W/qYnVkxn/V9YvQraVVhjxnEiwUwD4+voya9Ysw/tU1Go1r776KvPmzeObb74hPDzckLdx48ZERESwe/duIiMjsbW1pXjx4nz22WdZ/gNojkajYdq0aUydOpVVq1ZhbW1NvXr1mD9/Pm+99ZbZdSpVqsT777/P3LlzWb9+PY6OjnTv3p23337baDxDqVKlmD9/PjNnzmTFihWGl4p+/PHHdOjQIUdvQ//qq6+YPXs2Bw4cYOvWrRQvXpwRI0ZgZWXFxIkTTY7lzz//zPTp01m1apXhpaJffvmlUTCmN2TIEKpUqcLKlStZsWIFSUlJFCpUiLJlyzJmzBijvGPGjKFw4cJs2LCBmTNnUrx4cT7++GNu3bqV42DH09OT9u3bc/LkSUJCQkhLS6NIkSJ06tSJfv36Gd3kvfvuu5QvX57Vq1ezZMkSdDodnp6eNGjQwOKbwZzUs3379pQoUYJly5axfv164uLicHNzo2TJkgwfPtxkdprnaezYsRQpUoRNmzYxc+ZMSpYsydixY/n777+5cOFCjq6rnJyDnBw/S7z++uvMnj2bhISEHD2kqFq1KosXL+aXX35h9+7drFu3Djc3N5OZDwMCAli8eDGLFy9m+/btREVF4eLigq+vL7169bJ4wosnzZ49m2nTpjF//nySk5MNLxV9cnujR49GURRCQkLYt28flStXZtq0aWzYsMEo2PHz8+PSpUscOHCAiIgINBoNxYoV47333jN5d9HTsvSYWFImOzs73nzzTY4dO2aYic7Dw4PAwEAGDBiQ4zFRI0aMICYmhjVr1hAXF4eiKGzevNloJsrOnTuzfft2ihcvjp+fX6bbeuONN0hISGDVqlWGl4p+8MEHvPnmm0b5KlasyK+//srChQvZv38/69atw9HREW9vb9q1a0ft2rVzVAdRcKiAT0rFZBvsCPG8qJRnPYJXiH/o3ww/YcKEXG/jwoUL9OnTh3feeccwZbUQT+v999/n+PHj7Nu3z6IB60K8bM6dO0f//v15++23jV6krHfixAmGDRvG+PHjpUVGZK3rN7Au89ZoBUiPXITNLw6Z5qngBpcGyfP2nFBNSMo+0z+UCfbZZ/oPkTE74oX15HiIx8fOyKBYkRvmxhFduXKFw4cPU7t2bQl0RIG1evVqrKysJJART2/5+9nncbLnYhaNzqf65F1xhMiOhNXCSFpamkUDS93d3Z/5jWHPnj2pXbs25cqVIykpiQMHDnD69GmCgoLMvh+iIEhOTrbo5YiZTesssqZ/30iDBg1wd3cnNDSUDRs2YGVlxdChQwE5B5ZKTEzM9kWgGo0Gd/fsp58VpmJiYsxOKvI4Ozs7oxeiPikpKYn9+/dz/fp1tm/fTqdOnf7z163IAzaWTYtesagVjqp0Ep7oP+RfFBxt5fZTPD9ytQkjZ8+eZdiwYdnm27x58zMfZN64cWMOHDjAtm3b0Gq1FCtWjGHDhhXo7mu7du0yGUdkzuOzxQnLVapUiZCQEFatWkVMTAyOjo74+/szZMgQKlWqBMg5sNTSpUv56aefsszj7e1NcHDwcypRwfLhhx9y6tSpLPNk1y04KiqKTz/9FAcHB5o1a8bIkSPzuJRCZC3+AysW/5XO8N1gp4FDPaGyh9x6iudLxuwII7GxsVy4cCHbfDVq1MjRYG5hmYiICLPTAj9JuvE9O3IOLHP79u1M33ujZ2trazTxgbDchQsXspxOGjJeZFqmTJnnVCIhHqPunDE4x4xEwDp1VZ69GFdkUE007YadGWV83sxEWVBIsCOEEEIIISz3wQKYtsXsom0jahA04xMJdvKYBDu5JxMUCCGEEEIIy43tYjZZAe68UvT5lkWIbEiwI4QQQgghLFc485dRKxq5tRQvFrkihRBCCCGE5dRq8HI1SlKAvwJ986c8QmRBgh0hhBBCCJEz4QuhU21Qq8BKQ/qMARzp9Up+l0oIEzL/nxBCCCGEyLn1n/z7/2lpsHBh/pWloFPldwFeXtKyI4QQQgghhCiQJNgRQgghhBBCFEgS7AghhBBCCCEKJBmzI4QQQgghxAtNBu3klrTsCCGEEEKIPBWTrCMhVZffxRBCWnaEEEIIIUTe2BRVmS/GRlEiKppLRTyIdbTj72G2VCmqye+iif8oadkRQgghhBBPLUWnJuFPN+47O3G8VAliHezRpOuo+n1Sfhft5afKwUcYkWBHCCGEEEI8tcWX6rKvQjlQ/3N7qVKhtbICtZrENCV/Cyf+syTYEUIIIYQQuZeuJWj435x1Lg0qM00LKhVJaTJ+R+QPCXaEEEIIIUSuDeu5m30lKpFulflQcHtr6V8l8ocEO0IIIYQQInfS0llW1j/bbPZWEuyI/CHBjhBCCCGEyLUEO4ds86Sky5gdkT8k2BFCCCGEELmSojKeUtpKm079W5co++ieUXrkjbjnWSwhDOQ9O0IIIYQQIlcen4+g2r1bbP31G4rHRgKw5NWG9O84DEWtpu1XDzi11DWfSlkASC/AXJOWHSGEEEIIkSsaFaBkdFGbs3WhIdAB6Hv2IJ0vHAfgdJli+VE8ISTYEUIIIYQQuaMoiqF5p+6dqybL697+J00tt5wif8iVJ4QQQmQiODgYf39/Tpw4kd9FEeKFlK7827/qRLEyJstP+Pyb9sGO5OdSJiEeJ8GOEEII8QIKCQlh3rx5+V0MIbKkeWwsyTut+3PP6d9xOauq1mNtlboZXxSFaX/Ii0XF8ycTFAghhBAvoJCQELZs2cLQoUPzuyjiP+RqpJZCdlDIQZN9ZsBao0KTno7WyorTxUpT8v1ZNLh1mXtOrlwo6vtvRrUatVbLtWvxlC3r9IxKL4QpCXaEEEIIIfJYSrrCH3cVijmpqFDI8qm0ktIUjoQrlHBRUdZNxeE7Oo6EQ/cKCrfj1Virwc/LeHt/PtTxf3/oeJAA9tbgYQ9+nrDlGuy7DToFnG0gLR0SdPrWlSc69yhPvgcnI59ap8UuPQ2btDRKRj5k5tqVuCVouexWlPp3/8JdF81enzK81XEIVzy8SbWyZm+ZqqbbVqnQqdUE/JBAQFgoQWFXSLB15O/C3qToFBRvZ96sCFUq2lGmYRGsHS28RdXp4I9L4GgHNUpbto74T5FgRwghhMiGoigsXbqUtWvX8uDBA7y9vRk4cCBt27Y1yrdx40bWrFlDaGgoVlZWvPLKKwwePJgaNWoY5Tt48CBLlizh2rVrJCcn4+bmRpUqVXjnnXcoWbIkQ4YM4dSpUwD4+//7dvrx48fTrl07i8r88OFDli1bxvHjxwkPDyclJQUfHx/atGlDnz590GiMn9zfvXuX6dOnc+zYMQD8/Pz44IMPGDZsGN7e3syfP98o/9GjR1myZAl///03qamplChRgq5du9K1a1eLyleQnbin0Ha9lvuJGd/7VFGx6HU1alXWQc/B2wodN2l5lJTx3cMOIv4Z5vJBCIAWgPrFYFsXDQ5W0HSllkPhpttaesH4e3Sq/v8yGcGQSdl0GisSNVakaaz5Yu96GoefBaBSrD23qcojiqJJT6PU/QQ8I29ysEJJs9sunJiKd3wKVorCBW9vItxcCbz7kPrh17lRqARRj1LZcNya3w5GU+5/l3njy4oUb+yVyZH6R+gDaPklXL6b8b1Zddg0NiPwKWiyuXZE5iTYEUIIIbIxZ84cUlJS6Ny5MzY2Nqxdu5YJEybg6+trCGRmzZrFkiVLqFq1KiNGjCAxMZENGzYwdOhQvvvuOxo2bAjAyZMnGT16NGXLlmXAgAE4OTkRERHBsWPHCAsLo2TJkgwcOBBFUTh9+jRffvmloRzVq1e3uMxXrlxh7969NGnSBF9fX9LT0/njjz/4/vvvuXPnDp9++qkhb3R0NIMHD+bRo0d06dKF0qVLc/r0aYYNG0ZSUpLJttevX8/kyZOpVq0aAwcOxN7enqNHj/L1119z584dRo0alcsjXTCM2P1voAOw9LxCp/IKncpnfcM6dNe/gQ78G+g86fBd+O64jnLuKrOBzrMw6NgeOp87bvhuSxJFCCWcSmjj7Jm9eieL6lU3G+zYp2kpHvdvZRzTtETY2aNVqQktXBLvmHsoKi/uu1iRaGvLnUKF2PX5n/Tf64lak8Ux+2TZv4EOwJ4/Ye4O+LBjXlRZFBAS7AghhBDZSE1NZcmSJVhbWwPQrFkzOnTowOrVq6lRowahoaEsXbqUV199lR9//NGQr2PHjnTr1o0pU6YQEBCARqNh37596HQ65syZQ6FChQz7GDRokOH/69Wrx44dOzh9+jStW7fOVZlr1arFpk2bUD32RLhnz558/vnnbNq0iaFDh+Lh4QHA4sWLuX//Pl999RWvv/46AF27dmXmzJksXbrUaLsRERFMnTqVFi1a8H//93+G9G7dujF16lR+/fVXunTpgq+vLy+CyMhIHB0dsbW1BSA+Ph5FUXB2dgYyzm1cXByFCxc2rBMeHo63t3em3+/du4enp6fh2D65jxP3FJ58C+T+G4l0Ku+U6TavhYVz/lERi+t19E46t2MUntetXMDNyyZp9sQBUDQ+I0LbVcV0NjYA59R0kzSHdB1pajW2Oh06lRo1YK3VkmplRZpGQ3SiisT7STgVc8j8fBw3neqaE1fz5ZznxT7EsyGzsQkhhBDZ6NatmyGAAShatCglSpQgLCwMgH379qEoCn379jXKV6RIEdq1a0d4eDiXLl0CwMkp44b3999/Jz3d9CYwr9jZ2RluzNLS0oiJiSE6OpqAgAB0Oh3nz5835D1w4AAeHh60bNnSaBt9+vQx2e7u3btJTU2lQ4cOREdHG30aNWqETqczdIV7ERQqVMhwQwoZx19/QwpgY2NjdEMKmNyAPvndy8vLKIh8ch+1vUxbIxqXdshym2WLe1PVuBhZqudrRdNSNpav8JROFzMdD5OMo9F398SM1hsbbUZ3OxQF1+RUUjSmt5sKYPPP+KF0tRUKkP7Pu3hstOm4O4KDpz2QxfmoU960oLXL5cs5z4t9ZEmVg48wIi07QgghRDZ8fHxM0lxdXbl37x6QMd4FoGzZsib59Gl37tyhSpUqdO/enX379vH1118ze/ZsXn31VerXr0/Lli1xd3fPszKnp6ezaNEitm3bRlhYWMbLHx8TGxtr+P+7d+9StWpV1E+8+LFQoUJGN3AAoaGhAIwYMSLTfUdGRj5l6V9uc5traLtBy72EjO99q6hoXy77u9D5LTR02Kgl4p+ubEXs4aFpL0Ia+MBofzWO1vDDGfjjOXRl+zEgiC5/HaHBPy08adjykFKAlnQ0WAEDD51hX/mSpGnUdLh6m2KJyTilpvN3IRduODtjq8u4BhUUKkfGoAKckuN45FSIWDtbdCoVTsnJFIuMIujLall3YQOY3AtOXYdLdzK+N68OI15/VodAvKQk2BFCCCGy8WQQoPdkAGEJNzc3lixZwunTpzl69CinT59m2rRpzJs3j5kzZ+ZoXE5Wpk+fzqpVqwgKCmLgwIG4u7tjZWXFxYsXmT17dq7KDv/WeeLEiYZucE8yFxz+l/h5qbg5RMORu+DtBOXdLXvcXt9HRdhQDUfDobgzlHFTceSujqPh0K2CQli8Ghs11PT8d3uHe1lx8p6O707oSEwDWw04WENtLzh0J2NGNkUBGzXodOkkpOhI1ajJ6NyTzYxs/8yihqKQhIrOb77PnI0/UzvsFkqyK5DEfQ8dVx3L4hWbQvU793FKTiHKyYEdpbzxexBF4eQU7jjbc7GwI86pWuzStJSJiqFY7EPS0HDP3hmnxGQqq6IpW8eLcq+4UyawAtYOFtyiliwK52fC0SvgaAvVS1l0nMV/iwQ7QgghxFPS39xfu3bNZKzK9evXjfIAaDQa/P39DTOtXblyhd69e/PLL78wc+ZMAKMuM7mxbds2atWqxeTJk43S9V3vHuft7U1YWBg6nc4osIuMjCQuLs4ob/HixYGMoK1u3bpPVcaCzEajIrB4ztezs1LR+LH16hVTU69Yxv8Xcza/jp+XmuVtTQPyETWfTMn+ti81XUeSFlxtn9yeDeAAkz81Si2dmMz5wJ+56m5Hh+GDSbfK2EeKlYbDxYyD4QRrhZW9rXi9SknAzKxtuaFWQ0DFvNmWKJBkzI4QQgjxlAIDA1GpVCxdutRoHE5ERATBwcF4e3tTsWLGDVl0dLTJ+qVKlcLOzs6oa5m9fcZ4hZiYmFyVSa1Wm7TeJCUlsXz5crPlj4iI4LfffjNKf3JyAoCgoCBsbGyYN28eycmm04XFx8eTmppqki5eDjZWajOBTuaSUiHBxpbtlV8xBDqZKRIby+tV7J+2iELkiLTsCCGEEE+pVKlS9OnThyVLljB48GCCgoIMU08nJiby1VdfGd5rM2nSJB48eEDdunXx9vYmJSWFXbt2kZCQQJs2bQzbrFatGqtXr+brr7+mYcOGhvf2WNpFrFmzZqxfv55PPvmEOnXq8OjRI4KDg3F1dTXJ269fP3bs2MHEiRP5+++/KVWqFKdPn+bPP//Ezc3NqJXJ09OTsWPHMmnSJLp160br1q3x9vYmKiqKq1evEhISwpo1ayhWrNhTHlXxMnBws2Nm3VaEuWc9k5xKp2NuG9ss8wjxLEiwI4QQQuSBkSNHUrx4cdasWcP333+PtbU1VatWZdKkSdSs+W9/otatWxMcHMzWrVuJiorC0dGRMmXKMGXKFJo1a2bI17JlSy5dusTOnTvZs2cPOp2O8ePHWxzsjB49GkdHR3bt2sW+ffvw9PSkU6dOVKlSxWRyATc3N37++WdmzJjB5s2bUalU+Pn58eOPP9K3b1+jWacA2rdvT4kSJVi2bBnr168nLi4ONzc3SpYsyfDhw01moRIFV7pWl22gA4ACnZoXyj6fEHlMpeR2hKIQQgghCrTo6GiaN29O586dGTduXH4XR7ygVBOTMiYyyIqioIyXLmy5pZpseddQ5ZPnNyX5y0DG7AghhBDC7PibxYsXA8hEBCJTSWm67DMpiulMb0I8J9KNTQghhHhJJCcnEx8fn22+zKaEzsqoUaPw9vamUqVK6HQ6jh8/zoEDB6hevTpNmjTJRWnFf4HawlkDt7ypecYlEcI8CXaEEEKIl8SuXbuYOHFitvlOnDiR4203atSIrVu3snfvXlJSUvD09KR3794MHjzYMLmCEE+ytbIg2FGpaFZWrqGn83RT0f+XyZgdIYQQ4iURERHBtWvXss0n3c7E86SekIiSyYt3AVAUbr+nwcdVxpLklmpymsV5lU+sn2FJXj7SsiOEEEK8JDw8PHLVRU2IZ0qtzhiTk0WXNntradkR+UMmKBBCCCGEELkW/bENVipV5pMQqFRo5I5T5BO59IQQQgghRK652KpJ/ERDG7uTZperdVqs5I7z6ahy8BFG5NITQgghhBBPrb37edBqjRMVBZ1ag6ONdGMT+UOCHSGEEEIIkSfeK/SbcYJKxd/DZYi4yD9y9QkhhBBCiDxR2T6C1BEaknUaNCpwsJHn6iJ/SbAjhBBCCCHylLOtBDnixSBXohBCCCGEEKJAkpYdIYQQQgghXmQyy1quScuOEEIIIYQQokCSYEcIIYQQQghRIEmwI4QQQgghhCiQJNgRQgghhBBCFEgS7AghhBBCCCEKJAl2hBBCCCGEEAWSTD0thBBCCCHEi0ymns41adkRQgghhBBCFEgS7AghhBBCCCEKJAl2hBBCCCGEEAWSBDtCCCGEEEKIAkmCHSGEEEIIIUSBJMGOEEIIIYQQokCSqaeFEEIIIYR4kalk7unckpYdIYQQQggh/gMmTJiAk5NTfhfjuZJgRwghhBBCCFEgSTc2IYQQQgghXmTSiy3XpGVHCCGEEEIIwV9//UXLli1xdHTE1dWVrl27cuvWLcPyt956i0aNGhm+R0REoFarqV27tiEtPj4ea2tr1qxZ81zLnhkJdoQQQgghhPiPCwsLIzAwkEePHrFs2TJ+/PFHTp06RePGjYmLiwMgMDCQ48ePk5ycDMD+/fuxtbXl9OnThjyHDx8mPT2dwMDAfKvL46QbmxBCCCEKJEVRDDdg4tlKS0sjKSkJgNjYWKytrfO5RC8PZ2dnVC/AbGvTp08nLS2NnTt3UqhQIQBq1qxJlSpVWLRoEe+++y6BgYGkpKRw9OhRGjduzP79++nUqRM7d+7k0KFDtGrViv3791OhQgU8PT3zuUYZJNgRQgghRIEUFxeHq6trfhfjP+e9997L7yK8VGJiYnBxcckyjzLm2d+yHzhwgNdee80Q6ABUqlSJV199lYMHD/Luu+9SunRpfH192b9/vyHYGTZsGElJSezbt88Q7LworTogwY4QQgghCihnZ2diYmKe2/7i4+Np06YNW7du/c9N7wtS/9zW39nZ+RmWynJRUVHUqFHDJN3T05PIyEjDd32QExsby9mzZwkMDCQhIYG1a9eSkpLCsWPHGDx48HMsedYk2BFCCCFEgaRSqbJ9Yp6X1Go1Go0GFxeX/+TNvtT/5a5/oUKFePDggUn6/fv3qVChguF7YGAgo0ePJiQkBA8PDypVqkRCQgIff/wxe/fuJSUlxWgSg/wmExQIIYQQQgjxH9ewYUP27NlDVFSUIe3SpUv8+eefNGzY0JCmb8mZNm2aobtajRo1sLe35+uvv6Z48eKUKlXqeRc/U9KyI4QQQgghxH+EVqtl7dq1JumjRo1i4cKFtGjRgk8//ZTk5GQ+++wzSpQoQf/+/Q35KlWqRNGiRdm3bx+zZs0CQKPR0KBBA7Zv306vXr2eV1UsIsGOEEIIIUQesLGxYfDgwdjY2OR3UfKF1P/lqH9ycjLdunUzSV+6dCn79u1jzJgx9OrVC41GQ1BQENOmTTMZVxQYGMjatWuNJiJo3Lgx27dvf6EmJwBQKYqi5HchhBBCCCGEECKvyZgdIYQQQgghRIEkwY4QQgghhBCiQJJgRwghhBAil/bv38+bb75J/fr16dy5M5s3b852nbS0NGbOnMngwYNp2LAh/v7+REdHP/vCPoXQ0FBGjBhBw4YNadmyJTNnziQtLS3b9RRFYdGiRbRp04YGDRowYMAA/vrrr+dQ4ryV2/qvWbOG9957j+bNm+Pv78/u3bufQ2nF4yTYEUIIIYTIhTNnzvDhhx9SrVo1Zs2aRVBQEF999VW2N7TJycls3LgRGxsbatas+ZxKm3uxsbEMGzaM9PR0vv32W0aMGMGGDRuYNm1atusuXryYefPm0bNnT6ZPn46HhwfvvPMOt2/ffg4lzxtPU/+tW7cSHR1NgwYNnkNJhTkyG5sQQgghRC78/PPPVK1alXHjxgHg7+/P7du3mTdvHs2bN890PWdnZ37//XdUKhXBwcH88ccfz6vIubJu3ToSEhL49ttvcXV1BTKmL54yZQoDBw6kSJEiZtdLSUlh4cKF9O7d2zAdcc2aNencuTPLli1j7Nixz60OTyO39QdYsGABarWau3fvsnXr1udVZPEYadkRQgghhMih1NRUTpw4YRLUtGjRghs3bnD37t08sV6AAAAYaUlEQVQs11epVM+yeHnq8OHD1KlTx3CjDxAUFIROp+PIkSOZrvfnn3+SkJBgdIysra1p2rQphw4deqZlzku5rT+AWi232vlNzoAQQgghRA7dvn2b9PR0kzfFly5dGsgY41FQhIaGmtTT2dkZDw+PLOupX2buGN27d4/k5OS8Legzktv6ixeDBDtCCCGEEDkUGxsLYPKyRRcXF6PlBUFsbKxJPSGj7lnVMzY2FhsbG2xtbU3WUxSFuLi4PC/rs5Db+osXg4zZEUIIIYQA4uPjiYiIyDafj4/PcyiNECIvSLAjhBBCCAHs3r2bSZMmZZtv7dq1hhac+Ph4o2X6J/365QWBi4uLST0B4uLisqyni4sLqamppKSkGLXuxMXFoVKpzLaWvIhyW3/xYpBgRwghhBAC6NixIx07drQob2pqKlZWVoSGhhIQEGBIz2ycysusVKlSJmNT9K1gWdVTv+zmzZtUqFDBkB4aGoqXlxd2dnbPoLR5L7f1Fy8GGbMjhBBCCJFDNjY2+Pv7s2fPHqP0Xbt2Ubp0aYoVK5ZPJct79evX59ixY0ZjbHbv3o1araZevXqZrle9enUcHR2N3juUnp7O3r17X6r3zuS2/uLFIC07QgghhBC5MGjQIIYOHcrXX39N8+bNOXnyJDt27GDy5MlG+erWrUubNm344osvDGmHDh0iKSmJ8+fPA7B//34cHBwoU6YMZcqUea71yE6XLl1YtWoVH3zwAQMHDuTBgwfMnDmTzp07G71jZvjw4YSHh7Nx40YAbG1tGTBgAPPnz8fd3Z1y5cqxZs0aYmJi6N27dz7VJudyW3+A8+fPc/fuXaKjowE4d+4cAO7u7vj5+T3PavxnqRRFUfK7EEIIIYQQL6N9+/bxww8/cPPmTby8vOjfvz8dOnQwyuPv70/btm2ZMGGCIa1du3aEh4ebbG/w4MEMHTr0WRc7x27cuMG3337L2bNncXR0pE2bNowYMQJra2tDniFDhhAeHk5wcLAhTVEUFi1axNq1a4mKiqJChQqMHj2a6tWr50c1ci239Z8wYQJbtmwx2V6tWrWYP3/+cyn7f50EO0IIIYQQQogCScbsCCGEEEIIIQokCXaEEEIIIYQQBZIEO0IIIYQQQogCSYIdIYQQQgghRIEkwY4QQgghhBCiQJJgRwghhBBCCFEgSbAjhBBCCCGEKJAk2BFCCCGEEEIUSBLsCCGEEOKl1L9/f1QqVX4XA4Bz585hZWXFrl27DGkhISGoVCoWLVqUfwUTL4RFixahUqkICQnJ1fpyLZl35swZ1Go1+/btyzSPBDtCCCHEC+T69esMGTKESpUq4eDggLu7O5UrV6Zfv37s3bvXKG+pUqV45ZVXMt2WPhiIiIgwu/zChQuoVCpUKhUHDhzIdDv6PPqPnZ0d5cuXZ/To0URGRuauogXM6NGjadCgAUFBQfldlOciNDSUCRMmcObMmfwuinhOoqOjmTBhQq4DttzK6lqrUaMGHTt25IMPPkBRFLPrWz3j8gkhhBDCQidOnKBx48ZYW1vTt29fqlatSlJSEleuXGHnzp04OzvTtGnTPNvfL7/8grOzM/b29ixYsIBGjRplmrdGjRp88MEHAERGRrJt2zamT5/Orl27OHnyJDY2NnlWrpfNH3/8wa5du9i4caNRemBgIElJSVhbW+dPwZ6h0NBQJk6cSKlSpahRo0Z+F0c8B9HR0UycOBGAJk2aPLf9ZnetvffeezRu3Jht27bRpk0bk+US7AghhBAviIkTJ5KYmMiZM2d49dVXTZbfu3cvz/aVlpbG0qVL6datG66ursyfP59Zs2bh7OxsNr+Pjw+9e/c2fB85ciTt2rVjy5YtbNq0iW7duuVZ2V42c+fOxcPDg9atWxulq9Vq7Ozs8qlUQvw3NGrUiFKlSvHjjz+aDXakG5sQQgjxgrhy5QqFCxc2G+gAeHl55dm+goODefDgAf369aN///4kJCSwatWqHG2jZcuWAFy9ejXTPD/88AMqlYrNmzebLNPpdPj6+ho9rd25cydvvPEGZcqUwd7eHjc3N1q0aJFln/zHNWnShFKlSpmkh4aGolKpmDBhglG6oij88MMP+Pn54eDggJOTE02bNjXpMpiZ9PR0Nm7cSPPmzU1acMyNs3g8be7cuVSsWBE7OzuqVavGli1bAPjrr79o1aoVLi4uFC5cmJEjR5KWlma2ntevX6dDhw64urri4uJCp06duH79ulFenU7H//3f/xEYGIiXlxc2NjaUKFGC4cOH8+jRI7P1WrduHU2aNMHNzQ0HBwcqVqzIyJEjSU1NZdGiRYYWxgEDBhi6N1rytD80NJQ+ffrg6emJra0tZcuWZdy4cSQmJhrlmzBhAiqVikuXLjFu3Dh8fX2xtbXl1VdfZdu2bdnuB/4dJ7Nnzx6+/PJLSpYsib29PXXr1uXIkSMA7Nu3j4YNG+Lo6Ii3tzdfffWV2W1t3LiRBg0a4OjoiJOTEw0aNGDTpk1m8/70009UqlQJW1tbypUrx4wZMzLtYhUTE8PHH39MuXLlsLW1pUiRIrz55psm5zCnLD3OWY17U6lU9O/fH8i4bkuXLg1kPJTRn3P9b+3x39eKFSuoXr06dnZ2lChRggkTJpCenm60bUt/p5ZcayqVipYtW7Jjxw7i4+NNtiktO0IIIcQLomzZsly6dIn169fTuXNni9bRarWZjslJSUnJdL1ffvmF0qVL06hRI1QqFTVr1mTBggUMGjTI4vJeuXIFAA8Pj0zz9OjRg/fff58lS5bQvn17o2V79uzhzp07hu5xkHFzExkZSd++ffH19eXOnTv8/PPPNGvWjL1792bZ1S43+vTpw4oVK+jatSsDBgwgJSWFX3/9laCgINavX29S5iedPHmS+Ph46tSpk6P9zpkzh6ioKAYNGoSdnR2zZs2iU6dOrFmzhsGDB/Pmm2/SsWNHdu7cyezZsylatCifffaZ0TYSEhJo0qQJdevWZfLkyVy5coW5c+dy5MgRTp8+bQiOU1NT+fbbb+nSpQsdOnTA0dGR48eP88svv3Dw4EGTboiffvop//vf/6hSpQrvv/8+3t7eXLt2jXXr1vHll18SGBjIuHHj+N///seQIUMM58TT0zPLOt+8eZM6deoQExPDiBEjKF++PCEhIUyePJlDhw6xZ88erKyMb0379euHtbU1Y8aMITU1lRkzZtCxY0cuX75s9mbZnLFjx6LVahk1ahSpqal89913tGjRgiVLlvDWW28xZMgQevXqxerVq/niiy8oXbq0USvm3Llzefvtt6lUqRJffPEFkHGdduzYkXnz5jFkyBBD3hkzZvD+++/z6quv8r///Y/ExESmTp1K0aJFTcoVExND/fr1uXXrFgMHDqRq1aqEh4czd+5c6taty4kTJyhZsqRFdXza45ydypUrM336dN5//306depk+Pvk5ORklG/z5s1cv36dt99+Gy8vLzZv3szEiRO5efMmCxcuzHFdLL3WAgICmDdvHgcPHqRVq1bGG1GEEEII8UI4fPiwYm1trQBK+fLllQEDBihz585Vzp8/bzZ/yZIlFSDbz8OHD43Wu3PnjqLRaJTx48cb0mbMmKEAZvcFKC1atFAePnyoPHz4ULl8+bIybdo0xdraWnF1dVXu37+fZb26du2q2NraKpGRkUbpvXv3VqysrIzWj4+PN1n/3r17SuHChZXXX3/dKL1fv37Kk7cyjRs3VkqWLGmyjRs3biiAUZ3Xr1+vAMq8efOM8qalpSl+fn5KqVKlFJ1Ol2XdFixYoADKpk2bTJbt3btXAZSFCxeapBUrVkyJjo42pJ89e1YBFJVKpaxbt85oO7Vq1VK8vLxM6gkoo0aNMkrX12no0KGGNJ1OpyQmJpqU7+eff1YAZdWqVYa0o0ePKoDStGlTJSkpySi/TqczHA9zdctOz549FUDZunWrUfqYMWMUQPn5558NaePHj1cApU2bNkbn4NixYwqgjB07Ntv9LVy4UAGUmjVrKikpKYb0TZs2KYBiZWWlHD9+3JCekpKieHl5KfXq1TOkRUZGKo6OjkrZsmWVmJgYQ3pMTIxSpkwZxcnJSYmKilIURVGioqIUBwcHpXLlykpCQoIhb1hYmOLo6KgAyt69ew3pI0eOVOzs7JQzZ84YlTs0NFRxdnZW+vXrZ0jLyfHOyXE29xvSA4zKYO439OQytVqtnDx50pCu0+mUjh07KoDyxx9/GNJz8ju1pO4HDhxQAGXq1Kkmy6QbmxBCCPGCCAgI4OTJk/Tr14+YmBgWLlzIiBEjqFKlCoGBgWa7tpQqVYpdu3aZ/bRo0cLsfhYtWoROp6Nv376GtF69emFtbc2CBQvMrrNz506KFClCkSJFqFChAqNHj6ZKlSrs3LnT7FPrx/Xr14+UlBSjbnLx8fFs2LCBVq1aGa3v6OholOfRo0doNBrq1q3L0aNHs9xPTi1btgxnZ2c6duxIRESE4RMdHU27du0IDQ01tF5l5uHDhwAUKlQoR/vu378/rq6uhu/Vq1fHxcWFYsWKmbTqNWzYkHv37pntojN27Fij7506daJixYpGkyWoVCrs7e2BjJbA6OhoIiIieO211wCMjuuvv/4KwOTJk03GG+m7EOWGTqdj8+bN1KxZ02Rs0yeffIJarWbDhg0m640aNcpon7Vr18bJySnb8/K44cOHG7Vc6VsH6tati7+/vyHdxsaGOnXqGG17165dJCQkMHLkSFxcXAzpLi4ujBw5kvj4eHbv3g1k/EYSExN5++23cXBwMOT19fWlV69eRmVSFIVff/2VwMBAfHx8jK4/R0dH6tWrx86dOy2uo15uj3NeCQoKolatWobvKpWKjz76COCZ7rdw4cIAPHjwwGSZdGMTQgghXiDVqlUzjPG4efMm+/bt4+eff+bAgQN06NDBpMuRo6MjzZs3N7utZcuWmaQpisKCBQuoXr06Op3OaLxNgwYNWLp0KZMnTzbp5lK3bl0mTZoEgK2tLSVLlqREiRIW1Ukf0CxZsoRhw4YBGWNCEhISjAIugGvXrvHpp5/y22+/ER0dbbQsr9+pc+HCBeLi4rLsfnX//n0qVKiQ6XJ9mZRMxmRkpkyZMiZp7u7uFC9e3Gw6wKNHj4y6Dbm5uZkdx1W5cmU2btxIQkKCIXhcvXo13333HadPnzYZ/xMVFWX4/ytXrqBSqTIdN5ZbDx8+JD4+nqpVq5osK1SoEN7e3maDeXPHqXDhwpmONTLnyW3oj6d+DMqTyx7f9o0bNwDMllufpi+3/r+VKlUyyVulShWj7w8fPuTRo0eGhwjmqNU5b5PI7XHOK5UrVzZJ09f9We5X//sz9zdCgh0hhBDiBVWyZEn69u1Lnz59aNSoEYcOHeLYsWM0bNgw19vct28f165dA6B8+fJm82zZsoWOHTsapXl4eGQaVGXHysqKnj17MmPGDK5evUq5cuVYsmQJ7u7uRmNi4uPjCQwMJCEhgffee49q1arh7OyMWq1m8uTJ/P7779nuK7OA6MkB0pBxg1SkSBGWL1+e6fayeo8RYLhRzen7hjQaTY7SIecBld769et54403qFOnDjNnzqR48eLY2dmh1Wpp1aoVOp3OKP/TtODktcyOR06ORW6O9bOmL3/z5s35+OOP860cOfm9vMj71f/+zAWOEuwIIYQQLziVSkXdunU5dOgQd+7ceaptLViwAFtbW5YsWWL2yfHQoUP55ZdfTIKdp9WvXz9mzJjBkiVLGDx4MCEhIQwZMgRbW1tDnj179nD37l0WLFjAgAEDjNZ/cnB+ZgoVKsTJkydN0s09VS5fvjyXL1+mXr16JgOtLaUPhnLSrSqvREdHc+/ePZPWnQsXLlC0aFFDq87SpUuxs7Nj7969Rt2rLl68aLLNChUqsH37ds6ePZvlpAs5DYaKFCmCs7Mzf//9t8myqKgowsPDX8j39ehbhf7++2+aNWtmtOz8+fNGefT/vXjxYqZ59YoUKYKbmxuxsbG5fohgTk6Ps777ZWRkpFFXTHO/F0vO+YULF0zSnjxO+v1a+ju1ZL/6FmpzDydkzI4QQgjxgti1a5fZJ5tJSUmG/vtPdofJiZiYGNauXUuLFi3o3r07Xbt2Nfm0b9+e7du3Ex4enuv9mFOjRg2qV6/OsmXLWLp0KTqdjn79+hnl0T9pf/Kp/c6dOy0er1OhQgXi4uI4duyYIU2n0zF9+nSTvH379kWn0/HJJ5+Y3db9+/ez3V/NmjVxcXExTGX8vH399ddG3zds2MClS5eMglWNRoNKpTJqwVEUxdAt8XE9e/YEYNy4caSmppos158bfXBoaYuWWq2mXbt2nD59mh07dpjUQafT0alTJ4u29TwFBQXh6OjI7NmziYuLM6THxcUxe/ZsnJycCAoKMuS1t7dnzpw5RlM8375926T1UK1W06tXL44dO8batWvN7tvc+JPs5PQ467to6scd6X333Xcm27bknO/atYtTp04ZviuKwjfffANgdE3m5HdqyX6PHDmClZUVDRo0MFkmLTtCCCHEC+L999/n0aNHtG/fnmrVquHg4EBYWBjLly/n8uXL9O3bl2rVquV6+ytWrCApKYkuXbpkmqdLly4sWrSIxYsXmwx+f1r9+vXjgw8+YMqUKVSoUIF69eoZLW/YsCFeXl588MEHhIaG4uvry5kzZ1i6dCnVqlXjr7/+ynYfQ4YM4bvvvqNTp06MGjUKGxsb1q5dazaI1E83/f3333Pq1Cnatm2Lh4cHt2/f5o8//uDq1avZjjPQaDR07tyZjRs3kpKSYtRS9ax5eHiwfv167t69S5MmTQxTT3t6ehq9T6hr166sW7eO1157jb59+5KWlsbGjRtN3rkCUKdOHT7++GOmTJlCrVq1eOONN/Dy8uLGjRusXbuWY8eO4ebmRpUqVXB2dmbu3Lk4ODjg5uZG0aJFDZMemPO///2PXbt20bFjR0aMGEG5cuXYv38/q1atIjAw0CT4fRG4ubnxzTff8Pbbb1O3bl3De2cWLVrE1atXmTdvnmGiCXd3d7766ivGjBlD/fr16du3L4mJifz444+UL1+e06dPG237//7v/zh06BDdu3ene/fu1KtXDxsbG27evMm2bdvw8/MzekeTpXJynN98803GjRvHkCFDuHjxIoUKFWLHjh1mp7MvXLgw5cqVY+XKlZQtWxZPT08cHR1p166dIc+rr77Ka6+9xttvv423tzebNm1i9+7d9OnTh4CAAEO+nPxOs7vWFEVhx44dtGrVynwLbaZzuAkhhBDiufrtt9+UESNGKNWrV1cKFy6saDQapVChQkqTJk2UX375RdFqtUb5S5YsqVStWjXT7emnldVPPe3v769YWVmZTAH9uOTkZMXZ2VmpUKGCIY1/pgB+Wvfu3VOsrKwUQJk0aZLZPGfPnlVatmypuLm5KU5OTkrjxo2V/fv3m50iN7Npc7du3aq8+uqrio2NjeLt7a189NFHysWLFzOdNnfJkiVKw4YNFWdnZ8XW1lYpWbKk0qlTJ2XlypUW1Us/XfPatWuN0rOaetrcNLolS5ZUGjdubJKun4b5xo0bhjT91L3Xrl1T2rdvrzg7OytOTk5K+/btlStXrphsY/78+UrlypUVW1tbxcvLSxk8eLDy6NEjk+mF9ZYvX67Ur19fcXJyUhwcHJSKFSsqo0aNMprCeevWrUrNmjUVW1tbBTBb9iddv35d6d27t1KkSBHF2tpaKV26tPLJJ58YTdWcWZ2zO05P0k89/fh0z3qZ1Tuza2r9+vVKQECA4uDgoDg4OCgBAQHKhg0bzO73xx9/VCpUqKDY2NgoZcuWVaZPn26YovzJsiQkJChffvml8sorryh2dnaKk5OTUqlSJWXQoEHKkSNHDPlyOtW3pcdZURTlyJEjSv369RVbW1ulcOHCyuDBg5WoqCizx+jo0aNK/fr1FQcHBwUwTB/9+JTRy5cvV6pVq6bY2Ngovr6+yueff66kpqaa7Dcnv9OsrrWQkBAFULZs2WL2WKgUJZej3YQQQgghBJAx41xCQgIHDhx4Lvtr0qQJoaGhhIaGPpf9CZGV0NBQSpcuzfjx441aFZ+HTp06ERYWxvHjx82O75ExO0IIIYQQT+m7777jjz/+yNW7UYQQuXP69Gk2bdrEd999l+lEBjJmRwghhBDiKVWtWvWZT9crhDBWs2ZNk6nTnyQtO0IIIYQQQogCScbsCCGEEEIIIQokadkRQgghhBBCFEgS7AghhBBCCCEKJAl2hBBCCCGEEAWSBDtCCCGEEEKIAkmCHSGEEEIIIUSBJMGOEEIIIYQQokCSYEcIIYQQQghRIEmwI4QQQgghhCiQJNgRQgghhBBCFEj/D+m0vO89us0gAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## SHAP VALUES\n", - "\n", - "# SHAP requires that all features passed to Explainer be numeric (floats/ints)\n", - "X_test_shap = X_test_lasso.copy()\n", - "X_test_shap = X_test_shap.astype(float)\n", - "\n", - "# Function that returns the probability of the positive class\n", - "def model_predict(data):\n", - " return best_pipeline_lasso.predict_proba(data)[:, 1]\n", - "\n", - "# Ensure input to SHAP is numeric\n", - "X_test_shap = X_test_lasso.astype(float)\n", - "\n", - "# Create SHAP explainer\n", - "explainer = shap.Explainer(model_predict, X_test_shap)\n", - "\n", - "# Compute SHAP values\n", - "shap_values = explainer(X_test_shap)\n", - "\n", - "# Plot summary\n", - "shap.summary_plot(shap_values.values, X_test_shap)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Interpreting the SHAP Summary Plot\n", - "\n", - "Each point on a row represents a SHAP value for a single prediction (row = feature).\n", - "The x-axis shows how much the feature contributed to increasing or decreasing the prediction.\n", - "* Right (positive SHAP value): pushes prediction toward the positive class (i.e., higher chance of incident).\n", - "* Left (negative SHAP value): pushes prediction toward the negative class (i.e., lower chance of incident).\n", - "\n", - "Color shows the actual feature value for that point:\n", - "* Red = high value\n", - "* Blue = low value\n", - "\n", - "In other words:\n", - "* The position tells you impact.\n", - "* The color tells you feature value.\n", - "* The density (thickness) of dots shows how often a value occurs." - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "5e02ada3", - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "\n", - "def clean_colname(col):\n", - " return re.sub(r'[^A-Za-z0-9_]+', '_', col)\n", - "\n", - "X_train_lasso.columns = [clean_colname(col) for col in X_train_lasso.columns]\n", - "X_test_lasso.columns = X_train_lasso.columns # Keep them aligned" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "345467a8", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/xgboost/training.py:183: UserWarning: [15:47:04] WARNING: /workspace/src/learner.cc:738: \n", - "Parameters: { \"use_label_encoder\" } are not used.\n", - "\n", - " bst.update(dtrain, iteration=i, fobj=obj)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Info] Number of positive: 16843, number of negative: 16843\n", - "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002913 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 3256\n", - "[LightGBM] [Info] Number of data points in the train set: 33686, number of used features: 78\n", - "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", - "Random Forest: Average Precision = 0.2043\n", - "Extra Trees: Average Precision = 0.1996\n", - "XGBoost: Average Precision = 0.1267\n", - "LightGBM: Average Precision = 0.0924\n" - ] - } - ], - "source": [ - "from xgboost import XGBClassifier\n", - "from lightgbm import LGBMClassifier\n", - "from sklearn.ensemble import ExtraTreesClassifier\n", - "\n", - "# Updated model list\n", - "models = {\n", - " \"Random Forest\": RandomForestClassifier(class_weight='balanced', random_state=123),\n", - " \"XGBoost\": XGBClassifier(scale_pos_weight=(y_train_lasso.value_counts()[0] / y_train_lasso.value_counts()[1]),\n", - " use_label_encoder=False, eval_metric='logloss', random_state=123),\n", - " \"LightGBM\": LGBMClassifier(class_weight='balanced', random_state=123),\n", - " \"Extra Trees\": ExtraTreesClassifier(class_weight='balanced', random_state=123)\n", - "}\n", - "\n", - "results = {}\n", - "\n", - "for name, model in models.items():\n", - " pipeline = Pipeline([\n", - " ('classifier', model)\n", - " ])\n", - "\n", - " pipeline.fit(X_train_lasso, y_train_lasso)\n", - " y_pred_proba = pipeline.predict_proba(X_test_lasso)[:, 1]\n", - "\n", - " avg_precision = average_precision_score(y_test_lasso, y_pred_proba)\n", - " results[name] = avg_precision\n", - "\n", - "# Sort and display\n", - "sorted_results = dict(sorted(results.items(), key=lambda x: x[1], reverse=True))\n", - "for model, score in sorted_results.items():\n", - " print(f\"{model}: Average Precision = {score:.4f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "281689e7", - "metadata": {}, - "source": [ - "### Model 4 Extra Trees Classifier with Lasso features" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "id": "4ff9d4ca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 10.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 15.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 18.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 20.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 21.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 24.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 26.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 29.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 9.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 30.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 16.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 8.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 22.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 24.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 25.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 25.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 15.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 15.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 15.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 19.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 19.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 9.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 30.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 23.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 23.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 24.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 25.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 25.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 18.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 18.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, 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model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 11.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 26.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 26.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 17.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 27.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 29.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 17.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 17.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 29.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, 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model__min_samples_split=5, model__n_estimators=100; total time= 6.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 22.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 19.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 20.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 19.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 12.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 20.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 13.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 13.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 22.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 13.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 19.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 19.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 19.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 10.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 8.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 10.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 11.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 13.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 23.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 14.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 16.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 15.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 17.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 10.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 29.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 10.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 11.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 22.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 14.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 13.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 14.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 14.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 14.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 9.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 16.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 10.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 14.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 14.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 16.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 15.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 9.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 17.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 10.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 10.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 12.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 13.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 14.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 13.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 14.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 15.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 8.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 9.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 8.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 9.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 8.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 4.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 11.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 12.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 12.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 8.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 7.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 14.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 14.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 9.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 12.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 11.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 12.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 7.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 13.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 12.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 8.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 8.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 7.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 8.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 11.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 11.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 13.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 8.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 13.5s[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 7.9s\n", - "\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 8.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 8.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 8.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 13.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 11.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 11.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 11.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 11.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 12.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 9.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 9.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 14.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 9.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 8.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 15.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 15.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 17.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 24.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 22.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 24.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 18.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 29.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 18.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 10.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 10.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 10.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 11.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 25.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 26.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 28.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 27.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 27.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 15.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 16.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 18.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 17.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 8.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 23.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 6.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 26.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 24.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 14.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 15.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 17.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 27.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 28.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 8.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 22.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 24.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 13.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 15.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 26.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 26.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 12.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 17.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 26.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 16.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 7.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 20.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 23.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 23.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 23.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 15.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 13.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 25.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 16.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 7.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 20.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 19.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 22.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 21.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 23.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 11.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 13.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 14.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 6.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 7.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 17.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 20.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 17.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 19.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 12.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 18.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 11.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 10.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 13.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 13.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 12.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 12.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 11.4s\n", - "Best hyperparameters (Extra Trees): {'model__max_depth': None, 'model__max_features': 'log2', 'model__min_samples_leaf': 1, 'model__min_samples_split': 5, 'model__n_estimators': 200}\n" - ] - } - ], - "source": [ - "# Define pipeline (scaling numeric features only)\n", - "et_pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', ExtraTreesClassifier(class_weight='balanced', random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid for ExtraTrees\n", - "et_param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "et_grid_search = GridSearchCV(\n", - " estimator=et_pipeline,\n", - " param_grid=et_param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=5,\n", - " n_jobs=-1,\n", - " verbose=2\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "et_grid_search.fit(X_train_lasso, y_train_lasso)\n", - "\n", - "# Best model\n", - "best_et_pipeline_lasso = et_grid_search.best_estimator_\n", - "print(\"Best hyperparameters (Extra Trees):\", et_grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba_et_lasso = best_et_pipeline_lasso.predict_proba(X_test_lasso)[:, 1]\n", - "y_pred_et_lasso = best_et_pipeline_lasso.predict(X_test_lasso)" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "603b17b3", - "metadata": {}, - "outputs": [], - "source": [ - "# Actual and predicted\n", - "y_true_et_lasso = y_test_lasso\n", - "\n", - "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n", - "tn, fp, fn, tp = confusion_matrix(y_true_et_lasso, y_pred_et_lasso).ravel()\n", - "\n", - "# Total predictions\n", - "total = tp + tn + fp + fn\n", - "\n", - "# Compute all requested metrics\n", - "recall_et_lasso = recall_score(y_true_et_lasso, y_pred_et_lasso)\n", - "precision_et_lasso = precision_score(y_true_et_lasso, y_pred_et_lasso)\n", - "f1_et_lasso = fbeta_score(y_true_et_lasso, y_pred_et_lasso, beta=1)\n", - "f2_et_lasso = fbeta_score(y_true_et_lasso, y_pred_et_lasso, beta=2)\n", - "fpr_et_lasso = fp / (fp + tn) if (fp + tn) != 0 else 0\n", - "\n", - "# Scores relative to total\n", - "tp_score_et_lasso = tp / total\n", - "tn_score_et_lasso = tn / total\n", - "fp_score_et_lasso = fp / total\n", - "fn_score_et_lasso = fn / total\n", - "\n", - "# Create DataFrame\n", - "summary_df_et_lasso = pd.DataFrame([{\n", - " \"title\": \"Lasso ET\",\n", - " \"flagging_analysis_type\": \"RISK_VS_CLAIM using Extra Trees with Lasso Features\",\n", - " \"count_total\": total,\n", - " \"count_true_positive\": tp,\n", - " \"count_true_negative\": tn,\n", - " \"count_false_positive\": fp,\n", - " \"count_false_negative\": fn,\n", - " \"true_positive_score\": tp_score_et_lasso,\n", - " \"true_negative_score\": tn_score_et_lasso,\n", - " \"false_positive_score\": fp_score_et_lasso,\n", - " \"false_negative_score\": fn_score_et_lasso,\n", - " \"recall_score\": recall_et_lasso,\n", - " \"precision_score\": precision_et_lasso,\n", - " \"false_positive_rate_score\": fpr_et_lasso,\n", - " \"f1_score\": f1_et_lasso,\n", - " \"f2_score\": f2_et_lasso\n", - "}])" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "d10ae5b4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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SCi2r7MndTz/9JIQX7MpJ9Gl/5u/enr8MZcqUEZ03jY2NFXbvUyYvL4+cnJyE5efNmyfMy/9ktU+fPkJ4ixYthPANGzYUSjMtLU1hF/mSePLkiWibhoSEKI0bFxcnijtt2jRhnrLzGFHR3WSL65788uVL0bHi4eFBb9++FcW5f/++cLzOmjVLVJbdu3eL1jX/E/iWLVsK8wrWt7Vr1wrzCj6xjI2NJaJP+9TGxkYIHzx4sLDM51xHGPva+MkdY19Rq1at0Lt370LhL168QFhYmOgupCIPHjxQOq9Pnz7CUPcmJiYwMzPDkydPAEC4W/nu3TvhaRkAtGnTBlpaWsJ0ly5dsGTJkkJp//XXX6Inj126dBE9lQkLC8O0adOE6aSkJLi7uxdKp127dtDU1ASAQt+Hat26tZBmwUEw0tPToa+vr3TdFbl79y6GDRumcF5kZCRCQkIUzmvatCmqV69eKPzEiRPC/xMTE4t8KnXy5ElUrVpVuPMs16lTJ+H/jo6O8PHxweHDh4taDZEPHz7g4sWLwnS3bt0K3U3X1dWFrq5uidK8cuWKMB0aGgqZTCbKo2/fvsJ0UlKSwm2nbLsp8+bNG4UjlpbkadvXtG7dOgwcOFB4MqZIZmYmnj9/Dmtr60L7tlu3bsL/jY2N0bJlS4XfJyy4XNeuXYX/lytXDj4+Pgo/35C//qWnp8PU1FRpOU+ePImffvoJ58+fBxEJ4Z07dxb+r6mpiXbt2omeAqtq5syZmDBhguipZUH5z1Xe3t6QSCQgIixevBhnz55FpUqV4OLiAi8vLzRs2BCWlpYlLociGhoaGDhwIBo1aqTyMvXq1cPcuXMBAGPHjsWOHTtQsWJFuLi4oFatWqhXr57S4/3+/fvCOdPMzAz79++Hh4eHynlLJBKEh4cL+2HDhg348ccf8eLFC9HT34iICFF55Z90CQ8Px+LFi1GhQgW4uLjA29sbNWvWLFWfPVHk1KlTwtNvABg5ciT09PREcfKPiJr/yaK5uTmaNm0qTFtYWKBp06ZCDxRlPSo0NDTQvn17YdrR0VGIq6mpKTxZlUgkcHJywqNHjwD83/UX+LzrCGNfGzfuGPtM7du3R7Vq1XDy5Emhe8i6deuQlpaGAwcOiC6+3bt3L7ZhB3z6calMwcZS/kabvAvIq1evRHHk3X/klP3Ayt+wUxSv4HT+i1l+NjY2wv+lUqnSeQW7/cnL/7k0NDRgZmYGLy8vREZGChdhRSpWrKgwvOA2KIq8C1r+7a2vry9qNAHKt7cy6enpoh/rTk5OJVpelTQLlklXVxd6enrCD3ll+1bZdlPm5cuXChvfDg4On9W4y78OJXXhwgV069ZNpXomPwYLHksFu/kq6/ZbsE4UbIgrW+5L6x+g+vFelO3bt2PIkCHFxsvKyhL+X7NmTcycORPjxo3Du3fvcOHCBVHXQTMzM8TGxn7W98xGjx4NLS0tbNu2DZcvX0ZOTg6GDx+ODx8+qNxwbdu2LYYOHYp58+YhMzMTSUlJoh/4Dg4O+OOPP1C5cuUi09HX1/+sbRoeHo6JEyciLy8PSUlJSElJwd69e5GdnQ0AcHNzE30OZeDAgbhy5QrWr1+PzMxMJCYmim4IuLm5Yf/+/bC2ti5xWeRMTU0hlUqF/Xjv3j2lcQvOU5ZvwWO0qOtZcQoeD8WdC/PHV7SP8ocpO8dZWFiIrk35r2EWFhaihlr+ePnPK59zHDP2tXHjjrHP1KRJE4SHhwMAevfujcWLFwMADh06hLVr1wp37N+/fy96N6BRo0b4/fff4eDgAHV1ddSsWRNnz54tNj/5EzE5RXdu5e/hyMmHDZeTP+kryMTEpMh4BaeNjY1VKmN+it7j+hINGjT4rA9YK3vqZWJiImwvHx+fIj+jIH+XzcjISAh7+/YtMjIyRA08ZdtbGWNjY+EJCAAkJyeXaHlV0ixYpvfv34ue0CjbtyV5Wvi/JjY2VvgBJpFIsH79ejRv3hy6urrYvXs3goKCCi2Tf98Cn46l/MdJ/vfclC2nqE4oWy5/2tbW1hg8eLDS9ZE/sVBUxvxKWv8AiN7f1NPTw7Zt21CvXj1oa2tj4cKF6Nevn8LlBg4ciJ49e+LUqVO4fv06bt++jb179+L27dt4/vw5wsLCimxAKNOjRw84Ojpi2LBhqFu3Li5dugTg0zvCXbp0UflTGDExMRg7dixOnjyJmzdv4tatW9ixYwcePXqEe/fuoW/fvjhy5Eih5YyMjKClpYUnT54gOTkZ/v7+OHr0KMzMzFRehzJlysDPzw8HDhwAEWHjxo3Ys2ePMD//Uzvg07ly9erVmDFjBk6ePIm//voLf/31F+Li4pCeno5r165h5MiRWLVqlcplKEhdXR116tQR1vnKlSu4c+cOypUrVyhuwW/g1atXT/h//nOL/P1Budu3b392+Qpek5KTk4v8Hmj++Irqff6wb3n9+pzrCGNf3ffqD8rYv01Ro2Wmp6eToaGhMK9cuXLC+2UPHz4ULSf/NAAR0c2bN0UjcIWFhSnNr+BQ7QWHqZf7J965u3TpkjBP2TYpOJpj/nnFrZsyRY2WqepyBT8PIBcSEiLabq9fvy4U58OHD7Rq1Sph+lu/c6enp0e3b98uVIb879ytXbtWlJaid3Hc3d2F+cW9c7d9+/YSbbdv5WuOlpm/3hsZGYnegymYj7wuFnznbvz48cIyJXnnbvny5cK8ot65mz17thCuoaGh8H2cvLw8SkhIoLt37xLRp3PL137nLv8ooFWrVhXCc3NzqWHDhgrTe/jwocJ36i5cuCCK//z582LzL2q0zILnjfDwcNGyyt7junv3rsKRXbdt2yY61hSVwcHBgS5cuCB6p7d69eol/hTB+vXrRWnK95umpiY9ffpUFPfmzZsKj+P87za6ubkp3S6qnk8LfgohODhYeIdNbvXq1aI4BT+FkP84yP/5mb1794qWK3j+++GHH4R5NWrUKFS2gu/cVa9evdA2efjwoVDe/McPUPQ7d/nfLyzqPd389angvPwjjea/bn/OdYSxr42f3DH2FRgZGaFfv3745ZdfAAB37tzBpk2b0KlTJ1hYWMDIyEjoQjVp0iQ8ffoUOTk5WL58+Rd1XVGke/fuGD58uFCOOnXqoHnz5rh27Rq2bdumcBk1NTUMGjQI48aNA/DpnQQfHx80btwYN2/eFN25bdiwIapVq/ZVy/y/YMiQIYiPjwcR4c6dO3Bzc0Pr1q1haWmJ169f4+rVqzhy5Ajev38vvH/VokULmJubC91r+vTpg7NnzwqjZcq7XZXEyJEjhdFW3717B3d3d2G0zNTUVOzatQsLFy4U3ouztbUVLd+pUyfUrVsXampq6Nq1KywtLTFkyBDhSXJKSgpq1KghGi1TrkKFCgqfYv3bubi4CP9/9eoVgoKCULduXRw/flzpiHW1a9dGlSpVhFFpo6OjkZycjDJlymDz5s1Ku3a1bNkSFhYWwt373r1748yZMzA0NMTq1atF7xHlFx4ejkmTJuH58+fIycmBt7c3QkNDUa5cOWRmZuKvv/5CYmIinjx5gsOHD8PJyQk2NjZo2rSpMCrm2rVr8ebNG7i7u2PPnj24fv36Z20r+btgV65cQceOHeHq6oo9e/YoHVXy6NGj6Ny5M3x8fODq6gobGxvk5uaKzjdSqRQ6OjolLk9+vr6+qFu3rjDi6Nq1axEVFVXsyI2bNm3ChAkT4Ovri/Lly8Pa2hrv37/Hhg0bhDgFn4Lm5+Hhgbi4ODRt2hRZWVm4cOECgoKCsH//fpXXqVWrVsJ1IP8TzKCgIJibm4vizpo1C2vWrEGjRo3g5OQES0tLvHz5EqtXr1apvKrq0KED1q5dKzxF3LVrF9zc3ISRIU+fPi3qdWJsbIw5c+aI0qhRo4ZwDK1ZswYPHz6ETCYrdiTI/Oet8+fPY8CAAbC3t4dUKsVPP/0EY2Nj9OzZUxiR9cKFC6hUqRJCQkJgZGSEW7duIS4uDmlpaTAyMkJYWBiio6Px4sULAJ/eN4+MjISBgQHWr18v9E6QSCQYOHDgl224InzOdYSxr+57ty4Z+7co7jt3T58+FX1LqXLlysLollOnThUtK/9zc3MTfVPqazy5y8rKEn1LK/+fr6+vaLrgd+5CQ0MVLif/c3V1FX3fiqj0PLkjIlqwYEGR3yeS/+UXHx+v8JtU+vr6oqdwJfnOXVRUVLHfuZP7+PEjWVtbK4x39uxZIV5x37mzsbEp8jt3/+Yndy9evBCNbpf/T9mTOyLl37nT0tIiPz8/YdrJyUlUDmXfuTM2NqbatWsL0w0bNhQtd+LEiSK/c6fouL179y5ZWFgojJf/6YKq2/D27dukr69fKC0NDQ3q3LmzwvQKPq1U9Jd/REFV96Gic8OuXbtE8/OPMqnsyd2UKVOKLV/+EYeV1aNNmzaRmpqaMK9x48aFPqJdlD59+hTKd8eOHYXi9erVq8iyqqmpic4Bn3s+Jfr0rb3izvvApyf+586dK7R8QkKCwnOVqakp1axZU+n57+LFi6JtKf/T1dUV4mRkZBT6VmPBv4LfuTMyMipyu02fPl1Ujq/95I7o864jjH1NamCMfRXm5ub44YcfhOnr168L35UbMWIEFixYgAoVKkBTUxNWVlbo0aMHjhw5UmgEsC+lqamJ/fv3Y9iwYbC1tYVUKoWLiwtmzJghfLdHEXV1dWzevBmxsbFo1qyZ8HK5oaEhatWqhZiYGJw9e1Y0MEpp07dvX1y8eBE9e/ZEhQoVoKOjAw0NDVhaWqJBgwYYN24cLl++LFqmRYsWOHDgAOrXrw+ZTAYjIyO0bNkSp0+fRpUqVT6rHBMmTMCpU6cQFhYGZ2dnaGtrQ0dHB87OzujatSvc3NyEuFpaWti9ezcaN24MAwMDpWnOmDEDCQkJaNOmDWxsbKCpqQk9PT24u7tj3LhxuHLlSrEDSvxbmZiY4Pjx42jdujUMDAwgk8lQo0YNbNu2TXhvVhEvLy+cPHkSQUFB0NPTg56eHho1aoSjR4+ifPnyQryCT1GCg4Nx8OBBNGjQQFQnTp06JXovtuBydevWxfXr1zFu3Dh4enrCwMAA6urqMDIygqenJ3788UckJCSgfv36wjJOTk44deoU2rVrByMjI8hkMtSpUwc7d+4sct2UKVeuHI4ePYrGjRtDR0cHenp6aNCgAQ4ePAh/f3+Fy/j4+GDy5MkICgpC2bJloa+vDw0NDZibm6NRo0ZYuXIlZsyYUeKyKBIUFCTqObB8+XKkpaUVuUxISAjGjx8Pf39/ODo6Cse1tbU1goKCsGPHDvTv37/YvNu1ayd6crV//3506NBB+CZdcQq+W2dpaSka1VGue/fuGDFiBOrXrw97e3toa2tDKpXC3t4eoaGhOHLkiNLRgEtKT08PmzdvRmJiIsLDw1G+fHno6uoK16kmTZpg0aJFuHHjBjw9PQst7+/vj7i4OFSvXh1SqRSmpqbo3Lkzzp8/D1dXV6X5uru7Y8OGDahevTq0tbUVxtHW1sauXbuwefNmBAcHw8rKCpqamjAwMECVKlUwYMAA0ZPT+vXr49q1axgyZAgqV64MHR0dSKVSlClTBp07d8bJkydVGizoS33OdYSxr0lC9AVDkDHGGGOlVFZWFjQ0NKCmJr4P+u7dO7i5uQnd63r06CH67MPHjx8V/mB9+PAhKlWqhDdv3gAAJk+ejNGjR3/DNWCMMfZfw407xhhjTIFLly6hRYsW6Ny5MypVqgRjY2OkpKTgt99+E95nU1NTw4ULF0RPk7Zv346RI0eiY8eOqFChAnR1dXHr1i3MmzcP9+/fB/Dpicnt27eVfhqBMcYY+xw8oApjjDGmRGpqKqZOnapwnlQqxaJFixQOMPTXX38hKipK4XL6+vrYtGkTN+wYY4x9dfzkjjHGGFPgxYsXmDx5MhITE3H//n28fv0a2tracHJygq+vL/r27avw4+7JycmIiYnB0aNH8ejRI7x58wa6urooX748AgIC0K9fP9jZ2X2HNWKMMVbaceOOMcYYY4wxxkoBHi2TMcYYY4wxxkoBbtwxxhhjjDHGWCnAjTvGGGOMMcYYKwW4cccYY4wxxhhjpQA37hhjjDHGGGOsFODGHWOMMcYYY4yVAty4Y4wxxhhjjLFSgBt3jDHGGGOMMVYKcOOOMcYYY4wxxkoBbtwxxhhjjDHGWCnAjTvGGGOMMcYYKwW4cccYY4wxxhhjpQA37hhjjDHGGGOsFODGHWOMMcYYY4yVAty4Y4wxxhhjjLFSgBt3jDHGGGOMMVYKcOOOMcYYY4wxxkoBbtwxxhhjjDHGWCnAjTvGGGOMMcYYKwW4cccYY4wxxhhjpQA37hhjjDHGGGOsFODGHWOMMcYYY4yVAty4Y4wxxhhjjLFSgBt3jDHGGGOMMVYKcOOOMcYYY4wxxkoBbtwxxhhjjDHGWCnAjTvGGGOMMcYYKwW4cccYY4wxxhhjpQA37hhjjDHGGGOsFODGHWOMMcYYY4yVAty4Y4wxxhhjjLFSgBt3jDHGGGOMMVYKcOOOMcYYY4wxxkoBbtwxxhhjjDHGWCnAjTvGGGOMMcYYKwW4cccYY4wxxhhjpQA37hhjjDHGGGOsFODGHWOMMcYYY4yVAty4Y4wxxhhjjLFSgBt3jDHGGGOMMVYKcOOOMcYYY4wxxkoBbtwxxhhjjDHGWCnAjTvGGGOMMcYYKwW4cccYY4wxxhhjpQA37hhjjDHGGGOsFODGHWOMMcYYY4yVAty4Y4wxxhhjjLFSgBt3jDHGGGOMMVYKcOOOMcYYY4wxxkoBbtwxxhhjjDHGWCnwn2/chYeHQyKRFPqbPn06AGDlypWQSCTIycn5LuXz9fVFly5dvkve30Jqair8/f2hp6cHiUSCV69efe8i/edFRUXBzs7uq6YpkUiwdOnSr5omY4wxxhgrmsb3LsD/gqpVq2Lx4sWiMAcHh+9UGrGFCxdCS0vrexfjq4mOjkZycjLi4uKgr68PfX39710k9g0kJSWhbNmy37sYjDHGGGP/Kdy4A6Cvr4/atWt/72IoVKlSpe9dhK8iIyMDMpkMt27dgre3NwICAr5Keux/0//q8cQYY4wxVpr957tlltTbt2/Rp08flC1bFjKZDBUrVsSMGTNARKJ4f/zxB1xcXCCTyeDv749Dhw5BIpEgMTFRiHPr1i3Ur18f2tracHNzw6FDh2BnZ4eoqCghTsFumeHh4fDx8UF8fDxcXFygr6+PZs2aIS0trcT5FyTvgnru3DnUrFkT2tra8PT0xPnz50Xxnj9/ju7du8Pc3BwymQx+fn64fv26MD8lJQUSiQSbNm1Cu3btoK+vjz59+kAikeDIkSNYs2YNJBIJfH19AQDv3r1D7969hfQaNGhQKE+JRIJ58+ahV69eMDExQbNmzYR8tmzZgvbt20NPTw/ly5fHwYMHkZWVhb59+8LIyAhOTk7YuHGjKL3ly5ejdu3aMDQ0hJWVFdq3b48nT56I4jg6OmLs2LGYNGkSLC0tYW5ujv79+yM7O1sULyEhAXXr1oWOjg7MzMzQvHlzUVqJiYnw9vaGTCaDhYUFBg4ciMzMTKX74WuW7/r162jbti1sbGygp6eHGjVqYN++fUrzvXnzJiQSCU6cOCEKv3Pnjqj+HD16FHXr1oW+vj6MjIxQo0YNHDhwQIhfsFtmXFwcPDw8oKOjA1NTU9SrVw+XLl0qchswxhhjjLGS4cbd/5eTkyP85ebmKo33/v17aGpqYsaMGdi7dy/69euH6OhozJo1S4hz9+5dtG7dGq6urti2bRsCAgIQGRkpSicvLw/NmzfH8+fPsX79eowcORK9e/fGmzdvii3rnTt3EB0djSlTpmDp0qU4f/48fvzxxxLlX5ROnTohIiICsbGx0NLSQtOmTfHu3TsAQGZmJho1aoQTJ05gzpw52Lp1K9TV1eHv74/379+L0hk4cCDKlCmD7du3o2fPnkhKSkLVqlXRrFkzJCUlYeHChQA+NVg3bdqEyZMnY/PmzZBIJPDz88PTp09F6U2ePBlZWVnYuHEjxo0bJ4QPGTIElStXxrZt2+Ds7IzQ0FD06dMHWlpaiI2NhY+PD8LCwvD48WNhmdTUVPTq1Qvx8fFYtGgRHjx4gMaNGxdqpK9atQqXL1/GqlWrMGLECCxatAi//fabMD8hIQFNmzaFhYUFNm7ciOXLl8PBwQHp6ekAgGPHjiEgIADlypVDXFwcpkyZgnXr1mHo0KFF7oOvVb6HDx/Cw8MDS5cuRXx8PBo2bIigoCCcPXtWYb4VK1ZE7dq1sXr1alH46tWr4eDggAYNGuDNmzdo3rw5KlSogLi4OGzcuBGtW7fGy5cvFaZ5584dtG/fHv7+/vjjjz+watUq+Pr6it639PX1FRr7jDHGGGPsM9F/XFhYGAEQ/amrqwvzV6xYQQAoOzu70LJ5eXmUnZ1N0dHRVLlyZSF84MCBZGdnJ1pm6NChBIAOHz5MRETbt28nAHTjxg0hzq5duwgATZgwQQhr0KABde7cWVReTU1Nun//vhA2ffp00tDQoNzcXJXzV0S+rnPnzhXCXrx4QTKZjGbPnk1EREuWLCGZTCbK//3792RhYUGzZs0iIqLk5GQCICq3nLe3N4WFhQnTV69eJQAUGxsrhL17945MTU1p+PDhQhgA8vb2FqUlz6dv375C2I0bNwgABQcHC2Fv3rwhDQ0NWrZsmcL1zsnJoXv37hEAOnv2rBDu4OBAlSpVErYrEVFwcDD5+fkJ0zVq1KB69eopTFe+vkFBQaKw2NhYkkql9PjxY6XLfa3y5Zebm0vZ2dnk7+9P/fr1E8InTJhAtra2wvTixYvJyMiIPn78SESf6rmTkxONGzeOiIjOnj1LAOjNmzdKywyAlixZIqyviYlJkevo5+entNyMMcYYY0w1/OQOQLVq1XD27Fnh7/Tp00XGX7x4MSpXrgyZTAZNTU2MGzcOd+7cEeafP38ejRs3hobG/73SGBwcLErj/PnzcHZ2hqurqxDWpEkTqKurF1veChUqwN7eXph2dXVFTk6O8KRLlfyL0qJFC+H/JiYmqFu3rvCk5+DBg6hduzasra2FJ51SqRS1a9cu1JWyadOmxeZ1/vx5qKurIyQkRAjT1dVFs2bNCj1dUpaen5+f8H/5IB75nwLp6+vD3Nwcjx49EsIuX76MJk2awNTUFBoaGsIAOvn3IwA0bNgQamr/d5i4urri4cOHAD49xT137hy6du2qsFwfPnxAUlIS2rZtK3oy7Ovri6ysLFy7dk3ZZvkq5ZOXcejQoXBwcICmpiY0NTVx4MCBQunk1759e2RmZmLHjh0APj19TE5ORrdu3QB82sZ6enro3Lkzdu3ahbdv3ypNCwCqVKmC169fIzw8HAcOHMDHjx8LxTl48CAOHjxYZDqMMcYYY6xo3LgDoKenBy8vL+HP09NTadzNmzejd+/eCAoKwo4dO3DmzBmMHDlS9A7V06dPYWpqKlrOzMxMNK0ojrq6OoyNjYstr5GRkWhaKpUCgPCjWZX8i2Jubl5oWt6l8fnz5zh8+LDQUJD/7dixA6mpqaLlLCwsis0rLS0NxsbGooYoAFhaWhZ6j1BZeoaGhsL/5dsif5g8XL59Xr9+jSZNmuD9+/dYvHgxTp48KbxjVrDhoWhby+Okp6eDiGBtba2wXOnp6cjLy0NERIRoW8m3b8HtJfe1ygcAw4YNw7JlyzB06FAcOHAAZ8+eRUBAgMIGlpyhoSFat24tdM1cvXo16tati3LlygEAjI2NsW/fPmRkZKB169YwMzNDaGioqNtrfi4uLti+fTtu376NwMBAmJmZoUePHsU2ChljjDHGWMnwaJklFBcXh4YNG2LatGlC2M6dO0VxLCws8Pz5c1FYwWlFcXJzc4V3tb6EKvkX5dmzZ6JPQTx79gxWVlYAPj3J8/b2xuzZswstV/CzBhKJpNi8rK2tkZ6ejpycHFED78mTJ4UaTaqkp4pTp07h8ePHOH36NMqUKQPg03uKJWVsbAyJRFKoESpnZGQEiUSCKVOmoFGjRoXmOzo6ftPyAZ/q66BBg9C/f38h7OPHj6KnfYpERESgSZMmuH//PrZs2SKq7wBQt25dJCQk4P3799izZw8GDBiAfv36YevWrQrTCw4ORnBwMNLT07F9+3YMGDAABgYGmDFjxmetF2OMMcYYK4yf3JVQRkaG8HQI+DQwypYtW0RxPD09sX//ftGHz3ft2lUoTnJyMv78808hbO/evUUO5qIqVfIvirw7HgC8fPkSJ0+eRI0aNQB86gJ569YtlCtXTvS008vLCy4uLiUuq5eXF3JzcxEfHy+EffjwAbt370bNmjVLnJ4qMjIyAEC0Hzdv3lzidHR1dVGzZk2sXbtW6fxatWrhzp07hbaVl5eX0qepX6t88rTyp5OamopTp04Vu5yfnx9sbW3RuXNnZGZmol27dgrj6erqom3btujQoYOoLitjbGyMiIgIBAQEqBSfMcYYY4ypjp/clZCfnx8GDRqE2bNno2LFili8eDE+fPggitO/f38sXLgQbdu2Re/evXH58mXExsYCgPDERD7aYJs2bTBp0iRkZGRg4sSJ0NfXL/apSnFUyb8o8+bNg4aGBuzt7TFlyhTo6uoKo22GhYVh0aJF8PX1xeDBg+Hg4ICnT5/i2LFjqFmzpuizDaqoXLky2rRpg549eyI9PR1WVlaYMWMGsrOzMXjw4JKvvApq164NHR0d9OrVC/3798eFCxewYsWKz0pr8uTJCAwMROvWrREREQE1NTXs378fffv2hYuLC3799VcEBASAiBASEgKZTIa7d+8iPj4e69evh4GBwTctn5+fH+bOnQtnZ2eoq6tjwoQJsLGxKXY5iUSCsLAw/Pzzz2jXrp2o++cff/yBFStWICQkBPb29khJScHatWsRGhqqMK3Fixfj9OnTCAwMhJWVFa5fv469e/di4sSJQhz5k01+744xxhhj7PPxk7sS6tOnD/r06YPo6Gh06dIFjo6OGDFihCiOs7Mztm7dihs3biAkJAS7d+/G9OnTAUD4Ma+mpoadO3fCzMwMnTp1QnR0NObNmweJRKLwB39JqJJ/UdauXYtly5ahbdu2yMjIwO7du4Uul9ra2jh8+DC8vb0xatQoNG7cGEOGDMHz589RrVq1zyrvihUr0LZtW4wcORKhoaHIzc3FwYMHVXpn73NYWVlh/fr1+PPPP9G8eXP88ccfSrsTFqdRo0bYvXs3Hj58iNDQUISFhSElJUV4d7J+/fo4dOgQUlJS0LFjR4SEhGDevHmoVq2a0o+wf83yzZ8/H+7u7oiMjMTQoUMxcOBA+Pj4qLRs8+bNAXxq0OdXrlw5EBFGjhyJxo0bY/z48ejatStiYmIUplO1alU8fvwYAwYMQGBgIGbOnInRo0eLGu+5ublf5ak1Y4wxxth/mYSowIez2Dcxa9YsjBo1Ci9fvoSOjo7COBcvXkT16tVx4MABhe9ofev8V65ciYiICGRnZxca4IT990ydOhVz5szBgwcPVBrFlTHGGGOMfV/8C/4bGTZsGGrVqgUTExOcPXsW0dHRCAsLEzWsFixYAJlMBmdnZ6SkpODnn39GpUqVvsrHnFXJnzFFUlJS8Oeff2LmzJno3bs3N+wYY4wxxv4luHH3jbx79w4//fQTnj17BjMzM/zwww/49ddfRXE0NDQwdepU3L9/H1paWmjUqBFmz579VX5Mq5I/Y4pERUVhw4YN8Pf3x/Dhw793cRhjjDHGmIq4WyZjjDHGGGOMlQI8oApjjDHGGGOMlQLcuGOMMcYYY4yxUoAbd4wxxhhjjDFWCnDjjjHGGGOMMcZKAW7cMcYYY4wxxlgpwI07xhhjjDHGGCsFuHHHGGOMMcYYY6UAN+4YY4wxxhhjrBTgxh1jjDHGGGOMlQLcuGOMMcYYY4yxUoAbd4wxxhhjjDFWCnDjjjHGGGOMMcZKAW7cMcYYY4wxxlgpwI07xhhjjDHGGCsFuHHHGGOMMcYYY6UAN+4YY4wxxhhjrBTgxh1jjDHGGGOMlQLcuGOMMcYYY4yxUoAbd4wxxhhjjDFWCnDjjjHGGGOMMcZKAW7cMcYYY4wxxlgpwI07xhhjjDHGGCsFuHHHGGOMMcYYY6UAN+4YY4wxxhhjrBTgxh1jjDHGGGOMlQLcuGOMMcYYY4yxUoAbdyWQl5eH3377DR4eHtDR0YGJiQlatGiB8+fPF4qbmpoKf39/6OnpQSKR4NWrV5+VZ2JiIiQSCe7cuQMASElJgUQiwYEDB5Quc+nSJURFRSEvL08UHh4eDh8fn88qR3G+Zdqfk5dEIsHSpUv/kfL8r0hMTESVKlUglUrh7u6uUl35X7R06VJIJJLvXQyFUlJSEBUVhZcvXxYb19HRERKJBBKJBFpaWqhYsSJ++eUXZGdni+L5+vqiS5cuKpfB0dERY8eOLXHZ83vz5g3GjRsHT09P6Ovro0yZMujZsydevHhRKO6lS5dQr149yGQyODk5Yf78+SrlQURYuHAhKlWqBC0tLdja2uKnn34Sxbly5Qr8/Pwgk8lgZWWF4cOHIysrSxRn1KhRMDU1haurK06cOCGal56eDktLS9y8ebOEW+DzbN++HcuXL/9m6UdFRcHOzk6YXrlyJSQSCXJycr443QsXLnxp8f4xY8eOhaOjY5FxSnrcACU7fv/XvXr1ClFRUbh79+73Lgpj7H8MN+5KIDIyEgMGDEBgYCB27dqF5cuX4+PHj/D29sa+fftEcaOjo5GcnIy4uDgkJSVBX1//HyvnpUuXMHHixEKNu/+SpKQktGzZ8nsX4x/Vs2dPODg44NChQ1izZs33Lk6plJKSgokTJ6r84zA8PBxJSUnYt28fWrRogTFjxmDKlCmiOAsXLsTEiRO/RXGVun//PlauXImQkBDEx8dj6tSpOHjwIJo3b47c3Fwh3rNnzxAQEAADAwPs2rULffv2xcCBA1WqX6NGjcKYMWPQs2dP7N+/HzExMdDV1RXmv3r1Co0aNYJEIsGWLVswfvx4LFq0CIMHDxbi7N69G4sXL8bvv/+OoKAgdOzYUdQ4jo6ORtu2bVGxYsWvtGWK9q0bd9/KxIkT/1WNO1V8znFT0uP3f9mrV68wceJEbtwxxgrR+N4F+LeIjY3FqlWrsGbNGtHdwhYtWqBJkyYICwvD33//Lfx4uXXrFry9vREQEPC9ivyfVrt27e9dhH9UXl4e/v77b4wcOVJ4qpmSkvJ9C8Vga2sr1EVfX19cu3YNa9aswfjx44U4lSpV+sfL5eTkhFu3bkEmkwlhZcuWRe3atXH69GnUrVsXAPDbb79BIpEgNjYWOjo6aNSoEZKTkxEdHY2uXbsqTf/q1auIiYlBQkIC/Pz8FMZZuHAhcnNzER8fDz09PQCfnvYNGDAAo0ePho2NDQ4dOoSOHTuiTZs2CAkJwe+//45bt26hcuXK+Pvvv7FmzRrcuHHjK24ZVpyMjAxRvflevsdxwxhj/wb85E5F8+fPR8WKFdG5c2dRuJqaGiZOnIgnT54gNjYWwKcugUeOHMGaNWsgkUjg6+urMM23b9+iT58+KFu2LGQyGSpWrIgZM2aAiD67nCtXrkRERAQAQFNTExKJBOHh4aI48fHxcHFxgb6+Ppo1a4a0tDTR/OTkZLRt2xZGRkbQ09NDSEgIHjx4oFL+GzZsgLOzM2QyGUJCQvDs2TPR/L1798LT0xPa2tqwtbXFmDFjRE8KAGDNmjVwdXWFlpYWnJ2dMXv27CLzfPv2LXx8fFCzZk2h+2vBbpnyrmyTJk2CpaUlzM3N0b9//0Jd5KZMmQJLS0sYGBigV69emD9/vqiLYFZWFgYNGgQ7OztoaWnB3t4e3bp1U1q20aNHo2bNmsL0jRs3IJFIEBkZKYRt3rwZurq6Qter3NxcREdHw8nJCVpaWqhcuTK2b9+uNI/ExESoq6sjLy8P3bt3h0QiQVRUlMK4v/76K9zd3aGnpwd7e3v06tULb9++FcW5desW6tevD21tbbi5ueHQoUOws7MTpfnu3TuEhYVBT08PNjY2mDt3Lrp06VKoricmJsLb2xsymQwWFhYYOHAgMjMzRXGmT58OKysrGBgYoEePHvj48aPSdZU7fvw4mjVrBgsLCxgaGqJ+/fo4d+6cKM7Vq1fRuHFjGBkZQV9fH1WrVsX69euVplnc8ZiYmIiGDRsCAMqXL1/ksa1MlSpVCh1LBbuXlbTcf/75J6ytrdGrVy+Vzx26urqFfqBXrVoVwKfjX27fvn1o1qwZdHR0hLDQ0FDcvn27yCcGq1evRvny5ZU27ADg8uXLqF27ttCwAwB/f3/k5uZi//79AD4db/K81dXVIZVKhfozcuRIDB06FObm5iqtsyp1Bvh0DnN3d4e2tjasrKzQsWNHZGZmIjw8HKtWrcKJEyeE7rYrV64EoLgbeMHj4fr162jbti1sbGygp6eHGjVqFOrxUZRXr15BJpNh3bp1ovC3b99CV1dXKEtB8vNXjx49hHIDirt85uTkiNYL+HTuHD16NEaPHg0rKyuhUSWRSLBo0SIMGDAAxsbGsLW1xc8//1yoDm7ZsgUeHh7Q1taGnZ0doqOjRXHy8vIwfPhwGBsbw9TUFKNGjVKp10nB40beXV/Z9a2447ek1x1AtfOpIsVdBxW9enDgwAFIJBKkpKQgJSUFTk5OAICAgABIJBJRN9Zz586hcePG0NfXh5GRERo1aiTqulxc/vLuwYmJiahatSp0dHQQFBSEV69e4dq1a/D29oaenh58fX0Lnc++5PcDY+zr4MadCrKzs3Hq1CkEBQUpfBeoTp06MDU1xbFjxwB86hJYtWpVNGvWDElJSVi4cKHCdN+/fw9NTU3MmDEDe/fuRb9+/RAdHY1Zs2Z9dlmDgoKE93GOHz+OpKQkjBs3Tph/584dREdHY8qUKVi6dCnOnz+PH3/8UZj//Plz1KtXDw8ePMDy5cuxbt063L9/H0FBQcVecG/fvo3o6GjExMRgyZIlOH36tKjhc+nSJQQHB8PZ2RlxcXEYNGgQpk+fjjFjxghxdu3ahW7dusHX1xc7duxAx44dMXjwYPz2228K83zz5g0CAwORnZ2NhIQEGBkZKS3fqlWrcPnyZaxatQojRozAokWLROmuXbsWo0ePRkREBGJjY5GdnY2pU6eK0pgyZQo2btyIyZMnY//+/Zg2bRo0NTWV5unt7Y2LFy/iw4cPAIATJ05AW1sbx48fF+KcOHECtWrVgobGpwfpffv2xfTp0zFgwADs2rULjRo1Qps2bXDmzBmFeVSvXl1Ib+zYsUhKSsIPP/ygMO7jx48xbNgw/PHHH5g2bRqOHz8uumGRl5eH5s2b4/nz51i/fj1GjhyJ3r17482bN6J0Bg4ciLi4OEyfPh2LFy/Gpk2bkJiYKIpz7NgxBAQEoFy5coiLi8OUKVOwbt06DB06VIizefNmDBs2DF27dhW2+S+//KJ0e8rdv38f/v7+WLt2LWJjY1G2bFk0aNAAqampQpwWLVpAKpViw4YNiIuLQ/fu3Yt897W447F69epYsGABgE9P8os6tpVJTU0t9l2ikpT7+vXr8PX1RUhIiPCU7XMlJSUBAMqVKyeE3bp1q1CXR/n0X3/9pTStM2fOoHLlyhg/fjxMTEygra2N4OBg3L9/X4iTkZEBqVQqWk5LSwsAhB+i1atXR1xcHFJTU7F+/XpkZWWhQoUKOHnyJM6dO4cBAwaovH6q1Jnly5ejU6dO8PT0RFxcHBYuXAhNTU1kZWVh3LhxaNasGapWrYqkpCQkJSUhKChI5fwfPnwIDw8PLF26FPHx8WjYsCGCgoJw9uxZlZY3MjJCSEgIVq9eLQqPjY2FRCJB27ZtFS4n36+jRo0Syl1Sy5cvx9WrV7F8+XLhGACAX375BVlZWdi0aRO6deuGCRMmYNeuXcL8DRs2oH379sL5fOjQoZg6dSpmzpwpxJk+fTpmzZqF4cOHY+3atbh69arShmpxirq+FXX8lvS6I1fc+VQRVa6DxbG2tsa2bdsAAHPnzkVSUhLi4uIAANeuXUP9+vWRkZGB5cuXY8OGDahZsyYeP35covzfvHmDQYMGYezYsVi+fDlOnTqF3r17o1u3bujRowfWr1+Pe/fuoW/fvsIyX/L7gTH2FRErVlpaGgGgOXPmKI3j7u5OTZo0Eaa9vb0pLCxM5Tzy8vIoOzuboqOjqXLlykL44cOHCQDdvn2biIiSk5MJACUkJChNa8WKFQSAsrOzReFhYWGkqalJ9+/fF8KmT59OGhoalJubS0REY8aMIWtra3rz5o0Q58GDBySVSikuLk5pnmFhYQSArly5IoTt3r2bANClS5eIiKht27bk5uZGeXl5QpypU6eSTCajFy9eEBGRl5cXBQUFidLu3bs3WVtbC2UMCwsjb29vevXqFdWqVYtq1apFr169Ei0DgJYsWSJMOzg4UKVKlYQ0iIiCg4PJz89PmHZ3d6f27duL0vHy8qL8h0lQUBANHjxY6XYo6OXLlySRSOjgwYNC2Xv16kVqamr05MkTIiLy9PSkcePGERHRrVu3SCKRUGxsrCidoKAgatmypdJ8srOzCQCtWLFCCCuuruTk5NDRo0dJIpHQ06dPiYho+/btBIBu3LghxNu1axcBoAkTJhAR0dOnT0kqldKiRYuEOM+ePSNtbW1q0KCBEObt7V1oX8bGxpJUKqXHjx8TEVH16tWpdevWojjVq1enkpyacnNzKTs7m8qVK0cxMTFCeQrWx5JQ9XgsioODA40ePZqys7Pp7du3tHnzZpJKpbRmzRpRvAYNGlDnzp1VLreDgwONGTOGrly5Qubm5tS3b1/RMfU5srOzqUaNGlSzZk1RuIaGhmg/ExFlZGQQAFq3bp3S9CpUqEB6enpUqVIl2r59O23bto3KlStHHh4eQlkHDRpEZcqUoZycHGG5zZs3EwDq0aMHERFlZmZSo0aNCACpq6vT0qVLiYiodu3atH79+s9eX0V1Jjc3l6ysrKhLly5Kl5OfewoqeL4hIurcubPoeFCUv7+/P/Xr108InzBhAtna2grTBc/l+/btI3V1dXr06JEQp0GDBtS1a9ci11dR+RRdJxSdRxwcHMjBwaHQ9QQABQYGisLc3NwoMjKSiD4dQ/b29qL1IyKKiYkhMzMzysrKouzsbLKwsBCdUzMzM8nKyoocHByKXKf8xw2Ratc3ZcevKted4ig6nyqiynVQUT1LSEggAJScnExEys/voaGh5OzsTFlZWZ+d/4QJEwgAnTlzRogzfPhwAkBbtmwRwhYuXEhqampC3fjc3w+Msa/rP/3kjoiQk5Mj/H2PO0uLFy9G5cqVIZPJoKmpiXHjxgkjY34LFSpUgL29vTDt6uqKnJwcPH36FABw8OBBBAYGQiaTCdvF0tISFStWVDgqaH6Ojo6oUqWKMB0YGAipVCrcmT537hxat24teroQGhqKjIwMXLt2Dbm5ubh06RLatGkjSjc0NBRpaWmirh1v3rxBQEAA1NTUsH//fhgaGha77g0bNoSa2v9VeVdXVzx8+BDAp+5IV65cKXQnPjg4WDTt7u6OlStXYvr06bh+/XqxeRobG6NSpUrCU93jx4+jefPmcHV1xbFjx/Du3TtcvnwZ3t7eAIBDhw5BKpUiKChIVDf9/PyK3f6qOHLkCOrVqwdDQ0NoaGigfv36ICL8/fffAIDz58/D2dkZrq6uwjJNmjSBurq6MH316lVkZWWJtpWZmZnoPccPHz4gKSkJbdu2Fa2Hr68vsrKycO3aNeTk5ODy5cto0aKFqIzNmzcvdj2ePXuGH374Aba2ttDQ0ICmpibu3LkjHDsmJiawt7dHnz59EBsbq3AUSEW+9vH4yy+/QFNTE/r6+mjXrh0iIyOLHOFP1XJfvXoVfn5+aNeuHRYsWPDFo4sOGzYMN27cwLJly74oHbm8vDy8f/8eW7ZsQcuWLdGqVSts2LABFy9exMGDBwEAP/zwAx4+fIghQ4bg6dOnOHfuHEaNGgV1dXXhOJVKpThw4AD+/vtvPH/+HN27d8emTZuQl5eHDh064PDhw6hUqRIsLS0xatSoIstUXJ3566+/8Pjx4yK7WX+J9+/fY+jQoXBwcICmpiY0NTVx4MCBEtUvf39/WFtbY+3atQCAe/fu4ejRo9+szHIBAQFCz4KC5ckv/zn11q1bSE1NLXQOaNiwIZ4/f4779+8jNTUVT58+FZ0DpFIpAgMDP6ucxV3fFCnJdaeg4s6nihR3HfxSR44cQYcOHZT2KFE1f3nXYbmyZcsCgKgra9myZZGXlyc8FfyS3w+Msa/nP924O3LkiHCR1dTUFL0HlZ+pqSmkUqmoS1FBqampsLGxKVH+mzdvRu/evREUFIQdO3bgzJkzGDlyZKF3kr6mgt0W5d2i5O85PX/+HCtXrhRtF01NTVy5ckXUfUmRgu++qKmpwcTERDjxp6WlwcLCQhTH0tJSmPfs2TPk5OQUGUfu/v37OH/+PNq3bw8DAwNVVl3huudf77y8PJiamorimJmZiabHjh2LPn36YM6cOXBzc4OTk1OhblIF+fj44Pjx43jy5AmSk5Ph7e0Nb29vHD9+HKdPn0ZeXh7q1KkjlCMzMxM6Ojqi7T9kyBA8evToi25AJCcno1mzZjAxMcGaNWtw6tQp4T1R+XZ4+vRpoW2grq4OY2NjYVr+Q6mobZWeno68vDxERESI1kNeR1JTU/H8+XPk5uYWqjeqvEMVFhaGhIQETJw4EYmJiTh79izc3NyE9VBTU8O+fftgbm6OsLAwWFpaonHjxrh165bSNL/F8RgZGYmzZ8/i0KFDaNOmDX777TfEx8crja9quY8dO4ZXr14J79d+iUWLFmHu3LlYv3493NzcRPOMjY3x+vVrUZi8i2j+OlGQsbExLC0tRTcJvLy8oKenJwyAUqlSJSxYsABLly6FpaUl6tSpg27dusHExARWVlai9JydnWFkZITMzEyMGjUKM2bMQGZmJjp27IjJkyfj8uXL2Lx5M3bs2KG0TMXVGXlD2traupgt9nmGDRuGZcuWYejQoThw4ADOnj2LgIAAld4xlVNTU0NYWJgwWumaNWtga2tb5LuNX0PBc7JccedU4NNNtfznAC8vLwCfzgFPnjwBUPiYV/U9SlXKA6DIbVyS605+qpxPFSnuOvilXrx4UWQdVjX/gjdM5dsyf/jX/P3AGPt6/tOjZXp6eoredyj4Q15OU1MTtWvXxp49exATE1PoLvnp06fx4sUL1KtXr0T5x8XFoWHDhpg2bZoQtnPnzhKl8bWZmJggNDQUw4cPLzSv4I/5ggoOnpKXl4eXL18KP9Ssra0L3UGVX9ytra1hbm4ODQ2NIuPIubm5oXPnzujfvz/s7OwK3XUtKTMzM6ipqQk/SOQKTmtra2PSpEmYNGkS/vzzT8ycORPh4eGoVq0aqlWrpjBtb29vrFu3DkeOHEHlypVhZGQEHx8fzJkzB8bGxqhSpYrQQDUxMYFMJsPRo0cVppX/yWNJ7d+/H7m5uYiNjRUuyu/fvxfFsbCwKLTOubm5SE9PF8UBPm2bMmXKCOH5lzMyMoJEIsGUKVPQqFGjQmVxdHSEkZER1NXVC9WbgtMFZWRkYN++fVixYoXoiUXBRoirqyvi4uKQmZmJw4cPY8iQIejYsaPSO8jf4ni0trYWfszWr18f1atXx6hRo9CiRQulT9tUKXfv3r2RnJyMpk2b4sSJEyhfvvxnlW/37t3o378/YmJiCj1BBT49CSn4DTn5tIuLi9J0XV1dFd4MIyJRHe7Vqxe6du2Kv//+W7g5NmHCBNEgRPnNnTsX1atXh4+PDy5fvozc3Fy0atUKANCqVSskJiYqXA9V6oz8/JaWllaokVscqVRa6Pt8+Y8ZAML7Tf379xfCPn78WOJjOjw8HJMnT8bFixexZs0adO3a9bPOC/L3G7OysoSncgXLLPc5T4ZNTEwAfHrXWdHIli4uLsI5o6TngK+pJNed/FQ5nypS3HUQ+LRviqtPypiamhbZSFQl/8/1Jb8fGGNfz3/6yZ2+vj68vLyEv6IGOvjxxx9x48YNbNiwQRSel5eHCRMmwNLSEqGhoSXKv+CAAnl5ediyZUuJ0lBEnubnPHHw8/PD9evXUa1aNdG28fLyEkbnUiYlJQVXr14Vpvft24esrCzhx22NGjWwfft20UhpW7ZsgUwmg5ubG9TV1eHh4SG8KJ4/jrW1tejjvgDQp08fREVFoXPnzjh06FCJ1zU/DQ0NVK1aFbt37xaF5x8coCBXV1fMnDkTRFTk4BI+Pj549+4d5s+fL4yA5uPjg0uXLmHv3r2iUdEaNmyIjIwMZGZmFtr+8u34uTIyMqChoSHqYrl582ZRHE9PTyQnJ+PPP/8Uwvbu3SsaSU3+ofT82+rFixc4deqUMK2rq4tatWrhzp07CtfDzMwMGhoaqFatWqGnLcU1qDIzM5GXlyc6dk6dOqX0zrCWlhaaNGmC3r17i9arIFWOxy85ttTV1fHzzz/jzz//LLJeqVJuNTU1rF69Gp6enggICBC6wpXExYsX0b59e/Ts2VP0bbn8AgMDsXv3bmRkZAhhW7ZsQfny5eHs7Kw07WbNmuHJkyeizxScOXMG79+/F0bllNPR0UGVKlVgamqKRYsWwc7OTuEnZF68eIHp06fj119/FcLkdQGAMGiRIqrUGRcXF1GXR0Xyj9aZn52dnegc8O7dO5w+fVoUp2D9Sk1NFR0zqipXrhzq1auHn376Cbdu3UJYWFixy2hqahYqt/x8mr/cBw4cKHF5lHFxcYGNjQ1SU1MVngP09fVhb28PCwsL0TkgKyurRKOIloSi47ek1x05Vc6nihR3HQQ+7Zu7d++KRjItuG+UnYsaNmyITZs2FRoJuiT5f64v+f3AGPt6/tNP7koiNDQUYWFhiIiIwLVr1xAQEIA3b95g4cKFOHLkCOLj40Uf6FWFn58fBg0ahNmzZ6NixYpYvHhxkT9QVCW/oz5//nz4+fnB3Ny82BH65AYPHow1a9bA398fffv2hZWVFR49eoQDBw6gU6dOCp/CyFlYWKB9+/aIjo7Gx48fMXToUAQGBsLd3R0AMGbMGHh6eqJ9+/aIjIzE9evXMX78eAwcOFC4yzt+/Hg0b94c/fr1Q8uWLXHs2DH89ttvWLhwocK702PHjsWLFy8QEhKCw4cPw9PTs2QbK58hQ4aga9eucHJyQsOGDbFx40Y8evRIdNe6VatW8PLygoeHB6RSKVatWgUdHR3UqlVLabpOTk6wsbHBsWPH0Lt3byHM0tISSUlJojv5FStWRM+ePdG6dWuMGDEC7u7uwnt57969E/2wLSlfX198+PABvXv3Rvv27XHw4EFhyHm55s2bo0KFCmjTpg0mTZqEjIwMTJw4Efr6+sL2Nzc3R9euXTF8+HBIJBLY2tpiypQpMDExEe2jX3/9FQEBASAihISEQCaT4e7du4iPj8f69ethYGCAYcOGoWPHjhg2bBj8/f2xYcOGYrsmGRkZwcPDAxMnToSuri5ev36N8ePHi+46X7lyBcOGDUP79u3h7OyMp0+fYt68eUV2X1PleCxfvjzU1NSwdOlSdOzYEYaGhkU+wSqoRYsWqFSpEmbOnKnw3cKSlFtTUxNbt25FQEAAAgMDcezYMaGrpKOjI3x9fZWOOvjkyRMEBwfD1tYWnTt3FjUy7OzshB+0vXv3xty5c9GuXTsMHDgQFy9exOLFiwt9yFtDQwPjx48Xvt/Xpk0bVK1aVahHeXl5GDlyJOrXr4/69esD+PQkIiYmRri5If9g+fbt2xW+LzRx4kR07txZePfHxcUFMplM+NzIhg0blK6vKnVGTU0Nv/zyCyIiIiCVStG6dWtkZmYiPj4ec+fOhb6+PlxcXLB27VrEx8fD1tYWTk5OMDU1Fb7BV7VqVVhZWWHGjBmFPjXh5+eHuXPnwtnZGerq6pgwYUKJu/LLRUREIDIyEjVr1lSp/rm4uGDr1q1wd3eHlpYWvLy8UKtWLVhbW+PHH3/EuHHjkJqaKhoJ80upqakhJiYGERERSE9Ph7+/P9TU1HDr1i0kJCQgPj4eGhoaGDRoEMaNGwczMzO4u7t/lXdIlVF2/Jb0ugOodj5VRJXrYMuWLTF+/Hj06dMH7du3x5EjRwo1eK2srGBoaIi1a9fC1NQUurq6qFKlCsaPH48aNWrA398fP/30E/T09HDs2DH4+/vD19dXpfw/l6q/HwqeLxhjX9l3G8rlXyg3N5cWLVpE7u7upK2tTUZGRhQcHEznzp0rFFeV0TKzsrKoX79+ZGJiQqampjRw4EBauHChaKTAzxktk4ho5MiRZGVlRRKJRCiHKiNwERHdv3+funTpQmZmZqSlpUXOzs70ww8/0L1795TmJ0977dq15ODgQNra2tS8eXNhREi53bt3k4eHB0mlUrK2thZGE8xv1apVVLFiRdLU1CRHR0eaOXOmwrzk8vLyqFu3bmRubk43b94kIsWjZY4ZM0aUzpgxYwqNyDZ58mQyNzcnPT09CgsLo0mTJpGhoaEwf9q0aVS9enXS19cnAwMDqlevHiUmJirdLnKhoaEEQLQN5WH5R3cj+lTPYmJiyMXFhaRSKVlYWFBAQADFx8crTV/V0TIXL15M9vb2pKOjQ82bN6ejR48SADp8+LAQ56+//qJ69eqRlpYWubi40N69e8nAwIBmzZolxHn79i117dqVdHR0yNLSkqZNm0bNmzcvNKLn8ePHqVGjRqSnp0d6enpUpUoVGj16tGgkt19//ZUsLCxIT0+PIiIiaO7cucWOlnnjxg3y9vYmmUxGrq6uFB8fLzrmHj9+TJ06dSJHR0fS0tIia2tr6t69Oz1//lxpmqocj0REc+bMIXt7e1JTU1M6GiKR4jpHRLR69WoCQOfPnyci8ah/qpS7YLrp6elUpUoVqlOnDr1//56IiMzNzWnYsGFKyyY/ryj6k4+KKnfx4kXy9vYmLS0tKlOmDM2dO7dQeoqWe/ToEbVp04b09PTIwMCAOnbsKBpF8M2bN+Tv709GRkYkk8mobt26dODAAYXlvXXrFllaWtLLly9F4QkJCVSuXDkyNjamoUOHKl1fouLrjNy6devIzc2NpFIpWVpaUqdOnejjx49E9Glbt27dmgwNDUXH2+vXr6lTp05kZGREdnZ29NtvvxUaLfPhw4fUtGlT0tXVJUdHR1qyZEmhOMWNliknH1V1wYIFRa6z3KFDh4R1yl+fT548Se7u7sL2v3LlisLRMhXV44LnWCLFI4Tu3LmT6tSpQzKZjAwNDcnLy4umTZsmzM/JyaEhQ4aQoaEhGRsb07Bhw2jUqFGfNVqmKtc3ZcdvcdcdRVQ5nyqiynXw999/J0dHR9LV1aWOHTvS1q1bC63Lpk2bqFy5cqShoSHaXmfPniU/Pz+SyWRkZGREjRo1Eq6NquRfsB4SKa6LikYfVeX3g6LzBWPs65EQfcEXsxkr5Vq2bImXL18Ko13+F128eBHVq1fHgQMHlD65zcjIgLOzM3744QdER0f/wyVk+d27dw/Ozs64fft2kV0n2b/Xxo0bERYWhrS0tC9+2sIYY6x04W6ZjP1/Dx8+xNy5c1G/fn2oq6tjx44d2LFjB9avX/+9i/aPWrBgAWQyGZydnZGSkoKff/4ZlSpVEg2BvX//fly9ehXu7u548+YN5s2b99VGb2RfJikpCSEhIdywK4UePXqEv/76CxMmTED79u25YccYY6wQbtwx9v9pa2vj4sWLWLp0Kd6+fQsnJycsWrQIHTt2/N5F+0dpaGhg6tSpuH//PrS0tNCoUSPMnj1bNHCArq4uNmzYgKioKOTk5MDd3R179+7lBsX/gA4dOqBDhw7fuxjsG/j9998xadIk1KpV64vev2WMMVZ6cbdMxhhjjDHGGCsF/tOfQmCMMcYYY4yx0oIbd4wxxhhjjDFWCnDjjjHGGGOMMcZKAW7cMcYYY4wxxlgpwI07xhhjjDHGGCsFuHFXAnl5efjtt9/g4eEBHR0dmJiYoEWLFjh//nyhuKmpqfD394eenh4kEglevXr1WXkmJiZCIpHgzp07AICUlBRIJBIcOHBA6TKXLl1CVFQU8vLyROHh4eHw8fH5rHIU51um/Tl5SSQSLF269B8pT3Fmz56NQ4cOicJU2Y+qWrlyJbZt2/bF6XxrUVFRsLOzE6aV1dPPNXbsWDg6OhYZx9fXF126dPkq+X1P/5Z9zr6uxMREzJw5s1D416rXsbGxKFeuHDQ0NBASElLo+vNvocq5ICEhAe3bt4e9vb3S60VCQgJCQ0Nhb28PPT091KhRA/Hx8SqV4cWLF+jUqRMMDAxgamqKH3/8ERkZGYXizZ07F46OjpDJZKhXrx6uXLkimn/06FFUrFgRZmZmGDt2bKHlIyIiFIZ/roJ1QJHt27dj+fLlhcIdHR2/alkYY5+HG3clEBkZiQEDBiAwMBC7du3C8uXL8fHjR3h7e2Pfvn2iuNHR0UhOTkZcXBySkpKgr6//j5Xz0qVLmDhx4lf70fxvlJSUhJYtW37vYgBQ3Lj7mv6tP/S/Rz1duHAhJk6c+I/l9638W/c5+zLKGndfQ3Z2NiIiIuDr64sjR45g2rRp3ySf/xV79+7FjRs30KxZM6VxlixZgtzcXMTExCA+Ph516tRBSEgIdu/eXWz6bdq0wfnz57FmzRosWLAAW7ZswY8//iiKs2LFCgwePBh9+/bFrl27YGhoiICAALx48QIAkJWVhU6dOqFFixZYvHgx5s+fj7179wrLX7p0Cfv27cOIESM+cyuIqVoHlDXuGGP/G/gj5iqKjY3FqlWrsGbNGtEd0hYtWqBJkyYICwvD33//DV1dXQDArVu34O3tjYCAgO9V5P+02rVrf+8i/M8jImRmZkJbW/t7F+UfU6lSpe9dBFaKZWRkQCaTqRz+v+Thw4d4//49OnXqBG9vbwDAo0ePvnOpvp2YmBjMmDEDwKePwyuyaNEimJqaCtONGjXC7du3MXv27CIbhceOHcORI0dw5swZ1KhRAwCgpqaGjh07IioqCvb29gCASZMmoVevXhg+fDiAT9ctR0dHLF68GKNHj8atW7fw5s0bTJkyBerq6jh48CAOHjyIJk2aAACGDh2KqKior3bzWFEdYIz9+/CTOxXNnz8fFStWROfOnUXhampqmDhxIp48eYLY2FgAn7oEHjlyBGvWrIFEIoGvr6/CNN++fYs+ffqgbNmykMlkqFixImbMmIEv+a78ypUrERERAQDQ1NSERCJBeHi4KE58fDxcXFygr6+PZs2aIS0tTTQ/OTkZbdu2hZGREfT09BASEoIHDx6olP+GDRvg7OwMmUyGkJAQPHv2TDR/79698PT0hLa2NmxtbTFmzBjk5uaK4qxZswaurq7Q0tKCs7MzZs+eXWSeb9++hY+PD2rWrCl0fy3YzUbeXWTSpEmwtLSEubk5+vfvj+zsbFFaU6ZMgaWlJQwMDNCrVy/Mnz8fEolEmJ+VlYVBgwbBzs4OWlpasLe3R7du3ZSWzdHREffu3cPkyZMhkUggkUiQkpIizH/z5g26dOkCPT09ODk5FfqRsWPHDvj6+sLExASmpqZo1qyZqIuU/A7runXrhPQTExMVlmXlypWQSCQ4e/Ys6tSpA5lMho0bNwIAtmzZAg8PD2hra8POzg7R0dGienj16lU0btwYRkZG0NfXR9WqVbF+/XphvqJuTV26dFFa94urp8WVJy8vD8OHD4exsTFMTU0xatQolZ4AFuy+Ju/iW9wxUdDx48fRrFkzWFhYwNDQEPXr18e5c+eKzf/x48fo2LGjcGwFBwfj77//FuYr6wZnZ2eHqKgoYR2U7fN3795hwIABsLW1hba2NlxdXbFkyRKV85d3F96yZQvat28PPT09lC9fHgcPHkRWVhb69u0LIyMjODk5CXVHLjc3F9HR0XBycoKWlhYqV66M7du3F7tN8tu/fz80NDTw9u1bIczS0hLOzs7C9NOnTyGRSHDy5EkhrLj6osjX2labNm1Cu3btoK+vjz59+gj78MCBA2jcuDF0dHTw66+/Avi0f729vSGTyWBhYYGBAwciMzNTVK6EhATUrVsXOjo6MDMzQ/PmzfHkyRNERUVh4sSJePjwobDf5XUiv99++w2GhoaFugAuW7YMenp6ePfuXaFlVq5cCScnJwCfGjASiQQrV65UuN2GDBkCV1dX6OjooGzZshg1ahSysrJEcU6fPo3q1atDW1sbtWrVwvnz5wul+eTJE7Ro0QIymQxly5bFpk2b4OPjU+h69a3OBWpqxf/8yd+wk6tatSqSk5OLXG7fvn1wcHAQGnYA0LJlS2hoaCAhIQEAcOfOHdy9exehoaFCHF1dXTRr1kx4OpeVlQUtLS2oq6sDAHR0dIT68scff+Dx48fo3r17seshd+bMGdSvXx8ymQzm5ubo27cv3r9/D0D1OhAeHo5Vq1bhxIkTQj0sGK+462xxx4G8C39iYiKqVq0KHR0dBAUF4dWrV7h27Rq8vb2hp6cHX19flX+bMPafQqxYWVlZJJVKaciQIUrjmJqaUmRkJBERJSUlUdWqValZs2aUlJRE169fV7hMWloa9e/fn+Li4igxMZHmzp1LhoaGNGPGDCHO4cOHCQDdvn2biIiSk5MJACUkJChM8+nTpzR27FgCQMePH6ekpCS6c+cOERGFhYWRpaUleXp60tatW2njxo1kYWFBrVu3FpZ/9uwZ2draUq1atWjr1q20fft28vDwoKpVq1Jubq7S9Q8LCyMLCwtydXWlLVu20Jo1a8jKyoqaNGkixLl48SKpq6tT27Ztaffu3RQTE0NSqZRGjBghxNm5cycBoN69e9PevXtp9OjRJJFIaNGiRaK8vL29iYjo9evXVKdOHapZsya9evVKiAOAlixZIkw7ODiQnZ0dtW3blvbs2UMxMTGkrq5Oc+fOFeKsWbOGANCIESNo7969FBERQba2tpT/MImKiiIrKytauXIlJSYm0vr164X9rsiFCxfIysqKwsPDKSkpiZKSkujjx4/CfnRwcKAxY8bQ/v37KTIykgDQlStXhOXnzp1LCxcupAMHDtCuXbsoODiYrKys6N27d0REdP36dfLw8KDAwEAh/devXyssy4oVKwgAlS9fnubNm0eHDh2imzdv0vr160lNTY0GDhxI+/bto1mzZpGOjg5Nnz5dWNbR0ZGCgoJo9+7dlJCQQLNnz6YFCxYo3d5ERJ07d6YGDRoI0xMmTCBbW1siKrqeqlKeX3/9lTQ0NOiXX36h3bt3U1BQEFlbW5ODg4PSfUFE1KBBA+rcubMwrcoxoci6detoxowZtG/fPtq3bx+Fh4eTjo4O3b9/X+kyeXl55OXlRfb29rRu3TraunUrubm5kYODA338+JGICh/vcra2tjRhwgQiUr7Pc3NzqUGDBmRkZERz5syhAwcO0KJFi4TlVMlfXi/LlClDEydOpH379lHjxo3J2NiYIiMjaeDAgbR//37q0qULSaVSSktLE8rYs2dPMjAwoFmzZtH+/fupf//+pKamRqdPny5yW+b3+vVrUldXp3379hER0a1bt0hTU5PU1NTo4cOHRES0bds20tbWpszMTCJSrb4U9DW3lZWVFQ0ZMoQOHDhAJ06cEPahvb09TZo0iQ4dOkQXLlygo0ePkoaGBnXr1o327NlDS5cuJTMzM/rxxx+Fcu3fv5/U1dWpZcuWFB8fT/Hx8dSvXz/6888/KTU1lbp3707m5ubCfk9NTSUicb1OT08nbW1tWr9+vWid69WrR926dVO4PZ4+fUqxsbEEgBYsWEBJSUn09OlThfUxMjKSNm/eTImJibRs2TKytbWl/v37i/ahiYkJeXt7044dO2jRokXk4uJCAGjFihVCvEaNGpG1tTWtXbuWtm7dSpUrVyZLS0sKCwsT4nzLc0F+is5fytSrV090XVMkNDSUAgMDC4VXqFCBhg8fTkREf/zxBwEQHUNERL/88gtZWFgQEdHbt29JT0+PNm3aRPfu3SMnJydauXIl5eTkkKurK+3Zs0elMhN9+r2hp6dHvr6+tGPHDlq8eDEZGhpS+/btiUh5HSjozp071KxZM6patapQD+XxVLnOqnIcTJgwgfT19cnd3Z02bdpEGzZsIBMTE2rfvj15eHjQihUrKD4+nhwdHal58+YqbwPG/iu4caeCtLQ0AkBz5sxRGsfd3V10wvf29hZdpIqTl5dH2dnZFB0dTZUrVxbCS9q4I/q/H/HZ2dmi8LCwMNLU1BT9AJ0+fTppaGgIDbcxY8aQtbU1vXnzRojz4MEDkkqlFBcXpzTPsLCwQg2T3bt3EwC6dOkSERG1bduW3NzcKC8vT4gzdepUkslk9OLFCyIi8vLyoqCgIFHavXv3Jmtra6GM8sbdq1evqFatWlSrVi1Rw45IceOuUqVKogZqcHAw+fn5CdPu7u7ChU7Oy8tL1LgLCgqiwYMHK90OisgbcPnJ92OvXr2EsKysLDIxMaGff/5ZYTo5OTn04cMH0tHRodjYWCG8YINFGXm9yL9d8vLyyN7envr16yeKGxMTQ2ZmZpSVlUXPnj0rtG8LKmnjLn958tdTVcqTnZ1NFhYWov2QmZlJVlZWn9W4K+6YKE5ubi5lZ2dTuXLlKCYmRmm8Xbt2EQA6e/asEHb//n3S1NSkhQsXEpFqjTtF60H0fz8WDxw48Nn5y+tl3759hTg3btwgABQcHCyEvXnzhjQ0NGjZsmVE9KkRJpFIRPWS6NPx0rJlS6XbRBF3d3caN24cEX2qI7Vr16Zq1arRpk2biIhoyJAhVK9ePSJSrb4o8jW3VcH9IN+HBY95b2/vQue22NhYkkql9PjxYyIiqlGjhrBuihQ8huQK1oeOHTuKrkd3794liURCBw8eVJr27du3CQAdPny40LoUrI9y2dnZtHr1atLX1xeOl9mzZ5NMJqOXL18K8ebPny9q3F26dIkAiBonV69eJQDCdfNbnwvyU7Vxt337dgJAu3fvLjKev79/oWsJEVGtWrWoR48eRES0du1aAkAZGRmiOIsWLSJNTU1hevHixaSurk4AKCAggLKysmjhwoXUuHFjVVZNMHToUDIzM6MPHz4IYRs3biSJRCLcgFZUBxTJf4M1P1Wus6ocBxMmTCAAdObMGSHO8OHDCQBt2bJFCFu4cCGpqakV+q3D2H/df7pbJhEhJydH+PseA5AsXrwYlStXhkwmg6amJsaNG/dNRyarUKGC0N8fAFxdXZGTk4OnT58CAA4ePIjAwEDIZDJhu1haWqJixYoKRwXNz9HREVWqVBGmAwMDIZVKcfbsWQDAuXPn0Lp1a1E3x9DQUGRkZODatWvIzc3FpUuX0KZNG1G6oaGhSEtLE3W/ePPmDQICAqCmpob9+/fD0NCw2HVv2LChqCuOq6srHj58CADIycnBlStXEBQUJFomODhYNO3u7o6VK1di+vTpuH79erF5Fsff31/4v6amJsqWLSuUCfjURTY0NBSWlpbQ0NCAjo4OPnz48EV1pGnTpsL/b926hdTUVLRt21Z0LDRs2BDPnz/H/fv3YWJiAnt7e/Tp0wexsbHCy/7fgirlSU1NxdOnT9GiRQthOalUisDAwM/Ks7hjQpFnz57hhx9+gK2tLTQ0NKCpqYk7d+4UuV/OnTuHMmXKwMvLSwizt7dH7dq1hWPkSxw5cgQ2NjZo1KjRF+fv5+cn/L9s2bIAIOpiq6+vD3Nzc+GdrEOHDkEqlSIoKEi03/z8/Io9bxTk7e2NY8eOAfjU/dXHxwc+Pj6iMPn7QKrUF0W+5rbKfzwpC//w4QOSkpIKldPX1xdZWVm4du0a3r9/j3PnzqFr164qbinlIiIikJCQgMePHwP41NXdzs4ODRs2/OK0t23bBk9PT+jq6kJTUxPdunXD27dvhePl/PnzqFOnDoyNjYVlCp5Hz58/Dw0NDTRu3FgIc3NzE41w+T3OBUW5e/cuunfvjrCwMKX7/Fvo2bMnnj17hrt372L//v34+PEjoqOjMX36dKSnp6N169YwMTFBo0aNiuyieO7cOTRr1kz07qf8eqxKl3JVFXWdVeU4kJOPTiqn6DxUtmxZ5OXlCfWcMfbJf7pxd+TIEWhqagp/kZGRCuOZmppCKpUq/aEAfPr0gY2NTYny37x5M3r37o2goCDs2LEDZ86cwciRIwu9g/E1GRkZiaalUikA4OPHjwCA58+fY+XKlaLtoqmpiStXriA1NbXItM3NzUXTampqMDExEU68aWlpsLCwEMWxtLQU5j179gw5OTlFxpG7f/8+zp8/j/bt28PAwECVVVe47vnXOy8vr9A7FmZmZqLpsWPHok+fPpgzZw7c3Nzg5OSE1atXq5R/ScuUm5uLFi1a4ObNm5g5cyaOHz+Os2fPwsTERIjzOfJv3+fPnwP4dEHOv7/lP2pTU1OhpqaGffv2wdzcHGFhYbC0tETjxo1x69atzy6DMqqU58mTJwAK17eC06oq7phQJCwsDAkJCZg4cSISExNx9uxZuLm5FbmMovoPfKrfxb3jp4oXL17A2tr6q+Sf/2aJfHsUvIFS8PjJzMyEjo6OaL8NGTIEjx49KtGNMx8fH5w5cwbZ2dk4ceIE6tWrB29vbxw/fhwZGRm4cOGC8CkUVeqLIl9zWymKVzA8PT0deXl5iIiIEJVTXmdTU1ORnp4OIiqyXKpq1KgRbGxssG7dOgCfGnddu3YV3Vj7HElJSQgNDUW1atWwZcsWnD59GnPnzgXwf8fL06dPiz2PPn36FMbGxoXee8sf73ucC5RJT09HUFAQKlasiMWLFxcb39jYGK9fvy4U/urVK6HRK/+3YLz8cfKnJ38fbsqUKQgKCkKVKlUwceJEaGhoIDU1FVWqVMFPP/2ktEyK6rSmpiZMTEy+yvlHrqhrmirHgZyi803BcFXO1Yz9F/2nR8v09PQU3YUteAGS09TURO3atbFnzx7ExMQUukCePn0aL168QL169UqUf1xcHBo2bCgabnjnzp0lSuNrMzExQWhoqDB6V36KXi7Pr+DgKXl5eXj58iWsrKwAANbW1oWehsgvztbW1jA3N4eGhkaRceTc3NzQuXNn9O/fH3Z2doWe9pWUmZkZ1NTUhB8UcgWntbW1MWnSJEyaNAl//vknZs6cifDwcFSrVg3VqlX7ojIUdOfOHVy7dg1HjhxB/fr1AXwaqvpzv5kol7/+mpiYAABWrVqlcCRJFxcXAJ/uvsbFxSEzMxOHDx/GkCFD0LFjR+GpjFQqLTSoQnp6eonLpkp55PukYH0rOP2tZGRkYN++fVixYoVoMB1FP+byU1T/gU/1u1y5cgAALS0tACi0LVXZ56ampkX+SFMl/89lYmICmUyGo0ePKpyvyuAVct7e3vjw4QP2798vjDr84cMHdOnSBQcOHEBOTg7q1q0r5AsUX38L+prbSlmDKX+4kZERJBIJpkyZovBpofw7ZxKJ5Kv80FZTU0NYWBhWr16NOnXq4M6dOwgLC/vidHfs2AFHR0fRMPj5n7YAnxq1BZ8gFTyPWlhYCD/089eN/PH+V84FWVlZaN26NbKysrB9+3bhGC1KhQoVhIa1XGZmJpKTk4U6WaFCBQDAzZs3hRuY8mll9TY1NRVLlizB1atXAXy6QT1p0iTo6urihx9+EK4Tiiiq09nZ2Xj58uVXuaGgClWOA8bYl/tPP7nT19eHl5eX8FfUieXHH3/EjRs3sGHDBlF4Xl4eJkyYAEtLS9GoV6rIyMgQ7jzJ09qyZUuJ0lBEnubnPAH08/PD9evXUa1aNdG28fLyEu4cKpOSkiJcdIBPI4ZlZWUJd1pr1KiB7du3i0Y627JlC2QyGdzc3KCurg4PD49C3+/asmULrK2tRR/ABoA+ffogKioKnTt3/uLvyGloaKBq1aqFvl+0a9cupcu4urpi5syZICL89ddfSuNJpdLP2hfy0e7y15GtW7cWegryuekDn34g2djYIDU1tdD+9vLyKjTEtpaWFpo0aYLevXvjzz//FMLt7OxE2+Ddu3c4ffp0kXkrqqeqlMfe3h4WFhbYsWOHsFxWVlahb01+K5mZmcjLyxPtl1OnThX7ZLtGjRrCE2e5hw8f4tSpU6hZsyYACHU8/7ZMSkoSRrSTU7TPGzZsiEePHuHw4cOfnf/natiwITIyMpCZmalwv5WEvb09ypQpg19//RUuLi4wNTWFvb097OzsEBMTg0qVKglPNkpaf/OX95/cVrq6uqhVqxbu3LmjsJxmZmbQ1dVFzZo1sXbtWqXplORYDw8Px5UrVzBixAjUrl1baEx8iYyMDGhqaorCNm/eLJr29PTEqVOnRDd3Cp5HPT09kZOTg/379wth165dE40k/L9yLoiMjMTly5fxxx9/KL0BXFBgYCBSUlJE9Wfnzp3IyckRPo9Urlw5ODs7i675Hz58wO7du4VPHRQ0evRo/PTTT8INU/ky+f9VpkaNGtizZ4/oKVdcXByIqMTH6Odec1Q5DhhjX+4//eSuJEJDQxEWFoaIiAhcu3YNAQEBePPmDRYuXIgjR44gPj5e+Madqvz8/DBo0CDMnj1b6O5R3AlaFfK7fvPnz4efnx/Mzc1VviM2ePBgrFmzBv7+/ujbty+srKzw6NEjHDhwAJ06dVL6jgrw6W5s+/btER0djY8fP2Lo0KEIDAyEu7s7AGDMmDHw9PRE+/btERkZievXr2P8+PEYOHCgcJd2/PjxaN68Ofr164eWLVvi2LFj+O2337Bw4UKFd//Hjh2LFy9eICQkBIcPH4anp2fJNlY+Q4YMQdeuXeHk5ISGDRti48aNePTokegOfKtWreDl5QUPDw9IpVKsWrUKOjo6qFWrltJ0XVxcsHfvXgQHB0NXVxdVq1ZVqTwVK1aEtbU1hgwZgvHjxyMlJQVTp04t1A3VxcUFW7duxf79+2FiYiIM6a8KNTU1xMTEICIiAunp6fD394eamhpu3bqFhIQExMfH48qVKxg2bBjat28PZ2dnPH36FPPmzRO9lxUSEoLff/8dVatWhZWVFWbMmFHsd72U1dPiyqOhoYFBgwZh3LhxMDMzg7u7OxYsWPDFXc5UZWRkBA8PD0ycOBG6urp4/fo1xo8fX+zd76ZNm8LLywtt2rTBlClToK2tjaioKNjY2AifhbC3t0f16tUxatQo4ZMAv/76a6H9qWifBwYGomHDhmjbti2ioqLg5uaGv//+G48ePcL48eNVyv9zVaxYET179kTr1q0xYsQIuLu74927d7h8+TLevXsnfApAPpx//hs8inh7e2PDhg3o0aNHobCePXsKYarUX0W+x7b69ddfERAQACJCSEgIZDIZ7t69i/j4eKxfvx4GBgaYPHkyAgMD0bp1a0RERAjvFPft2xcuLi7C06pVq1ahcuXKsLGxUfo6QNmyZVG/fn0cPXoUCxcu/KwyF+Tn54c5c+Zg7NixaNCgATZv3oybN2+K4kRERODnn39G8+bNMXLkSDx48ADz5s0D8H9PcKtVq4ZGjRohMjISMTExkMlkGD9+PCwsLIQ4quzbLzkX3Lt3T9Rz5/z58zAyMoK5uTkaNGgA4NOQ/uvWrcOUKVPw6tUrnDp1Soif/1uq5cqVQ4MGDbBs2TIAQL169VC/fn107twZ06ZNQ0ZGBgYOHIjw8HDR+71jx45Fz5494eDggOrVq2PWrFmQSCTo1atXofKeP38eR44cEXULrVevHqZNmwZDQ0PMnDlTKLcigwcPxqJFixAUFIRBgwYhLS0Nw4cPR7t27Ur8/U8XFxesXbsW8fHxsLW1hZOTU7E9e+RUOQ6+hIaGBsaPH4/x48d/UTqM/at9v7Fc/n1yc3Np0aJF5O7uTtra2mRkZETBwcF07ty5QnFVGS0zKyuL+vXrRyYmJmRqakoDBw6khQsXikZn/JzRMomIRo4cSVZWViSRSIRyKBrhKiEhgQBQcnKyEHb//n3q0qULmZmZkZaWFjk7O9MPP/xA9+7dU5qfPO21a9eSg4MDaWtrU/PmzenJkyeieLt37yYPDw+SSqVkbW1No0ePLjTS1apVq6hixYqkqalJjo6ONHPmTIV5yeXl5VG3bt3I3Nycbt68SUSKR8ssOHrdmDFjCo2oNnnyZDI3Nyc9PT0KCwujSZMmkaGhoTB/2rRpVL16ddLX1ycDAwOqV68eJSYmKt0uRJ9GhqtVqxbJZDJhWyvbjwXrzYkTJ6hatWqkra1Nnp6edPLkyUIjJ967d48aNWpEenp6RY50pmwUVaJPn6CoU6cOyWQyMjQ0JC8vL5o2bRoRET1+/Jg6depEjo6OpKWlRdbW1tS9e3d6/vy5sPzr16+pU6dOZGRkRHZ2dvTbb78VO1omkeJ6Wlx5iD6NHDpkyBAyNDQkY2NjGjZsGI0aNeqzRstU5Zgo6MaNG+Tt7U0ymYxcXV0pPj5epWM+LS2N2rdvTwYGBqSjo0PNmjUrNBLhzZs3ycfHh3R0dKhq1ap07Ngxlff527dvqV+/fmRhYUFaWlrk6upKS5cuVTl/ZfWy4PFEVPiYys3NpZiYGHJxcSGpVEoWFhYUEBBA8fHxQpxhw4aRubl5kduIiGjBggUEgFatWlUobPXq1YXiF1dfFPlW26qoESaPHz8u7Dc9PT2qUqUKjR49WjSq5759+6hmzZqkpaVFpqam1KJFC+E8mpWVRZGRkWRqakoAhDqhbMTcmJgY0tLSEo1cqYyqo2VGRUWRhYUFGRgYULdu3Wjr1q2FjpdTp06Ru7s7SaVS8vDwoP379xMA0YjLaWlpFBwcTNra2uTg4ECrV6+mKlWq0IABA0Tl+lbnAvn5sOBf/nNWgwYNFMYp+NPJwcGh0LH/7Nkz6tChA+np6ZGxsTH17dtXNFKl3Jw5c6hMmTKkpaVF3t7ewujSBTVo0IBWrlwpCnvx4gUFBweTnp4e1a9fv8hrNBHR6dOnycfHR6hbvXv3Fj6rQ6T6aJnp6enUunVrMjQ0FI2Cqup1trjjQNG1QtH1S1H9zH9cMPZfJSH6gi9mM1bKtWzZEi9fvhRG6mOMfb6GDRvC19cXEyZM+N5F+U9o0qQJ9PX1ERsb+13LERcXh9atW+POnTvCqIcFpaWlwdnZGfPnzy/Rh7kZY4yJcbdMxv6/hw8fYu7cuahfvz7U1dWxY8cO7NixA+vXr//eRWPsXy83NxfXrl0r9I4W+/quXr2KEydOYP/+/Thw4MA/nv+kSZPg7OwMGxsbXL9+HRMnTkTjxo1FDbsNGzYgPT0drq6uePr0qdDlvG3btv94eRljrDThxh1j/5+2tjYuXryIpUuX4u3bt3BycsKiRYvQsWPH7100xv711NXV/7ERTf/rmjdvjhcvXmD48OGid2P/Kbm5uRg1ahQeP34MfX19BAcHY9asWaI4MpkMv/zyC/7++29IJBLUrVsXq1evVumbpYwxxpTjbpmMMcYYY4wxVgr8pz+FwBhjjDHGGGOlBTfuGGOMMcYYY6wU4MYdY4wxxhhjjJUC3LhjjDHGGGOMsVKAG3eMMcYYY4wxVgpw404FEomk2D8ACA8Ph4+Pz3cu7T/ra63zypUrIZFIkJOT8xVK9e0lJiZCIpHgzp07X5xWVFQULly48BVK9f2kpKQgKioKL1++FIVHRUXBzs7uO5WqeCtXrsS2bdu+S97/69vmf9HXPO6KsnTpUuG8rsx/8Xz/PYwePRpWVlaQSCSYPXu2wjibNm1CSEgILCwsIJFIFH7bT36NKfiXmJhYZP7nzp1Dt27dUK5cOUgkEowdO1ZhvMTERNSsWRPa2tpwcHDAr7/+ivyDkWdlZeGHH36AkZERvLy88Oeff4qWv337NiwsLPD8+fOiNwhjjBWDv3OngqSkJOH/Dx48QGhoKBYsWIDq1at/x1Kx0mLixImws7P7V9enlJQUTJw4EV26dIGJicn3Lo7KVq5cCTs7O7Ru3fofz/uHH35Ay5Yt//F8/82qV6+OpKQk2Nvbf++isH/AyZMnMWXKFMydOxc1atSAo6OjwnhbtmzBgwcP0KRJE6xZs6bINI8fPw51dXVhulKlSkXGP3HiBE6dOgUfHx+lDa+///4bTZo0QcuWLREdHY0LFy5g3LhxUFdXx9ChQwF8umFw8OBBrF+/Hhs2bEBERAROnTolpDFixAgMGzYMZmZmRZaHMcaKw407FdSuXVv4v/yOcaVKlUTh/2YZGRmQyWTfuxiM/afY2dnxkzsVEREyMzNhYGBQas67pcG3vnbcunULANCvXz+oqSnvaLRp0yaoqakhJSWl2MZdrVq1oKGh+k+f/v37Y8CAAQCgtHH566+/wtHRERs2bICamhoCAwPx8uVLTJ48Gf3794eWlhYOHTqEvn37olmzZvDy8oKlpSXev38PXV1dHDt2DJcuXcKGDRtULhdjjCnD3TK/gfj4eLi4uEBfXx/NmjVDWlqaaH5ycjLatm0LIyMj6OnpISQkBA8ePCgyTXkXoA0bNsDZ2RkymQwhISF49uyZEEdRN687d+4U6noikUgwb9489OrVCyYmJmjWrNlnl0uuqHI9fPgQ3bp1Q5kyZaCjo4OqVati7dq1Rab39u1b9OnTB2XLloVMJkPFihUxY8YMUTcXeRetY8eOISgoCLq6uqhYsSJ27NghSis7OxsTJkyAk5MTtLS04OzsjEmTJgnzc3NzER0dLcyvXLkytm/frtJ6p6SkoFGjRkIZ9+3bJ5r/4cMHDB48GLa2ttDS0kLNmjVx7NgxYb6861ePHj1EXXxtbGywcOFCId7w4cMhkUhw9OhRIaxZs2bo2bOnMK3K/isujqrbNL/ExEQ0bNgQAFC+fHlIJBL4+vqK4pw4cQIeHh7Q1dWFj48P/vrrL9H858+fo3v37jA3N4dMJoOfnx+uX78uijNp0iQ4OztDS0sL1tbWaNmyJT58+CDMv3z5MgIDA6GnpwcjIyOEhYXh1atXSsvt6+uLI0eOYN26daIuWp06dUK7du2EeLt374ZEIsHPP/8shE2bNg0VKlQQph8/foyOHTsK2zU4OBh///230ryBwsdreno6IiIiYGVlBW1tbTg7O2PEiBFFpvH777+jYsWKkMlksLCwQOPGjZGamgpAcVfnnJwcSCQSrFy5UghzdHTEmDFjMH78eJibm8PU1BSzZs0CACxcuBD29vYwMzPDyJEjRcef/Jy0ZcsWlCtXDnp6eujWrRuysrJw5MgRuLu7Q19fHyEhIaL9oMr5QF72s2fPok6dOpDJZNi4cWOhbpm+vr4Ku9pFRUUJaSUmJsLb21vYRgMHDkRmZqYov+nTp8PKygoGBgbo0aMHPn78WOR2z0/Zue/mzZuQSCQ4ceKEKL6ic3JBQ4YMgaurK3R0dFC2bFmMGjUKWVlZRZbj/v37aN26NczMzKCjo4OKFSsK+1Fu7ty5qFixIrS0tGBnZ4d+/foJ83JycjBq1CjY2tpCW1sbnp6eSEhIEC3v6OiI0aNHC10l5U+9VDl+FVmzZg1cXV2F83L+bpfh4eGIiIgAAKirq0MikSAlJUVhOkU1/L6UKmlfvnwZfn5+orj+/v549eqV0PMnKysLOjo6ACD8m5mZCSLCkCFDMGXKFGhpaX2DNWCM/ecQK5Hbt28TADp8+HCheWFhYWRpaUmenp60detW2rhxI1lYWFDr1q2FOM+ePSNbW1uqVasWbd26lbZv304eHh5UtWpVys3NVZpvWFgYWVhYkKurK23ZsoXWrFlDVlZW1KRJEyHOhAkTyNbWttjyAiBLS0sKDw+nffv20cGDB79puS5evEijRo2iHTt20KFDh+iXX34hTU1N2rp1qxBnxYoVBICys7OJiCgtLY369+9PcXFxlJiYSHPnziVDQ0OaMWOGsMzhw4cJAJUvX55iYmJo37591Lx5c9LW1qanT58K8bp27UpaWlo0adIkOnDgAK1YsYJ++uknYX7Pnj3JwMCAZs2aRfv376f+/fuTmpoanT59Wul6y/O2t7enX375hXbv3i3knZKSQkREeXl51KRJE7K2tqYlS5bQ3r17qUOHDiSTyej+/ftERJSUlEQAaNSoUZSUlERJSUlERNS2bVvq2LGjkF/dunVJW1ubJk+eLKRtZGREq1atIiLV6pUqcVTdpvm9fv2aFixYQAAoNjaWkpKS6Pr160T0qU7q6+tT1apVae3atbRjxw4qX748eXp6Cst//PiRqlatSi4uLrRu3Tr6448/yN/fn6ysrOjdu3dERLRy5UrS09OjhQsX0pEjRyg2Npa6d+9O6enpRET0119/kb6+PjVr1ox27NhBGzZsICcnJwoODla6D69fv04eHh4UGBgobPvXr1/T/PnzycbGRog3evRo0tbWpoCAACGsRYsWFBERIewLLy8vsre3p3Xr1tHWrVvJzc2NHBwc6OPHj0rzL3i8hoeHk4uLC23atIkSExNpxYoVNGjQIKXLJyYmkoaGBk2ZMoUSExMpLi6OBg4cSDdu3CCiwscUEVF2djYBoBUrVghhDg4OZGtrS506daK9e/fSoEGDCAANGTKEmjRpQrt376apU6cSANExKz/2a9WqRfHx8bR48WLS0tKiXr16kbu7O23atIk2bNhAJiYm1L9/f2G5kpwPypcvT/PmzaNDhw7RzZs3hfp5+/ZtYR/K911SUhLNmjWLANDatWuJiOjo0aOkoaFB3bp1oz179tDSpUvJzMyMfvzxRyGvTZs2EQAaOnQo7d27l8LCwsja2pqKuzyqcu6rXbs29ezZU7TcuHHjyMHBgfLy8pSmHRkZSZs3b6bExERatmwZ2drairahIr6+vlS7dm3avn07HTp0iH777Tf6+eefRfmqqanR0KFDad++fbRhwwbq1q2bMH/IkCGkpaVF06dPp927d1Pr1q1JQ0ODrly5IsRxcHAgS0tLCg4Opj/++IP++OMPlY5fRXbu3EkAqHfv3rR3714aPXo0SSQSWrRoERER3blzh0aNGkUAhP1b1PFERJScnEwAKCEhodA8eZ2ysLAgdXV1qlKlCsXGxhaZXkEODg40ZsyYQuFVqlShAQMGiMLkdVW+PhMmTKA6derQ06dPaerUqeTo6EhEROvWraPatWuXqByMMVYUbtyVUHGNO01NTeGHOxHR9OnTSUNDQ/jxPGbMGLK2tqY3b94IcR48eEBSqZTi4uKU5hsWFkYARBfa3bt3EwC6dOkSEZWsceft7S2K9y3LlV9eXh5lZ2dT9+7dKSgoSAhX9EO04DLR0dFUuXJlIVx+8ZwyZYoQ9uzZM5JIJLR69Woi+vTjDwAtXbpUYflv3bpFEomk0EU+KCiIWrZsqXS95XkPHjxYCMvKyiJbW1saOHAgERElJCQQADp79qxoXQr+EABAS5YsEaU/e/Zssre3J6JPjR8tLS3q3bs3NW3alIiIrl69SgDo77//JiLV9p8qcVTZpkVtD/mPbrkJEyYIP87ktmzZQgCE42TJkiWiBi8R0fv378nCwoJmzZpFRET9+vUT3SQpqHPnzlStWjXKyckRws6ePUsA6MKFC0qXa9CgAXXu3FkUdunSJdG2bdCgAfXu3Zv09fWF9M3MzGjZsmVERLRr165C+/n+/fukqalJCxcuVJp3weO1cuXKNHfuXKXxC4qJiaHq1asrnV+Sxl3lypWFxkZubi5ZWVmRubk5ffjwQYhXs2ZN6tq1qzAtP989ePBACGvXrh0BoHPnzglhw4cPF+pyQcWdDwoeF8rqGdGnempnZ0ddunQRwry9vUXpEhHFxsaSVCqlx48fExFR9erVC9Wt6tWrq9S4K+7ct3jxYjIyMhIaJXl5eeTk5ETjxo0rMu38srOzafXq1aSvr1/kjTZdXV3asWOHwnkvXrwgLS0tGjt2rML5z58/J21tbYqJiRHCcnNzqVKlStSuXTshzMHBgRwcHER1SpXjVxEvL69C+6Z3795kbW0trOeSJUuK3Q/5FdW427t3L02ePJkOHDhAO3fupJCQEAJA27dvVzl9ZY27Vq1aUd26dUVh06ZNIwDCDbkXL15QtWrVCADp6OjQ7t27KSMjgxwcHOjEiRMql4Exxorzn+6WSUTIyckR/vLy8r44zQoVKohe9nd1dUVOTg6ePn0KADh48CACAwMhk8mEfC0tLVGxYkWcP3++yLQdHR1RpUoVYTowMBBSqRRnz54tcTmbNm0qmv6W5crNzcUvv/yCcuXKQUtLC5qamli2bFmxI94tXrwYlStXhkwmg6amJsaNG6dwGX9/f+H/ZmZmMDMzw8OHDwEAR44cgZqaGrp06aIwj0OHDkEqlSIoKEhUF/z8/IpdbwBo0aKF8H9NTU00adJEWO+DBw/CyckJ7u7uQrq5ubnw9fUtNm1vb2+kpqbi3r17OHv2LAwMDBAZGYmTJ08iLy8Px48fh7W1NZydnYW8itt/JdnHRW3TktLV1RW9J+Xq6goAQnoHDx5E7dq1YW1tLZRLKpWidu3aQrnc3d2xe/duREVF4fz586LugfI0WrVqJTqm3d3dYWhoWOKRSKtUqQIDAwMcO3YM2dnZOHPmDPr27QuJRIJLly7h5s2beP78Oby9vQF8Gk2vTJky8PLyEtKwt7dH7dq1S3Rsuru7Y/r06Vi0aBHu3r2rUvyLFy9i8ODBOH78+BeNNNuwYUOhS7CamhqcnJxQq1Yt0ftUZcuWxaNHj0TLubi4wNbWVhRHT08Pnp6eorC0tDRhn5XkfFDwPKVMXl4eunTpAmNjYyxevBjApy7RSUlJaNu2rejY9vX1RVZWFq5du4acnBxcvnxZdBwDQPPmzVXKt7hzX/v27ZGZmSl0az527BiSk5PRrVu3ItPdtm0bPD09oaurC01NTXTr1g1v374VriOKuLu7Y9SoUVi9enWh/XT69GlkZmYqzffatWv4+PEj2rRpI4SpqamhTZs2hepwQECA6J01VY7fgnJzc3Hp0iVRfgAQGhqKtLQ0lV8HKInAwECMHj0ajRo1QnBwMOLi4uDj44Nffvnli9Pu1asXTp48iZiYGLx48QIHDx7EjBkzAPxft04TExNcuHABt27dwtOnT9G0aVPMmTMHNWvWRN26dbFp0yY4OzvDzs4Oc+fO/eIyMcb+u/7TjbsjR45AU1NT+IuMjPziNI2MjETTUqkUAIR3OJ4/f46VK1eK8tXU1MSVK1eEd2WUMTc3F02rqanBxMQEjx8/LnE5LSwsRNPfslwzZszAxIkT0b17d+zZswdnz55FWFhYke+1bN68Gb1790ZQUBB27NiBM2fOYOTIkYXelQEUb3N52i9evICxsbHSdxmeP3+OzMxM6OjoiNZ7yJAhePToUbEN/oLrbm5uLqz38+fPkZycXGibzps3r9ht6u7uDl1dXRw/fhwnTpyAj48PPDw8kJOTg6tXr+LEiRNC40KeV3H7ryT7uKhtWlKqHBOHDx8uVK4dO3YI5YqMjMSkSZOwYcMGeHl5wcbGBtOmTROtf1RUVKE0Xr9+Xey2LkhNTQ116tTB8ePHceHCBchkMri5uaF27drC/jA3N4eLiwsAIC0trdDxBACWlpaF3rctyvz589G8eXOMHz8eZcuWhZubG/bs2aM0vr+/P5YvX47Dhw+jXr16MDc3x/Dhw5GdnV2i9QUAQ0ND0bRUKlUYVrAOqLqc/MYGULLzgaLtqkh0dDSSkpKwdetW4X2m9PR05OXlISIiQlQn5Mdsamoqnj9/jtzcXIXHsSqKO/cZGhqidevWWL16NQBg9erVqFu3LsqVK6c0zaSkJISGhqJatWrYsmULTp8+LfzYL+oY3LRpEzw8PNC/f3/Y2tqidu3aOHPmDIBP50EAsLa2VrisvJ4W3N6K6rCia0dxx29Bz549Q05OjsL88pfnW2vZsiUuXbr0xekEBgZi/PjxGDNmDMzMzNC8eXOMHj0aAGBlZSXEU1NTQ/ny5aGrq4tnz55hxowZmDp1Kh4/fowffvgBGzduxOHDhxEVFYXLly9/cbkYY/9N/+nRMj09PUV3Jf+JIYhNTEwQGhqK4cOHF5pnampa5LL5BykBPt2tfvnypXDx0NLSKvTSfXp6usK0Cn7D6VuWKy4uDp07d8aoUaNEcYoSFxeHhg0bin7A79y5s8hlFDE1NUV6ejoyMzMVNvBMTEwgk8lEA5XkV9zL9AXX/dmzZ8J6m5iYwNnZGZs2bSq0nLyBo4yGhgZq1aqFY8eO4dGjR/D19RWFnThxAj/99JNoPYrbf1+yj78lExMTeHt7K/yGlb6+PoBP+2HIkCEYMmQIUlJSsGTJEowYMQKurq5o3rw5TExM0KFDB3Tt2rVQGjY2NiUuk7e3N9atWwdXV1d4e3tDIpHAx8cHx44dg4GBAerWrSvEtf5/7N13WBRX2wbwe5feO1JEEAuKvSHEgiAqdsUWS1RijcZEsXdsscVurLEbe0VF7DVi7zE27AVUVLAAUs73Bx/zsrCUxVVkc/+ui0t35szMs9N2nz1nzrG3V1qjEhUVle2X+IzMzc0xf/58zJs3D5cuXcK4ceMQEBCAhw8fZpnkdO3aFV27dkVkZCTWr1+PIUOGwMnJSeqhD0jtyCGtpiWr+8HXpMr9IKex5gDgwIEDmDBhAjZv3owSJUpI083NzSGTyTB58mTUrVs303IuLi4wNzeHlpaW0us4N3K69wFAYGAg/P398ejRI2zZskXhnqZMSEgIXFxcsHz5cmna9evXc4zF0dERa9asQXJyMk6dOoXhw4ejadOmePbsmXR9P3/+XGEfpUlL+l68eIGiRYtK06OiojIlhMo+O3K6fjOysbGBtrZ2pusmKipKIZ6CZNy4cQgKCsKjR4/g4uIi9fTp4eGhtHxwcDA6d+4MV1dX7Ny5E6VKlZLK+vr64tixY6hQocJXi5+INMd/OrkzMTFRaEr1Nfj6+iIkJAQVKlSAjo6OSss+ePAA165dk5oB7du3D58+fZLeQ+HChREdHY3o6Gjpw1zZYK5fO664uDiFZObDhw8IDQ2FsbFxluvMuExKSgq2bNmiUlxAam96KSkpWLdundTzWno+Pj6Ii4tDQkKCQk1YboWEhMDb2xtAam9zYWFhaNOmDYDUfTp79mxYWVkpfGHKSEdHR2mNZFpPhFFRUdLAuTVr1sSmTZtw//59hQGUc3P8PucY5yTtWCl7Hznx9fXF6NGjUbx48Uy1fMq4uLhg0qRJWLRoEf799180bdoUvr6+uHHjhsrXs66ubpb7fsyYMdi+fbvURK9mzZr4448/YGpqqtBLabVq1aTmomnNEZ8+fYrTp0+jQ4cOKsUDpH55rly5MiZOnIiQkBA8ePAgxxosOzs7DBgwAGvXrpUGR07rifPWrVuoVKkSgNzfD76kvNwPsvLkyRN07NgRQUFBaNmypcI8IyMjVK9eHXfv3s2219EKFSogJCQEXbp0kabl9oeknO59QOr57ejoiI4dOyIhIUGhJ1Zl4uLiMl2fmzZtylU8QGrPkrVq1cLQoUPRrFkzREdHw9PTE/r6+li7di3GjRuXaZmyZctCX18f27Ztw8CBAwGkPrawbdu2LJOT9O9Ples3LcZKlSph27ZtCvflLVu2wN7e/qsMESKEwPbt26VrQx3MzMykc2HRokXw8PBQOo7erVu3sHXrVty8eVOalr7n3/T/JyJS1X86ucsPQUFBWLNmDfz8/NCnTx/Y2dnh2bNnOHjwIDp06KD0F+Y0tra2aNeuHSZMmID4+HgMGjQIDRo0QMWKFQH873mPbt26oW/fvrh69apCl+f5FZevry+WLl0KDw8P2NjYYPr06Tl+kfP19cWAAQMwe/ZslCpVCosXL87TB17p0qXRtWtX9O3bF5GRkahevTqePXuG8+fPS+vu2bMnAgICMHToUFSsWBHv37/HlStX8P79e0ydOjXb9W/evBnW1taoWLEiFi5ciFevXkljItWvXx8+Pj6oW7cuhg4dCjc3N7x58wZnz56FtbW19CXKzc0NW7duRcWKFaGnpyd9MaxRowbGjx8PQ0NDaYDztGlGRkbS/gVyd/w+5xjnpESJEpDL5fjzzz/Rvn17mJmZSc0Wc9KlSxcsXLgQderUQVBQEJydnfHixQucOHECHh4e6NSpE3r16gUrKyt4enrCxMQEu3fvxtu3b6XEOjg4GNWqVUPLli3RuXNnmJub49GjR9JzemnP+WWUtu/3798PS0tLaQiTtLGwTp48KZ0D1atXR3R0NKKiohR+CGjYsCGqVq2KVq1aYfLkydDX10dwcDAcHByU/qCQlZo1ayIgIABly5ZFSkoK5syZA1tbW5QpU0Zp+eDgYLx+/Rre3t6wtrbG33//jStXrkg1YtWrV4e9vT1+/vlnjB49Go8fP8Yff/yR63i+lLzcD7ISGBgIY2NjNG/eXGFA6LQxBKdOnYp69epBCIEWLVrAwMAA9+7dw86dO7Fu3TqYmppi8ODBaN++PQYPHgw/Pz+sX78+180Cc7r3AanJepcuXTB+/Hi0bds2xwTI19cXc+bMwahRo+Dt7Y1NmzYpJAHKxMTEwN/fH507d0bJkiXx/v17/PbbbyhTpoz0w8CwYcOkONO66Q8NDcWKFStgZWWFvn37YtSoUZDJZHB3d8eyZctw69atHMdey831q8yYMWPQtGlT9O3bF82bN8eJEyewaNEiLFiwQOWhDW7cuIEbN25INaknTpzA27dv4eLiIt1PW7duDQ8PD5QvXx4JCQn4888/ER4erjDMy8OHD1GsWDEsX75cej7x5cuXOHbsGIDUpOvmzZvYsmULjIyMpGdCb9++jc2bN6NatWqIi4vDxo0bsXPnzixbhAwePBgjRoyQzgUPDw9ERERg9uzZsLCwwKFDh6RnAZXFRESUrXzszKVAyqm3zIy9UKb1mHj//n1p2qNHj0SnTp2EtbW10NPTE66urqJ79+7i4cOHWW43bd1r164Vzs7OQl9fXzRt2lRERUUplAsJCRFubm7CwMBA+Pv7i2PHjintLTNjL3RfMq6YmBjRvn17YWpqKuzt7cWkSZPEyJEjhbOzs1QmY89+nz59En379hWWlpbCyspK9O/fXyxYsECh57Sses5zdHQUY8eOlV4nJiaKUaNGCScnJ6GrqytcXV2lHsyESO0Vbvr06cLNzU3o6uoKW1tbUa9ePbFz584s33fatvfv3y+8vb2Fnp6eKFmypAgNDVUoFxcXJ4YPHy5cXFyEjo6OcHBwEM2bNxfHjx+Xyhw+fFiULVtW6OrqKry/2NhYoaWlJXx8fDJN8/X1zRRTbo5fTmVyu0+VmTNnjnBychJyuVx4e3sLIXLfg+vr169Fnz59hIODg9DV1RVOTk6iffv2Uk+EK1asEF5eXsLc3FwYGRlJw42kd/36ddGiRQthbm4uDAwMhJubm/j111/F69evs4z54cOHom7dusLY2DhTTNWqVRP6+voiISEh22lCpA7d0a5dO2FqaioMDQ1Fo0aNlPbomF7GfTNo0CBRpkwZYWhoKCwsLIS/v7+4cuVKlsvv2rVL+Pj4CCsrK6Gvry/KlCkjFi9erFDm1KlTomLFisLAwEB899134urVq0p7y8zYA6CyXkQz3t+U3e+UHe+M13Ze7gdpMp6fzs7OAkCmv/Tn6smTJ6VjbGxsLMqVKydGjBghPn36JJWZOnWqsLW1FcbGxiIwMFDMnTs3V71l5uaeLMT/em7ds2dPtutMExwcLGxtbYWpqano3Lmz2Lp1a6bPkfTi4+NFt27dRIkSJYS+vr6wsbERbdq0yVR+1qxZonjx4kJXV1cULlxYYXiFxMREMWzYMGFvby90dXVF5cqVxb59+xSWz6q3yJyu36ysWrVKlCpVSujo6AgXFxcxc+ZMhfm57S0zrVfejH9dunSRygwfPlyUKFFCGBgYSNdDxuOR1ttm+usj7ZzL+Jf+fL13756oUaOGMDExEUZGRqJ+/friwoULSmM9fPiwKFGihML5J4QQa9euFYULFxa2trYK+0FZTERE2ZEJkaHbOfomde3aFXfv3sXJkyfzOxQiIlLBlClTMGfOHDx58gRaWlr5HQ4REWkwNsskIiL6Ah48eIB///0XM2fORO/evZnYERHRF/efHgqBiIjoSwkODkaLFi1QrVo1pb3UEhERqRubZRIREREREWkA1twRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3udS1a1fUrFlTYdrmzZtRvHhxaGtro0WLFvkTmBooe295FRwcjMKFC0uvL1++jODgYKSkpHyxbWblwYMHkMlkOHjw4Geva/bs2Th8+LDCtLdv3yI4OBj37t377PXnpzp16qBTp06fvZ6Mx16Zo0ePQiaT4e7duwCUHyOZTIY///xTeq1s339Jjx8/hp+fH4yNjSGTyfD27dtcLXf37l3IZDIcPXpUpe0FBwfj4sWLqgeagwcPHiA4OBivX7/OtL2cjtPXNmLECNjZ2UEmk2H27Nn5HY5SX+o4fQuUXWPqvH9mpM51u7i4YNSoUWqI6us4ePAgZDIZHjx4kN+hqI067ikLFiyAv78/zM3NFT4j0ps2bRrKlSsHU1NTmJmZoUaNGjhw4ECO6964cSNatGgBW1vbbM+7y5cvo1atWjAwMEDRokUxf/58hfkxMTEICAiAqakp6tati2fPninMP3bsGIoVK4ZPnz6p8M6J1I/JXR4lJiYiMDAQderUwbFjxzBt2rT8DinPRo8ejSVLlqhlXd27d8euXbuk15cvX8a4ceMyJXfq3ObXkFVyN27cuAKf3H1NlStXRnh4OJycnLIsEx4ejubNm0uvv3ZyN2HCBNy/fx/bt29HeHg4TExMvuj2xo0b98WSu3HjxmVK7jJeo/nt1KlTmDx5MkaOHInw8HB8//33+R2SUl/qOH0LvvY1RppFHfeUNWvW4P3796hbt26WZWJjY9G1a1ds2rQJGzduROHChdG4cWOcP38+23Vv2bIFT548gb+/f5ZlXr58iXr16sHU1BS7d+9Gnz590L9/f6xZs0YqM2nSJDx8+BCbN2+GtrY2goKCpHlCCAwcOBBTpkyBrq6uCu+cSP208zuAgurp06f48OEDOnTogBo1auR3OHkSFxcHAwMDFCtW7LPXlZiYCLlcjsKFC+fqFzx1bFOTCCGQkJAAfX39b3J96mJqagpPT89sy+Q0/0u7ffs2atSogXr16uVrHF9Kbq/Rr+X27dsAgL59+0Iu5++NRPkh7ftAXqjjnvL3339DLpfj6NGj2LZtm9IyEydOVHhdv359FC1aFBs3bkTVqlWzXPfGjRshl8vx4MEDhWQtvUWLFkEmk2Hz5s0wNDRE3bp1cf/+fUyYMAE//PADAODw4cMYNmwYGjRoAAsLCzRu3Fhafu3atdDV1UWbNm1UfetEasdP0jxYuXIlihYtCgCoW7cuZDIZVq5cmanc/v37oa2tjXfv3knTChUqBFdXV+n1ixcvIJPJcOrUKWnali1bUKlSJejr66Nw4cKYMGECchprXiaTYf78+ejRowdMTU1hZ2eH6dOnK5RJa343e/ZsFClSBEZGRgCUN5Fcs2YNSpcuDT09Pbi6umZqKpW2zLp161CyZEno6+vj8ePHCs0zVq5cicDAQACAjo4OZDIZunbtqnSbjx49QkBAAKytrWFoaIhSpUph1qxZCttcuHChFFOxYsWwePHibPdJmtevX6NVq1YwMjKCi4tLppt7cnIyJkyYgKJFi0JPTw9lypTBjh07pPkuLi54+PAhJk2aBJlMJjWpSTsH6tWrB5lMBhcXF2mZK1euoEGDBjA2Noa5uTm6dOmi0Lxv5cqVkMlkOHfuHLy8vGBgYIANGzYojT9tX61fvx6urq4wMDBAixYt8PLly1ytb8aMGXB1dYWenh7c3d2xbt06pduZPXs2HBwcYGxsjB9//BEfP36U5v3zzz9o3bq1NL9atWrYt2+f0vUcPHgQZcqUgb6+PurUqaNQs5mxWaYy6ZtlKtv3ERERcHR0xKRJkxSWE0LA2dkZwcHBWa77/fv36N27N2xsbGBgYABvb29cuHBBYdvHjh3DmjVrIJPJUKdOnSzXtX//fpQuXRoGBgaoW7eu0mZWHz9+RFBQEBwdHaGnpwcPDw+cOHFCYXsA0KNHD+n9pbl//z5at24Nc3NzGBsbo0WLFnjy5InC+u/evYtWrVrBwsICxsbG8PLywt9//42jR4/Cx8cHAFCiRAmF96KsCVVYWBiqVKkCfX19ODo6YuTIkUhOTpbmpy3z999/o1KlSjAyMkLNmjVx69atLPdPmuzuJV27dpXuEVpaWtk2V1u+fDk8PT1hZmYGOzs7tGvXDlFRUTlu/+zZs6hduzYMDAxgY2ODPn364MOHD5neW3oZm9hmd5yyOga53X7atXv58mXp2q1ZsyYePXqEJ0+eSPeRKlWq4Pr16wpxvnr1Ct26dZPOZ19fX/zzzz857pP0srq/pYmNjUWnTp1gbGyMokWLKm1x8aXuzXk55iEhIahTpw4sLS1hZWWFRo0aZbrfpH0WLl26FEWKFIG5uTk6dOiA2NhYhXLnz59H/fr1YWJiAnNzc9StWxc3b96U5ud0nwdSv/S7uLjAyMgIrVq1ylSTrkxuPg/nzp2LUqVKQU9PD4ULF0bfvn2leUlJSRg+fDgcHR2hr6+PKlWqZGq+6OLighEjRkhNot3d3QHk7ZzKeA29efMGgYGBsLOzg76+PlxdXTF06NBs15GXH3bkcjnMzc2RmJj42evet28fGjVqBENDQ2lamzZtcOfOHekz7NOnT9J8Q0NDJCQkAEhNjEeNGoWZM2eq/B6IvghBudKlSxdRo0YNIYQQL168EJs3bxYAxB9//CHCw8PFixcvMi0TExMjtLS0xL59+4QQQty+fVvo6OgIuVwunj59KoQQYtu2bUJfX18kJCQIIYRYt26dkMvlon///mLfvn1i1qxZwtDQUPz+++/ZxgdA2Nvbiy5duoiwsDAxePBgAUBs2LBBKuPt7S0KFSokvLy8xI4dO8S2bdsyvTchhNi1a5cAIHr37i3CwsLEiBEjhEwmEwsXLlTYH9bW1sLd3V1s3LhR7NmzR7x9+1aMHTtWODo6Svtp1KhRAoA4efKkCA8PF3fv3lW6zTp16ghPT0+xY8cOcfjwYbFo0SIxfvx4af5vv/0m9PT0RHBwsDhw4IAYO3as0NLSElu2bMlyn9y/f18AEA4ODmLQoEEiLCxMBAYGCplMJsLDw6VyPXv2FKampmLWrFli//79ol+/fkIul4szZ84IIYS4ePGisLOzE127dhXh4eEiPDxcxMfHi23btgkAYu7cuSI8PFxcvHhRCCHErVu3hImJiWjUqJEICQkR69evF0WLFhVNmjSRtrlixQoBQJQoUULMmzdPHD58WNy8eVPp++jSpYuwtbUVpUuXFlu2bBFr1qwRdnZ2wt/fP8f1zZs3T8hkMjFy5EgRFhYmevbsKQCI0NBQhfPC3t5eeHp6ipCQELFw4UJhbGwsevfuLZXZt2+fmDhxotizZ484ePCgGDx4sNDS0hJnz56VyowdO1aYmJiIYsWKidWrV4utW7cKNzc3UaZMGZGcnCyEEOLIkSMCgLhz547CMTpw4IC0HgBi6dKl2e77YcOGCTc3N4X9dOjQISGTyURERESW50SrVq2Eubm5WLx4sQgJCRHe3t7C1NRUREVFCSGECA8PF+XLlxeNGjUS4eHh4p9//lG6nocPHwp9fX3RuHFjERoaKn777Tfh5OQkAIgjR44IIYRISUkR/v7+wt7eXixdulSEhYWJ77//XhgYGIhHjx5J2wMghg8fLr0/IYR4+fKlcHR0FNWrVxdbt24VO3bsEJUqVRLly5eX9uXz58+Fra2tcHd3F2vXrhX79+8XEyZMEBs2bBAxMTHijz/+EADE5s2bFd5L+mtUCCEuXboktLS0ROvWrUVoaKiYPn260NXVFUOHDs10bMuXLy/Wrl0rQkJCRIkSJUSVKlWy3NdC5HwvuXv3rhg+fLgAoHB8lQkODhbLly8XR44cEdu2bRPfffedKF++vEhJScly+8+fPxfGxsaiTp06IiQkRCxevFiYmZmJdu3aKby39PtDCCHu3LmjcCyzOk7ZHYPcbj/t2q1QoYJYvny52Llzp3BxcRE+Pj6iVq1aYs6cOWLv3r2icuXKoly5ctJy8fHxonz58sLNzU389ddfYs+ePcLPz0/Y2dmJ9+/fZ3tc0svqGku7Np2dncXIkSPF/v37xY8//igAiKtXr0rLf8l7c26OeVp8aebOnSsWLFggDh48KHbv3i2aNGmSaZ94e3uLwoULCx8fH7Fr1y6xdOlSYWRkJIKCgqQy165dEwYGBqJmzZpi06ZNIjQ0VAwbNkw6J3Jznw8PDxcymUz6XB44cKBwcHAQAMT9+/ez3D85fR6OHj1ayOVyMWjQILFv3z6xfv160blzZ2n+wIEDhZ6envj9999FaGioCAgIENra2grHzdnZWRQqVEg0adJE7NmzR+zZsyfP51TGa6hr167Czc1NbNy4URw9elSsWLFCDBgwIMvl08v4GaFMYmKiiI6OFnPmzBGGhobi0qVLuVq3ss+bNDY2NmLy5MkK0549e6bwedmlSxcREBAgoqOjRd++fUWdOnWEEEJMmjRJ4Zomym9M7nIpYzKS8cM/KxUrVhSjR48WQqR+iHt6eooKFSqIjRs3CiFSb8K1atUSQqR+GXRychJ9+/ZVWMf06dOFtbW1+PTpU5bbAZDpi1bbtm1FxYoVpdfe3t7C2NhYvH79Otv3VrVqVdG4cWOFMr179xb29vbSF8suXboIuVyeKSHJeJNP++KSmJiY7TaNjIxESEiI0vcWExMjDA0NxfTp0xWm9+3bV1SoUEHpMkL870YeEBCgMN3Dw0O0aNFCCJGacMtkMrF582aFMo0bNxbNmzeXXmf8ApF+/Rk/KDp27CgqVKggkpKSpGnnzp0TAKQEMG2/pCUx2enSpUumL1ShoaECgLh8+XKW60tKShJ2dnaZzid/f3/h4eEhvfb29hZ6enoKP1AsWLBA6OrqSklPesnJySIxMVH4+fkprHvs2LECgMJx/OeffwQAsWPHDiGE6smdEMr3/c2bNwUAcfr0aYX9lHYtKXPt2jUp2Unz/v17YWVlJYYMGSJNq1GjhujSpUuW6xFCiKCgIGFnZyf9KJM2Lf094cCBAwKAOHfunFQmJSVFlCtXTvz6669Zvl8hhBg5cqSwt7cXsbGx0rQnT54IXV1dsX37diGEEIMHDxZmZmYiOjpaaYxZfUnKeI22bt1alC1bVuEL85QpU4SBgYG07rRjm/6L95YtWwQAKVFVJjf3kqVLlwpVf2dMSkoSDx8+zLR/Mxo0aJCwtrYWHz9+lKZt2LBByGSyLJNdIZTf35Udp5yOQW62n3btbtq0SSqzYMECAUDhR720az7tx4ulS5cq/FAghBAfPnwQtra2YtasWVnuE2Wyu7/16tVLmvbp0ydhaWkpJRpf8t6cUVbHXFns6Zf5+PGjMDQ0VLjuvb29hZWVlULC8vPPPwtXV1fpdZs2bYSrq2uWn7u5uc8HBARk+lwOCAjIMbnL7vMwOjpa6OnpiVGjRimd/+rVK6Gvr69wTJKTk4W7u7to27atNM3Z2Vk4OzsrfDbn9ZzKeA2VKVNGzJ07N8vy2ckpuUv7oQWAMDQ0FLt27cr1urNL7rS1tRV+wBZCiLi4OAFA/PXXX0IIISIiIoSLi4sAIKytrcX58+dFVFSUsLa2zvZ4En1t/+lmmUIIJCUlSX8ZO/1Qhxo1akjNsE6ePImaNWuiZs2aCtPSntm7ffs2Hj9+jNatWyvE5ePjg1evXuHRo0fZbqtp06aZXl+9elWh5yZPT09YWFhkuY7k5GRcvnwZrVq1Upjepk0bPH/+XKFZmKurK9zc3HKxF3JWsWJFDB8+HKtXr87UA1V4eDg+fvyIVq1aZdov165dy7FnqmbNmim8btq0Kc6dOwcgtQ29rq4uGjdurLBuX19fheZ6qjh06BBatmypcH5VrFgRZmZmmTpkaNiwYa7W6eLignLlykmvGzRoAF1dXel9KFvfkydPEBkZqfRYXrp0SaHZnZeXF2xsbKTXTZs2xadPn3D16lUAwIcPHzBo0CA4OztDR0cHOjo6OHjwYKbmTtra2mjUqJH02t3dHcWKFcsU5+dyc3PDd999h9WrVwNIbf64detWdO7cOctlLly4AC0tLYWebY2MjNCoUSOV4zt//jz8/f0VHpzPeP0dOnQIRYsWRcWKFaXzIDk5GXXq1Mnx3Dp06BAaNGgAAwMDadlChQqhVKlS0rLHjh1D48aNYWlpqVLsyt5LQECAQlPDNm3aIC4uTqEZoJGRkcLzkKVLlwaQ+vyxMqrcS3LjypUr8Pf3h5WVFbS1teHs7AwA2TbxPX/+PBo1aqTwLFHae82pE4bcyOkYqLJ9X19f6f9pzySnbxacNi3t/njo0CF4enrC3t5eOkd0dXXh6emZ53uXMn5+ftL/dXR0UKxYMemYf8l7M5C3Y37//n20adMGhQoVgra2NgwNDfHx48dMy3h6ekqPJgCp53P6c/nYsWP4/vvvoaOjo3Q7ubnPnz9/Xunnck6y+zw8c+YMEhISsrzXXb9+HfHx8QrXnVwuR6tWrTLd5+rVqwdt7f91vaCuc6pixYr4/fffsXDhQrV3OFauXDmcO3cO+/fvR/v27dGuXTuFx1q+JFdXV9y5cwe3bt3CkydPUKVKFYwdOxaBgYFwcXHB7Nmz4ejoCFdXV2zevPmrxESkzH86uTt27Jj0RVVHRwc//vij2rdRs2ZNnD17FomJifj7779Rq1Yt1KhRAydPnkRcXBwuXrwoPXv26tUrAICPj49CXGkPCj9+/DjbbaX/cp72OiUlBS9evJCm2draZruOly9fIikpKVO5QoUKAQCeP3+e63WpYuPGjahUqRL69esHR0dHeHp64uzZswD+t19cXV0V9kvr1q2RkpKS6cMvI2X7Je25jVevXiEhIQGGhoYK6x44cCCePXuWp4T/1atXCA4OVlifjo4OYmJiMh3D3O7DjO9BLpfD0tISkZGRWa4v7VgpO5aJiYnSflW2/rTXaesfPHgwli1bhkGDBuHgwYM4d+4c6tWrh/j4eIXlLCwsoKWllWldGeNUh8DAQGzcuBGfPn3Ctm3bkJycjLZt22ZZ/vnz57CwsFD4MgOk7o/053VuREVFZbnP0rx69Qr379/PdB7Mmzcvx2v51atXWLlyZaZlr169Ki0bHR0Ne3t7leJW5vnz57m63s3NzRXKpCW2Gc+BNKrcS3ISExMDf39/fPjwAYsXL8apU6ek59qy2n7aNjJuX0dHB5aWliofc2VyOgaqbN/MzEz6f9q+VTYt7f2+evUKR44cyXSOhISE5Hh+qULZcU8fA/Bl7s15OebJyclo1qwZbt68iZkzZ+LkyZM4d+4cLC0tMy2j7H2lPUMF5Hxsc3Ofz819QpnsPg+jo6MBIMvYsrvvZzznMpZR1zk1f/58NG3aFGPGjEGxYsVQtmxZ7N27N9fLZ8fIyAhVq1ZFvXr18Oeff+K7777L9jnr3LKwsEBMTIzCtLTnJ9P/IK6trY2SJUtCT08PN27cwI4dOzBy5EhcunQJEyZMwLFjx7Bu3Tp069YtV88EE30J/+neMqtUqaLwS5a1tbXat1GjRg18/PgR+/fvl3rh+/jxIzp16oSDBw8iKSkJ3333HQBIv/6uWrVKerg5vZxqydJ3sJH2Wi6XK9zA0/86r4yNjQ20tbUVEkIA0k0q/QdKTutShaOjI9asWYPk5GScOnUKw4cPR9OmTfHs2TNpvxw4cCDTB3LGmJRRtl/SvmBaWlrCwMAAx48fV7psXh7ytrS0xPfffy/1sJWeg4ODwuvc7sOM7yElJQWvX7+GnZ1dlutL2y8vXrxAmTJlpOlRUVHQ0dFRON+V7SMA0vq3b9+OAQMGoF+/flKZ+Pj4TPvnzZs3SE5OVkjwXr58mSlOdWjbti1+/fVX7NmzB6tXr0aLFi1gamqaZXl7e3u8efMGSUlJCgleVFSUyklSoUKFstxnaSwtLeHq6oqNGzdmWj6nrrItLS3Rpk0bDBkyJNM8Kysr6V91JCj29va5ut5Vpcq9JCenT59GZGQkzpw5gyJFigBArmoElL23xMREvH79Wtq+np5ephqmN2/e5CqunI5BbrafV5aWlqhRo4bScQG/9PAd6WMAvsy9OS/H/O7du7h+/TqOHTuG2rVrA0jd37kdqzK9nI5tbu7zublPKJPd52Ha9f/8+XOUKFEi07Lp7/tpnX4Byu9zGT9/1HVOmZubY/78+Zg3bx4uXbqEcePGISAgAA8fPlTrj8JAai3h9u3bP3s9JUuWVOgsB4D0OqvvXoMHD8bIkSNhZmaG48ePw9fXF8WLF0fx4sVRsmRJnDlzJlPtNNHX8J9O7kxMTLLtPlcdnJycUKRIEUydOhVubm6wsrKClZUVChcujOnTp8Pd3V36VcjNzQ0ODg54/Phxts3LsrJr1y6MHTtW4XX58uVVGnNFS0sLlSpVwrZt26Re7IDUHjzt7e1V7u44bdsJCQmZakyy2n6tWrUwdOhQNGvWDNHR0fD09ISBgQGioqIUmgjlVkhICLp06SK93rVrF6pVqwYgtZY0Li4OCQkJ2Q5pkfFX3YzvLT1fX1/cuHFDrefWgwcPcO3aNalp5r59+/Dp06dst1G4cGHY2dlh27ZtUs+JQOqxrFy5skICFh4ejlevXkkJ365du6Crq4vy5csDSO0NLP159PjxY5w+fVr6YSJNUlISQkNDpaZHN27cQEREhLS/80LZvgdSh1Vo1aoVpk+fjjNnziA0NDTb9VStWhXJycnYuXOn1GTp48ePCA0NRffu3VWKqWrVqli3bh0+ffok7ZeM4zz5+vpi9uzZsLKyUviSlZGOjo7ScygkJAQVKlTIslmYj48PFi1ahDdv3ihtap3V+ZlRtWrVsGPHDgQHB0tf9rZs2QIDAwOULVs222Wzo857SVxcHADFpHjTpk05LletWjWsXLkS8fHx0rAg27dvhxBCunYKFy6M6OhoREdHS1+clQ1yrOw45XQMcrP9vPL19cXo0aNRvHhxpYmVKrK6xnLyJe/NeTnmypbZunVrnlpg+Pj4YOPGjVLtXEa5uc9XrVpV6edybmX1eaivr4+1a9di3LhxmZYpW7Ys9PX1sW3bNgwcOBBA6iMo27Ztg4eHR7bbU+c5BaQmj5UrV8bEiRMREhKCBw8eqDW5E0IgPDxcoafqvGrQoAHmz5+vMCTEli1bUKJECYUeztMcOnQIERER6N27tzQtfQ/T6f9P9LX9p5O7r6VGjRpYv349evTokWlaz549pWlyuRzTp09HYGAg3rx5Az8/P8jlcty+fRsHDhzAzp07s93Os2fP0LVrV7Rv3x6HDx/Gpk2bsH79epXjHTNmDJo2bYq+ffuiefPmOHHiBBYtWoQFCxaoXJOV9ovX/Pnz4evrCxsbm0w34rTmN507d0bJkiXx/v17/PbbbyhTpoz0QTB69Gj07t0bERER8PLyQmJiIm7cuIEbN25g+fLl2cZw+vRpDB48GH5+fti4cSPOnTuHkydPAgBKlSqFnj17IiAgAEOHDkXFihXx/v17XLlyBe/fv8fUqVOl9xEWFoYmTZrAyMgI5cuXh52dHczMzLB27VpYWVnByMgI5cqVQ3BwMKpVq4aWLVuic+fOMDc3x6NHjxAaGorg4GDpWSVV2Nraol27dpgwYQLi4+MxaNAgNGjQABUrVsxyGS0tLYwcORK//vorLCwsULNmTWzfvh179+7N1ETG0tISTZs2xYgRI/D06VMMGTIEgYGB0v739fXF3Llz4erqCi0tLYwdOzZTLSSQ+oPJgAEDEBMTA0NDQ4wYMQKlS5fO1XMmWVG279O+vAUGBsLX1xf29vY5frksU6YMWrVqhZ49e+LNmzews7PDjBkzkJiYqDAYbW788ssv+OOPPxAQEIC+ffvi8uXL2LJli0KZ+vXrw8fHB3Xr1sXQoUPh5uaGN2/e4OzZs7C2tpa+eLm5uWHr1q2oWLEi9PT0ULVqVQQFBWHNmjXw8/NDnz59YGdnh2fPnuHgwYPo0KED6tatiwEDBmDlypXw9vbG8OHDYWNjg/Pnz6No0aJo164dSpQoAblcjj///BPt27eHmZmZ0l+gR44ciSpVqqBdu3b48ccf8c8//2DMmDHo37//Zz/Pp657iaenJwwNDdGrVy/069cPFy9exIoVK3JcLigoCAsXLkTjxo0xYMAAPH/+HEOGDEHbtm2l1hFpz69269YNffv2xdWrV5UObaPsOOV0DHKz/bzq0qULFi5ciDp16iAoKAjOzs548eIFTpw4AQ8PD3Tq1AnA/57bSxvWQRll11huWFhYfLF7c16OealSpWBvb4+BAwdizJgxePDgAaZMmZJtjX5WxowZg2rVqsHPzw+//PILjI2NceLECfj5+aFOnTq5us8PHDgQNWvWlD6XDx48iNOnT2e73dx8Hg4bNkz6LPDz88Pbt28RGhqKFStWwMrKCn379sWoUaMgk8ng7u6OZcuW4datWzl+H8jtOZWTmjVrIiAgAGXLlkVKSgrmzJkDW1tbhRYkGZ0/fx4PHjyQhl0ICwuThmhwd3dHTEwMmjVrhh9++AGurq6IjY3F6tWrER4ervDD3urVq/Hjjz8iIiJCekYz7XxMqzU9ceIE3r59CxcXFyk57927N+bOnYu2bduif//+uHTpEhYvXqz0HE5JScHAgQMxbdo06UfrtCR89erVUpP8tGT62LFjqFu3Lg4dOgRvb+9c7UOiz5KPnbkUKHntLVMIIXVJvmrVqkzTVq9enan8rl27hJeXlzAwMBBmZmaiatWqYtq0adluA//fJX9gYKAwNjYWNjY2YsqUKQplvL29RceOHXN8b0IIsWrVKlGqVCmho6MjXFxcxMyZM3NcRgjlPc8NGzZM2NnZSV1CZ1w+Pj5edOvWTZQoUULo6+sLGxsb0aZNm0y9Ty1fvlxUqFBB6OnpCUtLS1GzZk2xfPnyLPdJWs9YGzZsEM2bNxcGBgbCyclJrFy5UqFccnKymD59unBzcxO6urrC1tZW1KtXT+zcuVMqc/nyZVG9enVhYGCg0NPZxo0bRfHixYW2trZwdnaWyl+/fl20aNFCmJubCwMDA+Hm5iZ+/fVXqafSrHoRVSZtX61du1Y4OzsLfX190bRpU4WeLLNb3/Tp04WLi4vQ0dERpUqVEmvXrlWYn3Ze/P7778LOzk4YGRmJLl26iA8fPkhlnj59Kho2bCiMjIyEi4uLWLp0qejYsaPw9vaWyqQd+3379olSpUoJPT09Ubt2bYVez/LSW2ZW+16I1N4nTUxMxODBg3Pcj0IIERsbK3r27CmsrKyEvr6+qFWrVqbeFnPTW6YQQuzdu1eULFlS6OnpCW9vb7F///5M94S4uDgxfPhwaf87ODiI5s2bi+PHj0tlDh8+LMqWLSt0dXUVeo189OiR6NSpk7C2thZ6enrC1dVVdO/eXTx8+FAqc/v2bdGsWTNhYmIijI2NhZeXl/j777+l+XPmzBFOTk5CLpdLx0rZNRoaGioqVaokdHV1hb29vRgxYoTCuZTbHiWVyelektveMnfs2CHdI2rXri31frpixYpslztz5oyoWbOm0NPTE1ZWVqJ3796ZunUPCQkRbm5uwsDAQPj7+4tjx45lem9ZHaecjkFO21d27SrrMVDZtfL69WvRp08f4eDgIHR1dYWTk5No3769Qs+61apVE23atMl2Hym7xrLqWVDZ9fGl7s25OeYZe8v8+++/RYUKFYS+vr6oUqWKOHXqlHB0dBRjx46Vyij7LFR2Hp47d074+voKAwMDYW5uLurWravQQ3RO93khUs//IkWKCAMDA9G8eXOxYcOGbHvLzO3n4axZs0Tx4sWFrq6uKFy4sOjXr580LzExUQwbNkzY29sLXV1dUblyZWlIpqz2W5rcnFMZZbw/DBo0SJQpU0YYGhoKCwsL4e/vL65cuZLl8kL8r1fojH9pxy0+Pl507txZuLi4CD09PWFnZycaNGggTp48qbCetOsp/f5K6+0341/G8/jSpUuiRo0aQk9PTxQpUiTLHj+XL18uDYOQ3vTp04Wtra1wcnIS69evl6anXc+5+b5IpA4yIXIYHZsKBJlMhqVLl6rcvIy+fV27dsXdu3elX7Tpf06fPg0vLy9cv34921+Fif6LPn36BFNTU+zbt481BkRE/xFslklEBU50dDRu3ryJIUOGoFatWkzsiJS4ePEiypYty8SOiOg/5D89FAIRFUy7du1CrVq18O7dOyxcuDC/wyH6Jnl6eqplPD8iIio42CyTiIiIiIhIA7DmjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IqLPsG7dOlSoUAGGhoaQyWQwNzf/Yts6evQoZDIZZDIZunbt+sW2o4nq1Kkj7bsHDx7kdzh54uLiIr0HVa1cuVJaNjg4WP3BERHRN4HJHREVOB8+fMCsWbNQu3ZtWFlZQV9fH0WLFkWTJk2wdu1afPr06avEER4ejk6dOuHq1auIi4v7Ktv8VgQHB0vJgkwmQ/369TOVuXDhgkIZmUyG+Pj4PG1vx44dCA4ORnBwcIFNztLLuP9kMhm0tbVha2sLf39/7N27N79D/Kbdu3cP/fr1Q6lSpWBkZAQjIyOUKlUKP//8M+7du/fZ6798+bJ0vh09evTzAyYi+kq08zsAIiJV3LhxA02bNs30Be7Bgwd48OAB9uzZg7Jly6JixYpfPJY9e/ZACAEA6NWrFzp27AgdHZ0vtr1KlSrhxIkTAIBChQp9se3kxaFDh/Dw4UM4OztL05YuXaq29e/YsQOrVq0CkFoL5+LiotLy8+bNQ0xMDADA3t5ebXGpU3JyMl6+fIl9+/Zh//792L59O5o3by7N37JlS56TY02yZcsW/PDDD5n2xa1bt3Dr1i0sW7YMa9asQevWrfO8jcuXL2PcuHHS6zp16uR5XUREXxOTOyIqMF6/fo2GDRvi0aNHAAAHBwcMHjwY5cqVw7t373Ds2DGsWLHiq8Xz7Nkz6f/t2rVDrVq1vuj2zMzMULNmzS+6jbxKSUnBsmXLMH78eACptavr1q3L56hS4zAyMkK5cuXyO5QsNWzYECNGjMCrV68QHByMK1euQAiBefPmKSR3VatWzccovw2XLl1Cx44dpdr5Zs2aoUePHgCAP//8Ezt37kR8fDw6deqE4sWLf5UfeYiIvimCiKiAGD58uAAgAAgzMzPx5MmTTGWioqJEdHS09DohIUFMmTJFVKhQQRgaGgoDAwNRvnx5MXnyZJGQkKCwrLOzs7T+58+fi06dOglzc3NhbGws2rZtK633/v37UrmMf97e3kIIIb12dnZW2Ia3t7c07/79+9L0LVu2iBo1aghTU1Oho6MjChUqJGrUqCGGDBkiUlJShBBCHDlyRFq2S5cuCut9/vy56Nevn3B1dRW6urrCzMxMeHt7i02bNimUSx+7t7e3OHv2rKhTp44wMDAQhQoVEiNHjhTJyck5HouxY8dK6zExMREAROHChaVlly1bpjAv7S8uLk5aR1BQkPDy8hJ2dnZCV1dXGBkZiUqVKonp06eLxMTEHPc1AHHkyJFM+/vq1avCz89PGBkZSccj435PSUkRvr6+0rTdu3dLcf3000/S9N9++y3HfZEX6fdf+mO5detWaXrJkiUVlkl/fqa3aNEiUaVKFWFkZCR0dXWFg4ODqFu3rpg6dapUZsWKFdKyY8eOlaZ369ZNmt6oUaNM10Safv36SeW2bdumMG/SpEnSvD/++EMIkXrc2rdvL+zt7YW2trYwMzMTpUuXFl27dhVXrlzJyy4TQgjRtGlTaVs+Pj7StSGEECkpKcLHx0ea37RpU2lely5dMp0zWe2X9Ps541/6fffo0SPRt29fUaxYMaGnpyfMzc2Fp6en2LBhg0LMFy5cEK1btxaFChWSru1WrVqJ8+fPK5TLGMv8+fOFs7OzMDQ0FA0bNhSPHj0ScXFx4pdffhFWVlaZ7kvpHT9+XDRt2lRYW1sLHR0d4eLiIgYMGCBev36d531PRAUDkzsiKjBcXV2lLz/BwcE5lo+Pjxe1a9fO8ota7dq1Fb7Mpv9Sl35baX8dO3YUQqg/uTt69KiQy+VZrjMt0ckqubt3756ws7PLcvmhQ4dKZdPHbm9vLwwMDDKVX7p0aY77Nn1y0rVrV6GjoyMAiD179gghhKhevboAIHr27Jllcqenp5dlzIGBgTnua2XJnZmZmbCyssp0PJTt9/v37wtjY2PpOL1//16cPHlSyGQyAUB4eHiIpKSkHPdFXmSV3G3ZskWaXqdOHYVllCV3q1evznLfODo6SuWUJTHpfyypW7euwrHJ6PTp01LZDh06KMyrVKmSACB0dHTEq1evRGJioihZsmSWceXm/FLm48ePQldXV1pP+oQ8za5du6T5urq60ntSd3J36dIlYWlpqbRM+uO5c+dO6drI+KejoyN27typNJZixYplKl++fHnRokWLLO9LaZYuXZrl/cTNzY0JHpGGY4cqRFQgvH//XuE5u9w0gZw9ezaOHz8OAHBycsK6deuwfv16FClSBABw/PhxzJo1S+mycXFxWLt2LRYsWABdXV0AwIYNGxATEwN7e3ucOHECDRs2lMrPnTsXJ06cwLx581R+b7t27UJKSgoA4LfffsOhQ4ewYcMGjBo1Cu7u7jn2jtinTx9ERkYCSH02KCQkBDNnzoS+vj4AYOrUqThz5kym5Z4/f47KlStj586d+OWXX6TpixcvVin+QoUKoUmTJgBSm8Zdu3ZN2l737t2zXG7kyJFYv349wsLCcPToUWzbtg3Vq1cHkNq745MnT7Ld1ydOnEClSpUU1hkTEwMtLS0sWbIE+/bty3b7Li4umD59OgDg4cOHGD58OHr27AkhBPT19bFq1SpoaWmptC/y4sWLFzh58iR27NiBCRMmSNN79eqV47I7d+4EAGhra2PRokU4dOgQ/vrrLwwcOBBFixbNcrk5c+Zg8uTJAFKvpZCQEOl8UaZ69eooXrw4AGD37t1ISEgAkNqxyaVLlwAA/v7+sLKyws2bN3H79m0AgJ+fH8LCwrB7927MmzcPDRs2hJ6eXo7vS5m7d+8qdJakrMll+mmfPn3C3bt3Vd7Oli1bMGLECOl1YGCgdL79+OOPEEKgc+fOeP36NQCgbNmyWLNmDfbs2YMxY8bAysoKQGqT4G7duiExMREA8NNPPyE0NBR9+vQBACQmJqJbt2748OFDphgiIiIwZMgQ7Ny5E46OjgCAq1evYvfu3fj999+xbt06GBgYAPjffQkAnj59ip9//hkpKSkwMTHBvHnzsG/fPgQGBgJIfS4x/XsjIg2U39klEVFuPHnyROEX6H///TfHZcqXLy+V37VrlzQ9/a/7FSpUkKan/8V++/bt0nR/f39p+uXLl6XpWdUGCKFazd2wYcOkaZs3bxavXr1S+n6U1dxFR0dLNU16enoKyw4cOFAq/+uvvwohFGvCdHV1RWRkpBBCiOTkZGFoaCgACHNz8xz3bfqap6FDh4o9e/ZItRFt27aVahrS7wtkqLk7efKkaN68ubCzsxPa2tqZahnS12pkt68zbmP//v2Z5mfVHFYIIfz8/DJte8aMGTnug/j4eHHixAmlf1FRUbnefxn/bG1txapVqzIto6zm7vvvvxcAhKGhoTh48KCIiYlRur30tUKVK1eWzpnq1auL2NjYHN9rxphDQkKEEEJMnTpVmpbWHPHmzZvStB9++EFERETkqqlvTk6ePKmwn5Q1IY2Pj1coc/LkSSGEajV32U0XIrXWLm2eqampePHihdJ4t23bJpWrUqWKwrwqVapkutek3+Z3330nle3bt6/C/kzTuHHjTPelWbNmSdMCAwOl8/H48ePS9W1mZqaW40FE3ybW3BFRgWBmZqbwOn1nJllJqz0AINUIAYCHh4fSMul5e3tL/0/7JR4A3r59m+N2VdWxY0epNqNNmzawtrZGoUKFEBAQgIMHD2a77J07d6QeO4sVK6YQa07vs1SpUlKvm3K5HBYWFgDy9h79/f3h5OSExMREbNq0CQCkji6UOXv2LHx8fLBz505ERkYiKSkpU5m8xKGvr4969eqptMyyZctgZGQkvfb09ET//v1zXO758+eoVauW0r/Q0FBVQ5e8fPkS//zzT67KBgYGQiaT4ePHj/Dz84OZmRmcnJzQqVMnnD9/XukyFy9ehBACJiYm2LNnD0xMTHK1rU6dOkn/37Jli8K/JiYmaNasGQCgRIkSUs36mjVrUKxYMRgbG8PLywvTp0+Xav1UZWpqqvD65cuXmcpknJbxvqEOGe8rNjY2uSqXXk7XZvr5lpaW0v/Td6pjbW0t/T/tWkm/rhUrVkjnY+3atfHx40cAqbXbubl/ElHBxOSOiAoEY2NjuLq6Sq///vvvPK8rN4NApyU6QGqTtzRpiVRuJScnK7x+9epVpjJly5bFhQsX8Msvv6B69eowMzPDixcvsH37djRo0ACnTp1SaZtpcnqf6d8joPg+VSWXy6WmX0BqkpU+Gcho0aJFUnO1Jk2aIDQ0FCdOnEDnzp2lMmlNVVVha2ur8jIPHz6UvvgCwKNHjxAbG6vyevKqS5cuSExMRFhYGAwNDSGEwLRp07Br164cl61fvz7+/vtv9OjRA5UqVYKhoSGePHmCv/76C97e3krHfEtravru3TuMGTMm13EWL15cSlJCQkJw9+5dnDt3DgAQEBAgNROUy+UIDQ3FjBkz4O/vjyJFiiAuLg6nT5/GkCFD8Ouvv+Z6mxm3n9ZEGkgdriCjK1euSP/X1dWVmpKmvxbSX5PKrsevIadrM31SKpf/76taxgQ3jar3JWVNQYlIMzC5I6ICo127dtL/Z86cqfTX5xcvXkjPwpQsWVKafvbsWen/6Z8/S19GndK+nEVHR0tJzIMHD3Dz5s1MZYUQKFOmDObMmYPTp0/j7du3Uo1ISkoKduzYkeV2ihcvLn1RjIiIQHR0tDTva7zP9H788Ufpi2irVq1gbm6eZdmnT59K/588eTIaNmyImjVrIioqSmn59F9ws0v6cpO4p/fx40cEBgZCCCElPc+ePctVzZ2LiwtEasdkmf66du2qUhza2tpo0KABhgwZIk0bPXp0jssJIeDl5YUlS5bg4sWLePfuHWbMmCG9t7CwsEzL/PTTT1LSs2DBAkyZMiXXcXbs2BFAak1R2rNjgGKtnhACxsbGCAoKwt69e/Hw4UO8ePFCegZw27Ztud5eegYGBqhfv770etasWQpJjRBC4RnaBg0aSM8Rpk+W0p5PBaB0/wDZn28Z7ytZJYhZ3X8yvlbntZl+XWPHjlV6bn748AFubm5q2yYRfVs4zh0RFRiDBg3CX3/9hUePHuHt27eoXr06Bg0aJI1zd/ToUaxYsQJHjx6FpaUlOnTogKtXrwIA+vbti3fv3kEmk2HYsGHSOtu3b/9FYi1evDguXLiAuLg4dOjQAbVr18aCBQsy1eQBwLRp03D06FE0btwYRYoUgZGREfbt2yfNz64Zm5WVFRo0aICwsDAkJCSgbdu2GDBgACIiIrBgwQKp3Jd6n+k5Ozvjjz/+QGRkZI4DSKcf7Hzy5Mno0qUL9u7dq/C+00tfy7h27VpoaWlBS0vrs8f9Gzp0KCIiIgCkdsCzc+dOHDx4EKtWrUKbNm3QuHHjz1q/qvr164dp06bh48ePuHLlCvbv36+Q0GT0yy+/4Pnz56hXrx6cnJygra0tDXQPKD93rKyssGfPHnh6euLNmzcYMWIEChcunG1Na5rvv/8eQUFBSEpKwoEDBwCkjjfp6+srlXn69Cn8/PzQtm1buLu7o1ChQrh//77UZDJ9TMHBwdJg4StWrMgxKQ4ODsa+ffuQmJiIQ4cOISAgAN26dYNMJsOyZctw6NAhAKm1dsHBwdJyacksAIwaNQpv377FqVOnpPIZpT/fwsLCULt2bejr66NcuXKoUKECypYti+vXryMmJgZ169bFkCFDYGlpiQsXLuDNmzeYMWMG6tevDysrK0RHR+P8+fP4+eef0bhxY4SGhkpNZq2trVVuRpyd1q1bY9iwYUhISMCUKVMgk8ng5eWFjx8/4v79+zhy5Aji4uKkY0dEGuhrPuBHRPS5/vnnH6XDFKT/u3TpkhAitXOFWrVqZVkuu6EQ0suqM4bsOvlYvHhxpu0ZGxuLwoULZ+rYY8KECVnGKJfLpU4hshoKISIiIk9DIaQNE5DT+1cmY4cq2UkfS1qHKmfOnJE69Uj7k8lkwsvLS3q9YsUKaR3pO8FJ/5dxGxk7sEmjrEOVI0eOSDF4eXmJ5ORkce/ePWFkZCQACAcHB/HmzZsc90VeZDUUghCKHWj4+flJ05Udn/Tj1GX8MzAwEBEREUII5R2EHDlyROqmX0dHRxw4cCBXsTds2FBhO0FBQQrzHz9+nO312atXL6X7If3xzs769euFvr5+luvX19fPNNbcq1evpGEv0v+VLl06034RQoiXL18qHaoj7Tq/cOGCMDc3V7r99Mdzx44deRoKIX0sWe2jrO4/2Q2FoOy6JyLNwmaZRFSguLu74+rVq5g5cyZq1qwJS0tL6OrqwsnJCQ0aNMCqVavg7u4OANDT08OBAwcwZcoUlC9fHgYGBtKv75MnT8b+/fsVnuFRp+7du2P48OGwtbWFgYEBfH19ceLECRQrVixT2UaNGqFXr14oW7YsLCwsoKWlBUtLS9SvXx/79u1DjRo1st2Wq6srLl68iJ9//hlFixaFjo4OTE1NUbt2bWzcuFGlZndfi4eHB7Zv345y5cpBX18fZcqUwebNm7OspWrSpAl+//13FCtW7LOeDUzz4cMHqVt7HR0dLF26FHK5HEWLFsWkSZMApDbPTD9ExNfSv39/qVngwYMHpaEGlOnYsSO6dOkCNzc3mJmZQUtLC7a2tmjRogVOnDih8JxqRnXq1JGGvUhMTESrVq0UnlnLSsYavoyvLS0tMXbsWHh7e8Pe3h46OjowMDBA+fLlMXHixDwNF5Le999/j+vXr6NPnz4oWbIkDAwMYGBggJIlS6JPnz64du2aQhNuILW2cseOHShfvjx0dXVRrFgx/PHHHwrNYNOztrbGjh07UKlSJelZwvQqV66MK1eu4KeffoKrqyt0dXVhbm4OT09PhWE7mjdvjvDwcLRu3Rq2trbQ1taGjY0NAgICcOrUKakTGnXq3r07jh8/joCAABQqVAja2tooVKgQPDw8MHr0aIUafSLSPDIhVHwKl4iIiIiIiL45rLkjIiIiIiLSAP/ZDlVSUlLw7NkzmJiYqNy7GhERERER0dcghMC7d+/g4OCg0JuvMv/Z5O7Zs2dwcnLK7zCIiIiIiIhy9PjxYxQuXDjbMv/Z5M7ExARA6k7KalBQIiIiIiKi/BQbGwsnJycpf8nOfza5S2uKaWpqCgE2yyQios9nZvq/D963Me/yMRIiItIUaRVRuXmUjB2qEBERERERaQAmd0RERERERBqAyR0REREREZEGYHJHRERERESkAZjcERERERERaQAmd0RERERERBqAyR0REREREZEGYHJHRERERESkAZjcERERERERaQAmd0RERERERBqAyR0REREREZEGYHJHRERERESkAZjcERERERERaQAmd0RERERERBqAyR0REREREZEGYHJHRERERESkAZjcERERERERaQAmd0RERERERBqAyR0REREREZEGYHJHRERERESkAZjcERERERERaYACkdxdv349y3k7duz4eoEQERERERF9owpEctegQQPcv38/0/StW7eiY8eO+RARERERERHRt6VAJHfdu3eHn58fIiMjpWkbN25E586dsXLlyvwLjIiIiIiI6Buhnd8B5Ma4cePw+vVr+Pn54fjx4wgLC0P37t2xZs0atGrVKr/DIyIiIiIiyncFIrkDgHnz5qFjx47w9PTE06dPsX79ejRv3jy/wyIiIiIiIvomfLPJXUhISKZpAQEBOHHiBNq3bw+ZTCaVadas2dcOj4iIiIiI6JsiE0KI/A5CGbk8d48DymQyJCcnq7z+2NhYmJmZISYmBgIylZcnIiLKyMzURPr/25h3+RgJERFpCrlMSHmLqalptmW/2Zq7lJSU/A6BiIiIiIiowCgQvWUq8/bt2/wOgYiIiIiI6JtRIJK7qVOnYuPGjdLrNm3awNLSEo6Ojrhy5Uo+RkZERERERPRtKBDJ3aJFi+Dk5AQAOHDgAA4ePIiwsDA0bNgQgwcPzufoiIiIiIiI8t83+8xdepGRkVJyt3v3brRt2xb169eHi4sLqlevns/RERERERER5b8CUXNnYWGBx48fAwDCwsLg5+cHABBC5KmnTCIiIiIiIk1TIGruAgIC0KFDB5QoUQLR0dFo2LAhAODSpUsoXrx4PkdHRERERESU/wpEcjdr1iy4uLjg8ePHmDZtGoyNjQEAz58/R58+ffI5OiIiIiIiovz3zQ5i/qVxEHMiIlI3DmJORETqphGDmIeEhKBhw4bQ0dFBSEhItmWbNWv2laIiIiIiIiL6Nn2zNXdyuRyRkZGwtbWFXJ51vy8ymSxPnaqw5o6IiNSNNXdERKRuGlFzl5KSovT/RERERERElFmBGAohO0+fPs3vEIiIiIiIiPJdgU3uIiMj0a9fP5QoUSK/QyEiIiIiIsp333Ry9+bNG7Rv3x7W1tZwcHDA3LlzkZKSgjFjxsDV1RXnzp3DihUr8jtMIiIiIiKifPfNPnMHAMOGDcOpU6fQtWtX7Nu3DwMGDEBYWBjkcjkOHz4MT0/P/A6RiIiIiIjom/BN19zt3bsXK1aswO+//45du3ZBCIGKFSti9+7dTOyIiIiIiIjS+aaTu2fPnqF06dIAABcXF+jr66NTp075HBUREREREdG355tO7oQQ0Nb+X8tRLS0tGBgY5GNERERERERE36Zv+pk7IQTq1q0rJXhxcXFo2rQpdHV1FcpdvHgxP8IjIiIiIiL6ZnzTyd3YsWMVXjdv3jyfIiEiIiIiIvq2yYQQIr+DyA+xsbEwMzNDTEwMBGT5HQ4REWkAM1MT6f9vY97lYyRERKQp5DIh5S2mpqbZl/1KMREREREREdEXxOSOiIiIiIhIAzC5IyIiIiIi0gBM7oiIiIiIiDRAgU3u3r59m98hEBERERERfTMKRHI3depUbNy4UXrdtm1bWFlZwdHREVeuXMnHyIiIiIiIiL4NBSK5W7RoEZycnAAABw4cwIEDB7B37140bNgQgwcPzufoiIiIiIiI8t83PYh5msjISCm52717N9q2bYv69evDxcUF1atXz+foiIiIiIiI8l+BqLmzsLDA48ePAQBhYWHw8/MDAAghkJycnJ+hERERERERfRMKRM1dQEAAOnTogBIlSiA6OhoNGzYEAFy6dAnFixfP5+iIiIiIiIjyX4GouZs1axZ+/vlnuLu748CBAzA2NgYAPH/+HH369Mnn6Igovd27dsHXp06O5TyqVcXRo0e/dDhERKTE7t27UNe3Tn6HkWfVPariWA6fIePHBWPwoIFfJyCib4RMCCHyO4j8EBsbCzMzM8TExEBAlt/h0H+AR7Wq2c7v3qMHevbs9VVi6d2rJy5evAgA0NXVhaOjI9q0aYvWbdp89rrj4+Px8eNHWFpaAgCWLFmMY0eP4a916xTKvXr1CqamptDV1f3sbRJ9K8xMTaT/v415l4+R0H/B+HHB2LNnd6bpW7Zul/oqyMru3bswa+YMHDp89IvEtnv3LkwYPw4AIJPJYG1jAw+P6vj5537S58PniH71Cib//xny7NkztGzRDGvW/oWSJd2kMu/fv4cQAiYmJtmsiejbJ5cJKW8xNTXNtmyBaJaZ5saNG3j06BE+ffqkML1Zs2b5FBFR7oXuDZP+f/DAASxevAibt2yVphkaGkr/T3ueVFv7y12iLVq0RM9evZAQH489oXswbdpUmJiaoEED/89ar76+PvT19XMsZ21t/VnbISIiwMvrO4wePUZhmrmFRT5Fo8jIyAibN29FihC4c+c2Jowfj1cvX2LuvPmfvW6rXHyGpLX0IvovKRDNMu/du4cKFSqgbNmyaNy4MVq0aIEWLVqgZcuWaNmyZX6HR5Qr1tbW0p+xsXHqL5n///rhwweo410bp/7+G51/6IQa33nhypXLGBccjEEZmpTMnDEDvXv1lF6npKRg5YoVaN68GWrVrIEOHdrj0KGDOcajr68Pa2trOBYujJ49e8GpSBGcOH4cQGoPtYMGBsG7di341PHG8OHDEB0dLS17+/Zt/NS7F+p414ZPHW90/qETbty4AUCxWebuXbvw59KluHPnNjyqVYVHtarYvWsXAMVmmd1+/BHz5s1ViO/Nmzfw8qwu1TB++vQJc2bPRuNGDVG7Vk0Edu2CCxfOS+WfP3+OoAEDUNfXB7Vr1US7tm3x998nc3FkiIgKLh0dHVhZWyv8aWlpYd1fa9GhfTt4166Jpk0aY9rUKfj48WOW67l9+zZ++qkXfOrUho+PNzp37oR///++DgCXL19Gzx7dUbtWDTRt0hgzfp+OuLi4bGOTyWSwsraGjY0NvvuuBtq1a4dz584iPj4eKSkp+PPPpWjSpBFq1vBCp44dEB5+Slo2MTER06dPRaOGDVCr5ndo3qwJVq5cIc1P3yyzZYvUH/l/6NQR1T2q4qfeqZ+R6Ztlbt++DY0b+SMlJUUhxkGDgjBhwjjp9bFjR9H5h46oVfM7tGzRHH8uXYKkpCQAqT+8Ll2yGM2aNkbNGl5o3MgfM36fnu0+IPraCkTN3a+//oqiRYvi0KFDKFq0KM6ePYvo6GgMHDgQv//+e36HR6Q28/+Yj19//RWOjoVz3Yxk5coVCNu7F8OGDUcRJydcunQJY8eMgYW5BSpXqZLrbevp6SExMREpKSkYNDAIBoaGWLR4CZKTkzFt2lSMHDEcixYvAQCMGT0Kbm5uGDpsOORyOW7fvq20ltGvXj1EREQgPPwU5v+xAIDyX1L9/f2xZs1q/PxzP8hkqc2kDxzYDxsbG1SqVAkAMH3aNNy/fw8TJ/0GGxsbHD1yBL/+8gvWrd+AIkWKYNq0qUhKTMTiJUthoK+Pe/fvw8DAMNO2iIj+C2RyOYIGDoaDgwOePX2KadOmYP68uRgydJjS8mPHjEJJNzcMHZp6X79z+za0/v++/uTJE/T/tR969f4Jo0aPwdu3b/D79GmYPn0axowZm+uY9PT0kJKSguTkZGzcsB7r/lqLYcNHwM3NDbtCQjBoYBDWb9iEIkWKYOPGDThx/Dgm/TYFdnZ2iIqKQlRUpNL1rli5CoFdu2D+/AVwdXWFto5OpjJ16/phxu/TceH8eVTz8AAAxMTE4HR4OGbNmgMgtaO+ccFjMXDgYFSsVBFPnjzB5N9+AwB079EThw8fwvr16zBx0m9wdS2G6OhXuHPnTq7fP9HXUCCSu/DwcBw+fBjW1taQy+WQy+WoWbMmJk+ejF9++QWXLl3K7xCJ1KJXr16oXt0z1+U/ffqElStWYP4fC1C+fHkAgGPhwrh85TK2bd+Wq+QuOTkZ+/ftw907d9CyRUucO3cWERER2LFjJwrZ2QEAgoPH4ft2bXHjn3/gXqYMoqKi0OmHznBxcQEAFClSROm69fX1YWBoAC0t7WybYfrVq4eZM2fg8uXLUjK3L2wf6tdvAJlMhsjISOzevQshu3bDxsYGANDphx8QHh6O3bt2oU/fvoiKjISPr6/Ug65j4cK524lERAXY33+fRB3vWtJrL6/vMHnKVLRv30Ga5uDggF69f8LUKZOzTO4io6LQqZPy+/qqlSvQwN9fWmeRIkUQNHAwfurdE0OHDoOenl6OcT569Ajbtm1F6dLuMDIywl9/rUXnzl1Qv34DAMDP/X7BhQvnsWHDegwZMhRRkZFwciqCihUrQiaTwd7ePst1m5unNkM1MzfLsrmmqakpvLy+w759YVJyd/jwIZibm6NK1dRn4pf9uRSdu3RF4yZNAACOjoXRq3dvzJ83F9179ERUZCSsrKzg4VEd2trasLOzQ5kyZXN870RfU4FI7pKTk6VaDGtrazx79gxubm5wdnbGrVu38jk6IvUpXdpdpfKPHz9GfHw8+v3cV2F6YmIi3Nzcslgq1ZYtm7Fz5w4kJiZCS0sL7Tt0QKvWrbF50ybYFiokJXYA4OrqChMTE9x/cB/uZcqgfYcOmDRxAvaGhsLDwwN1/fxQ+DOSKQsLC3h6eiIsbC8qVaqEp0+f4tq1qxg+YgQA4O7du0hOTkbrVgEKy3369AlmZmYAgLbtvsfUKZNx5vRpeHhUh4+vL0qUKJHnmIiICoIqVapgyNDh0msDAwMAwNmzZ7Bq5Uo8fPgAHz58QHJyMhISEhAfH6/0uegO7Ttg0qQJ2Ls3FNU8PFC37v/u63fu3MHdu3ewL+x/z44LIZCSkoJnz56haNGiSmN7//496njXQkpKCj59+oQKFSpi5MhReP/+PV6+fInyFSoolC9fvoJUE9a4SVP0+7kv2rRuBS8vL9SoWQuenrn/8VMZf/+G+O23iRgydBh0dXWxLywM9erVh1wu///3eRtXr17ByhXLpWVSUlKk/VbXzw8bNqxHyxbN4eXlhe++q4GatWp90efjiVRVIM7GsmXL4sqVKyhatCiqV6+OadOmQVdXF0uWLIGrq2t+h0ekNmkfymnkchmQoUPbtLb/AKTnHWbNmg0bW1uFcrpKmqWk5+/fEIE//gg9PT2pVjy3evbshQYN/PH33ycRfuoUlixZjImTfoOPj0+u15FRA/+GmPH7dAwePAT79oWhePHiUi1c3MeP0NLSwurVayDX0lJYLm2ftWjRAl6enjj590mcOX0GK1euwK/9+6Ndu+/zHBMR0bdOX98gU8+Yz549w8CgAQgIaIXeP/WBmakpLl+5jEkTJyAxMVFpctcj3X39VPgpLF2yGBMn/oY6Pj6Ii/uIli0D0FbJ/dQu3Q+BGRkaGWH16rWQy2WwsrKWtvv+/fsc31epUqWwfcdOhIefwrmzZzFyxDBU8/DAlCnTclw2KzVr1YIQAn//fRLu7u64fPkS+g8IkubHxcWhR4+eqOPjm2lZXV1dFCpkh02bt+LcubM4e+YMpk2bgrVr12DR4iVM8OibUSDOxFGjRuHDhw8AgPHjx6NJkyaoVasWrKyssHHjxnyOjujLMbewQEREhMK027dvSR8iRYsWha6uLiKjIlV6vg5IffZNWVfZLkVd8CIqClGRkVLt3b179/Du3TsULfq/H1OcnZ3h7OyMDh06YtTIEdi9K0Rpcqejo4OUlOQc4/H29sbk3yYh/NQp7Avbh0aNG0nzSrq5ITk5Ga/fvJGabSpTyM4OrVq1RqtWrfHH/PnYuWMHkzsi+s+5efNfpKSk4Nf+A6Qf7g4ePJDjckWcnVHE2RntO3TEqFEjsHt3COr4+MDNrRTu37+f4/AKGcllMqXLGBsbw8bGBlevXEHlyv/77Lp69Qrcy5RRKFevXn3Uq1cfvr518euv/RATEyO12Eij8/8/ZiYnK3aWkpGenh7q+PgiLGwvnjx+DGdnZ5QqVUqa7+bmhocPH2b7PvX19VGrVm3UqlUbrdu0Qds2rXH37l2F9RDlpwKR3DVo0ED6f/HixXHz5k28fv0aFhYWUucLRJqoatVqWLtmDfbs2Y1y5cojbO9eRERESE0ujYyM0LFTJ8yaORMpKQIVK1bE+/fvceXKZRgZGaPJ/z83oAoPj+ooVqwYRo8ZjaCggUhOTsLUqVNRuXJluLu7Iz4+HvPmzoFv3bpwcHDEixdRuHHjBnx8M//SCQD29g549uwZbt+6BdtChWBoaKh0bDsDAwN4e9fBokWL8ODBfYUhGZydneHv3xDBwWPR/9f+KOnmhrdv3+Dc2XMoXqIEatasiZkzZsDru+9QpEgRvHv3DhcunIeLi/KmQkREmqxwYSckJSVh06aNqFWrFq5cuYJt27dlWT4+Ph7z5s2Br+//7uv/3rgBn/+vwfqhcxd0+7Erpk+fiubNW0Bf3wD379/D2bNnMHjw0DzF2LHTD1i6ZDEcCxdGyZIlsXvXLty+fRvjxk8EAKz7ay2srK3h5lYKcpkMhw4dhJWVldLOxiwsLKCnp4fT4adga2sLPT29LIdB8Pf3x8CgAbh/7x78/RsqzOvWrQeCgvrDzs4Ovr51IZPLcefObdyLiEDvn/pg9+5dSElORpmyZaGvr4+9e/dCT08P9tnUXhJ9bQUiuVNGHQNgEn3rvLy80K1bd8ybOw+fPiWgabNmaNS4MSLu3pXK9O79EyzMLbBq5Qr89vQpTExM4OZWCl0DA/O0TZlMht9nzMTv06ehV88ekMvl8PTywqBBgwEAWlpaiImJQfDYsXj9+jXMzc1Rx8cnywHYfX19cfTIYfz0U2+8e/cOY8aMRZOmTZWW9ff3R//+v6JSpcqZmvqMGTsWy5ctw+w5s/HyxQuYm5ujbNlyqFkrtSOB5JRkTJ82FS9evICRkRE8vbwwIF1zGyKi/4qSJUuif/8BWLN6FRb8MR+VKlVGnz59MS5Yec+Waff1ccHp7ut1fNDj/+/rJUqUwKLFS7Bw4QL06tkDQgg4OhZGvXr18hxju3bf48P795gzZzbevH6NokVd8fuMmVJHLoZGRli7ZjUeP34MuVwOd/cymDV7jtJHCLS1tTFw4GAsW7YUS5YsRsWKFbFw0RKl261atRpMTU3x8OFDNPBXHNfV08sLM2fOxrJlS7F69Spoa2vDxcUFzZq3AACYGJtg1eqVmD17FlJSUlCsWHHMmDELZubmed4PROomEyLDAz3/EbGxsdJI7wKs/SMios9nZvq/WoW3Me/yMRIiItIUcpmQ8hZTU9Psy36lmIiIiIiIiOgLKrDNMlWVkJCAhIQE6XVsbGw+RkNERERERKRen1Vzl5ycLPVi+a2bPHkyzMzMpD9Ve3wiIiIiIiL6lqn0zF10dDTWrVuHAwcO4MyZM3j16hWA1LE/SpYsiVq1aqFNmzbw9vb+7MBCQkJyXbZZs2Y5llFWc+fk5MRn7oiISG34zB0REambKs/c5Sq5e/ToEcaMGYMNGzbA0tISnp6eqFChAqytraGnp4e3b9/iwYMHOH/+PC5cuICiRYti7Nix6NixY97fRIbekGQyGdKHmn4IhOTknMfQyogdqtB/Qc+ePRAQ0Ar+GXoEKwjevn2Ldm3bYPWatShUqFB+h0OUK0zu6L/o4cMH6N2rF7Zs3QYjI6P8Dkdlf8yfh7i4OAwaPCS/QyFSSu0dqri7u0Mmk+HAgQN4+vQptm3bhrFjx6Jv377o3r07Bg0ahPnz5+P06dN49uwZfv31V4wfPx5TpkzJ85tISUmR/vbv34+KFSti7969ePv2Ld6+fYvQ0FBUrlwZYWFhed4GUUHx4sULjBk9Gn5+dVGrZg20/74dbty4ke0yx48dw+voaNSvX1+alpCQgGlTp8LPry68a9fC0CGDER0dne16jhw+jH4/94WfX114VKuK27duZVlWCIFff/kFHtWq4ujRo9L0mJgYBA0YAO/atdCpYwfcunVTYblpU6fir7VrFaaZm5ujUaPGWLJkcbbxERFRzlauXIGuXTrDp05t+Deoh8GDBuLhwwcKZSZPnoSAls1Ru1YNNKjvh0GDgvDgwQOl60tvwR9/oG3btgqJ3Z07d9CzR3fUqvkdmjZpjDWrV+W4nuoeVTP97d+/T2nZK1cu4zuv6ujUsYPC9LCwvWjapDH86vpg9qyZCvOePXuG1q0C8P79e4XpHTv9gD2he/D06ZMcYyT61uUqufvnn3+wYsUK1KpVK8dBw21sbNC3b1/cvHkTP/zwg1qC7N+/P+bMmYMGDRrA1NQUpqamaNCgAWbOnIlffvlFLdsg+lbFxsaiR/du0NbWxpw5c7Bh4yb82n9Ajr/cbNy4EU2bNlOoBZ81ayZOnDiOyZOnYNHiJXj56hWGDhmc7Xri4uNQoUJF/PxzvxxjXb9+HZTdIlYsX46PHz9g9Zq1qFylCiZNmiTNu3btGq7/cx3ft2+fabkmTZtiX1gYYmJictw2ERFl7dLFi2jdpg2WLVuBufP+QFJyEn7p9zPi4uKkMqVKlcbo0WOxYeNmzJk7HxACv/Trm20LqcjISJw8eQKNm/xv/NL379/jl34/w97eHqtWrUG/X37B0qVLsD2bgdTTjB4zFqGhYdKft3edTGXevXuHccFjUbVqNYXpb9++xW+TJuKXX3/F3HnzERa2FydPnJDmT5s2BX1//jnTAOfm5ubwrO6JrVu35hgf0bcuV8mds7OzyiuWyWRwdHRUeTllIiIiYK5kgEgzM7Nc/aJEVJCtXrUKtoUKYczYsShTpiwcHR3h6emJwoULZ7nMmzdvcP78OWmAbyD1wzZk5070HzAA1apVQ+nSpTFmzFhcvXoV165dy3JdjRo1RvcePeDh4ZFtnLdv3cK6v/7CqNFjMs178OA+6tWvD2dnZ7RsGYAH9+8DAJKSkjBl8mQMGzYcWlpamZYrVqwYrK1tcPTokWy3TURE2Zszdx6aNGkK12LFULJkSYwZE4zIyEjc/PdfqUzLlgGoVLkyHBwcUKpUKfTq3QdRUVF4/vx5lus9ePAASpQoCVtbW2navrAwJCUlYtToMXAtVgz16zdAu3bfY/26v3KM08TYBFbW1tKfnp5epjJTpvyG+g38Ua5cOYXpT58+gZGRMerVqw939zKoUqUq7j9I/bzZty8M2tra8PHxVbrdWrVq4cD+/TnGR/Sty3Nvmc+ePcPZs2dx/PjxTH/qVq1aNQQFBSEqKkqaFhUVhcGDB+f4hZOooDtx4jhKly6NYcOGokH9eujUsQN2bN+e7TKXL1+Gvr4+ihYtKk37999/kZSUBA+P6tI0FxcX2NnZ4dq1q58VY3x8PEaPHoXBQ4bA2to60/wSJUri/PnzSEpKwunwcBQvUQIAsHr1KlSpUgXu7u5ZrrtMGXdcvnT5s+IjIiJFaU0TTc2UtwKJi4vD7l0hcHBwzPa558uXL6F06dIK065du4qKFStBR0dHmlbd0wsPHz7McSiq6dOnon69ugjs2hkhITuRsWuIXbtC8OzpU3Tv3iPTsk5ORRCfEI9bt24iJiYGN27cQIniJRAbG4slixdhcDbP1LmXKYsXL6Lw7NmzbOMj+tapPM7dvXv38MMPP+D06dMAkOmik8lkeergJDvLly9Hy5YtUaRIEWkIg8ePH6NEiRLYsWOHWrdF9K15+vQptm3dig4dOiIwMBA3/rmBGTN+h7aODpo0aaJ0mcjnz2FpaanQJDM6Oho6OjowMTFRKGtpaZnjc3c5mTVzBsqVL6+0+QwAdOnaFVOmTEZAyxawt3fAqFGj8ejRI+zZswfLli3H5Mm/4czpMyjtXhojR45SaDJjbWOT7XN+RESkmpSUFMyaOQPlK1RAsWLFFeZt2bIZ8+fNRVxcHJydnTFv/h8KSVpGkc8jUbq04g900a+j4eDgoDDN0tIydV50dJaPFfTs1RtVq1aFvr4+zpw+jenTpiIuLg7t2n0PILWDvz/+mI8li5dCWzvzV1hTU1OMHROMccFjkZCQgEaNGsHTywsTJ4xH6zZt8ezpMwwaGISkpCR079ETdev6Scum/TAZGfk8U+xEBYnKyV2PHj3w5MkTLF++HO7u7tDV1f0ScSkoXrw4rl69igMHDuDmzdSOGEqXLg0/P78cnwEkKuhSUlJQurQ7+vTtCwBwcyuFiHsR2LZta5bJXUJCAnR1Mzdl+RKOHzuG8+fPY83arJvbGBsbY+LESQrTfvqpN3755RfsCwvDs6dPsWXrVkyaOBF/Ll2K/gMGSOX09PQQHx//xeInIvqvmT5tKu7di8DiJX9mmufv3xAeHtUR/eoV/vprDUaMGIalS5cpbR4JAAkJ8Wr7LtitW3fp/25upRAXH4+1a9agXbvvkZycjDGjR6Fnj54oks3jQnV8fFDHx0d6ffHiBdy9exeDBg9Bq4AWmDBxEqysrBDYtQsqVaosJZ36+voAwM8bKvBUTu7Onj2LVatWISAg4EvEkyWZTIb69eujdu3a0NPTY1JH/xnW1tYo6lpUYZqLS1EcOXw4y2XMzc3x7p1i0xcrKyskJibi3bt3CrV3r1+/hpWVVZ7jO3/+PJ48eYK6vj4K04cNHYKKFSti0eIlmZbZFRICE2MTeHvXwZDBg+HtXQfa2tqo6+eHJYsXKZSNjY2FuYVFnuMjIqL/mT59Kk6ePInFi5cobW5pbGwMY2NjFClSBGXLlYNfXR8cPXoEDRooH1In9fNGcdgPK0srvI5+rTDt9evU16p83pQpUxbLl/2JT58+ISEhAf/+ewO3b9/C779PB5D646cQAt95VcfcufNRtZpiByufPn3CtKlTETxuPB4/fozk5GRUrlwFAFCkiDP++ec6atWqDQBSx10W5vy8oYJN5eTO0dFRaccHX1JKSgomTZqERYsWISoqCrdv34arqytGjx4NFxcXdOvW7avGQ/Q1la9QAQ8fPlSY9ujRQ9jZ2We5TEk3N0RHRyM2NlZq/lK6dGloa2vj3Lmz8PWtCwB4+OABIiMjUa5c+TzH17lLFzRv3lxhWvv232PAgCCFDl3SvHnzBn8u+xNLl6b+YpySkoykpCQAqR2sJCenKJSPiIhAlf//MCYiorwRQuD336fh2NGjWLBwMRxy0emdEAJCCCQmJmZZpqSbG+7fu6cwrVy58li0aAGSkpKk5pNnz56Bs7Nzjj09p3fn9i2YmppCV1cX2traWLd+g8L8rVu24Pz5c5g8ZSocHDK/n+XLl8HLywulSpXCrVs3FR4bSkpKQkq6z5t7ERHQ1tZGUVfXXMdH9C1SuUOVSZMmYcqUKdIvMF/DxIkTsXLlSkybNk2h6r9s2bL488/MTQqINEmH9h1w/do1rFixHI8fP0ZYWBh2bN+ONm3aZLmMm5sbzM3NceXKFWmasbExmjVvjtmzZuH8+fP4999/MX78eJQrV16hx7E2rVvhyJH/9U4ZExOD27du4f791A/vhw8f4vatW3j16hWA1JrFYsWLK/wBQCE7O6U95s6cMQMdO3aUelYrX6ECQveG4v79+9ixfRsqVKgglY2Pj8fNf/9FdU/PvOw6IiL6f9OnTUXY3r0YP2EijAwNEf3qFaJfvZKaIT59+gQrV67Av//+i8jISFy9egUjhg+Fnp4+vvuuRpbr9fT0wrXr1xQSpwb+/tDW1sHECeNxLyICBw7sx8YN69G+Q0epzNEjR9C2TSvp9YkTx7Fzxw5ERNzF48ePsXXLFqxcuQJt2rYDAMjlchQrVlzhz8LCArq6eihWrDgMDAwU4rp37x4OHjiAnr16AwCcnV0gk8kQsnMHTp48iYcPH6B0us68Ll++hIoVK0nNM4kKKpVr7lauXIknT57AxcUFFStWzDREgUwmw86dO9UVHwBg9erVWLJkCerWrYvevXtL0ytUqCA9g0ekqdzLlMG06b9jwR/zsezPP+Hg4ICgoIHwb9gwy2W0tLT+f4y4vaiVrvZswIAgyGVyDBs6BJ8+fYKnpxeGDB2qsOzDhw/xId0AryeOH8f48eOk1yNHjgAAdO/RAz179lLpvYSHh+PJk8cYN368NK1t23b4999/8WNgV7i7l0H3Hv/rAe3YsaOws7NDpUqVVNoOEREp2rp1CwDgp96K9+3RY8aiSZOm0NXVw+XLl7Bhw3q8i42FpaUVKlWqhD+XLZOeS1PGy+s7aGtp4dzZs/D08gKQ+mPi3HnzMX3aVHTp8gPMzM3RrVt3tGz5v0d63n94r9AqRVtbG1u2bMLs2TMhhEDhwk74tf8AtGjRUuX3KoTA5MmT0L//ACnp09fXx5gxwZg+fSo+fUrEoEFDFIZvOHBgP7r36Knytoi+NTKRsbvLHPj4+ORYJv2v/upgYGCAmzdvwtnZGSYmJrhy5QpcXV1x48YNeHh4SN35qiI2NhZmZmaIiYmBAJ/fI83z6tUrfP99O6xZsxb29lk34fyW/RjYFW3bfQ9/f+XPehB9a8xM//c869uYd9mUJNIcmzdvwonjxzF33vz8DiVPTp36G3PmzMZff61X2gsnUX6Ty4SUt+TUtFnlM1jdiVtuuLu748SJE5kGU9+yZQt/0SfKgrW1NUaNGo3IyMgCmdy9ffsWdXx80KBBg/wOhYiIstGyZQDev3uHDx8+wMjIKL/DUVlcXBxGjx7LxI40QoE4i8eMGYMuXbrg6dOnSElJwbZt23Dr1i2sXr0au3fvzu/wiL5ZderUye8Q8szc3BydO3fJ7zCIiCgH2traCPyx4HZul368O6KCTuUOVQDg0qVLaNOmDezt7aGnpwd7e3u0bdsWly5dUnd8AIDmzZtj165dOHjwIIyMjDBmzBj8+++/2LVrF+rVq/dFtklERERERFSQqPzM3YkTJ1CvXj3Y2dkhICAAhQoVQlRUFLZv347IyEgcOHAANWvW/FLxqg2fuSMiInXjM3dERKRuqjxzp3JyV6NGDZiYmGD37t0KbZOTk5PRuHFjvH//HidPnsxb5F8RkzsiIlI3JndERKRuX7RDlUuXLmHLli2ZHjrV0tLCL7/8gtatW6u6yhxZWFhAJsucgMlkMujr66N48eLo2rUrAgMD1b5tIiIiIiKigkDl5M7IyAgvXrxQOi8qKuqL9JI0ZswYTJo0CQ0bNoSHhwcA4OzZswgLC0Pfvn1x//59/PTTT0hKSkKPdGNkERERERER/VeonNw1bdoUQ4cOReHCheHn97/ehQ4ePIjhw4ejWbNmag0QAE6ePImJEycqDGAOAIsXL8b+/fuxdetWlC9fHnPnzmVyR0RERERE/0kqP3P35s0b+Pv74/z58zA1NYWtrS1evHiB2NhYVKtWDXv37oWFhYVagzQ2Nsbly5dRvHhxhel3795FxYoV8f79e0RERKB8+fL48OFDrtbJZ+6IiEjd+MwdERGp2xd95s7CwgLh4eHYvXs3Tp48iTdv3sDS0hI1a9ZE48aNIZfnaXSFbFlaWmLXrl0YMGCAwvRdu3bB0tISAPDhwweYmJgoW5yIiIiIiEjj5WkQc7lcjmbNmn2RJpjKjB49Gj/99BOOHDkiPXN37tw5hIaGYtGiRQCAAwcOwNvb+6vEQ0RERERE9K3JVbPM169fw9zcHHK5HK9fv85xpWm1aer0999/Y/78+bh16xYAwM3NDf369cN3332Xp/WxWSYREakbm2USEZG6qX2cOy0tLYSHh8PDwwNyuVzpsATpJScnqxZxPmByR0RE6sbkjoiI1E3tz9wtX74cxYoVk/6fU3KnbrGxsUqny2Qy6OnpQVdX96vGQ0RERERE9K1RubfM/JBTbWHhwoXRtWtXjB07NtcdurDmjoiI1I01d0REpG6q1Nyp3LWlq6srrly5onTe9evX4erqquoqc7Ry5Uo4ODhgxIgR2LFjB3bs2IERI0bA0dERCxcuRM+ePTF37lxMmTJF7dsmIiIiIiIqCFTuLfPBgwdISEhQOu/jx494/PjxZweV0apVqzBjxgy0bdtWmta0aVOUK1cOixcvxqFDh1CkSBFMmjQJI0aMUPv2iYiIiIiIvnW5Su7i4+Px8eNHpLXgjI2NzdRrZnx8PHbs2AEHBwe1B3nq1ClpyIP0KlWqhPDwcABAzZo18ejRI7Vvm4iIiIiIqCDIVXI3depUjB8/HkBqJyYNGjTIsmxwcLBaAkvPyckJy5Yty9TsctmyZXBycgIAREdHw8LCQu3bJiIiIiIiKghyldy1aNECLi4uEELgxx9/xKhRo6TeM9Po6uqidOnSqFixotqD/P3339GmTRvs3bsX1apVAwCcP38eN2/exJYtWwCkDmrerl07tW+biIiIiIioIFC5t8xVq1ahSZMmsLKy+lIxKfXgwQMsXrxYYRDzXr16wcXFJU/rY2+ZRESkbuwtk4iI1E3tg5in9/jxY7x8+RKVK1fONO/ixYuwtbVF4cKFVYs4HzC5IyIidWNyR0RE6qb2QczT++mnn1CiRAmlyd26detw584d7Ny5U9XV5srHjx/x6NEjfPr0SWF6+fLlv8j2iIiIiIiICgqVk7szZ86gV69eSuf5+Phg9erVnx1URi9fvkRgYCD27t2rdH5ycrLat0lERERERFSQqDyI+fv376Gjo6N8ZXI53r1TfzOU/v374+3btzhz5gwMDAwQFhaGVatWoUSJEggJCVH79oiIiIiIiAoalWvuSpcuje3bt8Pf3z/TvJ07d8LNzU0tgaV3+PBh7Ny5E1WrVoVcLoezszPq1asHU1NTTJ48GY0bN1b7NomIiIiIiAoSlZO7/v37o2vXrtDS0sKPP/4IBwcHPHv2DCtWrMDSpUuxfPlytQf54cMH2NraAgAsLCzw8uVLlCxZEuXKlcPFixfVvj0iIiIiIqKCRuXkrnPnzoiKisK4ceOwePFiabqBgQGmTJmCLl26qDVAIHXYg1u3bsHFxQUVKlTA4sWL4eLigkWLFsHe3l7t2yMiIiIiIipoVB4KIU1sbCzCw8MRHR0NKysreHl55dg1Z16tXbsWSUlJ6Nq1Ky5cuAB/f3+8fv0aurq6WLlyZZ4GL+dQCEREpG4cCoGIiNTti45z9y34+PEjbt68iSJFisDa2jpP62ByR0RE6sbkjoiI1E3t49xt27YNvr6+MDc3x7Zt23IsHxAQkLtI88jQ0FDpOHtERERERET/VbmquZPL5Th9+jQ8PDwgl2c/eoJMJlPLuHNBQUG5Ljtz5kyV18+aOyIiUjfW3BERkbqpvebu/v37Uscl9+/f//wIc+HSpUu5KieTMTEjIiIiIiIqkM/cqQNr7oiISN1Yc0dEROqm9pq7R48eqRRAkSJFVCqflXv37qFo0aKsnSMiIiIiIspBrpI7FxcXlRIsdTxzBwAlSpTA8+fPpQHM27Vrh7lz56JQoUJqWT8REREREZGmyFVyt337dun/79+/x7Bhw1CsWDG0atUKhQoVQmRkJLZu3Yp79+5h6tSpagsuY4vR0NBQTJ48WW3rJyIiIiIi0hQqP3PXo0cPJCcnY/ny5ZnmBQYGQiaTKZ2XF3K5HJGRkVLNnYmJCa5cuQJXV9fPXjefuSMiInXjM3dERKRuqjxzl/24Bkps3rwZ7du3Vzqvffv2CrV8n0smk2VqDsrn74iIiIiIiDLLVbPM9LS0tHDp0iXUq1cv07yLFy/mOA6eKoQQ6Nq1K/T09AAA8fHx6N27N4yMjBTK5WZgdSIiImpYMy8AAIIiSURBVCIiIk2mcnL3ww8/YMyYMYiLi0OLFi1ga2uLFy9eYPv27ZgyZQp69+6ttuC6dOmi8LpTp05qWzcREREREZEmUfmZu6SkJAwbNgwLFy5EfHy8NF1fXx+9e/fGlClToKOjo/ZA1Y3P3BERkbrxmTsiIlI3VZ65y/Mg5m/evMHVq1cRGRkJe3t7lCtXDhYWFnkKOD8wuSMiInVjckdEROqm9kHMlbGwsIC3t3deFyciIiIiIiI1ylPvJ69evcKwYcNQt25duLm54Z9//gEAzJkzB6dPn1ZrgERERERERJQzlZO7ixcvokSJEtiwYQMKFy6Mu3fvIiEhAQDw9OlTzJo1S+1BEhERERERUfZUTu4GDBgALy8v3LlzB8uWLUP6R/aqV6/OmjsiIiIiIqJ8oPIzd+fOncO2bdugo6OD5ORkhXk2NjZ48eKF2oIjIiIiIiKi3FG55s7IyAixsbFK5z169AhWVlafHRQRERERERGpRuXkrkGDBpg4cSKio6OlaTKZDHFxcZgzZw4aNWqk1gCJiIiIiIgoZyqPc/f06VPUqFEDsbGx8PHxwY4dO+Dv748bN25AJpPh9OnTsLW1/VLxqg3HuSMiInXjOHdERKRuqoxzp3LNnaOjIy5fvox+/frh+fPnKFasGKKjo9GxY0ecP3++QCR2REREREREmkalmrv4+HgMGTIEP/zwA6pVq/Yl4/riWHNHRETqxpo7IiJSty9Wc6evr4/ly5fj48ePnxUgERERERERqZfKzTK/++47jmVHRERERET0jVF5nLvx48ejY8eO0NLSQqNGjVCoUCHIZIrNGi0tLdUWIBEREREREeVM5d4y5fL/VfZlTOrSZBzc/FvEZ+6IiEjd+MwdERGpmyrP3Klcc7d8+fIskzoiIiIiIiLKHyond127dv0CYRAREREREdHnyHWHKkuXLkX58uVhYmICNzc3jB49Gp8+ffqSsREREREREVEu5Sq5W7FiBXr16oWEhAQ0btwY5ubmmDRpEoKCgr50fERERERERJQLuepQpXLlyihRogTWr18vdajy22+/Ydy4cfjw4QO0tVVu3Znv2KEKERGpGztUISIidVOlQ5VcJXempqbYsmUL6tevL017/fo1rK2tcfv2bRQvXvzzo/7K0id3Oe0kIiIiIiKi/KBK3pKrZpnv37+Hubm5wjQzMzNpY0RERERERJS/ct2e8tatWwrNL9PGsrt582amspUrV1ZDaERERERERJRbuWqWKZfLlY5tl7Zo2jwhBGQyWYEbxDw5Jb+jISIiTWBh/r/mMpXbTsrHSIiISFMc/bOfegcxP3LkiFoCIyIiIiIioi8jV8mdt7f3l46DiIiIiIiIPkOuBzEnIiIiIiKib1eukrvq1atjx44dSEnJ3cNpjx8/xqBBgzBz5szPCo6IiIiIiIhyJ1fNMjt37ow+ffqgZ8+eaN68OWrUqIHy5cvDxsYGenp6ePv2Le7fv48LFy5g7969OH36NJo1a4affvrpS8dPREREREREyGVy17dvX/z444/YsGEDVq9ejdWrVyMpKUmhjBAC9vb2aN26NRYsWIBy5cp9kYCJiIiIiIgos1yPc2dgYIDAwEAEBgYiPj4ely9fxvPnzxEfHw9LS0u4ubnBxcXlC4ZKREREREREWcl1cpeevr4+PD091R0LERERERER5RF7yyQiIiIiItIATO6IiIiIiIg0QIFI7tavX5/lvMGDB3/FSIiIiIiIiL5NBSK5++mnn7B3795M0wcMGIC1a9fmQ0RERERERETflgKR3P31119o3749Tp48KU3r168fNm3ahCNHjuRjZERERERERN8GlXvLfPToUZbz5HI5zMzMYGJi8llBZdS4cWMsWLAAzZo1w4EDB7Bs2TLs3LkTR44cQcmSJdW6LSIiIiIiooJI5eTOxcUFMpksxzL9+/dHv3798hxYRh06dMDbt29Ro0YN2NjY4NixYyhevLja1k9ERERERFSQqZzcrV27FsOHD0fJkiXRvHlz2Nra4sWLF9i+fTvu3LmDYcOGITw8HAMGDACAPCd4QUFBSqfb2NigcuXKWLBggTRt5syZedoGERERERGRplA5uTtx4gT8/f2xePFihek///wzevbsiQsXLmDNmjUwNTXFggUL8pzcXbp0Sen04sWLIzY2VpqfUy0iERERERHRf4HKyd2GDRuwadMmpfPatGmDtm3bYtmyZWjSpAmWLVuW58DYUQoREREREVHuqdxbplwux5UrV5TOu3z5MuTy1FVqaWnBwMDg86LLQmxsLHbs2IGbN29+kfUTEREREREVNCrX3HXs2BGjR4/Gp0+f0LRpU9jY2ODly5fYuXMnJk6ciF69egEALly4AHd3d7UE2bZtW9SuXRs///wz4uLiULVqVTx48ABCCGzYsAGtWrVSy3aIiIiIiIgKKpWTuxkzZkBbWxsTJ07E6NGjpel6enro27cvpk6dCgCoUaMGGjRooJYgjx8/jpEjRwIAtm/fDiEE3r59i1WrVmHixIlM7oiIiIiI6D9P5eROR0cHM2fOxOjRo3Ht2jU8f/4c9vb2KFu2LCwtLaVytWvXVluQMTEx0rrDwsLQqlUrGBoaonHjxhg8eLDatkNERERERFRQqZzcpbGwsFBrApcdJycnhIeHw9LSEmFhYdiwYQMA4M2bN9DX1/8qMRAREREREX3L8pTcvXnzBnv37sWTJ08QHx+vME8mkyk011SH/v37o2PHjjA2NoazszPq1KkDILW5Zrly5dS6LSIiIiIiooJI5eRu//79aN26Nd6/fw8DAwPo6uoqzP8SyV2fPn3g4eGBx48fo169elKPnK6urpg4caJat0VERERERFQQqZzcDRw4ENWqVcPy5cvh7Oz8JWJSqmrVqqhatarCtMaNG3+17RMREREREX3LVE7u7t27h5kzZ37xxC4oKAgTJkyAkZERgoKCsi07c+bMLxoLERERERHRt07l5K5y5cp4/Pjxl4hFwaVLl5CYmCj9n4iIiIiIiLKmcnK3cOFCdOrUCY6Ojqhbty60tfPc4Wa2jhw5ovT/RERERERElJnKmZmXlxcSExPRqFEjyOVyGBgYKMyXyWSIiYlRW4A52bJlC1q3bv3VtkdERERERPQtylOHKjKZ7EvEolRSUhJu3rwJXV1dlCxZUpq+c+dOjBkzBjdv3mRyR0RERERE/3kqJ3fBwcFfIAzlrl+/jiZNmkjP+DVv3hwLFy5E27Ztcf36dfTo0QN79uz5avEQERERERF9q77MA3NqMnToUBQvXhzz58/H+vXrsX79evz777/o1q0bwsLCMjUJJSIiIiIi+q/KVXLXrFkzzJgxAyVKlECzZs2yLSuTybBz5061BHfu3Dns378fFStWRK1atbB+/XqMGDECP/zwg1rWT0REREREpClyldy9e/cOycnJAIDY2Niv9szdq1ev4ODgAAAwMzODkZERPD09v8q2iYiIiIiICpJcJXfphyI4evTol4olE5lMhnfv3kFfXx9CCMhkMsTFxSE2NlahnKmp6VeLiYiIiIiI6Fv0TT9zJ4RQ6CFTCIFKlSopvJbJZFKtIhERERER0X+Vysnd+PHjs5wnl8thZmYmPSP3uTh4ORERERERUe6onNzNmjULnz59QlxcHABAX18f8fHxAAADAwMkJiYiOTkZlStXRmhoKGxsbPIcnLe3d56XJSIiIiIi+i+Rq7rA4cOH4ejoiDVr1iA2NhYfP35EbGwsVq1aBQcHBxw7dgz79+/HkydPMHjw4C8RMxEREREREWWgcs1d3759MXDgQHTs2FGaZmxsjB9++AEfPnxA//79cebMGYwaNSrbJpxERERERESkPirX3F26dAnOzs5K57m4uODatWsAgLJlyyImJubzoiMiIiIiIqJcUTm5c3Z2xp9//ql03pIlS6TELzo6GtbW1p8XHREREREREeWKys0yJ0+ejLZt28LNzQ1NmjSBjY0NXr58id27d+PevXvYvHkzAODQoUOoXbu22gN+8uQJAKBw4cJqXzcREREREVFBpXJy17JlS5w9exaTJ0/G9u3b8fz5c9jb26NatWrYuHEjKlasCAD4448/1BZkSkoKJk6ciBkzZuD9+/cAABMTEwwcOBAjR46EXK5yBSQREREREZFGydMg5pUqVcKmTZvUHUuWRo4ciWXLlmHKlCmoUaMGAODkyZMIDg5GfHw8Jk2a9NViISIiIiIi+hblKbn72latWoU///wTzZo1k6aVL18ejo6O6NOnD5M7IiIiIiL6z1M5ufP19c1ynlwuh5mZGSpVqoTAwEA4Ojp+VnBpXr9+jVKlSmWaXqpUKbx+/Vot2yAiIiIiIirIVH5YzczMDHfv3sWJEycQGxsLfX19xMbG4sSJE7h9+zbevHmDGTNmwN3dHRcvXlRLkBUqVMD8+fMzTZ8/fz4qVKiglm0QEREREREVZCrX3LVp0wYRERE4ceKEwnh3Dx48QNOmTdGlSxds3boVfn5+GD58OPbt2/fZQU6bNg2NGzfGwYMH4eXlBQAIDw/H48ePERoa+tnrJyIiIiIiKuhUrrkbN24cgoODMw1k7uLigrFjx2LChAmwsLDAoEGDcPr0abUE6e3tjdu3b6Nly5Z4+/Yt3r59i4CAANy6dQu1atVSyzaIiIiIiIgKMpVr7h49egSZTKZ0nkwmw9OnTwEADg4OSEpK+rzo0nFwcGDHKURERERERFlQObmrVq0axowZg6pVq8LJyUma/vDhQ4wdOxYeHh4AUptpfm6HKo8ePcpVuSJFinzWdoiIiIiIiAo6lZO7RYsWoV69eihWrBjKlSsHGxsbvHz5ElevXkWhQoWwefNmAEBUVBR69uz5WcG5uLgorSUUQkjTZTKZWmsIiYiIiIiICiKVkzt3d3dERERg+fLlOH/+PJ4/f44KFSqge/fuCAwMhL6+PgBgyJAhnx3cpUuXlE4XQmDDhg2YO3cujI2NP3s7REREREREBV2eBjHX19dHnz591B1LJsqGOTh48CCGDRuG27dvY8iQIRg4cOAXj4OIiIiIiOhbl6fkLj9cvHgRQ4cOxYkTJ9C9e3eEhobC1tY2v8MiIiIiIiL6Jqg8FAIArFmzBjVr1oStrS1MTU0z/alTREQE2rVrBw8PD9jY2ODGjRuYP38+EzsiIiIiIqJ0VE7u1q5dix49eqBs2bJ49eoV2rZti1atWkFXVxe2trYYNGiQ2oLr06cP3N3dERMTg/Pnz2PdunVwdXVV2/qJiIiIiIg0hcrNMmfMmIHRo0dj2LBhWLJkCfr06YPKlSvj3bt3qF+/vlo7OFm0aBH09fXx4sUL/Pjjj1mWu3jxotq2SUREREREVBCpnNzduXMHNWrUgJaWFrS0tBAbGwsAMDExwdChQ9G/f38EBQWpJbixY8eqZT1EBcXu3bswe9ZMHDx0JL9DyVfjxwfj/bv3mDb99/wOhYiICriLm0YiaPpmHD13O79DIfriVE7uzMzMkJCQAABwdHTEjRs3UKdOHQBAcnIyoqOj1RYckzsqiMaPD0bonj2Zpm/esg1OTk5fPZ70du/ehYkTxsPT0xOz58z7v/buOyqKqw0D+LNLWfpSBZWqIFZEo0axi7333mKJNfYee4mxG7tGY4vGjorB3rFrxI6VIs1CRzp7vz/4mLhSxIqQ53cOR7lz5847s7szvHvv3JHKY2Nj0aB+PaxctQbffffdV4snJCQEbdu0wpatf6JECWepfNSoMRBCfLU4iIjyi392/Zzj8rW7z2Ht7vNfJZZ107qjUhk7TFjqgWMX70vlXZtWRtemVdB86MqvEkeGAR1qok5lZ3QZt16tvEH/pYh5k/hVYyHKKx+c3FWqVAm3b99Go0aN0LJlS8yYMQMqlQpaWlr49ddfUbVq1S8RJ1G+UrVaNUyZMlWtzNjYJI+iUaehoYFr167hxvXr+K5SpbwOJ0t8fiURUdYa9F8q/b+hW2kM7FQLbYevkcriE5PV6mvIZUhTfbkvyxKTUzCkc22cuuKL1DTVF9vOpwiPfpPXIRB9NR+c3E2cOBEBAQEAgJkzZyIgIAAjRoyASqVC5cqVsXbt2s8eJFF+o62lDTMz80zl27dvw9+HPBEcHAwjIyPUqFkTQ4cOg56eXpbtPH70CEuWLIav7wMAMtjY2GDCxIkoVao0AMDHxwerV62Er+8DKJVK1K5TF4MHD4Gurm62senq6sLdvT5WrlqBP/7YlG29Fy/CsOy333DlymXI5XKUL++KkaNGo0iRIgCA1NRU/PbbUhz2+htyuQZatmqFiPBwxMX9O5zy0qWL2PjHH3j27Cnkcg2UK1cOI0eNhrW1NQCgbZtWAICePboDACpUrIjVq9eqDcvc77EP69f/joOef0Mu/3cOqLFjRkOpVGLy/5Poc2fPYv2G3+Hv5wdzc3M0bdYcvXv/AE1NTQghsH797zjkeRARERFQKpWoW88do0d/vgmgiIi+hrcTlbj4JED8W/ZdaVv8Pr0Hhv6yA0M614ajbSEMnr0dLeq4wFBfB6MX7JHWHdOrAUrYW+LHGX8CAGQyoHcrN7StXwFmxvoIDInA73u9cfKKb47xHL1wH7W+c0Ib9wrYfexGtvVqVyqBH9vXRDFrc7yKjMWhs3ewYZ+3lHjaFzHDlIHNULpYYQS/jMT8jcewZko3teGUw7rVRd3KzihkZoTwqDgc9r6H3/ecR2qaCi1qu2BAh1oA/u3dnLbSE55nb6sNy9w4qxdu+gZi2bZ/b38wNtTD0bXDMGjWNvzz4Dm0NDUwpEsdNK5eBoZ6Cjx5/grLtp3CjfuBAIDC5kYY37cxXJ2toaWpgZBX0Vj650lcuPk0dy8i0Rf0wcld1apVpd45Y2NjHDhwAElJSUhKSvrsj0EgKmjkMhlGjRqDwkWKICQkGAvmz8OKFcswbtyELOtPmzYFJUo4Y9z4CZDL5Xj8+BE0NdI/tkFBQRg5YhgGDBiInydPQVRUJBYuXICFC+ZjytSchzT36/8j2rdrg1MnT6Keu3um5ampqRg+bBjKlSuHNWt/h4aGBjZu3ICRI4bhz21/QUtLC1u3bsHRI0cwecpU2Ns7YOfOHTh79gy+++7f3sCEhER06doVjo5OSEiIx7p1azF+/Fhs3boNcrkcf2zchD4/9MbyFStRrFgxaGpqZYqlnnt9LFq0EDduXEflylUAANHR0bh8+RIWL1kKAPC5eRMzZkzDqNFj4OrqiqCgYPw695f0fe3XH6dPncKOv7Zj1uw5KFasOMLDw/H4Me+9IKKCaVjXuliy9SSCX0YiJi53wxH7tK6OpjXL4pffDyMwNAIVS9li9k+tEBkTj38eBGa73pv4JPzhcQH929eA59nbSExKyVSnQkkbzBzaAgs2HsPNB89hbWmCyQOaAgDW7TkPuUyGRWPbI+x1DHr+vBH6OtoY2bN+5m0lJGPaKk+8ioyDk20hTB7QFPEJSdh88DKOXbyP4rYWcCtfDINmbQfw/+T3HYe976JXy2pqyV0jt9J4HRmHfx48BwCM79sIxYqaY+JSD7yKjEXdKs5YMakLOo5Zh+dhkZjQtzE0NTXQb9pWJCSloJi1ORLe6TElyisf9Zy7dykUCiZ2RG+5cMEbdevUkn4mTUxP3jp36YrvKlVCkSJFUKlSZQwYMAgnT5zItp2wsBeoXKUK7O3tYWtrC3f3+nAqUQIAsHnzJjRq1Bidu3SFra0tXFzKY9SoMTh82Eu6LzY7FhYW6NSpM9asWYXU1NRMy48fPwYhVJj082Q4OjrCwcEBU6ZMQ1hYGP65kf7N7O5dO9GrVy/UqVMX9vb2GDNmLAwNDdXaqVevHurWrQcbGxuUKOGMyZOn4umTJ/Dzewbg36GqSqUSZmbmUCqVmWIxMjJCtWpuOHb0qFR2+tRJGBsbS4nk+g2/o2fPXmjWrDmKFrXG999/jx8HDMB+j33px/FFGMzMzFClyvewsrJCmTJl0Lp1mxyPERFRfrV61zlcueOHoBdRubrXTEtTA33auGHG6kO4dOsZgl9GwfPsbXidv4t2DSq8d/1dR28gOTkV3Zt/n+XyH9vXxKb9l3Do7B0Ev4zClTt+WL3zLNrVT2+7qosDrC1NMHXFQTwOeAmfh0FYueNMpnY27LuA24+CEfoqGuduPMZWz8toUC19JEtSSioSEpORphIIj36D8Og3SErJfH07dvEBzE0MUKHkv/fAN65RBkcu3AMAWJkZoWWd8hi3ZB9u+j5H0IsobPW8Ah/f52hVt3x6HXMj3HoYhCfPXyH4ZRTO//NESgyJ8lqueu6GDRuW6wZlMhl+++23jw4ot6KiomBsbPzFt0P0MSp+951ab1zGMMmrV69gy+ZNCAgIwJs3b5CWloakpCQkJiZCR0cnUztdunbFL3Nm4/BhL1SpXAX13OtLQxqfPH6EJ0+e4OjRI1J9IQRUKhVCQkLg4OCQY4w9evbC/v0eOOR5EO71G6gte/L4MYKCglCvbm218uTkZAQFB6FMXBwiIiJQukwZaZmGhgacS5aEeOvejsDAQPy+bi3u3buLqKhoCJF+P8aLsBcoXtwxx/je1qhxY8z9ZQ7GjhsPbW1tHD16BPUbNJCGaT55/Bh3bt/Gpk0bpXVUKpV0bN3d3bFzx19o26YVqlarBje36qhRoyY0NT948AIR0Tfv/tPQD6pvY2UCXR1trJrSVa1cS1MDvn5h710/JTUNq3edw7g+DbEni6GZJewLoXxJa/RtW10qk8tl0NHWgo62JuyKmOFFeIzakNN7T0IytdOwWil0blIZ1lYm0NPRhoZcjjcJOX+Z+a6o2Hhcvu2HJjXL4qbvcxSxUKK8szXmrPMCADjaFoKmhhz7fxuktp6Wpgai4xIAAH8dvo6J/RqjqosDrt7xx8krvngc+PKD4iD6UnL1l42np2euG/wSyd28efNgb2+PTp06AQA6duyIvXv3wsrKCl5eXihfvvxn3R7Rp9LV0c00M2ZISAjGjB6Ftm3bYeDAwTBSGuGWzy3MmTMLKSkpWSZ3/fv/iEaNGuHChQu4dPEifv99HWbNnoM6deoiPiEBrdu0RceOnTKtZ2Vl9d4YDQ0N0bNnb2zYsB7Va9RUWxafkADnkiUxY8asTOuZmOR+YpixY0bByqowJk76GebmFhBCha5dOiMlNfOwnZzUqFETQghcuOCN0qVLw8fHB8NH/PvIlYSEBPTr/yPq1KmbaV1tbW1YWlph5649uHbtKq5evYoF8+dh259bsXrNOiZ4RFTgJCSpDxEUApBBplamqfnv4C09HW0AwLC5O/EqIlatXnIWozuy4nX+Dnq2+B792tZAyKsotWW6OtpYu+scTl15mGm9rHrXsuLiVBSzh7XG2l3ncPHWM8TFJ6JR9TLokU1vYU4On7+LsT80xPw/jqJJjbJ4HPACT56/AgDo6WghNU2FbuM3QPXORDQZk9XsP+WDS7eeokZFJ1RzccAPbdyweMsJ7Dxy/YNjIfrccvVXjZ+f35eOI0dr1qzBtm3bAADHjx/H8ePHcfjwYezatQtjx47FsWPH8jQ+otzw9fWFSqXCsOEjpB6nEzkMycxga2sHW1s7dOnSFVMm/4xDhzxRp05dODs7w8/v2Sc9XqFDx47YtWsHdu7coVbu7OyMkyeOw9TEBPrZzFxpamqK+/fvo0KFigDSH4Xy8OFDlHBKHzYaHR2FgIAATJz4M1wrpA+98fHxUWtDSyv9HjvVe2ZYUygUqFOnLo4eOYKgoCDY2tmhZMmS0vISzs4IDAjI8Vjo6OigZs1aqFmzFtq3b49OHTvgyZMnau0QERVEkTFvUNzGQq2shJ2lNLvls6DXSEpORWFzoxzvr8uJEMDy7WewcEw77Dn2j9oy32dhsCtihucvIrNcNyAkHJZmRjBV6iPi/713ZYoXUavj4myN0FfR2OBxQSorbK4+lD8lNQ1yuXoSm5Uz1x9h8oCmcHMtjsY1yuDQuTv/xur/Apoacpgq9XHTN/uhli/CY7H3+D/Ye/wfDO1SB23dXZnc0Tfhs9xz96WFhYVJf7QdOnQIHTt2RMOGDTFu3Dhcu3Ytj6Mjyh0ba2ukpqZi966dCA4OwmEvL3js25dt/cTERCxcMB83btxAaGgobt26hQcP7sPePn24ZY8evXDn9m0sXDAfjx49RGBgIM6dPYuFC+bnOiaFQoH+/Qdg966dauWNGzeBUmmMsWPHwOfmTYSEBOPGjRtYtGghXr54AQDo0LETtmzehHNnzyIgwB9LFi9CbEwMZLL0C6uhoRGUSiX27/fA8+fPcf36NSz7bYnadkxMTKBQKHDp8iWE/3+mzew0atwYFy9ewCHPg2jUqLHasr59+8HL62+sX/87nj17Cj8/Pxw/dgxr1qwGkP58v4MHD+Dp0ycIDg7CkcOHoVAoULjw+3s4iYjyu2t3A1C6WGE0q1UONlYmGNihForb/pvsxScmY6vnZYzq1QDNa5eDtaUxSjpYoVPjSmheu1yut+N98wnuPg5B23fu0/t9rzea1SonzZbpUNQMDd1KY3Cn9KH/l2/7IehFJGYOaQEn20Io72yNwZ3Tl2U88jQwNAJW5kZo6FYa1pbG6NykEupWKaG2nZCX0ShayBgl7CxhbKgLLU2NLONMTErBmWsPMahTbTgUNcdR73vSssDQCHidv4OZQ1uiXhVnFLFQokzxIvihtRtqVEi/nWBMrwaoVr4YilgoUdLBCpXL2MMv+PM955noU+Sq587FxQXbt29H2bJlpbLt27ejadOmX+W+NxMTEzx//hw2NjY4cuQIZs+eDSD9/qK0tLQvvn2iz8GpRAkMHzESW7duwapVK1GhQgUMHjwEM2ZkPbOlhoYGoqOjMXPGNERERMDY2Bi169RF//4/prfn5ITVa9ZizerVGDjgRwghULSoNeo3aJBle9lp2qwZtm//U62HXkdHB2vWrsXKFSswYcI4xMfHw8LCApUqVYa+vj4AoEePnggPD8eMGdOgoaGBVq1bo2rValKvpFwux6zZc7B40SJ069oZtrZ2GDV6NAYPGihtR1NTE6NGj8EfG9bj93VrUd7VFatXZ/04lUqVKsPIyAgBAQFo1KiR2rKqVath0eIl2LBhPbZu2QxNTU3Y2dujZcv0Ry0YGhpiy+bN+G3pEqhUKhQv7oiFixZDqTT+oGNFRJQfXbr1DOv3emN493pQaGniwOlb+PvsHTjaFpLqrNp5FpEx8fihtRusLU0Q+yYRvn5h+OOtnrLcWLbtFDbN6Z1p+yPm7UL/djXQq1U1pKalwT84HPtP+QAAVEJg9II9mDKwGbbO/QHBL6Kw9M+T+G1CJyT/f9jmuRuPsf3vqxjfpxG0tTTg/c8TrN/rLT3+AABOXvFFve+dsW5aNxgZ6EqPQsiK1/l7WDGpHG7cD0BYeIzasumrDqFf2xoY2bM+CpkaIiomHnceB+P8jccA0u8XnNC3EQqZGuFNQhIu+jzFos3vH4lD9DXIhBDvfbKlXC7H5cuXUaVK+jTkaWlp0NbWxrVr11CxYsUvHuTQoUNx6NAhODk54ebNm/D394eBgQF27NiB+fPn459//nl/I++IiYmBUqlEdHQ0vtFnbhLlKyqVCp07dYC7e30MGDjo/SsQFUAmxv/OHF2x45w8jIQofyvvbI2Ns3qh5U8rEfQiKq/DIcpTZ9b/JOUt73tCwUfPJJCLnPCzWbJkCezt7fH8+XPMnz8fBv+/Byg0NBSDBw/+anEQ0b9CQ0Nx5cplVKxYEcnJKdizexdCQkLQ8J0hk0RERO9Tt7Iz4hOTERgWARsrE4zt3VB6FAER5V6+mCZOS0sLY8aMyVQ+cuTIXLeR8aD1DDExMTnUJqL3kctl+PvvQ1i+7DcIARQvXgzLV6x87yMYiIiI3qWnq41h3erCylyJqNh4XLnjhyVbTuZ1WET5Tq6Tu4xJEt5X9iXdv38fgYGBSE5Wn+K3ZcuW71137ty5mDFjxpcKjeg/x9LSCr//viGvwyAiogLg73N38Pdbs1YS0cfJ9T13enp60kQJABAXF5epDEhP+KKjoz9rkM+ePUObNm1w584dyGQyaUhoRnKZm0lVsuq5s7Gx4T13RET02fCeOyIi+tw++z1306ZlPZvf1zJ8+HA4ODjg5MmTcHBwwNWrVxEeHo7Ro0dj4cKFuWpDoVBAoVB84UiJvm0BAf4YNHAAdu/ZJ816mZ/s27cXFy54Y9GiJe+vTEREeU5TQ469SwZgyoqDuP0oOK/D+SIcippj1eQuaDNiDRKTUvI6HPqPyxfJ3aVLl3Dq1CmYm5tDLpdDLpejRo0amDt3LoYNG4abN2/maXxEX8qWzZuwatVKdOrUGSNHjc60XAiBkSOH4/KlS5g3fwFq166TY3urVq1Eh46dpMQuKSkJ8+bNxUNfX/j7+6N69RqYv+D9X5hER0dj0aIF8D7vDblchrp162HkqNHQ09MDkJ5Ezvv1V/j5+eHNmziYm5ujYaPG6NevPzQ10087V65cwcIF8xEeHo5atWrh58lTpIeax8XF4YfevbBs+QoULlxY2m6LFi2x8Y8N8Ll5U3owOhHRf1HvVtUwrFs9bP/7KhZuPp5tvWLW5hjUqTZKOVihSCFjLNx0DNu91J8RLJfJMKBjLTStWRZmxvp4FREHz7O3sX6vt1THVKmPYd3qoppLMRjo6+Dmg0DM++Monodl/WDyDO0bVkTwyyi1xK6kgxWGdauLMsWLIE2lwqkrD7Fo83EkvJUYlS5eGMO61kWpYoUhhMC9JyFYuu0UHge8zHI7Rvo6GNixFqqWLwYrcyNExsTjzLVHWL3jLOIS/h25VaWsPQZ1qg1HWwskJKXg0NnbWPnXGaSp0keFFbZQYtaQlihVzAoPnoVhysqDCH3174i038Z3xIEzt3DqykOpzC/4Ne48Dkb35t+rHTOivJAvHmKelpYGQ0NDAIC5uTlCQkIAAHZ2dnj48GFOqxLlW/fv34OHhwccHZ2yrbNjx1+QIXf3voaFheGCtzeaNWsulalUKigUOujQsRMqV66c69imTZsCv2fPsGz5CixctAQ3b97Er3N/kZZramqiadOmWLZsOXbu2oMRI0fhwP79+H3dWmm706ZORpu2bbF+wwY88H2A/fs9pPVXrlyBNm3bqiV2QPrkSg0bNsKudx66TkT0X1K6eGG0a1ARj/xfvLeujkILwS8isWz7abyKjMuyTu/W1dC+QUXM23AU7UauxbJtp9CrZVV0blJJqrN4bHtYFzLByAW70XXceoS+isaaKd2go9DKcfudGlXC/lO3pN/NTQywekpXPA+LRM9JGzH0lx0oZm2OGUNaSHV0FVpYMakzwl7HoOekjegzdQveJCZj5c9doKmR9Z+uFqaGsDA1xNKtJ9Fx9DpMX+kJt/LFMHVQM6mOk10hLJvYCRd9nqLruA2YsMQDtb8rgZ+61ZPqjOpZHy8jY9F53Hq8jorDyB7u0rKG1UpBJYRaYpfh4Onb6NCgIjTkX3c+CqJ35YvkrmzZsrh1K/3E8P3332P+/Pm4cOECZs6ciWLFiuVxdESfX3x8PKZNnYqJkybB0MgwyzqPHj3E9m3bMHnKlFy1efLEcTg5OaFQoX8fWqurq4vx4yegdes2MDUzy1U7fn5+uHzpEib9PBlly5aFq6srRo8Zg+PHj+HVq1cAgKJFrdG8RUs4lSiBwoULo1at2mjUuDF8fHwAAFFRUYiKikK7du1RrFhx1KxZC/7/f4j67du38OD+fXTq1DnL7deoWRPnz59DYmJiruIlIipIdBVamPNTK8xa+zdi3rz/PHj/aSiW/nkKxy7eR8r/Hwj+rvIlrHH2+iN433yC0FfROHnFF5dv+6GsYxEAgG1hU7iUsMYv6w/j/tNQBIRG4Jf1h6HQ1kTj6mWy3XapYoVhbWUC73+eSGW1KjohNTUNv244goDQCNx/Gopffj+M+lVLwcbSBABgX9QcxoZ6WL3rLAJCI/As6DXW7T4Pc2MDFDZXZrmtp89fYeyivTh34zGCXkTh2r0ArNxxBrW+c5ISrkZupfE44CV+3+uN5y8i8c+DQPy27RQ6NvoOejraAACHomY4dOY2nodF4uCZ23Aoag4AMNBTYHDnOvh1w5Est3/59jMYGejiu9J2Ob0cRF9cvkjuJk+eDJUqfdaTmTNnws/PDzVr1oSXlxeWLVuWx9ERfX4LF8xH9erVUaXK91kuT0xMxNQpUzB27DiYmZnnqk0fHx+ULFX6k2O7e+cODA0NUeqttipXrgK5XI579+5muc7z589x+dIlVKhYEQBgYmICc3NzXLlyGYmJibjlcxOOjk5ITU3F/HnzMGHCRGhoaGTZVqlSpZGWloZ79+598r4QEeU3E/o1hvfNJ7h6x/+ztXnrURCqlLWHbWFTAOk9XK7O1rhw8ykAQFsz/Xyc/FZyKASQnJIG15LW2bZbsZQNAkMiEJ/47yznWloaSElV4e3p/JKS09t1LWkDAAgICUdkTDxa13OFpoYcCi1NtK7nimdBrxDyKirX+2Wgp4M3CUnSkEstTQ21fQCAxOQU6GhroVQxKwDAo4CXqOLiAJkMqFbeAY8D04eBjujhjl1Hr+NFeGyW20pNU+GR/wtUKGWT6/iIvoR8kdw1atQIbdu2BQA4OjrC19cXr1+/xsuXL1GvXr33rE2Uvxw/dgwPH/pi0OAh2dZZumQxyrm4oFbt2rluNywsFBbmuUsEcxIeEQ4TExO1Mk1NTRgZGSE8PFytvH+/PqhVszo6tG8LV1dX/PjjAADpM93OnjMXG//YgC6dO6FECWe0aNkSWzZvwnfffQdthTb69++Ljh3aYffuXWpt6ujowMDAAGFhoZ+8L0RE+UlDt9Io6WCF5dtPf9Z2N+6/iKMX72PfkoG4sn0C/prXD9u9ruGwd/qXaP4h4Qh9FY2hXevCUF8Hmhpy9GpVDVbmRrAwNsi23cLmSryKVE+Grt31h5mxPnq2qApNDTkM9XXwU7e6ANKHbAJAfGIyfpzxJ5rWLItL28bDe+tYVHMthp9+2SElau9jbKiL/u1qYN8JH6ns0q1ncHG2RqPqpSGXyWBhYogf29VU2/aSLSfhUMQMh1YOha2VKZZsOYmKpWzgbGeJQ2fv4NeRbXBw+WBM6t8k0xDRV5Gx2fYsEn0t+eIh5lkxNTXN6xCIPrsXL8KwePEiLFu+ItvZXc+dO4vr169jy9Y/P6jtpKQkaGt/3RljZ8/5BfFv4vH48WMsX74M27b9iR49egIAXF1dsXHTFqluYGAAvLy8sGXrnxg48Ed06tQZ1aq5oVvXznB1rQAnp3/vPVQoFByWSUT/KZZmhhjbuwEGz/4LySnvfwTUh2hQrTSa1CiLScv249nzV3C2t8To3g3wKjIWh87eQWqaCmMW7sHUQc1xduNopKapcPWOH7z/eZLjM48V2ppIeifWZ0GvMW2lJ0b1qo+hXetCpVJhx+FreB0VB9X/u/MUWpqYOrAZfB4GYeJv+6Ehl6FHi6r4bUIn9Ji4EUnZDC/NoK+rjd8mdMKzoNdYu/ucVH75th+Wbj2JSf2bYNbQVkhJScXve71RsbQtxP+TxleRsRg+798vFbU0NbDy586YttIT/drVQHxCMtqOWIMVkzqjXYOK2HnkulQ3MTn1vfcgEn1p+Ta5IyqIfH19ERkZgd69ekhlaWlp8Ll5E3v27Ma58xdw4/p1BAcHoUF99V7riRPGo7yrK1avXptl20pjY8TGxnxyjGamZoiMVJ8dLTU1FTExMTB75749S8v0YS4OxYohTZWGX+f+gq5du2U55PLXuXMxbPhwCJUKjx4+hLt7fejo6KBChYq4efMfteQuJiYGJsYmmdogIiqoShUrDDNjA2yb11cq09SQo2IpW3RsXAlVu/4qJUcfakR3d2w6cBHHLt4HADx5/gpWFkr80NoNh86mP1j8gV8YuoxbDwNdBTQ1NRAVG4/Nc3rjwbPsR1FExSbA0bZQpvIjF+7hyIV7MFXqIyExGQJAt+bfI/hF+rWlcY0yKGKhRO/Jm6Thm5N+24+zG0ejduUSUpxZ0dPRxopJXRCfkIzRC3cj9Z2HGW/7+yq2/X0V5iYGiI1LRJFCSgzrVg9BL6OybK9vm+q4fNsPD/zCMHlAM6zaeQapaSqcuvoQlcvaqyV3SgNdBL3IefZQoi+NyR3RN6RSpcrYtv0vtbLZs2bCzs4ePXr2hIaGBnr26oWWrVqp1enWtQuGjxiJmjVrZtu2cwln+P1/0pJPUbZcOcTGxsL3wQOULFUKAHDj+nWoVCqUKVM22/WEEEhNTYXI4o+PgwcPwMjICLVq1UZMTHoCmpqaKv2rSvv3m9+goCAkJSWhhLPzJ+8LEVF+cfWOPzqMXqdWNn1Qc/iHhGPTgUsfndgBgI5CE6p3hjuqVALyLHrlMh4rYGNlgtLFC2P1zrPZtuvrF4b2DStmuzwi+g0AoFXd8khOTsXl237/j0cLKgG1+/KEEBBAljFl0NfVxsqfuyA5JQ0j5+/KsYfz9f9nDm1UvQxCX0fD91lYpjoORc3QuEYZdB63HgCgIZdB8/9fTmpqyCF/Z2bM4jYWOHH5QbbbJPoamNwRfUP09fVRvLijWpmOri6USqVUbmZmnuUkKlZWVihSpGi2bX9ftSp+mTMHaWlpaj1nfs+eISU1BTExMYh/E49Hj9KneC5RIj15unfvHmbOmIblK1ahUKFCcHBwQNVq1fDL3DkYP34iUlNTsXDhAjRo0BAWFhYAgCNHDkNTUxPFiztCW1sLDx48wOpVK1G/QQPpOXcZIiIisPGPP7Du9/SLp5GREeztHbBjx1/4/vvvcf36NfT+4Qepvo/PTRQtWhTW1tnfxE9EVNDEJybj6fNXamUJSSmIjk3IVP42TQ05ilmnn5u1NDVQyNQQJewskZCYjOf/72U6d+Mx+ratjrDXMXga9Aol7a3QvXkVHDj97yMM6lcticiYeIS9joGjbSGM7d0AZ649khKyrFy/FwA9HW0Ut7FQi7FTo0q49SgI8YnJqOrigOHd3bF8+ynExacnjldu+2FEd3dM6NsYO49cg0wmww+t3ZCWpsL1ewEAAAsTQ6yZ2hVTV3ji3tMQ6OtqY9XPXaGj0MTk5Qegr6uAvm76rQiRMfFS8tuzRVVc9HkKlRCo931J/NDaDeOX7MsyOZ78Y1Ms2nxcejC5z8MgtHV3RWBoOJrVcsHRC/9O7FXYQolCpoa4eufTv0Ql+hRM7oj+I6pVc4OmpgauXbuKqlWrSeUjR41AWOi/w2p69ugOALh8Jf0ht4mJiQgICJB60gBgxoxZWLRwAX4aOhgyWfpDzEeNHiMt19DQwNYtW/D8eSCEELCyskL79h3QuUvXTHEtWbwIXbt1kxJDAJgydRpmzpyOXTt3olu37ihd+t+pto8fO4ZWrVp/6uEgIiqQpg9ujiIWxvhxRvp92RamhtixoJ+0vGfLaujZshqu3wuQ6sz/4xgGd6qNif0aw0Sph1cRcdh7/CbW7TkvrWduYoBRPRvAzFgfryPjcOjcHfz+1vKsRMcl4PTVh2hSowxW/HVGKi/jWBgDOtaEno42/IPD8cs6L/x9/t/Zlv1DwjFi3i782KEmNs3uDZUQeOgXhqG//IXXUek9bpqacjgUNYeOIv1P2ZIOVihXIv0LzoPL1SckazZkhfQg8uoViqNv2+rQ0tLAY/+XGDl/Ny76PM0Ue7v6FRAe/Qbn33qMw9rd5/DLsNbYPOcHXLz1FLuO/jsks3H1Mrh8+xlCX3/67Q9En0Imshoj9Q04ePBgruu2bNnyg9uPiYmBUqlEdHQ03hmOTVRg7dm9C+fPn8dvy5bndSgf5dmzpxgyeDB279kLA4PsZ2gjyismxkbS/yt2nJOHkdB/1e/Tu+P6vQCs3Z1z4vW1ONkWwqrJXdDyp1VI+H8PWEGjqSHHgWWDMWnZftx6GJTX4VABdGb9T1LeYmRklGPdb7bnrnXr1mq/y2QytXt13p6dKS3t884aRVRQtW7TFrFxcXjz5g309fXzOpwP9vr1a0ybPp2JHRFRFgx0FbC2NMGwuTvzOhTJ48CXWLbtNIoWMsaTHIaP5meFzZX4w+MCEzv6Jnyzz7lTqVTSz7Fjx+Dq6orDhw8jKioKUVFR8PLyQsWKFXHkyJG8DpUo39DU1MQPP/TJl4kdAFSp8r3akFIiIvpXXEISmgxa/s31kHmevV1gEzsAeP4iEntP3MzrMIgAfMM9d28bMWIE1qxZgxo1akhljRo1gp6eHn788Uc8eMCZiYiIiIiI6L/tm+25e9vTp09hbGycqVypVMLf3/+rx0NERERERPStyRfJXeXKlTFq1Ci8ePFCKnvx4gXGjh2LKlWq5GFkRERERERE34Z8kdz98ccfCA0Nha2tLRwdHeHo6AhbW1sEBwdjw4YNeR0eERERERFRnssX99w5Ojri9u3bOH78OHx9fQEApUqVQv369dVmzSQiIiIiIvqvyhfJHZD+6IOGDRuiVq1aUCgUTOqIiIiIiIjeki+GZapUKsyaNQtFixaFgYEB/Pz8AABTpkzhsEwiIiIiIiLkk+Ru9uzZ2LRpE+bPnw9tbW2pvGzZsli/fn0eRkZERERERPRtyBfJ3ZYtW7Bu3Tp069YNGhoaUnn58uWle/CIiIiIiIj+y/JFchccHAxHR8dM5SqVCikpKXkQERERERER0bclXyR3pUuXxvnz5zOV79mzBxUqVMiDiIiIiIiIiL4t+WK2zKlTp6JXr14IDg6GSqXCvn378PDhQ2zZsgWHDh3K6/CIiIiIiIjyXL7ouWvVqhU8PT1x4sQJ6OvrY+rUqXjw4AE8PT3RoEGDvA6PiIiIiIgoz+WLnjsAqFmzJo4fP57XYRAREREREX2T8kXPHREREREREeUsX/TcmZiYQCaTZSqXyWTQ0dGBo6MjevfujR9++CEPoiMiIiIiIsp7+SK5mzp1KubMmYMmTZqgSpUqAICrV6/iyJEjGDJkCPz8/DBo0CCkpqaif//+eRwtERERERHR15cvkjtvb2/Mnj0bAwcOVCtfu3Ytjh07hr1798LFxQXLli1jckdERERERP9J+eKeu6NHj6J+/fqZyt3d3XH06FEAQNOmTfHs2bOvHRoREREREdE3IV8kd6ampvD09MxU7unpCVNTUwDAmzdvYGho+LVDIyIiIiIi+ibki2GZU6ZMwaBBg3D69Gnpnrtr167By8sLa9asAQAcP34ctWvXzsswiYiIiIiI8ky+SO769++P0qVLY8WKFdi3bx8AwNnZGWfPnoWbmxsAYPTo0XkZIhERERERUZ7KF8kdAFSvXh3Vq1fP6zCIiIiIiIi+SfkiuYuJicmyXCaTQaFQQFtb+ytHRERERERE9G3JF8mdsbFxlg8xz2BtbY3evXtj2rRpkMvzxRwxREREREREn1W+SO42bdqEn3/+Gb1791Z7iPnmzZsxefJkvHr1CgsXLoRCocCkSZPyOFoiIiIiIqKvL18kd5s3b8aiRYvQsWNHqaxFixYoV64c1q5di5MnT8LW1hZz5sxhckdERERERP9J+WIM48WLF1GhQoVM5RUqVMClS5cAADVq1EBgYODXDo2IiIiIiOibkC+SOxsbG2zYsCFT+YYNG2BjYwMACA8Ph4mJydcOjYiIiIiI6JuQL4ZlLly4EB06dMDhw4dRuXJlAMD169fh6+uLPXv2AEh/qHmnTp3yMkwiIiIiIqI8ky+Su5YtW+Lhw4dYu3YtHj58CABo0qQJ9u/fD3t7ewDAoEGD8jBCIiIiIiKivJUvkjsAsLe3x9y5c/M6DCIiIiIiom9SvknuACA+Ph6BgYFITk5WK3dxccmjiIiIiIiIiL4N+SK5e/XqFX744QccPnw4y+VpaWlfOSIiIiIiIqJvS76YLXPEiBGIiorClStXoKuriyNHjmDz5s1wcnLCwYMH8zo8IiIiIiKiPJcveu5OnTqFAwcOoFKlSpDL5bCzs0ODBg1gZGSEuXPnolmzZnkdIhERERERUZ7KFz13b968QaFChQAAJiYmePXqFQCgXLly+Oeff/IyNCIiIiIiom9CvkjunJ2dpUcglC9fHmvXrkVwcDDWrFmDwoUL53F0REREREREeS9fDMscPnw4QkNDAQDTpk1D48aNsW3bNmhra2PTpk15GxwREREREdE3IF8kd927d5f+/9133yEgIAC+vr6wtbWFubl5HkZGRERERET0bcgXyd279PT0ULFixbwOg4iIiIiI6JvxzSZ3o0aNynXdxYsXf8FIiIiIiIiIvn3fbHJ38+bNXNWTyWRfOBIiIiIiIqJv3zeb3J0+fTqvQyAiIiIiIso3vulHITx79gxCiLwOg4iIiIiI6Jv3TSd3Tk5O0gPLAaBTp0548eJFHkZERERERET0bfqmk7t3e+28vLzw5s2bPIqGiIiIiIjo2/VNJ3dERERERESUO990cieTyTLNhsnZMYmIiIiIiDL7ZmfLBNKHZfbu3RsKhQIAkJiYiIEDB0JfX1+t3r59+/IiPCIiIiIiom/GN53c9erVS+337t2751EkRERERERE37ZvOrnbuHFjXodARERERESUL3zT99wRERERERFR7jC5IyIiIiIiKgCY3BERERERERUATO6IiIiIiIgKACZ3REREREREBQCTOyIiIiIiogKAyR0REREREVEBwOSOiIiIiIioAPimH2L+JQkhAAAxMTEwMjLK42iIiKggiImJkf5/Zv1PeRgJEREVFBnXloz8JSf/2eQuNjYWAGBjY5PHkRAREREREeUsNjYWSqUyxzoykZsUsABSqVQICQmBoaEhZDJZXodD9M2KiYmBjY0Nnj9/zl5uIiL6LHhtIco9IQRiY2NRpEgRyOU531X3n+25k8vlsLa2zuswiPINIyMjXoCJiOiz4rWFKHfe12OXgROqEBERERERFQBM7oiIiIiIiAoAJndElCOFQoFp06ZBoVDkdShERFRA8NpC9GX8ZydUISIiIiIiKkjYc0dERERERFQAMLkjIiIiIiIqAJjcERERERERFQBM7oiIiIiIiAoAJndE/2EymQz79+/Pk22fOXMGMpkMUVFROdazt7fH0qVLv0pMRET04fLyWvI58XpDBQGTO6Kv4NKlS9DQ0ECzZs0+eN28vNj07t0bMpkMMpkM2tracHR0xMyZM5GamvrJbbu5uSE0NBRKpRIAsGnTJhgbG2eqd+3aNfz444+fvD0iovwuv19Lfv31V7Xy/fv3QyaTffV4eL2hgozJHdFXsGHDBvz00084d+4cQkJC8jqcD9K4cWOEhobi8ePHGD16NKZPn44FCxZ8crva2tqwsrJ674XdwsICenp6n7w9IqL8Lj9fS3R0dDBv3jxERkbmdSjZ4vWGCgImd0RfWFxcHHbu3IlBgwahWbNm2LRpU6Y6np6eqFy5MnR0dGBubo42bdoAAOrUqYOAgACMHDlS6kEDgOnTp8PV1VWtjaVLl8Le3l76/dq1a2jQoAHMzc2hVCpRu3Zt/PPPPx8cv0KhgJWVFezs7DBo0CDUr18fBw8eBABERkaiZ8+eMDExgZ6eHpo0aYLHjx9L6wYEBKBFixYwMTGBvr4+ypQpAy8vLwDqwzLPnDmDH374AdHR0dJ+Tp8+HYD6t81du3ZFp06d1OJLSUmBubk5tmzZAgBQqVSYO3cuHBwcoKuri/Lly2PPnj0fvN9ERN+S/H4tqV+/PqysrDB37twc63l7e6NmzZrQ1dWFjY0Nhg0bhjdv3kjLQ0ND0axZM+jq6sLBwQHbt2/P1Cu5ePFilCtXDvr6+rCxscHgwYMRFxcHALzeUIHH5I7oC9u1axdKliwJZ2dndO/eHX/88QeEENLyv//+G23atEHTpk1x8+ZNnDx5ElWqVAEA7Nu3D9bW1pg5cyZCQ0MRGhqa6+3GxsaiV69e8Pb2xuXLl+Hk5ISmTZsiNjb2k/ZHV1cXycnJANKH2ly/fh0HDx7EpUuXIIRA06ZNkZKSAgAYMmQIkpKScO7cOdy5cwfz5s2DgYFBpjbd3NywdOlSGBkZSfs5ZsyYTPW6desGT09P6SINAEePHkV8fLz0R8zcuXOxZcsWrFmzBvfu3cPIkSPRvXt3nD179pP2m4goL+X3a4mGhgZ++eUXLF++HEFBQVnWefr0KRo3box27drh9u3b2LlzJ7y9vTF06FCpTs+ePRESEoIzZ85g7969WLduHV6+fKnWjlwux7Jly3Dv3j1s3rwZp06dwrhx4wDwekP/AYKIvig3NzexdOlSIYQQKSkpwtzcXJw+fVpaXq1aNdGtW7ds17ezsxNLlixRK5s2bZooX768WtmSJUuEnZ1dtu2kpaUJQ0ND4enpKZUBEB4eHtmu06tXL9GqVSshhBAqlUocP35cKBQKMWbMGPHo0SMBQFy4cEGq//r1a6Grqyt27dolhBCiXLlyYvr06Vm2ffr0aQFAREZGCiGE2Lhxo1AqlZnqvb3/Gcdvy5Yt0vIuXbqITp06CSGESExMFHp6euLixYtqbfTt21d06dIl2/0kIvrWFZRrSdWqVUWfPn2EEEJ4eHiIt/8U7du3r/jxxx/V1j1//ryQy+UiISFBPHjwQAAQ165dk5Y/fvxYAMi0b2/bvXu3MDMzk37n9YYKMvbcEX1BDx8+xNWrV9GlSxcAgKamJjp16oQNGzZIdXx8fODu7v7Zt/3ixQv0798fTk5OUCqVMDIyQlxcHAIDAz+onUOHDsHAwAA6Ojpo0qQJOnXqhOnTp+PBgwfQ1NTE999/L9U1MzODs7MzHjx4AAAYNmwYZs+ejerVq2PatGm4ffv2J+2TpqYmOnbsiG3btgEA3rx5gwMHDqBbt24AgCdPniA+Ph4NGjSAgYGB9LNlyxY8ffr0k7ZNRJRXCsK1JMO8efOwefNm6Trxtlu3bmHTpk1q5+9GjRpBpVLBz88PDx8+hKamJipWrCit4+joCBMTE7V2Tpw4AXd3dxQtWhSGhobo0aMHwsPDER8fn+s4eb2h/EozrwMgKsg2bNiA1NRUFClSRCoTQkChUGDFihVQKpXQ1dX94HblcrnacBwA0lDIDL169UJ4eDh+++032NnZQaFQoFq1atKQytyqW7cuVq9eDW1tbRQpUgSamrk/bfTr1w+NGjXC33//jWPHjmHu3LlYtGgRfvrppw+K4W3dunVD7dq18fLlSxw/fhy6urpo3LgxAEjDZ/7++28ULVpUbT2FQvHR2yQiyksF4VqSoVatWmjUqBEmTpyI3r17qy2Li4vDgAEDMGzYsEzr2dra4tGjR+9t39/fH82bN8egQYMwZ84cmJqawtvbG3379kVycvIHTZjC6w3lR0zuiL6Q1NRUbNmyBYsWLULDhg3VlrVu3Rp//fUXBg4cCBcXF5w8eRI//PBDlu1oa2sjLS1NrczCwgJhYWEQQkg3xvv4+KjVuXDhAlatWoWmTZsCAJ4/f47Xr19/8H7o6+vD0dExU3mpUqWQmpqKK1euwM3NDQAQHh6Ohw8fonTp0lI9GxsbDBw4EAMHDsTEiRPx+++/Z5ncZbWfWXFzc4ONjQ127tyJw4cPo0OHDtDS0gIAlC5dGgqFAoGBgahdu/YH7ysR0bemoFxL3vbrr7/C1dUVzs7OauUVK1bE/fv3s7zmAICzszNSU1Nx8+ZNfPfddwDSe9DenoHzxo0bUKlUWLRoEeTy9AFqu3btUmuH1xsqyJjcEX0hhw4dQmRkJPr27Ss9yy1Du3btsGHDBgwcOBDTpk2Du7s7ihcvjs6dOyM1NRVeXl4YP348gPTZu86dO4fOnTtDoVDA3NwcderUwatXrzB//ny0b98eR44cweHDh2FkZCRtw8nJCVu3bkWlSpUQExODsWPHftQ3u9lxcnJCq1at0L9/f6xduxaGhoaYMGECihYtilatWgEARowYgSZNmqBEiRKIjIzE6dOnUapUqSzbs7e3R1xcHE6ePIny5ctDT08v229Yu3btijVr1uDRo0c4ffq0VG5oaIgxY8Zg5MiRUKlUqFGjBqKjo3HhwgUYGRmhV69en23/iYi+hoJ4LSlXrhy6deuGZcuWqZWPHz8eVatWxdChQ9GvXz/o6+vj/v37OH78OFasWIGSJUuifv36+PHHH7F69WpoaWlh9OjR0NXVlZJTR0dHpKSkYPny5WjRogUuXLiANWvWqG2H1xsq0PLyhj+igqx58+aiadOmWS67cuWKACBu3bolhBBi7969wtXVVWhrawtzc3PRtm1bqe6lS5eEi4uLUCgUajeer169WtjY2Ah9fX3Rs2dPMWfOHLWb4P/55x9RqVIloaOjI5ycnMTu3bsz3VCPD7gJPisRERGiR48eQqlUCl1dXdGoUSPx6NEjafnQoUNF8eLFhUKhEBYWFqJHjx7i9evXQojME6oIIcTAgQOFmZmZACCmTZsmhMh6EoD79+8LAMLOzk6oVCq1ZSqVSixdulQ4OzsLLS0tYWFhIRo1aiTOnj2b7X4QEX2rCuq1xM/PT2hra4t3/xS9evWqaNCggTAwMBD6+vrCxcVFzJkzR1oeEhIimjRpIhQKhbCzsxPbt28XhQoVEmvWrJHqLF68WBQuXFi6Lm3ZsoXXG/rPkAnxzmBrIiIiIqJ8ICgoCDY2NtIkKkT/dUzuiIiIiChfOHXqFOLi4lCuXDmEhoZi3LhxCA4OxqNHj6T74Yj+y3jPHRERERHlCykpKZg0aRKePXsGQ0NDuLm5Ydu2bUzsiP6PPXdEREREREQFAB9iTkREREREVAAwuSMiIiIiIioAmNwREREREREVAEzuiIiIiIiICgAmd0RERERERAUAkzsiIiIiIqICgMkdERERERFRAcDkjoiIiIiIqABgckdERERERFQAMLkjIiIiIiIqAJjcERERERERFQBM7oiIiIiIiAoAJndEREREREQFAJM7IqJv2PTp0yGTyaQfMzMz1KhRA15eXnkWU506ddC8efMPXm/p0qVZxm1vb4+hQ4d+jtByLS0tDStWrEDFihWhp6cHpVIJd3f3jz6uUVFRmD59Ou7fv/+ZI817d+7cgaGhIV69epVpmYeHB2QyGdzd3T+q7TNnzuCXX3751BBzNH36dBgYGEi/X7hwAebm5oiJifmi2yUiygtM7oiIvnG6urq4dOkSLl26hN9//x2JiYlo0aIFLl68mNehfZDskjsPDw+MGTPmq8WhUqnQrl07jBo1CvXq1YOnpyf+/PNPGBsbo1mzZli0aNEHtxkVFYUZM2YUyORu8uTJ6N27NywsLDIt27ZtG4D0JC0kJOSD2/4ayd27qlevjjJlynzU60xE9K1jckdE9I2Ty+WoWrUqqlatirZt2+LAgQMQQmDz5s15HdpnUaFCBdjb23+17a1YsQIHDhzAunXrsHDhQri7u6NFixbYu3cvevbsifHjx8PHx+erxfMte/bsGTw9PdGnT59My2JiYvD333+jfv36UKlU2LFjRx5E+HH69u2L1atXIyUlJa9DISL6rJjcERHlM0WLFoWFhQUCAwPVyi9duoR69epBX18fSqUSXbt2xcuXL9Xq/Prrr3B0dISOjg4sLCxQv359+Pn5ScsjIiLQp08fmJubQ1dXF25ubjh37lyO8fTu3Rtly5ZVK4uKioJMJsOmTZsApA+9DAgIwMqVK6Uhpm8ve3dY5r59++Dq6godHR0UKVIEo0aNQmJiorT8zJkzkMlkOH78OLp27QpDQ0PY2dlh/vz57z1+S5cuhbOzM3r27Jlp2cyZMyGTybB8+XKpLKv49u/fD5lMBn9/f/j7+8PBwQEA0KFDB2n//P39AQBJSUmYPHkyihUrBoVCAWtra/Tu3fuj9vfo0aPo2LEjDAwMYGtri+3btwMAli1bBltbW5iamqJfv35ISkpSaz8oKAjdu3eXXtdatWrhxo0b7z1WW7ZsQbFixVChQoVMy/bt24fExERMnz4d3333ndSL9zaVSoXFixejVKlSUCgUsLKyQocOHRAdHY3p06djxowZePPmjXTM6tSpAyB376mM+GrUqAFTU1OYmJigTp06uHr16nv3q3Xr1oiKisrT4c1ERF8CkzsionwmLi4OERERUkIBpCd2derUgVKpxM6dO7Fu3Tpcu3YNrVq1kups2bIFU6ZMQd++fXHkyBGsX78erq6u0r1HaWlpaNKkCTw9PTFv3jzs3r0bBgYGaNCgQa4SgZx4eHjAysoK7du3l4aYNmvWLMu6Bw8eRPv27VG6dGns378f48aNw5o1a9C9e/dMdQcOHIgSJUrAw8MDLVq0wPjx43HkyJFs43j+/Dn8/PzQrFkzyOWZL4F2dnZwcXF5b0L7tsKFC2Pfvn0AgF9++UXav8KFCwMA2rVrh8WLF6NPnz74+++/sWDBArx58+aj9nfQoEEoW7YsPDw8ULVqVfTo0QPjx4/H0aNHsWbNGsycORNbtmxRG3IYGRmJGjVqwMfHB8uXL8fevXuhr6+PevXqZUr+33XixAm4ublluWzbtm2wt7eHm5sbunbtin/++QcPHz5Uq/PTTz9h3LhxaN68OTw9PbFy5UoYGhoiLi4O/fr1Q9++fdWGHa9atSp3B/3//P390bNnT+zevRvbt2+Hra0tatWqhUePHuW4npGREcqUKYPjx49/0PaIiL55goiIvlnTpk0T+vr6IiUlRaSkpIiAgADRqVMnYWJiInx9faV6tWrVEm5ubkKlUkll9+7dEzKZTPz9999CCCGGDBkiKlasmO22Dhw4IACII0eOSGXJycnC1tZWtG3bViqrXbu2aNasmfR7r169RJkyZdTaioyMFADExo0bpTI7OzsxZMiQTNt9t7xChQqiWrVqanXWrl0rAIjbt28LIYQ4ffq0ACDGjh0r1VGpVMLe3l707ds32328dOmSACCWLl2abZ3WrVsLHR2dHOP28PAQAISfn58QQgg/Pz8BQOzevVut3rFjxwQAsX379my39yH7O27cOKlOVFSU0NDQEDY2NiI5OVkqb9eunXB1dZV+nzp1qlAqleLFixdSWWJiorC1tVU7fu9SqVRCoVCIBQsWZFoWGhoqNDQ0xIQJE4QQQgQHBwu5XC6mTJki1Xn48KGQyWTil19+yXYbGe/vd+X2PfW2tLQ0kZKSIpydncXEiRNztY1KlSplGxsRUX7Enjsiom/cmzdvoKWlBS0tLdjZ2WHPnj3YunUrnJ2dAQDx8fG4cOECOnTogLS0NKSmpiI1NRUlSpSAjY0Nrl27BgCoWLEibt68iVGjRsHb2zvT/Ubnz5+HkZERGjVqJJVpaWmhbdu28Pb2/ir7GhcXBx8fH7Rv316tvFOnTgCQKY6GDRtK/5fJZChVqhSCgoK+fKC5dPLkSejp6aFz585ZLv/Q/W3QoIH0f6VSiUKFCqFWrVrQ0tKSykuUKIHnz59Lvx87dgx169aFqamp9N7Q0NBA7dq1pfdGViIjI5GUlJTlRCo7d+5EWloaunbtCgAoUqQIateuLQ0TBYBTp05BCIG+fftmu41P9eDBA7Rp0waWlpbQ0NCAlpYWHj58+N6eOwAwNzdHaGjoF4uNiCgvMLkjIvrG6erq4tq1a7hy5Qr+/PNPFC5cGD179pT+MI2MjERaWhpGjhwpJYEZP4GBgdIf+r1798aSJUtw9OhR1KxZExYWFhg+fDgSEhKkdgoVKpRp+5aWloiIiPgq+xoVFQUhBCwtLdXKlUolFApFpjiMjY3VftfW1la7V+1dRYsWBYBM9yu+LTAwENbW1h8YedbCw8NRuHBhyGSyLJd/jv193zF4/fo19u/fn+m9sXXrVrUk8F0ZbSgUikzLtm3bBmdnZ9jY2CAqKgpRUVFo2bIlnj59iitXrkj7rqmpmeV76nOIjY1Fw4YNERAQgMWLF+P8+fO4du0aypcvn+N7IINCoZDe+0REBYVmXgdAREQ5k8vlqFSpEgCgSpUqcHZ2xvfff4+ZM2di9erVMDY2hkwmw6RJk9C6detM65ubm0vtDB8+HMOHD0dwcDB27NiBCRMmwNzcHFOmTIGpqWmW92C9ePECpqam2cano6OD5ORktbLIyMiP2teMfXk3jujoaCQlJeUYR27Y2NjAwcEBhw8fxsKFCzMlXYGBgbh9+7baZCufsn9mZmYIDQ2FECLLBO9L7y8AmJqaonHjxpg1a1amZVklbm+vB6QnoG978uSJ1ONnYmKSab1t27bh+++/h5mZGVJTU/Hy5csPTvByc8wvXbqEoKAgHDp0COXLl5fKo6Ojc5WcR0VFwczM7IPiIiL61rHnjogon6lUqRK6dOmCjRs3IiwsDPr6+qhWrRoePHiASpUqZfrJ6jEDRYsWxejRo+Hi4oIHDx4AAGrUqIGYmBgcO3ZMqpeamgoPDw/UqFEj23isra0RFBSEuLg4qeztNjK8r1cNAAwMDODq6oo9e/aole/atUuK8VONGDECDx48wNatWzMtmz59OoQQ+Omnn6Qya2tr6RhleHf/tLW1ASDT/tWvXx/x8fFS/O/6Gvtbv3593L9/H6VKlcr03ihXrly26+no6MDW1lZtNlUA2L59O2QyGTw8PHD69Gm1n0aNGklDNuvVqweZTIaNGzdmuw1tbe1MM3sCuXtPZfS6ZRx7ALh48aI0S+n7+Pv7S0ObiYgKCvbcERHlQ1OmTMGOHTuwdOlS/Prrr1iwYAHq1auHTp06oXPnzjAxMUFQUBCOHz+OH374AXXq1MGAAQNgYmKCqlWrwsTEBBcuXMCtW7cwePBgAECzZs1QpUoVdO/eHb/++issLS2xfPlyhIaGYtKkSdnG0rZtW0ydOhV9+vRB//79ce/ePaxfvz5TvVKlSuHUqVM4fvw4TExM4ODgkGXPyfTp09G6dWt0794d3bt3x8OHDzFp0iS0a9cux2Qkt4YOHYpTp06hX79+uHPnDpo0aYKEhARs2rQJe/bswcKFC+Hq6irVb9++PQYNGoQZM2bAzc0NXl5euHTpklqbVlZWMDY2xl9//QUHBwcoFAq4uLigfv36aNq0Kfr06YOnT5/i+++/R0REBPbs2YOdO3d+lf0dNWoUtm3bhtq1a2P48OGwtbXFq1evcOXKFRQpUgQjR47Mdt3q1atnmil1+/btqFmzZpa9xDExMWjVqhVOnDiBRo0aYeDAgZg8eTIiIiLg7u6O+Ph4/P3335g+fTqKFi2KUqVKITU1Fb/99hvc3NxgZGQEZ2fnXL2nqlatCgMDAwwZMgQTJkxAcHAwpk2bJg29fZ/r169j9OjRuapLRJRv5Ol0LkRElKPsZvoTQohu3boJIyMjERUVJYQQ4tq1a6Jp06ZCqVQKXV1d4eTkJAYOHCieP38uhBBi06ZNonr16sLU1FTo6OiI0qVLi2XLlqm1+fr1a9G7d29hamoqFAqFqFatmjhz5oxanXdnyxRCiC1btghHR0ehq6srGjRoIHx8fDLNbHj37l1Rs2ZNYWhoqLYsq9ko9+zZI1xcXIS2trawsrISI0aMEAkJCdLyjNkjr127prZeq1atRO3atXM+qEKI1NRUsWzZMuHq6ip0dXWFkZGRqFu3rjSz6NtSUlLEmDFjhKWlpVAqlWLAgAFi+/btarNlCpE+g2apUqWEQqFQW5aQkCAmTJggbG1thZaWlrC2thZ9+vT5LPub1bHL6j0TGhoq+vbtKwoXLiy0tbWFtbW1aN++vbhw4UKOx2nv3r1CR0dHxMTECCGEuH79ugAg1q9fn2X95ORkYWFhIXr06CGESJ/Bcv78+cLJyUloaWkJKysr0alTJxEdHS0d28GDBwtLS0shk8nUXrvcvKcOHz4sypQpI3R0dISLi4vw8vLK9P7M6njcuHFDyGQy8eTJkxz3n4gov5EJIUTepZZERET0rUpJSYGtrS3mzZuX5UPf86uxY8fixo0bOHXqVF6HQkT0WTG5IyIiomz99ttv2LJlyyc/yP5bERMTAzs7Oxw4cAC1atXK63CIiD4r3nNHRERE2Ro4cCBiYmLw+vVraebV/CwwMBCzZs1iYkdEBRJ77oiIiIiIiAoAPgqBiIiIiIioAGByR0REREREVAAwuSMiIiIiIioAmNwREREREREVAEzuiIiIiIiICgAmd0RERERERAUAkzsiIiIiIqICgMkdERERERFRAcDkjoiIiIiIqABgckdERERERFQAMLkjIiIiIiIqAJjcERERERERFQBM7oiIiIiIiAoAJndEREREREQFAJM7IiIiIiKiAoDJHRERERERUQHA5I6IiIiIiKgAYHJHRERERERUADC5IyIiIiIiKgCY3BERERERERUATO6IiIiIiIgKACZ3REREREREBQCTOyIiIiIiogKAyR0REREREVEBwOSOiIiIiIioAGByR0REREREVAAwuSMiIiIiIioAmNwREREREREVAEzuiIiIiIiICgAmd0RERERERAUAkzsiIiIiIqICgMkdERERERFRAcDkjoiIiIiIqABgckdERERERFQAMLkjIiIiIiIqAJjcERERERERFQBM7oiIiIiIiAoAJndEREREREQFAJM7IiIiIiKiAoDJHRERERERUQHA5I6IiIiIiKgAYHJHRERERERUADC5IyIiIiIiKgCY3BERERERERUATO6IiIiIiIgKACZ3REREREREBQCTOyIiIiIiogKAyR0REREREVEBwOSOiIiIiIioAGByR0REREREVAAwuSMiIiIiIioAmNwREREREREVAEzuiIiIiIiICgAmd/TJZDIZfHx88mTbvXv3xogRI7Jctm3bNri5uX3dgOizqlOnDs6cOZPXYXywM2fOwNjYWPq9Tp06WLp0aZ7F863LL69zYGAgDAwMEB0d/Vnq0cfJL+8XylubNm1C79698zoMoq+OyV0BZWBgIP1oaGhAoVBIvzdp0iTb9XJKlj7Gpk2boKGhIW27cOHCGDx4MJKSkj7bNrLTrVs3XLx48Ytuw9PTE7Vq1YKhoSHMzMxQpUoVrFmz5otuM4O9vT3279//UesKIVCjRg3IZDJERUUBAJKSktC/f384ODjA0NAQJUuWxB9//JFp3fXr18PZ2Rn6+vqwt7fHgQMHAACPHj1CmzZtYGVlBWNjY1SvXh0XLlz42N37LOzt7aGrqwsDAwOYm5ujWbNmePLkSZ7GlBtTpkxBuXLloKmpmenz+DHH+cGDB6hevTr09PRQokQJHDx4MNfLY2Ji0KxZMyiVSjRv3hxxcXHSsh07dqBHjx6fvsOf6N3XuUWLFnj69Oln346trS3i4uKgVCo/S73PITef25iYGHTt2hVGRkawtLTErFmz3ttudp9zADh+/DgqVqwIQ0NDlC5dGkeOHFHbVn56v2T8HDp0CACwYsUKVKpUCQqFAq1bt35vW1euXEHdunVhYmICY2NjuLi4YNOmTV92Bz7S3bt30ahRI5ibm6ud+zO8e702MDDA/Pnzs23v9OnTqFu3LpRKpdqXWRlevnyJzp07w8LCAhYWFhgzZgzS0tKk5UuXLkWhQoXg6OiIc+fOSeVRUVEoU6YMXr169cn7/CmmT58OTU1NtePRpUsXAMDmzZtRpUoVKJVKFC5cGH379s10PN8WGxuLwYMHo2jRojAwMICNjQ06d+78lfbkw/34449wdnaGXC7P9MVkUFAQ3NzcYGZmBqVSCVdXV3h4eGTbVmhoKFq2bIkiRYpk2xmwf/9+ODk5QU9PDzVq1ICvr6+07O7du3BxcYGpqSkmTJigtt7AgQOxYcOGT9rXgojJXQEVFxcn/dSsWRPz5s2Tfj98+PBXjaVcuXLStm/cuIELFy5g4cKFXzWGL2H16tXo1asX+vfvj6CgILx+/RqrV6+W/kj4lq1atQoKhUKtLDU1FYULF8aJEycQExODTZs2YfTo0Th27JhUZ926dVi0aBF27NiBuLg4XLlyBeXKlQOQfkFu0qQJ7ty5g/DwcPTu3RtNmzbF69evv+q+veuvv/5CXFwc/P39YWxsjD59+uRpPLnh6OiI+fPno2XLlpmWfehxTklJQYsWLeDu7o6IiAgsXrwYXbt2lZLc9y1fu3YtjIyMEB4eDl1dXaxdu1aKY9asWViyZMkXOgofJuN1fvbsGfT09NCzZ88s66Wmpn7lyL6s3Hxuf/rpJ0RERCAwMBDnz5/H77//ji1btmTbZk6f82fPnqFNmzaYOXMmoqOjMX/+fLRr1w7Pnj0DkP/eLxk/zZs3BwAUKVIEkydPRv/+/d/bRmxsLBo3boxOnTrh5cuXePXqFTZs2IBChQp99nhTUlI+uQ0tLS107Ngxx+Tz7et1XFwcxo0bl21dfX199OnTB4sXL85yeY8ePaBQKBAQEIBbt27h5MmTmDdvHgAgLCwMs2fPxq1bt7B48WIMGTJEWm/8+PEYM2YMLCwsPm5HP6OMLygyfv766y8AQHx8PObPn48XL17g3r17CA0NxeDBg7NtZ+TIkfD398c///yDuLg4XLp0CXXq1Pns8aampkII8cntlC9fHqtWrUKVKlUyLTMxMcGmTZvw6tUrREdHY9WqVejevTv8/PyybEsul6Nx48bZfhn98OFDdOvWDUuWLEFERATq1auHVq1aSefq8ePHY9CgQfDz88OuXbtw48YNAMCFCxfw6NGjfHFN/+oEFXi1a9cWS5YskX4/evSocHV1FUZGRqJChQri+PHjQgghfvvtN6GpqSm0tLSEvr6+KF26tBBCiK1bt4oyZcoIAwMDYWNjIyZPnixUKpXUHgBx8+bNLLe9ceNGUb58ebWysWPHii5duki/h4WFiQ4dOghzc3NhY2MjJk2aJFJSUt4brxBC9OrVSwwfPlwIIURKSoro1auXcHd3FzExMZm2bWdnJ+bNmye+//57YWBgIGrVqiUCAwOl5Xfv3pWW1alTR4wdO1bUrl07y/2KiYkRhoaGYuvWrVkuz03s774uN2/eFG9/JGvXri0mTJggGjZsKAwMDESFChXE7du3hRBCtG/fXshkMqGjoyP09fXFgAEDcozjbYGBgaJYsWLi+vXrAoCIjIzMtm6bNm3ElClThBBCpKamCktLS3H06NFcb8vExEScPHky1/XfVbt2bXH69OmPXt/Ozk54eHhIvx86dEjo6elJvycnJ4spU6aIYsWKCVNTU9GiRQsRHBwsLQ8NDRXdunUTVlZWQqlUipo1a4r4+HghRPr72NbWVhgYGIhSpUqJXbt2SeudPn1aKJVKtf14+7XOrbff3znJ6TifOHFCGBsbi+TkZKmsadOmYurUqblaPnDgQLFmzRohhBCrV68WgwYNEkII0b9/f7Fx48YP3qesfO7X2dPTUxgYGEhtjx07VjRo0EDo6emJgwcPitjYWDFkyBBhY2MjLCwsRI8ePURUVJS0/qNHj0SLFi2Eubm5MDExEW3atBFCCOHn56f2mTl27JgoV66cMDAwEIUKFRIDBw7Msl5ycrKYMGGCsLGxEebm5qJjx47i5cuX0vYAiNWrV4syZcoIQ0ND0aJFC7V4PtTbn9s3b94IbW1tce3aNWn5/PnzRa1atbJc932f85UrV4qaNWuqldWpU0dMmzZNCJE/3y9ZmTZtmmjVqlWOda5duya0tLREWlpatnVyOoc8fvxYNGzYUJiYmIhixYqpnSMyrl9Tp04VlpaWom3btkIIIf766y9Rrlw5oVQqRaVKlcSFCxdytc9ve/f9+e42P9S75zshhIiLixMymUw8ffpUKtu0aZOws7MTQghx+fJlUbVqVSGEEAkJCUJXV1cIIYS3t7eoW7fuB8eQlY0bN4pevXp99Pq5eQ9kOHDggLCxscl2eZkyZcSWLVuyXZ6WliZ+++034ezsLAwMDISjo6M4fPiwECJ354/ly5eLMmXKCG1tbRETEyOePHkimjdvLszNzYWtra2YNWtWju/T7Lzv2qVSqcSlS5eEQqEQp06dem97Wf29OHnyZNGsWTPp9+TkZGFsbCy1V7JkSeHr6yuEEKJTp05i586dIjk5WVSsWFEqJ3XsufuPefLkCVq1aoUpU6YgPDwckyZNQsuWLeHn54dhw4ahW7duGDx4MOLi4nDv3j0AgJmZGfbt24eYmBgcPHgQ69atw/bt2z9q+8+fP8eRI0dQvXp1qaxr167Q0tKCn58fzp8/j/3790tDQXKK921v3rxBy5YtkZCQAC8vLxgaGma5/T///BN//fUXXr16BX19fUyZMgVA+reiLVu2RJMmTRAeHo5ff/01yyGJGS5duoT4+Hh07Ngx2zq5jT0nW7duxfz58xEZGYlKlSrhp59+AgDs3r0btra20rfPGUNBBw8enOO3hwAwaNAgTJ8+HWZmZjnWS0xMxNWrV+Hi4gIg/du1Fy9e4J9//oG9vT2sra3Rv39/xMTEZLn+nTt3EBsbi9KlS+d6f7+k6OhobN26FSVKlJDKfv75Z1y4cAHe3t4IDQ1FiRIlpKEyKpUKLVq0gKamJu7fv4/Xr1/jl19+gVyeftosX748rl27hqioKEydOhU9evTI9Wvr4uLy0Z+hd73vON++fRtlypSBlpaWVObq6orbt2/nanm5cuVw6tQpJCUl4fTp0yhXrhy8vb3x9OnTb/J+lqioKGzZsgUVK1aUyjZt2oTZs2cjLi4O9evXR58+fRAREYHbt2/Dz88PKSkpGDp0KID0c0n9+vVRtmxZ+Pv7IywsTPrcvatXr14YO3YsYmNj8ezZs2yHHM6dOxeHDh2Ct7c3/Pz8IJPJ0K1bN7U6u3btwqlTpxAYGIigoCC1Hq7mzZvj119/zdX+Z/W5TU5Ohqurq1Tn7df3Xe/7nKtUqkw9AyqVKt++Xz5FiRIloFQq0blzZxw4cABhYWFqy3M6h6SmpqJ58+YoX748QkJC4OHhgfnz56udF+7evQtNTU0EBgZi69at8PLywpgxY7Bp0yZERERg4sSJaNGiBcLDwwEA27dvl173j/Xw4UMUKlQIDg4OGDx4cI5DDXMihJB+3j4eAQEBiImJgZOTE/z8/BAUFITjx4+jXLlySElJwbBhw77arQ2f09mzZ3M89tWrV8fMmTOxbt063L59O9NnaMWKFVi6dCm2bduGmJgYnDx5EnZ2dgByd/7Yvn07jh07hpiYGGhoaMDd3R3u7u4IDg7G+fPnsWPHDmzcuFGqb2xsDG9v70/aZxcXFygUClSrVg3Vq1dHzZo1P6qd27dvq52ftLS0ULp0abVzyvHjxxEVFYUbN26gbNmy0sgWZ2fnT9qHAitPU0v6Kt7+5mX27NmicePGassbNGgg5syZI4TIXU/B8OHDRb9+/aTf8Z6eO7lcLpRKpTAyMhIAhJubm4iOjhZCCBEUFCQAiLCwMGmdbdu2CScnp1zH261bN1GlShXx008/qX0zlVXP3erVq6Xf//zzT1G2bFkhhBDnzp0TSqVSrcdw8ODB2fbc/fnnn8LS0jK7Q5Sr2HPTczd+/Hjpd29vb6k3ImN/3vft87u2b98u3N3dhRDZf3srRPq3cd26dRN16tSRjun58+cFAOHu7i5evXolXr16Jdzd3UWfPn0yrR8ZGSlKly4t9f58rM/xDb2enp703itRooS4e/euECJ9H/X19YWPj49UPyEhQcjlchEYGCguX74s9PX1pW/Z36d8+fLizz//FEJ8vZ673BznmTNnqn0rKkR6z03G++B9yxMTE8WgQYNE2bJlxaBBg0RMTIyoUKGCePTokVi5cqWoVauWaNOmjVqP54f6XK+zsbGxKFKkiGjXrp3w9/eX2n77GL58+VLI5XIREREhlT169EhoaWmJ1NRUsWPHDlG8eHG10QkZ3v3M2NraiqlTp6p9i55VPUdHR7Fjxw5peXBwsAAgHTMA0rf0QqSfO5o3b/7BxyGrz+25c+eEvr6+Wr2rV68KDQ2NLNt43+fc19dXKBQK4eHhIVJSUoSHh4fQ0NDIl+8XpVIplEqlKFasWKY6ue21efz4sRgwYIAoVqyYkMlkokqVKuLGjRtCCJHjOcTb21sYGRmJpKQkqWzOnDmiQYMGQoj065epqanaNa1p06Zi6dKlau24ubnl2COUlezO/U+fPhWPHz8WaWlp4tmzZ8Ld3V20bNnyve1l1XMnhBC1atUS3bp1E7GxsSIgIECUL19eABDPnz8XQgixa9cu8d1334m6deuK27dvi1mzZomZM2eKu3fvisaNG4vatWuLffv2fdC+ve1z9NxpampK7xOlUimOHDmSqZ6Xl5cwMjKSRtZkJTExUSxcuFBUq1ZNKBQKYWFhIRYtWiQtL1mypNi8eXOW6+bm/PH23wK7du0Srq6uam2sW7dO1KtXL1f7/bb3XbuSkpKEp6enWLBgQZbnzHdl9fdivXr1xIIFC9TKmjZtKmbNmiWESB9t1Lx5c1G+fHmxfPly8fjxY1GhQgVpBEbNmjXF0KFD1Uaf/Nex5+4/JigoCPb29mplxYoVQ1BQULbrHD16FG5ubjA3N4dSqcSaNWs+6D6qcuXKISoqCtHR0YiNjUWVKlXQuHFjKR4dHR1YWlpmGU9u4j1x4gSePn2KiRMnSr0q2bGyspL+r6+vj9jYWABASEgIChcuDE1NTWm5ra1ttu2Ym5vj9evXSE5OzrbOxxzr98X79uQEHyoiIgITJkzA6tWrc6wnhMDgwYPx8OFD7N+/XzqmBgYGAICJEyfC3Nwc5ubmmDhxIjw9PdXWj46ORqNGjVCjRg1Mnz79o+P9XLZt24bo6Gj4+voiNTVVmmjj9evXePPmDWrVqgVjY2MYGxvDysoK2traeP78OQICAlC0aFHo6upm2e6SJUtQpkwZaTKBu3fvftX7C3N7nLOatTE6Olrq3X7fcoVCgVWrVuHOnTtYtWoVli5dirZt2yIlJQUrV67EsWPH0LJlS4wePfrz7uAH2rZtGyIjIxEcHIw9e/ZI33oD6p9lf39/qFQqODg4SK975cqVIZfLERYWhoCAABQvXhwymey92/Tw8MDdu3fh7OyMChUqYNeuXVnWe/dcUKRIESgUCrVzQXbnptzK6XMbHx+vdq/h26/vu973OXd2dsbOnTsxY8YMFCpUCBs2bEDnzp2lkQD56f0SFRWFqKioT5p8x9HREWvWrMHTp08RFBQER0dHtGzZEkKIHM8hQUFBKFKkCLS1taWyd68PRYsWVbum+fv7Y9KkSdL71tjYGD4+PggODv7o+N9WrFgxODo6Qi6Xw8HBAcuWLcOhQ4cQHx//Ue1t27YNCQkJcHR0RP369dG1a1fIZDKYmJgAADp06IDr16/j1KlT0NHRwb59+zB+/Hj07dsXEydOhIeHB4YNG4bIyMjPsn8fo1mzZtL7JCoqCo0aNVJbfurUKXTv3h379u2T7kvNikKhwOjRo3Hx4kVER0dj8eLFmDBhgnRvbEBAAJycnLJcNzfnj3fPcXfv3lV7n4wePTpTz/LnoK2tjebNm+P06dPYtm3bR7XxvmuQjY0NPD094ePjg6FDh2LQoEH47bff8OeffyI+Ph7nzp1DbGxsjqOt/muY3P3HWFtbw9/fX63M398f1tbWAJApOUpOTkbbtm0xYMAABAcHIzo6GgMHDvzoG3YNDAzQt29fXLp0CeHh4bC2tkZiYiJevHiRZTzvixcAOnfujCFDhqBOnToffZErUqQIwsLC1P4ACgwMzLa+m5sb9PT0sHv37mzrvC/2jD+6MoSGhn5QzO9LZN91+/ZthISEoFq1ajA3N5eGrRUvXhx79uwBkP4H4pAhQ3DlyhUcO3ZMbbY/Z2dn6Ojo5LiNjISjTJkyWLNmTa7+QP5anJ2dsXDhQgwaNAgJCQkwMzODnp4erly5onbxTkhIgJubG+zs7BAcHIzExMRMbXl7e2P69OnYsmULIiMjERUVhbJly36WG9lz40OOs4uLC+7du6c2IYOPj4/0h8j7lr/t0aNH2L9/P8aNG4c7d+6oDcu5devWZ9zDz+vtz4qNjQ3kcjlCQkLUXvfExEQULVoUdnZ2ePr0aa5ey4oVK2Lv3r14/fo1pkyZgq5du6qdyzK8ey4ICwtDUlKS2nnsU7zvc6ulpaX2+mT3+mbUf9/nvFWrVrh58yYiIiLg6emJx48fo3bt2pnq5df3y8cqUqQIJkyYgODgYEREROR4DrG2tkZISIja5+7da9u753gbGxssWrRI7X375s2bTDMIfi4Z2//Y85q1tTX27t2LsLAwPHr0CIaGhqhUqRL09fUz1R00aBCWLVsGbW1t3Lp1C99//z1MTExgbW2Nx48ff9J+fCmnTp1C+/btsX37dri7u+d6PYVCge7du6NcuXK4c+cOAMDOzi7bmZxzc/549xz33Xffqb1PYmJipFttvoSUlJSPfp1cXFzUZtBMSUnB/fv3szxHbdmyBfb29qhZs6b0PgFQYM8pH4vJ3X9Mp06dcObMGRw4cACpqanYt28fzp07J91nZGlpiWfPnkkn86SkJCQmJsLMzAwKhQJXrlz5pHuFEhISsHHjRhQpUgSmpqYoWrQo6tatizFjxuDNmzcIDAzEnDlz0KtXr1zFm2HGjBno1q0b6tSpg+fPn39wXFWrVoWxsTHmzp2LlJQUXLt2Ldtv4QHA0NAQ8+bNw7Bhw6Qx8kII+Pj4SDMcvi/2ihUrYt++fYiOjsbLly9znHI6K5aWlh/0jXO1atXg5+cHHx8f+Pj4wMvLCwBw/vx5NG3aFAAwdOhQXLhwAcePH5e+Xc2gq6uL7t27Y968eVJCM2/ePLRq1QpA+hTojRs3RokSJbB+/fpvKrHL0Lp1a5iZmWHlypWQy+UYOHAgRo8eLb1nwsPDsXPnTgBA5cqV4ezsLN13kpqaCm9vbyQlJUn3NVhYWEClUuGPP/7A3bt3P1ucKSkpSExMRFpaGtLS0pCYmCj9Efihx7lWrVowNTXFnDlzkJSUBC8vL5w5c0aaTfJ9y982ePBg6Q+wYsWK4erVq4iOjsbx48dRvHjxz7b/X5KVlRVat26NoUOHSj2tYWFh0lTezZo1Q1JSEqZOnYo3b94gOTkZp0+fztROcnIytm7disjISMjlcmkq+Ld7/zN0794dv/zyC54/f464uDiMGjUK9evXR5EiRT7LPuX0udXT00OnTp0wZcoUREdH4/Hjx1i+fDn69euXZVvv+5wDwPXr15GamorY2FjMnDkTERER0jn7bfnx/ZKamorExESkpqZCpVIhMTEx2xEavr6+mDdvntQbHBUVhRUrVqBEiRIwMzPL8RxSpUoVWFpaYurUqUhKSsLdu3exfPnyLI9jhiFDhmDBggW4ceMGhBCIj4/HiRMncj0aRAiBxMRE6VFEGdf3jOu9l5eX9CVjUFAQhg8fjsaNG2eZjAHIdHwSExPVEllfX19ERUUhLS0NZ86cwezZszFz5sxM7WzevBnFixdHjRo1AKT3IB4/fhwhISF4/PixWi/8t+LMmTNo164dtm7dmqk3LyszZszAxYsXkZCQgLS0NBw8eBD3799HtWrVAAADBgzAjBkz4OPjAyEEAgMD8eDBAwAffv5o3rw5Xrx4gVWrVknXkYcPH37QsyGTk5ORmJgIlUql9pkA0u8vvHTpEpKTk5GcnIxNmzbh9OnTaNCgQbbtvf3eeLvtjP07deoUvLy8kJSUhDlz5sDc3By1atVSayM8PBzz58+X/lYqVqwYTp06hZSUFJw6deqbPafkiTwYCkpf2btjpr28vET58uWFoaGhKF++vNoY8idPnoiKFSsKY2NjUa5cOSFE+oxnhQsXlmZxGzp0qNq9CMjFPXf6+vpCX19fmJiYiPr166vVDw0NFe3atRNmZmbC2tpajB8/Xm3sdE7xvntP0pw5c0SxYsWEv79/lvfcvT0u3cPDQ5q5Swghbt26JapUqSL09fVFnTp1xIgRI0TDhg1zPLYHDhwQNWrUEPr6+sLU1FRUrlxZrF27NlexR0REiObNmwtDQ0NRpkwZsXr16kz33OV0T97BgweFvb29UCqV0ox0AwYMyPXMme/ed+Hv7y8ACIVCIb1e787EGRcXJ3r16iWUSqUoVKiQ6Nevn4iJiRFCpM+EBkDo6emprZ9xH9rH+BKz4m3fvl1YWFiIuLg4kZSUJGbNmiUcHR2FgYGBsLOzU7uHMDg4WHTq1EkUKlRIKJVKUbt2bREfHy/S0tJE//79hZGRkbCwsBCjRo0StWrVkl6v991zV7p06RyPS69evQQAtZ+Me0dyc5z19fXFuXPnpN/v3bsn3NzchI6OjnB0dBT79+9X2977lguR/ll+9701ZswYYWJiIsqVKyfu3buX7f68z5ec/TCre0ZiYmLEyJEjhb29vTA0NBSOjo7i559/lpb7+vqKJk2aCFNTU2FqairatWsnhFD/zCQlJYnGjRsLU1NTYWBgIEqXLi127tyZqZ4Q6feljB07VhQtWlSYmZmJ9u3bq91n/O45dMmSJWr3+zZu3Fi6V/ddufncRkdHi86dOwsDAwNhYWEhZsyYodbGu+3n9DkXQoj69esLQ0NDYWRkJNq1ayfdQ/W2/Pp+mTZtWqbPXnb3XgcFBYlOnToJa2troa+vLwoVKiTatGkjHj58KNXJ7hwihBAPHz4UDRo0EMbGxsLBwUEsXLhQum8pu5krd+3aJSpUqCC9Ns2bNxcBAQFCiPR7wTNmuc5Kxvvy3R8/Pz8hRPrrY2lpKXR1dYW1tbUYOHCgCA8Pl9Z/t/3Tp09n2V6GVatWiUKFCgldXV3h4uKS5Xnl1atXokyZMmr3/50+fVo4ODgICwsLsWzZsmz3532+5GyZderUUfvbJuMnO7NnzxYuLi7C0NBQKJVKUaFCBbVzdlpamli4cKFwcnIS+vr6wsnJSfp74UPPH0Kk/y3Xtm1bYWlpKZRKpahYsaL466+/pOXvXiPeVbt27Uyva8aMuH///bc0S7CxsbGoUqWK2LNnj7RuQECA0NfXl96XGTG++/P2Z3jfvn3C0dFR6OjoCDc3N/HgwYNMMfXq1UttH6Kjo0WTJk2EkZGRaNasmYiNjc12f/5rZEJ8pXFERPnMgAEDoFKp8Pvvv+d1KP9ZderUwfTp07/I84Do28HXmT4E3y+UG5s2bcKZM2e+2YfKE30pHJZJ9H/nz5/H8+fPoVKpcPLkSWzbtg0dOnTI67CIiIiIiHIl880BRP9Rz549Q+fOnREZGQlra2v8+uuvaNiwYV6H9Z/Wu3fvTDOOUsHD15k+BN8vlBuurq7SvbBE/yUclklERERERFQAcFgmERERERFRAcDkjoiIiIiIqABgckdERERERFQAMLkjIiIiIiIqAJjcERERERERFQBM7oiIiIiIiAoAJndEREREREQFAJM7IiIiIiKiAoDJHRERERERUQHA5I6IiIiIiKgAYHJHRERERERUADC5IyIiIiIiKgCY3BERERERERUAmnkdABERfZvS0tJw/vx53L17F3K5HBoaGlAqlahTpw6srKzyOjwAwJkzZ1CjRg1oan745WzXrl0oUaIEXF1dP39gAPbv3w8rKytUrVr1i7T/rgMHDiAkJAQymQwaGhpwd3dHsWLFAABv3ryBh4cHIiMjoaGhgWbNmsHOzi5XbQYGBkJLSwva2tpo1KgRihYtCgBISUnBwYMHERwcDJlMBnd3d5QuXRoAcPLkSTx48ACampqQy+WoV68eHB0dAaS/ZteuXYOhoSEAoFChQmjbtu2XOCRERP85TO6IiChLBw4cQHJyMvr27QtdXV0AwLNnz/D69euvktwJIQAAMpks2zpnz55F1apVPyq5K2gaNWoEHR0dAEBoaCi2bNmCcePGQSaT4cSJE7C2tkb37t0RHByMnTt3Yvjw4dDQ0MixzZIlS6JFixaQy+V49OgRdu/ejREjRgAALl68CA0NDQwbNgyRkZFYv3497O3toaenB1tbW9SqVQtaWloICwvDpk2bMGrUKGhrawMAypUrh8aNG3/R40FE9F/EqyEREWUSHh4OX19fjBw5UkrsAEg9QRkuXryIe/fuQaVSQV9fH82bN4exsTHOnDmD169fIyUlBRERETAwMEDHjh2ltnJa7+XLl0hOTkZ0dDR69OiBy5cvIyAgAGlpaVAoFGjRogXMzc1x6NAhAMDGjRshk8nQo0cPaGpq4ujRo3jx4gVSU1NhbW2Npk2bQkNDA69fv8aBAweQlJQEU1NTpKSkZLv/t27dwsWLFwEASqUSzZs3h5GREXx8fHD79m3o6+vj5cuX0NDQQIcOHWBiYpLj8Txz5gwSExOlhObq1asICQlB69at39vmrVu3cO3aNaSlpUFbWxtNmjTJMrnOSOwAICkpSW3ZvXv3MGzYMABA0aJFYWhoiICAgEyv57ucnZ2l/1tbWyM2NhYqlQpyuRz37t1Dy5YtAQAmJiawt7eHr68vKlasCCcnJ2k9S0tLCCEQHx8vJXdERPRlMLkjIqJMwsLCYGpqqpbYvevOnTt4/fo1+vbtC7lcjlu3bsHLywtdu3YFAAQFBeHHH3+Enp4e9uzZg+vXr6NmzZrvXe/58+cYMGAADAwMAADVq1dHw4YNAQB3797FkSNH0L17dzRv3hw3btzADz/8ICU2np6esLOzQ8uWLSGEgKenJy5fvozq1avDw8MD3333HSpWrIgXL17g999/R7ly5TLt18uXL3H8+HH8+OOPMDIywrlz5+Dp6Ylu3boBAEJCQjBgwACYmJjgxIkT8Pb2RosWLT7peGfXZmBgIO7evYvevXtDU1MTAQEB2LdvHwYPHpxlOydOnMD9+/eRkJCAjh07QiaTIT4+HiqVSjqeAGBsbIzo6OgPivHy5ctwcnKCXJ5+u350dDSUSuV727x58yZMTEzU6t6/fx/+/v7Q1dVFrVq14ODg8EGxEBFR1pjcERHRe0VERGDXrl1ITU2FjY0NWrVqBV9fX4SEhGDdunUA/h1GmcHR0RF6enoA0nt9Xr58CQDvXc/JyUktEXn27BmuXr2KpKQkCCGQkJCQbZy+vr4ICgrCpUuXAACpqamQyWRISkpCWFiYdH+dpaUlbG1ts2zDz88Pjo6OMDIyAgBUrlwZ586dg0qlkvYlo1fN2toaV69efc/Re7/s2nz48CFevHiB9evXS3UTEhKQkpICLS2tTO3Ur18f9evXx7Nnz3DixAn06dPnk2MDgNu3b+P+/fvo3bv3B6337NkznD17Fj169JCG11aqVAk1a9aEhoYGAgMDsXPnTvTv3x/GxsafJVYiov8yJndERJSJlZUVIiIikJCQAF1dXZiammLgwIHw8fGBr6+vVK9GjRr47rvvsmzj7fvg5HK5lBy9b723h+5FR0fDy8sL/fv3h6mpKV68eIGNGzfmGHvHjh1hZmamVvbuMMUP8e49fzntV3berZeampqrNoUQKF++PNzd3T8o5mLFisHLywsvXrxAkSJFIJfLERcXJyXNUVFRaj1pObl79y7Onj2Lnj17qiXdSqUS0dHR0sQoUVFRKF68uLTc398fBw4cQJcuXWBubi6Vv92Gra0tChcujJCQECZ3RESfAR+FQEREmZiZmcHZ2RkHDx5EYmKiVJ6cnCz939nZGdevX5d60tLS0hAaGvretj9kvcTERGhoaMDQ0BBCiEy9ZNra2mrxOTs7w9vbW0qOEhISEBERAYVCASsrK9y6dQtA+tDLwMDALLfp4OCAJ0+eIDY2FgBw/fp1ODg4SMMRP4apqSlCQ0OhUqmQkpKCBw8e5Go9Z2dn3L59WxruKIRASEhIpnppaWmIiIiQfg8ODsabN2+k3sDSpUvj+vXr0rLY2FhptswTJ05k2/t47949nD59Gj169MiUDL7dZmRkJPz9/VGyZEkAQEBAADw8PNC5c+dM9wfGxMRI/w8PD0dYWBgsLS1zdTyIiChn7LkjIqIstW7dGufOncP69eshl8uho6MDfX19VK9eHQDg4uKChIQEbN68GQCgUqng6uqKwoUL59juh6xnaWmJMmXKYNWqVdDV1ZWShwzVqlXD1q1boaWlhR49eqBx48Y4ceIE1qxZA5lMBrlcjgYNGsDU1BRt2rTBgQMHcOnSJZiZmWX7KIBChQqhQYMG+PPPPwGk91B9zD11KpVK6pErVaoU7t+/j5UrV8LIyAhWVlY5TuiSwc7ODg0aNMDOnTuhUqmQlpYGJycnFClSJNO29u/fj8TERMjlcmhra6tNYFO/fn14eHhg+fLl0NDQQJs2baSZMjN697Kyb98+GBgYYMeOHVJZz549oaenBzc3Nxw8eBDLli2DTCZD06ZNpWG4Bw8eRFpaGg4cOCCt16ZNG1haWuLUqVMICQmBXC6HXC5H06ZNM/W0EhHRx5GJd292ICIiok+iUqmwbt06NGrU6JueLESlUmHDhg3o169fjo+cICKi/IHJHRER0WcUEBCAQ4cOwdbWFs2bN2fSREREXw2TOyIiIiIiogKAE6oQEREREREVAEzuiIiIiIiICgAmd0RERERERAUAkzsiIiIiIqICgMkdERERERFRAcDkjoiIiIiIqABgckdERERERFQAMLkjIiIiIiIqAJjcERERERERFQBM7oiIiIiIiAqA/wGDNKh6OjwbnQAAAABJRU5ErkJggg==", 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", 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", 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QgQMHii2rrOXup59+EsKLduUkKtifhbu3Fy7DN998I7puGhsbK+zep0x+fj45OjoKyy9YsECYV7hltV+/fkJ4mzZthPANGzYUSzMtLU1hF/myePLkiWibBgcHK40bGxsrivvrr78K85Rdx4hK7iZbWvfkly9fis4Vd3d3evv2rSjO/fv3hfN1zpw5orLEx8eL1rVwC3zbtm2FeUWPt7Vr1wrzirZYxsTEEFHBPrWxsRHChw4dKizzMfcRxj43brlj7DP69ttv0bdv32LhL168QEhIiOgppCIPHjxQOq9fv37CUPcmJiYwMzPDkydPAEB4WpmRkSG0lgFA+/btoaWlJUx3794dy5YtK5b2X3/9JWp57N69u6hVJiQkBL/++qswnZiYCDc3t2LpdOrUCZqamgBQ7PtQ7dq1E9IsOghGeno69PX1la67In///TdGjBihcF54eDiCg4MVzmvZsiVq165dLPzkyZPC/xMSEkpslTp16hRq1qwpPHmW69q1q/B/BwcHeHt748iRIyWthsj79+9x8eJFYbpnz57Fnqbr6upCV1e3TGleuXJFmO7YsSNkMpkoj/79+wvTiYmJCredsu2mzJs3bxSOWFqW1rbPad26dRg8eLDQMqZIVlYWnj9/Dmtr62L7tmfPnsL/jY2N0bZtW4XfJyy6XI8ePYT/V6pUCd7e3go/31D4+EtPT4epqanScp46dQo//fQTzp8/DyISwrt16yb8X1NTE506dRK1Aqtq9uzZmDRpkqjVsqjC1yovLy9IJBIQEZYuXYqkpCRUrVoVzs7O8PT0hK+vLywtLctcDkU0NDQwePBgNG3aVOVlGjVqhPnz5wMAxo8fj7i4OLi4uMDZ2Rn16tVDo0aNlJ7v9+/fF66ZZmZm2L9/P9zd3VXOWyKRIDQ0VNgPGzZswI8//ogXL16IWn/DwsJE5ZV/0iU0NBRLly5FlSpV4OzsDC8vL9StW7dcffZEkdOnTwut3wAwevRo6OnpieIUHhG1cMuiubk5WrZsKUxbWFigZcuWQg8UZT0qNDQ00LlzZ2HawcFBiKupqSm0rEokEjg6OuLRo0cA/u/+C3zcfYSxz40rd4x9pM6dO6NWrVo4deqU0D1k3bp1SEtLw8GDB0U33169epVasQMKflwqU7SyVLjSJu8C8urVK1EcefcfOWU/sApX7BTFKzpd+GZWmI2NjfB/qVSqdF7Rbn/y8n8sDQ0NmJmZwdPTE+Hh4cJNWBEXFxeF4UW3QUnkXdAKb299fX1RpQlQvr2VSU9PF/1Yd3R0LNPyqqRZtEy6urrQ09MTfsgr27fKtpsyL1++VFj5tre3/6jKXeF1KKsLFy6gZ8+eKh1n8nOw6LlUtJuvsm6/RY+JohVxZct96vEHqH6+l2T79u0YNmxYqfGys7OF/9etWxezZ8/GhAkTkJGRgQsXLoi6DpqZmSEmJuajvmc2duxYaGlpYdu2bbh8+TJyc3MxcuRIvH//XuWKa4cOHTB8+HAsWLAAWVlZSExMFP3At7e3x+7du1GtWrUS09HX1/+obRoaGorIyEjk5+cjMTERKSkp2Lt3L3JycgAA1atXF30OZfDgwbhy5QrWr1+PrKwsJCQkiB4IVK9eHfv374e1tXWZyyJnamoKqVQq7Md79+4pjVt0nrJ8i56jJd3PSlP0fCjtWlg4vqJ9VDhM2TXOwsJCdG8qfA+zsLAQVdQKxyt8XfmY85ixz40rd4x9pICAAISGhgIA+vbti6VLlwIADh8+jLVr1wpP7N+9eyd6N6Bp06b4/fffYW9vD3V1ddStWxdJSUml5idvEZNT9ORW/h6OnHzYcDl5S19RJiYmJcYrOm1sbKxSGQtT9B7Xp2jSpMlHfcBaWauXiYmJsL28vb1L/IyC/F02IyMjIezt27fIzMwUVfCUbW9ljI2NhRYQAEhOTi7T8qqkWbRM7969E7XQKNu3ZWkt/LeJiYkRfoBJJBKsX78eQUFB0NXVRXx8PAIDA4stU3jfAgXnUuHzpPB7bsqWU3RMKFuucNrW1tYYOnSo0vWRt1goKmNhZT3+AIje39TT08O2bdvQqFEjaGtrY/HixRgwYIDC5QYPHozevXvj9OnTuH79Om7fvo29e/fi9u3beP78OUJCQkqsQCjzww8/wMHBASNGjEDDhg1x6dIlAAXvCHfv3l3lT2FERUVh/PjxOHXqFG7evIlbt24hLi4Ojx49wr1799C/f38cPXq02HJGRkbQ0tLCkydPkJycjGbNmuHYsWMwMzNTeR2++eYb+Pn54eDBgyAibNy4EXv27BHmF261AwquldHR0Zg1axZOnTqFv/76C3/99RdiY2ORnp6Oa9euYfTo0Vi9erXKZShKXV0dDRo0ENb5ypUruHPnDipVqlQsbtFv4DVq1Ej4f+Fri/z9Qbnbt29/dPmK3pOSk5NL/B5o4fiKjvvCYV/y/vUx9xHGPruv1R+Usf81JY2WmZ6eToaGhsK8SpUqCe+XPXz4ULSc/NMAREQ3b94UjcAVEhKiNL+iQ7UXHaZe7p945+7SpUvCPGXbpOhojoXnlbZuypQ0WqaqyxX9PIBccHCwaLu9fv26WJz379/T6tWrhekv/c6dnp4e3b59u1gZCr9zt3btWlFait7FcXNzE+aX9s7d9u3by7TdvpTPOVpm4ePeyMhI9B5M0Xzkx2LRd+4mTpwoLFOWd+7++OMPYV5J79zNnTtXCNfQ0FD4Pk5+fj4dOHCA/v77byIquLZ87nfuCo8CWrNmTSE8Ly+PfH19Fab38OFDhe/UXbhwQRT/+fPnpeZf0miZRa8boaGhomWVvcf1999/KxzZddu2baJzTVEZ7O3t6cKFC6J3emvXrl3mTxGsX79elKZ8v2lqatLTp09FcW/evKnwPC78bmP16tWVbhdVr6dFP4XQunVr4R02uejoaFGcop9CKHweFP78zN69e0XLFb3+ff/998K8OnXqFCtb0XfuateuXWybPHz4UChv4fMHKPmdu8LvF5b0nm7h46novMIjjRa+b3/MfYSxz41b7hj7DIyMjDBgwAD8/PPPAIA7d+5g06ZN6Nq1KywsLGBkZCR0oZo6dSqePn2K3Nxc/PHHH5/UdUWRXr16YeTIkUI5GjRogKCgIFy7dg3btm1TuIyamhqGDBmCCRMmACh4J8Hb2xvNmzfHzZs3RU9ufX19UatWrc9a5n+DYcOGYceOHSAi3LlzB9WrV0e7du1gaWmJ169f4+rVqzh69CjevXsnvH/Vpk0bmJubC91r+vXrh6SkJGG0THm3q7IYPXq0MNpqRkYG3NzchNEyU1NTsWvXLixevFh4L87W1la0fNeuXdGwYUOoqamhR48esLS0xLBhw4SW5JSUFNSpU0c0WqZclSpVFLZi/a9zdnYW/v/q1SsEBgaiYcOGOHHihNIR6+rXr48aNWoIo9JOmTIFycnJ+Oabb7B582alXbvatm0LCwsL4el93759cfbsWRgaGiI6Olr0HlFhoaGhmDp1Kp4/f47c3Fx4eXmhY8eOqFSpErKysvDXX38hISEBT548wZEjR+Do6AgbGxu0bNlSGBVz7dq1ePPmDdzc3LBnzx5cv379o7aV/F2wK1euoEuXLnB1dcWePXuUjip57NgxdOvWDd7e3nB1dYWNjQ3y8vJE1xupVAodHZ0yl6cwHx8fNGzYUBhxdO3atYiIiCh15MZNmzZh0qRJ8PHxQeXKlWFtbY13795hw4YNQpyiraCFubu7IzY2Fi1btkR2djYuXLiAwMBA7N+/X+V1+vbbb4X7QOEWzMDAQJibm4vizpkzB2vWrEHTpk3h6OgIS0tLvHz5EtHR0SqVV1Xfffcd1q5dK7Qi7tq1C9WrVxdGhjxz5oyo14mxsTHmzZsnSqNOnTrCObRmzRo8fPgQMpms1JEgC1+3zp8/j0GDBsHOzg5SqRQ//fQTjI2N0bt3b2FE1gsXLqBq1aoIDg6GkZERbt26hdjYWKSlpcHIyAghISGYMmUKXrx4AaDgffPw8HAYGBhg/fr1Qu8EiUSCwYMHf9qGK8HH3EcY++y+du2Ssf8VpX3n7unTp6JvKVWrVk0Y3fKXX34RLSv/q169uuibUp+j5S47O1v0La3Cfz4+PqLpot+569ixo8Ll5H+urq6i71sRlZ+WOyKiRYsWlfh9IvlfYTt27FD4TSp9fX1RK1xZvnMXERFR6nfu5D58+EDW1tYK4yUlJQnxSvvOnY2NTYnfuftfbrl78eKFaHS7wn/KWu6IlH/nTktLi/z8/IRpR0dHUTmUfefO2NiY6tevL0z7+vqKljt58mSJ37lTdN7+/fffZGFhoTBe4dYFVbfh7du3SV9fv1haGhoa1K1bN4XpFW2tVPRXeERBVfehomvDrl27RPMLjzKprOVu+vTppZav8IjDyo6jTZs2kZqamjCvefPmxT6iXZJ+/foVyzcuLq5YvD59+pRYVjU1NdE14GOvp0QF39or7boPFLT4nzt3rtjyBw4cUHitMjU1pbp16yq9/l28eFG0LeV/urq6QpzMzMxi32os+lf0O3dGRkYlbreZM2eKyvG5W+6IPu4+wtjnpAbG2Gdhbm6O77//Xpi+fv268F25UaNGYdGiRahSpQo0NTVhZWWFH374AUePHi02Atin0tTUxP79+zFixAjY2tpCKpXC2dkZs2bNEr7bo4i6ujo2b96MmJgYtGrVSni53NDQEPXq1UNUVBSSkpJEA6OUN/3798fFixfRu3dvVKlSBTo6OtDQ0IClpSWaNGmCCRMm4PLly6Jl2rRpg4MHD6Jx48aQyWQwMjJC27ZtcebMGdSoUeOjyjFp0iScPn0aISEhcHJygra2NnR0dODk5IQePXqgevXqQlwtLS3Ex8ejefPmMDAwUJrmrFmzcODAAbRv3x42NjbQ1NSEnp4e3NzcMGHCBFy5cqXUASX+V5mYmODEiRNo164dDAwMIJPJUKdOHWzbtk14b1YRT09PnDp1CoGBgdDT04Oenh6aNm2KY8eOoXLlykK8oq0orVu3xqFDh9CkSRPRMXH69GnRe7FFl2vYsCGuX7+OCRMmwMPDAwYGBlBXV4eRkRE8PDzw448/4sCBA2jcuLGwjKOjI06fPo1OnTrByMgIMpkMDRo0wM6dO0tcN2UqVaqEY8eOoXnz5tDR0YGenh6aNGmCQ4cOoVmzZgqX8fb2xrRp0xAYGIiKFStCX18fGhoaMDc3R9OmTbFq1SrMmjWrzGVRJDAwUNRz4I8//kBaWlqJywQHB2PixIlo1qwZHBwchPPa2toagYGBiIuLw8CBA0vNu1OnTqKWq/379+O7774TvklXmqLv1llaWopGdZTr1asXRo0ahcaNG8POzg7a2tqQSqWws7NDx44dcfToUaWjAZeVnp4eNm/ejISEBISGhqJy5crQ1dUV7lMBAQFYsmQJbty4AQ8Pj2LLN2vWDLGxsahduzakUilMTU3RrVs3nD9/Hq6urkrzdXNzw4YNG1C7dm1oa2srjKOtrY1du3Zh8+bNaN26NaysrKCpqQkDAwPUqFEDgwYNErWcNm7cGNeuXcOwYcNQrVo16OjoQCqV4ptvvkG3bt1w6tQplQYL+lQfcx9h7HOSEH3CEGSMMcZYOZWdnQ0NDQ2oqYmfg2ZkZKB69epC97offvhB9NmHDx8+KPzB+vDhQ1StWhVv3rwBAEybNg1jx479gmvAGGPsv4Yrd4wxxpgCly5dQps2bdCtWzdUrVoVxsbGSElJwW+//Sa8z6ampoYLFy6IWpO2b9+O0aNHo0uXLqhSpQp0dXVx69YtLFiwAPfv3wdQ0GJy+/ZtpZ9GYIwxxj4GD6jCGGOMKZGamopffvlF4TypVIolS5YoHGDor7/+QkREhMLl9PX1sWnTJq7YMcYY++y45Y4xxhhT4MWLF5g2bRoSEhJw//59vH79Gtra2nB0dISPjw/69++v8OPuycnJiIqKwrFjx/Do0SO8efMGurq6qFy5Mvz9/TFgwABUqFDhK6wRY4yx8o4rd4wxxhhjjDFWDvBomYwxxhhjjDFWDnDljjHGGGOMMcbKAa7cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyh1jjDHGGGOMlQNcuWOMMcYYY4yxcoArd4wxxhhjjDFWDnDljjHGGGOMMcbKAa7cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyh1jjDHGGGOMlQNcuWOMMcYYY4yxcoArd4wxxhhjjDFWDnDljjHGGGOMMcbKAa7cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyh1jjDHGGGOMlQNcuWOMMcYYY4yxcoArd4wxxhhjjDFWDnDljjHGGGOMMcbKAa7cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyh1jjDHGGGOMlQNcuWOMMcYYY4yxcoArd4wxxhhjjDFWDnDljjHGGGOMMcbKAa7cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyh1jjDHGGGOMlQNcuWOMMcYYY4yxcoArd4wxxhhjjDFWDnDljjHGGGOMMcbKAa7cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyh1jjDHGGGOMlQNcuWOMMcYYY4yxcoArd4wxxhhjjDFWDnDljjHGGGOMMcbKgf985S40NBQSiaTY38yZMwEAq1atgkQiQW5u7lcpn4+PD7p37/5V8v4SUlNT0axZM+jp6UEikeDVq1dfu0j/eREREahQocJnTVMikWD58uWfNU3GGGOMMVYyja9dgH+DmjVrYunSpaIwe3v7r1QascWLF0NLS+trF+OzmTJlCpKTkxEbGwt9fX3o6+t/7SKxLyAxMREVK1b82sVgjDHGGPtP4codAH19fdSvX/9rF0OhqlWrfu0ifBaZmZmQyWS4desWvLy84O/v/1nSY/9O/9bziTHGGGOsPPvPd8ssq7dv36Jfv36oWLEiZDIZXFxcMGvWLBCRKN7u3bvh7OwMmUyGZs2a4fDhw5BIJEhISBDi3Lp1C40bN4a2tjaqV6+Ow4cPo0KFCoiIiBDiFO2WGRoaCm9vb+zYsQPOzs7Q19dHq1atkJaWVub8i5J3QT137hzq1q0LbW1teHh44Pz586J4z58/R69evWBubg6ZTAY/Pz9cv35dmJ+SkgKJRIJNmzahU6dO0NfXR79+/SCRSHD06FGsWbMGEokEPj4+AICMjAz07dtXSK9JkybF8pRIJFiwYAH69OkDExMTtGrVSshny5Yt6Ny5M/T09FC5cmUcOnQI2dnZ6N+/P4yMjODo6IiNGzeK0vvjjz9Qv359GBoawsrKCp07d8aTJ09EcRwcHDB+/HhMnToVlpaWMDc3x8CBA5GTkyOKd+DAATRs2BA6OjowMzNDUFCQKK2EhAR4eXlBJpPBwsICgwcPRlZWltL98DnLd/36dXTo0AE2NjbQ09NDnTp1sG/fPqX53rx5ExKJBCdPnhSF37lzR3T8HDt2DA0bNoS+vj6MjIxQp04dHDx4UIhftFtmbGws3N3doaOjA1NTUzRq1AiXLl0qcRswxhhjjLGy4crd/5ebmyv85eXlKY337t07aGpqYtasWdi7dy8GDBiAKVOmYM6cOUKcv//+G+3atYOrqyu2bdsGf39/hIeHi9LJz89HUFAQnj9/jvXr12P06NHo27cv3rx5U2pZ79y5gylTpmD69OlYvnw5zp8/jx9//LFM+Zeka9euCAsLQ0xMDLS0tNCyZUtkZGQAALKystC0aVOcPHkS8+bNw9atW6Guro5mzZrh3bt3onQGDx6Mb775Btu3b0fv3r2RmJiImjVrolWrVkhMTMTixYsBFFRYN23ahGnTpmHz5s2QSCTw8/PD06dPRelNmzYN2dnZ2LhxIyZMmCCEDxs2DNWqVcO2bdvg5OSEjh07ol+/ftDS0kJMTAy8vb0REhKCx48fC8ukpqaiT58+2LFjB5YsWYIHDx6gefPmxSrpq1evxuXLl7F69WqMGjUKS5YswW+//SbMP3DgAFq2bAkLCwts3LgRf/zxB+zt7ZGeng4AOH78OPz9/VGpUiXExsZi+vTpWLduHYYPH17iPvhc5Xv48CHc3d2xfPly7NixA76+vggMDERSUpLCfF1cXFC/fn1ER0eLwqOjo2Fvb48mTZrgzZs3CAoKQpUqVRAbG4uNGzeiXbt2ePnypcI079y5g86dO6NZs2bYvXs3Vq9eDR8fH9H7lj4+PkJlnzHGGGOMfST6jwsJCSEAoj91dXVh/sqVKwkA5eTkFFs2Pz+fcnJyaMqUKVStWjUhfPDgwVShQgXRMsOHDycAdOTIESIi2r59OwGgGzduCHF27dpFAGjSpElCWJMmTahbt26i8mpqatL9+/eFsJkzZ5KGhgbl5eWpnL8i8nWdP3++EPbixQuSyWQ0d+5cIiJatmwZyWQyUf7v3r0jCwsLmjNnDhERJScnEwBRueW8vLwoJCREmL569SoBoJiYGCEsIyODTE1NaeTIkUIYAPLy8hKlJc+nf//+QtiNGzcIALVu3VoIe/PmDWloaNCKFSsUrndubi7du3ePAFBSUpIQbm9vT1WrVhW2KxFR69atyc/PT5iuU6cONWrUSGG68vUNDAwUhcXExJBUKqXHjx8rXe5zla+wvLw8ysnJoWbNmtGAAQOE8EmTJpGtra0wvXTpUjIyMqIPHz4QUcFx7ujoSBMmTCAioqSkJAJAb968UVpmALRs2TJhfU1MTEpcRz8/P6XlZowxxhhjquGWOwC1atVCUlKS8HfmzJkS4y9duhTVqlWDTCaDpqYmJkyYgDt37gjzz58/j+bNm0ND4/9eaWzdurUojfPnz8PJyQmurq5CWEBAANTV1Ustb5UqVWBnZydMu7q6Ijc3V2jpUiX/krRp00b4v4mJCRo2bCi09Bw6dAj169eHtbW10NIplUpRv379Yl0pW7ZsWWpe58+fh7q6OoKDg4UwXV1dtGrVqljrkrL0/Pz8hP/LB/Eo3Aqkr68Pc3NzPHr0SAi7fPkyAgICYGpqCg0NDWEAncL7EQB8fX2hpvZ/p4mrqysePnwIoKAV99y5c+jRo4fCcr1//x6JiYno0KGDqGXYx8cH2dnZuHbtmrLN8lnKJy/j8OHDYW9vD01NTWhqauLgwYPF0imsc+fOyMrKQlxcHICC1sfk5GT07NkTQME21tPTQ7du3bBr1y68fftWaVoAUKNGDbx+/RqhoaE4ePAgPnz4UCzOoUOHcOjQoRLTYYwxxhhjJePKHQA9PT14enoKfx4eHkrjbt68GX379kVgYCDi4uJw9uxZjB49WvQO1dOnT2FqaipazszMTDStKI66ujqMjY1LLa+RkZFoWiqVAoDwo1mV/Etibm5ebFrepfH58+c4cuSIUFGQ/8XFxSE1NVW0nIWFRal5paWlwdjYWFQRBQBLS8ti7xEqS8/Q0FD4v3xbFA6Th8u3z+vXrxEQEIB3795h6dKlOHXqlPCOWdGKh6JtLY+Tnp4OIoK1tbXCcqWnpyM/Px9hYWGibSXfvkW3l9znKh8AjBgxAitWrMDw4cNx8OBBJCUlwd/fX2EFS87Q0BDt2rUTumZGR0ejYcOGqFSpEgDA2NgY+/btQ2ZmJtq1awczMzN07NhR1O21MGdnZ2zfvh23b99GixYtYGZmhh9++KHUSiFjjDHGGCsbHi2zjGJjY+Hr64tff/1VCNu5c6cojoWFBZ4/fy4KKzqtKE5eXp7wrtanUCX/kjx79kz0KYhnz57BysoKQEFLnpeXF+bOnVtsuaKfNZBIJKXmZW1tjfT0dOTm5ooqeE+ePClWaVIlPVWcPn0ajx8/xpkzZ/DNN98AKHhPsayMjY0hkUiKVULljIyMIJFIMH36dDRt2rTYfAcHhy9aPqDgeB0yZAgGDhwohH348EHU2qdIWFgYAgICcP/+fWzZskV0vANAw4YNceDAAbx79w579uzBoEGDMGDAAGzdulVheq1bt0br1q2Rnp6O7du3Y9CgQTAwMMCsWbM+ar0YY4wxxlhx3HJXRpmZmULrEFAwMMqWLVtEcTw8PLB//37Rh8937dpVLE5ycjL+/PNPIWzv3r0lDuaiKlXyL4m8Ox4AvHz5EqdOnUKdOnUAFHSBvHXrFipVqiRq7fT09ISzs3OZy+rp6Ym8vDzs2LFDCHv//j3i4+NRt27dMqeniszMTAAQ7cfNmzeXOR1dXV3UrVsXa9euVTq/Xr16uHPnTrFt5enpqbQ19XOVT55W4XRSU1Nx+vTpUpfz8/ODra0tunXrhqysLHTq1ElhPF1dXXTo0AHfffed6FhWxtjYGGFhYfD391cpPmOMMcYYUx233JWRn58fhgwZgrlz58LFxQVLly7F+/fvRXEGDhyIxYsXo0OHDujbty8uX76MmJgYABBaTOSjDbZv3x5Tp05FZmYmIiMjoa+vX2qrSmlUyb8kCxYsgIaGBuzs7DB9+nTo6uoKo22GhIRgyZIl8PHxwdChQ2Fvb4+nT5/i+PHjqFu3ruizDaqoVq0a2rdvj969eyM9PR1WVlaYNWsWcnJyMHTo0LKvvArq168PHR0d9OnTBwMHDsSFCxewcuXKj0pr2rRpaNGiBdq1a4ewsDCoqalh//796N+/P5ydnTFjxgz4+/uDiBAcHAyZTIa///4bO3bswPr162FgYPBFy+fn54f58+fDyckJ6urqmDRpEmxsbEpdTiKRICQkBJMnT0anTp1E3T93796NlStXIjg4GHZ2dkhJScHatWvRsWNHhWktXboUZ86cQYsWLWBlZYXr169j7969iIyMFOLIWzb5vTvGGGOMsY/HLXdl1K9fP/Tr1w9TpkxB9+7d4eDggFGjRoniODk5YevWrbhx4waCg4MRHx+PmTNnAoDwY15NTQ07d+6EmZkZunbtiilTpmDBggWQSCQKf/CXhSr5l2Tt2rVYsWIFOnTogMzMTMTHxwtdLrW1tXHkyBF4eXlhzJgxaN68OYYNG4bnz5+jVq1aH1XelStXokOHDhg9ejQ6duyIvLw8HDp0SKV39j6GlZUV1q9fjz///BNBQUHYvXu30u6EpWnatCni4+Px8OFDdOzYESEhIUhJSRHenWzcuDEOHz6MlJQUdOnSBcHBwViwYAFq1aql9CPsn7N8CxcuhJubG8LDwzF8+HAMHjwY3t7eKi0bFBQEoKBCX1ilSpVARBg9ejSaN2+OiRMnokePHoiKilKYTs2aNfH48WMMGjQILVq0wOzZszF27FhR5T0vL++ztFozxhhjjP2XSYiKfDiLfRFz5szBmDFj8PLlS+jo6CiMc/HiRdSuXRsHDx5U+I7Wl85/1apVCAsLQ05OTrEBTth/zy+//IJ58+bhwYMHKo3iyhhjjDHGvi7+Bf+FjBgxAvXq1YOJiQmSkpIwZcoUhISEiCpWixYtgkwmg5OTE1JSUjB58mRUrVr1s3zMWZX8GVMkJSUFf/75J2bPno2+fftyxY4xxhhj7H8EV+6+kIyMDPz000949uwZzMzM8P3332PGjBmiOBoaGvjll19w//59aGlpoWnTppg7d+5n+TGtSv6MKRIREYENGzagWbNmGDly5NcuDmOMMcYYUxF3y2SMMcYYY4yxcoAHVGGMMcYYY4yxcoArd4wxxhhjjDFWDnDljjHGGGOMMcbKAa7cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyh1jjDHGGGOMlQNcuWOMMcYYY4yxcoArd4wxxhhjjDFWDnDljjHGGGOMMcbKAa7cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyh1jjDHGGGOMlQNcuWOMMcYYY4yxcoArd4wxxhhjjDFWDnDljjHGGGOMMcbKAa7cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyh1jjDHGGGOMlQNcuWOMMcYYY4yxcoArd4wxxhhjjDFWDnDlrgzy8/Px22+/wd3dHTo6OjAxMUGbNm1w/vz5YnFTU1PRrFkz6OnpQSKR4NWrVx+VZ0JCAiQSCe7cuQMASElJgUQiwcGDB5Uuc+nSJURERCA/P18UHhoaCm9v748qR2m+ZNofk5dEIsHy5cv/kfL8WyQkJKBGjRqQSqVwc3NT6Vj5N1q+fDkkEsnXLoZCKSkpiIiIwMuXL0uN6+DgAIlEAolEAi0tLbi4uODnn39GTk6OKJ6Pjw+6d++uchkcHBwwfvz4Mpe9qG7dusHJyQkymQyWlpZo3749bt++LYrz4cMH/PTTT7CwsICuri78/f3x119/lZr2pk2bEBwcDAsLC6XH4OrVq+Hp6QkjIyPo6uqidu3a2LhxoyjO9evX4eHhASMjI/Tp06fYtouMjCzTtvsUr169QkREBP7+++8vlkfR69bn2NcJCQmYPXv2pxbtH5ObmwuJRIJVq1YpjVP0vqiqVatWYdu2bZ9Ywn+H7du3448//vjaxWCM/Qtx5a4MwsPDMWjQILRo0QK7du3CH3/8gQ8fPsDLywv79u0TxZ0yZQqSk5MRGxuLxMRE6Ovr/2PlvHTpEiIjI4tV7v5LEhMT0bZt269djH9U7969YW9vj8OHD2PNmjVfuzjlUkpKCiIjI1Wq3AEFDyISExOxb98+tGnTBuPGjcP06dNFcRYvXozIyMgvUdwS5efnY+zYsdi9ezcWLVqE5ORkNGvWDG/evBHi/Pjjj1i3bh2mT5+OTZs2ISMjA02bNsXr169LTHvLli148OABAgIClMZJT09HcHAw1q5dix07dqBhw4bo0qULtm/fLsQJCwtD9erVsW7dOuzduxcrVqwQ5qWlpWHhwoX4+eefP34jlMGrV68QGRn5RSt3X8L/WuVOFbVr10ZiYiLs7OzKtBxX7hhj/wUaX7sA/ytiYmKwevVqrFmzRvSkuE2bNggICEBISAju3r0LXV1dAMCtW7fg5eUFf3//r1Xk/7T69et/7SL8o/Lz83H37l2MHj1aaNVMSUn5uoVisLW1FY5FHx8fXLt2DWvWrMHEiROFOFWrVv0qZduwYYNo2s3NDZUrV8bx48cRGBiI1NRUrFy5EtHR0ejWrRsAoF69erC3t8fSpUsxcuRIpWlv2rQJampqSElJUfqgYfDgwaLpZs2a4dKlS1i3bh2Cg4ORkZGBpKQk7N69G+bm5hgwYAAOHTqEvn37AgDGjx+PH374Ad98880nbAVWFkSErKwsaGtrf9VyGBgY/Oeu8YwxpipuuVPRwoUL4eLiIvzIkVNTU0NkZCSePHmCmJgYAAVda44ePYo1a9ZAIpHAx8dHYZpv375Fv379ULFiRchkMri4uGDWrFkgoo8u56pVqxAWFgYA0NTUhEQiQWhoqCjOjh074OzsDH19fbRq1QppaWmi+cnJyejQoQOMjIygp6eH4OBgPHjwQKX8N2zYIHT1Cg4OxrNnz0Tz9+7dCw8PD2hra8PW1hbjxo1DXl6eKM6aNWvg6uoKLS0tODk5Ye7cuSXm+fbtW3h7e6Nu3bpC91dl3ZumTp0KS0tLmJubY+DAgcW6eU2fPh2WlpYwMDBAnz59sHDhQlEXwezsbAwZMgQVKlSAlpYW7Ozs0LNnT6VlGzt2LOrWrStM37hxAxKJBOHh4ULY5s2boauri9zcXABAXl4epkyZAkdHR2hpaaFatWqi1oyiEhISoK6ujvz8fPTq1QsSiQQREREK486YMQNubm7Q09ODnZ0d+vTpg7dv34ri3Lp1C40bN4a2tjaqV6+Ow4cPo0KFCqI0MzIyEBISAj09PdjY2GD+/Pno3r17sWM9ISEBXl5ekMlksLCwwODBg5GVlSWKM3PmTFhZWcHAwAA//PADPnz4oHRd5U6cOIFWrVrBwsIChoaGaNy4Mc6dOyeKc/XqVTRv3hxGRkbQ19dHzZo1sX79eqVplnY+JiQkwNfXFwBQuXLlEs9tZWrUqFHsXCraLbOs5f7zzz9hbW2NPn36fNK1w9TUFACEc+LatWvIz88XPaAyNzdHrVq1EB8fX2Jaamofd2sxNTUV8s/OzgYA6OjoCP/Kj50rV65gz549GDNmjMppDxs2DK6urtDR0UHFihUxZswYIQ+5J0+eIDQ0FObm5tDR0YGbmxt27NiBlJQUODo6AgD8/f0hkUjg4OAAAIiIiECFChVE6dy5cwcSiQQJCQlCmCrnXklGjx6NKlWqFAufMGECHB0dFe77iIgIREZG4uHDh0IXYfl5rKjL5/jx44X1AgruJxKJBElJSWjQoAFkMhk2btworPPJkyfh7u4OXV1deHt7F+uy+/z5c/Tq1Qvm5uaQyWTw8/PD9evXRXHOnz8PT09PaGtro27durh06VKp20LZ6wrbtm1D9+7doaenB0dHR/z+++/CMj4+Pjh69CjWrVsnbAv5/nn8+DG6dOki3PNat26Nu3fvlliG69evo0OHDrCxsYGenh7q1KlTrAePIhkZGejbt6+wTZo0aSJ6tUNZd3pvb2/hXh4aGorVq1fj5MmTwrrIu7Hm5ORg0qRJwv3DyckJU6dOVTl/oOD+uWjRIvTv3x+GhoawtbXFhg0bQESIiIiAhYUFbGxsMGfOnGLrt2TJEuH+XbFiRSxdurTUbcIY+7y4cqeCnJwcnD59GoGBgQrfBWrQoAFMTU1x/PhxAAVdAmvWrIlWrVohMTERixcvVpjuu3fvoKmpiVmzZmHv3r0YMGAApkyZovCCqarAwEDhhn3ixAkkJiZiwoQJwvw7d+5gypQpmD59OpYvX47z58/jxx9/FOY/f/4cjRo1woMHD/DHH39g3bp1uH//PgIDA0vt5nn79m1MmTIFUVFRWLZsGc6cOSOq+Fy6dAmtW7eGk5MTYmNjMWTIEMycORPjxo0T4uzatQs9e/aEj48P4uLi0KVLFwwdOhS//fabwjzfvHmDFi1aICcnBwcOHICRkZHS8q1evRqXL1/G6tWrMWrUKCxZskSU7tq1azF27FiEhYUhJiYGOTk5+OWXX0RpTJ8+HRs3bsS0adOwf/9+/Prrr9DU1FSap5eXFy5evIj3798DAE6ePAltbW2cOHFCiHPy5EnUq1cPGhoFDen9+/fHzJkzMWjQIOzatQtNmzZF+/btcfbsWYV51K5dW0hv/PjxSExMxPfff68w7uPHjzFixAjs3r0bv/76K06cOCF6YJGfn4+goCA8f/4c69evx+jRo9G3b19RVz2goNUlNjYWM2fOxNKlS7Fp0ybRD1kAOH78OPz9/VGpUiXExsZi+vTpWLduHYYPHy7E2bx5M0aMGIEePXoI21yVbnb3799Hs2bNsHbtWsTExKBixYpo0qQJUlNThTht2rSBVCrFhg0bEBsbi169epX47mtp52Pt2rWxaNEiAAUt+SWd28qkpqaKfjwrUpZyX79+HT4+PggODsZvv/1W5ncViQi5ublITU3F4MGDUbFiRTRv3hwAkJmZCQCQSqWiZbS0tHDz5s0y5VOS3NxcvHnzBps2bcKBAwfQp08fAICJiQns7e2xZMkSPH36FGvWrIGHhwcAYPjw4Zg4cWKZuru/evUKkydPxp49ezBu3DisWbNGdCy+f/8eTZo0waFDh/DLL79g586dCAkJQWpqKqytrYXufPPnz0diYiJiY2PLtJ6lnXulCQ0Nxe3bt5GYmCiEERHWrFmDnj17Ktz333//vVC5SkxMLPHaUJJu3bqhW7du2LNnDxo0aACg4Nrbv39/DB8+HBs3bsTTp09F65OVlYWmTZvi5MmTmDdvHrZu3Qp1dXU0a9YM7969A1BQ0WjZsiU0NTURExOD0NBQdO3atczlkxs6dCgcHBwQGxsLPz8/9OnTB1evXgVQ0P3Z3d0dLVq0ELZF7dq1QUQICgrCyZMnsXjxYkRHR+PevXto2rRpsQdRhT18+BDu7u5Yvnw5duzYAV9fXwQGBiIpKanEMoaGhmLTpk2YNm0aNm/eDIlEAj8/Pzx9+lTl9ZwwYQJatWqFmjVrCusSGBgIAOjVqxdmzJiB77//HvHx8Zg4caLoIauq+U+fPh1SqRQxMTHw9fVFz549MXjwYNy9exdr1qwR7s0XLlwQLTNkyBB899132L17N3r06IEBAwZg69atKq8bY+wzIFaqtLQ0AkDz5s1TGsfNzY0CAgKEaS8vLwoJCVE5j/z8fMrJyaEpU6ZQtWrVhPAjR44QALp9+zYRESUnJxMAOnDggNK0Vq5cSQAoJydHFB4SEkKampp0//59IWzmzJmkoaFBeXl5REQ0btw4sra2pjdv3ghxHjx4QFKplGJjY5XmGRISQgDoypUrQlh8fDwBoEuXLhERUYcOHah69eqUn58vxPnll19IJpPRixcviIjI09OTAgMDRWn37duXrK2thTKGhISQl5cXvXr1iurVq0f16tWjV69eiZYBQMuWLROm7e3tqWrVqkIaREStW7cmPz8/YdrNzY06d+4sSsfT05MKnyaBgYE0dOhQpduhqJcvX5JEIqFDhw4JZe/Tpw+pqanRkydPiIjIw8ODJkyYQEREt27dIolEQjExMaJ0AgMDqW3btkrzycnJIQC0cuVKIay0YyU3N5eOHTtGEomEnj59SkRE27dvJwB048YNId6uXbsIAE2aNImIiJ4+fUpSqZSWLFkixHn27Blpa2tTkyZNhDAvL69i+zImJoakUik9fvyYiIhq165N7dq1E8WpXbs2leXSlJeXRzk5OVSpUiWKiooSylP0eCwLVc/Hktjb29PYsWMpJyeH3r59S5s3byapVEpr1qwRxWvSpAl169ZN5XLb29vTuHHj6MqVK2Rubk79+/cXnVNlMX36dAJAAMjJyUm0XpcvXyYAtH//fiEsMzOTTE1NSVNTU6X0SzsG5ddWAKSurk5Lly4Vzd+5cyfJZDICQO7u7vTy5UuKj4+nqlWrUm5u7kescYGcnByKjo4mfX194ZqwaNEiUldXp5s3b5ZpXSZNmkS2traisNu3bxMAOnLkiMK0FJ17RIqvW+PGjROmGzRoQH379hWm5cfjnTt3lK6rovIpSpuo4Ppvb28vTMvvJYXLJE8TACUmJgphW7ZsIQDC/WXZsmUkk8lE95t3796RhYUFzZkzh4iI5s+fT1paWqJtMH/+/GLXsqKU3Rf79OkjxMnOziYTExOaPHmyEFb4XJOTX9+SkpKEsPv375OmpiYtXrxYaRkKk1+DmjVrRgMGDFAa7+rVqwRAdH3PyMggU1NTGjlypGhdih5nRX9TyO+DhV2/fp0A0PLlyz86f6KC47BVq1bC9Js3b0hDQ4Nq1KghXGvy8vLIyspKuHe9fv2adHR0hGuw3IABA6hWrVpKtwlj7PP7T7fc0f9/ai3/+xoDkCxduhTVqlWDTCaDpqYmJkyYUOYRwMqiSpUqopfQXV1dkZubKzy1O3ToEFq0aAGZTCZsF0tLS7i4uCgcFbQwBwcH1KhRQ5hu0aIFpFKp8CTz3LlzaNeunegJc8eOHZGZmYlr164hLy8Ply5dQvv27UXpduzYEWlpaaLubG/evIG/vz/U1NSwf/9+GBoalrruvr6+ou5irq6uePjwIYCC1oMrV64ITz/lWrduLZp2c3PDqlWrMHPmzGLdixQxNjZG1apVhVbdEydOICgoCK6urjh+/DgyMjJw+fJleHl5AQAOHz4MqVSKwMBA0bHp5+dX6vZXxdGjR9GoUSMYGhpCQ0MDjRs3BhEJXZDOnz8PJycnuLq6CssEBARAXV1dmL569Sqys7NF28rMzEz0Dsz79++RmJiIDh06iNbDx8cH2dnZuHbtGnJzc3H58mW0adNGVMagoKBS1+PZs2f4/vvvYWtrCw0NDWhqauLOnTvCuWNiYgI7Ozv069cPMTExePHihUrb53Ofjz///DM0NTWhr6+PTp06ITw8vMTRHVUt99WrV+Hn54dOnTph0aJFHz26aGhoKM6ePYvY2FjY2NggICBAyLNmzZqoX78+hgwZgitXruDx48cYMGAAXr169dHdLosyMzNDUlISDh8+jGHDhuHHH38UPeVv3bo1nj59ilu3buHcuXMwMDDAiBEjEBUVhdzcXHz//fcwMzND3bp1ce3atRLz2rZtGzw8PKCrqwtNTU307NkTb9++Fa59R48eRb169eDs7PxZ1q2o0s49VYSFhWHTpk1Cd9Lo6Gh4eXmhYsWKX6TMci1btiwWpqurKzrn5dcM+TX10KFDqF+/PqytrYXzXyqVon79+sK17Ny5c2jQoAHMzc2FdFQ5/5Vp1qyZ8H9NTU1UrFhRKI8y586dwzfffANPT08hzM7ODvXr1y+xFe7du3cYPnw47O3toampCU1NTRw8eLDE68X58+ehrq6O4OBgIUxXVxetWrUqtcVPFUePHoWamprSa0xZ8vfz8xP+r6+vD3NzczRp0kS41qipqcHR0RGPHj0CUNBj6f3792jfvr3omu/r6yvcMxhj/4z/dOXu6NGjwkVZU1NT9B5UYaamppBKpbh//77StFJTU2FjY1Om/Ddv3oy+ffsiMDAQcXFxOHv2LEaPHl1iV5BPVbTborzLlfw9p+fPn2PVqlWi7aKpqYkrV66IurwpUvgGDRRc/E1MTPD48WMABaPbWVhYiOJYWloK8549e4bc3NwS48jdv38f58+fR+fOnWFgYKDKqitc98LrnZ+fL7x3JGdmZiaaHj9+PPr164d58+ahevXqcHR0RHR0dIn5ent748SJE3jy5AmSk5Ph5eUFLy8vnDhxAmfOnEF+fr7Q1en58+fIysqCjo6OaPsPGzYMjx49+qQHEMnJyWjVqhVMTEywZs0anD59WnhPVL4dnj59WmwbqKurw9jYWJiW/xguaVulp6cjPz8fYWFhovWQHyOpqal4/vw58vLyih03RacVCQkJwYEDBxAZGYmEhAQkJSWhevXqwnqoqalh3759MDc3R0hICCwtLdG8eXPcunVLaZpf4nwMDw8XKi/t27fHb7/9hh07diiNr2q5jx8/jlevXgnv134sKysr1KlTB8HBwYiPj8fLly9F78isXr0aAFCrVi1YW1vj5s2b6NmzJ6ysrD4pXzkNDQ14enrC19cXM2bMQEhISLH36PT09FC5cmWoqalhxYoVsLa2RqtWrbBkyRLcvHkTd+/eRefOnUt89zUxMREdO3ZErVq1sGXLFpw5cwbz588H8H/H/osXL2Btbf1Z1qsoVc49VXTu3BkfPnzAzp07kZmZiS1btiAkJOSLlLmwotdkQLV7yZEjR4rdS+Li4oR7yZMnTz7q/FempGu8MoruS0DBfafo++iFjRgxAitWrMDw4cNx8OBBJCUlwd/fv8T80tLSYGxsLHTBVzUvVb148QLGxsbQ0tL65PyLPjCVSqUKwwrvbwBwcnIS7e8OHTogPz9fqAQyxr68//RomR4eHqKnVUV/yMtpamqifv362LNnD6Kiooo9JT9z5gxevHiBRo0alSn/2NhY+Pr64tdffxXCdu7cWaY0PjcTExN07NhR4Uh4RX/MF1V08JT8/Hy8fPlS+CFobW1drF//kydPhHnm5ubQ0NAoMY5c9erV0a1bNwwcOBAVKlQo1tpXVmZmZlBTUxNuUHJFp7W1tTF16lRMnToVf/75J2bPno3Q0FDUqlULtWrVUpi2l5cX1q1bh6NHj6JatWowMjKCt7c35s2bB2NjY9SoUUOooJqYmEAmk+HYsWMK0/qUFpP9+/cjLy8PMTExwg8x+bsvchYWFsXWOS8vD+np6aI4QMG2KTxSYeHljIyMIJFIMH36dDRt2rRYWRwcHGBkZAR1dfVix03R6aIyMzOxb98+rFy5UvSDvujw/K6uroiNjUVWVhaOHDmCYcOGoUuXLkpbQL/E+WhtbS20CDRu3Bi1a9fGmDFj0KZNG6WtbaqUu2/fvkhOTkbLli1x8uRJVK5c+ZPKCRQ8na9YsaJoqP8qVarg2rVrQmtEpUqVEBQUJBok6HNyc3PDypUrFc7LyMhAZGSkMJjL0aNH0aVLFxgaGqJPnz4YPnw43rx5o/BhT1xcHBwcHERDxxdt6TM1NVV54KjCtLS0irVKFD5fANXOPVUYGBigffv2iI6ORlZWFnJyctCpU6cyp6NqueU+pmXYxMQEXl5eCgfEkr8raWlpWeyhaWnn/+em6L4EFNx3KlWqpHQ5+XvjAwcOFMI+fPhQ4jXa2toa6enpyM3NFVWwnjx5Itzf5BUzVfdNYaampkhPT0dWVpbCCp4q+X8sExMTAFD67vuXenDCGCvuP91yp6+vD09PT+GvpIEOfvzxR9y4caPY8OH5+fmYNGkSLC0t0bFjxzLln5mZKRqsID8/H1u2bClTGorI0/yYFgf5aGa1atUSbRtPT09htDhlUlJShJfXAWDfvn3Izs4WftzWqVMH27dvF43qtmXLFshkMlSvXh3q6upwd3cv9h2iLVu2wNrautiIdP369UNERAS6deuGw4cPl3ldC9PQ0EDNmjWLjQK4a9cupcu4urpi9uzZIKISP+zs7e2NjIwMLFy4UPhMgbe3Ny5duoS9e/eKPsju6+uLzMxMZGVlFdv+hbsNfYzMzExoaGiIulhu3rxZFMfDwwPJycn4888/hbC9e/eKRjSVfyi98LZ68eIFTp8+LUzr6uqiXr16uHPnjsL1MDMzg4aGBmrVqoW4uDhRGUqrUGVlZSE/P1907pw+fVppy7KWlhYCAgLQt29f0XoVpcr5+Cnnlrq6OiZPnow///yzxONKlXKrqakhOjoaHh4e8Pf3L7XrmSpevHiBv/76S+F1sFKlSqhUqRLu3r2LAwcOfHKLoTKnTp1Seh2eMWMGAgICRA9R5AMVyf9VJjMzs9jAR0WPfV9fX5w5c6bYh9zllO37ChUq4MWLF6IutEVHOlTl3FNVWFgY9uzZg3nz5iE4OLjULulSqVTh8VqhQgXRdSs/Px9Hjhz5qDIp4ufnh1u3bqFSpUrFzn9511dPT08kJiaKHgx9yQecirZFnTp1hJ4gcg8fPsTp06dLfIhR9HqRmpoqugYq4unpiby8PFHr/fv37xEfHy/kZWlpCQ0NDdG+uX//frHjUtG6+Pj4ID8/X+nouqrk/7Hq168PmUyGJ0+eKLzmK2tNZIx9fv/plruy6NixI0JCQhAWFoZr167B398fb968weLFi3H06FHs2LFD+Madqvz8/DBkyBDMnTsXLi4uWLp0aak/UlQhv3EuXLgQfn5+MDc3L3WEPrmhQ4dizZo1aNasGfr37w8rKys8evQIBw8eRNeuXRW2wshZWFigc+fOmDJlCj58+IDhw4ejRYsWcHNzAwCMGzcOHh4e6Ny5M8LDw3H9+nVMnDgRgwcPFp76TZw4EUFBQRgwYADatm2L48eP47fffsPixYsVPhEdP348Xrx4geDgYBw5ckQYTe9jDBs2DD169ICjoyN8fX2xceNGPHr0SPTU+ttvv4Wnpyfc3d0hlUqxevVq6OjooF69ekrTdXR0hI2NDY4fPy58o8vR0RGWlpZITEwUPfl1cXFB79690a5dO4waNQpubm7Ce3kZGRmYMWPGR6+fj48P3r9/j759+6Jz5844dOgQ9u/fL4oTFBSEKlWqoH379pg6dSoyMzMRGRkJfX19Yfubm5ujR48eGDlyJCQSCWxtbTF9+nSYmJiI9tGMGTPg7+8PIkJwcDBkMhn+/vtv7NixA+vXrxfen+rSpQtGjBiBZs2aYcOGDaV2TzIyMoK7uzsiIyOhq6uL169fY+LEiaInw1euXMGIESPQuXNnODk54enTp1iwYIHoPZKiVDkf5d0Dly9fLrQaleUdrTZt2qBq1aqYPXu2wneLylJuTU1NbN26Ff7+/mjRogWOHz8udJ91cHCAj4+PMDx6Ubt378aGDRsQGBgIa2trpKamYtasWdDS0kKvXr2EePPmzYOFhQWsrKxw48YNTJ48GcHBwaJ3sKKjoxEeHo67d+/C3t4eQMEnP27cuCG0wsi7kTo4OAgPKXx9fdG+fXu4uLjgw4cPwnFRePh6uQcPHmDp0qW4fPmyENaoUSMsXLgQ1apVw6ZNm+Dm5qa0i7afnx/mzZuH8ePHo0mTJti8eXOxET9DQkIwf/58NG3aFBEREXBwcMC1a9egpqaGH3/8EVZWVjA0NMTatWthamoKXV1d1KhRQ3i3uFevXhgwYACuXLlSbLurcu6pytfXF7a2tjh79qzST54U5uzsjOfPn2P16tWoVq0abGxsYGNjg+DgYAwdOhSzZ89GtWrVsGLFCrx9+1ZUAf0UISEhWLJkCXx8fDB06FDY29vj6dOnOH78OOrWrYvu3bsjLCwMkydPRlBQEMaOHYsHDx5gwYIFnyV/RZydnbF161bs378fJiYmcHZ2RsuWLeHp6Yn27dtj+vTp0NbWRkREBGxsbEp8iOHn54f58+fDyckJ6urqmDRpUqmvZlSrVg3t27dH7969kZ6eDisrK8yaNQs5OTkYOnQogIIHN23atMGvv/4KW1tbaGpq4ueffxbukYXXZe3atdixYwdsbW3h6OgIV1dXhIaGYsCAAXj8+DHq1auHR48e4dy5c5g7d65K+X8sY2NjTJgwAX379sXdu3fRoEED5OTkCNcCeav55MmTMXnyZOHTP4yxL+BrjubyvyYvL4+WLFlCbm5upK2tTUZGRtS6dWs6d+5csbiqjJaZnZ1NAwYMIBMTEzI1NaXBgwfT4sWLRSMFfsxomUREo0ePJisrK5JIJEI5FI2udeDAAQJAycnJQtj9+/epe/fuZGZmRlpaWuTk5ETff/893bt3T2l+8rTXrl1L9vb2pK2tTUFBQcKIkHLx8fHk7u5OUqmUrK2thdEEC1u9ejW5uLiQpqYmOTg40OzZsxXmJZefn089e/Ykc3NzYaQ7lDLqHFHxkeGIiKZNm0bm5uakp6dHISEhNHXqVDI0NBTm//rrr1S7dm3S19cnAwMDatSoESUkJCjdLnIdO3YkAKJtKA8rPJocUcFxFhUVRc7OziSVSsnCwoL8/f1px44dStNXdbTMpUuXkp2dHeno6FBQUBAdO3as2Kh+f/31FzVq1Ii0tLTI2dmZ9u7dSwYGBsIId0REb9++pR49epCOjg5ZWlrSr7/+SkFBQcVG9Dxx4gQ1bdqU9PT0SE9Pj2rUqEFjx46l7OxsIc6MGTPIwsKC9PT0KCwsTBgtryQ3btwgLy8vkslk5OrqSjt27BCdc48fP6auXbuSg4MDaWlpkbW1NfXq1YueP3+uNE1Vzkcionnz5pGdnR2pqamJRgctStExR0QUHR1NAOj8+fNEJB7BT5VyF003PT2datSoQQ0aNKB3794REZG5uTmNGDGixO0XFBREVlZWJJVKydHRkUJDQyklJUUUb9q0afTNN9+QVColBwcHioyMFO07ov8bUbHwNUQ+mmLRv8LXxEGDBlGVKlVIJpORmZkZNW7cmHbu3KmwvD179qSIiAhRWGZmJvXo0YMMDAzIzc2NLl++rHR9iYgiIiLIwsKCDAwMqGfPnrR169Zi5X78+DF1796djI2NSSaTkZubm+i827RpE1WqVIk0NDRE1464uDhydnYmmUxGAQEBdPTo0WLnlSrnnirXLaKCEQitrKxUGjE0OzubwsPDydTUVDTqbVZWFg0cOJDMzMzI3NycIiMjlY6WWfQareoIoS9fvqT+/fuTjY0NSaVSsrOzoy5duohGgz179izVrl2btLS0qHbt2nT27NmPHi2ztBEm7927J1yPCpc1LS2NOnfuTAYGBqSjo0OtWrUqdUTchw8fUsuWLUlXV5ccHBxo2bJl1K1btxKvCUQFI0/27t2bTE1NSVtbmxo1aiQaqZOI6NGjR9SqVSvS09OjSpUqUWxsbLF1SU9Pp3bt2pGhoaFoe+Xk5ND48ePJzs6OpFIpOTk50bRp08qUf9HjkEjxsaho9NE//viDatWqRVpaWmRiYkLe3t70xx9/CPPl1wbG2JcjIfqEr94yVs61bdsWL1++FEa7/C+6ePEiateujYMHDyptuc3MzISTkxO+//57TJky5R8uISvs3r17cHJywu3bt+Hk5PS1i8O+ABcXFwQFBSEqKuprF4Uxxti/DHfLZOz/e/jwIebPn4/GjRtDXV0dcXFxiIuLU/r+Qnm1aNEiyGQyODk5ISUlBZMnT0bVqlXh4+MjxNm/fz+uXr0KNzc3vHnzBgsWLPgsozeyT5eYmIjg4GCu2JVD586dw+7du3Hnzh307t37axeHMcbYvxBX7hj7/7S1tXHx4kUsX74cb9++haOjI5YsWYIuXbp87aL9ozQ0NPDLL7/g/v370NLSQtOmTTF37lzRuzi6urrYsGEDIiIikJubCzc3N+zdu5crFP8C3333Hb777ruvXQz2BdSpUwcmJiaYM2fOZxkhlTHGWPnD3TIZY4wxxhhjrBz4T38KgTHGGGOMMcbKC67cMcYYY4wxxlg5wJU7xhhjjDHGGCsHuHLHGGOMMcYYY+UAV+4YY4wxxhhjrBzgyl0Z5Ofn47fffoO7uzt0dHRgYmKCNm3a4Pz588XipqamolmzZtDT04NEIsGrV68+Ks+EhARIJBLcuXMHAJCSkgKJRIKDBw8qXebSpUuIiIhAfn6+KDw0NBTe3t4fVY7SfMm0PyYviUSC5cuX/yPlKc3cuXNx+PBhUZgq+1FVq1atwrZt2z45nS8tIiICFSpUEKaVHacfa/z48XBwcCgxjo+PD7p37/5Z8vua/lf2Ofu8EhISMHv27GLhn+u4jomJQaVKlaChoYHg4OBi95//FapcCw4cOIDOnTvDzs6uxPvF7t27UbduXejp6cHW1hY//PADXrx4UWoZUlJS0KpVK+jq6sLa2hqTJk0qdq3Ly8vDxIkTYW1tDV1dXQQGBuLevXuiOFu2bIG9vT2sra2xePHiYvn4+vp+1nvdwoULYWdnBzU1NQwePFhhHGXXn3/TfZex/zKu3JVBeHg4Bg0ahBYtWmDXrl34448/8OHDB3h5eWHfvn2iuFOmTEFycjJiY2ORmJgIfX39f6ycly5dQmRk5Gf70fy/KDExEW3btv3axQCguHL3Of2v/tD/Gsfp4sWLERkZ+Y/l96X8r+5z9mmUVe4+h5ycHISFhcHHxwdHjx7Fr7/++kXy+bfYu3cvbty4gVatWimNk5SUhLZt26J69erYvn07pk+fjvj4eHTt2rXEtLOystC8eXO8fPkSW7duxeTJkzFr1ixMmzZNFG/y5MmYPXs2IiMjsXXrVrx8+RIBAQHIyckBADx//hwhISEYNGgQfv75ZwwdOhQ3btwQlo+Li8OLFy8QHh7+CVvi/zx48ACDBg1Cz549cfLkSQwZMkRhPL7+MPbvxh8xV1FMTAxWr16NNWvWiJ6QtmnTBgEBAQgJCcHdu3ehq6sLALh16xa8vLzg7+//tYr8n1a/fv2vXYR/PSJCVlYWtLW1v3ZR/jFVq1b92kVg5VhmZiZkMpnK4f8mDx8+xLt379C1a1d4eXkBAB49evSVS/XlREVFYdasWQCA33//XWGcLVu2wNbWFitWrIBEIgFQ0IMnPDwcr1+/hqGhocLlNm/ejJSUFBw5cgS2trYAgPT0dEydOhUjRoyAtrY2MjMzMXv2bEyaNAm9e/cGANSoUQMODg6IiYlB165dcfr0aTg4OGDo0KEACn6HHDlyBFWrVkVubi5GjhyJ+fPnQ03t8zynv3PnDvLz89GrVy84OTl9ljQZY/88brlT0cKFC+Hi4oJu3bqJwtXU1BAZGYknT54gJiYGQEHXhKNHj2LNmjWQSCTw8fFRmObbt2/Rr18/VKxYETKZDC4uLpg1axY+5bvyq1atQlhYGABAU1MTEokEoaGhojg7duyAs7Mz9PX10apVK6SlpYnmJycno0OHDjAyMoKenh6Cg4Px4MEDlfLfsGEDnJycIJPJEBwcjGfPnonm7927Fx4eHtDW1oatrS3GjRuHvLw8UZw1a9bA1dUVWlpacHJywty5c0vM8+3bt/D29kbdunWF7q9Fu4c4ODhg/PjxmDp1KiwtLWFubo6BAwcKT0jlpk+fDktLSxgYGKBPnz5YuHChcFMHgOzsbAwZMgQVKlSAlpYW7Ozs0LNnT6Vlc3BwwL179zBt2jRIJBJIJBKkpKQI89+8eYPu3btDT08Pjo6OxX5kxMXFwcfHByYmJjA1NUWrVq1EXaTkT9nXrVsnpJ+QkKCwLKtWrYJEIkFSUhIaNGgAmUyGjRs3Aij4EePu7g5tbW1UqFABU6ZMER2HV69eRfPmzWFkZAR9fX3UrFkT69evF+Yr6o7TvXt3pcd+acdpaeXJz8/HyJEjYWxsDFNTU4wZM0alFsCi3dfkXXxLOyeKOnHiBFq1agULCwsYGhqicePGOHfuXKn5P378GF26dBHOrdatW+Pu3bvCfGXd4CpUqICIiAhhHZTt84yMDAwaNAi2trbQ1taGq6srli1bpnL+8u7CW7ZsQefOnaGnp4fKlSvj0KFDyM7ORv/+/WFkZARHR0fh2JHLy8vDlClT4OjoCC0tLVSrVg3bt28vdZsUtn//fmhoaODt27dCmKWlpeiH5tOnTyGRSHDq1CkhrLTjRZHPta02bdqETp06QV9fH/369RP24cGDB9G8eXPo6OhgxowZAAr2r5eXF2QyGSwsLDB48GBkZWWJynXgwAE0bNgQOjo6MDMzQ1BQEJ48eYKIiAhERkbi4cOHwn6XHxOF/fbbbzA0NERmZqYofMWKFdDT00NGRkaxZVatWgVHR0cAQNOmTSGRSLBq1SqF223YsGFwdXWFjo4OKlasiDFjxiA7O1sU58yZM6hduza0tbVRr149nD9/vliaT548QZs2bSCTyVCxYkVs2rQJ3t7exe5XX+paoEqFKDc3V3i1Qs7Q0BBEVOLxtW/fPjRs2FCo2AFAx44d8fbtW+G4PXnyJDIyMtCxY0chjq2tLRo2bIi9e/cCKLjf6OjoCPN1dHSE42Xp0qVwdHRE8+bNS10PuZLuvxEREfD19QUAVKxYUem9pLR7Tk5ODgYNGgRjY2PY2tpi8uTJxbZVaftUfl3esmULKlWqBD09PfTs2RPZ2dk4evQo3NzcoK+vj+Dg4I9+5YWxco1YqbKzs0kqldKwYcOUxjE1NaXw8HAiIkpMTKSaNWtSq1atKDExka5fv65wmbS0NBo4cCDFxsZSQkICzZ8/nwwNDWnWrFlCnCNHjhAAun37NhERJScnEwA6cOCAwjSfPn1K48ePJwB04sQJSkxMpDt37hARUUhICFlaWpKHhwdt3bqVNm7cSBYWFtSuXTth+WfPnpGtrS3Vq1ePtm7dStu3byd3d3eqWbMm5eXlKV3/kJAQsrCwIFdXV9qyZQutWbOGrKysKCAgQIhz8eJFUldXpw4dOlB8fDxFRUWRVCqlUaNGCXF27txJAKhv3760d+9eGjt2LEkkElqyZIkoLy8vLyIiev36NTVo0IDq1q1Lr169EuIAoGXLlgnT9vb2VKFCBerQoQPt2bOHoqKiSF1dnebPny/EWbNmDQGgUaNG0d69eyksLIxsbW2p8GkSERFBVlZWtGrVKkpISKD169cL+12RCxcukJWVFYWGhlJiYiIlJibShw8fhP1ob29P48aNo/3791N4eDgBoCtXrgjLz58/nxYvXkwHDx6kXbt2UevWrcnKyooyMjKIiOj69evk7u5OLVq0ENJ//fq1wrKsXLmSAFDlypVpwYIFdPjwYbp58yatX7+e1NTUaPDgwbRv3z6aM2cO6ejo0MyZM4VlHRwcKDAwkOLj4+nAgQM0d+5cWrRokdLtTUTUrVs3atKkiTA9adIksrW1JaKSj1NVyjNjxgzS0NCgn3/+meLj4ykwMJCsra3J3t5e6b4gImrSpAl169ZNmFblnFBk3bp1NGvWLNq3bx/t27ePQkNDSUdHh+7fv690mfz8fPL09CQ7Oztat24dbd26lapXr0729vb04cMHIip+vsvZ2trSpEmTiEj5Ps/Ly6MmTZqQkZERzZs3jw4ePEhLliwRllMlf/lx+c0331BkZCTt27ePmjdvTsbGxhQeHk6DBw+m/fv3U/fu3UkqlVJaWppQxt69e5OBgQHNmTOH9u/fTwMHDiQ1NTU6c+ZMiduysNevX5O6ujrt27ePiIhu3bpFmpqapKamRg8fPiQiom3btpG2tjZlZWURkWrHS1Gfc1tZWVnRsGHD6ODBg3Ty5ElhH9rZ2dHUqVPp8OHDdOHCBTp27BhpaGhQz549ac+ePbR8+XIyMzOjH3/8USjX/v37SV1dndq2bUs7duygHTt20IABA+jPP/+k1NRU6tWrF5mbmwv7PTU1lYjEx3V6ejppa2vT+vXrRevcqFEj6tmzp8Lt8fTpU4qJiSEAtGjRIkpMTKSnT58qPB7Dw8Np8+bNlJCQQCtWrCBbW1saOHCgaB+amJiQl5cXxcXF0ZIlS8jZ2ZkA0MqVK4V4TZs2JWtra1q7di1t3bqVqlWrRpaWlhQSEiLE+ZLXgsIUXb+IiM6dO0dSqZQWLVpEr169oj///JM8PT2pS5cuJaZXp04d6tOnT7FwLS0tWrx4MRERLVq0iLS1tYvF6d27N9WtW5eIiO7evUtSqZSOHz9O165dI0NDQ0pISKBXr16RlZUVXbt2TeV1LO3+m5qaSvPnzycAtG3bNqX3kpLuOQCoQoUK1LdvX9q3bx+NHj2aAFBcXJywvCr7VP57ol69erRjxw5aunQpaWlpUZ8+fcjNzY02bdpEGzZsIBMTE9GxxxgrwJU7FaSlpREAmjdvntI4bm5uooqMl5eX6CZVmvz8fMrJyaEpU6ZQtWrVhPCyVu6I/u9HfE5Ojig8JCSENDU1RT9AZ86cSRoaGkLFbdy4cWRtbU1v3rwR4jx48ICkUinFxsYqzTMkJKRYxSQ+Pp4A0KVLl4iIqEOHDlS9enXKz88X4vzyyy8kk8noxYsXRETk6elJgYGBorT79u1L1tbWQhnllbtXr15RvXr1qF69eqKKHZHiyl3VqlVFFdTWrVuTn5+fMO3m5kadO3cWpePp6Smq3AUGBtLQoUOVbgdF5BW4wuT7sfAPgOzsbDIxMaHJkycrTCc3N5fev39POjo6FBMTI4QXrbAoIz8uCm+X/Px8srOzowEDBojiRkVFkZmZGWVnZ9OzZ8+K7duiylq5K1yewsepKuXJyckhCwsL0X7IysoiKyurj6rclXZOlCYvL49ycnKoUqVKFBUVpTTerl27CAAlJSUJYffv3ydNTU3hB58qlTtF60FEtHv3bgJABw8e/Oj85cdl//79hTg3btwgANS6dWsh7M2bN6ShoUErVqwgooJKmEQiER2XRAXnS9u2bZVuE0Xc3NxowoQJRFRwjNSvX59q1apFmzZtIiKiYcOGUaNGjYhIteNFkc+5rYruB/k+LHrOe3l5Fbu2xcTEkFQqpcePHxNRQaVAvm6KFD2H5IoeD126dBHdj/7++2+SSCR06NAhpWnfvn2bANCRI0eKrUvR41EuJyeHoqOjSV9fXzhf5s6dSzKZjF6+fCnEW7hwoahyd+nSJQJAe/bsEeJcvXqVAAj3zS99LShMWeWOqOA+pqenRwAIADVr1ozev39fYnqVKlUSPbSUs7S0pGnTphER0dSpU8nS0rJYnFGjRlHlypWFaflDMAAUGhpKREQjR46k3r17q7x+RKrdfw8cOEAAKDk5ucS0lN1zAFCLFi1EYdWrVxcegKp6vsqvyw8ePBDidOrUiQDQuXPnhLCRI0eSnZ2dCmvP2H/Lf7pbJhEhNzdX+PsaA5AsXboU1apVg0wmg6amJiZMmPBFRyarUqUK7OzshGlXV1fk5ubi6dOnAIBDhw6hRYsWkMlkwnaxtLSEi4uLwlFBC3NwcECNGjWE6RYtWkAqlSIpKQkAcO7cObRr107UxaVjx47IzMzEtWvXkJeXh0uXLqF9+/aidDt27Ii0tDRR19A3b97A398fampq2L9/v9J3Hwrz9fUVdcVxdXXFw4cPARR0v7ly5QoCAwNFy7Ru3Vo07ebmhlWrVmHmzJm4fv16qXmWplmzZsL/NTU1UbFiRaFMQEEX2Y4dO8LS0hIaGhrQ0dHB+/fvP+kYadmypfD/W7duITU1FR06dBCdC76+vnj+/Dnu378PExMT2NnZoV+/foiJiVFppLiPpUp5UlNT8fTpU7Rp00ZYTiqVokWLFh+VZ2nnhCLPnj3D999/D1tbW2hoaEBTUxN37twpcb+cO3cO33zzDTw9PYUwOzs71K9fXzhHPsXRo0dhY2ODpk2bfnL+fn5+wv8rVqwIAKIutvr6+jA3NxfeyTp8+DCkUikCAwNF+83Pz6/U60ZRXl5eOH78OICC7q/e3t7w9vYWhcnfCVPleFHkc26rwueTsvD3798jMTGxWDl9fHyQnZ2Na9eu4d27dzh37hx69Oih4pZSLiwsDAcOHMDjx48BFHR1r1ChgtDt7lNs27YNHh4e0NXVhaamJnr27Im3b98K58v58+fRoEEDGBsbC8sUvY6eP38eGhoaoi6F1atXF41w+TWuBUVdvXoVPXr0QFhYGA4fPoz169fj77//LrEr/uc2ZcoUPHnyBKmpqVi5ciXu3buHlStXYvLkybh//z78/PxgYmKC9u3bl9hFsbT77+dS+J4GiO+zZTlfnZ2dRd1aK1asCD09PXh4eIjC0tLSPulVFsbKo/905e7o0aPQ1NQU/pSNOGVqagqpVKr0hwJQ8OkDGxubMuW/efNm9O3bF4GBgYiLi8PZs2cxevToYu9gfE5GRkaiaalUCgD48OEDgILRuVatWiXaLpqamrhy5QpSU1NLTNvc3Fw0raamBhMTE+EHRlpaGiwsLERxLC0thXnPnj1Dbm5uiXHk7t+/j/Pnz6Nz584wMDBQZdUVrnvh9c7Pz4epqakojpmZmWh6/Pjx6NevH+bNm4fq1avD0dER0dHRKuVf1jLl5eWhTZs2uHnzJmbPno0TJ04gKSkJJiYmQpyPUXj7Pn/+HEBBxbfw/pb/qE1NTYWamhr27dsHc3NzhISEwNLSEs2bN8etW7c+ugzKqFKeJ0+eACh+vBWdVlVp54QiISEhOHDgACIjI5GQkICkpCRUr169xGUUHf9AwfFd2jt+qnjx4gWsra0/S/6FH5bIt0fRByhFz5+srCzo6OiI9tuwYcPw6NGjMj048/b2xtmzZ5GTk4OTJ0+iUaNG8PLywokTJ5CZmYkLFy4In0JR5XhR5HNuK0Xxioanp6cjPz8fYWFhonLKj9nU1FSkp6eDiEosl6qaNm0KGxsbrFu3DkBB5a5Hjx6iH/YfIzExER07dkStWrWwZcsWnDlzBvPnzwfwf+fL06dPS72OPn36FMbGxsXeeysc72tcC4qaMGEC3NzcMH/+fPj6+qJLly5Yu3YttmzZgrNnzypdztjYGK9fvy4W/urVK6HSq0ocOQsLC+EzMmPGjMGgQYNgaWmJn376Ce7u7sJxPnnyZKVlKu3++7mUdp8FVDtfFV1vFIXl5uYWe2+fsf+6//RomR4eHqKnsEVvQHKampqoX78+9uzZg6ioqGI3yDNnzuDFixdo1KhRmfKPjY2Fr6+vaMjpnTt3limNz83ExAQdO3bEyJEji80resMuqujgKfn5+Xj58iWsrKwAANbW1sVaQ+Q3Z2tra5ibm0NDQ6PEOHLVq1dHt27dMHDgQFSoUKFYa19ZmZmZQU1NTbj5yBWd1tbWxtSpUzF16lT8+eefmD17NkJDQ1GrVi3UqlXrk8pQ1J07d3Dt2jUcPXoUjRs3BlDwsvqnvkBe+Pg1MTEBAKxevVrhSJLOzs4ACp6+xsbGIisrC0eOHMGwYcPQpUsXoVVGKpUWG1QhPT29zGVTpTzyfVL0eCs6/aVkZmZi3759WLlypegJvqIfaoUpOv6BguO7UqVKAAAtLS0AKLYtVdnnpqamJf5IUyX/j2ViYgKZTIZjx44pnF+W0fy8vLzw/v177N+/Xxh1+P379+jevTsOHjyI3NxcNGzYUMgXKP34LepzbitlFabC4UZGRpBIJJg+fbrC1kIHBwfIZDJIJJLP8kNbTU0NISEhiI6ORoMGDXDnzh2EhIR8crpxcXFwcHDAH3/8IYQVbfWxsLAoNgBX0euohYWFUOEtfGwUjvdvuBbcunWrWMus/Dp/9+5d1K1bV+FyVapUwc2bN0VhKSkpyMrKEo7JKlWq4MOHD7h37x7s7e2FeDdv3lR63J49exYnTpzAihUrABQ8oJ42bRp0dXURFhaGiRMnKl2X0u6//4SPPV8ZY2Xzn26509fXh6enp/BX0kdPf/zxR9y4cQMbNmwQhefn52PSpEmwtLQUjXqliszMTOGpuDytLVu2lCkNReRpfkwLoJ+fH65fv45atWqJto2np6cwmpoyKSkpuHr1qjC9b98+ZGdnC0/l6tSpg+3bt4u6UGzZsgUymQzVq1eHuro63N3di30/Z8uWLbC2thZ9ABsA+vXrh4iICHTr1u2TvyOnoaGBmjVrIj4+XhS+a9cupcu4urpi9uzZICL89ddfSuNJpdKP2hfy0e4KHyNbt24t1grysekDBTdTGxsbpKamFtvfnp6exb7PqKWlhYCAAPTt2xd//vmnEF6hQgXRNsjIyMCZM2dKzFvRcapKeezs7GBhYYG4uDhhuezs7GLfmvxSsrKykJ+fL9ovp0+fLrVlu06dOkKLs9zDhw9x+vRp4Uei/BgvvC0TExPx7t07UVqK9rmvry8ePXqEI0eOfHT+H8vX1xeZmZnIyspSuN/Kws7ODt988w1mzJgBZ2dnmJqaws7ODhUqVEBUVBSqVq0qtGyU9fgtXN5/clvp6uqiXr16uHPnjsJympmZQVdXF3Xr1sXatWuVplOWcz00NBRXrlzBqFGjUL9+fVSpUqXM5S4qMzMTmpqaorDNmzeLpj08PHD69GnRw52i11EPDw/k5uZi//79Qti1a9dEIwn/G64FdnZ2uHjxoijswoULACCqkBXVokULnDx5UlRR37JlCwwMDIQHE15eXtDT0xPd8x89eoRTp04hICBAYbrDhw/HtGnTRJ/VeP/+vehfZUq7/5bFx95zPvZ8ZYyVzX+65a4sOnbsiJCQEISFheHatWvw9/fHmzdvsHjxYhw9ehQ7duwQvnGnKj8/PwwZMgRz586Fi4sLli5dWuoFWhXyp18LFy6En58fzM3NS6y4FjZ06FCsWbMGzZo1Q//+/WFlZYVHjx7h4MGD6Nq1q9J3VICCp7GdO3fGlClT8OHDBwwfPhwtWrSAm5sbAGDcuHHw8PBA586dER4ejuvXr2PixIkYPHiw8ERv4sSJCAoKwoABA9C2bVscP34cv/32GxYvXqzw6f/48ePx4sULBAcH48iRI6L++GU1bNgw9OjRA46OjvD19cXGjRvx6NEj0RP4b7/9Fp6ennB3d4dUKsXq1auho6ODevXqKU3X2dkZe/fuRevWraGrq4uaNWuqVB4XFxdYW1tj2LBhmDhxIlJSUvDLL78U64bq7OyMrVu3Yv/+/TAxMRGG9FeFmpoaoqKiEBYWhvT0dDRr1gxqamq4desWDhw4gB07duDKlSsYMWIEOnfuDCcnJzx9+hQLFiwQvZcVHByM33//HTVr1oSVlRVmzZpV6ne9lB2npZVHQ0MDQ4YMwYQJE2BmZgY3NzcsWrTok7ucqcrIyAju7u6IjIyErq4uXr9+jYkTJ5b69Ltly5bw9PRE+/btMX36dGhrayMiIgI2NjbCZyHs7OxQu3ZtjBkzRvgkwIwZM4rtT0X7vEWLFvD19UWHDh0QERGB6tWr4+7du3j06BEmTpyoUv4fy8XFBb1790a7du0watQouLm5ISMjA5cvX0ZGRobwKQD5cP6lvSPj5eWFDRs24IcffigWJv8mGKDa8avI19hWM2bMgL+/P4gIwcHBkMlk+Pvvv7Fjxw6sX78eBgYGmDZtGlq0aIF27dohLCxMeKe4f//+cHZ2FlqrVq9ejWrVqsHGxkbp6wAVK1ZE48aNcezYMSxevPijylyUn58f5s2bh/Hjx6NJkybYvHlzsRaqsLAwTJ48GUFBQRg9ejQePHiABQsWAPi/FtxatWqhadOmCA8PR1RUFGQyGSZOnAgLCwshjir79lOuBffu3RP13Dl//jyMjIxgbm6OJk2aAAB69+6NDh06oH///mjXrh3S0tIwceJE1K5dW3TN19DQwMSJE4WWs06dOmHKlClo164dIiIikJqaisjISIwcOVL4rqhMJsPQoUMREREBQ0ND2NnZITIyEhUrVlT4oHjbtm1CC7Zco0aNMGnSJAwePBgzZswQyq2IKvdfVX3sPedjz1dV3bt3DxUrVsQff/zxj74Xydi/ztcby+V/T15eHi1ZsoTc3NxIW1ubjIyMqHXr1qLRm+RUGS0zOzubBgwYQCYmJmRqakqDBw+mxYsXi0Zn/JjRMomIRo8eTVZWViSRSIRyFP6EgJyi0bHu379P3bt3JzMzM9LS0iInJyf6/vvv6d69e0rzk6e9du1asre3J21tbQoKCqInT56I4sXHx5O7uztJpVKytramsWPHFhvVc/Xq1eTi4kKamprk4OBAs2fPVpiXXH5+PvXs2ZPMzc3p5s2bRKR4tMyio9eNGzeu2Ihq06ZNI3Nzc9LT06OQkBCaOnUqGRoaCvN//fVXql27Nunr65OBgQE1atSIEhISlG4XooKR4erVq0cymUzY1sr2Y9Hj5uTJk1SrVi3S1tYmDw8POnXqVLGRE+/du0dNmzYVRnQrPNpdYcpGUSUq+ARFgwYNSCaTkaGhIXl6etKvv/5KRESPHz+mrl27koODA2lpaZG1tTX16tWLnj9/Liz/+vVr6tq1KxkZGVGFChXot99+K3W0TCLFx2lp5SEqGDl02LBhZGhoSMbGxjRixAgaM2bMR42Wqco5UdSNGzfIy8uLZDIZubq60o4dO1Q659PS0qhz585kYGBAOjo61KpVq2IjEd68eZO8vb1JR0eHatasScePH1d5n799+5YGDBhAFhYWpKWlRa6urrR8+XKV81d2XBY9n4iKn1N5eXkUFRVFzs7OJJVKycLCgvz9/WnHjh1CnBEjRpC5uXmJ24ioYJh4ALR69epiYdHR0cXil3a8KPKltlVJI0yeOHFC2G96enpUo0YNGjt2rGhUz3379lHdunVJS0uLTE1NqU2bNsJ1NDs7m8LDw8nU1JQACMeEstELo6KiSEtLSzRypTKqjpYZERFBFhYWZGBgQD179qStW7cWO19Onz5Nbm5uJJVKyd3dnfbv308ARCMup6WlUevWrUlbW5vs7e0pOjqaatSoQYMGDRKV60tdC+TXw6J/ha9ZRETR0dHk5uZGurq6ZGtrSz169KBHjx6J4hTeF3J///03BQQEkEwmI0tLS5owYUKxEXhzc3Np/PjxZGlpSTKZjFq2bEkpKSnFypqdnU2VKlUqdq9JTk4mb29v0tPTo6CgoFL3c2n3X1VHy1R2/VF0nSh6HyAqfZ8qui4run8UvafJz8nCn9xg7L9IQsTDDDGmTNu2bfHy5UthpD7G2Mfz9fWFj48PJk2a9LWL8p8QEBAAfX19xMTEfNVyxMbGol27drhz544w+mpRaWlpcHJywsKFC9GrV69/uISMMVZ+cLdMxv6/hw8fYv78+WjcuDHU1dURFxeHuLg4rF+//msXjbH/eXl5ebh27Vqxd7TY53f16lWcPHkS+/fvx8GDB//x/KdOnQonJyfY2Njg+vXriIyMRPPmzUUVuw0bNiA9PR2urq54+vSp0OW8Q4cO/3h5GWOsPOHKHWP/n7a2Ni5evIjly5fj7du3cHR0xJIlS9ClS5evXTTG/uepq6v/YyOa/tcFBQXhxYsXGDlypOjd2H9KXl4exowZg8ePH0NfXx+tW7fGnDlzRHFkMhl+/vln3L17FxKJBA0bNkR0dLRK3yxljDGmHHfLZIwxxhhjjLFy4D/9KQTGGGOMMcYYKy+4cscYY4wxxhhj5QBX7hhjjDHGGGOsHODKHWOMMcYYY4yVA1y5Y4wxxhhjjLFygCt3KpBIJKX+AUBoaCi8vb2/cmn/WZ9rnVetWgWJRILc3NzPUKovLyEhARKJBHfu3PnktCIiInDhwoXPUKqvJyUlBREREXj58qUoPCIiAhUqVPhKpSrdqlWrsG3btq+S97992/wbfc7zriTLly8XruvK/Bev91/D2LFjYWVlBYlEgrlz5xabn5eXh19++QUNGzaEkZERrKys0KlTJ9y7d09pmk+ePIGBgYHK9xwiwuLFi1G1alVoaWnB1tYWP/30U7F4hw4dgre3N3R0dGBiYoLmzZvj7du3wvwxY8bA1NQUrq6uOHnypGjZ9PR0WFpa4ubNm6WWhzHGSsKVOxUkJiYKfzExMQCARYsWicIZ+1iRkZHlonIXGRlZrHL3b/c1K3fff/89du7c+VXy/l9Vu3ZtJCYmws7O7msXhf0DTp06henTp2PcuHFITEzEd999VyxOZmYmoqKi0KhRI2zevBm//fYb7ty5Az8/P2RkZChMd+zYsZDJZCqXY8yYMRg3bhx69+6N/fv3IyoqCrq6uqI4u3fvRsuWLVG3bl3s2rULK1euhKurK3JycgAA8fHxWLp0KX7//XcEBgaiS5cuwjwAmDJlCjp06AAXFxeVy8UYY4rwR8xVUL9+feH/8ifGVatWFYX/L8vMzCzTjY4x9ukqVKjALXcqIiJkZWXBwMCg3Fx3y4Mvfe+4desWAGDAgAFQU1P8LFomk+Hu3bswMjISwurWrQs7OzvExcWha9euovgXLlzAtm3bMHbsWIwcObLUMly9ehVRUVE4cOCA0g/CZ2dno0+fPhg9ejQmT54shLdt21b4/+HDh9GlSxe0b98ewcHB+P3333Hr1i1Uq1YNd+/exZo1a3Djxo1Sy8MYY6XhlrsvYMeOHXB2doa+vj5atWqFtLQ00fzk5GR06NABRkZG0NPTQ3BwMB48eFBimvIuQBs2bICTkxNkMhmCg4Px7NkzIY6ibl537tyBRCJBQkKCECaRSLBgwQL06dMHJiYmaNWq1UeXS66kcj18+BA9e/bEN998Ax0dHdSsWRNr164tMb23b9+iX79+qFixImQyGVxcXDBr1iwQkRBH3kXr+PHjCAwMhK6uLlxcXBAXFydKKycnB5MmTYKjoyO0tLTg5OSEqVOnCvPz8vIwZcoUYX61atWwfft2ldY7JSUFTZs2Fcq4b98+0fz3799j6NChsLW1hZaWFurWrYvjx48L8+Vdv3744QdRF18bGxssXrxYiDdy5EhIJBIcO3ZMCGvVqhV69+4tTKuy/0qLo+o2LSwhIQG+vr4AgMqVK0MikcDHx0cU5+TJk3B3d4euri68vb3x119/ieY/f/4cvXr1grm5OWQyGfz8/HD9+nVRnKlTp8LJyQlaWlqwtrZG27Zt8f79e2H+5cuX0aJFC+jp6cHIyAghISF49eqV0nL7+Pjg6NGjWLdunbDtExIS0LVrV3Tq1EmIFx8fD4lEIvrR9uuvv6JKlSrC9OPHj9GlSxdhu7Zu3Rp3795VmjdQ/HxNT09HWFgYrKysoK2tDScnJ4waNarENH7//Xe4uLhAJpPBwsICzZs3R2pqKgDFXZ1zc3MhkUiwatUqIczBwQHjxo3DxIkTYW5uDlNTU8yZMwcAsHjxYtjZ2cHMzAyjR48WnX/ya9KWLVtQqVIl6OnpoWfPnsjOzsbRo0fh5uYGfX19BAcHi/aDKtcDedmTkpLQoEEDyGQybNy4sVi3TB8fH4Xd5CMiIoS0EhIS4OXlJWyjwYMHIysrS5TfzJkzYWVlBQMDA/zwww/48OFDidu9MGXXvps3b0IikRTrfqfomlzUsGHD4OrqCh0dHVSsWBFjxoxBdnZ2ieW4f/8+2rVrBzMzM+jo6MDFxUXYj3Lz58+Hi4sLtLS0UKFCBQwYMECYl5ubizFjxsDW1hba2trw8PDAgQMHRMs7ODhg7NixQlfJqlWrAlDt/FVkzZo1cHV1Fa7LhbtdhoaGIiwsDACgrq4OiUSClJSUYmmoq6uLKnZAwfXTzMwMycnJxeIPHjwYo0ePhrm5eanlA4Do6GhUrlxZacUOAA4cOICHDx+if//+SuNkZ2dDR0dHKLNUKhWOw9GjR2P48OEql4kxxkpErExu375NAOjIkSPF5oWEhJClpSV5eHjQ1q1baePGjWRhYUHt2rUT4jx79oxsbW2pXr16tHXrVtq+fTu5u7tTzZo1KS8vT2m+ISEhZGFhQa6urrRlyxZas2YNWVlZUUBAgBBn0qRJZGtrW2p5AZClpSWFhobSvn376NChQ1+0XBcvXqQxY8ZQXFwcHT58mH7++WfS1NSkrVu3CnFWrlxJACgnJ4eIiNLS0mjgwIEUGxtLCQkJNH/+fDI0NKRZs2YJyxw5coQAUOXKlSkqKor27dtHQUFBpK2tTU+fPhXi9ejRg7S0tGjq1Kl08OBBWrlyJf3000/C/N69e5OBgQHNmTOH9u/fTwMHDiQ1NTU6c+aM0vWW521nZ0c///wzxcfHC3mnpKQQEVF+fj4FBASQtbU1LVu2jPbu3UvfffcdyWQyun//PhERJSYmEgAaM2YMJSYmUmJiIhERdejQgbp06SLk17BhQ9LW1qZp06YJaRsZGdHq1auJSLXjSpU4qm7Twl6/fk2LFi0iABQTE0OJiYl0/fp1Iio4JvX19almzZq0du1aiouLo8qVK5OHh4ew/IcPH6hmzZrk7OxM69ato927d1OzZs3IysqKMjIyiIho1apVpKenR4sXL6ajR49STEwM9erVi9LT04mI6K+//iJ9fX1q1aoVxcXF0YYNG8jR0ZFat26tdB9ev36d3N3dqUWLFsK2f/36NS1cuJBsbGyEeGPHjiVtbW3y9/cXwtq0aUNhYWHCvvD09CQ7Oztat24dbd26lapXr0729vb04cMHpfkXPV9DQ0PJ2dmZNm3aRAkJCbRy5UoaMmSI0uUTEhJIQ0ODpk+fTgkJCRQbG0uDBw+mGzduEFHxc4qIKCcnhwDQypUrhTB7e3uytbWlrl270t69e2nIkCEEgIYNG0YBAQEUHx9Pv/zyCwEQnbPyc79evXq0Y8cOWrp0KWlpaVGfPn3Izc2NNm3aRBs2bCATExMaOHCgsFxZrgeVK1emBQsW0OHDh+nmzZvC8Xn79m1hH8r3XWJiIs2ZM4cA0Nq1a4mI6NixY6ShoUE9e/akPXv20PLly8nMzIx+/PFHIa9NmzYRABo+fDjt3buXQkJCyNramkq7Papy7atfvz717t1btNyECRPI3t6e8vPzlaYdHh5OmzdvpoSEBFqxYgXZ2tqKtqEiPj4+VL9+fdq+fTsdPnyYfvvtN5o8ebIoXzU1NRo+fDjt27ePNmzYQD179hTmDxs2jLS0tGjmzJkUHx9P7dq1Iw0NDbpy5YoQx97eniwtLal169a0e/du2r17t0rnryI7d+4kANS3b1/au3cvjR07liQSCS1ZsoSIiO7cuUNjxowhAML+Lel8KuzOnTskkUho48aNovBNmzaRvb09ZWZmKjw/FGncuDG1a9eOJkyYQMbGxqSlpUWBgYF07949Ic7EiRPJ1NSUYmJiyMnJidTV1cnd3V103125ciVVrFiR7t+/T+vWrSN9fX16+/YtnTx5khwcHCgzM1OldWOMsdJw5a6MSqvcaWpqCj/ciYhmzpxJGhoawo/ncePGkbW1Nb1580aI8+DBA5JKpRQbG6s035CQEAIgutHGx8cTALp06RIRla1y5+XlJYr3JctVWH5+PuXk5FCvXr0oMDBQCC/pRitfZsqUKVStWjUhXP5Db/r06ULYs2fPSCKRUHR0NBEV/PgDQMuXL1dY/lu3bpFEIqGYmBhReGBgILVt21bpesvzHjp0qBCWnZ1Ntra2NHjwYCIiOnDgAAGgpKQk0brUqFGDBg0aJIQBoGXLlonSnzt3LtnZ2RFRQeVHS0uL+vbtSy1btiQioqtXrxIAunv3LhGptv9UiaPKNi1pe8h/dMtNmjRJ+HEmt2XLFgIgnCfLli0TVXiJiN69e0cWFhY0Z84cIiIaMGCA6CFJUd26daNatWpRbm6uEJaUlEQA6MKFC0qXa9KkCXXr1k0UdunSJdG2bdKkCfXt25f09fWF9M3MzGjFihVERLRr165i+/n+/fukqalJixcvVpp30fO1WrVqNH/+fKXxi4qKiqLatWsrnV+Wyl21atWEykZeXh5ZWVmRubk5vX//XohXt25d6tGjhzAtv949ePBACOvUqRMBoHPnzglhI0eOFI7lokq7HhQ9L5QdZ0QFx2mFChWoe/fuQpiXl5coXSKimJgYkkql9PjxYyIiql27drFjq3bt2ipV7kq79i1dupSMjIyESkl+fj45OjrShAkTSky7sJycHIqOjiZ9ff0SH7Tp6upSXFycwnkvXrwgLS0tGj9+vML5z58/J21tbYqKihLC8vLyqGrVqtSpUychzN7enuzt7UXHlCrnryKenp7F9k3fvn3J2tpaWM9ly5aVuh8UCQ4OJjs7O1Fl8P3792Rvb0/r168nopLvOYVVqVKF9PT0qGrVqrR9+3batm0bVapUidzd3YVzpnfv3qStrU2mpqb0+++/0/79+6lVq1ako6NDDx8+JCKirKwsatq0KQEgdXV14Z5Uv359oUyMMfY5/Ke7ZRIRcnNzhb/8/PxPTrNKlSqil/1dXV2Rm5uLp0+fAigYTatFixaQyWRCvpaWlnBxccH58+dLTNvBwQE1atQQplu0aAGpVIqkpKQyl7Nly5ai6S9Zrry8PPz888+oVKkStLS0oKmpiRUrVpQ64t3SpUtRrVo1yGQyaGpqYsKECQqXadasmfB/MzMzmJmZ4eHDhwCAo0ePQk1NDd27d1eYx+HDhyGVShEYGCg6Fvz8/EpdbwBo06aN8H9NTU0EBAQI633o0CE4OjrCzc1NSDcvLw8+Pj6lpu3l5YXU1FTcu3cPSUlJMDAwQHh4OE6dOoX8/HycOHEC1tbWcHJyEvIqbf+VZR+XtE3LSldXV/SelKurKwAI6R06dAj169eHtbW1UC6pVIr69esL5XJzc0N8fDwiIiJw/vx5UfdAeRrffvut6Jx2c3ODoaFhmQerqVGjBgwMDHD8+HHk5OTg7Nmz6N+/PyQSCS5duoSbN2/i+fPn8PLyAgCcO3cO33zzDTw9PYU07OzsUL9+/TKdm25ubpg5cyaWLFmCv//+W6X4Fy9exNChQ3HixIlPGmnW19dX6BKspqYGR0dH1KtXT/Q+VcWKFfHo0SPRcs7OzrC1tRXF0dPTg4eHhygsLS1N2GdluR4UvU4pk5+fj+7du8PY2BhLly4FUNAlOjExER06dBCd2z4+PsjOzsa1a9eQm5uLy5cvi85jAAgKClIp39KufZ07d0ZWVpbQrfn48eNITk5Gz549S0x327Zt8PDwgK6uLjQ1NdGzZ0+8fftWuI8o4ubmhjFjxiA6OrrYfjpz5gyysrKU5nvt2jV8+PAB7du3F8LU1NTQvn37Ysewv78/NDT+73V9Vc7fovLy8nDp0iVRfgDQsWNHpKWlqfw6gCLz5s3Dzp07sXLlSmhpaQnh8q63igZlKUl+fj7evXuHLVu2oG3btvj222+xYcMGXLx4EYcOHRLifPjwAVOnTsUPP/wAf39/xMTEQFdXV+heL5VKcfDgQdy9e1foxrpp0ybk5+fju+++w5EjR1C1alVYWlpizJgxH73+jDH2n67cHT16FJqamsJfeHj4J6dZtO+/VCoFAOEdjufPn2PVqlWifDU1NXHlyhXhXRllivbHV1NTg4mJCR4/flzmclpYWIimv2S5Zs2ahcjISPTq1Qt79uxBUlISQkJCSnyvZfPmzejbty8CAwMRFxeHs2fPYvTo0cXelQEUb3N52i9evICxsbHoJl90vbOysqCjoyNa72HDhuHRo0elVviLrru5ubmw3s+fP0dycnKxbbpgwYJSt6mbmxt0dXVx4sQJnDx5Et7e/4+9+w6L4mrbAH4vHelNQEQQUexdBEFRRMWCBXuJLbHHxN4Lttg1llhjN7ZYURG7iQW7osYWKxawoIJKkXK+P/iYl4UFdnEV2dy/6+LSnTkz8+zMzuw+c86c440qVaogOTkZ169fx+nTp6XkIn1buR0/VY5xTvtUVcqcE8ePH88SV3BwsBRXz549MXXqVGzevBnVq1dHkSJFMGvWLLn3HxQUlGUdMTExue7rzLS0tODp6YlTp07h8uXLMDQ0RPny5eHh4SEdDxsbG7i5uQEAIiMjs5xPAGBra5vleducLF68GAEBAZgwYQJKlCiB8uXL48CBA9mW9/Pzw+rVq3H8+HHUrl0bNjY2GDFihFwPfMoyMzOTe62np6dwWubPgLLLpd/YAFS7Hijar4pMmTIFYWFh2LFjh/Rc09u3b5GamooePXrIfSbSz9knT57g9evXSElJUXgeKyO3a5+ZmRkCAwOxfv16AGnPb9WqVQuurq7ZrjMsLAxt27ZFpUqVsH37dpw7dw4LFy4EgBzPwa1bt6JKlSoYOHAgHBwc4OHhgfPnzwNIuw4CgL29vcJl0z+nmfe3os+wou+O3M7fzF69eoXk5GSF28sYj6r27duHoUOH4tdff0X9+vXltjdz5kyMHz8eMTExePfunfS8bkxMTI771cLCAra2ttJNKQCoXr06jI2NpQ5QLCwsAEB69hgAChUqBHd39yydpLi4uMDc3ByJiYkYPXo05s6di8TERHTs2BHTpk1DeHg4tm3bluNzzkREOflP95ZZrVo1ubuS1tbWX3yblpaWaNu2rcJeuqysrHJcNmMnJUDa3cI3b97Azs4OAKCvr5/lofu3b98qXFfmMZy+ZFy7du1C586d5e5G5pY07dq1C/Xq1ZP7AZ+XbuOtrKzw9u1bJCYmKkzwLC0tYWhoKNdRSUbZ9dCWLvN7f/XqlfS+LS0t4eLigq1bt2ZZLj3ByY6Ojg5q1qyJkydP4vnz56hbt67ctNOnT8uNs6TM8fucY/wlWVpawsvLS+EYViYmJgDSjsPQoUMxdOhQPHr0CCtXrsTIkSNRpkwZBAQEwNLSEh06dMB3332XZR1FihRROSYvLy/88ccfKFOmDLy8vCCTyeDt7Y2TJ0/C1NQUtWrVksra29srrFF58eJFjj/iMzM3N8fixYuxaNEiXLlyBZMmTUJgYCAeP36cbZLTvXt3dO/eHVFRUdi8eTNGjBgBR0dHDBw4UPq8f/r0Sappye568DWpcj3Ibaw5IK0ziylTpuDPP/9EyZIlpenm5uaQyWSYPn263A/9dM7OzjA3N4e2trbC81gZuV37AKBHjx7w9/dHREQEtm/fLndNUyQ4OBjOzs5YvXq1NO3GjRu5xuLg4IANGzYgJSUFZ86cwejRoxEQEIDnz59L53dkZKTcPkqXnvS9fPkSxYsXl6a/ePEiS0Ko6Lsjt/M3MxsbG+jo6GQ5b168eCEXjyouX76MDh06oH///vjxxx/l5j179gwfP35Es2bNsixnbW2NkSNHYsaMGQrXW6ZMGURERGSZLoSQvh/SE7/MLQqA7L9DFi5ciKpVq8Lb2xvh4eFISUlBq1atAACtWrXCiRMnstQoExEp4z+d3JmYmMg1pfoafH19ERwcjEqVKkFXV1elZR89eoTr169LzYAOHjyIT58+Se+haNGiiI6ORnR0tPRlfuTIkXyPKz4+Xi6Z+fjxI0JCQmBsbJztOjMvk5qaiu3bt6sUF5DWm15qaio2bdok9byWUb169RAfH4/ExES5mjBlBQcHw8fHB0Bab3OhoaFo27YtgLR9+uuvv8LKykruB1Nmurq6Cmsk03sifPHiBcaNGydN27ZtGx4+fCg3gLIyx+9zjnFu0o+VoveRG19fX4wfPx6urq5ZavkUcXZ2xrRp07Bs2TLcunULAQEB8PX1xc2bN1U+nzP2WJeRt7c3JkyYgF27dklN9Ly9vfHbb7/B1NRUrpfSGjVqSM1F05sjPnv2DGfPns3SDbsyZDIZqlatiqlTpyI4OBiPHj3KtQbLzs4OgwcPxsaNG3Hr1i0AkHrivHPnDqpUqQJA+evBl5SX60F2nj59is6dO2PIkCHSD+N0RkZGqFmzJu7du5djr6OVKlVCcHAwunXrJk1T9kZSbtc+IO3z7eDggM6dOyMxMVGuJ1ZF4uPjs5yf27ZtUyoeIK0nxtq1a2PkyJFo3rw5oqOj4eHhAQMDA2zcuBGTJk3Kskz58uVhYGCAnTt3YujQoQDSEpWdO3fC3d09x+2pev6mx1ilShXs3LlT7rq8fft22NvbqzxESEREBJo1a4Z69epl6SEUAFxdXXH8+HG5aaGhoZg5cyaOHDmS4/W5SZMmWL9+PW7evCn1Dnr+/Hl8/PgRFStWBAA0bNgQ2traOHbsmDROXVxcHM6dO4eBAwdmWWd0dDTmzJmDM2fOSNMSExORmpoKLS0txMXFwcDAQKV9QESU7j+d3OWHIUOGYMOGDfDz80P//v1hZ2eH58+f48iRI+jUqZPCO8zpChcujPbt22PKlClISEjAsGHD0KhRI1SuXBnA/573+P777zFgwABcu3ZNrsvz/IrL19cXK1euhLu7O2xsbDB79uxcf8j5+vpi8ODB+PXXX1G6dGksX75crtt7ZZUpUwbdu3fHgAEDEBUVhZo1a+L58+e4ePGitO7evXsjMDAQI0eOROXKlfHhwweEh4fjw4cPmDlzZo7r//PPP2FtbY3KlStj6dKleP36NX7++WcAaV/49erVQ/369TFy5Ei4ubnh7du3OH/+PKytraUfUW5ubtixYwcqV64MfX196Yehl5cXJk+ejEKFCqFq1apy04yMjKT9Cyh3/D7nGOemZMmS0NLSwu+//46OHTvCzMxMaraYm27dumHp0qWoW7cuhgwZAicnJ7x8+RInT56Eu7s7unTpgj59+sDKygoeHh4wMTHBvn378O7dOymxDgoKQo0aNdCqVSt07doV5ubmiIiIkJ7Ty9ikKqP0fX/o0CFYWlpKQ5jUrFkTOjo6OHXqlPQZqFmzJqKjo/HixQu5GwGNGzdG9erV0bp1a0yfPh0GBgYICgpCkSJFFN5QyI63tzcCAwNRvnx5pKamYsGCBShcuDDKlSunsHxQUBDevHkDHx8fWFtb4/Tp0wgPD5dqxGrWrAl7e3v8+OOPGD9+PJ48eYLffvtN6Xi+lLxcD7LTo0cPGBsbo0WLFjh79qw0PX0MwZkzZ6JBgwYQQqBly5YwNDTEgwcPsGfPHmzatAmmpqYYPnw4OnbsiOHDh8PPzw+bN29Wullgbtc+IC1Z79atGyZPnox27drlmgD5+vpiwYIFGDduHHx8fLBt2zbcvn07x2ViYmLg7++Prl27olSpUvjw4QN++eUXlCtXTroxMGrUKClOPz8/vHv3DiEhIVizZg2srKwwYMAAjBs3DjKZDGXLlsWqVatw584dbN68OcdtK3P+KjJhwgQEBARgwIABaNGiBU6ePIlly5ZhyZIlubaYyCg+Ph5NmzZFSkoKBg0aJNcax8bGRnoONPPwLOnDKvj4+Mg9Q6ijo4MJEyZgwoQJAIDWrVujYsWKaN26NaZOnYrU1FSMGjUKderUQZ06dQCk1Zr26dMHo0ePhkwmg7OzMxYsWICUlBS54SbSTZo0CZ07d0aJEiUApF2HDA0NMWbMGLi7u2Pz5s1y392ZYyIiylH+9eVSMOXWW2bmXijTe0x8+PChNC0iIkJ06dJFWFtbC319feHi4iJ++OEHua6Vs1v3xo0bhZOTkzAwMBABAQHixYsXcuWCg4OFm5ubMDQ0FP7+/uKvv/5S2Ftm5l7ovmRcMTExomPHjsLU1FTY29uLadOmibFjxwonJyepTOaeyz59+iQGDBggLC0thZWVlRg0aJBYsmSJXM9p2fWc5+DgICZOnCi9TkpKEuPGjROOjo5CT09PuLi4SEMKCJHWK9zs2bOFm5ub0NPTE4ULFxYNGjQQe/bsyfZ9p2/70KFDwsfHR+jr64tSpUqJkJAQuXLx8fFi9OjRwtnZWejq6ooiRYqIFi1aiL///lsqc+zYMVG+fHmhp6cn9/5iY2OFtra2qFevXpZpvr6+WWJS5vjlVkbZfarIggULhKOjo9DS0hI+Pj5CCOV7cH3z5o3o37+/KFKkiNDT0xOOjo6iY8eOUk+Ea9asEZ6ensLc3FwYGRlJw41kdOPGDdGyZUthbm4uDA0NhZubm/j555/Fmzdvso358ePHon79+sLY2DhLTDVq1BAGBgYiMTExx2lCpA3d0b59e2FqaioKFSokmjRporBHx4wy75thw4aJcuXKiUKFCgkLCwvh7+8vwsPDs11+7969ol69esLKykoYGBiIcuXKieXLl8uVOXPmjKhcubIwNDQUtWrVEteuXVPYW+bYsWPlllPUi2jm65ui652i45353M7L9SBd5s+nk5OTAJDlL+Nn9dSpU9IxNjY2FhUqVBBjxowRnz59ksrMnDlTFC5cWBgbG4sePXqIhQsXKtVbpjLXZCH+13Pr/v37c1xnuqCgIFG4cGFhamoqunbtKnbs2JHleySjhIQE8f3334uSJUsKAwMDYWNjI9q2bZul/Pz584Wrq6vQ09MTRYsWlRteISkpSYwaNUrY29sLPT09UbVqVXHw4EG55RV9VoTI/fzNzrp160Tp0qWFrq6ucHZ2FvPmzZObr0xvmQ8fPlT4GQAgunXrlu1y2X3GMn9+hBDi+fPnonXr1sLY2FiYmpqKjh07ZhkaJjExUQwfPlwULlxY6Ovrizp16ohLly5l2e7du3eFra1tluvS4cOHhaurq7CwsBDDhg3LNSYiouzIhFDQSJy+Od27d8e9e/dw6tSp/A6FiIhUMGPGDCxYsABPnz6FtrZ2fodDREQajM0yiYiIvoBHjx7h1q1bmDdvHvr27cvEjoiIvrj/9FAIREREX0pQUBBatmyJGjVqKOylloiISN3YLJOIiIiIiEgDsOaOiIiIiIhIAzC5IyIiIiIi0gBM7oiIiIiIiDQAkzsiIiIiIiINwOSOiIiIiIhIAzC5U1L37t3h7e0tN+3PP/+Eq6srdHR00LJly/wJTA0Uvbe8CgoKQtGiRaXXV69eRVBQEFJTU7/YNrPz6NEjyGQyHDly5LPX9euvv+LYsWNy0969e4egoCA8ePDgs9efn+rWrYsuXbp89noyH3tFTpw4AZlMhnv37gFQfIxkMhl+//136bWiff8lPXnyBH5+fjA2NoZMJsO7d++UWu7evXuQyWQ4ceKEStsLCgrC5cuXVQ80F48ePUJQUBDevHmTZXu5HaevbcyYMbCzs4NMJsOvv/6a3+Eo9KWO07dA0TmmzutnZupct7OzM8aNG6eGqL6OI0eOQCaT4dGjR/kditp87jUlNjYW48ePR7Vq1WBiYoJixYqhd+/eiI6OzlL26tWrqF27NgwNDVG8eHEsXrw41/Xfu3cPvXr1Qrly5aClpZXt992tW7fQrFkzWFlZwdLSEo0bN8b169flyowePRpWVlYoU6YMTp8+LTfv7du3sLW1xe3bt1V490Tqx+Quj5KSktCjRw/UrVsXf/31F2bNmpXfIeXZ+PHjsWLFCrWs64cffsDevXul11evXsWkSZOyJHfq3ObXkF1yN2nSpAKf3H1NVatWRVhYGBwdHbMtExYWhhYtWkivv3ZyN2XKFDx8+BC7du1CWFgYTExMvuj2Jk2a9MWSu0mTJmVJ7jKfo/ntzJkzmD59OsaOHYuwsDB06NAhv0NS6Esdp2/B1z7HSLN87jUlIiICa9euRcuWLbFnzx7MmDEDR48eRUBAAFJSUqRyr169QoMGDWBqaop9+/ahf//+GDRoEDZs2JDj+v/55x8cOnQIFSpUQLFixRSWiY2NRcOGDREdHY1169Zh/fr1iImJQcOGDfH27VsAQEhICJYvX44VK1agadOm6NixI5KSkqR1TJkyBW3atEHp0qXzvC+I1EEnvwMoqJ49e4aPHz+iU6dO8PLyyu9w8iQ+Ph6GhoYoUaLEZ68rKSkJWlpaKFq0qFJ38NSxTU0ihEBiYiIMDAy+yfWpi6mpKTw8PHIsk9v8L+3u3bvw8vJCgwYN8jWOL0XZc/RruXv3LgBgwIAB0NLi/Uai/JD+eyAvPveaUrx4cdy9e1du+yVKlICHhwfOnTuHWrVqAQCWLVsGmUyGP//8E4UKFUL9+vXx8OFDTJkyBd9991226w8ICJBuGNatW1dhmdOnT+Pp06c4ceKE9PukfPnyKF68OE6ePInmzZvj2LFj6NixI1q3bo2WLVtixYoVuHv3LsqVK4f79+9jw4YNuHnzZp73A5G68Js0D9auXYvixYsDAOrXrw+ZTIa1a9dmKXfo0CHo6Ojg/fv30jRbW1u4uLhIr1++fAmZTIYzZ85I07Zv344qVarAwMAARYsWxZQpU5DbWPMymQyLFy9Gr169YGpqCjs7O8yePVuuTHrzu19//RXFihWDkZERAMVNJDds2IAyZcpAX18fLi4uWZpKpS+zadMmlCpVCgYGBnjy5Ilc84y1a9eiR48eAABdXV3IZDJ0795d4TYjIiIQGBgIa2trFCpUCKVLl8b8+fPltrl06VIpphIlSmD58uU57pN0b968QevWrWFkZARnZ+csd/lSUlIwZcoUFC9eHPr6+ihXrhx2794tzXd2dsbjx48xbdo0yGQyqUlN+megQYMGkMlkcHZ2lpYJDw9Ho0aNYGxsDHNzc3Tr1k2ued/atWshk8lw4cIFeHp6wtDQEFu2bFEYf/q+2rx5M1xcXGBoaIiWLVvi1atXSq1v7ty5cHFxgb6+PsqWLYtNmzYp3M6vv/6KIkWKwNjYGD179kRcXJw0759//kGbNm2k+TVq1MDBgwcVrufIkSMoV64cDAwMULduXbmazczNMhXJ2CxT0b6/f/8+HBwcMG3aNLnlhBBwcnJCUFBQtuv+8OED+vbtCxsbGxgaGsLHxweXLl2S2/Zff/2FDRs2QCaTZftDAEg7v8uUKQNDQ0PUr19fYTOruLg4DBkyBA4ODtDX14e7uztOnjwptz0A6NWrl/T+0j18+BBt2rSBubk5jI2N0bJlSzx9+lRu/ffu3UPr1q1hYWEBY2NjeHp64vTp0zhx4gTq1asHAChZsqTce1HUhCo0NBTVqlWDgYEBHBwcMHbsWLk75unLnD59GlWqVIGRkRG8vb1x586dbPdPupyuJd27d5euEdra2jk2V1u9ejU8PDxgZmYGOzs7tG/fHi9evMh1++fPn0edOnVgaGgIGxsb9O/fHx8/fszy3jLK3MQ2p+OU3TFQdvvp5+7Vq1elc9fb2xsRERF4+vSpdB2pVq0abty4IRfn69ev8f3330ufZ19fX/zzzz+57pOMsru+pYuNjUWXLl1gbGyM4sWLK2xx8aWuzXk55sHBwahbty4sLS1hZWWFJk2aZLnepH8Xrly5EsWKFYO5uTk6deqE2NhYuXIXL15Ew4YNYWJiAnNzc9SvX1+uyV1u13kA2LhxI5ydnWFkZITWrVtnqUlXRJnvw4ULF6J06dLQ19dH0aJFMWDAAGlecnIyRo8eDQcHBxgYGKBatWo4fPiw3PLOzs4YM2aM1CS6bNmyAPL2mcp8Dr19+xY9evSAnZ0dDAwM4OLigpEjR2a7vJGRUZbEsmLFigDSroPpDh48iCZNmqBQoULStLZt2+Lff//NsQWNMjeNkpOTAaTdgExnZmYGANLvr0+fPknb1tbWhp6eHhITEwEAo0aNwrBhw2BjY5Prtoi+OEFK6datm/Dy8hJCCPHy5Uvx559/CgDit99+E2FhYeLly5dZlomJiRHa2tri4MGDQggh7t69K3R1dYWWlpZ49uyZEEKInTt3CgMDA5GYmCiEEGLTpk1CS0tLDBo0SBw8eFDMnz9fFCpUSMyZMyfH+AAIe3t70a1bNxEaGiqGDx8uAIgtW7ZIZXx8fIStra3w9PQUu3fvFjt37szy3oQQYu/evQKA6Nu3rwgNDRVjxowRMplMLF26VG5/WFtbi7Jly4qtW7eK/fv3i3fv3omJEycKBwcHaT+NGzdOABCnTp0SYWFh4t69ewq3WbduXeHh4SF2794tjh07JpYtWyYmT54szf/ll1+Evr6+CAoKEocPHxYTJ04U2traYvv27dnuk4cPHwoAokiRImLYsGEiNDRU9OjRQ8hkMhEWFiaV6927tzA1NRXz588Xhw4dEgMHDhRaWlri3LlzQgghLl++LOzs7ET37t1FWFiYCAsLEwkJCWLnzp0CgFi4cKEICwsTly9fFkIIcefOHWFiYiKaNGkigoODxebNm0Xx4sVFs2bNpG2uWbNGABAlS5YUixYtEseOHRO3b99W+D66desmChcuLMqUKSO2b98uNmzYIOzs7IS/v3+u61u0aJGQyWRi7NixIjQ0VPTu3VsAECEhIXKfC3t7e+Hh4SGCg4PF0qVLhbGxsejbt69U5uDBg2Lq1Kli//794siRI2L48OFCW1tbnD9/XiozceJEYWJiIkqUKCHWr18vduzYIdzc3ES5cuVESkqKEEKI48ePCwDi33//lTtGhw8fltYDQKxcuTLHfT9q1Cjh5uYmt5+OHj0qZDKZuH//frafidatWwtzc3OxfPlyERwcLHx8fISpqal48eKFEEKIsLAwUbFiRdGkSRMRFhYm/vnnH4Xrefz4sTAwMBBNmzYVISEh4pdffhGOjo4CgDh+/LgQQojU1FTh7+8v7O3txcqVK0VoaKjo0KGDMDQ0FBEREdL2AIjRo0dL708IIV69eiUcHBxEzZo1xY4dO8Tu3btFlSpVRMWKFaV9GRkZKQoXLizKli0rNm7cKA4dOiSmTJkitmzZImJiYsRvv/0mAIg///xT7r1kPEeFEOLKlStCW1tbtGnTRoSEhIjZs2cLPT09MXLkyCzHtmLFimLjxo0iODhYlCxZUlSrVi3bfS1E7teSe/fuidGjRwsAcsdXkaCgILF69Wpx/PhxsXPnTlGrVi1RsWJFkZqamu32IyMjhbGxsahbt64IDg4Wy5cvF2ZmZqJ9+/Zy7y3j/hBCiH///VfuWGZ3nHI6BspuP/3crVSpkli9erXYs2ePcHZ2FvXq1RO1a9cWCxYsEAcOHBBVq1YVFSpUkJZLSEgQFStWFG5ubuKPP/4Q+/fvF35+fsLOzk58+PAhx+OSUXbnWPq56eTkJMaOHSsOHTokevbsKQCIa9euSct/yWuzMsc8Pb50CxcuFEuWLBFHjhwR+/btE82aNcuyT3x8fETRokVFvXr1xN69e8XKlSuFkZGRGDJkiFTm+vXrwtDQUHh7e4tt27aJkJAQMWrUKOkzocx1PiwsTMhkMul7eejQoaJIkSICgHj48GG2+ye378Px48cLLS0tMWzYMHHw4EGxefNm0bVrV2n+0KFDhb6+vpgzZ44ICQkRgYGBQkdHR+64OTk5CVtbW9GsWTOxf/9+sX///jx/pjKfQ927dxdubm5i69at4sSJE2LNmjVi8ODB2S6vyNGjRwUAcfbsWWmajY2NmD59uly558+fZ/lOy4mPj4/o3LlzlukfP34Ujo6Oonv37iIyMlJERkaKHj16CBcXFxEXFyeESDtXS5QoISIiIsQff/whTExMxPv378Xp06eFs7OziI+PV+k9En0pTO6UlDkZyfzln53KlSuL8ePHCyHSLgweHh6iUqVKYuvWrUKItItw7dq1hRBpPwYdHR3FgAED5NYxe/ZsYW1tLT59+pTtdgBk+aHVrl07UblyZem1j4+PMDY2Fm/evMnxvVWvXl00bdpUrkzfvn2Fvb299MOyW7duQktLK0tCkvkin/7DJSkpKcdtGhkZieDgYIXvLSYmRhQqVEjMnj1bbvqAAQNEpUqVFC4jxP9+QAQGBspNd3d3Fy1bthRCpCXcMplM/Pnnn3JlmjZtKlq0aCG9zvwDIuP6MyYmQgjRuXNnUalSJZGcnCxNu3DhggAgJYDp+yU9iclJt27dsvygCgkJEQDE1atXs11fcnKysLOzy/J58vf3F+7u7tJrHx8foa+vL3eDYsmSJUJPT09KejJKSUkRSUlJws/PT27dEydOFADkjuM///wjAIjdu3cLIVRP7oRQvO9v376d5Yu/W7du0rmkyPXr16VkJ92HDx+ElZWVGDFihDTNy8tLdOvWLdv1CCHEkCFDhJ2dnXRTJn1axmvC4cOHBQBx4cIFqUxqaqqoUKGC+Pnnn7N9v0IIMXbsWGFvby9iY2OlaU+fPhV6enpi165dQgghhg8fLszMzER0dLTCGDPv63SZz9E2bdqI8uXLy/1gnjFjhjA0NJTWnX5sM/7w3r59uwAgJaqKKHMtWblypVD1PmNycrJ4/Phxlv2b2bBhw4S1tbX040wIIbZs2SJkMlm2ya4Qiq/vio5TbsdAme2nn7vbtm2TyixZskQAkLupl37Op9+8WLlypdyNAiHSfqAWLlxYzJ8/P9t9okhO17c+ffpI0z59+iQsLS2lRONLXpszy+6YK4o94zJxcXGiUKFCcue9j4+PsLKykktYfvzxR+Hi4iK9btu2rXBxccn2e1eZ63xgYGCW7+XAwMBck7ucvg+jo6OFvr6+GDdunML5r1+/FgYGBnLHJCUlRZQtW1a0a9dOmubk5CScnJzkvpvz+pnKfA6VK1dOLFy4MNvyuUlKShI1atSQ+54SQggdHR25m8xCCBEfHy8AiD/++EOpdWeX3AkhxP3790XJkiUFAOnGRsbrZ2Jioqhfv74AILS1tcXvv/8uhBDCw8NDbNq0SZW3SPRF/aebZQohkJycLP1l7vRDHby8vKRmWKdOnYK3tze8vb3lpqU/s3f37l08efIEbdq0kYurXr16eP36NSIiInLcVkBAQJbX165dw6dPn6RpHh4esLCwyHYdKSkpuHr1Klq3bi03vW3btoiMjJRrFubi4gI3Nzcl9kLuKleujNGjR2P9+vV4/vy53LywsDDExcWhdevWWfbL9evX5d6fIs2bN5d7HRAQgAsXLgAAjh07Bj09PTRt2lRu3b6+vnLN9VRx9OhRtGrVSu7zVblyZZiZmWXpkKFx48ZKrdPZ2RkVKlSQXjdq1Ah6enrS+1C0vqdPnyIqKkrhsbxy5YpcsztPT0+55iQBAQH49OkTrl27BgD4+PEjhg0bBicnJ+jq6kJXVxdHjhzJ0txJR0cHTZo0kV6XLVsWJUqUyBLn53Jzc0OtWrWwfv16AGnNH3fs2IGuXbtmu8ylS5egra0t17OtkZERmjRponJ8Fy9ehL+/P/T09KRpmc+/o0ePonjx4qhcubL0OUhJSUHdunVz/WwdPXoUjRo1gqGhobSsra0tSpcuLS37119/oWnTprC0tFQpdkXvJTAwUK6pYdu2bREfHy/XDNDIyEjuecgyZcoASHv+WBFVriXKCA8Ph7+/P6ysrKCjowMnJycAyLGJ78WLF9GkSRO5Jl/p7/XixYsqbV+R3I6BKtv39fWV/p/+zE/GZsHp09Kvj0ePHoWHhwfs7e2lz4ienh48PDzyfO1SxM/PT/q/rq4uSpQoIR3zL3ltBvJ2zB8+fIi2bdvC1tYWOjo6KFSoEOLi4rIs4+HhIT2aAKR9njN+lv/66y906NABurq6CrejzHX+4sWLCr+Xc5PT9+G5c+eQmJiY7bXuxo0bSEhIkDvvtLS00Lp16yzXuQYNGkBH539dL6jrM1W5cmXMmTMHS5cuzVOHY8OHD8fNmzexatUqlZfNqw8fPqBt27YoVqwYQkJCEBISAldXVzRt2lTqUEVPTw9HjhzB/fv3pearW7duRWpqKjp06IDjx4+jbNmysLW1xejRo79a7ESZ/aeTu7/++kv6oaqrq4uePXuqfRve3t44f/48kpKScPr0adSuXRteXl44deoU4uPjcfnyZenZs9evXwMA6tWrJxdX9erVAaR10Z6TzG29bWxskJqaipcvX0rTChcunOM6Xr16heTk5CzlbG1tAQCRkZFKr0sVW7duRZUqVTBw4EA4ODjAw8MD58+fB/C//eLi4iK3X9q0aYPU1NQsX36ZKdov6c9tvH79GomJiShUqJDcuocOHYrnz5/nKeF//fo1goKC5Nanq6uLmJiYLMdQ2X2Y+T1oaWnB0tISUVFR2a4v/VgpOpZJSUnSflW0/vTX6esfPnw4Vq1ahWHDhuHIkSO4cOECGjRogISEBLnlLCwsoK2tnWVdmeNUhx49emDr1q349OkTdu7ciZSUFLRr1y7b8pGRkbCwsJD7MQOk7Y+Mn2tlvHjxItt9lu7169d4+PBhls/BokWLcj2XX79+jbVr12ZZ9tq1a9Ky0dHRsLe3VyluRSIjI5U6383NzeXKpCe2mT8D6VS5luQmJiYG/v7++PjxI5YvX44zZ85Iz7Vlt/30bWTevq6uLiwtLVU+5orkdgxU2X768z3A//atomnp7/f169c4fvx4ls9IcHBwrp8vVSg67hljAL7MtTkvxzwlJQXNmzfH7du3MW/ePJw6dQoXLlyApaVllmUUva/056eA3I+tMtd5Za4TiuT0fZg+PEB2seV03c/8mctcRl2fqcWLFyMgIAATJkxAiRIlUL58eRw4cECpZZcuXYqFCxdi06ZNKF++vNw8CwsLxMTEyE1Lf8Yxp5vWyvj999/x4MED7N69G40bN0bjxo2xZ88evHr1CitXrpQr6+LiAnNzcyQmJmL06NGYO3cuEhMT0bFjR0ybNg3h4eHYtm0bgoODPysmorz6T/eWWa1aNbk7WdbW1mrfhpeXF+Li4nDo0CGpF764uDh06dIFR44cQXJystQTVPrd33Xr1kkPN2eUWy1Zxg420l9raWnJXcAz3p1XxMbGBjo6OnIJIQDpCzfjF0pu61KFg4MDNmzYgJSUFJw5cwajR49GQEAAnj9/Lu2Xw4cPZ/lCzhyTIor2S/oPTEtLSxgaGuLvv/9WuGxeeu+ztLREhw4dFPbeVaRIEbnXyu7DzO8hNTUVb968gZ2dXbbrS98vL1++RLly5aTpL168gK6urtznXdE+AiCtf9euXRg8eDAGDhwolUlISMiyf96+fYuUlBS5BO/Vq1dZ4lSHdu3a4eeff8b+/fuxfv16tGzZUu5h+Mzs7e3x9u1bJCcnyyV4L168UDlJsrW1zXafpbO0tISLiwu2bt2aZfmMNX6KWFpaom3bthgxYkSWeVZWVtK/6khQ7O3tlTrfVaXKtSQ3Z8+eRVRUFM6dOyd1Za5MjYCi95aUlIQ3b95I29fX189Sw5R+pz43uR0DZbafV5aWlvDy8lI4LuCXHr4jYwzAl7k25+WY37t3Dzdu3MBff/2FOnXqAEjb38qOVZlRbsdWmeu8MtcJRXL6Pkw//yMjI1GyZMksy2a87qd3+gUovs5l/v5R12fK3NwcixcvxqJFi3DlyhVMmjQJgYGBePz4cY43NENCQjBw4EDMnj07S60uAJQqVSrLGHLprz+3FdHdu3fh4uICY2NjaZqRkRFcXV1x//59hcssXLgQVatWhbe3N8LDw5GSkoJWrVoBAFq1aoUTJ04ofB9EX9p/OrkzMTGRasW+FEdHRxQrVgwzZ86Em5sbrKysYGVlhaJFi2L27NkoW7asdMfJzc0NRYoUwZMnT3JsXpadvXv3YuLEiXKvK1asmOsPyYy0tbVRpUoV7Ny5U+rFDkjrwdPe3l7l7o7Tt52YmJilxiS77deuXRsjR45E8+bNER0dDQ8PDxgaGuLFixdyTYSUFRwcjG7dukmv9+7dixo1agBIqyWNj49HYmJijkNaZL6rm/m9ZeTr64ubN2+q9bP16NEjXL9+XWqaefDgQXz69CnHbRQtWhR2dnbYuXOn1HMikHYsq1atKpeAhYWF4fXr11LCt3fvXujp6Uk9lsXHx8t9jp48eYKzZ89KNybSJScnIyQkRGp6dPPmTdy/f1/a33mhaN8Dab2atW7dGrNnz8a5c+cQEhKS43qqV6+OlJQU7NmzR2qyFBcXh5CQEPzwww8qxVS9enVs2rQJnz59kvZL5nGefH198euvv8LKykruR1Zmurq6Cj9DwcHBqFSpUrbNwurVq4dly5bh7du3Cu9aZ/f5zKxGjRrYvXs3goKCpB9727dvh6GhYZY756pQ57UkPj4egHxSvG3btlyXq1GjBtauXYuEhARpWJBdu3ZBCCGdO0WLFkV0dDSio6OlH86KBtdWdJxyOwbKbD+vfH19MX78eLi6uipMrFSR3TmWmy95bc7LMVe0zI4dO/LUAqNevXrYunWrVDuXmTLX+erVqyv8XlZWdt+HBgYG2LhxIyZNmpRlmfLly8PAwAA7d+7E0KFDAaQ9grJz5064u7vnuD11fqaAtOSxatWqmDp1KoKDg/Ho0aNsk7srV66gffv26N27N4YMGaKwTKNGjbB48WK5YRu2b9+OkiVLyvVCnheOjo7YtGkT3r9/LyWy79+/x7///ivXlD9ddHQ05syZI9fTeWJiIlJTU6GlpYW4uLhvbigi+u/4Tyd3X4uXlxc2b96MXr16ZZnWu3dvaZqWlhZmz56NHj164O3bt/Dz84OWlhbu3r2Lw4cPY8+ePTlu5/nz5+jevTs6duyIY8eOYdu2bdi8ebPK8U6YMAEBAQEYMGAAWrRogZMnT2LZsmVYsmSJyjVZ6XfTFi9eDF9fX9jY2MgNGQD8r/lN165dUapUKXz48AG//PILypUrJ30RjB8/Hn379sX9+/fh6emJpKQk3Lx5Ezdv3sTq1atzjOHs2bMYPnw4/Pz8sHXrVly4cAGnTp0CAJQuXRq9e/dGYGAgRo4cicqVK+PDhw8IDw/Hhw8fMHPmTOl9hIaGolmzZjAyMkLFihVhZ2cHMzMzbNy4EVZWVjAyMkKFChUQFBSEGjVqoFWrVujatSvMzc0RERGBkJAQBAUFSc8qqaJw4cJo3749pkyZgoSEBAwbNgyNGjVC5cqVs11GW1sbY8eOxc8//wwLCwt4e3tj165dOHDgQJYmMpaWlggICMCYMWPw7NkzjBgxAj169JD2v6+vLxYuXAgXFxdoa2tj4sSJWWohgbQbJoMHD0ZMTAwKFSqEMWPGoEyZMko9Z5IdRfs+/cdbjx494OvrC3t7+1x/XJYrVw6tW7dG79698fbtW9jZ2WHu3LlISkrK9sdEdn766Sf89ttvCAwMxIABA3D16lVs375drkzDhg1Rr1491K9fHyNHjoSbmxvevn2L8+fPw9raWvrh5ebmhh07dqBy5crQ19dH9erVMWTIEGzYsAF+fn7o378/7Ozs8Pz5cxw5cgSdOnVC/fr1MXjwYKxduxY+Pj4YPXo0bGxscPHiRRQvXhzt27dHyZIloaWlhd9//x0dO3aEmZmZwrvbY8eORbVq1dC+fXv07NkT//zzDyZMmIBBgwZ99vN86rqWeHh4oFChQujTpw8GDhyIy5cvY82aNbkuN2TIECxduhRNmzbF4MGDERkZiREjRqBdu3ZS64j051e///57DBgwANeuXVM4tI2i45TbMVBm+3nVrVs3LF26FHXr1sWQIUPg5OSEly9f4uTJk3B3d0eXLl0A/O+5vfRhHRRRdI4pw8LC4otdm/NyzEuXLg17e3sMHToUEyZMwKNHjzBjxowca/SzM2HCBNSoUQN+fn746aefYGxsjJMnT8LPzw9169ZV6jo/dOhQeHt7S9/LR44cwdmzZ3PcrjLfh6NGjZK+C/z8/PDu3TuEhIRgzZo1sLKywoABAzBu3DjIZDKULVsWq1atwp07d3L9PaDsZyo33t7eCAwMRPny5ZGamooFCxagcOHCci1IMnrx4gWaNWsGBwcHdO7cWW4fZRxDr2/fvli4cCHatWuHQYMG4cqVK1i+fHmWz5mOjg4mTJiACRMmAPjfTTwgreY0NTVVul63adMGANCpUydMmzYNzZs3l74P5s2bh+TkZIW1s5MmTULnzp2lZ2Hd3NxgaGiIMWPGwN3dHZs3b5a7jmSOieiLys/eXAqSvPaWKYSQuiRft25dlmnr16/PUn7v3r3C09NTGBoaCjMzM1G9enUxa9asHLeB/++Sv0ePHsLY2FjY2NiIGTNmyJXJrpeozO9NCCHWrVsnSpcuLXR1dYWzs7OYN29erssIobjnuVGjRgk7OzupS+jMyyckJIjvv/9elCxZUhgYGAgbGxvRtm3bLL2JrV69WlSqVEno6+sLS0tL4e3tLVavXp3tPknvkW3Lli2iRYsWwtDQUDg6Ooq1a9fKlUtJSRGzZ88Wbm5uQk9PTxQuXFg0aNBA7NmzRypz9epVUbNmTWFoaCjX09nWrVuFq6ur0NHREU5OTlL5GzduiJYtWwpzc3NhaGgo3NzcxM8//yz1VJpdL6KKpO+rjRs3CicnJ2FgYCACAgLkerLMaX2zZ88Wzs7OQldXV5QuXVps3LhRbn7652LOnDnCzs5OGBkZiW7duomPHz9KZZ49eyYaN24sjIyMhLOzs1i5cqXo3Lmz8PHxkcqkH/uDBw+K0qVLC319fVGnTh253sby0ltmdvteiLTeJ01MTMTw4cNz3Y9CCBEbGyt69+4trKyshIGBgahdu3aW3haV6S1TCCEOHDggSpUqJfT19YWPj484dOhQlmtCfHy8GD16tLT/ixQpIlq0aCH+/vtvqcyxY8dE+fLlhZ6enlyvkREREaJLly7C2tpa6OvrCxcXF/HDDz+Ix48fS2Xu3r0rmjdvLkxMTISxsbHw9PQUp0+fluYvWLBAODo6Ci0tLelYKTpHQ0JCRJUqVYSenp6wt7cXY8aMkfssKdujpCK5XUuU7S1z9+7d0jWiTp06Uu+na9asyXG5c+fOCW9vb6Gvry+srKxE3759s3TrHhwcLNzc3IShoaHw9/cXf/31V5b3lt1xyu0Y5LZ9Reeuop5OFZ0rb968Ef379xdFihQRenp6wtHRUXTs2FGuZ90aNWqItm3b5riPFJ1j2fUGrOj8+FLXZmWOeebeMk+fPi0qVaokDAwMRLVq1cSZM2eEg4ODmDhxolRG0Xehos/hhQsXhK+vrzA0NBTm5uaifv36cj1E53adFyLt81+sWDFhaGgoWrRoIbZs2ZJjb5nKfh/Onz9fuLq6Cj09PVG0aFExcOBAaV5SUpIYNWqUsLe3F3p6eqJq1arSkEzZ7bd0ynymMst8fRg2bJgoV66cKFSokLCwsBD+/v4iPDw82+XTP++K/jIeNyHShm7x8vIS+vr6olixYgp75cy8XPrnTdFfRmfPnhW+vr7CwsJCWFpaivr168sN95Pu7t27wtbWNkvP44cPHxaurq7CwsJCDBs2LMeYiL4kmRC5jI5NBYJMJsPKlStVbl5G377u3bvj3r170h1t+p+zZ8/C09MTN27cyPauMNF/1adPn2BqaoqDBw/Cx8cnv8MhIqKvgM0yiajAiY6Oxu3btzFixAjUrl2biR2RApcvX0b58uWZ2BER/Yf8p4dCIKKCae/evahduzbev3+PpUuX5nc4RN8kDw8PtYznR0REBQebZRIREREREWkA1twRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdERERERKQBmNwRERERERFpACZ3REREREREGoDJHRERERERkQZgckdE9Bk2bdqESpUqoVChQpDJZDA3N/9i2zpx4gRkMhlkMhm6d+/+xbajierWrSvtu0ePHuV3OHni7OwsvQdVrV27Vlo2KChI/cEREdE3gckdERU4Hz9+xPz581GnTh1YWVnBwMAAxYsXR7NmzbBx40Z8+vTpq8QRFhaGLl264Nq1a4iPj/8q2/xWBAUFScmCTCZDw4YNs5S5dOmSXBmZTIaEhIQ8bW/37t0ICgpCUFBQgU3OMsq8/2QyGXR0dFC4cGH4+/vjwIED+R3iN+3BgwcYOHAgSpcuDSMjIxgZGaF06dL48ccf8eDBg89e/9WrV6XP24kTJz4/YCKir0QnvwMgIlLFzZs3ERAQkOUH3KNHj/Do0SPs378f5cuXR+XKlb94LPv374cQAgDQp08fdO7cGbq6ul9se1WqVMHJkycBALa2tl9sO3lx9OhRPH78GE5OTtK0lStXqm39u3fvxrp16wCk1cI5OzurtPyiRYsQExMDALC3t1dbXOqUkpKCV69e4eDBgzh06BB27dqFFi1aSPO3b9+e5+RYk2zfvh3fffddln1x584d3LlzB6tWrcKGDRvQpk2bPG/j6tWrmDRpkvS6bt26eV4XEdHXxOSOiAqMN2/eoHHjxoiIiAAAFClSBMOHD0eFChXw/v17/PXXX1izZs1Xi+f58+fS/9u3b4/atWt/0e2ZmZnB29v7i24jr1JTU7Fq1SpMnjwZQFrt6qZNm/I5qrQ4jIyMUKFChfwOJVuNGzfGmDFj8Pr1awQFBSE8PBxCCCxatEguuatevXo+RvltuHLlCjp37izVzjdv3hy9evUCAPz+++/Ys2cPEhIS0KVLF7i6un6VmzxERN8UQURUQIwePVoAEACEmZmZePr0aZYyL168ENHR0dLrxMREMWPGDFGpUiVRqFAhYWhoKCpWrCimT58uEhMT5ZZ1cnKS1h8ZGSm6dOkizM3NhbGxsWjXrp203ocPH0rlMv/5+PgIIYT02snJSW4bPj4+0ryHDx9K07dv3y68vLyEqamp0NXVFba2tsLLy0uMGDFCpKamCiGEOH78uLRst27d5NYbGRkpBg4cKFxcXISenp4wMzMTPj4+Ytu2bXLlMsbu4+Mjzp8/L+rWrSsMDQ2Fra2tGDt2rEhJScn1WEycOFFaj4mJiQAgihYtKi27atUquXnpf/Hx8dI6hgwZIjw9PYWdnZ3Q09MTRkZGokqVKmL27NkiKSkp130NQBw/fjzL/r527Zrw8/MTRkZG0vHIvN9TU1OFr6+vNG3fvn1SXP369ZOm//LLL7nui7zIuP8yHssdO3ZI00uVKiW3TMbPZ0bLli0T1apVE0ZGRkJPT08UKVJE1K9fX8ycOVMqs2bNGmnZiRMnStO///57aXqTJk2ynBPpBg4cKJXbuXOn3Lxp06ZJ83777TchRNpx69ixo7C3txc6OjrCzMxMlClTRnTv3l2Eh4fnZZcJIYQICAiQtlWvXj3p3BBCiNTUVFGvXj1pfkBAgDSvW7duWT4z2e2XjPs581/GfRcRESEGDBggSpQoIfT19YW5ubnw8PAQW7ZskYv50qVLok2bNsLW1lY6t1u3bi0uXrwoVy5zLIsXLxZOTk6iUKFConHjxiIiIkLEx8eLn376SVhZWWW5LmX0999/i4CAAGFtbS10dXWFs7OzGDx4sHjz5k2e9z0RFQxM7oiowHBxcZF+/AQFBeVaPiEhQdSpUyfbH2p16tSR+zGb8Uddxm2l/3Xu3FkIof7k7sSJE0JLSyvbdaYnOtkldw8ePBB2dnbZLj9y5EipbMbY7e3thaGhYZbyK1euzHXfZkxOunfvLnR1dQUAsX//fiGEEDVr1hQARO/evbNN7vT19bONuUePHrnua0XJnZmZmbCysspyPBTt94cPHwpjY2PpOH348EGcOnVKyGQyAUC4u7uL5OTkXPdFXmSX3G3fvl2aXrduXbllFCV369evz3bfODg4SOUUJTEZb5bUr19f7thkdvbsWalsp06d5OZVqVJFABC6urri9evXIikpSZQqVSrbuJT5fCkSFxcn9PT0pPVkTMjT7d27V5qvp6cnvSd1J3dXrlwRlpaWCstkPJ579uyRzo3Mf7q6umLPnj0KYylRokSW8hUrVhQtW7bM9rqUbuXKldleT9zc3JjgEWk4dqhCRAXChw8f5J6zU6YJ5K+//oq///4bAODo6IhNmzZh8+bNKFasGADg77//xvz58xUuGx8fj40bN2LJkiXQ09MDAGzZsgUxMTGwt7fHyZMn0bhxY6n8woULcfLkSSxatEjl97Z3716kpqYCAH755RccPXoUW7Zswbhx41C2bNlce0fs378/oqKiAKQ9GxQcHIx58+bBwMAAADBz5kycO3cuy3KRkZGoWrUq9uzZg59++kmavnz5cpXit7W1RbNmzQCkNY27fv26tL0ffvgh2+XGjh2LzZs3IzQ0FCdOnMDOnTtRs2ZNAGm9Oz59+jTHfX3y5ElUqVJFbp0xMTHQ1tbGihUrcPDgwRy37+zsjNmzZwMAHj9+jNGjR6N3794QQsDAwADr1q2Dtra2SvsiL16+fIlTp05h9+7dmDJlijS9T58+uS67Z88eAICOjg6WLVuGo0eP4o8//sDQoUNRvHjxbJdbsGABpk+fDiDtXAoODpY+L4rUrFkTrq6uAIB9+/YhMTERQFrHJleuXAEA+Pv7w8rKCrdv38bdu3cBAH5+fggNDcW+ffuwaNEiNG7cGPr6+rm+L0Xu3bsn11mSoiaXGad9+vQJ9+7dU3k727dvx5gxY6TXPXr0kD5vPXv2hBACXbt2xZs3bwAA5cuXx4YNG7B//35MmDABVlZWANKaBH///fdISkoCAPTr1w8hISHo378/ACApKQnff/89Pn78mCWG+/fvY8SIEdizZw8cHBwAANeuXcO+ffswZ84cbNq0CYaGhgD+d10CgGfPnuHHH39EamoqTExMsGjRIhw8eBA9evQAkPZcYsb3RkQaKL+zSyIiZTx9+lTuDvStW7dyXaZixYpS+b1790rTM97dr1SpkjQ94x37Xbt2SdP9/f2l6VevXpWmZ1cbIIRqNXejRo2Spv3555/i9evXCt+Popq76OhoqaZJX19fbtmhQ4dK5X/++WchhHxNmJ6enoiKihJCCJGSkiIKFSokAAhzc/Nc923GmqeRI0eK/fv3S7UR7dq1k2oaMu4LZKq5O3XqlGjRooWws7MTOjo6WWoZMtZq5LSvM2/j0KFDWeZn1xxWCCH8/PyybHvu3Lm57oOEhARx8uRJhX8vXrxQev9l/itcuLBYt25dlmUU1dx16NBBABCFChUSR44cETExMQq3l7FWqGrVqtJnpmbNmiI2NjbX95o55uDgYCGEEDNnzpSmpTdHvH37tjTtu+++E/fv31eqqW9uTp06JbefFDUhTUhIkCtz6tQpIYRqNXc5TRcirdYufZ6pqal4+fKlwnh37twplatWrZrcvGrVqmW51mTcZq1ataSyAwYMkNuf6Zo2bZrlujR//nxpWo8ePaTP499//y2d32ZmZmo5HkT0bWLNHREVCGZmZnKvM3Zmkp302gMAUo0QALi7uyssk5GPj4/0//Q78QDw7t27XLerqs6dO0u1GW3btoW1tTVsbW0RGBiII0eO5Ljsv//+K/XYWaJECblYc3ufpUuXlnrd1NLSgoWFBYC8vUd/f384OjoiKSkJ27ZtAwCpowtFzp8/j3r16mHPnj2IiopCcnJyljJ5icPAwAANGjRQaZlVq1bByMhIeu3h4YFBgwblulxkZCRq166t8C8kJETV0CWvXr3CP//8o1TZHj16QCaTIS4uDn5+fjAzM4OjoyO6dOmCixcvKlzm8uXLEELAxMQE+/fvh4mJiVLb6tKli/T/7du3y/1rYmKC5s2bAwBKliwp1axv2LABJUqUgLGxMTw9PTF79myp1k9Vpqamcq9fvXqVpUzmaZmvG+qQ+bpiY2OjVLmMcjs3M863tLSU/p+xUx1ra2vp/+nnSsZ1rVmzRvo81qlTB3FxcQDSareVuX4SUcHE5I6ICgRjY2O4uLhIr0+fPp3ndSkzCHR6ogOkNXlLl55IKSslJUXu9evXr7OUKV++PC5duoSffvoJNWvWhJmZGV6+fIldu3ahUaNGOHPmjErbTJfb+8z4HgH596kqLS0tqekXkJZkZUwGMlu2bJnUXK1Zs2YICQnByZMn0bVrV6lMelNVVRQuXFjlZR4/fiz98AWAiIgIxMbGqryevOrWrRuSkpIQGhqKQoUKQQiBWbNmYe/evbku27BhQ5w+fRq9evVClSpVUKhQITx9+hR//PEHfHx8FI75lt7U9P3795gwYYLScbq6ukpJSnBwMO7du4cLFy4AAAIDA6VmglpaWggJCcHcuXPh7++PYsWKIT4+HmfPnsWIESPw888/K73NzNtPbyINpA1XkFl4eLj0fz09PakpacZzIeM5qeh8/BpyOzczJqVaWv/7qZY5wU2n6nVJUVNQItIMTO6IqMBo37699P958+YpvPv88uVL6VmYUqVKSdPPnz8v/T/j82cZy6hT+o+z6OhoKYl59OgRbt++naWsEALlypXDggULcPbsWbx7906qEUlNTcXu3buz3Y6rq6v0Q/H+/fuIjo6W5n2N95lRz549pR+irVu3hrm5ebZlnz17Jv1/+vTpaNy4Mby9vfHixQuF5TP+wM0p6VMmcc8oLi4OPXr0gBBCSnqeP3+uVM2ds7MzRFrHZFn+unfvrlIcOjo6aNSoEUaMGCFNGz9+fK7LCSHg6emJFStW4PLly3j//j3mzp0rvbfQ0NAsy/Tr109KepYsWYIZM2YoHWfnzp0BpNUUpT87BsjX6gkhYGxsjCFDhuDAgQN4/PgxXr58KT0DuHPnTqW3l5GhoSEaNmwovZ4/f75cUiOEkHuGtlGjRtJzhBmTpfTnUwEo3D9Azp+3zNeV7BLE7K4/mV+r89zMuK6JEycq/Gx+/PgRbm5uatsmEX1bOM4dERUYw4YNwx9//IGIiAi8e/cONWvWxLBhw6Rx7k6cOIE1a9bgxIkTsLS0RKdOnXDt2jUAwIABA/D+/XvIZDKMGjVKWmfHjh2/SKyurq64dOkS4uPj0alTJ9SpUwdLlizJUpMHALNmzcKJEyfQtGlTFCtWDEZGRjh48KA0P6dmbFZWVmjUqBFCQ0ORmJiIdu3aYfDgwbh//z6WLFkilftS7zMjJycn/Pbbb4iKisp1AOmMg51Pnz4d3bp1w4EDB+Ted0YZaxk3btwIbW1taGtrf/a4fyNHjsT9+/cBpHXAs2fPHhw5cgTr1q1D27Zt0bRp089av6oGDhyIWbNmIS4uDuHh4Th06JBcQpPZTz/9hMjISDRo0ACOjo7Q0dGRBroHFH92rKyssH//fnh4eODt27cYM2YMihYtmmNNa7oOHTpgyJAhSE5OxuHDhwGkjTfp6+srlXn27Bn8/PzQrl07lC1bFra2tnj48KHUZDJjTEFBQdJg4WvWrMk1KQ4KCsLBgweRlJSEo0ePIjAwEN9//z1kMhlWrVqFo0ePAkirtQsKCpKWS09mAWDcuHF49+4dzpw5I5XPLOPnLTQ0FHXq1IGBgQEqVKiASpUqoXz58rhx4wZiYmJQv359jBgxApaWlrh06RLevn2LuXPnomHDhrCyskJ0dDQuXryIH3/8EU2bNkVISIjUZNba2lrlZsQ5adOmDUaNGoXExETMmDEDMpkMnp6eiIuLw8OHD3H8+HHEx8dLx46INNDXfMCPiOhz/fPPPwqHKcj4d+XKFSFEWucKtWvXzrZcTkMhZJRdZww5dfKxfPnyLNszNjYWRYsWzdKxx5QpU7KNUUtLS+oUIruhEO7fv5+noRDShwnI7f0rkrlDlZxkjCW9Q5Vz585JnXqk/8lkMuHp6Sm9XrNmjbSOjJ3gZPzLvI3MHdikU9ShyvHjx6UYPD09RUpKinjw4IEwMjISAESRIkXE27dvc90XeZHdUAhCyHeg4efnJ01XdHwyjlOX+c/Q0FDcv39fCKG4g5Djx49L3fTr6uqKw4cPKxV748aN5bYzZMgQuflPnjzJ8fzs06ePwv2Q8XjnZPPmzcLAwCDb9RsYGGQZa+7169fSsBcZ/8qUKZNlvwghxKtXrxQO1ZF+nl+6dEmYm5sr3H7G47l79+48DYWQMZbs9lF215+chkJQdN4TkWZhs0wiKlDKli2La9euYd68efD29oalpSX09PTg6OiIRo0aYd26dShbtiwAQF9fH4cPH8aMGTNQsWJFGBoaSnffp0+fjkOHDsk9w6NOP/zwA0aPHo3ChQvD0NAQvr6+OHnyJEqUKJGlbJMmTdCnTx+UL18eFhYW0NbWhqWlJRo2bIiDBw/Cy8srx225uLjg8uXL+PHHH1G8eHHo6urC1NQUderUwdatW1Vqdve1uLu7Y9euXahQoQIMDAxQrlw5/Pnnn9nWUjVr1gxz5sxBiRIlPuvZwHQfP36UurXX1dXFypUroaWlheLFi2PatGkA0ppnZhwi4msZNGiQ1CzwyJEj0lADinTu3BndunWDm5sbzMzMoK2tjcKFC6Nly5Y4efKk3HOqmdWtW1ca9iIpKQmtW7eWe2YtO5lr+DK/trS0xMSJE+Hj4wN7e3vo6urC0NAQFStWxNSpU/M0XEhGHTp0wI0bN9C/f3+UKlUKhoaGMDQ0RKlSpdC/f39cv35drgk3kFZbuXv3blSsWBF6enooUaIEfvvtN7lmsBlZW1tj9+7dqFKlivQsYUZVq1ZFeHg4+vXrBxcXF+jp6cHc3BweHh5yw3a0aNECYWFhaNOmDQoXLgwdHR3Y2NggMDAQZ86ckTqhUacffvgBf//9NwIDA2FrawsdHR3Y2trC3d0d48ePl6vRJyLNIxNCxadwiYiIiIiI6JvDmjsiIiIiIiIN8J/tUCU1NRXPnz+HiYmJyr2rERERERERfQ1CCLx//x5FihSR681Xkf9scvf8+XM4OjrmdxhERERERES5evLkCYoWLZpjmf9scmdiYgIgbSdlNygoERERERFRfoqNjYWjo6OUv+TkP5vcpTfFNDU1hQCbZRIR0eczM/3fF29M7Pt8jISIiDRFekWUMo+SsUMVIiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDcDkjoiIiIiISAMwuSMiIiIiItIATO6IiIiIiIg0AJM7IiIiIiIiDVAgkrsbN25kO2/37t1fLxAiIiIiIqJvVIFI7ho1aoSHDx9mmb5jxw507tw5HyIiIiIiIiL6thSI5O6HH36An58foqKipGlbt25F165dsXbt2vwLjIiIiIiI6Buhk98BKGPSpEl48+YN/Pz88PfffyM0NBQ//PADNmzYgNatW+d3eERERERERPmuQCR3ALBo0SJ07twZHh4eePbsGTZv3owWLVrkd1hERERERETfhG82uQsODs4yLTAwECdPnkTHjh0hk8mkMs2bN//a4REREREREX1TZEIIkd9BKKKlpdzjgDKZDCkpKSqvPzY2FmZmZoiJiYGATOXliYiIMjMzNZH+HxP7Ph8jISIiTSGDkPIWU1PTHMt+szV3qamp+R0CERERERFRgVEgestU5N27d/kdAhERERER0TejQCR3M2fOxNatW6XXbdu2haWlJRwcHBAeHp6PkREREREREX0bCkRyt2zZMjg6OgIADh8+jCNHjiA0NBSNGzfG8OHD8zk6IiIiIiKi/PfNPnOXUVRUlJTc7du3D+3atUPDhg3h7OyMmjVr5nN0RERERERE+a9A1NxZWFjgyZMnAIDQ0FD4+fkBAIQQeeopk4iIiIiISNMUiJq7wMBAdOrUCSVLlkR0dDQaN24MALhy5QpcXV3zOToiIiIiIqL8VyCSu/nz58PZ2RlPnjzBrFmzYGxsDACIjIxE//798zk6IiIiIiKi/PfNDmL+pXEQcyIiUjcOYk5EROqmEYOYBwcHo3HjxtDV1UVwcHCOZZs3b/6VoiIiIiIiIvo2fbM1d1paWoiKikLhwoWhpZV9vy8ymSxPnaqw5o6IiNSNNXdERKRuGlFzl5qaqvD/RERERERElFWBGAohJ8+ePcvvEIiIiIiIiPJdgU3uoqKiMHDgQJQsWTK/QyEiIiIiIsp333Ry9/btW3Ts2BHW1tYoUqQIFi5ciNTUVEyYMAEuLi64cOEC1qxZk99hEhERERER5btv9pk7ABg1ahTOnDmD7t274+DBgxg8eDBCQ0OhpaWFY8eOwcPDI79DJCIiIiIi+iZ80zV3Bw4cwJo1azBnzhzs3bsXQghUrlwZ+/btY2JHRERERESUwTed3D1//hxlypQBADg7O8PAwABdunTJ56iIiIiIiIi+Pd90cieEgI7O/1qOamtrw9DQMB8jIiIiIiIi+jZ908/cCSFQv359KcGLj49HQEAA9PT05Mpdvnw5P8IjIiIiIiL6ZnzTyd3EiRPlXrdo0SKfIiEiIiIiIvq2yYQQIr+DyA+xsbEwMzNDTEwMBGT5HQ4REWkAM1MT6f8xse/zMRIiItIUMggpbzE1Nc2x7Df9zB0REREREREph8kdERERERGRBmByR0REREREpAGY3BEREREREWmAApvcvXv3Lr9DICIiIiIi+mYUiORu5syZ2Lp1q/S6Xbt2sLKygoODA8LDw/MxMiIiIiIiom9DgUjuli1bBkdHRwDA4cOHcfjwYRw4cACNGzfG8OHD8zk6IiIiIiKi/PdND2KeLioqSkru9u3bh3bt2qFhw4ZwdnZGzZo18zk6IiIiIiKi/Fcgau4sLCzw5MkTAEBoaCj8/PwAAEIIpKSk5GdoRERERERE34QCUXMXGBiITp06oWTJkoiOjkbjxo0BAFeuXIGrq2s+R0dERERERJT/CkTN3fz58/Hjjz+ibNmyOHz4MIyNjQEAkZGR6N+/fz5HR0QZ7du7F7716uZazr1GdZw4ceJLh0NERAooe63+VinzHTIpKAjDhg39OgERfSNkQgiR30Hkh9jYWJiZmSEmJgYCsvwOh/4D3GtUz3H+D716oXfvPl8llr59euPy5csAAD09PTg4OKBt23Zo07btZ687ISEBcXFxsLS0BACsWLEcf534C39s2iRX7vXr1zA1NYWent5nb5PoW2FmaiL9Pyb2fT5GQv8Fk4KCsH//vizTd+zcJfVVkJ19e/di3ry5OHb8xBeJbd/evZg8eRIAQCaTwcbGBu7uNfHjwIHS98PnyPgd8vz5c7Rs0RwbN/6BUm5uUpkPHz5ACAETE5Mc1kT07ZNBSHmLqalpjmULRLPMdDdv3kRERAQ+ffokN7158+b5FBGR8kIOhEr/P3L4MJYvX4Y/t++QphUqVEj6f/rzpDo6X+4UbdmyFXr36YPEhATsD9mPWbNmwsTUBI0a+X/Weg0MDGBgYJBrOWtr68/aDhERAZ6etTB+wgS5aRYWFvkUjTwjIyP8uX0HhBD49+5dTJ48Ga9ev8KiRYs/e93KfIekt/Qi+i8pEM0yHzx4gEqVKqF8+fJo2rQpWrZsiZYtW6JVq1Zo1apVfodHpBRra2vpz9jYGDKZTHr9+PEj1PWpgzOnT6Prd13gVcsT4eFXFTYpmTd3Lvr26S29Tk1Nxdo1a9CiRXPU9vZCp04dcfTokVzjMTAwgLW1NRyKFkXv3n3gWKwYTv79N4C0HmqHDR0Cnzq1Ua+uD0aPHoXo6Ghp2bt376Jf3z6o61MH9er6oOt3XXDz5k0A8k199u3di99XrsS//96Fe43qcK9RHfv27gUg36Tm+549sWjRQrn43r59C0+PmlIN46dPn7Dg11/RtElj1KntjR7du+HSpYtS+cjISAwZPBj1feuhTm1vtG/XDqdPn1LiyBARFVy6erpy3y/W1tbQ1tbGH39sRMcO7VGntjeaNW2KmTNmIC4uLtv15HRdB4CrV6+iV68fUNvbC82aNsWcObMRHx+fY2zp33M2Njao5eWF9h3a48L580hISEBqaip+X7kSzZo2gVctT3Tu1AlhZ85IyyYlJWH2rJlo7N8I3l610DygGdauWSPNz/gd0rJF2k3+Ll06w71Gdek7MuN36K6dO9GksT9SU1PlYhw2dAim/H8NIwD89dcJfNelM7y9aqFlixZYuXIFkpOTAaTdeF2xYjkCmjWFVy1PNGnsjzlzZue4D4i+tgJRc/fzzz+jePHiOHr0KIoXL47z588jOjoaQ4cOxZw5c/I7PCK1WfzbYvz8889wcCiqdDOStWvXIPTAAYwaNRrFHB1x5coVTJwwARbmFqharZrS29bX10dSUhJSU1MxbOgQGBYqhGXLVyAlJQWzZs3E2DGjsWz5CgDAhPHj4ObmhpGjRkNLSwt3795VWMvo16AB7t+/j7CwM1j82xIAiu+k+vv7Y8OG9fjxx4GQydKaSR8+fAg2NjaoUqUKAGD2rFl4+PABpk77BTY2Njhx/Dh+/uknbNq8BcWKFcOsWTORnJSE5StWwtDAAA8ePoShYaEs2yIi+i/Qkmlh6LDhKFKkCJ49e4ZZM2dg0cKFGDlqlMLyOV3Xnz59ip9/Goi+ffth/PgJePf2LWbPnoXZs2ZhwsSJSsekr6+P1NRUpKSkYMuWzfjjj40YPWYM3Eq5IXhvMIYOHYItW7ehWLFi2LplC/7++2/8Mn0G7Ozs8OLFC7x4EaVwvWvXrkP37t2w+LclcHFxga6ubpYy9f38MGfObFy8eBHu7u4AgJiYGISFhWH+rwsApHXUFzRxIoYOG44qlSvj6bOn+OWXXwAAvXr1xrFjR7F50yZMm/YLXEqUQHT0a/x791+l3z/R11AgkruwsDAcO3YM1tbW0NLSgpaWFry9vTF9+nT89NNPuHLlSn6HSKQWffr0Qc2aHkqX//TpE9auWYPFvy1BxYoVAQAORYviavhV7Ny1U6nkLiUlBYcOHsS9f/9Fq5atcOHCedy/fx+7d++BrZ0dACAoaBI6tG+Hm//8g7LlyuHFixfo8l1XODs7AwCKFSumcN0GBgYwLGQIbW2dHJvQ+DVogHnz5uLq1atSMncw9CAaNmwEmUyGqKgo7Nu3F8F798HGxgYA0OW77xAWFoZ9e/ei/4ABeBEVhXq+vlIPug5Fiyq3E4mICrDTp07Bp05t6bVnrVqYMWMmOnbqJE0rUqQI+vbrhxnTp2eb3OV0XV+7dg38/f2ldRYrVgxDhw1H3z69MXLUKOjr6+caZ0REBHbu2IEyZcrCyMgIf2zciK7duqFhw0YAgIEDf8KlixexZfNmjBg5ElEvouDoWAyVK1eGTCaDvb19tus2//9mqOZmZtl+15iamsKzVi0cPBgqJXfHjh6Fubk5qldPeyb+95Ur0a1bdzRr1gxA2vdI3z59sWjRQvTq1RtRUVGwsrKCe82a0NHRgZ2dHcqVK5/reyf6mgpEcpeSkiLVYlhbW+P58+dwc3ODk5MT7ty5k8/REalPmTJlVSr/5MkTJCQkYOCPA+SmJyUlwS3DQ+WKbN/+J/bs2Y2kpCRoa2ujY6dOaN2mDf7ctg2FbW2lxA4AXFxcYGJigoePHqJsuXLo2KkTpk2dggMhIXB3d0d9Pz8U/YxkysLCAh4eHggNPYAqVarg2bNnuH79GkaPGQMAuHfvHlJSUtCmdaDccp8+fYKZmRkAoF37Dpg5YzrOnT0Ld/eaqOfri5IlS+Y5JiKigqBatWoYOWq09NrQ0BAAcP7cOaxduxaPHz/Cx48fkZKSgsTERCQkJCh8Ljqn6/q/d//FvXv/IjT0f8+OCyGQmpqK58+fo3jx4gpj+/DhA3zq1EZqaio+ffqESpUrY+zYcfjw4QNevXqFShUryZWvWKkS/v03rSasWbMA/PjjALRp0xqenp7w9q4NDw/lb34q4u/fGL9Mm4qRI0dBT08PoaGhaNCgIbS00p5S+vffu7h2LRxr1qyWlklNTZX2m199P2zZvBktW7SAp6cnanl5oXbt2l/0+XgiVRWIT2P58uURHh6O4sWLo2bNmpg1axb09PSwYsUKuLi45Hd4RGqT/qWcTktLBmTq0Da97T8A6XmH+fN/hU3hwnLl9BQ0S8nI378xevTsCX19falWXFm9e/dBo0b+OH36FMLOnMGKFcsxddovqFevntLryKyRf2PMnTMbw4ePwMGDoXB1dZVq4eLj4qCtrY316zdAS1tbbrn0fdayZUt4enjg1OlTOHf2HNauXYOfBw1C+/Yd8hwTEdG3zsDQMEvPmM+fP8eQIYMR2Lo1+vXvD1NTU4SHX8XUKVOQlJSkMLnL6boeHx+HVoGBCq+ndhluBGZmZGSE9Rs2Qksmg5W1tbTdDx8+5Pq+Spcujd279yDszBmcP38eY0aPgru7O2bMnJXrstmpXbs2hBA4feoUypYti6tXr2DwkCHS/Pj4ePTq3Rv16vlmWVZPTw+2dnb4c/sOXDh/HufOn8OsmTOwccMGLF+xggkefTMKxCdx3Lhx+PjxIwBg8uTJaNasGWrXrg0rKyts3bo1n6Mj+nLMLSxw//59uWl3796RvkSKFy8OPT09RL2IUun5OiDt2TdFXWU7F3fGyxcv8CIqSqq9e/DgAd6/f4/ixf93M8XJyQlOTk7o1Kkzxo0dg317gxUmd7q6ukhNTck1Hh8fH0z/ZRrCzpzBwdCDaNK0iTSvlJsbUlJS8ObtW6nZpiK2dnZo3boNWrdug98WL8ae3buZ3BHRf87t27eQmpqKQYMGSzfujhw5nOty2V3X3dxK4+GDh7kOr5CZTCZTuIyxsTFsbGwQfi1c7rvrWng4ypUrJ1euQcOGaNCwIXzr18fPPw1ETEyM1GIjXfozdimZOkvJTF9fH/Xq+SI09ACePH0CJycnlC5dWprv5uaGx48f5/g+DQwMULtOHdSuUwdt27RF27ZtcO/ePbn1EOWnApHcNWrUSPq/q6srbt++jTdv3sDCwkLqfIFIE1WvXgMbN2zA/v37UKFCRYQeOID79+9LTS6NjIzQuUsXzJ83D6mpApUrV8aHDx8QHn4VRkbG0nMDqnB3r4kSJUpg/ITxGDJkKFJSkjFz5kxUrVoVZcuWRUJCAhYtXADf+vVRpIgDXr58gZs3b6Keb9Y7nQBgb18Ez58/x907d1DY1haFChVSOLadoaEhfHzqYtmyZXj06KHckAxOTk7w92+MoKCJGPTzIJRyc8O7d29x4fwFuJYsCW9vb8ybOxeetWqhWLFieP/+PS5dughnZ8VNhYiINFnRoo5ITk7Gtq1bUbt2bYRfC8eunTuzLZ/bdb1rt27o2aM7Zs+aiRYtWsLA0BAPHz7A+XPnMHzEyDzF2KXLd1ixYjmKOhRFqVKlsHfvXty9exeTp0wFAPzxx0ZYW1vDza00ZDIZjh49AisrK4WdjVlYWEBfXx9hYWdQuHBh6OvrZzsMgr+/P4YMGYwHDx7Av3FjuXnf/9ALQwYPgp2dHXx960NLSwv//nsX9+/fR79+/bFv716kpKagfLnyMDAwwIEDB6Cvr59j7SXR11YgkjtF1DEAJtG3ztPTE99//wMWLVyET58SEdC8OZo0bYr79+5JZfr27QcLcwusW7sGvzx7BhMTE7i5lUb3Hj3ytE2ZTIY5c+dhzuxZ6NO7F7S0tODh6Ylhw4YDALS1tRETE4OgiRPx5s0bmJubo269etkOwO7r64sTx4+hX7++eP/+PSZMmIhmAQEKy/r7+2PQoJ9RpUrVLF+WEyZOxOpVq/Drgl/x6uVLmJubo3z5CvCundaRQEpqCmbPmomXL1/CyMgIHp6eGDx4iKLNEBFptFKlSmHQ4MFYv34dfvttMapUqYr+AwYgKJueLXO7rpcsWRLLl6/A0qVL0Lt3Lwgh4FC0KBo0aJDnGNt36IAPHz9gwYJf8ebNGxQv7oK5c+dJHbkYFTLChvXr8eTJE2hpaaFs2XL4dcEChY8Q6OjoYOiw4Vj1+0qsWL4clStXlnp3zqx6jRowNTXF48eP4Z9pXFdPT0/Mm/8rVv2+EuvXrYOOjg6cnZ3RokVLAICxiQnWr1uLX+fPR2pqKkq4umLuvPkwNzfP834gUjeZEJke6PmPiI2NlUZ6F2DtHxERfT4z0//VKsTEvs/HSIiISFPIIKS8xdTUNMeyBWIQcyIiIiIiIspZgW2WqarExEQkJiZKr2NjY/MxGiIiIiIiIvX6rJq7lJQUqRfLb9306dNhZmYm/ana4xMREREREdG3TKVn7qKjo7Fp0yYcPnwY586dw+vXrwGkjf1RqlQp1K5dG23btoWPj89nBxYcHKx02ebNm+daRlHNnaOjI5+5IyIiteEzd0REpG6qPHOnVHIXERGBCRMmYMuWLbC0tISHhwcqVaoEa2tr6Ovr4927d3j06BEuXryIS5cuoXjx4pg4cSI6d+6c5zeRuTckmUyGjKFmHAIhJSX3MbQyY4cq9F/Qu3cvBAa2hr+/f+6FvzHv3r1D+3ZtsX7DRtja2uZ3OERKYXJH/0Xp1+s1a9ehSJEi+R2Oynbs2I7Tp05j3vz5+R0KkUJq71ClbNmykMlkOHz4MJ49e4adO3di4sSJGDBgAH744QcMGzYMixcvxtmzZ/H8+XP8/PPPmDx5MmbMmJHnN5Gamir9HTp0CJUrV8aBAwfw7t07vHv3DiEhIahatSpCQ0PzvA2iguDjx4+YN3cumgc0Q21vL3zfsydu/vNPrsv9/ddfeBMdjYYNG0rTEhMTMWvmTPj51YdPndoYOWI4oqOjs11HcnIyFi1aiI4d2qNObW80aeyPiRMn4NWrV3LlVq9ehe979kRtby/41qubZT0xMTEYMngwfOrURpfOnXDnzm25+bNmzsQfGzfKTTM3N0eTJk2xYsXyXN8rERHl7PLlyxgyeDCaNPaHe43qOHHiRI7lp0//Be41qmPzpk25rnvNmtWoU8dHLrGLiorC4EE/o7a3Fxo1bICFCxYgOTk5x/UMHTIYAc2awturFhr7N8LECePlvm8eP3qEfn37wL9RQ3h71ULLFi2wdOkSufWeO3cWrVsHol5dH0ycMB5JSUnSvA8fPqB160BERkbKbbd58xa4c+c2rly5kut7JfrWKZXc/fPPP1izZg1q166d66DhNjY2GDBgAG7fvo3vvvtOLUEOGjQICxYsQKNGjWBqagpTU1M0atQI8+bNw08//aSWbRB9q6ZNnYpz584haNJkbNq8BTU9amLAgP54+fJljstt3boVAQHN5WrB58+fh5Mn/8b06TOwbPkKvHr9GiNHDM92HQkJCbhz+zZ6fv8DNmzYiJmzZiPi8WMMHSo/flxyUjLq+9VH69ZtFK5nzerViIv7iPUbNqJqtWqYNm2aNO/69eu48c8NdOjYMctyzQICcDA0FDExMTm+VyIiyllCfDxKliqp1KDjx48fx43rN2BjY5P7ehMSELxnD5q3aCFNS0lJweBBPyMpKQmrVq3GxIlB2LdvL1Ysz/lmXbXq1fHL9Bn4c/sOzJw5C0+fPsOokf+LV0dHB02aNMXCRYvx5/YdGDJkCPbs3i2tNzU1FePHjUNgYCB+X7Uat27dwq5d/xu8ffHiRQgMDIS9vb3cdnV1ddGokT+2bt2S6/sl+tYpldw5OTmpvGKZTAYHBweVl1Pk/v37CgeINDMzw6NHj9SyDaJvUUJCAo4fP4aBP/2EqlWrwtHREb1794GjoyN27Nie7XJv377FxYsXpAG+gbQ7lsF79mDQ4MGoUaMGypQpgwkTJuLatWu4fv26wvUYGxtj8W9L0KBBAzg5O6NChQoYPnwEbt+6haioKKlc7z590KlTZ7i6uipcz6NHD9GgYUM4OTmhVatAPHr4EEBazeCM6dMxatRoaGtrZ1muRIkSsLa2wYkTx5XaX0REpFgtLy/069cf9erVy7Hcy5cvMXfObEyeMgU6Orl3qn769Cno6emhQoUK0rRzZ8/i4cOHmDR5Ckq5uaGWlxf69O2LP//cJleTllmnTp1RoUIF2Nvbo2KlSujWrRtu3Lgu1cw5FC2KgObNUapUKdjb26OOjw8a+fvjytW0Grf01l1t2rRFiRIlULtOHTx6+AgAcC08HDdv3kSHDllvJAKAd+3aOPn330hISMj1PRN9y/LcW+bz589x/vx5/P3331n+1K1GjRoYMmQIXrx4IU178eIFhg8fDnd3d7Vvj+hbkZKSgpSUFOjp6clN19fXR/jVq9kud/XqVRgYGKB48eLStFu3biE5ORnu7jWlac7OzrCzs8P169eUjunDhw+QyWQwNjZWepmSJUvh4sWLSE5OxtmwMLiWLAkAWL9+HapVq4ayZctmu2y5cmVx9cpVpbdFRER5k5qaiokTJ6BLl+9QokQJpZa5euUqSpcuIzft+vXrKFHCFVZWVtI0Dw9PfPz4EQ8e3FdqvTExMQgNDUXFihWzTTKfPHmCs2FhqFq1KgDAwsIC1tbWOHf2LBISEnD1ylW4lnRNu5E4cwZGjx6j8EYikPYIUkpKCv7554ZS8RF9q1Qe5+7Bgwf47rvvcPbsWQBA5v5YZDJZnjo4ycnq1avRqlUrFCtWTBrC4MmTJyhZsiR2796t1m0RfUuMjIxQoUJFrF71O4oXLw5LS0scOngQ169fR9GiRbNdLioyEpaWlnJNMqOjo6GrqwsTExO5spaWljk+d5dRYmIiFi9ehIYNG6mU3HXr3h0zZkxHYKuWsLcvgnHjxiMiIgL79+/HqlWrMX36Lzh39hzKlC2DsWPHya3b2sYGd+/cUXpbRESUN+vXrYOOtjbad+ig9DKRUZGwtrGWmxYdHQ1LK0u5aemJXvTraMAt+/UtWrQQf27bhoSEBJSvUAHz5mXt5OT7nj1x585tfPr0Ca1atUKfPn0BpP0G/WX6DMyfNw9z582FVy0vNG/eAuvWrkX1atWhp6eHH77viXfv3qFd+/Zo1669tE4DAwMYGxsjMjIqy/aIChKVk7tevXrh6dOnWL16NcqWLZulRuFLcHV1xbVr13D48GHcvp3WEUOZMmXg5+eX6zOARAXdpMmTMWXyZDRt0hja2tpwc3NDw4aNcPv2rWyXSUxMhJ6evlrjSE5OxpjRoyCEwMhRo1Ra1tjYGFOnTpOb1q9fX/z00084GBqK58+eYfuOHZg2dSp+X7kSgwYPlsrp6+uzmQwR0Rd269YtbNmyBRs2blTpt1ViYiL01fh98913XdG8eQtERUXi95UrMSloIubN/1Uupl9++QUf4+Lw7793sWjhQmzcuAFdu3YDAFSuXBnr1q+Xyj5+/BghIfuxYeMf6NO7F9p36IhatWqhY4f2qFKlKkr+f0sSgN83pBlUTu7Onz+PdevWITAw8EvEky2ZTIaGDRuiTp060NfXZ1JH/xlFixbF8hUrEB8fj48fP8La2hpjRo/O8ZlWc3NzvH8fKzfNysoKSUlJeP/+vVzt3Zs3b+SaziiSnJyM0aNHITIqCkuWLFWp1k6RvcHBMDE2gY9PXYwYPhw+PnWho6OD+n5+WLF8mVzZ2NhYmFtYfNb2iIgoZ1evXMHbt2/QPKCZNC0lJQULFvyKLVs2Y0/wXoXLmZuZI1bB980/mXp1Tm8hYmWd8/eNubk5zM3N4eTkBGfn4gho1hTXr19HxYoVpTK2dnYAABcXF6SmpOKXX6ahc+cuCptczpj+C37+eRBSU1Nx584d+Pn5wcDAAFWqVsXly5fkkrvY2FhYWJjnGB/Rt07lZ+4cHByyba/8paSmpmLKlClwcHCAsbExHv5/Zwzjx4/HqlWrvmosRPnF0NAQ1tbWiI2NxdmzYahTxyfbsqXc3BAdHY3Y2P994ZYpUwY6Ojq4cOG8NO3xo0eIiopChQoVFa0GwP8SuycREfjttyUKOzdSxdu3b/H7qt8xbHhaL52pqSnSw/LJyclISUmVK3///n24lcqhDQ8REX22xk2aYNOmzdi48Q/pz8bGBl26fIeFCxdlu5ybmxsePngoN61ChQq4f/8e3rx5I007f+4cjIyMULy4i9IxpT/6k5T0KdsyqSIVycnJWR4TAoA9e3bD1NQUdXx8kJqa9t2S8fsmNcP3zdOnT5GYmAg3t9JKx0f0LVI5uZs2bRpmzJghd8J+aVOnTsXatWsxa9YsuWag5cuXx++///7V4iDKD2FhYQg7cwbPnj3DuXNn0a9vXzg7OyOgefNsl3Fzc4O5uTnCw8OlacbGxmjeogV+nT8fFy9exK1btzB58mRUqFBRrpeztm1a4/jxtN4pk5OTMWrkCNy6eQuTp0xFSkoKXr9+jdevX8v1eBYVFYW7d+4gKioKqampuHvnDu7euYO4uLgssc2bOxedO3dG4cKFAQAVK1VCyIEQPHz4ELt37USlSpWksgkJCbh96xZqenjkfQcSERHi4uKkazMAPH/+TLpuA2k1ZiVcXeX+dHR0YGVlBSdn52zX6+HpiQcP7svdTKzp4YHixYtj4sQJuHv3LsLCwrBs2VK0bdtO+h33zz830LZNa2lYnxs3bmDbtq24e+cOIiMjceHCBYwbOwZFixaVbkCGHjiAw4cP4+HDh3j29CkOHz6MJb/9hgYNGmbpdOXNmzdYvXo1hg0fAQAwNTVF8eLFsXnzJly7dg0XL1yQ+765euUKHBwccnyenaggULlZ5tq1a/H06VM4OzujcuXKWe7iy2Qy7NmzR13xAQDWr1+PFStWoH79+ujbt680vVKlStIzeESa6sOHD1jy22K8fPkSpqam8PX1Rb/+A3LsolpbW/v/x4g7gNoZhkMYPHgItGRaGDVyBD59+gQPD0+MGCk/5tHjx4/x8cMHAGldYqf3gNulcye5ckuXLUO1atUBAMuXLcP+/fukeV26dM5SBkhLVJ8+fYJJkydL09q1a49bt26hZ4/uKFu2HH7o1Uua99dfJ2BnZ4cqVaoot7OIiEihW7duol+G31C/zk/rqKRp02aYGBSU5/W6urqidOnSOHLkMAIDWwNI+w6aN/9XzJwxHd/37AFDQ0M0bdoMvfv0kZZLSEjA48ePpZo0AwMDHD9+HCtWrEBCfDysrK3h6emJnj2/lxJCbW1tbFi/DhERERBCwM7OHm3btkPHTp2yxDVv7hx07txZbqy+CRMnYlJQELZu3YouXb5D2XLlpHkHDx1Ey5at8rwfiL4VMqGoHjsHuY2PAkC6668uhoaGuH37NpycnGBiYoLw8HC4uLjg5s2bcHd3x4f//yGqitjYWJiZmSEmJgYCfH6PNM/r16/RoUN7bNiwMcuArQVFzx7d0a59B/j7++d3KERKMTP93/OsMbHv8zESoq/n1KlTWLRwATZv2SrXS3NBcf/+fQzo3w/bd+z87GfKib4EGYSUt5iamuZYVuWaO3UnbsooW7YsTp48mWUw9e3bt/OOPlE2rK2tMW7ceERFRRXI5O7du3eoW68eGjVqlN+hEBFRDry9vfEkIgKvXr6UOjspSKJfv8bEoElM7EgjqJzc5YcJEyagW7duePbsGVJTU7Fz507cuXMH69evx759+3JfAdF/VN26dfM7hDwzNzeXurYmIqJvm6KmkQWFe82a+R0Ckdrkqe78ypUraNu2Lezt7aGvrw97e3u0a9cOV65cUXd8AIAWLVpg7969OHLkCIyMjDBhwgTcunULe/fuRYMGDb7INomIiIiIiAoSlZ+5O3nyJBo0aAA7OzsEBgbC1tYWL168wK5duxAVFYXDhw/D29v7S8WrNnzmjoiI1I3P3BERkbqp8sydysmdl5cXTExMsG/fPrne+lJSUtC0aVN8+PABp06dylvkXxGTOyIiUjcmd0REpG5ftEOVK1euYPv27Vm6YdfW1sZPP/2ENm3aqLrKXFlYWEAmy5qAyWQyGBgYwNXVFd27d0ePHj3Uvm0iIiIiIqKCQOXkzsjISBpwMrMXL17AyMjos4PKbMKECZg2bRoaN24Md3d3AMD58+cRGhqKAQMG4OHDh+jXrx+Sk5PRK8MYWURERERERP8VKid3AQEBGDlyJIoWLQo/Pz9p+pEjRzB69Gg0b95crQECaeOnTJ06VW4AcwBYvnw5Dh06hB07dqBixYpYuHAhkzsiIiIiIvpPUvmZu7dv38Lf3x8XL16EqakpChcujJcvXyI2NhY1atTAgQMHYGFhodYgjY2NcfXqVbi6uspNv3fvHipXrowPHz7g/v37qFixIj5+/KjUOvnMHRERqRufuSMiInX7os/cWVhYICwsDPv27cOpU6fw9u1bWFpawtvbG02bNoWWVp5GV8iRpaUl9u7di8GDB8tN37t3LywtLQEAHz9+hImJiaLFiYiIiIiINF6eBjHX0tJC8+bNv0gTTEXGjx+Pfv364fjx49IzdxcuXEBISAiWLVsGADh8+DB8fHy+SjxERERERETfGqWaZb558wbm5ubQ0tLCmzdvcl1pem2aOp0+fRqLFy/GnTt3AABubm4YOHAgatWqlaf1sVkmERGpG5tlEhGRuql9nDttbW2EhYXB3d0dWlpaCoclyCglJUW1iPMBkzsiIlI3JndERKRuan/mbvXq1ShRooT0/9ySO3WLjY1VOF0mk0FfXx96enpfNR4iIiIiIqJvjcq9ZeaH3GoLixYtiu7du2PixIlKd+jCmjsiIlI31twREZG6qVJzp3LXli4uLggPD1c478aNG3BxcVF1lblau3YtihQpgjFjxmD37t3YvXs3xowZAwcHByxduhS9e/fGwoULMWPGDLVvm4iIiIiIqCBQubfMR48eITExUeG8uLg4PHny5LODymzdunWYO3cu2rVrJ00LCAhAhQoVsHz5chw9ehTFihXDtGnTMGbMGLVvn4iIiIiI6FunVHKXkJCAuLg4pLfgjI2NzdJrZkJCAnbv3o0iRYqoPcgzZ85IQx5kVKVKFYSFhQEAvL29ERERofZtExERERERFQRKJXczZ87E5MmTAaR1YtKoUaNsywYFBaklsIwcHR2xatWqLM0uV61aBUdHRwBAdHQ0LCws1L5tIiIiIiKigkCp5K5ly5ZwdnaGEAI9e/bEuHHjpN4z0+np6aFMmTKoXLmy2oOcM2cO2rZtiwMHDqBGjRoAgIsXL+L27dvYvn07gLRBzdu3b6/2bRMRERERERUEKveWuW7dOjRr1gxWVlZfKiaFHj16hOXLl8sNYt6nTx84OzvnaX3sLZOIiNSNvWUSEZG6qX0Q84yePHmCV69eoWrVqlnmXb58GYULF0bRokVVizgfMLkjIiJ1Y3JHRETqpvZBzDPq168fSpYsqTC527RpE/7991/s2bNH1dUqJS4uDhEREfj06ZPc9IoVK36R7RERERERERUUKid3586dQ58+fRTOq1evHtavX//ZQWX26tUr9OjRAwcOHFA4PyUlRe3bJCIiIiIiKkhUHsT8w1HMv7kAAIebSURBVIcP0NXVVbwyLS28f6/+ZiiDBg3Cu3fvcO7cORgaGiI0NBTr1q1DyZIlERwcrPbtERERERERFTQq19yVKVMGu3btgr+/f5Z5e/bsgZubm1oCy+jYsWPYs2cPqlevDi0tLTg5OaFBgwYwNTXF9OnT0bRpU7Vvk4iIiIiIqCBRObkbNGgQunfvDm1tbfTs2RNFihTB8+fPsWbNGqxcuRKrV69We5AfP35E4cKFAQAWFhZ49eoVSpUqhQoVKuDy5ctq3x4REREREVFBo3Jy17VrV7x48QKTJk3C8uXLpemGhoaYMWMGunXrptYAgbRhD+7cuQNnZ2dUqlQJy5cvh7OzM5YtWwZ7e3u1b4+IiIiIiKigUXkohHSxsbEICwtDdHQ0rKys4OnpmWvXnHm1ceNGJCcno3v37rh06RL8/f3x5s0b6OnpYe3atXkavJxDIRARkbpxKAQiIlK3LzrO3bcgLi4Ot2/fRrFixWBtbZ2ndTC5IyIidWNyR0RE6qb2ce527twJX19fmJubY+fOnbmWDwwMVC7SPCpUqJDCcfaIiIiIiIj+q5SqudPS0sLZs2fh7u4OLa2cR0+QyWRqGXduyJAhSpedN2+eyutnzR0REakba+6IiEjd1F5z9/DhQ6njkocPH35+hEq4cuWKUuVkMiZmREREREREBfKZO3VgzR0REakba+6IiEjd1F5zFxERoVIAxYoVU6l8dh48eIDixYuzdo6IiIiIiCgXSiV3zs7OKiVY6njmDgBKliyJyMhIaQDz9u3bY+HChbC1tVXL+omIiIiIiDSFUsndrl27pP9/+PABo0aNQokSJdC6dWvY2toiKioKO3bswIMHDzBz5ky1BZe5xWhISAimT5+utvUTERERERFpCpWfuevVqxdSUlKwevXqLPN69OgBmUymcF5eaGlpISoqSqq5MzExQXh4OFxcXD573XzmjoiI1I3P3BERkbqp8sxdzuMaKPDnn3+iY8eOCud17NhRrpbvc8lksizNQfn8HRERERERUVZKNcvMSFtbG1euXEGDBg2yzLt8+XKu4+CpQgiB7t27Q19fHwCQkJCAvn37wsjISK6cMgOrExERERERaTKVk7vvvvsOEyZMQHx8PFq2bInChQvj5cuX2LVrF2bMmIG+ffuqLbhu3brJve7SpYva1k1ERERERKRJVH7mLjk5GaNGjcLSpUuRkJAgTTcwMEDfvn0xY8YM6Orqqj1QdeMzd0REpG585o6IiNRNlWfu8jyI+du3b3Ht2jVERUXB3t4eFSpUgIWFRZ4Czg9M7oiISN2Y3BERkbqpfRBzRSwsLODj45PXxYmIiIiIiEiN8tT7yevXrzFq1CjUr18fbm5u+OeffwAACxYswNmzZ9UaIBEREREREeVO5eTu8uXLKFmyJLZs2YKiRYvi3r17SExMBAA8e/YM8+fPV3uQRERERERElDOVk7vBgwfD09MT//77L1atWoWMj+zVrFmTNXdERERERET5QOVn7i5cuICdO3dCV1cXKSkpcvNsbGzw8uVLtQVHREREREREylG55s7IyAixsbEK50VERMDKyuqzgyIiIiIiIiLVqJzcNWrUCFOnTkV0dLQ0TSaTIT4+HgsWLECTJk3UGiARERERERHlTuVx7p49ewYvLy/ExsaiXr162L17N/z9/XHz5k3IZDKcPXsWhQsX/lLxqg3HuSMiInXjOHdERKRuqoxzp3LNnYODA65evYqBAwciMjISJUqUQHR0NDp37oyLFy8WiMSOiIiIiIhI06hUc5eQkIARI0bgu+++Q40aNb5kXF8ca+6IiEjdWHNHRETq9sVq7gwMDLB69WrExcV9VoBERERERESkXio3y6xVqxbHsiMiIiIiIvrGqDzO3eTJk9G5c2doa2ujSZMmsLW1hUwm36zR0tJSbQESERERERFR7lTuLVNL63+VfZmTunSZBzf/FvGZOyIiUjc+c0dEROqmyjN3KtfcrV69OtukjoiIiIiIiPKHysld9+7dv0AYRERERERE9DmU7lBl5cqVqFixIkxMTODm5obx48fj06dPXzI2IiIiIiIiUpJSyd2aNWvQp08fJCYmomnTpjA3N8e0adMwZMiQLx0fERERERERKUGpDlWqVq2KkiVLYvPmzVKHKr/88gsmTZqEjx8/QkdH5dad+Y4dqhARkbqxQxUiIlI3VTpUUSq5MzU1xfbt29GwYUNp2ps3b2BtbY27d+/C1dX186P+yjImd7ntJCIiIiIiovygSt6iVLPMDx8+wNzcXG6amZmZtDEiIiIiIiLKX0q3p7xz545c88v0sexu376dpWzVqlXVEBoREREREREpS6lmmVpaWgrHtktfNH2eEAIymazADWKeKvjMHRERfT5zs/89c1e13bR8jISIiDTFid8HqncQ8+PHj6slMCIiIiIiIvoylErufHx8vnQcRERERERE9BmUHsSciIiIiIiIvl1KJXc1a9bE7t27kZqaqtRKnzx5gmHDhmHevHmfFRwREREREREpR6lmmV27dkX//v3Ru3dvtGjRAl5eXqhYsSJsbGygr6+Pd+/e4eHDh7h06RIOHDiAs2fPonnz5ujXr9+Xjp+IiIiIiIigZHI3YMAA9OzZE1u2bMH69euxfv16JCcny5URQsDe3h5t2rTBkiVLUKFChS8SMBEREREREWWl9Dh3hoaG6NGjB3r06IGEhARcvXoVkZGRSEhIgKWlJdzc3ODs7PwFQyUiIiIiIqLsKJ3cZWRgYAAPDw91x0JERERERER5xN4yiYiIiIiINACTOyIiIiIiIg1QIJK7zZs3Zztv+PDhXzESIiIiIiKib1OBSO769euHAwcOZJk+ePBgbNy4MR8iIiIiIiIi+rYUiOTujz/+QMeOHXHq1Clp2sCBA7Ft2zYcP348HyMjIiIiIiL6NqjcW2ZERES287S0tGBmZgYTE5PPCiqzpk2bYsmSJWjevDkOHz6MVatWYc+ePTh+/DhKlSql1m0REREREREVRCond87OzpDJZLmWGTRoEAYOHJjnwDLr1KkT3r17By8vL9jY2OCvv/6Cq6ur2tZPRERERERUkKmc3G3cuBGjR49GqVKl0KJFCxQuXBgvX77Erl278O+//2LUqFEICwvD4MGDASDPCd6QIUMUTrexsUHVqlWxZMkSadq8efPytA0iIiIiIiJNoXJyd/LkSfj7+2P58uVy03/88Uf07t0bly5dwoYNG2BqaoolS5bkObm7cuWKwumurq6IjY2V5udWi0hERERERPRfoHJyt2XLFmzbtk3hvLZt26Jdu3ZYtWoVmjVrhlWrVuU5MHaUQkREREREpDyVe8vU0tJCeHi4wnlXr16FllbaKrW1tWFoaPh50WUjNjYWu3fvxu3bt7/I+omIiIiIiAoalWvuOnfujPHjx+PTp08ICAiAjY0NXr16hT179mDq1Kno06cPAODSpUsoW7asWoJs164d6tSpgx9//BHx8fGoXr06Hj16BCEEtmzZgtatW6tlO0RERERERAWVysnd3LlzoaOjg6lTp2L8+PHSdH19fQwYMAAzZ84EAHh5eaFRo0ZqCfLvv//G2LFjAQC7du2CEALv3r3DunXrMHXqVCZ3RERERET0n6dycqerq4t58+Zh/PjxuH79OiIjI2Fvb4/y5cvD0tJSKlenTh21BRkTEyOtOzQ0FK1bt0ahQoXQtGlTDB8+XG3bISIiIiIiKqhUTu7SWVhYqDWBy4mjoyPCwsJgaWmJ0NBQbNmyBQDw9u1bGBgYfJUYiIiIiIiIvmV5Su7evn2LAwcO4OnTp0hISJCbJ5PJ5JprqsOgQYPQuXNnGBsbw8nJCXXr1gWQ1lyzQoUKat0WERERERFRQaRycnfo0CG0adMGHz58gKGhIfT09OTmf4nkrn///nB3d8eTJ0/QoEEDqUdOFxcXTJ06Va3bIiIiIiIiKohUTu6GDh2KGjVqYPXq1XBycvoSMSlUvXp1VK9eXW5a06ZNv9r2iYiIiIiIvmUqJ3cPHjzAvHnzvnhiN2TIEEyZMgVGRkYYMmRIjmXnzZv3RWMhIiIiIiL61qmc3FWtWhVPnjz5ErHIuXLlCpKSkqT/ExERERERUfZUTu6WLl2KLl26wMHBAfXr14eOTp473MzR8ePHFf6fiIiIiIiIslI5M/P09ERSUhKaNGkCLS0tGBoays2XyWSIiYlRW4C52b59O9q0afPVtkdERERERPQtylOHKjKZ7EvEolBycjJu374NPT09lCpVSpq+Z88eTJgwAbdv32ZyR0RERERE/3kqJ3dBQUFfIAzFbty4gWbNmknP+LVo0QJLly5Fu3btcOPGDfTq1Qv79+//avEQERERERF9q77MA3NqMnLkSLi6umLx4sXYvHkzNm/ejFu3buH7779HaGholiahRERERERE/1VKJXfNmzfH3LlzUbJkSTRv3jzHsjKZDHv27FFLcBcuXMChQ4dQuXJl1K5dG5s3b8aYMWPw3XffqWX9REREREREmkKp5O79+/dISUkBAMTGxn61Z+5ev36NIkWKAADMzMxgZGQEDw+Pr7JtIiIiIiKigkSp5C7jUAQnTpz4UrFkIZPJ8P79exgYGEAIAZlMhvj4eMTGxsqVMzU1/WoxERERERERfYu+6WfuhBByPWQKIVClShW51zKZTKpVJCIiIiIi+q9SObmbPHlytvO0tLRgZmYmPSP3uTh4ORERERERkXJUTu7mz5+PT58+IT4+HgBgYGCAhIQEAIChoSGSkpKQkpKCqlWrIiQkBDY2NnkOzsfHJ8/LEhERERER/ZdoqbrAsWPH4ODggA0bNiA2NhZxcXGIjY3FunXrUKRIEfz11184dOgQnj59iuHDh3+JmImIiIiIiCgTlWvuBgwYgKFDh6Jz587SNGNjY3z33Xf4+PEjBg0ahHPnzmHcuHE5NuEkIiIiIiIi9VG55u7KlStwcnJSOM/Z2RnXr18HAJQvXx4xMTGfFx0REREREREpReXkzsnJCb///rvCeStWrJASv+joaFhbW39edERERERERKQUlZtlTp8+He3atYObmxuaNWsGGxsbvHr1Cvv27cODBw/w559/AgCOHj2KOnXqqD3gp0+fAgCKFi2q9nUTEREREREVVCond61atcL58+cxffp07Nq1C5GRkbC3t0eNGjWwdetWVK5cGQDw22+/qS3I1NRUTJ06FXPnzsWHDx8AACYmJhg6dCjGjh0LLS2VKyCJiIiIiIg0Sp4GMa9SpQq2bdum7liyNXbsWKxatQozZsyAl5cXAODUqVMICgpCQkICpk2b9tViISIiIiIi+hblKbn72tatW4fff/8dzZs3l6ZVrFgRDg4O6N+/P5M7IiIiIiL6z1M5ufP19c12npaWFszMzFClShX06NEDDg4OnxVcujdv3qB06dJZppcuXRpv3rxRyzaIiIiIiIgKMpUfVjMzM8O9e/dw8uRJxMbGwsDAALGxsTh58iTu3r2Lt2/fYu7cuShbtiwuX76sliArVaqExYsXZ5m+ePFiVKpUSS3bICIiIiIiKshUrrlr27Yt7t+//3/t3XVYFdkbB/DvpftSCigloliAuiYWdmGuii1rt66tq4Luuq7tqrvG2t0tdndjg0VIiqRKc8/vD37OeiXEJPb7eR6eh3vmzJl37h1meO85cwbnz59Xet5dQEAAWrVqhV69emHXrl1o1KgRJk6ciKNHj35xkLNnz0bLli1x4sQJ1KxZEwBw+fJlvHjxAt7e3l/cPhERERERUUH3yT1306ZNg5eXV6YHmdva2sLT0xO//vorjIyMMGbMGFy5cuWrBFmvXj08fvwY7dq1Q2xsLGJjY9G+fXv4+fmhTp06X2UbREREREREBdkn99wFBQVBJpNluUwmkyEkJAQAUKxYMaSlpX1ZdO8pVqwYJ04hIiIiIiLKxicnd1WrVsXUqVNRpUoVWFlZSeWBgYHw9PREtWrVAGQM0/zSCVWCgoJyVc/a2vqLtkNERERERFTQfXJyt2zZMjRu3BglS5aEo6MjihQpgsjISNy9exdmZmbYsWMHACAiIgL9+/f/ouBsbW2z7CUUQkjlMpnsq/YQEhERERERFUSfnNyVK1cOz549w+rVq3Hjxg2EhYXB2dkZffv2xU8//QQtLS0AwLhx4744uNu3b2dZLoTA1q1bsWjRIujp6X3xdoiIiIiIiAq6z3qIuZaWFgYPHvy1Y8kkq8ccnDhxAhMmTMDjx48xbtw4jB49+pvHQURERERElN99VnKXF27duoXx48fj/Pnz6Nu3L7y9vVG0aNG8DouIiIiIiChf+ORHIQDAhg0bULt2bRQtWhQGBgaZfr6mZ8+ewd3dHdWqVUORIkXw8OFDLFmyhIkdERERERHRez45udu4cSP69euHChUq4NWrV+jUqRN+/PFHaGhooGjRohgzZsxXC27w4MEoV64c4uLicOPGDWzevBl2dnZfrX0iIiIiIqLC4pOHZc6bNw9TpkzBhAkTsGLFCgwePBiVK1fG69ev0aRJk686wcmyZcugpaWFly9fonfv3tnWu3Xr1lfbJhERERERUUH0ycndkydPUKtWLaiqqkJVVRXx8fEAAH19fYwfPx4jR47EqFGjvkpwnp6eX6UdooLi4MEDWDB/Hk6eOpPXoeSp6dO88Pr1a8yZOy+vQyEiogLu1vZfMGrODpy5/jivQyH65j45uZPL5UhOTgYAFC9eHA8fPoSrqysAID09HVFRUV8tOCZ3VBBNn+aFQ4cOZirfuWsPrKys8iCifx08eAC/Tp+GGjVq4s9Fi6Xy169fo1HD+vh76TL88EOV7xZPaGgo2rVtjQ0bN6F0aQepfNToMRBCfLc4iIgKilvbf8lx+fId57B8x/nvEssKz+6oUt4GExbuwbFLD6Xyri2qomuLanAb+td3ieOdAR3rwLWqA7qMW6lU3rjfQsS/TfqusRDllU9O7qpUqYK7d++iadOmaN26NaZNmwaFQgF1dXX88ccfqFGjxreIk6hAqVnTBVOmTFUqMzQyyqNolKmqquL69Wu4ceMGqlT5foncp+DzK4mIsta430Lp9yYu5TDQvS7aj1gmlSUkpSjVV1WRIV3x7b4sS0pJxZDO9XDqqi/S0hXfbDtfIirubV6HQPTdfHJyN3HiRAQGBgIApk+fjsDAQIwcORIKhQJVq1bF8uXLv3qQRAWNuro6TExNM5Vv3rQRBw8eQEhICAwM5KhTpw6GDhsOHR2dLNt5/PgxFiyYB99HjwCZDFZWVpg4YRLKlisHAPDx8cHffy2Br+8jyOWGcHV1xeAhQ6GtrZ1tbNra2mjYqDH+/msxVq9Zl229iIhw/LlwIa5evQIVFRU4V6yIUaPGoFixYgCAtLQ0/LlwAby9D0FFRRVt2rRBVFQU3rx5Iw2nvHz5ElavXoXnz55BRUUVjo6OGDV6DCwtLQEA7dq2BgD06N4NAFC5cmUsXbZCaVjmnj27sfKfFThw0BsqKv/OATVmzCjI5XJMmZLRw3/27BmsWvkP/P39YWpaBC1btoTHT72hpqYGIURGGwf2Izo6GnK5HA0aNMToMWOz3X8iovzo/UTlTUIyIP4t+6GcNf7x6oGhv2/FkM71YG9dFIN/24xWrk7Q19XC6Dk7pXXH9GqM0rZm6D9tIwBAJgM82rigfaNKMDHURVBoNP7ZdQEnr/rmGM/Riw9R94dSaNewEnYcu5ltvXpVSqN/hzqwszRFZMxrHDx7D6t2X5AST9tiJpgysCXK2Vkg5GUMZq85hmVTuikNpxzerT7qV3VAURMDRMW+weELD/DPzvNIS1egVT0nDOhYF8C/vZuefx3AgbN3lYZlrvm1F277BmHRptNSbIb6Oji6fDgG/boJtx69gLqaKoZ0cUWzWuWhr6OJpy8isWjTKdx8GAQAsDA1wPg+zVDRwRLqaqoIjYzDwo0ncfH2s9x9iETf0CcndzVq1JB65wwNDbFv3z4kJycjOTn5qz8GgaiwkamoYNTosShWrBhCQ0Iwe/YfWLJ4EcaNn5Blfc+pk1HawQHjx0+EiooKnjx+DFW1jD/b4OBgjBwxDAMGDsLkKVMRGxuDuXNmY86c2Zg6Nechzf369ceP7dvi5MkTaNiwUablaWlpGD58GBwrOGL5ipVQVVXFmtWrMHLEMGzavBXq6urYsH4djhw5gilTPGFbogS2bd2Cs2fPKA3rTExMRNeu3WBvXwqJiQlYsXwZxo8bgw0bN0NFRQVr1q7DTx69sGTJ37Czs4OaunqmWBo2bIR5c+fg5o0bqFqtGgAgLi4OVy5fxoIFfwIAbt++jWlenhg9eiwqVqqI4OBgzPz9dwBA3379cerUSWzZshm/zfgddnYlERX1Ck+ePMnFJ0ZEVPAM71ofCzacRMjLGMS/yd1wxN5ta6FFnQr4/Z/DCAqLRuWy1vhtWBvExCfg1qOgbNd7m5CM1Xsuol+H2jhw9i6SklMz1alUxgrTh7bCnDXHcPvRC1iaGWHygBYAgBU7z0NFJsO8sR0Q/ioePX9ZA10tDfzcM/O16W1iCjz/PoDImDcoZV0Ukwe0QEJiMtbtv4Jjlx6ipHURuDjbYdCvmwH8P/n9wOEL99GrdU2l5K6pSzm8inmDW49eAADG92kKu+KmmLhwDyJjXqN+NQcsmdQFncaswIvwGEzo0wxqaqro67kBicmpsLM0ReIHPaZEeeWznnP3IU1NTSZ2RO+5ePECXOvVkX4mThgPAOjSpSuqVKmCYsWKoUrVqhgwcBBOnDiebTvhERGoVrU6bG1tYW1tjYaNGqF06dIAgHVr16Bps2bo0qUrrK2t4eTkjFGjx+Kw9yHpvtjsFClSBO6du2DZ0r+RlpaWafnx48cgFAr8MnkK7O3tUaJECUyZ6onw8HDcvJnxzez27dvRy8MDrvXrw9bWFmPGjoO+vr5SOw0aNET9+g1gZWWF0qUdMHmKJ54+fQp//+cAAEPDjKGqckM5TExNIZfLM8ViYGCAmjVdcPToEans1KmTMDQ0xA//H1a6auU/6NnLAy3d3FC8uCWqV6+BAQMHYs+e3QCAiPBwmJiYoFq16jA3N0f58hXQtm27HN8jIqKCaun2c7h6zx/BEbG5utdMXU0Vvdu5YNrSg7h85zlCXsbiwNm78D5/Hz82rvTR9bcfvYmUlDR0d6ue5fL+Hepg7d7LOHj2HkJexuLqPX8s3XYWPzbKaLuGUwlYmhlh6pL9eBL4Ej5+wfhr65lM7azafRF3H4cgLDIO524+wYYDV9C4ZsZIluTUNCQmpSBdIRAV9xZRcW+RnJr5+nbs0iOYGumhUpl/74FvVrs8jlx8AAAwNzFAa1dnjFuwG7d9XyA4IhYbDlyFj+8LtKnvnFHH1AB3/ILx9EUkQl7G4vytp1JiSJTXctVzN3z48Fw3KJPJ8Oeff352QLkVGxsLQ0PDb74dos/xww8/YNz4idLrd8Mkr127inVr1yIwMABv375Feno6kpOTkZSUBC0trUztdO3SFTNm/IrDh71RtVo1NGzYSBrS+OTJEzx9+gRHj/yb9AghoFAoEBoaihIlSuQYY8+evbB3z24cOLAfjRo1Vlr25MkTBAcHo75rXaXylJQUhAQH482bN4iOjkL5cuWlZaqqqihTpiwUin/vuQgKCsKKFcvw4P4DxMbFQvx/WXh4OEqWtM8xvvc1a9Ycv//+G8aNnwANDQ0cPXIEjRs3kYZpPnnyGHfv3sHaNauldRQKhfTeNmzUCFu3bkG7tm1Qs2ZNuLjUQu06daCm9smDF4iI8r2Hz8I+qb6VuRG0tTTw95SuSuXqaqrw9Q//6PqpaelYuv0cxvVugp1ZDM0sbVsUzmUs0ad9LalMRUUGLQ11aGmowaaYCSKi4pWGnD54GpqpnSY1y6Jz86qwNDeCjpYGVFVU8DYx5y8zPxT7OgFX7vqjeZ0KuO37AsWKyOHsYIkZK7wBAPbWRaGmqoK9fw5SWk9dTRVxbxIBAFsO38DEvs1Qw6kErt0LwMmrvngS9PKT4iD6VnL1n82BAwdy3eC3SO5mzZoFW1tbuLu7AwA6deqEXbt2wdzcHN7e3nB2dv6q2yP6Ulpa2plmxgwNDcXoUT+jffsfMXDQYMgNDOBzxwczfvsVqampWSZ3/foPQNOmzXDx4gVcunwJ/6xYjt9++x2u9esjMTEB7dq1Ryf3zpnWMzc3/2iM+vr66NnLA6tW/oPatesoLUtMSECZMmUwbfpvmdYz+oSJYcaM/hnm5haYNOkXmBYpAqFQoEsXd6Rl8W1qTmrXqQMhBC5evIBy5crBx+c2Rv787yNXEhMT0a9ff7jWb5BpXQ0NDZiZmWP7jl24fv0arl29itmz/8DGjRuwbPkKJnhEVOgkJisPERQCkEGmVKam9u/gLR0tDQDA8JnbEBn9WqleShajO7Liff4eeraqjr7tayM0MlZpmbaWBpZvP4dTV/0yrZdV71pWnEoVx2/D22L59nO4dOc53iQkoWmt8uiRTW9hTg6fv4+xPzXB7NVH0bx2BTwJjMDTF5EAAB0tdaSlK9Bt/CooPpiI5t1kNXtP+eDynWeoXbkUajqVwE/tXDB//QlsO3Ljk2Mh+tpy9V+Nv7//t44jR8uWLcOmTZsAAMePH8fx48dx+PBhbN++HWPHjsWxY8fyND6i3PD1fQSFQoERI3+WepxyGpL5jrWNDaxtbNClazdMnjwJBw/uh2v9+nBwKAN/f/8verxCp07u2L5tG7Zt3aJU7lCmDE6cOA4jI6NsZ640NjbBw0cPUalyZQAZj0Lx8/NFqVIZw0bjYmMRGBiIiZMmo1KljKE3Pj4+Sm2o//8eu/SPzLCmqakJ1/oNcOTIYQS/eAEbGxuUKVPm33gdHBAYGJjje6GlpYU6deqiTp266NCxIzp17ICnT58qtUNEVBjFxL9FSasiSmWlbcyk2S2fB79CckoaLEwNcry/LidCAIs3n8HcMT9i57FbSst8n4fDppgJXkTEZLluYGgUzEwMYCzXRfT/e+/KlyymVMfJwRJhkXFYteeiVGZhqjyUPzUtHSoqyklsVs7ceIzJA1rApWJJNKtdHgfP3fs31oAIqKmqwFiui9u+2Q+1jIh6jV3Hb2HX8VsY2sUV7RtWZHJH+cJXuefuWwsPD5f+aTt48CA6deqEJk2aYNy4cbh+/XoeR0eUO5aWVkhLS8P27dsQEhIMb+9D2P3/e8KykpSUhDlzZuHmzRsICwvDnTs+ePTwIWxtM4Zb9ujZC3fv3sGcObPw+LEfgoKCcPbsGcyZMyvXMWlqaqJf//7Yvn2bUnmzZs0hlxti7NjRuH37NkJDQnDz5g3MmzsHERERADJ60NetXYOzZ88gMDAA8+fPRXx8PGSyjAurvoEB5HI59u7djRcvXuDG9ev4c+F8pe0YGRlBU1MTVy5fkmbazE6zZs1w6eJFHDiwH02bNlNa1qdPP3h7H8LKf1bg+bNn8Pf3x7FjR7Fs6d8AMp7vt3/fXjx79hQhIcE4fPgwNDU1YZGLHk4iooLu+v1AlLOzQMu6jrAyN8LAjnVR0vrfZC8hKQUbDlzBqF6N4VbPEZZmhihTwhzuzarArZ5jrrdz4fZT3H8SivYf3Kf3z64LaFnXUZots0RxEzRxKYfB7vUAAFfu+iM4IgbTh7RCKeuicHawxODOGcvePfI0KCwa5qYGaOJSDpZmhujcvArqVyuttJ3Ql3EoXtQQpW3MYKivDXU11SzjTEpOxZnrfhjkXg8lipvi6IUH0rKgsGh4n7+H6UNbo0E1BxQrIkf5ksXwU1sX1K6UcTvBmF6NUdPZDsWKyFGmhDmqlreFf8jXe84z0ZfIVc+dk5MTNm/ejAoVKkhlmzdvRosWLb7LfW9GRkZ48eIFrKyscOTIEfz2W8ZQMSEE0tPTv/n2ib6G0qVLY+TIn7Fh/Tr8/dcSVKpUGYMHD8E0r6xntlRVVUVcXBymeXkiOjoahoaGcHWtj379BwAASpUqhWXLV2Dp0r8xoH8/CCFQvLglGjdunGV72WnZ0g2bN22SJjkBMnq5li9fgSVLFmPC+LFISEhAkSJFUKVqNejq6gLISC6joqIwzcsTqqqqaNu2HWrUqAkV1YzvjFRUVPDbjN8xb95cdO3iDmtrG4weMwaDBg6QtqOmpobRo8di1ap/sGLFclSsWBFLl63IMs4qVarCwMAAgYGBaNpMObmrUbMm5s9fiFWr/sH69eugpqYGW1tbtG7TFgCgr6ePdevXYuHCBVAoFChZ0h7z5i2AnPftEtF/wOU7z7Fy1wWM6N4Amupq2Hf6Dg6dvQd766JSnb+3nUVMfAJ+ausCSzMjvH6bBF//cKx+r6csNxZtOoW1MzwybX/krO3o92Nt9GpTE2np6QgIicLeUz4AAIUQGD1nJ6YMbIkNM39CSEQsFm48iT8nuCPl/8M2z918gs2HrmF876bQUFfFhVtPsXLXBenxBwBw8qovGlR3wArPbjDQ05YehZAV7/MPsGSSI24+DER4VLzSMq+/D6Jv+9r4uWcjFDXWR2x8Au49CcH5mxmzLKuoyDChT1MUNTbA28RkXPJ5hnnrTnzS+0T0rciEEB99sqWKigquXLmCav+fhjw9PR0aGhq4fv06Kv9/SNa3NHToUBw8eBClSpXC7du3ERAQAD09PWzduhWzZ8/GrVu3Pt7IB+Lj4yGXyxEXFweF+HgXPhHlTKFQwL1TBzRs1BgDBw76+ApEhZCh/N8ZYyt3mpGHkRAVbM4Olljzay+0HvYXgiNi8zocojx1ZuUwKW/52BMKPnsmgVzkhF/NggULYGtrixcvXmD27NnSPUBhYWEYPHjwd4uDiP4VFhaGq1evoHKlykhJTcWOHdsQGhqaacgkERHRx9Sv6oCEpBQEhUfDytwIYz2aSI8iIKLcKxDTxKmrq2PMmDGZyn/++edct/HuQevvxMfH51CbiD5GRUWGQwcPYNGfCyEAlLQriSVL/v7oIxiIiIg+pKOtgeHd6sPcVI7Y1wm4es8fC9afzOuwiAqcXCd37yZJ+FjZt/Tw4UMEBQUhJUV5it/WrVt/dN2ZM2di2rRp3yo0ov8cMzNz/LNy9ccrEhERfcShc/dw6L1ZK4no8+T6njsdHR1p+nYAePPmTaYyICPhi4uL+6pBPn/+HO3atcO9e/cgk8mkIaHvksvcTKqSVc+dlZUV77kjIqKvhvfcERHR1/bV77nz9Mx6Nr/vZcSIEShRogROnjyJEiVK4Nq1a4iKisLo0aMxd+7cXLWhqakJTU3NbxwpUf4WFxsLd/eOWL1mHYoVK/bxFfKZ58+fY/jwodixYxe0tbXzOhwiIvpEaqoq2LVgAKYs2Y+7j0PyOpxPZqivjZ3zB6Dr+FV4+cED34nygwKR3F2+fBmnTp2CqakpVFRUoKKigtq1a2PmzJkYPnw4bt++nafxEX0r69atxd9/LYF75y4YNWq0VL5nz24cO3oEvn5+SHj7FidOnoa+vn4OLWVYs2Y16tatp5TYhYeHY9asmbh54wZ0dHTQoqUbBg8eAjW17E8Pvr6++GvJIjx8+BAqKqqo36ABRo78GTo6Okr1Dh48gC2bNyEoKAi6urpo0LARxo0bDwAIDQ3FNC9P+Po+QpkyZeHpNU0prlE/j4Rbq1Zo0KChVGZnZ4cKFSpg8+ZN6NOn78ffQCKi/wiPNjUxvFsDbD50DXPXHc+2np2lKQa510PZEuYoVtQQc9cew2Zv5WcG62hpYLB7PdSv5gAjuQ78/CMwZ+0xPHwWJtXR1lTH8G4N4Fq1NOT62gh9GYsth29g1/GcZzDv0KQyQl7GKiV2ZUqYY3i3+ihfshjSFQqcuuqHeeuOIzE5VapTrqQFhnetj7J2FhBC4MHTUCzcdApPAl9mu60Vnt1RpbyNUtnO47fw+z+HAQAGulqYPrQ1qpS3QVBYNKYtPQi/gAip7oQ+TREcEYuNB69KZbGvE3Hw3D0M7FQX05cdynFfifJCgXiIeXp6uvSPq6mpKUJDQwEANjY28PPzy8vQiL6Zhw8fYM/u3bC3L5VpWVJSEmrUdIGHx0+5bi8pKQn79+9Dq9ZtpLL09HSM+nkE0lJTsXLVakz19MKhgwewYsXybNuJjIzEsKGDYWlphdVr1uLPRYvg//wZpk/3Uqq3edNGLFv6N3r29MCWrduxeMnfqFGjhrT8zz8XoEjRItiwcTNMTU2xaNFCadnx48egoiJTSuzecXNrjd27diItLS3X+05EVJiVK2mBHxtXxuP3EpPsaGmqIyQiBos2n0ZkzJss60wd2BLVnUpgypJ9cB/9D67cfY6lU7qiiNG/XyKO7tUYLhXtMHnxPvz483JsPnQd43s3Rd0fMl+z3ufetAr2nrojvTY10sPSKV3xIjwGPSetwdDft8LO0hTThrSS6mhrqmPJpM4IfxWPnpPWoPfU9XiblIK/fukCNdWc/5XdfeI2GvdbKP38ufHfSVr6tK8FHS0NdB2/CjcfBmLKgJbSMsdSxVDBvjg2H7qWqc39p++gee0KMNDVynHbRHmhQCR3FSpUwJ07GSeC6tWrY/bs2bh48SKmT58OOzu7PI6O6OtLSEjA1ClTMOmXX2BgkLlHrkuXrujVywMVKlTIdZsXL16AhoYGHB0dpbKrV6/A398fXtN+RenSDnBxqYUBAwZi547tSE1NzbKdCxfOQ1VNDWPHjYeNjS3KlSuP8RMm4fSpU3jx4gWAjHtaly1bCk/PaWjarBksLS1RqlQp1K1bT2onICAALVu6wdraGi3d3BDgHwAAeP36NZYtW4qx/+/h+1D16tURHx+P25/xfEsiosJGW1MdM4a1wa/LDyH+bdJH6z98FoaFG0/h2KWHSE3N/CWZproaGlQvgz83nsKtRy/wIiIGy3ecR3B4DDo2+ffZxk6li+PA2Xu4+TAIYZFx2H3yNp4ERqCCffZD/svaWcDS3AgXbj2VyupWLoW0tHT8seoIAsOi8fBZGH7/5zAa1SgLKzMjAIBtcVMY6utg6fazCAyLxvPgV1ix4zxMDfVgYSrPcX+TklMRFfdW+nmb+O+kfCWKm+LopYcICovG7hO3UaK4CYCMoaOT+rXAjH8OQ5HF1BTPg18hMuY16ldzyHHbRHmhQCR3kydPhkKhAABMnz4d/v7+qFOnDry9vbFo0aI8jo7o65szexZq1aqFatWqf7U2fXx8UKZMWaWye/fuoWRJe5iYmEhlNWrUxNu3b/H8+bMs20lNSYG6mrrSZErv7me9c8cHAHDt6lUIIRAZ+RLunTrAza0FJk2cgIiIcGmdUqVK4dq1a1AoFLh65QrsS9kDABYv+hMdOnSEmZl5lttXV1dHqdKl4ePD4dhERBP6NsOF209x7V7AV2lPVVUFaqoqSPkg8UtKSUPFMlbS67uPQ1Dvh1JSb16V8jawtjDGlbvPs227clkrBIVGIyHp3wRLXV0VqWkKvJ9DJadkbPvd9gJDoxATn4C2DSpCTVUFmupqaNugIp4HRyI0MjbH/WlepzxOrvwZ2+f2w9AurtDS+PeWg8eBEahawQaqKjLUdLbDk6CMIZ69WtfEzQeBePQ8LLtm8eBpKCqVtcp2OVFeKRDJXdOmTdG+fXsAgL29PXx9ffHq1Su8fPkSDRo0yOPoiL6uY8eOws/PF4OHDP2q7YaHhcHU1FSpLCoqCsbGxkplxv9P9KKiorJsp0qVqoiKeoUNG9YjNTUV8fHx+OuvxQCAV69eAQBCQkOgUCiwdu0a/PzzaMycOQvx8fEYNnSI1CM4fPhIBAYEoG2bVnjx4gWGDx+J27du4fHjx2jRoiUmTZyAdm3b4I+Zv2fqRSxiWgRh4eEgIvova+JSDmVKmGPx5tNfrc2EpBTc8QtG3x9rw9RIDyoyGVrUqQCn0sVhaqQn1Zu1+iieh7zC0eXDcXXzBCyZ1Bl/rDqKW49eZNu2hakckTHKk5Bcvx8AE0Nd9GxVA2qqKtDX1cKwbvUBQNpeQlIK+k/biBZ1KuDypvG4sGEsala0w7DftyJdkf2k70cuPMDkxfsxYNpGrNl7CS3rOuK3Yf/emrB272Wkpyuwf/EQ1K/mgGlLD8HK3Ahu9Rzxz64LmNSvOfYvHow/fm4HPW3lSfkiY958tNeQKC8UiIeYZ+XDf0iJCoOIiHDMnz8Pixf/9dVnd01OTobGV2jTrmRJeHpOw8KFC7D077+goqKCTu6dYWxsApX/P55EKBRIS0vDqNFjpfvsfv1tBlo0b4qbN26gRs2aKFq0KOYvWCi1m5KSghHDh2Kq5zSsWb0KOjo62LFzF0YMH4Y9u3ehk3tnqa6mpiaSkz4+/IiIqLAyM9HHWI/GGPzbFqSkfvyRUJ9iypJ98BzkhmPLRyAtXQFf/3AcvfgAZUtYSHU6N68Cx1LFMXLWdoRFxqFyWWtM6NMUkTGvs+1F1NRQQ/IHsT4PfgXPvw5gVK9GGNq1PhQKBbYevo5XsW+kIZGa6mqYOrAlfPyCMfHPvVBVkaFHqxr4c4I7ekxcg+QshpcCwO6T/47wePoiEq9i3mC5Z3dYmhkiOCIWbxKT8cuifUrrLJ/aDQs3nkLzOuVRvKgh2o9chskDWqBfh9pYsOHf+/WSU9Kgpan+Se8r0fdQYJM7osLI95EvYqKj0atnd6ksPT0dt2/fxs4d23H+wiWoqqp+VtuGhoZ4HR+vVGZiYoKHDx4olUX/v8fu/aGaH2rarBmaNmuGqKgoaGtrQyaTYcvmTShe3DJj3f/3EJYoUUJax8jICHJDQ4RHZN3jtnbtGlSvXgNly5bFzN9/w4CBg6CmpgbX+vVx48Z1peQuPj4exS2Lf8LeExEVLmXtLGBiqIdNs/pIZWqqKqhc1hqdmlVBja5/ZHm/WG4ER8Sin9dGaGmqQ09bE69i3+CPke0Q/DIWQEayNbRLfYyesxMXbmfcP/ck6CVK25qhZ6sa2SZ3sa8TYW9dNFP5kYsPcOTiAxjLdZGYlAIBoJtbdYRExAAAmtUuj2JF5PCYvFYavjnpz704u2Y06lUtjWOXHuZqv+49zZiQz8rcGMERsZmWt3Z1wuu3STh74zHmjv4RZ64/Rlq6Aicu+2KQe12lugZ6WoiNT8jVdom+JyZ3RPlIlapVsXnLVqWyX6dPh42tDXr27PXZiR0AlHZwwJHDh5XKHB0dsXbNakRHR0u94VevXYWuri5KlPj4ZEXvEsD9+/dBQ0MD1apn3CPo7OQMAAgKDISZmRkAIC4uDnGxsTA3t8jUjr+/P44ePYKNGzcDANLTFdJsmGlpaVCkK5TqP3v2jEOyieg/7dq9AHQcvUKpzGuQGwJCo7B23+XPTuzel5SciqTkVOjraqGmsx3+3HgKAKCmpgJ1NdVM21AoFJD9fwRHVnz9w9HhvUlZPhQd9xYA0Ka+M1JS0nDlrj+AjBk+FQJK9+UJISAAacRIbjjYZlyPXmUxS6ihvg76daiD3lPWAwBUVFSkmTjV1FSU7jMHAHurIrjxMCjX2yb6XgrEPXdE/xW6urooWdJe6UdbWwtyuSFKlrSX6kW9eoXHj/0Q/CIYAPD06VM8fuyHuLi4bNuuUaMmnj9/hvj3eu+qV6+BEiVKwMtzKh4/fowrly9j+bKl6NCxEzQ0NAAADx7cR6eOP+Lly3+fJbRj+zb4+voiKDAQO3Zsx9w5szF4yFDpkSXWNjaoW7ce5s+fi7t37+DZs6eYPs0LNja2qFKlilJcQgjM/H0Gfh45SnowubOzM/bt3Qt/f38c9j4EJ2dnqX5oaCgiI1+i6lecbIaIqKBJSErBsxeRSj+JyamIe52IZy8is11PTVUFpW3MUNrGDOpqqihqrI/SNmbSzJQAUNPZDi7OdihWRI7qjiWwwrM7AkKisP9MxszlbxNTcONBIEZ2b4AfylmjWBE5WtVzQst6jjh9LftHVN14EAgdLQ2UtCqiVO7etArKlDCHtYUxOjX9AeN6N8XiLafxJiEZAHD1rj8MdLUwoU8zlChuAjtLU3gNboX0dAVuPAgEABQx0seuBQNQvmTGbJ2WZobo+2NtlC1hDosictT9oRSmD2mNmw8DpYlT3jfGozE2Hrgq3RN4x+8FWtZ1RIniJmjfsBLu+P17L6GWhhrK2Fngyp3sJ48hyivsuSMqgHbv3oWVK/+RXg8c0A8AMGWqJ9zcWmW5jr29PcqUKYMTJ46jffsfAQCqqqqYN38hZs2aib59foK2tjZatHRD//4DpPWSkpIQGBio9Fy5Bw8eYMWKFUhMTICNjS0mTJyEFi1aKm3P02saFi6Yj1E/j4RMpoLKlSvjz0WLMj0cfc+e3TA2MUbtOnWksr79+mPqlMno3dsDNWvURMeOnaRlx44dRfXqNWBhkbkHkIiIlHkNdkOxIoboP20jAKCIsT62zukrLe/ZuiZ6tq6JGw8CpTp6OpoY2qU+zEz0EfcmCaeu+uKvLWeQ9t4oiokL92BY1/qYMbwtDPS0EBYZh7+2nMHOHB5iHvcmEaev+aF57fJYsuWMVF7e3gIDOtWBjpYGAkKi8PsKbxw6f19aHhAahZGztqN/xzpY+5sHFELAzz8cQ3/fglexGb1wamoqKFHcFFqaGdeY1LR0VHe0RdcWVaGtqYGIqHicuuqLlbsvZIqrprMdrMyNMGXJv/ffbTtyA2VLWmDdjJ/w4Gkolu84Ly2rV7U0wl/F4bZv9pPHEOUVmRBfod/+G9i/f3+u67Zu3fqT24+Pj4dcLkdcXBwUIvdd+kQF2YULF7B48Z/YsmVbpiEmBUFqaio6/NgO03/9Dc7OFfM6HKJMDOX/PpeycqcZeRgJUYZ/vLrjxoNApeQkL5WyLoq/J3dB62F/IzE56+ep5nfrfvPAlsPXceTig49XJvoKzqwcJuUtBgYGOdbNtz13bdu2VXotk8nwfh76/pju9PSvO0sUUWFVu3ZtvHgRhMjIl9k+Ry4/Cw8PRy+Pn5jYERHlgp62JizNjDB85ra8DkXyJOglFm06jeJFDfE0h+Gj+ZWhvjZOXfNlYkf5Vr796l6hUEg/x44dQ8WKFXH48GHExsYiNjYW3t7eqFy5Mo4cOZLXoRIVKF26dC2QiR0AWFlZSUNKiYgoZ28Sk9F80OJ810N24OzdApnYARkzfq7bfyWvwyDKVr7tuXvfyJEjsWzZMtSuXVsqa9q0KXR0dNC/f388evQoD6MjIiIiIiLKe/m25+59z549g6GhYaZyuVyOgICA7x4PERERERFRflMgkruqVati1KhRiIiIkMoiIiIwduxYVKtWLQ8jIyIiIiIiyh8KRHK3evVqhIWFwdraGvb29rC3t4e1tTVCQkKwatWqvA6PiIiIiIgozxWIe+7s7e1x9+5dHD9+HL6+vgCAsmXLolGjRkqzZhIREREREf1XFYjkDsh49EGTJk1Qt25daGpqMqkjIiIiIiJ6T4EYlqlQKPDrr7+iePHi0NPTg7+/PwBgypQpHJZJRERERESEApLc/fbbb1i7di1mz54NDQ0NqbxChQpYuXJlHkZGRERERESUPxSI5G79+vVYsWIFunXrBlVVVanc2dlZugePiIiIiIjov6xAJHchISGwt7fPVK5QKJCampoHEREREREREeUvBSK5K1euHM6fP5+pfOfOnahUqVIeRERERERERJS/FIjZMqdOnYpevXohJCQECoUCu3fvhp+fH9avX4+DBw/mdXhERERERER5rkD03LVp0wYHDhzAiRMnoKuri6lTp+LRo0c4cOAAGjdunNfhERERERER5bkC0XMHAHXq1MHx48fzOgwiIiIiIqJ8qUD03BEREREREVHOCkTPnZGREWQyWaZymUwGLS0t2Nvbw8PDAz/99FMeREdERERERJT3CkRyN3XqVMyYMQPNmzdHtWrVAADXrl3DkSNHMGTIEPj7+2PQoEFIS0tDv3798jhaIiIiIiKi769AJHcXLlzAb7/9hoEDByqVL1++HMeOHcOuXbvg5OSERYsWMbkjIiIiIqL/pAJxz93Ro0fRqFGjTOUNGzbE0aNHAQAtWrTA8+fPv3doRERERERE+UKBSO6MjY1x4MCBTOUHDhyAsbExAODt27fQ19f/3qERERERERHlCwViWOaUKVMwaNAgnD59Wrrn7vr16/D29sayZcsAAMePH0e9evXyMkwiIiIiIqI8UyCSu379+qFcuXJYsmQJdu/eDQBwcHDA2bNn4eLiAgAYPXp0XoZIRERERESUpwpEcgcAtWrVQq1atfI6DCIiIiIionypQCR38fHxWZbLZDJoampCQ0PjO0dERERERESUvxSI5M7Q0DDLh5i/Y2lpCQ8PD3h6ekJFpUDMEUNERERERPRVFYjkbu3atfjll1/g4eGh9BDzdevWYfLkyYiMjMTcuXOhqamJSZMm5XG0RERERERE31+BSO7WrVuHefPmoVOnTlJZq1at4OjoiOXLl+PkyZOwtrbGjBkzmNwREREREdF/UoEYw3jp0iVUqlQpU3mlSpVw+fJlAEDt2rURFBT0vUMjIiIiIiLKFwpEcmdlZYVVq1ZlKl+1ahWsrKwAAFFRUTAyMvreoREREREREeULBWJY5ty5c9GxY0ccPnwYVatWBQDcuHEDvr6+2LlzJ4CMh5q7u7vnZZhERERERER5pkAkd61bt4afnx+WL18OPz8/AEDz5s2xd+9e2NraAgAGDRqUhxESERERERHlrQKR3AGAra0tZs6cmddhEBERERER5UsFJrkDgISEBAQFBSElJUWp3MnJKY8iIiIiIiIiyh8KRHIXGRmJn376CYcPH85yeXp6+neOiIiIiIiIKH8pELNljhw5ErGxsbh69Sq0tbVx5MgRrFu3DqVKlcL+/fvzOjwiIiIiIqI8VyB67k6dOoV9+/ahSpUqUFFRgY2NDRo3bgwDAwPMnDkTLVu2zOsQiYiIiIiI8lSB6Ll7+/YtihYtCgAwMjJCZGQkAMDR0RG3bt3Ky9CIiIiIiIjyhQKR3Dk4OEiPQHB2dsby5csREhKCZcuWwcLCIo+jIyIiIiIiynsFYljmiBEjEBYWBgDw9PREs2bNsGnTJmhoaGDt2rV5GxwREREREVE+UCCSu+7du0u///DDDwgMDISvry+sra1hamqah5ERERERERHlDwUiufuQjo4OKleunNdhEBERERER5Rv5NrkbNWpUruvOnz//G0ZCRERERESU/+Xb5O727du5qieTyb5xJERERERERPlfvk3uTp8+ndchEBERERERFRj5+lEIz58/hxAir8MgIiIiIiLK9/J1cleqVCnpgeUA4O7ujoiIiDyMiIiIiIiIKH/K18ndh7123t7eePv2bR5FQ0RERERElH/l6+SOiIiIiIiIcidfJ3cymSzTbJicHZOIiIiIiCizfDtbJpAxLNPDwwOampoAgKSkJAwcOBC6urpK9Xbv3p0X4REREREREeUb+Tq569Wrl9Lr7t2751EkRERERERE+Vu+Tu7WrFmT1yEQEREREREVCPn6njsiIiIiIiLKHSZ3REREREREhQCTOyIiIiIiokKAyR0REREREVEhwOSOiIiIiIioEGByR0REREREVAgwuSMiIiIiIioEmNwREREREREVAvn6IebfkhACABAfHw8DA4M8joaIiAqD+Ph46fczK4flYSRERFRYvLu2vMtfcvKfTe5ev34NALCyssrjSIiIiIiIiHL2+vVryOXyHOvIRG5SwEJIoVAgNDQU+vr6kMlkeR0OUb4VHx8PKysrvHjxgr3cRET0VfDaQpR7Qgi8fv0axYoVg4pKznfV/Wd77lRUVGBpaZnXYRAVGAYGBrwAExHRV8VrC1HufKzH7h1OqEJERERERFQIMLkjIiIiIiIqBJjcEVGONDU14enpCU1NzbwOhYiICgleW4i+jf/shCpERERERESFCXvuiIiIiIiICgEmd0RERERERIUAkzsiIiIiIqJCgMkdERERERFRIcDkjug/TCaTYe/evXmy7TNnzkAmkyE2NjbHera2tli4cOF3iYmIiD5dXl5LviZeb6gwYHJH9B1cvnwZqqqqaNmy5Sevm5cXGw8PD8hkMshkMmhoaMDe3h7Tp09HWlraF7ft4uKCsLAwyOVyAMDatWthaGiYqd7169fRv3//L94eEVFBV9CvJX/88YdS+d69eyGTyb57PLzeUGHG5I7oO1i1ahWGDRuGc+fOITQ0NK/D+STNmjVDWFgYnjx5gtGjR8PLywtz5sz54nY1NDRgbm7+0Qt7kSJFoKOj88XbIyIq6ArytURLSwuzZs1CTExMXoeSLV5vqDBgckf0jb158wbbtm3DoEGD0LJlS6xduzZTnQMHDqBq1arQ0tKCqakp2rVrBwBwdXVFYGAgfv75Z6kHDQC8vLxQsWJFpTYWLlwIW1tb6fX169fRuHFjmJqaQi6Xo169erh169Ynx6+pqQlzc3PY2Nhg0KBBaNSoEfbv3w8AiImJQc+ePWFkZAQdHR00b94cT548kdYNDAxEq1atYGRkBF1dXZQvXx7e3t4AlIdlnjlzBj/99BPi4uKk/fTy8gKg/G1z165d4e7urhRfamoqTE1NsX79egCAQqHAzJkzUaJECWhra8PZ2Rk7d+785P0mIspPCvq1pFGjRjA3N8fMmTNzrHfhwgXUqVMH2trasLKywvDhw/H27VtpeVhYGFq2bAltbW2UKFECmzdvztQrOX/+fDg6OkJXVxdWVlYYPHgw3rx5AwC83lChx+SO6Bvbvn07ypQpAwcHB3Tv3h2rV6+GEEJafujQIbRr1w4tWrTA7du3cfLkSVSrVg0AsHv3blhaWmL69OkICwtDWFhYrrf7+vVr9OrVCxcuXMCVK1dQqlQptGjRAq9fv/6i/dHW1kZKSgqAjKE2N27cwP79+3H58mUIIdCiRQukpqYCAIYMGYLk5GScO3cO9+7dw6xZs6Cnp5epTRcXFyxcuBAGBgbSfo4ZMyZTvW7duuHAgQPSRRoAjh49ioSEBOmfmJkzZ2L9+vVYtmwZHjx4gJ9//hndu3fH2bNnv2i/iYjyUkG/lqiqquL333/H4sWLERwcnGWdZ8+eoVmzZvjxxx9x9+5dbNu2DRcuXMDQoUOlOj179kRoaCjOnDmDXbt2YcWKFXj58qVSOyoqKli0aBEePHiAdevW4dSpUxg3bhwAXm/oP0AQ0Tfl4uIiFi5cKIQQIjU1VZiamorTp09Ly2vWrCm6deuW7fo2NjZiwYIFSmWenp7C2dlZqWzBggXCxsYm23bS09OFvr6+OHDggFQGQOzZsyfbdXr16iXatGkjhBBCoVCI48ePC01NTTFmzBjx+PFjAUBcvHhRqv/q1Suhra0ttm/fLoQQwtHRUXh5eWXZ9unTpwUAERMTI4QQYs2aNUIul2eq9/7+v3v/1q9fLy3v0qWLcHd3F0IIkZSUJHR0dMSlS5eU2ujTp4/o0qVLtvtJRJTfFZZrSY0aNUTv3r2FEELs2bNHvP+vaJ8+fUT//v2V1j1//rxQUVERiYmJ4tGjRwKAuH79urT8yZMnAkCmfXvfjh07hImJifSa1xsqzNhzR/QN+fn54dq1a+jSpQsAQE1NDe7u7li1apVUx8fHBw0bNvzq246IiEC/fv1QqlQpyOVyGBgY4M2bNwgKCvqkdg4ePAg9PT1oaWmhefPmcHd3h5eXFx49egQ1NTVUr15dqmtiYgIHBwc8evQIADB8+HD89ttvqFWrFjw9PXH37t0v2ic1NTV06tQJmzZtAgC8ffsW+/btQ7du3QAAT58+RUJCAho3bgw9PT3pZ/369Xj27NkXbZuIKK8UhmvJO7NmzcK6deuk68T77ty5g7Vr1yqdv5s2bQqFQgF/f3/4+flBTU0NlStXltaxt7eHkZGRUjsnTpxAw4YNUbx4cejr66NHjx6IiopCQkJCruPk9YYKKrW8DoCoMFu1ahXS0tJQrFgxqUwIAU1NTSxZsgRyuRza2tqf3K6KiorScBwA0lDId3r16oWoqCj8+eefsLGxgaamJmrWrCkNqcyt+vXrY+nSpdDQ0ECxYsWgppb700bfvn3RtGlTHDp0CMeOHcPMmTMxb948DBs27JNieF+3bt1Qr149vHz5EsePH4e2tjaaNWsGANLwmUOHDqF48eJK62lqan72NomI8lJhuJa8U7duXTRt2hQTJ06Eh4eH0rI3b95gwIABGD58eKb1rK2t8fjx44+2HxAQADc3NwwaNAgzZsyAsbExLly4gD59+iAlJeWTJkzh9YYKIiZ3RN9IWloa1q9fj3nz5qFJkyZKy9q2bYstW7Zg4MCBcHJywsmTJ/HTTz9l2Y6GhgbS09OVyooUKYLw8HAIIaQb4318fJTqXLx4EX///TdatGgBAHjx4gVevXr1yfuhq6sLe3v7TOVly5ZFWloarl69ChcXFwBAVFQU/Pz8UK5cOamelZUVBg4ciIEDB2LixIn4559/skzustrPrLi4uMDKygrbtm3D4cOH0bFjR6irqwMAypUrB01NTQQFBaFevXqfvK9ERPlNYbmWvO+PP/5AxYoV4eDgoFReuXJlPHz4MMtrDgA4ODggLS0Nt2/fxg8//AAgowft/Rk4b968CYVCgXnz5kFFJWOA2vbt25Xa4fWGCjMmd0TfyMGDBxETE4M+ffpIz3J758cff8SqVaswcOBAeHp6omHDhihZsiQ6d+6MtLQ0eHt7Y/z48QAyZu86d+4cOnfuDE1NTZiamsLV1RWRkZGYPXs2OnTogCNHjuDw4cMwMDCQtlGqVCls2LABVapUQXx8PMaOHftZ3+xmp1SpUmjTpg369euH5cuXQ19fHxMmTEDx4sXRpk0bAMDIkSPRvHlzlC5dGjExMTh9+jTKli2bZXu2trZ48+YNTp48CWdnZ+jo6GT7DWvXrl2xbNkyPH78GKdPn5bK9fX1MWbMGPz8889QKBSoXbs24uLicPHiRRgYGKBXr15fbf+JiL6HwngtcXR0RLdu3bBo0SKl8vHjx6NGjRoYOnQo+vbtC11dXTx8+BDHjx/HkiVLUKZMGTRq1Aj9+/fH0qVLoa6ujtGjR0NbW1tKTu3t7ZGamorFixejVatWuHjxIpYtW6a0HV5vqFDLyxv+iAozNzc30aJFiyyXXb16VQAQd+7cEUIIsWvXLlGxYkWhoaEhTE1NRfv27aW6ly9fFk5OTkJTU1PpxvOlS5cKKysroaurK3r27ClmzJihdBP8rVu3RJUqVYSWlpYoVaqU2LFjR6Yb6vEJN8FnJTo6WvTo0UPI5XKhra0tmjZtKh4/fiwtHzp0qChZsqTQ1NQURYoUET169BCvXr0SQmSeUEUIIQYOHChMTEwEAOHp6SmEyHoSgIcPHwoAwsbGRigUCqVlCoVCLFy4UDg4OAh1dXVRpEgR0bRpU3H27Nls94OIKL8qrNcSf39/oaGhIT78V/TatWuicePGQk9PT+jq6gonJycxY8YMaXloaKho3ry50NTUFDY2NmLz5s2iaNGiYtmyZVKd+fPnCwsLC+m6tH79el5v6D9DJsQHg62JiIiIiAqA4OBgWFlZSZOoEP3XMbkjIiIiogLh1KlTePPmDRwdHREWFoZx48YhJCQEjx8/lu6HI/ov4z13RERERFQgpKamYtKkSXj+/Dn09fXh4uKCTZs2MbEj+j/23BERERERERUCfIg5ERERERFRIcDkjoiIiIiIqBBgckdERERERFQIMLkjIiIiIiIqBJjcERERERERFQJM7oiIiIiIiAoBJndERERERESFAJM7IiIiIiKiQoDJHRERERERUSHA5I6IiIiIiKgQYHJHRERERERUCDC5IyIiIiIiKgSY3BERERERERUCTO6IiPIxLy8vyGQy6cfExAS1a9eGt7d3nsXk6uoKNze3T15v4cKFWcZta2uLoUOHfo3Qci09PR1LlixB5cqVoaOjA7lcjoYNG372+xobGwsvLy88fPjwK0ea9+7duwd9fX1ERkZmWrZnzx7IZDI0bNjws9o+c+YMfv/99y8NMUdeXl7Q09OTXl+8eBGmpqaIj4//ptslIsoLTO6IiPI5bW1tXL58GZcvX8Y///yDpKQktGrVCpcuXcrr0D5Jdsndnj17MGbMmO8Wh0KhwI8//ohRo0ahQYMGOHDgADZu3AhDQ0O0bNkS8+bN++Q2Y2NjMW3atEKZ3E2ePBkeHh4oUqRIpmWbNm0CkJGkhYaGfnLb3yO5+1CtWrVQvnz5z/qciYjyOyZ3RET5nIqKCmrUqIEaNWqgffv22LdvH4QQWLduXV6H9lVUqlQJtra23217S5Yswb59+7BixQrMnTsXDRs2RKtWrbBr1y707NkT48ePh4+Pz3eLJz97/vw5Dhw4gN69e2daFh8fj0OHDqFRo0ZQKBTYunVrHkT4efr06YOlS5ciNTU1r0MhIvqqmNwRERUwxYsXR5EiRRAUFKRUfvnyZTRo0AC6urqQy+Xo2rUrXr58qVTnjz/+gL29PbS0tFCkSBE0atQI/v7+0vLo6Gj07t0bpqam0NbWhouLC86dO5djPB4eHqhQoYJSWWxsLGQyGdauXQsgY+hlYGAg/vrrL2mI6fvLPhyWuXv3blSsWBFaWlooVqwYRo0ahaSkJGn5mTNnIJPJcPz4cXTt2hX6+vqwsbHB7NmzP/r+LVy4EA4ODujZs2emZdOnT4dMJsPixYulsqzi27t3L2QyGQICAhAQEIASJUoAADp27CjtX0BAAAAgOTkZkydPhp2dHTQ1NWFpaQkPD4/P2t+jR4+iU6dO0NPTg7W1NTZv3gwAWLRoEaytrWFsbIy+ffsiOTlZqf3g4GB0795d+lzr1q2LmzdvfvS9Wr9+Pezs7FCpUqVMy3bv3o2kpCR4eXnhhx9+kHrx3qdQKDB//nyULVsWmpqaMDc3R8eOHREXFwcvLy9MmzYNb9++ld4zV1dXALk7pt7FV7t2bRgbG8PIyAiurq64du3aR/erbdu2iI2NzdPhzURE3wKTOyKiAubNmzeIjo6WEgogI7FzdXWFXC7Htm3bsGLFCly/fh1t2rSR6qxfvx5TpkxBnz59cOTIEaxcuRIVK1aU7j1KT09H8+bNceDAAcyaNQs7duyAnp4eGjdunKtEICd79uyBubk5OnToIA0xbdmyZZZ19+/fjw4dOqBcuXLYu3cvxo0bh2XLlqF79+6Z6g4cOBClS5fGnj170KpVK4wfPx5HjhzJNo4XL17A398fLVu2hIpK5kugjY0NnJycPprQvs/CwgK7d+8GAPz+++/S/llYWAAAfvzxR8yfPx+9e/fGoUOHMGfOHLx9+/az9nfQoEGoUKEC9uzZgxo1aqBHjx4YP348jh49imXLlmH69OlYv3690pDDmJgY1K5dGz4+Pli8eDF27doFXV1dNGjQIFPy/6ETJ07AxcUly2WbNm2Cra0tXFxc0LVrV9y6dQt+fn5KdYYNG4Zx48bBzc0NBw4cwF9//QV9fX28efMGffv2RZ8+fZSGHf/999+5e9P/LyAgAD179sSOHTuwefNmWFtbo27dunj8+HGO6xkYGKB8+fI4fvz4J22PiCjfE0RElG95enoKXV1dkZqaKlJTU0VgYKBwd3cXRkZGwtfXV6pXt25d4eLiIhQKhVT24MEDIZPJxKFDh4QQQgwZMkRUrlw5223t27dPABBHjhyRylJSUoS1tbVo3769VFavXj3RsmVL6XWvXr1E+fLlldqKiYkRAMSaNWukMhsbGzFkyJBM2/2wvFKlSqJmzZpKdZYvXy4AiLt37wohhDh9+rQAIMaOHSvVUSgUwtbWVvTp0yfbfbx8+bIAIBYuXJhtnbZt2wotLa0c496zZ48AIPz9/YUQQvj7+wsAYseOHUr1jh07JgCIzZs3Z7u9T9nfcePGSXViY2OFqqqqsLKyEikpKVL5jz/+KCpWrCi9njp1qpDL5SIiIkIqS0pKEtbW1krv34cUCoXQ1NQUc+bMybQsLCxMqKqqigkTJgghhAgJCREqKipiypQpUh0/Pz8hk8nE77//nu023h3fH8rtMfW+9PR0kZqaKhwcHMTEiRNztY0qVapkGxsRUUHEnjsionzu7du3UFdXh7q6OmxsbLBz505s2LABDg4OAICEhARcvHgRHTt2RHp6OtLS0pCWlobSpUvDysoK169fBwBUrlwZt2/fxqhRo3DhwoVM9xudP38eBgYGaNq0qVSmrq6O9u3b48KFC99lX9+8eQMfHx906NBBqdzd3R0AMsXRpEkT6XeZTIayZcsiODj42weaSydPnoSOjg46d+6c5fJP3d/GjRtLv8vlchQtWhR169aFurq6VF66dGm8ePFCen3s2DHUr18fxsbG0rGhqqqKevXqScdGVmJiYpCcnJzlRCrbtm1Deno6unbtCgAoVqwY6tWrJw0TBYBTp05BCIE+ffpku40v9ejRI7Rr1w5mZmZQVVWFuro6/Pz8PtpzBwCmpqYICwv7ZrEREeUFJndERPmctrY2rl+/jqtXr2Ljxo2wsLBAz549pX9MY2JikJ6ejp9//llKAt/9BAUFSf/oe3h4YMGCBTh69Cjq1KmDIkWKYMSIEUhMTJTaKVq0aKbtm5mZITo6+rvsa2xsLIQQMDMzUyqXy+XQ1NTMFIehoaHSaw0NDaV71T5UvHhxAMh0v+L7goKCYGlp+YmRZy0qKgoWFhaQyWRZLv8a+/ux9+DVq1fYu3dvpmNjw4YNSkngh961oampmWnZpk2b4ODgACsrK8TGxiI2NhatW7fGs2fPcPXqVWnf1dTUsjymvobXr1+jSZMmCAwMxPz583H+/Hlcv34dzs7OOR4D72hqakrHPhFRYaGW1wEQEVHOVFRUUKVKFQBAtWrV4ODggOrVq2P69OlYunQpDA0NIZPJMGnSJLRt2zbT+qamplI7I0aMwIgRIxASEoKtW7diwoQJMDU1xZQpU2BsbJzlPVgREREwNjbONj4tLS2kpKQolcXExHzWvr7blw/jiIuLQ3Jyco5x5IaVlRVKlCiBw4cPY+7cuZmSrqCgINy9e1dpspUv2T8TExOEhYVBCJFlgvet9xcAjI2N0axZM/z666+ZlmWVuL2/HpCRgL7v6dOnUo+fkZFRpvU2bdqE6tWrw8TEBGlpaXj58uUnJ3i5ec8vX76M4OBgHDx4EM7OzlJ5XFxcrpLz2NhYmJiYfFJcRET5HXvuiIgKmCpVqqBLly5Ys2YNwsPDoauri5o1a+LRo0eoUqVKpp+sHjNQvHhxjB49Gk5OTnj06BEAoHbt2oiPj8exY8ekemlpadizZw9q166dbTyWlpYIDg7GmzdvpLL323jnY71qAKCnp4eKFSti586dSuXbt2+XYvxSI0eOxKNHj7Bhw4ZMy7y8vCCEwLBhw6QyS0tL6T1658P909DQAIBM+9eoUSMkJCRI8X/oe+xvo0aN8PDhQ5QtWzbTseHo6JjtelpaWrC2tlaaTRUANm/eDJlMhj179uD06dNKP02bNpWGbDZo0AAymQxr1qzJdhsaGhqZZvYEcndMvet1e/feA8ClS5ekWUo/JiAgQBraTERUWLDnjoioAJoyZQq2bt2KhQsX4o8//sCcOXPQoEEDuLu7o3PnzjAyMkJwcDCOHz+On376Ca6urhgwYACMjIxQo0YNGBkZ4eLFi7hz5w4GDx4MAGjZsiWqVauG7t27448//oCZmRkWL16MsLAwTJo0KdtY2rdvj6lTp6J3797o168fHjx4gJUrV2aqV7ZsWZw6dQrHjx+HkZERSpQokWXPiZeXF9q2bYvu3buje/fu8PPzw6RJk/Djjz/mmIzk1tChQ3Hq1Cn07dsX9+7dQ/PmzZGYmIi1a9di586dmDt3LipWrCjV79ChAwYNGoRp06bBxcUF3t7euHz5slKb5ubmMDQ0xJYtW1CiRAloamrCyckJjRo1QosWLdC7d288e/YM1atXR3R0NHbu3Ilt27Z9l/0dNWoUNm3ahHr16mHEiBGwtrZGZGQkrl69imLFiuHnn3/Odt1atWplmil18+bNqFOnTpa9xPHx8WjTpg1OnDiBpk2bYuDAgZg8eTKio6PRsGFDJCQk4NChQ/Dy8kLx4sVRtmxZpKWl4c8//4SLiwsMDAzg4OCQq2OqRo0a0NPTw5AhQzBhwgSEhITA09NTGnr7MTdu3MDo0aNzVZeIqMDI0+lciIgoR9nN9CeEEN26dRMGBgYiNjZWCCHE9evXRYsWLYRcLhfa2tqiVKlSYuDAgeLFixdCCCHWrl0ratWqJYyNjYWWlpYoV66cWLRokVKbr169Eh4eHsLY2FhoamqKmjVrijNnzijV+XC2TCGEWL9+vbC3txfa2tqicePGwsfHJ9PMhvfv3xd16tQR+vr6Ssuymo1y586dwsnJSWhoaAhzc3MxcuRIkZiYKC1/N3vk9evXldZr06aNqFevXs5vqhAiLS1NLFq0SFSsWFFoa2sLAwMDUb9+fWlm0felpqaKMWPGCDMzMyGXy8WAAQPE5s2blWbLFCJjBs2yZcsKTU1NpWWJiYliwoQJwtraWqirqwtLS0vRu3fvr7K/Wb13WR0zYWFhok+fPsLCwkJoaGgIS0tL0aFDB3Hx4sUc36ddu3YJLS0tER8fL4QQ4saNGwKAWLlyZZb1U1JSRJEiRUSPHj2EEBkzWM6ePVuUKlVKqKurC3Nzc+Hu7i7i4uKk93bw4MHCzMxMyGQypc8uN8fU4cOHRfny5YWWlpZwcnIS3t7emY7PrN6PmzdvCplMJp4+fZrj/hMRFTQyIYTIu9SSiIiI8qvU1FRYW1tj1qxZWT70vaAaO3Ysbt68iVOnTuV1KEREXxWTOyIiIsrWn3/+ifXr13/xg+zzi/j4eNjY2GDfvn2oW7duXodDRPRV8Z47IiIiytbAgQMRHx+PV69eSTOvFmRBQUH49ddfmdgRUaHEnjsiIiIiIqJCgI9CICIiIiIiKgSY3BERERERERUCTO6IiIiIiIgKASZ3REREREREhQCTOyIiIiIiokKAyR0REREREVEhwOSOiIiIiIioEGByR0REREREVAgwuSMiIiIiIioEmNwREREREREVAkzuiIiIiIiICgEmd0RERERERIUAkzsiIiIiIqJCgMkdERERERFRIcDkjoiIiIiIqBBgckdERERERFQIMLkjIiIiIiIqBJjcERERERERFQJM7oiIiIiIiAoBJndERERERESFAJM7IiIiIiKiQoDJHRERERERUSHA5I6IiIiIiKgQYHJHRERERERUCDC5IyIiIiIiKgSY3BERERERERUCTO6IiIiIiIgKASZ3REREREREhQCTOyIiIiIiokKAyR0REREREVEhwOSOiIiIiIioEGByR0REREREVAgwuSMiIiIiIioEmNwREREREREVAkzuiIiIiIiICgEmd0RERERERIUAkzsiIiIiIqJCgMkdERERERFRIcDkjoiIiIiIqBBgckdERERERFQIMLkjIiIiIiIqBJjcERERERERFQJM7oiIiIiIiAoBJndERERERESFAJM7IiIiIiKiQoDJHRERERERUSHA5I6IiIiIiKgQYHJHRERERERUCDC5IyIiIiIiKgSY3BERERERERUCTO6IiIiIiIgKASZ3REREREREhQCTOyIiIiIiokKAyR19MZlMBh8fnzzZtoeHB0aOHJnlsk2bNsHFxeX7BkRflaurK86cOZPXYXyyM2fOwNDQUHrt6uqKhQsX5lk8+Z2HhwfWrl2b12Hkip6eHu7du/fV6tGnK6jnBfq+vLy84OXllddhEH13TO4KKT09PelHVVUVmpqa0uvmzZtnu15OydLnWLt2LVRVVaVtW1hYYPDgwUhOTv5q28hOt27dcOnSpW+6jQMHDqBu3brQ19eHiYkJqlWrhmXLln3Tbb5ja2uLvXv3fta6QgjUrl0bMpkMsbGxAIDk5GT069cPJUqUgL6+PsqUKYPVq1dnWnflypVwcHCArq4ubG1tsW/fPgDA48eP0a5dO5ibm8PQ0BC1atXCxYsXP3f3vgpbW1toa2tDT08PpqamaNmyJZ4+fZqnMeXGlClT4OjoCDU1tSz/Hi9cuIAaNWpALpejePHimDhxIhQKRbbthYaGokWLFtDV1YW1tTX++eefXC9PT09Hjx49YGhoiNq1ayM0NFRadunSJbi6ukII8eU7/QVcXV2lc5yxsTFcXV1x8+bNb7KtN2/ewNHR8avV+xpmzpwJOzs7GBgYwNzcHB4eHtLfNQA8e/YMzZs3h5GREYoXL47Zs2fn2F5Ox19ycjJcXV1RtGhRGBgYoEyZMlixYoW0PD4+Hi1btoRcLoebmxvevHkjLdu6dSt69OjxVfb5S7x/Xnj3c/DgQQDAkiVLUKVKFWhqaqJt27Yfbevq1auoX78+jIyMYGhoCCcnp3z7RcW6detQrVo1yOVyWFhYoE+fPkrHyfu6du2aqy9uL168iFq1akFPTw9FixbF1KlTlZbv378fFStWhK6uLooVK6Z0fRw7diyMjY3h7OyMhw8fSuXPnz9HxYoVkZSU9Nn7+jV4eHhAQ0ND6TgZP348AGDu3LlwcnKCgYEBLC0tMWbMGKSkpGTbVlhYGLp27Qpzc3Po6+vDzs4OP//88/falU9y5coVNG3aFKampjA2NkbTpk2VPh8g43N3dnaGjo4OKlasiMuXL2fbXlhYGFq3bo1ixYple0zNmDEDNjY2MDAwQKVKlXDs2DFp2f379+Hk5ARjY2NMmDBBab2BAwdi1apVX7bDhRCTu0LqzZs30k+dOnUwa9Ys6fXhw4e/ayyOjo7Stm/evImLFy9i7ty53zWGb2Hp0qXo1asX+vXrh+DgYLx69QpLly6V/knIz/7++29oamoqlaWlpcHCwgInTpxAfHw81q5di9GjRyudZFesWIF58+Zh69atePPmDa5evSr9AxsbG4vmzZvj3r17iIqKgoeHB1q0aIFXr15913370JYtW/DmzRsEBATA0NAQvXv3ztN4csPe3h6zZ89G69atMy1LT09HmzZt0KZNG0RHR+PixYvYunVrpoTtfV26dIG5uTlevnyJHTt2YOzYsTh79myulu/evRsBAQGIiIhA9erVMXPmTABAamoqhg0bhmXLlkEmk33ld+DTvTvHhYaGolKlSmjTpk2W9VJTU79zZN9ehw4dcPv2bcTHx+Px48dISUnBmDFjAGQcL61bt0blypXx8uVLnDp1CkuWLMHmzZuzbS+n409NTQ2LFy9GaGgo4uPjsXv3bkyZMgXnz58HACxfvhwGBgaIioqCtrY2li9fDiDj/PDrr79iwYIF3+Ad+HTvzgvvftzc3AAAxYoVw+TJk9GvX7+PtvH69Ws0a9YM7u7uePnyJSIjI7Fq1SoULVr0q8f7NY7bhIQEzJ49GxEREXjw4AHCwsIwePDgTPUOHTqEiIiIj7Z39+5dtGvXDmPHjkV0dDT8/f3RoUMHafmRI0cwePBgLFy4EPHx8Xjw4AFcXV0BANevX8fevXsREBCAPn36SEkTAAwePBjz58+HlpbWF+/zlxo8eLDScTJr1iwAGX9Xq1atQlRUFK5cuYIzZ87k2EvYo0cPaGlpwdfXF3FxcTh+/DgqVqz41eP9GsdJTEwMfvrpJzx9+hTh4eGoVq0amjVrhvT0dABAdHQ03NzcMHToUMTExGDIkCFwc3PL9osCFRUVNGvWLNsvo/fu3Yu5c+fi4MGDiIuLw6hRo9CuXTtER0cDAMaPH49BgwbB398f27dvl764u3jxIh4/flwgrunfnaBCr169emLBggXS66NHj4qKFSsKAwMDUalSJXH8+HEhhBB//vmnUFNTE+rq6kJXV1eUK1dOCCHEhg0bRPny5YWenp6wsrISkydPFgqFQmoPgLh9+3aW216zZo1wdnZWKhs7dqzo0qWL9Do8PFx07NhRmJqaCisrKzFp0iSRmpr60XiFEKJXr15ixIgRQgghUlNTRa9evUTDhg1FfHx8pm3b2NiIWbNmierVqws9PT1Rt25dERQUJC2/f/++tMzV1VWMHTtW1KtXL8v9io+PF/r6+mLDhg1ZLs9N7B9+Lrdv3xbv/0nWq1dPTJgwQTRp0kTo6emJSpUqibt37wohhOjQoYOQyWRCS0tL6OrqigEDBuQYx/uCgoKEnZ2duHHjhgAgYmJisq3brl07MWXKFCGEEGlpacLMzEwcPXo019syMjISJ0+ezHX9D9WrV0+cPn36s9e3sbERe/bskV4fPHhQ6OjoSK9TUlLElClThJ2dnTA2NhatWrUSISEh0vKwsDDRrVs3YW5uLuRyuahTp45ISEgQQmQcx9bW1kJPT0+ULVtWbN++XVrv9OnTQi6XK+3H+591br1/fL8TFRUlACjF2bdvXzFkyJAs23j69KlQUVER4eHhUtngwYNFz549c7X8jz/+EBMmTBBCCHH48GHRvHlzIYQQM2bMEJ6enp+8T1np1auXWLNmzWev/+H7e+/ePQFAvHr1SvTq1Uv07t1bdOzYUejr64tFixZ90ef+/vnu5s2bonr16kJfX1+YmJgINzc3qY336ykUCjF37lxhZ2cnjIyMRNOmTcWzZ8+kuh87N32KuLg40a1bN1G3bl0hhBAPHjwQqqqqIjk5Warj5eWV7bntfVkdfx96+PChMDMzE6tXrxZCCDFw4ECxbNkyIYQQS5cuFYMGDRJCCNGvX78v+ozf97XPC1nx9PQUbdq0ybHO9evXhbq6ukhPT8+2Tk7H0pMnT0STJk2EkZGRsLOzUzqG312/pk6dKszMzET79u2FEEJs2bJFODo6CrlcLqpUqSIuXryYq33Oyr59+4SVlZVSWXx8vChVqpR4/Phxjtd2ITKuQxMnTsx2eZUqVcTy5cuzXLZ161bRuXNnIYQQjx49EmXLlhVCCLFp0ybRq1evT9uRbHh6en7ROSo3x/87f/75p6hTp062y3V1dcW5c+eyXZ6cnCydk/T09ESFChXEzZs3hRAZn0m/fv2Eubm5MDc3FwMGDBBv3rwRQgjh7+8vAIjVq1eLkiVLiqJFiwohMs5Nrq6uwsjISJQsWVKsWLEil3udWVxcnAAgnbNWrlwpypcvr1SnXLly0jkgJ1kdU/PmzRONGzdWKlNXVxfXr18XQghRpkwZ4evrK4QQwt3dXWzbtk2kpKSIypUrS+WkjD13/zFPnz5FmzZtMGXKFERFRWHSpElo3bo1/P39MXz4cHTr1k36purBgwcAABMTE+zevRvx8fHYv38/VqxYkeO3vjl58eIFjhw5glq1akllXbt2hbq6Ovz9/XH+/Hns3btXGjaUU7zve/v2LVq3bo3ExER4e3tDX18/y+1v3LgRW7ZsQWRkJHR1dTFlyhQAGd92tW7dGs2bN0dUVBT++OOPLIckvnP58mUkJCSgU6dO2dbJbew52bBhA2bPno2YmBhUqVIFw4YNAwDs2LED1tbW0rfP74a6DB48OMtvYt83aNAgeHl5wcTEJMd6SUlJuHbtGpycnAAAfn5+iIiIwK1bt2BrawtLS0v069cP8fHxWa5/7949vH79GuXKlcv1/n5LcXFx2LBhA0qXLi2V/fLLL7h48SIuXLiAsLAwlC5dGp07dwYAKBQKtGrVCmpqanj48CFevXqF33//HSoqGadNZ2dnXL9+HbGxsZg6dSp69OiR68/Wycnps/+GjI2N0bt3b6xatQqpqal49uwZTpw4gZYtW2ZZ/+7du7CwsICZmZlUVrFiRdy9ezdXyx0dHXH+/HkkJibi5MmTcHR0xNOnT7Fjxw5MnDjxs/bhW0pISMDKlSthY2MjHeNbtmyRhqD16dPniz739w0dOhStWrVCbGwsQkJCMHbs2Cxj2rBhA+bPn4+9e/ciNDQU5cuXR6tWrZCWlibVye7cBOTu73rz5s0wMDCAXC7Hnj17pFjeDdcV7w2dVSgU0uf7udzc3KClpYVy5crBzMwM7dq1A5BxvJw6dQrJyck4ffo0HB0dceHCBTx79gweHh5ftM38pnTp0pDL5ejcuTP27duH8PBwpeU5HUtpaWlwc3ODs7MzQkNDsWfPHsyePVvpvHD//n2oqakhKCgIGzZsgLe3N8aMGYO1a9ciOjoaEydORKtWrRAVFQUg4xh4d77OjbNnz2aqP3HiRPTo0QOlSpXK1fopKSmoWLEiihQpgmbNmsHPzw9AxjX55s2bCAkJQenSpWFubo6OHTsiLCwMAFChQgXcuHEDsbGxOHHiBBwdHRETE4Pff/8d8+bNy/U+5BdZvZfvq1WrFkaOHIn169fj8ePHmZZPmDAB3t7eOHLkCOLj47Fz507p/DVixAg8ffoU9+/fx7179+Dr65tpSOf+/ftx48YN+Pv7Izw8HI0bN8agQYMQGRmJvXv3wtPTEydPngQABAUFwdDQEEFBQbneN0NDQ1hbWwPIuGZ82Ov4/jXjU7m7uyM8PBy3b99Geno61qxZA0tLS1SoUAFAxjnl+PHjiI2Nxc2bN1GhQgVpZIGDg8NnbbPQy+vskr6997/V/u2330SzZs2Uljdu3FjMmDFDCJG7b6pGjBgh+vbtK73GR3ruVFRUhFwuFwYGBgKAcHFxEXFxcUIIIYKDgwUApV6DTZs2iVKlSuU63m7duolq1aqJYcOGKX2DmlXP3dKlS6XXGzduFBUqVBBCCHHu3Dkhl8uVegwHDx6c7bfbGzduFGZmZtm9RbmKPTc9d+PHj5deX7hwQejp6Sntz8e+ff7Q5s2bRcOGDYUQ/37jl1XPnUKhEN26dROurq7Se3r+/HkBQDRs2FBERkaKyMhI0bBhQ9G7d+9M68fExIhy5cqJqVOnflJ8H/oa39Dr6OhIx17p0qXF/fv3hRAZ+6irqyt8fHyk+omJiUJFRUUEBQWJK1euCF1dXelb9o9xdnYWGzduFEJ82547IYQ4cuSIsLS0FKqqqgKAGDp0qFJv+vvWr1+f6VvW7du3i5IlS+ZquRBC/PLLL8LR0VF06dJFREdHi8aNG4uzZ8+KnTt3inr16olmzZqJhw8ffvL+vb+fX9pzp6WlJeRyuTAzMxNNmzYVd+7ckdp+vwfmSz/39893devWFf369RMvXrzIsV6jRo3EH3/8IS1LSkoS+vr6Uq9LTuemTxUYGCimTp0qHjx4IITI6J0uWbKkGDdunEhKShL379+Xjp2P+dj1IC0tTZw5c0ZMmzZNJCUlSfs2aNAgUaFCBTFo0CARHx8vKlWqJB4/fiz++usvUbduXdGuXTulntJP9bXOC3K5XMjlcmFnZ5epTm567oTI6H0bMGCAsLOzEzKZTFSrVk3qccnpWLpw4YIwMDBQ6lGdMWOG1IOxZs0aYWxsrHRNa9GihVi4cKFSOy4uLmL9+vW52u/3eXt7CwMDA2k0iBBCXLx4UZQvX16KKadruxBCqKqqiuLFi4t79+6JpKQkMW7cOOHg4CBSU1PFixcvBADh5OQkAgICxOvXr0W3bt1EgwYNpPUXL14snJ2dhZubmwgKChJ9+vQR69evF+fOnRP169cXDRo0EOfPn//kfXvna/TcaWhoSMeJXC7P8jy3YsUKYWZmJkJDQ7NtKy4uTnh6eopKlSoJNTU1YW1tLTZt2iSEyDgn6ejoiLNnz2ZaLz09XWhoaIgrV65IZRcvXhSampoiPT1duo6//znNnj1btG3bVqmdSZMmZXmt/pjAwEBhYWEhVq1aJZX17t0700iRwYMHiz59+ny0vayOqZSUFDFhwgShoqIiVFVVhYGBgThx4oS0PCgoSLi5uQlnZ2exePFi8eTJE1GpUiXx+vVrMWTIEFGnTh0xdOhQkZKS8sn7V1ix5+4/Jjg4GLa2tkpldnZ2CA4Oznado0ePwsXFBaamppDL5Vi2bNkn3Ufl6OiI2NhYxMXF4fXr19L47XfxaGlpKfUavB9PbuI9ceIEnj17hokTJ2b57fr7zM3Npd91dXXx+vVrABkTSlhYWEBNTU1a/u5bqqyYmpri1atXOd5A/Tnv9cfifX9ygk8VHR2NCRMmYOnSpTnWE0Jg8ODB8PPzw969e6X3VE9PD0DGN7umpqYwNTXFxIkTceDAAaX14+Li0LRpU9SuXTtfzFS2adMmxMXFwdfXF2lpaXj27BkA4NWrV3j79i3q1q0LQ0NDGBoawtzcHBoaGnjx4gUCAwNRvHhxaGtrZ9nuggULUL58ecjlchgaGuL+/fvf5f5CPz8/tGnTBgsWLEBSUhJCQ0Px6NGjTDeav6Onp4e4uDilsri4OKl3+2PLAeC3337D3bt3sXnzZhw8eBDW1taoUKECRowYgT179mD8+PF5ft/DzJkzERsbi/DwcBw5ckTpW/T3/5a/9HN/3+rVq5GUlIQffvgBZcqUwZIlS7Ks9+G5QFNTE8WKFVM6F2R3bvpU1tbWcHNzk+6XU1dXx759+3D79m0UL14c3bp1w08//fTRnvvcUFVVRb169RAREYE5c+YAyNi3v//+G/fu3cPff/+NhQsXon379khNTcVff/2FY8eOoXXr1hg9evQXb/9LbNq0CbGxsYiNjZXOCZ/D3t4ey5Ytw7NnzxAcHAx7e3u0bt0aQogcj6Xg4GAUK1YMGhoaUtmH14fixYsrXdMCAgIwadIk6bg1NDSEj48PQkJCPinmU6dOoXv37ti9e7d0z3RKSgr69++PpUuXKsWUEz09Pfz000+oUKECNDU1MX36dDx9+hSPHz+WrhfDhw+HjY0N9PT0MG3aNJw+fRpv374FkNHz7ePjgwMHDsDf3x9BQUHo1q0bunbtipUrV2LFihXo1q1bnk7YNGjQIOk4iY2NRdmyZZWWb9q0CZMnT8axY8dgYWGRbTsGBgbw8vLCrVu3EBMTg+HDh6Nnz5549OgRIiMjkZCQkGVvaWRkJFJSUpTOH3Z2dkhOTla63rx/jgsICIC3t7fScbJo0SKp1zS3goOD0bBhQwwdOlTp/J6ba8anmD59Ory9vaX7hfft2wd3d3dp4hUrKyscOHAAPj4+GDp0KAYNGoQ///wTGzduREJCAs6dO4fXr1/nONrqv4bJ3X+MpaUlAgIClMoCAgJgaWkJAJmSo5SUFLRv3x4DBgxASEgI4uLiMHDgwM8+2erp6aFPnz64fPkyoqKiYGlpiaSkJKWbt9+P52PxAkDnzp0xZMgQuLq6fvJF7p1ixYohPDxcaZhUTkMWXFxcoKOjgx07dmRb52Ox6+npISEhQVr2qSfejyWyH7p79y5CQ0NRs2ZNmJqaonLlygCAkiVLYufOnQAyErshQ4bg6tWrOHbsGORyubS+g4PDR29wf5fYlS9fPt9MtPGOg4MD5s6di0GDBiExMREmJibQ0dHB1atXlS7eiYmJcHFxgY2NDUJCQrKcse3ChQvw8vLC+vXrERMTg9jYWFSoUOG7/BNy7949WFpaokOHDlBTU4OFhQV69eqFQ4cOZVnfyckJoaGhePnypVTm4+Mj/VP3seXvi4qKwqxZszBnzhw8efIEVlZWMDIyQs2aNXHnzp2vvKdfz/t/K1/yuX+oZMmSWL9+PcLDw7Fy5UqMGTMmy1k6PzwXpKSkIDQ0VOk89jWlpqYiICBAmlyhfPnyOHbsGF69egUfHx8kJyejXr16X3V7T548yVT++PFj7N27F+PGjcO9e/fg5OQETU3NfH+8fK5ixYphwoQJCAkJQXR0dI7HkqWlJUJDQ5UmwPjw2vbhOd7Kygrz5s1TOm7fvn2b7Rc7WTl16hQ6dOiAzZs3o2HDhlL5uy+J2rVrJ315BwD169fH/Pnzs2zL2dlZ6fX75/v3h/F96MPzZEpKCkaOHIm///4bkZGRSEtLg52dHUqWLImUlBRERkbmev++p02bNmHkyJGZvkz6GD09PYwePRpyuRwPHz5EkSJFoKOjk+VMzkWKFIGGhobS+SMgIACamprSZwQoHytWVlZo166d0nHy+vVreHt75zrG4OBg1K9fH927d8ekSZOUljk5OWWa8TK7a0Zu3L59Gx07dkTJkiWhoqICV1dXODs748SJE5nqrl+/Hra2tqhTpw7u3LmD6tWrA0ChPad8LiZ3/zHu7u44c+YM9u3bh7S0NOzevRvnzp2T7jcxMzPD8+fPpZNvcnIykpKSYGJiAk1NTVy9evWz7xUCgMTERKxZswbFihWDsbExihcvjvr162PMmDF4+/YtgoKCMGPGDPTq1StX8b4zbdo0dOvWDa6urnjx4sUnx1WjRg0YGhpi5syZSE1NxfXr17F9+/Zs6+vr62PWrFkYPnw4Nm3ahPj4eAgh4OPjI31j/rHYK1eujN27dyMuLg4vX7786PTkHzIzM/ukb5xr1qwJf39/+Pj4wMfHRzrRnz9/Hi1atACQ8U3qxYsXcfz4cRgZGSmtr62tje7du2PWrFlSQjNr1ixpVsL4+Hg0a9YMpUuXxsqVK/NVYvdO27ZtYWJigr/++gsqKioYOHAgRo8eLR0zUVFR2LZtGwCgatWqcHBwwODBgxEbG4u0tDRcuHABycnJiI+Ph6qqKooUKQKFQoHVq1fj/v37Xy3O1NRUJCUlIT09Henp6UhKSpL+Cfzhhx8QGhqKvXv3QqFQIDIyEhs2bEClSpWybKtkyZKoVasWJk2ahISEBFy7dg2bNm1Cnz59crX8fWPGjMEvv/wCIyMj2NjY4PHjxwgJCcHx48dRsmTJr7b/39KXfO4fWr9+PSIiIiCTyWBoaAgVFRWoqqpmqte9e3csWbIEDx8+RHJyMiZPnozixYujWrVqX2Wfli1bJiXnz58/x4QJE9CgQQOoq6sDyPhi5+3bt0hJScHu3buxevVqTJ48Odv2cjr+fHx8cPz4cSQmJiItLQ2HDh3Cpk2b0LRp00ztDB48GIsWLYKGhgbs7Oxw7do1aabA/Hq8pKWlISkpCWlpaVAoFEhKSsp2hIavry9mzZqFgIAAKBQKxMbGYsmSJShdujRMTExyPJaqVasGMzMzTJ06FcnJybh//z4WL14sXfuyMmTIEMyZMwc3b96EEAIJCQk4ceJErkeDnDlzBj/++CM2bNiQ6fOysrJCYGCgdH1498/7tm3bsp05tH///li7di38/PyQmpqKadOmoVSpUtJ9zf3798fixYsREhKCxMRETJ8+HQ0bNpR69d6ZOXMmOnbsCHt7e5iamiI5ORl37tzB3bt3kZKS8lV6mb+2LVu2YPjw4Th8+HC25973jR07Fj4+PkhJSUFKSgpWrlyJt2/f4ocffoBMJkO/fv0wevRoPH36FEII+Pn5ITAwECoqKujatSt++eUXREdHS/fv9+jRI9sveHv06IFTp05h165dSE1NRWpqKnx8fHD9+vVc7VtoaCjq168Pd3d3eHp6Zlrerl07BAcHY9WqVUhJScGqVasQFhYm3XeblaSkJOlLjpSUFCQlJUn3A9esWRM7d+5EYGAghBC4ePEirl27lum+vqioKMyePVv6X8nOzg6nTp1CamoqTp06lW/PKXkib0aD0vf04f0+3t7ewtnZWejr6wtnZ2dx5MgRadnTp09F5cqVhaGhoXB0dBRCZMx4ZmFhIfT19UWrVq3E0KFDle5FQC7uudPV1RW6urrCyMhINGrUSKl+WFiY+PHHH4WJiYmwtLQU48ePVxo7nVO8H94TMmPGDGFnZycCAgKyvOfu/XvU9uzZI2xsbKTXd+7cEdWqVRO6urrC1dVVjBw5UjRp0iTH93bfvn2idu3aQldXVxgbG4uqVasqzQ6WU+zR0dHCzc1N6Ovri/Lly4ulS5dmuucup3vy9u/fL2xtbYVcLpdmpBswYECuZ8788J67gIAAAUBoampKn9eHM3G+efNG9OrVS8jlclG0aFHRt29fER8fL4QQYu3atQKA0NHRUVr/3X1on+NbzIq3efNmUaRIEfHmzRuRnJwsfv31V2Fvby/09PSEjY2N0n0JISEhwt3dXRQtWlTI5XJRr149kZCQINLT00W/fv2EgYGBKFKkiBg1apSoW7eu9Hl97J67cuXK5fi+9OrVSwBQ+nl/Brl9+/aJSpUqCQMDA1G0aFHRrVs3ERkZmW37wcHBolmzZkJHR0dYWlpmmjntY8vf7dOH95AuWLBAmJqaCjs7uyzvF8mtrz1b5odtf3jf2Od+7kIon+969OghzMzMhK6urrCzsxNLliyR2ni/nkKhELNmzRIlSpQQhoaGokmTJuLJkydS3Y+dmz72d92hQwdRpEgR6fPr37+/ePnypbT8l19+EcbGxkJHR0fUrFlTXLhwQWn9Zs2aSfcCv3vPsjv+rl+/LqpUqSL09fWFgYGBcHJykmbHfN+aNWsyxTxmzBhhZGQkHB0dpXsCP8e3nC3T09Mz075nd+91cHCwcHd3F5aWlkJXV1cULVpUtGvXTvj5+Ul1cjqW/Pz8ROPGjYWhoaEoUaKEmDt3rnTvbFYzTQuRcT9spUqVpHOwm5ubCAwMFEJk3Kv5bpbrrLi6uipdj9/9ZOfDa/uMGTMynQP++OMPYWFhIR3Xjx8/lpalpaWJUaNGCRMTE2FiYiI6dOggwsLClNb39fUVP/zwg9I1f+vWrcLCwkIUK1ZM7NixI9v4PuZbzpZpa2sr1NTUlN7HnN77YcOGCQcHB6GnpyeMjIyEi4uL0v8DSUlJYsKECcLGxkbo6ekJR0dHcevWLSFExv16ffr0EWZmZsLMzEz069dPuu5md+/8rVu3ROPGjYWJiYm0vXf3sQUGBgpdXV3puPmQl5eXAJDpOHl/ts/z588LR0dHoaWlJZycnJRmbc2q/Q//pgBIf8MpKSli1KhRwtLSUujp6YlSpUqJRYsWZYqrV69eYsuWLdLruLg40bx5c2FgYCBatmwpXr9+ne37/18jEyKPnz5LlE8NGDAACoUix+eH0bfl6uoKLy8v6dlIVDh5eHjA1dW10M2mSN8GzwuUG+/u+c4P934TfU8clkn0f+fPn8eLFy+gUChw8uRJbNq0CR07dszrsIiIiIiIckXt41WI/hueP3+Ozp07IyYmBpaWlvjjjz/QpEmTvA7rP83DwyPTjKNU+LRt25afM+UazwuUG+zZpf8qDsskIiIiIiIqBDgsk4iIiIiIqBBgckdERERERFQIMLkjIiIiIiIqBJjcERERERERFQJM7oiIiIiIiAoBJndERERERESFAJM7IiIiIiKiQoDJHRERERERUSHA5I6IiIiIiKgQYHJHRERERERUCDC5IyIiIiIiKgSY3BERERERERUCTO6IiIiIiIgKAbW8DoCIiPKn9PR0nD9/Hvfv34eKigpUVVUhl8vh6uoKc3PzvA4PAHDmzBnUrl0bamqffjnbvn07SpcujYoVK379wADs3bsX5ubmqFGjxjdp/0P79u1DaGgoZDIZVFVV0bBhQ9jZ2QEA3r59iz179iAmJgaqqqpo2bIlbGxsctVmUFAQ1NXVoaGhgaZNm6J48eIAgNTUVOzfvx8hISGQyWRo2LAhypUrBwA4efIkHj16BDU1NaioqKBBgwawt7cHkPGZXb9+Hfr6+gCAokWLon379t/iLSEi+s9hckdERFnat28fUlJS0KdPH2hrawMAnj9/jlevXn2X5E4IAQCQyWTZ1jl79ixq1KjxWcldYdO0aVNoaWkBAMLCwrB+/XqMGzcOMpkMJ06cgKWlJbp3746QkBBs27YNI0aMgKqqao5tlilTBq1atYKKigoeP36MHTt2YOTIkQCAS5cuQVVVFcOHD0dMTAxWrlwJW1tb6OjowNraGnXr1oW6ujrCw8Oxdu1ajBo1ChoaGgAAR0dHNGvW7Ju+H0RE/0W8GhIRUSZRUVHw9fXFzz//LCV2AKSeoHcuXbqEBw8eQKFQQFdXF25ubjA0NMSZM2fw6tUrpKamIjo6Gnp6eujUqZPUVk7rvXz5EikpKYiLi0OPHj1w5coVBAYGIj09HZqammjVqhVMTU1x8OBBAMCaNWsgk8nQo0cPqKmp4ejRo4iIiEBaWhosLS3RokULqKqq4tWrV9i3bx+Sk5NhbGyM1NTUbPf/zp07uHTpEgBALpfDzc0NBgYG8PHxwd27d6Grq4uXL19CVVUVHTt2hJGRUY7v55kzZ5CUlCQlNNeuXUNoaCjatm370Tbv3LmD69evIz09HRoaGmjevHmWyfW7xA4AkpOTlZY9ePAAw4cPBwAUL14c+vr6CAwMzPR5fsjBwUH63dLSEq9fv4ZCoYCKigoePHiA1q1bAwCMjIxga2sLX19fVK5cGaVKlZLWMzMzgxACCQkJUnJHRETfBpM7IiLKJDw8HMbGxkqJ3Yfu3buHV69eoU+fPlBRUcGdO3fg7e2Nrl27AgCCg4PRv39/6OjoYOfOnbhx4wbq1Knz0fVevHiBAQMGQE9PDwBQq1YtNGnSBABw//59HDlyBN27d4ebmxtu3ryJn376SUpsDhw4ABsbG7Ru3RpCCBw4cABXrlxBrVq1sGfPHvzwww+oXLkyIiIi8M8//8DR0THTfr18+RLHjx9H//79YWBggHPnzuHAgQPo1q0bACA0NBQDBgyAkZERTpw4gQsXLqBVq1Zf9H5n12ZQUBDu378PDw8PqKmpITAwELt378bgwYOzbOfEiRN4+PAhEhMT0alTJ8hkMiQkJEChUEjvJwAYGhoiLi7uk2K8cuUKSpUqBRWVjNv14+LiIJfLP9rm7du3YWRkpFT34cOHCAgIgLa2NurWrYsSJUp8UixERJQ1JndERPRR0dHR2L59O9LS0mBlZYU2bdrA19cXoaGhWLFiBYB/h1G+Y29vDx0dHQAZvT4vX74EgI+uV6pUKaVE5Pnz57h27RqSk5MhhEBiYmK2cfr6+iI4OBiXL18GAKSlpUEmkyE5ORnh4eHS/XVmZmawtrbOsg1/f3/Y29vDwMAAAFC1alWcO3cOCoVC2pd3vWqWlpa4du3aR969j8uuTT8/P0RERGDlypVS3cTERKSmpkJdXT1TO40aNUKjRo3w/PlznDhxAr179/7i2ADg7t27ePjwITw8PD5pvefPn+Ps2bPo0aOHNLy2SpUqqFOnDlRVVREUFIRt27ahX79+MDQ0/CqxEhH9lzG5IyKiTMzNzREdHY3ExERoa2vD2NgYAwcOhI+PD3x9faV6tWvXxg8//JBlG+/fB6eioiIlRx9b7/2he3FxcfD29ka/fv1gbGyMiIgIrFmzJsfYO3XqBBMTE6WyD4cpfooP7/nLab+y82G9tLS0XLUphICzszMaNmz4STHb2dnB29sbERERKFasGFRUVPDmzRspaY6NjVXqScvJ/fv3cfbsWfTs2VMp6ZbL5YiLi5MmRomNjUXJkiWl5QEBAdi3bx+6dOkCU1NTqfz9NqytrWFhYYHQ0FAmd0REXwEfhUBERJmYmJjAwcEB+/fvR1JSklSekpIi/e7g4IAbN25IPWnp6ekICwv7aNufsl5SUhJUVVWhr68PIUSmXjINDQ2l+BwcHHDhwgUpOUpMTER0dDQ0NTVhbm6OO3fuAMgYehkUFJTlNkuUKIGnT5/i9evXAIAbN26gRIkS0nDEz2FsbIywsDAoFAqkpqbi0aNHuVrPwcEBd+/elYY7CiEQGhqaqV56ejqio6Ol1yEhIXj79q3UG1iuXDncuHFDWvb69WtptswTJ05k2/v44MEDnD59Gj169MiUDL7fZkxMDAICAlCmTBkAQGBgIPbs2YPOnTtnuj8wPj5e+j0qKgrh4eEwMzPL1ftBREQ5Y88dERFlqW3btjh37hxWrlwJFRUVaGlpQVdXF7Vq1QIAODk5ITExEevWrQMAKBQKVKxYERYWFjm2+ynrmZmZoXz58vj777+hra0tJQ/v1KxZExs2bIC6ujp69OiBZs2a4cSJE1i2bBlkMhlUVFTQuHFjGBsbo127dti3bx8uX74MExOTbB8FULRoUTRu3BgbN24EkNFD9Tn31CkUCqlHrmzZsnj48CH++usvGBgYwNzcPMcJXd6xsbFB48aNsW3bNigUCqSnp6NUqVIoVqxYpm3t3bsXSUlJUFFRgYaGhtIENo0aNcKePXuwePFiqKqqol27dtJMme9697Kye/du6OnpYevWrVJZz549oaOjAxcXF+zfvx+LFi2CTCZDixYtpGG4+/fvR3p6Ovbt2yet165dO5iZmeHUqVMIDQ2FiooKVFRU0KJFi0w9rURE9Hlk4sObHYiIiOiLKBQKrFixAk2bNs3Xk4UoFAqsWrUKffv2zfGRE0REVDAwuSMiIvqKAgMDcfDgQVhbW8PNzY1JExERfTdM7oiIiIiIiAoBTqhCRERERERUCDC5IyIiIiIiKgSY3BERERERERUCTO6IiIiIiIgKASZ3REREREREhQCTOyIiIiIiokKAyR0REREREVEhwOSOiIiIiIioEGByR0REREREVAgwuSMiIiIiIioE/get3GLRNVlb6QAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_confusion_matrix_from_df(summary_df_kbest, 'RISK_VS_CLAIM using KBest Features from all features')\n", - "plot_confusion_matrix_from_df(summary_df_rfe, 'RISK_VS_CLAIM using RFE Features from all features')\n", - "plot_confusion_matrix_from_df(summary_df_lasso, 'RISK_VS_CLAIM using Lasso Features from all features')\n", - "plot_confusion_matrix_from_df(summary_df_et_lasso, 'RISK_VS_CLAIM using Extra Trees with Lasso Features')" - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "id": "8f25a5ae", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC Curve\n", - "fpr, tpr, _ = roc_curve(y_test_lasso, y_pred_proba_et_lasso)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')\n", - "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC Curve')\n", - "plt.legend(loc='lower right')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "id": "740e81cb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# PR Curve\n", - "precision, recall, _ = precision_recall_curve(y_test_lasso, y_pred_proba_et_lasso)\n", - "pr_auc = average_precision_score(y_test_lasso, y_pred_proba_et_lasso)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(recall, precision, color='green', lw=2, label=f'PR curve (AUC = {pr_auc:.4f})')\n", - "plt.xlabel('Recall')\n", - "plt.ylabel('Precision')\n", - "plt.title('Precision-Recall Curve')\n", - "plt.legend(loc='lower left')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 131, - "id": "81209bc0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Print a table to summarize the results\n", - "summary_table = pd.concat([summary_df_kbest, summary_df_rfe, summary_df_lasso, summary_df_et_lasso], ignore_index=True)\n", - "summary_table = summary_table[['title', 'count_true_positive', 'count_true_negative',\n", - " 'count_false_positive', 'count_false_negative', 'true_positive_score', 'true_negative_score',\n", - " 'false_positive_score', 'false_negative_score', 'recall_score', 'precision_score',\n", - " 'false_positive_rate_score', 'f1_score', 'f2_score']]\n", - "\n", - "# Rename them\n", - "summary_table.columns = ['Model', 'TP', 'TN', 'FP', 'FN',\n", - " 'TP Rate', 'TN Rate', 'FP Rate', 'FN Rate',\n", - " 'Recall', 'Precision', 'FPR', 'F1', 'F2']\n", - " \n", - "# summary_table.to_csv('flagging_analysis_summary.csv', index=False)\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Set up figure and axis\n", - "fig, ax = plt.subplots(figsize=(16, 4)) # Adjust width/height as needed\n", - "ax.axis('off') # Hide axes\n", - "\n", - "# Create table from DataFrame\n", - "table = ax.table(cellText=summary_table.round(3).values,\n", - " colLabels=summary_table.columns,\n", - " loc='center',\n", - " cellLoc='center')\n", - "\n", - "table.auto_set_font_size(False)\n", - "table.set_fontsize(10)\n", - "table.scale(1.2, 1.5) # Adjust cell size\n", - "\n", - "# Save as image\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "id": "8617110d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ModelTPTNFPFNTP RateTN RateFP RateFN RateRecallPrecisionFPRF1F2
0Kbest6418923440.0014080.9828720.0053970.0103240.120.2068970.0054610.1518990.131004
1RFE3131921020190.0072740.7489440.2393240.0044580.620.0294960.2421650.0563120.123901
2Lasso742048430.0016420.9863910.0018770.0100890.140.4666670.0018990.2153850.162791
3Lasso ET9419814410.0021120.9849840.0032850.0096200.180.3913040.0033240.2465750.201794
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" - ], - "text/plain": [ - " Model TP TN FP FN TP Rate TN Rate FP Rate FN Rate \\\n", - "0 Kbest 6 4189 23 44 0.001408 0.982872 0.005397 0.010324 \n", - "1 RFE 31 3192 1020 19 0.007274 0.748944 0.239324 0.004458 \n", - "2 Lasso 7 4204 8 43 0.001642 0.986391 0.001877 0.010089 \n", - "3 Lasso ET 9 4198 14 41 0.002112 0.984984 0.003285 0.009620 \n", - "\n", - " Recall Precision FPR F1 F2 \n", - "0 0.12 0.206897 0.005461 0.151899 0.131004 \n", - "1 0.62 0.029496 0.242165 0.056312 0.123901 \n", - "2 0.14 0.466667 0.001899 0.215385 0.162791 \n", - "3 0.18 0.391304 0.003324 0.246575 0.201794 " - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summary_table" - ] - }, - { - "cell_type": "markdown", - "id": "4bc285af", - "metadata": {}, - "source": [ - "### Conclusions\n", - "Using Random Forest Extra Trees with Lasso features appears to offer the best trade-off between catching positives and avoiding false alarms. RFE, while good for recall, introduces too much noise. Plain Lasso is a solid choice if minimizing false positives is the main goal.\n", - "\n", - "The ROC AUC score of 0.8145 indicates that the models are generally good at distinguishing between the two classes.\n", - "However, the Precision-Recall AUC is only 0.1720 due to class imbalance, making the performance on the positive (minority) class very limited.\n", - "\n", - "## Most Relevant Features\n", - "| **Feature** | **Importance** | **Insight** |\n", - "| ------------------------------ | -------------- | ------------------------------------------------------------------------------ |\n", - "| `number_of_listings_of_host` | 0.069 | Fewer listings ā†’ **higher** risk. Smaller hosts have more tendency to complain |\n", - "| `listing_number_of_bedrooms` | 0.066 | More bedrooms ā†’ **higher** risk. Larger properties involve higher risk. |\n", - "| `listing_number_of_bathrooms` | 0.063 | More bathrooms ā†’ **higher** risk. Related to larger properties. |\n", - "| `host_months_with_truvi` | 0.049 | Longer time ā†’ **higher** risk. |\n", - "| `host_account_type_PMC` | 0.035 | PMC hosts ā†’ **lower** risk. Professional management mitigates incidents. |\n", - "| `host_country_United Kingdom` | 0.035 | UK-based hosts ā†’ **lower** risk. Possibly due to operational/regional norms. |\n", - "| `number_of_nights` | 0.034 | Longer stays ā†’ **higher** risk. Increased time/possibility to issues. |\n", - "| `guest_age` | 0.033 | Younger guests ā†’ **higher** risk. May reflect behavioral trends. |\n", - "| `host_age` | 0.031 | Older hosts ā†’ **higher** risk. Possibly less adaptive to platform dynamics. |\n", - "| `host_active_pms_list_Hostify` | 0.030 | Hostify users ā†’ **lower** risk. |\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/data_driven_risk_assessment/experiments/ddra_joaquin_weighted.ipynb b/data_driven_risk_assessment/experiments/ddra_joaquin_weighted.ipynb deleted file mode 100644 index 5944822..0000000 --- a/data_driven_risk_assessment/experiments/ddra_joaquin_weighted.ipynb +++ /dev/null @@ -1,5581 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "84dcd475", - "metadata": {}, - "source": [ - "# DDRA Joaquin\n", - "\n", - "## General Idea\n", - "The idea is to start with a very simple model with basic Booking attributes. This should serve as a first understanding of what can bring value in the data-driven risk assessment of new dash protected bookings.\n", - "\n", - "## Initial setup\n", - "This first section just ensures that the connection to DWH works correctly." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "12368ce1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "šŸ”Œ Testing connection using credentials at: /home/joaquin/.superhog-dwh/credentials.yml\n", - "āœ… Connection successful.\n" - ] - } - ], - "source": [ - "# This script connects to a Data Warehouse (DWH) using PostgreSQL. \n", - "# This should be common for all Notebooks, but you might need to adjust the path to the `dwh_utils` module.\n", - "\n", - "import sys\n", - "import os\n", - "sys.path.append(os.path.abspath(\"../../utils\")) # Adjust path if needed\n", - "\n", - "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n", - "\n", - "# --- Connect to DWH ---\n", - "creds = read_credentials()\n", - "dwh_pg_engine = create_postgres_engine(creds)\n", - "\n", - "# --- Test Query ---\n", - "test_connection()" - ] - }, - { - "cell_type": "markdown", - "id": "c86f94f1", - "metadata": {}, - "source": [ - "## Data Extraction\n", - "In this section we extract the data for our first attempt on Basic Booking Attributes modelling.\n", - "\n", - "This SQL query retrieves a clean and relevant subset of booking data for our model. It includes:\n", - "- A **unique booking ID**\n", - "- Key **numeric features** such as number of services, time between booking creation and check-in, and number of nights\n", - "- Several **categorical (boolean) features** related to service usage\n", - "- A **target variable** (`has_resolution_incident`) indicating whether a resolution incident occurred\n", - "\n", - "Filters applied being:\n", - "1. Bookings from **\"New Dash\" users** with a valid deal ID\n", - "2. Only **protected bookings**, i.e., those with Protection or Deposit Management services\n", - "3. Bookings flagged for **risk categorisation** (excluding incomplete/rejected ones)\n", - "4. Bookings that are **already completed**\n", - "\n", - "The result is converted into a pandas DataFrame for further processing and modeling.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e3ed391", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialise all imports needed for the Notebook\n", - "from sklearn.model_selection import (\n", - " train_test_split, \n", - " GridSearchCV\n", - ")\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.feature_selection import RFE\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.utils.class_weight import compute_class_weight\n", - "from sklearn.feature_selection import SelectKBest, f_classif\n", - "import pandas as pd\n", - "import numpy as np\n", - "from datetime import date\n", - "from sklearn.metrics import (\n", - " roc_auc_score, \n", - " average_precision_score,\n", - " classification_report,\n", - " roc_curve, \n", - " auc,\n", - " precision_recall_curve,\n", - " precision_score,\n", - " recall_score,\n", - " fbeta_score,\n", - " confusion_matrix\n", - ")\n", - "import matplotlib.pyplot as plt\n", - "import shap\n", - "import math" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "db5e3098", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " id_booking days_from_booking_creation_to_check_in number_of_nights \\\n", - "0 919656 26.0 4.0 \n", - "1 926634 17.0 3.0 \n", - "2 931082 20.0 7.0 \n", - "3 931086 15.0 3.0 \n", - "4 931096 8.0 5.0 \n", - "\n", - " host_town host_country host_postcode host_age host_months_with_truvi \\\n", - "0 Madison CT United States 06443 125.0 8.0 \n", - "1 Madison CT United States 06443 125.0 8.0 \n", - "2 London United Kingdom N16 6DD 125.0 8.0 \n", - "3 London United Kingdom N16 6DD 125.0 8.0 \n", - "4 London United Kingdom N16 6DD 125.0 8.0 \n", - "\n", - " host_account_type host_active_pms_list ... \\\n", - "0 Host Hostaway ... \n", - "1 Host Hostaway ... \n", - "2 PMC - Property Management Company Hostify ... \n", - "3 PMC - Property Management Company Hostify ... \n", - "4 PMC - Property Management Company Hostify ... \n", - "\n", - " number_of_applied_upgraded_services number_of_applied_billable_services \\\n", - "0 2 2 \n", - "1 2 2 \n", - "2 1 1 \n", - "3 1 1 \n", - "4 1 1 \n", - "\n", - " booking_days_to_check_in booking_number_of_nights has_verification_request \\\n", - "0 87 4 False \n", - "1 109 3 False \n", - "2 50 7 False \n", - "3 15 3 False \n", - "4 8 5 False \n", - "\n", - " has_billable_services has_upgraded_screening_service_business_type \\\n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "\n", - " has_deposit_management_service_business_type \\\n", - "0 True \n", - "1 True \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_protection_service_business_type has_resolution_incident \n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "\n", - "[5 rows x 64 columns]\n", - "Total Bookings: 21,307\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_48568/805553034.py:455: DtypeWarning: Columns (50) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df_extraction = pd.read_csv(\"/home/joaquin/data-jupyter-notebooks/data_driven_risk_assessment/experiments/data.csv\")\n" - ] - } - ], - "source": [ - "# Query to extract data\n", - "data_extraction_query = \"\"\"\n", - "with\n", - " int_core__verification_requests as (\n", - " select *\n", - " from intermediate.int_core__verification_requests\n", - " where created_date_utc >= '2024-10-21'\n", - " ),\n", - " int_core__bookings as (\n", - " select *\n", - " from intermediate.int_core__bookings\n", - " where created_date_utc >= '2024-10-21'\n", - " ),\n", - " stg_core__verification as (\n", - " select *\n", - " from staging.stg_core__verification\n", - " where created_date_utc >= '2024-10-21'\n", - " ),\n", - " int_core__guest_journey_payments as (\n", - " select *\n", - " from intermediate.int_core__guest_journey_payments\n", - " where payment_due_date_utc >= '2024-10-21'\n", - " ),\n", - " filtered_bookings as (\n", - " select *\n", - " from intermediate.int_booking_summary\n", - " where\n", - " is_user_in_new_dash = true\n", - " and is_missing_id_deal = false\n", - " and (\n", - " has_protection_service_business_type\n", - " or has_deposit_management_service_business_type\n", - " )\n", - " and is_booking_flagged_as_risk is not null\n", - " and is_booking_past_completion_date = true\n", - " and booking_created_date_utc < '2025-06-25'\n", - " ),\n", - " previous_booking_counts as (\n", - " select\n", - " id_booking,\n", - " id_accommodation,\n", - " id_user_guest,\n", - " booking_check_in_date_utc,\n", - " booking_check_out_date_utc,\n", - " count(*) over (\n", - " partition by id_accommodation\n", - " order by booking_check_in_date_utc\n", - " rows between unbounded preceding and 1 preceding\n", - " ) as previous_bookings_in_listing_count,\n", - " count(*) over (\n", - " partition by id_user_guest\n", - " order by booking_check_in_date_utc\n", - " rows between unbounded preceding and 1 preceding\n", - " ) as previous_guest_bookings_count\n", - " from filtered_bookings\n", - " ),\n", - " listing_info as (\n", - " select\n", - " id_accommodation,\n", - " address_line_1 as listing_address,\n", - " town as listing_town,\n", - " country_name as listing_country,\n", - " postcode as listing_postcode,\n", - " number_of_bedrooms,\n", - " number_of_bathrooms,\n", - " friendly_name as listing_description,\n", - " id_user_host\n", - " from intermediate.int_core__accommodation\n", - " ),\n", - " host_info as (\n", - " select\n", - " scu.id_user as id_user_host,\n", - " icuh.account_type,\n", - " icuh.active_pms_list,\n", - " scc.country_name,\n", - " scu.billing_town,\n", - " scu.billing_postcode,\n", - " scu.id_billing_country,\n", - " extract(year from age(current_date, scu.date_of_birth)) as host_age,\n", - " extract(\n", - " month from age(current_date, scu.joined_date_utc)\n", - " ) as host_months_with_truvi\n", - " from staging.stg_core__user scu\n", - " left join\n", - " staging.stg_core__country scc on scu.id_billing_country = scc.id_country\n", - " left join\n", - " intermediate.int_core__user_host icuh on icuh.id_user_host = scu.id_user\n", - " ),\n", - " guest_info as (\n", - " select\n", - " scu.id_user as id_user_guest,\n", - " scc.country_name,\n", - " scu.billing_town,\n", - " scu.billing_postcode,\n", - " scu.id_billing_country,\n", - " extract(year from age(current_date, scu.date_of_birth)) as guest_age,\n", - " scu.email,\n", - " scu.phone_number\n", - " from staging.stg_core__user scu\n", - " left join\n", - " staging.stg_core__country scc on scu.id_billing_country = scc.id_country\n", - " ),\n", - " host_listing_counts as (\n", - " select id_user_host, count(*) as number_of_listings_of_host\n", - " from intermediate.int_core__accommodation\n", - " where is_active = true\n", - " group by id_user_host\n", - " ),\n", - " listing_incident_counts as (\n", - " select\n", - " i.created_date_utc::date as date_day,\n", - " i.id_accommodation,\n", - " count(*) over (\n", - " partition by i.id_accommodation\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_incidents_in_listing,\n", - " count(i.calculated_payout_amount_in_txn_currency) over (\n", - " partition by i.id_accommodation\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_payouts_in_listing\n", - " from intermediate.int_resolutions__incidents i\n", - " where\n", - " i.id_accommodation is not null\n", - " and i.created_date_utc::date between '2024-10-21' and current_date\n", - " order by i.id_accommodation, date_day\n", - " ),\n", - " guest_incident_counts as (\n", - " select\n", - " i.created_date_utc::date as date_day,\n", - " i.id_user_guest,\n", - " count(*) over (\n", - " partition by i.id_user_guest\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_incidents_of_guest\n", - " from intermediate.int_resolutions__incidents i\n", - " where\n", - " i.id_user_guest is not null\n", - " and i.created_date_utc::date between '2024-10-21' and current_date\n", - " order by i.id_user_guest, date_day\n", - " ),\n", - " host_incident_counts as (\n", - " select\n", - " i.created_date_utc::date as date_day,\n", - " i.id_user_host,\n", - " count(*) over (\n", - " partition by i.id_user_host\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_incidents_of_host,\n", - " count(i.calculated_payout_amount_in_txn_currency) over (\n", - " partition by i.id_user_host\n", - " order by i.created_date_utc::date\n", - " rows between unbounded preceding and current row\n", - " ) as number_of_previous_payouts_of_host\n", - " from intermediate.int_resolutions__incidents i\n", - " where\n", - " i.id_user_host is not null\n", - " and i.created_date_utc::date between '2024-10-21' and current_date\n", - " order by i.id_user_host, date_day\n", - " ),\n", - " verification_requests as (\n", - " select\n", - " icvr.id_verification_request,\n", - " extract(\n", - " day\n", - " from\n", - " age(\n", - " icvr.verification_estimated_started_date_utc,\n", - " icb.created_date_utc\n", - " )\n", - " ) as days_to_start_verification,\n", - " extract(\n", - " day\n", - " from\n", - " age(\n", - " icvr.verification_estimated_completed_date_utc,\n", - " icvr.verification_estimated_started_date_utc\n", - " )\n", - " ) as days_to_complete_verification,\n", - " -- CSAT Results\n", - " gsr.experience_rating as guest_csat_score,\n", - " gsr.guest_comments as guest_csat_comments,\n", - " -- GUEST_PRODUCT fields\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'GUEST_PRODUCT' then product_name\n", - " end\n", - " ) as guest_product_name,\n", - " max(\n", - " case when guest_journey_product_type = 'GUEST_PRODUCT' then currency end\n", - " ) as guest_currency,\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'GUEST_PRODUCT'\n", - " then total_amount_in_txn_currency\n", - " end\n", - " ) as guest_total_amount,\n", - " -- VERIFICATION_PRODUCT fields\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'VERIFICATION_PRODUCT'\n", - " then product_name\n", - " end\n", - " ) as verification_product_name,\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'VERIFICATION_PRODUCT'\n", - " then currency\n", - " end\n", - " ) as verification_currency,\n", - " max(\n", - " case\n", - " when guest_journey_product_type = 'VERIFICATION_PRODUCT'\n", - " then total_amount_in_txn_currency\n", - " end\n", - " ) as verification_total_amount,\n", - " -- Verification Results\n", - " max(\n", - " case when scv.verification = 'Screening' then id_verification_status end\n", - " ) as screening_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'GovernmentId' then id_verification_status\n", - " end\n", - " ) as government_id_status,\n", - " max(\n", - " case when scv.verification = 'Contract' then id_verification_status end\n", - " ) as contract_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'SelfieConfidenceScore'\n", - " then id_verification_status\n", - " end\n", - " ) as selfie_confidence_score_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'PaymentValidation'\n", - " then id_verification_status\n", - " end\n", - " ) as payment_validation_status,\n", - " max(\n", - " case when scv.verification = 'FirstName' then id_verification_status end\n", - " ) as first_name_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'DateOfBirth' then id_verification_status\n", - " end\n", - " ) as date_of_birth_status,\n", - " max(\n", - " case when scv.verification = 'LastName' then id_verification_status end\n", - " ) as last_name_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'AutohostPartner'\n", - " then id_verification_status\n", - " end\n", - " ) as autohost_partner_status,\n", - " max(\n", - " case\n", - " when scv.verification = 'CriminalRecord' then id_verification_status\n", - " end\n", - " ) as criminal_record_status\n", - " from int_core__verification_requests icvr\n", - " left join\n", - " int_core__bookings icb\n", - " on icb.id_verification_request = icvr.id_verification_request\n", - " left join\n", - " stg_core__verification scv\n", - " on scv.id_verification_request = icvr.id_verification_request\n", - " left join\n", - " int_core__guest_journey_payments gjp\n", - " on gjp.id_verification_request = icb.id_verification_request\n", - " left join\n", - " intermediate.int_core__guest_satisfaction_responses gsr\n", - " on gsr.id_verification_request = icvr.id_verification_request\n", - " and scv.verification in (\n", - " 'Screening',\n", - " 'GovernmentId',\n", - " 'Contract',\n", - " 'SelfieConfidenceScore',\n", - " 'PaymentValidation',\n", - " 'FirstName',\n", - " 'DateOfBirth',\n", - " 'LastName',\n", - " 'AutohostPartner',\n", - " 'CriminalRecord'\n", - " )\n", - " group by 1, 2, 3, 4, 5\n", - " )\n", - "select\n", - " fb.id_booking,\n", - " extract(day from age(fb.booking_check_in_date_utc, fb.booking_created_date_utc)) as days_from_booking_creation_to_check_in,\n", - " extract(day from age(fb.booking_check_out_date_utc, fb.booking_check_in_date_utc)) as number_of_nights,\n", - " -- Host Info\n", - " hi.billing_town as host_town,\n", - " hi.country_name as host_country,\n", - " hi.billing_postcode as host_postcode,\n", - " hi.host_age,\n", - " hi.host_months_with_truvi,\n", - " hi.account_type as host_account_type,\n", - " hi.active_pms_list as host_active_pms_list,\n", - " coalesce(hlc.number_of_listings_of_host, 0) as number_of_listings_of_host,\n", - " coalesce(\n", - " hic.number_of_previous_incidents_of_host, 0\n", - " ) as number_of_previous_incidents_of_host,\n", - " coalesce(\n", - " hic.number_of_previous_payouts_of_host, 0\n", - " ) as number_of_previous_payouts_of_host,\n", - " -- Guest Info\n", - " gi.billing_town as guest_town,\n", - " gi.country_name as guest_country,\n", - " gi.billing_postcode as guest_postcode,\n", - " gi.guest_age,\n", - " coalesce(\n", - " pbc.previous_guest_bookings_count, 0\n", - " ) as number_of_previous_bookings_of_guest,\n", - " coalesce(\n", - " gic.number_of_previous_incidents_of_guest, 0\n", - " ) as number_of_previous_incidents_of_guest,\n", - " case\n", - " when pbc.previous_bookings_in_listing_count > 0 then true else false\n", - " end as has_guest_previously_booked_same_listing,\n", - " -- Listing Info\n", - " li.listing_address,\n", - " li.listing_town,\n", - " li.listing_country,\n", - " li.listing_postcode,\n", - " li.number_of_bedrooms as listing_number_of_bedrooms,\n", - " li.number_of_bathrooms as listing_number_of_bathrooms,\n", - " li.listing_description,\n", - " coalesce(pbc.previous_bookings_in_listing_count, 0) as previous_bookings_in_listing_count,\n", - " coalesce(lic.number_of_previous_incidents_in_listing, 0) as number_of_previous_incidents_in_listing,\n", - " coalesce(lic.number_of_previous_payouts_in_listing, 0) as number_of_previous_payouts_in_listing,\n", - " -- Verification Info\n", - " case\n", - " when fb.id_verification_request is null then 0\n", - " else vr.days_to_start_verification\n", - " end as days_to_start_verification,\n", - " case \n", - " when vr.id_verification_request is null then 0\n", - " else vr.days_to_complete_verification\n", - " end as days_to_complete_verification,\n", - " vr.screening_status,\n", - " vr.government_id_status,\n", - " vr.contract_status,\n", - " vr.selfie_confidence_score_status,\n", - " vr.payment_validation_status,\n", - " vr.first_name_status,\n", - " vr.date_of_birth_status,\n", - " vr.last_name_status,\n", - " vr.autohost_partner_status,\n", - " vr.criminal_record_status,\n", - " vr.guest_csat_score,\n", - " vr.guest_csat_comments,\n", - " -- Boolean features\n", - " gi.email is not null as guest_has_email,\n", - " gi.phone_number is not null as guest_has_phone_number,\n", - " case \n", - " when gi.billing_town is null or li.listing_town is null then null \n", - " when gi.billing_town = li.listing_town \n", - " then true else false \n", - " end as is_guest_from_listing_town,\n", - " case \n", - " when gi.country_name is null or li.listing_country is null then null\n", - " when gi.country_name = li.listing_country \n", - " then true else false \n", - " end as is_guest_from_listing_country,\n", - " case \n", - " when gi.billing_postcode is null or li.listing_postcode is null then null\n", - " when gi.billing_postcode = li.listing_postcode \n", - " then true else false \n", - " end as is_guest_from_listing_postcode,\n", - " case \n", - " when hi.billing_town is null or li.listing_town is null then null\n", - " when hi.billing_town = li.listing_town \n", - " then true else false \n", - " end as is_host_from_listing_town,\n", - " case \n", - " when hi.country_name is null or li.listing_country is null then null\n", - " when hi.country_name = li.listing_country \n", - " then true else false \n", - " end as is_host_from_listing_country,\n", - " case \n", - " when hi.billing_postcode is null or li.listing_postcode is null then null\n", - " when hi.billing_postcode = li.listing_postcode \n", - " then true else false \n", - " end as is_host_from_listing_postcode,\n", - " case\n", - " when vr.days_to_complete_verification is null then false\n", - " else true\n", - " end as has_completed_verification,\n", - " -- Numeric features\n", - " fb.number_of_applied_services,\n", - " fb.number_of_applied_upgraded_services,\n", - " fb.number_of_applied_billable_services,\n", - " fb.booking_check_in_date_utc\n", - " - fb.booking_created_date_utc as booking_days_to_check_in,\n", - " fb.booking_number_of_nights,\n", - " -- Categorical features\n", - " fb.has_verification_request,\n", - " fb.has_billable_services,\n", - " fb.has_upgraded_screening_service_business_type,\n", - " fb.has_deposit_management_service_business_type,\n", - " fb.has_protection_service_business_type,\n", - " -- Target\n", - " fb.has_resolution_incident\n", - "from filtered_bookings fb\n", - "left join previous_booking_counts pbc on fb.id_booking = pbc.id_booking\n", - "left join listing_info li on li.id_accommodation = fb.id_accommodation\n", - "left join host_info hi on hi.id_user_host = fb.id_user_host\n", - "left join guest_info gi on gi.id_user_guest = fb.id_user_guest\n", - "left join host_listing_counts hlc on li.id_user_host = hlc.id_user_host\n", - "left join\n", - " lateral(\n", - " select *\n", - " from listing_incident_counts lic\n", - " where\n", - " lic.id_accommodation = fb.id_accommodation\n", - " and lic.date_day <= fb.booking_check_in_date_utc\n", - " order by lic.date_day desc\n", - " limit 1\n", - " ) lic\n", - " on true\n", - "left join\n", - " lateral(\n", - " select *\n", - " from guest_incident_counts gic\n", - " where\n", - " gic.id_user_guest = fb.id_user_guest\n", - " and gic.date_day <= fb.booking_check_in_date_utc\n", - " order by gic.date_day desc\n", - " limit 1\n", - " ) gic\n", - " on true\n", - "left join\n", - " lateral(\n", - " select *\n", - " from host_incident_counts hic\n", - " where\n", - " hic.id_user_host = fb.id_user_host\n", - " and hic.date_day <= fb.booking_check_in_date_utc\n", - " order by hic.date_day desc\n", - " limit 1\n", - " ) hic\n", - " on true\n", - "left join\n", - " verification_requests vr on vr.id_verification_request = fb.id_verification_request\n", - "\"\"\"\n", - "\n", - "# Retrieve Data from Query\n", - "# df_extraction = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n", - "df_extraction = pd.read_csv(\"/home/joaquin/data-jupyter-notebooks/data_driven_risk_assessment/experiments/data.csv\")\n", - "print(df_extraction.head())\n", - "print(f\"Total Bookings: {len(df_extraction):,}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b56a8530", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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id_bookingdays_from_booking_creation_to_check_innumber_of_nightshost_townhost_countryhost_postcodehost_agehost_months_with_truvihost_account_typehost_active_pms_list...number_of_applied_upgraded_servicesnumber_of_applied_billable_servicesbooking_days_to_check_inbooking_number_of_nightshas_verification_requesthas_billable_serviceshas_upgraded_screening_service_business_typehas_deposit_management_service_business_typehas_protection_service_business_typehas_resolution_incident
091965626.04.0Madison CTUnited States06443125.08.0HostHostaway...22874FalseTrueFalseTrueTrueFalse
192663417.03.0Madison CTUnited States06443125.08.0HostHostaway...221093FalseTrueFalseTrueTrueFalse
293108220.07.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify...11507FalseTrueFalseFalseTrueFalse
393108615.03.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify...11153FalseTrueFalseFalseTrueFalse
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5 rows Ɨ 64 columns

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" - ], - "text/plain": [ - " id_booking days_from_booking_creation_to_check_in number_of_nights \\\n", - "0 919656 26.0 4.0 \n", - "1 926634 17.0 3.0 \n", - "2 931082 20.0 7.0 \n", - "3 931086 15.0 3.0 \n", - "4 931096 8.0 5.0 \n", - "\n", - " host_town host_country host_postcode host_age host_months_with_truvi \\\n", - "0 Madison CT United States 06443 125.0 8.0 \n", - "1 Madison CT United States 06443 125.0 8.0 \n", - "2 London United Kingdom N16 6DD 125.0 8.0 \n", - "3 London United Kingdom N16 6DD 125.0 8.0 \n", - "4 London United Kingdom N16 6DD 125.0 8.0 \n", - "\n", - " host_account_type host_active_pms_list ... \\\n", - "0 Host Hostaway ... \n", - "1 Host Hostaway ... \n", - "2 PMC - Property Management Company Hostify ... \n", - "3 PMC - Property Management Company Hostify ... \n", - "4 PMC - Property Management Company Hostify ... \n", - "\n", - " number_of_applied_upgraded_services number_of_applied_billable_services \\\n", - "0 2 2 \n", - "1 2 2 \n", - "2 1 1 \n", - "3 1 1 \n", - "4 1 1 \n", - "\n", - " booking_days_to_check_in booking_number_of_nights has_verification_request \\\n", - "0 87 4 False \n", - "1 109 3 False \n", - "2 50 7 False \n", - "3 15 3 False \n", - "4 8 5 False \n", - "\n", - " has_billable_services has_upgraded_screening_service_business_type \\\n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "\n", - " has_deposit_management_service_business_type \\\n", - "0 True \n", - "1 True \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_protection_service_business_type has_resolution_incident \n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "\n", - "[5 rows x 64 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_extraction.head()" - ] - }, - { - "cell_type": "markdown", - "id": "e9a9da26", - "metadata": {}, - "source": [ - "## Exploratory Data Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f4545e95", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset size: 21,307 rows and 63 columns\n" - ] - } - ], - "source": [ - "# Copy dataset to make changes and drop id_booking column\n", - "df = df_extraction.copy().drop(columns=['id_booking'])\n", - "\n", - "# Check size of the dataset\n", - "print(f\"Dataset size: {df.shape[0]:,} rows and {df.shape[1]:,} columns\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "de574969", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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days_from_booking_creation_to_check_innumber_of_nightshost_townhost_countryhost_postcodehost_agehost_months_with_truvihost_account_typehost_active_pms_listnumber_of_listings_of_hostnumber_of_previous_incidents_of_hostnumber_of_previous_payouts_of_hostguest_townguest_countryguest_postcodeguest_agenumber_of_previous_bookings_of_guestnumber_of_previous_incidents_of_guesthas_guest_previously_booked_same_listinglisting_addresslisting_townlisting_countrylisting_postcodelisting_number_of_bedroomslisting_number_of_bathroomslisting_descriptionprevious_bookings_in_listing_countnumber_of_previous_incidents_in_listingnumber_of_previous_payouts_in_listingdays_to_start_verificationdays_to_complete_verificationscreening_statusgovernment_id_statuscontract_statusselfie_confidence_score_statuspayment_validation_statusfirst_name_statusdate_of_birth_statuslast_name_statusautohost_partner_statuscriminal_record_statusguest_csat_scoreguest_csat_commentsguest_has_emailguest_has_phone_numberis_guest_from_listing_townis_guest_from_listing_countryis_guest_from_listing_postcodeis_host_from_listing_townis_host_from_listing_countryis_host_from_listing_postcodehas_completed_verificationnumber_of_applied_servicesnumber_of_applied_upgraded_servicesnumber_of_applied_billable_servicesbooking_days_to_check_inbooking_number_of_nightshas_verification_requesthas_billable_serviceshas_upgraded_screening_service_business_typehas_deposit_management_service_business_typehas_protection_service_business_typehas_resolution_incident
026.04.0Madison CTUnited States06443125.08.0HostHostaway200NaNNaNNaNNaN10320True389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States054642.02.0Mountain Life Retreat at Smuggler's Notch Resort3000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse322874FalseTrueFalseTrueTrueFalse
117.03.0Madison CTUnited States06443125.08.0HostHostaway200NaNNaNNaNNaN19000True389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States054642.02.0Mountain Life Retreat at Smuggler's Notch Resort5000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse3221093FalseTrueFalseTrueTrueFalse
220.07.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify46700NaNNaNNaNNaN6100TrueTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EE12.012.0Mansion by the Sea, 12BR/12BA, Perfect for Events5000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse211507FalseTrueFalseFalseTrueFalse
315.03.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify46700NaNNaNNaNNaN1360TrueTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EE12.012.0Mansion by the Sea, 12BR/12BA, Perfect for Events2000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse211153FalseTrueFalseFalseTrueFalse
48.05.0LondonUnited KingdomN16 6DD125.08.0PMC - Property Management CompanyHostify46700NaNNaNNaNNaN730FalseAird House, 15 Wellesley Ct, Rockingham StreetGreater LondonUnited KingdomSE1 6PD2.01.0Your London Home: 2BR Flat with Modern Amenities0000.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNFalseTrueFalseFalse21185FalseTrueFalseFalseTrueFalse
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" - ], - "text/plain": [ - " days_from_booking_creation_to_check_in number_of_nights host_town \\\n", - "0 26.0 4.0 Madison CT \n", - "1 17.0 3.0 Madison CT \n", - "2 20.0 7.0 London \n", - "3 15.0 3.0 London \n", - "4 8.0 5.0 London \n", - "\n", - " host_country host_postcode host_age host_months_with_truvi \\\n", - "0 United States 06443 125.0 8.0 \n", - "1 United States 06443 125.0 8.0 \n", - "2 United Kingdom N16 6DD 125.0 8.0 \n", - "3 United Kingdom N16 6DD 125.0 8.0 \n", - "4 United Kingdom N16 6DD 125.0 8.0 \n", - "\n", - " host_account_type host_active_pms_list \\\n", - "0 Host Hostaway \n", - "1 Host Hostaway \n", - "2 PMC - Property Management Company Hostify \n", - "3 PMC - Property Management Company Hostify \n", - "4 PMC - Property Management Company Hostify \n", - "\n", - " number_of_listings_of_host number_of_previous_incidents_of_host \\\n", - "0 2 0 \n", - "1 2 0 \n", - "2 467 0 \n", - "3 467 0 \n", - "4 467 0 \n", - "\n", - " number_of_previous_payouts_of_host guest_town guest_country guest_postcode \\\n", - "0 0 NaN NaN NaN \n", - "1 0 NaN NaN NaN \n", - "2 0 NaN NaN NaN \n", - "3 0 NaN NaN NaN \n", - "4 0 NaN NaN NaN \n", - "\n", - " guest_age number_of_previous_bookings_of_guest \\\n", - "0 NaN 1032 \n", - "1 NaN 1900 \n", - "2 NaN 610 \n", - "3 NaN 136 \n", - "4 NaN 73 \n", - "\n", - " number_of_previous_incidents_of_guest \\\n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "\n", - " has_guest_previously_booked_same_listing \\\n", - "0 True \n", - "1 True \n", - "2 True \n", - "3 True \n", - "4 False \n", - "\n", - " listing_address listing_town \\\n", - "0 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "1 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "2 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "3 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "4 Aird House, 15 Wellesley Ct, Rockingham Street Greater London \n", - "\n", - " listing_country listing_postcode listing_number_of_bedrooms \\\n", - "0 United States 05464 2.0 \n", - "1 United States 05464 2.0 \n", - "2 United Kingdom BH1 3EE 12.0 \n", - "3 United Kingdom BH1 3EE 12.0 \n", - "4 United Kingdom SE1 6PD 2.0 \n", - "\n", - " listing_number_of_bathrooms \\\n", - "0 2.0 \n", - "1 2.0 \n", - "2 12.0 \n", - "3 12.0 \n", - "4 1.0 \n", - "\n", - " listing_description \\\n", - "0 Mountain Life Retreat at Smuggler's Notch Resort \n", - "1 Mountain Life Retreat at Smuggler's Notch Resort \n", - "2 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "3 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "4 Your London Home: 2BR Flat with Modern Amenities \n", - "\n", - " previous_bookings_in_listing_count \\\n", - "0 3 \n", - "1 5 \n", - "2 5 \n", - "3 2 \n", - "4 0 \n", - "\n", - " number_of_previous_incidents_in_listing \\\n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "\n", - " number_of_previous_payouts_in_listing days_to_start_verification \\\n", - "0 0 0.0 \n", - "1 0 0.0 \n", - "2 0 0.0 \n", - "3 0 0.0 \n", - "4 0 0.0 \n", - "\n", - " days_to_complete_verification screening_status government_id_status \\\n", - "0 0.0 NaN NaN \n", - "1 0.0 NaN NaN \n", - "2 0.0 NaN NaN \n", - "3 0.0 NaN NaN \n", - "4 0.0 NaN NaN \n", - "\n", - " contract_status selfie_confidence_score_status payment_validation_status \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " first_name_status date_of_birth_status last_name_status \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " autohost_partner_status criminal_record_status guest_csat_score \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "\n", - " guest_csat_comments guest_has_email guest_has_phone_number \\\n", - "0 NaN False False \n", - "1 NaN False False \n", - "2 NaN False False \n", - "3 NaN False False \n", - "4 NaN False False \n", - "\n", - " is_guest_from_listing_town is_guest_from_listing_country \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " is_guest_from_listing_postcode is_host_from_listing_town \\\n", - "0 NaN False \n", - "1 NaN False \n", - "2 NaN False \n", - "3 NaN False \n", - "4 NaN False \n", - "\n", - " is_host_from_listing_country is_host_from_listing_postcode \\\n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False \n", - "\n", - " has_completed_verification number_of_applied_services \\\n", - "0 False 3 \n", - "1 False 3 \n", - "2 False 2 \n", - "3 False 2 \n", - "4 False 2 \n", - "\n", - " number_of_applied_upgraded_services number_of_applied_billable_services \\\n", - "0 2 2 \n", - "1 2 2 \n", - "2 1 1 \n", - "3 1 1 \n", - "4 1 1 \n", - "\n", - " booking_days_to_check_in booking_number_of_nights \\\n", - "0 87 4 \n", - "1 109 3 \n", - "2 50 7 \n", - "3 15 3 \n", - "4 8 5 \n", - "\n", - " has_verification_request has_billable_services \\\n", - "0 False True \n", - "1 False True \n", - "2 False True \n", - "3 False True \n", - "4 False True \n", - "\n", - " has_upgraded_screening_service_business_type \\\n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_deposit_management_service_business_type \\\n", - "0 True \n", - "1 True \n", - "2 False \n", - "3 False \n", - "4 False \n", - "\n", - " has_protection_service_business_type has_resolution_incident \n", - "0 True False \n", - "1 True False \n", - "2 True False \n", - "3 True False \n", - "4 True False " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Remove columns limit to display all columns and rows\n", - "pd.set_option('display.max_columns', None)\n", - "pd.set_option('display.max_rows', None)\n", - "\n", - "# Preview of the dataset\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "de4c6753", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 21307 entries, 0 to 21306\n", - "Data columns (total 63 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 days_from_booking_creation_to_check_in 21307 non-null float64\n", - " 1 number_of_nights 21307 non-null float64\n", - " 2 host_town 21281 non-null object \n", - " 3 host_country 21300 non-null object \n", - " 4 host_postcode 15800 non-null object \n", - " 5 host_age 21307 non-null float64\n", - " 6 host_months_with_truvi 21307 non-null float64\n", - " 7 host_account_type 17831 non-null object \n", - " 8 host_active_pms_list 20363 non-null object \n", - " 9 number_of_listings_of_host 21307 non-null int64 \n", - " 10 number_of_previous_incidents_of_host 21307 non-null int64 \n", - " 11 number_of_previous_payouts_of_host 21307 non-null int64 \n", - " 12 guest_town 11676 non-null object \n", - " 13 guest_country 11677 non-null object \n", - " 14 guest_postcode 11676 non-null object \n", - " 15 guest_age 11677 non-null float64\n", - " 16 number_of_previous_bookings_of_guest 21307 non-null int64 \n", - " 17 number_of_previous_incidents_of_guest 21307 non-null int64 \n", - " 18 has_guest_previously_booked_same_listing 21307 non-null bool \n", - " 19 listing_address 21307 non-null object \n", - " 20 listing_town 21307 non-null object \n", - " 21 listing_country 21307 non-null object \n", - " 22 listing_postcode 21307 non-null object \n", - " 23 listing_number_of_bedrooms 21185 non-null float64\n", - " 24 listing_number_of_bathrooms 21185 non-null float64\n", - " 25 listing_description 21294 non-null object \n", - " 26 previous_bookings_in_listing_count 21307 non-null int64 \n", - " 27 number_of_previous_incidents_in_listing 21307 non-null int64 \n", - " 28 number_of_previous_payouts_in_listing 21307 non-null int64 \n", - " 29 days_to_start_verification 20084 non-null float64\n", - " 30 days_to_complete_verification 18500 non-null float64\n", - " 31 screening_status 9332 non-null float64\n", - " 32 government_id_status 8082 non-null float64\n", - " 33 contract_status 5856 non-null float64\n", - " 34 selfie_confidence_score_status 6622 non-null float64\n", - " 35 payment_validation_status 8047 non-null float64\n", - " 36 first_name_status 4810 non-null float64\n", - " 37 date_of_birth_status 4810 non-null float64\n", - " 38 last_name_status 4810 non-null float64\n", - " 39 autohost_partner_status 0 non-null float64\n", - " 40 criminal_record_status 2075 non-null float64\n", - " 41 guest_csat_score 3221 non-null float64\n", - " 42 guest_csat_comments 454 non-null object \n", - " 43 guest_has_email 21307 non-null bool \n", - " 44 guest_has_phone_number 21307 non-null bool \n", - " 45 is_guest_from_listing_town 11677 non-null object \n", - " 46 is_guest_from_listing_country 11677 non-null object \n", - " 47 is_guest_from_listing_postcode 11677 non-null object \n", - " 48 is_host_from_listing_town 21307 non-null bool \n", - " 49 is_host_from_listing_country 21300 non-null object \n", - " 50 is_host_from_listing_postcode 18102 non-null object \n", - " 51 has_completed_verification 21307 non-null bool \n", - " 52 number_of_applied_services 21307 non-null int64 \n", - " 53 number_of_applied_upgraded_services 21307 non-null int64 \n", - " 54 number_of_applied_billable_services 21307 non-null int64 \n", - " 55 booking_days_to_check_in 21307 non-null int64 \n", - " 56 booking_number_of_nights 21307 non-null int64 \n", - " 57 has_verification_request 21307 non-null bool \n", - " 58 has_billable_services 21307 non-null bool \n", - " 59 has_upgraded_screening_service_business_type 21307 non-null bool \n", - " 60 has_deposit_management_service_business_type 21307 non-null bool \n", - " 61 has_protection_service_business_type 21307 non-null bool \n", - " 62 has_resolution_incident 21307 non-null bool \n", - "dtypes: bool(11), float64(20), int64(13), object(19)\n", - "memory usage: 8.7+ MB\n" - ] - } - ], - "source": [ - "# View summary of dataset\n", - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9c79c06a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing Values (%):\n", - "autohost_partner_status 100.000000\n", - "guest_csat_comments 97.869245\n", - "criminal_record_status 90.261416\n", - "guest_csat_score 84.882902\n", - "date_of_birth_status 77.425259\n", - "last_name_status 77.425259\n", - "first_name_status 77.425259\n", - "contract_status 72.516075\n", - "selfie_confidence_score_status 68.921012\n", - "payment_validation_status 62.233069\n", - "government_id_status 62.068804\n", - "screening_status 56.202187\n", - "guest_postcode 45.201108\n", - "guest_town 45.201108\n", - "guest_country 45.196414\n", - "is_guest_from_listing_country 45.196414\n", - "is_guest_from_listing_postcode 45.196414\n", - "guest_age 45.196414\n", - "is_guest_from_listing_town 45.196414\n", - "host_postcode 25.845966\n", - "host_account_type 16.313887\n", - "is_host_from_listing_postcode 15.042005\n", - "days_to_complete_verification 13.174074\n", - "days_to_start_verification 5.739898\n", - "host_active_pms_list 4.430469\n", - "listing_number_of_bedrooms 0.572582\n", - "listing_number_of_bathrooms 0.572582\n", - "host_town 0.122026\n", - "listing_description 0.061013\n", - "host_country 0.032853\n", - "is_host_from_listing_country 0.032853\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "# View percentage of missing values\n", - "missing_values = df.isnull().mean() * 100\n", - "missing_values = missing_values[missing_values > 0].sort_values(ascending=False)\n", - "print(\"Missing Values (%):\")\n", - "print(missing_values)" - ] - }, - { - "cell_type": "markdown", - "id": "1837c541", - "metadata": {}, - "source": [ - "Despite the small amount of data with on CSAT, I want to check if there might be any interesting correlation with the incidents." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6e89712c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "guest_csat_score\n", - "1.0 0.010695\n", - "2.0 0.013761\n", - "3.0 0.018293\n", - "4.0 0.013105\n", - "5.0 0.022619\n", - "Name: has_resolution_incident, dtype: float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.groupby('guest_csat_score')['has_resolution_incident'].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ce9ed8a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlation: 0.02\n" - ] - } - ], - "source": [ - "correlation = df['guest_csat_score'].corr(df['has_resolution_incident'])\n", - "print(f\"Correlation: {correlation:.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8ac447bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dropping columns with more than 50% missing values: ['autohost_partner_status', 'guest_csat_comments', 'criminal_record_status', 'guest_csat_score', 'date_of_birth_status', 'last_name_status', 'first_name_status', 'contract_status', 'selfie_confidence_score_status', 'payment_validation_status', 'government_id_status', 'screening_status']\n" - ] - } - ], - "source": [ - "# Remove columns with more than 50% missing values\n", - "threshold = 50\n", - "columns_to_drop = missing_values[missing_values > threshold].index\n", - "print(f\"Dropping columns with more than {threshold}% missing values: {columns_to_drop.tolist()}\")\n", - "df.drop(columns=columns_to_drop, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "20bd5c86", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 18 categorical variables\n", - "\n", - "The categorical variables are: ['host_town', 'host_country', 'host_postcode', 'host_account_type', 'host_active_pms_list', 'guest_town', 'guest_country', 'guest_postcode', 'listing_address', 'listing_town', 'listing_country', 'listing_postcode', 'listing_description', 'is_guest_from_listing_town', 'is_guest_from_listing_country', 'is_guest_from_listing_postcode', 'is_host_from_listing_country', 'is_host_from_listing_postcode']\n" - ] - } - ], - "source": [ - "# Find categorical variables\n", - "categorical = df.select_dtypes(include=['object']).columns.tolist()\n", - "print(f'There are {len(categorical)} categorical variables\\n')\n", - "print('The categorical variables are:', categorical)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "67ddd437", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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host_townhost_countryhost_postcodehost_account_typehost_active_pms_listguest_townguest_countryguest_postcodelisting_addresslisting_townlisting_countrylisting_postcodelisting_descriptionis_guest_from_listing_townis_guest_from_listing_countryis_guest_from_listing_postcodeis_host_from_listing_countryis_host_from_listing_postcode
0Madison CTUnited States06443HostHostawayNaNNaNNaN389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States05464Mountain Life Retreat at Smuggler's Notch ResortNaNNaNNaNTrueFalse
1Madison CTUnited States06443HostHostawayNaNNaNNaN389 Mountain View Dr, Jeffersonville, VT 05464...CambridgeUnited States05464Mountain Life Retreat at Smuggler's Notch ResortNaNNaNNaNTrueFalse
2LondonUnited KingdomN16 6DDPMC - Property Management CompanyHostifyNaNNaNNaNTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EEMansion by the Sea, 12BR/12BA, Perfect for EventsNaNNaNNaNTrueFalse
3LondonUnited KingdomN16 6DDPMC - Property Management CompanyHostifyNaNNaNNaNTudor Grange Hotel, 31 Gervis RoadDorsetUnited KingdomBH1 3EEMansion by the Sea, 12BR/12BA, Perfect for EventsNaNNaNNaNTrueFalse
4LondonUnited KingdomN16 6DDPMC - Property Management CompanyHostifyNaNNaNNaNAird House, 15 Wellesley Ct, Rockingham StreetGreater LondonUnited KingdomSE1 6PDYour London Home: 2BR Flat with Modern AmenitiesNaNNaNNaNTrueFalse
\n", - "
" - ], - "text/plain": [ - " host_town host_country host_postcode \\\n", - "0 Madison CT United States 06443 \n", - "1 Madison CT United States 06443 \n", - "2 London United Kingdom N16 6DD \n", - "3 London United Kingdom N16 6DD \n", - "4 London United Kingdom N16 6DD \n", - "\n", - " host_account_type host_active_pms_list guest_town \\\n", - "0 Host Hostaway NaN \n", - "1 Host Hostaway NaN \n", - "2 PMC - Property Management Company Hostify NaN \n", - "3 PMC - Property Management Company Hostify NaN \n", - "4 PMC - Property Management Company Hostify NaN \n", - "\n", - " guest_country guest_postcode \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " listing_address listing_town \\\n", - "0 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "1 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n", - "2 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "3 Tudor Grange Hotel, 31 Gervis Road Dorset \n", - "4 Aird House, 15 Wellesley Ct, Rockingham Street Greater London \n", - "\n", - " listing_country listing_postcode \\\n", - "0 United States 05464 \n", - "1 United States 05464 \n", - "2 United Kingdom BH1 3EE \n", - "3 United Kingdom BH1 3EE \n", - "4 United Kingdom SE1 6PD \n", - "\n", - " listing_description \\\n", - "0 Mountain Life Retreat at Smuggler's Notch Resort \n", - "1 Mountain Life Retreat at Smuggler's Notch Resort \n", - "2 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "3 Mansion by the Sea, 12BR/12BA, Perfect for Events \n", - "4 Your London Home: 2BR Flat with Modern Amenities \n", - "\n", - " is_guest_from_listing_town is_guest_from_listing_country \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " is_guest_from_listing_postcode is_host_from_listing_country \\\n", - "0 NaN True \n", - "1 NaN True \n", - "2 NaN True \n", - "3 NaN True \n", - "4 NaN True \n", - "\n", - " is_host_from_listing_postcode \n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# view the categorical variables\n", - "df[categorical].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "841347ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "host_town 26\n", - "host_country 7\n", - "host_postcode 5507\n", - "host_account_type 3476\n", - "host_active_pms_list 944\n", - "guest_town 9631\n", - "guest_country 9630\n", - "guest_postcode 9631\n", - "listing_address 0\n", - "listing_town 0\n", - "listing_country 0\n", - "listing_postcode 0\n", - "listing_description 13\n", - "is_guest_from_listing_town 9630\n", - "is_guest_from_listing_country 9630\n", - "is_guest_from_listing_postcode 9630\n", - "is_host_from_listing_country 7\n", - "is_host_from_listing_postcode 3205\n", - "dtype: int64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check missing values in categorical variables\n", - "df[categorical].isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a58cd17e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_48568/2855830200.py:2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_guest_from_listing_town'] = df['is_guest_from_listing_town'].fillna(False)\n", - "/tmp/ipykernel_48568/2855830200.py:3: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_guest_from_listing_country'] = df['is_guest_from_listing_country'].fillna(False)\n", - "/tmp/ipykernel_48568/2855830200.py:4: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_guest_from_listing_postcode'] = df['is_guest_from_listing_postcode'].fillna(False)\n", - "/tmp/ipykernel_48568/2855830200.py:6: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_host_from_listing_country'] = df['is_host_from_listing_country'].fillna(False)\n", - "/tmp/ipykernel_48568/2855830200.py:7: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df['is_host_from_listing_postcode'] = df['is_host_from_listing_postcode'].fillna(False)\n" - ] - } - ], - "source": [ - "# For all missing values in listing location with both host and guest, we will fill with False\n", - "df['is_guest_from_listing_town'] = df['is_guest_from_listing_town'].fillna(False)\n", - "df['is_guest_from_listing_country'] = df['is_guest_from_listing_country'].fillna(False)\n", - "df['is_guest_from_listing_postcode'] = df['is_guest_from_listing_postcode'].fillna(False)\n", - "df['is_host_from_listing_town'] = df['is_host_from_listing_town'].fillna(False)\n", - "df['is_host_from_listing_country'] = df['is_host_from_listing_country'].fillna(False)\n", - "df['is_host_from_listing_postcode'] = df['is_host_from_listing_postcode'].fillna(False)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e5aefb50", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "host_town 26\n", - "host_country 7\n", - "host_postcode 5507\n", - "host_account_type 3476\n", - "host_active_pms_list 944\n", - "guest_town 9631\n", - "guest_country 9630\n", - "guest_postcode 9631\n", - "listing_address 0\n", - "listing_town 0\n", - "listing_country 0\n", - "listing_postcode 0\n", - "listing_description 13\n", - "is_guest_from_listing_town 0\n", - "is_guest_from_listing_country 0\n", - "is_guest_from_listing_postcode 0\n", - "is_host_from_listing_country 0\n", - "is_host_from_listing_postcode 0\n", - "dtype: int64" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Checking again missing values in categorical variables\n", - "df[categorical].isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "292eaad2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique values in 'host_account_type':\n", - "host_account_type\n", - "PMC - Property Management Company 12719\n", - "Host 5112\n", - "Name: count, dtype: int64 \n", - "\n", - "Unique values in 'host_active_pms_list':\n", - "host_active_pms_list\n", - "Hostify 6468\n", - "Hostaway 3675\n", - "Guesty 3108\n", - "Hospitable 2739\n", - "Hostfully 1905\n", - "Lodgify 1341\n", - "OwnerRez 649\n", - "Avantio 248\n", - "TrackHs 142\n", - "Uplisting 61\n", - "Hospitable Connect 15\n", - "Smoobu 12\n", - "Name: count, dtype: int64 \n", - "\n", - "Unique values in 'host_country':\n", - "host_country\n", - "United States 10962\n", - "United Kingdom 6707\n", - "Canada 2007\n", - "Australia 305\n", - "Mexico 273\n", - "New Zealand 154\n", - "Sweden 122\n", - "Norway 117\n", - "Bulgaria 117\n", - "Portugal 87\n", - "South Africa 78\n", - "Costa Rica 75\n", - "Puerto Rico 50\n", - "Belgium 50\n", - "Italy 35\n", - "Barbados 34\n", - "Spain 31\n", - "France 26\n", - "Jamaica 20\n", - "Egypt 19\n", - "Switzerland 10\n", - "Isle of Man 8\n", - "Bahamas 3\n", - "Guernsey 3\n", - "United Arab Emirates 2\n", - "Colombia 2\n", - "Germany 1\n", - "Greece 1\n", - "Hungary 1\n", - "Name: count, dtype: int64 \n", - "\n", - "Unique values in 'guest_country':\n", - "guest_country\n", - "United States 7409\n", - "Canada 1458\n", - "United Kingdom 1175\n", - "Australia 287\n", - "Colombia 151\n", - "Mexico 134\n", - "Germany 100\n", - "Ireland 77\n", - "New Zealand 70\n", - "France 56\n", - "Spain 53\n", - "Costa Rica 43\n", - "Netherlands 37\n", - "Brazil 36\n", - "Switzerland 34\n", - "Puerto Rico 31\n", - "Italy 29\n", - "Argentina 23\n", - "Singapore 23\n", - "China 21\n", - "Belgium 20\n", - "Ecuador 20\n", - "India 20\n", - "United Arab Emirates 20\n", - "Panama 19\n", - "Poland 17\n", - "Dominican Republic 15\n", - "Israel 14\n", - "Saudi Arabia 13\n", - "South Africa 12\n", - "Romania 11\n", - "Malaysia 11\n", - "El Salvador 10\n", - "Chile 9\n", - "Norway 9\n", - "Japan 9\n", - "Portugal 9\n", - "Sweden 8\n", - "Hong Kong 8\n", - "Austria 8\n", - "South Korea 8\n", - "United States Minor Outlying Islands 8\n", - "Finland 8\n", - "Philippines 7\n", - "Czech Republic 7\n", - "Guatemala 7\n", - "Hungary 6\n", - "Venezuela 6\n", - "Denmark 6\n", - "Honduras 6\n", - "Jamaica 5\n", - "Thailand 5\n", - "Peru 5\n", - "Taiwan 5\n", - "Russian Federation 5\n", - "French Polynesia 4\n", - "Turkey 4\n", - "Kazakhstan 4\n", - "Curacao 4\n", - "Martinique 3\n", - "Cayman Islands 3\n", - "Saint Pierre and Miquelon 3\n", - "Slovenia 3\n", - "Estonia 3\n", - "Iceland 3\n", - "Georgia 3\n", - "Indonesia 2\n", - "Qatar 2\n", - "Greece 2\n", - "Egypt 2\n", - "Latvia 2\n", - "Pakistan 2\n", - "Barbados 2\n", - "Bolivia 2\n", - "Aruba 2\n", - "Malta 2\n", - "Suriname 1\n", - "Lebanon 1\n", - "Nauru 1\n", - "Fiji 1\n", - "Cook Islands 1\n", - "Bahamas 1\n", - "Albania 1\n", - "Uruguay 1\n", - "Jersey 1\n", - "Croatia 1\n", - "Bulgaria 1\n", - "Belize 1\n", - "Nicaragua 1\n", - "DR Congo 1\n", - "Kuwait 1\n", - "Niger 1\n", - "Cyprus 1\n", - "Name: count, dtype: int64 \n", - "\n", - "Unique values in 'listing_country':\n", - "listing_country\n", - "United States 10067\n", - "United Kingdom 6574\n", - "Canada 1870\n", - "Colombia 599\n", - "Australia 305\n", - "Mexico 303\n", - "Ireland 168\n", - "New Zealand 153\n", - "Virgin Islands, U.s. 130\n", - "Bahamas 130\n", - "Norway 125\n", - "Sweden 122\n", - "Bulgaria 117\n", - "Costa Rica 108\n", - "Portugal 87\n", - "South Africa 83\n", - "Puerto Rico 50\n", - "Belgium 48\n", - "France 46\n", - "Italy 44\n", - "Spain 36\n", - "Barbados 34\n", - "Morocco 25\n", - "Jamaica 20\n", - "Egypt 19\n", - "Saint Lucia 10\n", - "Germany 10\n", - "Sint Maarten 9\n", - "Isle of Man 8\n", - "United Arab Emirates 2\n", - "Lithuania 2\n", - "Antigua and Barbuda 1\n", - "Greece 1\n", - "Hungary 1\n", - "Name: count, dtype: int64 \n", - "\n" - ] - } - ], - "source": [ - "# Check unique values in host_account_type, host_active_pms_list, host_country and guest_country with their counts\n", - "print(\"Unique values in 'host_account_type':\")\n", - "print(df['host_account_type'].value_counts(), \"\\n\")\n", - "print(\"Unique values in 'host_active_pms_list':\")\n", - "print(df['host_active_pms_list'].value_counts(), \"\\n\")\n", - "print(\"Unique values in 'host_country':\")\n", - "print(df['host_country'].value_counts(), \"\\n\")\n", - "print(\"Unique values in 'guest_country':\")\n", - "print(df['guest_country'].value_counts(), \"\\n\")\n", - "print(\"Unique values in 'listing_country':\")\n", - "print(df['listing_country'].value_counts(), \"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7289f9fd", - "metadata": {}, - "outputs": [], - "source": [ - "# Due to the many unique values in host_country, guest_country and listing_country, we will only keep the top 10 most frequent values and set the rest to 'Other'\n", - "top_host_countries = df['host_country'].value_counts().nlargest(10).index\n", - "top_guest_countries = df['guest_country'].value_counts().nlargest(10).index\n", - "top_listing_countries = df['listing_country'].value_counts().nlargest(10).index\n", - "\n", - "df['host_country'] = df['host_country'].where(df['host_country'].isin(top_host_countries), 'Other')\n", - "df['guest_country'] = df['guest_country'].where(df['guest_country'].isin(top_guest_countries), 'Other')\n", - "df['listing_country'] = df['listing_country'].where(df['listing_country'].isin(top_listing_countries), 'Other')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7348866c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New columns created from one-hot encoding: ['host_account_type_Host', 'host_account_type_PMC - Property Management Company', 'host_active_pms_list_Avantio', 'host_active_pms_list_Guesty', 'host_active_pms_list_Hospitable', 'host_active_pms_list_Hospitable Connect', 'host_active_pms_list_Hostaway', 'host_active_pms_list_Hostfully', 'host_active_pms_list_Hostify', 'host_active_pms_list_Lodgify', 'host_active_pms_list_OwnerRez', 'host_active_pms_list_Smoobu', 'host_active_pms_list_TrackHs', 'host_active_pms_list_Uplisting', 'host_country_Australia', 'host_country_Bulgaria', 'host_country_Canada', 'host_country_Mexico', 'host_country_New Zealand', 'host_country_Norway', 'host_country_Other', 'host_country_Portugal', 'host_country_Sweden', 'host_country_United Kingdom', 'host_country_United States', 'guest_country_Australia', 'guest_country_Canada', 'guest_country_Colombia', 'guest_country_France', 'guest_country_Germany', 'guest_country_Ireland', 'guest_country_Mexico', 'guest_country_New Zealand', 'guest_country_Other', 'guest_country_United Kingdom', 'guest_country_United States', 'listing_country_Australia', 'listing_country_Bahamas', 'listing_country_Canada', 'listing_country_Colombia', 'listing_country_Ireland', 'listing_country_Mexico', 'listing_country_New Zealand', 'listing_country_Other', 'listing_country_United Kingdom', 'listing_country_United States', 'listing_country_Virgin Islands, U.s.']\n" - ] - } - ], - "source": [ - "# Lets one hot encode host_account_type, host_active_pms_list, host_country, guest_country and listing_country\n", - "df = pd.get_dummies(df, columns=['host_account_type', 'host_active_pms_list', 'host_country', 'guest_country', 'listing_country'], drop_first=False)\n", - "# Check the new columns created\n", - "new_columns = df.columns[df.columns.str.startswith(('host_account_type_', 'host_active_pms_list_', 'host_country', 'guest_country', 'listing_country'))]\n", - "print(f\"New columns created from one-hot encoding: {new_columns.tolist()}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "b443ccf4", - "metadata": {}, - "outputs": [], - "source": [ - "# Drop the original categorical columns and the ones we are not going to use like postcodes and towns\n", - "df.drop(columns=['host_postcode', 'guest_postcode', 'listing_postcode', 'listing_town', 'host_town', 'guest_town', 'listing_description', 'listing_address'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a31ae1fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 22 numerical variables\n", - "\n", - "The numerical variables are : ['days_from_booking_creation_to_check_in', 'number_of_nights', 'host_age', 'host_months_with_truvi', 'number_of_listings_of_host', 'number_of_previous_incidents_of_host', 'number_of_previous_payouts_of_host', 'guest_age', 'number_of_previous_bookings_of_guest', 'number_of_previous_incidents_of_guest', 'listing_number_of_bedrooms', 'listing_number_of_bathrooms', 'previous_bookings_in_listing_count', 'number_of_previous_incidents_in_listing', 'number_of_previous_payouts_in_listing', 'days_to_start_verification', 'days_to_complete_verification', 'number_of_applied_services', 'number_of_applied_upgraded_services', 'number_of_applied_billable_services', 'booking_days_to_check_in', 'booking_number_of_nights']\n" - ] - } - ], - "source": [ - "# Find numerical variables\n", - "numerical = df.select_dtypes(include=[np.number]).columns.tolist()\n", - "print('There are {} numerical variables\\n'.format(len(numerical)))\n", - "print('The numerical variables are :', numerical)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "cf795d45", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Summary statistics of numerical variables:\n" - ] - }, - { - "data": { - "text/html": [ - "
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days_from_booking_creation_to_check_innumber_of_nightshost_agehost_months_with_truvinumber_of_listings_of_hostnumber_of_previous_incidents_of_hostnumber_of_previous_payouts_of_hostguest_agenumber_of_previous_bookings_of_guestnumber_of_previous_incidents_of_guestlisting_number_of_bedroomslisting_number_of_bathroomsprevious_bookings_in_listing_countnumber_of_previous_incidents_in_listingnumber_of_previous_payouts_in_listingdays_to_start_verificationdays_to_complete_verificationnumber_of_applied_servicesnumber_of_applied_upgraded_servicesnumber_of_applied_billable_servicesbooking_days_to_check_inbooking_number_of_nights
count21307.00000021307.00000021307.00000021307.00000021307.00000021307.00000021307.00000011677.00000021307.00000021307.021185.00000021185.00000021307.00000021307.00000021307.00000020084.00000018500.00000021307.00000021307.00000021307.00000021307.00000021307.000000
mean8.7400383.87680196.5330175.482142152.8758152.7184960.75130242.3178902175.9998120.02.0529621.6018416.2150940.1233870.0435070.9967640.7131353.7215942.7216881.86520917.5922474.144507
std8.3892423.33561543.6163412.714314179.0288295.5828572.95705313.2125093038.8374960.01.7452811.2977396.7278960.5374640.2709943.4233032.7684741.5536121.5536290.94985723.5729014.799364
min-20.0000000.00000019.0000000.0000000.0000000.0000000.00000018.0000000.0000000.00.0000000.0000000.0000000.0000000.0000000.0000000.0000002.0000001.0000000.000000-48.0000000.000000
25%1.0000002.00000039.0000004.0000009.0000000.0000000.00000032.0000000.0000000.01.0000001.0000001.0000000.0000000.0000000.0000000.0000002.0000001.0000001.0000002.0000002.000000
50%6.0000003.000000125.0000005.00000072.0000001.0000000.00000041.0000000.0000000.02.0000001.0000004.0000000.0000000.0000000.0000000.0000004.0000003.0000002.0000008.0000003.000000
75%15.0000004.000000125.0000008.000000247.0000003.0000001.00000051.0000004302.5000000.03.0000002.0000009.0000000.0000000.0000000.0000000.0000005.0000004.0000003.00000024.0000005.000000
max30.00000030.000000125.00000011.000000467.00000085.00000062.00000089.0000009629.0000000.015.00000017.00000041.0000009.0000006.00000030.00000030.0000008.0000007.0000005.000000218.000000116.000000
\n", - "
" - ], - "text/plain": [ - " days_from_booking_creation_to_check_in number_of_nights host_age \\\n", - "count 21307.000000 21307.000000 21307.000000 \n", - "mean 8.740038 3.876801 96.533017 \n", - "std 8.389242 3.335615 43.616341 \n", - "min -20.000000 0.000000 19.000000 \n", - "25% 1.000000 2.000000 39.000000 \n", - "50% 6.000000 3.000000 125.000000 \n", - "75% 15.000000 4.000000 125.000000 \n", - "max 30.000000 30.000000 125.000000 \n", - "\n", - " host_months_with_truvi number_of_listings_of_host \\\n", - "count 21307.000000 21307.000000 \n", - "mean 5.482142 152.875815 \n", - "std 2.714314 179.028829 \n", - "min 0.000000 0.000000 \n", - "25% 4.000000 9.000000 \n", - "50% 5.000000 72.000000 \n", - "75% 8.000000 247.000000 \n", - "max 11.000000 467.000000 \n", - "\n", - " number_of_previous_incidents_of_host \\\n", - "count 21307.000000 \n", - "mean 2.718496 \n", - "std 5.582857 \n", - "min 0.000000 \n", - "25% 0.000000 \n", - "50% 1.000000 \n", - "75% 3.000000 \n", - "max 85.000000 \n", - "\n", - " number_of_previous_payouts_of_host guest_age \\\n", - "count 21307.000000 11677.000000 \n", - "mean 0.751302 42.317890 \n", - "std 2.957053 13.212509 \n", - "min 0.000000 18.000000 \n", - "25% 0.000000 32.000000 \n", - "50% 0.000000 41.000000 \n", - "75% 1.000000 51.000000 \n", - "max 62.000000 89.000000 \n", - "\n", - " number_of_previous_bookings_of_guest \\\n", - "count 21307.000000 \n", - "mean 2175.999812 \n", - "std 3038.837496 \n", - "min 0.000000 \n", - "25% 0.000000 \n", - "50% 0.000000 \n", - "75% 4302.500000 \n", - "max 9629.000000 \n", - "\n", - " number_of_previous_incidents_of_guest listing_number_of_bedrooms \\\n", - "count 21307.0 21185.000000 \n", - "mean 0.0 2.052962 \n", - "std 0.0 1.745281 \n", - "min 0.0 0.000000 \n", - "25% 0.0 1.000000 \n", - "50% 0.0 2.000000 \n", - "75% 0.0 3.000000 \n", - "max 0.0 15.000000 \n", - "\n", - " listing_number_of_bathrooms previous_bookings_in_listing_count \\\n", - "count 21185.000000 21307.000000 \n", - "mean 1.601841 6.215094 \n", - "std 1.297739 6.727896 \n", - "min 0.000000 0.000000 \n", - "25% 1.000000 1.000000 \n", - "50% 1.000000 4.000000 \n", - "75% 2.000000 9.000000 \n", - "max 17.000000 41.000000 \n", - "\n", - " number_of_previous_incidents_in_listing \\\n", - "count 21307.000000 \n", - "mean 0.123387 \n", - "std 0.537464 \n", - "min 0.000000 \n", - "25% 0.000000 \n", - "50% 0.000000 \n", - "75% 0.000000 \n", - "max 9.000000 \n", - "\n", - " number_of_previous_payouts_in_listing days_to_start_verification \\\n", - "count 21307.000000 20084.000000 \n", - "mean 0.043507 0.996764 \n", - "std 0.270994 3.423303 \n", - "min 0.000000 0.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 0.000000 0.000000 \n", - "75% 0.000000 0.000000 \n", - "max 6.000000 30.000000 \n", - "\n", - " days_to_complete_verification number_of_applied_services \\\n", - "count 18500.000000 21307.000000 \n", - "mean 0.713135 3.721594 \n", - "std 2.768474 1.553612 \n", - "min 0.000000 2.000000 \n", - "25% 0.000000 2.000000 \n", - "50% 0.000000 4.000000 \n", - "75% 0.000000 5.000000 \n", - "max 30.000000 8.000000 \n", - "\n", - " number_of_applied_upgraded_services \\\n", - "count 21307.000000 \n", - "mean 2.721688 \n", - "std 1.553629 \n", - "min 1.000000 \n", - "25% 1.000000 \n", - "50% 3.000000 \n", - "75% 4.000000 \n", - "max 7.000000 \n", - "\n", - " number_of_applied_billable_services booking_days_to_check_in \\\n", - "count 21307.000000 21307.000000 \n", - "mean 1.865209 17.592247 \n", - "std 0.949857 23.572901 \n", - "min 0.000000 -48.000000 \n", - "25% 1.000000 2.000000 \n", - "50% 2.000000 8.000000 \n", - "75% 3.000000 24.000000 \n", - "max 5.000000 218.000000 \n", - "\n", - " booking_number_of_nights \n", - "count 21307.000000 \n", - "mean 4.144507 \n", - "std 4.799364 \n", - "min 0.000000 \n", - "25% 2.000000 \n", - "50% 3.000000 \n", - "75% 5.000000 \n", - "max 116.000000 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# View summary statistics of numerical variables\n", - "print(\"\\nSummary statistics of numerical variables:\")\n", - "df[numerical].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "2cf714c9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Select numeric columns\n", - "numerical = df.select_dtypes(include='number').columns\n", - "n_cols = 3\n", - "n_rows = math.ceil(len(numerical) / n_cols)\n", - "\n", - "# Create subplots\n", - "fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, 4 * n_rows))\n", - "axes = axes.flatten()\n", - "\n", - "# Plot each numeric column\n", - "for i, col in enumerate(numerical):\n", - " axes[i].hist(df[col].dropna(), bins=30, edgecolor='black')\n", - " axes[i].set_title(f'Distribution of {col}')\n", - " axes[i].set_xlabel(col)\n", - " axes[i].set_ylabel('Frequency')\n", - "\n", - "# Hide any unused subplots\n", - "for j in range(i + 1, len(axes)):\n", - " fig.delaxes(axes[j])\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "311da64d", - "metadata": {}, - "outputs": [], - "source": [ - "# We see that there are some outliers in host_age with ages above 100, we will remove those\n", - "df['host_age'] = df['host_age'].where(df['host_age'] <= 100, np.nan)\n", - "\n", - "# We drop number_of_previous_incidents_of_guest as it has only 0 values\n", - "df.drop(columns=['number_of_previous_incidents_of_guest'], inplace=True)\n", - "numerical = df.select_dtypes(include='number').columns" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "692854bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing Values (%):\n", - "host_age 69.826817\n", - "guest_age 45.196414\n", - "days_to_complete_verification 13.174074\n", - "days_to_start_verification 5.739898\n", - "listing_number_of_bathrooms 0.572582\n", - "listing_number_of_bedrooms 0.572582\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "# Check missing values for the remaining columns\n", - "missing_values = df.isnull().mean() * 100\n", - "missing_values = missing_values[missing_values > 0].sort_values(ascending=False)\n", - "print(\"Missing Values (%):\")\n", - "print(missing_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "9f333fd5", - "metadata": {}, - "outputs": [], - "source": [ - "# We will fill the remaining missing values with the median for numerical columns\n", - "for col in numerical:\n", - " df[col] = df[col].fillna(df[col].median())" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "ccd46ddc", - "metadata": {}, - "outputs": [], - "source": [ - "# Convert all boolean columns to int\n", - "bool_columns = df.select_dtypes(include='bool').columns\n", - "for col in bool_columns:\n", - " df[col] = df[col].astype(int)" - ] - }, - { - "cell_type": "markdown", - "id": "2c84ebe5", - "metadata": {}, - "source": [ - "### Feature Relevance Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "74a582c8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Check correlation matrix\n", - "import seaborn as sns\n", - "\n", - "# 1. Move 'has_resolution_incident' to the end\n", - "target_col = 'has_resolution_incident'\n", - "if target_col in df.columns:\n", - " columns = [col for col in df.columns if col != target_col] + [target_col]\n", - " df = df[columns]\n", - "\n", - "# 2. Create short column names (truncate to, say, 15 chars)\n", - "short_columns = [col[:15] for col in df.columns]\n", - "\n", - "# 3. Compute correlation matrix\n", - "correlation_matrix = df.corr()\n", - "\n", - "# 4. Plot with Seaborn\n", - "plt.figure(figsize=(14, 12))\n", - "sns.heatmap(\n", - " correlation_matrix,\n", - " xticklabels=short_columns,\n", - " yticklabels=short_columns,\n", - " cmap='coolwarm',\n", - " annot=False,\n", - " fmt=\".2f\",\n", - " square=True,\n", - " cbar_kws={'shrink': 0.6}\n", - ")\n", - "plt.title('Correlation Matrix (Truncated Labels)', fontsize=16)\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "a6f7988d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "number_of_previous_incidents_in_listing 0.101702\n", - "number_of_previous_payouts_in_listing 0.096180\n", - "host_account_type_Host 0.073745\n", - "number_of_listings_of_host 0.070200\n", - "listing_number_of_bedrooms 0.065542\n", - "listing_country_United States 0.062555\n", - "host_active_pms_list_Hostify 0.060898\n", - "host_country_United States 0.055897\n", - "has_deposit_management_service_business_type 0.055543\n", - "host_country_United Kingdom 0.049846\n", - "listing_country_United Kingdom 0.048641\n", - "guest_country_United States 0.047742\n", - "number_of_applied_billable_services 0.045234\n", - "host_account_type_PMC - Property Management Company 0.044632\n", - "has_completed_verification 0.040583\n", - "guest_age 0.039814\n", - "is_guest_from_listing_country 0.038440\n", - "listing_number_of_bathrooms 0.038292\n", - "listing_country_New Zealand 0.036971\n", - "is_guest_from_listing_town 0.036880\n", - "host_country_New Zealand 0.036791\n", - "previous_bookings_in_listing_count 0.035117\n", - "guest_has_email 0.033928\n", - "number_of_applied_services 0.032459\n", - "number_of_applied_upgraded_services 0.032452\n", - "guest_country_New Zealand 0.031652\n", - "guest_country_Other 0.031166\n", - "guest_has_phone_number 0.030379\n", - "booking_number_of_nights 0.026738\n", - "has_guest_previously_booked_same_listing 0.026621\n", - "host_active_pms_list_Hospitable 0.025430\n", - "host_active_pms_list_Hostfully 0.025058\n", - "number_of_previous_bookings_of_guest 0.024027\n", - "number_of_nights 0.023304\n", - "guest_country_Canada 0.022773\n", - "host_active_pms_list_Hostaway 0.021299\n", - "booking_days_to_check_in 0.020963\n", - "host_country_Canada 0.020417\n", - "has_upgraded_screening_service_business_type 0.020254\n", - "has_verification_request 0.019356\n", - "listing_country_Colombia 0.018607\n", - "listing_country_Canada 0.018591\n", - "is_host_from_listing_country 0.018029\n", - "number_of_previous_incidents_of_host 0.017803\n", - "number_of_previous_payouts_of_host 0.017717\n", - "days_from_booking_creation_to_check_in 0.016637\n", - "host_active_pms_list_OwnerRez 0.015977\n", - "is_host_from_listing_town 0.015359\n", - "is_host_from_listing_postcode 0.014238\n", - "host_active_pms_list_Avantio 0.011872\n", - "host_active_pms_list_Lodgify 0.010976\n", - "guest_country_Australia 0.009813\n", - "listing_country_Ireland 0.009753\n", - "listing_country_Mexico 0.009473\n", - "guest_country_Colombia 0.009243\n", - "is_guest_from_listing_postcode 0.009204\n", - "host_active_pms_list_TrackHs 0.008961\n", - "has_protection_service_business_type 0.008933\n", - "guest_country_Mexico 0.008703\n", - "host_country_Mexico 0.008603\n", - "listing_country_Bahamas 0.008572\n", - "host_country_Sweden 0.008302\n", - "host_country_Bulgaria 0.008129\n", - "guest_country_Germany 0.007512\n", - "guest_country_United Kingdom 0.007411\n", - "host_months_with_truvi 0.007277\n", - "host_active_pms_list_Guesty 0.007083\n", - "host_country_Portugal 0.007005\n", - "guest_country_Ireland 0.006589\n", - "host_active_pms_list_Uplisting 0.005862\n", - "guest_country_France 0.005616\n", - "host_country_Other 0.004820\n", - "has_billable_services 0.004251\n", - "listing_country_Other 0.003930\n", - "days_to_start_verification 0.003879\n", - "listing_country_Virgin Islands, U.s. 0.002997\n", - "host_age 0.002981\n", - "host_active_pms_list_Hospitable Connect 0.002904\n", - "host_active_pms_list_Smoobu 0.002597\n", - "host_country_Norway 0.002255\n", - "host_country_Australia 0.001435\n", - "listing_country_Australia 0.001435\n", - "days_to_complete_verification 0.001179\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "# Compute correlation with the target variable\n", - "correlation_with_target = df.corrwith(df['has_resolution_incident'])\n", - "\n", - "# Drop the target itself (its correlation with itself is always 1)\n", - "correlation_with_target = correlation_with_target.drop(labels='has_resolution_incident')\n", - "\n", - "# Sort by absolute correlation, descending\n", - "correlation_sorted = correlation_with_target.abs().sort_values(ascending=False)\n", - "\n", - "# Print the sorted correlations (you can keep the original signs too if preferred)\n", - "print(correlation_sorted)" - ] - }, - { - "cell_type": "markdown", - "id": "2caec836", - "metadata": {}, - "source": [ - "### Weighted classes" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "e6d091fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: np.float64(1.0119419188492333), 1: np.float64(84.73863636363637)}" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# We will use weight classes due to the inbalance of the target variable\n", - "X = df.drop(columns=['has_resolution_incident'])\n", - "y = df['has_resolution_incident']\n", - "\n", - "# 1. Split data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.3, random_state=123, stratify=y\n", - ")\n", - "\n", - "# Compute label distribution on the training set\n", - "label_distribution = y_train.value_counts(normalize=True)\n", - "\n", - "# Calculate inverse weights\n", - "weights = {\n", - " 0: 1 / label_distribution[0],\n", - " 1: 1 / label_distribution[1]\n", - "}\n", - "weights" - ] - }, - { - "cell_type": "markdown", - "id": "ab8f7646", - "metadata": {}, - "source": [ - "### Feature Selection\n", - "\n", - "Since we have many columns, weā€™ll apply feature selection techniques like KBest, RFE (Recursive Feature Elimination), and Lasso (L1 regularization), to reduce the number of fields used in our predictive model. This helps:\n", - "- Avoid overfitting\n", - "- Potentially improve model performance (simpler models often generalize better)\n", - "- Reduce training time\n", - "\n", - "We'll also experiment with different numbers of features to determine which combination produces the model best suited to our objectives." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "0246eb6c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected Features:\n", - "Index(['number_of_nights', 'number_of_listings_of_host', 'guest_age',\n", - " 'has_guest_previously_booked_same_listing',\n", - " 'listing_number_of_bedrooms', 'listing_number_of_bathrooms',\n", - " 'previous_bookings_in_listing_count',\n", - " 'number_of_previous_incidents_in_listing',\n", - " 'number_of_previous_payouts_in_listing', 'guest_has_email',\n", - " 'is_guest_from_listing_town', 'is_guest_from_listing_country',\n", - " 'has_completed_verification', 'number_of_applied_services',\n", - " 'number_of_applied_upgraded_services',\n", - " 'number_of_applied_billable_services', 'booking_number_of_nights',\n", - " 'has_deposit_management_service_business_type',\n", - " 'host_account_type_Host',\n", - " 'host_account_type_PMC - Property Management Company',\n", - " 'host_active_pms_list_Hospitable', 'host_active_pms_list_Hostify',\n", - " 'host_country_New Zealand', 'host_country_United Kingdom',\n", - " 'host_country_United States', 'guest_country_Canada',\n", - " 'guest_country_United States', 'listing_country_New Zealand',\n", - " 'listing_country_United Kingdom', 'listing_country_United States'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "selector = SelectKBest(score_func=f_classif, k=30)\n", - "X_new = selector.fit_transform(X_train, y_train)\n", - "selected_features_kbest = X_train.columns[selector.get_support()]\n", - "\n", - "print(\"Selected Features:\")\n", - "print(selected_features_kbest)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "736a8d68", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected Features using RFE:\n", - "Index(['has_guest_previously_booked_same_listing',\n", - " 'number_of_previous_payouts_in_listing', 'guest_has_email',\n", - " 'is_guest_from_listing_town', 'is_guest_from_listing_country',\n", - " 'is_host_from_listing_country', 'is_host_from_listing_postcode',\n", - " 'has_completed_verification', 'has_verification_request',\n", - " 'has_upgraded_screening_service_business_type',\n", - " 'has_deposit_management_service_business_type',\n", - " 'host_account_type_Host',\n", - " 'host_account_type_PMC - Property Management Company',\n", - " 'host_active_pms_list_Avantio', 'host_active_pms_list_Hostify',\n", - " 'host_active_pms_list_TrackHs', 'host_country_Bulgaria',\n", - " 'host_country_Canada', 'host_country_New Zealand',\n", - " 'guest_country_Australia', 'guest_country_Canada',\n", - " 'guest_country_Germany', 'guest_country_Mexico', 'guest_country_Other',\n", - " 'listing_country_Bahamas', 'listing_country_Canada',\n", - " 'listing_country_Colombia', 'listing_country_Ireland',\n", - " 'listing_country_New Zealand', 'listing_country_United States'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "# Recursive Feature Elimination (RFE) with Logistic Regression\n", - "model = LogisticRegression(max_iter=1000)\n", - "rfe = RFE(model, n_features_to_select=30)\n", - "rfe.fit(X_train, y_train)\n", - "selected_features_rfe = X_train.columns[rfe.support_]\n", - "\n", - "print(\"Selected Features using RFE:\")\n", - "print(selected_features_rfe)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "484786aa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected Features using Lasso Regression:\n", - "Index(['days_from_booking_creation_to_check_in', 'number_of_nights',\n", - " 'host_age', 'host_months_with_truvi', 'number_of_listings_of_host',\n", - " 'number_of_previous_incidents_of_host',\n", - " 'number_of_previous_payouts_of_host', 'guest_age',\n", - " 'number_of_previous_bookings_of_guest',\n", - " 'has_guest_previously_booked_same_listing',\n", - " 'listing_number_of_bedrooms', 'listing_number_of_bathrooms',\n", - " 'previous_bookings_in_listing_count',\n", - " 'number_of_previous_incidents_in_listing',\n", - " 'number_of_previous_payouts_in_listing', 'days_to_start_verification',\n", - " 'days_to_complete_verification', 'is_guest_from_listing_town',\n", - " 'is_guest_from_listing_country', 'is_host_from_listing_town',\n", - " 'is_host_from_listing_postcode', 'has_completed_verification',\n", - " 'number_of_applied_services', 'number_of_applied_billable_services',\n", - " 'booking_days_to_check_in', 'booking_number_of_nights',\n", - " 'has_verification_request',\n", - " 'has_upgraded_screening_service_business_type',\n", - " 'has_deposit_management_service_business_type',\n", - " 'has_protection_service_business_type', 'host_account_type_Host',\n", - " 'host_account_type_PMC - Property Management Company',\n", - " 'host_active_pms_list_Guesty', 'host_active_pms_list_Hospitable',\n", - " 'host_active_pms_list_Hostaway', 'host_active_pms_list_Hostfully',\n", - " 'host_active_pms_list_Hostify', 'host_active_pms_list_Lodgify',\n", - " 'host_active_pms_list_OwnerRez', 'host_country_New Zealand',\n", - " 'guest_country_Canada', 'guest_country_Other',\n", - " 'guest_country_United Kingdom', 'guest_country_United States',\n", - " 'listing_country_Colombia', 'listing_country_New Zealand',\n", - " 'listing_country_United States'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "# Lasso Regression for feature selection\n", - "model = LogisticRegression(penalty='l1', solver='liblinear')\n", - "model.fit(X_train, y_train)\n", - "\n", - "# Check which features have non-zero coefficients\n", - "selected_features_lasso = X_train.columns[model.coef_[0] != 0]\n", - "print(\"Selected Features using Lasso Regression:\")\n", - "print(selected_features_lasso)" - ] - }, - { - "cell_type": "markdown", - "id": "04010a1e", - "metadata": {}, - "source": [ - "## Processing\n", - "Processing in this notebook is quite straight-forward: we just drop id booking, split the features and target and apply a scaling to numeric features.\n", - "Afterwards, we split the dataset between train and test and display their sizes and target distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "f735b111", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training set size: 14914 rows\n", - "Test set size: 6393 rows\n", - "\n", - "Training target distribution:\n", - "has_resolution_incident\n", - "0 0.988199\n", - "1 0.011801\n", - "Name: proportion, dtype: float64\n", - "\n", - "Test target distribution:\n", - "has_resolution_incident\n", - "0 0.988112\n", - "1 0.011888\n", - "Name: proportion, dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_48568/2398832410.py:8: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " X_train_kbest[selected_features_kbest] = X_train_kbest[selected_features_kbest].astype(float)\n" - ] - } - ], - "source": [ - "# Separate features and target\n", - "X_train_kbest = X_train[selected_features_kbest] # Use the features selected by SelectKBest\n", - "y_train_kbest = y_train\n", - "X_test_kbest = X_test[selected_features_kbest]\n", - "y_test_kbest = y_test\n", - "\n", - "# Scale numeric features\n", - "X_train_kbest[selected_features_kbest] = X_train_kbest[selected_features_kbest].astype(float)\n", - "\n", - "print(f\"Training set size: {X_train_kbest.shape[0]} rows\")\n", - "print(f\"Test set size: {X_test_kbest.shape[0]} rows\")\n", - "\n", - "print(\"\\nTraining target distribution:\")\n", - "print(y_train_kbest.value_counts(normalize=True))\n", - "\n", - "print(\"\\nTest target distribution:\")\n", - "print(y_test_kbest.value_counts(normalize=True))" - ] - }, - { - "cell_type": "markdown", - "id": "897eb678", - "metadata": {}, - "source": [ - "### Using RFE Features" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "301a8fb2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training set size: 14914 rows\n", - "Test set size: 6393 rows\n", - "\n", - "Training target distribution:\n", - "has_resolution_incident\n", - "0 0.988199\n", - "1 0.011801\n", - "Name: proportion, dtype: float64\n", - "\n", - "Test target distribution:\n", - "has_resolution_incident\n", - "0 0.988112\n", - "1 0.011888\n", - "Name: proportion, dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_48568/2877144001.py:8: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " X_train_rfe[selected_features_rfe] = X_train_rfe[selected_features_rfe].astype(float)\n" - ] - } - ], - "source": [ - "# Separate features and target\n", - "X_train_rfe = X_train[selected_features_rfe] # Use the features selected by RFE\n", - "y_train_rfe = y_train\n", - "X_test_rfe = X_test[selected_features_rfe]\n", - "y_test_rfe = y_test\n", - "\n", - "# Scale numeric features\n", - "X_train_rfe[selected_features_rfe] = X_train_rfe[selected_features_rfe].astype(float)\n", - "\n", - "print(f\"Training set size: {X_train_rfe.shape[0]} rows\")\n", - "print(f\"Test set size: {X_test_rfe.shape[0]} rows\")\n", - "\n", - "print(\"\\nTraining target distribution:\")\n", - "print(y_train_rfe.value_counts(normalize=True))\n", - "\n", - "print(\"\\nTest target distribution:\")\n", - "print(y_test_rfe.value_counts(normalize=True))" - ] - }, - { - "cell_type": "markdown", - "id": "2bbc1524", - "metadata": {}, - "source": [ - "### Using Lasso Features" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "f4b9c01a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training set size: 14914 rows\n", - "Test set size: 6393 rows\n", - "\n", - "Training target distribution:\n", - "has_resolution_incident\n", - "0 0.988199\n", - "1 0.011801\n", - "Name: proportion, dtype: float64\n", - "\n", - "Test target distribution:\n", - "has_resolution_incident\n", - "0 0.988112\n", - "1 0.011888\n", - "Name: proportion, dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_48568/1333565449.py:8: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " X_train_lasso[selected_features_lasso] = X_train_lasso[selected_features_lasso].astype(float)\n" - ] - } - ], - "source": [ - "# Separate features and target\n", - "X_train_lasso = X_train[selected_features_lasso] # Use the features selected by lasso\n", - "y_train_lasso = y_train\n", - "X_test_lasso = X_test[selected_features_lasso]\n", - "y_test_lasso = y_test\n", - "\n", - "# Scale numeric features\n", - "X_train_lasso[selected_features_lasso] = X_train_lasso[selected_features_lasso].astype(float)\n", - "\n", - "print(f\"Training set size: {X_train_lasso.shape[0]} rows\")\n", - "print(f\"Test set size: {X_test_lasso.shape[0]} rows\")\n", - "\n", - "print(\"\\nTraining target distribution:\")\n", - "print(y_train_lasso.value_counts(normalize=True))\n", - "\n", - "print(\"\\nTest target distribution:\")\n", - "print(y_test_lasso.value_counts(normalize=True))" - ] - }, - { - "cell_type": "markdown", - "id": "d36c9276", - "metadata": {}, - "source": [ - "## Classification Model with Random Forest\n", - "\n", - "We define a machine learning pipeline that includes:\n", - "- **Scaling numeric features** with `StandardScaler`\n", - "- **Training a Random Forest classifier** with balanced class weights to handle the imbalanced dataset\n", - "\n", - "We then use `GridSearchCV` to perform a **grid search with cross-validation** over a range of key hyperparameters (e.g., number of trees, max depth, etc.). \n", - "The model is evaluated using **Average Precision**, which is better suited for imbalanced classification tasks.\n", - "\n", - "The best combination of parameters is selected, and the resulting model is used to make predictions on the test set.\n" - ] - }, - { - "cell_type": "markdown", - "id": "fe3351be", - "metadata": {}, - "source": [ - "### Model 1 with Kbest Features" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "943ef7d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 7.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "Best hyperparameters: {'model__max_depth': None, 'model__max_features': 'log2', 'model__min_samples_leaf': 2, 'model__min_samples_split': 2, 'model__n_estimators': 300}\n" - ] - } - ], - "source": [ - "# Define pipeline (scaling numeric features only)\n", - "pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', RandomForestClassifier(class_weight=weights, # We have an imbalanced dataset\n", - " random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid\n", - "param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "grid_search = GridSearchCV(\n", - " estimator=pipeline,\n", - " param_grid=param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=5, # 5-fold cross-validation\n", - " n_jobs=-1, # Use all available cores\n", - " verbose=2 # Verbose output for progress tracking\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "grid_search.fit(X_train_kbest, y_train_kbest)\n", - "\n", - "# Best model\n", - "best_pipeline_kbest = grid_search.best_estimator_\n", - "print(\"Best hyperparameters:\", grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba_kbest = best_pipeline_kbest.predict_proba(X_test_kbest)[:, 1]\n", - "y_pred_kbest = best_pipeline_kbest.predict(X_test_kbest)\n" - ] - }, - { - "cell_type": "markdown", - "id": "672444f7", - "metadata": {}, - "source": [ - "### Model 2 with RFE Features" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "49cb625c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 0.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 0.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.8s[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.5s\n", - "Best hyperparameters: {'model__max_depth': 10, 'model__max_features': 'sqrt', 'model__min_samples_leaf': 2, 'model__min_samples_split': 5, 'model__n_estimators': 100}\n" - ] - } - ], - "source": [ - "# Define pipeline (scaling numeric features only)\n", - "pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', RandomForestClassifier(class_weight=weights, # We have an imbalanced dataset\n", - " random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid\n", - "param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "grid_search = GridSearchCV(\n", - " estimator=pipeline,\n", - " param_grid=param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=5, # 5-fold cross-validation\n", - " n_jobs=-1, # Use all available cores\n", - " verbose=2 # Verbose output for progress tracking\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "grid_search.fit(X_train_rfe, y_train_rfe)\n", - "\n", - "# Best model\n", - "best_pipeline_rfe = grid_search.best_estimator_\n", - "print(\"Best hyperparameters:\", grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba_rfe = best_pipeline_rfe.predict_proba(X_test_rfe)[:, 1]\n", - "y_pred_rfe = best_pipeline_rfe.predict(X_test_rfe)\n" - ] - }, - { - "cell_type": "markdown", - "id": "b763f4cd", - "metadata": {}, - "source": [ - "### Model 3 with Lasso Features" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "47c6ab43", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.2s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.5s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.5s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n", - "[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.2s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n", - "[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n", - "[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.5s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.2s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n", - "[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n", - "Best hyperparameters: {'model__max_depth': None, 'model__max_features': 'log2', 'model__min_samples_leaf': 2, 'model__min_samples_split': 2, 'model__n_estimators': 200}\n" - ] - } - ], - "source": [ - "# Define pipeline (scaling numeric features only)\n", - "pipeline = Pipeline([\n", - " ('scaler', StandardScaler()),\n", - " ('model', RandomForestClassifier(class_weight=weights, # We have an imbalanced dataset\n", - " random_state=123))\n", - "])\n", - "\n", - "# Define parameter grid\n", - "param_grid = {\n", - " 'model__n_estimators': [100, 200, 300],\n", - " 'model__max_depth': [None, 10, 20],\n", - " 'model__min_samples_split': [2, 5],\n", - " 'model__min_samples_leaf': [1, 2],\n", - " 'model__max_features': ['sqrt', 'log2']\n", - "}\n", - "\n", - "# GridSearchCV\n", - "grid_search = GridSearchCV(\n", - " estimator=pipeline,\n", - " param_grid=param_grid,\n", - " scoring='average_precision', # For imbalanced classification\n", - " cv=5, # 5-fold cross-validation\n", - " n_jobs=-1, # Use all available cores\n", - " verbose=2 # Verbose output for progress tracking\n", - ")\n", - "\n", - "# Fit the grid search on training data\n", - "grid_search.fit(X_train_lasso, y_train_lasso)\n", - "\n", - "# Best model\n", - "best_pipeline_lasso = grid_search.best_estimator_\n", - "print(\"Best hyperparameters:\", grid_search.best_params_)\n", - "\n", - "# Predict on test set\n", - "y_pred_proba_lasso = best_pipeline_lasso.predict_proba(X_test_lasso)[:, 1]\n", - "y_pred_lasso = best_pipeline_lasso.predict(X_test_lasso)\n" - ] - }, - { - "cell_type": "markdown", - "id": "fc2fcc89", - "metadata": {}, - "source": [ - "## Evaluation\n", - "This section aims to evaluate how good the new model is vs. the actual Resolution Incidents.\n", - "\n", - "We start by computing and displaying the classification report, ROC Curve, PR Curve and the respective Area Under the Curve (AUC)." - ] - }, - { - "cell_type": "markdown", - "id": "76099daf", - "metadata": {}, - "source": [ - "### Model 1 evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "78887f46", - "metadata": {}, - "outputs": [], - "source": [ - "# Actual and predicted\n", - "y_true_kbest = y_test_kbest\n", - "\n", - "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n", - "tn, fp, fn, tp = confusion_matrix(y_true_kbest, y_pred_kbest).ravel()\n", - "\n", - "# Total predictions\n", - "total = tp + tn + fp + fn\n", - "\n", - "# Compute all requested metrics\n", - "recall_kbest = recall_score(y_true_kbest, y_pred_kbest)\n", - "precision_kbest = precision_score(y_true_kbest, y_pred_kbest)\n", - "f1_kbest = fbeta_score(y_true_kbest, y_pred_kbest, beta=1)\n", - "f2_kbest = fbeta_score(y_true_kbest, y_pred_kbest, beta=2)\n", - "fpr_kbest = fp / (fp + tn) if (fp + tn) != 0 else 0\n", - "\n", - "# Scores relative to total\n", - "tp_score_kbest = tp / total\n", - "tn_score_kbest = tn / total\n", - "fp_score_kbest = fp / total\n", - "fn_score_kbest = fn / total\n", - "\n", - "# Create DataFrame\n", - "summary_df_kbest = pd.DataFrame([{\n", - " \"title\": \"Kbest\",\n", - " \"flagging_analysis_type\": \"RISK_VS_CLAIM using KBest Features from all features\",\n", - " \"count_total\": total,\n", - " \"count_true_positive\": tp,\n", - " \"count_true_negative\": tn,\n", - " \"count_false_positive\": fp,\n", - " \"count_false_negative\": fn,\n", - " \"true_positive_score\": tp_score_kbest,\n", - " \"true_negative_score\": tn_score_kbest,\n", - " \"false_positive_score\": fp_score_kbest,\n", - " \"false_negative_score\": fn_score_kbest,\n", - " \"recall_score\": recall_kbest,\n", - " \"precision_score\": precision_kbest,\n", - " \"false_positive_rate_score\": fpr_kbest,\n", - " \"f1_score\": f1_kbest,\n", - " \"f2_score\": f2_kbest\n", - "}])" - ] - }, - { - "cell_type": "markdown", - "id": "ea079e83", - "metadata": {}, - "source": [ - "### Model 2 evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "03c83137", - "metadata": {}, - "outputs": [], - "source": [ - "# Actual and predicted\n", - "y_true_rfe = y_test_rfe\n", - "\n", - "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n", - "tn, fp, fn, tp = confusion_matrix(y_true_rfe, y_pred_rfe).ravel()\n", - "\n", - "# Total predictions\n", - "total = tp + tn + fp + fn\n", - "\n", - "# Compute all requested metrics\n", - "recall_rfe = recall_score(y_true_rfe, y_pred_rfe)\n", - "precision_rfe = precision_score(y_true_rfe, y_pred_rfe)\n", - "f1_rfe = fbeta_score(y_true_rfe, y_pred_rfe, beta=1)\n", - "f2_rfe = fbeta_score(y_true_rfe, y_pred_rfe, beta=2)\n", - "fpr_rfe = fp / (fp + tn) if (fp + tn) != 0 else 0\n", - "\n", - "# Scores relative to total\n", - "tp_score_rfe = tp / total\n", - "tn_score_rfe = tn / total\n", - "fp_score_rfe = fp / total\n", - "fn_score_rfe = fn / total\n", - "\n", - "# Create DataFrame\n", - "summary_df_rfe = pd.DataFrame([{\n", - " \"title\": \"RFE\",\n", - " \"flagging_analysis_type\": \"RISK_VS_CLAIM using RFE Features from all features\",\n", - " \"count_total\": total,\n", - " \"count_true_positive\": tp,\n", - " \"count_true_negative\": tn,\n", - " \"count_false_positive\": fp,\n", - " \"count_false_negative\": fn,\n", - " \"true_positive_score\": tp_score_rfe,\n", - " \"true_negative_score\": tn_score_rfe,\n", - " \"false_positive_score\": fp_score_rfe,\n", - " \"false_negative_score\": fn_score_rfe,\n", - " \"recall_score\": recall_rfe,\n", - " \"precision_score\": precision_rfe,\n", - " \"false_positive_rate_score\": fpr_rfe,\n", - " \"f1_score\": f1_rfe,\n", - " \"f2_score\": f2_rfe\n", - "}])" - ] - }, - { - "cell_type": "markdown", - "id": "8c2f75c9", - "metadata": {}, - "source": [ - "### Model 3 evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "7d34f389", - "metadata": {}, - "outputs": [], - "source": [ - "# Actual and predicted\n", - "y_true_lasso = y_test_lasso\n", - "\n", - "# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n", - "tn, fp, fn, tp = confusion_matrix(y_true_lasso, y_pred_lasso).ravel()\n", - "\n", - "# Total predictions\n", - "total = tp + tn + fp + fn\n", - "\n", - "# Compute all requested metrics\n", - "recall_lasso = recall_score(y_true_lasso, y_pred_lasso)\n", - "precision_lasso = precision_score(y_true_lasso, y_pred_lasso)\n", - "f1_lasso = fbeta_score(y_true_lasso, y_pred_lasso, beta=1)\n", - "f2_lasso = fbeta_score(y_true_lasso, y_pred_lasso, beta=2)\n", - "fpr_lasso = fp / (fp + tn) if (fp + tn) != 0 else 0\n", - "\n", - "# Scores relative to total\n", - "tp_score_lasso = tp / total\n", - "tn_score_lasso = tn / total\n", - "fp_score_lasso = fp / total\n", - "fn_score_lasso = fn / total\n", - "\n", - "# Create DataFrame\n", - "summary_df_lasso = pd.DataFrame([{\n", - " \"title\": \"Lasso\",\n", - " \"flagging_analysis_type\": \"RISK_VS_CLAIM using Lasso Features from all features\",\n", - " \"count_total\": total,\n", - " \"count_true_positive\": tp,\n", - " \"count_true_negative\": tn,\n", - " \"count_false_positive\": fp,\n", - " \"count_false_negative\": fn,\n", - " \"true_positive_score\": tp_score_lasso,\n", - " \"true_negative_score\": tn_score_lasso,\n", - " \"false_positive_score\": fp_score_lasso,\n", - " \"false_negative_score\": fn_score_lasso,\n", - " \"recall_score\": recall_lasso,\n", - " \"precision_score\": precision_lasso,\n", - " \"false_positive_rate_score\": fpr_lasso,\n", - " \"f1_score\": f1_lasso,\n", - " \"f2_score\": f2_lasso\n", - "}])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "09609773", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_confusion_matrix_from_df(df, flagging_analysis_type):\n", - "\n", - " # Subset - just retrieve one row depending on the flagging_analysis_type\n", - " row = df[df['flagging_analysis_type'] == flagging_analysis_type].iloc[0]\n", - "\n", - " # Define custom x-axis labels and wording\n", - " if flagging_analysis_type == 'RISK_VS_CLAIM':\n", - " x_labels = ['With Submitted Claim', 'Without Submitted Claim']\n", - " outcome_label = \"submitted claim\"\n", - " elif flagging_analysis_type == 'RISK_VS_SUBMITTED_PAYOUT':\n", - " x_labels = ['With Submitted Payout', 'Without Submitted Payout']\n", - " outcome_label = \"submitted payout\"\n", - " else:\n", - " x_labels = ['Actual Positive', 'Actual Negative'] \n", - " outcome_label = \"outcome\"\n", - "\n", - " # Confusion matrix structure\n", - " cm = np.array([\n", - " [row['count_true_positive'], row['count_false_positive']],\n", - " [row['count_false_negative'], row['count_true_negative']]\n", - " ])\n", - "\n", - " # Create annotations for the confusion matrix\n", - " labels = [['True Positives', 'False Positives'], ['False Negatives', 'True Negatives']]\n", - " counts = [[f\"{v:,}\" for v in [row['count_true_positive'], row['count_false_positive']]],\n", - " [f\"{v:,}\" for v in [row['count_false_negative'], row['count_true_negative']]]]\n", - " percentages = [[f\"{round(100*v,2):,}\" for v in [row['true_positive_score'], row['false_positive_score']]],\n", - " [f\"{round(100*v,2):,}\" for v in [row['false_negative_score'], row['true_negative_score']]]]\n", - " annot = [[f\"{labels[i][j]}\\n{counts[i][j]} ({percentages[i][j]}%)\" for j in range(2)] for i in range(2)]\n", - "\n", - " # Scores formatted as percentages\n", - " recall = row['recall_score'] * 100\n", - " precision = row['precision_score'] * 100\n", - " f1 = row['f1_score'] * 100\n", - " f2 = row['f2_score'] * 100\n", - "\n", - " # Set up figure and axes manually for precise control\n", - " fig = plt.figure(figsize=(9, 8))\n", - " grid = fig.add_gridspec(nrows=4, height_ratios=[2, 2, 15, 2])\n", - "\n", - " \n", - " ax_main_title = fig.add_subplot(grid[0])\n", - " ax_main_title.axis('off')\n", - " ax_main_title.set_title(f\"Random Predictor - Flagged as Risk vs. {outcome_label.title()}\", fontsize=14, weight='bold')\n", - " \n", - " # Business explanation text\n", - " ax_text = fig.add_subplot(grid[1])\n", - " ax_text.axis('off')\n", - " business_text = (\n", - " f\"Flagging performance analysis:\\n\\n\"\n", - " f\"- Of all the bookings we flagged as at Risk, {precision:.2f}% actually turned into a {outcome_label}.\\n\"\n", - " f\"- Of all the bookings that resulted in a {outcome_label}, we correctly flagged {recall:.2f}% of them.\\n\"\n", - " f\"- The pure balance between these two is summarized by a score of {f1:.2f}%.\\n\"\n", - " f\"- If we prioritise better probability of detection of a {outcome_label}, the balanced score is {f2:.2f}%.\\n\"\n", - " )\n", - " ax_text.text(0.0, 0.0, business_text, fontsize=10.5, ha='left', va='bottom', wrap=False, linespacing=1.5)\n", - "\n", - " # Heatmap\n", - " ax_heatmap = fig.add_subplot(grid[2])\n", - " ax_heatmap.set_title(f\"Confusion Matrix ā€“ Risk vs. {outcome_label.title()}\", fontsize=12, weight='bold', ha='center', va='center', wrap=False)\n", - "\n", - " cmap = sns.light_palette(\"#315584\", as_cmap=True)\n", - "\n", - " sns.heatmap(cm, annot=annot, fmt='', cmap=cmap, cbar=False,\n", - " xticklabels=x_labels,\n", - " yticklabels=['Flagged as Risk', 'Flagged as No Risk'],\n", - " ax=ax_heatmap,\n", - " linewidths=1.0,\n", - " annot_kws={'fontsize': 10, 'linespacing': 1.2})\n", - " ax_heatmap.set_xlabel(\"Resolution Outcome (Actual)\", fontsize=11, labelpad=10)\n", - " ax_heatmap.set_ylabel(\"Flagging (Prediction)\", fontsize=11, labelpad=10)\n", - " \n", - " # Make borders visible\n", - " for _, spine in ax_heatmap.spines.items():\n", - " spine.set_visible(True)\n", - "\n", - " # Footer with metrics and date\n", - " ax_footer = fig.add_subplot(grid[3])\n", - " ax_footer.axis('off')\n", - " metrics_text = f\"Total Booking Count: {row['count_total']} | Recall: {recall:.2f}% | Precision: {precision:.2f}% | F1 Score: {f1:.2f}% | F2 Score: {f2:.2f}%\"\n", - " date_text = f\"Generated on {date.today().strftime('%B %d, %Y')}\"\n", - " ax_footer.text(0.5, 0.7, metrics_text, ha='center', fontsize=9)\n", - " ax_footer.text(0.5, 0.1, date_text, ha='center', fontsize=8, color='gray')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "7cc4a1d2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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w4IDCsqqe5P3yyy9CeP4unkSft2febu95y1CiRAnRedPU1FRptz9VcnNzycXFRVh+7ty5wry8T1p79uwphDdr1kwI37Bhg0KaT548Udp1viiePXsmqtOQkBCVcWNjY0Vxp02bJsxTdR4jKrj7bGHdll+9eiU6VipVqkTv3r0Txbl//75wvM6aNUtUlvj4eNG65n0i37x5c2Fe/v1t7dq1wrz8TzBjYmKI6PM2tbOzE8IHDhwoLPMl1xHGvjV+ksfYd9CiRQv06NFDIfzly5cICwsT3ZVU5uHDhyrn9ezZUxgi38zMDBYWFnj27BkACHcv379/Lzw9A4BWrVpBR0dHmO7YsSOWLFmikPaNGzdETyI7duwoekoTFhaGadOmCdNJSUnw8vJSSKdNmzaQSqUAoPB9qZYtWwpp5h8s4/Xr1zA0NFS57srcuXMHQ4YMUTovMjISISEhSuc1atQIlStXVgg/ceKE8P+EhIQCn1KdPHkSFSpUEO5Ey7Vv3174v7OzM/z8/HDkyJGCVkMkPT0dFy5cEKY7d+6scHddX18f+vr6RUrz0qVLwnRoaChkMpkoj169egnTSUlJSutOVb2p8vbtW6UjnBbl6du3tG7dOvTv3194UqbMp0+fkJqaCltbW4Vt27lzZ+H/pqamaN68udLvG+ZfrlOnTsL/S5UqBT8/P6Wffci7/71+/Rrm5uYqy3ny5En88ssvOHfuHIhICO/QoYPwf6lUijZt2oieCqtr5syZGDdunOgpZn55z1W+vr6QSCQgIixevBjJyckoW7Ys3Nzc4OPjg4CAAFhbWxe5HMpoaWmhf//+qFu3rtrL1KpVC3PmzAEAjB49GnFxcXB3d4ebmxuqVauGWrVqqTze79+/L5wzLSwssH//flSqVEntvCUSCcLDw4XtsGHDBvTp0wcvX74UPQ2OiIgQlVf+KZjw8HAsXrwYZcqUgZubG3x9fVG1atVi9bkUZU6dOiU8DQeA4cOHw8DAQBQn7wiqeZ80WlpaolGjRsK0lZUVGjVqJPRIUdXDQktLC23bthWmnZ2dhbhSqVR40iqRSODi4oLHjx8D+L/rL/Bl1xHGvjVu5DH2ldq2bYuKFSvi5MmTQreRdevW4cmTJzh48KDoItylS5dCG3jA5x+ZquRvNOVtvMm7hrx580YUR94tSE7VD628DTxl8fJP572o5WVnZyf8X1tbW+W8/N0B5eX/UlpaWrCwsICPjw8iIyOFi7Ey7u7uSsPz10FB5F3T8ta3oaGhqPEEqK5vVV6/fi360e7i4lKk5dVJM3+Z9PX1YWBgIPygV7VtVdWbKq9evVLaCHdycvqiRl7edSiq8+fPo3PnzmrtZ/JjMP+xlL/7r6ruwPn3ifwNclXLfe3+B6h/vBdk+/btGDRoUKHxMjMzhf9XrVoVM2fOxJgxY/D+/XucP39e1KXQwsICMTExX/Q9tJEjR0JHRwfbtm3Dn3/+iezsbAwdOhTp6elqN2Bbt26NwYMHY+7cufj06ROSkpJEP/SdnJywe/dulCtXrsB0DA0Nv6hOw8PDMX78eOTm5iIpKQl3797F3r17kZWVBQDw9PQUfUalf//+uHTpEtavX49Pnz4hISFBdGPA09MT+/fvh62tbZHLImdubg5tbW1hO967d09l3PzzVOWb/xgt6HpWmPzHQ2HnwrzxlW2jvGGqznFWVlaia1Pea5iVlZWowZY3Xt7zypccx4x9a9zIY+wrNWzYEOHh4QCAHj16YPHixQCAw4cPY+3atcId/A8fPojeHahbty7++OMPODk5QVNTE1WrVkVycnKh+cmfkMkpu5Mrf09HTj7cuJz8yV9+ZmZmBcbLP21qaqpWGfNS9p7X16hTp84XfQhb1VMwMzMzob78/PwK/PyC/F03ExMTIezdu3fIyMgQNfRU1bcqpqamwhMRAEhJSSnS8uqkmb9MHz58ED2xUbVti/L08H9NTEyM8ENMIpFg/fr1aNq0KfT19REfH4/g4GCFZfJuW+DzsZT3OMn7Hpyq5ZTtE6qWy5u2ra0tBg4cqHJ95E8wlJUxr6LufwBE73caGBhg27ZtqFWrFnR1dbFgwQL07t1b6XL9+/dHt27dcOrUKVy9ehW3bt3C3r17cevWLaSmpiIsLKzAhoQqXbt2hbOzM4YMGYKaNWvi4sWLAD6/Q9yxY0e1P6ERHR2N0aNH4+TJk7h+/Tpu3ryJuLg4PH78GPfu3UOvXr2QmJiosJyJiQl0dHTw7NkzpKSkICgoCEePHoWFhYXa61CiRAkEBgbi4MGDICJs3LgRe/bsEebnfYoHfD5Xrl69GjNmzMDJkydx48YN3LhxA7GxsXj9+jWuXLmC4cOHY9WqVWqXIT9NTU3UqFFDWOdLly7h9u3bKFWqlELc/N/Qq1WrlvD/vOcW+fuFcrdu3fri8uW/JqWkpBT4PdG88ZXt93nDvuf160uuI4x9cz+qnyhj/1YFja75+vVrMjY2FuaVKlVKeP/s0aNHouXknxQgIrp+/bpoxK6wsDCV+eUf4j3/8PZy/8Q7eRcvXhTmqaqT/KM/5p1X2LqpUtDomuoul/+zAnIhISGiektLS1OIk56eTqtWrRKmv/c7eQYGBnTr1i2FMuR9J2/t2rWitJS9q+Pl5SXML+ydvO3btxep3r6Xbzm6Zt793sTERPSeTP585Pti/nfyxo4dKyxTlHfyli9fLswr6J282bNnC+FaWlpK39fJzc2lAwcO0J07d4jo87nlW7+Tl3fU0AoVKgjhOTk5FBAQoDS9R48eKX3n7vz586L4qampheZf0Oia+c8b4eHhomVVved1584dpSPBbtu2TXSsKSuDk5MTnT9/XvTOb+XKlYv8CYP169eL0pRvN6lUSs+fPxfFvX79utLjOO+7j56enirrRd3zaf5PKDRp0kR4x01u9erVojj5P6GQ9zjI+9mavXv3ipbLf/77+eefhXlVqlRRKFv+d/IqV66sUCePHj0Sypv3+AEKficv7/uHBb3Hm3d/yj8v78ikea/bX3IdYexb4yd5jH1DJiYm6N27NyZPngwAuH37NjZt2oT27dvDysoKJiYmQteqiRMn4vnz58jOzsby5cu/qkuLMl26dMHQoUOFctSoUQNNmzbFlStXsG3bNqXLaGhoYMCAARgzZgyAz+8s+Pn5oX79+rh+/broTm5AQAAqVqz4Tcv8v2DQoEHYsWMHiAi3b9+Gp6cnWrZsCWtra6SlpeHy5ctITEzEhw8fhPezmjVrBktLS6HbTc+ePZGcnCyMrinvjlUUw4cPF0Znff/+Pby8vITRNR88eIBdu3ZhwYIFwntz9vb2ouXbt2+PmjVrQkNDA506dYK1tTUGDRokPFm+e/cuqlSpIhpdU65MmTJKn2r927m5uQn/f/PmDYKDg1GzZk0cP35c5Qh31atXR/ny5YVRbCdMmICUlBSUKFECmzdvVtnlq3nz5rCyshLu5vfo0QNnzpyBsbExVq9eLXrPKK/w8HBMnDgRqampyM7Ohq+vL0JDQ1GqVCl8+vQJN27cQEJCAp49e4YjR47AxcUFdnZ2aNSokTCK5tq1a/H27Vt4eXlhz549uHr16hfVlfxdsUuXLqFdu3bw8PDAnj17VI5CefToUXTo0AF+fn7w8PCAnZ0dcnJyROcbbW1t6OnpFbk8efn7+6NmzZrCCKVr165FVFRUoSM9btq0CePGjYO/vz9Kly4NW1tbfPjwARs2bBDi5H8qmlelSpUQGxuLRo0aITMzE+fPn0dwcDD279+v9jq1aNFCuA7kfaIZHBwMS0tLUdxZs2ZhzZo1qFu3LlxcXGBtbY1Xr15h9erVapVXXT/99BPWrl0rPFXctWsXPD09hZEkT58+LeqFYmpqit9//12URpUqVYRjaM2aNXj06BFkMlmhI0fmPW+dO3cO/fr1g6OjI7S1tfHLL7/A1NQU3bp1E0ZwPX/+PMqWLYuQkBCYmJjg5s2biI2NxZMnT2BiYoKwsDBMmDABL1++BPD5ffTIyEgYGRlh/fr1Qm8FiUSC/v37f13FFeBLriOMfXM/upXJ2L9NYd/Je/78uehbTOXKlRNGw5w6dapoWfmfp6en6JtU3+JJXmZmpuhbXHn//P39RdP5v5MXGhqqdDn5n4eHh+j7WETF50keEdH8+fML/L6R/C+vHTt2KP2mlaGhoeipXFG+kxcVFVXod/LkPn78SLa2tkrjJScnC/EK+06enZ1dgd/J+zc/yXv58qVoNLy8f6qe5BGp/k6ejo4OBQYGCtMuLi6icqj6Tp6pqSlVr15dmA4ICBAtd+LEiQK/k6fsuL1z5w5ZWVkpjZf3aYO6dXjr1i0yNDRUSEtLS4s6dOigNL38Ty+V/eUdgVDdbajs3LBr1y7R/LyjUqp6kjdlypRCy5d3hGJV+9GmTZtIQ0NDmFe/fn2Fj3EXpGfPngr5xsXFKcTr3r17gWXV0NAQnQO+9HxK9PlbfYWd94HPPQDOnj2rsPyBAweUnqvMzc2patWqKs9/Fy5cENWl/E9fX1+Ik5GRofCtx/x/+b+TZ2JiUmC9TZ8+XVSOb/0kj+jLriOMfUsaYIx9U5aWlvj555+F6atXrwrfpRs2bBjmz5+PMmXKQCqVwsbGBl27dkViYqLCiGFfSyqVYv/+/RgyZAjs7e2hra0NNzc3zJgxQ/jujzKamprYvHkzYmJi0LhxY+EldGNjY1SrVg3R0dFITk4WDaBS3PTq1QsXLlxAt27dUKZMGejp6UFLSwvW1taoU6cOxowZgz///FO0TLNmzXDw4EHUrl0bMpkMJiYmaN68OU6fPo3y5ct/UTnGjRuHU6dOISwsDK6urtDV1YWenh5cXV3RqVMneHp6CnF1dHQQHx+P+vXrw8jISGWaM2bMwIEDB9CqVSvY2dlBKpXCwMAAXl5eGDNmDC5dulTowBP/VmZmZjh+/DhatmwJIyMjyGQyVKlSBdu2bRPeq1XGx8cHJ0+eRHBwMAwMDGBgYIC6devi6NGjKF26tBAv/1OVJk2a4NChQ6hTp45onzh16pTovdn8y9WsWRNXr17FmDFj4O3tDSMjI2hqasLExATe3t7o06cPDhw4gNq1awvLuLi44NSpU2jTpg1MTEwgk8lQo0YN7Ny5s8B1U6VUqVI4evQo6tevDz09PRgYGKBOnTo4dOgQgoKClC7j5+eHSZMmITg4GCVLloShoSG0tLRgaWmJunXrYuXKlZgxY0aRy6JMcHCwqCfB8uXL8eTJkwKXCQkJwdixYxEUFARnZ2fhuLa1tUVwcDDi4uLQt2/fQvNu06aN6EnW/v378dNPPwnftCtM/nfvrK2tRaNAynXp0gXDhg1D7dq14ejoCF1dXWhra8PR0RGhoaFITExUOXpwURkYGGDz5s1ISEhAeHg4SpcuDX19feE61bBhQyxcuBDXrl2Dt7e3wvJBQUGIjY1F5cqVoa2tDXNzc3To0AHnzp2Dh4eHyny9vLywYcMGVK5cGbq6ukrj6OrqYteuXdi8eTOaNGkCGxsbSKVSGBkZoXz58ujXr5/oSWrt2rVx5coVDBo0COXKlYOenh60tbVRokQJdOjQASdPnlRrUKGv9SXXEca+JQnRVwxVxhhjjBVzmZmZ0NLSgoaG+L7o+/fv4enpKXS769q1q+hzER8/flT6w/XRo0coW7Ys3r59CwCYNGkSRo4c+R3XgDHG2H8NN/IYY4yxAly8eBHNmjVDhw4dULZsWZiamuLu3btYtGiR8L6bhoYGzp8/L3q6tH37dgwfPhzt2rVDmTJloK+vj5s3b2Lu3Lm4f/8+gM9PUG7duqXykwqMMcbYl+CBVxhjjLFCPHjwAFOnTlU6T1tbGwsXLlQ6ENGNGzcQFRWldDlDQ0Ns2rSJG3iMMca+OX6SxxhjjBXg5cuXmDRpEhISEnD//n2kpaVBV1cXLi4u8Pf3R69evZR+JD4lJQXR0dE4evQoHj9+jLdv30JfXx+lS5dGvXr10Lt3bzg4OPyANWKMMVbccSOPMcYYY4wxxooRHl2TMcYYY4wxxooRbuQxxhhjjDHGWDHCjTzGGGOMMcYYK0a4kccYY4wxxhhjxQg38hhjjDHGGGOsGOFGHmOMMcYYY4wVI9zIY4wxxhhjjLFihBt5jDHGGGOMMVaMcCOPMcYYY4wxxooRbuQxxhhjjDHGWDHCjTzGGGOMMcYYK0a4kccYY4wxxhhjxQg38hhjjDHGGGOsGOFGHmOMMcYYY4wVI9zIY4wxxhhjjLFihBt5jDHGGGOMMVaMcCOPMcYYY4wxxooRbuQxxhhjjDHGWDHCjTzGGGOMMcYYK0a4kccYY4wxxhhjxQg38hhjjDHGGGOsGOFGHmOMMcYYY4wVI9zIY4wxxhhjjLFihBt5jDHGGGOMMVaMcCOPMcYYY4wxxooRbuQxxhhjjDHGWDHCjTzGGGOMMcYYK0a4kccYY4wxxhhjxQg38hhjjDHGGGOsGOFGHmOMMcYYY4wVI9zIY4wxxhhjjLFihBt5jDHGGGOMMVaMcCOPMcYYY4wxxooRbuQxxhhjjDHGWDHCjTzGGGOMMcYYK0a4kccYY4wxxhhjxQg38hhjjDHGGGOsGOFGHmOMMcYYY4wVI9zIY4wxxhhjjLFihBt5jDHGGGOMMVaMcCOPMcYYY4wxxooRbuQxxhhjjDHGWDHCjTzGGGOMMcYYK0a4kccYY4wxxhhjxQg38hhjjDHGGGOsGOFGHmOMMcYYY4wVI9zIY4wxxhhjjLFihBt5jDHGGGOMMVaMcCOPMcYYY4wxxooRbuQxxhhjjDHGWDHyn2/khYeHQyKRKPxNnz4dALBy5UpIJBJkZ2f/kPL5+/ujY8eOPyTv7+HBgwcICgqCgYEBJBIJ3rx586OL9J8XFRUFBweHb5qmRCLB0qVLv2majDHGGGNMPVo/ugD/CypUqIDFixeLwpycnH5QacQWLFgAHR2dH12Mb2bChAlISUlBbGwsDA0NYWho+KOLxL6DpKQklCxZ8kcXgzHGGGPsP4kbeQAMDQ1RvXr1H10MpcqWLfuji/BNZGRkQCaT4ebNm/D19UW9evW+SXrsf9P/6vHEGGOMMfZf8J/vrllU7969Q8+ePVGyZEnIZDK4u7tjxowZICJRvN27d8PNzQ0ymQxBQUE4fPgwJBIJEhIShDg3b95E7dq1oaurC09PTxw+fBgODg6IiooS4uTvrhkeHg4/Pz/s2LEDbm5uMDQ0ROPGjfHkyZMi55+fvGvq2bNnUbVqVejq6sLb2xvnzp0TxUtNTUWXLl1gaWkJmUyGwMBAXL16VZh/9+5dSCQSbNq0CW3atIGhoSF69uwJiUSCxMRErFmzBhKJBP7+/gCA9+/fo0ePHkJ6derUUchTIpFg7ty56N69O8zMzNC4cWMhny1btqBt27YwMDBA6dKlcejQIWRmZqJXr14wMTGBi4sLNm7cKEpv+fLlqF69OoyNjWFjY4O2bdvi2bNnojjOzs4YPXo0Jk6cCGtra1haWqJv377IysoSxTtw4ABq1qwJPT09WFhYoGnTpqK0EhIS4OvrC5lMBisrK/Tv3x+fPn1SuR2+ZfmuXr2K1q1bw87ODgYGBqhSpQr27dunMt/r169DIpHgxIkTovDbt2+L9p+jR4+iZs2aMDQ0hImJCapUqYKDBw8K8fN314yNjUWlSpWgp6cHc3Nz1KpVCxcvXiywDhhjjDHG2JfhRt7/l52dLfzl5OSojPfhwwdIpVLMmDEDe/fuRe/evTFhwgTMmjVLiHPnzh20bNkSHh4e2LZtG+rVq4fIyEhROrm5uWjatClSU1Oxfv16DB8+HD169MDbt28LLevt27cxYcIETJkyBUuXLsW5c+fQp0+fIuVfkPbt2yMiIgIxMTHQ0dFBo0aN8P79ewDAp0+fULduXZw4cQK///47tm7dCk1NTQQFBeHDhw+idPr3748SJUpg+/bt6NatG5KSklChQgU0btwYSUlJWLBgAYDPDddNmzZh0qRJ2Lx5MyQSCQIDA/H8+XNRepMmTUJmZiY2btyIMWPGCOGDBg1CuXLlsG3bNri6uiI0NBQ9e/aEjo4OYmJi4Ofnh7CwMDx9+lRY5sGDB+jevTt27NiBhQsX4uHDh6hfv75CY33VqlX4888/sWrVKgwbNgwLFy7EokWLhPkHDhxAo0aNYGVlhY0bN2L58uVwcnLC69evAQDHjh1DvXr1UKpUKcTGxmLKlClYt24dBg8eXOA2+Fble/ToESpVqoSlS5dix44dCAgIQHBwMJKTk5Xm6+7ujurVq2P16tWi8NWrV8PJyQl16tTB27dv0bRpU5QpUwaxsbHYuHEjWrZsiVevXilN8/bt22jbti2CgoKwe/durFq1Cv7+/qL3Mf39/YVGP2OMMcYY+0r0HxcWFkYARH+amprC/BUrVhAAysrKUlg2NzeXsrKyaMKECVSuXDkhvH///uTg4CBaZvDgwQSAjhw5QkRE27dvJwB07do1Ic6uXbsIAI0bN04Iq1OnDnXo0EFUXqlUSvfv3xfCpk+fTlpaWpSTk6N2/srI13XOnDlC2MuXL0kmk9Hs2bOJiGjJkiUkk8lE+X/48IGsrKxo1qxZRESUkpJCAETllvP19aWwsDBh+vLlywSAYmJihLD379+Tubk5DR06VAgDQL6+vqK05Pn06tVLCLt27RoBoCZNmghhb9++JS0tLVq2bJnS9c7OzqZ79+4RAEpOThbCnZycqGzZskK9EhE1adKEAgMDhekqVapQrVq1lKYrX9/g4GBRWExMDGlra9PTp09VLvetypdXTk4OZWVlUVBQEPXu3VsIHzduHNnb2wvTixcvJhMTE/r48SMRfd7PXVxcaMyYMURElJycTADo7du3KssMgJYsWSKsr5mZWYHrGBgYqLLcjDHGGGOsaPhJHoCKFSsiOTlZ+Dt9+nSB8RcvXoxy5cpBJpNBKpVizJgxuH37tjD/3LlzqF+/PrS0/u+VxyZNmojSOHfuHFxdXeHh4SGENWzYEJqamoWWt0yZMnB0dBSmPTw8kJ2dLTz5Uif/gjRr1kz4v5mZGWrWrCk8+Tl06BCqV68OW1tb4cmntrY2qlevrtDFslGjRoXmde7cOWhqaiIkJEQI09fXR+PGjRWeNqlKLzAwUPi/fLCPvE+FDA0NYWlpicePHwthf/75Jxo2bAhzc3NoaWkJA+3k3Y4AEBAQAA2N/ztMPDw88OjRIwCfn+qePXsWnTp1Ulqu9PR0JCUloXXr1qInxf7+/sjMzMSVK1dUVcs3KZ+8jIMHD4aTkxOkUimkUikOHjyokE5ebdu2xadPnxAXFwfg89PIlJQUdO7cGcDnOjYwMECHDh2wa9cuvHv3TmVaAFC+fHmkpaUhPDwcBw8exMePHxXiHDp0CIcOHSowHcYYY4wxph5u5AEwMDCAj4+P8Oft7a0y7ubNm9GjRw8EBwcjLi4OZ86cwfDhw0XvWD1//hzm5uai5SwsLETTyuJoamrC1NS00PKamJiIprW1tQFA+PGsTv4FsbS0VJiWd3VMTU3FkSNHhAaD/C8uLg4PHjwQLWdlZVVoXk+ePIGpqamoQQoA1tbWCu8ZqkrP2NhY+L+8LvKGycPl9ZOWloaGDRviw4cPWLx4MU6ePCm8g5a/AaKsruVxXr9+DSKCra2t0nK9fv0aubm5iIiIENWVvH7z15fctyofAAwZMgTLli3D4MGDcfDgQSQnJ6NevXpKG1pyxsbGaNmypdBlc/Xq1ahZsyZKlSoFADA1NcW+ffuQkZGBli1bwsLCAqGhoaLusHm5ublh+/btuHXrFho0aAALCwt07dq10MYhY4wxxhj7Mjy6ZhHFxsYiICAA06ZNE8J27twpimNlZYXU1FRRWP5pZXFycnKEd7m+hjr5F+TFixeiT0i8ePECNjY2AD4/2fP19cXs2bMVlsv/OQSJRFJoXra2tnj9+jWys7NFDb1nz54pNJ7USU8dp06dwtOnT3H69GmUKFECwOf3GIvK1NQUEolEoTEqZ2JiAolEgilTpqBu3boK852dnb9r+YDP++uAAQPQt29fIezjx4+ip3/KREREoGHDhrh//z62bNki2t8BoGbNmjhw4AA+fPiAPXv2oF+/fujduze2bt2qNL0mTZqgSZMmeP36NbZv345+/frByMgIM2bM+KL1YowxxhhjqvGTvCLKyMgQnhYBnwdQ2bJliyiOt7c39u/fL/qA+q5duxTipKSk4K+//hLC9u7dW+CgL+pSJ/+CyLvpAcCrV69w8uRJVKlSBcDnrpE3b95EqVKlRE8/fXx84ObmVuSy+vj4ICcnBzt27BDC0tPTER8fj6pVqxY5PXVkZGQAgGg7bt68ucjp6Ovro2rVqli7dq3K+dWqVcPt27cV6srHx0fl09VvVT55WnnTefDgAU6dOlXocoGBgbC3t0eHDh3w6dMntGnTRmk8fX19tG7dGj/99JNoX1bF1NQUERERqFevnlrxGWOMMcZY0fGTvCIKDAzEgAEDMHv2bLi7u2Px4sVIT08Xxenbty8WLFiA1q1bo0ePHvjzzz8RExMDAMITFPnohK1atcLEiRORkZGB8ePHw9DQsNCnLIVRJ/+CzJ07F1paWnB0dMSUKVOgr68vjM4ZFhaGhQsXwt/fHwMHDoSTkxOeP3+OY8eOoWrVqqLPPaijXLlyaNWqFbp164bXr1/DxsYGM2bMQFZWFgYOHFj0lVdD9erVoaenh+7du6Nv3744f/48VqxY8UVpTZo0CQ0aNEDLli0REREBDQ0N7N+/H7169YKbmxt+++031KtXD0SEkJAQyGQy3LlzBzt27MD69ethZGT0XcsXGBiIOXPmwNXVFZqamhg3bhzs7OwKXU4ikSAsLAy//vor2rRpI+oWunv3bqxYsQIhISFwdHTE3bt3sXbtWoSGhipNa/HixTh9+jQaNGgAGxsbXL16FXv37sX48eOFOPInnfxeHmOMMcbY1+MneUXUs2dP9OzZExMmTEDHjh3h7OyMYcOGieK4urpi69atuHbtGkJCQhAfH4/p06cDgPCjXkNDAzt37oSFhQXat2+PCRMmYO7cuZBIJEp/+BeFOvkXZO3atVi2bBlat26NjIwMxMfHC10xdXV1ceTIEfj6+mLEiBGoX78+Bg0ahNTUVFSsWPGLyrtixQq0bt0aw4cPR2hoKHJycnDo0CG13un7EjY2Nli/fj3++usvNG3aFLt371bZzbAwdevWRXx8PB49eoTQ0FCEhYXh7t27wruVtWvXxuHDh3H37l20a9cOISEhmDt3LipWrKjyY+7fsnzz5s2Dl5cXIiMjMXjwYPTv3x9+fn5qLdu0aVMAnxv2eZUqVQpEhOHDh6N+/foYO3YsOnXqhOjoaKXpVKhQAU+fPkW/fv3QoEEDzJw5EyNHjhQ14nNycr7JU2zGGGOMMQZIiPJ9eIt9F7NmzcKIESPw6tUr6OnpKY1z4cIFVK5cGQcPHlT6Dtf3zn/lypWIiIhAVlaWwkAo7L9n6tSp+P333/Hw4UO1Rn1ljDHGGGP/G/iX/HcyZMgQVKtWDWZmZkhOTsaECRMQFhYmamDNnz8fMpkMrq6uuHv3Ln799VeULVv2m3wUWp38GVPm7t27+OuvvzBz5kz06NGDG3iMMcYYY/8y3Mj7Tt6/f49ffvkFL168gIWFBX7++Wf89ttvojhaWlqYOnUq7t+/Dx0dHdStWxezZ8/+Jj+q1cmfMWWioqKwYcMGBAUFYejQoT+6OIwxxhhjrIi4uyZjjDHGGGOMFSM88ApjjDHGGGOMFSPcyGOMMcYYY4yxYoQbeYwxxhhjjDFWjHAjjzHGGGOMMcaKEW7kMcYYY4wxxlgxwo08xhhjjDHGGCtGuJHHGGOMMcYYY8UIN/IYY4wxxhhjrBjhRh5jjDHGGGOMFSPcyGOMMcYYY4yxYoQbeYwxxhhjjDFWjHAjjzHGGGOMMcaKEW7kMcYYY4wxxlgxwo08xhhjjDHGGCtGuJHHGGOMMcYYY8UIN/IYY4wxxhhjrBjhRh5jjDHGGGOMFSPcyGOMMcYYY4yxYoQbeYwxxhhjjDFWjHAjjzHGGGOMMcaKEW7kMcYYY4wxxlgxwo08xhhjjDHGGCtGuJHHGGOMMcYYY8UIN/IYY4wxxhhjrBjhRh5jjDHGGGOMFSPcyGOMMcYYY4yxYoQbeUWQm5uLRYsWoVKlStDT04OZmRmaNWuGc+fOKcR98OABgoKCYGBgAIlEgjdv3nxRngkJCZBIJLh9+zYA4O7du5BIJDh48KDKZS5evIioqCjk5uaKwsPDw+Hn5/dF5SjM90z7S/KSSCRYunTpP1Ke/xUJCQkoX748tLW14eXlpda+8r9o6dKlkEgkP7oYSt29exdRUVF49epVoXGdnZ0hkUggkUigo6MDd3d3TJ48GVlZWaJ4/v7+6Nixo9plcHZ2xujRo4tc9vw6dOgAV1dXyGQyWFtbo1WrVrh161aBy6SlpaFFixZwdHSErq4u7O3tERERgSdPnijEvXPnDkJDQ2FqagoDAwNUqVIFycnJwvwtW7bAyckJtra2WLBggcLyAQEB/9gxrOqc+a18yXlcHStXrsS2bdu+RRH/EQcPHoREIsHdu3dVxomKioKDg0OR046KisL58+e/onT/O2bPno3Dhw//6GIwxv7luJFXBJGRkejXrx8aNGiAXbt2Yfny5fj48SN8fX2xb98+UdwJEyYgJSUFsbGxSEpKgqGh4T9WzosXL2L8+PHf7QfLv0FSUhKaN2/+o4vxj+rWrRucnJxw+PBhrFmz5kcXp1i6e/cuxo8fr1YjD/h8QyIpKQn79u1Ds2bNMGrUKEyZMkUUZ8GCBRg/fvz3KG6BcnNzMXLkSOzevRvz589HSkoKgoKC8PbtW5XLfPr0CYaGhpg0aRL27t2LqVOn4ujRo2jatClycnKEePfu3UONGjXw8eNHrFmzBtu2bUNISAjS09MBAKmpqQgLC0O/fv0wefJkDBw4ENeuXROWj4uLw8uXLxEZGfn9KiCPf+s589/WyFPHzz//jJ07dxZ5ufHjx3MjjzHG8tD60QX4t4iJicGqVauwZs0a0V33Zs2aoWHDhggLC8Pff/8NfX19AMDNmzfh6+uLevXq/agi/6dVr179RxfhH5Wbm4u///4bw4cPF55yFnS3nP0z7O3thX3R398fV65cwZo1azB27FghTtmyZX9I2TZs2CCa9vLyQunSpXHs2DEEBwcrXcbKygqrV68Whdna2qJevXq4fv06ypUrBwAYPHgwPDw8sGPHDmhofL6XWL9+fWGZU6dOwdnZGQMHDgTw+fx65MgRlC1bFtnZ2Rg6dCjmzJkjLMu+v6ysLGhoaEBTU/OHlsPBweGLnuQxxhgT4yuomubNmwd3d3d06NBBFK6hoYHx48fj2bNniImJAfC5q2BiYiLWrFkDiUQCf39/pWm+e/cOPXv2RMmSJSGTyeDu7o4ZM2aAiL64nCtXrkRERAQAQCqVQiKRIDw8XBRnx44dcHNzg6GhIRo3bqzQ1SolJQWtW7eGiYkJDAwMEBISgocPH6qV/4YNG4QuYCEhIXjx4oVo/t69e+Ht7S109Ro1apToCQAArFmzBh4eHtDR0YGrqytmz55dYJ7v3r2Dn58fqlatKnSLzd9dU97FbeLEibC2toalpSX69u2r0HVuypQpsLa2hpGREbp374558+aJug5mZmZiwIABcHBwgI6ODhwdHdG5c2eVZRs5ciSqVq0qTF+7dg0SiUT0hGLz5s3Q19dHdnY2ACAnJwcTJkyAi4sLdHR0UK5cOWzfvl1lHgkJCdDU1ERubi66dOkCiUSCqKgopXF/++03eHl5wcDAAI6OjujevTvevXsninPz5k3Url0burq68PT0xOHDh+Hg4CBK8/379wgLC4OBgQHs7OwwZ84cdOzYUWFfT0hIgK+vL2QyGaysrNC/f398+vRJFGf69OmwsbGBkZERunbtio8fP6pcV7njx4+jcePGsLKygrGxMWrXro2zZ8+K4ly+fBn169eHiYkJDA0NUaFCBaxfv15lmoUdjwkJCQgICAAAlC5dusBjW5Xy5csrHEv5u2sWtdx//fUXbG1t0b179686d5ibmwOAwjFR1OXevHmD7du3o0ePHiobaZmZmdDT0xOm9fT0hP1i8eLFcHFxETUKC/Lo0SN07twZJUqUgJ6eHipUqIC1a9cqxDt79izq168PQ0NDmJiYoG7durh+/XqB50xlXWnzdyf+Fufx6tWro2vXrgrhtWrVUjh/y/n7+yMxMRHr1q0TugUnJCSo7Arq5+cnSkve7X39+vUoU6YMdHV18eDBA2GdlyxZghIlSsDExATt27dXeML7559/okGDBjAwMICJiQnCwsIUXktYu3YtnJ2doa+vj1atWqn1BDx/d82VK1dCIpHgypUr8PPzg56eHipVqoSkpCQhjnx7dO3aVagLuTNnzqB27dqQyWSwtLREr1698OHDhwLLEBcXB39/f5iZmcHc3ByNGzcWutoW5NatWwgODhbqpH379nj+/LnCusjP9QCQnZ0NiUSClStXAvh8rbp37x4mTZokrIv8ht2zZ88QHh4OS0tL6OnpwcvLCzt27FA7f3m34SNHjqBBgwZCGn/++SfS0tLQtm1bGBoawt3dHQkJCaJ1S09Px8CBA2Fvbw8dHR1UrVoVx44dK7ROGGM/Djfy1JCVlYVTp04hODhY6btCNWrUgLm5uXDCS0pKQoUKFdC4cWMkJSUpfd8EAD58+ACpVIoZM2Zg79696N27NyZMmIBZs2Z9cVmDg4OF93WOHz+OpKQkjBkzRph/+/ZtTJgwAVOmTMHSpUtx7tw59OnTR5ifmpqKWrVq4eHDh1i+fDnWrVuH+/fvIzg4uNCuTLdu3cKECRMQHR2NJUuW4PTp06IG0MWLF9GkSRO4uroiNjYWAwYMwPTp0zFq1Cghzq5du9C5c2f4+/sjLi4O7dq1w8CBA7Fo0SKleb59+xYNGjRAVlYWDhw4ABMTE5XlW7VqFf7880+sWrUKw4YNw8KFC0Xprl27FiNHjkRERARiYmKQlZWFqVOnitKYMmUKNm7ciEmTJmH//v2YNm0apFKpyjx9fX1x4cIFoZvaiRMnoKuri+PHjwtxTpw4gWrVqkFL6/OD9V69emH69Ono168fdu3ahbp166JVq1Y4c+aM0jwqV64spDd69GgkJSXh559/Vhr36dOnGDJkCHbv3o1p06bh+PHjohsXubm5aNq0KVJTU7F+/XoMHz4cPXr0UPiB179/f8TGxmL69OlYvHgxNm3apPCj4NixY6hXrx5KlSqF2NhYTJkyBevWrcPgwYOFOJs3b8aQIUPQqVMnoc4nT56ssj7l7t+/j6CgIKxduxYxMTEoWbIk6tSpgwcPHghxmjVrBm1tbWzYsAGxsbHo0qVLge/GFnY8Vq5cGfPnzwfw+clTQce2Kg8ePICzs3OBcYpS7qtXr8Lf3x8hISFYtGhRkd9lJCJkZ2fjwYMH6N+/P0qWLKlW4yo3NxdZWVm4ffs2Ro4ciZo1a6JixYoAgAsXLiA7OxtEhBo1akBLSwtOTk6iuvLy8sKlS5dw/PhxXL16FQcPHoS3tzfS0tIwceJETJ8+Xe11ePHiBRwcHDB//nzs3r0b7dq1Q2RkpKgb45UrV1C7dm1kZGRg+fLl2LBhA6pWrYqnT58Wes4szLc4j8vPOXlvcNy5cwfHjx9HWFiY0mUWLFiASpUqoUGDBkhKSkJSUhIqV66sdp4AcOPGDUyaNAkTJ07Ezp07YWpqCgBITEzEhg0bsGDBAkyfPh1xcXGiLsU3b95ErVq1oKWlhQ0bNmDRokU4duwYOnXqJMQ5deqUcC7ftm0bXFxcMGDAgCKVL6+OHTuic+fO2LZtG3R1ddGqVStkZmYCgNDgGzFihFAXwOfzXd26daGpqYnNmzdj0qRJWL9+Pbp06VJgXvfu3UPbtm0RExOD1atXQ1NTE7Vq1SqwcZiRkYHAwEA8fPgQq1evxvz583Hs2LEivzYQGxsLGxsboat3UlISbG1tkZ6ejjp16uDQoUOYOnUqdu7cibCwMOGcV5T8u3fvjuDgYGzbtg2amppo06YNIiIi4Onpia1bt8LJyQlt2rQRbrwQEVq1aoWNGzdi/PjxiIuLQ8mSJdGgQQPROZcx9j+GWKGePHlCAOj3339XGcfLy4saNmwoTPv6+lJYWJjaeeTm5lJWVhZNmDCBypUrJ4QfOXKEANCtW7eIiCglJYUA0IEDB1SmtWLFCgJAWVlZovCwsDCSSqV0//59IWz69OmkpaVFOTk5REQ0atQosrW1pbdv3wpxHj58SNra2hQbG6syz7CwMAJAly5dEsLi4+MJAF28eJGIiFq3bk2enp6Um5srxJk6dSrJZDJ6+fIlERH5+PhQcHCwKO0ePXqQra2tUMawsDDy9fWlN2/eULVq1ahatWr05s0b0TIAaMmSJcK0k5MTlS1bVkiDiKhJkyYUGBgoTHt5eVHbtm1F6fj4+FDewyQ4OJgGDhyosh7ye/XqFUkkEjp06JBQ9u7du5OGhgY9e/aMiIi8vb1pzJgxRER08+ZNkkgkFBMTI0onODiYmjdvrjKfrKwsAkArVqwQwgrbV7Kzs+no0aMkkUjo+fPnRES0fft2AkDXrl0T4u3atYsA0Lhx44iI6Pnz56StrU0LFy4U4rx48YJ0dXWpTp06Qpivr6/CtoyJiSFtbW16+vQpERFVrlyZWrZsKYpTuXJlKsqpKScnh7KysqhUqVIUHR0tlCf//lgU6h6PBXFycqKRI0dSVlYWvXv3jjZv3kza2tq0Zs0aUbw6depQhw4d1C63k5MTjRo1ii5dukSWlpbUq1cv0TFVFFOmTCEABIBcXV3VWi8iou7duwvLVa5cWdh/iIjWr19PAMjIyIhGjBhBhw4dokGDBhEA0Tlk9OjRQhrh4eFERDR06FDq1q3bF60L0f9tty5duoj2vdDQUHJ1daXMzEyly6k6Z+bdNnJLlixRuX9+6Xn8zZs3JJPJaOPGjcIy48aNIycnpwK3rbLyqTru81+TwsLCSENDg65fv66Qprm5Ob1//14I69OnD7m6ugrTHTp0oIoVK1J2drYQlpycTADo/PnzRETUsmVL8vb2FqXdsmVLAkApKSkq12ncuHFkb28vTMu3zYYNG4Sws2fPEgA6evSoEJb/nE9ENHjwYLKwsKD09HQhbOPGjSSRSOjq1asqy5BXdnY2paenk56ensJ5Oa958+aRVCqlR48eCWGnTp0iABQfHy9al7z7mbJzt/wYz2v+/PmkqampsL2Kkr98P5w2bZoQR36d7tOnjxB27do1AiBctw4cOEAAKDk5WYiTm5tL5cuXp379+qmsE8bYj/WffpJH//8utvzvR7x0v3jxYpQrVw4ymQxSqRRjxoxRq1vIlypTpgwcHR2FaQ8PD2RnZwtdOg4dOoQGDRpAJpMJ9WJtbQ13d3elo4jm5ezsjPLlywvTDRo0gLa2tjCi3tmzZ9GyZUvR04bQ0FBkZGTgypUryMnJwcWLF9GqVStRuqGhoXjy5Imom9vbt29Rr149aGhoYP/+/TA2Ni503QMCAkTdxzw8PPDo0SMAn7vMXLp0SeFdpCZNmoimvby8sHLlSkyfPh1Xr14tNE9TU1OULVtWeMp7/PhxNG3aFB4eHjh27Bjev3+PP//8E76+vgCAw4cPQ1tbG8HBwaJ9MzAwsND6V0diYiJq1aoFY2NjaGlpoXbt2iAi/P333wCAc+fOwdXVFR4eHsIyDRs2FL2nc/nyZWRmZorqysLCQvQeZHp6OpKSktC6dWvRevj7+yMzMxNXrlxBdnY2/vzzTzRr1kxUxqZNmxa6Hi9evMDPP/8Me3t7aGlpQSqV4vbt28KxY2ZmBkdHR/Ts2RMxMTF4+fKlWvXzrY/HyZMnQyqVwtDQEG3atEFkZGSBI2mqW+7Lly8jMDAQbdq0wfz58794NNLw8HCcOXMGsbGxsLOzQ8OGDdWqq5EjR+L06dNYv349Pn36hCZNmghPVeTn0UaNGmHy5MkIDAzE9OnTERwcLHpCN2HCBDx79gwPHjzAihUrcO/ePaxYsQK//vor7t+/j8DAQJiZmaFVq1YFPoHNycnB5MmTUapUKejo6EAqlWLZsmWi7ZaYmIiffvqpwKfuX+Nr9xtjY2O0aNFC9L6j/P3v7znSrKurK9zc3BTCq1evLrxfDojPlcDn60SLFi1E11AvLy8YGxsLg5+cPXtW4VhW59hWJSgoSFQeAKIyKXP27Fk0btwYMplMCJNfg/J3784rJSUFoaGhsLa2hpaWFvT09JCenl7gNj179ixq1KgBOzs7IaxatWpwdHQUjSr7pRITE1GtWjWl26uo+QcGBgr/L1myJACIup7Lwx4/fgzg8/Z2cXGBl5eXsL1zcnLg7+//Ta5LjLHv4z/dyEtMTIRUKhX+VI3kZm5uDm1tbdy/f19lWg8ePBCdXNWxefNm9OjRA8HBwYiLi8OZM2cwfPhwhXeWvqX83Rm1tbUBQOgmlJqaipUrV4rqRSqV4tKlS4V2y7C0tBRNa2howMzMDE+fPgUAPHnyBFZWVqI41tbWwrwXL14gOzu7wDhy9+/fx7lz59C2bVsYGRmps+pK1z3veufm5grvF8lZWFiIpkePHo2ePXvi999/h6enJ1xcXBQGosjPz88Px48fx7Nnz5CSkgJfX1/4+vri+PHjOH36NHJzc1GjRg2hHJ8+fYKenp6o/gcNGoTHjx9/1Y2IlJQUNG7cGGZmZlizZg1OnTolvEcqr4fnz58r1IGmpqbQjUseB0CBdfX69Wvk5uYiIiJCtB7yfeTBgwdITU1FTk6Own6Tf1qZsLAwHDhwAOPHj0dCQgKSk5Ph6ekprIeGhgb27dsHS0tLhIWFwdraGvXr18fNmzdVpvk9jsfIyEgkJyfj8OHDaNWqFRYtWiR6hyY/dct97NgxvHnzRniX7EvZ2NigSpUqCAkJQXx8PF69eoXFixcXulyJEiVQtWpVtGvXDnv37kVycjK2bNkCAMK+In9/Uc7f3180gibweSAX+ftXI0aMQL9+/WBtbY1ffvkFlSpVEs45v/76q8qyzJgxA+PHj0eXLl2wZ88eJCcnIywsTNT18eXLl7C1tVWjRoruW+03ERER2L9/P549e4bjx4/jzp07Bb7v+y3kP9fKKTtX5l2f1NRUREVFKVwn0tLShG327NmzLzq2VclbpvzXLVWUXXOkUinMzMyUfvYD+HzToFmzZrh+/TpmzpyJ48ePIzk5GWZmZgXmpywv4PP1S1VeRVHYPlyU/PPeFJXXpbKwvNfHlJQUhe09d+5c7q7J2P+w//Tomt7e3qI7XPl/0MtJpVJUr14de/bsQXR0tMKd1dOnT+Ply5eoVatWkfKPjY1FQEAApk2bJoR9ydDR35KZmRlCQ0MxdOhQhXn5f9Tnl3+QldzcXLx69Qo2NjYAPo/Cl/clcODzDwH5PEtLS2hpaRUYR87T0xMdOnRA37594eDgoPD0r6gsLCygoaGB1NRUUXj+aV1dXUycOBETJ07EX3/9hZkzZyI8PBwVK1YU3knKz9fXF+vWrUNiYiLKlSsHExMT+Pn54ffff4epqSnKly8vNFTNzMwgk8lw9OhRpWl9zWiD+/fvR05ODmJiYoSLeP53TKysrBTWOScnB69fvxbFAT7XTYkSJYTwvMuZmJhAIpFgypQpqFu3rkJZnJ2dYWJiAk1NTYX9Jv90fhkZGdi3bx9WrFgh+hGclpYmiufh4YHY2Fh8+vQJR44cwaBBg9CuXTuVd56/x/Foa2sLHx8fAEDt2rVRuXJljBgxAs2aNVP5hEadcvfo0QMpKSlo1KgRTpw4gdKlS39VOQHA0NAQJUuWxJ07d4q0nIODA8zNzYXl5E9ZSMnAI6r23zNnzuD48eNYtmwZgM834CZNmgR9fX1ERESIRiPNLzY2Fh06dMCIESOEsPw3Q8zNzb/oh7aOjo7whFIu77Egz/9b7DeBgYGws7PD+vXr8ddff6FGjRooU6bMF5UZQKHlBvDFTwnNzMzw008/id7Bk5Pf7LS2ti7ysf2tKbvmZGVl4dWrVyobTLdv38aVK1eQmJiI2rVrC8sU9q1bW1tbpcfOs2fPhLzybhv5O9jKtosy5ubmBQ6Apk7+X8rMzAyurq7YtGmTwjz5tYQx9r/nP/0kz9DQED4+PsJfQQMi9OnTB9euXVMYdjw3Nxfjxo2DtbU1QkNDi5R/RkaG6ASZm5sr3A3/GvI0v+QJRGBgIK5evYqKFSuK6sbHxwcuLi4FLnv37l1cvnxZmN63bx8yMzOFH7lVqlTB9u3bRT/+tmzZAplMBk9PT2hqaqJSpUoK333asmULbG1tFYbV7tmzJ6KiotChQ4ev/qaQlpYWKlSogPj4eFH4rl27VC7j4eGBmTNngohw48YNlfH8/Pzw/v17zJs3T/i8gZ+fHy5evIi9e/eKPuweEBCAjIwMfPr0SaH+5fX4pTIyMqClpSXqerl582ZRHG9vb6SkpOCvv/4Swvbu3SsaAVX+wfW8dfXy5UucOnVKmNbX10e1atVw+/ZtpethYWEBLS0tVKxYEXFxcaIyFPYD+dOnT8jNzRUdO6dOnVJ5R1lHRwcNGzZEjx49ROuVnzrH49ccW5qamvj111/x119/FbhfqVNuDQ0NrF69Gt7e3qhXr16h3dbU8fLlS9y4caPQgWHyu3nzJlJTU4XlXFxc4O7urnBMHj58GBUqVFCaxuDBgzFp0iRRtzr5YEXyf1XJv90+fPigcBwHBARg06ZNKkcOVbVdHRwcFI7t/KNWfqvzuIaGBsLCwrB8+XLExMSoHHAlf7nzl1nexTBvue/fv1/oh+6LIjAwENeuXVN6bMsbeT4+PgrH8ve8iSmVShXqokqVKtizZ4/oCVxsbCyISOX5NCMjA4C48bJ169ZCe1FUqVIFp06dEt1MOHPmDB48eCCMsCy/huXdNvn3J3ne+dclICAAp0+fVrkd1cn/S8kHdDE3N1fY3qqOacbYj/effpJXFKGhoQgLC0NERASuXLmCevXq4e3bt1iwYAESExOxY8cO0TsM6ggMDMSAAQMwe/ZsuLu7Y/HixYX+oFGHvM/+vHnzEBgYCEtLS7V/uA0cOBBr1qxBUFAQevXqBRsbGzx+/BgHDx5E+/btlT6VkbOyskLbtm0xYcIEfPz4EYMHD0aDBg3g5eUFABg1ahS8vb3Rtm1bREZG4urVqxg7diz69+8PMzMzAMDYsWPRtGlT9O7dG82bN8exY8ewaNEiLFiwQOlTgNGjR+Ply5cICQnBkSNH4O3tXbTKymPQoEHo1KkTXFxcEBAQgI0bN+Lx48eiu90tWrSAj48PKlWqBG1tbaxatQp6enqoVq2aynRdXFxgZ2eHY8eOoUePHkKYtbU1kpKS0LdvXyGuu7s7unXrhpYtW2LYsGHw8vIS3tt7//49fvvtty9eP39/f6Snp6NHjx5o27YtDh06hP3794viNG3aFGXKlEGrVq0wceJEZGRkYPz48TA0NBTq39LSEp06dcLQoUMhkUhgb2+PKVOmwMzMTLSNfvvtN9SrVw9EhJCQEMhkMty5cwc7duzA+vXrYWRkhCFDhqBdu3YYMmQIgoKCsGHDhkKfuJiYmKBSpUoYP3489PX1kZaWhrFjx4ruVl+6dAlDhgxB27Zt4erqiufPn2Pu3Lmid1HyU+d4LF26NDQ0NLB06VK0a9cOxsbGKt+RUaZZs2YoW7YsZs6cqfT9pKKUWyqVYuvWrahXrx4aNGiAY8eOCV0lnZ2d4e/vLwzLnt/u3buxYcMGBAcHw9bWFg8ePMCMGTOgo6MjGnmwS5cuSExMFN5F+uOPP3Dx4kUEBATA0tISN2/exNSpU1GqVCm0bNlSWO7XX39F27ZtMWzYMAQFBSE+Ph579uzBoUOHFMqybds2pKeni95VrFWrFsaNG4f+/fvjt99+Q506dVTWaWBgIJYsWYKqVavC0tIS0dHRMDAwEMUZO3YsqlSpgqCgIPzyyy8wMDDAsWPHEBQUBH9/f5XnzJCQECxfvhyjRo2Cv78/YmNjFbqcfsvzeHh4OCZOnAhtbW20bdu20Phubm7YunUr9u/fDzMzM+HTOM2aNcO0adNgb28PqVSKyZMnC+fYbyEqKgpVqlRBixYt0LlzZ5iYmOD+/fuIj49HVFQUPDw8MGjQIOGzDe3atcPBgwdFN4K+NXldeHl5QUdHBz4+Phg4cCAWLlyI4OBgDBgwAE+ePMHQoUPRpk0bld+ndHd3h62tLQYNGoSxY8fi7t27mDp1aqGvBURGRmLKlClo1KgRxo0bh48fP2LYsGGoXr06GjZsCODzO3K2trbo06cPxowZgwcPHggj9uZfl71796JJkybQ19dHhQoVEBYWhjlz5qBu3bqIioqCs7Mzrly5Ag0NDfTp00et/L9U/fr1ERAQgLp162LYsGFwc3PD69evcebMGVhYWGDQoEEAIPw+UHacM8Z+gB835su/T05ODi1cuJC8vLxIV1eXTExMqEmTJnT27FmFuOqMrpmZmUm9e/cmMzMzMjc3p/79+9OCBQtEI7d9yeiaRETDhw8nGxsbkkgkQjnko1LmJR81K+9oZ/fv36eOHTuShYUF6ejokKurK/3888907949lfnJ0167di05OTmRrq4uNW3aVBhBUi4+Pp4qVapE2traZGtrK4w+mNeqVavI3d2dpFIpOTs708yZM5XmJZebm0udO3cmS0tLYeQxKBldM/9oZaNGjSInJydR2KRJk8jS0pIMDAwoLCyMJk6cSMbGxsL8adOmUeXKlcnQ0JCMjIyoVq1alJCQoLJe5EJDQwmAqA7lYXlHOyX6vJ9FR0eTm5sbaWtrk5WVFdWrV4927NihMn11R9dcvHgxOTo6kp6eHjVt2pSOHj1KAOjIkSNCnBs3blCtWrVIR0eH3NzcaO/evWRkZESzZs0S4rx79446depEenp6ZG1tTdOmTaOmTZsqjAB6/Phxqlu3LhkYGJCBgQGVL1+eRo4cKRrl8LfffiMrKysyMDCgiIgImjNnTqGja167do18fX1JJpORh4cH7dixQ3TMPX36lNq3b0/Ozs6ko6NDtra21KVLF0pNTVWZpjrHIxHR77//To6OjqShoSEaTTQ/ZfscEdHq1asJAJ07d46IxCMkqlPu/Om+fv2aypcvTzVq1KAPHz4QEZGlpSUNGTKkwPpr2rQp2djYkLa2Nrm4uFB4eDjdvXtXFC8sLEx0jBw/fpyCgoKEc0Pp0qWpX79+9OLFC4U8li9fTqVKlSKpVEru7u60fv16hTiZmZlUqlQphWMoJSWF/Pz8yMDAgJo2bUqvXr1SuS5paWnUrl07MjIyIltbW5o0aZLSYzs5OZkCAwNJJpORiYkJ1a1bVzRSobJzZm5uLv36669kZ2dHxsbG1KdPH5o/f75on/jW5/Fy5cpRaGioyvXN6969e8Lxlfc4fvz4MTVu3JgMDAyoVKlSFBsbq3R0zfzXAyL1RxS9cuUKhYSEkImJCclkMnJzc6N+/fqJttWqVauoRIkSJJPJqHnz5rRx48YvHl2zsBEpDx8+TJ6enqStrS0q6+nTp8nPz490dHTI3NycevToIRo5VJkTJ05QxYoVSVdXl7y9venkyZNkb28vjDCsyo0bN6hRo0akp6dHRkZG1K5dO4Vr4MmTJ8nLy4tkMhnVrFmTLl26pLAuFy9epGrVqpFMJhPV19OnT6ljx45kampKMpmMvLy8RNeFwvJXNjqwqn0x/zU0IyODRowYQc7OziSVSsnOzo6aN28uGuG0Tp06BZ4TGWP/LAnRV3w9l7Firnnz5nj16tV/+qOvFy5cQOXKlXHw4EGVT3IzMjLg6uqKn3/+GRMmTPiHS8jyunfvHlxdXXHr1i24urr+6OKwInj48CGcnZ2xY8cOhZF+GWOMsaLg7pqM/X+PHj3CnDlzULt2bWhqaiIuLg5xcXFYv379jy7aP2r+/PmQyWRwdXXF3bt38euvv6Js2bKiIbb379+Py5cvw8vLC2/fvsXcuXO/yWiP7OslJSUhJCSEG3j/Iu/evcPVq1fx22+/oUSJEl/dvY4xxhjjRh5j/5+uri4uXLiApUuX4t27d3BxccHChQvRrl27H120f5SWlhamTp2K+/fvQ0dHB3Xr1sXs2bNFA7bo6+tjw4YNiIqKEr6RtXfvXm5Y/A/46aef8NNPP/3oYrAiOHfuHAICAuDq6ooVK1aIjjXGGGPsS3B3TcYYY4wxxhgrRv7Tn1BgjDHGGGOMseKGG3mMMcYYY4wxVoxwI48xxhhjjDHGihFu5DHGGGOMMcZYMcKNPMYYY4wxxhgrRriRVwS5ublYtGgRKlWqBD09PZiZmaFZs2Y4d+6cQtwHDx4gKCgIBgYGkEgkePPmzRflmZCQAIlEgtu3bwMA7t69C4lEgoMHD6pc5uLFi4iKikJubq4oPDw8HH5+fl9UjsJ8z7S/JC+JRIKlS5f+I+UpzOzZs3H48GFRmDrbUV0rV67Etm3bvjqd7y0qKgoODg7CtKr99EuNHj0azs7OBcbx9/dHx44dv0l+P9K/ZZuzbyshIQEzZ85UCP9W+3VMTAxKlSoFLS0thISEKFx//i3UORcAwPv37zFo0CDY29tDV1cXbm5uWLFihSjO7t27UbVqVRgYGMDe3h5du3bFy5cv1S7Ls2fPYGRkBIlEguzsbCH8xo0b6NmzJ9zc3KCnpwd3d3f89ttvojgAMHfuXNja2sLZ2RmxsbGieVlZWXBzc/sm1xG5kSNHwsbGBhKJBLNnz1Ya53tf0xhj3wY38oogMjIS/fr1Q4MGDbBr1y4sX74cHz9+hK+vL/bt2yeKO2HCBKSkpCA2NhZJSUkwNDT8x8p58eJFjB8//pv9eP43SkpKQvPmzX90MQAovyB+S//WH/w/Yj9dsGABxo8f/4/l9738W7c5+zqqGnnfQlZWFiIiIuDv74/ExERMmzbtu+TzvyI7OxuNGjXC3r17ER0djT179qB///7IysoS4iQnJ6N58+bw9PTE9u3bMWXKFMTHx6N9+/Zq5zNy5EjIZDKF8AMHDuDs2bMYMGAA4uPj0a1bN0yYMAEjR44U4ly9ehVDhgzBlClT0KdPH3Tq1AmpqanC/IULF6JUqVIICgr6wloQO3nyJKZMmYJRo0YhKSlJ5fc2v/c1jTH2bfDH0NUUExODVatWYc2aNaI7ps2aNUPDhg0RFhaGv//+G/r6+gCAmzdvwtfXF/Xq1ftRRf5Pq169+o8uwv88IsKnT5+gq6v7o4vyjylbtuyPLgIrxjIyMpT+oFcV/r/k0aNH+PDhA9q3bw9fX18AwOPHj39wqb6fxYsX49q1a7hx4wYsLCwAAAEBAaI4W7Zsgb29PZYtWwaJRALgc4+eyMhIpKWlwdjYuMA8zp8/j23btmHkyJEYOnSoaF67du3Qp08fYdrf3x9ZWVmIiorClClToKmpiSNHjqBu3boIDw8HACxfvhynT59GcHAw3rx5g8mTJ3/TxtbNmzcBAL1794aGBj8DYOzfjo9iNc2bNw/u7u7o0KGDKFxDQwPjx4/Hs2fPEBMTA+BzV8HExESsWbMGEokE/v7+StN89+4devbsiZIlS0Imk8Hd3R0zZszA13yffuXKlYiIiAAASKVSSCQS4QIht2PHDri5ucHQ0BCNGzfGkydPRPNTUlLQunVrmJiYwMDAACEhIXj48KFa+W/YsAGurq6QyWQICQnBixcvRPP37t0Lb29v6Orqwt7eHqNGjUJOTo4ozpo1a+Dh4QEdHR24urqq7DIi9+7dO/j5+aFq1apCt9j83TWdnZ0xevRoTJw4EdbW1rC0tETfvn1Fd20BYMqUKbC2toaRkRG6d++OefPmCRd3AMjMzMSAAQPg4OAAHR0dODo6onPnzirL5uzsjHv37mHSpEmQSCSQSCS4e/euMP/t27fo2LEjDAwM4OLigj/++EO0fFxcHPz9/WFmZgZzc3M0btxY1HVKftd93bp1QvoJCQlKy7Jy5UpIJBIkJyejRo0akMlk2LhxI4DPP2YqVaoEXV1dODg4YMKECaL98PLly6hfvz5MTExgaGiIChUqYP369cJ8Zd1jO3bsqHLfL2w/Law8ubm5GDp0KExNTWFubo4RI0ao9UQwf7c2edffwo6J/I4fP47GjRvDysoKxsbGqF27Ns6ePVto/k+fPkW7du2EY6tJkyb4+++/hfmqusc5ODggKipKWAdV2/z9+/fo16+f0P3Mw8MDS5YsUTt/eZerLVu2oG3btjAwMEDp0qVx6NAhZGZmolevXjAxMYGLi4uw78jl5ORgwoQJcHFxgY6ODsqVK4ft27cXWid57d+/H1paWnj37p0QZm1tDVdXV2H6+fPnkEgkOHnypBBW2P6izLeqq02bNqFNmzYwNDREz549hW148OBB1K9fH3p6evjtt98AfN6+vr6+kMlksLKyQv/+/fHp0ydRuQ4cOICaNWtCT08PFhYWaNq0KZ49e4aoqCiMHz8ejx49Era7fJ/Ia9GiRTA2NkZGRoYofNmyZTAwMMD79+8Vllm5ciVcXFwAAHXr1oVEIsHKlSuV1tugQYPg4eEBPT09lCxZEiNGjEBmZqYozunTp1G5cmXo6uqiWrVqOHfunEKaz549Q7NmzSCTyVCyZEls2rQJfn5+Cter73UuWLlyJUJDQ4UGnjLZ2dnCKxdyxsbGICK1rtP9+/fH8OHDYWlpqTDP3NxcIaxChQr4+PEjnj59CuDz9UZPT0+Yr6enJ+wvkyZNQosWLYp046qga2t4eLhwTtbU1FS4Vsl97TUN+PwEUl6OkiVLYvHixaL58vP0/Pnz4eDgACMjIwwZMgREhK1bt6JMmTIwMTFBZGSkwvHDGMuDWKEyMzNJW1ubBg0apDKOubk5RUZGEhFRUlISVahQgRo3bkxJSUl09epVpcs8efKE+vbtS7GxsZSQkEBz5swhY2NjmjFjhhDnyJEjBIBu3bpFREQpKSkEgA4cOKA0zefPn9Po0aMJAB0/fpySkpLo9u3bREQUFhZG1tbW5O3tTVu3bqWNGzeSlZUVtWzZUlj+xYsXZG9vT9WqVaOtW7fS9u3bqVKlSlShQgXKyclRuf5hYWFkZWVFHh4etGXLFlqzZg3Z2NhQw4YNhTgXLlwgTU1Nat26NcXHx1N0dDRpa2vTsGHDhDg7d+4kANSjRw/au3cvjRw5kiQSCS1cuFCUl6+vLxERpaWlUY0aNahq1ar05s0bIQ4AWrJkiTDt5OREDg4O1Lp1a9qzZw9FR0eTpqYmzZkzR4izZs0aAkDDhg2jvXv3UkREBNnb21PewyQqKopsbGxo5cqVlJCQQOvXrxe2uzLnz58nGxsbCg8Pp6SkJEpKSqKPHz8K29HJyYlGjRpF+/fvp8jISAJAly5dEpafM2cOLViwgA4ePEi7du2iJk2akI2NDb1//56IiK5evUqVKlWiBg0aCOmnpaUpLcuKFSsIAJUuXZrmzp1Lhw8fpuvXr9P69etJQ0OD+vfvT/v27aNZs2aRnp4eTZ8+XVjW2dmZgoODKT4+ng4cOECzZ8+m+fPnq6xvIqIOHTpQnTp1hOlx48aRvb09ERW8n6pTnt9++420tLRo8uTJFB8fT8HBwWRra0tOTk4qtwURUZ06dahDhw7CtDrHhDLr1q2jGTNm0L59+2jfvn0UHh5Oenp6dP/+fZXL5Obmko+PDzk6OtK6deto69at5OnpSU5OTvTx40ciUjze5ezt7WncuHFEpHqb5+TkUJ06dcjExIR+//13OnjwIC1cuFBYTp385ftliRIlaPz48bRv3z6qX78+mZqaUmRkJPXv35/2799PHTt2JG1tbXry5IlQxm7dupGRkRHNmjWL9u/fT3379iUNDQ06ffp0gXWZV1paGmlqatK+ffuIiOjmzZsklUpJQ0ODHj16RERE27ZtI11dXfr06RMRqbe/5Pct68rGxoYGDRpEBw8epBMnTgjb0NHRkSZOnEiHDx+m8+fP09GjR0lLS4s6d+5Me/bsoaVLl5KFhQX16dNHKNf+/ftJU1OTmjdvTjt27KAdO3ZQ79696a+//qIHDx5Qly5dyNLSUtjuDx48ICLxfv369WvS1dWl9evXi9a5Vq1a1LlzZ6X18fz5c4qJiSEANH/+fEpKSqLnz58r3R8jIyNp8+bNlJCQQMuWLSN7e3vq27evaBuamZmRr68vxcXF0cKFC8nNzY0A0IoVK4R4devWJVtbW1q7di1t3bqVypUrR9bW1hQWFibE+V7ngk+fPpGWlhb9+uuv1LZtW9LR0SFTU1Pq3bu3sH2JiM6ePUva2to0f/58evPmDf3111/k4+ND7dq1U5m23KZNm8jJyYkyMjKEc29WVlaBy4wZM4YMDQ0pMzOTiD6fD0xNTenq1auUmJhIUqmU/v77b7pz5w5ZWFjQs2fPCi2HXGHX1tu3b9OIESMIgOhald/XXtMmT55MOjo6FBUVRQcOHKBx48aRpqYmbdmyRYhTp04dsrOzo/r161N8fDxNnTqVAFD//v2pWrVqtGPHDlq8eDHp6OiIfi8xxsS4kaeGJ0+eEAD6/fffVcbx8vISNWh8fX1FF6vC5ObmUlZWFk2YMIHKlSsnhBe1kUdEKi8oYWFhJJVKRT9Ep0+fTlpaWkIDbtSoUWRra0tv374V4jx8+JC0tbUpNjZWZZ5hYWEKJ/P4+HgCQBcvXiQiotatW5Onpyfl5uYKcaZOnUoymYxevnxJREQ+Pj4UHBwsSrtHjx5ka2srlFHeyHvz5g1Vq1aNqlWrJmrgESlv5JUtW1bUUG3SpAkFBgYK015eXtS2bVtROj4+PqJGXnBwMA0cOFBlPSgjv+jlJd+O3bt3F8IyMzPJzMyMfv31V6XpZGdnU3p6Ounp6VFMTIwQnr/hoop8v8hbL7m5ueTo6Ei9e/cWxY2OjiYLCwvKzMykFy9eKGzb/IrayMtbnrz7qTrlycrKIisrK9F2+PTpE9nY2HxRI6+wY6IwOTk5lJWVRaVKlaLo6GiV8Xbt2kUAKDk5WQi7f/8+SaVSWrBgARGp18hTth5ERLt37yYAdPDgwS/OX75f9urVS4hz7do1AkBNmjQRwt6+fUtaWlq0bNkyIvrcGJNIJKL9kujz8dK8eXOVdaKMl5cXjRkzhog+7yPVq1enihUr0qZNm4iIaNCgQVSrVi0iUm9/UeZb1lX+7SDfhvmPeV9fX4VzW0xMDGlra9PTp0+JiKhKlSrCuimT/xiSy78/tGvXTnQ9unPnDkkkEjp06JDKtG/dukUA6MiRIwrrkn9/lMvKyqLVq1eToaGhcLzMnj2bZDIZvXr1Sog3b948USPv4sWLBID27NkjxLl8+TIBEK6b3/Nc8PjxYwJABgYGFBISQgcOHKD58+eTvr4+9evXTxQ3Pj6eDAwMCAABoKCgIEpPT1eZNhFReno6OTk5CQ1tdRp59+7dIyMjIxo6dKgovFOnTkLe8nNAmzZtaPLkyQWWIT91rq1LliwRXe9U+dJrWlpaGunp6SmcJ3v37k0VK1YUpuvUqUMWFhb04cMHIaxq1aqkra0t3Owh+lwPBR0vjP3X/ae7axIRsrOzhb8fMVDJ4sWLUa5cOchkMkilUowZM+a7jmRWpkwZODo6CtMeHh7Izs7G8+fPAQCHDh1CgwYNIJPJhHqxtraGu7u70lFE83J2dkb58uWF6QYNGkBbWxvJyckAgLNnz6Jly5airi+hoaHIyMjAlStXkJOTg4sXL6JVq1aidENDQ/HkyRNRl9G3b9+iXr160NDQwP79+wt9NwL4/L5F3vcMPDw88OjRIwCfu+VcunQJwcHBomWaNGkimvby8sLKlSsxffp0XL16tdA8C5P3hXmpVIqSJUsKZQI+d50NDQ2FtbU1tLS0oKenh/T09K/aRxo1aiT8/+bNm3jw4AFat24tOhYCAgKQmpqK+/fvw8zMDI6OjujZsydiYmKKNLJcUalTngcPHuD58+do1qyZsJy2tjYaNGjwRXkWdkwo8+LFC/z888+wt7eHlpYWpFIpbt++XeB2OXv2LEqUKAEfHx8hzNHREdWrVxeOka+RmJgIOzs71K1b96vzDwwMFP5fsmRJABB1vTU0NISlpaXwztbhw4ehra2N4OBg0XYLDAws9LyRn6+vL44dOwbgc7dYPz8/+Pn5icLk74yps78o8y3rKu/xpCo8PT0dSUlJCuX09/dHZmYmrly5gg8fPuDs2bPo1KmTmjWlWkREBA4cOCB0+1uzZg0cHBwU3jn7Etu2bYO3tzf09fUhlUrRuXNnvHv3Tjhezp07hxo1asDU1FRYJv959Ny5c9DS0kL9+vWFME9PT9GImN/zXCC/1puammLz5s0ICgpCr169EBUVhYULF+LDhw8APndT79SpEyIiInD48GGsX78ed+7cKbCLPgBMnz4dNjY2Kgcuye/jx49o27YtHB0dMXbsWNG81atX48GDB0KX3VOnTuH06dMYMGAALl26hCpVqsDCwgJdu3ZV6DYrV5Rr69cq6JqWlJSE9PR0tGrVSmGbXr58WVT+6tWri7qqlixZEmXKlIGdnZ0orDi/N8rY1/pPN/ISExMhlUqFv8jISKXxzM3Noa2trfIHA/D5kwl5Tz7q2Lx5M3r06IHg4GDExcXhzJkzGD58+HftY25iYiKa1tbWBvD5IgMAqampWLlypahepFIpLl26hAcPHhSYdv73DjQ0NGBmZib80Hjy5AmsrKxEcaytrYV5L168QHZ2doFx5O7fv49z586hbdu2MDIyUmfVla573vXOzc1VeE8i//sao0ePRs+ePfH777/D09MTLi4uWL16tVr5F7VMOTk5aNasGa5fv46ZM2fi+PHjSE5OhpmZmRDnS+StX/lIbQEBAaLtLf9x++DBA2hoaGDfvn2wtLREWFgYrK2tUb9+feEl/W9JnfI8e/YMgOL+puy9F3UUdkwoExYWhgMHDmD8+PFISEhAcnIyPD09C1xG2f4PfN6/C3sHUB0vX76Era3tN8k/700TeX3kv5GS//j59OkT9PT0RNtt0KBBePz4cZFuoPn5+eHMmTPIysrCiRMnUKtWLfj6+uL48ePIyMjA+fPnhU+oqLO/KPMt60pZvPzhr1+/Rm5uLiIiIkTllO+zDx48wOvXr0FEBZZLXXXr1oWdnR3WrVsH4HMjr1OnTqIbbF8iKSkJoaGhqFixIrZs2YLTp09jzpw5AP7veHn+/Hmh59Hnz5/D1NRUYXCPvPG+57lAfsz7+vpCKpUK4fJGt/zdyzFjxsDLywtz5sxBQEAA2rVrh7Vr12LLli04c+aM0rRfvHiB3377DWPGjEFaWhrevHmD9PR0AEBaWprCOYKIEBERgb///hs7d+4UBm/Ly8HBQdifBg0ahEmTJkFXVxedO3dGhw4d8Pfff+PKlStYtGiRyjKpe239WoVdZwHA1dVVtE1bt26N3NxcUYNN2fmmoHMQY0zRf3p0TW9vb9FdWVUvYEulUlSvXh179uxBdHS0woXy9OnTePnyJWrVqlWk/GNjYxEQECAaqnrnzp1FSuNbMzMzQ2hoqMJIYIDyF8Xzyj/ISm5uLl69egUbGxsAgK2trcLTEflF2tbWFpaWltDS0iowjpynpyc6dOiAvn37wsHBQeEOZVFZWFhAQ0NDNDw1AIVpXV1dTJw4ERMnTsRff/2FmTNnIjw8HBUrVkTFihW/qgz53b59G1euXEFiYiJq164N4PMw51/6zUW5vPuvmZkZAGDVqlVKX+B3c3MD8PnpVmxsLD59+oQjR45g0KBBaNeunfCURltbW+Eu8uvXr4tcNnXKI98m+fe3/NPfS0ZGBvbt24cVK1aI7uinpaUVuJyy/R/4vH+XKlUKAKCjowMACnWpzjY3Nzcv8MeaOvl/KTMzM8hkMhw9elTp/KKM1Ofr64v09HTs379fGKU4PT0dHTt2xMGDB5GdnY2aNWsK+QKF77/5fcu6UtVwyhtuYmICiUSCKVOmKH166OzsDJlMBolE8k1+cGtoaCAsLAyrV69GjRo1cPv2bYSFhX11unFxcXB2dsby5cuFsCtXrojiWFlZKTwZyn8etbKyEhq+efeNvPG+57lAX18fJUqUUDl4irxMN2/eVHhSKz/P//3336hatarCsvJRSvM/vQQ+X2eGDRuGqVOnCmGjRo1CbGwsDh8+LAx+o0pMTAwyMzPRvn17pKWl4c8//0TXrl2hr6+PDh064PDhw/jll18UlivKtfV7km/TAwcOKDQG/8lyMPZf8Z9+kmdoaAgfHx/hr6CPp/bp0wfXrl3Dhg0bROG5ubkYN24crK2tERoaWqT8MzIyhLvk8rS2bNlSpDSUkaf5JU8EAwMDcfXqVVSsWFFUNz4+PoVegO7evYvLly8L0/v27UNmZqZw57VKlSrYvn276MK6ZcsWyGQyeHp6QlNTE5UqVVL4/teWLVtga2sr+pA2APTs2RNRUVHCxe1raGlpoUKFCoiPjxeF79q1S+UyHh4emDlzJogIN27cUBlPW1v7i7aFfHS8vPvI1q1bFZ6KfGn6wOcfSnZ2dnjw4IHC9vbx8VH4vqOOjg4aNmyIHj164K+//hLCHRwcRHXw/v17nD59usC8le2n6pTH0dERVlZWiIuLE5bLzMxU+Fbl9/Lp0yfk5uaKtsupU6cKfdJdpUoV4Qm03KNHj3Dq1Cnhx6J8H89bl0lJSUL3MTll2zwgIACPHz/GkSNHvjj/LxUQEICMjAx8+vRJ6XYrCkdHR5QoUQK//fYb3NzcYG5uDkdHRzg4OCA6Ohply5YVugIWdf/NW95/sq709fVRrVo13L59W2k5LSwsoK+vj6pVq2Lt2rUq0ynKsR4eHo5Lly5h2LBhqF69OsqUKVPkcueXkZEhevIFfO6Rkpe3tzdOnTolusmT/zzq7e2N7Oxs7N+/Xwi7cuWKaJTG730uaNy4MU6cOCG6oXL48GHIZDKhIe/o6IgLFy6Iljt//jwAwMnJSWm6pUqVwpEjR0R/w4YNAwAcPHgQ3bp1E+IuWbIEU6dOxcqVK4UbF6pkZmZixIgRmDFjhugGgvwpofxfZYp6bS3Ml15zqlevDplMhmfPnindpvKbXIyxb+M//SSvKEJDQxEWFoaIiAhcuXIF9erVw9u3b7FgwQIkJiZix44dSrtZFCQwMBADBgzA7Nmz4e7ujsWLFxd4olaX/O71vHnzEBgYCEtLywIbsHkNHDgQa9asEd5RsLGxwePHj3Hw4EG0b99e5TsswOe7s23btsWECRPw8eNHDB48GA0aNICXlxeAz3csvb290bZtW0RGRuLq1asYO3Ys+vfvL9zhGzt2LJo2bYrevXujefPmOHbsGBYtWoQFCxYofRowevRovHz5EiEhIThy5Ai8vb2LVll5DBo0CJ06dYKLiwsCAgKwceNGPH78WHRBbdGiBXx8fFCpUiVoa2tj1apV0NPTQ7Vq1VSm6+bmhr1796JJkybQ19dHhQoV1CqPu7s7bG1tMWjQIIwdOxZ3797F1KlTFbqnurm5YevWrdi/fz/MzMyETwGoQ0NDA9HR0YiIiMDr168RFBQEDQ0N3Lx5EwcOHMCOHTtw6dIlDBkyBG3btoWrqyueP3+OuXPnit7bCgkJwR9//IEKFSrAxsYGM2bMKPS7YKr208LKo6WlhQEDBmDMmDGwsLCAl5cX5s+f/9Vd0dRlYmKCSpUqYfz48dDX10daWhrGjh1b6F3oRo0awcfHB61atcKUKVOgq6uLqKgo2NnZCUOXOzo6onLlyhgxYoTwKYHffvtNYXsq2+YNGjRAQEAAWrdujaioKHh6euLvv//G48ePMXbsWLXy/1Lu7u7o1q0bWrZsiWHDhsHLywvv37/Hn3/+iffv3wufEJB/BkDVExQ5X19fbNiwAV27dlUIy/sjWZ39V5kfUVe//fYb6tWrByJCSEgIZDIZ7ty5gx07dmD9+vUwMjLCpEmT0KBBA7Rs2RIRERHCO8e9evWCm5ub8PRq1apVKFeuHOzs7FS+JlCyZEnUrl0bR48exYIFC76ozPkFBgbi999/x+jRo1GnTh1s3rwZ169fF8WJiIjAr7/+iqZNm2L48OF4+PAh5s6dC+D/npBVrFgRdevWRWRkJKKjoyGTyTB27FhYWVkJcdTZtl9zLhgyZAjWrl2L1q1bo1evXrh16xbGjx+PwYMHC98O7datmzC/ZcuWePLkCcaOHYvKlSuLzvlaWloYO3Ysxo4dCwMDA4VPx8gbr3Xq1IGW1uefXUeOHEGvXr3QunVrODs749SpU0L8smXLKpzn582bhwoVKgi9OoyNjVG+fHkMGTIEbdq0wYIFCzBgwACV61vUa2tBvvSaZmpqijFjxqBHjx74+++/UaNGDWRlZeHatWu4du2a6Anxl0hMTETdunVx6NAh1KlT56vSYqxY+HFjvvz75OTk0MKFC8nLy4t0dXXJxMSEmjRpQmfPnlWIq87ompmZmdS7d28yMzMjc3Nz6t+/Py1YsEA0utWXjK5JRDR8+HCysbEhiUQilCPvpwfkDhw4QAAoJSVFCLt//z517NiRLCwsSEdHh1xdXennn3+me/fuqcxPnvbatWvJycmJdHV1qWnTpgpDPMfHx1OlSpVIW1ubbG1taeTIkQojjq1atYrc3d1JKpWSs7MzzZw5U2lecrm5udS5c2eytLSk69evE5Hy0TXzjwY2atQohRHYJk2aRJaWlmRgYEBhYWE0ceJEMjY2FuZPmzaNKleuTIaGhmRkZES1atWihIQElfVC9HkkuWrVqpFMJhPqWtV2zL/fnDhxgipWrEi6urrk7e1NJ0+eVBhp8d69e1S3bl1hBLi8o+PlVdAIbzt37qQaNWqQTCYjY2Nj8vHxoWnTphER0dOnT6l9+/bk7OxMOjo6ZGtrS126dKHU1FRh+bS0NGrfvj2ZmJiQg4MDLVq0qNDRNYmU76eFlYfo80ijgwYNImNjYzI1NaUhQ4bQiBEjvmh0TXWOifyuXbtGvr6+JJPJyMPDg3bs2KHWMf/kyRNq27YtGRkZkZ6eHjVu3Fhh5MLr16+Tn58f6enpUYUKFejYsWNqb/N3795R7969ycrKinR0dMjDw4OWLl2qdv6q9sv8xxOR4jGVk5ND0dHR5ObmRtra2mRlZUX16tWjHTt2CHGGDBlClpaWBdYREdH8+fMJAK1atUohbPXq1QrxC9tflPledVXQiJTHjx8XtpuBgQGVL1+eRo4cKRoFdN++fVS1alXS0dEhc3NzatasmXAezczMpMjISDI3NxeNtqhqhN3o6GjS0dERjXSpirqja0ZFRZGVlRUZGRlR586daevWrQrHy6lTp8jLy4u0tbWpUqVKtH//fgIgGqH5yZMn1KRJE9LV1SUnJydavXo1lS9fXmF0y+91LiAiOnnyJFWvXl04r40aNYqys7NFcVavXk1eXl6kr69P9vb21KlTJ3r8+LEoTt5toYyyc++4ceOEUTPz/+U/h798+ZKsrKzo5s2bovALFy5QxYoVycjIiMLCwpR+8iCvwq6t6o6u+TXXNCKi5cuXU8WKFUlHR4fMzMzIz8+Pli9fLsxXtj8rO1fnv6bI91dV10DG/mskRF/x5W3GirnmzZvj1atXwsh+jLEvFxAQAH9/f4wbN+5HF+U/oWHDhjA0NERMTMwPLUdsbCxatmyJ27dvC6O15vfkyRO4urpi3rx56NKlyz9cQsYYK364uyZj/9+jR48wZ84c1K5dG5qamoiLi0NcXBzWr1//o4vG2L9eTk4Orly5ovAOF/v2Ll++jBMnTmD//v04ePDgP57/xIkT4erqCjs7O1y9ehXjx49H/fr1RQ28DRs24PXr1/Dw8MDz58+FruitW7f+x8vLGGPFETfyGPv/dHV1ceHCBSxduhTv3r2Di4sLFi5ciHbt2v3oojH2r6epqfmPjYD6X9e0aVO8fPkSQ4cOFb07+0/JycnBiBEj8PTpUxgaGqJJkyaYNWuWKI5MJsPkyZPx999/QyKRoGbNmli9erVa3zxljDFWOO6uyRhjjDHGGGPFyH/6EwqMMcYYY4wxVtxwI48xxhhjjDHGihFu5DHGGGOMMcZYMcKNPMYYY4wxxhgrRriRxxhjjDHGGGPFCDfy1CCRSAr9A4Dw8HD4+fn94NL+s77VOq9cuRISiQTZ2dnfoFTfX0JCAiQSCW7fvv3VaUVFReH8+fPfoFQ/zt27dxEVFYVXr16JwqOiouDg4PCDSlW4lStXYtu2bT8k7//1uvlf9C2Pu4IsXbpUOK+r8l883/8II0eOhI2NDSQSCWbPnq0y3vPnz9GlSxdYWVlBJpOhfPny2L17t9r5LFiwABKJBB07dhSFnz17Fp07d0apUqUgkUgwevRohWUfPXqEgIAAGBkZoVWrVnj37p1o/qpVq3hfYYz94/g7eWpISkoS/v/w4UOEhoZi/vz5qFy58g8sFSsuxo8fDwcHh3/1/nT37l2MHz8eHTt2hJmZ2Y8ujtpWrlwJBwcHtGzZ8h/P++eff0bz5s3/8Xz/zSpXroykpCQ4Ojr+6KKwf8DJkycxZcoUzJkzB1WqVIGzs7PSeGlpaahduzbMzc2xcOFCmJiY4NKlS/j48aNa+bx+/Rrjxo2DpaWlwrwTJ07g1KlT8PPzQ2pqqtLlBwwYAF1dXcTExGDEiBGYNGkSpk6dCgBIT0/H6NGjsXXrVvVWmjHGvhFu5KmhevXqwv/ld5DLli0rCv83y8jIgEwm+9HFYOw/xcHBgZ/kqYmI8OnTJxgZGRWb825x8L2vHTdv3gQA9O7dGxoaqjseTZw4Ebm5uTh06BB0dXUBAHXr1lU7n/HjxyMoKAhPnjxRmNe3b1/069cPAFQ2Mg8fPow9e/agSpUqSEtLQ3R0tDBvxowZqFWrFqpWrap2eRhj7Fvg7prfwY4dO+Dm5gZDQ0M0btxY4cKRkpKC1q1bw8TEBAYGBggJCcHDhw8LTFPeNWjDhg1wdXWFTCZDSEgIXrx4IcRR1v3r9u3bkEgkSEhIEMIkEgnmzp2L7t27w8zMDI0bN/7icskVVK5Hjx6hc+fOKFGiBPT09FChQgWsXbu2wPTevXuHnj17omTJkpDJZHB3d8eMGTNAREIcedetY8eOITg4GPr6+nB3d0dcXJworaysLIwbNw4uLi7Q0dGBq6srJk6cKMzPycnBhAkThPnlypXD9u3b1Vrvu3fvom7dukIZ9+3bJ5qfnp6OgQMHwt7eHjo6OqhatSqOHTsmzJd3Cevatauo66+dnR0WLFggxBs6dCgkEgmOHj0qhDVu3BjdunUTptXZfoXFUbdO80pISEBAQAAAoHTp0pBIJPD39xfFOXHiBCpVqgR9fX34+fnhxo0bovmpqano0qULLC0tIZPJEBgYiKtXr4riTJw4Ea6urtDR0YGtrS2aN2+O9PR0Yf6ff/6JBg0awMDAACYmJggLC8ObN29Ultvf3x+JiYlYt26dUPcJCQlo37492rRpI8SLj4+HRCLBr7/+KoRNmzYNZcqUEaafPn2Kdu3aCfXapEkT/P333yrzBhSP19evXyMiIgI2NjbQ1dWFq6srhg0bVmAaf/zxB9zd3SGTyWBlZYX69evjwYMHAJR3gc7OzoZEIsHKlSuFMGdnZ4waNQpjx46FpaUlzM3NMWvWLACfu7A5OjrCwsICw4cPFx1/8nPSli1bUKpUKRgYGKBz587IzMxEYmIivLy8YGhoiJCQENF2UOd8IC97cnIyatSoAZlMho0bNyp01/T391fafT4qKkpIKyEhAb6+vkId9e/fH58+fRLlN336dNjY2MDIyAhdu3ZV+wkQoPrcd/36dUgkEpw4cUIUX9k5Ob9BgwbBw8MDenp6KFmyJEaMGIHMzMwCy3H//n20bNkSFhYW0NPTg7u7u7Ad5ebMmQN3d3fo6OjAwcEBvXv3FuZlZ2djxIgRsLe3h66uLry9vXHgwAHR8s7Ozhg5cqTQhbJs2bIA1Dt+lVmzZg08PDyE83Le7pjh4eGIiIgAAGhqakIikeDu3bsKaRARVq1ahcjISKGBVxTXr1/HsmXLhCdv+RXUuJTLzMyEnp4eAEBPT0/Yv549e4Y5c+ZgypQpRS4XY4x9NWJFcuvWLQJAR44cUZgXFhZG1tbW5O3tTVu3bqWNGzeSlZUVtWzZUojz4sULsre3p2rVqtHWrVtp+/btVKlSJapQoQLl5OSozDcsLIysrKzIw8ODtmzZQmvWrCEbGxtq2LChEGfcuHFkb29faHkBkLW1NYWHh9O+ffvo0KFD37VcFy5coBEjRlBcXBwdPnyYJk+eTFKplLZu3SrEWbFiBQGgrKwsIiJ68uQJ9e3bl2JjYykhIYHmzJlDxsbGNGPGDGGZI0eOEAAqXbo0RUdH0759+6hp06akq6tLz58/F+J16tSJdHR0aOLEiXTw4EFasWIF/fLLL8L8bt26kZGREc2aNYv2799Pffv2JQ0NDTp9+rTK9Zbn7ejoSJMnT6b4+Hgh77t37xIRUW5uLjVs2JBsbW1pyZIltHfvXvrpp59IJpPR/fv3iYgoKSmJANCIESMoKSmJkpKSiIiodevW1K5dOyG/mjVrkq6uLk2aNElI28TEhFatWkVE6u1X6sRRt07zSktLo/nz5xMAiomJoaSkJLp69SoRfd4nDQ0NqUKFCrR27VqKi4uj0qVLk7e3t7D8x48fqUKFCuTm5kbr1q2j3bt3U1BQENnY2ND79++JiGjlypVkYGBACxYsoMTERIqJiaEuXbrQ69eviYjoxo0bZGhoSI0bN6a4uDjasGEDubi4UJMmTVRuw6tXr1KlSpWoQYMGQt2npaXRvHnzyM7OTog3cuRI0tXVpXr16glhzZo1o4iICGFb+Pj4kKOjI61bt462bt1Knp6e5OTkRB8/flSZf/7jNTw8nNzc3GjTpk2UkJBAK1asoAEDBqhcPiEhgbS0tGjKlCmUkJBAsbGx1L9/f7p27RoRKR5TRERZWVkEgFasWCGEOTk5kb29PbVv35727t1LAwYMIAA0aNAgatiwIcXHx9PUqVMJgOiYlR/71apVox07dtDixYtJR0eHunfvTl5eXrRp0ybasGEDmZmZUd++fYXlinI+KF26NM2dO5cOHz5M169fF/bPW7duCdtQvu2SkpJo1qxZBIDWrl1LRERHjx4lLS0t6ty5M+3Zs4eWLl1KFhYW1KdPHyGvTZs2EQAaPHgw7d27l8LCwsjW1pYKuzyqc+6rXr06devWTbTcmDFjyMnJiXJzc1WmHRkZSZs3b6aEhARatmwZ2dvbi+pQGX9/f6pevTpt376dDh8+TIsWLaJff/1VlK+GhgYNHjyY9u3bRxs2bKDOnTsL8wcNGkQ6Ojo0ffp0io+Pp5YtW5KWlhZdunRJiOPk5ETW1tbUpEkT2r17N+3evVut41eZnTt3EgDq0aMH7d27l0aOHEkSiYQWLlxIRES3b9+mESNGEABh+yo7nu7cuUMAaMGCBVS/fn2SSqVkbW1N48aNK/DaJdeoUSMaNmwYERHVqVOHOnTooDKuk5MTjRo1SiG8Tp069Msvv9DLly+pefPmFB4eTkREXbt2FdJmjLF/GjfyiqiwRp5UKhV+wBMRTZ8+nbS0tISLzahRo8jW1pbevn0rxHn48CFpa2tTbGysynzDwsIIgOiCGx8fTwDo4sWLRFS0Rp6vr68o3vcsV165ubmUlZVFXbp0oeDgYCFc2Q/S/MtMmDCBypUrJ4TLf/BNmTJFCHvx4gVJJBJavXo1EX3+EQiAli5dqrT8N2/eJIlEQjExMaLw4OBgat68ucr1luc9cOBAISwzM5Ps7e2pf//+RER04MABAkDJycmidSlfvjz169dPCANAS5YsEaU/e/ZscnR0JKLPjSAdHR3q0aMHNWrUiIiILl++TADo77//JiL1tp86cdSp04LqQ/7jW27cuHHCjzS5LVu2EADhOFmyZImo4UtE9OHDB7KysqJZs2YREVHv3r1FN0vy69ChA1WsWJGys7OFsOTkZAJA58+fV7mcsh91Fy9eFNVtnTp1qEePHmRoaCikb2FhQcuWLSMiol27dils5/v375NUKqUFCxaozDv/8VquXDmaM2eOyvj5RUdHU+XKlVXOL0ojr1y5ckKjIycnh2xsbMjS0pLS09OFeFWrVqVOnToJ0/Lz3cOHD4WwNm3aEAA6e/asEDZ06FBhX86vsPNB/uNC1X5G9Hk/dXBwoI4dOwphvr6+onSJiGJiYkhbW5uePn1KRESVK1dW2LcqV66sViOvsHPf4sWLycTERGic5ObmkouLC40ZM6bAtPPKysqi1atXk6GhYYGNFn19fYqLi1M67+XLl6Sjo0OjR49WOj81NZV0dXUpOjpaCMvJyaGyZctSmzZthDAnJydycnIS7VPqHL/K+Pj4KGybHj16kK2trbCeS5YsKXQ7nDx5kgCQgYEBde3alQ4dOkSTJk0iLS2tAvMnItq9ezdZWlpSWloaEX15I+/06dNkbm5OAMjFxYVSUlLoypUrZGNjI6TNGGP/tP90d00iQnZ2tvCXm5v71WmWKVNGNCiAh4cHsrOz8fz5cwDAoUOH0KBBA8hkMiFfa2truLu749y5cwWm7ezsjPLlywvTDRo0gLa2NpKTk4tczkaNGommv2e5cnJyMHnyZJQqVQo6OjqQSqVYtmxZoSPkLV68GOXKlYNMJoNUKsWYMWOULhMUFCT838LCAhYWFnj06BEAIDExERoaGgojpskdPnwY2traCA4OFu0LgYGBha43ADRr1kz4v1QqRcOGDYX1PnToEFxcXODl5SWkm5OTA39//0LT9vX1xYMHD3Dv3j0kJyfDyMgIkZGROHnyJHJzc3H8+HHY2trC1dVVyKuw7VeUbVxQnRaVvr6+6D0qDw8PABDSO3ToEKpXrw5bW1uhXNra2qhevbpQLi8vL8THxyMqKgrnzp0TdRuUp9GiRQvRMe3l5QVjY+Mij1xavnx5GBkZ4dixY8jKysKZM2fQq1cvSCQSXLx4EdevX0dqaip8fX0BfB59r0SJEvDx8RHScHR0RPXq1Yt0bHp5eWH69OlYuHAh7ty5o1b8CxcuYODAgTh+/PhXjUwbEBAgdBXW0NCAi4sLqlWrJnrfqmTJknj8+LFoOTc3N9jb24viGBgYwNvbWxT25MkTYZsV5XyQ/zylSm5uLjp27AhTU1MsXrwYwOeu0klJSWjdurXo2Pb390dmZiauXLmC7Oxs/Pnnn6LjGACaNm2qVr6Fnfvatm2LT58+Cd2djx07hpSUFHTu3LnAdLdt2wZvb2/o6+tDKpWic+fOePfunXAdUcbLywsjRozA6tWrFbbT6dOn8enTJ5X5XrlyBR8/fkSrVq2EMA0NDbRq1UphH65Xrx60tP7vdX51jt/8cnJycPHiRVF+ABAaGoonT56o/ZoAAOG6Xa5cOfzxxx8IDAzEyJEj0b17d0yfPl3lcllZWRg4cCDGjx8PIyMjtfNTpmrVqnj48CGuX7+OmzdvwtnZGYMHD8aYMWNgaGiIYcOGwdraGuXKlUNiYuJX5cUYY+r6TzfyEhMTIZVKhb/IyMivTtPExEQ0ra2tDQDCOx6pqalYuXKlKF+pVIpLly4J79Kokn/kLw0NDZiZmeHp06dFLqeVlZVo+nuWa8aMGRg/fjy6dOmCPXv2IDk5GWFhYQW+97J582b06NEDwcHBiIuLw5kzZzB8+HCFd2kA5XUuT/vly5cwNTWFjo6O0nxSU1Px6dMn6OnpidZ70KBBePz4caEN//zrbmlpKax3amoqUlJSFOp07ty5hdapl5cX9PX1cfz4cZw4cQJ+fn6oVKkSsrOzcfnyZZw4cUJoZMjzKmz7FWUbF1SnRaXOMXHkyBGFcsXFxQnlioyMxMSJE7Fhwwb4+PjAzs4O06ZNE61/VFSUQhppaWmF1nV+GhoaqFGjBo4fP47z589DJpPB09MT1atXF7aHpaUl3NzcAABPnjxROJ4AwNraWulADqrMmzcPTZs2xdixY1GyZEl4enpiz549KuMHBQVh+fLlOHLkCGrVqgVLS0sMHToUWVlZRVpfADA2NhZNa2trKw3Lvw+ou5z8BgdQtPOBsnpVZsKECUhKSsLWrVuFd6Nev36N3NxcREREiPYJ+TH74MEDpKamIicnR+lxrI7Czn3Gxsb/j737joriasMA/iy99yogiChWwI6CYkHFbmyJJfbYe+9i1Ng1do2x967YsHextxhbLFhBBAVEpN/vDz5GFhZYCIiQ53eOR3bmztx3ys7uu3PnXrRq1QobNmwAAGzYsAE1atSAk5NThusMCAhA27Zt4erqil27duHKlStYtGgRAGT6Hty+fTsqVKiAgQMHwsbGBu7u7rh69SqA5OsgAFhbWytcNuU8Tbu/FZ3Dij47snr/pvX+/XskJCQorC91PMowNjYGAOm54BS1a9fGmzdvEBkZqXC5VatWIT4+Hm3btkV4eDjCw8ORkJCA+Ph4hIeHZ/tHXy0tLTg7O0NNTQ3Hjh1DYGAgevXqhf3792PPnj24e/cupkyZgvbt2yv8HCMiym3/6d41K1WqJPcrpZmZWZ7XaWJigrZt22LUqFHp5pmamma6bOrOTIDkXzA/fPgAKysrAICmpma6h/M/fvyocF1px4DKy7j27t2Ljh07YuzYsXJlMrN3717UqVNH7ov8gQMHMl1GEVNTU3z8+BGxsbEKEz0TExNoa2vLdWiSWlYP3afd9vfv30vbbWJiAkdHR2zfvj3dcimJTkbU1NRQrVo1nD9/Hm/fvkXt2rXlpl28eBGDBg2S246sjt+/OcZ5ycTEBB4eHgrHwNLX1weQfByGDx+O4cOHIzAwEKtWrcLo0aNRunRpNGvWDCYmJvjpp5/w888/p1tHkSJFsh2Th4cHNm/ejNKlS8PDwwMymQyenp44f/48DAwMUKNGDamstbW1wjss7969y/TLfFpGRkZYsmQJFi9ejFu3bmHKlClo1aoVXrx4kWGy07VrV3Tt2hXBwcHYunUrRo0aBTs7OwwcOFA63+Pi4qQ7LxldD76l7FwPshqrDgCOHz+OqVOnYufOnShRooQ03cjICDKZDDNmzFDY06KDgwOMjIygqqqq8H2sjKyufQDQrVs3+Pj44OXLl9i1a5fcNU0RPz8/ODg4YM2aNdK0e/fuZRmLjY0NNm7ciMTERFy6dAljx45Fs2bN8PbtW+n9HRQUJLePUqQkfyEhIShWrJg0/d27d+kSQ0WfHVm9f9MyNzeHmppauvfNu3fv5OJRRvHixaGhoZHu7n6KjK7hjx8/xrNnzxQm9Dt27MCDBw9QqlQppeNIkZSUhBEjRmDOnDlQU1PD2bNn0bJlS1haWqJNmzbo06cPHj16BBcXl2yvm4goO/7TSZ6+vr5cE6tvoW7duvDz84OrqyvU1dWztWxgYCD++usvqXnQ0aNHERcXJ22Dra0twsLCEBYWJn2onzhxIt/j+vLli1xS8/nzZxw+fBh6enoZrjPtMklJSdi1a1e24gKSf81NSkrCli1bpJ7aUqtTpw6+fPmC2NhYuTtjyvLz84OXlxeA5N7p/P390bZtWwDJ+/T333+Hqamp3BentNTV1RX+spvSc+G7d++kAXg9PT2xY8cOPH/+XG5wXWWO3785xllJOVY5+YW6bt26mDhxIpycnNLd9VPEwcEB06dPx4oVK/DgwQM0a9YMdevWxf3797P9ftbQ0Mhw30+aNAl79+6Vmu55enpi6dKlMDAwkOvVtEqVKlIz0pRmim/evMHly5fRoUOHbMUDJH+JrlixIqZNmwY/Pz8EBgZmeUfLysoKQ4cOxaZNm/DgwQMAkHrufPToESpUqABA+etBXsrJ9SAjr1+/RseOHTFs2DD88MMPcvN0dXVRrVo1PHnyJNNeSl1dXeHn54cuXbpI05T9QSmrax+QfH7b2NigY8eOiI2Nleu5VZEvX76ke3/u2LFDqXiA5J4oa9asidGjR6N58+YICwuDu7s7tLS0sGnTJkyZMiXdMuXKlYOWlhb27NmD4cOHA0h+nGHPnj1Zdv2f3fdvSowVKlTAnj175K7Lu3btgrW1dbaGFtHU1ETdunVx+vRpuemnTp1CsWLFMjyvBgwYgJYtW8pNGzJkCMzNzTF+/HgULVpU6RhSW7t2LczNzdG0aVNpWkovwImJibyLR0TfzH86ycsPw4YNw8aNG+Ht7Y1+/frBysoKb9++xYkTJ9ChQ4dMx/axsLDAjz/+iKlTpyImJgYjRoxAw4YN4ebmBuDr8yA9evRA//79cffuXbmu0vMrrrp162LVqlWoWrUqzM3NMWfOnCy/0NWtWxdDhw7F77//jlKlSmHlypVy3eUrq3Tp0ujatSv69++P4OBgVKtWDW/fvsX169eldffq1QutWrXC6NGj4ebmhqioKNy5cwdRUVGYNWtWpuvfuXMnzMzM4ObmhuXLlyM0NFQaU6lBgwaoU6cO6tWrh9GjR8PZ2RkfP37E1atXYWZmJn2ZcnZ2xu7du+Hm5gZNTU3pC6KHhwd+/fVX6OjoSAOlp0zT1dWV9i+g3PH7N8c4KyVKlICKigr+/PNPtG/fHoaGhlJzxqx06dIFy5cvR+3atTFs2DDY29sjJCQE58+fR9WqVdGpUyf07t0bpqamcHd3h76+Pg4ePIjw8HApwfb19UWVKlXwww8/oHPnzjAyMsLLly+l5/hSngNMK2XfHzt2DCYmJtLQJ9WqVYOamhouXLggnQPVqlVDWFgY3r17J/eDQKNGjVC5cmW0bt0aM2bMgJaWFnx9fVGkSBGFPyxkxNPTE61atUK5cuWQlJSEhQsXwsLCAmXLllVY3tfXFx8+fICXlxfMzMxw8eJF3LlzR7pDVq1aNVhbW2PAgAGYOHEiXr16haVLlyodT17JyfUgI926dYOenh5atGiBy5cvS9NTxiCcNWsW6tevDyEEWrZsCW1tbTx79gz79+/Hli1bYGBggJEjR6J9+/YYOXIkvL29sXXrVqWbC2Z17QOSk/YuXbrg119/Rbt27bJMhOrWrYuFCxdiwoQJ8PLywo4dO/Dw4cNMl4mIiICPjw86d+6MkiVLIioqCr/99hvKli0r/UAwZswYKU5vb2+Eh4fj8OHDWLt2LUxNTdG/f39MmDABMpkMZcqUwerVq/Ho0SNs3bo107qVef8qMmnSJDRr1gz9+/dHixYtcP78eaxYsQLLli1TatiCtOuqVasWevTogR9//BFXrlzBH3/8gT/++EMqs2HDBnTv3h1Pnz6Fvb09nJyc0t1pNzIygqWlpdwQMO/fv5eeo4uOjsbDhw+xa9cu6Orqpntm9PPnz5g8eTIOHjwoTatZsyZ++eUX1KtXD5cvX4aenp50bUwbExFRrsrHTl8KpKx610zba2VKD4vPnz+Xpr18+VJ06tRJmJmZCU1NTeHo6Ch69uwpXrx4kWG9KevetGmTsLe3F1paWqJZs2bi3bt3cuX8/PyEs7Oz0NbWFj4+PuLs2bMKe9dM22tdXsYVEREh2rdvLwwMDIS1tbWYPn26GD9+vLC3t5fKpO0JMC4uTvTv31+YmJgIU1NTMWTIELFs2TK5ntYy6mnPxsZGTJ48WXodHx8vJkyYIOzs7ISGhoZwdHSUhiIQIrkXuTlz5ghnZ2ehoaEhLCwsRP369cX+/fsz3O6Uuo8dOya8vLyEpqamKFmypDh8+LBcuS9fvoixY8cKBwcHoa6uLooUKSJatGghzp07J5U5deqUKFeunNDQ0JDbvsjISKGqqirq1KmTblrdunXTxaTM8cuqjLL7VJGFCxcKOzs7oaKiIry8vIQQyvf4+uHDB9GvXz9RpEgRoaGhIezs7ET79u2lngvXrl0rqlevLoyMjISurq40TElq9+7dEy1bthRGRkZCW1tbODs7i8GDB4sPHz5kGPOLFy9EvXr1hJ6eXrqYqlSpIrS0tERsbGym04RIHvLjxx9/FAYGBkJHR0c0btxYYQ+QqaXdNyNGjBBly5YVOjo6wtjYWPj4+Ig7d+5kuPyBAwdEnTp1hKmpqdDS0hJly5YVK1eulCtz6dIl4ebmJrS1tUWNGjXE3bt3FfaumbbHQEW9DKa9vim63ik63mnf2zm5HqRIe37a29sLAOn+pT5XL1y4IB1jPT09Ub58eTFu3DgRFxcnlZk1a5awsLAQenp6olu3bmLRokVK9a6pzDVZiK89vR46dCjTdabw9fUVFhYWwsDAQHTu3Fns3r073edIajExMaJHjx6iRIkSQktLS5ibm4u2bdumK79gwQLh5OQkNDQ0hK2trdywDPHx8WLMmDHC2tpaaGhoiIoVK4qjR4/KLZ9R75JZvX8zsn79elGqVCmhrq4uHBwcxPz58+XmK9O7ZooDBw6I8uXLCw0NDeHg4CB+//13ufkp51RG+1AIxed9yjmX9l/q8zXFpEmTpOETUiQlJYlhw4YJY2NjUaJECXHy5MlsxURElFMyITJoyE7fla5du+LJkye4cOFCfodCRETZMHPmTCxcuBCvX7+GqqpqfodDRET/AWyuSURElAcCAwPx4MEDzJ8/H3369GGCR0RE38x/eggFIiKivOLr64uWLVuiSpUqCnu1JSIiyitsrklERERERFSI8E4eERERERFRIcIkj4iIiIiIqBBhkkdERERERFSIMMkjIiIiIiIqRJjkERERERERFSJM8pTUtWtXeHp6yk3buXMnnJycoKamhpYtW+ZPYLlA0bbllK+vL2xtbaXXt2/fhq+vL5KSkvKszowEBgZCJpPhxIkT/3pdv//+O06dOiU3LTw8HL6+vnj27Nm/Xn9+ql27Njp16vSv15P22Cty5swZyGQyPHnyBIDiYySTyfDnn39KrxXt+7z06tUreHt7Q09PDzKZDOHh4Uot9+TJE8hkMpw5cyZb9fn6+uLmzZvZDzQLgYGB8PX1xYcPH9LVl9Vx+tbGjRsHKysryGQy/P777/kdjkJ5dZy+B4reY7l5/UwrN9ft4OCACRMm5EJU38aJEycgk8kQGBiY36Hkmty4pixbtgw+Pj4wMjKS+4zIyOHDhyGTyZT+HnHy5ElUrFgRWlpaKF26NHbs2CE3P+WcTPvP19dXKhMXF4eePXvCyMgIlStXxoMHD+TW8c8//8DCwgKhoaHKbTRRHmOSl0Px8fHo1q0bateujbNnz2L27Nn5HVKOTZw4EX/88UeurKtnz544cOCA9Pr27duYMmVKuiQvN+v8FjJK8qZMmVLgk7xvqWLFiggICICdnV2GZQICAtCiRQvp9bdO8qZOnYrnz59j7969CAgIgL6+fp7WN2XKlDxL8qZMmZIuyUv7Hs1vly5dwowZMzB+/HgEBATgp59+yu+QFMqr4/Q9+NbvMSpccuOasnHjRkRFRaFevXpZlo2Pj8ewYcNgYWGh1LofPHiApk2bwsXFBUeOHEGLFi3Qvn17nD17Nl3ZRYsWISAgQPrXs2dPad6ff/6JkydPYsuWLShdujS6desmt+zo0aMxcuRImJmZKRUXUV5Ty+8ACqo3b97g8+fP6NChAzw8PPI7nBz58uULtLW1Ubx48X+9rvj4eKioqMDW1lapX/Ryo87CRAiB2NhYaGlpfZfryy0GBgZwd3fPtExW8/Pa48eP4eHhgfr16+drHHlF2ffot/L48WMAQP/+/aGiwt8difJDyveBnMiNa8rFixehoqKCM2fOYM+ePZmWXbp0KUxMTODu7p7lHT8AmDt3LpycnLBmzRqoqKigTp06uHv3LqZPnw4vLy+5sqVLl87wM+jUqVPo168fGjdujMqVK8PS0hKfP3+Grq4uzp8/j9u3b2Pr1q3KbzRRHuMnag6sW7cOxYoVAwDUq1cPMpkM69atS1fu2LFjUFNTw6dPn6RplpaWcHR0lF6HhIRAJpPh0qVL0rRdu3ahQoUK0NLSgq2tLaZOnYqsxqyXyWRYsmQJfvnlFxgYGMDKygpz5syRK5PSLO/3339H0aJFoaurC0Bx08mNGzeidOnS0NTUhKOjY7omVCnLbNmyBSVLloSWlhZevXol12xj3bp10i9d6urqkMlk6Nq1q8I6X758iVatWsHMzAw6OjooVaoUFixYIFfn8uXLpZiKFy+OlStXZrpPUnz48AGtW7eGrq4uHBwcsHHjRrn5iYmJmDp1KooVKwZNTU2ULVsW+/btk+Y7ODjgxYsXmD59utSEIzAwUDoH6tevD5lMBgcHB2mZO3fuoGHDhtDT04ORkRG6dOki1+xv3bp1kMlkuHbtGqpXrw5tbW1s27ZNYfwp+2rr1q1wdHSEtrY2WrZsiffv3yu1vnnz5sHR0RGampooU6YMtmzZorCe33//HUWKFIGenh66d++O6Ohoad7ff/+NNm3aSPOrVKmCo0ePKlzPiRMnULZsWWhpaaF27dpydzrTNtdUJHVzTUX7/unTp7CxscH06dPllhNCwN7eXq55TVpRUVHo06cPzM3Noa2tDS8vL9y4cUOu7rNnz2Ljxo2QyWSoXbt2hus6duwYSpcuDW1tbdSrV09h86vo6GgMGzYMNjY20NTURNWqVXH+/Hm5+gDgl19+kbYvxfPnz9GmTRsYGRlBT08PLVu2xOvXr+XW/+TJE7Ru3RrGxsbQ09ND9erVcfHiRZw5cwZ16tQBAJQoUUJuWxQ1rfL390elSpWgpaUFGxsbjB8/HomJidL8lGUuXryIChUqQFdXF56ennj06FGG+ydFZteSrl27StcIVVXVTJuxrVmzBu7u7jA0NISVlRV+/PFHvHv3Lsv6r169ilq1akFbWxvm5ubo168fPn/+nG7bUkvb9Daz45TRMVC2/pT37u3bt6X3rqenJ16+fInXr19L15FKlSrh3r17cnGGhoaiR48e0vlct25d/P3331nuk9Qyur6liIyMRKdOnaCnp4dixYopbIGRV9fmnBxzPz8/1K5dGyYmJjA1NUXjxo3TXW9SPgtXrVqFokWLwsjICB06dEBkZKRcuevXr6NBgwbQ19eHkZER6tWrh4cPH0rzs7rOA8CmTZvg4OAAXV1dtG7dOt2ddUWU+TxctGgRSpUqBU1NTdja2qJ///7SvISEBIwdOxY2NjbQ0tJCpUqVcPz4cbnlHRwcMG7cOKmpdJkyZQDk7JxK+x76+PEjunXrBisrK2hpacHR0RGjR4/OdB3K/sATGhqKX3/9FfPnz1eqPAAcPXoUrVu3lqujbdu2OHPmDGJjY5VeT1xcHHR0dABA+j82NhZCCAwfPhwzZsyApqam0usjynOClNKlSxfh4eEhhBAiJCRE7Ny5UwAQS5cuFQEBASIkJCTdMhEREUJVVVUcPXpUCCHE48ePhbq6ulBRURFv3rwRQgixZ88eoaWlJWJjY4UQQmzZskWoqKiIIUOGiKNHj4oFCxYIHR0dMXfu3EzjAyCsra1Fly5dhL+/vxg5cqQAILZt2yaV8fLyEpaWlqJ69epi3759Ys+ePem2TQghDhw4IACIPn36CH9/fzFu3Dghk8nE8uXL5faHmZmZKFOmjNi+fbs4dOiQCA8PF5MnTxY2NjbSfpowYYIAIC5cuCACAgLEkydPFNZZu3Zt4e7uLvbt2ydOnTolVqxYIX799Vdp/m+//SY0NTWFr6+vOH78uJg8ebJQVVUVu3btynCfPH/+XAAQRYoUESNGjBD+/v6iW7duQiaTiYCAAKlcr169hIGBgViwYIE4duyYGDhwoFBRURFXrlwRQghx8+ZNYWVlJbp27SoCAgJEQECAiImJEXv27BEAxKJFi0RAQIC4efOmEEKIR48eCX19fdG4cWPh5+cntm7dKooVKyaaNm0q1bl27VoBQJQoUUIsXrxYnDp1Sjx8+FDhdnTp0kVYWFiI0qVLi127domNGzcKKysr4ePjk+X6Fi9eLGQymRg/frzw9/cXvXr1EgDE4cOH5c4La2tr4e7uLvz8/MTy5cuFnp6e6NOnj1Tm6NGjYtq0aeLQoUPixIkTYuTIkUJVVVVcvXpVKjN58mShr68vihcvLjZs2CB2794tnJ2dRdmyZUViYqIQQojTp08LAOKff/6RO0bHjx+X1gNArFq1KtN9P2bMGOHs7Cy3n06ePClkMpl4+vRphudE69athZGRkVi5cqXw8/MTXl5ewsDAQLx7904IIURAQIBwcXERjRs3FgEBAeLvv/9WuJ4XL14ILS0t0aRJE3H48GHx22+/CTs7OwFAnD59WgghRFJSkvDx8RHW1tZi1apVwt/fX/z0009CW1tbvHz5UqoPgBg7dqy0fUII8f79e2FjYyOqVasmdu/eLfbt2ycqVKggXFxcpH0ZFBQkLCwsRJkyZcSmTZvEsWPHxNSpU8W2bdtERESEWLp0qQAgdu7cKbctqd+jQghx69YtoaqqKtq0aSMOHz4s5syZIzQ0NMTo0aPTHVsXFxexadMm4efnJ0qUKCEqVaqU4b4WIutryZMnT8TYsWMFALnjq4ivr69Ys2aNOH36tNizZ4+oUaOGcHFxEUlJSRnWHxQUJPT09ETt2rWFn5+fWLlypTA0NBQ//vij3Lal3h9CCPHPP//IHcuMjlNmx0DZ+lPeu66urmLNmjVi//79wsHBQdSpU0fUrFlTLFy4UBw5ckRUrFhRlC9fXlouJiZGuLi4CGdnZ7F582Zx6NAh4e3tLaysrERUVFSmxyW1jN5jKe9Ne3t7MX78eHHs2DHRvXt3AUDcvXtXWj4vr83KHPOU+FIsWrRILFu2TJw4cUIcPHhQNG3aNN0+8fLyEra2tqJOnTriwIEDYtWqVUJXV1cMGzZMKvPXX38JbW1t4enpKXbs2CEOHz4sxowZI50TylznAwIChEwmkz6Xhw8fLooUKSIAiOfPn2e4f7L6PJw4caJQUVERI0aMEEePHhVbt24VnTt3luYPHz5caGpqirlz54rDhw+LVq1aCTU1NbnjZm9vLywtLUXTpk3FoUOHxKFDh3J8TqV9D3Xt2lU4OzuL7du3izNnzoi1a9eKoUOHZrh8amk/I9Lq27ev9P5J+z1CkaioKAFAbN26VW76pUuXBABx//59IcTXc9LMzEyoqKgIOzs7MWXKFJGQkCC3ndWrVxchISFi5syZwsHBQQghxObNm4W7u7tS20f0LTHJU1Lai0naLwEZcXNzExMnThRCJH+Yu7u7C1dXV7F9+3YhRPLFuGbNmkKI5C+FdnZ2on///nLrmDNnjjAzMxNxcXEZ1gMg3Reudu3aCTc3N+m1l5eX0NPTEx8+fMh02ypXriyaNGkiV6ZPnz7C2tpa+oLZpUsXoaKiki4xSXuxT/kCEx8fn2mdurq6ws/PT+G2RURECB0dHTFnzhy56f379xeurq4KlxHi60W7VatWctOrVq0qWrZsKYRITrxlMpnYuXOnXJkmTZqIFi1aSK/TfpFIvf7UCYoQQnTs2FG4urrKfThcu3ZNAJASwZT9kpLMZKZLly7pvlgdPnxYABC3b9/OcH0JCQnCysoq3fnk4+MjqlatKr328vISmpqacj9ULFu2TGhoaEjJT2qJiYkiPj5eeHt7y6178uTJAoDccfz7778FALFv3z4hRPaTPCEU7/uHDx8KAOLy5cty+ynlvaTIX3/9JSU9KaKiooSpqakYNWqUNM3Dw0N06dIlw/UIIcSwYcOElZWV9ONMyrTU14Tjx48LAOLatWtSmaSkJFG+fHkxePDgDLdXCCHGjx8vrK2tRWRkpDTt9evXQkNDQ+zdu1cIIcTIkSOFoaGhCAsLUxhjRl+W0r5H27RpI8qVKyf3xXnmzJlCW1tbWnfKsU39BXzXrl0CgJSwKqLMtWTVqlUiu783JiQkiBcvXqTbv2mNGDFCmJmZiejoaGnatm3bhEwmyzDpFULx9V3RccrqGChTf8p7d8eOHVKZZcuWCQByP+6lvOdTfsRYtWqV3A8GQgjx+fNnYWFhIRYsWJDhPlEks+tb7969pWlxcXHCxMRESjjy8tqcVkbHXFHsqZeJjo4WOjo6cu97Ly8vYWpqKpe4DBgwQDg6Okqv27ZtKxwdHTP83FXmOt+qVat0n8utWrXKMsnL7PMwLCxMaGpqigkTJiicHxoaKrS0tOSOSWJioihTpoxo166dNM3e3l7Y29vLfTbn9JxK+x4qW7asWLRoUYblM5NZknf37l2hq6sr7TtlkrzXr18LAOLIkSNy0x88eCAAiIsXLwohhHj79q0YOHCgOHDggDh58qQYOXKkUFNTE0OGDJGWCQsLE66urgKA0NHREYcPHxZfvnwR9vb20nqIvif/6eaaQggkJCRI/9J2DpIbPDw8pOZZFy5cgKenJzw9PeWmpTzT9/jxY7x69Qpt2rSRi6tOnToIDQ3Fy5cvM62rWbNm6V7fvXsXcXFx0jR3d3cYGxtnuI7ExETcvn0brVu3lpvetm1bBAUFyTUXc3R0hLOzsxJ7IWtubm4YO3YsNmzYgLdv38rNCwgIQHR0NFq3bp1uv/z1119y26dI8+bN5V43a9YM165dA5Dcxl5DQwNNmjSRW3fdunXlmvFlx8mTJ/HDDz/InV9ubm4wNDRM13FDo0aNlFqng4MDypcvL71u2LAhNDQ0pO1QtL7Xr18jODhY4bG8deuWXHO86tWrw9zcXHrdrFkzxMXF4e7duwCAz58/Y8SIEbC3t4e6ujrU1dVx4sSJdM2g1NTU0LhxY+l1mTJlULx48XRx/lvOzs6oUaMGNmzYACC5WeTu3bvRuXPnDJe5ceMGVFVV5XrC1dXVRePGjbMd3/Xr1+Hj4wMNDQ1pWtr338mTJ1GsWDG4ublJ50FiYiJq166d5bl18uRJNGzYENra2tKylpaWKFWqlLTs2bNn0aRJE5iYmGQrdkXb0qpVK7kmiG3btsWXL1/kmgfq6urKPatSunRpAMnPJyuSnWuJMu7cuQMfHx+YmppCTU0N9vb2AJBp09/r16+jcePGcs8apWzr9evXs1W/Ilkdg+zUX7duXenvlGeWUzcXTpmWcn08efIk3N3dYW1tLZ0jGhoacHd3z/G1SxFvb2/pb3V1dRQvXlw65nl5bQZydsyfP3+Otm3bwtLSEmpqatDR0UF0dHS6Zdzd3aVHFoDk8zn1uXz27Fn89NNPUFdXV1iPMtf569evK/xczkpmn4dXrlxBbGxshte6e/fuISYmRu59p6KigtatW6e7ztWvXx9qal+7Zsitc8rNzQ1z587F8uXLc7VjsiFDhqB///5yj0bkFmtrayxatAhNmzZF3bp1MXv2bIwePRpLly5FREQEAMDExAQ3b97E48ePERISgkaNGmHhwoWoWrUqatSoge3bt8PR0RG2trZYtGhRrsdIlF3/6STv7Nmz0hdWdXV1dO/ePdfr8PT0xNWrVxEfH4+LFy+iZs2a8PDwwIULF/DlyxfcvHlTejYtpdvdOnXqyMVVuXJlAMldu2cm9Zf0lNdJSUkICQmRpmXVG9X79++RkJCQrpylpSUAICgoSOl1Zcf27dtRoUIFDBw4EDY2NnB3d8fVq1cBfN0vjo6OcvulTZs2SEpKSvchmJai/ZLyXEdoaChiY2Oho6Mjt+7hw4fj7du3OUr8Q0ND4evrK7c+dXV1REREpDuGyu7DtNugoqICExMTBAcHZ7i+lGOl6FjGx8fLdfOsaB8BkNY/cuRIrF69GiNGjMCJEydw7do11K9fHzExMXLLGRsbQ1VVNd260saZG7p164bt27cjLi4Oe/bsQWJiItq1a5dh+aCgIBgbG8t9qQGS90fq81oZ7969y3CfpQgNDcXz58/TnQeLFy/O8r0cGhqKdevWpVv27t270rJhYWGwtrbOVtyKBAUFKfV+NzIykiuTkuCmPQdSZOdakpWIiAj4+Pjg8+fPWLlyJS5duiQ995ZR/Sl1pK1fXV0dJiYm2T7mimR1DLJTv6GhofR3yr5VNC1le0NDQ3H69Ol054ifn1+W51d2KDruqWMA8ubanJNjnpiYiObNm+Phw4eYP38+Lly4gGvXrsHExCTdMoq2K/XzWVkdW2Wu88pcJxTJ7PMwLCwMADKMLbPrftpzLm2Z3DqnlixZgmbNmmHSpEkoXrw4ypUrhyNHjii9vCKHDx/GzZs30a9fP4SHhyM8PBxxcXFITExEeHg4EhISFC6XcpxTErUUKc9OZvajd8uWLREfHy/3TKKKigpKlCgBXV1dvH//HvPmzcPMmTMRHByMnj17Ytu2bTh9+jR8fX1x586df7XNRP/Wf7p3zUqVKsn9spUX3d56eHggOjoax44dk3rti46ORqdOnXDixAkkJCSgRo0aACD9Grx+/XrpIejUsrprlrojjpTXKioqchfy1L/WK2Jubg41NTW5xBCA9MGb+oMlq3Vlh42NDTZu3IjExERcunQJY8eORbNmzfD27Vtpvxw/fjzdB3PamBRRtF9SvmiamJhAW1sb586dU7hsTnr7MzExwU8//YSff/453bwiRYrIvVZ2H6bdhqSkJHz48AFWVlYZri9lv4SEhKBs2bLS9Hfv3kFdXV3ufFe0jwBI69+7dy+GDh2KgQMHSmViYmLS7Z+PHz8iMTFRLtF7//59ujhzQ7t27TB48GAcOnQIGzZsQMuWLWFgYJBheWtra3z8+BEJCQlyid67d++ynSxZWlpmuM9SmJiYwNHREdu3b0+3fOo7gIqYmJigbdu2GDVqVLp5pqam0v+5kahYW1sr9X7PruxcS7Jy+fJlBAcH48qVKyhatCgAKHWHQNG2xcfH48OHD1L9mpqa6e44ffz4Uam4sjoGytSfUyYmJvDw8FA4rmBeD/uROgYgb67NOTnmT548wb1793D27FnUqlULQPL+Vnasy9SyOrbKXOeVuU4oktnnYcr7PygoCCVKlEi3bOrrfkrnYIDi61zaz5/cOqeMjIywZMkSLF68GLdu3cKUKVPQqlUrvHjxIsc/Dj9+/Bjh4eEK7+IZGxvjyJEj8PHxSTdPV1cXNjY2ch3mAMDDhw+hrq4u1xFeRjL6nPb19UXnzp3h6OiI/fv3o1SpUqhatSqA5DvzZ8+ehaurqxJbR5Q3/tNJnr6+vnSXLK/Y2dmhaNGimDVrFpydnWFqagpTU1PY2tpizpw5KFOmjPRLkrOzM4oUKYJXr15l2uwsIwcOHMDkyZPlXru4uGT5hTI1VVVVVKhQAXv27JEbA2bXrl2wtrbOdjfJKXXHxsamu4OSUf01a9bE6NGj0bx5c4SFhcHd3R3a2tp49+6dXNMhZfn5+aFLly7S6wMHDqBKlSoAku+afvnyBbGxsZkOhZH2V96025Za3bp1cf/+/Vw9twIDA/HXX39JTTaPHj2KuLi4TOuwtbWFlZUV9uzZI/W0CCQfy4oVK8olYgEBAQgNDZUSvwMHDkBDQwMuLi4AkrvXTn0evXr1CpcvX5Z+oEiRkJCAw4cPS02S7t+/j6dPn0r7OycU7XsgeTiG1q1bY86cObhy5QoOHz6c6XoqV66MxMRE7N+/X2rKFB0djcOHD8uNhaSMypUrY8uWLYiLi5P2S9pxourWrYvff/8dpqamcl+20lJXV1d4Dvn5+cHV1TXD5mJ16tTBihUr8PHjR4W/Rmd0fqZVpUoV7Nu3D76+vtKXmV27dkFbWxvlypXLdNnM5Oa15MuXLwDkk+O0gxkrUqVKFaxbtw4xMTHScCJ79+6FEEJ679ja2iIsLAxhYWHSF2hFg3QrOk5ZHQNl6s+punXrYuLEiXByclKYYGVHRu+xrOTltTknx1zRMrt3785Ri4w6depg+/bt0t26tJS5zleuXFnh57KyMvo81NLSwqZNmzBlypR0y5QrVw5aWlrYs2cPhg8fDiD50ZQ9e/ZICUhGcvOcApKTo4oVK2LatGnw8/NDYGBgjpO8Nm3awM3NTW7azJkz8ebNGyxevFj6rFKkYcOG2LNnDyZNmiR3jatdu3amvWHu2bMHGhoacj+Spnj06BF2794tlzym7pE69d9E+eU/neR9Kx4eHti6dSt++eWXdNN69eolTVNRUcGcOXPQrVs3fPz4Ed7e3lBRUcHjx49x/Phx7N+/P9N63r59i65du6J9+/Y4deoUduzYkaMxWyZNmoRmzZqhf//+aNGiBc6fP48VK1Zg2bJl2b6zlXL3ccmSJahbty7Mzc3T/RKX0iync+fOKFmyJKKiovDbb7+hbNmy0gfCxIkT0adPHzx9+hTVq1dHfHw87t+/j/v372PNmjWZxnD58mWMHDkS3t7e2L59O65du4YLFy4AAEqVKoVevXqhVatWGD16NNzc3BAVFYU7d+4gKioKs2bNkrbD398fTZs2ha6uLlxcXGBlZQVDQ0Ns2rQJpqam0NXVRfny5eHr64sqVarghx9+QOfOnWFkZISXL1/i8OHD8PX1lZ5lyg4LCwv8+OOPmDp1KmJiYjBixAg0bNgw3Ydeaqqqqhg/fjwGDx4MY2NjeHp6Yu/evThy5Ei6pjMmJiZo1qwZxo0bhzdv3mDUqFHo1q2btP/r1q2LRYsWwdHREaqqqpg8eXK6u5JA8g8nQ4cORUREBHR0dDBu3DiULl1aqedQMqJo36d8ievWrRvq1q0La2vrLL9kli1bFq1bt0avXr3w8eNHWFlZYd68edLAutkxaNAgLF26FK1atUL//v1x+/Zt7Nq1S65MgwYNUKdOHdSrVw+jR4+Gs7MzPn78iKtXr8LMzEz6Aubs7Izdu3fDzc0NmpqaqFy5MoYNG4aNGzfC29sb/fr1g5WVFd6+fYsTJ06gQ4cOqFevHoYOHYp169bBy8sLY8eOhbm5Oa5fv45ixYrhxx9/RIkSJaCiooI///wT7du3h6GhocLWAOPHj0elSpXw448/onv37vj7778xadIkDBky5F8/75db1xJ3d3fo6Oigd+/eGDhwIG7evIm1a9dmudywYcOwfPlyNGnSBEOHDkVQUBBGjRqFdu3aSa0lUp5v7dGjB/r374+7d+8qHBJH0XHK6hgoU39OdenSBcuXL0ft2rUxbNgw2NvbIyQkBOfPn0fVqlXRqVMnAF+f60sZDkIRRe8xZRgbG+fZtTknx7xUqVKwtrbG8OHDMWnSJAQGBmLmzJmZ3uHPyKRJk1ClShV4e3tj0KBB0NPTw/nz5+Ht7Y3atWsrdZ0fPnw4PD09pc/lEydO4PLly5nWq8zn4ZgxY6TPAm9vb4SHh+Pw4cNYu3YtTE1N0b9/f0yYMAEymQxlypTB6tWr8ejRoyy/Dyh7TmXF09MTrVq1Qrly5ZCUlISFCxfCwsJCYbKU4vr16wgMDJSaRvr7+0tDO5QpU0bhWHzr1q1DVFSU3LOrZ8+eRb169XDy5ElpDLwRI0agYsWK6NGjBzp37oyjR4/iyJEjOHXqlLTclClT8OnTJ3h4eEBXVxf+/v5YtGgRhg0bpvD8GTlyJMaNGyclw1WrVsXTp0/x+++/w9jYGCdPnsRvv/0GAHjx4gWKFy+ONWvW5OgHfKIcy89eXwqSnPauKYSQujJfv359umkbNmxIV/7AgQOievXqQltbWxgaGorKlSuL2bNnZ1oH/t+Vf7du3YSenp4wNzcXM2fOlCvj5eUlOnbsmOW2CSHE+vXrRalSpYS6urpwcHAQ8+fPz3IZIRT3VDdmzBhhZWUldSWddvmYmBjRo0cPUaJECaGlpSXMzc1F27Zt0/U+tmbNGuHq6io0NTWFiYmJ8PT0FGvWrMlwn6T04LZt2zbRokULoa2tLezs7MS6devkyiUmJoo5c+YIZ2dnoaGhISwsLET9+vXF/v37pTK3b98W1apVE9ra2nI9o23fvl04OTkJNTU1YW9vL5W/d++eaNmypTAyMhLa2trC2dlZDB48WOrZNKNeRxVJ2VebNm0S9vb2QktLSzRr1kyu58vM1jdnzhzh4OAg1NXVRalSpcSmTZvk5qecF3PnzhVWVlZCV1dXdOnSRXz+/Fkq8+bNG9GoUSOhq6srHBwcxKpVq0THjh2Fl5eXVCbl2B89elSUKlVKaGpqilq1asn1kpaT3jUz2vdCJPdWqa+vL0aOHJnlfhRCiMjISNGrVy9hamoqtLS0RM2aNdP1zqhM75pCCHHkyBFRsmRJoampKby8vMSxY8fSXRO+fPkixo4dK+3/IkWKiBYtWohz585JZU6dOiXKlSsnNDQ05HqZfPnypejUqZMwMzMTmpqawtHRUfTs2VO8ePFCKvP48WPRvHlzoa+vL/T09ET16tXlenlbuHChsLOzEyoqKtKxUvQePXz4sKhQoYLQ0NAQ1tbWYty4cXLnkrI9UCqS1bVE2d419+3bJ10jatWqJfWWunbt2kyXu3LlivD09BSamprC1NRU9OnTJ1138H5+fsLZ2Vloa2sLHx8fcfbs2XTbltFxyuoYZFW/oveuoh4GFb1XPnz4IPr16yeKFCkiNDQ0hJ2dnWjfvr1cT7xVqlQRbdu2zXQfKXqPZdR7sKL3R15dm5U55ml717x48aJwdXUVWlpaolKlSuLSpUvCxsZGTJ48WSqj6LNQ0Xl47do1UbduXaGtrS2MjIxEvXr15HqUzuo6L0Ty+V+0aFGhra0tWrRoIbZt25Zp75rKfh4uWLBAODk5CQ0NDWFraysGDhwozYuPjxdjxowR1tbWQkNDQ1SsWFEayimj/ZZCmXMqrbTXhxEjRoiyZcsKHR0dYWxsLHx8fMSdO3cyXF6Ir71Ip/2X+rgpWibt95CU907a69KJEyeEm5ub0NDQkIZ3SG3r1q2iYsWKwsDAQPqsnDdvntQLcGqnTp0SJUqUSNfz6qZNm4Stra2wsLCQu86lnO9ZXauIcptMiCxG2aYCQSaTYdWqVdludkbfv65du+LJkyfSL9z01eXLl1G9enXcu3cv01+Jif6L4uLiYGBggKNHj0p3NYiI6L+BzTWJqMAJCwvDw4cPMWrUKNSsWZMJHpECN2/eRLly5ZjgERH9B/2nh1AgooLpwIEDqFmzJj59+oTly5fndzhE3yV3d/dcGQ+QiIgKHjbXJCIiIiIiKkR4J4+IiIiIiKgQYZJHRERERERUiDDJIyIiIiIiKkSY5BERERERERUiTPKIiIiIiIgKESZ5REREREREhQiTPCIiIiIiokKESR4REREREVEhwiSPiIiIiIioEGGSR0REREREVIgwySMiIiIiIipEmOQREREREREVIkzyiIiIiIiIChEmeURERERERIUIkzwiIiIiIqJChEkeERERERFRIcIkj4iIiIiIqBBhkkdERERERFSIMMkjIiIiIiIqRJjkERERERERFSJM8oiIiIiIiAoRJnlERERERESFCJM8IiIiIiKiQoRJHhERERERUSHCJI+IiIiIiKgQYZJHRERERERUiDDJIyIiIiIiKkSY5BERERERERUiTPKIiIiIiIgKESZ5REREREREhQiTPCIiIiIiokKESR4REREREVEhwiSPiIiIiIioEGGSR0REREREVIgwySMiIiIiIipEmOQREREREREVIkzyiIiIiIiIChEmeURERERERIUIkzwiIiIiIqJChEkeERERERFRIcIkj4iIiIiIqBBhkkdERERERFSIMMkjIiIiIiIqRJjkERERERERFSJM8oiIiIiIiAoRJnlERERERESFCJM8IiIiIiKiQoRJHhERERERUSHCJI+IiIiIiKgQYZJHRERERERUiDDJIyIiIiIiKkSY5BERERERERUiTPKIiIiIiIgKESZ5REREREREhQiTPCIiIiIiokKESR4REREREVEhwiSPiIiIiIioEGGSR0REREREVIgwySMiIiIiIipEmOQREREREREVIkzyiIiIiIiIChEmeURERERERIUIkzwiIiIiIqJChEkeERERERFRIcIkj4iIiIiIqBBhkkdERERERFSIMMkjIiIiIiIqRJjkERERERERFSJM8oiIiIiIiAoRJnlERERERESFCJM8IiIiIiKiQoRJHhERERERUSHCJI+IiIiIiKgQYZJHRERERERUiDDJIyIiIiIiKkSY5BERERERERUiTPKIiIiIiIgKESZ5REREREREhQiTPCIiIiIiokKESR4REREREVEhwiSPiIiIiIioEGGSR0REREREVIgwySMiIiIiIipEmOQREREREREVIkzyiIiIiIiIChEmeURERERERIUIkzwiIiIiIqJChEkeERERERFRIcIkj4iIiIiIqBBhkkdERERERFSIMMkjIiIiIiIqRJjkERERERERFSJM8oiIiIiIiAoRJnlERERERESFCJM8IiIiIiKiQoRJHhERERERUSHCJI+IiIiIiKgQYZJHRERERERUiDDJIyIiIiIiKkSY5BERERERERUiTPKIiIiIiIgKESZ5REREREREhQiTPCIiIiIiokKESR4REREREVEhwiSPiIiIiIioEGGSR0REREREVIgwySMiIiIiIipEmOQREeWCLVu2wNXVFTo6OpDJZDAyMsqzus6cOQOZTAaZTIauXbvmWT2FUe3ataV9FxgYmN/h5IiDg4O0Ddm1bt06aVlfX9/cD46IiL4LTPKIqMD6/PkzFixYgFq1asHU1BRaWlooVqwYmjZtik2bNiEuLu6bxBEQEIBOnTrh7t27+PLlyzep83vh6+srJQ0ymQwNGjRIV+bGjRtyZWQyGWJiYnJU3759++Dr6wtfX98Cm6Sllnb/yWQyqKmpwcLCAj4+Pjhy5Eh+h/hde/bsGQYOHIhSpUpBV1cXurq6KFWqFAYMGIBnz5796/Xfvn1bOt/OnDnz7wMmIvpG1PI7ACKinLh//z6aNWuW7otcYGAgAgMDcejQIZQrVw5ubm55HsuhQ4cghAAA9O7dGx07doS6unqe1VehQgWcP38eAGBpaZln9eTEyZMn8eLFC9jb20vTVq1alWvr37dvH9avXw8g+a6cg4NDtpZfvHgxIiIiAADW1ta5FlduSkxMxPv373H06FEcO3YMe/fuRYsWLaT5u3btynGSXJjs2rULP//8c7p98ejRIzx69AirV6/Gxo0b0aZNmxzXcfv2bUyZMkV6Xbt27Ryvi4joW2KSR0QFzocPH9CoUSO8fPkSAFCkSBGMHDkS5cuXx6dPn3D27FmsXbv2m8Xz9u1b6e8ff/wRNWvWzNP6DA0N4enpmad15FRSUhJWr16NX3/9FUDy3dYtW7bkc1TJcejq6qJ8+fL5HUqGGjVqhHHjxiE0NBS+vr64c+cOhBBYvHixXJJXuXLlfIzy+3Dr1i107NhRulvfvHlz/PLLLwCAP//8E/v370dMTAw6deoEJyenb/JjDxHRd0UQERUwY8eOFQAEAGFoaChev36drsy7d+9EWFiY9Do2NlbMnDlTuLq6Ch0dHaGtrS1cXFzEjBkzRGxsrNyy9vb20vqDgoJEp06dhJGRkdDT0xPt2rWT1vv8+XOpXNp/Xl5eQgghvba3t5erw8vLS5r3/PlzafquXbuEh4eHMDAwEOrq6sLS0lJ4eHiIUaNGiaSkJCGEEKdPn5aW7dKli9x6g4KCxMCBA4Wjo6PQ0NAQhoaGwsvLS+zYsUOuXOrYvby8xNWrV0Xt2rWFtra2sLS0FOPHjxeJiYlZHovJkydL69HX1xcAhK2trbTs6tWr5eal/Pvy5Yu0jmHDhonq1asLKysroaGhIXR1dUWFChXEnDlzRHx8fJb7GoA4ffp0uv199+5d4e3tLXR1daXjkXa/JyUlibp160rTDh48KMXVt29fafpvv/2W5b7IidT7L/Wx3L17tzS9ZMmScsukPj9TW7FihahUqZLQ1dUVGhoaokiRIqJevXpi1qxZUpm1a9dKy06ePFma3qNHD2l648aN070nUgwcOFAqt2fPHrl506dPl+YtXbpUCJF83Nq3by+sra2FmpqaMDQ0FKVLlxZdu3YVd+7cyckuE0II0axZM6muOnXqSO8NIYRISkoSderUkeY3a9ZMmtelS5d050xG+yX1fk77L/W+e/nypejfv78oXry40NTUFEZGRsLd3V1s27ZNLuYbN26INm3aCEtLS+m93bp1a3H9+nW5cmljWbJkibC3txc6OjqiUaNG4uXLl+LLly9i0KBBwtTUNN11KbVz586JZs2aCTMzM6Guri4cHBzE0KFDxYcPH3K874moYGCSR0QFjqOjo/QlyNfXN8vyMTExolatWhl+YatVq5bcl9rUX+5S15Xyr2PHjkKI3E/yzpw5I1RUVDJcZ0rCk1GS9+zZM2FlZZXh8qNHj5bKpo7d2tpaaGtrpyu/atWqLPdt6iSla9euQl1dXQAQhw4dEkIIUa1aNQFA9OrVK8MkT1NTM8OYu3XrluW+VpTkGRoaClNT03THQ9F+f/78udDT05OOU1RUlLhw4YKQyWQCgKhatapISEjIcl/kREZJ3q5du6TptWvXlltGUZK3YcOGDPeNjY2NVE5RMpP6R5N69erJHZu0Ll++LJXt0KGD3LwKFSoIAEJdXV2EhoaK+Ph4UbJkyQzjUub8UiQ6OlpoaGhI60mdmKc4cOCANF9DQ0PaptxO8m7duiVMTEwUlkl9PPfv3y+9N9L+U1dXF/v371cYS/HixdOVd3FxES1btszwupRi1apVGV5PnJ2dmegRFXLseIWICpSoqCi55/CUaRr5+++/49y5cwAAOzs7bNmyBVu3bkXRokUBAOfOncOCBQsULvvlyxds2rQJy5Ytg4aGBgBg27ZtiIiIgLW1Nc6fP49GjRpJ5RctWoTz589j8eLF2d62AwcOICkpCQDw22+/4eTJk9i2bRsmTJiAMmXKZNmbYr9+/RAcHAwg+dkhPz8/zJ8/H1paWgCAWbNm4cqVK+mWCwoKQsWKFbF//34MGjRImr5y5cpsxW9paYmmTZsCSG4y99dff0n19ezZM8Plxo8fj61bt8Lf3x9nzpzBnj17UK1aNQDJvUG+fv060319/vx5VKhQQW6dERERUFVVxR9//IGjR49mWr+DgwPmzJkDAHjx4gXGjh2LXr16QQgBLS0trF+/HqqqqtnaFzkREhKCCxcuYN++fZg6dao0vXfv3lkuu3//fgCAmpoaVqxYgZMnT2Lz5s0YPnw4ihUrluFyCxcuxIwZMwAkv5f8/Pyk80WRatWqwcnJCQBw8OBBxMbGAkjuAOXWrVsAAB8fH5iamuLhw4d4/PgxAMDb2xv+/v44ePAgFi9ejEaNGkFTUzPL7VLkyZMncp0qKWqKmXpaXFwcnjx5ku16du3ahXHjxkmvu3XrJp1v3bt3hxACnTt3xocPHwAA5cqVw8aNG3Ho0CFMmjQJpqamAJKbCvfo0QPx8fEAgL59++Lw4cPo168fACA+Ph49evTA58+f08Xw9OlTjBo1Cvv374eNjQ0A4O7duzh48CDmzp2LLVu2QFtbG8DX6xIAvHnzBgMGDEBSUhL09fWxePFiHD16FN26dQOQ/Nxi6m0jokIov7NMIqLseP36tdwv0g8ePMhyGRcXF6n8gQMHpOmpf+13dXWVpqf+BX/v3r3SdB8fH2n67du3pekZ3R0QInt38saMGSNN27lzpwgNDVW4PYru5IWFhUl3njQ1NeWWHT58uFR+8ODBQgj5O2MaGhoiODhYCCFEYmKi0NHREQCEkZFRlvs29Z2o0aNHi0OHDkl3J9q1ayfdeUi9L5DmTt6FCxdEixYthJWVlVBTU0t31yH1XY7M9nXaOo4dO5ZufkbNZIUQwtvbO13d8+bNy3IfxMTEiPPnzyv89+7dO6X3X9p/FhYWYv369emWUXQn76effhIAhI6Ojjhx4oSIiIhQWF/qu0QVK1aUzplq1aqJyMjILLc1bcx+fn5CCCFmzZolTUtppvjw4UNp2s8//yyePn2qVBPgrFy4cEFuPylqWhoTEyNX5sKFC0KI7N3Jy2y6EMl38VLmGRgYiJCQEIXx7tmzRypXqVIluXmVKlVKd61JXWeNGjWksv3795fbnymaNGmS7rq0YMECaVq3bt2k8/HcuXPS+9vQ0DBXjgcRfZ94J4+IChRDQ0O516k7PclIyt0EANIdIgCoWrWqwjKpeXl5SX+n/DIPAOHh4VnWm10dO3aU7m60bdsWZmZmsLS0RKtWrXDixIlMl/3nn3+kHj6LFy8uF2tW21mqVCmpl04VFRUYGxsDyNk2+vj4wM7ODvHx8dixYwcASB1iKHL16lXUqVMH+/fvR3BwMBISEtKVyUkcWlpaqF+/fraWWb16NXR1daXX7u7uGDJkSJbLBQUFoWbNmgr/HT58OLuhS96/f4+///5bqbLdunWDTCZDdHQ0vL29YWhoCDs7O3Tq1AnXr19XuMzNmzchhIC+vj4OHToEfX19perq1KmT9PeuXbvk/tfX10fz5s0BACVKlJDutG/cuBHFixeHnp4eqlevjjlz5kh3AbPLwMBA7vX79+/TlUk7Le11Izekva6Ym5srVS61rN6bqeebmJhIf6fufMfMzEz6O+W9knpda9eulc7HWrVqITo6GkDy3W5lrp9EVDAxySOiAkVPTw+Ojo7S64sXL+Z4XcoMJp2S8ADJTeFSpCRUykpMTJR7HRoamq5MuXLlcOPGDQwaNAjVqlWDoaEhQkJCsHfvXjRs2BCXLl3KVp0pstrO1NsIyG9ndqmoqEhNwoDkZCt1UpDWihUrpGZsTZs2xeHDh3H+/Hl07txZKpPShDU7LCwssr3MixcvpC/AAPDy5UtERkZmez051aVLF8THx8Pf3x86OjoQQmD27Nk4cOBAlss2aNAAFy9exC+//IIKFSpAR0cHr1+/xubNm+Hl5aVwzLiUJqifPn3CpEmTlI7TyclJSlb8/Pzw5MkTXLt2DQDQqlUrqfmgiooKDh8+jHnz5sHHxwdFixbFly9fcPnyZYwaNQqDBw9Wus609ac0nQaShzlI686dO9LfGhoaUhPT1O+F1O9JRe/HbyGr92bq5FRF5etXtrSJborsXpcUNRElosKBSR4RFTg//vij9Pf8+fMV/hodEhIiPStTsmRJafrVq1elv1M/n5a6TG5K+ZIWFhYmJTOBgYF4+PBhurJCCJQtWxYLFy7E5cuXER4eLt0hSUpKwr59+zKsx8nJSfrC+PTpU4SFhUnzvsV2pta9e3fpC2nr1q1hZGSUYdk3b95If8+YMQONGjWCp6cn3r17p7B86i+6mSV/yiTwqUVHR6Nbt24QQkjJz9u3b5W6k+fg4ACR3JFZun9du3bNVhxqampo2LAhRo0aJU2bOHFilssJIVC9enX88ccfuHnzJj59+oR58+ZJ2+bv759umb59+0rJz7JlyzBz5kyl4+zYsSOA5DtHKc+WAfJ3+YQQ0NPTw7Bhw3DkyBG8ePECISEh0jOCe/bsUbq+1LS1tdGgQQPp9YIFC+SSGyGE3DO2DRs2lJ4zTJ00pTy/CkDh/gEyP9/SXlcyShQzuv6kfZ2b783U65o8ebLCc/Pz589wdnbOtTqJ6PvCcfKIqMAZMWIENm/ejJcvXyI8PBzVqlXDiBEjpHHyzpw5g7Vr1+LMmTMwMTFBhw4dcPfuXQBA//798enTJ8hkMowZM0ZaZ/v27fMkVicnJ9y4cQNfvnxBhw4dUKtWLSxbtizdnT0AmD17Ns6cOYMmTZqgaNGi0NXVxdGjR6X5mTVvMzU1RcOGDeHv74/Y2Fi0a9cOQ4cOxdOnT7Fs2TKpXF5tZ2r29vZYunQpgoODsxyIOvWg6TNmzECXLl1w5MgRue1OLfVdx02bNkFVVRWqqqr/etzA0aNH4+nTpwCSO+rZv38/Tpw4gfXr16Nt27Zo0qTJv1p/dg0cOBCzZ89GdHQ07ty5g2PHjsklNmkNGjQIQUFBqF+/Puzs7KCmpobz589L8xWdO6ampjh06BDc3d3x8eNHjBs3Dra2tpneeU3x008/YdiwYUhISMDx48cBJI9XWbduXanMmzdv4O3tjXbt2qFMmTKwtLTE8+fPpaaUqWPy9fWVBh1fu3Ztlsmxr68vjh49ivj4eJw8eRKtWrVCjx49IJPJsHr1apw8eRJA8l08X19fabmUpBYAJkyYgPDwcFy6dEkqn1bq883f3x+1atWClpYWypcvD1dXV5QrVw737t1DREQE6tWrh1GjRsHExAQ3btzAx48fMW/ePDRo0ACmpqYICwvD9evXMWDAADRp0gSHDx+WmtKamZllu3lxZtq0aYMxY8YgNjYWM2fOhEwmQ/Xq1REdHY3nz5/j9OnT+PLli3TsiKgQ+pYPABIR5Za///5b4fAGqf/dunVLCJHcCUPNmjUzLJfZEAqpZdRpQ2adgaxcuTJdfXp6esLW1jZdByBTp07NMEYVFRWp84iMhlB4+vRpjoZQSBleIKvtVyRtxyuZSR1LSscrV65ckTr/SPknk8lE9erVpddr166V1pG6s5zU/9LWkbajmxSKOl45ffq0FEP16tVFYmKiePbsmdDV1RUARJEiRcTHjx+z3Bc5kdEQCkLId7Th7e0tTVd0fFKPc5f2n7a2tnj69KkQQnFHIqdPn5a691dXVxfHjx9XKvZGjRrJ1TNs2DC5+a9evcr0/dm7d2+F+yH18c7M1q1bhZaWVobr19LSSjdWXWhoqDRcRup/pUuXTrdfhBDi/fv3Cof4SHmf37hxQxgZGSmsP/Xx3LdvX46GUEgdS0b7KKPrT2ZDKCh63xNR4cLmmkRUIJUpUwZ3797F/Pnz4enpCRMTE2hoaMDOzg4NGzbE+vXrUaZMGQCApqYmjh8/jpkzZ8LFxQXa2trSr/EzZszAsWPH5J7xyU09e/bE2LFjYWFhAW1tbdStWxfnz59H8eLF05Vt3LgxevfujXLlysHY2BiqqqowMTFBgwYNcPToUXh4eGRal6OjI27evIkBAwagWLFiUFdXh4GBAWrVqoXt27dnqznet1K1alXs3bsX5cuXh5aWFsqWLYudO3dmeNeqadOmmDt3LooXL/6vnh1M8fnzZ6k7fHV1daxatQoqKiooVqwYpk+fDiC52WbqoSW+lSFDhkjNBU+cOCENUaBIx44d0aVLFzg7O8PQ0BCqqqqwsLBAy5Ytcf78ebnnWNOqXbu2NFxGfHw8WrduLfdMW0bS3vFL+9rExASTJ0+Gl5cXrK2toa6uDm1tbbi4uGDatGk5GmYktZ9++gn37t1Dv379ULJkSWhra0NbWxslS5ZEv3798Ndff8k17QaS717u27cPLi4u0NDQQPHixbF06VK55rGpmZmZYd++fahQoYL0rGFqFStWxJ07d9C3b184OjpCQ0MDRkZGcHd3lxvuo0WLFggICECbNm1gYWEBNTU1mJubo1WrVrh06ZLUWU1u6tmzJ86dO4dWrVrB0tISampqsLS0RNWqVTFx4kS5O/xEVPjIhMjmU7pERERERET03eKdPCIiIiIiokLkP9vxSlJSEt6+fQt9ff1s98JGRERERET0rQkh8OnTJxQpUkSuB+C0/rNJ3tu3b2FnZ5ffYRAREREREWXLq1evYGtrm+H8/2ySp6+vDyB5B2U0qCgREREREdH3IjIyEnZ2dlIuk5H/bJKX0kTTwMAASYLNNYmIKHcYGX794D1yLuteKomIiJTl4VYMALJ83IwdrxARERERERUiTPKIiIiIiIgKESZ5REREREREhQiTPCIiIiIiokKESR4REREREVEhwiSPiIiIiIioEGGSR0REREREVIgwySMiIiIiIipEmOQREREREREVIkzyiIiIiIiIChEmeURERERERIUIkzwiIiIiIqJChEkeERERERFRIcIkj4iIiIiIqBBhkkdERERERFSIMMkjIiIiIiIqRJjkERERERERFSJM8oiIiIiIiAoRJnlERERERESFCJM8IiIiIiKiQoRJHhERERERUSFSIJK8e/fuZThv37593y4QIiIiIiKi71yBSPIaNmyI58+fp5u+e/dudOzYMR8iIiIiIiIi+j4ViCSvZ8+e8Pb2RnBwsDRt+/bt6Ny5M9atW5d/gREREREREX1n1PI7AGVMmTIFHz58gLe3N86dOwd/f3/07NkTGzduROvWrfM7PCIiIiIiou9GgUjyAGDx4sXo2LEj3N3d8ebNG2zduhUtWrTI77CIiIiIiIi+K99tkufn55duWqtWrXD+/Hm0b98eMplMKtO8efNvHR4REREREdF3SSaEEPkdhCIqKso9LiiTyZCYmJjt9UdGRsLQ0BARERFIErJsL09ERKSIkaG+9PeRc3fyMRIiIipsPNyKSTmMgYFBhuW+2zt5SUlJ+R0CERERERFRgVMgetdUJDw8PL9DICIiIiIi+u4UiCRv1qxZ2L59u/S6bdu2MDExgY2NDe7cYVMYIiIiIiKiFAUiyVuxYgXs7OwAAMePH8eJEyfg7++PRo0aYeTIkfkcHRERERER0ffju30mL7Xg4GApyTt48CDatWuHBg0awMHBAdWqVcvn6IiIiIiIiL4fBeJOnrGxMV69egUA8Pf3h7e3NwBACJGjnjWJiIiIiIgKqwJxJ69Vq1bo0KEDSpQogbCwMDRq1AgAcOvWLTg5OeVzdERERERERN+PApHkLViwAA4ODnj16hVmz54NPT09AEBQUBD69euXz9ERERERERF9P77bwdDzGgdDJyKivMDB0ImIKK8U+MHQ/fz80KhRI6irq8PPzy/Tss2bN/9GUREREREREX3fvtskr2XLlggODoaFhQVatmyZYTmZTMbOV4iIiIiIiP7vu03ykpKSFP5NREREREREGSsQQyhk5s2bN/kdAhERERER0XejwCZ5wcHBGDhwIEqUKJHfoRAREREREX03vusk7+PHj2jfvj3MzMxQpEgRLFq0CElJSZg0aRIcHR1x7do1rF27Nr/DJCIiIiIi+m58t8/kAcCYMWNw6dIldO3aFUePHsXQoUPh7+8PFRUVnDp1Cu7u7vkdIhERERER0Xflu76Td+TIEaxduxZz587FgQMHIISAm5sbDh48yASPiIiIiIhIge86yXv79i1Kly4NAHBwcICWlhY6deqUz1ERERERERF9v77rJE8IATW1ry1KVVVVoa2tnY8RERERERERfd++62fyhBCoV6+elOh9+fIFzZo1g4aGhly5mzdv5kd4RERERERE353vOsmbPHmy3OsWLVrkUyREREREREQFQ4FK8oiIiIiIiChz3/UzeURERERERJQ9TPKIiIiIiIgKESZ5REREREREhQiTPCIiIiIiokKkwCZ54eHh+R0CERERERHRd6dAJHmzZs3C9u3bpdft2rWDqakpbGxscOfOnXyMjIiIiIiI6PtSIJK8FStWwM7ODgBw/PhxHD9+HEeOHEGjRo0wcuTIfI6OiIiIiIjo+/Fdj5OXIjg4WEryDh48iHbt2qFBgwZwcHBAtWrV8jk6IiIiIiKi70eBuJNnbGyMV69eAQD8/f3h7e0NABBCIDExMT9DIyIiIiIi+q4UiDt5rVq1QocOHVCiRAmEhYWhUaNGAIBbt27Byckpn6MjIiIiIiL6fhSIO3kLFizAgAEDUKZMGRw/fhx6enoAgKCgIPTr1y+foyOitA4ePIB6dWtnWa5a1co4e+ZMnsdDRESKXbl4BmMGdc3vMHJsyC/tcPfW1UzLbF6zFH8unf2NIiL6PsiEECK/g8gPkZGRMDQ0REREBJKELL/Dof+IalUrZzq/Z89f8Euv3t8klr59euHmzZsAAA0NDRSxsUHbtu3Qpk3bf73umJgYREdHw8TEBACw6o+VOHv2LDZt3iJXLiw0FPoGBtDQ0PjXdRJ9L4wM9aW/j5xjD9CU9zavWYprAWfTTR8/fRHMLawyXfbKxTPYu30dZi5alyexXbl4BlvXLQMAyGQyGBgaw7mMC5q17gh9A8N/vf7IiHDo6OhCTV0dYaEhmDp2AEZMnA3bog5SmS/R0RAQ0NHR/df1EeU3D7diUg5jYGCQYbkC0Vwzxf379/Hy5UvExcXJTW/evHk+RUSUPYcP+0t/Hz9xHH+sXIGdO3dL07R1dKS/U545VVPLu7dpi5Y/oHev3oiJjcHhQ4cwZ/Ys6Ovro2FDn3+1Xi0tLWhpaWVZztTM7F/VQ0REyUqVc0OHrvKtm/T0M/4C+C1paWtj3NSFECIJb169wNZ1yxAR/hF9h47/1+s2MDTKskzqz1ai/4oCkeQ9e/YMP/zwA/766y/IZDKk3HyUyZLvwLHzFSooUic1enp6kMlk0rQbN66jX98+WPD7QqxYsRxPnzzBosVLcOjgQXz69Alz5s6Tlp0/fx7+efwIy1f8AQBISkrChg3rsW/vXnz4EAY7u6Lo3qMH6tXzzjQeLS0tqf5fevXG0aNHcf78OTRs6IPg4GDMnTsb169dg0xFBdXdq2P4iJEwNTUFADx+/BgLFszDwwcPAJkMdnZ2GDtmHEqXKYODBw9gwfx5OHnqDA4ePIA//1wF4OudzImTJqNp02aoVrUyZs+eC6/atdGzR3e4ublhwMBBUnwfP35Ek8Y+WLp0OSpUrIi4uDgsX74Mx48dxadPn1C8eHH0HzAQlSolrzcoKAhz58zGnTu3ER8fD2vrIhg4aBA8PDz/1XEjIvreqampKUx4Th87iKuXTiPsfQh0dPVQ1rUSmrfuBM0Mfoh78yoQe7evx6vAp4BMBnMLK7T7uReKOhQHADz75yEO7tmCVy+eQlfPAOUrVEHTVh2gqZnZD3syKTZDIxPUqtsIh/dvR1xcHNTU1HDs0B4EnDuBqKhIWFrZoFnrjihdzg0AkJCQgH071uPuzSuI/vwZ+gaGqOFVH/Ub/wAgublm934j4FKhKqaOHQAAmDt1FACgeMkyGDjSF5vXLMWXL5/Rs/8oXDp3Av5+O+E7ezlUVL4+tfTnktnQ0dOTEuW/bl/D0QO7EPz2NQyNjFGluhfqN2kFVVVVCCHgf2Anrlw8jU+REdDV1YdrpWpo3b67soeLKM8ViCRv8ODBKFasGE6ePIlixYrh6tWrCAsLw/DhwzF37tz8Do8oVy1dsgSDBg+GjY0t9PX1s14AwPp1a+HvfwRjxoyFXVE73Lp1C76TJ8HY2BgVK1ZSum5NTU3Ex8cjKSkJI0cMg7a2Dpav+AOJiYmYM3sWJowfKyWWkydNQElnZ4wePRYqKir45/FjqCq46+jtXR9Pnz7F5YBLWLIkucmO7v+fq02toY8PNm3cgP4DBko/4Jw4fgzm5uZwq1ABADB3zmw8f/4MU6f9BnNzc5w5cxpDBg/C5i3bULRoUcyZPQvxCfFYsXIVtLW18PzZc+ho8xdcIvrvkqnI0OqnbjAxs0DY+xDs2vIn/HZvQtuOPRWW3/jnYtgWdUDbjj2hoqKCN68CoaqqCgAIDQnGioXT0bjlT2jftS+iPkVi99Y12L1lDTp0U76PBHUNDQghkJSUiHMnj+HM8QNo16kXbIoWw5ULp/DnklkYM2U+zC2tce7kYdy7cx1deg2FsakZwj+E4eOHUIXrHTbuN8z/bRz6DZsIqyJ2Cj+T3Cq5Y/fWNXjy6G+ULF0eAPD5cxQe/H0bvQeNBQA8ffwAm9csQaufusGxRGmEvX+H7RtWAgB8mrfFnZtXcPbEIXT+ZQisi9ghMjIcb18FKr39RN9CgUjyAgICcOrUKZiZmUFFRQUqKirw9PTEjBkzMGjQINy6dSu/QyTKNb1690a1au5Kl4+Li8O6dWuxZMkylHdxAQDY2Njizu3b2Ltnj1JJXmJiIo4dO4onT/5Byx9+wLVrV/H06VPs3bcflpbJz3NM9p2C9j+1w/37f6NMmbIIfvcOnTp1hoODAwCgaNGiCtetpaUFHW1tqKqqZdo809u7PhbMn4fbt2+jwv+TuqNHj6JBg4aQyWQIDg7GwYMHsN/vIMzNzQEAnTr9jMsBATh48AD69euPd++CUadOXanXXRsbW+V2IhFRAXf/7k2MGvCz9Lp0uQro1mcYans3kaaZmlmgccufsHPTqgyTvI8fQlG3YTNYWtsAAMwtraV5J47sQ6VqNaV1mltao9VP3bB4zmS07dQT6upZP1/9/l0QLp49Djv74tDS0sbpYwdQr2ELVKzqAQBo3qYTnjz6G2dPHEKbjj3x8UMozC2s4ViiFGQyGUxMzTNct+7/m6fq6Opn2IxTR1cPpcu54caVC1KSd+fGZejp6cPJuSwA4OjBXajn0xJVa9QGAJiZW6Jxyx/ht2szfJq3xcewUOgbGMG5dHmoqqnB2NQM9sXY2zt9XwpEkpeYmCjd0TAzM8Pbt2/h7OwMe3t7PHr0KJ+jI8pdpUuXyVb5169eISYmBgMH9pebHh8fD2dn50yX3b1rJ/z270N8fDxUVVXRvn0HtG7dBjt37oCFhaWU4AGAo6Mj9PX1Efj8OcqUKYsO7Ttg+vSpOHLkMKpUrYp69bxha5vzpMrY2BjV3N1x1P8IKlSogLdv3uCvv+5izNhxAIAnT54gMTERbdu0klsuLi4OhobJD++3a/cTZs2agStXLqNK1WqoU6cuSpQokeOYiIgKCifnsmjb6RfptYaGJgDg0f27OHFkH0KC3yDmyxckJSUiPj4ecbGx0NDUTLee2vWbYNuGlbh++TxKli4Pt0ruMPt/5y1vXr3A2zcvcOPK+a8LiORnyMNCQ2BlrfgzIOZLNEYN+BlCCCTEx6OYUyn81Lk3Yr5EIyL8I4o5lZIrX8zJGW9fvQAAVK1RG8sXTMNvE4agVDlXlHWphFJlXf/VvqpcrSa2bViJth17Qk1dHTeunEeFKh5S8803rwLx/MlDHD+85+tmJiVJ+82tsjvOnjyEqeMGolQ5V5QpVxFlXStJdzyJvgcFIskrV64c7ty5g2LFiqFatWqYPXs2NDQ08Mcff8DR0TG/wyPKVdra2nKvZSoyCMh3gpuQkCD9Hf3lCwBg/oLfYW5uIVdOQ0M907oa+jRCt27doampKd0pV9YvvXqjYUMfXLx4AZcCLmHVHysxbdpvqF2njtLrSMunYSPMmzcHI0aOwtGj/nBycpLuyn35Eg1VVVWs37ARKiryH6Q6/99nLVq2hHt1d1y8cAFXrlzB+nVrMXjwELT78accx0REVBBoaGqm60kzLDQEqxbPgkft+mjS8ifo6Orh2ZOH2LZ+BRISE6CB9Eleo+btUKmqJ+7/dRMP7t3GEb8d6PLLELhUrIrY2BjUqOWNWvUap1vO2CTjlhqaWtoYMXGW1LtmSo/KMV+is9wuO3tHTJqxBPfv3cbjB3exbuUCOJcuj259h2e5bEbKulYCIPD3XzdR1KE4nv3zEC3bdZHmx8XGwKd5O7hUrJZuWTV1dRibmGHc1IV4/OAuHt2/i11b/sSpY34YOMJXYRNRovxQIM7ECRMm4PPnzwCAX3/9FU2bNkXNmjVhamqK7du353N0RHnL2MgYz54+lZv2z+NHUq+bxYoVg4aGBoKDg7P1/B2Q3PmLnZ1duukODg4ICXmHd++Cpbt5z549w6dPn1Cs2NcfVora26OovT3ad+iICRPG4eBBP4VJnrq6OpKSsu4gqZaXF2bMmI6AgEs4evQoGjf++kXCuaQzEhMT8eHDR6k5pyKWllZo1boNWrVug6VLl2D//n1M8ojoP+n1i2cQIgkt2naWfsS7fT0gy+UsrIrAwqoIatdvivV//I4rl07DpWJV2BUthndBb7IcliEt2f87cElLS1sHhkbGeP7kIZycv7Zief7kEYqmav6opa2DilVqoGKVGnCt6I6VC3/D589R0NWVf7475XNRiKRM41FX14BLhWq4ceU8QkOCYW5ZBHb2Xz/bbIs6IiT4babbqaGhgXKulVHOtTI86/hgxsQhePvmpdx6iPJTgUjyGjZsKP3t5OSEhw8f4sOHDzA2NpY6aCAqrCpXroJNmzbi8KGDKFfeBf7+R/D06VOpKaauri46duyE3xfMh0gScHVzQ1RUFO7euQ1dXT00ado023VWrVoNxYsXx6SJEzF02HAkJiZg9qxZqFixIkqXKYOYmBgsXrwQdevWQ5EiNggJeYcH9++jTp26CtdnbV0Eb9++xePHj2BhYQkdHR2FY+Npa2vDy6s2Vq5YgcDA52iQaiiHovb28PFphCm+kzF4yBA4l3TGx/CPuHbtGpycSsDT0xPz589Djeo1ULRoUUR++oQbN67DwaFYtrefiKgwMLOwQmJiIs6f8kdZ10p4/uQRLp49nmH5uLg4+O3aCNdK7jA1s0D4xzC8Cnwq3dGq16gFFswYj11bVsPdsx40NTURHPQaj+7fRZsOPXIUY52GzeHvtwOmFlawsXPA1Yun8eZVIH7umdzT8uljB2FgaATbosUgk8lw58ZlGBgaQVtBp1p6+oZQ19DAg3u3YWRsAjU1jQyHT6hUzROrFs9C8NvXqFytpty8Bk1bY9WSWTA2MYNrJXfIVGR4++oFgt68QpMffsKVi2cgkpJg7+gEdQ1N3Lh8DuoaGpk+L0j0rRWIJE+RlEGWiQo79+rV0b1HTyxevBhxcbFo1qw5GjdugqdPn0hlevfpCyNjY6xfvxZvfnsDfX19ODuXQteu3XJUp0wmw5y58zF37mz06f2L3BAKAKCqqoqIiAhM8Z2MDx8+wMjICLVr18lwIPc6devi9JlT6Ne3Dz59+iQNoaBIQx8fDB0yGBUqVISVlfyvqBMnTcaaNaux8Pff8f59CIyMjFCuXHl4eiZ/QCclJmLOnFkICQmBrq4u3N2rY+jQYTnaB0REBZ2NnQNatuuMk/77cXDvFhQvURpNW3XA5jVLFJZXUVHB56hP2LxmCT5FRkBPTx8uFaqhUYt2AIAitvYYOMIXh/Ztw+LZkyAgYGZuhQqVq+c4xlp1GyEmOhr7d2xA1KcIWFrboueA0VKHL1paWjh11A/vQ4KgoqKCog5O6DVorMLHC1RVVdHqp244enAXjuzfDscSpTFwpK/CekuUKgcdXT2EBL9FxWryw+yULueGXwaMxrGDu3Hy6H6oqqrCwsoG1T2Tf8jU1tHBySP7sW/neiQlJcHapih+GTAaunrK9YhN9C3IRMqgc/8xkZGR0mjxSYJ3A4mIKHcYGX79onfk3J18jISIiAobD7diUg5jYGCQYTnle1kgIiIiIiKi716Bba6ZXbGxsYiNjZVeR0ZG5mM0REREREREeeNf3clLTEyUer383s2YMQOGhobSP0U9ChIRERERERV02XomLywsDFu2bMHx48dx5coVhIaGAkjuRrZkyZKoWbMm2rZtCy8vr38dmJ+fn9JlmzdvnmUZRXfy7Ozs+EweERHlKj6TR0REeUXZZ/KUaq758uVLTJo0Cdu2bYOJiQnc3d3Rr18/mJmZQVNTE+Hh4QgMDMT169excuVKFCtWDJMnT0bHjh1zvAEtW7aUey2TyZA6H009dEJiYtbjb2lqakJTM/2gn0T/NS9eBKJP797YtXsPdHV18zucbAsIuISlS5Zgw8ZN2Rq8nYiI8s/nqE+YMWkoho77DaZmFvkdTp44sHsz4mJj0bpD9/wOhUi5JK9MmTJo27Ytjh8/Dk9Pz0zHpnv//j127NiBX3/9Fa9evcKYMWNyFFhS0teBLE+cOIHRo0fjt99+Q/Xqyd30BgQEYMKECfjtt99ytH6i79mqP1bizz9XyU2zt7fHjp27AQARERFY9cdKXLlyGe/evYORkRG8vGqjd5++0NPTU7RKybKlS9GuXTu5BO+ff/7BnNmz8ODBfRgZGaNdu3b4uXOXTNdz//7fWLpkCR4+fACZTIYyZcpiwMBBKFmyJIDku+ezZs7Aw4cPEBgYCA8PT8yZO09uHY8ePcS0qcnXikqVKmPSZF8YGhoCABISEtC9e1eMHj0GZcuWk5apXr0GVq5YAX//I2jcuEkWe5KIqPB5+vg+Th31w6sXzxEZ8RHd+42AS4WqSi//7MlDLJnjC6sidhg1eY40PSbmCw7v246/bl1F1KcI2BQthlY/dpUbmHzzmqW4FnBWbn2lyrqiz5DxmdZ57NAelHOrLCV4b14F4sSRfXj+5BE+R0XC2NQCHl714eXd+Guc/zzEgd2b8S74DeLjYmFsao4atbxRu37G47++C36LnZtWIfjta8R8iYahkTEqVvWET7M2UFX7+rX3zIlDuHjmGMI/hEJXzwCulaqhaasOUFdPHsP1+uXzOLhnC2JjY1C1Rm388OPXz8Sw0BCsWDAdwyfMgFaq8frqNGiGqeMGwqt+E5iZW2a6P4jymlJJ3t9//w17e3ulVmhubo7+/fujX79+ePv27b8KLsWQIUOwYsUKeHp+HcekYcOG0NHRQa9evfDgwYNcqYfoe+Lo6IglS5ZJr1N/OIWGvsf70PcYNHgIihVzRHBQEGbOnIH3oe8xc+bsDNcZHByMCxfOS+PdAUBUVBQGDRyAqlWrYsyYsXjy9AmmTf0Vevr6+OGHVgrXEx0djcGDBqFmrVoYNXo0EhMS8ceqlRg8aCAOHDwENTU1JCUlQVNTE+1+/AmnT51SuJ7p06ehcuUqmD59BqZPn4Z169Zi8OAhAIAtmzfB1cVVLsFL0aRpU+zYvp1JHhH9J8XGxqKIrQOqedTFmuVzs7VsdPRnbF6zFCVKlcenyHC5edvWr0Dwm1fo1GMADIxMcP3yOSxbMBVjpiyAkfHX8YlLlXNDh679pNdqapl/nYyLjcWVi6fQZ/DXRPDVi2fQNzBEpx4DYWRiisCnj7B94x9QUVFBzbo+AAANTU141mmIIrb20NDUxPMnD7Fj4ypoaGqhRi1vhXWpqqqiSvVasC1aDNo6unj76gW2bVgJIZLQtFUHAMCNKxdwcPcWtO/aFw7FS+L9uyBsWbsMgAw//NgFUZ8isX3DCrTv1h9mZhb4Y/FMlCxVDmVdKwEAdm1ejaatO8gleACgp2+AUmVdcfHMMbRo+3PmB4IojymV5Cmb4KUmk8lgY2OT7eUUefr0KYyMjNJNNzQ0RGBgYK7UQfS9UVVVg6mZmcJ5xYs7Ydasr7++2traom/ffpg8eSISEhIy/MA9ceI4SpQoCQuLr01ljvr7IyEhHhMmToK6ujocixfHP48fY+uWzRkmeS8CAxEZGYHevXvD0jJ5wPKePXuhY4efEBQUBDs7O2hra2P0mLEAgLt37uDTp0/p1hP4/Dl+/XUaitrbo0GDhrhw8TwA4M2b1/Dz88P6DRsV1l+zZi3MnTMbr1+/hq2trcIyRESFVZnyFVCmfIUcLbtz0ypUquoBmYoK/rp1TZoeFxeHuzevoEf/UShesgwAoFHzdvj7zg1cPHMMTX74SSqrpqYGA0Mjpeu8f+8W1NTU4VC8pDTN/f8Di6cwM7dE4NPHuHvzipTk2RYtBtuixaQypmYWuHvzKp798yDDJM/M3FLuLpqJqTkqPfobz/55KE17/vQRijk5o9L/B0E3NbNAxaoeePH8HwDJd+q0tHVQsUoNAICTc1m8C36Dsq6VcOPKBaiqqsK1YjWF9Zd1qYTD+7YxyaN8l+MHWt6+fYurV6/i3Llz6f7ltipVqmDYsGF49+6dNO3du3cYOXIkqlZVvnkCUUHy6tVLNGnsgx9atsCkiRMQHBycafmoqCjo6upm+ovq7du3ULp0ablpf/11F25uFaCuri5Nq+ZeHS9evMhwqJGi9vYwNDSE3/79iI+PR0xMDPz89sOhWDFYW1srvY0lSpTE1SuXkZCQgGvXr8LJqQQAYObMGRg4cFCGzwxaWVnBxMQUt2/fUrouIqL/uisXTyPs/Ts0bNY23bykpEQkJSXJfRYAgLqGBp49eSg37cmj+5gwrCemTxiMHZtW4XNU+h/xUnv2zwPY2jtmGd+XL9HQ0c34kYPXL5/j+dNHUhKqjPchwXj49225ZYoVd8arF8/w4vkTAEDo+3e4/9ctlCmXnDibW1ghLi4Or18+x+fPUXgV+BTWNkUR/TkKR/Zvz/SZO/tiTgj/GIaw0BClYyTKC9keJ+/Zs2f4+eefcfnyZQBA2s45ZTKZUh2hZMeaNWvwww8/oGjRotLQB69evUKJEiWwb9++XK2L6HtQtlw5TJrki6L29ggLDcWff65C7149sWXrdoWJT3h4ONas+RMtW/6Q6XqDg4JRurT8h2PYhzAUKVJEbpqJSXKznLCwMIU9N+nq6mL5ipUYNXIE1qxZDQCws7PDwkVLsmy2k9r48RMwe/YsbN68CS4urujSpSsOHz4ELU0tlC5TBoMGDsCbN69Rv34D9OnbT25Zc3MzBAcFKV0XEdF/2ft3QTiwewsGjZoCVVXVdPO1tLThULwkjh7cDUtrG+gbGOHm1QsIfPoYZhZWUrnS5dzgWrEaTMwsEPo+GIf2bsXKhb9hyNjpGXaG9SHsPQwNjTON7/mTR7h1PQC9Bqbvy2HyyD6IiopEUmIifJq3RfWa9bLc3t9nTsDrF8+RkBCP6rW80ahFO2lepWqeiIqKxKJZEyEAJCUmooZXfdRvktx6RUdXDx279cfmNUsQHxeHytVroXQ5N2xdtxyedX3wITQEfy6ZjcT/x+NWyV1at6FR8nZ+DAsttB3MUMGQ7STvl19+wevXr7FmzRqUKVMGGhoaeRGXHCcnJ9y9exfHjx/Hw4fJvyaVLl0a3t7emXYCQ1RQ1ajhIf1dokQJlC1XDi2aN8XJE8fRvEVLubJRUVEYNnQwihVzxC+9eme63tjYmFx5z8bExGD6tKlwcXHF1GnTkZiYhM2bN2LY0MFYu24DtLS0lFqPY/HiWLHyD+l1RHg4Vv3xB1as/APz5s6Bi4sLZs2eg65dO6NsuXKoWbOWVFZTUxMxMTH/eluIiAq7pKQkbFi1CI2at4WFVZEMy3XqPgBb1y/H5JF9oKKiAtuixVCxqgdevXgulalY9evnUxHboihia49p4wbiyaO/UbJ0eYXrjY+Lh7qRusJ5ABD05iX+XDobPk3boFRZ13TzB436FbGxMXjx7DEO7NkCM3MrqallRrr0GoLYmBi8eRUIv12bcNr8AOr5tAAA/PPob5w4vBdtOvaEfbESCA0Jxp7ta3H04C40bNoGAOBSsSpcKn5tLfbk0X28ff0Srdt3x7Txg9D5l8HQNzTCgt/GoniJ0tA3SO40LKXjlri4WBDlp2wneVevXsX69evRqpXiZ3XyikwmQ4MGDVCrVi1oamoyuaP/FH19fRQtao9Xr1/LTf/8+TOGDB4EHR1dzJo9J8u7aEZGRumejTM1McWHsA9y0z58SH5tamqqcD3HjvrjbVAQ/ly9VvrldurU6fCuVwfnzp1FgwYNs7V9KX7/fQF+at8elpaWuHnzBnr36QttbW14eHji5o0bckleZGQkjIwz/2WYiIiSe8189eIp3rx6jt1b1wBIboklhMCw3j+hz5AJKFm6HMwsrDBw5BTExsYg5ssXGBoZY93KBTAzz/iOlJm5JXT19PE+JDjDJE9PTx/R0Z8Vzgt++xpL501FjVreaNC0tcIypv+vv4htUXyKjID/gZ1ZJnnGJsnPtFsVsYUQSdi+8Q/UadAMKioqOLJvOyq715LuCBaxLYq4uBhs3/gH6jdule6OZEJ8PHZt/hMdewxA6PtgJCUlwsk5uVWMuUURvHj+D8q5VgYAREdHJW+zfsbjlxF9C9l+Js/Gxkbhbf68lJSUhKlTp8LGxgZ6enp4/jz5F6WJEydi9erV3zQWovwQHR2NN29ewyxVRywpvWKqq6th7rz5So0DWdLZGc+fPZObVr68C27fvoWEhARp2tWrV2Bvb5/hIJsxMTFQkcnkfmyR/f+1SBIKl8nKtatXERj4HG3bJjepSUxMlGJKSEhAYqphVWJjY/H69Ws4l3TOUV1ERP8lWlraGO07FyMnzZb+1fCqDwurIhg5aTbsHZ3kymtqasHQyBjRn6Pw8O87KOdWJcN1h38IQ/TnqEybY9oUdUDw29fppge9eYUlc6egag0vNPmhvVLbkiSE3OeVUsskCSQmJkL8/3MkLi423c0CmSzjr8THDu1GqXJusLN3RFJSEpJSPZaUmJggN+xX0JtXUFVVhVURu2zFSJTbsp3kTZ8+HTNnzpR+6f8Wpk2bhnXr1mH27NlyTc3KlSuHP//885vFQfStLFz4O27evIG3b9/i7t07GD1qBFRUVKQ7ZFFRURg0aABiYr5g/IRJ+BwVhbDQUISFhmb6TKy7e3X8de8vuTINfXygpqaOaVN/xbOnT3H8+DFs37YV7Tt0lMqcOX0a7dp+/YW1ajV3fPr0CXNmz8Lz58/x7OlTTJ2a/JxHpcqVpXLPnj3D48ePEBkZgajPUXj8+BEeP36ULq7Y2FjMnTsbY8eNl35BdXF1xa5dO/H48WOcPnUKri5fm/Dcu/cXNDQ0UN7FJQd7l4ioYIuNicHrl4F4/TIQAPAhNASvXwbiY1iowvIqKiqwtikq909P3wBqauqwtikKTc3kJvYP7t3Gg3u3EfY+BI/u38WSuVNgaWWDajVqS/Xu37kRgU8fIyw0BI8f/IU/l86GmbmVwmaWKUqVdUNw0GtEf46SpgW9eYml86agVFkX1K7fFJER4YiMCEfUp68dfp0/7Y97d67j/bsgvH8XhMvnT+H0sQOoXK3m1zKn/LF03q/S6+uXz+PWtUsIDnqN0PfvcOvaJRzauwUVKleXhiIq61oJF88ex82rF6VtPbJ/O8q6VEp3Fy/47WvcuhYgPdNnYWUDmYoKLp8/hb/v3kRI8FsUdfiaJD/95wEcS5T+Jo8zEWUm2801161bh9evX8PBwQFubm7phjaQyWTYv39/bsUHANiwYQP++OMP1KtXD3369JGmu7q6Ss/oERUmISHvMHHCeERERMDI2Biurq5YvWYdjP/fPPHRo4f4+949AEDrVi3llt27zy9dRyopqlevATVVVVy7ehXu1asDAPT09LBo8RLMmT0LXbr8DEMjI/To0VNu+ISoz1F48eKF9NrBwQFz583Hn3+uQs8e3aCiooKSJZ3x+8LFcncbhw0djKBUnaP83Ck5cbxy9bpcXH/+uQo1PDxRMtWdueHDR2LSxPHo0/sX+Pg0Qp26X7vbPnbsKBo29FH62T8iosLk5YunWDp3ivR6344NAIAq1b3QsXt/AMARvx24euksJs9cqvR6Y75E4+DerQj/GAZdXT24VKyGJi3bS8mRTEUFb1+/xLWAs/gS/RkGRiYoVcYFjVv+CDX1jJ+5K2JbFLZFi+HW9QB4eNUHANy+cRlRnyJx/fJ5XL98XiprbGouxSySBA7u2YoPoSFQUVWBmbkVmrXuKDd8QlRUJELff+19XUVVFSf99+P9uyAICJiYmMOzjg9q1/86rmqDJq0hgwyH921DRPgH6OoboJxLJTROczdRCIHtG1eiZbvOUiKsoaGBDl37YdeW1UhIiEfrDt3lxhC8de0SfBT0Xkr0rclE2u4xs1CnTp0sy5w+fTrHASmira2Nhw8fwt7eHvr6+rhz5w4cHR1x//59VK1aFVFRUVmvJI3IyEgYGhoiIiICSYLP99F/x86dO3D+3DksWrwkv0PJkfDwcLRt0xrr129AkVwai5MoNxkZ6kt/Hzl3Jx8jof+yzWuWAJBJSV9++/vuTfjt2ojRvvMy7IWzoLv/1y3s37kBoybP/eaPNtF/h4dbMSmHyeixGiAHd/JyO4FTRpkyZXD+/Pl0g7Lv2rULFSrkbDBQov+qH35ohahPn/D58+cMx6H7ngW9fYtRo0czwSMiyoAQAk8e3ceg0b9mXfgbKetSEe9DghAR/kHqFKWwiYuLRfuu/Zjg0Xch20lefpg0aRK6dOmCN2/eICkpCXv27MGjR4+wYcMGHDx4ML/DIypQ1NTU0K17j/wOI8dKlymD0mWUHwiXiOi/RiaTYfKsZfkdRjq1vZtkXagASz1eHlF+y9H98lu3bqFt27awtraGpqYmrK2t0a5dO9y6dSu34wMAtGjRAgcOHMCJEyegq6uLSZMm4cGDBzhw4ADq16+fJ3USEREREREVRNm+k3f+/HnUr18fVlZWaP//8azevXuHvXv3okaNGjh+/Dg8PTMfuyQnatasiePHj+f6eomIiIiIiAqTbHe84uHhAX19fRw8eFBu4OXExEQ0adIEUVFRuHDhQq4HmtvY8QoREeUFdrxCRER5Jc86Xrl16xZ27doll+ABgKqqKgYNGoQ2bdpkP9osGBsbpxu0Ekhuc66lpQUnJyd07doV3bp1y/W6iYiIiIiICpJsJ3m6uroICQlROO/du3d50lvfpEmTMH36dDRq1AhVq1YFAFy9ehX+/v7o378/nj9/jr59+yIhIQG//PJLrtdPRERERERUUGQ7yWvWrBlGjx4NW1tbeHt/HYzyxIkTGDt2LJo3b56rAQLAhQsXMG3aNLmB0AFg5cqVOHbsGHbv3g0XFxcsWrSISR4REREREf2nZfuZvI8fP8LHxwfXr1+HgYEBLCwsEBISgsjISFSpUgVHjhyBsbFxrgapp6eH27dvw8nJSW76kydP4ObmhqioKDx9+hQuLi74/PmzUuvkM3lERJQX+EweERHllTx7Js/Y2BgBAQE4ePAgLly4gI8fP8LExASenp5o0qQJVFRyNCpDpkxMTHDgwAEMHTpUbvqBAwdgYmICAPj8+TP09fUVLU5ERERERPSfkaPB0FVUVNC8efM8aZqpyMSJE9G3b1+cPn1aeibv2rVrOHz4MFasWAEAOH78OLy8vL5JPERERERERN8rpZK8Dx8+wMjICCoqKvjw4UOW5VPurp/AydIAAIrzSURBVOWWX375BWXKlMGSJUuwZ88eAICzszPOnj2LGjVqAACGDx+eq3USEREREREVREoleebm5ggICEDVqlVhZmamcDiD1BITE3MluNQ8PDzg4eGR6+slIiIiIiIqTJRK8tasWYPixYtLf2eV5OW2yMhIhdNlMhk0NTWhoaHxTeMhIiIiIiL6XimV5HXp0kX6u2vXrnkVS4aMjIwyTSxtbW3RtWtXTJ48OU86fiEiIiIiIioosp0ROTo64s4dxV1C37t3D46Ojv86qLTWrVuHIkWKYNy4cdi3bx/27duHcePGwcbGBsuXL0evXr2waNEizJw5M9frJiIiIiIiKkiy3btmYGAgYmNjFc6Ljo7Gq1ev/nVQaa1fvx7z5s1Du3btpGnNmjVD+fLlsXLlSpw8eRJFixbF9OnTMW7cuFyvn4iIiIiIqKBQKsmLiYlBdHQ0UsZNj4yMTNfLZkxMDPbt24ciRYrkepCXLl2ShkpIrUKFCggICAAAeHp64uXLl7leNxERERERUUGiVJI3a9Ys/PrrrwCSOztp2LBhhmV9fX1zJbDU7OzssHr16nTNMVevXg07OzsAQFhYGIyNjXO9biIiIiIiooJEqSSvZcuWcHBwgBAC3bt3x4QJE6TeNlNoaGigdOnScHNzy/Ug586di7Zt2+LIkSOoUqUKAOD69et4+PAhdu3aBSB5cPQff/wx1+smIiIiIiIqSJRK8lxdXeHq6gog+U5e06ZNYWpqmqeBpda8eXM8evQIK1euxKNHjwAAjRo1wr59++Dg4AAA6Nu37zeLh4iIiIiI6HuV7Y5X6tatixcvXihM8m7evAkLCwvY2trmSnCpOTg4YMaMGbm+XiIiIiIiosIk20le3759UaJECVSsWDHdvC1btuCff/7B/v37cyW4tKKjo/Hy5UvExcXJTXdxccmT+oiIiIiIiAqabCd5V65cQe/evRXOq1OnDjZs2PCvg0rr/fv36NatG44cOaJwfmJiYq7XSUREREREVBBlezD0qKgoqKurK16Zigo+ffr0r4NKa8iQIQgPD8eVK1egra0Nf39/rF+/HiVKlICfn1+u10dERERERFRQZftOXunSpbF37174+Pikm7d//344OzvnSmCpnTp1Cvv370flypWhoqICe3t71K9fHwYGBpgxYwaaNGmS63USEREREREVRNlO8oYMGYKuXbtCVVUV3bt3R5EiRfD27VusXbsWq1atwpo1a3I9yM+fP8PCwgIAYGxsjPfv36NkyZIoX748bt68mev1ERERERERFVTZTvI6d+6Md+/eYcqUKVi5cqU0XVtbGzNnzkSXLl1yNUAAcHZ2xqNHj+Dg4ABXV1esXLkSDg4OWLFiBaytrXO9PiIiIiIiooIq20keAIwcORK9e/dGQEAAwsLCYGpqiurVq8PAwCC34wMADB48GEFBQQCAyZMnw8fHB5s3b4aGhgbWrVuXJ3USEREREREVRDIhhMjvILIrOjoaDx8+RNGiRWFmZpajdURGRsLQ0BARERFIErJcjpCIiP6rjAz1pb+PnLuTj5EQEVFh4+FWTMphMrvBptSdvD179qBu3bowMjLCnj17sizfqlUr5SPNAR0dHYXj9BEREREREf3XKZXktWnTBpcvX0bVqlXRpk2bTMvKZLJcGbdu2LBhSpedP3/+v66PiIiIiIioMFAqyXv+/LnUwcnz58/zNKAUt27dUqqcTMamlkRERERERCmUSvLs7e0V/p2XTp8+/U3qISIiIiIiKkyUSvJevnyZrZUWLVo0R8Gk9ezZMxQrVox364iIiIiIiJSkVJLn4OCQrUQrN57JA4ASJUogKChIGgj9xx9/xKJFi2BpaZkr6yciIiIiIipslEry9u7dK/0dFRWFMWPGoHjx4mjdujUsLS0RHByM3bt349mzZ5g1a1auBZd2dIfDhw9jxowZubZ+IiIiIiKiwkapJK9FixbS37/88gvq16+PNWvWyJUZNGgQunXrhhMnTqBDhw65GyUREREREREpRSW7C+zcuRPt27dXOK99+/Zyd/3+LZlMlq6ZKJ/PIyIiIiIiyphSd/JSU1VVxa1bt1C/fv10827evAkVlWznjRkSQqBr167Q1NQEAMTExKBPnz7Q1dWVK6fMAO1ERERERET/BdlO8n7++WdMmjQJX758QcuWLWFhYYGQkBDs3bsXM2fORJ8+fXItuC5dusi97tSpU66tm4iIiIiIqDCSibS9m2QhISEBY8aMwfLlyxETEyNN19LSQp8+fTBz5kyoq6vneqC5LTIyEoaGhoiIiECSYBNQIiLKHUaG+tLfR87dycdIiIiosPFwKyblMAYGBhmWy3aSl+Ljx4+4e/cugoODYW1tjfLly8PY2DjHAX9rTPKIiCgvMMkjIqK8omySl+3mmimMjY3h5eWV08WJiIiIiIgoD+Sol5TQ0FCMGTMG9erVg7OzM/7++28AwMKFC3H58uVcDZCIiIiIiIiUl+0k7+bNmyhRogS2bdsGW1tbPHnyBLGxsQCAN2/eYMGCBbkeJBERERERESkn20ne0KFDUb16dfzzzz9YvXo1Uj/SV61aNd7JIyIiIiIiykfZfibv2rVr2LNnD9TV1ZGYmCg3z9zcHCEhIbkWHBEREREREWVPtu/k6erqIjIyUuG8ly9fwtTU9F8HRURERERERDmT7SSvYcOGmDZtGsLCwqRpMpkMX758wcKFC9G4ceNcDZCIiIiIiIiUl+0kb9asWYiMjESJEiXQrl07yGQyTJgwAWXKlEFYWBimTZuWF3ESERERERGRErKd5NnY2OD27dsYOHAggoKCULx4cYSFhaFjx464fv06LCws8iJOIiIiIiIiUkK2Ol6JiYnBqFGj8PPPP2PKlCmYMmVKXsVFREREREREOZCtO3laWlpYs2YNoqOj8yoeIiIiIiIi+hey3VyzRo0aHAuPiIiIiIjoO5XtcfJ+/fVXdOzYEaqqqmjcuDEsLS0hk8nkypiYmORagERERERERKQ8mRBCZGcBFZWvN//SJncp0g6S/j2KjIyEoaEhIiIikCQUbwcREVF2GRnqS38fOXcnHyMhIqLCxsOtmJTDGBgYZFgu23fy1qxZk2FyR0RERERERPkr20le165d8yAMIiIiIiIiyg1Kd7yyatUquLi4QF9fH87Ozpg4cSLi4uLyMjYiIiIiIiLKJqWSvLVr16J3796IjY1FkyZNYGRkhOnTp2PYsGF5HR8RERERERFlg1JJ3uLFi9G2bVs8ePAA27Ztw5UrVzBt2jSsWrUKCQkJeR0jERERERERKUmp3jUNDAywa9cuNGjQQJr24cMHmJmZ4fHjx3BycsrTIPNC6t41M+uZhoiIiIiI6HugbA6j1J28qKgoGBkZyU0zNDSUKiIiIiIiIqLvg9K9az569Ahqal+Lp4yF9/Dhw3RlK1asmAuhERERERERUXYp1VxTRUVF4dh4KYumzBNCQCaTFbjB0AU47h8REeUOQ4Ovg6FXbDc9HyMhIqLC5syfA3NvMPTTp0/nWmBERERERESUd5RK8ry8vPI6DiIiIiIiIsoFSg+GTkRERERERN8/pZK8atWqYd++fUhKSlJqpa9evcKIESMwf/78fxUcERERERERZY9SzTU7d+6Mfv36oVevXmjRogU8PDzg4uICc3NzaGpqIjw8HM+fP8eNGzdw5MgRXL58Gc2bN0ffvn3zOn4iIiIiIiJKRakkr3///ujevTu2bduGDRs2YMOGDUhISJArI4SAtbU12rRpg2XLlqF8+fJ5EjARERERERFlTOlx8rS1tdGtWzd069YNMTExuH37NoKCghATEwMTExM4OzvDwcEhD0MlIiIiIiKirCid5KWmpaUFd3f33I6FiIiIiIiI/iX2rklERERERFSIMMkjIiIiIiIqRApEkrd169YM540cOfIbRkJERERERPR9KxBJXt++fXHkyJF004cOHYpNmzblQ0RERERERETfpwKR5G3evBnt27fHhQsXpGkDBw7Ejh07cPr06XyMjIiIiIiI6PuS7d41X758meE8FRUVGBoaQl9f/18FlVaTJk2wbNkyNG/eHMePH8fq1auxf/9+nD59GiVLlszVuoiIiIiIiAqybCd5Dg4OkMlkWZYZMmQIBg4cmOPA0urQoQPCw8Ph4eEBc3NznD17Fk5OTrm2fiIiIiIiosIg20nepk2bMHbsWJQsWRItWrSAhYUFQkJCsHfvXvzzzz8YM2YMAgICMHToUADIcaI3bNgwhdPNzc1RsWJFLFu2TJo2f/78HNVBRERERERU2GQ7yTt//jx8fHywcuVKuekDBgxAr169cOPGDWzcuBEGBgZYtmxZjpO8W7duKZzu5OSEyMhIaX5WdxWJiIiIiIj+S7Kd5G3btg07duxQOK9t27Zo164dVq9ejaZNm2L16tU5DowdqhAREREREWVftnvXVFFRwZ07dxTOu337NlRUklepqqoKbW3tfxddBiIjI7Fv3z48fPgwT9ZPRERERERUUGX7Tl7Hjh0xceJExMXFoVmzZjA3N8f79++xf/9+TJs2Db179wYA3LhxA2XKlMmVINu1a4datWphwIAB+PLlCypXrozAwEAIIbBt2za0bt06V+ohIiIiIiIq6LKd5M2bNw9qamqYNm0aJk6cKE3X1NRE//79MWvWLACAh4cHGjZsmCtBnjt3DuPHjwcA7N27F0IIhIeHY/369Zg2bRqTPCIiIiIiov/LdpKnrq6O+fPnY+LEifjrr78QFBQEa2trlCtXDiYmJlK5WrVq5VqQERER0rr9/f3RunVr6OjooEmTJhg5cmSu1UNERERERFTQZTvJS2FsbJyriVxm7OzsEBAQABMTE/j7+2Pbtm0AgI8fP0JLS+ubxEBERERERFQQ5CjJ+/jxI44cOYLXr18jJiZGbp5MJpNrxpkbhgwZgo4dO0JPTw/29vaoXbs2gORmnOXLl8/VuoiIiIiIiAqybCd5x44dQ5s2bRAVFQVtbW1oaGjIzc+LJK9fv36oWrUqXr16hfr160s9eDo6OmLatGm5WhcREREREVFBlu0kb/jw4ahSpQrWrFkDe3v7vIhJocqVK6Ny5cpy05o0afLN6iciIiIiIioIsp3kPXv2DPPnz8/zBG/YsGGYOnUqdHV1MWzYsEzLzp8/P09jISIiIiIiKiiyneRVrFgRr169yotY5Ny6dQvx8fHS30RERERERJS1bCd5y5cvR6dOnWBjY4N69epBTS3HHXRm6vTp0wr/JiIiIiIiooxlO0OrXr064uPj0bhxY6ioqEBbW1tuvkwmQ0RERK4FmJVdu3ahTZs236w+IiIiIiKi71mOOl6RyWR5EYtCCQkJePjwITQ0NFCyZElp+v79+zFp0iQ8fPjwf+3ddVhU2RsH8O/QPaR0qCAWYq+iiN3dDXbHrrm7KlhrrLXGGqtrYuei6Nrdgd0SIqhII82c3x+s9+dICK6KsN/P8/DonHPuve+duTN33rnnnMskj4iIiIiI6B/5TvJ8fHy+QBjZu3PnDlq2bCmNAWzTpg2WL1+Ozp07486dOxgwYAAOHDjw1eIhIiIiIiL61n2ZAXWfyYQJE+Do6IilS5diy5Yt2LJlC+7fv49+/frh0KFDWbqKEhERERER/dflKclr3bo15s+fDycnJ7Ru3TrXtjKZDPv27fsswV25cgWHDx9GxYoV4e7uji1btuCnn35Cr169Psv6iYiIiIiIipo8JXnx8fHIyMgAAMTFxX21MXlv3ryBlZUVAEAul0NXVxc1atT4KtsmIiIiIiIqjPKU5L1/C4OTJ09+qViykMlkiI+Ph5aWFoQQkMlkSEpKQlxcnFI7AwODrxYTERERERHRt+ybHpMnhFCaUVMIgUqVKik9lslk0lVGIiIiIiKi/7p8J3nTpk3LsU5FRQVyuVwaQ/dv8SboRERERERE+ZPvJG/hwoVITU1FUlISAEBLSwvJyckAAG1tbaSlpSEjIwOVK1eGv78/zMzMPjk4Dw+PT16WiIiIiIjov0glvwscP34c1tbW2LhxI+Li4pCYmIi4uDisX78eVlZWOHXqFA4fPozQ0FCMGzfuS8RMREREREREOcj3lbxhw4ZhzJgx6NGjh1Smp6eHXr164e3btxg9ejQuXbqESZMm5dq1k4iIiIiIiD6/fF/Ju3HjBuzt7bOtc3BwwO3btwEA5cuXR2xs7L+LjoiIiIiIiPIl30mevb09Vq9enW3dqlWrpAQwMjISpqam/y46IiIiIiIiypd8d9ecNWsWOnfuDGdnZ7Rs2RJmZmaIiIjA/v378ezZM+zYsQMAcOzYMdSpU+ezBxwaGgoAsLGx+ezrJiIiIiIiKuzyneS1a9cOly9fxqxZs7Bnzx6Eh4fD0tIS1apVw7Zt21CxYkUAwLJlyz5bkAqFAjNmzMD8+fORkJAAANDX18eYMWPw888/Q0Ul3xckiYiIiIiIiqRPuhl6pUqVsH379s8dS45+/vlnrFmzBrNnz0atWrUAAGfPnoWPjw+Sk5Mxc+bMrxYLERERERHRt+yTkryvbf369Vi9ejVat24tlVWoUAHW1tYYOnQokzwiIiIiIqJ/5DvJq1+/fo51KioqkMvlqFSpEvr06QNra+t/Fdw7UVFRKF26dJby0qVLIyoq6rNsg4iIiIiIqCjI92A2uVyOJ0+e4MyZM4iLi4OWlhbi4uJw5swZPHr0CNHR0Zg/fz7Kli2L69evf5YgXV1dsXTp0izlS5cuhaur62fZBhERERERUVGQ7yt5nTp1wtOnT3HmzBml++UFBQWhVatW8PT0xK5du9CwYUP8+OOP+Pvvv/91kHPnzkWLFi1w9OhR1KxZEwBw4cIFPH/+HP7+/v96/UREREREREVFvq/kTZ06FT4+PlluiO7g4ABvb29Mnz4dRkZGGDt2LC5evPhZgvTw8MCjR4/Qrl07xMTEICYmBu3bt8fDhw/h7u7+WbZBRERERERUFOT7Sl5ISAhkMlm2dTKZDC9evAAAWFlZIT09/d9F9x4rKytOsEJERERERPQR+U7yqlWrhilTpqBq1aqwtbWVyoODg+Ht7Y3q1asDyOy++W8nXgkJCclTOzs7u3+1HSIiIiIioqIi30neihUr0KhRI5QsWRIuLi4wMzNDREQEbt26BXNzc+zYsQMA8OrVKwwcOPBfBefg4JDtVUMhhFQuk8k+6xVDIiIiIiKiwizfSV7ZsmXx9OlT/Pnnn7h69SrCw8Ph6uqK/v37o0+fPtDS0gIAjB8//l8Hd+PGjWzLhRDYunUrFi9eDD09vX+9HSIiIiIioqLik26GrqWlhaFDh37uWLLI7vYIR48excSJE/Ho0SOMHz8eY8aM+eJxEBERERERFRaflOQVhOvXr2PChAk4c+YM+vfvD39/fxQrVqygwyIiIiIiIvqm5PsWCgCwceNG1K5dG8WKFYOBgUGWv8/p6dOn6NKlC6pXrw4zMzPcu3cPS5cuZYJHRERERESUjXwneZs2bcKAAQNQvnx5vHnzBp07d0aHDh2goaGBYsWKYezYsZ8tuKFDh6Js2bKIjY3F1atXsXnzZpQoUeKzrZ+IiIiIiKioyXd3zfnz52Py5MmYOHEiVq1ahaFDh6Jy5cqIj49H48aNP+tEKCtWrICWlhZev36Nvn375tju+vXrn22bREREREREhVm+k7zHjx+jVq1aUFVVhaqqKuLi4gAA+vr6mDBhAkaPHo0ffvjhswTn7e39WdZDVJjs9/PDggXzcfzEyYIOpUBN9fFBfEI85s2bX9ChEBFREXB9+8/44dcdOHnlUUGHQvTF5TvJk8vlSElJAQBYW1vj3r17qFu3LgAgIyMDkZGRny04JnlUWE318cGBA/uzlO/avQe2trYFENH/7ffzw7RpU1GjRk0sXrJEKo+Pj0eD+vWwfMUKVKlS9avFExYWhrZtWmPTJl+UcnaWyseMHQshxFeLg4ioMLm+/edc61fuOI2VO858lVhWefdE1XL2mLhoDw6fvyeVd29eDd2bV0fL4cu+ShzvDOrkjrrVnNFt/Gql8kYDFiHubfJXjYWooOQ7yatatSpu3bqFJk2aoHXr1pg6dSoUCgXU1dUxe/Zs1KhR40vESVTo1KzphslTpiiVGRkZFVA0ylRVVXHlymVcvXoVVat+vYQuP3gPTCKinDUasEj6f2O3shjcpQ7aj1ohlSUmpyq1V1WRIUPx5X44S05Nw7CuHjh+6QHSMxRfbDv/RmTs24IOgeiryXeS9+OPPyI4OBgAMG3aNAQHB2P06NFQKBSoVq0aVq5c+dmDJCqM1DXUYWpqmqXc13cT9vv54cWLFzAwkMPd3R0jRo6Ejo5Otut59OgRFi6Yj/v370Mmk8HW1hYTf/wJZcuWBQAEBARg2bKleHD/PuRyQ9StVxfDhg2HtrZ2jrFpa2ujYcNGWLZ0CdauW59ju1cvX2LRb4tw6eJFqKiooGLFivhhzFhYWVkBANLT07Fo0UL4HzgAFVVVtGnTBpGRkUhISJC6WV44fx5//rkGT58+hYqqKlxcXDBmzFjY2NgAANq2aQ0A6NmzBwCgcuXKWLFylVJ3zT27d+OPP1Zh/wF/qKj8f76osWN+gFwux+QpmVf9T506idV//IHAwECYmpqhRcsW6NOnL9TU1CCEwB9/rILfX38hKioKcrkc9Rs0wNix43LcfyKib9X7CUtCYgog/l9Wpawd/vDpheG/bMWwrh5wtCuGoTM2o1XdCtDX1cKYX3dKy471bIRSDuYYOHUTAEAmA7zauKF9w0owMdRFSFgU/th1FscuPcg1nr/P3UOdKk5o16ASdhy+lmM7j6qlMLCjO0rYmCIiOh77T93Gmt1npQTUwcoEkwe3QNkSlnjxOhpz1x7Gisk9lLpZjuxRD/WqOaOYiQEiYxJw8Oxd/LHzDNIzFGjlUQGDOtUB8P+rnd7L/OB36pZSd8210z1x40EIFvuekGIz1NfB3ytHYsh0X1y//xzqaqoY1q0umtYqB30dTTx5HoHFvsdx7V4IAMDS1AAT+jVFRWcbqKupIiwiFos2HcO5G0/z9iISfUH5TvJq1KghXa0zNDTEvn37kJKSgpSUlM9++wSiokhFpoIxY8fBysoKL168wNw5s7Fk8WJMmDgx2/ZTJk+Cs7MzJkz8ESoqKnj06BHU1DLfuqGhoRg1cgQGDx6CyZOnICY6Gr/+Ohe/zp2LKR/p7jxg4EC0b9cWx44dRYMGDbPUp6enY+TIESjv4oJVf6yGqqoq/lyzBqNGjsDmLVuhrq6ODRvW49ChQ5g8xRvFixfH1q1bcOrkSVR57+pgUnISunfvAUcnJyQlJmLlyhUYP24sNvluhoqKCtatWw8vL08sXfY7SpQoAXV19SyxNGjYEPPm/YqrV6+ievXqAIDY2FhcuHABCxf9BgC4ceMGfLy9MWbsOFSqWBGhL0Lxyy+/ZO7rgIE4fvwYtmzejJkzf0GJkiURGfkGjx89zsMrRkRUOI3sXg8LNx7Di9fRiEvIWzfFvm1robl7efzyx0GEhEehchk7zBjRBtFxibh+PyTH5d4mpuDPPecwoGNt+J26heSUtCxtKpW2xbThrfDr2sO4cf85bMyNMGlQcwDAqp1noCKTYf64jnj5Jg69f14LXS0NfN876/npbVIqvH/3Q0R0ApzsimHSoOZITErB+r8u4vD5eyhpZwY31xIYMn0zgH+S4A8cPHsHnq1rKiV5TdzK4k10Aq7ffw4AmNCvCUpYm+LHRXsQER2PetWdsfSnbug8dhWev4zGxH5Noaamiv7eG5GUkoYSNqZI+uAKKlFB+aT75H1IU1OTCR7RB86dPQuPOu7S38SJEwAA3bp3R9WqVWFlZYVq1aph8JAhOHr0SI7refXqFapV/w4ODg6ws7NDw4YNUapUKQDAunVr0bRpU3Tr3h12dnao4OqKMWPHwd//gDR2NidmZmbo2rUblv/+O9LT07PUHzl8GAqFApMmTYajoyOKFy+OKd7eePnyJa5dy/yVdvu27fDy9EK9evXg4OCAcePGQ19fX2k99es3QL369WFra4tSzs6YPMUbT548QeCzZwAAw3+6sBrK5TA1NYVcLs8Si4GBAWq6ueHvvw9JZcePHYOhoaHU3XT1H3/A09MLLVu2hLWNDb77rgYGDxqMPbt3AwBevnwJExMTVP/uO1hYWKBcufJo265drs8REVFhtnz7aVy6HYjQVzF5GoumrqaKvu3cMHX5fly4+QwvXsfA79Qt+J+5gw6NKn10+e1/X0Nqajp6tvwu2/qBHd2xbu8F7D91Gy9ex+DS7UAs33YKHRpmrrtGheKwMTfClKV/4XHwawQ8DMWyrSezrGfN7nO49egFwiNicfraY2z0u4hGNTN7t6SkpSMpORUZCoHI2LeIjH2LlLSs57jD5+/D1EgPlUr/f5x809rlcOjcXQCAhYkBWtd1xfiFu3HjwXOEvorBRr9LCHjwHG3quWa2MTXAzYehePI8Ai9ex+DM9SdSgkhU0PJ0JW/kyJF5XqFMJsNvv/32yQHlVUxMDAwNDb/4dog+VZUqVTBh4o/S43fdJy9fuoR169YhODgIb9++RUZGBlJSUpCcnAwtLa0s6+nWvTtmzpiOg/7+qF69Oho0bCh1dXz86DGePHmMQ4f+n/wIIaBQKBAWFobixYvnGmNvT0/s2bMbfn/9hYaNGinVPX78GKGhoajrUUepPDU1FaGhoUhISEBUVCTKlisn1amqqqJ06TJQiP+PxwgJCcGqlStw585dxMbGQKHIrHv56iVKOjrmGt/7mjZthl9mzsCECROhoaGBQ4cOoVGjxlL3zcePH+HWrZtYu/ZPaRmFQiE9tw0bNMTWLVvQtk0b1KxZE261asHd3V26KkpEVNTcexqer/a2FkbQ1tLA75O7K5Wrq6niQeDLjy6flp6B5dtPY3zfxtiZTZfNUg7F4FraBv3a15LKVFRk0NJQh5aGGuytTPAqMk6pK+rdJ2FZ1tO4Zhl0bVYNNhZG0NHSgKqKCt4m5f7D5odi4hNx8VYgmrmXx40Hz2FlJoersw1mrvIHADjaFYOaqgr2/jZEaTl1NVXEJiQBALYcvIof+zdFjQrFcfl2EI5deoDHIa/zFQfRl5Knbzd+fn55XuGXSPLmzJkDBwcHdOnSBQDQuXNn7Nq1CxYWFvD394erq+tn3R7R56ClrZ1lJs2wsDD88MP3aN+hA4YMHQoDAwPcvBmAGdOnIy0tLdskb+DAQWjSpCnOnTuLC+fPY9WqlZgx8xfUq1cPSUmJaNe+Pbp06ZplOQsLi4/GqK+vD08vL6xe/Qdqu7sr1SUmJaJ06dKYNn1GluXyM4HMmB++h4WlJX76+WeYmZlBoVCgW9cuSMvml9XcuLu7QwiBc2fPomzZsggIuIHv37tdS1JSEgYMHIh69epnWVZDQwPmFhbYsXMXrly+jEuXL2HunNnYtHEjVq5axUSPiIqkpBTlroNCADLIlMrU1P7fqUtHSwMAMHLWNkRExSu1S82mx0d2/M/cRu9W36F/+9oIi4hRqtPW0sDK7adx/NLDLMtld7UtOxWcrDFjZFus3H4a528+Q0JiMprUKodeOVw9zM3BM3cwrk9jzP3zbzSrXR6Pg1/hyfMIAICOljrSMxToMWENFB9MWPNuUpu9xwNw4eZT1K7shJoViqNPOzcs2HAU2w5dzXcsRJ9bnr7ZBAYGfuk4crVixQr4+voCAI4cOYIjR47g4MGD2L59O8aNG4fDhw8XaHxEefXgwX0oFAqMHv29dAUqt66a79jb28Pe3h7du/fApJ9/wn6/v1CvXj04O5dG4LPAf3Vbhs6du2Db1m3YunWLUnlp59I4euQIjIyMcpzp0tjYBPfv3UPlypUBZN5G5eHDB3D6pztpTEwMgoOD8dPPk1CpUmZ3nICAAKV1vBuDl6HIfTY2TU1N1KtXH4cOHcTz0Oewt7dH6dKlpXpnZ2cEBwfn+lxoaWnBvU4duNepg04dO6FTp4548uSJ0nqIiIqq6Li3KGlrplRWyt5cmg3zWegbpKSmw9LUINfxd7kRAliy+STmje2AnYevK9U9ePYS9lYmeP4qOttlg8MiYW5iAGO5LqL+uZpXrqSVUpsKzjYIj4jFmj3npDJLU+Vu/mnpGVBRUU5ms3Py6iNMGtQcbhVLomntcth/+vb/Yw16BTVVFRjLdXHjQc5dMF9FxmPXkevYdeQ6hneri/YNKjLJo2/CZxmT96W9fPlS+uK2f/9+dO7cGY0bN8b48eNx5cqVAo6OKO9sbGyRnp6O7du24UVoKPz9D0hjxrKTnJyMX+fOwbVrVxEeHo6bNwNw7949OPzTDbO3pydu3bqJX+fOwaOHDxESEoJTp07i17lz8hyTpqYmBg4aiO3btimVN23WDHJDQ4wbOwY3btzAixcvcO3aVcyb9ytevXoFAOjcpTPWrVuLU6dOIjgoCAvmz0NcXBxkssyTq4GBAeRyOfbu2Y3nz5/jypUrWLRwgdJ2jIyMoKmpiQsXzkszc+akadOmOHfuHPz++gtNmjZVquvXfwD8DxzAH3+swtOnTxEYGIjDh//G8uW/A8i8P+C+fXvx9MkTvAgNxcGDB6GpqZmnK55EREXBlTvBKFvCEi3quMDWwgiDO9VBSbv/J32JyanY6HcRP3g2QksPF9iYG6J0cQt0aVoVLT1c8rydszee4M7jMLT/YBzfH7vOokUdF2l2zeLWJmjsVhZDu3gAAC7eCkToq2hMG9YKTnbF4Opsg6FdM+ve3TY1JDwKFqYGaOxWFjbmhujarCrqVS+ltJ2w17GwLmaIUvbmMNTXhrqaarZxJqek4eSVhxjSxQPFrU3x99m7Ul1IeBT8z9zGtOGtUb+6M6zM5ChX0gp92rqhdqXMoQZjPRuhpmsJWJnJUbq4BaqVc0Dgi893v2iifyNPV/IqVKiAzZs3o3z58lLZ5s2b0bx5868yLs7IyAjPnz+Hra0tDh06hBkzMruPCSGQkZHxxbdP9LmUKlUKo7//Hhs2rMeyZUtRqVJlDB02DD45zISpqqqK2NhY+Hh7IyoqCoaGhqhbrx4GDhwEAHBycsLKlauwfPnvGDhwAIQQsLaxQaMPxtd9TIsWLeG7yReBgc+kMi0tLaxcuQpLly7BhPHjkJiYCDMzM1SrVh26uroAgN69PREZGQkfb2+oqqqibdt2qFGzJlT/uUqpoqKCmTN/wfz589CtaxfY2dtj7JixGDx4kLQdNTU1jBk7DmtW/4FVK1eiYsWKWLFyVbZxVq1WDQYGBggODkbTJspJXs2aNbFg4SKsWf0HNqxfDzU1NTg4OKBNm7YAAD19fWxYvw6LFi6EQqFASUdHzF+wkGN7ieg/48LNZ1i96yxG9awPTXU17DtxEwdO3YajXTGpze/bTiE6LhF92rrBxtwI8W+T8SDwJf5878pZXiz2PY51M72ybH/0nO0Y0KE2PNvURHpGBoJeRGLv8QAAgEIIjPl1JyYPboGNs/rgxasYLNp0DL9N7ILUf7pznr72GJsPXMaEvk2goa6Ks9efYPWus9JtEwDg2KUHqP+dM1Z594CBnrZ0C4Xs+J+5i6U/ueDavWC8jIxTqvP5fT/6t6+N73s3RDFjfcTEJeL24xc4cy1zZmYVFRkm9muCYsYGeJuUgvMBTzF//dF8PU9EX4pMCPHRO2OqqKjg4sWL0tTlGRkZ0NDQwJUrV6RuWl/S8OHDsX//fjg5OeHGjRsICgqCnp4etm7dirlz5+L69esfX8kH4uLiIJfLERsbC4GPX9Inoo9TKBTo3KkjGjZshMFDhnx8AaIiSG7w/xlmK3eeWYCREBV+rs42WDvdE61HLEPoq5iCDoeowJ1cPULKYXK7u8EnzzaQh9zws1m4cCEcHBzw/PlzzJ07VxofFB4ejqFDh361OIhIWXh4OC5dvIhKlSsjLS0NO7ZvQ1hYWJaulERERHlRr5ozEpNTEfIyCrYWRhjn1Vi6hQER5V2hmFJOXV0dY8eOzVL+/fff53kd727Y/k5cXFwurYkoL1RkMuzf74ffflsEAChRoiSWLvv9o7duICIiyo6OtgZG9qgHC1M5YuITcel2IBZuOFbQYREVOnlO8t5NpPCxsi/p3r17CAkJQWqq8pTArVu3/uiys2bNwtSpU79UaET/SeYWFli95s+PNyQiIsqDA6dv48B7s1wS0afJ85g8HR0dacp3AEhISMhSBmQmfrGxsZ81yGfPnqFdu3a4ffs2ZDKZ1FX0XZKZl8lXsruSZ2tryzF5RET0WXFMHhERfSmfdUyedw4z/30to0aNQvHixXHs2DEUL14cly9fRmRkJMaMGYN58+blaR2amprQ1NT8wpESffuCg4IwaNAg7Nq9W5olszDZtWsnzp09hwULFxZ0KERElEf2lsb4Y2ovtB25XLqZeFGzfoYX1vtdyPZm70RfW6FI8i5cuIDjx4/D1NQUKioqUFFRQe3atTFr1iyMHDkSN27cKND4iD63dWvX4sSJEwgODoKmpiZcKlTAiOEjYO/gkKWtEAKjR43ChQvnMffXeahbt26u6162bBk6d+mslOA9fvwYc+fOwf1792BoaITOXTqjd2/PHNcRExODKZMn48mTx4iNjYWRkTE8POpgyNBhSjdOP3TwIDZs3IDnISHQ09ODm5sbRowcJd224NKli5g7dy6iIiNRp04dTJo8Rbo5ekJCAjw9e2Pp0mWwtLSU1tm6dRv8uWYNbty4Id1gnYjov8qrTU2M7FEfmw9cxrz1R3Jt2715NXRsXAUWpgaIiUvCsUv3sWTzCaSmZfaI6tPWDfWrO8PB2gQpqem4+SgUizcdR3B4FADAQFcLgzvXQQ3XErAwNUB0XCJOXnmE5VtPISEpJbdNY0T3eth28KqU4A3q5K5024N3kpJTUav3rwCAVh4VMHVYK6X6lNR01OyZt3vBujrb4A+fXnj6PALdxq+Wyjs2qoxOjSvD0swQAPAsNAKrdp7F+YCnUpsfejdEq7oVkJSchiWbj+Pge/fQa1ijNFp6VMDoOduVtrd691mM8WyEE5cf4ivOT0iUrUIx8UpGRgb09TO7v5iamiIsLAzOzs6wt7fHw4f8tYSKnuvXr6NTp04oU7YsMjIysPz3ZRgxYji2bd8BbW1tpbZbtmxGXofHvnz5EmfPnsHYceOksoSEBIwYPhzVq1fHxIk/4unTJ5g+bRr09fTRrn37bNejoqKCOh4eGDxkiHQfy1/nzkFsXBxmzMjsnnbzZgB8fLzx/fc/oLa7OyIiIjB71i/4ZeZMzP31VygUCkyeNAmeXl6oUaMmfpw4AXv27Ebnzl0AAEuXLkH79u2VEjwgcyKmJk2aYtu2rUzyiOg/rWxJS3RoVBmPgl59tG3TWuUwont9TF2+HzcfhcLe0hhTh7aCEMCCDZn3dqtS1g7b/76Gu0/DoKqqguHd6uH3Sd3R4YeVSE5Jg5mxPsyM9bFo4zE8C42ApakcPw1oBjMjPYxfsDvHbVuYGMC9ihPm/HlYKtvw10XsPKx8C6wVU3rg7tMwpbL4xGS0H7VCepzX3ElPRxPThrXGlduBMDbUU6p7HRWPxZtPICQ8CjKZDK08KmDh+E7oNn41noW+QZ0qTmhauxyGztgCO0sjeA9piQs3nyEmPgl62poY1rUuhkzfnGWb5248xeTBLVCroiPO3niSx0iJvoxCkeSVL18eN2/eRPHixfHdd99h7ty50NDQwKpVq1CiRImCDo/os1u8ZInS4ynePmjSuBHu37+vdG/KRw8fYrOvL9at34DmzT5+24KjR47AyakUihX7/41vDx06hPT0NEyeknkVrWTJknj08BE2b/bNMckzMDBAx44dpceWlpbo2LETNm7cKJXdvnUblpaW6NK1KwDA2toa7dq3x8YNGwBkXg2MiYlBx46doKmpCfc6dRAUGAQAuHXzJu7du4dx48Znu/3a7u4YMXwYkpOToaWl9dH9JiIqarQ11TFzRBtMX3kA/dvX/mh7V2cb3Hz4HIfOZV6RCo+IxaFzd1HeyVpqM/yXrUrLeC/zw/E136NsCQtcv/8cT59HYNz8XVJ96KsYLNt6EjNGtIGqigwZiuxTsEZuZfAo6BUiouOlsqSUNCSlpEmPneyLoaStGX7546DywgKIjH370f370M8DmuHQubtQKBSoW81Zqe70Pzczf2fZ1pPo2LgyXJys8Sz0DYpbm+Da3WDcfxaO+8/CMdazEayKGSImPgmjetbHziPXs9w4Hci8mfu5G0/RpFZZJnlU4FQ+3qTgTZo0CQqFAgAwbdo0BAYGwt3dHf7+/li8eHEBR0f05SUkJAAA5O8NsE1OTsbkyZMwbvx4mJqa5mk9AQE3UKZsGaWy27dvoWKlSlI3SQCoUbMmgoOD83yrkYiICJw4cVwpAXWp4IJXr17h3LmzEEIgMjISx48dh5tbLQCAkZERTE1NceniRSQnJyPgRgAcnRyRnp6O2XNm48cff4Kqqmq22yv7zxXOu3fv5Ck+IqKiZmL/pjh74wku3w7KU/ubD0NRpoQlypW0AgBYFzNE7UqOOJdLMqKvkzmXQWxCco5t9HS08DYpJccEDwAqlbbDvWfhucbXrn5FBIVF4saD50rl2loaOLBsOPx/H4EF4zqhhM3Hz3et61aAtbkRVu04/dG2KjIZGruVhbamOm49egEAeBT8GmVLWkJfVwtliltAU0Mdz19Go6KzDUoXt8AW/ys5ru/OkzBUKm370e0SfWmF4kpekyZNpP87OjriwYMHiIqKgpGR0Ve/jQPR16ZQKLBgwXy4urqipKOjVL5wwXy4VKgAD4+6eV5XePhLlClTVqksKjISVlZWSmXGxsYAgMjIyFxnbpr08084deoUUlJS4O7ujp8nTZLqXF0rYtr0Gfj5p5+QkpKCjIwMuLu7Y/yECQAyZ8f9ZdZsLFywAPMXzEctt1po3boN1q9bh6pVqkJDQwP9+/VFTEwMOnfpInXjBAAtLS3o6ekhPPxlnvediKioaOxWFqWLW6DXj3m/hc2hc3dhaKCNP6f3BgCoq6lix+Fr+HPP+Wzby2TAWK9GuPEg8wpedgz1tTGgQ23sPhqQ67YtzeS5Jnka6qpo5l4e6/YqxxIcFompy/fjcfBr6OloonfrGlg7wxOdfliF11Hx2a7L1sIII7rXQz/vjbkmno62Zlg30wsa6mpISk7FmHk7EfjiDQDgws1n8D9zB5tm9UFyajq8l/khKTkVPw5oBp9lfujUuAq6NKuKmLhEzFjlj2ehb6T1RkTFw9zUADIZOC6PClShSPKy8+5LKFFRN3fuHDx7+hSr/vj/oPHTp07h6tWr2LjJN1/rSklJhoamxmeLbfT3P6D/gIEICQ7GsmXLsGjhQkyYOBFA5q1PFsyfh379+6NGjZp48+YNliz+DbNm/YLJk6cAACpWrIj1/3TfBIDg4GD4+x/Axk2+GDRwALp07QY3Nzd069oFlSpVhpOTk9RWU1MTyck5/7pMRFQUmZvoY5xXIwydsUWaMCUvqpS1Q992tTBr9SHcefwCthbGGNunEfp3qI3Vu85maT+xX1OUtDVD3ykbslkboKutgd8mdsGz0DdY+ZErZpoaakhNTc+xvl51Z+hoacDvlPL98W49foFbj1/8//GjUOxaOAgdGlXG8m2nsqxHRSbDLyPbYsWOMwj5Z7KYnASFRaLbuNXQ09FEgxqlMW1YK/T33iQleit3nMHKHWek9gM7uuPy7UCkZyjQr0MtdB7zB+pUccT04a3RY+L/k+2U1HSoqqhAQ00NKWk57zPRl1Zokzyi/4Jf587B2TNnsXLVKpibm0vlV69eRWhoKBrUr6fUfuKE8ahYsSJWrFyV7foMDQ0RH6f866exiQkio5RPhlH/PDYxMck1PlNTU5iamsLBwQEGcjkGDuiPfv37w9TUFOvXrUUFV1f06pX5q7GTkxO0tbUxcEB/DBkyNNsuprNn/YJRo0ZDoVDg4cOHaNiwIbS0tFCpcmVcv35NKcmLi4uDkZFhrvERERU1ZUpYwsRQD75z+kllaqoqqFzGDp2bVkWN7rOhyOYS0tAuHvA/fRt7jwcAAJ48j4C2ljp+Htgca3afVbrqNKFvE7hXdkJ/7w3ZXjHT0dLA0p+6ITEpFWPm7UB6hiLXmGPiEmGgl/P46Xb1K+LM9SeI+sjYu/QMBR4EvoKthVG29TraGijnaAXn4haY0DezF5iKTAYVFRkub/kRw2ZsxpW7wdK6nr+KBgDcD3yJciWt0L15Ncz8cEwgAAcrEzR3L49u41ejTX1XXL8fgpj4RBy+cB8+Q1tBR0tDmjVUrqeFxORUJnhU4JjkEX2DhBCY9+tcnDx5EstXrIS1tbVSfW9PT7Rp00aprFu3rtJMljlxdnbGs8BnSmUuLhWwYvnvSE9Ph5pa5kfC5UuXYG9vn2tXzQ+9Gzebmpp5oktOToaqqvJHjIqKirR/H9q3by8MDAxQx8NDGguYnp4u/at470tEaGgoUlJS4OxcOs/xEREVBZdvB6HTGOUf8nyGtERQWCTW7buQbYIHAFqa6lnqMv753JZBBvHPvJUT+jZBverOGOCzEWERsVnWo6utgWU/d0NqWga+n7s9T1cTHwa9QvEcxtJZmclRtZwDvp+7Pdv696nIZHC0M8O5G0+zrX+blJLluenUuAqqlbfH+AW78eJ1TM7rVpFBXT37ceA/D2yGBRuOIiklDaoqKlD7Z7y4mqqKtOw7Je2K4WEghxJQwSsUE68Q/dfMnTMHBw8exPTpM6Cjo4M3b97gzZs3UvdEU1NTlHR0VPoDAHMLiywJ4ftq1KiJO7dvIyPj/yflpk2bQk1NHdOnT8PTp09x5PBhbN26Bd2795DanDhxAp06dpAenzt3Fn5//YWnT54gLCwMZ8+exZzZs+Dq6iqN73N3r4MTJ45j586deBEaips3AzB/3jyUK1cOZmZmSnFFRUXhzz//xNh/ZtM0MDBA8eLFsWXLZty6dQtXr1yBq6ur1D7gxg1YW1vDxsbmU59iIqJCKTE5FU+fRyj9JaWkITY+Kcexc0DmjJIdG1VBY7eysDKT4zuX4hjaxQNnrj2Wkr+J/ZqiuXt5/PTbXiQmpcJErgsTuS401TN/sNPV1sDvP3eHtqY6pq3YD11tTamNSi5zJFy4+QwVnKyzbdOmfkW8iUnINnEb0KE2alQoDutihihd3AIzRraBpZkce44FSG2Gd6uLaf/cS08IZHluouPeIjUtA0+fRyD5n9k8h3eri8plbGFpJoejrRmGd6uLKmXtcfDM3SwxtGtQEdFxidKMnAEPQlGtvD1cnKzQs8V3ePo8AgmJ/79HYKXStrhwKzDH54Loa+GVPKJv0K5dOwEAgwcPUiqfMsUbLVu1ym6RPKnp5gZVVVVcvnwZNWvWBADo6elhydKlmDt3Djx794KhoSH69e+vdPuEtwkJCA4Olh5ramph7969WLhwAdLS0lDM3Bz16taDp5eX1KZlq1Z4m/gWO7Zvx2+LFkJfXx9Vq1bD8BEjssS1YP489OjRQyn5m+Ltjak+Pti2bRt69uyFsuXKSXV/H/4bbdu2++TngYioqPMZ2hJWZoYYOHUTAGD1rswumcO6esDMWB/RcYk4c+0xlm45KS3TuUmVzLZTeymty3uZH/xO3ULp4hZwKZX5Q+JfS4YptWkxbCnCs7nyBwDnbjxBhkLguwrFceHm/3uTyGSZNzz3O3kr2yuQBnpamDyoBUwMdRH3Nhn3n71En0nrpXFzAGBqpAcLU3k+nhnAWK6LacNaw9RIDwmJKXgc/BrDZm7BpduBWdr1a1cLXpPXS2V3n4Zhk98l/DaxC6JjEzFlmZ9UZ2akD1dnG0xasi9f8RB9CTKRXb+pb8Bff/2V57atW7fO9/rj4uIgl8sRGxsLAc7QSf8dO7Zvx+kzp7FkydKCDuWTPH36FMOGDsHOXbuhp6f38QWIvjK5gb70/8qdZxZgJPRf9odPT1y9G6w0eUhB6tykCjyqOGHYB/fiK0pG9qgHA11tzFjlX9ChUBF2cvUIKYfJbVjNN3slr23btkqPZTKZ0jie92+d8H7XMyLKXbv27RGfEI+3b99CV1e3oMPJt8g3b+DtM5UJHhFRDvS0NWFjboSRs7YVdCiSXUeuQ19HS2mSkqImKjYRm/ZfLugwiAB8w2PyFAqF9Hf48GFUrFgRBw8eRExMDGJiYuDv74/KlSvj0KFDBR0qUaGipqaGvn37FcoEDwCqf/ed1NWUiIiySkhKQbMhS5D0zxi0b0GGQmDNnnNFNsEDgE37L310hlCir+WbvZL3vtGjR2PFihWoXbu2VNakSRPo6Ohg4MCBuH//fgFGR0RERERE9O34Zq/kve/p06cwNDTMUi6XyxEUFPTV4yEiIiIiIvpWFYokr1q1avjhhx/w6tUrqezVq1cYN24cqlevXoCRERERERERfVsKRZL3559/Ijw8HHZ2dnB0dISjoyPs7Ozw4sULrFmzpqDDIyIiIiIi+mYUijF5jo6OuHXrFo4cOYIHDx4AAMqUKYOGDRsqzbJJRERERET0X1cokjwg85YJjRs3Rp06daCpqcnkjoiIiIiIKBuForumQqHA9OnTYW1tDT09PQQGBgIAJk+ezO6aRERERERE7ykUSd6MGTOwbt06zJ07FxoaGlJ5+fLlsXr16gKMjIiIiIiI6NtSKJK8DRs2YNWqVejRowdUVVWlcldXV2mMHhERERERERWSJO/FixdwdHTMUq5QKJCWllYAEREREREREX2bCkWSV7ZsWZw5cyZL+c6dO1GpUqUCiIiIiIiIiOjbVChm15wyZQo8PT3x4sULKBQK7N69Gw8fPsSGDRuwf//+gg6PiIiIiIjom1EoruS1adMGfn5+OHr0KHR1dTFlyhTcv38ffn5+aNSoUUGHR0RERERE9M0oFFfyAMDd3R1Hjhwp6DCIiIiIiIi+aYXiSh4RERERERHlTaG4kmdkZASZTJalXCaTQUtLC46OjvDy8kKfPn0KIDoiIiIiIqJvR6FI8qZMmYKZM2eiWbNmqF69OgDg8uXLOHToEIYNG4bAwEAMGTIE6enpGDBgQAFHS0REREREVHAKRZJ39uxZzJgxA4MHD1YqX7lyJQ4fPoxdu3ahQoUKWLx4MZM8IiIiIiL6TysUY/L+/vtvNGzYMEt5gwYN8PfffwMAmjdvjmfPnn3t0IiIiIiIiL4phSLJMzY2hp+fX5ZyPz8/GBsbAwDevn0LfX39rx0aERERERHRN6VQdNecPHkyhgwZghMnTkhj8q5cuQJ/f3+sWLECAHDkyBF4eHgUZJhEREREREQFrlAkeQMGDEDZsmWxdOlS7N69GwDg7OyMU6dOwc3NDQAwZsyYggyRiIiIiIjom1AokjwAqFWrFmrVqlXQYRAREREREX3TCkWSFxcXl225TCaDpqYmNDQ0vnJERERERERE36ZCkeQZGhpmezP0d2xsbODl5QVvb2+oqBSKuWSIiIiIiIi+iEKR5K1btw4///wzvLy8lG6Gvn79ekyaNAkRERGYN28eNDU18dNPPxVwtERERERERAWnUCR569evx/z589G5c2eprFWrVnBxccHKlStx7Ngx2NnZYebMmUzyiIiIiIjoP61Q9G08f/48KlWqlKW8UqVKuHDhAgCgdu3aCAkJ+dqhERERERERfVMKRZJna2uLNWvWZClfs2YNbG1tAQCRkZEwMjL62qERERERERF9UwpFd8158+ahU6dOOHjwIKpVqwYAuHr1Kh48eICdO3cCyLw5epcuXQoyTCIiIiIiogJXKJK81q1b4+HDh1i5ciUePnwIAGjWrBn27t0LBwcHAMCQIUMKMEIiIiIiIqJvQ6FI8gDAwcEBs2bNKugwiIiIiIiIvmmFJskDgMTERISEhCA1NVWpvEKFCgUUERERERER0belUCR5ERER6NOnDw4ePJhtfUZGxleOiIiIiIiI6NtUKGbXHD16NGJiYnDp0iVoa2vj0KFDWL9+PZycnPDXX38VdHhERERERETfjEJxJe/48ePYt28fqlatChUVFdjb26NRo0YwMDDArFmz0KJFi4IOkYiIiIiI6JtQKK7kvX37FsWKFQMAGBkZISIiAgDg4uKC69evF2RoRERERERE35RCkeQ5OztLt05wdXXFypUr8eLFC6xYsQKWlpYFHB0REREREdG3o1B01xw1ahTCw8MBAN7e3mjatCl8fX2hoaGBdevWFWxwRERERERE35BCkeT17NlT+n+VKlUQHByMBw8ewM7ODqampgUYGRERERER0belUCR5H9LR0UHlypULOgwiIiIiIqJvzjeb5P3www95brtgwYIvGAkREREREVHh8c0meTdu3MhTO5lM9oUjISIiIiIiKjy+2STvxIkTBR0CERERERFRofNN30Lh2bNnEEIUdBhERERERESFxjed5Dk5OUk3PgeALl264NWrVwUYERERERER0bftm07yPryK5+/vj7dv3xZQNERERERERN++bzrJIyIiIiIiovz5ppM8mUyWZfZMzqZJRERERESUs292dk0gs7uml5cXNDU1AQDJyckYPHgwdHV1ldrt3r27IMIjIiIiIiL65nzTSZ6np6fS4549exZQJERERERERIXDN53krV27tqBDICIiIiIiKlS+6TF5RERERERElD9M8oiIiIiIiIoQJnlERERERERFCJM8IiIiIiKiIoRJHhERERERURHCJI+IiIiIiKgIYZJHRERERERUhDDJIyIiIiIiKkK+6Zuhf0lCCABAXFwcDAwMCjgaIiIqKuLi4qT/n1w9ogAjISKioubdOeZdLpOT/2ySFx8fDwCwtbUt4EiIiIiIiIjyLj4+HnK5PMd6mfhYGlhEKRQKhIWFQV9fHzKZrKDDIfqmxcXFwdbWFs+fP+eVbyIi+qx4jiHKOyEE4uPjYWVlBRWVnEfe/Wev5KmoqMDGxqagwyAqVAwMDHgCJiKiL4LnGKK8ye0K3juceIWIiIiIiKgIYZJHRERERERUhDDJI6KP0tTUhLe3NzQ1NQs6FCIiKmJ4jiH6/P6zE68QEREREREVRbySR0REREREVIQwySMiIiIiIipCmOQREREREREVIUzyiIiIiIiIihAmeUT/cTKZDHv37i2QbZ88eRIymQwxMTG5tnNwcMCiRYu+SkxERPRpCvJ88jnxnENFAZM8oq/kwoULUFVVRYsWLfK9bEGecLy8vCCTySCTyaChoQFHR0dMmzYN6enp/3rdbm5uCA8Ph1wuBwCsW7cOhoaGWdpduXIFAwcO/NfbIyIqCgr7+WT27NlK5Xv37oVMJvvq8fCcQ0UZkzyir2TNmjUYMWIETp8+jbCwsIIOJ1+aNm2K8PBwPH78GGPGjIGPjw9+/fXXf71eDQ0NWFhYfPTkbmZmBh0dnX+9PSKioqAwn0+0tLQwZ84cREdHF3QoOeI5h4oCJnlEX0FCQgK2bduGIUOGoEWLFli3bl2WNn5+fqhWrRq0tLRgamqKdu3aAQDq1q2L4OBgfP/999IVNQDw8fFBxYoVldaxaNEiODg4SI+vXLmCRo0awdTUFHK5HB4eHrh+/Xq+49fU1ISFhQXs7e0xZMgQNGzYEH/99RcAIDo6Gr1794aRkRF0dHTQrFkzPH78WFo2ODgYrVq1gpGREXR1dVGuXDn4+/sDUO6uefLkSfTp0wexsbHSfvr4+ABQ/uW5e/fu6NKli1J8aWlpMDU1xYYNGwAACoUCs2bNQvHixaGtrQ1XV1fs3Lkz3/tNRPStKeznk4YNG8LCwgKzZs3Ktd3Zs2fh7u4ObW1t2NraYuTIkXj79q1UHx4ejhYtWkBbWxvFixfH5s2bs1ylXLBgAVxcXKCrqwtbW1sMHToUCQkJAMBzDhV5TPKIvoLt27ejdOnScHZ2Rs+ePfHnn39CCCHVHzhwAO3atUPz5s1x48YNHDt2DNWrVwcA7N69GzY2Npg2bRrCw8MRHh6e5+3Gx8fD09MTZ8+excWLF+Hk5ITmzZsjPj7+X+2PtrY2UlNTAWR2v7l69Sr++usvXLhwAUIING/eHGlpaQCAYcOGISUlBadPn8bt27cxZ84c6OnpZVmnm5sbFi1aBAMDA2k/x44dm6Vdjx494OfnJ52oAeDvv/9GYmKi9EVm1qxZ2LBhA1asWIG7d+/i+++/R8+ePXHq1Kl/td9ERAWtsJ9PVFVV8csvv2DJkiUIDQ3Nts3Tp0/RtGlTdOjQAbdu3cK2bdtw9uxZDB8+XGrTu3dvhIWF4eTJk9i1axdWrVqF169fK61HRUUFixcvxt27d7F+/XocP34c48ePB8BzDv0HCCL64tzc3MSiRYuEEEKkpaUJU1NTceLECam+Zs2aokePHjkub29vLxYuXKhU5u3tLVxdXZXKFi5cKOzt7XNcT0ZGhtDX1xd+fn5SGQCxZ8+eHJfx9PQUbdq0EUIIoVAoxJEjR4SmpqYYO3asePTokQAgzp07J7V/8+aN0NbWFtu3bxdCCOHi4iJ8fHyyXfeJEycEABEdHS2EEGLt2rVCLpdnaff+/r97/jZs2CDVd+vWTXTp0kUIIURycrLQ0dER58+fV1pHv379RLdu3XLcTyKiwqConE9q1Kgh+vbtK4QQYs+ePeL9r6T9+vUTAwcOVFr2zJkzQkVFRSQlJYn79+8LAOLKlStS/ePHjwWALPv2vh07dggTExPpMc85VJTxSh7RF/bw4UNcvnwZ3bp1AwCoqamhS5cuWLNmjdQmICAADRo0+OzbfvXqFQYMGAAnJyfI5XIYGBggISEBISEh+VrP/v37oaenBy0tLTRr1gxdunSBj48P7t+/DzU1NXz33XdSWxMTEzg7O+P+/fsAgJEjR2LGjBmoVasWvL29cevWrX+1T2pqaujcuTN8fX0BAG/fvsW+ffvQo0cPAMCTJ0+QmJiIRo0aQU9PT/rbsGEDnj59+q+2TURUkIrC+eSdOXPmYP369dK54n03b97EunXrlD7DmzRpAoVCgcDAQDx8+BBqamqoXLmytIyjoyOMjIyU1nP06FE0aNAA1tbW0NfXR69evRAZGYnExMQ8x8lzDhVWagUdAFFRt2bNGqSnp8PKykoqE0JAU1MTS5cuhVwuh7a2dr7Xq6KiotRFB4DURfIdT09PREZG4rfffoO9vT00NTVRs2ZNqatlXtWrVw/Lly+HhoYGrKysoKaW94+O/v37o0mTJjhw4AAOHz6MWbNmYf78+RgxYkS+Ynhfjx494OHhgdevX+PIkSPQ1tZG06ZNAUDqUnPgwAFYW1srLaepqfnJ2yQiKmhF4XzyTp06ddCkSRP8+OOP8PLyUqpLSEjAoEGDMHLkyCzL2dnZ4dGjRx9df1BQEFq2bIkhQ4Zg5syZMDY2xtmzZ9GvXz+kpqbma2IVnnOoMGKSR/QFpaenY8OGDZg/fz4aN26sVNe2bVts2bIFgwcPRoUKFXDs2DH06dMn2/VoaGggIyNDqczMzAwvX76EEEIaPB8QEKDU5ty5c/j999/RvHlzAMDz58/x5s2bfO+Hrq4uHB0ds5SXKVMG6enpuHTpEtzc3AAAkZGRePjwIcqWLSu1s7W1xeDBgzF48GD8+OOP+OOPP7JN8rLbz+y4ubnB1tYW27Ztw8GDB9GpUyeoq6sDAMqWLQtNTU2EhITAw8Mj3/tKRPQtKirnk/fNnj0bFStWhLOzs1J55cqVce/evWzPOwDg7OyM9PR03LhxA1WqVAGQeUXt/Rk7r127BoVCgfnz50NFJbPj2vbt25XWw3MOFWVM8oi+oP379yM6Ohr9+vWT7gX3TocOHbBmzRoMHjwY3t7eaNCgAUqWLImuXbsiPT0d/v7+mDBhAoDMmb5Onz6Nrl27QlNTE6ampqhbty4iIiIwd+5cdOzYEYcOHcLBgwdhYGAgbcPJyQkbN25E1apVERcXh3Hjxn3Sr7w5cXJyQps2bTBgwACsXLkS+vr6mDhxIqytrdGmTRsAwOjRo9GsWTOUKlUK0dHROHHiBMqUKZPt+hwcHJCQkIBjx47B1dUVOjo6Of7a2r17d6xYsQKPHj3CiRMnpHJ9fX2MHTsW33//PRQKBWrXro3Y2FicO3cOBgYG8PT0/Gz7T0T0tRTF84mLiwt69OiBxYsXK5VPmDABNWrUwPDhw9G/f3/o6uri3r17OHLkCJYuXYrSpUujYcOGGDhwIJYvXw51dXWMGTMG2traUpLq6OiItLQ0LFmyBK1atcK5c+ewYsUKpe3wnENFWkEOCCQq6lq2bCmaN2+ebd2lS5cEAHHz5k0hhBC7du0SFStWFBoaGsLU1FS0b99eanvhwgVRoUIFoampqTQ4ffny5cLW1lbo6uqK3r17i5kzZyoNlL9+/bqoWrWq0NLSEk5OTmLHjh1ZBt0jHwPlsxMVFSV69eol5HK50NbWFk2aNBGPHj2S6ocPHy5KliwpNDU1hZmZmejVq5d48+aNECLrxCtCCDF48GBhYmIiAAhvb28hRPYTBdy7d08AEPb29kKhUCjVKRQKsWjRIuHs7CzU1dWFmZmZaNKkiTh16lSO+0FE9C0rqueTwMBAoaGhIT78Snr58mXRqFEjoaenJ3R1dUWFChXEzJkzpfqwsDDRrFkzoampKezt7cXmzZtFsWLFxIoVK6Q2CxYsEJaWltK5acOGDTzn0H+GTIgPOmETERERERUioaGhsLW1lSZbIfqvY5JHRERERIXK8ePHkZCQABcXF4SHh2P8+PF48eIFHj16JI2XI/ov45g8IiIiIipU0tLS8NNPP+HZs2fQ19eHm5sbfH19meAR/YNX8oiIiIiIiIoQ3gydiIiIiIioCGGSR0REREREVIQwySMiIiIiIipCmOQREREREREVIUzyiIiIiIiIihAmeUREREREREUIkzwiIiIiIqIihEkeERERERFREcIkj4iIiIiIqAhhkkdERERERFSEMMkjIiIiIiIqQpjkERERERERFSFM8oiIiIiIiIoQJnlERIWAj48PZDKZ9GdiYoLatWvD39+/wGKqW7cuWrZsme/lFi1alG3cDg4OGD58+OcILc8yMjKwdOlSVK5cGTo6OpDL5WjQoMEnP68xMTHw8fHBvXv3PnOkBe/27dvQ19dHRERElro9e/ZAJpOhQYMGn7TukydP4pdffvm3IebKx8cHenp60uNz587B1NQUcXFxX3S7REQFgUkeEVEhoa2tjQsXLuDChQv4448/kJycjFatWuH8+fMFHVq+5JTk7dmzB2PHjv1qcSgUCnTo0AE//PAD6tevDz8/P2zatAmGhoZo0aIF5s+fn+91xsTEYOrUqUUyyZs0aRK8vLxgZmaWpc7X1xdAZrIWFhaW73V/jSTvQ7Vq1UK5cuU+6XUmIvrWMckjIiokVFRUUKNGDdSoUQPt27fHvn37IITA+vXrCzq0z6JSpUpwcHD4attbunQp9u3bh1WrVmHevHlo0KABWrVqhV27dqF3796YMGECAgICvlo837Jnz57Bz88Pffv2zVIXFxeHAwcOoGHDhlAoFNi6dWsBRPhp+vXrh+XLlyMtLa2gQyEi+qyY5BERFVLW1tYwMzNDSEiIUvmFCxdQv3596OrqQi6Xo3v37nj9+rVSm9mzZ8PR0RFaWlowMzNDw4YNERgYKNVHRUWhb9++MDU1hba2Ntzc3HD69Olc4/Hy8kL58uWVymJiYiCTybBu3ToAmV0yg4ODsWzZMqnr6ft1H3bX3L17NypWrAgtLS1YWVnhhx9+QHJyslR/8uRJyGQyHDlyBN27d4e+vj7s7e0xd+7cjz5/ixYtgrOzM3r37p2lbtq0aZDJZFiyZIlUll18e/fuhUwmQ1BQEIKCglC8eHEAQKdOnaT9CwoKAgCkpKRg0qRJKFGiBDQ1NWFjYwMvL69P2t+///4bnTt3hp6eHuzs7LB582YAwOLFi2FnZwdjY2P0798fKSkpSusPDQ1Fz549pde1Tp06uHbt2kefqw0bNqBEiRKoVKlSlrrdu3cjOTkZPj4+qFKlinRV730KhQILFixAmTJloKmpCQsLC3Tq1AmxsbHw8fHB1KlT8fbtW+k5q1u3LoC8HVPv4qtduzaMjY1hZGSEunXr4vLlyx/dr7Zt2yImJqZAuz0TEX0JTPKIiAqphIQEREVFSYkFkJng1a1bF3K5HNu2bcOqVatw5coVtGnTRmqzYcMGTJ48Gf369cOhQ4ewevVqVKxYURqblJGRgWbNmsHPzw9z5szBjh07oKenh0aNGuUpIcjNnj17YGFhgY4dO0pdT1u0aJFt27/++gsdO3ZE2bJlsXfvXowfPx4rVqxAz549s7QdPHgwSpUqhT179qBVq1aYMGECDh06lGMcz58/R2BgIFq0aAEVlaynQnt7e1SoUOGjie37LC0tsXv3bgDAL7/8Iu2fpaUlAKBDhw5YsGAB+vbtiwMHDuDXX3/F27dvP2l/hwwZgvLly2PPnj2oUaMGevXqhQkTJuDvv//GihUrMG3aNGzYsEGpK2J0dDRq166NgIAALFmyBLt27YKuri7q16+f5UeADx09ehRubm7Z1vn6+sLBwQFubm7o3r07rl+/jocPHyq1GTFiBMaPH4+WLVvCz88Py5Ytg76+PhISEtC/f3/069dPqTvy77//nrcn/R9BQUHo3bs3duzYgc2bN8POzg516tTBo0ePcl3OwMAA5cqVw5EjR/K1PSKib54gIqJvnre3t9DV1RVpaWkiLS1NBAcHiy5duggjIyPx4MEDqV2dOnWEm5ubUCgUUtndu3eFTCYTBw4cEEIIMWzYMFG5cuUct7Vv3z4BQBw6dEgqS01NFXZ2dqJ9+/ZSmYeHh2jRooX02NPTU5QrV05pXdHR0QKAWLt2rVRmb28vhg0blmW7H5ZXqlRJ1KxZU6nNypUrBQBx69YtIYQQJ06cEADEuHHjpDYKhUI4ODiIfv365biPFy5cEADEokWLcmzTtm1boaWllWvce/bsEQBEYGCgEEKIwMBAAUDs2LFDqd3hw4cFALF58+Yct5ef/R0/frzUJiYmRqiqqgpbW1uRmpoqlXfo0EFUrFhRejxlyhQhl8vFq1evpLLk5GRhZ2en9Px9SKFQCE1NTfHrr79mqQsPDxeqqqpi4sSJQgghXrx4IVRUVMTkyZOlNg8fPhQymUz88ssvOW7j3fH9obweU+/LyMgQaWlpwtnZWfz444952kbVqlVzjI2IqDDilTwiokLi7du3UFdXh7q6Ouzt7bFz505s3LgRzs7OAIDExEScO3cOnTp1QkZGBtLT05Geno5SpUrB1tYWV65cAQBUrlwZN27cwA8//ICzZ89mGY905swZGBgYoEmTJlKZuro62rdvj7Nnz36VfU1ISEBAQAA6duyoVN6lSxcAyBJH48aNpf/LZDKUKVMGoaGhXz7QPDp27Bh0dHTQtWvXbOvzu7+NGjWS/i+Xy1GsWDHUqVMH6urqUnmpUqXw/Plz6fHhw4dRr149GBsbS8eGqqoqPDw8pGMjO9HR0UhJScl2wpVt27YhIyMD3bt3BwBYWVnBw8ND6j4KAMePH4cQAv369ctxG//W/fv30a5dO5ibm0NVVRXq6up4+PDhR6/kAYCpqSnCw8O/WGxERAWBSR4RUSGhra2NK1eu4NKlS9i0aRMsLS3Ru3dv6QtqdHQ0MjIy8P3330vJ4Lu/kJAQ6Qu/l5cXFi5ciL///hvu7u4wMzPDqFGjkJSUJK2nWLFiWbZvbm6OqKior7KvMTExEELA3NxcqVwul0NTUzNLHIaGhkqPNTQ0lMayfcja2hoAsoxnfF9ISAhsbGzyGXn2IiMjYWlpCZlMlm3959jfjz0Hb968wd69e7McGxs3blRKBj/0bh2amppZ6nx9feHs7AxbW1vExMQgJiYGrVu3xtOnT3Hp0iVp39XU1LI9pj6H+Ph4NG7cGMHBwViwYAHOnDmDK1euwNXVNddj4B1NTU3p2CciKirUCjoAIiLKGxUVFVStWhUAUL16dTg7O+O7777DtGnTsHz5chgaGkImk+Gnn35C27ZtsyxvamoqrWfUqFEYNWoUXrx4ga1bt2LixIkwNTXF5MmTYWxsnO0YrVevXsHY2DjH+LS0tJCamqpUFh0d/Un7+m5fPowjNjYWKSkpucaRF7a2tihevDgOHjyIefPmZUm+QkJCcOvWLaVJWf7N/pmYmCA8PBxCiGwTvS+9vwBgbGyMpk2bYvr06Vnqskvg3l8OyExE3/fkyRPpCqCRkVGW5Xx9ffHdd9/BxMQE6enpeP36db4Tvbw85xcuXEBoaCj2798PV1dXqTw2NjZPSXpMTAxMTEzyFRcR0beOV/KIiAqpqlWrolu3bli7di1evnwJXV1d1KxZE/fv30fVqlWz/GV3ewJra2uMGTMGFSpUwP379wEAtWvXRlxcHA4fPiy1S09Px549e1C7du0c47GxsUFoaCgSEhKksvfX8c7HrrIBgJ6eHipWrIidO3cqlW/fvl2K8d8aPXo07t+/j40bN2ap8/HxgRACI0aMkMpsbGyk5+idD/dPQ0MDALLsX8OGDZGYmCjF/6Gvsb8NGzbEvXv3UKZMmSzHhouLS47LaWlpwc7OTmn2VQDYvHkzZDIZ9uzZgxMnTij9NWnSROrKWb9+fchkMqxduzbHbWhoaGSZCRTI2zH17ircu+ceAM6fPy/NavoxQUFBUpdnIqKiglfyiIgKscmTJ2Pr1q1YtGgRZs+ejV9//RX169dHly5d0LVrVxgZGSE0NBRHjhxBnz59ULduXQwaNAhGRkaoUaMGjIyMcO7cOdy8eRNDhw4FALRo0QLVq1dHz549MXv2bJibm2PJkiUIDw/HTz/9lGMs7du3x5QpU9C3b18MGDAAd+/exerVq7O0K1OmDI4fP44jR47AyMgIxYsXz/ZKio+PD9q2bYuePXuiZ8+eePjwIX766Sd06NAh16Qkr4YPH47jx4+jf//+uH37Npo1a4akpCSsW7cOO3fuxLx581CxYkWpfceOHTFkyBBMnToVbm5u8Pf3x4ULF5TWaWFhAUNDQ2zZsgXFixeHpqYmKlSogIYNG6J58+bo27cvnj59iu+++w5RUVHYuXMntm3b9lX294cffoCvry88PDwwatQo2NnZISIiApcuXYKVlRW+//77HJetVatWlplVN2/eDHd392yvGsfFxaFNmzY4evQomjRpgsGDB2PSpEmIiopCgwYNkJiYiAMHDsDHxwfW1tYoU6YM0tPT8dtvv8HNzQ0GBgZwdnbO0zFVo0YN6OnpYdiwYZg4cSJevHgBb29vqUvux1y9ehVjxozJU1siokKjQKd9ISKiPMlpZkAhhOjRo4cwMDAQMTExQgghrly5Ipo3by7kcrnQ1tYWTk5OYvDgweL58+dCCCHWrVsnatWqJYyNjYWWlpYoW7asWLx4sdI637x5I7y8vISxsbHQ1NQUNWvWFCdPnlRq8+HsmkIIsWHDBuHo6Ci0tbVFo0aNREBAQJaZEO/cuSPc3d2Fvr6+Ul12s1fu3LlTVKhQQWhoaAgLCwsxevRokZSUJNW/m23yypUrSsu1adNGeHh45P6kCiHS09PF4sWLRcWKFYW2trYwMDAQ9erVk2YifV9aWpoYO3asMDc3F3K5XAwaNEhs3rxZaXZNITJn3CxTpozQ1NRUqktKShITJ04UdnZ2Ql1dXdjY2Ii+fft+lv3N7rnL7pgJDw8X/fr1E5aWlkJDQ0PY2NiIjh07inPnzuX6PO3atUtoaWmJuLg4IYQQV69eFQDE6tWrs22fmpoqzMzMRK9evYQQmTNezp07Vzg5OQl1dXVhYWEhunTpImJjY6XndujQocLc3FzIZDKl1y4vx9TBgwdFuXLlhJaWlqhQoYLw9/fPcnxm93xcu3ZNyGQy8eTJk1z3n4iosJEJIUTBpZhERET0rUtLS4OdnR3mzJmT7c3jC6tx48bh2rVrOH78eEGHQkT0WTHJIyIioo/67bffsGHDhizdNguruLg42NvbY9++fahTp05Bh0NE9FlxTB4RERF91ODBgxEXF4c3b95IM7UWZiEhIZg+fToTPCIqknglj4iIiIiIqAjhLRSIiIiIiIiKECZ5RERERERERQiTPCIiIiIioiKESR4REREREVERwiSPiIiIiIioCGGSR0REREREVIQwySMiIiIiIipCmOQREREREREVIUzyiIiIiIiIihAmeUREREREREUIkzwiIiIiIqIihEkeERERERFREcIkj4iIiIiIqAhhkkdERERERFSEMMkjIiIiIiIqQpjkERERERERFSFM8oiIiIiIiIoQJnlERERERERFCJM8IiIiIiKiIoRJHhERERERURHCJI+IiIiIiKgIYZJHRERERERUhDDJIyIiIiIiKkKY5BERERERERUhTPKIiIiIiIiKECZ5RERERERERQiTPCIiIiIioiKESR4REREREVERwiSPiIiIiIioCGGSR0REREREVIQwySMiIiIiIipCmOQREREREREVIUzyiIiIiIiIihAmeUREREREREUIkzwiIiIiIqIihEkeERERERFREcIkj4iIiIiIqAhhkkdERERERFSEMMkjIiIiIiIqQpjkERERERERFSFM8oiIiIiIiIoQJnlERERERERFCJM8IiIiIiKiIoRJHhERERERURHCJI+IiIiIiKgIYZJHRERERERUhDDJIyIiIiIiKkKY5BERERERERUhTPKIiIiIiIiKECZ5RERERERERQiTPCIiIiIioiKESR4REREREVERwiSPiIiIiIioCGGSR1+NTCZDQEBAgWzby8sLo0ePzrbO19cXbm5uXzcg+qzq1q2LkydPFnQY+Xby5EkYGhpKj+vWrYtFixYVWDzfOgcHBwQFBRV0GB915swZ2NjYfLZ2lL3CcjzQl+Xj4wMfH5+CDoPom8Mk7z9OT09P+lNVVYWmpqb0uFmzZjkul1vS9CnWrVsHVVVVaduWlpYYOnQoUlJSPts2ctKjRw+cP3/+i27Dz88PderUgb6+PkxMTFC9enWsWLHii27zHQcHB+zduzffy507dw61atWCnp4eihUrhilTpkh1I0aMgK2tLQwMDGBtbY3Ro0cjNTVVqr927Rpq164NAwMDlChRAhs2bJDqUlJSULduXRQrVgwGBgYoXbo0Vq1a9a/28d9ycHCAtrY29PT0YGpqihYtWuDJkycFGlNerV69Gs7OztDV1YWDgwP27dsn1Z09exY1atSAXC6HtbU1fvzxRygUio+u886dO9DQ0EDbtm2lsosXL6JJkyYwNTWFsbExmjRpgnv37kn1L168QK1atWBoaAhPT0+l7cyePRuTJ0/+PDv8L8hkMujo6EBPTw/m5ubo3r07IiIiPvt23N3dERoa+tnafS4dO3aEpaUlDAwMULx4ccyYMSPHtnl5n6akpGDs2LGwtLSEnp4eXFxcpKSrsB0P7/5u374NAJg8eTJcXFygpqaWp3PdwYMHUb16dcjlchgZGaFatWrw9/f/wnvw6e7du4cmTZpAX18fxsbG6Nev30eXEUKgdu3akMlkiImJkcqXLl2KqlWrQlNTU+kz451x48bB2NgYrq6uSp8Zz549Q8WKFZGcnPw5dumTeXl5QUNDQ+k4mDBhAgBg3rx5qFChAgwMDGBjY4OxY8cqnes+FB4eju7du8PCwgL6+vooUaIEvv/++6+1K/mSmpqKjh07wsHBATKZLNfvCatWrYJMJvvoj5B79+6Fk5MTdHR0ULt2bTx48ECqO3nyJGQymdLzPHz4cKn+zp07qFChAoyNjTFx4kSl9Q4ePBhr1qz5pP38L2OS9x+XkJAg/bm7u2POnDnS44MHD37VWFxcXKRtX7t2DefOncO8efO+agxfwvLly+Hp6YkBAwYgNDQUb968wfLly7F///6CDi1Ht27dQrt27TBu3DhERUUhMDAQHTt2lOqHDh2KBw8eIC4uDjdv3sTNmzcxd+5cAEBMTAyaN2+Onj17Ijo6Glu2bMGIESNw9uxZAICamhqWLFmCsLAwxMXFYffu3Zg8eTLOnDlTIPv6zpYtW5CQkICgoCAYGhqib9++BRpPXqxatQrz58/H1q1bkZCQgEuXLsHFxQUAkJGRgTZt2qBNmzaIiorCuXPnsHXrVvzxxx+5rlOhUGDAgAGoVauWUnl0dDT69OmDJ0+e4OXLl6hevTqaNm2KjIwMAMAvv/wCd3d3vHz5Eo8fP8aePXsAZH6R27p1KyZNmvQFnoH8O3/+PBISEnD79m28ePEi2y/wQghpv4oSb29vBAUFIS4uDqdOncLmzZuxadOmbNvm5X3ap08fPH36FNeuXUN8fDx27NghXZkubMfDu7937x9HR0fMnTsXrVu3/ug6nj59ik6dOuGnn35CVFQUwsPDMW/ePOjr63/2eNPS0v71OsLCwlC/fn107twZr1+/Rnh4OIYNG/bR5X7//XdoampmKbeyssKkSZMwYMCALHVXrlzB3r17ERQUhH79+knJE5B5HlmwYAG0tLT+3Q59BkOHDlU6DubMmQMg83N0zZo1iIyMxMWLF3Hy5Mlcrxr26tULWlpaePDgAWJjY3HkyBFUrFjxs8f7OY4DAKhduzY2btyYa4+CsLAw/Prrr9J7IycPHz5Ejx49sHDhQkRFRaF+/fpo06YN0tPTpTZyuVzpeV66dKlUN2HCBAwZMgSBgYHYvn07rl27BiDzB+dHjx4VinPyt4ZJHuXo8OHDqFSpEuRyOSpXroyjR48CABYvXgxfX1/8/vvv0NPTQ7ly5QAAmzZtQvny5aGvrw87OztMnjwZQohP2raVlRWaNGmCu3fvSmWvXr1C586dYWZmBjs7O/z8889KHx45xfuh9PR0eHl5oWHDhoiPj8e6deuUPoQdHBwwd+5c1KhRA/r6+vDw8MDz58+l+rt370p19erVw/jx41G3bt1stxUfH48JEyZg8eLF6NWrF+RyOWQyGapUqaKU5OUW+4dd+AICAiCTyZTqf/zxR+lX2cqVK0u/Rnfq1AkhISHo1q0b9PT0MHjw4Nyf+H9Mnz4d/fv3R9u2baGhoQFdXV1UqFBBqi9Tpgx0dXUBZH4hVlFRwePHjwFkfmnS1NTE4MGDoaqqiu+++w7t27fH6tWrAQCqqqrSL+RA5q/pMpnsm7lypqenh+7du0snGCDzhDplyhSULFkSJiYmaN26NcLCwqT6ly9fomfPnrC0tIShoSHq1KmDpKQkAMD48eNhb28PfX19lC1bFjt27PgscWZkZGDKlCn47bffUKlSJchkMpibm6NEiRIAgNjYWERFRcHT0xOqqqpwcHBAw4YNpWMjJ4sXL0aZMmXg4eGhVN6sWTN07doVhoaG0NDQwLhx4/D8+XMEBwcDyPzyXq9ePWhpaaFOnTp4+vQpAGDIkCFYuHBhtl8OC1KxYsXQqVMn3Lp1C0Dm+37WrFmoUaMGdHR0cO/ePbx+/Ro9evSApaUlrKysMHr0aKXeBdeuXUP9+vVhbGwMMzMzjBgxAkDWbri+vr5wcnKCvr4+rK2tMX369GzbxcfHY+DAgbC0tISlpSUGDx6Mt2/fAgCCgoIgk8mwceNGODo6wtDQEF5eXvn6sufi4iK9DjKZTOl9+6GPvU/v3r2Lffv24c8//4SVlRVkMhlKly4t7U9hOx4+5OnpiWbNmsHAwOCjbW/cuAFzc3O0bdsWqqqq0NLSgoeHB9zd3aU2jx8/RuvWrWFmZgZjY2O0b99eqrt69ap01bNs2bLYsmWLVOfj44OWLVtiyJAh0hUOIQQWL14sPd9169bF/fv387xvCxcuRP369dGvXz9oa2tDU1MTlStXznWZ58+fY8GCBdKPee9r37492rZtC1NT0yx1z549Q9WqVWFgYIDGjRtLx8HmzZthYWGB+vXr5znugjBhwgRUq1YN6urqsLGxQe/evaUfLLNz8eJF9OnTB4aGhlBRUUHJkiXh6ekp1aempkrnEn19fbi4uOD69esA8vb+X7t2LRwdHaWk7Pr166hXrx6MjY3h6Oj40R/x3qehoYHRo0fD3d0dqqqqObYbNmwYJk+eDGNj41zXt2nTJtSrVw8tW7aElpYWJk+ejNevX+f5B9xnz56hfv36kMvlqF69Op4+fYq0tDSMHDkSy5cvV/reQ3nDJI+y9eTJE7Rp0waTJ09GZGQkfvrpJ7Ru3RqBgYEYOXIkevToIf3y9S4RMzExwe7duxEXF4e//voLq1atwubNmz9p+8+fP8ehQ4eUriZ0794d6urqCAwMxJkzZ7B3717phJNbvO97+/YtWrdujaSkJPj7++f4S+umTZuwZcsWREREQFdXV+palJaWhtatW6NZs2aIjIzE7Nmz8eeff+a4HxcuXEBiYiI6d+6cY5u8xp6bjRs3Yu7cuYiOjkbVqlWlL5s7duyAnZ2ddJXqXRfRoUOHYujQoTmu79SpU0hNTUXFihVhZmaGpk2b4uHDh0ptZs+eLXXlvHnzprRNhUKRJblXKBTSl+l33p0IypYtC3Nzc7Rr1y7P+/slxcbGYuPGjShVqpRU9vPPP+PcuXM4e/YswsPDUapUKXTt2hVA5r61atUKampquHfvHt68eYNffvkFKiqZH6+urq64cuUKYmJiMGXKFPTq1SvPr22FChVyfA89fPgQr169wvXr1+Hg4AAbGxsMGDAAcXFxAABjY2P07dsXa9asQVpaGp4+fYqjR4+iRYsWOW4vODgYv/32G3799dePxnbq1CkYGhrCzs4OQGYCcfToUSQlJeHMmTNwcXGBr68vrKysUK9evTzt79f08uVLbN++XenL7bp167B+/XokJCSgVKlSaN26NSwsLPD06VPcvn0bN2/elLo4vnjxAvXr10fHjh0RFhaG4ODgbN/nb9++hZeXF9asWYP4+HjcvXsXTZs2zTamUaNG4cmTJ7hz5w5u376NBw8eZOnqdfDgQdy4cQP37t3DsWPH4OvrK9Xldry8M3ToUOjo6MDOzg4JCQnw8vLKtX1O79NTp07BwcEBkyZNgpmZGZycnJQSgMJ2PPwbVapUQVhYGIYMGYJDhw4hKipKqf7t27do2LAhypcvj6CgILx8+VL6vIyJiUHTpk3RtWtXREREYPny5RgwYADOnTsnLX/o0CF89913eP36NaZPn47ly5djzZo18PPzw5s3b9C+fXu0atVK6kY4e/ZstGzZMsd4T506BT09PdSqVQsmJiZwd3fHpUuXct3HIUOGwMfHByYmJvl6bsqXL4+rV68iJiYGR48ehYuLC6Kjo/HLL79g/vz5+VrXt+DUqVNKP3h+qFatWhg9ejQ2bNiAR48eZamfOHEi/P39cejQIcTFxWHnzp3Sc5qX9/9ff/2Fq1evIjAwEC9fvkSjRo0wZMgQREREYO/evfD29saxY8cAACEhITA0NERISMgn7+/OnTsRFxeH3r17f7TtrVu3lH4wV1dXR9myZZXO/QkJCbCysoKNjQ169OiBFy9eSHUuLi44cuQIYmJicO3aNZQvX166mu7s7PzJ+/CfJoj+4eHhIRYuXCiEEGLGjBmiadOmSvWNGjUSM2fOFEII4enpKUaNGpXr+kaNGiX69+8vPQYgbty4kW3btWvXChUVFSGXy4WBgYEAINzc3ERsbKwQQojQ0FABQLx8+VJaxtfXVzg5OeU53h49eojq1auLESNGiIyMDKVtu7q6So/t7e3F8uXLpcebNm0S5cuXF0IIcfr0aSGXy0VaWppUP3ToUOHh4ZHtfm3atEmYm5vn9BTlKfb3XxchhLhx44Z4/63r4eEhJkyYID0+e/as0NPTU9qfPXv25BrDh1RVVYW1tbW4ffu2SE5OFuPHjxfOzs5K+/3OvXv3xM8//yyeP38uhBDizZs3wsjISCxZskSkpqaKs2fPCn19fVGyZMksy6anp4uTJ0+KqVOniuTk5HzF+D4PDw9x4sSJT17e3t5e6OjoSMdeqVKlxJ07d4QQQigUCqGrqysCAgKk9klJSUJFRUWEhISIixcvCl1dXZGYmJinbbm6uopNmzYJIYQ4ceKEkMvlSvvx/mudmzNnzggAokGDBiIiIkJERESIBg0aiL59+0ptDh06JGxsbISqqqoAIIYPHy4UCkWO62zSpInYsGGDEEIIb29v0aZNm2zbBQcHC0tLS7FmzRqpLCoqSnTv3l24uLiISZMmicjISFGuXDnx5s0bMWXKFOHu7i569eolvac/hb29vQgMDPzk5QEIPT09YWhoKGxtbYWXl5eIjIyU1v3+c3/58mVhbGys9Flx+PBhUaJECSGEELNnzxb16tXLdjvvv64JCQlCW1tbrFixIsu+v98uIyNDaGhoiIsXL0r1586dE5qamiIjI0MEBgYKAOL+/ftSff/+/cXw4cPz/TxkZGSIK1euiMmTJ4vo6OiPts/ufTp9+nQBQIwfP14kJSWJO3fuCCsrK+n4KUzHg1wuF3K5XNStWzdLm7yc64QQ4tq1a6Jnz57C2tpaqKioiIYNG4qnT58KIYTYunWrKFmyZLbvvU2bNonSpUsrlQ0YMEAMGDBACJH5Pnz//CSEEGXLlhV79+5VKrOyshKnT5/+aJxCCFGyZEmhp6cnzp49K1JSUsSSJUuEqampiIqKyrb95s2bRYMGDYQQQjoOsztucvrMWLJkiXB1dRUtW7YUISEhol+/fmLDhg3i9OnTol69eqJ+/frizJkzeYo9O97e3sLb2/uTl/f09BQaGhrScSCXy8W9e/eytFu1apUwNzcXYWFhOa4rNjZWeHt7i0qVKgk1NTVhZ2cnfH19hRCZ5xIdHR1x6tSpLMvl9f3//neouXPnirZt2yqt56efflI6B+RVdt8ToqKihIODg3j06JEQ4uPnp/r164tff/1Vqax58+Zi+vTpQgghwsPDxe3bt0V6eroIDw8X3bp1E5UqVZI+Y0NCQkTLli2Fq6urWLJkiXj8+LGoVKmSiI+PF8OGDRPu7u5i+PDhIjU1Nd/791/FK3mUrdDQUDg4OCiVlShRItdJAv7++2+4ubnB1NQUcrkcK1aswJs3b/K8TRcXF8TExCA2Nhbx8fHSmJ938WhpacHc3DzbePIS79GjR/H06VP8+OOP0lWWnFhYWEj/19XVRXx8PIDMvumWlpZSFyYA0pWM7JiamuLNmze5DtT+lOf6Y/EmJCTkedns6OnpoU+fPihfvjw0NTUxbdo0PHnyJNtfJsuUKQNXV1fpioCJiQn8/Pyk7jgTJ05Enz59sv0FWFVVFR4eHnj16lWerh59Sb6+voiNjcWDBw+Qnp4udSt68+YN3r59izp16sDQ0BCGhoawsLCAhoaG1F3R2toa2tra2a534cKFKFeuHORyOQwNDXHnzp18vS9yoqenBwD48ccfYWpqClNTU/z444/w8/MDkHmlr02bNli4cCGSk5MRFhaG+/fvZxnQ/s6mTZuQnp6OXr165brd0NBQNGjQAMOHD1caI2FkZARfX1/cunUL06dPx7hx4zBx4kRcuXIF586dw8mTJ1GiRAnMmjXrX+/7v3HmzBlER0cjJCQEa9euVeqC9P57OSgoCDExMTA2NpZe944dO+LVq1cAMq96Ojk5fXR7urq68PPzw759+2Bra4vatWvjxIkTWdpFREQgNTVV6bOgRIkSSElJUTpecvpsyg8VFRVUrVoV+vr6GDt27EfbZ/c+fTdZ17Rp06ClpYVy5cqhb9++0vFXmI6HmJgYxMTEZPu65FXlypWxceNGhIaG4tGjRxBCoGfPngAyj5WSJUtm290sL5//H55jgoKC0LNnT+m4NDQ0RHR0dJ7PGXp6emjbti1q1aoFDQ0NDB8+HFpaWrhw4UKWtlFRUZg4cSKWL1+ep3VnZ/jw4QgICICfnx8CAwMREhKCHj16oHv37li9ejVWrVqFHj16fPLwjs9hyJAh0nEQExODMmXKKNX7+vpi0qRJOHz4MCwtLXNcj4GBAXx8fHD9+nVER0dj5MiR6N27N+7fv4+IiAgkJiZm+7mR1/f/h59R/v7+SsfB4sWLER4e/i+eif8bN24c+vXrl6fPOSDzuIqNjVUqi42NlXpMWVhYoHz58lBVVYWFhQVWrVqFmzdvSt8rbG1t4efnh4CAAAwfPhxDhgzBb7/9hk2bNiExMRGnT59GfHx8rr2nSBmTPMqWjY1Nlqmpg4KCpH7gHyZJqampaN++PQYNGoQXL14gNjYWgwcP/uQPbT09PfTr1w8XLlxAZGQkbGxskJycLH3B+jCej8ULAF27dsWwYcNQt25dpS4C+WFlZYWXL18qjQXMrSuEm5sbdHR0ch2H9bHY9fT0kJiYKNXl9wP8YwltdlxdXZUef6wvfFpamtLYnlq1auH8+fOIjIzEmTNn8PLlyyxjvHJbviA5Oztj3rx5GDJkCJKSkmBiYgIdHR1cunRJ6UtAUlIS3NzcYG9vjxcvXmQ7Q9zZs2fh4+ODDRs2IDo6GjExMShfvvxn+TLj7Oyc64QFt2/fho2NDTp27Ag1NTVYWlrC09MTBw4cyLb90aNHcenSJSlhnDt3Lg4ePKiUVISGhqJevXro2bMnfvrppxy3ferUKYSGhqJnz564efMmqlWrBhUVFdSsWRM3b9789J3+wt5/r9ja2qJYsWJKr3lsbKz0A4q9vX2ex5E2aNAA/v7+ePPmDTp16oS2bdtmmeXUzMwMGhoaSp8FQUFB0NTUzHas0+eQ3/fd++3ffUbkZZxMYT0ePlXJkiUxatQoafyrvb09nj59mu37Pi/nrg8/w21tbbFjxw6lYzMxMRHdunXLU3wffr7n5tatWwgLC0PNmjVhamoqdW8uWbIkdu7cmef1AJnfE0aPHo3ff/8dERERSE9PR4kSJVCyZEmkpqZ+kZluPwdfX1+MHj0ahw4dyrWr5of09PQwZswYyOVy3Lt3D2ZmZtDR0cn2cyOv7/8PP6PatWundBzEx8d/tlldjx49igULFkjnhHPnzmHSpEno0KFDtu0rVKigdJustLQ03Lt3L8cJW3L77NiwYQMcHBzg7u6Omzdv4rvvvgOAIvuZ8aUwyaNsdenSBSdPnsS+ffuQnp6O3bt34/Tp09I4JHNzczx79kw6aaWkpCA5ORkmJibQ1NTEpUuXPnk8HgAkJSVh7dq1sLKygrGxMaytrVGvXj2MHTsWb9++RUhICGbOnCkNaP5YvO9MnToVPXr0QN26dZUmU8mrGjVqwNDQELNmzUJaWhquXLmC7du359heX18fc+bMwciRI+Hr64u4uDgIIRAQECDN2vax2CtXrozdu3cjNjYWr1+/znbge27Mzc2lq1J5NXDgQKxbtw4PHz5EWloapk6dCicnJ5QqVQoJCQlYu3YtYmJiIITA7du3MWPGDDRp0kRa/saNG0hJSUFSUhL++OMPnDx5UprFMCAgAEeOHEFSUhLS09Nx4MAB+Pr6Ki1f0Nq2bQsTExMsW7YMKioqGDx4MMaMGSMdM5GRkdi2bRsAoFq1anB2dsbQoUMRExOD9PR0nD17FikpKYiLi4OqqirMzMygUCjw559/4s6dO58lRm1tbfTs2RNz5syREsg5c+agTZs2AP4/Tmjv3r1QKBSIiIjAxo0bUalSpWzXt3DhQty/fx8BAQEICAjA4MGDUa9ePWkCmrCwMNSrVw9dunSBt7d3jnGlpKTg+++/l375L1GiBE6fPo2UlBQcPXoUJUuW/Cz7/6VVq1YNtra2mDRpEuLj4yGEQHBwsDTrcI8ePXD58mWsWLECKSkpSExMzHaCgVevXmHPnj2Ij4+HmpoaDAwMlHoCvKOiooLu3bvj559/RlRUlDQ+t1evXp/0Q82HgoODsWvXLiQkJEChUOD8+fNYvHhxju+7j71P69SpAycnJ0ydOhVpaWl4+PAh1q1bJx1/7xTW4yEtLQ3JycnIyMhARkYGkpOTc5zk5syZM/j999+lyZhevnyJP/74Q7r/aosWLZCSkoIpU6bg7du3SE1Nla4aNm/eHK9fv8bvv/+O9PR0nDlzBr6+vrmOgRo2bBimTJkijZOOi4vDvn378nxVd8CAAdi3bx8uXbqEjIwM6RjO7n6xNWvWRGBgoPS58C6BOHPmDJo3bw4gczKz5ORkpKenQ6FQIDk5OdveK7NmzUKnTp3g6OgIU1NTpKSk4ObNm7h16xZSU1PzPd7va9iyZQtGjhyJgwcP5vjZ+b5x48YhICAAqampSE1NxerVq/H27VtUqVIFMpkMAwYMwJgxY/DkyRMIIfDw4UMEBwd/0vu/V69eOH78OHbt2oW0tDSkpaUhICAAV65cyfP+vfvuJoRQOuaBzElkbt26Jb32VatWxbhx47By5cps19WzZ08cP34c/v7+SElJwcyZM2Fqaoo6deoAAE6cOIHAwEAIIRAZGYkhQ4agXLlyWa4URkZGYu7cudJ3nRIlSuD48eNIS0vD8ePHv9nPjG9SwfQSpW/Rh/2t/f39haurq9DX1xeurq7i0KFDUt2TJ09E5cqVhaGhoXBxcRFCCLF8+XJhaWkp9PX1RatWrcTw4cOV+ucjD2PydHV1ha6urjAyMhINGzZUah8eHi46dOggTExMhI2NjZgwYYJS3+zc4v1wXMXMmTNFiRIlRFBQULZj8t7vm75nzx5hb28vPb5586aoXr260NXVFXXr1hWjR48WjRs3zvW53bdvn6hdu7bQ1dUVxsbGolq1amLlypV5ij0qKkq0bNlS6Ovri3Llyonly5dnGZOX25i9v/76Szg4OAi5XC6GDBkihBBi0KBBYtCgQbnGPHv2bGFpaSkMDQ1F48aNpX75CQkJomHDhsLY2Fjo6uqK4sWLi7Fjx4q3b99Ky3p5eQm5XC50dXVFo0aNpPFtQghx5coVUbVqVaGvry8MDAxEhQoVxIoVK3KN5WM+x5i8D8cjbN68WZiZmYmEhASRkpIipk+fLhwdHYWenp6wt7dXGvfw4sUL0aVLF1GsWDEhl8uFh4eHSExMFBkZGWLAgAHCwMBAmJmZiR9++EHUqVNHer0+NiavbNmy0vi97CQkJAhPT08hl8tFsWLFRP/+/UVcXJxUv2/fPlGpUiVhYGAgihUrJnr06CEiIiLytP4Px9f4+PgIANJ79N3fh+OAvL29xaxZs6TH6enpokePHsLAwEDUqlVLvHjxIsf9+ZjPMQYrp8+g7I6BV69eCS8vL2FtbS29/xYvXizVX7p0Sbi7uwu5XC7MzMzEyJEjhRDKr2tYWJjw8PAQcrlc6OvriypVqojjx49naSdE5niefv36CXNzc2Fubi4GDBggvZ7ZjYUaNWqU8PT0lB7n9noGBQWJ2rVrS3E4OzuLGTNmKI05fH/5vLxPHz16JOrVqyd0dHSEg4NDlvE4QhTe48HT01MAUPp7/7l+3+3bt0Xr1q2FhYWF0NHREZaWlqJ3794iPDxcavPgwQPRrFkzYWxsLIyNjUWHDh2kukuXLomaNWsKAwMDUbp0abFx40apLrtxbgqFQixbtkyULVtW6OvrCysrK9G5c2fpWJk5c2aWcd4fWrdunXBwcBB6enqiZs2a4vLly1Jdbstndxx6e3tnea4+HKf+4MEDUaVKFaVz9tatW4WlpaWwsrISO3bsyDXe3HyOMXk5jbt0cHAQampqSp95ZcuWzXFdI0aMEM7OzkJPT08YGRkJNzc3pfN5cnKymDhxorC3txd6enrCxcVFXL9+XQiR//e/EEJcv35dNGrUSJiYmEjbO3r0qBAic+y0rq6uCA4OzjFee3v7LK/d2rVrs2374fnp9OnTQldXV6nN7t27haOjo9DS0hJubm5KY4jnz58vbGxshI6OjrCwsBDdunXLNjZPT0+xZcsW6XFsbKxo1qyZMDAwEC1atBDx8fE57g8pkwlRgJ2giYqAQYMGQaFQ5GvqYvq86tatCx8fnxxvZUFFg4ODA06ePJllDBP9N/F4IADSfetyu38d0X8Ru2sS5dOZM2fw/PlzKBQKaQrzTp06FXRYREREREQAgKwDA4goV8+ePUPXrl0RHR0NGxsbzJ49G40bNy7osP7TvLy8+Gv+f8Do0aOVbh5O/208HggAe3AQ5YDdNYmIiIiIiIoQdtckIiIiIiIqQpjkERERERERFSFM8oiIiIiIiIoQJnlERERERERFCJM8IiIiIiKiIoRJHhERERERURHCJI+IiIiIiKgIYZJHRERERERUhDDJIyIiIiIiKkKY5BERERERERUhTPKIiIiIiIiKECZ5RERERERERQiTPCIiIiIioiJEraADICKib1tGRgbOnDmDO3fuQEVFBaqqqpDL5ahbty4sLCwKOjwAwMmTJ1G7dm2oqeX/tLZ9+3aUKlUKFStW/PyBAdi7dy8sLCxQo0aNL7L+D+3btw8hISFQV1eHhoYGmjRpAmtra6n+ypUruHz5MlRUVCCTydC/f/+PPm/Hjh3D/fv3oaamBhUVFdSvXx+Ojo4AACEEDh48iCdPngAAatSogerVqwMALl26hGvXrkEmkwEAatWqhQoVKgAAAgICcOjQIRgaGgIAtLW14enp+VmfCyKi/yomeURElKt9+/YhNTUV/fr1g7a2NgDg2bNnePPmzVdJ8oQQACAlCtk5deoUatSo8UlJXlFTunRptGrVCioqKnj06BF27NiB0aNHAwAePHiA27dvo1+/ftDS0sLbt2+hovLxTj12dnaoU6cO1NXV8fLlS6xbtw4//PADNDQ0cOvWLbx58wbDhw9HSkoKVq5cCQcHBxQrVgxmZmbo27cvtLS0EBsbi5UrV8LGxgbGxsYAAAcHB3Tt2vVLPh1ERP9JPBsSEVGOIiMj8eDBA3z//fdSggcAJUqUUGp3/vx53L17FwqFArq6umjZsiUMDQ1x8uRJvHnzBmlpaYiKioKenh46d+4srSu35V6/fo3U1FTExsaiV69euHjxIoKDg5GRkQFNTU20atUKpqam2L9/PwBg7dq1kMlk6NWrF9TU1PD333/j1atXSE9Ph42NDZo3bw5VVVW8efMG+/btQ0pKCoyNjZGWlpbj/t+8eRPnz58HAMjlcrRs2RIGBgYICAjArVu3oKuri9evX0NVVRWdOnWCkZFRrs/nyZMnkZycjKZNmwIALl++jLCwMLRt2/aj67x58yauXLmCjIwMaGhooFmzZtkm2c7OztL/bWxsEB8fD4VCARUVFZw/fx4eHh7Q0tICAOjq6uYa7ztOTk7S/83NzSGEQGJiIjQ0NHD37l1UrlwZKioq0NbWRrly5XDnzh3Ur19f6TiRy+XQ09NDXFyclOQREdGXwSSPiIhy9PLlSxgbGysleB+6ffs23rx5g379+kFFRQU3b96Ev78/unfvDgAIDQ3FwIEDoaOjg507d+Lq1atwd3f/6HLPnz/HoEGDoKenByCzq1/jxo0BAHfu3MGhQ4fQs2dPtGzZEteuXUOfPn2k5MXPzw/29vZo3bo1hBDw8/PDxYsXUatWLezZswdVqlRB5cqV8erVK/zxxx9wcXHJsl+vX7/GkSNHMHDgQBgYGOD06dPw8/NDjx49AABhYWEYNGgQjIyMcPToUZw9exatWrX6V893TusMCQnBnTt34OXlBTU1NQQHB2P37t0YOnRoruu7ePEinJycpKt1ERERCAsLw6lTp5Ceng5XV1d89913+Yrxxo0bMDIyglwuBwDExsZK/wcAQ0NDhIaGZlnu2bNnSE5OhpWVlVQWEhKCFStWQF1dHTVq1EC5cuXyFQsREWWPSR4REeVZVFQUtm/fjvT0dNja2qJNmzZ48OABwsLCsGrVKgD/7175jqOjI3R0dABkXll6/fo1AHx0OScnJynBAzKThMuXLyMlJQVCCCQlJeUY54MHDxAaGooLFy4AANLT0yGTyZCSkoKXL19K4+/Mzc1hZ2eX7ToCAwPh6OgIAwMDAEC1atVw+vRpKBQKaV/eXWWzsbHB5cuXP/LsfVxO63z48CFevXqF1atXS22TkpKQlpYGdXX1bNd169Yt3Lt3D15eXlKZQqFAdHQ0vLy8kJycjHXr1sHIyAilSpXKU3zPnj3DqVOn0KtXr1y7z37o1atX2LdvHzp27AgNDQ0AQKlSpVCuXDmoq6sjIiICmzZtglwuh42NTZ7XS0RE2WOSR0REObKwsEBUVBSSkpKgra0NY2NjDB48GAEBAXjw4IHUrnbt2qhSpUq263h/nJyKioqUJH1suXfJAJB5tcjf3x8DBgyAsbExXr16hbVr1+Yae+fOnWFiYqJUlpKSkusyufkwqcltv3LyYbv09PQ8rVMIAVdXVzRo0CBPsd65cwenTp1C7969lRJluVwOFxcXqKioQEdHB46OjggNDc1TkhcUFIR9+/ahW7duMDU1VVpnbGwsbG1tAQAxMTFKV/YiIiKwZcsWtG7dWimhfpf4A4CZmRkcHR0REhLCJI+I6DPgLRSIiChHJiYmcHZ2xl9//YXk5GSpPDU1Vfq/s7Mzrl69Kl1Zy8jIQHh4+EfXnZ/lkpOToaqqCn19fQghslw109DQUIrP2dkZZ8+elZKkpKQkREVFQVNTExYWFrh58yaAzC6ZISEh2W6zePHiePLkCeLj4wEAV69eRfHixfM0UUlOjI2NER4eDoVCgbS0NNy/fz9Pyzk7O+PWrVuIjY0FkJn0hYWFZdv27t27OHHiBHr16qWUbAGAi4uLNAtmWloagoODYW5uDiBzfODRo0ezXWdwcDD27NmDrl27ZhkHWLZsWVy/fh0KhQJJSUm4e/eu1O0yIiICvr6+aNmyJUqWLKm0XFxcnPT/hIQEBAUFwdLSMk/PBxER5Y5X8oiIKFdt27bF6dOnsXr1aqioqEBLSwu6urqoVasWAKBChQpISkrC+vXrAWR2CaxYseJHv7DnZzlzc3OUK1cOv//+O7S1tVG6dGml+po1a2Ljxo1QV1dHr1690LRpUxw9ehQrVqyATCaDiooKGjVqBGNjY7Rr1w779u3DhQsXYGJiAnt7+2zjK1asGBo1aoRNmzYByLxi9Slj7hQKhXSFrkyZMrh37x6WLVsGAwMDWFhY5Drxyzv29vZo1KgRtm3bBoVCgYyMDDg5OSmNb3tn9+7d0NPTw9atW6Wy3r17Q0dHBzVr1sT+/fuxbNkyyGQylClTRikhy2nimL/++gsZGRnYt2+fVNauXTuYm5ujQoUKePHiBZYsWQKZTIYaNWpIieOhQ4eQkpKCo0ePSglkw4YN4ejoiCtXruDhw4dQUVGBEAI1atRA8eLF8/isEhFRbmTiw0EQRERE9FkoFAqsWrUKTZo0+eYTmD///BM9evSApqZmQYdCRET/EpM8IiKiLyA4OBj79++HnZ0dWrZsma+JSoiIiP4NJnlERERERERFCCdeISIiIiIiKkKY5BERERERERUhTPKIiIiIiIiKECZ5RERERERERQiTPCIiIiIioiKESR4REREREVERwiSPiIiIiIioCGGSR0REREREVIQwySMiIiIiIipCmOQREREREREVIf8DB4e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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot confusion matrix for claim scenario\n", - "plot_confusion_matrix_from_df(summary_df_kbest, 'RISK_VS_CLAIM using KBest Features from all features')\n", - "plot_confusion_matrix_from_df(summary_df_rfe, 'RISK_VS_CLAIM using RFE Features from all features')\n", - "plot_confusion_matrix_from_df(summary_df_lasso, 'RISK_VS_CLAIM using Lasso Features from all features')" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "30786f7c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Print a table to summarize the results\n", - "summary_table = pd.concat([summary_df_kbest, summary_df_rfe, summary_df_lasso], ignore_index=True)\n", - "summary_table = summary_table[['title', 'count_true_positive', 'count_true_negative',\n", - " 'count_false_positive', 'count_false_negative', 'true_positive_score', 'true_negative_score',\n", - " 'false_positive_score', 'false_negative_score', 'recall_score', 'precision_score',\n", - " 'false_positive_rate_score', 'f1_score', 'f2_score']]\n", - "\n", - "# Rename them\n", - "summary_table.columns = ['Model', 'TP', 'TN', 'FP', 'FN',\n", - " 'TP Rate', 'TN Rate', 'FP Rate', 'FN Rate',\n", - " 'Recall', 'Precision', 'FPR', 'F1', 'F2']\n", - " \n", - "# summary_table.to_csv('flagging_analysis_summary.csv', index=False)\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Set up figure and axis\n", - "fig, ax = plt.subplots(figsize=(16, 4)) # Adjust width/height as needed\n", - "ax.axis('off') # Hide axes\n", - "\n", - "# Create table from DataFrame\n", - "table = ax.table(cellText=summary_table.round(3).values,\n", - " colLabels=summary_table.columns,\n", - " loc='center',\n", - " cellLoc='center')\n", - "\n", - "table.auto_set_font_size(False)\n", - "table.set_fontsize(10)\n", - "table.scale(1.2, 1.5) # Adjust cell size\n", - "\n", - "# Save as image\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d731d0c5", - "metadata": {}, - "source": [ - "### Interpreting the Classification Report\n", - "\n", - "The **Classification Report** provides key metrics to evaluate how well the model performed on each class.\n", - "\n", - "It includes the following metrics for each class (0 and 1):\n", - "* Metric: Meaning\n", - "* Precision: Out of all predicted positives, how many were actually positive?\n", - "* Recall: Out of all actual positives, how many did we correctly identify?\n", - "* F1-score: Harmonic mean of precision and recall (balances both)\n", - "* Support: Number of true samples of that class in the test data\n", - "\n", - "Interpretation:\n", - "* Class 0 = No incident\n", - "* Class 1 = Has resolution incident (rare, but important!)\n", - "\n", - "A few explanatory cases:\n", - "* A high recall for class 1 means we're catching most incidents.\n", - "* A high precision for class 1 means when we predict an incident, we're often correct.\n", - "* The F1-score gives a single balanced measure (good for imbalanced data).\n", - "\n", - "Special note for imbalanced data:\n", - "Since class 1 (or just True) is rare (1% in our case), metrics for that class are more critical.\n", - "We want to maximize recall to catch as many real incidents as possible ā€” without letting precision drop too low (to avoid too many false alarms)." - ] - }, - { - "cell_type": "markdown", - "id": "c366cfe7", - "metadata": {}, - "source": [ - "### Results Summary\n", - "\n", - "- Model 1 (Kbest) best in F1 Score (0.227), but has a moderate recall.\n", - "- Model 2 (RFE) provides the highest recall (0.875) and the best F2 score (0.345), meaning it's most effective at capturing positives while tolerating more false positives.\n", - "- Model 3 (Lasso) offers the highest precision (0.9) and the lowest FPR, though it misses most real incidents (low recall)." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "4b4da914", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ROC Curve\n", - "fpr, tpr, _ = roc_curve(y_test_rfe, y_pred_proba_rfe)\n", - "roc_auc = auc(fpr, tpr)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')\n", - "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.title('ROC Curve')\n", - "plt.legend(loc='lower right')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e403edb1", - "metadata": {}, - "source": [ - "### Interpreting the ROC Curve\n", - "\n", - "The **Receiver Operating Characteristic (ROC) curve** shows how well the model distinguishes between the positive and negative classes across all decision thresholds.\n", - "\n", - "A quick reminder of the definitions:\n", - "* True Positive Rate (TPR) = Recall\n", - "* False Positive Rate (FPR) = Proportion of negatives wrongly classified as positives\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is False Positive Rate\n", - "* The y-axis is True Positive Rate\n", - "\n", - "The curve shows how TPR and FPR change as the threshold varies\n", - "\n", - "It's important to note that:\n", - "* A model with no skill will produce a diagonal line (AUC = 0.5)\n", - "* A model with perfect discrimination will hug the top-left corner (AUC = 1.0)\n", - "\n", - "The Area Under the Curve (ROC AUC) gives a single performance score:\n", - "* Closer to 1 means better at ranking positive cases higher than negative ones\n", - "\n", - "**Important!**\n", - "\n", - "While useful, the ROC curve can sometimes overestimate performance when the dataset is imbalanced, because it includes negatives (which dominate in our case, around 99%!). Thatā€™s why we also MUST check the Precision-Recall curve." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "6790d41d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# PR Curve\n", - "precision, recall, _ = precision_recall_curve(y_test_rfe, y_pred_proba_rfe)\n", - "pr_auc = average_precision_score(y_test_rfe, y_pred_proba_rfe)\n", - "\n", - "plt.figure(figsize=(6, 5))\n", - "plt.plot(recall, precision, color='green', lw=2, label=f'PR curve (AUC = {pr_auc:.4f})')\n", - "plt.xlabel('Recall')\n", - "plt.ylabel('Precision')\n", - "plt.title('Precision-Recall Curve')\n", - "plt.legend(loc='lower left')\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c111a266", - "metadata": {}, - "source": [ - "### Interpreting the Precision-Recall (PR) Curve\n", - "\n", - "The **Precision-Recall (PR) curve** helps evaluate model performance, especially on imbalanced datasets like ours (where positive cases are rare).\n", - "\n", - "A quick reminder of the definitions:\n", - "* Precision = How many of the predicted positives are actually positive\n", - "* Recall = How many of the actual positives the model correctly identifies\n", - "\n", - "What we display in this plot is:\n", - "* The x-axis is Recall \n", - "* The y-axis is Precision \n", - "\n", - "The curve shows the trade-off between them at different model thresholds\n", - "\n", - "In imbalanced datasets, accuracy can be misleading ā€” the PR curve focuses only on the positive class, making it much more meaningful:\n", - "* A higher curve means better performance\n", - "* The area under the curve (PR AUC) summarizes this: closer to 1 is better" - ] - }, - { - "cell_type": "markdown", - "id": "1c83ddcd", - "metadata": {}, - "source": [ - "## Feature Importance\n", - "Understanding what drives the prediction is useful for future experiments and business knowledge. Here we track both the native feature importances of the trees, as well as a more heavy SHAP values analysis.\n", - "\n", - "Important! Be aware that SHAP analysis might take quite a bit of time." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "d66ffe2c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## BUILT-IN\n", - "\n", - "# Get feature importances from the model\n", - "importances = best_pipeline_rfe.named_steps['model'].feature_importances_\n", - "\n", - "# Create a Series and sort\n", - "feat_series = pd.Series(importances, index=selected_features_rfe).sort_values(ascending=True) # ascending=True for horizontal plot\n", - "\n", - "# Plot Feature Importances\n", - "plt.figure(figsize=(8, 5))\n", - "feat_series.plot(kind='barh', color='skyblue')\n", - "plt.title('Feature Importances')\n", - "plt.xlabel('Importance')\n", - "plt.grid(axis='x')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3897f25c", - "metadata": {}, - "source": [ - "### Interpreting the Feature Importance Plot\n", - "The **feature importance plot** shows how much each feature contributes to the modelā€™s overall decision-making.\n", - "\n", - "For tree-based models like Random Forest, importance is based on how often and how effectively a feature is used to split the data across all trees.\n", - "A higher score means the feature plays a bigger role in improving prediction accuracy.\n", - "\n", - "In the graph you will see that:\n", - "* Features are ranked from most to least important.\n", - "* The values are relative and model-specific ā€” not directly interpretable as weights or probabilities.\n", - "\n", - "This helps us identify which features the model relies on most when making predictions.\n", - "\n", - "**Important!**\n", - "Unlike SHAP values, native importance doesn't show how a feature affects predictions ā€” only how useful it is to the model overall. For deeper interpretability (e.g., direction and context), SHAP is better (but it takes more time to run)." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "e2197cea", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "PermutationExplainer explainer: 6394it [13:25, 7.93it/s] \n", - "/tmp/ipykernel_29610/4064815753.py:21: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n", - " shap.summary_plot(shap_values.values, X_test_shap)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## SHAP VALUES\n", - "\n", - "# SHAP requires that all features passed to Explainer be numeric (floats/ints)\n", - "X_test_shap = X_test_rfe.copy()\n", - "X_test_shap = X_test_shap.astype(float)\n", - "\n", - "# Function that returns the probability of the positive class\n", - "def model_predict(data):\n", - " return best_pipeline_rfe.predict_proba(data)[:, 1]\n", - "\n", - "# Ensure input to SHAP is numeric\n", - "X_test_shap = X_test_rfe.astype(float)\n", - "\n", - "# Create SHAP explainer\n", - "explainer = shap.Explainer(model_predict, X_test_shap)\n", - "\n", - "# Compute SHAP values\n", - "shap_values = explainer(X_test_shap)\n", - "\n", - "# Plot summary\n", - "shap.summary_plot(shap_values.values, X_test_shap)" - ] - }, - { - "cell_type": "markdown", - "id": "e9ae2701", - "metadata": {}, - "source": [ - "### Interpreting the SHAP Summary Plot\n", - "\n", - "Each point on a row represents a SHAP value for a single prediction (row = feature).\n", - "The x-axis shows how much the feature contributed to increasing or decreasing the prediction.\n", - "* Right (positive SHAP value): pushes prediction toward the positive class (i.e., higher chance of incident).\n", - "* Left (negative SHAP value): pushes prediction toward the negative class (i.e., lower chance of incident).\n", - "\n", - "Color shows the actual feature value for that point:\n", - "* Red = high value\n", - "* Blue = low value\n", - "\n", - "In other words:\n", - "* The position tells you impact.\n", - "* The color tells you feature value.\n", - "* The density (thickness) of dots shows how often a value occurs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "345467a8", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/data_driven_risk_assessment/experiments/pablo_eda.ipynb b/data_driven_risk_assessment/experiments/pablo_eda.ipynb deleted file mode 100644 index 5d83496..0000000 --- a/data_driven_risk_assessment/experiments/pablo_eda.ipynb +++ /dev/null @@ -1,5600 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "84dcd475", - "metadata": {}, - "source": [ - "# DDRA - Pablo's EDA\n", - "\n", - "General fuck around to understand what might drive claims.\n", - "\n", - "A handful of ideas I want to give a first shot to:\n", - "- Time features\n", - " - Length of stay\n", - " - Check In and Check out dates\n", - " - Lead time between creation and check out\n", - " - checkin as week/month/year cycle, checkout as week/month/year cycle\n", - " - Duration between starting GJ and booking checkin\n", - "- Same country, same town features\n", - "- Tokenize listing names and correlate them\n", - " - And specifically get bedrooms with regex\n", - "- Number of active listings of host\n", - "- Number of bookings created by host in last 12 months (and monthly/per listing average?)\n", - "- Number of bookings cancelled on the host in last 12 months (and monthly/per listing average?)\n", - "- Number of claims created by host in last 12 months (and monthly/per listing average?)\n", - "- Number of claims with positive settlemend by host in last 12 months (and monthly/per listing average?)\n", - "- Total invoiced to host in last 12 months (and monthly/per listing average?)\n", - "- Guest age\n", - "- Paid for waiver\n", - "- Paid for CIH\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "12368ce1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "šŸ”Œ Testing connection using credentials at: /home/pablo/.superhog-dwh/credentials.yml\n", - "āœ… Connection successful.\n" - ] - } - ], - "source": [ - "import sys\n", - "import os\n", - "sys.path.append(os.path.abspath(\"../../utils\")) # Adjust path if needed\n", - "\n", - "from dwh_utils import read_credentials, create_postgres_engine, query_to_dataframe, test_connection\n", - "\n", - "# --- Connect to DWH ---\n", - "creds = read_credentials()\n", - "dwh_pg_engine = create_postgres_engine(creds)\n", - "\n", - "# --- Test Query ---\n", - "test_connection()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "385c350b", - "metadata": {}, - "outputs": [], - "source": [ - "# Other imports\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "from sklearn.decomposition import TruncatedSVD\n", - "import string" - ] - }, - { - "cell_type": "markdown", - "id": "78cbf43d", - "metadata": {}, - "source": [ - "# Getting data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba87ff74", - "metadata": {}, - "outputs": [], - "source": [ - "# Get all bookings and their claims\n", - "df_bookings_and_claims = query_to_dataframe(\n", - " engine=dwh_pg_engine, \n", - " query=\"\"\"\n", - " select \n", - " -- Unique ID --\n", - " ibs.id_booking,\n", - " -- Target (Boolean) --\n", - " ibs.has_resolution_incident,\n", - " -- Various features -- \n", - " ibs.booking_created_date_utc, \n", - " ibs.booking_check_in_date_utc,\n", - " ibs.booking_check_out_date_utc,\n", - " vr.link_used_date_utc as guest_journey_started_date_utc,\n", - "\n", - " -- Other --\n", - " ibs.id_accommodation\n", - "\n", - "from intermediate.int_booking_summary ibs\n", - "left join intermediate.int_core__verification_requests vr\n", - " on ibs.id_verification_request = vr.id_verification_request\n", - "where \n", - " -- 1. Bookings from New Dash users with Id Deal\n", - " ibs.is_user_in_new_dash = True and \n", - " ibs.is_missing_id_deal = False and\n", - " -- 2. Protected Bookings with a Protection or a Deposit Management service\n", - " (ibs.has_protection_service_business_type or \n", - " ibs.has_deposit_management_service_business_type) and\n", - " -- 3. Bookings with flagging categorisation (this excludes Cancelled/Incomplete/Rejected bookings)\n", - " ibs.is_booking_flagged_as_risk is not null and \n", - " -- 4. Booking is completed\n", - " ibs.is_booking_past_completion_date = True\n", - " \"\"\"\n", - ")\n", - "\n", - "# Get listing details\n", - "\n", - "df_listing_details = query_to_dataframe(\n", - " engine=dwh_pg_engine,\n", - " query=\"\"\"\n", - " select\n", - " a.id_accommodation,\n", - " a.friendly_name\n", - " from intermediate.int_core__accommodation a\n", - " where a.id_accommodation in (\n", - " select distinct id_accommodation\n", - " from intermediate.int_booking_summary ibs\n", - " where \n", - " -- 1. Bookings from New Dash users with Id Deal\n", - " ibs.is_user_in_new_dash = True and \n", - " ibs.is_missing_id_deal = False and\n", - " -- 2. Protected Bookings with a Protection or a Deposit Management service\n", - " (ibs.has_protection_service_business_type or \n", - " ibs.has_deposit_management_service_business_type) and\n", - " -- 3. Bookings with flagging categorisation (this excludes Cancelled/Incomplete/Rejected bookings)\n", - " ibs.is_booking_flagged_as_risk is not null and \n", - " -- 4. Booking is completed\n", - " ibs.is_booking_past_completion_date = True)\n", - " \"\"\"\n", - ")\n", - "\n", - "# Get last 12 months host KPIs \n", - "\n", - "# Get guest data\n", - "\n", - "# Get host data\n", - "\n", - "# Get guest journey sales\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9848916e", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "id_booking", - "rawType": "int64", - "type": "integer" - }, - { - "name": "has_resolution_incident", - "rawType": "bool", - "type": "boolean" - }, - { - "name": "booking_created_date_utc", - "rawType": "object", - "type": "unknown" - }, - { - "name": "booking_check_in_date_utc", - "rawType": "object", - "type": "unknown" - }, - { - "name": "booking_check_out_date_utc", - "rawType": "object", - "type": "unknown" - }, - { - "name": "guest_journey_started_date_utc", - 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111059HIL-1
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32774694000 sqft Lakefront Retreat | Private Hot Tub #NH
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.........
3627197269Luxury Glamping | Hot Tub, Firepit & Grill
3628198130Brick Haven House: 10min Walk to Shakespeare F...
3629205403NO FEES! Pool+Hot Tub/Volley&Bocce Ball+Firepit
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Pool+Hot Tub/Volley&Bocce Ball+Firepit\n", - "3630 263762 Brasada Ranch | Hot Tub | Guest Casita | 5 Bed\n", - "3631 267589 10% Off July 6-10 ā€¢ Creek ā€¢ 3 Dogs ā€¢ Fenced Yard\n", - "\n", - "[3632 rows x 2 columns]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_listing_details" - ] - }, - { - "cell_type": "markdown", - "id": "5acb3488", - "metadata": {}, - "source": [ - "# Processing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fe386f8", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "# Ensure date columns are datetime objects\n", - "df = df_bookings_and_claims.copy()\n", - "df['booking_created_date_utc'] = pd.to_datetime(df['booking_created_date_utc'])\n", - "df['booking_check_in_date_utc'] = pd.to_datetime(df['booking_check_in_date_utc'])\n", - "df['booking_check_out_date_utc'] = pd.to_datetime(df['booking_check_out_date_utc'])\n", - "df['guest_journey_started_date_utc'] = pd.to_datetime(df['guest_journey_started_date_utc'])\n", - "\n", - "# 1. Length of stay (in days)\n", - "df['length_of_stay_days'] = (df['booking_check_out_date_utc'] - df['booking_check_in_date_utc']).dt.days\n", - "\n", - "# 2. Lead time between creation and check-in (in days)\n", - "df['lead_time_to_checkin_days'] = (df['booking_check_in_date_utc'] - df['booking_created_date_utc']).dt.days\n", - "\n", - "# 3. Cyclical transformations\n", - "def add_cyclical_features(df, col, prefix, period):\n", - " df[f'{prefix}_cycle_sin'] = np.sin(2 * np.pi * df[col] / period)\n", - " df[f'{prefix}_cos'] = np.cos(2 * np.pi * df[col] / period)\n", - " return df\n", - "\n", - "# Check-in and check-out day-of-year\n", - "df['checkin_doy'] = df['booking_check_in_date_utc'].dt.dayofyear\n", - "df['checkout_doy'] = df['booking_check_out_date_utc'].dt.dayofyear\n", - "\n", - "# Apply cyclical encoding for week, month, and year cycles\n", - "df = add_cyclical_features(df, 'checkin_doy', 'checkin_week_cycle', 7)\n", - "df = add_cyclical_features(df, 'checkin_doy', 'checkin_month_cycle', 30)\n", - "df = add_cyclical_features(df, 'checkin_doy', 'checkin_year_cycle', 365)\n", - "\n", - "df = add_cyclical_features(df, 'checkout_doy', 'checkout_week_cycle', 7)\n", - "df = add_cyclical_features(df, 'checkout_doy', 'checkout_month_cycle', 30)\n", - "df = add_cyclical_features(df, 'checkout_doy', 'checkout_year_cycle', 365)\n", - "\n", - "# 4. Time in days between GJ start and check-in\n", - "df['gj_start_to_checkin_days'] = (df['booking_check_in_date_utc'] - df['guest_journey_started_date_utc']).dt.days\n", - "\n", - "# Clean up temporary columns if needed\n", - "df.drop(['checkin_doy', 'checkout_doy'], axis=1, inplace=True)\n", - "\n", - "# Final transformed DataFrame\n", - "df_bookings_and_claims = df\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "95814ea4", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "id_booking", - "rawType": "int64", - "type": "integer" - }, - { - "name": "has_resolution_incident", - "rawType": "bool", - "type": "boolean" - }, - { - "name": "booking_created_date_utc", - "rawType": "object", - "type": "unknown" - }, - { - "name": "booking_check_in_date_utc", - "rawType": "object", - "type": "unknown" - }, - { - "name": "booking_check_out_date_utc", - 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20280 rows Ɨ 7 columns

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" - ], - "text/plain": [ - " id_booking has_resolution_incident booking_created_date_utc \\\n", - "0 975057 False 2024-12-04 \n", - "1 975059 False 2024-12-04 \n", - "2 975060 False 2024-12-04 \n", - "3 975061 False 2024-12-04 \n", - "4 975062 False 2024-12-04 \n", - "... ... ... ... \n", - "20275 931096 False 2024-10-31 \n", - "20276 931086 False 2024-10-31 \n", - "20277 931082 False 2024-10-31 \n", - "20278 926634 False 2024-10-27 \n", - "20279 919656 False 2024-10-21 \n", - "\n", - " booking_check_in_date_utc booking_check_out_date_utc \\\n", - "0 2024-12-05 2025-03-31 \n", - "1 2024-12-06 2024-12-08 \n", - "2 2025-01-26 2025-01-29 \n", - "3 2024-12-15 2025-03-15 \n", - "4 2024-12-11 2025-03-10 \n", - "... ... ... \n", - "20275 2024-11-08 2024-11-13 \n", - "20276 2024-11-15 2024-11-18 \n", - "20277 2024-12-20 2024-12-27 \n", - "20278 2025-02-13 2025-02-16 \n", - "20279 2025-01-16 2025-01-20 \n", - "\n", - " guest_journey_started_date_utc id_accommodation \n", - "0 None 196871 \n", - "1 2024-12-07 196875 \n", - "2 2024-12-07 196876 \n", - "3 None 196872 \n", - "4 2024-12-10 196875 \n", - "... ... ... \n", - "20275 None 187560 \n", - "20276 None 187585 \n", - "20277 None 187585 \n", - "20278 None 185004 \n", - "20279 None 185004 \n", - "\n", - "[20280 rows x 7 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_bookings_and_claims " - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "2c9927b0", - "metadata": {}, - "outputs": [], - "source": [ - "# Tokenizing friendly name\n", - "\n", - "\n", - "# Minimal preprocessing: lowercase, remove punctuation\n", - "def preprocess(text):\n", - " text = str(text).lower()\n", - " text = text.translate(str.maketrans('', '', string.punctuation))\n", - " return text\n", - "\n", - "df_listing_details['clean_name'] = df_listing_details['friendly_name'].apply(preprocess)\n", - "\n", - "# Basic length features\n", - "df_listing_details['char_count'] = df_listing_details['clean_name'].apply(len)\n", - "df_listing_details['word_count'] = df_listing_details['clean_name'].apply(lambda x: len(x.split()))\n", - "df_listing_details['unique_word_count'] = df_listing_details['clean_name'].apply(lambda x: len(set(x.split())))\n", - "\n", - "# Vectorize with TF-IDF (unigrams + bigrams)\n", - "vectorizer = TfidfVectorizer(ngram_range=(1,2), max_features=1000)\n", - "X_tfidf = vectorizer.fit_transform(df_listing_details['clean_name'])\n", - "\n", - "# Dimensionality reduction to get dense features\n", - "svd = TruncatedSVD(n_components=30, random_state=42)\n", - "X_reduced = svd.fit_transform(X_tfidf)\n", - "\n", - "# Append the SVD components as features\n", - "for i in range(X_reduced.shape[1]):\n", - " df_listing_details[f'tfidf_svd_{i}'] = X_reduced[:, i]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "0e845d47", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - 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"type": "float" - }, - { - "name": "tfidf_svd_7", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_8", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_9", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_10", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_11", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_12", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_13", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_14", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_15", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_16", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_17", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_18", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_19", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_20", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_21", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_22", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_23", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_24", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_25", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_26", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_27", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_28", - "rawType": "float64", - "type": "float" - }, - { - "name": "tfidf_svd_29", - "rawType": "float64", - "type": "float" - } - ], - "ref": "85040f21-ec13-4afa-b027-48d2b85f98ba", - "rows": [ - [ - "0", - "10368", - "Maddox St", - "maddox st", - "9", - "2", - "2", - "0.010854894473435336", - "-0.029763070635827105", - "-0.02105066582524067", - "0.009316318557873451", - 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010368Maddox Stmaddox st9220.010855-0.029763-0.0210510.009316...0.004607-0.0106690.0225640.009577-0.0049640.0023770.0081820.0202710.0113620.014186
111059HIL-1hil14110.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
214345SUS-2sus24110.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
32774694000 sqft Lakefront Retreat | Private Hot Tub #NH4000 sqft lakefront retreat private hot tub nh47880.118079-0.331729-0.036329-0.111210...-0.036141-0.0524050.0932270.019269-0.0067740.0700270.102554-0.0073780.0412810.015410
428561LAN-3lan34110.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
..................................................................
3627197269Luxury Glamping | Hot Tub, Firepit & Grillluxury glamping hot tub firepit grill39660.086114-0.296208-0.030307-0.076856...0.1007070.100131-0.0259850.0492670.0095510.0436390.076095-0.0094650.0574980.044279
3628198130Brick Haven House: 10min Walk to Shakespeare F...brick haven house 10min walk to shakespeare fest48880.152979-0.1829200.2480660.178298...0.0188350.034150-0.0333400.000108-0.0010940.0281100.066771-0.0083120.0078320.015454
3629205403NO FEES! Pool+Hot Tub/Volley&Bocce Ball+Firepitno fees poolhot tubvolleybocce ballfirepit42550.005290-0.0075610.000197-0.004329...0.004708-0.0055170.004417-0.006661-0.006363-0.003132-0.0058540.012305-0.005357-0.006805
3630263762Brasada Ranch | Hot Tub | Guest Casita | 5 Bedbrasada ranch hot tub guest casita 5 bed43880.076370-0.220398-0.006391-0.042772...0.0332480.0353030.0259650.0630880.0148740.003055-0.013777-0.012340-0.0182230.016823
363126758910% Off July 6-10 ā€¢ Creek ā€¢ 3 Dogs ā€¢ Fenced Yard10 off july 610 ā€¢ creek ā€¢ 3 dogs ā€¢ fenced yard4612100.015270-0.0465310.000051-0.017550...-0.074968-0.004176-0.051906-0.114376-0.010725-0.0713840.044820-0.035996-0.0385470.000560
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3632 rows Ɨ 36 columns

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" - ], - "text/plain": [ - " id_accommodation friendly_name \\\n", - "0 10368 Maddox St \n", - "1 11059 HIL-1 \n", - "2 14345 SUS-2 \n", - "3 277469 4000 sqft Lakefront Retreat | Private Hot Tub #NH \n", - "4 28561 LAN-3 \n", - "... ... ... \n", - "3627 197269 Luxury Glamping | Hot Tub, Firepit & Grill \n", - "3628 198130 Brick Haven House: 10min Walk to Shakespeare F... \n", - "3629 205403 NO FEES! Pool+Hot Tub/Volley&Bocce Ball+Firepit \n", - "3630 263762 Brasada Ranch | Hot Tub | Guest Casita | 5 Bed \n", - "3631 267589 10% Off July 6-10 ā€¢ Creek ā€¢ 3 Dogs ā€¢ Fenced Yard \n", - "\n", - " clean_name char_count \\\n", - "0 maddox st 9 \n", - "1 hil1 4 \n", - "2 sus2 4 \n", - "3 4000 sqft lakefront retreat private hot tub nh 47 \n", - "4 lan3 4 \n", - "... ... ... \n", - "3627 luxury glamping hot tub firepit grill 39 \n", - "3628 brick haven house 10min walk to shakespeare fest 48 \n", - "3629 no fees poolhot tubvolleybocce ballfirepit 42 \n", - "3630 brasada ranch hot tub guest casita 5 bed 43 \n", - "3631 10 off july 610 ā€¢ creek ā€¢ 3 dogs ā€¢ fenced yard 46 \n", - "\n", - " word_count unique_word_count tfidf_svd_0 tfidf_svd_1 tfidf_svd_2 \\\n", - "0 2 2 0.010855 -0.029763 -0.021051 \n", - "1 1 1 0.000000 0.000000 0.000000 \n", - "2 1 1 0.000000 0.000000 0.000000 \n", - "3 8 8 0.118079 -0.331729 -0.036329 \n", - "4 1 1 0.000000 0.000000 0.000000 \n", - "... ... ... ... ... ... \n", - "3627 6 6 0.086114 -0.296208 -0.030307 \n", - "3628 8 8 0.152979 -0.182920 0.248066 \n", - "3629 5 5 0.005290 -0.007561 0.000197 \n", - "3630 8 8 0.076370 -0.220398 -0.006391 \n", - "3631 12 10 0.015270 -0.046531 0.000051 \n", - "\n", - " tfidf_svd_3 ... tfidf_svd_20 tfidf_svd_21 tfidf_svd_22 \\\n", - "0 0.009316 ... 0.004607 -0.010669 0.022564 \n", - "1 0.000000 ... 0.000000 0.000000 0.000000 \n", - "2 0.000000 ... 0.000000 0.000000 0.000000 \n", - "3 -0.111210 ... -0.036141 -0.052405 0.093227 \n", - "4 0.000000 ... 0.000000 0.000000 0.000000 \n", - "... ... ... ... ... ... \n", - "3627 -0.076856 ... 0.100707 0.100131 -0.025985 \n", - "3628 0.178298 ... 0.018835 0.034150 -0.033340 \n", - "3629 -0.004329 ... 0.004708 -0.005517 0.004417 \n", - "3630 -0.042772 ... 0.033248 0.035303 0.025965 \n", - "3631 -0.017550 ... -0.074968 -0.004176 -0.051906 \n", - "\n", - " tfidf_svd_23 tfidf_svd_24 tfidf_svd_25 tfidf_svd_26 tfidf_svd_27 \\\n", - "0 0.009577 -0.004964 0.002377 0.008182 0.020271 \n", - "1 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "2 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "3 0.019269 -0.006774 0.070027 0.102554 -0.007378 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "... ... ... ... ... ... \n", - "3627 0.049267 0.009551 0.043639 0.076095 -0.009465 \n", - "3628 0.000108 -0.001094 0.028110 0.066771 -0.008312 \n", - "3629 -0.006661 -0.006363 -0.003132 -0.005854 0.012305 \n", - "3630 0.063088 0.014874 0.003055 -0.013777 -0.012340 \n", - "3631 -0.114376 -0.010725 -0.071384 0.044820 -0.035996 \n", - "\n", - " tfidf_svd_28 tfidf_svd_29 \n", - "0 0.011362 0.014186 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.041281 0.015410 \n", - "4 0.000000 0.000000 \n", - "... ... ... \n", - "3627 0.057498 0.044279 \n", - "3628 0.007832 0.015454 \n", - "3629 -0.005357 -0.006805 \n", - "3630 -0.018223 0.016823 \n", - "3631 -0.038547 0.000560 \n", - "\n", - "[3632 rows x 36 columns]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_listing_details" - ] - }, - { - "cell_type": "markdown", - "id": "18bbd6f2", - "metadata": {}, - "source": [ - "# Relating vars" - ] - }, - { - "cell_type": "markdown", - "id": "2d4d57c0", - "metadata": {}, - "source": [ - "## Time related vars" - ] - }, - { - "cell_type": "markdown", - "id": "5a701a5f", - "metadata": {}, - "source": [ - "### Boruta" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "63a15eb4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: \t1 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t15\n", - "Rejected: \t0\n", - "Iteration: \t2 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t15\n", - "Rejected: \t0\n", - "Iteration: \t3 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t15\n", - "Rejected: \t0\n", - "Iteration: \t4 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t15\n", - "Rejected: \t0\n", - "Iteration: \t5 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t15\n", - "Rejected: \t0\n", - "Iteration: \t6 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t15\n", - "Rejected: \t0\n", - "Iteration: \t7 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t15\n", - "Rejected: \t0\n", - "Iteration: \t8 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t2\n", - "Rejected: \t13\n", - "Iteration: \t9 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t2\n", - "Rejected: \t13\n", - "Iteration: \t10 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t2\n", - "Rejected: \t13\n", - "Iteration: \t11 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t2\n", - "Rejected: \t13\n", - "Iteration: \t12 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t2\n", - "Rejected: \t13\n", - "Iteration: \t13 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t2\n", - "Rejected: \t13\n", - "Iteration: \t14 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t2\n", - "Rejected: \t13\n", - "Iteration: \t15 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t2\n", - "Rejected: \t13\n", - "Iteration: \t16 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t17 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t18 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t19 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t20 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t21 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t22 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t23 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t24 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t25 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t26 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t27 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t28 / 100\n", - "Confirmed: \t1\n", - "Tentative: \t1\n", - "Rejected: \t13\n", - "Iteration: \t29 / 100\n", - "Confirmed: \t2\n", - "Tentative: \t0\n", - "Rejected: \t13\n", - "\n", - "\n", - "BorutaPy finished running.\n", - "\n", - "Iteration: \t30 / 100\n", - "Confirmed: \t2\n", - "Tentative: \t0\n", - "Rejected: \t13\n", - "\n", - "šŸ“Š Boruta Feature Selection Results:\n", - " feature rank status\n", - "12 length_of_stay_days 1 Selected āœ…\n", - "13 lead_time_to_checkin_days 1 Selected āœ…\n", - "5 checkin_year_cycle_cos 2 Rejected āŒ\n", - "14 gj_start_to_checkin_days 2 Rejected āŒ\n", - "4 checkin_year_cycle_sin 3 Rejected āŒ\n", - "11 checkout_year_cycle_cos 4 Rejected āŒ\n", - "3 checkin_month_cycle_cos 6 Rejected āŒ\n", - "10 checkout_year_cycle_sin 6 Rejected āŒ\n", - "8 checkout_month_cycle_sin 7 Rejected āŒ\n", - "2 checkin_month_cycle_sin 8 Rejected āŒ\n", - "9 checkout_month_cycle_cos 9 Rejected āŒ\n", - "1 checkin_week_cycle_cos 10 Rejected āŒ\n", - "6 checkout_week_cycle_sin 11 Rejected āŒ\n", - "0 checkin_week_cycle_sin 12 Rejected āŒ\n", - "7 checkout_week_cycle_cos 13 Rejected āŒ\n" - ] - }, - { - "data": { - "image/png": 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zXxlW5OTJk8LU1FS0bt1a+poylPKVYbdv31ZpW9LXsD148ECMGjVKVKlSRSgUCtG9e3eRnJwsAIj58+eXeX2IiCoTmRDcsYKIiIiIKr/4+Hh4eXnhp59+woABAyo6HCIijfCdbiIiIiKqdPLz84sdW7p0KXR0dFQ2wSMiquz4TjcRERERVToLFy7E2bNn0bZtW+jp6WH//v3Yv38/hg8frtFXqhERVRZcXk5ERERElc7hw4cxY8YMJCYmIi8vD/b29hg4cCCmTJkCPT0+NyKitweTbiIiIiIiIiIt4TvdRERERERERFrCpJuIiIiIiIhIS/hCDNErUCqV+PPPP2FqagqZTFbR4RARERERkZYIIXD//n3Y2tpCR0fz59dMuolewZ9//skdVImIiIiI3iE3btxArVq1NK7PpJvoFZiamgJ49h+emZlZBUdDRERERETakpubCzs7OykH0BSTbqJXULSk3MzMjEk3EREREdE74GVfK+VGakRERERERERawqSbiIiIiIiISEuYdBMRERERERFpCZNuIiIiIiIiIi1h0k1ERERERESkJUy6iYiIiIiIiLSESTcRERERERGRljDpJiIiIiIiItISJt1EREREREREWsKkm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLdGr6ACIiIiIiIjo7SebIavoELTrUfma8Uk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLXnnkm4fHx+MHTu2osNATEwMZDIZsrOztTbGX3/9hfbt28PExAQWFhZaG+dNCg8PR8OGDSs6DCIiIiIiIo28c0l3RaioRH/JkiXIzMxEfHw8UlJSXqkvR0dHLF269PUERkRERERE9I7Qq+gASHtSU1PRuHFjODs7V3QoRERERERE76R3+kl3QUEBJkyYgJo1a8LExATNmzdHTEyMdD4yMhIWFhY4ePAgXF1doVAo0LFjR2RmZkp1nj59ipCQEFhYWMDKygqhoaEIDAxE9+7dAQBBQUE4duwYli1bBplMBplMhvT0dKn92bNn0aRJExgbG6Nly5ZITk7WOP6VK1eiTp06MDAwQN26dbFu3TrpnKOjI7Zu3Yq1a9dCJpMhKCiozL6EEAgPD4e9vT3kcjlsbW0REhIC4NmT+oyMDHzxxRfSHADg7t276N+/P2rWrAljY2O4u7sjKipK6nPt2rWwsrJCQUGByljdu3fHwIEDNZrj/PnzYW1tDVNTUwwdOhSPHj1SOX/69Gm0b98eVatWhbm5Odq0aYNz585J54cMGYIuXbqotHny5AmqV6+OH374AQDw888/w93dHUZGRrCysoKfnx8ePHigUXxERERERERleaeT7tGjR+PEiRPYuHEjLly4gN69e6Njx464cuWKVOfhw4dYtGgR1q1bh19//RXXr1/HhAkTpPMLFizA+vXrERERgbi4OOTm5mLHjh3S+WXLlqFFixYYNmwYMjMzkZmZCTs7O+n8lClTsHjxYpw5cwZ6enoYMmSIRrFv374dn3/+OcaPH48//vgDI0aMwODBg3H06FEAz5LRjh07ok+fPsjMzMSyZcvK7G/r1q1YsmQJvv/+e1y5cgU7duyAu7s7AGDbtm2oVasWZs6cKc0BAB49eoTGjRtj7969+OOPPzB8+HAMHDgQp06dAgD07t0bhYWF2LVrlzROVlYW9u7dq9E8N2/ejPDwcMydOxdnzpyBjY0NVqxYoVLn/v37CAwMxPHjx/H777/D2dkZnTp1wv379wEAwcHBOHDggMofSvbs2YOHDx+ib9++yMzMRP/+/TFkyBAkJSUhJiYGH330EYQQauMjIiIiIiJSRybesezCx8cHDRs2xLhx41C7dm1cv34dtra20nk/Pz80a9YMc+fORWRkJAYPHoyrV6+iTp06AIAVK1Zg5syZ+OuvvwAANWrUwIQJE6REvLCwELVr14aXl5eUfBeN+fw70TExMWjbti2OHDkCX19fAMC+ffvQuXNn5Ofnw9DQsMx5eHt7w83NDatWrZKO9enTBw8ePMDevXsBPHuibGFhgcjISLXX5euvv8b333+PP/74A/r6+sXOOzo6YuzYsWrfTe/SpQvq1auHRYsWAQBGjhyJ9PR07Nu3Txrnu+++w9WrV6Un5qVp2bIlvLy88N1330nH3n//fTx69Ajx8fEltlEqlbCwsMCGDRukJ9xubm4IDAzEpEmTAAAffvghrKysEBERgXPnzqFx48ZIT0+Hg4NDmfEAz1ZHPP/kPjc3F3Z2dsjJyYGZmZna9kRERERE/1ayGWX/fv/WewRgPl76d/939kn3xYsXUVhYCBcXFygUCqkcO3YMqampUj1jY2Mp4QYAGxsbZGVlAXh2sf/++280a9ZMOq+rq4vGjRtrHIeHh4dK3wCk/suSlJQEb29vlWPe3t5ISkrSeOzn9e7dG/n5+ahduzaGDRuG7du34+nTp2W2KSwsxKxZs+Du7o4qVapAoVDg4MGDuH79ulRn2LBhOHToEG7dugXg2ZL9oKAgtQk38GyOzZs3VznWokULlc9///03hg0bBmdnZ5ibm8PMzAx5eXkqMQQHByMiIkKqv3//fulJu6enJ3x9feHu7o7evXtj9erVuHfvXqkxzZs3D+bm5lJ5ftUCERERERHRi97ZpDsvLw+6uro4e/Ys4uPjpZKUlKSyFPvFp74ymey1Lj1+vv+iRFSpVL62/jVlZ2eH5ORkrFixAkZGRhg5ciRat26NJ0+elNrmq6++wrJlyxAaGoqjR48iPj4e/v7+ePz4sVTHy8sLnp6eWLt2Lc6ePYtLly6pfb/8ZQQGBiI+Ph7Lli3Db7/9hvj4eFhZWanEMGjQIFy7dg0nTpzATz/9hPfeew+tWrUC8OyPJIcPH8b+/ftRv359fPPNN6hbty7S0tJKHG/y5MnIycmRyo0bN17bXIiIiIiI6N/nnU26vby8UFhYiKysLDg5OamUGjVqaNSHubk5rK2tcfr0aelYYWGhykZeAGBgYIDCwsLXGr+rqyvi4uJUjsXFxaF+/frl7tPIyAhdu3bF8uXLERMTgxMnTuDixYsASp5DXFwcunXrhk8++QSenp6oXbt2iV9NFhwcjMjISERERMDPz0/jp8Ourq44efKkyrHff/+9WAwhISHo1KkT3NzcIJfLcefOHZU6VlZW6N69OyIiIqRXBp4nk8ng7e2NGTNm4Pz58zAwMMD27dtLjEkul8PMzEylEBERERERlead/cowFxcXDBgwAIMGDcLixYvh5eWF27dvIzo6Gh4eHujcubNG/YwZMwbz5s2Dk5MT6tWrh2+++Qb37t1TWT7t6OiIkydPIj09HQqFAlWqVHnl+CdOnIg+ffrAy8sLfn5+2L17N7Zt24YjR46Uq7/IyEgUFhaiefPmMDY2xk8//QQjIyPpPWdHR0f8+uuv6NevH+RyOapWrQpnZ2f8/PPP+O2332BpaYmvv/4af//9d7HE/+OPP8aECROwevVqrF27VuOYPv/8cwQFBaFJkybw9vbG+vXrcenSJdSuXVuq4+zsjHXr1qFJkybIzc3FxIkTYWRkVKyv4OBgdOnSBYWFhQgMDJSOnzx5EtHR0ejQoQOqV6+OkydP4vbt23B1dX3ZS0hERERERFTMO/ukGwAiIiIwaNAgjB8/HnXr1kX37t1x+vRp2Nvba9xHaGgo+vfvj0GDBqFFixZQKBTw9/dX2QhtwoQJ0NXVRf369VGtWjWV943Lq3v37li2bBkWLVoENzc3fP/994iIiICPj0+5+rOwsMDq1avh7e0NDw8PHDlyBLt374aVlRUAYObMmUhPT0edOnVQrVo1AMDUqVPRqFEj+Pv7w8fHBzVq1JC+Ku155ubm6NmzJxQKRYnnS9O3b19MmzYNkyZNQuPGjZGRkYHPPvtMpc4PP/yAe/fuoVGjRhg4cCBCQkJQvXr1Yn35+fnBxsYG/v7+KhvnmZmZ4ddff0WnTp3g4uKCqVOnYvHixQgICNA4TiIiIiIiotK8c7uXa5tSqYSrqyv69OmDWbNmVXQ4lYavry/c3NywfPnyChk/Ly8PNWvWREREBD766KPX1m9ubi7Mzc25ezkRERERvfO4e3nJ3tnl5a9LRkYGDh06hDZt2qCgoADffvst0tLS8PHHH1d0aJXCvXv3EBMTg5iYmGLfsf0mKJVK3LlzB4sXL4aFhQU+/PDDNx4DERERERG9u97p5eWvg46ODiIjI9G0aVN4e3vj4sWLOHLkyCu/E+zm5qbyVWbPl/Xr1790f+vXry+1Pzc3t1eKtSxeXl4ICgrCggULULduXZVzr3uOJbl+/Tqsra2xYcMG/Pjjj9DT49+ZiIiIiIjozeHy8koqIyOj1K/rsra2hqmp6Uv1d//+ffz9998lntPX15c2THuTXvccKwKXlxMRERERPcPl5SXjY79K6nUnwaamppUuia2IRJ+IiIiIiOhN4vJyIiIiIiIiIi1h0k1ERERERESkJVxeTkRERERERK9MhP27twvLzc2F+Xzzl27HJ91EREREREREWsKkm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCXcvZyIiIiIiIhemWyGrKJD0K5H5WvGJ91EREREREREWsKkm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi05KWSbh8fH4wdO1ZLobyeMSIjI2FhYfHa4nlbODo6YunSpVodQyaTYceOHRUWQ0xMDGQyGbKzs7U2BhERERER0ev0Vj/pLinJ69u3L1JSUiomoP8vPDwcDRs2rNAYKsLp06cxfPjwig6DiIiIiIio0tCr6ABeNyMjIxgZGVV0GO+katWqVXQIRERERERElUq5n3QXFBRgwoQJqFmzJkxMTNC8eXPExMRI5+/evYv+/fujZs2aMDY2hru7O6KiolT6ePDgAQYNGgSFQgEbGxssXrxY4/F9fHyQkZGBL774AjKZDDKZDEDx5eVFT51//PFH2NvbQ6FQYOTIkSgsLMTChQtRo0YNVK9eHXPmzFHpPzs7G8HBwahWrRrMzMzQrl07JCQkqI0rMjISM2bMQEJCghRXZGQkAOD69evo1q0bFAoFzMzM0KdPH/z9998az3n37t1o2rQpDA0NUbVqVfTo0UPl/MOHDzFkyBCYmprC3t4eq1atUjl/48YN9OnTBxYWFqhSpQq6deuG9PR0lTo//vgj3NzcIJfLYWNjg9GjR5caT1hYGGxsbHDhwgUAxVceyGQyrFmzBj169ICxsTGcnZ2xa9cujee7b98+uLi4wMjICG3bti0Wq7qfsbVr18LKygoFBQUq7bp3746BAwcCABISEtC2bVuYmprCzMwMjRs3xpkzZzSOkYiIiIiIqCzlTrpHjx6NEydOYOPGjbhw4QJ69+6Njh074sqVKwCAR48eoXHjxti7dy/++OMPDB8+HAMHDsSpU6ekPiZOnIhjx45h586dOHToEGJiYnDu3DmNxt+2bRtq1aqFmTNnIjMzE5mZmaXWTU1Nxf79+3HgwAFERUXhhx9+QOfOnXHz5k0cO3YMCxYswNSpU3Hy5EmpTe/evZGVlYX9+/fj7NmzaNSoEXx9ffHPP/+UGVffvn0xfvx4uLm5SXH17dsXSqUS3bp1wz///INjx47h8OHDuHbtGvr27avRfPfu3YsePXqgU6dOOH/+PKKjo9GsWTOVOosXL0aTJk1w/vx5jBw5Ep999hmSk5MBAE+ePIG/vz9MTU0RGxuLuLg4KBQKdOzYEY8fPwYArFy5EqNGjcLw4cNx8eJF7Nq1C05OTsViEUJgzJgxWLt2LWJjY+Hh4VFq3DNmzECfPn1w4cIFdOrUCQMGDFB7DYFnfyD46KOP0LVrV8THxyM4OBhffvmlSh11P2O9e/dGYWGhSqKflZWFvXv3YsiQIQCAAQMGoFatWjh9+jTOnj2LL7/8Evr6+mrjIyIiIiIi0kS5lpdfv34dERERuH79OmxtbQEAEyZMwIEDBxAREYG5c+eiZs2amDBhgtRmzJgxOHjwIDZv3oxmzZohLy8PP/zwA3766Sf4+voCAP73v/+hVq1aGsVQpUoV6OrqwtTUFDVq1CizrlKpxI8//ghTU1PUr18fbdu2RXJyMvbt2wcdHR3UrVsXCxYswNGjR9G8eXMcP34cp06dQlZWFuRyOQBg0aJF2LFjB37++ecy31s2MjKCQqGAnp6eSlyHDx/GxYsXkZaWBjs7OwDPnsS6ubnh9OnTaNq0aZlzmDNnDvr164cZM2ZIxzw9PVXqdOrUCSNHjgQAhIaGYsmSJTh69Cjq1q2LTZs2QalUYs2aNdKqgIiICFhYWCAmJgYdOnTA7NmzMX78eHz++edSny/G9fTpU3zyySc4f/48jh8/jpo1a5YZd1BQEPr37w8AmDt3LpYvX45Tp06hY8eOZbZbuXIl6tSpI61+qFu3Li5evIgFCxZIddT9jBkZGeHjjz9GREQEevfuDQD46aefYG9vDx8fHwDPfpYnTpyIevXqAQCcnZ3LjKugoEDlyXlubm6Z9YmIiIiI6N1WrqT74sWLKCwshIuLi8rxgoICWFlZAQAKCwsxd+5cbN68Gbdu3cLjx49RUFAAY2NjAM+ePj9+/BjNmzeX2lepUgV169Yt71xK5ejoCFNTU+mztbU1dHV1oaOjo3IsKysLwLMlx3l5edJciuTn5yM1NbVcMSQlJcHOzk5KuAGgfv36sLCwQFJSktqkOz4+HsOGDSuzzvNPnGUyGWrUqKEyp6tXr6pcB+DZ0+LU1FRkZWXhzz//lP4AUpovvvgCcrkcv//+O6pWrVpm3RdjMjExgZmZmRRTWZKSklR+NgCgRYsWKp/V/YwBwLBhw9C0aVPcunULNWvWRGRkJIKCgqQ/PIwbNw7BwcFYt24d/Pz80Lt3b9SpU6fUuObNm6fyhw8iIiIiIqKylCvpzsvLg66uLs6ePQtdXV2VcwqFAgDw1VdfYdmyZVi6dCnc3d1hYmKCsWPHSkuZ36QXlwvLZLISjymVSgDP5mdjY6PyjnqRivo6Mk02h1M3p8aNG2P9+vXF2lWrVk3lDxBlad++PaKionDw4EEMGDDglWJ6VZr8jHl5ecHT0xNr165Fhw4dcOnSJezdu1c6Hx4ejo8//hh79+7F/v37ERYWho0bNxZ7X77I5MmTMW7cOOlzbm6uyh9SiIiIiIiInleupNvLywuFhYXIyspCq1atSqwTFxeHbt264ZNPPgHwbIl3SkoK6tevDwCoU6cO9PX1cfLkSdjb2wMA7t27h5SUFLRp00ajOAwMDFBYWFieKZSpUaNG+Ouvv6CnpwdHR8eXbl9SXK6urrhx4wZu3LghJWmJiYnIzs6WrklZPDw8EB0djcGDB790PMCzOW3atAnVq1eHmZlZiXUcHR0RHR2Ntm3bltrPhx9+iK5du+Ljjz+Grq4u+vXrV6541HF1dS226drvv/+u8lndz1iR4OBgLF26FLdu3YKfn1+xJNnFxQUuLi744osv0L9/f0RERJSadMvlcumVAyIiIiIiInXKtZGai4sLBgwYgEGDBmHbtm1IS0vDqVOnMG/ePOkporOzMw4fPozffvsNSUlJGDFihMpO3QqFAkOHDsXEiRPxyy+/4I8//kBQUJDGT1yBZ0nir7/+ilu3buHOnTvlmUqJ/Pz80KJFC3Tv3h2HDh1Ceno6fvvtN0yZMkWjna0dHR2RlpaG+Ph43LlzBwUFBfDz84O7uzsGDBiAc+fO4dSpUxg0aBDatGmDJk2aqO0zLCwMUVFRCAsLQ1JSUrH3m9UZMGAAqlatim7duiE2NhZpaWmIiYlBSEgIbt68CeDZU9/Fixdj+fLluHLlCs6dO4dvvvmmWF89evTAunXrMHjwYPz8888ax/AyPv30U1y5cgUTJ05EcnIyNmzYIO0CX0Tdz1iRjz/+GDdv3sTq1aulDdSAZ68LjB49GjExMcjIyEBcXBxOnz4NV1dXrcyJiIiIiIjePeXevTwiIgKDBg3C+PHjUbduXXTv3h2nT5+WnlpPnToVjRo1gr+/P3x8fFCjRg10795dpY+vvvoKrVq1QteuXeHn54cPPvgAjRs31jiGmTNnIj09HXXq1Hmt3xEtk8mwb98+tG7dGoMHD4aLiwv69euHjIwMWFtbq23fs2dPdOzYEW3btkW1atUQFRUFmUyGnTt3wtLSEq1bt4afnx9q166NTZs2aRSTj48PtmzZgl27dqFhw4Zo166dyk7w6hgbG+PXX3+Fvb09PvroI7i6umLo0KF49OiR9OQ7MDAQS5cuxYoVK+Dm5oYuXbpIu9G/qFevXvjf//6HgQMHYtu2bRrHoSl7e3ts3boVO3bsgKenJ/773/9i7ty5KnU0+RkDAHNzc/Ts2RMKhULlvK6uLu7evYtBgwbBxcUFffr0QUBAAN/ZJiIiIiKi10YmhBAVHQSRtvn6+sLNzQ3Lly9/rf3m5ubC3NwcOTk5pS7bJyIiIiJ6F8hmyCo6BO16BGA+Xvp3/3K90030trh37x5iYmIQExODFStWVHQ4RERERET0jin38nJti42NhUKhKLVUJDc3t1LjKml38IrqszL79NNPS53vp59++trG8fLyQlBQEBYsWKCVr6MjIiIiIiIqS6VdXp6fn49bt26Vet7JyekNRqMqIyMDT548KfGctbV1se/Crqg+K7OsrCzk5uaWeM7MzAzVq1d/wxGVD5eXExERERE9w+XlJau0y8uNjIwqNLEui4ODw1vRZ2VWvXr1tyaxJiIiIiIiKq9Ku7yciIiIiIiI6G3HpJuIiIiIiIhISyrt8nIiIiIiIiJ6e4iwSrld2GuTm5sL8/nmL92OT7qJiIiIiIiItIRJNxEREREREZGWMOkmIiIiIiIi0hIm3URERERERERawqSbiIiIiIiISEu4ezkREREREdGbIJNVdARUAfikm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZa8VUl3eno6ZDIZ4uPjtTZGTEwMZDIZsrOzKyyGd4GjoyOWLl1a0WEQERERERFpFb+n+yXZ2dkhMzMTVatWrehQiIiIiIiIqJJ7q550Vwa6urqoUaMG9PTe7r9XPHnypKJDICIiIiIi+terlEm3UqnEwoUL4eTkBLlcDnt7e8yZM0c6f+3aNbRt2xbGxsbw9PTEiRMnVNofP34crVq1gpGREezs7BASEoIHDx5I5wsKChAaGgo7OzvI5XI4OTnhhx9+KDGWhw8fIiAgAN7e3sjOzi62vLxoOXp0dDSaNGkCY2NjtGzZEsnJyWrnmZ6eDh0dHZw5c0bl+NKlS+Hg4AClUgkA+OOPPxAQEACFQgFra2sMHDgQd+7ckeofOHAAH3zwASwsLGBlZYUuXbogNTVVZRyZTIZNmzahTZs2MDQ0xPr169XGFxcXBx8fHxgbG8PS0hL+/v64d+8e1q5dCysrKxQUFKjU7969OwYOHCh93r17N5o2bQpDQ0NUrVoVPXr0KHWs7OxsBAcHo1q1ajAzM0O7du2QkJCgNkZNxrp37x4GDRoES0tLGBsbIyAgAFeuXJHOZ2RkoGvXrrC0tISJiQnc3Nywb98+jccmIiIiIiIqTaVMuidPnoz58+dj2rRpSExMxIYNG2BtbS2dnzJlCiZMmID4+Hi4uLigf//+ePr0KQAgNTUVHTt2RM+ePXHhwgVs2rQJx48fx+jRo6X2gwYNQlRUFJYvX46kpCR8//33UCgUxeLIzs5G+/btoVQqcfjwYVhYWJQa85QpU7B48WKcOXMGenp6GDJkiNp5Ojo6ws/PDxERESrHIyIiEBQUBB0dHWRnZ6Ndu3bw8vLCmTNncODAAfz999/o06ePVP/BgwcYN24czpw5g+joaOjo6KBHjx5S0l7kyy+/xOeff46kpCT4+/uXGVt8fDx8fX1Rv359nDhxAsePH0fXrl1RWFiI3r17o7CwELt27ZLqZ2VlYe/evdK89+7dix49eqBTp044f/48oqOj0axZs1LH6927N7KysrB//36cPXsWjRo1gq+vL/755x+111HdWEFBQThz5gx27dqFEydOQAiBTp06SU/7R40ahYKCAvz666+4ePEiFixYUOLPAxERERER0UsTlUxubq6Qy+Vi9erVxc6lpaUJAGLNmjXSsUuXLgkAIikpSQghxNChQ8Xw4cNV2sXGxgodHR2Rn58vkpOTBQBx+PDhEsc/evSo1J+Hh4fo2bOnKCgoKBbD+fPnVeofOXJEqrN3714BQOTn56ud76ZNm4SlpaV49OiREEKIs2fPCplMJtLS0oQQQsyaNUt06NBBpc2NGzcEAJGcnFxin7dv3xYAxMWLF1ViXrp0qdp4ivTv3194e3uXev6zzz4TAQEB0ufFixeL2rVrC6VSKYQQokWLFmLAgAGltndwcBBLliwRQjy7P2ZmZtI1KFKnTh3x/fffq421rLFSUlIEABEXFycdu3PnjjAyMhKbN28WQgjh7u4uwsPD1Y4jhBCPHj0SOTk5Uim6Fzk5ORq1JyIiIqJ3GMDyFpccoFy/+1e6J91JSUkoKCiAr69vqXU8PDykf9vY2AB49qQVABISEhAZGQmFQiEVf39/KJVKpKWlIT4+Hrq6umjTpk2ZcbRv3x5OTk7YtGkTDAwM1MZdVkxl6d69O3R1dbF9+3YAQGRkJNq2bQtHR0dpPkePHlWZT7169QBAWkJ+5coV9O/fH7Vr14aZmZnU9vr16ypjNWnSRG08RYqedJdm2LBhOHToEG7duiXFHRQUBJlMplH75yUkJCAvLw9WVlYq80xLS1NZJl+eWJOSkqCnp4fmzZtLx6ysrFC3bl0kJSUBAEJCQjB79mx4e3sjLCwMFy5cKHWsefPmwdzcXCp2dnYazZGIiIiIiN5NlW43MCMjI7V19PX1pX8XJXlFS6nz8vIwYsQIhISEFGtnb2+Pq1evahRH586dsXXrViQmJsLd3f2VYiqLgYEBBg0ahIiICHz00UfYsGEDli1bJp3Py8tD165dsWDBgmJti5L7rl27wsHBAatXr4atrS2USiUaNGiAx48fq9Q3MTFRG08RdffBy8sLnp6eWLt2LTp06IBLly5h7969Grd/Xl5eHmxsbBATE1PsXFlL+sszVkmCg4Ph7++PvXv34tChQ5g3bx4WL16MMWPGFKs7efJkjBs3Tvqcm5vLxJuIiIiIiEpV6Z50Ozs7w8jICNHR0eVq36hRIyQmJsLJyalYMTAwgLu7O5RKJY4dO1ZmP/Pnz0dgYCB8fX2RmJhYrlg0FRwcjCNHjmDFihV4+vQpPvroI5X5XLp0CY6OjsXmY2Jigrt37yI5ORlTp06Fr68vXF1dce/evVeOycPDQ+09CA4ORmRkJCIiIuDn56eSfGrSvkijRo3w119/QU9Pr9gcNflqtrLGcnV1xdOnT3Hy5EnpWNE1q1+/vnTMzs4On376KbZt24bx48dj9erVJfYnl8thZmamUoiIiIiIiEpT6ZJuQ0NDhIaGYtKkSVi7di1SU1Px+++/l7q7+ItCQ0Px22+/YfTo0YiPj8eVK1ewc+dOaSM1R0dHBAYGYsiQIdixYwfS0tIQExODzZs3F+tr0aJFGDBgANq1a4fLly+/1nk+z9XVFe+//z5CQ0PRv39/lSe3o0aNwj///IP+/fvj9OnTSE1NxcGDBzF48GAUFhbC0tISVlZWWLVqFa5evYpffvlF5UlseU2ePBmnT5/GyJEjceHCBVy+fBkrV65U2TX9448/xs2bN7F69epiG8eFhYUhKioKYWFhSEpKkjYoK4mfnx9atGiB7t2749ChQ0hPT8dvv/2GKVOmFNvZvSRljeXs7Ixu3bph2LBhOH78OBISEvDJJ5+gZs2a6NatGwBg7NixOHjwINLS0nDu3DkcPXoUrq6u5b10REREREREkkqXdAPAtGnTMH78eEyfPh2urq7o27evRu9HA8+eeh47dgwpKSlo1aoVvLy8MH36dNja2kp1Vq5ciV69emHkyJGoV68ehg0bpvKVYs9bsmQJ+vTpg3bt2iElJeW1zK8kQ4cOxePHj4slr7a2toiLi0NhYSE6dOgAd3d3jB07FhYWFtDR0YGOjg42btyIs2fPokGDBvjiiy/w1VdfvXI8Li4uOHToEBISEtCsWTO0aNECO3fuVPl+cnNzc/Ts2RMKhQLdu3dXae/j44MtW7Zg165daNiwIdq1a4dTp06VOJZMJsO+ffvQunVrDB48GC4uLujXrx8yMjJUdq0vjbqxIiIi0LhxY3Tp0gUtWrSAEAL79u2TXgkoLCzEqFGj4Orqio4dO8LFxQUrVqwox1UjIiIiIiJSJXu2iR5VtFmzZmHLli1lbuJVGfn6+sLNzQ3Lly+v6FAqRG5uLszNzZGTk8Ol5kRERERUtv+/9xO9nXIBmAMv/bt/pdtI7V2Tl5eH9PR0fPvtt5g9e3ZFh6Oxe/fuISYmBjExMXwqTEREREREVIpKubz838TNzU3la7CeL+vXr8fo0aPRuHFj+Pj4FFtark0BAQGlxjV37ly17b28vBAUFIQFCxagbt26Wo1V3TUkIiIiIiKqrLi8XMsyMjLw5MmTEs9ZW1vD1NT0DUf0zK1bt5Cfn1/iuSpVqqBKlSpvOKLSVdZrCHB5ORERERG9BC4vf6txeXkl5eDgUNEhlKhmzZoVHYLGKus1JCIiIiIiUofLy4mIiIiIiIi0hEk3ERERERERkZZweTkREREREdGbwO203m65uYC5+Us345NuIiIiIiIiIi1h0k1ERERERESkJUy6iYiIiIiIiLSESTcRERERERGRljDpJiIiIiIiItIS7l5ORERERET0JshkFR0BVQA+6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLWHSTURERERERKQllS7p9vHxwdixYys6jLfSm7h2jo6OWLp0aYXFkJ6eDplMhvj4eK2NQURERERE9LpUuqR727ZtmDVr1iv387qTP3XJ5osiIyNhYWHx2sZ/W7yu+0dERERERPRvoFfRAbyoSpUqFR2CisePH8PAwKCiw3hrVLb7R0REREREVJHe+JPu+/fvY8CAATAxMYGNjQ2WLFmi8lT6ZZ5Qr1ixAs7OzjA0NIS1tTV69eoFAAgKCsKxY8ewbNkyyGQyyGQypKeno7CwEEOHDsV7770HIyMj1K1bF8uWLVPpMygoCN27d8ecOXNga2uLunXrwsfHBxkZGfjiiy+k/soSExODwYMHIycnR6ofHh4OALh37x4GDRoES0tLGBsbIyAgAFeuXNH4+sXFxcHHxwfGxsawtLSEv78/7t27J51XKpWYNGkSqlSpgho1akjjFsnOzkZwcDCqVasGMzMztGvXDgkJCSp1du/ejaZNm8LQ0BBVq1ZFjx49So1nzZo1sLCwQHR0NIDi98/R0RFz587FkCFDYGpqCnt7e6xatUrj+Z46dQpeXl4wNDREkyZNcP78eZXz6u7pr7/+Cn19ffz1118q7caOHYtWrVoBADIyMtC1a1dYWlrCxMQEbm5u2Ldvn8YxEhERERERleaNJ93jxo1DXFwcdu3ahcOHDyM2Nhbnzp176X7OnDmDkJAQzJw5E8nJyThw4ABat24NAFi2bBlatGiBYcOGITMzE5mZmbCzs4NSqUStWrWwZcsWJCYmYvr06fjPf/6DzZs3q/QdHR2N5ORkHD58GHv27MG2bdtQq1YtzJw5U+qvLC1btsTSpUthZmYm1Z8wYQKAZ0n9mTNnsGvXLpw4cQJCCHTq1AlPnjxRO+f4+Hj4+vqifv36OHHiBI4fP46uXbuisLBQqvO///0PJiYmOHnyJBYuXIiZM2fi8OHD0vnevXsjKysL+/fvx9mzZ9GoUSP4+vrin3/+AQDs3bsXPXr0QKdOnXD+/HlER0ejWbNmJcazcOFCfPnllzh06BB8fX1LjXvx4sVSwjxy5Eh89tlnSE5OVjvfvLw8dOnSBfXr18fZs2cRHh4uXcci6u5p69atUbt2baxbt05q8+TJE6xfvx5DhgwBAIwaNQoFBQX49ddfcfHiRSxYsAAKhUJtfERERERERGqJNyg3N1fo6+uLLVu2SMeys7OFsbGx+Pzzz4UQQrRp00b6d1m2bt0qzMzMRG5ubonnNe1n1KhRomfPntLnwMBAYW1tLQoKClTqOTg4iCVLlqjtr0hERIQwNzdXOZaSkiIAiLi4OOnYnTt3hJGRkdi8ebPaPvv37y+8vb1LPd+mTRvxwQcfqBxr2rSpCA0NFUIIERsbK8zMzMSjR49U6tSpU0d8//33QgghWrRoIQYMGFDqGEXXYdKkScLGxkb88ccfxWJ4/ro7ODiITz75RPqsVCpF9erVxcqVK8uerBDi+++/F1ZWViI/P186tnLlSgFAnD9/vtR2L97TBQsWCFdXV+nz1q1bhUKhEHl5eUIIIdzd3UV4eLjaeIQQ4tGjRyInJ0cqN27cEABETk6ORu2JiIiI6B0GsLzFJQco1+/+b/RJ97Vr1/DkyROVJ6fm5uaoW7fuS/fVvn17ODg4oHbt2hg4cCDWr1+Phw8fqm333XffoXHjxqhWrRoUCgVWrVqF69evq9Rxd3fXynvcSUlJ0NPTQ/PmzaVjVlZWqFu3LpKSktS2L3rSXRYPDw+VzzY2NsjKygIAJCQkIC8vD1ZWVlAoFFJJS0tDamqqxmMsXrwYq1evxvHjx+Hm5qY27udjkslkqFGjhhRTWZKSkuDh4QFDQ0PpWIsWLYrVU3dPg4KCcPXqVfz+++8Anm1y16dPH5iYmAAAQkJCMHv2bHh7eyMsLAwXLlwoNaZ58+bB3NxcKnZ2dmrnQURERERE765Kt3u5pkxNTXHu3DlERUXBxsYG06dPh6enJ7Kzs0tts3HjRkyYMAFDhw7FoUOHEB8fj8GDB+Px48cq9YqSscrGyMhIbR19fX2VzzKZDEqlEsCz5do2NjaIj49XKcnJyZg4caLGY7Rq1QqFhYXFluWXJ6ZXpck9rV69Orp27YqIiAj8/fff2L9/v7S0HACCg4Nx7do1DBw4EBcvXkSTJk3wzTfflDje5MmTkZOTI5UbN268lnkQEREREdG/0xtNumvXrg19fX2cPn1aOpaTk4OUlJRy9aenpwc/Pz8sXLgQFy5cQHp6On755RcAgIGBgcq7zsCzTchatmyJkSNHwsvLC05OTtITXnVK6u9l67u6uuLp06c4efKkdOzu3btITk5G/fr11fbp4eEhbVhWHo0aNcJff/0FPT09ODk5qZSqVatqPEazZs2wf/9+zJ07F4sWLSp3POq4urriwoULePTokXSs6Gl1EU3vaXBwMDZt2oRVq1ahTp068Pb2VjlvZ2eHTz/9FNu2bcP48eOxevXqEmOSy+UwMzNTKURERERERKV5o0m3qakpAgMDMXHiRBw9ehSXLl3C0KFDoaOjo3ZH8Bft2bMHy5cvR3x8PDIyMrB27VoolUppqbqjoyNOnjyJ9PR03LlzB0qlEs7Ozjhz5gwOHjyIlJQUTJs2TeUPAGVxdHTEr7/+ilu3buHOnTsa1c/Ly0N0dDTu3LmDhw8fwtnZGd26dcOwYcNw/PhxJCQk4JNPPkHNmjXRrVs3tX1OnjwZp0+fxsiRI3HhwgVcvnwZK1eu1CgeAPDz80OLFi3QvXt3HDp0COnp6fjtt98wZcoUnDlzBgAQFhaGqKgohIWFISkpSdpY7EUtW7bEvn37MGPGjJf6/vKX8fHHH0Mmk2HYsGFITEzEvn37iiX5mt5Tf39/mJmZYfbs2Rg8eLDKubFjx+LgwYNIS0vDuXPncPToUbi6umplTkRERERE9G5548vLv/76a7Ro0QJdunSBn58fvL294erqqvLeriYsLCywbds2tGvXDq6urvjvf/+LqKgo6R3jCRMmQFdXF/Xr10e1atVw/fp1jBgxAh999BH69u2L5s2b4+7duxg5cqRG482cORPp6emoU6cOqlWrprZ+y5Yt8emnn6Jv376oVq0aFi5cCACIiIhA48aN0aVLF7Ro0QJCCOzbt6/YEuySuLi44NChQ0hISECzZs3QokUL7Ny5E3p6mn3dukwmw759+9C6dWsMHjwYLi4u6NevHzIyMmBtbQ3g2Vd+bdmyBbt27ULDhg3Rrl07nDp1qsT+PvjgA+zduxdTp04tdTn2q1AoFNi9ezcuXrwILy8vTJkypdgfADS9pzo6OggKCkJhYSEGDRqkcq6wsBCjRo2Cq6srOnbsCBcXF6xYseK1z4eIiIiIiN49smeb6FWcBw8eoGbNmli8eDGGDh2KFi1awNfXF7Nnz67IsOhfaOjQobh9+zZ27dr12vrMzc2Fubk5cnJyuNSciIiIiMr2kqt7qXLJBWAOvPTv/po9In2Nzp8/j8uXL6NZs2bIycnBzJkzAQBdunTBmTNncOnSJYSEhLzpsOhfLCcnBxcvXsSGDRtea8JNRERERESkToXsXr5o0SJ4enrCz88PDx48QGxsLE6cOIF27drhww8/RK9evRAbG6vytVYvlooWEBBQamxz586tNH1WZnPnzi11vgEBAa9tnG7duqFDhw749NNP0b59+9fWLxERERERkToVvry8NPn5+bh161ap552cnN5gNMXdunUL+fn5JZ6rUqUKqlSpUin6rMz++ecf/PPPPyWeMzIyQs2aNd9wRC+Py8uJiIiISGNcXv5WK+/y8kqbdBO9DZh0ExEREZHGmHS/1cqbdFfI8nIiIiIiIiKidwGTbiIiIiIiIiIteeO7lxMREREREb2T+Gbv2y03FzA3f+lmfNJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrC3cuJiIiIiKhykMkqOgKi145PuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWnJW5t0p6enQyaTIT4+XmtjxMTEQCaTITs7u8JieBc4Ojpi6dKlWh8nMjISFhYWWh+HiIiIiIioyFubdFcGdnZ2yMzMRIMGDSo6FNJA3759kZKSUtFhEBERERHRO0SvogN4m+nq6qJGjRoVHcYre/LkCfT19Ss6DK0zMjKCkZFRRYdBRERERETvkEr/pFupVGLhwoVwcnKCXC6Hvb095syZI52/du0a2rZtC2NjY3h6euLEiRMq7Y8fP45WrVrByMgIdnZ2CAkJwYMHD6TzBQUFCA0NhZ2dHeRyOZycnPDDDz+UGMvDhw8REBAAb29vZGdnF1teXrQcPTo6Gk2aNIGxsTFatmyJ5ORktfNMT0+Hjo4Ozpw5o3J86dKlcHBwgFKpBAD88ccfCAgIgEKhgLW1NQYOHIg7d+5I9Q8cOIAPPvgAFhYWsLKyQpcuXZCamqoyjkwmw6ZNm9CmTRsYGhpi/fr1auOLi4uDj48PjI2NYWlpCX9/f9y7dw9r166FlZUVCgoKVOp3794dAwcOlD7v3r0bTZs2haGhIapWrYoePXqUOlZ2djaCg4NRrVo1mJmZoV27dkhISFAbIwAkJCSgbdu2MDU1hZmZGRo3bixd0xeXl4eHh6Nhw4ZYt24dHB0dYW5ujn79+uH+/fsajUVERERERKROpU+6J0+ejPnz52PatGlITEzEhg0bYG1tLZ2fMmUKJkyYgPj4eLi4uKB///54+vQpACA1NRUdO3ZEz549ceHCBWzatAnHjx/H6NGjpfaDBg1CVFQUli9fjqSkJHz//fdQKBTF4sjOzkb79u2hVCpx+PDhMt8NnjJlChYvXowzZ85AT08PQ4YMUTtPR0dH+Pn5ISIiQuV4REQEgoKCoKOjg+zsbLRr1w5eXl44c+YMDhw4gL///ht9+vSR6j948ADjxo3DmTNnEB0dDR0dHfTo0UNK2ot8+eWX+Pzzz5GUlAR/f/8yY4uPj4evry/q16+PEydO4Pjx4+jatSsKCwvRu3dvFBYWYteuXVL9rKws7N27V5r33r170aNHD3Tq1Annz59HdHQ0mjVrVup4vXv3RlZWFvbv34+zZ8+iUaNG8PX1xT///KP2Og4YMAC1atXC6dOncfbsWXz55ZdlPsVPTU3Fjh07sGfPHuzZswfHjh3D/Pnz1Y5DRERERESkEVGJ5ebmCrlcLlavXl3sXFpamgAg1qxZIx27dOmSACCSkpKEEEIMHTpUDB8+XKVdbGys0NHREfn5+SI5OVkAEIcPHy5x/KNHj0r9eXh4iJ49e4qCgoJiMZw/f16l/pEjR6Q6e/fuFQBEfn6+2vlu2rRJWFpaikePHgkhhDh79qyQyWQiLS1NCCHErFmzRIcOHVTa3LhxQwAQycnJJfZ5+/ZtAUBcvHhRJealS5eqjadI//79hbe3d6nnP/vsMxEQECB9Xrx4sahdu7ZQKpVCCCFatGghBgwYUGp7BwcHsWTJEiHEs/tjZmYmXYMiderUEd9//73aWE1NTUVkZGSJ5yIiIoS5ubn0OSwsTBgbG4vc3Fzp2MSJE0Xz5s1L7f/Ro0ciJydHKkXXPycnR21sRERERKQGwMJSaUsOIMrzu3+lftKdlJSEgoIC+Pr6llrHw8ND+reNjQ2AZ09agWdLjSMjI6FQKKTi7+8PpVKJtLQ0xMfHQ1dXF23atCkzjvbt28PJyQmbNm2CgYGB2rjLiqks3bt3h66uLrZv3w7g2XLotm3bwtHRUZrP0aNHVeZTr149AJCWkF+5cgX9+/dH7dq1YWZmJrW9fv26ylhNmjRRG0+RoifdpRk2bBgOHTqEW7duSXEHBQVBJpNp1P55CQkJyMvLg5WVlco809LSVJbJl2bcuHEIDg6Gn58f5s+fr7aNo6MjTE1Npc82NjZl3qt58+bB3NxcKnZ2dhrNi4iIiIiI3k2VeiM1TTa9en7pcFGSV7SUOi8vDyNGjEBISEixdvb29rh69apGcXTu3Blbt25FYmIi3N3dXymmshgYGGDQoEGIiIjARx99hA0bNmDZsmXS+by8PHTt2hULFiwo1rYoue/atSscHBywevVq2NraQqlUokGDBnj8+LFKfRMTE7XxFFF3H7y8vODp6Ym1a9eiQ4cOuHTpEvbu3atx++fl5eXBxsYGMTExxc5p8nVf4eHh+Pjjj7F3717s378fYWFh2LhxY6nvkL+49Fwmk5V5ryZPnoxx48ZJn3Nzc5l4ExERERFRqSp10u3s7AwjIyNER0cjODj4pds3atQIiYmJcHJyKvG8u7s7lEoljh07Bj8/v1L7mT9/PhQKBXx9fRETE4P69eu/dCyaCg4ORoMGDbBixQo8ffoUH330kXSuUaNG2Lp1KxwdHaGnV/zW3b17F8nJyVi9ejVatWoF4NlGcq/Kw8MD0dHRmDFjRplxL126FLdu3YKfn59KIlrUfvDgwWrHatSoEf766y/o6elJT+lflouLC1xcXPDFF1+gf//+iIiIKHPjtpchl8shl8tfS19ERERERPTvV6mXlxsaGiI0NBSTJk3C2rVrkZqait9//73U3cVfFBoait9++w2jR49GfHw8rly5gp07d0obqTk6OiIwMBBDhgzBjh07kJaWhpiYGGzevLlYX4sWLcKAAQPQrl07XL58+bXO83murq54//33ERoaiv79+6s8JR41ahT++ecf9O/fH6dPn0ZqaioOHjyIwYMHo7CwEJaWlrCyssKqVatw9epV/PLLLypPZctr8uTJOH36NEaOHIkLFy7g8uXLWLlypcqu6R9//DFu3ryJ1atXF9s4LiwsDFFRUQgLC0NSUhIuXrxY4tN6APDz80OLFi3QvXt3HDp0COnp6fjtt98wZcqUYju7vyg/Px+jR49GTEwMMjIyEBcXh9OnT8PV1fWVrwEREREREVF5VOqkGwCmTZuG8ePHY/r06XB1dUXfvn01ej8aePaE9dixY0hJSUGrVq3g5eWF6dOnw9bWVqqzcuVK9OrVCyNHjkS9evUwbNgwla8Ue96SJUvQp08ftGvXDikpKa9lfiUZOnQoHj9+XCx5tbW1RVxcHAoLC9GhQwe4u7tj7NixsLCwgI6ODnR0dLBx40acPXsWDRo0wBdffIGvvvrqleNxcXHBoUOHkJCQgGbNmqFFixbYuXOnytN2c3Nz9OzZEwqFAt27d1dp7+Pjgy1btmDXrl1o2LAh2rVrh1OnTpU4lkwmw759+9C6dWsMHjwYLi4u6NevHzIyMlR2rS+Jrq4u7t69i0GDBsHFxQV9+vRBQEBAmU/oiYiIiIiItEn2bJNAqkxmzZqFLVu24MKFCxUdykvx9fWFm5sbli9fXtGhvDG5ubkwNzdHTk4OzMzMKjocIiIiorfb/98PiagyygVgDrz07/6V+p3ud01eXh7S09Px7bffYvbs2RUdjsbu3buHmJgYxMTEYMWKFRUdDhERERERUaVR6ZeX/5u4ubmpfA3W82X9+vUYPXo0GjduDB8fn2JLy7UpICCg1Ljmzp2rtr2XlxeCgoKwYMEC1K1bV6uxqruGRERERERElQmXl79BGRkZePLkSYnnrK2tVb4v+k26desW8vPzSzxXpUoVVKlS5Q1HVLrKdg25vJyIiIjoNeLycqrEuLz8LeDg4FDRIZSoZs2aFR2CxirrNSQiIiIiIioJl5cTERERERERaQmTbiIiIiIiIiIt4fJyIiIiIiKqHLjdFFVmubmAuflLN+OTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEu5eTkRERET0tpDJKjoCInpJfNJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhISypF0p2eng6ZTIb4+HitjRETEwOZTIbs7GytjUGa4b0gIiIiIqJ3RaVIuv8tIiMjYWFhUdFhEBERERERUSXBpPsd9Pjx44oOgYiIiIiI6J3wRpNupVKJhQsXwsnJCXK5HPb29pgzZ450/tq1a2jbti2MjY3h6emJEydOqLQ/fvw4WrVqBSMjI9jZ2SEkJAQPHjyQzhcUFCA0NBR2dnaQy+VwcnLCDz/8UGIsDx8+REBAALy9vZGdnQ2lUomZM2eiVq1akMvlaNiwIQ4cOCDVL2lJdHx8PGQyGdLT0xETE4PBgwcjJycHMpkMMpkM4eHhZV6PmTNnokGDBsWON2zYENOmTZM+r1mzBq6urjA0NES9evWwYsUKlfqhoaFwcXGBsbExateujWnTpuHJkyfS+fDwcDRs2BBr1qzBe++9B0NDwzLjAsq+V+3atcPo0aNV6t++fRsGBgaIjo4G8HL3AlB/b8uibqxjx46hWbNmkMvlsLGxwZdffomnT59K53/++We4u7vDyMgIVlZW8PPz03hsIiIiIiKiMok3aNKkScLS0lJERkaKq1evitjYWLF69WqRlpYmAIh69eqJPXv2iOTkZNGrVy/h4OAgnjx5IoQQ4urVq8LExEQsWbJEpKSkiLi4OOHl5SWCgoKk/vv06SPs7OzEtm3bRGpqqjhy5IjYuHGjEEKIo0ePCgDi3r174t69e6Jly5aiQ4cO4sGDB0IIIb7++mthZmYmoqKixOXLl8WkSZOEvr6+SElJKda+yPnz5wUAkZaWJgoKCsTSpUuFmZmZyMzMFJmZmeL+/ftlXo8bN24IHR0dcerUKenYuXPnhEwmE6mpqUIIIX766SdhY2Mjtm7dKq5duya2bt0qqlSpIiIjI6U2s2bNEnFxcSItLU3s2rVLWFtbiwULFkjnw8LChImJiejYsaM4d+6cSEhIKPe9EkKI9evXC0tLS/Ho0SOp/tdffy0cHR2FUql8qXuh6b0tS1lj3bx5UxgbG4uRI0eKpKQksX37dlG1alURFhYmhBDizz//FHp6euLrr78WaWlp4sKFC+K7775Te++K5OTkCAAiJydHo/pERERErwRgYWGpoJIDiPL87g8t/c9BMbm5uUIul0uJ2/OKku41a9ZIxy5duiQAiKSkJCGEEEOHDhXDhw9XaRcbGyt0dHREfn6+SE5OFgDE4cOHSxy/KNFLSkoSHh4eomfPnqKgoEA6b2trK+bMmaPSpmnTpmLkyJEq7UtLuoUQIiIiQpibm2t8TYQQIiAgQHz22WfS5zFjxggfHx/pc506dcSGDRtU2syaNUu0aNGi1D6/+uor0bhxY+lzWFiY0NfXF1lZWRrFVNa9EkKI/Px8YWlpKTZt2iQd8/DwEOHh4UIIofG9KLqW6u5tWdSN9Z///EfUrVtX+mOAEEJ89913QqFQiMLCQnH27FkBQKSnp5c5TpFHjx6JnJwcqdy4caNc/+ERERERlUslSDxYWN7VUt6k+40tL09KSkJBQQF8fX1LrePh4SH928bGBgCQlZUFAEhISEBkZCQUCoVU/P39oVQqkZaWhvj4eOjq6qJNmzZlxtG+fXs4OTlh06ZNMDAwAADk5ubizz//hLe3t0pdb29vJCUllWu+mho2bBiioqLw6NEjPH78GBs2bMCQIUMAAA8ePEBqaiqGDh2qMu/Zs2cjNTVV6mPTpk3w9vZGjRo1oFAoMHXqVFy/fl1lHAcHB1SrVk2jmNTdK0NDQwwcOBA//vgjAODcuXP4448/EBQUBAAa34si6u5tWdSNlZSUhBYtWkAmk0nHvL29kZeXh5s3b8LT0xO+vr5wd3dH7969sXr1aty7d6/U8ebNmwdzc3Op2NnZaTRHIiIiIiJ6N+m9qYGMjIzU1tHX15f+XZQkKZVKAEBeXh5GjBiBkJCQYu3s7e1x9epVjeLo3Lkztm7disTERLi7u2vUBgB0dJ79fUIIIR17/r3p8uratSvkcjm2b98OAwMDPHnyBL169QLwbM4AsHr1ajRv3lylna6uLgDgxIkTGDBgAGbMmAF/f3+Ym5tj48aNWLx4sUp9ExMTjWPS5F4FBwejYcOGuHnzJiIiItCuXTs4ODho3P556u7tq8ZaFl1dXRw+fBi//fYbDh06hG+++QZTpkzByZMn8d577xWrP3nyZIwbN076nJuby8SbiIiIiIhK9caedDs7O8PIyEjaaOtlNWrUCImJiXBycipWDAwM4O7uDqVSiWPHjpXZz/z58xEYGAhfX18kJiYCAMzMzGBra4u4uDiVunFxcahfvz4ASE+JMzMzpfMvfq+4gYEBCgsLX2peenp6CAwMREREBCIiItCvXz8pkbS2toatrS2uXbtWbM5FCeFvv/0GBwcHTJkyBU2aNIGzszMyMjJeKoYXaXKv3N3d0aRJE6xevVrl6XzROU3uRRF197Ys6sZydXXFiRMnVP5YEhcXB1NTU9SqVQvAsz/weHt7Y8aMGTh//jwMDAywffv2EvuTy+UwMzNTKURERERERKXSyrsmpQgPDxeWlpbif//7n7h69ao4ceKEWLNmjfRO9/nz56W69+7dEwDE0aNHhRBCJCQkCCMjIzFq1Chx/vx5kZKSInbs2CFGjRoltQkKChJ2dnZi+/bt4tq1a+Lo0aPSe8cvvkc8duxYYW1tLb0zvmTJEmFmZiY2btwoLl++LEJDQ1U2Unv8+LGws7MTvXv3FikpKWLPnj2ibt26Avi/d7rj4uIEAHHkyBFx+/ZtaZM2dVJSUoSurq7Q1dUVv//+u8q51atXCyMjI7Fs2TKRnJwsLly4IH788UexePFiIYQQO3fuFHp6eiIqKkpcvXpVLFu2TFSpUkXl3fKwsDDh6empUSxFSrtXz1u1apUwMDAQlpaWxd69fpl7ocm9LUtZYxVtpDZq1CiRlJQkduzYobKR2u+//y7mzJkjTp8+LTIyMsTmzZuFgYGB2Ldvn0ZjcyM1IiIieqMqwXutLCzvaqn0G6kJIURhYaGYPXu2cHBwEPr6+sLe3l7MnTtXo6RbCCFOnTol2rdvLxQKhTAxMREeHh4qm5/l5+eLL774QtjY2AgDAwPh5OQkfvzxRyFEyRuhjRkzRtjY2Ijk5GRRWFgowsPDRc2aNYW+vr7w9PQU+/fvV4n/+PHjwt3dXRgaGopWrVqJLVu2iOeTbiGE+PTTT4WVlZUAICV2mmjVqpVwc3Mr8dz69etFw4YNpQS3devWYtu2bdL5iRMnCisrK6FQKETfvn3FkiVLXjnpLu1ePe/+/fvSzuAvetl7oe7elqWssYQQIiYmRjRt2lQYGBiIGjVqiNDQUGlX/MTEROHv7y+qVasm5HK5cHFxEd98843G14lJNxEREb1RlSDxYGF5V0t5k27Zs/92qSIJIeDs7IyRI0eqvC9c2aWnp6NOnTo4ffo0GjVqVNHhVIjc3FyYm5sjJyeHS82JiIhI+57bHJaI3qxcAObAS//u/8Y2UqOS3b59Gxs3bsRff/2FwYMHV3Q4Gnny5Anu3r2LqVOn4v33339nE24iIiIiIiJ1mHRrUWxsLAICAko9n5eXh+rVq6Nq1apYtWoVLC0t30hc169flzaIK0liYmKZu4bHxcWhbdu2cHFxwc8//6yNECWaXEMiIiIiIqLKisvLtSg/Px+3bt0q9byTk9MbjOb/PH36FOnp6aWed3R0hJ5e5fh7TGW9hkW4vJyIiIjeKC4vJ6ow5V1ezqSb6BUw6SYiIqI3ikk3UYUpb9L9xr6nm4iIiIiIiOhdw6SbiIiIiIiISEsqx4u7RERERESkHt8MJao4ubmAuflLN+OTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEu5eTkRERPQukckqOgIioncKn3QTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSkgpJutPT0yGTyRAfH6+1MWJiYiCTyZCdnV1hMVQkdfOvaDKZDDt27KjoMIiIiIiIiLTqnX3SbWdnh8zMTDRo0KCiQ3llPj4+GDt2bEWHQURERERERC/Qq+gAKoquri5q1KhR0WEQERERERHRv5hWn3QrlUosXLgQTk5OkMvlsLe3x5w5c6Tz165dQ9u2bWFsbAxPT0+cOHFCpf3x48fRqlUrGBkZwc7ODiEhIXjw4IF0vqCgAKGhobCzs4NcLoeTkxN++OGHEmN5+PAhAgIC4O3tjezs7GLLy4uWY0dHR6NJkyYwNjZGy5YtkZycrNFcw8PD0bBhQ/z444+wt7eHQqHAyJEjUVhYiIULF6JGjRqoXr26yvwB4Pr16+jWrRsUCgXMzMzQp08f/P3338X6XbduHRwdHWFubo5+/frh/v37AICgoCAcO3YMy5Ytg0wmg0wmQ3p6utT+7Nmz5ZoPAOzevRtNmzaFoaEhqlatih49egAAZs6cWeIKgYYNG2LatGnS5x9//BFubm6Qy+WwsbHB6NGjSx3rxo0b6NOnDywsLFClShV069ZNZR7qlDWWumuckJCAtm3bwtTUFGZmZmjcuDHOnDmj8dhERERERESl0WrSPXnyZMyfPx/Tpk1DYmIiNmzYAGtra+n8lClTMGHCBMTHx8PFxQX9+/fH06dPAQCpqano2LEjevbsiQsXLmDTpk04fvy4SjI1aNAgREVFYfny5UhKSsL3338PhUJRLI7s7Gy0b98eSqUShw8fhoWFRakxT5kyBYsXL8aZM2egp6eHIUOGaDzf1NRU7N+/HwcOHEBUVBR++OEHdO7cGTdv3sSxY8ewYMECTJ06FSdPngTw7I8S3bp1wz///INjx47h8OHDuHbtGvr27Vus3x07dmDPnj3Ys2cPjh07hvnz5wMAli1bhhYtWmDYsGHIzMxEZmYm7OzsXnk+e/fuRY8ePdCpUyecP38e0dHRaNasGQBgyJAhSEpKwunTp6X658+fx4ULFzB48GAAwMqVKzFq1CgMHz4cFy9exK5du+Dk5FTiWE+ePIG/vz9MTU0RGxuLuLg4KBQKdOzYEY8fP1Yba1ljaXKNBwwYgFq1auH06dM4e/YsvvzyS+jr62t0nYiIiIiIiMoktCQ3N1fI5XKxevXqYufS0tIEALFmzRrp2KVLlwQAkZSUJIQQYujQoWL48OEq7WJjY4WOjo7Iz88XycnJAoA4fPhwieMfPXpU6s/Dw0P07NlTFBQUFIvh/PnzKvWPHDki1dm7d68AIPLz89XONywsTBgbG4vc3FzpmL+/v3B0dBSFhYXSsbp164p58+YJIYQ4dOiQ0NXVFdevXy92HU6dOlVqvxMnThTNmzeXPrdp00Z8/vnnJc6/vPNp0aKFGDBgQKnnAwICxGeffSZ9HjNmjPDx8ZE+29raiilTppTaHoDYvn27EEKIdevWibp16wqlUimdLygoEEZGRuLgwYNqYy1rLE2usampqYiMjFQ7jhBCPHr0SOTk5Ejlxo0bAoDIycnRqD0REVGFA1hYWFhYylFyAFGe3/219qQ7KSkJBQUF8PX1LbWOh4eH9G8bGxsAQFZWFoBnS34jIyOhUCik4u/vD6VSibS0NMTHx0NXVxdt2rQpM4727dvDyckJmzZtgoGBgdq4y4pJHUdHR5iamkqfra2tUb9+fejo6KgcK+ovKSkJdnZ2Kk+m69evDwsLCyQlJZXar42NjcYxlXc+8fHxZd67YcOGISoqCo8ePcLjx4+xYcMG6Sl6VlYW/vzzzzLbPy8hIQFXr16FqampdK+rVKmCR48eITU1tcy26sbS5BqPGzcOwcHB8PPzw/z588scc968eTA3N5fK8/0SERERERG9SGtJt5GRkdo6zy/hlclkAJ4tBwaAvLw8jBgxAvHx8VJJSEjAlStXUKdOHY36B4DOnTvj119/RWJiokb1y4rpZdoWtS/pmKb9ldVveWJ6mfmou75du3aFXC7H9u3bsXv3bjx58gS9evXSqO2L8vLy0LhxY5V7HR8fj5SUFHz88cevFKcmwsPDcenSJXTu3Bm//PIL6tevj+3bt5dYd/LkycjJyZHKjRs3Xnl8IiIiIiL699Ja0u3s7AwjIyNER0eXq32jRo2QmJgIJyenYsXAwADu7u5QKpU4duxYmf3Mnz8fgYGB8PX11TjxflNcXV1x48YNlcQtMTER2dnZqF+/vsb9GBgYoLCw8LXG5uHhUea909PTQ2BgICIiIhAREYF+/fpJCbCpqSkcHR01vveNGjXClStXUL169WL32tzcvMy26sbS9Bq7uLjgiy++wKFDh/DRRx8hIiKixP7kcjnMzMxUChERERERUWm0lnQbGhoiNDQUkyZNwtq1a5Gamorff/+91N3FXxQaGorffvsNo0ePRnx8PK5cuYKdO3dKG6k5OjoiMDAQQ4YMwY4dO5CWloaYmBhs3ry5WF+LFi3CgAED0K5dO1y+fPm1zvNV+Pn5wd3dHQMGDMC5c+dw6tQpDBo0CG3atEGTJk007sfR0REnT55Eeno67ty589JP0ksSFhaGqKgohIWFISkpCRcvXsSCBQtU6gQHB+OXX37BgQMHim3QFh4ejsWLF2P58uW4cuUKzp07h2+++abEsQYMGICqVauiW7duiI2Nle5lSEgIbt68qTbWssZSd43z8/MxevRoxMTEICMjA3FxcTh9+jRcXV3LeeWIiIiIiIj+j1Z3L582bRrGjx+P6dOnw9XVFX379n2pd5GPHTuGlJQUtGrVCl5eXpg+fTpsbW2lOitXrkSvXr0wcuRI1KtXD8OGDVP5SrHnLVmyBH369EG7du2QkpLyWub3qmQyGXbu3AlLS0u0bt0afn5+qF27NjZt2vRS/UyYMAG6urqoX78+qlWrhuvXr79ybD4+PtiyZQt27dqFhg0bol27djh16pRKHWdnZ7Rs2RL16tVD8+bNVc4FBgZi6dKlWLFiBdzc3NClSxdcuXKlxLGMjY3x66+/wt7eHh999BFcXV0xdOhQPHr0SKMnyWWNpe4a6+rq4u7duxg0aBBcXFzQp08fBAQEYMaMGeW5bERERERERCpkzzaxJHp5Qgg4Oztj5MiRGDduXEWHUyFyc3Nhbm6OnJwcLjUnIqK3w//f44WIiF5OLgBz4KV/99fTWkT0r3b79m1s3LgRf/31l/Td3ERERERERKRKq8vL/03c3NxUvr7s+bJ+/fqKDu+lvep8qlevjpkzZ2LVqlWwtLTUaqylxalQKBAbG6vVsYmIiIiIiF4Fn3RraN++fXjy5EmJ56ytrd9wNK/uVefzJt9KiI+PL/VczZo131gcREREREREL4tJt4YcHBwqOoTX6m2aj5OTU0WHQEREREREVC5cXk5ERERERESkJUy6iYiIiIiIiLSEy8uJiIiI3iX8tlgiovLJzQXMzV+6GZ90ExEREREREWkJk24iIiIiIiIiLWHSTURERERERKQlTLqJiIiIiIiItIRJNxEREREREZGWcPdyIiIioneJTFbRERARvVP4pJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLWHSTURERERERKQlTLqJiIiIiIiItIRJNxEREREREZGWVMqkOz09HTKZDPHx8VobIyYmBjKZDNnZ2VobgzTzJu+Fj48Pxo4dq/VxiIiIiIiIAH5Pt1ZFRkZi7NixTOwrkW3btkFfX7+iwyAiIiIioncEk27C48ePYWBgUNFhvBFVqlSp6BCIiIiIiOgdUqHLy5VKJRYuXAgnJyfI5XLY29tjzpw50vlr166hbdu2MDY2hqenJ06cOKHS/vjx42jVqhWMjIxgZ2eHkJAQPHjwQDpfUFCA0NBQ2NnZQS6Xw8nJCT/88EOJsTx8+BABAQHw9vZGdnY2lEolZs6ciVq1akEul6Nhw4Y4cOCAVL+kJdHx8fGQyWRIT09HTEwMBg8ejJycHMhkMshkMoSHh5d5PWbOnIkGDRoUO96wYUNMmzZN+rxmzRq4urrC0NAQ9erVw4oVK1Tqh4aGwsXFBcbGxqhduzamTZuGJ0+eSOfDw8PRsGFDrFmzBu+99x4MDQ3LjAso+161a9cOo0ePVql/+/ZtGBgYIDo6GsDL3QtA/b0ty4oVK+Ds7AxDQ0NYW1ujV69e0rkXl5c7Ojpi7ty5GDJkCExNTWFvb49Vq1ZpNA4REREREZFaogJNmjRJWFpaisjISHH16lURGxsrVq9eLdLS0gQAUa9ePbFnzx6RnJwsevXqJRwcHMSTJ0+EEEJcvXpVmJiYiCVLloiUlBQRFxcnvLy8RFBQkNR/nz59hJ2dndi2bZtITU0VR44cERs3bhRCCHH06FEBQNy7d0/cu3dPtGzZUnTo0EE8ePBACCHE119/LczMzERUVJS4fPmymDRpktDX1xcpKSnF2hc5f/68ACDS0tJEQUGBWLp0qTAzMxOZmZkiMzNT3L9/v8zrcePGDaGjoyNOnTolHTt37pyQyWQiNTVVCCHETz/9JGxsbMTWrVvFtWvXxNatW0WVKlVEZGSk1GbWrFkiLi5OpKWliV27dglra2uxYMEC6XxYWJgwMTERHTt2FOfOnRMJCQnlvldCCLF+/XphaWkpHj16JNX/+uuvhaOjo1AqlS91LzS9t6U5ffq00NXVFRs2bBDp6eni3LlzYtmyZdL5Nm3aiM8//1z67ODgIKpUqSK+++47ceXKFTFv3jyho6MjLl++rHYsIYTIyckRAEROTo5G9YmIiCocwMLCwsJSjpIDiPL87g8t/c+5Wrm5uUIul0uJ2/OKku41a9ZIxy5duiQAiKSkJCGEEEOHDhXDhw9XaRcbGyt0dHREfn6+SE5OFgDE4cOHSxy/KNFLSkoSHh4eomfPnqKgoEA6b2trK+bMmaPSpmnTpmLkyJEq7UtLuoUQIiIiQpibm2t8TYQQIiAgQHz22WfS5zFjxggfHx/pc506dcSGDRtU2syaNUu0aNGi1D6/+uor0bhxY+lzWFiY0NfXF1lZWRrFVNa9EkKI/Px8YWlpKTZt2iQd8/DwEOHh4UIIofG9KLqW6u5tWbZu3SrMzMxEbm5uiedLSro/+eQT6bNSqRTVq1cXK1euLLH9o0ePRE5OjlRu3LhRrv/wiIiIKkwl+MWVhYWF5W0s5U26K2x5eVJSEgoKCuDr61tqHQ8PD+nfNjY2AICsrCwAQEJCAiIjI6FQKKTi7+8PpVKJtLQ0xMfHQ1dXF23atCkzjvbt28PJyQmbNm2S3mvOzc3Fn3/+CW9vb5W63t7eSEpKKtd8NTVs2DBERUXh0aNHePz4MTZs2IAhQ4YAAB48eIDU1FQMHTpUZd6zZ89Gamqq1MemTZvg7e2NGjVqQKFQYOrUqbh+/brKOA4ODqhWrZpGMam7V4aGhhg4cCB+/PFHAMC5c+fwxx9/ICgoCAA0vhdF1N3bsrRv3x4ODg6oXbs2Bg4ciPXr1+Phw4dltnn+50wmk6FGjRrSz9mL5s2bB3Nzc6nY2dlpNCciIiIiIno3VdhGakZGRmrrPL/LtEwmA/Ds3WIAyMvLw4gRIxASElKsnb29Pa5evapRHJ07d8bWrVuRmJgId3d3jdoAgI7Os79XCCGkY8+/N11eXbt2hVwux/bt22FgYIAnT55I7yTn5eUBAFavXo3mzZurtNPV1QUAnDhxAgMGDMCMGTPg7+8Pc3NzbNy4EYsXL1apb2JionFMmtyr4OBgNGzYEDdv3kRERATatWsHBwcHjds/T929LYupqSnOnTuHmJgYHDp0CNOnT0d4eDhOnz4NCwuLEtu8uJu5TCaTfs5eNHnyZIwbN076nJuby8SbiIiIiIhKVWFJt7OzM4yMjBAdHY3g4OCXbt+oUSMkJibCycmpxPPu7u5QKpU4duwY/Pz8Su1n/vz5UCgU8PX1RUxMDOrXrw8zMzPY2toiLi5O5elsXFwcmjVrBgDSU+LMzExYWloCQLHvFTcwMEBhYeFLzUtPTw+BgYGIiIiAgYEB+vXrJyWt1tbWsLW1xbVr1zBgwIAS2//2229wcHDAlClTpGMZGRkvFcOLNLlX7u7uaNKkCVavXo0NGzbg22+/VTmnyb0oou7eqqOnpwc/Pz/4+fkhLCwMFhYW+OWXX/DRRx+Vq7/nyeVyyOXyV+6HiIiIiIjeDRWWdBsaGiI0NBSTJk2CgYEBvL29cfv2bVy6dKnMJedFQkND8f7772P06NEIDg6GiYkJEhMTcfjwYXz77bdwdHREYGAghgwZguXLl8PT0xMZGRnIyspCnz59VPpatGgRCgsL0a5dO8TExKBevXqYOHEiwsLCUKdOHTRs2BARERGIj4/H+vXrAQBOTk6ws7NDeHg45syZg5SUlGJPkx0dHZGXl4fo6Gh4enrC2NgYxsbGaucWHBwMV1dXAM8S/efNmDEDISEhMDc3R8eOHVFQUIAzZ87g3r17GDduHJydnXH9+nVs3LgRTZs2xd69e7F9+3a1Y5alrHs1dOhQlbhHjx4NExMT9OjRQ+U6aHovAPX3tix79uzBtWvX0Lp1a1haWmLfvn1QKpWoW7fuK10DIiIiIiKictHODh2aKSwsFLNnzxYODg5CX19f2Nvbi7lz50obqZ0/f16qe+/ePQFAHD16VDp26tQp0b59e6FQKISJiYnw8PBQ2fwsPz9ffPHFF8LGxkYYGBgIJycn8eOPPwohSt4IbcyYMcLGxkYkJyeLwsJCER4eLmrWrCn09fWFp6en2L9/v0r8x48fF+7u7sLQ0FC0atVKbNmyRQD/t5GaEEJ8+umnwsrKSgAQYWFhGl+bVq1aCTc3txLPrV+/XjRs2FAYGBgIS0tL0bp1a7Ft2zbp/MSJE4WVlZVQKBSib9++YsmSJSobuoWFhQlPT0+NYxGi9Hv1vPv37wtjY2Nps7nnvey9UHdvSxMbGyvatGkjLC0thZGRkfDw8FDZ4K2kjdSWLFmi0oenp6fG94q7lxMR0VunEmxGxMLCwvI2lvJupCZ79r+9VJkIIeDs7IyRI0eqvD9c2aWnp6NOnTo4ffo0GjVqVNHhvBG5ubkwNzdHTk4OzMzMKjocIiIi9f7/PjlERPRycgGYAy/9u3+FLS+nkt2+fRsbN27EX3/9hcGDB1d0OBp58uQJ7t69i6lTp+L9999/ZxJuIiIiIiIidZh0v0GxsbEICAgo9XxeXh6qV6+OqlWrYtWqVdIGbdp2/fp11K9fv9TziYmJZe4aHhcXh7Zt28LFxQU///yzNkKUaHINiYiIiIiIKgsuL3+D8vPzcevWrVLPl3e37lf19OlTpKenl3re0dERenqV4+8zle0acnk5ERG9dbi8nIioXMq7vJxJN9ErYNJNRERvHSbdRETlUt6kW0drERERERERERG945h0ExEREREREWlJ5XhRl4iIiIjeDL5ZSERUPrm5gLn5Szfjk24iIiIiIiIiLWHSTURERERERKQlTLqJiIiIiIiItIRJNxEREREREZGWMOkmIiIiIiIi0hLuXk5ERET0PJmsoiMgIqJ/ET7pJiIiIiIiItISJt1EREREREREWsKkm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVaTbrT09Mhk8kQHx+vtTFiYmIgk8mQnZ2ttTH+DYKCgtC9e/eKDqNEkZGRsLCweCNjOTo6YunSpW9kLCIiIiIiIj7p1sCbTApf1Zv4Q8fb7PTp0xg+fHhFh0FERERERO8IvYoOgOhNqlatWkWHQERERERE75DX8qRbqVRi4cKFcHJyglwuh729PebMmSOdv3btGtq2bQtjY2N4enrixIkTKu2PHz+OVq1awcjICHZ2dggJCcGDBw+k8wUFBQgNDYWdnR3kcjmcnJzwww8/lBjLw4cPERAQAG9vb2RnZ0OpVGLmzJmoVasW5HI5GjZsiAMHDkj1S1qeHh8fD5lMhvT0dMTExGDw4MHIycmBTCaDTCZDeHi42mvi6OiI2bNnY9CgQVAoFHBwcMCuXbtw+/ZtdOvWDQqFAh4eHjhz5oxKu61bt8LNzQ1yuRyOjo5YvHhxsX7nzp2LIUOGwNTUFPb29li1apV0/r333gMAeHl5QSaTwcfHR6X9okWLYGNjAysrK4waNQpPnjxROxeg9HsghICTkxMWLVqkUr/oGl69ehUAkJ2djREjRsDa2hqGhoZo0KAB9uzZU+p4O3fuRKNGjWBoaIjatWtjxowZePr0qdo4hRAIDw+Hvb095HI5bG1tERISIp1/cXm5TCbDmjVr0KNHDxgbG8PZ2Rm7du3S6JoQERERERGpJV6DSZMmCUtLSxEZGSmuXr0qYmNjxerVq0VaWpoAIOrVqyf27NkjkpOTRa9evYSDg4N48uSJEEKIq1evChMTE7FkyRKRkpIi4uLihJeXlwgKCpL679Onj7CzsxPbtm0Tqamp4siRI2Ljxo1CCCGOHj0qAIh79+6Je/fuiZYtW4oOHTqIBw8eCCGE+Prrr4WZmZmIiooSly9fFpMmTRL6+voiJSWlWPsi58+fFwBEWlqaKCgoEEuXLhVmZmYiMzNTZGZmivv376u9Jg4ODqJKlSriv//9r0hJSRGfffaZMDMzEx07dhSbN28WycnJonv37sLV1VUolUohhBBnzpwROjo6YubMmSI5OVlEREQIIyMjERERUazf7777Tly5ckXMmzdP6OjoiMuXLwshhDh16pQAII4cOSIyMzPF3bt3hRBCBAYGCjMzM/Hpp5+KpKQksXv3bmFsbCxWrVql0T0u6x7MmTNH1K9fX6V+SEiIaN26tRBCiMLCQvH+++8LNzc3cejQIZGamip2794t9u3bJ4QQIiIiQpibm0ttf/31V2FmZiYiIyNFamqqOHTokHB0dBTh4eFq49yyZYswMzMT+/btExkZGeLkyZMqc3RwcBBLliyRPgMQtWrVEhs2bBBXrlwRISEhQqFQSNdNnZycHAFA5OTkaFSfiIjeAgALCwsLC0uxkgOI8vzuj1f9/6Xc3Fwhl8vF6tWri50rSrrXrFkjHbt06ZIAIJKSkoQQQgwdOlQMHz5cpV1sbKzQ0dER+fn5Ijk5WQAQhw8fLnH8oqQ5KSlJeHh4iJ49e4qCggLpvK2trZgzZ45Km6ZNm4qRI0eqtC8t6RaieFKoCQcHB/HJJ59InzMzMwUAMW3aNOnYiRMnBACRmZkphBDi448/Fu3bt1fpZ+LEiSoJ7Yv9KpVKUb16dbFy5UohxP9d8/Pnz6v0ExgYKBwcHMTTp0+lY7179xZ9+/ZVOxd19+DWrVtCV1dXnDx5UgghxOPHj0XVqlVFZGSkEEKIgwcPCh0dHZGcnFxi+xevr6+vr5g7d65KnXXr1gkbGxu1sS5evFi4uLiIx48fl3i+pKR76tSp0ue8vDwBQOzfv7/E9o8ePRI5OTlSuXHjRrn+wyMiokqsEvxix8LCwsJS+Up5k+5XXl6elJSEgoIC+Pr6llrHw8ND+reNjQ0AICsrCwCQkJCAyMhIKBQKqfj7+0OpVCItLQ3x8fHQ1dVFmzZtyoyjffv2cHJywqZNm2BgYAAAyM3NxZ9//glvb2+Vut7e3khKSirXfF/G8/O2trYGALi7uxc7VnQtkpKSSoz1ypUrKCwsLLFfmUyGGjVqSH2Uxc3NDbq6utJnGxsbjdqpuwe2trbo3LkzfvzxRwDA7t27UVBQgN69e0vta9WqBRcXF7VjAc9+JmbOnKnyMzFs2DBkZmbi4cOHZbbt3bs38vPzUbt2bQwbNgzbt29Xuyz9+etpYmICMzOzUq/LvHnzYG5uLhU7OzuN5kRERERERO+mV066jYyM1NbR19eX/i2TyQA8ew8cAPLy8jBixAjEx8dLJSEhAVeuXEGdOnU06h8AOnfujF9//RWJiYkvFb+OzrNLIISQjmn6nrM6Jc27rGtRnn6L+tGkj/K20+QeBAcHY+PGjcjPz0dERAT69u0LY2Njjds/Ly8vDzNmzFD5mbh48SKuXLkCQ0PDMtva2dkhOTkZK1asgJGREUaOHInWrVuXeU9f5rpMnjwZOTk5Urlx48ZLzY2IiIiIiN4tr5x0Ozs7w8jICNHR0eVq36hRIyQmJsLJyalYMTAwgLu7O5RKJY4dO1ZmP/Pnz0dgYCB8fX2lxNvMzAy2traIi4tTqRsXF4f69esD+L/drDMzM6XzL37dloGBgcqTZm1xdXUtMVYXFxeVJ9RlKXrK/zrj1eQedOrUCSYmJli5ciUOHDiAIUOGSOc8PDxw8+ZNpKSkaDReo0aNkJycXOLPRNEfScpiZGSErl27Yvny5YiJicGJEydw8eJFjcZWRy6Xw8zMTKUQERERERGV5pW/MszQ0BChoaGYNGkSDAwM4O3tjdu3b+PSpUtlLjkvEhoaivfffx+jR49GcHAwTExMkJiYiMOHD+Pbb7+Fo6MjAgMDMWTIECxfvhyenp7IyMhAVlYW+vTpo9LXokWLUFhYiHbt2iEmJgb16tXDxIkTERYWhjp16qBhw4aIiIhAfHw81q9fDwBwcnKCnZ0dwsPDMWfOHKSkpJS4Y3heXh6io6Ph6ekJY2Nj6Snu6zR+/Hg0bdoUs2bNQt++fXHixAl8++23WLFihcZ9VK9eHUZGRjhw4ABq1aoFQ0NDmJubv1JcmtwDXV1dBAUFYfLkyXB2dkaLFi2k9m3atEHr1q3Rs2dPfP3113BycsLly5chk8nQsWPHYuNNnz4dXbp0gb29PXr16gUdHR0kJCTgjz/+wOzZs8uMNTIyEoWFhWjevDmMjY3x008/wcjICA4ODq90DYiIiIiIiMrjtXxl2LRp0zB+/HhMnz4drq6u6Nu3r0bvCgPPnoIeO3YMKSkpaNWqFby8vDB9+nTY2tpKdVauXIlevXph5MiRqFevHoYNG6bylWLPW7JkCfr06YN27dohJSUFISEhGDduHMaPHw93d3ccOHAAu3btgrOzM4BnS4ujoqJw+fJleHh4YMGCBcUSu5YtW+LTTz9F3759Ua1aNSxcuLCcV6psjRo1wubNm7Fx40Y0aNAA06dPx8yZMxEUFKRxH3p6eli+fDm+//572Nraolu3bq8lNk3uwdChQ/H48WMMHjy4WPutW7eiadOm6N+/P+rXr49JkyaV+jTe398fe/bswaFDh9C0aVO8//77WLJkiUaJs4WFBVavXg1vb294eHjgyJEj2L17N6ysrMo3cSIiIiIiolcgE8+/zEz0CmJjY+Hr64sbN25Im8T92+Xm5sLc3Bw5OTlcak5E9G/x//dcISIiel4uAHPgpX/3f+Xl5UQFBQW4ffs2wsPD0bt373cm4SYiIiIiIlLntSwvf9fExsaqfJ3Vi+Vt86rziYqKgoODA7Kzs7W29L7I+vXrS43Tzc1Nq2MTERERERG9LC4vL4f8/HzcunWr1PNOTk5vMJpX9zbN5/79+/j7779LPKevr//GN0zj8nIion8hLi8nIqISlHd5OZNuolfApJuI6F+ISTcREZWgvEk3l5cTERERERERaQmTbiIiIiIiIiIt4e7lRERERM/jm3dERFSS3FzA3Pylm/FJNxEREREREZGWMOkmIiIiIiIi0hIm3URERERERERawqSbiIiIiIiISEuYdBMRERERERFpCXcvJyIiIs3JZBUdARER0VuFT7qJiIiIiIiItIRJNxEREREREZGWMOkmIiIiIiIi0hIm3URERERERERawqSbiIiIiIiISEuYdBMRERERERFpSYUn3enp6ZDJZIiPj9faGDExMZDJZMjOzq6wGCqSuvlXNJlMhh07dmh9nPDwcDRs2FDr4xARERERERWp8KS7MrCzs0NmZiYaNGhQ0aG8Mh8fH4wdO7aiw6iUJkyYgOjo6IoOg4iIiIiI3iF6FR1AZaCrq4saNWpUdBikZQqFAgqFoqLDICIiIiKid8gbe9KtVCqxcOFCODk5QS6Xw97eHnPmzJHOX7t2DW3btoWxsTE8PT1x4sQJlfbHjx9Hq1atYGRkBDs7O4SEhODBgwfS+YKCAoSGhsLOzg5yuRxOTk744YcfSozl4cOHCAgIgLe3N7Kzs4stLy9ajh0dHY0mTZrA2NgYLVu2RHJyskZzLVrG/OOPP8Le3h4KhQIjR45EYWEhFi5ciBo1aqB69eoq8weA69evo1u3blAoFDAzM0OfPn3w999/F+t33bp1cHR0hLm5Ofr164f79+8DAIKCgnDs2DEsW7YMMpkMMpkM6enpUvuzZ8+Waz4AsHv3bjRt2hSGhoaoWrUqevToAQCYOXNmiSsEGjZsiGnTpkmff/zxR7i5uUEul8PGxgajR48udawbN26gT58+sLCwQJUqVdCtWzeVeZQlJiYGzZo1g4mJCSwsLODt7Y2MjAwAxZeXBwUFoXv37li0aBFsbGxgZWWFUaNG4cmTJxqNRUREREREpM4bS7onT56M+fPnY9q0aUhMTMSGDRtgbW0tnZ8yZQomTJiA+Ph4uLi4oH///nj69CkAIDU1FR07dkTPnj1x4cIFbNq0CcePH1dJ3AYNGoSoqCgsX74cSUlJ+P7770t8qpmdnY327dtDqVTi8OHDsLCwKDXmKVOmYPHixThz5gz09PQwZMgQjeebmpqK/fv348CBA4iKisIPP/yAzp074+bNmzh27BgWLFiAqVOn4uTJkwCe/VGiW7du+Oeff3Ds2DEcPnwY165dQ9++fYv1u2PHDuzZswd79uzBsWPHMH/+fADAsmXL0KJFCwwbNgyZmZnIzMyEnZ3dK89n79696NGjBzp16oTz588jOjoazZo1AwAMGTIESUlJOH36tFT//PnzuHDhAgYPHgwAWLlyJUaNGoXhw4fj4sWL2LVrF5ycnEoc68mTJ/D394epqSliY2MRFxcHhUKBjh074vHjx2XG+fTpU3Tv3h1t2rTBhQsXcOLECQwfPhwymazUNkePHkVqaiqOHj2K//3vf4iMjERkZGSp9QsKCpCbm6tSiIiIiIiISiXegNzcXCGXy8Xq1auLnUtLSxMAxJo1a6Rjly5dEgBEUlKSEEKIoUOHiuHDh6u0i42NFTo6OiI/P18kJycLAOLw4cMljn/06FGpPw8PD9GzZ09RUFBQLIbz58+r1D9y5IhUZ+/evQKAyM/PVzvfsLAwYWxsLHJzc6Vj/v7+wtHRURQWFkrH6tatK+bNmyeEEOLQoUNCV1dXXL9+vdh1OHXqVKn9Tpw4UTRv3lz63KZNG/H555+XOP/yzqdFixZiwIABpZ4PCAgQn332mfR5zJgxwsfHR/psa2srpkyZUmp7AGL79u1CCCHWrVsn6tatK5RKpXS+oKBAGBkZiYMHD5YZ5927dwUAERMTU+L5sLAw4enpKX0ODAwUDg4O4unTp9Kx3r17i759+5Y6RlhYmABQrOTk5JQZGxHRvwbAwsLCwsLyTpYclO93/zfypDspKQkFBQXw9fUttY6Hh4f0bxsbGwBAVlYWACAhIQGRkZHSO7kKhQL+/v5QKpVIS0tDfHw8dHV10aZNmzLjaN++PZycnLBp0yYYGBiojbusmNRxdHSEqamp9Nna2hr169eHjs7/a+/O46Kq/v+BvwaEYVgGQRQhWVJcEVDSTFFB0dCstNRMTXFNcyvLND+WYLllmktmuRSooZmZVprmkhAgrQIuIChCmpJLsggiKPP+/eHP+3VkmQEd0Xw9H4/348HMueee97mXct7ce8+Y6b13c3+pqalwc3PTuzLdokUL1K5dG6mpqRXu18XFxeicqjufpKSkSs/d6NGjsXHjRly9ehUlJSXYsGGDchX9/PnzOHv2bKX9b5WcnIwTJ07Azs5OOdeOjo64evUqMjIyKu3r6OiIYcOGISQkBM888wyWLl2K7OzsSvt4e3vD3NxceW3oeE6fPh15eXlKnD592qh5ERERERHRw+meLKSm0WgMbmNhYaH8fPN2YJ1OBwAoKCjAmDFjMGnSpDL93N3dceLECaPy6NWrF7Zs2YKUlBT4+PjcUU5V6Xuzf3nvGbu/yvZbnZyqMh9D5++ZZ56BWq3G1q1bYWlpiWvXrqFfv35G9b1dQUEBHnvsMURFRZVpq1u3rsH+ERERmDRpEnbt2oVNmzbh7bffxp49e/DEE0+Uu31Vj6darYZarTaYBxEREREREXCPnulu3LgxNBpNtb+uyd/fHykpKfDy8ioTlpaW8PHxgU6nQ0xMTKX7mT9/PkJDQxEcHIyUlJRq5WIqzZs3x+nTp/WunKakpCA3NxctWrQwej+WlpYoLS29q7n5+vpWeu5q1aqF0NBQREREICIiAi+++KJSbNvZ2cHT09Poc+/v74/jx4+jXr16Zc61vb29Ufto3bo1pk+fjgMHDqBly5bYsGGDUf2IiIiIiIjutntSdFtZWWHatGmYOnUq1q1bh4yMDPzyyy8Vri5+u2nTpuHAgQOYMGECkpKScPz4cXz77bfKQmqenp4IDQ3FiBEjsG3bNmRmZiI6OhpfffVVmX0tXLgQgwcPRteuXXHs2LG7Os870a1bN/j4+GDw4ME4ePAgfvvtNwwdOhSBgYFo06aN0fvx9PTEr7/+iqysLFy8eLHKV9LLExYWho0bNyIsLAypqak4fPgw3n//fb1tRo0ahZ9++gm7du0qs0BbeHg4Fi1ahGXLluH48eM4ePAgPvroo3LHGjx4MJycnNC7d2/ExsYq53LSpEn4+++/K80zMzMT06dPR0JCAv766y/s3r0bx48fR/Pmze/sABAREREREVXTPVu9/J133sEbb7yBmTNnonnz5hgwYECVnkWOiYlBeno6OnXqhNatW2PmzJlwdXVVtvnkk0/Qr18/jBs3Ds2aNcPo0aP1vlLsVosXL8YLL7yArl27Ij09/a7M706pVCp8++23cHBwQOfOndGtWzc0bNgQmzZtqtJ+pkyZAnNzc7Ro0QJ169bFqVOn7ji3oKAgbN68Gd999x1atWqFrl274rffftPbpnHjxujQoQOaNWuGdu3a6bWFhoZiyZIlWLFiBby9vfH000/j+PHj5Y5lbW2Nn3/+Ge7u7nj++efRvHlzjBw5ElevXoVWq600T2traxw7dgx9+/ZFkyZN8PLLL2P8+PEYM2bMnR0AIiIiIiKialLdWIiU6M6ICBo3boxx48bh9ddfr+l07pn8/HzY29sjLy/P4B8FiIj+Eyr5GkYiIqL/snwA9kCVP/vfk4XU6L/twoUL+PLLL/HPP/8o381NRERERERE9/D28v8Sb29vva8vuzXKW3X7fnen86lXrx7effddrFq1Cg4ODibNtaI8bW1tERsba9KxiYiIiIiIqopXuqvhhx9+wLVr18ptc3Z2vsfZ3Lk7nc+9fEIhKSmpwrZHHnnknuVBRERERERkDBbd1eDh4VHTKdxVD9J8vLy8ajoFIiIiIiIio/H2ciIiIiIiIiITYdFNREREREREZCK8vZyIiIiMx28aJSKih1V+PmBvX+VuvNJNREREREREZCIsuomIiIiIiIhMhEU3ERERERERkYmw6CYiIiIiIiIyERbdRERERERERCbC1cuJiIjuJpWqpjMgIiKi+wivdBMRERERERGZCItuIiIiIiIiIhNh0U1ERERERERkIiy6iYiIiIiIiEyERTcRERERERGRibDoJiIiIiIiIjKRu1p0Z2VlQaVSISkp6W7uVk90dDRUKhVyc3NNNsZ/wbBhw9CnT5+aTqNckZGRqF27dk2nQUREREREZHK80l2OB6kovBd/6CAiIiIiIqLqYdFNREREREREZCLVKrp1Oh0WLFgALy8vqNVquLu7Y86cOUr7yZMn0aVLF1hbW8PPzw8JCQl6/ePi4tCpUydoNBq4ublh0qRJKCwsVNqLi4sxbdo0uLm5Qa1Ww8vLC5999lm5uVy5cgU9e/ZEQEAAcnNzodPp8O6776JBgwZQq9Vo1aoVdu3apWxf3u3pSUlJUKlUyMrKQnR0NIYPH468vDyoVCqoVCqEh4cbPCaenp6YPXs2hg4dCltbW3h4eOC7777DhQsX0Lt3b9ja2sLX1xd//PGHXr8tW7bA29sbarUanp6eWLRoUZn9zp07FyNGjICdnR3c3d2xatUqpf3RRx8FALRu3RoqlQpBQUF6/RcuXAgXFxfUqVMH48ePx7Vr1wzOBaj4HIgIvLy8sHDhQr3tbx7DEydOAAByc3MxZswYODs7w8rKCi1btsT27dsrHO/bb7+Fv78/rKys0LBhQ8yaNQvXr183KldDYxk6xitWrEDjxo1hZWUFZ2dn9OvXz6hxiYiIiIiIDJJqmDp1qjg4OEhkZKScOHFCYmNjZfXq1ZKZmSkApFmzZrJ9+3ZJS0uTfv36iYeHh1y7dk1ERE6cOCE2NjayePFiSU9Pl/j4eGndurUMGzZM2f8LL7wgbm5u8s0330hGRobs3btXvvzySxER2b9/vwCQnJwcycnJkQ4dOsiTTz4phYWFIiLy4YcfilarlY0bN8qxY8dk6tSpYmFhIenp6WX635SYmCgAJDMzU4qLi2XJkiWi1WolOztbsrOz5fLlywaPiYeHhzg6Osqnn34q6enp8sorr4hWq5UePXrIV199JWlpadKnTx9p3ry56HQ6ERH5448/xMzMTN59911JS0uTiIgI0Wg0EhERUWa/H3/8sRw/flzmzZsnZmZmcuzYMRER+e233wSA7N27V7Kzs+Xff/8VEZHQ0FDRarUyduxYSU1Nle+//16sra1l1apVRp3jys7BnDlzpEWLFnrbT5o0STp37iwiIqWlpfLEE0+It7e37N69WzIyMuT777+XH374QUREIiIixN7eXun7888/i1arlcjISMnIyJDdu3eLp6enhIeHG8zT0FiGjvHvv/8u5ubmsmHDBsnKypKDBw/K0qVLKxzv6tWrkpeXp8Tp06cFgOTl5Rl1XInoIQAwGAwGg8H4D0YeINX57I+qfpbIz88XtVotq1evLtN2s+hes2aN8t7Ro0cFgKSmpoqIyMiRI+Xll1/W6xcbGytmZmZSVFQkaWlpAkD27NlT7vg3i+bU1FTx9fWVvn37SnFxsdLu6uoqc+bM0evTtm1bGTdunF7/iopukbJFoTE8PDzkpZdeUl5nZ2cLAHnnnXeU9xISEgSAZGdni4jIoEGDpHv37nr7efPNN/UK2tv3q9PppF69evLJJ5+IyP8d88TERL39hIaGioeHh1y/fl15r3///jJgwACDczF0Ds6cOSPm5uby66+/iohISUmJODk5SWRkpIiI/Pjjj2JmZiZpaWnl9r/9+AYHB8vcuXP1tlm/fr24uLgYzNXQWIaO8ZYtW0Sr1Up+fr7BsUREwsLCBP//P7Zbg0U3ESnugw8FDAaDwWAw7n5Ut+iu8u3lqampKC4uRnBwcIXb+Pr6Kj+7uLgAAM6fPw8ASE5ORmRkJGxtbZUICQmBTqdDZmYmkpKSYG5ujsDAwErz6N69O7y8vLBp0yZYWloCAPLz83H27FkEBATobRsQEIDU1NSqTrXKbp23s7MzAMDHx6fMezePRWpqarm5Hj9+HKWlpeXuV6VSoX79+so+KuPt7Q1zc3PltYuLi1H9DJ0DV1dX9OrVC59//jkA4Pvvv0dxcTH69++v9G/QoAGaNGlicCzgxu/Eu+++q/c7MXr0aGRnZ+PKlSsGc61sLEPHuHv37vDw8EDDhg0xZMgQREVFVTrm9OnTkZeXp8Tp06eNmiMRERERET2cqlx0azQag9tYWFgoP6tUKgA3ngMHgIKCAowZMwZJSUlKJCcn4/jx42jUqJFR+weAXr164eeff0ZKSkqV8jczuzFlEVHeM/Y5Z0PKm3dlx6I6+725H2P2Ud1+xpyDUaNG4csvv0RRUREiIiIwYMAAWFtbG93/VgUFBZg1a5be78Thw4dx/PhxWFlZ3XGulbGzs8PBgwexceNGuLi4YObMmfDz86vwK+nUajW0Wq1eEBERERERVaTKRXfjxo2h0Wiwb9++ag3o7++PlJQUeHl5lQlLS0v4+PhAp9MhJiam0v3Mnz8foaGhCA4OVgpvrVYLV1dXxMfH620bHx+PFi1aAADq1q0LAMjOzlbab/+6LUtLS70rzabSvHnzcnNt0qSJ3hXqyty8yn838zXmHDz11FOwsbHBJ598gl27dmHEiBFKm6+vL/7++2+kp6cbNZ6/vz/S0tLK/Z24+UeSihgay5hjXKtWLXTr1g0LFizAoUOHkJWVhZ9++smo3ImIiIiIiCpTq6odrKysMG3aNEydOhWWlpYICAjAhQsXcPTo0UpvOb9p2rRpeOKJJzBhwgSMGjUKNjY2SElJwZ49e7B8+XJ4enoiNDQUI0aMwLJly+Dn54e//voL58+fxwsvvKC3r4ULF6K0tBRdu3ZFdHQ0mjVrhjfffBNhYWFo1KgRWrVqhYiICCQlJSEqKgoA4OXlBTc3N4SHh2POnDlIT08vd8XwgoIC7Nu3D35+frC2tlau4t5Nb7zxBtq2bYv33nsPAwYMQEJCApYvX44VK1YYvY969epBo9Fg165daNCgAaysrGBvb39HeRlzDszNzTFs2DBMnz4djRs3Rvv27ZX+gYGB6Ny5M/r27YsPP/wQXl5eOHbsGFQqFXr06FFmvJkzZ+Lpp5+Gu7s7+vXrBzMzMyQnJ+PIkSOYPXt2pbkaGsvQMd6+fTtOnjyJzp07w8HBAT/88AN0Oh2aNm16R8eQiIiIiIgIAFCdNWJKS0tl9uzZ4uHhIRYWFuLu7i5z584td1GvnJwcASD79+9X3vvtt9+ke/fuYmtrKzY2NuLr66u3+FlRUZFMnjxZXFxcxNLSUry8vOTzzz8XkfIXQps4caK4uLhIWlqalJaWSnh4uDzyyCNiYWEhfn5+snPnTr384+LixMfHR6ysrKRTp06yefNmAf5vITURkbFjx0qdOnUEgISFhRk8Jh4eHrJ48WK99wDI1q1bldflHZ+vv/5aWrRooRzHDz74wOB+/fz89HJavXq1uLm5iZmZmQQGBorIjYXUevfurdfv1VdfVdoNqewc3JSRkSEAZMGCBWX6//vvvzJ8+HCpU6eOWFlZScuWLWX79u0iUv5Cdbt27ZIOHTqIRqMRrVYrjz/+uNErrVc2lkjlxzg2NlYCAwPFwcFBNBqN+Pr6yqZNm4waV0QkLy+vWospENF/2H2w0AuDwWAwGIy7H9VdSE114/MBUdXFxsYiODgYp0+fVhaJe9jk5+fD3t4eeXl5fL6biG74/+t3EBER0X9LPgB7oMqf/at8ezlRcXExLly4gPDwcPTv3/+hLbiJiIiIiIgMqfJCag+j2NhYva+zuj0eNHc6n40bN8LDwwO5ublYsGCBSXONioqqME9vb2+Tjk1ERERERHSneHu5EYqKinDmzJkK2728vO5hNnfuQZrP5cuXce7cuXLbLCws4OHhcY8z0sfby4moDN5eTkRE9J9U3dvLWXQT3QEW3URUBotuIiKi/6TqFt28vZyIiIiIiIjIRFh0ExEREREREZkIVy8nIiK6m/jUFhER0X9Tfj5gb1/lbrzSTURERERERGQiLLqJiIiIiIiITIRFNxEREREREZGJsOgmIiIiIiIiMhEW3UREREREREQmwtXLiYjo3lKpajoDIiIionuGV7qJiIiIiIiITIRFNxEREREREZGJsOgmIiIiIiIiMhEW3UREREREREQmwqKbiIiIiIiIyERYdBMRERERERGZyH1ZdGdlZUGlUiEpKclkY0RHR0OlUiE3N7fGcjCl+zn/8PBwtGrVqqbTICIiIiIiMjl+T3cF3NzckJ2dDScnp5pOhYiIiIiIiB5QLLorYG5ujvr169d0GkRERERERPQAq9Hby3U6HRYsWAAvLy+o1Wq4u7tjzpw5SvvJkyfRpUsXWFtbw8/PDwkJCXr94+Li0KlTJ2g0Gri5uWHSpEkoLCxU2ouLizFt2jS4ublBrVbDy8sLn332Wbm5XLlyBT179kRAQAByc3PL3J5983b0ffv2oU2bNrC2tkaHDh2QlpZmcJ55eXkwNzfHH3/8oczb0dERTzzxhLLNF198ATc3N+X16dOn8cILL6B27dpwdHRE7969kZWVpbffNWvWoHnz5rCyskKzZs2wYsWKCnMoLS3FiBEj0KxZM5w6dcpgzrm5uRgzZgycnZ1hZWWFli1bYvv27SgsLIRWq8XXX3+tt/22bdtgY2ODy5cvAwD+/vtvDBw4EI6OjrCxsUGbNm3w66+/VjheVeZyO0NjffLJJ2jUqBEsLS3RtGlTrF+/XmkTEYSHh8Pd3R1qtRqurq6YNGmS0WMTERERERFVpkavdE+fPh2rV6/G4sWL0bFjR2RnZ+PYsWNK+4wZM7Bw4UI0btwYM2bMwMCBA3HixAnUqlULGRkZ6NGjB2bPno3PP/8cFy5cwIQJEzBhwgREREQAAIYOHYqEhAQsW7YMfn5+yMzMxMWLF8vkkZubi169esHW1hZ79uyBtbV1hc96z5gxA4sWLULdunUxduxYjBgxAvHx8ZXO097eHq1atUJ0dDTatGmDw4cPQ6VSITExEQUFBbC1tUVMTAwCAwMBANeuXUNISAjat2+P2NhY1KpVC7Nnz0aPHj1w6NAhWFpaIioqCjNnzsTy5cvRunVrJCYmYvTo0bCxsUFoaKje+MXFxRg4cCCysrIQGxuLunXrVpqvTqdDz549cfnyZXzxxRdo1KgRUlJSYG5uDhsbG7z44ouIiIhAv379lD43X9vZ2aGgoACBgYF45JFH8N1336F+/fo4ePAgdDpdueNVZS63MzTW1q1b8eqrr2LJkiXo1q0btm/fjuHDh6NBgwbo0qULtmzZgsWLF+PLL7+Et7c3/vnnHyQnJ1c4XnFxMYqLi5XX+fn5leZHREREREQPOakh+fn5olarZfXq1WXaMjMzBYCsWbNGee/o0aMCQFJTU0VEZOTIkfLyyy/r9YuNjRUzMzMpKiqStLQ0ASB79uwpd/z9+/cr+/P19ZW+fftKcXFxmRwSExP1tt+7d6+yzY4dOwSAFBUVGZzv66+/Lr169RIRkSVLlsiAAQPEz89Pdu7cKSIiXl5esmrVKhERWb9+vTRt2lR0Op3Sv7i4WDQajfz4448iItKoUSPZsGGD3hjvvfeetG/fXi//2NhYCQ4Olo4dO0pubq7BPEVEfvzxRzEzM5O0tLRy23/99VcxNzeXs2fPiojIuXPnpFatWhIdHS0iIitXrhQ7Ozv5999/y+0fFhYmfn5+ymtDc6mMobE6dOggo0eP1nuvf//+8tRTT4mIyKJFi6RJkyZSUlJicKybuQMoE3l5eUb1JyIRARgMBoPBYDAeuMhD9T7719jt5ampqSguLkZwcHCF2/j6+io/u7i4AADOnz8PAEhOTkZkZCRsbW2VCAkJgU6nQ2ZmJpKSkmBubq5cPa5I9+7d4eXlhU2bNsHS0tJg3pXlVJnAwEDExcWhtLQUMTExCAoKQlBQEKKjo3H27FmcOHECQUFBytxOnDgBOzs7ZW6Ojo64evUqMjIyUFhYiIyMDIwcOVJv/rNnz0ZGRobeuAMHDkRhYSF2794Ne3t7g3kCQFJSEho0aIAmTZqU2/7444/D29sba9euBXDj1ngPDw907txZ6d+6dWs4OjoaHKsqc6ko18rGSk1NRUBAgN57AQEBSE1NBQD0798fRUVFaNiwIUaPHo2tW7fi+vXrFY43ffp05OXlKXH69GmDORIRERER0cOrxm4v12g0BrexsLBQflapVACg3DZcUFCAMWPGlPv8rbu7O06cOGFUHr169cKWLVuQkpICHx+fO8qpMp07d8bly5dx8OBB/Pzzz5g7dy7q16+P+fPnw8/PD66urmjcuLEyt8ceewxRUVFl9lO3bl0UFBQAAFavXo127drptZubm+u9fuqpp/DFF18gISEBXbt2NZgnYNy5GTVqFD7++GO89dZbiIiIwPDhw5XjYUz/m6oyl+rmWhk3NzekpaVh79692LNnD8aNG4cPPvgAMTExeuf6JrVaDbVafUdjEhERERHRw6PGrnQ3btwYGo0G+/btq1Z/f39/pKSkwMvLq0xYWlrCx8cHOp0OMTExle5n/vz5CA0NRXBwMFJSUqqVizFq164NX19fLF++HBYWFmjWrBk6d+6MxMREbN++Xe+KvL+/P44fP4569eqVmZu9vT2cnZ3h6uqKkydPlml/9NFH9cZ95ZVXMH/+fDz77LMGj8VNvr6++Pvvv5Genl7hNi+99BL++usvLFu2DCkpKXrPXvv6+iIpKQmXLl0yOFZV5lJRrpWN1bx58zLP3MfHx6NFixbKa41Gg2eeeQbLli1DdHQ0EhIScPjwYYNjExERERERGWSiJ/aMEh4eLg4ODrJ27Vo5ceKEJCQkyJo1a8o8Ty0ikpOTIwBk//79IiKSnJwsGo1Gxo8fL4mJiZKeni7btm2T8ePHK32GDRsmbm5usnXrVjl58qTs379fNm3aJCL/94x2Tk6OiIi89tpr4uzsrDwzXtEz3Te3FxFJTEwUAJKZmWnUfF977TUxNzeXAQMGKO/5+fmJubm5fPrpp8p7hYWF0rhxYwkKCpKff/5ZyX3ixIly+vRpERFZvXq1aDQaWbp0qaSlpcmhQ4fk888/l0WLFpWb/+LFi8XW1lZiY2ONyjUoKEhatmwpu3fvlpMnT8oPP/ygPH9+06BBg8TS0lJ69Oih935xcbE0adJEOnXqJHFxcZKRkSFff/21HDhwQETKPtNtaC6VMTTW1q1bxcLCQlasWCHp6emyaNEiMTc3V36PIiIiZM2aNXL48GHJyMiQt99+WzQajVy8eNGo45SXl1et5zqIHmr3wTNZDAaDwWAwGFWN6j7TDRN9pDJKaWmpzJ49Wzw8PMTCwkLc3d1l7ty5RhXdIiK//fabdO/eXWxtbcXGxkZ8fX1lzpw5SntRUZFMnjxZXFxcxNLSUry8vOTzzz8XkfKL6IkTJ4qLi4ukpaWZpOjeunWrAJBPPvlEee/VV18VAHLs2DG9bbOzs2Xo0KHi5OQkarVaGjZsKKNHj9Y7wVFRUdKqVSuxtLQUBwcH6dy5s3zzzTciUrboFrmxaJidnZ3Ex8cbzPXff/+V4cOHS506dcTKykpatmwp27dv19tm3759AkC++uqrMv2zsrKkb9++otVqxdraWtq0aSO//vqriJQtug3NxZDKxhIRWbFihTRs2FAsLCykSZMmsm7dOqVt69at0q5dO9FqtWJjYyNPPPGE3mJ5hrDoJqqG++AfTQaDwWAwGIyqRnWLbtWNzz9EVbd+/XpMnjwZZ8+eNWoRuv+i/Px82NvbIy8vD1qttqbTIXow/P/1H4iIiIgeJPkA7IEqf/av0e/ppgfTlStXkJ2djfnz52PMmDEPbcFNRERERERkSI0tpPZf4+3trfeVV7dGeauQ16SoqKgKc/X29jbYf8GCBWjWrBnq16+P6dOnmzTXuXPnVphrz549TTo2ERERERHRneLt5XfJX3/9hWvXrpXb5uzsDDs7u3ucUcUuX76Mc+fOldtmYWEBDw+Pe5xRxS5dulThyuQajQaPPPLIPc5IH28vJ6oG3l5OREREDyDeXl7D7qdC1RA7O7v76o8AlXF0dISjo2NNp0FERERERFQtvL2ciIiIiIiIyERYdBMRERERERGZCG8vJyKie4tLiRAREdGDKD8fsLevcjde6SYiIiIiIiIyERbdRERERERERCbCopuIiIiIiIjIRFh0ExEREREREZkIi24iIiIiIiIiE+Hq5URE9xuVqqYzICIiIqK7hFe6iYiIiIiIiEyERTcRERERERGRibDoJiIiIiIiIjIRFt1EREREREREJsKim4iIiIiIiMhEWHQTERERERERmUiNFt1ZWVlQqVRISkoy2RjR0dFQqVTIzc012RgPgvDwcLRq1aqm0yiXp6cnlixZYvJxIiMjUbt2bZOPQ0REREREdBOvdN8FLOYeDAMGDEB6enpNp0FERERERA+RWjWdANG9otFooNFoajoNIiIiIiJ6iNyTK906nQ4LFiyAl5cX1Go13N3dMWfOHKX95MmT6NKlC6ytreHn54eEhAS9/nFxcejUqRM0Gg3c3NwwadIkFBYWKu3FxcWYNm0a3NzcoFar4eXlhc8++6zcXK5cuYKePXsiICAAubm50Ol0ePfdd9GgQQOo1Wq0atUKu3btUrYv7/b0pKQkqFQqZGVlITo6GsOHD0deXh5UKhVUKhXCw8MrPR7Lly9Hy5Ytldfbtm2DSqXCp59+qrzXrVs3vP3228rrb7/9Fv7+/rCyskLDhg0xa9YsXL9+XWnPzc3FqFGjULduXWi1WnTt2hXJyckV5pCRkYGGDRtiwoQJEJFK8wWA+Ph4BAUFwdraGg4ODggJCUFOTg7WrVuHOnXqoLi4WG/7Pn36YMiQIcrr77//Hm3btoWVlRWcnJzw3HPPVThWVedyq+TkZHTp0gV2dnbQarV47LHH8McffwAoe0fCzVvu169fD09PT9jb2+PFF1/E5cuXjRqLiIiIiIjIkHtSdE+fPh3z58/HO++8g5SUFGzYsAHOzs5K+4wZMzBlyhQkJSWhSZMmGDhwoFJQZmRkoEePHujbty8OHTqETZs2IS4uDhMmTFD6Dx06FBs3bsSyZcuQmpqKlStXwtbWtkweubm56N69O3Q6Hfbs2YPatWtj6dKlWLRoERYuXIhDhw4hJCQEzz77LI4fP27U3Dp06IAlS5ZAq9UiOzsb2dnZmDJlSqV9AgMDkZKSggsXLgAAYmJi4OTkhOjoaADAtWvXkJCQgKCgIABAbGwshg4dildffRUpKSlYuXIlIiMj9f5w0b9/f5w/fx47d+7En3/+CX9/fwQHB+PSpUtlxj906BA6duyIQYMGYfny5VCpVJXmm5SUhODgYLRo0QIJCQmIi4vDM888g9LSUvTv3x+lpaX47rvvlO3Pnz+PHTt2YMSIEQCAHTt24LnnnsNTTz2FxMRE7Nu3D48//niF41VlLrcbPHgwGjRogN9//x1//vkn3nrrLVhYWFS4fUZGBrZt24bt27dj+/btiImJwfz58yvcvri4GPn5+XpBRERERERUITGx/Px8UavVsnr16jJtmZmZAkDWrFmjvHf06FEBIKmpqSIiMnLkSHn55Zf1+sXGxoqZmZkUFRVJWlqaAJA9e/aUO/7+/fuV/fn6+krfvn2luLhYaXd1dZU5c+bo9Wnbtq2MGzdOr39OTo7SnpiYKAAkMzNTREQiIiLE3t7e6GOi0+mkTp06snnzZhERadWqlcybN0/q168vIiJxcXFiYWEhhYWFIiISHBwsc+fO1dvH+vXrxcXFRTkeWq1Wrl69qrdNo0aNZOXKlSIiEhYWJn5+fhIfHy8ODg6ycOFCo/MdOHCgBAQEVNj+yiuvSM+ePZXXixYtkoYNG4pOpxMRkfbt28vgwYMr7O/h4SGLFy82ei6VsbOzk8jIyHLbbj9PYWFhYm1tLfn5+cp7b775prRr167C/YeFhQmAMpGXl2cwNyKjAQwGg8FgMBiM+yzyUL3P/ia/0p2amori4mIEBwdXuI2vr6/ys4uLC4AbV0uBG7cLR0ZGwtbWVomQkBDodDpkZmYiKSkJ5ubmCAwMrDSP7t27w8vLC5s2bYKlpSUAID8/H2fPnkVAQIDetgEBAUhNTa3WfI2hUqnQuXNnREdHIzc3FykpKRg3bhyKi4tx7NgxxMTEoG3btrC2tgZw4xi8++67esdg9OjRyM7OxpUrV5CcnIyCggLUqVNHb5vMzExkZGQo4546dQrdu3fHzJkz8cYbbxid780r3RUZPXo0du/ejTNnzgC4cRv3sGHDlCvohvrfyti5VOT111/HqFGj0K1bN8yfP99gH09PT9jZ2SmvXVxclN+98kyfPh15eXlKnD592qh5ERERERHRw8nkC6kZs3DVrbf/3izUdDodAKCgoABjxozBpEmTyvRzd3fHiRMnjMqjV69e2LJlC1JSUuDj42NUHwAwM7vxdwkRUd67du2a0f0rEhQUhFWrViE2NhatW7eGVqtVCvGYmBi9PyIUFBRg1qxZeP7558vsx8rKCgUFBXBxcVFuT7/Vrc8w161bF66urti4cSNGjBgBrVZrVK6GzmHr1q3h5+eHdevW4cknn8TRo0exY8cOo/vfyti5VCQ8PByDBg3Cjh07sHPnToSFheHLL7+s8Bny2289V6lUyu9eedRqNdRqtcE8iIiIiIiIgHvwTHfjxo2h0Wiwb9++avX39/dHSkoKvLy8yoSlpSV8fHyg0+kQExNT6X7mz5+P0NBQBAcHIyUlBQCg1Wrh6uqK+Ph4vW3j4+PRokULADcKVQDIzs5W2m//XnFLS0uUlpZWaV43n+vevHmz8ux2UFAQ9u7dqyxadusxSEtLK/cYmJmZwd/fH//88w9q1apVpt3JyUnZj0ajwfbt22FlZYWQkBCjFwzz9fU1eP5GjRqFyMhIREREoFu3bnBzc6tS/1vnasxcKtOkSRNMnjwZu3fvxvPPP4+IiAij+hEREREREd1tJi+6raysMG3aNEydOhXr1q1DRkYGfvnllwpXF7/dtGnTcODAAUyYMAFJSUk4fvw4vv32W2UhNU9PT4SGhmLEiBHYtm0bMjMzER0dja+++qrMvhYuXIjBgweja9euOHbsGADgzTffxPvvv49NmzYhLS0Nb731FpKSkvDqq68CALy8vODm5obw8HAcP34cO3bswKJFi/T26+npiYKCAuzbtw8XL17ElStXDM7L19cXDg4O2LBhg17RvW3bNhQXF+vd8j5z5kysW7cOs2bNwtGjR5Gamoovv/xSWd28W7duaN++Pfr06YPdu3cjKysLBw4cwIwZM5SVu2+ysbHBjh07UKtWLfTs2RMFBQUGc50+fTp+//13jBs3DocOHcKxY8fwySef4OLFi8o2gwYNwt9//43Vq1crC6jdFBYWho0bNyIsLAypqak4fPgw3n///XLHqspcbldUVIQJEyYgOjoaf/31F+Lj4/H777+jefPmBudIRERERERkEqZZBUhfaWmpzJ49Wzw8PMTCwkLc3d1l7ty5ykJqiYmJyrY5OTkCQPbv36+899tvv0n37t3F1tZWbGxsxNfXV2/xs6KiIpk8ebK4uLiIpaWleHl5yeeffy4i5S+ENnHiRHFxcZG0tDQpLS2V8PBweeSRR8TCwkL8/Pxk586devnHxcWJj4+PWFlZSadOnWTz5s0C/N9CaiIiY8eOlTp16ggACQsLM+q49O7dW2rVqiWXL19WjpODg4M88cQTZbbdtWuXdOjQQTQajWi1Wnn88cdl1apVSnt+fr5MnDhRXF1dxcLCQtzc3GTw4MFy6tQpEfm/hdRuunz5snTo0EE6d+4sBQUFBnONjo6WDh06iFqtltq1a0tISIjeMRURGTJkiDg6OpZZBE1EZMuWLdKqVSuxtLQUJycnef7555W2WxdSM2YuFSkuLpYXX3xR3NzcxNLSUlxdXWXChAlSVFQkIuUvpHbrMRERWbx4sXh4eBg8Hjfl5eVVazEFokrdBwuFMBgMBoPBYDD0o7oLqalufL4junPBwcHw9vbGsmXLajqVeyY/Px/29vbIy8sz+hl5IoMMfI0fEREREd17+QDsgSp/9jf5Qmr035eTk4Po6GhER0djxYoVNZ0OERERERHRfcPkz3Q/jGJjY/W+7ur2uN/07Nmzwlznzp1rsH/r1q0xbNgwvP/++2jatKlJc/X29q4w16ioKJOOTUREREREVFW8vdwEioqKlO+sLo+Xl9c9zMawM2fOoKioqNw2R0dHODo63uOMKvbXX39V+JVtzs7Oet+5fS/w9nIyCd5eTkRERHTfqe7t5Sy6ie4Ai24yCRbdRERERPed6hbdvL2ciIiIiIiIyERYdBMRERERERGZCFcvJyK63/CpHyIiIqL7T34+YG9f5W680k1ERERERERkIiy6iYiIiIiIiEyERTcRERERERGRibDoJiIiIiIiIjIRFt1EREREREREJsLVy4nowaNS1XQGRERERERG4ZVuIiIiIiIiIhNh0U1ERERERERkIiy6iYiIiIiIiEyERTcRERERERGRibDoJiIiIiIiIjIRFt1EREREREREJvJAFN1ZWVlQqVRISkoy2RjR0dFQqVTIzc2tsRxM6X7OPzw8HK1atbonY6lUKmzbtu2ejEVERERERMTv6TaSm5sbsrOz4eTkVNOp0B3Izs6Gg4NDTadBREREREQPCRbdRjI3N0f9+vVrOg26QzyHRERERER0L91Xt5frdDosWLAAXl5eUKvVcHd3x5w5c5T2kydPokuXLrC2toafnx8SEhL0+sfFxaFTp07QaDRwc3PDpEmTUFhYqLQXFxdj2rRpcHNzg1qthpeXFz777LNyc7ly5Qp69uyJgIAA5Obmlrk9++bt6Pv27UObNm1gbW2NDh06IC0tzeA88/LyYG5ujj/++EOZt6OjI5544gllmy+++AJubm7K69OnT+OFF15A7dq14ejoiN69eyMrK0tvv2vWrEHz5s1hZWWFZs2aYcWKFRXmUFpaihEjRqBZs2Y4deqUwZxzc3MxZswYODs7w8rKCi1btsT27dtRWFgIrVaLr7/+Wm/7bdu2wcbGBpcvXwYA/P333xg4cCAcHR1hY2ODNm3a4Ndff61wvKrM5VYlJSWYMGECXFxcYGVlBQ8PD8ybN09pv/X28pvn9Jtvvqn094qIiIiIiKi67quie/r06Zg/fz7eeecdpKSkYMOGDXB2dlbaZ8yYgSlTpiApKQlNmjTBwIEDcf36dQBARkYGevTogb59++LQoUPYtGkT4uLiMGHCBKX/0KFDsXHjRixbtgypqalYuXIlbG1ty+SRm5uL7t27Q6fTYc+ePahdu3aFOc+YMQOLFi3CH3/8gVq1amHEiBEG52lvb49WrVohOjoaAHD48GGoVCokJiaioKAAABATE4PAwEAAwLVr1xASEgI7OzvExsYiPj4etra26NGjB0pKSgAAUVFRmDlzJubMmYPU1FTMnTsX77zzDtauXVtm/OLiYvTv3x9JSUmIjY2Fu7t7pfnqdDr07NkT8fHx+OKLL5CSkoL58+fD3NwcNjY2ePHFFxEREaHXJyIiAv369YOdnR0KCgoQGBiIM2fO4LvvvkNycjKmTp0KnU5X7nhVmcvtli1bhu+++w5fffUV0tLSEBUVBU9Pz0r7VPZ7dbvi4mLk5+frBRERERERUYXkPpGfny9qtVpWr15dpi0zM1MAyJo1a5T3jh49KgAkNTVVRERGjhwpL7/8sl6/2NhYMTMzk6KiIklLSxMAsmfPnnLH379/v7I/X19f6du3rxQXF5fJITExUW/7vXv3Ktvs2LFDAEhRUZHB+b7++uvSq1cvERFZsmSJDBgwQPz8/GTnzp0iIuLl5SWrVq0SEZH169dL06ZNRafTKf2Li4tFo9HIjz/+KCIijRo1kg0bNuiN8d5770n79u318o+NjZXg4GDp2LGj5ObmGsxTROTHH38UMzMzSUtLK7f9119/FXNzczl79qyIiJw7d05q1aol0dHRIiKycuVKsbOzk3///bfc/mFhYeLn56e8NjSXykycOFG6du2qd6xuBUC2bt0qIsb9XpWXK4AykZeXZzA3uosABoPBYDAYDAbjnkYeqvfZ/7650p2amori4mIEBwdXuI2vr6/ys4uLCwDg/PnzAIDk5GRERkbC1tZWiZCQEOh0OmRmZiIpKQnm5ubK1eOKdO/eHV5eXti0aRMsLS0N5l1ZTpUJDAxEXFwcSktLERMTg6CgIAQFBSE6Ohpnz57FiRMnEBQUpMztxIkTsLOzU+bm6OiIq1evIiMjA4WFhcjIyMDIkSP15j979mxkZGTojTtw4EAUFhZi9+7dsLe3N5gnACQlJaFBgwZo0qRJue2PP/44vL29lSvRX3zxBTw8PNC5c2elf+vWreHo6GhwrKrMpTzDhg1DUlISmjZtikmTJmH37t0G+1TlHE6fPh15eXlKnD592uD+iYiIiIjo4XXfLKSm0WgMbmNhYaH8rFKpAEC5RbmgoABjxozBpEmTyvRzd3fHiRMnjMqjV69e2LJlC1JSUuDj43NHOVWmc+fOuHz5Mg4ePIiff/4Zc+fORf369TF//nz4+fnB1dUVjRs3Vub22GOPISoqqsx+6tatq9ySvnr1arRr106v3dzcXO/1U089hS+++AIJCQno2rWrwTwB487NqFGj8PHHH+Ott95CREQEhg8frhwPY/rfVJW5lMff3x+ZmZnYuXMn9u7dixdeeAHdunUr88z5rapyDtVqNdRqtcE8iIiIiIiIgPuo6G7cuDE0Gg327duHUaNGVbm/v78/UlJS4OXlVW67j48PdDodYmJi0K1btwr3M3/+fNja2iI4OBjR0dFo0aJFlXMxRu3ateHr64vly5fDwsICzZo1Q7169TBgwABs375d74q8v78/Nm3ahHr16kGr1ZbZl729PVxdXXHy5EkMHjy40nFfeeUVtGzZEs8++yx27Nhh8Mo/cONK8N9//4309PQKr3a/9NJLmDp1KpYtW4aUlBSEhobq9V+zZg0uXbpk8Gq3s7Oz0XOpiFarxYABAzBgwAD069cPPXr0MGpsIiIiIiKiu+2+ub3cysoK06ZNw9SpU7Fu3TpkZGTgl19+qXB18dtNmzYNBw4cwIQJE5CUlITjx4/j22+/VRZS8/T0RGhoKEaMGIFt27YhMzMT0dHR+Oqrr8rsa+HChRg8eDC6du2KY8eO3dV53iooKAhRUVFK4evo6IjmzZtj06ZNesXw4MGD4eTkhN69eyM2NlbJfdKkSfj7778BALNmzcK8efOwbNkypKen4/Dhw4iIiMCHH35YZtyJEydi9uzZePrppxEXF2cwz8DAQHTu3Bl9+/bFnj17lCvJu3btUrZxcHDA888/jzfffBNPPvkkGjRooLQNHDgQ9evXR58+fRAfH4+TJ09iy5YtFa4SXpW53O7DDz/Exo0bcezYMaSnp2Pz5s2oX79+pYvhERERERERmcp9U3QDwDvvvIM33ngDM2fORPPmzTFgwACjno8GblxNjYmJQXp6Ojp16oTWrVtj5syZcHV1Vbb55JNP0K9fP4wbNw7NmjXD6NGj9b5S7FaLFy/GCy+8gK5duyI9Pf2uzO92gYGBKC0tVZ7dBm4U4re/Z21tjZ9//hnu7u54/vnn0bx5c4wcORJXr15VrnyPGjUKa9asQUREBHx8fBAYGIjIyEg8+uij5Y792muvYdasWXjqqadw4MABg7lu2bIFbdu2xcCBA9GiRQtMnToVpaWletuMHDkSJSUlZVZwt7S0xO7du1GvXj089dRT8PHxUVY/L09V53IrOzs7LFiwAG3atEHbtm2RlZWFH374AWZm99WvOhERERERPSRUNxYCJrpz69evx+TJk3H27FmjFqH7L8jPz4e9vT3y8vLKvfWfTOT/P3tPRERERHSv5AOwB6r82f++eaabHlxXrlxBdnY25s+fjzFjxjw0BTcREREREZEhvOfWRLy9vfW+8urWKG8V8poUFRVVYa7e3t4G+y9YsADNmjVD/fr1MX36dJPmOnfu3Apz7dmzp0nHJiIiIiIiqireXm4if/31F65du1Zum7OzM+zs7O5xRhW7fPkyzp07V26bhYUFPDw87nFGFbt06RIuXbpUbptGo8EjjzxyT/Ph7eU1hLeXExEREdE9xtvL7zP3U6FqiJ2d3X31R4DKODo68qu/iIiIiIjogcHby4mIiIiIiIhMhEU3ERERERERkYnw9nIievBwKQoiIiIiutfy8wF7+yp345VuIiIiIiIiIhNh0U1ERERERERkIiy6iYiIiIiIiEyERTcRERERERGRibDoJiIiIiIiIjIRrl5O9F+kUtV0BkREREREBF7pJiIiIiIiIjIZFt1EREREREREJsKim4iIiIiIiMhEWHQTERERERERmQiLbiIiIiIiIiITYdFNREREREREZCL3vOjOysqCSqVCUlKSycaIjo6GSqVCbm6uycZ4EISHh6NVq1Y1nUa5PD09sWTJkppOg4iIiIiIyKR4pbuaIiMjUbt27ZpOg4iIiIiIiO5jLLqJiIiIiIiITMRkRbdOp8OCBQvg5eUFtVoNd3d3zJkzR2k/efIkunTpAmtra/j5+SEhIUGvf1xcHDp16gSNRgM3NzdMmjQJhYWFSntxcTGmTZsGNzc3qNVqeHl54bPPPis3lytXrqBnz54ICAhAbm4udDod3n33XTRo0ABqtRqtWrXCrl27lO3Luz09KSkJKpUKWVlZiI6OxvDhw5GXlweVSgWVSoXw8PBKj8fy5cvRsmVL5fW2bdugUqnw6aefKu9169YNb7/9tvL622+/hb+/P6ysrNCwYUPMmjUL169fV9pzc3MxatQo1K1bF1qtFl27dkVycnKFOWRkZKBhw4aYMGECRKTSfAEgPj4eQUFBsLa2hoODA0JCQpCTk4N169ahTp06KC4u1tu+T58+GDJkiPL6+++/R9u2bWFlZQUnJyc899xzFY5V1bncrrKxcnJyMHToUDg4OMDa2ho9e/bE8ePHlfa//voLzzzzDBwcHGBjYwNvb2/88MMPRo9NRERERERUEZMV3dOnT8f8+fPxzjvvICUlBRs2bICzs7PSPmPGDEyZMgVJSUlo0qQJBg4cqBSUGRkZ6NGjB/r27YtDhw5h06ZNiIuLw4QJE5T+Q4cOxcaNG7Fs2TKkpqZi5cqVsLW1LZNHbm4uunfvDp1Ohz179qB27dpYunQpFi1ahIULF+LQoUMICQnBs88+q1eIVaZDhw5YsmQJtFotsrOzkZ2djSlTplTaJzAwECkpKbhw4QIAICYmBk5OToiOjgYAXLt2DQkJCQgKCgIAxMbGYujQoXj11VeRkpKClStXIjIyUu8PF/3798f58+exc+dO/Pnnn/D390dwcDAuXbpUZvxDhw6hY8eOGDRoEJYvXw6VSlVpvklJSQgODkaLFi2QkJCAuLg4PPPMMygtLUX//v1RWlqK7777Ttn+/Pnz2LFjB0aMGAEA2LFjB5577jk89dRTSExMxL59+/D4449XOF5V5nI7Q2MNGzYMf/zxB7777jskJCRARPDUU0/h2rVrAIDx48ejuLgYP//8Mw4fPoz333+/3N8l4MYfe/Lz8/WCiIiIiIioQmIC+fn5olarZfXq1WXaMjMzBYCsWbNGee/o0aMCQFJTU0VEZOTIkfLyyy/r9YuNjRUzMzMpKiqStLQ0ASB79uwpd/z9+/cr+/P19ZW+fftKcXGx0u7q6ipz5szR69O2bVsZN26cXv+cnBylPTExUQBIZmamiIhERESIvb290cdEp9NJnTp1ZPPmzSIi0qpVK5k3b57Ur19fRETi4uLEwsJCCgsLRUQkODhY5s6dq7eP9evXi4uLi3I8tFqtXL16VW+bRo0aycqVK0VEJCwsTPz8/CQ+Pl4cHBxk4cKFRuc7cOBACQgIqLD9lVdekZ49eyqvFy1aJA0bNhSdTiciIu3bt5fBgwdX2N/Dw0MWL15s9FwqU9lY6enpAkDi4+OV9y5evCgajUa++uorERHx8fGR8PBwg+OI3DimAMpEXl6eUf3vGYDBYDAYDAaDwWDcxchD9T77m+RKd2pqKoqLixEcHFzhNr6+vsrPLi4uAG5cLQWA5ORkREZGwtbWVomQkBDodDpkZmYiKSkJ5ubmCAwMrDSP7t27w8vLC5s2bYKlpSUAID8/H2fPnkVAQIDetgEBAUhNTa3WfI2hUqnQuXNnREdHIzc3FykpKRg3bhyKi4tx7NgxxMTEoG3btrC2tgZw4xi8++67esdg9OjRyM7OxpUrV5CcnIyCggLUqVNHb5vMzExkZGQo4546dQrdu3fHzJkz8cYbbxid780r3RUZPXo0du/ejTNnzgC4sbDcsGHDlCvohvrfyti5VCfX1NRU1KpVC+3atVPeq1OnDpo2baqc70mTJmH27NkICAhAWFgYDh06VOFY06dPR15enhKnT582ao5ERERERPRwqmWKnWo0GoPbWFhYKD/fLNR0Oh0AoKCgAGPGjMGkSZPK9HN3d8eJEyeMyqNXr17YsmULUlJS4OPjY1QfADAzu/G3CBFR3rt5K/KdCAoKwqpVqxAbG4vWrVtDq9UqhXhMTIzeHxEKCgowa9YsPP/882X2Y2VlhYKCAri4uCi3p9/q1lXV69atC1dXV2zcuBEjRoyAVqs1KldD57B169bw8/PDunXr8OSTT+Lo0aPYsWOH0f1vZexcqpurIaNGjUJISAh27NiB3bt3Y968eVi0aBEmTpxYZlu1Wg21Wn1H4xERERER0cPDJFe6GzduDI1Gg3379lWrv7+/P1JSUuDl5VUmLC0t4ePjA51Oh5iYmEr3M3/+fISGhiI4OBgpKSkAAK1WC1dXV8THx+ttGx8fjxYtWgC4UagCQHZ2ttJ++/eKW1paorS0tErzuvlc9+bNm5Vnt4OCgrB3715l0bJbj0FaWlq5x8DMzAz+/v74559/UKtWrTLtTk5Oyn40Gg22b98OKysrhISE4PLly0bl6uvra/D8jRo1CpGRkYiIiEC3bt3g5uZWpf63ztWYuVQn1+bNm+P69ev49ddflff+/fdfpKWlKecbANzc3DB27Fh88803eOONN7B69WqjciciIiIiIqqUaR4oFQkPDxcHBwdZu3atnDhxQhISEmTNmjXKM92JiYnKtjk5OQJA9u/fLyIiycnJotFoZPz48ZKYmCjp6emybds2GT9+vNJn2LBh4ubmJlu3bpWTJ0/K/v37ZdOmTSJS9pns1157TZydnZVnxhcvXixarVa+/PJLOXbsmEybNk0sLCwkPT1dRERKSkrEzc1N+vfvL+np6bJ9+3Zp2rSpAP/3THd8fLwAkL1798qFCxeUZ7Ero9PpxNHRUczNzWXnzp0icuNZcXNzc6lVq5YUFBQo2+7atUtq1aol4eHhcuTIEUlJSZGNGzfKjBkzlH117NhR/Pz85Mcff5TMzEyJj4+X//3vf/L777+LyP890y0icvnyZenYsaMEBATI5cuXDeaalpYmlpaW8sorr0hycrKkpqbKihUr5MKFC8o2ubm5Ym1tLZaWlvLll1/q9d+/f7+YmZnJzJkzJSUlRQ4dOiTz589X2m99ptuYuVTG0Fi9e/eWFi1aSGxsrCQlJUmPHj3Ey8tLSkpKRETk1VdflV27dsnJkyflzz//lHbt2skLL7xgcFwRkby8vGo912Fy98EzLwwGg8FgMBgMxn8pqvtMN0z0kV9KS0tl9uzZ4uHhIRYWFuLu7i5z5841qugWEfntt9+ke/fuYmtrKzY2NuLr66u3+FlRUZFMnjxZXFxcxNLSUry8vOTzzz8XkfIXQps4caK4uLhIWlqalJaWSnh4uDzyyCNiYWEhfn5+ShF8U1xcnPj4+IiVlZV06tRJNm/eLLcW3SIiY8eOlTp16ggACQsLM+q49O7dW2rVqqUUvqWlpeLg4CBPPPFEmW137dolHTp0EI1GI1qtVh5//HFZtWqV0p6fny8TJ04UV1dXsbCwEDc3Nxk8eLCcOnVKRPSLbpEbhXeHDh2kc+fOegV+RaKjo6VDhw6iVquldu3aEhISondMRUSGDBkijo6OZRZBExHZsmWLtGrVSiwtLcXJyUmef/55pe3WotuYuRhS2ViXLl2SIUOGiL29vWg0GgkJCVH+wCIiMmHCBGnUqJGo1WqpW7euDBkyRC5evGjUuCy6GQwGg8FgMBiMhyOqW3Srbnw+J6qe4OBgeHt7Y9myZTWdSo3Iz8+Hvb098vLyjH5e/p4w8JVwRERERERUNfkA7IEqf/Y3yUJq9N+Xk5OD6OhoREdHY8WKFTWdDhERERER0X3JJAupPYxiY2P1vu7q9rjf9OzZs8Jc586da7B/69atMWzYMLz//vto2rSpSXP19vauMNeoqCiTjk1ERERERHQneHv5XVJUVKR8Z3V5vLy87mE2hp05cwZFRUXltjk6OsLR0fEeZ1Sxv/76q8KvbHN2doadnd09zuj/8PZyIiIiIqKHQ3VvL2fRTXQHWHQTERERET0cqlt08/ZyIiIiIiIiIhNh0U1ERERERERkIly9nOi/iE+NEBERERHdXfn5gL19lbvxSjcRERERERGRibDoJiIiIiIiIjIRFt1EREREREREJsKim4iIiIiIiMhEWHQTERERERERmQiLbiIiIiIiIiITYdFNREREREREZCIsuomIiIiIiIhMhEU3ERERERERkYmw6CYiIiIiIiIyERbdRERERERERCbCopuIiIiIiIjIRFh0ExEREREREZkIi24iIiIiIiIiE2HRTURERERERGQiLLqJiIiIiIiITIRFNxEREREREZGJsOgmIiIiIiIiMhEW3UREREREREQmwqKbiIiIiIiIyERYdBMRERERERGZSK2aToDoQSYiAID8/PwazoSIiIiIiEzp5mf+mzWAsVh0E92By5cvAwDc3NxqOBMiIiIiIroX/v33X9jb2xu9vUqqWqYTkUKn0+Hs2bOws7ODSqWq6XQeCvn5+XBzc8Pp06eh1WprOh2qIp6/BxvP34OP5/DBxvP3YOP5e/Dl5eXB3d0dOTk5qF27ttH9eKWb6A6YmZmhQYMGNZ3GQ0mr1fIfrAcYz9+Djefvwcdz+GDj+Xuw8fw9+MzMqrY0GhdSIyIiIiIiIjIRFt1EREREREREJsKim4geKGq1GmFhYVCr1TWdClUDz9+Djefvwcdz+GDj+Xuw8fw9+Kp7DrmQGhEREREREZGJ8Eo3ERERERERkYmw6CYiIiIiIiIyERbdRERERERERCbCopuIHhhz5sxBhw4dYG1tjdq1a5dpT05OxsCBA+Hm5gaNRoPmzZtj6dKl9z5RKpeh8wcAp06dQq9evWBtbY169erhzTffxPXr1+9tomS09PR09O7dG05OTtBqtejYsSP2799f02lRFezYsQPt2rWDRqOBg4MD+vTpU9MpURUVFxejVatWUKlUSEpKqul0yEhZWVkYOXIkHn30UWg0GjRq1AhhYWEoKSmp6dSoAh9//DE8PT1hZWWFdu3a4bfffjO6L4tuInpglJSUoH///njllVfKbf/zzz9Rr149fPHFFzh69ChmzJiB6dOnY/ny5fc4UyqPofNXWlqKXr16oaSkBAcOHMDatWsRGRmJmTNn3uNMyVhPP/00rl+/jp9++gl//vkn/Pz88PTTT+Off/6p6dTICFu2bMGQIUMwfPhwJCcnIz4+HoMGDarptKiKpk6dCldX15pOg6ro2LFj0Ol0WLlyJY4ePYrFixfj008/xf/+97+aTo3KsWnTJrz++usICwvDwYMH4efnh5CQEJw/f96o/ly9nIgeOJGRkXjttdeQm5trcNvx48cjNTUVP/30k+kTI6NUdP527tyJp59+GmfPnoWzszMA4NNPP8W0adNw4cIFWFpa1kC2VJGLFy+ibt26+Pnnn9GpUycAwOXLl6HVarFnzx5069athjOkyly/fh2enp6YNWsWRo4cWdPpUDXt3LkTr7/+OrZs2QJvb28kJiaiVatWNZ0WVdMHH3yATz75BCdPnqzpVOg27dq1Q9u2bZULOTqdDm5ubpg4cSLeeustg/15pZuI/tPy8vLg6OhY02mQERISEuDj46MU3AAQEhKC/Px8HD16tAYzo/LUqVMHTZs2xbp161BYWIjr169j5cqVqFevHh577LGaTo8MOHjwIM6cOQMzMzO0bt0aLi4u6NmzJ44cOVLTqZGRzp07h9GjR2P9+vWwtrau6XToLuBnlvtTSUkJ/vzzT70/JpuZmaFbt25ISEgwah8suonoP+vAgQPYtGkTXn755ZpOhYzwzz//6BXcAJTXvF35/qNSqbB3714kJibCzs4OVlZW+PDDD7Fr1y44ODjUdHpkwM0raeHh4Xj77bexfft2ODg4ICgoCJcuXarh7MgQEcGwYcMwduxYtGnTpqbTobvgxIkT+OijjzBmzJiaToVuc/HiRZSWlpb7GcXYzycsuomoRr311ltQqVSVxrFjx6q83yNHjqB3794ICwvDk08+aYLMCTDd+aOaY+w5FRGMHz8e9erVQ2xsLH777Tf06dMHzzzzDLKzs2t6Gg8tY8+fTqcDAMyYMQN9+/bFY489hoiICKhUKmzevLmGZ/HwMvb8ffTRR7h8+TKmT59e0ynTbarz7+KZM2fQo0cP9O/fH6NHj66hzMmUatV0AkT0cHvjjTcwbNiwSrdp2LBhlfaZkpKC4OBgvPzyy3j77bfvIDsy5G6ev/r165dZCfTcuXNKG90bxp7Tn376Cdu3b0dOTg60Wi0AYMWKFdizZw/Wrl1r1DNudPcZe/5u/mGkRYsWyvtqtRoNGzbEqVOnTJkiVaIq//0lJCRArVbrtbVp0waDBw/G2rVrTZglVaaq/y6ePXsWXbp0QYcOHbBq1SoTZ0fV4eTkBHNzc+UzyU3nzp0z+vMJi24iqlF169ZF3bp179r+jh49iq5duyI0NBRz5sy5a/ul8t3N89e+fXvMmTMH58+fR7169QAAe/bsgVar1SsMyLSMPadXrlwBcOO5tluZmZkpV1Hp3jP2/D322GNQq9VIS0tDx44dAQDXrl1DVlYWPDw8TJ0mVcDY87ds2TLMnj1beX327FmEhIRg06ZNaNeunSlTJAOq8u/imTNn0KVLF+VOk9v/f0r3B0tLSzz22GPYt2+f8rWKOp0O+/btw4QJE4zaB4tuInpgnDp1CpcuXcKpU6dQWlqqfB+pl5cXbG1tceTIEXTt2hUhISF4/fXXledszM3N72phT9Vj6Pw9+eSTaNGiBYYMGYIFCxbgn3/+wdtvv43x48eXuZpDNa99+/ZwcHBAaGgoZs6cCY1Gg9WrVyMzMxO9evWq6fTIAK1Wi7FjxyIsLAxubm7w8PDABx98AADo379/DWdHhri7u+u9trW1BQA0atQIDRo0qImUqIrOnDmDoKAgeHh4YOHChbhw4YLSxru77j+vv/46QkND0aZNGzz++ONYsmQJCgsLMXz4cON2IERED4jQ0FABUCb2798vIiJhYWHltnt4eNRo3nSDofMnIpKVlSU9e/YUjUYjTk5O8sYbb8i1a9dqLmmq1O+//y5PPvmkODo6ip2dnTzxxBPyww8/1HRaZKSSkhJ54403pF69emJnZyfdunWTI0eO1HRaVA2ZmZkCQBITE2s6FTJSREREuf8msjy7f3300Ufi7u4ulpaW8vjjj8svv/xidF9+TzcRERERERGRifDBASIiIiIiIiITYdFNREREREREZCIsuomIiIiIiIhMhEU3ERERERERkYmw6CYiIiIiIiIyERbdRERERERERCbCopuIiIiIiIjIRFh0ExEREREREZkIi24iIiJ6KKhUKmzbtq3K/Tp37owNGzbc8X4eNMOGDUOfPn1qOg2DSkpK4OnpiT/++KOmUyEiKheLbiIiovvUsGHDoFKpysSJEyfuyv4jIyNRu3btu7Kv6rp1jhYWFnj00UcxdepUXL16tUbzuum7777DuXPn8OKLL9Z0Kg+k6OhoqFQq5Obm6r0fFBSE11577a6MYWlpiSlTpmDatGl3ZX9ERHcbi24iIqL7WI8ePZCdna0Xjz76aE2nVca1a9eq3ffmHE+ePInFixdj5cqVCAsLu4vZVd+yZcswfPhwmJndvx+ZSkpKajqFGnNz7oMHD0ZcXByOHj1awxkREZV1//4LQkRERFCr1ahfv75emJubAwC+/fZb+Pv7w8rKCg0bNsSsWbNw/fp1pe+HH34IHx8f2NjYwM3NDePGjUNBQQGAG1cghw8fjry8POVKc3h4OIDyb5+uXbs2IiMjAQBZWVlQqVTYtGkTAgMDYWVlhaioKADAmjVr0Lx5c1hZWaFZs2ZYsWKF0XN0c3NDnz590K1bN+zZs0dp//fffzFw4EA88sgjsLa2ho+PDzZu3Ki3j6CgIEyaNAlTp06Fo6Mj6tevr8ynImFhYXBxccGhQ4fKbb9w4QJ++uknPPPMM5Xu5/Dhw+jatSs0Gg3q1KmDl19+WTnOR44cgZmZGS5cuAAAuHTpEszMzPSunM+ePRsdO3ZUXh85cgQ9e/aEra0tnJ2dMWTIEFy8eFFvrhMmTMBrr70GJycnhISEVJrfnZo1axbq1q0LrVaLsWPH6hX5Op0O8+bNw6OPPgqNRgM/Pz98/fXXAG78nnTp0gUA4ODgAJVKhWHDhmHYsGGIiYnB0qVLld+9rKysO5q7g4MDAgIC8OWXX5r0WBARVQeLbiIiogdQbGwshg4dildffRUpKSlYuXIlIiMjMWfOHGUbMzMzLFu2DEePHsXatWvx008/YerUqQCADh06YMmSJdBqtcoV9ClTplQph7feeguvvvoqUlNTERISgqioKMycORNz5sxBamoq5s6di3feeQdr1641ep9HjhzBgQMHYGlpqbx39epVPPbYY9ixYweOHDmCl19+GUOGDMFvv/2m13ft2rWwsbHBr7/+igULFuDdd9/VK95vEhFMnDgR69atQ2xsLHx9fcvNJS4uDtbW1mjevHmF+RYWFiIkJAQODg74/fffsXnzZuzduxcTJkwAAHh7e6NOnTqIiYkBcOO83foaAGJiYhAUFAQAyM3NRdeuXdG6dWv88ccf2LVrF86dO4cXXnihzFwtLS0RHx+PTz/9tNzcoqKiYGtrW2nExsZWODcA2LdvH1JTUxEdHY2NGzfim2++waxZs5T2efPmYd26dfj0009x9OhRTJ48GS+99BJiYmLg5uaGLVu2AADS0tKQnZ2NpUuXYunSpWjfvj1Gjx6t/O65ubnd8dwff/xxg/MhIqoRQkRERPel0NBQMTc3FxsbGyX69esnIiLBwcEyd+5cve3Xr18vLi4uFe5v8+bNUqdOHeV1RESE2Nvbl9kOgGzdulXvPXt7e4mIiBARkczMTAEgS5Ys0dumUaNGsmHDBr333nvvPWnfvr1Rc1Sr1QJAzMzM5Ouvv66wj4hIr1695I033lBeBwYGSseOHfW2adu2rUybNk1vXps3b5ZBgwZJ8+bN5e+//650jMWLF0vDhg3LvH/r8Vm1apU4ODhIQUGB0r5jxw4xMzOTf/75R0REnn/+eRk/fryIiLz22mvy5ptvioODg6SmpkpJSYlYW1vL7t27ReTG8XryySf1xjt9+rQAkLS0NGWurVu3rjR3EZH8/Hw5fvx4pXHlypUK+4eGhoqjo6MUFhYq733yySdia2srpaWlcvXqVbG2tpYDBw7o9Rs5cqQMHDhQRET2798vACQnJ0dvm8DAQHn11Vf13rvTuS9dulQ8PT0rPSZERDWhVs2V+0RERGRIly5d8MknnyivbWxsAADJycmIj4/Xu7JdWlqKq1ev4sqVK7C2tsbevXsxb948HDt2DPn5+bh+/bpe+51q06aN8nNhYSEyMjIwcuRIjB49Wnn/+vXrsLe3N2qOhYWFWLx4MWrVqoW+ffvqzWvu3Ln46quvcObMGZSUlKC4uLjMHG6/Yu3i4oLz58/rvTd58mSo1Wr88ssvcHJyqjSvoqIiWFlZVbpNamoq/Pz8lPMCAAEBAdDpdEhLS4OzszMCAwOxatUqADeuas+dOxfp6emIjo7GpUuXcO3aNQQEBAC4cV73798PW1vbMmNlZGSgSZMmAIDHHnus0rwAwM7ODnZ2dga3q4yfn5/ecW7fvj0KCgpw+vRpFBQU4MqVK+jevbten5KSErRu3brKY93p3DUaDa5cuVLlcYmITI1FNxER0X3MxsYGXl5eZd4vKCjArFmz8Pzzz5dps7KyQlZWFp5++mm88sormDNnDhwdHREXF4eRI0eipKSk0qJbpVJBRPTeK2+htFsLzZvPMK9evRrt2rXT2+7mM+jGzPHzzz+Hn58fPvvsM4wcORIA8MEHH2Dp0qVYsmSJ8oz6a6+9VmYBMQsLizLz0Ol0eu91794dGzduxI8//ojBgwdXmpeTkxNycnIq3cYYN1fqPn78OFJSUtCxY0ccO3YM0dHRyMnJQZs2bZTzUVBQgGeeeQbvv/9+mf24uLgoP9967CsSFRWFMWPGVLrNzp070alTpyrO6Iab53zHjh145JFH9NrUanW19ncnc7906RLq1q1b5XGJiEyNRTcREdEDyN/fH2lpaeUW5ADw559/QqfTYdGiRcrK21999ZXeNpaWligtLS3Tt27dusjOzlZeHz9+3OAVRGdnZ7i6uuLkyZMGi9nKmJmZ4X//+x9ef/11DBo0CBqNBvHx8ejduzdeeuklADcW70pPT0eLFi2qvP9nn30WzzzzDAYNGgRzc/NKvwqsdevW+Oeff5CTkwMHB4dyt2nevDkiIyNRWFioFIPx8fEwMzND06ZNAQA+Pj5wcHDA7Nmz0apVK9ja2iIoKAjvv/8+cnJylOe5gRvndcuWLfD09EStWnf2Me3ZZ58t8weQ291eLN8uOTkZRUVF0Gg0AIBffvkFtra2cHNzg6OjI9RqNU6dOoXAwMBy+998Nv/237PyfvfudO5Hjhyp1hV2IiJT40JqRERED6CZM2di3bp1mDVrFo4ePYrU1FR8+eWXePvttwEAXl5euHbtGj766COcPHkS69evL7PglqenJwoKCrBv3z5cvHhRKay7du2K5cuXIzExEX/88QfGjh1b5ipyeWbNmoV58+Zh2bJlSE9Px+HDhxEREYEPP/ywSnPr378/zM3N8fHHHwMAGjdujD179uDAgQNITU3FmDFjcO7cuSrt81bPPfcc1q9fj+HDhysrbZendevWcHJyQnx8fIXbDB48GFZWVggNDcWRI0ewf/9+TJw4EUOGDIGzszOAG1fcO3fujKioKKXA9vX1RXFxMfbt26dXsI4fPx6XLl3CwIED8fvvvyMjIwM//vgjhg8fXu4fSCpjZ2cHLy+vSuNmMV2RkpISjBw5EikpKfjhhx8QFhaGCRMmwMzMDHZ2dpgyZQomT56MtWvXIiMjAwcPHsRHH32kLJ7n4eEBlUqF7du348KFC8rVcU9PT/z666/IysrCxYsXodPp7njusbGxePLJJ6t0jIiI7gUW3URERA+gkJAQbN++Hbt370bbtm3xxBNPYPHixfDw8ABw41ncDz/8EO+//z5atmyJqKgozJs3T28fHTp0wNixYzFgwADUrVsXCxYsAAAsWrQIbm5u6NSpEwYNGoQpU6YY9Qz4qFGjsGbNGkRERMDHxweBgYGIjIys8veK16pVCxMmTMCCBQtQWFiIt99+G/7+/ggJCUFQUBDq16+PPn36VGmft+vXrx/Wrl2LIUOG4Jtvvil3G3NzcwwfPlz5OrTyWFtb48cff8SlS5fQtm1b9OvXD8HBwVi+fLnedoGBgSgtLVWKbjMzM3Tu3BkqlUp5nhsAXF1dER8fj9LSUjz55JPw8fHBa6+9htq1a9fId4UHBwejcePG6Ny5MwYMGIBnn31W76vY3nvvPbzzzjuYN28emjdvjh49emDHjh3KOX/kkUcwa9YsvPXWW3B2dlZWdZ8yZQrMzc3RokUL1K1bF6dOnbqjuSckJCAvLw/9+vUz2bEgIqouldz+0BYRERERAQD++ecfeHt74+DBg8ofNOj+M2DAAPj5+eF///tfTadCRFQGr3QTERERVaB+/fr47LPPcOrUqZpOhSpQUlICHx8fTJ48uaZTISIqF690ExEREREREZkIr3QTERERERERmQiLbiIiIiIiIiITYdFNREREREREZCIsuomIiIiIiIhMhEU3ERERERERkYmw6CYiIiIiIiIyERbdRERERERERCbCopuIiIiIiIjIRFh0ExEREREREZkIi24iIiIiIiIiE/l/YhtV4AbMHYYAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Boruta for time related features\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from boruta import BorutaPy\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Prepare your feature matrix and target\n", - "# Only keep valid rows (no NaNs in features or target)\n", - "features = [\n", - " # check-in cyclical\n", - " 'checkin_week_cycle_sin', 'checkin_week_cycle_cos',\n", - " 'checkin_month_cycle_sin', 'checkin_month_cycle_cos',\n", - " 'checkin_year_cycle_sin', 'checkin_year_cycle_cos',\n", - " \n", - " # check-out cyclical\n", - " 'checkout_week_cycle_sin', 'checkout_week_cycle_cos',\n", - " 'checkout_month_cycle_sin', 'checkout_month_cycle_cos',\n", - " 'checkout_year_cycle_sin', 'checkout_year_cycle_cos',\n", - " \n", - " # scalar features\n", - " 'length_of_stay_days',\n", - " 'lead_time_to_checkin_days',\n", - " 'gj_start_to_checkin_days'\n", - "]\n", - "df_model = df_bookings_and_claims[features + ['has_resolution_incident']].dropna()\n", - "\n", - "X = df_model[features].values\n", - "y = df_model['has_resolution_incident'].values.astype(int)\n", - "# Random Forest\n", - "rf = RandomForestClassifier(\n", - " n_estimators=100,\n", - " max_depth=5,\n", - " random_state=42,\n", - " n_jobs=-1,\n", - " class_weight='balanced'\n", - ")\n", - "\n", - "# Boruta setup\n", - "boruta_selector = BorutaPy(\n", - " estimator=rf,\n", - " n_estimators='auto',\n", - " verbose=2,\n", - " random_state=42\n", - ")\n", - "\n", - "# Fit selector\n", - "boruta_selector.fit(X, y)\n", - "\n", - "# Prepare results\n", - "feature_rankings = pd.DataFrame({\n", - " 'feature': features,\n", - " 'rank': boruta_selector.ranking_,\n", - " 'selected': boruta_selector.support_,\n", - " 'tentative': boruta_selector.support_weak_,\n", - "})\n", - "\n", - "# Status column\n", - "def determine_status(row):\n", - " if row['selected']:\n", - " return 'Selected āœ…'\n", - " elif row['tentative']:\n", - " return 'Tentative šŸ¤”'\n", - " else:\n", - " return 'Rejected āŒ'\n", - "\n", - "feature_rankings['status'] = feature_rankings.apply(determine_status, axis=1)\n", - "\n", - "# Sort by rank\n", - "feature_rankings = feature_rankings.sort_values(by='rank')\n", - "\n", - "# Show results\n", - "print(\"\\nšŸ“Š Boruta Feature Selection Results:\")\n", - "print(feature_rankings[['feature', 'rank', 'status']])\n", - "\n", - "# Optional plot\n", - "plt.figure(figsize=(10, 6))\n", - "colors = feature_rankings['status'].map({\n", - " 'Selected āœ…': 'green',\n", - " 'Tentative šŸ¤”': 'orange',\n", - " 'Rejected āŒ': 'red'\n", - "})\n", - "\n", - "plt.barh(\n", - " feature_rankings['feature'],\n", - " -feature_rankings['rank'], # Negative to flip axis: rank 1 = top\n", - " color=colors\n", - ")\n", - "plt.xlabel(\"Feature Rank (lower = better)\")\n", - "plt.title(\"Boruta Feature Ranking\")\n", - "plt.gca().invert_yaxis()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "11deba4b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Ensure clean data\n", - "df_viz = df_bookings_and_claims[[\n", - " 'length_of_stay_days',\n", - " 'lead_time_to_checkin_days',\n", - " 'has_resolution_incident'\n", - "]].dropna()\n", - "\n", - "# Setup\n", - "sns.set(style=\"whitegrid\", palette=\"muted\")\n", - "\n", - "# Create subplots\n", - "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", - "fig.suptitle(\"Distributions and Boxplots of Key Duration Features vs. Resolution Incident\", fontsize=16)\n", - "\n", - "# Length of Stay - KDE/Histogram\n", - "sns.histplot(\n", - " data=df_viz,\n", - " x='length_of_stay_days',\n", - " hue='has_resolution_incident',\n", - " element='step',\n", - " common_norm=False,\n", - " ax=axes[0, 0],\n", - " stat='probability'\n", - ")\n", - "axes[0, 0].set_title(\"Length of Stay Distribution\")\n", - "\n", - "# Length of Stay - Boxplot\n", - "sns.boxplot(\n", - " data=df_viz,\n", - " x='has_resolution_incident',\n", - " y='length_of_stay_days',\n", - " ax=axes[0, 1]\n", - ")\n", - "axes[0, 1].set_title(\"Length of Stay by Incident\")\n", - "\n", - "# Lead Time - KDE/Histogram\n", - "sns.histplot(\n", - " data=df_viz,\n", - " x='lead_time_to_checkin_days',\n", - " hue='has_resolution_incident',\n", - " element='step',\n", - " common_norm=False,\n", - " ax=axes[1, 0],\n", - " stat='probability'\n", - ")\n", - "axes[1, 0].set_title(\"Lead Time Distribution\")\n", - "\n", - "# Lead Time - Boxplot\n", - "sns.boxplot(\n", - " data=df_viz,\n", - " x='has_resolution_incident',\n", - " y='lead_time_to_checkin_days',\n", - " ax=axes[1, 1]\n", - ")\n", - "axes[1, 1].set_title(\"Lead Time by Incident\")\n", - "\n", - "# Adjust\n", - "for ax in axes.flat:\n", - " ax.set_xlabel(\"\")\n", - " ax.set_ylabel(\"\")\n", - "\n", - "plt.tight_layout(rect=[0, 0, 1, 0.96])\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "7774f0eb", - "metadata": {}, - "source": [ - "It seems that:\n", - "- Longer stays (specially beyond two weeks) have higher chances of claiming.\n", - "- Longer lead times have less chances of claiming (although it looks like we simply need to have more samples)." - ] - }, - { - "cell_type": "markdown", - "id": "2a6fb142", - "metadata": {}, - "source": [ - "## Listing NPL features" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "b8d9a007", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration: \t1 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t30\n", - "Rejected: \t0\n", - "Iteration: \t2 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t30\n", - "Rejected: \t0\n", - "Iteration: \t3 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t30\n", - "Rejected: \t0\n", - "Iteration: \t4 / 100\n", - "Confirmed: \t0\n", - "Tentative: \t30\n", - "Rejected: \t0\n", - "Iteration: \t5 / 100\n", - "Confirmed: 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"Confirmed: \t14\n", - "Tentative: \t8\n", - "Rejected: \t8\n", - "\n", - "\n", - "BorutaPy finished running.\n", - "\n", - "Iteration: \t100 / 100\n", - "Confirmed: \t14\n", - "Tentative: \t7\n", - "Rejected: \t9\n", - "\n", - "šŸ“Š Boruta Feature Selection Results:\n", - " feature rank status\n", - "1 tfidf_svd_1 1 Selected āœ…\n", - "7 tfidf_svd_7 1 Selected āœ…\n", - "5 tfidf_svd_5 1 Selected āœ…\n", - "4 tfidf_svd_4 1 Selected āœ…\n", - "11 tfidf_svd_11 1 Selected āœ…\n", - "10 tfidf_svd_10 1 Selected āœ…\n", - "9 tfidf_svd_9 1 Selected āœ…\n", - "8 tfidf_svd_8 1 Selected āœ…\n", - "12 tfidf_svd_12 1 Selected āœ…\n", - "19 tfidf_svd_19 1 Selected āœ…\n", - "18 tfidf_svd_18 1 Selected āœ…\n", - "29 tfidf_svd_29 1 Selected āœ…\n", - "24 tfidf_svd_24 1 Selected āœ…\n", - "17 tfidf_svd_17 1 Selected āœ…\n", - "6 tfidf_svd_6 2 Tentative šŸ¤”\n", - "13 tfidf_svd_13 2 Tentative šŸ¤”\n", - "3 tfidf_svd_3 2 Tentative šŸ¤”\n", - "26 tfidf_svd_26 2 Tentative šŸ¤”\n", - "25 tfidf_svd_25 2 Tentative šŸ¤”\n", - "14 tfidf_svd_14 2 Tentative šŸ¤”\n", - "23 tfidf_svd_23 2 Tentative šŸ¤”\n", - "15 tfidf_svd_15 3 Rejected āŒ\n", - "16 tfidf_svd_16 4 Rejected āŒ\n", - "2 tfidf_svd_2 4 Rejected āŒ\n", - "0 tfidf_svd_0 6 Rejected āŒ\n", - "21 tfidf_svd_21 7 Rejected āŒ\n", - "27 tfidf_svd_27 8 Rejected āŒ\n", - "28 tfidf_svd_28 9 Rejected āŒ\n", - "22 tfidf_svd_22 10 Rejected āŒ\n", - "20 tfidf_svd_20 11 Rejected āŒ\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = df_bookings_and_claims.merge(\n", - " df_listing_details[['id_accommodation'] + [f'tfidf_svd_{i}' for i in range(30)]],\n", - " on='id_accommodation',\n", - " how='left'\n", - ")\n", - "\n", - "\n", - "svd_features = [f'tfidf_svd_{i}' for i in range(30)]\n", - "all_features = svd_features\n", - "\n", - "df_model = df[all_features + ['has_resolution_incident']].dropna()\n", - "\n", - "X = df_model[all_features].values\n", - "y = df_model['has_resolution_incident'].values.astype(int)\n", - "\n", - "# --- Step 4: Boruta setup and fit ---\n", - "rf = RandomForestClassifier(\n", - " n_estimators=100,\n", - " max_depth=5,\n", - " random_state=42,\n", - " n_jobs=-1,\n", - " class_weight='balanced'\n", - ")\n", - "\n", - "boruta_selector = BorutaPy(\n", - " estimator=rf,\n", - " n_estimators='auto',\n", - " verbose=2,\n", - " random_state=42\n", - ")\n", - "\n", - "boruta_selector.fit(X, y)\n", - "\n", - "# --- Step 5: Prepare and show results ---\n", - "feature_rankings = pd.DataFrame({\n", - " 'feature': all_features,\n", - " 'rank': boruta_selector.ranking_,\n", - " 'selected': boruta_selector.support_,\n", - " 'tentative': boruta_selector.support_weak_,\n", - "})\n", - "\n", - "def determine_status(row):\n", - " if row['selected']:\n", - " return 'Selected āœ…'\n", - " elif row['tentative']:\n", - " return 'Tentative šŸ¤”'\n", - " else:\n", - " return 'Rejected āŒ'\n", - "\n", - "feature_rankings['status'] = feature_rankings.apply(determine_status, axis=1)\n", - "feature_rankings = feature_rankings.sort_values(by='rank')\n", - "\n", - "print(\"\\nšŸ“Š Boruta Feature Selection Results:\")\n", - "print(feature_rankings[['feature', 'rank', 'status']])\n", - "\n", - "# Optional plot\n", - "plt.figure(figsize=(10, 8))\n", - "colors = feature_rankings['status'].map({\n", - " 'Selected āœ…': 'green',\n", - " 'Tentative šŸ¤”': 'orange',\n", - " 'Rejected āŒ': 'red'\n", - "})\n", - "\n", - "plt.barh(\n", - " feature_rankings['feature'],\n", - " -feature_rankings['rank'], # Flip axis so rank 1 is top\n", - " color=colors\n", - ")\n", - "plt.xlabel(\"Feature Rank (lower = better)\")\n", - "plt.title(\"Boruta Feature Ranking Including Text SVD Features\")\n", - "plt.gca().invert_yaxis()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/requirements.txt b/requirements.txt index 26fc698..fd154dc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,4 @@ sqlalchemy psycopg2-binary seaborn statsmodels -scikit-learn -shap -boruta \ No newline at end of file +scikit-learn \ No newline at end of file