{ "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": [ "🔌 Testing connection using credentials at: /home/joaquin/.superhog-dwh/credentials.yml\n", "✅ 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
\n", "
" ], "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
\n", "
" ], "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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n3d13320+/+qrrwxJxm233WakpaWZ5atWrTIkGQsXLjTL/Pz8jCFDhhTYZn59GzJkiOHn52c+//TTTw1JxosvvmgR99BDDxk2NjbGzz//bJZJMuzt7S3Kvv/+e0OSsXjx4lz7utaCBQsMScb7779vlmVmZhrBwcGGi4uLxdj9/PyMsLCwfNszDMNISEgwJBlPPvmkRXn//v0NScYLL7xgll28eDHX9nFxcYYk49133zXL3nzzTUOScfjwYYt+1qxZ0+K1d3NzMyIjIwvsY36OHTtmSDJeffVVi/Kcz2O/fv0synPGO3z4cIvyZ555xpBkbNmyxSzz8/MzJBk7d+40yzZs2GBIMpycnIzjx4/nGvNXX31V6L4X9nObmZlpeHl5Gc2bNzcuXbpkxq1Zs8aQZEybNs0sa9WqleHl5WWcOXPGLPv++++NKlWqGIMHD871+vz555/G4cOHDV9fX+POO+80zp49a9HH6z/rOa+3p6enRexnn31mSDK++OILsywwMNCoU6eO8ddff5llW7duNSRZtFmQK1euGP7+/oafn59x7tw5i7rs7GyLvkoy/vWvf1nEfP3114YkIyYmxqJ8/fr1ucrz+ow/8cQTRrVq1YzLly+bZWFhYXmOIef1ufZ7o6jvybBhwyzafPDBBw1PT89c+8pPZGSkxfdxjqJ8VxWkU6dORvXq1S3+HRiG5XtS2DH99ttvRtWqVY2XXnrJIm7//v2Gra2tWV6Uz4Kzs7NhGIaxY8cOw9XV1QgLC7N4DwsjZy48duyYWZbzvbB9+3az7PTp04aDg4MxYcKEIrWPioljpFvnGKkofbp+3Dmuf+9z2nRwcLD4bsk5jvDx8bHo65QpU3J9D+W8/3PnzjXLMjIyzPkmMzPTMAzDeO+994wqVaoYX3/9tcX+o6KiDEnGN998Y9GnKlWqGAcPHizU65KjKMcohfm3k5ec7UJCQiy+68eNG2dUrVrVOH/+vFl2/Wd17ty5hiTj008/NcsuXbpkNGnSJNdxW87reu0xbUZGhuHj42OEh4ebZTf6/O/bt8+QZKxevbpI4wNQuXHcwHFDXn3q3bu3YW9vb/zyyy9m2cmTJ43q1asbnTp1MstatmxZ4L5vdO5XGHmd/86ePduwsbGxOM8r7Ll9Uc69gfzcUkvH5HBxccn3Dtk5Vx9+9tlnN31zCwcHBw0dOrTQ8YMHD1b16tXN5w899JBq166tdevW3dT+C2vdunWqWrWqnnrqKYvyCRMmyDAMffnllxblISEhFlfxtGjRQq6urvr1118L3I+Pj4/69etnltnZ2empp55Senq6tm3bdlN9l5Sr79ff5FGSxV+us7KydObMGTVs2FDu7u4Wy7088sgjcnR0VExMjFm2YcMGpaSkaODAgWaZu7u7du/erZMnTxa534U1cuRIi+c54x0/frxFec5fp9euXWtRHhAQoODgYPN5zl+Wu3Tponr16uUqL+g9zEtBn9u9e/fq9OnTevLJJy3WSg0LC1OTJk3MPp86dUoJCQl67LHH5OHhYca1aNFC9957b57/Dg4cOKC7775b9evX16ZNm1SjRo1C9fnRRx+1iM25MiFn/CdPntT+/fs1ePBgi6u97777bgUGBhZqHzn27dunY8eOaezYsbnWdc3rZ3GjRo2yeL569Wq5ubnp3nvvVUpKivkICgqSi4uLxVVt137G//rrL6WkpKhjx466ePGifvzxxyL1W7q59+T6z2zHjh115swZpaWlFXn/1yvqd9WN/Pnnn9q+fbuGDRtm8e9Ayvs9KWhMH3/8sbKzs/XII49YvEc+Pj5q1KiR+R4V9bPw1VdfKTQ0VF27dtXHH39cYjc1DQgIMD/z0t9XtTRu3Pim/v2jcuIY6f9U5GOk4vYpP127drW4YjznOCI8PNzifbrR8YWtra2eeOIJ87m9vb2eeOIJnT59WvHx8ZL+nv+aNm2qJk2aWHy3dunSRZJyXdV99913F3kN2sIeo5SEESNGWHzXd+zYUVevXtXx48dvuM369et122236f777zfLHB0db3jVqIuLi8Wxqr29vf75z38W6r3OuWJ9w4YNRVryDQA4bvg/t8Jxw9WrV7Vx40b17t1b//jHP8y42rVrq3///tqxY4d5nuTu7q6DBw/q6NGjN92f/Fx7/nvhwgWlpKTorrvukmEY5tK4RTm3L8q5N5CfWzLRnp6ebvHFe71HH31U7du31/Dhw+Xt7a2+fftq1apVRZoYbrvttiLdnKNRo0YWz21sbNSwYcNCraFVHMePH5evr2+u16Np06Zm/bWuT0xJUo0aNQpcs+r48eNq1KhRrqVJbrSfwva9SpUquX6+27hx41yxly5d0rRp08y10mrWrKlatWrp/PnzFmtPuru7q1evXlq5cqVZFhMTo9tuu808uZOkOXPm6MCBA6pbt67++c9/avr06SWeqPL397d4njPehg0bWpT7+PjI3d29wPcq5ySqbt26eZbfzLpjBX1uc/qU13vSpEkTsz6/uKZNmyolJUUXLlywKO/Vq5eqV6+uDRs2yNXVtdB9vv51yUm654w/py/Xv843KsvPL7/8Iklq3rx5gbG2traqU6eORdnRo0eVmpoqLy8v1apVy+KRnp5u3pxN+vsnfA8++KDc3Nzk6uqqWrVqmSfcN7O+6s28JwW9tsVR1O+qG8n5d1qY90QqeExHjx6VYRhq1KhRrvfo8OHD5ntUlM/C5cuXFRYWpjvuuEOrVq0q0Rs93ex3OG4dHCP9n4p8jFTcPhWlzaIeX/j6+ua62fftt98uSeZ7evToUR08eDDX92pO3LXzn5T7mKkwCnuMUhJuZn48fvy4GjRokOuPsTc6FqlTp06u2MK+1/7+/ho/frzeeust1axZU6GhoVq6dCnrswMoEMcN/+dWOG74888/dfHixRueI2ZnZ5vr4c+cOVPnz5/X7bffrsDAQE2cOFE//PDDTffteomJieZFYS4uLqpVq5buvvtuSf93/luUc/uinHsD+bnl1mj//ffflZqamm/CzMnJSdu3b9dXX32ltWvXav369frwww/VpUsXbdy4UVWrVi1wP8VdRzcvN7oxxNWrVwvVp5Jwo/0YN3EzwtI0ZswYLV++XGPHjlVwcLDc3NxkY2Ojvn375prkBw8erNWrV2vnzp0KDAzU559/rieffNJiInvkkUfUsWNHffLJJ9q4caNeffVVvfLKK/r444/VvXv3EunzjT5Dhb1ByI3eq4r6Hl4vPDxcK1asUExMjMWVcQUpr+N3cHDIdbCUnZ0tLy8vi19YXCtnjb3z58/r7rvvlqurq2bOnKkGDRrI0dFR3333nSZPnnzTV48UVXl9bYujoDFlZ2fLxsZGX375ZZ6x+a2BfyMODg7q0aOHPvvsM61fv149e/Yschs3UhnfI5QcjpGKpzz++ypMn/J77YrSZkmOPzs7W4GBgZo3b16e9dcn9a3xmSpJpfHZKO4+5s6dq8cee0yfffaZNm7cqKeeekqzZ8/Wrl27cl0IAAASxw3FVVGPGwqrU6dO+uWXX8x55a233tL8+fMVFRWl4cOHF6ufV69e1b333quzZ89q8uTJatKkiZydnfXHH3/oscceu6nz38KeewMFueUS7Tk3WQgNDc03rkqVKuratau6du2qefPm6eWXX9Zzzz2nr776SiEhIUW6G3JhXP9zGsMw9PPPP6tFixZmWY0aNXT+/Plc2x4/ftziZztF6Zufn582bdqkv/76y+IvrzlLTfj5+RW6rYL288MPPyg7O9simVic/fj5+Sk7O1u//PKLxV9Ujxw5kiv2o48+0pAhQzR37lyz7PLly3m+nvfdd59q1aqlmJgYtW3bVhcvXtSgQYNyxdWuXVtPPvmknnzySZ0+fVqtW7fWSy+9VGKJ9uvljPfo0aPmX6slKTk5WefPny+x96ooCvrc5vTpyJEjFr8IyCnLqb827no//vijatasmevqt1dffVW2trbmjVr69+9fImPK6cvPP/+cqy6vsvzk/NriwIEDCgkJKXJfGjRooE2bNql9+/b5HmBu3bpVZ86c0ccff6xOnTqZ5ceOHcsVW9jvh5t5T0rCjfpXUt9VOd+VBw4cKGZP/9agQQMZhiF/f3/zSssbxeXst6DPgo2NjWJiYvTAAw/o4Ycf1pdffqnOnTuXSH+B/HCMZKkiHyMVRX6vnTWcPHlSFy5csJhDfvrpJ0kyl6Rp0KCBvv/+e3Xt2rXEP085CnuMUlb8/Px06NAhGYZh8RoU9VjkWgW9loGBgQoMDNTUqVO1c+dOtW/fXlFRUXrxxRdvep8AKi+OGyzdCscNtWrVUrVq1W54jlilShWLP4Z7eHho6NChGjp0qNLT09WpUydNnz7dTLTf7Hu/f/9+/fTTT1qxYoUGDx5slsfGxlrEFeXcvrDn3kBBbqmlY7Zs2aJZs2bJ399fAwYMuGHc2bNnc5W1atVKkpSRkSFJ5slBXl/ON+Pdd9+1WNvso48+0qlTpyyStg0aNNCuXbuUmZlplq1Zs8b8aU6OovStR48eunr1qpYsWWJRPn/+fNnY2JRY0rhHjx5KSkrShx9+aJZduXJFixcvlouLi/kTn6LI6duiRYssyhcsWJArtmrVqrn+Crt48eI8r9aytbVVv379tGrVKkVHRyswMNBiUr569Wqun9J6eXnJ19fX/HxYQ48ePSTlHl/O1V5hYWFW2/eNFPS5bdOmjby8vBQVFWXx2nz55Zc6fPiw2efatWurVatWWrFihcXn9sCBA9q4caM59mvZ2Nho2bJleuihhzRkyBB9/vnnJTImX19fNW/eXO+++67S09PN8m3btmn//v1Faqt169by9/fXggULcv17LMxVAY888oiuXr2qWbNm5aq7cuWK2WbOlQfXtpmZmanXX38913bOzs6F+in4zbwnJeFG318l9V1Vq1YtderUSe+8844SExMt6m7mSo0+ffqoatWqmjFjRq7tDcPQmTNnJBX9s2Bvb6+PP/5Yd95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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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" ] }, "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+5Rhuo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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, we’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 – 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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" ] }, "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 — 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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fQaK4Qz7/HSRyhnyWRqmpqQgPD0dSUhKUSiV69+6NRo0ayV3WUzFgEBFRqZCVBVy75oZ16xS4csU0SBR3yGfeIFGrlmEUhzURQiAyMhJJSUlwdnZGWFgYqlSpIndZRcKAQUREJSbvkM9/X41ISLCDXt+pyOfy9ATq1Mk/AVVQUPGHfJY2CoUCvXv3xtatWzFgwAB4Pm1SjFKEAYOIiCSXnW1Y5TPvxFM5j+/dK+yo/D0mFQrDHBH/DhG1a0s35LO0EUIgKSkJfn5+AAyLeo4dOxYKK/tmGTCIiKjY/j3kM+fx5cuGeSWKysUFqFVLDze3RHTs6Ie6dVWoXRuoUcOyQz5Lm6ysLKxfvx6XLl3C6NGjjbdDrC1cAAwYRET0FHq9YZXPf0+FfeECkJxs3rkqVy54Aip/fyA7W4dt206gZ08f2NuX0h6XFvTgwQOsWbMGd+7cgUqlwqNHj+Qu6ZkwYBAREQDDkM+8q3zmhIi4OPOHfOZd5bO0Dfksja5du4a1a9ciPT0drq6uGDp0KPz9/eUu65kwYBAR2RAhgKSkghfnyrP0U5F4exe8rkZpHvJZGp04cQJbt26FXq+Hr68vhg4dCnd3d7nLemYMGEREZVBWVuGrfJpz5d3ODggOLvi2hrUN+SyNrly5gs2bNwMAQkJC0K9fP9mXWZcKAwYRkRUrbJXPhATzVvn09Cz4akRZGvJZGgUFBSEkJAQVK1ZEu3btrLIzZ2EYMIiISrns7MJX+Sx8yGd+OUM+C1pXo6wO+SyN7t27Bzc3N9jb20OhUGDgwIFlKljkYMAgIiolHj4sfJVPc4d8FjQddvXqwD+re5NM4uPjERkZiaCgIAwaNAgKhaJMhguAAYOIqETlDPksKEgUd8jnv4OEvz+vRpQ2QggcO3YMO3bsgBACqampyMrKgmMZnuSDAYOIyAJyhnwWtMpnRkbRz5N3yGfeIFGzJuDmZrn6STo6nQ7bt2/H8ePHAQANGjRA7969YWdXtj+Cy/Z3R0RkQTmrfOadEjvn61mHfOaEiapVOeTTmqWnpyMyMhJXr14FAISGhqJVq1Zl9rZIXgwYRERPkTPkM+/ViPPnVTh3ricyMoo+xEKlyl3lM2+QqFULKF/egt8AyUIIgfDwcNy4cQNqtRoDBw5EzZo15S6rxDBgEBH94++/gfPn89/WiI8vaMin8p+v/Aoa8lmrlmHIp1pt4W+CSg2FQoEuXbpg06ZNGDx4MLy9veUuqUQxYBCRzUlJAc6dA2Jjc/+MjQVu3y76ORQKAW/vdDRq5IQ6dZQmQcLbm50sbZUQAvfv30f5fy5JBQQE4I033oBSWXAYLcsYMIiozLpzJ3+IOHcOuHu36OcwrPKZ/4pE1arZ+P333ejZsyfs7W3vw4Pyy87OxpYtWxAbG4uXX34ZlSpVAgCbDBcAAwYRWTkhDFce8gaInMcpKUU/j7c3EBIC1Klj+HrakE9z5qWgsi8tLQ0RERG4efMmFAoFkpKSjAHDVjFgEJFVEMIwT0TeEJHzpzmzWfr4AHXrGsJE3bq5X15elqudyrbk5GSsWbMGqampcHR0xKBBgxAcHCx3WbJjwCCiUiVn6GdBVyTu3y/6eXx9c0NE3jDB0RokpfPnz2P9+vXQarWoUKEChg0bhgoVKshdVqnAgEFEshACSEzM3z8iNtYwZXZR+fsXfEWCK32SpV2+fBlr164FAAQHB2PgwIFw4lzsRgwYRGRxKSnAX3+ZhonYWCA1tejnqFw5/xWJOnUMQ0KJ5BAUFISgoCBUrFgRXbt2tdnOnIVhwCAii/rpJ2DCBCAzs2j7V6lS8BUJd3fL1klUFGlpaXBycoJKpYJSqcTw4cOh4lSrBWLAICKL0OmAqVOBL78s+PWqVQu+IsH1Nai0SkxMRHh4OGrXro1evXoBAMPFEzBgEJHk0tKAESOATZtyt734IhAamhskXF3lq4/IXGfOnMGmTZuQnZ2Na9euISsrCw4ODnKXVaoxYBCRpG7cAPr0AU6dMjxXqYBvvwXeeEPeuoiKQwiB33//Hfv27QMA1KxZEwMGDGC4KAIGDCKSzLFjQN++hvkqAMDDA4iMBLp0kbcuouLQaDRYv349Lly4AABo3bo1OnXqxM6cRcSAQUSSiIoCRo7M7cwZFARs2WK4HUJkbYQQWLVqFa5fvw6VSoU+ffqgQYMGcpdlVRgwiOiZCAHMmQNMn567rU0bYP16zo5J1kuhUKB169a4f/8+Bg8ejICAALlLsjoMGERUbFlZwCuvAL/8krtt1Cjgxx8B3qIma/T48WO4uLgAMPS3eOutt2Bvby9zVdaJN5KIqFju3gU6dzYNF3PnAsuWMVyQ9dHr9di5cycWLlyI+3nmpGe4KD5ewSAis8XGAr17AwkJhudOTsDKlcDAgfLWRVQcmZmZWLduHS5fvgwAuHLlCpo2bSpzVdaPAYOIzLJzJzBkSO40376+wObNQJMm8tZFVBz37t3DmjVrkJKSAjs7O/Tv3x8hISFyl1UmMGAQUZEtXAi8/Tag1xueN2pkmEyrcmV56yIqjoSEBERGRiIjIwNubm4YOnQo/Pz85C6rzGDAIKKnys4GJk0Cvvsud1v//obbIpyRk6xRfHw8Vq1aBb1eD39/f4SFhcGN89RLigGDiJ7o4UMgLMxwayTHe+8ZOnRyviGyVgEBAfDx8UGFChXQt29f2Nnx41BqbFEiKlRCgqEzZ2ys4bm9PfDDD8DYsfLWRVQcmZmZcHBwgEKhgL29PUaNGgW1Wg2FQiF3aWUSf/8gogIdOAA0b54bLsqXB6KjGS7IOt29exc//vgj9u7da9yWEzbIMhgwiCifX34BOnUCUlIMz2vVAo4cAdq3l7cuouK4dOkSfvrpJ9y/fx+nT59GVlaW3CXZBN4iISIjvR6YORP49NPcbZ07GxYsK1dOvrqIikMIgcOHDyM6OhpCCFSpUgVDhgzhSqglhAGDiAAA6enAmDGGMJHj1VcNI0c4mSFZm+zsbGzduhUxMTEAgIYNG6J3795QqVTyFmZDGDCICElJQL9+huXWAUChAL76CvjPfwyPiayJEAJr1qxBfHw8FAoFunbtihYtWrC/RQljwCCycTExQJ8+wM2bhueursCaNYbRI0TWSKFQoEGDBkhMTMSgQYNQvXp1uUuySQwYRDYqNhaYP98wWZZGY9hWpYph2u/69eWtjag4NBoN1Go1AKB+/fqoXr06nJ2dZa7KdnEUCZENEQL4/XcFevUCQkKAn37KDRctWhhGijBckLURQmDfvn343//+h7S0NON2hgt58QoGkQ3QaoFVqxT4+OP2SEgw/Wfv7g68+SYwY4ZhVVQia6LVarF582acOXMGAHDu3Dm0aNFC5qoIYMAgKtMePAAWLwa++QZITLQD4Gl8rUoVw/oiL78McAkGskaPHj1CREQEEhMToVQq0aNHDy6zXoowYBCVQVevGkLFkiVAnivGAICmTfWYMkWJgQMBLr9A1urWrVsIDw/Ho0eP4OTkhMGDB6NatWpyl0V58L8XojLk6FHD8NKoqNwl1QHDUNNevfRo2fIgpkxpAbWa3a/IeiUkJGD16tXIzs5GxYoVMXToUJQvX17usuhfGDCIrJxebxj58dVXwL59pq85Ohomz5o0CahWTYdt2/7mvBZk9Xx9feHh4YHy5ctj4MCBnJmzlGLAILJSmZmGNUPmzQMuXjR9zdsbmDABeOMNwMvLsE2rLfkaiaSSnZ0NlUoFhUIBR0dHjBkzBs7OzlAqeTWutGLAILIyDx4AixYZ+lgkJ5u+VqcOMHky8OKLhqsXRGXBw4cPER4ejoYNGxpHiLi6uspcFT0NAwaRlUhMBBYsAH74AXj0yPS1Dh2AKVOAHj0A/kJHZcmNGzcQERGBx48fY//+/WjUqJFxMi0q3RgwiEq52FjDbZBVq0xvcygUwIABwHvvAc2by1cfkaWcOnUKmzdvhk6nQ6VKlTB06FCGCysi++86CxcuRGBgIBwdHdGiRQscPXr0ifsvWLAAtWrVgpOTEwICAjBp0iRkZmaWULVEJUMIYP9+wxohISHAsmW54cLBAXjtNSAuzjBahOGCyhq9Xo/o6Ghs2LABOp0OtWvXxksvvQRPT0+5SyMzyHoFIyIiApMnT8aiRYvQokULLFiwAN26dUNcXBy8vb3z7b969WpMnToVS5cuRatWrXDx4kWMGTMGCoUC8+fPl+E7IJKWXg9s2gR88QVw6JDpa56ehhk3334bqFRJlvKILE4IgV9//RUX/+m53K5dO3To0IEroVohWQPG/PnzMW7cOIwdOxYAsGjRImzduhVLly7F1KlT8+1/8OBBtG7dGsOHDwcABAYGYtiwYThy5EiJ1k30JEIA16/nn+DqaQ4fNtwKiYsz3V65sqHj5iuvcMZNKvsUCgWqVKmC+Ph49OvXD88995zcJVExyRYwNBoNjh8/jmnTphm3KZVKhIaG4tC/f3X7R6tWrfDLL7/g6NGjaN68OeLj47Ft2zaMHDmy0PfJyspCVlaW8XlqaioAw/z1WonG7eWcR6rzkfW26blzwKRJKuzd++x3H+vWFXjnHR3CwgRybjs/S3NYa5uWVmxPaen1euh0OgBAw4YNUbNmTXh6erJ9n4ElfkbNOZdsASMlJcXYcSevSpUq4cKFCwUeM3z4cKSkpKBNmzYQQiA7Oxuvv/46Pvjgg0LfZ+7cuZg9e3a+7bt27ZJ8pb3o6GhJz0fW06YZGXaIiKiJzZuDodM9W7gICUnBCy9cRpMmt6FQALt3S1TkP6ylTa0F2/PZpaSkICUlBTVq1IBKpcJuqX/obZyUP6Pp6elF3teqRpHs3bsXc+bMwf/+9z+0aNECly9fxsSJE/HJJ5/go48+KvCYadOmYfLkycbnqampCAgIQNeuXeHu7i5JXVqtFtHR0ejSpQvs7e0lOaets5Y2FQKIjFTgvfdUuHUr9x5xtWoCHToIs87l6iowZIhAixYeAJpIXKn1tKm1YHs+O71ej927d+PmzZsAgAoVKuDBgwdsU4lY4mc05y5AUcgWMLy8vKBSqXD79m2T7bdv34aPj0+Bx3z00UcYOXIkXnnlFQBAvXr18PjxY7z66qv48MMPC5zRzcHBocBpZO3t7SX/AbbEOW1daW7T8+cNs2X+9lvuNkdHYNo04L33FHB0LJ2d0kpzm1ojtmfxZGRkICoqCvHx8QCAjh074vnnn8f27dvZphKTsj3NOY9sw1TVajWaNGmCPXv2GLfp9Xrs2bMHLVu2LPCY9PT0fCFCpVIBMPQ8JioJaWmGuSfq1zcNF717G/pgzJjBWTSJniQlJQVLlixBfHw87O3tMWTIELRr144jRcoYWW+RTJ48GaNHj0bTpk3RvHlzLFiwAI8fPzaOKhk1ahT8/f0xd+5cAECfPn0wf/58NGrUyHiL5KOPPkKfPn2MQYPIUgy3QwwjOhITc7dXq2aYtrtPH/lqI7IW169fx+rVq5GVlQUPDw8MHTq00KvWZN1kDRhhYWG4e/cuZsyYgeTkZDRs2BA7duwwdvy8fv26yRWL6dOnQ6FQYPr06UhMTETFihXRp08ffPbZZ3J9C2QjLlwA3nrLtMOlgwMwdSrw/vuAk5N8tRFZk/Lly8PBwQHe3t4ICwuDi4uL3CWRhcjeyXPChAmYMGFCga/t3bvX5LmdnR1mzpyJmTNnlkBlRAb79wOhoUCe0c7o2RP4v/8DgoPlq4vIWgghjLc/XF1dMXr0aLi7u8POTvaPILIg2acKJyrNHj8GRo/ODRdVqwIbNwJbtjBcEBVFeno6li9fjtOnTxu3lS9fnuHCBvBvmOgJPvwQ+KeTO1q3BnbtAiSePoWozLpz5w7WrFmDBw8eICUlBbVr1+ZiZTaEAYOoEAcOGG6DAIZRIT//zHBBVFRxcXH49ddfodFoUK5cOQwbNozhwsYwYBAVICMDeOklw8gRAPj0U6BGDXlrIrIGQggcPHjQOBtnYGAgBg8eLPnMyVT6MWAQFWDmTOCfxRzRogXwn//IWg6RVRBCYOPGjTh16hQAoEmTJujRowenEbBRDBhE/3LkCPDVV4bHajWwdCnA/x+Jnk6hUMDd3R0KhQI9evRAs2bN5C6JZMSAQZRHVpbh1oheb3g+axZQt66sJRGVenmHoXbs2BF16tSBr6+vzFWR3DhMlSiPjz8GYmMNj5s0Ad59V956iEq7c+fOYcWKFcZlvBUKBcMFAWDAIDI6cQL4738Nj+3tDaNGOFSfqGBCCOzduxdRUVG4evUqjh07JndJVMrwv08iABoNMHYsoNMZnk+fDtSrJ29NRKWVVqvFhg0bEPvP5b7nn38ezz//vMxVUWnDgEEEYO5cIGeiwfr1DWuMEFF+qampCA8PR1JSEpRKJXr37o1GjRrJXRaVQgwYZPNOnzbMcwEYRov8/LNh9AgRmbp16xbWrFmDtLQ0ODs7IywsDFWqVJG7LCqlGDDIpmm1hlsj2dmG51OnAo0by1sTUWnl6OgInU4Hb29vDBs2DJ6ennKXRKUYAwbZLJ0OmDjR0LkTAEJCgI8+krcmotKsfPnyGDVqFMqXL89pv+mpOIqEbFJ6OjBoEPD994bnSqVhQi0HB3nrIipNsrKyEBERgUuXLhm3+fj4MFxQkTBgkM25cwfo2BHYsMHwXKUCfvoJaN5c1rKISpX79+9j6dKluHDhAjZu3Gic54KoqHiLhGxKXBzQs2fuEuxubkBUFNC1q7x1EZUm165dw9q1a5Geng5XV1eEhYXB3t5e7rLIyjBgkM3Ytw/o3x+4d8/w3N8f2LbNMCyViAxOnDiBrVu3Qq/Xw9fXF0OHDoW7u7vcZZEVYsAgmxARAYwaZZhQCzCEiq1bgcqV5a2LqLQQQmDnzp04cuQIACAkJAT9+vXjlQsqNgYMKtOEAL74wnTirK5dgchIgL+UEZnK/me8docOHdCuXTvjAmZExcGAQWXa11+bhouXXgIWLTKsNUJEuXKWWK9bty6CgoLkLofKAI4ioTIrLS13hk4A+OQTYMkShguiHPHx8Vi3bh30ej0AQKVSMVyQZHgFg8qspUuB+/cNj0eONCxgRkSG/hbHjh3Djh07IISAn58fWrZsKXdZVMYwYFCZlJ0NzJ+f+/zdd+Wrhag00el02L59O44fPw4AaNCgAZo1ayZzVVQWMWBQmRQZCVy7ZnjcoweXXicCgPT0dERGRuLq1asAgNDQULRq1YqdOckiGDCozBECmDcv9/l778lXC1FpcffuXaxZswb379+HWq3GwIEDUbNmTbnLojKMAYPKnD17gJMnDY+bNgXat5e3HqLSQKfTIS0tDZ6enhg2bBi8vb3lLonKOAYMKnPyXr14912AV3+JDIuUDR8+HN7e3nB2dpa7HLIBHKZKZcqpU8CuXYbHQUHAgAHy1kMkl+zsbGzatAk3btwwbgsMDGS4oBLDgEFlSt6rF5MnA3a8Rkc2KC0tDcuXL8fJkycRGRnJlVBJFvzvl8qMa9eA8HDD4woVgLFj5a2HSA7JyclYs2YNUlNT4eDgwPVESDYMGFRmLFgA6HSGxxMmALwSTLbm/PnzWL9+PbRaLSpUqIChQ4fCy8tL7rLIRjFgUJlw/z6weLHhsZMTMH68vPUQlSQhBPbt24fff/8dABAcHIyBAwfCyclJ5srIljFgUIkSArhyBcjKevJ+Wi1w/bobzp0r2tohK1cCjx8bHo8dC1Ss+Oy1ElmT27dvAwCaN2+Obt26QalkFzuSFwMGlRghgOHDc/tJPJk9gE5mv4dSaejcSWRLFAoF+vXrh7p16yIkJETucogAcBQJlaD164saLopv0CAgONiy70FUGiQmJmL79u0QQgAA1Go1wwWVKryCQSXi0SPg7bdznw8aBHh4FL6/Xq/HjRs3EBAQUORLvV5eXNSMbMOZM2ewadMmZGdnw8vLi4uVUanEgEElYtYsIDHR8LhHD2Dt2ifPsKnV6rBtWwx69vSDvT0vtBEBhs6cv/32G/bv3w8AqFmzJurXry9zVUQFe6aAkZmZCUdHR6lqoTIqJgb45hvDY0dH4LvvOH03kbk0Gg1+/fVXxMXFAQBat26NTp06sTMnlVpm/2Tq9Xp88skn8Pf3h6urK+Lj4wEAH330EX766SfJCyTrptcDr7+eOz/FRx8ZpvAmoqJ78OABli5diri4OKhUKvTv3x+hoaEMF1Sqmf3T+emnn2LZsmX44osvoFarjdufe+45LFmyRNLiyPpkZADnzuV+ffklcOSI4bU6dYApU+Stj8gaPXz4EHfv3oWLiwvGjBmDBg0ayF0S0VOZfYtkxYoV+PHHH9G5c2e8/vrrxu0NGjTAhQsXJC2OrMvNm0DDhsDffxf8+vffA3kyKREVUdWqVTFo0CD4+fnB40m9o4lKEbOvYCQmJqJ69er5tuv1ei6oY+NWriw8XIwaBbRvX7L1EFkrvV6PPXv24M6dO8ZtderUYbggq2L2FYy6deti3759qFq1qsn2qKgoNGrUSLLCyPps25b7eNSo3Bk4fX2BqVPlqYnI2mRmZmLdunW4fPkyzp07hzfffBN2XBaYrJDZP7UzZszA6NGjkZiYCL1eb+zVvGLFCmzZssUSNZIVuH8fOHTI8LhWLWD5cnnrIbJG9+7dw5o1a5CSkgI7Ozt07tyZ4YKsltm3SPr164fNmzdj9+7dcHFxwYwZM3D+/Hls3rwZXbp0sUSNZAWio3NHivToIW8tRNYoISEBS5YsQUpKCtzc3PDSSy9xZk6yasWKxm3btkV0dLTUtZAV27499zEDBpF5jh07Zpz229/fH2FhYXBzc5O7LKJnYvYVjKCgIPxdQE++Bw8eIIgTHNgkvR7YscPw2NkZaNdO3nqIrIler8f58+chhEC9evUwZswYhgsqE8y+gnH16lXocq6F55GVlYXEnLmgyabExADJyYbHnToZZuskoqJRKpUYPHgwzpw5g2bNmkHBaW6pjChywNi0aZPx8c6dO02GS+l0OuzZsweBgYGSFkfWIe/tkZ495auDyFqkpKQgNjYW7f653Ofk5ITmzZvLXBWRtIocMPr37w8AUCgUGD16tMlr9vb2CAwMxFdffSVpcWQd8g5PZf8Loie7fPkyoqKikJWVBXd3dzRs2FDukogsosgBQ6/XAwCqVauGY8eOwcvLy2JFkfW4dw84fNjwuHZtgBexiAomhMDhw4cRHR0NIQSqVKmCGjVqyF0WkcWY3QcjISHBEnWQldq1y9DJE+DtEaLCZGdnY+vWrYiJiQEANGzYEL1794ZKpZK3MCILKtYw1cePH+OPP/7A9evXodFoTF57++23JSmMrAOHpxI92ePHj7F27Vpcv34dCoUCXbt2RYsWLdiZk8o8swPGyZMn0bNnT6Snp+Px48coX748UlJS4OzsDG9vbwYMK6XXA5cvA+YsJyNE7vBUFxegbVvL1EZkzW7duoXr16/DwcEBgwYNKnAtJ6KyyOyAMWnSJPTp0weLFi2Ch4cHDh8+DHt7e7z44ouYOHGiJWokCxMC6N3b9GqEuTp3BhwcpKuJqKyoUaMGevfujapVq7LvGtkUsyfaiomJwTvvvAOlUgmVSoWsrCwEBATgiy++wAcffGCJGsnCDh9+tnABAC+8IE0tRNZOCIFDhw7hwYMHxm1NmjRhuCCbY/YVDHt7eyiVhlzi7e2N69evG5cRvnHjhuQFkuX9+GPu4x49AD8/846vU8eweiqRrdNqtdi8eTPOnDmDmJgYjBs3jouVkc0y+ye/UaNGOHbsGGrUqIH27dtjxowZSElJwcqVK/Hcc89ZokayoAcPgIgIw2MPDyAqyjDdNxGZ59GjRwgPD8etW7egUCjQtGlThguyaWbfIpkzZw58fX0BAJ999hnKlSuHN954A3fv3sUPP/xgdgELFy5EYGAgHB0d0aJFCxw9evSJ+z948ADjx4+Hr68vHBwcULNmTWzLO9MTmWXlSiAjw/B45EiGC6LiuHXrFhYvXoxbt27ByckJI0eORLNmzeQui0hWZsfrpk2bGh97e3tjR84wgmKIiIjA5MmTsWjRIrRo0QILFixAt27dEBcXB29v73z7azQadOnSBd7e3oiKioK/vz+uXbsGT0/PYtdgy4QA8mbC116TrxYiaxUbG4stW7YgOzsbXl5eGDZsGMqXLy93WUSyM/sKRmFOnDiB3r17m3XM/PnzMW7cOIwdOxZ169bFokWL4OzsjKVLlxa4/9KlS3Hv3j1s2LABrVu3RmBgINq3b48GDRpI8S3YBL0euHABOHcOWLLE8CcAtGoF8A4XkXmEEDhy5Aiys7NRo0YNvPzyywwXRP8w6wrGzp07ER0dDbVajVdeeQVBQUG4cOECpk6dis2bN6Nbt25FPpdGo8Hx48cxbdo04zalUonQ0FAcOnSowGM2bdqEli1bYvz48di4cSMqVqyI4cOH4/333y90RrysrCxkZWUZn6empgIwdMbSmjPpwxPknEeq81mKXg+0b6/CkSP5c+XLL2dDqxUyVFUwa2lTa8I2lZZWq4VCoUC/fv1w7tw5tG7dGkqlku37DPgzKi1LtKc55ypywPjpp58wbtw4lC9fHvfv38eSJUswf/58vPXWWwgLC8PZs2dRp06dIr9xSkoKdDodKlWqZLK9UqVKuHDhQoHHxMfH47fffsOIESOwbds2XL58GW+++Sa0Wi1mzpxZ4DFz587F7Nmz823ftWsXnCXucBAdHS3p+aSWmOiCI0dC8213d8+Cq+subNuml6GqJyvtbWqN2KbPRqPR4NGjR6hQoQIA4PA/i/E8y+1iMsWfUWlJ2Z7p6elF3rfIAeObb77Bf//7X7z77rtYt24dBg8ejP/97384c+YMKleuXKxCzaXX6+Ht7Y0ff/wRKpUKTZo0QWJiIubNm1dowJg2bRomT55sfJ6amoqAgAB07doV7u7uktSl1WoRHR2NLl26wN7eXpJzWsK+faZTE48dq4daLTBqlArNmnWXqaqCWUubWhO26bO7efMm1q1bh8ePH6NRo0a4fv0621NC/BmVliXaM+cuQFEUOWBcuXIFgwcPBgAMGDAAdnZ2mDdvXrHDhZeXF1QqFW7fvm2y/fbt2/Dx8SnwGF9fX9jb25vcDqlTpw6Sk5Oh0WigVqvzHePg4ACHAqaYtLe3l/wH2BLnlFJKSu7jL74A3n1Xsi44FlPa29QasU2LJyYmBlu2bDFeeQ0ICMD169fZnhbANpWWlO1pznmK/AmTkZFhvKWgUCjg4OBgHK5aHGq1Gk2aNMGePXuM2/R6Pfbs2YOWLVsWeEzr1q1x+fJl49LxAHDx4kX4+voWGC7IVN4s9687U0RUCL1ej+joaGzcuBE6nQ61a9fGSy+9BA8PD7lLIyrVzOrkuWTJEri6ugIwLD+8bNmyfNPfmrPY2eTJkzF69Gg0bdoUzZs3x4IFC/D48WOMHTsWADBq1Cj4+/tj7ty5AIA33ngD3333HSZOnIi33noLly5dwpw5c7jAWhHlDRiFXCQiojyysrKwbt06XLp0CQDQrl07dOjQAQqFgh0RiZ6iyAGjSpUqWLx4sfG5j48PVq5cabKPQqEw68M+LCwMd+/exYwZM5CcnIyGDRtix44dxo6f169fN05LDgABAQHYuXMnJk2ahPr168Pf3x8TJ07E+++/X+T3tGW8gkFknitXruDSpUuws7NDv379OFsxkRmKHDCuXr1qkQImTJiACRMmFPja3r17821r2bKlsdc2mSc5OfcxAwbR09WtWxedOnVCUFAQ/P395S6HyKqU/l5+JJmcKxgKBcCFHYkKdurUKTx+/Nj4vG3btgwXRMXAgGFDcgJGxYoA12AiMqXX67F9+3Zs2LABa9euhU6nk7skIqvGjxkbIURuwODtESJTGRkZiIqKQnx8PACgevXqJv2/iMh8DBg24uFDIGfGdAYMolwpKSlYs2YN7t27B3t7e7zwwgtmzUpMRAVjwLARHEFClN+VK1cQGRmJrKwseHh4YOjQoYVO9EdE5inWNcArV65g+vTpGDZsGO7cuQMA2L59O87lLM1JpQ7nwCAypdfrsXPnTmRlZSEgIADjxo1juCCSkNkB448//kC9evVw5MgR/Prrr0hLSwNg6Hld2HogJD9ewSAypVQqERYWhmbNmmHUqFFwcXGRuySiMsXsgDF16lR8+umnxmXbc3Tq1InzU5RinAODyLAS5Pnz543PK1SogJ49e8KOw6qIJGd2wDhz5gxeeOGFfNu9vb2Rknc1LSpVeAWDbN2dO3ewePFiREZGGkeLEJHlmB0wPD09kZSUlG/7yZMnORlNKcaAQbYsLi4OP/30Ex48eABPT0+4ubnJXRJRmWd2wBg6dCjef/99JCcnQ6FQQK/X48CBA5gyZQpGjRpliRpJAuzkSbZICIH9+/cjPDwcGo0GgYGBeOWVV1CxYkW5SyMq88y+8ThnzhyMHz8eAQEB0Ol0qFu3LnQ6HYYPH47p06dbokaSQE4fDE4TTrYiOzsbmzdvxunTpwEATZo0QY8ePaBSqWSujMg2mB0w1Go1Fi9ejI8++ghnz55FWloaGjVqhBo1aliiPpJAZiZw7JjhsZcXpwkn23Du3DmcPn0aCoUCPXr0QLNmzeQuicimmP1Rs3//frRp0wZVqlRBlSpVLFETSejOHSAkJPc5+1+Qrahfvz6SkpJQs2ZNBAUFyV0Okc0xuw9Gp06dUK1aNXzwwQeIjY21RE0koc2bgbyDe+rXl68WIku7ePEisv6ZE1+hUKB79+4MF0QyMTtg3Lp1C++88w7++OMPPPfcc2jYsCHmzZuHmzdvWqI+ekZXruQ+rl8f+OIL+WohshQhBPbu3Ys1a9Zg3bp10Ov1cpdEZPPMDhheXl6YMGECDhw4gCtXrmDw4MFYvnw5AgMD0alTJ0vUSM8gb8CIjAQ4kpjKGq1Wi6ioKPzxxx8ADJNnEZH8nqm7X7Vq1TB16lQ0aNAAH330kfEfOJUeOQFDqQQCA2UthUhyqampCA8PR1JSEpRKJXr16oXGjRvLXRYR4RkCxoEDB7Bq1SpERUUhMzMT/fr1w9y5c6WsjSSQEzACAoA8M7sTWb2bN28iIiICaWlpcHZ2xpAhQ1C1alW5yyKif5gdMKZNm4bw8HDcunULXbp0wTfffIN+/frB2dnZEvXRM7h/H3jwwPCY/dyoLNHpdMbFFr29vTFs2DB4enrKXRYR5WF2wPjzzz/x7rvvYsiQIfDijE2lWt7+F8HB8tVBJDWVSoVBgwbh4MGD6NOnDxwcHOQuiYj+xeyAceDAAUvUQRbAgEFlSVZWFpKSkhD4T2ciPz8/DBo0SN6iiKhQRQoYmzZtQo8ePWBvb49NmzY9cd++fftKUhg9OwYMKivu37+P8PBw3Lt3D2PGjOHCikRWoEgBo3///khOToa3tzf69+9f6H4KhQI6nU6q2ugZMWBQWXDt2jWsXbsW6enpcHV1lbscIiqiIgWMvJPWcAIb68GAQdbuxIkT2Lp1K/R6PXx9fTF06FC4u7vLXRYRFYHZE22tWLHCOBVvXhqNBitWrJCkKJJGTsCoUAHw8JC3FiJz6PV67NixA5s3b4Zer0dISAjGjh3LcEFkRcwOGGPHjsXDhw/zbX/06BHGjh0rSVH0bDIygFOngMREw3MOUSVrExMTgyNHjgAAOnTogIEDB8Le3l7mqojIHGaPIhFCQKFQ5Nt+8+ZNePDXZNndugXUqwfcu5e7jbdHyNo0bNgQCQkJqFOnDurWrSt3OURUDEUOGI0aNYJCoYBCoUDnzp1hZ5d7qE6nQ0JCArp3726RIqno1q0zDRcA0KyZPLUQmePGjRvw9fWFnZ0dlEolBg4cKHdJRPQMihwwckaPxMTEoFu3bia9udVqNQIDA/kfQikQE5P7eOBAoHFj4PXXZSuH6KmEEDh27Bh27NiB+vXro1+/fgVeJSUi61LkgDFz5kwAQGBgIMLCwuDo6Gixoqj4Tp0y/KlUAitWAJzBnUoznU6H7du34/jx4wAMYUOv10OlUslcGRE9K7P7YIwePdoSdZAEsrOBs2cNj2vUYLig0i09PR2RkZG4evUqACA0NBStWrXi1QuiMqJIAaN8+fK4ePEivLy8UK5cuSf+B3Dv3x0AqMTExQE5I4gbNpS1FKInunPnDsLDw3H//n2o1WoMHDgQNWvWlLssIpJQkQLG119/DTc3N+Nj/oZROuXtf9GggWxlED2RTqfDmjVr8ODBA3h6emLYsGHw9vaWuywikliRAkbe2yJjxoyxVC30jPIGDF7BoNJKpVKhb9++2LdvHwYNGgRn3ssjKpPMnmjrxIkTOHPmjPH5xo0b0b9/f3zwwQfQaDSSFkfm+eOP3MeNGslXB9G/ZWdnIzk52fi8WrVqGDlyJMMFURlmdsB47bXXcPHiRQBAfHw8wsLC4OzsjMjISLz33nuSF0hFc+cO8Ndfhsf16wM+PvLWQ5QjLS0NK1aswLJly5CSkmLczlutRGWb2QHj4sWLaPjP9ffIyEi0b98eq1evxrJly7Bu3Tqp66Mi2rkTEMLwuEcPeWshypGcnIwlS5bgxo0bUCgUSEtLk7skIiohxZoqPGdF1d27d6N3794AgICAAJPfTqhkbd+e+7hnT/nqIMpx/vx5rF+/HlqtFhUqVMCwYcNQoUIFucsiohJidsBo2rQpPv30U4SGhuKPP/7A999/DwBISEhApUqVJC+Qnk6nM1zBAAB3d6BlS3nrIdsmhMC+ffvw+++/AwCCg4MxcOBAODk5yVwZEZUkswPGggULMGLECGzYsAEffvghqlevDgCIiopCq1atJC+Qni4yMnf9ka5dAS46SXI6efKkMVw0b94c3bp1g1Jp9t1YIrJyZgeM+vXrm4wiyTFv3jxO7yuDd98Fvvwy9zn7X5DcGjRogLNnzyIkJARNmjSRuxwikonZASPH8ePHcf78eQBA3bp10bhxY8mKoqLR64Effsh9rlYzYJA87t69iwoVKkCpVEKlUmHkyJEcJUJk48wOGHfu3EFYWBj++OMPeHp6AgAePHiAjh07Ijw8HBUrVpS6RirE5cvAo0e5zzduBHx95auHbNOZM2ewceNGNGvWDN26dQPAIahEVIxhqm+99RbS0tJw7tw53Lt3D/fu3cPZs2eRmpqKt99+2xI1UiFy5r0AgE8+Abp3l68Wsj1CCOzZswe//vordDod7t27B51OJ3dZRFRKmH0FY8eOHdi9ezfq1Klj3Fa3bl0sXLgQXbt2lbQ4erJ/VrgGAPBWN5UkjUaDX3/9FXFxcQCAVq1aoXPnzuzMSURGZgcMvV4P+wKGKdjb2xvnx6CSwYBBcnjw4AHCw8Nx+/ZtqFQq9OnTBw24uh4R/YvZv2506tQJEydOxK1bt4zbEhMTMWnSJHTu3FnS4qhwej1w4oThcUAAwMUoqSTodDosX74ct2/fhouLC0aPHs1wQUQFMjtgfPfdd0hNTUVgYCCCg4MRHByMatWqITU1Fd9++60laqQCXLqU28GzaVN5ayHboVKp0KVLF/j4+GDcuHEICAiQuyQiKqXMvkUSEBCAEydOYM+ePcZhqnXq1EFoaKjkxVHhDh7MfcyAQZak1+vx8OFDlCtXDoChz1Xt2rXZ34KInsisgBEREYFNmzZBo9Ggc+fOeOuttyxVFz3Fn3/mPm7bVr46qGzLzMzEunXrkJycjHHjxsHd3R0AGC6I6KmKHDC+//57jB8/HjVq1ICTkxN+/fVXXLlyBfPmzbNkfVSIP/4w/OngADRvLm8tVDbdu3cPa9asQUpKCuzs7HDnzh1jwCAiepoi/xry3XffYebMmYiLi0NMTAyWL1+O//3vf5asjQpx4waQkGB4/PzzhpBBJKWEhAQsXrwYKSkpcHNzw9ixY43rDhERFUWRA0Z8fDxGjx5tfD58+HBkZ2cjKSnJIoVR4fbty33crp18dVDZdOzYMaxcuRKZmZnw9/fHuHHj4OfnJ3dZRGRlinyLJCsrCy4uLsbnSqUSarUaGRkZFimMCpaaCuS9cNS+vXy1UNlz4sQJbNu2DQBQr1499O3bF3Z2xV6yiIhsmFn/c3z00UdwdnY2PtdoNPjss8/g4eFh3DZ//nzpqiMTp08DLVsC6emG53Z2hlskRFJ57rnncOzYMYSEhKB169ZcU4SIiq3IAaNdu3bGaYFztGrVCvHx8cbn/M/Istavzw0XgOH2SJ6LSkTFkpqaCjc3NygUCqjVarzyyitQqVRyl0VEVq7IAWPv3r0WLIOK4sGD3MfPPw8sXixbKVRGXL58GVFRUWjTpg3atGkDAAwXRCQJDma3Ig8f5j5esgQICpKvFrJuQggcOnQIq1evRlZWFi5fvsy1hIhIUqUiYCxcuBCBgYFwdHREixYtcPTo0SIdFx4eDoVCgf79+1u2wFIiNTX3MacjoOLKzs7Gpk2bsGvXLggh0KhRI4wcOZKTZxGRpGT/HyUiIgKTJ0/GzJkzceLECTRo0ADdunXDnTt3nnjc1atXMWXKFLS1oWks817ByNOvlqjItFotVq9ejZiYGCgUCnTr1g19+vThbREikpzsAWP+/PkYN24cxo4di7p162LRokVwdnbG0qVLCz1Gp9NhxIgRmD17NoJs5D6BRgMcOmR4rFAArq7y1kPWJzs7G5cuXcLNmzfh4OCA4cOH4/nnn2fnbCKyCFkHuGs0Ghw/fhzTpk0zblMqlQgNDcWhnE/TAnz88cfw9vbGyy+/jH15Z50qQFZWFrKysozPU/+5z6DVaqHVap/xO4DxXHn/lFpqKtCggR0ePzZ8ELi5Ceh02dDpLPJ2pYKl29QWCSHg7e2NtLQ0DBkyBF5eXmzfZ8CfUemxTaVlifY051zFChj79u3DDz/8gCtXriAqKgr+/v5YuXIlqlWrZuyJXhQpKSnQ6XSoVKmSyfZKlSrhwoULBR6zf/9+/PTTT4iJiSnSe8ydOxezZ8/Ot33Xrl0mc3pIITo6WtLz5ThyxAeJiS2MzytVeoBt2/58whFlh6Xa1FYIIZCdnQ17e3sAgJeXF8qXL1/kfk70dPwZlR7bVFpStmd63rkSnsLsgLFu3TqMHDkSI0aMwMmTJ41XBx4+fIg5c+YYZwG0hEePHmHkyJFYvHgxvLy8inTMtGnTMHnyZOPz1NRUBAQEoGvXrpIt3KTVahEdHY0uXboY/yOX0p07uZewHR0FoqJcUadOT8nfpzSxdJvaAq1Wi23btiExMRFjxoyBvb09oqOj0a1bN7apBPgzKj22qbQs0Z6peUcbPIXZAePTTz/FokWLMGrUKISHhxu3t27dGp9++qlZ5/Ly8oJKpcLt27dNtt++fRs+Pj759r9y5QquXr2KPn36GLflDK2zs7NDXFwcgoODTY5xcHCAQwGrgdnb20v+A2yJcwKmnTtXrFCgfn3b+YdnqTYt6x49eoSIiAgkJiZCqVQiOTnZ2F+JbSottqf02KbSkrI9zTmP2Z084+Li0K6AFbY8PDzwIO9MUEWgVqvRpEkT7Nmzx7hNr9djz549aNmyZb79a9eujTNnziAmJsb41bdvX3Ts2BExMTEICAgw99uxCn//nfu4QgX56iDrcOvWLSxevBiJiYlwcnLCiy++iFq1asldFhHZGLOvYPj4+ODy5csIDAw02b5///5ijeiYPHkyRo8ejaZNm6J58+ZYsGABHj9+jLFjxwIARo0aBX9/f8ydOxeOjo547rnnTI739PQEgHzby5J793Ifly8vXx1U+p07dw4bNmxAdnY2vLy8MGzYMJTnDw0RycDsgDFu3DhMnDgRS5cuhUKhwK1bt3Do0CFMmTIFH330kdkFhIWF4e7du5gxYwaSk5PRsGFD7Nixw9jx8/r16zY/ARADBhXFqVOnsGHDBgBAjRo1MGDAADg6OspbFBHZLLMDxtSpU6HX69G5c2ekp6ejXbt2cHBwwJQpU/DWW28Vq4gJEyZgwoQJBb72tDVQli1bVqz3tCa8RUJFUaNGDZQrVw61a9dGaGiozQdzIpKX2QFDoVDgww8/xLvvvovLly8jLS0NdevWhStnfrKYnCsYajUg8chasnKZmZnGqxTOzs549dVXedWCiEqFYk+0pVarUbduXSlroULkBIzy5Q2zeBIBwI0bNxAREYGOHTuiSZMmAMBwQUSlhtkBo2PHjk+cWvi33357poIov5xbJLw9QjliYmKwZcsW6HQ6nDhxAo0aNeItESIqVcwOGA0bNjR5rtVqERMTg7Nnz2L06NFS1UX/yMwEciZOYwdP0uv12L17t3Eq/dq1a+OFF15guCCiUsfsgPH1118XuH3WrFlIS0t75oLIVN4RJLyCYduysrKwbt06XLp0CQDQtm3bp15RJCKSi2SLnb344oto3rw5vvzyS6lOaRP0euDiRRS6cNnZs7mPeQXDdmVnZ2Pp0qW4c+cO7Ozs0LdvX9SrV0/usoiICiVZwDh06BA7mJlJCKBtW+DgwaLtz4Bhu+zs7PDcc8/h6NGjGDp0KPz9/eUuiYjoicwOGAMGDDB5LoRAUlIS/vrrr2JNtGXLrl0rergAAA7asT0ajQZqtRoA0KZNGzRp0kTyVYCJiCzB7IDh4eFh8lypVKJWrVr4+OOP0bVrV8kKswUZGabPX3658H1r1waGD7dsPVR66PV67Ny5E9euXcNLL70EtVoNhULBcEFEVsOsgKHT6TB27FjUq1cP5cqVs1RNNiMzM/fxa68BixbJVwuVHhkZGYiKikJ8fDwAwyrCderUkbkqIiLzmDW2TaVSoWvXrmavmkoFy8rKfczuKwQAKSkpWLJkCeLj42Fvb48hQ4YwXBCRVTL7Fslzzz2H+Ph4VKtWzRL12JS8AcPBQb46qHS4cuUKIiMjkZWVBQ8PDwwdOhQ+Pj5yl0VEVCxmz87z6aefYsqUKdiyZQuSkpKQmppq8kVFd/t27mMGDNt25swZrFq1CllZWQgICMC4ceMYLojIqhX5CsbHH3+Md955Bz179gQA9O3b12SCHyEEFAoFdIVN6EAmDh0CwsJyn/MWiW2rUqUKnJ2dUaNGDfTq1Qt2dpKNICcikkWR/xebPXs2Xn/9dfz++++WrMdmREaaPg8OlqcOkk92drYxSHh4eOC1116Dq6srZ+YkojKhyAFDCAEAaN++vcWKsSV5b4+MHw/8a3oRKuPu3LmD8PBwdOnSxdiJ083NTeaqiIikY1YfDP5mJZ07d3Iff/YZYG8vXy1UsuLi4vDTTz/h/v37+OOPP6DX6+UuiYhIcmbd6K1Zs+ZTQ8a9vKtzUaFyAoZaDbi7y1sLlQwhBA4ePIjdu3cDAAIDAzF48GCuhEpEZZJZAWP27Nn5ZvKk4skJGN7eAC8MlX3Z2dnYvHkzTp8+DQBo0qQJevToAZVKJXNlRESWYVbAGDp0KLy9vS1VS5mm1QKXLhkWONPrgeRkw3Y2Z9mXnZ2N5cuX4+bNm1AoFOjRoweaNWsmd1lERBZV5IDB/hfFl5EBhIQACQn5X2PAKPvs7Ozg7++PlJQUDB48GEFBQXKXRERkcWaPIiHzHTxYcLgAgOeeK9laqOTo9Xpj/4quXbvi+eefh6enp7xFERGVkCIHDPZ0L75/L92Ss2qqvz/w9tslXg5ZmBACf/75JxISEjBy5EioVCoolUqGCyKyKZwusATknUF90SLDyqlUNmm1WmzYsAGxsbEAgAsXLiAkJETmqoiISh4DRgl4+DD3MYekll2pqakIDw9HUlISlEolevXqxXBBRDaLAaME5L2CwVG+ZdPNmzcRERGBtLQ0ODs7Y8iQIahatarcZRERyYYBw8L0euDIkdznDBhlz/nz57Fu3TrodDp4e3tj2LBh7G9BRDaPAcOC9HqgeXPg+PHcbbxFUvZUrFgRdnZ2qF69Ol544QU4ODjIXRIRkewYMCwoLs40XDg5AQEB8tVD0hFCGOeG8fLywiuvvIIKFSpwvhgion9wEQQLevTI9PmGDQCvnFu/+/fv48cff0RCnslNvLy8GC6IiPJgwLCgx49zH7//PtC1q3y1kDSuXbuGJUuWIDk5Gdu3b+cEdEREheAtEgvKGzBcXOSrg6Rx4sQJbN26FXq9Hr6+vhg6dCivWhARFYIBw4IYMMoGvV6PXbt24cg/w4FCQkLQr18/2Nvby1wZEVHpxYBhQXn7YDBgWCetVouIiAhcuXIFANChQwe0a9eOVy6IiJ6CAcNC9Hpg3Ljc5wwY1snOzg4uLi6ws7PDCy+8gLp168pdEhGRVWDAsJB/fuE1qllTnjqoeHKGoSoUCvTp0wetW7eGt7e33GUREVkNjiKxkH8vPtu8uTx1kHmEEDh69CgiIyONI0Ts7OwYLoiIzMQrGCVg9Gi5K6Ci0Ol02L59O47/MztabGwsFysjIiomBgwiAOnp6YiMjMTVq1cBAF26dGF/CyKiZ8CAQTbvzp07CA8Px/3796FWqzFw4EDUZKcZIqJnwoBBNu3y5cuIjIyERqOBp6cnhg0bxv4WREQSYMAgm+bk5ASdToeqVatiyJAhcHZ2lrskIqIygQGDbJq/vz/GjBkDX19fqFQqucshIiozOEyVbEpaWhpWrlyJW7duGbdVrlyZ4YKISGIMGGQzkpOTsXjxYsTHx2Pjxo1cCZWIyIJ4i4Rswvnz57F+/XpotVpUqFABgwcP5noiREQWxIBBZZoQAn/++Sf27t0LAAgODsbAgQPh5OQkb2FERGUcAwaVWdnZ2diwYQPOnTsHAGjevDm6desGpZJ3BomILI0Bw0JmzJC7AlIqldBoNFAqlejZsyeaNGkid0lERDaDAcNCjhzJfRwYKFsZNk2pVGLgwIG4c+cOAgIC5C6HiMim8FqxheS9Cj9xonx12JqzZ89i69atxhEiDg4ODBdERDLgFQwLyVmu3ccHKFdO3lpsgRACv//+O/bt2wcAqFatGhcrIyKSEQOGheQEDPYntDyNRoP169fjwoULAIDWrVujdu3aMldFRGTbGDAsJCdgcKoFy3rw4AHCw8Nx+/ZtqFQq9OnTBw0aNJC7LCIim8eAYSE5k0TyCoblXL9+HREREUhPT4eLiwvCwsLY34KIqJRgwLAQ3iKxPK1Wi4yMDPj4+GDo0KHw8PCQuyQiIvoHA4aFMGBYXnBwMIYNG4aqVatCrVbLXQ4REeXBjz8LYcCQXmZmJtatW4e///7buK1GjRoMF0REpRA//iyEAUNa9+7dw08//YSzZ88iKiqKK6ESEZVypeLjb+HChQgMDISjoyNatGiBo0ePFrrv4sWL0bZtW5QrVw7lypVDaGjoE/eXCwOGdBISErB48WKkpKTAzc0Nffr04UqoRESlnOwffxEREZg8eTJmzpyJEydOoEGDBujWrRvu3LlT4P579+7FsGHD8Pvvv+PQoUMICAhA165dkZiYWMKVPxmHqUrj+PHjWLlyJTIzM+Hv749x48bBz89P7rKIiOgpZA8Y8+fPx7hx4zB27FjUrVsXixYtgrOzM5YuXVrg/qtWrcKbb76Jhg0bonbt2liyZAn0ej327NlTwpUXTAjg4kUgNdXwnFcwiken0+HmzZvYuXMnhBCoV68eRo8eDTc3N7lLIyKiIpB1FIlGo8Hx48cxbdo04zalUonQ0FAcOnSoSOdIT0+HVqtF+fLlC3w9KysLWVlZxuep/3zya7VaaLXaZ6g+V855tFot+vZVYceO3FShUAhotdmSvI8t0Wg0yMjIAAB06NABLVu2BADJ/s5sUd6fU3p2bE/psU2lZYn2NOdcsgaMlJQU6HQ6VKpUyWR7pUqVjNM+P837778PPz8/hIaGFvj63LlzMXv27Hzbd+3aBWdnZ/OLfoL163/Hjh29TLY5Od3Gtm1HCjmCniQwMBDp6el48OABtm/fLnc5ZUZ0dLTcJZQpbE/psU2lJWV7pqenF3lfq54H4/PPP0d4eDj27t0LR0fHAveZNm0aJk+ebHyemppq7Lfh7u4uSR1arRbR0dFo06ajyfZJk3R4/fUKqFatpyTvU9ZduXIFt27dQtu2bY1tOmjQINjb28tdWpmQ06ZdunRhm0qA7Sk9tqm0LNGeOXcBikLWgOHl5QWVSoXbt2+bbL99+zZ8fHyeeOyXX36Jzz//HLt370b9+vUL3c/BwQEODg75ttvb20v+A6xQ5J7vhReA+fNVAFSSvkdZJITA4cOHER0dDSEEKleujGrVqgGwzN+TrWObSovtKT22qbSkbE9zziNrF0S1Wo0mTZqYdNDM6bCZc8+9IF988QU++eQT7NixA02bNi2JUoskO09XCxVzRZFkZ2dj06ZN2LVrF4QQaNSoEYKDg+Uui4iInpHst0gmT56M0aNHo2nTpmjevDkWLFiAx48fY+zYsQCAUaNGwd/fH3PnzgUA/Pe//8WMGTOwevVqBAYGIjk5GQDg6uoKV1dX2b4PwDRg2MnesqXf48ePERERgRs3bkChUKBr165o0aIFFAoF9DnjfImIyCrJ/jEYFhaGu3fvYsaMGUhOTkbDhg2xY8cOY8fP69evQ5lnrOf3338PjUaDQYMGmZxn5syZmDVrVkmWno9Ol/uYAePJbt++jTVr1uDhw4dwcHDAoEGDUL16dbnLIiIiiZSKj8EJEyZgwoQJBb62d+9ek+dXr161fEHF9O23uUGIAePJUlJS8PDhQ5QvXx7Dhg2Dl5eX3CUREZGE+DEooRMncqftrFpVxkKsQEhICLKzs1GzZk04OTnJXQ4REUmM80xKKG/Hzrfekq+O0kir1WL79u0mQ5waNGjAcEFEVEYxYFiIp6fcFZQejx49wvLly3H06FFERkZyJVQiIhvAWyRkUbdu3UJ4eDgePXoEJycndO7cmSuhEhHZAAYMspizZ89i48aNyM7ORsWKFTF06NBC14whIqKyhQFDQklJ/M0cMMzMuXfvXvz5558AgBo1amDgwIEFzqhKRERlEwOGRLZsqYaEBAYMwNChM2exupYtWyI0NNRkLhMiIir7GDAkcuCAv/Gxvz9gy5+narUaw4YNw7Vr19CgQQO5yyEiIhkwYEgk78CIdesAW+vHeOPGDSQnJ6NZs2YAAE9PT3hyKA0Rkc1iwLCAfz5jbcapU6ewefNm6HQ6VKhQAUFBQXKXREREMmPAoGLLWfn24MGDAIDatWujcuXKMldFRESlAQMGFUtWVhbWrVuHS5cuAQDatm2Ljh07co4LIiICwIAhGb3edj5Y79+/jzVr1uDu3buws7ND3759Ua9ePbnLIiKiUoQBQwI6HXDxou1MIBUfH4+7d+/C1dUVQ4cOhb+//9MPIiIim8KAIYG4ONPnZf0uQZMmTaDRaBASEgJ3d3e5yyEiolLIhmdrkI5en/u4QYOyFzD0ej3++OMPZGRkGLe1bNmS4YKIiArFgCGxsjZENSMjA6tWrcLevXsRFRXFlVCJiKhIeIuECpWSkoI1a9bg3r17sLe3R9OmTTlKhIiIioQBgwp0+fJlREVFISsrCx4eHhg6dCh8fHzkLouIiKwEAwaZEELgyJEj2LVrF4QQCAgIQFhYGFxcXOQujYiIrAgDhgQWLlTJXYJkNBoNjhw5AiEEGjZsiF69esHOjj8mRERkHn5ySODUqdzHVavKV4cUHBwcMGzYMMTHx6NFixbsc0FERMXCgCGBvL/gT5ggXx3FdefOHdy9exchISEAAG9vb3h7e8tcFRERWTMGDIl5eMhdgXni4uLw66+/Ijs7G+7u7ggICJC7JCIiKgMYMGyUEAIHDhzAnj17AADVqlVDhQoVZK6KiIjKCgYMG5SdnY3Nmzfj9OnTAICmTZuie/fuUKnKTmdVIiKSFwOGjUlLS0N4eDgSExOhUCjQo0cPNCtr048SEZHsGDBszJkzZ5CYmAhHR0cMHjwYQUFBcpdERERlEAOGjXn++efx+PFjNGrUiH0uiIjIYrjYWRknhMBff/0FjUYDAFAoFAgNDWW4ICIii2LAKMO0Wi2ioqKwdetWbNy4kSuhEhFRieEtkjIqNTUV4eHhSEpKglKpRPXq1TkrJxERlRgGjDLo5s2biIiIQFpaGpydnREWFoYqVarIXRYREdkQBowy5vTp09i0aRN0Oh28vb0xbNgweHp6yl0WERHZGAYMCRw+XDq6smRlZWHXrl3Q6XSoVasWXnjhBTg4OMhdFhER2SAGDAn4+wskJsrfv8HBwQFhYWG4dOkSOnbsyD4XREQkGwYMCeRdTbWkP9Pv37+Pe/fuITg4GAAQEBDABcuIiEh2pePafhnh41Oyw0CvXbuGJUuWICIiArdv3y7R9yYiInoSXsGwUidOnMDWrVuh1+vh6+sLJycnuUsiIiIyYsCwMnq9Hrt27cKRI0cAACEhIejXrx/s7e1lroyIiCgXA4YVyczMRFRUFK5cuQIA6NChA9q1a8fOnEREVOowYEjg2rWS+YA/evQorly5Ant7e/Tv3x9169YtkfclIiIyFwOGBBQKASEUSE62bNBo06YN7t+/j+bNm8PX19ei70VERPQsOIpEAjnDVB0dpR1FIoRAbGwsdDodAECpVKJfv34MF0REVOoxYEiodm3pzqXT6bB161ZERkZi+/btXAmViIisCm+RlELp6emIjIzE1atXAQDlypWTtyAiIiIzMWCUMnfu3EF4eDju378PtVqNgQMHombNmnKXRUREZBYGjFLk4sWLWLduHTQaDTw9PTFs2DB4e3vLXRYREZHZGDBKiczMTKxfvx4ajQZVq1bFkCFD4OzsLHdZRJIRQiA7O9vYadnaabVa2NnZITMzs8x8T3Jjm0qruO1pb28PlUr1zO/PgFFKODo6YsCAAYiLi0OPHj0k+cslKi00Gg2SkpKQnp4udymSEULAx8cHN27c4GR3EmGbSqu47alQKFC5cmW4uro+0/szYMgoLS0NDx8+hL+/PwCgRo0aqFGjhsxVEUlLr9cjISEBKpUKfn5+UKvVZeLDQ6/XIy0tDa6urlAqOSBPCmxTaRWnPYUQuHv3Lm7evIkaNWo80y+7DBgySU5Oxpo1a6DVajFu3DiOFKEyS6PRQK/XIyAgoEzd9tPr9dBoNHB0dOSHoUTYptIqbntWrFgRV69ehVarZcCwNufPn8f69euh1WpRoUIF6PV6uUsisjh+YBBZB6muMDJglCAhBPbt24fff/8dABAcHIyBAwdyqXUiIipzGDBKiFarxaZNm3D27FkAQPPmzdGtWzf+VkdERGUSP91KyP79+3H27FkolUr07t0bPXr0YLggojIrLi4OPj4+ePTokdylUB4ajQaBgYH466+/LP5e/IQrIW3atEH16tUxcuRINGnSRO5yiKgIxowZA4VCAYVCAXt7e1SrVg3vvfceMjMz8+27ZcsWtG/fHm5ubnB2dkazZs2wbNmyAs+7bt06dOjQAR4eHnB1dUX9+vXx8ccf4969exb+jkrOtGnT8NZbb8HNzS3fa7Vr14aDgwOSk5PzvVa/fn188803+bbPmjULDRs2NNmWnJyMt956C0FBQXBwcEBAQAD69OmDPXv2SPZ9FCQyMhK1a9eGo6Mj6tWrh23btj31mFWrVqFBgwZwdnaGr68vXnrpJfz999/G18+dO4eBAwciMDAQCoUCCxYsyHcOnU6Hjz76CNWqVYOTkxOCg4PxySefmKxVlZaWhgkTJqBy5cpwcXHB888/j0WLFhlfV6vVmDJlCt5///1na4QiYMCwoKtXrxr/4u3t7TFixAgEBgbKWxQRmaV79+5ISkpCfHw8vv76a/zwww+YOXOmyT7ffvst+vXrh9atW+PIkSM4ffo0hg4ditdffx1Tpkwx2ffDDz9EWFgYmjVrhu3bt+Ps2bP46quvcOrUKaxcubLEvi+NRmOxc1+/fh1btmzBmDFj8r22f/9+ZGRkYNCgQVi+fHmx3+Pq1ato0qQJfvvtN8ybNw9nzpzBjh070LFjR4wfP/4Zqn+ygwcPYtiwYXj55Zdx8uRJ9O/fH/379zfe/i7IgQMHMGrUKLz88ss4d+4cIiMjcfToUYwbN864T3p6OoKCgvD555/Dx8enwPP897//xffff4/vvvsO58+fx3//+1988cUX+Pbbb437TJ48GTt27MAvv/yCc+fO4fXXX8fbb7+NTZs2GfcZMWIE9u/fj3PnzknQIk8gbMzDhw8FAPHw4UPJzmlvrxeAEA0b6oUQQuj1erFnzx4xa9YssWfPHsnex5ZoNBqxYcMGodFo5C6lzJCrTTMyMkRsbKzIyMgo0feVwujRo0W/fv1Mtg0YMEA0atRI6HQ6cf/+fXH16lVhb28vJk+enO/4//u//xMAxOHDh4UQQhw5ckQAEAsWLCjw/e7fv19oLTdu3BBDhw4V5cqVE87OzqJJkybG8xZU58SJE0X79u2Nz9u3by/Gjx8vJk6cKCpUqCA6dOgghg0bJoYMGWJynEajERUqVBDLly8XQgih0+nEnDlzRGBgoHB0dBT169cXkZGRhdYphBDz5s0TTZs2LfC1MWPGiKlTp4rt27eLmjVrmrym0+lEQECAmD9/fr7jZs6cKRo0aGB83qNHD+Hv7y/S0tLy7fukdnxWQ4YMEb169TLZ1qJFC/Haa68Vesy8efNEUFCQybb/+7//E/7+/gXuX7VqVfH111/n296rVy/x0ksvmWwbMGCAGDFihPF5SEiI+Pjjj4UQwvgz2rhxY/Hhhx+aHNexY0cxffr0At//Sf9mzfkMLRVXMBYuXIjAwEA4OjqiRYsWOHr06BP3L87lqZKi0Wiwdu1a7Nu3D4BhHLLgUutE+TRtClSuXPJfTZsWv+azZ8/i4MGDUKvVxm3r1q2DVqvNd6UCAF577TW4urpizZo1AAyXyV1dXfHmm28WeH5PT88Ct6elpaF9+/ZITEzEpk2bcOrUKbz33ntmD3Ffvnw51Go1Dhw4gEWLFmHEiBHYvHkz0tLSjPvs3LkT6enpeOGFFwAAc+fOxYoVK7Bo0SKcO3cOkyZNwosvvog//vij0PfZt28fmhbQ0I8ePUJkZCRefPFFdOnSBQ8fPjT+X2mOe/fuYceOHRg/fjxcXFzyvV5YOwK5fwdP+npSTYcOHUJoaKjJtm7duuHQoUOFHtOyZUvcuHED27ZtgxACt2/fRlRUFHr27Pn0bzaPVq1aYc+ePbh48SIA4NSpU9i/fz969Ohhss+mTZuQmJhoHLl48eJFdO3a1eRczZs3L1bbm0P2USQRERGYPHkyFi1ahBYtWmDBggXo1q0b4uLiClzoK+fy1Ny5c9G7d2+sXr0a/fv3x4kTJ/Dcc8/J8B3kcnJ6gKVLI3D79m2oVCr06dMHDRo0kLUmotIqORlITJS7iqfbsmULXF1dkZ2djaysLCiVSnz33XfG1y9evAgPDw/4+vrmO1atViMoKMj4gXDp0iUEBQXB3t7erBpWr16Nu3fv4tixYyhfvjwAoHr16mZ/LzVq1MAXX3xhfB4cHAwXFxesX78eI0eONL5X37594ebmhqysLMyZMwe7d+9Gy5YtAQBBQUHYv38/fvjhB7Rv377A97l27VqBASM8PBw1atRASEgIAGDo0KH46aef0LZtW7O+j8uXL0MIgdq1a5t1HAD07dsXLVq0eOI+ObMrFyQ5ORmVKlUy2VapUqUC+5PkaN26NVatWoWwsDBkZmYiOzsbffr0wcKFC82qferUqUhNTUXt2rWhUqmg0+nw2WefYcSIEcZ9vv32W7z66quoXLky7OzsoFQq8cMPP6Bdu3Ym5/Lz88O1a9fMen9zyR4w5s+fj3HjxmHs2LEAgEWLFmHr1q1YunQppk6dmm//b775Bt27d8e7774LAPjkk08QHR2N7777zqQjS0mrUuU6OnSIwO3b6XBxcUFYWBgCAgJkq4eotCvkNnOpe9+OHTvi+++/x+PHj/H111/Dzs4OAwcOLNYEecW9mhkTE4NGjRoZw0Vx/buDuZ2dHYYMGYJVq1Zh5MiRePz4MTZu3Ijw8HAAhg/y9PR0dOnSxeQ4jUaDRo0aFfo+GRkZcHR0zLd96dKlePHFF43PX3zxRbRv3x7ffvttgZ1BC/MsV4Xd3NzMei8pxMbGYuLEiZgxYwa6deuGpKQkvPvuu3j99dfx008/Ffk8a9euxapVq7B69WqEhIQgJiYG//nPf+Dn54fRo0cDMASMw4cPY9OmTQgICMCuXbvw1ltvoXLlyiZXXpycnCy+NpCsAUOj0eD48eOYNm2acZtSqURoaGihl5sOHTqEyZMnm2zr1q0bNmzYUOD+WVlZyMrKMj5PTU0FYJiXQqvVPuN3YODoqMXw4avh4JCFSpUqYfDgwXB3d5fs/LYop+3YhtKRq021Wi2EENDr9SYfyk+5E2pRRc0GQgg4OzsjKCgIALBkyRI0atQIixcvxksvvQTAcFXg4cOHuHnzJvz8/EyO12g0uHLlCjp06AC9Xo8aNWpg//79yMrKMusqRs6HdWGhRqFQ5GvfnE6cebc5OzvnO8ewYcPQsWNHJCcnIzo6Gk5OTujatSv0er3x/8vNmzfn+63ewcGh0Hq8vLxw7949k9djY2Nx+PBhHD161GQEg06nw+rVqzFu3DgIIeDm5oYHDx7kO/f9+/fh4eEBvV6P4OBgKBQKnD9/Hv369Su40QqxatUqvPHGG0/cZ+vWrYVeVfHx8UFycrJJfcnJyfDx8Sm0PebMmYNWrVrhnXfeAQA899xz+O6779C+fXt8/PHHBV79yvk3k9e7776L999/H0OGDAEAhISE4OrVq5g7dy5GjhyJjIwMfPDBB1i3bh169eoFIQQCAwMRFxeHefPmoVOnTsZz/f3336hYsWKBNefc2i9oqnBz/v+QNWCkpKRAp9MVeLnpwoULBR5j7uWpuXPnYvbs2fm279q1S7J1ETIy+mDbtp5o3PgM6tVzwv79+yU5LwHR0dFyl1DmlHSb2tnZwcfHB2lpaRYduWAJWq0W2dnZxg9aAJg4cSKmT5+O3r17Gz+M7e3t8fnnn+PTTz81Of6HH37A48eP0adPH6SmpqJv37749ttv8fXXX+P111/P934PHz6Eh4dHvu01atTAkiVLcO3atQLXLXJ3d8fp06dN6jx+/Djs7e2N27Kzs6HRaEz2AQwfdv7+/lixYgWio6PRt29fZGRkICMjA5UrV4aDgwPi4uIKvGLx73PlqFu3br56Fi1ahFatWmHevHkm+65evRpLlixBWFiY8Xs9evRovnMfO3YMNWrUQGpqKuzs7NCpUycsXLgQo0ePztcPo7B2BIAOHTrgzz//LPC1HL6+voV+b02bNsXOnTuNV90BYMeOHWjcuHGhx+TUnPf1nKHOqamp+erX6/XIzMzMd77Hjx8jKyvLZHven9HU1FRotdp8x+p0Omi1WpNtJ0+eREhISIE1azQaZGRk4M8//0R2drbJa+Zc9ZD9FomlTZs2zeSKR2pqKgICAtC1a1e4u7tL8h6jRulw/boHKlcehD59SkW/Waun1WoRHR2NLl26mH2/mgomV5tmZmbixo0bcHV1LfCyeWlmb28POzs7k/8rRo0ahVmzZmHlypV49dVXUbduXfz3v//FlClT4O7ujhdffBH29vbYtGkTZs6cicmTJxt/c+zUqRPeffddTJ8+HX///Tf69+8PPz8/XL58GT/88APatGmDt99+O18dY8eOxYIFCzB69Gh89tln8PX1xcmTJ+Hn54eWLVuie/fu+Pbbb7Fhwwa0bNkSq1atwoULF9CoUSNj7XZ2dlCr1QX+vzdixAgsX74cFy9exJ49e4z7uLu745133sH06dPh4OCANm3a4OHDhzh48CDc3NyMl+X/rXfv3nj11Vfh4uIClUoFrVaLtWvXYtasWXj++edN9vXw8MDChQtx48YN1K1bF2+88QZ69uyJ7777Di+88AJ0Oh3Cw8Nx7NgxLFq0yFjbokWL0LZtW3Tt2hWzZs1C/fr1kZ2djd27dxs7pBbE3d39iX0snmby5Mno2LEjlixZgp49eyIiIgIxMTFYsmSJsbYPPvgAiYmJxmG4/fv3x2uvvYZVq1YZb5F8+OGHaN68OWrVqgXA8KEeGxsLwBAG//77b8THx8PV1dXY36ZPnz74+uuvUbNmTYSEhODkyZP4/vvvMXbsWLi7u8Pd3R3t27fH7NmzUaFCBVSpUgW7du1CREQEvvzyS5O/+yNHjmD27NkF/jxkZmbCyckJ7dq1y/dvtrAQVaCnjjOxoKysLKFSqcT69etNto8aNUr07du3wGMCAgLyDd+ZMWOGqF+/fpHe0xLDVDmkUnpsU+lxmKr5Chr+KYQQc+fOFRUrVhQ3b94UOp1OCCHExo0bRdu2bYWLi4twdHQUTZo0EUuXLi3wvBEREaJdu3bCzc1NuLi4iPr164uPP/74icMrr169KgYOHCjc3d2Fs7OzaNq0qThy5Ijx9RkzZohKlSoJDw8PMWnSJDFhwoR8w1QnTpxY4LljY2MFAFG1alWh1+tNXtPr9WLBggWiVq1awt7eXlSsWFF069ZN/PHHH4XWqtVqhZ+fn9ixY4cQQoioqCihVCpFcnJygfvXqVNHTJo0yTiscvv27aJ169aiXLlyxiG1Bb3frVu3xPjx40XVqlWFWq0W/v7+om/fvuL3338vtDYprF27VtSsWVOo1WoREhIitm7davL66NGjTdpeCMOw1Lp16wonJyfh6+srRowYIW7evGl8PSEhQQDI95X3PKmpqWLixImiSpUqwtHRUQQFBYkPP/xQZGVlGfdJSkoSY8aMEX5+fsLR0VHUqFFDfPnllyZ/rwcPHhSenp4iPT29wO9PqmGqss+D0bx5czFhwgTjc51OJ/z9/cXcuXML3H/IkCGid+/eJttatmz5xDHIeTFgWAe2qfQYMKSV82GYEzDI1HfffSe6du1q1jFsU2kV1p5DhgwRn332WaHHSRUwZL9FMnnyZIwePRpNmzZF8+bNsWDBAjx+/Nh4f2vUqFHw9/fH3LlzARjuf7Zv3x5fffUVevXqhfDwcPz111/48ccf5fw2iIgoj9deew0PHjzAo0ePSnzUBhVOo9GgXr16mDRpksXfS/aAERYWhrt372LGjBlITk5Gw4YNsWPHDmNHzuvXr5ssCtaqVSusXr0a06dPxwcffIAaNWpgw4YNss+BQUREuezs7PDhhx/KXQb9i1qtxvTp00vkvWQPGAAwYcIETJgwocDX9u7dm2/b4MGDMXjwYAtXRURERMXFIQ9EREQkOQYMIioRgmvyEFkFqf6tMmAQkUXlzLlh6WmJiUgaORPi/XsWT3OVij4YRFR2qVQqeHp64s6dOwAM01UrFAqZq3p2er0eGo0GmZmZJh3RqfjYptIqTnvq9XrcvXsXzs7OsLN7tojAgEFEFufzzwpjOSGjLBBCICMjA05OTmUiMJUGbFNpFbc9lUolqlSp8sx/BwwYRGRxCoUCvr6+8Pb2LjML2Gm1Wvz5559o164dp7OXCNtUWsVtT7VaLckVJAYMIioxKpXqme/rlhYqlQrZ2dlwdHTkh6FE2KbSkrs9eZOLiIiIJMeAQURERJJjwCAiIiLJ2VwfjJwJRMxa0/4ptFot0tPTkZqayvuGEmGbSo9tKi22p/TYptKyRHvmfHYWZTIumwsYjx49AgAEBATIXAkREZF1evToETw8PJ64j0LY2Py9er0et27dgpubm2TjrFNTUxEQEIAbN27A3d1dknPaOrap9Nim0mJ7So9tKi1LtKcQAo8ePYKfn99Th7La3BUMpVKJypUrW+Tc7u7u/EchMbap9Nim0mJ7So9tKi2p2/NpVy5ysJMnERERSY4Bg4iIiCTHgCEBBwcHzJw5Ew4ODnKXUmawTaXHNpUW21N6bFNpyd2eNtfJk4iIiCyPVzCIiIhIcgwYREREJDkGDCIiIpIcAwYRERFJjgGjiBYuXIjAwEA4OjqiRYsWOHr06BP3j4yMRO3ateHo6Ih69eph27ZtJVSp9TCnTRcvXoy2bduiXLlyKFeuHEJDQ5/6d2BrzP0ZzREeHg6FQoH+/ftbtkArZG6bPnjwAOPHj4evry8cHBxQs2ZN/tvPw9z2XLBgAWrVqgUnJycEBARg0qRJyMzMLKFqS78///wTffr0gZ+fHxQKBTZs2PDUY/bu3YvGjRvDwcEB1atXx7JlyyxXoKCnCg8PF2q1WixdulScO3dOjBs3Tnh6eorbt28XuP+BAweESqUSX3zxhYiNjRXTp08X9vb24syZMyVceellbpsOHz5cLFy4UJw8eVKcP39ejBkzRnh4eIibN2+WcOWlk7ntmSMhIUH4+/uLtm3bin79+pVMsVbC3DbNysoSTZs2FT179hT79+8XCQkJYu/evSImJqaEKy+dzG3PVatWCQcHB7Fq1SqRkJAgdu7cKXx9fcWkSZNKuPLSa9u2beLDDz8Uv/76qwAg1q9f/8T94+PjhbOzs5g8ebKIjY0V3377rVCpVGLHjh0WqY8BowiaN28uxo8fb3yu0+mEn5+fmDt3boH7DxkyRPTq1ctkW4sWLcRrr71m0Tqtiblt+m/Z2dnCzc1NLF++3FIlWpXitGd2drZo1aqVWLJkiRg9ejQDxr+Y26bff/+9CAoKEhqNpqRKtCrmtuf48eNFp06dTLZNnjxZtG7d2qJ1WquiBIz33ntPhISEmGwLCwsT3bp1s0hNvEXyFBqNBsePH0doaKhxm1KpRGhoKA4dOlTgMYcOHTLZHwC6detW6P62pjht+m/p6enQarUoX768pcq0GsVtz48//hje3t54+eWXS6JMq1KcNt20aRNatmyJ8ePHo1KlSnjuuecwZ84c6HS6kiq71CpOe7Zq1QrHjx833kaJj4/Htm3b0LNnzxKpuSwq6c8mm1vszFwpKSnQ6XSoVKmSyfZKlSrhwoULBR6TnJxc4P7JyckWq9OaFKdN/+3999+Hn59fvn8stqg47bl//3789NNPiImJKYEKrU9x2jQ+Ph6//fYbRowYgW3btuHy5ct48803odVqMXPmzJIou9QqTnsOHz4cKSkpaNOmDYQQyM7Oxuuvv44PPvigJEoukwr7bEpNTUVGRgacnJwkfT9ewSCr8/nnnyM8PBzr16+Ho6Oj3OVYnUePHmHkyJFYvHgxvLy85C6nzNDr9fD29saPP/6IJk2aICwsDB9++CEWLVokd2lWae/evZgzZw7+97//4cSJE/j111+xdetWfPLJJ3KXRkXEKxhP4eXlBZVKhdu3b5tsv337Nnx8fAo8xsfHx6z9bU1x2jTHl19+ic8//xy7d+9G/fr1LVmm1TC3Pa9cuYKrV6+iT58+xm16vR4AYGdnh7i4OAQHB1u26FKuOD+jvr6+sLe3h0qlMm6rU6cOkpOTodFooFarLVpzaVac9vzoo48wcuRIvPLKKwCAevXq4fHjx3j11Vfx4YcfQqnk78fmKuyzyd3dXfKrFwCvYDyVWq1GkyZNsGfPHuM2vV6PPXv2oGXLlgUe07JlS5P9ASA6OrrQ/W1NcdoUAL744gt88skn2LFjB5o2bVoSpVoFc9uzdu3aOHPmDGJiYoxfffv2RceOHRETE4OAgICSLL9UKs7PaOvWrXH58mVjWAOAixcvwtfX16bDBVC89kxPT88XInLCm+ASWsVS4p9NFuk6WsaEh4cLBwcHsWzZMhEbGyteffVV4enpKZKTk4UQQowcOVJMnTrVuP+BAweEnZ2d+PLLL8X58+fFzJkzOUz1X8xt088//1yo1WoRFRUlkpKSjF+PHj2S61soVcxtz3/jKJL8zG3T69evCzc3NzFhwgQRFxcntmzZIry9vcWnn34q17dQqpjbnjNnzhRubm5izZo1Ij4+XuzatUsEBweLIUOGyPUtlDqPHj0SJ0+eFCdPnhQAxPz588XJkyfFtWvXhBBCTJ06VYwcOdK4f84w1XfffVecP39eLFy4kMNUS4Nvv/1WVKlSRajVatG8eXNx+PBh42vt27cXo0ePNtl/7dq1ombNmkKtVouQkBCxdevWEq649DOnTatWrSoA5PuaOXNmyRdeSpn7M5oXA0bBzG3TgwcPihYtWggHBwcRFBQkPvvsM5GdnV3CVZde5rSnVqsVs2bNEsHBwcLR0VEEBASIN998U9y/f7/kCy+lfv/99wL/X8xpx9GjR4v27dvnO6Zhw4ZCrVaLoKAg8fPPP1usPi7XTkRERJJjHwwiIiKSHAMGERERSY4Bg4iIiCTHgEFERESSY8AgIiIiyTFgEBERkeQYMIiIiEhyDBhEREQkOQYMojJm2bJl8PT0lLuMYlMoFNiwYcMT9xkzZgz69+9fIvUQUfEwYBCVQmPGjIFCocj3dfnyZblLw7Jly4z1KJVKVK5cGWPHjsWdO3ckOX9SUhJ69OgBALh69SoUCgViYmJM9vnmm2+wbNkySd6vMLNmzTJ+nyqVCgEBAXj11Vdx7949s87DMES2isu1E5VS3bt3x88//2yyrWLFijJVY8rd3R1xcXHQ6/U4deoUxo4di1u3bmHnzp3PfO7Clu/Oy8PD45nfpyhCQkKwe/du6HQ6nD9/Hi+99BIePnyIiIiIEnl/ImvGKxhEpZSDgwN8fHxMvlQqFebPn4969erBxcUFAQEBePPNN5GWllboeU6dOoWOHTvCzc0N7u7uaNKkCf766y/j6/v370fbtm3h5OSEgIAAvP3223j8+PETa1MoFPDx8YGfnx969OiBt99+G7t370ZGRgb0ej0+/vhjVK5cGQ4ODmjYsCF27NhhPFaj0WDChAnw9fWFo6Mjqlatirlz55qcO+cWSbVq1QAAjRo1gkKhQIcOHQCYXhX48ccf4efnZ7JMOgD069cPL730kvH5xo0b0bhxYzg6OiIoKAizZ89Gdnb2E79POzs7+Pj4wN/fH6GhoRg8eDCio6ONr+t0Orz88suoVq0anJycUKtWLXzzzTfG12fNmoXly5dj48aNxqshe/fuBQDcuHEDQ4YMgaenJ8qXL49+/frh6tWrT6yHyJowYBBZGaVSif/7v//DuXPnsHz5cvz222947733Ct1/xIgRqFy5Mo4dO4bjx49j6tSpsLe3BwBcuXIF3bt3x8CBA3H69GlERERg//79mDBhglk1OTk5Qa/XIzs7G9988w2++uorfPnllzh9+jS6deuGvn374tKlSwCA//u//8OmTZuwdu1axMXFYdWqVQgMDCzwvEePHgUA7N69G0lJSfj111/z7TN48GD8/fff+P33343b7t27hx07dmDEiBEAgH379mHUqFGYOHEiYmNj8cMPP2DZsmX47LPPivw9Xr16FTt37oRarTZu0+v1qFy5MiIjIxEbG4sZM2bggw8+wNq1awEAU6ZMwZAhQ9C9e3ckJSUhKSkJrVq1glarRbdu3eDm5oZ9+/bhwIEDcHV1Rffu3aHRaIpcE1GpZrF1Womo2EaPHi1UKpVwcXExfg0aNKjAfSMjI0WFChWMz3/++Wfh4eFhfO7m5iaWLVtW4LEvv/yyePXVV0227du3TyiVSpGRkVHgMf8+/8WLF0XNmjVF06ZNhRBC+Pn5ic8++8zkmGbNmok333xTCCHEW2+9JTp16iT0en2B5wcg1q9fL4QQIiEhQQAQJ0+eNNnn38vL9+vXT7z00kvG5z/88IPw8/MTOp1OCCFE586dxZw5c0zOsXLlSuHr61tgDUIIMXPmTKFUKoWLi4twdHQ0LoU9f/78Qo8RQojx48eLgQMHFlprznvXqlXLpA2ysrKEk5OT2Llz5xPPT2Qt2AeDqJTq2LEjvv/+e+NzFxcXAIbf5ufOnYsLFy4gNTUV2dnZyMzMRHp6OpydnfOdZ/LkyXjllVewcuVK42X+4OBgAIbbJ6dPn8aqVauM+wshoNfrkZCQgDp16hRY28OHD+Hq6gq9Xo/MzEy0adMGS5YsQWpqKm7duoXWrVub7N+6dWucOnUKgOH2RpcuXVCrVi10794dvXv3RteuXZ+prUaMGIFx48bhf//7HxwcHLBq1SoMHToUSqXS+H0eOHDA5IqFTqd7YrsBQK1atbBp0yZkZmbil19+QUxMDN566y2TfRYuXIilS5fi+vXryMjIgEajQcOGDZ9Y76lTp3D58mW4ubmZbM/MzMSVK1eK0QJEpQ8DBlEp5eLigurVq5tsu3r1Knr37o033ngDn332GcqXL4/9+/fj5ZdfhkajKfCDctasWRg+fDi2bt2K7du3Y+bMmQgPD8cLL7yAtLQ0vPbaa3j77bfzHVelSpVCa3Nzc8OJEyegVCrh6+sLJycnAEBqaupTv6/GjRsjISEB27dvx+7duzFkyBCEhoYiKirqqccWpk+fPhBCYOvWrWjWrBn27duHr7/+2vh6WloaZs+ejQEDBuQ71tHRsdDzqtVq49/B559/jl69emH27Nn45JNPAADh4eGYMmUKvvrqK7Rs2RJubm6YN28ejhw58sR609LS0KRJE5Ngl6O0dOQlelYMGERW5Pjx49Dr9fjqq6+Mv53n3O9/kpo1a6JmzZqYNGkShg0bhp9//hkvvPACGjdujNjY2HxB5mmUSmWBx7i7u8PPzw8HDhxA+/btjdsPHDiA5s2bm+wXFhaGsLAwDBo0CN27d8e9e/dQvnx5k/Pl9HfQ6XRPrMfR0REDBgzAqlWrcPnyZdSqVQuNGzc2vt64cWPExcWZ/X3+2/Tp09GpUye88cYbxu+zVatWePPNN437/PsKhFqtzld/48aNERERAW9vb7i7uz9TTUSlFTt5ElmR6tWrQ6vV4ttvv0V8fDxWrlyJRYsWFbp/RkYGJkyYgL179+LatWs4cOAAjh07Zrz18f777+PgwYOYMGECYmJicOnSJWzcuNHsTp55vfvuu/jvf/+LiIgIxMXFYerUqYiJicHEiRMBAPPnz8eaNWtw4cIFXLx4EZGRkfDx8SlwcjBvb284OTlhx44duH37Nh4+fFjo+44YMQJbt27F0qVLjZ07c8yYMQMrVqzA7Nmzce7cOZw/fx7h4eGYPn26Wd9by5YtUb9+fcyZMwcAUKNGDfz111/YuXMnLl68iI8++gjHjh0zOSYwMBCnT59GXFwcUlJSoNVqMWLECHh5eaFfv37Yt28fEhISsHfvXrz99tu4efOmWTURlVpydwIhovwK6hiYY/78+cLX11c4OTmJbt26iRUrVggA4v79+0II006YWVlZYujQoSIgIECo1Wrh5+cnJkyYYNKB8+jRo6JLly7C1dVVuLi4iPr16+frpJnXvzt5/ptOpxOzZs0S/v7+wt7eXjRo0EBs377d+PqPP/4oGjZsKFxcXIS7u7vo3LmzOHHihPF15OnkKYQQixcvFgEBAUKpVIr27dsX2j46nU74+voKAOLKlSv56tqxY4do1aqVcHJyEu7u7qJ58+bixx9/LPT7mDlzpmjQoEG+7WvWrBEODg7i+vXrIjMzU4wZM0Z4eHgIT09P8cYbb4ipU6eaHHfnzh1j+wIQv//+uxBCiKSkJDFq1Cjh5eUlHBwcRFBQkBg3bpx4+PBhoTURWROFEELIG3GIiIiorOEtEiIiIpIcAwYRERFJjgGDiIiIJMeAQURERJJjwCAiIiLJMWAQERGR5BgwiIiISHIMGERERCQ5BgwiIiKSHAMGERERSY4Bg4iIiCT3/6FE2DY5QwuaAAAAAElFTkSuQmCC", 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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%!). That’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 — 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 model’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 — 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 — 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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", 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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, model__min_samples_leaf=2, model__min_samples_split=5, 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=5, model__n_estimators=200; total time= 17.8s\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= 18.2s\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= 10.6s\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= 10.5s\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= 10.2s\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= 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, 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=log2, 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=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=None, model__max_features=log2, model__min_samples_leaf=1, 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=1, model__min_samples_split=2, model__n_estimators=300; total time= 22.7s\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= 25.1s\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= 23.6s\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= 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=200; total time= 14.4s\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= 14.3s\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= 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=200; total time= 14.8s\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= 14.9s\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= 27.4s\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= 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= 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= 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=100; total time= 8.3s\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= 18.7s\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= 20.6s\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= 22.1s\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= 21.9s\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= 13.0s\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= 22.3s\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= 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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", 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", 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", 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", "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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", 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" ] }, "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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ly5eLvUy1pO+5yMhIAUAoFApx48YNo2OuXLkihg0bJlxdXYVKpRIeHh7ixRdfFJs2bSrV+yIqD4UQJexzJSIiIioHnoNBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQ17kZbWq0Wt2/fhq2tbYlPpiQiIiJ9QghkZGTA3d3d4HlLj6pxAeP27dvw9PQ0dRlERETV1o0bNwyeGP2oGhcwbG1tATxojp2dnZR1ajQa7N69W3frZ3py7Kl87Klc7Kd87KlcFdHP9PR0eHp66v6WlqTGBYyiwyJ2dnZSA4a1tTXs7Oz4QyEJeyofeyoX+ykfeypXRfazNKcY8CRPIiIiko4Bg4iIiKRjwCAiIiLpGDCIiIhIOgYMIiIiko4Bg4iIiKRjwCAiIiLpGDCIiIhIOgYMIiIiko4Bg4iIiKQzacDYv38/+vXrB3d3dygUCmzZsuWxy0RHR6Ndu3ZQq9Vo3Lgx1q5dW+F1EhERUdmYNGBkZWWhdevWWLJkSanGx8XFoW/fvujWrRtiYmLw/vvvY+TIkdi1a1cFV0pERERlYdKHnfXu3Ru9e/cu9fjly5ejQYMG+PLLLwEAzz77LA4ePIivvvoKQUFBFVXmY51LPof4nHj8nfw3VOYV94AeM4UZmjk2g5mCR7aIiKhqq1ZPUz18+DACAgL0pgUFBeH9998vdpm8vDzk5eXpXqenpwN48JQ5jUYjpa4OqzpAo9UAF6SsrkStXVrjyJtHnvqQUfRvI+vfiNhT2dhP+dhTuSqin2VZV7UKGAkJCXBxcdGb5uLigvT0dOTk5MDKyspgmbCwMMyaNctg+u7du2FtbS2lLiGElPWUxunE01i5eSU8LT0rbZumFBkZaeoSnjrsqVzsp3zsqVwy+5mdnV3qsdUqYJTHlClTEBISonudnp4OT09PBAYGws7OTso2hmmHIf5mPDw8PGBmVjF7Fg7eOIhL9y4BAJ7v8jxaOLWokO1UFRqNBpGRkejZsydUqoo77FSTsKdysZ/ysadyVUQ/i44ClEa1Chiurq5ITEzUm5aYmAg7Ozujey8AQK1WQ61WG0xXqVTSGr78xeWIiIhAnz59KuyHYuTWkbqAoTKXV3tVJ/PfiR5gT+ViP+VjT+WS2c+yrKdaHcj38/NDVFSU3rTIyEj4+fmZqCIiIiIyxqQBIzMzEzExMYiJiQHw4DLUmJgYxMfHA3hweGPYsGG68e+88w6uXr2KSZMmITY2FkuXLsVPP/2ECRMmmKJ8IiIiKoZJA8bx48fRtm1btG3bFgAQEhKCtm3bYubMmQCAO3fu6MIGADRo0ADbt29HZGQkWrdujS+//BLffvutSS9RJSIiIkMmPQeja9euJV6BYewunV27dsWpU6cqsCoiIiJ6UtXqHAwiIiKqHhgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikM3nAWLJkCby8vGBpaQlfX18cPXq0xPELFy5Es2bNYGVlBU9PT0yYMAG5ubmVVC0RERGVhkkDxoYNGxASEoLQ0FCcPHkSrVu3RlBQEJKSkoyO/+GHHzB58mSEhobi/PnzWLVqFTZs2ICpU6dWcuVERERUEpMGjAULFmDUqFEYMWIEmjdvjuXLl8Pa2hqrV682Ov6PP/5A586dMXjwYHh5eSEwMBCDBg167F4PIiIiqlzmptpwfn4+Tpw4gSlTpuimmZmZISAgAIcPHza6TKdOnfD999/j6NGj6NixI65evYqIiAgMHTq02O3k5eUhLy9P9zo9PR0AoNFooNFopLyXovXIWp8xWq32n+0VyKu9qqqMntY07Klc7Kd87KlcFdHPsqzLZAEjJSUFhYWFcHFx0Zvu4uKC2NhYo8sMHjwYKSkpeP755yGEQEFBAd55550SD5GEhYVh1qxZBtN3794Na2vrJ3sTj4iMjJS6vofduHFD9/WB/Qdw3ep6hW2rKqnIntZU7Klc7Kd87KlcMvuZnZ1d6rEmCxjlER0djdmzZ2Pp0qXw9fXF5cuXMX78eHz66aeYMWOG0WWmTJmCkJAQ3ev09HR4enoiMDAQdnZ2UurSaDSIjIxEz549oVKppKzzUb9u/xW49+DrLi90QQunFhWynaqiMnpa07CncrGf8rGnclVEP4uOApSGyQKGo6MjlEolEhMT9aYnJibC1dXV6DIzZszA0KFDMXLkSABAq1atkJWVhbfffhvTpk2DmZnhKSVqtRpqtdpgukqlkv4NXBHrLPLwe1OZV9x2qpqK7GlNxZ7KxX7Kx57KJbOfZVmPyU7ytLCwgI+PD6KionTTtFotoqKi4OfnZ3SZ7OxsgxChVCoBAEKIiiuWiIiIysSkh0hCQkIQHByM9u3bo2PHjli4cCGysrIwYsQIAMCwYcPg4eGBsLAwAEC/fv2wYMECtG3bVneIZMaMGejXr58uaBAREZHpmTRgDBw4EMnJyZg5cyYSEhLQpk0b7Ny5U3fiZ3x8vN4ei+nTp0OhUGD69Om4desWnJyc0K9fP3z++eemegtERERkhMlP8hw3bhzGjRtndF50dLTea3Nzc4SGhiI0NLQSKiMiIqLyMvmtwomIiOjpw4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCQdAwYRERFJx4BBRERE0jFgEBERkXQMGERERCSdyQPGkiVL4OXlBUtLS/j6+uLo0aMljk9NTcXYsWPh5uYGtVqNpk2bIiIiopKqJSIiotIwN+XGN2zYgJCQECxfvhy+vr5YuHAhgoKCcOHCBTg7OxuMz8/PR8+ePeHs7IxNmzbBw8MD169fh729feUXT0RERMUyacBYsGABRo0ahREjRgAAli9fju3bt2P16tWYPHmywfjVq1fj3r17+OOPP6BSqQAAXl5elVnyU29f3D7cSL+BIa2GQGmmNHU5RERUTZksYOTn5+PEiROYMmWKbpqZmRkCAgJw+PBho8ts3boVfn5+GDt2LH799Vc4OTlh8ODB+Oijj6BUGv9jmJeXh7y8PN3r9PR0AIBGo4FGo5HyXorWI2t9xmi12n+2VyCv9of9ceMPBHwfAK3QIisvCyPbjpS+jdKqjJ7WNOypXOynfOypXBXRz7Ksy2QBIyUlBYWFhXBxcdGb7uLigtjYWKPLXL16FXv37sWQIUMQERGBy5cvY8yYMdBoNAgNDTW6TFhYGGbNmmUwfffu3bC2tn7yN/KQyMhIqet72I0bN3RfH9h/ANetrktdvxACUy5PgVY8CDJ7TuyB+x13qdsoj4rsaU3FnsrFfsrHnsols5/Z2dmlHmvSQyRlpdVq4ezsjG+++QZKpRI+Pj64desWvvjii2IDxpQpUxASEqJ7nZ6eDk9PTwQGBsLOzk5KXRqNBpGRkejZs6fu0I1sv27/Fbj34OsuL3RBC6cWUtf/28XfEHv6n2DXsFFD9OnWR+o2yqIyelrTsKdysZ/ysadyVUQ/i44ClIbJAoajoyOUSiUSExP1picmJsLV1dXoMm5ublCpVHqHQ5599lkkJCQgPz8fFhYWBsuo1Wqo1WqD6SqVSvo3cEWss4iZ2T8X/KjM5W6nUFuIGb/P0JumNFNWiR/wiuxpTcWeysV+yseeyiWzn2VZj8kuU7WwsICPjw+ioqJ007RaLaKiouDn52d0mc6dO+Py5ct65yNcvHgRbm5uRsMFlc53p7/DueRzpi6DiIieIia9D0ZISAhWrlyJdevW4fz583j33XeRlZWlu6pk2LBheieBvvvuu7h37x7Gjx+PixcvYvv27Zg9ezbGjh1rqrdQ7eVocjAzeqapyyAioqeMSc/BGDhwIJKTkzFz5kwkJCSgTZs22Llzp+7Ez/j4eL1DA56enti1axcmTJiA5557Dh4eHhg/fjw++ugjU72Fau+/R/+Lm+k3AQAutVyQmJX4mCWIiIgez+QneY4bNw7jxo0zOi86Otpgmp+fH/78888KrqpmuJ9zH2EHwwAACigw/YXp+L8d/2fiqoiI6GlQroBRWFiItWvXIioqCklJSXrnRADA3r17pRRHFWvuobm4n3sfADC09VC0cm5l4oqIiOhpUa6AMX78eKxduxZ9+/ZFy5YtoVAoZNdF5RB3Pw5X7l9BjwY9Hvtvciv9FhYdWQQAsFBa4JOun+Ba6rVKqJKIiGqCcgWM8PBw/PTTT+jTx3T3SSB9Kdkp6PhtR6Rkp2BRr0V4z/e9Esd/HP0xcgtyAQDjOozDM/bPMGAQEZE05bqKxMLCAo0bN5ZdCz2Bn8/9jJTsFADAqYRTJY6NTYnF6pjVAAA7tR2mdpla4fUREVHNUq6A8cEHH2DRokUQQsiuh8pp47mNpR47NWqq7pbgH3X+CHWt61ZUWUREVEOV6xDJwYMHsW/fPuzYsQMtWrQwuLPXL7/8IqU4Kp2krCTsu7avVGP/vPknNsduBgC42rhivO/4iiyNiIhqqHIFDHt7e7z88suya6Fy2nx+s26PREmEEPhozz/3DPnY/2PUsqhVkaUREVENVa6AsWbNGtl10BP46dxPpRq34/IO7L++HwDQtG5TvNn2zYosi4iIarAnutFWcnIyLly4AABo1qwZnJycpBRFpZeUlYToa9GlGvvZ/s90X3/e/XOolHyYEBERVYxyneSZlZWFN998E25ubnjhhRfwwgsvwN3dHW+99VaZnhVPT+6X87+U6vDIsVvHcPjmYQBAK+dWePXZVyu6NCIiqsHKFTBCQkLw+++/47fffkNqaipSU1Px66+/4vfff8cHH3wgu0YqwU9/l+7wyOKji3Vfj/cdz5ujERFRhSrXIZKff/4ZmzZtQteuXXXT+vTpAysrKwwYMADLli2TVR+VIDEzEb9f/x0AYGluqbtx1qMSMhMQfjYcAFDHqg4GtxpcaTUSEVHNVK49GNnZ2bonnj7M2dmZh0gq0cOHR/o0Kf6uqt+c+AYarQYA8Ha7t2GlsqqU+oiIqOYqV8Dw8/NDaGgocnP/+R9zTk4OZs2aBT8/P2nFUckevrnW681fNzomvzAfy44/2KOkVCjxbod3K6U2IiKq2cp1iGTRokUICgpCvXr10Lp1awDA6dOnYWlpiV27dkktkIx7+PBIkzpN0NqltdFxm85tQkJmAgCgv3d/1K9dv9JqJCKimqtcAaNly5a4dOkS1q9fj9jYWADAoEGDMGTIEFhZcfd7ZXj48MjrzV8v9qTNr498rfv6cQ9AIyIikqXc98GwtrbGqFGjZNZCZfDwzbUGtBhgdMyRm0dw5NYRAEBrl9boUr9LpdRGRERU6oCxdetW9O7dGyqVClu3bi1x7EsvvfTEhVHxEjITdHfkbFKnCZ5zeQ4X7l4wGPfwpanv+b7HS1OJiKjSlDpg9O/fHwkJCXB2dkb//v2LHadQKFBYWCijNirGw4dHBrQYYDQ43Mm4o7tHRl2ruhjUclCl1khERDVbqQOGVqs1+jVVvp/P/6z7urjDIytOrPjn0lQfXppKRESVq1yXqRqTmpoqa1X0GIlZiQAePLCslXMrg/l5BXlYfnw5gP9/aWp7XppKRESVq1wBY+7cudiwYYPu9euvv446derAw8MDp0+fllYclWxAc+OHRyIuRehCyCvPvgLP2p6VXRoREdVw5QoYy5cvh6fngz9akZGR2LNnD3bu3InevXvjww8/lFogFe/1FsZvrpWWl6b7mpemEhGRKZTrMtWEhARdwNi2bRsGDBiAwMBAeHl5wdfXV2qBZFyzus2MHh55WFvXtujs2bmSKiIiIvpHufZgODg44MaNGwCAnTt3IiAgAAAghOAVJJWkuKtHHsZLU4mIyFTKtQfjlVdeweDBg9GkSRPcvXsXvXv3BgCcOnUKjRs3llogGVfcs0eKOFo74j8t/1NJ1RAREekrV8D46quv4OXlhRs3bmDevHmwsbEBANy5cwdjxoyRWiAZ8nb0RkvnliWOGe0zGpbmlpVUERERkb5yBQyVSoWJEycaTJ8wYcITF0SPV9KzRwBemkpERKbHW4VXE861nAEACigwsMVAg/l1repCAQUEBF5v8To87Dwqu0QiIiId3iq8mnj/X+8jKz8L7d3bo4VzC4P5TrWcsObfa3Ds9jHM6jrLBBUSERH9g7cKryacazljUe9FJY4JbhOM4DbBlVQRERFR8aTdKpyIiIioSLkCxnvvvYevv/7aYPp///tfvP/++09aExEREVVz5QoYP//8Mzp3NrxDZKdOnbBp06YnLoqIiIiqt3IFjLt376J27doG0+3s7JCSkvLERREREVH1Vq6A0bhxY+zcudNg+o4dO9CwYcMnLoqIiIiqt3LdaCskJATjxo1DcnIyunfvDgCIiorCl19+iYULF8qsj4iIiKqhcgWMN998E3l5efj888/x6aefAgC8vLywbNkyDBs2TGqBREREVP2UK2AAwLvvvot3330XycnJsLKy0j2PhIiIiKjc98EoKCjAnj178Msvv0AIAQC4ffs2MjMzpRVHRERE1VO59mBcv34dvXr1Qnx8PPLy8tCzZ0/Y2tpi7ty5yMvLw/Lly2XXSURERNVIufZgjB8/Hu3bt8f9+/dhZWWlm/7yyy8jKipKWnFERERUPZVrD8aBAwfwxx9/wMLCQm+6l5cXbt26JaUwIiIiqr7KtQdDq9UafWLqzZs3YWtr+8RFERERUfVWroARGBiod78LhUKBzMxMhIaGok+fPrJqIyIiomqqXIdI5s+fj169eqF58+bIzc3F4MGDcenSJTg6OuLHH3+UXSMRERFVM+UKGJ6enjh9+jQ2bNiA06dPIzMzE2+99RaGDBmid9In1UxHbh5BTkEOunp1NXUpRERkImUOGBqNBt7e3ti2bRuGDBmCIUOGVERdVE39fO5nvLbxNQDArjd2IbBRoIkrIiIiUyjzORgqlQq5ubkVUQtVc9dTr2PkbyN1r88lnzNhNUREZErlOslz7NixmDt3LgoKCmTXQ1Xcj2d+xDMLn8Fn+z/Tm16gLcDgXwYjNTfVNIUREVGVUq5zMI4dO4aoqCjs3r0brVq1Qq1atfTm//LLL1KKo6rl6v2reGvrW8gpyMGcg3Mw/YXpunkfR3+MP278YcLqiIioKilXwLC3t8err74quxaqwoQQGL1tNHIKcgAAuQX/HCbbG7cXsw/MNlVpRERUBZUpYGi1WnzxxRe4ePEi8vPz0b17d3z88ce8cqQG+N9f/8Oeq3sMpidnJeONX96AwIMH3nX06Iijt45WdnlERFTFlOkcjM8//xxTp06FjY0NPDw88PXXX2Ps2LEVVRtVEclZyQjZFWIwXSu0GP7rcNzJvAMA6NmwJ973fb+SqyMioqqoTAHju+++w9KlS7Fr1y5s2bIFv/32G9avXw+tVltR9VEVMGHXBNzNuWswfdGfixBxKQIA4FzLGd+9/B3MFOU6b5iIiJ4yZfprEB8fr3cr8ICAACgUCty+fVt6YVQ17Lq8C+vPrAcAOFg6wMveCwBQKArx0Z6PdOO+6/8dXG1cTVEiERFVQWUKGAUFBbC0tNSbplKpoNFopBZFVUNWfhbe2f6O7vX8wPlwtHbUvdZoH/y7T/SbiKDGQZVeHxERVV1lOslTCIHhw4dDrVbrpuXm5uKdd97Ru1SVl6k+HWbum4lrqdcAAN28umFEmxFYdnyZ3pgO7h3weY/PTVAdERFVZWUKGMHBwQbT3njjDWnFUNVx4s4J7I3bCwBQK9VY8eIKKBQKvTG2Frb48dUfYaG0MEWJRERUhZUpYKxZs6ai6qAq5uFLUkP9Q9GkbhMAgKX5P4fIlr+4HI3qNCrzutfFrMOSY0vwYacP8XqL15+8WCIiqnKqxCn/S5YsgZeXFywtLeHr64ujR0t3H4Xw8HAoFAr079+/YguswZ5zeQ4TO03UvQ75VwgaOTRCqH8oBrcaXOb1bfx7I4b/OhzHbh/DjH0zZJZKRERVSLnu5CnThg0bEBISguXLl8PX1xcLFy5EUFAQLly4AGdn52KXu3btGiZOnIguXbpUYrU1iwIKrOy3EiqlSjft5WdfxsvPvlyu9R29dRTDtgzTvc7Iz3jiGomIqGoy+R6MBQsWYNSoURgxYgSaN2+O5cuXw9raGqtXry52mcLCQgwZMgSzZs1Cw4YNK7HamuU93/fQ0aOjlHVdT72Ol358Se8W40RE9PQy6R6M/Px8nDhxAlOmTNFNMzMzQ0BAAA4fPlzscp988gmcnZ3x1ltv4cCBAyVuIy8vD3l5ebrX6enpAACNRiPt8tqi9VT3y3WFVui+rm9XH6FdQsv8ngoK/3nCbmFhITQaDdLz0vHiDy8iMSvxkQ0W37OnpadVCXsqF/spH3sqV0X0syzrMmnASElJQWFhIVxcXPSmu7i4IDY21ugyBw8exKpVqxATE1OqbYSFhWHWrFkG03fv3g1ra+sy11ySyMhIqeurbHnaPNRT10OKJgWjnEZh/579ZV5HzP0Y3dfnzp3Db8m/YXbcbJxNPwsAcLVwRVZhFjIKM5Cbl4uIiIgS11fde1oVsadysZ/ysadyyexndnZ2qcea/ByMssjIyMDQoUOxcuVKODo6Pn4BAFOmTEFIyD/P0UhPT4enpycCAwNhZ2cnpS6NRoPIyEj07NkTKpXq8QtUYf369IMQQu+8i7LIPJcJXH/wdfPmzbEvdR9OpJ8AANhb2iMyOBJ9fuyDjPQMWKot9e4M+7CnqadVBXsqF/spH3sqV0X0s+goQGmYNGA4OjpCqVQiMVF/13liYiJcXQ1vO33lyhVcu3YN/fr1000reg6Kubk5Lly4gEaN9C+bVKvVejcGK6JSqaR/A1fEOiubCk9Wv7nyn2+p789+j5iEmAfTzczxy4Bf0NK15T+DFXhsv56GnlY17Klc7Kd87KlcMvtZlvWY9CRPCwsL+Pj4ICoqSjdNq9UiKioKfn5+BuO9vb1x5swZxMTE6D5eeukldOvWDTExMfD09KzM8ukxisIFACzvuxzdGnQzXTFERFSpTH6IJCQkBMHBwWjfvj06duyIhQsXIisrCyNGjAAADBs2DB4eHggLC4OlpSVatmypt7y9vT0AGEynqmNSp0l4q91bpi6DiIgqkckDxsCBA5GcnIyZM2ciISEBbdq0wc6dO3UnfsbHx8PMzORX01I5vez9MsICwkxdBhERVTKTBwwAGDduHMaNG2d0XnR0dInLrl27Vn5BVG61LWvrvm7n1g7/e/l/MFMwIBIR1TRVImDQ0yOgYQDeavsWMvMz8VXQV6hlUevxCxER0VOHAYOkMjczx7cvfWvqMoiIyMS475qIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6RgwiIiISDoGDCIiIpKOAYOIiIikY8AgIiIi6apEwFiyZAm8vLxgaWkJX19fHD16tNixK1euRJcuXeDg4AAHBwcEBASUOJ6IiIgqn8kDxoYNGxASEoLQ0FCcPHkSrVu3RlBQEJKSkoyOj46OxqBBg7Bv3z4cPnwYnp6eCAwMxK1btyq5ciIiIiqOyQPGggULMGrUKIwYMQLNmzfH8uXLYW1tjdWrVxsdv379eowZMwZt2rSBt7c3vv32W2i1WkRFRVVy5URERFQcc1NuPD8/HydOnMCUKVN008zMzBAQEIDDhw+Xah3Z2dnQaDSoU6eO0fl5eXnIy8vTvU5PTwcAaDQaaDSaJ6j+H0XrkbW+GkMU3zP2VD72VC72Uz72VK6K6GdZ1mXSgJGSkoLCwkK4uLjoTXdxcUFsbGyp1vHRRx/B3d0dAQEBRueHhYVh1qxZBtN3794Na2vrshddgsjISKnre1rl5OQAAHLzchEREVHiWPZUPvZULvZTPvZULpn9zM7OLvVYkwaMJzVnzhyEh4cjOjoalpaWRsdMmTIFISEhutfp6em68zbs7Oyk1KHRaBAZGYmePXtCpVJJWefTzOqqFaABLNWW6NOnj9Ex7Kl87Klc7Kd87KlcFdHPoqMApWHSgOHo6AilUonExES96YmJiXB1dS1x2fnz52POnDnYs2cPnnvuuWLHqdVqqNVqg+kqlUr6N3BFrPOppsBj+8WeyseeysV+yseeyiWzn2VZj0lP8rSwsICPj4/eCZpFJ2z6+fkVu9y8efPw6aefYufOnWjfvn1llEpERERlYPJDJCEhIQgODkb79u3RsWNHLFy4EFlZWRgxYgQAYNiwYfDw8EBYWBgAYO7cuZg5cyZ++OEHeHl5ISEhAQBgY2MDGxsbk70PIiIi+ofJA8bAgQORnJyMmTNnIiEhAW3atMHOnTt1J37Gx8fDzOyfHS3Lli1Dfn4+XnvtNb31hIaG4uOPP67M0omIiKgYJg8YADBu3DiMGzfO6Lzo6Gi919euXav4goiIiOiJmPxGW0RERPT0YcCgKksIYeoSiIionBgwqErRCi1WnliJxv9tjPEXxuN+zn1Tl0REROXAgEFVxrFbx/Cvb/+Ft7e9jfj0eMTnxmPvtb2mLouIiMqhSpzkSTXb3ey7mBo1FStProSA/mERjZbPJCAiqo64B4NMRiu0+ObEN2j636b45uQ3unChVhreefVRZ5POYvWp1UjPK/1ta4mIqPJwDwaZTEJmAkZvG617bWthi4+7fgwhBCZGTjS6TGxKLEKjQ/HT3z8BAHZf2Y3w18IrpV4iIio97sGgKmFwq8GIHReLEL8QqJSG97qPux+H4VuGo8XSFrpwAQAX716szDKJiKiUuAeDKl1ty9q4kX4DANDCqQWW9FkCfy9/o2NvZ9zGu9vexbenvkWBtqAyyyQioifAgEGVbl7APCw8shB9GvfBmA5jjO6xKDJ572S91w6WDpjUeRKm752OQlFY0aUSEVE5MWBQpevdpDd6N+ldpmVsLGwQ8q8QTPCbAHtLe8zcN5MBg4ioCmPAoCrHuZaz7msrcyuM6zgOkzpPgqO1owmrIiKismDAoCqnv3d/TOk8BZcvX8b8gfNR36G+9G0UaAuw+8puXL1/FcGtg2GrtpW+DSKimowBg6ocS3NLzPKfhYisCLjZuEld9/XU61h1ahVWn1qNWxm3AADnks9had+lUrdDRFTTMWDQU09TqMFvF3/DypMrsevyLoO7hV5LvWaawoiInmIMGPTUunLvCr49+S3WxKxBYlai3jwFFAZBg4iI5GHAoKdKXkEetsRuwcqTKxEVF2Uw38veCyPbjkR/7/5ouaylCSokIqoZGDDoqXAh5QJWnlyJdafXISU7RW+euZk5+nv3x6h2oxDQMABmCjM+Bp6IqIIxYFC1djvjNvzX+mP/9f0G8xrXaYxR7UYhuHUwXGxcTFAdEVHNxYBB1VpiVqLe+RUWSgu8+uyrGNVuFLp6dYVCoTBhdURENRcDBlVLluaW0ORrdK+9Hb3xdru3MbT10Eq5Idet9FuIuBSBiMsRSM9Lx5I+S+Dt6F3h2yUiqi4YMKhamvz8ZCw/vhzdGnTDqHaj0Nmzs5S9FUIIXLh7AZFXIhF5NRJ/3vwTnTw74ecBP+PoraPYfmk7tl/ajpiEGL3llhxdgsV9Fj/x9omInhYMGFQtTe0yFVO7TJWyroz8DISfDdeFiqInvRb59cKvMP+05B+VTE2mlFqIiJ4WDBhU4x2MP4iD8QfLtIyPmw/aubXDypMrK6gqIqLqjQGDaiSlmdLodEtzS3Sp3wWBjQJxJukMvjv9HYAHT3MNbBSIvk36onfj3nCzdUNsSiwDBhFRMRgwqEayU9vhlWdfwS/nf0Fb17bo2bAnejbqiefrPw9Lc0sAgFZo8Yr3K7BV2+L5+s/DQmlh4qqJiKoPBgyqsX4e8DMKtYXF7s0wU5jh397/rpBt38u5h8M3DuPwzQcf5mbm+Lbft/Cs7WkwNjM/E9dSr6FJnSZQm6t104UQiEuNw4nbJ3DyzknUta6L8b7joVKqKqRmIqKyYMCgGq24cCGTVmgRmxKLwzcO448bf+CPm38gNiXWYNzamLWY/PxknEk6g6O3juLoraM4dvsYziWfg1Zo0bRuU3zW7TMcv30cJ+48CBX3c/XvSOph64FBrQZV+HsiInocBgwiiYQQuJF+A8dvH8fx28dx7PYxHL99HKm5qY9ddtGRRfj8wOfIK8wzOv/i3YsYsGlAiet49KFuRESmwoBBJMGRm0fQ94e+OH77OJKykkocqzJToa1bW3Sq1wnWKmvMPjgbAHA3567BWHMzcxRoC4yux9XGFe3d20MBBX67+Fux20vLTUNsSixup98uNrwQEcnGgEEkwfmU8zifct7oPJdaLvhXvX+hk2cndPLsBB83H1iprAAASVlJ+Pro18jMf3AfjSZ1mqCjR0d0cO+Ajh4d0ca1DQ7EH8C0vdPgUssFPm4+aO/eHj7uPnC3dQcAbDi7QRcwziadxeIji3E+5TxiU2IRmxKLO5l3dLW0t2uPV/FqRbaCiAgAAwZRuTnXcoaZwgxaodVNc7B0QAePDmjv1h4dPDqgg3sHuNu6F3uXUedazvh7zN+Iux+HVi6tUMeqjsGYwEaBCGwUWKqaVp1aVeL8qzlXS7UeIqInxYBBVE51rOogYnAEDt04hBZOLdDBowMa2Dco8y3L69euj/q165e7jtqWtYud52TthGednsXRW0eRW5Bb7m0QEZUVAwbREwhqHISgxkEmraF7g+6Y8K8JuHL/CprWaQpvR2886/QsmtVthrrWdQEA9b+qb3ALdCKiisSAQVTNWSgtsCBoQbmW1QotbqTdwKV7l3D1/lU0cmiEHg17SK6QiGoiBgyiGuSe5h4+ivoIV1Kv4NLdB6Hi0StLjow8go4eHXWv0/PSEXc/DlfvX0VmfiZ6NOyBlOwUxN2PQ1xq3D+fU+NwI+0G2rm1Q8SQCN0dUYmoZmLAIKphvjryVYnzZ/0+C7YWtrh6/yqu3r9q9PLZkuy7tg/Hbx/H8/Wff5IyiaiaY8AgqgHq2dUzOAdDrVSjUZ1GaFKnCe7l3MOB+AMAgIhLEU+8vfzCfAAPbjyWnJ2MG2k3cCP9Bm6k3UChKMSbbd+EndruibdDRFUXAwZRDbD+lfX4/vT3uH3lNvp36Y9nXZ5FPbt6MFOYAQC2XdymCxhFFFCgnl09NHRoiIYODfHbxd9wN/suujzTBQ3sGzz4cPjn85KjSzDn0BwAwPid45GtycbN9Ju6sPGw2JRYLH9xucH0/MJ83Mu5Bydrp0q5jTsRVRwGDKIaoIFDA0zuPBkRaRHo3qA7VCr9B6L1bdIXWwZuwe2M27pAUb92fb2Hqz1OUVgBHtzwqyR74/ZiUuQkJGQm4E7mHSRkJiAhMwEp2SkAgJbOLXFq9CmYmxn+isrR5EChUJT7HA+t0CI9Lx21VLWgUqoghEBaXhruZt9FSnYK7ubcxd3su4hLjYObjRsszS2RnJ2MpKwk3UdydjKSMpNgVWCFdpnt4Olg+JA6opqOAYOIoFAonvjJsV29umLOoTm6G4/ZW9qjnl09eNp5wtPOE3Wt6yLsYBgA4NK9S/jijy+KXdfZpLMYuXUkHCwd9AJIQmYC0vLSoIAC6/qvw7/q/Qupuam4n3sf93Pu6z7rpj36Ouc+0vLS9G6OplQoUSgKy/2+t17cirG+Y8u9PNHTigGDiKTo2agnro2/hsz8TNSzqwdbta3e/AJtAb49+S2Ss5MNllUr1XCzdcO11Gu6aetOryt2WwICw7YMk1L3k4QLALhy/wrOJZ9Del460vPSkZabpvtaaabE681fh5utm5RaiaoTBgwiksazdvGHCszNzLF/xH5EXY1CHas6cLVxhZutG1xtXFFbXRsKhQKf7f8MM/bNMLq8jYUNFFAgIz+jzHUpFUrYW9rDwcoB1ipr/JX4F9RKNVxtXFHXui7qWtWFo7Uj6lrVRV3rusjKz8KZpDPo4N4BzrWc4VTLCc61nB98be2EPVf34I3NbwB4cFVOSVfmbL+0Hbve2FXmmomqOwYMIqo03o7e8Hb0Lnb+pM6T4GrjiryCPLjauOo+XGxcYGNhAwD4+sjX2HRuE2wsbOBg5QAHS4cH4cHSQff64c/2lvawtbAt8y3cS1L0oLnSOHXnFI7dOoaM/Axk5GXofc7Mz0QH9w7o16yftNqIqgoGDCOEECgoKEBhYel2nWo0GpibmyM3N7fUy1DJ2FP5qnpPlUolVOYqjGw3ssRx7/m+h/d836ukqozz9/LHR50+ws6/dqKBRwPYW9mjtro27NR2sFPboba6NiZHTca9nHtIzk5Gx287lri+k2+fRFu3tpVUPVHlYMB4RH5+Pu7cuYPs7OxSLyOEgKurK27cuCH1f0k1GXsqX3XoqbW1Ndzc3GBhYWHqUkpkpjDDp10/hV+2H/r06WNwVQ4ArI5ZjT9v/lmq9cWmxDJg0FOHAeMhWq0WcXFxUCqVcHd3h4WFRal+EWu1WmRmZsLGxgZmZmaPHU+Px57KV5V7KoRAfn4+kpOTERcXhyZNmlS5GstqaZ+lWHJsCTRaDWwtbB98qP/5/Pv137E2Zi0A4NP9n+JM0hnkFuQiR5ODnIIHH7kFubCxsMFHnT/Ccy7PmfYNEZURA8ZD8vPzodVq4enpCWtr61Ivp9VqkZ+fD0tLy2r/S7GqYE/lq+o9tbKygkqlwvXr13V1Vmdt3dri25e+LXZ+el461mItAOB8ynmcP3i+2LEn75zET6/9hMz8TGTmZyJLk6X7OluTDf9n/OHj7mN0WSEEAOj9Z0krtMjKz9KtJ68gD96O3ry5GUnFgGFEVfzlS1QT1KSfvU6enUo9NjYlFs8tL3kPhpuNGxysHJBbkKvbE1L0tcCDkOFk7YTM/EzkFOQYLN/SuSViRscwZJA0DBhERCbQ3r09zo89jzOJZ2ClsoKVuZXBZ+8l3sgtyC3V+u5k3sGdzDsljjF2D5IiZ5PO4uLdi3jW6VlohRb5hfnIL8xHXkEe8gvzYW9pj1oWtSCE+CfEFOTogoxWaNHCuYXeHV2pZmPAICIykcddthsxOALfnPwGCihQS1ULNhY2eh8302/qnv9ibmYOS3NLWJpbwsrc6sFnlRX+SvwLCijgVMtJt45aFv//s6oW/kr8C3GpcQCA5kubw9zMHAXagnK9n65eXbEveF+5lqWnDwMGPXVWrVqFDRs2YPfu3aYuhR4yefJkZGVlYfHixaYupdro1qAbujXoVuKYz3t8Dq3QGn1uS2mE7ArBV3/+c6Ow8oYLAIi+Fo17OfdQx6pOuddBTw8GjKfE8OHDsW7dg1srq1Qq1K9fH8OGDcPUqVNhbm6O6OhodOv2zy8qR0dHdOjQAXPnzkWrVq1MVbZ0ubm5mDFjBjZu3Ggw7+bNm2jYsCGaNm2Ks2f1H8Z17do1NGjQAKdOnUKbNm305nXt2hVt2rTBwoULddNOnTqF2bNnY//+/UhLS4Onpye6du2KDz/8EE2bNq2ItwYhBEJDQ7Fy5Uqkpqaic+fOWLZsGZo0aVLsMvv378cXX3yBEydO4M6dO/j+++8xaNAgvTHFXSk1b948fPjhh7h27Ro+/fRT7N27FwkJCXB3d8cbb7yBadOm6V1O+tdff2Hs2LE4duwYnJyc8H//93+YNGmSbv7EiRPRsGFDTJgwAQ0bNnzCblARM4XZEx2WeKf9O4hJiMHN9JuwUFpAba5+8Fmp1r3ednEbGjo0hJ3azuhekt+v/Y5bGbcAAJ1Xd0ZtdW0UaAtQKApRoC3QfVy+dxnv+74PFxsXFGoL9cYUaguRX5CPyzcvY9euXRAQ+vMfGlf02tg6cgpykJabhn5N+8HR2hFOtZzgaO1o8MFDORWPAeMp0qtXL6xZswZ5eXmIiIjA2LFjoVKpMGXKFN2YCxcuwM7ODrdv38aHH36Ivn374vLly5V63wGNRmP0vgEybNq0CXZ2dujcubPBvLVr12LAgAHYv38/jhw5Al9f33JtY9u2bXj11VcRFBSE9evXo1GjRkhKSsLGjRsxY8YMbNiw4UnfhlHz5s3D119/jXXr1qFBgwaYMWMGgoKCcO7cuWKvuMjKykLr1q3x5ptv4pVXXjE65s4d/eP2O3bswFtvvYVXX30VABAbGwutVosVK1agcePGOHv2LEaNGoWsrCzMnz8fAJCeno7AwEAEBARg+fLlOHPmDN58803Y29vj7bffBvAg1AYFBWHZsmX44oviH3RGlatp3abYG7z3idbxyoZXsDl2M4AHJ6SWZOGRhY9fYcoTlVOq7cwLmAdHa0eolCpYKC2gMlNBpVRBZaaCcy1ntHFtU2XvF1NdMGA8RdRqNVxdXQEA7777LjZv3oytW7fqBQxnZ2fY29vD1dUV77//Pl566SXExsbiueeKP0P90KFDmDZtGo4ePQq1Wo2OHTsiPDwcDg4O8PLywvvvv4/3339fN75Nmzbo378/Pv74YwAP/oe8dOlS7NixA1FRUfjggw+wevVqTJs2De+++65uuVOnTsHHxwdxcXHw9PREWloaPvjgA2zduhV5eXlo3749vvrqK7Ru3brYWsPDw9Gvn+Ftl4UQWLNmDZYuXYp69eph1apV5QoY2dnZGDFiBPr06YPNmzfrpjdo0AC+vr5ITU0t8zpLQwiBhQsXYvr06fj3vx889fS7776Di4sLtmzZgv/85z9Gl+vduzd69+5d4rqLvmeK/Prrr+jWrZtuL0OvXr3Qq1cv3fyGDRviwoULWLZsmS5grF+/Hvn5+Vi9ejUsLCzQokULxMTEYMGCBbqAAQD9+vXDtGnTGDCeMm+1fQvR16JxP/e+bppSoYS5mTnMzcyRpckyYXXGTdoz6bFjPun6CRysHFDHqg4cLP//5///2t7SvtyHpWoKdqcU2n/THgmZCSWOEUJIT7uuNq44/vbxci9vZWWFu3fvGp2XlpaG8PBwAChx70VMTAx69OiBN998E4sWLYK5uTn27dtX5ltNf/zxx5gzZw4WLlwIc3Nz5OTk4IcfftALGOvXr0fnzp3xzDPPQKvVYvjw4bCxscGOHTtQu3ZtrFixAj169MDFixdRp47xY7wHDx7E0KFDDabv27cP2dnZCAgIgIeHBzp16oSvvvoKtWrVKtP72LVrF1JSUvR2/T/M3t6+2GXfeecdfP/99yWuPzMz0+j0uLg4JCQkICAgQDetdu3a8PX1xeHDh4sNGGWVmJiI7du36w63FSctLU3v3+Dw4cN44YUX9L6XgoKCMHfuXNy/fx8ODg4AgI4dO+LmzZu4du0avLy8pNRMpte3aV8kfZiEQm0hlGZKKBVKg9+HdzLu4ED8ASgVSijNHoSPohBS9FoUChz98yi6PN8FlhaWevMfHVvc64z8DCRmJiIlO8XgI+JyBM4lnyv1+5oZPbPE+XZqO90zb/RCyCNh5NExdmq7GrF3hAGjFBIyE3THF6sDIQSioqKwa9cu/N///Z/evHr16gF4sOscAF566SV4exd/Fvu8efPQvn17LF26VDetRYsWZa5p8ODBGDFihO71kCFD8OWXXyI+Ph7169eHVqtFeHg4pk+fDuBBUDhx4gQSExNhZWUFAJg/fz62bNmCTZs26f2vuEhqairS0tLg7m74IKpVq1bhP//5D5RKJVq2bImGDRti48aNGD58eJnex6VLlwCgxJ4V55NPPsHEiRPLvBwAJCQ8CLguLi56011cXHTzZFi3bh1sbW2LPZwCAJcvX8bixYt1ey+K6mvQoIFBbUXzigJG0b/N9evXGTCeMkV7K4rjZuuGAS0GlLgOjUaD1DOp8HHzKfdhVHtLe9hb2qMZmhnM+yLwC/yd9DdO3jkJjVYDTaEGGq0G+YX5uq+Le5qvMel56UjPS8f1tOtlqrHo6b4GIcRIMHn0tZXKqkzbMiUGjFJwtXF97JiK2oNRFtu2bYONjQ00Gg20Wi0GDx6sO0xR5MCBA7C2tsaff/6J2bNnY/ny5SWuMyYmBq+//npZSzfQvn17vddt2rTBs88+ix9++AGTJ0/G77//jqSkJN22/vrrL2RlZcHJyUlvuZycHFy5csXoNnJyHtw86NHzEVJTU/HLL7/g4MGDumlvvPEGVq1aVeaAUXRXxPJwdnaGs7NzuZevDKtXr8aQIUOKPafj1q1b6NWrF15//XWMGjWqzOsvCotledYPkUwtnFughXPx/0ma/sJ0XL53GUlZSbiXcw/3c+4/+Jz7yOdHppfl6ptCUYi7OXdxN8f4HuaSqJVq4yHEyJ4UW5UtbuXeQlpuGhxVjmXe1pNiwCiFxx2m0Gq1SE9Ph52dnUnvRNitWzcsW7YMFhYWcHd3h7m54T9vgwYNYG9vj2bNmiEpKQkDBw7E/v37i11n0R+E4piZmRn80dVoNAbjjB2KGDJkiC5g/PDDD+jVqxfq1q0L4MGhAldXV+zbt8+gp8Udhqhbty4UCgXu37+vN/2HH35Abm6u3jkXQghotVpcvHgRTZs2hZ2dHYAHu/4flZqaitq1awOA7gqR2NhY+Pn5Ga2jOE9yiKToPInExES4ubnppicmJhpc9VJeBw4cwIULF4o9SfX27dvo1q0bOnXqhG+++cagvsTERL1pRa8fPsfj3r17AGAQHImqksZ1GqNxncalHi+EQJYmyzCMlBBOir5Oy03T3Wm1NPIK80p1U7WHnbE+g2X9lpV6vCxVImAsWbIEX3zxBRISEtC6dWssXrwYHTsW/3jjorP1r127hiZNmmDu3Lno06dPJVZcNdWqVQuNG5f+h2Ls2LEICwvD5s2b8fLLLxsd89xzzyEqKgqzZs0yOt/JyUnvKoT09HTExcWVavuDBw/G9OnTceLECWzatElvb0rbtm2RmJgIc3PzUl/SaGFhgebNm+PcuXMIDAzUTV+1ahU++OADg70VY8aMwerVqzFnzhzUqVMHjo6OOHHiBPz9/fXez+XLl3XBIjAwEI6Ojpg3b57eSZ5FUlNTiw1AT3KIpEGDBnB1dUVUVJQuUKSnp+PIkSN657E8iVWrVsHHx8foSbS3bt1Ct27d4OPjgzVr1hiEPj8/P0ybNk3vCqHIyEg0a9ZMd3gEAM6ePQuVSlWuw2xEVZVCodDd/MyztmeZli3UFiItL61U4eTRadma0u0JtLe0L8e7kkCYWHh4uLCwsBCrV68Wf//9txg1apSwt7cXiYmJRscfOnRIKJVKMW/ePHHu3Dkxffp0oVKpxJkzZ0q1vbS0NAFApKWlGczLyckR586dEzk5OWV6D4WFheL+/fuisLCwTMvJFBwcLP79738XO3/fvn0CgLh//77e9EmTJolWrVoJrVZrdLkLFy4ICwsL8e6774rTp0+L8+fPi6VLl4rk5GQhhBCTJ08Wrq6uYv/+/eKvv/4S/fv3FzY2NiI0NFS3DgBi8+bNRtffuXNn0bp1a2Frayuys7N10wsKCsS//vUv0bp1a7Fr1y4RFxcnDh06JKZOnSqOHTtW7PsMCQkRr776qu71qVOnBABx/vx5g7FLly4Vrq6uQqPRCCGEmD17tqhbt674/vvvxeXLl8WRI0fEiy++KLy8vPRq27Jli1CpVKJfv34iMjJSxMXFiWPHjokPP/xQDBw4sNjantScOXOEvb29+PXXX8Vff/0l/v3vf4sGDRrofb92795dLF68WPc6IyNDnDp1SteHzz//XJw4cUJcv35db91paWnC2tpaLFu2zGC7N2/eFI0bNxY9evQQN2/eFHfu3NF9FElNTRUuLi5i6NCh4uzZsyI8PFxYW1uLFStW6K0rNDRUdO/evdj3WN6fQVPIz88XW7ZsEfn5+aYu5anBnpZNriZX3Mm4I84lnRMHrx8Uv134TayLWScWHl4oZu6dKcZuGyv8F/mLn878JG2bJf0NfZTJA0bHjh3F2LFjda8LCwuFu7u7CAsLMzp+wIABom/fvnrTfH19xejRo0u1PQaM+3rT4+Pjhbm5udiwYUOxy0ZHR4tOnToJtVot7O3tRVBQkG49aWlpYuDAgcLOzk54enqKtWvXitatW5c6YCxdulQAEMOGDdObXlhYKOLj48W4ceOEu7u7UKlUwtPTUwwZMkTEx8cXW+vff/8trKysRGpqqhBCiHHjxonmzZsbHXvnzh1hZmYmfv31VyHEg1Dz9ddfi1atWglra2tRr149MXDgQBEXF2ew7LFjx8Qrr7winJychFqtFo0bNxZvv/22uHTpUrG1PSmtVitmzJghXFxchFqtFj169BAXLlzQG/PMM8/o9b7o3/3Rj+DgYL3lVqxYode3h61Zs8boOh79/8np06fF888/L9RqtfDw8BBz5swxWFezZs3Ejz/+WOx7ZMCo2dhTuSqin2UJGAohnuCstSeUn58Pa2trbNq0Cf3799dNDw4ORmpqKn799VeDZerXr4+QkBC9+y6EhoZiy5YtOH36tMH4vLw85OXl6V6np6fD09MTKSkpuuPuRXJzc3Hjxg14eXmV6VHRQghkZGTA1ta2Rlx6VBmepKcDBgxAu3btMHny5Aqqrnoy9ffpjh078OGHHyImJsbo+UHAg5/Ba9euwdPTs8o/rl2j0SAyMhI9e/assBvH1TTsqVwV0c/09HQ4OjoiLS3N4G/oo0x6DkZKSgoKCwuNXnoXG2v8bnAJCQllulQvLCzM6PkDu3fvhrW1td40c3NzuLq6IjMzE/n5+WV5KwCAjIyMMi9DJStPT2fOnImdO3ciPT29Aiqq/kz1fZqSkoKvv/66xCtI8vPzkZOTg/3796OgoPzPxKhMkZGRpi7hqcOeyiWzn2W5AqxKnORZkaZMmYKQkBDd66I9GIGBgcXuwbCxseEeDBN7kp62bNkSLVu2rKDKqi9Tf58auwHao3Jzc2FlZYUXXniBezBqIPZUrorag1FaJg0Yjo6OUCqVRi9ve/T2xUWKuxyuuPFqtRpqtdpgukqlMmh4YWEhFAoFzMzMynS5qVarBQDdsvTk2FP5qkNPzczMoFAojP58VlXVqdbqgj2VS2Y/y7Iek/6WsbCwgI+PD6KionTTtFotoqKiir3HgJ+fn9544MHun7Lek4CIiIgqjskPkYSEhCA4OBjt27dHx44dsXDhQmRlZeluKz1s2DB4eHggLCwMADB+/Hj4+/vjyy+/RN++fREeHo7jx48b3PjnSZjwvFeiGo0/e0RPD5MHjIEDByI5ORkzZ85EQkIC2rRpg507d+pO5IyPj9fbndupUyf88MMPmD59OqZOnYomTZpgy5YtUo65F+36yc7OfuwdLIlIvqITyLh7nKj6M3nAAIBx48Zh3LhxRudFR0cbTHv99delPB/jUUqlEvb29khKSgIAWFtbl+pkOK1Wi/z8fOTm5lbZY9vVDXsqX1XuqRAC2dnZSEpKgr29PZRKpalLIqInVCUCRlVSdLJoUcgoDSEEcnJyYGVlxatIJGFP5asOPbW3ty/2hG0iql4YMB6hUCjg5uYGZ2dnow/tMkaj0WD//v144YUXuGtXEvZUvqreU5VKxT0XRE8RBoxiKJXKUv+yUyqVKCgogKWlZZX8xV0dsafysadEVJmq1oFYIiIieiowYBAREZF0DBhEREQkXY07B6PoRj4yH4Sl0WiQnZ2N9PR0HtuWhD2Vjz2Vi/2Ujz2VqyL6WfS3szQ3xatxAaPoSZKenp4mroSIiKh6ysjIQO3atUscoxA17N68Wq0Wt2/flvpEyaIntN64ccPgCa1UPuypfOypXOynfOypXBXRz6KnMru7uz/2hn01bg+GmZkZ6tWrVyHrtrOz4w+FZOypfOypXOynfOypXLL7+bg9F0V4kicRERFJx4BBRERE0jFgSKBWqxEaGgq1Wm3qUp4a7Kl87Klc7Kd87Klcpu5njTvJk4iIiCoe92AQERGRdAwYREREJB0DBhEREUnHgEFERETSMWCU0pIlS+Dl5QVLS0v4+vri6NGjJY7fuHEjvL29YWlpiVatWiEiIqKSKq0+ytLTlStXokuXLnBwcICDgwMCAgIe+29Q05T1e7RIeHg4FAoF+vfvX7EFVkNl7WlqairGjh0LNzc3qNVqNG3alD/7DylrPxcuXIhmzZrBysoKnp6emDBhAnJzcyup2qpv//796NevH9zd3aFQKLBly5bHLhMdHY127dpBrVajcePGWLt2bcUVKOixwsPDhYWFhVi9erX4+++/xahRo4S9vb1ITEw0Ov7QoUNCqVSKefPmiXPnzonp06cLlUolzpw5U8mVV11l7engwYPFkiVLxKlTp8T58+fF8OHDRe3atcXNmzcrufKqqaz9LBIXFyc8PDxEly5dxL///e/KKbaaKGtP8/LyRPv27UWfPn3EwYMHRVxcnIiOjhYxMTGVXHnVVNZ+rl+/XqjVarF+/XoRFxcndu3aJdzc3MSECRMqufKqKyIiQkybNk388ssvAoDYvHlzieOvXr0qrK2tRUhIiDh37pxYvHixUCqVYufOnRVSHwNGKXTs2FGMHTtW97qwsFC4u7uLsLAwo+MHDBgg+vbtqzfN19dXjB49ukLrrE7K2tNHFRQUCFtbW7Fu3bqKKrFaKU8/CwoKRKdOncS3334rgoODGTAeUdaeLlu2TDRs2FDk5+dXVonVSln7OXbsWNG9e3e9aSEhIaJz584VWmd1VZqAMWnSJNGiRQu9aQMHDhRBQUEVUhMPkTxGfn4+Tpw4gYCAAN00MzMzBAQE4PDhw0aXOXz4sN54AAgKCip2fE1Tnp4+Kjs7GxqNBnXq1KmoMquN8vbzk08+gbOzM956663KKLNaKU9Pt27dCj8/P4wdOxYuLi5o2bIlZs+ejcLCwsoqu8oqTz87deqEEydO6A6jXL16FREREejTp0+l1Pw0quy/TTXuYWdllZKSgsLCQri4uOhNd3FxQWxsrNFlEhISjI5PSEiosDqrk/L09FEfffQR3N3dDX5YaqLy9PPgwYNYtWoVYmJiKqHC6qc8Pb169Sr27t2LIUOGICIiApcvX8aYMWOg0WgQGhpaGWVXWeXp5+DBg5GSkoLnn38eQggUFBTgnXfewdSpUyuj5KdScX+b0tPTkZOTAysrK6nb4x4MqnbmzJmD8PBwbN68GZaWlqYup9rJyMjA0KFDsXLlSjg6Opq6nKeGVquFs7MzvvnmG/j4+GDgwIGYNm0ali9fburSqqXo6GjMnj0bS5cuxcmTJ/HLL79g+/bt+PTTT01dGpUS92A8hqOjI5RKJRITE/WmJyYmwtXV1egyrq6uZRpf05Snp0Xmz5+POXPmYM+ePXjuuecqssxqo6z9vHLlCq5du4Z+/frppmm1WgCAubk5Lly4gEaNGlVs0VVceb5H3dzcoFKpoFQqddOeffZZJCQkID8/HxYWFhVac1VWnn7OmDEDQ4cOxciRIwEArVq1QlZWFt5++21MmzYNZmb8/3FZFfe3yc7OTvreC4B7MB7LwsICPj4+iIqK0k3TarWIioqCn5+f0WX8/Pz0xgNAZGRkseNrmvL0FADmzZuHTz/9FDt37kT79u0ro9Rqoaz99Pb2xpkzZxATE6P7eOmll9CtWzfExMTA09OzMsuvksrzPdq5c2dcvnxZF9YA4OLFi3Bzc6vR4QIoXz+zs7MNQkRReBN8hFa5VPrfpgo5dfQpEx4eLtRqtVi7dq04d+6cePvtt4W9vb1ISEgQQggxdOhQMXnyZN34Q4cOCXNzczF//nxx/vx5ERoaystUH1HWns6ZM0dYWFiITZs2iTt37ug+MjIyTPUWqpSy9vNRvIrEUFl7Gh8fL2xtbcW4cePEhQsXxLZt24Szs7P47LPPTPUWqpSy9jM0NFTY2tqKH3/8UVy9elXs3r1bNGrUSAwYMMBUb6HKycjIEKdOnRKnTp0SAMSCBQvEqVOnxPXr14UQQkyePFkMHTpUN77oMtUPP/xQnD9/XixZsoSXqVYFixcvFvXr1xcWFhaiY8eO4s8//9TN8/f3F8HBwXrjf/rpJ9G0aVNhYWEhWrRoIbZv317JFVd9ZenpM888IwAYfISGhlZ+4VVUWb9HH8aAYVxZe/rHH38IX19foVarRcOGDcXnn38uCgoKKrnqqqss/dRoNOLjjz8WjRo1EpaWlsLT01OMGTNG3L9/v/ILr6L27dtn9PdiUR+Dg4OFv7+/wTJt2rQRFhYWomHDhmLNmjUVVh8f105ERETS8RwMIiIiko4Bg4iIiKRjwCAiIiLpGDCIiIhIOgYMIiIiko4Bg4iIiKRjwCAiIiLpGDCIiIhIOgYMInoqKBQKbNmyBQBw7do1KBQKPo6eyIQYMIjoiQ0fPhwKhQIKhQIqlQoNGjTApEmTkJuba+rSiMhE+Lh2IpKiV69eWLNmDTQaDU6cOIHg4GAoFArMnTvX1KURkQlwDwYRSaFWq+Hq6gpPT0/0798fAQEBiIyMBPDg0dxhYWFo0KABrKys0Lp1a2zatElv+b///hsvvvgi7OzsYGtriy5duuDKlSsAgGPHjqFnz55wdHRE7dq14e/vj5MnT1b6eySi0mPAICLpzp49iz/++AMWFhYAgLCwMHz33XdYvnw5/v77b0yYMAFvvPEGfv/9dwDArVu38MILL0CtVmPv3r04ceIE3nzzTRQUFAAAMjIyEBwcjIMHD+LPP/9EkyZN0KdPH2RkZJjsPRJRyXiIhIik2LZtG2xsbFBQUIC8vDyYmZnhv//9L/Ly8jB79mzs2bMHfn5+AICGDRvi4MGDWLFiBfz9/bFkyRLUrl0b4eHhUKlUAICmTZvq1t29e3e9bX3zzTewt7fH77//jhdffLHy3iQRlRoDBhFJ0a1bNyxbtgxZWVn46quvYG5ujldffRV///03srOz0bNnT73x+fn5aNu2LQAgJiYGXbp00YWLRyUmJmL69OmIjo5GUlISCgsLkZ2djfj4+Ap/X0RUPgwYRCRFrVq10LhxYwDA6tWr0bp1a6xatQotW7YEAGzfvh0eHh56y6jVagCAlZVViesODg7G3bt3sWjRIjzzzDNQq9Xw8/NDfn5+BbwTIpKBAYOIpDMzM8PUqVMREhKCixcvQq1WIz4+Hv7+/kbHP/fcc1i3bh00Go3RvRiHDh3C0qVL0adPHwDAjRs3kJKSUqHvgYieDE/yJKIK8frrr0OpVGLFihWYOHEiJkyYgHXr1uHKlSs4efIkFi9ejHXr1gEAxo0bh/T0dPznP//B8ePHcenSJfzvf//DhQsXAABNmjTB//73P5w/fx5HjhzBkCFDHrvXg4hMi3swiKhCmJubY9y4cZg3bx7i4uLg5OSEsLAwXL16Ffb29mjXrh2mTp0KAKhbty727t2LDz/8EP7+/lAqlWjTpg06d+4MAFi1ahXefvtttGvXDp6enpg9ezYmTpxoyrdHRI+hEEIIUxdBRERETxceIiEiIiLpGDCIiIhIOgYMIiIiko4Bg4iIiKRjwCAiIiLpGDCIiIhIOgYMIiIiko4Bg4iIiKRjwCAiIiLpGDCIiIhIOgYMIiIiku7/ATOml2BNA4wUAAAAAElFTkSuQmCC", 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" ] }, "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 → **higher** risk. Smaller hosts have more tendency to complain |\n", "| `listing_number_of_bedrooms` | 0.066 | More bedrooms → **higher** risk. Larger properties involve higher risk. |\n", "| `listing_number_of_bathrooms` | 0.063 | More bathrooms → **higher** risk. Related to larger properties. |\n", "| `host_months_with_truvi` | 0.049 | Longer time → **higher** risk. |\n", "| `host_account_type_PMC` | 0.035 | PMC hosts → **lower** risk. Professional management mitigates incidents. |\n", "| `host_country_United Kingdom` | 0.035 | UK-based hosts → **lower** risk. Possibly due to operational/regional norms. |\n", "| `number_of_nights` | 0.034 | Longer stays → **higher** risk. Increased time/possibility to issues. |\n", "| `guest_age` | 0.033 | Younger guests → **higher** risk. May reflect behavioral trends. |\n", "| `host_age` | 0.031 | Older hosts → **higher** risk. Possibly less adaptive to platform dynamics. |\n", "| `host_active_pms_list_Hostify` | 0.030 | Hostify users → **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 }