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data-jupyter-notebooks/data_driven_risk_assessment/experiments/ddra_joaquin_weighted.ipynb
Joaquin Ossa 6ece1d9d27 Merged PR 5574: EDA DDRA Joaquin 
I have 2 notebooks
`ddra_joaquin.ipynb` is the main one, there are still things I want to try maybe adding a few a the features Uri used like `number_of_bookings_30_days_before_cid` and trying it to do it contactless.
`ddra_joaquin_weighted.ipynb` is a basically a copy where I try new stuff, not worth checking

Related work items: #31120
2025-06-30 13:47:52 +00:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "84dcd475",
"metadata": {},
"source": [
"# DDRA Joaquin\n",
"\n",
"## General Idea\n",
"The idea is to start with a very simple model with basic Booking attributes. This should serve as a first understanding of what can bring value in the data-driven risk assessment of new dash protected bookings.\n",
"\n",
"## Initial setup\n",
"This first section just ensures that the connection to DWH works correctly."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "12368ce1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔌 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": null,
"id": "3e3ed391",
"metadata": {},
"outputs": [],
"source": [
"# Initialise all imports needed for the Notebook\n",
"from sklearn.model_selection import (\n",
" train_test_split, \n",
" GridSearchCV\n",
")\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.feature_selection import RFE\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"from sklearn.feature_selection import SelectKBest, f_classif\n",
"import pandas as pd\n",
"import numpy as np\n",
"from datetime import date\n",
"from sklearn.metrics import (\n",
" roc_auc_score, \n",
" average_precision_score,\n",
" classification_report,\n",
" roc_curve, \n",
" auc,\n",
" precision_recall_curve,\n",
" precision_score,\n",
" recall_score,\n",
" fbeta_score,\n",
" confusion_matrix\n",
")\n",
"import matplotlib.pyplot as plt\n",
"import shap\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "db5e3098",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id_booking days_from_booking_creation_to_check_in number_of_nights \\\n",
"0 919656 26.0 4.0 \n",
"1 926634 17.0 3.0 \n",
"2 931082 20.0 7.0 \n",
"3 931086 15.0 3.0 \n",
"4 931096 8.0 5.0 \n",
"\n",
" host_town host_country host_postcode host_age host_months_with_truvi \\\n",
"0 Madison CT United States 06443 125.0 8.0 \n",
"1 Madison CT United States 06443 125.0 8.0 \n",
"2 London United Kingdom N16 6DD 125.0 8.0 \n",
"3 London United Kingdom N16 6DD 125.0 8.0 \n",
"4 London United Kingdom N16 6DD 125.0 8.0 \n",
"\n",
" host_account_type host_active_pms_list ... \\\n",
"0 Host Hostaway ... \n",
"1 Host Hostaway ... \n",
"2 PMC - Property Management Company Hostify ... \n",
"3 PMC - Property Management Company Hostify ... \n",
"4 PMC - Property Management Company Hostify ... \n",
"\n",
" number_of_applied_upgraded_services number_of_applied_billable_services \\\n",
"0 2 2 \n",
"1 2 2 \n",
"2 1 1 \n",
"3 1 1 \n",
"4 1 1 \n",
"\n",
" booking_days_to_check_in booking_number_of_nights has_verification_request \\\n",
"0 87 4 False \n",
"1 109 3 False \n",
"2 50 7 False \n",
"3 15 3 False \n",
"4 8 5 False \n",
"\n",
" has_billable_services has_upgraded_screening_service_business_type \\\n",
"0 True False \n",
"1 True False \n",
"2 True False \n",
"3 True False \n",
"4 True False \n",
"\n",
" has_deposit_management_service_business_type \\\n",
"0 True \n",
"1 True \n",
"2 False \n",
"3 False \n",
"4 False \n",
"\n",
" has_protection_service_business_type has_resolution_incident \n",
"0 True False \n",
"1 True False \n",
"2 True False \n",
"3 True False \n",
"4 True False \n",
"\n",
"[5 rows x 64 columns]\n",
"Total Bookings: 21,307\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_48568/805553034.py:455: DtypeWarning: Columns (50) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" df_extraction = pd.read_csv(\"/home/joaquin/data-jupyter-notebooks/data_driven_risk_assessment/experiments/data.csv\")\n"
]
}
],
"source": [
"# Query to extract data\n",
"data_extraction_query = \"\"\"\n",
"with\n",
" int_core__verification_requests as (\n",
" select *\n",
" from intermediate.int_core__verification_requests\n",
" where created_date_utc >= '2024-10-21'\n",
" ),\n",
" int_core__bookings as (\n",
" select *\n",
" from intermediate.int_core__bookings\n",
" where created_date_utc >= '2024-10-21'\n",
" ),\n",
" stg_core__verification as (\n",
" select *\n",
" from staging.stg_core__verification\n",
" where created_date_utc >= '2024-10-21'\n",
" ),\n",
" int_core__guest_journey_payments as (\n",
" select *\n",
" from intermediate.int_core__guest_journey_payments\n",
" where payment_due_date_utc >= '2024-10-21'\n",
" ),\n",
" filtered_bookings as (\n",
" select *\n",
" from intermediate.int_booking_summary\n",
" where\n",
" is_user_in_new_dash = true\n",
" and is_missing_id_deal = false\n",
" and (\n",
" has_protection_service_business_type\n",
" or has_deposit_management_service_business_type\n",
" )\n",
" and is_booking_flagged_as_risk is not null\n",
" and is_booking_past_completion_date = true\n",
" and booking_created_date_utc < '2025-06-25'\n",
" ),\n",
" previous_booking_counts as (\n",
" select\n",
" id_booking,\n",
" id_accommodation,\n",
" id_user_guest,\n",
" booking_check_in_date_utc,\n",
" booking_check_out_date_utc,\n",
" count(*) over (\n",
" partition by id_accommodation\n",
" order by booking_check_in_date_utc\n",
" rows between unbounded preceding and 1 preceding\n",
" ) as previous_bookings_in_listing_count,\n",
" count(*) over (\n",
" partition by id_user_guest\n",
" order by booking_check_in_date_utc\n",
" rows between unbounded preceding and 1 preceding\n",
" ) as previous_guest_bookings_count\n",
" from filtered_bookings\n",
" ),\n",
" listing_info as (\n",
" select\n",
" id_accommodation,\n",
" address_line_1 as listing_address,\n",
" town as listing_town,\n",
" country_name as listing_country,\n",
" postcode as listing_postcode,\n",
" number_of_bedrooms,\n",
" number_of_bathrooms,\n",
" friendly_name as listing_description,\n",
" id_user_host\n",
" from intermediate.int_core__accommodation\n",
" ),\n",
" host_info as (\n",
" select\n",
" scu.id_user as id_user_host,\n",
" icuh.account_type,\n",
" icuh.active_pms_list,\n",
" scc.country_name,\n",
" scu.billing_town,\n",
" scu.billing_postcode,\n",
" scu.id_billing_country,\n",
" extract(year from age(current_date, scu.date_of_birth)) as host_age,\n",
" extract(\n",
" month from age(current_date, scu.joined_date_utc)\n",
" ) as host_months_with_truvi\n",
" from staging.stg_core__user scu\n",
" left join\n",
" staging.stg_core__country scc on scu.id_billing_country = scc.id_country\n",
" left join\n",
" intermediate.int_core__user_host icuh on icuh.id_user_host = scu.id_user\n",
" ),\n",
" guest_info as (\n",
" select\n",
" scu.id_user as id_user_guest,\n",
" scc.country_name,\n",
" scu.billing_town,\n",
" scu.billing_postcode,\n",
" scu.id_billing_country,\n",
" extract(year from age(current_date, scu.date_of_birth)) as guest_age,\n",
" scu.email,\n",
" scu.phone_number\n",
" from staging.stg_core__user scu\n",
" left join\n",
" staging.stg_core__country scc on scu.id_billing_country = scc.id_country\n",
" ),\n",
" host_listing_counts as (\n",
" select id_user_host, count(*) as number_of_listings_of_host\n",
" from intermediate.int_core__accommodation\n",
" where is_active = true\n",
" group by id_user_host\n",
" ),\n",
" listing_incident_counts as (\n",
" select\n",
" i.created_date_utc::date as date_day,\n",
" i.id_accommodation,\n",
" count(*) over (\n",
" partition by i.id_accommodation\n",
" order by i.created_date_utc::date\n",
" rows between unbounded preceding and current row\n",
" ) as number_of_previous_incidents_in_listing,\n",
" count(i.calculated_payout_amount_in_txn_currency) over (\n",
" partition by i.id_accommodation\n",
" order by i.created_date_utc::date\n",
" rows between unbounded preceding and current row\n",
" ) as number_of_previous_payouts_in_listing\n",
" from intermediate.int_resolutions__incidents i\n",
" where\n",
" i.id_accommodation is not null\n",
" and i.created_date_utc::date between '2024-10-21' and current_date\n",
" order by i.id_accommodation, date_day\n",
" ),\n",
" guest_incident_counts as (\n",
" select\n",
" i.created_date_utc::date as date_day,\n",
" i.id_user_guest,\n",
" count(*) over (\n",
" partition by i.id_user_guest\n",
" order by i.created_date_utc::date\n",
" rows between unbounded preceding and current row\n",
" ) as number_of_previous_incidents_of_guest\n",
" from intermediate.int_resolutions__incidents i\n",
" where\n",
" i.id_user_guest is not null\n",
" and i.created_date_utc::date between '2024-10-21' and current_date\n",
" order by i.id_user_guest, date_day\n",
" ),\n",
" host_incident_counts as (\n",
" select\n",
" i.created_date_utc::date as date_day,\n",
" i.id_user_host,\n",
" count(*) over (\n",
" partition by i.id_user_host\n",
" order by i.created_date_utc::date\n",
" rows between unbounded preceding and current row\n",
" ) as number_of_previous_incidents_of_host,\n",
" count(i.calculated_payout_amount_in_txn_currency) over (\n",
" partition by i.id_user_host\n",
" order by i.created_date_utc::date\n",
" rows between unbounded preceding and current row\n",
" ) as number_of_previous_payouts_of_host\n",
" from intermediate.int_resolutions__incidents i\n",
" where\n",
" i.id_user_host is not null\n",
" and i.created_date_utc::date between '2024-10-21' and current_date\n",
" order by i.id_user_host, date_day\n",
" ),\n",
" verification_requests as (\n",
" select\n",
" icvr.id_verification_request,\n",
" extract(\n",
" day\n",
" from\n",
" age(\n",
" icvr.verification_estimated_started_date_utc,\n",
" icb.created_date_utc\n",
" )\n",
" ) as days_to_start_verification,\n",
" extract(\n",
" day\n",
" from\n",
" age(\n",
" icvr.verification_estimated_completed_date_utc,\n",
" icvr.verification_estimated_started_date_utc\n",
" )\n",
" ) as days_to_complete_verification,\n",
" -- CSAT Results\n",
" gsr.experience_rating as guest_csat_score,\n",
" gsr.guest_comments as guest_csat_comments,\n",
" -- GUEST_PRODUCT fields\n",
" max(\n",
" case\n",
" when guest_journey_product_type = 'GUEST_PRODUCT' then product_name\n",
" end\n",
" ) as guest_product_name,\n",
" max(\n",
" case when guest_journey_product_type = 'GUEST_PRODUCT' then currency end\n",
" ) as guest_currency,\n",
" max(\n",
" case\n",
" when guest_journey_product_type = 'GUEST_PRODUCT'\n",
" then total_amount_in_txn_currency\n",
" end\n",
" ) as guest_total_amount,\n",
" -- VERIFICATION_PRODUCT fields\n",
" max(\n",
" case\n",
" when guest_journey_product_type = 'VERIFICATION_PRODUCT'\n",
" then product_name\n",
" end\n",
" ) as verification_product_name,\n",
" max(\n",
" case\n",
" when guest_journey_product_type = 'VERIFICATION_PRODUCT'\n",
" then currency\n",
" end\n",
" ) as verification_currency,\n",
" max(\n",
" case\n",
" when guest_journey_product_type = 'VERIFICATION_PRODUCT'\n",
" then total_amount_in_txn_currency\n",
" end\n",
" ) as verification_total_amount,\n",
" -- Verification Results\n",
" max(\n",
" case when scv.verification = 'Screening' then id_verification_status end\n",
" ) as screening_status,\n",
" max(\n",
" case\n",
" when scv.verification = 'GovernmentId' then id_verification_status\n",
" end\n",
" ) as government_id_status,\n",
" max(\n",
" case when scv.verification = 'Contract' then id_verification_status end\n",
" ) as contract_status,\n",
" max(\n",
" case\n",
" when scv.verification = 'SelfieConfidenceScore'\n",
" then id_verification_status\n",
" end\n",
" ) as selfie_confidence_score_status,\n",
" max(\n",
" case\n",
" when scv.verification = 'PaymentValidation'\n",
" then id_verification_status\n",
" end\n",
" ) as payment_validation_status,\n",
" max(\n",
" case when scv.verification = 'FirstName' then id_verification_status end\n",
" ) as first_name_status,\n",
" max(\n",
" case\n",
" when scv.verification = 'DateOfBirth' then id_verification_status\n",
" end\n",
" ) as date_of_birth_status,\n",
" max(\n",
" case when scv.verification = 'LastName' then id_verification_status end\n",
" ) as last_name_status,\n",
" max(\n",
" case\n",
" when scv.verification = 'AutohostPartner'\n",
" then id_verification_status\n",
" end\n",
" ) as autohost_partner_status,\n",
" max(\n",
" case\n",
" when scv.verification = 'CriminalRecord' then id_verification_status\n",
" end\n",
" ) as criminal_record_status\n",
" from int_core__verification_requests icvr\n",
" left join\n",
" int_core__bookings icb\n",
" on icb.id_verification_request = icvr.id_verification_request\n",
" left join\n",
" stg_core__verification scv\n",
" on scv.id_verification_request = icvr.id_verification_request\n",
" left join\n",
" int_core__guest_journey_payments gjp\n",
" on gjp.id_verification_request = icb.id_verification_request\n",
" left join\n",
" intermediate.int_core__guest_satisfaction_responses gsr\n",
" on gsr.id_verification_request = icvr.id_verification_request\n",
" and scv.verification in (\n",
" 'Screening',\n",
" 'GovernmentId',\n",
" 'Contract',\n",
" 'SelfieConfidenceScore',\n",
" 'PaymentValidation',\n",
" 'FirstName',\n",
" 'DateOfBirth',\n",
" 'LastName',\n",
" 'AutohostPartner',\n",
" 'CriminalRecord'\n",
" )\n",
" group by 1, 2, 3, 4, 5\n",
" )\n",
"select\n",
" fb.id_booking,\n",
" extract(day from age(fb.booking_check_in_date_utc, fb.booking_created_date_utc)) as days_from_booking_creation_to_check_in,\n",
" extract(day from age(fb.booking_check_out_date_utc, fb.booking_check_in_date_utc)) as number_of_nights,\n",
" -- Host Info\n",
" hi.billing_town as host_town,\n",
" hi.country_name as host_country,\n",
" hi.billing_postcode as host_postcode,\n",
" hi.host_age,\n",
" hi.host_months_with_truvi,\n",
" hi.account_type as host_account_type,\n",
" hi.active_pms_list as host_active_pms_list,\n",
" coalesce(hlc.number_of_listings_of_host, 0) as number_of_listings_of_host,\n",
" coalesce(\n",
" hic.number_of_previous_incidents_of_host, 0\n",
" ) as number_of_previous_incidents_of_host,\n",
" coalesce(\n",
" hic.number_of_previous_payouts_of_host, 0\n",
" ) as number_of_previous_payouts_of_host,\n",
" -- Guest Info\n",
" gi.billing_town as guest_town,\n",
" gi.country_name as guest_country,\n",
" gi.billing_postcode as guest_postcode,\n",
" gi.guest_age,\n",
" coalesce(\n",
" pbc.previous_guest_bookings_count, 0\n",
" ) as number_of_previous_bookings_of_guest,\n",
" coalesce(\n",
" gic.number_of_previous_incidents_of_guest, 0\n",
" ) as number_of_previous_incidents_of_guest,\n",
" case\n",
" when pbc.previous_bookings_in_listing_count > 0 then true else false\n",
" end as has_guest_previously_booked_same_listing,\n",
" -- Listing Info\n",
" li.listing_address,\n",
" li.listing_town,\n",
" li.listing_country,\n",
" li.listing_postcode,\n",
" li.number_of_bedrooms as listing_number_of_bedrooms,\n",
" li.number_of_bathrooms as listing_number_of_bathrooms,\n",
" li.listing_description,\n",
" coalesce(pbc.previous_bookings_in_listing_count, 0) as previous_bookings_in_listing_count,\n",
" coalesce(lic.number_of_previous_incidents_in_listing, 0) as number_of_previous_incidents_in_listing,\n",
" coalesce(lic.number_of_previous_payouts_in_listing, 0) as number_of_previous_payouts_in_listing,\n",
" -- Verification Info\n",
" case\n",
" when fb.id_verification_request is null then 0\n",
" else vr.days_to_start_verification\n",
" end as days_to_start_verification,\n",
" case \n",
" when vr.id_verification_request is null then 0\n",
" else vr.days_to_complete_verification\n",
" end as days_to_complete_verification,\n",
" vr.screening_status,\n",
" vr.government_id_status,\n",
" vr.contract_status,\n",
" vr.selfie_confidence_score_status,\n",
" vr.payment_validation_status,\n",
" vr.first_name_status,\n",
" vr.date_of_birth_status,\n",
" vr.last_name_status,\n",
" vr.autohost_partner_status,\n",
" vr.criminal_record_status,\n",
" vr.guest_csat_score,\n",
" vr.guest_csat_comments,\n",
" -- Boolean features\n",
" gi.email is not null as guest_has_email,\n",
" gi.phone_number is not null as guest_has_phone_number,\n",
" case \n",
" when gi.billing_town is null or li.listing_town is null then null \n",
" when gi.billing_town = li.listing_town \n",
" then true else false \n",
" end as is_guest_from_listing_town,\n",
" case \n",
" when gi.country_name is null or li.listing_country is null then null\n",
" when gi.country_name = li.listing_country \n",
" then true else false \n",
" end as is_guest_from_listing_country,\n",
" case \n",
" when gi.billing_postcode is null or li.listing_postcode is null then null\n",
" when gi.billing_postcode = li.listing_postcode \n",
" then true else false \n",
" end as is_guest_from_listing_postcode,\n",
" case \n",
" when hi.billing_town is null or li.listing_town is null then null\n",
" when hi.billing_town = li.listing_town \n",
" then true else false \n",
" end as is_host_from_listing_town,\n",
" case \n",
" when hi.country_name is null or li.listing_country is null then null\n",
" when hi.country_name = li.listing_country \n",
" then true else false \n",
" end as is_host_from_listing_country,\n",
" case \n",
" when hi.billing_postcode is null or li.listing_postcode is null then null\n",
" when hi.billing_postcode = li.listing_postcode \n",
" then true else false \n",
" end as is_host_from_listing_postcode,\n",
" case\n",
" when vr.days_to_complete_verification is null then false\n",
" else true\n",
" end as has_completed_verification,\n",
" -- Numeric features\n",
" fb.number_of_applied_services,\n",
" fb.number_of_applied_upgraded_services,\n",
" fb.number_of_applied_billable_services,\n",
" fb.booking_check_in_date_utc\n",
" - fb.booking_created_date_utc as booking_days_to_check_in,\n",
" fb.booking_number_of_nights,\n",
" -- Categorical features\n",
" fb.has_verification_request,\n",
" fb.has_billable_services,\n",
" fb.has_upgraded_screening_service_business_type,\n",
" fb.has_deposit_management_service_business_type,\n",
" fb.has_protection_service_business_type,\n",
" -- Target\n",
" fb.has_resolution_incident\n",
"from filtered_bookings fb\n",
"left join previous_booking_counts pbc on fb.id_booking = pbc.id_booking\n",
"left join listing_info li on li.id_accommodation = fb.id_accommodation\n",
"left join host_info hi on hi.id_user_host = fb.id_user_host\n",
"left join guest_info gi on gi.id_user_guest = fb.id_user_guest\n",
"left join host_listing_counts hlc on li.id_user_host = hlc.id_user_host\n",
"left join\n",
" lateral(\n",
" select *\n",
" from listing_incident_counts lic\n",
" where\n",
" lic.id_accommodation = fb.id_accommodation\n",
" and lic.date_day <= fb.booking_check_in_date_utc\n",
" order by lic.date_day desc\n",
" limit 1\n",
" ) lic\n",
" on true\n",
"left join\n",
" lateral(\n",
" select *\n",
" from guest_incident_counts gic\n",
" where\n",
" gic.id_user_guest = fb.id_user_guest\n",
" and gic.date_day <= fb.booking_check_in_date_utc\n",
" order by gic.date_day desc\n",
" limit 1\n",
" ) gic\n",
" on true\n",
"left join\n",
" lateral(\n",
" select *\n",
" from host_incident_counts hic\n",
" where\n",
" hic.id_user_host = fb.id_user_host\n",
" and hic.date_day <= fb.booking_check_in_date_utc\n",
" order by hic.date_day desc\n",
" limit 1\n",
" ) hic\n",
" on true\n",
"left join\n",
" verification_requests vr on vr.id_verification_request = fb.id_verification_request\n",
"\"\"\"\n",
"\n",
"# Retrieve Data from Query\n",
"# df_extraction = query_to_dataframe(engine=dwh_pg_engine, query=data_extraction_query)\n",
"df_extraction = pd.read_csv(\"/home/joaquin/data-jupyter-notebooks/data_driven_risk_assessment/experiments/data.csv\")\n",
"print(df_extraction.head())\n",
"print(f\"Total Bookings: {len(df_extraction):,}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b56a8530",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id_booking</th>\n",
" <th>days_from_booking_creation_to_check_in</th>\n",
" <th>number_of_nights</th>\n",
" <th>host_town</th>\n",
" <th>host_country</th>\n",
" <th>host_postcode</th>\n",
" <th>host_age</th>\n",
" <th>host_months_with_truvi</th>\n",
" <th>host_account_type</th>\n",
" <th>host_active_pms_list</th>\n",
" <th>...</th>\n",
" <th>number_of_applied_upgraded_services</th>\n",
" <th>number_of_applied_billable_services</th>\n",
" <th>booking_days_to_check_in</th>\n",
" <th>booking_number_of_nights</th>\n",
" <th>has_verification_request</th>\n",
" <th>has_billable_services</th>\n",
" <th>has_upgraded_screening_service_business_type</th>\n",
" <th>has_deposit_management_service_business_type</th>\n",
" <th>has_protection_service_business_type</th>\n",
" <th>has_resolution_incident</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>919656</td>\n",
" <td>26.0</td>\n",
" <td>4.0</td>\n",
" <td>Madison CT</td>\n",
" <td>United States</td>\n",
" <td>06443</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>Host</td>\n",
" <td>Hostaway</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>87</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>926634</td>\n",
" <td>17.0</td>\n",
" <td>3.0</td>\n",
" <td>Madison CT</td>\n",
" <td>United States</td>\n",
" <td>06443</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>Host</td>\n",
" <td>Hostaway</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>109</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>931082</td>\n",
" <td>20.0</td>\n",
" <td>7.0</td>\n",
" <td>London</td>\n",
" <td>United Kingdom</td>\n",
" <td>N16 6DD</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>PMC - Property Management Company</td>\n",
" <td>Hostify</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>50</td>\n",
" <td>7</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>931086</td>\n",
" <td>15.0</td>\n",
" <td>3.0</td>\n",
" <td>London</td>\n",
" <td>United Kingdom</td>\n",
" <td>N16 6DD</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>PMC - Property Management Company</td>\n",
" <td>Hostify</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>931096</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>London</td>\n",
" <td>United Kingdom</td>\n",
" <td>N16 6DD</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>PMC - Property Management Company</td>\n",
" <td>Hostify</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 64 columns</p>\n",
"</div>"
],
"text/plain": [
" id_booking days_from_booking_creation_to_check_in number_of_nights \\\n",
"0 919656 26.0 4.0 \n",
"1 926634 17.0 3.0 \n",
"2 931082 20.0 7.0 \n",
"3 931086 15.0 3.0 \n",
"4 931096 8.0 5.0 \n",
"\n",
" host_town host_country host_postcode host_age host_months_with_truvi \\\n",
"0 Madison CT United States 06443 125.0 8.0 \n",
"1 Madison CT United States 06443 125.0 8.0 \n",
"2 London United Kingdom N16 6DD 125.0 8.0 \n",
"3 London United Kingdom N16 6DD 125.0 8.0 \n",
"4 London United Kingdom N16 6DD 125.0 8.0 \n",
"\n",
" host_account_type host_active_pms_list ... \\\n",
"0 Host Hostaway ... \n",
"1 Host Hostaway ... \n",
"2 PMC - Property Management Company Hostify ... \n",
"3 PMC - Property Management Company Hostify ... \n",
"4 PMC - Property Management Company Hostify ... \n",
"\n",
" number_of_applied_upgraded_services number_of_applied_billable_services \\\n",
"0 2 2 \n",
"1 2 2 \n",
"2 1 1 \n",
"3 1 1 \n",
"4 1 1 \n",
"\n",
" booking_days_to_check_in booking_number_of_nights has_verification_request \\\n",
"0 87 4 False \n",
"1 109 3 False \n",
"2 50 7 False \n",
"3 15 3 False \n",
"4 8 5 False \n",
"\n",
" has_billable_services has_upgraded_screening_service_business_type \\\n",
"0 True False \n",
"1 True False \n",
"2 True False \n",
"3 True False \n",
"4 True False \n",
"\n",
" has_deposit_management_service_business_type \\\n",
"0 True \n",
"1 True \n",
"2 False \n",
"3 False \n",
"4 False \n",
"\n",
" has_protection_service_business_type has_resolution_incident \n",
"0 True False \n",
"1 True False \n",
"2 True False \n",
"3 True False \n",
"4 True False \n",
"\n",
"[5 rows x 64 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_extraction.head()"
]
},
{
"cell_type": "markdown",
"id": "e9a9da26",
"metadata": {},
"source": [
"## Exploratory Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f4545e95",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset size: 21,307 rows and 63 columns\n"
]
}
],
"source": [
"# Copy dataset to make changes and drop id_booking column\n",
"df = df_extraction.copy().drop(columns=['id_booking'])\n",
"\n",
"# Check size of the dataset\n",
"print(f\"Dataset size: {df.shape[0]:,} rows and {df.shape[1]:,} columns\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "de574969",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>days_from_booking_creation_to_check_in</th>\n",
" <th>number_of_nights</th>\n",
" <th>host_town</th>\n",
" <th>host_country</th>\n",
" <th>host_postcode</th>\n",
" <th>host_age</th>\n",
" <th>host_months_with_truvi</th>\n",
" <th>host_account_type</th>\n",
" <th>host_active_pms_list</th>\n",
" <th>number_of_listings_of_host</th>\n",
" <th>number_of_previous_incidents_of_host</th>\n",
" <th>number_of_previous_payouts_of_host</th>\n",
" <th>guest_town</th>\n",
" <th>guest_country</th>\n",
" <th>guest_postcode</th>\n",
" <th>guest_age</th>\n",
" <th>number_of_previous_bookings_of_guest</th>\n",
" <th>number_of_previous_incidents_of_guest</th>\n",
" <th>has_guest_previously_booked_same_listing</th>\n",
" <th>listing_address</th>\n",
" <th>listing_town</th>\n",
" <th>listing_country</th>\n",
" <th>listing_postcode</th>\n",
" <th>listing_number_of_bedrooms</th>\n",
" <th>listing_number_of_bathrooms</th>\n",
" <th>listing_description</th>\n",
" <th>previous_bookings_in_listing_count</th>\n",
" <th>number_of_previous_incidents_in_listing</th>\n",
" <th>number_of_previous_payouts_in_listing</th>\n",
" <th>days_to_start_verification</th>\n",
" <th>days_to_complete_verification</th>\n",
" <th>screening_status</th>\n",
" <th>government_id_status</th>\n",
" <th>contract_status</th>\n",
" <th>selfie_confidence_score_status</th>\n",
" <th>payment_validation_status</th>\n",
" <th>first_name_status</th>\n",
" <th>date_of_birth_status</th>\n",
" <th>last_name_status</th>\n",
" <th>autohost_partner_status</th>\n",
" <th>criminal_record_status</th>\n",
" <th>guest_csat_score</th>\n",
" <th>guest_csat_comments</th>\n",
" <th>guest_has_email</th>\n",
" <th>guest_has_phone_number</th>\n",
" <th>is_guest_from_listing_town</th>\n",
" <th>is_guest_from_listing_country</th>\n",
" <th>is_guest_from_listing_postcode</th>\n",
" <th>is_host_from_listing_town</th>\n",
" <th>is_host_from_listing_country</th>\n",
" <th>is_host_from_listing_postcode</th>\n",
" <th>has_completed_verification</th>\n",
" <th>number_of_applied_services</th>\n",
" <th>number_of_applied_upgraded_services</th>\n",
" <th>number_of_applied_billable_services</th>\n",
" <th>booking_days_to_check_in</th>\n",
" <th>booking_number_of_nights</th>\n",
" <th>has_verification_request</th>\n",
" <th>has_billable_services</th>\n",
" <th>has_upgraded_screening_service_business_type</th>\n",
" <th>has_deposit_management_service_business_type</th>\n",
" <th>has_protection_service_business_type</th>\n",
" <th>has_resolution_incident</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>26.0</td>\n",
" <td>4.0</td>\n",
" <td>Madison CT</td>\n",
" <td>United States</td>\n",
" <td>06443</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>Host</td>\n",
" <td>Hostaway</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1032</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>389 Mountain View Dr, Jeffersonville, VT 05464...</td>\n",
" <td>Cambridge</td>\n",
" <td>United States</td>\n",
" <td>05464</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>Mountain Life Retreat at Smuggler's Notch Resort</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>87</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>17.0</td>\n",
" <td>3.0</td>\n",
" <td>Madison CT</td>\n",
" <td>United States</td>\n",
" <td>06443</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>Host</td>\n",
" <td>Hostaway</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1900</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>389 Mountain View Dr, Jeffersonville, VT 05464...</td>\n",
" <td>Cambridge</td>\n",
" <td>United States</td>\n",
" <td>05464</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>Mountain Life Retreat at Smuggler's Notch Resort</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>109</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20.0</td>\n",
" <td>7.0</td>\n",
" <td>London</td>\n",
" <td>United Kingdom</td>\n",
" <td>N16 6DD</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>PMC - Property Management Company</td>\n",
" <td>Hostify</td>\n",
" <td>467</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>610</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>Tudor Grange Hotel, 31 Gervis Road</td>\n",
" <td>Dorset</td>\n",
" <td>United Kingdom</td>\n",
" <td>BH1 3EE</td>\n",
" <td>12.0</td>\n",
" <td>12.0</td>\n",
" <td>Mansion by the Sea, 12BR/12BA, Perfect for Events</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>50</td>\n",
" <td>7</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>15.0</td>\n",
" <td>3.0</td>\n",
" <td>London</td>\n",
" <td>United Kingdom</td>\n",
" <td>N16 6DD</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>PMC - Property Management Company</td>\n",
" <td>Hostify</td>\n",
" <td>467</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>136</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>Tudor Grange Hotel, 31 Gervis Road</td>\n",
" <td>Dorset</td>\n",
" <td>United Kingdom</td>\n",
" <td>BH1 3EE</td>\n",
" <td>12.0</td>\n",
" <td>12.0</td>\n",
" <td>Mansion by the Sea, 12BR/12BA, Perfect for Events</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>London</td>\n",
" <td>United Kingdom</td>\n",
" <td>N16 6DD</td>\n",
" <td>125.0</td>\n",
" <td>8.0</td>\n",
" <td>PMC - Property Management Company</td>\n",
" <td>Hostify</td>\n",
" <td>467</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>73</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>Aird House, 15 Wellesley Ct, Rockingham Street</td>\n",
" <td>Greater London</td>\n",
" <td>United Kingdom</td>\n",
" <td>SE1 6PD</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>Your London Home: 2BR Flat with Modern Amenities</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" days_from_booking_creation_to_check_in number_of_nights host_town \\\n",
"0 26.0 4.0 Madison CT \n",
"1 17.0 3.0 Madison CT \n",
"2 20.0 7.0 London \n",
"3 15.0 3.0 London \n",
"4 8.0 5.0 London \n",
"\n",
" host_country host_postcode host_age host_months_with_truvi \\\n",
"0 United States 06443 125.0 8.0 \n",
"1 United States 06443 125.0 8.0 \n",
"2 United Kingdom N16 6DD 125.0 8.0 \n",
"3 United Kingdom N16 6DD 125.0 8.0 \n",
"4 United Kingdom N16 6DD 125.0 8.0 \n",
"\n",
" host_account_type host_active_pms_list \\\n",
"0 Host Hostaway \n",
"1 Host Hostaway \n",
"2 PMC - Property Management Company Hostify \n",
"3 PMC - Property Management Company Hostify \n",
"4 PMC - Property Management Company Hostify \n",
"\n",
" number_of_listings_of_host number_of_previous_incidents_of_host \\\n",
"0 2 0 \n",
"1 2 0 \n",
"2 467 0 \n",
"3 467 0 \n",
"4 467 0 \n",
"\n",
" number_of_previous_payouts_of_host guest_town guest_country guest_postcode \\\n",
"0 0 NaN NaN NaN \n",
"1 0 NaN NaN NaN \n",
"2 0 NaN NaN NaN \n",
"3 0 NaN NaN NaN \n",
"4 0 NaN NaN NaN \n",
"\n",
" guest_age number_of_previous_bookings_of_guest \\\n",
"0 NaN 1032 \n",
"1 NaN 1900 \n",
"2 NaN 610 \n",
"3 NaN 136 \n",
"4 NaN 73 \n",
"\n",
" number_of_previous_incidents_of_guest \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" has_guest_previously_booked_same_listing \\\n",
"0 True \n",
"1 True \n",
"2 True \n",
"3 True \n",
"4 False \n",
"\n",
" listing_address listing_town \\\n",
"0 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n",
"1 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n",
"2 Tudor Grange Hotel, 31 Gervis Road Dorset \n",
"3 Tudor Grange Hotel, 31 Gervis Road Dorset \n",
"4 Aird House, 15 Wellesley Ct, Rockingham Street Greater London \n",
"\n",
" listing_country listing_postcode listing_number_of_bedrooms \\\n",
"0 United States 05464 2.0 \n",
"1 United States 05464 2.0 \n",
"2 United Kingdom BH1 3EE 12.0 \n",
"3 United Kingdom BH1 3EE 12.0 \n",
"4 United Kingdom SE1 6PD 2.0 \n",
"\n",
" listing_number_of_bathrooms \\\n",
"0 2.0 \n",
"1 2.0 \n",
"2 12.0 \n",
"3 12.0 \n",
"4 1.0 \n",
"\n",
" listing_description \\\n",
"0 Mountain Life Retreat at Smuggler's Notch Resort \n",
"1 Mountain Life Retreat at Smuggler's Notch Resort \n",
"2 Mansion by the Sea, 12BR/12BA, Perfect for Events \n",
"3 Mansion by the Sea, 12BR/12BA, Perfect for Events \n",
"4 Your London Home: 2BR Flat with Modern Amenities \n",
"\n",
" previous_bookings_in_listing_count \\\n",
"0 3 \n",
"1 5 \n",
"2 5 \n",
"3 2 \n",
"4 0 \n",
"\n",
" number_of_previous_incidents_in_listing \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" number_of_previous_payouts_in_listing days_to_start_verification \\\n",
"0 0 0.0 \n",
"1 0 0.0 \n",
"2 0 0.0 \n",
"3 0 0.0 \n",
"4 0 0.0 \n",
"\n",
" days_to_complete_verification screening_status government_id_status \\\n",
"0 0.0 NaN NaN \n",
"1 0.0 NaN NaN \n",
"2 0.0 NaN NaN \n",
"3 0.0 NaN NaN \n",
"4 0.0 NaN NaN \n",
"\n",
" contract_status selfie_confidence_score_status payment_validation_status \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"\n",
" first_name_status date_of_birth_status last_name_status \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"\n",
" autohost_partner_status criminal_record_status guest_csat_score \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"\n",
" guest_csat_comments guest_has_email guest_has_phone_number \\\n",
"0 NaN False False \n",
"1 NaN False False \n",
"2 NaN False False \n",
"3 NaN False False \n",
"4 NaN False False \n",
"\n",
" is_guest_from_listing_town is_guest_from_listing_country \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
" is_guest_from_listing_postcode is_host_from_listing_town \\\n",
"0 NaN False \n",
"1 NaN False \n",
"2 NaN False \n",
"3 NaN False \n",
"4 NaN False \n",
"\n",
" is_host_from_listing_country is_host_from_listing_postcode \\\n",
"0 True False \n",
"1 True False \n",
"2 True False \n",
"3 True False \n",
"4 True False \n",
"\n",
" has_completed_verification number_of_applied_services \\\n",
"0 False 3 \n",
"1 False 3 \n",
"2 False 2 \n",
"3 False 2 \n",
"4 False 2 \n",
"\n",
" number_of_applied_upgraded_services number_of_applied_billable_services \\\n",
"0 2 2 \n",
"1 2 2 \n",
"2 1 1 \n",
"3 1 1 \n",
"4 1 1 \n",
"\n",
" booking_days_to_check_in booking_number_of_nights \\\n",
"0 87 4 \n",
"1 109 3 \n",
"2 50 7 \n",
"3 15 3 \n",
"4 8 5 \n",
"\n",
" has_verification_request has_billable_services \\\n",
"0 False True \n",
"1 False True \n",
"2 False True \n",
"3 False True \n",
"4 False True \n",
"\n",
" has_upgraded_screening_service_business_type \\\n",
"0 False \n",
"1 False \n",
"2 False \n",
"3 False \n",
"4 False \n",
"\n",
" has_deposit_management_service_business_type \\\n",
"0 True \n",
"1 True \n",
"2 False \n",
"3 False \n",
"4 False \n",
"\n",
" has_protection_service_business_type has_resolution_incident \n",
"0 True False \n",
"1 True False \n",
"2 True False \n",
"3 True False \n",
"4 True False "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove columns limit to display all columns and rows\n",
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.max_rows', None)\n",
"\n",
"# Preview of the dataset\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "de4c6753",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 21307 entries, 0 to 21306\n",
"Data columns (total 63 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 days_from_booking_creation_to_check_in 21307 non-null float64\n",
" 1 number_of_nights 21307 non-null float64\n",
" 2 host_town 21281 non-null object \n",
" 3 host_country 21300 non-null object \n",
" 4 host_postcode 15800 non-null object \n",
" 5 host_age 21307 non-null float64\n",
" 6 host_months_with_truvi 21307 non-null float64\n",
" 7 host_account_type 17831 non-null object \n",
" 8 host_active_pms_list 20363 non-null object \n",
" 9 number_of_listings_of_host 21307 non-null int64 \n",
" 10 number_of_previous_incidents_of_host 21307 non-null int64 \n",
" 11 number_of_previous_payouts_of_host 21307 non-null int64 \n",
" 12 guest_town 11676 non-null object \n",
" 13 guest_country 11677 non-null object \n",
" 14 guest_postcode 11676 non-null object \n",
" 15 guest_age 11677 non-null float64\n",
" 16 number_of_previous_bookings_of_guest 21307 non-null int64 \n",
" 17 number_of_previous_incidents_of_guest 21307 non-null int64 \n",
" 18 has_guest_previously_booked_same_listing 21307 non-null bool \n",
" 19 listing_address 21307 non-null object \n",
" 20 listing_town 21307 non-null object \n",
" 21 listing_country 21307 non-null object \n",
" 22 listing_postcode 21307 non-null object \n",
" 23 listing_number_of_bedrooms 21185 non-null float64\n",
" 24 listing_number_of_bathrooms 21185 non-null float64\n",
" 25 listing_description 21294 non-null object \n",
" 26 previous_bookings_in_listing_count 21307 non-null int64 \n",
" 27 number_of_previous_incidents_in_listing 21307 non-null int64 \n",
" 28 number_of_previous_payouts_in_listing 21307 non-null int64 \n",
" 29 days_to_start_verification 20084 non-null float64\n",
" 30 days_to_complete_verification 18500 non-null float64\n",
" 31 screening_status 9332 non-null float64\n",
" 32 government_id_status 8082 non-null float64\n",
" 33 contract_status 5856 non-null float64\n",
" 34 selfie_confidence_score_status 6622 non-null float64\n",
" 35 payment_validation_status 8047 non-null float64\n",
" 36 first_name_status 4810 non-null float64\n",
" 37 date_of_birth_status 4810 non-null float64\n",
" 38 last_name_status 4810 non-null float64\n",
" 39 autohost_partner_status 0 non-null float64\n",
" 40 criminal_record_status 2075 non-null float64\n",
" 41 guest_csat_score 3221 non-null float64\n",
" 42 guest_csat_comments 454 non-null object \n",
" 43 guest_has_email 21307 non-null bool \n",
" 44 guest_has_phone_number 21307 non-null bool \n",
" 45 is_guest_from_listing_town 11677 non-null object \n",
" 46 is_guest_from_listing_country 11677 non-null object \n",
" 47 is_guest_from_listing_postcode 11677 non-null object \n",
" 48 is_host_from_listing_town 21307 non-null bool \n",
" 49 is_host_from_listing_country 21300 non-null object \n",
" 50 is_host_from_listing_postcode 18102 non-null object \n",
" 51 has_completed_verification 21307 non-null bool \n",
" 52 number_of_applied_services 21307 non-null int64 \n",
" 53 number_of_applied_upgraded_services 21307 non-null int64 \n",
" 54 number_of_applied_billable_services 21307 non-null int64 \n",
" 55 booking_days_to_check_in 21307 non-null int64 \n",
" 56 booking_number_of_nights 21307 non-null int64 \n",
" 57 has_verification_request 21307 non-null bool \n",
" 58 has_billable_services 21307 non-null bool \n",
" 59 has_upgraded_screening_service_business_type 21307 non-null bool \n",
" 60 has_deposit_management_service_business_type 21307 non-null bool \n",
" 61 has_protection_service_business_type 21307 non-null bool \n",
" 62 has_resolution_incident 21307 non-null bool \n",
"dtypes: bool(11), float64(20), int64(13), object(19)\n",
"memory usage: 8.7+ MB\n"
]
}
],
"source": [
"# View summary of dataset\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9c79c06a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing Values (%):\n",
"autohost_partner_status 100.000000\n",
"guest_csat_comments 97.869245\n",
"criminal_record_status 90.261416\n",
"guest_csat_score 84.882902\n",
"date_of_birth_status 77.425259\n",
"last_name_status 77.425259\n",
"first_name_status 77.425259\n",
"contract_status 72.516075\n",
"selfie_confidence_score_status 68.921012\n",
"payment_validation_status 62.233069\n",
"government_id_status 62.068804\n",
"screening_status 56.202187\n",
"guest_postcode 45.201108\n",
"guest_town 45.201108\n",
"guest_country 45.196414\n",
"is_guest_from_listing_country 45.196414\n",
"is_guest_from_listing_postcode 45.196414\n",
"guest_age 45.196414\n",
"is_guest_from_listing_town 45.196414\n",
"host_postcode 25.845966\n",
"host_account_type 16.313887\n",
"is_host_from_listing_postcode 15.042005\n",
"days_to_complete_verification 13.174074\n",
"days_to_start_verification 5.739898\n",
"host_active_pms_list 4.430469\n",
"listing_number_of_bedrooms 0.572582\n",
"listing_number_of_bathrooms 0.572582\n",
"host_town 0.122026\n",
"listing_description 0.061013\n",
"host_country 0.032853\n",
"is_host_from_listing_country 0.032853\n",
"dtype: float64\n"
]
}
],
"source": [
"# View percentage of missing values\n",
"missing_values = df.isnull().mean() * 100\n",
"missing_values = missing_values[missing_values > 0].sort_values(ascending=False)\n",
"print(\"Missing Values (%):\")\n",
"print(missing_values)"
]
},
{
"cell_type": "markdown",
"id": "1837c541",
"metadata": {},
"source": [
"Despite the small amount of data with on CSAT, I want to check if there might be any interesting correlation with the incidents."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "6e89712c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"guest_csat_score\n",
"1.0 0.010695\n",
"2.0 0.013761\n",
"3.0 0.018293\n",
"4.0 0.013105\n",
"5.0 0.022619\n",
"Name: has_resolution_incident, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('guest_csat_score')['has_resolution_incident'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ce9ed8a0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correlation: 0.02\n"
]
}
],
"source": [
"correlation = df['guest_csat_score'].corr(df['has_resolution_incident'])\n",
"print(f\"Correlation: {correlation:.2f}\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8ac447bb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dropping columns with more than 50% missing values: ['autohost_partner_status', 'guest_csat_comments', 'criminal_record_status', 'guest_csat_score', 'date_of_birth_status', 'last_name_status', 'first_name_status', 'contract_status', 'selfie_confidence_score_status', 'payment_validation_status', 'government_id_status', 'screening_status']\n"
]
}
],
"source": [
"# Remove columns with more than 50% missing values\n",
"threshold = 50\n",
"columns_to_drop = missing_values[missing_values > threshold].index\n",
"print(f\"Dropping columns with more than {threshold}% missing values: {columns_to_drop.tolist()}\")\n",
"df.drop(columns=columns_to_drop, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "20bd5c86",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 18 categorical variables\n",
"\n",
"The categorical variables are: ['host_town', 'host_country', 'host_postcode', 'host_account_type', 'host_active_pms_list', 'guest_town', 'guest_country', 'guest_postcode', 'listing_address', 'listing_town', 'listing_country', 'listing_postcode', 'listing_description', 'is_guest_from_listing_town', 'is_guest_from_listing_country', 'is_guest_from_listing_postcode', 'is_host_from_listing_country', 'is_host_from_listing_postcode']\n"
]
}
],
"source": [
"# Find categorical variables\n",
"categorical = df.select_dtypes(include=['object']).columns.tolist()\n",
"print(f'There are {len(categorical)} categorical variables\\n')\n",
"print('The categorical variables are:', categorical)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "67ddd437",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>host_town</th>\n",
" <th>host_country</th>\n",
" <th>host_postcode</th>\n",
" <th>host_account_type</th>\n",
" <th>host_active_pms_list</th>\n",
" <th>guest_town</th>\n",
" <th>guest_country</th>\n",
" <th>guest_postcode</th>\n",
" <th>listing_address</th>\n",
" <th>listing_town</th>\n",
" <th>listing_country</th>\n",
" <th>listing_postcode</th>\n",
" <th>listing_description</th>\n",
" <th>is_guest_from_listing_town</th>\n",
" <th>is_guest_from_listing_country</th>\n",
" <th>is_guest_from_listing_postcode</th>\n",
" <th>is_host_from_listing_country</th>\n",
" <th>is_host_from_listing_postcode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Madison CT</td>\n",
" <td>United States</td>\n",
" <td>06443</td>\n",
" <td>Host</td>\n",
" <td>Hostaway</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>389 Mountain View Dr, Jeffersonville, VT 05464...</td>\n",
" <td>Cambridge</td>\n",
" <td>United States</td>\n",
" <td>05464</td>\n",
" <td>Mountain Life Retreat at Smuggler's Notch Resort</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Madison CT</td>\n",
" <td>United States</td>\n",
" <td>06443</td>\n",
" <td>Host</td>\n",
" <td>Hostaway</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>389 Mountain View Dr, Jeffersonville, VT 05464...</td>\n",
" <td>Cambridge</td>\n",
" <td>United States</td>\n",
" <td>05464</td>\n",
" <td>Mountain Life Retreat at Smuggler's Notch Resort</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>London</td>\n",
" <td>United Kingdom</td>\n",
" <td>N16 6DD</td>\n",
" <td>PMC - Property Management Company</td>\n",
" <td>Hostify</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Tudor Grange Hotel, 31 Gervis Road</td>\n",
" <td>Dorset</td>\n",
" <td>United Kingdom</td>\n",
" <td>BH1 3EE</td>\n",
" <td>Mansion by the Sea, 12BR/12BA, Perfect for Events</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>London</td>\n",
" <td>United Kingdom</td>\n",
" <td>N16 6DD</td>\n",
" <td>PMC - Property Management Company</td>\n",
" <td>Hostify</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Tudor Grange Hotel, 31 Gervis Road</td>\n",
" <td>Dorset</td>\n",
" <td>United Kingdom</td>\n",
" <td>BH1 3EE</td>\n",
" <td>Mansion by the Sea, 12BR/12BA, Perfect for Events</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>London</td>\n",
" <td>United Kingdom</td>\n",
" <td>N16 6DD</td>\n",
" <td>PMC - Property Management Company</td>\n",
" <td>Hostify</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Aird House, 15 Wellesley Ct, Rockingham Street</td>\n",
" <td>Greater London</td>\n",
" <td>United Kingdom</td>\n",
" <td>SE1 6PD</td>\n",
" <td>Your London Home: 2BR Flat with Modern Amenities</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" host_town host_country host_postcode \\\n",
"0 Madison CT United States 06443 \n",
"1 Madison CT United States 06443 \n",
"2 London United Kingdom N16 6DD \n",
"3 London United Kingdom N16 6DD \n",
"4 London United Kingdom N16 6DD \n",
"\n",
" host_account_type host_active_pms_list guest_town \\\n",
"0 Host Hostaway NaN \n",
"1 Host Hostaway NaN \n",
"2 PMC - Property Management Company Hostify NaN \n",
"3 PMC - Property Management Company Hostify NaN \n",
"4 PMC - Property Management Company Hostify NaN \n",
"\n",
" guest_country guest_postcode \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
" listing_address listing_town \\\n",
"0 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n",
"1 389 Mountain View Dr, Jeffersonville, VT 05464... Cambridge \n",
"2 Tudor Grange Hotel, 31 Gervis Road Dorset \n",
"3 Tudor Grange Hotel, 31 Gervis Road Dorset \n",
"4 Aird House, 15 Wellesley Ct, Rockingham Street Greater London \n",
"\n",
" listing_country listing_postcode \\\n",
"0 United States 05464 \n",
"1 United States 05464 \n",
"2 United Kingdom BH1 3EE \n",
"3 United Kingdom BH1 3EE \n",
"4 United Kingdom SE1 6PD \n",
"\n",
" listing_description \\\n",
"0 Mountain Life Retreat at Smuggler's Notch Resort \n",
"1 Mountain Life Retreat at Smuggler's Notch Resort \n",
"2 Mansion by the Sea, 12BR/12BA, Perfect for Events \n",
"3 Mansion by the Sea, 12BR/12BA, Perfect for Events \n",
"4 Your London Home: 2BR Flat with Modern Amenities \n",
"\n",
" is_guest_from_listing_town is_guest_from_listing_country \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
" is_guest_from_listing_postcode is_host_from_listing_country \\\n",
"0 NaN True \n",
"1 NaN True \n",
"2 NaN True \n",
"3 NaN True \n",
"4 NaN True \n",
"\n",
" is_host_from_listing_postcode \n",
"0 False \n",
"1 False \n",
"2 False \n",
"3 False \n",
"4 False "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# view the categorical variables\n",
"df[categorical].head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "841347ea",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"host_town 26\n",
"host_country 7\n",
"host_postcode 5507\n",
"host_account_type 3476\n",
"host_active_pms_list 944\n",
"guest_town 9631\n",
"guest_country 9630\n",
"guest_postcode 9631\n",
"listing_address 0\n",
"listing_town 0\n",
"listing_country 0\n",
"listing_postcode 0\n",
"listing_description 13\n",
"is_guest_from_listing_town 9630\n",
"is_guest_from_listing_country 9630\n",
"is_guest_from_listing_postcode 9630\n",
"is_host_from_listing_country 7\n",
"is_host_from_listing_postcode 3205\n",
"dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check missing values in categorical variables\n",
"df[categorical].isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "a58cd17e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_48568/2855830200.py:2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
" df['is_guest_from_listing_town'] = df['is_guest_from_listing_town'].fillna(False)\n",
"/tmp/ipykernel_48568/2855830200.py:3: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
" df['is_guest_from_listing_country'] = df['is_guest_from_listing_country'].fillna(False)\n",
"/tmp/ipykernel_48568/2855830200.py:4: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
" df['is_guest_from_listing_postcode'] = df['is_guest_from_listing_postcode'].fillna(False)\n",
"/tmp/ipykernel_48568/2855830200.py:6: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
" df['is_host_from_listing_country'] = df['is_host_from_listing_country'].fillna(False)\n",
"/tmp/ipykernel_48568/2855830200.py:7: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
" df['is_host_from_listing_postcode'] = df['is_host_from_listing_postcode'].fillna(False)\n"
]
}
],
"source": [
"# For all missing values in listing location with both host and guest, we will fill with False\n",
"df['is_guest_from_listing_town'] = df['is_guest_from_listing_town'].fillna(False)\n",
"df['is_guest_from_listing_country'] = df['is_guest_from_listing_country'].fillna(False)\n",
"df['is_guest_from_listing_postcode'] = df['is_guest_from_listing_postcode'].fillna(False)\n",
"df['is_host_from_listing_town'] = df['is_host_from_listing_town'].fillna(False)\n",
"df['is_host_from_listing_country'] = df['is_host_from_listing_country'].fillna(False)\n",
"df['is_host_from_listing_postcode'] = df['is_host_from_listing_postcode'].fillna(False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e5aefb50",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"host_town 26\n",
"host_country 7\n",
"host_postcode 5507\n",
"host_account_type 3476\n",
"host_active_pms_list 944\n",
"guest_town 9631\n",
"guest_country 9630\n",
"guest_postcode 9631\n",
"listing_address 0\n",
"listing_town 0\n",
"listing_country 0\n",
"listing_postcode 0\n",
"listing_description 13\n",
"is_guest_from_listing_town 0\n",
"is_guest_from_listing_country 0\n",
"is_guest_from_listing_postcode 0\n",
"is_host_from_listing_country 0\n",
"is_host_from_listing_postcode 0\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking again missing values in categorical variables\n",
"df[categorical].isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "292eaad2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Unique values in 'host_account_type':\n",
"host_account_type\n",
"PMC - Property Management Company 12719\n",
"Host 5112\n",
"Name: count, dtype: int64 \n",
"\n",
"Unique values in 'host_active_pms_list':\n",
"host_active_pms_list\n",
"Hostify 6468\n",
"Hostaway 3675\n",
"Guesty 3108\n",
"Hospitable 2739\n",
"Hostfully 1905\n",
"Lodgify 1341\n",
"OwnerRez 649\n",
"Avantio 248\n",
"TrackHs 142\n",
"Uplisting 61\n",
"Hospitable Connect 15\n",
"Smoobu 12\n",
"Name: count, dtype: int64 \n",
"\n",
"Unique values in 'host_country':\n",
"host_country\n",
"United States 10962\n",
"United Kingdom 6707\n",
"Canada 2007\n",
"Australia 305\n",
"Mexico 273\n",
"New Zealand 154\n",
"Sweden 122\n",
"Norway 117\n",
"Bulgaria 117\n",
"Portugal 87\n",
"South Africa 78\n",
"Costa Rica 75\n",
"Puerto Rico 50\n",
"Belgium 50\n",
"Italy 35\n",
"Barbados 34\n",
"Spain 31\n",
"France 26\n",
"Jamaica 20\n",
"Egypt 19\n",
"Switzerland 10\n",
"Isle of Man 8\n",
"Bahamas 3\n",
"Guernsey 3\n",
"United Arab Emirates 2\n",
"Colombia 2\n",
"Germany 1\n",
"Greece 1\n",
"Hungary 1\n",
"Name: count, dtype: int64 \n",
"\n",
"Unique values in 'guest_country':\n",
"guest_country\n",
"United States 7409\n",
"Canada 1458\n",
"United Kingdom 1175\n",
"Australia 287\n",
"Colombia 151\n",
"Mexico 134\n",
"Germany 100\n",
"Ireland 77\n",
"New Zealand 70\n",
"France 56\n",
"Spain 53\n",
"Costa Rica 43\n",
"Netherlands 37\n",
"Brazil 36\n",
"Switzerland 34\n",
"Puerto Rico 31\n",
"Italy 29\n",
"Argentina 23\n",
"Singapore 23\n",
"China 21\n",
"Belgium 20\n",
"Ecuador 20\n",
"India 20\n",
"United Arab Emirates 20\n",
"Panama 19\n",
"Poland 17\n",
"Dominican Republic 15\n",
"Israel 14\n",
"Saudi Arabia 13\n",
"South Africa 12\n",
"Romania 11\n",
"Malaysia 11\n",
"El Salvador 10\n",
"Chile 9\n",
"Norway 9\n",
"Japan 9\n",
"Portugal 9\n",
"Sweden 8\n",
"Hong Kong 8\n",
"Austria 8\n",
"South Korea 8\n",
"United States Minor Outlying Islands 8\n",
"Finland 8\n",
"Philippines 7\n",
"Czech Republic 7\n",
"Guatemala 7\n",
"Hungary 6\n",
"Venezuela 6\n",
"Denmark 6\n",
"Honduras 6\n",
"Jamaica 5\n",
"Thailand 5\n",
"Peru 5\n",
"Taiwan 5\n",
"Russian Federation 5\n",
"French Polynesia 4\n",
"Turkey 4\n",
"Kazakhstan 4\n",
"Curacao 4\n",
"Martinique 3\n",
"Cayman Islands 3\n",
"Saint Pierre and Miquelon 3\n",
"Slovenia 3\n",
"Estonia 3\n",
"Iceland 3\n",
"Georgia 3\n",
"Indonesia 2\n",
"Qatar 2\n",
"Greece 2\n",
"Egypt 2\n",
"Latvia 2\n",
"Pakistan 2\n",
"Barbados 2\n",
"Bolivia 2\n",
"Aruba 2\n",
"Malta 2\n",
"Suriname 1\n",
"Lebanon 1\n",
"Nauru 1\n",
"Fiji 1\n",
"Cook Islands 1\n",
"Bahamas 1\n",
"Albania 1\n",
"Uruguay 1\n",
"Jersey 1\n",
"Croatia 1\n",
"Bulgaria 1\n",
"Belize 1\n",
"Nicaragua 1\n",
"DR Congo 1\n",
"Kuwait 1\n",
"Niger 1\n",
"Cyprus 1\n",
"Name: count, dtype: int64 \n",
"\n",
"Unique values in 'listing_country':\n",
"listing_country\n",
"United States 10067\n",
"United Kingdom 6574\n",
"Canada 1870\n",
"Colombia 599\n",
"Australia 305\n",
"Mexico 303\n",
"Ireland 168\n",
"New Zealand 153\n",
"Virgin Islands, U.s. 130\n",
"Bahamas 130\n",
"Norway 125\n",
"Sweden 122\n",
"Bulgaria 117\n",
"Costa Rica 108\n",
"Portugal 87\n",
"South Africa 83\n",
"Puerto Rico 50\n",
"Belgium 48\n",
"France 46\n",
"Italy 44\n",
"Spain 36\n",
"Barbados 34\n",
"Morocco 25\n",
"Jamaica 20\n",
"Egypt 19\n",
"Saint Lucia 10\n",
"Germany 10\n",
"Sint Maarten 9\n",
"Isle of Man 8\n",
"United Arab Emirates 2\n",
"Lithuania 2\n",
"Antigua and Barbuda 1\n",
"Greece 1\n",
"Hungary 1\n",
"Name: count, dtype: int64 \n",
"\n"
]
}
],
"source": [
"# Check unique values in host_account_type, host_active_pms_list, host_country and guest_country with their counts\n",
"print(\"Unique values in 'host_account_type':\")\n",
"print(df['host_account_type'].value_counts(), \"\\n\")\n",
"print(\"Unique values in 'host_active_pms_list':\")\n",
"print(df['host_active_pms_list'].value_counts(), \"\\n\")\n",
"print(\"Unique values in 'host_country':\")\n",
"print(df['host_country'].value_counts(), \"\\n\")\n",
"print(\"Unique values in 'guest_country':\")\n",
"print(df['guest_country'].value_counts(), \"\\n\")\n",
"print(\"Unique values in 'listing_country':\")\n",
"print(df['listing_country'].value_counts(), \"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7289f9fd",
"metadata": {},
"outputs": [],
"source": [
"# Due to the many unique values in host_country, guest_country and listing_country, we will only keep the top 10 most frequent values and set the rest to 'Other'\n",
"top_host_countries = df['host_country'].value_counts().nlargest(10).index\n",
"top_guest_countries = df['guest_country'].value_counts().nlargest(10).index\n",
"top_listing_countries = df['listing_country'].value_counts().nlargest(10).index\n",
"\n",
"df['host_country'] = df['host_country'].where(df['host_country'].isin(top_host_countries), 'Other')\n",
"df['guest_country'] = df['guest_country'].where(df['guest_country'].isin(top_guest_countries), 'Other')\n",
"df['listing_country'] = df['listing_country'].where(df['listing_country'].isin(top_listing_countries), 'Other')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "7348866c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"New columns created from one-hot encoding: ['host_account_type_Host', 'host_account_type_PMC - Property Management Company', 'host_active_pms_list_Avantio', 'host_active_pms_list_Guesty', 'host_active_pms_list_Hospitable', 'host_active_pms_list_Hospitable Connect', 'host_active_pms_list_Hostaway', 'host_active_pms_list_Hostfully', 'host_active_pms_list_Hostify', 'host_active_pms_list_Lodgify', 'host_active_pms_list_OwnerRez', 'host_active_pms_list_Smoobu', 'host_active_pms_list_TrackHs', 'host_active_pms_list_Uplisting', 'host_country_Australia', 'host_country_Bulgaria', 'host_country_Canada', 'host_country_Mexico', 'host_country_New Zealand', 'host_country_Norway', 'host_country_Other', 'host_country_Portugal', 'host_country_Sweden', 'host_country_United Kingdom', 'host_country_United States', 'guest_country_Australia', 'guest_country_Canada', 'guest_country_Colombia', 'guest_country_France', 'guest_country_Germany', 'guest_country_Ireland', 'guest_country_Mexico', 'guest_country_New Zealand', 'guest_country_Other', 'guest_country_United Kingdom', 'guest_country_United States', 'listing_country_Australia', 'listing_country_Bahamas', 'listing_country_Canada', 'listing_country_Colombia', 'listing_country_Ireland', 'listing_country_Mexico', 'listing_country_New Zealand', 'listing_country_Other', 'listing_country_United Kingdom', 'listing_country_United States', 'listing_country_Virgin Islands, U.s.']\n"
]
}
],
"source": [
"# Lets one hot encode host_account_type, host_active_pms_list, host_country, guest_country and listing_country\n",
"df = pd.get_dummies(df, columns=['host_account_type', 'host_active_pms_list', 'host_country', 'guest_country', 'listing_country'], drop_first=False)\n",
"# Check the new columns created\n",
"new_columns = df.columns[df.columns.str.startswith(('host_account_type_', 'host_active_pms_list_', 'host_country', 'guest_country', 'listing_country'))]\n",
"print(f\"New columns created from one-hot encoding: {new_columns.tolist()}\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "b443ccf4",
"metadata": {},
"outputs": [],
"source": [
"# Drop the original categorical columns and the ones we are not going to use like postcodes and towns\n",
"df.drop(columns=['host_postcode', 'guest_postcode', 'listing_postcode', 'listing_town', 'host_town', 'guest_town', 'listing_description', 'listing_address'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "a31ae1fd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 22 numerical variables\n",
"\n",
"The numerical variables are : ['days_from_booking_creation_to_check_in', 'number_of_nights', 'host_age', 'host_months_with_truvi', 'number_of_listings_of_host', 'number_of_previous_incidents_of_host', 'number_of_previous_payouts_of_host', 'guest_age', 'number_of_previous_bookings_of_guest', 'number_of_previous_incidents_of_guest', 'listing_number_of_bedrooms', 'listing_number_of_bathrooms', 'previous_bookings_in_listing_count', 'number_of_previous_incidents_in_listing', 'number_of_previous_payouts_in_listing', 'days_to_start_verification', 'days_to_complete_verification', 'number_of_applied_services', 'number_of_applied_upgraded_services', 'number_of_applied_billable_services', 'booking_days_to_check_in', 'booking_number_of_nights']\n"
]
}
],
"source": [
"# Find numerical variables\n",
"numerical = df.select_dtypes(include=[np.number]).columns.tolist()\n",
"print('There are {} numerical variables\\n'.format(len(numerical)))\n",
"print('The numerical variables are :', numerical)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "cf795d45",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Summary statistics of numerical variables:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>days_from_booking_creation_to_check_in</th>\n",
" <th>number_of_nights</th>\n",
" <th>host_age</th>\n",
" <th>host_months_with_truvi</th>\n",
" <th>number_of_listings_of_host</th>\n",
" <th>number_of_previous_incidents_of_host</th>\n",
" <th>number_of_previous_payouts_of_host</th>\n",
" <th>guest_age</th>\n",
" <th>number_of_previous_bookings_of_guest</th>\n",
" <th>number_of_previous_incidents_of_guest</th>\n",
" <th>listing_number_of_bedrooms</th>\n",
" <th>listing_number_of_bathrooms</th>\n",
" <th>previous_bookings_in_listing_count</th>\n",
" <th>number_of_previous_incidents_in_listing</th>\n",
" <th>number_of_previous_payouts_in_listing</th>\n",
" <th>days_to_start_verification</th>\n",
" <th>days_to_complete_verification</th>\n",
" <th>number_of_applied_services</th>\n",
" <th>number_of_applied_upgraded_services</th>\n",
" <th>number_of_applied_billable_services</th>\n",
" <th>booking_days_to_check_in</th>\n",
" <th>booking_number_of_nights</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>11677.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.0</td>\n",
" <td>21185.000000</td>\n",
" <td>21185.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>20084.000000</td>\n",
" <td>18500.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" <td>21307.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>8.740038</td>\n",
" <td>3.876801</td>\n",
" <td>96.533017</td>\n",
" <td>5.482142</td>\n",
" <td>152.875815</td>\n",
" <td>2.718496</td>\n",
" <td>0.751302</td>\n",
" <td>42.317890</td>\n",
" <td>2175.999812</td>\n",
" <td>0.0</td>\n",
" <td>2.052962</td>\n",
" <td>1.601841</td>\n",
" <td>6.215094</td>\n",
" <td>0.123387</td>\n",
" <td>0.043507</td>\n",
" <td>0.996764</td>\n",
" <td>0.713135</td>\n",
" <td>3.721594</td>\n",
" <td>2.721688</td>\n",
" <td>1.865209</td>\n",
" <td>17.592247</td>\n",
" <td>4.144507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.389242</td>\n",
" <td>3.335615</td>\n",
" <td>43.616341</td>\n",
" <td>2.714314</td>\n",
" <td>179.028829</td>\n",
" <td>5.582857</td>\n",
" <td>2.957053</td>\n",
" <td>13.212509</td>\n",
" <td>3038.837496</td>\n",
" <td>0.0</td>\n",
" <td>1.745281</td>\n",
" <td>1.297739</td>\n",
" <td>6.727896</td>\n",
" <td>0.537464</td>\n",
" <td>0.270994</td>\n",
" <td>3.423303</td>\n",
" <td>2.768474</td>\n",
" <td>1.553612</td>\n",
" <td>1.553629</td>\n",
" <td>0.949857</td>\n",
" <td>23.572901</td>\n",
" <td>4.799364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-20.000000</td>\n",
" <td>0.000000</td>\n",
" <td>19.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>18.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-48.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>39.000000</td>\n",
" <td>4.000000</td>\n",
" <td>9.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>32.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.000000</td>\n",
" <td>3.000000</td>\n",
" <td>125.000000</td>\n",
" <td>5.000000</td>\n",
" <td>72.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>41.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>8.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>15.000000</td>\n",
" <td>4.000000</td>\n",
" <td>125.000000</td>\n",
" <td>8.000000</td>\n",
" <td>247.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>51.000000</td>\n",
" <td>4302.500000</td>\n",
" <td>0.0</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>9.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>4.000000</td>\n",
" <td>3.000000</td>\n",
" <td>24.000000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>30.000000</td>\n",
" <td>30.000000</td>\n",
" <td>125.000000</td>\n",
" <td>11.000000</td>\n",
" <td>467.000000</td>\n",
" <td>85.000000</td>\n",
" <td>62.000000</td>\n",
" <td>89.000000</td>\n",
" <td>9629.000000</td>\n",
" <td>0.0</td>\n",
" <td>15.000000</td>\n",
" <td>17.000000</td>\n",
" <td>41.000000</td>\n",
" <td>9.000000</td>\n",
" <td>6.000000</td>\n",
" <td>30.000000</td>\n",
" <td>30.000000</td>\n",
" <td>8.000000</td>\n",
" <td>7.000000</td>\n",
" <td>5.000000</td>\n",
" <td>218.000000</td>\n",
" <td>116.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" days_from_booking_creation_to_check_in number_of_nights host_age \\\n",
"count 21307.000000 21307.000000 21307.000000 \n",
"mean 8.740038 3.876801 96.533017 \n",
"std 8.389242 3.335615 43.616341 \n",
"min -20.000000 0.000000 19.000000 \n",
"25% 1.000000 2.000000 39.000000 \n",
"50% 6.000000 3.000000 125.000000 \n",
"75% 15.000000 4.000000 125.000000 \n",
"max 30.000000 30.000000 125.000000 \n",
"\n",
" host_months_with_truvi number_of_listings_of_host \\\n",
"count 21307.000000 21307.000000 \n",
"mean 5.482142 152.875815 \n",
"std 2.714314 179.028829 \n",
"min 0.000000 0.000000 \n",
"25% 4.000000 9.000000 \n",
"50% 5.000000 72.000000 \n",
"75% 8.000000 247.000000 \n",
"max 11.000000 467.000000 \n",
"\n",
" number_of_previous_incidents_of_host \\\n",
"count 21307.000000 \n",
"mean 2.718496 \n",
"std 5.582857 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 1.000000 \n",
"75% 3.000000 \n",
"max 85.000000 \n",
"\n",
" number_of_previous_payouts_of_host guest_age \\\n",
"count 21307.000000 11677.000000 \n",
"mean 0.751302 42.317890 \n",
"std 2.957053 13.212509 \n",
"min 0.000000 18.000000 \n",
"25% 0.000000 32.000000 \n",
"50% 0.000000 41.000000 \n",
"75% 1.000000 51.000000 \n",
"max 62.000000 89.000000 \n",
"\n",
" number_of_previous_bookings_of_guest \\\n",
"count 21307.000000 \n",
"mean 2175.999812 \n",
"std 3038.837496 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 4302.500000 \n",
"max 9629.000000 \n",
"\n",
" number_of_previous_incidents_of_guest listing_number_of_bedrooms \\\n",
"count 21307.0 21185.000000 \n",
"mean 0.0 2.052962 \n",
"std 0.0 1.745281 \n",
"min 0.0 0.000000 \n",
"25% 0.0 1.000000 \n",
"50% 0.0 2.000000 \n",
"75% 0.0 3.000000 \n",
"max 0.0 15.000000 \n",
"\n",
" listing_number_of_bathrooms previous_bookings_in_listing_count \\\n",
"count 21185.000000 21307.000000 \n",
"mean 1.601841 6.215094 \n",
"std 1.297739 6.727896 \n",
"min 0.000000 0.000000 \n",
"25% 1.000000 1.000000 \n",
"50% 1.000000 4.000000 \n",
"75% 2.000000 9.000000 \n",
"max 17.000000 41.000000 \n",
"\n",
" number_of_previous_incidents_in_listing \\\n",
"count 21307.000000 \n",
"mean 0.123387 \n",
"std 0.537464 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 9.000000 \n",
"\n",
" number_of_previous_payouts_in_listing days_to_start_verification \\\n",
"count 21307.000000 20084.000000 \n",
"mean 0.043507 0.996764 \n",
"std 0.270994 3.423303 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 6.000000 30.000000 \n",
"\n",
" days_to_complete_verification number_of_applied_services \\\n",
"count 18500.000000 21307.000000 \n",
"mean 0.713135 3.721594 \n",
"std 2.768474 1.553612 \n",
"min 0.000000 2.000000 \n",
"25% 0.000000 2.000000 \n",
"50% 0.000000 4.000000 \n",
"75% 0.000000 5.000000 \n",
"max 30.000000 8.000000 \n",
"\n",
" number_of_applied_upgraded_services \\\n",
"count 21307.000000 \n",
"mean 2.721688 \n",
"std 1.553629 \n",
"min 1.000000 \n",
"25% 1.000000 \n",
"50% 3.000000 \n",
"75% 4.000000 \n",
"max 7.000000 \n",
"\n",
" number_of_applied_billable_services booking_days_to_check_in \\\n",
"count 21307.000000 21307.000000 \n",
"mean 1.865209 17.592247 \n",
"std 0.949857 23.572901 \n",
"min 0.000000 -48.000000 \n",
"25% 1.000000 2.000000 \n",
"50% 2.000000 8.000000 \n",
"75% 3.000000 24.000000 \n",
"max 5.000000 218.000000 \n",
"\n",
" booking_number_of_nights \n",
"count 21307.000000 \n",
"mean 4.144507 \n",
"std 4.799364 \n",
"min 0.000000 \n",
"25% 2.000000 \n",
"50% 3.000000 \n",
"75% 5.000000 \n",
"max 116.000000 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# View summary statistics of numerical variables\n",
"print(\"\\nSummary statistics of numerical variables:\")\n",
"df[numerical].describe()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "2cf714c9",
"metadata": {},
"outputs": [
{
"data": {
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cLf9l/HsHyf/sL/BVjmVl/mkPr+P5lSMlHg8gA1CQPFDe2xf8PSjQdpr+Kg8wR5ecguZnoOzkaOYTzecTkZFWoPkDQOrDqwXKP5mJ/yLp3A7s378fDg4O+baPi4vDx4MGIy1VvXheKdixT67Qwy8//5Rjv3mbylb7qAjty2JMZapeVUyfoYEymqMFCSGEePDggQAgTp8+rTJ+ypQponnz5jnaz5w5U+DV0ZQDBw4cOHAoluHevXullfbKFeZoDhw4cOCg6YE5OqeC5mchmKM5cODAgUPxD2UpR/OK9v9nbm4ObW1txMXFqYyPi4uDlZVVjvbTp0/HpEmTpL+zsrLw5MkTmJmZQSaTFTqOpKQk2Nra4t69e1AqlYWeT0XGPsof+yh/7KP8sY/yV1x9JITAs2fPYGNjU4zRVRzM0ZrDda7461zZ1hfgOnOdC4Y5+u0Kmp+BksvRb6qM23xe2B85sU9yYp/kxD5RVdb6oyzmaBba/59cLoebmxsOHTqEnj17AniV9A8dOgQ/P78c7RUKRY77cJmYmBRbPEqlskxstGUZ+yh/7KP8sY/yxz7KX3H0kbGxcTFFU/EwR2se17niq2zrC3CdKwvm6JJT0PwMlHyOflNl3Obzwv7IiX2SE/skJ/aJqrLUH2UtR7PQ/ppJkyZhyJAhaNasGZo3b45ly5bh+fPn0hPUiYiISDOYo4mIiMoe5mciIqL/sND+mn79+uHff//FjBkzEBsbi8aNG2Pfvn1qP6SCiIiISgZzNBERUdnD/ExERPQfFtrf4Ofn99afuZUGhUKBmTNn5vg5Hf2HfZQ/9lH+2Ef5Yx/lj31UupijSx/XueKrbOsLcJ0ri8q4zpqi6fycG77/qtgfObFPcmKf5MQ+UcX+yJ9MCCE0HQQRERERERERERERUXmlpekAiIiIiIiIiIiIiIjKMxbaiYiIiIiIiIiIiIiKgIV2IiIiIiIiIiIiIqIiYKG9jLhz5w58fHxgb28PfX191KlTBzNnzkRaWppKu4sXL6Jt27bQ09ODra0tFixYoKGINePrr79Gq1atYGBgABMTk1zbREdHw8vLCwYGBrCwsMCUKVOQkZFRuoFq2OrVq1GrVi3o6emhRYsW+PvvvzUdksYcP34c3bt3h42NDWQyGXbu3KkyXQiBGTNmwNraGvr6+vDw8MCNGzc0E6wGzJs3D++88w6qVKkCCwsL9OzZE5GRkSptXr58CV9fX5iZmcHIyAi9e/dGXFychiIufWvXroWrqyuUSiWUSiXc3d2xd+9eaXpl75/KpDIdW/39/SGTyVQGR0dHTYdVrCpbfshvfYcOHZrjPe/cubNmgi0GlTG/qbPOHTp0yPE+jx49WkMRFx1zdOVWGffzgpg/fz5kMhkmTJggjauM/fHgwQN8/PHHMDMzg76+PlxcXBAaGipNr2j5Pj+ZmZn43//+p1J7mjNnDl5/fGNF75PiOAd88uQJvL29oVQqYWJiAh8fHyQnJ5fiWhSvvPokPT0d06ZNg4uLCwwNDWFjY4PBgwfj4cOHKvOoaH1SWCy0lxHXrl1DVlYW1q9fj4iICCxduhTr1q3DF198IbVJSkpCp06dYGdnh7CwMCxcuBD+/v7YsGGDBiMvXWlpaejTpw/GjBmT6/TMzEx4eXkhLS0Np0+fxsaNGxEYGIgZM2aUcqSas3XrVkyaNAkzZ87E+fPn0ahRI3h6eiI+Pl7ToWnE8+fP0ahRI6xevTrX6QsWLMCKFSuwbt06nD17FoaGhvD09MTLly9LOVLNOHbsGHx9fXHmzBkEBwcjPT0dnTp1wvPnz6U2EydOxF9//YVt27bh2LFjePjwIXr16qXBqEtXjRo1MH/+fISFhSE0NBTvvfceevTogYiICADsn8qiMh5bGzRogJiYGGk4efKkpkMqVpUtP+S3vgDQuXNnlff8119/LcUIi1dlzG/qrDMAjBw5UuV9Ls8X7jBHV26VcT9X17lz57B+/Xq4urqqjK9s/fH06VO0bt0aurq62Lt3L65cuYLFixejatWqUpuKlu/z8+2332Lt2rVYtWoVrl69im+//RYLFizAypUrpTYVvU+K4xzQ29sbERERCA4Oxq5du3D8+HGMGjWqtFah2OXVJy9evMD58+fxv//9D+fPn8eOHTsQGRmJDz74QKVdReuTQhNUZi1YsEDY29tLf69Zs0ZUrVpVpKamSuOmTZsmHBwcNBGeRgUEBAhjY+Mc4/fs2SO0tLREbGysNG7t2rVCqVSq9FtF1rx5c+Hr6yv9nZmZKWxsbMS8efM0GFXZAED8/vvv0t9ZWVnCyspKLFy4UBqXkJAgFAqF+PXXXzUQoebFx8cLAOLYsWNCiFf9oaurK7Zt2ya1uXr1qgAgQkJCNBWmxlWtWlV8//337J9KpLIdW2fOnCkaNWqk6TBKTWXLD2+urxBCDBkyRPTo0UMj8ZSGypjf3lxnIYRo3769GD9+vOaCKgXM0ZVXZdzPc/Ps2TNRr149ERwcrLLPV8b+mDZtmmjTps1bp1f0fJ8bLy8vMXz4cJVxvXr1Et7e3kKIytcnhTkHvHLligAgzp07J7XZu3evkMlk4sGDB6UWe0nJ7TzxTX///bcAIO7evSuEqPh9UhC8or0MS0xMhKmpqfR3SEgI2rVrB7lcLo3z9PREZGQknj59qokQy5yQkBC4uLjA0tJSGufp6YmkpCTpypaKLC0tDWFhYfDw8JDGaWlpwcPDAyEhIRqMrGyKiopCbGysSn8ZGxujRYsWlba/EhMTAUA69oSFhSE9PV2ljxwdHVGzZs1K2UeZmZnYsmULnj9/Dnd3d/ZPJVFZj603btyAjY0NateuDW9vb0RHR2s6pFJTWfPD0aNHYWFhAQcHB4wZMwaPHz/WdEjFpjLmtzfXOdumTZtgbm6Ohg0bYvr06Xjx4oUmwit2zNFUGffz3Pj6+sLLy0tlvYHK2R9//vknmjVrhj59+sDCwgJNmjTBd999J02vjPm+VatWOHToEK5fvw4A+Oeff3Dy5El06dIFQOXsk9eps/4hISEwMTFBs2bNpDYeHh7Q0tLC2bNnSz1mTUhMTIRMJpNu6cw++Y+OpgOg3N28eRMrV67EokWLpHGxsbGwt7dXaZddUI6NjVX5+VNlFRsbq1JkB1T7qKJ79OgRMjMzc+2Da9euaSiqsit7m8itvyrD9vKmrKwsTJgwAa1bt0bDhg0BvOojuVye45kIla2PLl26BHd3d7x8+RJGRkb4/fff4ezsjPDwcPZPJVAZj60tWrRAYGAgHBwcEBMTg1mzZqFt27a4fPkyqlSpounwSlxlzA+dO3dGr169YG9vj1u3buGLL75Aly5dEBISAm1tbU2HVySVMb/lts4AMHDgQNjZ2cHGxgYXL17EtGnTEBkZiR07dmgw2qJhjiagcu7nudmyZQvOnz+Pc+fO5ZhWGfvj9u3bWLt2LSZNmoQvvvgC586dw7hx4yCXyzFkyJBKme8///xzJCUlwdHREdra2sjMzMTXX38Nb29vAJXzHOh16qx/bGwsLCwsVKbr6OjA1NS0UvTRy5cvMW3aNAwYMABKpRIA++R1LLSXsM8//xzffvttnm2uXr2q8oCxBw8eoHPnzujTpw9GjhxZ0iFqXGH6iIiKn6+vLy5fvlzh7sNcHBwcHBAeHo7ExERs374dQ4YMwbFjxzQdFlGJyb6qCQBcXV3RokUL2NnZISgoCD4+PhqMjEpK//79pf+7uLjA1dUVderUwdGjR9GxY0cNRlZ0lTG/vW2dX79XqouLC6ytrdGxY0fcunULderUKe0wiwVzNAGVcz9/07179zB+/HgEBwdDT09P0+GUCVlZWWjWrBm++eYbAECTJk1w+fJlrFu3DkOGDNFwdJoRFBSETZs2YfPmzWjQoAHCw8MxYcIE2NjYVNo+IfWlp6ejb9++EEJg7dq1mg6nTGKhvYRNnjwZQ4cOzbNN7dq1pf8/fPgQ7777Llq1apXjIadWVlY5ngie/beVlVXxBKwBBe2jvFhZWeHvv/9WGVcR+khd5ubm0NbWznU7qQzrX1DZfRIXFwdra2tpfFxcHBo3bqyhqDTDz89PemBJjRo1pPFWVlZIS0tDQkKCytUvlW2bksvlqFu3LgDAzc0N586dw/Lly9GvXz/2TyXAYytgYmKC+vXr4+bNm5oOpVQwP7w69zI3N8fNmzfLdaG9Mua3t61zblq0aAHg1a9py2uhnTmaKuN+npuwsDDEx8ejadOm0rjMzEwcP34cq1atwv79+ytVfwCAtbU1nJ2dVcY5OTnht99+A1A58/2UKVPw+eefS1+wu7i44O7du5g3bx6GDBlSKfvkdeqsv5WVFeLj41Vel5GRgSdPnlTYfQn4r8h+9+5dHD58WLqaHai8fZIb3qO9hFWrVg2Ojo55Dtn3XH/w4AE6dOgANzc3BAQEQEtL9e1xd3fH8ePHkZ6eLo0LDg6Gg4NDub5tTEH6KD/u7u64dOmSyg4eHBwMpVKZI8FWRHK5HG5ubjh06JA0LisrC4cOHYK7u7sGIyub7O3tYWVlpdJfSUlJOHv2bKXpLyEE/Pz88Pvvv+Pw4cM5bk/l5uYGXV1dlT6KjIxEdHR0pemj3GRlZSE1NZX9U0nw2AokJyfj1q1bKh84KjLmB+D+/ft4/PhxuX3PK2N+y2+dcxMeHg4A5fZ9zg1zdOVRGffzvHTs2BGXLl1CeHi4NDRr1gze3t7S/ytTfwBA69atERkZqTLu+vXrsLOzA1A58/2LFy9y1Jq0tbWRlZUFoHL2yevUWX93d3ckJCQgLCxManP48GFkZWVJX2BXNNlF9hs3buDgwYMwMzNTmV4Z++StNPooVpLcv39f1K1bV3Ts2FHcv39fxMTESEO2hIQEYWlpKQYNGiQuX74stmzZIgwMDMT69es1GHnpunv3rrhw4YKYNWuWMDIyEhcuXBAXLlwQz549E0IIkZGRIRo2bCg6deokwsPDxb59+0S1atXE9OnTNRx56dmyZYtQKBQiMDBQXLlyRYwaNUqYmJiI2NhYTYemEc+ePZO2EwBiyZIl4sKFC9LTsefPny9MTEzEH3/8IS5evCh69Ogh7O3tRUpKioYjLx1jxowRxsbG4ujRoyrHnRcvXkhtRo8eLWrWrCkOHz4sQkNDhbu7u3B3d9dg1KXr888/F8eOHRNRUVHi4sWL4vPPPxcymUwcOHBACMH+qSwq27F18uTJ4ujRoyIqKkqcOnVKeHh4CHNzcxEfH6/p0IpNZcsPea3vs2fPxGeffSZCQkJEVFSUOHjwoGjatKmoV6+eePnypaZDL5TKmN/yW+ebN2+K2bNni9DQUBEVFSX++OMPUbt2bdGuXTsNR154zNGVW2Xczwuqffv2Yvz48dLfla0//v77b6GjoyO+/vprcePGDbFp0yZhYGAgfvnlF6lNRcv3+RkyZIioXr262LVrl4iKihI7duwQ5ubmYurUqVKbit4nxXEO2LlzZ9GkSRNx9uxZcfLkSVGvXj0xYMAATa1SkeXVJ2lpaeKDDz4QNWrUEOHh4SrH29TUVGkeFa1PCouF9jIiICBAAMh1eN0///wj2rRpIxQKhahevbqYP3++hiLWjCFDhuTaR0eOHJHa3LlzR3Tp0kXo6+sLc3NzMXnyZJGenq65oDVg5cqVombNmkIul4vmzZuLM2fOaDokjTly5Eiu28yQIUOEEEJkZWWJ//3vf8LS0lIoFArRsWNHERkZqdmgS9HbjjsBAQFSm5SUFDF27FhRtWpVYWBgID788EOVLwEruuHDhws7Ozshl8tFtWrVRMeOHaUP8EKwfyqTynRs7devn7C2thZyuVxUr15d9OvXT9y8eVPTYRWrypYf8lrfFy9eiE6dOolq1aoJXV1dYWdnJ0aOHFmuv0iqjPktv3WOjo4W7dq1E6ampkKhUIi6deuKKVOmiMTERM0GXgTM0ZVbZdzPC+rNQntl7I+//vpLNGzYUCgUCuHo6Cg2bNigMr2i5fv8JCUlifHjx4uaNWsKPT09Ubt2bfHll1+qFEwrep8Uxzng48ePxYABA4SRkZFQKpVi2LBh0gWg5VFefRIVFfXW4+3rtbiK1ieFJRNCiCJcEE9EREREREREREREVKnxHu1EREREREREREREREXAQjsRERERERERERERURGw0E5EREREREREREREVAQstBMRERERERERERERFQEL7URERERERERERERERcBCOxERERERERERERFREbDQTkRERERERERERERUBCy0ExEREREREREREREVAQvtVGw6dOiACRMmaDoMvHjxAr1794ZSqYRMJkNCQoKmQ5IEBgbCxMSkxJfj7++Pxo0bazyOsurOnTuQyWQIDw/XdCglpjTe4/y2s7xU9m2QiOhNZeU8KjfXrl1Dy5YtoaenV+jj/usKkwOGDh2Knj17FnnZREREhVGW8zQRlR0stFOFs3HjRpw4cQKnT59GTEwMjI2NNR1SmdOvXz9cv35d02GUitw+mNva2iImJgYNGzYstThkMhl27txZassr6yrTNkhEVN7NnDkThoaGiIyMxKFDh4o8v5LKAbVq1cKyZcuKfb5EREQliRchEVUcOpoOgKi43bp1C05OTnkWUdPS0iCXy0sxqrJFX18f+vr6mg7jrYQQyMzMhI5OyRyitLW1YWVlVSLzJvWU9W2QiKgiyMzMhEwmg5ZW0a6tuXXrFry8vGBnZ1cscTEHEBEREVFFxCvaqVCeP3+OwYMHw8jICNbW1li8eLHK9J9//hnNmjVDlSpVYGVlhYEDByI+Ph7AqyJq3bp1sWjRIpXXhIeHQyaT4ebNmxBCwN/fHzVr1oRCoYCNjQ3GjRuXb1wdOnTA4sWLcfz4cchkMnTo0AHAqyuc5syZg8GDB0OpVGLUqFEAgN9++w0NGjSAQqFArVq1cqxHrVq1MHfuXGld7ezs8Oeff+Lff/9Fjx49YGRkBFdXV4SGhhao/3bu3Il69epBT08Pnp6euHfvnsr0tWvXok6dOpDL5XBwcMDPP/+sMj06OlpavlKpRN++fREXF/fW5d26dQu1a9eGn58fhBA5vjHPvgXIzz//jFq1asHY2Bj9+/fHs2fPpDbPnj2Dt7c3DA0NYW1tjaVLlxbo53OpqamYNm0abG1toVAoULduXfzwww8AgKNHj0Imk2Hv3r1wc3ODQqHAyZMnkZWVhXnz5sHe3h76+vpo1KgRtm/fLs0zMzMTPj4+0nQHBwcsX75cZb02btyIP/74AzKZDDKZDEePHs311jHHjh1D8+bNoVAoYG1tjc8//xwZGRnS9A4dOmDcuHGYOnUqTE1NYWVlBX9/f7XWvVatWgCADz/8EDKZTPobyP+9zktCQgI++eQTWFpaQk9PDw0bNsSuXbtU2uzfvx9OTk4wMjJC586dERMTozL9+++/h5OTE/T09ODo6Ig1a9aoTL9//z4GDBgAU1NTGBoaolmzZjh79myu8by5neWlMNsgEVFpyOt4n1v+SEhIkPIL8F9O279/P5o0aQJ9fX289957iI+Px969e+Hk5ASlUomBAwfixYsXKsvOyMiAn58fjI2NYW5ujv/9738qx9PU1FR89tlnqF69OgwNDdGiRQtpucB/x9Y///wTzs7OUCgUiI6OznN9s7KyMHv2bNSoUQMKhQKNGzfGvn37pOkymQxhYWGYPXs2ZDJZvrkvu4927NiBd999FwYGBmjUqBFCQkJyxPm6uXPnwsLCAlWqVMGIESPw+eef53qbmkWLFsHa2hpmZmbw9fVFeno6gFfv2927dzFx4kQp5wPA3bt30b17d1StWhWGhoZo0KAB9uzZk+c6EBER5SYrK+utnwfz+4z+zz//4N1330WVKlWgVCrh5uaG0NBQHD16FMOGDUNiYqKUv9T5nJlXzSXbn3/+KdUd3n33XWzcuDHH7XVPnjyJtm3bQl9fH7a2thg3bhyeP39e1K4iqrwEUSGMGTNG1KxZUxw8eFBcvHhRdOvWTVSpUkWMHz9eCCHEDz/8IPbs2SNu3bolQkJChLu7u+jSpYv0+q+//lo4OzurzHPcuHGiXbt2Qgghtm3bJpRKpdizZ4+4e/euOHv2rNiwYUO+cT1+/FiMHDlSuLu7i5iYGPH48WMhhBB2dnZCqVSKRYsWiZs3b4qbN2+K0NBQoaWlJWbPni0iIyNFQECA0NfXFwEBAdL87OzshKmpqVi3bp24fv26GDNmjFAqlaJz584iKChIREZGip49ewonJyeRlZWVb3wBAQFCV1dXNGvWTJw+fVqEhoaK5s2bi1atWkltduzYIXR1dcXq1atFZGSkWLx4sdDW1haHDx8WQgiRmZkpGjduLNq0aSNCQ0PFmTNnhJubm2jfvr00j5kzZ4pGjRoJIYT4559/hJWVlfjyyy9V4jA2NlZpb2RkJHr16iUuXbokjh8/LqysrMQXX3whtRkxYoSws7MTBw8eFJcuXRIffvihynuen759+wpbW1uxY8cOcevWLXHw4EGxZcsWIYQQR44cEQCEq6urOHDggLh586Z4/PixmDt3rnB0dBT79u0Tt27dEgEBAUKhUIijR48KIYRIS0sTM2bMEOfOnRO3b98Wv/zyizAwMBBbt24VQgjx7Nkz0bdvX9G5c2cRExMjYmJiRGpqqoiKihIAxIULF4QQQty/f18YGBiIsWPHiqtXr4rff/9dmJubi5kzZ0rxt2/fXiiVSuHv7y+uX78uNm7cKGQymThw4EC+6x4fHy8AiICAABETEyPi4+PVeq/zkpmZKVq2bCkaNGggDhw4IG7duiX++usvsWfPHiHEf9uah4eHOHfunAgLCxNOTk5i4MCB0jx++eUXYW1tLX777Tdx+/Zt8dtvvwlTU1MRGBgo9V/t2rVF27ZtxYkTJ8SNGzfE1q1bxenTp4UQ+W9neSnMNkhEVBryOt6/mT+EEOLp06cCgDhy5IgQ4r+c1rJlS3Hy5Elx/vx5UbduXdG+fXvRqVMncf78eXH8+HFhZmYm5s+fr7JcIyMjMX78eHHt2jUpp71+/jNixAjRqlUrcfz4cXHz5k2xcOFCoVAoxPXr14UQ/x37W7VqJU6dOiWuXbsmnj9/nuf6LlmyRCiVSvHrr7+Ka9euialTpwpdXV1pnjExMaJBgwZi8uTJIiYmRjx79izP+WX3kaOjo9i1a5eIjIwUH330kbCzsxPp6elSnK/ngF9++UXo6emJH3/8UURGRopZs2YJpVIp5RghhBgyZIhQKpVi9OjR4urVq+Kvv/5S6Z/Hjx+LGjVqiNmzZ0s5XwghvLy8xPvvvy8uXrwo5cpjx47luQ5ERERvyuv8QJ3P6A0aNBAff/yxuHr1qrh+/boICgoS4eHhIjU1VSxbtkwolUopf+WXa4XIv+Zy+/ZtoaurKz777DNx7do18euvv4rq1asLAOLp06dCCCFu3rwpDA0NxdKlS8X169fFqVOnRJMmTcTQoUOLu/uIKg0W2qnAnj17JuRyuQgKCpLGPX78WOjr67+16Hru3DkBQEoYDx48ENra2uLs2bNCiFcFU3Nzc6nAt3jxYlG/fn2RlpZW4PjGjx+vktCEeFUw79mzp8q4gQMHivfff19l3JQpU1S+ALCzsxMff/yx9HdMTIwAIP73v/9J40JCQgQA6QNdXgICAgQAcebMGWnc1atXBQCpL1q1aiVGjhyp8ro+ffqIrl27CiGEOHDggNDW1hbR0dHS9IiICAFA/P3330KI/wqgp06dElWrVhWLFi3KEcebRU4DAwORlJSk0hctWrQQQgiRlJQkdHV1xbZt26TpCQkJwsDAQK1Ce2RkpAAggoODc52eXZTYuXOnNO7ly5fCwMBAKupm8/HxEQMGDHjrsnx9fUXv3r2lv4cMGSJ69Oih0ubNQskXX3whHBwcVL4sWb16tTAyMhKZmZlCiFcnVm3atFGZzzvvvCOmTZv29hV/DQDx+++/q4zL773Oy/79+4WWlpaIjIzMdXr2tnbz5k1p3OrVq4WlpaX0d506dcTmzZtVXjdnzhzh7u4uhBBi/fr1okqVKtIXVm/KbzvLS0G3QSKi0pLX8b4ghfaDBw9KbebNmycAiFu3bknjPvnkE+Hp6amy3De/uJ82bZpwcnISQghx9+5doa2tLR48eKASW8eOHcX06dOFEP8d+8PDw9VeXxsbG/H111/nWN+xY8dKfzdq1Ejly+e8ZPfR999/L43LPk+5evWqFOfrOaBFixbC19dXZT6tW7fOUWi3s7MTGRkZ0rg+ffqIfv36SX/b2dmJpUuXqszHxcVF+Pv7qxU7ERHR2+R1fqDOZ/QqVapI9Y43vZkXC+PNmsu0adNEw4YNVdp8+eWXKoV2Hx8fMWrUKJU2J06cEFpaWiIlJaVI8RBVVrx1DBXYrVu3kJaWhhYtWkjjTE1N4eDgIP0dFhaG7t27o2bNmqhSpQrat28PANLPl21sbODl5YUff/wRAPDXX38hNTUVffr0AQD06dMHKSkpqF27NkaOHInff/9d5TYehdGsWTOVv69evYrWrVurjGvdujVu3LiBzMxMaZyrq6v0f0tLSwCAi4tLjnFv/kzrbXR0dPDOO+9Ifzs6OsLExARXr17NM67Xp9va2sLW1laa7uzsrDIP4FVfv//++5gxYwYmT56cb1y1atVClSpVpL+tra2ldbp9+zbS09PRvHlzabqxsbHKe56X8PBwaGtrS9vB27z+Ht28eRMvXrzA+++/DyMjI2n46aefcOvWLand6tWr4ebmhmrVqsHIyAgbNmzI92fyb7p69Src3d2ln5kDr/o8OTkZ9+/fl8a9vi0Aqn1UGPm913kJDw9HjRo1UL9+/be2MTAwQJ06dXKN9/nz57h16xZ8fHxU+nfu3LlS/4aHh6NJkyYwNTV96zIKup3lJa9tkIioNBXH8f7N8wcDAwPUrl1bZdyb82zZsqVKLnJ3d5fOSy5duoTMzEzUr19f5bh97Ngxlbwol8tzxP82SUlJePjwYaFzUV5ej8Ha2hrA28+VIiMjVc4xAOT4GwAaNGgAbW1tlfnm976MGzcOc+fORevWrTFz5kxcvHhR7XUgIiJ63dvOD9T5jD5p0iSMGDECHh4emD9/vkruLoz8ai6RkZEqdQcgZ279559/EBgYqHJe4enpiaysLERFRRUpPqLKioV2KnbPnz+Hp6cnlEolNm3ahHPnzuH3338H8OohpNlGjBiBLVu2ICUlBQEBAejXrx8MDAwAALa2toiMjMSaNWugr6+PsWPHol27dtJ9OAvD0NCwUK/T1dWV/p/94Te3cVlZWYWOrSRUq1YNzZs3x6+//oqkpKR827++TsCr9SqudVL3gWevv0fJyckAgN27dyM8PFwarly5It2nfcuWLfjss8/g4+ODAwcOIDw8HMOGDVPZzopTSfZRQanTp7nFK/7/Xr/Z/fvdd9+p9O/ly5dx5swZtZdR0O2soPGWtf2KiCqHtx2Psh8qKl67b/rbzk3ePFco6jEuOTkZ2traCAsLUzluX716VeX5JPr6+irFek0piXOlwvThiBEjcPv2bQwaNAiXLl1Cs2bNsHLlyiLFQURElVNRcrm/vz8iIiLg5eWFw4cPw9nZWaqTFJS6NZf8JCcn45NPPlE5r/jnn39w48YNlQu2iEh9LLRTgdWpUwe6uroqD0R8+vQprl+/DgC4du0aHj9+jPnz56Nt27ZwdHTM9Wqjrl27wtDQEGvXrsW+ffswfPhwlen6+vro3r07VqxYgaNHjyIkJASXLl0qtvVwcnLCqVOnVMadOnUK9evXV7laqrhlZGSoPDw1MjISCQkJcHJyyjMuZ2dnafq9e/dUHqB65coVJCQkSG2AV/23a9cu6YGrRXmoZO3ataGrq4tz585J4xITE6X3PD8uLi7IysrCsWPH1F7m6w9xq1u3rsqQfaXAqVOn0KpVK4wdOxZNmjRB3bp1c1wZIJfLVX6hkBsnJyeEhISoFE5OnTqFKlWqoEaNGmrHnBddXd0cceT3XufF1dUV9+/fV/s9eJOlpSVsbGxw+/btHP1rb28vLSM8PBxPnjx563yKczsjIirrqlWrBgAqD5Z+/cGoRfXmw6bPnDmDevXqQVtbG02aNEFmZibi4+NzHLetrKwKtTylUgkbG5tC56Li4uDgoHKOASDH3+p4W863tbXF6NGjsWPHDkyePBnfffddoWMlIiJ6k7qf0evXr4+JEyfiwIED6NWrFwICAgCo95n1derUXBwcHFTqDkDO3Nq0aVNcuXIlx3lF3bp1IZfL1Y6HiP7DQjsVmJGREXx8fDBlyhQcPnwYly9fxtChQ6WrvGrWrAm5XI6VK1fi9u3b+PPPPzFnzpwc89HW1sbQoUMxffp01KtXD+7u7tK0wMBA/PDDD7h8+TJu376NX375Bfr6+rCzsyu29Zg8eTIOHTqEOXPm4Pr169i4cSNWrVqFzz77rNiWkRtdXV18+umnOHv2LMLCwjB06FC0bNlS+hnXlClTEBgYiLVr1+LGjRtYsmQJduzYIcXl4eEBFxcXeHt74/z58/j7778xePBgtG/fPsftcQwNDbF7927o6OigS5cu0lXMBVWlShUMGTIEU6ZMwZEjRxAREQEfHx9oaWmpddVcrVq1MGTIEAwfPhw7d+5EVFQUjh49iqCgoDyX+dlnn2HixInYuHEjbt26hfPnz2PlypXYuHEjAKBevXoIDQ3F/v37cf36dfzvf//LcfJQq1YtXLx4EZGRkXj06FGuVx6OHTsW9+7dw6effopr167hjz/+wMyZMzFp0iRpuy6qWrVq4dChQ4iNjcXTp08B5P9e56V9+/Zo164devfujeDgYERFRWHv3r3Yt2+f2jHNmjUL8+bNw4oVK3D9+nVcunQJAQEBWLJkCQBgwIABsLKyQs+ePXHq1Cncvn0bv/32G0JCQlTmU1zbGRFRWaevr4+WLVti/vz5uHr1Ko4dO4avvvqq2OYfHR2NSZMmITIyEr/++itWrlyJ8ePHA3j14dzb2xuDBw/Gjh07EBUVhb///hvz5s3D7t27C73MKVOm4Ntvv8XWrVsRGRmJzz//HOHh4dJyS8Onn36KH374ARs3bsSNGzcwd+5cXLx4scBX5teqVQvHjx/HgwcP8OjRIwDAhAkTsH//fkRFReH8+fM4cuSIdHEDERFRccjvM3pKSgr8/Pxw9OhR3L17F6dOncK5c+ekfFSrVi0kJyfj0KFDePToEV68eJHn8tSpuXzyySe4du0apk2bhuvXryMoKAiBgYEA/vul2bRp03D69Gn4+fkhPDwcN27cwB9//AE/P7/i7ySiSoKFdiqUhQsXom3btujevTs8PDzQpk0buLm5AXh1tVdgYCC2bdsGZ2dnzJ8/H4sWLcp1Pj4+PkhLS8OwYcNUxpuYmOC7775D69at4erqioMHD+Kvv/6CmZlZsa1D06ZNERQUhC1btqBhw4aYMWMGZs+ejaFDhxbbMnJjYGCAadOmYeDAgWjdujWMjIywdetWaXrPnj2xfPlyLFq0CA0aNMD69esREBCADh06AHiVFP/44w9UrVoV7dq1g4eHB2rXrq0yj9cZGRlh7969EELAy8sLz58/L1TcS5Ysgbu7O7p16wYPDw+0bt0aTk5O0NPTU+v1a9euxUcffYSxY8fC0dERI0eOzDeWOXPm4H//+x/mzZsHJycndO7cGbt375auuP7kk0/Qq1cv9OvXDy1atMDjx48xduxYlXmMHDkSDg4OaNasGapVq5bjqj0AqF69Ovbs2YO///4bjRo1wujRo+Hj41OsxZPFixcjODgYtra2aNKkCYD83+v8/Pbbb3jnnXcwYMAAODs7Y+rUqQW6EmLEiBH4/vvvERAQABcXF7Rv3x6BgYFS/8rlchw4cAAWFhbo2rUrXFxcMH/+/Fx/8VFc2xkRUVn3448/IiMjA25ubpgwYQLmzp1bbPMePHgwUlJS0Lx5c/j6+mL8+PEYNWqUND0gIACDBw/G5MmT4eDggJ49e+LcuXOoWbNmoZc5btw4TJo0CZMnT4aLiwv27duHP//8E/Xq1SuOVVKLt7c3pk+fjs8++wxNmzZFVFQUhg4dqvY5RrbZs2fjzp07qFOnjvTrg8zMTPj6+krnEfXr18eaNWtKYjWIiKiSyu8zura2Nh4/fozBgwejfv366Nu3L7p06YJZs2YBAFq1aoXRo0ejX79+qFatGhYsWJDn8tSpudjb22P79u3YsWMHXF1dsXbtWnz55ZcAAIVCAeDVL5iPHTuG69evo23btmjSpAlmzJgBGxub4u4iokpDJl6/VwJRKTtx4gQ6duyIe/fuSQ8VpfLh+fPnqF69OhYvXgwfHx9Nh0NEREQVyPvvvw8rKyv8/PPPmg6FiIioQvj666+xbt06lVvcEFHx0tF0AFQ5paam4t9//4W/vz/69OnDIns5cOHCBVy7dg3NmzdHYmIiZs+eDQDo0aOHhiMjIiKi8uzFixdYt24dPD09oa2tjV9//RUHDx5EcHCwpkMjIiIqt9asWYN33nkHZmZmOHXqFBYuXMjbwhCVMN46hjTi119/hZ2dHRISEvL9WdTrTpw4ASMjo7cOmtalS5e3xvbNN99oOrwiW7RoERo1agQPDw88f/4cJ06cgLm5eZl/X0rapk2b3rruDRo0KDPzLE0VfV8gIiov8srPJ06cKPD8vvnmm7fOr0uXLoWKUSaTYc+ePWjXrh3c3Nzw119/4bfffoOHh0eh5kdERFSeFdfn6xs3bqBHjx5wdnbGnDlzMHnyZPj7+5dc4ETEW8dQ+ZKSkoIHDx68dXrdunVLMZqcHjx4gJSUlFynmZqawtTUtJQjKh1l/X0pac+ePUNcXFyu03R1dQv1EN+SmGdpqqz7AhFRWXPz5s23TqtevTr09fULNL8nT57gyZMnuU7T19dH9erVCzQ/IiIiUlXZP18TlWcstBMRERERERERERERFQFvHUNEREREREREREREVAQstBMRERERERERERERFQEL7URERERERERERERERcBCOxERERERERERERFREbDQTkRERERERERERERUBCy0ExEREREREREREREVAQvtRERERERERERERERFwEI7EREREREREREREVERsNBORERERERERERERFQELLQTERERERERERERERUBC+1EREREREREREREREXAQjsRERERERERERERURGw0E5EREREREREREREVAQstFO+/P39IZPJSmVZHTp0QIcOHaS/jx49CplMhu3bt5fK8ocOHYpatWqVyrIKKzk5GSNGjICVlRVkMhkmTJjw1rYymQx+fn6lF1wlEhgYCJlMhtDQUE2HkieZTAZ/f3+121ak7SX7+HH06FFNh0KUL+basqUgubYs6tChAxo2bKjpMApl3759aNy4MfT09CCTyZCQkJDva3I73hf3dvbmflMRZGRkYOrUqbC1tYWWlhZ69uyp1uvK8/ZFmsEcV7Ywx2lOYXJcWXTnzh3IZDIEBgZqOpR8lXT+LsjxrSCfzcu7ynyOwUJ7JZNdHMwe9PT0YGNjA09PT6xYsQLPnj0rluU8fPgQ/v7+CA8PL5b5FaeyHJs6vvnmGwQGBmLMmDH4+eefMWjQIE2HpGLPnj0VKnmsWbOmXJxAqOv06dPw9/cv0ZO6Fy9ewN/fnwVuqrSYa8t2bOoo67m2onr8+DH69u0LfX19rF69Gj///DMMDQ1LbflXrlyBv78/7ty5U2rL1KQff/wRCxcuxEcffYSNGzdi4sSJmg7prSrbe1OWMceV7djUwRynGZrOcVQ5bN68GcuWLdN0GJX7HENQpRIQECAAiNmzZ4uff/5Z/Pjjj+Kbb74RnTp1EjKZTNjZ2Yl//vlH5TXp6ekiJSWlQMs5d+6cACACAgIK9LrU1FSRmpoq/X3kyBEBQGzbtq1A8ylsbGlpaeLly5fFtqyS0KJFC9G6dWu12gIQvr6+JRyRKl9fX1GRDi0NGjQQ7du3zzE+e186d+5c6QdVACkpKSI9PV36e+HChQKAiIqKytG2uLaXf//9VwAQM2fOLPK8iiIzM1OkpKSIzMxMjcZBlQ9zbeXKtWVR+/btRYMGDTQdRoHt3btXABDBwcEFel32NnzkyBFpXGG2s23btuWYT7Y395uKoF+/fqJ69eoFfp0mtq+83hsqXcxxzHGaVtlyXFmUlZUlUlJSREZGhqZDyVdJ5++CHN9K4zOyl5eXsLOzK9FlqKMyn2PoFE+5nsqbLl26oFmzZtLf06dPx+HDh9GtWzd88MEHuHr1KvT19QEAOjo60NEp2U3lxYsXMDAwgFwuL9Hl5EdXV1ejy1dHfHw8nJ2dNR0GlRN6enqaDiFfz58/L5GrObS0tMrF+lPFxVybO+baiiErKwtpaWnFepyNj48HAJiYmBR5XsW9nWl6vykJ8fHxxdLXVDkxx+WOOa5iKOs5Tl1CCLx8+VLaF4tL9q9ZyoOSPiaUxvGtPKrU5xjFUq6nciO/q3C/+eYbAUBs2LBBGjdz5swcVygfOHBAtG7dWhgbGwtDQ0NRv359MX36dCHEf1cNvDlkf+Of/Q1VaGioaNu2rdDX1xfjx4+Xpr1+9XD2vLZs2SKmT58uLC0thYGBgejevbuIjo5WicnOzk4MGTIkxzq9Ps/8YhsyZEiOb/+Sk5PFpEmTRI0aNYRcLhf169cXCxcuFFlZWSrt8P9XA//++++iQYMGQi6XC2dnZ7F3795c+/pNcXFxYvjw4cLCwkIoFArh6uoqAgMDc/TFm0NuVyYXJqbz58+Lzp07iypVqghDQ0Px3nvviZCQEJU2aWlpwt/fX9StW1coFAphamoqWrduLQ4cOCD1X24xqit72/jnn39Eu3bthL6+vqhTp450BcrRo0dF8+bNhZ6enqhfv36uVwOosx7Z+8HJkyfFxIkThbm5uTAwMBA9e/YU8fHxUjs7O7sc65K9Lak7DyFeXfXSqVMnYWZmJvT09EStWrXEsGHD1O6X5cuXCy0tLfH06VNp3KJFiwQAMXHiRGlcRkaGMDIyElOnTpXG4bVvzbP35bdtQ0XdhoUQIioqKtdlZMcwZMgQYWhoKG7evCm6dOkijIyMRI8ePYQQ6u3DsbGxQltbW/j7++dod+3aNQFArFy5UgiR+xWORKWBuZa5NreYcltvIXJ/77PnGRQUJJycnISenp5o2bKluHjxohBCiHXr1ok6deoIhUIh2rdvnyO+199/d3d3KfesXbs2x/JfvnwpZsyYIerUqSPkcrmoUaOGmDJlSo4rMrNj+uWXX4Szs7PQ0dERv//++1v75U1BQUGiadOmQk9PT5iZmQlvb29x//59lZjf7PfctrXc5Ha8z62/f/31V9G0aVNhZGQkqlSpIho2bCiWLVsmhPhvv31zyJ7n2/abrVu3irlz54rq1asLhUIh3nvvPXHjxo0cMa5atUrY29sLPT098c4774jjx4/nmKcQQqxYsUI4OzsLfX19YWJiItzc3MSmTZvU6ods+e1Pb8vV6ubL7O0rIiJCdOjQQejr6wsbGxvx7bff5mib3z6XrSjvDZUu5jjmOOa4nEoyx2Xvc8eOHROjRo0SpqamokqVKmLQoEHiyZMnKm3t7OyEl5eX2Ldvn3BzcxMKhUIsXbpUCCHE06dPxfjx46XtsE6dOmL+/PnSr3/T0tJE1apVxdChQ3PEkJiYKBQKhZg8ebIQ4r888uavOg4dOiTatGkjDAwMhLGxsfjggw/ElStXVNoUZFvJ6zihrqLm7zNnzoguXboIExMTYWBgIFxcXKT89La4X758KSZMmCDMzc2FkZGR6N69u7h3716uV7Tfv39fDBs2TFhYWEj71w8//KDSRt2Yc9vOXu9rnmOUzjkGv3YhFYMGDcIXX3yBAwcOYOTIkbm2iYiIQLdu3eDq6orZs2dDoVDg5s2bOHXqFADAyckJs2fPxowZMzBq1Ci0bdsWANCqVStpHo8fP0aXLl3Qv39/fPzxx7C0tMwzrq+//hoymQzTpk1DfHw8li1bBg8PD4SHhxfo21l1YnudEAIffPABjhw5Ah8fHzRu3Bj79+/HlClT8ODBAyxdulSl/cmTJ7Fjxw6MHTsWVapUwYoVK9C7d29ER0fDzMzsrXGlpKSgQ4cOuHnzJvz8/GBvb49t27Zh6NChSEhIwPjx4+Hk5ISff/4ZEydORI0aNTB58mQAQLVq1fJcZ3ViioiIQNu2baFUKjF16lTo6upi/fr16NChA44dO4YWLVoAePWgj3nz5mHEiBFo3rw5kpKSEBoaivPnz+P999/HJ598gocPHyI4OBg///yzem/KG54+fYpu3bqhf//+6NOnD9auXYv+/ftj06ZNmDBhAkaPHo2BAwdK9/u6d+8eqlSpUqD1yPbpp5+iatWqmDlzJu7cuYNly5bBz88PW7duBQAsW7YMn376KYyMjPDll18CQI5tNb95xMfHo1OnTqhWrRo+//xzmJiY4M6dO9ixY4fafdK2bVtkZWXh5MmT6NatGwDgxIkT0NLSwokTJ6R2Fy5cQHJyMtq1a5frfHr16oXr16/j119/xdKlS2Fubg5AdRsq7DacrVq1ali7di3GjBmDDz/8EL169QIAuLq6Sm0yMjLg6emJNm3aYNGiRTAwMFC7LywtLdG+fXsEBQVh5syZKtO2bt0KbW1t9OnTR+35EWkCc62qypJrC+rEiRP4888/4evrCwCYN28eunXrhqlTp2LNmjUYO3Ysnj59igULFmD48OE4fPiwyuufPn2Krl27om/fvhgwYACCgoIwZswYyOVyDB8+HMCrK/Y++OADnDx5EqNGjYKTkxMuXbqEpUuX4vr169i5c6fKPA8fPoygoCD4+fnB3Nxc7Qf+BQYGYtiwYXjnnXcwb948xMXFYfny5Th16hQuXLgAExMTfPnll3BwcMCGDRswe/Zs2Nvbo06dOoXqu9wEBwdjwIAB6NixI7799lsAwNWrV3Hq1CmMHz8e7dq1w7hx47BixQp88cUXcHJyAgDp37eZP38+tLS08NlnnyExMRELFiyAt7c3zp49K7VZu3Yt/Pz80LZtW0ycOBF37txBz549UbVqVdSoUUNq991332HcuHH46KOPMH78eLx8+RIXL17E2bNnMXDgQLXWU539qVq1avj555/x9ddfIzk5GfPmzVNrXV/39OlTdO7cGb169ULfvn2xfft2TJs2DS4uLujSpQsA9fY5oOTeG9IM5jhVzHG5Y44reI7z8/ODiYkJ/P39ERkZibVr1+Lu3bvSA3+zRUZGYsCAAfjkk08wcuRIODg44MWLF2jfvj0ePHiATz75BDVr1sTp06cxffp0xMTEYNmyZdDV1cWHH36IHTt2YP369SpXgu/cuROpqano37//W+M7ePAgunTpgtq1a8Pf3x8pKSlYuXIlWrdujfPnzxf4IcH5HSeKSp38HRwcjG7dusHa2hrjx4+HlZUVrl69il27dkk5LDcjRozAL7/8goEDB6JVq1Y4fPgwvLy8crSLi4tDy5YtIZPJ4Ofnh2rVqmHv3r3w8fFBUlJSjgcV5xfzl19+icTERNy/f186thgZGQHgOUapnmMUukRP5ZI695U2NjYWTZo0kf5+8xu6pUuXCgDi33//fes88rpvXfa3bOvWrct1Wm7fNlavXl0kJSVJ44OCggQAsXz5cmmcOlcg5Bfbm9+u7ty5UwAQc+fOVWn30UcfCZlMJm7evCmNAyDkcrnKuH/++UflCtu3WbZsmQAgfvnlF2lcWlqacHd3F0ZGRirrnv0ttTrUjalnz55CLpeLW7duSeMePnwoqlSpItq1ayeNa9SoUb7LLso92rO3jc2bN0vjsq9S1tLSEmfOnJHG79+/P8f7qO56ZO8HHh4eKleSTJw4UWhra4uEhARpXH73aM9vHr///nu++1x+MjMzhVKplK5Uz8rKEmZmZqJPnz5CW1tbPHv2TAghxJIlS3Jc+Y43vjXP7x7thd2GX5fXPdqzf/Xw+eef55im7j68fv16AUBcunRJpZ2zs7N47733pL95RTtpCnMtc21uMRX0aj+FQqFyrM4+9llZWanEOn369BzH9ez3f/HixdK41NRU0bhxY2FhYSHS0tKEEEL8/PPPQktLS5w4cUJl+evWrRMAxKlTp1Ri0tLSEhEREWr1S7a0tDRhYWEhGjZsqHIP0127dgkAYsaMGdK4wj7/RJ0r2sePHy+USmWe95PN6x6db9tvnJycVO79unz5cpUclZqaKszMzMQ777yj8syUwMBAAUBlnj169CjyfUkLsj8V9j6o2dvXTz/9JI1LTU0VVlZWonfv3tI4dfe5or43VLqY45jjmOP+Uxo5Lvt1bm5u0roJIcSCBQsEAPHHH39I47J/kb1v3z6VecyZM0cYGhqK69evq4z//PPPhba2tvTrjuzP2H/99ZdKu65du4ratWtLf+d2RXt2/z9+/Fga988//wgtLS0xePBgaZy624o6xwl1FDZ/Z2RkCHt7e2FnZ6fy+VoIofL5/824w8PDBQAxduxYldcMHDgwx2dkHx8fYW1tLR49eqTStn///sLY2Fi8ePGiQDEL8fZ7tPMco/TOMbTyKsJT5WRkZJTn0+Kz77P0xx9/ICsrq1DLUCgUGDZsmNrtBw8eLF21DAAfffQRrK2tsWfPnkItX1179uyBtrY2xo0bpzJ+8uTJEEJg7969KuM9PDxUvpl2dXWFUqnE7du3812OlZUVBgwYII3T1dXFuHHjkJycjGPHjhV6HfKLKTMzEwcOHEDPnj1Ru3ZtqZ21tTUGDhyIkydPIikpCcCr9z4iIgI3btwodDz5MTIyUvmm3MHBASYmJnByclK5Ij37/4VZj2yjRo1S+fa/bdu2yMzMxN27d9WON795ZO8vu3btQnp6utrzfZ2WlhZatWqF48ePA3j1Dezjx4/x+eefQwiBkJAQAK+uDGnYsGGR7oVW2G24oMaMGVPo1/bq1Qs6OjrSrwYA4PLly7hy5Qr69etXHOERlTjm2v9UhlxbGB07dlS5+is77/Xu3VvlfXozH2bT0dHBJ598Iv0tl8vxySefID4+HmFhYQCAbdu2wcnJCY6Ojnj06JE0vPfeewCAI0eOqMyzffv2Bb6vb2hoKOLj4zF27FiV+7l6eXnB0dERu3fvLtD8CsvExATPnz9HcHBwsc532LBhKlf9ZV/dmv1+hIaG4vHjxxg5cqTKPVy9vb1RtWrVHDHev38f586dK3Q8Bd2fCsvIyAgff/yx9LdcLkfz5s1VtkN197mSem9Ic5jj/sMclzvmuIIbNWqUyjMAxowZAx0dnRzbsL29PTw9PVXGbdu2DW3btkXVqlVV+sLDwwOZmZnS58z33nsP5ubmKp+znj59iuDg4Dw/Z8XExCA8PBxDhw6FqampNN7V1RXvv/9+ofaz4jhO5CW//H3hwgVERUVhwoQJOT5fv/75/03Z6/rmPv/m1elCCPz222/o3r07hBAq74unpycSExNx/vz5AsWcF55jlN45BgvtlENycrJKcntTv3790Lp1a4wYMQKWlpbo378/goKCCnTwq169eoEeSlGvXj2Vv2UyGerWrYs7d+6oPY/CuHv3LmxsbHL0R/bPSN4syNasWTPHPKpWrYqnT5/mu5x69epBS0t1l3zbcgoiv5j+/fdfvHjxAg4ODjnaOTk5ISsrC/fu3QMAzJ49GwkJCahfvz5cXFwwZcoUXLx4sdCx5aZGjRo5EpexsTFsbW1zjANQqPXI9mbfZH/gze/9Ksg82rdvj969e2PWrFkwNzdHjx49EBAQgNTUVLWXAbxKomFhYUhJScGJEydgbW2Npk2bolGjRtLtY06ePCkl28Iq7DZcEDo6Oio/lS8oc3NzdOzYEUFBQdK4rVu3QkdHR7pVDVFZx1z7n8qQa4tjntl5L798mM3GxibHg6br168PANJ7euPGDURERKBatWoqQ3a77Ae3ZbO3ty/wemT3a2752dHRsUj9XhBjx45F/fr10aVLF9SoUQPDhw/Hvn37ijzf/M4Dstevbt26Ku10dHRy/Ix+2rRpMDIyQvPmzVGvXj34+voW+CfyBd2fCiu387U3t3l197mSem9Ic5jj/sMcp948mePy9+Y2bGRkBGtr6xzbcG7rcePGDezbty9HX3h4eAD4ry90dHTQu3dv/PHHH9Ln1R07diA9PT3PQnte/eDk5IRHjx7h+fPn6q8siuc4kZf88vetW7cAAA0bNizQfO/evQstLa0ctwZ6s2/+/fdfJCQkYMOGDTnel+wvEd/cRotSv+A5RumdY7DQTiru37+PxMTEHB8GXqevr4/jx4/j4MGDGDRoEC5evIh+/frh/fffR2ZmplrLKe6nXgNv/1ZR3ZiKg7a2dq7jhRClFsObijOmdu3a4datW/jxxx/RsGFDfP/992jatCm+//77ooYpeVu8JdG3xTHP/OYhk8mwfft2hISEwM/PDw8ePMDw4cPh5uaG5ORktZfTpk0bpKenIyQkBCdOnJAK6m3btsWJEydw7do1/Pvvv0UutJfGNqxQKHIkRKBg+3D//v1x/fp1hIeHAwCCgoLQsWNH6b7zRGUZc23RlNdcW9C+K418mJWVBRcXFwQHB+c6jB07VqV9SWxTpcXCwgLh4eH4888/pfuLdunSBUOGDCnSfIvz/XByckJkZCS2bNmCNm3a4LfffkObNm1yPJOkLCjO9S6p94Y0gzmuaJjjmOOKKrf1yMrKwvvvv//Wvujdu7fUtn///nj27Jl0dXJQUBAcHR3RqFGjYolP3W2lOI4TedH0vpb9hcHHH3/81veldevWKq8pSsw8xyi9cwwW2klF9gMs3/yp0Zu0tLTQsWNHLFmyBFeuXMHXX3+Nw4cPSz+/yuunNIXx5q1KhBC4efOmypVAVatWRUJCQo7XvvmtWkFis7Ozw8OHD3P89PHatWvS9OJgZ2eHGzdu5Ph2triXk5tq1arBwMAAkZGROaZdu3YNWlpaKlcWmJqaYtiwYfj1119x7949uLq6wt/fX5pe3O+9ugq6HuoqrvVp2bIlvv76a4SGhmLTpk2IiIjAli1b1H598+bNIZfLceLECZVCe7t27XD27FkcOnRI+jsvpfH+FHYZ6u7DANCzZ0/I5XJs3boV4eHhuH79ep4P5yEqS5hrVVWGXAsU7BhXHB4+fJjj6rHr168DgPSe1qlTB0+ePEHHjh3h4eGRY8jtyrSCyu7X3PJzZGRkiff76+RyObp37441a9bg1q1b+OSTT/DTTz/h5s2bAEomR2avX/YysmVkZOR6Ja2hoSH69euHgIAAREdHw8vLC19//TVevnyp9vJKY39SNxZ19zlNvDdUMpjjVDHHMccVlze34eTkZMTExKj1kNE6deogOTk5137w8PBQuVK6Xbt2sLa2xtatW/Ho0SMcPnw439tz5tUP165dg7m5ufQLhIJsK/kdJ0pS9hXply9fLtDr7OzskJWVJV0Rn+3NvqlWrRqqVKmCzMzMt74vFhYWBY47r+MTzzFK5xyDhXaSHD58GHPmzIG9vT28vb3f2u7Jkyc5xjVu3BgApJ8XZR9EczuAFsZPP/2ksjNv374dMTEx0hOHgVcHwjNnziAtLU0at2vXrhy3CylIbF27dkVmZiZWrVqlMn7p0qWQyWQqyy+Krl27IjY2VuVeaBkZGVi5ciWMjIzQvn37YllObrS1tdGpUyf88ccfKh/44uLisHnzZrRp0wZKpRIA8PjxY5XXGhkZoW7duiq3QSnu915dBVmPgjA0NCzSujx9+jTHN69v7i/q0NPTwzvvvINff/0V0dHRKle0p6SkYMWKFahTpw6sra3znE9pvD8GBgaFWoa6+zDw6l5rnp6eCAoKwpYtWyCXy9GzZ8+ihE1UKphrc6oMuRZ41XeJiYkqt1yLiYnB77//XiLLy8jIwPr166W/09LSsH79elSrVg1ubm4AgL59++LBgwf47rvvcrw+JSWlwD/zzk2zZs1gYWGBdevWqeS9vXv34urVq/Dy8iryMtTx5jmMlpYWXF1dAZTcPgW8Wn8zMzN89913yMjIkMZv2rQpx0+934xRLpfD2dkZQgi1n/NSWvuTurGos89p6r2h4scclxNzHHNccdmwYYNKLli7di0yMjLU2ob69u2LkJAQ7N+/P8e0hIQElfykpaWFjz76CH/99Rd+/vlnZGRk5Ftot7a2RuPGjbFx40aV/eLy5cs4cOAAunbtKo1Td1tR5zhRkpo2bQp7e3ssW7Ysx76e15XV2e/HihUrVMYvW7ZM5W9tbW307t0bv/32W67F/H///bdQcRsaGiIxMTHHeJ5jvFIa5xg6+Tehimjv3r24du0aMjIyEBcXh8OHDyM4OBh2dnb4888/VR7k8abZs2fj+PHj8PLygp2dHeLj47FmzRrUqFEDbdq0AfDq4GliYoJ169ahSpUqMDQ0RIsWLQp13zPg1VXUbdq0wbBhwxAXF4dly5ahbt26GDlypNRmxIgR2L59Ozp37oy+ffvi1q1b+OWXX3LcG6sgsXXv3h3vvvsuvvzyS9y5cweNGjXCgQMH8Mcff2DChAk55l1Yo0aNwvr16zF06FCEhYWhVq1a2L59O06dOoVly5bleY/D4jB37lwEBwejTZs2GDt2LHR0dLB+/XqkpqZiwYIFUjtnZ2d06NABbm5uMDU1RWhoKLZv3w4/Pz+pTfaJzbhx4+Dp6Qltbe1Su9JY3fUoCDc3N6xduxZz585F3bp1YWFhIT1ARx0bN27EmjVr8OGHH6JOnTp49uwZvvvuOyiVSpUTDnW0bdsW8+fPh7GxMVxcXAC8+imUg4MDIiMjMXToULXWBwC+/PJL9O/fH7q6uujevXuOexwWhb6+PpydnbF161bUr18fpqamaNiwYb73t1N3H87Wr18/fPzxx1izZg08PT2L9BBYopLAXMtc+7r+/ftj2rRp+PDDDzFu3Di8ePECa9euRf369XM87Ko42NjY4Ntvv8WdO3dQv3596RdAGzZskB6mNmjQIAQFBWH06NE4cuQIWrdujczMTFy7dg1BQUHYv38/mjVrVqQ4dHV18e2332LYsGFo3749BgwYgLi4OCxfvhy1atXCxIkTi2N18zVixAg8efIE7733HmrUqIG7d+9i5cqVaNy4sXQ/z8aNG0NbWxvffvstEhMToVAo8N577xXqirJscrkc/v7++PTTT/Hee++hb9++uHPnDgIDA1GnTh2Vq6g6deoEKysrtG7dGpaWlrh69SpWrVoFLy8vtbfP0tqf1KHuPqep94aKhjmOOe51zHEln+PS0tLQsWNH9O3bF5GRkVizZg3atGmDDz74IN/XTpkyBX/++Se6deuGoUOHws3NDc+fP8elS5ewfft23LlzR+UWnP369cPKlSsxc+ZMuLi4SMfivCxcuBBdunSBu7s7fHx8kJKSgpUrV8LY2FjlF/DqbivqHCdKkpaWFtauXYvu3bujcePGGDZsGKytrXHt2jVERETk+qUF8CpfDRgwAGvWrEFiYiJatWqFQ4cO5fhlGwDMnz8fR44cQYsWLTBy5Eg4OzvjyZMnOH/+PA4ePJjrlw35cXNzw9atWzFp0iS88847MDIyQvfu3XmOUZrnGIIqlYCAAAFAGuRyubCyshLvv/++WL58uUhKSsrxmpkzZ4rXN5VDhw6JHj16CBsbGyGXy4WNjY0YMGCAuH79usrr/vjjD+Hs7Cx0dHQEABEQECCEEKJ9+/aiQYMGucbXvn170b59e+nvI0eOCADi119/FdOnTxcWFhZCX19feHl5ibt37+Z4/eLFi0X16tWFQqEQrVu3FqGhoTnmmVdsQ4YMEXZ2diptnz17JiZOnChsbGyErq6uqFevnli4cKHIyspSaQdA+Pr65ojJzs5ODBkyJNf1fV1cXJwYNmyYMDc3F3K5XLi4uEhxvTk/Ly+vfOdX0JjOnz8vPD09hZGRkTAwMBDvvvuuOH36tEqbuXPniubNmwsTExOhr68vHB0dxddffy3S0tKkNhkZGeLTTz8V1apVEzKZTBTkMPO2beNt65zb+qmzHtn7wblz51TGZ29vR44ckcbFxsYKLy8vUaVKFQFA2pbUncf58+fFgAEDRM2aNYVCoRAWFhaiW7duIjQ0VN1ukezevVsAEF26dFEZP2LECAFA/PDDDzleA0DMnDlTZdycOXNE9erVhZaWlgAgoqKipLZF2YZfd/r0aeHm5ibkcrlKDEOGDBGGhoZvfZ26+7AQQiQlJQl9fX0BQPzyyy85puf2fhKVBubavGOrzLn2wIEDomHDhkIulwsHBwfxyy+/5Hjv3zbPqKgoAUAsXLhQZXz2+7dt2zZpXPb7HxoaKtzd3YWenp6ws7MTq1atyhFnWlqa+Pbbb0WDBg2EQqEQVatWFW5ubmLWrFkiMTEx3/VU19atW0WTJk2EQqEQpqamwtvbW9y/f1+lzdtya35yO96/uZ1t375ddOrUSVhYWAi5XC5q1qwpPvnkExETE6Myr++++07Url1baGtrq8zzbfvN6/0uxH/v05vb1YoVK4SdnZ1QKBSiefPm4tSpU8LNzU107txZarN+/XrRrl07YWZmJhQKhahTp46YMmWKyvugDnX3p7yOE3l52+ty27fV2eeK+t5Q6WKOyzs25jjmuOLOcdmvO3bsmBg1apSoWrWqMDIyEt7e3uLx48cqbfN6b589eyamT58u6tatK+RyuTA3NxetWrUSixYtUvk8L4QQWVlZwtbWVgAQc+fOzTGvt+W6gwcPitatWwt9fX2hVCpF9+7dxZUrV3K8Xp1tRd3jRH6Kmr9Pnjwp3n//fVGlShVhaGgoXF1dxcqVK6XpuW3jKSkpYty4ccLMzEwYGhqK7t27i3v37uX62TwuLk74+voKW1tboaurK6ysrETHjh3Fhg0bChVzcnKyGDhwoDAxMREApOMRzzFK7xxDJoQGn6pBRERERERUyWRlZaFatWro1atXrrc1ICIiAoDAwEAMGzYM586dK/IV+ERU8niPdiIiIiIiohLy8uXLHPdz/emnn/DkyRN06NBBM0ERERERUbHjPdqJqMQ9efJE5aFCb9LW1ka1atVKMaKyozz1TWZmZr4PZTEyMoKRkVEpRURERJpU0jksJSUl1wd6vc7U1BRyubzQyygNZ86cwcSJE9GnTx+YmZnh/Pnz+OGHH9CwYUP06dNHrXmURg4uT+ckREQlrazkOMrdv//+i8zMzLdOl8vl7D818RyjeLHQTkQlrlevXjh27Nhbp9vZ2eHOnTulF1AZUp765t69e/k+gGrmzJkqD7shIqKKq6Rz2NatWzFs2LA82xw5cqTMXxVeq1Yt2NraYsWKFXjy5AlMTU0xePBgzJ8/X+0vCUojB5encxIiopJWVnIc5e6dd97B3bt33zq9ffv2OHr0aOkFVI7xHKN48R7tRFTiwsLC8PTp07dO19fXR+vWrUsxorKjPPXNy5cvcfLkyTzb1K5dG7Vr1y6liIiISJNKOofFxMQgIiIizzZubm6oWrVqoZdRXpRGDi5P5yRERCWNOa5sO3XqFFJSUt46vWrVqnBzcyvFiMovnmMULxbaiYiIiIiIiIiIiIiKgA9DJSIiIiIiIiIiIiIqAt6jvZhkZWXh4cOHqFKlCmQymabDISKickQIgWfPnsHGxgZaWvwOvLgxRxMRUWGVxRx9/PhxLFy4EGFhYYiJicHvv/+Onj17qrS5evUqpk2bhmPHjiEjIwPOzs747bffULNmTQCvbhUwefJkbNmyBampqfD09MSaNWtgaWkpzSM6OhpjxozBkSNHYGRkhCFDhmDevHnQ0fmvjHD06FFMmjQJERERsLW1xVdffYWhQ4eqvS7M0UREVFhlMUez0F5MHj58CFtbW02HQURE5di9e/dQo0YNTYdR4TBHExFRUZWlHP38+XM0atQIw4cPR69evXJMv3XrFtq0aQMfHx/MmjULSqUSERER0NPTk9pMnDgRu3fvxrZt22BsbAw/Pz/06tULp06dAgBkZmbCy8sLVlZWOH36NGJiYjB48GDo6urim2++AQBERUXBy8sLo0ePxqZNm3Do0CGMGDEC1tbW8PT0VGtdmKOJiKioylKO5j3ai0liYiJMTExw7949KJVKTYdDRETlSFJSEmxtbZGQkABjY2NNh1PhMEcTEVFhlfUcLZPJclzR3r9/f+jq6uLnn3/O9TWJiYmoVq0aNm/ejI8++ggAcO3aNTg5OSEkJAQtW7bE3r170a1bNzx8+FC6yn3dunWYNm0a/v33X8jlckybNg27d+/G5cuXVZadkJCAffv2qRU/czQRERVWWczRvKK9mGT/zE2pVPIEgYiICoU/mS4ZzNFERFRU5SVHZ2VlYffu3Zg6dSo8PT1x4cIF2NvbY/r06VIxPiwsDOnp6fDw8JBe5+joiJo1a0qF9pCQELi4uKjcSsbT0xNjxoxBREQEmjRpgpCQEJV5ZLeZMGGC2vEyRxMRUVGVpRxdNm5gQ0RERERERERFEh8fj+TkZMyfPx+dO3fGgQMH8OGHH6JXr144duwYACA2NhZyuRwmJiYqr7W0tERsbKzU5vUie/b07Gl5tUlKSkJKSkqu8aWmpiIpKUllICIiqih4RTsRERERERFRBZCVlQUA6NGjByZOnAgAaNy4MU6fPo1169ahffv2mgwP8+bNw6xZszQaAxERUUnhFe1EREREREREFYC5uTl0dHTg7OysMt7JyQnR0dEAACsrK6SlpSEhIUGlTVxcHKysrKQ2cXFxOaZnT8urjVKphL6+fq7xTZ8+HYmJidJw7969wq0oERFRGcRCOxEREREREVEFIJfL8c477yAyMlJl/PXr12FnZwcAcHNzg66uLg4dOiRNj4yMRHR0NNzd3QEA7u7uuHTpEuLj46U2wcHBUCqVUhHf3d1dZR7ZbbLnkRuFQiHdj533ZSciooqGt44hIiIiIiIiKieSk5Nx8+ZN6e+oqCiEh4fD1NQUNWvWxJQpU9CvXz+0a9cO7777Lvbt24e//voLR48eBQAYGxvDx8cHkyZNgqmpKZRKJT799FO4u7ujZcuWAIBOnTrB2dkZgwYNwoIFCxAbG4uvvvoKvr6+UCgUAIDRo0dj1apVmDp1KoYPH47Dhw8jKCgIu3fvLvU+ISIiKgtYaCciIiIiIiIqJ0JDQ/Huu+9Kf0+aNAkAMGTIEAQGBuLDDz/EunXrMG/ePIwbNw4ODg747bff0KZNG+k1S5cuhZaWFnr37o3U1FR4enpizZo10nRtbW3s2rULY8aMgbu7OwwNDTFkyBDMnj1bamNvb4/du3dj4sSJWL58OWrUqIHvv/8enp6epdALREREZY9MCCE0HURFkJSUBGNjYyQmJvLnb0REVCDMISWL/UtERIXFHFKy2L9ERFRYZTGH8B7tRERERERERERERERFwEI7EREREREREREREVERsNBORERERERERERERFQELLQTERERERERERERERUBC+1EREREREREREREREWgo+kAiIjolejoaDx69KhArzE3N0fNmjVLKCIiehP3UyIiorKpoDma+ZmIiIobC+1ERGVAdHQ0HByd8DLlRYFep6dvgMhrV/khgagUcD8lIiIqmwqTo5mfiYiouLHQTkRUBjx69AgvU17ArNtk6JrZqvWa9Mf38HjXYjx69IgfEIhKAfdTIiKisqmgOZr5mYiISgIL7UREZYiumS0UVnU1HQYR5YH7KRERUdnEHE1ERJrEh6ESERERERERERERERUBC+1EREREREREREREREXAQjsREREVyoMHD/Dxxx/DzMwM+vr6cHFxQWhoqDRdCIEZM2bA2toa+vr68PDwwI0bN1Tm8eTJE3h7e0OpVMLExAQ+Pj5ITk5WaXPx4kW0bdsWenp6sLW1xYIFC0pl/YiIiIiIiIjUxUI7ERERFdjTp0/RunVr6OrqYu/evbhy5QoWL16MqlWrSm0WLFiAFStWYN26dTh79iwMDQ3h6emJly9fSm28vb0RERGB4OBg7Nq1C8ePH8eoUaOk6UlJSejUqRPs7OwQFhaGhQsXwt/fHxs2bCjV9SUiIiIiIiLKCx+GSkRERAX27bffwtbWFgEBAdI4e3t76f9CCCxbtgxfffUVevToAQD46aefYGlpiZ07d6J///64evUq9u3bh3PnzqFZs2YAgJUrV6Jr165YtGgRbGxssGnTJqSlpeHHH3+EXC5HgwYNEB4ejiVLlqgU5ImIiIiIiIg0iVe0ExERUYH9+eefaNasGfr06QMLCws0adIE3333nTQ9KioKsbGx8PDwkMYZGxujRYsWCAkJAQCEhITAxMREKrIDgIeHB7S0tHD27FmpTbt27SCXy6U2np6eiIyMxNOnT3ONLTU1FUlJSSoDERERERERUUlioZ2IiIgK7Pbt21i7di3q1auH/fv3Y8yYMRg3bhw2btwIAIiNjQUAWFpaqrzO0tJSmhYbGwsLCwuV6To6OjA1NVVpk9s8Xl/Gm+bNmwdjY2NpsLW1LeLaEhEREREREeWNhXYiIiIqsKysLDRt2hTffPMNmjRpglGjRmHkyJFYt26dpkPD9OnTkZiYKA337t3TdEhERERERERUwbHQTkRERAVmbW0NZ2dnlXFOTk6Ijo4GAFhZWQEA4uLiVNrExcVJ06ysrBAfH68yPSMjA0+ePFFpk9s8Xl/GmxQKBZRKpcpAREREREREVJJYaCciIqICa926NSIjI1XGXb9+HXZ2dgBePRjVysoKhw4dkqYnJSXh7NmzcHd3BwC4u7sjISEBYWFhUpvDhw8jKysLLVq0kNocP34c6enpUpvg4GA4ODigatWqJbZ+RERERERERAXBQjsREREV2MSJE3HmzBl88803uHnzJjZv3owNGzbA19cXACCTyTBhwgTMnTsXf/75Jy5duoTBgwfDxsYGPXv2BPDqCvjOnTtj5MiR+Pvvv3Hq1Cn4+fmhf//+sLGxAQAMHDgQcrkcPj4+iIiIwNatW7F8+XJMmjRJU6tORERERERElIOOpgMgIiKi8uedd97B77//junTp2P27Nmwt7fHsmXL4O3tLbWZOnUqnj9/jlGjRiEhIQFt2rTBvn37oKenJ7XZtGkT/Pz80LFjR2hpaaF3795YsWKFNN3Y2BgHDhyAr68v3NzcYG5ujhkzZmDUqFGlur5EREREREREeWGhnYiIiAqlW7du6Nat21uny2QyzJ49G7Nnz35rG1NTU2zevDnP5bi6uuLEiROFjpOIiIiIiIiopPHWMURERERERERERERERcBCOxERERERERERERFREbDQTkRERERERERERERUBCy0ExEREREREREREREVQZkvtPv7+0Mmk6kMjo6O0vSXL1/C19cXZmZmMDIyQu/evREXF6cyj+joaHh5ecHAwAAWFhaYMmUKMjIyVNocPXoUTZs2hUKhQN26dREYGFgaq0dERERERERERERE5VyZL7QDQIMGDRATEyMNJ0+elKZNnDgRf/31F7Zt24Zjx47h4cOH6NWrlzQ9MzMTXl5eSEtLw+nTp7Fx40YEBgZixowZUpuoqCh4eXnh3XffRXh4OCZMmIARI0Zg//79pbqeRERERERERERERFT+lItCu46ODqysrKTB3NwcAJCYmIgffvgBS5YswXvvvQc3NzcEBATg9OnTOHPmDADgwIEDuHLlCn755Rc0btwYXbp0wZw5c7B69WqkpaUBANatWwd7e3ssXrwYTk5O8PPzw0cffYSlS5dqbJ2JiIiIiIiI3nT8+HF0794dNjY2kMlk2Llz51vbjh49GjKZDMuWLVMZ/+TJE3h7e0OpVMLExAQ+Pj5ITk5WaXPx4kW0bdsWenp6sLW1xYIFC3LMf9u2bXB0dISenh5cXFywZ8+e4lhFIiKicqlcFNpv3LgBGxsb1K5dG97e3oiOjgYAhIWFIT09HR4eHlJbR0dH1KxZEyEhIQCAkJAQuLi4wNLSUmrj6emJpKQkRERESG1en0d2m+x5EBEREREREZUFz58/R6NGjbB69eo82/3+++84c+YMbGxsckzz9vZGREQEgoODsWvXLhw/fhyjRo2SpiclJaFTp06ws7NDWFgYFi5cCH9/f2zYsEFqc/r0aQwYMAA+Pj64cOECevbsiZ49e+Ly5cvFt7JERETliI6mA8hPixYtEBgYCAcHB8TExGDWrFlo27YtLl++jNjYWMjlcpiYmKi8xtLSErGxsQCA2NhYlSJ79vTsaXm1SUpKQkpKCvT19XPElZqaitTUVOnvpKSkIq8rERERERERUV66dOmCLl265NnmwYMH+PTTT7F//354eXmpTLt69Sr27duHc+fOoVmzZgCAlStXomvXrli0aBFsbGywadMmpKWl4ccff4RcLkeDBg0QHh6OJUuWSAX55cuXo3PnzpgyZQoAYM6cOQgODsaqVauwbt26ElhzIiKisq3MX9HepUsX9OnTB66urvD09MSePXuQkJCAoKAgjcY1b948GBsbS4Otra1G4yEiIiIiIiLKysrCoEGDMGXKFDRo0CDH9JCQEJiYmEhFdgDw8PCAlpYWzp49K7Vp164d5HK51MbT0xORkZF4+vSp1Ia/DCciIvpPmS+0v8nExAT169fHzZs3YWVlhbS0NCQkJKi0iYuLg5WVFQDAysoKcXFxOaZnT8urjVKpzPVqdgCYPn06EhMTpeHevXvFsXpEREREREREhfbtt99CR0cH48aNy3V6bGwsLCwsVMbp6OjA1NS0WH4Znj09N6mpqUhKSlIZiIiIKopyV2hPTk7GrVu3YG1tDTc3N+jq6uLQoUPS9MjISERHR8Pd3R0A4O7ujkuXLiE+Pl5qExwcDKVSCWdnZ6nN6/PIbpM9j9woFAoolUqVgYiIiIiIiEhTwsLCsHz5cgQGBkImk2k6nBz4y3AiIqrIynyh/bPPPsOxY8dw584dnD59Gh9++CG0tbUxYMAAGBsbw8fHB5MmTcKRI0cQFhaGYcOGwd3dHS1btgQAdOrUCc7Ozhg0aBD++ecf7N+/H1999RV8fX2hUCgAvHoS++3btzF16lRcu3YNa9asQVBQECZOnKjJVSciIiIiIiJS24kTJxAfH4+aNWtCR0cHOjo6uHv3LiZPnoxatWoBePWL7tcvRAOAjIwMPHnypFh+GZ49PTf8ZTgREVVkZb7Qfv/+fQwYMAAODg7o27cvzMzMcObMGVSrVg0AsHTpUnTr1g29e/dGu3btYGVlhR07dkiv19bWxq5du6CtrQ13d3d8/PHHGDx4MGbPni21sbe3x+7duxEcHIxGjRph8eLF+P777+Hp6Vnq60tERERERERUGIMGDcLFixcRHh4uDTY2NpgyZQr2798P4NUvuhMSEhAWFia97vDhw8jKykKLFi2kNsePH0d6errUJjg4GA4ODqhatarUhr8MJyIi+o+OpgPIz5YtW/Kcrqenh9WrV2P16tVvbWNnZ4c9e/bkOZ8OHTrgwoULhYqRiIiIiIiIqDQkJyfj5s2b0t9RUVEIDw+HqakpatasCTMzM5X2urq6sLKygoODAwDAyckJnTt3xsiRI7Fu3Tqkp6fDz88P/fv3h42NDQBg4MCBmDVrFnx8fDBt2jRcvnwZy5cvx9KlS6X5jh8/Hu3bt8fixYvh5eWFLVu2IDQ0FBs2bCiFXiAiIip7yvwV7URERERERET0SmhoKJo0aYImTZoAACZNmoQmTZpgxowZas9j06ZNcHR0RMeOHdG1a1e0adNGpUBubGyMAwcOICoqCm5ubpg8eTJmzJiBUaNGSW1atWqFzZs3Y8OGDWjUqBG2b9+OnTt3omHDhsW3skREROVImb+inYiIiIiIiIhe6dChA4QQare/c+dOjnGmpqbYvHlznq9zdXXFiRMn8mzTp08f9OnTR+1YiIiIKjJe0U5EREREREREREREVAQstBMRERERERERERERFQEL7URERERERERERERERcBCOxERERERERERERFREbDQTkRERERERERERERUBCy0ExEREREREREREREVAQvtRERERERERERERERFwEI7EREREREREREREVERsNBORERERERERERERFQELLQTERERERERERERERUBC+1EREREREREREREREXAQjsRERERERERERERURGw0E5EREREREREREREVAQstBMRERERERERERERFQEL7URERERERERERERERcBCOxERERERERERERFREbDQTkRERERERERERERUBCy0ExEREREREREREREVAQvtRERERERERERERERFwEI7EREREREREREREVERsNBOREREBebv7w+ZTKYyODo6StNfvnwJX19fmJmZwcjICL1790ZcXJzKPKKjo+Hl5QUDAwNYWFhgypQpyMjIUGlz9OhRNG3aFAqFAnXr1kVgYGBprB4RERERERFRgbDQTkRERIXSoEEDxMTESMPJkyelaRMnTsRff/2Fbdu24dixY3j48CF69eolTc/MzISXlxfS0tJw+vRpbNy4EYGBgZgxY4bUJioqCl5eXnj33XcRHh6OCRMmYMSIEdi/f3+pricRERERERFRfnQ0HQARERGVTzo6OrCyssoxPjExET/88AM2b96M9957DwAQEBAAJycnnDlzBi1btsSBAwdw5coVHDx4EJaWlmjcuDHmzJmDadOmwd/fH3K5HOvWrYO9vT0WL14MAHBycsLJkyexdOlSeHp6luq6EhEREREREeWFV7QTERFRody4cQM2NjaoXbs2vL29ER0dDQAICwtDeno6PDw8pLaOjo6oWbMmQkJCAAAhISFwcXGBpaWl1MbT0xNJSUmIiIiQ2rw+j+w22fMgIiIiIiIiKit4RTsREREVWIsWLRAYGAgHBwfExMRg1qxZaNu2LS5fvozY2FjI5XKYmJiovMbS0hKxsbEAgNjYWJUie/b07Gl5tUlKSkJKSgr09fVzjS01NRWpqanS30lJSUVaVyIiIiIiIqL8sNBOREREBdalSxfp/66urmjRogXs7OwQFBT01gJ4aZk3bx5mzZql0RiIiIiIiIiocuGtY4iIiKjITExMUL9+fdy8eRNWVlZIS0tDQkKCSpu4uDjpnu5WVlaIi4vLMT17Wl5tlEplnsX86dOnIzExURru3btX1NUjIiIiIiIiyhOvaCfSkOjoaDx69Ejt9ubm5qhZs2YJRkREVHjJycm4desWBg0aBDc3N+jq6uLQoUPo3bs3ACAyMhLR0dFwd3cHALi7u+Prr79GfHw8LCwsAADBwcFQKpVwdnaW2uzZs0dlOcHBwdI83kahUEChUBT3KhIRERERERG9FQvtRBoQHR0NB0cnvEx5ofZr9PQNEHntKovtRFQmfPbZZ+jevTvs7Ozw8OFDzJw5E9ra2hgwYACMjY3h4+ODSZMmwdTUFEqlEp9++inc3d3RsmVLAECnTp3g7OyMQYMGYcGCBYiNjcVXX30FX19fqUg+evRorFq1ClOnTsXw4cNx+PBhBAUFYffu3ZpcdSIiIiIiIqIceOsYIg149OgRXqa8gFm3ybAasizfwazbZLxMeVGgK+CJiErS/fv3MWDAADg4OKBv374wMzPDmTNnUK1aNQDA0qVL0a1bN/Tu3Rvt2rWDlZUVduzYIb1eW1sbu3btgra2Ntzd3fHxxx9j8ODBmD17ttTG3t4eu3fvRnBwMBo1aoTFixfj+++/h6enZ6mvLxERUVlx/PhxdO/eHTY2NpDJZNi5c6c0LT09HdOmTYOLiwsMDQ1hY2ODwYMH4+HDhyrzePLkCby9vaFUKmFiYgIfHx8kJyertLl48SLatm0LPT092NraYsGCBTli2bZtGxwdHaGnpwcXF5ccv0QjIiKqTHhFO5EG6ZrZQmFVV9NhEBEV2JYtW/Kcrqenh9WrV2P16tVvbWNnZ5fvB/IOHTrgwoULhYqRiIioInr+/DkaNWqE4cOHo1evXirTXrx4gfPnz+N///sfGjVqhKdPn2L8+PH44IMPEBoaKrXz9vZGTEwMgoODkZ6ejmHDhmHUqFHYvHkzACApKQmdOnWCh4cH1q1bh0uXLmH48OEwMTHBqFGjAACnT5/GgAEDMG/ePHTr1g2bN29Gz549cf78eTRs2LD0OoSIiKiMYKGdiIiIiIiIqJzo0qULunTpkus0Y2NjBAcHq4xbtWoVmjdvjujoaNSsWRNXr17Fvn37cO7cOTRr1gwAsHLlSnTt2hWLFi2CjY0NNm3ahLS0NPz444+Qy+Vo0KABwsPDsWTJEqnQvnz5cnTu3BlTpkwBAMyZMwfBwcFYtWoV1q1bV4I9QEREVDbx1jFEREREREREFVRiYiJkMhlMTEwAACEhITAxMZGK7ADg4eEBLS0tnD17VmrTrl07yOVyqY2npyciIyPx9OlTqY2Hh4fKsjw9PRESEvLWWFJTU5GUlKQyEBERVRQstBMRERERERFVQC9fvsS0adMwYMAAKJVKAEBsbCwsLCxU2uno6MDU1BSxsbFSG0tLS5U22X/n1yZ7em7mzZsHY2NjabC1tS3aChIREZUhLLQTERERERERVTDp6eno27cvhBBYu3atpsMBAEyfPh2JiYnScO/ePU2HREREVGx4j3YiIiIiIiKiCiS7yH737l0cPnxYupodAKysrBAfH6/SPiMjA0+ePIGVlZXUJi4uTqVN9t/5tcmenhuFQgGFQlH4FSMiIirDeEU7ERERERERUQWRXWS/ceMGDh48CDMzM5Xp7u7uSEhIQFhYmDTu8OHDyMrKQosWLaQ2x48fR3p6utQmODgYDg4OqFq1qtTm0KFDKvMODg6Gu7t7Sa0aERFRmcZCOxEREREREVE5kZycjPDwcISHhwMAoqKiEB4ejujoaKSnp+Ojjz5CaGgoNm3ahMzMTMTGxiI2NhZpaWkAACcnJ3Tu3BkjR47E33//jVOnTsHPzw/9+/eHjY0NAGDgwIGQy+Xw8fFBREQEtm7diuXLl2PSpElSHOPHj8e+ffuwePFiXLt2Df7+/ggNDYWfn1+p9wkREVFZwEI7ERERERERUTkRGhqKJk2aoEmTJgCASZMmoUmTJpgxYwYePHiAP//8E/fv30fjxo1hbW0tDadPn5bmsWnTJjg6OqJjx47o2rUr2rRpgw0bNkjTjY2NceDAAURFRcHNzQ2TJ0/GjBkzMGrUKKlNq1atsHnzZmzYsAGNGjXC9u3bsXPnTjRs2LD0OoOIiKgM4T3aiYiIiIiIiMqJDh06QAjx1ul5TctmamqKzZs359nG1dUVJ06cyLNNnz590KdPn3yXR0REVBnwinYiIiIiIiIiIiIioiJgoZ2IiIiIiIiIiIiIqAhYaCciIiIiIiIiIiIiKoJyVWifP38+ZDIZJkyYII17+fIlfH19YWZmBiMjI/Tu3RtxcXEqr4uOjoaXlxcMDAxgYWGBKVOmICMjQ6XN0aNH0bRpUygUCtStWxeBgYGlsEZERERERESUn+joaJw/f75AQ3R0tKbDJiIiokqk3DwM9dy5c1i/fj1cXV1Vxk+cOBG7d+/Gtm3bYGxsDD8/P/Tq1QunTp0CAGRmZsLLywtWVlY4ffo0YmJiMHjwYOjq6uKbb74BAERFRcHLywujR4/Gpk2bcOjQIYwYMQLW1tbw9PQs9XUlIiIiIiKiV6Kjo+Hg6ISXKS8K9Do9fQNEXruKmjVrllBkRERERP8pF4X25ORkeHt747vvvsPcuXOl8YmJifjhhx+wefNmvPfeewCAgIAAODk54cyZM2jZsiUOHDiAK1eu4ODBg7C0tETjxo0xZ84cTJs2Df7+/pDL5Vi3bh3s7e2xePFiAICTkxNOnjyJpUuXstBORERERESkQY8ePcLLlBcw6zYZuma2ar0m/fE9PN61GI8ePWKhnYiIiEpFubh1jK+vL7y8vODh4aEyPiwsDOnp6SrjHR0dUbNmTYSEhAAAQkJC4OLiAktLS6mNp6cnkpKSEBERIbV5c96enp7SPHKTmpqKpKQklYGIiIiIiIhKhq6ZLRRWddUa1C3IExERERWXMn9F+5YtW3D+/HmcO3cux7TY2FjI5XKYmJiojLe0tERsbKzU5vUie/b07Gl5tUlKSkJKSgr09fVzLHvevHmYNWtWodeLiIiIiIiIiIiIiCqGMn1F+7179zB+/Hhs2rQJenp6mg5HxfTp05GYmCgN9+7d03RIRERERERERERERKQBZbrQHhYWhvj4eDRt2hQ6OjrQ0dHBsWPHsGLFCujo6MDS0hJpaWlISEhQeV1cXBysrKwAAFZWVoiLi8sxPXtaXm2USmWuV7MDgEKhgFKpVBmIiIiIiIiIiIiIqPIp04X2jh074tKlSwgPD5eGZs2awdvbW/q/rq4uDh06JL0mMjIS0dHRcHd3BwC4u7vj0qVLiI+Pl9oEBwdDqVTC2dlZavP6PLLbZM+DiIiIiIiIiIiIiOhtyvQ92qtUqYKGDRuqjDM0NISZmZk03sfHB5MmTYKpqSmUSiU+/fRTuLu7o2XLlgCATp06wdnZGYMGDcKCBQsQGxuLr776Cr6+vlAoFACA0aNHY9WqVZg6dSqGDx+Ow4cPIygoCLt37y7dFSYiIiIiIiIiIiKicqdMF9rVsXTpUmhpaaF3795ITU2Fp6cn1qxZI03X1tbGrl27MGbMGLi7u8PQ0BBDhgzB7NmzpTb29vbYvXs3Jk6ciOXLl6NGjRr4/vvv4enpqYlVIiIiIiIiIiIiIqJypNwV2o8eParyt56eHlavXo3Vq1e/9TV2dnbYs2dPnvPt0KEDLly4UBwhEhEREREREREREVElUqbv0U5EREREREREREREVNax0E5EREREREREREREVAQstBMRERERERERERERFQEL7URERERERERERERERcBCOxERERERERERERFREbDQTkRERERERERERERUBCy0ExEREREREREREREVAQvtRERERERERERERERFwEI7EREREREREREREVERsNBORERERERERERERFQELLQTERERERERERERERUBC+1EREREREREREREREXAQjsRERERERERERERURGw0E5EREREREREREREVAQstBMRERERERGVE8ePH0f37t1hY2MDmUyGnTt3qkwXQmDGjBmwtraGvr4+PDw8cOPGDZU2T548gbe3N5RKJUxMTODj44Pk5GSVNhcvXkTbtm2hp6cHW1tbLFiwIEcs27Ztg6OjI/T09ODi4oI9e/YU+/oSERGVFyy0ExERUZHNnz8fMpkMEyZMkMa9fPkSvr6+MDMzg5GREXr37o24uDiV10VHR8PLywsGBgawsLDAlClTkJGRodLm6NGjaNq0KRQKBerWrYvAwMBSWCMiIqKy6fnz52jUqBFWr16d6/QFCxZgxYoVWLduHc6ePQtDQ0N4enri5cuXUhtvb29EREQgODgYu3btwvHjxzFq1ChpelJSEjp16gQ7OzuEhYVh4cKF8Pf3x4YNG6Q2p0+fxoABA+Dj44MLFy6gZ8+e6NmzJy5fvlxyK09ERFSGsdBORERERXLu3DmsX78erq6uKuMnTpyIv/76C9u2bcOxY8fw8OFD9OrVS5qemZkJLy8vpKWl4fTp09i4cSMCAwMxY8YMqU1UVBS8vLzw7rvvIjw8HBMmTMCIESOwf//+Uls/IiKisqRLly6YO3cuPvzwwxzThBBYtmwZvvrqK/To0QOurq746aef8PDhQ+nK96tXr2Lfvn34/vvv0aJFC7Rp0wYrV67Eli1b8PDhQwDApk2bkJaWhh9//BENGjRA//79MW7cOCxZskRa1vLly9G5c2dMmTIFTk5OmDNnDpo2bYpVq1aVSj8QERGVNSy0ExERUaElJyfD29sb3333HapWrSqNT0xMxA8//IAlS5bgvffeg5ubGwICAnD69GmcOXMGAHDgwAFcuXIFv/zyCxo3bowuXbpgzpw5WL16NdLS0gAA69atg729PRYvXgwnJyf4+fnho48+wtKlSzWyvkRERGVZVFQUYmNj4eHhIY0zNjZGixYtEBISAgAICQmBiYkJmjVrJrXx8PCAlpYWzp49K7Vp164d5HK51MbT0xORkZF4+vSp1Ob15WS3yV5OblJTU5GUlKQyEBERVRQstBMREVGh+fr6wsvLK8cH7bCwMKSnp6uMd3R0RM2aNVU+6Lu4uMDS0lJq4+npiaSkJEREREhtCvohnoiIqLKKjY0FAJXcmv139rTY2FhYWFioTNfR0YGpqalKm9zm8foy3tYme3pu5s2bB2NjY2mwtbUt6CoSERGVWTqaDoCIiIjKpy1btuD8+fM4d+5cjmmxsbGQy+UwMTFRGf/mB/3CfohPSkpCSkoK9PX1cyw7NTUVqamp0t+8Wo6IiKhsmD59OiZNmiT9nZSUxGI7ERFVGLyinYiIiArs3r17GD9+PDZt2gQ9PT1Nh6OCV8sREVFlZWVlBQA5Hj4eFxcnTbOyskJ8fLzK9IyMDDx58kSlTW7zeH0Zb2uTPT03CoUCSqVSZSAiIqooWGgnIiKiAgsLC0N8fDyaNm0KHR0d6Ojo4NixY1ixYgV0dHRgaWmJtLQ0JCQkqLzuzQ/6hf0Qr1Qqc72aHXh1tVxiYqI03Lt3rzhWmYiIqMyzt7eHlZUVDh06JI1LSkrC2bNn4e7uDgBwd3dHQkICwsLCpDaHDx9GVlYWWrRoIbU5fvw40tPTpTbBwcFwcHCQnsni7u6uspzsNtnLISIiqmxYaCciIqIC69ixIy5duoTw8HBpaNasGby9vaX/6+rqqnwAj4yMRHR0tMoH/UuXLqlcVRccHAylUglnZ2epTUE/xPNqOSIiqsiSk5Ol3Au8egBqeHg4oqOjIZPJMGHCBMydOxd//vknLl26hMGDB8PGxgY9e/YEADg5OaFz584YOXIk/v77b5w6dQp+fn7o378/bGxsAAADBw6EXC6Hj48PIiIisHXrVixfvlzlti/jx4/Hvn37sHjxYly7dg3+/v4IDQ2Fn59faXcJERFRmVBi92i/ffs2ateuXVKzJyIiokIqjhxdpUoVNGzYUGWcoaEhzMzMpPE+Pj6YNGkSTE1NoVQq8emnn8Ld3R0tW7YEAHTq1AnOzs4YNGgQFixYgNjYWHz11Vfw9fWFQqEAAIwePRqrVq3C1KlTMXz4cBw+fBhBQUHYvXt3keInIiIqbcX1GTk0NBTvvvuu9Hd28XvIkCEIDAzE1KlT8fz5c4waNQoJCQlo06YN9u3bp3Krt02bNsHPzw8dO3aElpYWevfujRUrVkjTjY2NceDAAfj6+sLNzQ3m5uaYMWMGRo0aJbVp1aoVNm/ejK+++gpffPEF6tWrh507d+Y4PyAiIqosSqzQXrduXbRv3x4+Pj746KOPytz9W4mIiCqr0srRS5culT68p6amwtPTE2vWrJGma2trY9euXRgzZgzc3d1haGiIIUOGYPbs2VIbe3t77N69GxMnTsTy5ctRo0YNfP/99/D09CyRmImIiEpKceXfDh06QAjx1ukymQyzZ89WyadvMjU1xebNm/NcjqurK06cOJFnmz59+qBPnz55B0xERFRJlNitY86fPw9XV1dMmjQJVlZW+OSTT/D333+X1OKIiIhITSWVo48ePYply5ZJf+vp6WH16tV48uQJnj9/jh07duR4QJqdnR327NmDFy9e4N9//8WiRYugo6N6HUCHDh1w4cIFpKam4tatWxg6dGiRYyUiIipt/IxMRERUsZVYob1x48ZYvnw5Hj58iB9//BExMTFo06YNGjZsiCVLluDff/8tqUUTERFRHpijiYiISh/zLxERUcVW4g9D1dHRQa9evbBt2zZ8++23uHnzJj777DPY2tpi8ODBiImJKekQiIiIKBfM0URERKWP+ZeIiKhiKvFCe2hoKMaOHQtra2ssWbIEn332GW7duoXg4GA8fPgQPXr0KOkQiIiIKBfM0URERKWP+ZeIiKhiKrGHoS5ZsgQBAQGIjIxE165d8dNPP6Fr167Q0npV27e3t0dgYCBq1apVUiEQERFRLpijiYiISh/zLxERUcVWYoX2tWvXYvjw4Rg6dCisra1zbWNhYYEffvihpEIgIiKiXDBHExERlT7mXyIiooqtxArtN27cyLeNXC7HkCFDSioEIiIiygVzNBERUelj/iUiIqrYSuwe7QEBAdi2bVuO8du2bcPGjRtLarFERESUD+ZoIiKi0sf8S0REVLGVWKF93rx5MDc3zzHewsIC33zzTUktloiIiPLBHE1ERFT6mH+JiIgqthIrtEdHR8Pe3j7HeDs7O0RHR5fUYomIiCgfzNFERESlj/mXiIioYiuxQruFhQUuXryYY/w///wDMzOzklosERER5YM5moiIqPQx/xIREVVsJVZoHzBgAMaNG4cjR44gMzMTmZmZOHz4MMaPH4/+/fuX1GKJiIgoH8zRREREpY/5l4iIqGLTKakZz5kzB3fu3EHHjh2ho/NqMVlZWRg8eDDvP0dERKRBzNFERESlj/mXiIioYiuxQrtcLsfWrVsxZ84c/PPPP9DX14eLiwvs7OxKapFERESkBuZoIiKi0sf8S0REVLGVWKE9W/369VG/fv2SXgwREREVEHM0ERFR6WP+JSIiqphKrNCemZmJwODUZOsAAQAASURBVMBAHDp0CPHx8cjKylKZfvjw4ZJaNBEREeWBOZqIiKj0Mf8SERFVbCVWaB8/fjwCAwPh5eWFhg0bQiaTldSiiIiIqACYo4mIiEof8y8REVHFVmKF9i1btiAoKAhdu3YtqUUQERFRITBHExERlT7mXyIioopNq6RmLJfLUbdu3ZKaPRERERUSczQREVHpY/4lIiKq2Eqs0D558mQsX74cQogizWft2rVwdXWFUqmEUqmEu7s79u7dK01/+fIlfH19YWZmBiMjI/Tu3RtxcXEq84iOjoaXlxcMDAxgYWGBKVOmICMjQ6XN0aNH0bRpUygUCtStWxeBgYFFipuIiKisKq4cTUREROpj/iUiIqrYSuzWMSdPnsSRI0ewd+9eNGjQALq6uirTd+zYodZ8atSogfnz56NevXoQQmDjxo3o0aMHLly4gAYNGmDixInYvXs3tm3bBmNjY/j5+aFXr144deoUgFcPnPHy8oKVlRVOnz6NmJgYDB48GLq6uvjmm28AAFFRUfDy8sLo0aOxadMmHDp0CCNGjIC1tTU8PT2Lt2OIiIg0rLhyNBEREamP+ZeIiKhiK7FCu4mJCT788P/Yu/OwqKr/D+DvYRuGZUBAGFBAcgU3DE1RcyVR0dwyTVTcIg1S1NQsdzPUcs/UNrSS3DVzxz0VTUlyRy0VM5ZQAVFkPb8//HG/jmwzwDCA79fzzKNz77nnfs6Zyzn3nrlzbp9S59OzZ0+19/PmzcOqVatw+vRp1KxZE9999x3Cw8PRqVMnAEBYWBjc3d1x+vRptGrVCgcOHMCVK1dw8OBBODg4wNPTE3PnzsWUKVMwa9YsmJiYYPXq1XBzc8OiRYsAAO7u7jhx4gSWLFnCgXYiIqpyyqqPJiIiIs2x/yUiIqradDbQHhYWVuZ55uTkYPPmzXj8+DG8vb0RFRWFrKws+Pj4SGkaNGgAFxcXREZGolWrVoiMjETjxo3h4OAgpfH19cWYMWNw+fJlNGvWDJGRkWp55KUJCQkp8zIQERHpmy76aCIiIioa+18iIqKqTWdztANAdnY2Dh48iDVr1uDRo0cAgH///RdpaWla5XPx4kVYWFhALpdj9OjR2L59Ozw8PBAfHw8TExNYW1urpXdwcEB8fDwAID4+Xm2QPW993rqi0qSmpiI9Pb3AmDIyMpCamqr2IiIiqizKqo8mIiIizbH/JSIiqrp0dkf7nTt30LVrV8TGxiIjIwNvvPEGLC0tsWDBAmRkZGD16tUa51W/fn1ER0cjJSUFW7ZsQUBAAI4dO6ar0DUSGhqK2bNn6zUGIiKikijLPpqIiIg0w/6XiIioatPZHe3jxo1D8+bN8fDhQygUCml5nz59cOjQIa3yMjExQZ06deDl5YXQ0FA0bdoUy5Ytg0qlQmZmJpKTk9XSJyQkQKVSAQBUKhUSEhLyrc9bV1QapVKpFvvzpk6dipSUFOl19+5drcpERESkL2XZRxMREZFm2P8SERFVbTq7o/23337DqVOnYGJiora8Vq1auHfvXqnyzs3NRUZGBry8vGBsbIxDhw6hX79+AICYmBjExsbC29sbAODt7Y158+YhMTER9vb2AICIiAgolUp4eHhIafbs2aO2j4iICCmPgsjlcsjl8lKVg4iISB902UcTERFRwdj/EhERVW06G2jPzc1FTk5OvuX//PMPLC0tNc5n6tSp6NatG1xcXPDo0SOEh4fj6NGj2L9/P6ysrDBy5EhMmDABNjY2UCqV+OCDD+Dt7Y1WrVoBALp06QIPDw8MGTIECxcuRHx8PKZNm4agoCBpoHz06NH48ssvMXnyZIwYMQKHDx/Gpk2bsHv37rKpDCIiogqkrPpoIiIi0hz7XyIioqpNZ1PHdOnSBUuXLpXey2QypKWlYebMmejevbvG+SQmJmLo0KGoX78+OnfujLNnz2L//v144403AABLlixBjx490K9fP7Rr1w4qlQrbtm2Ttjc0NMSuXbtgaGgIb29vDB48GEOHDsWcOXOkNG5ubti9ezciIiLQtGlTLFq0CN9++y18fX1LXxFEREQVTFn10URERKS58up/c3JyMH36dLi5uUGhUKB27dqYO3cuhBBSGiEEZsyYAUdHRygUCvj4+ODGjRtq+Tx48AD+/v5QKpWwtrbGyJEj8z209cKFC3j99ddhamoKZ2dnLFy4sMzKQUREVNno7I72RYsWwdfXFx4eHnj69CkGDRqEGzduwM7ODj///LPG+Xz33XdFrjc1NcXKlSuxcuXKQtO4urrmmxrmRR06dMD58+c1jouIiKiyKqs+moiIiDRXXv3vggULsGrVKqxbtw4NGzbEuXPnMHz4cFhZWWHs2LEAgIULF2L58uVYt24d3NzcMH36dPj6+uLKlSswNTUFAPj7+yMuLg4RERHIysrC8OHDERgYiPDwcABAamoqunTpAh8fH6xevRoXL17EiBEjYG1tjcDAwDIrDxERUWWhs4H2mjVr4s8//8SGDRtw4cIFpKWlYeTIkfD39y/0AaNERESke+yjiYiIyl959b+nTp1Cr1694OfnB+DZHPA///wzfv/9dwDP7mZfunQppk2bhl69egEAfvjhBzg4OGDHjh0YOHAgrl69in379uHs2bNo3rw5AGDFihXo3r07vvjiCzg5OWH9+vXIzMzE999/DxMTEzRs2BDR0dFYvHgxB9qJiOilpLOBdgAwMjLC4MGDdbkLIiIiKgH20UREROWvPPrf1q1b4+uvv8b169dRr149/Pnnnzhx4gQWL14MALh16xbi4+Ph4+MjbWNlZYWWLVsiMjISAwcORGRkJKytraVBdgDw8fGBgYEBzpw5gz59+iAyMhLt2rVTe7irr68vFixYgIcPH6JatWo6LScREVFFo7OB9h9++KHI9UOHDtXVromIiKgI7KOJiIjKX3n1vx999BFSU1PRoEEDGBoaIicnB/PmzYO/vz8AID4+HgDg4OCgtp2Dg4O0Lj4+Hvb29mrrjYyMYGNjo5bGzc0tXx556woaaM/IyEBGRob0PjU1tTRFJSIiqlB0NtA+btw4tfdZWVl48uQJTExMYGZmxot4IiIiPWEfTUREVP7Kq//dtGkT1q9fj/DwcGk6l5CQEDg5OSEgIKBM9lFSoaGhmD17tl5jICIi0hUDXWX88OFDtVdaWhpiYmLQtm1bPmiNiIhIj9hHExERlb/y6n8nTZqEjz76CAMHDkTjxo0xZMgQjB8/HqGhoQAAlUoFAEhISFDbLiEhQVqnUqmQmJiotj47OxsPHjxQS1NQHs/v40VTp05FSkqK9Lp7924pS0tERFRx6GygvSB169bF/Pnz832TT0RERPrFPpqIiKj86aL/ffLkCQwM1C/1DQ0NkZubCwBwc3ODSqXCoUOHpPWpqak4c+YMvL29AQDe3t5ITk5GVFSUlObw4cPIzc1Fy5YtpTTHjx9HVlaWlCYiIgL169cvdH52uVwOpVKp9iIiIqoqynWgHXg2r9u///5b3rslIiKiYrCPJiIiKn9l3f/27NkT8+bNw+7du3H79m1s374dixcvRp8+fQAAMpkMISEh+PTTT7Fz505cvHgRQ4cOhZOTE3r37g0AcHd3R9euXfHuu+/i999/x8mTJxEcHIyBAwfCyckJADBo0CCYmJhg5MiRuHz5MjZu3Ihly5ZhwoQJZVYWIiKiykRnc7Tv3LlT7b0QAnFxcfjyyy/Rpk0bXe2WiIiIisE+moiIqPyVV/+7YsUKTJ8+He+//z4SExPh5OSE9957DzNmzJDSTJ48GY8fP0ZgYCCSk5PRtm1b7Nu3D6amplKa9evXIzg4GJ07d4aBgQH69euH5cuXS+utrKxw4MABBAUFwcvLC3Z2dpgxYwYCAwPLrCxERESVic4G2vO+Cc8jk8lQvXp1dOrUCYsWLdLVbomIiKgY7KOJiIjKX3n1v5aWlli6dCmWLl1aaBqZTIY5c+Zgzpw5haaxsbFBeHh4kftq0qQJfvvtt5KGSkREVKXobOqY3NxctVdOTg7i4+MRHh4OR0dHXe2WiIiIilEWffSqVavQpEkTaX5Vb29v7N27V1r/9OlTBAUFwdbWFhYWFujXr1++B6bFxsbCz88PZmZmsLe3x6RJk5Cdna2W5ujRo3j11Vchl8tRp04drF27ttTlJyIi0gdeIxMREVVt5T5HOxEREVV+NWvWxPz58xEVFYVz586hU6dO6NWrFy5fvgwAGD9+PH799Vds3rwZx44dw7///ou+fftK2+fk5MDPzw+ZmZk4deoU1q1bh7Vr16r9rP3WrVvw8/NDx44dER0djZCQEIwaNQr79+8v9/ISERERERERFUVnU8do8wCUxYsX6yoMIiIiekFZ9NE9e/ZUez9v3jysWrUKp0+fRs2aNfHdd98hPDwcnTp1AgCEhYXB3d0dp0+fRqtWrXDgwAFcuXIFBw8ehIODAzw9PTF37lxMmTIFs2bNgomJCVavXg03Nzfp5/Tu7u44ceIElixZAl9f3xKWnoiISD94jUxERFS16Wyg/fz58zh//jyysrJQv359AMD169dhaGiIV199VUonk8l0FQIREREVoKz76JycHGzevBmPHz+Gt7c3oqKikJWVBR8fHylNgwYN4OLigsjISLRq1QqRkZFo3LgxHBwcpDS+vr4YM2YMLl++jGbNmiEyMlItj7w0ISEhRcaTkZGBjIwM6X1qaqpG5SAiItIlXiMTERFVbTobaO/ZsycsLS2xbt06VKtWDQDw8OFDDB8+HK+//jomTpyoq10TERFREcqqj7548SK8vb3x9OlTWFhYYPv27fDw8EB0dDRMTExgbW2tlt7BwQHx8fEAgPj4eLVB9rz1eeuKSpOamor09HQoFIoC4woNDcXs2bM1KgMREVF54TUyERFR1aazOdoXLVqE0NBQ6QQCAKpVq4ZPP/20TJ+oTkRERNopqz66fv36iI6OxpkzZzBmzBgEBATgypUrughZK1OnTkVKSor0unv3rr5DIiIi4jUyERFRFaezO9pTU1Px33//5Vv+33//4dGjR7raLRERERWjrPpoExMT1KlTBwDg5eWFs2fPYtmyZRgwYAAyMzORnJysdld7QkICVCoVAEClUuH3339Xyy8hIUFal/dv3rLn0yiVykLvZgcAuVwOuVyucTmIiIjKA6+RiYiIqjadDbT36dMHw4cPx6JFi/Daa68BAM6cOYNJkyahb9++utotVVKxsbFISkrSOL2dnR1cXFx0GBERUdWlqz46NzcXGRkZ8PLygrGxMQ4dOoR+/foBAGJiYhAbGwtvb28AgLe3N+bNm4fExETY29sDACIiIqBUKuHh4SGl2bNnj9o+IiIipDyIiIgqE14jExERVW06G2hfvXo1PvzwQwwaNAhZWVnPdmZkhJEjR+Lzzz/X1W6pEoqNjUX9Bu54mv5E421MFWaIuXaVg+1ERCVQFn301KlT0a1bN7i4uODRo0cIDw/H0aNHsX//flhZWWHkyJGYMGECbGxsoFQq8cEHH8Db2xutWrUCAHTp0gUeHh4YMmQIFi5ciPj4eEybNg1BQUHS3eijR4/Gl19+icmTJ2PEiBE4fPgwNm3ahN27d+umYoiIiHSI18hERERVm84G2s3MzPDVV1/h888/x19//QUAqF27NszNzXW1S6qkkpKS8DT9CWx7TISxrXOx6bPu38X9XYuQlJTEgXYiohIoiz46MTERQ4cORVxcHKysrNCkSRPs378fb7zxBgBgyZIlMDAwQL9+/ZCRkQFfX1989dVX0vaGhobYtWsXxowZA29vb5ibmyMgIABz5syR0ri5uWH37t0YP348li1bhpo1a+Lbb7+Fr69vGdUEERFR+eE1MhERUdWms4H2PHFxcYiLi0O7du2gUCgghIBMJtP1bqkSMrZ1hlxVR99hEBG9NErTR3/33XdFrjc1NcXKlSuxcuXKQtO4urrmmxrmRR06dMD58+c1iomIiKgy4DUyERFR1WSgq4zv37+Pzp07o169eujevTvi4uIAACNHjsTEiRN1tVsiIiIqBvtoIiKi8sf+l4iIqGrT2UD7+PHjYWxsjNjYWJiZmUnLBwwYgH379ulqt0RERFQM9tFERETlj/0vERFR1aazqWMOHDiA/fv3o2bNmmrL69atizt37uhqt0RERFQM9tFERETlj/0vERFR1aazO9ofP36s9i19ngcPHkAul+tqt0RERFQM9tFERETlj/0vERFR1aazgfbXX38dP/zwg/ReJpMhNzcXCxcuRMeOHXW1WyIiIioG+2giIqLyx/6XiIioatPZ1DELFy5E586dce7cOWRmZmLy5Mm4fPkyHjx4gJMnT+pqt0RERFQM9tFERETlj/0vERFR1aazO9obNWqE69evo23btujVqxceP36Mvn374vz586hdu7audktERETFYB9NRERU/tj/EhERVW06uaM9KysLXbt2xerVq/HJJ5/oYhdERERUAuyjiYiIyh/7XyIioqpPJ3e0Gxsb48KFC7rImoiIiEqBfTQREVH5Y/9LRERU9els6pjBgwfju+++01X2REREVELso4mIiMof+18iIqKqTWcPQ83Ozsb333+PgwcPwsvLC+bm5mrrFy9erKtdExERURHYRxMREZU/9r9ERERVW5kPtP/999+oVasWLl26hFdffRUAcP36dbU0MpmsrHdLRERExWAfTUREVP7Y/xIREb0cynygvW7duoiLi8ORI0cAAAMGDMDy5cvh4OBQ1rsiIiIiLbCPJiIiKn/sf4mIiF4OZT5HuxBC7f3evXvx+PHjst4NERERaYl9NBERUflj/0tERPRy0NnDUPO8eFJBREREFQP7aCIiovLH/peIiKhqKvOBdplMlm9+Oc43R0REpH/so4mIiMof+18iIqKXQ5nP0S6EwLBhwyCXywEAT58+xejRo/M9UX3btm1lvWsiIiIqAvtoIiKi8sf+l4iI6OVQ5ne0BwQEwN7eHlZWVrCyssLgwYPh5OQkvc97ERERUfliH01ERFT+9NH/3rt3D4MHD4atrS0UCgUaN26Mc+fOSeuFEJgxYwYcHR2hUCjg4+ODGzduqOXx4MED+Pv7Q6lUwtraGiNHjkRaWppamgsXLuD111+HqakpnJ2dsXDhwjItBxERUWVS5ne0h4WFlXWWREREVAbYRxMREZW/8u5/Hz58iDZt2qBjx47Yu3cvqlevjhs3bqBatWpSmoULF2L58uVYt24d3NzcMH36dPj6+uLKlSswNTUFAPj7+yMuLg4RERHIysrC8OHDERgYiPDwcABAamoqunTpAh8fH6xevRoXL17EiBEjYG1tjcDAwHItMxERUUVQ5gPtRERERERERKQfCxYsgLOzs9oAv5ubm/R/IQSWLl2KadOmoVevXgCAH374AQ4ODtixYwcGDhyIq1evYt++fTh79iyaN28OAFixYgW6d++OL774Ak5OTli/fj0yMzPx/fffw8TEBA0bNkR0dDQWL17MgXYiInoplfnUMURERERERESkHzt37kTz5s3Rv39/2Nvbo1mzZvjmm2+k9bdu3UJ8fDx8fHykZVZWVmjZsiUiIyMBAJGRkbC2tpYG2QHAx8cHBgYGOHPmjJSmXbt2MDExkdL4+voiJiYGDx8+1HUxiYiIKhwOtBMRERERERFVEX///TdWrVqFunXrYv/+/RgzZgzGjh2LdevWAQDi4+MBAA4ODmrbOTg4SOvi4+Nhb2+vtt7IyAg2NjZqaQrK4/l9vCgjIwOpqalqLyIioqqCU8cQERERERERVRG5ublo3rw5PvvsMwBAs2bNcOnSJaxevRoBAQF6jS00NBSzZ8/WawxERES6wjvaiYiIiIiIiKoIR0dHeHh4qC1zd3dHbGwsAEClUgEAEhIS1NIkJCRI61QqFRITE9XWZ2dn48GDB2ppCsrj+X28aOrUqUhJSZFed+/eLUkRiYiIKiTe0U5EFVZsbCySkpI0Tm9nZwcXFxcdRkREREREVLG1adMGMTExasuuX78OV1dXAM8ejKpSqXDo0CF4enoCAFJTU3HmzBmMGTMGAODt7Y3k5GRERUXBy8sLAHD48GHk5uaiZcuWUppPPvkEWVlZMDY2BgBERESgfv36qFatWoGxyeVyyOXyMi8zERFRRVDh72gPDQ1FixYtYGlpCXt7e/Tu3TvfScPTp08RFBQEW1tbWFhYoF+/fvm+WY+NjYWfnx/MzMxgb2+PSZMmITs7Wy3N0aNH8eqrr0Iul6NOnTpYu3atrotHRIWIjY1F/Qbu8PLy0vhVv8H/7tQhIiIiInoZjR8/HqdPn8Znn32GmzdvIjw8HF9//TWCgoIAADKZDCEhIfj000+xc+dOXLx4EUOHDoWTkxN69+4N4Nkd8F27dsW7776L33//HSdPnkRwcDAGDhwIJycnAMCgQYNgYmKCkSNH4vLly9i4cSOWLVuGCRMm6KvoREREelXh72g/duwYgoKC0KJFC2RnZ+Pjjz9Gly5dcOXKFZibmwN4diKxe/dubN68GVZWVggODkbfvn1x8uRJAEBOTg78/PygUqlw6tQpxMXFYejQoTA2Npbmrbt16xb8/PwwevRorF+/HocOHcKoUaPg6OgIX19fvZWf6GWVlJSEp+lPYNtjIoxtnYtNn3X/Lu7vWoSkpCTe1U5EVAT+WoiIqGpr0aIFtm/fjqlTp2LOnDlwc3PD0qVL4e/vL6WZPHkyHj9+jMDAQCQnJ6Nt27bYt28fTE1NpTTr169HcHAwOnfuDAMDA/Tr1w/Lly+X1ltZWeHAgQMICgqCl5cX7OzsMGPGDAQGBpZreYmIiCqKCj/Qvm/fPrX3a9euhb29PaKiotCuXTukpKTgu+++Q3h4ODp16gQACAsLg7u7O06fPo1WrVrhwIEDuHLlCg4ePAgHBwd4enpi7ty5mDJlCmbNmgUTExOsXr0abm5uWLRoEYBn3+CfOHECS5Ys4UA7kR4Z2zpDrqqj7zCIiKqEvF8LPU1/ovE2pgozxFy7ysF2IqJKpEePHujRo0eh62UyGebMmYM5c+YUmsbGxgbh4eFF7qdJkyb47bffShwnERFRVVLhB9pflJKSAuBZpw8AUVFRyMrKgo+Pj5SmQYMGcHFxQWRkJFq1aoXIyEg0btwYDg4OUhpfX1+MGTMGly9fRrNmzRAZGamWR16akJCQAuPIyMhARkaG9D41NbWsikhERESkE/y1EBERERERkW5UqoH23NxchISEoE2bNmjUqBEAID4+HiYmJrC2tlZL6+DggPj4eCnN84Pseevz1hWVJjU1Fenp6VAoFGrrQkNDMXv27DIrGxEREVF54a+FiIiIiIiIylaFfxjq84KCgnDp0iVs2LBB36Fg6tSpSElJkV53797Vd0hEREREREREREREpAeV5o724OBg7Nq1C8ePH0fNmjWl5SqVCpmZmUhOTla7qz0hIQEqlUpK8/vvv6vll5CQIK3L+zdv2fNplEplvrvZAUAul0Mul5dJ2YiIiIiIiIiIiIio8qrwd7QLIRAcHIzt27fj8OHDcHNzU1vv5eUFY2NjHDp0SFoWExOD2NhYeHt7AwC8vb1x8eJFJCYmSmkiIiKgVCrh4eEhpXk+j7w0eXkQERERERERERERERWkwt/RHhQUhPDwcPzyyy+wtLSU5lS3srKCQqGAlZUVRo4ciQkTJsDGxgZKpRIffPABvL290apVKwBAly5d4OHhgSFDhmDhwoWIj4/HtGnTEBQUJN2VPnr0aHz55ZeYPHkyRowYgcOHD2PTpk3YvXu33spORERERERERERERBVfhb+jfdWqVUhJSUGHDh3g6OgovTZu3CilWbJkCXr06IF+/fqhXbt2UKlU2LZtm7Te0NAQu3btgqGhIby9vTF48GAMHToUc+bMkdK4ublh9+7diIiIQNOmTbFo0SJ8++238PX1LdfyEhEREREREREREVHlUuHvaBdCFJvG1NQUK1euxMqVKwtN4+rqij179hSZT4cOHXD+/HmtYyQiIiIiIiIiIiKil1eFv6OdiIiIiIiIiIiIiKgi40A7EREREREREREREVEpcKCdiIiItBYaGooWLVrA0tIS9vb26N27N2JiYtTSPH36FEFBQbC1tYWFhQX69euHhIQEtTSxsbHw8/ODmZkZ7O3tMWnSJGRnZ6ulOXr0KF599VXI5XLUqVMHa9eu1XXxiIiIiIiIiLTCgXYiIiLS2rFjxxAUFITTp08jIiICWVlZ6NKlCx4/fiylGT9+PH799Vds3rwZx44dw7///ou+fftK63NycuDn54fMzEycOnUK69atw9q1azFjxgwpza1bt+Dn54eOHTsiOjoaISEhGDVqFPbv31+u5SUiIiIiIiIqSoV/GCoRERFVPPv27VN7v3btWtjb2yMqKgrt2rVDSkoKvvvuO4SHh6NTp04AgLCwMLi7u+P06dNo1aoVDhw4gCtXruDgwYNwcHCAp6cn5s6diylTpmDWrFkwMTHB6tWr4ebmhkWLFgEA3N3dceLECSxZsgS+vr7lXm4iIiIiIiKigvCOdiIiIiq1lJQUAICNjQ0AICoqCllZWfDx8ZHSNGjQAC4uLoiMjAQAREZGonHjxnBwcJDS+Pr6IjU1FZcvX5bSPJ9HXpq8PAqSkZGB1NRUtRcRERERERGRLnGgnYiIiEolNzcXISEhaNOmDRo1agQAiI+Ph4mJCaytrdXSOjg4ID4+Xkrz/CB73vq8dUWlSU1NRXp6eoHxhIaGwsrKSno5OzuXuoxEREREREREReFAOxEREZVKUFAQLl26hA0bNug7FADA1KlTkZKSIr3u3r2r75CIiIiIiIioiuMc7URERFRiwcHB2LVrF44fP46aNWtKy1UqFTIzM5GcnKx2V3tCQgJUKpWU5vfff1fLLyEhQVqX92/esufTKJVKKBSKAmOSy+WQy+WlLhsRERERERGRpnhHOxEREWlNCIHg4GBs374dhw8fhpubm9p6Ly8vGBsb49ChQ9KymJgYxMbGwtvbGwDg7e2NixcvIjExUUoTEREBpVIJDw8PKc3zeeSlycuDiIiIiIiIqCLgHe1ERESktaCgIISHh+OXX36BpaWlNKe6lZUVFAoFrKysMHLkSEyYMAE2NjZQKpX44IMP4O3tjVatWgEAunTpAg8PDwwZMgQLFy5EfHw8pk2bhqCgIOmO9NGjR+PLL7/E5MmTMWLECBw+fBibNm3C7t279VZ2IiIiIiIiohfxjnYiIiLS2qpVq5CSkoIOHTrA0dFRem3cuFFKs2TJEvTo0QP9+vVDu3btoFKpsG3bNmm9oaEhdu3aBUNDQ3h7e2Pw4MEYOnQo5syZI6Vxc3PD7t27ERERgaZNm2LRokX49ttv4evrW67lJSIiIiIiIioK72gnIiIirQkhik1jamqKlStXYuXKlYWmcXV1xZ49e4rMp0OHDjh//rzWMRIRERERERGVFw60ExEREenY1atXNU5rZ2cHFxcXHUZDpC42NhZJSUlabcPjlIiIiIhIHQfaiYiIiHQkJ+0hIJNh8ODBGm9jqjBDzLWrHMSkchEbG4v6DdzxNP2JVtvxOCUiIiIiUseBdiIiIiIdyc1IA4SAbY+JMLZ1LjZ91v27uL9rEZKSkjiASeUiKSkJT9OfaHyMAjxOiYiIiIgKwoF2IgKg/c/G+ZNxIiLNGds6Q66qo+8wiArFY5SIiIiIqHQ40E5EJfrZOH8yTkRERERERERE9AwH2olI65+N8yfjRERERERERERE/8OBdiKS8GfjRERERERERERE2jPQdwBERERERERERERERJUZB9qJiIiIiIiIqqj58+dDJpMhJCREWvb06VMEBQXB1tYWFhYW6NevHxISEtS2i42NhZ+fH8zMzGBvb49JkyYhOztbLc3Ro0fx6quvQi6Xo06dOli7dm05lIiIiKhi4kA7ERERERERURV09uxZrFmzBk2aNFFbPn78ePz666/YvHkzjh07hn///Rd9+/aV1ufk5MDPzw+ZmZk4deoU1q1bh7Vr12LGjBlSmlu3bsHPzw8dO3ZEdHQ0QkJCMGrUKOzfv7/cykdERFSRcI52IiIiogrm6tWrGqe1s7Pjg6mJiCiftLQ0+Pv745tvvsGnn34qLU9JScF3332H8PBwdOrUCQAQFhYGd3d3nD59Gq1atcKBAwdw5coVHDx4EA4ODvD09MTcuXMxZcoUzJo1CyYmJli9ejXc3NywaNEiAIC7uztOnDiBJUuWwNfXVy9lJiIi0icOtBMRERFVEDlpDwGZDIMHD9Z4G1OFGWKuXeVgOxERqQkKCoKfnx98fHzUBtqjoqKQlZUFHx8faVmDBg3g4uKCyMhItGrVCpGRkWjcuDEcHBykNL6+vhgzZgwuX76MZs2aITIyUi2PvDTPT1HzooyMDGRkZEjvU1NTy6CkREREFQMH2omIiIgqiNyMNEAI2PaYCGNb52LTZ92/i/u7FiEpKYkD7UREJNmwYQP++OMPnD17Nt+6+Ph4mJiYwNraWm25g4MD4uPjpTTPD7Lnrc9bV1Sa1NRUpKenQ6FQ5Nt3aGgoZs+eXeJyERERVWQcaCciIiKqYIxtnSFX1dF3GEREVAndvXsX48aNQ0REBExNTfUdjpqpU6diwoQJ0vvU1FQ4Oxf/xTIREVFlwIehEhEREREREVURUVFRSExMxKuvvgojIyMYGRnh2LFjWL58OYyMjODg4IDMzEwkJyerbZeQkACVSgUAUKlUSEhIyLc+b11RaZRKZYF3swOAXC6HUqlUexEREVUVHGgnIiIiIiIiqiI6d+6MixcvIjo6Wno1b94c/v7+0v+NjY1x6NAhaZuYmBjExsbC29sbAODt7Y2LFy8iMTFRShMREQGlUgkPDw8pzfN55KXJy4OIiOhlw6ljiIiIiIiIiKoIS0tLNGrUSG2Zubk5bG1tpeUjR47EhAkTYGNjA6VSiQ8++ADe3t5o1aoVAKBLly7w8PDAkCFDsHDhQsTHx2PatGkICgqCXC4HAIwePRpffvklJk+ejBEjRuDw4cPYtGkTdu/eXb4FJiIiqiA40E5ERERERET0ElmyZAkMDAzQr18/ZGRkwNfXF1999ZW03tDQELt27cKYMWPg7e0Nc3NzBAQEYM6cOVIaNzc37N69G+PHj8eyZctQs2ZNfPvtt/D19dVHkYiIiPSOA+1EREREREREVdjRo0fV3puammLlypVYuXJlodu4urpiz549RebboUMHnD9/vixCJCIiqvQ4RzsRERERERERERERUSlwoJ2IiIiIiIiIiIiIqBQ40E5EREREREREREREVAqco52IiIiIKo3Y2FgkJSVptY2dnR1cXFx0FBEREREREREH2omIiIiokoiNjUX9Bu54mv5Eq+1MFWaIuXaVg+1ERERERKQzHGgnIiIiokohKSkJT9OfwLbHRBjbOmu0Tdb9u7i/axGSkpI40E5ERERERDrDgXYiIiIiqlSMbZ0hV9XRdxhEREREREQSPgyViIiIiIiIiIiIiKgUeEc7EREVStuHDvKBg0RERERERET0MuJAOxERFagkDx3kAweJiIiIiIiI6GXEgXYiIiqQtg8d5AMHiYiIiIiIiOhlxYF2IiIqEh86SERERERERERUtAr/MNTjx4+jZ8+ecHJygkwmw44dO9TWCyEwY8YMODo6QqFQwMfHBzdu3FBL8+DBA/j7+0OpVMLa2hojR45EWlqaWpoLFy7g9ddfh6mpKZydnbFw4UJdF42IiIiIiIiIiIiIqoAKP9D++PFjNG3aFCtXrixw/cKFC7F8+XKsXr0aZ86cgbm5OXx9ffH06VMpjb+/Py5fvoyIiAjs2rULx48fR2BgoLQ+NTUVXbp0gaurK6KiovD5559j1qxZ+Prrr3VePiIiIiIiIiIiIiKq3Cr81DHdunVDt27dClwnhMDSpUsxbdo09OrVCwDwww8/wMHBATt27MDAgQNx9epV7Nu3D2fPnkXz5s0BACtWrED37t3xxRdfwMnJCevXr0dmZia+//57mJiYoGHDhoiOjsbixYvVBuTp5RIbG4ukpCSN09vZ2XFeaiIiIiIiIiIiopdQhR9oL8qtW7cQHx8PHx8faZmVlRVatmyJyMhIDBw4EJGRkbC2tpYG2QHAx8cHBgYGOHPmDPr06YPIyEi0a9cOJiYmUhpfX18sWLAADx8+RLVq1fLtOyMjAxkZGdL71NRUHZWS9CE2Nhb1G7jjafoTjbcxVZgh5tpVDrYTERERERERERG9ZCr1QHt8fDwAwMHBQW25g4ODtC4+Ph729vZq642MjGBjY6OWxs3NLV8eeesKGmgPDQ3F7Nmzy6YgVOEkJSXhafoT2PaYCGNb52LTZ92/i/u7FiEpKYkD7URERERERERERC+ZSj3Qrk9Tp07FhAkTpPepqalwdi5+QJYqF2NbZ8hVdfQdBhEREREREREREVVgFf5hqEVRqVQAgISEBLXlCQkJ0jqVSoXExES19dnZ2Xjw4IFamoLyeH4fL5LL5VAqlWovIiIiIiIiIiIiInr5VOqBdjc3N6hUKhw6dEhalpqaijNnzsDb2xsA4O3tjeTkZERFRUlpDh8+jNzcXLRs2VJKc/z4cWRlZUlpIiIiUL9+/QKnjSEiIiLg+PHj6NmzJ5ycnCCTybBjxw619UIIzJgxA46OjlAoFPDx8cGNGzfU0jx48AD+/v5QKpWwtrbGyJEjkZaWppbmwoULeP3112FqagpnZ2csXLhQ10UjIiIiIiIi0kqFnzomLS0NN2/elN7funUL0dHRsLGxgYuLC0JCQvDpp5+ibt26cHNzw/Tp0+Hk5ITevXsDANzd3dG1a1e8++67WL16NbKyshAcHIyBAwfCyckJADBo0CDMnj0bI0eOxJQpU3Dp0iUsW7YMS5Ys0UeRiYiIKoXHjx+jadOmGDFiBPr27Ztv/cKFC7F8+XKsW7dO6qN9fX1x5coVmJqaAgD8/f0RFxeHiIgIZGVlYfjw4QgMDER4eDiAZ1+gd+nSBT4+Pli9ejUuXryIESNGwNraGoGBgeVaXiIiIqparl69qlV6Ozs7PpOLiIgKVeEH2s+dO4eOHTtK7/PmRQ8ICMDatWsxefJkPH78GIGBgUhOTkbbtm2xb98+6QIeANavX4/g4GB07twZBgYG6NevH5YvXy6tt7KywoEDBxAUFAQvLy/Y2dlhxowZvIAnIiIqQrdu3dCtW7cC1wkhsHTpUkybNg29evUCAPzwww9wcHDAjh07MHDgQFy9ehX79u3D2bNn0bx5cwDAihUr0L17d3zxxRdwcnLC+vXrkZmZie+//x4mJiZo2LAhoqOjsXjxYvbTREREVCI5aQ8BmQyDBw/WajtThRlirl3lYDsRERWowg+0d+jQAUKIQtfLZDLMmTMHc+bMKTSNjY2NdGdcYZo0aYLffvutxHESERHR/9y6dQvx8fHw8fGRlllZWaFly5aIjIzEwIEDERkZCWtra2mQHQB8fHxgYGCAM2fOoE+fPoiMjES7du1gYmIipfH19cWCBQvw8OHDAqd4y8jIQEZGhvQ+NTVVR6UkIiKiyig3Iw0QArY9JsLY1lmjbbLu38X9XYuQlJTEgXYiIipQhR9oJyKqKGJjY5GUlKRxev60lF5m8fHxAAAHBwe15Q4ODtK6+Ph42Nvbq603MjKCjY2NWho3N7d8eeStK2igPTQ0FLNnzy6bghAREVGVZWzrDLmqjr7DICKiKoID7UREGoiNjUX9Bu54mv5E423401Ii/Zg6dao01Rzw7I52Z2fN7lYjIiIiIiIiKgkOtBMRaSApKQlP059o/PNS/rSUXnYqlQoAkJCQAEdHR2l5QkICPD09pTSJiYlq22VnZ+PBgwfS9iqVCgkJCWpp8t7npXmRXC6HXC4vk3IQERERERERaYID7RWQttNTAJyigqi88OelRJpxc3ODSqXCoUOHpIH11NRUnDlzBmPGjAEAeHt7Izk5GVFRUfDy8gIAHD58GLm5uWjZsqWU5pNPPkFWVhaMjY0BABEREahfv36B08YQERERERER6QMH2iuYkkxPAXCKCiIiKn9paWm4efOm9P7WrVuIjo6GjY0NXFxcEBISgk8//RR169aFm5sbpk+fDicnJ/Tu3RsA4O7ujq5du+Ldd9/F6tWrkZWVheDgYAwcOBBOTk4AgEGDBmH27NkYOXIkpkyZgkuXLmHZsmVYsmSJPopMREREREREVCAOtFcw2k5PAXCKCiIi0o9z586hY8eO0vu8edEDAgKwdu1aTJ48GY8fP0ZgYCCSk5PRtm1b7Nu3D6amptI269evR3BwMDp37gwDAwP069cPy5cvl9ZbWVnhwIEDCAoKgpeXF+zs7DBjxgwEBgaWX0GJiIgqkdDQUGzbtg3Xrl2DQqFA69atsWDBAtSvX19K8/TpU0ycOBEbNmxARkYGfH198dVXX6k9xDw2NhZjxozBkSNHYGFhgYCAAISGhsLI6H/DCEePHsWECRNw+fJlODs7Y9q0aRg2bFh5FpeIiKjC4EB7BcXpKYiIqKLr0KEDhBCFrpfJZJgzZw7mzJlTaBobGxuEh4cXuZ8mTZrgt99+K3GcREREL5Njx44hKCgILVq0QHZ2Nj7++GN06dIFV65cgbm5OQBg/Pjx2L17NzZv3gwrKysEBwejb9++OHnyJAAgJycHfn5+UKlUOHXqFOLi4jB06FAYGxvjs88+A/Dsl2x+fn4YPXo01q9fj0OHDmHUqFFwdHSEr6+v3spPRESkLxxoJyIiIiIiIqoi9u3bp/Z+7dq1sLe3R1RUFNq1a4eUlBR89913CA8PR6dOnQAAYWFhcHd3x+nTp9GqVSscOHAAV65cwcGDB+Hg4ABPT0/MnTsXU6ZMwaxZs2BiYoLVq1fDzc0NixYtAvBsSrgTJ05gyZIlHGgnIqKXkoG+AyAiIiIiIiIi3UhJSQHw7FdkABAVFYWsrCz4+PhIaRo0aAAXFxdERkYCACIjI9G4cWO1qWR8fX2RmpqKy5cvS2mezyMvTV4eRERELxve0U5ERERERERUBeXm5iIkJARt2rRBo0aNAADx8fEwMTGBtbW1WloHBwfEx8dLaZ4fZM9bn7euqDSpqalIT0+HQqHIF09GRgYyMjKk96mpqaUrIBERUQXCO9qJiIiIiIiIqqCgoCBcunQJGzZs0HcoAJ49qNXKykp6OTs76zskIiKiMsOBdiIiIiIiIqIqJjg4GLt27cKRI0dQs2ZNablKpUJmZiaSk5PV0ickJEClUklpEhIS8q3PW1dUGqVSWeDd7AAwdepUpKSkSK+7d++WqoxEREQVCQfaiYiIiIiIiKoIIQSCg4Oxfft2HD58GG5ubmrrvby8YGxsjEOHDknLYmJiEBsbC29vbwCAt7c3Ll68iMTERClNREQElEolPDw8pDTP55GXJi+PgsjlciiVSrUXERFRVcE52omIiIiIiIiqiKCgIISHh+OXX36BpaWlNKe6lZUVFAoFrKysMHLkSEyYMAE2NjZQKpX44IMP4O3tjVatWgEAunTpAg8PDwwZMgQLFy5EfHw8pk2bhqCgIMjlcgDA6NGj8eWXX2Ly5MkYMWIEDh8+jE2bNmH37t16KzsREZE+8Y52IiIiIiIioipi1apVSElJQYcOHeDo6Ci9Nm7cKKVZsmQJevTogX79+qFdu3ZQqVTYtm2btN7Q0BC7du2CoaEhvL29MXjwYAwdOhRz5syR0ri5uWH37t2IiIhA06ZNsWjRInz77bfw9fUt1/ISERFVFLyjnYiIiIiIiKiKEEIUm8bU1BQrV67EypUrC03j6uqKPXv2FJlPhw4dcP78ea1jJCIiqop4RzsRERERERERERERUSlwoJ2IiIiIiIiIiIiIqBQ40E5EREREREREREREVAocaCciIiIiIiIiIiIiKgU+DJWIiOg5sbGxSEpK0ji9nZ0dXFxcdBgREREREREREVV0HGgnIqJKQ9eD4LGxsajfwB1P059ovI2pwgwx165ysJ2IiIiIiIjoJcaBdiIiqhTKYxA8KSkJT9OfwLbHRBjbOhebPuv+XdzftQhJSUkcaCe9unr1apmmIyIiIiIiIu1woP0lxakRiKiyKc9BcGNbZ8hVdUoaKlG5yUl7CMhkGDx4sL5DISIiIiIieqlxoP0lxKkRiKgy4yA40f/kZqQBQmj8BVT63+eQ8ttP5RAZERERERHRy4UD7S8hTo1ARERUtWj6BVTW/bvlEA0REREREdHLhwPtLzHeFUpERERERERERERUehxoJyIiIiIiIiIqY3w2GhHRy4UD7UREREREREREZYjPRiMievlwoJ2IiIiIiIiIqAzx2WhERC8fDrQTERERkd5o87P6q1ev6jgaIiKissVnoxERvTw40E5EREREelGSn9UTERERERFVRBxoJyIiIiK90PZn9el/n0PKbz+VQ2RERERERETa4UA7EREREemVpj+rz7p/txyiISIiIiIi0h4H2omIiIiIiIiINKDp80L4XBEiopcPB9qJiIiIiIiIiIqQk/YQkMkwePBgfYdCREQVFAfaiYiIiIiIiIiKkJuRBgih8+eKaHMnvJ2dHVxcXLTeBxER6QYH2omIiIiIiIiINKCr54qU5I55udwUW7dugaOjo0bpOTBPRKRbHGgnIiIiIipHsbGxSEpK0jg9B0aIiKo+be+Yf/rPZSQf/hY9evTQeB+mCjPEXLvKPoWISEc40E5EREREVE5iY2NRv4E7nqY/0XgbDowQEb08tLpjXouB+az7d3F/1yIkJSWxPyEi0hEOtBMRERERlZOkpCQ8TX/CgREiIioTmg7MExGR7nGgnYiIiIjKjDbTomjzwLeqhgMjRERERERVCwfaiYiIiKhMlGRaFCp7/LKDiIiIiKj8caCdiIiIiMqEttOipP99Dim//VQOkb08+GUHEREREZF+cKD9BStXrsTnn3+O+Ph4NG3aFCtWrMBrr72m77CIiIheeuyjKw+tHuRGZYpfdhBReWP/XLlo80smOzs7Ph+EiEgLHGh/zsaNGzFhwgSsXr0aLVu2xNKlS+Hr64uYmBjY29vrOzwiIqKXFvtoIu3wyw4iKg/snyuPnLSHgEyGwYMHa7yNqcIMMdeucrCdiEhDHGh/zuLFi/Huu+9i+PDhAIDVq1dj9+7d+P777/HRRx/pOToiIqKXF/to/dL07reKPN+3Lu/g45zoRPSyYv9ceeRmpAFCaPyLp6z7d3F/1yIkJSVxoJ2ISEMcaP9/mZmZiIqKwtSpU6VlBgYG8PHxQWRkpB4jIyIiermxj9afktz9VtHo+g4+zomuGW2+jACAjIwMyOVyjdNzegOi8sf+uXLS9BdPJaHrth5ge09EFRsH2v9fUlIScnJy4ODgoLbcwcEB165dy5c+IyMDGRkZ0vuUlBQAQGpqaqniSEtLe5Z//E3kZj7VaJusB/8AAKKioqTtixITE6PVPrTNH3h2gpWbm6tR2ooWT3nEVNHKXB7xVMSYKlI82uZfkn0AFavMFTGeku4jLS2tVO1/3rZCiBLnUZVV6j76/6fl0PiYqmDpM/69CggBZYu+MLSqXmz6zH+v4/GVIxUmfkD7MuSk/IfUs9uwf/9+1K9fv9j0MTExeJr+RHd1pOtzjBLUqbYxJSQkYPCQocjM0Cz/Z2QANG8TTeSm+OnHH/K1E4XR9txQ1+krYkwVKX1pzpPYR+uOtv0zUHH66BK1fRWsj9Z5+grY1gOVv72vaOkrYkyVPX1FjKmipVepVFCpVBqnL0yF7KMFCSGEuHfvngAgTp06pbZ80qRJ4rXXXsuXfubMmQLPegS++OKLL774KpPX3bt3y6vbq1TYR/PFF1988aXvF/vo/LTtn4VgH80XX3zxxVfZvypSH8072v+fnZ0dDA0NkZCQoLY8ISGhwG9Zpk6digkTJkjvc3Nz8eDBA9ja2kImk5U4jtTUVDg7O+Pu3btQKpUlzqcqYF08w3p4hvXwDOvhf6pSXQgh8OjRIzg5Oek7lAqJfXTlxnorGdZbybDeSob1Vjj20YXTtn8G2EdXNqxX3WC9lj3WqW5U9HqtiH00B9r/n4mJCby8vHDo0CH07t0bwLNO/9ChQwgODs6XXi6X55tLzNrausziUSqVFfIg1gfWxTOsh2dYD8+wHv6nqtSFlZWVvkOosNhHVw2st5JhvZUM661kWG8FYx9dMG37Z4B9dGXFetUN1mvZY53qRkWu14rWR3Og/TkTJkxAQEAAmjdvjtdeew1Lly7F48ePpSeoExERkX6wjyYiIqp42D8TERH9DwfanzNgwAD8999/mDFjBuLj4+Hp6Yl9+/Zp/JANIiIi0g320URERBUP+2ciIqL/4UD7C4KDgwv9mVt5kMvlmDlzZr6f072MWBfPsB6eYT08w3r4H9bFy4d9dOXEeisZ1lvJsN5KhvVGpaHv/hngMawrrFfdYL2WPdapbrBetScTQgh9B0FEREREREREREREVFkZ6DsAIiIiIiIiIiIiIqLKjAPtRERERERERERERESlwIF2IiIiIiIiIiIiIqJS4EA7EREREREREREREVEpcKC9glm5ciVq1aoFU1NTtGzZEr///ru+QypXoaGhaNGiBSwtLWFvb4/evXsjJiZG32Hp3fz58yGTyRASEqLvUPTi3r17GDx4MGxtbaFQKNC4cWOcO3dO32GVq5ycHEyfPh1ubm5QKBSoXbs25s6di6r+POvjx4+jZ8+ecHJygkwmw44dO9TWCyEwY8YMODo6QqFQwMfHBzdu3NBPsFTlvex99IvK4u/zwYMH8Pf3h1KphLW1NUaOHIm0tLRyLEX50uQ85+nTpwgKCoKtrS0sLCzQr18/JCQkqKWJjY2Fn58fzMzMYG9vj0mTJiE7O7s8i1KuVq1ahSZNmkCpVEKpVMLb2xt79+6V1rPONFPQ+STrjqoK9tElV1Z9ExWtpG0wqStubIDXh9rTZKyB9ao5DrRXIBs3bsSECRMwc+ZM/PHHH2jatCl8fX2RmJio79DKzbFjxxAUFITTp08jIiICWVlZ6NKlCx4/fqzv0PTm7NmzWLNmDZo0aaLvUPTi4cOHaNOmDYyNjbF3715cuXIFixYtQrVq1fQdWrlasGABVq1ahS+//BJXr17FggULsHDhQqxYsULfoenU48eP0bRpU6xcubLA9QsXLsTy5cuxevVqnDlzBubm5vD19cXTp0/LOVKq6thH51cWf5/+/v64fPkyIiIisGvXLhw/fhyBgYHlVYRyp8l5zvjx4/Hrr79i8+bNOHbsGP7991/07dtXWp+TkwM/Pz9kZmbi1KlTWLduHdauXYsZM2boo0jlombNmpg/fz6ioqJw7tw5dOrUCb169cLly5cBsM40Udj5JOuOqgL20aVTFn0TFa2kbTCp02RsgNeH2tNkrIH1qgVBFcZrr70mgoKCpPc5OTnCyclJhIaG6jEq/UpMTBQAxLFjx/Qdil48evRI1K1bV0RERIj27duLcePG6TukcjdlyhTRtm1bfYehd35+fmLEiBFqy/r27Sv8/f31FFH5AyC2b98uvc/NzRUqlUp8/vnn0rLk5GQhl8vFzz//rIcIqSpjH120kvx9XrlyRQAQZ8+eldLs3btXyGQyce/evXKLXZ9ePM9JTk4WxsbGYvPmzVKaq1evCgAiMjJSCCHEnj17hIGBgYiPj5fSrFq1SiiVSpGRkVG+BdCjatWqiW+//ZZ1poHCzidZd1RVsI8uWyXpm6hwpWmDSV1xYwO8PiyZ4sYaWK/a4R3tFURmZiaioqLg4+MjLTMwMICPjw8iIyP1GJl+paSkAABsbGz0HIl+BAUFwc/PT+24eNns3LkTzZs3R//+/WFvb49mzZrhm2++0XdY5a5169Y4dOgQrl+/DgD4888/ceLECXTr1k3PkenPrVu3EB8fr/b3YWVlhZYtW77U7SaVPfbR2tPk7zMyMhLW1tZo3ry5lMbHxwcGBgY4c+ZMucesDy+e50RFRSErK0ut3ho0aAAXFxe1emvcuDEcHBykNL6+vkhNTZXu8K7KcnJysGHDBjx+/Bje3t6sMw0Udj7JuqOqgH102StJ30SFK00bTOqKGxvg9WHJFDfWwHrVjpG+A6BnkpKSkJOTo3YSCwAODg64du2anqLSr9zcXISEhKBNmzZo1KiRvsMpdxs2bMAff/yBs2fP6jsUvfr777+xatUqTJgwAR9//DHOnj2LsWPHwsTEBAEBAfoOr9x89NFHSE1NRYMGDWBoaIicnBzMmzcP/v7++g5Nb+Lj4wGgwHYzbx1RWWAfrT1N/j7j4+Nhb2+vtt7IyAg2NjYvxd9wQec58fHxMDExgbW1tVraF+utoHrNW1dVXbx4Ed7e3nj69CksLCywfft2eHh4IDo6mnVWhKLOJ3m8UVXAPrpslbRvooKVtg0mdcWNDfD6sGSKG2tgvWqHA+1UYQUFBeHSpUs4ceKEvkMpd3fv3sW4ceMQEREBU1NTfYejV7m5uWjevDk+++wzAECzZs1w6dIlrF69+qUaaN+0aRPWr1+P8PBwNGzYENHR0QgJCYGTk9NLVQ9ERFXFy3yeUxL169dHdHQ0UlJSsGXLFgQEBODYsWP6DqtC4/kkEWmLfVPZYRtc9jg2oBscayhbnDqmgrCzs4OhoWG+J0wnJCRApVLpKSr9CQ4Oxq5du3DkyBHUrFlT3+GUu6ioKCQmJuLVV1+FkZERjIyMcOzYMSxfvhxGRkbIycnRd4jlxtHRER4eHmrL3N3dERsbq6eI9GPSpEn46KOPMHDgQDRu3BhDhgzB+PHjERoaqu/Q9CavbWS7SbrGPlp7mvx9qlSqfA+qy87OxoMHD6p8vRZ2nqNSqZCZmYnk5GS19C/WW0H1mreuqjIxMUGdOnXg5eWF0NBQNG3aFMuWLWOdFaG480kHBwfWHVV67KPLTmn6JsqvLNpgUlfc2ACvD0umuLEG1qt2ONBeQZiYmMDLywuHDh2SluXm5uLQoUPw9vbWY2TlSwiB4OBgbN++HYcPH4abm5u+Q9KLzp074+LFi4iOjpZezZs3h7+/P6Kjo2FoaKjvEMtNmzZtEBMTo7bs+vXrcHV11VNE+vHkyRMYGKg32YaGhsjNzdVTRPrn5uYGlUql1m6mpqbizJkzL1W7SbrHPlp7mvx9ent7Izk5GVFRUVKaw4cPIzc3Fy1btiz3mMtDcec5Xl5eMDY2Vqu3mJgYxMbGqtXbxYsX1b6kiIiIgFKpzHfxWZXl5uYiIyODdVaE4s4nmzdvzrqjSo99dOmVRd9E+ZVFG0zqihsb4PVhyRQ31sB61ZKeH8ZKz9mwYYOQy+Vi7dq14sqVKyIwMFBYW1uL+Ph4fYdWbsaMGSOsrKzE0aNHRVxcnPR68uSJvkPTu+efUP4y+f3334WRkZGYN2+euHHjhli/fr0wMzMTP/30k75DK1cBAQGiRo0aYteuXeLWrVti27Ztws7OTkyePFnfoenUo0ePxPnz58X58+cFALF48WJx/vx5cefOHSGEEPPnzxfW1tbil19+ERcuXBC9evUSbm5uIj09Xc+RU1XDPjq/svj77Nq1q2jWrJk4c+aMOHHihKhbt65455139FUkndPkPGf06NHCxcVFHD58WJw7d054e3sLb29vaX12drZo1KiR6NKli4iOjhb79u0T1atXF1OnTtVHkcrFRx99JI4dOyZu3bolLly4ID766CMhk8nEgQMHhBCsM228eD7JuqOqgH106ZRF30Sa0bYNJnWajA3w+lB7mow1sF41x4H2CmbFihXCxcVFmJiYiNdee02cPn1a3yGVKwAFvsLCwvQdmt69rAPtQgjx66+/ikaNGgm5XC4aNGggvv76a32HVO5SU1PFuHHjhIuLizA1NRWvvPKK+OSTT0RGRoa+Q9OpI0eOFNgmBAQECCGEyM3NFdOnTxcODg5CLpeLzp07i5iYGP0GTVXWy95Hv6gs/j7v378v3nnnHWFhYSGUSqUYPny4ePTokR5KUz40Oc9JT08X77//vqhWrZowMzMTffr0EXFxcWr53L59W3Tr1k0oFAphZ2cnJk6cKLKyssq5NOVnxIgRwtXVVZiYmIjq1auLzp07S4PsQrDOtPHi+STrjqoK9tElV1Z9ExWvJG0wqStubIDXh9rTZKyB9ao5mRBClMed80REREREREREREREVRHnaCciIiIiIiIiIiIiKgUOtBMRERERERERERERlQIH2omIiIiIiIiIiIiISoED7UREREREREREREREpcCBdiIiIiIiIiIiIiKiUuBAOxERERERERERERFRKXCgnYiIiIiIiIiIiIioFDjQTqShDh06ICQkRN9hVFjDhg1D79699R0G1q5dC2tr62LTyWQy7NixQ+fxlJXKFi8RUWVUkfv6a9euoVWrVjA1NYWnp2eRaW/fvg2ZTIbo6GgAwNGjRyGTyZCcnFzi/VfkuinMrFmz4ODgoFEfWlHOY4iI9KEit/Ha9H/6VpGu2cr6M501a1ax9V9V+tIdO3agTp06MDQ0LLYONR1/oJcHB9qJKoDK1Di/ePFe0QwYMADXr1+X3mtyQqCtshiw0FZcXBy6detWbvsjIqKKZebMmTA3N0dMTAwOHTqk1batW7dGXFwcrKysik1bWB+3bds2zJ07V6v96tPVq1cxe/ZsrFmzpkL0ofo4dyAiqgpK0/+Vt4rQ3+Qp6377ww8/1Ev96+J6vjjvvfce3nrrLdy9e7dCnPtUpC9wqHhG+g6AiKgsKRQKKBQKfYcBAMjMzISJiUmZ5KVSqcokHyIiKl85OTmQyWQwMCjd/S1//fUX/Pz84OrqqvW2JiYmpe5HbGxsSrV9efvrr78AAL169YJMJtNzNEREL5+K0P9pQgiBnJwcGBmVfnisIl2zlXW/bWFhAQsLizLNsyJKS0tDYmIifH194eTkpO9wqBLiHe1EWsjNzcXkyZNhY2MDlUqFWbNmSetiY2PRq1cvWFhYQKlU4u2330ZCQoK0/s8//0THjh1haWkJpVIJLy8vnDt3DkePHsXw4cORkpICmUwGmUymlm9hatWqhU8//RRDhw6FhYUFXF1dsXPnTvz3339SHE2aNMG5c+fUttu6dSsaNmwIuVyOWrVqYdGiRfny/eyzzzBixAhYWlrCxcUFX3/9tbTezc0NANCsWTPIZDJ06NBBbfsvvvgCjo6OsLW1RVBQELKysqR1X331FerWrQtTU1M4ODjgrbfeKracu3btgrW1NXJycgAA0dHRkMlk+Oijj6Q0o0aNwuDBgwGo/zpg7dq1mD17Nv7880+pbteuXSttl5SUhD59+sDMzAx169bFzp07i43n9u3b6NixIwCgWrVqkMlkGDZsGIBnP88LDg5GSEgI7Ozs4OvrW+AvAJKTkyGTyXD06FHk5uaiZs2aWLVqldp+zp8/DwMDA9y5cwcAv8UmopdLhw4dMHbs2AL73OLaVeB/dw/v378fzZo1g0KhQKdOnZCYmIi9e/fC3d0dSqUSgwYNwpMnT9T2nZ2djeDgYFhZWcHOzg7Tp0+HEEJan5GRgQ8//BA1atSAubk5WrZsKe0X+F8/tHPnTnh4eEAulyM2NrbI8ubm5mLOnDmoWbMm5HI5PD09sW/fPmm9TCZDVFQU5syZo/F5wvNevJv6zp076NmzJ6pVqwZzc3M0bNgQe/bsKbaPe/7n08WdLwDAqVOn4OnpCVNTUzRv3hw7duxQ++wePnwIf39/VK9eHQqFAnXr1kVYWJhGZbp48SI6deoEhUIBW1tbBAYGIi0tDcCzu9969uwJADAwMNBqoL2o85iHDx9i6NChqFatGszMzNCtWzfcuHFDWl+SeiUieh77v9L1f3l1tGHDBrRu3RqmpqZo1KgRjh07JqXJq6O9e/fCy8sLcrkcJ06cQG5uLkJDQ+Hm5gaFQoGmTZtiy5YtUpwluWYrqq8CCp7epXfv3mp9REmuoQvKW5N++59//sE777wDGxsbmJubo3nz5jhz5gyA/HeW5+TkYMKECbC2toatrS0mT56sdrzk1VthdQr877M4dOgQmjdvDjMzM7Ru3RoxMTEACr+eF0Jg1qxZcHFxgVwuh5OTE8aOHatRvRTVlx89ehSWlpYAgE6dOqn9bRVn//79cHd3h4WFBbp27Yq4uDi1eijqOM/MzERwcDAcHR1hamoKV1dXhIaGAnj2uQFAnz59IJPJpPdUgQki0kj79u2FUqkUs2bNEtevXxfr1q0TMplMHDhwQOTk5AhPT0/Rtm1bce7cOXH69Gnh5eUl2rdvL23fsGFDMXjwYHH16lVx/fp1sWnTJhEdHS0yMjLE0qVLhVKpFHFxcSIuLk48evSo2HhcXV2FjY2NWL16tbh+/boYM2aMUCqVomvXrmLTpk0iJiZG9O7dW7i7u4vc3FwhhBDnzp0TBgYGYs6cOSImJkaEhYUJhUIhwsLC8uW7cuVKcePGDREaGioMDAzEtWvXhBBC/P777wKAOHjwoIiLixP3798XQggREBAglEqlGD16tLh69ar49ddfhZmZmfj666+FEEKcPXtWGBoaivDwcHH79m3xxx9/iGXLlhVbzuTkZGFgYCDOnj0rhBBi6dKlws7OTrRs2VJKU6dOHfHNN98IIYQICwsTVlZWQgghnjx5IiZOnCgaNmwo1e2TJ0+EEEIAEDVr1hTh4eHixo0bYuzYscLCwkIqT2Gys7PF1q1bBQARExMj4uLiRHJyshDi2TFiYWEhJk2aJK5duyauXbsmbt26JQCI8+fPS3k8fPhQABBHjhwRQgjx4YcfirZt26rtZ+LEiWrLAIjt27cXW19ERFVBUX2uJu3qkSNHBADRqlUrceLECfHHH3+IOnXqiPbt24suXbqIP/74Qxw/flzY2tqK+fPnq+3XwsJCjBs3Tly7dk389NNPan2ZEEKMGjVKtG7dWhw/flzcvHlTfP7550Iul4vr168LIZ71Q8bGxqJ169bi5MmT4tq1a+Lx48dFlnfx4sVCqVSKn3/+WVy7dk1MnjxZGBsbS3nGxcWJhg0biokTJ2p0nvBiHeXVx8OHD4UQQvj5+Yk33nhDXLhwQfz111/i119/FceOHSu2jxs3bpy0j+LOF1JSUoSNjY0YPHiwuHz5stizZ4+oV6+eWlxBQUHC09NTnD17Vty6dUtERESInTt3Flk2IYRIS0sTjo6Oom/fvuLixYvi0KFDws3NTQQEBAghhHj06JEICwsTAKT+vzjFnccIIcSbb74p3N3dxfHjx0V0dLTw9fUVderUEZmZmSWuVyKi57H/K5v+r2bNmmLLli3iypUrYtSoUcLS0lIkJSWp1VGTJk3EgQMHxM2bN8X9+/fFp59+Kho0aCD27dsn/vrrLxEWFibkcrk4evSoEEL7a7bi+qq8en++bxVCiF69eklpSnoNXVDexfXbjx49Eq+88op4/fXXxW+//SZu3LghNm7cKE6dOiWEEGLmzJmiadOmUn4LFiwQ1apVE1u3bhVXrlwRI0eOFJaWlqJXr15SmuLqNO+zaNmypTh69Ki4fPmyeP3110Xr1q2FEIVfz2/evFkolUqxZ88ecefOHXHmzBm1Y7UoRfXlGRkZIiYmRgAQW7duFXFxcSIjI6PI/PKOex8fH3H27FkRFRUl3N3dxaBBg6Q0xR3nn3/+uXB2dhbHjx8Xt2/fFr/99psIDw8XQgiRmJgoAIiwsDARFxcnEhMTNSon6Q8H2ok01L59+3wda4sWLcSUKVPEgQMHhKGhoYiNjZXWXb58WQAQv//+uxBCCEtLS7F27doC835+cFhTrq6uYvDgwdL7uLg4AUBMnz5dWhYZGSldZAohxKBBg8Qbb7yhls+kSZOEh4dHofnm5uYKe3t7sWrVKiFE/ov3PAEBAcLV1VVkZ2dLy/r37y8GDBgghBBi69atQqlUitTUVK3KKYQQr776qvj888+FEEL07t1bzJs3T5iYmIhHjx6Jf/75RwBQO8F7vi5fPCHIA0BMmzZNep+WliYAiL179xYbz4sDFnnat28vmjVrprZMkxPi8+fPC5lMJu7cuSOEECInJ0fUqFFDqvO8eDnQTkQvi6L6XG0GGg4ePCilCQ0NFQDEX3/9JS177733hK+vr9p+n/+CWgghpkyZItzd3YUQQty5c0cYGhqKe/fuqcXWuXNnMXXqVCGEkAZ4o6OjNS6vk5OTmDdvXr7yvv/++9L7pk2bipkzZ2qUX3ED7Y0bNxazZs0qcNui+rgXL9iLOl9YtWqVsLW1Fenp6VKab775Ri2unj17iuHDh2tUpud9/fXXolq1aiItLU1atnv3bmFgYCDi4+OFEEJs375daHNPUXHnMdevXxcAxMmTJ6X1SUlJQqFQiE2bNgkhSlavRETPY/9XNv3f818iZGVliZo1a4oFCxYIIf5XRzt27JDSPH36VJiZmUmDynlGjhwp3nnnHSGE9tdsmvRVxQ20l+YaWtt+e82aNcLS0rLQG89evK52dHQUCxculN7n1XPeQLsmdVrQ8bp7924BQDp/KOh6ftGiRaJevXrSF92a0qQvf/Fvqjh5x/3NmzelZStXrhQODg7S++KO8w8++EB06tRJ7e/veRwLqFw4dQyRFpo0aaL23tHREYmJibh69SqcnZ3h7OwsrfPw8IC1tTWuXr0KAJgwYQJGjRoFHx8fzJ8/X5o7tKzicXBwAAA0btw437LExEQAzx4M1qZNG7U82rRpgxs3bkhTs7yYr0wmg0qlkvIoSsOGDWFoaCi9z6sfAHjjjTfg6uqKV155BUOGDMH69evz/VyxMO3bt8fRo0chhMBvv/2Gvn37wt3dHSdOnMCxY8fg5OSEunXrapTX854vp7m5OZRKpUblLIqXl5fW23h6esLd3R3h4eEAgGPHjiExMRH9+/cvVSxERJVZYX1uSfNwcHCAmZkZXnnlFbVlL+bZqlUrtalGvL29pX7y4sWLyMnJQb169aS5Si0sLHDs2DG1ft3ExCRf/IVJTU3Fv//+W2D/nHcOUdbGjh2LTz/9FG3atMHMmTNx4cKFEuVT1PlCTEwMmjRpAlNTUynNa6+9prb9mDFjsGHDBnh6emLy5Mk4deqURvu9evUqmjZtCnNzc2lZmzZtkJubK/3cvCSKOo+5evUqjIyM0LJlS2m9ra0t6tevL31OZVWvRPRyY/9X+v7P29tb+r+RkRGaN2+eL8/mzZtL/7958yaePHmCN954Q618P/zwg1Q+ba/ZyqKvKs01dEGK6rejo6PRrFkzjeZ2T0lJQVxcnFqfmFfPeTSp04LicnR0BIAij/n+/fsjPT0dr7zyCt59911s374d2dnZxcatSV9eEmZmZqhdu7ZaGfLi1+Q4HzZsGKKjo1G/fn2MHTsWBw4cKHEspH8caCfSgrGxsdp7mUyG3NxcjbadNWsWLl++DD8/Pxw+fBgeHh7Yvn17mcWTd1JU0DJNYywo37x8NMmjqO0sLS3xxx9/4Oeff4ajoyNmzJiBpk2bSvPFFqVDhw44ceIE/vzzTxgbG6NBgwbo0KEDjh49imPHjqF9+/aaF07DeEvq+RMpANLDf8Rz89U9P99rHn9/f+mkLTw8HF27doWtrW2pYiEiqswKa6M1bVdfzEMmk5W63U9LS4OhoSGioqIQHR0tva5evYply5ZJ6RQKRYV+AOeoUaPw999/Y8iQIbh48SKaN2+OFStWaJ1PaeuzW7duuHPnDsaPH49///0XnTt3xocffqh1HGWltOUpq3olopcb+7/y8fx1W9686bt371Yr35UrV9TmFC/razYDA4N885o//5mW5hq6IEUdBwqFokR5FkbTOn0xLk3GMJydnRETE4OvvvoKCoUC77//Ptq1a1fo34OuFVSvL36uRXn11Vdx69YtzJ07F+np6Xj77bc1noufKh4OtBOVAXd3d9y9exd3796Vll25cgXJycnw8PCQltWrVw/jx4/HgQMH0LdvX+mBXyYmJmp3lOsyzpMnT6otO3nyJOrVq6d2B1dRTExMAKBE8RoZGcHHxwcLFy7EhQsXcPv2bRw+fLjY7V5//XU8evQIS5YskQbV8wbajx49mu+BrC/GW9Z1q00dVK9eHQDUHoby/AOM8gwaNAiXLl1CVFQUtmzZAn9//7IJloioitG0XS2pvId+5Tl9+jTq1q0LQ0NDNGvWDDk5OUhMTESdOnXUXiqVqkT7UyqVcHJyKrB/fv4coqw5Oztj9OjR2LZtGyZOnIhvvvkGQOn6+efVr18fFy9eREZGhrTs7Nmz+dJVr14dAQEB+Omnn7B06dJ8D2YriLu7O/788088fvxYWnby5EkYGBigfv36pYq7qH1mZ2erHR/3799HTEyM2uek63olopcX+z/NnT59Wvp/dnY2oqKi4O7uXmj65x/e+mL5nv/VujbXbJr0VdWrV1f7PHNycnDp0iW1fEp6Da2tJk2aIDo6Gg8ePCg2rZWVFRwdHdWOmbx6zqNpnRansOt5hUKBnj17Yvny5Th69CgiIyNx8eLFIvPStC8vS5oe50qlEgMGDMA333yDjRs3YuvWrdJnYWxszPOHSsRI3wEQVQU+Pj5o3Lgx/P39sXTpUmRnZ+P9999H+/bt0bx5c6Snp2PSpEl466234Obmhn/++Qdnz55Fv379ADx7knRaWhoOHTqEpk2bwszMDGZmZmUe58SJE9GiRQvMnTsXAwYMQGRkJL788kt89dVXGudhb28PhUKBffv2oWbNmjA1NYWVlVWx2+3atQt///032rVrh2rVqmHPnj3Izc3V6IK4WrVqaNKkCdavX48vv/wSANCuXTu8/fbbyMrKKvKO9lq1auHWrVuIjo5GzZo1YWlpCblcrnF5C+Lq6gqZTIZdu3ahe/fuUCgUsLCwKDCtQqFAq1atMH/+fLi5uSExMRHTpk0rMM7WrVtj5MiRyMnJwZtvvlmqGImIqipN29WSio2NxYQJE/Dee+/hjz/+wIoVK7Bo0SIAz74w9/f3x9ChQ7Fo0SI0a9YM//33Hw4dOoQmTZrAz8+vRPucNGkSZs6cidq1a8PT0xNhYWGIjo7G+vXry6xczwsJCUG3bt1Qr149PHz4EEeOHJEGILTp44oyaNAgfPLJJwgMDMRHH32E2NhYfPHFFwD+d7fajBkz4OXlhYYNGyIjIwO7du0qciAkj7+/P2bOnImAgADMmjUL//33Hz744AMMGTJEmjavrNWtWxe9evXCu+++izVr1sDS0hIfffQRatSogV69egEon3olopcX+z/NrVy5EnXr1oW7uzuWLFmChw8fYsSIEYWmt7S0xIcffojx48cjNzcXbdu2RUpKCk6ePAmlUomAgAAA2l2zadJXderUCRMmTMDu3btRu3ZtLF68WO1u9dJcQ2vrnXfewWeffYbevXsjNDQUjo6OOH/+PJycnNSm4skzbtw4zJ8/H3Xr1kWDBg3yxa5pnRanoOv5n3/+GTk5OWjZsiXMzMzw008/QaFQwNXVtci8NOnLdaG443zx4sVwdHREs2bNYGBggM2bN0OlUsHa2lqqg0OHDqFNmzaQy+WoVq2azmKl0uMd7URlQCaT4ZdffkG1atXQrl07+Pj44JVXXsHGjRsBAIaGhrh//z6GDh2KevXq4e2330a3bt0we/ZsAEDr1q0xevRoDBgwANWrV8fChQt1Euerr76KTZs2YcOGDWjUqBFmzJiBOXPmYNiwYRrnYWRkhOXLl2PNmjVwcnLSuEOytrbGtm3b0KlTJ7i7u2P16tX4+eef0bBhQ422b9++PXJycqS7121sbODh4QGVSlXkiUa/fv3QtWtXdOzYEdWrV8fPP/+s0f6KUqNGDcyePRsfffQRHBwcEBwcXGT677//HtnZ2fDy8kJISAg+/fTTAtP5+/vjzz//RJ8+fcr8p3tERFWJpu1qSQwdOhTp6el47bXXEBQUhHHjxiEwMFBaHxYWhqFDh2LixImoX78+evfujbNnz8LFxaXE+xw7diwmTJiAiRMnonHjxti3bx927txZouePaCInJwdBQUFwd3dH165dUa9ePelLd237uMIolUr8+uuviI6OhqenJz755BPMmDEDAKR5201MTDB16lQ0adIE7dq1g6GhITZs2FBs3mZmZti/fz8ePHiAFi1a4K233kLnzp2lL+N1JSwsDF5eXujRowe8vb0hhMCePXukn4yXR70S0cuN/Z9m5s+fj/nz56Np06Y4ceIEdu7cCTs7uyK3mTt3LqZPn47Q0FCpHd+9ezfc3NzU0ml6zaZJXzVixAgEBARg6NChaN++PV555RV07NhRWl/aa2htmJiY4MCBA7C3t0f37t3RuHFjzJ8/v9Bfvk+cOBFDhgxBQEAAvL29YWlpiT59+qil0bROi1LQ9by1tTW++eYbtGnTBk2aNMHBgwfx66+/ajSNT3F9uS4Ud5xbWlpi4cKFaN68OVq0aIHbt29jz5490nRRixYtQkREBJydndGsWTOdxUllQya0mTiIiIiIiIioBNavX4/hw4cjJSWFXygTEVGZu337Ntzc3HD+/Hl4enrqOxwieglx6hgiIiIiIipzP/zwA1555RXUqFEDf/75J6ZMmYK3336bg+xEREREVCVx6hiiCui3336DhYVFoa+qJDY2tsiyxsbGlntMo0ePLjSe0aNHl3s8RERUuRXVz/32229a5/fZZ58Vml+3bt10UIKSiY+Px+DBg+Hu7o7x48ejf//+Gj3sVBflK+vPgIiIivey9n+6VhGvoSsCXYyjdOvWrdD8PvvsszIuAVUFnDqGqAJKT0/HvXv3Cl1fp06dcoxGt7Kzs3H79u1C19eqVQtGRuX745vExESkpqYWuE6pVMLe3r5c4yEiosrt5s2bha6rUaOG1nd4P3jwAA8ePChwnUKhQI0aNbTKr6LRRfnK+jMgIqLisf/TjYp4DV0R6GIc5d69e0hPTy9wnY2NDWxsbLTOk6o2DrQTEREREREREREREZUCp44hIiIiIiIiIiIiIioFDrQTEREREREREREREZUCB9qJiIiIiIiIiIiIiEqBA+1ERERERERERERERKXAgXYiIiIiIiIiIiIiolLgQDsRERERERERERERUSlwoJ2IiIiIiIiIiIiIqBQ40E5EREREREREREREVAocaCciIiIiIiIiIiIiKgUOtBMRERERERERERERlQIH2omIiIiIiIiIiIiISoED7UREREREREREREREpcCBdiIiIiIiIiIiIiKiUuBAOxERERERERERERFRKXCgvZRmzZoFmUxWLvvq0KEDOnToIL0/evQoZDIZtmzZUi77HzZsGGrVqlUu+yqptLQ0jBo1CiqVCjKZDCEhIfoOSSsdOnRAo0aN9B1Giezbtw+enp4wNTWFTCZDcnKyvkMqkdu3b0Mmk2Ht2rX6DoX0IDs7G5MnT4azszMMDAzQu3dvjbarzH+7RBUZz7Mqlsp+nkVEusd2u2Kp7O12ZT7H5vVx+atVqxZ69OhR7vvNq6MvvviiyHR5bdTRo0fLJ7BycvbsWbRu3Rrm5uaQyWSIjo7Wd0h6xYH256xduxYymUx6mZqawsnJCb6+vli+fDkePXpUJvv5999/MWvWrAp58FXk2DTx2WefYe3atRgzZgx+/PFHDBkyRN8hvRTu37+Pt99+GwqFAitXrsSPP/4Ic3NzfYdFlUh4eDiWLl2q7zDw/fff4/PPP8dbb72FdevWYfz48foOqVBXrlzBrFmzcPv2bX2HQqQRnmdV7Ng0UZXOsz777DPs2LFD32EQVWhstyt2bJqoSu12ZcLrY3pZZGVloX///njw4AGWLFmCH3/8Ea6urvoOq9RK0/YblX04ld+cOXPg5uaGrKwsxMfH4+jRowgJCcHixYuxc+dONGnSREo7bdo0fPTRR1rl/++//2L27NmoVasWPD09Nd7uwIEDWu2nJIqK7ZtvvkFubq7OYyiNw4cPo1WrVpg5c6a+Q3mpnD17Fo8ePcLcuXPh4+Oj73BKxdXVFenp6TA2NtZ3KC+V8PBwXLp0Se932Rw+fBg1atTAkiVL9BqHJq5cuYLZs2ejQ4cOFf5uKqLn8TyL51kVwWeffYa33npL418uEb3M2G6z3Sbt8PqYCtKuXTukp6fDxMRE36GUmb/++gt37tzBN998g1GjRuk7nDJT0n4J4EB7gbp164bmzZtL76dOnYrDhw+jR48eePPNN3H16lUoFAoAgJGREYyMdFuNT548gZmZmd7/GCtDw5qYmAgPDw99h1Gh5ebmIjMzE6ampmWWZ2JiIgDA2tq6zPIsjhACT58+lf4Wy0re3Tr0ckpMTCzX45joZcTzrILxPIuIKiq22wVju1018Pq4aLw+LjsGBgZVri71caxXdJw6RkOdOnXC9OnTcefOHfz000/S8oLmoIuIiEDbtm1hbW0NCwsL1K9fHx9//DGAZ3MytWjRAgAwfPhw6Wd4efNd5c1BFhUVhXbt2sHMzEza9sU56PLk5OTg448/hkqlgrm5Od58803cvXtXLU2tWrUwbNiwfNs+n2dxsRU0B93jx48xceJEODs7Qy6Xo379+vjiiy8ghFBLJ5PJEBwcjB07dqBRo0aQy+Vo2LAh9u3bV3CFvyAxMREjR46Eg4MDTE1N0bRpU6xbt05anzfX1a1bt7B7924p9qKmVNA0psLm3ivos8/Lc/PmzfDw8IBCoYC3tzcuXrwIAFizZg3q1KkDU1NTdOjQodD4oqKi0Lp1aygUCri5uWH16tX50mRkZGDmzJmoU6cO5HI5nJ2dMXnyZGRkZBQY0/r169GwYUPI5XKN6x0ANm/eDC8vLygUCtjZ2WHw4MG4d++etL5Dhw4ICAgAALRo0QIymazAY60geT9HPX78ON577z3Y2tpCqVRi6NChePjwoVravPnW9u/fj+bNm0OhUGDNmjUAgOTkZISEhEjHYZ06dbBgwQLpDpOsrCzY2Nhg+PDh+WJITU2FqakpPvzwQwCFz0F3+PBhvP766zA3N4e1tTV69eqFq1evqqXR5lgpqp3QVF6dHDhwQJr/z8PDA9u2bVNL9+DBA3z44Ydo3LgxLCwsoFQq0a1bN/z5559SmrS0NJibm2PcuHH59vPPP//A0NAQoaGh0rK///4b/fv3h42NDczMzNCqVSvs3r1bbbu8z/fF4/zFuek6dOiA3bt3486dO9Lf7vP1uGLFCjRs2BBmZmaoVq0amjdvjvDwcK3qqri2Ku9zP3LkCC5fvizFoe38eVeuXEHHjh1hZmaGGjVqYOHChfnSFNee5dmwYQO8vLxgaWkJpVKJxo0bY9myZQCe1W3//v0BAB07dixxvEQVBc+zqt55Vnp6OsaOHQs7OztYWlrizTffxL179yCTyTBr1iwpnTZ9JwD89NNP0nmJjY0NBg4cmO/zuHHjBvr16weVSgVTU1PUrFkTAwcOREpKilRfjx8/xrp166SyaHruAgBffPEFWrduDVtbWygUCnh5eRU4J7SmdQAA9+7dw4gRI+Dg4CB9ft9//73GMRGVN7bbVa/d5vWxZnh9/ExFvD7OU9z1MaDZ9Syg+bXbi4QQCAwMhImJibT/guZoz2vjNLmOvHPnDt58802Ym5vD3t4e48ePx/79+/PlWdx5kKaK+4yHDRuG9u3bAwD69+8PmUxWYJtcGE3Lo0l7nUfTdqA0/VJxeEe7FoYMGYKPP/4YBw4cwLvvvltgmsuXL6NHjx5o0qQJ5syZA7lcjps3b+LkyZMAAHd3d8yZMwczZsxAYGAgXn/9dQBA69atpTzu37+Pbt26YeDAgRg8eDAcHByKjGvevHmQyWSYMmUKEhMTsXTpUvj4+CA6OlqrbzM1ie15Qgi8+eabOHLkCEaOHAlPT0/s378fkyZNwr179/JNvXDixAls27YN77//PiwtLbF8+XL069cPsbGxsLW1LTSu9PR0dOjQATdv3kRwcDDc3NywefNmDBs2DMnJyRg3bhzc3d3x448/Yvz48ahZsyYmTpwIAKhevXqRZS5pTEX57bffsHPnTgQFBQEAQkND0aNHD0yePBlfffUV3n//fTx8+BALFy7EiBEjcPjwYbXtHz58iO7du+Ptt9/GO++8g02bNmHMmDEwMTHBiBEjADz71v3NN9/EiRMnEBgYCHd3d1y8eBFLlizB9evX8805evjwYWzatAnBwcGws7PTeJqJtWvXYvjw4WjRogVCQ0ORkJCAZcuW4eTJkzh//jysra3xySefoH79+vj666+ln5XWrl1bqzoLDg6GtbU1Zs2ahZiYGKxatQp37tyROqI8MTExeOedd/Dee+/h3XffRf369fHkyRO0b98e9+7dw3vvvQcXFxecOnUKU6dORVxcHJYuXQpjY2P06dMH27Ztw5o1a9TuftmxYwcyMjIwcODAQuM7ePAgunXrhldeeQWzZs1Ceno6VqxYgTZt2uCPP/7QetqO4toJbdy4cQMDBgzA6NGjERAQgLCwMPTv3x/79u3DG2+8AeDZScSOHTvQv39/uLm5ISEhAWvWrEH79u1x5coVODk5wcLCAn369MHGjRuxePFiGBoaSvv4+eefIYSAv78/ACAhIQGtW7fGkydPMHbsWNja2mLdunV48803sWXLFvTp00erMnzyySdISUnBP//8I7UbFhYWAJ79JHfs2LF46623MG7cODx9+hQXLlzAmTNnMGjQII3y16Stql69On788UfMmzcPaWlp0pcK7u7uGpfj4cOH6Nq1K/r27Yu3334bW7ZswZQpU9C4cWN069YNgGbtGfCs43/nnXfQuXNnLFiwAABw9epVnDx5EuPGjUO7du0wduxYLF++HB9//LEUpzbxElU0PM9SV9nPs4YNG4ZNmzZhyJAhaNWqFY4dOwY/Pz+N66sg8+bNw/Tp0/H2229j1KhR+O+//7BixQq0a9dOOi/JzMyEr68vMjIy8MEHH0ClUuHevXvYtWsXkpOTYWVlhR9//BGjRo3Ca6+9hsDAQADQ6txl2bJlePPNN+Hv74/MzExs2LAB/fv3x65du9TKqGkdJCQkoFWrVtLgT/Xq1bF3716MHDkSqampep9WjagwbLfVVfZ2uzQxFYXXx7w+Lk55Xx9rej2r6bXbi3JycjBixAhs3LgR27dvL/b8R5PryMePH6NTp06Ii4vDuHHjoFKpEB4ejiNHjqjlpcl5kCY0+Yzfe+891KhRA5999hnGjh2LFi1aFNs+59G0PNrQtB0oi36pSIIkYWFhAoA4e/ZsoWmsrKxEs2bNpPczZ84Uz1fjkiVLBADx33//FZrH2bNnBQARFhaWb1379u0FALF69eoC17Vv3156f+TIEQFA1KhRQ6SmpkrLN23aJACIZcuWSctcXV1FQEBAsXkWFVtAQIBwdXWV3u/YsUMAEJ9++qlaurfeekvIZDJx8+ZNaRkAYWJiorbszz//FADEihUr8u3reUuXLhUAxE8//SQty8zMFN7e3sLCwkKt7K6ursLPz6/I/LSN6cVy53nxs8/LUy6Xi1u3bknL1qxZIwAIlUqlFuvUqVMFALW0eZ//okWLpGUZGRnC09NT2Nvbi8zMTCGEED/++KMwMDAQv/32m9r+V69eLQCIkydPqsVkYGAgLl++rFG95MnMzBT29vaiUaNGIj09XVq+a9cuAUDMmDFDWqbJ305B8rbz8vKSyiaEEAsXLhQAxC+//CItc3V1FQDEvn371PKYO3euMDc3F9evX1db/tFHHwlDQ0MRGxsrhBBi//79AoD49ddf1dJ1795dvPLKK9L7W7du5fsbyKv/+/fvS8v+/PNPYWBgIIYOHSot0/RY0aSd0ERenWzdulValpKSIhwdHdXaqadPn4qcnBy1bW/duiXkcrmYM2eOtCyvjvbu3auWtkmTJmrtREhIiACgdvw9evRIuLm5iVq1akn7yvt8nz/Ghfhf23XkyBFpmZ+fX4F116tXL9GwYcNi66Io2rRV7du3L9H+8v52f/jhB2lZRkaGUKlUol+/ftIyTduzcePGCaVSKbKzswvd5+bNm/PVI1FFxvOsl+c8KyoqSgAQISEhasuHDRsmAIiZM2cWWu48L372t2/fFoaGhmLevHlq6S5evCiMjIyk5efPnxcAxObNm4uM0dzcvMDPTBNPnjxRe5+ZmSkaNWokOnXqJC3Tpg5GjhwpHB0dRVJSklragQMHCisrq3z7IyovbLdfnnZbm5h4fczr48p+fazp9aymf2t5dfT555+LrKwsMWDAAKFQKMT+/fvV4ivoOljT68hFixYJAGLHjh3SsvT0dNGgQQO1PDU9DyqOpp9xXpm03Z+m5RFC8/Za03agtP1ScTh1jJYsLCyKfLp63rxEv/zyS4kfjCKXywv8CU9hhg4dCktLS+n9W2+9BUdHR+zZs6dE+9fUnj17YGhoiLFjx6otnzhxIoQQ2Lt3r9pyHx8ftW9ymzRpAqVSib///rvY/ahUKrzzzjvSMmNjY4wdOxZpaWk4duxYictQ0piK0rlzZ7VvcFu2bAkA6Nevn9rnlLf8xX0ZGRnhvffek96bmJjgvffeQ2JiIqKiogA8+7mau7s7GjRogKSkJOnVqVMnAMj3LWD79u21npvv3LlzSExMxPvvv682j5ifnx8aNGhQ4M+qSiowMFBtjsMxY8bAyMgo3zHs5uYGX19ftWWbN2/G66+/jmrVqqnVhY+PD3JycnD8+HEAz37eamdnh40bN0rbPnz4EBERERgwYEChscXFxSE6OhrDhg2DjY2NtLxJkyZ44403SvR3VhbtRB4nJye1O8jzflp4/vx5xMfHA3jWphgYPGvuc3JycP/+fennUX/88Ye0rY+PD5ycnLB+/Xpp2aVLl3DhwgUMHjxYWrZnzx689tpraNu2rbTMwsICgYGBuH37Nq5cuVKqMj3P2toa//zzD86ePVviPLRtq0rKwsJCrZ5MTEzw2muvqf2Na9qeWVtb4/Hjx4iIiCiT2IgqC55n/U9lPs/K+wn++++/r7b8gw8+0DqvPNu2bUNubi7efvtttf5epVKhbt260rlP3p1a+/fvx5MnT0q8v6I8f0fsw4cPkZKSgtdff12tT9W0DoQQ2Lp1K3r27AkhhFrZfH19kZKSopYvUUXDdvt/KnO7XdqYisLrY+3x+rjkNLk+1vR6Vtu/tczMTOkXbnv27EGXLl00ilmT68h9+/ahRo0aePPNN6Vlpqam+X5NVBbnQbr4jF+kaXm0oWk7UJbHW0E40K6ltLQ0tc7gRQMGDECbNm0watQoODg4YODAgdi0aZNWH16NGjW0erBL3bp11d7LZDLUqVOnyPnXysKdO3fg5OSUrz7ypi64c+eO2nIXF5d8eVSrVi3fXGMF7adu3brSQGFx+9FGSWPSJs+8hs7Z2bnA5S/uy8nJCebm5mrL6tWrBwDSZ3rjxg1cvnwZ1atXV3vlpct7IEUeNzc3rcuRV6/169fPt65BgwalqvcXvXgMW1hYwNHRMd8xXFA5bty4gX379uWri7ynu+fVhZGREfr164dffvlFmp9r27ZtyMrKKvJEoqh6cHd3R1JSEh4/fqx5YVE27USeOnXq5Jvf7sXjJTc3F0uWLEHdunUhl8thZ2eH6tWr48KFC2rztBkYGMDf3x87duyQOuX169fD1NRUmg8ceFYnhdVH3vqyMmXKFFhYWOC1115D3bp1ERQUpPVPCLVtq0qqZs2a+T6LF9sTTduz999/H/Xq1UO3bt1Qs2ZNjBgxQqu5I4kqK55n/U9lPs+6c+cODAwM8vXbderU0TqvPDdu3IAQAnXr1s3X51+9elXq793c3DBhwgR8++23sLOzg6+vL1auXKn1vKRF2bVrF1q1agVTU1PY2NigevXqWLVqldo+NK2D//77D8nJyfj666/zlStvYPHF8zqiioTt9v9U5na7tDFpkyevj4vH62PdXh9rej2r7d9aaGgoduzYgS1btmg1V7mm15G1a9fOl+7F84qyOA/SxWdc0D40KY82NG0HyvJ4KwjnaNfCP//8g5SUlCI/eIVCgePHj+PIkSPYvXs39u3bh40bN6JTp044cOCA2rzHReVR1gp6mBTw7O5WTWIqC4XtR7zwYJjypElMRdWdNnmWZflzc3PRuHFjLF68uMD1L5606OKY0oeCypGbm4s33ngDkydPLnCbvEYVAAYOHIg1a9Zg79696N27NzZt2oQGDRqgadOmZRKfpsdKWbQT2vjss88wffp0jBgxAnPnzoWNjQ0MDAwQEhKSrzMZOnQoPv/8c+zYsQPvvPMOwsPD0aNHD43ncnuetn87BXF3d0dMTAx27dqFffv2YevWrfjqq68wY8YMzJ49W+uYdKks/8bt7e0RHR2N/fv3Y+/evdi7dy/CwsIwdOhQjR6+Q1QZ8TyrdCrieZYmNO0rcnNzIZPJsHfv3gLLmvdsDwBYtGgRhg0bhl9++QUHDhzA2LFjERoaitOnT6NmzZqlive3337Dm2++iXbt2uGrr76Co6MjjI2NERYWpvWDuvPKBQCDBw+WHp73oiZNmpQqZiJdYbtdOhWx3eb1ceXC6+OKxdfXF/v27cPChQvRoUMHtV8+FKWs2wJdngfpg6bttabtgK6PNw60a+HHH38EgHw/zXmRgYEBOnfujM6dO2Px4sX47LPP8Mknn+DIkSPw8fEp9CApqRs3bqi9F0Lg5s2baifl1apVQ3Jycr5t79y5g1deeUV6r01srq6uOHjwIB49eqT2rf21a9ek9WXB1dUVFy5cQG5urto3iWW9n8IUVXe68O+//+Lx48dq39pfv34dAKSf3NWuXRt//vknOnfuXObHU568eo2JiZF+apMnJiamTOv9xo0b6Nixo/Q+LS0NcXFx6N69e7Hb1q5dG2lpadI39EVp164dHB0dsXHjRrRt2xaHDx/GJ598UuQ2z9fDi65duwY7Ozvps9LmWCmundDUzZs3IYRQOw5ePF62bNmCjh074rvvvlPbNjk5GXZ2dmrLGjVqhGbNmmH9+vWoWbMmYmNjsWLFCrU0rq6uhdZH3nrgWX3k7ed5BdVHUcexubk5BgwYgAEDBiAzMxN9+/bFvHnzMHXqVI1OXsqrrdKENu2ZiYkJevbsiZ49eyI3Nxfvv/8+1qxZg+nTpxd4pwZRZcfzLHWV+TzL1dUVubm5uHXrltpdeTdv3syXVtO+s3bt2hBCwM3NTW2QoDCNGzdG48aNMW3aNJw6dQpt2rTB6tWr8emnnwLQ7rN43tatW2Fqaor9+/dDLpdLy8PCwtTSaVoH1atXh6WlJXJycrTq/4kqArbb6ipzu60NXh/z+hio3NfHml7Pavu31qpVK4wePRo9evRA//79sX37dhgZlc2wq6urK65cuZKvbAWdWwHFnwcVty9As8+4pLQpj6bttTbtgC77JU4do6HDhw9j7ty5cHNzg7+/f6HpHjx4kG+Zp6cnAEg/x8k7IAs6UErihx9+UJsXb8uWLYiLi5OeTgw8O+BOnz6NzMxMadmuXbtw9+5dtby0ia179+7IycnBl19+qbZ8yZIlkMlkavsvje7duyM+Pl5t7rDs7GysWLECFhYWaN++fZnspzC1a9dGSkoKLly4IC2Li4vD9u3bdbK/7OxsrFmzRnqfmZmJNWvWoHr16vDy8gIAvP3227h37x6++eabfNunp6eX+mc8ANC8eXPY29tj9erV0rELAHv37sXVq1eLfXK2Nr7++mtkZWVJ71etWoXs7GyNjqG3334bkZGR2L9/f751ycnJyM7Olt4bGBjgrbfewq+//ooff/wR2dnZRf4sDgAcHR3h6emJdevWqf1dXLp0CQcOHFA72dH0WNGkndDUv//+q5Z/amoqfvjhB3h6ekKlUgF49g35i9+Gb968Gffu3SswzyFDhuDAgQNYunQpbG1t830O3bt3x++//47IyEhp2ePHj/H111+jVq1a0nyHefM75s0DCDz71vnrr7/Ot09zc/MCf852//59tfcmJibw8PCAEELtmClKebVVmsaiSXv2YrkNDAyki0Nd9SVE+sTzrPwq83lW3qDbV199pbb8xS9uAc37zr59+8LQ0BCzZ8/O16cJIaR2MzU1Va3vB55dbBoYGKj1sebm5iU6RgwNDSGTydTuxrt9+zZ27Nihlk7TOjA0NES/fv2wdetWXLp0Kd/+/vvvP61jJCoPbLfzq8zttjZ4fczr48p+fazp9WxJ/tZ8fHywYcMG7Nu3D0OGDCmz6Uh8fX1x79497Ny5U1r29OnTfMe8pudBRdHmMy4pTcsDaN5ea9oO6Lpf4h3tBdi7dy+uXbuG7OxsJCQk4PDhw4iIiICrqyt27txZ5B2Uc+bMwfHjx+Hn5wdXV1ckJibiq6++Qs2aNaUHLdSuXRvW1tZYvXo1LC0tYW5ujpYtW5ZonjAAsLGxQdu2bTF8+HAkJCRg6dKlqFOnjtpDBEaNGoUtW7aga9euePvtt/HXX3/hp59+UnvQibax9ezZEx07dsQnn3yC27dvo2nTpjhw4AB++eUXhISE5Mu7pAIDA7FmzRoMGzYMUVFRqFWrFrZs2YKTJ09i6dKlRc4JWBYGDhyIKVOmoE+fPhg7diyePHmCVatWoV69ejp5OJWTkxMWLFiA27dvo169eti4cSOio6Px9ddfSw9EGTJkCDZt2oTRo0fjyJEjaNOmDXJycnDt2jVs2rQJ+/fvR/PmzUsVh7GxMRYsWIDhw4ejffv2eOedd5CQkIBly5ahVq1aGD9+fFkUF8Czk6XOnTvj7bffRkxMDL766iu0bdtW7cEYhZk0aRJ27tyJHj16YNiwYfDy8sLjx49x8eJFbNmyBbdv31a7a3vAgAFYsWIFZs6cicaNG0vzqxXl888/R7du3eDt7Y2RI0ciPT0dK1asgJWVFWbNmiWl0/RY0aSd0FS9evUwcuRInD17Fg4ODvj++++RkJCgdmddjx49MGfOHAwfPhytW7fGxYsXsX79erVvf583aNAgTJ48Gdu3b8eYMWPUHsQDAB999BF+/vlndOvWDWPHjoWNjQ3WrVuHW7duYevWrdK3/Q0bNkSrVq0wdepUPHjwADY2NtiwYUO+jh8AvLy8sHHjRkyYMAEtWrSAhYUFevbsiS5dukClUqFNmzZwcHDA1atX8eWXX8LPz0/jv/3yaqs0oWl7NmrUKDx48ACdOnVCzZo1cefOHaxYsQKenp7SMevp6QlDQ0MsWLAAKSkpkMvl6NSpE+zt7cutPEQlwfOsqn+e5eXlhX79+mHp0qW4f/8+WrVqhWPHjkl3lD1/l5CmfWft2rXx6aefYurUqbh9+zZ69+4NS0tL3Lp1C9u3b0dgYCA+/PBDHD58GMHBwejfvz/q1auH7Oxs/Pjjj9KA9vMxHjx4EIsXL4aTkxPc3NykB/EVxc/PD4sXL0bXrl0xaNAgJCYmYuXKlahTp47aQII2dTB//nwcOXIELVu2xLvvvgsPDw88ePAAf/zxBw4ePFjgBSFReWK7XfXbbW3w+pjXx5X9+ljT69mS/q317t1bmvZTqVSqfVFUUu+99x6+/PJLvPPOOxg3bhwcHR2l56kB/zuv0PQ8qDiafsa6Lg+geXutaTug835JkCQsLEwAkF4mJiZCpVKJN954Qyxbtkykpqbm22bmzJni+Wo8dOiQ6NWrl3BychImJibCyclJvPPOO+L69etq2/3yyy/Cw8NDGBkZCQAiLCxMCCFE+/btRcOGDQuMr3379qJ9+/bS+yNHjggA4ueffxZTp04V9vb2QqFQCD8/P3Hnzp182y9atEjUqFFDyOVy0aZNG3Hu3Ll8eRYVW0BAgHB1dVVL++jRIzF+/Hjh5OQkjI2NRd26dcXnn38ucnNz1dIBEEFBQflicnV1FQEBAQWW93kJCQli+PDhws7OTpiYmIjGjRtLcb2Yn5+fX7H5aRvTgQMHRKNGjYSJiYmoX7+++Omnn/J99oXleevWLQFAfP7552rL8z6/zZs3S8vyPv9z584Jb29vYWpqKlxdXcWXX36ZL87MzEyxYMEC0bBhQyGXy0W1atWEl5eXmD17tkhJSSm2nJrauHGjaNasmZDL5cLGxkb4+/uLf/75Ry1N3t/O2bNntco7b7tjx46JwMBAUa1aNWFhYSH8/f3F/fv31dIW9dk+evRITJ06VdSpU0eYmJgIOzs70bp1a/HFF1+IzMxMtbS5ubnC2dlZABCffvppvrzyPq8Xj6+DBw+KNm3aCIVCIZRKpejZs6e4cuVKvu01OVY0bSeKk1cn+/fvF02aNBFyuVw0aNBA7ZgSQoinT5+KiRMnCkdHR6FQKESbNm1EZGRkgX//ebp37y4AiFOnThW4/q+//hJvvfWWsLa2FqampuK1114Tu3btKjCdj4+PkMvlwsHBQXz88cciIiJCABBHjhyR0qWlpYlBgwYJa2trAUBqa9asWSPatWsnbG1thVwuF7Vr1xaTJk1SO8Y1oWlbVVQbXJTCtiuo3dSkPduyZYvo0qWLsLe3FyYmJsLFxUW89957Ii4uTi3dN998I1555RVhaGiYr06JKhqeZxUdW1U7z3r8+LEICgoSNjY2wsLCQvTu3VvExMQIAGL+/PlqaTU9zxJCiK1bt4q2bdsKc3NzYW5uLho0aCCCgoJETEyMEEKIv//+W4wYMULUrl1bmJqaChsbG9GxY0dx8OBBtXyuXbsm2rVrJxQKhQCgUT3l+e6770TdunWlfjcsLKzAeLWpg4SEBBEUFCScnZ2FsbGxUKlUonPnzuLrr7/WOC6issZ2u+jYqlq7zetjzfD6+JnKfH0shObXs5r8rRV2TH/11VcCgPjwww+FEP87xp+/ZtPmOvLvv/8Wfn5+QqFQiOrVq4uJEyeKrVu3CgDi9OnTUhpNzoM0oclnXNDfraY0KU8eTdtrTdqB0vZLxZEJUcGfkEREVdbatWsxfPhwnD17ttR3GLyMatWqhUaNGmHXrl1lnnefPn1w8eLFQud8IyIi0lZ0dDSaNWuGn376qcipJqoy1gERERWG18ekraVLl2L8+PH4559/UKNGDX2HU2pVoTyco52IiNTExcVh9+7dGDJkiL5DISKiSio9PT3fsqVLl8LAwADt2rXTQ0Tlj3VAREREZeXF84qnT59izZo1qFu3bqUclK5q5cnDOdqJXkIPHjxQe5DEiwwNDVG9evUS55+enl7ggy2fZ2NjU+L8q7r//vtP7SFrLzIxMdFJ/d26dQsnT57Et99+C2NjY7z33ntlvo+ylJOTU+xD4iwsLGBhYVHifej6b4WIqKpauHAhoqKi0LFjRxgZGWHv3r3Yu3cvAgMD4ezsrO/w8tFFn1LZ6oCI6GXF6+OKTV/XxxVN37594eLiAk9PT6SkpOCnn37CtWvXsH79eo3zSElJKfBGgOflPTS2JDIzM4t9toyVlRUUCkWZlKci4kA70Uuob9++OHbsWKHrXV1dcfv27RLnv3HjRgwfPrzINEeOHClx/lVdixYtcOfOnULXt2/fHkePHi3z/R47dgzDhw+Hi4sL1q1bV6oOtjzcvXu32IeRzJw5s1QPa9H13woRUVXVunVrREREYO7cuUhLS4OLiwtmzZqFTz75RN+hFUgXfUplqwMiopcVr48rNn1dH1c0vr6++Pbbb7F+/Xrk5OTAw8MDGzZswIABAzTOY9y4cVi3bl2RaUozw/ipU6fQsWPHItOEhYVh2LBhZVKeiohztBO9hKKiovDw4cNC1ysUCrRp06bE+cfFxeHy5ctFpvHy8kK1atVKvI+q7OTJk0V+y1ytWjV4eXmVY0QV09OnT3HixIki07zyyit45ZVXSrwPXf+tEBFRxVAefQoREVVMvD6u2Hh9XHauXLmCf//9t8g0Pj4+Jc7/4cOHiIqKKjJNw4YN4ejoWOJ9VHQcaCciIiIiIiIiIiIiKgW9Pgw1NDQULVq0gKWlJezt7dG7d2/ExMSopXn69CmCgoJga2sLCwsL9OvXDwkJCWppYmNj4efnBzMzM9jb22PSpEnIzs5WS3P06FG8+uqrkMvlqFOnDtauXZsvnpUrV6JWrVowNTVFy5Yt8fvvv5d5mYmIiIiIiIiIiIioatHrHO3Hjh1D0P+xd+dxVZb5/8ffB2XTBASDAxMYZbnvFmJmloyo1Gg5loppRTo5MLmNmpOZS0VZbpXJ16nUvulYzrecRg0l3DLJBcUtJEsLKw8OKhAuiHD//vDHPZ7cEA7b4fV8PO5H3vf1Off9ue6T93XOx/vcV2ys7rrrLl24cEF/+9vf1LNnT33zzTeqX7++JGnMmDFavXq1VqxYIW9vb8XFxemRRx7RV199JenixEVRUVGyWq3aunWrjh07pqFDh8rV1VWvvPKKpIsT/EVFRemZZ57R0qVLlZycrKefflqBgYGKjIyUdPGZWWPHjlVCQoLCwsI0d+5cRUZGKiMjQ/7+/tftS3FxsX755Rc1aNBAFoulgs4YAMAZGYahX3/9VUFBQXJxqdJ/A3dKjNEAgLJijK5YjNEAgLKqlmO0UY0cP37ckGRs2rTJMAzDyMnJMVxdXY0VK1aYMenp6YYkIyUlxTAMw1izZo3h4uJi2Gw2M2bBggWGl5eXUVBQYBiGYUyYMMFo2bKl3bEee+wxIzIy0ly/++67jdjYWHO9qKjICAoKMuLj40uV+9GjRw1JLCwsLCwsZV6OHj16gyMnSoMxmoWFhYWlvAtjdMVgjGZhYWFhKe9SncboKr2j/bdyc3MlSb6+vpIuTkhRWFho9yD+Zs2aKSQkRCkpKercubNSUlLUunVrBQQEmDGRkZEaOXKkDhw4oPbt2yslJeWyh/lHRkZq9OjRkqTz588rNTVVkyZNMttdXFwUERGhlJSUUuXeoEEDSdLRo0fl5eV1450HANRaeXl5Cg4ONscSOBZjNACgrBijKxZjNACgrKrjGF1tCu3FxcUaPXq07rnnHrVq1UqSZLPZ5ObmJh8fH7vYgIAA2Ww2M+bSIntJe0nbtWLy8vJ09uxZnTp1SkVFRVeMOXjw4BXzLSgoUEFBgbn+66+/SpK8vLz4gAAAKBN+Ml0xSs4rYzQAoKwYoysGYzQAoLyq0xhdTR5gI8XGxmr//v1avnx5VadSKvHx8fL29jaX4ODgqk4JAAAAAAAAAFAFqkWhPS4uTqtWrdKGDRt0yy23mNutVqvOnz+vnJwcu/isrCxZrVYzJisr67L2krZrxXh5ecnT01ONGjVSnTp1rhhTso/fmjRpknJzc83l6NGjN95xAAAAAAAAAECNV6WFdsMwFBcXp08//VTr169XaGioXXvHjh3l6uqq5ORkc1tGRoYyMzMVHh4uSQoPD9e+fft0/PhxMyYpKUleXl5q0aKFGXPpPkpiSvbh5uamjh072sUUFxcrOTnZjPktd3d38+dt/MwNAAAAAAAAAGqvKi20x8bG6sMPP9SyZcvUoEED2Ww22Ww2nT17VpLk7e2tmJgYjR07Vhs2bFBqaqqefPJJhYeHq3PnzpKknj17qkWLFnr88ce1Z88erV27VpMnT1ZsbKzc3d0lSc8884wOHz6sCRMm6OD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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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"text/plain": [
"<Figure size 1500x3200 with 22 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Select numeric columns\n",
"numerical = df.select_dtypes(include='number').columns\n",
"n_cols = 3\n",
"n_rows = math.ceil(len(numerical) / n_cols)\n",
"\n",
"# Create subplots\n",
"fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, 4 * n_rows))\n",
"axes = axes.flatten()\n",
"\n",
"# Plot each numeric column\n",
"for i, col in enumerate(numerical):\n",
" axes[i].hist(df[col].dropna(), bins=30, edgecolor='black')\n",
" axes[i].set_title(f'Distribution of {col}')\n",
" axes[i].set_xlabel(col)\n",
" axes[i].set_ylabel('Frequency')\n",
"\n",
"# Hide any unused subplots\n",
"for j in range(i + 1, len(axes)):\n",
" fig.delaxes(axes[j])\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "311da64d",
"metadata": {},
"outputs": [],
"source": [
"# We see that there are some outliers in host_age with ages above 100, we will remove those\n",
"df['host_age'] = df['host_age'].where(df['host_age'] <= 100, np.nan)\n",
"\n",
"# We drop number_of_previous_incidents_of_guest as it has only 0 values\n",
"df.drop(columns=['number_of_previous_incidents_of_guest'], inplace=True)\n",
"numerical = df.select_dtypes(include='number').columns"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "692854bb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing Values (%):\n",
"host_age 69.826817\n",
"guest_age 45.196414\n",
"days_to_complete_verification 13.174074\n",
"days_to_start_verification 5.739898\n",
"listing_number_of_bathrooms 0.572582\n",
"listing_number_of_bedrooms 0.572582\n",
"dtype: float64\n"
]
}
],
"source": [
"# Check missing values for the remaining columns\n",
"missing_values = df.isnull().mean() * 100\n",
"missing_values = missing_values[missing_values > 0].sort_values(ascending=False)\n",
"print(\"Missing Values (%):\")\n",
"print(missing_values)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "9f333fd5",
"metadata": {},
"outputs": [],
"source": [
"# We will fill the remaining missing values with the median for numerical columns\n",
"for col in numerical:\n",
" df[col] = df[col].fillna(df[col].median())"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "ccd46ddc",
"metadata": {},
"outputs": [],
"source": [
"# Convert all boolean columns to int\n",
"bool_columns = df.select_dtypes(include='bool').columns\n",
"for col in bool_columns:\n",
" df[col] = df[col].astype(int)"
]
},
{
"cell_type": "markdown",
"id": "2c84ebe5",
"metadata": {},
"source": [
"### Feature Relevance Analysis"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "74a582c8",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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uXbh48SIyMzM1ffOdO3cOQMl+ee2118ot16NHjwqv4z59+qB+/fq4f/8+Tp48ifbt29d4X1WmcePGMDY2xq+//ooFCxZgxIgRcHJykrS9pddF6XXCGGOMvQy4gJAxxtgLydLSEgCQnp5OXvbWrVsASn4EmpiYVBjj4uKiFfu4yvrWkxKTkpICABWOuvy4u3fvVltAmJOTg7feegvbt2+vMu7Ro0fVrk+K0n1S1Y/tqvaftbV1pX0rmpiY4OHDh8jPz69x/lxcXNCpUyccPnwYycnJ2LBhA2QymaTBOP766y8MHToUqamplcY8yX6Uct5UZdKkSfjll1+wd+9eCIKAzZs3o169euR0MjMzAaDS878mnnTbqkI9Z65fvw4ApBGh6+LY379/X7OMnZ2dpPhGjRrh5s2bAKq+xpycnJ5KAeHcuXOxYMECFBUVVRpT2X6pKv+Ojo64f/++Zltruq8qY2xsjKioKIwaNQoff/wxPv74Y1hbW6Nt27bo1atXlYXHpddFRYMtMcYYY/9VXEDIGGPshdSyZUt88803OHnyJFQqVbkBQuqaUqmscUxpzb5BgwbB0NCwyjSk1PCaPXs2tm/fjsaNG2PRokVo1aoVLCwsNINftG/fHkePHoUoitWm9TTIZLI6X8fo0aNx6NAhTJ06FX///Td69OhRbaFDbm4uBgwYgDt37mDUqFF499134erqChMTE8jlcly6dAkeHh5PtB+lnDdVuXz5smbUXlEUcfz4cbRt25acjpmZGYDaKzQGnmzbHh8h+HF1fc7U1bEvu10V1VZ+nEKhIOe9Lm3btg1hYWEwMjLCqlWr0KVLF9jY2ECpVEIQBHz44YcIDw9/omuidNm62FcDBw5Et27dsHPnThw5cgRxcXHYvn07tm/fjk8++QT79u1Ds2bNyi1XWoBek8J3xhhj7EXFBYSMMcZeSH369MG0adOQkZGBnTt34vXXX5e8bGmtk9IaKxXVoiqt5VdVDZWasrOzw+XLlzFz5kz4+fk9cXrff/89AGDLli0VNvm9fPnyE6+jrNJ9UrqPKlKX+0+KQYMGaUaDBkoKDKtz+PBh3LlzBy1atKhwlNTa3o9U+fn5GDJkCLKyshAYGIitW7dixowZaN++Pfk8atCgAYCSa+BpKC2sLm2q+7jSGn+1xd7eHgBw8eJFSfF1dewtLCygVCqRl5eHpUuXSh7VufS6uXbtWqUxVc2rLaX3lgULFmDs2LHl5le3X65evVrpvNL829raAqj5vqqOqakp3nrrLbz11lsASka3njRpEn766SdMnDgRhw4dKrdM6XXRsGHDWskDY4wx9iKo+1f4jDHGWB1wcXHB8OHDAQAhISF48OBBlfHp6ema/sNsbW01TWCjo6PLxYqiqJneuXPn2sv0/7z66qsA/v3x/aRKt93BwaHcvN9++w337t2rcLnSQpvS/sSk6tSpE2QyGRITE3Hq1Kly89PS0jR9wNXF/pPCwMAAwcHBqF+/PpycnDBgwIBqlyndj6WFS4/79ttvK122tPkrdV9SvP/++0hMTETnzp3x9ddfIyIiAoWFhRgyZAgyMjJIabVo0QIAcP78+TrIaXmlBV6VFdiV9stZW0r7K/zuu++Qk5NTbXxdHXu5XI7u3bsDoF3vnTp1giAIOHnyZIX77NSpU0+leXFV95b09HTs27evyuX37t1bYTcQv/76K+7fvw9jY2O0bNkSQM33FZWdnR3mzp0LAEhMTKww5uzZswCgyRtjjDH2MuACQsYYYy+slStXwtXVFVevXkWHDh3wxx9/lIspLCzEhg0b4OvriwsXLmimT58+HQDw6aefahVyiaKI+fPnIzExEWZmZnjnnXdqPd8zZsyAmZkZli1bpinkedzVq1erLJQoy9PTE0DJ/igrKSkJ48ePr3S50po7pQMNSGVvb4/BgwdDFEWMGzdOqxZaTk4Oxo4di/z8fLRv377cACVP0+eff4579+4hJSVFUnPE0v144MCBcgVn69atw5YtWypdtqb7UqpNmzZh3bp1aNiwITZt2gSZTIb33nsPgwYNwtWrVyXVkCyrtOC2tLlyXWvdujVMTExw/vx5fPPNN1rzfvjhB6xYsaJW19evXz/4+vri9u3bGDx4cLmakvn5+ZoBMYC6PfahoaHQ09PDjBkzEBMTU2Fz6rNnz2Lbtm2a/+3t7fH6669DrVbj3Xff1WoK/vDhQ0yYMOGpdBlQul/WrVundZ/KzMzEyJEjNU1xK5OXl4d3330XeXl5mmm3b99GSEgIAGD8+PFaA43UZF9VJiEhAVu2bNFad6nSmsUVFXwCwJ9//gkA6NKlS7XrYYwxxv4zns3gyYwxxljtuHPnjhgQECACEAGITk5OYv/+/cXhw4eLXbp0EY2MjEQAoomJiXjs2DHNcmq1WnzrrbdEAKKOjo7YtWtXcfjw4aKHh4cIQFQqleKvv/5abn0ODg4iAPHq1auV5qk0L1U5dOiQaGFhIQIQGzRoIHbp0kUMDAwU+/TpI7q4uIgAxDZt2mgtExoaKgIQQ0NDtab/+OOPoiAIIgCxWbNm4rBhw8QuXbqIurq6YpcuXcT27duLAMSDBw9qLXfq1ClRJpOJMplM7Natmzhq1ChxzJgx4k8//VTt9t67d09s3ry5CEA0NTUVBwwYIA4aNEi0tLTUHIfHl7l69aoIQHRwcKh0v0jZv4975ZVXRADiN998I3mZ0mN048YNren9+/cXAYh6enpijx49xGHDhomNGzcWBUEQP/roo0rzP336dBGAaGFhIQ4ZMkQcM2aMOGbMGPHevXuiKIpiVFSUCEAcOXKkpHyVdfHiRdHIyEiUyWTigQMHtOZlZGSIzs7OIgBx+fLlkrdfFEXR29tbBCCeP3++2tiDBw9Wel5Xdl4+LjIyUpNGu3btxEGDBolNmjQRBUEQ58yZU+G+fZJz5tq1a5rr2cDAQOzRo4c4fPhwsVOnTqKpqWm5NOvq2IuiKH7//feigYGBCEC0tbUVe/ToIQYGBoqvvvqqaGtrKwIQhw4dqpVuWlqa5l5gbm4uvvHGG+Lrr78umpmZiS4uLmK/fv1EAGJUVFSV+72s0vNQT09PbNOmTaWfd999VxRFUUxJSRHNzMxEAGKjRo3EgQMHiv369RNNTU1Fa2trcfTo0RUe+9JzIigoSDQ3NxetrKzEwYMHi3379hUNDQ0150Bubm65PNZkX40cObLcvti+fbvmXu7v7y8OGzZMHDRokOac0NPTE3fv3l1u/enp6aKOjo5oY2MjFhUVSd63jDHG2IuOCwgZY4z9J+zevVsMCgoSXV1dRSMjI1FXV1e0srISu3fvLi5fvly8f/9+hctt2rRJDAgIEM3MzERdXV3Rzs5ODA4OFi9evFhhfG0VEIpiSeHmnDlzxBYtWojGxsainp6eaGtrK7Zv314MDQ0VT58+rRVfVUHM4cOHxa5du4oWFhaigYGB2LRpU3HBggViQUGBpgDt8QJCUSz5Ee3v7y8aGxtrChnLpl/V9ubk5Ijh4eGij4+PaGBgIOrr64uenp7ihx9+KD548KBc/ItQQFhYWCh+9tlnYrNmzUQDAwPR3Nxc7NGjh7h3794q85+Xlyd+8MEHoqurq6inp6dJv3Q7alpAmJubKzZr1qzKAri///5bVCgUop6ennj8+HHJ+2DdunUiAPGDDz6oNrY2CghFURRjYmLEFi1aiPr6+qKJiYnYpUsXcd++fZXu2yc9Z7KyssTFixeLrVq1Eo2NjUWFQiE6ODiI/fr1Ezdv3qwVW1fHvuy2TJ06VWzatKloaGgo6uvriw4ODmJAQIC4aNEi8cqVK+XSvnfvnjhp0iTR1tZWc38YP368ePfu3QoLxapTeh5W93nllVe08h0YGCja29tr9t/48ePFf/75p9JjX3Z6SkqKOHz4cLFhw4ainp6e6OrqKn7yySdiTk5Opfmk7quK9kVaWpq4aNEi8bXXXhOdnJxEAwMD0cTERPTy8hLfe++9Su/xy5YtEwGIc+fOlbxfGWOMsf8CQRSfkyENGWOMMcbYU5ObmwtHR0fo6Ojg2rVrmj4pGXtZiaKI5s2b48qVK0hJSYGVldWzzhJjjDH21HAfhIwxxhhjLyEDAwMsWLAAaWlpWLdu3bPODmPP3NatW3HmzBnMnDmTCwcZY4y9dLgGIWOMMcbYS0qtVqN169a4efMmkpOTYWho+KyzxNgzoVKp0KRJE+Tl5eHixYtQKpXPOkuMMcbYU8UFhIwxxhhjjDHGGGOMvcS4iTFjjDHGGGOMMcYYYy8xLiBkjDHGGGOMMcYYY+wlxgWEjDHGGGOMMcYYY4y9xOq0gDAgIABTpkypy1VIkpubi4EDB8LExASCICAjI+NZZ0kjOjoaZmZmdb6esLAw+Pj4PPN8MMYYY4wxxhhjjLHni86zzsDTEBMTgyNHjuDPP/+EhYUFTE1Nn3WWnjtDhw7Fa6+9Rlpml64HKd77/HZSfI7MhBRvWnRPcmyWrjkp7Vw1bSQ7Y3kWKV5RnEuKVwtyybG6qgJS2nm6xqR4HXUhKV5PlS85NlPXgpQ2VYPsFFK8Sq5Hir+pcJUcKwNtvChVHVcAVwjSj6sIgZS2UqCd79Rz7JFQjxSfUUg75yn0ZMWk+KZ/LiPFp3cKlBz7T3FDUtrGOrTjlFlkRIrXl9OOa32Z9Hu8SqB9vckVaSP3GgrZpHiKhyra+WsgzyPF64H2TFAT7zWFokJ6XgRaXgSBdp+k5IVKJUp/DgOAHuGeCtD3e47KgBRvKJd+fQvE55MuaNtaDF1SvAwqQqyalDZVIWjnGHVfZhVLv68q5dK/XwH0c5L6rC8Wpd+HZULdHielmEOKzxVozzPKeUY9B+QC7XvEgyLabxzKeVOgpn0PptxnakIB2jkvE6XfO/IF2j1VDtpxKgJtX3q6NCLFM1ZTL0UBYXJyMjw9PdG0adNKYwoLC6GnR7tQ/0uUSiWUSlohGGOMMcYYY4wxxhh78dVaFZScnBwEBQXByMgI1tbWiIiI0Jr/zTffwM/PD8bGxrCyssKIESOQnp4OABBFEa6urli6dKnWMomJiRAEAVeuXIEoiggLC4O9vT0UCgVsbGwwefLkavMVEBCAiIgIHD58GIIgICAgAADg6OiITz/9FEFBQTAxMcHYsWMBAD/++COaNGkChUIBR0fHctvh6OiI+fPna7bVwcEBO3fuxN27d9G/f38YGRnB29sbf//9N2n/7dixA25ubtDX10fPnj1x48YNrflr1qyBi4sL9PT04OHhgW+++UZrfmpqqmb9JiYmGDJkCO7cuVPp+pKTk+Hs7IyJEydCFEVuYswYY4wxxhhjjDH2kqq1AsIZM2bg0KFD+Omnn7B3717Exsbi5MmTmvlFRUX49NNPcerUKezYsQPXrl1DcHAwAEAQBIwePRpRUVFaaUZFRaFTp05wdXXFjz/+iMjISKxduxaXL1/Gjh070KxZs2rztW3bNrzzzjto164d0tLSsG3bNs28pUuXonnz5khISMCcOXMQHx+PIUOGYNiwYThz5gzCwsIwZ84cREdHa6UZGRkJf39/JCQkoHfv3njrrbcQFBSEN998EydPnoSLiwuCgoIgitKqkOfm5mLBggX4+uuvERcXh4yMDAwbNkwzf/v27Xj//fcREhKCs2fPYty4cRg1ahQOHjwIAFCr1ejfvz8ePHiAQ4cOYd++fUhJScHQoUMrXN/p06fRoUMHjBgxAqtWrYIg0JoMMMYYY4wxxhhjjLH/jlppYpydnY3/+7//w7fffouuXbsCKOn3z9bWVhMzevRozd/Ozs5YsWIFWrVqhezsbBgZGSE4OBiffPIJjh8/jtatW6OoqAibNm3S1CpMTU2FlZUVunXrBl1dXdjb26N169bV5s3c3BwGBgbQ09ODlZWV1rwuXbogJCRE839gYCC6du2KOXPmAADc3d1x/vx5fPbZZ5rCTAB47bXXMG7cOADAJ598gjVr1qBVq1YYPHgwAGDmzJlo164d7ty5U26dFSkqKsKqVavQpk0bzb7z9PTU7IulS5ciODgYEyZMAABMmzYNf/31F5YuXYrOnTvjwIEDOHPmDK5evQo7OzsAwNdff40mTZrgxIkTaNWqlWZdf/75J/r06YOPPvpIa9sZY4wxxhhjjDHG2MupVmoQJicno7CwUFPABZQUzHl4/DuIRXx8PPr27Qt7e3sYGxvjlVdeAVBS8AcANjY26N27NzZs2AAA+Pnnn1FQUKApdBs8eDDy8vLg7OyMd955B9u3b0dxMa0z0Mf5+flp/X/hwgX4+/trTfP398fly5ehUv3bqam3t7fm74YNSzp9L1ubsXRaaRPq6ujo6GgV4jVu3BhmZma4cOFClfkqO9/Ozk5TOAgAXl5eWmkAJfu6e/fu+OSTT8iFgwUFBXj06JHWp0is2w6FGWOMMcYYY4wxxljdq9thMP8nJycHPXv2hImJCTZu3IgTJ05g+/aSEW0LC/8dQevtt9/G5s2bkZeXh6ioKAwdOhQGBiUjCNnZ2SEpKQmrV6+GUqnEhAkT0KlTJxQVFdU4X4aGtFELS+nq/jvaWmnz3IqmqdXPVwGapaUlWrduje+++w6PHj0iLRseHg5TU1Otz/fqB3WUU8YYY4wxxhhjjDH2tNRKAaGLiwt0dXVx7NgxzbSHDx/i0qVLAICLFy/i/v37WLRoETp27IjGjRtXWLvutddeg6GhIdasWYM9e/ZoNUsGSkba7du3L1asWIHY2FgcPXoUZ86cqY1NAAB4enoiLi5Oa1pcXBzc3d0hl8trbT2PKy4u1hrUJCkpCRkZGfD09KwyX15eXpr5N27c0BrY5Pz588jIyNDEACX775dfftEMhJKVlSU5j7Nnz0ZmZqbWZ4jMvEbbyxhjjDHGGGOMMcaeH7XSB6GRkRHGjBmDGTNmoH79+mjQoAE++ugjyGQl5Y/29vbQ09PDypUrMX78eJw9exaffvppuXTkcjmCg4Mxe/ZsuLm5oV27dpp50dHRUKlUaNOmDQwMDPDtt99CqVTCwcGhNjYBABASEoJWrVrh008/xdChQ3H06FGsWrUKq1evrrV1VERXVxeTJk3CihUroKOjg4kTJ6Jt27aaPhZnzJiBIUOGwNfXF926dcPPP/+Mbdu2Yf/+/QCAbt26oVmzZggMDMTy5ctRXFyMCRMm4JVXXinXjNrQ0BC7du3Cq6++ildffRV79uyBkZFRtXlUKBRQKBTa+RaeSgVUxhhjjDHGGGOMMVaHaq2E57PPPkPHjh3Rt29fdOvWDR06dEDLli0BlDRtjY6Oxg8//AAvLy8sWrRIM/jI48aMGYPCwkKMGjVKa7qZmRm++uor+Pv7w9vbG/v378fPP/+M+vXr19YmoEWLFvj++++xefNmNG3aFJ988gnmzZunNUBJXTAwMMDMmTMxYsQI+Pv7w8jICFu2bNHMHzBgAD7//HMsXboUTZo0wdq1axEVFYWAgAAAJU2af/rpJ9SrVw+dOnVCt27d4OzsrJVGWUZGRti9ezdEUUTv3r2Rk5NTp9vHGGOMMcYYY4wxxp5fgiiK4rPORFlHjhxB165dcePGDc1gH+z5tEvXo/qgMrzPbyfF58hMSPGmRfckx2bp0ppH56qVpHhjufTm2wCgKM4lxasF6U3edVUFpLTzdI1J8TrqwuqDytBT5UuOzdS1IKVN1SA7hRSvkuuR4m8qXCXHykC7FavquAtZhSD9uIoQSGkrBdr5Tj3HHgn1SPEZhbRznkJPRhtMq+mfy0jx6Z0CJcf+U0x7phrr0I5TZlH1tdHL0pfTjmt9mfR7vEqgNZDIFWl9EhsK2aR4iocq2vlrIM8jxeuB9kxQE+81haKi+qDSvAi0vAgC7T5JyQuVSqR1PaNHuKcC9P2eozIgxRvKpV/fAvH5pAvathZDt/qgMmRQVR+kia3b/sALQTvHqPsyq1j6fVUpl/79CqCfk9RnfbEo/T4sE+r2OClFWmWIXIH2PKOcZ9RzQC7Qvkc8KKL9xqGcNwVq2vdgyn2mJhSgnfMyUfq9I1+g3VPloB2nItD2padLI1I8YzX13LQRLSgowM2bNxEWFobBgwdLLhwMCAjAlClT6jZzNXTx4kW0bdsW+vr68PHxeeL0oqOjYWZmRlomODgYAwYMeOJ1M8YYY4wxxhhjjLH/puemBmF0dDTGjBkDHx8f7Ny5E40aSSsl9/X1xdmzZ8v1j1cqO7vu3vJXZ+jQodi/fz/y8vIgCIJmdONSH374IT788EPJ6eXl5SErKwsNGjSQvExwcDAyMjKwY8eOSmMcHR0xZcoUckHrjcvnSfGnvV4nxbte3E+Kp9QglKtpo18/VFiR4qlv+wTiZUipUZUp0N4kUt9sUt/OU99AU9T1G2i1SHuncr+AVguWwkr/Pin+dp4lKb6RsvxAUpVRgVaThnrOFIq0t6yZhbQ3/9RaSRQ6xHPSRnaj+qAyVEs/lhx7dfJ3pLQt9TNI8XfzzUjx1G8fjXWTJMfegBMpbXvQahPn69BqHOYJ0uOp9xnqfa9ITautJRek17gAAPPiO5Jjs3VptSWLiV1nywk1zQDavYa6X6jPVup5QK1xqC9Ir3lK3Y/5oLW6oNY4pNyzZSLt+tAvov1uuCk4kuKNdWjpk2rhEZ+t1HOYmn6RKP1eQ72PUa8nizzas/WGwo0Un6+Sfu8w06W1MqLWfswUafdVPZn06y9fTasxayij1SCk1sITBdpvCqOCh5JjU2UupLSp17YOaL9FXV1o32sYq6laGaSkNgQHB9eorz9jY2MEBgbi44+l/0iqjkqlgiAImkFWaio5ORlBQUF47733Kpxvbk6sAq5UQqmkfelijDHGGGOMMcYYY6wqtdbEOCAgAJMnT8YHH3wAc3NzWFlZISwsDABw7do1CIKAxMRETXxGRgYEQUBsbCwAIDY2FoIg4LfffoOvry+USiW6dOmC9PR07N69G56enjAxMcGIESOQm/vv2wiZTAYjIyMsX74cLVu2RNu2bRETEwMXFxe4upb0/1VQUIDp06ejUaNGMDQ0RJs2bTTrBf5turtz5054eXlBoVAgNTW1yu1Vq9WYN28ebG1toVAo4OPjgz179mjmC4KA+Ph4LF++HG5ubvj222/h6uqq9SlbQFi6j7Zt24bOnTvDwMAAzZs3x9GjR8vls6z58+ejQYMGMDY2xttvv41Zs2ZV2Jx56dKlsLa2Rv369fHee++hqKhIc9yuX7+OqVOnVljLkTHGGGOMMcYYY4z9t9VqH4QxMTEwNDTEsWPHsGTJEsybNw/79u0jpREWFoZVq1bhzz//xI0bNzBkyBAsX74cmzZtwq5du7B3716sXLmy3Hp1dHRw/PhxfP7551i2bBnWr1+vmT9x4kQcPXoUmzdvxunTpzF48GD06tULly9f1sTk5uZi8eLFWL9+Pc6dO1dtM97PP/8cERERWLp0KU6fPo2ePXuiX79+mjTT0tLQpEkThISEIC0tDdOnT5e0/R999BGmT5+OxMREuLu7Y/jw4Sgurri69caNG7FgwQIsXrwY8fHxsLe3x5o1a8rFHTx4EMnJyTh48CBiYmIQHR2N6OhoAMC2bdtga2uLefPmIS0tDWlpaZLyyRhjjDHGGGOMMcb+G2q1ibG3tzdCQ0MBAG5ubli1ahUOHDgANzfp/TjMnz8f/v7+AIAxY8Zg9uzZSE5OhrOzMwBg0KBBOHjwIGbOnKlZxs7ODpGRkRAEAR4eHjhz5gwiIyPxzjvvIDU1FVFRUUhNTYWNjQ0AYPr06dizZw+ioqKwcOFCAEBRURFWr16N5s2bS8rn0qVLMXPmTAwbNgwAsHjxYhw8eBDLly/HF198ASsrK+jo6MDIyAhWVtL7r5s+fTp69+4NAJg7dy6aNGmCK1euoHHjxuViV65ciTFjxmDUqFEAgE8++QR79+4t1+9ivXr1sGrVKsjlcjRu3Bi9e/fGgQMH8M4778Dc3BxyuRzGxsakfDLGGGOMMcYYY4yx/4ZarUHo7e2t9b+1tTXS06V3dP94Gg0bNoSBgYGmcLB02uNptm3bVqtpbLt27XD58mWoVCqcOXMGKpUK7u7uMDIy0nwOHTqE5ORkzTJ6enrl8l+ZR48e4fbt25qCzFL+/v64cOECaXsfVzYP1tbWAFDpPkxKSkLr1q21pj3+PwA0adIEcvm/gwjU5LgUFBTg0aNHWp+CQlqn0owxxhhjjDHGGGPs+VOrNQh1dbVHqxIEAWq1WjPYR9kBk0v7wKsqDUEQKk1TquzsbMjlcsTHx2sVkgGAkdG/I10qlcrnov+9x7cfAGl7q0uzNF1qmuHh4Zg7d67WtCkTJ2Da5IoHYGGMMcYYY4wxxhhjL4ZarUFYGUtLSwDQ6t+u7IAlT+rYsWNa///1119wc3ODXC6Hr68vVCoV0tPTyw0SUtMmtSYmJrCxsUFcXJzW9Li4OHh5edV4O6g8PDxw4sQJrWmP/y+Fnp4eVCpVlTGzZ89GZmam1ue98e+Q18UYY4wxxhhjjDHGni+1WoOwMkqlEm3btsWiRYvg5OSE9PR0fPzxx7WWfmpqKqZNm4Zx48bh5MmTWLlyJSIiIgAA7u7uCAwMRFBQECIiIuDr64u7d+/iwIED8Pb21vT3RzVjxgyEhobCxcUFPj4+iIqKQmJiIjZu3Fhr21WdSZMm4Z133oGfnx/at2+PLVu24PTp01pNsqVwdHTE4cOHMWzYMCgUClhYWJSLUSgUUCgUWtMy9fSeKP+MMcYYY4wxxhhj7Nl7KgWEALBhwwaMGTMGLVu2hIeHB5YsWYIePXrUStpBQUHIy8tD69atIZfL8f7772Ps2LGa+VFRUZg/fz5CQkJw69YtWFhYoG3btujTp0+N1zl58mRkZmYiJCQE6enp8PLyws6dO0kDsjypwMBApKSkYPr06cjPz8eQIUMQHByM48ePk9KZN28exo0bBxcXFxQUFGg1BWeMMcYYY4wxxhhj/22CyKVB/yndu3eHlZUVvvnmmzpf143L50nxp71eJ8W7XtxPijctuic5Vq6uuA/MyjxU0Jqjy4ViUrxAvAx11NIHiMkUzGl5AS0vMtD6sxRRd319yoQn66+zOmqR1ivD/QKTOsoJYKV/nxR/O8+SFN9IKX0gIxXk1QeVQT1nCkVabeXMQqPqg8oQhLp7DOoQz0kb2Q1SvGqp9Nr4Vyd/R0rbUj+DFH8334wUT/320Vg3SXLsDTiR0rZHCik+X8eQFJ8nSI+n3meo970itW71QWXIhaq7IHmcefEdybHZuvVIaRcT32vLQcs75V5D3S/UZyv1PFATew3SF/Ikx1L3Yz6UpHhd0Aa9o9yzZSLt+tAvyibF3xQcSfHGOrT0i0Xp5zz12Uo9h6npF4nS7zXU+xj1erLIoz1bbyhoFT7yVdLvHWa6WaS0lWIOKT5TpN1X9WTSr798taL6oDIMZbmkeDlov59E4vgBRgUPJcemylxIaVOvbR3Qfou6utC+1zBWU0+tBuGLKiAgAD4+Pli+fPmzzko5ubm5+PLLL9GzZ0/I5XJ899132L9/P/bt2/ess8YYY4wxxhhjjDHGXhBcg7ASpSMc5+XlQSaTafW/t3v3bnTs2JGU3sKFC7Fw4UKtaUVFRSgsLESvXr2we/duch7z8vLQt29fJCQkID8/Hx4eHvj444/xxhtvkNOqiYvJN0nx1Ld9Vxp3I8VTahzezi3fz2KVaetfJcWrBFrZe4FAe9tO2ZePio1JaZvqPCLFF4i0t4lZRQaSY630pNdiA4Bi0GrGUGtcUM9hSk1SUaS9BVUTa+1R806hJ+aT4gsFfVL8vULa2/AGCum1iQFASawxQiEQa6881GlAiqfUutBDASlt6pt5uZr25l8u0uJvqRpJjnUSr5DSztel1TrNgikpXodwL6jrGoTUWkANMy+R4hMV7SXHOiho3yNEgbZvqM8EyvOMckwBoNHDs6T4dHN3UryimFZTJ1cuvYY7tZY4lZ5Auzflq6V/Z6I++woJtd5qwjH7DCk+01j6fS8HtO97ugKtBhMVpcVIXbYuAQCVSGzpQLyvFqql1yB0yjxJSpt6L9BT0b6TZQj1JccqiNcqFfVeQ619nK2W/qyXE88Bak1rag1eDxc7UjxjNcU1CCtROspyYGAgPD09tQZVadRI+sO61Pjx4zFkyBCtaT/++CMWLFiA9evX1yiPSqUS+/fTmuEyxhhjjDHGGGOMMVYW7VXsS8TV1RWurq5QKpUwMTHBunXr0Lp1a3To0AGLFy/WxKWmpqJ///4wMjKCiYkJhgwZgjt3/u1/59SpU+jcuTMcHBzQokULDB06FBkZGbh58yZmzZqFrKws2NraQhAEhIWFVZuvb775Bn5+fjA2NoaVlRVGjBiB9HTtWlalg6Xo6+ujc+fOiImJgSAIyMjI0MT88ccf6NixI5RKJezs7DB58mTk5ND6uGCMMcYYY4wxxhhjLz4uIJQgJiYGhoaGOHbsGJYsWYJ58+Zh3759UKvV6N+/Px48eIBDhw5h3759SElJwdChQzXLBgYGwtbWFidOnEB8fDxmzZoFXV1dtG/fHsuXL4eJiQnS0tKQlpaG6dOnV5uXoqIifPrppzh16hR27NiBa9euITg4WDP/6tWrGDRoEAYMGIBTp05h3Lhx+Oijj7TSSE5ORq9evTBw4ECcPn0aW7ZswR9//IGJEyfW2j5jjDHGGGOMMcYYYy8GbmIsgbe3N0JDQwEAbm5uWLVqFQ4cOAAAOHPmDK5evQo7u5J+Ab7++ms0adIEJ06cQKtWrZCamooZM2agcePGmuVLmZqaQhAEWFlJHyF39OjRmr+dnZ2xYsUKtGrVCtnZ2TAyMsLatWvh4eGBzz77DADg4eGBs2fPYsGCBZrlwsPDERgYiClTpmjytGLFCrzyyitYs2YN9PVp/YMxxhhjjDHGGGOMsRcX1yCUwNvbW+t/a2trpKen48KFC7Czs9MUDgKAl5cXzMzMcOHCBQDAtGnT8Pbbb6Nbt25YtGgRkpOTnygv8fHx6Nu3L+zt7WFsbIxXXnkFQElTZwBISkpCq1attJZp3bq11v+nTp1CdHQ0jIyMNJ+ePXtCrVbj6tWKB+MoKCjAo0ePtD6FBXXbUS1jjDHGGGOMMcYYq3tcQCiBrq72iGaCIECtljayUVhYGM6dO4fevXvj999/h5eXF7Zv316jfOTk5KBnz54wMTHBxo0bceLECU1ahYXSR3HKzs7GuHHjkJiYqPmcOnUKly9fhouLS4XLhIeHw9TUVOuz7ssvarQdjDHGGGOMMcYYY+z5wU2Mn4Cnpydu3LiBGzduaGoRnj9/HhkZGfDy8tLEubu7w93dHVOnTsXw4cMRFRWF119/HXp6elCppA9xfvHiRdy/fx+LFi3SrO/vv//WivHw8MCvv/6qNe3EiRNa/7do0QLnz5+Hq6ur5HXPnj0b06ZN05p27eZdycszxhhjjDHGGGOMsecT1yB8At26dUOzZs0QGBiIkydP4vjx4wgKCsIrr7wCPz8/5OXlYeLEiYiNjcX169cRFxeHEydOwNPTEwDg6OiI7OxsHDhwAPfu3UNubm6V67O3t4eenh5WrlyJlJQU7Ny5E59++qlWzLhx43Dx4kXMnDkTly5dwvfff4/o6GgAJTUfAWDmzJn4888/MXHiRCQmJuLy5cv46aefqhykRKFQwMTEROujp1A8wd5jjDHGGGOMMcYYY88DLiB8AoIg4KeffkK9evXQqVMndOvWDc7OztiyZQsAQC6X4/79+wgKCoK7uzuGDBmCV199FXPnzgUAtG/fHuPHj8fQoUNhaWmJJUuWVLk+S0tLREdH44cffoCXlxcWLVqEpUuXasU4OTlh69at2LZtG7y9vbFmzRrNKMaK/xXoeXt749ChQ7h06RI6duwIX19ffPLJJ7CxsantXcQYY4wxxhhjjDHGnnOCKIris84Eq1sLFizAl19+iRs3btRquheTb5LiBdBOtSuNu5HiXS/ulxx7O9eClrZ+xYO3VEYl0FrvFwhKUjxlXz4qNialbarziBRfINJqkmYVGUiOtdJLJ6VdDN3qg8pQE9+RUM9huVAsOVYUBVLaashJ8dS8U+iJ+aT4QoE2Uvq9wnqk+AaKe6R4ZVE2KZ5CEKX1V1vqoU4DUrxMkJ6+HmgDS4kC7ZyUq6Wf7wAgF2nxt1SNJMc6iVdIaefrGpHis2BKitch3AvUIu2+RDkHAEAGWnzDzEuk+ERFe8mxDgra9whRoO0b6jOB8jyjHFMAaPTwLCk+3dydFK8orroVyuNy5SaSY1XE5w2VnkC7N+WrpX9noj77CkXaOUPlmH2GFJ9pLP2+lwPa9z1doYgUTyVC+jOEElsTKpF2DlPvq4VqPcmxTpknSWlT7wV6Ktp3sgyhvuRYBfFapaLea3QhvQ9+AMhWS3/Wy4nngBzSuw0rSZ8W7+FiV30QY7Wg1moQBgQEYMqUKbWV3H9OcHAwBgwY8FTWtXr1apw4cQIpKSn45ptv8Nlnn2HkyJEAgOjoaJiZmT2VfDDGGGOMMcYYY4yx598LMUhJdHQ0pkyZgoyMjGedlWpdu3YNTk5OSEhIgI+PD2nZI0eO4NVXX610fna2tFouly9fxvz58/HgwQPY29sjJCQEs2fPBgAMHToUr732miY2LCwMO3bsQGJiIimvAGBaRKulQ32LS6kRCNBqHHbdOomU9q1mr1UfVEa9nNuk+LsGDqR4q4wLkmNlZrQ3j5kqWs0YfRntbaKrWnreHwmWpLRlxLd3eSpazU018Q330WTp+SdW1kJrp/uk+LNp0t8QA0AbW+k1jqm1qRoWpJLis3RotbuOpDqT4uuyHr2FCa2WUUuj83WUE2DzxWak+AFNU0jxPye5keKpgi1/rT7of5LrtSal7XY3jhSfY0lLn/L8S82m1XC3NXpAis9T0WrwFpo0JcW3ub5VcuwFe9qz1URGq+FOrThNqcWUp6btx2tmvqR4UxXtuB594FV9UBlt6idJjs0A7flhJMsixeuoaLWAsgg15QxltJqVzlf3kuKPWg0lxcuMaNdTykPp+96lHu17eR02LAAAFIvSf2YqiN8li4g1PV2TdpDi91m9TYq3NMyRHEu9F+QX0Vrq3Myk1SS1NZV+vRYKtP1eX0Y7Jx+JZqR4Q9CeCY2TD0iO/dt+OCnthsTWK3VdM5uxmnohCghfFn5+fjUqqHtcZGQkIiMjK5ynVCqhVNIKRRhjjDHGGGOMMcbYf1etDlKiVqvxwQcfwNzcHFZWVggLC9PMS01NRf/+/WFkZAQTExMMGTIEd+7c0cw/deoUOnfuDGNjY5iYmKBly5b4+++/ERsbi1GjRiEzMxOCIEAQBK10K+Po6Ij58+cjKCgIRkZGcHBwwM6dO3H37l1NPry9vfH3339rLffjjz+iSZMmUCgUcHR0RERERLl0Fy5ciNGjR8PY2Bj29vZYt26dZr6TkxMAwNfXF4IgICAgQGv5pUuXwtraGvXr18d7772HoqJ/+/+IiorCq6++iqZNm8Lf3x+zZs2Cq6ur5lORX375BWZmZlCpSmpOJSYmQhAEzJo1SxPz9ttv48033wSg3cQ4Ojoac+fOxalTpzT7tnTEY8YYY4wxxhhjjDH2cqjVAsKYmBgYGhri2LFjWLJkCebNm4d9+/ZBrVajf//+ePDgAQ4dOoR9+/YhJSUFQ4f+Wy0/MDAQtra2OHHiBOLj4zFr1izo6uqiffv2WL58OUxMTJCWloa0tDRMnz5dUn4iIyPh7++PhIQE9O7dG2+99RaCgoLw5ptv4uTJk3BxcUFQUBBKx2mJj4/HkCFDMGzYMJw5cwZhYWGYM2dOuUKziIgI+Pn5ISEhARMmTMC7776LpKSSphrHjx8HAOzfvx9paWnYtm2bZrmDBw8iOTkZBw8eRExMDKKjozVp//3335g8eTLmzZuHpKQk7NmzB506dap2Gzt27IisrCwkJCQAAA4dOgQLCwvExsZqYg4dOlSuoBIoaW4cEhKCJk2aaPZt2WPCGGOMMcYYY4wxxv77arWJsbe3N0JDQwEAbm5uWLVqFQ4cKGnrf+bMGVy9ehV2diUj8Hz99ddo0qQJTpw4gVatWiE1NRUzZsxA48aNNcuXMjU1hSAIsLKyIuXntddew7hx4wAAn3zyCdasWYNWrVph8ODBAICZM2eiXbt2uHPnDqysrLBs2TJ07doVc+bMAQC4u7vj/Pnz+OyzzxAcHKyV7oQJEzRpREZG4uDBg/Dw8IClZUmfY/Xr1y+X33r16mHVqlWQy+Vo3LgxevfujQMHDuCdd95BamoqDA0N0adPHxgbG8PBwQG+vtX3UWFqagofHx/ExsbCz88PsbGxmDp1KubOnYvs7GxkZmbiypUreOWVV8otq1QqYWRkBB0dHfK+ZYwxxhhjjDHGGGP/DbVag9Db21vrf2tra6Snp+PChQuws7PTFA4CgJeXF8zMzHDhQsmABdOmTcPbb7+Nbt26YdGiRUhOTq7V/DRs2BAA0KxZs3LT0tPTAQAXLlyAv7+/Vhr+/v64fPmypgnv4+mWFlyWplGVJk2aQC7/t0PS0v0DAN27d4eDgwOcnZ3x1ltvYePGjcjNldax8iuvvILY2FiIoogjR47gjTfegKenJ/744w8cOnQINjY2WgWuNVFQUIBHjx5pfQoKaZ1KM8YYY4wxxhhjjLHnT60WEOrqao9sJAgC1Gq1pGXDwsJw7tw59O7dG7///ju8vLywffv2WsuP8L8hQiuaJjWPFaVbmo6UNKpaztjYGCdPnsR3330Ha2trfPLJJ2jevLmkkZsDAgLwxx9/4NSpU9DV1UXjxo0REBCA2NhYHDp0qMLag1Th4eEwNTXV+qxct+GJ02WMMcYYY4wxxhhjz1atFhBWxtPTEzdu3MCNGzc0086fP4+MjAx4eXlpprm7u2Pq1KnYu3cv3njjDURFRQEA9PT0tGrw1WU+4+LitKbFxcXB3d1dq+ZfVfT09ACgRvnV0dFBt27dsGTJEpw+fRrXrl3D77//Xu1ypf0QRkZGagoDSwsIY2NjK+x/sGx+peR19uzZyMzM1PpMGjta8rYxxhhjjDHGGGOMsefTUykg7NatG5o1a4bAwECcPHkSx48fR1BQEF555RX4+fkhLy8PEydORGxsLK5fv464uDicOHECnp6eAEpGDs7OzsaBAwdw7949yU1vqUJCQnDgwAF8+umnuHTpEmJiYrBq1SrJg6IAQIMGDaBUKrFnzx7cuXMHmZmZkpb75ZdfsGLFCiQmJuL69ev4+uuvoVar4eHhUe2y9erVg7e3NzZu3KgpDOzUqRNOnjyJS5cuVVmD0NHREVevXkViYiLu3buHgoKCCuMUCgVMTEy0Por/FYYyxhhjjDHGGGOMsRfXUykgFAQBP/30E+rVq4dOnTqhW7ducHZ2xpYtWwAAcrkc9+/fR1BQENzd3TFkyBC8+uqrmDt3LgCgffv2GD9+PIYOHQpLS0ssWbKkTvLZokULfP/999i8eTOaNm2KTz75BPPmzdMaoKQ6Ojo6WLFiBdauXQsbGxv0799f0nJmZmbYtm0bunTpAk9PT3z55Zf47rvv0KRJE0nLv/LKK1CpVJoCQnNzc3h5ecHKyqrKQsaBAweiV69e6Ny5MywtLfHdd99JWh9jjDHGGGOMMcYY+2+otVGMY2Njy03bsWOH5m97e3v89NNPFS6rp6dXbcHUmjVrsGbNGsn5uXbtWrlppYN5DBgwAEBJ7TlRFLViBg4ciIEDB5LSTUxM1Pr/7bffxttvv601LTo6utxyy5cv1/xtYWGB/Px85ObmonHjxuXSfDwPTk5OSEhI0Ixg/Pnnn+Phw4cwMzOrNF8AEBwcrFXgqVAosHXrVgQEBMDHx4dUGMoYY4wxxhhjjDHGXny1VkDInkxoaCgMDQ2RlJQEIyMj0rLt27dHWloaTE1Nq42NjY1F586dyxUmbtu2rdwgKtXJ0jUnxdcr+IcUfzvXghTfdeskybEHBq0kpe17thMpXiWnNb/OVStJ8fLCPMmx5jk3SWlfUTcixVsakMJhfPWk5Ngk5yBS2nqyYlJ8sUirRC1ArD6ojLv3pedHLhdoeXEm5uUhLb6okfT7wb386u89ZSkNaNd2Zg7tJLtzjzbwlFpN2zcU9x/SzjFnb0tSvJn6nuTYW7do3XPIm9CupwcPafH37km/jwEAGkrrCxgADl2knWOuN/4ixev0bEGKv5ZtJTm2SEU7Z2Sgne9pWcakeEGgfSfJmLZccmx2zDBS2rl6+qT4enqPSPEUekIRKT5PRct7ppz2HauhMe16uic2kBwrE2jn2CO1CSm+WE37OWKkkyM5VibQ+gS/t/NXUvxfHd8ixff0kX4fA4AGRtLv24ZCNintAtDOSTXxOxPlvFGJtP1Cpb57hxRv7pRPir+QJv17UDMb2vWkkBWS4hsY0e4FNsKN6oP+564g/VkGAPfVtGexnHi9/qOyIcXLfvlNcuzhjqNIab/awpAUz9jz6oUsIDxy5AheffXVSudnZ9MekE9CpVJBEATIZE/WWjs5ORm9e/eGg4OD1vTU1FStgVyAf0ddbteuHZKSkmBvbw8rK9oN+3Hm5rQvoowxxhhjjDHGGGPsv+Gp9EH4uICAAEyePBkffPABzM3NYWVlhbCwMAAlzWcFQdBqHpuRkQFBEDTNmHNzc5GTk4OVK1fC3t4excXFaNasGQ4cOIDPP/8cnp6eMDExwYgRI8oNaFJcXIyJEyfC1NQUFhYWmDNnjlYz44KCAkyfPh2NGjWCoaEh2rRpo9V8Ojo6GmZmZti5cye8vLygUCiQmppa5faq1WrMmzcPtra2UCgU8PHxwZ49ezTzBUFAfHw85s2bB0EQNPsCAGxsbJCYmKj1+fXXkrebW7ZsgY2NDWJjYyEIAjIyMgAA169fR9++fVGvXj0YGhqiSZMm+PXXX3Ht2jV07twZQMnAJoIgaJoUBwQEYMqUKdUcOcYYY4wxxhhjjDH2X/PMahDGxMRg2rRpOHbsGI4ePYrg4GD4+/vDzc2t2mUVCgUAYN26dfjqq69gYGCAIUOGYObMmVAoFNi0aROys7Px+uuvY+XKlZg5c6bWeseMGYPjx4/j77//xtixY2Fvb4933nkHADBx4kScP38emzdvho2NDbZv345evXrhzJkzmrzl5uZi8eLFWL9+PerXr48GDapupvH5558jIiICa9euha+vLzZs2IB+/frh3LlzcHNzQ1paGrp164ZevXph+vTpWk2MdXR04OrqqpWejk7JYbO3t9f8XdZ7772HwsJCHD58GIaGhjh//jyMjIxgZ2eHH3/8EQMHDkRSUhJMTEygVNKatjLGGGOMMcYYY4yx/5ZnVkDo7e2N0NBQAICbmxtWrVqFAwcOSCogLDV//nz4+/sDAMaMGYPZs2cjOTkZzs7OAIBBgwbh4MGDWgWEdnZ2iIyMhCAI8PDwwJkzZxAZGYl33nkHqampiIqKQmpqKmxsSvo0mD59Ovbs2YOoqCgsXLgQAFBUVITVq1ejefPmkvK5dOlSzJw5E8OGlfS1s3jxYhw8eBDLly/HF198ASsrK+jo6MDIyOiJmwoDJc2SBw4ciGbNmgGAZn8A/zYlbtCggVYfhIwxxhhjjDHGGGPs5fRMCwjLsra2Rnp6eo3TaNiwIQwMDLQKwxo2bIjjx49rLdO2bVsIwr+DAbRr1w4RERFQqVQ4c+YMVCoV3N3dtZYpKChA/fr1Nf/r6emVy39lHj16hNu3b2sKMkv5+/vj1KlTktKgmjx5Mt59913s3bsX3bp1w8CBAyXntzIFBQUoKCjQmlZYUAC9/9XmZIwxxhhjjDHGGGMvpmfSByGAciPmCoIAtVqtGeyjbL+ARUUVjxRXNg1BECpNU6rs7GzI5XLEx8dr9fl34cIFfP7555o4pVKpVcj4vHn77beRkpKCt956C2fOnIGfnx9WrqSN2vu48PBwmJqaan3Wfrm6lnLMGGOMMcYYY4wxxp6VZ1ZAWBlLS0sAQFpammZa2QFLntSxY8e0/v/rr7/g5uYGuVwOX19fqFQqpKenw9XVVetT06a/JiYmsLGxQVxcnNb0uLi4cqMT1yY7OzuMHz8e27ZtQ0hICL766isAJbUfgZLRlylmz56NzMxMrc+48RNqPd+MMcYYY4wxxhhj7Ol6Zk2MK6NUKtG2bVssWrQITk5OSE9Px8cff1xr6aempmLatGkYN24cTp48iZUrVyIiIgIA4O7ujsDAQAQFBSEiIgK+vr64e/cuDhw4AG9vb/Tu3btG65wxYwZCQ0Ph4uICHx8fREVFITExERs3bqy17SprypQpePXVV+Hu7o6HDx/i4MGD8PT0BAA4ODhAEAT88ssveO2116BUKrUGRamMQqHQDA5TSk+RURfZZ4wxxhhjjDHGGGNP0XNXQAgAGzZswJgxY9CyZUt4eHhgyZIl6NGjR62kHRQUhLy8PLRu3RpyuRzvv/8+xo4dq5kfFRWF+fPnIyQkBLdu3YKFhQXatm2LPn361HidkydPRmZmJkJCQpCeng4vLy/s3LmTNCALhUqlwnvvvYebN2/CxMQEvXr1QmRkJACgUaNGmDt3LmbNmoVRo0YhKCgI0dHRdZIPxhhjjDHGGGOMMfb8eyYFhLGxseWm7dixQ/O3p6cn/vzzT635ZfskDAgI0PofAIKDgxEcHKw1LSwsDGFhYRWud82aNRXmTVdXF3PnzsXcuXMrnF/Reqojk8kQGhqqGbW5IpRm1I6OjlXuj+r6G5wzZw7mzJmjNa2iY8IYY4wxxhhjjDHG/vueuz4IqQICAjBlypRnnY0KXbx4EW3btoW+vj58fHyedXaqJAiCViEtY4wxxhhjjDHGGHs5COLjVfFeMAEBAfDx8cHy5cufWR4q68MvPz8fzZs3x7Zt22BkZIT69etLSm/hwoVYuHBhhfM6duyI3bt31zivlfnnn39Qr169cv0MViXx8l3SOgxkeaR4pTqbFF8o15eedhEt7YSmg0nxvhNbkOJvj6eNCG0pT5ccqxJoFYUfqU1I8SJoI3qbyjIlxza4e46UtiBKH7UcAG42aEmKp9IRKh6BvSKiSNuPashJ8dTjRImXgzbo0e18S1K8Qi59PwJAfd0MUrxSlUWKp8iV066nHLUBKV4py5cca6h+REq7WKZHy0sRbT8Wy3RJ8efz3CXH6slp56Rf9j5SfEr9tqR4haxAcuydfHNS2pb6GaR4lUi7d6hF2rtkx0cJkmNTTGjPSpui66T4BwraAHPFovTnpQy0540g0L5uU49TdrGSFG+oI/3eoUt4lgG0/QjQn0+UfU9NW0coJsXXK7xDis/Qa0CKzyquvp/wUnbqlDrNC3VfUs5hfeJvhCKR9nyqa3qQfo8vgPTfKwD9XpBLvBc0vb9fcmxyww6ktKnfbfVkhaT47GJDUryxjvTff7qg5aUYtO80AmjPBDcXB1I8YzX1XPZB+KypVCoIggCZTNqX4sqaB7/++uvo2bMnHBxoF/T48eMxZMiQCucplf/e9EVRhEqlgo7Okx/Gmo7SzBhjjDHGGGOMMcZebLXWxDggIACTJ0/GBx98AHNzc1hZWWn6/7t27RoEQdAqSMvIyIAgCJq+72JjYyEIAn777Tf4+vpCqVSiS5cuSE9Px+7du+Hp6QkTExOMGDECubm5WusuLi7GxIkTYWpqCgsLC8yZM0erT76CggJMnz4djRo1gqGhIdq0aaPV5150dDTMzMywc+dOeHl5QaFQIDU1tcrtVavVmDdvHmxtbdGkSRMMGjQIV65cgaurK1xdXeHm5oazZ88iPDwcgiBo9YVYkdJ9tHnzZvTp0wdNmzbFgAEDcOvWLU2aN2/ehK2tLXbv3o2WLVtCoVDgjz/+gFqtRnh4OJycnKBUKtG8eXNs3bpVk09bW9tyfS4mJCRAJpPh+vWSt/HcxJgxxhhjjDHGGGPs5VSrfRDGxMTA0NAQx44dw5IlSzBv3jzs20drthMWFoZVq1bhzz//xI0bNzBkyBAsX74cmzZtwq5du7B3795yg3DExMRAR0cHx48fx+eff45ly5Zh/fr1mvkTJ07E0aNHsXnzZpw+fRqDBw9Gr169cPnyZU1Mbm4uFi9ejPXr1+PcuXNo0KDqaveff/45IiIisHTpUpw+fRo9e/ZEv379NGmmpaWhSZMmCAkJQVpaGqZPny5p+2fMmIGQkBAkJCSgXbt26Nu3L+7fv68VM2vWLCxatAgXLlyAt7c3wsPD8fXXX+PLL7/EuXPnMHXqVLz55ps4dOgQZDIZhg8fjk2bNmmlsXHjRvj7+5NrNzLGGGOMMcYYY4yx/5ZaLSD09vZGaGgo3NzcEBQUBD8/Pxw4cICUxvz58+Hv7w9fX1+MGTMGhw4dwpo1a+Dr64uOHTti0KBBOHjwoNYydnZ2iIyMhIeHBwIDAzFp0iRERkYCAFJTUxEVFYUffvgBHTt2hIuLC6ZPn44OHTogKipKk0ZRURFWr16N9u3bw8PDAwYGVfcDtXTpUsycORPDhg2Dh4cHFi9erNUXopWVFXR0dGBkZAQrK6tK+yl83MSJEzFw4EB4enpizZo1MDU1xf/93/9pxcybNw/du3eHi4sLDA0NsXDhQmzYsAE9e/aEs7MzgoOD8eabb2Lt2rUAgMDAQMTFxWlqRarVamzevBmBgYGS8sQYY4wxxhhjjDHG/rtqtQ9Cb29vrf+tra2Rni59MIXH02jYsCEMDAzg7OysNe348eNay7Rt2xaC8G8nqO3atUNERARUKhXOnDkDlUoFd3ftzs0LCgq0Bg3R09Mrl//KPHr0CLdv34a/v7/WdH9/f5w6dUpSGpVp166d5m8dHR34+fnhwoULWjF+fn6av69cuYLc3Fx0795dK6awsBC+vr4AAB8fH3h6emLTpk2YNWsWDh06hPT0dAweLH3gjYKCAhQUaHfAW1hYAD096YOaMMYYY4wxxhhjjLHnT60WEOrqao/eIwgC1Gq1ZrCPsv0CFhVVPCJa2TQEQag0Tamys7Mhl8sRHx8PuVx7FKiytfqUSqVWIePzzNDw3xGbsrNLRmPatWsXGjVqpBVXdkTiwMBATQHhpk2b0KtXL8mjKgNAeHg45s6dqzVt3MTpGD/5g5psAmOMMcYYY4wxxhh7TtRqE+PKWFpaAijpl69UZSP/1sSxY8e0/v/rr7/g5uYGuVwOX19fqFQqpKenawb7KP3UdOReExMT2NjYIC4uTmt6XFwcvLy8arwdpXkvVVxcjPj4eHh6elYaX3ZQlce3z87OThM3YsQInD17FvHx8di6dSu5efHs2bORmZmp9Rk9/n36BjLGGGOMMcYYY4yx50qt1iCsjFKpRNu2bbFo0SI4OTkhPT0dH3/8ca2ln5qaimnTpmHcuHE4efIkVq5ciYiICACAu7s7AgMDERQUhIiICPj6+uLu3bs4cOAAvL290bt37xqtc8aMGQgNDYWLiwt8fHwQFRWFxMREbNy48Ym25YsvvoCbmxs8PT0RGRmJhw8fYvTo0ZXGGxsbY/r06Zg6dSrUajU6dOiAzMxMxMXFwcTEBCNHjgQAODo6on379hgzZgxUKhX69etHypdCodCqkQgAenoFlUQzxhhjjDHGGGOMsRfFUykgBIANGzZgzJgxaNmyJTw8PLBkyRL06NGjVtIOCgpCXl4eWrduDblcjvfffx9jx47VzI+KisL8+fMREhKCW7duwcLCAm3btkWfPn1qvM7JkycjMzMTISEhSE9Ph5eXF3bu3Ak3N7cn2pZFixZh0aJFSExMhKurK3bu3AkLC4sql/n0009haWmJ8PBwpKSkwMzMDC1atMCHH36oFRcYGIgJEyYgKCgISqXyifLJGGOMMcYYY4wxxv4bBLFsx4Dsmbl27RqcnJyQkJAAHx+fZ50dSRIv3yXFG8jySPFKdTYpvlCuLz3tIlraCU2lD+gCAL4TW5Dib49fTYq3lEsf/Ecl0N4DPFKbkOJF0PruNJVlSo5tcPccKW1BlN4/KQDcbNCSFE+lI1Tc12pFRJG2H9WQVx9UNn3icaLEy6EipX0735IUr5BL348AUF83gxSvVGWR4ily5bTrKUdtQIpXyvIlxxqqH5HSLpbp0fJSRNuPxTLd6oPKOJ/nXn3Q/+jJaeekX/Y+UnxK/bakeIVMeo37O/nmpLQt9TNI8SqRdu9Qi7TeaBwfJUiOTTGhPSttiq6T4h8oaF3JFIvSn5cy0J43gkD7uk09TtnFtBe/hjrS7x26hGcZQNuPAP35RNn31LR1hGJSfL3CO6T4DL0GpPisYqPqg/7HTp1Sp3mh7kvKOaxP/I1QJNKeT3VND9Lv8QWQ/nsFoN8Lcon3gqb390uOTW7YgZQ29butnqyQFJ9dbFh9UBnGOtJ//+mClpdi0L7TCKA9E9xcHEjxjNXUU+mDsC4FBARgypQpzzobFbp48SLatm0LfX39577QTxAE7Nix41lngzHGGGOMMcYYY4w9ZU+tifGLpuwIx4/bvXs3OnbsWG0aoaGhMDQ0RFJSEr766qtK0+zYsSPWrFlT47zWhrS0NNSrV4+0jLG87mrdAPSab/VybktPW05780itEZiw6iQp3vE96W/yAcAw/4Hk2AwlrQYF9U2lTKDVojAszJAeTBxZXP5Aes1KACi2rNtaDplFtNpjFCa6tOsvV0V7o2wgl/42X0181+SouEGKfyjSalQ9UhmT4u+qaOlTKECreWMkzyHFU2rS/KO2IaVtLpd+nwGA2zJ7Ujz1XmNvJP36pp7veSa0+6RcoNVQLFArqg/6HyNd2vOAWguBGk+tUZVj2JAUT6HMptXWArEGIeV6otYIpO53ag1FPRntOFGOK6V2FEB/JpBrEBK+d1BrMNUvkP5dEgBu6jqT4g2RS4qXy6Tfa+7LrUlpU8+xujxO1BqBOsRnq0Ex7TtThrzq7p0elwPpNT2pNXKpx0mHeC9IaegvOZbaYkSQ0e571HuNHrGlg3m+9Ov7H4UjKW3qvqGeB4w9LVxAWAGVSoWTJ09CJqv4C06jRo0kpZOcnIzevXvDwcEB06ZNQ3BwcIVxSqUSjRo1ArW1tyiKUKlU0NF58sNY0xGdGWOMMcYYY4wxxtiLrdaaGAcEBGDy5Mn44IMPYG5uDisrK4SFhQEo6V9PEAQkJiZq4jMyMiAIAmJjYwEAsbGxEAQBv/32G3x9faFUKtGlSxekp6dj9+7d8PT0hImJCUaMGIHcXO23b8XFxZg4cSJMTU1hYWGBOXPmaBW2FRQUYPr06WjUqBEMDQ3Rpk0bzXoBIDo6GmZmZti5cye8vLygUCigp6cHV1fXCj9KpRJqtRrz5s2Dra0tFAoFfHx8sGfPHk2agiAgPj4e8+bNgyAIWLFiRaXpNWrUSLOPNm/ejPbt20NfXx9NmzbFoUOHNGmW7qPdu3ejZcuWUCgU+OOPP6BWqxEeHg4nJycolUo0b94cW7duBQCo1WrY2tqWq6GYkJAAmUyG69eva/LLTYwZY4wxxhhjjDHGXj612gdhTEwMDA0NcezYMSxZsgTz5s3Dvn20jr/DwsKwatUq/Pnnn7hx4waGDBmC5cuXY9OmTdi1axf27t2LlStXlluvjo4Ojh8/js8//xzLli3D+vXrNfMnTpyIo0ePYvPmzTh9+jQGDx6MXr164fLly5qY3NxcLF68GOvXr8e5c+fQoEHVHfd+/vnniIiIwNKlS3H69Gn07NkT/fr106SZlpaGJk2aICQkBGlpaZg+fbqk7Z8xYwZCQkKQkJCAdu3aoW/fvrh//75WzKxZs7Bo0SJcuHAB3t7eCA8Px9dff40vv/wS586dw9SpU/Hmm2/i0KFDkMlkGD58ODZt2qSVxsaNG+Hv7w8HB+7wlDHGGGOMMcYYY+xlVqsFhN7e3ggNDYWbmxuCgoLg5+eHAwcOkNKYP38+/P394evrizFjxuDQoUNYs2YNfH190bFjRwwaNAgHDx7UWsbOzg6RkZHw8PBAYGAgJk2ahMjISABAamoqoqKi8MMPP6Bjx45wcXHB9OnT0aFDB0RFRWnSKCoqwurVq9G+fXt4eHjAwKDqkSSXLl2KmTNnYtiwYfDw8MDixYvh4+OD5cuXAyhpsqujowMjIyNYWVlV2adhWRMnTsTAgQPh6emJNWvWwNTUFP/3f/+nFTNv3jx0794dLi4uMDQ0xMKFC7Fhwwb07NkTzs7OCA4Oxptvvom1a9cCAAIDAxEXF4fU1FQAJbUKN2/ejMDAQEl5YowxxhhjjDHGGGP/XbXaB6G3t7fW/9bW1khPpw0aUDaNhg0bwsDAAM7OzlrTjh8/rrVM27ZtIZQZzKBdu3aIiIiASqXCmTNnoFKp4O7urrVMQUEB6tevr/lfT0+vXP4r8+jRI9y+fRv+/tqduvr7++PUqVOS0qhMu3btNH/r6OjAz88PFy5c0Irx8/PT/H3lyhXk5uaie/fuWjGFhYXw9fUFAPj4+MDT0xObNm3CrFmzcOjQIaSnp2Pw4MGS81VQUICCgoJy0xQK6R2vM8YYY4wxxhhjjLHnT60WEOrq6mr9LwgC1Gq1ZrCPsv0CFhVVPHJP2TQEQag0Tamys7Mhl8sRHx8PuVx7xMSytfqUSqVWIePzzNDQUPN3dnY2AGDXrl3lBk8pW3gXGBioKSDctGkTevXqpVVAWp3w8HDMnTtXa9qkyZPx/vvv12QTGGOMMcYYY4wxxthzolabGFfG0tISQEm/fKXKDljypI4dO6b1/19//QU3NzfI5XL4+vpCpVIhPT293OAgNR2518TEBDY2NoiLi9OaHhcXBy8vrxpvR2neSxUXFyM+Ph6enp6VxpcOqpKamlpu++zs7DRxI0aMwNmzZxEfH4+tW7eSmxfPnj0bmZmZWp/x48fTN5AxxhhjjDHGGGOMPVdqtQZhZZRKJdq2bYtFixbByckJ6enp+Pjjj2st/dTUVEybNg3jxo3DyZMnsXLlSkRERAAA3N3dERgYiKCgIERERMDX1xd3797FgQMH4O3tjd69e9donTNmzEBoaChcXFzg4+ODqKgoJCYmYuPGjU+0LV988QXc3Nzg6emJyMhIPHz4EKNHj6403tjYGNOnT8fUqVOhVqvRoUMHZGZmIi4uDiYmJhg5ciQAwNHREe3bt8eYMWOgUqnQr18/Ur4UCkW55sSKe/foG8gYY4wxxhhjjDHGnitPpYAQADZs2IAxY8agZcuW8PDwwJIlS9CjR49aSTsoKAh5eXlo3bo15HI53n//fYwdO1YzPyoqCvPnz0dISAhu3boFCwsLtG3bFn369KnxOidPnozMzEyEhIQgPT0dXl5e2LlzJ9zc3J5oWxYtWoRFixYhMTERrq6u2LlzJywsLKpc5tNPP4WlpSXCw8ORkpICMzMztGjRAh9++KFWXGBgICZMmICgoCAolconyidjjDHGGGOMMcYY+28QxLIdA7Jn5tq1a3ByckJCQgJ8fHyedXYkSU5JqdP05epiUrxJnvQBcVRyPVLaRV9FkuITVp0kxTteiCXFN8y/Jjk2Q0lrSp+pMiXFywTpfYICgE3xdcmxho9uk9LWuZ9WfVAZye59SfEiaP2U5hbXXUG8iW4WKT5XRcuLgTxPcqwA2mNEX8wlxT8UzUnxKlFefVAZBSrd6oNqSCGvuL/dyhjJc0jxMki//jKKade2ue4DUnwmMX3qcTLWyZYcSz3f7YqTSfHpenbVB5VB2dZi4n6hXKvUvAD067tesfRn8R15o+qDynB5eLz6oDJu1W9OileL0nveEQTafqHuR0peACBfTRs0TinPlxyrgPRYAMgXadcf9dlK+d4hirS0LQtvkuJv6jpXH1SGoYz2/MtRG0iOVQiFpLSp39+ox4mCen3ogPZsNSimfWfKkFddOeNxKki/r+oKtLzX9b1ATyY9P5TvHAD9Pkm912SrjUnx1oVXJcf+o3AkpS2HihRPPQ/cXBxI8YzV1FPpg5AxxhhjjDHGGGOMMfZ84hqElSg7wvHjdu/ejY4dO5LSW7hwIRYuXFhuemFhIfT19XH69OkXrgbhzUtnSfGFctob5SLQavlR5KppeVGpaTUuKG/mAeCaZwAp3u78EcmxOgKtJiY1nvrWN1/Ur7O0qfEKgXacVMReGZRq6TWeRIH2vuae2pIUby6n1QbLE6XXWpALtLemBmram/wiGe1tuFykncO5QuX3+6fNtPg+KT5XR/rbcx2R9rY6C7QagYaC9PMdANTEd5TFkF7Tk1rLoUik1SLVl9Fq7clE6fnJEWnnI/X6U4q0Wqpqgfb8Sy+SXvPGjFgTmlq7pADSnzcA7byhnr/Uc5KaPrVmKOVZLwftnkq5VoG6rYWeL0h/lj0N1H1ZQPjOVEi8jxnJiPeCOqxXQr0HK4QCUnyWinZfrSd/SIrXUUuvvZkno+Wlru8FOcXSrxFT3UektKmo14dI3DeUfakLWo1c6u9W6jnf1LVmg6syRvXU+iB80VQ1ynKjRrQmMQAwfvx4DBkypNz0FStW4MCBA3B0dASX1TLGGGOMMcYYY4yxp+2lb2KclZWFwMBAGBoawtraGpGRkQgICMCqVavg6uoKNzc3nD17Fq6urpqPtbU1oqOjNWncuHEDQ4YMgZmZGczNzdG/f39cu3ZNMz82Nha9evVC8+bN4efnh5EjR0JXVxd//PEHVq5cifPnz0MQBAiCoJVuZZYtW4ZmzZrB0NAQdnZ2mDBhArKztWtufPXVV7Czs4OBgQFef/11LFu2DGZmZloxP/30E1q0aAF9fX04Oztj7ty5KC6mvblhjDHGGGOMMcYYYy+2l76AcNq0aYiLi8POnTuxb98+HDlyBCdPSh9goqioCD179oSxsTGOHDmCuLg4GBkZoVevXigsLERxcTEGDBiAV155BadPn8bRo0cxduxYCIKAoUOHIiQkBE2aNEFaWhrS0tIwdOjQatcpk8mwYsUKnDt3DjExMfj999/xwQcfaObHxcVh/PjxeP/995GYmIju3btjwYIFWmkcOXIEQUFBeP/993H+/HmsXbsW0dHR5eIYY4wxxhhjjDHG2H/bS93EOCsrCzExMdi0aRO6du0KAIiKioKNjY3kNLZs2QK1Wo3169dDEARNGmZmZoiNjYWfnx8yMzPRp08fuLi4AAA8PT01yxsZGUFHRwdWVtL7FZgyZYrmb0dHR8yfPx/jx4/H6tWrAQArV67Eq6++iunTpwMA3N3d8eeff+KXX37RLDd37lzMmjULI0eOBAA4Ozvj008/xQcffIDQ0FDJeWGMMcYYY4wxxhhjL7aXuoAwJSUFRUVFaN26tWaaqakpPDw8JKdx6tQpXLlyBcbG2h3F5+fnIzk5GT169EBwcDB69uyJ7t27o1u3bhgyZAisra1rnO/9+/cjPDwcFy9exKNHj1BcXIz8/Hzk5ubCwMAASUlJeP3117WWad26tVYB4alTpxAXF6dVY1ClUmmlU1ZBQQEKCrQ7BC4oLIRCr+4GEmGMMcYYY4wxxhhjde+lb2JcHUEQyg0eUlT070iQ2dnZaNmyJRITE7U+ly5dwogRIwCU1Cg8evQo2rdvjy1btsDd3R1//fVXjfJz7do19OnTB97e3vjxxx8RHx+PL774AkDJiMhSZWdnY+7cuVp5PnPmDC5fvgx9/fIjpoWHh8PU1FTr88Xa9TXaBsYYY4wxxhhjjDH2/HipaxA6OztDV1cXJ06cgL29PQAgMzMTly5dQqdOnQAAlpaWSEtL0yxz+fJl5Obmav5v0aIFtmzZggYNGsDExKTSdfn6+sLX1xezZ89Gu3btsGnTJrRt2xZ6enpQqVSS8xwfHw+1Wo2IiAjIZCXlu99//71WjIeHB06cOKE17fH/W7RogaSkJLi6ukpa7+zZszFt2jStaXdTr0jON2OMMcYYY4wxxhh7Pr3UBYTGxsYYOXIkZsyYAXNzczRo0AChoaGQyWSa/gS7dOmCVatWoV27dlCpVJg5cyZ0dXU1aQQGBuKzzz5D//79MW/ePNja2uL69evYtm0bPvjgAxQVFWHdunXo168fbGxskJSUhMuXLyMoKAhASR+CV69eRWJiImxtbWFsbAyFQlFpnl1dXVFUVISVK1eib9++iIuLw5dffqkVM2nSJHTq1AnLli1D37598fvvv2P37t2abQKATz75BH369IG9vT0GDRoEmUyGU6dO4ezZs5g/f3659SoUinL5esTNixljjDHGGGOMMcZeeC99E+Nly5ahXbt26NOnD7p16wZ/f394enpqmtlGRETAzs4OHTt2xIgRIzB9+nSt/vkMDAxw+PBh2Nvb44033oCnpyfGjBmD/Px8mJiYwMDAABcvXsTAgQPh7u6OsWPH4r333sO4ceMAAAMHDkSvXr3QuXNnWFpa4rvvvqsyv82bN8eyZcuwePFiNG3aFBs3bkR4eLhWjL+/P7788kssW7YMzZs3x549ezB16lStpsM9e/bEL7/8gr1796JVq1Zo27YtIiMj4eDgUFu7ljHGGGOMMcYYY4y9AATx8Q72XnI5OTlo1KgRIiIiMGbMmGednVrzzjvv4OLFizhy5EitpXnz0llSfKFcSYovQt3VUMxV0/KiUstJ8Up5Pin+mmcAKd7uvPTjqCMUk9Kmxgug3ULyxfJ9XNZW2tR4hUA7TipipWulOltyrCjQ3tfcU1uS4s3lD0jxeaJB9UH/Ixekd5MAAAbqLFJ8kazyWtUVkYu0czhXMCLF1yXT4vuk+Fwd4+qD/kdHLKo+qIwsmJLiDQXp5zsAqInvKIuhW33Q/8igJqVdJEpPGwD0ZXmkeJkoPT85Iu18pF5/SjGHFK8WaM+/9CILybFmurR7gQK0e3YBpD9vANp5Qz1/qeckNX2VSDtOlGe9HLR7KuVaBejPbn0xt/qg/8kXpD/LngbqviwgfGcqJN7HjGTEe0Ed1iuh3oMVQkH1QWVkqWj31Xryh6R4HbX0fuDzZLS81PW9IKdY+jViqvuIlDYV9foQifuGsi91If2YAvTfrdRzvqmrFSmesZp64WsQBgQEYMqUKTVePiEhAd999x2Sk5Nx8uRJBAYGAgD69+//xHm7ePEi2rZtC319ffj4+DxxehRLly7VjLC8cuVKxMTEYOTIkZXGC4KAHTt2PL0MMsYYY4wxxhhjjLHnwkvdB2GppUuXIikpCXp6emjZsiWOHDkCCwvpb8ArExoaCkNDQyQlJcHISNrboo0bN2qaHz/OwcEB586dk5TO8ePHsWTJEmRlZcHZ2RkrVqzA22+/XWl8Wloa6tWrJyntUtRaBZS3awBQLKO9WbHKuCA5Vl5Iq/1xz9KTFG+YT6utpSLUCASAG14dJcd6n99OSvuuuiEpXl9Ge4vrcuug5NhrjTqR0qbWfryd34AUr1LT3qmsWCu9touugna+zxhLy8virfVJ8RMHSj+u+qBdT48E2r2mQEV7K/v5t7Sacr5+tHOeor6ZUH1QGb3q3yTFy3Wkv/lfvd+OlPbo7rTajBuO0NJ3daQd1/71DkmOTdb3puUlN4EUn2VEe5N/R21Niqcw0qHVAspG5QOqVUQgNjTxzD1RfdD/pJu6kdLOFQ1J8dRnggrSv9dQa70Vi7Sv3PoC7b76UE27rxrIpddoLCTUKAeAeuq7pHjq98lcmfSa02ZFtLwYZt8hxf+l25kUb66kXa8P8qSf85YGtNpdBSKtdr5apH3vEATp14iKmLZIrB1ssewdUnzyextJ8YdPSb++B7elnZPGYgYp/paqESneXDdTcmy+mrbfdWW072PUe411fgopXpEnvWZonG43UtrOxv+Q4qm1yhl7Wl76AkJfX1/Ex8drTVOpVFCr1ZpRgmsqOTkZvXv3JvXr169fP7Rp06bCeWUHRwEAURShUqmgo1P+MD4+snF1rKy42jJjjDHGGGOMMcbYy6jWmhgHBARg8uTJ+OCDD2Bubg4rKyuEhYUBAK5duwZBEJCYmKiJz8jIgCAIiI2NBQDExsZCEAT89ttv8PX1hVKpRJcuXZCeno7du3fD09MTJiYmGDFiBHJztfseKS4uxsSJE2FqagoLCwvMmTMHZbtWLCgowPTp09GoUSMYGhqiTZs2mvUCQHR0NMzMzLBz5054eXlBoVAgNTW1yu1Vq9WaUYsVCgV8fHywZ88ezXxBEBAfH4958+ZBEATNvqhM6T7atWsXgoKC0LRpUwwYMAC3bt2Cq6srXF1dcfXqVQiCgN27d6Nly5ZQKBT4448/oFarER4eDicnJyiVSjRv3hxbt27V5NPW1hZr1qzRWl9CQgJkMhmuX7+uyS83MWaMMcYYY4wxxhh7+dRqH4QxMTEwNDTEsWPHsGTJEsybNw/79u0jpREWFoZVq1bhzz//xI0bNzBkyBAsX74cmzZtwq5du7B3716sXLmy3Hp1dHRw/PhxfP7551i2bBnWr1+vmT9x4kQcPXoUmzdvxunTpzF48GD06tULly9f1sTk5uZi8eLFWL9+Pc6dO4cGDapuevj5558jIiICS5cuxenTp9GzZ0/069dPk2ZaWhqaNGmCkJAQpKWlYfr06ZK2f8aMGQgJCUFCQgLatWuHvn374v597WZes2bNwqJFi3DhwgV4e3sjPDwcX3/9Nb788kucO3cOU6dOxZtvvolDhw5BJpNh+PDh2LRpk1YaGzduhL+/P49azBhjjDHGGGOMMfaSq9UCQm9vb4SGhsLNzQ1BQUHw8/PDgQMHSGnMnz8f/v7+8PX1xZgxY3Do0CGsWbMGvr6+6NixIwYNGoSDB7X7MLOzs0NkZCQ8PDwQGBiISZMmITIyEgCQmpqKqKgo/PDDD+jYsSNcXFwwffp0dOjQAVFRUZo0ioqKsHr1arRv3x4eHh4wMKi6D4SlS5di5syZGDZsGDw8PLB48WL4+Phg+fLlAEqa7Oro6MDIyAhWVlaS+yCcOHEiBg4cCE9PT6xZswampqb4v//7P62YefPmoXv37nBxcYGhoSEWLlyIDRs2oGfPnnB2dkZwcDDefPNNrF27FgAQGBiIuLg4Ta1ItVqNzZs3awZkkaKgoACPHj3S+hQU0voUZIwxxhhjjDHGGGPPn1ovICzL2toa6enpNU6jYcOGMDAwgLOzs9a0x9Ns27YtBOHfjuDbtWuHy5cvQ6VS4cyZM1CpVHB3d4eRkZHmc+jQISQnJ2uW0dPTK5f/yjx69Ai3b9+Gv7+/1nR/f39cuCB9oIyKtGvXTvO3jo4O/Pz8yqXp5+en+fvKlSvIzc1F9+7dtbbv66+/1myfj48PPD09NbUIDx06hPT0dAwePFhyvsLDw2Fqaqr1Wf3luifZVMYYY4wxxhhjjDH2HKjVQUoeH0RDEAStwT7K9gtYVFTxqEZl0xAEodI0pcrOzoZcLkd8fDzkcu1R0srW6lMqlVqFjM8zQ8N/RzTLzs4GAOzatQuNGmmPWqVQ/Ds6WWBgIDZt2oRZs2Zh06ZN6NWrF+rXlz6q6ezZszFt2jStaXduXK1J9hljjDHGGGOMMcbYc6RWaxBWxtLSEkBJv3ylyg5Y8qSOHTum9f9ff/0FNzc3yOVy+Pr6QqVSIT09XTPYR+mnpiP3mpiYwMbGBnFxcVrT4+Li4OXlVePtKM17qeLiYsTHx8PT07PS+LKDqjy+fXZ2dpq4ESNG4OzZs4iPj8fWrVtJzYuBksJGExMTrY9CT4++gYwxxhhjjDHGGGPsuVKrNQgro1Qq0bZtWyxatAhOTk5IT0/Hxx9/XGvpp6amYtq0aRg3bhxOnjyJlStXIiIiAgDg7u6OwMBABAUFISIiAr6+vrh79y4OHDgAb29v9O7du0brnDFjBkJDQ+Hi4gIfHx9ERUUhMTERGzdufKJt+eKLL+Dm5gZPT09ERkbi4cOHGD16dKXxxsbGmD59OqZOnQq1Wo0OHTogMzMTcXFxMDExwciRIwEAjo6OaN++PcaMGQOVSoV+/fo9UT4ZY4wxxhhjjDHG2H/DUykgBIANGzZgzJgxaNmyJTw8PLBkyRL06NGjVtIOCgpCXl4eWrduDblcjvfffx9jx47VzI+KisL8+fMREhKCW7duwcLCAm3btkWfPn1qvM7JkycjMzMTISEhSE9Ph5eXF3bu3Ak3N7cn2pZFixZh0aJFSExMhKurK3bu3AkLC4sql/n0009haWmJ8PBwpKSkwMzMDC1atMCHH36oFRcYGIgJEyYgKCgISqXyifLJGGOMMcYYY4wxxv4baq2AMDY2tty0jIwMODo6AgA8PT3x559/as0v2ydhQECA1v8AEBwcjODgYK1pYWFhCAsLq3C9a9asqTBvurq6mDt3LubOnVvh/IrWUx2ZTIbQ0FCEhoZWGlOTZtSenp7lmkyXqmgfASX9Mr7//vt4//33q0z73XffxbvvvlvhvKioKAQHByMjI4OcZ8YYY4wxxhhjjDH24hLEikqcaklAQAB8fHywfPnyulrFc00QBGzfvh0DBgyoNvbatWtwcnJCQkICfHx86jxvj8vLy0NWVhYaNGggeZm0i4mkddzTsSbFF6hofRya6WRIjjXPuUlKO8PQhhSvJnbvWSgqqg8qw1jMkBx72ut1Uto25+OqDypDIRSS4p0TvpMce6slLe9UBWp9UryeUECKz1IbS46VQ/rgSwCgK6t4oKfKFKl1qw8qQ0colhwrgjbAk1xQkeIF0B5ThSLt3pFTTDsPKOQCLe8Oqiuk+Bs6zpJjC1W0d4I2umnVB5WRrmpIiqceV3OdB5Jjc0XD6oPKkBGvv+xiWvrGutmSYxXIJ6VdCNrzI0dlQIqncslJlBybZuJBSlslyqsPKoN6r1GL0p/d1PseNV5XoN3jqecw5T5JeR4AgA5oeU8vrLqlzOMMdKRfI5RjCgB2xcmk+Fu6TqR4PeJxpXzvEIjPmyLis5KKcs5TnwfUeH0xlxSfDRNSfJEo/fkqF2jXqh7xezZ13+hCevpFoJ0zBcTfN9RtvVdQjxTvjvOSY2/ouJDSNpE/IsUXiLTvnk1cab+jGauppzJIyYvIyMio0s+RI0fI6S1cuLDS9F599dUa57OwkHYjrYxSqSQVDjLGGGOMMcYYY4yx/4Y6LyBUq9X44IMPYG5uDisrK63mwcuWLUOzZs1gaGgIOzs7TJgwAdnZ/75lv379Ovr27Yt69erB0NAQTZo0wa+//ippvaX9Aerr66Nz586IiYmBIAiaJrRhYWHlauotX75c0yQ6MTERiYmJ+PDDD9GwYUMUFRWhYcOGmDFjBvz8/ACUFM5NnDgR1tbW0NfXh4ODA8LDwwFAk87rr78OQRDw5ZdfatJ8/LN+/Xo4OjoiNDQUwcHBWLt2Lezs7GBgYIAhQ4YgMzNTk8fg4GAMGDAACxYsgI2NDTw8St6+37hxA0OGDIGZmRnMzc3Rv39/XLt2DQCwd+9e6Ovrl2s+/P7776NLly4AgOjoaJiZmUnat4wxxhhjjDHGGGPsv6POCwhjYmJgaGiIY8eOYcmSJZg3bx727dtXsnKZDCtWrMC5c+cQExOD33//HR988IFm2ffeew8FBQU4fPgwzpw5g8WLF8PIyKjadV69ehWDBg3CgAEDcOrUKYwbNw4fffQRKd+urq44duwYVq1ahc8++wwXL17EZ599hhUrVuD7778HAKxYsQI7d+7E999/j6SkJGzcuFFTMHjixAkAJX37paWlIT4+Hq6urhV+GjVqpFnvlStX8P333+Pnn3/Gnj17kJCQgAkTJmjl7cCBA0hKSsK+ffvwyy+/oKioCD179oSxsTGOHDmCuLg4GBkZoVevXigsLETXrl1hZmaGH3/8UZOGSqXCli1bEBgYSNovjDHGGGOMMcYYY+y/pc5HMfb29tYM5OHm5oZVq1bhwIED6N69O6ZMmaKJc3R0xPz58zF+/HisXr0aAJCamoqBAweiWbNmAABnZ2l9La1duxYeHh747LPPAAAeHh44e/YsFixYQMp7aGgoIiIi8MYbbwAAnJyccP78eaxduxYjR45Eamoq3Nzc0KFDBwiCAAcHB82ylpaWAAAzMzNYWVlJXmd+fj6+/vprTaHhypUr0bt3b0RERGjSMTQ0xPr166GnV9IPxLfffgu1Wo3169dDEEr6+4iKioKZmRliY2PRo0cPDBs2DJs2bcKYMWMAlBQyZmRkYODAgaR9whhjjDHGGGOMMcb+W55KAWFZ1tbWSE9PBwDs378f4eHhuHjxIh49eoTi4mLk5+cjNzcXBgYGmDx5Mt59913s3bsX3bp1w8CBA8ulV5GkpCS0atVKa1rr1q1J+c7JyUFycjLGjBmDd955RzO9uLgYpqamAEqa+3bv3h0eHh7o1asX+vTpgx49epDW8zh7e3utGoXt2rWDWq1GUlKSpoCwWbNmmsJBADh16hSuXLkCY2PtARHy8/ORnFzS2XJgYCDatm2L27dvw8bGBhs3bkTv3r0lNysuKChAQYF2R8kFhYVQ6NVtB8eMMcYYY4wxxhhjrG7VeRNjXV3tkTMFQYBarca1a9fQp08feHt748cff0R8fDy++OILAP8OvPH2228jJSUFb731Fs6cOQM/Pz+sXLmyVvIlk8nw+ADORUX/jixW2hfiV199pdVf4NmzZ/HXX38BAFq0aIGrV6/i008/RV5eHoYMGYJBgwbVSv6qYmioPXpidnY2WrZsWa5vw0uXLmHEiBEAgFatWsHFxQWbN29GXl4etm/fTmpeHB4eDlNTU63PynUbanW7GGOMMcYYY4wxxtjTV+c1CCsTHx8PtVqNiIgIyGQl5ZSlffuVZWdnh/Hjx2P8+PGYPXs2vvrqK0yaNKnKtD08PMoNZlLaJ2ApS0tL/PPPPxBFUdMsNzExUTO/YcOGsLGxQUpKSpUFaSYmJhg6dCiGDh2KQYMGoVevXnjw4AHMzc2hq6sLlUpVZV4fl5qaqqnlBwB//fUXZDKZZjCSirRo0QJbtmxBgwYNYGJiUmlcYGAgNm7cCFtbW8hkMvTu3VtyvmbPno1p06ZpTXtw7aLk5RljjDHGGGOMMcbY86nOaxBWxtXVFUVFRVi5ciVSUlLwzTff4Msvv9SKmTJlCn777TdcvXoVJ0+exMGDB+Hp6Vlt2uPGjcPFixcxc+ZMXLp0Cd9//z2io6MBQFMYGBAQgLt372LJkiVITk7GF198gd27d2ulM3fuXISHh2PFihW4dOkSzpw5g6ioKCxbtgxAySjM3333HS5evIhLly7hhx9+gJWVlabZrqOjIw4cOIB//vkHDx8+lLRf9PX1MXLkSJw6dQpHjhzB5MmTMWTIkCr7MQwMDISFhQX69++PI0eO4OrVq4iNjcXkyZNx8+ZNrbiTJ09iwYIFGDRoEBQKhaQ8AYBCoYCJiYnWh5sXM8YYY4wxxhhjjL34nlkBYfPmzbFs2TIsXrwYTZs2xcaNGxEeHq4Vo1Kp8N5778HT0xO9evWCu7u7ZgCTqjg5OWHr1q3Ytm0bvL29sWbNGs0oxqWFYp6enli9ejW++OILNG/eHMePH8f06dO10nn77bexfv16REVFoVmzZnjllVcQHR0NJycnAICxsTGWLFkCPz8/tGrVCteuXcOvv/6qqREZERGBffv2wc7ODr6+vpL2i6urK9544w289tpr6NGjB7y9vavdZgMDAxw+fBj29vZ444034OnpiTFjxiA/P1+rRqGrqytat26N06dP8+jFjDHGGGOMMcYYYwwAIIiPd8T3H7VgwQJ8+eWXuHHjxrPOSqXCwsKwY8cOrabOz7O0i4mk+Hs61qT4AhWthqKZTobkWPOcm9UHlZFhaEOKVxPL3gtF6bU5AcBYzJAce9rrdVLaNufjSPEKoZAU75zwneTYWy1peacqUOuT4vWEguqDyshSG1cf9D9yqElp68qKqg8qo0itW31QGTpCseRYEQIpbblA63pBAO0xVSjS7h05xbTzgEIu0PLuoLpCir+h4yw5tlBF61XERjeNFJ+uakiKpx5Xc50HkmNzRcPqg8qQEa+/7GJa+sa62ZJjFcgnpV0I2vMjR2VAiqdyyUmUHJtmUnn3KRVRiXJSPPVeoxalP7up9z1qvK5Au8dTz2HKfZLyPAAAHdDynl5oQYo30JF+jVCOKQDYFSeT4m/pOpHi9YjHlfK9QyA+b4qIz0oqyjlPfR5Q4/XFXFJ8NirvsqkiRaL056tcoF2resTv2dR9owvp6ReBds4UEH/fULf1XkE9Urw7zkuOvaHjQkrbRP6IFF8g0r57NnGl/Y5mrKaeSg3CgIAATJkyBUBJs9vly5fXOK3o6GhJI++uXr0aJ06c0DRf/uyzzzBy5Mgar/dZedL9xRhjjDHGGGOMMcZYVZ76ICUnTpwoNwpvZRwdHTFlyhRN4SIADB06FAcPHoSRkVGFy7z55pv48ssvcfnyZcyfPx8PHjyAvb09QkJCMHv27NrYhBpr0qQJrl+/XuG8tWvXPuXcPLk8Xem1owBAIFZWNdWhvYnJVJlKjr2ibkRK20qdSYqn1nIwlkuvXQIAd9XSa+pQawTe9vInxbsPob1hi/+o/GBElXHLpw2EU6hDexsnl9FqRahBO672BZckx1Jrl2QoK++XtCINCiq+91TmvtJWcqycWLtEJtLenlP3jXkhreabXj7tXkNBfZOfaUy7N1Fq8DrlnyOlfUvXnRTvmn+KFF9MvF4f6VpKjrXKu0pK+7zgTYq306edY4YFGZJj9fPuk9K+W8+NFG8tSq+JCQCKItrz6S+hg+TYh//QajZbGNFqlzgY3SHFFxO+FlNr+CnFHFJ8jkj7jmUgZpHiRZn0+6octHt8nkirpWquR/uORbmv6hCP06n85qT4ZsJZUjz1u7NcLX3fZ8ukfw9+GnQI5w31OU91Nd+eFG+tvEeKtyi6LTk2X7fi37CVuVvcgBRvA1pruYdy6c9WSgsmAOSqSDLQan3X06N9f0vI8ZEc6ysmkNK+AVdSvIWQTooHuAYhezqeegGhpaX0m1BFlEolPvvsM8yZM6fC+aV97kVGRiIyMvKJ1lXbfv31VxQVVfxFpWHDhjA2NkZYWFid56OwsBB6PMAIY4wxxhhjjDHGGMMzGKSkbJNZURQRFhYGe3t7KBQK2NjYYPLkyQBKmiVfv34dU6dOhSAImtGHo6Oj4e7uDldXV7i6uuLbb7/FoEGDcPToUXTr1g1ubm4YNmwYsrL+fZOalZWFwMBAGBoawtraGpGRkVrNnqXkeeHChRg9ejSMjY1hb2+PdevWaebHxsZCEARkZGRopiUmJkIQBFy7dk2T7+bNm+PixYvo3bs3vL29MWvWLNjY2CAuLg7NmjVDvXr1MHnyZKhU2m9PsrKyMHz4cBgaGqJRo0b44osvtOZnZGTg7bffhqWlJUxMTNClSxecOvVvTY6wsDD4+Phg/fr1cHJygr5+3fW3xRhjjDHGGGOMMcZeLM9sFGMA+PHHHxEZGYm1a9fi8uXL2LFjB5o1awYA2LZtG2xtbTFv3jykpaUhLa3y5jzJycnYsWMHfvnlF/zyyy84dOgQFi1apJk/bdo0xMXFYefOndi3bx+OHDmCkydPkvIaEREBPz8/JCQkYMKECXj33XeRlJRESiM3NxcrVqzA5s2bsWfPHsTGxuL111/Hr7/+il9//RXffPMN1q5di61bt2ot99lnn6F58+ZISEjArFmz8P7772Pfvn2a+YMHD0Z6ejp2796N+Ph4tGjRAl27dsWDB/82I7py5Qp+/PFHbNu27YUZBIUxxhhjjDHGGGOM1b2n3sS4rNTUVFhZWaFbt27Q1dWFvb09WrduDQAwNzeHXC6HsbExrKyq7mtLrVYjOjoaxsYl/Xq89dZbOHDgABYsWICsrCzExMRg06ZN6Nq1KwAgKioKNja0UWlfe+01TJgwAQAwc+ZMREZG4uDBg/DwkD4CX1FREdasWQMXl5I+2wYNGoRvvvkGd+7cgZGREby8vNC5c2ccPHgQQ4cO1Szn7++PWbNmAQDc3d0RFxeHyMhIdO/eHX/88QeOHz+O9PR0KBQlI0UtXboUO3bswNatWzF27FgAJc2Kv/766ydu4s0YY4wxxhhjjDHG/lueaQ3CwYMHIy8vD87OznjnnXewfft2FBfTOkIGSpoAlxYOAoC1tTXS00s6/kxJSUFRUZGm4BEATE1NSQV7AODt/W8H5oIgwMrKSrMOqQwMDDSFg0BJv4OOjo5aA640bNiwXLrt2rUr9/+FCxcAAKdOnUJ2djbq168PIyMjzefq1atITk7WLOPg4PBEhYMFBQV49OiR1qegoKDG6THGGGOMMcYYY4yx58MzLSC0s7NDUlISVq9eDaVSiQkTJqBTp06VDuRRGV1d7VHwBEGAWk0bJfNJ1iGTlexGscwovRVtQ0VpPGnes7OzYW1tjcTERK1PUlISZsyYoYmTOnJ0ZcLDw2Fqaqr1+fLLL58oTcYYY4wxxhhjjDH27D3TJsZAyajEffv2Rd++ffHee++hcePGOHPmDFq0aAE9Pb1yA3ZQOTs7Q1dXFydOnIC9fckQ95mZmbh06RI6depUG5ugqZmXlpaGevXqAUCt9vP3119/lfvf09MTANCiRQv8888/0NHRgaOjY62t83GzZ8/GtGnTtKbdunmzztbHGGOMMcYYY4wxxp6OZ1pAGB0dDZVKhTZt2sDAwADffvstlEolHBwcAJQ0HT58+DCGDRsGhUIBCwsL8jqMjY0xcuRIzJgxA+bm5mjQoAFCQ0Mhk8k0IyM/KVdXV9jZ2SEsLAwLFizApUuXEBERUStpA0BcXByWLFmCAQMGYN++ffjhhx+wa9cuAEC3bt3Qrl07DBgwAEuWLIG7uztu376NXbt24fXXX4efn1+t5EGhUGj6OCx177H/GWOMMcYYY4wxxtiL55k2MTYzM8NXX30Ff39/eHt7Y//+/fj5559Rv359AMC8efNw7do1uLi4PFH/ecuWLUO7du3Qp08fdOvWDf7+/vD09IS+vn6tbIeuri6+++47XLx4Ed7e3li8eDHmz59fK2kDQEhICP7++2/4+vpi/vz5WLZsGXr27AmgpEnyr7/+ik6dOmHUqFFwd3fHsGHDcP36dTRs2LDW8sAYY4wxxhhjjDHG/psEsWzHeS+JnJwcNGrUCBERERgzZsyzzs4LK6XMIChS5Iq0fhAVAm0QlEcq4+qD/udurlH1QWVYGWaS4lWinBRvLM8mxWerpO9LQaBd4re9/Enx7kNcqg8qI/2j7yXHuuEiKe1CHVqhf7FMjxSvFmjH1TTvjuRYEbQazRnKqkd3f1y9vDRS/H2lreRYuUAbXEom0vqIpe4b44L7pHi9/EekeAoBtOsv07gRLV4wlxxrk3uZlPYtA3dSvG0O7XotJl6vj5TSXxSa5kq/9gDgvOBdfVAZdvq068mwIENyrH4e7fy9W8+NFK8soj1vFMT4E8XSWy48zNGtPqgMC6NCUryDEe08KBClt4zQFWj9ZSvFHFJ8DqR/pwEAI5H2PSVPJv17kBy0e3yeaECKp+5Lyn1Vh5j21WzaPbiZ7llSfJ4u7bjKRen7PltmSkq7rukQzhvqc57qdh6toom18h4p3qxQ+sCV+bq03yB3VQ1I8Ta4QYp/KJe+b4zFDFLa+TLabz8ZaF2LFRLu2QBwM0d6a0RfnQRS2jd0XUnxFgJtsFN7N09SPGM19VRqEAYEBGDKlCkASpoNL1++vMZpRUdHw8zMjLRMQkICvvvuOyQnJ+PkyZMIDAwEAPTv37/G+XhannR/McYYY4wxxhhjjDFWlafeB+GJEyckj6jr6OiIKVOmaAoXAWDo0KF47bXXyOtdunQpkpKSoKenh5YtW+LIkSOwsLDAkSNH8Oqrr1a6XHY27c35iyA1NRVeXl6Vzj9//rxmQJeq6Khpb/JlgpIUT3mTDwD6Muk1Di1pL7fJbzZlAq2GFLWWEWVbqag1Ai99T6tJaheWKzm2ALQDpRJotzTqfheJ/ZbmKsykp008x4pBq3mTr0ertUBKW027tk3VtBpSBTq08yBXj1aLQny2vW1okYm0t+eUe02evhkxNzS5Sum1GQH69aooln7vKNKhnZNyNe1eoBZo54xaRqh9TEybSiWj7XfqvclCKb2m3INsM1LaKpGWF/KzG7RnN0UB8TsQNS/U+xilZp1I3O/UZys1nrJvqDWM9HVotSWL5bSWCIUCrea0UpT+O4R6zqjr+NmXR/huoJTlkdKm5t1IN58Un0v8XmNEOA+o9wLqfYz6XVVPkP577naxDSltSznt+x75XkNsJWWsJ/08KJLV7XGitmJi7Gl56gWET9KXIFAy6rFSSbtgfX19ER8fX+E8Pz+/Wh1x+EVgYWFR5Tbb2NBu/owxxhhjjDHGGGPsxfXUq02UbTIriiLCwsJgb28PhUIBGxsbTJ48GUBJs+Tr169j6tSpEARBM+Lw402Mw8LC4OPjg2+++QaOjo4wNTXFsGHDkJWVpYnJyspCYGAgDA0NYW1tjcjISE2zZ6VSCVdX10o/pXleuHAhRo8eDWNjY9jb22PdunWa9GNjYyEIAjIyMjTTEhMTIQgCrl27ppXvX375BR4eHjAwMMCgQYOQm5uLmJgYODo6ol69epg8eTJUKu0aJFlZWRg+fDgMDQ3RqFEjfPHFF1rzMzIy8Pbbb8PS0hImJibo0qULTp06VW4frV+/Hk5OTjAyMqpym3V0nung1owxxhhjjDHGGGPsKXqm7ap+/PFHREZGYu3atbh8+TJ27NiBZv/P3p3HRVX1fwD/3JlhBpgZQBRZFEFWRxRFcEHSUDHcsVQwScRILTM1l5SeR0WsNHMtc3myQHvcWtSsrDQLI0RFENwQEResSMwFZB+Y+f3Bz4lRkPtVEJ/8vl+veb2YO98599zLzL13zv2eczp2BADs3LkTrVu3RmxsLPLy8pCXV/eA4Dk5Odi9eze++eYbfPPNNzh48CCWLFlieH3GjBlISkrCnj17sH//fiQmJiItLY1U1+XLl8PPzw/Hjx/H5MmT8corryArK4tURklJCd5//31s374d33//PRISEvDss89i79692Lt3Lz799FNs2LABX3zxhdH73nvvPXTq1AnHjx/H3LlzMW3aNOzfv9/w+qhRo5Cfn4/vvvsOqamp6NKlC/r164cbN24YYs6fP48vv/wSO3fufOIyJhljjDHGGGOMMcZY3Zo0VSw3Nxd2dnYICgqCiYkJ2rRpg27dugEArK2tIZVKoVarYWd3/9k6dTod4uPjoVZXj7E1duxYHDhwAG+//TZu376NTZs2YevWrejXrx8AIC4ujtyNdtCgQZg8eTIAYM6cOVi5ciV+/vlneHp6ii5Dq9Vi3bp1cHWtHuNt5MiR+PTTT3H16lWoVCq0b98effr0wc8//4ywsDDD+wICAjB37lwAgIeHB5KSkrBy5Ur0798fv/76K44ePYr8/HwoFNXjrCxbtgy7d+/GF198gYkTJwIAKioqsHnz5ofu4s0YY4wxxhhjjDHG/lmaNINw1KhRKC0thYuLCyZMmIBdu3ahspI2ODBQ3QX4TuMgANjb2yM/v3rq8AsXLkCr1RoaHgHA0tKS1LAHAN7e3oa/BUGAnZ2dYR1imZubGxoHAcDW1hbOzs5QqVRGy+4u19/f/57nmZmZAICMjAwUFRWhefPmUKlUhsfFixeRk/P35BFOTk4P1ThYXl6OwsJCo0d5BW2SEsYYY4wxxhhjjDH2+GnSBkJHR0dkZWVh7dq1MDMzw+TJk9G7d29oteJnVgMAExPj2TwFQYBO17Az0d1vHRJJ9W7U6/+eSam2baitjIete1FREezt7ZGenm70yMrKwuzZsw1xYmeOrsvixYthaWlp9Fi7/j/1v5ExxhhjjDHGGGOMPdaafDYKMzMzDB06FEOHDsWrr76Kdu3a4eTJk+jSpQvkcvk9E3ZQubi4wMTEBCkpKWjTpg0AoKCgAOfOnUPv3r0bYhMMmXl5eXlo1qwZADToOH+HDx++57lGowEAdOnSBX/++SdkMhmcnZ0bbJ13i46OxowZM4yWXb1ysdHWxxhjjDHGGGOMMcYejSZtIIyPj0dVVRW6d+8Oc3Nz/Pe//4WZmRmcnJwAVHcd/uWXXzB69GgoFAq0aNGCvA61Wo1x48Zh9uzZsLa2RsuWLbFgwQJIJBLDzMgPy83NDY6OjoiJicHbb7+Nc+fOYfny5Q1SNgAkJSVh6dKlGD58OPbv34/PP/8c3377LQAgKCgI/v7+GD58OJYuXQoPDw/88ccf+Pbbb/Hss8/Cz8+vQeqgUCgMYxzecUsub5CyGWOMMcYYY4wxxljTadIuxlZWVvjoo48QEBAAb29v/Pjjj/j666/RvHlzAEBsbCwuXboEV1fXhxo/b8WKFfD398eQIUMQFBSEgIAAaDQamJqaNsh2mJiYYNu2bTh79iy8vb3x7rvv4q233mqQsgFg5syZOHbsGHx8fPDWW29hxYoVCA4OBlDdJXnv3r3o3bs3xo8fDw8PD4wePRqXL1+Gra1tg9WBMcYYY4wxxhhjjP0zCfqaA+c9IYqLi9GqVSssX74cUVFRTV2d/1m52Zmk+CLBkhSvI7ZfCxD/US7TKeoPqkEiNOyYlndTSYpJ8Vq9Sf1BD8hkwUuk+HOf5dQfVIPjmUTRsc3wF6nsKoGWFE35zABApYS23+VVZaJj9aBlNJdI1PUH1aCuukmKL5aK/75SP4+Wuuuk+HKZOSlepqONY2tWcZsU35i0MtqxqUDSXHQsdb/fkLQkxVvraBN3Ub+vEr34IUekOtqEZxd0bqR4O1PatiorCkTHmpfQjnvXrFzrD6qBclwCALNy8XUHgEsm7UTHnrtmRSrbWkX7brdVXyXFV+rFfyalAm0IHAlo1xHUayAzPe06gnI+0+tp56cyvRkp3kSg/V8p+7KS2FnqerkVKb6t7AIpnnJuBQAzXZHoWOp1AfUzRqXVif+MmUlKSWVT6367UlV/UA1SCe373VKfJzqW+hkoqLQgxTsIV0jxJVLx5V/XWpHKtpHTrjuoxxotaL3ZblWI/444SS6Rys4TWpPim0lp1+UurrRzPWMPqkkzCBtDZGQkhg8fbrTs+PHj2LZtG3JycpCWlobw8HAAQEhISKPWJT4+HlZWVo26DgCIiYlB586dm7wejDHGGGOMMcYYY+x/T5NPUtLQVq9ejdqSIpctW4asrCzI5XL4+voiMTERLVq0QGJiIgYOHFhneUVF4u/cPa7CwsIwaNAgw/Pc3Fy0b9++zvgzZ84YJnS5H2oWgl5mRYq/raVlDbnpxGc0qi+mkcr+Q/MMKV5ZcYsUn29Cu+vk+vvPomMl+b+Tyk7912ekeMeYElL8lfa9RMeant1PKlsq0LKGiqtod5Tl+gpS/IWSVqJjqUOitlLSsozOlruT4u3NboiOpd7Jr5DSskvKdbThIHJL7UnxpdrGOxXKpLQs1W7lv5Lir6vEDyVx9Fbdx/3adG5Om4gqtZBWvkxCy6jyM80QHXte8CSV3VpOO05e09KyK28KzUTHWliKzwoFgCq9lBR/tZI2ZItO4kSK98vZIjpW7j6cVHYlcVupxyYtIYOQWnaVnhYvJ2bVlQu042qlTvy2UjPczQVaNuNfWtoY46bSctGxOmLdffP3kOJ/thhJim9jQcuokkjE/1+pvSKo8VQmEvGf4SoQv9vE75NbqfjzBwDkKmnns3Pl4rPQm5nSfluaSWm/t0oEWsYhZd9bm9Ayyst0tOMS1e1K2m/FTvl7Rcf+YvkcqWwX9Z+k+BK9khTP2KPy2DQQVlRUQN4Ak15YWt6btu3j44PU1NRa4/38/Bp0xuHHkZmZGczM/j5AOzg43HebHRwcHkGtGGOMMcYYY4wxxtjjoNG6GAcGBmLKlCmYMmUKLC0t0aJFC8ybN8+Q3efs7IxFixYhIiICFhYWmDhxIgDg119/Ra9evWBmZgZHR0dMnToVxcXVdyLffPNNdO/e/Z51derUCbGxsQDu7WJcXl6OqVOnomXLljA1NcVTTz2FlJQUw+s7duyAn58f3NzcDI9Tp07B3d0dbm7Vd4MyMjLQp08fqNVqWFhYwNfXF8eOHRO9L3bv3g13d3eYmpoiODgYV64Yjw2xbt06uLq6Qi6Xw9PTE59++qnR67m5uQgJCYFKpYKFhQVCQ0Nx9Wrd4+rk5OTAxcUFU6ZMgV6vv6eL8VtvvYWRI0ciOTkZQUFB8PX1xb///W/Y2trCzc0NMtlj027MGGOMMcYYY4wxxhpZo45BuGnTJshkMhw9ehSrV6/GihUrsHHjRsPry5YtQ6dOnXD8+HHMmzcPOTk5GDBgAEaMGIETJ05gx44d+PXXXzFlyhQAQHh4OI4ePYqcnL8nRTh9+jROnDiBMWPG1FqHN954A19++SU2bdqEtLQ0uLm5ITg4GDduiO8+Fx4ejtatWyMlJQWpqamYO3cuTEzEDbxbUlKCt99+G5s3b0ZSUhJu3bqF0aNHG17ftWsXpk2bhpkzZ+LUqVOYNGkSxo8fj59/ru5CqtPpEBISghs3buDgwYPYv38/Lly4gLCwsFrXd+LECTz11FMYM2YM1qxZA6GOfos5OTnYvXs3vvnmG3zzzTc4ePAglixZInqfMMYYY4wxxhhjjLF/hkZNFXN0dMTKlSshCAI8PT1x8uRJrFy5EhMmTAAA9O3bFzNnzjTEv/TSSwgPD8f06dMBAO7u7nj//ffx9NNPY926dfDy8kKnTp2wdetWzJs3DwCwZcsWdO/e3ZDtV1NxcTHWrVuH+Ph4wziDH330Efbv34+PP/4Ys2fPFrUdubm5mD17Ntq1a2eol1harRZr1qwxZD5u2rQJGo0GR48eRbdu3bBs2TJERkZi8uTJAIAZM2bg8OHDWLZsGfr06YMDBw7g5MmTuHjxIhwdHQEAmzdvhpeXF1JSUtC1a1fDug4dOoQhQ4bgX//6l9F+rY1Op0N8fDzU6urZnMaOHYsDBw7g7bffFr1tjDHGGGOMMcYYY+x/X6NmEPbo0cMog83f3x/Z2dmoqqqeOt7Pz88oPiMjA/Hx8VCpVIZHcHAwdDodLl6sHiw9PDwcW7duBQDo9Xps27bNMCvx3XJycqDVahEQEGBYZmJigm7duiEzU/yEFjNmzMBLL72EoKAgLFmyxCiDsT4ymcyoEa9du3awsrIyrD8zM9OofgAQEBBg9Lqjo6OhcRAA2rdvb1QGUN2I2b9/f8yfP7/exkGguov3ncZBALC3t0d+fn6d8eXl5SgsLDR6lFfQJmxgjDHGGGOMMcYYY4+fRm0grI9SaTx7T1FRESZNmoT09HTDIyMjA9nZ2XB1dQUAPP/888jKykJaWhoOHTqEK1eu1NndVgyJRHLPrMdarfGsWzExMTh9+jQGDx6Mn376Ce3bt8euXbseeJ2NwcbGBt26dcO2bdtQWFhYb/zdXaQFQYBOV/eMkosXL4alpaXR44P/fPLQ9WaMMcYYY4wxxhhjTatRGwiPHDli9Pzw4cNwd3eHVFr7dOpdunTBmTNnjCYMufO4M8Nx69at8fTTT2PLli3YsmUL+vfvj5YtW9Za3p2JP5KSkgzLtFotUlJS0L599fT1NjY2uH37tmEiFAC1zvDr4eGB119/Hfv27cNzzz2HuLg4UfugsrLSaEKTrKws3Lp1CxqNBgCg0WiM6gcASUlJhvppNBpcuXLFaGKTM2fO4NatW4YYoHqm4m+++cYwEcrt27dF1U+s6OhoFBQUGD1em/hig66DMcYYY4wxxhhjjD16jdpAmJubixkzZiArKwvbtm3DBx98gGnTptUZP2fOHBw6dAhTpkxBeno6srOz8dVXXxkmKbkjPDwc27dvx+eff15n92KgOkPxlVdewezZs/H999/jzJkzmDBhAkpKShAVFQUA6N69O8zNzfHmm28iJycHW7duRXx8vKGM0tJSTJkyBQkJCbh8+TKSkpKQkpJiaOCrj4mJCV577TUcOXIEqampiIyMRI8ePdCtWzcAwOzZsxEfH49169YhOzsbK1aswM6dOzFr1iwAQFBQEDp27Ijw8HCkpaXh6NGjiIiIwNNPP31PF22lUolvv/0WMpkMAwcORFFRkag6iqFQKGBhYWH0UPx/oy1jjDHGGGOMMcYY+9/VqA2EERERKC0tRbdu3fDqq69i2rRpmDhxYp3x3t7eOHjwIM6dO4devXrBx8cH8+fPh4ODg1HcyJEjcf36dZSUlGD48OH3rcOSJUswYsQIjB07Fl26dMH58+fxww8/oFmzZgAAa2tr/Pe//8XevXvRsWNHbNu2DTExMYb3S6VSXL9+HREREfDw8EBoaCgGDhyIhQsXitoH5ubmmDNnDsaMGYOAgACoVCrs2LHD8Prw4cOxevVqLFu2DF5eXtiwYQPi4uIQGBgIoLrr71dffYVmzZqhd+/eCAoKgouLi1EZNalUKnz33XfQ6/UYPHiwUWYkY4wxxhhjjDHGGGN3E/R3D8DXQAIDA9G5c2esWrWqMYp/5Ot5EGfPnkVkZCTS09PRrl27WrsuPy4EQcCuXbvqbXCt6c+zx0nruC6zI8UXapX1B9XgphM/8Yz6Yhqp7D80z5DilRW3SPH5Jq1J8a6//yw6VpL/O6nsVK9JpHi1SQkp/kr7XqJj3c/uJ5UtFSpJ8cVVKlK8XEKbmOdKkY3o2BrzOYnSSvkXKf6PkuakeHuzG6Jjq1D7sBF1UQq07OYyvRkp/mppM1J8qVZGiqeQSWmn2G66X0nxuaoOomNzbtI+A52bXyTFn7zZlhQvk9Q97m1t/EwzRMeeFzxJZbcy+YMUf62q9qFNGoKFjDZECPX7d7PCghSv09PuJftd2iI69pz7cFLZlXratppJy0jx5TrxPSOkAu3zW0Xcj3JBW39QDTLi+a9SL/64pwftBGUm0K4L/tK2IMWbSstFx+qIdff8/UdS/M8WI0nxbSyuk+JNhVLRsTrisUDXyEPRUz43AmjnSupxyaHoHCk+V9m+/qAarpep6w/6f81MaddAMqGKFK8QxH8/ANo5RALacU+rN6k/6CHcrjQnxXtf/U507C+Wz5HKdlH/SYqv0NN64nVwo/2OZuxBNd6vIoYFCxZAqVQiKysLKhWtIeJRy8vLM2RVilVgQrugI577YSeve1bl2hQK4htjslwiSGV3vnaAFE9t7RGatyLFX2rVW3SswpH2A8m97Cwpvhy0k7MpodEvu11/UtmacHdSvHzeelo8aBdd7czFz3hO/QFWAVNSvIfpBVJ8iSD+Ylch0D5jVYQfpg/C1fQSKV4ho/2YpZDoaRf2VxW0z7CUUL5fsyxS2WWg3aTxtThDijeppH2frsodRcd2KDxMKvsUepDirUxojXiU77eZjvbjsVhCa/CzkYtv/AcAGWgNVeku4ieOay65RSpbpb1Jir8ttSbFUxrlJMQGQrlAbAAhNt6U6WjnBIWE9v2jKCeen1QyWm8XgbgvKU7bDyDF+1WJv3EBALcE2s2FUr34aywTYqMytbGHivIZptaF+hm4YN6RFK+U0K4LPOXiG4duS2i/t6jHglId7aaqiUT854ZyYwGg37igUsnEN6ADQKa9+ISPLpUnSGUXgnYT1rqK9jsX4AZC9mhwA2EtqqqqIAgCJJL7H5AHDhyIxMTEWl+7M6bh4MGD4eTk1BjVhF6vR1VVFWSyh/832tnxQYcxxhhjjDHGGGPsSdRgueWBgYGYOnUq3njjDVhbW+Ps2bOwsrICAFy6dAmCIBh1sb116xYEQUBCQgIAICEhAYIg4IcffoCPjw/MzMzQt29f5Ofn47vvvoNGo4GFhQXGjBmDkhLjuzqVlZWYMmUKLC0t0aJFC8ybNw81e06Xl5dj1qxZaNWqFZRKJbp3725YLwDEx8fDysoKe/bsQfv27aFQKJCbm3vf7dXpdPDy8oJKpUJFRQUcHR3x/vvvIz09Henp6fjXv/6F1NRUxMbGQhAEo3ENa3NnH23fvh09e/aEqakpOnTogIMHDxpi7uyj7777Dr6+vlAoFPj111+h0+mwePFitG3bFmZmZujUqRO++OILQz1bt26NdevWGa3v+PHjkEgkuHz5MoDqLsa7d+++bx0ZY4wxxhhjjDHG2D9Pgw4+sWnTJiiVShw5cgRLly5FbGws9u+njSEWExODNWvW4NChQ7hy5QpCQ0OxatUqbN26Fd9++y327duHDz744J71ymQyHD16FKtXr8aKFSuwceNGw+tTpkxBcnIytm/fjhMnTmDUqFEYMGAAsrOzDTElJSV49913sXHjRpw+fRotW96/C8Dq1avx0UcfYdWqVTh58iSGDRuGl19+GXq9Hm5ubsjLy4OXlxdmzpyJvLw8w6zE9Zk9ezZmzpyJ48ePw9/fH0OHDsX168bjlcydOxdLlixBZmYmvL29sXjxYmzevBnr16/H6dOn8frrr+OFF17AwYMHIZFI8Pzzz2Pr1q1GZWzZsgUBAQGNlt3IGGOMMcYYY4wxxv43NGgDobe3NxYsWAB3d3dERETAz88PBw7Qxm576623EBAQAB8fH0RFReHgwYNYt24dfHx80KtXL4wcORI//2w8QYOjoyNWrlwJT09PhIeH47XXXsPKlSsBALm5uYiLi8Pnn3+OXr16wdXVFbNmzcJTTz2FuLg4QxlarRZr165Fz5494enpCXPz+4/5sWzZMsyZMwejR4+Gp6cn3n33XaPJUuzs7CCTyaBSqWBnZyd6DMIpU6ZgxIgR0Gg0WLduHSwtLfHxxx8bxcTGxqJ///5wdXWFUqnEO++8g08++QTBwcFwcXFBZGQkXnjhBWzYsAEAEB4ejqSkJENWpE6nw/bt2xEeHi6qTowxxhhjjDHGGGPsn6tBxyD09vY2em5vb4/8fNoAnDXLsLW1hbm5OVxcXIyWHT161Og9PXr0gFBjUgh/f38sX74cVVVVOHnyJKqqquDh4WH0nvLycjRv/vdgonK5/J7616WwsBB//PEHAgICjJYHBAQgI4M2WPHd/P39DX/LZDL4+fkhM9N4dl4/Pz/D3+fPn0dJSQn69zeezKGiogI+Pj4AgM6dO0Oj0WDr1q2YO3cuDh48iPz8fIwaNUp0vcrLy1FebjygdUV5OeQKhegyGGOMMcYYY4wxxtjjp0EbCE1MjKcyFwQBOp3OMNlHzXEBtdraZ0yqWYYgCHWWKVZRURGkUilSU1MhlRpP414zq8/MzMyokfFxplT+PbNkUVH1bIfffvstWrUynglXUaPxLjw83NBAuHXrVgwYMMCogbQ+ixcvxsKFC42WvfradLw2bcaDbAJjjDHGGGOMMcYYe0w0aBfjutjY2AAA8vLyDMtqTljysI4cOWL0/PDhw3B3d4dUKoWPjw+qqqqQn58PNzc3o8eDztxrYWEBBwcHJCUlGS1PSkpC+/btH3g77tT9jsrKSqSmpkKj0dQZX3NSlbu3z9HR0RA3ZswYnDp1Cqmpqfjiiy/I3Yujo6NRUFBg9Jj08qv0DWSMMcYYY4wxxhhjj5UGzSCsi5mZGXr06IElS5agbdu2yM/Px7///e8GKz83NxczZszApEmTkJaWhg8++ADLly8HAHh4eCA8PBwRERFYvnw5fHx8cO3aNRw4cADe3t4YPHjwA61z9uzZWLBgAVxdXdG5c2fExcUhPT0dW7Zseaht+fDDD+Hu7g6NRoOVK1fi5s2bePHFF+uMV6vVmDVrFl5//XXodDo89dRTKCgoQFJSEiwsLDBu3DgAgLOzM3r27ImoqChUVVVh2LBhpHopFAqjjEQAkCsK6BvIGGOMMcYYY4wxxh4rj6SBEAA++eQTREVFwdfXF56enli6dCmeeeaZBik7IiICpaWl6NatG6RSKaZNm4aJEycaXo+Li8Nbb72FmTNn4vfff0eLFi3Qo0cPDBky5IHXOXXqVBQUFGDmzJnIz89H+/btsWfPHri7uz/UtixZsgRLlixBeno63NzcsGfPHrRo0eK+71m0aBFsbGywePFiXLhwAVZWVujSpQvefPNNo7jw8HBMnjwZERERMDMze6h6MsYYY4wxxhhjjLF/BkFfc2BA1mQuXbqEtm3b4vjx4+jcuXNTV0eUrJwrpPhKPa092lwoJsXrBPE95vPKbElldy6kzcYN4niWl5p3JcVLIH4cToWkjFS2uuwGKb7c5P4zft+tVCJuRm8AyG7Xv/6gGjThtAb6innrSfFylNcfVIOJTny8HrTPTIXElBSv0JWS4kskatGxMqH2MWXrotNL6w+qQas3qT+oBpWelt2sqCwhxVNI9FWk+BsKe1I85bhqob9JKrtMoqw/qAbzqkJSvEkl7ft0Ve5Yf9D/cyo8QSr7lFkPUryVyW1SPOX7Tf0/FUssSPFS0D6TMtC+31e1NqJjm5vcIpWt0tL2zW0Ta1I85fskEcSfhwFAAO1yW0ccBUirox0nFRLa94+CWne9nnb+E4TG++lSXEm7pmlTlUOKv6VoSYov14ufDNCEeC6mXEs+CMrngFoX6jVTcRXt/6qU0q4L1JXij023Zc1IZTf2scBEIv5zQ/2uSgXa+YaKen1YrpOLjnWsvEAqu1Ahfmx/ADCvpF1HOHiKm0yVsYf1SMYgbEyBgYGYPn16U1ejVmfPnkWPHj1gamr62Df6CYKA3bt3N3U1GGOMMcYYY4wxxtgj9si6GP+vqTnD8d2+++479OrVq94yFixYAKVSiaysLHz00Ud1ltmrVy+sW7fugevaEPLy8tCsGe2OFhX1bnslaHeFJISsCLmkklS2oKfVXXojn1Z+c9rdcJlAqz9FhYyWmVYl0A4jUkLdqRmBmVuySfGu80jh9Lu4EvF3/qn0xLpUSsTfNQXo2S4UUtA+v1risYC6rY15JpTpaBkdVJTjaqVA3C9E1M+7IKV9xiiZbxVy8ZnKAKCQ0v5P1AwmnV7891Un0DJsGxslOx8AZJTPJPHLJ9NVkOKpx+zGzExrbNS6m0D8vtQTe0VUELLeHgTl/ETNNJMRrw8rBNo10/8y6veJgnrNQY03If5fqZ+bSgntOqUxUX9vUc5P1LKpPUyoqBmElIxGrYx2HKP+bmXsccUNhLWoqqpCWloaJJLaD5itWrUSVU5OTg4GDx4MJycnzJgxA5GRkbXGmZmZoVWrVqD29tbr9aiqqoJM9vD/xged0ZkxxhhjjDHGGGOM/W9rsFtBgYGBmDp1Kt544w1YW1vDzs4OMTExAKrH1xMEAenp6Yb4W7duQRAEJCQkAAASEhIgCAJ++OEH+Pj4wMzMDH379kV+fj6+++47aDQaWFhYYMyYMSgpMR4XorKyElOmTIGlpSVatGiBefPmGTW2lZeXY9asWWjVqhWUSiW6d+9uWC8AxMfHw8rKCnv27EH79u2hUCggl8vh5uZW68PMzAw6nQ6xsbFo3bo1FAoFOnfujO+//95QpiAISE1NRWxsLARBwPvvv19nea1atTLso+3bt6Nnz54wNTVFhw4dcPDgQUOZd/bRd999B19fXygUCvz666/Q6XRYvHgx2rZtCzMzM3Tq1AlffPEFAECn06F169b3ZCgeP34cEokEly9fNtSXuxgzxhhjjDHGGGOMPXkaNFd806ZNUCqVOHLkCJYuXYrY2Fjs37+fVEZMTAzWrFmDQ4cO4cqVKwgNDcWqVauwdetWfPvtt9i3bx8++OCDe9Yrk8lw9OhRrF69GitWrMDGjRsNr0+ZMgXJycnYvn07Tpw4gVGjRmHAgAHIzv67O2JJSQneffddbNy4EadPn0bLlvcfRHj16tVYvnw5li1bhhMnTiA4OBjDhg0zlJmXlwcvLy/MnDkTeXl5mDVrlqjtnz17NmbOnInjx4/D398fQ4cOxfXr141i5s6diyVLliAzMxPe3t5YvHgxNm/ejPXr1+P06dN4/fXX8cILL+DgwYOQSCR4/vnnsXXrVqMytmzZgoCAADg5OYmqF2OMMcYYY4wxxhj7Z2rQBkJvb28sWLAA7u7uiIiIgJ+fHw4coM3++tZbbyEgIAA+Pj6IiorCwYMHsW7dOvj4+KBXr14YOXIkfv75Z6P3ODo6YuXKlfD09ER4eDhee+01rFy5EgCQm5uLuLg4fP755+jVqxdcXV0xa9YsPPXUU4iLizOUodVqsXbtWvTs2ROenp4wN7//bFfLli3DnDlzMHr0aHh6euLdd99F586dsWrVKgDVXXZlMhlUKhXs7OzuO6ZhTVOmTMGIESOg0Wiwbt06WFpa4uOPPzaKiY2NRf/+/eHq6gqlUol33nkHn3zyCYKDg+Hi4oLIyEi88MIL2LBhAwAgPDwcSUlJyM3NBVCdVbh9+3aEh4eLqhNjjDHGGGOMMcYY++dq0DEIvb2Np9+2t7dHfj5tsoaaZdja2sLc3BwuLi5Gy44ePWr0nh49ekCoMYCyv78/li9fjqqqKpw8eRJVVVXw8PAwek95eTmaN/97OnK5XH5P/etSWFiIP/74AwEBAUbLAwICkJGRIaqMuvj7+xv+lslk8PPzQ2ZmplGMn5+f4e/z58+jpKQE/fv3N4qpqKiAj48PAKBz587QaDTYunUr5s6di4MHDyI/Px+jRo0SXa/y8nKUl5cbr6O8HHJF4w5EzRhjjDHGGGOMMcYaV4NmEJqYGM/eIwgCdDqdYbKPmuMCarW1z2pUswxBEOosU6yioiJIpVKkpqYiPT3d8MjMzMTq1asNcWZmZkaNjI8zpVJp+LuoqAgA8O233xpt35kzZwzjEALVWYR3uhlv3boVAwYMMGogrc/ixYthaWlp9Niw/sMG2iLGGGOMMcYYY4yx/z2//PILhg4dCgcHB9HzOyQkJKBLly5QKBRwc3NDfHz8PTEffvghnJ2dYWpqiu7du9+TLNfQGm+++hpsbGwAVI/Ld0fNCUse1pEjR4yeHz58GO7u7pBKpfDx8UFVVRXy8/PvmRzkQWfutbCwgIODA5KSkoyWJyUloX379g+8HXfqfkdlZSVSU1Oh0WjqjL8zqUpubu492+fo6GiIGzNmDE6dOoXU1FR88cUX5O7F0dHRKCgoMHpMevlV+gYyxhhjjDHGGGOM/UMUFxejU6dO+PBDcUlUFy9exODBg9GnTx+kp6dj+vTpeOmll/DDDz8YYnbs2IEZM2ZgwYIFSEtLQ6dOnRAcHEzupUvRoF2M62JmZoYePXpgyZIlaNu2LfLz8/Hvf/+7wcrPzc3FjBkzMGnSJKSlpeGDDz7A8uXLAQAeHh4IDw9HREQEli9fDh8fH1y7dg0HDhyAt7c3Bg8e/EDrnD17NhYsWABXV1d07twZcXFxSE9Px5YtWx5qWz788EO4u7tDo9Fg5cqVuHnzJl588cU649VqNWbNmoXXX38dOp0OTz31FAoKCpCUlAQLCwuMGzcOAODs7IyePXsiKioKVVVVGDZsGKleCoUCiru6E8sVBfQNZIwxxhhjjDHGGPuHGDhwIAYOHCg6fv369Wjbtq2h3Uqj0eDXX3/FypUrERwcDABYsWIFJkyYgPHjxxve8+233+KTTz7B3LlzG34j8IgaCAHgk08+QVRUFHx9feHp6YmlS5fimWeeaZCyIyIiUFpaim7dukEqlWLatGmYOHGi4fW4uDi89dZbmDlzJn7//Xe0aNECPXr0wJAhQx54nVOnTkVBQQFmzpyJ/Px8tG/fHnv27IG7u/tDbcuSJUuwZMkSpKenw83NDXv27EGLFi3u+55FixbBxsYGixcvxoULF2BlZYUuXbrgzTffNIoLDw/H5MmTERERATMzs4eqJ2OMMcYYY4wxxhijSU5ORlBQkNGy4OBgTJ8+HUD1nBKpqamIjo42vC6RSBAUFITk5ORGq5egrzkwIGsyly5dQtu2bXH8+HF07ty5qasjSlbOFVK8jtijXYZKUrxEqBIde71C/PiLAOB18+f6g2qQ3qSl/eZ4DCXFmwi1j+FZa10E2n5UVJaQ4islclJ8lUT8fQkhdiqp7Mwt2aR417O0WdZlEL/fAUAC8eOlUlUR7+9Q666F+P8r9TMmEE87ZXraDQ1ToZQUL9NVkOJpZdP2+21ZM1I85bhqCtp+qYRJ/UE1mOjL6w+qQV5VRoq/Jb3/zbKaWpZdJpV9We5JijeX0vZllV4qOlYNWnZ+qaCsP6gGKcSfKwHauRUAbmrFf4aVMtr5pkX576T46woHUrwe4seiFkA7jlHjqddMlXraOUEpFImO1RPH6K7Q0yaw0+tp5UsE8edWyv8UAMp1tGuaFvqrpPgSqQUpvpywLynXhgD9GoX6maSgHpeoyvSmpHjqvjTX3RYdWypRkcqm7nfK+QagHZso3z0AkAu06wKqUp05Kb6SsG9s9H+Syr4tpV2/WVTeIMU7eIqbTJU1nNomaa2tV2VtBEHArl27MHz48DpjPDw8MH78eKMGwL1792Lw4MEoKSnBzZs30apVKxw6dMhoIts33ngDBw8evGeYvYbyyDII6xIYGIjOnTtj1apVTV0VxhhjjDHGGGOMMdbEvjWh3cBtSCn/eh4LFy40WrZgwQLExMQ0TYUekSZvIHxcODs7Y/r06YaUTpWq7rs73333HXr16kUq/5133sE777xjeF5aWgqJRAKFQoFevXph3bp1D1TvO+6u/6PQsugCKf5PlRspnnrHrLRKfJZRpZ5W9m8tfUnxlTa0r5ZCoGXS/FHWUnRsC+JYkVIJMRuMmBVRXCX+zql83npS2a7zSOHIadePFO9z6nNSvKpYfGaBTqDd8b1qQRvOwEwr/u42AGhNxGfZavW0jAsd8ftHvWNtfZuW3ayV0+5AU5TLaGXb3s4hxeepxX8Omt+ilX3Fkna3unkpbb9XSmkZHeWEbOVCM/HHSAAw0dGOe9RsF5Ve/HH4tmBFKpuaBUTNqJIS9w3l+009f1AzAqnlmwjis4mpxz3q/4maBUT9TFKusaj7kZoRSM58I2S1UrMZy6qI/1fi+YmacS8nfF8Velpms0RP+8xUSWhZ5RUQv++lxF5DVNfLaJmbjuZ59QfVQOl9U0685iAfC4hZ3+Z68dnExXo1qWzq+UYQqMds2rGjpFL8b0W9hFZ36rYWyqxJ8bSzH2sI0dHRmDFjhtEyMdmDYtnZ2eHqVePfilevXoWFhQXMzMwglUohlUprjXnQyXbF4AbCOtxvluVWrVqRy3v55ZcRGhpqeB4eHg6NRoN///vfMDMzQ6tWrfAgvb0rKiogl9MuZhhjjDHGGGOMMcYeV4IJreG1IYntTvyg/P39sXfvXqNl+/fvN3Qnlsvl8PX1xYEDBwxdlXU6HQ4cOIApU6Y0Wr0abzCJWhQXFyMiIgIqlQr29vaGGVvu+PTTT+Hn5we1Wg07OzuMGTPGMIWzXq+Hm5sbli1bZvSe9PR0CIKA8+fPQ6/XIyYmBm3atIFCoYCDgwOmTq1/DLPAwEBcvnwZr7/+OgRBgCAIcHNzg5ubGzIyMhASEgIvLy8EBQXhq6++Ej3Bx9q1a+Hu7g5TU1NoNBrMnTsXbm5ueOutt3D06FFs2rQJ7u7uaN26NS5duoSqqipERUWhbdu2MDMzg6enJ1avXm1UZmRkJIYPH463334bDg4O8PT0rLX+91NYWAgzMzN89913Rst37doFtVqNkhLamECMMcYYY4wxxhhjT6KioiKkp6cbEs0uXryI9PR05ObmAqjOSIyIiDDEv/zyy7hw4QLeeOMNnD17FmvXrsVnn32G119/3RAzY8YMfPTRR9i0aRMyMzPxyiuvoLi42DCrcWN4pBmEs2fPxsGDB/HVV1+hZcuWePPNN5GWlmaYlEOr1WLRokXw9PREfn4+ZsyYgcjISOzduxeCIODFF19EXFwcZs2aZSgzLi4OvXv3hpubG7744gusXLkS27dvh5eXF/78809kZGTUW6+dO3eiU6dOmDhxIiZMmGBYnpqaitDQUMTExCAsLAyHDh3C5MmT0bx5c0RGRt63zGPHjmHq1Kn49NNP0bNnT9y4cQOJiYkAgNWrV+PcuXPo0KEDYmNjAQA2NjbQ6XRo3bo1Pv/8czRv3hyHDh3CxIkTYW9vb5R9eODAAVhYWGD//v0AAHt7+1rrXxcLCwsMGTIEW7duNZqKe8uWLRg+fDjMzRuvux1jjDHGGGOMMcbYP8WxY8fQp08fw/M73ZPHjRuH+Ph45OXlGRoLAaBt27b49ttv8frrr2P16tVo3bo1Nm7ciODgYENMWFgYrl27hvnz5+PPP/9E586d8f3338PW1rbRtuORNRAWFRXh448/xn//+1/061c97temTZvQunVrQ8yLL75o+NvFxQXvv/8+unbtiqKiIqhUKkRGRmL+/Pk4evQounXrBq1Wi61btxqyCnNzc2FnZ4egoCCYmJigTZs26NatW711s7a2hlQqNWQu3rFixQr069cP8+ZVD2zm4eGBM2fO4L333qu3gTA3NxdKpRJDhgyBWq2Gk5MTfHx8AACWlpaQy+UwNzc3Wp9UKjUaCLNt27ZITk7GZ599ZtRAqFQqsXHjRqOuxbXV/37Cw8MxduxYlJSUwNzcHIWFhfj222+xa9cuUe9njDHGGGOMMcYYawwSWdN1MaYKDAy875Bx8fHxtb7n+PHj9y13ypQpjdql+G6PrItxTk4OKioq0L17d8Mya2treHr+PTNNamoqhg4dijZt2kCtVuPpp58GAENLq4ODAwYPHoxPPvkEAPD111+jvLwco0aNAgCMGjUKpaWlcHFxwYQJE7Br1y5UVj74oLeZmZkICAgwWhYQEIDs7GxUVd1/ANj+/fvDyckJLi4uGDt2LLZs2SKq6+6HH34IX19f2NjYQKVS4T//+Y9RSzMAdOzY8aHHHRw0aBBMTEywZ88eAMCXX34JCwsLBAUF1RpfXl6OwsJCo0d5hfjBvBljjDHGGGOMMcbY4+mRjkF4P8XFxQgODoaFhQW2bNmClJQUQzZbRY2GqJdeegnbt29HaWkp4uLiEBYWZugS6+joiKysLKxduxZmZmaYPHkyevfuDa2WNsNRQ1Cr1UhLS8O2bdtgb2+P+fPno1OnTrh161ad79m+fTtmzZqFqKgo7Nu3D+np6Rg/frzR9gPVGYQPSy6XY+TIkdi6dSsAYOvWrQgLC4NMVntS6eLFi2FpaWn0WPnxloeuB2OMMcYYY4wxxlhNgomkyR5Pqke25a6urjAxMcGRI0cMy27evIlz584BAM6ePYvr169jyZIl6NWrF9q1a2eYoKSmQYMGQalUYt26dfj++++NuiUDgJmZGYYOHYr3338fCQkJSE5OxsmTJ+utn1wuvycrUKPRICkpyWhZUlISPDw8IJXWP+W8TCZDUFAQli5dihMnTuDSpUv46aef6lxfUlISevbsicmTJ8PHxwdubm7Iycmpdz11lVef8PBwfP/99zh9+jR++uknhIeH1xkbHR2NgoICo8frUXXHM8YYY4wxxhhjjLH/DY9sDEKVSoWoqCjMnj0bzZs3R8uWLfGvf/0LEkl1G2WbNm0gl8vxwQcf4OWXX8apU6ewaNGie8qRSqWIjIxEdHQ03N3dDdNAA9X9uquqqtC9e3eYm5vjv//9L8zMzODk5FRv/ZydnfHLL79g9OjRUCgUaNGiBWbOnImuXbti0aJFCAsLQ3JyMtasWYO1a9fWW94333yDCxcuoHfv3mjWrBn27t0LnU5n6FLt7OyMI0eO4NKlS1CpVLC2toa7uzs2b96MH374AW3btsWnn36KlJQUtG3b9oHqX5/evXvDzs4O4eHhaNu2rVH377vVNs131UN2c2aMMcYYY4wxxhhjTe+R5k6+99576NWrF4YOHYqgoCA89dRT8PX1BVA9i298fDw+//xztG/fHkuWLDFMPnK3qKgoVFRU3DO9s5WVFT766CMEBATA29sbP/74I77++ms0b9683rrFxsbi0qVLcHV1hY2NDQCgS5cu+Oyzz7B9+3Z06NAB8+fPR2xsbL0TlNypy86dO9G3b19oNBqsX78e27Ztg5eXFwBg1qxZkEqlaN++PWxsbJCbm4tJkybhueeeQ1hYGLp3747r169j8uTJ9a6rrvrXRxAEPP/888jIyLhv9iBjjDHGGGOMMcbYoyKRCU32eFIJ+vtNtfKYSkxMRL9+/XDlypVGneKZ3d/NjIOk+D9VbqR4CXSk+HKdov6g/1dUaUYqu7n8Fim+Uk9LzlUIZaT4P8paio5toSgglW2OIlK8ANohpEDfTHSsXNK4E+HktOtHivc59TkpXlV8VXSsTqh/2IKarlq4k+IttX+R4gtN6r+xcoeOeK9Jp6fFSwTascDu9nlSvFZuToqnKJfRylaV0P5PeWrxn4NWt06Ryr5i6U2Kty8VNyTGHZVSU1L87ybOomMtJbTjXpFOTYo3ldCO2WY68cfV24IVqWzq94N6bpXraduaXyn+uszC5DapbCrq+UkmiB+zWqun9aKQgjYMjFZvQoqnkgvlomMFgbYfy3W077YJYb8DgEQQvy8r9OKvDQGgQKsixbcRLpPiS01o5VcSPgcKfSmpbIme9pmsktA+kxUQv+9N0LjXe7mlDqR4R/M8Uryq/Kbo2EJ5/b28aqIeC6jfV3O9+PNTMWjnSspxBqDXnfL9AIACrYXoWAfJFVLZt6Xif98A9PNTO9fWpPh/iv22HZps3f2v0q6b/ykaNYMwMDAQ06dPb7DyysvL8dtvvyEmJgajRo3ixsFaxMfHw8rKqqmrwRhjjDHGGGOMMfZABBOhyR5Pqkc2BmFD2LZtG6KiotC5c2ds3rxZ9Pvs7e1x48YNmJjUfpehqIiWMQVUZzEOHDiwztcfpMyGNHDgQCQmJtb62ptvvok333zT8FwQBOzatQvDhw8nraNKSrt7Ts0akhLuEAOADuK/yNS7NlR6Ql0AoIr4VazSid+X1Lt3OtAy2fQCbVvlevF3ieWg1p32GaNmBB7vMIoU3+vw+6JjqXdrqFlA0ira3XldI87eRf0/mYKWFSGtKCbFlytod8QbU2NmM1bJaFk9MqGSFK8XiJmhOlrWUKVO/HFSkFCzEGjHPapyifisdep3m3o+o2bqVElo5ydKRmNjn4up5VcRs/8bEzWThpyhCPHXcDI97VhArXsV8bqD8m+lZ5HSvn864nFPr6ddM1GyWstB6x0jJR7jqf9Xyq6nXgdTr7MrCdfNACAQO9hJdLR92ZhkoNWF0oOF+n2iHGcA+rGminjulkrEHyepGbPk/f5oR3pjTLTH50pIhMjISFHj/91NLpdj1qxZ94xZ+DD8/PyQnp7eYOU1tI0bN6K0tPYf1dbW1o+4NowxxhhjjDHGGGPscdVgTdfFxcWIiIiASqWCvb09li9fbvT6p59+Cj8/P6jVatjZ2WHMmDHIz88HAOj1eri5ud0zKUl6ejoEQcD58+eh1+sRExODNm3aQKFQwMHBAVOnTq23XoGBgcjNzcU777wDd3d3uLu7w83NDW5ubsjIyEBISAgUCgWcnZ3vqfP9SCQSfPTRR+jTpw+8vLwwYMAAHDx40FD2wYMH0a1bNygUCtjb22Pu3LmorPz7zkJgYCBee+01TJ8+Hc2aNYOtrS0++ugjFBcXY/z48VCr1XBzc8N3331neE9CQgIEQcC3334Lb29vmJqaokePHjh16t7+8a1atTLU5fTp0wgNDUWHDh3wzDPP4IMPPjDUxdnZGQDw7LPPQhAEw3PGGGOMMcYYY4yxpsCTlDx6DdZAOHv2bBw8eBBfffUV9u3bh4SEBKSlpRle12q1WLRoETIyMrB7925cunTJkA0oCAJefPFFxMXFGZUZFxeH3r17w83NDV9++SVWrlyJDRs2IDs7G7t370bHjh3rrdfOnTvRunVrxMbGIi8vD3l51YPOpqamIjQ0FKNHj8bJkycRExODefPmIT4+XtT2RkREYNu2bXj//feRmZmJDRs2QKWqHnj4999/x6BBg9C1a1dkZGRg3bp1+Pjjj/HWW28ZlbFp0ya0aNECR48exWuvvYZXXnkFo0aNQs+ePZGWloZnnnkGY8eORUlJyT37evny5UhJSYGNjQ2GDh0Krbb2LgiJiYmIiIjAtGnTcObMGWzYsAHx8fF4++23AQApKSmGfZ2Xl2d4zhhjjDHGGGOMMcaeDA3SxbioqAgff/wx/vvf/6Jfv+pZQTdt2oTWrf+ebefFF180/O3i4oL3338fXbt2RVFREVQqFSIjIzF//nwcPXoU3bp1g1arxdatWw1Zhbm5ubCzs0NQUBBMTEzQpk0bdOvWrd66WVtbQyqVGjIX71ixYgX69euHefPmAQA8PDxw5swZvPfee/V2Yz537hw+++wz7N+/H0FBQYZtumPt2rVwdHTEmjVrIAgC2rVrhz/++ANz5szB/PnzIZFUt8t26tQJ//73vwEA0dHRWLJkCVq0aIEJEyYAAObPn49169bhxIkT6NGjh6H8BQsWoH///kb7edeuXQgNDb2nrgsXLsTcuXMxbtw4Qz0XLVqEN954AwsWLICNjQ0AwMrKymj/MMYYY4wxxhhjjDWFJ3mykKbSIBmEOTk5qKioQPfu3Q3LrK2t4enpaXiempqKoUOHok2bNlCr1Xj66acBVDf8AYCDgwMGDx6MTz75BADw9ddfo7y8HKNGVU8KMGrUKJSWlsLFxQUTJkzArl27jLrsUmVmZiIgIMBoWUBAALKzs1FVdf8BTNPT0yGVSg3bUFvZ/v7+EGpM3hAQEICioiL89ttvhmXe3t6Gv6VSKZo3b26UFXlnluY7XbHv8Pf3N/x9Zz9nZmbWWpeMjAzExsZCpVIZHhMmTEBeXt49mYn3U15ejsLCQqNHeQVtoHPGGGOMMcYYY4wx9vh5JNPnFBcXIzg4GBYWFtiyZQtSUlKwa9cuAEBFjUaml156Cdu3b0dpaSni4uIQFhYGc/PqGR0dHR2RlZWFtWvXwszMDJMnT0bv3r3r7FrbmMzMaLOE1eXuWZUFQTBadqeBUaejzahWU1FRERYuXIj09HTD4+TJk8jOzoapqfgZLRcvXgxLS0ujx+qN4meSZowxxhhjjDHGGGOPpwbpYuzq6goTExMcOXIEbdq0AQDcvHkT586dw9NPP42zZ8/i+vXrWLJkCRwdHQEAx44du6ecQYMGQalUYt26dfj+++/xyy+/GL1uZmaGoUOHYujQoXj11VfRrl07nDx5El26dLlv/eRy+T1ZgRqNBklJSUbLkpKS4OHhAan0/lOmd+zYETqdDgcPHjR0Mb677C+//BJ6vd7QyJeUlAS1Wm3U7fpBHT58+J79rNFoao3t0qULsrKy4ObmVmd5JiYm9WZNRkdHY8aMGUbLbp8/Tqw5Y4wxxhhjjDHG2P09yZOFNJUGaSBUqVSIiorC7Nmz0bx5c7Rs2RL/+te/DGPttWnTBnK5HB988AFefvllnDp1CosWLbqnHKlUisjISERHR8Pd3d2oK218fDyqqqrQvXt3mJub47///S/MzMzg5ORUb/2cnZ3xyy+/YPTo0VAoFGjRogVmzpyJrl27YtGiRQgLC0NycjLWrFmDtWvXiipv3LhxePHFF/H++++jU6dOuHz5MvLz8xEaGorJkydj1apVeO211zBlyhRkZWVhwYIFmDFjhmGfPIzY2Fg0b94ctra2+Ne//oUWLVpg+PDhtcbOnz8fQ4YMQZs2bTBy5EhIJBJkZGTg1KlThklTnJ2dceDAAQQEBEChUKBZs2b3lKNQKKBQKIyWVcjlD70tjDHGGGOMMcYYY6xpNVgX4/feew+9evXC0KFDERQUhKeeegq+vr4AABsbG8THx+Pzzz9H+/btsWTJEsPkI3eLiopCRUUFxo8fb7TcysoKH330EQICAuDt7Y0ff/wRX3/9NZo3b15v3WJjY3Hp0iW4uroaJuXo0qULPvvsM2zfvh0dOnTA/PnzERsbW+8EJXesW7cOI0eOxOTJk9GuXTtMmDABxcXFAIBWrVph7969OHr0KDp16oSXX34ZUVFRhglJHtaSJUswbdo0+Pr64s8//8TXX38NeR2NdcHBwfjmm2+wb98+dO3aFT169MDKlSuNGlaXL1+O/fv3w9HRET4+Pg1SR8YYY4wxxhhjjLEHIUiFJns8qQS9Xq9v6krUlJiYiH79+uHKlSuGSTpYtYSEBPTp0wc3b96ElZVVU1cHf51KJsVfNXUmxZsItPElS3Tix4YsrVTUH1SDtbyAFK/Vm9QfVAN1W/8oaSE61tH8T1LZEtDGvNQLtAOoVi8+81SOclLZOuI9D/PK26T44x1GkeJ7HX5ffLBAq/tVSw9SvFUZ7XNw3bQVKZ6iUk9LXjcXiknxNtfPkuJL1I03g7tWSjvWKCrFTx4FAH8pxP+f7IrOk8rOV7mQ4luUXiHF60E7dlw0qX0ojVrrIvuLVPbNqnsz5+9HJaV9JiW4/zAeNVWCdv4QQLuMIx9Xicemm1rx+1ItKyKVTUXdNxTU842U8BkAgEpiJx9q+VW4/3A6NclAmxSQWnfq/4myrZTtBICiSiUp3k74nRRfJqWVLwji900l8dpTSvy/UuoC0OpDvvYknj9yi2m/KV2Uv9UfVIO6TPw555Yp7ZqjsX9TyCA+vgwNMw5/XajHGuq+KdWJH3+/heQaqewKQXzZAP0c4unqSIr/p/ilQ9MlL/U+9WQOp/ZIJikRo7y8HL/99htiYmIwatSoR9o4KAgCdu/e/cjW97hwdnbGqlWrDM+f1P3AGGOMMcYYY4wx9iRrkDEIG8K2bdsQFRWFzp07Y/Nm8bPjJiYmYuDAgYbnFRUVqKqqMsw0XFREvzt9d5l3e5AyH0cpKSlQKml3Mmv6TVH3xCe1KSw3J8W3MrtKik/OsREde+067Q5VuD8tG6VAa0GKt5PeJMW/v6FMdOz8abTMmDbl50jxJQorUvyFEvEZT+3Mc0hlayW0bC1VMe0zRsoIBJDYY6roWLk17S6od8ImUvyfc+aS4tWrNoiOLRVoxxHq3W1lxS1S/MnJMaR4t2AvUjyFtWv94+TWlPnUNFJ8RYX403jFnHmksk3WbCfFX387lhRv17UdKT6nx0rRse5maaSyD5e4kuK9bStI8faluaJjqyS0YwE1G8WkSvz5A6BndyWfbys6to8nLZvRUn+DFF8iUZPiTSD+/9rYGYSFVbS6t9Fmk+JvmDqIjpXraZ+ZQp346zEAUEho3ycZ4RxSUqUilX0w04oU/6rZTlL8TSdfUry6QHxm9k1r2nGsUkIbS1yio32GKRmKEj2tbJ1AywyN+ddhUvzeGNpnvuyLLaJjZS/NJ5VdqKMdC+zKaNfON1Xir8tVVbQeVWUS2vUh9VjzFyFjHQASz4g/HkxV7SKVfdZlGCleLaX1YnpSSZ7grr5N5bFpIIyMjBQ9/l9Nfn5+SE9PNzx///33sX//fnz99dcPXJe7y3xcBAYGoiF7hN8Zj5ExxhhjjDHGGGOMPbkavIvx7du3ER4eDqVSCXt7e6xcuRKBgYGYPn06gNq7sVpZWSE+Pt7w/MqVKwgNDYWVlRWsra0REhKCS5cuGV5PSEhAt27dDOsYN24cTExM8Ouvv+KDDz7A2bNn4e7uDnd3d6Ny7+evv/7Cs88+C3Nzc3h7e+PMmTNwc3ODm5sb2rZti8WLF6N///7o2LEjPD09sXr1aqP316yTlZUVAgICcPnyZVHr/uqrr9ClSxeYmprCxcUFCxcuRGXl33fdBEHAhg0bMGTIEJibm0Oj0SA5ORnnz59HYGAglEolevbsiZycv+8Y5eTkICQkBLa2tlCpVOjatSt+/PFHo/Xe3cWYMcYYY4wxxhhjrKkJEqHJHk+qBm8gnDFjBpKSkrBnzx7s378fiYmJSEsT39VHq9UiODgYarUaiYmJSEpKgkqlwoABA1BRUYHKykoMHz4cTz/9NE6cOIHk5GRMnDgRgiAgLCwMM2fOhJeXF/Ly8pCXl4ewsDBR6124cCFCQ0Nx4sQJDBo0COHh4bhxo7ori06nQ+vWrfH555/jzJkzmD9/Pt5880189tlnAHDfOtUnMTERERERmDZtGs6cOYMNGzYgPj4eb7/9tlHcokWLEBERgfT0dLRr1w5jxozBpEmTEB0djWPHjkGv12PKlCmG+KKiIgwaNAgHDhzA8ePHMWDAAAwdOhS5ueK7OTHGGGOMMcYYY4yxf74G7WJ8+/ZtbNq0CVu3bkW/fv0AAHFxcXBwED/OyY4dO6DT6bBx40ZDA1tcXBysrKyQkJAAPz8/FBQUYMiQIXB1rR5rQ6P5e2ZDlUoFmUwGOzvamDyRkZF4/vnnAQDvvPMO3n//fRw9ehQDBgyAiYkJFi5caIht27YtkpOT8dlnnyE0NBSFhYX3rdP9LFy4EHPnzsW4ceMAAC4uLli0aBHeeOMNLFiwwBA3fvx4hIaGAgDmzJkDf39/zJs3D8HBwQCAadOmYfz48Yb4Tp06oVOnTobnixYtwq5du7Bnzx6jhkSxysvLUV5uPFZQRUU55HLamG+MMcYYY4wxxhhj7PHSoBmEFy5cgFarRbdu3QzLLC0t4enpKbqMjIwMnD9/Hmq1GiqVCiqVCtbW1igrK0NOTg6sra0RGRmJ4OBgDB06FKtXr0ZeXt5D193b29vwt1KphIWFBfLz8w3LPvzwQ/j6+sLGxgYqlQr/+c9/DNl4D1OnjIwMxMbGGrZVpVJhwoQJyMvLQ0lJSa31uzPDc8eOHY2WlZWVobCwEEB1BuGsWbOg0WhgZWUFlUqFzMzMB84gXLx4MSwtLY0en6xfXf8bGWOMMcYYY4wxxggEqaTJHk+qR77lgiDcM9GGVvv3bGRFRUXw9fVFenq60ePcuXMYM2YMgOqMwuTkZPTs2RM7duyAh4cHDh+mzU51NxMT41kDBUGATqcDAGzfvh2zZs1CVFQU9u3bh/T0dIwfPx4VFX/PuvagdSoqKsLChQuNtvXkyZPIzs6GqalprfW7k1lZ27I7dZ41axZ27dqFd955B4mJiUhPT0fHjh2N6kwRHR2NgoICo8eLL9Nm22SMMcYYY4wxxhhjj58G7WLs4uICExMTpKSkoE2bNgCAgoICnDt3Dr179wZQPXNuzey67Oxso0y5Ll26YMeOHWjZsiUsLCzqXJePjw98fHwQHR0Nf39/bN26FT169IBcLkdVVVVDbhaSkpLQs2dPTJ482bCs5oQg9dXpfrp06YKsrCy4ubk1eJ0jIyPx7LPPAqhuiKw50QuVQqGAQmHcnVguL68jmjHGGGOMMcYYY+zBSKRP7mQhTaVBMwjVajXGjRuH2bNn4+eff8bp06cRFRUFiURiyHDr27cv1qxZg+PHj+PYsWN4+eWXjTLhwsPD0aJFC4SEhCAxMREXL15EQkICpk6dit9++w0XL15EdHQ0kpOTcfnyZezbtw/Z2dmGMf+cnZ1x8eJFpKen46+//rpn3LwH4e7ujmPHjuGHH37AuXPnMG/ePKSkpBher69O9zN//nxs3rwZCxcuxOnTp5GZmYnt27fj3//+90PXeefOnUhPT0dGRgbGjBljyC5kjDHGGGOMMcYYY+yOBu9ivGLFCvj7+2PIkCEICgpCQEAANBqNobvs8uXL4ejoiF69emHMmDGYNWsWzM3NDe83NzfHL7/8gjZt2uC5556DRqNBVFQUysrKYGFhAXNzc5w9exYjRoyAh4cHJk6ciFdffRWTJk0CAIwYMQIDBgxAnz59YGNjg23btj30Nk2aNAnPPfccwsLC0L17d1y/ft0om7C+Ot1PcHAwvvnmG+zbtw9du3ZFjx49sHLlSjg5OT1UnVesWIFmzZqhZ8+eGDp0KIKDg9GlS5eHKpMxxhhjjDHGGGOM/fM0aBdjoDqLcMuWLYbnxcXFWLhwISZOnAgAcHBwwA8//GD0nlu3bhk9t7Ozw6ZNm2ot38LCArt27apz/QqFAl988UWdrwuCgF27dmH48OGGZXePiXh3nRQKBeLi4hAXF2cUs3jxYgDVE4Tcr071CQ4ONsxGHBkZiVu3bmHChAl11s/Z2fmeZYGBgUbLnJ2d8dNPPxnFvPrqq0bP7+5yXNt+YIwxxhhjjDHGGHuUBAl3MX7UGryB8Pjx4zh79iy6deuGgoICxMbGAgBCQkIaelWIiYnB7t27kZ6e3uBls/pJ0LgNino97YAgEMKlxPEMqHWh0gu0ZF4ThUn9Qf9PClrXcj2I+4YYT/k/Ucum0glSUjw15VpuLf7/VHFDW39QDdTPjExBO9xT983jRK6kbatE2ojbSixbIB5XKd8R6mdAEGh1IZdP3DcSSn2I3w+qxj42PU6o2yp5jC7mdcSjNjX+fxllWxv9XKwn7vdGrM7jNnEl9Vz/v4q6ndRrFKmJ+OsxAIBAm9RRQi2/MVEutAEIjZggQj2mNvZvEOrvP8aeRI1y1lm2bBk6deqEoKAgFBcXIzExES1atGiMVdVry5YtUKlUhgcAjB49GiqVCl5eXo2+fi8vL6P113zUzLRkjDHGGGOMMcYYY9WTlDTV40n1UA2Et2/fRnh4OJRKJezt7bFy5Uq8/vrr6NWrF4qKinDz5k28+uqr6Nixo+E9VlZWiI+PNzy/cuUKQkNDYWVlBWtra4SEhBh1fU1ISEC3bt2gVCphZWWFgIAAXL58GfHx8Vi4cCEyMjIgCAIEQTAq945hw4YhPT3d8ACqJwbp2bMnLly4AHd3d+zZs8cQX1VVhaioKLRt2xZmZmbw9PTE6tWrjcqsq0612bt3r2HdL774IhwdHTFnzhyo1WpMmDABoaGhKCgouOd9y5Ytg729PZo3b45XX30VWu3f2UU3b95EREQEmjVrBnNzcwwcOBDZ2dmG1+Pj42FlZYUffvgBGo0GKpUKAwYMMJo9GgA2btxoGB+yXbt2WLt2ba3bwBhjjDHGGGOMMcb+uR6qgXDGjBlISkrCnj17sH//fiQmJiItLU30+7VaLYKDg6FWq5GYmIikpCRDY1ZFRQUqKysxfPhwPP300zhx4gSSk5MxceJECIKAsLAwzJw5E15eXsjLy0NeXh7CwsLuWYdarYabm5vhAQDr1q3D+PHjcfLkSQwaNAjh4eG4ceMGAECn06F169b4/PPPcebMGcyfPx9vvvkmPvvsMwC4b51q4+TkZFi3tbU1rly5goSEBHz33Xf4/vvvcfz4caMJTwDg559/Rk5ODn7++Wds2rQJ8fHxRo2fkZGROHbsGPbs2YPk5GTo9XoMGjTIqBGxpKQEy5Ytw6effopffvkFubm5mDVrluH1LVu2YP78+Xj77beRmZmJd955B/Pmzatz7EfGGGOMMcYYY4wx9s/0wGMQ3r59G5s2bcLWrVvRr18/AEBcXBwcHBxEl7Fjxw7odDps3LjR0MAWFxcHKysrJCQkwM/PDwUFBRgyZAhcXV0BABqNxvB+lUoFmUwGOzs7Ut0jIyPx/PPPAwDeeecdvP/++zh69CgGDBgAExMTLFy40BDbtm1bJCcn47PPPkNoaCgKCwvvW6f6lJWVYfPmzWjVqhUA4IMPPsDgwYOxfPlyw3Y0a9YMa9asgVQqRbt27TB48GAcOHAAEyZMQHZ2Nvbs2YOkpCT07NkTQHVjn6OjI3bv3o1Ro0YBqG58Xb9+vaGOU6ZMMYwHCQALFizA8uXL8dxzzxm288yZM9iwYQPGjRtH2p+MMcYYY4wxxhhjDUV4grv6NpUHbiC8cOECtFotunXrZlhmaWkJT09P0WVkZGTg/PnzUKvVRsvLysqQk5ODZ555BpGRkQgODkb//v0RFBSE0NBQ2NvbP2i1AQDe3t6Gv5VKJSwsLJCfn29Y9uGHH+KTTz5Bbm4uSktLUVFRgc6dOwMArK2tH6pObdq0MTQOAoC/vz90Oh2ysrIMDYReXl6Q1hi83d7eHidPngQAZGZmQiaToXv37obXmzdvDk9PT2RmZhqWmZubGxoH75RxZxuLi4uRk5ODqKgoo9mSKysrYWlpWWu9y8vLUV5ebrSsoqIccrlC1HYzxhhjjDHGGGOMscdTo06NJQgC9HfNjFSzG2xRURF8fX2NxghMT0/HuXPnMGbMGADVGYXJycno2bMnduzYAQ8PDxw+fPih6mVy10xTgiBAp6ue6XX79u2YNWsWoqKisG/fPqSnp2P8+PGoqPh7NqvGqJPY+j1MGXf+F0VFRQCAjz76yGi/nzp1qs7tWLx4MSwtLY0eH69/n1QnxhhjjDHGGGOMsfoIEkmTPZ5UD7zlLi4uMDExQUpKimFZQUEBzp07Z3huY2NjNDFGdnY2SkpKDM+7dOmC7OxstGzZ0micQDc3N6NMNh8fH0RHR+PQoUPo0KEDtm7dCgCQy+Woqqp60E2o1Z2uu5MnT4aPjw/c3NyQk5NzT1xddapPbm4u/vjjD8Pzw4cPQyKRiM681Gg0qKysxJEjRwzLrl+/jqysLLRv315UGba2tnBwcMCFCxfu2e9t27at9T3R0dEoKCgwekS9PFXU+hhjjDHGGGOMMcbY4+uBGwjVajXGjRuH2bNn4+eff8bp06cRFRUFiURiGE+wb9++WLNmDY4fP45jx47h5ZdfNspsCw8PR4sWLRASEoLExERcvHgRCQkJmDp1Kn777TdcvHgR0dHRSE5OxuXLl7Fv3z5kZ2cbxvxzdnbGxYsXkZ6ejr/++uueLrAPwt3dHceOHcMPP/yAc+fOYd68eUaNoPXVqT6mpqYYN24cMjIykJiYiKlTpyI0NFT0OIru7u4ICQnBhAkT8OuvvyIjIwMvvPACWrVqhZCQENHbuXDhQixevBjvv/8+zp07h5MnTyIuLg4rVqyoNV6hUMDCwsLowd2LGWOMMcYYY4wxxv73PVTu5IoVK+Dv748hQ4YgKCgIAQEB0Gg0MDU1BQAsX74cjo6O6NWrF8aMGYNZs2bB3Nzc8H5zc3P88ssvaNOmDZ577jloNBpERUWhrKwMFhYWMDc3x9mzZzFixAh4eHhg4sSJePXVVzFp0iQAwIgRIzBgwAD06dMHNjY22LZt28NsDgBg0qRJeO655xAWFobu3bvj+vXrRrMM11en+ri5ueG5557DoEGD8Mwzz8Db2xtr164l1TEuLg6+vr4YMmQI/P39odfrsXfv3nu6Fd/PSy+9hI0bNyIuLg4dO3bE008/jfj4+DozCBljjDHGGGOMMcYeBUEiNNnjSfXAk5QA1VmEW7ZsMTwvLi7GwoULMXHiRACAg4MDfvjhB6P33Lp1y+i5nZ0dNm3aVGv5FhYW2LVrV53rVygU+OKLL0h1vntMxLvrpFAoEBcXh7i4OKOYxYsXA6junnu/Oonxyiuv4JVXXqn1tfj4+HuWrVq1yuh5s2bNsHnz5jrLj4yMRGRkpNGy4cOH37PtY8aMMYz1yBhjjDHGGGOMMcaeTA+VQXj8+HFs27YNOTk5SEtLQ3h4OADct6trYGAgpk+f/jCr/Z9VVFSEnJwcKJVKWFlZNXV1jMTExBhmamaMMcYYY4wxxhhrKhKp0GSPJ5Wgry2lTqTjx4/jpZdeQlZWFuRyOXx9fbFixQp07NixzvfcuHEDJiYmUKvVD7raOm3ZsqXOrr5OTk44ffp0g60rJiYGu3fvRnp6umGZl5cXLl++XGv8hg0bsHbtWqSlpeHEiROwtLREy5YtG6w+D6u27anP8ey/SOtQSkrqD3oIRVVK0bGCQPvYU+uuI7a9F1eZ1x9Us3y9+PJVsmJS2SaoqD+ohkqI79oOAHqIP+BS66In7nfq/0kC2mziFhXivyN6gVaX1I6jSfHd0uvOOq6NecHvomPzbb1JZeuI21quMyXFW1deJcULetr/laJKQvt+5EvsSfHmklLRsWrtDVLZRSbNSPEq7U1SfKVUToovkljWH/T/ZKgklV2mp33GZAKtfAHizznmutuksksktOspKWiTu1GO2QBQRvi+KiS0saOpdaGilN/Y+5Ear66iff8KpdaiY830tOuIMoF2TUPdVsr3iapMRxtfm3IMBujbalWRLzr2ptyWVPbjhLpfLKpo57NLlbQhlOxMxe93AJDqxJ8TygUzUtnUa1XLyuuk+FvSFqJjpQLtuEetO/VYUwza+U+rF995UiUpopUN2jWNAmWkeBdXV1L8P0X6M72abN2d9yU22bqb0kN1Mfbx8UFqairpPdbW4i9IqIYNG4bu3bvX+hplfL4HtXfvXmi12lpfu9M1OTQ0FO7u7nWWodVqH0ldGWOMMcYYY4wxxhgDHrKL8YOo2cV47dq1cHd3h6mpKWxtbTFy5EhRZdy+fRvh4eFQKpWwt7fHypUrERgYiHnz5sHNzQ1ubm5wd3fHqVOnDM+dnJxgZWVlNMbflStXEBoaCisrK1hbWyMkJASXLl0yvJ6QkIBu3boZugQHBATg8uXLiI+Px8KFC5GRkQFBECAIAuLj4+Hk5GRY392Pjh074ssvv8TmzZshCIJhjEBBELBu3ToMGzYMSqUSb7/9NgBg3bp1cHV1hVwuh6enJz799FOjfSAIAjZs2IAhQ4bA3NwcGo0GycnJOH/+PAIDA6FUKtGzZ0/k5OQ88P+KMcYYY4wxxhhj7FHjSUoevUfeQHjHsWPHMHXqVMTGxiIrKwvff/89evfuLeq9M2bMQFJSEvbs2YP9+/cjMTERaWlppPVrtVoEBwdDrVYjMTERSUlJUKlUGDBgACoqKlBZWYnhw4fj6aefxokTJ5CcnIyJEydCEASEhYVh5syZ8PLyQl5eHvLy8hAWFnbf9aWkpGDAgAEIDQ1FXl4eVq9ebXgtJiYGzz77LE6ePIkXX3wRu3btwrRp0zBz5kycOnUKkyZNwvjx4/Hzzz8blblo0SJEREQgPT0d7dq1w5gxYzBp0iRER0fj2LFj0Ov1mDJlCmm/MMYYY4wxxhhjjLEny0N1MX4Yubm5UCqVGDJkCNRqNZycnODj41Pv+27fvo1NmzZh69at6NevHwAgLi4ODg4OpPXv2LEDOp0OGzduhCAIhnKsrKyQkJAAPz8/FBQUYMiQIXD9/z7/Go3G8H6VSgWZTAY7OztR67OxsYFCoYCZmdk97xkzZgzGjx9veP78888jMjISkydPBlDdIHr48GEsW7YMffr0McSNHz8eoaGhAIA5c+bA398f8+bNQ3BwMABg2rRpRuUyxhhjjDHGGGOMPe4ESZPlsz2xmmyP9+/fH05OTnBxccHYsWOxZcsWlJTUPxHEhQsXoNVq0a1bN8MyS0tLeHp6ktafkZGB8+fPQ61WQ6VSQaVSwdraGmVlZcjJyYG1tTUiIyMRHByMoUOHYvXq1cjLyyNvpxh+fn5GzzMzMxEQEGC0LCAgAJmZmUbLvL3/nhjA1rZ6YOKaE8TY2tqirKwMhYWFD13H8vJyFBYWGj0qKmiDizPGGGOMMcYYY4yxx0+TNRCq1WqkpaVh27ZtsLe3x/z589GpUyfcunWrQcoXBAF3T9BccwKRoqIi+Pr6Ij093ehx7tw5jBkzBkB1RmFycjJ69uyJHTt2wMPDA4cPH26Q+tWkVIqffbemmpOZ3MmCrG2ZTvfwM3QuXrwYlpaWRo9P1q+u/42MMcYYY4wxxhhjBDwG4aPXpDmbMpkMQUFBWLp0KU6cOIFLly7hp59+uu97XFxcYGJigpSUFMOygoICnDt3zijOxsbGKOMvOzvbKEOxS5cuyM7ORsuWLe+ZUMTS0tIQ5+Pjg+joaBw6dAgdOnTA1q1bAQByuRxVVbSp3sXSaDRISkoyWpaUlIT27ds3yvrEiI6ORkFBgdHjxZenNVl9GGOMMcYYY4wxxljDaLIxCL/55htcuHABvXv3RrNmzbB3717odLp6uwqr1WqMGzcOs2fPhrW1NVq2bIkFCxZAIpEYMuYAoG/fvlizZg38/f1RVVWFOXPmGGXXhYeH47333kNISAhiY2PRunVrXL58GTt37sQbb7wBrVaL//znPxg2bBgcHByQlZWF7OxsREREAACcnZ1x8eJFpKeno3Xr1lCr1VAoFA2yb2bPno3Q0FD4+PggKCgIX3/9NXbu3Ikff/yxQcp/EAqF4p7tk8srmqg2jDHGGGOMMcYYY6yhNFkGoZWVFXbu3Im+fftCo9Fg/fr12LZtG7y8vOp974oVK+Dv748hQ4YgKCgIAQEB0Gg0MDU1NcQsX74cjo6O6NWrF8aMGYNZs2bB3Nzc8Lq5uTl++eUXtGnTBs899xw0Gg2ioqJQVlYGCwsLmJub4+zZsxgxYgQ8PDwwceJEvPrqq5g0aRIAYMSIERgwYAD69OkDGxsbbNu2rcH2zfDhw7F69WosW7YMXl5e2LBhA+Li4hAYGNhg62CMMcYYY4wxxhh7HEmkQpM9nlSC/u6B+v4HFRcXo1WrVli+fDmioqKaujpPjOPZf5HilZL6J6F5GEVV4sdyFATax55adx2x7b24yrz+oJrl68WXr5IVk8o2AS0ztBIm9QfVoIf4Ay61Lnrifqf+nySgjedpUSH+O6IXaHVJ7TiaFN8tfTMp3rzgd9Gx+bbe9QfVoCNua7nOtP6gGqwrr5LiBf3Dj9NalyoJ7fuRL7EnxZtLSkXHqrU3SGUXmTQjxau0N0nxlVI5Kb5IYll/0P+ToZJUdpme9hmTCbTyBYg/55jrbpPKLpGoSfFS0IZHoRyzAaCM8H1VSGiTnVHrQkUpv7H3IzVeXUX7/hVKrUXHmulp1xFlAu2ahrqtlO8TVZmO1huIcgwG6NtqVZEvOvam3JZU9uOEul8sqmjns0uVbUnxdqbi9zsASHXizwnlghmpbOq1qmXldVL8LWkL0bFSgXbco9adeqwpBu38p9WL7zypkhTRygbtmkaBMlK8i6srKf6f4nRI3yZbt9dX9x/67p/qkWcQBgYGYvr06Q9VxvHjx7Ft2zbk5OQgLS0N4eHhAICQkJAGqGHj+fPPP9G/f38olUpYWVk1dXWMxMTEoHPnzk1dDcYYY4wxxhhjjD3heJKSR++Rj0G4c+dOo7EA75abm3vfyTjOnDkDAFi2bBmysrIgl8vh6+uLxMREtGgh/g7Iw4qJicHu3buRnp4OANiyZYuh+/HdnJyccPr0aaxcuRJ5eXlIT083mgjlUfDy8sLly5drfW3Dhg2PpA5/lNqQ4luZ0e7encprLjr22k3a3echHWl3iEuqaHcHraW0O6HvfiF+Wyc/R8tgalle++ekLmVy2t27s+XuomM9TC+Qyq6U0O7emWlpmTrSKlpG459z5oqOlSloh2NqRuDRzhGk+A6Ze0TH/lVJO/a21WaS4nXELLziZbGk+ObebqR4CpOWtOPeH74vk+KvacVnASk3zCaVLUxdQov/eBkp3qZLR1J8ZvvXRcf6V9DG7P389iBSvHcrWraWS/kpUjxFuZKWrSUhZoCoymnb+vMVX9GxvVz+IJVtVUXruVAoE//9AGjZK1US2jFbQsxUvqWzIsVbV9KyMcsFwvmSmE5AzcKr1NH2pUIi/lxcRaz8r+fEX18BwATQrqH/9KRlw5jf/lN0bHkz2rGgQKB9P6oIvVcAwEwiPkOKmhVaLqVt6/z5Z0jxn79J+z6Vxa0VHaufsohUNvVY0FJLy3wrFVqLjpVLtKSyqZ8Z6rEmv8SKFH/6ivgM95eKadfZZzpFkuIVUloGIWOPyiNvILS2vv/JyMHBwdDoVtfrbdq0QWpqagPX7OEMGzYM3bt3r/W1Ow2iOTk58PX1hbt73Q0kWq32vg2oD2rv3r3Qams/qNva2mL58uUNvk7GGGOMMcYYY4wx9vhr0i7Ga9euhbu7O0xNTWFra4uRI0dCJpPBzc2tzodMJsPt27cRHh4OpVIJe3t7rFy58p6uy4IgYPfu3UbrtrKyQnx8vOH5lStXEBoaCisrK1hbWyMkJASXLl0yvJ6QkIBu3boZugQHBATg8uXLiI+Px8KFC5GRkQFBECAIAr788ss66+zk5ARnZ2d8+eWX2Lx5MwRBQGRkpKGe69atw7Bhw6BUKvH2228DANatWwdXV1fI5XJ4enri008/NdoWQRCwYcMGDBkyBObm5tBoNEhOTsb58+cRGBgIpVKJnj17IicnB0B1FmNd9VOraRlgjDHGGGOMMcYYY41FkEia7PGkarItP3bsGKZOnYrY2FhkZWXh+++/R+/evUW9d8aMGUhKSsKePXuwf/9+JCYmIi0tjbR+rVaL4OBgqNVqJCYmIikpCSqVCgMGDEBFRQUqKysxfPhwPP300zhx4gSSk5MxceJECIKAsLAwzJw5E15eXsjLy0NeXh7CwsLuu76UlBQMGDAAoaGhyMvLw+rVqw2vxcTE4Nlnn8XJkyfx4osvYteuXZg2bRpmzpyJU6dOYdKkSRg/fjx+/vlnozIXLVqEiIgIpKeno127dhgzZgwmTZqE6OhoHDt2DHq9HlOmTCHtF8YYY4wxxhhjjDH2ZHnkXYzvyM3NhVKpxJAhQ6BWq+Hk5AQfH59633f79m1s2rQJW7duRb9+/QAAcXFxcHBwIK1/x44d0Ol02LhxIwRBMJRjZWWFhIQE+Pn5oaCgAEOGDIHr/88apNFoDO9XqVSQyWSws7MTtT4bGxsoFAqYmZnd854xY8Zg/PjxhufPP/88IiMjMXnyZADVDaKHDx/GsmXL0KdPH0Pc+PHjERoaCgCYM2cO/P39MW/ePAQHBwMApk2bZlQuY4wxxhhjjDHG2OPuSZ4spKk0WQZh//794eTkBBcXF4wdOxZbtmxBSUlJve+7cOECtFotunXrZlhmaWkJT09P0vozMjJw/vx5qNVqqFQqqFQqWFtbo6ysDDk5ObC2tkZkZCSCg4MxdOhQrF69Gnl5eeTtFMPPz8/oeWZmJgICAoyWBQQEIDPTeGB/b29vw9+2trYAgI4dOxotKysrQ2Fh4UPXsby8HIWFhUaPigraAL6MMcYYY4wxxhhj7PHTZA2EarUaaWlp2LZtG+zt7TF//nx06tQJt27dapDyBUGAXm88I1bNSTqKiorg6+uL9PR0o8e5c+cwZswYANUZhcnJyejZsyd27NgBDw8PHD58uEHqV5NSqXyg99WczOROFmRty3Q62sx5tVm8eDEsLS2NHp+sX13/GxljjDHGGGOMMcbYY61JR1+UyWQICgrC0qVLceLECVy6dAk//fTTfd/j4uICExMTpKSkGJYVFBTg3LlzRnE2NjZGGX/Z2dlGGYpdunRBdnY2WrZsec+kHZaWloY4Hx8fREdH49ChQ+jQoQO2bt0KAJDL5aiqqnqo7a+LRqNBUlKS0bKkpCS0b9++UdYnRnR0NAoKCoweL748rcnqwxhjjDHGGGOMsX8mQSI02eNJ1WRjEH7zzTe4cOECevfujWbNmmHv3r3Q6XT1dhVWq9UYN24cZs+eDWtra7Rs2RILFiyARCIxZMwBQN++fbFmzRr4+/ujqqoKc+bMMcquCw8Px3vvvYeQkBDExsaidevWuHz5Mnbu3Ik33ngDWq0W//nPfzBs2DA4ODggKysL2dnZiIiIAAA4Ozvj4sWLSE9PR+vWraFWq6FQKBpk38yePRuhoaHw8fFBUFAQvv76a+zcuRM//vhjg5T/IBQKxT3bJ5dXNFFtGGOMMcYYY4wxxlhDabIMQisrK+zcuRN9+/aFRqPB+vXrsW3bNnh5edX73hUrVsDf3x9DhgxBUFAQAgICoNFoYGpqaohZvnw5HB0d0atXL4wZMwazZs2Cubm54XVzc3P88ssvaNOmDZ577jloNBpERUWhrKwMFhYWMDc3x9mzZzFixAh4eHhg4sSJePXVVzFp0iQAwIgRIzBgwAD06dMHNjY22LZtW4Ptm+HDh2P16tVYtmwZvLy8sGHDBsTFxSEwMLDB1sEYY4wxxhhjjDH2OOIMwkfvkWcQJiQk1Po3hVqtxpYtWwzPi4uLsXDhQkycONGwzMHBAT/88IPR++4e39DOzg6bNm2qdR0WFhbYtWtXnXVQKBT44osvSPXevXv3PcvuHifxjldeeQWvvPJKnWXd/T5nZ+d7lgUGBtZZ/t1iYmIQExMjKpYxxhhjjDHGGGOM/XM88gzCwMBATJ8+/aHKOH78OLZt24acnBykpaUhPDwcABASEtIANWw8f/75J/r37w+lUgkrK6umro6RmJgYdO7cuamrwRhjjDHGGGOMMcYeMUEvNsWsgdy4cQMmJiZQq9W1vp6bm3vfyTjOnDmD69ev46WXXkJWVhbkcjl8fX2xYsUKdOzYsbGqfY+YmBjs3r0b6enpAIAtW7YYuh/fzcnJCadPn8acOXPw7bffYteuXbC0tETLli0fWX29vLxw+fLlWl/bsGEDsrOzjbZHjDPn/yDVwUTQ1h/0EHSE9m6t3qT+oBpkQiW1OiQ6Pa2tnrKtEtBmsW7sba3Ui09clgq0iYAENO7hjLLfAcBUX1J/0J2yBSmpbNurGaT4v1rWP3xDTac0w0THepzdRyqbirrf5SinlS803r0ygXiKLdWb1x9Ug1wQPxasDLRjsBZyUjy1/CpiJ4YqiP+OSEE7dlDKfhCU4zD1OEb9flDLlxD3ZbnetP6g/0c9xlPPZ3rQughR4qmfMSlo59YK0Ma1pl5HUM711M9AJWjXWNT/K/UzT0G5RgEAM4n48zxAPyeUQ/z3ifrdpsZTv08Ujf19KtLV/ruzLnIJbZx1BcpEx1L3I/VYQC2/Ma+dqd9t6rGGeh1B+f1H/W5r9bRrJuq+8XBtQ4r/p8gOH9Rk63bfsrfJ1t2UHnkXY2tr6/u+7uDgcN9GKgcHB7Rp0wapqakNXLOHM2zYMHTv3r3W1+5MjpKTkwNfX1+4u7vXWY5WqzWaTKWh7N27F1pt7T/ebG1tsXz58gZfJ2OMMcYYY4wxxhh7/DVpF+O1a9fC3d0dpqamsLW1xciRIyGTyeDm5lbnQyaT4fbt2wgPD4dSqYS9vT1Wrlx5T9dlQRDuGfPPysoK8fHxhudXrlxBaGgorKysYG1tjZCQEFy6dMnwekJCArp162boEhwQEIDLly8jPj4eCxcuREZGBgRBgCAI+PLLL+uss5OTE5ydnfHll19i8+bNEAQBkZGRhnquW7cOw4YNg1KpxNtvvw0AWLduHVxdXSGXy+Hp6YlPP/3UaFsEQcCGDRswZMgQmJubQ6PRIDk5GefPn0dgYCCUSiV69uyJnJwcANVZjHXVr65sTsYYY4wxxhhjjLFHTSIVmuzxpGqyWYyPHTuGqVOnIjY2FllZWfj+++/Ru3dvUe+dMWMGkpKSsGfPHuzfvx+JiYlIS0sjrV+r1SI4OBhqtRqJiYlISkqCSqXCgAEDUFFRgcrKSgwfPhxPP/00Tpw4geTkZEycOBGCICAsLAwzZ86El5cX8vLykJeXh7CwsPuuLyUlBQMGDEBoaCjy8vKwevVqw2sxMTF49tlncfLkSbz44ovYtWsXpk2bhpkzZ+LUqVOYNGkSxo8fj59//tmozEWLFiEiIgLp6elo164dxowZg0mTJiE6OhrHjh2DXq/HlClTSPuFMcYYY4wxxhhjjD1ZHnkX4ztyc3OhVCoxZMgQqNVqODk5wcfHp9733b59G5s2bcLWrVvRr18/AEBcXBwcHBxI69+xYwd0Oh02btwIQRAM5VhZWSEhIQF+fn4oKCjAkCFD4OrqCgDQaDSG96tUKshkMtjZ2Ylan42NDRQKBczMzO55z5gxYzB+/HjD8+effx6RkZGYPHkygOoG0cOHD2PZsmXo06ePIW78+PEIDQ0FAMyZMwf+/v6YN28egoODAQDTpk0zKpcxxhhjjDHGGGOMsbs1WQZh//794eTkBBcXF4wdOxZbtmxBSUn9g4FeuHABWq0W3bp1MyyztLSEp6cnaf0ZGRk4f/481Go1VCoVVCoVrK2tUVZWhpycHFhbWyMyMhLBwcEYOnQoVq9ejby8PPJ2iuHn52f0PDMzEwEBAUbLAgICkJmZabTM29vb8LetrS0AGE3UYmtri7KyMhQWFj50HcvLy1FYWGj0qCinTQTAGGOMMcYYY4wxVh9BIjTZ40nVZA2EarUaaWlp2LZtG+zt7TF//nx06tQJt27dapDyBUHA3RM015yko6ioCL6+vkhPTzd6nDt3DmPGjAFQnVGYnJyMnj17YseOHfDw8MDhw4cbpH41KZXKB3pfzclM7mRB1rZMp6PNklSbxYsXw9LS0ujx0YY1D10uY4wxxhhjjDHG2P+yDz/8EM7OzjA1NUX37t1x9OjROmMDAwMN81nUfAwePNgQExkZec/rAwYMaNRtaLIGQgCQyWQICgrC0qVLceLECVy6dAk//fTTfd/j4uICExMTpKSkGJYVFBTg3LlzRnE2NjZGGX/Z2dlGGYpdunRBdnY2WrZsec+kHZaWloY4Hx8fREdH49ChQ+jQoQO2bt0KAJDL5aiqok3FLpZGo0FSUpLRsqSkJLRv375R1idGdHQ0CgoKjB4TJvH4howxxhhjjDHGGGtYgkTSZA+qHTt2YMaMGViwYAHS0tLQqVMnBAcHIz8/v9b4nTt3GuazyMvLw6lTpyCVSjFq1CijuAEDBhjFbdu27YH2pVhNNgbhN998gwsXLqB3795o1qwZ9u7dC51OV29XYbVajXHjxmH27NmwtrZGy5YtsWDBAkgkEkPGHAD07dsXa9asgb+/P6qqqjBnzhyj7Lrw8HC89957CAkJQWxsLFq3bo3Lly9j586deOONN6DVavGf//wHw4YNg4ODA7KyspCdnY2IiAgAgLOzMy5evIj09HS0bt0aarUaCoWiQfbN7NmzERoaCh8fHwQFBeHrr7/Gzp078eOPPzZI+Q9CoVDcs31yRVET1YYxxhhjjDHGGGOs6a1YsQITJkwwzAGxfv16fPvtt/jkk08wd+7ce+Ktra2Nnm/fvh3m5ub3NBAqFArR8140hCbLILSyssLOnTvRt29faDQarF+/Htu2bYOXl1e9712xYgX8/f0xZMgQBAUFISAgABqNBqampoaY5cuXw9HREb169cKYMWMwa9YsmJubG143NzfHL7/8gjZt2uC5556DRqNBVFQUysrKYGFhAXNzc5w9exYjRoyAh4cHJk6ciFdffRWTJk0CAIwYMQIDBgxAnz59YGNj06AtucOHD8fq1auxbNkyeHl5YcOGDYiLi0NgYGCDrYMxxhhjjDHGGGOMGattDobyOuZgqKioQGpqKoKCggzLJBIJgoKCkJycLGp9H3/8MUaPHn3P8HMJCQlo2bIlPD098corr+D69esPvlEiCPq7B+prAoGBgejcuTNWrVr1QO8vLi6GpaUl+vTpg/379zds5R5CSUkJxo4di/379+P27du4efMmrKysmrpaAID4+HhMnz7dMOZjTEwMdu/ejfT0dNFlnDn/B2mdJoK2/qCHoCO0d2v1JvUH1SATKqnVIdHpaW31lG2VgDYGZWNva6VefOKyVKB14xfQuIczyn4HAFN9/RMvGcoWpKSyba9mkOL/aln/zZeaTmmGiY71OLuPVDYVdb/LQZtASSc03r0ygXiKLdWb1x9Ug1yoEB0rA+0YrIWcFE8tv4rYiaEK4r8jUtCOHZSyHwTlOEw9jlG/H9TyJcR9Wa43rT/o/1GP8dTzmR60QcYp8dTPmBS0c2sFaL1SqNcRlHM99TNQCdo1FvX/Sv3MU1CuUQDATCL+PA/QzwnlEP99on63qfHU7xNFY3+finRqUrxcIv7cCgAKlImOpe5H6rGAWn5jXjtTv9vUYw31OoLy+4/63dbqaddM1H3j4dqGFP9PcemlkCZbd3xrHyxcuNBo2YIFCxATE3NP7B9//IFWrVrh0KFD8Pf3Nyx/4403cPDgQRw5cuS+6zp69Ci6d++OI0eOGE3GeyersG3btsjJycGbb74JlUqF5ORkSKWNc+3aZF2Ma9q5c6dR99/6HD9+HGfPnkW3bt1QUFCA2NhYANXjEzaWuxvUxNi0aRMSExNx6NAhtGjRwmhsw8fNrFmz8Nprr5HeQz0BUX+AmemLSfG3IX7//lVG+1+0Mqt97IC6UC9eqT+STFEqOpba4CAlNhCW6cxI8ZR9oxDEX3A9COrJnKpUeLAJiMTIt/WuP6iGvypbkOIpjX7n2j1DKtvxTCIpvnVpFin+T3NXUjz1hzUFtcGdeqFOOa5WED/vWuIPZZlAK99Sf4MUfxPiP8MqyW1S2SZ6WqMy9f9UKRF/jVNJvIFFRa27XEc7Dl8pE98Npo0yr/6gGsp14htLAPq5lXIs0AvEH/nE75/T9WOk+AvW3UnxlGs452tppLIv23SrP6gGle4WKV5RKf6H+zV5a1LZFvqbpPgrZY6k+JYKWuaHVif+eEA935gK4q8lAfpn3qL0mujYawrafrSquEqKJ7axwUxLO4dcQVvRsUoZsVGZer4hnrsp3H8/QIrPad2HFN/2Gm0y0HIV7dr2L3PxjWzXKpqTyraQ0X63Us9P7NGLjo7GjBkzjJY11JByd/v444/RsWNHo8ZBABg9erTh744dO8Lb2xuurq5ISEhAv379GqUuj0UDYc3+17m5ufedjOPMmTMAgGXLliErKwtyuRy+vr7w8fGBmRmtoaIhbdmyxdD9+I7y8nLodDqEhYXh9OnTtb6voqICcnnjNlIAgJeXFy5fvmx4rtVqUVFRAZVKhQ0bNiA8PBwqlarR68EYY4wxxhhjjDF2P4Kk8TKX61PbHAx1adGiBaRSKa5eNb55cfXq1XrHDywuLsb27dsNSW/34+LighYtWuD8+fON1kDYpLMY3xEYGIjp06cDAL766iu0aNECWq0WpqameOqpp5Cenm54ODg4wMfHB6mpqSgqKsKNGzewf/9+KJVK6HQ6vPHGG7C2toadnd096Z+5ubkICQmBSqWChYUFQkNDjf6JGRkZ6NOnD9RqNSwsLODr64tjx44hISEB4wflnLIAAQAASURBVMePR0FBgWF66bvLHjZsmFE9vby8UFlZCZ1OhzNnzhjGD3R2dsaiRYsQEREBCwsLTJw4EQDw5ZdfwsvLCwqFAs7Ozli+fLlR+c7OznjrrbcQEREBlUoFJycn7NmzB9euXTNsk7e3N44dq/2O8969e43qFxsbC7VajfT0dAwbNgwxMTHo3LnzA/8PGWOMMcYYY4wxxp4kd5LWDhz4O8tWp9PhwIEDRl2Oa/P555+jvLwcL7zwQr3r+e2333D9+nXY29s/dJ3r8lg0EN5x7NgxvP7661i8eDHOnTuH/fv3Y9CgQXBzczM8ZLK6kx43bdoEpVKJI0eOYOnSpYiNjTWMSajT6RASEoIbN27g4MGD2L9/Py5cuICwsDDD+8PDw9G6dWukpKQgNTUVc+fOhYmJCXr27IlVq1bBwsLCML30rFmzjNatVquN6vndd99hwoQJ8Pf3R15eHnbu3GmIXbZsGTp16oTjx49j3rx5SE1NRWhoKEaPHo2TJ08iJiYG8+bNQ3x8vNE6Vq5ciYCAABw/fhyDBw/G2LFjERERgRdeeAFpaWlwdXVFREQEahtW0snJyah+tra2kEgkcHNzg1pNG5eDMcYYY4wxxhhjjAEzZszARx99hE2bNiEzMxOvvPIKiouLDbMaR0REIDo6+p73ffzxxxg+fDiaNzfu1l5UVITZs2fj8OHDuHTpEg4cOICQkBC4ubkhODi40bbjsehifEdubi6USiWGDBkCtVoNJycn+Pj4iH6/t7c3FixYAABwd3fHmjVrcODAAfTv3x8HDhzAyZMncfHiRTg6Vo9zsXnzZnh5eSElJQVdu3ZFbm4uZs+ejXbt2hnKuMPS0hKCIIieYtra2hrm5uaQy+X3vKdv376YOXOm4Xl4eDj69euHefPmAQA8PDxw5swZvPfee4iMjDTEDRo0yNCNef78+Vi3bh26du1qmAp7zpw58Pf3F5XKyhhjjDHGGGOMMfY4EiSPVT7bfYWFheHatWuYP38+/vzzT3Tu3Bnff/89bG1tAVS3dUnu2p6srCz8+uuv2Lfv3rHfpVIpTpw4gU2bNuHWrVtwcHDAM888g0WLFjXaWIjAY9ZA2L9/fzg5OcHFxQUDBgzAgAED8Oyzz8LcXNwkC97exoP229vbIz+/enKJzMxMODo6GhoHAaB9+/awsrJCZmYmunbtihkzZuCll17Cp59+iqCgIIwaNQqurrTB7sXw8/Mzep6ZmYmQEOMZegICArBq1SpUVVUZZqipuX13PmgdO3a8Z1l+fn6DNxCWl5ffM613RXk55I344WSMMcYYY4wxxhh73E2ZMgVTpkyp9bWEhIR7lnl6etba+xMAzMzM8MMPPzRk9UR5rJpk1Wo10tLSsG3bNtjb22P+/Pno1KmT6JmD754JWRAE6HTipxCPiYnB6dOnMXjwYPz0009o3749du3aRdkEUZTKB5vZtOb2Cf8/k1htyyjbLNbixYthaWlp9PhowwcNvh7GGGOMMcYYY4w92QSJ0GSPJ9Vj1UAIADKZDEFBQVi6dClOnDiBS5cu4aeffnrocjUaDa5cuYIrV64Ylp05cwa3bt0ymjXZw8MDr7/+Ovbt24fnnnsOcXFxAKoHnqyqapzpyDUaDZKSkoyWJSUlwcPDw5A92NSio6NRUFBg9Jgw6bWmrhZjjDHGGGOMMcYYe0iPVRfjb775BhcuXEDv3r3RrFkz7N27FzqdDp6eng9ddlBQEDp27Ijw8HCsWrUKlZWVmDx5Mp5++mn4+fmhtLQUs2fPxsiRI9G2bVv89ttvSElJwYgRIwBUzyJcVFSEAwcOoFOnTjA3Nxfd9bk+M2fORNeuXbFo0SKEhYUhOTkZa9aswdq1axuk/IZQ2zTfckVxE9WGMcYYY4wxxhhjjDWUxyqD0MrKCjt37kTfvn2h0Wiwfv16bNu2DV5eXg9dtiAI+Oqrr9CsWTP07t0bQUFBcHFxwY4dOwBUDwJ5/fp1REREwMPDA6GhoRg4cCAWLlwIAOjZsydefvllhIWFwcbGBkuXLn3oOt3RpUsXfPbZZ9i+fTs6dOiA+fPnIzY21miCEsYYY4wxxhhjjLEngSCRNNnjSSXo6xoVkbF6nD6fR4qXCrQu2mZ6WobibViKjv2rTHwsALQyyyfF6xq57d0EFaJjS/W0TFdTSSkpvkxnRoqn7BtzSeNmqWr18kYtX4/GG7/CFLT/01+VLUjxVrIC0bHn2j1DKtvxTCIpvnVpFin+T3Pa5FKN+X2VCZWkeJ2eVheJIH7M2So9bcgKrZ7WyUBGPMZb6m+Q4m9C/GdYJblNKluip9VdAO3SqVJiUn/QnVi9+NgHIQFtnGKFroQUn1PmLDq2jZJ2HVGuMyXFU687KN8RQaB9Bqjfbafrx0jxF6y7k+Ip+8b52hFS2ZdtupHiLXS0Y4GiUvxn8pq8NalslV78uQ8Armgd6w+qoaXiOim+lHCNRT3fmAq06wi9QLumsSi9Jjr2moK2H20qfiPF31Y0J8WbaWnnkCtoKzpWKaMdU8nnG+K5m8L99wOk+JzWfUjxba8dJsWXq2jXtn+ZtxEde0NL+61oIaP9ZjERtKR4d1cnUvw/xW9TRjXZuluv+bzJ1t2UHoum0cDAQEyfPr3Jy2hoJSUlGDFiBCwsLCAIgujJVh6F+Ph4WFlZGZ7HxMSgc+fOTVYfxhhjjDHGGGOMMQCAIDTd4wn1WIxBuHPnzntmIK4pNzfXaCKRu505c6YxqmUkPj4e06dPNzTyJSYmYuDAgXXGFxUVYdOmTUhMTMShQ4fQokULWFrS7kQ8jIEDByIxsfYsnTfffBMODg5Gy2bNmoXXXqNNOmIm0O6AVYKWFVEh0DIFbMtzRceamdPuOP1WakeKd1ZcqT+oBmr2SqHQTHQsNYNCoqdll1jqaHfDK6Ti74ZXEe+CStG42VrUTDPq3UEKnUCrS1ttJin+psyh/qD/R80IvNK+FyneIvNrUjw1K+JWlRUpnkImpX0mG/MzU6ZX1B9UgwkxG6VcR8vIvS7YkOLb3s4QHVuoFv/5BYA/dbR4M2kZKd5aKz4LnZqlUyylXVtQMwJ1Ai3zVCUvFx1LPbe2NvuTFF9QScwAkRaKjtWBtl/kEvH7BQDyWniT4k1AO3bIBfH1+aOlT6PWpURqQYovlaobrS6yKlq8VNI4ExjeYSHcEh179JobqezOLWlZeJU62jXZRWk70bGSKtq1500F7dhRRsw+LpPRese0qbwgOrZYQjsuHc0Tn50IAE+1pF3vLfyspejYgJ7Pk8rujj9I8XktO5HiC6tox442pedEx9qX0doXvi4LJsV3tRf/u5WxR+mxaCC0tra+7+sODg5IT0+/7+uPmp+f333rBAA5OTnQaDTo0KFDnTEVFRWQyxu+m+PGjRtRWlr7j2Rra2vs2bPHaJlKpYJKpWrwejDGGGOMMcYYY4yxx9tj18V47dq1cHd3h6mpKWxtbTFy5EjIZDK4ubnV+ZDJqts5dTod3njjDVhbW8POzg4xMTFG68nNzUVISAhUKhUsLCwQGhqKq1evGl7PyMhAnz59oFarYWFhAV9fXxw7dgwJCQkYP348CgoKIAgCBEHAu+++e986BQYGYvny5fjll18gCAICAwMBVM+GvGjRIkRERMDCwgITJ04EAHz55Zfw8vKCQqGAs7Mzli9fblR3Z2dnvPXWW4iIiIBKpYKTkxP27NmDa9euGbbJ29sbx45Vj1nTqlWrOutWW4MsdzFmjDHGGGOMMcbY40CQCE32eFI9Fg2Edxw7dgxTp05FbGwssrKy8P3336N3796i379p0yYolUocOXIES5cuRWxsLPbv3w+guvEwJCQEN27cwMGDB7F//35cuHABYWFhhveHh4ejdevWSElJQWpqKubOnQsTExP07NkTq1atgoWFBfLy8pCXl4dZs2bdty47d+7EhAkT4O/vj7y8POzcudPw2rJly9CpUyccP34c8+bNQ2pqKkJDQzF69GicPHkSMTExmDdvHuLj443KXLlyJQICAnD8+HEMHjwYY8eORUREBF544QWkpaXB1dUVERER4HlnGGOMMcYYY4wxxphYj0UX4ztyc3OhVCoxZMgQqNVqODk5wcdH/Lgn3t7eWLBgAQDA3d0da9aswYEDB9C/f38cOHAAJ0+exMWLF+HoWD1T1ubNm+Hl5YWUlBR07doVubm5mD17Ntq1a2co4w5LS0sIggA7O3FjXlhbW8Pc3Bxyufye9/Tt2xczZ840PA8PD0e/fv0wb948AICHhwfOnDmD9957D5GRkYa4QYMGYdKkSQCA+fPnY926dejatStGjaqe3WfOnDnw9/fH1atXRdeTMcYYY4wxxhhj7HEiSB6rfLYnwmO1x/v37w8nJye4uLhg7Nix2LJlC0pKxA+m7e1tPKCzvb098vOrBwbPzMyEo6OjoXEQANq3bw8rKytkZlYP5jpjxgy89NJLCAoKwpIlS5CTk9MAW3UvPz8/o+eZmZkICAgwWhYQEIDs7GxUVf09+HHN7bO1tQUAdOzY8Z5ld7a5IZWXl6OwsNDoUV5OG3CbMcYYY4wxxhhjjD1+HqsGQrVajbS0NGzbtg329vaYP38+OnXqZJg5uD53z4QsCAJ0OvGzYsXExOD06dMYPHgwfvrpJ7Rv3x67du2ibIIoSqXygd5Xc/uE/5/lsLZllG0Wa/HixbC0tDR6rF+/vsHXwxhjjDHGGGOMMcYerceqgRAAZDIZgoKCsHTpUpw4cQKXLl3CTz/99NDlajQaXLlyBVeuXDEsO3PmDG7duoX27dsblnl4eOD111/Hvn378NxzzyEuLg4AIJfLjbL5GpJGo0FSUpLRsqSkJHh4eEAqlTbKOqmio6NRUFBg9Hj55ZebulqMMcYYY4wxxhj7h+FJSh69x2oMwm+++QYXLlxA79690axZM+zduxc6nQ6enp4PXXZQUBA6duyI8PBwrFq1CpWVlZg8eTKefvpp+Pn5obS0FLNnz8bIkSPRtm1b/Pbbb0hJScGIESMAVM8iXFRUhAMHDqBTp04wNzeHubn5Q9cLAGbOnImuXbti0aJFCAsLQ3JyMtasWYO1a9c2SPkNQaFQQKFQGC37667njDHGGGOMMcYYY+x/z2OVQWhlZYWdO3eib9++0Gg0WL9+PbZt2wYvL6+HLlsQBHz11Vdo1qwZevfujaCgILi4uGDHjh0AAKlUiuvXryMiIgIeHh4IDQ3FwIEDsXDhQgBAz5498fLLLyMsLAw2NjZYunTpQ9fpji5duuCzzz7D9u3b0aFDB8yfPx+xsbFGE5QwxhhjjDHGGGOMPQkEiaTJHk8qQa/X65u6Eux/0wXiJC6VMKk/qAYBtI+mVflV0bGFpi1IZf9WSpsV2llxpf6gGiR6Wvf1QqGZ6FipQCtbgTJSvEkVLb5CaiY6toqY5CxFJSm+TC++LgCgI95TMRG0pHgKGbFsyxLx3w8AuGnmIDq2VEfbj1fa9yLFe2V+TYrXCbShGW5VWZHiKcylpaR4KRpnKAsAKNbRst5NBNr3qVwnJ8VLBNp4uW1vZ4iOLVSL//wCwJ86WryZlHbcs64SP3mYXqB1aymWWpLiTXXFpHjq9+laVUvRsSWVtM9Ma7M/SfEFlbR9YyEtFB2rA22/SInfJ52eVn4l8XwpF8RPNEetC5UetM889fqQQllZQIrP1TuR4pub3CLFm+jF/5+OXnMjld255W+k+Eo97TNWVCl+zHXq+cBCdpsUX6YzJcXLiN9X60rx11jFctpx6WheW1L8Uy0zSfELPxN/zA7oSfv91N3xD1I85bgEAIVVFqT4NhXnRMcqymjHgq/LgknxXe1zSfHurrRjzT/Fn7NfaLJ127333yZbd1N6LJpGAwMDMX369CYvo6GVlJRgxIgRsLCwgCAIoidbeRTi4+NhZWVleB4TE4POnTs3WX0YY4wxxhhjjDHGWNN4LMYg3Llz5z0zENeUm5trNJHI3c6cOdMY1TISHx+P6dOnGxr5EhMTMXDgwDrji4qKsGnTJiQmJuLQoUNo0aIFLC1pd4wexsCBA5GYmFjra2+++SYcHIyzJWbNmoXXXnuNtA6ZroIUXyLQZm++raVlu9yWqUTHFhTTyjYzoW3rTb01Kd5CIj5rAQDKq8RnXSilJaSyqXfyy2W0fVlOvItLoSVmqVLvWJuClg2mrLhFiqe4bmJPitdJaPuGki3ZujSLVLYFMSPwtGYoKd735HZSvETWeFl7sirasYOarXVbsBIda1v1O6nsEjntznyzqmukeJNKWhZesVJ8loNWQhsj968i2vnJ2YKWhWf1V7bo2Iu2PUllU7NOb4CWAWIp0LIoCivEH+Nbmf9FKluipx2zqee/Cr34z40g0LLYKnW0S25qBpNWRzzGC+KP8dRzpV5Pu46w1F0nxRdKxV9jUeuSVeFOiu+sO0qKz5PRyi8l9HToYEPLsKVe75Gz/ExoWX4UVcQMXupvCrUJ7dhhViL+M3xDZksqu5NtHin+FpqT4qeFiT+HKARa1ptWTzsuaUHLKreS3iLFn9R3Fh3bRZJUf1ANne1o/ydq1veT6kmeLKSpPBafTGvr+5/oHRwckJ6eft/XHzU/P7/71gkAcnJyoNFo0KFDhzpjKioqIJfTDoZibNy4EaWltTdmWFtbY8+ePUbLVCoVVCrxDWyMMcYYY4wxxhhj7J/hsetivHbtWri7u8PU1BS2trYYOXIkZDIZ3Nzc6nzIZNXtnDqdDm+88Qasra1hZ2eHmJgYo/Xk5uYiJCQEKpUKFhYWCA0NxdWrf48ZkZGRgT59+kCtVsPCwgK+vr44duwYEhISMH78eBQUFEAQBAiCgHffffe+dQoMDMTy5cvxyy+/QBAEBAYGAqieDXnRokWIiIiAhYUFJk6cCAD48ssv4eXlBYVCAWdnZyxfvtyo7s7OznjrrbcQEREBlUoFJycn7NmzB9euXTNsk7e3N44dOwYAaNWqVZ11q61BlrsYM8YYY4wxxhhj7HEgSIQmezypHosGwjuOHTuGqVOnIjY2FllZWfj+++/Ru3dv0e/ftGkTlEoljhw5gqVLlyI2Nhb79+8HUN14GBISghs3buDgwYPYv38/Lly4gLCwMMP7w8PD0bp1a6SkpCA1NRVz586FiYkJevbsiVWrVsHCwgJ5eXnIy8vDrFmz7luXnTt3YsKECfD390deXh527txpeG3ZsmXo1KkTjh8/jnnz5iE1NRWhoaEYPXo0Tp48iZiYGMybNw/x8fFGZa5cuRIBAQE4fvw4Bg8ejLFjxyIiIgIvvPAC0tLS4OrqioiICPC8M4wxxhhjjDHGGGNMrMeii/Edubm5UCqVGDJkCNRqNZycnODj4yP6/d7e3liwYAEAwN3dHWvWrMGBAwfQv39/HDhwACdPnsTFixfh6OgIANi8eTO8vLyQkpKCrl27Ijc3F7Nnz0a7du0MZdxhaWkJQRBgZyduNltra2uYm5tDLpff856+ffti5syZhufh4eHo168f5s2bBwDw8PDAmTNn8N577yEyMtIQN2jQIEyaNAkAMH/+fKxbtw5du3bFqFGjAABz5syBv78/rl69KrqejDHGGGOMMcYYY+zJ9lhlEPbv3x9OTk5wcXHB2LFjsWXLFpSUiB8k1tvb2+i5vb098vPzAQCZmf/H3r3HxZT/fwB/nZma6V5KKimhJpVu5FJtZGXltm7rsqLNdUNLSNYuSnxFK7dcl5/KZRffr8vaL+uykSUk2dIqaZEs0bpUUqaamd8ffc02Kp0PJbvez8fjPJiZ13zO55yZOXP6zOd8PlmwsLBQNg4CgL29PQwMDJCVVTUd/KxZszBx4kT4+Phg2bJluHHjRgNsVU1ubm4qt7OysuDp6alyn6enJ3JyciCT/TVwbPXtMzGpGuDW0dGxxn0vtrkhSaVSFBcXqyzScrbB9wkhhBBCCCGEEELqJRA03fKeeqe2XFdXF5cvX8b3338PMzMzLFy4EM7OzsqZg+vz8kzIHMdBLuc/41Z4eDiuXr2K/v374+TJk7C3t8eBAwdYNoEXbW222RJfqL59HMfVeR/LNvMVGRkJfX19lWXDpm8bfD2EEEIIIYQQQggh5O16pxoIAUBNTQ0+Pj6IiorClStXkJubi5MnT75xuXZ2drhz5w7u3LmjvC8zMxOFhYWwt7dX3ieRSDBz5kwcP34cQ4cORWxsLABAJBKp9OZrSHZ2dkhKUp1KPSkpCRKJBEKhsFHWyWrevHkoKipSWaYGTm7qahFCCCGEEEIIIeQf5sUEsU2xvK/eqTEI//vf/+LmzZvo3r07mjVrhiNHjkAul8PW1vaNy/bx8YGjoyP8/PywevVqVFZWYurUqejRowfc3NxQVlaGOXPm4JNPPkGbNm3wxx9/ICUlBcOGDQNQNYtwSUkJEhIS4OzsDC0tLWhpab1xvQBg9uzZ6Ny5MxYvXoyRI0fi/PnzWLduHTZs2NAg5TcEsVgMsViscl+hSNREtSGEEEIIIYQQQgghDeWd6kFoYGCA/fv348MPP4SdnR02bdqE77//Hg4ODm9cNsdx+OGHH9CsWTN0794dPj4+aNu2Lfbs2QMAEAqFePToEfz9/SGRSDBixAj07dsXixYtAgB4eHggMDAQI0eOhLGxMaKiot64Ti907NgRe/fuxe7du9GhQwcsXLgQERERKhOUEEIIIYQQQgghhBDSGDiFQqFo6kqQv6e8nCymfDHXjCn/tIKth6ZYWME7WyRlK1tTnW1CFg0hW15PUMyUL5QZ8M5qC/lP9AMA6mCru4KxC7ZUrsGUb0wCjm28ThGkTHnt8kKmPItH6mZM+ebl95jyT8QmvLPGz3KZyi7W4l82AFy1G8iU75Sxmyn/XO31xoXlQ03O9nmSc2zDSjzlDHhnDSsfMJVdKtJjymtWlDDl1WXPmfIVQv7HDtbXNLvYkilvpcc2GVjr+xd4Z2+ZeDCVLeTYhj95LhfXH6pGX1DElL9Z2op31lzrIVPZYrC9Z8rBtq0yBf/PH8exnT4rFGzflWpcJVP+OeN3K8v7hvW7knVb9eWPmPLFQsNGq8uDMrZzVRf5RaZ8vrYNU75Swf9iL9b3DOuxQ4F355I71ro8luoz5XXV2c6dWz+9wjt7V8++/tAbkKHxhqcSc2znwRUK9fpD1bAea1i/E26XtuSd7ViRVH+oGtbXlfU7xK6dOVP+n+LhwglNtu7mEf/XZOtuSq/dg9Db2xvBwcENWJW/v6bcJ3FxcTAwMGiSdRNCCCGEEEIIIYSQv693agzCuuTl5alMJPKyzMxMWFqy/fr/ps6cOYO+ffuq3FdWVoakpCRs3boVJSX196SIi4tDcHAw71maWfTt2xdnzpyp9bGvvvoKX3311Ruvg7VHYFG5DlPeVIOth8aZvLa8sw8esv1CNdSVrS7FMl2mvFDB9qvvmp38e0uG+LONFWlYns+ULxWx/SqbV8a/51s7jVymsisFjNv69E79oWqE5c+Y8hlTw3lnRdpsh+NWm9nGKH22IoIpL/p6Je/sfa12TGVrcGVMedYegamOo5jyHyz9iCnPQtyKf28qALjmNp4p/7yS/3tec9l8prLLF2xmyotj5jLltRwlTPljrot5Zz8U/cJU9tPnbZjycl2231dL9fn3Wmhewdbbt1jUnCnfQs52jGftgXX9Lv+ebKbWbD1d1OWMPQiFbD0IWY5Ncsbf2NXA/3sbAB7LjJjyrSpvMeVZeomLFWzH7MdytrrLBcZMeTFDb/4SBVtv4mt3NZny3cR/MOUNxWw9s7We8T//zDdg68GkxrG9J4VytnNVlh7xAkXjTAT5wpIotl6qe6az9biX/bSfd1ZrJNvfrKyfJ8vy60z5h5oWvLMiBdsxmPUqI9Zjzf1KtitSMv/g//l2F7F9F1fqOjHldbinTPn3FSd4d3ouvy/+Fg2ELVu2RFpa2isff9vc3Nxq1MnPzw92dnaYP5/tj7DGsHXrVpSV1X6QNTTkf2kGIYQQQgghhBBCCPlne6NJSuRyOUJDQ2FoaAhTU1OEh4crH1u5ciUcHR2hra0NCwsLTJ06VaVX3e3btzFw4EA0a9YM2tracHBwwJEjR2pdj5qaGqytrZWLVCpFcHAwOnbsCFdXV/Ts2RM3btxQ1ikiIgKtWrWCWCyGi4sLjh49qiwrNzcXHMdh79698PLygqamJjp37ozr168jJSUFbm5u0NHRQd++ffHnn38qnxcQEIDBgwdj0aJFMDY2homJCVasWAFLS0tlvTQ1NWFgYABra2sAgFQqRUhICMzNzaGtrY2uXbsiMTERAJCYmIhx48ahqKhIOZX2i/33que9EBcXB0tLS2hpaWHIkCF49Ej1lzFzc3OVffZikcvlMDIywrVr11Tyq1atQrt2bL2ACCGEEEIIIYQQQsjf3xs1EMbHx0NbWxvJycmIiopCREQETpw4UVWwQIC1a9fi6tWriI+Px8mTJxEaGqp87rRp0yCVSvHLL78gIyMDy5cvh45O/Zeg3r17F927d4dYLMbJkyeRmpqK8ePHo7Kyqtv7mjVrEB0djRUrVuDKlSvo06cPPv74Y+Tk5KiUExYWhvnz5+Py5ctQU1PD6NGjERoaijVr1uDMmTP4/fffsXDhQpXnJCQkICsrC4mJifj++++xf/9+5SzHtQkKCsL58+exe/duXLlyBcOHD4evry9ycnLg4eGB1atXQ09PD/n5+cjPz0dISEi9zwOA5ORkTJgwAUFBQUhLS0PPnj2xZMkSHq8YIJFI4Obmhl27dqncv2vXLowePZpXGYQQQgghhBBCCCGNRiBouuU99UaXGDs5OSEsLAwAYGNjg3Xr1iEhIQG9e/dWmazDysoKS5YsQWBgIDZsqBo3Ky8vD8OGDYOjoyMAoG1bfuPHrV+/Hvr6+ti9ezfU1atmRpJI/hrHaMWKFZg7dy5Gjaoag2r58uU4deoUVq9ejfXr1ytzISEh6NOnDwBgxowZ+PTTT5GQkABPT08AwIQJExAXF6eybpFIhG3btkFLSwsODg6IiIjAnDlzsHjxYgheehPl5eUhNjYWeXl5ykugQ0JCcPToUcTGxmLp0qXQ19cHx3EwNTVlet6aNWvg6+urbHCVSCQ4d+6cSk/JV/Hz88O6deuweHHVeE7Xr19Hamoqdu7cyev5hBBCCCGEEEIIIeSf440bCKszMzNDQUHVYLo///wzIiMjce3aNRQXF6OyshLPnz9HaWkptLS0MH36dEyZMgXHjx+Hj48Phg0bVqO82qSlpcHLy0vZOFhdcXEx7t27p2zke8HT0xPp6el11t3EpGqA0xeNlS/ue7EtLzg7O0NLS0t5293dHSUlJbhz5w5at26tks3IyIBMJlNpvASqLh82Mqp7sFk+z8vKysKQIUNUHnd3d+fdQDhq1CiEhITgwoUL6NatG3bt2oWOHTuiffv2dT5HKpVCKlUdFLpcKoVIzDYAOCGEEEIIIYQQQsir0CQlb98b9Z18uZGO4zjI5XLk5uZiwIABcHJywr59+5CamqrsvVdeXg4AmDhxIm7evImxY8ciIyMDbm5uiImJqXedmppss4vxqTv3vxmWXr5PLmeb6ba6kpISCIVCpKamIi0tTblkZWVhzZo1Df48Fqampvjwww/x3XffAQC+++47+Pn5vfI5kZGR0NfXV1m2bq7/9SKEEEIIIYQQQggh77ZGubg6NTUVcrkc0dHR6NatGyQSCe7dqzlVuIWFBQIDA7F//37Mnj0bW7ZsqbdsJycnnDlzBhUVFTUe09PTQ8uWLZGUlKRyf1JSEuzt7V9/g/4nPT1dZWbgCxcuQEdHBxYWNaeHd3V1hUwmQ0FBQY2JQl5cUiwSiSCTyZifZ2dnh+TkZJXnXbhwgWlb/Pz8sGfPHpw/fx43b95UXpJdl3nz5qGoqEhlmfj5F0zrJIQQQgghhBBCCCHvnkZpILS2tkZFRQViYmJw8+ZN7NixA5s2bVLJBAcH49ixY7h16xYuX76MU6dOwc7Ort6yg4KCUFxcjFGjRuHSpUvIycnBjh07kJ2dDQCYM2cOli9fjj179iA7Oxtffvkl0tLSMGPGjDfervLyckyYMAGZmZk4cuQIwsLCEBQUVGP8QaBqXEA/Pz/4+/tj//79uHXrFi5evIjIyEgcPnwYQNXYjCUlJUhISMDDhw9RWlrK63nTp0/H0aNHsWLFCuTk5GDdunW8Ly9+YejQoXj69CmmTJmCnj17Ksc7rItYLIaenp7KQpcXE0IIIYQQQgghpKFxnKDJlvdVo2y5s7MzVq5cieXLl6NDhw7YtWsXIiMjVTIymQzTpk2DnZ0dfH19IZFIlBOYvIqRkRFOnjyJkpIS9OjRA506dcKWLVuUlwdPnz4ds2bNwuzZs+Ho6IijR4/i0KFDsLGxeePt6tWrF2xsbNC9e3eMHDkSH3/8McLDw+vMx8bGwt/fH7Nnz4atrS0GDx6MlJQUWFpaAgA8PDwQGBiIkSNHwtjYGFFRUbye161bN2zZsgVr1qyBs7Mzjh8/jvnz5zNti66uLgYOHIj09PR6Ly8mhBBCCCGEEEIIIf9crz1JSWJiYo37Dh48qPz/zJkzMXPmTJXHx44dq/w/n/EG6+Lk5IRjx47V+phAIEBYWJhyduWXWVlZQaFQqNzn7e1d476AgAAEBATUeP6iRYuwaNGiWst+eZ+oq6u/Mg8AGzduxMaNG5mfN378eIwfP17lvtmzZ9eZr82ePXuwZ88epucQQgghhBBCCCGENCqapOSt+0f0nfT29kZwcHBTV6NW165dQ7du3aChoQEXF5emro7Sy/vMysoKq1evbrL6EEIIIYQQQgghhJCm8do9CBtLYGAgdu7cWetjY8aMqTGW4bsuLCwM2trayM7Oho6OzltZp4ODA27fvl3rY5s3b671kuKUlBRoa2szraewXJcpL+DYZoXWrChhyr/UCfSV5HKGMABN2VOm/J8yQ6Z8qZDtveHqZsI7+6yymKls0XO2vILxd4ayCv6HHbFaKVPZrEe0CpEWU14qZnvPW/dx4J0VCIVMZcsUbJ8nIydrpvyfDGNvyBVs74FCmQFTXqAmqz9UzQdLP2LKn/3qOFOehVFHfaa8dBvb5E+awpoTdtVFwXjcMyy/z5R/8qCIKa9l+pAp396Qf3244kqmsjnGH6ilchFTvlDTlHe25b1LTGU/acn/+wAACoXNmfKmZTeZ8rra/N9nCrDteA5s72HW8ksV/M+DRFw5U9kVjOMZiQT8P9sAoFuQw5QvtGjBlGfBut/VOLbPa4mc/+ukULDVJTevrP5QNZw52/lhqboeU96gMIN3VlPPkqns5wK2834F44FSDv7nNcJG7iVk7WLFlP9Dj+042bbtZd7ZhxzbGO6snyed/Gy28s35v06FYrbvGwHYzlVZCTm274Tbtxk+3+3Y6s4x1oX180TI2/LONRBGREQgJCSk1sf09Ni+VN+ETCYDx3HKCUji4uJeq5wbN26gf//+aN26dQPW7tWOHDlS6yzPAGBiUvuB3djYuDGrRAghhBBCCCGEEMILV8tksKRxNege9/b2xvTp0xEaGgpDQ0OYmpoqJ/HIzc0Fx3FIS0tT5gsLC8FxnHLsvsTERJiYmODGjRsYPnw4HB0dMXnyZOjp6SEnJwc9evSAnp4eRo8ejdJS1Z5FlZWVCAoKgr6+Ppo3b44FCxaojCsolUoREhICc3NzaGtro2vXripjBsbFxcHAwACHDh2Cvb09xGIx8vLyXrm9crkcERERaNWqFcRiMVxcXFRmE+Y4DqmpqYiIiADHca+c0OSFuXPnQiKRQEtLC23btsWCBQtUGvvCw8Ph4uKCzZs3w8LCAlpaWhgxYgSKiv7quREWFoaQkBDs2rUL7u7u6NixI1asWAFLS0vo6tbeA4ouMSaEEEIIIYQQQgh5PzV4D8L4+HjMmjULycnJOH/+PAICAuDp6ck0i3B4eDjWrVunbPwaMWIExGIxvvvuO5SUlGDIkCGIiYnB3LlzVdY7YcIEXLx4EZcuXcLkyZNhaWmJSZMmAQCCgoKQmZmJ3bt3o2XLljhw4AB8fX2RkZGhrFtpaSmWL1+OrVu3wsjICC1avPryizVr1iA6OhqbN2+Gq6srtm3bho8//hhXr16FjY0N8vPz4ePjA19fX4SEhPC6xFhXVxdxcXFo2bIlMjIyMGnSJOjq6iI0NFSZ+f3337F37178+OOPKC4uxoQJEzB16lTs2rVLmUlISICGhgYSExORm5uLcePGwcjICP/61794vw6EEEIIIYQQQgghbxtHk5S8dQ3eQOjk5KScQdjGxgbr1q1DQkICUwPhkiVL4OnpCQCYMGEC5s2bhxs3bqBt27YAgE8++QSnTp1SaSC0sLDAqlWrwHEcbG1tkZGRgVWrVmHSpEnIy8tDbGws8vLy0LJlSwBASEgIjh49itjYWCxduhQAUFFRgQ0bNsDZ2ZlXPVesWIG5c+di1KhRAIDly5fj1KlTWL16NdavXw9TU1OoqalBR0cHpqb8xh+aP3++8v9WVlYICQnB7t27VRoInz9/ju3bt8Pc3BxA1YzQ/fv3R3R0tHI9IpEI27Ztg5aWFhwcHBAREYE5c+Zg8eLFysumWUilUkilUpX7yssrIRKxjaNBCCGEEEIIIYQQQt4tDX5Rt5OTk8ptMzMzFBQUvHYZJiYmysttq9/3cpndunUDV22wT3d3d+Tk5EAmkyEjIwMymQwSiQQ6OjrK5fTp07hx44byOSKRqEb961JcXIx79+4pGzJf8PT0RFZWFtP2Vrdnzx54enrC1NQUOjo6mD9/fo1LnS0tLZWNgy+2VS6XIzv7r0FpnZ2doaWlpZIpKSnBnTt3XqtekZGR0NfXV1l2blnxWmURQgghhBBCCCGEkHdHg/cgVFdXV7nNcRzkcrmy11r1cQHrmkijehkcx9VZJl8lJSUQCoVITU2F8KWZQqtf9qupqanSyPi2nT9/Hn5+fli0aBH69OkDfX197N69G9HR0U1WpxfmzZuHWbNmqdx36SbbrHOEEEIIIYQQQggh9eJokpK37a3NYvxiltz8/Hy4uroCgMqEJW8qOTlZ5faFCxdgY2MDoVAIV1dXyGQyFBQUwMvLq0HWp6enh5YtWyIpKQk9evRQ3p+UlIQuXbq8Vpnnzp1D69at8fXXXyvvu337do1cXl4e7t27p7xc+sKFCxAIBLC1tVVm0tPTUVZWBk1NTWVGR0cHFhYWr1U3sVgMsVj1cmKR6NlrlUUIIYQQQgghhBBC3h1vrYFQU1MT3bp1w7Jly9CmTRsUFBSojLf3pvLy8jBr1ix8/vnnuHz5MmJiYpQ97yQSCfz8/ODv74/o6Gi4urrizz//REJCApycnNC/f//XWuecOXMQFhaGdu3awcXFBbGxsUhLS1OZLISFjY0N8vLysHv3bnTu3BmHDx/GgQMHauQ0NDTw2WefYcWKFSguLsb06dMxYsQIlXEOy8vLMWHCBMyfPx+5ubkICwtDUFDQa40/SAghhBBCCCGEEPK20CQlb99bayAEgG3btmHChAno1KkTbG1tERUVhY8++qhByvb390dZWRm6dOkCoVCIGTNmYPLkycrHY2NjsWTJEsyePRt3795F8+bN0a1bNwwYMOC11zl9+nQUFRVh9uzZKCgogL29PQ4dOsQ0IUt1H3/8MWbOnImgoCBIpVL0798fCxYsQHh4uErO2toaQ4cORb9+/fD48WMMGDAAGzZsUMn06tULNjY26N69O6RSKT799NMa5RBCCCGEEEIIIYQQ0qANhImJiTXuO3jwoPL/dnZ2OHfunMrj1cck9Pb2VrkNAAEBAQgICFC5Lzw8vEZjl7q6OlavXo2NGzfWWjd1dXUsWrQIixYtqvXx2tZTH4FAgLCwMOWszbXZvXs3AgICsGzZMrRv377ey6qjoqIQFRWlcl9wcHCN3JQpUzBlypRXlvWq7SWEEEIIIYQQQgghBHjLPQjfR2FhYdDW1kZ2drbKhCjvmpSUFGhrazd1NQghhBBCCCGEEPK+o+HR3jra43WQyWTQ1taGjo5OrcuZM2d4lXPjxg188MEHaN26NTZv3lxneX379m3kLXo1Y2NjaGlpNWkdCCGEEEIIIYQQQsjb16ANhN7e3pg+fTpCQ0NhaGgIU1NT5aXAubm54DhO5RLbwsJCcBynvDQ5MTERHMfh2LFjcHV1haamJj788EMUFBTgp59+gp2dHfT09DB69GiUlpaqrLuyshJBQUHQ19dH8+bNsWDBApXLlaVSKUJCQmBubg5tbW107dpV5ZLouLg4GBgY4NChQ7C3t4dYLMbhw4eRlpZW6+Lm5ga5XI6IiAi0atUKYrEYLi4uOHr0qLJMjuOQmpqKiIgIcByHoqKiOsvbunUrAGDu3LmQSCTQ0tJC27ZtsWDBAlRUVNTY15s3b4aFhQW0tLQwYsQIFBUV1cgsWrQIxsbG0NPTQ2BgIMrLy+t87aysrLB69eo6HyeEEEIIIYQQQgh5GziOa7LlfdXglxjHx8dj1qxZSE5Oxvnz5xEQEABPT0+miTvCw8Oxbt06ZePXiBEjIBaL8d1336GkpARDhgxBTEwM5s6dq7LeCRMm4OLFi7h06RImT54MS0tLTJo0CQAQFBSEzMxM7N69Gy1btsSBAwfg6+uLjIwMZd1KS0uxfPlybN26FUZGRsoGuLqsWrUK0dHR2Lx5M1xdXbFt2zZ8/PHHuHr1KmxsbJCfnw8fHx/4+voiJCRE2VvwVXR1dREXF4eWLVsiIyMDkyZNgq6uLkJDQ5WZ33//HXv37sWPP/6I4uJiTJgwAVOnTlWZPTkhIQEaGhpITExEbm4uxo0bByMjI/zrX//i/ToQQgghhBBCCCGEkH++Br/E2MnJCWFhYbCxsYG/vz/c3NyQkJDAVMaSJUvg6ekJV1dXTJgwAadPn8bGjRvh6uoKLy8vfPLJJzh16pTKcywsLLBq1SrY2trCz88PX3zxBVatWgUAyMvLQ2xsLP7973/Dy8sL7dq1Q0hICD744APExsYqy6ioqMCGDRvg4eEBW1vbei+5XbFiBebOnYtRo0bB1tYWy5cvh4uLi7InnqmpKdTU1KCjowNTU1NeYxDOnz8fHh4esLKywsCBAxESEoK9e/eqZJ4/f47t27fDxcUF3bt3R0xMDHbv3o379+8rMyKRCNu2bYODgwP69++PiIgIrF27FnK5vN46EEIIIYQQQgghhJD3R4P3IHRyclK5bWZmhoKCgtcuw8TERHm5bfX7Ll68qPKcbt26qXQFdXd3R3R0NGQyGTIyMiCTySCRSFSeI5VKYWRkpLwtEolq1L8uxcXFuHfvHjw9PVXu9/T0RHp6Oq8yarNnzx6sXbsWN27cQElJCSorK6Gnp6eSsbS0hLm5ufK2u7s75HI5srOzYWpqCgBwdnZWaeB0d3dHSUkJ7ty5g9atWzPXSyqVQiqVqtxXXl4JkUjMXBYhhBBCCCGEEEJInWiSkreuwRsI1dXVVW5zHAe5XA7B/17c6uMC1ja23stlcBxXZ5l8lZSUQCgUIjU1FUKhUOWx6r36NDU1m/R68/Pnz8PPzw+LFi1Cnz59oK+vj927dyM6OrrJ6vRCZGQkFi1apHLfuKnzMH7a101UI0IIIYQQQgghhBDSEBq8gbAuxsbGAID8/Hy4uroCgMqEJW8qOTlZ5faFCxdgY2MDoVAIV1dXyGQyFBQUwMvLq0HWp6enh5YtWyIpKQk9evRQ3p+UlIQuXbq8Vpnnzp1D69at8fXXfzW63b59u0YuLy8P9+7dQ8uWLQFUbatAIICtra0yk56ejrKyMmhqaiozOjo6sLCweK26zZs3D7NmzVK579LNytcqixBCCCGEEEIIIaQunOD9nSykqby1BkJNTU1069YNy5YtQ5s2bVBQUID58+c3WPl5eXmYNWsWPv/8c1y+fBkxMTHKnncSiQR+fn7w9/dHdHQ0XF1d8eeffyIhIQFOTk7o37//a61zzpw5CAsLQ7t27eDi4oLY2FikpaWpTBbCwsbGBnl5edi9ezc6d+6Mw4cP48CBAzVyGhoa+Oyzz7BixQoUFxdj+vTpGDFihPLyYgAoLy/HhAkTMH/+fOTm5iIsLAxBQUHKnpysxGIxxGLVy4lFomevVRYhhBBCCCGEEEIIeXe8tQZCANi2bRsmTJiATp06wdbWFlFRUfjoo48apGx/f3+UlZWhS5cuEAqFmDFjBiZPnqx8PDY2FkuWLMHs2bNx9+5dNG/eHN26dcOAAQNee53Tp09HUVERZs+ejYKCAtjb2+PQoUNMMzZX9/HHH2PmzJkICgqCVCpF//79sWDBAoSHh6vkrK2tMXToUPTr1w+PHz/GgAEDsGHDBpVMr169YGNjg+7du0MqleLTTz+tUQ4hhBBCCCGEEEIIIQ3aQJiYmFjjvoMHDyr/b2dnh3Pnzqk8Xn1MQm9vb5XbABAQEICAgACV+8LDw1Uau6qvd+PGjbXWTV1dHYsWLaoxjt6r1lMfgUCAsLAwhIWF1ZlhvYw6KioKUVFRKvcFBwfXyE2ZMgVTpkx5ZVmv2t6XX6vc3FyWahJCCCGEEEIIIYQ0Do4mKXnb/hF73Nvbu9ZGtHfBtWvX0K1bN2hoaMDFxaWpq6P08j6zsrLC6tWrm6w+hBBCCCGEEEIIIaRpvNVLjP9uqs9w/LKffvqJ14QnYWFh0NbWRnZ2NrZs2VJnmV5eXvjpp59eu65vKiUlBdra2kzPEQkad5ISTsF/pmoAaK7Hvz6PnrC1jZcK9ZjyYtQ+Q3dDMTLgP2CrkFPUH6qGA1uelZqQf/kChYytbDnbfpeqaTHlWRm2a80//NIM6/UpFKjXH6pGvYUxU55T8H+d1Di2Y4GakDEvK2fKi1u1YsobddRnyrN4dLmIKW8kZHsPCzj+x8kWndozla0ofciU5yQtmfJadmz1MXuWwzur/pSt7s8Zf6GWKdg+r2J5Ge+sXKTBVLZcwVb3SgXbqZ9AxvZ5VSj4fz8JwPY9L6rkvx8BQCE0ZMqXy/kfV0VCtuOSEGz7UcSxDcwu06z7nLU2LO8DTQVb3YUc23c363uyrFJcf+h/tNSeM5WtpcX4p5GQLc96jlWpbcA7KxWwndOw7nc1xrkC5Cz9UFjrwnie3bIl2984JZWM58L/mxSSD9ZjAcv3PADItXSZ8kVitvNDFpWMTQ0aisYd315bh6E+crbjGOv3mYDx79z3Fk1S8tZRA2EdZDIZLl++XOekHubm5rzKuXHjBvr374/WrVtj1qxZdV7GrMnzi+Xly6trExcXx6us6l7MMk0IIYQQQgghhBBC3i8Neomxt7c3pk+fjtDQUBgaGsLU1FTZmJWbmwuO41TG5CssLATHccrx8BITE8FxHI4dOwZXV1doamriww8/REFBAX766SfY2dlBT08Po0ePRmlpqcq6KysrERQUBH19fTRv3hwLFixQGc9QKpUiJCQE5ubm0NbWRteuXVXG4YuLi4OBgQEOHToEe3t7iMViiEQiWFtb17poampCLpcjIiICrVq1glgshouLC44ePaosk+M4pKamIiIiAhzHYe3atXWW96LBce7cuZBIJNDS0kLbtm2xYMECVFT89StZeHg4XFxcsHnzZlhYWEBLSwsjRoxAUdFfvVQCAgIwePBgLFq0CMbGxtDT00NgYCDKy+v+tZsuMSaEEEIIIYQQQgh5PzV4D8L4+HjMmjULycnJOH/+PAICAuDp6ck0s294eDjWrVunbPwaMWIExGIxvvvuO5SUlGDIkCGIiYnB3LlzVdY7YcIEXLx4EZcuXcLkyZNhaWmJSZMmAQCCgoKQmZmJ3bt3o2XLljhw4AB8fX2RkZGhrFtpaSmWL1+OrVu3wsjICC1atHhlPdesWYPo6Ghs3rwZrq6u2LZtGz7++GNcvXoVNjY2yM/Ph4+PD3x9fRESEvLKS5Zf0NXVRVxcHFq2bImMjAxMmjQJurq6CA0NVWZ+//137N27Fz/++COKi4sxYcIETJ06Fbt27VJmEhISoKGhgcTEROTm5mLcuHEwMjLCv/71L96vAyGEEEIIIYQQQsjbxtEkJW9dg+9xJycnhIWFwcbGBv7+/nBzc0NCQgJTGUuWLIGnpydcXV0xYcIEnD59Ghs3boSrqyu8vLzwySef4NSpUyrPsbCwwKpVq2Braws/Pz988cUXWLVqFQAgLy8PsbGx+Pe//w0vLy+0a9cOISEh+OCDDxAbG6sso6KiAhs2bICHhwdsbW2hpfXqsTxWrFiBuXPnYtSoUbC1tcXy5cvh4uKi7IlnamoKNTU16OjowNTUlFcD4fz58+Hh4QErKysMHDgQISEh2Lt3r0rm+fPn2L59O1xcXNC9e3fExMRg9+7duH//vjIjEomwbds2ODg4oH///oiIiMDatWshl9N4B4QQQgghhBBCCCHkLw3eg9DJyUnltpmZGQoKCl67DBMTE+XlttXvu3jxospzunXrBq7agM7u7u6Ijo6GTCZDRkYGZDIZJBKJynOkUimMjIyUt0UiUY3616W4uBj37t2Dp6enyv2enp5IT0/nVUZt9uzZg7Vr1+LGjRsoKSlBZWUl9PRUJ8iwtLRUGQPR3d0dcrkc2dnZMDU1BQA4OzurNHC6u7ujpKQEd+7cQevWDBMn/I9UKoVUKlW5r7xcCpGI/0DRhBBCCCGEEEIIIfWiSUreugZvIFRXV50BjuM4yOVy5WQf1ccFrD62Xl1lcBxXZ5l8lZSUQCgUIjU1FcKXZgqt3qtPU1NTpZHxbTt//jz8/PywaNEi9OnTB/r6+ti9ezeio6ObrE4vREZGYtGiRSr3TZw2F5OC5jVRjQghhBBCCCGEEEJIQ3hrF3W/mCU3Pz9feV/1CUveVHJyssrtCxcuwMbGBkKhEK6urpDJZCgoKKgxOciLHnes9PT00LJlSyQlJancn5SUBHt7+9cq89y5c2jdujW+/vpruLm5wcbGBrdv366Ry8vLw71795S3L1y4AIFAAFtbW+V96enpKCsrU8no6OjAwsLiteo2b948FBUVqSyfTZ71WmURQgghhBBCCCGE/FOsX78eVlZW0NDQQNeuXWtc9VpdXFwcOI5TWTQ0NFQyCoUCCxcuhJmZGTQ1NeHj44OcnJxG3YYG70FYF01NTXTr1g3Lli1DmzZtUFBQgPnz5zdY+Xl5eZg1axY+//xzXL58GTExMcqedxKJBH5+fvD390d0dDRcXV3x559/IiEhAU5OTujfv/9rrXPOnDkICwtDu3bt4OLigtjYWKSlpalMFsLCxsYGeXl52L17Nzp37ozDhw/jwIEDNXIaGhr47LPPsGLFChQXF2P69OkYMWKESmNneXk5JkyYgPnz5yM3NxdhYWEICgpS9uRkJRaLIRarXk4sEinqSBNCCCGEEEIIIYS8Hu412y6awp49ezBr1ixs2rQJXbt2xerVq9GnTx9kZ2fXOfmtnp4esrOzlbdfvpo1KioKa9euRXx8PNq0aYMFCxagT58+yMzMrNGY2FDeWgMhAGzbtg0TJkxAp06dYGtri6ioKHz00UcNUra/vz/KysrQpUsXCIVCzJgxA5MnT1Y+HhsbiyVLlmD27Nm4e/cumjdvjm7dumHAgAGvvc7p06ejqKgIs2fPRkFBAezt7XHo0CGmGZur+/jjjzFz5kwEBQVBKpWif//+WLBgAcLDw1Vy1tbWGDp0KPr164fHjx9jwIAB2LBhg0qmV69esLGxQffu3SGVSvHpp5/WKIcQQgghhBBCCCGEvL6VK1di0qRJGDduHABg06ZNOHz4MLZt24Yvv/yy1udwHFfnFa0KhQKrV6/G/PnzMWjQIADA9u3bYWJigoMHD2LUqFGNsh0N2kCYmJhY476DBw8q/29nZ4dz586pPF59TEJvb2+V2wAQEBCAgIAAlfvCw8NVGruqr3fjxo211k1dXR2LFi2qMY7eq9ZTH4FAgLCwMISFhdWZYb2MOioqClFRUSr3BQcH18hNmTIFU6ZMeWVZr9rel1+r3NxclmoSQgghhBBCCCGENI4mnB+itklaa7uqEqi6ejM1NRXz5v01P4NAIICPjw/Onz9f5zpKSkrQunVryOVydOzYEUuXLoWDgwMA4NatW7h//z58fHyUeX19fXTt2hXnz59vtAbCBuuz6e3tXWtDVkOysrLC6tWrm7QO9UlMTATHcSgsLGzSehBCCCGEEEIIIYQQNpGRkdDX11dZIiMja80+fPgQMpkMJiYmKvebmJjg/v37tT7H1tYW27Ztww8//ICdO3dCLpfDw8MDf/zxBwAon8dSZkN4q5cYN7b9+/fXmPH4TVSf4fhlP/30E7y8vJjKW7p0KZYuXVrrY15eXvjpp5+YyqtPXFwcgoODG62xssO5lUz5xx8MZ8o/EdR+rX5dOulk8s62dTJmKvuZXIspryN8xpTXrXzClPc1+oN3ViET1h+qpkjXnCkvUMiY8l2kZ3lnH4hf73J9vkye3mDKV4jY3gdZH8zgneXANqangOM/kzsA3OsUyJTXVDznnWWtuzpX+wz2dZFzbO/ha27jmfLSbV8w5VkYCdm29ZFjF6a8Q9aPvLO/fVR3b/fa6KmzHccejf6AKa+jzv89BgDG3APe2ce6bBNydZA9ZKuLgu3ETCrkf+zIbubJVLYWV1Z/qBptQQlT/oFuO6a8o/afvLMGjPu9WKM5U57x0AQNgbT+0P9UKNjONxWMvSAUYMvnGLoz5TU4/tv6jNNjKruZ4jFTXsppMuWNRfzrrl1ZxFT2p+5sn6c8sA2VxPq6ZjXrzjurpWCru5hjOwbLwfZdLFfw74fC+vmQM/ZxGeuSxZQvFhgy5W92+IR3Vgi282YT+b36Q9Vcbd6LKa+h4P95kjG8pgAgFpQz5Z8J2I41JsivP1SNnzv/MdvuwKf+UDVisH2eyhRsf1OQt2/evHmYNUt1Utbaeg++Lnd3d7i7//Xd7eHhATs7O2zevBmLFy9usPWw+kc1EBoash3M6/Oqy4PNzdkaUQAgMDAQI0aMqPUxTU1+J0cvX15dm7i4OJV/CSGEEEIIIYQQQv42mnCSkrouJ65N8+bNIRQK8eCB6o/ZDx48qHOMwZepq6vD1dUVv//+OwAon/fgwQOYmZmplOni4sKrzNfRoHu8srISQUFB0NfXR/PmzbFgwQLlmIJPnjyBv78/mjVrBi0tLfTt27fGFM379u2Dg4MDxGIxrKyslLMQ12Xr1q0wMDBAQkICgJqXGFtZWWHp0qUYP348dHV1YWlpiW+//ValjHPnzsHFxQUaGhpwc3PDwYMHwXEc0tLSYG1tXefyokHvyJEjkEgk0NTURM+ePWuM5ffo0SN8+umnMDc3R6tWrTBkyBCkpKQoyzl37hy6du2K5s1VfxUfPHgwxo4dCwBIT09Hz549oaurCz09PXTq1AmXLl165b5JTEzEuHHjUFRUpJw2+0XDIp/XghBCCCGEEEIIIYTUTSQSoVOnTsp2KQCQy+VISEhQ6SX4KjKZDBkZGcrGwDZt2sDU1FSlzOLiYiQnJ/Mu83U0aANhfHw81NTUcPHiRaxZswYrV67E1q1bAVRNAnLp0iUcOnQI58+fh0KhQL9+/VBRUXX5VWpqKkaMGIFRo0YhIyMD4eHhWLBgQZ294KKiovDll1/i+PHj6NWr7q7U0dHRcHNzw6+//oqpU6diypQpyqmki4uLMXDgQDg6OuLy5ctYvHgx5s6dy3t779y5g6FDh2LgwIFIS0vDxIkTa8xQ8/z5c3Tq1AmHDx/Gb7/9hsmTJ2Ps2LG4ePEiAGD48OGQyWQ4dOiQ8jkFBQU4fPgwxo+vukzOz88PrVq1QkpKClJTU/Hll1/Weym1h4cHVq9eDT09PeTn5yM/Px8hISEA6n8tCCGEEEIIIYQQQpoMxzXdwmjWrFnYsmUL4uPjkZWVhSlTpuDZs2fKWY39/f1VJjGJiIjA8ePHcfPmTVy+fBljxozB7du3MXHixP9tOofg4GAsWbIEhw4dQkZGBvz9/dGyZUsMHjy4QXZvbRr0EmMLCwusWrUKHMfB1tYWGRkZWLVqFby9vXHo0CEkJSXBw8MDALBr1y5YWFjg4MGDGD58OFauXIlevXphwYIFAACJRILMzEx88803NWYXnjt3Lnbs2IHTp08rZ3mpS79+/TB16lTl81atWoVTp07B1tYW3333HTiOw5YtW6ChoQF7e3vcvXsXkyZN4rW9GzduRLt27ZQ9HV9s8/Lly5UZc3NzZcMcAHzxxRc4duwY9u7diy5dukBTUxOjR49GbGwshg+vGqNv586dsLS0hLe3NwAgLy8Pc+bMQfv27QEANjb1j8smEomgr69fY+rsnJycel8LQgghhBBCCCGEEFK/kSNH4s8//8TChQtx//59uLi44OjRo8pJRvLy8iCodsn0kydPMGnSJNy/fx/NmjVDp06dcO7cOdjb2yszoaGhePbsGSZPnozCwkJ88MEHOHr0KDQ0+I+nyapBGwi7desGrlprq7u7O6Kjo5GZmQk1NTV07dpV+ZiRkRFsbW2RlVU1aGxWVhYGDRqkUp6npydWr14NmUwGobBqYNzo6Gg8e/YMly5dQtu2beutk5OTk/L/LxrLCgoKAADZ2dlwcnJS2cFduvAfID4rK0tlm15sc3UymQxLly7F3r17cffuXZSXl0MqlUJL66+BSSdNmoTOnTvj7t27MDc3R1xcHAICApT7ctasWZg4cSJ27NgBHx8fDB8+HO3asQ0aXr3O9b0Wtaltmm9ZRSXE6v+oYSwJIYQQQgghhBBCmAQFBSEoKKjWxxITE1Vur1q1CqtWrXpleRzHISIiAhEREQ1VxXo13aiPr8nLywsymQx79+7llX/5UlyO4yCXs83++Sa++eYbrFmzBnPnzsWpU6eQlpaGPn36oLz8r1mdXF1d4ezsjO3btyM1NRVXr15V6TUZHh6Oq1evon///jh58iTs7e1x4MCBt7YNQO3TfK84wn82WkIIIYQQQgghhBA+OIGgyZb3VYNueXJyssrtCxcuwMbGBvb29qisrFR5/NGjR8jOzlZ2obSzs0NSUpLK85OSkiCRSJS9B4GqHn4//fQTli5dihUrVrxRfV9cEly9Z1xKSgrv59vZ2SnHEnzhwoULKreTkpIwaNAgjBkzBs7Ozmjbti2uX79eo6yJEyciLi4OsbGx8PHxgYWFhcrjEokEM2fOxPHjxzF06FDExsbWWz+RSASZTFajzvW9FrWZN28eioqKVJaQfh/UWwdCCCGEEEIIIYQQ8m5r0AbCvLw8zJo1C9nZ2fj+++8RExODGTNmwMbGBoMGDcKkSZNw9uxZpKenY8yYMTA3N1deVjx79mwkJCRg8eLFuH79OuLj47Fu3TqV8fte8PDwwJEjR7Bo0SKsXr36tes7evRoyOVyTJ48GVlZWTh27Jiy0ZHjMTBlYGAgcnJyMGfOHGRnZ+O7776rMamKjY0NTpw4gXPnziErKwuff/55jemvX9Tljz/+wJYtW5STkwBAWVkZgoKCkJiYiNu3byMpKQkpKSmws7Ort35WVlYoKSlBQkICHj58iNLSUl6vRW3EYjH09PRUFrq8mBBCCCGEEEIIIQ2OEzTd8p5q0C339/dHWVkZunTpgmnTpmHGjBmYPHkyACA2NhadOnXCgAED4O7uDoVCgSNHjigvAe7YsSP27t2L3bt3o0OHDli4cCEiIiJqTFDywgcffIDDhw9j/vz5iImJea366unp4ccff0RaWhpcXFzw9ddfY+HChQDAa+BHS0tL7Nu3DwcPHoSzszM2bdqEpUuXqmTmz5+Pjh07ok+fPvD29oapqWmts87o6+tj2LBh0NHRUXlcKBTi0aNH8Pf3h0QiwYgRI9C3b18sWrSo3vp5eHggMDAQI0eOhLGxMaKiogDU/1oQQgghhBBCCCGEkPcHp1AoFE1diXfJrl27MG7cOBQVFUFTU/OtrrtXr15wcHDA2rVr3+p6X1fptjCm/OMP2GZILhYYMuUNZA95Z58IjZnKLlewNZ5qCcqY8rqVT5jy6pX8y1dwwvpD1UjVteoPVSNQyOoPVaP97E/e2Qd69c/Y/SZMnt5gyleI2PZNnpo17ywHtkOxgGMbS7VSztbjV1P4nHeWte7qXAVTXiwvZco/RnOmfFG5NlOehVjItq2PHPlPlAUADlk/8s7mV5jWH6pGT/0ZU/6RVI8pr6PO/z0GAMZczd73dSkXss3uVixjq7ux4j5TXqrG/9hRWGnAVLaWkO37RsRJ6w9VU8n4/Vcm57/vW8jz2cpW12HKlyvETHmFov6rR5RZ8M8CgBpXyVYXxvJZ9jsAaAj4vw9Yj/GaCrZjh5RjO99mqY92ZRFT2SVqBkx5Vqyva3GlLu8s67FAzLEdg+VgO5+sVPA/7+A4tvcY63tSR1bIlGf9G4SFkGM7b2b9G+EuLJnyGkL+xwKZgq1vkVhQXn+oGtbXVUv+lClfLuB/nFQw9qPiwHZezvr9ZG/dkin/T1G2c2n9oUaiOearJlt3U2J653t7eyM4OLiRqlLFysrqlZcNN3Qdtm/fjrNnz+LWrVs4ePAg5s6dixEjRrzVxsEnT57gwIEDSExMxLRp05T3JyYmguM4FBYWvrW6EEIIIYQQQgghhDQpAdd0y3vqbzeI3P79+xv0Utj79+9j4cKFuH//PszMzDB8+HD861//AlA1xuDOnTtrfd6YMWOwadOmBqmDq6srnjx5guXLl8PW1pb38/r27YszZ87U+thXX32Fr75q3Fbvgu5+THkuKpQpL5i7jinPwkDOv7chADwVNmPKCxh/RSpV4/8LMQAIGXqjPJax/Qoq5th+7WPtyfZIx4R3VsjYO5G1Lvm6jdtDsbyc/yGWtVdBcxHbL8p/VrC9D/TVinlnZYy9Clg95QyY8s8rRUx5TcZefixY35MsPQIB4KrdQN5Zwyv8J+F6HWoCtm19LmN7nTgh//KLKvWZyjZV3GXKN2ZPNk0BW68e1h4XUsaeZqw9ewzB//uVtbeWTMF2rFEDW6+9cvB/T7J+tlmP8aznEeqMPRRZevnJBGx/LpSC7fPB2quVpacn6/mVpryEKf+ngv85DQBoC9l6xDdmb34Z45+BrOWzHDuYr6Jg/HyIKtmOq5WM46yXy/n/baqjxtbD9rka21UOYjnbeTxb72a2/aIBtl6tcsZx3yoFbOcRGjL++74AZkxlN3ZvfkLelr9dA6GhYcN2+Q4NDUVoaO0NVxEREbVOkgJUjV/YUHJzc1/reVu3bkVZWe0Ho1ftp/LycohEbAdUQgghhBBCCCGEkLeBe48nC2kqzHu8srISQUFB0NfXR/PmzbFgwQK8GMbwyZMn8Pf3R7NmzaClpYW+ffsiJydH5fn79u2Dg4MDxGIxrKysEB0d/cr1bd26FQYGBkhISABQ8xJjKysrLF26FOPHj4euri4sLS3x7bffqpRx7tw5uLi4QENDA25ubjh48CA4jkNaWtor192iRQv88ccfsLGxwe3btzFq1Cg4OTnB398fT5781XsnICCgxsQjwcHB8Pb2Vt729vbGF198geDgYDRr1gwmJibYsmULnj17hnHjxkFXVxfW1tb46aefatQjKSkJTk5O0NDQQLdu3fDbb78BAMzNzWFtbY379+9j3LhxcHR0RM+ePbF27VqIxX/1VrCyssLixYvh7+8PPT095cQxhBBCCCGEEEIIIYQwNxDGx8dDTU0NFy9exJo1a7By5Ups3boVQFVD2aVLl3Do0CGcP38eCoUC/fr1Q0VF1eVbqampGDFiBEaNGoWMjAyEh4djwYIFiIuLq3VdUVFR+PLLL3H8+HH06tWrzjpFR0fDzc0Nv/76K6ZOnYopU6YgOzsbAFBcXIyBAwfC0dERly9fxuLFizF37lzWzcbXX3+N6OhoXLp0CWpqahg/fjxzGfHx8WjevDkuXryIL774AlOmTMHw4cPh4eGBy5cv46OPPsLYsWNRWqp6+cGcOXMQHR2NlJQUGBsbY+DAgcp9euPGDfj6+mLYsGG4cuUK9uzZg7NnzyIoKEiljBUrVsDZ2Rm//vorFixYwFx3QgghhBBCCCGEEPLPxHyJsYWFBVatWgWO42Bra4uMjAysWrUK3t7eOHToEJKSkuDh4QGgakZgCwsLHDx4EMOHD8fKlSvRq1cvZQOVRCJBZmYmvvnmGwQEBKisZ+7cudixYwdOnz4NBweHV9apX79+mDp1qvJ5q1atwqlTp2Bra4vvvvsOHMdhy5Yt0NDQgL29Pe7evYtJkyYxbfe//vUv9OjRAwDw5Zdfon///nj+/Dk0NPiP5ePs7Iz58+cDAObNm4dly5ahefPmyrosXLgQGzduxJUrV9CtWzfl88LCwtC7d28AVY2MrVq1woEDBzBixAhERkbCz89P2avSxsYGa9euRY8ePbBx40Zl/T788EPMnj2baZsJIYQQQgghhBBC3rr3eLKQpsLcg7Bbt27guL9eKHd3d+Tk5CAzMxNqamro2rWr8jEjIyPY2toiKysLAJCVlQVPT0+V8jw9PZGTkwOZ7K/JCKKjo7FlyxacPXu23sZBAHByclL+n+M4mJqaoqCgAACQnZ2tvDz3hS5dujButeo6zMyqBi19sY7XKUMoFMLIyAiOjo7K+0xMTGot193dXfl/Q0NDlX2anp6OuLg46OjoKJc+ffpALpfj1q1byue5ubkx1fVlUqkUxcXFKotUyjYILiGEEEIIIYQQQgh597yToz56eXlBJpNh7969vPIvz2rMcRzkcrbZrVjW8aKB9MU6BAKBchzGF15cAlxfPV9VLh8lJSX4/PPPkZaWplzS09ORk5ODdu3aKXPa2mwzYL0sMjIS+vr6KsvGzZvfqExCCCGEEEIIIYSQGjhB0y3vKeZLjJOTk1VuX7hwATY2NrC3t0dlZSWSk5OVlxg/evQI2dnZsLe3BwDY2dkhKSlJ5flJSUmQSCQQCoXK+7p06YKgoCD4+vpCTU2tzpmE+bC1tcXOnTshlUqVE3ekpKS8dnm1MTY2Vk4c8kJaWlqNBsHXdeHCBVhaWgKomgjm+vXrsLOzAwB07NgRmZmZsLa2bpB11WXevHmYNWuWyn35d/IadZ2EEEIIIYQQQgghpPExN43m5eVh1qxZyM7Oxvfff4+YmBjMmDEDNjY2GDRoECZNmoSzZ88iPT0dY8aMgbm5OQYNGgQAmD17NhISErB48WJcv34d8fHxWLduXa0NgB4eHjhy5AgWLVqE1atXv/YGjh49GnK5HJMnT0ZWVhaOHTuGFStWAIDKpdJv4sMPP8SlS5ewfft25OTkICwsrEaD4ZuIiIhAQkICfvvtNwQEBKB58+bKWZPnzp2Lc+fOISgoCGlpacjJycEPP/xQY5KSNyUWi6Gnp6eyiMWiBl0HIYQQQgghhBBCCHn7mBsI/f39UVZWhi5dumDatGmYMWMGJk+eDACIjY1Fp06dMGDAALi7u0OhUODIkSPKnnQdO3bE3r17sXv3bnTo0AELFy5EREREjQlKXvjggw9w+PBhzJ8/HzExMa+1gXp6evjxxx+RlpYGFxcXfP3111i4cCEAME0w8ip9+vTBggULEBoais6dO+Pp06fw9/dvkLIBYNmyZZgxYwY6deqE+/fv48cff4RIVNU45+TkhNOnT+P69evw8vKCq6srFi5ciJYtWzbY+gkhhBBCCCGEEELeGo5ruuU9xSleHjyviXh7e8PFxeWNegvytWvXLowbNw5FRUXQ1NRstPW8vE2lpaUYO3YsTpw4gadPn+LJkydwcXFBcHCwchbixpCYmIiePXviyZMnMDAwaLByc3+/zpTnokKZ8mVz1zHl9Sof884qGD/0T4XNmPJCyOoPVcOBbcxMlvIfywyZyhYL2CafETDWvVLBf2QDIce2HwVcw449+qYKy3V5ZxVge082Fz1hyv8pZXsftBA/4p2VQVh/qBrWz0e5gq238tNKLaZ8Y44ywvqebC78kyl/1W4g76zhFbbhNfTUnzHliyp0mPKsLIW3eWcLYMZUtqniLlO+TJ1tW8sVYt5ZhYLtWMD6HpMr2N7xHMd2mqgtL+adfS5gGx+Z9VijhkqmPMuxhnW/c2Dbj6zfrazHSR3wf51kArYRiVje7wAg4qRMedbPCAuxvIwp/6fChCmvLSxlylco+A9XxPqeYT3HYn0Ps3xeG/vzYSBlm1jyvrolU75czv910lFj+24Vge3zUSLnf+4JAOqCmuPm14XlHB4AdLinTHk547hvHGMzhkjG//PNeh6hJWQ7dqiB/34HAOt2bZjy/xTP/x3dZOvWGD67ydbdlJjHIPw72r59O9q2bQtzc3Okp6dj7ty5GDFiRKM2DgLA/v37VcYhjI+Px5kzZ3Du3Dk0b94c+vr6SElJeeMJRKqrraHVw8MD+fn50NfXb7D1AMD9SraToufTv2fKW+IeU373Ncf6Q/9z9y7bCVpwb/5/mALAfTlbD05jIduJy4afLXhnh/Rg+wJq8/wqU75Mw4Apf7HQnnfWrVk2U9mVHNsfSEaFN5jyMjW2XsflcxfwzqqJ2Q7HustXMuW1N89hypdP/xf/LOMfps8Z/3g0kbE13mgum8+UV8gb73eyFp3aM+V/+yiMKc/S6PfYqTNT2aKr55nyGPshU7xdL7Z9s7/nDt5ZP+NjTGX/t+wjpryLaT5T3rTsJlOexUMttj9kdeVsPy4I5GyNCDuuOvHO9nVmaxBvJmfLlwnZ/lBmajRjPIVmbQB5WNmcKW9VyfZ9+UzM/4dPdRlbA8VjOdsPUgZqbA25LH9YP6o0Yir74Hlzpvx83fVM+UeOPkx53ZL7vLOFuq2YymY9ZxIo2I4FCobGHtay5RzbjwWBmwyY8tvGZzLlhad/5J19OGAKU9msx4J20gymfKUa/7+Hn6uz/c2qYPwJVihn+5ulQG7KlN9/lv8PyAuNtzKVndl+FFNeV8j2nn9vCd7fyUKaynuxx+/fv48xY8bAzs4OM2fOxPDhw/Htt98CAAIDA6Gjo1PrEhgY+FrrKy+v6oFlaGgIXd2/Tk5v3LgBOzs7dOjQAaampuA4DsbGxtDSYuvtwkokEinXBwBLly6tc5v79u3bqHUhhBBCCCGEEEIIIe+Wd6qBUC6XIzQ0FIaGhjA1NUV4eLjysZUrV8LR0RHa2tqwsLDA1KlTUVJSonz89u3bGDhwIJo1awZtbW04ODjgyJEjAIDQ0FDk5ubi+fPnuHXrFlatWgUtLS3I5XIcOnQIc+bMQVpamnLZtWsXSktLMWnSJABAYWEhJk6cCGNjY+jp6eHDDz9Eenq6ct3h4eFwcXHB1q1b0aZNG+XYht7e3spLh729vREdHY1ffvkFHMfB29sbAGBlZaXS26+wsBCff/45TExMoKGhgQ4dOuC///0vgKpZoT/99FOYm5tDS0sLjo6O+P77v3rlBQQE4PTp01izZg04jgPHccjNzUViYiI4jkNhYSGAqkbRZcuWwczMDOXl5TAwMMAXX3yBtLQ0bN3K9msJIYQQQgghhBBCCPl7e6caCOPj46GtrY3k5GRERUUhIiICJ06cAAAIBAKsXbsWV69eRXx8PE6ePInQ0L/GtJs2bRqkUil++eUXZGRkYPny5dDRefUYQQKBAH5+fvj5559hbW2tXM6cOQNPT0906tQJADB8+HAUFBTgp59+QmpqKjp27IhevXrh8eO/xrz7/fffsW/fPuzfvx9paWk11rV//35MmjQJ7u7uyM/Px/79+2tk5HI5+vbti6SkJOzcuROZmZlYtmwZhMKqbvTPnz9Hp06dcPjwYfz222+YPHkyxo4di4sXLwIA1qxZA3d3d0yaNAn5+fnIz8+HhUXNS1Fv3bqFGTNmwN/fH7/99huWLFmCNWvW4OzZszA3Z7u0ghBCCCGEEEIIIaRBcYKmW95T79QYhE5OTggLqxp/ycbGBuvWrUNCQgJ69+6tMomHlZUVlixZgsDAQGzYsAEAkJeXh2HDhsHRsWocurZt2/Jap5+fH6Kjo5GXlwdLS0vI5XLs3r0b8+dXjWF19uxZXLx4EQUFBRCLq8bNWrFiBQ4ePIj//Oc/yhmcy8vLsX37dhgbG9e6HkNDQ2hpaSkv963Nzz//jIsXLyIrKwsSiaTGdpibmyMkJER5+4svvsCxY8ewd+9edOnSBfr6+hCJRNDS0qpzHUBVb8xevXphwYKq8dEkEgkyMzPxzTff1DmjNCGEEEIIIYQQQgj5Z3qnmkadnFQHtjYzM0NBQdXkDT///DN69eoFc3Nz6OrqYuzYsXj06BFKS6smm5g+fTqWLFkCT09PhIWF4cqVK7zW6eLiAjs7O3z33XcAgNOnT6OgoADDhw8HAKSnp6OkpARGRkYqY/XdunULN278NcFB69at62wc5CstLQ2tWrVSNg6+TCaTYfHixXB0dIShoSF0dHRw7Ngx5OXlMa0nKysLnp6eKvd5enoiJycHMlntA6ZKpVIUFxerLOXlbINWE0IIIYQQQgghhNRLwDXd8p56pxoIq8/4CwAcx0EulyM3NxcDBgyAk5MT9u3bh9TUVKxfXzVr2IsJQSZOnIibN29i7NixyMjIgJubG2JiYnit18/PT9lA+N1338HX1xdGRlWznpWUlMDMzExljMK0tDRkZ2djzpy/ZgVtiJmI65tV+ZtvvsGaNWswd+5cnDp1CmlpaejTp49yHzSmyMhI6Ovrqyzbv2WbRZUQQgghhBBCCCGEvHveqQbCuqSmpkIulyM6OhrdunWDRCLBvXv3auQsLCwQGBiI/fv3Y/bs2diyZQuv8kePHo3ffvsNqamp+M9//gM/Pz/lYx07dsT9+/ehpqamMk6htbU1mjdnm3a+Pk5OTvjjjz9w/fr1Wh9PSkrCoEGDMGbMGDg7O6Nt27Y1siKRqM5egC/Y2dkhKSmpRtkSiUQ53uHL5s2bh6KiIpXFf/Ishq0jhBBCCCGEEEIIIe+iv0UDobW1NSoqKhATE4ObN29ix44d2LRpk0omODgYx44dw61bt3D58mWcOnUKdnZ2vMq3srKCh4cHJkyYAJlMho8//lj5mI+PD9zd3TF48GAcP34cubm5OHfuHL7++mtcunSpQbezR48e6N69O4YNG4YTJ07g1q1b+Omnn3D06FEAVeMynjhxAufOnUNWVhY+//xzPHjwoMa2JCcnIzc3Fw8fPoRcLq+xntmzZyMhIQGLFy/G9evXER8fj3Xr1qmMb/gysVgMPT09lUUkEjfo9hNCCCGEEEIIIYTQJCVv399iy52dnbFy5UosX74cHTp0wK5duxAZGamSkclkmDZtGuzs7ODr6wuJRKKcwIQPPz8/pKenY8iQISqX+nIchyNHjqB79+4YN24cJBIJRo0ahdu3b8PExKTBtvGFffv2oXPnzvj0009hb2+P0NBQZY/A+fPno2PHjujTpw+8vb1hamqKwYMHqzw/JCQEQqEQ9vb2MDY2rnV8wo4dO2Lv3r3YvXs3OnTogIULFyIiIoImKCGEEEIIIYQQQgh5D70zsxgnJibWuO/gwYPK/8+cORMzZ85UeXzs2LHK//Mdb7AuU6ZMwZQpU2p9TFdXF2vXrsXatWtrfTw8PBzh4eE17n95m1avXl0jk5ubq3Lb0NAQ27Ztq3U9hoaGKvukNhKJBOfPn1e5z8rKCgqFQuW+YcOGYdiwYa8sixBCCCGEEEIIIeSt497fyUKaSpP1IPT29kZwcPBbX29iYiI4jkNhYWGdmbi4OBgYGChvh4eHw8XFRXk7ICCgRs+9V8nNzQXHcUhLS3ujehFCCCGEEEIIIYQQ0tDemR6EjSUwMBA7d+5U3n5xua65uTnGjh1bYyxDABg5ciT69ev31ur4d6WrVtqoeQXjLwaDO9zknRU6VDKVXcmJmPKGwsdM+adyfab8+N6PeGc1Fc+Yyr6rLmHKs3IxusU7+xxvPjv4q9zRd2LKq3Fs7xv1dbt5ZzlOUX+omhKw1YWbvowpL0ftExbVpkLB9lWizrgfS0V6TPnyBZuZ8obl95nyLBSlD5nyeupsn1cWoqvn6w9Vc9/BnSnfOX0XU/6OWlum/Kiy07yzt7VdmMruZPAHU/7iXQumvEer57yzBVK2CdAMUcSUlwq1mPIKNbbv4k+drvHOPuPYPtvPhWzfCXLG38GlnGb9odfEge0Yr69WzJR/qsb2vpEx/AnwHBpMZbPWPa+Ubcie4+f578sJvf5kKvtzz3ym/F3hIKa8XMH/uxUAyvR1+JfN8L0NAM/lbK+rkHv1JIgvK5er885qCcqYyq5kPO9YNqOCKZ8PG6a8cMAM3tnGPhY8UTdnyrO8JxVg+z5g3VbWY82tJ2x/P03pzv/76Q8BW1uADtjO31i/nwh5W/7xDYQREREqk28kJydjzJgxOHPmDFq1alXrczQ1NVXGISSEEEIIIYQQQgghb4mAGlLftibd43K5HKGhoTA0NISpqanKOH4rV66Eo6MjtLW1YWFhgalTp6KkpET5+O3btzFw4EA0a9YM2tracHBwwJEjR2qso0WLFrC2tlYu5uZVv6rk5+fDx8cHGhoa6NatG3777Tflc16+xLg+R48exQcffAADAwMYGRlhwIABuHHjRo3ctWvX4OHhAQ0NDXTo0AGnT7+6N8TZs2fh5eUFTU1NWFhYYPr06Xj2jN+vExs2bICNjQ00NDRgYmKCTz75RPmYXC5HZGQk2rRpA01NTTg7O+M///kP7+0lhBBCCCGEEEIIIf8cTdpAGB8fD21tbSQnJyMqKgoRERE4ceJEVcUEAqxduxZXr15FfHw8Tp48idDQUOVzp02bBqlUil9++QUZGRlYvnw5dHT4d8OfM2cOoqOjkZKSAmNjYwwcOBAVFWzdz1949uwZZs2ahUuXLiEhIQECgQBDhgyBXC6vsc7Zs2fj119/hbu7OwYOHIhHj2q/VPTGjRvw9fXFsGHDcOXKFezZswdnz55FUFBQvfW5dOkSpk+fjoiICGRnZ+Po0aPo3r278vHIyEhs374dmzZtwtWrVzFz5kyMGTOm3gZLQgghhBBCCCGEkEbHcU23vKea9BJjJycnhIWFAQBsbGywbt06JCQkoHfv3ioTmFhZWWHJkiUIDAzEhg0bAAB5eXkYNmwYHB0dAQBt27KNZRQWFobevXsDqGqobNWqFQ4cOIARI0Ywb8fLswFv27YNxsbGyMzMRIcOHZT3BwUFKbMbN27E0aNH8X//938qDZ8vREZGws/PT7kfbGxssHbtWvTo0QMbN26EhkbdYzTk5eVBW1sbAwYMgK6uLlq3bg1XV1cAgFQqxdKlS/Hzzz/D3b1qfKm2bdvi7Nmz2Lx5M3r06MG8/YQQQgghhBBCCCHk76vJGwirMzMzQ0FBAQDg559/RmRkJK5du4bi4mJUVlbi+fPnKC0thZaWFqZPn44pU6bg+PHj8PHxwbBhw2qU9yovGscAwNDQELa2tsjKynqt7cjJycHChQuRnJyMhw8fKnsO5uXlqTQQVl+nmpoa3Nzc6lxneno6rly5gl27/hr0XaFQQC6X49atW7Czs6uzPr1790br1q3Rtm1b+Pr6wtfXF0OGDIGWlhZ+//13lJaWKhtHXygvL1c2ItZGKpVCKpWqPkcqhUgsrvM5hBBCCCGEEEIIIeTd16SXGKurq85uxXEc5HI5cnNzMWDAADg5OWHfvn1ITU3F+vXrAVQ1ZAHAxIkTcfPmTYwdOxYZGRlwc3NDTEzMW98GABg4cCAeP36MLVu2IDk5GcnJySp1fR0lJSX4/PPPkZaWplzS09ORk5ODdu3avfK5urq6uHz5Mr7//nuYmZlh4cKFcHZ2RmFhoXIcx8OHD6uUnZmZ+cpxCCMjI6Gvr6+ybNncNPubEEIIIYQQQggh/2CcoOmW99Q7OYtxamoq5HI5oqOjIfjfzDV79+6tkbOwsEBgYCACAwMxb948bNmyBV988QWvdVy4cAGWlpYAgCdPnuD69euv7JVXl0ePHiE7OxtbtmyBl5cXgKrJRepa54uxACsrK5GamlrnmIIdO3ZEZmYmrK2tmesEVPVQ9PHxgY+PD8LCwmBgYICTJ0+id+/eEIvFyMvLY7qceN68eZg1a5bKfTfuPH6tuhFCCCGEEEIIIYSQd8c72UBobW2NiooKxMTEYODAgUhKSsKmTZtUMsHBwejbty8kEgmePHmCU6dOMTXwRUREwMjICCYmJvj666/RvHlzDB48mLmuzZo1g5GREb799luYmZkhLy8PX375Za3Z9evXw8bGBnZ2dli1ahWePHmC8ePH15qdO3cuunXrhqCgIEycOBHa2trIzMzEiRMnsG7dulfW6b///S9u3ryJ7t27o1mzZjhy5AjkcjlsbW2hq6uLkJAQzJw5E3K5HB988AGKioqQlJQEPT09fPbZZ7WWKRaLIX7pcmKRmN+MyoQQQgghhBBCCCG8Cd7fnnxN5Z3c487Ozli5ciWWL1+ODh06YNeuXYiMjFTJyGQyTJs2DXZ2dvD19YVEIlFOYMLHsmXLMGPGDHTq1An379/Hjz/+CJFIxFxXgUCA3bt3IzU1FR06dMDMmTPxzTff1LnOZcuWwdnZGWfPnsWhQ4fQvHnzWrNOTk44ffo0rl+/Di8vL7i6umLhwoVo2bJlvXUyMDDA/v378eGHH8LOzg6bNm3C999/DwcHBwDA4sWLsWDBAkRGRir33+HDh9GmTRvm7SeEEEIIIYQQQgghf2+cQqFQNHUlyN/T1d/zG7V8DUEZU55jeCsLFZVMZcs5IWOere29VK7NlFfnKnhnNRVsPT2LYcCUZ6XDPeWdrYR6/aE3UK5g+1FAjWN738gU/N83HMd2KFYDW104yJnycvCve6lck6lsdcb9yHosKFewTZ5kWH6fKc9Cs/QhU/6OPv/Jtlg9l7Ptl/sO7vWHqumcvqv+UDV31Noy5duWZfDO/qHdnqlsEcc2ZvDFuxZMeY9WN3lnC6S1/3BYF0NREVNeHWzbquA4prxI9px39plAj6lsNfD/7gMAGeOFMhwa75SYtWwF2PZ7Y+6b5woNprLFnLT+UDV3Slsw5Y+f578vJ/T6k6lsTXkJU75cyLZv5AznBQDbdzfL9zYAPJez1V3IyZjy5Qr+53BajN/zlQq2zzbLeTMAyBj3pRD8901jHwuEjOdYLO9J1rqwbivrsebaQ2OmfDeja7yzUgHbuS3r5491X9q2Yzvv+Kd4fnRrk61bw3dik627Kb12D0Jvb28EBwc3YFX+2eLi4mBgYPDG5XAch4MHD75xOYQQQgghhBBCCCHvJI5ruuU99U6OQfgmAgMDsXPnzlofGzNmTI2xDP+OMjMzMWbMmDoffzFTcWMrqtBhypfL2N5uVhr8e5oBwI/ZNryzj5+w/br2udtvTPl7AkumvIGwkCm/7Qz/X5EGuZcylW39PJ0pX6ppyJRPLbbnne2kl8lUdoWArYeUUdkdpryCsWfoo39F8M6qidk+H5pfLmHKc/+3gilfGriId1aNY+uJKZWz5ZvJ2HqAiGPmMuWfPGDrgcWCk9Q/LER1j0Z/wJRXEzD0DB37IVPZrD0CU5z9mPIeEb2Y8ns68h9GZHTzY0xlH37+EVPezuQJU96oKJd3VkeDrewisPWgEMvYvhNYeucDwKFc/r1gPds9YCpbV862b0qFbD0UxXL++0YmYOvhzinYenE/lLG9rm0qspjyz8TNeGfVGXsEPqxkq7uJZiFTfqQ3/33/VMZ2rrovzZwpP1N7C1P+kY0nU1736T3e2UI9tnNPTsD22RYw9iDUVPD/PLFe2SPj2M6Zvt7M9nldP4Z/r28AEJ07wjv70GccU9msxwLrMrbz+Ap1/j3lnot0mcpm7SXHeqxpoctWn+2ptryzs412MJWdZTWAKa8rYPs7l5C35R/XQBgREYGQkJBaH9PTYztRfFl5eflrjVPY0Nq1a4e0tLSmrgYhhBBCCCGEEEJIw2PsnEHe3BvtcblcjtDQUBgaGsLU1BTh4eHKx1auXAlHR0doa2vDwsICU6dOVenZdvv2bQwcOBDNmjWDtrY2HBwccORI/b++1Hap7sGDB8H9rxtoixYtsHPnTnzyySdISEhAz5494eTkhK+++kplFt7KykpMnz4dBgYGMDIywty5c/HZZ5+pzGTs7e2NoKAgBAcHo3nz5ujTpw+vbXtRT0tLS2hpaWHIkCF49OhRjW354Ycf0LFjR2hoaKBt27ZYtGgRKiv/+gUtJycH3bt3h4aGBuzt7XHixAkAVTMKW1tb17kAVY2ZQUFBMDMzg4aGBlq3bq0y0UthYSE+//xzmJiYQENDAx06dMB///vfevc/IYQQQgghhBBCCPlneaMGwvj4eGhrayM5ORlRUVGIiIhQNmIJBAKsXbsWV69eRXx8PE6ePInQ0FDlc6dNmwapVIpffvkFGRkZWL58OXR02C4DeJXff/8de/fuxY8//oijR4/i119/xdSpU5WPL1++HLt27UJsbCySkpJQXFxc69h+8fHxEIlESEpKUl6eXN+2JScnY8KECQgKCkJaWhp69uyJJUtULwc8c+YM/P39MWPGDGRmZmLz5s2Ii4vDv/71LwBVja9Dhw6FSCRCcnIyNm3ahLlz+V82t3btWhw6dAh79+5FdnY2du3aBSsrK2XZffv2RVJSEnbu3InMzEwsW7YMQiHb4KqEEEIIIYQQQggh5O/vjS4xdnJyQlhYGADAxsYG69atQ0JCAnr37q0ygYmVlRWWLFmCwMBAbNhQNYZQXl4ehg0bBkdHRwBA27ZssxnW5/nz59i+fTvMzavGEomJiUH//v0RHR0NU1NTxMTEYN68eRgyZAgAYN26dbX2YLSxsUFUVJTKffVt25o1a+Dr66tsNJRIJDh37hyOHj2qfN6iRYvw5Zdf4rPPPgNQtf2LFy9GaGgowsLC8PPPP+PatWs4duwYWrasGsNq6dKl6Nu3L6/tz8vLg42NDT744ANwHIfWrVsrH/v5559x8eJFZGVlQSKRKNdPCCGEEEIIIYQQ0uTe48lCmsobNxBWZ2ZmhoKCAgBVjVCRkZG4du0aiouLUVlZiefPn6O0tBRaWlqYPn06pkyZguPHj8PHxwfDhg2rUd6bsLS0VDYOAoC7uzvkcjmys7OhqamJBw8eoEuXLsrHhUIhOnXqBLlcdRDpTp061Si7vm3LyspSNjxWX3/1BsL09HQkJSUpewwCgEwmU5aTlZUFCwsLZePgizL4CggIQO/evWFrawtfX18MGDAAH31UNQh7WloaWrVqpWwc5EMqlUIqVR04tmpMRrZJIQghhBBCCCGEEELIu+WNLjFWV1edEYrjOMjlcuTm5mLAgAFwcnLCvn37kJqaivXr1wOoalQCgIkTJ+LmzZsYO3YsMjIy4ObmhpiYmPorLBBA8dKMehUVFW+yGa+kra2tcpvPtvFRUlKCRYsWIS0tTblkZGQgJycHGhoab1zvjh074tatW1i8eDHKysowYsQIfPLJJwAATU3+s1W9EBkZCX19fZVlx7fRb1xPQgghhBBCCCGEEBUCQdMt76lG2fLU1FTI5XJER0ejW7dukEgkuHfvXo2chYUFAgMDsX//fsyePRtbtmypt2xjY2M8ffoUz549U95X24y+eXl5Kuu8cOECBAIBbG1toa+vDxMTE6SkpCgfl8lkuHz5coNsm52dHZKTk1Xuu3Dhgsrtjh07Ijs7u9ZJRgQCAezs7HDnzh3k5+fXWUZ99PT0MHLkSGzZsgV79uzBvn378PjxYzg5OeGPP/7A9evXeZc1b948FBUVqSxjJ89mqg8hhBBCCCGEEEIIefe80SXGdbG2tkZFRQViYmIwcOBAlQk+XggODkbfvn0hkUjw5MkTnDp1CnZ2dvWW3bVrV2hpaeGrr77C9OnTkZycjLi4uBo5DQ0NfPbZZ1ixYgWKi4sxffp0jBgxAqampgCAL774ApGRkbC2tkb79u0RExODJ0+eKGdDfpNtmz59Ojw9PbFixQoMGjQIx44dU7m8GAAWLlyIAQMGwNLSEp988gkEAgHS09Px22+/YcmSJfDx8YFEIsFnn32Gb775BsXFxfj666/r3T8vrFy5EmZmZnB1dYVAIMC///1vmJqawsDAAD169ED37t0xbNgwrFy5EtbW1rh27Ro4joOvr2+t5YnFYpVZoAFAJHrKuz6EEEIIIYQQQggh5N3UKD0InZ2dsXLlSixfvhwdOnTArl27EBkZqZKRyWSYNm0a7Ozs4OvrC4lEopzk41UMDQ2xc+dOHDlyBI6Ojvj+++8RHh5eI2dtbY2hQ4eiX79++Oijj+Dk5KRS/ty5c/Hpp5/C398f7u7u0NHRQZ8+feq9vJfPtnXr1g1btmzBmjVr4OzsjOPHj2P+/PkqmT59+uC///0vjh8/js6dO6Nbt25YtWqVcjIRgUCAAwcOoKysDF26dMHEiRNVxiusj66uLqKiouDm5obOnTsjNzcXR44cgeB/3WX37duHzp0749NPP4W9vT1CQ0Mhk8l4l08IIYQQQgghhBDSGBQc12TL++q1exAmJibWuO/gwYPK/8+cORMzZ85UeXzs2LHK//MZb7AugwcPxuDBg1XumzRpUo3clClTMGXKlFrLUFNTQ0xMjLIecrkcdnZ2GDFihDJT2zYC9W8bAIwfPx7jx49XuW/2bNVLcvv06YM+ffrUug6gavbjM2fOqNz38viLdZk0aVKt++QFQ0NDbNu2jVdZhBBCCCGEEEIIIeSf67V7EHp7eyM4OLgBq/L6EhMTwXEcCgsLeT/n9u3b2LJlC65fv46MjAxMmTIFt27dwujRoxukTnFxcTAwMGiQsgghhBBCCCGEEELeG5yg6Zb3VKOMQfgmAgMDsXPnzlofGzNmTI3x/l6XQCBAXFwcQkJCoFAo0KFDB/z888+8xkHkY+TIkejXr5/ydnh4OA4ePFjrhCqvY+nSpVi6dGmtj3l5eeGnn35qkPW8ioaQ/6zNACCtZHu7CRWVTHkWDx+WMeUrBer1h6qRKYRMeTljW721lYh3lsOz+kPVVKqxzaIt49heVzWBnHdWvVLKVDYn5NfD9oVKIdu2CuRsM6abdm7PO8sJ2d4zhUL+7wEAMO7oyJR/yvD1oK94zFT2I86YKa9e+Zwpr+UoYcubPmTKM5Vtx/89AAA66mzb+lzG/33QrhdbXe6otWXKe0T0YsqfW5jAlC/ezf/Y8dzCiKlsbQX/sgFAS8j2HXJD24V31qYklanspyJDprz+41tM+ec6bJ/Xoqf896UQbEObKNC4l/woGP4gYK1LJSeuP1RNc+GfTHlhKduxQ6qpxTsrUrCV3Uz9CVNeXFnKlK9g+O5+WNmcqexnpYzD7TRjO48oF2oy5eUM558s718AEILtPJvjeRXT62A9z2ati6SDGVMeuM2UFtp24J2tYDwWsB5rhIznTOUinUarS7mA7fOhrmD727K1+A+m/PHS1ryzClO210muYPv8qSnY/qYg5G155xoIIyIiEBISUutjenp6vMoIDw+vdVzC6iwsLJCUlMRaPd40NTWhqcl2EsAiMDBQ5XLol9dNCCGEEEIIIYQQQggfb9R3Ui6XIzQ0FIaGhjA1NVVplFu5ciUcHR2hra0NCwsLTJ06FSUlJcrHb9++jYEDB6JZs2bQ1taGg4MDjhw5ghYtWsDa2rrWpUWLFgCAI0eOQCKRQFNTEz179kRubm6Nup09exZeXl7Q1NSEhYUFpk+fjmfP/upJZWVlhcWLF+PTTz+FtrY2zM3NsX79epUy8vLyMGjQIOjo6EBPTw8jRozAgwcPlI+np6ejZ8+e0NXVhZ6eHjp16oRLly4BUL3EOC4uDosWLUJ6ejo4jgPHcbXOvPwyjuOwefNmDBgwAFpaWrCzs8P58+fx+++/Y+jQoXB2doa/vz84jlPuI47jMHXqVJiYmEBHRwedO3fGzz//rFKulZUVli5divHjx0NXVxeWlpb49ttv660PIYQQQgghhBBCSKOjS4zfujfa8vj4eGhrayM5ORlRUVGIiIjAiRMnqgoWCLB27VpcvXoV8fHxOHnyJEJDQ5XPnTZtGqRSKX755RdkZGRg+fLl0NGpv4vznTt3MHToUAwcOBBpaWmYOHEivvzyS5XMjRs34Ovri2HDhuHKlSvYs2cPzp49i6CgIJXcN998A2dnZ/z666/48ssvMWPGDGX95XI5Bg0ahMePH+P06dM4ceIEbt68iZEjRyqf7+fnh1atWiElJQWpqan48ssvoa5es4v8yJEjMXv2bDg4OCA/Px/5+fkq5bzK4sWL4e/vj7S0NLRv3x6jR4/G559/jnnz5uHSpUtQKBQq21VSUoJ+/fohISEBv/76K3x9fTFw4EDk5eWplBsdHQ03Nzf8+uuvmDp1KqZMmYLs7GxedSKEEEIIIYQQQggh/xxvdImxk5MTwsLCAAA2NjZYt24dEhIS0Lt3b5UJTKysrLBkyRIEBgZiw4YNAKp65w0bNgyOjlXjYrVty2+so40bN6Jdu3aIjo4GANja2iobGF+IjIyEn5+fsg42NjZYu3YtevTogY0bN0JDo2o8BE9PT2XjokQiQVJSElatWoXevXsjISEBGRkZuHXrFiwsLAAA27dvh4ODA1JSUtC5c2fk5eVhzpw5aN++vXI9tdHU1ISOjg7U1NRgamrKaztfGDdunPJS4rlz58Ld3R0LFixQzn48Y8YMjBs3Tpl3dnaGs7Oz8vbixYtx4MABHDp0SKUhsV+/fpg6daqy3FWrVuHUqVOwtbVlqh8hhBBCCCGEEEJIQ1JwjTv2MKnpjXoQOjk5qdw2MzNDQUEBAODnn39Gr169YG5uDl1dXYwdOxaPHj1CaWnVQMTTp0/HkiVL4OnpibCwMFy5coXXOrOystC1a1eV+9zd3VVup6enIy4uDjo6OsqlT58+kMvluHXrVp3Pc3d3R1ZWlnI9FhYWysZBALC3t4eBgYEyM2vWLEycOBE+Pj5YtmwZbty4wWsbWFTfxyYmJgCgbFR9cd/z589RXFwMoKoHYUhICOzs7GBgYAAdHR1kZWXV6EFYvVyO42Bqaqp87WojlUpRXFysspSXs00gQQghhBBCCCGEEELePW/UQPjy5bQcx0EulyM3NxcDBgyAk5MT9u3bh9TUVOX4fuXlVbMTTZw4ETdv3sTYsWORkZEBNzc3xMTEvEl1lEpKSvD5558jLS1NuaSnpyMnJwft2rVrkHUAVZOhXL16Ff3798fJkydhb2+PAwcONFj5gOo+5v7Xgl7bfXJ51ayBISEhOHDgAJYuXYozZ84gLS0Njo6Oyv1eW7kvynlRRm0iIyOhr6+vssRuXv1G20YIIYQQQgghhBBCml6jzGKcmpoKuVyO6OhoCARVbZB79+6tkbOwsEBgYCACAwMxb948bNmyBV988cUry7azs8OhQ4dU7rtw4YLK7Y4dOyIzMxPW1tavLOvl5124cAF2dnbK9dy5cwd37txR9iLMzMxEYWEh7O3tlc+RSCSQSCSYOXMmPv30U8TGxmLIkCE11iUSiSCTyV5Zn4aQlJSEgIAAZR1KSkpqncSF1bx58zBr1iyV+zLzSupIE0IIIYQQQgghhLym93iykKbSKHvc2toaFRUViImJwc2bN7Fjxw5s2rRJJRMcHIxjx47h1q1buHz5Mk6dOqVsnHuVwMBA5OTkYM6cOcjOzsZ3331XY0bguXPn4ty5cwgKCkJaWhpycnLwww8/1JikJCkpCVFRUbh+/TrWr1+Pf//735gxYwYAwMfHB46OjvDz88Ply5dx8eJF+Pv7o0ePHnBzc0NZWRmCgoKQmJiI27dvIykpCSkpKXVug5WVFW7duoW0tDQ8fPgQUmnjXJ5rY2OD/fv3K3tNjh49+pU9A/kSi8XQ09NTWUQicQPUmBBCCCGEEEIIIYQ0pUZpIHR2dsbKlSuxfPlydOjQAbt27UJkZKRKRiaTYdq0abCzs4Ovry8kEolyApNXsbS0xL59+3Dw4EE4Oztj06ZNWLp0qUrGyckJp0+fxvXr1+Hl5QVXV1csXLgQLVu2VMnNnj0bly5dgqurK5YsWYKVK1cqJ//gOA4//PADmjVrhu7du8PHxwdt27bFnj17AABCoRCPHj2Cv78/JBIJRowYgb59+2LRokW11nvYsGHw9fVFz549YWxsjO+//573/mSxcuVKNGvWDB4eHhg4cCD69OmDjh07Nsq6CCGEEEIIIYQQQhocxzXd8p567UuMExMTa9x38OBB5f9nzpyJmTNnqjw+duxY5f/fZLzBAQMGYMCAASr3VZ/JFwA6d+6M48ePv7IcPT29Wi99fsHS0hI//PBDrY+JRKJXNvIFBAQgICBAeVssFuM///nPK+vzMoVCoXLbysqqxn3e3t4q91lZWeHkyZMqmWnTpqncru2S47S0NKa6EUIIIYQQQgghhJB/hrdyUbe3tzeCg4Pfxqr+kRITE8FxHAoLC5u6KoQQQgghhBBCCCHkH4ZTvNwlrRF4e3vDxcUFq1evrjcbGBiInTt31vrYmDFjaoxl+LqsrKwQHBz82g2XcXFxCA4Ofu1Gu127duHzzz+vcX9ZWRkMDAzw6NEj5X3l5eV4/PgxTExMlLMWvwtu/57NlBdVlrGVjzZMeYfis/zDAiFT2efVP2TKW+oUMOVZmZTc4J19otOKqWwF43tMXFnKlBdVPuedfSCyYCpbCLaJgKQKEVO+Us7W6frGI33eWQHHdih2bJHPlL9TYsyUZ3kPl8vZ9mPrp1eY8s+0WzDlLxR1YMq3N7zPlGdh9iyHKV+ixfY6cQr+Y8z+eJNtv4wyO82U33O/B1O+uIRtfFyHUfWPU/xCy8wkprKFYKuLuqCCKa9f+aj+0P8IFGzHsUdqpkz528XNmfLt9Nk+H/oVD3lnH6uZMJUtZ/xdW8D4uj4p1+Od1VArZypbxPie0eGeMuU1KtgmjmN532hxz5jKfiJrxpRnJZPzP4czVv+TqWzNCrb9fkvRjq18NbYxyA3B//P0RyXb+Z6OGtt5uYBj+zxJGc4N1LlKprI1BPzPJQHAvOAyU/6QfBBT3rp5Ee+shpDtPcB6LGA9L2c5Z2qhw/aeMRCx1Z31WFMk53+eDQCtyvn//XRfbMVUNuvrJIUGU759O7bP9z9FadK+Jlu3luewJlt3U2qUWYzfREREBEJCQmp9TE+P/4lbfeqa2Vcmk4HjOOXsy43l448/RteuXWvc7+fnBwcHB5X7RCIRTE3Z/gAghBBCCCGEEEIIIYSPtzZvtFwuR2hoKAwNDWFqaorw8HDlYytXroSjoyO0tbXRqVMnrFy5EqamprC2toa1tTXU1dUxc+ZM2NraQltbGw4ODjhy5Ei963xxae7hw4fh5OQEDQ0NdOvWDb/99psyExcXBwMDAxw6dAj29vYQi8XIy8vDkydP4O/vj2bNmkFLSwt9+/ZFTk6Ostxx48ahqKgIHMeB4zjl9kilUoSEhMDc3Bza2tro2rVrjfEak5KSMHDgQDg5OaFz586YNm0ajIyMsGTJEly8eBGxsbHKcnNzc2u9xHjfvn1wcHCAWCyGlZUVoqOjVdZhZWWFpUuXYvz48dDV1YWlpSW+/fZbtheNEEIIIYQQQggh5C1TcFyTLe+rt9ZAGB8fD21tbSQnJyMqKgoRERE4ceJEVSUEAqxduxZXr15FfHw8Tp48idDQUOVzp02bBqlUil9++QUZGRlYvnw5dHR0eK97zpw5iI6ORkpKCoyNjTFw4EBUVPx1qUdpaSmWL1+OrVu34urVq2jRogUCAgJw6dIlHDp0COfPn4dCoUC/fv1QUVEBDw8PrF69Gnp6esjPz0d+fr6y12NQUBDOnz+P3bt348qVKxg+fDh8fX2VjYtpaWno1asX7O3tcf78eZw9exYDBw6ETCbDmjVr4O7ujkmTJinLtbCoeYllamoqRowYgVGjRiEjIwPh4eFYsGAB4uLiVHLR0dFwc3PDr7/+iqlTp2LKlCnIzma7LJgQQgghhBBCCCGE/LO9tUuMnZycEBYWBgCwsbHBunXrkJCQgN69e6uMA2hlZYUlS5YgMDAQGzZsAADk5eVh2LBhcHR0BAC0bduWad1hYWHo3bs3gKqGylatWuHAgQMYMWIEAKCiogIbNmyAs7MzACAnJweHDh1CUlISPDw8AFSNGWhhYYGDBw9i+PDh0NfXB8dxKpf+5uXlITY2Fnl5eWjZsiUAICQkBEePHkVsbCyWLl2KqKgouLm5KbcNgMolxSKRCFpaWq+8pHjlypXo1asXFixYAACQSCTIzMzEN998ozJzcr9+/TB16lQAwNy5c7Fq1SqcOnUKtra2TPsPqOoZKZVKX7qvHGIx29hjhBBCCCGEEEIIIeTd8tZ6EDo5OancNjMzQ0FB1SD4P//8M3r16gVzc3Po6upi7NixePToEUpLqwZZnT59OpYsWQJPT0+EhYXhyhW2Ae7d3d2V/zc0NIStrS2ysrKU94lEIpX6ZWVlQU1NTWWMQCMjoxrPe1lGRgZkMhkkEgl0dHSUy+nTp3HjRtWgqC96EL6JrKwseHp6qtzn6emJnJwcyGR/DW5efZteNGa+2OesIiMjoa+vr7Js2Lz59TaAEEIIIYQQQgghpC6coOmW99Rb23J1dXWV2xzHQS6XIzc3FwMGDICTkxP27duH1NRUrF+/HkDV7L0AMHHiRNy8eRNjx45FRkYG3NzcEBMT02B109TUbJDZgUtKSiAUCpGamoq0tDTlkpWVhTVr1ijX9bbUtc9fx7x581BUVKSyTK1lFmZCCCGEEEIIIYSQ98n69ethZWUFDQ0NdO3aFRcvXqwzu2XLFnh5eaFZs2Zo1qwZfHx8auQDAgKUc1O8WHx9fRt1G5q8aTQ1NRVyuRzR0dHo1q0bJBIJ7t27VyNnYWGBwMBA7N+/H7Nnz8aWLVt4r+PChQvK/z958gTXr1+HnZ1dnXk7OztUVlYiOTlZed+jR4+QnZ0Ne3t7AFW9Dqv31gMAV1dXyGQyFBQUKCdYebG8uGTYyckJCQkJda67tnJrq19SUpLKfUlJSZBIJBAKha987usSi8XQ09NTWejyYkIIIYQQQgghhDQ0BSdosoXVnj17MGvWLISFheHy5ctwdnZGnz596ryCMzExEZ9++ilOnTqF8+fPw8LCAh999BHu3r2rkvP19VXOT5Gfn4/vv//+tfYlX03eQGhtbY2KigrExMTg5s2b2LFjBzZt2qSSCQ4OxrFjx3Dr1i1cvnwZp06demUD38siIiKQkJCA3377DQEBAWjevDkGDx5cZ97GxgaDBg3CpEmTcPbsWaSnp2PMmDEwNzfHoEGDAFSNlVhSUoKEhAQ8fPgQpaWlkEgk8PPzg7+/P/bv349bt27h4sWLiIyMxOHDhwFU9cRLSUnB1KlTceXKFVy7dg0bN27Ew4cPleUmJycjNzcXDx8+rLXH3+zZs5GQkIDFixfj+vXriI+Px7p165QTpRBCCCGEEEIIIYSQxrdy5UpMmjQJ48aNg729PTZt2gQtLS1s27at1vyuXbswdepUuLi4oH379ti6dSvkcnmNzmRisRimpqbKpVmzZo26HU3eQOjs7IyVK1di+fLl6NChA3bt2oXIyEiVjEwmw7Rp02BnZwdfX19IJBKVST7qs2zZMsyYMQOdOnXC/fv38eOPP0IkenXvt9jYWHTq1AkDBgyAu7s7FAoFjhw5orxs18PDA4GBgRg5ciSMjY0RFRWlfJ6/vz9mz54NW1tbDB48GCkpKbC0tARQNaHI8ePHkZ6eji5dusDd3R0//PAD1NSq5osJCQmBUCiEvb09jI2NkZeXV6NuHTt2xN69e7F792506NABCxcuREREhMoEJYQQQgghhBBCCCGk8ZSXlyM1NRU+Pj7K+wQCAXx8fHD+/HleZZSWlqKiogKGhoYq9ycmJqJFixawtbXFlClT8OjRowat+8s4hUKhaIyCvb294eLigtWrVzdG8bwkJiaiZ8+eePLkCQwMDJqsHv9Ut3/PZsqLKsvYykcbprxD8Vn+YQHbpdjn1T9kylvqvN5kMHyZlNzgnX2i04qpbAXjeJziylKmvKjyOe/sA5EFU9lCvPry/JdJFWyXyVfK2SZ+v/FIn3dWwLEdih1b5DPl75QYM+VZ3sPlcrb92Pop20RTz7RbMOUvFHVgyrc3vM+UZ2H2LIcpX6LF9jpxCv7jyv54k22/jDI7zZTfc78HU764hG1MXIdR/K8caJmZVH+oGiHY6qIuqGDK61fyP5kTKNiOY4/UTJnyt4ubM+Xb6bN9PvQrHvLOPlYzYSpbzvi7toDxdX1Srsc7q6FWzlS2iPE9o8M9ZcprVJQw5VneN1rcM6ayn8gat3eDTM7/HM5Y/U+msjUr2Pb7LUU7tvLVpEx5Q/D/PP1RyXa+p6PGdl4u4Ng+T1KGcwN1rpKpbA0B/3NJADAvuMyUPyQfxJS3bl7EO6shZHsPsB4LWM/LWc6ZWuiwvWcMRGx1Zz3WFMn5n2cDQKty/n8/3RdbMZXN+jpJocGUb9+O7fP9T1GS/GOTrVvd5SNIpaqfV7FYDLFYXCN77949mJub49y5cyoT5IaGhuL06dMqQ9fVZerUqTh27BiuXr0KDY2q98fu3buhpaWFNm3a4MaNG/jqq6+go6OD8+fPN9rQcmx/7b4j4uLiEBwcjMLCwqauylvHcRwOHDjwykukqwsPD8fBgweRlpbW4HWRcWxvnzuMDX5tFL8z5W8068I7e/oa2x9Izm3YTkRKZWyT0egI2b4Qb2g41R/6Hw0F24mIadktpnyFGtu2/s7Z8s52KL5Qf6iacpEOU75Yk63hiROwNeLZaDKckDKOdfEn4+fJvfxnpvx9OPDO6gjYToqKdVsy5SsENb+IX+VD0S9Mea6Y7Q8TFupP+f9xBwCPddkaxYsq+Z8c+xkfYyr7trYLU350c7byn1sYMeXvMjT63bP3ZCp7/0KGH5gAfDGK8ccFgXr9of8RKNiOBax/UNnps+V1y9gaWA4+cK8/9D+DTOs/YX4TJRps7zGZiP/JtohjbCAE23dxiUKXKZ+vYGtsNeT4N2g8V7B9z1tU3mTK/ykyZ8orBPx/yGTdj8mP2Rr8Bj+t/dKxutyx9qk/VM39Cv7fl7rqbA1DaoyNchzYzoEEAv4NikKO7YcR1h8LftHoz5R31H3AlG/3xyne2fsWnZnKfqZgO7e9L2c7t7XQL+adFQsYj3uMx0nWY42WgK3B8lQx/33/8eNvmcq+1X4gU15Hzv8YTJpGZGQkFi1apHJfWFgYwsPDG3xdy5Ytw+7du5GYmKhsHASAUaNGKf/v6OgIJycntGvXDomJiejVq1eD1wN4By4xfhOBgYHQ0dGpdQkMDGzq6r2zli5dWud+69u3b1NXjxBCCCGEEEIIIe+xppykZN68eSgqKlJZ5s2bV2s9mzdvDqFQiAcPVH9cePDggXKy2rqsWLECy5Ytw/Hjx+Hk9OpOQG3btkXz5s3x++9sHalYNGoDoVwuR2hoKAwNDWFqaqrS2pqXl4dBgwZBR0cHenp6GDFihMoOTU9PR8+ePaGrqws9PT106tQJly5dQmJiIsaNG4eioiJs3rwZz549w/jx45GWlqayREREwNvbGwqFAgYGBtixYwfc3Nygq6sLU1NTjB49usaMMlevXsWAAQOgp6cHXV1deHl54caNv7oib9u2DQ4ODhCLxTAzM0NQUBDv7QkICKjR6y84OBje3t7K297e3pg+fXqd+8zKygoAMGTIEHAcp7xdl7i4OCxatAjp6enKabHj4uJw9epVdO7cWWV/paSkQCwWN1pLNCGEEEIIIYQQQsi7TiwWQ09PT2Wp7fJiABCJROjUqZPKBCMvJhypfsnxy6KiorB48WIcPXoUbm5u9dbpjz/+wKNHj2BmZsa+QTw1agNhfHw8tLW1kZycjKioKERERODEiROQy+UYNGgQHj9+jNOnT+PEiRO4efMmRo4cqXyun58fWrVqhZSUFKSmpuLLL7+Euro6PDw8sHr1aujp6Smnel66dCmsra1VlhYtVLtXV1RUYPHixUhPT8fBgweRm5urMqnH3bt30b17d4jFYpw8eRKpqakYP348Kiurut9v3LgR06ZNw+TJk5GRkYFDhw7B2toaAHhtz5vuMwBISUkBUDURSn5+vvJ2XUaOHInZs2fDwcFBua9GjhyJadOm4cyZM9DW1lbur2vXrkEqlVLPS0IIIYQQQgghhBCeZs2ahS1btiA+Ph5ZWVmYMmUKnj17hnHjxgEA/P39VXogLl++HAsWLMC2bdtgZWWF+/fv4/79+ygpqRpTuKSkBHPmzMGFCxeQm5uLhIQEDBo0CNbW1ujTp0+jbUejjkHo5OSEsLAwAICNjQ3WrVunbFXNyMjArVu3YGFRNd7S9u3b4eDggJSUFHTu3Bl5eXmYM2cO2rdvr3z+C/r6+uA4rt7umtWNHz9e+f+2bdti7dq16Ny5M0pKSqCjo4P169dDX18fu3fvVs5ULJFIlM9ZsmQJZs+ejRkzZijv69y5ahyDhISEerfnTfdZ7969YWxcNXi9gYEBr23X1NSEjo4O1NTUVPIeHh6wtbXFjh07EBoaCqCq0XH48OHQ0WEb54IQQgghhBBCCCGkQTFOntmURo4ciT///BMLFy7E/fv34eLigqNHj8LEpGqM4Ly8PAgEf/XP27hxI8rLy/HJJ5+olPNinEOhUIgrV64gPj4ehYWFaNmyJT766CMsXry4zp6MDaHRGwirMzMzQ0FBAbKysmBhYaFsTAMAe3t7GBgYICsrC507d8asWbMwceJE7NixAz4+Phg+fDjatWMbOLi61NRUhIeHIz09HU+ePIFcXjVwbl5eHuzt7ZGWlgYvLy9l42B1BQUFuHfvXp2X3/LZHr7q2mcNbeLEifj2228RGhqKBw8e4KeffsLJkyfrzEul0hqz+Eil0kZ9cxJCCCGEEEIIIYS864KCglSGoasuMTFR5XZubu4ry9LU1MSxY2wTADaERr3E+OXGNo7jlA1z9QkPD8fVq1fRv39/nDx5Evb29jhw4MBr1ePZs2fo06cP9PT0sGvXLqSkpCjLKi+vml1JU7PuWZNe9RhfAoEACoXq7F8VFRU1cm+yz1j4+/vj5s2bOH/+PHbu3Ik2bdrAy8urznxkZCT09fVVlk2bNjV4vQghhBBCCCGEEPKe4wRNt7ynmmTL7ezscOfOHdy5c0d5X2ZmJgoLC2Fvb6+8TyKRYObMmTh+/DiGDh2K2NhYAFWDQMpkMt7ru3btGh49eoRly5bBy8sL7du3r9Erz8nJCWfOnKm10U5XVxdWVlYqg06ybo+xsTHy8/NVnpeWlsZ7G15QV1dn2va69pWRkREGDx6M2NhYxMXFKa+Nr0tts/jQeIWEEEIIIYQQQgghf39N0kDo4+MDR0dH+Pn54fLly7h48SL8/f3Ro0cPuLm5oaysDEFBQUhMTMTt27eRlJSElJQU2NnZAaiazbekpAQJCQl4+PAhSktLX7k+S0tLiEQixMTE4ObNmzh06BAWL16skgkKCkJxcTFGjRqFS5cuIScnBzt27EB2djaAqh6N0dHRWLt2LXJycnD58mXExMTw2h4A+PDDD3Hp0iVs374dOTk5CAsLw2+//ca87140VN6/fx9Pnjzhlb916xbS0tLw8OFDlcuEJ06cqBxE87PPPntlOSyz+BBCCCGEEEIIIYSQv48maSDkOA4//PADmjVrhu7du8PHxwdt27bFnj17AABCoRCPHj2Cv78/JBIJRowYgb59+2LRokUAqibZCAwMxMiRI2FsbIyoqKhXrs/Y2BhxcXH497//DXt7eyxbtgwrVqxQyRgZGeHkyZMoKSlBjx490KlTJ2zZskV5ye9nn32G1atXY8OGDXBwcMCAAQOQk5PDa3sAoE+fPliwYAFCQ0PRuXNnPH36FP7+/sz7Ljo6GidOnICFhQVcXV3rzQ8bNgy+vr7o2bMnjI2N8f333ysf8/HxgZmZGfr06YOWLVsy14UQQgghhBBCCCGkoSk4rsmW9xWneHlgPPLeKCkpgbm5OWJjYzF06FDm59+8cYMp/7CiOVO+teJ3pny+miXv7OlrbHVxbvOcKa8nenWv1pfpCJ8x5YsrdXlnNYTS+kPVmD6/xZSvUGMbo/OWgv9kQx3KLjCVXS5im4W7WLMFU54D2+GyWXEeQ+Fsv9f8qduGKW/6+CpT/r6hA++sEJVMZavL2d6TFQK23sqGT+/UH6qGk7PVn4X604dM+XyLLkz5okp93lnrp5eYyr6t78KUb12UxpR/rmnElL+rbsU7e8/ek6ns/QvPMuW/GMUUhyH4vw8ECv7DiABAhVCDrTKMdMv+ZMoffODOOzvINJm1OkxKNNjeYyUK/t+tIq6cqWwR2I57LHUBgKcVWkx5Q1ER76xcwfb91Lz8HlP+T5E5U14B/n+4sWQB4GoB23nB4KfbmPJ3rH2Y8k8qDHhnNdXYzlXVOLbvPtZzIJb3jZBjO+6xvq63ik2Y8ha6j5jy7f44xTt734L/5JUA8EzBdm7LeixQE/Af614sYDvuaQjY3pOVCrb5U1nfN+kF/I81Hz/+lqnsW+0HMuV15PyPwQBgYWNff+gfqDj17U/S8YJepz5Ntu6m1Gg9CL29vREcHNxYxZM3IJfLUVBQgMWLF8PAwAAff/xxU1eJEEIIIYQQQgghpApNUvLWsTXTvyPi4uIQHByMwsJCAMCZM2fQt2/fOvMlJSVvqWaNj+M4HDhwAIMHDwYAODg44Pbt27VmN2/ejJycHBw8eFBlQpS8vDy0adMGrVq1QlxcHNTUXu9tUKrQZspb4iZT/rk62y9mNn8m8c5a32HrmQZjD6Z4mZ4pU75AYMGUty79lXf2nm57prIzOSemvFDO9otyK9Fd3tnf0I2pbLGw5iRDr6LO2HOsUiFkyl8o5d9bklUHnYL6Q9X8+2k/pryH4R+8s+oKtp4x9+VsQxo8LGE71jx9zta7sjGvInjOeILRQcbW49BUwf/z9N+yj5jK7mTA/z0AAIefs5WvreDfawEArPT59+hg7RE4NOIDprzxyP8w5XPKrXln84vYegQ6mdxnyrN6KtZjyo/K/pJ39nzzRUxlG2qy9bbXYDw26Sse885KObZeOuVg6wmth0KmfAFDb2IAEIsYevYwHiPzhGzffcZg+z5j6T3G2uvt45zNTPk404VM+V6o/Zy9LoVS/ldpmKixHQvKwXaskTP2KxEz9B6TsfYcY7xyoefDXUz5x1odmfIH1Ph3K+8Etu9WXbD1NLtXbsiUd1VP4519LmD726ycY3uPqYOthyLHeB4x8PelvLNbjb5mKrtrJdvrVClke8+z/aVIyOv7WzYQvszNze21ZgT+Jzhy5EitMy8DgImJCaKjo2vcb2VlBbqynBBCCCGEEEIIIYQAjTxJiVwuR2hoKAwNDWFqaorw8HDlY3l5eRg0aBB0dHSgp6eHESNG4MGDB8rH09PT0bNnT+jq6kJPTw+dOnXCpUuXkJiYiHHjxqGoqAgcx4HjOCxfvhzW1tZ1LgCwY8cOuLm5QVdXF6amphg9ejQKClR/sbx69SoGDBgAPT096OrqwsvLCzeqjbO3bds2ODg4QCwWw8zMDEFBQby3JyAgQNnr74Xg4GB4e3srb3t7e2P69Ol17jMrKysAwJAhQ8BxHKysrNC6des6t3vfvn1YtGgR0tPTlfsqLi4O48ePx4ABA1TqUlFRgRYtWuD//u//eL22hBBCCCGEEEIIIY1BAa7JlvdVozYQxsfHQ1tbG8nJyYiKikJERAROnDgBuVyOQYMG4fHjxzh9+jROnDiBmzdvYuTIkcrn+vn5oVWrVkhJSUFqaiq+/PJLqKurw8PDA6tXr4aenh7y8/ORn5+PkJCQeutSUVGBxYsXIz09HQcPHkRubi4CAgKUj9+9exfdu3eHWCzGyZMnkZqaivHjx6OysqoL+8aNGzFt2jRMnjwZGRkZOHTokLLxkc/2vOk+A4CUlBQAQGxsLPLz85W36zJy5EjMnj0bDg4Oyn01cuRITJw4EUePHkV+fr4y+9///helpaWvVWdCCCGEEEIIIYQQ8vfVqJcYOzk5ISwsDABgY2ODdevWISEhAQCQkZGBW7duwcKi6or67du3w8HBASkpKejcuTPy8vIwZ84ctG/fXvn8F/T19cFxHExN+Y/zNn78eOX/27Zti7Vr16Jz584oKSmBjo4O1q9fD319fezevRvq6uoAAIlEonzOkiVLMHv2bMyYMUN5X+fOVbNQJSQk1Ls9b7rPevfuDWNjYwCAgYEBr23X1NSEjo4O1NTUVPIeHh6wtbXFjh07EBoaCqCq0XH48OHQ0WEbW4IQQgghhBBCCCGkISne48lCmkqj7nEnJ9XJDszMzFBQUICsrCxYWFgoG9MAwN7eHgYGBsjKygIAzJo1CxMnToSPjw+WLVumcqnv60hNTcXAgQNhaWkJXV1d9OjRA0DVpcEAkJaWBi8vL2XjYHUFBQW4d+8eevXqVWvZfLaHr7r2WUObOHEiYmNjAQAPHjzATz/9pNKI+jKpVIri4mKVpVzKNvg3IYQQQgghhBBCCHn3NGoD4cuNbRzHQS7nN9tQeHg4rl69iv79++PkyZOwt7fHgQMHXqsez549Q58+faCnp4ddu3YhJSVFWVZ5edVsSZqadc8S9qrH+BIIBDUmBqltcpE32Wcs/P39cfPmTZw/fx47d+5EmzZt4OXlVWc+MjIS+vr6KsvWzTENXi9CCCGEEEIIIYQQ8nY1SZ9NOzs73LlzB3fu3FHel5mZicLCQtjb2yvvk0gkmDlzJo4fP46hQ4cqe7yJRCLIZDLe67t27RoePXqEZcuWwcvLC+3bt6/RK8/JyQlnzpyptdFOV1cXVlZWysujX2d7jI2NVcb8A/BaMy+rq6szbXtd+8rIyAiDBw9GbGws4uLiMG7cuFeWM2/ePBQVFaksEz//grn+hBBCCCGEEEIIIa/ECZpueU81yZb7+PjA0dERfn5+uHz5Mi5evAh/f3/06NEDbm5uKCsrQ1BQEBITE3H79m0kJSUhJSUFdnZ2AKpm8y0pKUFCQgIePnyI0tLSV67P0tISIpEIMTExuHnzJg4dOoTFixerZIKCglBcXIxRo0bh0qVLyMnJwY4dO5CdnQ2gqkdjdHQ0/p+9f4+K6toXvO/vKu5KFQiygR1u2ygIHDlshHMeMYCJnESjicaOBVrjMXhJ4s6hbeOQvDFJb4n0iGyGOmxM4rZ9346XgfGWJ2h6EBtbHgzqjgIRkhjFGxcTLwQNkBIEoer9g5NKSkFrKohJfp8x1lCqfjXnXJdaa9Vc85Kfn8+ZM2f44osvWLdunUPrA/DEE09QUVHBli1bOHPmDMuXL+frr79W3nY/VVRevnyZH374waH42tpaqqqqaGpqouMX3YIXLFjA5s2bOXnyJC+88MId03Fzc8NgMNgtrm5uyuUXQgghhBBCCCGEEA+XQakg1DSNPXv2MGzYMJKTk0lNTWXEiBHs2LEDACcnJ65evcqcOXMIDw/HaDQyefJk3n77baBnko2FCxeSlpaGn58feXl5d8zPz8+PTZs2sWvXLqKiosjNzWXVqlV2Mb6+vpSUlGA2m0lJSWHs2LFs3LjR1uX3hRdeYO3atbz//vtER0czdepUzpw549D6ADz11FP81//6X3nttddISEjgxx9/ZM6cOcrbbvXq1ezfv5/g4GD+/Oc/3zX+P/2n/8SkSZN4/PHH8fPz48MPP7S9l5qaSmBgIE899RR//OMflcsihBBCCCGEEEII0d+smjZoy+/VgM1iXFpaettrhYWFtv+HhISwZ8+eXj/r6upqV5HVm/Xr17N+/XqHyzNr1ixmzZpl99qtYwLGxMTwv//3/+4zjZdffpmXX3651/futD4/efvtt22VnL252zYDeOaZZ3jmmWfumM8vubm5sXv37l7fu379Oj/88APz5893OD0hhBBCCCGEEEII8dvSry0IJ0yYwOLFi/szyYdOXV0dmqbd0/iBDwuLxUJjYyM5OTl4e3vz7LPPDnaRhBBCCCGEEEIIIcQg0ay3NqO7DxMmTCA2Npa1a9f2V5I2mzZtYvHixTQ3N9/2XllZGZMnT+7zs2az+Z7yzMjIoLm52a4VX3d3N99//z3Dhw/H2XnAGmA6LDo6mvr6+l7f27BhAyaT6bbX6+rq+NOf/kRQUBCbNm1i4sSJ95R37bmzSvGaVW02ZjMGpXgdjqfvrHUppX3D4q4U76Q5PpEMgLuuXSnetfuGw7GXuh9RStvP5XuleIviIK4/3BzmcOwQJ7XtomlqpzMn1PaTqptWl7sH/Qcrak3ZVY+x611DlOI9na87HOthdTwW4Ee8lOJV19ViVTsmOyyuSvEquq1OSvGB2rdK8R3Oju/XDsXzWOVFtaEnIv3vPi7uLw3k91t1n/pZLyvFV0U/rxQfcvIzpXgVrlqnUnyHRW38YJ2mdu3utDh+3nPTqZVdQ/Ecr3juUD0PD2TaquvaZVW7J1XZNqplUaW6bf74/+Q6HHujqVkp7e/mr1WKV+Xh5Pj9G4DV6vi2Uf2uutJx96BflkWxy51Lt+Ppt+n0SmmrUl3XbsVOdjesjl9fVX+DDPS5QOX3k+oxNtDnvbCvC5Xizypcu9u71e6Z9M5q9Q2q2ybyUbXfc78V174sG7S8fWKSBi3vwTT4NVz9ID4+/oG16HNyciIgIOCB5OWIoqKiXmdeBvD39+/19bCwsNu6VwshhBBCCCGEEEKI36d+n6TEYrHw2muv4ePjQ0BAANnZ2bb3GhoamDZtGp6enhgMBoxGI1euXLG9X11dzeOPP45er8dgMDB27FgqKiooLS1l7ty5tLS0oGkamqbZpevh4cHIkSNvW/7xj3+Qnp6OXq8nICCA2bNn09jYaFfeEydOMHXqVAwGA3q9nqSkJM6dO0d2djabN29mz549tjxLS0vtuhhbLBaCgoJuGwvx+PHj6HQ6W8u+5uZmFixYgJ+fHwaDgSeeeILq6mqHtmd2djaxsbFs2LCB4OBghgwZgtFopKWlBYDQ0FD+23/7byxdupSdO3cyfvx44uPj2bZtGx4eHmRlZeHj40NQUBAffPCBLd3Ozk4yMzMJDAzE3d2d0NBQVq5c6VCZhBBCCCGEEEIIIQaMpg3e8jvV7xWEmzdvZujQoRw9epS8vDxWrFjB/v37sVgsTJs2jWvXrnHw4EH279/P+fPnSUtLs33WZDIRFBREeXk5lZWVvP7667i4uJCYmMjatWsxGAxcunSJS5cusXTp0ruW5ebNm+Tk5FBdXU1hYSF1dXVkZGTY3v/uu+9ITk7Gzc2NkpISKisrmTdvHl1dXSxduhSj0cikSZNseSYmJtqlr9PpmDVrFtu2bbN7vaCggPHjxxMaGgrAzJkzaWxs5NNPP6WyspK4uDgmTpzItWvXHNqmZ8+eZefOnXzyySfs27eP48eP88orr9jFlJSUcPHiRT777DPWrFnD8uXLmTp1KsOGDePo0aMsXLiQl19+mW+/7em+lp+fz969e9m5cyc1NTUUFBQQFhbmUHmEEEIIIYQQQgghxG9Hv3cxjomJYfny5QCMGjWKd999lwMHDgDw1VdfUVtbS3BwMABbtmwhOjqa8vJyEhISaGhoICsri9GjR9s+/xMvLy80TVPq3jtv3jzb/0eMGEF+fj4JCQmYzWY8PT1577338PLyYvv27bi49IyZEx4ebvuMh4cHHR0dd8zTZDKxevVqGhoaCAkJwWKxsH37dt566y0ADh06xLFjx2hsbMTNrWfcn1WrVlFYWMju3bt56aWX7roeN27cYMuWLTzySM/YA+vWrWPKlCmsXr3aVjYfHx/y8/PR6XRERESQl5dHW1sbb7zxBgDLli0jNzeXQ4cOkZ6eTkNDA6NGjeKxxx5D0zRbZaYQQgghhBBCCCGE+H3p9xaEMTExdn8HBgbS2NjIyZMnCQ4OtlUOAkRFReHt7c3JkycBWLJkCQsWLCA1NZXc3FzOnTt3X2WprKzkmWeeISQkBL1eT0pKCtDT1RmgqqqKpKQkW+XgvYiNjSUyMtLWivDgwYM0NjYyc+ZMoKfbtNlsxtfXF09PT9tSW1vr8PqFhITYKgcBxo0bh8VioaamxvZadHQ0Ot3Pu9Pf358xY8bY/nZycsLX19fWxTojI4OqqioiIiJYtGgRxcXFdyxDR0cHra2tdktHh9qAv0IIIYQQQgghhBB3Y9V0g7b8XvX7mt9a2aZpGhaLYzMeZWdnc+LECaZMmUJJSQlRUVF8/PHH91SO69ev89RTT2EwGCgoKKC8vNyWVmdnz6x5Hh4e95T2rUwmk62CcNu2bUyaNAlfX1+gZwblwMBAqqqq7JaamhqysrL6JX/ofbvfaV/ExcVRW1tLTk4O7e3tGI1Gnn++75mdVq5ciZeXl92y/u8b+q38QgghhBBCCCGEEGJwPLCq0cjISC5cuMCFCxdsr33zzTc0NzcTFRVley08PJxXX32V4uJiZsyYYZtYw9XVle7ubofzO3XqFFevXiU3N5ekpCRGjx592wQlMTExlJWV9TkLsKN5zp49m6+//prKykp2796NyWSyvRcXF8fly5dxdna+bRKV4cOHO7QuDQ0NXLx40fb3559/butKfD8MBgNpaWls3LiRHTt28NFHH/U5LuKyZctoaWmxW/6y8OX7yl8IIYQQQgghhBDiVla0QVt+rx5YBWFqaipjxozBZDLxxRdfcOzYMebMmUNKSgrx8fG0t7eTmZlJaWkp9fX1HD58mPLyciIjIwEICwvDbDZz4MABmpqaaGtru2N+ISEhuLq6sm7dOs6fP8/evXvJycmxi8nMzKS1tZX09HQqKio4c+YMW7dutXXdDQsL48svv6SmpoampqY+KxLDwsJITExk/vz5dHd38+yzz9qt97hx45g+fTrFxcXU1dVx5MgR3nzzTSoqKhzadu7u7rzwwgtUV1dTVlbGokWLMBqNSuMx3mrNmjV8+OGHnDp1itOnT7Nr1y4CAgLw9vbuNd7NzQ2DwWC3/DSmohBCCCGEEEIIIYT49XpgFYSaprFnzx6GDRtGcnIyqampjBgxgh07dgA9Y+RdvXqVOXPmEB4ejtFoZPLkybz99tsAJCYmsnDhQtLS0vDz8yMvL++O+fn5+bFp0yZ27dpFVFQUubm5rFq1yi7G19eXkpISzGYzKSkpjB07lo0bN9q65r744otEREQQHx+Pn58fhw8f7jM/k8lEdXU1zz33nF3XZU3TKCoqIjk5mblz5xIeHk56ejr19fX4+/s7tO1GjhzJjBkzePrpp3nyySeJiYnh/fffd+izfdHr9eTl5REfH09CQgJ1dXUUFRXZjWMohBBCCCGEEEIIIX77NKvVah3sQoi+ZWdnU1hYSFVV1WAX5Ta1584qxWtWx8ai/IkZg1K8DsfTd9a6lNK+YXFXinfSHO8OD+Cua1eKd+2+4XDspe5H7h70C34u3yvFWxQHcf3h5jCHY4c4qW0XTVM7nTmhtp9U3bQ6PgGSalN21WPsetcQpXhP5+sOx3pYHY8F+BEvpXjVdbVY1Y7JDourUryKbquTUnyg9q1SfIez4/u1Q/E8Vnnxj0rxkf4/KMUP5PdbdZ/6WS8rxVdF9z1mb29CTn6mFK/CVetUiu+wqLX+12lq1+5Oi+PnPTedWtk1FM/xiueOgexSpJq26rp2WZ2V4lW2jWpZVKlumz/+P7kOx95oalZK+7v5a5XiVXk4OX7/BmC1Or5tVL+rrqhNNmjV1PaTS7fj6bfp9Eppq1Jd127Uvk83rI5fX1V/gwz0uUDl95PqMTbQ572wrwuV4s8qXLvbu9XumfTOZqV41W0T+aja77nfiu9PHB20vP2i/3XQ8h5M/dpcbMKECSxevLg/k3zo1NXVoWnaQ1lhJ4QQQgghhBBCCCGEKrVHDINo06ZNLF68mObmZgDKysqYPHlyn/Fms1otfm8yMjJobm6msLDQ9lpwcDCXLl1yeIKRu4mOjqa+vr7X9zZs+G3NEtyuDVWKd2bgnrDVmdXGbwz1vKIUr9xCQ7F15RVLoMOxehe178LQjmaleItOrYXUD5rjLQhVn66pthzztLYoxXfo1GY+D2xvUIpX0TgkTCl+RMfXSvFNzqEOx3bpHG8xBOBzs/HuQb/g3XRGKb7NS63lW7PHvY/nejduFrVWch1Oai09O62On2sC2s8rpZ0YpNbSxbelTin+3NBYpfhAq+OtK1WPyTOdI5XiVVsENkQmOxzrWXVcKe0/uKu13GxsV2vBO8RFrZXf5R8dv9YHGX5USttZp3ZfoNpKVaXljUqrGwAvy1Wl+Gad2n2maqtZLyfHr383rGrXPtVWQENROw6+fe4NpXgVN26qbcdRlm+U4n90UtuvrVbHe9M4K/aKUO11odqQtENzvAWWags/lWsfQMMNtfsCbze1e+dHm8sdjm31ClZKW/VcoNKKG9SOAxfF32YeujvPGXCrdovaPVDtPz2nFP9jp+PpR9ysVkr7kpPafYRqC/ffLcWWy+L+/WoqCG8VHx8/KK34nJyc7mtykFsVFRX1OfmJv78/er2e7OzsfstPCCGEEEIIIYQQQohf6vcZKSwWC6+99ho+Pj4EBATYVW41NDQwbdo0PD09MRgMGI1Grlz5uWVWdXU1jz/+OHq9HoPBwNixY6moqKC0tJS5c+fS0tKCpmlomsbf/vY3Ro4c2ecCsHXrVuLj49Hr9QQEBDB79mwaG+1brpw4cYKpU6diMBjQ6/UkJSVx7tw5srOz2bx5M3v27LHlWVpaatfF2GKxEBQUxPr16+3SPH78ODqdztYysLm5mQULFuDn54fBYOCJJ56gurrnqURoaGif6/BT5WBsbCwbNmwgODiYIUOGYDQaaWn5+clvRkYG06dP55133sHf3x9vb29WrFhBV1cXWVlZ+Pj4EBQUxAcffGD7TGdnJ5mZmQQGBuLu7k5oaCgrV67sn4NACCGEEEIIIYQQQvxq9HsF4ebNmxk6dChHjx4lLy+PFStWsH//fiwWC9OmTePatWscPHiQ/fv3c/78edLS0myfNZlMBAUFUV5eTmVlJa+//jouLi4kJiaydu1aDAYDly5d4tKlSyxduvSuZbl58yY5OTlUV1dTWFhIXV0dGRkZtve/++47kpOTcXNzo6SkhMrKSubNm0dXVxdLly7FaDQyadIkW56JiYl26et0OmbNmsW2bdvsXi8oKGD8+PGEhvZ00Zs5cyaNjY18+umnVFZWEhcXx8SJE7l27ZpD2/Ts2bPs3LmTTz75hH379nH8+HFeeeUVu5iSkhIuXrzIZ599xpo1a1i+fDlTp05l2LBhHD16lIULF/Lyyy/z7bc9XbTy8/PZu3cvO3fupKamhoKCAsLCwhwqjxBCCCGEEEIIIcRAsaIbtOX3qt+7GMfExLB8+XIARo0axbvvvsuBAwcA+Oqrr6itrSU4uGfshS1bthAdHU15eTkJCQk0NDSQlZXF6NGjbZ//iZeXF5qmKXXvnTdvnu3/I0aMID8/n4SEBMxmM56enrz33nt4eXmxfft2XFx6xmsIDw+3fcbDw4OOjo475mkymVi9ejUNDQ2EhIRgsVjYvn07b731FgCHDh3i2LFjNDY24ubWM17GqlWrKCwsZPfu3bz00kt3XY8bN26wZcsWHnmkZ/aidevWMWXKFFavXm0rm4+PD/n5+eh0OiIiIsjLy6OtrY033ugZp2XZsmXk5uZy6NAh0tPTaWhoYNSoUTz22GNommarzBRCCCGEEEIIIYQQvy/9XjUaExNj93dgYCCNjY2cPHmS4OBgW+UgQFRUFN7e3pw8eRKAJUuWsGDBAlJTU8nNzeXcuXP3VZbKykqeeeYZQkJC0Ov1pKSkAD1dnQGqqqpISkqyVQ7ei9jYWCIjI22tCA8ePEhjYyMzZ84EerpNm81mfH198fT0tC21tbUOr19ISIitchBg3LhxWCwWampqbK9FR0ej0/28O/39/RkzZoztbycnJ3x9fW1drDMyMqiqqiIiIoJFixZRXFx8xzJ0dHTQ2tpqt3R0qA0oLIQQQgghhBBCCHE3Vk0btOX3qt8rCG+tbNM0DYvFsZnesrOzOXHiBFOmTKGkpISoqCg+/vjjeyrH9evXeeqppzAYDBQUFFBeXm5Lq7OzZ0Y+Dw+1Wdn6YjKZbBWE27ZtY9KkSfj6+gI9sykHBgZSVVVlt9TU1JCVldUv+UPv2/1O+yIuLo7a2lpycnJob2/HaDTy/PPP95n+ypUr8fLyslvW//23NcuyEEIIIYQQQgghxO/RA+tcHRkZyYULF7hw4YLttW+++Ybm5maioqJsr4WHh/Pqq69SXFzMjBkzbBNruLq60t3t+HTgp06d4urVq+Tm5pKUlMTo0aNvm6AkJiaGsrKyPmcRdjTP2bNn8/XXX1NZWcnu3bsxmUy29+Li4rh8+TLOzs63TUIyfLhj09Y3NDRw8eJF29+ff/65rSvx/TAYDKSlpbFx40Z27NjBRx991Oe4iMuWLaOlpcVu+cvCl+8rfyGEEEIIIYQQQggx+B5YBWFqaipjxozBZDLxxRdfcOzYMebMmUNKSgrx8fG0t7eTmZlJaWkp9fX1HD58mPLyciIjIwEICwvDbDZz4MABmpqaaGtru2N+ISEhuLq6sm7dOs6fP8/evXvJycmxi8nMzKS1tZX09HQqKio4c+YMW7dutXXdDQsL48svv6SmpoampqY+KxLDwsJITExk/vz5dHd38+yzz9qt97hx45g+fTrFxcXU1dVx5MgR3nzzTSoqKhzadu7u7rzwwgtUV1dTVlbGokWLMBqNSuMx3mrNmjV8+OGHnDp1itOnT7Nr1y4CAgLw9vbuNd7NzQ2DwWC3/DSmohBCCCGEEEIIIUR/sWq6QVt+rx7Ymmuaxp49exg2bBjJycmkpqYyYsQIduzYAfSMkXf16lXmzJlDeHg4RqORyZMn8/bbbwOQmJjIwoULSUtLw8/Pj7y8vDvm5+fnx6ZNm9i1axdRUVHk5uayatUquxhfX19KSkowm82kpKQwduxYNm7caOua++KLLxIREUF8fDx+fn4cPny4z/xMJhPV1dU899xzdl2XNU2jqKiI5ORk5s6dS3h4OOnp6dTX1+Pv7+/Qths5ciQzZszg6aef5sknnyQmJob333/foc/2Ra/Xk5eXR3x8PAkJCdTV1VFUVGQ3jqEQQgghhBBCCCGE+O3TrFartb8SmzBhArGxsaxdu7a/knzo1NXV8ac//Ynjx48TGxs74PllZ2dTWFhIVVXVgOelqvbcWaX4G6iN+aihdmiqxNebHauc/Umo5xWl+A6LWutKva5VKf5ql6/DsUOd79za9la+HZeU4i06J6X4b7Uwh2M9nG4opW1FbUBZL2vvXer70qFTO4Z92r5TilfROCRMKf4P12uV4puGOj6zuZPWpZS2x02zUrx30xml+DavPyrFN3vce2vsu3GztCvFdzq5q8VbHT/X+LepHQM/DFXbjr4tdUrx54bGKsUHWr91OLZLpzb52PmOMKV4X3e1c3ZDZLLDsZ5Vx5XS/oP7D0rx3113bHiTnwxx6VSKv/zjUIdjgww/KqXtrFM71wxxUvv+dVmdHY7V4dj42j/xslxVim/Wqe2nG4r3HV5OLY6nbR3Y+7ehqB0H19ErxatouempFD/K8o1S/I/uavu11WJwONZZc3woJgAXrfeeUf2l2+r4/aGrpjbxocq1D6DxxjCleG83tfuUR5vLHY5t9Qq+e9AvqJ4LOiyuSvGa5vj31UX1fk+n9huk3TJEKd5J8Zhv7nT83BFxs1op7UtDRirFq5Z99KNBSvG/FZdOVQ1a3oGjYwct78Hk+J3QINu0aROLFy+mubn5geWZkZFBc3MzhYWFtteCg4O5dOmSw+MH/pb90K12sXXTqf3IUL3BbDA7vk9udqu1lLxyw0cp3tNFrWLrulXthlSFG2plcW9X+xGDYhNsg5fjlZseFrUbNIumVln5o+atFK/6g7BbsZJCxRCL2g8qVSrfvy6r2nqqzgxW65+oFD/85sW7B/3CHy86NtzDvbC4qlX41QwbrxTvoVP7fqto7FC7znkqVlSNMlcqxbcNcbw8OqvaeelSi9p+Uq0gVKn0M8f+WSlt/1MHlOKdNLXzmOq1WKfwY1Plh+m9xKtSXVcV13R/UIoP/vGEUrz7KccrKADOJGQ4HKtakaRSMQTQhto9kMp+Un1w2GhWq6DwHqZW2WNRPDeN/uIDh2PPjf2/1cqi2JFM9R5I5fvaYVU7BzsrVlSpnjtU99O3Pv/scKzKgwhQ3+6RR9crxZ/6vxYqxatoszj+wAjUzzXOqMV/f93x8vgOc/xBOYDq5UO17EI8KL+aCsJblZWVMXny5D7fN5vVKhYc5eTkdF9j/90qOjqa+vr6Xt/bsEFmCRZCCCGEEEIIIYQQA6vfB5yzWCy89tpr+Pj4EBAQQHZ2tu29hoYGpk2bhqenJwaDAaPRyJUrP3fdrK6u5vHHH0ev12MwGBg7diwVFRWUlpYyd+5cWlpa0DQNTdPYt28fVVVVfS4AW7duJT4+Hr1eT0BAALNnz75tJuMTJ04wdepUDAYDer2epKQkzp07R3Z2Nps3b2bPnj22PEtLS6mrq0PTNKqqqrBYLAQFBbF+vf2TmuPHj6PT6WwVf83NzSxYsAA/Pz8MBgNPPPEE1dU9zZaLior6XIdfTnayYcMGgoODGTJkCEajkZaWn7uGZGRkMH36dN555x38/f3x9vZmxYoVdHV1kZWVhY+PD0FBQbYZoQE6OzvJzMwkMDAQd3d3QkNDWbly5f3tfCGEEEIIIYQQQoj7JJOUPHj93oJw8+bNLFmyhKNHj/KPf/yDjIwMxo8fz8SJE22VgwcPHqSrq4t///d/Jy0tjdLSUqBnoo8///nPrF+/HicnJ6qqqnBxcSExMZG1a9fy17/+1TbDsKenJ56ed+6ScPPmTXJycoiIiKCxsZElS5aQkZFBUVERAN999x3JyclMmDCBkpISDAYDhw8fpquri6VLl3Ly5ElaW1ttFWs+Pj5cvPhz1zWdTsesWbPYtm0bf/nLX2yvFxQUMH78eEJDe5omz5w5Ew8PDz799FO8vLzYsGEDEydO5PTp07aYOzl79iw7d+7kk08+obW1lfnz5/PKK69QUFBgiykpKSEoKIjPPvuMw4cPM3/+fI4cOUJycjJHjx5lx44dvPzyy/zbv/0bQUFB5Ofns3fvXnbu3ElISAgXLlzgwoULDuxhIYQQQgghhBBCCPFb0u8VhDExMSxfvhyAUaNG8e6773LgQM/4OF999RW1tbUEB/eM17Flyxaio6MpLy8nISGBhoYGsrKyGD16tO3zP/Hy8kLTNKXuvfPmzbP9f8SIEeTn55OQkIDZbMbT05P33nsPLy8vtm/fbpu5ODw83PYZDw8POjo67pinyWRi9erVNDQ0EBISgsViYfv27bz11lsAHDp0iGPHjtHY2IibW8+AuqtWraKwsJDdu3fz0ksv3XU9bty4wZYtW3jkkUcAWLduHVOmTGH16tW2svn4+JCfn49OpyMiIoK8vDza2tp44403AFi2bBm5ubkcOnSI9PR0GhoaGDVqFI899hiapjlUUSmEEEIIIYQQQggx0FTHLRf3r9/bTsbExNj9HRgYSGNjIydPniQ4ONhWOQgQFRWFt7c3J0+eBGDJkiUsWLCA1NRUcnNzOXfu3H2VpbKykmeeeYaQkBD0ej0pKSlAT1dngKqqKpKSkmyVg/ciNjaWyMhItm3bBsDBgwdpbGxk5syZQE+3abPZjK+vr63Vo6enJ7W1tQ6vX0hIiK1yEGDcuHFYLBZba0roGctQp/t5d/r7+zNmzBjb305OTvj6+tq6WGdkZFBVVUVERASLFi2iuLj4jmXo6OigtbXVbunsVJtxTAghhBBCCCGEEEI8fPq9gvDWyjZN07BYHJt9KTs7mxMnTjBlyhRKSkqIiori448/vqdyXL9+naeeegqDwUBBQQHl5eW2tDo7e2bT9fDwuKe0b2UymWwVhNu2bWPSpEn4+vbM1Go2mwkMDLxtfMGamhqysrL6JX/ofbvfaV/ExcVRW1tLTk4O7e3tGI1Gnn/++T7TX7lyJV5eXnbLBxvW9lv5hRBCCCGEEEIIIcTgeGCjL0ZGRt42zt0333xDc3MzUVFRttfCw8N59dVXKS4uZsaMGbbx/1xdXenu7nY4v1OnTnH16lVyc3NJSkpi9OjRt01QEhMTQ1lZGTdv9j7NuKN5zp49m6+//prKykp2796NyWSyvRcXF8fly5dxdnZm5MiRdsvw4cMdWpeGhga7sQ8///xzW1fi+2EwGEhLS2Pjxo3s2LGDjz76iGvXrvUau2zZMlpaWuyWuS8vvq/8hRBCCCGEEEIIIW5lRRu05ffqgVUQpqamMmbMGEwmE1988QXHjh1jzpw5pKSkEB8fT3t7O5mZmZSWllJfX8/hw4cpLy8nMjISgLCwMMxmMwcOHKCpqYm2trY75hcSEoKrqyvr1q3j/Pnz7N27l5ycHLuYzMxMWltbSU9Pp6KigjNnzrB161Zb192wsDC+/PJLampqaGpq6rMiMSwsjMTERObPn093d7fd7MOpqamMGzeO6dOnU1xcTF1dHUeOHOHNN9+koqLCoW3n7u7OCy+8QHV1NWVlZSxatAij0ag0HuOt1qxZw4cffsipU6c4ffo0u3btIiAgAG9v717j3dzcMBgMdourq9s95y+EEEIIIYQQQgghHg4PrIJQ0zT27NnDsGHDSE5OJjU1lREjRrBjxw6gZ4y8q1evMmfOHMLDwzEajUyePJm3334bgMTERBYuXEhaWhp+fn7k5eXdMT8/Pz82bdrErl27iIqKIjc3l1WrVtnF+Pr6UlJSgtlsJiUlhbFjx7Jx40Zb19wXX3yRiIgI4uPj8fPz4/Dhw33mZzKZqK6u5rnnnrPruqxpGkVFRSQnJzN37lzCw8NJT0+nvr4ef39/h7bdyJEjmTFjBk8//TRPPvkkMTExvP/++w59ti96vZ68vDzi4+NJSEigrq6OoqIiu3EMhRBCCCGEEEIIIR40q6YbtOX3SrNardbBLoToW3Z2NoWFhVRVVQ12UW7zxemrSvFuuk6leA21Q7PB7FiXbYCb3Wpfei93tQlZPF1uKMW76npvndqXG92Ot970ce6923hfhjefVYpH8QR62cvxrvEeFrNS2hbNSSn+OnqleJ3m2HiqP/G98Z1SvIp2V4NSvGd7k1L81aEhDsdaFJ81eXY3K8U36xz/bgMMv3nx7kG/oG86rxSvwuLqrhRfM2y8UryHzvFzTUC72nrWOMfcPegXRnBGKd6jXe3c1DbE8eOgS6c2+VjZ99FK8f/k33j3oF+41uH4ucYc+2eltB89dUAp/lKbr1K8u7Pa9emKeajDsY8YflRK21nXpRSv8v0A6LY6fg1RvUfpRu36FPzjCaV491PlSvFnEjIcjnXR1I4Ble0I6ttShWo3sTM//EEpPnzYFaV41evlyMotDseeG/t/K6Wtek+jQy1eZV0tVrXt4qypnQsu31A77+ld2pXiPZwcP9d0WZ2V0lbd7iP+8f9Tij/1fy10ONZFcburfv9UzzXOqMV/84Pj97ajh6ndw9+0qt13uGlq16eRj/5JKf634sKZbwYt7+BRUXcP+g3q16rRCRMmsHjx4v5M8qFTV1eHpmkPZYWdEEIIIYQQQgghhBCq+rUF4YQJE4iNjWXt2rX9laTNpk2bWLx4Mc3NzQCUlZUxefLkPuPNZrWWR73JyMigubmZwsJC22vd3d18//33DB8+HGdntSdAvYmOjqa+vr7X9zZs2MCZM2ce2haEp859qxSv+hRJ9YmyyhM51adxmqZWFtWyD/S2UeGiqbX0VNVpdbz140Cu571QLY/qcaZCtTWKE45P8gTqrRxUqG5H1bKorqvqtlSh2ipiIM81qsfjQJ+XdIr7qcPqeGvMIdp1pbTbrI63egP11isqx7Dqdjw3eqJS/IhTJUrxA3k9G+hz/MN0DRno79PDdB+h6mEqu+r1xgW1e6Zu1H47PEzfp18z1f2qui1VWrJ1odbSbKDPBSoetvOSavoq8a6aWu+xLsUWhKoiHg0e0PQfVg1nTg5a3iGjIgct78F0/zVcgyQ+Pn5QKs2cnJzua3KQWxUVFfU5+Ym/vz96vZ7s7Ox+y08IIYQQQgghhBBCiF/q9yYiFouF1157DR8fHwICAuwqtxoaGpg2bRqenp4YDAaMRiNXrvw8dkd1dTWPP/44er0eg8HA2LFjqaiooLS0lLlz59LS0oKmaWiaxt/+9jdGjhzZ5wKwdetW4uPj0ev1BAQEMHv2bBob7ccNOnHiBFOnTsVgMKDX60lKSuLcuXNkZ2ezefNm9uzZY8uztLTUrouxxWIhKCiI9evX26V5/PhxdDqdrWVgc3MzCxYswM/PD4PBwBNPPEF1dTUAoaGhfa7DT5WDsbGxbNiwgeDgYIYMGYLRaKSlpcWWX0ZGBtOnT+edd97B398fb29vVqxYQVdXF1lZWfj4+BAUFMQHH3xg+0xnZyeZmZkEBgbi7u5OaGgoK1eu7J+DQAghhBBCCCGEEOIeySQlD16/r/nmzZsZOnQoR48eJS8vjxUrVrB//34sFgvTpk3j2rVrHDx4kP3793P+/HnS0tJsnzWZTAQFBVFeXk5lZSWvv/46Li4uJCYmsnbtWgwGA5cuXeLSpUssXbr0rmW5efMmOTk5VFdXU1hYSF1dHRkZGbb3v/vuO5KTk3Fzc6OkpITKykrmzZtHV1cXS5cuxWg0MmnSJFueiYmJdunrdDpmzZrFtm3b7F4vKChg/PjxhIaGAjBz5kwaGxv59NNPqaysJC4ujokTJ3LtmmMDtJ89e5adO3fyySefsG/fPo4fP84rr7xiF1NSUsLFixf57LPPWLNmDcuXL2fq1KkMGzaMo0ePsnDhQl5++WW+/banW3B+fj579+5l586d1NTUUFBQQFhYmEPlEUIIIYQQQgghhBC/Hf3exTgmJobly5cDMGrUKN59910OHOiZYe+rr76itraW4OCePvRbtmwhOjqa8vJyEhISaGhoICsri9GjR9s+/xMvLy80TVPq3jtv3jzb/0eMGEF+fj4JCQmYzWY8PT1577338PLyYvv27bi49IwbEB4ebvuMh4cHHR0dd8zTZDKxevVqGhoaCAkJwWKxsH37dt566y0ADh06xLFjx2hsbMTNrWfstVWrVlFYWMju3bt56aWX7roeN27cYMuWLTzyyCMArFu3jilTprB69Wpb2Xx8fMjPz0en0xEREUFeXh5tbW288cYbACxbtozc3FwOHTpEeno6DQ0NjBo1isceewxN02yVmUIIIYQQQgghhBDi96XfWxDGxMTY/R0YGEhjYyMnT54kODjYVjkIEBUVhbe3NydP9gw+uWTJEhYsWEBqaiq5ubmcO3fuvspSWVnJM888Q0hICHq9npSUFKCnqzNAVVUVSUlJtsrBexEbG0tkZKStFeHBgwdpbGxk5syZQE+3abPZjK+vL56enraltrbW4fULCQmxVQ4CjBs3DovFQk1Nje216OhodLqfd6e/vz9jxoyx/e3k5ISvr6+ti3VGRgZVVVVERESwaNEiiouL71iGjo4OWltb7ZbODrXBW4UQQgghhBBCCCHuxoo2aMvvVb9XEN5a2aZpGhaLYzMnZmdnc+LECaZMmUJJSQlRUVF8/PHH91SO69ev89RTT2EwGCgoKKC8vNyWVmdnz4xjHh4e95T2rUwmk62CcNu2bUyaNAlfX1+gZzblwMBAqqqq7JaamhqysrL6JX/ofbvfaV/ExcVRW1tLTk4O7e3tGI1Gnn/++T7TX7lyJV5eXnbL//j7e/1WfiGEEEIIIYQQQggxOB7YLMaRkZFcuHCBCxcu2FoRfvPNNzQ3NxMVFWWLCw8PJzw8nFdffZVZs2bxwQcf8Nxzz+Hq6kp3d7fD+Z06dYqrV6+Sm5try6+iosIuJiYmhs2bN3Pz5s1eWxE6mufs2bN56623qKysZPfu3fz973+3vRcXF8fly5dxdna+5zH+GhoauHjxIn/84x8B+Pzzz21die+HwWAgLS2NtLQ0nn/+eSZNmsS1a9fw8fG5LXbZsmUsWbLE7rW6b7+/r/yFEEIIIYQQQgghbvV7nixksDywLZ6amsqYMWMwmUx88cUXHDt2jDlz5pCSkkJ8fDzt7e1kZmZSWlpKfX09hw8fpry8nMjISADCwsIwm80cOHCApqYm2tra7phfSEgIrq6urFu3jvPnz7N3715ycnLsYjIzM2ltbSU9PZ2KigrOnDnD1q1bbV13w8LC+PLLL6mpqaGpqYmbN2/2mldYWBiJiYnMnz+f7u5unn32Wbv1HjduHNOnT6e4uJi6ujqOHDnCm2++eVuFZV/c3d154YUXqK6upqysjEWLFmE0GpXGY7zVmjVr+PDDDzl16hSnT59m165dBAQE4O3t3Wu8m5sbBoPBbnH9jzEVhRBCCCGEEEIIIcSv1wOrINQ0jT179jBs2DCSk5NJTU1lxIgR7NixA+gZI+/q1avMmTOH8PBwjEYjkydP5u233wYgMTGRhQsXkpaWhp+fH3l5eXfMz8/Pj02bNrFr1y6ioqLIzc1l1apVdjG+vr6UlJRgNptJSUlh7NixbNy40daa8MUXXyQiIoL4+Hj8/Pw4fPhwn/mZTCaqq6t57rnn7Loua5pGUVERycnJzJ07l/DwcNLT06mvr8ff39+hbTdy5EhmzJjB008/zZNPPklMTAzvv/++Q5/ti16vJy8vj/j4eBISEqirq6OoqMhuHEMhhBBCCCGEEEKIB03GIHzwNKvVah3sQoi+ZWdnU1hYSFVV1WAX5Tanzn2rFK/6RdNQOzS7rI73mNfh2LiYtrJoamVRLftAbxsVLlrngKUN0Gl1vOXpQK7nvVAtj+pxpqIbJ6V4JxwfogHAMoDPj1S3o2pZVNdVdVuqsFjVyj6Q5xrV43Ggz0s6xf3UYXV3OHaIdl0p7TbrUKV4Z61LKV7lGFbdjudGT1SKH3GqRCl+IK9nA32Of5iuIQP9fXqY7iNUPUxlV73euKB2z9StOLrTw/R9+jVT3a+q29KZ3nuZ9aYLtckxB/pcoOJhOy+ppq8S76qpTcbZZb33SU8dEfFo8N2DfoPO3+ektfdjxKOPDlreg6lffwFOmDCBxYsX92eSD526ujo0TXsoK+yEEEIIIYQQQgghhFD1wCYpuV+bNm1i8eLFNDc3A1BWVsbkyZP7jDebzfedZ0ZGBs3NzRQWFtpeCw4O5tKlSwwfPvy+0weIjo6mvr6+1/c2bNjQL3kMFFfUnqyYLXqleHfdDaX49m7HW5dc+lGtLCO81SZkUX0CNpQfleLNGByO7bC4KqUdaL2mFN+tUzuNXOnyczjWz1WtLKpUnzyqthRw6VY7hlW0O6kdwzpNrbWW1TpwrRbcLHceQ/ZW11A73/7Bckkpvtmpf87nvVFp2QwwVKd27eqwOH7e01t+UEvbaYhSvFu32n71ularFP+Za9/X/FtFeqm1IFTVYVEbg7ex3cvhWCdNraWnaovA86OfUIr3qv5CKf7bZsePm9Bhatc+Vye1lptDnNqV4rutjrcmHuieCKrnDg+d2vfP0O74fY3Oqnb9UNXi4diQOz9ptzp+jKle57sUjgGAbk0t3l1TOyZV1lX1mPTQ1I4Z1daPXQrxqvdXbha17djEH5TiR137h1J8re+/OByr2gJ9oM8FLV2OX59cdY63lATw7+j9N25fVM81ZndfpfgfrLdPxNl3YZSSHtBWp79nVu3329V3sPxqKghvFR8fPyit+JycnO5rcpBbFRUV9Tn5ib+/P3q9nuzs7H7LTwghhBBCCCGEEEKIX+r3QaYsFguvvfYaPj4+BAQE2FVuNTQ0MG3aNDw9PTEYDBiNRq5cuWJ7v7q6mscffxy9Xo/BYGDs2LFUVFRQWlrK3LlzaWlpQdM0NE3jb3/7GyNHjuxzAdi6dSvx8fHo9XoCAgKYPXs2jY2NduU9ceIEU6dOxWAwoNfrSUpK4ty5c2RnZ7N582b27Nljy7O0tNSui7HFYiEoKIj169fbpXn8+HF0Op2tZWBzczMLFizAz88Pg8HAE088QXV1NQChoaF9rsNPlYOxsbFs2LCB4OBghgwZgtFopKWlxZZfRkYG06dP55133sHf3x9vb29WrFhBV1cXWVlZ+Pj4EBQUxAcffGD7TGdnJ5mZmQQGBuLu7k5oaCgrV67sn4NACCGEEEIIIYQQ4h5ZrdqgLb9X/V5BuHnzZoYOHcrRo0fJy8tjxYoV7N+/H4vFwrRp07h27RoHDx5k//79nD9/nrS0NNtnTSYTQUFBlJeXU1lZyeuvv46LiwuJiYmsXbsWg8HApUuXuHTpEkuXLr1rWW7evElOTg7V1dUUFhZSV1dHRkaG7f3vvvuO5ORk3NzcKCkpobKyknnz5tHV1cXSpUsxGo1MmjTJlmdiYqJd+jqdjlmzZrFt2za71wsKChg/fjyhoaEAzJw5k8bGRj799FMqKyuJi4tj4sSJXLvmWPfJs2fPsnPnTj755BP27dvH8ePHeeWVV+xiSkpKuHjxIp999hlr1qxh+fLlTJ06lWHDhnH06FEWLlzIyy+/zLff9kwskp+fz969e9m5cyc1NTUUFBQQFhbmUHmEEEIIIYQQQgghxG9Hv3cxjomJYfny5QCMGjWKd999lwMHDgDw1VdfUVtbS3Bwzyw8W7ZsITo6mvLychISEmhoaCArK4vRo0fbPv8TLy8vNE1T6t47b9482/9HjBhBfn4+CQkJmM1mPD09ee+99/Dy8mL79u24uPTMPBQeHm77jIeHBx0dHXfM02QysXr1ahoaGggJCcFisbB9+3beeustAA4dOsSxY8dobGzEza1nzKJVq1ZRWFjI7t27eemll+66Hjdu3GDLli088sgjAKxbt44pU6awevVqW9l8fHzIz89Hp9MRERFBXl4ebW1tvPHGGwAsW7aM3NxcDh06RHp6Og0NDYwaNYrHHnsMTdNslZlCCCGEEEIIIYQQ4vel31sQxsTE2P0dGBhIY2MjJ0+eJDg42FY5CBAVFYW3tzcnT54EYMmSJSxYsIDU1FRyc3M5d5/TWldWVvLMM88QEhKCXq8nJSUF6OnqDFBVVUVSUpKtcvBexMbGEhkZaWtFePDgQRobG5k5cybQ023abDbj6+uLp6enbamtrXV4/UJCQmyVgwDjxo3DYrFQU1Njey06Ohqd7ufd6e/vz5gxY2x/Ozk54evra+tinZGRQVVVFRERESxatIji4uI7lqGjo4PW1la7paNDbZISIYQQQgghhBBCiLuxohu05feq39f81so2TdOwWBybVSs7O5sTJ04wZcoUSkpKiIqK4uOPP76ncly/fp2nnnoKg8FAQUEB5eXltrQ6O3tmyvLw8LintG9lMplsFYTbtm1j0qRJ+Pr2zKpkNpsJDAykqqrKbqmpqSErK6tf8ofet/ud9kVcXBy1tbXk5OTQ3t6O0Wjk+eef7zP9lStX4uXlZbf8/e9/77fyCyGEEEIIIYQQQojB8cCqRiMjI7lw4QIXLlywvfbNN9/Q3NxMVFSU7bXw8HBeffVViouLmTFjhm1iDVdXV7q7HZ/6/NSpU1y9epXc3FySkpIYPXr0bROUxMTEUFZW1ucswo7mOXv2bL7++msqKyvZvXs3JpPJ9l5cXByXL1/G2dn5tklIhg8f7tC6NDQ0cPHiRdvfn3/+ua0r8f0wGAykpaWxceNGduzYwUcffdTnuIjLli2jpaXFblm4cOF95S+EEEIIIYQQQghxKyvaoC334r333iMsLAx3d3f+9V//lWPHjt0xfteuXYwePRp3d3fGjBlDUVGR/fpbrfz1r38lMDAQDw8PUlNTOXPmzD2VzVEPrIIwNTWVMWPGYDKZ+OKLLzh27Bhz5swhJSWF+Ph42tvbyczMpLS0lPr6eg4fPkx5eTmRkZEAhIWFYTabOXDgAE1NTbS1td0xv5CQEFxdXVm3bh3nz59n79695OTk2MVkZmbS2tpKeno6FRUVnDlzhq1bt9q67oaFhfHll19SU1NDU1NTnxWJYWFhJCYmMn/+fLq7u3n22Wft1nvcuHFMnz6d4uJi6urqOHLkCG+++SYVFRUObTt3d3deeOEFqqurKSsrY9GiRRiNRqXxGG+1Zs0aPvzwQ06dOsXp06fZtWsXAQEBeHt79xrv5uaGwWCwW34aU1EIIYQQQgghhBDi92jHjh0sWbKE5cuX88UXX/DP//zPPPXUU7c1UvvJkSNHmDVrFvPnz+f48eNMnz6d6dOn8/XXX9ti8vLyyM/P5+9//ztHjx5l6NChPPXUU9y4cWPA1uOBVRBqmsaePXsYNmwYycnJpKamMmLECHbs2AH0jJF39epV5syZQ3h4OEajkcmTJ/P2228DkJiYyMKFC0lLS8PPz4+8vLw75ufn58emTZvYtWsXUVFR5ObmsmrVKrsYX19fSkpKMJvNpKSkMHbsWDZu3Gjrmvviiy8SERFBfHw8fn5+HD58uM/8TCYT1dXVPPfcc3ZdlzVNo6ioiOTkZObOnUt4eDjp6enU19fj7+/v0LYbOXIkM2bM4Omnn+bJJ58kJiaG999/36HP9kWv15OXl0d8fDwJCQnU1dVRVFRkN46hEEIIIYQQQgghhOjbmjVrePHFF5k7dy5RUVH8/e9/Z8iQIfzP//k/e43/7//9vzNp0iSysrKIjIwkJyeHuLg43n33XaCn9eDatWt56623mDZtGjExMWzZsoWLFy9SWFg4YOuhWa1W64ClLu5bdnY2hYWFVFVVDXZRbnNecRIZs0WvFO+uU6sZb+kyOBx76Ue1sozw/l4pXkPtazWUH5XizTi+rh0WV6W0A63fKsV369QmQ7/QFXz3oP/g59p7l/f+otp83IVOtfjugXu60+6kdgzrNMeHaADotjq+X1WPdzfLnVuA3+oajg3H8JM/WC4pxTc7qaWvokthOwIMdTIrxXdY3B2O9bJcVUvbaYhSvFu32n71ularFP+Z62SHYyO96pXSbrMOVYrvtjopxTe2ezkc66Q5Nm7zTwKGqJ0nz49+Qineq/oLpfhvmx0/bkKHqV37XJ26lOKHOLUrxavsVx1q+0nT1M6TqseYh07t+2dod/y+RmdVu36oavFw7IH5T9qtjh9jqtf5LsXt7qx4bXXX1I5JlXVVPSY9NLVjphu161mXQrzq/ZWbRW07NvEHpfhR1/6hFF/r+y8OxzprauexgT4XtHQ5fn1y1fXem64v/h1q12LVc43Z3Vcp/gerj8Oxbjq1Y1L1XtgZtW058tE/KcX/VtScu3D3oAESFvSH2yZldXNz67UXZWdnJ0OGDGH37t1Mnz7d9voLL7xAc3Mze/bsue0zISEhLFmyhMWLF9teW758OYWFhVRXV3P+/HkeffRRjh8/TmxsrC0mJSWF2NhY/vt//+/3vY696dfmYhMmTLBbwd+iuro6NE17KCvshBBCCCGEEEIIIcS9622S1pUrV/Ya29TURHd39209RP39/bl8+XKvn7l8+fId43/6VyXN/qD2KGgQbdq0icWLF9Pc3AxAWVkZkyf33ZrAbFZrhdGbjIwMmpub7ZpwBgcHc+nSJYcnGLmb6Oho6ut7f7qyYcOGfsljoFgU65edFJ+y+recVorvNPyTw7Ga5qmUtsWqtq6qTwctmtrTQW0AG/663VT77qg+nbfoQh2OVX26ZtEUj0mL4lNcxdaSKk8TVbejarxnxw9K8T+6Ov6U1dWi1lJS9Xj30lqU4q1WtW0T0H5eKV6FrlvtGLuif1QpXqVVks6idg62OqttR9Xz0g1PP6X4Rz0cvxnSK7SOAvjRzfFW2QA6xVZ+Q1wcb4mg2gpBNV61RWDLP8cpxXuXf+VwrGqLQBfFa6vqtlFpgaXaInDYzd7HIOrLNWfFVnUWtRa/Xe5/dDxWsSW06nZ3Qu3cpJK+alma2ocpxcdb1FqaXR+i1uLpppPjvUBU71U7URtLXHVbqlyLVctyU6fWO+bmTRel+AbfsUrxfl0X7x50j1TPBTcsHncP+gWVVrCqx8BV90eU4lVbS6p/vx2/1v9L12dKaX9riFaKv8c5MH537nWykP6wbNkylixZYvfa72EOhl9NBeGt4uPjB6UVn5OT031NDnKroqKiPic/8ff3R6/Xk52d3W/5CSGEEEIIIYQQQoje9dWduDfDhw/HycmJK1eu2L1+5cqVPuuOAgIC7hj/079XrlwhMDDQLuaXXY77W7/PSGGxWHjttdfw8fEhICDArnKroaGBadOm4enpicFgwGg02m2U6upqHn/8cfR6PQaDgbFjx1JRUUFpaSlz586lpaUFTdPQNI2//e1vjBw5ss8FYOvWrcTHx6PX6wkICGD27Nm3zSJz4sQJpk6disFgQK/Xk5SUxLlz58jOzmbz5s3s2bPHlmdpaaldF2OLxUJQUBDr16+3S/P48ePodDpby8Dm5mYWLFiAn58fBoOBJ554gurqagBCQ0P7XIefKgdjY2PZsGEDwcHBDBkyBKPRSEvLzy1qMjIymD59Ou+88w7+/v54e3uzYsUKurq6yMrKwsfHh6CgID744APbZzo7O8nMzCQwMBB3d3dCQ0P7bDIrhBBCCCGEEEIIIey5uroyduxYDhw4YHvNYrFw4MABxo0b1+tnxo0bZxcPsH//flv8n/70JwICAuxiWltbOXr0aJ9p9od+ryDcvHkzQ4cO5ejRo+Tl5bFixQr279+PxWJh2rRpXLt2jYMHD7J//37Onz9PWlqa7bMmk4mgoCDKy8uprKzk9ddfx8XFhcTERNauXYvBYODSpUtcunSJpUuX3rUsN2/eJCcnh+rqagoLC6mrqyMjI8P2/nfffUdycjJubm6UlJRQWVnJvHnz6OrqYunSpRiNRiZNmmTLMzEx0S59nU7HrFmz2LZtm93rBQUFjB8/ntDQnq6UM2fOpLGxkU8//ZTKykri4uKYOHEi1645NrD42bNn2blzJ5988gn79u3j+PHjvPLKK3YxJSUlXLx4kc8++4w1a9awfPlypk6dyrBhwzh69CgLFy7k5Zdf5ttveyagyM/PZ+/evezcuZOamhoKCgoICwtzqDxCCCGEEEIIIYQQA8WKNmiLqiVLlrBx40Y2b97MyZMn+ctf/sL169eZO3cuAHPmzGHZsmW2+P/yX/4L+/btY/Xq1Zw6dYrs7GwqKirIzMwEQNM0Fi9ezH/7b/+NvXv38tVXXzFnzhz++Mc/2k2E0t/6vYtxTEwMy5cvB2DUqFG8++67tlrPr776itraWoKDe2Yx3bJlC9HR0ZSXl5OQkEBDQwNZWVmMHj3a9vmfeHl5oWmaUvfeefPm2f4/YsQI8vPzSUhIwGw24+npyXvvvYeXlxfbt2/HxaVnbIrw8HDbZzw8POjo6LhjniaTidWrV9PQ0EBISAgWi4Xt27fz1ltvAXDo0CGOHTtGY2OjrYnqqlWrKCwsZPfu3bz00kt3XY8bN26wZcsWHnmkZxyHdevWMWXKFFavXm0rm4+PD/n5+eh0OiIiIsjLy6OtrY033ngD6OlDn5uby6FDh0hPT6ehoYFRo0bx2GOPoWmarTJTCCGEEEIIIYQQQjgmLS2N77//nr/+9a9cvnyZ2NhY9u3bZ5tkpKGhAZ3u5/Z5iYmJbNu2jbfeeos33niDUaNGUVhYyD/908/zKrz22mtcv36dl156iebmZh577DH27duHu7v7gK1Hv7cgjImJsfs7MDCQxsZGTp48SXBwsK1yECAqKgpvb29OnjwJ9NS6LliwgNTUVHJzczl37tx9laWyspJnnnmGkJAQ9Ho9KSkpQM/OAaiqqiIpKclWOXgvYmNjiYyMtLUiPHjwII2NjcycORPo6TZtNpvx9fXF09PTttTW1jq8fiEhIbbKQehpjmqxWKipqbG9Fh0dbXfA+fv7M2bMGNvfTk5O+Pr62rpYZ2RkUFVVRUREBIsWLaK4uPiOZejo6KC1tdVuuXXabyGEEEIIIYQQQoj7ZbVqg7bci8zMTOrr6+no6ODo0aP867/+q+290tJSNm3aZBc/c+ZMampq6Ojo4Ouvv+bpp5+2e1/TNFasWMHly5e5ceMG/+f//B+7Bm0Dod8rCG+tbNM0DYvFsVnhsrOzOXHiBFOmTKGkpISoqCg+/vjjeyrH9evXeeqppzAYDBQUFFBeXm5Lq7OzZzZBDw+1WZ76YjKZbBWE27ZtY9KkSfj69sxSZjabCQwMpKqqym6pqakhKyurX/KH3rf7nfZFXFwctbW15OTk0N7ejtFo5Pnnn+8z/d6m+d7w9/V9xgshhBBCCCGEEEKIX4d+ryDsS2RkJBcuXODChQu217755huam5uJioqyvRYeHs6rr75KcXExM2bMsE2s4erqSne349Ownzp1iqtXr5Kbm0tSUhKjR4++bYKSmJgYysrK+pxF2NE8Z8+ezddff01lZSW7d+/GZDLZ3ouLi+Py5cs4OzvfNgnJ8OHDHVqXhoYGLl68aPv7888/t3Ulvh8Gg4G0tDQ2btzIjh07+Oijj/ocF3HZsmW0tLTYLS8v/Mt95S+EEEIIIYQQQgghBt8DqyBMTU1lzJgxmEwmvvjiC44dO8acOXNISUkhPj6e9vZ2MjMzKS0tpb6+nsOHD1NeXk5kZCQAYWFhmM1mDhw4QFNTE21tbXfMLyQkBFdXV9atW8f58+fZu3cvOTk5djGZmZm0traSnp5ORUUFZ86cYevWrbauu2FhYXz55ZfU1NTQ1NTUZ0ViWFgYiYmJzJ8/n+7ubp599lm79R43bhzTp0+nuLiYuro6jhw5wptvvklFRYVD287d3Z0XXniB6upqysrKWLRoEUajUWk8xlutWbOGDz/8kFOnTnH69Gl27dpFQEAA3t7evca7ublhMBjsFken/RZCCCGEEEIIIYRw1K9pkpLfigdWQahpGnv27GHYsGEkJyeTmprKiBEj2LFjB9AzRt7Vq1eZM2cO4eHhGI1GJk+ezNtvvw30DOK4cOFC0tLS8PPzIy8v7475+fn5sWnTJnbt2kVUVBS5ubmsWrXKLsbX15eSkhLMZjMpKSmMHTuWjRs32rrmvvjii0RERBAfH4+fnx+HDx/uMz+TyUR1dTXPPfecXddlTdMoKioiOTmZuXPnEh4eTnp6OvX19bYBK+9m5MiRzJgxg6effponn3ySmJgY3n//fYc+2xe9Xk9eXh7x8fEkJCRQV1dHUVGR3TiGQgghhBBCCCGEEOK3T7NardbBLoToW3Z2NoWFhVRVVQ12UW5z9lytUnyHVW22naDWE0rxFwz/dPeg/1Db4lj37p+EGnrvet0XZ61LKd5da1eKb7cOcTj2hkWtpWdY5ymleNUnLKd0MXcP+g+PuF1SStuiqVVwO1nU9lO3Tm3id/eu6w7Hqm7HNieDUrxX5/dK8T+6+jgc62q5oZS2RXNSircq7lfXbrXvk0dHi1K8Cl232jF2Rf+oUrxF4Tmf9021Y6DNVe0Y87hpVop3ven49wPgqkeQw7G+7d8qpX3RTW276zTHxlb+SevNoQ7Haqjdlg1zbVWKb+oYphTf8s9xSvFO5V85HOs3RO2YcVG8trrqOpXiLVbHv0+aprifbjbePegXrjk79hD5JyrnAgAXrfdeMb3psqpd+1SPYSfN8SGEALqtatcQFRfbfJXi4y3/UIq/PkQt/VYnx6/FKscvqG931f06kPtJ9fv3401PpXhPZ7Xrk0/XFaV4FarnAtX7SZX9pHrMDPR3W/WYVPl+/0vXZ0ppf2uIVop31dQm+xz56J+U4n8rvj57edDy/qeR995b89esX5uLTZgwgcWLF/dnkg+duro6NE17KCvshBBCCCGEEEIIIYRQpfZIcBBt2rSJxYsX09zcDEBZWRmTJ0/uM95sVnsq3ZuMjAyam5spLCy0vRYcHMylS5ccnmDkbqKjo6mvr+/1vQ0bNvRLHgOl06rWMk316VqVW6JS/L/W73Y4tnnJWqW0w/7nSqX460PVnvbVo/ZUKLKt3OFY1dZXn2uPKcUP91B7yhp/rsDh2KoRaUppOyu26lF92q7aaugfZx3frzqd2hPfpJFqrVH+3wtjleIfG+F4680LN9SesHm6qj01be1Ua318+ju1eP3QgWtIb7Wq7dcxQ9Va+fnQ5HDs1hOOt94FmBWj1pp4b51a+i0/qn2fXvinaodjC6+MU0o7veZ1pfjTqVlK8Zd/dLwFoU6xZYy3z49K8d82O94CHcBboUUgQHfCGIdjW7+oUkrb3dnxVm8ALq5q8Sot5XSoHb/f6UKV4l2saq0lL173Vor/wxDHjxvVa59OsVXP9W61Y9LdyfFriOo5+F9KlinFF/3LaqX48CFqPVJa2x3fNqOd1c7ZA90yTSVepUUrwE2ri1J8zDeblOKPRSxQiq+57vi2DPFW67Wgei643OatFJ/Yvs/h2LO+atfWDourUrzqucZ8U+3c8S//5//jcOzOf16rlrZe7b68U/EYFuJB+dVUEN4qPj5+UFrxOTk53dfkILcqKirqc/ITf39/9Ho92dnZ/ZafEEIIIYQQQgghxMPs9zxZyGDp9xkpLBYLr732Gj4+PgQEBNhVbjU0NDBt2jQ8PT0xGAwYjUauXPm5VVl1dTWPP/44er0eg8HA2LFjqaiooLS0lLlz59LS0oKmaWiaxt/+9jdGjhzZ5wKwdetW4uPj0ev1BAQEMHv2bBob7Wv3T5w4wdSpUzEYDOj1epKSkjh37hzZ2dls3ryZPXv22PIsLS2162JssVgICgpi/fr1dmkeP34cnU5naxnY3NzMggUL8PPzw2Aw8MQTT1Bd3dMSIjQ0tM91+KlyMDY2lg0bNhAcHMyQIUMwGo20tPz89CkjI4Pp06fzzjvv4O/vj7e3NytWrKCrq4usrCx8fHwICgrigw8+sH2ms7OTzMxMAgMDcXd3JzQ0lJUr1VrJCSGEEEIIIYQQQohfv36vINy8eTNDhw7l6NGj5OXlsWLFCvbv34/FYmHatGlcu3aNgwcPsn//fs6fP09a2s9dCE0mE0FBQZSXl1NZWcnrr7+Oi4sLiYmJrF27FoPBwKVLl7h06RJLly69a1lu3rxJTk4O1dXVFBYWUldXR0ZGhu397777juTkZNzc3CgpKaGyspJ58+bR1dXF0qVLMRqNTJo0yZZnYqJ9l1edTsesWbPYtm2b3esFBQWMHz+e0NCe7iQzZ86ksbGRTz/9lMrKSuLi4pg4cSLXrjnWzeDs2bPs3LmTTz75hH379nH8+HFeeeUVu5iSkhIuXrzIZ599xpo1a1i+fDlTp05l2LBhHD16lIULF/Lyyy/z7bc9A7fn5+ezd+9edu7cSU1NDQUFBYSFhTlUHiGEEEIIIYQQQoiBYrVqg7b8XvV7F+OYmBiWL18OwKhRo3j33Xc5cOAAAF999RW1tbUEBwcDsGXLFqKjoykvLychIYGGhgaysrIYPXq07fM/8fLyQtM0pe698+bNs/1/xIgR5Ofnk5CQgNlsxtPTk/feew8vLy+2b9+Oi0vPOADh4eG2z3h4eNDR0XHHPE0mE6tXr6ahoYGQkBAsFgvbt2/nrbfeAuDQoUMcO3aMxsZG3Nx6xuxbtWoVhYWF7N69m5deeumu63Hjxg22bNnCI488AsC6deuYMmUKq1evtpXNx8eH/Px8dDodERER5OXl0dbWxhtvvAHAsmXLyM3N5dChQ6Snp9PQ0MCoUaN47LHH0DTNVpnZl46ODjo67Md86ezowNVNbRxCIYQQQgghhBBCCPFw6fcWhDEx9oOUBwYG0tjYyMmTJwkODrZVDgJERUXh7e3NyZMnAViyZAkLFiwgNTWV3Nxczp07d19lqays5JlnniEkJAS9Xk9KSgrQ09UZoKqqiqSkJFvl4L2IjY0lMjLS1orw4MGDNDY2MnPmTKCn27TZbMbX1xdPT0/bUltb6/D6hYSE2CoHAcaNG4fFYqGmpsb2WnR0NDrdz7vT39+fMWN+HijcyckJX19fWxfrjIwMqqqqiIiIYNGiRRQXF9+xDCtXrsTLy8tu2bjhXYfKL4QQQgghhBBCCCEeXv1eQXhrZZumaVgsjs1IlJ2dzYkTJ5gyZQolJSVERUXx8ccf31M5rl+/zlNPPYXBYKCgoIDy8nJbWp2dnUBPC8H+YDKZbBWE27ZtY9KkSfj6+gI9sykHBgZSVVVlt9TU1JCVpTYL4p30tt3vtC/i4uKora0lJyeH9vZ2jEYjzz//fJ/pL1u2jJaWFrvlxZcz+638QgghhBBCCCGEEAAWtEFbfq/6vYKwL5GRkVy4cIELFy7YXvvmm29obm4mKirK9lp4eDivvvoqxcXFzJgxwzaxhqurK93d3Q7nd+rUKa5evUpubi5JSUmMHj36tglKYmJiKCsr63MWYUfznD17Nl9//TWVlZXs3r0bk8lkey8uLo7Lly/j7Ox82yQkw4cPd2hdGhoauHjxou3vzz//3NaV+H4YDAbS0tLYuHEjO3bs4KOPPupzXEQ3NzcMBoPdIt2LhRBCCCGEEEIIIX79HlgFYWpqKmPGjMFkMvHFF19w7Ngx5syZQ0pKCvHx8bS3t5OZmUlpaSn19fUcPnyY8vJyIiMjAQgLC8NsNnPgwAGamppoa2u7Y34hISG4urqybt06zp8/z969e8nJybGLyczMpLW1lfT0dCoqKjhz5gxbt261dd0NCwvjyy+/pKamhqampj4rEsPCwkhMTGT+/Pl0d3fz7LPP2q33uHHjmD59OsXFxdTV1XHkyBHefPNNKioqHNp27u7uvPDCC1RXV1NWVsaiRYswGo1K4zHeas2aNXz44YecOnWK06dPs2vXLgICAvD29r7nNIUQQgghhBBCCCHulxVt0JbfqwdWQahpGnv27GHYsGEkJyeTmprKiBEj2LFjB9AzRt7Vq1eZM2cO4eHhGI1GJk+ezNtvvw1AYmIiCxcuJC0tDT8/P/Ly8u6Yn5+fH5s2bWLXrl1ERUWRm5vLqlWr7GJ8fX0pKSnBbDaTkpLC2LFj2bhxo61r7osvvkhERATx8fH4+flx+PDhPvMzmUxUV1fz3HPP2XVd1jSNoqIikpOTmTt3LuHh4aSnp1NfX4+/v79D227kyJHMmDGDp59+mieffJKYmBjef/99hz7bF71eT15eHvHx8SQkJFBXV0dRUZHdOIZCCCGEEEIIIYQQ4rev32cxvlVhYaHt/yEhIezZs6fXOFdXVz788MM7prV+/XrWr1/vcN6zZs1i1qxZdq9ZrVa7v2NiYvjf//t/9/p5Pz+/2ybvqKur6zX2L3/5C3/5y196fU+v15Ofn09+fr6DJVdLf9OmTbe9Vlpaettrvyz7iy++yIsvvnjP5RFCCCGEEEIIIYQQvw2a9dYas/swYcIEYmNjWbt2bX8labNp0yYWL15Mc3Nzv6fdl4yMDJqbm+0qObu7u/n+++8ZPnw4zs4DXr9KdnY2hYWFVFVVDXheqs6eq1WKtyo2WHWxdijF/2D1cTj2ynWDUtoBQ1uU4lW569TW1QnHx+PsUnwOUHX5j0rxqmeQf/L/3uFYD127Utqq66qhVnjV+JvWe58h/W5ctN6HPOhLt9VJKV6nOTa5VE9ZOpXS/rZdbXgEf/fex0bti+q6DmQ3Ah2Ob0cA7+4mpXizs7fDsR1WtXFjVY8x1eNd5Tymyq/jwt2DfqGqK1Yp/o9D1Y7J611DHI7VNLXzjN7ZrBT/Q6fa9c/VqUspvrXD8Qng2uJildIOOfmZUrzqMWyxOn6forqfVI/3Lqva9cxZU9tPKt9X1WufKg/tzsMG3eoGjh9jqmW/3u34dxXgD9pltfR1at+/GxbHz9uuise7kzZw52BVFsXfCKrXVhfU7lOsmtp9QafC9VX1XBD07RGl+FOB/6YU76Jz/NyhWnbV+yvV76urpvb7qbXb8e/fMKcflNL2azqpFH9x+D8rxYc/GqIU/1vxxemrg5Z3XLjvoOU9mAa+hmuAlJWVMXny5D7fN5vVbpgd5eTkdF9j/90qOjqa+vr6Xt/bsGFDv+UjhBBCCCGEEEIIIURv+n3AOYvFwmuvvYaPjw8BAQFkZ2fb3mtoaGDatGl4enpiMBgwGo1cuXLF9n51dTWPP/44er0eg8HA2LFjqaiooLS0lLlz59LS0oKmaWiaxr59+6iqqupzAdi6dSvx8fHo9XoCAgKYPXv2bTMZnzhxgqlTp2IwGNDr9SQlJXHu3Dmys7PZvHkze/bsseVZWlpKXV0dmqZRVVWFxWIhKCjotm7Px48fR6fT2Sr+mpubWbBgAX5+fhgMBp544gmqq6sBKCoq6nMdfjnZyYYNGwgODmbIkCEYjUZaWn5u0ZaRkcH06dN555138Pf3x9vbmxUrVtDV1UVWVhY+Pj4EBQXZZoQG6OzsJDMzk8DAQNzd3QkNDWXlypX3t/OFEEIIIYQQQggh7pNMUvLg9XsLws2bN7NkyRKOHj3KP/7xDzIyMhg/fjwTJ060VQ4ePHiQrq4u/v3f/520tDTbeHkmk4k///nPrF+/HicnJ6qqqnBxcSExMZG1a9fy17/+1TbDsKenJ56enncsy82bN8nJySEiIoLGxkaWLFlCRkYGRUVFAHz33XckJyczYcIESkpKMBgMHD58mK6uLpYuXcrJkydpbW21Vaz5+Phw8eJFW/o6nY5Zs2axbds2u/EBCwoKGD9+PKGhoQDMnDkTDw8PPv30U7y8vNiwYQMTJ07k9OnTtpg7OXv2LDt37uSTTz6htbWV+fPn88orr1BQUGCLKSkpISgoiM8++4zDhw8zf/58jhw5QnJyMkePHmXHjh28/PLL/Nu//RtBQUHk5+ezd+9edu7cSUhICBcuXODCBbUuWUIIIYQQQgghhBDi16/fKwhjYmJYvnw5AKNGjeLdd9/lwIEDAHz11VfU1tYSHBwMwJYtW4iOjqa8vJyEhAQaGhrIyspi9OjRts//xMvLC03TlLr3zps3z/b/ESNGkJ+fT0JCAmazGU9PT9577z28vLzYvn27bebi8PBw22c8PDzo6Oi4Y54mk4nVq1fT0NBASEgIFouF7du389ZbbwFw6NAhjh07RmNjI25uPeNTrFq1isLCQnbv3s1LL7101/W4ceMGW7Zs4ZFHHgFg3bp1TJkyhdWrV9vK5uPjQ35+PjqdjoiICPLy8mhra+ONN94AYNmyZeTm5nLo0CHS09NpaGhg1KhRPPbYY2ia5lBFpRBCCCGEEEIIIYT47en3LsYxMTF2fwcGBtLY2MjJkycJDg62VQ4CREVF4e3tzcmTPYN6LlmyhAULFpCamkpubi7nzp27r7JUVlbyzDPPEBISgl6vJyUlBejp6gxQVVVFUlKSrXLwXsTGxhIZGcm2bdsAOHjwII2NjcycORPo6TZtNpvx9fW1tXr09PSktrbW4fULCQmxVQ4CjBs3DovFYmtNCT1jGep0P+9Of39/xowZY/vbyckJX19fWxfrjIwMqqqqiIiIYNGiRbfN1nyrjo4OWltb7ZaODrWBYYUQQgghhBBCCCHuxmrVBm35ver3CsJbK9s0TcNicWymqezsbE6cOMGUKVMoKSkhKiqKjz/++J7Kcf36dZ566ikMBgMFBQWUl5fb0urs7JnJysPD8VnQ7sRkMtkqCLdt28akSZPw9e2Z9cZsNhMYGHjb+II1NTVkZWX1S/7Q+3a/076Ii4ujtraWnJwc2tvbMRqNPP/8832mv3LlSry8vOyWDX9f32e8EEIIIYQQQgghhPh16PcKwr5ERkbeNs7dN998Q3NzM1FRUbbXwsPDefXVVykuLmbGjBm28f9cXV3p7nZ8avVTp05x9epVcnNzSUpKYvTo0bdNUBITE0NZWRk3b97sNQ1H85w9ezZff/01lZWV7N69G5PJZHsvLi6Oy5cv4+zszMiRI+2W4cOHO7QuDQ0NdmMffv7557auxPfDYDCQlpbGxo0b2bFjBx999BHXrl3rNXbZsmW0tLTYLS8v/EuvsUIIIYQQQgghhBD3SiYpefAeWAVhamoqY8aMwWQy8cUXX3Ds2DHmzJlDSkoK8fHxtLe3k5mZSWlpKfX19Rw+fJjy8nIiIyMBCAsLw2w2c+DAAZqammhra7tjfiEhIbi6urJu3TrOnz/P3r17ycnJsYvJzMyktbWV9PR0KioqOHPmDFu3brV13Q0LC+PLL7+kpqaGpqamPisSw8LCSExMZP78+XR3d9vNPpyamsq4ceOYPn06xcXF1NXVceTIEd58800qKioc2nbu7u688MILVFdXU1ZWxqJFizAajUrjMd5qzZo1fPjhh5w6dYrTp0+za9cuAgIC8Pb27jXezc0Ng8Fgt/w0pqIQQgghhBBCCCGE+PV6YBWEmqaxZ88ehg0bRnJyMqmpqYwYMYIdO3YAPWPkXb16lTlz5hAeHo7RaGTy5Mm8/fbbACQmJrJw4ULS0tLw8/MjLy/vjvn5+fmxadMmdu3aRVRUFLm5uaxatcouxtfXl5KSEsxmMykpKYwdO5aNGzfauua++OKLREREEB8fj5+fH4cPH+4zP5PJRHV1Nc8995xd12VN0ygqKiI5OZm5c+cSHh5Oeno69fX1+Pv7O7TtRo4cyYwZM3j66ad58skniYmJ4f3333fos33R6/Xk5eURHx9PQkICdXV1FBUV2Y1jKIQQQgghhBBCCCF++zSr1Wod7EKIvmVnZ1NYWEhVVdVgF+U2Z8/VKsVbFeujXaxqk6D8YPVxOPbKdYNS2gFDW5TiVbnr1NbVCce723cpTlZedfmPSvGqZ5B/8v/e4VgPXbtS2qrrqqFWeNX4m9Z7nwDpbly03ls096Xb6qQUr9McGzu2pyydSml/267W+tnfvfehD/qiuq4D2Y1Ah+PbEcC7u0kp3uzs7XBsh1Wt1bfqMaZ6vKucx1T5dVy4e9AvVHXFKsX/cajaMXm9a4jDsZqmdp7RO5uV4n/oVLv+uTp1KcW3djg+vnNbXKxS2iEnP1OKVz2GLVbH71NU95Pq8d5lVbueOWtq+0nl+6p67VPlod25V9CtbuD4MaZa9uvdjn9XAf6gXVZLX6f2/bthcfy87ap4vDtpA3cOVmVR/I2gem11Qe0+xaqp3Rd0KlxfVc8FQd8eUYo/FfhvSvEuOsfPHaplV72/Uv2+umpqv59aux3//g1z+kEpbb+mk0rxF4f/s1J8+KMhSvG/FcdODexv8Dv5l9Feg5b3YOrX5mITJkxg8eLF/ZnkQ6eurg5N0x7KCjshhBBCCCGEEEIIIVSpPZ4cRJs2bWLx4sU0NzcDUFZWxuTJk/uMN5vVnqj3JiMjg+bmZgoLC22vBQcHc+nSJYcnGLmb6Oho6uvre31vw4YN/ZLHQFF9en5T+Wm42pNQg67V4dg2V3eltP94s/d91BcP8xWl+CZftQln2qxDHY5VfXo33FPtKWu34jTwXQqtuzxvqj29c7aolf2qm1prSVVeVrVWRiqua2qtEAZ14bHaAAEAAElEQVS0ZZpF7fsU5KHW4kJnVWwpYLmhFD+QrWNcu9Rawba6q11bVFpLDrM43noX4IaT4+cZAL1F7fuqem5q1vXPdbc3Ph7XleJVjxlnhRYaqtdWVaotAl0UW6a5Ozt+7R6u2CKwITJZKf7RUweU4lW2veoxoNoiUPU4aLM43qoO1FqbDfQxqXJPA2ot3FXPMzctai3Qh95Uu87/6OGtFO+ksK6q+0l12wx0azAVqmX5oXuYUrybTu1+0r/D8d8J37sFK6VdH5SkFO9sUWvlp9IaU/lcoBiumn6HVe3+s9Pi+HnYu/07pbQvDI9TindG7dr6e6X2C0D0h19NBeGt4uPjB6UVn5OT031NDnKroqKiPic/8ff3R6/Xk52d3W/5CSGEEEIIIYQQQgjxS/0+I4XFYuG1117Dx8eHgIAAu8qthoYGpk2bhqenJwaDAaPRyJUrP7e0qq6u5vHHH0ev12MwGBg7diwVFRWUlpYyd+5cWlpa0DQNTdP429/+xsiRI/tcALZu3Up8fDx6vZ6AgABmz55NY2OjXXlPnDjB1KlTMRgM6PV6kpKSOHfuHNnZ2WzevJk9e/bY8iwtLbXrYmyxWAgKCmL9+vV2aR4/fhydTmdrGdjc3MyCBQvw8/PDYDDwxBNPUF1dDUBoaGif6/BT5WBsbCwbNmwgODiYIUOGYDQaaWn5uT9+RkYG06dP55133sHf3x9vb29WrFhBV1cXWVlZ+Pj4EBQUxAcffGD7TGdnJ5mZmQQGBuLu7k5oaCgrV67sn4NACCGEEEIIIYQQQvxq9HsF4ebNmxk6dChHjx4lLy+PFStWsH//fiwWC9OmTePatWscPHiQ/fv3c/78edLS0myfNZlMBAUFUV5eTmVlJa+//jouLi4kJiaydu1aDAYDly5d4tKlSyxduvSuZbl58yY5OTlUV1dTWFhIXV0dGRkZtve/++47kpOTcXNzo6SkhMrKSubNm0dXVxdLly7FaDQyadIkW56JiYl26et0OmbNmsW2bdvsXi8oKGD8+PGEhoYCMHPmTBobG/n000+prKwkLi6OiRMncu2aY90Szp49y86dO/nkk0/Yt28fx48f55VXXrGLKSkp4eLFi3z22WesWbOG5cuXM3XqVIYNG8bRo0dZuHAhL7/8Mt9++y0A+fn57N27l507d1JTU0NBQQFhYWEOlUcIIYQQQgghhBBioFit2qAtv1f93sU4JiaG5cuXAzBq1CjeffddDhzoGQPmq6++ora2luDgnrEXtmzZQnR0NOXl5SQkJNDQ0EBWVhajR4+2ff4nXl5eaJqm1L133rx5tv+PGDGC/Px8EhISMJvNeHp68t577+Hl5cX27dtxcemZzS08PNz2GQ8PDzo6Ou6Yp8lkYvXq1TQ0NBASEoLFYmH79u289dZbABw6dIhjx47R2NiIm1vPDFerVq2isLCQ3bt389JLL911PW7cuMGWLVt45JFHAFi3bh1Tpkxh9erVtrL5+PiQn5+PTqcjIiKCvLw82traeOONNwBYtmwZubm5HDp0iPT0dBoaGhg1ahSPPfYYmqbZKjOFEEIIIYQQQgghxO9Lv7cgjImJsfs7MDCQxsZGTp48SXBwsK1yECAqKgpvb29OnuyZFnzJkiUsWLCA1NRUcnNzOXfu3H2VpbKykmeeeYaQkBD0ej0pKSlAT1dngKqqKpKSkmyVg/ciNjaWyMhIWyvCgwcP0tjYyMyZM4GebtNmsxlfX188PT1tS21trcPrFxISYqscBBg3bhwWi4Wamhrba9HR0eh0P+9Of39/xowZY/vbyckJX19fWxfrjIwMqqqqiIiIYNGiRRQXF9+xDB0dHbS2ttotHR1qU8sLIYQQQgghhBBC3I0VbdCW36t+ryC8tbJN0zQsFsfmn8nOzubEiRNMmTKFkpISoqKi+Pjjj++pHNevX+epp57CYDBQUFBAeXm5La3Ozp6ZqTw81GZ864vJZLJVEG7bto1Jkybh6+sL9MymHBgYSFVVld1SU1NDVlZWv+QPvW/3O+2LuLg4amtrycnJob29HaPRyPPPP99n+itXrsTLy8tu+fvf/95v5RdCCCGEEEIIIYQQg6PfKwj7EhkZyYULF7hw4YLttW+++Ybm5maioqJsr4WHh/Pqq69SXFzMjBkzbBNruLq60t3t+LTtp06d4urVq+Tm5pKUlMTo0aNvm6AkJiaGsrKyPmcRdjTP2bNn8/XXX1NZWcnu3bsxmUy29+Li4rh8+TLOzs63TUIyfPhwh9aloaGBixcv2v7+/PPPbV2J74fBYCAtLY2NGzeyY8cOPvrooz7HRVy2bBktLS12y8KFC+8rfyGEEEIIIYQQQggx+B5YBWFqaipjxozBZDLxxRdfcOzYMebMmUNKSgrx8fG0t7eTmZlJaWkp9fX1HD58mPLyciIjIwEICwvDbDZz4MABmpqaaGtru2N+ISEhuLq6sm7dOs6fP8/evXvJycmxi8nMzKS1tZX09HQqKio4c+YMW7dutXXdDQsL48svv6SmpoampqY+KxLDwsJITExk/vz5dHd38+yzz9qt97hx45g+fTrFxcXU1dVx5MgR3nzzTSoqKhzadu7u7rzwwgtUV1dTVlbGokWLMBqNSuMx3mrNmjV8+OGHnDp1itOnT7Nr1y4CAgLw9vbuNd7NzQ2DwWC3/DSmohBCCCGEEEIIIUR/kUlKHrwHVkGoaRp79uxh2LBhJCcnk5qayogRI9ixYwfQM0be1atXmTNnDuHh4RiNRiZPnszbb78NQGJiIgsXLiQtLQ0/Pz/y8vLumJ+fnx+bNm1i165dREVFkZuby6pVq+xifH19KSkpwWw2k5KSwtixY9m4caOta+6LL75IREQE8fHx+Pn5cfjw4T7zM5lMVFdX89xzz9l1XdY0jaKiIpKTk5k7dy7h4eGkp6dTX1+Pv7+/Q9tu5MiRzJgxg6effponn3ySmJgY3n//fYc+2xe9Xk9eXh7x8fEkJCRQV1dHUVGR3TiGQgghhBBCCCGEEOK3T7NardbBLoToW3Z2NoWFhVRVVQ12UW5z7vx5pfhOq1qLQ3faleItmuOVm5dv/EEp7VGcUor3MF9Rim/yVesu3mYd6nCs6iCrl64PU4rvVnzC8ochPzocG2i5cPegX3C2dCrFX3X7o1K8htrpcojF8XVVdV1nUIo3dPU+fEBfzM7eDsd2WZ2V0nbT3VCK11kdG8f2Jy7daumr7lcVrl1q57FWd8eGnviJynlV3/2DUto3nBw/zwB4dJuV4lXPTc06x7eNf0e9UtoNLqOU4j0Uj+EbFsf3k6apHY+qZbnePUQp3kXrUku/y93hWE8Xte9HQ2SyUvyjpw4oxasck6rnDYtV7QGs6nHQYXFVinfVeu8V0x9lUaW6bXSa2jVBRctNT6X48JtfKcVf9hihFN+Nk8OxzorfVdVjWPWcrZL+QKYN6uc9N53a/aTKNed7t+C7B/2C6vfvpkVt8k0nzfEhvFS/e6qtsFTXVfXc8WOX48dBREeVUtqXPUcqxTuj9n0d9WioUvxvxaFvrg9a3o9Fqd0H/1b0a3OxCRMmsHjx4v5M8qFTV1eHpmkPZYWdEEIIIYQQQgghhBCq1Jp9DKJNmzaxePFimpubASgrK2Py5Ml9xpvNai0ZepORkUFzczOFhYW214KDg7l06ZLDE4zcTXR0NPX1vT912rBhQ7/kMVBUWwQ64fgTKoAu1J6AqTxMHObaqpT0NU1xvEe3ex8f0hEqT4ktis8BQj3VWj+qPvVVKc+PTj4DljaoP4FWjW/T6R2OHeiytzqrbUuV9FWePgO0dHkpxQ91uvOYs7fqdFI7N6kew0ppKx7Dqo0ZVZ5Atzs5fjyC+jHZ5qTWqlWVSktSs7uvUtru1g6leNVjfoiTWks5FcotmxXLopq+i6tCyzTFtFVbBJ4bPVEpfuSp/6MUr0L1mFHlrlM7hgey5bSq4R3fKsVfc1dr/a/Cy0Xt98NlZ7UWgarHgQ7Hz3sDvU+9b36vFN/i4vhvpYEuu95Jbb+qXv+a3IMcjlX9PaTKVbH1owrl/TTAw7h5dV9Vite5OH5vcMlZrWeB6n5VPcaEeFB+NRWEt4qPjx+UVnxOTk73NTnIrYqKivqc/MTf3x+9Xk92dna/5SeEEEIIIYQQQgjxMLM8PM+yfjf6veraYrHw2muv4ePjQ0BAgF3lVkNDA9OmTcPT0xODwYDRaOTKlZ9bK1VXV/P444+j1+sxGAyMHTuWiooKSktLmTt3Li0tLWiahqZp/O1vf2PkyJF9LgBbt24lPj4evV5PQEAAs2fPprGx0a68J06cYOrUqRgMBvR6PUlJSZw7d47s7Gw2b97Mnj17bHmWlpbadTG2WCwEBQWxfv16uzSPHz+OTqeztQxsbm5mwYIF+Pn5YTAYeOKJJ6iurgYgNDS0z3X4qXIwNjaWDRs2EBwczJAhQzAajbS0tNjyy8jIYPr06bzzzjv4+/vj7e3NihUr6OrqIisrCx8fH4KCgvjggw9sn+ns7CQzM5PAwEDc3d0JDQ1l5cqV/XMQCCGEEEIIIYQQQohfjX6vINy8eTNDhw7l6NGj5OXlsWLFCvbv34/FYmHatGlcu3aNgwcPsn//fs6fP09aWprtsyaTiaCgIMrLy6msrOT111/HxcWFxMRE1q5di8Fg4NKlS1y6dImlS5fetSw3b94kJyeH6upqCgsLqaurIyMjw/b+d999R3JyMm5ubpSUlFBZWcm8efPo6upi6dKlGI1GJk2aZMszMTHRLn2dTsesWbPYtm2b3esFBQWMHz+e0NCewURnzpxJY2Mjn376KZWVlcTFxTFx4kSuXXNs0oCzZ8+yc+dOPvnkE/bt28fx48d55ZVX7GJKSkq4ePEin332GWvWrGH58uVMnTqVYcOGcfToURYuXMjLL7/Mt9/2dOPIz89n79697Ny5k5qaGgoKCggLC3OoPEIIIYQQQgghhBADxYo2aMvvVb93MY6JiWH58uUAjBo1infffZcDB3rGjPnqq6+ora0lOLhn9qYtW7YQHR1NeXk5CQkJNDQ0kJWVxejRo22f/4mXlxeapil17503b57t/yNGjCA/P5+EhATMZjOenp689957eHl5sX37dlxcesa7Cw8Pt33Gw8ODjo6OO+ZpMplYvXo1DQ0NhISEYLFY2L59O2+99RYAhw4d4tixYzQ2NuLm1jMu1qpVqygsLGT37t289NJLd12PGzdusGXLFh555BEA1q1bx5QpU1i9erWtbD4+PuTn56PT6YiIiCAvL4+2tjbeeOMNAJYtW0Zubi6HDh0iPT2dhoYGRo0axWOPPYamabbKTCGEEEIIIYQQQgjx+9LvLQhjYmLs/g4MDKSxsZGTJ08SHBxsqxwEiIqKwtvbm5MnTwKwZMkSFixYQGpqKrm5uZw7d+6+ylJZWckzzzxDSEgIer2elJQUoKerM0BVVRVJSUm2ysF7ERsbS2RkpK0V4cGDB2lsbGTmzJlAT7dps9mMr68vnp6etqW2ttbh9QsJCbFVDgKMGzcOi8VCTU2N7bXo6Gh0up93p7+/P2PGjLH97eTkhK+vr62LdUZGBlVVVURERLBo0SKKi4vvWIaOjg5aW1vtls4OtQGxhRBCCCGEEEIIIcTDp98rCG+tbNM0DYvFsRm4srOzOXHiBFOmTKGkpISoqCg+/vjjeyrH9evXeeqppzAYDBQUFFBeXm5Lq7OzZ3YnDw+Pe0r7ViaTyVZBuG3bNiZNmoSvb88sSWazmcDAQKqqquyWmpoasrKy+iV/6H2732lfxMXFUVtbS05ODu3t7RiNRp5//vk+01+5ciVeXl52y//4+7v9Vn4hhBBCCCGEEEIIAKtVG7Tl9+qBza8dGRnJhQsXuHDhgu21b775hubmZqKiomyvhYeH8+qrr1JcXMyMGTNsE2u4urrS3e349OGnTp3i6tWr5ObmkpSUxOjRo2+boCQmJoaysrI+ZxF2NM/Zs2fz9ddfU1lZye7duzGZTLb34uLiuHz5Ms7OzrdNQjJ8+HCH1qWhoYGLFy/a/v78889tXYnvh8FgIC0tjY0bN7Jjxw4++uijPsdFXLZsGS0tLXbLSwsz7yt/IYQQQgghhBBCCDH4HlgFYWpqKmPGjMFkMvHFF19w7Ngx5syZQ0pKCvHx8bS3t5OZmUlpaSn19fUcPnyY8vJyIiMjAQgLC8NsNnPgwAGamppoa2u7Y34hISG4urqybt06zp8/z969e8nJybGLyczMpLW1lfT0dCoqKjhz5gxbt261dd0NCwvjyy+/pKamhqampj4rEsPCwkhMTGT+/Pl0d3fz7LPP2q33uHHjmD59OsXFxdTV1XHkyBHefPNNKioqHNp27u7uvPDCC1RXV1NWVsaiRYswGo1K4zHeas2aNXz44YecOnWK06dPs2vXLgICAvD29u413s3NDYPBYLe4/seYikIIIYQQQgghhBD9xWodvOX36oFVEGqaxp49exg2bBjJycmkpqYyYsQIduzYAfSMkXf16lXmzJlDeHg4RqORyZMn8/bbbwOQmJjIwoULSUtLw8/Pj7y8vDvm5+fnx6ZNm9i1axdRUVHk5uayatUquxhfX19KSkowm82kpKQwduxYNm7caOua++KLLxIREUF8fDx+fn4cPny4z/xMJhPV1dU899xzdl2XNU2jqKiI5ORk5s6dS3h4OOnp6dTX1+Pv7+/Qths5ciQzZszg6aef5sknnyQmJob333/foc/2Ra/Xk5eXR3x8PAkJCdTV1VFUVGQ3jqEQQgghhBBCCCGE+O3TrNbfc/3owy87O5vCwkKqqqoGuyi3OXnuO6V4JxzvIg6gMXCH5k2r2sQ0Os2xcTQfFJVtY1F8DqC6n1SngVcpj2pZVNdV9RhTjVfZNgNddlUDmf717iFK8UOd7txi/H6pHsMPS9oAznQNWNqqx6SOgT1Pqpy3PTS1Y6bNOlQp3llT2+4DeRyoflcH+lyjsq4DfR47N3qiUvzIU/9ngEoy8FSPsYHe9ip8bly8e9AvXHP/4wCVRP37oTpOlZM2cPdYA71PvW42KcW3uDg2lNKDMNDnSZX9NNDXyofpejPQ9F0/KMW3OPs6HDvQ321VEY8G3z3oN+j//ap90PJ+fEz/zFfxa9OvzcUmTJjA4sWL+zPJh05dXR2apj2UFXZCCCGEEEIIIYQQv3YWtEFbfq+cB7sAjtq0aROLFy+mubkZgLKyMiZPntxnvNlsvu88MzIyaG5uprCw0PZacHAwly5dcniCkbuJjo6mvr6+1/c2bNjQL3k8LDqtrgOa/kA+MbNYB7YljWprlG6cHI5VLXuX4mlBdV1vWh1P31XrfdzPvmia2pNNF61TKb5boewALjievurT6puofZ88rNeV4m9ojrfyUz3GDE6tSvGdVrXxTt01taeNqq3HVHRa1Foru+s61NJXOA48UdvuHZrak1M3i1qrPaumdtw0djr+5L/b1fFzJICXtfcJuvrSpumV4rsUzh2qLTSUrx9WtW2jeo5XWVfVsque41VbBJ4dnepw7KOnDiilPdBU9+tAt3ZR0eQepBQ/oD1MFM/Zw1BrVXddMyjFq1xfB7q3S7OL34CmP5BUf4OonpuGWhy/vrbp1K4fqlTvyVTPqw+TZie13+NdFsevTz7WxrsH/YLZyVspXoiH1a+mgvBW8fHxg9KKz8nJ6b4mB7lVUVFRn5Of+Pv7o9fryc7O7rf8hBBCCCGEEEIIIR5mql29xf3r9xkpLBYLr732Gj4+PgQEBNhVbjU0NDBt2jQ8PT0xGAwYjUauXLlie7+6uprHH38cvV6PwWBg7NixVFRUUFpayty5c2lpaUHTNDRN429/+xsjR47scwHYunUr8fHx6PV6AgICmD17No2N9k8DTpw4wdSpUzEYDOj1epKSkjh37hzZ2dls3ryZPXv22PIsLS2162JssVgICgpi/fr1dmkeP34cnU5naxnY3NzMggUL8PPzw2Aw8MQTT1BdXQ1AaGhon+vwU+VgbGwsGzZsIDg4mCFDhmA0GmlpabHll5GRwfTp03nnnXfw9/fH29ubFStW0NXVRVZWFj4+PgQFBfHBBx/YPtPZ2UlmZiaBgYG4u7sTGhrKypUr++cgEEIIIYQQQgghhBC/Gv1eQbh582aGDh3K0aNHycvLY8WKFezfvx+LxcK0adO4du0aBw8eZP/+/Zw/f560tDTbZ00mE0FBQZSXl1NZWcnrr7+Oi4sLiYmJrF27FoPBwKVLl7h06RJLly69a1lu3rxJTk4O1dXVFBYWUldXR0ZGhu397777juTkZNzc3CgpKaGyspJ58+bR1dXF0qVLMRqNTJo0yZZnYmKiXfo6nY5Zs2axbds2u9cLCgoYP348oaGhAMycOZPGxkY+/fRTKisriYuLY+LEiVy75liXprNnz7Jz504++eQT9u3bx/Hjx3nllVfsYkpKSrh48SKfffYZa9asYfny5UydOpVhw4Zx9OhRFi5cyMsvv8y3334LQH5+Pnv37mXnzp3U1NRQUFBAWFiYQ+URQgghhBBCCCGEEL8d/d7FOCYmhuXLlwMwatQo3n33XQ4c6Bmn5auvvqK2tpbg4J5ZeLZs2UJ0dDTl5eUkJCTQ0NBAVlYWo0ePtn3+J15eXmiaptS9d968ebb/jxgxgvz8fBISEjCbzXh6evLee+/h5eXF9u3bcXHpGXMkPDzc9hkPDw86OjrumKfJZGL16tU0NDQQEhKCxWJh+/btvPXWWwAcOnSIY8eO0djYiJtbzzhaq1atorCwkN27d/PSSy/ddT1u3LjBli1beOSRRwBYt24dU6ZMYfXq1bay+fj4kJ+fj06nIyIigry8PNra2njjjTcAWLZsGbm5uRw6dIj09HQaGhoYNWoUjz32GJqm2SozhRBCCCGEEEIIIQaT9dc7ROavVr+3IIyJibH7OzAwkMbGRk6ePElwcLCtchAgKioKb29vTp48CcCSJUtYsGABqamp5Obmcu7cufsqS2VlJc888wwhISHo9XpSUlKAnq7OAFVVVSQlJdkqB+9FbGwskZGRtlaEBw8epLGxkZkzZwI93abNZjO+vr54enraltraWofXLyQkxFY5CDBu3DgsFgs1NTW216Kjo9Hpft6d/v7+jBkzxva3k5MTvr6+ti7WGRkZVFVVERERwaJFiyguLr5jGTo6OmhtbbVbOjvUBtMXQgghhBBCCCGEEA+ffq8gvLWyTdM0LBbHZtXKzs7mxIkTTJkyhZKSEqKiovj444/vqRzXr1/nqaeewmAwUFBQQHl5uS2tzs6emUU9PNRmaOyLyWSyVRBu27aNSZMm4evbM9ui2WwmMDCQqqoqu6WmpoasrKx+yR963+532hdxcXHU1taSk5NDe3s7RqOR559/vs/0V65ciZeXl93yP/7+br+VXwghhBBCCCGEEALAijZoy+9Vv1cQ9iUyMpILFy5w4cIF22vffPMNzc3NREVF2V4LDw/n1Vdfpbi4mBkzZtgm1nB1daW7u9vh/E6dOsXVq1fJzc0lKSmJ0aNH3zZBSUxMDGVlZX3OIuxonrNnz+brr7+msrKS3bt3YzKZbO/FxcVx+fJlnJ2db5uEZPhwx6Zmb2ho4OLFi7a/P//8c1tX4vthMBhIS0tj48aN7Nixg48++qjPcRGXLVtGS0uL3fLSwsz7yl8IIYQQQgghhBBCDL4HVkGYmprKmDFjMJlMfPHFFxw7dow5c+aQkpJCfHw87e3tZGZmUlpaSn19PYcPH6a8vJzIyEgAwsLCMJvNHDhwgKamJtra2u6YX0hICK6urqxbt47z58+zd+9ecnJy7GIyMzNpbW0lPT2diooKzpw5w9atW21dd8PCwvjyyy+pqamhqampz4rEsLAwEhMTmT9/Pt3d3Tz77LN26z1u3DimT59OcXExdXV1HDlyhDfffJOKigqHtp27uzsvvPAC1dXVlJWVsWjRIoxGo9J4jLdas2YNH374IadOneL06dPs2rWLgIAAvL29e413c3PDYDDYLa7/MaaiEEIIIYQQQgghhPj1emAVhJqmsWfPHoYNG0ZycjKpqamMGDGCHTt2AD1j5F29epU5c+YQHh6O0Whk8uTJvP322wAkJiaycOFC0tLS8PPzIy8v7475+fn5sWnTJnbt2kVUVBS5ubmsWrXKLsbX15eSkhLMZjMpKSmMHTuWjRs32rrmvvjii0RERBAfH4+fnx+HDx/uMz+TyUR1dTXPPfecXddlTdMoKioiOTmZuXPnEh4eTnp6OvX19fj7+zu07UaOHMmMGTN4+umnefLJJ4mJieH999936LN90ev15OXlER8fT0JCAnV1dRQVFdmNYyiEEEIIIYQQQgjxoFmsg7f8XmlWa//NDTNhwgRiY2NZu3ZtfyX50Kmrq+NPf/oTx48fJzY2dsDzy87OprCwkKqqqgHPS9XJc98pxXdbnQaoJD0GcqwADbWviQ7Hxt38ibPWpRTfjePb0mJVq/S1KD43UF3Xm1bHJ0931XpvtdsXTVPbT66a2kQ73QplB3DG8fKrbvebuCrFe1ivK8Xf0IY4HKt6jKlu906rWmtld61dKb7NOlQpXkWnRW0SLHed2rZROe950qqUdoemNk6vu0XtGLNqasfNt52P3D3oPxhc1criZe19eI2+tOn0SvFdCucO1euN6vXjplXtmFQ9x6usq2rZVc/xqmU/OzrV4dhHTx1QSnugqd5jOWmOD9sz0FTv31S/Iyo6LWrX1mE0KcVf1xmU4lWurzpN7Xj/PVE976mem4ZaHL++ql4/VKnek6mcVwfyu3cvVNdV5frkY228e9AvmJ28leJVjX40aEDTf1jtq+octLwnxapdD34r1H7tDqJNmzaxePFimpubH1ieGRkZNDc3U1hYaHstODiYS5cuOTx+4G/ZQN+Mql6E2i3uDscqVzwplkX1R4xq5ZBKeVRvvF0Ut40qlXUd6Jvdm9aBPfGr7lcVTqh9n7p1aqd7J6vj6Vs1tWPMolDBDQP/fXLVBu7mw9VJLW3lyhuF70j3AF/yu3VqZVc9N7k7O74tVfdph0KF+L1QragaSKplGchKOdW0B/rHqUql37nREwcs7Xvxa64cepgqHQxas1J8K8OU4lWv3arfEaW0Fbf7wzRo/0A/uB92U61y6AeXPzgcO9DH+8N2Xh1IqutqoNnh2FbNRynth+k6/1titT48553fi19NBeGtysrKmDx5cp/vm83mAcnXycnpvsb+u1V0dDT19fW9vrdhw4Z+y0cIIYQQQgghhBBCiN70e/MWi8XCa6+9ho+PDwEBAWRnZ9vea2hoYNq0aXh6emIwGDAajVy5csX2fnV1NY8//jh6vR6DwcDYsWOpqKigtLSUuXPn0tLSgqZpaJrGvn37qKqq6nMB2Lp1K/Hx8ej1egICApg9e/ZtMxmfOHGCqVOnYjAY0Ov1JCUlce7cObKzs9m8eTN79uyx5VlaWkpdXR2aplFVVYXFYiEoKIj169fbpXn8+HF0Op2t4q+5uZkFCxbg5+eHwWDgiSeeoLq6GoCioqI+1+GXk51s2LCB4OBghgwZgtFopKWlxfZeRkYG06dP55133sHf3x9vb29WrFhBV1cXWVlZ+Pj4EBQUZJsRGqCzs5PMzEwCAwNxd3cnNDSUlStX3t/OF0IIIYQQQgghhBC/Ov3egnDz5s0sWbKEo0eP8o9//IOMjAzGjx/PxIkTbZWDBw8epKuri3//938nLS2N0tJSoGeijz//+c+sX78eJycnqqqqcHFxITExkbVr1/LXv/7VNsOwp6cnnp6edyzLzZs3ycnJISIigsbGRpYsWUJGRgZFRUUAfPfddyQnJzNhwgRKSkowGAwcPnyYrq4uli5dysmTJ2ltbbVVrPn4+HDx4kVb+jqdjlmzZrFt2zb+8pe/2F4vKChg/PjxhIaGAjBz5kw8PDz49NNP8fLyYsOGDUycOJHTp0/bYu7k7Nmz7Ny5k08++YTW1lbmz5/PK6+8QkFBgS2mpKSEoKAgPvvsMw4fPsz8+fM5cuQIycnJHD16lB07dvDyyy/zb//2bwQFBZGfn8/evXvZuXMnISEhXLhwgQsXLjiwh4UQQgghhBBCCCEGTv/NliEc1e8VhDExMSxfvhyAUaNG8e6773LgQM+4K1999RW1tbUEBwcDsGXLFqKjoykvLychIYGGhgaysrIYPXq07fM/8fLyQtM0pe698+bNs/1/xIgR5Ofnk5CQgNlsxtPTk/feew8vLy+2b99um7k4PDzc9hkPDw86OjrumKfJZGL16tU0NDQQEhKCxWJh+/btvPXWWwAcOnSIY8eO0djYiJtbz0D7q1atorCwkN27d/PSSy/ddT1u3LjBli1beOSRngHa161bx5QpU1i9erWtbD4+PuTn56PT6YiIiCAvL4+2tjbeeOMNAJYtW0Zubi6HDh0iPT2dhoYGRo0axWOPPYamaQ5VVAohhBBCCCGEEEKI355+72IcExNj93dgYCCNjY2cPHmS4OBgW+UgQFRUFN7e3pw8eRKAJUuWsGDBAlJTU8nNzeXcuXP3VZbKykqeeeYZQkJC0Ov1pKSkAD1dnQGqqqpISkqyVQ7ei9jYWCIjI9m2bRsABw8epLGxkZkzZwI93abNZjO+vr62Vo+enp7U1tY6vH4hISG2ykGAcePGYbFYbK0poWcsQ53u593p7+/PmDFjbH87OTnh6+tr62KdkZFBVVUVERERLFq0iOLi4juWoaOjg9bWVruls0Nttk0hhBBCCCGEEEKIu7GgDdrye9XvFYS3VrZpmobF4tisPtnZ2Zw4cYIpU6ZQUlJCVFQUH3/88T2V4/r16zz11FMYDAYKCgooLy+3pdXZ2TO7oYeHxz2lfSuTyWSrINy2bRuTJk3C19cX6JksJTAw8LbxBWtqasjKyuqX/KH37X6nfREXF0dtbS05OTm0t7djNBp5/vnn+0x/5cqVeHl52S3/3w3r+q38QgghhBBCCCGEEGJw9HsFYV8iIyNvG+fum2++obm5maioKNtr4eHhvPrqqxQXFzNjxgzb+H+urq50d3c7nN+pU6e4evUqubm5JCUlMXr06NsmKImJiaGsrIybN2/2moajec6ePZuvv/6ayspKdu/ejclksr0XFxfH5cuXcXZ2ZuTIkXbL8OHDHVqXhoYGu7EPP//8c1tX4vthMBhIS0tj48aN7Nixg48++ohr1671Grts2TJaWlrslgUv/+f7yl8IIYQQQgghhBBCDL4HVkGYmprKmDFjMJlMfPHFFxw7dow5c+aQkpJCfHw87e3tZGZmUlpaSn19PYcPH6a8vJzIyEgAwsLCMJvNHDhwgKamJtra2u6YX0hICK6urqxbt47z58+zd+9ecnJy7GIyMzNpbW0lPT2diooKzpw5w9atW21dd8PCwvjyyy+pqamhqampz4rEsLAwEhMTmT9/Pt3d3XazD6empjJu3DimT59OcXExdXV1HDlyhDfffJOKigqHtp27uzsvvPAC1dXVlJWVsWjRIoxGo9J4jLdas2YNH374IadOneL06dPs2rWLgIAAvL29e413c3PDYDDYLa7/MaaiEEIIIYQQQgghRH+xWgdvGUjXrl3DZDJhMBjw9vZm/vz5mM3mO8b/5//8n4mIiMDDw4OQkBAWLVpES0uLXZymabct27dvVyrbA6sg1DSNPXv2MGzYMJKTk0lNTWXEiBHs2LED6Bkj7+rVq8yZM4fw8HCMRiOTJ0/m7bffBiAxMZGFCxeSlpaGn58feXl5d8zPz8+PTZs2sWvXLqKiosjNzWXVqlV2Mb6+vpSUlGA2m0lJSWHs2LFs3LjR1jX3xRdfJCIigvj4ePz8/Dh8+HCf+ZlMJqqrq3nuuefsui5rmkZRURHJycnMnTuX8PBw0tPTqa+vx9/f36FtN3LkSGbMmMHTTz/Nk08+SUxMDO+//75Dn+2LXq8nLy+P+Ph4EhISqKuro6ioyG4cQyGEEEIIIYQQQgjRP0wmEydOnGD//v38r//1v/jss8/uOHntxYsXuXjxIqtWreLrr79m06ZN7Nu3j/nz598W+8EHH/D/Z+/ew6q47sX/v4cNbOWyuXgDgYgICBgvrZdUPRFReryeeEmCtfSAIqbWYH/EGMU2UTE5mtgmxxgbtY0F1MZoj4l6rLGhphBLqKAW9UTURFFQiXdABLnt/fuDrztuuc0yoCZ+Xs8zz6N7PrNmzezZM8Oaz6xVXFxsnSZOnKhUN81ikcGjH2ZLlixh+/bt5OXlPeiqNPB/X32tFG/Q9L8iDqChdmhWmtvpjnXUGs8Gba26aJpivGL5KmotaoOV22u1bVSTejUW/YMCOSh+Tw8be9qu/mYMSvEGxe/VbNFffi1qx5gBtXNBneK2OlCtFK+6L9uSyu8DwE7T18cvgJFbSmXXolYX1f1uUewA+nqdh+5YF8NNpbLt0L8fQf2YNFva7uGb6rW1TuG3DerXM5Xy2/q+QDVe5Zg8FTJSqewex/cqxatS/T215X3Hd1k7S/NvKN3tJq5K8arHvMr3+jD9Ptqaat1V74U71Kr9jXPdobPu2Lb+7T1K5wLVbW1v0X9vUIGLUtkq92P3IqSHb5uW/7DaeUDtnNmanhrQNn8f5OfnExYWRm5uLgMGDABgz549jB07lnPnztG1a1dd5fz5z3/mZz/7GTdv3sTevv4cp2kaH330kXKj4J1a/Y51+PDhJCYmtnaxQgghhBBCCCGEEEK0qaqqKsrKymymqqqqb11udnY27u7u1sZBqO+Wzs7Ojv379+sup7S0FJPJZG0cvO3555+nY8eODBo0iD/+8Y+o5gOqPU55wFJTU0lMTKSkpIR9+/YxZsyYJmObe4f7fsvIyCAiIoLr16836OOvV69enD17ttHl1q1bd8/rzM7O5t/+7d8YPXo0f/nLX+65nOY4aqpZOmrt0T7X/08p/oz7D3THVtbpzzYEcLZXe6Ks+jRONXtF5UloWz5dA6jS1EYDr1PIpHFUzFxRpbrfValmyqmoUzx921nUtlXlGK62OCqV7WindnGtNStmwSpmbtZobZjdhVrmpkVT+70+TNklmuIxVqup9WPraKf/e3VE7RirRq0uqudVN/NV3bHX7PRnooD6eUw1I9Cj5lLLQXc4b9dNd6yrQe1eTTULSDVbS4VqRmBbZxyqZoaq7JtHKePpqlnfAIK3VdWpZVr3SJ2tFH9lZvNdKt2pLbMTHzZtnf14zV5fN1C3VdTqvxe++PiPlMpu63OByjVE9fqhSvV7Vc3OV/l915jV9qO7ww2l+O9y5uajYvny5dbu7m5bvHgxS5Ys+Vblfv3113TubHuvZ29vj6enJ19/rS97+cqVK7z66qsNXkteunQpI0aMwMnJiU8++YTZs2dTXl7OL3/5S931+041EN5pwIABD+Vrt6p2795tHfykuroaR8dv/tDu0qULrq6u93QQrl+/njlz5rB+/XouXLigO1VVCCGEEEIIIYQQ4kEyP8B21IULFzJ37lybz4zNDNKalJTEG2+80WyZ+fn537peZWVljBs3jrCwsAbtRK+88or13z/4wQ+4efMmv/nNb5QaCNskbcJsNjN//nw8PT3x8vKyqXhhYSETJkzAxcUFk8lEVFQUFy9etM4/fPgwERERuLq6YjKZ6N+/PwcOHCAjI4Pp06dTWlqKpmk4OTmxadMmAgMDG51uq6qqYsGCBfj5+WE0GgkMDGT9+vXW+ZmZmQwaNAij0Yi3tzdJSUnU1n6T8eHv78/KlStttq9fv34226RpGu+99x6TJk3CycmJoKAgdu7cCcCZM2eIiIgAwMPDA03TmDZtGlD/OvZvfvMbVq9ezY9+9COef/55li1bRmJiIoGBgbi61vdrUlNTQ+fOnW3q3Zzy8nK2bNnCL37xC8aNG0dqaqrN/NTU1AaZjNu3b0dTzFwRQgghhBBCCCGE+D4xGo2YTCabqbkGwhdffJH8/Pxmp4CAALy8vLh0yfbtjNraWq5du4aXl1ezdbpx4wajR4/G1dWVjz76yDq4blOeeOIJzp07p/RqdJtkEKalpTF37lz2799PdnY206ZNY+jQoYwcOdLaOJiZmUltbS3PP/88U6ZMISMjA6gf0eUHP/gBa9aswWAwkJeXh4ODA0OGDGHlypUsWrSIEydOAODi0nLnoTExMWRnZ7Nq1Sr69u1LQUEBV65cAeD8+fOMHTuWadOmsWHDBo4fP87MmTNp166dctZecnIyK1as4De/+Q3vvPMO0dHRnD17Fj8/P7Zt28bTTz/NiRMnMJlMNqMcp6Wl8Ytf/MI6QvLVq1cZNmwYxcXFeHt7A7Br1y4qKiqYMmWKrrps3bqVkJAQevbsyc9+9jMSExNZuHChNAAKIYQQQgghhBDiofddGk63U6dOdOrUqcW4wYMHU1JSwsGDB+nfvz8An376KWazmSeeeKLJ5crKyhg1ahRGo5GdO3fSrl3LXabl5eXh4eHRbMPm3dqkgbBPnz4sXrwYgKCgIFavXs3evfX9Jxw9epSCggL8/PwA2LBhA7169SI3N5eBAwdSWFjISy+9REhIiHX529zc3NA0rcWW1dtOnjzJ1q1bSU9PJzIyEoCAgADr/HfffRc/Pz9Wr16NpmmEhIRw4cIFFixYwKJFi7Cz059gOW3aNKZOnQrAsmXLWLVqFTk5OYwePRpPT08AOnfu3CBzLygoiBUrbPsX6dmzJxs3bmT+/PlA/VDVzz77rK4GUah/vfhnP/sZAKNHj6a0tJTMzEyGDx+ue3uEEEIIIYQQQgghROsIDQ1l9OjRzJw5k7Vr11JTU0NCQgI/+clPrN3CnT9/npEjR7JhwwYGDRpEWVkZ//7v/05FRQWbNm2yDpoC9Q2TBoOB//3f/+XixYv86Ec/ol27dqSnp7Ns2TLmzZunVL82ecW4T58+Nv/39vbm0qVL5Ofn4+fnZ20cBAgLC8Pd3d36PvbcuXOJj48nMjKS119/nVOnTt1zPfLy8jAYDISHhzc6Pz8/n8GDB9tk1g0dOpTy8nLOnTuntK47t9nZ2RmTydQgdbQxt1uN7xQfH09KSgoAFy9e5OOPPyYuLk5XPU6cOEFOTo61sdLe3p4pU6bofj25KY2N4lPdCqP4CCGEEEIIIYQQQjwK/vSnPxESEsLIkSMZO3Ys//Zv/8bvf/976/yamhpOnDhBRUX9QKmHDh1i//79HD16lMDAQLy9va1TUVERAA4ODvzud79j8ODB9OvXj3Xr1vHWW29ZE/f0apMMwrvfhdY0DbNZ3whJS5Ys4ac//Sl/+ctf+Pjjj1m8eDEffPABkyZNUq7Hna/y3is7O7sGQ0PfHlTkTve6zc7Ozg0+i4mJISkpiezsbD7//HO6d+/Ok08+qau+69evp7a21mZQEovFgtFoZPXq1bi5uenepjs1NopPwpxE5vx/L+iqlxBCCCGEEEIIIYQe3+XR1pvj6enJ+++/3+R8f39/m/aa4cOHN2i/udvo0aMZPXr0t65bm2QQNiU0NJSioiJrKyfAsWPHKCkpISwszPpZcHAwL7zwAp988gmTJ0+2ZtM5OjpSV1ene329e/fGbDaTmZnZZH2ys7NtdnZWVhaurq74+voC9SmbxcXF1vllZWUUFBTorsPtegO6696hQwcmTpxISkoKqampTJ8+XddytbW1bNiwgTfffJO8vDzrdPjwYbp27crmzZut23Tjxg1u3rxpXbalEaEXLlxIaWmpzfTzWbN11UsIIYQQQgghhBBCPLzuawNhZGQkvXv3Jjo6mkOHDpGTk0NMTAzh4eEMGDCAyspKEhISyMjI4OzZs2RlZZGbm0toaChQ35JaXl7O3r17uXLlijXlsin+/v7ExsYSFxfH9u3bKSgoICMjg61btwIwe/ZsioqKmDNnDsePH2fHjh0sXryYuXPnWvsfHDFiBBs3bmTfvn0cPXqU2NhYDAaD0nZ369YNTdPYtWsXly9fpry8vMVl4uPjSUtLIz8/n9jYWF3r2bVrF9evX2fGjBk8/vjjNtPTTz9tfc34iSeewMnJiV/96lecOnWK999/v8FIx3drbBQfR4XOLoUQQgghhBBCCCH0MFse3PSouq8NhJqmsWPHDjw8PBg2bBiRkZEEBASwZcsWAAwGA1evXiUmJobg4GCioqIYM2aM9dXWIUOGMGvWLKZMmUKnTp0aDO7RmDVr1vDMM88we/ZsQkJCmDlzpjVzzsfHh927d5OTk0Pfvn2ZNWsWM2bM4OWXX7Yuv3DhQsLDwxk/fjzjxo1j4sSJ9OjRQ2m7fXx8SE5OJikpiS5dupCQkNDiMpGRkXh7ezNq1Cib14Wbs379eiIjI3Fzc2sw7+mnn+bAgQMcOXIET09PNm3axO7du+nduzebN29WHrVZCCGEEEIIIYQQQnw/aJaWXmYWD0R5eTk+Pj6kpKQwefLkB12dRp08VagUb1Zsj/a5/n9K8Wfcf6A7tqrOUalsZ/vms1XvpqH2s7JDXx+dt9Va9HcfWodaxquJEqX4Kk2tr88Ks/749na3lMpWpbrfVdnTfN+e30Y1ahm8jqgNKqTS50elxUmp7HZ2lUrxVeZ2SvFOtJylfacare2yoQ3UKsXXoHZuUvmejKj9nmpxaDnoDg4WtWNMdb/fsug/Dly0G0plq/6eVK9nprprumOv2XVWKttBUzvPqPbn41HT8oBrdzpv1013rKtB7beqcu0DMGj6u6WBtu3r6FTISKX4Hsf3KsW35b5RvadRpbrf27I+N+vUrmdVdWrnyR6pat3zXJnZcjLEbarH+3eZ6v1btUXt2qpafkWd/nvbi4//SKnstj4XqGyrprXtuUD1t11nUfsb55ZZ/7W+xqxWtruD2n2H6rb27OHXctD30P/sb9u/1ZrzzBP3NZfuodHqWz18+HASExNbu9hHhtls5tKlS7z66qu4u7vz1FNPPegqCSGEEEIIIYQQQtw3FsuDmx5VbTKKcVtJTU0lMTGRkpISAPbt28eYMWOajNfT19/9kJGRQUREBNevX8fd3b3Z2MLCQrp3746vry+pqanY29vbzLtzMJe7HTt2jEWLFpGWlmb9zNPTk4EDB7JixQr69OnzrbflTqoZFGaLWvwlz2CleDeFDI1Sg6dS2apPqJQz0xSTFtpp+jOwVDNjblpcleJVt9VRIdtF9RhTpfq9qj45LatT25cqnA1qWa0lZneleJXszW5XDyiVXdxR7Vxkr6ll4V2r66AU72jXdpmejpraj1s1k0bl93eltqNS2W72ZUrxV+o6KcV3NFxWirdXOHeUK57HVDOnKzVnpfgSO/373u/GF0plXzQFKcWrZpdcs++iFO9g0f97Va1LW2evtCXVLCDVjMPu+X9Xim/rrMDvKgfF641mUNuP159brlb+o/xXajNU7w/dLPr/RgAot2vYXVNzVO4jVM8FqlTvy1XOqw/beaPH2XSl+JOP6R/h1c7Qtplr39fRecV333eqgfBuAwYMaHH03e+S6urqBkNa36lr167Nbu/tvgpHjx5tHfn566+/5uWXX2b8+PEUFqq9EiyEEEIIIYQQQghxv8kzkvuvTdJzzGYz8+fPx9PTEy8vL5sBMAoLC5kwYQIuLi6YTCaioqK4ePGidf7hw4eJiIjA1dUVk8lE//79OXDgABkZGUyfPp3S0lI0TUPTNN544w0CAwObnACqqqpYsGABfn5+GI1GAgMDraP5AmRmZjJo0CCMRiPe3t4kJSVRW/vN00N/f39Wrlxps339+vWz2SZN03jvvfeYNGkSTk5OBAUFsXPnTgDOnDlDREQEAB4eHmiaxrRp04D617ETEhJITEykY8eOjBo1iri4OMaPH2+zvpqaGjp37kxaWlqz23s729BoNOLl5YWXlxf9+vUjKSmJoqIiLl+uz9bIyMhA0zRrJiZAXl4emqZx5swZ/V+0EEIIIYQQQgghhPjOa5MGwrS0NJydndm/fz8rVqxg6dKlpKenYzabmTBhAteuXSMzM5P09HROnz7NlClTrMtGR0fj6+tLbm4uBw8eJCkpCQcHB4YMGcLKlSsxmUwUFxdTXFzMvHnzWqxLTEwMmzdvZtWqVeTn57Nu3TpcXFwAOH/+PGPHjmXgwIEcPnyYNWvWsH79el577TXlbU5OTiYqKoojR44wduxYoqOjuXbtGn5+fmzbtg2AEydOUFxczNtvv22zrxwdHcnKymLt2rXEx8ezZ88eiouLrTG7du2ioqLCZj/pVV5ezqZNmwgMDKRDB7XX7oQQQgghhBBCCCHE91+bvGLcp08fFi9eDEBQUBCrV69m7976/haOHj1KQUEBfn71I/Fs2LCBXr16kZuby8CBAyksLOSll14iJCTEuvxtbm5uaJqGl5eXrnqcPHmSrVu3kp6eTmRkJAABAQHW+e+++y5+fn6sXr0aTdMICQnhwoULLFiwgEWLFmFnp7/9dNq0aUydOhWAZcuWsWrVKnJychg9ejSenvX93XXu3LlBH4RBQUGsWGE7QlnPnj3ZuHEj8+fPByAlJYVnn33W2rDZkl27dlljb968ibe3N7t27VLaHiGEEEIIIYQQQogHwWyRvhrvtzZpMbp7MAxvb28uXbpEfn4+fn5+1sZBgLCwMNzd3cnPzwdg7ty5xMfHExkZyeuvv86pU6fuuR55eXkYDAbCw8MbnZ+fn8/gwYPR7uhEfujQoZSXl3Pu3Dmldd25zc7OzphMJi5dutTicv3792/wWXx8vLUPwYsXL/Lxxx8TFxenuy4RERHk5eWRl5dHTk4Oo0aNYsyYMZw9e1Z3GXerqqqirKzMZqquqrrn8oQQQgghhBBCCCHEw6FNGggdHBxs/q9pGmazvpGAlixZwhdffMG4ceP49NNPCQsL46OPPrqnerRv3/6elruTnZ1dg0FDamoajlR1r9vs7NxwJMSYmBhOnz5NdnY2mzZtonv37jz55JO66+zs7Gztl3DgwIG899573Lx5kz/84Q/WbQJstquxbbrT8uXLcXNzs5l+v/Z3uuskhBBCCCGEEEIIoYfF8uCmR9V9fec0NDSUoqIiioqKrJ8dO3aMkpISwsLCrJ8FBwfzwgsv8MknnzB58mRrNp2joyN1dXW619e7d2/MZjOZmZlN1ic7O9umoSwrKwtXV1d8fX0B6NSpk01/gGVlZRQUFOiuw+16A7rr3qFDByZOnEhKSgqpqalMnz5daX130zQNOzs7KisrgfptAmy2q6XRoBcuXEhpaanN9Nys579VvYQQQgghhBBCCCHEg3dfGwgjIyPp3bs30dHRHDp0iJycHGJiYggPD2fAgAFUVlaSkJBARkYGZ8+eJSsri9zcXEJDQ4H6EYXLy8vZu3cvV65coaKiotn1+fv7ExsbS1xcHNu3b6egoICMjAy2bt0KwOzZsykqKmLOnDkcP36cHTt2sHjxYubOnWvNshsxYgQbN25k3759HD16lNjYWAwGg9J2d+vWDU3T2LVrF5cvX6a8vLzFZeLj40lLSyM/P5/Y2Fil9VVVVfH111/z9ddfk5+fz5w5cygvL+c//uM/AAgMDMTPz48lS5bw5Zdf8pe//IU333yz2TKNRiMmk8lmcjQaleolhBBCCCGEEEIIIR4+97WBUNM0duzYgYeHB8OGDSMyMpKAgAC2bNkCgMFg4OrVq8TExBAcHExUVBRjxowhOTkZgCFDhjBr1iymTJlCp06dGgzu0Zg1a9bwzDPPMHv2bEJCQpg5cyY3b94EwMfHh927d5OTk0Pfvn2ZNWsWM2bM4OWXX7Yuv3DhQsLDwxk/fjzjxo1j4sSJ9OjRQ2m7fXx8SE5OJikpiS5dupCQkNDiMpGRkXh7ezNq1Ci6du2qtL49e/bg7e2Nt7c3TzzxBLm5ufz5z39m+PDhQP3r0Js3b+b48eP06dOHN954455GbhZCCCGEEEIIIYRobfKK8f2nWe7uYE88FMrLy/Hx8SElJYXJkyc/6Oo06vgptYFczBa19mij3S2leIc6/YOmlGqeSmVrqP1M7NDX56Y1XlOLd0T/tlajlump+j2p1l2lfE1r29OTRXFkLNX6VNR9+35Qm+JsaD6D+m6V5nZK8e0Vfn++V/6lVHZxxz4tB93Bgtr3VF7XsG/X5jjaNd8H67fhqFUrxZsVn9upnGtKa01KZbvZlynFl9W6KsV3NFxWirdo+vdNuUWtLiZKlOIrNbVjrM6i/80Dnxv5SmVfNAUpxddYHFoOuoPq9azWYq871l6rVSpb9RysWnfVc01bOhUyUim+e/7fleJVr91tSXW/q96Tqag2OyrF1yr8tgGcDJVK8Sr3TA/Td/qwcTarXc/K7dyU4mvM+s+rjnZq9wWqVO/jVc6rbfnbuxf+Zz5Vij/52GjdsXWK92Mq9833IqSHb5uW/7B6/x8P7pj76b89PPcE91OrZxAOHz6cxMTE1i72kWE2m7l06RKvvvoq7u7uPPXUUw+6SkIIIYQQQgghhBD3jdny4KZHlf7HvA+B1NRUEhMTKSkpAWDfvn2MGTOmyXg9ff3dDxkZGURERHD9+nXc3d2bjS0sLKR79+74+vqSmpqKvb29zbw7B3O527Fjx3jsscf4+uuv+a//+i/+8pe/cP78eTp37ky/fv1ITExk5Ei1J9LNuVnnpBRvVHxiZqxVy5DKvtb0vrlbF1e1J7iOBrUsB0c7tXjVfXPd7KE71tWg9jtwstxQircoPmeo0vRn1d1SzHpTzS4xoH/Qo3uJf6zmS6V4FdWo/f48a/VnnQJccdTftcFpzyeUynZALWNP5ck8gG+t2kBSrpfa7nuqa++iFP+l52CleAeFDCz/2hNKZd+w76gU371GLfPNUKH2tL2yvf7M72JLF6WyL9WqZYt0aV+iFF+lkJXU7niuUtmWQcFK8e3tVLOP1c41F266644NcP1aqewKs1pWdjs7tfOeSqanaraWStmgnhFYEBqhFN8tv/EB/BqjmjWkGl+H2r5RvRarOF+u//4K4MoNtetT/64XlOLbL5ulO7biV+uUyjZqbZvxVIv+fdPWb6+cremmFO/soPZ3wuUK/Rn6XZ2vKZXd7fohpfj9kxYoxXf++17dsQZN7bfX1tnBx/zGKcVfLNf/PZVWqjWT9Olc3HKQEN8B36kGwrsNGDCgxdF3v0uqq6vx9/enqbe+u3bt2uz2du3alTNnzjB06FDc3d35zW9+Q+/evampqeGvf/0rzz//PMePH2+j2gshhBBCCCGEEEKI76I2GaTEbDYzf/58PD098fLyYsmSJdZ5hYWFTJgwARcXF0wmE1FRUVy8eNE6//Dhw0RERODq6orJZKJ///4cOHCAjIwMpk+fTmlpKZqmoWkab7zxBoGBgU1OUD+i74IFC/Dz88NoNBIYGMj69eut68vMzGTQoEEYjUa8vb1JSkqitvabjAx/f39Wrlxps339+vWz2SZN03jvvfeYNGkSTk5OBAUFsXPnTgDOnDlDRET9U10PDw80TWPatGlA/evYCQkJJCYm0rFjR0aNGkVcXBzjx4+3WV9NTQ2dO3cmLS2t2e21t7dn9uzZaJpGTk4OTz/9NMHBwfTq1Yu5c+fyz3/+01rmW2+9Re/evXF2dsbPz4/Zs2c/NBmXQgghhBBCCCGEeHRZLNoDmx5VbdJAmJaWhrOzM/v372fFihUsXbqU9PR0zGYzEyZM4Nq1a2RmZpKens7p06eZMmWKddno6Gh8fX3Jzc3l4MGDJCUl4eDgwJAhQ1i5ciUmk4ni4mKKi4uZN29ei3WJiYlh8+bNrFq1ivz8fNatW4eLS/0rX+fPn2fs2LEMHDiQw4cPs2bNGtavX39PI/omJycTFRXFkSNHGDt2LNHR0Vy7dg0/Pz+2bdsGwIkTJyguLubtt9+22VeOjo5kZWWxdu1a4uPj2bNnD8XF36Qp79q1i4qKCpv91Jhr166xZ88enn/+eZydG3agfufrzXZ2dqxatYovvviCtLQ0Pv30U+bPn6+83UIIIYQQQgghhBDiu61NXjHu06cPixcvBiAoKIjVq1ezd299/wZHjx6loKAAPz8/ADZs2ECvXr3Izc1l4MCBFBYW8tJLLxESEmJd/jY3Nzc0TcPLy0tXPU6ePMnWrVtJT08nMjISgICAAOv8d999Fz8/P1avXo2maYSEhHDhwgUWLFjAokWLsLPT3346bdo0pk6dCsCyZctYtWoVOTk5jB49Gk/P+n6TOnfu3KAPwqCgIFasWGHzWc+ePdm4caO1wS4lJYVnn33W2rDZlK+++gqLxWLdd825cyAZf39/XnvtNWbNmsW7777b4rJCCCGEEEIIIYQQbaWJntdEG2qTDMI+ffrY/N/b25tLly6Rn5+Pn5+ftXEQICwsDHd3d/Lz6zs3nzt3LvHx8URGRvL6669z6tSpe65HXl4eBoOB8PDwRufn5+czePBgNO2bFNKhQ4dSXl7OuXPnlNZ15zY7OztjMpm4dOlSi8v179+/wWfx8fGkpKQAcPHiRT7++GPi4uJaLKupvgsb87e//Y2RI0fi4+ODq6sr//mf/8nVq1epqGi88/KqqirKyspspupqtc6/hRBCCCGEEEIIIcTDp00aCB0cbEet0jQNs1nfaFNLlizhiy++YNy4cXz66aeEhYXx0Ucf3VM92rdXG+2uMXZ2dg0a3mpqGo7Aea/b3NirwDExMZw+fZrs7Gw2bdpE9+7defLJJ1ssKygoCE3TWhyI5MyZM4wfP54+ffqwbds2Dh48yO9+9zugfqCUxixfvhw3NzebKWXdyhbrJIQQQgghhBBCCCEebm3SQNiU0NBQioqKKCoqsn527NgxSkpKCAsLs34WHBzMCy+8wCeffMLkyZOt2XSOjo7U1ekfXr13796YzWYyMzObrE92drZNA2BWVhaurq74+voC0KlTJ5v+AMvKyigoKNBdh9v1BnTXvUOHDkycOJGUlBRSU1OZPn26ruU8PT0ZNWoUv/vd77h582aD+SUlJQAcPHgQs9nMm2++yY9+9COCg4O5cOFCs2UvXLiQ0tJSm2n6zxN11UsIIYQQQgghhBBCL7PlwU2PqvvaQBgZGUnv3r2Jjo7m0KFD5OTkEBMTQ3h4OAMGDKCyspKEhAQyMjI4e/YsWVlZ5ObmEhoaCtT3lVdeXs7evXu5cuVKk6/D3ubv709sbCxxcXFs376dgoICMjIy2Lp1KwCzZ8+mqKiIOXPmcPz4cXbs2MHixYuZO3eutf/BESNGsHHjRvbt28fRo0eJjY3FYDAobXe3bt3QNI1du3Zx+fJlXaMFx8fHk5aWRn5+PrGxsbrX9bvf/Y66ujoGDRrEtm3b+PLLL8nPz2fVqlUMHjwYgMDAQGpqanjnnXc4ffo0GzduZO3atc2WazQaMZlMNpOjo1F3vYQQQgghhBBCCCHEw+m+NhBqmsaOHTvw8PBg2LBhREZGEhAQwJYtWwAwGAxcvXqVmJgYgoODiYqKYsyYMSQnJwMwZMgQZs2axZQpU+jUqVODwT0as2bNGp555hlmz55NSEgIM2fOtGbX+fj4sHv3bnJycujbty+zZs1ixowZvPzyy9blFy5cSHh4OOPHj2fcuHFMnDiRHj16KG23j48PycnJJCUl0aVLFxISElpcJjIyEm9vb0aNGkXXrl11rysgIIBDhw4RERHBiy++yOOPP86Pf/xj9u7dy5o1awDo27cvb731Fm+88QaPP/44f/rTn1i+fLnSNgkhhBBCCCGEEEK0BYvlwU2PKs2iMrKFuG/Ky8vx8fEhJSWFyZMnP+jqNOrgyWtK8Ua7xvs3bIq7+YpSfPa1sJaD/p8urpVKZTsaatXi7dTiVffNLbP+7E1XQ8sZq3dqZ274enpzLJrac4YqTX/foLfM7ZTK1jS105kB/V0WANhrat+rx63iloPuUbWDk1K8Q63aoEJXHPU/mKi12KvVxa5hP67NqTE7tBx0B6+aQqV410tfKsWrqGvf/Ojzd/vSc7BSvIPCMdml6qxS2TfadVSKd6u8qBRvqLmlFF/Z3lN3bIFF7UFeZa3aMdalfYlSfJXZUXdsr4N/UCr7zKBopXgHTe16U2lWO9ecK9f/PQW4fq1UdoVZrW/pdnZq5706i/43ROw0fX1r30vZAHaolV8QGqEU3y2/8e53GqOhdm1Vja9Dbd+oXrtVnL3RSSn+yg21c0f/rs136XO39stm6Y6t+NU6pbKNmto5WFUt+veN2aJ2L6n6+7tS5aEU7+yg9nfC5QqT7tiuzmp/P3W7fkgpfv+kBUrxnf++V3esQVP77VnQWg66g+q5Q+XaCnDxpv7vqbRS7d62T+e2u+cHCAvUf1/+fZLy9we37ulql9XvjVbJIBw+fDiJiYmtUdQjz2w2c+nSJV599VXc3d156qmnHnSVhBBCCCGEEEIIIcT3mFrT+AOQmppKYmKidYCNO+3bt48xY8Y0uayevv7aSkZGBhEREVy/fh13d3fdyxUWFtK9e3d8fX1JTU3F3t7eZt6dg7ncVlVVRW1tLdHR0WzatMlm3vPPP8+7775LbGwsqamp97o5jXI2NN8H5N3sUcsaqjDof8oD8ESHE7pjr1g6K5XtoJgRqJppppop4GTQH19tUXu6ZrFTe9pnr6l9r7Vm/acdo2L2hwNqmTFmxWckNajty2vt9D/tU62LqipNre5Omv6n56pPiB01te/VrJilet3YRSm+xE/tfKBCNbuyneK+aW/Rn/F706iWQVGneIugWn5Ve7XMtCqL/sxpT61UqWyjo1omzS3UMtncDPrr8+XAaUplOyqe90yVl5XiaxXOYwCdnW7ojq2xqGVfOSpeb1SzUVSzY9qybNW6q2QEApwNDdcd2+O4/gyje9HWGYoqujiXKcW7t1M7hlXfdKj8VfN9hN/JQfH3YVbM3GzL/e5/MVspvtDrCaX4Tka1rD3VjF9fZ/1vPakeA+c9+yjFe2WkK8Xbof9vFtXMzQsjxyrFd927WyleNUtc5ffdob3aPZDqvhH6yLuu999D30DYnAEDBpCXl/egq/GtVFdXW0c5hvqBVZp667tr166Nbu/8+fP55z//ya5du6isrKR9+/o/XG7dusX777/PY4891iZ1F0IIIYQQQgghhBDffa2WsmI2m5k/fz6enp54eXmxZMkS67zCwkImTJiAi4sLJpOJqKgoLl78pq+iw4cPExERgaurKyaTif79+3PgwAEyMjKYPn06paWlaJqGpmk25bZv357AwMAGk5+fH3/4wx/w8/PDaDQSGBjI+vXrrctlZmYyaNAgjEYj3t7eJCUlUVv7zdMTf39/Vq5cabN9/fr1s1m3pmm89957TJo0CScnJ4KCgti5cycAZ86cISKi/qV1Dw8PNE1j2rRpQP3r2AkJCSQmJtKxY0dGjRpFXFwc48ePt1lfTU0NnTt3tqm3vb19o9trMpkYNGgQfn5+fPjhh9b4Dz/8kMcee4wf/OAHDb6r5cuX0717d9q3b0/fvn35n//5n2a+XSGEEEIIIYQQQoj7w2x5cNOjqtUaCNPS0nB2dmb//v2sWLGCpUuXkp6ejtlsZsKECVy7do3MzEzS09M5ffo0U6ZMsS4bHR2Nr68vubm5HDx4kKSkJBwcHBgyZAgrV67EZDJRXFxMcXEx8+bNa7EuMTExbN68mVWrVpGfn8+6detwcanvKP78+fOMHTuWgQMHcvjwYdasWcP69et57bXXlLc5OTmZqKgojhw5wtixY4mOjubatWv4+fmxbds2AE6cOEFxcTFvv/22zb5ydHQkKyuLtWvXEh8fz549eygu/qZz0127dlFRUWGzn1oSFxdHSkqK9f9//OMfmT59eoO45cuXs2HDBtauXcsXX3zBCy+8wM9+9jMyM9VeTxFCCCGEEEIIIYQQ332t9opxnz59WLx4MQBBQUGsXr2avXvr+yw5evQoBQUF+Pn5AbBhwwZ69epFbm4uAwcOpLCwkJdeeomQkBDr8re5ubmhaRpeXl666nHy5Em2bt1Keno6kZGRAAQEBFjnv/vuu/j5+bF69Wo0TSMkJIQLFy6wYMECFi1ahJ2d/jbTadOmMXXqVACWLVvGqlWryMnJYfTo0Xh61o/i17lz5wZ9EAYFBbFixQqbz3r27MnGjRuZP38+ACkpKTz77LPWhk09fvazn7Fw4ULOnq0fqTIrK4sPPviAjIwMa0xVVRXLli3jb3/7G4MH14+WGRAQwD/+8Q/WrVtHeHjjfdJUVVVRVWXbz0N1VRWORv19QgkhhBBCCCGEEEKIh0+rZRD26WPbgaq3tzeXLl0iPz8fPz8/a+MgQFhYGO7u7uTn5wMwd+5c4uPjiYyM5PXXX+fUqVP3XI+8vDwMBkOTDV35+fkMHjwYTfumU/2hQ4dSXl7OuXPnlNZ15zY7OztjMpm4dOlSi8v179+/wWfx8fHW7L+LFy/y8ccfExcXp1SfTp06MW7cOFJTU0lJSWHcuHF07NjRJuarr76ioqKCH//4x7i4uFinDRs2NLvfly9fjpubm830+7W/U6qfEEIIIYQQQgghREsslgc3PapaLYPQwcF2FC9N0zCb9Y3ms2TJEn7605/yl7/8hY8//pjFixfzwQcfMGnSJOV63B6g49uws7NrMFBITU3DkcHudZudnZ0bfBYTE0NSUhLZ2dl8/vnndO/enSeffFKx5vWvGSckJADwu981bMC7PbLzX/7yF3x8fGzmGZvJBly4cCFz5861+ezMObWREIUQQgghhBBCCCHEw6fNRzEODQ2lqKiIoqIiaxbhsWPHKCkpISwszBoXHBxMcHAwL7zwAlOnTiUlJYVJkybh6OhIXV2d7vX17t0bs9lMZmam9RXju+uzbds2LBaLNYswKysLV1dXfH19gfpMvDv7AywrK6OgoEBpu2+PTKy37h06dGDixImkpKSQnZ3daN+BeowePZrq6mo0TWPUqFEN5oeFhWE0GiksLGwyy7IxRqOxQQOio1H/UPFCCCGEEEIIIYQQeujMNxOtqNVeMW5KZGQkvXv3Jjo6mkOHDpGTk0NMTAzh4eEMGDCAyspKEhISyMjI4OzZs2RlZZGbm0toaChQP6JweXk5e/fu5cqVK1RUVDS7Pn9/f2JjY4mLi2P79u0UFBSQkZHB1q1bAZg9ezZFRUXMmTOH48ePs2PHDhYvXszcuXOt/Q+OGDGCjRs3sm/fPo4ePUpsbCwGg0Fpu7t164amaezatYvLly9bM/eaEx8fT1paGvn5+cTGxiqt7zaDwUB+fj7Hjh1rtM6urq7MmzePF154gbS0NE6dOsWhQ4d45513SEtLu6d1CiGEEEIIIYQQQojvrjZvINQ0jR07duDh4cGwYcOIjIwkICCALVu2APUNWlevXiUmJobg4GCioqIYM2YMycnJAAwZMoRZs2YxZcoUOnXq1GBwj8asWbOGZ555htmzZxMSEsLMmTO5efMmAD4+PuzevZucnBz69u3LrFmzmDFjBi+//LJ1+YULFxIeHs748eMZN24cEydOpEePHkrb7ePjQ3JyMklJSXTp0sX62m9zIiMj8fb2ZtSoUXTt2lVpfXcymUyYTKYm57/66qu88sorLF++nNDQUEaPHs1f/vIXunfvfs/rFEIIIYQQQgghhBDfTZrl7s72voXhw4fTr18/Vq5c2VpFPlLKy8vx8fEhJSWFyZMnP+jqtOj4KbVBXexp2I9jc8yoZW22M9/UHXvF0lmpbAe7WqV4e00t3g61/GmzQtt+nUVtPzpoat+TvWJ8lbmd7liDpr97AQAHqpXiVfYjQA2OSvEq36tqXVRVmdXq7mRXqTu2xuLQctAd2imUDXDLrNa3rOrvT6PteiKutaj15KFpanVpb9F/3lNVo6mNUm80N5/hf7cqOye1eIv++qgeA0ZuKcXfQu2YVDk3VVrU9oujpnbe87x1QSn+Wju1B5Y36/TX32inVnfV36rqNcSC1nLQPVItW3VbVc81Z0P1dzPT4/hepbJVtfW+UVFRp/bbrqpTu/65O95QirdY9O8b1fOeKtX9XqdwH//Y1/uVyi70ekIpXrnuivfOKlSv8219LlC5V7XT1P5euTByrFJ81727leLNFrV751tm/fcRtWa1/WhyUPttqwrp4dum5T+s1v71wa17VsPe2h4Jbd4HYWtJTU0lMTGRkpKSB10VJRkZGURERHD9+nXc3d0bjTGbzVy5coU333wTd3d3nnrqqXta17Rp00hLS2P58uUkJSVZP9++fTuTJk1qMPDKt6V8o47ajbpqY0wJHXTHql7gVBvNHKlSildtHKpW+ANSte4G1G4wVW5eoY3/ANPa9o8Me4vavnG06G90aMv9Aijni9sp/F79Lx9SKvtC5x+o1UXx92q0qDVAtqX2isfMTa3p7O/G1Nnpv4w71Kmdl26hvzEfwEFTK1/l9wFgUPjj95ZFccAyxZ+f6rlDpT6q52xVdha1a7HyH5sKv1fV/aj6h/XDpC0bte6lfJVGv1MhI9us7IeN8v2hndrvybXuulJ8mZ2n7ljVe0nVh9OqVI5J1QY/v7+tUYo/M7LlN7nupHocqDRUPUz7Hdr2vKra4Kd6rgk4/qlSvMr3alD8batS/VtRiPulzV8xbm379u3DxcWlyem7qLCwkC5duvD+++/zxz/+EXt7e5t5zW1vYWGhTVnt2rXjjTfe4Pp1tRsQIYQQQgghhBBCiIeBxfLgpkdVqzcQms1m5s+fj6enJ15eXixZssQ6r7CwkAkTJuDi4oLJZCIqKoqLFy9a5x8+fJiIiAhcXV0xmUz079+fAwcOkJGRwfTp0yktLWXYsGHcvHmTuLg48vLyGkx3qqqqYsGCBfj5+WE0GgkMDGT9+vXW+ZmZmQwaNAij0Yi3tzdJSUnU1n6ToeDv79/gdel+/frZbJOmabz33ntMmjQJJycngoKC2LlzJwBnzpwhIiICAA8PDzRNY9q0aUD969gJCQkkJiYyYMAAhg8fzo9//GP++7//22Z9nTp1wmg08utf/7rR7b27r8LIyEi8vLxYvnx5s9/TP/7xD5588knat2+Pn58fv/zlL639NAohhBBCCCGEEEKIR0erNxCmpaXh7OzM/v37WbFiBUuXLiU9PR2z2cyECRO4du0amZmZpKenc/r0aaZMmWJdNjo6Gl9fX3Jzczl48CBJSUk4ODgwZMgQVq5ciclkori4mOLiYpYtW0ZgYGCD6U4xMTFs3ryZVatWkZ+fz7p166xZhufPn2fs2LEMHDiQw4cPs2bNGtavX89rr72mvM3JyclERUVx5MgRxo4dS3R0NNeuXcPPz49t27YBcOLECYqLi3n77bdt9pWjoyNZWVmsXbuW+Ph49uzZQ3FxsTVmz549VFVVMWfOnEa3985sQ6gf9GXZsmW88847nDvXeB+Bp06dYvTo0Tz99NMcOXKELVu28I9//EPXQCpCCCGEEEIIIYQQ4vul1fsg7NOnD4sXLwYgKCiI1atXs3dvfV8kR48epaCgAD8/PwA2bNhAr169yM3NZeDAgRQWFvLSSy8REhJiXf42Nzc3NE3Dy8tLVz1OnjzJ1q1bSU9PJzIyEoCAgADr/HfffRc/Pz9Wr16NpmmEhIRw4cIFFixYwKJFi7Cz0992Om3aNKZOnQrAsmXLWLVqFTk5OYwePRpPz/q+Qzp37tygD8KgoKAGozL37NmTjRs3Mn/+fABSUlJ49tlnlV6fnjRpEv369WPx4sU2GZO3LV++nOjoaBITE631WLVqFeHh4axZs4Z27dT6nRJCCCGEEEIIIYRoLeZH+FXfB6XVMwj79Olj839vb28uXbpEfn4+fn5+1sZBgLCwMNzd3cnPzwdg7ty5xMfHExkZyeuvv86pU6fuuR55eXkYDAbCwxsfpS0/P5/Bgwej3TGowdChQykvL28y864pd26zs7MzJpOJS5cutbhc//79G3wWHx9PSkoKABcvXuTjjz8mLi5OqT4Ab7zxBmlpadZ9e6fDhw+Tmppq05fhqFGjMJvNFBQUNFpeVVUVZWVlNlN1lXSuKoQQQgghhBBCCPFd1+oNhA4ODjb/1zQNs1nfiEFLlizhiy++YNy4cXz66aeEhYXx0Ucf3VM92rdXHMGwEXZ2dg1G/q2paTi64L1us7Ozc4PPYmJiOH36NNnZ2WzatInu3bvz5JNPKtYchg0bxqhRo1i4cGGDeeXl5fz85z+36cvw8OHDfPnll/To0aPR8pYvX46bm5vNtG7t75TrJYQQQgghhBBCCNEci8XywKZHVau/YtyU0NBQioqKKCoqsmYRHjt2jJKSEsLCwqxxwcHBBAcH88ILLzB16lRSUlKYNGkSjo6O1NXpH268d+/emM1mMjMzra8Y312fbdu2YbFYrFmEWVlZuLq64uvrC9QPEHJnf4BlZWVNZtg1xdHREUB33Tt06MDEiRNJSUkhOzub6dOnK63vTq+//jr9+vWjZ8+eNp//8Ic/5NixYw36bGzOwoULmTt3rs1nZ8+1nCUphBBCCCGEEEIIIR5urZ5B2JTIyEh69+5NdHQ0hw4dIicnh5iYGMLDwxkwYACVlZUkJCSQkZHB2bNnycrKIjc3l9DQUKB+ROHy8nL27t3LlStXqKioaHZ9/v7+xMbGEhcXx/bt2ykoKCAjI4OtW7cCMHv2bIqKipgzZw7Hjx9nx44dLF68mLlz51r7HxwxYgQbN25k3759HD16lNjYWAwGg9J2d+vWDU3T2LVrF5cvX6a8vLzFZeLj462vB8fGxiqt70639/eqVatsPl+wYAGff/45CQkJ5OXl8eWXX7Jjx45mBykxGo2YTCabydFovOe6CSGEEEIIIYQQQoiHw31rINQ0jR07duDh4cGwYcOIjIwkICCALVu2APWj7169epWYmBiCg4OJiopizJgxJCcnAzBkyBBmzZrFlClT6NSpU4PBPRqzZs0annnmGWbPnk1ISAgzZ87k5s2bAPj4+LB7925ycnLo27cvs2bNYsaMGbz88svW5RcuXEh4eDjjx49n3LhxTJw4sclXcJvi4+NDcnIySUlJdOnSRddIwZGRkXh7ezNq1Ci6du2qtL67LV26tMHrzn369CEzM5OTJ0/y5JNP8oMf/IBFixZ963UJIYQQQgghhBBCfFsWy4ObHlWa5VF+wfohVV5ejo+PDykpKUyePPlBV6dJJ04VKcU7ojaoyS3U+pGsMTu0HPT/mNFaDrpDOzu1uqtuq1mxrb7S4qQ71kFr2G9mc+xRi1dVZdE/Sra9VqtUtr3itqoyW9QyiB0tt3THWhSPSVWVWsM+T5vjQLXuWO9Lh5XKvtD5B0rxtYq9YbS33FSKb0sGi9oxfNPOpBTvoOn/nhzq1M5LpZqnUrwrpUrxGmq3H3Wa/uPglkXt+tFea/6thLtVof88BlBr0V931fOe6n7sWKl27f66XXel+BqFbXVUPGdrmtq22qGvD+zb2vo83JbqFK9PBk1/tz2nQkYqld3j+F6leNX9rnrMq7hlVns7ptasdn3y0s4rxZfZ6T8P22lqx7vq70N1v6vc26oeA35/W6MUf2Zkywkad1Ldl2aL/m1V+e3dC9Vzgcq2tuVvD9TPNQHHP1WKrzI76o5V3Y/OBrX7CNW/FQMUk5S+L975y4Nrqpoz7rt7T/BttGofhMOHD6dfv36sXLmyNYt9ZJjNZq5cucKbb76Ju7s7Tz311IOukhBCCCGEEEIIIcR9pXOsW9GK7tsgJd9WamoqiYmJlJSUALBv3z7GjBnTZLyevv7uh4yMDCIiIrh+/Tru7u7NxhYWFtK9e3d8fX1JTU3F3t7eZt6dg7nc7dixYyxatIi0tDSWL19OUlKSdd727duZNGlSq4/Go5JhBOoZgapc7G7oji0zq2XpqGR/gHpGoOqTSg/zZd2xtwxqmWMq2Ymg/jTRSdOf3aWapVNtUXvyb7EoZi0oZq+UmTspxatQfVKpmhWh2enf1rOdBimV7aCYpar6PV0zd1Arvw2zhlQzBTws15TiK3DRHXvNrJYR6GZfphR/pVbtePdwuK4UX1LrrjvWr/a0UtmFBrUn8y72almqKudJ1awF1YxDVarneLs2zjARjWvLzB7VjMC2zgJqS6rHr+r9W7nBXSm+LX9OqveqD5OiyF8oxZvMatebcs1NKV7lvN3WGYRtneXXllTPNf5n1OJPPjZad6xF8betqgb92YxC3E/fmQbCuw0YMIC8vLwHXY1WU11djb+/f5ONeF27dm12e2/3H9iuXTveeOMNfv7zn+Ph4dEWVRVCCCGEEEIIIYQQ3yOt/ujIbDYzf/58PD098fLyYsmSJdZ5hYWFTJgwARcXF0wmE1FRUVy8eNE6//Dhw0RERODq6orJZKJ///4cOHCAjIwMpk+fTmlpKZqmoWkab7zxBoGBgU1OAFVVVSxYsAA/Pz+MRiOBgYGsX7/eur7MzEwGDRqE0WjE29ubpKQkamu/eRLv7+/f4HXpfv362WyTpmm89957TJo0CScnJ4KCgti5cycAZ86cISIiAgAPDw80TWPatGlA/evYCQkJJCYm0rFjR0aNGkVcXBzjx4+3WV9NTQ2dO3cmLS2t2e29nW0YGRmJl5cXy5cvb/Z72rZtG7169cJoNOLv78+bb77ZbLwQQgghhBBCCCHE/SCDlNx/rd5AmJaWhrOzM/v372fFihUsXbqU9PR0zGYzEyZM4Nq1a2RmZpKens7p06eZMmWKddno6Gh8fX3Jzc3l4MGDJCUl4eDgwJAhQ1i5ciUmk4ni4mKKi4uZN29ei3WJiYlh8+bNrFq1ivz8fNatW4eLS/3rWOfPn2fs2LEMHDiQw4cPs2bNGtavX89rr72mvM3JyclERUVx5MgRxo4dS3R0NNeuXcPPz49t27YBcOLECYqLi3n77bdt9pWjoyNZWVmsXbuW+Ph49uzZQ3FxsTVm165dVFRU2Oyn5hgMBpYtW8Y777zDuXPnGo05ePAgUVFR/OQnP+Ho0aMsWbKEV155hdTUVOVtF0IIIYQQQgghhBDfba3+inGfPn1YvHgxAEFBQaxevZq9e+v7Bzh69CgFBQX4+fkBsGHDBnr16kVubi4DBw6ksLCQl156iZCQEOvyt7m5uaFpGl5eXrrqcfLkSbZu3Up6ejqRkZEABAQEWOe/++67+Pn5sXr1ajRNIyQkhAsXLrBgwQIWLVqEnZ3+ttNp06YxdepUAJYtW8aqVavIyclh9OjReHrW9/nUuXPnBn0QBgUFsWLFCpvPevbsycaNG5k/fz4AKSkpPPvss9aGTT0mTZpEv379WLx4sU3G5G1vvfUWI0eO5JVXXgEgODiYY8eO8Zvf/Maa4SiEEEIIIYQQQgjxIJgf4Uy+B6XVMwj79Olj839vb28uXbpEfn4+fn5+1sZBgLCwMNzd3cnPzwdg7ty5xMfHExkZyeuvv86pU6fuuR55eXkYDAbCw8MbnZ+fn8/gwYPRtG86px86dCjl5eVNZt415c5tdnZ2xmQycenSpRaX69+/f4PP4uPjSUlJAeDixYt8/PHHxMXFKdUH4I033iAtLc26b++Un5/P0KFDbT4bOnQoX375JXV1jXecW1VVRVlZmc1UVaU2PLsQQgghhBBCCCGEePi0egOhg4ODzf81TcOsc3zqJUuW8MUXXzBu3Dg+/fRTwsLC+Oijj+6pHu3bf/sRc+3s7BoMGlJT03D0zXvdZmfnhqPLxsTEcPr0abKzs9m0aRPdu3fnySefVKw5DBs2jFGjRrFw4ULlZRuzfPly3NzcbKa1a9e2StlCCCGEEEIIIYQQ4sG5b+Pbh4aGUlRURFFRkfWzY8eOUVJSQlhYmPWz4OBgXnjhBT755BMmT55szaZzdHRsMrutMb1798ZsNpOZmdlkfbKzs20aALOysnB1dcXX1xeATp062fQHWFZWRkFBge463K43oLvuHTp0YOLEiaSkpJCamsr06dOV1nen119/nf/93/8lOzvb5vPQ0FCysrJsPsvKyiI4OBiDwdBoWQsXLqS0tNRmmjVr1j3XTQghhBBCCCGEEKIxMkjJ/XffGggjIyPp3bs30dHRHDp0iJycHGJiYggPD2fAgAFUVlaSkJBARkYGZ8+eJSsri9zcXEJDQ4H6EYXLy8vZu3cvV65coaKiotn1+fv7ExsbS1xcHNu3b6egoICMjAy2bt0KwOzZsykqKmLOnDkcP36cHTt2sHjxYubOnWvtf3DEiBFs3LiRffv2cfToUWJjY5tsQGtKt27d0DSNXbt2cfnyZcrLy1tcJj4+3vp6cGxsrNL67nR7f69atcrm8xdffJG9e/fy6quvcvLkSdLS0li9enWzA78YjUZMJpPNZDQa77luQgghhBBCCCGEEOLhcN8aCDVNY8eOHXh4eDBs2DAiIyMJCAhgy5YtQP3ou1evXiUmJobg4GCioqIYM2YMycnJAAwZMoRZs2YxZcoUOnXq1GBwj8asWbOGZ555htmzZxMSEsLMmTO5efMmAD4+PuzevZucnBz69u3LrFmzmDFjBi+//LJ1+YULFxIeHs748eMZN24cEydOpEePHkrb7ePjQ3JyMklJSXTp0oWEhIQWl4mMjMTb25tRo0bRtWtXpfXdbenSpQ1ed/7hD3/I1q1b+eCDD3j88cdZtGgRS5culQFKhBBCCCGEEEII8cBZzJYHNj2qNMvdneyJB668vBwfHx9SUlKYPHnyg65Ok04rDiJTRbs2qkk9I7d0x5aZTUpl22v6X28HsENfv5vWeE0t3qWuRHfsLUPDvi6bU2lxUorXUDuFOGk3dce29TFjsWgtB91B09S2taLu2/eF2hRnQ/NZ1HerNKvty/Z2+n9PZovasyYHrWFfrs2psTi0HHSHW2a17GYLaseBCoPiucNDu6YUX6HpH+G+vFbtXOBmX6YUX1brqhTv4XBdKb6k1l13rF/taaWyCw1qD/9c7PWfxwDqLGpvH6iw12qV4jtWFrUcdIeL7fyV4mst9rpjVeuueg5WvRa35bmgrameh1XvO1ScChmpFB9w/FOleNX7DhXVZkel+FrF37aToVIpXuV7bcvv9LvO2ax2PSu3c1OKrzHrv09xtKtWKluV6rlA5bzalr+9e+F/Ru3ccfKx0bpj6xTzqFTum0F9X/bs4ddy0PfQbz98cOe1eZPvWy7dQ6XNtnr48OEkJia2VfHfS2azmUuXLvHqq6/i7u7OU0899aCrJIQQQgghhBBCCCG+5/Q/5n2IpKamMmfOnAYjDN9JT19/91tGRgYRERFcv34dd3f3BvMLCwvp3r07vr6+pKamYm9vbzPvzsFc7nbs2DEee+wxm8+KiopYvHgxe/bs4cqVK3h7ezNx4kQWLVpEhw4drHH+/v4kJiYqN+jWopbV44DaEzPVTAH7Ov3l15rVDn2DoW2zEFSfIpk1/U+sL1V3VCrb07FUKV617ldq9NdHNUtHlWomWx1qmQJGhafEqk98VY8x1WPezk7/Me9iLlEqu8KglsHrZr6qFG+266QUr5rFpEIlmwqgSlPLOnXUqnTHuturbWdhRRel+C7tS5TijbVqWbDgrjvysqOPUsmduKQUX4H+zE0AZ260Wdmq54LS9mrfqwG1LNibdfqz0F0Vs1QrLGpZsB2rzinFX2nnqztW9drX1tmJqtentswEUs0IPB0yQim+x/G9umNV93uVYgZhZa3avXB7g1qWUVseNypv3oD6Mab6d4KKts5kU70nU6H6napmQqt+TwaLwjn+IUuyLm9iMNKmVEXrT76prlO7f2tn1H8/Burf66PqEX7T94H5TjYQQn2fhQcOHHjQ1WhV/v7+WCwWqqurraMf39a1a1fy8vKaXPbuvgpPnz7N4MGDCQ4OZvPmzXTv3p0vvviCl156iY8//ph//vOfeHp6tsVmCCGEEEIIIYQQQojvkDZ9sdpsNjN//nw8PT3x8vJiyZIl1nmFhYVMmDABFxcXTCYTUVFRXLx40Tr/8OHDRERE4Orqislkon///hw4cICMjAymT59OaWkpQUFBBAUFsWnTJgIDA22mu1VVVbFgwQL8/PwwGo0EBgayfv166/zMzEwGDRqE0WjE29ubpKQkamu/ybbw9/dn5cqVNmX269fPZps0TeO9995j0qRJODk5ERQUxM6dOwE4c+YMERERAHh4eKBpmnVQkOHDh5OQkEBiYiIdO3Zk1KhRxMXFMX78eGvZ9vb2dOvWjSFDhpCZmdlge+/MNgR4/vnncXR05JNPPiE8PJzHHnuMMWPG8Le//Y3z58/z61//2rrus2fP8sILL6BpGpr2kD0aEkIIIYQQQgghxCPFYnlw06OqTRsI09LScHZ2Zv/+/axYsYKlS5eSnp6O2WxmwoQJXLt2jczMTNLT0zl9+jRTpkyxLhsdHY2vry+5ubkcPHiQpKQkHBwcGDJkCCtXrsRkMlFcXExxcTHz5s1rsS4xMTFs3ryZVatWkZ+fz7p163BxqX+N5/z584wdO5aBAwdy+PBh1qxZw/r163nttdeUtzk5OZmoqCiOHDnC2LFjiY6O5tq1a/j5+bFt2zYATpw4QXFxMW+//bbNvnJ0dCQrK4u1a9cSHx/Pnj17KC4utsbs2rWLiooKm/3UmGvXrvHXv/6V2bNn07697etqXl5eREdHs2XLFiwWCx9++CG+vr4sXbrUuj+FEEIIIYQQQgghxKOjTV8x7tOnD4sXLwYgKCiI1atXs3dvfb8hR48epaCgAD+/+hF5NmzYQK9evcjNzWXgwIEUFhby0ksvERISYl3+Njc3NzRNw8vLS1c9Tp48ydatW0lPTycyMhKAgIAA6/x3330XPz8/Vq9ejaZphISEcOHCBRYsWMCiRYuws9Pfjjpt2jSmTp0KwLJly1i1ahU5OTmMHj3a+kpv586dG/RBGBQUxIoVK2w+69mzJxs3bmT+/PkApKSk8Oyzz1obNpvy5ZdfYrFYCA0NbXR+aGgo169f5/Lly3Tu3BmDwYCrq6vu/SmEEEIIIYQQQgghvj/aNIOwT58+Nv/39vbm0qVL5Ofn4+fnZ20cBAgLC8Pd3Z38/HwA5s6dS3x8PJGRkbz++uucOnXqnuuRl5eHwWAgPDy80fn5+fkMHjzY5vXaoUOHUl5ezrlzah1c37nNzs7OmEwmLl1qufPz/v37N/gsPj6elJQUAC5evMjHH39MXFyc7ro0N4iLqqqqKsrKymym6iq1zliFEEIIIYQQQgghWmI2Wx7Y9Khq0wZCBwfb0as0TcNs1jdiz5IlS/jiiy8YN24cn376KWFhYXz00Uf3VI+7X7O9F3Z2dg0a3GpqGo6Aeq/b7OzccGS+mJgYTp8+TXZ2Nps2baJ79+48+eSTLZYVGBiIpmnWxta75efn4+HhQadO+kf6XL58OW5ubjbTurXv6l5eCCGEEEIIIYQQQjyc2rSBsCmhoaEUFRVRVFRk/ezYsWOUlJQQFhZm/Sw4OJgXXniBTz75hMmTJ1uz6RwdHamr0z8ke+/evTGbzWQ2MRR6aGgo2dnZNg2AWVlZuLq64uvrC0CnTp1s+ucrKyujoKBAdx1u1xvQXfcOHTowceJEUlJSSE1NZfr06bqX+/GPf8y7775LZWWlzbyvv/6aP/3pT0yZMsWaMalnfy5cuJDS0lKb6eezZuuqjxBCCCGEEEIIIYRe39dBSq5du0Z0dDQmkwl3d3dmzJhBeXl5s8sMHz7cOqjs7WnWrFk2MYWFhYwbNw4nJyc6d+7MSy+9ZDPwrh4PpIEwMjKS3r17Ex0dzaFDh8jJySEmJobw8HAGDBhAZWUlCQkJZGRkcPbsWbKyssjNzbX2qefv7095eTl79+7lypUrVFRUNLs+f39/YmNjiYuLY/v27RQUFJCRkcHWrVsBmD17NkVFRcyZM4fjx4+zY8cOFi9ezNy5c639D44YMYKNGzeyb98+jh49SmxsLAaDQWm7u3XrhqZp7Nq1i8uXL7d4EED9a8ZpaWnk5+cTGxure12rV6+mqqqKUaNG8dlnn1FUVMSePXv48Y9/jI+PD//1X/9ls38+++wzzp8/z5UrVxotz2g0YjKZbCZHo1F3fYQQQgghhBBCCCEeZdHR0XzxxRekp6eza9cuPvvsM5577rkWl5s5c6Z1YNni4mKbMSzq6uoYN24c1dXVfP7556SlpZGamsqiRYuU6vZAGgg1TWPHjh14eHgwbNgwIiMjCQgIYMuWLQAYDAauXr1KTEwMwcHBREVFMWbMGJKTkwEYMmQIs2bNYsqUKXTq1KnB4B6NWbNmDc888wyzZ88mJCSEmTNncvPmTQB8fHzYvXs3OTk59O3bl1mzZjFjxgxefvll6/ILFy4kPDyc8ePHM27cOCZOnEiPHj2UttvHx4fk5GSSkpLo0qULCQkJLS4TGRmJt7c3o0aNomvXrrrXFRQUxIEDBwgICCAqKooePXrw3HPPERERQXZ2tnXAFIClS5dy5swZevToofTasRBCCCGEEEIIIYRoWX5+Pnv27OG9997jiSee4N/+7d945513+OCDD7hw4UKzyzo5OeHl5WWdTCaTdd4nn3zCsWPH2LRpE/369WPMmDG8+uqr/O53v6O6ulp3/TRLa45kIVpdeXk5Pj4+pKSkMHny5AddHRsnTxUqxduh/7VwAE1TOzQd6vQPmnLJrDZis9Gg/0cFYIe+vjZvs9fUUn/bm1vOPr3tXK2vUtmejqVK8Rpq31NpjanloP/Hxf6mUtmqHLSG/Yg2pw61rOFai/6B4s0Wtec1DnZqdS+vbdjPaXPc7Mt0xzrV6Y8FqDDoPwYAnOvUjsnrdmoPOlR/fypUjgEAR03tXGNQqLvZonb8FlZ0UYrv0r5EKd7d3HjGelMuov8hWTs7tUG0XFA8hjUXpfj2Fv3nsgrUyrbT1K43jqjtmxocleLLal11x3a0VzsGKixq57HOVWr3KVfa6b9eql77LGgtB30LqueatjzvqTodMkIpvsfxvbpjVfe76rWystah5aA7dGpXohSvcm9g0NTus43cUopXvgdCbd+oUP39OZlvKMWXaR5K8XUK11fV+zfVvymqLWrnbIPC32eqf5u1tU5paplRBdFv6o6trlM7p3oa1e5VVfY7QHCPx5Tivy/+6wO1/dSafv0TtXOeXn/84x958cUXuX79uvWz2tpa2rVrx5///GcmTZrU6HLDhw/niy++wGKx4OXlxX/8x3/wyiuv4OTkBMCiRYvYuXMneXl51mUKCgoICAjg0KFD/OAHP9BVP7UjX8Hw4cPp168fK1eubKtVfK+ZzWauXLnCm2++ibu7O0899dSDrpIQQgghhBBCCCHE91pVVRVVVbYPVY1GI8Zv2c3a119/TefOnW0+s7e3x9PTk6+//rrJ5X7605/SrVs3unbtypEjR1iwYAEnTpzgww8/tJbbpYvtw/3b/2+u3Lu1WQNhW0pNTSUxMZGSkhIA9u3bx5gxY5qM19PX3/2QkZFBREQE169fx93dvdnYwsJCunfvjq+vL6mpqdjb29vMu3Mwl7sdO3aMRYsWkZaWBtSPrPzYY48RExPDr371K5uyvo22zgi8ZVYbffoG+rMWVDPTVJ4MgnpGRztL8/1o3q3CTv+2OtmrPSFWfSqr+mSznUF/9orqMaNcd8Wn7YrFY6+SoaiYXKL6ZN5op5aZZlbogcJYq3b8Vhr0H78AZQbPloPuYFTMkCo3q2WMqKisVbuJ6OSoVneLRf+BY49a1sIn2WoH/JThasdkjaGdUnxdjf7zsMVO7QelmmXU9cPXleLPTfqV7ljV85hqfKXFqU3LVznH30LtOq96bb3WTn/WKahv68NSNqhno7R1fVSoZAQCnAoZ2WZlqxy/APZ2apmYqvtdNStQqWyzYiabYl1ca67pjr3uqJaxrnrOvmmn9uaCE2p/J9xUyPxu6+xj5XOBwr32w3TeAHD68WileJXft2qmp+q+UbnPfpSZH+DLrsuXL7d2cXfb4sWLWbJkSaPxSUlJvPHGG82WmZ+ff8/1ubOPwt69e+Pt7c3IkSM5deqUctd3zflONhDebcCAATaplN911dXV+Pv709Tb3127dm12e2/3VTh69GhSUlKoqqpi9+7dPP/88zg4OLBw4cK2qLYQQgghhBBCCCHEd9rChQuZO3euzWfNZQ+++OKLTJs2rdkyAwIC8PLy4tKlSzaf19bWcu3aNby89HeD9sQTTwDw1Vdf0aNHD7y8vMjJybGJuXjxIoBSuW3adG02m5k/fz6enp54eXnZtLYWFhYyYcIEXFxcMJlMREVFWTcA4PDhw0RERODq6orJZKJ///4cOHCAjIwMpk+fTmlpqXV45zfeeIPAwMAmJ6hPEV2wYAF+fn4YjUYCAwNZv369dX2ZmZkMGjQIo9GIt7c3SUlJNkNC+/v7N3hdul+/fjbbpGka7733HpMmTcLJyYmgoCB27twJwJkzZ4iIiADAw8MDTdOsB9Dw4cNJSEggMTGRjh07MmrUKOLi4hg/frzN+mpqaujcuTNpaWnNbu/tDEGj0YiXlxfdunXjF7/4BZGRkdb6XL9+nZiYGDw8PHBycmLMmDF8+eWX9/AtCyGEEEIIIYQQQnw/GI1GTCaTzdRcA2GnTp0ICQlpdnJ0dGTw4MGUlJRw8OBB67KffvopZrPZ2uinx+2EMW9vbwAGDx7M0aNHbRof09PTMZlMzb59erc2bSBMS0vD2dmZ/fv3s2LFCpYuXUp6ejpms5kJEyZw7do1MjMzSU9P5/Tp00yZMsW6bHR0NL6+vuTm5nLw4EGSkpJwcHBgyJAhrFy5EpPJZB3eed68eS3WJSYmhs2bN7Nq1Sry8/NZt24dLi716eDnz59n7NixDBw4kMOHD7NmzRrWr1/Pa6+9przNycnJREVFceTIEcaOHUt0dDTXrl3Dz8+Pbdu2AXDixAmKi4t5++23bfaVo6MjWVlZrF27lvj4ePbs2UNxcbE1ZteuXVRUVNjsJxXt27e3jmAzbdo0Dhw4wM6dO8nOzsZisTB27FhqatTSqYUQQgghhBBCCCFak8X84Ka2EhoayujRo5k5cyY5OTlkZWWRkJDAT37yE+uboOfPnyckJMSaEXjq1CleffVVDh48yJkzZ9i5cycxMTEMGzaMPn36APDv//7vhIWF8Z//+Z8cPnyYv/71r7z88ss8//zzSv0mtukrxn369GHx4sUABAUFsXr1avbure8L5OjRoxQUFODn5wfAhg0b6NWrF7m5uQwcOJDCwkJeeuklQkJCrMvf5ubmhqZpulMlT548ydatW0lPTycyMhKoT++87d1338XPz4/Vq1ejaRohISFcuHCBBQsWsGjRIuzs9LejTps2jalTpwKwbNkyVq1aRU5ODqNHj8bTs74frc6dOzfogzAoKIgVK1bYfNazZ082btzI/PnzAUhJSeHZZ5+1NmzqZbFY2Lt3L3/961+ZM2cOX375JTt37iQrK4shQ4YA8Kc//Qk/Pz+2b9/Os88+q1S+EEIIIYQQQgghhGjen/70JxISEhg5ciR2dnY8/fTTrFq1yjq/pqaGEydOUFFR38+7o6Mjf/vb31i5ciU3b97Ez8+Pp59+mpdfftm6jMFgYNeuXfziF79g8ODBODs7Exsby9KlS5Xq1uYNhHfy9vbm0qVL5Ofn4+fnZ20cBAgLC8Pd3Z38/HwGDhzI3LlziY+PZ+PGjURGRvLss8/ec+eLeXl5GAwGwsPDG52fn5/P4MGD0bRvOoEdOnQo5eXlnDt3jsce0z+s+J3b7OzsjMlkavCOeWP69+/f4LP4+Hh+//vfM3/+fC5evMjHH3/Mp59+qrsuu3btwsXFhZqaGsxmMz/96U9ZsmQJe/fuxd7e3iaFtUOHDvTs2bPJjjMbG8WnqqrqW4/iI4QQQgghhBBCCHGnpsZk+K7z9PTk/fffb3L+3eNR+Pn5kZmZ2WK53bp1Y/fu3d+qbm36irGDg+2IhpqmYTbry9dcsmQJX3zxBePGjePTTz8lLCyMjz766J7q0b692ih5jbGzs2twgDb2Ou69brOzc8NRNGNiYjh9+jTZ2dls2rSJ7t278+STT+quc0REBHl5eXz55ZdUVlZaX/m+F8uXL8fNzc1mWrd2zT2VJYQQQgghhBBCCCEeHg9kfO3Q0FCKioooKiqyfnbs2DFKSkpsOlAMDg7mhRde4JNPPmHy5MmkpKQA9SmWdXX6h3Dv3bs3ZrO5yVbX0NBQaz98t2VlZeHq6oqvry9Q3+nknf0BlpWVUVBQoLsOt+sN6K57hw4dmDhxIikpKaSmpjJ9+nSl9Tk7OxMYGMhjjz1mHbgE6re3traW/fv3Wz+7evUqJ06caLIDy4ULF1JaWmoz/XzWL5TqI4QQQgghhBBCCCEePg+kgTAyMpLevXsTHR3NoUOHyMnJISYmhvDwcAYMGEBlZSUJCQlkZGRw9uxZsrKyyM3NJTQ0FKhPuSwvL2fv3r1cuXLF+m52U/z9/YmNjSUuLo7t27dTUFBARkYGW7duBWD27NkUFRUxZ84cjh8/zo4dO1i8eDFz58619j84YsQINm7cyL59+zh69CixsbEYDAal7e7WrRuaprFr1y4uX75MeXl5i8vEx8eTlpZGfn4+sbGxSutrSlBQEBMmTGDmzJn84x//4PDhw/zsZz/Dx8eHCRMmNLqM6ig+QgghhBBCCCGEEPfCbH5w06PqgTQQaprGjh078PDwYNiwYURGRhIQEMCWLVuA+g4Wr169SkxMDMHBwURFRTFmzBiSk5MBGDJkCLNmzWLKlCl06tSpweAejVmzZg3PPPMMs2fPJiQkhJkzZ3Lz5k0AfHx82L17Nzk5OfTt25dZs2YxY8YMm04fFy5cSHh4OOPHj2fcuHFMnDhRuU9EHx8fkpOTSUpKokuXLiQkJLS4TGRkJN7e3owaNco6qk1rSElJoX///owfP57BgwdjsVjYvXt3g1ekhRBCCCGEEEIIIcT3m2ZppZ4fhw8fTr9+/Vi5cmVrFCf+n/Lycnx8fEhJSWHy5MkPujo2vjql9oq1pqkdarfMan1H1lj0j7nTzq6q5aA71FnUskXtNLXHDk6WlrNJ71Sp6e9L8pZZLdOzvd0tpXg71La1UuF7dbBr2M9nczTUjjEHrVop3qx4HGiK+0ZFLWqN+dVmR6V4Rzv9+6ZD1QWlskuMXZTiaxXH07KnVim+3HxvfbPqUVmr9vvr5HhNKV7lGDOgv2sOgLV7fZTipwxvPpv/bh6G60rxl2s66Y51sq9UKtuFMqV4j4/eUYo/N+lXumNVz2MGTe17rVW4VoJ6ferQf56019R+q6pUr08WtJaDHlJmi9ozf9X7lIfJqZCRumN7HN+rVHaNRe3aWqt4X6B6j9WWnMw3lOItmtox1q5G/73tdUe1+4K2ZkTte7ppcdEd29bnPdVzgcrfZ6rXg7b22IVspfhT3sN0x9Yp7kfVvy1VhfTwbdPyH1aL0tT+VmtNS2PV/nb6vmjTUYxbS2pqKomJiZSUlDzoqijJyMggIiKC69ev4+7urrSs2WzmypUrvPnmm7i7u/PUU081G3/58mUWLVrEX/7yFy5evIiHhwd9+/Zl0aJFDB069FtsRdNUb7w1xbZo1YuQs53+P07tlP+gclKKt1jU/si4pamV715zWXfsBTv9o3AD2GtqjXLVFrUGEHMb/gGm+sedat1Vj8mKOv03jKqcDGoNIHVtmDB+2VHtpsUBtWNM9fdUblFr8FMtX4WTvdofGc61pUrxFfauumOv1nZQKnvGSP3nGYAbisf7ldqOSvGdHPTXp9yif7+A+m/71pUSpXgVbd1IpVq+6r5R+T2pPjj8Ljfgiaapfq8qjX4qjYkAfsf2KcW35fUD1Bp7VBt9K+zUzpOqKh3b7uGbKmez2kOgG5q7UrxSo5ycxlqN3a2bSvEqv1fVhlYhvi++Ew2ELdm3bx9jxoxpcr6evv4epOrqausAJrcVFhbSvXt3fH19SU1NtRlkpLCwsMFgIpWV9Y0Fjo6O/P3vf8fe3p69e/dy9erVtt8AIYQQQgghhBBCCPGd1apN42azmfnz5+Pp6YmXlxdLliyxzissLGTChAm4uLhgMpmIiori4sWL1vmHDx8mIiICV1dXTCYT/fv358CBA2RkZDB9+nRKS0vRNA1N02zKBRgwYAB5eXk20/79+/npT3+Kq6srRqORwMBA1q9fb10mMzOTQYMGYTQa8fb2Jikpidrab1K+/f39G7wu3a9fP5t1a5rGe++9x6RJk3ByciIoKIidO3cCcObMGSIiIgDw8PBA0zSmTZsG1L+OnZCQQGJiIh07dmTUqFHExcUxfvx4m/VXV1dTVVXFmTNnbOrRtWtXm2397LPPMJvNbNiwgaNHjzJo0CAGDRrEwoULrZmH8+bNsyl/5cqVaJrGnj17rJ8FBgby3nvvNfHtCiGEEEIIIYQQQrQ9s+XBTY+qVm0gTEtLw9nZmf3797NixQqWLl1Keno6ZrOZCRMmcO3aNTIzM0lPT+f06dNMmTLFumx0dDS+vr7k5uZy8OBBkpKScHBwYMiQIaxcuRKTyURxcTHFxcXMmzfPZr3t27cnMDDQZlq6dCl79uzh3XffJT8/n3Xr1uHiUv/60/nz5xk7diwDBw7k8OHDrFmzhvXr1/Paa68pb3NycjJRUVEcOXKEsWPHEh0dzbVr1/Dz82Pbtm0AnDhxguLiYt5++22bfeXo6EhWVhZr164lPj6ePXv2UFxcbI3ZtWsXFRUVNvsJwN7e3mZb+/Tpg4uLCwcOHMDPz88m2/C28PBw/vGPf1BXV/9qbWZmJh07diQjI8O6T06dOsXw4cOV94EQQgghhBBCCCGE+O5q1VeM+/Tpw+LFiwEICgpi9erV7N1b30/I0aNHKSgowM/PD4ANGzbQq1cvcnNzGThwIIWFhbz00kuEhIRYl7/Nzc0NTdPw8vLSVY+TJ0+ydetW0tPTiYyMBCAgIMA6/91338XPz4/Vq1ejaRohISFcuHCBBQsWsGjRIuzs9LebTps2jalTpwKwbNkyVq1aRU5ODqNHj8bT0xOAzp07N+iDMCgoqMHoyz179mTjxo3Mnz8fqB9p+Nlnn7U2bDbF3t6e1NRUZs6cydq1a/nhD39IeHg4P/nJT+jTpw8ATz75JDdu3OBf//oX/fv357PPPuOll15i+/btQH1/iT4+PgQGBurediGEEEIIIYQQQojWZnmUU/kekFbNILzdGHWbt7c3ly5dIj8/Hz8/P2vjIEBYWBju7u7k5+cDMHfuXOLj44mMjOT111/n1KlT91yPvLw8DAYD4eHhjc7Pz89n8ODBaNo3HZUOHTqU8vJyzp07p7SuO7fZ2dkZk8nEpUuXWlyuf//+DT6Lj48nJSUFgIsXL/Lxxx8TFxenqx5PP/00Fy5cYOfOnYwePZqMjAx++MMfkpqaCoC7uzt9+/YlIyODo0eP4ujoyHPPPce//vUvysvLyczMbHJ/AVRVVVFWVmYzVVW17WhNQgghhBBCCCGEEKLttWoDoYODg83/NU3DbNY3otaSJUv44osvGDduHJ9++ilhYWF89NFH91SP9u3b39Nyd7Kzs8Ny16i7NTUNR9681212dm44sldMTAynT58mOzubTZs20b17d5588knddW7Xrh0//vGPeeWVV/j888+ZNm2aNaMT6vs+zMjIsDYGenp6Ehoayj/+8Y8WGwiXL1+Om5ubzbR27VrddRNCCCGEEEIIIYQQD6f7Mn53aGgoRUVFFBUVWT87duwYJSUlNqPxBgcH88ILL/DJJ58wefJkazado6Ojte88PXr37o3ZbCYzM7PJ+mRnZ9s0AGZlZeHq6oqvry8AnTp1sukPsKysjIKCAt11uF1vQHfdO3TowMSJE0lJSSE1NZXp06crre9uYWFh3Lz5zfDvt/sh3Lt3r7WvweHDh7N582ZOnjzZbP+DCxcupLS01GaaNWvWt6qfEEIIIYQQQgghxN0slgc3ParuSwNhZGQkvXv3Jjo6mkOHDpGTk0NMTAzh4eEMGDCAyspKEhISyMjI4OzZs2RlZZGbm0toaChQP6JveXk5e/fu5cqVK1RUVDS7Pn9/f2JjY4mLi2P79u0UFBSQkZHB1q1bAZg9ezZFRUXMmTOH48ePs2PHDhYvXszcuXOt/Q+OGDGCjRs3sm/fPo4ePUpsbCwGg0Fpu7t164amaezatYvLly9TXl7e4jLx8fGkpaWRn59PbGysrvVcvXqVESNGsGnTJo4cOUJBQQF//vOfWbFiBRMmTLDGDRs2jBs3brBr1y6bBsI//elPeHt7Exwc3OQ6jEYjJpPJZjIajbrqJ4QQQgghhBBCCCEeXvelgVDTNHbs2IGHhwfDhg0jMjKSgIAAtmzZAoDBYODq1avExMQQHBxMVFQUY8aMITk5GYAhQ4Ywa9YspkyZQqdOnRoM7tGYNWvW8MwzzzB79mxCQkKYOXOmNZvOx8eH3bt3k5OTQ9++fZk1axYzZszg5Zdfti6/cOFCwsPDGT9+POPGjWPixIn06NFDabt9fHxITk4mKSmJLl26kJCQ0OIykZGReHt7M2rUKLp27aprPS4uLjzxxBP893//N8OGDePxxx/nlVdeYebMmaxevdoa5+HhQe/evenUqZN1MJhhw4ZhNpubfb1YCCGEEEIIIYQQ4n4xmy0PbHpUaZa7O9oTD1R5eTk+Pj6kpKQwefLkB12dZp1WHEhGQ+1Qq7Q4KcU7aA37iGyKnab/lXWASrNaXezQ1/fmbQbF+rjVXNEde8HuMbWy7UuV4qstapmkVWZH3bFGu2qlstua6jFcUfft+0NtipOhUim+0txOKb693S3dsWaL2rMmld8qQI3FoeWgO1RZ9B9jABaL1nLQPbLT1M4Fnc3FLQfdocLeVXdsSa27Utkm+xtK8TfqXJTiVY+bjvb6z3vlFv37BcBEiVJ8uz+8phR/ZWbLDzbvlb1WqxSv+ntSvZ7VWux1xzoqnuMtqP1WVeuuWv7DRPX3pHpuakuq+13lWnwqZKRS2X7H9inF11rU3i5SvXarfK8P03f6sHE2lynF39DcleLb8rynSvVcoGn6f0+q98Ftzf/035TiT/qP1R3b1r9tVSE9fNu0/IdV0h/0/z3S2l6fqfa30/dFq2YQDh8+nMTExNYs8pFhNpu5dOkSr776Ku7u7jz11FMPukpCCCGEEEIIIYQQ4hGg/3HHA5aamkpiYiIlJSXs27ePMWPGNBmrp6+/+yUjI4OIiAiuX7+Ou7t7k3GFhYV0794dX19fUlNTsbe3t5l352Audzt27BiPPfYYw4cPp1+/fqxcudJm/p377kFqV6P2vZQa3JTiAwo+0R17ZedupbJrEt9Siu9QdUEp/prRWyneufyi7lg/J7VsrcO3+irFt7NXy17pf2mn7tgvvEcrlW1vp1aXW3VqmWb2ik/nM/PddccaFB/XDA/Vn00F8I+THZTiI3rqP8ZMlutKZdvXqR2TJ6qDlOKPn1fL3DxT2HZPfZ2c1C6zUwer1aW9Wf95dXu2j1LZPx+qls24LU+t/JsVapnTCT/Sn9G4/5palyBPfblOKf74jJVK8bdq9J9rLpWrZaz39PxaKV41K+JKpYdS/KBPF+qO/XLsyy0H3aHGrFZ3Nwe1+44as/7sSpNWolT2VXNHpXgHxczQ8+Vq31MXZ/0ZVaqZaXaKWUYqbxYAtDNU6Y5VzQgsCntSKf7Pv/5MKf7Fn6ll8F6pNOmO9Windry3s9O/H0E909PJor8+pRa141f1zZtOeR8rxXfo4qcU/86lZ3THRvYpUSpb9Vxw4aa7Urx7O/33HQHmL5XKLrIPUIpX/V5zuj6rFF/Rf4Du2P9dlK1U9s8nqJ3HhD7ysuv9951pILzTgAEDyMvLe9DVaFVdu3Zt8gfQtWvXZrdXb1+FQgghhBBCCCGEEELcrdUHKTGbzcyfPx9PT0+8vLxYsmSJdV5hYSETJkzAxcUFk8lEVFQUFy9+k6Vy+PBhIiIicHV1xWQy0b9/fw4cOEBGRgbTp0+ntLQUTdNwcnJi06ZNBAYGNjrdVlVVxYIFC/Dz88NoNBIYGMj69eut8zMzMxk0aBBGoxFvb2+SkpKorf3mKY2/v3+DbLx+/frZbJOmabz33ntMmjQJJycngoKC2LmzPkPqzJkzREREAPUDhGiaxrRp04D617ETEhJITEykY8eOjBo1iri4OMaPH2+zvpqaGrp27UpmZmaT23tntqEe06ZNY+LEifz2t7/F29ubDh068Pzzz1NTo5bVI4QQQgghhBBCCNHaLOYHNz2qWr2BMC0tDWdnZ/bv38+KFStYunQp6enpmM1mJkyYwLVr18jMzCQ9PZ3Tp08zZcoU67LR0dH4+vqSm5vLwYMHSUpKwsHBgSFDhrBy5UpMJhPFxcUUFxczb968FusSExPD5s2bWbVqFfn5+axbtw4Xl/pO1M+fP8/YsWMZOHAghw8fZs2aNaxfv57XXlPreBwgOTmZqKgojhw5wtixY4mOjubatWv4+fmxbds2AE6cOEFxcTFvv/22zb5ydHQkKyuLtWvXEh8fz549eygu/ua1rl27dlFRUWGzn1rD3//+d06dOsXf//530tLSSE1NJTU1tVXXIYQQQgghhBBCCCEefq3+inGfPn1YvHgxAEFBQaxevZq9e/cCcPToUQoKCvDzq+/XYcOGDfTq1Yvc3FwGDhxIYWEhL730EiEhIdblb3Nzc0PTNLy8vHTV4+TJk2zdupX09HQiIyMBCAj4ph+Ed999Fz8/P1avXo2maYSEhHDhwgUWLFjAokWLsLPT33Y6bdo0pk6dCsCyZctYtWoVOTk5jB49Gk9PTwA6d+7coA/CoKAgVqywHdmwZ8+ebNy4kfnz5wOQkpLCs88+a23YbC0eHh6sXr0ag8FASEgI48aNY+/evcycObNV1yOEEEIIIYQQQgghHm6tnkHYp08fm/97e3tz6dIl8vPz8fPzszYOAoSFheHu7k5+fj4Ac+fOJT4+nsjISF5//XVOnTp1z/XIy8vDYDAQHh7e6Pz8/HwGDx6Mpn3T4e7QoUMpLy/n3LlzSuu6c5udnZ0xmUxcunSpxeX69+/f4LP4+HhSUlIAuHjxIh9//DFxcXFK9dGjV69eGAzfdPZ9+3tqSlVVFWVlZTZTVZVa58ZCCCGEEEIIIYQQLTFbLA9selS1egOhg4Pt6FyapmE263uJe8mSJXzxxReMGzeOTz/9lLCwMD766KN7qkf79mqjWDbGzs6uwcAhjfXTd6/b7Ozs3OCzmJgYTp8+TXZ2Nps2baJ79+48+aS+EdVMJhOlpaUNPi8pKcHNzXZEYNU6L1++HDc3N5tp7dq1uuolhBBCCCGEEEIIIR5erd5A2JTQ0FCKioooKiqyfnbs2DFKSkoICwuzfhYcHMwLL7zAJ598wuTJk63ZdI6OjtTV6R/6vHfv3pjNZjIzM5usT3Z2tk0DYFZWFq6urvj6+gLQqVMnm/4Ay8rKKCgo0F2H2/UGdNe9Q4cOTJw4kZSUFFJTU5k+fbrudfXs2ZNDhw41+PzQoUMEBwfrLqcxCxcupLS01GaaNWvWtypTCCGEEEIIIYQQ4m4Wi+WBTY+q+9ZAGBkZSe/evYmOjubQoUPk5OQQExNDeHg4AwYMoLKykoSEBDIyMjh79ixZWVnk5uYSGhoK1I8oXF5ezt69e7ly5QoVFRXNrs/f35/Y2Fji4uLYvn07BQUFZGRksHXrVgBmz55NUVERc+bM4fjx4+zYsYPFixczd+5ca/+DI0aMYOPGjezbt4+jR48SGxtr81quHt26dUPTNHbt2sXly5cpLy9vcZn4+HjS0tLIz88nNjZW97p+8YtfcPLkSX75y19y5MgRTpw4wVtvvcXmzZt58cUXlep9N6PRiMlkspmMRuO3KlMIIYQQQgghhBBCPHj3rYFQ0zR27NiBh4cHw4YNIzIykoCAALZs2QKAwWDg6tWrxMTEEBwcTFRUFGPGjCE5ORmAIUOGMGvWLKZMmUKnTp0aDO7RmDVr1vDMM88we/ZsQkJCmDlzJjdv3gTAx8eH3bt3k5OTQ9++fZk1axYzZszg5Zdfti6/cOFCwsPDGT9+POPGjWPixIn06NFDabt9fHxITk4mKSmJLl26kJCQ0OIykZGReHt7M2rUKLp27ap7XQEBAXz22WccP36cyMhInnjiCbZu3cqf//xnRo8erVRvIYQQQgghhBBCCPFoaNVRjDMyMhp8tn37duu/H3vsMXbs2NHoso6OjmzevLnZ8tesWcOaNWt016ddu3a89dZbvPXWW43ODw8PJycnp8nlTSYTH3zwgc1nd2f0NZZ+WlJSYvP/V155hVdeecXms8b21W03b97k+vXrzJgxo8mYpgwcOJBPPvmk2ZjU1NQGn61cuVJ5XUIIIYQQQgghhBCtzWx+dF/1fVBaNYNw+PDhJCYmtmaRjxSz2cylS5d49dVXcXd356mnnnrQVRJCCCGEEEIIIYQQ33OtmkHYllJTU0lMTLRm5+3bt48xY8Y0Ga+nr7/7ISMjg4iICK5fv467u3uzsYWFhXTv3h1fX19SU1Oxt7e3mXfnYC53O3bsGDExMfTr169BNuDd+661VKPWB+FVrYNSvAMNR4xuTrbXFN2x/3zyP5XKjqvOU4o/5xCgFO9EpVL8Px0idMd2cihTKru39n9K8bUGR6X4v5ue0R07oO6wUtnVWjuleDtN3wjrt5k1tWcqz7f/UCleRaHdj5XiZ7JOKf683b/rji265adUtsFO/4BTAP3MTWd6N+ZHxnNK8ZrPDaV4JQa1y2wh+vc7wGVLF92xL7v+Tqns84YJSvEvOP9BKR4Ptd/rvyyTdcdOvPFHpbJTvRYpxQ/iqlJ8kPmY7lh3D7XfkwPVSvF1mlpfygPM2Urxuwe9qTt2qJavVLZzzTWl+K/t1a7FHlzRHVuGh1LZVXUOSvGaQS1r4soNtfLd2+mPd1A8Z6teWytr1epub1erO9Zi0ZTK/vOvP1OKf/a/hinFV/5E7XpWXq1/3wS2u6xUdplF7RjWUDsmr1s8dcc62andB98yq10/NndU64vdy1Xtb5D4T5/THXui138rlY3aKZtr5Wq/p0EG/cfkacdeSmWr/v5U42vMajvnfxbs0x07/U8jlcr2mLBRKb5OU22G6awY//3wCI8V8sB8ZxoI7zZgwADy8vIedDVaTXV1Nf7+/k2OmNO1a9dmt1elr0IhhBBCCCGEEEIIIW5r9UFKzGYz8+fPx9PTEy8vL5YsWWKdV1hYyIQJE3BxccFkMhEVFcXFixet8w8fPkxERASurq6YTCb69+/PgQMHyMjIYPr06ZSWlqJpGpqm8cYbbxAYGNjkBFBVVcWCBQvw8/PDaDQSGBjI+vXrrevLzMxk0KBBGI1GvL29SUpKorb2m6eS/v7+DbLx+vXrZ7NNmqbx3nvvMWnSJJycnAgKCmLnzp0AnDlzhoiI+kwvDw8PNE1j2rRpQP3r2AkJCSQmJtKxY0dGjRpFXFwc48ePt1lfTU0NnTt3Ji0trdntvTPbsCXTpk1j4sSJ/Pa3v8Xb25sOHTrw/PPPU1Oj9rRMCCGEEEIIIYQQQnz3tXoDYVpaGs7Ozuzfv58VK1awdOlS0tPTMZvNTJgwgWvXrpGZmUl6ejqnT59mypRvXguNjo7G19eX3NxcDh48SFJSEg4ODgwZMoSVK1diMpkoLi6muLiYefPmtViXmJgYNm/ezKpVq8jPz2fdunW4uLgAcP78ecaOHcvAgQM5fPgwa9asYf369bz22mvK25ycnExUVBRHjhxh7NixREdHc+3aNfz8/Ni2bRsAJ06coLi4mLfffttmXzk6OpKVlcXatWuJj49nz549FBcXW2N27dpFRUWFzX5qDX//+985deoUf//730lLSyM1NbXRwUuEEEIIIYQQQggh7ieL2fLApkdVq79i3KdPHxYvXgxAUFAQq1evZu/evQAcPXqUgoIC/Pzq+9fZsGEDvXr1Ijc3l4EDB1JYWMhLL71ESEiIdfnb3Nzc0DQNLy8vXfU4efIkW7duJT09ncjISAACAr7pi+bdd9/Fz8+P1atXo2kaISEhXLhwgQULFrBo0SLs7PS3nU6bNo2pU6cCsGzZMlatWkVOTg6jR4/G07O+/43OnTs36IMwKCiIFStW2HzWs2dPNm7cyPz58wFISUnh2WeftTZsthYPDw9Wr16NwWAgJCSEcePGsXfvXmbOnNmq6xFCCCGEEEIIIYQQD7dWzyDs06ePzf+9vb25dOkS+fn5+Pn5WRsHAcLCwnB3dyc/v76T6rlz5xIfH09kZCSvv/46p06duud65OXlYTAYCA8Pb3R+fn4+gwcPRtO+6Qx16NChlJeXc+6cWgf3d26zs7MzJpOJS5cutbhc//79G3wWHx9PSkoKABcvXuTjjz8mLi5OqT569OrVC4Phm45db39PTamqqqKsrMxmqq6qavV6CSGEEEIIIYQQ4tFmtlge2PSoavUGQgcH25GTNE3DbNY3mtmSJUv44osvGDduHJ9++ilhYWF89NFH91SP9u3b39Nyd7Kzs2swaEhj/fTd6zY7Ozs3+CwmJobTp0+TnZ3Npk2b6N69O08++aSu+ppMJkpLSxt8XlJSgpub27eq8/Lly3Fzc7OZfr9WbURMIYQQQgghhBBCCPHwafUGwqaEhoZSVFREUVGR9bNjx45RUlJCWFiY9bPg4GBeeOEFPvnkEyZPnmzNpnN0dKSurk73+nr37o3ZbCYzM7PJ+mRnZ9s0AGZlZeHq6oqvry8AnTp1sukPsKysjIKCAt11uF1vQHfdO3TowMSJE0lJSSE1NZXp06frXlfPnj05dOhQg88PHTpEcHCw7nIas3DhQkpLS22m52Y9/63KFEIIIYQQQgghhBAP3n1rIIyMjKR3795ER0dz6NAhcnJyiImJITw8nAEDBlBZWUlCQgIZGRmcPXuWrKwscnNzCQ0NBepHFC4vL2fv3r1cuXKFioqKZtfn7+9PbGwscXFxbN++nYKCAjIyMti6dSsAs2fPpqioiDlz5nD8+HF27NjB4sWLmTt3rrX/wREjRrBx40b27dvH0aNHiY2NtXktV49u3bqhaRq7du3i8uXLlJeXt7hMfHw8aWlp5OfnExsbq3tdv/jFLzh58iS//OUvOXLkCCdOnOCtt95i8+bNvPjii0r1vpvRaMRkMtlMjkbjtypTCCGEEEIIIYQQ4m4ySMn9d98aCDVNY8eOHXh4eDBs2DAiIyMJCAhgy5YtABgMBq5evUpMTAzBwcFERUUxZswYkpOTARgyZAizZs1iypQpdOrUqcHgHo1Zs2YNzzzzDLNnzyYkJISZM2dy8+ZNAHx8fNi9ezc5OTn07duXWbNmMWPGDF5++WXr8gsXLiQ8PJzx48czbtw4Jk6cSI8ePZS228fHh+TkZJKSkujSpQsJCQktLhMZGYm3tzejRo2ia9euutcVEBDAZ599xvHjx4mMjOSJJ55g69at/PnPf2b06NFK9RZCCCGEEEIIIYQQj4ZWHcU4IyOjwWfbt2+3/vuxxx5jx44djS7r6OjI5s2bmy1/zZo1rFmzRnd92rVrx1tvvcVbb73V6Pzw8HBycnKaXN5kMvHBBx/YfHZ3Rt/dfRRCfZ9/d3rllVd45ZVXbD5rbF/ddvPmTa5fv86MGTOajGnKwIED+eSTT5qNSU1NbfDZypUrldclhBBCCCGEEEII0doe5Uy+B6VVGwjFt2M2m7ly5Qpvvvkm7u7uPPXUUw+6SkIIIYQQQgghhBDie06zNJYC9x2wb98+xowZ0+Dz6upq6urqlAY0eRikpqbyy1/+khs3buDr60tqaiojR460zi8sLLQZzOVux44d47HHHlNa5+bNm/nZz37GrFmz+N3v1EckPnGqqOWgO1jQlOK9yk4qxRe6PK47ttai1peks6H5Pi/vpqH2s7JD7XgtrXNrOej/aW93S6lsJ1ruJ/NO1Vo7tXiLo+5YR61aqWxVBq1WKd5iUTuGnWsajireWsocOijFGy2VSvFmTf9vpK6NnzXVWBxaDrqDZ+1FpfgKB5NSvArVc0E1bde3q0tdiVJ8hUFtvzjVlSnFVxvaK8WXmvWf95zt1M7ZqmoVj3kj+s/DFRZnpbIdtBqleANq5732dWrXhIvo7xrFyaB4XrKo9Yxj0NSurar3KSo8fr9QKf76c8uV4lX3jabpPze51l1XKrvc4K4Ur7rfVc+rKlSvN5W1avdAl3sPUor3z8/QHat6vD9KSmpcleJd7dWuIbfM+q/dvuv/P6Wy2/pcoEL1eqN6rVQ915TZeSrF11r016fKrP/vFQBXe7VrpR1mpfjgHmp/539fPP/bkge27t/Nc39g636QvrMZhAMGDCAvL6/B56tWrSI9Pf3+V6gV2NnZNfrKMkDXrl3Jy8ujurraOjLy3fNVrV+/nvnz57Nu3TrefPNN2rVTu8kRQgghhBBCCCGEaG3yhvH91+qPGG7cuEF0dDTOzs54e3vz3//93wwfPpzExESgfrCSO/slBHB3d7fpF6+oqIioqCjc3d3x9PRkwoQJnDlzxjo/IyOD8PBw+vbty4ABA4iNjcXBwYF//OMfvPPOOxw/fhxN09A0rdH+9u5WUlLCz3/+c7p06UK7du14/PHH2bVrl3X+tm3b6NWrF0ajEX9/f958802b5VvapjNnzqBpGh9++CERERE4OTnRt29fsrOzrdszffp0SktLrfVesmQJUD8a86uvvkpcXBw//OEPWbFiBc899xwrV64kMDDQOl2/fh1HR0f27t3b4vYCFBQU8Pnnn5OUlERwcDAffvihruWEEEIIIYQQQgghxPdLqzcQzp07l6ysLHbu3El6ejr79u3j0KFDupevqalh1KhRuLq6sm/fPrKysnBxcWH06NFUV1dTW1vLxIkTCQ8P58iRI2RnZ/Pcc8+haRpTpkzhxRdfpFevXhQXF1NcXMyUKVOaXZ/ZbGbMmDFkZWWxadMmjh07xuuvv47BUP963cGDB4mKiuInP/kJR48eZcmSJbzyyiu6Gh7v9utf/5p58+aRl5dHcHAwU6dOpba2liFDhrBy5UpMJpO13vPmzbMu99vf/pa+ffvyr3/9i1deeYX4+Hjef/99qqqqrDGbNm3Cx8eHESNG6KpLSkoK48aNw83NjZ/97GesX79eeXuEEEIIIYQQQgghWpvFbHlg06OqVV8xvnHjBmlpabz//vvW/vNSUlKUXn/dsmULZrOZ9957D03TrGW4u7uTkZHBgAEDKC0tZfz48fTo0QOA0NBQ6/IuLi7Y29vj5eWla31/+9vfyMnJIT8/n+DgYAACAgKs89966y1GjhxpHYU4ODiYY8eO8Zvf/IZp06bp3i6AefPmMW7cOACSk5Pp1asXX331FSEhIbi5uaFpWqP1HjFiBC+++KL1/z4+PiQkJLBjxw6ioqKA+j4Mp02bZt1nzTGbzaSmpvLOO+8A8JOf/IQXX3yRgoICunfvrrRNQgghhBBCCCGEEOK7rVUzCE+fPk1NTQ2DBn3TAa+bmxs9e/bUXcbhw4f56quvcHV1xcXFBRcXFzw9Pbl16xanTp3C09OTadOmMWrUKP7jP/6Dt99+m+Li4nuuc15eHr6+vtbGwbvl5+czdOhQm8+GDh3Kl19+qTwQSp8+faz/9vb2BuDSpUstLjdgwACb/7dr147//M//5I9//CMAhw4d4v/+7/90N1imp6dz8+ZNxo4dC0DHjh358Y9/bC2vMVVVVZSVldlM1XdkMAohhBBCCCGEEEKI76a2G+aoCZqmNRiIo6bmmxGRysvL6d+/P3l5eTbTyZMn+elPfwrUZxRmZ2czZMgQtmzZQnBwMP/85z/vqT7t26uNotiYlrbpNgeHb0ZGu53pZza3PIKRs3PDEQ3j4+NJT0/n3LlzpKSkMGLECLp166arvuvXr+fatWu0b98ee3t77O3t2b17N2lpaU3WZ/ny5bi5udlM69aqj3wshBBCCCGEEEII0RyLxfLApkdVqzYQBgQE4ODgQG5urvWz0tJSTp48af1/p06dbDL+vvzySyoqvhlK/oc//CFffvklnTt3thmEIzAwEDc3N2vcD37wAxYuXMjnn3/O448/zvvvvw+Ao6OjUmZfnz59OHfunE0d7xQaGkpWVpbNZ1lZWQQHB1v7KWxpm/RQrXfv3r0ZMGAAf/jDH3j//feJi4vTtdzVq1fZsWMHH3zwgU0D7L/+9S+uX7/OJ5980uhyCxcupLS01Gb6+azndddXCCGEEEIIIYQQQjycWrUPQldXV2JjY3nppZfw9PSkc+fOLF68GDs7O2vG3IgRI1i9ejWDBw+mrq6OBQsW2GTWRUdH85vf/IYJEyawdOlSfH19OXv2LB9++CHz58+npqaG3//+9zz11FN07dqVEydO8OWXXxITEwPUj/pbUFBgfXXY1dUVo9HYZJ3Dw8MZNmwYTz/9NG+99RaBgYHWUZBHjx7Niy++yMCBA3n11VeZMmUK2dnZrF69mnfffddaRkvbpIe/vz/l5eXs3buXvn374uTkhJOTU7PLxMfHk5CQgLOzM5MmTdK1no0bN9KhQweioqIa9Fc4duxY1q9fz+jRoxssZzQaG+xHR2OprnUKIYQQQgghhBBC6GV+hAcLeVBa/RXjt956i8GDBzN+/HgiIyMZOnQooaGhtGvXDoA333wTPz8/nnzySX76058yb948m4YwJycnPvvsMx577DEmT55MaGgoM2bM4NatW5hMJpycnDh+/DhPP/00wcHBPPfcczz//PP8/Oc/B+Dpp59m9OjRRERE0KlTJzZv3txinbdt28bAgQOZOnUqYWFhzJ8/35rN98Mf/pCtW7fywQcf8Pjjj7No0SKWLl1q099fS9ukx5AhQ5g1axZTpkyhU6dOrFixosVlpk6dir29PVOnTrXu35b88Y9/ZNKkSY0OZvL000+zc+dOrly5olR3IYQQQgghhBBCCPHdpVna+AXrmzdv4uPjw5tvvsmMGTPaclWPnDNnztCjRw9yc3P54Q9/eN/Xf+JUkVK8hZZHWL6TV1njr303pdDlcd2xtRaDUtnOBrVXxjXUflZ2qA14U1rn1nLQ/9Pe7pZS2U6UK8VXa/oap63xFkfdsY5atVLZqgxarVK8xaJ2DDvXtF2WbZlDB6V4o6VSKd6s6f+N1LVuMnoDNRa1jGzP2otK8RUOJqV4Farngmqaznj/tlzqSpTiKwxq+8Wprkwpvtqg1gdwqVn/ec/ZTu2crapW8Zg3ov88XGFp2O9wcxy0hn0eN8eA2nmvfZ3aNeEiXXXHOhkUz0sWtefaBk3t2qp6n6LC4/cLleKvP7dcKV5132ia/nOTa911pbLLDe5K8ar7XfW8qkL1elNZq3YPdLn3oJaD7uCfn6E7VvV4f5SU1Lgqxbvaq11Dbpn1X7t91/9/SmW39blAher1RvVaqXquKbPzVIqvteivT5VZ/98rAK72atdKO1oeh+BOwT0eU4r/vpi57OoDW/cffqX2t9b3RaufQf71r3+xefNmTp06xaFDh4iOjgZgwoQJrb2qRi1ZsoR+/frdl3W1ptTUVNzd3XXF1tTU8PXXX/Pyyy/zox/96J4aB7/66iumT5+Or68vRqOR7t27M3XqVA4cOKBclhBCCCGEEEIIIURrkUFK7r82Sfv47W9/y4kTJ3B0dKR///7s27ePjh07tsWqWvSnP/3J+vrx3bp168YXX3xxn2v07VRXV/P5558TERFBcHAw//M//2Mzf9++fYwZM6bJ5cvLyzlw4AAjR47k8ccfZ926dYSEhHDjxg127NjBiy++SGZmpq663Kh1Uap7O0OVUnypq49S/Onr+lv5O7uoPRlUzlqwU3uK66j4RO5apf4Mk+4uallsBrNidolF8YmZnf6soUqL2qv6qhwVsxbsFb8n11L9WbYWTe0Ys3NVO8acbnytFH+uQz/dsTVmtYwLk1aiFF9pUcs0c7p5SSneveSoUryKWmd3pfh8j2FK8e0N+jPTXMvVjoFKN7VzvOuNC0rxZju148bg4q079usa/VlsACVVaseYr7NaVxxlFv3ZmCGHUpTKPjPgp0rxqufVGoNaFkVZpf7y7YxqGRQGTS1eNUND5VqvkoEHcGVmy13H2JSv+MdJ+2WzlOIrf7VWd6xqlo5qgp9qBqFKppzq/duVSrXM6fJqtfNYgEJGIMCZ0OG6Y7vn/12pbNVjWPUtCpXy27JsgOIyteuZ0aPt7svbtfG5wP4VfQNX3lb9aqru2DrU3sBS/V5VzzWq9bl6S38maWWNWjOJs0nxb8vWz9MSolW0+pEZGBhISEgIFosFo9HI2LFjmTNnDomJiQBomsb27dttlnF3dyc1NdX6/6KiIqKionB3d8fT05MJEyZw5swZ6/yMjAwGDRqEs7Mz7u7uDB06lLNnz5KamkpycjKHDx9G0zQ0TePGjRs2o/XeOe3evRuAkpISfv7zn9OlSxfatWvH448/zq5du6zr27ZtG7169cJoNOLv78+bb75pU/+WtunMmTNomsaHH35IREQETk5O9O3bl+zsbOv2TJ8+ndLSUmu9lyxZAtQPXvLqq68SExODyWTiueeeY+nSpTz//POcOHGC3r17A3D58mUcHR2b3d68vDwsFgvTpk0jKCiIffv2MW7cOHr06EG/fv1YvHgxO3bsuIdvXQghhBBCCCGEEKJ1WMyWBzY9qlo9g3Du3LlkZWWxc+dOunTpwqJFizh06JDu135ramoYNWoUgwcPZt++fdjb2/Paa68xevRojhw5gp2dHRMnTmTmzJls3ryZ6upqcnJy0DSNKVOm8H//93/s2bOHv/3tbwC4ubnRvn3TmQFms5kxY8Zw48YNNm3aRI8ePTh27BgGQ/0TiYMHDxIVFcWSJUuYMmUKn3/+ObNnz6ZDhw42A5Xo8etf/5rf/va3BAUF8etf/5qpU6fy1VdfMWTIEFauXMmiRYs4ceIEAC4u3zzp+u1vf8uiRYtYvHgxAPv37ychIYE333zTOrLwpk2b8PHxYcyYMY0OQHLbv/71L7744gvef/997Owatg/rfc1ZCCGEEEIIIYQQQnw/tGoD4Y0bN0hLS+P9999n5MiRAKSkpNC1q/5XfbZs2YLZbOa9996zNnSlpKTg7u5ORkYGAwYMoLS0lPHjx9OjRw8AQkNDrcu7uLhgb2+Pl5eXrvX97W9/Iycnh/z8fIKDgwEICAiwzn/rrbcYOXIkr7zyCgDBwcEcO3aM3/zmN8oNhPPmzWPcuHEAJCcn06tXL7766itCQkJwc3ND07RG6z1ixAhefPFF6/99fHxISEhgx44dREVFAfV9GE6bNq3ZxkGAL7/8EoCQkBClugshhBBCCCGEEEKI76dWfcX49OnT1NTUMGjQ/8/e3YdVUe6L/38Pz88QioqoECCK4jNRyjEF+W61TC12EPJVSfDhmLvjVnrgVxpqpadErePOOmXAN8P0ZJl50hN6RNlIWxMwH1BQwaJNqZUIKrBgrd8fbFcuBZnbIGn3eV3XXBdrrc/cc8+sWTPDPZ+5759H6HJ3d6dPnz66yzh8+DCnTp3C1dUVFxcXXFxc8PT0pLa2ltOnT+Pp6UlCQgJjx47loYce4rXXXqOysvK261xUVESPHj3MjYM3Ki4uJjw83OK98PBwSktLaWxU6wNs4MCB5r+9vZv6Ujp3rvW+skJDQy1eOzg4MHXqVN59910ACgoKOHr0qK4Gy9vtcLOuro5Lly5ZTPX1an0KCiGEEEIIIYQQQrRGHjH+9f3qvWNqmnZTI5XB8HNHsDU1NQwbNuymvvNKSkqYMqWpI+709HTy8/MZMWIEmzZtIigoiC+++OK26nOrx4/1am2drrG1/bkj42uZfkZj6x1oOzvf3PFtUlIS2dnZVFRUkJ6eTmRkJL6+vq2Wda0h9MSJE63GXm/58uW4u7tbTBlvrVYqQwghhBBCCCGEEEJ0PG3aQOjv74+trS0HDx40v1dVVUVJSYn5tZeXl0XGX2lpKVeu/Dzqz9ChQyktLaVLly4EBgZaTO7u7ua4IUOGkJKSwv79+wkJCSErKwsAOzs7pcy+gQMHUlFRYVHH6wUHB5OXl2fxXl5eHkFBQeZ+CltbJz1U6z1gwABCQ0N5++23ycrKYsYMfSNWDR48mH79+pGWltZs4+TFixebnS8lJYWqqiqLKWH2n3XXVwghhBBCCCGEEEIPo8l0x6bfqzZtIHR1dWX69Ok89dRT7Nmzh2PHjpGYmIiVlZU5Yy4yMpK1a9dSWFjIl19+yZw5cywy6+Lj4+ncuTOTJk0iNzeXsrIycnJyePLJJ6moqKCsrIyUlBTy8/M5e/Ysn3/+OaWlpeZ+CP38/CgrK6OoqIgLFy5QV3frx2BHjRrF/fffT3R0NNnZ2ZSVlbFjxw527twJwMKFC9m9ezfLli2jpKSEzMxM1q5dS3JysrmM1tZJDz8/P2pqati9ezcXLlzQ1cCYlJTEihUrMJlMPPzww7qWo2ka6enplJSUMHLkSD777DPOnDnDV199xUsvvcSkSZOanc/e3h43NzeLyc7OXmkdhRBCCCGEEEIIIUTH0+aPGK9atYrhw4czYcIEoqKiCA8PJzg4GAcHBwDS0tLo2bMnI0eOZMqUKSQnJ+Pk5GSe38nJiX379tGrVy8eeeQRgoODSUxMpLa2Fjc3N5ycnDhx4gTR0dEEBQUxa9YsnnjiCWbPng1AdHQ048aNIyIiAi8vLzZu3Nhqnbds2cI999xDXFwc/fr14+mnnzZn8w0dOpTNmzfzwQcfEBISwuLFi1m6dKlFf3+trZMeI0aMYM6cOcTGxuLl5cUrr7zS6jxxcXHY2NgQFxdn3r56hIWF8eWXXxIYGMjMmTMJDg5m4sSJHDt2jDVr1ijVWwghhBBCCCGEEEL8trXpKMbQlEX4/vvvm19fvnyZJUuWMGvWLAC6d+/O//zP/1jMc+Njrd26dSMzM7PZ8t3c3Pj4449bXL69vT0ffvihUp09PT3NA340Jzo6mujo6BY/b22d/Pz8buqj0MPD46b31q1bx7p16yzeKy8vb3G5Fy5coLa2lsTExBZjWhIUFNTiNhZCCCGEEEIIIYS4U37Pg4XcKW3eQFhYWMiJEycICwujqqqKpUuXArT46KpQZzAY+OGHH3j++ee57777GDp06J2ukhBCCCGEEEIIIYT4jdJMN6ax/UKFhYUkJSVx8uRJ7OzsGDZsGKtWrWLAgAFtuZgWpaamsnXrVoqKigB4//33zY8f38jX15djx479KvVqTUZGBvPnz29xkJDr5eTkEBERQVBQEB9++KHFts3NzWX8+PEtzltTUwPAqVOneOmll8jOzub8+fN0796d++67j4ULFxIaGqqrzsdOVbYedB2N9r0D0Ii17lhnrUapbOf6i0rxP9h6K8XXmeyU4lW2pat1tVLZ9Sa1viWtaH0k7uup1N3YzgOtO5jUBhOq09RGPXcyqm17FZet3JTiVetyxcpVd6zRpPY9HT3fTSk+xOs7pXhrTf+ATwCORrXjgYo6K7XuJurb8VjgYqpSKrvWylkp3sF4WSnepKntN98auuuOdbVV+227cVEp/qqmtm0aTPrvx6qeK220hnary+3o1KD/9/qjTVelsjVNbduonp9MaErxHUmjSf81EICtZtAd297n4o6k1qh2DdSJ80rxqudulfNrWXCEUtkBJ3Yrxf+WXW5UOxc7WtUqxdeb9Pc972B16/7xfynVY0FAwfutB/1D+bDHlMpWPZ+pHoNVrz9Vzn+Nisc91X1GVd+AHu1afkc1bZFae0Nb+n/L1P6f/2fR5leJQ4YM4dChQ21d7G2bOHEi9957b7OfqQ4k0hHU19czevTomx5PviY0NNTcONqSL7/8kjFjxhASEsJbb71F3759qa6u5pNPPmHhwoXs3bu3HWouhBBCCCGEEEIIITqiNr8lWF1dTXx8PM7Oznh7e7N69WpGjx7N/PnzgaZRdLdu3Woxj4eHBxkZGebX33zzDTExMXh4eODp6cmkSZMs+uLLyckhLCwMZ2dnPDw8CA8P5+zZs2RkZLBkyRIOHz6MpmlomsaWLVsIDAxsdvL19QWa+gucPXs2Xbt2xcHBgZCQELZv325e3pYtW+jfvz/29vb4+fmRlpZmUf/W1qm8vBxN0/joo4+IiIjAycmJQYMGkZ+fb16fxx9/nKqqKnO9U1NTgab+C5ctW8a0adNwc3Nj1qxZREZGMm/ePIvlnT9/Hjs7O/bv39/i+gYGBmIymUhISKB3797k5uby4IMPEhAQwODBg3nhhRf45JNPFL5tIYQQQgghhBBCCPFb1+YNhAsWLCAvL49t27aRnZ1Nbm4uBQUFuuc3GAyMHTsWV1dXcnNzycvLw8XFhXHjxlFfX09DQwOTJ09m1KhRfPXVV+Tn5zNr1iw0TSM2NpaFCxfSv39/KisrqaysJDY29pbLMxqNjB8/nry8PDZs2MDx48dZsWIF1tZN6dmHDh0iJiaGxx57jCNHjpCamsqiRYssGjT1eu6550hOTqaoqIigoCDi4uJoaGhgxIgRrFmzBjc3N3O9k5OTzfOtXLmSQYMGUVhYyKJFi0hKSiIrK4u6up9T1Dds2ICPjw+RkZG3rENRURHHjh1j4cKFWFnd/PV7eHgor5cQQgghhBBCCCFEWzEaTXds+r1q00eMq6uryczMJCsrizFjxgCQnp5O9+76+w3atGkTRqORd955B03TzGV4eHiQk5NDaGgoVVVVTJgwgYCAAACCg4PN87u4uGBjY0O3bvr6uNq1axcHDhyguLiYoKAgAPz9/c2fr1q1ijFjxrBo0SKgafTf48eP8+qrr5KQkKB7vQCSk5N58MEHAViyZAn9+/fn1KlT9O3bF3d3dzRNa7bekZGRLFy40Pzax8eHefPm8cknnxATEwM09WGYkJBg3mYtKS0tBaBv375KdRdCCCGEEEIIIYQQ/5zaNIPwzJkzGAwGwsLCzO+5u7vTp08f3WUcPnyYU6dO4erqiouLCy4uLnh6elJbW8vp06fx9PQkISGBsWPH8tBDD/Haa69RWXn7nVcWFRXRo0cPc+PgjYqLiwkPD7d4Lzw8nNLSUhob1TrCHzhwoPlvb++mTi/PnTvX6nw3Dhri4ODA1KlTeffddwEoKCjg6NGjuhosb3dMmrq6Oi5dumQx1de1bye7QgghhBBCCCGE+P0xGU13bPq9+tWHJdM07aZGKoPh55HUampqGDZsGEVFRRZTSUkJU6ZMAZoyCvPz8xkxYgSbNm0iKCiIL7744rbq4+ioNippc1pbp2uuHxTlWqaf0dj6CHvOzjePlpiUlER2djYVFRWkp6cTGRlp7lPxVq41hJ44caLV2OstX74cd3d3i+ntt/5DqQwhhBBCCCGEEEII0fG0aQOhv78/tra2HDx40PxeVVUVJSUl5tdeXl4WGX+lpaVcuXLF/Hro0KGUlpbSpUuXmwbYcHd3N8cNGTKElJQU9u/fT0hICFlZWQDY2dkpZfYNHDiQiooKizpeLzg4mLy8PIv38vLyCAoKMvdT2No66aFa7wEDBhAaGsrbb79NVlYWM2bM0DXf4MGD6devH2lpac02Tl68eLHZ+VJSUqiqqrKYZs7+k+76CiGEEEIIIYQQQoiOqU0bCF1dXZk+fTpPPfUUe/bs4dixYyQmJmJlZWXOmIuMjGTt2rUUFhby5ZdfMmfOHIvMuvj4eDp37sykSZPIzc2lrKyMnJwcnnzySSoqKigrKyMlJYX8/HzOnj3L559/TmlpqbkfQj8/P8rKyigqKuLChQsWA3k0Z9SoUdx///1ER0eTnZ1NWVkZO3bsYOfOnQAsXLiQ3bt3s2zZMkpKSsjMzGTt2rUWg4i0tk56+Pn5UVNTw+7du7lw4YKuBsakpCRWrFiByWTi4Ycf1rUcTdNIT0+npKSEkSNH8tlnn3HmzBm++uorXnrpJSZNmtTsfPb29ri5uVlMdvb2SusohBBCCCGEEEII0RqTyXTHpvb0448/Eh8fj5ubGx4eHiQmJlJTU9NifHl5OZqmNTv913/9lzmuuc8/+OADpbq1+SPGq1atYvjw4UyYMIGoqCjCw8MJDg7GwcEBgLS0NHr27MnIkSOZMmUKycnJODk5med3cnJi37599OrVi0ceeYTg4GASExOpra3Fzc0NJycnTpw4QXR0NEFBQcyaNYsnnniC2bNnAxAdHc24ceOIiIjAy8uLjRs3tlrnLVu2cM899xAXF0e/fv14+umnzdl8Q4cOZfPmzXzwwQeEhISwePFili5datHfX2vrpMeIESOYM2cOsbGxeHl58corr7Q6T1xcHDY2NsTFxZm3rx5hYWF8+eWXBAYGMnPmTIKDg5k4cSLHjh1jzZo1SvUWQgghhBBCCCGEEK2Lj4/n2LFjZGdns337dvbt28esWbNajO/ZsyeVlZUW05IlS3BxcWH8+PEWsenp6RZxkydPVqqbZmrn5tHLly/j4+NDWloaiYmJ7bmo353y8nICAgI4ePAgQ4cO/dWXf+yU2uAwGu3bEt+Ite5YZ63lFvpm4+svKsX/YOutFF9nslOKV9mWrtbVSmXXm9QyQ61ovR/N66nU3djO3aQ6mNS6AqjT1PosdTKqbXsVl63clOJV63LFylV3rNGk9j0dPa9vlPlrQry+U4q31tQGkHI0qh0PVNRZqd0sqm/HY4GLqUqp7Fqrm/u/vRUH42WleJOmtt98a+iuO9bVVu237cZFpfirmtq2aTDZ6I5VPVfaaA3tVpfb0alB/+/1R5uuSmVrmtq2UT0/mdCU4juSRpP+ayAAW+3mvrJb0t7n4o6k1qh2DdSJ80rxqudulfNrWXCEUtkBJ3Yrxf+WXW5UOxc7WtUqxdeb9D855mDVvoM8qh4LAgre1x1bPuwxpbJVz2eqx2DV60+V81+j4nFPdZ9R1TegR7uW31HFPf31HVv2xld6tUu5xcXF9OvXj4MHD5oHo925cycPPPAAFRUVdO+u73p3yJAhDB06lPXr15vf0zSNjz/+WLlR8HptfsYvLCxk48aNnD59moKCAuLj4wFafHRVqDMYDHz33Xc8//zz3HfffXekcVAIIYQQQgghhBBC6JOfn4+Hh4e5cRAgKioKKysr/va3v+kq49ChQxQVFTWbgPfEE0/QuXNnwsLCePfdd5Ufl26X28grV67k5MmT2NnZMWzYMHJzc+ncuXN7LKpZqampbN26laKiIt5//33z48c38vX15dixY79avVqTkZHB/PnzWxwo5Jq8vDwiIiIICgriww8/tPgsNzf3pjTT6117tn306NHs3bv3ps8NBgM2Nvp2CzutXlfcNap3oFXv/KvcpKpD/yPZAAa7LkrxqnV30dQyb+oUsvwMihlJqlS/V5W7icr7gCIrk1qmmbVqpo5V+257FVWap1K8Hfp/3w7aVaWyB3epUIpXvaNso5AZA+qZcipUs7XsNbU70I0Kp/EGTW1/rDWqHSc1K7ULEGvUfk8uNvr3M9WsunrFc4IdahkgKplv7Z2t5aipZVfWo5ZRpZIVqJrt25Ey/No7M0aV6rHDqPDUheq5WHUftket7tZG/cd4lWx4UM/uumS6SyneGrV9XuXYoZoReLrvGKV41fJVfiOqvw/V35+t4jlBNVvZRvF7bU+qxwLVrEAV7Z3FrbqudZr+c71qdqL451NXV3fTeBb29vbY/8JxGL777ju6dLFsW7CxscHT05PvvtP3FMb69esJDg5mxIgRFu8vXbqUyMhInJyc+Pzzz5k7dy41NTU8+eSTuuvX5g2EQ4YM4dChQ21d7G2bOHEi9957b7OfqQ4k0lGMGDGixZbg0NBQioqKdJUzc+ZMli5davGe3sZBIYQQQgghhBBCiPZgNLZvF2W3snz5cpYsWWLx3gsvvEBqamqz8c8++yz//u//fssyi4uLf3G9rl69SlZWFosWLbrps+vfGzJkCJcvX+bVV19VaiBsl6bx6upq4uPjcXZ2xtvbm9WrVzN69Gjmz58PND0bvXXrVot5PDw8yMjIML/+5ptviImJwcPDA09PTyZNmkR5ebn585ycHMLCwnB2dsbDw4Pw8HDOnj1LRkYGS5Ys4fDhw2iahpubG3/9618JDAy8afL19TWXd/HiRWbPnk3Xrl1xcHAgJCSE7du3mz/fsmUL/fv3x97eHj8/P9LS0izq39o6XRt55qOPPiIiIgInJycGDRpEfn6+eX0ef/xxqqqqzCPOXNv5/Pz8WLZsGdOmTcPNzY1Zs2YRGRnJvHnzLJZ3/vx53N3dOXv2bLPrGxgYaBHv5OREt27dLCYhhBBCCCGEEEKI36uUlBSqqqosppSUlBbjFy5cSHFx8S0nf39/unXrxrlz5yzmbWho4Mcff9TVHvPhhx9y5coVpk2b1mrsvffeS0VFxU2ZkLfSLuliCxYsIC8vj23bttG1a1cWL15MQUEBgwcP1jW/wWBg7NixDB8+nNzcXGxsbHjxxRcZN24cX331FVZWVkyePJmZM2eyceNG6uvrOXDgAJqmERsby9GjR9m5cye7du0CwN3d/ZbLMxqNjB8/nurqajZs2EBAQADHjx/H2rrp8YtDhw4RExNDamoqsbGx7N+/n7lz59KpUyeL0Yz1eO6551i5ciW9e/fmueeeIy4ujlOnTjFixAjWrFnD4sWLOXnyJAAuLi7m+VauXMnixYt54YUXAPjb3/7GvHnzSEtLM6e5btiwAR8fHyIjI5XqJIQQQgghhBBCCNFRtPN4urek+jixl5cXXl5ercYNHz6cixcvcujQIYYNGwbA//7v/2I0Glt88vV669evZ+LEibqWVVRUxF133aW0Hm3eQFhdXU1mZiZZWVmMGdPUt0V6erru0VgANm3ahNFo5J133kHTNHMZHh4e5OTkEBoaSlVVFRMmTCAgIACA4OBg8/wuLi7Y2NjozojbtWsXBw4coLi4mKCgIAD8/f3Nn69atYoxY8aYUzaDgoI4fvw4r776qnIDYXJyMg8++CAAS5YsoX///pw6dYq+ffvi7u6OpmnN1jsyMpKFCxeaX/v4+DBv3jw++eQTYmJigKY+DBMSEszbrDVvvPEG77zzjvn17Nmzb8qMFEIIIYQQQgghhBC/THBwMOPGjWPmzJm8+eabGAwG5s2bx2OPPWZuM/v2228ZM2YM/+///T/CwsLM8546dYp9+/bx2Wef3VTup59+yvfff899992Hg4MD2dnZvPzyyyQnJyvVr80bCM+cOYPBYLBYEXd3d/r06aO7jMOHD3Pq1ClcXS07Fq6treX06dP84Q9/ICEhgbFjx/J//s//ISoqipiYGLy9vW+rzkVFRfTo0cPcOHij4uLim0ZhDg8PZ82aNTQ2NpozDfUYOHCg+e9r9T137hx9+/a95XzXj3ID4ODgwNSpU3n33XeJiYmhoKCAo0ePsm3bNt11iY+P57nnnjO/9vDwaDG2uU466+vqsPuFnXQKIYQQQgghhBBCXM90B/sgbE/vv/8+8+bNY8yYMVhZWREdHc3rr79u/txgMHDy5EmuXLEcWO7dd9+lR48e/OEPf7ipTFtbW/7yl7/w5z//GZPJRGBgIKtWrWLmzJlKdbsjI1JomnZTuqjB8POIZDU1NQwbNoz333//pnmvpVKmp6fz5JNPsnPnTjZt2sTzzz9PdnY29913n3J9HB0dlee5UWvrdM31A6Ncy/QzGlsf4cnZ+eZRNpOSkhg8eDAVFRWkp6cTGRlp0a9ia9zd3W/ql7AlzXXSOe9P/8aT//Zn3csTQgghhBBCCCGE+L3y9PQkKyurxc/9/Pyafbz65Zdf5uWXX252nnHjxjFu3LhfXLc2H6TE398fW1tbDh48aH6vqqqKkpIS82svLy8qKyvNr0tLSy1aR4cOHUppaSldunS5aZCN6/sTHDJkCCkpKezfv5+QkBDzRrazs6OxUf9w8wMHDqSiosKijtcLDg4mLy/P4r28vDyCgoLM2YOtrZMeqvUeMGAAoaGhvP3222RlZTFjxgyl5alorpPOOXPmttvyhBBCCCGEEEIIIcSvo80bCF1dXZk+fTpPPfUUe/bs4dixYyQmJmJlZWXOmIuMjGTt2rUUFhby5ZdfMmfOHIvMuvj4eDp37sykSZPIzc2lrKyMnJwcnnzySSoqKigrKyMlJYX8/HzOnj3L559/TmlpqbkfQj8/P8rKyigqKuLChQutjtoyatQo7r//fqKjo8nOzqasrIwdO3awc+dOoGlEmt27d7Ns2TJKSkrIzMxk7dq1Fs9zt7ZOevj5+VFTU8Pu3bu5cOGCrgbGpKQkVqxYgclk4uGHH1Zangp7e3vc3NwsJnm8WAghhBBCCCGEEG3NZDTdsen3qs0bCKFpUI/hw4czYcIEoqKiCA8PJzg4GAcHBwDS0tLo2bMnI0eOZMqUKSQnJ+Pk5GSe38nJiX379tGrVy8eeeQRgoODSUxMpLa2Fjc3N5ycnDhx4gTR0dEEBQUxa9YsnnjiCWbPng1AdHQ048aNIyIiAi8vLzZu3Nhqnbds2cI999xDXFwc/fr14+mnnzZn8w0dOpTNmzfzwQcfEBISwuLFi1m6dKnFACWtrZMeI0aMYM6cOcTGxuLl5cUrr7zS6jxxcXHY2NgQFxdn3r5CCCGEEEIIIYQQQuilmX6FsaMvX76Mj48PaWlpJCYmtvfiflfKy8sJCAjg4MGDDB069Fdddunps0rxRsX2aCta75vxegaT/oxNTVPb7VXrYkLfSNLXWKP/0XKAOpPCUOVag1LZqnVXpaF/26tud1UOxstK8fVWao3wqt+rijrU6qLy+wCw0+p1x9qiPxagAbW6qO6TNtrN/b/eSqOp/brjbVAs2067dcb7jRoVuhJW/Z5qjK6tB13H0eqqUrw1ascmlfrYWqntAzaKdVGtuwE73bHtfa5U3Q/qUXtawGjSX39rTe0YqXosUDnfqJbfnmXfDhvU9nkj+gfYU6W6D9tTqxRvbdS/rles1I5jqhpNatuxPfd51X3ydN8xSvEBJ3YrxavUp71/23VGteOY6jlEZT9QvS5XpXosUL0mU6F6Hay6H6iez1SunVXOZQBWWvv+z9I3oEe7lt9RPfLkqTu27I9e1zdWwz+bdvmvqLCwkBMnThAWFkZVVRVLly4FuGkkYHH7DAYDP/zwA88//zz33Xffr944KIQQQgghhBBCCCH+ObRb2sTKlSs5efIkdnZ2DBs2jNzcXDp37txeiwMgNTWVrVu3UlRUZPH++++/b378+Ea+vr4cO3asXevVmoyMDObPn8/Fixd1z5OXl0dERARBQUF8+OGHFp/l5uYyfvz4FuctKiri5ZdfZteuXXz//fd07tyZvn37MmPGDGJjY7Gx0bdbqN7lae9sMBWqd4VUMw5Vt43q3Xal+rdv0kKHorodG63U7poqZ54a2y+DUPV7bVTc502a/gWoxAI0GNVOPap3Za2NilmzivVXYaNYtGpWj1JGrkltf1TNdLFSjNcUH2BQ2Q9Us0uUs/ZU9xmF6rT3uVIl6xQ6VqacatkehvNK8RdtvdqtLh2N6vfanhoVj3uqxxoV7Z3JpspkUsggVLxGUc0IbO+MQxWq31Oj4jFeNaeuI12XK1+rmjrO/2fK525N8dpW4fckxO9VuzQQDhkyhEOHDrVH0bdl4sSJ3Hvvvc1+pjqQyJ1UX1+PnV3TY0qjR49uduhrgNDQ0JsaSa85fPgwQ4cOpX///vzlL3+hb9++AHz55Zf85S9/ISQkhEGDBrVL/YUQQgghhBBCCCFa83seLOROadNBSqqrq4mPj8fZ2Rlvb29Wr17N6NGjmT9/PgCaprF161aLeTw8PMjIyDC//uabb4iJicHDwwNPT08mTZpEeXm5+fOcnBzCwsJwdnbGw8OD8PBwzp49S0ZGBkuWLOHw4cNomoamaeZyXV1dCQwMbHZyd3dn9uzZdO3aFQcHB0JCQti+fbt5eVu2bKF///7Y29vj5+dHWlqaRf1bW6fy8nI0TeOjjz4iIiICJycnBg0aRH5+vnl9Hn/8caqqqsz1Tk1NBZpGNV62bBnTpk3Dzc2NWbNmERkZybx58yyWd/78eezs7Ni9u+lOnaOjY7PrGhAQwKJFiwgKCiIvL4+HHnqI3r1707t3b+Li4vjrX//KwIEDdX7bQgghhBBCCCGEEOKfQZs2EC5YsIC8vDy2bdtGdnY2ubm5FBQU6J7fYDAwduxYXF1dyc3NJS8vDxcXF8aNG0d9fT0NDQ1MnjyZUaNG8dVXX5Gfn8+sWbPQNI3Y2FgWLlxI//79qayspLKyktjY2Fsuz2g0Mn78ePLy8tiwYQPHjx9nxYoVWFs3Pepw6NAhYmJieOyxxzhy5AipqaksWrTIokFTr+eee47k5GSKiooICgoiLi6OhoYGRowYwZo1a3BzczPXOzk52TzfypUrGTRoEIWFhSxatIikpCSysrKoq/u5I/sNGzbg4+NDZGTkLetQVFREcXExycnJWFk1/9Vr7fionRBCCCGEEEIIIURrTEbTHZt+r9rsEePq6moyMzPJyspizJimPirS09Pp3r277jI2bdqE0WjknXfeMTdUpaen4+HhQU5ODqGhoVRVVTFhwgQCAgIACA4ONs/v4uKCjY0N3bp107W8Xbt2ceDAAYqLiwkKCgLA39/f/PmqVasYM2YMixYtAiAoKIjjx4/z6quvkpCQoHu9AJKTk3nwwQcBWLJkCf379+fUqVP07dsXd3d3NE1rtt6RkZEsXLjQ/NrHx4d58+bxySefEBMTAzT1YZiQkNBq415JSQkAffr0Mb937tw5i3V+5ZVXmDt3rtK6CSGEEEIIIYQQQojfrjbLIDxz5gwGg4GwsDDze+7u7haNUa05fPgwp06dwtXVFRcXF1xcXPD09KS2tpbTp0/j6elJQkICY8eO5aGHHuK1116jsrLytutcVFREjx49zI2DNyouLiY8PNzivfDwcEpLS2lsVOsg+fpHd729vYGmxrnWhIaGWrx2cHBg6tSpvPvuuwAUFBRw9OhR5QbLazp16kRRURFFRUV4eHhQX9/8cPF1dXVcunTJYqq/LotRCCGEEEIIIYQQQvw2tekjxq3RNO2mgTUMBoP575qaGoYNG2ZusLo2lZSUMGXKFKApozA/P58RI0awadMmgoKC+OKLL26rPo6Ojre/Mv/Q2jpdc/1gKNcy/YzG1keNcnZ2vum9pKQksrOzqaioID09ncjISHx9fVstq3fv3gCcPHnS/J61tbW5j8JbjV68fPly3N3dLaa33nyj1WUKIYQQQgghhBBCqDCZTHds+r1qswZCf39/bG1tOXjwoPm9qqoq82OtAF5eXhYZf6WlpVy5csX8eujQoZSWltKlS5dmBxO5ZsiQIaSkpLB//35CQkLIysoCwM7OTimzb+DAgVRUVFjU8XrBwcHk5eVZvJeXl0dQUJC5n8LW1kkP1XoPGDCA0NBQ3n77bbKyspgxY4au+YYMGULfvn1ZuXKlrsbJ66WkpFBVVWUxzZ4jjyILIYQQQgghhBBC/Na1WQOhq6sr06dP56mnnmLPnj0cO3aMxMRErKyszBlzkZGRrF27lsLCQr788kvmzJljkVkXHx9P586dmTRpErm5uZSVlZGTk8OTTz5JRUUFZWVlpKSkkJ+fz9mzZ/n8888pLS0190Po5+dHWVkZRUVFXLhwwWIgj+aMGjWK+++/n+joaLKzsykrK2PHjh3s3LkTgIULF7J7926WLVtGSUkJmZmZrF271mIQkdbWSQ8/Pz9qamrYvXs3Fy5c0NXAmJSUxIoVKzCZTDz88MO6lqNpGunp6Zw8eZLw8HC2bdtGaWkpx48f58033+T8+fPmhs8b2dvb4+bmZjHZ2dsrracQQgghhBBCCCFEa4xG4x2bfq/a9BHjVatWMXz4cCZMmEBUVBTh4eEEBwfj4OAAQFpaGj179mTkyJFMmTKF5ORknJyczPM7OTmxb98+evXqxSOPPEJwcDCJiYnU1tbi5uaGk5MTJ06cIDo6mqCgIGbNmsUTTzzB7NmzAYiOjmbcuHFERETg5eXFxo0bW63zli1buOeee4iLi6Nfv348/fTT5my+oUOHsnnzZj744ANCQkJYvHgxS5cutejvr7V10mPEiBHMmTOH2NhYvLy8eOWVV1qdJy4uDhsbG+Li4szbV4/77ruPQ4cO0adPH5544gn69evHiBEj2LhxI6tXr+Zf//VfleouhBBCCCGEEEIIIX7bNFM7PmB9+fJlfHx8SEtLIzExsb0W87tUXl5OQEAABw8eZOjQoXekDiWnv1aK12jfZ/kNJv2ZmyZuPeLzjaw1tUFpVMu3Qu0uRZ1Rf/amnVXzA8+0RLXuqlT2A9XtYlS852FPrVr5mlr5Nsab+yNtK1e1m/snvZVahX0GwMFK/yBEtpraPlZn1H9TA8BKU9sPHExq3Tw0aGpZ3yqMNJ+VfSfYmdT298u4KsU7WF1VircyqX2vl00uumNttAalslXPTzaa2m+7QeH81N7a+7jaYGq5P+MbqX5PqnW5y9D6YHDXu2jrpRTfkdigtk+a2rEb8kbF455q3W1N+s85qudK1Wsgk0nxek/xfGY06f+eVMtWdbrvGKX4gBO726km6q4qXneoXAMBGIz6j/Gq1+WqVK/JGhWO2ara+38/K8X/z+pN+q+F2/u3rapvQI92Lb+jemh28R1b9qdvBd+xZd9JbXpEKCws5MSJE4SFhVFVVcXSpUsBmDRpUlsu5nfNYDDwww8/8Pzzz3PffffdscZBIYQQQgghhBBCiPZgMv5+Bwu5U9r8lsHKlSs5efIkdnZ2DBs2jNzcXDp37tzWi9Hl/fffZ/bs2dTX19PY2GgxarGvry/Hjh27I/XSKyMjg/nz53Px4kXze3l5eURERBAUFMSHH35oEZ+bm8v48eNbLK+mpqbFz0aPHs3gwYNZs2aN7vqpZAncDtXMApX6qN7laTSp3Q1v77tImqb/YKl6N9wGte1+1ag2Gritlf5MAdVsEVX1KPajqXiOslbclu3J0Uote0xlv3G7el6p7DLrvkrxbrbVSvFGTe332p5Zfqr7sEq2CCgeCxQzYOsVMiIAHBUzN1XVGe10x1pZqR2D7RV/H7aNatkldZr+7BWV7xTUMwIbFC/9VLMo2jMLXTUbpcr2zlx/3gkNtF+WantnAanW3dXwo+7Yq3ZqGYROppavlZvzk8lTKd5BUzt2qB4PlMpW/F5VMwJVMg7bO9uwvffh9vyeVBlM+s+Vqny/yVWKP9tzpFK86vdkhVoGocr5sk4h2xDATjFzU4iOqk1beIYMGcKhQ4fasshfZOLEidx77728/vrrZGdn8+mnn5o/Ux1IpKMYPXr0TcNu19fXY2dnR2hoKEVFRXemYkIIIYQQQgghhBBtwKTYHY345do3PecfqquriY+Px9nZGW9vb1avXs3o0aOZP38+0DS67tatWy3m8fDwICMjw/z6m2++ISYmBg8PDzw9PZk0aRLl5eXmz3NycggLC8PZ2RkPDw/Cw8P58ccf+etf/8p//Md/cOLECXr37k3v3r3561//iq+v7y3rfPHiRWbPnk3Xrl1xcHAgJCSE7du3mz/fsmUL/fv3x97eHj8/P9LS0izmb22dysvL0TSNjz76iIiICJycnBg0aBD5+fnm9Xn88cepqqpC0zQ0TSM1NRVoGvV42bJlTJs2DTc3N2bNmkVkZCRPPfUUgYGB5snd3Z1+/fpx9uxZAgMDb/0lCSGEEEIIIYQQQojfpV+lgXDBggXk5eWxbds2srOzyc3NpaCgQPf8BoOBsWPH4urqSm5uLnl5ebi4uDBu3Djq6+tpaGhg8uTJjBo1iq+++or8/HxmzZqFpmnExsaycOFC+vfvT2VlJZWVlcTGxt5yeUajkfHjx5OXl8eGDRs4fvw4K1aswNq66TG0Q4cOERMTw2OPPcaRI0dITU1l0aJFFg2aej333HMkJydTVFREUFAQcXFxNDQ0MGLECNasWYObm5u53snJyeb5Vq5cyaBBgygsLGTRokUkJSWRlZVFXd3Pjy5s2LABHx8fIiMjleslhBBCCCGEEEIIIX4f2rcTOZqyBzMzM8nKymLMmKa+KNLT0+nevbvuMjZt2oTRaOSdd95B0zRzGR4eHuTk5BAaGkpVVRUTJkwgICAAgODgn0edcXFxwcbGhm7duula3q5duzhw4ADFxcUEBQUB4O/vb/581apVjBkzhkWLFgEQFBTE8ePHefXVV0lISNC9XgDJyck8+OCDACxZsoT+/ftz6tQp+vbti7u7O5qmNVvvyMhIFi5caH7t4+PDvHnz+OSTT4iJiQGa+jBMSEgwbzMhhBBCCCGEEEKIjk4GKfn1tXsG4ZkzZzAYDISFhZnfc3d3p0+fPrrLOHz4MKdOncLV1RUXFxdcXFzw9PSktraW06dP4+npSUJCAmPHjuWhhx7itddeo7Ky8rbrXFRURI8ePcyNgzcqLi4mPDzc4r3w8HBKS0tpbFTrLHXgwIHmv729vQE4d+5cq/OFhoZavHZwcGDq1Km8++67ABQUFHD06FHlBsuW1NXVcenSJYupvk6to2UhhBBCCCGEEEII0fH8Ko8Yt0bTtJsG3jAYfh7ptKamhmHDhlFUVGQxlZSUMGXKFKApozA/P58RI0awadMmgoKC+OKLL26rPtePdny7Wluna64fLOVapp/R2HpnnM7ON4/KlpSURHZ2NhUVFaSnpxMZGdlqX4t6LV++HHd3d4vp7bfWtknZQgghhBBCCCGEENeYjKY7Nv1etXsDob+/P7a2thw8eND8XlVVFSUlJebXXl5eFhl/paWlXLlyxfx66NChlJaW0qVLF4tBOK4NxHHNkCFDSElJYf/+/YSEhJCVlQWAnZ2dUmbfwIEDqaiosKjj9YKDg8nLy7N4Ly8vj6CgIHM/ha2tkx6q9R4wYAChoaG8/fbbZGVlMWPGDKXl3UpKSgpVVVUW08zZ89qsfCGEEEIIIYQQQghxZ7R7A6GrqyvTp0/nqaeeYs+ePRw7dozExESsrKzMGXORkZGsXbuWwsJCvvzyS+bMmWORWRcfH0/nzp2ZNGkSubm5lJWVkZOTw5NPPklFRQVlZWWkpKSQn5/P2bNn+fzzzyktLTX3Q+jn50dZWRlFRUVcuHDBYiCP5owaNYr777+f6OhosrOzKSsrY8eOHezcuROAhQsXsnv3bpYtW0ZJSQmZmZmsXbvWYhCR1tZJDz8/P2pqati9ezcXLlzQ1cCYlJTEihUrMJlMPPzww0rLuxV7e3vc3NwsJjt7+zYrXwghhBBCCCGEEELcGb/KI8arVq1i+PDhTJgwgaioKMLDwwkODsbBwQGAtLQ0evbsyciRI5kyZQrJyck4OTmZ53dycmLfvn306tWLRx55hODgYBITE6mtrcXNzQ0nJydOnDhBdHQ0QUFBzJo1iyeeeILZs2cDEB0dzbhx44iIiMDLy4uNGze2WuctW7Zwzz33EBcXR79+/Xj66afN2XxDhw5l8+bNfPDBB4SEhLB48WKWLl1q0d9fa+ukx4gRI5gzZw6xsbF4eXnxyiuvtDpPXFwcNjY2xMXFmbevEEIIIYQQQgghxG+F0WS8Y9PvlWa6saO8X8Hly5fx8fEhLS2NxMTEX3vx/9TKy8sJCAjg4MGDDB06tF2XdfzU39u1fButQSm+1qi/QdRKU/vRa6j9TFTLt0Itvt5kpztWdTvaoBZ/1ajWZ6et1c19cbZEdburau/y7U1X263sWk3thoPquprQP/p5p9pvlcous+6rFO9mW60Ub4faAEoNqGV3qzAq3oczmtTiNU3/9+qA2v540eihFO9uVaUUr7pP/tjoqTvW0apWqWx7xXjbRrV9rEZzbz3oH1S+U1A/f6jukyaT/mOBavm2mv7zAagdl6B9j3u/Jx1tO95V/73u2J/suiqV7Wi6rBT/k0n/cQnAwUrt2KGyLVW/p/b+Xk/3HaM7NuDEbqWyVdUa1Z56sreqV4pvMNnojlU97nUkvt/kKsWf7TlSKV51n7RR3JYNJv3Xe3WK+4yd4j6jqm9Aj3Ytv6MaO73oji37fzIH37Fl30n6j2a/QGFhISdOnCAsLIyqqiqWLl0KwKRJk9qk/NTUVLZu3UpRUVGblPdrycjIYP78+Vy8ePEXl2UwGPjhhx94/vnnue+++25qHBw9ejR79+5l+fLlPPvssxafPfjgg3z22We88MILpKam6l6maiOYKtULEXuFi65Gk7Vi2Wr/PBoUGvCa4tUaKBoVGhFsFf/ZVN3ujlZqjQ6N6N/2qv/4ql5YWCs2hjYqHjKtTPr7EDVpiv+0t/M/ytbor/t5+55KZVs1tu+xQ2W7A1hbteM/swr/NACYtPb7XlW3i5Pib9vapPZ7arBSO+7ZKtzssNbU1rVR8XsyWKkd41UaretMatn/qudiW9T+ialH7Z8klX9+VRsrVbX3TaCORPXmgt/3+bpjv+52r2p12pVqo5+KKtNdSvGqx8n2bKBXvbnQ3g25Ko1+Ko2JqmWD+u9D9caI2nW5UtHKVNe1k6Gy9aB/UG3w61XxV6X4r3v8i1J8vUnt/GQw6r/uaO//c4U+v+fBQu6UX20U45UrVzJo0CCioqK4fPkyubm5dO7c+ddavIX3338fFxeXZqf+/fvfkTrdSn196xfzeXl5eHt7c/DgQd58802Lz3Jzc8nNzUXTNP6//+//s1hfJycndu/ejbe3d3tVXwghhBBCCCGEEEJ0YG3SQFhdXU18fDzOzs54e3uzevVqRo8ezfz584GmPvsWLVpETU0NP/74I9nZ2YwcOZKMjAxzGd988w0xMTF4eHjg6enJpEmTKC8vN3+ek5NDWFgYzs7OeHh4EB4eztmzZ8nIyGDJkiUcPnwYTdPQNM2i3OZMnDiRffv2MWHCBBwcHDAYDHTv3p1Vq1bx2WefAU19EPbv3x97e3v8/PxIS0uzKEPTNLZu3WrxnoeHh3nZ5eXlaJrGRx99REREBE5OTgwaNIj8/Hzz+jz++ONUVVWZ630te8/Pz49ly5Yxbdo03NzcmDVrFpGRkcybZzlq8Pnz57Gzs2P37t2MHj0ak8nEyZMnGTBggEVcaGgooaGhxMXF4eHhwfr16ykqKqKoqIh//dd/5Q9/+ANdunS55TYTQgghhBBCCCGEEP+c2qSBcMGCBeTl5bFt2zays7PJzc2loKBA9/wGg4GxY8fi6upKbm4ueXl5uLi4MG7cOOrr62loaGDy5MmMGjWKr776ivz8fGbNmoWmacTGxrJw4UL69+9PZWUllZWVxMbG3nJ5zs7OPPHEExw9epSNGzdSXFzMqlWr6NmzJ76+vhw6dIiYmBgee+wxjhw5QmpqKosWLWq14bE5zz33HMnJyRQVFREUFERcXBwNDQ2MGDGCNWvW4ObmZq739aMgX8u4LCwsZNGiRSQlJZGVlWUxAvOGDRvw8fEhMjLylnVwdHTE0dERLy8vpk6dSnZ2NoGBgQQGBvLpp58yY8YM5fUSQgghhBBCCCGEaA8mo/GOTb9Xv7gPwurqajIzM8nKymLMmKb+I9LT0+nevbvuMjZt2oTRaOSdd95B+0f/S+np6Xh4eJCTk0NoaChVVVVMmDCBgIAAAIKDg83zu7i4YGNjQ7du3XQtb9euXRw4cIDi4mKCgoIA8Pf3N3++atUqxowZw6JFiwAICgri+PHjvPrqqxYjFeuRnJzMgw8+CMCSJUvo378/p06dom/fvri7u6NpWrP1joyMZOHChebXPj4+zJs3j08++YSYmBigqQ/DhIQE8zbTY8aMGYwcOZLXXnuNQ4cOmberSt+DQgghhBBCCCGEEOKfxy/OIDxz5gwGg4GwsDDze+7u7vTp00d3GYcPH+bUqVO4urqa+8bz9PSktraW06dP4+npSUJCAmPHjuWhhx7itddeo7JSf4eqNyoqKqJHjx7mxsEbFRcXEx4ebvFeeHg4paWlNDaqdXw+cOBA89/X+vk7d+5cq/OFhoZavHZwcGDq1Km8++67ABQUFHD06FHlBstBgwbRu3dvPvzwQ959912mTp2KjU3r7cR1dXVcunTJYqqvUxuJTQghhBBCCCGEEKI1JqPpjk2/V7/KICWapmEyWW5kg+HnUe5qamoYNmyYuV+8a1NJSQlTpkwBmjIK8/PzGTFiBJs2bSIoKIgvvvjiturj6Oh4+yvzD62t0zW2tj+PlnQt08+oI2XV2dn5pveSkpLIzs6moqKC9PR0IiMj8fX1Va06M2bM4C9/+Qsffvih7seLly9fjru7u8X0n2/+RXnZQgghhBBCCCGEEKJj+cUNhP7+/tja2nLw4EHze1VVVZSUlJhfe3l5WWT8lZaWcuXKFfProUOHUlpaSpcuXcx9412b3N3dzXFDhgwhJSWF/fv3ExISQlZWFgB2dnZKmX0DBw6koqLCoo7XCw4OJi8vz+K9vLw8goKCsLa21rVOeqjWe8CAAYSGhvL222+TlZV1230HTpkyhSNHjhASEkK/fv10zZOSkkJVVZXFNGvOE7e1fCGEEEIIIYQQQgjRcfziPghdXV2ZPn06Tz31FJ6ennTp0oUXXngBKysrc8ZcZGQka9euZfjw4TQ2NvLMM89YZNbFx8fz6quvMmnSJJYuXUqPHj04e/YsH330EU8//TQGg4H//M//ZOLEiXTv3p2TJ09SWlrKtGnTgKZRf8vKysyPDru6umJvb99inUeNGsX9999PdHQ0q1atIjAwkBMnTqBpGuPGjWPhwoXcc889LFu2jNjYWPLz81m7di1vvPGGuYzW1kkPPz8/ampq2L17N4MGDcLJyQknJ6dbzpOUlMS8efNwdnbm4YcfVlreNXfddReVlZVK9bW3t79pm9rZX7qt5QshhBBCCCGEEEK0xGT6/Q4Wcqe0ySPGq1atYvjw4UyYMIGoqCjCw8MJDg7GwcEBgLS0NHr27MnIkSOZMmUKycnJFg1hTk5O7Nu3j169evHII48QHBxMYmIitbW1uLm54eTkxIkTJ4iOjiYoKIhZs2bxxBNPMHv2bACio6MZN24cEREReHl5sXHjxlbrvGXLFu655x7i4uLo168fTz/9tDmbb+jQoWzevJkPPviAkJAQFi9ezNKlSy36+2ttnfQYMWIEc+bMITY2Fi8vL1555ZVW54mLi8PGxoa4uDjz9r0dHh4ezT7GLIQQQgghhBBCCCF+XzTTjR3ptYHLly/j4+NDWloaiYmJbV3871p5eTkBAQEcPHiQoUOH3tG6nDhd0a7la7Rf56CNJmuleHurWqV4g8lOKb7BpJbM26BQfwcrtcFkrFC7U6P6PTWiv+7tXRdrGpTiGxWTru2N+rsdMGlq92uuaC5K8arbUiXeYFLLnr7SqNYPrIvNZaV4B5Nadw+NVr84mb7lshV/2yq/D1Db5x1NatuxVlO76aW63Rus1Paby43693l7xeOe6rHDqHh/1Yab+yhuSZ1J7eafjaZ2HFM97tWbWn4io9nyNf1dp6huR1Wqxz0TWjvVpP0ZTWrb0u/7fN2xX3e7V7U6v1n1RrXrNwfF60PVfV7le7XSfruZNqf7jlGKDzixWyle9bpD9dq5XuE6SLVsVarHgk4G/QN//mTXVansXhV/VYr/use/KMWrHrMNRv3fk+rvSfVcrKpvQI92Lb+jioj52x1b9p7Nv59z3/Xa5L+iwsJCTpw4QVhYGFVVVSxduhSASZMmtUXxgqYBUH744Qeef/557rvvvjveOCiEEEIIIYQQQggh/jm0WdrEypUrOXnyJHZ2dgwbNozc3Fw6d+7cVsXrkpqaytatW3nqqafMjx/fyNfXl2PHjv2q9WpNRkYG8+fP5+LFiy3G5OXlERERQVBQEB9++KHFZ7m5uYwfP77FeWtqasx/jx49mr1797Jx40Yee+wx8/tr1qxhzZo1lJeX3/Z6tKY9MwJBPYtJrWy1O8oq2SKglnEBYEJ/hkl7b/f2vBuuaWp1b+91Vb1TadT0Z4OpxAK4Nf6oFF9nrZYNpsKj/nul+J/suynFq2bVdSSqxwLV35NKhpTqPqaa2dyoqcVrig8wqGTqqG5H1aw61fJVsvDaOwvB3nhVKd5gpXb+UzkX22pqv4+OlOGner5p7yxV1WwXlazAnrvWKZX9TdS/KsWrbpv23A9Ur8dqjWoZv3ZW9UrxqtdBSmV3oO2umhGomnHoc1wtk011u1srnItVM5vb+1igkhVojdrvQzUjsL2PNR0pw91WUzsWCPFraZMGwiFDhnDo0KG2KKpNTJw4kXvvbf7CR3UgkY5i9OjR1NXVYWd384V6aGgoRUVFustycHDg+eefJzo6+je7PYQQQgghhBBCCPHPyWT87Xad8FvVpk3j1dXVxMfH4+zsjLe3N6tXr2b06NHMnz8fAE3T2Lp1q8U8Hh4eZGRkmF9/8803xMTE4OHhgaenJ5MmTbLIasvJySEsLAxnZ2c8PDwIDw/n7NmzZGRksGTJEg4fPoybmxu9e/fmr3/9K4GBgRaTr6+vxfIvXrzI7Nmz6dq1Kw4ODoSEhLB9+3bz51u2bKF///7Y29vj5+dHWlqaxfytrVN5eTmapvHRRx8RERGBk5MTgwYNIj8/37w+jz/+OFVVVWiahqZppKamAk2jHC9btoxp06bh5ubGrFmziIyMZN68eRbLq6mpoV+/fpw9e/am9Q0MDLzpe4qLi+PixYu8/fbbzX2NQgghhBBCCCGEEOJ3pE0bCBcsWEBeXh7btm0jOzub3NxcCgoKdM9vMBgYO3Ysrq6u5ObmkpeXh4uLC+PGjaO+vp6GhgYmT57MqFGj+Oqrr8jPz2fWrFlomkZsbCwLFy6kf//+VFZWUllZSWxs7C2XZzQaGT9+PHl5eWzYsIHjx4+zYsUKrK2bHsM6dOgQMTExPPbYYxw5coTU1FQWLVpk0aCp13PPPUdycjJFRUUEBQURFxdHQ0MDI0aMYM2aNbi5uZnrnZycbJ5v5cqVDBo0iMLCQhYtWkRSUhJZWVnU1f3cwe2GDRvw8fEhMjJSV13c3Nx47rnnWLp0KZcvq3VaL4QQQgghhBBCCNGeTEbTHZt+r9qsD8Lq6moyMzPJyspizJimfiHS09Pp3r277jI2bdqE0WjknXfeQdM0cxkeHh7k5OQQGhpKVVUVEyZMICAgAIDg4GDz/C4uLtjY2NCtm77+rXbt2sWBAwcoLi4mKCgIAH9/f/Pnq1atYsyYMSxatAiAoKAgjh8/zquvvkpCQoLu9QJITk7mwQcfBGDJkiX079+fU6dO0bdvX9zd3dE0rdl6R0ZGsnDhQvNrHx8f5s2bxyeffEJMTAzQ1IdhQkKCeZvpMXfuXF577TVWrVplXj8hhBBCCCGEEEII8fvTZhmEZ86cwWAwEBYWZn7P3d2dPn366C7j8OHDnDp1CldXV1xcXHBxccHT05Pa2lpOnz6Np6cnCQkJjB07loceeojXXnuNykr9Q7PfqKioiB49epgbB29UXFxMeHi4xXvh4eGUlpbS2KjWSevAgQPNf3t7ewNw7ty5VucLDQ21eO3g4MDUqVN59913ASgoKODo0aPKDZb29vYsXbqUlStXcuHChVbj6+rquHTpksVUf10WoxBCCCGEEEIIIYT4bWrf4XluoGkaphtGLTQYfh7BrqamhmHDhlFUVGQxlZSUMGXKFKApozA/P58RI0awadMmgoKC+OKLL26rPo6Ojre/Mv/Q2jpdc/1gINcy/Yw6Ot10dna+6b2kpCSys7OpqKggPT2dyMjIm/pW1OP//t//i6+vLy+++GKrscuXL8fd3d1i+s83/6K8TCGEEEIIIYQQQohbMZmMd2z6vWqzBkJ/f39sbW05ePCg+b2qqipKSkrMr728vCwy/kpLS7ly5Yr59dChQyktLaVLly43DbTh7u5ujhsyZAgpKSns37+fkJAQsrKyALCzs1PK7Bs4cCAVFRUWdbxecHAweXl5Fu/l5eURFBRk7qewtXXSQ7XeAwYMIDQ0lLfffpusrCxmzJihtLxrrKysWL58OevWrbMYCKY5KSkpVFVVWUyz5jxxW8sVQgghhBBCCCGEEB1HmzUQurq6Mn36dJ566in27NnDsWPHSExMxMrKypwxFxkZydq1ayksLOTLL79kzpw5Fpl18fHxdO7cmUmTJpGbm0tZWRk5OTk8+eSTVFRUUFZWRkpKCvn5+Zw9e5bPP/+c0tJScz+Efn5+lJWVUVRUxIULFywG8mjOqFGjuP/++4mOjiY7O5uysjJ27NjBzp07AVi4cCG7d+9m2bJllJSUkJmZydq1ay0GEWltnfTw8/OjpqaG3bt3c+HCBV0NjElJSaxYsQKTycTDDz+stLzrPfjgg9x777289dZbt4yzt7fHzc3NYrKzt7/t5QohhBBCCCGEEEI0RwYp+fW16SPGq1atYvjw4UyYMIGoqCjCw8MJDg7GwcEBgLS0NHr27MnIkSOZMmUKycnJODk5med3cnJi37599OrVi0ceeYTg4GASExOpra3Fzc0NJycnTpw4QXR0NEFBQcyaNYsnnniC2bNnAxAdHc24ceOIiIjAy8uLjRs3tlrnLVu2cM899xAXF0e/fv14+umnzdl8Q4cOZfPmzXzwwQeEhISwePFili5datHfX2vrpMeIESOYM2cOsbGxeHl58corr7Q6T1xcHDY2NsTFxZm37+3693//d2pra39RGUIIIYQQQgghhBDit0kz3diBXhu6fPkyPj4+pKWlkZiY2F6L+V0qLy8nICCAgwcPMnTo0DtShxOnK5TiNdq3Jd5gUsvcVGGjNajFc3M/lLdiUmyrrzXpbxS20+qVylb9nkzoHz0boNFkrTvWWlMbDMgKtf4irFH7XhtQ28fsTPob3o2a/u0CYN+o1pVBnbXajQsVrnU/KMX/ZK9vpPlrGlHbNo6my2rlW9koxavQFE+x9ahlZqvs87aoHQuumG7uA/dWHDW1fVKVyu/PqHhMVT1mqx4LjCb99VE97qlyMlYrxddYubcedB2VY7ytpnquVDvftOf5TLVs1XjVfbg99dy1Tin+m6h/VYpv7+sOFSr77+3E21mpHYc70j7Znttd1em+Y5TifY7/VSneXvF7ajDpv46w19QGeexIxwJr1M5Pqtdv7X2sUfm9qm531fOZreL/ZwH+/krx/yxGTsq9Y8vO/WTkHVv2ndSm/xUVFhZy4sQJwsLCqKqqYunSpQBMmjSpLRfzu2YwGPjhhx94/vnnue++++5Y46AQQgghhBBCCCFEezDpGNRVtK02T5tYuXIlJ0+exM7OjmHDhpGbm0vnzp3bejHNSk1NZevWrRQVFQHw/vvvmx8/vpGvry/Hjh37VerVmoyMDObPn8/Fixdbjc3LyyMiIoKgoCA+/PBDi89yc3MZP358i/PW1NQAcOzYMZYsWcKePXu4dOkSvr6+PPbYYzz77LNKj0erZum41KplGX3v4KcUH3hyq+5Y4/nvlcr+eqRaBqxTg1qGxneaj1J851UzdceakpcrlV1W20sp3sVW7fH0wKuHdceecRqgVLatlVpG4A+1bkrxDUa1u4mpz+kfYd1ase/Sl18cqBS/ePFxpfjVy/z0Byt2R1prVOsWodqglv344itqx5rAwX5K8Sq6d1fLwps6uFgp3q5B/+9vzpseSmWv+De1u+HPvaW2DweFeCvFP937v3XH7nN4UKnsiAvvK8X/3V/trvLXtd11x2qaWlZPF4eflOIv0EUp3mBQ+14HHs/QHfv3QROUyv6p8S6leFfrGqX4epOd7ljVjHXV7Ct3049K8WcNvkrxXvb6yy8fM0+pbDej2j6p6rKV/nO3s/GSUtleRTuU4jd2XqgUH+xdpRRfeclFd2wvD7WybRWfjmlUzKhSyVBUybIG9YzAb/v9i1K86W9q/yNu/vi87tinp+s/zgB4GC8oxZfWBSjF93T6TndsjVH//gjgYKWWLal6rOl6tVwp3uWY/my09zo9rVT2H/xOKsXXmNT+BxHi19KmDYRDhgzh0KFDbVnkLzJx4kTuvffeZj9THUikI6ivr2f06NG09FR4aGiouXG0JV988QVRUVFERUXx3//933Tt2pUDBw6YB2TZs2cPdnZqJy4hhBBCCCGEEEKItvLXT0fd6Sr87rRppwbV1dXEx8fj7OyMt7c3q1evZvTo0cyfPx8ATdPYunWrxTweHh5kZGSYX3/zzTfExMTg4eGBp6cnkyZNory83Px5Tk4OYWFhODs74+HhQXh4OGfPniUjI4MlS5Zw+PBhNE1D0zS2bNlCYGBgs5Ovb9Od1osXLzJ79my6du2Kg4MDISEhbN++3by8LVu20L9/f+zt7fHz8yMtLc2i/q2tU3l5OZqm8dFHHxEREYGTkxODBg0iPz/fvD6PP/44VVVV5nqnpqYCTaMbL1u2jGnTpuHm5sasWbOIjIxk3jzLuyvnz5/Hzs6O/fv3t7i+gYGBmEwmEhMTCQ4O5qOPPiIsLAxfX18effRRPv30U/Lz81m9erXCNy6EEEIIIYQQQgghfuvatIFwwYIF5OXlsW3bNrKzs8nNzaWgoED3/AaDgbFjx+Lq6kpubi55eXm4uLgwbtw46uvraWhoYPLkyYwaNYqvvvqK/Px8Zs2ahaZpxMbGsnDhQvr3709lZSWVlZXExsbecnlGo5Hx48eTl5fHhg0bOH78OCtWrMDauqkD00OHDhETE8Njjz3GkSNHSE1NZdGiRRYNmno999xzJCcnU1RURFBQEHFxcTQ0NDBixAjWrFmDm5ubud7Jycnm+VauXMmgQYMoLCxk0aJFJCUlkZWVRV3dzynbGzZswMfHh8jIyFvWoaioiOPHj7NgwQKsrCy/+kGDBhEVFaVr5GchhBBCCCGEEEII8c+jzR4xrq6uJjMzk6ysLMaMaRpZKj09ne7d9fe7s2nTJoxGI++88w6appnL8PDwICcnh9DQUKqqqpgwYQIBAU39KwQHB5vnd3FxwcbGhm7d9I2QuWvXLg4cOEBxcTFBQUEA+F83QtCqVasYM2YMixYtAiAoKIjjx4/z6quvkpCQoHu9AJKTk3nwwaY+kZYsWUL//v05deoUffv2xd3dHU3Tmq13ZGQkCxf+3L+Jj48P8+bN45NPPiEmJgZo6sMwISHBvM1aUlJSAlhus+sFBwfz17+q9ekhhBBCCCGEEEIIIX7b2iyD8MyZMxgMBsLCwszvubu706dPH91lHD58mFOnTuHq6oqLiwsuLi54enpSW1vL6dOn8fT0JCEhgbFjx/LQQw/x2muvUVlZedt1LioqokePHubGwRsVFxcTHh5u8V54eDilpaU0NqoN8z5w4M8DCnh7N3XMfu7cuVbnCw0NtXjt4ODA1KlTeffddwEoKCjg6NGjSg2WLfVheCt1dXVcunTJYqqrUxueXQghhBBCCCGEEEJ0PG36iHFrNE27qXHKYPh5lMSamhqGDRtGUVGRxVRSUsKUKVOApozC/Px8RowYwaZNmwgKCuKLL/SPFHo9R0fH21+Zf2htna65flCUa5l+Rh3Ddjs73zwCZlJSEtnZ2VRUVJCenk5kZKS5T8VbudYQWlzc/CiZ12dS3mj58uW4u7tbTG+89VaryxRClSBJpwABAABJREFUCCGEEEIIIYQQHVubNRD6+/tja2vLwYMHze9VVVWZH2sF8PLyssj4Ky0t5cqVK+bXQ4cOpbS0lC5dutw0wIa7u7s5bsiQIaSkpLB//35CQkLIysoCwM7OTimzb+DAgVRUVFjU8XrBwcHk5eVZvJeXl0dQUJC5n8LW1kkP1XoPGDCA0NBQ3n77bbKyspgxY4au+QYPHkzfvn1ZvXr1TY2Thw8fZteuXcTFxTU7b0pKClVVVRbT3NmzdddZCCGEEEIIIYQQQnRMbdZA6OrqyvTp03nqqafYs2cPx44dIzExESsrK3PGXGRkJGvXrqWwsJAvv/ySOXPmWGTWxcfH07lzZyZNmkRubi5lZWXk5OTw5JNPUlFRQVlZGSkpKeTn53P27Fk+//xzSktLzX3q+fn5UVZWRlFRERcuXLAYyKM5o0aN4v777yc6Oprs7GzKysrYsWMHO3fuBGDhwoXs3r2bZcuWUVJSQmZmJmvXrrUYRKS1ddLDz8+Pmpoadu/ezYULF3Q1MCYlJbFixQpMJhMPP/ywruVomsb69es5fvw40dHRHDhwgK+//pr/+q//4qGHHmL48OHmEadvZG9vj5ubm8Vkb2+nsppCCCGEEEIIIYQQogNq00eMV61axfDhw5kwYQJRUVGEh4cTHByMg4MDAGlpafTs2ZORI0cyZcoUkpOTcXJyMs/v5OTEvn376NWrF4888gjBwcEkJiZSW1uLm5sbTk5OnDhxgujoaIKCgpg1axZPPPEEs/+RyRYdHc24ceOIiIjAy8tL14i8W7Zs4Z577iEuLo5+/frx9NNPm7P5hg4dyubNm/nggw8ICQlh8eLFLF261KK/v9bWSY8RI0YwZ84cYmNj8fLy4pVXXml1nri4OGxsbIiLizNvX73L+uKLL7C2tmb8+PEEBgaSkpLC9OnTyc7Oxt7eXqnuQgghhBBCCCGEEOK3rc1GMYamLML333/f/Pry5cssWbKEWbNmAdC9e3f+53/+x2KeixcvWrzu1q0bmZmZzZbv5ubGxx9/3OLy7e3t+fDDD5Xq7OnpaR7woznR0dFER0e3+Hlr6+Tn53dTH4UeHh43vbdu3TrWrVtn8V55eXmLy71w4QK1tbUkJia2GNOSAQMGKG8nIYQQQgghhBBCCPHPqU0bCAsLCzlx4gRhYWFUVVWxdOlSACZNmtSWi/ldMxgM/PDDDzz//PPcd999DB069E5XSQghhBBCCCGEEEL8hmmmG1PZfoHCwkKSkpI4efIkdnZ2DBs2jFWrVjFgwIC2WsQtpaamsnXrVoqKigB4//33zY8f38jX15djx479KvVqTUZGBvPnz78pm7I5OTk5REREEBQUxIcffmixbXNzcxk/fnyL89bU1ODn58f8+fNv6mvwxm2nR/Hpb3XHAtQZ1fosdLC6dR+SNyq92E13rKdjrVLZnewvKsUbTWpP7ztqagPblF/x0R3bxfGiUtk2WoNS/BWj2mjg1rQ+evc19or7gAlNKd5Wq1eK1xQPl50vnlIoXG2fOe8RoBR/V43a7/Wc8926Y90NF5TK/smmi1J8g0ntXpZf9WGl+Aq3/krxKmoabh6J/lZcbC4rxatsm16XjyuVXenaWym+6+UzSvGq/qf6X3THDuj6vVLZno3nlOIv27i3HnR9vFF/1yOq5w8n66tK8T0uFCrFf91pmFL8hTr928bHUe17uqqwHUH9fKZyDrnLoLbP/GjTVSle09TON7VGtS5i7BXOf6p1UaW8z2v6j5NXTWr7jN/f/6oUv9dVXz/g1/RyVTtf1pv092uu8p1C+3+vKkwmtes31bqfrlL8/d2rdl3gVFCkO7ar409KZatS2WcAbDT9A2V2v1KqVPZ3TmrXqqrfq+p+43v+gO7Y/U4t/0/dHB/nH5TirTT9/w8B9A3ooRQvxO1q0wzCIUOGcOjQobYs8heZOHEi9957b7OfqQ4k0hHU19czevTomx5PviY0NFSpgU8IIYQQQgghhBBCiDYdpKS6upr4+HicnZ3x9vZm9erVjB492pytpmkaW7dutZjHw8ODjIwM8+tvvvmGmJgYPDw88PT0ZNKkSRZ98eXk5BAWFoazszMeHh6Eh4dz9uxZMjIyWLJkCYcPH0bTNDRNY8uWLQQGBjY7+fr6Ak39Bc6ePZuuXbvi4OBASEgI27dvNy9vy5Yt9O/fH3t7e/z8/EhLS7Oof2vrVF5ejqZpfPTRR0RERODk5MSgQYPIz883r8/jjz9OVVWVud6pqalAU/+Fy5YtY9q0abi5uTFr1iwiIyOZN2+exfLOnz+PnZ0d+/fvb3F9AwMDFb5JIYQQQgghhBBCCPF70aYNhAsWLCAvL49t27aRnZ1Nbm4uBQUFuuc3GAyMHTsWV1dXcnNzycvLw8XFhXHjxlFfX09DQwOTJ09m1KhRfPXVV+Tn5zNr1iw0TSM2NpaFCxfSv39/KisrqaysJDY29pbLMxqNjB8/nry8PDZs2MDx48dZsWIF1tbWABw6dIiYmBgee+wxjhw5QmpqKosWLbJo0NTrueeeIzk5maKiIoKCgoiLi6OhoYERI0awZs0a3NzczPVOTk42z7dy5UoGDRpEYWEhixYtIikpiaysLOrqfn70csOGDfj4+BAZGalcLyGEEEIIIYQQQgjx+9ZmjxhXV1eTmZlJVlYWY8aMASA9PZ3u3bvrLmPTpk0YjUbeeecdNE0zl+Hh4UFOTg6hoaFUVVUxYcIEAgKa+jQIDg42z+/i4oKNjQ3duunri27Xrl0cOHCA4uJigoKCAPD39zd/vmrVKsaMGcOiRYsACAoK4vjx47z66qskJCToXi+A5ORkHnzwQQCWLFlC//79OXXqFH379sXd3R1N05qtd2RkJAsXLjS/9vHxYd68eXzyySfExMQATX0YJiQkmLeZEEIIIYQQQgghhBB6tVkG4ZkzZzAYDISFhZnfc3d3p0+fPrrLOHz4MKdOncLV1RUXFxdcXFzw9PSktraW06dP4+npSUJCAmPHjuWhhx7itddeo7Ky8rbrXFRURI8ePcyNgzcqLi4mPDzc4r3w8HBKS0tpbNTfoSvAwIEDzX97e3sDcO5c6x1ch4aGWrx2cHBg6tSpvPvuuwAUFBRw9OhR5QZLVXV1dVy6dMliqq9TG0BCCCGEEEIIIYQQQnQ8bfqIcWs0TbtpgA2DwWD+u6amhmHDhlFUVGQxlZSUMGXKFKApozA/P58RI0awadMmgoKC+OKLL26rPo6OaqOvNqe1dbrm+kFRrmX6GY2tj17k7HzzCJhJSUlkZ2dTUVFBeno6kZGR5j4VW+Pm5kZVVdVN71+8eBF395ZHHly+fDnu7u4W03++uVbXMoUQQgghhBBCCCFEx9VmDYT+/v7Y2tpy8OBB83tVVVWUlJSYX3t5eVlk/JWWlnLlyhXz66FDh1JaWkqXLl1uGmDj+sarIUOGkJKSwv79+wkJCSErKwsAOzs7pcy+gQMHUlFRYVHH6wUHB5OXl2fxXl5eHkFBQeZ+CltbJz1U6z1gwABCQ0N5++23ycrKYsaMGbrn7dOnT7MjTRcUFLSYSQmQkpJCVVWVxTRrzrwW44UQQgghhBBCCCHEb0ObNRC6uroyffp0nnrqKfbs2cOxY8dITEzEysrKnDEXGRnJ2rVrKSws5Msvv2TOnDkWmXXx8fF07tyZSZMmkZubS1lZGTk5OTz55JNUVFRQVlZGSkoK+fn5nD17ls8//5zS0lJzP4R+fn6UlZVRVFTEhQsXLAbyaM6oUaO4//77iY6OJjs7m7KyMnbs2MHOnTsBWLhwIbt372bZsmWUlJSQmZnJ2rVrLQYRaW2d9PDz86Ompobdu3dz4cIFXQ2MSUlJrFixApPJxMMPP6x7WX/+85/57//+b1566SWKi4s5evQozz33HPn5+fzbv/1bi/PZ29vj5uZmMdnZ2+terhBCCCGEEEIIIYTomNr0EeNVq1YxfPhwJkyYQFRUFOHh4QQHB+Pg4ABAWloaPXv2ZOTIkUyZMoXk5GScnJzM8zs5ObFv3z569erFI488QnBwMImJidTW1uLm5oaTkxMnTpwgOjqaoKAgZs2axRNPPMHs2bMBiI6OZty4cURERODl5cXGjRtbrfOWLVu45557iIuLo1+/fjz99NPmbL6hQ4eyefNmPvjgA0JCQli8eDFLly616O+vtXXSY8SIEcyZM4fY2Fi8vLx45ZVXWp0nLi4OGxsb4uLizNtX77J27NjBjh07CA8PZ/To0ezfv5/du3cTEhKiVG8hhBBCCCGEEEII8dunmW7sQK8NXb58GR8fH9LS0khMTGyvxfwulZeXExAQwMGDBxk6dOgdqUPx6W+V4uuMdkrxDlZqg6CUXtQ3ejWAp2OtUtmd7C8qxRtNam3vjpraY+nlV3x0x3ZxvKhUto3WoBR/xajWl6c1rfe9eY294j5gQm0kb1utXileUzxcdr54SqFwtX3mvEeAUvxdNWq/13POd+uOdTdcUCr7J5suSvENJhuleL/qw0rxFW79leJV1DTc3I/srbjYXFaKV9k2vS4fVyq70rW3UnzXy2eU4lX9T/W/6I4d0PV7pbI9G1sfNOx6l21a7rO32Xij/huHqucPJ+urSvE9LhQqxX/daZhS/IU6/dvGx1Hte7qqsB1B/Xymcg65y6C2z/xo01UpXtPUzje1RrWnOuwVzn+qdVGlvM9r+o+TV01q+4zf3/+qFL/XVf9TPAC9XNXOl/Um/U8lqXyn0P7fqwqTSe36TbXup6sUf3/3ql0XOBUU6Y7t6viTUtmqVPYZABtNfzdX3a+UKpX9nZPatarq96q63/ieP6A7dr/TeKWyfZx/UIq30vT/PwTQN6CHUrwQt6tNMwgLCwvZuHEjp0+fpqCggPj4eAAmTZrUlotpUWpqKoMHD/5VltWWMjIy8PDw0BVrMBj47rvveP7557nvvvuUGwf9/PxYs2bNTe//VredEEIIIYQQQgghhPhl1NIydFi5ciUnT57Ezs6OYcOGkZubS+fOndt6Mbq8//775sePb+Tr68uxY8d+5Rr9MvX19ezfv5+IiAiCgoL48MMPLT7Pzc1l/PiW73bU1NS0aX2sFDLBAGob1TIIrVC7i+TlrP+OcnGlWvbHqF5qWQ6XcVGKtzGq3fXdd1j/T3fSvWo/886GvyvFu1irfa8ldYG6Y/vYfadUdoOV2l1T+wa1zE0ro1o2Su2H7+svW7HvUutpz6jVJf0NpXj7ect0x36D/mxDgF4NaplmjlfU7so27vhIKd7fv0ApXomjWobtmZA/KsXXG/XvN9Z7P1Uq23pCy/3SNsdu/2dq5fdR69YisHuV7tiAij1KZX9s85hS/IBuatljARcPth70DxWeg5TKtsGgFF/WKUwp3qtB7Zxw8rL+TB0vB7Wst651Z5XiLzioZVw4Gy/pjv3JVi0T+kqD2rHAzkrtez1/xU0pvoez/kw21Qy/RpO1UrwqlWss1br/xzm1Y3DS/85Sii+f0no3Qtf78ar+LHQvJ/37L4AN+jPHQH1bqmSDNSqWrfI0CsDmj88rxT+ukBEIcGXoYP2xR79QKtvGSu3aU/VY0MlR//+HXzv2VSpbNatVdR+7ZFB7SmNd5UTdsVP/Nl2p7PMzX1WKd0TtfxAhfi1tmkEYGBhI3759MZlM2Nvb88ADD/CnP/2J+fPnA6BpGlu3brWYx8PDg4yMDPPrb775hpiYGDw8PPD09GTSpEmUl5ebP8/JySEsLAxnZ2c8PDwIDw/n7NmzZGRksGTJEg4fPoymaWiaRnV1NUVFRc1On33W9I/MxYsXmT17Nl27dsXBwYGQkBC2b99uXt6WLVvo378/9vb2+Pn5kZaWZlH/1tapvLwcTdP46KOPiIiIwMnJiUGDBpGfn29en8cff5yqqipzvVNTU4GmbL9ly5Yxbdo03NzcmDVrFkuXLuWJJ57g5MmTDBgwAIDz589jZ2d3y/UtKipS/0KFEEIIIYQQQgghxD+9Ns0gXLBgAXl5eWzbto2uXbuyePFiCgoKdD+6ajAYGDt2LMOHDyc3NxcbGxtefPFFxo0bx1dffYWVlRWTJ09m5syZbNy4kfr6eg4cOICmacTGxnL06FF27tzJrl27AHB3d8fxFtkbRqOR8ePHU11dzYYNGwgICOD48eNYWzfd+Tx06BAxMTGkpqYSGxvL/v37mTt3Lp06dbIYqESP5557jpUrV9K7d2+ee+454uLiOHXqFCNGjGDNmjUsXryYkydPAuDi8vOd0ZUrV7J48WJeeOEFAP72t78xb9480tLSsP/HKMIbNmzAx8eH8ePHm0eMFkIIIYQQQgghhBBCjzZrIKyuriYzM5OsrCzGjBkDQHp6Ot27d9ddxqZNmzAajbzzzjvmhq709HQ8PDzIyckhNDSUqqoqJkyYQEBAU6enwcHB5vldXFywsbGhWzd9g1Xs2rWLAwcOUFxcTFBQEAD+/v7mz1etWsWYMWNYtGgRAEFBQRw/fpxXX31VuYEwOTmZBx98EIAlS5bQv39/Tp06Rd++fXF3d0fTtGbrHRkZycKFC82vfXx8mDdvHp988gkxMTFAUx+GCQkJ0jgohBBCCCGEEEIIIZS12SPGZ86cwWAwEBb2c9827u7u9OnTR3cZhw8f5tSpU7i6uuLi4oKLiwuenp7U1tZy+vRpPD09SUhIYOzYsTz00EO89tprVFZW3nadi4qK6NGjh7lx8EbFxcWEh4dbvBceHk5paSmNjWr9dgwcOND8t7e3NwDnzrXeh1FoaKjFawcHB6ZOncq7774LQEFBAUePHlVusFRVV1fHpUuXLKb6OrURZoUQQgghhBBCCCFEx9OmfRC2RtM0TCbLDmsNhp87Ya6pqWHYsGE39Z1XUlLClClTgKaMwvz8fEaMGMGmTZsICgriiy/UOnu95laPH+vV2jpdY3vdAATXMv2MxtY72HV2vrnz1aSkJLKzs6moqCA9PZ3IyEh8fX111dfNzY2qqps7er948SLu7i0P3LF8+XLc3d0tprfe/IuuZQohhBBCCCGEEEKIjqvNGgj9/f2xtbXl4MGfR+qrqqqipKTE/NrLy8si46+0tJQrV34ewWfo0KGUlpbSpUsXAgMDLabrG6+GDBlCSkoK+/fvJyQkhKysLADs7OyUMvsGDhxIRUWFRR2vFxwcTF5ensV7eXl5BAUFmfspbG2d9FCt94ABAwgNDeXtt98mKyuLGTNm6J63T58+HDp06Kb3CwoKWsykBEhJSaGqqspimj3nCd3LFUIIIYQQQgghhBAdU5s1ELq6ujJ9+nSeeuop9uzZw7Fjx0hMTMTKysqcMRcZGcnatWspLCzkyy+/ZM6cORaZdfHx8XTu3JlJkyaRm5tLWVkZOTk5PPnkk1RUVFBWVkZKSgr5+fmcPXuWzz//nNLSUnM/hH5+fpSVlVFUVMSFCxeoa+UR2FGjRnH//fcTHR1NdnY2ZWVl7Nixg507dwKwcOFCdu/ezbJlyygpKSEzM5O1a9eSnJxsLqO1ddLDz8+Pmpoadu/ezYULF3Q1MCYlJbFixQpMJhMPP/yw7mX9+c9/5r//+7956aWXKC4u5ujRozz33HPk5+fzb//2by3OZ29vj5ubm8Vk949BUoQQQgghhBBCCCHEb1ebPmK8atUqhg8fzoQJE4iKiiI8PJzg4GAcHBwASEtLo2fPnowcOZIpU6aQnJyMk5OTeX4nJyf27dtHr169eOSRRwgODiYxMZHa2lrc3NxwcnLixIkTREdHExQUxKxZs3jiiSeYPXs2ANHR0YwbN46IiAi8vLzYuHFjq3XesmUL99xzD3FxcfTr14+nn37anM03dOhQNm/ezAcffEBISAiLFy9m6dKlFv39tbZOeowYMYI5c+YQGxuLl5cXr7zySqvzxMXFYWNjQ1xcnHn76l3Wjh072LFjB+Hh4YwePZr9+/eze/duQkJClOothBBCCCGEEEIIIX772mwUY2jKInz//ffNry9fvsySJUuYNWsWAN27d+d//ud/LOa5ePGixetu3bqRmZnZbPlubm58/PHHLS7f3t6eDz/8UKnOnp6e5gE/mhMdHU10dHSLn7e2Tn5+fjf1Uejh4XHTe+vWrWPdunUW75WXl7e43AsXLlBbW0tiYmKLMS35wx/+wB/+8Afl+YQQQgghhBBCCCHEP582zSAsLCxk48aNnD59moKCAuLj4wGYNGnSLecbPXo08+fPB5oa1NasWXPbdcjIyMDDw+O25+/oDAYD3333Hc8//zz33XcfQ4cOvdNVEkIIIYQQQgghhBC/YZrpxlS2X6CwsJCkpCROnjyJnZ0dw4YNY9WqVQwYMOCW840ePZrBgwezZs0azp8/j7Ozs67HdP38/Jg/f765cRHg6tWrVFdX06VLF95//33z48c38vX15dixY0rr1xHk5OQQERFBUFAQH374oXnbXnvfycnJ3OfjjWpqatq0LiWnv1aKNyq2R/v8dFQpvtxjiO7YukY7pbKdbdQGntFQ+1lZ0fqI1tdrMOlP/m3EWqlsNy4qxddpaqOBXzHqj3e0qlUqW5Xqdldlw80jmreVetT6ALXj1n2y3shE88eR5lw1qXWr4GB1VSm+zqi/GwUAJ9SOdQat/fpTtaZBKd6A2rFJ5XuyR+331IBaf7q2JrV9THW715r07wcuWrVS2aq/J9XzmVvjj7pjf7TqolS2raZ2nFHZZwDuMpxTiv/Wyld3rKu12m9V5dwHYK3pHwAO1LeNitN9xyjFB5zYrRTfnttG9ZpGlep2b8/6XG5UO5/VNaodJwMy5irFX5jZerdD16ju779lqtdv9Sa1c6tq+Vca9V/bfh9yn1LZ7X0sUFlXTWvfY4Hqb7vRpPY/Tq1R/7neYFQr28NW7bpDdV37BPRUihfidrXpI8ZDhgxpdoRcFV5eXr9ofkdHRxwdmw7SEydO5N577202TnUgkY5i9OjRNz2efL3c3Fzc3Nxa/Ly+vh47O7WTpBBCCCGEEEIIIYT459Wmjxi3hesfMTaZTKSmptKrVy/s7e3p3r07Tz75JNDUUHb27Fn+/Oc/o2maOWvu+keMXV1d2bBhA3/84x/Jz88nKiqKYcOG8fzzz+Pp6WleZnV1NfHx8Tg7O+Pt7c3q1astHntuTV1dHc888ww9e/bE3t6ewMBA1q9fb/587969hIWFYW9vj7e3N88++ywNDT9nlTT3WPXgwYNJTU01v9Y0jXfeeYeHH34YJycnevfuzbZt24CmvgojIiIAGDZsGL179+bFF18kMDCQpKQk1qxZw9q1a+ncuTNjx45lxowZTJgwwWJ5BoOBLl26WNRbCCGEEEIIIYQQQvzz63ANhNfbsmULq1ev5q233qK0tJStW7eaH6n96KOP6NGjB0uXLqWyspLKysoWyzl9+jRbt25l+/btbN++nb1797JixQrz5wsWLCAvL49t27aRnZ1Nbm4uBQUFuus5bdo0Nm7cyOuvv05xcTFvvfUWLi4uAHz77bc88MAD3HPPPRw+fJh169axfv16XnzxReXtsWTJEmJiYvjqq6944IEHiI+P58cff6Rnz55s2bIFgJMnT1JZWclrr71mni8zMxM7Ozvy8vJ48803SUpKYufOnRbbbPv27Vy5coXY2FjlegkhhBBCCCGEEEKI3642fcS4rX399dd069aNqKgobG1t6dWrF2FhYUDT6MPW1ta4urrSrVu3W5ZjNBrJyMjA1dUVgKlTp7J7925eeuklqquryczMJCsrizFjmvqHSU9Pp3v37rrqWFJSwubNm8nOziYqKgoAf39/8+dvvPEGPXv2ZO3atWiaRt++ffn73//OM888w+LFi7Gy0t9Gm5CQQFxcHAAvv/wyr7/+OgcOHGDcuHHmjMguXbrcNEhL7969eeUVyz5M+vTpw3vvvcfTTz9tXudHH33U3LAphBBCCCGEEEIIIX4fOnQG4aOPPsrVq1fx9/dn5syZfPzxxxaP5url5+dnbhwE8Pb25ty5pk63z5w5g8FgMDc8Ari7u9OnTx9dZRcVFWFtbc2oUaOa/by4uJjhw4dbDBwSHh5OTU0NFRUVSusxcOBA89/Ozs64ubmZ1+NWhg0bdtN7SUlJpKenA/D999+zY8cOZsyY0WIZdXV1XLp0yWKqr1PrkF4IIYQQQgghhBBCdDwduoGwZ8+enDx5kjfeeANHR0fmzp3L/fffj8GgNmLfjQOSaJqG0dg2o5deGxDll7Cysrpp4JHm1vF218PZ2fmm96ZNm8aZM2fIz89nw4YN3H333YwcObLFMpYvX467u7vF9Nabb7S6bCGEEEIIIYQQQgjRsXXoBkJoaoB76KGHeP3118nJySE/P58jR44AYGdnR2Nj4y8q39/fH1tbWw4ePGh+r6qqipKSEl3zDxgwAKPRyN69e5v9PDg4mPz8fIsGwLy8PFxdXenRowfQNHLz9f0BXrp0ibKyMqX1uDYysd7t0alTJyZPnkx6ejoZGRk8/vjjt4xPSUmhqqrKYpo9Z65SHYUQQgghhBBCCCFEx9OhGwgzMjJYv349R48e5cyZM2zYsAFHR0d8fX2BpkeH9+3bx7fffsuFCxduaxmurq5Mnz6dp556ij179nDs2DESExOxsrKyeCy4JX5+fkyfPp0ZM2awdetWysrKyMnJYfPmzQDMnTuXb775hj/96U+cOHGCTz75hBdeeIEFCxaY+x+MjIzkvffeIzc3lyNHjjB9+nSsra2V1sPX1xdN09i+fTvnz5+npqam1XmSkpLIzMykuLiY6dOn3zLW3t4eNzc3i8nO3l6pjkIIIYQQQgghhBCi4+nQDYQeHh68/fbbhIeHM3DgQHbt2sWnn35Kp06dAFi6dCnl5eUEBATg5eV128tZtWoVw4cPZ8KECURFRREeHk5wcDAODg665l+3bh1//OMfmTt3Ln379mXmzJlcvnwZAB8fHz777DMOHDjAoEGDmDNnDomJiTz//PPm+VNSUhg1ahQTJkzgwQcfZPLkyQQEBCitg4+PD0uWLOHZZ5+la9euzJs3r9V5oqKi8Pb2ZuzYsboHZRFCCCGEEEIIIYQQ/1w0042d3wkuX76Mj48PaWlpJCYm3unqtJuamhp8fHxIT0/nkUceUZ6/5PTXSvFGxfZon5+OKsWXewzRHVvXaKdUtrPNFaV4DbWflRVqfWI2mPQPQN6IWjaqGxeV4us0tX44rxj1xzta1SqVrUp1u6uyQa2/VBX1qGXw2qE2qJCJ1jOor7lqclIq28HqqlJ8nVHfzZprnGg9g/p6Bq39sqGtURtYy4DasUnle7JH7ffUgG3rQdexNantY6rbvdakfz9w0aqVylb9Pamez9waf9Qd+6NVF6WybTW144zKPgNwl6H1wdCu962Vr+5YV2u136rKuQ/AWlPrhkZ126g43XeMUnzAid1K8e25bVSvaVSpbvf2rM/lRrXzWV2j2nEyIEOte54LM1/RHau6v/+WqV6/1ZvUzq2q5V9p1H9t+33IfUplt/exQGVdNa19jwWqv+1Gk9r/OLVG/ed6g1GtbA9btesO1XXtE9BTKV6I29UhMghHjx7N/PnzgaZHdtesWXPbZWVkZODh4aE0T2FhIRs3buT06dMUFBQQHx8PwKRJk267Hh2Z0Wjk3LlzLFu2DA8PDyZOnHinqySEEEIIIYQQQggh7hC1Wwy/goMHDzY76m5z/Pz8mD9/vrlxESA2NpYHHnhAebkrV67k5MmT2NnZMWzYMHJzc+ncuTO5ubmMHz++xfn09PX3a8jJySEiIoKffvqp1QbSr7/+mrvvvpsePXqQkZGBjc3t7QbWmlpmjL1RLcvhnGeQUnytQf9dIXureqWyVe9QKWemKSYtuJou6o69YuWqVPb5BrXsFdU7/47W+rOYVLN0VKl+r6ouGdW2vQona7UsvItGD6V4lexN1bugByrvVoof1LWy9aDr/GjspBTfnllDVprasaCr8e9K8bU2+s6XABcaOiuV7W5zSSn+QqNaVx+q272zjf6+hi+bXJTKdqVKKf6qpn+7A1y00r/tVc8fqr8/1eySH226KsXbmvRfG/So2K9U9tkeI5XiOxLVLCBVvj8VKMV/6zmwnWqiTnWfb89jtq3ita3iQxr8NGu5UrwmD3o1S/X60MOo1lf9JStPpXgbK/37jeqxQDX7+O7iPUrxKlmB7Z1NrEr1Gkvl961ZdazMaSF+LR2ugfCX9CUITaMeOzqqPfI4ZMgQDh061OxnoaGhFBUV/aI6dST19fX4+fkhT5YLIYQQQgghhBBCCOggjxhf7/pHjE0mE6mpqfTq1Qt7e3u6d+/Ok08+CTQ9lnz27Fn+/Oc/o2maecThGx8xTk1NZfDgwbz33nv4+fnh7u7OY489RnX1z/0EVFdXEx8fj7OzM97e3qxevdr82LOjoyOBgYEtTgB1dXU888wz9OzZE3t7ewIDA1m/fr25/L179xIWFoa9vT3e3t48++yzNDT8fAejuceqBw8eTGpqqvm1pmm88847PPzwwzg5OdG7d2+2bdsGQHl5OREREQDcddddaJpGQkKCeTvNmzeP+fPn07lzZ8aOHcuMGTOYMGGCxfIMBgNdunSxqLcQQgghhBBCCCGE+OfX4RoIr7dlyxZWr17NW2+9RWlpKVu3bmXAgAEAfPTRR/To0YOlS5dSWVlJZWXLj6CdPn2arVu3sn37drZv387evXtZsWKF+fMFCxaQl5fHtm3byM7OJjc3l4IC/Y9qTJs2jY0bN/L6669TXFzMW2+9hYtL0+NN3377LQ888AD33HMPhw8fZt26daxfv54XX3xReXssWbKEmJgYvvrqKx544AHi4+P58ccf6dmzJ1u2bAHg5MmTVFZW8tprr5nny8zMxM7Ojry8PN58802SkpLYuXOnxTbbvn07V65cITY2VrleQgghhBBCCCGEEOK3q8M9Yny9r7/+mm7duhEVFYWtrS29evUiLCwMAE9PT6ytrXF1daVbt263LMdoNJKRkYGra1N/YFOnTmX37t289NJLVFdXk5mZSVZWFmPGNPXxkJ6eTvfu3XXVsaSkhM2bN5OdnU1UVBQA/v7+5s/feOMNevbsydq1a9E0jb59+/L3v/+dZ555hsWLF2Nlpb+NNiEhgbi4OABefvllXn/9dQ4cOMC4cePw9GzqK6NLly439UHYu3dvXnnFchS0Pn368N577/H000+b1/nRRx81N2wKIYQQQgghhBBCiN+HDp1B+Oijj3L16lX8/f2ZOXMmH3/8scWjuXr5+fmZGwcBvL29OXfuHABnzpzBYDCYGx4B3N3d6dOnj66yi4qKsLa2ZtSoUc1+XlxczPDhw82PQAOEh4dTU1NDRUWF0noMHPhzh9LOzs64ubmZ1+NWhg0bdtN7SUlJpKenA/D999+zY8cOZsyY0WIZdXV1XLp0yWKqq6tTqr8QQgghhBBCCCGE6Hg6dANhz549OXnyJG+88QaOjo7MnTuX+++/H4NBbTRcW1tbi9eapmE0Ko4y2wLVAVGaY2VlddOgIc2t4+2uR3OjQk+bNo0zZ86Qn5/Phg0buPvuuxk5suXRAZcvX467u7vF9Oabb7a6bCGEEEIIIYQQQgjRsXXoBkJoaoB76KGHeP3118nJySE/P58jR44AYGdnR2Nj4y8q39/fH1tbWw4ePGh+r6qqipKSEl3zDxgwAKPRyN69e5v9PDg4mPz8fIsGwLy8PFxdXenRowfQNHLz9f0BXrp0ibKyMqX1sLOzA9C9PTp16sTkyZNJT08nIyODxx9//JbxKSkpVFVVWUxz5sxRqqMQQgghhBBCCCGE6Hg6dANhRkYG69ev5+jRo5w5c4YNGzbg6OiIr68v0PTo8L59+/j222+5cOHCbS3D1dWV6dOn89RTT7Fnzx6OHTtGYmIiVlZWFo8Ft8TPz4/p06czY8YMtm7dSllZGTk5OWzevBmAuXPn8s033/CnP/2JEydO8Mknn/DCCy+wYMECc/+DkZGRvPfee+Tm5nLkyBGmT5+OtbW10nr4+vqiaRrbt2/n/Pnz1NTUtDpPUlISmZmZFBcXM3369FvG2tvb4+bmZjHZ29sr1VEIIYQQQgghhBBCdDwduoHQw8ODt99+m/DwcAYOHMiuXbv49NNP6dSpEwBLly6lvLycgIAAvLy8bns5q1atYvjw4UyYMIGoqCjCw8MJDg7GwcFB1/zr1q3jj3/8I3PnzqVv377MnDmTy5cvA+Dj48Nnn33GgQMHGDRoEHPmzCExMZHnn3/ePH9KSgqjRo1iwoQJPPjgg0yePJmAgACldfDx8WHJkiU8++yzdO3alXnz5rU6T1RUFN7e3owdO1b3oCxCCCGEEEIIIYQQ4p+LZrqx8zvB5cuX8fHxIS0tjcTExDtdnXZTU1ODj48P6enpPPLII8rznz5zRinexqjWd2SDlW3rQdf50eCpO9beql6pbCtNrc9KKxTjFct3Nl7SHXvFyrX1oOtUN6iNZG2i9Uzb6zla1+qOtdZ+WRcCrTGZ1Oqu6qpR302G2+FkfVUpvtaolvHraKX/ezKa1O41Hf7eWyl+UNfK1oOuo7quqvuwCtXfdlfj35Xia21u7mO2JT80dFIq291G/3EG4FKD2rFGdbt3ttH/pMBlk9pxzJUqpfirmv7tDtBoUnsqQIWNpjZ4m8Gkdm5VPZ81mGx0xwb+/X+Vyj7bo+W+kpujWvf2PBa0N58fv1KK/9ZzYOtBvxINtX9F2vN7qjfaKcUbFPZ3AGfrK0rxKudX1fPN74lL40Wl+EtW+v+nAKhXOK46WKkN8ni67xil+LuL9yjFa5r+35/qb7W9qR4LDEb931OD4nlb9bpcVd+AHu1avhDXdIgMwtGjRzN//nyg6ZHdNWvW3HZZGRkZeHh4KM1TWFjIxo0bOX36NAUFBcTHxwMwadKk265HR2Y0Gjl37hzLli3Dw8ODiRMn3ukqCSGEEEIIIYQQQog7RO2216/g4MGDzY662xw/Pz/mz59vblwEiI2N5YEHHlBe7sqVKzl58iR2dnYMGzaM3NxcOnfuTG5uLuPHj29xPj19/f0acnJyiIiI4Keffmq1gfTrr7/m7rvvpkePHmRkZGBjc3u7gUrGHqhn7d3VqNavZEWV/uyVLi5qd3lsrdQy2Wys1DI6VLfNt40+umM9rdQyY7rzjVK8SUdfnde7ornpjr1qVBslXPXuuWqGog1q32u32tP6gxW3Y62d/u0I0MWgdqw65+irO1YlYwjgX7oUK8VfRC3zrVe9vkGmrnGpPKkUr8LopJZVd6yzWqaAvVH/sSOg7ohS2T/Z6j/OAARePawUb92gP0sVoNpVf1cY3xm7KJX993q181l35x+V4usVshaC/7ZOqeyzIxKU4h2t1DKYahWPw99d8dAd2+D9f5TKtjGqHbPtFM+tKtlaKlk3oJ5Fqpr9+LeHn1GK75aTrTtWNWtINb4RtW1jTfs9XfD3yx5K8T/WqGXkDu6mdtyzWTRDd6xhWbpS2faaWl1U93mDSX82puqTCKrXe6V1al033eWgds10/or+azJv55+UylbNCCwLjlCK73k8V3es6nWzaoaf6rFDJXMT4PvL+r+nS1fVrm1DvNQyQ4XoqDpcA+Ev6UsQmkY9dnRUu5gdMmQIhw4davaz0NBQioqKflGdOpL6+nr8/PyQJ8uFEEIIIYQQQgghBHSQR4yvd/0jxiaTidTUVHr16oW9vT3du3fnySefBJoeSz579ix//vOf0TTNPOLwjY8Yp6amMnjwYN577z38/Pxwd3fnscceo7q62hxTXV1NfHw8zs7OeHt7s3r1avNjz46OjgQGBrY4AdTV1fHMM8/Qs2dP7O3tCQwMZP369eby9+7dS1hYGPb29nh7e/Pss8/S0PBzJlJzj1UPHjyY1NRU82tN03jnnXd4+OGHcXJyonfv3mzbtg2A8vJyIiKa7hbdddddaJpGQkKCeTvNmzeP+fPn07lzZ8aOHcuMGTOYMGGCxfIMBgNdunSxqLcQQgghhBBCCCGE+OfX4RoIr7dlyxZWr17NW2+9RWlpKVu3bmXAgAEAfPTRR/To0YOlS5dSWVlJZWXLndifPn2arVu3sn37drZv387evXtZsWKF+fMFCxaQl5fHtm3byM7OJjc3l4KCAt31nDZtGhs3buT111+nuLiYt956CxeXpg7Sv/32Wx544AHuueceDh8+zLp161i/fj0vvvii8vZYsmQJMTExfPXVVzzwwAPEx8fz448/0rNnT7Zs2QLAyZMnqays5LXXXjPPl5mZiZ2dHXl5ebz55pskJSWxc+dOi222fft2rly5QmxsrHK9hBBCCCGEEEIIIcRvV4d7xPh6X3/9Nd26dSMqKgpbW1t69epFWFgYAJ6enlhbW+Pq6kq3bt1uWY7RaCQjIwNX16a+oKZOncru3bt56aWXqK6uJjMzk6ysLMaMaer7KT09ne7d9fV1VFJSwubNm8nOziYqKgoAf39/8+dvvPEGPXv2ZO3atWiaRt++ffn73//OM888w+LFi7Gy0t9Gm5CQQFxcHAAvv/wyr7/+OgcOHGDcuHF4ejb1n9SlS5eb+iDs3bs3r7zyisV7ffr04b333uPpp582r/Ojjz5qbtgUQgghhBBCCCGEEL8PHTqD8NFHH+Xq1av4+/szc+ZMPv74Y4tHc/Xy8/MzNw4CeHt7c+7cOQDOnDmDwWAwNzwCuLu706dPH11lFxUVYW1tzahRo5r9vLi4mOHDh5sfgQYIDw+npqaGiooKpfUYOHCg+W9nZ2fc3NzM63Erw4YNu+m9pKQk0tObOjP+/vvv2bFjBzNmtNwRcl1dHZcuXbKY6uulM1YhhBBCCCGEEEKI37oO3UDYs2dPTp48yRtvvIGjoyNz587l/vvvx2AwKJVja2s5wpGmaRiNaqNftUR1QJTmWFlZ3TRoSHPreLvr0dyo0NOmTePMmTPk5+ezYcMG7r77bkaOHNliGcuXL8fd3d1iynhrdavLFkIIIYQQQgghhBAdW4duIISmBriHHnqI119/nZycHPLz8zly5AgAdnZ2NDaqDbd+I39/f2xtbTl48KD5vaqqKkpKSnTNP2DAAIxGI3v37m328+DgYPLz8y0aAPPy8nB1daVHjx5A08jN1/cHeOnSJcrKypTWw87ODkD39ujUqROTJ08mPT2djIwMHn/88VvGp6SkUFVVZTElzP6zUh2FEEIIIYQQQgghRMfToRsIMzIyWL9+PUePHuXMmTNs2LABR0dHfH19gaZHh/ft28e3337LhQsXbmsZrq6uTJ8+naeeeoo9e/Zw7NgxEhMTsbKysngsuCV+fn5Mnz6dGTNmsHXrVsrKysjJyWHz5s0AzJ07l2+++YY//elPnDhxgk8++YQXXniBBQsWmPsfjIyM5L333iM3N5cjR44wffp0rK2tldbD19cXTdPYvn0758+fp6amptV5kpKSyMzMpLi4mOnTp98y1t7eHjc3N4vJzs5eqY5CCCGEEEIIIYQQouPp0A2EHh4evP3224SHhzNw4EB27drFp59+SqdOnQBYunQp5eXlBAQE4OXlddvLWbVqFcOHD2fChAlERUURHh5OcHAwDg4OuuZft24df/zjH5k7dy59+/Zl5syZXL58GQAfHx8+++wzDhw4wKBBg5gzZw6JiYk8//zz5vlTUlIYNWoUEyZM4MEHH2Ty5MkEBAQorYOPjw9Llizh2WefpWvXrsybN6/VeaKiovD29mbs2LG6B2URQgghhBBCCCGEEP9cNNONnd8JLl++jI+PD2lpaSQmJt7p6rSbmpoafHx8SE9P55FHHlGe/+DJi0rx9lb1SvF3oZYV+uVP+gaWAejiclWpbFsrtUfZbazUBtNR3TbVDU66Yz1tq5TKdmm4qBRv0pFpe70r1m66Y68a1fr4tNLU+ha11hS/V9S+106Xv9YfrLgda+30b0cAe0PrWcXXO+foqzvWYLRtPeg6qr/ti1onpXjv2jNK8S6VJ5XiVRidXFsPus6xzmOU4lWOHT2uqq3nTy4+SvGdLins74B1Q61SfLWr/htZZ41+amXX67sheE135x+V4usVfiPBf1unVPbZEQlK8baa2vmmVvE4/O1l/b/Xzo7VSmXbKB6z7RTPrUaT/vvmmqZ2+dxoUnsyxAq189nfR49Viu+Wk607VkNtXVXjG1HbNtb8su6FbuXrms5K8T/WqJ3/BnerbD3oOjaLWh5A8EaGZelKZdtrasdg1X3eYLLTHavy2wP1673KK2rXEXc5qF0znb+i/5rM2/knpbJVjwVlwRFK8T2P5+qOVb1uNqF2bat67Kg3qf3+vr+s/3u6dNVGqewQr++U4lX1C5RkHvHr6BAZhKNHj2b+/PlA0yO7a9asue2yMjIy8PDwUJqnsLCQjRs3cvr0aQoKCoiPjwdg0qRJt12PjsxoNHLu3DmWLVuGh4cHEydOvNNVEkIIIYQQQgghhBB3iFrT+K/g4MGDzY662xw/Pz/mz59vblwEiI2N5YEHHlBe7sqVKzl58iR2dnYMGzaM3NxcOnfuTG5uLuPHj29xPj19/f0acnJyiIiI4Keffmq1gfTrr7/m7rvvpkePHmRkZGBjc3u7gaO12p1HG00t++qiSe1uXw93/ZkI3bVvlMq+67y+QWuuOdM1XCn+skJGIKhlBdqilkHxk7Xa4/p2itkoKpkCtlZqI5ar3oF2Mqn9fo2aWpaDSgaWppjMXWtSy+q5qvVQinfmilK8iiWbuyjF/1us2h3rC449leJNPmrfq4oqe7Xfk4OpTile5bjaYKO2zxgVM54Mtmrl19u5KMV/URWiO7an+yWlsofYFinFn0ft96SSeXPivjlKZTuidi6uanBXilfN2htxdafu2DLne5XKVs2kUaXyPalmuqjWXTVbq8ue3UrxVgoZ8ap1UWVtUtvH2rM+Hg5qT5iEWR9Qiv8Of6X4+mUZumMDCzYolV0+7DGleCtT+/3+OhnUMit/suuqFN/TSS27S/Uaq5Oj/uvJ9j4WqGQEAnzTb6TuWP8T/6tUtsmkmEGouK62ik/2qPy+HW3VshNV6666bYT4tXS4BsJf0pcgNI167OiodlAfMmQIhw4davaz0NBQioqKflGdOpL6+nr8/PyQJ8uFEEIIIYQQQgghBHSQR4yvd/0jxiaTidTUVHr16oW9vT3du3fnySefBJoeSz579ix//vOf0TTNPOLwjY8Yp6amMnjwYN577z38/Pxwd3fnscceo7r652yz6upq4uPjcXZ2xtvbm9WrV5sfe3Z0dCQwMLDFCaCuro5nnnmGnj17Ym9vT2BgIOvXrzeXv3fvXsLCwrC3t8fb25tnn32Whoaf73g091j14MGDSU1NNb/WNI133nmHhx9+GCcnJ3r37s22bdsAKC8vJyKiqb+Ju+66C03TSEhIMG+nefPmMX/+fDp37szYsWOZMWMGEyZMsFiewWCgS5cuFvUWQgghhBBCCCGEEP/8OlwD4fW2bNnC6tWreeuttygtLWXr1q0MGDAAgI8++ogePXqwdOlSKisrqaxsOTX99OnTbN26le3bt7N9+3b27t3LihUrzJ8vWLCAvLw8tm3bRnZ2Nrm5uRQUFOiu57Rp09i4cSOvv/46xcXFvPXWW7i4ND069e233/LAAw9wzz33cPjwYdatW8f69et58cUXlbfHkiVLiImJ4auvvuKBBx4gPj6eH3/8kZ49e7JlyxYATp48SWVlJa+99pp5vszMTOzs7MjLy+PNN98kKSmJnTt3Wmyz7du3c+XKFWJjY5XrJYQQQgghhBBCCCF+uzrcI8bX+/rrr+nWrRtRUVHY2trSq1cvwsLCAPD09MTa2hpXV1e6det2y3KMRiMZGRm4ujaNJjl16lR2797NSy+9RHV1NZmZmWRlZTFmTNPokenp6XTvrm+koJKSEjZv3kx2djZRUVEA+Pv/3L/IG2+8Qc+ePVm7di2aptG3b1/+/ve/88wzz7B48WKsrPS30SYkJBAXFwfAyy+/zOuvv86BAwcYN24cnp6eAHTp0uWmPgh79+7NK6+8YvFenz59eO+993j66afN6/zoo4+aGzaFEEIIIYQQQgghxO9Dh84gfPTRR7l69Sr+/v7MnDmTjz/+2OLRXL38/PzMjYMA3t7enDt3DoAzZ85gMBjMDY8A7u7u9OnTR1fZRUVFWFtbM2rUqGY/Ly4uZvjw4eZHoAHCw8OpqamhoqJCaT0GDhxo/tvZ2Rk3NzfzetzKsGHDbnovKSmJ9PR0AL7//nt27NjBjBkzWiyjrq6OS5cuWUz1dWqd6QshhBBCCCGEEEKIjqdDNxD27NmTkydP8sYbb+Do6MjcuXO5//77MRjURja1vWEUIk3TMBrbZiQu1QFRmmNlZXXToCHNrePtrkdzo0JPmzaNM2fOkJ+fz4YNG7j77rsZObLlUayWL1+Ou7u7xfTOW//R6rKFEEIIIYQQQgghRMfWoRsIoakB7qGHHuL1118nJyeH/Px8jhw5AoCdnR2NjY2/qHx/f39sbW05ePCg+b2qqipKSkp0zT9gwACMRiN79+5t9vPg4GDy8/MtGgDz8vJwdXWlR48eQNPIzdf3B3jp0iXKysqU1sPOzg5A9/bo1KkTkydPJj09nYyMDB5//PFbxqekpFBVVWUxJc3+k1IdhRBCCCGEEEIIIUTH06EbCDMyMli/fj1Hjx7lzJkzbNiwAUdHR3x9fYGmR4f37dvHt99+y4ULF25rGa6urkyfPp2nnnqKPXv2cOzYMRITE7GysrJ4LLglfn5+TJ8+nRkzZrB161bKysrIyclh8+bNAMydO5dvvvmGP/3pT5w4cYJPPvmEF154gQULFpj7H4yMjOS9994jNzeXI0eOMH36dKytrZXWw9fXF03T2L59O+fPn6empqbVeZKSksjMzKS4uJjp06ffMtbe3h43NzeLyc7eXqmOQgghhBBCCCGEEKLj6dANhB4eHrz99tuEh4czcOBAdu3axaeffkqnTp0AWLp0KeXl5QQEBODl5XXby1m1ahXDhw9nwoQJREVFER4eTnBwMA4ODrrmX7duHX/84x+ZO3cuffv2ZebMmVy+fBkAHx8fPvvsMw4cOMD/z96dx0VV9X8A/8wMMOyLIopsw+Iui2mmD7ljuODSYlqooGiZqaG5PqUCrpWCUbnkAoip+aSiklZqgshDjyaCpojKIi645MIuIHN+f/DjPgwzwD1jY1PP9/16zesFdz73zLmXO3cuZ849x9vbG9OmTUNISAg+/vhjYf1FixahX79+CAgIwPDhwzF69Gi4u7tzbYODgwPCw8OxcOFCtG7dGjNmzGh2HT8/P9jb28Pf31/0pCyEEEIIIYQQQggh5O9Fr2cxHj16NEaPHt3o87169UJmZmaTZYSFhSEsLExlWWhoKEJDQ4XfLSws8M033wi/l5WVITw8HO+8846oehobGyMyMhKRkZEan+/Xrx9Onz7d6PqWlpbYvXu3yrKGPfoajlEIAI8fP1b5ffHixVi8eLHKsqSkpEZft6ysDI8ePUJISEijGUIIIYQQQgghhBDy96Z3DYRnzpzROKmGJgqFQq2xb+zYsRg2bBjXa547dw6XL19Gz549UVRUhIiICADAqFGjuMr5MyUlJWHAgAF49OgRrK2tm8wqlUr8/vvvWLt2LaytrTFy5EitXrNSacSVfyrhu23aVFrBla+SGDYf+n/3JW24yn7Y2o4rLwPf2JhWhsVc+SdKcb1bAXD3E7Zgj7nyt5/y9T5tYVgkOvuU8Z2ipBK+yYfKmEXzoXokUG+ob4p5jfht5VUt5Xv/GUn5Jnfi0e7Wca687z/e4srLJQVceSP2hCv/WN6aK69LNYy3Y7/498gTQ3GfrXUYmh9mQ6V8I773E2/5dubiPxPk0iqusp9IzbnyvOcaQzzlyvPgPS/xngt4y7/Wsrf4uoCzLhK+uvDWXZd0XXeZhO+6g/cY1im+U4FO/65uyqtc+VyjLlx5Kwnf9V4NxF8753cfx1W2rrncSBGdve7U+ESJmvBeZ5cq+c7xzhWXufIFJh1FZ/XtXOB2+WfR2dyOA3VWNsC/rVLwnceMZeKvDQylfPuRt+68xwEhz4veNRA+y63CQO2kJtrMLLxmzRpkZ2fDyMgI3bt3R0pKCmxtbZGSkoKhQ4c2up6Ysf70SVVVFW7fvg1XV1c4OjoiNjYWBgZ6dxgQQgghhBBCCCGEkOdE78YgVCgUWLduHYDa22rDwsLg7OwMuVyOtm3bYtasWQCA/v374/r165g9ezYkEokwoUhsbKxKD7qwsDD4+PggPj4eCoUCVlZWGDduHEpKSoSMh4cHOnbsCMYY5HI5hg0bhpkzZyI0NBQ9evRARkZGow8AqKysxIIFC+Dk5AS5XA4PDw9s3bpVKD85ORk9e/aEXC6Hvb09Fi5ciKdP/9uboP421/Hx8VG5NVoikWDLli149dVXYWpqinbt2uHgwYMAgPz8fAwYMAAAYGNjA4lEguDgYGE/zZgxA6GhobC1tYW/vz8iIiIwfPhw3LhxA4MGDQIAVFdXw87OTqXehBBCCCGEEEIIIeTvT6+7ju3duxdRUVHYvXs3unTpgjt37ghjDu7btw/e3t545513MHXq1CbLycnJQUJCAhITE/Ho0SO8+eabWL16NVasWAEAmDNnDlJTU3Hw4EG0bt0aS5YsQXp6Onx8fGBiYgIPD48my584cSLS0tIQHR0Nb29v5OXlCbMq37p1C8OGDUNwcDC2b9+Oy5cvY+rUqTA2NlYbG7E54eHh+PTTT/HZZ5/hiy++QGBgIK5fvw4nJyfs3bsXr7/+OrKzs2FpaanSizIuLg7vvfceUlNTAQAPHjxA3759UVhYCHt7ewBAYmIiysvLMXbsWK46EUIIIYQQQgghhJC/Nr1uICwoKECbNm3g5+cHQ0NDODs7o2fPngCAFi1aQCaTwcLCAm3aND2enFKpRGxsLCwsasdHmjBhAo4fP44VK1agpKQEcXFx2Llzp9CbLiYmRvSsvleuXMGePXtw9OhR+Pn5AQDc3NyE59evXw8nJyd8+eWXkEgk6NixI27fvo0FCxZgyZIlkErFd+IMDg7GW2/Vjt21cuVKREdH4/Tp0xgyZAhatGgBALCzs1Mbg7Bdu3b49NNPVZZ16NAB8fHxmD9/vrDNY8aMgbk53xgdhBBCCCGEEEIIIeSvTe9uMa5vzJgxqKiogJubG6ZOnYr9+/er3JorlkKhEBoHAcDe3h737t0DAOTm5qK6ulpoeAQAKysrdOjQQVTZGRkZkMlk6Nevn8bns7Ky0Lt3b+EWaADw9fVFaWkpbt68ybUdXl5ews9mZmawtLQUtqMp3bt3V1s2ZcoUxMTEAADu3r2LI0eOYPLkyY2WUVlZieLiYpVHVVUlV/0JIYQQQgghhBBCiP7R6wZCJycnZGdnY/369TAxMcH06dPRt29fVFfzzXpnaKg6u61EIoFS+cfM3qbNhCgNSaVSMKY6k5GmbdR2OzTNCj1x4kTk5uYiLS0NO3bsgKurK/r0aXwGsVWrVsHKykrlEbNpXbOvTQghhBBCCCGEEEL0m143EAK1DXAjRoxAdHQ0kpKSkJaWhgsXLgAAjIyMUFPDNwV5Q25ubjA0NMSZM2eEZUVFRbhy5Yqo9T09PaFUKpGcnKzx+U6dOiEtLU2lATA1NRUWFhZwdHQEUDtzc2FhofB8cXEx8vLyuLbDyMgIAETvj5YtW2L06NGIiYlBbGwsJk2a1GR+0aJFKCoqUnlMejeUq46EEEIIIYQQQgghRP/odQNhbGwstm7dit9++w25ubnYsWMHTExM4OLiAqD21uGTJ0/i1q1bwqQgvCwsLBAUFIR58+bhxIkTuHjxIkJCQiCVSlVuC26MQqFAUFAQJk+ejISEBOTl5SEpKQl79uwBAEyfPh03btzAzJkzcfnyZRw4cABLly7FnDlzhPEHBw4ciPj4eKSkpODChQsICgqCTCbj2g4XFxdIJBIkJibi/v37KC0tbXadKVOmIC4uDllZWQgKCmoyK5fLYWlpqfIwMpJz1ZEQQgghhBBCCCGE6B+9biC0trbG5s2b4evrCy8vLxw7dgyHDh1Cy5YtAQARERHIz8+Hu7s7WrVqpfXrREZGonfv3ggICICfnx98fX3RqVMnGBsbi1p/w4YNeOONNzB9+nR07NgRU6dORVlZGQDAwcEBhw8fxunTp+Ht7Y1p06YhJCQEH3/8sbD+okWL0K9fPwQEBGD48OEYPXo03N3dubbBwcEB4eHhWLhwIVq3bo0ZM2Y0u46fnx/s7e3h7+8velIWQgghhBBCCCGEEPL3ImENB78jKCsrg4ODA9auXYuQkJA/uzo6U1paCgcHB8TExOC1117jXv/slYdceZmE73ZwU2kFV76kRvwMzLx1kUn4xqyUgbd8vnylUnzvTWPpE66yjZVlXPnbNXyNyy0Mi0RnnzK+idYlEr7TGWPN9xJ+lvLNleK3lVe51KL5UD1VzIgrbygRP9ary40UrrJ3s7e48r7OBVx5I8Z3zFdL9Kc3dJnSlCvPc26yYnzn7CdS9fFrm2KibL7nen0MfO+/gmpn0VlLQ77zmA347kIok1py5XnPZTx43qsAUM0Mmw/VIwHfea+aY1uNOOvOew6Wgu+zm/eY1GXZvPu9hvHdecJ73aFLut43PFpU3uHKF8j4vtC3MijmyteA7++qS7zvJ6cbqaKz150aHwddE97rbN7PVueKy1z5ApOOorNyaRVX2bo+F0g5riNyOw7kKtvt8s9ced5tVTK+vk5VHJ9/vPvRTFbOlefV0d1Rp+UTUkcvehD2798foaGhAGpv2V23bp3WZcXGxsLa2pprnXPnzmHXrl3IyclBeno6AgMDAQCjRo3Suh76TKlU4t69e1i2bBmsra0xcuTIP7tKhBBCCCGEEEIIIeRPoruvtLV05swZjbPuaqJQKBAaGio0LgLA2LFjMWzYMO7XXbNmDbKzs2FkZITu3bsjJSUFtra2SElJwdChQxtdT8xYf89DUlISBgwYgEePHjXbQFpQUABXV1c4OjoiNjYWBgbaHQa835TwftvH+41yS6n4HiAPlLZcZUs5v9GSSHXbMddQKr7XRSXj7B3F+bVBK9kDrvwTpfiZvw0kT7nKNuDsjcJ7jFWDrxceTw8sJeeO583XcH7LKuf4RjnHcQBX2S/hNleet8cTEzF+bH28vSJ4POX8mOXtWWAM8T2tGecxw/tNPu/7qUoqbhiPOtZGJaKzRhK+/Vgl4asLb+9jE6n4z8tyJV/PTd793rryOlf+gbEDV75SKf48yfte5e44prsOgXqH9zi4PUj8tXLb44d5q/OXdcPAjSvPey7g/UzgKZ+3V6guP/sAvl6BzjdPcZVd4PgyV95YWsmVv2PK1zNUzvmZo0u85wKeY4y3RyBvj0P3y8e58rzbytMrsFrJ2XtXfzr7EvJM9K6B8FnGEgRqZz02MRHfAAEA3bp1w9mzZzU+16NHD2RkZDxTnfRJVVUVFAoF6M5yQgghhBBCCCGEEALoyS3G9dW/xZgxhrCwMDg7O0Mul6Nt27aYNWsWgNrbkq9fv47Zs2dDIpEIMw43vMU4LCwMPj4+iI+Ph0KhgJWVFcaNG4eSkv/2RCgpKUFgYCDMzMxgb2+PqKgo4bZnExMTeHh4NPoAgMrKSixYsABOTk6Qy+Xw8PDA1q1bhfKTk5PRs2dPyOVy2NvbY+HChXj69L89ozTdVu3j44OwsDDhd4lEgi1btuDVV1+Fqakp2rVrh4MHDwIA8vPzMWBAbQ8eGxsbSCQSBAcHC/tpxowZCA0Nha2tLfz9/TF58mQEBASovF51dTXs7OxU6k0IIYQQQgghhBBC/v70roGwvr179yIqKgqbNm3C1atXkZCQAE9PTwDAvn374OjoiIiICBQWFqKwsLDRcnJycpCQkIDExEQkJiYiOTkZq1evFp6fM2cOUlNTcfDgQRw9ehQpKSlIT08XXc+JEydi165diI6ORlZWFjZt2gRz89oJM27duoVhw4bhxRdfRGZmJjZs2ICtW7di+fLl3PsjPDwcb775Js6fP49hw4YhMDAQDx8+hJOTE/bu3QsAyM7ORmFhIT7//HNhvbi4OBgZGSE1NRUbN27ElClT8MMPP6jss8TERJSXl2Ps2LHc9SKEEEIIIYQQQgghf116d4txfQUFBWjTpg38/PxgaGgIZ2dn9OzZEwDQokULyGQyWFhYoE2bNk2Wo1QqERsbCwuL2lk/J0yYgOPHj2PFihUoKSlBXFwcdu7ciUGDBgEAYmJi0LatuJlZr1y5gj179uDo0aPw8/MDALi5/Xf8kvXr18PJyQlffvklJBIJOnbsiNu3b2PBggVYsmQJpFLxbbTBwcF4663a2T9XrlyJ6OhonD59GkOGDEGLFi0AAHZ2dmpjELZr1w6ffvqpyrIOHTogPj4e8+fPF7Z5zJgxQsMmIYQQQgghhBBCCPnfoNc9CMeMGYOKigq4ublh6tSp2L9/v8qtuWIpFAqhcRAA7O3tce/ePQBAbm4uqqurhYZHALCyskKHDh1ElZ2RkQGZTIZ+/fppfD4rKwu9e/cWboEGAF9fX5SWluLmzZtc2+Hl5SX8bGZmBktLS2E7mtK9e3e1ZVOmTEFMTAwA4O7duzhy5AgmT57caBmVlZUoLi5WeVRV8g34SwghhBBCCCGEEEL0j143EDo5OSE7Oxvr16+HiYkJpk+fjr59+6K6mm+mUkND1RkwJRIJlMo/ZuYu3glRNJFKpWqThmjaRm23Q9Os0BMnTkRubi7S0tKwY8cOuLq6ok+fxmcbW7VqFaysrFQeX2/8qtnXJoQQQgghhBBCCCH6Ta8bCIHaBrgRI0YgOjoaSUlJSEtLw4ULFwAARkZGqKmpeaby3dzcYGhoiDNnzgjLioqKcOXKFVHre3p6QqlUIjk5WePznTp1QlpamkoDYGpqKiwsLODo6Aigdubm+uMBFhcXIy8vj2s7jIyMAED0/mjZsiVGjx6NmJgYxMbGYtKkSU3mFy1ahKKiIpXHO9Pe56ojIYQQQgghhBBCCNE/et1AGBsbi61bt+K3335Dbm4uduzYARMTE7i4uACovXX45MmTuHXrFn7//XetXsPCwgJBQUGYN28eTpw4gYsXLyIkJARSqVTltuDGKBQKBAUFYfLkyUhISEBeXh6SkpKwZ88eAMD06dNx48YNzJw5E5cvX8aBAwewdOlSzJkzRxh/cODAgYiPj0dKSgouXLiAoKAgyGQyru1wcXGBRCJBYmIi7t+/j9LS0mbXmTJlCuLi4pCVlYWgoKAms3K5HJaWlioPI7mcq46EEEIIIYQQQgghRP/odQOhtbU1Nm/eDF9fX3h5eeHYsWM4dOgQWrZsCQCIiIhAfn4+3N3d0apVK61fJzIyEr1790ZAQAD8/Pzg6+uLTp06wdjYWNT6GzZswBtvvIHp06ejY8eOmDp1KsrKygAADg4OOHz4ME6fPg1vb29MmzYNISEh+Pjjj4X1Fy1ahH79+iEgIADDhw/H6NGj4e7uzrUNDg4OCA8Px8KFC9G6dWvMmDGj2XX8/Pxgb28Pf39/0ZOyEEIIIYQQQgghhJC/FwlrOPgdQVlZGRwcHLB27VqEhIT82dXRmdLSUjg4OCAmJgavvfYa9/qXc/gmWZGB73ZwhuZ7cNZnxJ6Izj5Q2nKVbSjhmxzHUMo3TqYUfGNiKjna9p8yvsnK5RK+yWdknPvmiVL8uJ0GnGUbSPj2O2N8x1g1jLjyBhBff56/qTb5SiVf3U2lFaKzVYyvbN7jnfdcwHvc8NaHx1Pwvf94GUP830nK+M7BVRJxX5LVkSvL+cqX8pX/hIk/dxhJqrjK5j2PVTG+HvRGHOfVcqX6uMFNMeQ877V8cosr/8DYgStfXiP+7ySX8v2dJOC7ZJVJdHvdocuyebeV97P+nt8rorNtjx/mKpuXrvcNj7IaU668kvF9FpsblHHlea5TeI93XX72AUANxN8N5XzzFFfZBY4v81aHC+/fVSLR3TGp63MBz3HAu525HQdy5d0vH+fK1zC+O+6eKMV/dlcr+cq2NizhyvPq6O6o0/IJqaMXPQj79++P0NBQALW37K5bt07rsmJjY2Ftbc21zrlz57Br1y7k5OQgPT0dgYGBAIBRo0ZpXQ99plQqce/ePSxbtgzW1tYYOXLkn10lQgghhBBCCCGEEPIn0W3XBi2cOXNG46y7migUCoSGhgqNiwAwduxYDBs2jPt116xZg+zsbBgZGaF79+5ISUmBra0tUlJSMHTo0EbXEzPW3/OQlJSEAQMG4NGjR802kBYUFMDV1RWOjo6IjY2FgcHzOQxkHL2pAKASfL1Lipm1+LpwfstqxNnLwQh8vfB4e4NVMfHfcPP2pJHy9vTk7IWnT3i/CTVgfMcwT69WXfZcAcD9dRDPceB6/xeusgvtvLnyvD035Ux8rzpdM2Z8vUXKpJZceaVE/B9WpuTrafaE8xxsyNn72JBxnpsk4ns58PQ2BABD6LYnW4VS/Dmbt0cgb114e5Ly9tDg+Tvx1l2XvXR0TZe93rQpn6dXYE7HQVxl8/YC0ifcvU45r4Esah5x5YulLcTXhfM6Qtc9N3nyvD0CnY5t4MrnD2p+6KX6eM5jAF+PQ95jjJcuz6u8ZfOeC3jPNa5ZJ7jyXPtex92oeP9XJOR50bsGwmcZSxConfXYxITvH4Nu3brh7NmzGp/r0aMHMjIynqlO+qSqqgoKhQJ0ZzkhhBBCCCGEEEIIAfTkFuP66t9izBhDWFgYnJ2dIZfL0bZtW8yaNQtA7W3J169fx+zZsyGRSIQZhxveYhwWFgYfHx/Ex8dDoVDAysoK48aNQ0nJf8cJKCkpQWBgIMzMzGBvb4+oqCjhtmcTExN4eHg0+gCAyspKLFiwAE5OTpDL5fDw8MDWrVuF8pOTk9GzZ0/I5XLY29tj4cKFePr0vz2RNN1W7ePjg7CwMOF3iUSCLVu24NVXX4WpqSnatWuHgwcPAgDy8/MxYMAAAICNjQ0kEgmCg4OF/TRjxgyEhobC1tYW/v7+mDx5MgICAlRer7q6GnZ2dir1JoQQQgghhBBCCCF/f3rXQFjf3r17ERUVhU2bNuHq1atISEiAp6cnAGDfvn1wdHREREQECgsLUVhY2Gg5OTk5SEhIQGJiIhITE5GcnIzVq1cLz8+ZMwepqak4ePAgjh49ipSUFKSnp4uu58SJE7Fr1y5ER0cjKysLmzZtgrm5OQDg1q1bGDZsGF588UVkZmZiw4YN2Lp1K5YvX869P8LDw/Hmm2/i/PnzGDZsGAIDA/Hw4UM4OTlh7969AIDs7GwUFhbi888/F9aLi4uDkZERUlNTsXHjRkyZMgU//PCDyj5LTExEeXk5xo4dy10vQgghhBBCCCGEEPLXpXe3GNdXUFCANm3awM/PD4aGhnB2dkbPnj0BAC1atIBMJoOFhQXatGnTZDlKpRKxsbGwsLAAAEyYMAHHjx/HihUrUFJSgri4OOzcuRODBtWOexATE4O2bduKquOVK1ewZ88eHD16FH5+fgAANzc34fn169fDyckJX375JSQSCTp27Ijbt29jwYIFWLJkCaRS8W20wcHBeOuttwAAK1euRHR0NE6fPo0hQ4agRYvaMUrs7OzUxiBs164dPv30U5VlHTp0QHx8PObPny9s85gxY4SGTUIIIYQQQgghhBDyv0GvexCOGTMGFRUVcHNzw9SpU7F//36VW3PFUigUQuMgANjb2+PevXsAgNzcXFRXVwsNjwBgZWWFDh06iCo7IyMDMpkM/fr10/h8VlYWevfuLdwCDQC+vr4oLS3FzZs3ubbDy8tL+NnMzAyWlpbCdjSle/fuasumTJmCmJgYAMDdu3dx5MgRTJ48udEyKisrUVxcrPKoqqTBVQkhhBBCCCGEEEL+6vS6gdDJyQnZ2dlYv349TExMMH36dPTt2xfV1Xwz/BkaGqr8LpFIoFTyzU7VGN4JUTSRSqVqk4Zo2kZtt0PTrNATJ05Ebm4u0tLSsGPHDri6uqJPnz6NlrFq1SpYWVmpPL7e+FWzr00IIYQQQgghhBBC9JteNxACtQ1wI0aMQHR0NJKSkpCWloYLFy4AAIyMjFBT82xTxbu5ucHQ0BBnzpwRlhUVFeHKlSui1vf09IRSqURycrLG5zt16oS0tDSVBsDU1FRYWFjA0dERQO3MzfXHAywuLkZeXh7XdhgZGQGA6P3RsmVLjB49GjExMYiNjcWkSZOazC9atAhFRUUqj3emvc9VR0IIIYQQQgghhBCif/S6gTA2NhZbt27Fb7/9htzcXOzYsQMmJiZwcXEBUHvr8MmTJ3Hr1i38/vvvWr2GhYUFgoKCMG/ePJw4cQIXL15ESEgIpFKpym3BjVEoFAgKCsLkyZORkJCAvLw8JCUlYc+ePQCA6dOn48aNG5g5cyYuX76MAwcOYOnSpZgzZ44w/uDAgQMRHx+PlJQUXLhwAUFBQZDJZFzb4eLiAolEgsTERNy/fx+lpaXNrjNlyhTExcUhKysLQUFBTWblcjksLS1VHkZyOVcdCSGEEEIIIYQQQoj+0esGQmtra2zevBm+vr7w8vLCsWPHcOjQIbRs2RIAEBERgfz8fLi7u6NVq1Zav05kZCR69+6NgIAA+Pn5wdfXF506dYKxsbGo9Tds2IA33ngD06dPR8eOHTF16lSUlZUBABwcHHD48GGcPn0a3t7emDZtGkJCQvDxxx8L6y9atAj9+vVDQEAAhg8fjtGjR8Pd3Z1rGxwcHBAeHo6FCxeidevWmDFjRrPr+Pn5wd7eHv7+/qInZSGEEEIIIYQQQgghfy8S1nDwO4KysjI4ODhg7dq1CAkJ+bOrozOlpaVwcHBATEwMXnvtNe71L+fwTbJiBL5JTSohroG2TrXSsPnQ/1Oi+d6h9RlL+erOu61Kzrb6CmYqvi6SKq6yZeCfCIhHJRP/dzWQ8NXFQMI3PikvJePr2WvEnojOMs5jkleFRH0s0qYYQvxxY38vk6vsQjtvrnw1jLjyJqyMK69LMsZ3DJdJLbnyhhzvb8MavvNSkaQFV94CRVx5CfguP2okBqKzTxjf+MAmknKuPO/n01Mmvu685z3e/WhbcYMrf8fYlStfzbGtRpznbImEb1ul4BtvWtfnYV2q4fx8kknED9GT03EQV9nul49z5Xn3O+8xz+OJku/umKdK8cc7ALSR3OLKF0vFn4elEr7jnff9wbvfea5teY8Bp2MbuPL5g5rvOFEf775UMvHbyvPe0wbvuYBnW3X53gP4zzWuWSe48lVM/P+KvPvRTMZ3HcH7v6IbZ+chQrSlFz0I+/fvj9DQUAC1t+yuW7dO67JiY2NhbW3Ntc65c+ewa9cu5OTkID09HYGBgQCAUaNGaV0PfaZUKnHv3j0sW7YM1tbWGDly5J9dJUIIIYQQQgghhBDyJ+H72us5OHPmjMZZdzVRKBQIDQ0VGhcBYOzYsRg2bBj3665ZswbZ2dkwMjJC9+7dkZKSAltbW6SkpGDo0KGNridmrL/nISkpCQMGDMCjR4+abSAtKCiAq6srHB0dERsbCwMD7Q4DOcT3jgIApYSvPdqQ8fV8M0Ox6OydGr5bqnm/ITaScvZ4klZw5e2f5IrOZks9ucq2MRK/HwH+Hh0lT8X3fjQ34Nsv1RzfDAKAIWfvFd5vE3+vthGd5f323MqQ79xzr9yaK9/WVPy4rpXmtlxlF9fw9ZKzlj3myt952porL+M8hnWpNQqbD9XzlONcc0/ZhqvsvEdWXHk7CwuuvIucrxd6kVJ8fUw5z6kSxtdbRHExgSuf1/VV0VkD8J2XnoLvvFdq3JIrz9tjpLRa/Dm+jfweV9k8PdABwKrmAVf+sUz8uYz3s4+nhxEAuF8/ypW/5DScK89zZ4Tb5Z+5ylbk8/UgLG1kor/GmA4eIjorfcLXo/x02zFc+Wol33WBqQlfz+wacJTP+VEml+j2Ol4K8T3lqhhfz80bfu9x5VtX5HPl7xm7cOWLq8XfpWFpyHdM8vZm5OklBwCGHHcO6bpXNm+PwLxOA7jyFhnporPl1Xz70dSC77qjCjSWP9FPetdA+CxjCQK1sx6bmPDdWtStWzecPXtW43M9evRARkbGM9VJn1RVVUGhUIDuLCeEEEIIIYQQQgghgJ7cYlxf/VuMGWMICwuDs7Mz5HI52rZti1mzZgGovS35+vXrmD17NiQSiTDjcMNbjMPCwuDj44P4+HgoFApYWVlh3LhxKCkpETIlJSUIDAyEmZkZ7O3tERUVJdz2bGJiAg8Pj0YfAFBZWYkFCxbAyckJcrkcHh4e2Lp1q1B+cnIyevbsCblcDnt7eyxcuBBPn/732xpNt1X7+PggLCxM+F0ikWDLli149dVXYWpqinbt2uHgwYMAgPz8fAwYUPsNio2NDSQSCYKDg4X9NGPGDISGhsLW1hb+/v6YPHkyAgICVF6vuroadnZ2KvUmhBBCCCGEEEIIIX9/etdAWN/evXsRFRWFTZs24erVq0hISICnZ+3tkvv27YOjoyMiIiJQWFiIwsLGb8vKyclBQkICEhMTkZiYiOTkZKxevVp4fs6cOUhNTcXBgwdx9OhRpKSkID1dfBfkiRMnYteuXYiOjkZWVhY2bdoEc3NzAMCtW7cwbNgwvPjii8jMzMSGDRuwdetWLF++nHt/hIeH480338T58+cxbNgwBAYG4uHDh3BycsLevXsBANnZ2SgsLMTnn38urBcXFwcjIyOkpqZi48aNmDJlCn744QeVfZaYmIjy8nKMHTuWu16EEEIIIYQQQggh5K9L724xrq+goABt2rSBn58fDA0N4ezsjJ49ewIAWrRoAZlMBgsLC7Rp0/S4SkqlErGxsbD4//GRJkyYgOPHj2PFihUoKSlBXFwcdu7ciUGDamdOiomJQdu24saou3LlCvbs2YOjR4/Cz88PAODm5iY8v379ejg5OeHLL7+ERCJBx44dcfv2bSxYsABLliyBVCq+jTY4OBhvvfUWAGDlypWIjo7G6dOnMWTIELRoUTumiZ2dndoYhO3atcOnn36qsqxDhw6Ij4/H/PnzhW0eM2aM0LBJCCGEEEIIIYQQQv436HUPwjFjxqCiogJubm6YOnUq9u/fr3JrrlgKhUJoHAQAe3t73LtXOzB2bm4uqqurhYZHALCyskKHDh1ElZ2RkQGZTIZ+/fppfD4rKwu9e/cWboEGAF9fX5SWluLmTb4B2r28vISfzczMYGlpKWxHU7p37662bMqUKYiJiQEA3L17F0eOHMHkyZMbLaOyshLFxcUqj8pKvunZCSGEEEIIIYQQQoj+0esGQicnJ2RnZ2P9+vUwMTHB9OnT0bdvX1RX883wZ2ioOguRRCKBUsk3C1NjeCdE0UQqlapNGqJpG7XdDk2zQk+cOBG5ublIS0vDjh074Orqij59+jRaxqpVq2BlZaXy2LBxU7OvTQghhBBCCCGEEEL0m143EAK1DXAjRoxAdHQ0kpKSkJaWhgsXLgAAjIyMUFNT80zlu7m5wdDQEGfOnBGWFRUV4cqVK6LW9/T0hFKpRHJyssbnO3XqhLS0NJUGwNTUVFhYWMDR0RFA7czN9ccDLC4uRl5eHtd2GBkZAYDo/dGyZUuMHj0aMTExiI2NxaRJk5rML1q0CEVFRSqP96a9y1VHQgghhBBCCCGEEKJ/9LqBMDY2Flu3bsVvv/2G3Nxc7NixAyYmJnBxcQFQe+vwyZMncevWLfz+++9avYaFhQWCgoIwb948nDhxAhcvXkRISAikUqnKbcGNUSgUCAoKwuTJk5GQkIC8vDwkJSVhz549AIDp06fjxo0bmDlzJi5fvowDBw5g6dKlmDNnjjD+4MCBAxEfH4+UlBRcuHABQUFBkMlkXNvh4uICiUSCxMRE3L9/H6Wlpc2uM2XKFMTFxSErKwtBQUFNZuVyOSwtLVUecrmcq46EEEIIIYQQQgghRP/odQOhtbU1Nm/eDF9fX3h5eeHYsWM4dOgQWrZsCQCIiIhAfn4+3N3d0apVK61fJzIyEr1790ZAQAD8/Pzg6+uLTp06wdjYWNT6GzZswBtvvIHp06ejY8eOmDp1KsrKygAADg4OOHz4ME6fPg1vb29MmzYNISEh+Pjjj4X1Fy1ahH79+iEgIADDhw/H6NGj4e7uzrUNDg4OCA8Px8KFC9G6dWvMmDGj2XX8/Pxgb28Pf39/0ZOyEEIIIYQQQgghhJC/FwlrOPgdQVlZGRwcHLB27VqEhIT82dXRmdLSUjg4OCAmJgavvfYa9/p5Ode48koJX3s0Y8334KzPSPlEdPZODV+DqFTCN2alkZRvnEwTaQVX3qbituhsttSTr2yjYq68RMJ3CimqFj9TtrkB337hZSjh+zvVML6evTzbysB3vFsZNt9LuL57FdZc+bam4ntltyq/zlV2vrwTV95a9pgrf6/alisv4zyGdam1tLD5UD1PpUaisw+ftuAqO++RFVfezkL8ORgAXOR8E3U9YuLrb8p5TpUzvnybiz9x5fO6vio6a4gqrrKfwrD5UD0mrIwrXwaL5kP1PKwSf9y0kTc/0Vp9lUzcF7d1bGruc+Ufy8SfO3g/+5SM7xrI/fpRrvwlp+FceWOp+InmeLfVNf84V760kWF6GmM6eIjorPQJ3/F+uu0Yrny1ku+6wN7kIVe+BuLLl4Fv2CUTSTlXnvc6nkcV47srSQK+Y9K24gZX/p6xC1ee53rP0pDvmOT9H+SJkm9fGkrETwAqBV9deK9tef+ueZ0GcOUtMtJFZ8ur+T5bXSz4Pm94t7WjuyNXnhBt6UUPwv79+yM0NBRA7S2769at07qs2NhYWFtbc61z7tw57Nq1Czk5OUhPT0dgYCAAYNSoUVrXQ58plUrcu3cPy5Ytg7W1NUaOHPlnV4kQQgghhBBCCCGE/EkM/uwKNHTmzBmNs+5qolAoEBoaKjQuAsDYsWMxbNgw7tdds2YNsrOzYWRkhO7duyMlJQW2trZISUnB0KFDG11PzFh/z0NSUhIGDBiAR48eNdtAWlBQAFdXVzg6OiI2NhYGBtodBlLG902laVURV77QwJkr3zFH/DfW0sQfucoumx3JlW/xRHwPPwB4aMzXo1Fe8Uh0tr3ZJa6yz5X5cOUtjPh6DXnfOyw6m2X/ClfZMgnfMVn+lG8WcpmUr/yUS+K/UZbJ+L5lHdSFb79fvMHX86ZVe/HfnP5uyvdeda4QNwlUnQvMhyt/6Sbf3/X6dd31VDUz5zu/Bvbm+zsZ14jvibDvlClX2e/1vcyV3362A1f+p3K+HhqhL2WIzp4ofpGr7BHXVnLlr/WewpUvqRK/7++XibsGqtPO5g5XnqcnJgD8XmHJle95bIHo7LWAxVxlVyn53k9Sw5Zc+acc5VviMVfZD5R8PZuvOIvvJQcAd0v5/k6tzcTfLcDbg4m37pWBfF9UG8vE937kvRulvHsPrvx3C1K48rPe5DuGHzwR34O3pXEJV9mVEr7PG959ydPbrFrJ11uL93rP/CLf38nE+S5XfkOh+GN4QFfxPfYAvh5+AHC3jO9cYG0s/hrIWMbXw533zhvevytPj0AAKPF5QXT2yCenucp+ZwTfMUyIvtK7BsJnGUsQqJ312MSE75/Dbt264ezZsxqf69GjBzIyMp6pTvqkqqoKCoUCdGc5IYQQQgghhBBCCAH05Bbj+urfYswYQ1hYGJydnSGXy9G2bVvMmjULQO1tydevX8fs2bMhkUiEGYcb3mIcFhYGHx8fxMfHQ6FQwMrKCuPGjUNJyX+/ZSspKUFgYCDMzMxgb2+PqKgo4bZnExMTeHh4NPoAgMrKSixYsABOTk6Qy+Xw8PDA1q1bhfKTk5PRs2dPyOVy2NvbY+HChXj69L/fBmm6rdrHxwdhYWHC7xKJBFu2bMGrr74KU1NTtGvXDgcPHgQA5OfnY8CA2jEYbGxsIJFIEBwcLOynGTNmIDQ0FLa2tvD398fkyZMREBCg8nrV1dWws7NTqTchhBBCCCGEEEII+fvTuwbC+vbu3YuoqChs2rQJV69eRUJCAjw9aydc2LdvHxwdHREREYHCwkIUFjY+sHtOTg4SEhKQmJiIxMREJCcnY/Xq1cLzc+bMQWpqKg4ePIijR48iJSUF6eniuyxPnDgRu3btQnR0NLKysrBp0yaYm9feWnjr1i0MGzYML774IjIzM7FhwwZs3boVy5cv594f4eHhePPNN3H+/HkMGzYMgYGBePjwIZycnLB3714AQHZ2NgoLC/H5558L68XFxcHIyAipqanYuHEjpkyZgh9++EFlnyUmJqK8vBxjx47lrhchhBBCCCGEEEII+evSu1uM6ysoKECbNm3g5+cHQ0NDODs7o2fPngCAFi1aQCaTwcLCAm3atGmyHKVSidjYWFhY1I7jMWHCBBw/fhwrVqxASUkJ4uLisHPnTgwaNAgAEBMTg7ZtxY0Jd+XKFezZswdHjx6Fn58fAMDNzU14fv369XBycsKXX34JiUSCjh074vbt21iwYAGWLFkCqVR8G21wcDDeeustAMDKlSsRHR2N06dPY8iQIWjRonZMITs7O7UxCNu1a4dPP/1UZVmHDh0QHx+P+fPnC9s8ZswYoWGzocrKSlRWVjZYVgW5XPwMmoQQQgghhBBCCCFE/+h1D8IxY8agoqICbm5umDp1Kvbv369ya65YCoVCaBwEAHt7e9y7dw8AkJubi+rqaqHhEQCsrKzQoYO4wdYzMjIgk8nQr18/jc9nZWWhd+/ewi3QAODr64vS0lLcvHmTazu8vLyEn83MzGBpaSlsR1O6d++utmzKlCmIiYkBANy9exdHjhzB5MmTGy1j1apVsLKyUnms37SJq/6EEEIIIYQQQgghRP/odQOhk5MTsrOzsX79epiYmGD69Ono27cvqqurucoxNFSdVUgikUCp5JuRrTG8E6JoIpVK1SYN0bSN2m6HplmhJ06ciNzcXKSlpWHHjh1wdXVFnz59Gi1j0aJFKCoqUnlMf/fdZl+bEEIIIYQQQgghhOg3vW4gBGob4EaMGIHo6GgkJSUhLS0NFy5cAAAYGRmhpoZvOvSG3NzcYGhoiDNnzgjLioqKcOXKFVHre3p6QqlUIjk5WePznTp1QlpamkoDYGpqKiwsLODo6Aigdubm+uMBFhcXIy8vj2s7jIxqb/UVuz9atmyJ0aNHIyYmBrGxsZg0aVKTeblcDktLS5UH3V5MCCGEEEIIIYQQ8ten1w2EsbGx2Lp1K3777Tfk5uZix44dMDExgYuLC4DaW4dPnjyJW7du4ffff9fqNSwsLBAUFIR58+bhxIkTuHjxIkJCQiCVSlVuC26MQqFAUFAQJk+ejISEBOTl5SEpKQl79uwBAEyfPh03btzAzJkzcfnyZRw4cABLly7FnDlzhPEHBw4ciPj4eKSkpODChQsICgqCTCbj2g4XFxdIJBIkJibi/v37KC0tbXadKVOmIC4uDllZWQgKCuJ6PUIIIYQQQgghhBDy96DXDYTW1tbYvHkzfH194eXlhWPHjuHQoUNo2bIlACAiIgL5+flwd3dHq1attH6dyMhI9O7dGwEBAfDz84Ovry86deoEY2NjUetv2LABb7zxBqZPn46OHTti6tSpKCsrAwA4ODjg8OHDOH36NLy9vTFt2jSEhITg448/FtZftGgR+vXrh4CAAAwfPhyjR4+Gu7s71zY4ODggPDwcCxcuROvWrTFjxoxm1/Hz84O9vT38/f1FT8pCCCGEEEIIIYQQQv5e9HoW49GjR2P06NGNPt+rVy9kZmY2WUZYWBjCwsJUloWGhiI0NFT43cLCAt98843we1lZGcLDw/HOO++IqqexsTEiIyMRGRmp8fl+/frh9OnTja5vaWmJ3bt3qyxr2KOv4RiFAPD48WOV3xcvXozFixerLEtKSmr0dcvKyvDo0SOEhIQ0miGEEEIIIYQQQgghf28Spqnl6Tnr378/fHx8sG7dOty/fx9mZmYwNTVtdj2FQqHW2FdRUYGSkhLY2dmJfv1z587h8uXL6NmzJ4qKihAREYGkpCRcu3YNtra22mzSc5eUlIQBAwbg0aNHsLa2bjKrVCrx+++/Y+3atdi9ezdycnJgYMDfVpydc4MrX/zUovlQPcaySq58UZW56OzJC+J6h9YZ1yOfK1/GxNcFAMwkzd8SXt+VEifRWTvTEq6ynaqvceWrDfgm6jlf0VF09gWj85x1kXPlGZofRqC+Gqlh86F6Wuec4srzuOkxkCvvmL6PK3+r+6uis/erWnKV3aU0jSuvlPKdn+SPbnPl8QdNWqW5bL5xcm94+HHlS2rEn2s6X97dfKiem12GceUdczWPxdsYZsj3fr3mKP6Yb5e9n6vsLRK+Sbdeci/iyrtXXhCd/d3MhatsmeQpV76K8e13h6JLXPk9jwaLzg5S8H3eWJfe4soXmrXjylsqH4rOFktbcJVd/FR9wrimyGV8E/Jl3+c7D7u1KBadlUn5zmNSCd85tbya75rMzLBCdFbJ+G6W2nqQb4ift78ZxJU32n2cK3+/TPxxY2/Bd16SS6q48ryeMvGf3bzHDO/12y/5rbnyilZ8/4N47ZwiOps36Uuuso2kfOf4aw9suPKtLZ+Izpobis8CQLWS7/1kyHmuuV/O9/9W4s/izx1DF/TkKtv+4r+58i2lfMOjuXh04MoToi2960H4LLcKA7WTmmgzs/CaNWuQnZ0NIyMjdO/eHSkpKbC1tUVKSgqGDh3a6HpixvrTJ1VVVbh9+zZcXV3h6OiI2NhYrRoHCSGEEEIIIYQQQsjfg96NQahQKLBu3ToAtbfVhoWFwdnZGXK5HG3btsWsWbMA1PY6vH79OmbPng2JRCJMKBIbG6vSgy4sLAw+Pj6Ij4+HQqGAlZUVxo0bh5KS//aq8vDwQMeOHcEYg1wux7BhwzBz5kyEhoaiR48eyMjIaPQBAJWVlViwYAGcnJwgl8vh4eGBrVu3CuUnJyejZ8+ekMvlsLe3x8KFC/H06X+/Daq/zXV8fHxUbo2WSCTYsmULXn31VZiamqJdu3Y4ePAgACA/Px8DBgwAANjY2EAikSA4OFjYTzNmzEBoaChsbW3h7++PiIgIDB8+HDdu3MCgQbXffFZXV8POzk6l3oQQQgghhBBCCCHk70+vu47t3bsXUVFR2L17N7p06YI7d+4IYw7u27cP3t7eeOeddzB16tQmy8nJyUFCQgISExPx6NEjvPnmm1i9ejVWrFgBAJgzZw5SU1Nx8OBBtG7dGkuWLEF6ejp8fHxgYmICDw+PJsufOHEi0tLSEB0dDW9vb+Tl5QmzKt+6dQvDhg1DcHAwtm/fjsuXL2Pq1KkwNjZWGxuxOeHh4fj000/x2Wef4YsvvkBgYCCuX78OJycn7N27F6+//jqys7NhaWmp0osyLi4O7733HlJTUwEADx48QN++fVFYWAh7e3sAQGJiIsrLyzF27FiuOhFCCCGEEEIIIYSQvza9biAsKChAmzZt4OfnB0NDQzg7O6Nnz9rxAFq0aAGZTAYLCwu0adOmyXKUSiViY2NhYVE7Bt6ECRNw/PhxrFixAiUlJYiLi8POnTuF3nQxMTGiZ/W9cuUK9uzZg6NHj8LPr3bsKDc3N+H59evXw8nJCV9++SUkEgk6duyI27dvY8GCBViyZAmkUvGdOIODg/HWW28BAFauXIno6GicPn0aQ4YMQYsWtWPh2NnZqY1B2K5dO3z66acqyzp06ID4+HjMnz9f2OYxY8bA3JxvLAdCCCGEEEIIIYQQ8temd7cY1zdmzBhUVFTAzc0NU6dOxf79+1VuzRVLoVAIjYMAYG9vj3v37gEAcnNzUV1dLTQ8AoCVlRU6dBA3EGhGRgZkMhn69eun8fmsrCz07t1buAUaAHx9fVFaWoqbN29ybYeXl5fws5mZGSwtLYXtaEr37t3Vlk2ZMgUxMTEAgLt37+LIkSOYPHlyo2VUVlaiuLhY5VFVyTeALyGEEEIIIYQQQgjRP3rdQOjk5ITs7GysX78eJiYmmD59Ovr27Yvqar6Z3QwNVWcelUgkUP5Bs1ZqMyFKQ1KpFA0nk9a0jdpuh5mZ+gxoEydORG5uLtLS0rBjxw64urqiT58+jZaxatUqWFlZqTw2bfyq2dcmhBBCCCGEEEIIIfpNrxsIgdoGuBEjRiA6OhpJSUlIS0vDhQsXAABGRkaoqeGbDr0hNzc3GBoa4syZM8KyoqIiXLlyRdT6np6eUCqVSE5O1vh8p06dkJaWptIAmJqaCgsLCzg6OgKonbm5sLBQeL64uBh5eXlc22FkZAQAovdHy5YtMXr0aMTExCA2NhaTJk1qMr9o0SIUFRWpPN6d9j5XHQkhhBBCCCGEEEKI/tHrBsLY2Fhs3boVv/32G3Jzc7Fjxw6YmJjAxcUFQO2twydPnsStW7eESUF4WVhYICgoCPPmzcOJEydw8eJFhISEQCqVqtwW3BiFQoGgoCBMnjwZCQkJyMvLQ1JSEvbs2QMAmD59Om7cuIGZM2fi8uXLOHDgAJYuXYo5c+YI4w8OHDgQ8fHxSElJwYULFxAUFASZTMa1HS4uLpBIJEhMTMT9+/dRWlra7DpTpkxBXFwcsrKyEBQU1GRWLpfD0tJS5WEkl3PVkRBCCCGEEEIIIYToH71uILS2tsbmzZvh6+sLLy8vHDt2DIcOHULLli0BABEREcjPz4e7uztatWql9etERkaid+/eCAgIgJ+fH3x9fdGpUycYGxuLWn/Dhg144403MH36dHTs2BFTp05FWVkZAMDBwQGHDx/G6dOn4e3tjWnTpiEkJAQff/yxsP6iRYvQr18/BAQEYPjw4Rg9ejTc3d25tsHBwQHh4eFYuHAhWrdujRkzZjS7jp+fH+zt7eHv7y96UhZCCCGEEEIIIYQQ8veiF7MYJyUlCT/n5+cLP48ePRqjR49udL1evXohMzNTZVlwcDCCg4OF38PCwhAWFqaSCQ0NRWhoqPC7hYUFvvnmG+H3srIyhIeH45133hFVf2NjY0RGRiIyMlLj8/369cPp06cbXd/S0hK7d+9WWdawR1/DMQoB4PHjxyq/L168GIsXL1ZZVn/fNlRWVoZHjx4hJCSk0QwhhBBCCCGEEEII+XvTix6E/fv3FxrsFAoF1q1bp3VZsbGxsLa25lrn3Llz2LVrF3JycpCeno7AwEAAwKhRo7Suhz5TKpW4d+8eli1bBmtra4wcOfLPrhIhhBBCCCGEEEII+ZNImKauac9Z//794ePjg3Xr1uH+/fswMzODqalps+spFAq13oAVFRUoKSmBnZ2d6Nc/d+4cpkyZguzsbBgZGaF79+6IjIyEp6cnUlJSMHTo0EbXFTPW3/OQlJSEAQMG4NGjR802kObn58PV1RWOjo6IjY3FoEGDtHrNazl8E6k8hWHzoXqUjK/92lAifnbrUqX6zM5NMZOWc+WfMr7OuTIJ32Q7Eoh/20rBV3ZxjSVXnqH5sTrrs5CJf8/w1p33GOOtuwGecuXLlM2fx7Qll1Zx5StqxA2ZUMdUViE6y/tezbhjz5X3aVPYfKge3vefRKK7j0Epmp9pvj655AlXnueYL68x4SrbXFbGlec93nmPmxayh6KzT8C3raVP+T4TLAz4PvtrGN+4wjwMJHznJd7zHu/fief95/bwP1xl37B9gSvP+/7j3Tf6hPfvJJXw7Rtd4t3vPNdAvKqURlx5G/CNgV4m5bvG4vm76tPfVN+YMM7PM1hw5ZUcfW54z9m8eM8FPNdAunzvaYP33FGtFH/N9JTzc7uwyz+48h6Xj3HlO7g7ceUJ0ZZe3GJc37OMJQjUznpsYsL3j0G3bt1w9uxZjc/16NEDGRkZz1QnfVJVVQWFQqHxlmVCCCGEEEIIIYQQ8r9HL24xrq/+LcaMMYSFhcHZ2RlyuRxt27bFrFmzANT2Orx+/Tpmz54NiUQizDjc8BbjsLAw+Pj4ID4+HgqFAlZWVhg3bhxKSkqETElJCQIDA2FmZgZ7e3tERUUJtz2bmJjAw8Oj0QcAVFZWYsGCBXBycoJcLoeHhwe2bt0qlJ+cnIyePXtCLpfD3t4eCxcuxNOn//32SNNt1T4+PipjJ0okEmzZsgWvvvoqTE1N0a5dOxw8eBBAbY/AAQMGAABsbGwgkUiEcRj79++PGTNmIDQ0FLa2tvD398fkyZMREBCg8nrV1dWws7NTqTchhBBCCCGEEEII+fvTuwbC+vbu3YuoqChs2rQJV69eRUJCAjw9PQEA+/btg6OjIyIiIlBYWIjCwsZvQcvJyUFCQgISExORmJiI5ORkrF69Wnh+zpw5SE1NxcGDB3H06FGkpKQgPT1ddD0nTpyIXbt2ITo6GllZWdi0aRPMzc0BALdu3cKwYcPw4osvIjMzExs2bMDWrVuxfPly7v0RHh6ON998E+fPn8ewYcMQGBiIhw8fwsnJCXv37gUAZGdno7CwEJ9//rmwXlxcHIyMjJCamoqNGzdiypQp+OGHH1T2WWJiIsrLyzF27FjuehFCCCGEEEIIIYSQvy69u8W4voKCArRp0wZ+fn4wNDSEs7MzevbsCQBo0aIFZDIZLCws0KZNmybLUSqViI2NhYVF7XgSEyZMwPHjx7FixQqUlJQgLi4OO3fuFMbii4mJQdu2bUXV8cqVK9izZw+OHj0KPz8/AICbm5vw/Pr16+Hk5IQvv/wSEokEHTt2xO3bt7FgwQIsWbIEUqn4Ntrg4GC89dZbAICVK1ciOjoap0+fxpAhQ9CiRQsAgJ2dndoYhO3atcOnn36qsqxDhw6Ij4/H/PnzhW0eM2aM0LBJCCGEEEIIIYQQQv436HUPwjFjxqCiogJubm6YOnUq9u/fr3JrrlgKhUJoHAQAe3t73Lt3DwCQm5uL6upqoeERAKysrNChQwdRZWdkZEAmk6Ffv34an8/KykLv3r2FW6ABwNfXF6Wlpbh58ybXdnh5eQk/m5mZwdLSUtiOpnTv3l1t2ZQpUxATEwMAuHv3Lo4cOYLJkyc3WkZlZSWKi4tVHpWVlVz1J4QQQgghhBBCCCH6R68bCJ2cnJCdnY3169fDxMQE06dPR9++fVFdLX62WgAwNFSdsUgikUCp/GNm+uKdEEUTqVSqNmmIpm3UdjvMzNRnZ5w4cSJyc3ORlpaGHTt2wNXVFX369Gm0jFWrVsHKykrlsWnjhmZfmxBCCCGEEEIIIYToN71uIARqG+BGjBiB6OhoJCUlIS0tDRcuXAAAGBkZoaam5pnKd3Nzg6GhIc6cOSMsKyoqwpUrV0St7+npCaVSieTkZI3Pd+rUCWlpaSoNgKmpqbCwsICjoyOA2pmb648HWFxcjLy8PK7tMDIyAgDR+6Nly5YYPXo0YmJiEBsbi0mTJjWZX7RoEYqKilQe7057j6uOhBBCCCGEEEIIIUT/6HUDYWxsLLZu3YrffvsNubm52LFjB0xMTODi4gKg9tbhkydP4tatW/j999+1eg0LCwsEBQVh3rx5OHHiBC5evIiQkBBIpVKV24Ibo1AoEBQUhMmTJyMhIQF5eXlISkrCnj17AADTp0/HjRs3MHPmTFy+fBkHDhzA0qVLMWfOHGH8wYEDByI+Ph4pKSm4cOECgoKCIJPJuLbDxcUFEokEiYmJuH//PkpLS5tdZ8qUKYiLi0NWVhaCgoKazMrlclhaWqo85HI5Vx0JIYQQQgghhBBCiP7R6wZCa2trbN68Gb6+vvDy8sKxY8dw6NAhtGzZEgAQERGB/Px8uLu7o1WrVlq/TmRkJHr37o2AgAD4+fnB19cXnTp1grGxsaj1N2zYgDfeeAPTp09Hx44dMXXqVJSVlQEAHBwccPjwYZw+fRre3t6YNm0aQkJC8PHHHwvrL1q0CP369UNAQACGDx+O0aNHw93dnWsbHBwcEB4ejoULF6J169aYMWNGs+v4+fnB3t4e/v7+oidlIYQQQgghhBBCCCF/LxLWcPA7grKyMjg4OGDt2rUICQn5s6ujM6WlpXBwcEBMTAxee+017vWv5fDdBv0Uhs2H6lEyvvZrQ4n4sSlLlerjMjbFTFrOlX/K+CYIl0n4bpWXQPzbVgq+sotrLLnyDM33tK3PQtZ879Y6vHXnPcZ4624AvkmSypSmXHkecmkVV76iRtwXHnVMZRWis7zv1Yw79lx5nzaFzYfq4X3/SSS6+xiUgm+8W7nkCVee55gvr+EbM9dcVsaV5z3eeY+bFrKHorNPwLetpU/5PhMsDMSfxwCghvHdFcDDQMJ3XuI97/H+nXjef24P/8NV9g3bF7jyvO8/3n2jT3j/TlLJHzMW9x+Bd7/zXAPxqlIaceVtwHcHU5mU7xqL5++qT39TfWPCOD/PYNF8qB4lR58b3nM2L95zAc81kC7fe9rgPXdUK8VfMz3l/Nwu7PIPrrzH5WNc+Q7uTlx5QrT13HsQ9u/fH6Ghoc/7ZZt07tw57Nq1Czk5OUhPT0dgYCAAYNSoUc9cdnBwMEaPHv3M5YSFhcHHx+eZywEApVKJe/fuYdmyZbC2tsbIkSP/kHIJIYQQQgghhBBCyF8PXzeLv7E1a9YgOzsbRkZG6N69O1JSUmBra4uUlBQMHTq00fXEjPXHSyKRYP/+/SoNi3PnzsXMmTP/kPILCgrg6uoKR0dHxMbGwsBAu8OgGnzfshqCr8dTjYSvXjXQXQ8N3m/MeHozAkAl4xvPkadXBOPsVWcruceVfyrlOw7KmfieOi1q+OrCq9igBVee5xtiALCQlXDleUg4O3/LZbrrmcbbA/ZF+wLOuvCdC8wlfPudiRhvVltSxtejo4Lx9cIzklSKzlrI+P5O3Me7lG+/GzC+8+QTiN835soirrKfyviOMd5eCzzvEQPw7ZcazvcHb/m8neqqmPhzx21bb66yeXtx8x7DusR7HaFPvRmNIP48A/BfH/L29NSnvyvvtepfeVv1iaGE73+KUsbXc5P3usYE4u80qgLfNb+uzwWMiS9fl3dcAPznGt59yaOllK93sBlnj8BrHf248h2qs7nyhGjrf7aBkDGGmpoaGBgYoFu3bjh79qzGXI8ePZCRkfF8K6eBubk5zM3N/5CyFAoF6M5yQgghhBBCCCGEEAL8SZOUKJVKzJ8/Hy1atECbNm0QFhYmPBcZGQlPT0+YmZnByckJ06dPV+mld/36dYwYMQI2NjYwMzNDly5dcPjw4WZfMykpCRKJBEeOHEH37t0hl8tx6tQpKJVKrFq1Cq6urjAxMYG3tze+++47Yb0nT55g6dKl6N27Nzw9PTF06FCkpKTAw8MDHh4euHDhAgYOHAgTExO0bNkS77zzTpO9ChUKBdatW6eyzMfHR9gHCoUCAPDqq69CIpEIvze8xVipVCIiIgKOjo6Qy+Xw8fHBDz/8IDyfn58PiUSCffv2YcCAATA1NYW3tzfS0tKa3VeEEEIIIYQQQggh5H/Hn9JAGBcXBzMzM/znP//Bp59+ioiICBw9erS2QlIpoqOjcfHiRcTFxeHnn3/G/PnzhXXff/99VFZW4uTJk7hw4QI++eQTrp51CxcuxOrVq5GVlQUvLy+sWrUK27dvx8aNG3Hx4kXMnj0b48ePR3JyMgBg8eLFuHTpEo4cOYKsrCxs2LABtra2AGonM/H394eNjQ3OnDmDf/3rXzh27JioGYQbc+bMGQBATEwMCgsLhd8b+vzzz7F27VqsWbMG58+fh7+/P0aOHImrV6+q5D766CPMnTsXGRkZaN++Pd566y08farbwXEJIYQQQgghhBBCyF/Hn3KLsZeXF5YuXQoAaNeuHb788kscP34cgwcPVpnARKFQYPny5Zg2bRrWr18PoHb8vNdffx2enp4AADc3N67XjoiIwODBgwEAlZWVWLlyJY4dO4bevXsL5Z06dQqbNm1Cv379UFBQgG7duqFHjx5Cners3LkTT548wfbt22FmVjuu2pdffokRI0bgk08+QevWrbn3TatWrQAA1tbWaNOmTaO5NWvWYMGCBRg3bhwA4JNPPsGJEyewbt06fPXVV0Ju7ty5GD58OAAgPDwcXbp0wbVr19CxY0fuuhFCCCGEEEIIIYSQv58/rYGwPnt7e9y7VzsZwbFjx7Bq1SpcvnwZxcXFePr0KZ48eYLy8nKYmppi1qxZeO+99/DTTz/Bz88Pr7/+ulp5Talr6AOAa9euoby8XGgwrFNVVYVu3boBAN577z28/vrrSE9PxyuvvILRo0fjH/+oncY8KysL3t7eQuMgAPj6+kKpVCI7O1urBkIxiouLcfv2bfj6+qos9/X1RWZmpsqy+vvG3t4eAHDv3j3uBsLKykpUVqoOHFtVWQkjue4GhyWEEEIIIYQQQgghuven3GJsaKg6w51EIoFSqUR+fj4CAgLg5eWFvXv34uzZs0JvuKqq2tmqpkyZgtzcXEyYMAEXLlxAjx498MUXX4h+7fqNeXVjBX7//ffIyMgQHpcuXRLGIRw6dCiuX7+O2bNn4/bt2xg0aBDmzp2r9bZLpVK1CUKqqzlnFORQf19L/n+mTqWSbwY1AFi1ahWsrKxUHl9v/PIPqychhBBCCCGEEEII+XP8KQ2EjTl79iyUSiXWrl2LXr16oX379rh9+7ZazsnJCdOmTcO+ffvw4YcfYvPmzVq9XufOnSGXy1FQUCBMOlL3cHJyEnKtWrVCUFAQduzYgXXr1uHrr78GAHTq1AmZmZkoKysTsqmpqZBKpejQoYPG12zVqhUKCwuF34uLi5GXl6eSMTQ0RE1NTaP1trS0RNu2bZGamqqyPDU1FZ07dxa/AzgsWrQIRUVFKo93pmk/1iIhhBBCCCGEEEII0Q9/yi3GjfHw8EB1dTW++OILjBgxAqmpqdi4caNKJjQ0FEOHDkX79u3x6NEjnDhxAp06ddLq9SwsLDB37lzMnj0bSqUSL7/8MoqKipCamgpLS0sEBQVhyZIl6N69O7p06YLKykokJiYKrxcYGIilS5ciKCgIYWFhuH//PmbOnIkJEyY0envxwIEDERsbixEjRsDa2hpLliyBTCZTySgUChw/fhy+vr6Qy+WwsbFRK2fevHlYunQp3N3d4ePjg5iYGGRkZOCbb77Ral80Ry6XQ97gdmIjeYlOXosQQgghhBBCCCGEPD961UDo7e2NyMhIfPLJJ1i0aBH69u2LVatWYeLEiUKmpqYG77//Pm7evAlLS0sMGTIEUVFRWr/msmXL0KpVK6xatQq5ubmwtrbGCy+8gH/+858AACMjIyxatAj5+fkwMTFBnz59sHv3bgCAqakpfvzxR3zwwQd48cUXYWpqitdffx2RkZGNvt6iRYuQl5eHgIAAWFlZYdmyZWo9CNeuXYs5c+Zg8+bNcHBwQH5+vlo5s2bNQlFRET788EPcu3cPnTt3xsGDB9GuXTut9wUhhBBCCCGEEEII+d8jYQ0HxCNEpKycW1x5Q1Rx5Ws4268ZJKKzFUpjrrLNpWXNh55BJeOb7EUK8eNI8uwXALBgj7nyT6VGXPlyZtZ86P+1qLnHVTavYoMWXHkJ+E6XBhLdjS8q0fGp+ykMmw/9P979wusp57lAjidceSbhe4/wkDK+MV8rmClX3khS2Xzo/zHOUUWUnHkZnnLlDRjf++OJRPy+MVMWc5X9WNKSK28g4dtWnveIAfj2C+9nJe/fiVep0lx01lRawVU277mG9xjm/bzkwVt33rooGd+2SiXiz01GEH+eAYBq8F0X6PrvyqNKyVd3S8ljvvIlfNefT5n49zfP3/SvzlDC9z/FE6UJV14maXyoJ014rjuqwHfNr+tzAQ9dH2O85xrefcnz/raSPOIqm+caBQCudfTjyg+vzubKE6ItvRqDkBBCCCGEEEIIIYQQ8pyxv4l3332XmZmZaXy8++67f3b19M6QIUMa3V8rVqzQutwnT56wpUuXsidPnvzl8vpUF968PtVF13l9qgtvXp/qouu8PtWFN69PddF1Xp/qwpvXp7roOq9PdeHN61NddJ3Xp7rw5vWpLrrO61NdePP6VBdd5/WpLrx5faqLrvP6VBfevD7VRZs8Ibrwt2kgvHv3Lrt69arGx927d//s6umdmzdvNrq/Hjx4oHW5RUVFDAArKir6y+X1qS68eX2qi67z+lQX3rw+1UXXeX2qC29en+qi67w+1YU3r0910XVen+rCm9enuug6r0914c3rU110ndenuvDm9akuus7rU1148/pUF13n9akuvHl9qos2eUJ0Qa8mKXkWdnZ2sLOz+7Or8Zfh4ODwZ1eBEEIIIYQQQgghhOgBGoOQEEIIIYQQQgghhJD/YdRASAghhBBCCCGEEELI/zBqICR/KLlcjqVLl0IuFzftvD7l9akuvHl9qouu8/pUF968PtVF13l9qgtvXp/qouu8PtWFN69PddF1Xp/qwpvXp7roOq9PdeHN61NddJ3Xp7rw5vWpLrrO61NdePP6VBdd5/WpLrx5faqLNnlCdEHCGGN/diUIIYQQQgghhBBCCCF/DupBSAghhBBCCCGEEELI/zBqICSEEEIIIYQQQggh5H8YNRASQgghhBBCCCGEEPI/jBoICSGEEEIIIYQQQgj5H0YNhITooSdPnvzZVRDt5s2bUCqVOss/D3+l/U0IIYSQv6eHDx8+t9fSt2sxQgghfz6axZj8ZVRVVSEvLw/u7u4wMDBoNLd9+3aMHTtWbYr4qqoq7N69GxMnTtR1VbWiVCqxYsUKbNy4EXfv3sWVK1fg5uaGxYsXQ6FQICQk5M+uokaWlpbIyMiAm5ubTvLPg6WlJV577TUEBgZi0KBBkEob/+7Ezc0NZ86cQcuWLVWWP378GC+88AJyc3Nx8OBB0a89cuRItWXx8fHYuHEj8vLykJaWBhcXF6xbtw6urq4YNWqU+A0jpIHhw4djy5YtsLe3F5bVXQZIJJI/q1o6UbetP/zwA8aOHQtTU9Nm14mJiRGdbczjx49hbW2tF3l9qouu8/pUF23yfzVir8nq/F3PM3+Un376CVu2bMGhQ4dQUVEBADh79izmzp2LAwcOwNLSUiVfVFSE0aNHY926dfD29tYqL5PJUFhYCDs7OwDAvHnzsGjRIrRo0UJjHZVKJS5evAhPT08AwMaNG1FVVSU8L5PJ8N5770EqlWLOnDmitjsyMhIAEBERISq/ZMkSUTlNnj59iqioKOzatQtXrlwBALRv3x5vv/02PvjgAxgaGgIAunXrpvE4tbKyQvv27REaGopOnToJy3nz9fF85vB+PulTXp/qouv8H3Edocnq1asxbdq0v/XnCtEP1EBItNKiRQtcuXIFtra2sLGxafKCr+G3oQ0vSOo8ePAAdnZ2qKmpUVleXl6OmTNnIi4uDgCEhrOZM2fCwcEBCxcu5C5/zpw5WLZsGczMzJq9iOnfvz+GDh0KQ0PDZht+Ro4ciYMHD3Ll60RERCAuLg4RERGYOnUqfvvtN7i5ueHbb7/FunXrkJaWBgBal68rFhYWyMzMFN3gV5dv3bo1Vq9ejePHj+PevXtq32TXNbSJ3VYAWu+X/fv3Y+fOnfj+++9hZWWFsWPHYvz48ejRo4faulKpFHfu3FE7vu7evQtnZ2dUVlY22cBYn0QiUTveN2zYgCVLliA0NBQrVqwQjoPY2FjExcXhxIkTosomgIeHB8aPH4+3334b7du315iJjo7GO++8A2NjY0RHRzdZ3qxZs7jyALjKbqimpgYJCQnIysoCAHTp0gUjR46ETCZr9nUbU//9un37dnz22We4evUqgNp/lObNm4cJEyaorZeSkoJNmzYhJycH3333HRwcHBAfHw9XV1e8/PLLKtnDhw9DJpPB399fZfmPP/4IpVKJoUOHqpV/9epVnDhxQuO5QNt/COu2tXfv3qioqMCYMWMQEhKCf/zjH42u07p1a9FZAPjkk0+gUCgwduxYAMCbb76JvXv3ok2bNjh8+LDwj/jzyOtTXWhbm87zKCgoaPJ5Z2fnRp978uSJSgMOALWGI4Dv/c17TcZznuG9PgT4zpPbtm3DgAED4OrqqvacpnJjY2MbvUb5+eefnykPANevX8e2bdsQFxeHR48eYejQoXj99dcxZswYAMDbb7+NTp06YfHixRrruHLlSly6dAk7duzQKt/weqa5L3B37tyJjRs34uTJkwBqz7HW1tZCA/Hvv/+OdevWISQkBAMGDNBYRn0SiUTYL1KpFG3btoWdnR0a+9dUIpEgPT0dAODq6tpsY7NEIkFOTg4AoKKiAoMHD0ZaWhr8/PyEBrusrCwcO3YMvr6++Omnn2BsbIzw8HCN5T1+/Bjp6en45Zdf8PPPP8PX1xcAuPP18Xzm8H4+6VNen+qi6zxv2WLpYwcL8jfFCNFCbGwse/LkCWOMsZiYGBYbG9vooyGJRMLu3r2rtvzWrVvM2NhYbfmsWbNY9+7dWUpKCjMzM2M5OTmMMcYSEhKYj4+PxvLv3buntjwjI4PZ2Ngwxhjr378/e/TokfBzU4/69ZVIJI0+pFKp2vaJyddxd3dnx44dY4wxZm5uLmxnVlYWs7a21rj/xJRvY2PD7t+/zxhjzNramtnY2DT60Eb9uvLkx40bx+zt7dn8+fNZVFQUW7duncqDd1u13e/1FRcXs23btrHBgwczmUzG2rVrx8LDwxljjB04cIAdOHCASSQStn37duH3AwcOsH379rH333+ftW/fXqt9WF+nTp3Y/v37VfYVY4xduHCBtWzZkjHGmI+PD+vWrZuox/PId+vWjT18+FD0umKzvGU3FBkZyXr06MGkUinr0aMHW7duHSssLFTJKBQK9vvvvws/N/ZwdXXlzvOWXd/Vq1dZ+/btmampqbB9pqamrEOHDuzatWt8B1U9dcfU2rVrmampKZs/f75wHM+bN4+ZmpqyyMhIlXW+++47ZmJiwqZMmcLkcrlwTH7xxRds6NChaq/h6enJvv/+e7XlR44cYV5eXmrLv/76ayaTyVjr1q2Zt7c38/HxER6a/q6821pdXc327dvHRo4cyQwNDVmHDh3Y6tWr1Y4FxhhXlrHav2tqaipjjLGffvqJWVtbsx9//JGFhISwwYMHP9e8PtWFtrXxfN3nUGOPhnjzZWVl7P3332etWrUSled9f/Nck/GcZ+q2lef6kPc86eHhwaRSKXNycmLjx49nmzdvZlevXlXLMcbY+++/z8zMzNibb77JPvjgAxYaGqry0DZfWVnJdu3axQYNGsSMjY1ZQEAAk8lk7Pz582plurm5sczMTI31Y4yx8+fPq3x+8OYb7u/mruf8/PzY7t27G81v2LCB9e/fv9H1mzJs2DBmbGzMRo0axQ4cOMBqamqazDe8Zqz/CA0NZSYmJirH+5IlS5izs7PG/ZORkcGcnZ3Z0qVLRdX1n//8Jxs4cKDobWsqr8vPJ33K61Nd9G1bxeL9f4sQbVEPQqJTFRUVMDExAQChB83s2bOxbNkymJubC7mamhqcPHkS+fn5OHfunEoZLi4u+Pbbb9GrVy+V3i/Xrl3DCy+8gOLiYgD/7eKfmZmJLl26qNzyUlNTg7y8PAwZMgR79uzR9WZrxcTEBJcvX4aLi4vKdl66dAk9e/ZEaWmpVuXGxcVh3LhxkMvliI2NbfIb16CgIO7yte1B+MILL+D777/X+I2qPrh06RICAwNx/vx51NTUCL0CJRKJ2rfbhoaGUCgUWLt2LQICAp7pdRs7Dq5evQovLy9UVFQ0+m21JkuXLn0u+Xnz5sHU1FTUumKzvGUvXbpU4/IrV67gm2++wa5du5CXl4cBAwZg/PjxejvcAAAMGzYMjDF88803wu1eDx48wPjx4yGVSvH9999rVW7dMTVo0CCEh4er7YO4uDiEhYUhLy9PWNatWzfMnj0bEydOVDkmz507h6FDh+LOnTsqZZiYmCArKwsKhUJleX5+Prp06YKysjKV5S4uLpg+fToWLFig1TY1t631z013797Fjh07EBcXh8uXL2PIkCEICQnBiBEj1Hr+ismamJjgypUrcHJywgcffIAnT55g06ZNuHLlCl566SU8evRIpUxd5vWpLrStjecPHDigsm51dTXOnTuHuLg4hIeHqw0nkpmZqTEfGRmJFStW4LXXXlN5/v3338eJEyewbNkyTJgwAV999RVu3bqFTZs2YfXq1QgMDFTJ876/xV6TAbW9vMScZ7S9PtTmPHnr1i0kJSXh5MmTSE5OxtWrV2Fvb4/+/fsLPesAwNbWFtu3b8ewYcPUytBETH7mzJnYtWsX2rVrh/Hjx2PcuHFo2bIlDA0NkZmZic6dO6vkjY2NkZWV1WiPx7y8PHTu3Fm4JZk337AHYXPXc05OTkhKSoK7u7vGfFZWFnx9fbUaR9HS0hI//vgjkpKSEBsbi+LiYkycOBGTJ09Ghw4dRJXx8OFDLFu2DBs2bMBLL72ETz75BL169QIAdOjQAStXrsTrr7+ucd1//etf+Oijj4Rbj5ty8eJFDBgwAPfu3RNVL7H5P/rzSV/z+lQXfdvWpvD+v0WI1v7U5knytzBz5kyNy0tLS1W+SazrLSORSJiTk5NKD5r27duzV155hf3yyy9q5ZiYmAjfmNT/9iQjI4NZWloKubCwMBYWFsYkEgmbO3eu8HtYWBhbuXIl27lzJ6usrFQrf9KkSay4uFhj/SdNmqSyrKCgQMQe0S7/wgsvsPj4eMaY6naGh4ezl19++ZnL1xVtexAqFAp26dIl0evxbKu2+6WiooJ9++23bNSoUUwulzNnZ2e2YMEClYxCoRB6ZDbm888/ZxUVFcLPTT0a6tSpE0tISGCMqe7b6OjoZ+pNtXPnTlZaWqo3+T9LWloa8/HxabInqT4wNTXV2KMkIyODmZmZaV1u3TEll8s19py5cuUKk8vlKstMTExYXl6eyvqMMaGchlq3bs2OHz+utvzo0aOsVatWasstLCx08q14Y+emX375hb3zzjtMLpczhULBrKysmEKhYCdOnODO2tvbCz3H2rdvz/bs2cMYY+zy5cvMwsJCrTxd5vWpLrStTec1+eabb9jIkSNFZRljLDExkfXr109tuZOTk3B8WlhYCO/z7du3a+wRyPv+FntNxhgTfZ7R9vrwWc6TZWVl7IcffmBBQUHMwMCAyWQyleft7e1ZdnZ2k2Xw5mUyGfvnP/+pds1pYGDALl68qJZ3dHRkR44cabS8w4cPM0dHR63zEomEvfvuu2z27Nls9uzZzMjIiE2ePFn4ve5RRy6Xq/TMvHfvnkpPv6tXrzIjI6NGX78pDc/XycnJLDg4mFlYWLB//OMfrLy8vNF1y8vL2fLly5m1tTXz9vbW2INdLpc3eW1YUFCg8XjXJCsrS7ij44/O/5GfT/qc16e66Nu2NoZ6EJLnhRoIyTNzc3NjS5YsUVlWWlrKXn75ZY0NW/379xduGRSjT58+LDo6mjFWe3LMzc1ljDE2Y8YM5u/vr5aPjY0VGmfEkEqlGm9puX//vtoFo1QqZX379mVff/21qG3gySckJDArKyu2evVqZmpqyj777DM2ZcoUZmRkxH766adnLp8xxgYNGsRiYmJYUVFRs1mxeP+5r8vHx8ezN954g5WVlYlaj2dbeffLDz/8wCZOnMgsLS1ZixYt2DvvvMOSk5ObXa+x4+xZbi3dvHkzc3BwYLt372ZmZmZs165dbPny5cLP2tL276Sr/PP2n//8h33wwQesTZs2zNTUlI0dO5YxxtT+EWrqwZvnLbs+GxsbocGhvlOnTmk9HABj/73A7NKlC1uxYoXa88uWLWNdu3ZVWebq6sqOHj2qsj5jjMXFxbFOnTqplfHOO+8wT09PlX8kr169yry8vFhISIhafvLkyWzDhg1ab1Nj6tf1zp077LPPPmOdO3dmxsbGbNy4ccI2lZaWsvnz5zNnZ2fu7Pvvv89cXFyYn58fa9myJSspKWGMMbZr1y6NDfq6zOtTXWhbm85rkpOTw9X4f/XqVWZqaqq23MzMjF2/fp0xxpiDgwP7z3/+wxhjLDc3V2P5vO9vnmsynvMMY/zXh7znyR9//JEtWrSI9e7dmxkbG7Nu3bqx0NBQlpCQoPa6a9asYdOnT2dKpVJUXcTkd+7cyfz8/IRbkQ8dOsSePn3aaANhcHBwo18QK5VK5uvry4KDg7XO9+vXr9lhdgYMGCDknZ2dNTa+1Tl48KBwbuTVsPGjvLycxcXFsZ49ezITExON161Pnz5lGzZsYG3atGEKhYJt37690f3fqlUr9uuvvzb6+qdPn2a2trai6rpixQrWp08fUVkxeV19PulbXp/qom/bKgY1EJLnhRoIyTO7du0as7e3Z1FRUYyx2nHcevfuzfr06fOH9ChKSUlh5ubmbNq0aczY2Jh98MEHbPDgwczMzKzJD/vmFBUVscePHzOJRMKuXbvGioqKhMfDhw9ZXFwcs7e3V1knPT2dzZ07lzk6OjK5XM5GjRrF/vWvfwnjMTbEmz958iTz8/NjrVq1YiYmJszX15f9+OOPjW4Db/mzZs1ibdq0YSYmJuyNN95gCQkJrKqqSuQe00zbHoQ+Pj7MwsKCmZubs65duzY7nhzPtvLuFxMTEzZmzBhR+6OmpoZFRESwtm3bMplMJmz7xx9/zLZs2SJ6PzRlx44dzMPDQxg30cHB4ZnL1vbvJIa1tTUDwKysrJoc49LGxqbZcTAbjonJm68vOzubLVmyhLVr144ZGBiwV155hcXFxQn/vDPW/BikDf9J4snzll3fhAkTWJcuXdgvv/zClEolUyqVLC0tjXXt2pUFBQWJ/js2VPd3/e6775hMJmP+/v4sIiKCRUREMH9/f2ZgYMD27dunss7KlStZ586d2S+//MIsLCxYSkoK27FjB2vVqpXQUFDf48ePWa9evZiBgYHQGG5gYMAGDBggjP3asHxbW1sWFBTE1qxZ02wvW95tDQgIYIaGhqxLly4sKiqKPXjwQC179+5dJpFIuLKMMVZVVcU+++wzNmvWLJaeni5kIiMj2ebNm9XW1WVen+pC29p0vqHy8nL2wQcfaBzHtv61Sd11S1ZWFhs7dizz9vZWy3t6erKkpCTGWO2Xgh9++CFjrLY3u4ODg1qe9/3Nc03Gc56p7/79+8321GeM/zwpkUiYnZ0d++STTzSei+obPXo0s7KyYq6uriwgIIC9+uqrKo9nyefm5gpj4tna2jKpVMr+9a9/qZV57do1ZmVlxXr27Mm+/fZblpGRwTIyMtju3bvZiy++yKysrFR6aPLmeU2aNIn94x//0PicUqlkvXv3VrvzRqy68/W///1vNmXKFGZpacl69OjBvvrqK41/q2+//Za1a9eOtWrViq1bt07jHUL1vfnmm+y1115r9PnXXnuNjRkzhjHW+J0fERERbNSoUczAwEBo7NEmX58uP5/0Ka9PddG3bRWLGgjJ80JjEJI/xPnz5zFgwAAsXboUu3btglwux/fffw8zMzO1rDYzveXk5GD16tXIzMxEaWkpXnjhBSxYsACenp4AtJtVWSqVNpmTSCQIDw/HRx99pPYcYwxJSUnYuXMn9u7dC6VSiddeew3btm3TWBZvnhdP+UqlEseOHcPOnTuxf/9+yGQyvPHGGwgMDES/fv0A1I5xZGJigoyMDHTt2rXJ175x4wZatWoFc3Nz0fm2bdti+fLlTeYaG0+OZ1vFZktKSmBhYdFkfQBg9erVKC4uxrffftvsbNN/hPLycpSWlqrN6KgNbceKFJOPi4vDu+++i2XLlv0hda0TFBQkzJQpNl+fVCrFiy++iLfffhvjxo1D69at/7C66drjx48RFBSEQ4cOwdDQEADw9OlTjBw5EjExMbC2thay1dXVePfdd7F48eJmZ+dctWoV3nvvPVhbW+Ps2bOIiooSZv/s1KkTPvzwQ3Tr1k1lHcYYVq5ciVWrVqG8vBwAIJfLMXfuXCxbtkzj6zDGcPToUWRmZsLExAReXl7o27evxmxTdZZIJMjNzW1ymxpTt60ffvghpkyZgt69ezeaZYyhoKAAERERorMuLi5a1Yv8b2t4jcIYQ0lJCUxNTbFjxw6MHDlSJa/pWoUxBicnJ+zevVvtWI2KioJMJsOsWbNw7NgxjBgxAowxVFdXIzIyEh988IFaWbzv7+auyeoTe555/PgxPvroI3z77bfCmI02NjYYN24cli9frnLOq79OY+fJ2NhYWFlZqeTXrVuHkydP4uTJk5DL5ejXrx/69++P/v37q810P2nSJI3bXicmJuaZ8kDtvv/pp5+wdetWHDx4ELa2tnjttddUZr3/9ddfERwcjEuXLgnHAWMMnTt3RkxMDF588UWVMnnzPMzNzSGRSNC5c2fMnTtX2GfZ2dlYs2YNsrOzcfbsWXh4eHCXLZfL4ezsjKKiIgQGBmLSpEnw8vJqNC+VSmFiYoK33npL48zcdSIjIwHUjiv90ksvoUuXLpgzZw46duwIxhiysrIQFRWFS5cu4ZdffkGXLl0a/UyytLREhw4dMHv2bJX3HW++vpCQEJ19PvGUrev8X7nuut5WsYYNG4atW7fC3t5e9DqEaIMaCMkfJi0tDYMHD8ZLL72ExMREYXKShmbMmIHY2FgMHz4c9vb2ahe+UVFR3K+tzUQcycnJYIxh4MCB2Lt3rzDANQAYGRnBxcUFbdu2bfa109PTERISIkxm8UfnefGU/+TJExw6dAgrVqzAhQsXVPJubm7Yv38/vL29Rb0ub/6PwLOtf8R+t7S0hI2NDbZt24ZBgwapNKJdvnwZvXv3Vhu8HgBu3ryJgwcPoqCgAFVVVSrP1V286pouGwi1ya9evRrTpk3T+E/fs2Tr5+/fv4927do1m9+1axdGjhyp8oXGjRs3ANQOyi4GT54ne+3aNZV/rBv7x8vKygoZGRnNNhA+i6qqKly7dg2lpaXo3LmzykQCzXn8+LHov58YV69exYkTJzR+ybRkyRKV37dv346xY8dCLperLK+qqsLu3btVJlDgyda5ffs2Tp06pbEus2bNeq55faoLbavmfMMvPaRSKVq1aoWXXnoJNjY2auUmJydrzHt4eKhMxtaY69evC402TTW4PMv7+1k9fPgQvXv3xq1btxAYGIhOnToBqG3U2blzJ5ycnPDvf/9b4/4BxJ8n67tw4QKSk5Px888/IzExEXZ2drh58+Yft1GcHj58iO3btyMmJkZtYhoAyMjIwNWrV8EYQ/v27eHj49Nkebx5MSwsLLB9+3Z89NFHuHz5skoDZMeOHRETE4OXXnpJq7IlEgkcHBwwevRoGBkZNZqru2bq379/k9f6dWXW73Twyy+/ICQkBFlZWWp137p1a5MNOrpQXV2NIUOGYOPGjc1eq/Bk9S2vT3XRdZ637DpKpRLXrl3T+PnR2JerhOiMTvsnkr8tHx8ftVtCu3Xrxlq0aME6duzY5K2iLVu2bHIMk4Ya3l5T9yguLm72lgIx8vPzRY8xU+fGjRvsk08+Yd7e3kwmk7GXX365yTG0xOQbu5WyRYsWrG3btqxv375s27Ztf0h9GGOssLCQRUVFse7duzOJRMJeeukllee3bNnChg0bprFrvCa8eW3xbKs2+6Up5ubmTC6Xs/z8fOH3uu7+Fy9e1Di+07Fjx5ipqSnr2rUrMzAwYD4+Psza2ppZWVlpvLX0zp07bPz48cze3p7JZDImlUpVHs9Sd13dYqxNnmfMwuc1fmJ1dTX7+OOPmaWlpbC/LS0t2UcffaTx1nOePG/Z4eHhGsfnLC8vZ+Hh4WrLJ06cyCIjI5vczvpjODV2Xq17NFfO/v37G51kaPXq1Wz37t3C72PGjGFSqZS1bduWZWRkNFm2GF9//TWTyWSsdevWzNvbm/n4+AgPTZ85jY0z+/vvv6u9p3iyjDEWExPDjIyMmLm5OXNxcWl2jFFd5vWpLrStTecbc+HCBdHZv5MPPviAde3ald25c0ftucLCQubp6clCQ0OFZTU1NWz16tXsH//4B+vRowdbsGBBk5NY1KdUKtnZs2fZ2rVrWUBAALO2tmYymYz5+Pj8YdvzR9G3sYPrf86fO3eOffvtt+zbb79Vua1eW1KplL300kvcw3Foo37dz50798zlPct+t7W1ZVeuXBG1Hk9W3/L6VBdd53nLTktLY66urkwqlQpDC9U99H1CPfL3RA2ERCv1Zwhu7tEQ78xwdSfIxh7Ozs5syZIlwkxqEyZMYNu2bVMZIL8pR44cYSkpKcLvX375JfP29mZvvfWW2qDVGzduZH379mUymYx16dKFrVy5Umgs0oQnHxkZyVq2bMnGjx/PoqOjWXR0NBs/fjyztbVlK1asYFOmTGFyuZx9/fXXWtenqKiIbdu2jfn5+TEDAwPWvn17Fh4ernFf+fj4CA1i7du3b3aMQDF5GxsbYVyh5saWe5Z9ybtfxDI3N2ddunThmm36xRdfFCbxqcuXlJSwkSNHsvXr16vlhwwZwjp37szWr1/P9u/fzxISElQez1J3fWog5Mk/r7pMmzaN2dnZsY0bN7LMzEyWmZnJNm7cyNq0acOmTZumth5Pnrds3oaqZcuWMWtra/b666+zlStXahzHr36ZjZ1XNV2Qjhkzhn3xxReMsdoGyvbt2zNDQ0NmYGDAvvvuO7W6KBQKYeKAn376iVlbW7Mff/yRhYSEsMGDB2v8G9y4cYN99dVXbMGCBc1O4OLs7MxWr16tsRxNJBIJu3fvntryjIwMtXMNT5ax2llDly9frjKTZ1N0mdenuug6r0910SZfX3FxMdu0aRN78cUXNb636ya8Yqx2ptXFixezuXPnqk2kdfz4cdapUyeNDfyPHz9mnTt3ZidPnhSWTZo0SdSjTnPXYlKpVJjYjSfLGGMuLi7shx9+aHQfHTlyhLm4uAi/R0REMKlUyl555RU2atQoZmxsLGrsu4CAAGZjY8NkMhl74YUX2Jw5c9iBAwdUxrhr7NpEoVCwV155RW3SON48D336HNYmb2FhwXbs2CEqm5KS0ug40X8EXTaePst+DA0NZQsWLBC1Hk9W3/L6VBdd53nL9vb2ZmPGjGGXLl1ijx49Yo8fP1Z5EPK8NX9vAiEaNDY+nBgffvghPv/8c3z55ZfN3h4AALGxsfjoo48QHByMnj17AgBOnz6NuLg4fPzxx7h//z7WrFkDuVyOf/7znzAyMsKqVasQEhICBwcHYYyZfv36aezuPW/ePHzyyScAam85mTNnDj788EOcOHECc+bMURk3Zvny5XjrrbcQHR0t6lZanvypU6ewfPlyTJs2TWX5pk2b8NNPP2Hv3r3w8vJCdHQ0pk6dqlV9WrduDRsbG4wdOxarVq1Cjx49Gs2OHj262fJ481FRUcJYf1FRUaL+/nV4tpV3v/CYOXMmZsyYgVu3bkGpVGLfvn3Izs7G9u3bkZiYqJbPysrCrl27AAAGBgaoqKiAubk5IiIiMGrUKLz33nsq+VOnTiElJeUPuR2oPhcXF2GMJn3I66OdO3di9+7dGDp0qLDMy8sLTk5OeOutt7Bhwwat87xlM8Y0vj8yMzNVhkOos3XrVmFcwbNnz6o8J5FIMGvWLPz888/CuidOnBCzSwAAJ0+eFMZi3b9/P5RKJR4/foy4uDgsX74cr7/+ukr+zp07wu3TiYmJePPNN/HKK69AoVBovP3s+PHjGDlypHCrfteuXZGfnw/GGF544QW1/KNHjzBmzJhm692tWzdIJBJIJBIMGjRI5XbMmpoa5OXlYciQIdzZ+srLyzFu3DhIpdJm66PrvD7VRdd5faqLNnmg9n21detW7N27F23btsVrr72Gr776Snj+woULGDFiBG7cuIF27dph9+7dGDJkCMrKyiCVShEVFYXvvvtO+Oxdt24dpk6dqnE8NisrK7z77ruIjIxEnz59ANReW7m4uKBbt25gIkYb2r9/f6PPpaWlITo6Wrg1jicLAIWFhejSpUuj63Tt2hV37twRft++fTvWr1+Pd999FwBw7NgxDB8+HFu2bGnyb9CxY0e8++676NOnj9r4hHXWrVuncfnjx49x9uxZBAQE4LvvvsOIESO0yv8vYYxh8uTJWLJkCSZNmoSgoKBGh9Z4+eWXucq2tLRERkaG6GFNxBzjz5LX1tOnT7Ft2zYcO3YM3bt3Vxu7vf4wNDxZfcvrU130bVuvXr2K7777TquxOwnRBWogJH+Ys2fPCmPAdOnSRWUA6tdee00l+/PPP+PIkSPo0qWLWqPCvn37VH6Pi4vD2rVr8eabbwrLRowYAU9PT2zatAnHjx+Hs7MzVqxYgX/+85/YsmULAODWrVs4efIkkpOTsXbtWrz77ruwt7dXG2MmLy8PnTt3BgDs3bsXI0aMwMqVK5Geno5hw4apZAsKCrgatXjyP/74o9BQWd+gQYPw4YcfAqgdoHbhwoVa1+fgwYMYNGiQqH9ieBuBxeTrTyIRHBzcaK6iokJtGc+28u4XHoMHD0bnzp0REREBMzMzLFmyBC+88AIOHTqEwYMHq+XNzMyEcQft7e2Rk5Mj/CP0+++/q+WdnJy0vjAtLS1VG7uk7p/F33777bnn/2rkcjkUCoXacldXV41jIvHkxWbrJjCQSCRo3769ynFcU1OD0tJStS8RgNrzWHPqJiGqe10nJyeNkx/UjZFYp6ioSGhY/OGHH/D666/D1NQUw4cPx7x589Rex8bGBjdu3ICTkxN++OEHYUIixpjG8T8XLVqEuXPnIjw8HBYWFti7dy/s7OwQGBiosVFuzJgx+OmnnzTuh/rqGk4yMjLg7++vMqaakZERFAqF0LjJk60vJCQE//rXv1TOy03RZV6f6qLrvD7VhSd/584dxMbGYuvWrSguLsabb76JyspKJCQkCNchdebPnw9PT0988803iI+PR0BAAIYPH47NmzcDqP2yavXq1cKxm5mZqfEaos4rr7yCNWvWCL+/99572LVrF/Ly8jBp0iSMHz9e45cPdUaNGqW2LDs7GwsXLsShQ4cQGBiIiIgI7iwA2NraIj8/H46OjhpfOy8vT6VuBQUFKtdnfn5+kEgkuH37dqNlAMBnn33W6HN1Gk521ZCPjw9WrVolNPjx5v/KtLmuSk1NRUpKCuLi4hAeHo6BAwciJCSk2bEGm/O8GvB07bfffhO+CLty5cofltW3vD7VRdd53rJfeuklXLt2jRoIid6gSUrIM7t37x7GjRuHpKQkYRD6x48fY8CAAdi9ezdatWrV7Oxu9TWc6c3ExATnz59X6/139epVeHt7o7y8HHl5eejSpYswAx9Q+43+qVOncOLECSQlJSE9PR2dO3fGuXPnVMpp0aIFTp06hc6dO+Pll1/GxIkT8c477yA/Px+dO3dWKRMAUlJSsGnTJuTk5OC7776Dg4MD4uPj4erqqvEbULF5Z2dnzJ49G7Nnz1ZZPyoqClFRUSgoKMD58+fxyiuvqHyTzlufp0+fIikpCTk5OXj77bdhYWGB27dvw9LSUm1Q8sePH+O7775DTk4O5s2bhxYtWiA9PR2tW7eGg4ODWtk8+VmzZqnM1FenrKwMAQEBGns48Wwr734Rg3ciDqC24WH48OGYOnUq5s6diwMHDiA4OBj79u2DjY0Njh07ppL/6aefsHbtWmzatEljg1JDeXl5mDFjBpKSkvDkyRNheV0PtIYNMrrOi8WzL5/XBCs7duzA5cuXERMTI0xSUVlZiZCQELRr106tETwiIkJ0Xmw2Li5O6HWxbt06lV4udQ1VTQ2kXlVVhby8PLi7uzc5gYFMJkNhYaHarNMPHjyAnZ2dyt+1ffv2WL58OYYPHw5XV1fs3r0bAwcORGZmJgYNGqTW0D1jxgwkJiaiXbt2OHfuHPLz82Fubo7du3fj008/RXp6utr+z8jIgLu7O2xsbHDq1Cl06dIFmZmZGDVqFPLz81Xyq1atQmRkJIYPHw5PT0+1L5kaTiARFxeHsWPHwtjYuNH9AdROVlNSUoKJEyeKyo4cORLGxsYICAhARUWFxro07ClQU1Ojs7wuy9a3vD7VRWx+xIgROHnyJIYPHy40fstkMhgaGiIzM1OtgdDW1hY///wzvLy8UFpaCktLS5w5cwbdu3cHAFy+fBm9evXC48ePAQDGxsb47bffGv0n89q1a/D09FT5Aq6yshL79u3Dtm3b8O9//xvDhw9HSEgIXnnllSYbg27fvo2lS5ciLi4O/v7+WLVqFbp27ap1dvLkycjJycHRo0fVGo0qKyvh7+8PNzc3bNu2DUDt+evOnTto1aqVkLOwsMD58+c1TtSUlpaGBw8eICAgQFi2fft2LF26FGVlZRg9ejS++OILtcmJNLly5Qp69eqFhw8fNpvVJl+fvk0u9qz59PR0xMTECHdVvP322wgJCdHqTg992lZd14X8ve3fvx8ff/wx5s2bp/Hzo6nJpQjRBepBSJ7ZzJkzUVJSgosXL6rMPBcUFIRZs2Zh165dao1+PJycnLB161asXr1aZfnWrVuFWxUePHggzG73z3/+E0lJSTh37hw6deqEfv36YeHChejbt6/GGfBefvllzJkzB76+vjh9+jS+/fZbALUXdQ2/id67dy8mTJiAwMBAnDt3DpWVlQBqe9isXLkShw8f1jq/ePFivPfeezhx4oRwK/WZM2dw+PBhbNy4EQBw9OhRlV5AvPW5fv06hgwZgoKCAlRWVmLw4MGwsLDAJ598gsrKSuF1AOD8+fPw8/ODlZUV8vPzMXXqVLRo0QL79u1DQUEBtm/frlI2b/7777+HjY0NwsPDhWVlZWUaewzxbivvfhGrT58+jc7O3ZjIyEiUlpYCAMLDw1FaWopvv/0W7dq10ziD8dixY1FeXg53d3eYmpqqXSg0/Cdj/PjxYIxh27ZtaN26dbPf8Os6/1d27tw5HD9+HI6OjsI/LJmZmaiqqsKgQYNUekLv27ePK88Y4yrb1dUVvr6+zc5SWjdjs5GREWbOnCnMkHrlyhW4ublh5syZcHBwUOvZ1NgtzKWlpWqNY6GhoQgMDIS5uTlcXFzQv39/ALW3SHp6eqqVERUVBYVCgRs3buDTTz8VvngoLCzE9OnT1fK8vWy//vprmJubIzk5WW2G17rbqetrrodPnXfffRcZGRnNNg7WZV966SXs3LkTP/74Izp06CC8fv26NLRq1Sqd5XVZtr7l9akuYvNHjhzBrFmz8N5774ma3fLhw4do06YNAMDc3BxmZmYq1zA2NjYoKSkRfndwcGiygfD8+fOwt7dXWSaXy/HWW2/hrbfewvXr1xEbG4vp06fj6dOnuHjxotqXhnWfoV988QV8fHxw/Phx4ZblhniyERER6NGjB9q1a4f3338fHTt2BGMMWVlZWL9+PSorKxEfHy/kGWMIDg5WadB78uQJpk2bpnI7X90dKREREejfv7/QQHjhwgWEhIQgODgYnTp1wmeffYa2bdsiLCxMY/3qq6ys5Or5xpuvj/fzVtv8iRMnMGDAgGbzR44c0fjlsFgvvPAC2rRpg5YtW2L16tXYtm0b1q9fj969e2Pjxo1N3mauz57l7zR58mR8/vnnwvA7dcrKyjBz5kyhUZw3q295faqLvm1r3d0JkydPFpZJJJJn/iKeEK09v+EOyd+VpaUlO336tNry//znP8zKyuqZyz9w4AAzMjJiXl5eLCQkhIWEhDBvb28ml8vZoUOHGGOMrV+/XhjMXiKRMDs7O7Zq1SpRk6Fcv36dDR8+nHl5ebEtW7YIy0NDQ9nMmTNVsj4+PiwuLo4xpjrIcHp6OmvdurVa2bz5U6dOsXHjxgkTe4wbN04Y7F8T3vJHjRrFxo8fzyorK1XyJ06cYB4eHirZQYMGsXnz5qmVnZqaqjJYuLb5a9euMXt7exYVFcUYqx2ovXfv3qxPnz6stLT0mbaVd780NyFEcxOqNDW5Cq/Y2NgmHw2ZmZmxy5cviy5f13mxhg4dym7fvv2HZ7XJd+nShRUUFLDg4GDRD8YYV563bLHqBlKfNWsW6969O0tJSWFmZmbCMZ+QkKAyO2fdxB9SqZS9++67KpOBzJo1i7300kvsH//4h9rr/Prrr2zfvn2spKREWJaYmMhOnTrFVd/6hg0bxm7fvs1GjRolTL704YcfMg8PD7Z8+XL2wgsvsEGDBmldPi9tJs6xtrZmMTExol9Dl3l9qouu8/pUF7H5tLQ0NmXKFGZhYcF69uzJvvjiC3b//n1mYGDALl68qJZvOGGOubk5y83NFX6/c+eOyqQmM2bMYF27dmUVFRVqZZWXl7OuXbuqXdPUV1BQwMLDw5mrqytzcHBQea8zxtgnn3zCWrRowTp37tzshFk82Tq5ublsyJAhKjN5SqVS5u/vz65evaqS5T2XtmnThp05c0b4/Z///Cfz9fUVft+zZw/r1KmTqHp+8MEHzN/fX1RWm3x9z2vSESMjI+bm5saWLVvGCgoKtKmqRnWfT1VVVexf//oXGzp0KDMwMGC9evVimzdvZqWlpSwvL48FBgaK3v8N685bF13kn+Xv1Nj15/3791Um8uHN6lten+qi6zxv2fn5+U0+CHneqAcheWZKpVLj5ASGhoZq45UB/x0MviGJRAJjY2N4eHggODhY+DZz5MiRyM7OxqZNm5CdnQ0AGDp0KBISEoRbMOtP9HDu3DkkJycjKSkJa9euhZGRkTBRSf/+/dG+fXuV13V2dtY4uURUVJTasuzsbPTt21dtuZWVlXCbz7PkfX194evrq7a8Mbzlp6Sk4N///rfat9kKhQK3bt1SWXbmzBls2rRJrQwHBweVW5y1zbu7u+OHH37AgAEDIJVKsWvXLsjlcnz//fdqA/rybivvfmGNjLRQ981/Y4OQ82pqDL86Yns71XnxxRdx48YNoefKn52vc+/ePdy7d09te+tulajfi5Mnq02+OXXjJ/L2dH6WntF/lLpjNyEhAd9++y169eqlcn7t0qULcnJyhN/rhlhgjOHChQsq5wIjIyN4e3tj7ty5aq/TvXt34dbGOsOHD1f5nXfQ+JMnT6KiooK7l219ddv/Z/VslcvlXOdsXeb1qS66zutTXcTme/XqhV69emHdunX49ttvsW3bNsyZMwdKpRJHjx6Fk5OTWo+T+r3kGvaQq+sZX+fjjz/Gvn370L59e8yYMUM4Z1++fBlfffUVampqhMmG6tS/xfjUqVMICAjAl19+iSFDhqiNVbxw4UKYmJjAw8MDcXFxQm/lhvbt28eVrePq6oojR47g0aNHuHr1KgDAw8ND47iIy5YtQ9u2bUVPCvPo0SOV25GTk5NVJoyq+5wDgDlz5mgso6ioCOnp6bhy5QpOnjwpLOfNA/w99nSdv3XrFuLj43UyTmBYWBgOHz4MxhgmTJiATz/9VOUWczMzM6xZswZt27blKpv3nN/YdV5T+bS0NFGfZ9rsdwsLCxQVFYExhpKSEpWe6zU1NTh8+LAwBEhxcTEYY6Ky+pbXp7ro27bWcXFxUVtGyJ+JGgjJMxs4cCA++OAD7Nq1S/iAv3XrFmbPno1Bgwap5YcMGYINGzbA09NT5Vba8+fPIzg4GJcuXYKfnx/27dsnDHStUCiwatUqUfXx9vaGt7e3cJtZZmYmoqKi8P7770OpVKKmpgbFxcVCw0xxcXGT5dVvwGnTpg2uXbumNjbcqVOnNF5E8ObrPHnyRLjlTlM9tC2/bvsbunnzpto/JnK5XOO+uXLlisqFtrZ5oLZBJzExEYMHD8ZLL72ExMTERm/h5dlWsdm6MRAlEgm2bNmicjtVTU0NTp48iY4dO3I32tWnzRh+NTU1SEhIUJn0Z+TIkZDJZGrZLVu2YNq0abh16xa6du3a7Nglus6fPXsWQUFByMrKUmm80bS9PFmx+bpJPsRobEyoe/fuCV9GdOjQQeMFnbZ53rLFun//vsayysrKVPZH3diekyZNwueff67xvKIt3n/A6tR/T5qZmakMddCY7du347PPPhMaEtq3b4958+ZhwoQJWtVBWx988AG++OILjeOpPu+8PtVF13l9qgtv3szMDJMnT8bkyZORnZ0tDKGycOFCDB48GAcPHgSg/mXR+PHj1cqaOHGi8HPr1q3x73//G++99x4WLVqkco709/fHV199hdatWwv56dOnY/fu3XBycsLkyZOxa9cu2NraNlrviRMnij638mQbsrGxEa4NG9O5c2euLyOqqqqQlpYGFxcXVFVVIT09XWV4k5KSEuGzreE41XUsLS0xePBgYQiIOrx5oPY62NHRUfTMvrrOy+VyYQzsunECp0+fjunTp2scJ5CnIWzx4sX44osv8NprrzU6xqOtra3GcaebUnd867LxdNCgQVwzMPPsd6lUqjIhWUMSiUQ4Rq2trUVn9S2vT3XRt22tLycnB+vWrROu+zt37owPPvgA7u7uGvOE6BJNUkKe2Y0bNzBy5EhcvHhR+DC8ceMGunbtioMHD6qN4zd16lQ4Oztj8eLFKsuXL1+O69evY/PmzVi6dCm+//57/Prrr8Lz5eXlKCgoUGs4a9hAwRjDuXPnkJSUhKSkJJw6dQrFxcXw8vJCv379EBUVpTJAf92HdEOaGilWrVqFHTt2YNu2bRg8eDAOHz6M69evY/bs2Vi8eDFmzpypUgZPvry8HPPnz8eePXvw4MEDtfpoakjirc/YsWNhZWWFr7/+WhjQu1WrVhg1ahScnZ1VekRNmTIFDx48wJ49e9CiRQucP38eMpkMo0ePRt++fdV61YnJN9Z79Pr167Czs1NpHGw4kQHPtorN1l20X79+HY6OjioNcHUTQkREROCll15S+1vs379f5YN81KhRGseL8/X1BWMMH3zwgcYx/OqPKQnUDiQ/bNgw3Lp1S+gBkp2dDScnJ3z//fdqFwu//PIL3n77bZWJHJpqZNN13tvbG+7u7liwYIHG7a3/TSlPVmy+fk+VBw8eYPny5fD39xcm9khLS8OPP/6IxYsXq00IVFxcjPfffx+7d+8Wtksmk2Hs2LH46quvVCYM4c3zli1W3WDnwcHBGDNmDGbOnKkyWP/MmTNx9epV/PDDD1qVr01dnmWgdjG9bCMjI7F48WLMmDFD6LV16tQpfPXVV1i+fLna31UX9a/Lfvjhh/j555/RsmVLdOnSRa0BvX4PKQB49dVXdZbXZdn6ltenumiTb6impgaHDh3Ctm3bhAbCmzdvcvWSq59/9OgRrl27BsYY2rVrp3H8ZalUCmdn50Y/l8XWXUx9/sgswH+uMTQ0hKenJz7//HMkJCQgLi4Ot2/fFnrHffPNN1i3bh3OnDkjqrxnqfvNmzdhZGSEb775BnFxcbh48WKzPfZ+//13oYefLvIN3b59G19//TVWr14NAwMDPHnyRGWcQLlcLqohrLq6Gu+++y4WL16scfIYTcQ24J06dQovvvgiLC0tRdWljti6A7rd78nJyWCMYeDAgdi7d69KT1kjIyO4uLgInS54svqW16e66Nu21vnxxx8xcuRI+Pj4CNc0qampyMzMxKFDhzB48GC1dQjRJWogJH8IxhiOHTuGy5cvAwA6deoEPz8/jVkrKyucPXtWbSDta9euoXv37igqKsLly5fx4osvoqSkBPfv38ekSZNw5MgRjeU1bKCwsbFBaWkpvL29hVuL+/TpI8ywDNSexOsmAGg4yH1D9RtwGGNYuXIlVq1aJcxuLJfLMXfuXCxbtkzjfhGbf//993HixAksW7YMEyZMwFdffYVbt25h06ZNWL16NQIDA5+pfKC24XbIkCFgjOHq1avo0aMHrl69CltbW5w8eVKlB1JRURHeeOMN/PrrrygpKUHbtm1x584d9O7dG4cPH1a7DVhMvrFvzjRpOGMsz7by7pcBAwZg//79KsdIYy5evIiRI0fizp07QgNeXS/JQ4cOqc3OaG5ujrNnz4q+RXfYsGFgjOGbb74RLi4ePHiA8ePHQyqV4vvvv1fJd+7cGZ06dcL8+fNFNbLpOm9hYYFz5841OlC+tllt8q+//joGDBiAGTNmqCz/8ssvcezYMSQkJKgsHzt2LM6dO4cvvvhCpUHxgw8+gI+PD3bv3q11nrdsser+Ub59+zaGDh2K8ePHIzY2Fu+++y4uXbqEf//730hOTla7PRgAfv31V+zZs0fjFy/aNApo20AokUi4etm6uroiPDxcpfcUUDtbcVhYGPLy8rjrzlv/uqym80l9DW9DnzRpks7yuixb3/L6VBdt8mLw3rLPmzc0NMSoUaPU7h5oSNuhFHjqw1t33nONubk5OnfujF9//RXm5uaIi4vDq6++Kjw/aNAg9OrVCytWrBBV3rPUvWGed2ZfXeWrq6tx4MABbNu2DUePHkWPHj0QEhKCt956C/fv38fHH3+M9PR0XLp0iashzMrKChkZGaIbCHka8IDn13iqq/1+/fp1ODk5iWpg5snqW16f6qLrPG/Z3bp1g7+/v9pknAsXLsRPP/2k1mGCEF2jBkLy3LVu3RqfffaZ2j9327dvx7x583D37l1cunQJ/fr1w/379xEYGIjr169j3bp16N+/P/bv34+7d+9i+fLlWLt2rdo4WN9//z369OnT7K1zvN/61tTUIDU1FV5eXjA1NcW1a9dQWlqKzp07q830p03e2dkZ27dvR//+/WFpaYn09HR4eHggPj4eu3btUhtXjbf8Ok+fPsW3336LzMxMlJaW4oUXXkBgYGCjt/aeOnUK58+fF7KNNfxqmxeDZ1t590t1dTU6duyIxMREYRbupvTu3RutWrVCXFyc0Cvj0aNHCA4Oxv379/Hvf/9bJT9gwAB89NFHoveDmZkZfvnlF7XZYTMzM+Hr6yuM1VY/n5mZKbrRTNf50aNHY8KECcKsbH9UVpu8ubk5MjIyNH4Z4ePjo3Ff/vjjj8LtQnVSUlIwZMgQlJWVaZ3nLVus+v8o5+TkYPXq1Srv7QULFmicaXj37t2YOHEi/P398dNPP+GVV17BlStXcPfuXbz66qtaNQpo20A4YcIErl62xsbGGmdrvXr1Kjw9PVUaGXl07doVR44cafYfUt4sIdr4I3rk/pF5Xfbye17b2rJlS5ibm6sN1/Hw4UOYm5sLDUQ82/pH1L25HnsN/dH5mTNnYteuXcI4gVOmTFH7svPOnTto27atWg/v5hrCgoKC4OPjI7pn97P0ftR1Y6uu/k6PHz/G6dOnNY6t3PB/JZ6svuX1qS76tK3Gxsa4cOGC2uz2V65cgZeXl9bXNIRoTdvZTQipLykpiQUEBDB3d3fm7u7ORowYwU6ePKkxu2zZMmZiYsJmzZrF4uPjWXx8PJs1axYzNTVly5cvZ4wxFhkZyfz8/BhjtbPP/ec//2GM1c4qVjcz8YEDB1RmoeORmZnJTE1N2ffff88yMzObfNQnl8tVZhFsDk/ezMyMXb9+nTHGmIODg7DNubm5zMzM7JnLr6qqYm5ubuzSpUui8s/Lr7/+KhwH6enpjeZ4tpX379S2bVvR+8XY2Jj99ttvassvXLjAjI2N1ZZfu3aN+fn5sdjYWPbrr782eXwxxpiNjY3GmatPnTqlcZbkgIAA9t1334mq+/PI379/nw0bNoyFhYWx7777jh04cEDloW1Wm7yzszNbs2aN2vI1a9YwZ2dnteVOTk7s/PnzasszMzOZg4PDM+V5yxaLd8bmOp6enuzLL79kjP13RkWlUsmmTp3KlixZolVdeGeJrHtd3pmyu3TpwlasWKG2fNmyZaxr165qy11dXdnvv/+utvzRo0fM1dVV62x9d+/eZSdPnmQnT57UOHvh88zrU110ndenumiTb8rzmsFWLG3f37qoiz5tq7Z14Z3ZV5f5gQMHsp07d7InT540Wu/q6mqWlJSk8blbt26xpUuXMrlczszMzJhMJmMvv/wy++2339iyZcuYtbU1e/3119nKlSvZ559/rvJoytmzZ9mMGTNYy5YtWcuWLdnMmTNZRkZGk+s0VRdt8rr+Ox08eJBZWFgwiUTCrKysmLW1tfBoeM3Hk9W3vD7VRd+21dHRke3Zs0dt+bfffsucnJzUlhOia9RASJ5ZfHw8MzAwYG+++abwgT9mzBhmaGjIvvnmG43r7Nixg/Xq1YvZ2NgwGxsb1qtXL5VseXk5q6ioYIzVXqjl5eUxxmr/4T916hRjrLbhzMTERKs6SyQSBoBJJJImH1KpVGW97t27s2PHjol+HZ68p6encPE1aNAg9uGHHzLGGPv8888bbUDgrQ9PQxhjjB07dowNHz6cubm5MTc3NzZ8+HB29OjRPyR/9+5dNmDAACaRSITjQCKRsIEDB7J79+6p5Xm2lXe/rFixggUFBbHq6upms15eXuz48eNqy48fP66xcSItLY25urqqHVeaji/GGJswYQLr0qUL++WXX5hSqWRKpZKlpaX9H3vnHRfF1f3/zy69LEVBo0hVQEEUFCvGHns3FrBgj53YsQP2AmKLJUaKiT3W2Asi9g52EEGwxS5BUSnn9we/nS/D7MLehSH7PM9+Xq95KXffc/fcu+3OmXPPoZo1a5K/v7+A37BhA9na2tLcuXNVcpqJzR84cIDMzc1V+jyxsOrwERERpKOjQ506daJ58+bRvHnzqFOnTqSrq0sREREKx9q6dWt68eIF1/bixQtq06YNrV+/vkQ8a9/Xr1/nORT37dtHXbt2penTp9PXr18FPBFRTk4O7dq1i0JCQigkJIR2796t9D1tbGzMfa+WK1eOe6579+7Rd999p/Cc4sR6obxw4UJ6//49NW/evMjvlcLavXs36ejoUNu2bbmxtm3blnR1dWnPnj0CXiKRKHTWvHz5kvT19dVmiYg+fvxI/fv3J11dXe69qKurS/369aMPHz6UKa9JtmjHWjSvijTNaSYmL7azUtOcmwMGDKDy5ctTuXLlKCAggG7fvi3gXrx4QRKJhIiIc5KJwX/79o0GDx7MdFOVSHVHmIODg9KjqJsucqni8BPLiSfmvMvl7OxMAQEB9OnTp2LngoXVNF6TbBGbZ+07ODiYLCwsaPHixdwNpkWLFpGFhQWFhISo1IdWWpWmtA5CrUqs6tWrU1hYmKA9NDSUqlevXuL+vb296ejRo0RE1LlzZxowYAA9ffqUpk6dSk5OTmr1mZqaSsbGxnT27FlKTU0t8iioI0eOkKenJx08eJCeP39OHz9+5B2FxcKHhYVxd1NPnDhBhoaGZGBgQFKplMLDwxWOg9UeFkfY2rVrSVdXl/r27cs5fn19fUlPT4+LPCoJ37t3b/L29uY5LO/evUve3t7Ut2/fEo2VdV66detGMpmMKlWqRG3atKHu3bvzjoI6dOgQubu7065duyg9PZ3S09Np165d5OHhQYcOHRI8T40aNahHjx506dIlSklJKfL9RZQfqdSlSxeSSCSkr69P+vr6JJVKqVu3bgovNFkc3GXB29vb05gxY+jly5eCx0rCqsMTEV26dIn8/PzIy8uLvLy8yM/Pjy5duqSQ9fT0JFNTU9LT0+OiofX09MjU1JQ7X36w8qx9e3t7c5GbycnJZGhoSL6+vlStWjUKCAgQ2H7nzh1ycnIiY2Njrh8TExNycHBQeJFiY2PDOQU9PDxo69atRER04cIFMjMzUzg/X79+pQcPHij8/ti/fz8tWrRIoRO5OKcya5QtUX7kcb9+/ahOnTpUp04d6tevnyACWf58EomEoqOjeTbs2bOHxowZQy4uLsxsQfXu3ZucnZ3p6NGj3Of+6NGj5OrqSn369ClTXpNs0Y61aF4VaZIDT2xek2xh5dXpu1GjRkwRe6wRfqy8mZkZk4NQHUcYi1gcfmI68cSed6L8m3Wqvn9YWE3jNckWsXnWvvPy8igsLIxsbGy49bWNjQ2Fh4dTXl6eyv1opVVpSesg1KrE0tfXp6SkJEF7UlISGRgYlLj/LVu2cNE+165dIysrK5JKpWRoaEjbt29Xu1/WRV2HDh0EzhH5oYpzRRW+oFJTU+nPP/9UeoGsTv8sjjAbGxtavXq1oI81a9ZQ5cqVBe2svJmZGV25ckXQfvnyZTI3Ny/RWFnnZdCgQUUexfWt6G/58xgbGyv8fBSnxMREOnDgAB04cECt8/8tmZqa0qNHj0qdVYdnVVBQkMoHK8/at5mZGTfWxYsXU5s2bYgof6t5lSpVBLY3bNiQOnfuTO/evePa3r17R126dKFGjRoJeF9fXwoNDSUiopCQELK2tqZhw4aRvb294Lvg06dPNGTIENLR0SEdHR3ue3Ps2LG0aNEiIhI6kgt+Lgp/DguLNcpWVSmzRe58d3FxoYMHDzKzBWVsbExxcXGC9rNnz5KxsXGZ8ppki9i8JtmiDq+KxI6SEzOqjrV/Obt582aVom7S0tIoJyeHmVdVLGNVZx5//PFHpvQwLBF+6kQEDhw4UOGNfmVicYQFBwcrfI0+f/5MwcHBgnZWh59YTryTJ0+KPu9ERN27d6cdO3aUOqtpvCbZIjbP2ndBZWRkUEZGhlrnaqVVaUn3386BqNV/vmxtbXHq1ClBwviTJ09yCdzLlSuHxMREWFlZwdLSUpCEvqDevXvH+7t///7c/+vWrYsnT57gwYMHsLOzg5WVVSmOpGidPXsWf/zxh8IS9coUExOj9vPZ29sLKsQCgIeHBw4fPgxbW1vm/i0sLFQu8PDhwwe0a9dO0N6mTRtMmzatxHxeXh709PQE7Xp6eoKEvgDbXLLOC0tBBta+W7ZsyVTkQy5nZ2dBwuL/BPXo0QMxMTGoWrVqqbLq8ACQnJyMiIgIPH78GOHh4ahQoQKOHDkCOzs7QVLxwpWzixMrzyIi4j4HJ0+eRKdOnQDkf9++efNGwN+6dQvXrl3jCucA+RXdFyxYgHr16gn4NWvWcImvZ86cCT09PVy4cAE9e/bErFmzeOz06dMRHx+PM2fO8D7jrVu3RlBQEAIDA3mf2ZMnT2LatGlYuHAhr2LzrFmzsHDhQoEtQ4YMgZeXF7Zt26awSAkAZGRkcIWnMjIylMxavuSc3CZHR0dcvXq1yN8LFragypcvD3Nzc0G7ubk577UoC16TbBGb1yRb1OFVETHWEBSbZxVL/3I2MDAQAQEB6NWrF4YOHYrGjRsr5OVrS1ZeDKkzj0ePHsXSpUtVYvX09PDnn39i9uzZovBA/nojJCQE58+fR926dWFiYsJ7fPz48dz/s7OzYW9vj4YNG8LAwEBpn7q6umjWrBlatmyJkSNHwtjYmPf458+fERwcjDlz5vDa7927h9WrV6NHjx5K+7eyskJMTAyzLSx8q1at0KNHD1HnHQA6duyIKVOm4N69e/Dw8BCsi7t06aIWq2m8JtmiaWMtqOKqymulVZnoX3ROavVfol9++YX09fVp5MiRFB0dTdHR0fTTTz+RgYEBl1srMjKSu1sXGRlZ5KGuNO3uuVgqKzt8fX1p6dKlgvZly5Yp3DLFynfp0oWaNm1Kz54949qePn1KzZo1o27dupXQes0Raw6/nJwc2rRpE/n6+lKrVq2oRYsWvEORWIoEic3Pnz+frKysyN/fn5YvX15kQnIWVh3+zJkzZGRkRK1btyZ9fX3uc7No0SLq2bOn0vFqglq0aEEDBw6k6Oho0tPT46JIz5w5Q/b29gKeNTcmi+zs7OjixYtExP/+SUpKIplMJuDd3d2VRlMpSjuhSpStVCrlcgMWjkhkicwubYmZt5KV1yRbtGNVzosdJSdWVJ06UX6bNm1isiU7O5v27NlDXbp0IT09PXJ1daXFixfz5rWgWHlVJZPJaMmSJaLN+4ABA5gi9lgj/Fh51jyBLFuSJRKJwrzSp06dIisrK16bOlF4rNujWXix552ILY2LpqWU+V+xXYyxenl5cTs+PD09BalmCqed0UqrspSESORbiFr9T2jv3r0IDQ3F/fv3AQA1atTAlClT0LVr1zKzQSaTIT4+Hk5OTqLylpaW+O2337ixurm5YfDgwShXrpzC896/f8/Es9qtTv+vXr3Cw4cPAQCurq6oUKECAGDVqlUck5GRgeXLl8PHx4eLArp06RLOnz+PSZMmYdasWcx8QaWnp6NLly64e/cud3c/PT0dNWvWxIEDB1ClShWB3SxjZZ2X3bt3Y+fOnUhLS8O3b994j924cYP395cvX5CQkIBXr14Joh0L3xmUSqUKnw8AJBIJcnNzeW1jx45FZGQkOnbsiEqVKgmiqVasWMH7+/fff8fgwYPRo0cP+Pj4AADOnz+PvXv3IjIyEn5+fmXKOzo6Fjnex48fq8Wqwzdq1Ai9evXCxIkTeZ+bK1euoEePHnj69CmPz83NxYoVK5S+DwpHN7PwrH3Hx8ejX79+SE9Px8SJE7loxXHjxuHt27fYunUrL5Lu3LlzmDp1KoKCgtCwYUMA+Z+/kJAQLF68GB06dOD1f/jwYejo6KBt27a89uPHjyM3Nxft27fn2oyNjXHnzh04OTnx5jE+Ph5NmzbFx48feX0YGRnh6tWrqFmzJq89ISEBDRo0QFZWFq+9c+fOGDRoUJHRzbGxsfDx8YGuri5iY2OVcgDQrFkzQdupU6dw6tQphZ/ZzZs3q816eXnh0aNH+Pr1K+zs7AAAaWlpMDAwEEQA37hxQ1SeiDTGFu1YlfPPnj1DVlZWsVFvclWsWFFUXlXJP/uNGjVSuf+S2PL333/j999/R1RUFB48eIB27dph6NCh6Ny5s8LfVVa+uLEaGBjg27dvosz7/PnzERoailatWhUbsVcWPKv8/f3h6emJCRMmKGXku4U+fvwIMzMz3lomNzcXmZmZGDlyJNauXcs7z9zcHLdu3Sry957VFnV5TZt3rf57FBwcjClTpsDY2BhBQUFF7qwTc7eKVlopktZBqNW/ory8PDx69EjhBVjTpk3V6lOZwy8jIwOnT5+Gq6sratSowbWnp6ejcuXK0NHRUbn/X3/9FT/99BPMzc3h7e0NALh+/To+fPiAgwcPCmw/e/YsOnfurDLPOk7W/jMyMjBmzBhs376dc0zp6OigT58+WLt2LTw9PVWyQe6MUXUBp8h5A+Rf5J08eRIPHjwAkO9Ybt26tcI+WMbKOi+rVq3CzJkzMWjQIGzcuBGDBw9GcnIyrl69ijFjxmDBggUce/ToUQwcOFDhNk9FDj9WWVlZITo6WuDQUaYaNWpgxIgRgsVuWFgYfv31V85BWla8JsnU1BS3b9+Go6Mj73OTmpqK6tWrc1ts5ZozZw42bdrEObRnzpyJ1NRU7Nu3D3PmzBEs7ll41r6V6cuXL9DR0YGenh6kUilvUSn/OZe3Ffy78PuyVq1aCh2HR48exbRp0xAfH8+1NW3aFL169cK4ceMgk8mQkJAAR0dHjBs3DklJSTh69Civj6ZNm8LQ0BBbtmxBxYoVAeRfvA8cOBBfvnwROPg2btyI+fPnY8iQISptx0lLS4Otra1gQU1ESE9P5xw0cgUHByMkJATe3t4Kne579+5Vi5Xzqmru3Lmi8iwS2xbtWJVr5syZOHjwICIjI3HkyBE4OTlh8ODB8Pf3x3fffSfgc3JyROUjIiLQp08fwRbQwpKvmYhI5f5ZbSmsy5cvY/PmzYiKikKlSpXw/v17WFpaIiIiAs2bN2fmWcZaoUIFHD58WJR5F/vmGCsfEhKCyZMnC+YlKysLy5YtE2wDVsURFhUVBSLCkCFDEB4eztuCr6+vDwcHB+5mckGxOvzEdOKJPe9aaaWVVpoorYNQq1LTtWvXeNFadevWVchdunQJfn5+ePLkiSB3S0kcLHIHQGBgIJo2bYqxY8ciKysLtWvXRmpqKogI27dvVzkHn6L+K1eujGbNmmHdunWcYzE3NxejR4/GhQsXcPv2bd45Hh4eaNSokco8yzidnJyY++/Tpw9u3ryJ1atX83KDBQQEwNPTE9u3b2e2p6zEMlbWealevTrmzp0LX19f3vzOmTMH7969w5o1azjW2dkZbdq0wZw5czjnh6r68uULDA0Ni2QqV66MM2fOwMXFRaU+DQwMcPfuXUGOw0ePHqFmzZoCJ5jYfEEVdlgVJRZWVb5KlSrYuXMnGjduzHtd9+7di8mTJyM5OZnHV61aFatWrULHjh0hk8lw69Ytru3SpUvYunWr2jxr3/7+/hg6dGiRNxGKi6QrqMJRdUZGRrh//z4cHBx47ampqXB3d8enT5+4tnPnzqF9+/bo378/IiMj8dNPP+HevXu4cOECYmNjBd/1jx49Qvfu3ZGYmMiLDnZ2dsa+ffsE7yXWKFsdHR28ePGCi3yW6+3bt6hQoYKAr1SpEpYuXYoBAwYofR51WBZt27YNXbp0EVyM/hu8JtkiNq9JtijixY6SU4Uvqyg/Vdm///4bW7Zs4XLHduvWDUOHDkXr1q3x6dMnhISEYPv27Xjy5Akzr+5Y/81oxrIQ63cqiyMsNjYWjRs3VphzWpFYHX7/6U68kJCQIh8v6JxlYTWN1yRbxOZZ+3ZycsLVq1dRvnx5XvuHDx9Qp04drWNZq7JXGW9p1uq/UOnp6dSkSROSSCRkaWlJlpaWJJFIyMfHh9LT0wV87dq1qVevXnTv3j16//49ffjwgXeoK3lurIoVK9KtW7eIiOiPP/6gatWq0adPn+iXX34hT0/PEvVvYGBADx48EDz24MEDMjQ0FLQbGhoy8araIc8Bxtq/GJUWSyKW3HYsY2WdFyMjI0pNTSUiImtra+79k5iYSOXKleOxMpmMqZJuTk4OhYSEUOXKlXkVYGfNmkWbNm0S8MuXL6fRo0dTXl6eSv1XrVpVYU6sdevWUbVq1cqcJyKKioqimjVrkoGBARkYGJCHhwdFR0eXmGXlJ02aRE2aNKEXL16QTCajpKQkOnfuHDk5OXHVggvK2NiYnjx5QkRE3333HV2/fp2IiJKTk8nMzKxEPGvfXbt2JT09PapWrRotWLCAnj59qnRO1FHFihUV5iw8ceIEWVtbC9ofPXpEw4YNo3r16lGNGjWoX79+lJCQoLT/vLw8OnbsGJcf8vjx4yq/p4uTspxWqampCr/HypUrp/JnloVlkSZVpNUkW8TmNckWZfylS5doxIgRZGBgQA4ODmRubk4ODg4UExOjsI/S5kuax4/FnuLYTp06kZ6eHrm7u9OKFSvo7du3gj7+/vtvroItK1+SsZbmvLNW9hWbZ8kTqI5yc3Pp4cOHFBcXR7GxsbyjsFjzIYopseedKD//XMHD3d2djI2NyczMTJB/joXVNF6TbNG0sUokEi7HckG9fPmS9PT0BO1aaSW2tA5CrUqstm3bUoMGDXgOmQcPHlCjRo2obdu2Al6VhPTqSL7wNjQ0pLS0NCIiGjBgAE2bNo2IiJ48eUImJiZExE8Oq+wHvbAWLlxI9evXp7179woe27t3LzVo0EDQ3rhxYyZeFRV0ELL2b2trq/CiPj4+nmxsbHhteXl5tHPnTho1ahT17NmTunfvzjsKi5XfsmUL6erqUu/evTknQq9evUhPT4/++OMPAc8yVtZ5cXR0pBs3bhARUd26dTmH2LFjx8jS0pLHDh48WKFjT5mCg4PJycmJfv/9dzIyMuJeu+3bt1PDhg2JiARzZW5uTo6OjtSpU6di51GVIkFlyYeGhpKxsTFNnTqVK8QyZcoUMjY2FiTvZmHV4b9+/UrDhg0jXV1dkkgkpKenR1KplPr3768wOb+LiwtdunSJiIh8fHxo0aJF3GulyGnGwrP2TUT06tUrCg0NpVq1apGuri61a9eOdu7cSd++fVPIZ2Vl0eXLl+ngwYPFFsMZMWIEeXh48JxhSUlJVKtWLRo6dKjC/tVRVlZWqTkGy5cvT8OGDSOpVEo//fQTTZgwgTvGjx9PDRo0oMaNGwvOmzp1KoWEhKj0HCwsi1iLS4nJa5ItYvOaZEtB/uXLl7Rs2TJyc3MjQ0ND6tu3L504cYKIiDIzM2nq1KlkZ2fHnSc2X/C85cuXk4eHB+np6VHnzp1p3759lJubK+BU7Z+FHTJkCF24cKHIOczLy+Nu6LHyrGMVa94LFl0qqDdv3igskCAWb2FhQZaWliSVSrn/yw8zMzOSSqU0evRoQT8sjrCLFy+So6MjV0iquGIQrBLTiSf266RMHz9+pO7duxd5s1QdVtN4TbJFbF4RK1+fSSQSio6O5q3Z9uzZQ2PGjCEXFxeVbNFKq9KU1kGoVYllaGjIOVcK6tq1a2RkZCRob9GiBR05cqTU7ZAvvJ2dnWnHjh2UmZlJ1tbWXJTMrVu3qHz58pzN8uhGZT/oirR9+3ays7OjZcuWUVxcHMXFxdGyZcvIwcGBtm/fTvHx8dzBykdFRXGVngvq69evFBUVxf39xx9/UGZmplr2sFRaHD9+PBkYGFC7du3I39+fBg0axDsKi5WvXr26UieQokqnLGNlnZehQ4dyEWVr1qzhKt9aWFjQkCFDeHZ8+vSJOnTooHIl3apVq9LJkyeJiH8xef/+fbKwsCAiEsxVUYci7dmzh3x8fKhcuXJUrlw58vHxoX379ilkxeYdHBx471e5IiMjycHBQW1WHV6utLQ0OnToEO3YsYMSExOVctOmTaMFCxYQUf77TVdXl6pVq0b6+vrcjQZ1eda+C+v69es0duxYMjQ0JCsrK/r55595Yzly5AhZW1urXJHvw4cP1LBhQ9LV1eWiM3R1dalFixb0/v17+vjxo8pHYeXm5jJFzaoqHR0datCgAUkkEmrcuDE1b96cO9q0aUMjRoxQ+PqOHz+eLCwsqGnTpjR27FieY3HChAlqsyzSJEeVJtkiNq9Jtsj5li1biholx8oXVmlG+bGw3759o5YtWxb5HV1QrDzrWMWcd9aIPbH4yMhIioiIIIlEQitXrqTIyEju2Lp1q1LnK4sjjHXXEKvDT0wnntivU1FKSEgge3v7Umc1jdckW8TmC7MF12iF1236+vrk4uJCBw8eVNkWrbQqLWkdhFqVWM7OznT58mVB++XLl6lq1aqC9j179pCbmxtFRETQtWvXeM4aucOmoFRdLMTFxdGXL19o7dq1pKurSxYWFlS7dm3ubvCqVauoefPmRETUsGFDat26NQUFBZFEIqEpU6ZQcHCwwqOgiipdX/BLXr7IYOHVufPIao+npyeZmpqSnp4et61XT0+PTE1NycvLi3dYWlrSoUOHFD6vIrHy+vr6CiNJk5KSyMDAoERjZZ2X3Nxcys7O5p5r27ZtNG7cOFq1ahV9/fqVZ8emTZtIV1eXTE1Nyd7evtjtL4aGhlz0QsGLybt373IRreoqOzubgoODFW7l/zd4IiIDAwOFr2tiYqLgdWVh1eHV2e5TUBcvXqTQ0FA6cOBAsSwrz8I+f/6cFi9eTK6urmRiYkIDBw6kVq1aka6uLudkr1atGo0ePZpevnypkq1E/7cNeOnSpbR69Wredq+C30vKDmXOR1WiZtWR/PMzaNAghY5JZSroSCx8tGjRQm1WHds1gdckW8TmNckWOd+rVy9Ro+TUiaoTK8qP1RYrKysmhx8rT6T6WMWYdwsLCzI3N1c5Yo81wk/diMAzZ84ojUxXJBZHGOuuIda1sBhOvLKa96IUFxfH3UQuTVbTeE2yRWxeGevg4ECvX79W+Tm10kps6f7bORC1+s/XsmXLMG7cOKxdu5arGHvt2jUEBARg+fLlAl5eJGTIkCFcm0QiAREpTEgfHByMkSNHCqqrff78GcHBwVyy1yZNmgAARo8ejfr16yM9PR0//PADlxDayckJ8+fPBwBERkZi7ty5+OuvvyCRSHDkyBHo6go/DhKJhJdMNiUlhWluWHhHR0eFBReePn3Kq/6mbv8A0K1bN5XZyMhIQUXoomRubs7E29ra4tSpU4KCBSdPnuQKGxQU61hZ9PTpU95z9u3bF3379lVYFXXmzJkIDg5GYGCgSsnG3dzcEBcXB3t7e1777t274eXlpfS8V69e4eHDhwAAV1dXQfJwANDV1cXSpUsxcODAYu0oCx4AqlWrhp07d2LGjBm89h07dsDZ2VltVh1e1e8OZWrYsCEaNmwoaO/YsSM2bdqESpUqqc0Xx1pZWeHAgQOIiIjA8ePHUatWLfz888/w8/ODmZkZgPyKukOGDMGECRPw999/Y+LEiUyFcyQSCdq0aYM2bdoIHouJiVG5n8KKjo7Gxo0b0apVK4wcOZJrr127NlexvCQKDw9HTk6OoP3du3fQ1dXl5kculrGUZNxaaVWciAhPnz6FlZVVkZxEIoG9vT2ys7ORmpoqGg8AnTt3xrFjx+Di4oLhw4dj4MCBKFeuHMeamJhg0qRJWLZsGVP/lStXZralf//++O2337B48eIiz5GLlVd1rEuXLhVl3sPDw7nKvsHBwcVW9hWbl6tZs2bIy8tDYmIiXr16hby8PN7j8mJZlpaWkEgkkEgkcHFx4a1Zc3NzkZmZyfvOB4AGDRrg0aNHgrWeMsmvBQorPj6e91qx2sLCl9W8A8CqVasE43/x4gW2bNmC9u3bq81qGq9JtmjaWMW8vtFKK3WkdRBqpZbkP7Ryffr0CQ0aNOCcbDk5OdDV1cWQIUMETinWL0JVFwsF5e3tzTkr5erYsSP3f1dXV65ir1QqxalTpxQ6YAqrsJNHmeQX+arwXl5eePz4MSQSCVq1asVzVObm5iIlJQXt2rUrFXvmzp2rEr9t2zZMnz4dwcHB2Lx5M4yMjIo9JygoiImfNGkSxo8fj1u3bnGVBM+fP4/IyEisXLlSwLOOtbDzpijW0dFRYQW/d+/ewdHRkee0/vbtG/r06aNyJcI5c+bA398fz549Q15eHvbs2YOHDx8iOjoaf/31l4DPyMjAmDFjsH37du55dXR00KdPH6xdu1bgLG7VqhViY2MF1WiVSWw+ODgYffr0wdmzZ+Hj4wMg/3U9deoUdu7cqTarDq/Od4cqOnv2LLKyskTh5WylSpWQl5cHX19fXLlyBZ6engK2RYsWsLCwAAD8+OOPOHPmDKpWraq071WrVmHEiBEwNDQULGALq3ClSBY9e/ZM4cVgXl4esrOz1e5Xrr59+6Jz584YPXo0r33nzp04cOAADh8+XOLn0EorMSSRSLgbP6pIT08PCQkJovEAUKFCBcTGxip1YACAtbU1UlJSmPpXx5acnBxs3rwZJ0+eVFjBNiwsrES8qmNNTU0VrCGLkqpj9ff3B5B/Q1iVyr5i83JdunQJfn5+ePLkCYiI91jBG/fqOMLGjRuHSZMm4eXLl/Dw8BDYVKtWLQDsDj8xnXhlNe8AsGLFCt7fUqkU1tbW8Pf3x/Tp09VmNY3XJFs0baysVY+10kpsaR2EWqml8PBwtc9V1dGjp6cHU1NTpjuV8sciIyNx6tQphXdCT58+zfu78OOlIRaHQLdu3bBw4UIQEdq2bQtTU1PuMfmiRR51WRb2AMBPP/2ES5cuYc+ePahQoQIcHBwEC54bN27w/u7duze2bdumMj9q1Ch89913CA0N5Zw7NWrUwI4dO9C1a1eW4fGkjjNGmSMpMzMThoaGvDZ/f3/s2LFDEMWmTF27dsXBgwcREhICExMTzJkzB3Xq1MHBgwfxww8/CPjhw4fj5s2b+Ouvv7jF6sWLFxEQEICffvqJc2zL1b59ewQGBuL27dsKL5C6dOlSpnzPnj1x5coVhIWFYd++fQDyX9crV64IIiZZWBZenSgHTdOKFSvQq1cvwfuvoCwsLLgbLmvWrEGvXr0QFxen8CJs/PjxWLFiBfr16wdDQ0PBAragJBKJwEH4/v17/Pbbb7h//z6A/MjYwYMHK3S0qhs1q6ouX74suPAHgObNm2PmzJmC9hYtWij8fMtV8DeBhWWRvb0900WjmLwm2SI2r0m2yPmGDRuKGiXHwqsTccjSP6vtd+7cQZ06dQAAiYmJpcqzjlXMeVc1Yq+s+JEjR8Lb2xuHDh1CpUqVlH4HquMIU3XXEKvDryyceGLPO8AWNCHmLiaxeU2yRWyete+9e/fy/s7OzkZKSgp0dXVRtWpVrYNQqzKXhArfKtJKK5G0ePFijBw5kot4KU6GhoYICQlBYGAgwsPDVQ7ZHzt2LCIjI9GxY0eFCx1FF8bJyckIDw/nXfwGBAQUGY1TlGQyGeLj41XeciuTyTBnzhyMHz8eBgYGaj1nadvTtGlTXLlyBT/++CMqVqwomMfC0Yi9e/dGTEyMyrxYYhmrvr4+BgwYgMjISAwfPpy3FTU3NxeXL1+Gjo4Ozp8/z7WPHz8e0dHRqF27NmrVqiVYZCpyXKiibdu2oUuXLqhQoQKOHTvGbZmXKy4uDu3atcOnT5947UVFMirasi8mn52djZ9++gmzZ8+Go6Oj0vNYWVY+KiqKu9Bg+e5QVep8nlTlWfuW67fffsPIkSNhaGiI8uXL8z5/EokEjx8/xsePH5WmKyhKZ8+eRefOnWFubs5F1Vy/fh0fPnzAwYMHBRc9+/fv5+6Uh4SEIDg4mBc1q8gxrorkc+Ph4YFLly7Bw8OD9/jt27fRoEEDfP78mdc+YcIE3t/Z2dm4desW7ty5A39/f17EMgsr14cPH7B7924kJydjypQpKFeuHG7cuIGKFSvCxsamTHlNskU7VsX8uHHjEB0dDWdnZ5Wi3sTmra2tceHCBYVpGhSJpX9WW8QWy1jFnHdVI/bKijcxMUF8fLzK24CB/Jvrjx49KtYR9uTJkyL7KXwjKTY2ljkKT1VbWHmx572wnj59CgCoUqVKkRwrq2m8JtkiNs/at1wZGRkYNGgQunfvjgEDBjCdq5VWJZaYCQ610qqgZDKZWsm/z5w5wysgUZzKly/PVCzj6NGjpK+vT/Xr1+cqVdavX58MDAzo+PHjKvdTUOokLo+Li+MVg7h8+TIFBATQhg0b1LKhpPYYGRlRXFycyucYGxsz8XJdvXqVoqOjKTo6mq5du8Z8fmGxjFWdqqhiFTGQfz5sbW0pISFB8Hh8fDzZ2Nio3X9ZyczMjB4/flzqrDo863eHqiqrogpXr16lKVOmUJ8+fah79+68o7AqVqxICxYs4IoyKVLB5O/yasWqqGbNmjR8+HDKycnh2nJycmjEiBFUs2ZNheecPXuWWrduTdbW1mRkZEQ+Pj507NgxhSxrBffmzZvT2LFjBfzo0aOpSZMmKo2JiGju3Lk0adKkErHx8fFkbW1N1apVI11dXe61mzlzJg0YMKBMeU2yRTtW5XxRvyHyQmplyf/8888qVVJXp39WWwYPHkwZGRmC9szMTBo8eHCJeZaxijnvrJV9xeZbtGhBR44cUWleiPKLbDk6OiosCqesqB6LcnNz6eHDhxQXF0exsbG8o6S2sPBiz7t8rMHBwVwxE6lUSubm5hQSEiL4PWdhNY3XJFs0bazKxFpRWSutSktaB6FWZSZ1L6qvX7/Oc5js27ePunbtStOnTxdUmCUiqlSpEj18+FDl5/H09FS4YJw2bRp5eXmp3I8i21l4b29vio6OJiKiFy9ekEwmo0aNGpGVlZVKFVdL2x4nJyeFVaWVydXVlYlPT0+nJk2akEQi4aq9SSQS8vHxYaqaW1jqOGNYq6KKIbktGzZsoNatW9OLFy+4x168eEFt2rSh9evXE1F+xWh5xTNlF0gFJTZfUAMHDuQq65Ymqw5PRPTo0SOaOXMm9e3bl3OOHT58mO7cucPUT0GVhYNw27ZtpKenR506dSJ9fX3q1KkTubi4kLm5OQ0aNEhwnqWlJT169KjIvs3MzOjevXtEpLySoyIZGhrSgwcPBO0PHjwgQ0NDlfooSqxVK8+dO0eGhob0/fffU1BQEAUFBdH3339PhoaGdPbsWZWfNykpiSwtLUvEtmrViqZMmUJE/Nf5/PnzChf2YvKaZIt2rEXzmqSxY8eSmZkZ1a1bl0aMGMHdKJUfZSll3wWvX78mHR2dEvOaMlbWyr5i83v27CE3NzeKiIiga9euUXx8PO8oLBZHWFRUVJFHYbE6/MR04ok970REgYGBZG1tTb/88gs332vXriVra2uaMWOG2qym8Zpki6aNVZlYKyprpVVpSesg1KrMpO5Ftbe3N+3evZuIiJKTk8nAwIB8fX2pWrVqFBAQIDhv+fLlNHr0aMrLy1PpeQwMDARRYkREDx8+JAMDA5XtVWQ7C29mZsZdhK9cuZIaN25MRETHjh0jR0dHtewoiT2bNm2itm3bUkpKikrn/PXXX0x827ZtqUGDBjzHw4MHD6hRo0bUtm1blW0tLHWjtQrq48ePtHfvXrp//36R56enp5fImanIFk9PTzI1NSU9PT2qWrUqVa1alfT09MjU1JS8vLzIy8uLpFIpubm5EVH+BVJxjh4TExNunGLwBTVv3jyysLCgnj170sKFC2nlypW8Q11WHf7MmTNkZGRErVu3Jn19fW5MixYtop49e6o8psIqCwehh4cHrVmzhteWl5dHw4cPpzlz5gjO+/nnn2nBggVF9t2jRw+qWLEiNW/enHPGt2jRQuFRUI0bN6a9e/cK+tu7dy81aNBApXEVJWXOylu3bil14N28eZN8fX3Jzc2N6tatS4MHD1b4PV6UoqOjqVKlSiVizczMOMdswdc5NTVV4e+HmLwm2aIdq3Je7Cg5Vl7MKD9V2Y8fP9KHDx9IIpHQo0eP6OPHj9zx7t07ioqK4n3+WHl1xirmvLNG7InNF3bEyZ1xypxyLI4wCwsL3mFiYkISiYQMDAwUfr+zOvzEdOKJPe9E+UEN+/fvF7Tv27ePKleurDarabwm2SI2z9p34bVseHg4TZs2jSpXrky+vr4CXiutxJa2SIlWGq/ExESugueuXbvQrFkzbN26FefPn0ffvn0RHh6OHj168M45ffo0jhw5And3d0Eekz179vD+tra2xq1btwQ5aW7duqVSZePSUk5ODpd/8OTJk1zxh+rVq+PFixdlZodcEydOxJcvX1C1alUYGxsL5vHdu3e8v/v374/Pnz+rzMfGxuLChQtwdXXl2lxdXbF69Wp8//33pTyaotW7d280bdoUY8eORVZWFry9vZGamgoiwvbt23lFYvLy8jB//nyEhoYiMzMTQH5utEmTJmHmzJkqVzdWpsJVvxXp48ePePPmDQYPHgwiwvjx45VWjt68eTMaNWqEbt26oW7duqLwBfXbb7/BwsIC169fx/Xr13mPFS5+wcKqwwcGBmL+/PmYOHEiZDIZ196yZUusWbMGAFCnTh2cOnUKlpaWCAkJweTJk3m5KAurTp06mDBhAsqVK1csL+97xowZ2LRpE2bNmlVk3wAwY8YMlCtXDsnJyVzldX19fXz69AkSiQQTJkxAy5YtERwczDsvNzcXS5cuxbFjx5Tmxvz9998RFRWF5ORkxMbGwt3dXak9BStyjh8/HgEBAXj06BEaNmwIID/X0tq1a7mE/IUr2xcl+XeBl5cXV0yGtYK7p6cntm7dqtLzFf59ICK8ePEC165dw+zZs9VmAcDAwAAZGRmC9sTERFhbW5cpr0m2iM1rki2sfFRUFBYvXsz7TgKArKwsREdHC75TxeZjYmIEdhcllv5VZS0sLHiFpQpLIpHwvvNYeXXGKua8q1rZt6x41qIKDRo0wKNHj1TKWfj+/XtBW1JSEkaNGoUpU6YofGz37t0q50NksYWVF3vegfzfw+rVqwvaq1evLlg3s7CaxmuSLWLzrH2zVj3WSiuxpXUQaqXxIiIuifDJkyfRqVMnAICtrS3evHkDAILE+927d1e5/+HDh2PEiBF4/PgxGjduDAA4f/48lixZgokTJ4rqQJBrxowZ+PPPP7F+/Xp07NgRJ06cwLx58wAAz58/R/ny5Xn9s9gj719RtVFlsre3x5AhQ3jPW5xYK1vb2toiOztb0J6bm4vKlSsDYBtrSZwxZ8+e5aqf7t27F0SEDx8+ICoqCvPnz+c5CGfOnMlVKvTx8QEAnDt3DkFBQfjy5QsWLFjANA+FpUoxl5EjR2L48OH48OEDJBIJPn78iC9fvijlf//9d6xYsQLJycmi8BkZGTAzMwNQ/IUGC6sOX1C3b99W6ESqUKEC991x//59fPr0CZaWlggODsbIkSOLfN/cv38fBw4cgIWFRbG8vO/p06dDR0cHP//8c7HvSfli0NLSEv/88w8AwMbGBnfu3IGHhwc+fPggKMIhH6u8QvCdO3d4j8kdd0ZGRlz15mvXrmHJkiVKi0ZJpVKu0qRcU6dOFXB+fn7o06cP7/P/9u1bzJ8/H23btuVV4j527BjPySZ3ht+6dYu5gntycjIiIiLw+PFjhIeHo0KFCjhy5Ajs7Ozg7u7OYwv/PkilUri6uiIkJARt2rRRmwXyq3iHhIRwldglEgnS0tIwbdo0hbaLyWuSLdqxCvmMjAxQ/s4d/PPPP7wK5bm5uTh8+DDvpqTYvFxDhgzBypUrBY6tT58+Ydy4cZxji6V/VltiYmJARGjZsiX+/PNP3npFX18f9vb23LpAHZ5lrGUx76pW9i0rvnChkOKkjiOsoJydnbF48WL0798fDx484D3G6vAT04kn9rwDQO3atbFmzRqsWrWK175mzRrUrl1bbVbTeE2yRdPGyrq21Uor0VUWYYpaaeXl5cVtWwwODqZPnz4Ve87ChQvp/fv31KJFCxo4cCBFR0eTnp4etzXgzJkzpZLbJy8vj8LCwsjGxobbWmFjY0Ph4eGUl5dHhoaG3DZSZfluCoqVlysmJoYsLCxIKpXytqNMnz6dV5RA3f6JiBwdHenNmzeC9vfv35d4GzOr9u3bR/Xr16erV69ybVevXqWGDRty2xlZxlqSeTE0NKS0tDQiIhowYACXk/LJkydkYmLCY1m3Dqgq1m2r8qImDg4OCl9TZRKDZyl+wVooQ93CGkRENjY2dP78eSLiz++ePXvIycmJiIgaNmxIrVu3pqCgIJJIJDRlyhQKDg5WeLDyrH0XlK+vL4WGhhIRUUhICFlbW9OwYcPI3t5eYZGS0lZqaqrKR2H16NGDVq9eLWhfvXo1de3aVdAeGRlJWVlZKtsm1tZxdfThwwdq3bo1WVhYkI6ODtna2pKenh41bdqUMjMzy5TXJFu0YxXy8u2ayg4dHR2aP38+16fYvFyq5vFj6V9dW1JTU5mS+LPyqoy1LOad9TtVbJ41TyDrlmRFunnzJslkMkE7az5EVltYeLHnnSj/98zExIRq1KhBQ4YMoSFDhlCNGjXI1NRUkFOXhdU0XpNs0bSxaqWVpklCVKgOu1ZaiSAjIyM0bNgQW7duRZUqVfDixQuVt+8mJCSgX79+SEtLw8SJE7kIq3HjxuHt27eCCKGWLVtiz549gsiYjIwMdOvWDadPn1b6XPKInYJ3lxs1agRTU1PY2dkhIiICkydP5kW6FNScOXM4vkmTJggODi6WL6jc3FxkZGTA0tKSa0tNTYWxsTE3XyXpXyqV4uXLl4K5//vvv2FnZ4evX79ybWlpaQr7lMvOzo73typ84W2Inz59Qk5ODre1UP5/ExMTvHv3jmmsR44cUXteXFxcMH/+fHTs2BGOjo7Yvn07WrZsifj4eLRq1YqLNgMAQ0NDJCQkCLY1PXz4EJ6ensjKyipyHpSpZs2aOHLkCGxtbVXiZTIZ4uPj4eTkpBLv4eGBw4cPq9w/C29ubo4KFSrg9OnTsLe3x99//61wG56cvXTpEmrUqAGpVFokqw5fUJMnT8bly5exa9cuuLi44MaNG/j7778xcOBADBw4EHPnzsXDhw8xd+5cJCcn48aNGw3F7F4AAIgxSURBVHBzc+NtdZVLIpHgxo0bTPy2bduY+i6od+/e4cuXL6hcuTLy8vKwdOlSXLhwAc7Ozpg1axbvO0JVTZw4EfPmzYOJiQkmTpxYJBsWFsbcv1ympqa4deuWIALk0aNH8PT05Lbmq6tGjRqhV69e3NZx+efgypUr6NGjB54+farwvOvXr+P+/fsAAHd3dy7isqQskB9FnJCQgMzMTNSpUwetW7f+13hNskVsXpNsKY6PjY1linoTm5dHvllaWiIpKYn3vZqbm4uDBw8iMDAQz58/Z+6f1ZaC+vDhA65cuYJXr15xu0fkGjhwoFo8y1jFnndNVOHfk+zsbHz+/Bn6+vowNjYWbIt88uRJkf0VjEg8cOAA7zH6/2kb1qxZA1tbWxw5coT3uKI0LUVF4bHYog5fFnr27Bl++eUXLpqyRo0aGD16tML3DQurabwm2fJvj7VwOpOiVDg1llZaiS2tg1CrUtOrV68ULtBq1apVIqeWMn358gU6OjqC7QHKnGCvXr2CjY2Nwm2tRUnuENi1axcAlKoDobBTICcnB2fOnEFycjL8/Pwgk8nw/PlzmJmZcfOlTv/yBVq3bt0QFRXF20aXm5uLU6dO4cSJE3j48CHXLt9iqEyFF2mq8FFRUUofLyx/f/8yc8b88ssvCAgIgKmpKezt7XHjxg1IpVKsXr0ae/bs4eUtatCgARo0aCDYOjBu3DhcvXoVly5d4rWnp6dDIpGgSpUqAIArV65g69atcHNzw4gRI1Sej8JidRCKyffs2RN79+5F/fr1ceXKFTRu3Bj6+voKWUtLS5w/fx41atRAbGxskezp06fRs2dPJr6gvn37hjFjxiAyMhK5ubnQ1dVFbm4u/Pz8EBkZCR0dHR6v7LtDmVh41r5Z1aJFiyI/f6dPn0aLFi2wd+9eWFhYoHnz5kp5iUSi8EbKvXv3kJaWhm/fvvHa5flS5bK3t8f48eMxadIkXntoaChWrVqFJ0+eoFy5ckhMTISVlVWx+QsLX5yampri9u3bcHR05L1PU1NTUb16dcF2+FevXqFv3744c+YMd+Pow4cPaNGiBbZv385zFrCwQP7nW1Wnu9i8JtkiNq9JtrDyT548ga2trcr5asXii/vNlufxk6ffUMceVtsPHjyIfv36ITMzE2ZmZjz7JBKJ4LtAVV6dsYr5OkVHRxf5eGFHqNi8IhXME9i2bdtieWUqPB8SiQTW1tZo2bIlQkNDUalSJd7jmuTA+zfmXav/DQ0ePFhlNiIiQkRLtNJKKK2DUKsS6/r16/D398f9+/e5fFWF7/aVxGlW8HnkER1ubm6oU6cO73F5Un1PT0+cPn2adxc3NzcXR48exYYNG5CamqrWOGUyGT59+iSaA+HJkydo164d0tLS8PXrVyQmJsLJyQkBAQH4+vUr1q9fr3b/8gVa4ZxiAKCnpwcHBweEhoZy+R0BID4+nsdlZ2fj5s2bCAsLw4IFCwR3v1h5VbV48WKMHDkS5cqVE9UZc/36daSlpeGHH37gnLGHDh2ChYUFl2sQyI8W6NixI+zs7Hj51dLT03H48GFBgZXvv/8eI0aMwIABA/Dy5Uu4urrC3d0dSUlJGDdunMoO8cLSJAdhVlYWLC0tMWDAAPz2228YPny40lx7Cxcu5AplhIaGFsmuWLECWVlZTLwipaWl4c6dO8jMzISXl5egIJGmSFGRA2WS52WUa8KECby/s7OzcevWLdy5cwf+/v5YuXKl2nY9fvwY3bt3x+3bt3nfIfIL7sI3CyIjIzFs2DC0b98eDRo0AABcvnwZR48exa+//opBgwYhKioKffv2hYGBASIjI4u8ePf39+f9XaVKFezcuRONGzfmvU/37t2LyZMnIzk5mcf36dMHjx8/RnR0NGrUqAEg39np7++PatWqYdu2bWqxAKCjo4MmTZqgf//++PHHH4uN7BST1yRbxOY1yRZ1eDGi5Fj5soryY2FdXFzQoUMHLFy4sNh8rSy8umMV63VijdgTm1ema9euKcwTqEmOMDGdeGUx7xERETA1NUWvXr147bt27cLnz595v38srKbxmmSLpo1VK600TmW4nVmr/1LVqlWLunfvTpcuXaKUlJRic25IJBKm/HB///03NW/enCQSCVlaWpKlpSVJJBJq2bIlvXr1itevPOeLohwjxsbG9Ntvv6k9TtYccazq2rUr9e/fn75+/cp7rpiYGKpWrVqpPIeDgwO9fv26RH389ddf1KxZM9H4wpLn2tMEyW15+vQpzZgxg3r06EE9evSgmTNn0rNnzxSeY2FhQQ8ePCAiopUrV1Ljxo2JiOjYsWMlyvvI+n4sK7558+Yq5wlkYdXh1dGjR49o7Nix1KpVK2rVqhWNGzeOHj16VCp8cWxxuawKHqpq7ty5NGnSJF7bt2/fSEdHh27fvq1SH506daKuXbvS69evydTUlO7du0dxcXFUv359pfl0Ll26RH5+fuTl5UVeXl7k5+dHly5dUtnuojRp0iRq0qQJvXjxgmQyGSUlJdG5c+fIycmJgoKCBLyZmRlduXJF0H758mUyNzdXmyUiunHjBk2ePJmqVKlCBgYG1LVrV9q1axd9+fJFoe1i8ppki3asyvkDBw6QTCYjiURC5ubmZGFhwR2WlpZlzrPm8WPpn9UWY2Njpt8dVp5lrGLPe2ElJiZSq1at6OjRoyrZJzZPpDxPYMGxWVhYkImJCUkkEjIwMChyrHl5eZSXl1fkc7LmQ2S1RV3b5SrteXd2dqbTp08L2s+cOUMuLi5qs5rGa5ItYvOsfcv16tUriouLo7i4ON71rVZalbW0DkKtSixTU1OucIgY6t27N3l7e9O9e/e4trt375K3tzf17duXa0tNTaWUlBSSSCR09epVnpPy+fPnlJOTUyI75A4QsRwI5cqV4xxJBZ0zKSkpZGRkVOL+lYnV4ZKUlETGxsai8YVVcC5K0xlTUltUlYmJCaWkpBARUefOnWnx4sVElF8AxdDQsMxs0TSexfHLwk6YMIH09PRoyJAhNGHChCKPwjp69Cjp6+tT/fr1OaZ+/fpkYGBAx48fLxGvCnvmzBnuiIyMpO+++44CAwNp//79tH//fgoMDKRKlSpRZGSkSnNBlP/5U3TR4+joSLdu3VKpj/Lly3PJ4c3MzLjvqVOnTpGnp6fKtijT9evXKSEhgft737591LVrV5o+fTp9/fpVwH/9+pWGDRtGurq6JJFISE9Pj6RSKfXv31/h97ypqSndvHlT0H7jxg3BxS8LW1B5eXl0+vRpGjZsGFlaWpK5uTmv2FRZ8ppki3asQt7Z2ZkCAgJUKtZWFjxR/jrg2LFjtGXLlmIdMiz9s9rSvXt32rFjh8p2s/JEqo+1LOa9sK5evUqurq5lzst/Y+THvn37aN26deTu7k7t2rVTqe+iHGFRUVFUs2ZNMjAwIAMDA/Lw8KDo6GiF/ZTUgVecLaXBl+brZGBgwK0RCyolJUWwRmRhNY3XJFvE5ln7zszMpMGDB5OOjg4X1KKrq0tDhgwp0feJVlqpK62DUKsSq2vXrrR7926mc1icN6wRHWLJ1NSUIiIiRHMgWFhY0N27d7nnkjtE4uLiqEKFCgptYnVoLF68mLZv3879/eOPP5JEIqHKlSsLnAUfP37kHR8+fKD79+9Tnz59qHbt2oK+WXlVJZ+L0nbGqGvLkiVLaOfOnYLHdu7cqdBxU79+fZo2bRqdPXuWDA0NuXm+ePEi2djYqG2Lu7s7V3lZVds1yUHIwrOwzZs3Jx0dHWrQoAE1b95c6dGiRQvBuZ6enlwF64KaNm0aeXl5lYhn7btly5a0detWQfsff/zBFJEbHR1NlSpVErRv2rSJOnToQG/fvi22DwsLC3r8+DERETk5OXF3xh89elTkzQsioqysLMF3Q2F5e3tzvyHJyclkYGBAvr6+VK1aNQoICFDad1paGh06dIh27NhBiYmJSrkuXbpQ06ZNeVG+T58+pWbNmlG3bt3UZpXp+vXr5OnpqXKkp5i8JtkiNq9JthTFix0lx8qLGeXHasumTZvIzs6O5s6dS7t37xY4rkrKs4xV7HlXJGURe2Lziqr6VqxYkXx9fen58+cq96/IERYaGkrGxsY0depU7nWZMmUKGRsbU1hYmEr9qhP9KKaztTRfJ1tbW4Xv1X379gnWiCyspvGaZIvYPGvfI0aMICcnJzp8+DC3Tjp06BBVrVqVRo4cKeC10kpsaR2EWpVYr1+/pg4dOlBQUJBKCzRW5406ER2JiYm0YcMGmjdvHgUHB/MOdSWTycjNzU00B0Lv3r1p+PDhRJQ/5sePH9M///xDLVu2pEGDBim0idXp4ODgQOfPnyciouPHj5OFhQUdO3aMhg4dSj/88AOPVbTdUSKRkJ2dHV24cEHQNyuvquTOITGdMSy2ODg4MG0diImJIQsLC5JKpbxIkunTp1P37t0FfFpaGqWnp3N/X758mQICAmjDhg1q2y23XVMcfqy82LbIZWBgoNDR9PDhQzIwMCgRz9q3kZGRUl6RU6579+68o1u3btSgQQPS0dFRuO3W09OTTE1NycDAgFxcXLitwPKjoJo0aUJ79+4lIiJfX19q164dnTt3jgYOHEju7u6Cvj99+kRjxowha2trlbZHm5mZcTeIFi9eTG3atCEionPnzlGVKlUEfHBwsMK76p8/f1b4HZ+Wlkaenp6kp6dHTk5O5OTkRHp6euTl5cX7rLGyBZWenk5Lliyh2rVrk46ODjVp0oTWrVv3r/CaZIt2rEJe7Cg5Vl7MKD9WWxSlhynotCopzzJWMeedNWJPbL60pMgR5uDgoDASNTIykhwcHFTum9XhVxpOvLKY96lTp5K9vT2dPn2acnJyKCcnh06dOkX29vaC9CAsrKbxmmSLpo21fPnyFBMTI2g/ffo0WVlZCdq10kpsaR2EWpVYBw4cIHNzc5UXaKzOG9aIjo0bN5KOjg5VrFiRateuTZ6entxRuP+8vDx68uQJZWVlFTtOU1NT0tfXF82BkJ6eTm5ublSjRg3S1dWlhg0bUvny5cnV1VVpzkZWp4OhoSEXdTZ+/HgaMWIEx1tYWPDYgtsdz5w5Q2fPnqX79+9Tdna2QltYeVUld/aI6YxhsUVfX59p6wARUU5ODr17907AK3pdmzRpwm29efHiBZmZmVGjRo3IyspKofMjKipKYZ6rr1+/8hblf/zxB2VmZorOq6qydhCmpaUVG3FZpUoVhdGhO3bsIFtb2xLxrH27uLjQlClTBO1TpkxR6IgeNGgQ7xgyZAhNmzaNjh07JmCJiIKCgoo8Curo0aP0559/ElH+lmVXV1eSSCRkZWVFp06dEvQ9evRoqlGjBu3evZuMjIxo8+bNNG/ePKpSpQr9/vvvAl4mk3Gf19atW1N4eDgRKd+GL5VKFX523rx5ozS6Ky8vj44fP06rVq2iVatW0YkTJxRyrOz69eupadOmpKOjQ+7u7rRw4UKFuXfLgtckW7RjVc6LHSXHyosZ5cdqi9hiGauY884asSc2X1Cq5AlkcYQZGBgoTEOUmJjItB5T5vAT04lXFvP+9etX6t27N5cuQ09Pj3R0dGjw4MGCFBssrKbxmmSLpo3VyMiIl0ZLrjt37pQoRZNWWqkrrYNQqxLL3t6exowZQy9fvlSJZ3XeKIro0NXVVRrRYWdnx+V5K065ubmkp6dX5Pa0ghLTgUBElJ2dTVu2bKEpU6bQqFGj6Ndff6XPnz+Xmj2VKlXiIghdXFy4cx88eMB0p7UsJXf2iOmMUVUymYwqVarEtHWAVaxFTVidJWLzqqosHITZ2dk0a9YsMjMz46LXzMzMaObMmfTt2zfBecHBwWRhYUGLFy+ms2fP0tmzZ2nRokVkYWFBISEhJeJZ+z506BAZGhpSzZo1aejQoTR06FDy8PAgQ0NDOnTokMpzIZbevn2r9CLS1taWuxsuLyJClL/duX379gK+RYsWNHDgQIqOjiY9PT2OP3PmDNnb2wt4iUSiMIH3qVOnyvxue5UqVWjKlCkq53MUk9ckW8TmNckWVl7sKDlWXswoP1ZbxBbLWMWed00TS55AFkeYu7s7LViwQNDHvHnzqGbNmoJ2VodfWTpPxVRiYiLt3LmTDh48WOTNCFZW03hNskVsXlW2ZcuW1KtXL16wyufPn6lXr17UqlWrYm3SSqvSlu6/XUVZq/98vX37FhMmTEDFihVV4q2trXHr1i04Ozvz2m/duoUKFSoIeFtbW9y4cQOnTp3C/fv3AQA1atRA69atFfb//v17QWl5ZZJKpXB2dsbbt28F9ijS8OHDMWLECDx+/BiNGzcGAJw/fx5LlizBxIkTS8zr6uqif//+KtmuTv89evSAn58fN+b27dsDAG7evIlq1aoJ+KSkJMTExODVq1fIy8vjPTZnzpwS86ro+++/h5GREdNYWedFVREROnfujPHjx0Mmk6Fp06YAgNjYWAQEBKBv374Kz9u9ezd27tyJtLQ0fPv2jffYjRs3eH9nZ2fDwMAAAHDy5El06dIFAFC9enW8ePFCoU0SiUTQ/vTpU5ibm5c5r6oU9VkabEF+3Lhx2LNnD5YuXYpGjRoBAC5evIigoCC8ffsW69at4503e/ZsyGQyhIaGYvr06QCAypUrIygoCOPHjxc8DwvP2neHDh2QlJSEdevWcd97nTt3xsiRI2Fra6t07N++fVP4+bOzsxOwHz58wO7du5GcnIwpU6agXLlyuHHjBipWrAgbGxulzwEA5cqVU/rYu3fv4OTkBAAwMzPDu3fvAABNmjTBqFGjBHx4eDj69euHffv2YebMmdx30e7du7nPLwBYWlpCIpFAIpHAxcWF977Izc1FZmYmRo4cKeh//PjxqFatmmCe16xZg0ePHiE8PFwtFgDS0tKY3p9i8ppki9i8JtnCyhf+bP7bfMeOHTFlyhTcu3cPHh4e0NPT4z0u/w1Sp39WW0JCQop8vPA6gpVnGavY8y4XEQFQ/XdODD4sLAyzZ8/G2LFj4ePjAwA4d+4cRo4ciTdv3mDChAk8nmWswcHB6NOnD86ePcv1ff78eZw6dQo7d+4U8N26deP9LZFIYG1tjZYtWyI0NFTA/7e8Ts7OzkVeh5iZmeHWrVtwcnJiYln7FpvXJFs0ZawrV65E27ZtUaVKFdSuXRsAEB8fD0NDQxw7dkzp+VppJZYkJP8G00orNeXv74/vv/8ew4YNU4kPCQnBihUrEBgYqNB5M3v2bME5p06dwqlTpxRe+G7evJn399ChQ1GvXj2FF4qKdPDgQSxduhTr1q1DzZo1i2SJCOHh4QgNDcXz588B5F/kT5kyBePHjxcsBFj46OjoIp974MCBJbYnOzsbK1euRHp6OgYNGgQvLy8AwIoVKyCTyXiv4a+//opRo0bBysoK3333Ha8viUQicGyx8jdu3ICenh48PDwAAPv370dERATc3NwQFBQEfX19tcfKOi+Fn0duc2GdO3cOtWvXxrBhw7Br1y7o6ubfY8nLy8PAgQOxfv16gd2rVq3CzJkzMWjQIGzcuBGDBw9GcnIyrl69ijFjxmDBggU8vkGDBmjRogU6duyINm3a4NKlS6hduzYuXbqEH3/8EU+fPgUAeHl5QSKRID4+Hu7u7pwtQL6zJCUlBe3ateMW4GLzrJLJZIiPj+cWXaXFFuS9vLywfft2zhEu1+HDh+Hr64uPHz8q7eOff/7h+iqs8+fPw9vbm3PksvKsfRel0aNHIyQkBO/evcPQoUNx4cIF3uNyB29ubi6vPSEhAa1bt4a5uTlSU1Px8OFDODk5YdasWUhLS0NmZqZKzw8Ae/bs4f1dq1YtrF69Gs2aNUPr1q3h6emJ5cuXY9WqVVi6dCn3Hi5OX758gY6ODncRHxUVBSLCkCFDEB4eznNQ6+vrw8HBgXMEF5SNjQ0OHDiAunXr8tpv3LiBLl268OxRhU1ISEDNmjUhlUqRkJBQ5Bhq1aolKg9AY2zRjrVoXlMllUqVPqbou0NMydckcmVnZyMlJQW6urqoWrWqYB3BymvSWKOjo7Fs2TIkJSUBAFxcXDBlyhQMGDCgzHlHR0cEBwcL1plRUVEICgpCSkqK0nGo4gi7fv06VqxYwbvJP2nSJMHrV1KJ4cQT+3VSVWWxZtIEXpNsEZsvyH7+/Bl//PEHHjx4ACD/M9KvXz8YGRmp9LxaaVWa0joItSqxFixYgPDwcHTs2FHhHdnCkRiszpvg4GCEhITA29sblSpVEjy+d+9e3t+LFi1CWFiYyvZYWlri8+fPyMnJgb6+vuDLWB79Ulil7UDo1KkTry07OxufP3+Gvr4+jI2Nldqhrj3Fyd7eHqNHj8a0adNE4evVq4fAwED07NkTjx8/hru7O7p3746rV6+iY8eOgkidghLDGcO6qEtKSsKtW7dgZGQEDw8P2NvbK+SqV6+OuXPnwtfXl7cYmDNnDt69e4c1a9bw+DNnzqB79+7IyMiAv78/5wCfMWMGHjx4wDljgoODuX8nTZoEU1NTrg+5s6Rnz56cw1JsXq6QkBBMnjwZxsbGvPasrCwsW7aMi+g4d+4cjh8/jsDAQJXYevXqYcmSJSr3Xa9ePdja2iI2NhY1atTg8ffv30fTpk3x+vVrqKPCd5RLk1e37wEDBkBXVxeBgYEKvyfld6Xlat26NerUqYOlS5fy3pcXLlyAn58fWrRoodLzA0BERATv7xUrVkBHRwfjx4/HyZMn0blzZxARsrOzERYWhoCAAIX9XL9+nbuAdHNzQ506dRRysbGxaNy4seC7XZkMDQ1x584dQZT0o0ePULNmTXz58oWJlUqlePnyJSpUqACpVAqJRIKCSyn533KHg5g8EWmMLdqxFs3LJXaUHCvPKpb+S8OWjIwMDBo0CN27d1fJycLKK5OY864sYm/t2rWYP3++IGJPbF7Z915SUhI8PDx435FyieUIKyhVHX5iOfHEnncW/a842TTJFrF51r610qrMVKoblrX6n5SDg4PSQ1HOtILKyMigjIwMhY+dO3eOvnz5Qt99953SPCilYU9kZGSRhzqSyWRMOdOU8YmJidSqVSs6evSoWnYo6j86Opp8fHyoUqVKXE6MFStW0L59+1SySZXnUEWslUvFsEPOhoaGkrGxMU2dOpXLezNlyhQyNjamsLCwEtlhZGTEzbO1tTWXoyoxMZHKlSun8FyWoiaRkZEKi4gok9g8S85CsfMhBgcHk6+vL8/+L1++UL9+/RRW9lVVmlix2djYmO7fv6/yeQU/fwWfMzU1tUSFfBQpNTWV/vzzT4qPj1f4+N9//03NmzcniURClpaWZGlpSRKJhFq2bKkw1yBR/mdk9+7dNG/ePJo3bx7t2bOHcnJyFLLu7u60evVqQfuqVauoRo0azGxqaiqXfzE1NbXIQ2xek2zRjrVoXq6ChdM8PT3J3d2djI2NyczMTGGhNrF5VrH0X1q2JCQkKMxHWlq8Iok576yVfcXmWfMEsqyZDh06pHANe/ToUTp8+LCgnYgtHyLr+o2FF3veWVTWhd3+LV6TbBGbl7ORkZH0119/ce1Tpkwhc3NzatSokUp5EbXSqrSldRBqpbGSO1jKlSvHXcj+p6g0f1CuXr1Krq6upWLPL7/8QlZWVjR//nwyMjLinjMiIoKaN2/OO2fIkCG0bt06lZ+DlWetXKqq1PlxFmNRJ+/b0dGRbty4QUREdevWpfXr1xNRftERS0tLtfouqLS0NF6xnsuXL1NAQABt2LDhX+ElDAUkWFh1+G7dupFMJiMrKytq1aoVtWrViqysrMjMzIy6d+/OO1ikiYtjb29viouLU/k8a2tr7n1Z8DmPHz9eIgf9t2/fqGXLlioXfiIi6t27N3l7e/Oq+N29e5e8vb2pb9++Aj4pKYmcnZ3J2NiYvLy8yMvLi4yNjcnV1VXhb8Vvv/1GRkZGNGfOHK7K+uzZs8nY2Jg2btyoNktEFBsbq7BSe3Z2NsXGxpYpr0m2iM1rki3q8IX18eNH6t69u8o3Q0uTDw4OLvIobXtYbSciiouLIwsLixLzJR1rac07a2Vfsfndu3eTjo4OtW3blkJCQigkJITatm1Lurq6tGfPHgHPsmby8PBQWFjryJEjVKtWLUE7q8NPTCee2PPOov8VJ5sm2SI2L2ddXFzo1KlTRER04cIFMjIyog0bNlDnzp2Z16daaVUa0joItSpV5eXlKa1sySr5F+fUqVMVVvksTXsePXpEM2fOpL59+3IRSocPH6Y7d+6o9byl+YNy8+bNElcYlvdfo0YN2rt3r+A5b9++TeXLl+eds3DhQrKysiJ/f39avnw5rVy5kncUFivPWrmUdawsrBiLOnnfQ4cO5aLV1qxZQ0ZGRtS6dWuysLCgIUOGKDx3165d1KtXL2rQoAHnAJEfhdWkSRPu4uPFixckk8moUaNGZGVlpfCCRyzewsKCLC0tSSqVcv+XH/IqwqNHj2Zm1eHlGjRokMoHizRxcXzq1Clq1KgRxcTE0Js3b+jjx4+8o7CGDh1K3bp1o2/fvpGpqSk9fvyYnjx5Ql5eXhQQEECenp6C956yo7CsrKyYHIRmZmZ05coVQfvly5fJ3Nxc0N6+fXtq164dvX37lmt78+YNtWvXjjp06KDwOX755ReysbHhKlc6OjoqvEhkZTWpirgm2SI2r0m2qMMrkthRcsr4fyPKTxlbeN0QHh5O06ZNo8qVK5Ovr2+J+dIYa2nMO2vEntg8EdG1a9eoX79+VKdOHapTpw7169ePu4lUWCxrJkNDQ0pJSRGwKSkpZGxsLGhndfiJ6cQri3lXVersjhGjb7F5TbJFbL7gTqMnT54QEdHUqVNpwIABRER0584dhTe/tdJKbGmrGGtVKirtXCQTJ07E169fsWDBAshkMmzcuBEnT55ErVq1BHmnwsLCSmRPbGws2rdvDx8fH5w9exYLFixAhQoVEB8fj99++w27d+9WawysOnnyJL777jvubyLCixcvsGbNGi6XSUmVkpKiMCm0gYEBPn36xGvbuHEjTE1NERsbi9jYWN5jEolEkMuRlVe1cmlZqFq1ati5cydmzJjBa9+xY4dK1a2L0saNG7nCOmPGjEH58uVx4cIFdOnSBT/99JOAL1jUZP/+/YKiJoV1584d1K9fHwCwc+dOeHh44Pz58zh+/DhGjhwpyJMkFh8eHs4VkAgODi6ygAQLqw4vV+HceP/Nkld1b9WqFa+dlBQpCQ0NxY8//ogKFSogKysLzZo1w8uXL9GoUSMsWLAAy5cvV9uW/v3747fffsPixYtV4vPy8hTmE9TT01NYcTI2NhaXLl3iVVIuX748Fi9erPS7ctSoURg1ahRev34NIyMjXk7NkrDy+S2st2/fwsTEpEx5TbJFbF6TbFGHV6SPHz8WWThJLP7mzZuCtoJ5/MSwRxm7YsUK3t9SqRTW1tbw9/fnqr+XhC+NsZbGvLNW9hWbB4C6devi999/V2lMLGsmc3NzPH78GA4ODrz2R48eKfxsvHjxQuEasHHjxnjx4kWJbGHly2LeVRUxlAxgYTWN1yRbxOblrKmpKd6+fQs7OzscP34cEydOBJCfGzQrK4vp+bXSqjSkdRBqVWIpS8o7cuRIvHnzRq2kvDdv3kReXh7u3r0LIyMjeHp6Ash3VhSUogU5qz2BgYGYP38+Jk6cyCtk0bJlS0EBCTFVuOqyRCKBtbU1WrZsidDQ0FJ5DkdHR9y6dUtQUOPo0aOCQg5FVa1TJFa+Vq1auH37tqB92bJl0NHRYeqrpBJzUSeVSnmVE/v27Yu+ffsq5X/55Rds3LgRvr6+iIyMxNSpU3lFTQorOzubK8hy8uRJdOnSBUB+cRRFi2mxeH9/fwD57zEfHx9e1ePCYmHV4cWWqhUS1eFZ+5YrJiaGiTc3N8eJEydw7tw5JCQkIDMzE3Xq1OEcjXPnzlXLDgDIycnB5s2bcfLkSdStW1dwEVj4pk7Lli0REBCAbdu2oXLlygCAZ8+eYcKECQKHJ5B/Q0NefKigMjMzBUVzgPzvppycHDg7O8Pa2pprT0pKgp6eHu/CVVW2R48eAPJfr0GDBvEKQOXm5iIhIYF3kSsmr0m2aMdaNA/k3wQqKPnNwC1btggqrpcFr0hmZmYIDg5G586dBTdWWfpntUXsdYciKRurmPPes2dPXL58GStWrMC+ffsA5FctvXLlisKbuGLzhw8fho6ODtq2bctrP3bsGPLy8gT2s6yZunbtip9//hl79+5F1apVAeQ7BydNmsStJwqK1eEnphNP7Hln0ZEjR2BjY1PqrKbxmmSL2Lyc/eGHHzBs2DB4eXkhMTERHTp0AADcvXtX4FjXSquykNZBqFWJtXr1aqxbtw4DBw7k2rp06QJ3d3cEBQWp5SCMiYmBTCbD1q1bmas7sdpz+/ZtbN26VdBPhQoV8ObNG2bbAfUcCI8ePRKtkpXcnokTJ2LMmDH48uULiAhXrlzBtm3bsGjRImzatEmtvku7oquhoaFadsiljjNGjEVdQTs+fPiAK1eu4NWrV4KIqILvUwBIS0vjLiiNjIw4R8iAAQPQsGFDgdPa3d0d69evR8eOHXHixAnMmzcPAPD8+XOUL19eYJfYvEwmw/379+Hh4QEA2L9/PyIiIuDm5oagoCCeA4eFVYd/+/Yt5syZg5iYGIVzX9DhSkRIT09HhQoVin0PUn56DqSlpanE5+Xl4fnz56hcubJKfbOof//+MDMzQ7NmzVTiR48ejZCQEGRlZcHW1hZNmjRBkyZNmJ6zON25c4erQJyYmMh7TNHnc82aNejSpQscHBxga2sLAEhPT0fNmjUVRrR06tQJI0aMwG+//cZFt16+fBkjR45UeME5aNAgDBkyRHBxefnyZWzatAlnzpxhZuVRrEQEmUwGIyMjjtXX10fDhg0xfPhwrk1MXpNs0Y61aB4QP0qOlVem0ojyK4ktT58+BQBUqVJFJXtZ+YJSNFax550lYk9sPjAwUGHENxEhMDCwRA7OpUuXol27dqhevTr32jx9+hTff/+9wkh1Voef2E48sV8nebRYYUkkEhgaGqJatWro2rUrmjRpwsSy9i02r0m2aNpY165di1mzZiE9PR1//vknt7a+fv06fH19FfallVaiqlQ3LGv1PymxkvKy5n1Q1x4bGxs6f/48EfHzf+3Zs4ecnJw4Li8vj548eUJZWVnF2mBqakqPHj1i4ocMGUITJkwQHBMnTqQZM2bQ5s2beTm3WO2Rj+v333+natWqcfm1bGxsaNOmTcX2oUrfLLxEIiGpVKr0KCiWsZqYmFBcXBzzvJS25H0fOHCAZDIZSSQSMjc3JwsLC+5QVKSEtahJTEwMWVhYkFQqpcGDB3Pt06dPV5jcWGze29ubdu/eTUTE5Xf09fWlatWqUUBAgNqsOnz79u3J2dmZFi9eTBEREUVWKM/NzSU9PT2Vc+ex8Kx9ExHFx8crPBISEigxMZGpsnRByb9XpVIpNW3alDZu3CiomE1EZGlpSa9fvyYiEuR9LHyUhvLy8uj48eO0atUqWrVqFZ04cUIp+/79e+rSpQtJJBLS19cnfX19kkql1K1bN/rw4YPCMSv6TUhKShLkOGRhiYiCgoLo06dPxQ+wDHhNskVsXpNsUYfXJLHm8RNTubm5FBwczOWWlUqlZG5uTiEhIZSbm1tiXlPGylrZV2yeNU8gq/Ly8ujYsWO0dOlSWr16dbGFe1jyIYopseediKh58+ZkZmZGJiYm3HhNTU3J3NycGjRowP3+3r17l4ll7VtsXpNs0bSxaqWVpknrINSqxGJJyquuU0sse4iIJk2aRE2aNOEKMCQlJdG5c+fIycmJKy5BJK4DgYj9x0odp0NBffr0SWFSdVap6yDct28f79i1axfNmDFDocNSbGeMugnmiyqCExcXR1++fCFnZ2cKCAhQ+eJRnaImOTk5AidPSkqK0tdXTN7MzIyrJLt48WJq06YNERGdO3dOUB2XhVWHNzU1pVu3bikckyK5ubnRxYsXReFZ+y7OgW5gYEADBw5U6bu0oOSfvxs3btDkyZOpSpUqZGBgQF27dqVdu3ZxjsfIyEje/4s6ylLy35DPnz9TUlISHThwgA4cOKDQqSeXmZmZwovLa9eukampqdosEdHjx48VftckJiYqvOAWk9ckW8TmNckWdXi50tPTeVXii5MYvIODA+9wcnKiBg0a0PTp0ykjI6PU7FGFDQwMJGtra/rll1+4myJr164la2trmjFjRol5dcda2vPOWtlXbL5ixYpcFdWCOnHiBFlbWwva1XGEFaeaNWtSWloa83liOvHEnnciohUrVlCPHj14xcQ+fPhAP/74I4WHh9OnT5+oa9eu1KZNGyaWtW+xeU2yRdPGSkR09uxZ6tevHzVq1IiePn1KRETR0dEUFxen8H2jlVZiSusg1KrE2r17N+no6FDbtm0pJCSEQkJCqG3btqSrq0t79uzhsSV1apW2PUREX79+pWHDhpGuri5JJBLS09MjqVRK/fv3p5ycHB4rpgNBnR8UVnvEUGlXdP3jjz+oS5cugnaxnTGKnF3Pnj0jQ0NDQXtUVBTVrFmTDAwMyMDAgDw8PLhKv4VlbGzMND+5ubmUnZ3N/b1t2zYaN24crVq1ir5+/arwnOzsbDpx4gStX7+eu8h59uwZ/fPPP2XOy2Qy7vPdunVrCg8PJyKiJ0+eCOaShVWH9/b2ZnofHDhwgJo0aUK3b98udZ6173379pGrqytt2rSJEhISKCEhgTZt2kQ1atSg7du30++//05VqlShSZMmqdSfXIU/f3l5eXT69GkaNmwYWVpakrm5OS9SVB1169aNunfvLjh69OhBfn5+NGfOHHrw4AHvnJMnT1LHjh3JycmJnJycqGPHjgqjCNX5DenUqRP16tWL932ek5NDPXv2pHbt2qnNEhE1bdpUoZN0y5Yt1KxZszLlNckWsXlNsoWVFztKjpVnFUv/rLZUqlSJ9u/fL2jft28fVa5cucQ8i8Scd9aIPbH5ESNGkIeHB3cDjig/arpWrVo0dOhQAa+OI6w4yX+bWB1+YjrxxJ53IqLKlSsrjCS7c+cO9x6+fv06lS9fnoll7VtsXpNs0bSx7t69m4yMjGjYsGFkYGDArdFWr15N7du3F/SjlVZiS+sg1KpUdP36dZW3A5SFU0ud7QlPnjyhQ4cO0Y4dO5RefIrpQGD9QVHHHgcHB3J0dFR6qKPSdhAmJyeTiYmJoF0MZ4x8m5FUKqUFCxbwth6FhYVRt27dyNPTk3dOaGgoGRsb09SpU2n//v20f/9+mjJlChkbG1NYWJjgObp37047duwo1mZ1lZqaStWrVydjY2PS0dHh5nb8+PH0008/lTnfokULGjhwIEVHR5Oenh4X2XXmzBmyt7dXm1WHv3LlCrVs2ZLOnDlDb968oY8fP/KOwrKwsOC2qxoaGha7lZaFZ+27Xr16Si+S6tWrR0REe/fu5aVBUEVFff6uX79Onp6eCqNmc3JyaNeuXdxNl927d/Mc2QXl7+9P5ubmZG9vTz169KAePXqQg4MDWVhYUO/evcnV1ZUMDAzo3LlzRES0du1a0tXVpb59+3KfP19fX9LT06M1a9YI+mf9Dbl79y6VL1+eqlatSoMGDaJBgwZR1apVydraWvAdwcISsW9JFpPXJFvE5jXJFlZe7Cg5Vr6gSjvKj9UWAwMDevjwoaD9wYMHCm8CsfIsYxVz3lkj9sTmP3z4QA0bNiRdXV0uulJXV5datGhB79+/F/BibEmW/zaxOvzEdOKJPe9E+WlxYmJiBO0xMTFc1HpycjLJZDImlrVvsXlNskXTxurp6UlRUVFExF+j3bhxgypWrCjoRyutxJbWQahVifTt2zcaPHgwPX78WOVzWJ1aZamitowSietAYP1BUcee8PBw3rFs2TLy8/OjcuXK0aJFi1SdJp5Yc0UWxX/+/JkCAgLIxcVF8JgYzhj5QlgikZCtrS1v65GLiwu1adOGLl26xOvbwcGB+yEvqMjISHJwcCAi4hyH+/fvp02bNpGdnR3NnTuXdu/ezXtMUeQDUX6OtWPHjtGWLVsoKiqKdxRW165dqX///vT161fewiImJoaqVatW5nx8fDzVrFmTzMzMeFv0x44dK8jxxMKqwycmJpK3t7dge658+25hsW6lZeFZ+zY0NKT79+8L2u/fv89d+KakpJCRkZGAKUqFHYTp6em0ZMkSql27Nuno6FCTJk1o3bp1vHPu3LlDTk5OZGxsTF5eXuTl5UUmJibk4OCg8Ht82rRpNGrUKF7UTG5uLo0dO5amT59OeXl5NGLECPLx8SGi/Dywq1evFvSzZs0ahVFA6vyGPHv2jKZPn04dOnSgnj17UnBwMC+nq7os65ZkMXlNskVsXpNsYeXFjpJj5cWM8mO1pX79+jRu3DhB+9ixY6lBgwYl5lnGKua8s0bsic0TseUJVMcRVpzkv02sDj8xnXhlMe9+fn7k6OhIe/bs4ZzW8hzo/fv3J6L8nSR169ZlYln7FpvXJFs0baxGRkbce77gGk2ea1srrcpaWgehViWWmZkZk4OQ1amlrv7++2+6ffu2IMm/Im3atInc3d25hPfu7u7066+/CjgxHQisPyjq2KNMa9asoUGDBqnMF5S6EYSFCx9YWFiQjo4OyWQyhYtsMZ0xzZs3V1ioQZFUKYIjLwBT3KHIScVa1KRcuXLcds2Cr4Uy55HYvDJlZWXRt2/fSp0tiq9Xrx41atSItm/fTjExMXTmzBneocny9PQkf39/3rbyb9++kb+/PxfVeu7cOc4prarkr+H69eupadOmpKOjQ+7u7rRw4UJKTU1VeE7Dhg2pc+fOvM/Iu3fvqEuXLtSoUSMBb2VlpTCq5+HDh1wEdEJCAhdZZWJiovQzpSiauKx+Q1QR65ZkMXlNskU7VuW82FFyrLyYUX6stpw5c4ZMTEyoRo0aNGTIEBoyZAjVqFGDTE1N6ezZsyXmWcYq5ryzRuyJzasqeZ5AdRxhxUn+28Tq8BPTiVcW8/7PP//QsGHDuN80qVRK+vr6NHz4cMrMzCQiops3b9LNmzeZWNa+xeY1yRZNG6ujoyOXUqXgOjsqKopq1Kih8H2jlVZiSkJE9G9XUtbqP1v+/v7w9PTEhAkTVOKjoqKK7a8kun79Ovz9/XH//n0UfntLJBLk5uby2ubMmYOwsDCMGzcOjRo1AgBcvHgRa9aswYQJExASElIie1RVZmYmJkyYgOjoaOTk5AAAdHV14e/vjxUrVsDExAS3bt0CAHh6epbqcz9+/Bienp7IyMhgPvfcuXOoV68eDAwMmPjt27fz2qVSKaytrdGgQQNYWloy21EWMjMzw3fffYdBgwZhxowZvMfmz5+PHTt24Pbt2yV6DhcXF3To0AELFy6EsbFxsbylpSXOnz8PNzc3yGQyxMfHw8nJCefOnUPPnj3x999/lykv1/Xr13H//n0AgJubG+rUqaN0DCwsC29sbIybN2/C1dW1yP4KKjk5GREREUhOTsbKlStRoUIFHDlyBHZ2dnB3dy8Rz8JeuHABXbp0gVQqRa1atQAAt2/fRm5uLv766y80bNgQW7ZswcuXLzFlyhSVxzdq1CjMmzcPXl5e8PX1Rb9+/VC7du0izzEyMsK1a9cENt65cwf16tVDVlYWr93S0hJRUVHo0qULr/3AgQPw9/fH+/fvkZSUhPr16+P9+/fw8/ODl5eXYBzLly/HtWvXBN8V6v6GfP78GWlpafj27RuvXT6/6rD37t1D06ZNYWFhge+//x4AEBcXh4yMDJw+fRo1a9YsM16TbNGOVTnfoEEDNGjQAKtWreL1MW7cOFy9ehWXLl3itYvNV65cGevXrxd8Xvfv34/Ro0fj2bNnavfPagsAPHv2DL/88gsePHgAAKhRowZGjx6NypUrC1hWnmWsYs87EeHEiROIj4+HkZERatWqhaZNmyocY1nwqki+BihfvjzatWuHa9euoUqVKgCAp0+f4vvvv8eePXtgYWGhdt9LlizBxYsXsXfvXlStWhUA8OjRI/Ts2RP16tXDpk2beOd9/PiRyRZWvqzmPTMzE48fPwYAODk5wdTUtFRYTeM1yRaxeVXZRYsW4ffff8fmzZvxww8/4PDhw3jy5AkmTJiA2bNnY9y4cUXapJVWpS2tg1CrEmv+/PkIDQ1Fq1atULduXZiYmPAeHz9+fJnaU7t2bVStWhXTpk1DxYoVIZFIeI/b29vz/ra2tsaqVavg6+vLa9+2bRvGjRuHN2/e8NrFdCAA7D9WrP0r0tKlS/HLL78gNTWVa5s4caJCViKRwNDQENWqVUPXrl1Rrlw5tXh1JJYzRlXJZDIsWbIE48ePR+vWreHj4wMAOH/+PE6dOoWdO3eie/fuavXt4eGBw4cPo3r16rh9+zacnJxUOq9Pnz4wNzfHxo0bIZPJkJCQAGtra3Tt2hV2dnaIiIgoU/7Vq1fo06cPYmNjuYX2hw8f0KJFC2zfvh3W1tZqserwTZs2xZw5c9C6dWuV5jI2Nhbt27eHj48Pzp49i/v378PJyQmLFy/GtWvXsHv3brV51r4B4J9//sEff/yBxMREAICrqyv8/Pwgk8kEbEJCgsIxyT9/dnZ2PCc+EQm+GxVp9OjROHv2LFatWoWWLVvyHjt9+jQCAgIETvHx48dj27ZtmDFjBurVqwcAuHr1KhYuXAg/Pz+sXLkSmzZtQmRkJM6dO4f58+dj+fLl8PHx4W7SXLp0CefPn8ekSZNgZmbG65tVr1+/xuDBg3HkyBGFjxe8acTCyvX8+XOsWbOGd0E4duxYpd91YvKaZIt2rIr52NhYdOzYEXZ2drybkunp6Th8+DDnYCwr3tDQEAkJCXBxceG1P3z4EJ6enoIbACz9s9oitljGKva8qyL5usDW1lYj+II3CUvbAVkS5+O/7TwVe961+u8WEWHhwoVYtGgRPn/+DAAwMDDA5MmTMW/evH/ZOq3+J/UvRC1q9V+mgnnbCh/KCl88evSIZs6cSX379uWqxx4+fJju3LlTYntMTU0VbldTJnNzc4VFSR4+fChILn7mzBkyMjKi1q1bk76+PhcGvmjRIurZs6egD1aeVaz9e3p6cjnEvLy8yNPTk7777jvS0dGhDRs28NjmzZuTmZkZmZiYcMVeTE1NydzcnBo0aMBteZUXVmHlifJz7S1fvpyGDh1KQ4cOpbCwMPrw4UOJxyrWvMtD/9UpgqNq36xFTdLT08nNzY1q1KhBurq61LBhQypfvjy5uroqrMwsNt+7d2/y9vame/fucW13794lb29v6tu3r9qsOvzOnTvJzc2NIiIi6Nq1a8WmG2jYsCGFhoYSEX+bx+XLl8nGxqZEPGvfrJJvWVd2GBgY0MCBAykrK0ul/uSFXExNTbkUDLt27eLSH+zatUtpMvmcnByaP38+fffdd9x2+u+++44WLFjAbcF88uQJVyCgqN8QZb8nLEVT/Pz8yMfHh65evUomJiZ0/Phx2rJlC7m6utJff/2lNquVVurq6dOnNGPGDK6Iz8yZM+nZs2f/Cs+ax4+1fxZ28+bNtHPnTkH7zp07FaYHYeVZxyr261ScSrsIXFnzNWvWpLCwMPry5Yvgsa9fv/LyKv/xxx/c9kuWfIgstqSlpYnCl2QeMzMzadasWdSoUSOqWrVqkcUDWVhN4zXJFk0aa05ODsXGxtL79+/p69evdPfuXbp8+TL9888/Ahu00qqspI0g1EotZWRk8KI6WKROJA2LunXrhgEDBqBnz54q8ePGjYOenh7CwsJ47ZMnT0ZWVhbWrl3LtTVq1Ai9evXCxIkTeXdSr1y5gh49euDp06e8Plh5VrH2HxwczPtbvq23efPmqF69Ou+x8PBwxMXFISIignutP378iGHDhqFJkyYYPnw4/Pz8kJWVhWPHjjHz165dQ9u2bWFkZIT69esDyI8yysrKwvHjxwXbRlnGKta8F+yrNHXgwAH06dMHK1euhI6ODkJCQjB48GB4eHhAT0+PxxbeGgUAOTk52L59OxISEpCZmYk6deqgX79+MDIyUvh8YvLm5uY4efIkFzkm15UrV9CmTRt8+PBBLVYdXiqVCuyTSCRc9FzhaDBTU1Pcvn0bjo6OvNc6NTUV1atXx5cvX9TmWfsGgKSkJMTExODVq1fIy8vjPTZnzhze3/v378e0adMwZcoU7vN05coVhIaGYu7cucjJyUFgYCD69OmD5cuXC56rsKRSKSQSCfLy8iCVSrl0DfKow4J/K4qqk0uetkDd3wtFunv3Lrp06YKXL19y28cTExNhbW2NgwcPCrZ/VqpUCfv370f9+vVhZmaGa9euwcXFBQcOHMDSpUtx7tw5tdiCYtm+LDavSbaIzWuSLerwmiBNivJzcXHBhg0b0KJFC4GNI0aMwMOHD0vEa9JYVRHrmkMT+c+fP+PFixeoUKEC77G3b9+iQoUKRf5+FKWSRD+WNl+Svn19fREbG4sBAwagUqVKgsj+gIAA7v8srKbxmmSLpo3V0NAQ9+/fh6OjI7TSSiP0r7ontfqPlVQq5aKHWJMeix1J8/r1a+rQoQMFBQUprRo7YcIE7hg3bhzJZDJyd3fnItnklVLHjh3L69vExIQryFK4YIOiSlOsPKvE7L9y5cq8aD+57ty5w1Xku379Old0gJVv0qQJDRo0iBf1k52dTf7+/vT9998L+mEZq1jzYmpqynvvF9SbN28UFh1RRRKJhACoVdRE02RqasolXi6oGzdu8Cpws7Lq8KmpqUUehWVjY0Pnz5/nnkv+vpEXCyoJz9r3xo0bSUdHhypWrEi1a9cmT09P7vDy8hLw9erVo6NHjwrajx49SvXq1SMior179yp8LkWSF3IxMjKirVu3Cgq8qFLs5e+//6azZ8/S2bNn6dWrV8U+59evX+nBgwdKIwHlYi2aIpPJuAqBdnZ2dO7cOSIievz4saDQDgtLRPTq1Svq2LGj0sjNsuQ1yRbtWJXzYkfJsfJE4kX5sdpiYGCgtIKtssIgLDyR6mMti3kvTpoWEagOL5FIFH7/37p1q0RFpTRprCXp29zcnPudKU4srKbxmmSL2Dxr33Xr1qWTJ0+qzGulldjSOgi1UktmZmbcNj9lP/7KJLbT7MCBA2Rubl6kg6V58+YqHS1atOD1LaYDQR2x9i/fNlj4yMjI4FVLJcp/nWJiYgR9xMTEkKmpKRERJScnc44ZVt7Q0JDu378v4O/evavwQlxMZ4yqklcXVuQgfPbsmdILElXEusCUKyoqqsijrPkuXbpQ06ZNeRdcT58+pWbNmlG3bt3UZtXhWTVp0iRq0qQJvXjxgmQyGSUlJdG5c+fIycmJgoKCSsSz9m1nZ0eLFy9W2XZln6f79+9z70vWytNEbO/LUaNG0evXrykjI4P69+9POjo63Hevrq4u9evXT2EKgU+fPtGQIUNIR0eHdHR0uOcbO3YsLVq0SMAbGhoqTEdx+/ZthZ9Bb29vznnauXNnGjBgAD19+pSmTp0q+D5gYYnYtySLyWuSLdqxKuednZ3p9OnTgj7OnDlDLi4ugnaxeVax9M9qi62tLXcjt6D27dun8AYyK88iTZh3TXKCsfKenp6cg9zDw4OX3qZWrVokk8moV69eKj93SWwRmy9J3w4ODry0KUWJhdU0XpNsEZtn7fvIkSPk6elJBw8epOfPnwuu0bTSqqyldRBqpZZ69OhBFStWpObNm5NEIiEfHx9q0aKFwqOwxHaa2dvb05gxY+jly5cl7quwxHQglIU9xeUps7Ozozlz5lBubi75+fmRo6Mj7dmzh8s7Jn+N+vfvT0RE27Zto7p16xIRMfMVKlSgY8eOCWw8evQoVahQoURjFWPeV65cSfr6+iSVSmnBggW0cuVK7ggLC6Nu3bqRp6enWn0TqZfbJy0tjSwsLHiHiYkJSSQSMjAwUHh3Xmw+LS2NPD09SU9Pj5ycnMjJyYn09PTIy8uLyzmnDqsOT5Sf73Ts2LHUqlUratWqFY0bN44ePXqkkP369SsNGzaMdHV1SSKRkJ6eHkmlUurfvz+XO09dnrVvmUzG9H7w9PQkf39/nqP/27dv5O/vz70vz507Rw4ODir3ScT2vpTb3Lt3b3J2dqajR49yC9yjR4+Sq6sr9enTR3De+PHjqW7duhQXF0cmJibc8+3bt0/hZ6pWrVp06tQpQfupU6eoZs2agvYtW7ZQREQEERFdu3aNrKysSCqVkqGhIW3fvl1tlojou+++o8uXL3Pjf/jwIRER7d+/n3x8fMqU1yRbtGNVzosdJcfKixnlx2rL1KlTyd7enk6fPk05OTmUk5NDp06dInt7e5o0aVKJeZaxlkU0Y3EqKydYVFQUc57A4hQUFET6+vokkUho8uTJFBQUxB0LFy6krVu3Cm5Ms+i/xUG4ZcsW+vHHH+nTp0/FnsfCahqvSbaIzbP2XTiQRX78p+wc0uq/T1oHoVZq6fPnz7Ru3TqaPHkySSQSGjFiBP38888Kj8IS22lmamqq1AFQUonpQCgLe6KioqhKlSo0a9YsOnDgAB04cIBmzZpFtra2tGHDBpo/fz5ZWFjQggUL6J9//qFhw4ZxTjGpVEr6+vo0fPhwboF48+ZNbssnKz9u3DiqUqUKbd++ndLS0igtLY22bdtGVapUoYCAgBKNtaTznpOTQzdv3uRtYXRwcKBKlSqRRCIhW1tbXvEEFxcXatOmDV26dEmVl02hSnPxmpiYSK1atVK45bQs+Ly8PDp+/DitWrWKVq1aRSdOnFDaFwvLyh89epT09fWpfv36XEqB+vXrk4GBAR0/flzpeU+ePKFDhw7Rjh07FBYwKgmvKjtkyBBat25dsc8t1/nz56l8+fJkbW3NOUMrVKhA5cuXp4sXLxIRUXR0NC1dulTlPonUu0gyNjamuLg4weNnz54lY2NjQbudnR1nY8HnS0pKUrh1/NChQ0UWTSnuzvunT5/o+vXr9Pr162LHVBzLuiVZTF6TbNGOVTkvdpQcKy9mlB+rLV+/fqXevXtzv9t6enqko6NDgwcPVuhMYuVZxqoJ0Yxl5QQTI3WKvP+lS5cqdD6WVP8tDkJPT0+SyWRkampKNWvW5EVaFk4nwsJqGq9JtmjaWItK4VJUGhettBJLuv92DkSt/jNlZGSEkSNHAgCuXbuGJUuWwMLCQqVzFy5ciDFjxsDW1ha5ublwc3NDbm4u/Pz8MGvWrBLb1qNHD8TExKBq1aoq8V++fMHq1auVFgO4ceMG9399fX38+uuvmD17Nu7cuYPMzEx4eXnB2dlZYd+sPKtY+4+KikJoaCh69+7NtXXu3BkeHh7YsGEDTp06BTs7OyxYsAAzZszAr7/+ihUrVuDx48cAACcnJ5iamnLnenp6cv83NTVl4pcvXw6JRIKBAwciJycHAKCnp4dRo0Zh8eLFJRor67z8/PPP8PDwwNChQ5Gbm4tmzZrhwoULMDY2xl9//YXmzZsjJSUFANCiRQvs2bMHlpaWCvsqrOjoaPTp0wcGBga89m/fvmH79u0YOHAgAGDDhg2oWLGiSn0WJ2dnZyxevBj9+/fHgwcPypyXSCT44Ycf8MMPPxTbFwvLygcGBmLChAmC91NgYCCmTZumtA87Ozsu8Xnh5NIl5VVlq1WrhtmzZ+PSpUsKi9WMHz+e93fjxo2RkpKCP/74A4mJiQCAXr16wc/PDzKZDAAwYMCAYsdSWP3792cuMFK+fHmYm5sL2s3NzRV+bl6/fi1IYA8Anz59UjhHnTp1AgD07t1bUDSlc+fO3N/KCqgYGxsLiiAB+YVUbt26xUs0Xxzr6uqKhw8fwsHBAbVr18aGDRvg4OCA9evXo1KlSoLzxOQ1yRbtWJXzvr6+GD9+PGQyGZo2bQogv3hGQEAA+vbtK+hbbD4tLU1hcnx7e3ukpaWVqH9WW/T19bFjxw7Mnz8ft27dgpGRETw8PGBvby9g1eFZxirmvLOuC8Tm5d+XhfX06VOF3+UsatSoEV6/fo0qVaoAyC+etXXrVri5uWHEiBEl6lts7dmzB+PGjRNt3oH8woqqioXVNF6TbBGbZ+27WbNmKnGjR49GSEgIrKysmPrXSitm/csOSq3+h1R4yxxrlI6qmj9/PllZWZG/vz8tX76ctxV05cqVAt7Pz4+srKxo5MiRNHfuXN42iKIiGvPy8igvL09lu1h5VqnSv6GhocK5TkxM5KIclEVIiKVPnz5RQkICJSQkqByOzzKXqrA2NjZ09epVIsov5FC5cmV6+PAhzZo1ixo3bqzS8xSW/P0u5p35ou5Y37x5U2H0VVnwJ0+epOnTp9PQoUNp8ODBvKMkLCtvYGCg8P3+8OFDpflON23aRO7u7qSvr0/6+vrk7u5Ov/76q9J5YOFZ2IIRqoUPR0dHpfaoqiNHjvCi/NasWUO1a9cmX19fXuQsi+TvyQ0bNlDr1q3pxYsX3GMvXrygNm3a0Pr16wXnff/997Rq1SquD3mO2rFjx1Lbtm0FfHF329W9865OxAjrlmQxeU2yRTtW5bzYUXKsvJhRfqy2qCrWFAxynmWsYs4767pALF5e9Io1TyDLluRGjRpRdHQ0ERG3c6hRo0ZkZWVFwcHBgj5Ulbrbo1l4sV8nrbRiEev3nlZaqSutg1CrMpOiiy8xnGasF9ZmZmZM1abEdCCoI5b+nZ2dadq0aYL2adOmcdtrrl69SpUrV6bMzEyaNWsWNWrUiKpWrUqOjo68o7BYebHHysIaGBhwOeyGDx/ObXF+/Pgxk9OsoOTvd7Er+BWu0r1v3z5at24dubu7U7t27QTnic0HBQWRVCql+vXrU9euXalbt268Q11WHb5KlSoK803t2LGDbG1tBe2zZ88mExMTCgwM5MYbGBhIpqamNHv27BLxrH2ro8TERNqwYQPNmzePgoODeUdh1axZkw4dOkRERAkJCWRgYEDTp0+nhg0b0qBBg9R6fvl70tPTk0xNTUlPT4+qVq1KVatWJT09PTI1NVW41SYuLo5MTU1p5MiRZGhoSAEBAfTDDz+QiYkJXbt2Te35kBdNYbW/JCzL9mWxeU2yRWxek2xRlU9MTKSdO3fSwYMHFVZVLyueNY+fOvaw2l6c1N3Sqc5YxZh31nWBWLz8RjhrnkAWR5iFhQU9ePCAiPJzOctvuh47dkzh2pDV4SemE0/s10krrVikbjFDrbRilXaLsVb/in777TesWLECSUlJAPK3LP78888YNmxYifuWbwVVVTY2NtwWvOI0Z84chIWFYdy4cWjUqBEA4OLFi5gwYQLS0tIQEhJSIp5VrP0vX74cvXr1wpEjR1CvXj0A+VvEHzx4gN27dwMArl69ij59+mDYsGGIjY3FgAEDUKlSpWK3TqrC9+jRA5GRkTAzM0OPHj2K7G/Pnj1qj5V1XipWrIh79+6hUqVKOHr0KNatWwcA+Pz5M3R0dIq0U5k+f/6MLl26QCKRoFWrVtDV/b+v29zcXKSkpKBdu3Zq9V1QhbcySCQSWFtbo2XLlggNDS1zfv369YiMjFRpOysLqw4/fPhwjBgxAo8fP0bjxo0BAOfPn8eSJUswceJEAb9u3Tr8+uuv8PX15dq6dOmCWrVqYdy4cYL3DQvP2jerfv31V4waNQpWVlb47rvveJ8/iUSCOXPm8PiUlBS4ubkBAP7880906tQJCxcuxI0bN9ChQ4cS2cK6vaZJkya4desWFi9eDA8PDxw/fhx16tTBxYsX4eHhobYdv//+OyZPnlym23FYti+LzWuSLdqxCnlnZ+ci040U7lssft68eUhNTeX9TuXl5WHgwIFYuHCh0vNZ7GG1XSypM9bSnHcvLy8kJCSovC7w8vKCRCIRjZ87dy4AwMHBAX379hVsjVUmYtiSnJ2dzfV78uRJdOnSBQBQvXp1vHjxQtDH4MGD0a5dO0HaiX/++QeDBw/mtun6+fkx26IqL/a8lytXDomJibCysoKlpWWx62tV2Xfv3jH1LTb/n2y72GPVSitNltZBqFWZS2ynmaqSL+pCQ0Mxbdo0rF+/XmneGrnEdCCoI9b+u3TpgocPH2LDhg14+PAhAKB9+/bYt28fHBwcAACjRo0CAFhYWODQoUPw8fFRyZYjR44Uy5ubm3M/mqx5bcR0xgwePBi9e/fmHJutW7cGAFy+fBnVq1dnslMuXV1dtG7dGvfu3UPbtm15uRj19fXh4OCAnj17qtV3QRXOmflv89++feOccaXJqsPPnj0bMpkMoaGhmD59OgCgcuXKCAoKEuTwA/IvZLy9vQXtdevW5fJkqsurwk6cOBHz5s2DiYmJQgdmQYWFhfH+nj9/PhYsWIBp06YVeZ5c+vr6+Pz5M4D8izb5RVe5cuWQkZGhUh+FJc9XKL/oZFHVqlXx66+/qvW8ykT/Py+hJojVFjF5TbJFbF6TbGHly8oW1jx+YthTVp9VMcbKYnu3bt1w7949ZGdnq7QukN9suXXrlii8XC1btlQpTyCrIwwA3N3dsX79enTs2BEnTpzAvHnzAADPnz9H+fLlBbao6vAT04kn9ryvWLGCC0xYsWJFsY4nFpa1b7F5TbJFk8aqlVaaLK2DUKsyl9hOM1UlX9R5e3vjy5cvcHJygrGxsaAYQME7PWI6ENSROv07ODhg0aJFRfY7evRomJubo1y5cirbYmlpWSwfERGh8P+qqLSdMQUVFBSEmjVrIj09Hb169eLuduvo6CAwMJDJTrn09fUxfvx4eHl5Md2Z/zeKmpSmhg0bhq1bt2L27NmlyqrDSyQSTJgwARMmTMA///wDAEVGCw8YMADr1q0TON82btyIfv36lYhXhb158yays7O5/7Po/fv36NWrl8p8kyZNMHHiRPj4+ODKlSvYsWMHgPw74PKLRLmOHj0KU1NTNGnSBACwdu1a/Prrr3Bzc8PatWu5wiPyyFtVxOKEZC2Soq5YFvTaxb9W/23SlCi/stC/Nda5c+di+fLlmD9/PsaPH1/suoA1wk/diEA/Pz+MGDECAwYMwMuXL9G6dWvUrFkTf/zxB16+fMlFoKvjgFyyZAm6d++OZcuWwd/fH7Vr1wYAHDhwAPXr1+c4VoefmE48sefd39+f+/+gQYOKZAtKFZa1b7F5TbJFk8aqlVaaLK2DUKsyk/yCSmynGat8fX3x7NkzLFy4EBUrVizywk9MB4I6Eqv/33//HUFBQZgzZw6ioqJgbGxc7Dnz5s1j4ufPn49+/foprCioSKXtjCmox48f48cffxS0F1wcsEr+PlL1zrxcrNtrlEWaSSQSGBoaolq1aujatSvnvBWDv3r1KgwNDQHkRxxu3LgRJ0+eRK1atQQO94Iqjg0LC+M9vyp8QaWkpCAnJwfOzs48x2BSUhL09PTg4ODA618ikWDTpk04fvw4GjZsCCA/ijQtLY2bdxaete+YmBiOL/h/VdSrVy8cP36cqy5fnNasWYPRo0dj9+7dWLduHWxsbADkRwIXjgCZMmUKlixZAgC4ffs2Jk2ahIkTJyImJgYTJ04UOPulUmmR36O5ubmwsLBQ2cmmqBKxGNLEiCettNIUadJ7ntVBz8qLPVbWyr6s6whW/s6dO5yzbufOnfDw8MD58+e53xS5g1AdB2Tz5s3x5s0bZGRk8KrYjxgxgrdWZHX4lYXzVOx5B/JvRL948UKw5nv79i0qVKjA+/1jYTWN1yRbNG2sWmmladI6CLUqM8kXXGI7zVh14cIFXLx4kburWVhiOhDUkdj9A/mv1W+//Yb09HRUrFgRDg4OAmfMjRs3eH+HhoYiOTlZZX7Xrl2YO3cuGjRogP79+6N3796CXGFiOmMKqlq1amjWrBmGDh2KH3/8kXN2lUTy97uqd+YLnseST+fmzZu4ceMGcnNz4erqCiA/CkxHRwfVq1fHL7/8gkmTJuHcuXNwc3MThc/MzISXlxdMTEwAAJ6engDyLzoKSiKRCC68imLl42PhC2rQoEEYMmSIIFLk8uXL2LRpE86cOSPov27dugCA5ORkAICVlRWsrKxw9+5dhfYUxRcea3F9F1RMTAxatGghaAfyI/jGjBnDa6tWrRpmz56NS5cuwcPDQ/D5K7yl2s7ODn/99Zeg7xUrVgjaWPMV7t27l/d3dnY2bt68iaioKAQHB3Pjkys1NRWBgYEYNGgQL+1EVFRUsdHOqigkJASTJ08W3LjIysrCsmXLuM/gkSNHEBkZicDAQJVYuVNVK620KluJvf1abE2YMAHjx49XeV3Auo5g5VnzBLI6wogI169fR3JyMvz8/CCTyaCvr8/7nlU3+lFMJ57Y8y6fG0X6+vUr9PX11WY1jdckW8TmWftWVfI0LlppJba0DkKtSl25ubm4ffs27O3tubuFEydORIcOHbBy5UrRnFrqqnr16sjKylL6uJgOBHUkdv9y/fDDD7y7vcWJtTBBfHw87t69iz/++APLly/Hzz//jB9++AH9+vVDt27dYGxsXGbOmBs3biAiIgITJ07E2LFj0adPHwwdOpS3/UWZFL3fgf9zIKh6Z16d3D4AuGi/iIgIbuHw8eNHDBs2DE2aNMHw4cPh5+eHCRMm4NixY6LxWVlZOHbsWLHzJdfTp09RuXJlSKXSIrmYmBiV2cJ937x5U2FOzIYNG2Ls2LFc/yxi5dVVjx49cPLkSe59LNfKlSsxe/ZsgYNw48aNMDU1RWxsLGJjY3mPSSQShTkX5fry5Qu+ffvGayu4CGXNV9i1a1dB248//gh3d3fs2LEDQ4cORbNmzbjHQkJCEBYWJkg74eHhgY0bN5YokhcAgoODMXLkSIHT7/PnzwgODuY+g02aNEGzZs0wduxYlVgWiR3xJOb26P9kXpNsYeU1yRZ1pM5YWZz5NjY2zLwYUmfeExMTVVoXyKXqOkJdnjVPIIsj7MmTJ2jXrh3S0tLw9etX/PDDD5DJZFiyZAm+fv2K9evX8/pmdfiJ6cQTc95XrVoF4P9uaBeMmMzNzcXZs2e5HNgsrKbxmmSLpo1VLjHSuGilVYlU6nWRtfqfU0BAAG3atImIiHJycsjHx4ckEgmZmJhQTEwMERE1b95cpaNFixZlZrdMJqPk5GQ6duwYNW7cmGJiYujNmzf08eNH3vG/KFNTU0pOTi7T5zx37hyNHj2arK2tSSaTlelzy5WdnU1//vknde7cmfT09Mjd3Z1CQ0Pp1atXHKPK+72gTExMKCUlhYiIOnfuTIsXLyYioidPnpChoSHHBQUFUVBQEEkkEpo8eTL3d1BQEC1cuJC2bt1KX79+FfRfuXJlunv3rqD9zp07VLlyZSIiun79OpUvX75MeFUl//yVNluQNzMzoxs3bggev3btGpmamqrc37+hX3/9laytren+/ftc2/Lly8nMzIzOnj1b4v4zMzNpzJgxZG1tTVKpVHAUVOfOnalt27YUEhJCenp69PTpUyIiOnbsGDk7O6v8nMnJyWRiYiJoNzIyosTEREH7w4cPycjIiHFk/6eRI0fS69evSSKR8D7Dcp06dYqsrKx4bSwsi1i/U8XkNckWsXlNsoWV1yRbxOblrFQqpb///lvw+Js3bwTfS0TEzKsqsV8nY2NjldYFcqm6jlCXj4mJIQsLC5JKpTR48GCuffr06dS9e3cBb2FhQQ8ePCAiopUrV1Ljxo2JKP83wdHRkcd27dqV+vfvT1+/fuXNVUxMDFWrVk3Qd5MmTSg6OpqIiF68eEEymYwaNWpEVlZWFBwcXCJbWHkx593BwYEcHBxIIpGQra0t97eDgwO5uLhQmzZt6NKlS8yspvGaZIumjVWumjVr0qFDh4iIKCEhgQwMDGj69OnUsGFDGjRokIDXSiuxpXUQalVi2djY0NWrV4mIaO/evVS5cmV6+PAhzZo1i/vh1UTJFyoSiYQkEongAlne9r+of8NBePPmTZo0aRLZ2NgoXHiVpb58+UJhYWFkYGBAEomEDAwMaMCAAfT8+XPm93v9+vVp2rRpdPbsWTI0NKRbt24REdHFixfJxsZGwEdGRtKXL19UtlWZYzImJoZzgiUnJ3NOV7F5VVUWF8qdOnWiXr16UU5ODvdYTk4O9ezZk9q1ayc4Lysri5YuXUrt27enunXrkpeXF+8oCc/aNxHRkiVLyMbGhlJSUmjx4sVkZmZG586dU3keitLo0aOpRo0atHv3bjIyMqLNmzfTvHnzqEqVKvT777/z2CdPnlDHjh2pVq1anHOciOjnn3+mcePGqfR8nz9/poCAAHJxcRE85uLiQlOmTBG0T5kyRSF/5MgRiouL4/5es2YN1a5dm3x9fendu3dcu4WFBVlaWpJUKuX+Lz/MzMxIKpXS6NGjmdmCCg4Opk+fPikcb8GL2bi4OPry5Yuo/OzZszXGFu1Y/3NsV1XyGy9ijpXVQS+WQ18mk9HPP/8s2rzXq1ePaV3Auo5g5YnyfxsLfn8SEaWkpCh0wLI4wsqVK8c55Ar+lqekpCi8AcTq8BPTiVcW8968eXPBvCsTC6tpvCbZIjbP2nfB9+TcuXOpZ8+eRJR/871ixYoq96OVVqUlrYNQqxLLwMCA0tPTiYho+PDhFBAQQEREjx8//lciwVgXx2fOnCnyKCgxHQjqqLT7t7S0pNevX5OpqSmZmZnxLpILHwV5IhJcVCviC+vx48c0f/58cnNzIx0dHWrZsiVt2rSJPnz4UKKxqjsvV69epVGjRpGlpSVVqVKFZs6cSY8fP6azZ89Sq1atqF69eszvd9Y782lpaVz/RESXL1+mgIAA2rBhg0Kb/fz8yNHRkfbs2UPp6emUnp5Oe/bsIScnJ+rfvz8REW3bto3q1q1bJryqKgsH4d27d6l8+fJUtWpVGjRoEA0aNIiqVq1K1tbWdPv2bcF5fn5+ZGVlRSNHjqS5c+fyojiDgoJKxLP2LdfUqVOpfPnyZGFhQRcvXuQ9NmHCBMrMzOT+X9RRWLa2tpzjVyaTUVJSEhERRUdHU/v27Yue4GJU+LvAwsKCdHR0SCaT0f79+wX8oUOHyNDQkGrWrElDhw6loUOHkoeHBxkaGnJ31QtK1bvtkZGRFBERQRKJhFauXEmRkZHcsXXrVrpw4YJabEGJHfHEwmuSLWLzmmTLf7rtqkqdKD9VWVYHvboOfZaxijnvrOsCsXmi/B0UJ06coPXr11NGRgYRET179oz++ecfAcviCLOwsOB2IRT8LY+Li6MKFSoI+mZ1+InpxCuLeS+snJwcunnzpkoOJhZW03hNskVsvjjW0tKS+4z4+Phw631lTnSttBJb2hyEWpVYFStWxL1791CpUiUcPXqUy5Hw+fNn6OjoCPgvX75g9erViImJwatXr5CXl8d7vHAxC1ax5JsCwMuFVZyGDh2K48eP48cff0T9+vWLzT3DyrOqtPtfsWIFZDIZ+vfvDzc3t2KT4cp5+f9Znr9hw4a4evUqatWqhcGDB8PX17fIHEEsY2Wdl7CwMERERODhw4fo0KEDoqOj0aFDBy7nnaOjIyIjI+Hg4AAbGxum97uqFfzkYs2ns2HDBkyYMAF9+/blqoDr6urC39+fKzhRvXp1bNq0qUx4TZKbmxsSEhKwZs0axMfHw8jICAMHDsTYsWO5Ks0F9ddff+Hw4cMK8xYqEguvCivPX1NQNjY2MDY2RtOmTXHlyhVcuXIFQH7RkZs3byI7OxuAMDdpcXr37h2cnJwA5OcbfPfuHYD878VRo0YpPa+4fIUAEB4ezvtbKpXC2toaDRo0UJjXtEOHDkhKSsK6detw//59AEDnzp0xcuRI2NraCnhVi6bIcxc6OjrCx8eHl9ezsFjYgiIlRYXi4+MVvsfE5DXJFrF5TbLlP9121jx+Yow1PDwcRIQhQ4YgODiYV5BLXsFWXsBIHV6dsTZt2lS0eWddF4jNs+YJXLJkCbp3745ly5bB39+fK/J34MABQe7mNm3aIDw8HBs3bgSQn58tMzMTc+fOVVjkijUfIostrLzY8w4AP//8Mzw8PDB06FDk5uaiadOmuHjxIoyNjfHXX3+hefPmarGaxmuSLZo21iZNmmDixInw8fHBlStXsGPHDgD5BQHluTi10qpMVbb+SK3+GzV37lwyNzen6tWrk52dHbdl5bfffqOGDRsKeHUjaVQV65aT2NjYIo+CYt3iV5pbAkujf1W35pWFZsyYoTC3nTKxjJV1XqpVq0YLFy6k58+fK2W+fv1KkZGRzO93IrY786zba+T6559/KD4+nuLj4xX2m56eTrm5uWXGFydNysU1atQoev36NdWoUYPi4+NVPo+FV4UtmK+mqKOo94Gq8vDw4CKkW7VqRZMmTSKi/Pdc4SgKlnyFLJLPOyvPerf9+vXrlJCQwP29b98+6tq1K02fPl2Q11NVVuyIJzG3R/8n85pky3+67XKJEeWnri1nzpyh7OxsQbsysfKqjLWs5p1lXSA2z5onkEj1Lcnp6enk5uZGNWrUIF1dXWrYsCGVL1+eXF1dFb4W6kThsWyPZuXFfp0qV66sctoaFlbTeE2yRdPGWhppXLTSqjQlIVJSi1srrRi0e/dupKeno1evXtzdjqioKFhYWAgqWpqbmzNF6agqS0tLSCQSfPz4EWZmZrw7ubm5ucjMzMTIkSOxdu1a3nmKKqMWPlcuNzc3bN++HbVq1VLJJlaeVaz9e3h4YMmSJejQoQNu376NevXqYeLEiYiJiUH16tURERHBsTo6Onjx4gUqVKjA6+Pt27eoUKECb17U4eX69u0bUlJSULVq1SKjdljGKva8s7zfC9+ZT0xMhJOTEwICAhTemTc1NcWdO3fg4OCALl26wMfHB9OmTUNaWhpcXV2LrLhdlMzMzHDr1i0uauw/iS8rWx4+fIhVq1Zh/fr1sLe3L/a8I0eOqMyzsOooJiYGLVq0UPjY2rVrBVWPV6xYAR0dHYwfPx4nT55E586dQUTIzs5GWFgYAgICOHbMmDGIiYnBvHnzMGDAAKxduxbPnj3Dhg0bsHjxYvTr108tm9V9nX7++Wd8+/YNPj4+mDdvHlJSUmBjY4Pjx49j7NixSExM5J1Xr149BAYGomfPnnj8+DHc3NzQo0cPXL16FR07duRFPKrKRkVFcRFM4eHhxUYwiclrki3asf7n2C6XVCrF33//DWtra1776dOn0adPH7x+/Vr0scp148YN6OnpwcPDAwCwf/9+REREwM3NDUFBQdDX1y8Rr8pYy2LeWdcFYvPly5fHhQsX4OrqCplMhvj4eDg5OSE1NRVubm5cFfuCysnJwZkzZ5CcnAw/Pz/IZDI8f/4cZmZmvOqtcnb79u1ISEhAZmYm6tSpg379+sHIyEjQL5C/7i4chZeamgpjY2PBGpPVFhZe7HkHAENDQzx69AhVqlThIg3Dw8ORkpKC2rVrIyMjQy1W03hNskXTxqqVVhqnsvRGavXfKdZiFqxROqpK3RxSHz584B2vX7+m48ePU4MGDejkyZM89vDhw9SuXTtKTU1VySZWnlWs/bMkwpVIJArvpj579kxhHhhW/vPnzzRkyBDS0dEhHR0d7n00duxYWrRokYBnGau68/7p0ye6f/8+FyknPwqK9f3OemdenSTXqkiTKlay8mVly6tXr6h58+YklUrJ1NS02DyaLDxr3wWVl5dHeXl5RTIWFhZ07do1QXt4eLhKuWBTU1Ppzz//VPjdLFa+QnVfJ9a77WZmZvTo0SMiIlq8eDG1adOGiPIrp1epUkVtlkj8iCcWXpNsEZvXJFtYeU2xpSyi/Fht9/b2pt27dxNR/m+tgYEB+fr6UrVq1bh8v+rw6oxVzNeJdV0gNs+aJzA1NZWqV69OxsbGvPXb+PHj6aefflJpDooSSxQeqy0svNjzTkRkZ2dHx44do5ycHLK1taW//vqLiIju3LlDFhYWarOaxmuSLZo21oLKysqijx8/8g6ttCpraXMQalViVatWDc2aNcPQoUPx448/wtDQsEg+NDQU06ZNK/VIGnVzSBW82yvXDz/8AH19fUycOBHXr1/n2r29vfHlyxc4OTnB2NgYenp6vPPkebzU5VnF2r++vj53J/jkyZMYOHAgAKBcuXLcHS15HjSJRIJNmzbx7qbm5ubi7NmzqF69OtfGyssVGBiI+Ph4nDlzBu3atePaW7dujaCgIAQGBqo9VtZ5ef36NQYNGoSjR48K7JSPQy7W93tcXBwuXLggiGRwcHDAs2fPBDxrPh1NV0ZGBk6fPg1XV1fUqFGDa7937x4qV66sNqsOX5x8fX3x7NkzLFy4EBUrViw2dyULz9o3AERHR2PZsmVISkoCALi4uGDKlCkYMGCAgF22bBnat2/P+7yFhoYiJCQEhw4dUtj/qVOncOrUKYW5YDdv3sz9X918hWLJzs4Of/31l6BdnhOzsIiIG9/JkyfRqVMnAICtrS3evHmjNgsAMpkM9+/fVzmCSUxek2zRjlXzbVc3j5+YY01MTISnpycAYNeuXWjWrBm2bt2K8+fPo2/fvoL8pqry6oxVzNeJdV0gNs+aJzAgIADe3t6Ij4/n5QXs3r07hg8fzmOjo6MF5xeUfB0qF2s+RBZbWHmx5x0ABg8ejN69e6NSpUqQSCRo3bo1AODy5cuCtTMLq2m8JtmiaWP99OkTpk2bhp07d+Lt27eCx5XtwNJKK9H07/ontfpv0M2bN2n8+PFkbW1N5ubmNGLECLp8+bJSviSRNKqIJd9UUbp//z6ZmJjw2lq1akXOzs60ePFiioiI4EUpRkZGCvpg5VnF2n/nzp2pbdu2FBISQnp6evT06VMiys9v5+zsTET/lwdNIpGQra0tL/eZi4sLtWnThi5dusT1ycrLZWdnx1VlLXinNSkpSWHEE8tYWefFz8+PfHx86OrVq2RiYkLHjx+nLVu2kKurK3fnTy7W9zvrnXki9nw6qqisovB69epFq1evJqL8KFFnZ2fS09MjXV1dLtJDLhZWHZ7VdiMjIy5iUxWx8Kx9h4aGkrGxMU2dOpX2799P+/fvpylTppCxsTGFhYUpPGfJkiVkY2NDKSkptHjx4iJzcQYFBZFUKqX69etT165dqVu3bryjoFjyFbKoNN6Tqtxtb9GiBQ0cOJCio6NJT0+Pi4A8c+YM2dvbq80SiRfxpA6vSbZox/qfY7uYUX6stshkMkpMTCQiotatW1N4eDgRKa9gy8qzjFXMeWddF4jNs+YJLFeuHJcruWD/ivLAWlhY8A4TExOSSCRkYGCgcM3PGoXHYos6tos573Lt2rWLwsLCKD09nWuLjIykffv2lYjVNF6TbNGksY4ePZpq1KhBu3fvJiMjI9q8eTPNmzePqlSpQr///rtCW7TSSkxpHYRalZqys7Ppzz//pM6dO5Oenh65u7tTaGiooGCI2E4z1kVd4e2kt27doiNHjlCzZs3Ix8eHx4rpQFBHrP2zbM1r3rw5U+ESVv7/tXfuUVGW2x//DqCR3FVWSolIoKaJqGhmpXiJPHqATp6lKaaZZnoSCVO7Ha2jqxOpgJGmWRaXErtKqZV5QVKPCQKi4PEWKWKdvBSGokuw/fuDxfwY3hl898w8OMT+rDVryTPfeWY/m9fhmf0+e+9bb73VuHmqv5E6cOAAeXp6mtWrCsZ06NDBGOTz8PCgo0ePEhHRF198obkG6tB7vY8dO5aefPJJIqpdZ2lpKVVWVtKwYcPo8ccftzg3p8i1HpoqQHjbbbcZff/hhx9SUFAQXb58md566y0KDQ01eQ1Ha42ea3ufPn2MQWs9cPTcuQMCAigtLU0znpqaSgEBARZfN3/+fGrXrh15e3s3+n4dOnSg9PR0XbYkJSXRG2+8QUREW7duJVdXV7rlllvIycnJ+IXcGqy9xrhNU4qKiujuu+8mT09Pk0ZYs2bNovHjx1utJeKnJKvUO5ItstbmYzv3pqrKtXID9Fw9Z60q/c7dF6jWE9XuOzIyMmjevHk0c+ZMeuedd6iqqsqs1tpAWB3Hjh2j4cOH0zfffKN5jhvwUxnEawq/C4KqMi6CYC2SYizYDRcXFzzyyCMYPXo03nrrLbzwwguYO3cuXnzxRYwdOxavv/46OnbsiP/85z/Yu3evMXXS3nBTVEJDQ2EwGEAN+vUMHDjQJM0OALp3785qEsHVc+HOz0nNy87ONvn5+vXrOHToEDp37mxSONpafVhYGDZv3ozY2FgA/98Y5t133zWb1sRZK9cvly9fNha+9vHxwblz59C1a1f06tULBQUFZl+j93pPTEzEQw89hB49euDq1auYMGECjh8/jvbt2yMzM1MzLze9Ri96Ulrtob948SLatm0LAPjmm28wZswYtGnTBqNHj8a8efNMXsPRWqPnkpCQgGeffRavvvoqevXqpUlN9/T0tFrPnfvnn3/GoEGDNDYOGjQIP//8M4D/T++vz+233442bdpg8ODByM3NRW5uLgBg9uzZJrpr166Znd8c8fHxxn+PGDECR44cQX5+PoKCgpQ1AmqM+fPnIzs7G6tWrTLbNKUhISEhOHTokGZ86dKlcHZ2tloL8FOSVeodyRZZa/Ox/amnnsLzzz+PXr16obS0FOPGjcMjjzyCTz75BFVVVZo9k8q1Ll++HDExMcjKysJLL72EoKAgALWNwcx9XnH1nLWq9Dt3X6BaD9TuaSZOnGj2uYZwU5IbEhwcjISEBEycOBFHjhwxee6PP/4wm1JZXl4ODw8Pm23h6FX5PSUlBdOnT4erq6vZv+MN0audPXs2a27V+uZsu+q11sfRyrgIgpwgFOxGXl4ezZw5k3x8fOiOO+6gl156iUpLS+m7776j4cOHU//+/YmIf5KGCzfl5OTJkyaPsrIyunLlitm5t2zZQoMGDaLs7Gw6f/78DVPbuHoutsx/o9S8uLg44ynDmpoaGjRoEBkMBnJzczPe6bJFv2vXLnJ3d6cZM2aQq6srxcXF0YMPPkhubm5mmy1w1sr1S1hYmPFOdmRkJD322GNUXl5O8+fPp8DAQLP+03u9E/HuzFtT5FoPTXWCMDg4mD766CO6dOkS+fr60vbt24mo9mRou3btTF7D0Vqj18uMGTPo3LlzZDAYyGAwaE6k1Y01hKPnzt2zZ0969dVXNeOLFy+mu+++m4jIJJ2/sUeXLl0088yfP58WLVqk20fbtm2jF154gaZOnUpTpkwxeVhLnd+5emvvtu/fv58yMjIoIyOD8vPzG30vvVrVJ55Upkc3Z70j2dLcbVd5yo9riyWuXLlC165ds1nPWatqv3P2Bar1aWlpjT4awk1JNkdhYaHZcjLcU3hcW7h6FX4PCAig8+fPG//d2N9ujpY7t2q9I9niaGutj6oyLoJgLRIgFGwmMTGR7r77bmrVqhVFR0fTxo0b6fr16yaa06dPk7OzMxGpD5pZsyHV++VXZQDBGrjzc1Lz/Pz8KC8vj4iINmzYQH5+fnT06FH65z//SYMGDdLMzdUT1aaAT5s2jfr370933XUXxcTEmKT/WLtWrl8yMjLo/fffJ6LawED79u2NNXLWr19vouVe71y46TUNuXjxIm3YsIEOHz5sMl5WVkY1NTXK9StXriQXFxfy9vamkJAQo29SUlIoPDzc5DUcrTX6r7/+mnbt2mX8ecWKFdS7d28aP3682XT4nTt3NvqwRc+d+9NPPyVnZ2djzdBFixbRQw89RC4uLvT5559r9HqIj483PuLi4sjb25sGDx5Ms2bNMnkuPj7e5HWceoVEfL9z9W5ubnTq1CkiIrr99tuN5QFKS0s1dWOJiH755RcKDw8ng8FgrHVrMBho2LBhmpIAHC0RPyVZpd6RbJG1Nh/buTdVVa61Dk4wn6PnrFW13x0Jbp1AIv2Bs7oaunWPrKwsWrVqFfXs2ZNGjhyp0VsTfFQdbBUElagq4yII1mIgapBXKQhMgoOD8cQTT+Dxxx9Hx44dzWquXbuGzMxMTJ48GU5OTgC0KYxEBIPBYHO3poMHDyImJgZlZWWYM2cOXn75ZQBAbGwsLly4gHXr1pno//Wvf2HRokUICwszdpyqz4YNG4z/zsnJafS9hwwZYvIzV8+FO//TTz+N7OxsLF682GxqXkxMjFHr6uqKEydO4I477sD06dPRpk0bLF++HD/++CN69+5t7Hpsjb66uhpPPfUUFixYgC5duth9rbb6vaqqCkeOHIG/vz/at29v8hz3eud28PPx8cGePXvQo0cPeHh4oKioCIGBgdi9ezfGjBmDX375xUQ/duxYDB48GLNmzcKVK1fQu3dvnDx5EkSE9evXY8yYMU2qB4D8/HyUlZUhIiICbm5uAIDNmzfDx8dHk/bF0XL1vXr1wuuvv45Ro0bh0KFD6N+/P+bMmYPs7Gx0794d77//vsXfiyOQn5+P5ORk/Pe//wUA3HXXXXj22WfRp0+fRl9X92e94WfZ0KFDdb2vwWDAjh07jD937NgRS5YsMds92Rxcv3P1ISEhePPNNzFkyBCMGDECoaGhWLZsGVJSUrBkyRKUl5eb6MeNG4fS0lKkp6cbu10fPnwYkydPRlBQkEnqF0fbGFevXoWzs7Mmlfxm6B3JFtV6R7KFq79ZtgwbNgydOnXCiBEjMHXqVBw+fBhBQUHIycnB5MmTcfLkSbvbY0l79uxZjBs3Djk5OfD29gYAVFRUYOjQoVi/fj18fX1t0ttjrfbwO3dfoFpvjuPHj2PmzJmYN28eHnrooRvqLVG356/DYDDA19cXw4YNQ2Jiotl9VE1NDdavX4+DBw/i0qVL6Nu3L2JiYnDrrbdabYc1qPL7nDlzdL2/ufJHjWkTExNZc6vWN2fbVa+1MU6dOnVTy7gIggQIhSZHddDMEpY2ddwvv80Zf39/pKenIzw8HJ6enigoKEBQUBAyMjKQmZmJr776yqjt3Lkz3nnnHQwfPhxdunTBqlWrMHr0aJSUlOD+++/Hb7/9ZjI3V+/l5YUDBw7oDhDaG71/+AEgKSnJ6vdpWH+xuroaVVVVaN26Ndq0aWOsNVLHuHHj4OXlhTVr1sDDwwMHDx6Er68voqOj4e/vrwmWdOjQAVu2bEHv3r2xbt06vPzyyygqKkJaWhrWrFmDwsJC5fo5c+Zg8eLFcHNz0+VXvdqkpCTW3A1/T+7u7iguLkZAQABeeeUVFBcX49NPP0VBQQFGjRqF//3vfyb67777rtH5Bw8ebLWeO7c1pKenY+nSpTh+/DgAoGvXrpg3b57Nn23t2rVDbm4u7rzzTl16rt+5+uTkZDg7O2P27NnYtm0bIiMjQUSorq5GUlIS4uLiTPReXl7Ytm0b+vfvbzKem5uLiIgIVFRUWKWtT35+vjGQ26NHD/Tt27dRH6nUO5ItqvWOZEtztZ17U9Uae/RquQF6rt6atarwO3dfoFpvif3795utE2iPAKS9UBk8VeV3zs06TuBpx44d7BuBKvXN2XbVa23I9u3bsX37dpw9e9ZYy7SOhvXwBUE10qREsBtVVVUoKyvDtWvXTMYb3v1QFQBsiN5NHadYv8oAgjVw5+cUwp0yZQrGjh1rPFU5YsQIAMC+ffvQvXt3zXtx9Q8//DCysrJMGiDYa616tA0DYQUFBaipqUG3bt0A1Da7cXZ2Rr9+/czOofd6bxgYBUzvzDeEWxRbdaMPPfrCwkJUV1cb/22Juk2UXi137oa0bt0aVVVVAGqLxtdt/Nu2bas5AQsA4eHhjc7b8HQzR8+du24sKyvL+DnWs2dPREVFmW2WkZSUhAULFmDWrFm47777AAC7d+/GjBkzcP78ed3/z8wxbdo0rFu3DgsWLNCl5/qdq+c2Tfnjjz/MnvZp1aqVZiPO0QLqTzxx9I5ki6y1+djObcyjcq3ffPMNtm3bZgz2AbX7t5UrVyIiIkJjC1fPWatKv3P3Bar1lnBxccFPP/2kGW94E6ZhIKx+kM3SjT2DwQBXV1cEBQUhOjrauM/gBvw4tnD1qvzesKmfPeHOrVqvcm5H01vLjTLZBKHJafKkZuFPx9mzZ2nUqFFma9qZq/eWk5PT6MNWuDWkOMX662rb1X80tlaungt3fm4h3E8++YSSkpLo9OnTxrHU1FTKysoyaw9Hv3jxYvL29qYxY8bQv//9b3rjjTdMHrasleuXxMREioyMNKl39uuvv1J0dDQtW7bMRMu93i2Rl5dH3bp1M/scpz6O6kYfqhqDNAWRkZHGGn6tWrWi8vJyIqqtgxocHKzRV1RUmDzOnTtH3377Ld1zzz20bds2m/TcuY8fP05du3alNm3aUJ8+fahPnz7Upk0b6tatm7HIfn0CAgLMFpNPTU2lgIAA3T6rw9p6hUR8v3P1RLymKVFRUTR48GA6c+aMcay8vJyGDBmiqaHI0RLVFtQPCwszqclZUlJCYWFh9Oijjzap3pFskbU2H9vr0FvHT+Va3d3dqbCwUDNeUFBgtqEFV1+HnrU2ld/r09i+QKWeWyfQHMeOHaPhw4cbG77VER4eTp6enuTm5kZ9+/alvn37kru7O3l5edE999xD3t7e5OPjQyUlJURkXT1EvbbYS6/69yS0LDp06EDp6ek32wxBMCIBQsFmJkyYQPfddx/l5eWRm5sbffvtt5SRkUHdunWjTZs2afSqg2bcTdrs2bN1f/lVGUCwBu78jlQIl9vlS2Uwxs/Pj4qLizXjhw4doo4dO5qMca93S1jq4MdFdaMPrt6ROHXqFI0ePZpCQkKMHbaJiJ555hmKjY3VPc/OnTupb9++SvSWtH/5y19o5MiRdOHCBePY+fPnaeTIkTRq1CiN/pZbbjE2ZKrPsWPH6JZbbtFtex3h4eG6HkOHDtW8lut3rp7bNKWsrIxCQ0OpVatWFBgYSIGBgeTi4kJ9+vQxuZnB1RLVdkXNzc3VjO/bt4+8vLyaVO9ItqjWO5Itzd127k1VlWvlBui5es5aVfvdHNx9gb305vbkt912G40fP55++ukn3fObC4QlJyfTI488YtKEsKKigv7+97/T8uXL6fLlyxQdHW3sKG0ObgDPki320qv+PQkti7Zt25q98SsINwtJMRZsZseOHfjiiy8QFhYGJycndO7cGQ8++CA8PT3x2muvYfTo0Sb6hkfwq6urUVhYiAULFuDVV1+12R5uysnBgwcRGhoKACguLjZ5ruExby8vL83rH3zwQbRu3Rpz5sxBfn6+TXou3PlvlJqXkpKC6dOnw9XVFSkpKY2+9+zZs9n6+vz444/Gf5OFpgr14ayV65fff/8d586d07zm3LlzqKysNBnjXu9ffvmlyc9EhJ9//hkrVqwwpoLWh5te849//AP33HOPsXFHXUHwwMBAs/+fVOsdCX9/f2zatEkznpyczJrntttuw9GjR5XoLWlzcnLw/fffG9OugNpagAkJCWavm6CgIHz88cd48cUXTcY/+ugjBAcH67a9DltSa7h+5+pXr16N1NRU3bUVO3XqhIKCAmzfvt2k4UtdGQRrtQA/JVml3pFsUa13JFuau+2xsbG4dOkSSkpKNHX8Zs+erSltoXKtK1asQFRUFAICAtCpUycAQFlZGXr16oUPPvjAZj1nrSr9zt0XqNabW481mEtJXrp0KbZu3QpPT0/jmJeXF1555RVEREQgLi4OCxcuNLs/ryM4OBgJCQlm6yFybOHqVftdEAB+GRdBUI00KRFsxtPTEwcPHkRAQAA6d+6MdevW4b777sOPP/6Inj17GmtL3YicnBy7BM08PDywa9cuY9CvjsLCQgwZMsRsTStbOXLkCMLCwnDp0iUlenva01gh3OzsbOzfvx/t2rVrtHmIwWBAaWkpunTpwtI3ZO3atUhOTjY2VQgODsYzzzyDadOm6V0qy5eWtJMmTcKuXbuQmJiIAQMGAKitnThv3jw88MADSEtLM2q51zu3g5+eItfcxh2q9c2Bq1evaupF1v/CAtTeLKhP3eY+ISEBNTU12L17t9V67txt27bFpk2bNPVR9+zZg8jISE1x9M8++wzjxo3DiBEjjF9E9uzZg+3bt+Pjjz/G3/72N7N+UY0ev3P13KYpAK8AOEcbHR2NiooKZGZmws/PDwBw5swZxMTEwMfHBxs2bGgyvSPZImttPrZzG/OoXCtQ+9moN0DP1XPWqtLv3H2Baj23TmBjgbBOnTrh66+/Nj7n7u6OTZs2aerw7ty5E5GRkaisrERpaSlCQ0Mb3Z8fOHAAgwcP1mg4tnD1qv0utFzq/5/7448/kJaWhpCQEISEhGhuNDSXfbbw50FOEAo2061bNxw9ehQBAQHo3bs33n77bQQEBGD16tWsP4bcUzqWGDZsGOLi4jSbtPj4eAwfPtymuRv7kt8wIGmNXrU9NyqEW/9UX/1/W4Krr8/ChQuRlJSE2NhY3HvvvQCAvXv3Ij4+HmVlZVi0aJGJnrNWrl9Wr16NuXPnYsKECcaGGC4uLpg6dSqWLl1qouVe79w783qKXHMbd6jWOyqXL1/Gc889h48//hgXLlzQPN+wMUhoaKjZbnQDBw4020WOo+fO/de//hXTp0/H2rVrTYLWM2bMQFRUlEY/ZswY7Nu3D8nJycjKygJQ+0U5NzcXffr00ehVwvU7V8+9284pAM4tFq76xBNH70i2yFqbj+0qT/lxbQFqT+nv2LHDGKAvLCw0dhc291nJ0XPWqtLv3H2Ban1hYSEKCgpw/fp1TaO27t2746233sKzzz6L3bt3o0ePHnj44YdNXt8wEFaf6OhoPPHEE0hMTDQGZvPy8jB37lzjPLm5uejatSsA/ik8ji1cvWq/Cy2XhntrvZlsgtAUyAlCwWY++OAD1NTU4PHHH0d+fj5GjhyJCxcuoHXr1khLS8O4ceNM9NyTNFxOnz6NqKgolJSUaDZpX375Je644w6r53Zycmr0S37Dbr1cvWp7OnbsiCVLllhMzbvRabE6DAYDEhMT2fr6+Pr6IiUlBePHjzcZz8zMRGxsLM6fP28yzlmrtX6/fPkyfvjhBwDAnXfeCTc3N42Ge71z78xbYv/+/az0GgF4+umnkZ2djcWLF+Oxxx7DypUrcebMGbz99ttISEhATEyMif7UqVMmPzs5OcHX1xeurq5m5+fouXNXVFRg8uTJ2Lhxo/ELbXV1NaKjo5Gammo2jd5R4Ppdj96Wu+03+tyzVluHyhNPXL0j2SJrbR62qz7lx9HeKEDf0BaunrtWVX7n7gtU65cvX45du3bh/fffN57YvnjxIqZNm4b7778fTz75JCZMmIArV65gy5YtFtdvjkuXLiE+Ph7p6emoqakBUHsDdvLkyUhOToabmxsOHDgAoDZI4kin8FT7XRAEwRGRAKFgd6qqqnDkyBH4+/ujffv2mudVB80A/qZOLyoDCE1hz41S84YOHarrfQ0GA3bs2MHW18fb2xt5eXma+mjHjh3DgAEDNGlNKoMxtnCj633o0KGN3pk/evQoDAaD8c68JSyl1wiW8ff3R3p6OsLDw+Hp6YmCggIEBQUhIyMDmZmZ+OqrrzSv4aSXcvXcuQHgxIkTOHz4MIDaWqpBQUEW13v9+nVkZWUZP/d69uyJqKgoODs7W3yNCrh+16O35bOGk5KsOn1Ztd6RbJG1Ng/brbmpqmqt3AA9V89dqyq/c/cFqvW33347tm7dqtmDlJSUICIiAmfOnEFBQQEiIiJw/vx5qwJhly5dMpaaCQwMhLu7u4XfEg+VQTzVfhcEQXBEJMVYsAq9J8cA7WmOhqmoKoI33BQVvXTu3Jm1YeTqVdtzo9Q8bmMCWxoZPPbYY1i1apXm+lizZo3mhBHAW6u9/W7L9V630bzRnfn4+Hhs2bJFilzbkV9//RWBgYEAauvY1dXtu//++zFz5kyNnpteqjJ1FeDV6Dxx4gRGjx6N8vJy4xeT1157DZ06dcLmzZtZAS9b4fpdj96WzxpOSrLK9GXVekeyRbXekWxp7rZzG/OoXOu1a9c0dVftqeesVaXfufsC1fqLFy/i7NmzmqDVuXPnjDclvb29jfVhuSnJQG0twpCQkMZ/QeAH/Li2cPSq/S4IguCIyAlCwSoanuYoKChATU2N5o9tv379NKc5AOtO0uiFm3Kicm6Vtuid31EL4cbGxiI9PR2dOnXCwIEDAdTWWCsrK8OkSZNMbEtKSmL50t5+t+V6596Zd6T0muZOSEgI3nzzTQwZMgQjRoxAaGgoli1bhpSUFCxZsgTl5eUmepWnV7hzW6rRuWLFCsTHx2tqdI4aNQpEhA8//ND4xenChQuYOHEinJycsHnzZl3vaw+4fufq9cD53KuPyvRl1XpHskW13pFs4eodyZY6VJ3y49ry3HPPwd3dXXeAnqsH9K9Vpd+5+wLV+piYGOzdu9dsncBBgwYhIyMD69evx7Jly7B//36lKcncU3hcWzj64uJipX4XBEFwROQEoWAV9U9zJCUlwcPDA2lpacYOrL/99humTJmCBx54QPNaa07ScFi9ejVSU1NZm2NVc6u0Re/8jloIt7i4GH379gUAY92/9u3bo3379ia21dnF8aW9/W7L9c69My9Fru3HlClTUFRUhCFDhuD5559HZGQkVqxYgerqarPBcJWnV7hzr1q1Cu+8845Jjc6oqCiEhIQgNjZWEyDMycnB999/b5LS1a5dOyQkJDT5yVOu37l6PXA+9xreJ+V8Rqo+8aTyGmvOekeyhat3JFsAtaf89GgbBvPXrFmDbdu2WQzQc/X14axVpd+5+wLV+rfffhvx8fF49NFHzdYJBIDu3bvj3XffBQAsXboUW7duNekw7+XlhVdeeQURERGIi4vDwoULERERocsf9eGewuPawtFfvXpVqd8FQRAcEhIEG/Hz86Pi4mLN+KFDh6hjx46a8Q4dOlB6eroye9q2bUsnTpxwiLlV2tIU8zsSnLWq9Av3ep8wYQJ16dKFPv/8czp9+jSdPn2aPv/8cwoMDKSJEycSEVFmZib169ePiIji4+PNPubMmUMvvvgivffee3ThwgUla/uzc/LkSfrss8+oqKjI7PPz58+nRYsW6Z6Po+fO7eXlRceOHdOMHz16lLy8vDTjPj4+tGfPHs347t27ycfHR/f7quBGfrdVfzNRec1w9Y5ki2q9I9nC1TuSLUT8PZm91xoeHq7rMXToUKv01q5Vpd+5+wLV+joqKyupqKiIioqKqLKy0qL9bm5ulJ2drRnPzs4md3d3IiL64YcfyMPDQ5c/6uPn50clJSWa8eLiYvLz8yMiovz8fGrXrp1VtnD0TeV3QRAER0JSjAWb8fDwwMaNGxEeHm4ynp2djaioKFRWVpqMW1MEnoM1KSeq5lZpS1PM70hw1qrSL9zrndvBT4pc2xdO6lxcXBzS09N1p5dy9Ny5Y2Nj0apVK8343LlzceXKFaxcudJkfNKkSSgoKMDatWsxYMAAALUp+08++ST69euH1NRUc+5RhuomDDcTbtkGlfr63GxbZK3Nx/b66NmTNVXKvmputNam8jt3X6Baz4WbkszB3d0dmzZt0uyxdu7cicjISFRWVqK0tBShoaH4/fff2bZw9Dt37nQovwuCIDQFEiAUbGbSpEnYtWsXEhMTTb6Yzps3Dw888ADS0tJM9CqCN01VZ09lAKEp7GnOqAzGcOBe73Xo7eCnsrZPS4Nbi7KxTrnmuuNy9Hq09T/HampqkJqaCn9/f7M1Ot98802TOSoqKjB58mRs3LjReL1XV1cjOjoaqamp8PLysvj+9sbRarXaG9Xd3jl6vVu4prBF1mpZ72i210fPnkzlWpuSG621Kf0O8Dv7qtbrRWUgjBvwa4rgqaP4XRAEoSmQAKFgM1VVVZg7dy7ee+89VFdXA6j9Yzt16lQsXboUbm5uJnoVwRtbN2n2eB9bAwhNYU9zxt7BGGvhXu9cpMi1/bCmWP/NxB6fYydOnMDhw4cBAD169EBQUJDd7NNLUzRVEATBehy1eZkKWtJamxoVgTBrg48SxBMEQbAPEiAU7Mbly5eNzSbuvPNOi4GSlhTUEv686L3euXDTawTLqC5n4GisXbsWycnJOH78OAAgODgYzzzzDKZNm9akdnD93tJ+T4Jws2mqm6qOQEta658JCeAJgiDcHCRAKAiC4ECorO3T0mhJNToXLlyIpKQkxMbG4t577wUA7N27FytWrEB8fLym67FKHK1WqyAIgiAIgiAIN0YChIIgCA6EFLm2jZaaTubr64uUlBSMHz/eZDwzMxOxsbHK09GbsqmCIAiCIAiCIAj2RwKEgiAIDoik11hHS00n8/b2Rl5eHoKDg03Gjx07hgEDBqCiokLp+zd1cX9BEARBEARBEOyLBAgFQRAEoZkTGxuLVq1aaU7bzZ07F1euXMHKlStvkmWCIAiCIAiCIDQHXG62AYIgCIIg8KmfpmswGPDuu+/i22+/xcCBAwEA+/btQ1lZGSZNmnSzTBQEQRAEQRAEoZkgJwgFQRAEoRkiabqCIAiCIAiCINgLCRAKgiAIgiAIgiAIgiAIQgvG6WYbIAiCIAiCIAiCIAiCIAjCzUMChIIgCIIgCIIgCIIgCILQgpEAoSAIgiAIgiAIgiAIgiC0YCRAKAiCIAiCIAiCIAiCIAgtGAkQCoIgCIIgCIIgCIIgCEILRgKEgiAIgiAIgiAIgiAIgtCCkQChIAiCIAiCIAiCIAiCILRgJEAoCIIgCIIgCIIgCIIgCC2Y/wNcBdZwh8YTGwAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 1400x1200 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Check correlation matrix\n",
"import seaborn as sns\n",
"\n",
"# 1. Move 'has_resolution_incident' to the end\n",
"target_col = 'has_resolution_incident'\n",
"if target_col in df.columns:\n",
" columns = [col for col in df.columns if col != target_col] + [target_col]\n",
" df = df[columns]\n",
"\n",
"# 2. Create short column names (truncate to, say, 15 chars)\n",
"short_columns = [col[:15] for col in df.columns]\n",
"\n",
"# 3. Compute correlation matrix\n",
"correlation_matrix = df.corr()\n",
"\n",
"# 4. Plot with Seaborn\n",
"plt.figure(figsize=(14, 12))\n",
"sns.heatmap(\n",
" correlation_matrix,\n",
" xticklabels=short_columns,\n",
" yticklabels=short_columns,\n",
" cmap='coolwarm',\n",
" annot=False,\n",
" fmt=\".2f\",\n",
" square=True,\n",
" cbar_kws={'shrink': 0.6}\n",
")\n",
"plt.title('Correlation Matrix (Truncated Labels)', fontsize=16)\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "a6f7988d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number_of_previous_incidents_in_listing 0.101702\n",
"number_of_previous_payouts_in_listing 0.096180\n",
"host_account_type_Host 0.073745\n",
"number_of_listings_of_host 0.070200\n",
"listing_number_of_bedrooms 0.065542\n",
"listing_country_United States 0.062555\n",
"host_active_pms_list_Hostify 0.060898\n",
"host_country_United States 0.055897\n",
"has_deposit_management_service_business_type 0.055543\n",
"host_country_United Kingdom 0.049846\n",
"listing_country_United Kingdom 0.048641\n",
"guest_country_United States 0.047742\n",
"number_of_applied_billable_services 0.045234\n",
"host_account_type_PMC - Property Management Company 0.044632\n",
"has_completed_verification 0.040583\n",
"guest_age 0.039814\n",
"is_guest_from_listing_country 0.038440\n",
"listing_number_of_bathrooms 0.038292\n",
"listing_country_New Zealand 0.036971\n",
"is_guest_from_listing_town 0.036880\n",
"host_country_New Zealand 0.036791\n",
"previous_bookings_in_listing_count 0.035117\n",
"guest_has_email 0.033928\n",
"number_of_applied_services 0.032459\n",
"number_of_applied_upgraded_services 0.032452\n",
"guest_country_New Zealand 0.031652\n",
"guest_country_Other 0.031166\n",
"guest_has_phone_number 0.030379\n",
"booking_number_of_nights 0.026738\n",
"has_guest_previously_booked_same_listing 0.026621\n",
"host_active_pms_list_Hospitable 0.025430\n",
"host_active_pms_list_Hostfully 0.025058\n",
"number_of_previous_bookings_of_guest 0.024027\n",
"number_of_nights 0.023304\n",
"guest_country_Canada 0.022773\n",
"host_active_pms_list_Hostaway 0.021299\n",
"booking_days_to_check_in 0.020963\n",
"host_country_Canada 0.020417\n",
"has_upgraded_screening_service_business_type 0.020254\n",
"has_verification_request 0.019356\n",
"listing_country_Colombia 0.018607\n",
"listing_country_Canada 0.018591\n",
"is_host_from_listing_country 0.018029\n",
"number_of_previous_incidents_of_host 0.017803\n",
"number_of_previous_payouts_of_host 0.017717\n",
"days_from_booking_creation_to_check_in 0.016637\n",
"host_active_pms_list_OwnerRez 0.015977\n",
"is_host_from_listing_town 0.015359\n",
"is_host_from_listing_postcode 0.014238\n",
"host_active_pms_list_Avantio 0.011872\n",
"host_active_pms_list_Lodgify 0.010976\n",
"guest_country_Australia 0.009813\n",
"listing_country_Ireland 0.009753\n",
"listing_country_Mexico 0.009473\n",
"guest_country_Colombia 0.009243\n",
"is_guest_from_listing_postcode 0.009204\n",
"host_active_pms_list_TrackHs 0.008961\n",
"has_protection_service_business_type 0.008933\n",
"guest_country_Mexico 0.008703\n",
"host_country_Mexico 0.008603\n",
"listing_country_Bahamas 0.008572\n",
"host_country_Sweden 0.008302\n",
"host_country_Bulgaria 0.008129\n",
"guest_country_Germany 0.007512\n",
"guest_country_United Kingdom 0.007411\n",
"host_months_with_truvi 0.007277\n",
"host_active_pms_list_Guesty 0.007083\n",
"host_country_Portugal 0.007005\n",
"guest_country_Ireland 0.006589\n",
"host_active_pms_list_Uplisting 0.005862\n",
"guest_country_France 0.005616\n",
"host_country_Other 0.004820\n",
"has_billable_services 0.004251\n",
"listing_country_Other 0.003930\n",
"days_to_start_verification 0.003879\n",
"listing_country_Virgin Islands, U.s. 0.002997\n",
"host_age 0.002981\n",
"host_active_pms_list_Hospitable Connect 0.002904\n",
"host_active_pms_list_Smoobu 0.002597\n",
"host_country_Norway 0.002255\n",
"host_country_Australia 0.001435\n",
"listing_country_Australia 0.001435\n",
"days_to_complete_verification 0.001179\n",
"dtype: float64\n"
]
}
],
"source": [
"# Compute correlation with the target variable\n",
"correlation_with_target = df.corrwith(df['has_resolution_incident'])\n",
"\n",
"# Drop the target itself (its correlation with itself is always 1)\n",
"correlation_with_target = correlation_with_target.drop(labels='has_resolution_incident')\n",
"\n",
"# Sort by absolute correlation, descending\n",
"correlation_sorted = correlation_with_target.abs().sort_values(ascending=False)\n",
"\n",
"# Print the sorted correlations (you can keep the original signs too if preferred)\n",
"print(correlation_sorted)"
]
},
{
"cell_type": "markdown",
"id": "2caec836",
"metadata": {},
"source": [
"### Weighted classes"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "e6d091fb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: np.float64(1.0119419188492333), 1: np.float64(84.73863636363637)}"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We will use weight classes due to the inbalance of the target variable\n",
"X = df.drop(columns=['has_resolution_incident'])\n",
"y = df['has_resolution_incident']\n",
"\n",
"# 1. Split data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.3, random_state=123, stratify=y\n",
")\n",
"\n",
"# Compute label distribution on the training set\n",
"label_distribution = y_train.value_counts(normalize=True)\n",
"\n",
"# Calculate inverse weights\n",
"weights = {\n",
" 0: 1 / label_distribution[0],\n",
" 1: 1 / label_distribution[1]\n",
"}\n",
"weights"
]
},
{
"cell_type": "markdown",
"id": "ab8f7646",
"metadata": {},
"source": [
"### Feature Selection\n",
"\n",
"Since we have many columns, 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": 41,
"id": "0246eb6c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected Features:\n",
"Index(['number_of_nights', 'number_of_listings_of_host', 'guest_age',\n",
" 'has_guest_previously_booked_same_listing',\n",
" 'listing_number_of_bedrooms', 'listing_number_of_bathrooms',\n",
" 'previous_bookings_in_listing_count',\n",
" 'number_of_previous_incidents_in_listing',\n",
" 'number_of_previous_payouts_in_listing', 'guest_has_email',\n",
" 'is_guest_from_listing_town', 'is_guest_from_listing_country',\n",
" 'has_completed_verification', 'number_of_applied_services',\n",
" 'number_of_applied_upgraded_services',\n",
" 'number_of_applied_billable_services', 'booking_number_of_nights',\n",
" 'has_deposit_management_service_business_type',\n",
" 'host_account_type_Host',\n",
" 'host_account_type_PMC - Property Management Company',\n",
" 'host_active_pms_list_Hospitable', 'host_active_pms_list_Hostify',\n",
" 'host_country_New Zealand', 'host_country_United Kingdom',\n",
" 'host_country_United States', 'guest_country_Canada',\n",
" 'guest_country_United States', 'listing_country_New Zealand',\n",
" 'listing_country_United Kingdom', 'listing_country_United States'],\n",
" dtype='object')\n"
]
}
],
"source": [
"selector = SelectKBest(score_func=f_classif, k=30)\n",
"X_new = selector.fit_transform(X_train, y_train)\n",
"selected_features_kbest = X_train.columns[selector.get_support()]\n",
"\n",
"print(\"Selected Features:\")\n",
"print(selected_features_kbest)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "736a8d68",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n",
"/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n",
"/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n",
"/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n",
"/home/joaquin/data-jupyter-notebooks/.venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected Features using RFE:\n",
"Index(['has_guest_previously_booked_same_listing',\n",
" 'number_of_previous_payouts_in_listing', 'guest_has_email',\n",
" 'is_guest_from_listing_town', 'is_guest_from_listing_country',\n",
" 'is_host_from_listing_country', 'is_host_from_listing_postcode',\n",
" 'has_completed_verification', 'has_verification_request',\n",
" 'has_upgraded_screening_service_business_type',\n",
" 'has_deposit_management_service_business_type',\n",
" 'host_account_type_Host',\n",
" 'host_account_type_PMC - Property Management Company',\n",
" 'host_active_pms_list_Avantio', 'host_active_pms_list_Hostify',\n",
" 'host_active_pms_list_TrackHs', 'host_country_Bulgaria',\n",
" 'host_country_Canada', 'host_country_New Zealand',\n",
" 'guest_country_Australia', 'guest_country_Canada',\n",
" 'guest_country_Germany', 'guest_country_Mexico', 'guest_country_Other',\n",
" 'listing_country_Bahamas', 'listing_country_Canada',\n",
" 'listing_country_Colombia', 'listing_country_Ireland',\n",
" 'listing_country_New Zealand', 'listing_country_United States'],\n",
" dtype='object')\n"
]
}
],
"source": [
"# Recursive Feature Elimination (RFE) with Logistic Regression\n",
"model = LogisticRegression(max_iter=1000)\n",
"rfe = RFE(model, n_features_to_select=30)\n",
"rfe.fit(X_train, y_train)\n",
"selected_features_rfe = X_train.columns[rfe.support_]\n",
"\n",
"print(\"Selected Features using RFE:\")\n",
"print(selected_features_rfe)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "484786aa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected Features using Lasso Regression:\n",
"Index(['days_from_booking_creation_to_check_in', 'number_of_nights',\n",
" 'host_age', 'host_months_with_truvi', 'number_of_listings_of_host',\n",
" 'number_of_previous_incidents_of_host',\n",
" 'number_of_previous_payouts_of_host', 'guest_age',\n",
" 'number_of_previous_bookings_of_guest',\n",
" 'has_guest_previously_booked_same_listing',\n",
" 'listing_number_of_bedrooms', 'listing_number_of_bathrooms',\n",
" 'previous_bookings_in_listing_count',\n",
" 'number_of_previous_incidents_in_listing',\n",
" 'number_of_previous_payouts_in_listing', 'days_to_start_verification',\n",
" 'days_to_complete_verification', 'is_guest_from_listing_town',\n",
" 'is_guest_from_listing_country', 'is_host_from_listing_town',\n",
" 'is_host_from_listing_postcode', 'has_completed_verification',\n",
" 'number_of_applied_services', 'number_of_applied_billable_services',\n",
" 'booking_days_to_check_in', 'booking_number_of_nights',\n",
" 'has_verification_request',\n",
" 'has_upgraded_screening_service_business_type',\n",
" 'has_deposit_management_service_business_type',\n",
" 'has_protection_service_business_type', 'host_account_type_Host',\n",
" 'host_account_type_PMC - Property Management Company',\n",
" 'host_active_pms_list_Guesty', 'host_active_pms_list_Hospitable',\n",
" 'host_active_pms_list_Hostaway', 'host_active_pms_list_Hostfully',\n",
" 'host_active_pms_list_Hostify', 'host_active_pms_list_Lodgify',\n",
" 'host_active_pms_list_OwnerRez', 'host_country_New Zealand',\n",
" 'guest_country_Canada', 'guest_country_Other',\n",
" 'guest_country_United Kingdom', 'guest_country_United States',\n",
" 'listing_country_Colombia', 'listing_country_New Zealand',\n",
" 'listing_country_United States'],\n",
" dtype='object')\n"
]
}
],
"source": [
"# Lasso Regression for feature selection\n",
"model = LogisticRegression(penalty='l1', solver='liblinear')\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Check which features have non-zero coefficients\n",
"selected_features_lasso = X_train.columns[model.coef_[0] != 0]\n",
"print(\"Selected Features using Lasso Regression:\")\n",
"print(selected_features_lasso)"
]
},
{
"cell_type": "markdown",
"id": "04010a1e",
"metadata": {},
"source": [
"## Processing\n",
"Processing in this notebook is quite straight-forward: we just drop id booking, split the features and target and apply a scaling to numeric features.\n",
"Afterwards, we split the dataset between train and test and display their sizes and target distribution."
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "f735b111",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set size: 14914 rows\n",
"Test set size: 6393 rows\n",
"\n",
"Training target distribution:\n",
"has_resolution_incident\n",
"0 0.988199\n",
"1 0.011801\n",
"Name: proportion, dtype: float64\n",
"\n",
"Test target distribution:\n",
"has_resolution_incident\n",
"0 0.988112\n",
"1 0.011888\n",
"Name: proportion, dtype: float64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_48568/2398832410.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X_train_kbest[selected_features_kbest] = X_train_kbest[selected_features_kbest].astype(float)\n"
]
}
],
"source": [
"# Separate features and target\n",
"X_train_kbest = X_train[selected_features_kbest] # Use the features selected by SelectKBest\n",
"y_train_kbest = y_train\n",
"X_test_kbest = X_test[selected_features_kbest]\n",
"y_test_kbest = y_test\n",
"\n",
"# Scale numeric features\n",
"X_train_kbest[selected_features_kbest] = X_train_kbest[selected_features_kbest].astype(float)\n",
"\n",
"print(f\"Training set size: {X_train_kbest.shape[0]} rows\")\n",
"print(f\"Test set size: {X_test_kbest.shape[0]} rows\")\n",
"\n",
"print(\"\\nTraining target distribution:\")\n",
"print(y_train_kbest.value_counts(normalize=True))\n",
"\n",
"print(\"\\nTest target distribution:\")\n",
"print(y_test_kbest.value_counts(normalize=True))"
]
},
{
"cell_type": "markdown",
"id": "897eb678",
"metadata": {},
"source": [
"### Using RFE Features"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "301a8fb2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set size: 14914 rows\n",
"Test set size: 6393 rows\n",
"\n",
"Training target distribution:\n",
"has_resolution_incident\n",
"0 0.988199\n",
"1 0.011801\n",
"Name: proportion, dtype: float64\n",
"\n",
"Test target distribution:\n",
"has_resolution_incident\n",
"0 0.988112\n",
"1 0.011888\n",
"Name: proportion, dtype: float64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_48568/2877144001.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X_train_rfe[selected_features_rfe] = X_train_rfe[selected_features_rfe].astype(float)\n"
]
}
],
"source": [
"# Separate features and target\n",
"X_train_rfe = X_train[selected_features_rfe] # Use the features selected by RFE\n",
"y_train_rfe = y_train\n",
"X_test_rfe = X_test[selected_features_rfe]\n",
"y_test_rfe = y_test\n",
"\n",
"# Scale numeric features\n",
"X_train_rfe[selected_features_rfe] = X_train_rfe[selected_features_rfe].astype(float)\n",
"\n",
"print(f\"Training set size: {X_train_rfe.shape[0]} rows\")\n",
"print(f\"Test set size: {X_test_rfe.shape[0]} rows\")\n",
"\n",
"print(\"\\nTraining target distribution:\")\n",
"print(y_train_rfe.value_counts(normalize=True))\n",
"\n",
"print(\"\\nTest target distribution:\")\n",
"print(y_test_rfe.value_counts(normalize=True))"
]
},
{
"cell_type": "markdown",
"id": "2bbc1524",
"metadata": {},
"source": [
"### Using Lasso Features"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "f4b9c01a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set size: 14914 rows\n",
"Test set size: 6393 rows\n",
"\n",
"Training target distribution:\n",
"has_resolution_incident\n",
"0 0.988199\n",
"1 0.011801\n",
"Name: proportion, dtype: float64\n",
"\n",
"Test target distribution:\n",
"has_resolution_incident\n",
"0 0.988112\n",
"1 0.011888\n",
"Name: proportion, dtype: float64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_48568/1333565449.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X_train_lasso[selected_features_lasso] = X_train_lasso[selected_features_lasso].astype(float)\n"
]
}
],
"source": [
"# Separate features and target\n",
"X_train_lasso = X_train[selected_features_lasso] # Use the features selected by lasso\n",
"y_train_lasso = y_train\n",
"X_test_lasso = X_test[selected_features_lasso]\n",
"y_test_lasso = y_test\n",
"\n",
"# Scale numeric features\n",
"X_train_lasso[selected_features_lasso] = X_train_lasso[selected_features_lasso].astype(float)\n",
"\n",
"print(f\"Training set size: {X_train_lasso.shape[0]} rows\")\n",
"print(f\"Test set size: {X_test_lasso.shape[0]} rows\")\n",
"\n",
"print(\"\\nTraining target distribution:\")\n",
"print(y_train_lasso.value_counts(normalize=True))\n",
"\n",
"print(\"\\nTest target distribution:\")\n",
"print(y_test_lasso.value_counts(normalize=True))"
]
},
{
"cell_type": "markdown",
"id": "d36c9276",
"metadata": {},
"source": [
"## Classification Model with Random Forest\n",
"\n",
"We define a machine learning pipeline that includes:\n",
"- **Scaling numeric features** with `StandardScaler`\n",
"- **Training a Random Forest classifier** with balanced class weights to handle the imbalanced dataset\n",
"\n",
"We then use `GridSearchCV` to perform a **grid search with cross-validation** over a range of key hyperparameters (e.g., number of trees, max depth, etc.). \n",
"The model is evaluated using **Average Precision**, which is better suited for imbalanced classification tasks.\n",
"\n",
"The best combination of parameters is selected, and the resulting model is used to make predictions on the test set.\n"
]
},
{
"cell_type": "markdown",
"id": "fe3351be",
"metadata": {},
"source": [
"### Model 1 with Kbest Features"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "943ef7d6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
"\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 7.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n",
"Best hyperparameters: {'model__max_depth': None, 'model__max_features': 'log2', 'model__min_samples_leaf': 2, 'model__min_samples_split': 2, 'model__n_estimators': 300}\n"
]
}
],
"source": [
"# Define pipeline (scaling numeric features only)\n",
"pipeline = Pipeline([\n",
" ('scaler', StandardScaler()),\n",
" ('model', RandomForestClassifier(class_weight=weights, # We have an imbalanced dataset\n",
" random_state=123))\n",
"])\n",
"\n",
"# Define parameter grid\n",
"param_grid = {\n",
" 'model__n_estimators': [100, 200, 300],\n",
" 'model__max_depth': [None, 10, 20],\n",
" 'model__min_samples_split': [2, 5],\n",
" 'model__min_samples_leaf': [1, 2],\n",
" 'model__max_features': ['sqrt', 'log2']\n",
"}\n",
"\n",
"# GridSearchCV\n",
"grid_search = GridSearchCV(\n",
" estimator=pipeline,\n",
" param_grid=param_grid,\n",
" scoring='average_precision', # For imbalanced classification\n",
" cv=5, # 5-fold cross-validation\n",
" n_jobs=-1, # Use all available cores\n",
" verbose=2 # Verbose output for progress tracking\n",
")\n",
"\n",
"# Fit the grid search on training data\n",
"grid_search.fit(X_train_kbest, y_train_kbest)\n",
"\n",
"# Best model\n",
"best_pipeline_kbest = grid_search.best_estimator_\n",
"print(\"Best hyperparameters:\", grid_search.best_params_)\n",
"\n",
"# Predict on test set\n",
"y_pred_proba_kbest = best_pipeline_kbest.predict_proba(X_test_kbest)[:, 1]\n",
"y_pred_kbest = best_pipeline_kbest.predict(X_test_kbest)\n"
]
},
{
"cell_type": "markdown",
"id": "672444f7",
"metadata": {},
"source": [
"### Model 2 with RFE Features"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "49cb625c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 0.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 0.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 2.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 2.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 1.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 1.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 1.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 1.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 2.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.1s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n",
"\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 1.8s[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n",
"\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 3.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 4.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 2.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 2.5s\n",
"Best hyperparameters: {'model__max_depth': 10, 'model__max_features': 'sqrt', 'model__min_samples_leaf': 2, 'model__min_samples_split': 5, 'model__n_estimators': 100}\n"
]
}
],
"source": [
"# Define pipeline (scaling numeric features only)\n",
"pipeline = Pipeline([\n",
" ('scaler', StandardScaler()),\n",
" ('model', RandomForestClassifier(class_weight=weights, # We have an imbalanced dataset\n",
" random_state=123))\n",
"])\n",
"\n",
"# Define parameter grid\n",
"param_grid = {\n",
" 'model__n_estimators': [100, 200, 300],\n",
" 'model__max_depth': [None, 10, 20],\n",
" 'model__min_samples_split': [2, 5],\n",
" 'model__min_samples_leaf': [1, 2],\n",
" 'model__max_features': ['sqrt', 'log2']\n",
"}\n",
"\n",
"# GridSearchCV\n",
"grid_search = GridSearchCV(\n",
" estimator=pipeline,\n",
" param_grid=param_grid,\n",
" scoring='average_precision', # For imbalanced classification\n",
" cv=5, # 5-fold cross-validation\n",
" n_jobs=-1, # Use all available cores\n",
" verbose=2 # Verbose output for progress tracking\n",
")\n",
"\n",
"# Fit the grid search on training data\n",
"grid_search.fit(X_train_rfe, y_train_rfe)\n",
"\n",
"# Best model\n",
"best_pipeline_rfe = grid_search.best_estimator_\n",
"print(\"Best hyperparameters:\", grid_search.best_params_)\n",
"\n",
"# Predict on test set\n",
"y_pred_proba_rfe = best_pipeline_rfe.predict_proba(X_test_rfe)[:, 1]\n",
"y_pred_rfe = best_pipeline_rfe.predict(X_test_rfe)\n"
]
},
{
"cell_type": "markdown",
"id": "b763f4cd",
"metadata": {},
"source": [
"### Model 3 with Lasso Features"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "47c6ab43",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 7.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 5.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 3.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.9s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.2s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 5.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.8s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 8.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.5s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=None, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.5s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.3s\n",
"[CV] END model__max_depth=10, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 0.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.2s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.7s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.5s\n",
"[CV] END model__max_depth=10, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 5.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.1s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 7.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.4s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.5s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 7.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 5.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 4.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=100; total time= 1.6s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.7s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.1s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.2s\n",
"[CV] END model__max_depth=20, model__max_features=sqrt, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=200; total time= 3.7s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 4.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=2, model__n_estimators=300; total time= 5.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=200; total time= 4.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.1s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 5.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=1, model__min_samples_split=5, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.1s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=200; total time= 4.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 1.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=100; total time= 2.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 5.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=2, model__n_estimators=300; total time= 6.0s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.8s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.6s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=200; total time= 3.4s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.5s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.2s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 4.3s\n",
"[CV] END model__max_depth=20, model__max_features=log2, model__min_samples_leaf=2, model__min_samples_split=5, model__n_estimators=300; total time= 3.9s\n",
"Best hyperparameters: {'model__max_depth': None, 'model__max_features': 'log2', 'model__min_samples_leaf': 2, 'model__min_samples_split': 2, 'model__n_estimators': 200}\n"
]
}
],
"source": [
"# Define pipeline (scaling numeric features only)\n",
"pipeline = Pipeline([\n",
" ('scaler', StandardScaler()),\n",
" ('model', RandomForestClassifier(class_weight=weights, # We have an imbalanced dataset\n",
" random_state=123))\n",
"])\n",
"\n",
"# Define parameter grid\n",
"param_grid = {\n",
" 'model__n_estimators': [100, 200, 300],\n",
" 'model__max_depth': [None, 10, 20],\n",
" 'model__min_samples_split': [2, 5],\n",
" 'model__min_samples_leaf': [1, 2],\n",
" 'model__max_features': ['sqrt', 'log2']\n",
"}\n",
"\n",
"# GridSearchCV\n",
"grid_search = GridSearchCV(\n",
" estimator=pipeline,\n",
" param_grid=param_grid,\n",
" scoring='average_precision', # For imbalanced classification\n",
" cv=5, # 5-fold cross-validation\n",
" n_jobs=-1, # Use all available cores\n",
" verbose=2 # Verbose output for progress tracking\n",
")\n",
"\n",
"# Fit the grid search on training data\n",
"grid_search.fit(X_train_lasso, y_train_lasso)\n",
"\n",
"# Best model\n",
"best_pipeline_lasso = grid_search.best_estimator_\n",
"print(\"Best hyperparameters:\", grid_search.best_params_)\n",
"\n",
"# Predict on test set\n",
"y_pred_proba_lasso = best_pipeline_lasso.predict_proba(X_test_lasso)[:, 1]\n",
"y_pred_lasso = best_pipeline_lasso.predict(X_test_lasso)\n"
]
},
{
"cell_type": "markdown",
"id": "fc2fcc89",
"metadata": {},
"source": [
"## Evaluation\n",
"This section aims to evaluate how good the new model is vs. the actual Resolution Incidents.\n",
"\n",
"We start by computing and displaying the classification report, ROC Curve, PR Curve and the respective Area Under the Curve (AUC)."
]
},
{
"cell_type": "markdown",
"id": "76099daf",
"metadata": {},
"source": [
"### Model 1 evaluation"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "78887f46",
"metadata": {},
"outputs": [],
"source": [
"# Actual and predicted\n",
"y_true_kbest = y_test_kbest\n",
"\n",
"# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n",
"tn, fp, fn, tp = confusion_matrix(y_true_kbest, y_pred_kbest).ravel()\n",
"\n",
"# Total predictions\n",
"total = tp + tn + fp + fn\n",
"\n",
"# Compute all requested metrics\n",
"recall_kbest = recall_score(y_true_kbest, y_pred_kbest)\n",
"precision_kbest = precision_score(y_true_kbest, y_pred_kbest)\n",
"f1_kbest = fbeta_score(y_true_kbest, y_pred_kbest, beta=1)\n",
"f2_kbest = fbeta_score(y_true_kbest, y_pred_kbest, beta=2)\n",
"fpr_kbest = fp / (fp + tn) if (fp + tn) != 0 else 0\n",
"\n",
"# Scores relative to total\n",
"tp_score_kbest = tp / total\n",
"tn_score_kbest = tn / total\n",
"fp_score_kbest = fp / total\n",
"fn_score_kbest = fn / total\n",
"\n",
"# Create DataFrame\n",
"summary_df_kbest = pd.DataFrame([{\n",
" \"title\": \"Kbest\",\n",
" \"flagging_analysis_type\": \"RISK_VS_CLAIM using KBest Features from all features\",\n",
" \"count_total\": total,\n",
" \"count_true_positive\": tp,\n",
" \"count_true_negative\": tn,\n",
" \"count_false_positive\": fp,\n",
" \"count_false_negative\": fn,\n",
" \"true_positive_score\": tp_score_kbest,\n",
" \"true_negative_score\": tn_score_kbest,\n",
" \"false_positive_score\": fp_score_kbest,\n",
" \"false_negative_score\": fn_score_kbest,\n",
" \"recall_score\": recall_kbest,\n",
" \"precision_score\": precision_kbest,\n",
" \"false_positive_rate_score\": fpr_kbest,\n",
" \"f1_score\": f1_kbest,\n",
" \"f2_score\": f2_kbest\n",
"}])"
]
},
{
"cell_type": "markdown",
"id": "ea079e83",
"metadata": {},
"source": [
"### Model 2 evaluation"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "03c83137",
"metadata": {},
"outputs": [],
"source": [
"# Actual and predicted\n",
"y_true_rfe = y_test_rfe\n",
"\n",
"# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n",
"tn, fp, fn, tp = confusion_matrix(y_true_rfe, y_pred_rfe).ravel()\n",
"\n",
"# Total predictions\n",
"total = tp + tn + fp + fn\n",
"\n",
"# Compute all requested metrics\n",
"recall_rfe = recall_score(y_true_rfe, y_pred_rfe)\n",
"precision_rfe = precision_score(y_true_rfe, y_pred_rfe)\n",
"f1_rfe = fbeta_score(y_true_rfe, y_pred_rfe, beta=1)\n",
"f2_rfe = fbeta_score(y_true_rfe, y_pred_rfe, beta=2)\n",
"fpr_rfe = fp / (fp + tn) if (fp + tn) != 0 else 0\n",
"\n",
"# Scores relative to total\n",
"tp_score_rfe = tp / total\n",
"tn_score_rfe = tn / total\n",
"fp_score_rfe = fp / total\n",
"fn_score_rfe = fn / total\n",
"\n",
"# Create DataFrame\n",
"summary_df_rfe = pd.DataFrame([{\n",
" \"title\": \"RFE\",\n",
" \"flagging_analysis_type\": \"RISK_VS_CLAIM using RFE Features from all features\",\n",
" \"count_total\": total,\n",
" \"count_true_positive\": tp,\n",
" \"count_true_negative\": tn,\n",
" \"count_false_positive\": fp,\n",
" \"count_false_negative\": fn,\n",
" \"true_positive_score\": tp_score_rfe,\n",
" \"true_negative_score\": tn_score_rfe,\n",
" \"false_positive_score\": fp_score_rfe,\n",
" \"false_negative_score\": fn_score_rfe,\n",
" \"recall_score\": recall_rfe,\n",
" \"precision_score\": precision_rfe,\n",
" \"false_positive_rate_score\": fpr_rfe,\n",
" \"f1_score\": f1_rfe,\n",
" \"f2_score\": f2_rfe\n",
"}])"
]
},
{
"cell_type": "markdown",
"id": "8c2f75c9",
"metadata": {},
"source": [
"### Model 3 evaluation"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "7d34f389",
"metadata": {},
"outputs": [],
"source": [
"# Actual and predicted\n",
"y_true_lasso = y_test_lasso\n",
"\n",
"# Compute confusion matrix: [ [TN, FP], [FN, TP] ]\n",
"tn, fp, fn, tp = confusion_matrix(y_true_lasso, y_pred_lasso).ravel()\n",
"\n",
"# Total predictions\n",
"total = tp + tn + fp + fn\n",
"\n",
"# Compute all requested metrics\n",
"recall_lasso = recall_score(y_true_lasso, y_pred_lasso)\n",
"precision_lasso = precision_score(y_true_lasso, y_pred_lasso)\n",
"f1_lasso = fbeta_score(y_true_lasso, y_pred_lasso, beta=1)\n",
"f2_lasso = fbeta_score(y_true_lasso, y_pred_lasso, beta=2)\n",
"fpr_lasso = fp / (fp + tn) if (fp + tn) != 0 else 0\n",
"\n",
"# Scores relative to total\n",
"tp_score_lasso = tp / total\n",
"tn_score_lasso = tn / total\n",
"fp_score_lasso = fp / total\n",
"fn_score_lasso = fn / total\n",
"\n",
"# Create DataFrame\n",
"summary_df_lasso = pd.DataFrame([{\n",
" \"title\": \"Lasso\",\n",
" \"flagging_analysis_type\": \"RISK_VS_CLAIM using Lasso Features from all features\",\n",
" \"count_total\": total,\n",
" \"count_true_positive\": tp,\n",
" \"count_true_negative\": tn,\n",
" \"count_false_positive\": fp,\n",
" \"count_false_negative\": fn,\n",
" \"true_positive_score\": tp_score_lasso,\n",
" \"true_negative_score\": tn_score_lasso,\n",
" \"false_positive_score\": fp_score_lasso,\n",
" \"false_negative_score\": fn_score_lasso,\n",
" \"recall_score\": recall_lasso,\n",
" \"precision_score\": precision_lasso,\n",
" \"false_positive_rate_score\": fpr_lasso,\n",
" \"f1_score\": f1_lasso,\n",
" \"f2_score\": f2_lasso\n",
"}])"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "09609773",
"metadata": {},
"outputs": [],
"source": [
"def plot_confusion_matrix_from_df(df, flagging_analysis_type):\n",
"\n",
" # Subset - just retrieve one row depending on the flagging_analysis_type\n",
" row = df[df['flagging_analysis_type'] == flagging_analysis_type].iloc[0]\n",
"\n",
" # Define custom x-axis labels and wording\n",
" if flagging_analysis_type == 'RISK_VS_CLAIM':\n",
" x_labels = ['With Submitted Claim', 'Without Submitted Claim']\n",
" outcome_label = \"submitted claim\"\n",
" elif flagging_analysis_type == 'RISK_VS_SUBMITTED_PAYOUT':\n",
" x_labels = ['With Submitted Payout', 'Without Submitted Payout']\n",
" outcome_label = \"submitted payout\"\n",
" else:\n",
" x_labels = ['Actual Positive', 'Actual Negative'] \n",
" outcome_label = \"outcome\"\n",
"\n",
" # Confusion matrix structure\n",
" cm = np.array([\n",
" [row['count_true_positive'], row['count_false_positive']],\n",
" [row['count_false_negative'], row['count_true_negative']]\n",
" ])\n",
"\n",
" # Create annotations for the confusion matrix\n",
" labels = [['True Positives', 'False Positives'], ['False Negatives', 'True Negatives']]\n",
" counts = [[f\"{v:,}\" for v in [row['count_true_positive'], row['count_false_positive']]],\n",
" [f\"{v:,}\" for v in [row['count_false_negative'], row['count_true_negative']]]]\n",
" percentages = [[f\"{round(100*v,2):,}\" for v in [row['true_positive_score'], row['false_positive_score']]],\n",
" [f\"{round(100*v,2):,}\" for v in [row['false_negative_score'], row['true_negative_score']]]]\n",
" annot = [[f\"{labels[i][j]}\\n{counts[i][j]} ({percentages[i][j]}%)\" for j in range(2)] for i in range(2)]\n",
"\n",
" # Scores formatted as percentages\n",
" recall = row['recall_score'] * 100\n",
" precision = row['precision_score'] * 100\n",
" f1 = row['f1_score'] * 100\n",
" f2 = row['f2_score'] * 100\n",
"\n",
" # Set up figure and axes manually for precise control\n",
" fig = plt.figure(figsize=(9, 8))\n",
" grid = fig.add_gridspec(nrows=4, height_ratios=[2, 2, 15, 2])\n",
"\n",
" \n",
" ax_main_title = fig.add_subplot(grid[0])\n",
" ax_main_title.axis('off')\n",
" ax_main_title.set_title(f\"Random Predictor - Flagged as Risk vs. {outcome_label.title()}\", fontsize=14, weight='bold')\n",
" \n",
" # Business explanation text\n",
" ax_text = fig.add_subplot(grid[1])\n",
" ax_text.axis('off')\n",
" business_text = (\n",
" f\"Flagging performance analysis:\\n\\n\"\n",
" f\"- Of all the bookings we flagged as at Risk, {precision:.2f}% actually turned into a {outcome_label}.\\n\"\n",
" f\"- Of all the bookings that resulted in a {outcome_label}, we correctly flagged {recall:.2f}% of them.\\n\"\n",
" f\"- The pure balance between these two is summarized by a score of {f1:.2f}%.\\n\"\n",
" f\"- If we prioritise better probability of detection of a {outcome_label}, the balanced score is {f2:.2f}%.\\n\"\n",
" )\n",
" ax_text.text(0.0, 0.0, business_text, fontsize=10.5, ha='left', va='bottom', wrap=False, linespacing=1.5)\n",
"\n",
" # Heatmap\n",
" ax_heatmap = fig.add_subplot(grid[2])\n",
" ax_heatmap.set_title(f\"Confusion Matrix – Risk vs. {outcome_label.title()}\", fontsize=12, weight='bold', ha='center', va='center', wrap=False)\n",
"\n",
" cmap = sns.light_palette(\"#315584\", as_cmap=True)\n",
"\n",
" sns.heatmap(cm, annot=annot, fmt='', cmap=cmap, cbar=False,\n",
" xticklabels=x_labels,\n",
" yticklabels=['Flagged as Risk', 'Flagged as No Risk'],\n",
" ax=ax_heatmap,\n",
" linewidths=1.0,\n",
" annot_kws={'fontsize': 10, 'linespacing': 1.2})\n",
" ax_heatmap.set_xlabel(\"Resolution Outcome (Actual)\", fontsize=11, labelpad=10)\n",
" ax_heatmap.set_ylabel(\"Flagging (Prediction)\", fontsize=11, labelpad=10)\n",
" \n",
" # Make borders visible\n",
" for _, spine in ax_heatmap.spines.items():\n",
" spine.set_visible(True)\n",
"\n",
" # Footer with metrics and date\n",
" ax_footer = fig.add_subplot(grid[3])\n",
" ax_footer.axis('off')\n",
" metrics_text = f\"Total Booking Count: {row['count_total']} | Recall: {recall:.2f}% | Precision: {precision:.2f}% | F1 Score: {f1:.2f}% | F2 Score: {f2:.2f}%\"\n",
" date_text = f\"Generated on {date.today().strftime('%B %d, %Y')}\"\n",
" ax_footer.text(0.5, 0.7, metrics_text, ha='center', fontsize=9)\n",
" ax_footer.text(0.5, 0.1, date_text, ha='center', fontsize=8, color='gray')\n",
"\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "7cc4a1d2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 900x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 900x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 900x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot confusion matrix for claim scenario\n",
"plot_confusion_matrix_from_df(summary_df_kbest, 'RISK_VS_CLAIM using KBest Features from all features')\n",
"plot_confusion_matrix_from_df(summary_df_rfe, 'RISK_VS_CLAIM using RFE Features from all features')\n",
"plot_confusion_matrix_from_df(summary_df_lasso, 'RISK_VS_CLAIM using Lasso Features from all features')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "30786f7c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1600x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Print a table to summarize the results\n",
"summary_table = pd.concat([summary_df_kbest, summary_df_rfe, summary_df_lasso], ignore_index=True)\n",
"summary_table = summary_table[['title', 'count_true_positive', 'count_true_negative',\n",
" 'count_false_positive', 'count_false_negative', 'true_positive_score', 'true_negative_score',\n",
" 'false_positive_score', 'false_negative_score', 'recall_score', 'precision_score',\n",
" 'false_positive_rate_score', 'f1_score', 'f2_score']]\n",
"\n",
"# Rename them\n",
"summary_table.columns = ['Model', 'TP', 'TN', 'FP', 'FN',\n",
" 'TP Rate', 'TN Rate', 'FP Rate', 'FN Rate',\n",
" 'Recall', 'Precision', 'FPR', 'F1', 'F2']\n",
" \n",
"# summary_table.to_csv('flagging_analysis_summary.csv', index=False)\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Set up figure and axis\n",
"fig, ax = plt.subplots(figsize=(16, 4)) # Adjust width/height as needed\n",
"ax.axis('off') # Hide axes\n",
"\n",
"# Create table from DataFrame\n",
"table = ax.table(cellText=summary_table.round(3).values,\n",
" colLabels=summary_table.columns,\n",
" loc='center',\n",
" cellLoc='center')\n",
"\n",
"table.auto_set_font_size(False)\n",
"table.set_fontsize(10)\n",
"table.scale(1.2, 1.5) # Adjust cell size\n",
"\n",
"# Save as image\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "d731d0c5",
"metadata": {},
"source": [
"### Interpreting the Classification Report\n",
"\n",
"The **Classification Report** provides key metrics to evaluate how well the model performed on each class.\n",
"\n",
"It includes the following metrics for each class (0 and 1):\n",
"* Metric: Meaning\n",
"* Precision: Out of all predicted positives, how many were actually positive?\n",
"* Recall: Out of all actual positives, how many did we correctly identify?\n",
"* F1-score: Harmonic mean of precision and recall (balances both)\n",
"* Support: Number of true samples of that class in the test data\n",
"\n",
"Interpretation:\n",
"* Class 0 = No incident\n",
"* Class 1 = Has resolution incident (rare, but important!)\n",
"\n",
"A few explanatory cases:\n",
"* A high recall for class 1 means we're catching most incidents.\n",
"* A high precision for class 1 means when we predict an incident, we're often correct.\n",
"* The F1-score gives a single balanced measure (good for imbalanced data).\n",
"\n",
"Special note for imbalanced data:\n",
"Since class 1 (or just True) is rare (1% in our case), metrics for that class are more critical.\n",
"We want to maximize recall to catch as many real incidents as possible — without letting precision drop too low (to avoid too many false alarms)."
]
},
{
"cell_type": "markdown",
"id": "c366cfe7",
"metadata": {},
"source": [
"### Results Summary\n",
"\n",
"- Model 1 (Kbest) best in F1 Score (0.227), but has a moderate recall.\n",
"- Model 2 (RFE) provides the highest recall (0.875) and the best F2 score (0.345), meaning it's most effective at capturing positives while tolerating more false positives.\n",
"- Model 3 (Lasso) offers the highest precision (0.9) and the lowest FPR, though it misses most real incidents (low recall)."
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "4b4da914",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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/fmjevPnTBVUOmGAQEVGF8/Dh46843LgB3L6tv0LxNGxt9QuJ+foWfP932csLKOuUFEIIxMTEICUlBQ4ODggLC0PNmjWfLuhywgSDiIgshk4H3Lnz5C4LI2a0LlGVKiUnDflld3cUO2W3VBQKBfr164fY2FgMGTIEbm5upjuZxJhgEBGRWVCrgVu37LF/vwK3bpXcZfG0i60qlYC3d8lJQ40agI8PINdcVUIIpKSkwMfHB4B+Uc/x48dDYcpMxgSYYBARkUkJob+i8KQuizt3bAD0eqpz2duXnDTk11WvDlib6adfbm4u1q5di4sXLyIiIsLQHWJpyQXABIOIiJ6CVqsfy/CkLouMjKc/V9WqT+6ycHMzbZeFKd2/fx+RkZG4ffs2rKys8PDhQ7lDeipMMIiIqFg5OfouicfdZZGSop+R8mlYW+u7LHx8dLCySkHLll7w87MqlED4+OivTlRUV69excqVK5GVlQUnJyeEh4fD19dX7rCeChMMIqIKaP58ICZGPyjSGELo53S4cQP4+++nj8PR8cldFp6e+ls1NRotNm/+C3379oWNzRPu3axAjh07htjYWOh0Onh7eyM8PBwuLi5yh/XUmGAQEVUw584Br75q+vNUq/bkLgsXF8vtsigPly9fxsaNGwEADRs2xMCBA2VfZl0qTDCIiCqY6Oin29/GRt8l8bi5HXx89HNA0NMJCAhAw4YNUa1aNXTu3NkiB3OWhAkGEVEFExNTUE5M1I9vMIZKpb+Vk0zj7t27cHZ2ho2NDRQKBYYOHVqhEot8TDCIiCzI/fv6tTFKkpQE/LNcBTp0AGrXLo+oqLQSExMRExODgIAADBs2DAqFokImFwATDCIii7FxIzBsmH5CqtIYMcK08VDpCSFw5MgRbN26FUIIpKenIzc3F3Z2dnKHZjJMMIiILMSKFaVPLmxsgKFDTRsPlY5Wq8WWLVtw9OhRAEDTpk3Rr18/WJvrbF8SqdivjoioArl3r6AcFlbyAlpWVvorHRY+jUKFkJWVhZiYGCQlJQEAQkJC0L59+wrbLfIoJhhERBbiwYOC8vLlT17mm+QlhEBUVBSuX78OlUqFoUOHIjg4WO6wyg0TDCKip5CXB0RGAmfOmP5cly7pvzs5MbmwBAqFAj179sSGDRswfPhweHp6yh1SuWKCQUT0FNavVyAionzP6epavuej0hNC4N69e3B3dwcA+Pn54dVXX4WyEt73W/leMRGRhBISyr8vfcCAcj8llUJeXh7Wr1+P+fPn49atW4b6yphcALyCQUT0VB5d6OvLL4G2bU17PhcXoGlT056DjJeRkYHo6GjcuHEDCoUCKSkpqF69utxhyYoJBhHRU3g0wWjeHOjcWb5YSB6pqamIjIxEeno67OzsMGzYMAQGBsodluyYYBCRWbh2TT8x1IULckdSWtbQaPpAoym4/F3BpzWgYpw7dw5r166FRqNB1apVMXLkSFStWlXusMwCfx2IyCwsXgwcOiR3FMZQAFAVqqlSRZ5ISB6XLl3CypUrAQCBgYEYOnQo7O3tZY7KfDDBICKz8MiYONSqZf4rdQohkJmZCUdHRyiVCvTtCzRpIndUVJ4CAgIQEBCAatWqoVevXpV2MGdJmGAQkVlITy8ox8UBQUHyxVIaGk0eNm/eib59+8KmpCk1qcLJyMiAvb09rKysoFQqMWrUKFhxUpJiMcEgonKXlgb88QcgREFdQkJBmfM8kDm6efMmoqKiUK9ePTz77LMAwOTiMZhgEFG5ysoCgoMLr6vxby4u5RcPUWmcOnUKGzZsQF5eHq5evYrc3FzYmns/nsyYYBBRuTp79vHJRaNGQAVewZosjBACu3btwt69ewEAwcHBGDJkCJOLUmCCQUSyad8eGDy44LGdXeHHRHJSq9VYu3Ytzp8/DwDo0KEDunfvzsGcpcQEg4hk06IF8PbbckdBVJQQAsuXL8e1a9dgZWWF/v37oymnUDUKEwwiKtbdu8BHHwGXL0t73EeXHCcyVwqFAh06dMC9e/cwfPhw+Pn5yR2SxWGCQUTFmj8f+OEH056Dd3eSucmf2wTQj7d4/fXXeRtyGbEjiYiKlZho2uNXrw6MGmXacxCVlk6nw7Zt2zBv3jzce2QUMpOLsuMVDCIq1qNdGadOAVJfIXZ05NodZB5ycnKwevVqXLp0CQBw+fJltGrVSuaoLB9/vYmoiIcPgdOnCx7XqMHJr6hiunv3LiIjI5GWlgZra2sMGjQIDRs2lDusCoEJBhEVEhMDjBkD5OYW1Dk7yxcPkalcuXIFMTExyM7OhrOzM8LDw+Hj4yN3WBUGEwwiKmTp0sLJRUAAwNmQqaJJTEzE8uXLodPp4Ovri7CwMDgzk5YUEwwiKuTRRcfCwoA33pAvFiJT8fPzg5eXF6pWrYoBAwbAmgOCJMcWJaJCMjP1362sgMhIQKGQNx4iqeTk5MDW1hYKhQI2NjYYO3YsVCoVFHyTmwQTDKIK4ORJYN06IC+v5G20WiUuXaqHw4eVj+3yuHpV/93RkckFVRx37txBZGQkGjdujG7dugEA1xMxMSYYRBYuJwfo3h34++8nbWkFoG6pj/vPXENEFu/ixYtYvXo1cnNzcfLkSbRv357JRTlggkFk4W7cKE1yYbw+faQ/JlF5EkLgzz//RFxcHIQQqFmzJkaMGMHkopwwwSCycI9OiDVgADBpUvHb5eXl4fDhw2jTps0TB7Q5OwNt2kgYJFE5y8vLQ2xsLOLj4wEAzZo1Q79+/WDFW6LKDRMMIgv3aILRoAHQs2fx22k0Amr1HYSECK4BQhWaEAKRkZFITEyEQqFAr1690LZtWw7mLGdMMIgszJIlwCefAFlZ+sc5OQXPcbZNIv1KqE2bNsXNmzcxbNgw1KlTR+6QKiUmGEQWZsoUIDW1+Oe8vcs3FiJzolaroVKpAABNmjRBnTp14ODgIHNUlRcTDCILolYXJBe2toCXV8FzzZsDQ4fKExeRnIQQ2LdvH44ePYoXX3wRTk5OAMDkQmZMMIgsyJ07BeXevfVzXxBVZhqNBhs3bsSpU6cAAGfOnEHbtm1ljooAJhhEFiE1FfjrLyApqaDO01O2cIjMwsOHDxEdHY2bN29CqVSiT58+XGbdjDDBIDJzN24AgYH67pFHVa8uTzxE5iA5ORlRUVF4+PAh7O3tMXz4cNSuXVvusOgRTDCIzNzBg0WTC4DzVFDldeXKFaxYsQJ5eXmoVq0awsPD4e7uLndY9C9MMIgsSO/eQMeOQMOGwLPPyh0NkTy8vb3h6uoKd3d3DB06lDNzmikmGEQWpGdPYPJkuaMgKn95eXmwsrKCQqGAnZ0dxo0bBwcHByiVSrlDoxIwwSAyUytXAlFRwLVrckdCJK8HDx4gKioKzZo1M9whkn8rKpkvJhhEZujiRWDkSECnK1zPKb6psrl+/Tqio6ORmZmJffv2oXnz5obJtMi8McEgMkNffVU0ufD1Bfr3lyceIjmcOHECGzduhFarRfXq1REeHs7kwoLI3nk1b948+Pv7w87ODm3btsXhw4cfu/3cuXNRt25d2Nvbw8/PD2+++SZyHl2MgcjCpabq1xsB9KuaJiYCt27p58Dw95czMqLyodPpEBcXh3Xr1kGr1aJevXp4/vnn4ebmJndoZARZr2BER0dj8uTJmD9/Ptq2bYu5c+ciNDQUCQkJ8CxmFqEVK1ZgypQpWLRoEdq3b48LFy5g3LhxUCgUmDNnjgyvgKhkQgD37gF5ecbt99VXQG6uvvzKKwBv7afKRAiBNWvW4MKFCwCAzp07o2vXrlwJ1QLJmmDMmTMHEyZMwPjx4wEA8+fPR2xsLBYtWoQpU6YU2f7AgQPo0KEDRo0aBQDw9/fHyJEjcejQoXKNm+hJ1GpgyBAgNrbsx1CpgP/+V7KQiCyCQqFAzZo1kZiYiIEDB6JRo0Zyh0RlJFuCoVarcfToUUydOtVQp1QqERISgoMHDxa7T/v27fHbb7/h8OHDaNOmDRITE7F582aMGTOmxPPk5uYiN//fQQDp6ekA9PPXazQaSV5L/nGkOh5ZdpsKAbzwghViY5+uB3L0aB2qVdNCqiaw5DY1R2xPael0Omi1WgBAs2bNEBwcDDc3N7bvUzDFe9SYY8mWYKSlpRkG7jyqevXqOH/+fLH7jBo1CmlpaejYsSOEEMjLy8Mrr7yC999/v8TzzJo1CzNmzChSv337dslX2ouLi5P0eGSZbRoVVRdRUfUAACqVFk2b3oaxV3fd3XPQo8dZbN5sZP9KKVhim5oztufTS0tLQ1paGoKCgmBlZYUdO3bIHVKFIuV7NCsrq9TbWtRdJLt378bMmTPxww8/oG3btrh06RLeeOMNfPrpp/joo4+K3Wfq1KmY/MjMROnp6fDz80OvXr3g4uIiSVwajQZxcXHo2bMnbHgfoSQstU2XLlUgKkr/a6VQCCxdKjBkiEcZj1ZDusBguW1qrtieT0+n02HHjh24ceMGAKBq1aq4f/8+21QipniP5vcClIZsCYaHhwesrKxw69atQvW3bt2Cl5dXsft89NFHGDNmDF588UUAQOPGjZGZmYmXXnoJH3zwQbEzutna2hY7jayNjY3kb2BTHLOys6Q23bULePXVgsezZysQFmZ+ObwltaklYHuWTXZ2NlatWoXExEQAQLdu3fDMM89gy5YtbFOJSdmexhxHtttUVSoVWrZsiZ07dxrqdDoddu7ciXbt2hW7T1ZWVpEkwsrKCoB+5DGRXM6eBQYPhmG8xGuvcUpvopKkpaVh4cKFSExMhI2NDUaMGIHOnTvzTpEKRtZ/ryZPnoyIiAi0atUKbdq0wdy5c5GZmWm4q2Ts2LHw9fXFrFmzAAD9+/fHnDlz0Lx5c0MXyUcffYT+/fsbEg2i8paaCvTtCzx4oH/87LPAt9/C6HEXRJXBtWvXsGLFCuTm5sLV1RXh4eElXrUmyyZrghEWFoY7d+7g448/RmpqKpo1a4atW7caBn5eu3at0BWLDz/8EAqFAh9++CFu3ryJatWqoX///vj888/leglUyWVm6mfXvHpV/7h5c/36Idbm1zNCZBbc3d1ha2sLT09PhIWFwdHRUe6QyERk/zM4adIkTJo0qdjndu/eXeixtbU1pk2bhmnTppVDZESPp9UCo0cDf/2lf+znB2zaBHANJqLChBCG7g8nJydERETAxcUF1szEKzTZpwonslRvvQWsX68vu7joJ9Xy8ZE3JiJzk5WVhSVLluDkyZOGOnd3dyYXlQB/wkRl8O23+i9A3x2yejXQuLG8MRGZm9u3byMyMhL3799HWloa6tWrx8XKKhEmGERGWr8eePPNgsc//QSEhMgXD5E5SkhIwJo1a6BWq1GlShWMHDmSyUUlwwSDyAhHjgAjR+qnAweADz8Enn9e3piIzIkQAgcOHDDMxunv74/hw4dLPnMymT8mGESllJgI9OsHZGfrH48aBXzyibwxEZkTIQTWr1+PEydOAABatmyJPn36cBqBSooJBtFj3L+v7xJZuRLYvr1g6fXOnYFFizjXBdGjFAoFXFxcoFAo0KdPH7Ru3VrukEhGTDCI/iU9HdiwQZ9UbNumX3r9UXXrAmvXAsXMQE9UKT16G2q3bt1Qv359eHt7yxwVyY0JBhGAhw/1c1isXAls2QLk5hbdpkYNYMQI4L33AHf38o+RyBydOXMGf/31F0aNGgUbGxsoFAomFwSACQZVYpmZ+rkrVq7Uf8/JKbqNjw8wfLg+sXjmGaCY9fSIKiUhBPbs2YM9e/YAAI4cOYL27dvLHBWZEyYYVCncugV88QXwz6rQyMoCdu/Wf/83Ly9g2DB9UtGhA5MKon/TaDRYt24dzp49CwB45pln8Mwzz8gcFZkbJhhUKXz5JTB3bsnPV6tWkFR06gRw0DtR8dLT0xEVFYWUlBQolUr069cPzZs3lzssMkNMMKhSSEwsWle1KjB0qD6p6NKFC5QRPUlycjIiIyORkZEBBwcHhIWFoWbNmnKHRWaKf1KpUsjMLCifPatfO6R6dSYVRMaws7ODVquFp6cnRo4cCTc3N7lDIjPGP69U4eXmAjdvFjwODmYXCFFZuLu7Y+zYsXB3d+e03/REHL5GFdrKlfpbSv8ZiwZbWyYXRKWVm5uL6OhoXLx40VDn5eXF5IJKhQkGVWhff134ThF2FxOVzr1797Bo0SKcP38e69evh0ajkTsksjDsIqEKS60G/lkSAa6uQI8ewBtvyBsTkSW4evUqVq5ciaysLDg5OSEsLAw2NjZyh0UWhgkGVVhnzhTMyPnss8Dy5fLGQ2QJjh07htjYWOh0Onh7eyM8PBwuLi5yh0UWiAkGWTyNBlixArh9u3D98eMF5VatyjcmIksjhMC2bdtw6NAhAEDDhg0xcOBAXrmgMmOCQRbvo4+A//u/x2/DBIPoyfL+WS64a9eu6Ny5s2EBM6KyYIJBFu3vv4Hvv3/8NjVrAlw1mujx8pdYb9CgAQICAuQOhyoAJhhk0b7/vmASrREjgLCwws9bWQEdOwJ2duUfG5G5S0xMxPHjxzF48GAolUpYWVkxuSDJMMEgi5WZCfzvf/qylZW+m8TfX9aQiCyCEAJHjhzB1q1bIYSAj48P2rVrJ3dYVMEwwSCLtXAhcPeuvjxqFJMLotLQarXYsmULjh49CgBo2rQpWrMPkUyACQZZJLUa+OqrgsfvvitfLESWIisrCzExMUhKSgIAhISEoH379hzMSSbBBIMs0ooVwI0b+nL//kCjRvLGQ2Tu7ty5g8jISNy7dw8qlQpDhw5FcHCw3GFRBcYEgyyOTlf4ttQpU+SLhchSaLVaZGRkwM3NDSNHjoSnp6fcIVEFxwSDLM6GDcD58/py585A+/byxkNkCby8vDBq1Ch4enrCwcFB7nCoEuBiZ2RRhABmzSp4zKsXRMXLy8vDhg0bcP36dUOdv78/kwsqN0wwyKLs3g0cPqwvN2kC9O4tazhEZikjIwNLlizB8ePHERMTw5VQSRbsIiGL8sUXBeUpUwAOficqLDU1FZGRkUhPT4etrS3XEyHZMMEgi3HsGLB9u74cEAAMHy5vPETm5ty5c1i7di00Gg2qVq2K8PBweHh4yB0WVVJMMEgWUVFAbKx+TEVxdDorJCe3QHS0FZT/dOSdOFHw/DvvANZ89xIB0M/MuXfvXuzatQsAEBgYiKFDh8Le3l7myKgy459oKndXr+pn3iwpudBTAvAr9pnq1YFx40wQGJEFu3XrFgCgTZs2CA0NhVLJIXYkLyYYVO6Skp6UXJRModDfRcLFy4gKKBQKDBw4EA0aNEDDhg3lDocIABMMkkFubkF50iTgv/8tuo1Go8Hu3bvRtWvXQgPU3NyAqlVNHiKR2bt58yZOnjyJ3r17Q6FQQKVSMbkgs8IEg8pdTk5B2csLCAwsuo1GAyQkZCEwEOAAeKLCTp06hQ0bNiAvLw8eHh5crIzMEhMMKnePXsFgVwdR6Qkh8Pvvv2Pfvn0AgODgYDRp0kTmqIiK91QJRk5ODuz4CUFGyl+kDACcneWLg8iSqNVqrFmzBgkJCQCADh06oHv37hzMSWbL6HemTqfDp59+Cl9fXzg5OSExMREA8NFHH+GXX36RPECqeHbuLChzHRGiJ7t//z4WLVqEhIQEWFlZYdCgQQgJCWFyQWbN6HfnZ599hsWLF+PLL7+ESqUy1Ddq1AgLFy6UNDiqeDQaYM8efbl6dYBj0oie7MGDB7hz5w4cHR0xbtw4NG3aVO6QiJ7I6C6SpUuX4ueff0aPHj3wyiuvGOqbNm2K8/lLXBKV4MgRICNDX+7enVN9E5VGrVq1MGzYMPj4+MDV1VXucIhKxegrGDdv3kSdOnWK1Ot0Oi6oQ0/0z0SDAIAePeSLg8ic6XQ67Ny5E7dv3zbU1a9fn8kFWRSjE4wGDRpg7969RepXrVqF5s2bSxIUVVz/jE8DALRtK18cROYqJycHkZGR2LdvH6KiopCXlyd3SERlYnQXyccff4yIiAjcvHkTOp3OMKp56dKl2LRpkylipArk+vWCcq1a8sVBZI7u3r2LyMhIpKWlwdraGj169IA1F90hC2X0FYyBAwdi48aN2LFjBxwdHfHxxx/j3Llz2LhxI3r27GmKGKkCuXZN/93NjbeoEj3qypUrWLhwIdLS0uDs7Iznn3+eM3OSRStTatypUyfExcVJHQtVcDpdwRWMmjXljYXInBw5cgRbtmyBEAK+vr4ICwuDMzNwsnBGX8EICAjA33//XaT+/v37CAgIkCQoqphOn9bfpgowwSDKp9PpcO7cOQgh0LhxY4wbN47JBVUIRl/BSEpKglarLVKfm5uLmzdvShIUVUy//lpQ7tVLvjiIzIlSqcTw4cNx6tQptG7dGgreu00VRKkTjA0bNhjK27ZtK3S7lFarxc6dO+Hv7y9pcFRx5OYCy5bpy7a2wOjR8sZDJKe0tDScPXsWnTt3BgDY29ujTZs2MkdFJK1SJxiDBg0CACgUCkRERBR6zsbGBv7+/vj6668lDY4qjg0bgPyetcGDAXd3eeMhksulS5ewatUq5ObmwsXFBc2aNZM7JCKTKHWCodPpAAC1a9fGkSNH4OHhYbKgqOJ5dJmaF16QLw4iuQgh8OeffyIuLg5CCNSsWRNBQUFyh0VkMkaPwbhy5Yop4qAK7No1YPt2fdnfXz9FOFFlkpeXh9jYWMTHxwMAmjVrhn79+sHKykrewIhMqEy3qWZmZmLPnj24du0a1Gp1oef+85//SBIYVRyLFwNC6MvjxwNcAJIqk8zMTKxcuRLXrl2DQqFAr1690LZtWw7mpArP6ATj+PHj6Nu3L7KyspCZmQl3d3ekpaXBwcEBnp6eTDCoEJ2u4O4RhQIYN07WcIjKXXJyMq5duwZbW1sMGzas2LWciCoio/+XfPPNN9G/f3/cu3cP9vb2+PPPP3H16lW0bNkSX331lSliJAv2++9AUpK+3KsX57+gyicoKAj9+vXDiy++yOSCKhWjE4z4+Hi89dZbUCqVsLKyQm5uLvz8/PDll1/i/fffN0WMZME4uJMqGyEEDh48iPv37xvqWrZsyYHxVOkYnWDY2NhA+U8nuqenJ679s7iEq6srrj+6khVVenfvAmvX6stVqwIDBsgbD5GpaTQarF27Ftu3b0dkZCRXQqVKzegxGM2bN8eRI0cQFBSELl264OOPP0ZaWhqWLVuGRo0amSJGslArVugn2AKAMWP0E2wRVVQPHz5EVFQUkpOToVAo0KpVK66ESpWa0VcwZs6cCW9vbwDA559/jipVquDVV1/FnTt38NNPPxkdwLx58+Dv7w87Ozu0bdsWhw8ffuz29+/fx8SJE+Ht7Q1bW1sEBwdj8+bNRp+XTI/dI1RZJCcnY8GCBUhOToa9vT3GjBmD1q1byx0WkayMTq9btWplKHt6emLr1q1lPnl0dDQmT56M+fPno23btpg7dy5CQ0ORkJAAT0/PItur1Wr07NkTnp6eWLVqFXx9fXH16lW4ubmVOQYyjWPHgH9u+Ufr1gAvblFFdfbsWWzatAl5eXnw8PDAyJEj4c6paomMv4JRkmPHjqFfv35G7TNnzhxMmDAB48ePR4MGDTB//nw4ODhg0aJFxW6/aNEi3L17F+vWrUOHDh3g7++PLl26oGnTplK8BJIQr15QZSCEwKFDh5CXl4egoCC88MILTC6I/mHUFYxt27YhLi4OKpUKL774IgICAnD+/HlMmTIFGzduRGhoaKmPpVarcfToUUydOtVQp1QqERISgoMHDxa7z4YNG9CuXTtMnDgR69evR7Vq1TBq1Ci89957Jc6Il5ubi9z8gQAA0tPTAegHY2ny1w5/SvnHkep4li47G1i+3BqAAvb2AkOH5sHYpmGbSo9tKi2NRgOFQoGBAwfizJkz6NChA5RKJdv3KfA9Ki1TtKcxxyp1gvHLL79gwoQJcHd3x71797Bw4ULMmTMHr7/+OsLCwnD69GnUr1+/1CdOS0uDVqtF9erVC9VXr14d58+fL3afxMRE/P777xg9ejQ2b96MS5cu4bXXXoNGo8G0adOK3WfWrFmYMWNGkfrt27fDwcGh1PGWRlxcnKTHs1R79tTAgwctAQDPPHMd+/cfL/Ox2KbSY5s+HbVajYcPH6Jq1aoAgD///BMAnqq7mArje1RaUrZnVlZWqbdVCJE/ifPjNWnSBGPGjME777yD1atXY/jw4XjmmWewcuVK1KhRw+ggk5OT4evriwMHDqBdu3aG+nfffRd79uzBoUOHiuwTHByMnJwcXLlyxXDFYs6cOZg9ezZSUlKKPU9xVzD8/PyQlpYGFxcXo+MujkajQVxcHHr27AkbGxtJjmnJevWywu7d+t63nTvz0KlTqd5ihbBNpcc2fXo3btzA6tWrkZmZiQEDBuDatWtsTwnxPSotU7Rneno6PDw88ODBgyd+hpb6Csbly5cxfPhwAMCQIUNgbW2N2bNnlym5AAAPDw9YWVnh1q1bhepv3boFLy+vYvfx9vaGjY1Noe6Q+vXrIzU1FWq1GiqVqsg+tra2sC3m/kgbGxvJ38CmOKaluXwZ2L1bXw4KArp1s8bTLLnANpUe27Rs4uPjsWnTJsOVVz8/P1y7do3taQJsU2lJ2Z7GHKfUgzyzs7MNXQoKhQK2traG21XLQqVSoWXLlti5c6ehTqfTYefOnYWuaDyqQ4cOuHTpkmHpeAC4cOECvL29i00uqPzlrzsCAM8/j6dKLojMgU6nQ1xcHNavXw+tVot69erh+eefh6urq9yhEZk1owZ5Lly4EE5OTgD0yw8vXry4yPS3xix2NnnyZERERKBVq1Zo06YN5s6di8zMTIwfPx4AMHbsWPj6+mLWrFkAgFdffRXff/893njjDbz++uu4ePEiZs6cyQXWzIRWq185FQCsrICICFnDIXpqubm5WL16NS5evAgA6Ny5M7p27QqFQsGBiERPUOoEo2bNmliwYIHhsZeXF5YtW1ZoG4VCYdSHfVhYGO7cuYOPP/4YqampaNasGbZu3WoY+Hnt2jXDtOQA4Ofnh23btuHNN99EkyZN4OvrizfeeAPvvfdeqc9JprNtG3Dzpr7cty/wFBe4iMzC5cuXcfHiRVhbW2PgwIGcrZjICKVOMJLyl8SU2KRJkzBp0qRin9ud35n/iHbt2hlGbZN54dwXVNE0aNAA3bt3R0BAAHx9feUOh8iiSDbRFlVut28DGzboy9Wr669gEFmiEydOIDMz0/C4U6dOTC6IyoAJBkli2TIgf+HIiAiAA8DJ0uh0OmzZsgXr1q3DypUrodVq5Q6JyKJxqT96akIU7h55/nn5YiEqi+zsbKxatQqJiYkAgDp16hQa/0VExmOCQU/tzz+Bc+f05Y4dgbp15Y2HyBhpaWmIjIzE3bt3YWNjg8GDBxs1KzERFY8JBj01Du4kS3X58mXExMQgNzcXrq6uCA8PL3GiPyIyTpmuAV6+fBkffvghRo4cidu3bwMAtmzZgjNnzkgaHJm/jAwgOlpfdnYG/pnslcjs6XQ6bNu2Dbm5ufDz88OECROYXBBJyOgEY8+ePWjcuDEOHTqENWvWICMjA4B+5HVJC45RxbVypT7JAIDwcMDRUd54iEpLqVQiLCwMrVu3xtixY+HINy+RpIxOMKZMmYLPPvvMsGx7vu7du3N+ikqI3SNkSbKysnAuf8AQgKpVq6Jv376wtmZvMZHUjE4wTp06hcGDBxep9/T0RFpamiRBkWU4fx44cEBfbtgQaNNG3niIHuf27dtYsGABYmJiDHeLEJHpGJ1guLm5Fbs0+vHjxzkZTSWzaFFB+YUXuLAZma+EhAT88ssvuH//Ptzc3ODs7Cx3SEQVntEJRnh4ON577z2kpqZCoVBAp9Nh//79ePvttzF27FhTxEhmSKMBlizRl21sgDFj5I2HqDhCCOzbtw9RUVFQq9Xw9/fHiy++iGrVqskdGlGFZ3TH48yZMzFx4kT4+flBq9WiQYMG0Gq1GDVqFD788ENTxEhmKDZWPz04AAwcCPxrUV0i2eXl5WHjxo04efIkAKBly5bo06cPrKysZI6MqHIwOsFQqVRYsGABPvroI5w+fRoZGRlo3rw5goKCTBEfmSkO7iRzd+bMGZw8eRIKhQJ9+vRB69at5Q6JqFIxOsHYt28fOnbsiJo1a6JmzZqmiInMXHIysHmzvuznB/TsKW88RMVp0qQJUlJSEBwcjICAALnDIap0jB6D0b17d9SuXRvvv/8+zp49a4qYyMwtWQLodPryuHEArziTubhw4QJyc3MBAAqFAr1792ZyQSQToxOM5ORkvPXWW9izZw8aNWqEZs2aYfbs2bhx44Yp4iMzI0Thu0fGj5cvFqJ8Qgjs3r0bkZGRWL16NXT5GTARycboBMPDwwOTJk3C/v37cfnyZQwfPhxLliyBv78/unfvbooYyYz88Qdw6ZK+3KMHULu2vPEQaTQarFq1Cnv27AGgnzyLiOT3VNPX1a5dG1OmTEHTpk3x0UcfGX7BqeLi4E4yJ+np6YiKikJKSgqUSiWeffZZtGjRQu6wiAhPkWDs378fy5cvx6pVq5CTk4OBAwdi1qxZUsZGZubBA2DVKn3ZzQ0YNEjOaKiyu3HjBqKjo5GRkQEHBweMGDECtWrVkjssIvqH0QnG1KlTERUVheTkZPTs2RPffvstBg4cCAcHB1PER2YkMhLIztaXR48G7O3ljYcqL61Wa1hs0dPTEyNHjoSbm5vcYRHRI4xOMP744w+88847GDFiBDw4u1Klwu4RMhdWVlYYNmwYDhw4gP79+8PW1lbukIjoX4xOMPbv32+KOMjMnTwJ/PWXvty8uf6LqDzl5uYiJSUF/v7+AAAfHx8MGzZM3qCIqESlSjA2bNiAPn36wMbGBhs2bHjstgMGDJAkMDIvvHpBcrp37x6ioqJw9+5djBs3jgsrElmAUiUYgwYNQmpqKjw9PTHoMSP7FAoFtFqtVLGRmcjNBX77TV+2tQVGjZI3Hqpcrl69ipUrVyIrKwtOTk5yh0NEpVSqBOPRSWs4gU3ls24dcPeuvjx0KFCliqzhUCVy7NgxxMbGQqfTwdvbG+Hh4XBxcZE7LCIqBaMn2lq6dKlhKt5HqdVqLF26VJKgyLywe4TKm06nw9atW7Fx40bodDo0bNgQ48ePZ3JBZEGMTjDGjx+PBw8eFKl/+PAhxnPe6Arn6lVgxw59uXZtoGtXWcOhSiI+Ph6HDh0CAHTt2hVDhw6FjY2NzFERkTGMvotECAGFQlGk/saNG3B1dZUkKDIfixfr1x8BgOefB5RGp6RExmvWrBmuXLmC+vXro0GDBnKHQ0RlUOoEo3nz5lAoFFAoFOjRowesrQt21Wq1uHLlCnr37m2SIEkeOh3w66/6slKpXzmVyFSuX78Ob29vWFtbQ6lUYujQoXKHRERPodQJRv7dI/Hx8QgNDS00mlulUsHf359/ECqYnTv1XSQAEBoK1KghbzxUMQkhcOTIEWzduhVNmjTBwIEDi71KSkSWpdQJxrRp0wAA/v7+CAsLg52dncmCIvPAwZ1kalqtFlu2bMHRo0cB6JMNnU4HKysrmSMjoqdl9BiMiIgIU8RBZubvv4G1a/XlatWA/v3ljYcqnqysLMTExCApKQkAEBISgvbt2/PqBVEFUaoEw93dHRcuXICHhweqVKny2D8Ad/MnTCCLtnw5oFbry2PGACqVvPFQxXL79m1ERUXh3r17UKlUGDp0KIKDg+UOi4gkVKoE45tvvoGzs7OhzP8wKjYh2D1CpqPVahEZGYn79+/Dzc0NI0eOhKenp9xhEZHESpVgPNotMo63ElR4R4/qFzcDgGeeAXiXIEnJysoKAwYMwN69ezFs2DA4ODjIHRIRmYDRsxocO3YMp06dMjxev349Bg0ahPfffx/q/GvqZNF49YKklpeXh9TUVMPj2rVrY8yYMUwuiCowoxOMl19+GRcuXAAAJCYmIiwsDA4ODoiJicG7774reYBUvrKygBUr9GVHRyAsTN54yPJlZGRg6dKlWLx4MdLS0gz17GolqtiMTjAuXLiAZs2aAQBiYmLQpUsXrFixAosXL8bq1auljo/K2erVQHq6vjxiBPDP0BuiMklNTcXChQtx/fp1KBQKZGRkyB0SEZWTMk0Vnr+i6o4dO9CvXz8AgJ+fX6H/TsgysXuEpHLu3DmsXbsWGo0GVatWxciRI1G1alW5wyKicmJ0gtGqVSt89tlnCAkJwZ49e/Djjz8CAK5cuYLq1atLHiCVn0uXgD179OW6dYH27eWNhyyTEAJ79+7Frl27AACBgYEYOnQo7O3tZY6MiMqT0QnG3LlzMXr0aKxbtw4ffPAB6tSpAwBYtWoV2vMTyaItWlRQfuEFgF3kVBbHjx83JBdt2rRBaGgolFwlj6jSMTrBaNKkSaG7SPLNnj2b0/tasLw8/cqpAGBtDYwdK2s4ZMGaNm2K06dPo2HDhmjZsqXc4RCRTIxOMPIdPXoU586dAwA0aNAALVq0kCwoKn9btwIpKfpyv34Ae7vIGHfu3EHVqlWhVCphZWWFMWPG8C4RokrO6ATj9u3bCAsLw549e+Dm5gYAuH//Prp164aoqChUq1ZN6hipHHBwJ5XVqVOnsH79erRu3RqhoaEAeAsqEZXhNtXXX38dGRkZOHPmDO7evYu7d+/i9OnTSE9Px3/+8x9TxEgmdusWsGmTvuztDfTuLW88ZBmEENi5cyfWrFkDrVaLu3fvQqvVyh0WEZkJo69gbN26FTt27ED9+vUNdQ0aNMC8efPQq1cvSYOj8rF0qX4MBgCMG6cfg0H0OGq1GmvWrEFCQgIAoH379ujRowcHcxKRgdEfJTqdDjY2NkXqbWxsDPNjkGXZsKGgPH68fHGQZbh//z6ioqJw69YtWFlZoX///mjatKncYRGRmTH6343u3bvjjTfeQHJysqHu5s2bePPNN9GjRw9Jg6Py8fff+u9OTkBQkLyxkHnTarVYsmQJbt26BUdHR0RERDC5IKJiGZ1gfP/990hPT4e/vz8CAwMRGBiI2rVrIz09Hd99950pYiQTy8zUf3dykjcOMn9WVlbo2bMnvLy8MGHCBPj5+ckdEhGZKaO7SPz8/HDs2DHs3LnTcJtq/fr1ERISInlwVD7yEwxHR3njIPOk0+nw4MEDVKlSBYB+zFW9evU43oKIHsuoBCM6OhobNmyAWq1Gjx498Prrr5sqLipH+etPMcGgf8vJycHq1auRmpqKCRMmwMXFBQCYXBDRE5U6wfjxxx8xceJEBAUFwd7eHmvWrMHly5cxe/ZsU8ZHJpaWBuTm6sv//INKBAC4e/cuIiMjkZaWBmtra9y+fduQYBARPUmp/w35/vvvMW3aNCQkJCA+Ph5LlizBDz/8YMrYqBwcPVpQbt5cvjjIvFy5cgULFixAWloanJ2dMX78eMO6Q0REpVHqBCMxMRERERGGx6NGjUJeXh5S8ueXJov0118F5Vat5IuDzMeRI0ewbNky5OTkwNfXFxMmTICPj4/cYRGRhSl1F0lubi4cH+mkVyqVUKlUyM7ONklgVD4eTTBat5YvDjIPx44dw+bNmwEAjRs3xoABA2DNmdeIqAyM+svx0UcfwcHBwfBYrVbj888/h6urq6Fuzpw50kVHJpefYLi4ALwCTo0aNcKRI0fQsGFDdOjQgWuKEFGZlTrB6Ny5s2Fa4Hzt27dHYmKi4TH/GFmW1FTgxg19uWVLgDcGVE7p6elwdnaGQqGASqXCiy++CCsrK7nDIiILV+oEY/fu3SYMg+TA8Rd06dIlrFq1Ch07dkTHjh0BgMkFEUmC/7NWYkwwKi8hBA4ePIgVK1YgNzcXly5d4lpCRCQps0gw5s2bB39/f9jZ2aFt27Y4fPhwqfaLioqCQqHAoEGDTBtgBcUEo3LKy8vDhg0bsH37dggh0Lx5c4wZM4aTZxGRpGT/ixIdHY3Jkydj2rRpOHbsGJo2bYrQ0FDcvn37sfslJSXh7bffRqdOncop0oonPl7/vUoVoHZtWUOhcqLRaLBixQrEx8dDoVAgNDQU/fv3Z7cIEUlO9gRjzpw5mDBhAsaPH48GDRpg/vz5cHBwwKJFi0rcR6vVYvTo0ZgxYwYCAgLKMdqK48ED4OZNfblhQ4Djcyu+vLw8XLx4ETdu3ICtrS1GjRqFZ555hoOzicgkZL3BXa1W4+jRo5g6daqhTqlUIiQkBAcPHixxv08++QSenp544YUXsHfv3seeIzc3F7n5c2FDP2Ie0P8np9FonvIVwHCsR79bgtOnFcj/8detq4NGo5U3oH+xxDY1d0IIeHp6IiMjAyNGjICHhwfb9ynwPSo9tqm0TNGexhyrTAnG3r178dNPP+Hy5ctYtWoVfH19sWzZMtSuXdswEr000tLSoNVqUb169UL11atXx/nz54vdZ9++ffjll18Qn399/wlmzZqFGTNmFKnfvn17oTk9pBAXFyfp8UwpKqougHoAACHOYPPmxMfvIBNLalNzJIRAXl4ebGxsAAAeHh5wd3cv9TgnejK+R6XHNpWWlO2ZlZVV6m2NTjBWr16NMWPGYPTo0Th+/Ljh6sCDBw8wc+ZMwyyApvDw4UOMGTMGCxYsgIeHR6n2mTp1KiZPnmx4nJ6eDj8/P/Tq1UuyhZs0Gg3i4uLQs2dPwx9yc3blChAVVRDn4MH1ERpaT8aIirK0NjVHGo0Gmzdvxs2bNzFu3DjY2NggLi4OoaGhbFMJ8D0qPbaptEzRnvm9AKVhdILx2WefYf78+Rg7diyioqIM9R06dMBnn31m1LE8PDxgZWWFW7duFaq/desWvLy8imx/+fJlJCUloX///oa6/FvrrK2tkZCQgMDAwEL72NrawtbWtsixbGxsJH8Dm+KYphAdXfhxu3bWMNewLaVNzc3Dhw8RHR2NmzdvQqlUIjU11TBeiW0qLban9Nim0pKyPY05jtGDPBMSEtC5c+ci9a6urrh//75Rx1KpVGjZsiV27txpqNPpdNi5cyfatWtXZPt69erh1KlTiI+PN3wNGDAA3bp1Q3x8PPz8/Ix9OZXS6tUF5c2bgapV5YuFpJecnIwFCxbg5s2bsLe3x3PPPYe6devKHRYRVTJGX8Hw8vLCpUuX4O/vX6h+3759ZbqjY/LkyYiIiECrVq3Qpk0bzJ07F5mZmRg/fjwAYOzYsfD19cWsWbNgZ2eHRo0aFdrfzc0NAIrUU/EuXwZOnNCX27QB+vSRNx6S1pkzZ7Bu3Trk5eXBw8MDI0eOhLu7u9xhEVElZHSCMWHCBLzxxhtYtGgRFAoFkpOTcfDgQbz99tv46KOPjA4gLCwMd+7cwccff4zU1FQ0a9YMW7duNQz8vHbtGicAktCjVy+GDJEvDpLeiRMnsG7dOgBAUFAQhgwZAjs7O3mDIqJKy+gEY8qUKdDpdOjRoweysrLQuXNn2Nra4u2338brr79epiAmTZqESZMmFfvck9ZAWbx4cZnOWVk9mmAMHSpfHCS9oKAgVKlSBfXq1UNISAgTcyKSldEJhkKhwAcffIB33nkHly5dQkZGBho0aAAnJydTxEcSun4dyL87sUkTLs9eEeTk5BiuUjg4OOCll17iVQsiMgtlnmhLpVKhQYMGUsZCJrZmTUGZVy8s3/Xr1xEdHY1u3bqhZcuWAMDkgojMhtEJRrdu3R47tfDvv//+VAGR6bB7pOKIj4/Hpk2boNVqcezYMTRv3pxdIkRkVoxOMJo1a1bosUajQXx8PE6fPo2IiAip4iKJ3boF7NunL9etC/Dik2XS6XTYsWOHYSr9evXqYfDgwUwuiMjsGJ1gfPPNN8XWT58+HRkZGU8dEJnGunWAEPry0KFc3MwS5ebmYvXq1bh48SIAoFOnTk+8okhEJBfJ/u157rnnHrsCKsmL3SOWLS8vD4sWLcLFixdhbW2NIUOGoHv37kwuiMhsSZZgHDx4kAPMzNTdu8CuXfqyvz/QvLms4VAZWFtbo1GjRnBycsK4cePQuHFjuUMiInoso7tIhvxrdiYhBFJSUvDXX3+VaaItMr0NG4C8PH2Z3SOWRa1WQ6VSAQA6duyIli1bSr4KMBGRKRidYLi6uhZ6rFQqUbduXXzyySfo1auXZIGRdNg9Ynl0Oh22bduGq1ev4vnnn4dKpYJCoWByQUQWw6gEQ6vVYvz48WjcuDGqVKliqphIQunpwPbt+rKPD9C2rbzx0JNlZ2dj1apVSExMBKBfRbh+/foyR0VEZByjxmBYWVmhV69eRq+aSvKJjQXUan158GCAdzOat7S0NCxcuBCJiYmwsbHBiBEjmFwQkUUyuoukUaNGSExMRO3atU0RD0mMs3dajsuXLyMmJga5ublwdXVFeHg4vLy85A6LiKhMjP5/9rPPPsPbb7+NTZs2ISUlBenp6YW+yHxkZQGbN+vLHh5Ap07yxkMlO3XqFJYvX47c3Fz4+flhwoQJTC6IyKKV+grGJ598grfeegt9+/YFAAwYMKDQPfhCCCgUCmi1WumjpDLZtk2fZADAoEGAdZlXniFTq1mzJhwcHBAUFIRnn30W1vxhEZGFK/VfsRkzZuCVV17BrvwJFcjs8e4R85aXl2dIJFxdXfHyyy/DycmJk2cRUYVQ6gRD/DPPdJcuXUwWDEknNxfYuFFfdnUFuneXNx4q7Pbt24iKikLPnj0NgzidnZ1ljoqISDpGjcHgf1aWY+dO/S2qADBgAPDPXE1kBhISEvDLL7/g3r172LNnD3Q6ndwhERFJzqiO3uDg4CcmGXfv3n2qgEga7B4xP0IIHDhwADt27AAA+Pv7Y/jw4VwJlYgqJKMSjBkzZhSZyZPMT14esH69vuzoCHCCVfnl5eVh48aNOHnyJACgZcuW6NOnD6ysrGSOjIjINIxKMMLDw+Hp6WmqWEgif/wB/P23vvzss4C9vbzxVHZ5eXlYsmQJbty4AYVCgT59+qB169Zyh0VEZFKlTjA4/sJysHvEvFhbW8PX1xdpaWkYPnw4AgIC5A6JiMjkjL6LhMybTgesXasv29oCffrIG09lptPpDOMrevXqhWeeeQZubm7yBkVEVE5KPbpMp9Oxe8QCHDwIpKToy6GhAO98LH9CCOzZswdLly41TDynVCqZXBBRpcLpAisYdo/IS6PRYN26dTh79iwA4Pz582jYsKHMURERlT8mGBWIEAWLm1lbA/37yxtPZZOeno6oqCikpKRAqVTi2WefZXJBRJUWE4wKIjMTOHQIuHpV/7hHD6BKFXljqkxu3LiB6OhoZGRkwMHBASNGjECtWrXkDouISDZMMCqAJUuACRMAjaagjt0j5efcuXNYvXo1tFotPD09MXLkSI63IKJKjwmGhRMC+PTTwsmFSgUMHChfTJVNtWrVYG1tjTp16mDw4MGwtbWVOyQiItkxwbBwJ04Aly/ryzVqAG3aAM89B/CGH9MSQhjmhvHw8MCLL76IqlWrcr4YIqJ/cBEEC7dqVUH5/ff1d5EMHixfPJXBvXv38PPPP+PKlSuGOg8PDyYXRESPYIJhwYQoSDAUCiYW5eHq1atYuHAhUlNTsWXLFk5AR0RUAnaRWLCzZ4GEBH25UyfAy0veeCq6Y8eOITY2FjqdDt7e3ggPD+dVCyKiEjDBsGCPdo/wrhHT0el02L59Ow4dOgQAaNiwIQYOHAgbGxuZIyMiMl9MMCzYo7N2DhkiXxwVmUajQXR0NC7/M5K2a9eu6Ny5M69cEBE9ARMMC5WQAJw6pS+3a6e/g4SkZ21tDUdHR1hbW2Pw4MFo0KCB3CEREVkEJhgWimuOmFb+bagKhQL9+/dHhw4duNgfEZEReBeJhWKCYRpCCBw+fBgxMTGGO0Ssra2ZXBARGYlXMCxQYiJw7Ji+3KoV4O8vazgVhlarxZYtW3D06FEAwNmzZ7lYGRFRGTHBsECPXr0YNky+OCqSrKwsxMTEICkpCQDQs2dPjrcgInoKTDAsELtHpHX79m1ERUXh3r17UKlUGDp0KIKDg+UOi4jIojHBsDDXrumXZQeApk2BOnXkjcfSXbp0CTExMVCr1XBzc8PIkSM53oKISAJMMCzMmjUFZXaPPD17e3totVrUqlULI0aMgIODg9whERFVCEwwLAzHX0jL19cX48aNg7e3N6ysrOQOh4iowuBtqhYkORnYv19fbtAAqFdP3ngsUUZGBpYtW4bk5GRDXY0aNZhcEBFJjAmGhTh9GvD11a+gCvDqRVmkpqZiwYIFSExMxPr167kSKhGRCbGLxALodMDw4YXrmGAY59y5c1i7di00Gg2qVq2K4cOHcz0RIiITYoJhAWJjgfPnCx4PHgw0aiRfPJZECIE//vgDu3fvBgAEBgZi6NChsLe3lzcwIqIKjgmGBfj664LywoXACy/IF4slycvLw7p163DmzBkAQJs2bRAaGgqlkj2DRESmxgTDDOl0QHq6vnzqFLBnj75cty4wfrx8cVkapVIJtVoNpVKJvn37omXLlnKHRERUaTDBMDO3bumXX79ypehzb70F8J/v0lMqlRg6dChu374NPz8/ucMhIqpU+HFlZhYtKj65qFYNeO658o/H0pw+fRqxsbGGO0RsbW2ZXBARyYBXMMzMli0F5Z49ASsrwN4eePNN/XcqnhACu3btwt69ewEAtWvX5mJlREQyYoJhRu7fBw4c0JeDg4Ht22UNx2Ko1WqsXbsW5/+51aZDhw6ox1nIiIhkxQTDjOzYAWi1+nKfPvLGYinu37+PqKgo3Lp1C1ZWVujfvz+aNm0qd1hERJUeEwwz8mj3CBOMJ7t27Rqio6ORlZUFR0dHhIWFcbwFEZGZYIJhJoQAtm7Vl+3tgS5d5I3HEmg0GmRnZ8PLywvh4eFwdXWVOyQiIvoHEwwzcfKkfjEzAOjWDbCzkzceSxAYGIiRI0eiVq1aUKlUcodDRESP4G2qZoLdI0+Wk5OD1atX4++//zbUBQUFMbkgIjJDTDDMBBOMx7t79y5++eUXnD59GqtWreJKqEREZs4sEox58+bB398fdnZ2aNu2LQ4fPlzitgsWLECnTp1QpUoVVKlSBSEhIY/d3hI8eADs368vBwUBgYHyxmNurly5ggULFiAtLQ3Ozs7o378/V0IlIjJzsicY0dHRmDx5MqZNm4Zjx46hadOmCA0Nxe3bt4vdfvfu3Rg5ciR27dqFgwcPws/PD7169cLNmzfLOXLp7NxZcHtq797yxmJujh49imXLliEnJwe+vr6YMGECfHx85A6LiIieQPYEY86cOZgwYQLGjx+PBg0aYP78+XBwcMCiRYuK3X758uV47bXX0KxZM9SrVw8LFy6ETqfDzp07yzly6bB7pCitVosbN25g27ZtEEKgcePGiIiIgLOzs9yhERFRKch6F4larcbRo0cxdepUQ51SqURISAgOHjxYqmNkZWVBo9HA3d292Odzc3ORm5treJz+zzKlGo0GGo3mKaIvkH+cshxPCGDLFmsACtjZCXTokAeJwrJoarUa2dnZAICuXbuiXbt2AMrWxqT3NO9TKortKT22qbRM0Z7GHEvWBCMtLQ1arRbVq1cvVF+9enXDtM9P8t5778HHxwchISHFPj9r1izMmDGjSP327dvh4OBgfNCPERcXZ/Q+SUnOuHmzOwCgQYPb2LXrT0ljsmT+/v7IysrC/fv3seXRyzz0VMryPqWSsT2lxzaVlpTtmZWVVeptLXoejC+++AJRUVHYvXs37EqYOGLq1KmYPHmy4XF6erph3IaLi4skcWg0GsTFxaFnz56wsbExat+vviropXruOQ/07dtXkpgs0eXLl5GcnIxOnToZ2nTYsGFGtykV72nep1QU21N6bFNpmaI983sBSkPWBMPDwwNWVla4detWofpbt27By8vrsft+9dVX+OKLL7Bjxw40adKkxO1sbW1ha2tbpN7GxkbyN3BZjvnogmb9+lnBxsZK0pgsgRACf/75J+Li4iCEQI0aNVC7dm0Apvk5VXZsU2mxPaXHNpWWlO1pzHFkHeSpUqnQsmXLQgM08wds5ve5F+fLL7/Ep59+iq1bt6JVq1blEapJpKcD+/bpy4GB+ltUK5u8vDxs2LAB27dvhxACzZs3RyDv0yUisniyd5FMnjwZERERaNWqFdq0aYO5c+ciMzMT48ePBwCMHTsWvr6+mDVrFgDg//7v//Dxxx9jxYoV8Pf3R2pqKgDAyckJTk5Osr2Osti5E8jL05cr490jmZmZiI6OxvXr16FQKNCrVy+0bdsWCoUCOp1O7vCIiOgpyJ5ghIWF4c6dO/j444+RmpqKZs2aYevWrYaBn9euXYNSWXCh5ccff4RarcawYcMKHWfatGmYPn16eYb+1Crz7am3bt1CZGQkHjx4AFtbWwwbNgx16tSROywiIpKI7AkGAEyaNAmTJk0q9rndu3cXepyUlGT6gMqB/vZUfdnWFujaVdZwyl1aWhoePHgAd3d3jBw5Eh4eHnKHREREEjKLBKMyOnMGuHFDX+7SBZD4jlmz17BhQ+Tl5SE4OBj29vZyh0NERBKTfSbPymrr1oJyZege0Wg02LJlS6FbnJo2bcrkgoiogmKCIZPKNP7i4cOHWLJkCQ4fPoyYmBiuhEpEVAmwi0QGanXB6qn+/kBwsKzhmFRycjKioqLw8OFD2Nvbo0ePHlwJlYioEmCCIYP4eCB/eZROnYCK+nl7+vRprF+/Hnl5eahWrRrCw8NLXDOGiIgqFiYYMjh0qKD8zDPyxWEqQgjs3r0bf/zxBwAgKCgIQ4cOLXZGVSIiqpiYYMggKqqg3LatfHGYikajMSxW165dO4SEhBSay4SIiCo+JhjlLDcXOHBAX7azAx6zjIrFUqlUGDlyJK5evYqmTZvKHQ4REcmACUY5S04uKOfkABVlPZ/r168jNTUVrVu3BgC4ubnBzc1N3qCIiEg2TDBkNGiQ3BFI48SJE9i4cSO0Wi2qVq2KgIAAuUMiIiKZMcGQkaXPMZW/8u2Bf/p86tWrhxo1asgcFRERmQMmGFQmubm5WL16NS5evAgA6NSpE7p168Y5LoiICAATjHJ37lxBWaWSL46nce/ePURGRuLOnTuwtrbGgAED0LhxY7nDIiIiM8IEoxwJAXz0UcHjfv3ki+VpJCYm4s6dO3ByckJ4eDh8fX3lDomIiMwME4xytGYNcOyYvtysGTBkiKzhlFnLli2hVqvRsGFDuLi4yB0OERGZIc5+VE60WuDDDwsef/45YClzT+l0OuzZswfZ2dmGunbt2jG5ICKiElnIR5zli4oC/pncEh06WM4KqtnZ2Vi+fDl2796NVatWcSVUIiIqFXaRlJPVqwvKn3xiGQucpaWlITIyEnfv3oWNjQ1atWrFu0SIiKhUmGCUA60W+P13fdndHejaVdZwSuXSpUtYtWoVcnNz4erqivDwcHh5eckdFhERWQgmGOXgr7+ABw/05R49zHvshRAChw4dwvbt2yGEgJ+fH8LCwuDo6Ch3aEREZEGYYJSDHTsKyj17yhdHaajVahw6dAhCCDRr1gzPPvssrK35NiEiIuPwk6McxMUVlENC5IujNGxtbTFy5EgkJiaibdu2HHNBRERlwgTDxDIzC5ZnDwgAateWN57i3L59G3fu3EHDhg0BAJ6envD09JQ5KiIismRMMExs715Ao9GXzbF7JCEhAWvWrEFeXh5cXFzg5+cnd0hERFQBMMEwMXPtHhFCYP/+/di5cycAoHbt2qhatarMURERUUXBBMPE8gd4KhRA9+7yxpIvLy8PGzduxMmTJwEArVq1Qu/evWFlZSVzZEREVFEwwTChW7eAfz7D0bKlfg4MuWVkZCAqKgo3b96EQqFAnz590Lp1a7nDIiKiCoYJhgn90/sAwHzGX5w6dQo3b96EnZ0dhg8fjoCAALlDIiKiCogJhgmZ4/iLZ555BpmZmWjevDnHXBARkcmY8ZySlk2IgvEX9vZA+/ZyxSHw119/Qa1WAwAUCgVCQkKYXBARkUkxwTCRhATgxg19uVMnwM6u/GPQaDRYtWoVYmNjsX79eq6ESkRE5YZdJCby6PTgcnSPpKenIyoqCikpKVAqlahTpw5n5SQionLDBMNEHh1/Ud4DPG/cuIHo6GhkZGTAwcEBYWFhqFmzZvkGQURElRoTDBPIywN27dKXq1UDmjQpv3OfPHkSGzZsgFarhaenJ0aOHAk3N7fyC4CIiAhMMEziyBHg4UN9uTyXZ8/NzcX27duh1WpRt25dDB48GLa2tuVzciIiokcwwTABuW5PtbW1RVhYGC5evIhu3bpxzAUREcmGCYYJPDrA09TjL+7du4e7d+8iMDAQAODn58cFy4iISHa8TVViDx8CBw/qy0FBgCnHVl69ehULFy5EdHQ0bt26ZboTERERGYlXMCT2xx/6QZ6AabtHjh07htjYWOh0Onh7e8Pe3t50JyMiIjISEwyJmbp7RKfTYfv27Th06BAAoGHDhhg4cCBsbGykPxkREVEZMcGQ2NmzBeUOHaQ9dk5ODlatWoXLly8DALp27YrOnTtzMCcREZkdJhgS+/tv/XelEvDwkPbYhw8fxuXLl2FjY4NBgwahQYMG0p6AiIhIIkwwJJafYLi7Sz//RceOHXHv3j20adMG3t7e0h6ciIhIQryLRGJ37+q/u7s//bGEEDh79iy0Wi0AQKlUYuDAgUwuiIjI7DHBkJBaDaSn68tPuxq6VqtFbGwsYmJisGXLFq6ESkREFoVdJBK6fr2g7Otb9uNkZWUhJiYGSUlJAIAqVao8XWBERETljAmGhJKSCu7mqF27bMe4ffs2oqKicO/ePahUKgwdOhTBwcESRUhERFQ+mGBI6MqVgnJAgPH7X7hwAatXr4ZarYabmxtGjhwJT09P6QIkIiIqJ0wwJHTlStmvYOTk5GDt2rVQq9WoVasWRowYAQcHB4kjJJKXVquFRqOROwxJaDQaWFtbIycnxzAQm54O21RaZW1PGxsbWFlZPfX5mWBIKDm5IMEwdg0SOzs7DBkyBAkJCejTp48kP1wic5KRkYEbN25UmAHLQgh4eXnh+vXrnOxOImxTaZW1PRUKBWrUqAEnJ6enOj8TDAk9+o+Znd2Tt8/IyMCDBw/g+8+I0KCgIAQFBZkoOiL5aLVa3LhxAw4ODqhWrVqF+PDQ6XTIyMiAk5MTlFJPelNJsU2lVZb2FELgzp07uHHjBoKCgp7qn10mGBJ69AqU9RNaNjU1FZGRkdBoNJgwYQLvFKEKTaPRQAiBatWqVZiF+XQ6HdRqNezs7PhhKBG2qbTK2p7VqlVDUlISNBoNEwxzkb+KKvD4BOPcuXNYu3YtNBoNqlatCp1OZ/rgiMxARbhyQVTRSfV7ygRDQo8mGMUlfUII7N27F7t27QIABAYGYujQoRXmPzoiIqJ8TDAk9LguEo1Ggw0bNuD06dMAgDZt2iA0NJSXAYmIqELip5uEHpdg7Nu3D6dPn4ZSqUS/fv3Qp08fJhdEVGElJCTAy8sLDx8+lDsUesTWrVvRrFmzcuma5yechB7XRdKxY0fUqVMHY8aMQcuWLcs3MCIqk3HjxkGhUEChUMDGxga1a9fGu+++i5ycnCLbbtq0CV26dIGzszMcHBzQunVrLF68uNjjrl69Gl27doWrqyucnJzQpEkTfPLJJ7ibv1piBTB16lS8/vrrcHZ2LvJcvXr1YGtri9TU1CLPNWnSBN9++22R+unTp6NZs2aF6lJTU/H6668jICAAtra28PPzQ//+/bFz507JXkdxYmJiUK9ePdjZ2aFx48bYvHnzY7d/9H306FfDhg0N2zx8+BD//e9/UatWLdjb26N9+/Y4cuRIoeNMnz4d9erVg6OjI6pUqYKQkBAcOnSo0DYDBgxAzZo1YWdnB19fX7z88stITk42PN+7d2/Y2Nhg+fLlErTE4zHBkEhGhjV27SpoTmtrICkpyXDPv42NDUaPHg1/f3+ZIiSisujduzdSUlKQmJiIb775Bj/99BOmTZtWaJvvvvsOAwcORIcOHXDo0CGcPHkS4eHheOWVV/D2228X2vaDDz5AWFgYWrdujS1btuD06dP4+uuvceLECSxbtqzcXpdarTbZsa9du4ZNmzZh3LhxRZ7bt28fsrOzMWzYMCxZsqTM50hKSkLLli3x+++/Y/bs2Th16hS2bt2Kbt26YeLEiU8R/eMdOHAAI0eOxAsvvIDjx49j0KBBGDRokKH7uzjffvstUlJSDF/Xr1+Hu7s7hg8fbtjmxRdfRFxcHJYtW4ZTp06hV69eCAkJwc2bNw3bBAcH4/vvv8epU6ewb98++Pv7o1evXrhz545hm27dumHlypVISEhATEwMrly5ghEjRhSKZ9y4cfjf//4nYauUQFQyDx48EADEgwcPJDumWq0W48adEoAQgBAKhU7Exe0U06dPFzt37pTsPJWJWq0W69atE2q1Wu5QKgw52zQ7O1ucPXtWZGdnl/u5n0ZERIQYOHBgobohQ4aI5s2bC61WK+7duyeSkpKEjY2NmDx5cpH9//e//wkA4s8//xRCCHHo0CEBQMydO7fY8927d6/EWK5fvy7Cw8NFlSpVhIODg2jZsqXhuMXF+cYbb4guXboYHnfp0kVMnDhRvPHGG6Jq1aqia9euYuTIkWLEiBGF9lOr1aJq1apiyZIlQgghtFqtmDlzpvD39xd2dnaiSZMmIiYmpsQ4hRBi9uzZolWrVsU+N27cODFlyhSxZcsWERwcXOg5rVYr/Pz8xJw5c4rsN23aNNG0aVPD4z59+ghfX1+RkZFRZNvHtePTGjFihHj22WcL1bVt21a8/PLLpT7G2rVrhUKhEElJSUIIIbKysoSVlZXYtGlToe1atGghPvjggxKPk/95tmPHjmKf12q1Yvny5UKhUBT6vb969aoAIC5dulTsfo/7fTXmM9QsrmDMmzcP/v7+sLOzQ9u2bXH48OHHbm/s5anycPOmfsYzlUqN119fif379wLQ34csKsjMhURSatUKqFGj/L9atSp7zKdPn8aBAwegUqkMdatXr4ZGoylypQIAXn75ZTg5OSEyMhIAsHz5cjg5OeG1114r9vhubm7F1mdkZKBLly64efMmNmzYgBMnTuDdd981uh99yZIlUKlU2L9/P+bPn4/Ro0dj48aNyMjIMGyzbds2ZGVlYfDgwQCAWbNmYenSpZg/fz7OnDmDN998E8899xz27NlT4nn27t2LVsU09MOHDxETE4PnnnsOPXv2xIMHD7B3716jXgMA3L17F1u3bsXEiRPh6OhY5PmS2hEo+Bk87utxMR08eBAhISGF6kJDQ3Hw4MFSx//LL78gJCQEtWrVAgDk5eVBq9XC7l8zNNrb22Pfvn3FHkOtVuPnn3+Gq6srmjZtWuw2d+/exapVq9C+fXvY2NgY6mvWrInq1auXqe2NIftdJNHR0Zg8eTLmz5+Ptm3bYu7cuQgNDUVCQkKxC33lX56aNWsW+vXrhxUrVmDQoEE4duwYGjVqJMMr0Lt3zw6urvcxcmQU3N1vwcrKCv379y/xB09U2aWmAo9c/TVbmzZtgpOTE/Ly8pCbmwulUonvv//e8PyFCxfg6uoKb2/vIvuqVCoEBATgwoULAICLFy8iICCg0B/70lixYgXu3LmDI0eOwN3dHQBQp04do19LUFAQvvzyS8PjwMBAODo6Yu3atRgzZozhXAMGDICzszNyc3Mxc+ZM7NixA+3atQMABAQEYN++ffjpp5/QpUuXYs9z9erVYhOMqKgoBAUFGcYehIeH45dffkGnTp2Meh2XLl2CEAL16tUzaj9AP0ahbdu2j90mf3bl4qSmpqJ69eqF6qpXr17seJLiJCcnY8uWLVixYoWhztnZGe3atcOnn36K+vXro3r16oiMjMTBgweL/Jw3bdqE8PBwZGVlwdvbG3FxcfDw8Ci0zXvvvYfvv/8eWVlZaN26NWJjY4vE4ePjg6tXr5Yq5rKSPcGYM2cOJkyYgPHjxwMA5s+fj9jYWCxatAhTpkwpsv23336L3r1745133gEAfPrpp4iLi8P333+P+fPnl2vs+c6eBf7+OxsvvbQMjo5ZcHR0RFhYGPz8/GSJh8gSeHlZxnm7deuGH3/8EZmZmfjmm29gbW2NoUOHlmkUflmvZsbHx6N58+aG5KKs/j3A3NraGiNGjMDy5csxZswYZGZmYv369YiKigKg/yDPyspCz549C+2nVqvRvHnzEs+TnZ1d5L9xAFi0aBGee+45w+PnnnsOXbp0wXfffVfsYNCSPM1VYWdnZ6POJbUlS5bAzc0NgwYNKlS/bNkyPP/88/D19YWVlRVatGiBkSNH4ujRo4W269atG+Lj45GWloYFCxZgxIgROHToUKF/yN955x288MILuHLlCqZNm4aIiAjExsYWmkDL3t4eWVlZJn2tsiYYarUaR48exdSpUw11SqUSISEhJV5uOnjwICZPnlyoLjQ0FOvWrSt2+9zcXOTm5hoep6enA9DPSyHVqo6dOmkwcWI07OxykZbmhUmThsHFxaXCrBoph/y2YxtKR842zZ8qXKfTGT6Yn9ATalKlzQ2EEHBwcEBAQAAAYOHChWjevDkWLFiA559/HoD+qsCDBw9w48YN+Pj4FNpfrVbj8uXL6Nq1K3Q6HYKCgrBv3z7k5uYadRUj/8O6pKRGoVAUatv8c/97HwcHhyLHGDlyJLp164bU1FTExcXB3t4evXr1gk6nM/y93LhxY5H/6m1tbUuMx8PDA3fv3i30/NmzZ/Hnn3/i8OHDeO+99wz1Wq0WK1aswIQJEyCEgLOzM+7fv1/k2Pfu3YOrqyt0Oh0CAwOhUChw7tw5DBw4sPhGK8Hy5cvx6quvPnab2NjYEq+qeHl5ITU1tVB8qamp8PLyemLSKYQwJFnW1taFtq9duzZ27dqFzMxMpKenw9vbG+Hh4ahdu3ah7ezt7REQEICAgAC0adMGdevWxcKFCwv9Q+7u7g53d3cEBgaiRo0aaNSoEQ4cOGC4CgXou088PDyKjTm/a7+4qcKN+fsha4KRlpYGrVZb7OWm8+fPF7uPsZenZs2ahRkzZhSp3759u2TLoWdn98fmzX1Rt24Crl59psQ+MzJeXFyc3CFUOHK0qbW1Nby8vJCRkWHSuxekptFokJeXZ/igBYA33ngDH374Ifr162f4MLaxscEXX3yBzz77rND+P/30EzIzM9G/f3+kp6djwIAB+O677/DNN9/glVdeKXK+Bw8ewNXVtUh9UFAQFi5ciKtXrxa7bpGLiwtOnjxZKM6jR4/CxsbGUJeXlwe1Wl1oGwBo1KgRfH19sXTpUsTFxWHAgAHIzs5GdnY2atSoAVtbWyQkJBR7xeLfx8rXoEGDIvHMnz8f7du3x+zZswttu2LFCixcuBBhYWGG13r48OEixz5y5AiCgoKQnp4Oa2trdO/eHfPmzUNERESRcRgltSMAdO3aFX/88Uexz+Xz9vYu8bW1atUK27ZtM1x1B/RzS7Ro0aLEffLt27cPly5dwogRIx67raOjI65du4Zt27ZhxowZj902//1Z0jb5CcS9e/cM2+Tk5ODy5csIDg4udj+1Wo3s7Gz88ccfyHt0/gXAqKsesneRmNrUqVMLXfFIT0+Hn58fevXqBRcXF0nOMXasFteuucLbeyA++0yB2rX7SnLcykyj0SAuLg49e/Y0ur+aiidnm+bk5OD69etwcnIq9tK5ubKxsYG1tXWhvxVjx47F9OnTsWzZMrz00kto0KAB/u///g9vv/02XFxc8Nxzz8HGxgYbNmzAtGnTMHnyZHTv3h0A0L17d7zzzjv48MMP8ffff2PQoEHw8fHBpUuX8NNPP6Fjx474z3/+UySO8ePHY+7cuYiIiMDnn38Ob29vHD9+HD4+PmjXrh169+6N7777DuvWrUO7du2wfPlynD9/Hs2bNzfEbm1tDZVKVezfvdGjR2PJkiW4cOECdu7cadjGxcUFb731Fj788EPY2tqiY8eOePDgAQ4cOABnZ2dEREQU2279+vXDSy+9BEdHR1hZWUGj0WDlypWYPn06nnnmmULburq6Yt68ebh+/ToaNGiAV199FX379sX333+PwYMHQ6vVIioqCkeOHMH8+fMNsc2fPx+dOnVCr169MH36dDRp0gR5eXnYsWOHYUBqcVxcXB47xuJJJk+ejG7dumHhwoXo27cvoqOjER8fj4ULFxpie//993Hz5s0it+FGRUWhbdu2RdoA0A+uFUKgbt26uHTpEt577z3Ur18fr776KmxsbJCZmYmZM2eif//+8Pb2RlpaGn744QekpKRg9OjRcHFxwaFDh/DXX3+hQ4cOqFKlCi5duoQPP/wQgYGB6NGjB2xtbQEAx44dg62tLXr06FHsP9o5OTmwt7dH586di/y+PimJKuSJ95mYUG5urrCyshJr164tVD927FgxYMCAYvfx8/MT33zzTaG6jz/+WDRp0qRU5zTVbaq8pVJabFPp8TZV4xV3+6cQQsyaNUtUq1ZN3LhxQ2i1WiGEEOvXrxedOnUSjo6Ows7OTrRs2VIsWrSo2ONGR0eLzp07C2dnZ+Ho6CiaNGkiPvnkk8feXpmUlCSGDh0qXFxchIODg2jVqpU4dOiQ4fmPP/5YVK9eXbi6uoo333xTTJo0qchtqm+88Uaxxz579qwAIGrVqiV0Ol2h53Q6nZg7d66oW7eusLGxEdWqVROhoaFiz549Jcaq0WiEj4+P2Lp1qxBCiFWrVgmlUilSU1OL3b5+/frizTffNNz6u2XLFtGhQwdRpUoVwy21xZ0vOTlZTJw4UdSqVUuoVCrh6+srBgwYIHbt2lVibFJYuXKlCA4OFiqVSjRs2FDExsYWej4iIqJQ2wshxP3794W9vb34+eefiz1mdHS0CAgIECqVSnh5eYmJEyeK+/fvG57Pzs4WgwcPFj4+PkKlUglvb28xYMAAcfjwYcM2J0+eFN26dRPu7u7C1tZW+Pv7i/Hjx4tr164VOtdLL7302NtqpbpNVfZ5MNq0aSMmTZpkeKzVaoWvr6+YNWtWsduPGDFC9OvXr1Bdu3btSn0PMhMMy8A2lR4TDGnlfxjmJxhU2Pfffy969epl1D5sU2kV15537twR7u7uIjExscT9pEowZO8imTx5MiIiItCqVSu0adMGc+fORWZmpqF/a+zYsfD19cWsWbMA6Ps/u3Tpgq+//hrPPvssoqKi8Ndff+Hnn3+W82UQEdEjXn75Zdy/fx8PHz6U9a4NKiwpKQk//PADateubfJzyZ5ghIWF4c6dO/j444+RmpqKZs2aYevWrYaBnNeuXSu0KFj79u2xYsUKfPjhh3j//fcRFBSEdevWyToHBhERFWZtbY0PPvhA7jDoX1q1alXsHCWmIHuCAQCTJk3CpEmTin1u9+7dReqGDx9eaA53IiIiMi9mMVU4ERERVSxMMIio3Aiuy0Nk9qT6PWWCQUQmlz8boCVNskVUWeX/nv57Fk9jmcUYDCKq2KytreHg4IA7d+7Axsam0MBtS6XT6aBWq5GTk1MhXo85YJtKqyztqdPpcOfOHTg4OMDa+ulSBCYYRGRyCoUC3t7euHLlislXcCwvQghkZ2fD3t6+0CJSVHZsU2mVtT2VSiVq1qz51D8DJhhEVC5UKhWCgoIqTDeJRqPBH3/8gc6dO3M6e4mwTaVV1vZUqVSSXEFigkFE5UapVFrUWiSPY2Vlhby8PNjZ2fHDUCJsU2nJ3Z7s5CIiIiLJMcEgIiIiyTHBICIiIslVujEY+ROIGLWm/RNoNBpkZWUhPT2d/YYSYZtKj20qLban9Nim0jJFe+Z/dpZmMq5Kl2A8fPgQAODn5ydzJERERJbp4cOHcHV1few2ClHJ5u7V6XRITk6Gs7OzZPdZp6enw8/PD9evX4eLi4skx6zs2KbSY5tKi+0pPbaptEzRnkIIPHz4ED4+Pk+8lbXSXcFQKpWoUaOGSY7t4uLCXwqJsU2lxzaVFttTemxTaUndnk+6cpGPgzyJiIhIckwwiIiISHJMMCRga2uLadOmwdbWVu5QKgy2qfTYptJie0qPbSotuduz0g3yJCIiItPjFQwiIiKSHBMMIiIikhwTDCIiIpIcEwwiIiKSHBOMUpo3bx78/f1hZ2eHtm3b4vDhw4/dPiYmBvXq1YOdnR0aN26MzZs3l1OklsOYNl2wYAE6deqEKlWqoEqVKggJCXniz6CyMfY9mi8qKgoKhQKDBg0ybYAWyNg2vX//PiZOnAhvb2/Y2toiODiYv/uPMLY9586di7p168Le3h5+fn548803kZOTU07Rmr8//vgD/fv3h4+PDxQKBdatW/fEfXbv3o0WLVrA1tYWderUweLFi00XoKAnioqKEiqVSixatEicOXNGTJgwQbi5uYlbt24Vu/3+/fuFlZWV+PLLL8XZs2fFhx9+KGxsbMSpU6fKOXLzZWybjho1SsybN08cP35cnDt3TowbN064urqKGzdulHPk5snY9sx35coV4evrKzp16iQGDhxYPsFaCGPbNDc3V7Rq1Ur07dtX7Nu3T1y5ckXs3r1bxMfHl3Pk5snY9ly+fLmwtbUVy5cvF1euXBHbtm0T3t7e4s033yznyM3X5s2bxQcffCDWrFkjAIi1a9c+dvvExETh4OAgJk+eLM6ePSu+++47YWVlJbZu3WqS+JhglEKbNm3ExIkTDY+1Wq3w8fERs2bNKnb7ESNGiGeffbZQXdu2bcXLL79s0jgtibFt+m95eXnC2dlZLFmyxFQhWpSytGdeXp5o3769WLhwoYiIiGCC8S/GtumPP/4oAgIChFqtLq8QLYqx7Tlx4kTRvXv3QnWTJ08WHTp0MGmclqo0Cca7774rGjZsWKguLCxMhIaGmiQmdpE8gVqtxtGjRxESEmKoUyqVCAkJwcGDB4vd5+DBg4W2B4DQ0NASt69sytKm/5aVlQWNRgN3d3dThWkxytqen3zyCTw9PfHCCy+UR5gWpSxtumHDBrRr1w4TJ05E9erV0ahRI8ycORNarba8wjZbZWnP9u3b4+jRo4ZulMTERGzevBl9+/Ytl5grovL+bKp0i50ZKy0tDVqtFtWrVy9UX716dZw/f77YfVJTU4vdPjU11WRxWpKytOm/vffee/Dx8Snyy1IZlaU99+3bh19++QXx8fHlEKHlKUubJiYm4vfff8fo0aOxefNmXLp0Ca+99ho0Gg2mTZtWHmGbrbK056hRo5CWloaOHTtCCIG8vDy88soreP/998sj5AqppM+m9PR0ZGdnw97eXtLz8QoGWZwvvvgCUVFRWLt2Lezs7OQOx+I8fPgQY8aMwYIFC+Dh4SF3OBWGTqeDp6cnfv75Z7Rs2RJhYWH44IMPMH/+fLlDs0i7d+/GzJkz8cMPP+DYsWNYs2YNYmNj8emnn8odGpUSr2A8gYeHB6ysrHDr1q1C9bdu3YKXl1ex+3h5eRm1fWVTljbN99VXX+GLL77Ajh070KRJE1OGaTGMbc/Lly8jKSkJ/fv3N9TpdDoAgLW1NRISEhAYGGjaoM1cWd6j3t7esLGxgZWVlaGufv36SE1NhVqthkqlMmnM5qws7fnRRx9hzJgxePHFFwEAjRs3RmZmJl566SV88MEHUCr5/7GxSvpscnFxkfzqBcArGE+kUqnQsmVL7Ny501Cn0+mwc+dOtGvXrth92rVrV2h7AIiLiytx+8qmLG0KAF9++SU+/fRTbN26Fa1atSqPUC2Cse1Zr149nDp1CvHx8YavAQMGoFu3boiPj4efn195hm+WyvIe7dChAy5dumRI1gDgwoUL8Pb2rtTJBVC29szKyiqSROQnb4JLaJVJuX82mWToaAUTFRUlbG1txeLFi8XZs2fFSy+9JNzc3ERqaqoQQogxY8aIKVOmGLbfv3+/sLa2Fl999ZU4d+6cmDZtGm9T/Rdj2/SLL74QKpVKrFq1SqSkpBi+Hj58KNdLMCvGtue/8S6Sooxt02vXrglnZ2cxadIkkZCQIDZt2iQ8PT3FZ599JtdLMCvGtue0adOEs7OziIyMFImJiWL79u0iMDBQjBgxQq6XYHYePnwojh8/Lo4fPy4AiDlz5ojjx4+Lq1evCiGEmDJlihgzZoxh+/zbVN955x1x7tw5MW/ePN6mag6+++47UbNmTaFSqUSbNm3En3/+aXiuS5cuIiIiotD2K1euFMHBwUKlUomGDRuK2NjYco7Y/BnTprVq1RIAinxNmzat/AM3U8a+Rx/FBKN4xrbpgQMHRNu2bYWtra0ICAgQn3/+ucjLyyvnqM2XMe2p0WjE9OnTRWBgoLCzsxN+fn7itddeE/fu3Sv/wM3Url27iv27mN+OERERokuXLkX2adasmVCpVCIgIED8+uuvJouPy7UTERGR5DgGg4iIiCTHBIOIiIgkxwSDiIiIJMcEg4iIiCTHBIOIiIgkxwSDiIiIJMcEg4iIiCTHBIOIiIgkxwSDqIJZvHgx3Nzc5A6jzBQKBdatW/fYbcaNG4dBgwaVSzxEVDZMMIjM0Lhx46BQKIp8Xbp0Se7QsHjxYkM8SqUSNWrUwPjx43H79m1Jjp+SkoI+ffoAAJKSkqBQKBAfH19om2+//RaLFy+W5HwlmT59uuF1WllZwc/PDy+99BLu3r1r1HGYDFFlxeXaicxU79698euvvxaqq1atmkzRFObi4oKEhATodDqcOHEC48ePR3JyMrZt2/bUxy5p+e5Hubq6PvV5SqNhw4bYsWMHtFotzp07h+effx4PHjxAdHR0uZyfyJLxCgaRmbK1tYWXl1ehLysrK8yZMweNGzeGo6Mj/Pz88NprryEjI6PE45w4cQLdunWDs7MzXFxc0LJlS/z111+G5/ft24dOnTrB3t4efn5++M9//oPMzMzHxqZQKODl5QUfHx/06dMH//nPf7Bjxw5kZ2dDp9Phk08+QY0aNWBra4tmzZph69athn3VajUmTZoEb29v2NnZoVatWpg1a1ahY+d3kdSuXRsA0Lx5cygUCnTt2hVA4asCP//8M3x8fAotkw4AAwcOxPPPP294vH79erRo0QJ2dnYICAjAjBkzkJeX99jXaW1tDS8vL/j6+iIkJATDhw9HXFyc4XmtVosXXngBtWvXhr29PerWrYtvv/3W8Pz06dOxZMkSrF+/3nA1ZPfu3QCA69evY8SIEXBzc4O7uzsGDhyIpKSkx8ZDZEmYYBBZGKVSif/97384c+YMlixZgt9//x3vvvtuiduPHj0aNWrUwJEjR3D06FFMmTIFNjY2AIDLly+jd+/eGDp0KE6ePIno6Gjs27cPkyZNMiome3t76HQ65OXl4dtvv8XXX3+Nr776CidPnkRoaCgGDBiAixcvAgD+97//YcOGDVi5ciUSEhKwfPly+Pv7F3vcw4cPAwB27NiBlJQUrFmzpsg2w4cPx99//41du3YZ6u7evYutW7di9OjRAIC9e/di7NixeOONN3D27Fn89NNPWLx4MT7//PNSv8akpCRs27YNKpXKUKfT6VCjRg3ExMTg7Nmz+Pjjj/H+++9j5cqVAIC3334bI0aMQO/evZGSkoKUlBS0b98eGo0GoaGhcHZ2xt69e7F//344OTmhd+/eUKvVpY6JyKyZbJ1WIiqziIgIYWVlJRwdHQ1fw4YNK3bbmJgYUbVqVcPjX3/9Vbi6uhoeOzs7i8WLFxe77wsvvCBeeumlQnV79+4VSqVSZGdnF7vPv49/4cIFERwcLFq1aiWEEMLHx0d8/vnnhfZp3bq1eO2114QQQrz++uuie/fuQqfTFXt8AGLt2rVCCCGuXLkiAIjjx48X2ubfy8sPHDhQPP/884bHP/30k/Dx8RFarVYIIUSPHj3EzJkzCx1j2bJlwtvbu9gYhBBi2rRpQqlUCkdHR2FnZ2dYCnvOnDkl7iOEEBMnThRDhw4tMdb8c9etW7dQG+Tm5gp7e3uxbdu2xx6fyFJwDAaRmerWrRt+/PFHw2NHR0cA+v/mZ82ahfPnzyM9PR15eXnIyclBVlYWHBwcihxn8uTJePHFF7Fs2TLDZf7AwEAA+u6TkydPYvny5YbthRDQ6XS4cuUK6tevX2xsDx48gJOTE3Q6HXJyctCxY0csXLgQ6enpSE5ORocOHQpt36FDB5w4cQKAvnujZ8+eqFu3Lnr37o1+/fqhV69eT9VWo0ePxoQJE/DDDz/A1tYWy5cvR3h4OJRKpeF17t+/v9AVC61W+9h2A4C6detiw4YNyMnJwW+//Yb4+Hi8/vrrhbaZN28eFi1ahGvXriE7OxtqtRrNmjV7bLwnTpzApUuX4OzsXKg+JycHly9fLkMLEJkfJhhEZsrR0RF16tQpVJeUlIR+/frh1Vdfxeeffw53d3fs27cPL7zwAtRqdbEflNOnT8eoUaMQGxuLLVu2YNq0aYiKisLgwYORkZGBl19+Gf/5z3+K7FezZs0SY3N2dsaxY8egVCrh7e0Ne3t7AEB6evoTX1eLFi1w5coVbNmyBTt27MCIESMQEhKCVatWPXHfkvTv3x9CCMTGxqJ169bYu3cvvvnmG8PzGRkZmDFjBoYMGVJkXzs7uxKPq1KpDD+DL774As8++yxmzJiBTz/9FAAQFRWFt99+G19//TXatWsHZ2dnzJ49G4cOHXpsvBkZGWjZsmWhxC6fuQzkJXpaTDCILMjRo0eh0+nw9ddfG/47z+/vf5zg4GAEBwfjzTffxMiRI/Hrr79i8ODBaNGiBc6ePVskkXkSpVJZ7D4uLi7w8fHB/v370aVLF0P9/v370aZNm0LbhYWFISwsDMOGDUPv3r1x9+5duLu7Fzpe/ngHrVb72Hjs7OwwZMgQLF++HJcuXULdunXRokULw/MtWrRAQkKC0a/z3z788EN0794dr776quF1tm/fHq+99pphm39fgVCpVEXib9GiBaKjo+Hp6QkXF5enionIXHGQJ5EFqVOnDjQaDb777jskJiZi2bJlmD9/fonbZ2dnY9KkSdi9ezeuXr2K/fv348iRI4auj/feew8HDhzApEmTEB8fj4sXL2L9+vVGD/J81DvvvIP/+7//Q3R0NBISEjBlyhTEx8fjjTfeAADMmTMHkZGROH/+PC5cuICYmBh4eXkVOzmYp6cn7O3tsXXrVty6dQsPHjwo8byjR49GbGwsFi1aZBjcme/jjz/G0qVLMWPGDJw5cwbnzp1DVFQUPvzwQ6NeW7t27dCkSRPMnDkTABAUFIS//voL27Ztw4ULF/DRRx/hyJEjhfbx9/fHyZMnkZCQgLS0NGg0GowePRoeHh4YOHAg9u7diytXrmD37t34z3/+gxs3bhgVE5HZknsQCBEVVdzAwHxz5swR3t7ewt7eXoSGhoqlS5cKAOLevXtCiMKDMHNzc0V4eLjw8/MTKpVK+Pj4iEmTJhUawHn48GHRs2dP4eTkJBwdHUWTJk2KDNJ81L8Hef6bVqsV06dPF76+vsLGxkY0bdpUbNmyxfD8zz//LJo1ayYcHR2Fi4uL6NGjhzh27JjheTwyyFMIIRYsWCD8/PyEUqkUXbp0KbF9tFqt8Pb2FgDE5cuXi8S1detW0b59e2Fvby9cXFxEmzZtxM8//1zi65g2bZpo2rRpkfrIyEhha2srrl27JnJycsS4ceOEq6urcHNzE6+++qqYMmVKof1u375taF8AYteuXUIIIVJSUsTYsWOFh4eHsLW1FQEBAWLChAniwYMHJcZEZEkUQgghb4pDREREFQ27SIiIiEhyTDCIiIhIckwwiIiISHJMMIiIiEhyTDCIiIhIckwwiIiISHJMMIiIiEhyTDCIiIhIckwwiIiISHJMMIiIiEhyTDCIiIhIcv8PeIJOb1AUzq8AAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 600x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ROC Curve\n",
"fpr, tpr, _ = roc_curve(y_test_rfe, y_pred_proba_rfe)\n",
"roc_auc = auc(fpr, tpr)\n",
"\n",
"plt.figure(figsize=(6, 5))\n",
"plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (AUC = {roc_auc:.4f})')\n",
"plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC Curve')\n",
"plt.legend(loc='lower right')\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "e403edb1",
"metadata": {},
"source": [
"### Interpreting the ROC Curve\n",
"\n",
"The **Receiver Operating Characteristic (ROC) curve** shows how well the model distinguishes between the positive and negative classes across all decision thresholds.\n",
"\n",
"A quick reminder of the definitions:\n",
"* True Positive Rate (TPR) = Recall\n",
"* False Positive Rate (FPR) = Proportion of negatives wrongly classified as positives\n",
"\n",
"What we display in this plot is:\n",
"* The x-axis is False Positive Rate\n",
"* The y-axis is True Positive Rate\n",
"\n",
"The curve shows how TPR and FPR change as the threshold varies\n",
"\n",
"It's important to note that:\n",
"* A model with no skill will produce a diagonal line (AUC = 0.5)\n",
"* A model with perfect discrimination will hug the top-left corner (AUC = 1.0)\n",
"\n",
"The Area Under the Curve (ROC AUC) gives a single performance score:\n",
"* Closer to 1 means better at ranking positive cases higher than negative ones\n",
"\n",
"**Important!**\n",
"\n",
"While useful, the ROC curve can sometimes overestimate performance when the dataset is imbalanced, because it includes negatives (which dominate in our case, around 99%!). That’s why we also MUST check the Precision-Recall curve."
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "6790d41d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# PR Curve\n",
"precision, recall, _ = precision_recall_curve(y_test_rfe, y_pred_proba_rfe)\n",
"pr_auc = average_precision_score(y_test_rfe, y_pred_proba_rfe)\n",
"\n",
"plt.figure(figsize=(6, 5))\n",
"plt.plot(recall, precision, color='green', lw=2, label=f'PR curve (AUC = {pr_auc:.4f})')\n",
"plt.xlabel('Recall')\n",
"plt.ylabel('Precision')\n",
"plt.title('Precision-Recall Curve')\n",
"plt.legend(loc='lower left')\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "c111a266",
"metadata": {},
"source": [
"### Interpreting the Precision-Recall (PR) Curve\n",
"\n",
"The **Precision-Recall (PR) curve** helps evaluate model performance, especially on imbalanced datasets like ours (where positive cases are rare).\n",
"\n",
"A quick reminder of the definitions:\n",
"* Precision = How many of the predicted positives are actually positive\n",
"* Recall = How many of the actual positives the model correctly identifies\n",
"\n",
"What we display in this plot is:\n",
"* The x-axis is Recall \n",
"* The y-axis is Precision \n",
"\n",
"The curve shows the trade-off between them at different model thresholds\n",
"\n",
"In imbalanced datasets, accuracy can be misleading — the PR curve focuses only on the positive class, making it much more meaningful:\n",
"* A higher curve means better performance\n",
"* The area under the curve (PR AUC) summarizes this: closer to 1 is better"
]
},
{
"cell_type": "markdown",
"id": "1c83ddcd",
"metadata": {},
"source": [
"## Feature Importance\n",
"Understanding what drives the prediction is useful for future experiments and business knowledge. Here we track both the native feature importances of the trees, as well as a more heavy SHAP values analysis.\n",
"\n",
"Important! Be aware that SHAP analysis might take quite a bit of time."
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "d66ffe2c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## BUILT-IN\n",
"\n",
"# Get feature importances from the model\n",
"importances = best_pipeline_rfe.named_steps['model'].feature_importances_\n",
"\n",
"# Create a Series and sort\n",
"feat_series = pd.Series(importances, index=selected_features_rfe).sort_values(ascending=True) # ascending=True for horizontal plot\n",
"\n",
"# Plot Feature Importances\n",
"plt.figure(figsize=(8, 5))\n",
"feat_series.plot(kind='barh', color='skyblue')\n",
"plt.title('Feature Importances')\n",
"plt.xlabel('Importance')\n",
"plt.grid(axis='x')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3897f25c",
"metadata": {},
"source": [
"### Interpreting the Feature Importance Plot\n",
"The **feature importance plot** shows how much each feature contributes to the 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": 48,
"id": "e2197cea",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"PermutationExplainer explainer: 6394it [13:25, 7.93it/s] \n",
"/tmp/ipykernel_29610/4064815753.py:21: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
" shap.summary_plot(shap_values.values, X_test_shap)\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x950 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## SHAP VALUES\n",
"\n",
"# SHAP requires that all features passed to Explainer be numeric (floats/ints)\n",
"X_test_shap = X_test_rfe.copy()\n",
"X_test_shap = X_test_shap.astype(float)\n",
"\n",
"# Function that returns the probability of the positive class\n",
"def model_predict(data):\n",
" return best_pipeline_rfe.predict_proba(data)[:, 1]\n",
"\n",
"# Ensure input to SHAP is numeric\n",
"X_test_shap = X_test_rfe.astype(float)\n",
"\n",
"# Create SHAP explainer\n",
"explainer = shap.Explainer(model_predict, X_test_shap)\n",
"\n",
"# Compute SHAP values\n",
"shap_values = explainer(X_test_shap)\n",
"\n",
"# Plot summary\n",
"shap.summary_plot(shap_values.values, X_test_shap)"
]
},
{
"cell_type": "markdown",
"id": "e9ae2701",
"metadata": {},
"source": [
"### Interpreting the SHAP Summary Plot\n",
"\n",
"Each point on a row represents a SHAP value for a single prediction (row = feature).\n",
"The x-axis shows how much the feature contributed to increasing or decreasing the prediction.\n",
"* Right (positive SHAP value): pushes prediction toward the positive class (i.e., higher chance of incident).\n",
"* Left (negative SHAP value): pushes prediction toward the negative class (i.e., lower chance of incident).\n",
"\n",
"Color shows the actual feature value for that point:\n",
"* Red = high value\n",
"* Blue = low value\n",
"\n",
"In other words:\n",
"* The position tells you impact.\n",
"* The color tells you feature value.\n",
"* The density (thickness) of dots shows how often a value occurs."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "345467a8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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