diff --git a/Project-1/Part-2/Project 1-(Part-II)-IaaS.pdf b/Project-1/Part-2/Project 1-(Part-II)-IaaS.pdf
new file mode 100644
index 0000000..ddb0015
Binary files /dev/null and b/Project-1/Part-2/Project 1-(Part-II)-IaaS.pdf differ
diff --git a/Project-1/Part-2/get_info.py b/Project-1/Part-2/get_info.py
new file mode 100644
index 0000000..e829b9a
--- /dev/null
+++ b/Project-1/Part-2/get_info.py
@@ -0,0 +1,47 @@
+import boto3
+from pprint import pprint
+
+# creds from ~/.aws/credentials
+session = boto3.Session(profile_name="dev")
+
+region1="us-east-1"
+dev_ec2_client = session.client("ec2", region_name=region1)
+dev_s3_client = session.client("s3", region_name=region1)
+dev_sqs_client = session.client("sqs", region_name=region1)
+
+# Get all instances' information
+all_instances = dev_ec2_client.describe_instances()
+with open("output.txt", "w") as out_f:
+	pprint(all_instances, stream=out_f)
+
+print("Capacity Reservations:", len(all_instances["Reservations"]))
+for idx, reservation in enumerate(all_instances["Reservations"]):
+    print("Reservation", idx + 1, ":-")
+    print("\tGroups:", len(reservation["Groups"])) # TODO: add grp info logging
+    print("\tInstances:", len(reservation["Instances"]))
+    for inst_idx, instance in enumerate(reservation["Instances"]):
+        print("\tInstance", inst_idx + 1, ":-")
+        print("\t\tInstance ID:", instance["InstanceId"])
+        print("\t\tInstance state:", instance["State"]["Name"])
+        print(f"\t\t{instance["InstanceType"]} in {instance["Placement"]["AvailabilityZone"]}")
+        print(f"\t\t{instance["CpuOptions"]["CoreCount"]}vCPU, {instance["Hypervisor"]} hypervisor")
+        print(f"\t\t{instance["PlatformDetails"]} on {instance["RootDeviceType"]} volume")
+        print("\t\tPublic IP address:", instance["PublicIpAddress"] if "PublicIpAddress" in instance else "N/A")
+
+# Get all buckets' information
+all_buckets = dev_s3_client.list_buckets()
+with open("output.txt", "a") as out_f:
+	pprint(all_buckets, stream=out_f)
+
+print("Buckets:", len(all_buckets["Buckets"]))
+for bucket in all_buckets["Buckets"]:
+    print("\tName:", bucket["Name"])
+
+# Get all queues' information
+all_queues = dev_sqs_client.list_queues()
+with open("output.txt", "a") as out_f:
+	pprint(all_queues, stream=out_f)
+
+print("Queues:", len(all_queues["QueueUrls"]))
+for queue in all_queues["QueueUrls"]:
+    print("\tQueue URL:", queue)
diff --git a/Project-1/Part-2/output.txt b/Project-1/Part-2/output.txt
new file mode 100644
index 0000000..c9407be
--- /dev/null
+++ b/Project-1/Part-2/output.txt
@@ -0,0 +1,221 @@
+{'Reservations': [{'Groups': [],
+                   'Instances': [{'AmiLaunchIndex': 0,
+                                  'Architecture': 'x86_64',
+                                  'BlockDeviceMappings': [{'DeviceName': '/dev/sda1',
+                                                           'Ebs': {'AttachTime': datetime.datetime(2024, 3, 17, 23, 57, 30, tzinfo=tzutc()),
+                                                                   'DeleteOnTermination': True,
+                                                                   'Status': 'attached',
+                                                                   'VolumeId': 'vol-0b9d38863d1b6a517'}}],
+                                  'CapacityReservationSpecification': {'CapacityReservationPreference': 'open'},
+                                  'ClientToken': '2460e820-09e2-47f0-b6a2-2bead38887bc',
+                                  'CpuOptions': {'CoreCount': 1,
+                                                 'ThreadsPerCore': 1},
+                                  'CurrentInstanceBootMode': 'legacy-bios',
+                                  'EbsOptimized': False,
+                                  'EnaSupport': True,
+                                  'EnclaveOptions': {'Enabled': False},
+                                  'HibernationOptions': {'Configured': False},
+                                  'Hypervisor': 'xen',
+                                  'ImageId': 'ami-05afaa0d246abf7cf',
+                                  'InstanceId': 'i-000668a9b8c594d20',
+                                  'InstanceType': 't2.micro',
+                                  'KeyName': 'cse546-dev',
+                                  'LaunchTime': datetime.datetime(2024, 3, 17, 23, 57, 30, tzinfo=tzutc()),
+                                  'MaintenanceOptions': {'AutoRecovery': 'default'},
+                                  'MetadataOptions': {'HttpEndpoint': 'enabled',
+                                                      'HttpProtocolIpv6': 'disabled',
+                                                      'HttpPutResponseHopLimit': 1,
+                                                      'HttpTokens': 'optional',
+                                                      'InstanceMetadataTags': 'disabled',
+                                                      'State': 'pending'},
+                                  'Monitoring': {'State': 'disabled'},
+                                  'NetworkInterfaces': [{'Association': {'IpOwnerId': 'amazon',
+                                                                         'PublicDnsName': 'ec2-54-226-176-42.compute-1.amazonaws.com',
+                                                                         'PublicIp': '54.226.176.42'},
+                                                         'Attachment': {'AttachTime': datetime.datetime(2024, 3, 17, 23, 57, 30, tzinfo=tzutc()),
+                                                                        'AttachmentId': 'eni-attach-07debb5dda48fe9e1',
+                                                                        'DeleteOnTermination': True,
+                                                                        'DeviceIndex': 0,
+                                                                        'NetworkCardIndex': 0,
+                                                                        'Status': 'attached'},
+                                                         'Description': '',
+                                                         'Groups': [{'GroupId': 'sg-019686b391a1be806',
+                                                                     'GroupName': 'default'}],
+                                                         'InterfaceType': 'interface',
+                                                         'Ipv6Addresses': [],
+                                                         'MacAddress': '0a:ff:f3:35:32:df',
+                                                         'NetworkInterfaceId': 'eni-061ec190f81f40db0',
+                                                         'OwnerId': '146064153251',
+                                                         'PrivateDnsName': 'ip-172-31-30-6.ec2.internal',
+                                                         'PrivateIpAddress': '172.31.30.6',
+                                                         'PrivateIpAddresses': [{'Association': {'IpOwnerId': 'amazon',
+                                                                                                 'PublicDnsName': 'ec2-54-226-176-42.compute-1.amazonaws.com',
+                                                                                                 'PublicIp': '54.226.176.42'},
+                                                                                 'Primary': True,
+                                                                                 'PrivateDnsName': 'ip-172-31-30-6.ec2.internal',
+                                                                                 'PrivateIpAddress': '172.31.30.6'}],
+                                                         'SourceDestCheck': True,
+                                                         'Status': 'in-use',
+                                                         'SubnetId': 'subnet-08c8975406f48de21',
+                                                         'VpcId': 'vpc-0248a892aa9493bd8'}],
+                                  'Placement': {'AvailabilityZone': 'us-east-1b',
+                                                'GroupName': '',
+                                                'Tenancy': 'default'},
+                                  'PlatformDetails': 'Linux/UNIX',
+                                  'PrivateDnsName': 'ip-172-31-30-6.ec2.internal',
+                                  'PrivateDnsNameOptions': {'EnableResourceNameDnsAAAARecord': False,
+                                                            'EnableResourceNameDnsARecord': False,
+                                                            'HostnameType': 'ip-name'},
+                                  'PrivateIpAddress': '172.31.30.6',
+                                  'ProductCodes': [],
+                                  'PublicDnsName': 'ec2-54-226-176-42.compute-1.amazonaws.com',
+                                  'PublicIpAddress': '54.226.176.42',
+                                  'RootDeviceName': '/dev/sda1',
+                                  'RootDeviceType': 'ebs',
+                                  'SecurityGroups': [{'GroupId': 'sg-019686b391a1be806',
+                                                      'GroupName': 'default'}],
+                                  'SourceDestCheck': True,
+                                  'State': {'Code': 32,
+                                            'Name': 'shutting-down'},
+                                  'StateReason': {'Code': 'Client.UserInitiatedShutdown',
+                                                  'Message': 'Client.UserInitiatedShutdown: '
+                                                             'User initiated '
+                                                             'shutdown'},
+                                  'StateTransitionReason': 'User initiated '
+                                                           '(2024-03-18 '
+                                                           '05:12:55 GMT)',
+                                  'SubnetId': 'subnet-08c8975406f48de21',
+                                  'Tags': [{'Key': 'Name',
+                                            'Value': 'app-tier-instance'}],
+                                  'UsageOperation': 'RunInstances',
+                                  'UsageOperationUpdateTime': datetime.datetime(2024, 3, 17, 23, 57, 30, tzinfo=tzutc()),
+                                  'VirtualizationType': 'hvm',
+                                  'VpcId': 'vpc-0248a892aa9493bd8'}],
+                   'OwnerId': '146064153251',
+                   'ReservationId': 'r-037c5b8ea7529f040'},
+                  {'Groups': [],
+                   'Instances': [{'AmiLaunchIndex': 0,
+                                  'Architecture': 'x86_64',
+                                  'BlockDeviceMappings': [{'DeviceName': '/dev/sda1',
+                                                           'Ebs': {'AttachTime': datetime.datetime(2024, 3, 18, 5, 13, 9, tzinfo=tzutc()),
+                                                                   'DeleteOnTermination': True,
+                                                                   'Status': 'attaching',
+                                                                   'VolumeId': 'vol-09f549973ead49491'}}],
+                                  'CapacityReservationSpecification': {'CapacityReservationPreference': 'open'},
+                                  'ClientToken': '303fb65a-631d-468a-aea3-77a34152b976',
+                                  'CpuOptions': {'CoreCount': 1,
+                                                 'ThreadsPerCore': 1},
+                                  'CurrentInstanceBootMode': 'legacy-bios',
+                                  'EbsOptimized': False,
+                                  'EnaSupport': True,
+                                  'EnclaveOptions': {'Enabled': False},
+                                  'HibernationOptions': {'Configured': False},
+                                  'Hypervisor': 'xen',
+                                  'ImageId': 'ami-0c7217cdde317cfec',
+                                  'InstanceId': 'i-001fcad9b4bda4cbe',
+                                  'InstanceType': 't2.micro',
+                                  'KeyName': 'cse546-dev',
+                                  'LaunchTime': datetime.datetime(2024, 3, 18, 5, 13, 8, tzinfo=tzutc()),
+                                  'MaintenanceOptions': {'AutoRecovery': 'default'},
+                                  'MetadataOptions': {'HttpEndpoint': 'enabled',
+                                                      'HttpProtocolIpv6': 'disabled',
+                                                      'HttpPutResponseHopLimit': 1,
+                                                      'HttpTokens': 'optional',
+                                                      'InstanceMetadataTags': 'disabled',
+                                                      'State': 'pending'},
+                                  'Monitoring': {'State': 'disabled'},
+                                  'NetworkInterfaces': [{'Association': {'IpOwnerId': 'amazon',
+                                                                         'PublicDnsName': 'ec2-54-167-113-39.compute-1.amazonaws.com',
+                                                                         'PublicIp': '54.167.113.39'},
+                                                         'Attachment': {'AttachTime': datetime.datetime(2024, 3, 18, 5, 13, 8, tzinfo=tzutc()),
+                                                                        'AttachmentId': 'eni-attach-0aac12b5548bcf9fc',
+                                                                        'DeleteOnTermination': True,
+                                                                        'DeviceIndex': 0,
+                                                                        'NetworkCardIndex': 0,
+                                                                        'Status': 'attaching'},
+                                                         'Description': '',
+                                                         'Groups': [{'GroupId': 'sg-019686b391a1be806',
+                                                                     'GroupName': 'default'}],
+                                                         'InterfaceType': 'interface',
+                                                         'Ipv6Addresses': [],
+                                                         'MacAddress': '0a:ff:d6:7b:8c:b7',
+                                                         'NetworkInterfaceId': 'eni-073f511c623a388ac',
+                                                         'OwnerId': '146064153251',
+                                                         'PrivateDnsName': 'ip-172-31-31-11.ec2.internal',
+                                                         'PrivateIpAddress': '172.31.31.11',
+                                                         'PrivateIpAddresses': [{'Association': {'IpOwnerId': 'amazon',
+                                                                                                 'PublicDnsName': 'ec2-54-167-113-39.compute-1.amazonaws.com',
+                                                                                                 'PublicIp': '54.167.113.39'},
+                                                                                 'Primary': True,
+                                                                                 'PrivateDnsName': 'ip-172-31-31-11.ec2.internal',
+                                                                                 'PrivateIpAddress': '172.31.31.11'}],
+                                                         'SourceDestCheck': True,
+                                                         'Status': 'in-use',
+                                                         'SubnetId': 'subnet-08c8975406f48de21',
+                                                         'VpcId': 'vpc-0248a892aa9493bd8'}],
+                                  'Placement': {'AvailabilityZone': 'us-east-1b',
+                                                'GroupName': '',
+                                                'Tenancy': 'default'},
+                                  'PlatformDetails': 'Linux/UNIX',
+                                  'PrivateDnsName': 'ip-172-31-31-11.ec2.internal',
+                                  'PrivateDnsNameOptions': {'EnableResourceNameDnsAAAARecord': False,
+                                                            'EnableResourceNameDnsARecord': False,
+                                                            'HostnameType': 'ip-name'},
+                                  'PrivateIpAddress': '172.31.31.11',
+                                  'ProductCodes': [],
+                                  'PublicDnsName': 'ec2-54-167-113-39.compute-1.amazonaws.com',
+                                  'PublicIpAddress': '54.167.113.39',
+                                  'RootDeviceName': '/dev/sda1',
+                                  'RootDeviceType': 'ebs',
+                                  'SecurityGroups': [{'GroupId': 'sg-019686b391a1be806',
+                                                      'GroupName': 'default'}],
+                                  'SourceDestCheck': True,
+                                  'State': {'Code': 0, 'Name': 'pending'},
+                                  'StateTransitionReason': '',
+                                  'SubnetId': 'subnet-08c8975406f48de21',
+                                  'Tags': [{'Key': 'Name',
+                                            'Value': 'web-instance'}],
+                                  'UsageOperation': 'RunInstances',
+                                  'UsageOperationUpdateTime': datetime.datetime(2024, 3, 18, 5, 13, 8, tzinfo=tzutc()),
+                                  'VirtualizationType': 'hvm',
+                                  'VpcId': 'vpc-0248a892aa9493bd8'}],
+                   'OwnerId': '146064153251',
+                   'ReservationId': 'r-0678419e9bba10da6'}],
+ 'ResponseMetadata': {'HTTPHeaders': {'cache-control': 'no-cache, no-store',
+                                      'content-type': 'text/xml;charset=UTF-8',
+                                      'date': 'Mon, 18 Mar 2024 05:13:14 GMT',
+                                      'server': 'AmazonEC2',
+                                      'strict-transport-security': 'max-age=31536000; '
+                                                                   'includeSubDomains',
+                                      'transfer-encoding': 'chunked',
+                                      'vary': 'accept-encoding',
+                                      'x-amzn-requestid': 'a1fff5d0-40e6-4c66-b06a-3d2a3f22800c'},
+                      'HTTPStatusCode': 200,
+                      'RequestId': 'a1fff5d0-40e6-4c66-b06a-3d2a3f22800c',
+                      'RetryAttempts': 0}}
+{'Buckets': [{'CreationDate': datetime.datetime(2024, 3, 17, 23, 17, 37, tzinfo=tzutc()),
+              'Name': '1229569564-in-bucket'},
+             {'CreationDate': datetime.datetime(2024, 3, 17, 23, 18, 24, tzinfo=tzutc()),
+              'Name': '1229569564-out-bucket'}],
+ 'Owner': {'DisplayName': 'rknarayan02',
+           'ID': 'd9dd1c35ead8c3223e43ab9d0c96cce689c3295fd2799e8c9076d272a36d85a8'},
+ 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'application/xml',
+                                      'date': 'Mon, 18 Mar 2024 05:13:17 GMT',
+                                      'server': 'AmazonS3',
+                                      'transfer-encoding': 'chunked',
+                                      'x-amz-id-2': '7gCwSENarAQMBf7ShdAXTiB5NDX/k5uydWL9nivR9D/23H2Ps8nDJVIzh34dJfxPGCEC3X5LpXg=',
+                                      'x-amz-request-id': 'EEDAR281YM3D3SEC'},
+                      'HTTPStatusCode': 200,
+                      'HostId': '7gCwSENarAQMBf7ShdAXTiB5NDX/k5uydWL9nivR9D/23H2Ps8nDJVIzh34dJfxPGCEC3X5LpXg=',
+                      'RequestId': 'EEDAR281YM3D3SEC',
+                      'RetryAttempts': 0}}
+{'QueueUrls': ['https://sqs.us-east-1.amazonaws.com/146064153251/1229569564-req-queue',
+               'https://sqs.us-east-1.amazonaws.com/146064153251/1229569564-resp-queue'],
+ 'ResponseMetadata': {'HTTPHeaders': {'connection': 'keep-alive',
+                                      'content-length': '427',
+                                      'content-type': 'text/xml',
+                                      'date': 'Mon, 18 Mar 2024 05:13:17 GMT',
+                                      'x-amzn-requestid': 'ab25ecea-b352-5c95-a779-b29b7cffb16a'},
+                      'HTTPStatusCode': 200,
+                      'RequestId': 'ab25ecea-b352-5c95-a779-b29b7cffb16a',
+                      'RetryAttempts': 0}}
diff --git a/Project-1/Part-2/requirements.txt b/Project-1/Part-2/requirements.txt
new file mode 100644
index 0000000..015641e
--- /dev/null
+++ b/Project-1/Part-2/requirements.txt
@@ -0,0 +1,4 @@
+boto3
+numpy
+pandas
+requests
\ No newline at end of file
diff --git a/Project-1/Part-2/run_single_app_tier.py b/Project-1/Part-2/run_single_app_tier.py
new file mode 100644
index 0000000..7e90b18
--- /dev/null
+++ b/Project-1/Part-2/run_single_app_tier.py
@@ -0,0 +1,24 @@
+import boto3
+from pathlib import Path
+
+
+# creds from ~/.aws/credentials
+session = boto3.Session(profile_name="dev")
+dev_ec2_client = session.client("ec2", region_name="us-east-1")
+
+# Ubuntu Server 22.04 LTS, 8GB SSD on EBS, 64-bit (x86) 1vCPU
+# pip installed, torch installed, model loaded
+app_tier_ami_id = "ami-05afaa0d246abf7cf"
+reservation = dev_ec2_client.run_instances(
+    ImageId=app_tier_ami_id,
+    InstanceType="t2.micro",
+	KeyName='cse546-dev',
+    MinCount=1,
+    MaxCount=1,
+    TagSpecifications=[
+        {"ResourceType": "instance", "Tags": [{"Key": "Name", "Value": "app-tier-instance"}]}
+    ],
+	# UserData=user_data
+)
+
+print("Instances allocated successfully:", "Yes" if reservation else "No")
diff --git a/Project-1/Part-2/run_web_tier.py b/Project-1/Part-2/run_web_tier.py
new file mode 100644
index 0000000..baa12e2
--- /dev/null
+++ b/Project-1/Part-2/run_web_tier.py
@@ -0,0 +1,44 @@
+import boto3
+import argparse
+from pathlib import Path
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--eip-id", type=str,help="Allocation ID of elastic IP address")
+args = vars(parser.parse_args())
+eip_id = str(args["eip-id"]) if args.get("eip-id") else None
+
+# creds from ~/.aws/credentials
+session = boto3.Session(profile_name="dev")
+dev_ec2_client = session.client("ec2", region_name="us-east-1")
+
+with open(Path(__file__) / ".." / "web_tier_instance_run", "r") as setup_f:
+	user_data = setup_f.read()
+	
+# Create capacity reservation of instances
+ami_id = "ami-0c7217cdde317cfec" # Ubuntu Server 22.04 LTS, 8GB SSD on EBS, 64-bit (x86) 1vCPU
+reservation = dev_ec2_client.run_instances(
+    ImageId=ami_id,
+    InstanceType="t2.micro",
+	KeyName='cse546-dev',
+    MinCount=1,  # if available instances are less than min_count, abort with no allocation
+    MaxCount=1,  # try to allocate max_count instances. we only need one for now
+    TagSpecifications=[
+        {"ResourceType": "instance", "Tags": [{"Key": "Name", "Value": "web-instance"}]}
+    ],
+	# UserData=user_data
+)
+
+print("Instances allocated successfully:", "Yes" if reservation else "No")
+new_inst_id = ''
+
+# Allocate EIP on successful instance launch
+if reservation and eip_id is not None:
+	print("Waiting for instance to start running...")
+	waiter = dev_ec2_client.get_waiter('instance_running')
+	waiter.wait(InstanceIds=[new_inst_id])
+	assoc = dev_ec2_client.associate_address(
+		AllocationId=eip_id,
+		InstanceId=new_inst_id
+    )
+	if assoc["ResponseMetadata"]["HTTPStatusCode"] == 200:
+		print("Allocated EIP successfully")
diff --git a/Project-1/Part-2/terminate_setup.py b/Project-1/Part-2/terminate_setup.py
new file mode 100644
index 0000000..a4a77d1
--- /dev/null
+++ b/Project-1/Part-2/terminate_setup.py
@@ -0,0 +1,94 @@
+import boto3
+import botocore
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--web-instance-name",
+    type=str,
+    help="Value of 'Name' tag of the web tier instance to be terminated",
+)
+parser.add_argument(
+    "--in-bucket-name",
+    type=str,
+    help="Value of 'Name' tag of the S3 input bucket to be terminated",
+)
+parser.add_argument(
+    "--out-bucket-name",
+    type=str,
+    help="Value of 'Name' tag of the S3 output bucket to be terminated",
+)
+parser.add_argument(
+    "--in-queue-name",
+    type=str,
+    help="Value of 'Name' tag of the SQS request queue to be terminated",
+)
+parser.add_argument(
+    "--out-queue-name",
+    type=str,
+    help="Value of 'Name' tag of the SQS response queue to be terminated",
+)
+args = vars(parser.parse_args())
+
+
+def noneparser(obj, key):
+    return str(obj[key]) if obj.get(key) else None
+
+
+target_web_instance_name = noneparser(args, "web_instance_name")
+target_in_bucket_name = noneparser(args, "in_bucket_name")
+target_out_bucket_name = noneparser(args, "out_bucket_name")
+target_in_queue_name = noneparser(args, "in_queue_name")
+target_out_queue_name = noneparser(args, "out_queue_name")
+
+print(
+    target_web_instance_name,
+    target_in_bucket_name,
+    target_out_bucket_name,
+    target_in_queue_name,
+    target_out_queue_name,
+)
+
+# creds from ~/.aws/credentials
+session = boto3.Session(profile_name="dev")
+
+region1 = "us-east-1"
+dev_ec2_client = session.client("ec2", region_name=region1)
+dev_s3_client = session.client("s3", region_name=region1)
+
+
+def get_instance_id_by_name_tag(name):
+    all_instances = dev_ec2_client.describe_instances()
+    for reservation in all_instances["Reservations"]:
+        for instance in reservation["Instances"]:
+            for tag in instance["Tags"]:
+                if tag["Key"] == "Name" and tag["Value"] == name:
+                    return instance["InstanceId"]
+    return -1
+
+# web tier
+if target_web_instance_name is not None:
+    target_inst_id = get_instance_id_by_name_tag(target_web_instance_name)
+    if target_inst_id == -1:
+        print("Instance does not exist")
+    else:
+        print("Found web-instance ID: ", target_inst_id)
+        response = dev_ec2_client.terminate_instances(InstanceIds=[target_inst_id])
+        if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
+            print("Terminated instance successfully")
+
+# buckets
+if target_in_bucket_name is not None:
+    try:
+        response = dev_s3_client.delete_bucket(Bucket=target_in_bucket_name)
+        if response["ResponseMetadata"]["HTTPStatusCode"] == 204:
+            print("Deleted input bucket successfully")
+    except dev_s3_client.exceptions.NoSuchBucket as e:
+        print("Input bucket does not exist")
+if target_out_bucket_name is not None:
+    try:
+        response = dev_s3_client.delete_bucket(Bucket=target_out_bucket_name)
+        if response["ResponseMetadata"]["HTTPStatusCode"] == 204:
+            print("Deleted output bucket successfully")
+    except dev_s3_client.exceptions.NoSuchBucket as e:
+        print("Output bucket does not exist")
\ No newline at end of file
diff --git a/Project-1/Part-2/web-tier-express/README.md b/Project-1/Part-2/web-tier-express/README.md
new file mode 100644
index 0000000..be36e44
--- /dev/null
+++ b/Project-1/Part-2/web-tier-express/README.md
@@ -0,0 +1,4 @@
+# Web tier with ExpressJS
+
+- Run `npm i` to install all dependencies
+- Start: `npm run dev` or `prod`
diff --git a/Project-1/Part-2/web-tier-express/data/face_images_1000/test_000.jpg b/Project-1/Part-2/web-tier-express/data/face_images_1000/test_000.jpg
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diff --git a/Project-1/Part-2/web-tier-express/index.js b/Project-1/Part-2/web-tier-express/index.js
new file mode 100644
index 0000000..1e4372f
--- /dev/null
+++ b/Project-1/Part-2/web-tier-express/index.js
@@ -0,0 +1,51 @@
+const fs = require("fs");
+const path = require("path");
+const express = require("express");
+const cors = require("cors");
+const multer = require("multer");
+
+const logger = require("./utils/logger")(module);
+
+const app = express();
+const upload = multer({});
+
+app.use(cors());
+app.disable("x-powered-by");
+
+// Data loading
+const fastCSV = require("fast-csv");
+let lookup = [];
+fs.createReadStream(path.resolve(__dirname, "data", "results.csv"))
+	.pipe(fastCSV.parse({ headers: true }))
+	.on('error', error => console.error(error))
+	.on('data', row => lookup.push(row))
+	.on('end', (rowCount) => console.log(`Parsed ${rowCount} rows`));
+
+// Controllers
+
+const imageRecognition = (req, res) => {
+	try {
+		const fileName = req.files.inputFile[0].originalname.split(".jpg")[0];
+		return res.status(200).send(
+			fileName + ":" + lookup.filter((row) => row["Image"] === fileName)[0]["Results"]
+		);
+	} catch (error) {
+		logger.error("imageRecognition", { error });
+		return res.status(500).send({ message: "Server Error. Try again." });
+	}
+};
+
+// Routes
+app.post("/", upload.fields([
+	{ name: "inputFile", maxCount: 1 },
+]), imageRecognition);
+
+app.use((_req, res) => {
+	return res.status(200).send("Web tier on ExpressJS.");
+});
+
+const port = process.env.PORT || 5000;
+
+app.listen(port, () => {
+	logger.info(`App Listening on port ${port}`);
+});
diff --git a/Project-1/Part-2/web-tier-express/package-lock.json b/Project-1/Part-2/web-tier-express/package-lock.json
new file mode 100644
index 0000000..2738667
--- /dev/null
+++ b/Project-1/Part-2/web-tier-express/package-lock.json
@@ -0,0 +1,1655 @@
+{
+  "name": "web-tier-express",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "web-tier-express",
+      "version": "1.0.0",
+      "dependencies": {
+        "cors": "^2.8.5",
+        "dotenv-flow": "^3.2.0",
+        "express": "^4.18.2",
+        "express-validator": "^7.0.1",
+        "fast-csv": "^4.3.6",
+        "helmet": "^7.0.0",
+        "multer": "^1.4.5-lts.1",
+        "winston": "^3.8.2"
+      },
+      "devDependencies": {
+        "cross-env": "^7.0.3",
+        "nodemon": "^2.0.20"
+      }
+    },
+    "node_modules/@colors/colors": {
+      "version": "1.6.0",
+      "resolved": "https://registry.npmjs.org/@colors/colors/-/colors-1.6.0.tgz",
+      "integrity": "sha512-Ir+AOibqzrIsL6ajt3Rz3LskB7OiMVHqltZmspbW/TJuTVuyOMirVqAkjfY6JISiLHgyNqicAC8AyHHGzNd/dA==",
+      "engines": {
+        "node": ">=0.1.90"
+      }
+    },
+    "node_modules/@dabh/diagnostics": {
+      "version": "2.0.3",
+      "resolved": "https://registry.npmjs.org/@dabh/diagnostics/-/diagnostics-2.0.3.tgz",
+      "integrity": "sha512-hrlQOIi7hAfzsMqlGSFyVucrx38O+j6wiGOf//H2ecvIEqYN4ADBSS2iLMh5UFyDunCNniUIPk/q3riFv45xRA==",
+      "dependencies": {
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diff --git a/Project-1/Part-2/web-tier-express/package.json b/Project-1/Part-2/web-tier-express/package.json
new file mode 100644
index 0000000..899c6d8
--- /dev/null
+++ b/Project-1/Part-2/web-tier-express/package.json
@@ -0,0 +1,27 @@
+{
+  "name": "web-tier-express",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "dev": "cross-env NODE_ENV=development nodemon --exitcrash index.js",
+    "staging": "cross-env NODE_ENV=staging node index.js",
+    "prod": "cross-env NODE_ENV=production node index.js"
+  },
+  "author": "Kaushik Ravishankar <rknarayan02@gmail.com>",
+  "license": "",
+  "dependencies": {
+    "cors": "^2.8.5",
+    "dotenv-flow": "^3.2.0",
+    "express": "^4.18.2",
+    "express-validator": "^7.0.1",
+    "fast-csv": "^4.3.6",
+    "helmet": "^7.0.0",
+    "multer": "^1.4.5-lts.1",
+    "winston": "^3.8.2"
+  },
+  "devDependencies": {
+    "cross-env": "^7.0.3",
+    "nodemon": "^2.0.20"
+  }
+}
diff --git a/Project-1/Part-2/web-tier-express/typedefs.js b/Project-1/Part-2/web-tier-express/typedefs.js
new file mode 100644
index 0000000..20044de
--- /dev/null
+++ b/Project-1/Part-2/web-tier-express/typedefs.js
@@ -0,0 +1,13 @@
+// Type definitions to enable better auto-complete features.
+
+/**
+ * @typedef {import("module")} Module
+ * 
+ * @typedef {import("express").Request} Req
+ * @typedef {import("express").Response} Res
+ * @typedef {import("express").NextFunction} Next
+ * 
+ * @typedef {import("winston").Logger} Logger
+ */
+
+exports.unused = {};
\ No newline at end of file
diff --git a/Project-1/Part-2/web-tier-express/utils/README.md b/Project-1/Part-2/web-tier-express/utils/README.md
new file mode 100644
index 0000000..5747518
--- /dev/null
+++ b/Project-1/Part-2/web-tier-express/utils/README.md
@@ -0,0 +1 @@
+## General, reusable functionalities unrelated to API
diff --git a/Project-1/Part-2/web-tier-express/utils/logger.js b/Project-1/Part-2/web-tier-express/utils/logger.js
new file mode 100644
index 0000000..3241387
--- /dev/null
+++ b/Project-1/Part-2/web-tier-express/utils/logger.js
@@ -0,0 +1,57 @@
+// Whole thing is winston logger stuff, if you want to learn read the docs
+
+const path = require("path");
+
+const { createLogger, transports, config, format } = require("winston");
+const { combine, label, timestamp, printf } = format;
+
+
+const getLabel = (callingModule) => {
+  const parts = callingModule.filename.split(path.sep);
+  return path.join(parts[parts.length - 2], parts.pop());
+};
+
+const logMetaReplacer = (key, value) => {
+  if (key === "error") {
+    return value.name + ": " + value.message;
+  }
+  return value;
+}
+
+const metaFormat = (meta) => {
+  if (Object.keys(meta).length > 0)
+    return "\n" + JSON.stringify(meta, logMetaReplacer) + "\n";
+  return "\n";
+}
+
+const logFormat = printf(({ level, message, label, timestamp, ...meta }) => {
+  if (meta.error) {
+    for (const key in meta.error) {
+      if (typeof key !== "symbol" && key !== "message" && key !== "name") {
+        delete meta.error[key]
+      }
+    }
+  }
+  return `${timestamp} [${label}] ${level}: ${message}${metaFormat(meta)}`;
+});
+
+/**
+ * Creates a curried function, and call it with the module in use to get logs with filename 
+ */
+const logger = (callingModule) => {
+  return createLogger({
+    levels: config.npm.levels,
+    format: combine(
+      label({ label: getLabel(callingModule) }),
+      timestamp({ format: "YYYY-MM-DD HH:mm:ss" }),
+      logFormat,
+    ),
+    transports: [
+      new transports.Console(),
+      new transports.File({ filename: __dirname + "/../logs/common.log" }),
+      new transports.File({ filename: __dirname + "/../logs/error.log", level: "error" }),
+    ]
+  });
+}
+
+module.exports = logger;
diff --git a/Project-1/Part-2/web_tier_instance_run b/Project-1/Part-2/web_tier_instance_run
new file mode 100644
index 0000000..c196264
--- /dev/null
+++ b/Project-1/Part-2/web_tier_instance_run
@@ -0,0 +1,29 @@
+#!/bin/bash
+# https://stackoverflow.com/a/71648116/7630441
+sudo -u ubuntu -i <<'EOF'
+
+# initializing instance
+cd ~
+sudo apt-get update
+sudo apt-get install python3-pip
+sudo apt-get upgrade -y
+
+# Project specific setup
+curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash
+echo -e '\nexport NVM_DIR="$HOME/.nvm"\n[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"  # This loads nvm \n[ -s "$NVM_DIR/bash_completion" ] && \. "$NVM_DIR/bash_completion"  # This loads nvm bash_completion\n' >> .bashrc
+source ~/.bashrc
+export NVM_DIR="$HOME/.nvm"
+[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"  # This loads nvm
+[ -s "$NVM_DIR/bash_completion" ] && \. "$NVM_DIR/bash_completion"  # This loads nvm bash_completion
+nvm install v20.11.0
+
+npm i -g pm2
+git clone https://github.com/20kaushik02/CSE546_Cloud_Computing_Projects repo
+cd repo/Project-1/Part-2/web-tier-express
+npm i
+
+# Start
+pm2 start npm --name webTier -- run prod
+pm2 save
+
+EOF
diff --git a/Tooling/LICENSE b/Tooling/LICENSE
new file mode 100644
index 0000000..b541651
--- /dev/null
+++ b/Tooling/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 visa-lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/Tooling/README.md b/Tooling/README.md
new file mode 100644
index 0000000..7d52aff
--- /dev/null
+++ b/Tooling/README.md
@@ -0,0 +1,2 @@
+# CSE546-Cloud-Computing
+This is a public repository to facilitate project development for CSE546 Cloud Computing students. 
diff --git a/Tooling/dataset/Classification Results on Face Dataset (100 images).csv b/Tooling/dataset/Classification Results on Face Dataset (100 images).csv
new file mode 100644
index 0000000..6d4a91e
--- /dev/null
+++ b/Tooling/dataset/Classification Results on Face Dataset (100 images).csv	
@@ -0,0 +1,101 @@
+Image,Results
+test_00,Paul
+test_01,Emily
+test_02,Bob
+test_03,German
+test_04,Emily
+test_05,Gerry
+test_06,Gerry
+test_07,Ranil
+test_08,Bill
+test_09,Wang
+test_10,Paul
+test_11,Emily
+test_12,Bob
+test_13,German
+test_14,Emily
+test_15,Gerry
+test_16,Gerry
+test_17,Ranil
+test_18,Bill
+test_19,Wang
+test_20,Paul
+test_21,Emily
+test_22,Bob
+test_23,German
+test_24,Emily
+test_25,Gerry
+test_26,Gerry
+test_27,Ranil
+test_28,Bill
+test_29,Wang
+test_30,Paul
+test_31,Emily
+test_32,Bob
+test_33,German
+test_34,Emily
+test_35,Gerry
+test_36,Gerry
+test_37,Ranil
+test_38,Bill
+test_39,Wang
+test_40,Paul
+test_41,Emily
+test_42,Bob
+test_43,German
+test_44,Emily
+test_45,Gerry
+test_46,Gerry
+test_47,Ranil
+test_48,Bill
+test_49,Wang
+test_50,Paul
+test_51,Emily
+test_52,Bob
+test_53,German
+test_54,Emily
+test_55,Gerry
+test_56,Gerry
+test_57,Ranil
+test_58,Bill
+test_59,Wang
+test_60,Paul
+test_61,Emily
+test_62,Bob
+test_63,German
+test_64,Emily
+test_65,Gerry
+test_66,Gerry
+test_67,Ranil
+test_68,Bill
+test_69,Wang
+test_70,Paul
+test_71,Emily
+test_72,Bob
+test_73,German
+test_74,Emily
+test_75,Gerry
+test_76,Gerry
+test_77,Ranil
+test_78,Bill
+test_79,Wang
+test_80,Paul
+test_81,Emily
+test_82,Bob
+test_83,German
+test_84,Emily
+test_85,Gerry
+test_86,Gerry
+test_87,Ranil
+test_88,Bill
+test_89,Wang
+test_90,Paul
+test_91,Emily
+test_92,Bob
+test_93,German
+test_94,Emily
+test_95,Gerry
+test_96,Gerry
+test_97,Ranil
+test_98,Bill
+test_99,Wang
\ No newline at end of file
diff --git a/Tooling/dataset/Classification Results on Face Dataset (1000 images).csv b/Tooling/dataset/Classification Results on Face Dataset (1000 images).csv
new file mode 100644
index 0000000..2b8393c
--- /dev/null
+++ b/Tooling/dataset/Classification Results on Face Dataset (1000 images).csv	
@@ -0,0 +1,1001 @@
+Image,Results
+test_000,Paul
+test_001,Emily
+test_002,Bob
+test_003,German
+test_004,Emily
+test_005,Gerry
+test_006,Gerry
+test_007,Ranil
+test_008,Bill
+test_009,Wang
+test_010,Paul
+test_011,Emily
+test_012,Bob
+test_013,German
+test_014,Emily
+test_015,Gerry
+test_016,Gerry
+test_017,Ranil
+test_018,Bill
+test_019,Wang
+test_020,Paul
+test_021,Emily
+test_022,Bob
+test_023,German
+test_024,Emily
+test_025,Gerry
+test_026,Gerry
+test_027,Ranil
+test_028,Bill
+test_029,Wang
+test_030,Paul
+test_031,Emily
+test_032,Bob
+test_033,German
+test_034,Emily
+test_035,Gerry
+test_036,Gerry
+test_037,Ranil
+test_038,Bill
+test_039,Wang
+test_040,Paul
+test_041,Emily
+test_042,Bob
+test_043,German
+test_044,Emily
+test_045,Gerry
+test_046,Gerry
+test_047,Ranil
+test_048,Bill
+test_049,Wang
+test_050,Paul
+test_051,Emily
+test_052,Bob
+test_053,German
+test_054,Emily
+test_055,Gerry
+test_056,Gerry
+test_057,Ranil
+test_058,Bill
+test_059,Wang
+test_060,Paul
+test_061,Emily
+test_062,Bob
+test_063,German
+test_064,Emily
+test_065,Gerry
+test_066,Gerry
+test_067,Ranil
+test_068,Bill
+test_069,Wang
+test_070,Paul
+test_071,Emily
+test_072,Bob
+test_073,German
+test_074,Emily
+test_075,Gerry
+test_076,Gerry
+test_077,Ranil
+test_078,Bill
+test_079,Wang
+test_080,Paul
+test_081,Emily
+test_082,Bob
+test_083,German
+test_084,Emily
+test_085,Gerry
+test_086,Gerry
+test_087,Ranil
+test_088,Bill
+test_089,Wang
+test_090,Paul
+test_091,Emily
+test_092,Bob
+test_093,German
+test_094,Emily
+test_095,Gerry
+test_096,Gerry
+test_097,Ranil
+test_098,Bill
+test_099,Wang
+test_100,Paul
+test_101,Emily
+test_102,Bob
+test_103,German
+test_104,Emily
+test_105,Gerry
+test_106,Gerry
+test_107,Ranil
+test_108,Bill
+test_109,Wang
+test_110,Paul
+test_111,Emily
+test_112,Bob
+test_113,German
+test_114,Emily
+test_115,Gerry
+test_116,Gerry
+test_117,Ranil
+test_118,Bill
+test_119,Wang
+test_120,Paul
+test_121,Emily
+test_122,Bob
+test_123,German
+test_124,Emily
+test_125,Gerry
+test_126,Gerry
+test_127,Ranil
+test_128,Bill
+test_129,Wang
+test_130,Paul
+test_131,Emily
+test_132,Bob
+test_133,German
+test_134,Emily
+test_135,Gerry
+test_136,Gerry
+test_137,Ranil
+test_138,Bill
+test_139,Wang
+test_140,Paul
+test_141,Emily
+test_142,Bob
+test_143,German
+test_144,Emily
+test_145,Gerry
+test_146,Gerry
+test_147,Ranil
+test_148,Bill
+test_149,Wang
+test_150,Paul
+test_151,Emily
+test_152,Bob
+test_153,German
+test_154,Emily
+test_155,Gerry
+test_156,Gerry
+test_157,Ranil
+test_158,Bill
+test_159,Wang
+test_160,Paul
+test_161,Emily
+test_162,Bob
+test_163,German
+test_164,Emily
+test_165,Gerry
+test_166,Gerry
+test_167,Ranil
+test_168,Bill
+test_169,Wang
+test_170,Paul
+test_171,Emily
+test_172,Bob
+test_173,German
+test_174,Emily
+test_175,Gerry
+test_176,Gerry
+test_177,Ranil
+test_178,Bill
+test_179,Wang
+test_180,Paul
+test_181,Emily
+test_182,Bob
+test_183,German
+test_184,Emily
+test_185,Gerry
+test_186,Gerry
+test_187,Ranil
+test_188,Bill
+test_189,Wang
+test_190,Paul
+test_191,Emily
+test_192,Bob
+test_193,German
+test_194,Emily
+test_195,Gerry
+test_196,Gerry
+test_197,Ranil
+test_198,Bill
+test_199,Wang
+test_200,Paul
+test_201,Emily
+test_202,Bob
+test_203,German
+test_204,Emily
+test_205,Gerry
+test_206,Gerry
+test_207,Ranil
+test_208,Bill
+test_209,Wang
+test_210,Paul
+test_211,Emily
+test_212,Bob
+test_213,German
+test_214,Emily
+test_215,Gerry
+test_216,Gerry
+test_217,Ranil
+test_218,Bill
+test_219,Wang
+test_220,Paul
+test_221,Emily
+test_222,Bob
+test_223,German
+test_224,Emily
+test_225,Gerry
+test_226,Gerry
+test_227,Ranil
+test_228,Bill
+test_229,Wang
+test_230,Paul
+test_231,Emily
+test_232,Bob
+test_233,German
+test_234,Emily
+test_235,Gerry
+test_236,Gerry
+test_237,Ranil
+test_238,Bill
+test_239,Wang
+test_240,Paul
+test_241,Emily
+test_242,Bob
+test_243,German
+test_244,Emily
+test_245,Gerry
+test_246,Gerry
+test_247,Ranil
+test_248,Bill
+test_249,Wang
+test_250,Paul
+test_251,Emily
+test_252,Bob
+test_253,German
+test_254,Emily
+test_255,Gerry
+test_256,Gerry
+test_257,Ranil
+test_258,Bill
+test_259,Wang
+test_260,Paul
+test_261,Emily
+test_262,Bob
+test_263,German
+test_264,Emily
+test_265,Gerry
+test_266,Gerry
+test_267,Ranil
+test_268,Bill
+test_269,Wang
+test_270,Paul
+test_271,Emily
+test_272,Bob
+test_273,German
+test_274,Emily
+test_275,Gerry
+test_276,Gerry
+test_277,Ranil
+test_278,Bill
+test_279,Wang
+test_280,Paul
+test_281,Emily
+test_282,Bob
+test_283,German
+test_284,Emily
+test_285,Gerry
+test_286,Gerry
+test_287,Ranil
+test_288,Bill
+test_289,Wang
+test_290,Paul
+test_291,Emily
+test_292,Bob
+test_293,German
+test_294,Emily
+test_295,Gerry
+test_296,Gerry
+test_297,Ranil
+test_298,Bill
+test_299,Wang
+test_300,Paul
+test_301,Emily
+test_302,Bob
+test_303,German
+test_304,Emily
+test_305,Gerry
+test_306,Gerry
+test_307,Ranil
+test_308,Bill
+test_309,Wang
+test_310,Paul
+test_311,Emily
+test_312,Bob
+test_313,German
+test_314,Emily
+test_315,Gerry
+test_316,Gerry
+test_317,Ranil
+test_318,Bill
+test_319,Wang
+test_320,Paul
+test_321,Emily
+test_322,Bob
+test_323,German
+test_324,Emily
+test_325,Gerry
+test_326,Gerry
+test_327,Ranil
+test_328,Bill
+test_329,Wang
+test_330,Paul
+test_331,Emily
+test_332,Bob
+test_333,German
+test_334,Emily
+test_335,Gerry
+test_336,Gerry
+test_337,Ranil
+test_338,Bill
+test_339,Wang
+test_340,Paul
+test_341,Emily
+test_342,Bob
+test_343,German
+test_344,Emily
+test_345,Gerry
+test_346,Gerry
+test_347,Ranil
+test_348,Bill
+test_349,Wang
+test_350,Paul
+test_351,Emily
+test_352,Bob
+test_353,German
+test_354,Emily
+test_355,Gerry
+test_356,Gerry
+test_357,Ranil
+test_358,Bill
+test_359,Wang
+test_360,Paul
+test_361,Emily
+test_362,Bob
+test_363,German
+test_364,Emily
+test_365,Gerry
+test_366,Gerry
+test_367,Ranil
+test_368,Bill
+test_369,Wang
+test_370,Paul
+test_371,Emily
+test_372,Bob
+test_373,German
+test_374,Emily
+test_375,Gerry
+test_376,Gerry
+test_377,Ranil
+test_378,Bill
+test_379,Wang
+test_380,Paul
+test_381,Emily
+test_382,Bob
+test_383,German
+test_384,Emily
+test_385,Gerry
+test_386,Gerry
+test_387,Ranil
+test_388,Bill
+test_389,Wang
+test_390,Paul
+test_391,Emily
+test_392,Bob
+test_393,German
+test_394,Emily
+test_395,Gerry
+test_396,Gerry
+test_397,Ranil
+test_398,Bill
+test_399,Wang
+test_400,Paul
+test_401,Emily
+test_402,Bob
+test_403,German
+test_404,Emily
+test_405,Gerry
+test_406,Gerry
+test_407,Ranil
+test_408,Bill
+test_409,Wang
+test_410,Paul
+test_411,Emily
+test_412,Bob
+test_413,German
+test_414,Emily
+test_415,Gerry
+test_416,Gerry
+test_417,Ranil
+test_418,Bill
+test_419,Wang
+test_420,Paul
+test_421,Emily
+test_422,Bob
+test_423,German
+test_424,Emily
+test_425,Gerry
+test_426,Gerry
+test_427,Ranil
+test_428,Bill
+test_429,Wang
+test_430,Paul
+test_431,Emily
+test_432,Bob
+test_433,German
+test_434,Emily
+test_435,Gerry
+test_436,Gerry
+test_437,Ranil
+test_438,Bill
+test_439,Wang
+test_440,Paul
+test_441,Emily
+test_442,Bob
+test_443,German
+test_444,Emily
+test_445,Gerry
+test_446,Gerry
+test_447,Ranil
+test_448,Bill
+test_449,Wang
+test_450,Paul
+test_451,Emily
+test_452,Bob
+test_453,German
+test_454,Emily
+test_455,Gerry
+test_456,Gerry
+test_457,Ranil
+test_458,Bill
+test_459,Wang
+test_460,Paul
+test_461,Emily
+test_462,Bob
+test_463,German
+test_464,Emily
+test_465,Gerry
+test_466,Gerry
+test_467,Ranil
+test_468,Bill
+test_469,Wang
+test_470,Paul
+test_471,Emily
+test_472,Bob
+test_473,German
+test_474,Emily
+test_475,Gerry
+test_476,Gerry
+test_477,Ranil
+test_478,Bill
+test_479,Wang
+test_480,Paul
+test_481,Emily
+test_482,Bob
+test_483,German
+test_484,Emily
+test_485,Gerry
+test_486,Gerry
+test_487,Ranil
+test_488,Bill
+test_489,Wang
+test_490,Paul
+test_491,Emily
+test_492,Bob
+test_493,German
+test_494,Emily
+test_495,Gerry
+test_496,Gerry
+test_497,Ranil
+test_498,Bill
+test_499,Wang
+test_500,Paul
+test_501,Emily
+test_502,Bob
+test_503,German
+test_504,Emily
+test_505,Gerry
+test_506,Gerry
+test_507,Ranil
+test_508,Bill
+test_509,Wang
+test_510,Paul
+test_511,Emily
+test_512,Bob
+test_513,German
+test_514,Emily
+test_515,Gerry
+test_516,Gerry
+test_517,Ranil
+test_518,Bill
+test_519,Wang
+test_520,Paul
+test_521,Emily
+test_522,Bob
+test_523,German
+test_524,Emily
+test_525,Gerry
+test_526,Gerry
+test_527,Ranil
+test_528,Bill
+test_529,Wang
+test_530,Paul
+test_531,Emily
+test_532,Bob
+test_533,German
+test_534,Emily
+test_535,Gerry
+test_536,Gerry
+test_537,Ranil
+test_538,Bill
+test_539,Wang
+test_540,Paul
+test_541,Emily
+test_542,Bob
+test_543,German
+test_544,Emily
+test_545,Gerry
+test_546,Gerry
+test_547,Ranil
+test_548,Bill
+test_549,Wang
+test_550,Paul
+test_551,Emily
+test_552,Bob
+test_553,German
+test_554,Emily
+test_555,Gerry
+test_556,Gerry
+test_557,Ranil
+test_558,Bill
+test_559,Wang
+test_560,Paul
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diff --git a/Tooling/model/Readme.md b/Tooling/model/Readme.md
new file mode 100644
index 0000000..8f9d89b
--- /dev/null
+++ b/Tooling/model/Readme.md
@@ -0,0 +1,19 @@
+## Face Recognition Model Repository with Inference Code and Pretrained Weights
+
+### Folder structure:
+ - facenet_pytorch: This is a repository for Inception Resnet (V1) models in pytorch, pretrained on VGGFace2 and CASIA-Webface. 
+ - face_recognition.py: Model Inference code 
+ - data.pt: Saved model weights 
+
+### Prerequisites
+  - You need to install the PyTorch CPU version to use the code.
+    ```
+    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+    ```
+
+### Sample Output:
+
+```
+(cse546) kjha9@en4113732l:~/git/Project-1/part-2/model$ python3 face_recognition.py ../../face_images_1000/test_000.jpg
+Paul
+(cse546) kjha9@en4113732l:~/git/Project-1/part-2/model$
diff --git a/Tooling/model/data.pt b/Tooling/model/data.pt
new file mode 100644
index 0000000..066b55a
Binary files /dev/null and b/Tooling/model/data.pt differ
diff --git a/Tooling/model/face_recognition.py b/Tooling/model/face_recognition.py
new file mode 100644
index 0000000..2fb326c
--- /dev/null
+++ b/Tooling/model/face_recognition.py
@@ -0,0 +1,36 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import os
+import csv
+import sys
+import torch
+from PIL import Image
+from facenet_pytorch import MTCNN, InceptionResnetV1
+from torchvision import datasets
+from torch.utils.data import DataLoader
+
+mtcnn = MTCNN(image_size=240, margin=0, min_face_size=20) # initializing mtcnn for face detection
+resnet = InceptionResnetV1(pretrained='vggface2').eval() # initializing resnet for face img to embeding conversion
+test_image = sys.argv[1]
+
+def face_match(img_path, data_path): # img_path= location of photo, data_path= location of data.pt
+    # getting embedding matrix of the given img
+    img = Image.open(img_path)
+    face, prob = mtcnn(img, return_prob=True) # returns cropped face and probability
+    emb = resnet(face.unsqueeze(0)).detach() # detech is to make required gradient false
+
+    saved_data = torch.load('data.pt') # loading data.pt file
+    embedding_list = saved_data[0] # getting embedding data
+    name_list = saved_data[1] # getting list of names
+    dist_list = [] # list of matched distances, minimum distance is used to identify the person
+
+    for idx, emb_db in enumerate(embedding_list):
+        dist = torch.dist(emb, emb_db).item()
+        dist_list.append(dist)
+
+    idx_min = dist_list.index(min(dist_list))
+    return (name_list[idx_min], min(dist_list))
+
+result = face_match(test_image, 'data.pt')
+print(result[0])
diff --git a/Tooling/model/facenet_pytorch/.gitignore b/Tooling/model/facenet_pytorch/.gitignore
new file mode 100644
index 0000000..62acda3
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/.gitignore
@@ -0,0 +1,14 @@
+__pycache__
+.vscode
+.ipynb_checkpoints
+runs
+build
+dist
+*.egg-info
+*tmp*
+.coverage
+htmlcov
+test.py
+.cache
+.ipython
+.local
diff --git a/Tooling/model/facenet_pytorch/.travis.yml b/Tooling/model/facenet_pytorch/.travis.yml
new file mode 100644
index 0000000..4eab7f9
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/.travis.yml
@@ -0,0 +1,25 @@
+language: python
+matrix:
+  include:
+
+    - name: "Python 3.7 on Linux"
+      python: 3.7
+      dist: xenial
+
+    - name: "Python 3.6 on Linux"
+      python: 3.6
+      dist: xenial
+
+    - name: "Python 3.5 on Linux"
+      python: 3.5
+      dist: xenial
+      
+install: pip3 install -r tests/travis_requirements.txt || pip3 install --user -r tests/travis_requirements.txt
+script:
+  - python3 --version || python --version
+  - echo "import tests.travis_test" > test.py
+  - coverage run --source models,examples test.py && coverage report
+
+after_success:
+  - codecov
+  
diff --git a/Tooling/model/facenet_pytorch/LICENSE.md b/Tooling/model/facenet_pytorch/LICENSE.md
new file mode 100644
index 0000000..501bd49
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/LICENSE.md
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Timothy Esler
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/Tooling/model/facenet_pytorch/README.md b/Tooling/model/facenet_pytorch/README.md
new file mode 100644
index 0000000..f185f18
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/README.md
@@ -0,0 +1,246 @@
+# Face Recognition Using Pytorch 
+[![Downloads](https://pepy.tech/badge/facenet-pytorch)](https://pepy.tech/project/facenet-pytorch)
+
+[![Code Coverage](https://img.shields.io/codecov/c/github/timesler/facenet-pytorch.svg)](https://codecov.io/gh/timesler/facenet-pytorch)
+
+| Python | 3.7 | 3.6 | 3.5 |
+| :---: | :---: | :---: | :---: |
+| Status | [![Build Status](https://travis-ci.com/timesler/facenet-pytorch.svg?branch=master)](https://travis-ci.com/timesler/facenet-pytorch) | [![Build Status](https://travis-ci.com/timesler/facenet-pytorch.svg?branch=master)](https://travis-ci.com/timesler/facenet-pytorch) | [![Build Status](https://travis-ci.com/timesler/facenet-pytorch.svg?branch=master)](https://travis-ci.com/timesler/facenet-pytorch) |
+
+[![xscode](https://img.shields.io/badge/Available%20on-xs%3Acode-blue?style=?style=plastic&logo=appveyor&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAMAAACdt4HsAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAAZQTFRF////////VXz1bAAAAAJ0Uk5T/wDltzBKAAAAlUlEQVR42uzXSwqAMAwE0Mn9L+3Ggtgkk35QwcnSJo9S+yGwM9DCooCbgn4YrJ4CIPUcQF7/XSBbx2TEz4sAZ2q1RAECBAiYBlCtvwN+KiYAlG7UDGj59MViT9hOwEqAhYCtAsUZvL6I6W8c2wcbd+LIWSCHSTeSAAECngN4xxIDSK9f4B9t377Wd7H5Nt7/Xz8eAgwAvesLRjYYPuUAAAAASUVORK5CYII=)](https://xscode.com/timesler/facenet-pytorch)
+
+This is a repository for Inception Resnet (V1) models in pytorch, pretrained on VGGFace2 and CASIA-Webface.
+
+Pytorch model weights were initialized using parameters ported from David Sandberg's [tensorflow facenet repo](https://github.com/davidsandberg/facenet).
+
+Also included in this repo is an efficient pytorch implementation of MTCNN for face detection prior to inference. These models are also pretrained. To our knowledge, this is the fastest MTCNN implementation available.
+
+## Table of contents
+
+* [Table of contents](#table-of-contents)
+* [Quick start](#quick-start)
+* [Pretrained models](#pretrained-models)
+* [Example notebooks](#example-notebooks)
+  + [*Complete detection and recognition pipeline*](#complete-detection-and-recognition-pipeline)
+  + [*Face tracking in video streams*](#face-tracking-in-video-streams)
+  + [*Finetuning pretrained models with new data*](#finetuning-pretrained-models-with-new-data)
+  + [*Guide to MTCNN in facenet-pytorch*](#guide-to-mtcnn-in-facenet-pytorch)
+  + [*Performance comparison of face detection packages*](#performance-comparison-of-face-detection-packages)
+  + [*The FastMTCNN algorithm*](#the-fastmtcnn-algorithm)
+* [Running with docker](#running-with-docker)
+* [Use this repo in your own git project](#use-this-repo-in-your-own-git-project)
+* [Conversion of parameters from Tensorflow to Pytorch](#conversion-of-parameters-from-tensorflow-to-pytorch)
+* [References](#references)
+
+## Quick start
+
+1. Install:
+    
+    ```bash
+    # With pip:
+    pip install facenet-pytorch
+    
+    # or clone this repo, removing the '-' to allow python imports:
+    git clone https://github.com/timesler/facenet-pytorch.git facenet_pytorch
+    
+    # or use a docker container (see https://github.com/timesler/docker-jupyter-dl-gpu):
+    docker run -it --rm timesler/jupyter-dl-gpu pip install facenet-pytorch && ipython
+    ```
+    
+1. In python, import facenet-pytorch and instantiate models:
+    
+    ```python
+    from facenet_pytorch import MTCNN, InceptionResnetV1
+    
+    # If required, create a face detection pipeline using MTCNN:
+    mtcnn = MTCNN(image_size=<image_size>, margin=<margin>)
+    
+    # Create an inception resnet (in eval mode):
+    resnet = InceptionResnetV1(pretrained='vggface2').eval()
+    ```
+    
+1. Process an image:
+    
+    ```python
+    from PIL import Image
+    
+    img = Image.open(<image path>)
+
+    # Get cropped and prewhitened image tensor
+    img_cropped = mtcnn(img, save_path=<optional save path>)
+
+    # Calculate embedding (unsqueeze to add batch dimension)
+    img_embedding = resnet(img_cropped.unsqueeze(0))
+
+    # Or, if using for VGGFace2 classification
+    resnet.classify = True
+    img_probs = resnet(img_cropped.unsqueeze(0))
+    ```
+
+See `help(MTCNN)` and `help(InceptionResnetV1)` for usage and implementation details.
+
+## Pretrained models
+
+See: [models/inception_resnet_v1.py](models/inception_resnet_v1.py)
+
+The following models have been ported to pytorch (with links to download pytorch state_dict's):
+
+|Model name|LFW accuracy (as listed [here](https://github.com/davidsandberg/facenet))|Training dataset|
+| :- | :-: | -: |
+|[20180408-102900](https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt) (111MB)|0.9905|CASIA-Webface|
+|[20180402-114759](https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt) (107MB)|0.9965|VGGFace2|
+
+There is no need to manually download the pretrained state_dict's; they are downloaded automatically on model instantiation and cached for future use in the torch cache. To use an Inception Resnet (V1) model for facial recognition/identification in pytorch, use:
+
+```python
+from facenet_pytorch import InceptionResnetV1
+
+# For a model pretrained on VGGFace2
+model = InceptionResnetV1(pretrained='vggface2').eval()
+
+# For a model pretrained on CASIA-Webface
+model = InceptionResnetV1(pretrained='casia-webface').eval()
+
+# For an untrained model with 100 classes
+model = InceptionResnetV1(num_classes=100).eval()
+
+# For an untrained 1001-class classifier
+model = InceptionResnetV1(classify=True, num_classes=1001).eval()
+```
+
+Both pretrained models were trained on 160x160 px images, so will perform best if applied to images resized to this shape. For best results, images should also be cropped to the face using MTCNN (see below).
+
+By default, the above models will return 512-dimensional embeddings of images. To enable classification instead, either pass `classify=True` to the model constructor, or you can set the object attribute afterwards with `model.classify = True`. For VGGFace2, the pretrained model will output logit vectors of length 8631, and for CASIA-Webface logit vectors of length 10575.
+
+## Example notebooks
+
+### *Complete detection and recognition pipeline*
+
+Face recognition can be easily applied to raw images by first detecting faces using MTCNN before calculating embedding or probabilities using an Inception Resnet model. The example code at [examples/infer.ipynb](examples/infer.ipynb) provides a complete example pipeline utilizing datasets, dataloaders, and optional GPU processing.
+
+### *Face tracking in video streams*
+
+MTCNN can be used to build a face tracking system (using the `MTCNN.detect()` method). A full face tracking example can be found at [examples/face_tracking.ipynb](examples/face_tracking.ipynb).
+
+![](examples/tracked.gif)
+
+### *Finetuning pretrained models with new data*
+
+In most situations, the best way to implement face recognition is to use the pretrained models directly, with either a clustering algorithm or a simple distance metrics to determine the identity of a face. However, if finetuning is required (i.e., if you want to select identity based on the model's output logits), an example can be found at [examples/finetune.ipynb](examples/finetune.ipynb).
+
+### *Guide to MTCNN in facenet-pytorch*
+
+This guide demonstrates the functionality of the MTCNN module. Topics covered are:
+
+* Basic usage
+* Image normalization
+* Face margins
+* Multiple faces in a single image
+* Batched detection
+* Bounding boxes and facial landmarks
+* Saving face datasets
+
+See the [notebook on kaggle](https://www.kaggle.com/timesler/guide-to-mtcnn-in-facenet-pytorch).
+
+### *Performance comparison of face detection packages*
+
+This notebook demonstrates the use of three face detection packages:
+
+1. facenet-pytorch
+1. mtcnn
+1. dlib
+
+Each package is tested for its speed in detecting the faces in a set of 300 images (all frames from one video), with GPU support enabled. Performance is based on Kaggle's P100 notebook kernel. Results are summarized below.
+
+|Package|FPS (1080x1920)|FPS (720x1280)|FPS (540x960)|
+|---|---|---|---|
+|facenet-pytorch|12.97|20.32|25.50|
+|facenet-pytorch (non-batched)|9.75|14.81|19.68|
+|dlib|3.80|8.39|14.53|
+|mtcnn|3.04|5.70|8.23|
+
+![](examples/performance-comparison.png)
+
+See the [notebook on kaggle](https://www.kaggle.com/timesler/comparison-of-face-detection-packages).
+
+### *The FastMTCNN algorithm*
+
+This algorithm demonstrates how to achieve extremely efficient face detection specifically in videos, by taking advantage of similarities between adjacent frames.
+
+See the [notebook on kaggle](https://www.kaggle.com/timesler/fast-mtcnn-detector-55-fps-at-full-resolution).
+
+## Running with docker
+
+The package and any of the example notebooks can be run with docker (or nvidia-docker) using:
+
+```bash
+docker run --rm -p 8888:8888
+    -v ./facenet-pytorch:/home/jovyan timesler/jupyter-dl-gpu \
+    -v <path to data>:/home/jovyan/data
+    pip install facenet-pytorch && jupyter lab 
+```
+
+Navigate to the examples/ directory and run any of the ipython notebooks.
+
+See [timesler/jupyter-dl-gpu](https://github.com/timesler/docker-jupyter-dl-gpu) for docker container details.
+
+## Use this repo in your own git project
+
+To use this code in your own git repo, I recommend first adding this repo as a submodule. Note that the dash ('-') in the repo name should be removed when cloning as a submodule as it will break python when importing:
+
+`git submodule add https://github.com/timesler/facenet-pytorch.git facenet_pytorch`
+
+Alternatively, the code can be installed as a package using pip:
+
+`pip install facenet-pytorch`
+
+## Conversion of parameters from Tensorflow to Pytorch
+
+See: [models/utils/tensorflow2pytorch.py](models/tensorflow2pytorch.py)
+
+Note that this functionality is not needed to use the models in this repo, which depend only on the saved pytorch `state_dict`'s. 
+
+Following instantiation of the pytorch model, each layer's weights were loaded from equivalent layers in the pretrained tensorflow models from [davidsandberg/facenet](https://github.com/davidsandberg/facenet).
+
+The equivalence of the outputs from the original tensorflow models and the pytorch-ported models have been tested and are identical:
+
+---
+
+`>>> compare_model_outputs(mdl, sess, torch.randn(5, 160, 160, 3).detach())`
+
+```
+Passing test data through TF model
+
+tensor([[-0.0142,  0.0615,  0.0057,  ...,  0.0497,  0.0375, -0.0838],
+        [-0.0139,  0.0611,  0.0054,  ...,  0.0472,  0.0343, -0.0850],
+        [-0.0238,  0.0619,  0.0124,  ...,  0.0598,  0.0334, -0.0852],
+        [-0.0089,  0.0548,  0.0032,  ...,  0.0506,  0.0337, -0.0881],
+        [-0.0173,  0.0630, -0.0042,  ...,  0.0487,  0.0295, -0.0791]])
+
+Passing test data through PT model
+
+tensor([[-0.0142,  0.0615,  0.0057,  ...,  0.0497,  0.0375, -0.0838],
+        [-0.0139,  0.0611,  0.0054,  ...,  0.0472,  0.0343, -0.0850],
+        [-0.0238,  0.0619,  0.0124,  ...,  0.0598,  0.0334, -0.0852],
+        [-0.0089,  0.0548,  0.0032,  ...,  0.0506,  0.0337, -0.0881],
+        [-0.0173,  0.0630, -0.0042,  ...,  0.0487,  0.0295, -0.0791]],
+       grad_fn=<DivBackward0>)
+
+Distance 1.2874517096861382e-06
+```
+
+---
+
+In order to re-run the conversion of tensorflow parameters into the pytorch model, ensure you clone this repo _with submodules_, as the davidsandberg/facenet repo is included as a submodule and parts of it are required for the conversion.
+
+## References
+
+1. David Sandberg's facenet repo: [https://github.com/davidsandberg/facenet](https://github.com/davidsandberg/facenet)
+
+1. F. Schroff, D. Kalenichenko, J. Philbin. _FaceNet: A Unified Embedding for Face Recognition and Clustering_, arXiv:1503.03832, 2015. [PDF](https://arxiv.org/pdf/1503.03832)
+
+1. Q. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman. _VGGFace2: A dataset for recognising face across pose and age_, International Conference on Automatic Face and Gesture Recognition, 2018. [PDF](http://www.robots.ox.ac.uk/~vgg/publications/2018/Cao18/cao18.pdf)
+
+1. D. Yi, Z. Lei, S. Liao and S. Z. Li. _CASIAWebface: Learning Face Representation from Scratch_, arXiv:1411.7923, 2014. [PDF](https://arxiv.org/pdf/1411.7923)
+
+1. K. Zhang, Z. Zhang, Z. Li and Y. Qiao. _Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks_, IEEE Signal Processing Letters, 2016. [PDF](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf)
diff --git a/Tooling/model/facenet_pytorch/__init__.py b/Tooling/model/facenet_pytorch/__init__.py
new file mode 100644
index 0000000..2c00284
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/__init__.py
@@ -0,0 +1,11 @@
+from .models.inception_resnet_v1 import InceptionResnetV1
+from .models.mtcnn import MTCNN, PNet, RNet, ONet, prewhiten, fixed_image_standardization
+from .models.utils.detect_face import extract_face
+from .models.utils import training
+
+import warnings
+warnings.filterwarnings(
+    action="ignore", 
+    message="This overload of nonzero is deprecated:\n\tnonzero()", 
+    category=UserWarning
+)
\ No newline at end of file
diff --git a/Tooling/model/facenet_pytorch/codecov.yml b/Tooling/model/facenet_pytorch/codecov.yml
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index 0000000..35cde5c
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+++ b/Tooling/model/facenet_pytorch/codecov.yml
@@ -0,0 +1,4 @@
+coverage:
+  status:
+    project: off
+    patch: off
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diff --git a/Tooling/model/facenet_pytorch/custom/test2.py b/Tooling/model/facenet_pytorch/custom/test2.py
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--- /dev/null
+++ b/Tooling/model/facenet_pytorch/custom/test2.py
@@ -0,0 +1,59 @@
+# importing libraries
+from facenet_pytorch import MTCNN, InceptionResnetV1
+import torch
+from torchvision import datasets
+from torch.utils.data import DataLoader
+from PIL import Image
+
+mtcnn = MTCNN(image_size=240, margin=0, min_face_size=20) # initializing mtcnn for face detection
+resnet = InceptionResnetV1(pretrained='vggface2').eval() # initializing resnet for face img to embeding conversion
+
+dataset=datasets.ImageFolder('../data/test_images/') # photos folder path 
+idx_to_class = {i:c for c,i in dataset.class_to_idx.items()} # accessing names of peoples from folder names
+
+def collate_fn(x):
+    return x[0]
+
+loader = DataLoader(dataset, collate_fn=collate_fn)
+
+#face_list = [] # list of cropped faces from photos folder
+#name_list = [] # list of names corrospoing to cropped photos
+#embedding_list = [] # list of embeding matrix after conversion from cropped faces to embedding matrix using resnet
+
+#for img, idx in loader:
+#    face, prob = mtcnn(img, return_prob=True) 
+#    if face is not None and prob>0.90: # if face detected and porbability > 90%
+#        emb = resnet(face.unsqueeze(0)) # passing cropped face into resnet model to get embedding matrix
+#        embedding_list.append(emb.detach()) # resulten embedding matrix is stored in a list
+#        name_list.append(idx_to_class[idx]) # names are stored in a list
+#
+#
+#data = [embedding_list, name_list]
+#torch.save(data, 'data.pt') # saving data.pt file
+
+def face_match(img_path, data_path): # img_path= location of photo, data_path= location of data.pt 
+    # getting embedding matrix of the given img
+    img = Image.open(img_path)
+    face, prob = mtcnn(img, return_prob=True) # returns cropped face and probability
+    emb = resnet(face.unsqueeze(0)).detach() # detech is to make required gradient false
+    
+    saved_data = torch.load('data.pt') # loading data.pt file
+    embedding_list = saved_data[0] # getting embedding data
+    name_list = saved_data[1] # getting list of names
+    dist_list = [] # list of matched distances, minimum distance is used to identify the person
+    
+    for idx, emb_db in enumerate(embedding_list):
+        dist = torch.dist(emb, emb_db).item()
+        dist_list.append(dist)
+        
+    idx_min = dist_list.index(min(dist_list))
+    return (name_list[idx_min], min(dist_list))
+
+
+#result = face_match('../data/test_images/angelina_jolie/1.jpg', 'data.pt')
+result = face_match('face_images_100/test_1.jpg', 'data.pt')
+#result = face_match('test_jolie.jpg', 'data.pt')
+#result = face_match('test_paul.jpg', 'data.pt')
+
+print('Face matched with: ',result[0], 'With distance: ',result[1])
+
diff --git a/Tooling/model/facenet_pytorch/custom/travis_test.py b/Tooling/model/facenet_pytorch/custom/travis_test.py
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index 0000000..47d503c
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/custom/travis_test.py
@@ -0,0 +1,88 @@
+from PIL import Image, ImageDraw
+import torch
+from torch.utils.data import DataLoader
+from torchvision import transforms, datasets
+import numpy as np
+#import pandas as pd
+from time import time
+import sys, os
+import glob
+
+from facenet_pytorch import MTCNN, InceptionResnetV1
+
+#  #### TEST MTCNN ####
+#  mtcnn = MTCNN() # Using default values for size and margin # AW
+#  resnet = InceptionResnetV1(pretrained='vggface2').eval()
+#  img = Image.open("../data/test_images/angelina_jolie/1.jpg")
+#  img_cropped = mtcnn(img, save_path="./temp.png")
+#  img_embedding = resnet(img_cropped.unsqueeze(0))
+#  
+#  resnet.classify = True
+#  img_probs = resnet(img_cropped.unsqueeze(0))
+#  print(img_probs)
+#  print(img_probs.shape)
+#  
+#  
+#  # Prediction output
+#  idx_to_class = {i:c for c,i in dataset.class_to_idx.items()} # accessing names of peoples from folder names
+#  
+mtcnn = MTCNN(image_size=240, margin=0, min_face_size=20) # initializing mtcnn for face detection
+resnet = InceptionResnetV1(pretrained='vggface2').eval() # initializing resnet for face img to embeding conversion
+print("1")
+dataset=datasets.ImageFolder('../data/test_images/') # photos folder path
+idx_to_class = {i:c for c,i in dataset.class_to_idx.items()} # accessing names of peoples from folder names
+
+print("2")
+def collate_fn(x):
+    return x[0]
+
+loader = DataLoader(dataset, collate_fn=collate_fn)
+
+face_list = [] # list of cropped faces from photos folder
+name_list = [] # list of names corrospoing to cropped photos
+embedding_list = [] # list of embeding matrix after conversion from cropped faces to embedding matrix using resnet
+
+print("3")
+for img, idx in loader:
+    face, prob = mtcnn(img, return_prob=True)
+    if face is not None and prob>0.90: # if face detected and porbability > 90%
+        emb = resnet(face.unsqueeze(0)) # passing cropped face into resnet model to get embedding matrix
+        embedding_list.append(emb.detach()) # resulten embedding matrix is stored in a list
+        name_list.append(idx_to_class[idx]) # names are stored in a list
+
+
+print("4")
+
+
+data = [embedding_list, name_list]
+torch.save(data, 'data.pt') # saving data.pt file
+
+
+
+def face_match(img_path, data_path): # img_path= location of photo, data_path= location of data.pt 
+    # getting embedding matrix of the given img
+    img = Image.open(img_path)
+    face, prob = mtcnn(img, return_prob=True) # returns cropped face and probability
+    emb = resnet(face.unsqueeze(0)).detach() # detech is to make required gradient false
+    
+    saved_data = torch.load('data.pt') # loading data.pt file
+    embedding_list = saved_data[0] # getting embedding data
+    name_list = saved_data[1] # getting list of names
+    dist_list = [] # list of matched distances, minimum distance is used to identify the person
+    
+    for idx, emb_db in enumerate(embedding_list):
+        dist = torch.dist(emb, emb_db).item()
+        dist_list.append(dist)
+        
+    idx_min = dist_list.index(min(dist_list))
+    return (name_list[idx_min], min(dist_list))
+
+
+result = face_match('../data/test_images/angelina_jolie/1.jpg', 'data.pt')
+
+print('Face matched with: ',result[0], 'With distance: ',result[1])
+
+
+
+
+
diff --git a/Tooling/model/facenet_pytorch/data/.gitignore b/Tooling/model/facenet_pytorch/data/.gitignore
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+++ b/Tooling/model/facenet_pytorch/data/.gitignore
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+2018*
+*.json
+profile.txt
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diff --git a/Tooling/model/facenet_pytorch/examples/face_tracking.ipynb b/Tooling/model/facenet_pytorch/examples/face_tracking.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Face tracking pipeline\n",
+    "\n",
+    "The following example illustrates how to use the `facenet_pytorch` python package to perform face detection and tracking on an image dataset using MTCNN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from facenet_pytorch import MTCNN\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import mmcv, cv2\n",
+    "from PIL import Image, ImageDraw\n",
+    "from IPython import display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Determine if an nvidia GPU is available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on device: cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
+    "print('Running on device: {}'.format(device))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define MTCNN module\n",
+    "\n",
+    "Note that, since MTCNN is a collection of neural nets and other code, the device must be passed in the following way to enable copying of objects when needed internally.\n",
+    "\n",
+    "See `help(MTCNN)` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mtcnn = MTCNN(keep_all=True, device=device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get a sample video\n",
+    "\n",
+    "We begin by loading a video with some faces in it. The `mmcv` PyPI package by mmlabs is used to read the video frames (it can be installed with `pip install mmcv`). Frames are then converted to PIL images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<video src=\"video.mp4\" controls  width=\"640\" >\n",
+       "      Your browser does not support the <code>video</code> element.\n",
+       "    </video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Video object>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "video = mmcv.VideoReader('video.mp4')\n",
+    "frames = [Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) for frame in video]\n",
+    "\n",
+    "display.Video('video.mp4', width=640)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Run video through MTCNN\n",
+    "\n",
+    "We iterate through each frame, detect faces, and draw their bounding boxes on the video frames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tracking frame: 105\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "frames_tracked = []\n",
+    "for i, frame in enumerate(frames):\n",
+    "    print('\\rTracking frame: {}'.format(i + 1), end='')\n",
+    "    \n",
+    "    # Detect faces\n",
+    "    boxes, _ = mtcnn.detect(frame)\n",
+    "    \n",
+    "    # Draw faces\n",
+    "    frame_draw = frame.copy()\n",
+    "    draw = ImageDraw.Draw(frame_draw)\n",
+    "    for box in boxes:\n",
+    "        draw.rectangle(box.tolist(), outline=(255, 0, 0), width=6)\n",
+    "    \n",
+    "    # Add to frame list\n",
+    "    frames_tracked.append(frame_draw.resize((640, 360), Image.BILINEAR))\n",
+    "print('\\nDone')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Display detections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UBEHC/b3dbrd7fnomAkVRLHk5nY6ZHQmKMCmAVC6Hw40ojvTMkMUEPLHzznFduzzP0zQ1hpAyNRs1ysuOq6pi5qIoHj586JyDyGAgImMS9dbImE67zTEGrKMs66qejsfz05NRq5WnabYx3OptbLQ7nU6no15xkuV2vjw5OBTnSC4gUt8BvP21xlvhrotvAHhj1b83WPXGFRA1+mnIiPcWME+z1KZZkmQ2M3nLV5UI27wl1oiw81x7x8AazyAAw5gQJdEp/O6vl0DUUbWFAEC4ck3UU2FABhAE8b6uXOXY2yRNs2x1/AWF2TsX50CIaGNzY3NzE9ZlhAACGILUGmGZTKrjw4PR+blGFIJwFVl7MxEZwKDAEZAAUQAFBBiE1w3bZt+whPgIABChSSwiiXjv/bPnz67dvN7v961NENEzO/aGvPdeaVmKdnY6HYOYZTmiUbMBgBHh5q2bGxsbn3zy2aNHj+7f//revXv9fr/f7xfF8Wg0+uSTT46Ojrz3k8nEWpumaSufMWO73Z5PJ4iIQMYk0/EkMWY4GIgwCFprq9pVZblYLCpXDYaDXr9r5mYym6sV5r3XGHPhXFUVVV3l/ZY1ASJrttC3iWkMWioEcBENMxJaCCwiAWEKM8YizWcIQBq8TNizcyRISOortvKc0IgACnCEk0FVGgYSFQDM5/NlsdSvNta0Wq00yZVkoDe07gVHIw3Y82g0qqoKBIw1Gxsb/X6fiJhFMELwa2i1gKgWBuDJfHp6flZWpQJo7U57Y7iZpy11biVYp8peQgqsHkHAYAhL4/YirLHYAtSgWBAgXRIGK/xMLvz38qJcXiO5eD4lRHTCll7HA986FOTiNdDujQP/3f/8Q4fgJQH2twURxOe6LIXXv1+C9f1dz4iIaZqmaYKEgGKs6XQ68/l8uVx6ESAS5tF4+uvffJLl2c0bezbGdxGABDpZ8ic/ee98PHry/DkwI9JsNv/tbz8bDPr9flsBHAAggH43f/+9dw5eH8zdHAScc0+fPf/gg7u9XiZgSNldCMNBd2trYzwaMasbUHp2AszsWDw7P5/PrU1Um4oIUSDYqD1de3bOKVKaJEkAwtaMBmONOrhJkhCSZ4+I1lpEMoYQTCM2RYSBmQKK671nDwAOEYuimEwmxtiT4zNK0jTPsizL8zzPW9YmJslOz07U9VwfEo1dUoSJCMlEYCz8ibcrFALX+nZGREJDaEita8VcIQipJjiNgfjCAEIUMB8MDqUCafEEhcNPiIiEgoAmgm3CzOCRAIEMEQKC5GnazlupSRIyeZJmJkmTJAHDAOxqARJrxRjmmr3n2jEDh2AsYogWW0ICFiQwSKSEPFDf3BMRe46oMDOieZuxYEVYoHlaYPaeGZBskiZJFvS20OpRdf8LENH+3n6r3Yp+mu4KJiADkFpbl+XTx8/u3/9qNhtba4kMEkX5H5HT4NrqzwS0wroBeT10QIjGGCICQO+9q+qqqr33AGCThIwB8SKyWCyqqqprl6beGOucc86lNtX4AVmbEOluzpKk1WoRUlDAAqen5x//+jfdbk8lOzM/efLk+vXr7XZ7a2truVyen58vFwvlWwkzES2XSz0tIgLAZekQCJGSxEwm406nnefdw8OD2bzY3Nq8evUKCCzni42Nzf3dvTyfHh+fLOsCEZyrT0+PF7OZAO9e2Wu1cudFt7Hiz5GU22AVUSIFm0f9RdFpFEZES4S6mQECLbPBXVVd66EkBkRg9q6qFSIGFEJq5RkheL4o/6JPqRLAez9fzEUUwMA0zXr9vk7dmjxsEIuV7phOJ5PJWIQBsd1pD4dDpaoKXnxf85QoAIJIRVmNx6NlsQRga2yr3d7Y3M7zHAU4wPEa9cGGt0YiDUgvqs+Eo7Kl1aHAJmr9XWPtZVn7+9Ltrv3jsoJmWYvA6Xu/wwPWV75T5f3tesjSBOPjv/8uh1rll+IY3wFDA4IxptvpEJmyKp2rFGzsdrsislwuhdUBwtOTs9/85lNLf3r16o6SRgLgIrC12fmTn75/fHY6n8wQCQFfvnj1+PGzjz56z65lBBjCm9ev7O3tPn78BBBE5PDo6Ph41O3uYQCiAAHaebqzvfX08TP2DCBce+9qRCYilvro+Ojo6OjqlSuAJEqyFj1khsFRvIi1KvxCngMhraBBkJRsIyTXBOy6y6M7G0EEJJK2BBqwWp9ImKuqAs+VqxeLwtqZTZJWu9MfbpRVySsUWsL/VzsXVS0QUZDnAfAC9evYOWAxYAhFFW04gBA19EqDgKxt+vgt8pbjFsSuXNwPmikR5IA6AALACAwCiNYag8jOJ8a20yy31gIaFkQBx4zg2TOh5lCEu3IMzgtztKYliOS1yJEhNEROj3aUroTEAgAsAgyMARKMBh8iIlhlFyMgAOuM6TJ78BBsHBQARGT2zjnFbAXEGrO3vZNnqWnMcwSCYCOm1pTL4v7rg+OTI5tQnucmwOwXTheRaSL8iMEzExFmD8gcmXorq1gEQBS9986r76i8WV0a9syB6OSV81w7x3q2CFOT5GkGAHVV+boWFhD9Rgak6XT+y1/+KkyoMb1ezxgznU6tTfM8N8Z474nIEFljEDG1loypna9dJQUDiHcsQgCY5Rpfac9mk7IoXe0AcGdnxxgzGo1GZ+d2y2xvbLDzJ3xW1fV0MplOxpPx6Oatmzdu3+j0uvPZUoF9tRybqcNvlz/B+Hr7i2+8udkHgoBUV845JjIqcpPEtvL87TSq5uQAVFW5WMwhar52q93r9sNuigpG1jScuqTeufPRuQYmjDWD4bDVaYuGai7ePhExMxKBeHUgxqPRYrFAYQHJ89ZwuNHK27h2j9HiRggGSUDpEQQQvHOenWcmAmutTuxK/30fN+SvAbhd4nOvEyCVX/H7XvA/u/EdO/7tbzcCOJ/PPNfCDEiGbLvdzrJsMplMp9OqrAkQ0R4cHH/88SdJ8rP9/c2VGYOAANev773/wbuf/uYzVzgELEv39Vff3Lx5ZWdrsH4aep383p1bL168rMoKABaLxZNnL27e2DGWArBCkCR0/er+J9aWdQ0ahlMSK4pz1Xw2NYayPEOlwa4/iYToT5JaEVGRr/6iMp/XdyOuydYL5jmARvpAA7FrWQkRMIOAixIhoTEEiIiq7U273cnarTTPyRLjSsGv32X8+y3WKwICCzKw89EoR7z4rkuABq602vpXiYJFIhcQJX3XionVPN3am1RfhU8iZWmWGJK6ahFSajNjUkHDjOi5hlrEe48AYEhQHcDIH4pfg4goUc2v3XPzzPE/iEgIHH6lloKIBuCaz1rvvSZNojCLgEiapdZaX9bx0VeTHkwRYQAZDobb29v4hlAiBAJIUgKR6WTqPSepAVDfH0mTm9YD0mtRwsZyDEbWhSSWNx41LDkqUa3ZbcrWds5BxFa982mSJtYQspGwKhK3IwQnU/I839vbOz09nU6nCmRVVeWcEwEio4C/IarKMkmSJElF1PkmAHGu9t6zF+/BGDMY9NPULovF69evl8siyVo3rl8fDgdVVVlrnj55+uTh4x+99/7u/tUsb718/erg8PXJ4aE1tLe3uznYwNQWRa3x0ni0foDoj3NkDL0htRAF17bq+n5BAHS1DwRDFEFGi2SIgQFJrTm5fDlAwOWyqOtalb4x1OsPkiRpzk5wmCV8DShqJDKbz2azqUqQVqvT7w81+nUpp1XXmohQQAC892dnZ5PJiL1DgCxNNzc2O+0ukZXGilybqPDxIMCZvS/KwjnHwOonEVHM8IqTLHE+BJsd+gdnYFx8KEFaRYOQgeHyilwYvOZKQJjIt1AB/vYGRS/mP8kQCCdgpVQkej7f9hHxs9kssUmSWsV1NZcmTVNlCE/Hs7pyIOCdf/78Vbvd6vZ+1um09OP6oGliPnjv3vHRyYsnL8QjIRwfn3/91eP+Lz7KMtPcnDF46+bV4WBwdHwiInVdP336bPzRe5sbXYiICwJsDYedVqtYLIERkVSbAqJ3vtfrbW9v53mORMBqNl54XoXrmnAbRJUQ7nZN6a6p5Eg0WgE/IiLOOaVf4ZpQwdUBIGNMYi2QJZumWdbvD3b39nf397uD/iefuhfPnsQZojhRlz2qN1dF2RvK/IrTFnGu1ZIFLBvWTqNOnZ5q5sbNFfU8EZCIvPgAWYMoCAEIhiKnigiIAikTScloaZqmiZW6TgDRJpkxmTEJELB4carjrQlhCRYW9lVVOecBQYG1xl/Xp41322C1qxVpOMurGSJEovXtG1jQ+vAhHokhXtvsyKgShZlRkJnzduvHH364ubmpTr6+AQUw7gFj0Bgjwt55gEQRc4nXWoGh8TmahVSo1XsWcMwMAk2EGKIDQQDivasq3Vl6rnR52XsW8d4rG1AFnQhba7OsheTromIRICAgslZ1KgAgsDX23t27e3t7o9FoNpttbW055w4PD+fzuZKwRMQgJEmCSIR0Zq01SXfQy7IUooFZ1y6x1lpMkrQolqenJwCYiZydn26c94louZx7dscnR5PZ9Gc//8X+/tWy3Pjqi9+Nz0fXr1+zZCwlhNagASCF/ymyoBGFiEQuC6AGShKRsOVBMduLJ+StAxEAmL2yP8K2sNYktsFSwmlrTCJAZVZPZ1MvrFBMmubdbg/RqH0WZbYENkqwtMU7f3Z2WpYlACdptrm12coz5Td85w3KfD6dTsfO1wBgjBkMNzrdLhkTuC7yhnUuQqC5fVjXfjqbLBZzERAEYynL8iSxADYaCzFUJXHL//X07ipqFYa/eAZRMP7iH33f7x/NBm50kmrfb93VAuz8fDZttds26RiFRAnVlG3lreHtQVXWr16+Ho0mzMzinzx9urE5/OlP308S21yVADb73T/58Qez0fT8bCJgfO0f3H9y69aN6ze2GieHEDaG3Zs3r52cn3HNInB6evry1dHGsBuMOQFAaHda/X7v7PSMBfVQ6+NUVZXneZ7nK4bphUcJM6DRGYh6VDTROHiKF3ySMEUksvKlEQDqulZfAhGTJFGPC1ZGvaAmAVtrEmtsmmat4cbG3t7V3SvXNrd38lbe7w20tkkQLWtu0ht3Hcvd6B9AdeXX0Opv8R3Xr9K8GSBUNREGZQVdUHMXMKTgPoRNo0YFMSCRTdM2ppaZ0UJGFkqXOkjR5iZJyRCgsLBnEAGKcoRFOGQGBQELAQxWCczCEh3F+J7mLtZN5pAwQoAUr84S4K+Y4IXBQVbXEAGI0FqNYgdRioDsvYAg4qDXv3XjRpalzNxoaL1QdLI0bUvYexAwxtgkCYK7qTbUKOS1hVTxzcIxfhmXKgIyIAhKKHJe4/UrGAXCkQWAqqq898zQ6/a6nX6v0yUS5wpx3nu2SWLJpHlmNE6CAIiz6eRXv/pVludEpKwN3antdofIJknNzMIuS3NrkqKoqrIupF4UZbfXzvMMEVnYOe9dfX5+KiKRT27qqvzmm6/mi8ne3l5dVzs7W1f29l88f/Ef/+N/+PDDn1y9dt0am2cZilgy1pBzDkGKYllVVZZnFtEYS2QATGOyvDmandc4WGvbm+CiYmj298pmRmwYN8aq94CX0pcQdRuhQarqqq5q/UIAaLXbaZoLgA/7cqUQA3+bEDwsl4v5fKYCot1uDwYDY4iZNWobgt4I4ROAAIwAZVlMxueuKlVPdnu9wXDTUBIAKFiRlFDFns4SIoLUVTmZTubzWV3XRITGtLNWu91OkoSMwWau1PO9sJffMsdvzGFz2N4CwUXE4aJpvP76+gs/dLyJYfxe7//ucRlh+vbL/DUNiMtT9MM/+AYM87YhIiDeF0VhjMmSJEkTAMzzrN1uDweDnZ1hlqUHBzf+6q9+e3x85D0ul+XvPv+y1+29++5NMCtbMzF44/re+++/+6tffVKVXpDOR5Nvvn64szPIc9t8X5KYd+7e+ubBw/PxBJDKsnzy5Pm7797MUgvBg4dWKx0MBtEhFfWOPDMRKXqHymNC8BHDCU7hmgkSvFglc6/NxqUpQWwUNaguFBFmjwjGGGNI6y6sLYcQISCiITCExhhjOp32zvbu5uZOnuWEZIxNkyzkukjjBq77gTHo2WjWIG0a3CIiuOGoxXh0c9JE/Q1BwAbzaEwuCAQkZhYWAURjDEaPsZkHCRyP5nuFDObdztbe3v6NG2jtwevX47MzciJlTQwGyQoic8SZY2olBHfTew8soOi1amBUKSkRQg0/eM/hflkUFl9xLUF9PGwSVNS5V+zLAqEa7T56Uew9MBNAliREzTsRUbOPwBizsbHRbre9fzuAhgh1xa52ANDgHolNrEmITIOONibeuqJobBelFDTMO1mvIBiIaBiedW0PaoxEwDsvIpwk6Y9/8uPM5p12J0lwPp+8fPFiNJrkeU6AaZIEgA+RCNnz2dkZru1ORc0FhMgGN5RSaxJEm+eGOfPeL8tyOpnWdd1qta0xBIIsdem8dwKxWgrzYjF7/PDh4evXvV7v6tWr7bx9587t4uv7/+7f/burV68VyyV7v5jNvXOuroqqApHlcuG9zzGHsHrY6JawsnHyo3+JpPy/tTlBDYAiIbAIoOA6r7X5SSLhXAgFUAiFVvZaA1wTIIia8eDKyle13o0Sy4lMPHYYYz0Y1w1FoK7ryWRS1xWAJEnS7fXTNI0HufGyV9gOCBCQq4vR+dlysVA6cKvdHm5up2mOSCKAIBzvktRBClQGRIS6qsajs+VyGeL31rY67Xa7k6YpKiME1RVQ3iRfRMYax3h9vOmorNSoCIL41WuyHgMOxvLFj/8evnYUxHzxl+Zb3n7p9n74aGziC6bCRRgcLu29H6Aa4ycvvfE7SQtv85AuuL/f9r3eucVikRjb6XS2tjY3hsPNjWGrlVgLCHDzxrZzP/nLv/z12dmZCEyni9/89rNOt3P9ynbDVUWALLE/evfOy1eHTx4/FwQv8ujps7v3bt6+vd/cAiJcvbpz/frV88lEEEXw5atX56PZ7u5QRa2qgyTJiEjYIyGS+No5z3neMsZCFHrfIkujy4kozHpJBkJh5fg3G0xEEClwcOKvleSvVSOj4rsQ6VG1QkiGDBEBGjRJmrfbnV6S5spqBoGEEmBEJjBBv62AbJVNQXcbFVCr3YIgIHVdKwQtsFK6gqY5OYiIQAE6w5Ue1LX27D17L+y9F2YENBS43857ARYETRMS0BoSwsAibIi2N7du37zV6Q9KV7eMXQgiC3pGIEJg9uIkapcoihTOZC9iFJzDkE+BgWMmCCziGWStNojOd1xy1IUKAhtJKW8iwOHAoAABhjzgpvwFR4ITCJOmTDfWlIYZAIwxw+FGmqYAl4IAwb5hD0eH59PpGFGU7I5grEmtSVRtUlgXFnaAsG6RQVCvCIAcvdvGGtLYSSC2sYhwXTvn6pU4IBQQ7z0iG2O2trbu3r09Gc+tSa5f3/J+t9VuffP1/U6748sqy1ObGDVII1qN7Oq48Oi8RzSICOSECBEIybMQeiQz3NxstVqz2Ww+nzrnimWZZXliU2Oo2+0m1k6nk6oqmUWwFs9cS1ks5tNpVRQ3b97a2t6+c/d2UZSff/5pXVW9ThvBn5+dAPvU2iRJ22m+KJauqiJ/rRF8HOoBIasACwdUM4gohI4hgseXhRiuZL6qSADw3jdwAxEmaRJN7DVkAUBtaq1As1gsvXeIKCBpmrbbHSJ0K/5znFAJ3jcJjKfjs7MT7x0gdnq9jY2hsSaIu1W8P3xRc5nZfD6ZjJkdAKZpsrGx0Wq1gJBj/vOaxo6qHBERnatH4/FsNhNhIrJJ0ul12522MRYCQTRiSnrsxUBU5xS96G8Lf75V9Ks9fBHR+Wt6ij/wG/9zG5dU8lsMGgz+kKvK8Xi0t7d3787NTjtFWJnW1uCd23vO/cmvf/3b8/NzFjk9G33y6ReD7l/0+nlzzohgY9D5yY/fOzo6mc8XADibzh988+Tq/nbWWjnBaZLcvHn96wePqtID4XwxPzg43t4eGINV5ZdLd3wymU7nAMTg1Kjz3tXOt6mDkUSjhBQKgpoFOJJfVYwHyY6EGiIU1rrUl00UbOQ1rI7USsAqHhOR6/hhIrKo8UkiJAIkx8gQhDBFx51DVqEeNIyIVcA0g8Z8y4qhc86zbzzeeEDjLUNzcPGN9QVEg0ISk1IlItEsLCLOOUFiVnY3CYhnibFXJKJ23jKAxWQymc3q+SIFJDQJGaOSUwRYIBT5VqtF85qwdo6QsHbeOYlAL2kpIVyFA97cooFudQEDiB5GKAmyMuvthU9qnRRRSlGNKIGaJay1iz17FsnSfGNj01rrnLy1Smhd8etXx7PZQnNA1GUyxhqbKH9PQr6JaE3wlX65vHqX/60oSnx0AYC6rlztIrwTPWBhInPt2pWf/+xn29u96WTh2SNhltq9/d3RaLw53CCB5WLZ7/fPz050QUEMRgPy0ldLk9mKkQHC3Ot2b9y8qRVqsiw7ODh88fxl7dgmqbUZGb1b47zz3oM4YQbAwrnjo+ra1SvOVYbwX/yLf3716pX/8X/8H0bjc0Pw8tULYb+zu2+TbGM4nL6cVfOKjG23je6Ptyx5MKEC/oOaAEgYYd3AoJagpAiUxNcApwIAoFsWtZoVJXnaQUmE5VKPAQQCYURiL8tloYcKCbMsz7J8/Y40IqL/ExREZOfHk0lZVQCQWDsYDPI8j9aANDevCjVgIABVVYzHZ947ALDW9AfDXm9AZMLmj6IBQBBJhQMRCkJRLieTyXw+q73Tuj/9waDdbiNRg4Wtiy81guVt+/mNcUG/RvNUkxEDd/IHXOQ/zfgBCux73vD3anyb/627yTn38uXLq3u779y7Hog1UR5aS/fuXXPe//KXH89nC+f4+bPnX25u/OmffZBlNti3AoRw/frelav7Dx8+8SzC/OzFi5OTd65d324kBCHs7+32Br3Tk3PwWFb1o0dP93Z3yZiz0/FiWU2ms6Io9U7V1atrDwKEhGuVD1j9BxYW8Coi1YRGZuGwr5o02Ug75AtCAVl8rFpFgIAK8hAR6RmPO3/l28RJwWas5ilGNSV6h3plJUyq8kVQ6o/erqz/Ub0soCFPadBNWa1QVL4xMyneXriDoPWYkZmEgVl5riKo1bWYY+aPeuoSay/pbRsii1QtirqulrOZlHWGxtiQwisiCAxqmiGCAAe6NDILAntw4Bw7r84uqvsRpQ4iXnSNwhIQIjdFHmMaiKxNcng2FbYSCO7BRGJmRGDvvfNxFjTOCoTEntM02d7eGg4HIQAd+diyct9hsahevz5w3jWMISJK0tQYg0DBxVCEREQNNM2sBRbfdJshRAHkeGGBJg1uhfYDe+8ABETNNSRD1lok2dvb/fnP//za1X3V9c7VKq6TNGl3Opubw61h1yDs7u49e/pUhAFQgNmLZ48YkL0ID2jqtqgHTAZRedQgxlC73Wq1Wvt7V+7cvvtx+ptv7t+fzGb6Ge+cMQYQfO29Y2ZPZAGgrt2jRw+ns9ne/pU8vfbTj35iCf+H/9//dzodf/nlF//m//lv/tV//b/MW93ZbOada3e7aZpGu2kldyRmZxEiRKKabodA8I1lJS8ZE9HaXCFFaniFODCqr5gikgQS7iXphgDoXF1VpYZyEE2eta1N9CReenuwlgGXxXI2m+n+bnW63V6fjF3PiwgRhfAZQUDv3Wh8vliET7Xb7f5wSCYJSHPkEqhFAYFLIoKghQXm87lzNRHleT4YDNrtjgnF0OParjxnFTdNkoPaKOHXFx8/ijsBYQl/rb12OSj3j+M/zdDjjuPR6NPffmpA7ty5btMgW3SJEkv37t6Yzhaffvp5VdZFUX3x5Vdbm4M7d2+Yte4L7Tz58P13Dg+PJpOZCI/GkwcPn+7ubiRZI6lg2O9e298/Ox0xigg8f/mi+7vusL/BLJ5R+xEplVedtrIo2+0OQegio8IsnN9QOBZJ6RfIWpJIBY66kgAr+lWQfNHVBQilSmLNhaAY9SCv+6DRTlRrnOOshLxHWTuYIMCeQRCEVnIoKEhYEy8YTy828U7VNxoRXBdh1OiLYAlDbBsQOMwrN1GLOwEyEUTJzyLeM6suxgYHX1lj+pxkjKuqydm5MPuqMiyEZMgSajBNNLc34BAiBqkp8aRpT+Ccr2tmr9UO9COkqgZDVtjaros2hsayNBoYtpwEzY1BuumwEguPEhGLeFa6l7CwIWpc51hKCPb2r/zkT/+kPxw6CATdN9xUGI3mo/GYPZMlAGDNZrM2TVOkZunVfGMRSJLcq6FEpJ0KoDFVo98elI1C0MIsHgOY440hYw3XtS7lcHPjxs1rN2/e2NnbQUJ2YCwtl7UwK5NfZ9sa7PY67U4PiEAIiAXEMzvnRATRRzjUEZIxBITGUBI0BAHAspjXdWFMIsKA0ul2PvzJj02WPnj4YDKbpkmSWG26aI2va+HYeA4R4ezsdL5YJEly++atJE3397avXNl79OjR69evTs9Oj45Pb9y68/zFy+293cFgsJYqjc3Bi+uNMS8FMRAYgh8YopuximP4ePT6wukjBEEMTYqiAx0q6QRQQi5m66vaqr2vfKWVNIhsu90xZFeHGKBxugWBAFlkPJ1UdcUiibXdwSBt5ayGeIR5BYOtHbxg5vlsNplMvBcik6bJYGMzy1rQREMapzmk9gfhUhTFeDyez+fa9TLLsl6v12q1mmJDsGYsXjRgL+jOhsd90cWKu5fXMxDDK5ev8J19RP5x/OHjzemVtVXQRPqz07PffPxbAL5776axZt2kzDLz4w/vjUfTR4+eeO/H09lnv/tyc2tjc7MXXAoBQrhxfe/e7Vuf/+6LmsV5/+jx0/feu7u/v9GIvjy179y9df/hw+WyBsFlUR0dn+RZGwFrJ6PRZDqZhaQUwaKolsuy3e5KUC4h9oeIJAZEUMSEg7YS38yiLnizFfUUaMaKCkYtK6ScnpAcSEAx6c65mr3HNQwZAIWiUxd1B8UfEGM9OMTKOQEJDO417Eh97JVX/QZwGH+IoHBwcRVe19Ouupag+VZEiKYJACBAQsYiARpDRkBYHGtVaGaDRMagFzKEhMAhnkqIKoCL+aLGUjWlpeDqaqlcZhYUkiBGtA8Vrkp7IDQedgS5WBQUXE8PbdyZMCGgM8qCEf5dbdfoV0IApMFq6UECYzAR8MKemZ3z7Fn961ApiwQQGGRre+vO3evem7Cd1qNkAoDgaj48PJ5OJmmSBqyYmQgTa/v9XpqmUWaFFBcBSZKEQQSNw4qqWoV+AzLrTkDAWOY06g9ERNQ2IMYY71CAvMD1Gzf+6X/x83Y7RyJmQARjTYhxBhdFdCdYQ0liQVaeLijcw07EI1oAYPEgrMWUvEfnPFGNRGTs6Pz89cFBu9XZ3NxUy7Td6fzko496/d7vPv98Mh5LYijPjSFjEzLknAA7iDZvWZaPHj7Y3tra3d2bz2edTqfX6zHLcrF8cP+bW7fuXL9+PWu3lY2sa7wuz1fqJ+jhwE9uTFtNwTBkEQ2qPQ0Aun3WJZfeDjFaQAIQIUKbJHFPrfRumDgyCFjWVV3XWrfMGqtgcpR9IcNBU8OUa1DW1WI+E2YkzFqtfr9PxgCEHKXVBkUADCUl67oej0dlUTKDtabX67fbHc2ygtBLSgGtxgYUIahcPRqPp9Op1jPPsqzf77dabUMGGyhMrfQ1QRZnY10Tr+jNCi8HsxhDjxFoLvXtOO3fgfb9Xoh4/Ra+41bXruff/O1F3t5brrBCEX5PAOBSP2MJ4ml1N5dIZ28d36KGw8FglrPR+W8//SzJkhs3rjZ9kECAELrd7KOP3jsfnR8dHbGX1wdHX3zxzc9//tM8TyJqCHluf/Te3WfPn52engPC+Wj09OmL7Z1h03cNCba3Nnu9/rI4ASDv+fTsfDgY5Gnr/Hx6dHBQVaUKNRYvIp1OJ00sCEsj7EA9HxZgEa+ySAsAN7EWRCCbQCQZ6L6lxqcIKoFkpU+BCCxRnuXdbnc0Ol8sFgodUYjEaGqrCWIkuLBqdoTQlYKjtasFhBAZRZSZqwUoEULEk7Vnom9E91t3AoYwquAFH3iVaEpEZAwQAMaFJwx2BAIq2UqY2bFnFMmyzBrjQYiEjAHgNM/a7bZ2c0pM4p3X+lBkTGN7hE4MEkiWq92i+0KCmSLikdmS0WIEErltFGPoTcpxFNEYs/QFQgAvSEUKklAtjlCNBwVt89VEiB69mhbee+/AUHNngsAARNTqtEX9dGBV9RdtfphOy5OT06IsEi0/GYoIgrXU63XTNFkRxQS8cM0ODFrMnJAwoim0gQ9AYHsHR1ygqW8lIt47Zo+IokVQjWFAg5hYu7211Wq3AoFXzVgiRDQIBNpmkbQIh7EmzVIRAEERLRMC5EnEBMtMd4cS3yDEBgCBBIVhsShePH9BhvIXraPj4x+9936vP7A2vXXrTmLTr7788vjggLnMswyJiBJC57xjLcDqPAAcH598/vlnt27N1IbodrtJktW1e+fdH/3r/+Zfn02mn3/xJQeRh1EJRusp7O9Y+U0rGK/ODBJqDS1jjCEyElGoxmrTJ4ouboCvQAPtymeOKEoUcytjr6xKJ8FvTtM0SZIgEWClDZsjBwDz2Ww5X4iIMTTo91t5vq7zUBo2bLiCCC8Wi8Vy6dkTUbvTGQw2rE1YgIWVihiFzOp0s/eT6XQ2mynnOc9bw2E/z3MyRrQT10pMqS2+rvtXrVLXxEVjwkfBQivLMNgafxjm/N0a6xI/8Xvf/5/baDglzW9wvX1IMAlFAE5Oz37zySdJmuzv7xizmlVB2Nsf/OQn7/3lX85ms1lZuQcPH+/t7967e10lqzp9u7vDW7dujkYj79ixf/L0xXvv3R1udMJtIPT7nStX9rUiByAsy+V0MvOZPz48ODk6SBID4oF9tSyEpd/tWkMATjx5WN2wxNrgiKAk/1UbIqVvEBIKBPhFsRxCtIiBYoYxGqtuCxkkNGQSQVrj/mBIzwfVquFXcUYgAtfBIQZQCHr1hubg4bpYWN+Y0kw/QCDMSmNJQNBVwW6gmLtCK1eIwjev0OXgt6jZKyKaExy8Mw8irBhyr93b2Nja3tjsdfp51rbGmpjDAo21EFOIAIAZkIRFkALHCjz6ui5dIcZYLZ4SwGAQAVJRhRdM22bjBcxYC4tGgQJBNq5cQJTA8rS0BgEggHjv6qqsSmTWgovN9yCCsTZrtbwAa5ubN+x79nB+NplMJt7VAqzeKosHZDRq3ah4VV8evGfPAoDWWGNArBBZpeSty5nGwINYbptZNGzJzNZaQgIQROy028PBIAZtw65IUsuRE66ZcM55ETDGdjodRPLsQVDQpElCZJx3LOHmhdkgKUCkH1RCGZFhltq5VpIByqtXr0bjyd133rl9+26StG7cuNXvDR8/fPDo4YP5fJlliaHUJsJcMGtEQXFdeP78+fn5aH9/DwCSxBBhp935sz/7s/feu3t0Nnn09OlsNlOK/5osvuybBlUU1yOgUoiEuqy6m5UHIZEW0KifsOmDvkEwxhKaxq9G4OjdoqaSA0tdVeorE1GD7l44fGqxCSAgez+bTmtXA0Capv1+31h78c3YfEpHVVWj8biua0C0WdrrD9K8BWACXXHl9MRCbYIiMl8sxuNxU3BgMBi0223t/tB8TVS96j1/n5MaARNodDDHu5boJV/UiP8IOf8dj0sruI5nYIDZgpI+eH38V3/18c9+/mdXr4YeDAKhZ8K9e9ePj8+++N2XzrnJZP7ZZ19sbw03hr3msmlq33n3zpOnT89OzkTk6OTk5auTwbDTfHlmzdWre5//7ouavSJzk+lkOZ0fHr6azyb9fheAmV1ZLm1IxgVh8OCkgUBFZvN5VZVpYq2xKn9UrJdlJSx53kJjtYELCCApOhpVVsiHgEhnJjL6S/TMi8XSXeym8IYd1/Scx9U5BNCz77y7uNEvAFdvH2s62HuvJydMOuLaFzUYXnA1vERlGKpTQAyFiUT/o6qrsighgvMsXFU1e+m023tbOzubW71Ov5W3rUmNtkxYM3HU7Q8kEpWSKhxZ4TtxRTGbjH1dDQcDayxGCyAWvdco8NpUwOpJ475DQe135ZXY5QEFxINQYI2EciUhuEgxVKj9gIuyyEySpCkiKoytNDdjTZalhOBB+TaIFyEk9jKdLbXRnrBXTjt7LwyEBhGtucC7jjeMiMYaAGZDloyB4AdBiGUiNkAxAAAYZnFVLSxiwNhEokvUaXe6nZaNOh4FGSCxVv9tALyGFxDYy3QyX8xnCEyIIEyIvd7Ae8fiPYhX9rJnbdiRZVmappPJLE3aP/rgQ2vss2fPXr16uZgv+4PutWs3BOnrr74Sxlu377Ty9vb2Vr/bGfT7n3/++WQySjNDZIxJQICJnKvVOq+q8vysnM+mWZZZm5Ax1hoRqapajTREJDLWJqoUL2DCIOo3Rrtx1flEiXHBuoSgaSKxktcjl9Gai9sIsKkzHuzcKCBCgjyAZ66rSpiJxJLJ8hytiQ75WgY6oaYyFGU5nc2U39fpdFrtDjb2Mgakbe2hQJgnk+lyMRNhMqbVarU7nTX7o+F7C0pTnQaXy+V4PK7rWk0ljftGKlk8F+H4NxtpTVy86VlyECHqBkVGGq5RFP7+O6OXvcM31dVbP7baD38MDve36GCFd3TnMwAy88HB8W8+/jRLf7Gzu4EIyCHfrJUmP/7g3ZPj04ODA8/86vXR118/+tnPPzJa+FAAEfZ3N+/evTMaTbzjZVE+efL87t2rrTxRCUwEu9ubnU57NJoIiDBPJiOp/XR2XrmSIWf2y+XcOZemiTGooJRCobosrq7H5+dJmhSLhXO1NUZr75MhALA2TdKs08qITFGUznmJaaZRAavLoUkQFGOhofIGs1fXdW3rYtP7JBbao8brXM2t7iHNkgi7f933WvGf1n2A8EkVNwyVdimmlbl80XEg/SUyKhsMAZR20/Rt0GX1gaAjzlVFsUAyTpvGO+eqOrfJld39q3tXNvvDLG0ZkyAa5Vutcz503kKkmNBYq+Re9iLOLRbz8dn5bDYxBrutVruTWmvWxKNusAvbDdaOSfNIGDJrRHsoqQ4NbQm1Bw4LgKzUoaaACUtVlsvFIu32szQjIgjeqiBCnuftPA8Uc0BCNCEkBzF0I65SBxKUO4OIzntmAZXs1oLQKrlF1BJBY4xxzETWGq0mIdp+ChGBAktdCNUzAwRGV/u6qpN2liRW+WmI2M5brTRRRdsABRraZM8IhgCAYTaZjU7OP//ks+dPHyN4QhYQzRsmIidcV/VisSiWy1pKAQ+I1tp2u7NcVkme//ijP7l58+arl6/+u//u//7syYP5YioMH/74w8Taj3/9q/Fk/OMPf9Jpd7JWfvfdd1rdzmeffnJ4+EonOW91ssxOpuNiuWR2CN6z1FU1X8zTNMtb7TTFv/yrv7p9551bd+4pH4pCz2OdaomlUcIMhsSisPBESAGMjg5uKFwXC+BdFl5EIACETABAKERoVckFxhheqKKFiBJ6PLOAkMEsTQjAxxSL0P0SUTTnX/xiMS/LpQinadbr9ZMkvXwPFwXoclmMz89qVyNClmW93iBNMgzlYSl+JHix6tAXZXF2frJcLgFAtW+/31/zy1cWK70lcLJ2mNZR6JBTxCLq6QZ+aigkFzcwXr7AJYf4LQov0GQuYuB/N+MHKtQ/Cr371hGNs6hIYulKEUEkZnn9+uDTTz//i3/y5/1+B0IQAQRge7P353/6k//wH5fnZ2Pn/Df3H964ef3KlS29LAlkqXnn3u1Hj56cnJwxy6vXB2dn06tXNhsId7Pf29naHo8mAoAgVbmsiqIsF947Eee9K4qlMSYgixFw1owiESzLIs+z7e2txXzune90OmVZFEVBxmibYCJqt9tpmie2XCzmZVkxe3UqnXNNxhHGcJRqTQdK3+VQxymANipFqWkyvhZGxqamFUfgxzmHCndr3k4AoihqXngLJhsHgzjvRSLYDdCUp42HOPwSAsiGEYsOFyYEEGbnvHN6IvXYk5Zrdg48J8bsbO/cunZ9e7jRynIyCYIBQIU4ggrXGorCAmDIGrWDdD0EalctF7Pjw4Px2Zmr6yRNq2GJvT7G9bqIPAZzZ90DjmaI2n+xVuU6x0W1mHBTBdIiMAqDeEIwRleK2THEiuGrbxVIjA00wgB2NJDC6p6cZxbhUAUaEDEUjARQRbv+EAKiidNk0CB5RBN61FgMjakDyi+r0ishYs/MVV21RFRFqQ1njLGGENSpCr6bsSSiTUgSBBSWFy9fnR6dPHjwsFgWoegnAArmeTvNcyfeFRUC1VVd+AJQmL0XQWNqz+LZZmmn193b38/zlvfinZ9Ox+fnZ+/96L29nZ1vHjz65Xz+049+urOzZ4y5du1aliW/+e3Hz549FSdZmgJRmqbeu7JY1toGgQGASscVQ1vMy4PXH3/y2+39KzZJEu8NkjWhEvslr2vNLg2+shbjAkKJZwQI4Tu0giAiieYYqAqPMXhAEDBAAQpSi4ZEvNNINgKIsdRqpQhCIY0o7HUEJEFC8Z5ns4lnhwjtdqvX6xGtxZvWNrZ+ynk3nU3KqhSRJLHdbrfb7UIMnUBEq+LnBIGcr6fTcSjWbUy73b6ofePxiY0vY6Q61G2W1YaOod2gJkOddwkVaEUDciLNXePbNO76v94MECPCGgDxvTDe9/Gom0T8b9eYq4/LWnJVc/03Lvj72QTN0X7z6y698Ts+vvr2Rpz9sLv4TkNhje8AwU8AAGZ+8uRpp9P5+c//JElW3YCJ8Pat/fHkg1//6pNiuRyPpp9//vVg8LNuJ2sebHdn4969u+PxpK7deDp58uj53s7QJqR7IU/ttb39J4+eeu+VhVPXtbpAzFyWpQhnWaaHi732zwzWqvd+uVy0Wm0AYOE8T9PUFgWnaWJsuiwL9dWyLMvzVppkWZZNp9PlcqGMHRGnEGrAnRGJDBGSMUhOIz4I2nqVNIlSmqOEIUpFZJvuG6IpqSLNDgu4LQRZA0iC6DUiSygqZyJs3kw6IhAAOxeDRRJsc8D4/xDMCTsnnjnVOBovZWBX13VZcuVQG9XqioSeC5SlWbezceXWzd3dvU67DcYGppf6A9oNPRSJDMdAb88agwLs2ZeVOM9ltZhMJudnrnatdsvVtX6BqKURumWoJBAw+qRN9eW42dTlIwKvfmuQKqi+oIqX6LJqvyQP4JEEEdkzO8/OI4MNcUdNqwZCzPMsTaxubKXsYLha+HrnfV3VrE0B2SOzoqIiIszW2na73cR3ERGE2dXiWQJ6QlaLHuvqBJkogoImljqJphoz18457xs4Q/du0zZZtbcBSIlMCBLDYlEcH50cHZ/MZnPvFKBHddHD+mBQZKlN0iQzZA1ZRKprh4BV7WpXa9cMnRxNyBbB8WhydHS8t7f3L/7n/7zVyj///LODg1cijAg7Ozt//ud/du/ePWPM+Xg8Hk8EaTDcyNudyvGyqOZFNVtW80U5msxOzs5n88Xr168PD19bhMySJSRhg2JISIAESP3eNWkfdiRKYF6F1te0Oj9yWVWET6DRP3o4Qx7ehQKBuA40EaJzXvFkREwSa9MkbK6V+UwUkH6sq2q+WIiAssxarYZ+dfH6scbNcjmfTkaeHRJked7t9NMkfUPUNtsDPPNkOpnOppF4lff7/TRNoycETUF+iDBZ/EpoLishRZMhkDu07gGwMEcMKfxppHo4SGsvvf0PXvwDb37739B467fD+rfL2jb4lv3whw/8lj+/3+d/4JBvGc3rDMzIgtzIMUSsqvqrr766f/+RvxgWtZbefef21avXkGzt5eHjx0+fvmK/mqUsMXfv3eoPegJSe/fk+fPpdNG8mhi6dmUvT1sohEgs4n0tzIaInZtPpyBgyBAZJEMmVYJkkiTGGO2XICKz2Xw2mznvinI5X8yXZTmbz8uiFsFOu5tlubU2b2WDYf/q1Ss3blzf3BwCcGB3oiYqea15qCUmjUnSJM+yjMjE2hhqM4UzLbGNjrwNkYkbVfc+rAr4xzT8mG0j0Njtl+Fo0UcLGhdEmpT/yw6FYuSsxrHqTxSpq7Iuy7osvHPIWuRRvGfHbJO01x/s71+5e+/u9evXO92eoFkvnCqiiS3MDOxEdQ2SFkky7P34fHTw8uXJ4VG5WEhduXJRLGbFYuqqZUKQJYmxxtDlgq9NYQ0BZAAGZKRVr0QkodVWviSlA4jJCEK2EUAggtj0Pgx2mZ5ainDZoNdPk6RRvRSrDIpoiwRYLOuiLMqi9M4LexavzZd0Alqt1v7+fpqmblk3IIx291VPDgQpFMCMJFUR1JqU6s6HyAYAgvdei4TFzHEAEe89c1NWBdVQSBATRFXOJ4enz58+q6qqqkoA0PLFrJVoggRGzyBASZq3Wm42myt+DqHPBBqbKCUqTRJjLAixJ0Tb7fYnk+mnn352586dH3/4wfMXL776+ovlcn716tUsy7a3t3/xi19sbm5++umn4/F55uo0GQyHG0VR9gcWyDonDGBskmUtBDg8Ovz6q6+yNDEAAJy3Wp3eAIGARYvQSDDFMEIhiACkKUdqAZM0SX3fJmgb9EdJ4uu7hYi8jxiGroYWtxLwvmbtbE6QZClawxQQBwXTVgWQEefL5bIsAdEmSbfXMyY0D1+5OFo2FQkBvK8mk3FZFiKSplmv2293OhhtOIh260o2iDShX9BSWf1enq9qckGE5xsXEaNWkuhBK51yhS4EkE5EreZIw3oDflBUky/JLLn4nt/PnXzb+NtQk3+8CPPvPxorBEC1jgAQFUXx299+2uv2blzfW7cI2930vffvnp6ejMbnRVnef/Do5vX9Xr/VfHxnZ3jtxtWT8zPPcnh2+vrgeDjsYOSWbmz0ut3ufL4AhFj5l4nMbDYdjUbb29tqrHMof0hkEiJxzi2LwjPPFwvvGYEXy8X0aMbsO50+gDCLAopFsSyKMt6MMntVXAuihHJ4alQrncTYdruzubHZbrefPnu8WCwgnCCJ8CUKUMz+ClZ0c/2gS0XUr0FlLgcgqinaHBWqmtDB1W0wTmCR2nu9YkjmCS9JXJmoyoMGlyb3TA++907rCWrhAfU/BRHIdvvDwWCzPxy0el2TZQJGrS3igDWJlsBsgqYMZIzWvaiqanJ+dnZ8UhVlt91up1a49q5wrvDsiDqddtZuZ2QplNtsSg2sDcQQogWQIG4BOfp/0ESIcWVyrx++2I4wAmvapkMkOJrQWMoCiJjYJCGyAJ6BECxIU7lRBGrHJyejxWJZVRUASCAXoETS787Ozr1797IsXy4XAEEWKluKCByvPVM02BEAgDHUamEi0EauQFi52nnPCGCb3CvwnjXyQc2pEzCERFRXDA7ms2VVlLV3VVUTkTG0JlUREFjYeY8iNk1anbY5N+Cc5gIgUZamSZoo6B1bBokin/v7+61W69mzZ59//vn1m9euXLmaJvbBgwej0ejdd9/pdLu9Xu+jjz4aDAa//vWvT44PsywfDgd1Xf/5z3/xs5/9xXyxXCyL7mCwu7P76OGjzz799PGjx3mWindVVbTb7c2tbRbc2trtdvsQdYTzIpW0Wi0kKYsCRdLExp1m1P8jemPXxM0dzrA0vlosXHc5WoxRY2rzYwciascnaUZoYmQTUWIV5XjOp4uZYw8IWStvtVtCMbTcYL7QoGBQVNVsHpKI2u12vz/UbHIItSpx/c4RpKzL6XRc1xUAW2v7/X6n073kYUdXOMJOAACX1M+azxQ9qOAlXPCoListVANvfaLwzaTVSx7dW7Js/5DR3HlzWv9mr3/JeP/e2/i9nF0AuNTeWOKmWF3uDzZhRLP444UE1MeC0Wj0m99+0sr/yfbOEKNHaARu3Nh5551bn3wyLSt38Prw+fPX7394hyjwBtLE3L17+8HDR9PpoizLh4+e3LlzrdVO9cqtdj4Ybr4+PAYtUUOGkERguVwgYpKkRIYFCGJveWHvxXufpNlmq60pgyDsXF0UNZHp9XrOcVFW3vOLFy9EYv5q0L4C6I2xSZIomt3Iej3cSZLsbG/fvn2n2+2enJycHJ8iIJIhDMWN46la+Wu4NtYnUetXqEQPnvK3bzZlqXBkbYWOBStPQIKPG3OmGq86uhKNDAEQZs+w6uUgEuknrV7PtjoGTZKmYEgQGSjk+6BIU7MYQv9Za8igIUPO8+j8fDweLabTqihQxPs0WthaB0JtGjAGKZI1QpBab6wJhSFKg5+GZ5Am5YuicGnQyog6hGEvIVEiohVGQEL9FBWV7B0Anx0fLcezTpJlPqS4EIHh4E8U8/r16+Oqqj17QGFhIiJrgL0gIuFgMOh2e8ZoopEA+DClLKGpC5EgYoCgYQVoaFM6EARIjEUEBljWZahuZSxGCr7IiuKLqAcNiJCQRueTTp7MxgsSAkbhCzU4dZaIIskLAJCsTY0x7FeVlrUSOot4YSdM1jZ7JEnsrVs309Q+fvz4+dNnxWJ5/fr1d+7effz48W+n03d+9O7O7q619ubNW9Ykh0cHnVY2m03Oz8fPnj2/c+fdn/7Jn4nAaDL23u/s7rz//vvPnj7d3Bi6ujo7OyVrWBgQmV1ZFlVVWWuIDHs/mUwWSTYcDqezCZG5evUKijcgBAJoDFIULm8R0CvxGn9PkVdxgQmpUdNYGcd7HwF+NDHcEt1yoOYkAzjnloulMJChdrubpBkAhAqza5fXQIoAz2bTqqoQ0VrbaXezLGusyAu3jUIgzH42m87nc+210Ol0+r2+IcPCjexoCNPhtmT9ahiNzpCyJwjC+hvR5mgXp2utatfqIt+r8P64fc2/6bv/fdXpZZz8jfjFX3+IyMHBwee/++IXv/izbjf4uASQp+aD9+4evD58/uLlsii+uf/w+vX9/rCtOxEBrl7ZuXr1yjdfP/TCLw8ODk/Obt7aBwBBSBKzMdwAsCIsQmQsENV1jUjb2zsaGRFQIhOjgYZokKWZAtFltdRmAK12DwGdB0BKksyYELi1sU4kABhDiSVEZGmIyqRAmCGDSNYmed7KsixUlwzkKyQ0iHpMMMqHhmSD6z8DgLCyVCTq01imCmM4sDGJG+0ZCcwYxGOjn2IaX6ytryvRLK4GjMINaeYuMzuvTQAZ0SMyGkwzm7fybi9Vii42jbZRtWE0s8J9EpnEpqlNiHC5XIxGo+Pj47qqxNVaqhECOzpGiUOnh7oqC3Y1MGPse3DpRIT0ZQBY8Sso+jMYqhKFR9dc2NhpAwUA7JpVgsHg16KlII3WEWHwHrg+e/Xy+Tff8PhqO++0Oz2btwwa44EMeIZy7s/PJmXlvLCgOGAhIGvRuyRJWu1Ou9vO8zxJEq3MFLilrB2TOIhJMI1RHEFHiG42gIpUQgEpq8qzR0RrDQFqsnftfZCh4c2AHPbjowePDw8Oj4+OvK/ULCMCa0OdKTV/jKEsSQXQOefY13UtmgoFGApZCwJLXZbOecceYg6ZiBdhm5n96/s2s08ePzk8PFwul9ev3/jwww8fPXr0249/8/6HH1y/cdOQ2dzc6nTaWZIcHb8WkQf3H8xmy+l0/pOPfnp8eJSkyXC40e6079y5IyDFck4G5/N5WZWI9PLli7r2xpirV68NBoM8T8uymExGzLX3fjabTsZnSWLrqtQssqaizdtH4xYEzAcbAmRzxCQCRQ0lb61wtEnTXHWVCqCLJZOwqqqyrADQWjsYDIzRwi8YNyhE+xcAYLlcjscT7z0BtFqtbrerTRdWVww+uJ4Vv1wupuOJdw4QW1k+7A0SY4EDbNWIhIgINbv8wilqAJ5VNDj8yI1FvPbn4smL748w2ltHgy78gxw/3PHFN374/nf/tdX/+rYJmuHS6iEiovf+8ZMng+HwJz95L9FK0QgIMBh233//3bOzs/lifnBw9OTJ85989CMyYce2s/Sde3efPnm+LMrJfPby9cG167tkSAC8VsgV4FiXlVnq2qU2zbJc8WeAkFgg4DWkaozV6BKI3hoQGbIm4sRgIydDCyhFexoBRakWUjuFIaNXhsZYo55M4EaJl0hrBWnULkAoNKTXDNV7KGROYHSpvXPrKyNr2xoDih3LPEXTPRaeAAEObYyb8M/6RdZXam394j8RQDuMAQAYa0yStPPh/vWbw539pJV7D45B7QNR6RwKYKBw5KVaIotpkhLRYjE/PT2ZjMdVXYmwCDvnvHcdaaswZ6+mBgiLq+tiuayKJde1kj848D6U6KLaMahZ7VKgIHsTtYTmEdZ856AyUEgk9gNuqJkraSPGGFDbChiEPbv5eHz+6iCZOxLqb2xs7+93h4PUZiZF5+HkZDQ6n4K4sipUsYp2ejCUtbNrN693u60Gk9F7EUFmDe+GwEJT3jr682HPUCzthERap4lDdhOo3aWpIa6uxWu5NG1/G8o7MctsPi+r5XQ+qVxNRCIeCUxDMVetIJDYUB2s9rVjByJECBLexsKudlVVsTAZSrNU+zgDIBFp5+Ptne00TSfnk8PDw/v379++fefevXsvX7788osv57P5zRs3jTWEOB6PFovF/v5+UVbXrl0zZKbT6WQ8zvP86OiY2W9vbY9GI23ZZExyfn62XBZ53gLBLEu9r5ldnreHw761dHZ2VlXVycnJ+fl5ktj+oL+7u5uQMcQIHjgGci8IogbKldCMGkFLZxGSbqy1ExPgKVGDGEAAyFArbyuvcs2ODccLEYqi0AKieZa1221FU6RZWFCfVBT3mYzHxXIBIsYm/X6v1W4FvFgaFCca3ABVWY9Go6osgSXVVKU8F4zbId7ImuqLYS4B0IOkdM5wPkLKETZ6OBZxjQEeCDjPhXj65SSkiwPXTJLmRqTZ23+z428KfP5uj/PytwSCQUhyg+9CJXH1/D9grMDKv5bp8sZsRINvddiDoVYUxTfffLO7u3312m7zojF4587Vp0+uPHj4YFkVXz94dOPmtY2NLsY+ldf39zY3hi9fHXrnjw5OF4va2qQo3GS8nE7nENrsCIh4z1VVtfKWMeZCSj2gZ1JevXI2RDgxxhj03kcIE/ni42MonhjkpJbEh2gKijBZNsYk1hhD1lKSGNQkT2tDooiea2YkhBA2jq4sAAFpJgWiCPhQLC/AEKHsmxAKUCgzqydIxTCHOtMRA1bAKKBngrGnHmhguOkG/C0LvIbThtRKNDbNu/2Nwdbw+u07WbvrvHestcMCMs3CwD5yj7UidGqtQYK6rhaL5fj8dD6bVlXtva/rqqpq75z3ru0rT4EGRELAQh649uI8sAQcGIHJi8Y9CQCRA6c8PCyIZoM08D4GwYgARGjIGEPWMDgO727ygDHkjemmDLwetZIQBYiRmKEoi+V4wtACxtGinJ6NBrvbV25eH+71y9q9fP5qPDpPU+udR0BgZC/WGDImzbKt7WGrpQ4NMYuiC+o8htMbqp5AeIEARWIKMCjpVq05Y4xGajW0gLGLCAC4umKOKalxTCbL1wdHtfcmJQZw7NLQVWFV0B+BhKGu66osPYghAKFaAz+CmiFNZADA164sSu99kiSdTueCCFY1gbCxsbG7vZum6aeffvr111/u7u5eu3a91+s9fvz45Pj49u1b/X6vLIvzszMAaLc7+/v7d+7eFRZEcuxfvHoJnuuqds6lSb5cFMLCDOfn473dnAxUdVHVhfMVSwoASZJOJlPvfa83QKSiWBweHkynk1u3bmWZFXaGSIgk9NSKVZR1QjFWslD2ljLa4g4CEaCGe6ABD9GkfkQxxmiZoQZs0cPVyLn5fOE9E1G310vTdBVHbgRx5CoUVTmbzdgzoWnl7W6nb41VLG4lLuN9sveL+XKx0NqWptvpdDodQ4mSXeP5Da1E4gMEWS4r6EuichdQHCocitVFGmV8cY1XP38/Ihqiwt+aefwPZsRZ+nvt67/VTBmNRl988eVg2GuAaETodLIfvXf39eHryXRydHz89Pmr/uBdivWPe53W1Sv7rw+OmOHg8OzLL553e/2qqnS3U5QbIKApA9R0qg8SB5QUBBJK+mqFxVU5+lUJyguzKlHHxX3FwiyCzOy90zasCWBibCvL81Y7y1p5nmulW2PMyjhdDcaGOUWBJR7r7sW5ajZ5ePp1s5wEjYLepNV+ZY1yEuz70OZvzSlcC/yHJWlOnDQR4fDYAV3HJEk3Nja7vc5g2OtvbHpE77l2tbBXt16AAUKFRAK0aZIkaZbmSDCdT8/Hp5PxqCorYF+UZeldWVa18ogQhAwDVHUtLMjag8qL8+AFAelSVoiKIWNWjuv608SUiihJBCAEL621YIh8sO1BY8AQMBGvDxz6qwlQEK8gAsJAQFXlpifnLN2UEkm81Dypj6WS8WRrCf71y5ez2SRJrJLOld2sWlwgRLNVzkMTTBNEMSIUgfeAwstKNkb5G1QbJsYSITJBIObhym/WbF/nw1YHBIDa85NnL18dvLZkERFZcK1PRawaE9k2zHVVeWE0JAGcCXiNFqwREedcXdciYq1Nk/TCjg4em5ZztdeuXyvL8tmzZ6enp5PJ9PbtOx9+8ME39+//5je/vXnzerfb7XS7Z2fn09n82bPnve6g1+s7Vy+LJSJOplMiun379mw6e/jg8d7e3t7u/mQ8McYkqT09PamqKs/zbqfLwsYYEbA26ff73W739Oz4fHRWV9XR0dFkMrHGbmxuIyIBMTR8PIjlbxAQlRZIa2GgRo9FKEo9OtJEhyhMCDSa0AhfjTABAIJzvFgsJaTz9hS3gEs+JICW5p7P52VZIqK1ttvt5nm+fs3gZ4Vuh1JW5Xgy0uIAeZZ1u93EWpVMEP5DCBTer3Z644yGv0IHNta0NWkc4VWle7UWBLTlxPd4u/9Qx/c41gG+f4us/ns7gke3lmDtvX/x4sWjR7s//vF7Zq349rVre3fv3v3sd58vi+L+w8e3bl0fDtpqElpD167s/+53XxeFG00X3zx8eufuXa3ZjwAoDMyAwMzO1dpbTL2kINpWUKPmCmpxIhbApsqL5gZIk5cb4KB4OEUQwLM62S62HlGuZ2/QHwwGG61ur9ftDzc3N4YbZVVSLKr/ljlZm50LcaQ33i2opOcglgX0gBFiYCuF3N14XUEAFvYcThOuBNAaC3r1ZbL2lZGYoewvIGM6g4FNoN1r2zwX9lxXLA5CS2BgEc/svEOALEmzrJXY1Ht3fnJ+cnZUlAtXV96z876oytI759kBmCztdDqtXk+QXEhX8gDC7L1zVVWydyHiBho5pcaOQSJBrRS1MjvWSOGs6LLOlIYVkEwtTt1aAAks6GCWEAEih7RcbZ2IAIwCKESC4mVyPjpemjYlg82tHIygn51NTmez02L2+uB1WRauJu999DWYjEEA9kHpr0fmVFaKiGfvkbUgSNycAQ+EUAYOEMMEkzEiYJDEc4MP6Owzs/e+Zi/QlFmD5aI8PDipa2dTY4CYOdK01Ocj5sCcFmTPrqyKuqoRkaz1zoW0dYWD1FXybMgQ2TxrZXkL8c1NLdrYMEvT2zdvbG9tLhfLTz759LNPfvv++++//6N3n794+uTxw8Ggf+PGzes3rn3xxZevXjwfj0fvvPPu9u6uc7VFLMuiWKbz2ezZ0ydHhwd5nvS6rZ3tzaKsFqNpURRpms7n80M5NMa28s6tW7em02lZlqcnJ2enp9sbW7u7Ow8fPnxyfm7RtPNO2uoQAazxdBs4VwJZOtZANwSoVEedw2AAhap8Il4C6ELGGDSETe0KiHaPIEJVVVqaKkuzTrvTrMn6sdd7qOtaOygYQ1mWdns9Y40iIyC4ltSHAOKcm81ny7IARGtMp9/PO23tAxyD1wSAQtG7ViEX98TqVmXl/mLYb+HX68InXOKy2fCP449yxDgLBfAs6uCyrL7+6pvdnZ39/e3mzXme/ujdey9fvzo6PDo6Onnx/GDQvwOh9ins7m4PBoOiPGXm+XxRLIsszebz2cHha881IIv42Ww2m82Hwx4RRq+CEZGaNr4IpPtSKzI0m/IS8T4ANkAq/UGAUcFN8aD9G9I07fV63W4vyzJjLSIZIEsI7IW9r2rwsQMArPpev22SLoBTzVxBPDjBIn7zYyIcqz/h6p164jheOp4naVzHALW+dbFkzcE0ic2spQSSdsvmqasqFoldQVFiXU8CY43JsjxJkqqszk9PT06Pi2rJ7LzzRVlVniv2HgUT22m3B8PhsD9o2xQZFEsAAETwzi2Xy+VyURaFU0wXAIHQaHEiDEtBGNozRINdIum7sbVElJTs15QzIiAhWBaJ7gVqIDmUVgFRRDIsEwIAsHDlebKcLSuW0m3WPhmKQygcHh4dLBYzL54BhB1iCIyr4+VKpzNujFUkRH139ZO998IeEJCYwQOpZ8K6JwlElHYlzMyWtIkGeOfYe4gtIfURnHOurhkgtt6G2XS5XCw10K1tnGWlK9BaAgIGBgAGXlZL7xYatrTWCoigFxQRtiaNcD4ihFz6dt4iJK0fDrAWMBMBZgQhg4NBr9/vdh7kL189/+Wv/nJ/f//q1StXruzr6ray7OrVK3mWt7v9LMtEGEHyLL2yu1PX7qsvPpvPZqnF08ODXrs17PUeHD2czOdbW1vG0JMnT7rdzs7ObppkeZ4T0YsXz09Pjlt5a3dntygW8/mMiJLECnNdlEBkktRYu4pwrsBnigV0kMhA6J3CTehXXWIMOYEqC1C17/qBgaipRLCqKmWx5WmaJqnA5SQdtV1EuCgWRbEQ8ES21++2Om0JrTmDuSYghAFUXiznk+lY+1W3Ou1ur4uBAcAxErkWgND8Ymk8elmrr7cm6dYC3pelwNt++Y/jj3RIlP8al5DVppazs9GXX94fDPqtViiVigg7O8Nbt26enJwVRfnw8bM7t691OhkAIEG/19rb2Tk6OkYwVVEUiyU7//DBwydPHnbaqUHxzIvFwnuvuRUS/AuCpoNmU1lJAenIUSI0sdT+hRMjIAoiC2NEblBAlDsMALPZdD5fEBqbJMbYNM3zLGu1Wq1Wy1AyGY1A1KZt9KcwxlpN4RfCoWoDr52O4LgryAgR1cRG0QbQFfTDICzMzUsKAwQ4M2jk6AOvubtvWy2RcMNK82aLhIBkrbEWq0q0lw83AR601ibWWmsQaTqZnJ+dT87Pa1eKd3VVlJVb1nXJAtYm7Xxje6s3HOTttjWGGMk1WE7IwvTeu6r2TrvTqug3gKJUJDJGy44ZNIYMYKziFW6cVaaE8hHCwKy3K1ENo4BVewWQRAL7RlhAQq/EyIIJudbMUpMURtjCeDmbv1jQ+UnnxtW6n89mY+9KCR4LCwTA1hoDiLV3zgsg5GlqbRKeUwtk+dDPAggk1G0TFh9qIUAow7seViAMbTNVKWvbD2b2zK6uNGMdYrCiKEoRQEKWFUFdT6KIpGmiPCwRYfZVVXqvHwmtfiTCRUSx9AvEkIZIlqbGGHZOwAOywQCjR+QcRUShfzLkXD2dTk5PT54+fbK3tzcYDEajkc5vlmUbGxtZms4mU67r/f394Z3bn37ySblY5GnqvZ/PZ8vFvN/rzmcza0xijaur2XRsDZVFeXBwYIzd3Nza3tr0dQksp6fHh4evl8vF/t6V4XB4dnZ6ejr2Itdu3tjd3Wdcqagm2KNaFhFEK6MBogho2CDg/wIAGm3Ss2XsqkfTm9FQHwvA5nnL2mgRXQimCBGy8Hg8LssSANIsHQw2EmuD9SwIyBi6kwoCVnU1m041xmGzpNvvJUmiGhoQKJIBZJViIUGqhKOlJdK1FXcwr9fNbFnRAXX7NHDJd0qKtUe6xBqSFYsmTPLv5Ul/DwL8Ay7w/VHq33NcvODl6uKX/3nBh7l4nRCix0vvvxTSudQwmL9z/r/tYQVicaRYSS++HyDahc7VT5483t3d/uCDd4zRIoyQJObu3VsPHjw+PxsfHB4dHJzeu3dVr5Mmdn9v94svvmYB591kMkaER4/ujyfn7dauIHjvyJrNrc0kSWO2fcim5CacgsHPW00dkrHGhI2HpPk6MUQnwl78qiqkemThCJNSJjyKq2oiLko/n8+tMWmSWZssFguGGO0DREAOGzIan6J8W2HvOaQecTOrkavLDEDiRLTEkugdqv5d+bURNgOA2rnlciksqIX4VZas4sEX1m5F1YvQlCJYIsDee2AyNuRicChZIcyEaAwmSZJkCSFUVTmbzieT8Ww649o7Vy+LebFc1p4rQSbTamWbuzudwQATW2lLPUrAgLE2OumoLfbYe1eX7EMLOxCO6U4i3lPDlbqwZ0VtGJTmOCAACDN7F8PJAYS1K8AhQsMheqy/pgaBAwF0wFOp0jTLEqxZqKyK8WzQSVK7VRQFs9eKwAJeg0PMbJNECfqq0a0hox2gwoOyl9qDZ/ACRkmo7D2woKiMVKLZGlsqxsO1p68AIhlE8t57h3Xta+e0piIIVJWMpwvQulBqcOkkhh4THFGCMEmqM9nVqjyMQRHWyKjeORlwXuU3CECSJISKBTWuVWzgu/IdEQC893VdK0g+mUyMMUVRxKeQVy9fmm++SdJcCYivX+8O+/3Dw0NV/M55Y6l21XQ2cb4GdsfHR2mabm1uDIYb0+lkNl3krRaz39wcbgy7jx8/evz44XK5JLL9Xmc6Hb948XoxLwcbGzfptiGCaNI0swoARAbAh0wErXMTqPXNvmriZxo/hsQmpCEQjsdOzVxBRCzL0ntvyLRaLSJiFa8XByJq3wsAIKJOp9vK2wRGlAZIwfeNJ1OWy/liuXDekaF2u5XnLYzZ/EKi23XNjg1G9pr7qweWI9NBd4Q0eyAKu+b9K7ANV29YjXUYM/zzH7rPvP7If4h9EKyfv/PpWgN4LwwiWi6X9+8/uH79ynDYjQgsbG8Nb926OR7/brlYPn368tat/cRqvxPY29vu9/uj0cz76uz8kNlNp2fCDpBFpKpKY5DQBju+sQYRMNQ3VTwcQFCLYqrQdM4zOwERRlFznwUj0Zb1sIaKsUYb+TTKjEVAxBiSUJ6BwFhMLCN4ZAYPwhR2qW57C9GdhagwJFAgIoNfRJoTfnHSUKssUCRzBivhwvRqzeDwfomAs+Y+ybr3DdCA4zEbITCwPQCL996BoAcJLDXVDqHjU5raNEvJ4GI2PT4+nEymZV17733tysViqVX3ASFJ03ZrsL3V2Rh4wKqupITUmKRFOUaPFlAESBDYgXjvnWcv4iHmcYGIOM9GtIa2AHp18FWBxnjBxXlQD5a1LFWYSBHbMFoBWLvGqgJGFFgVzUc1VWuQpa/m5GsyTsQSzqqqnI86U1P5CiAkQwctJ+KZkdDahEgdayCtPLWCQRjRC2hrLRNPQjSDmBmRmY0JYGU0JFFdZ+9FgIxJyBj9Wu/ZOTEIAjBbuMOD8enJCNFYm/i64rByofSglk3GJrFdAAGTxKZJUonUde1EQLwQGptoY6KwxVGJ9UDWXJzo1dnmmLsW9rRIXdda3FEZiQCQJAkilmXhPRMCe79cLAzhw/F5URZJknRanXa3nSSptXY0Gs1nM++cgHjvCGE+ny+XpddAjbPT+UzQjc+Ozs9P6rpi9og0Ho9OT09Hk/ne7tX33n9/d3dn3U2IOlgTARt2dNMhJSAgMXAUNhZpbhmZ1KZEtIbZiGgHJNQmpiUzt1oxAUmTclb4vyhwtlwstJBkmqb9/tCYtVbBKBTSowBFSldNp9O6rgCklef9ft9aKxJ7lhECQdN8MG7DcJybhVCjGWK9UgnQWGOsNlzuHzouHbM3PvsPUB//wX75t47/JJMVPDwRADg5OXn8+NlPf/q+MSH6lqTm7t2bjx89mU5mr16+Gp+/t73T16OwudHb3tqajOfMfj6fVuUC0BkrgCLMVVVaa1FAFbCwoKBWHBJkAUEyRm3dkP8m6mcKO+8cACk3RSNEEAp4aPtao518CNCAgZAZGFRkAG4RjUmMMWRMkmZkEKagxRYQQFgvFVJC10zI+Ne6gyrivcd1KKP5KeBMEJrsKSlWX2leB62K2Pxy7S0SxI5End9cP0wHoHqO3rGIePbkFJniCC6F/itJakV4MZsevn41noxq52rvy6ouirKuSu12l6RpezDIer202y7YV7WrnTNoTJqJF69kpMb5jg5ZqEMUalGKgHgWpFC4Q+E3Bhb1Vy7jQ9CUwwrOC3P0QwQiC7pBINVpDkRQQ4SErDUmABDAMZfMS+SKpUJE4IXUi2K6nBknsazV2gppMawkSVBAQoMHCjs7DhYODeqbFRdU80arycS/VyXW1IkVRjUmvOeIoCgVS0SgrPjB/ZfPn7+sqhoAibBm9t5rXe7gqgI0NV9W20k4sVZrd7BXpekJCUFCy+vAIUMAiHz9i+c57trGV5BQQE4aKnXE9gUAkiRptVrtVodsWhRFt9M+Pz89Oj5yzuVp1uq0e71elmUQK2CQMXmeC/P4/Hw2X3T7g83tbfb11uZwPp8+uP9gdH6W57kxZj5fPHz0MEmS4XD73r07V67sGUM+tDBT6kdovRxD2CHdWlUoCEBjDF0Qjoghd1E1t8EAJ6x8I2au65oQ8yzPsgxjvFci9qGqXpink6nmT3c6nXarpfOzHqKGGLBbLBeLxYKdTxPb7/bbeWzsofmN2m00eK1BkMiqvctqrPA2AIh8eIALmjPqGIHfRxk3xsraL/4BKuC/vRFtvDUd/zcNoX/XtyPWdf3o0aNbt69tbgwgqqGd3Y1r165+Nb0/mUxfvjza3O7roc8zO9wYaIs7YF9WJSkGI76qqqqqCMlaCyCudsBCaA2F8u1AbBG14npdV865unZEqi8TAQFBgyQNSRZFkDRCQmQIDRmyZBOTEFFZlXVViwgikVH9nKZJlmbZYKN//fr1JLHz2axYLNcykZS4SxC6I33LVg9mawhvihJwMBAcMZw1Du0FMFoIOp/6F4umOAWIKggThPC7YCMzxtZtzXIAoBaO8oyxKnsARtULRlBhmOeZgEzHk+Ojw/FoVJTL2tdFVS+KsnIeCW2WtrSv+GBAWVailMulq2sJUV0BEfDiqprZaZkAItLWf8FL0bxpAAZGDAQCEUFaOfEYdElQ3eruRXUezI4YHQ+fCd5GU/QKVFR5FWvWoF1Tiw1oiw7EMwu7BbgUPIgTFKEof1YGXdBwwsxattmaWH45YCSemb1nL4YAwaCg98pLZonm3BqnBogCh0stFQxRTE0YBmaPALWTZ8+Pn794WZQFoUFCULZzXbP30SWFRhGun0AEAfEI3hIwknfIXnPsJIRCsLEFMbFJ9BI1H5wClgCMwngxet2sEBFWVaXecIBaicbjKaIBpMlk4n2dt1rOewCpnVsWxXK5bFhS2kGlLMvZbMoCnU4bQZyry2I5OT9HxMVizszaemixWIpQt9NutfLJeNTp9Y21QHihnlyzRWKGW2OTXXoAfWxjjLLaQ2eM6PWtlgmDAkakVquVJIk0SEC8HAkYoLIqFIrXJr7WJm9KXE2eqLybTiaaqtRqtXvdnjGGOdhDvKJsv6lMV7cv0gQltW/xRec1tHhaPezflqP3j+Pv0QgRVSUqi8DZ2fmjR0/6f/qRNjZFgDxLb92++eTxs2pZvHp18P4Ht7PcAoIxuLO9RYZc7UECNyJJDDOXRUFEYAwixcCGCHgWcc7X7JgdIrJzy2XR2P/tdlsDxkGcADW2aFBiAur4EhGhTYxNrUVLzlc1AAASgkE0SNYYayjPs43h1vbWLhlEtAhEGocOSoLemAlq9HPA+fTH+MsgwTDqmnCugxLSIpLNp0ABdC/AQpF5Bo20WGuJ3VBz1m9nJU3WjiSHEBIiEJHBlLIsFeDZbHJ2djKbTouymM+ny7KsXO0Exdg0y7Jup9XtpHkLksQRlnVdlpW6HYw6284xOO8kog4Iq+bK0pRQCa6+8JrLoQhdFJ+XHiLqr5XhElJwNfYe8oBD/gmSsLjaswgIWpMkNm2QOi0BKJ4RgAE8AqPUBgB9Kg4QQ5YpCPogZvW7rbGrQFrUVoBea0AHe8azscrAiG4uBARRXSIvosrNGgOE4KPAFUjIEGgSMHPNwjKfFU8ePy+qCoxhNIGX48VXtXcekYQR0aCyENZmi4jAs1bzCDArUS3gPXPws7EJkK/XHF7bw81muhAta7fba74yeu+YPQb+hT6yIbJIpqpKa6k/GJC1aEjpyNPJ1JeVtYn2xZrP52qtG5uUVaW9EZ+/eN5ptd5//wMEefbsmXOu3W53Ou0kSYzF8ejs5PTk5p27vX5/MV+Ssa12lwhJq5ZH6xUiay+q1Qj6Y4BXyJAx0Z4LcY8AxITDgqj3VpYlEqZpaqxZ5T5BSL1QcvJivqiqigymWZLnHSQrazBOyFdAEeD5fDabz0XE2qTd7mRZHoEyAc1HVpImCEDo2qQvwgqB8I1f28QFYD37CEXQN9/eoHHfMd7cAN893gRvf98r/L7j94WL/8bv57sv2PBX1xHP73r734JRFHYMCKIBlqqqHj96duvW7e3tAQIgggG4sr+9tbX58vnLw8PD8Wi+sz/Q+9jZ2ei0O5PxCAKRAomoKAptKIJo9OmtNewBGGpXn49GtXfMrtNuE1JVVcPhMMsy7z0ipqna9FLXXksVBP2tCagQ1AKRMWSttdoILravU7oimhAlMtZYYy2Rda72zmPoAHFhsjXRM/p5TWHEyJiQcBeN2gz5CNgs2WrtGlSpWS0UsETWmJikE5SVvryGZCNiqAgi60TGQEZR/xOlIdywGMCEDBG4up6cT8ej8/FoNJ9Pp7PxfD6tXC1EZJO0lXU3hp1Bn6z1DDUKIGpvJYo6kQibnnyg7WQQjSFrLAA479hz8FKENH4fMPMgQGL5xgbZWzt3IR9WhEEooOpAWmeKxUZTJvSwExHnnGMGAC1o1kDxEL3GRr8wCBj0yIyeIPq/EqiMXtg5j0BZnhuiuq6VOU+BCwcN2N6cAkIwBpPEkjHEpDPS7AFQzR2eTe+KWbxNTGINARok75x3brko1VUKOXcCRATCZeECH1aAkCRQGOKzhfOtPQ29RlnQEBBq1Diwdmuvpi4L2yR5m3xp7L8QNUDE5MI7Vxq6eXwRL4wKfWvlWJuleatlrc3zVl2Wx4eHs+mUBcRpkWpBRM98fHgACOx5Z3f32v6uq+tWq7WxsXF8fLxYLLrdbpqmVVWOxufLotrZ22PvDw4PW53OlWvX0zQ1RN47JAjKF0lbE8qaB4gQDThANBhhYd1uoOEniodKt2NVVa52hJS2stASwYdLQSh+RyCyLApmBoJ2q5W3WrGu1uX5rOrq/Pxc+zRkWdbtdBFJgCHW18AwqRiK9TWflJUYkWg/Nf9Y176qgFdr88PG371C/cfxNzgQVgUDNB9EcxzPz8+fPnm+sdHTeBki9Lr5zVvXX79+NZlOjo5Pt3YHYEAEsizN0hRW2wBdVc3GkzRNszRT0crCRGDIsJf5YuG86/U6ztUbw42yLLWLFzM754wxw+EwSbLpdDoZT71nDluUY4NYxcvIUCwtSSZ2HWh8ntDaCBFDTjxiqLcvFx4dINapiZ+IpChZN1JFYrnH8MG1/6wN1h6BF/f/Wh7jJQamBOV/KbIsoQpi1GgqoQkDYG6MMYhG34Ys1bKYTafTyWSxmM0Xs8lkvFjOnK8ZhAxlrVZ3OOgON2wrZYG6rNTp8yzR5FBI0RpEFGZtm6ihSAEWRkADJt6mRIGjUw0i4pkNkGpWjYWtmFO4mrMLNo862YCIEFdxzepnNQbUgSCN06MwAqEIOO8EVhCuuiuh3J/EdRAGQGasXa0aqK6qly8PjLF17Rx7z4yhZIrRi0cPSRCArDaEV1NPMPo2+rQeVllRGiAgQmMCpd97XxbVfL4E4RA1B0ZQ1IS8OJW0ygXXuscIMQQa8HpsiHyI2MQ+Qi01NU5drdXbtJl2NHkAAp0NAURpi0FXXWy7xCxuVdwc4gYlNZCIUo24tPNWrzc0Nul0u+1We2O4ff+br0bnp9FbDfXn6qoAgLqugetvkLMsq6qq1+t5709PT8fjsQoR9iJIk/G5F5jNZvP5fGtrx/QNIXEAXPTAhppijY0aFVqzS9byjuK20Q4Zq2cBdM4JiLU2TzNC8qLuvoSVAAQA77ksKjWP2u22NWZ9QvT7dcaWy4Ui1caYbrfXanX0GMS3SUz8Dx9pWA4iIsiRS99kHoXLQrNsf2uson8cf1QjnHqNBN++fWNra6AvGIPXru13Op3JaPz68Pjd929bIvZQld47B+o3ooBwsSwQIUtS7YXAiEAoTIgCDGVd5a0sSazGuqq6StO0dnXtXFXXebudtzppklhjEcx4MvF1LcKBO6gyHgTAG6M1L40xDKBpuwDrRRMxWqTqvoaOfmt4gwTti/hGiRyAACMBAAB77139tqmKX6WGAFkyFjGUqWhug9k752BVEkpVX/x/sOMv30D4PSGyUadJ9UGaJIjiXV0vFrPz8/FoVCzmZbFcFIvpYjpbzCpXgzZizFvtXq/b6ydZoilQnoEISFYHHgG0aLclY9Brdz0I6oaVq66oMWmIewWrAUQIjUNCN0QVcQGhD+Aaaq3siGgrtgtgmzcG4yoSQlXrGUPr6IKIxL4W0TG9YPFExzSuMHvP7CeTWbvTGZ+fvwR8+OjhdDKRUGPcaOxWJOSxoNL7mjaIgAGFhACDOBALgSckIMxegBFtYEEDeOeXi+VyWchaYRJoVOzK21YtedleW99RAuruadaUBh4CobGqaxEmxDRNYvlW9F7LCZPAWu5nlOzrdC1EiAHgQMuKq0CErnaOSlqWhWdhMe1Op93ptPJW50qLEB48+Prk9DhsDZFQUFGY2U8m4/l83uv10jS11m5vb29sbJydnQFAUZbz+VGWt46PjvuDgYhsbgzbnY5oMU6tzhMN6jdL1gUFDY2tFn7Qdh2N9dGcRYTwgGmSJInVqYYmlwyCui6KoigLBEisbbVagFq16vKKeOem05kCdK1Wq9fTjr/S5E8Gv1sAAChwtVfKNSSrra4ngekvIrr/YuOzN636C/b5P46//RHBrW99/W/vq9VAbITz6enp48dPB4OfWBuax2xv9vf3985H4+cvX794eTocDqbT4vXB8WK5VPCPiLSGxWAwyJIMAVlAROsRogeo6qp2zhgqipII5/MFM5dltVwWapQ7L8WiEhZAcM4rPpQmBglFBAkJbZBO2uemyW8SlLDJMRikREDU+JghE/qNHR6PZPNPXiuXEyR5KMn+RnjzLZciBEIhElrFjzVddP0ABvdaOMjl5gprl8KgqhBAhBmREmsToiwzIL4uy2I5Oz89Oj06dlXhfb2symVVLKvSCWetPO+08nY7zTKbJKjkNQEWb9CSztdadKrhM8fOcVEIYaCn4YW7W90hrKnydZuj+VGiye8jFIdCsYMDAKJdv1bYhaAhBQmwpAgAhUCcyIUJ08WOpYCxkWwgFBwLRsRiucxsMp/OfO2OD498Xce6mU0t6xU9WEQotHpq5KsuPAGSVrkgIibP7J2rPPuEDJGqanHezRaL+XzGWpBSV5GBvZeAjYfdCKygd9AlEuO1SCiojHpCY8gYYy1oZnrIhQPnnB6SJElXHrAGlKWJjgYeRTNlzSQrTLQKEghD5LiDiQqi4pPT05OzsbV2f38/+yDd3dm5eu1akpovvvzd8fFRXdcYCpVxVMPgnJtMJtYaVVRbW5udTufw+HgymdS11CwvXr0cLstbt2/funM3STOJNbCUTKXTs+4mNrtuZaVF5Ry3zapUpF4DATVpD0DIEBoTrEI1ezAeVJGiLJ1zgpCkaZbnb55v/chyuZzPZ7H7RTfPWnERg8QJhzWE6MMPDfAMsSCtmpAgqzMDABfLrP/j+CMfkUQC8Jak8+8dKghUfHrvnz9/cffu7c3Nvr6aZcnNG9ce3n9yejL6zcdf3r5zixkmk2ldl8xsjJITHbNPktTaBATFexRGQYPGe++95LHHl1r2CRkRIdIyijAZT89ORg2YaQwlSeItkUBsk6taKWQMWms7nY61yXg8rqoa1jRCcBy0ZhGQMAoTiHYH1ys0YDKu4K31uQwo6LrTEm8ggLEBxFXfDZtq8rEyo77dOeecU2cz8qfl4p+1b4yj6aKqh9gYoiTJDIqvJuMponduWSwm09FJXS0rVxd1Xfi6BLCt3OZZkmeUJIIGyah/59h571LbahxUJcWxMLCEXqu8Kk9GZKyx625sUBuhwmNonb4e39J5uiBSApgYHVbRqvShCxEi2NU7G6NDIkgQCjKsY/9BvAUvQ0DWSoHED66m1LlaQ7PMPDofVXU1Ho+FZZXmomU31nR7MEAgtBGMoHpg92vUwCB5TfeMJp2J7abLsnz+/Hl30PXgGSk4RgLOOx+6REVtAeHm15dfRJDIx52HhpRxpHnMjULVMh3CErVvuHf15uPVtKMJyVp4Jr4EAMGZbvBdja8qE1+T99M0z9KWMWY2nvzu88/de+/tX9m7cuUKs0/T9PDwcLFYUEB7EIBEtEyVL0tZLossS1utVpqm21tbRPbkZFSWdVX7zc3tm7fu2DxvumUCCKkRo9xovV4wf6HRqsFTDOXiMW6ZuEMRZNUiU7x3iqColwyBrL7aSSxSusoLC0CrtY4/X5CbzDybLeraAWCW5d1Ol8isndR1qaEmOjeocrPnAYBXNnhzXlY4Rfyv9t4Q+EGye00wfde4bFT8gCv/UY83JuTiLxDf+iaQ75/Jv+GB2ITNVnIAcbW5Tk5Onj9/GSs5AxHs7e20W+2z8fR8dL6/vAKA89msKApESSx5z0VRIKAhg2gAxBB5DqIGBNI0NYl1rlZ/Qxv7MTAhWINpknc7fcQV94WCg8KAICpzZC3wgpjn2Y0bNzc3Nz///PPFYhEkKKDWrGxEKggAqBJfe0wI0aWV3wkQgo5ImumkjI8IfAoJAQhrvovwyq9V+UCEFPiZQTBEvSABzL1Iul6TvRgFCgSxQjFLCoLrT2QQ6+ViPjt3rmq3U2TPdVWVi+ViVtbVvK4K8dRu5Xli0wSQRJ35JobILDGVVL1v9TWbfWeIrA1iEGJySmKttVr+OEoTAWzKziMAoKbRqjBvtOJqXtb2m4ZWPUggml3ygIlASzmLhNgqIIgoeYYgOPK8bqisKZUg9iAS3cQHjWUMonBdubPz8/PTM1dVGPe+NGCghISXsGNAEZWAW677jqCVOggDTiqCALHfCDjnHj16tHdtP2+39QMoIMx1XYl4AEYyRiRok0a7AEAsnaFrjkQ2SYhMIKdRqIKuGleNP5GggHkVRwy4MIg2dmQ0hkQQIU2bArOrQUQARjc8IBljjaE0ybNW7pxfLJZlVVpjh8PB/v6V09MTz/WVK3tXrlwdDDZevnz19VdfLhYzQlGFF2rDMHvvnXMvX75i5q2tnSRNt7e3ECwD/uLnf/HjH//U5tliWQD5NANjLZIkaUKFEREypLQHAYjePMcTEgCPUMUGyRqLodFQCH0jIAF6Ae8FkdI0TZJE96lOOgooT6qq6/l8ziLGmFbextAe9ILyE4C6rpfLhXdibdJpd1utNmgod71ITDi6zQFpSrTHTRXd4igKGphaoh0XbjHWSv/eEZmawfp4C3Ie33jxahcU/9/HcQnjakbjjnz3xy9Nw5uWTEx+C7McxEYkHTR9Rf+aA1cCVVDeFtpcv1UERAh9elb2VPOky6J4/PjJ3bu3er22fmQwaG9tb55PpsWymM2m1tqjg8P5ZDIY9IShKsqqLLu9HhAygjIZVc54CRV4SMggeW2lCgBAJrizoORlEVCqL4TaWYoner8y8wNYSkRplvUG/d6g70FY66WHV0woVqzNVbSWsshaqYt4ojE0mcf4GzWbLVlCE7rMIbKPJQ3iEQ1ln+IbQFv3EDYZoQDNEuuuj+ofL5cvhSg3Av0q7pAgaClUxKvLYnpyPJuepanNk65BBPHCzrOrfV1zVSEmBGgIQJDZeIbaSeXRWK0GgISEUeoDR5HVhFPJaMe8uD0IKbWptSkTailnlV4EREIozD70QdceFCLIIE1/+1jrB5rHoYDskosUJbvai/pfiAqRxRgTS/g2WAHGzhgYNL0EQa08PAk6EQCEAJk9iNf2ryLiymqxmHtfN+dY1yZGMxkDNyoG8aMpBY2g0xJdoPoX2f//2fvTJUmSJE0QY2YRUVU73PwMD48rr6quq7u2pxsLImBpQHiDxU+A8IAA4QEWD7D7ZxcDzOx0VXdlHXlFZBwefru5mamqiDDvDxYRVfOIyMo6+kQLRUZ6uJmpqcrBx8fMH6fcwORdCcQYz8/O7m6XzWRCiCIELDGEvuskxkxsnFdXAN+h0UjUTiLGkNY36cX1g9badHMCwmjI5odTt7gc5DTKlteLSI6a53tG0FwzIkPOuqqq3NGDB4+ePEGk5XL5xe++OD87E4g//dmPZ7P5l19+cXe3/PjjT/b2DqbTnUkz+/tf/v3tzaVAkMH5FgWlN5v1i2+fb9r25NHj2Wzn6OignswfPXo8nU3BGO9jHz0AOOcAzM7OjlLxiYgWamMpAs4pkhqQ0A2VSMQwH9v82Ljt7htjgAafspxzAPDBe98LsLV1owr4nWMpIuv1um03asHM53Mic+9dKU1gpDjVEsrgjyAjS0yQRHJ+JZ+MxEiZPP1/cifs38c/71CLjd4xuhCRDMUYLy4vLy+vigKuKntycvzV19+E4K+vrxDx1csXfbcBmQXvb66vWGIzaaTgXrm0D3NDgWJ8DzeQgrhAJN6HnBQiBJmkIvf5SX4zqL5DY4y1Fg0JISMwAQKxiCGljyNSBI8MasImx+QaJJg9uXoEBGgYc4GwOqBZL+Vb3c6kEMrdIkrlUvKa7k1j+iFlbr7PHno3qKmThmUOBUl86Nrl7cXZ6Wp109SuqbCuHSIjM0gUkEiYmj2JADOEAEAQPMRemARQmIGByDBAFMnp5aD54Wkd3mVVwkTHm0qzciRd7Q41sJCEhxKqnPyWwdwMsyAa45wFQImcLCzIkciCVajfplrWWmNtgiAU+ZDC6KSQAievJs1kdoMBNJQIMXAIIe84adtWGS2yKNa9FSUB0ck6TnDo2FNUz4lzDLe4Zjk5v6gBratr267reuCEGvS+Dz5wudtifW/vCMpD1QSh0TK7nHKkmBEQAkcGQcq2QmLtYob7A8cX395yWDAWEITUHgFFkMhUVb27u/v06dPDo0NmbtvNF1/8brm8efz48Wq1+uUvf3l1edPU088++8HPfvqz+XyX49CNS/WMascQwtXV5emb1zc3N2SM9/53X3xxenpqDDlnLCFy6Lu2aWprbV3XdV0r3WbBuIaDhGPdmuLrWH65DbQAgHLb5mS6bKxhaXEEPvSRAyC6qnJVNQ5KDV/KslottTxjOp02TZPXDbKVzSpCEpsfDj4wSCGJF0iNy1MvSsnu730HLaXWf5cavv+4/z7+ccY/wTwPXfZw+ANltxMZY9u+f/HtqxASUTMRHhzuWkfMXk/W1dVVCB6QQ/DMsZk0yrYrwiyRh2YYWI6GqsMQQgwhxhhj+r/3WgGqLVU5akuEDCzqB0WAkJyzVV0554whJOISFcolvIPiy8GtlJyDRall/tbhIJdYcDnYkORujHpyS7rvaInyRVSBjKDRpLcRkr4YjSJY8nuT3sCs/nHoTK5PJcDSrtfLq5vr84uby4t2veLQcwggrNiBdQ4JBaJwIGYSIVGNLMCs3XuYRXmcFCrYLlgEDpHjVnGKbhJWRihFB7L7pB6UMQmNRRFRLjCNjSIZMopuFo8/VW8bg6PkOMred74VHFBUQkIyeVtKFqyFSWqAjqVMYZpfSGglMGIKSgsIS9DeUTy8OScQcQIqBhWel4oIzKhUKnlR2peKiMhonwRJJpr43vd933WdXigy910Xoo/sk+NbMgNFlCmz7ANmtsYBIAgSGkfOYM4hUgYTFALxmpSPSBrjzPecxXv2EwEgA9RaMTCYkx+G8jgKCJFxTTOrXK3Kveu63/3udxcX548fPxKRX/zi705PXxtDT58+/qu/+qtHjx8BYNd1mWBLyJBRMFlgebs8PX1zeXnpe391dXl9c9O1PQJaJBTu2/XtzY2W2I7uEMcj3RkhEjEzoDZJ0v4NOVM9q+cymTACALIZlc45g2hrCiKsmwat5WHNy06E0Peb9VpErLXz+dw6C/cHjicb0r4roEqBOblo1rSJkL9foPc934fbP/z7+McYcm9d/0m+6z62Asgs3756vVpvym/39haTyYRZNpv2dnnb9xuWwMzeh6qZTCYzSdmoqct4EskIxthUggciktoNKRV/lj3K5ph8oWy0CivSGlQ9M5FxrplMZ9r9t2kaq7WU2T8j0OQX9btBQFTl52faEtTFTRu7Upj90GF+hvAy0njjJyedSihPxetQB6juZnLb3rOe9361rUkAlR0QMJnuAtH7btNGHxTlJUAD5MhaZWiKzCEo3SNqW78YVEtFAQFUsk+QRGifCPAhWQmR799Oie+WToPJ3CBF3Q2Z3LtAJBGnISKaEg6Hkt5bLA9MVc1SmLBgCDCojwPqUxiVsKPZUgWMrBRWoO2g1alVQyt1qAAAgBBCZDbWECWCFQZAQ1JQw7wlSvxSRJgjQPKIRjitOnkMGfpIFqaAQXTOAWm0l733WnkmwmhsZG1tpB2MFGFGFhSQNIPZN40xMosxFgtWDEhABgwkjQSGEBBi8MnR1FbEJYYoAvlJ1ZmykDonqibLaun+LkQcSm81a5+IrLPWVSLQ1JOnT58ul8tXr17t7+9//PHHZ28vfvWrX3Vdd/zgwdOnT6bTBlC+/uqL2+Wdq0xdVzo/HDhGBgjxbuXD6br1D44f9Rw2/cYaq1Rf0fvXl5dKdamLu16vRWQ6nRKS5PJZIqLcHHpAJtLUZUIvHMx8PVnWagfPjN7oiwTCoi1CiYzSVheQZ1BsIn3XxhAQsWma6XSa9GdOIHzvuGcLQjICGFLxO2mxproJ3y3fh8f4wEvw4Tf8+/jXO3Rxda9HkNvb27Ozq/lsppxw0+lksdhdLtsYY9dtBCKiiTF0XWvQ2soZQ1qyCAAgBCKIWkIKiAYk5vpdAUiE+8NXJ1cVAEoLAGCQyDGyTCbT/b393b293d3d/f393d3d2Xy+e3DQc3TOiUjiqBy5sCIA2ktIuDBcSQp/DojzKBMmo+LDfIiIcOSUoCiZGAABUjJMPgK5ywACaE+W5FNBauD34ej+fZs23VAyBNI9aUQbooiRGFgiq8IziEYUsEaIAoEpChIDas8LVSzIkQWEjI0+sEDmrEbQ5rtqLWVHsIg45uQqSO7cSiKaHaWON5FJPSjyfWcMhXAr1XyYYk6doQFwpICxbBEgEWRGFDTkEIyI5pASQGrpQwQiSt4VIyNzNEQppSp1cUcACMyBI1mTPNmUzbSVjGeJErzMorS/43uFAU4BAInKQQxASAAS1diB1KcIEu4oANrVK1gyEmMIAdW314cVTqJeAEqFcN4H27txmBlCJAIyJrWgEgCR3Pw57VMFN1iN3JRAAZq+ldLE3t16GkpAQSq9NLBQc2tcsqqrx48fi8iLFy/evn2LSI8fPavr+te//vV6tXr65MnRg+O//m/+Q127v/u7/9J1rSGpakfIEaCqqslkDkQC1Labtt8wh9X6rqnq2aQBkL7v27ZF5cphJsBu067vVmFvdzadiRAgOeeyBw+GHKEFFLUfU21hSazAZIZpvMoYC6lDAmYbJQU5QoiqR60x2i9BTfRU3SAgzHd3d8ysVEHGWgCFeobelCP1J2r+FxO2uOC6LRToAQBm3clFAL0bOBitTtoZ+I6evQ9f3ztH/3LGYBr+Ie///q++a+58r/dvl6QWx+zPOHl5G73nittw3X3cBQGUWIhQWLDrupcvXz17+tg5AoCmtrt7e69enTGH3EoOmIOIWGsIjaDW4KWgqLbLNVneRAAQcM5p16+tRlwJFcccHENR1RujiFhXTZqJq2pm6ft+tVo7V1tX394uV+u1isFh/pSpCLAUfUg+AAOEkwUd5aIn0gzMTICs04FohDklX4LWaCIBoRBK4nLOvrp+AaqwLFmKEll4aEEuKUtLCfxJK3owoabZgBYQvStJaVjJ01aHXkRiSJ4uMCFoApfeCKoqiYIEZI2xlhG0TR4RWUs+aANgi4QSszmBhMi6BJoAo4yfkAwINgiY2B6VFlFUgxoi2SKzQiJgUbJu1fEEoFczKVCQMFiJyDZjC9mlQSIy+fJkjZWtQ4Exxs1mreRKMTJLwAgxBoSUP4YplRRF4d8QmqrK6zMiPRhv+AJPFBWZ8eG8VcracMIXMZUwMYuxxlqb+mEp2IsILBwiUJDI2sIv7zcBYUr8n8BROIqahghEqCdl8NiISMU+IzIgGQuk1eYgkloCDfsb070jgLDqC4DELPqevknvyAsFagdMqqoqPQnGGO2JNJlMXr9+8/LVi8ODw6fPnrx+86oP/aeffLJ/sPfRRx+9fPni4uJCRHbmC2ZGpJ///K8fHD9crTeXNzeRYbF3wBwuLs4kModYV5WIuKZppk2MgUMkoMpaJuPb/qYLb0/Pp/PZx59+WkxCxf0ZKJFS5mfOcWPICfsafTBEJiEkolzvispI6cyoJJ2D/k5TAX3v71ZrBpg2zXQ6JVKXAUZAwrYfnDbPtgKAooohac0/wmMdOHz+ffwbH2VDJSNPIIqcnZ23bWftBADImL3dXUzMAamsxXuvolMjgSX/AAEAjUQmIgRkJe/LaB8zcxTeqsAEAEy1i6j7joxBQrJAwYery0tA5SawtmpcVTnnmPnm4pq045B6Crm6X4tzIRNKZL+LUAgY0WQHOIUNVf6MnB9IbpwwUG4Or2oU81FNXgxCiksVhCy/l1kLDz7s/iJiArHHX721HAXYIhGLWBFxCKHzKl1R2AgYTp5ZbrcMhoyzlQcE9hKjIWuAMNWyAhHFlA+bTAOO6lUKaJ6pydRU+WYKUKc3Ndx++hlHEeJUO5v5XdS5KAIo6Tlb9GL6eBKvKSXM2TpPnIhmTgt4770XnVnNqdE2eYJkUFOYtMIKAaCwa2YVnKOk6Uul5HwVZFZ45Mhsm6jMMrSW1H1DiNaQs4qDZl9UOEbfdUSk9TkpooLle6Hcwxi0HN8eZCMx2QaAAkTGIhhhQdWTOa6ZLyJJ5ivELsNKjUyk94+8gAlHiDF67421hKlZNwA0TfP06dPZbPbtty9fvnpxcnLy7NnTN29ef/7r9WeffHJ4eLhY7N7c3M5ms5/85GcvX768vLxcLPYePnwkSA/bFtA0szkZuLm5Xt7e9ptWmDebzdHx8fHJQxCQyD74EAKRcRN7c319+ubNj378kxRVSkSSBPmYA1DOlpasObMhKoyI1pq0IYXL02Mi0YwA4JzbUsBJ2SGA9L7z0YtIVdXOOY2sbW2JvIzKqZKgacnBsLTfNIli7OZuKe3yEwF8ly/87+Pf7tg+lltWGhEh883N7fX1zc7ORAS0iFe1r7WmqarI3vc9kgUiVH76LEOWd3feh5QtIcwiiGKso+T+qqCn7Gzptyb3Ub1JARGJwBE5yRBV7iFy6Lo++Mo5EOAoODRSH4JcWbmithoof/KDF7N6gHzfhS2YC0my+qBY8AXQkHMusMnXAZFEbUGohTOQfdzcBaDcxpAagojpqfPN3F8mRdGdNfWkMdZASr6GXPIjKVlXCTWElc8xefQiRm0SREJMeLZKcSLMfkOx4RWuhdw9Ld1BiXDf2zSZYzf7KErehZnXaHAs9d+QGC9h8IDLtSJLZBYRrUOG9EjqygkZquuamUPwkZmFwTAzj9EFTAHUJIpT27jtXK2isThZHZGFtdC77KDk5OedhFpZq4+YbDsEEI5av5TeqaoreI+4qZ1BCSBR91/2LUcGx8gj19TEjNzcd3d0lZWcUvsKI0FKVSvgyZYFnWccQERTMH6PDoZk5jBLjByQAYQZWFC0YbIgOOeOjh5UVfPixfNXr97s7e0+efL05bcvv/r6m5/+5MePHj158eLbEON8Z8c6Z6yzzkUGstQ0E2Pd7t6ucNzc3U3rpiZ7e3vje88++LYzZCwZYWlX69lsNq2rt+vN/mIxm9Sx76hypMEFTIRzCYVSUGjYUiMbNhmGKQFKMTG1dtXCQMTKubquxjOg51mEu67VlhhNM0EyUKybYbKKGpaibCmZQapMuex5QckVioNV/cflYf3bHu8KhHdf/dc+tjwtxBK5TCe4aF9EQUGk9Wbz1dcvdnf3Nht/c70+P7uIMabkJuH1ai2C03mFxqToLwIBrjabm5sbay1znEwmfd+JiHO1QHSaDYvIzIhWy09lJPS0uTuoBuAQIVWLZPZmBCEl6nO2AkQ0JEE90STAcgJWqtcddVMYRga/s94tdPjj9yBE5lhKPO67srnoYOtXox850+hD8ljSj1mdKmKtxJlZZW/p8/trB2idq5uJq8gYKwlXzxFdlJHGFBABjkBgiIwC7Dmht9gdCaEkA8g4qH8pnpX2H1DQc8t+yQ+rKHmC4wQHIHc0HYiJpzt1rM/eipWc+pSnNxks+hujXePGz49IZJBAxIjvfIwYueu8NcYZmzBXAUUnmaMIqxs6qJ/BZxeAGKIXYJ02fYNJpEjJJ5HsPibNq7n4hgQgRk2cEmtsqkXRCYvR9x1zbBqnzZyFo/JxF3VbDuG9HyhnDL4rg5iBIJMlAYtEASFURmtQppoMoCYFT/lcvYPAiEhUupy8RALImikROXRdC1AHDiwck41SdjItdhc/qH748uXL09PTruuPHhzfXF2dnV0cHj549uzjy6vL9WZjnXv0+NFid8+HGPqemYmo7zuOnkMgAHLmo4+e1VUDgL3v281md7G7szcXZiK8vbnhGBY7U4MCEgiZkFCTIBEVnUlxLkiHWERKnhZk+0Yp60q7BB2ccj7BWJOXO4N+iITgfVit7gSkrqrZbDYKAuFoO8p4NkdI8yDIyi9F3j3L98cHX///Pz1d1PAYMfq3N/IWBciLLCKirA3pHQAAkfmrL7+qq6auJn0fV+s1MyMBEvkQlqtV1UxcVWm2sYgQEbJsNu10NnPWIELTNHd30DQNAK43LSIqdCwiyp+RfcSUdqpVUGiIkCS6rgOGKALaJIcAkJAMNU3z4MGxqd3derXpW6YU4gHFBkslMWIi79PnS03PBmc3y/+80FrInJntZLvAMgV+k/6i4VwlJV68cIZMd1haTSU0dTS7OSVIta7RXJOSSbalfbKJZI11rrLWEFoCi6JFR0RAiXebhRLRRcouskiVMYjKS4CEFtGolaI7HIcv1K9K/rDWieXfFbWFg9WClBq9JcM/FVqOHjP9nDHSHJsABBDrIzMgGsuArKUp2qtVC62N8o9uGYyKUZM1ZEzsfQhhvV4jQFPXdVU7Zw2QUdHLwpGtsSGkArIc4c/To6tI5RgwiBJVZkQzu6kFW4cspgHSrkIA0uxkFb8hAjNzkBC7rpWYcrUA32Pdv98rzdI6YUSA+jel6c7zCWKIFAkR3XAD5MyJlCT/ylhbNI3kgYltM1ldGjBAgMTznKjWtRZtDN0IAjRN8/HHHxtjfvfbL19++3pvd7dtu8mkOT4+3t3bnUwms9ms7/vLy8tmtiMiWsMQgw/BA4D3fjqdiMiGNsYYMrRZr+7ulj/6wV+cnBy/fPXy9PSUmXd2Zoud+XQ+N6TsHKJNToFBDOZ4CqVSC520xGdD2agnFmZkKT0cEJMMIa20xlzZIJCDwIk/FsBamzDqtO8JUEQ4f1kq/cW0wVOV78iZGB0CTZ4oi5ytcCiK+t4uABUatP3L7TcW+T3sHbn/+ta/3+OFbL0O35Uo8EeM3+ez4jv/GBvv73z23dRx3L7MOzj+H6S9y90OIuze/L1zWre9jbGDpiAcqdp6LynW+LNREzhHNmW+CIHEu7u7y4uLg4Pjvo+ru7sYo5YnarLhZDq11kYREKhsJSx3q2W72VR1FXxf1+729kYPdQjch1A3TdM0IQTvvTAUWggVj8pkaYwha42xKMwcur6VROGnBrAxxk4m06MHR/Vk8uUXXyQbtBQAoVYwlHQq1AxfTXcREAbGVIaqtn3UitABnEzONrJATn1O14KkfBT0HqSeHsAUR8uK2PuO2SNGQADNq1GijYRj64zracsuVlZmirFxlpDKI1k8xhHsrS/ivdOZSlYFrXYcJENaIWSMMSZ7/wICRkt0yeBom6nTIgBoCAJkwgcQQEACARACMUUBZ90s5Y8UJxsIRbOyC1YqAGB771mEjMlLhcYYkIS3Zr7pvFkFENE6K2CIgot1FZkwldKGEGNcO28nk8YCCTKIhBAQwQBYfXgy465zOQWO8t0iSG4llA2GvOTpJ0JE0HxXCT7k6PywPUQkxhBjBJa7uyUIKIbAIMwxn+tkAhaWq1wBxZhrz3PhVwk1CoKotky7VcQaQ6meYAt30J2VIVABkMSKPKzTaA5Gf3TkEvzEq6KkdUTEOUFAh6YH13X91VdfLW9vP/ro6cH+wdu3byfTqdJWfPnlV2dnb+d9Z6zRrI/gPccIgOv1OgZfArF7e3sh+ufPv9nc3f3lX/5sd3f2zfPV7c3t3d2ShX/04x8xVALSTKfWUk7zQCUtwaw3y1SVVA4VFnqa1azLqfmKr1GpU4Jcg6TP1/e9DwEA6rp2lQNMcw8ABb5JaFuK7Iu2x9A3FAWcTgyk0jgByNnWkPV9xn7e0TXjtR/98p6+/CO8w+/WiPde/SfwPsdfcV9PvV/djh78nrP4PurNP2n8odMxEgQAoOxt35V6N9bBkoVDuQCCWpvAHO9W6/mOX96uLy8uQ9/bxqmrMJvNq7pObqvqa2Br3cHRUcJWc1aECHStX6/Xq7s7jonNxpBTU1skSXwQAGCJgTiyiyLiJSSkFbIvkj0TQ866WgoLFbK600pmnFib0ubPCKoeH0gOmQCP3bPixiZ4OFmno3VWT7ewlmy7tfot6eCDAIg2qEh8WVrSqfeUd5Gefr1KmorBZRQh9FGzzVm7BKV1TqnUavSnm5EMeEvGghHQIAmS1XRlSGa1wss6pcU5NERDzRQlHyyZKAJqKkEURhCjydECA6mJIgZIpDRERbCk1BmEHKGGNO+IYtXLHgGzmhYVVXmTLakBAmV9NLfVWDSumUwl4aNag9v3fQcgpkYwVpijD8hgkBxZYqBRDFjtjMwPmJgC05oKYEqj2ZaMSShi8vyVFQRR2yZyvpbqGl1CQuSok0TZ5IHBPhvZ1FLYrLROHkQdUkalNBSG1He+vBM1mA+odhlsx5DS3YAIsNJiZKdwWwQki2D4XEmMzBplSOtI+MXwYsIkQgh97w8ODx49enR69qZt2+l02jR1123CZUBD1iCw9F0HgES2a9vo/bpdo8BkOjGGLi7ODdHLVy9ul1effvqJ993l1bn3vFjMHxwfnl9d+RA++eyzyXRmDcUg2ko6wSnpL0QBJZVk0TIkA0ODxtKsIZ0gJdPTsFDOvUqgUAiRIxsy0+mskIPCuwMTxJV2zkjQ5snnoTskbsmRtAnH/tI95/U93/ePPrbzxSD3If9HH4Nn+Z0ec7EyC83yMLfvuB//EkZ2577rDfA+x7q8TgTGUIxxtV7fLpdnb8/O3r6Zz6cCpBWizXRG5JiTB8bAQNg0TVW7rtuE0EWlTwA17tkYo+mNAnBweLjY2ctojgozAlBmZcLQEztjTG6eyVBKDzCxPmddSIA0SDUiAOKkHwkkh9VGQmPsLZRrSiotYRnerv5MyHoJksTCLLTSGSr7AYvSVzBhRBGYXxxk+GDBvtcABkAU4cigTFwQEwEGWSQDQgkUyLlgCb7IyLcwg4ghAqI+RhRBwRhCjIwxKFGHPlJO1YZcLwOofeaVGxGSf8+RgZkoywqBEgJPAjuNzDFfSr90euH+ZrMAEGOsqgoV7RcRiSwSmQXBWlucG0yKpuSpYUXEYjgyxxhjRAREFwL2XX/npakqQooxUir/Tc0Kx3NMpXXTPU2bXZnUFwLIQIqKlzOTHUqhLOhFWIRi9N57gEZS5l+6f9WPhdmjbDwoj5ezc3NtXvI7VaeiaJ4iYvGaRBDRakLj+KjjSKnDwP76Xu2r06DJCVpIILmpUQLYt/O0swGb/8XahkGstYTUdd3u7u71zaXv+7VI5dztZnl9dR1imM0mJLBercBQVVUiwMLxVjgEQnr98oUhU9e1Ieza9f/3P/2nTdt2XVdXjQi8eP7i5vZ2trtou7aZTE2m2bqP5QMSKYymKXImWdNoEDmZ8IiIUB6NRnAIYlkR9gl/riaTSVlxgBzXGWHGg4FalqCo5G3L/btGQore6+/9C1Qrf86xhV2879UtB+ed94/ipQobApeuV/8CZk6PD+abkT/KSkAEQVy367Ozt+cXb1fr69lORSQxBmvddDozxkDacToiEoYuLO/uNus7RdpQ8yeEmqaq6wqQJN1ewp/zHgYApVEySGhTq2xoN2utapYctEul+US5R17arQKABEJS+BOzn6lHLB+lcfAka2AoeVhIgCbHdoailHyiCnCceZEwHfrytnLamTmDtOoAyShSPDLghEVi8vQU6UMklcFMGuJFFouGUOkPLeR4TXJ2JcNr6VHSdBAZQQWK1eVjEQ5hmG4RVs2nNBxKZ0LJwsliDkUgskRkTkioVkdLOR/60Ft038p/AjlJTEB4mzDbwpgTFQBScwyIklseDjOfzRUhGNyeITUHCCGAJSGDHLltOwJq2w7RICoEDJhZJjKsnXLD1OulbP+nJIBhLceHAQFAmI2r1F9Xa1ESZqJgclT1DSyc0IS0sQrfE24PEVG69Mhc7ucdlynjDVp9g4hG2/kBiIQQywmEbX2ZWSnMh3XwMNR91JRsjlE/Ekszsq13MiBH9ojiKusqc3l5vlwuRTh47rvO9z3HIBxQmL3XCmnmGEPHWukUtU8jtxvS2sKjo6Of/PgnV1dX/+v/+ner1UoY3r59W1X1fL4zmU6dVUYOawyn03FvIJIx2phMN5D2VEuUQFSkQHqzoe0MLP2PMfjILNY65yoYKeAPTdjWv1PzX3k3mnpPl4wv8d73f+Dt/0ZGsUPTecMxXJDGO9t/+Ow7F3vvJ/6FjsFE3k4XuO8KJ34hAJYQupvbbrO5jewBInOIMdR1o2n8zDHx8UPCA32M1rn5fCfGgALKwqvzi4iCOSMEOMNyWmkEBDa3M6K9vb0nT5++efPm5voa1ANSnysfF2OIQJCFWAgROWm47JYqqpTCcCrZjP4WyQzhn2y6AiCRMSOmjWxZDPMzMrvHP42kKRWNjJB6sQPrEyfgs5Ts5GfPjuTAnp1VaS410qILyjkzhgwagtGeTGXXQCah7igMnGo4U+I1pq8QBV8BRaPCiCgcWVWDAAqapDBTI3FMsXBJnj8DMCNnIJ1VxyRSJQQShtzhAQDIkEndokq8GABKGZLkEo4s5VUfbTmo+neKjQLmroRJ2ZMBEowCxCCWIzJE5sB93wMk0uC8wsOyiXCMMef3CmgpCxKAVt9Kdibz8uf/CQpiThAXQaS8LYuWUpMiz7raVRwRLYxwgLEyAEgk1VIQnoTBDMim2n0swigMoAVlOoFKfgnlu3UHZB9Kt+P2xh3ts+0NrfaBljQoB2cGz7ekA+Z8cjIQQv/tt89dZdq2VWQMSmGVCBF63zMZhQqYRbOc+hCAGRFCYO8DIk6n0xDCxx9/vFyu/uHzz9fr9e3tTdcdG2s+/vjjv/lv/3dv3pydnV/GyGlHjg1qEKUliRxYIuZMSUJMTS3T4qZtRkTGWY1tZwWMAhhZfO8RqK5rJTf40FDLN2VpYjoa3wGiDvZ/HgJD8CXBTPmlZF98yDf8VzgKlgWQoCwlGm3vVpu71e7Oop5O2hC5ZF9+57VSpigOh3rAm4bxffVxBhz/oAf6Hpd9P6oxmGI0ItJ5/wV01xIwiw+dDx1CjDF0XRdCsNZmrzG5dyovWAQRqqriQNoiBYFQY5gaqMpXHp4+e2wlWdEaszOdHR0c3lxfq0tQMpsACliNCJjSjAcJUmSQ6uFcnFwQUUk5x0VlJh9Nmyy5yhijOmz4tkHLagaVVg7RSLDnb8wUjLpDckeJ7x5lbxa9XEinh+If7QJFhKTJRGQK7JA0oeSU3qzF9LL6k7qKRW/pWqExKd1VHcLRHYmIUpHlX+Qyr7RhYEQyv53mp7+QtBsQASh5mnlPpZHqwZP4k4QaSLrjwkWV75hEV10ThFLKoTq2gggGCMRAlIAogtpDkQWBjBGQEGOM4d4RU7iVY2pUk+wnAGBhZOJkxoieExjsGEmRFYDEOUyQGgLm+1WLCCm1703dKoCA0qRlj7x8RkTIWmMtM6sfl15DYeTAwVaNIPTBB46s7BKpa7LmGqSJ0oIyyZiqHhJMRXCDqVhcvvxuzJZHGupuxqiUzohIIjxC9jBnZQNzvL29JQPKFRpCUKc5Z1CroYcgEIWVwQsS5orIShcWiOjmZnlxcTmdzheLxYOjo7enZ6vV6vb29tlHzz7++ONPPn5GaN+enSNiCAFBBFi3r8ZztIJAgRZK+XJJ0kASGVsOx312MEQC7Lzve0+kCti8c3iTjQggzBKDF4lEJm34dB4TkiJQoh7JWLjn4mS5r8uhNm76Qk55GN81Pug5/bONkgqab0zGrw33qT8QYPT+/PSNtP0UwXJYt5uAMN3btalJCZYUnjR76hAy69kp4FkJCr8n0fv3jeHj5fa+tyM9nvmRrZ6vvC37h8sWKTxa4/cBVCofhBlJBAUkRkLs+46Z1ZkU1hqhFEHU1rAsyr6HIQTf99nxAhak3Fi9CF7YWpf0T2PIOYupmyFpnQHlKsBMZaytxJGzT6yeIgAKA4rB1O6IEoNC6hRRsOjkPSkHYqZXMEQWkTTBF0sJ8jBLCWtUp4KUE2CQaeU9aS+WI6aHljGXcm6Zu3pXMOYqVumr9ZdJlSXU1RBZSr17M6KQcszSLJA2jEcEQk7kw0LWkLXapxHJEAdGHDYslpbGKohBIjNI4mJQBmcQhihIgiQoQMIQGQLruqPJfl0xfVRhiWCyyVKtTOrmeL8fMEDiwARmEJaEr8Kwr7V1Yn63JMo0TilnAKjdi4RjFFaaKiUdEcIu9L32ERpdlEtThREWhIiBGUCYhoyktMW1t0GhIRQBAC0cizJe12IOju1CGe2StBFJgyhZciu4IZqoTmTIaHhBj5a2XSrWJKKhxBdaeiOW+RzpWMBMcEPvkA+LSBx8bExcdakgwRhjTOlApXebvNv0LKRa3xhTVVZAmFmZ6ooT7L2HHG7XtmfMQ2a1IQohsIhyjHRd/+WXXy2Xd/P5fGc+36w2xrhnz559/PEnALBetzuLRVVVGn8SjkAmFZQlxAEAxFpjDMUIJbMOcQgolLCGfntZEck19apBiaiqKkBMvGMJlin7Vb8vatJfXZfejjLY//BBv+beKMBPXoO0W7j8I497//zeauKfcmAJkb/HKBDQuUvghEDv/frmdm7r/nZFITbOBiIKgYzBFCDIE6LKFxEAfAib1WY6nVpr1QjUOjl+z1f+ix6ST31xhe+9rooqiwclI8Kua4kckQXAGGIIHomMNaqDlRPXadmCsk2CoGaSiDAAgUleAiSTTyAF+FiiEtdWdVXXdVXV1lhjbLoLSbqORkByStkZti+iEIFBjZum4IK+SAIyqqVkRFtKYJMeTseS1JZGBQ5HKCBkDwiyjCshXUkUTKlm8p5LmT6brzBo6w8tDdxP4xBQ7NOQsQLALImBmLJnWvQYoCTwGhUwFkRyFpXnMZmimB0QLs+VlHyJDcvgfw93odOhwAdHpbsYT1DaRWMjoyDLI0MCAFMMuCAVKNlsESHEyjkkgoGGDPPV05cXkzI55MwphoyIysqBKACaLMAiIXKGhQdNWGRfWS7N69LiII6GDSdXCVXRaxIyQs5eK330BOBeRHAAm1RlQqJlDiFgZi9BbdADwsxaMZyNzOS8qnWlbiioLmUiMJVxztg8LdkESRTiaUmVwkpPOL7rHmwPfTwFBMyoa8UAmwwtRxBzQ0YiCjFe39zoVlDqSszJEdrCDBGRzGw2cVXFQopCd23Xd63W7Ktv470/Ozu7vb09OTlxzrmqmjRTa62AbDabN6dvQUwRUn3fC0vlnNYjESJKzMWF5UDg6Kwmy1jteqNZ0OXZM+IFal4YrOpaEXiVPYOCzWCcEtMbQ9niwiQx8fuq3nem/1/HSDUg73nClHD0nocXkZjc1r7ve98bovl8zt775cpTL0Fq51xdr0J/d77aPTpyk1nbdb7zFslYI4i2coIQCZy1vaH1ejWfzy0aSSR29/f3eydUPvzSP/H4fpski7PBdJbgPU7RGkNkNptNDBGJlaFVZbiGUfs+Ioh2CyzJ/EqirmETQlKeQUaQyIBYOTudTPYP9nYWu/PZfH//cLHYtdamgqoBPktaDzPJzXC2BiKp5AKIiHAUYSxvS4QdtFV9AZBl/KBREVIctPwyi+vRAiLg9nricLn7U/kOPsLbl7u/LwQACLWro3bRUftAxps8A85y/5N6I8gIrISgiMwoDFqTmzEPhXKBI+eCaIkgyuLFHCFH0/RqI2w+5evw8F3Jl9y6FxFtR46Y8lJBNI9oBEFvowdas4VkDI6FGZZ1yns39d9TSIVjCOmOQaxzYPSdjADGUFU5TE2ayhfpRuJMPDKo1RACEiNiNOScAWHA5PwRYQ6tayB9aGSrWoQjlydKzyUAubA6E60UPyo9mWpOUlxOkrGvabrKkKE6GkkIwAo6oMo5OxA5FZuu8ApL+Uu/F75zSCqJ55h6fwrl8qrkBJeKs8I9mi8rzF3XinDBi8rxAxCAYIy11tnZ/OTk8XS+IOMq5/q2++rL311cnLEYEVb4WkRijC9evKjrmsiGEK+ur3vv+65/+/qUI7TrjfYzVXitI0NEk2YyaRpMFD2ojvv46TDDAvlfREQW6V0S5silpbFJXTOymQdZQqhVFCP7EK11kOogt+3lP0IF/77xL8TlVT1HuBWwQACM0REyEMP9jF9mWV5cUZCmssvb23XfNpPJzFW1sVPr4u3merlub25mh4vYWDZoDSBwd7c6e3VKDHt7ewJQzyaTvYVWj8wmzfJmuVkudxcLApOBvt8/6F/MNH6PgXqcEUl9EmbxPkync63sJzLKacXMIQbd/LndQmRmaw2ZiWrv5OmyCIAZYF2MIhwYAGbT6d7uwWw2Wyx2d3YWu/sHR4cPprOpcw4QtRFPckSUOMoYY6n3gbPbkIRQLjcAlZbMIcQU4MvBznRcBoU3qFdJPpqAOu7CCnEXJZDO4Rj8xyJwfu98JlgwS4JtTf7Ou9WeZoHM6Q85m1VvFqVcDwfOqJH0SyTygoBWq7ZYmFmBWwFETJnKmZFEECJKFFaSqsiRaJTrYCg1aiBCIvUGBVKGqYZ8s8IcyyJIDjzSqHQAbXFWxtJKP4yoj0qpdRRgwrZEhnoira6V0uFQOPMkA6KxBhGYBRCU1LeuVmqGgMK/SDio8hg5oGAUFokcAwozEsfIMWquc8KLgERKfhWSsa5uEiU4mgIWDTZgngxJQZdEPyYZqs6rVyCUHKIpCynZuRbGzFVJgFomVqZ4NIeYOcFB66+IyDoHhKMsv/Gey79S3UmEiCxsnUMiDoG3F1Qvkc0RdZSFmWP0CScbjXRdYUqMdzSbz62rps2kqerFfPYPv/r7V69faDvCkiLOzF3XEZrNpm27NgbuuxDCXdeHvve68YP3MUbNPA8huMoZtJpGMhDLYHYhtm8JldM0Y/V55tMeI9IW23ZkbOo0MQiV3Dbvg3KWAyhuk4CgDPT8mx0FGKHsPaBA8H17eb07mVbTeTQYEeJotxFgA6Zf3bW3wYLsUEVAGHlWN7Nm+ub5m6lzhBKv2c4mtDMxhAZx2tTzSRPXnazbGHnjw3Q+AzIAYNHMm8nZ6amJsre7ZwCiRAbwHBDJVZWem3c5iEeY1O97TCxyGvI++X0tM+7pgD94G7xHhRTnTPd513Wz2U5uooB1XVdVtdlsrpZXkByx6L2vqsoawwLK6ZZEUtZjJCksW7QhIoYQb25ulsvl2dmFtXYym83nu5Pp5PTt2ySykowa3Jckx4Tfd/OImedImA0RoaT2PoggOc+uhORyNlkaUL5RleCgGGTUQDADvUkFS6Y4VMBckourI7fgTboi0yePxV/2H7H8kgU0HJndM81P1k70IICcKoupUIrkAAxCTlhGYRTIjle+39Sal7O4GBACANQ6l+xqcvpclmRqag0oemHdhOHuEzCKWjYjGj5VhgtEFkTJEHSeoHTfCXMxWnBiEYxybiVa3wGyYNRvS/Y2giAhsSpvtM5YROLIpIUpxljnNMyd1lhtkxGBBgIAMCKwMAmLIIvEGIgQkDgTgeUUMwBAJKrqWqV5ynMrs5Ih5rQY2nBZC6LTYwIWuuYSiSwRx7IV0+4A0GiwoJKybht9+YgNcSPVNKIRTeULA0GQe+wKhGDKsqvusZm6MlsSMLqNLZlCNAShQ/AhRObB+JDc1ZHIeB9FsDo7i4A7i10UmNbN8fGxQOz79TfPv0kp00hAaIjK+Q0h3tzcLu/Wvfdd19u6nk4nqvJT02Xm29sb7/vDoyMzX+TvBtTojBqa9yu/wOS+jmXyMUEYAiBEQxI+lKK00bRxjH3XI2Z+02JKJiAE3h33bB+Aeyt4b7wHMPu948NX+zOPsRojAfLh+uWbm9en/Xzn4OHJ7GA/EnlhPZooELoublqKESIjQgQxgg4NASDzYmf+8eMnYDAQmKa6CRu/6rGm5dWNtvgMbWucs4ZiiOiMHtRp0xzs7l2endPGWyCtfOv7rppOF8+esDORICDc84y3WlOV6cKtf448GRpPafkxpVwY0AKfdNbe6fjJ/L1WcHzM700zAiASoDAhIHZtR8ZaV+mx0lQMEOl9G0IwxuiljDGTySSGyOBVAxKRYIpGKelj6sGDaIBEggh43wcficgYD0irzebq+tY413Xd2FXId4WQAKIkjVLacGK0LQm6aQsQGULRQqXsccC7O7z4gVn25UJqla7J2C1rURTlSG/lSzIIAPkuSAAQUpLtbSGwlTUvDMgMMYCwASbh8ZIbo2oMABEIgbHI4NFzDM5HdrZJcxcQ0RpNSCrfXeQ8A6QcZGtMRIIUihUelYBKFmulhiqtNRmTXGE1H8plQZ1dFMIIEAQQIEYUVo1uEG3ybaWcZ2ZhAUBjJ9O5MWYs84o+e2evbqsEpMq6h4cPUcB3niMSOkPWWmeNU8oI7XyhrmXMqGlWwECpcdSQOqRIq0DqMKXEZtmpR6ONEBOwoYRfUpYXUyI4MEQWhgwmIKJm00HWaoqa4si/3HpIZkj2hmhQgRFx8ICLvM4KGNQAUt7jrSr1HMBINsAwwwCorbHN0KUAsuE11lVlaJ8lESmGRKYyT0umMf4QmSHAet2+evny9I1z1XQy/eFnP/jBp588fHj81//hr8nQl19+2fc9GWONWywWzrmdncWjxx8dHD+4W63v1pt200YO88UO4r4xxMErp0aIgZmZY11VjXMppFwOJYyeIx8ovUdKVD4Z01JgWWICmhR3yx/LRqquqXD0MfhEwwLDwybwAhFLN48Pj2Jv/esdiEgC7e3d6uwKNv1qcxnXfU223l8gQRBJzBjRd+3a9NECIBHHWE/JIt2eXUjXPzw4mFjXc4+CU9f0fWgvbmkhvN5YAY7sfY8GffDCTKlcEGNgw0BtOD9/iSwowjHEGGCnvyaaHu5VsylY6gtP4h/+aB9anZKNiBn9/BOm8DvvISNbKm0D83Q6q6rKGJMkoXDbre/u7kQk+B4BiMhZByy+74XBGVfOb+ZL1b5JqoBFgEk4+MAMRADKKZGZBsAQGGQUILQZVs3OGkBiPsgxqTIPJIBRK4ZERCAWtSQpnW6wOcbTJ0p5jIP5GWNUvTYo3i2JVfzJ7AfDcHcCEJhZWDDTR9w7kep3YzGxQUHF9G9MeKsKC4RU4w/q1AIrPxayaLp0SuMkSvLFICKCEAIQkDF2K1KsbhyLBJaKARlTDwooiaWqezULGpX1QrjocGPQGooSATTTOWvd5DwoYiCiYXRNMCpyH0BELOn6sCLeJBA1YQyJjHNkEvMZQdYYucNb2Z3pQUZzSmiaavrZJz+IPn75xVfBRwQCbQmVcuvK3G+DHimxOBWAIjBqEblQZLYFWtCEiIQhj5zXVEWSSuLeM0RgoNAqGPgWeeSwsxABBu+qvEpIonV3IoDg6lrRf23MdP8bM9z6gdtRcGd4d3JoMDXPKv7rFkYzosfSIFMysRGNUdLUdA7KOwGJkax11tV1XTdNU1UVgHzzzZeb1e0P/+IHx8cP//f/bVPXzW9/8xsAmkxm8/kCAA4OHjw8eVTPpn3vV6t117aX15ezuyURVZVzxlRV1fZdqTy+ur7crFaVtcH3Jaf/naFzkkPxownXu1Wcv64qa60i2ETEHMuRBgBEUcxcQ+AFQ9E/aa4gx6u+7/jXh1ojohG0AuvrW/Z+1kyMAMd4ffq26jeTvR1qHAAKgqtcVdebu1sRaZq6ds4R+fX68tXr/va2BbpcrciaiLC6WraRYeLQhwYJDYoTjAEQXO3quorZeO59v1wuOUYE2JnPg+83K19bVxuzubnxvp/t7eFs4nYmMTP8/IEjWdjwPjuKSLv9CP6By/yHDkTUgj5tqNA0jXFW0t5OYaD5zjzEwDE6azVIGWMUZmNM5SqOgspulHa/OsLKHa0Vw67vur4PqnoQEQiNNVVT7+7vX11drdbr994YFhYN2DJwcl6UoJaDCjNEhBHYiYWpvlyrgNJZxCOiNoOR5EZ/4B6yTVDiIVJeg6IXt8BmGO5XcrpCATW3vcJ0b4YycyXH4PugRBNb9yKgMLMKlsIyoiFeLekcyehUpEkshYQnq0qtkQaEGGKMiVMjAwfZBgIgIutsF8O92UhKk7JhMigYyEnV6ZFtVVlEEOEQAxhkcQKMCJqKLuq7YZ4sQWZmEaNXSX6Lqpm0CdQOq2y1M98hoOf0PPqIQNktQwSCXBOWtC+IpMx1Uec1aM4RQa45khhjiLEaXEwAEMkNiNTw02S03Kx3kP4jmENnzWSEYaQds6YsOlhycUIhz9LUa4X+Ez0bYjNp9BD0vY+jlcgTDsnP/7Aazl+XAgiQ1DxqwmrKOODhSjkbOGkgIqPJZdbZTG+nio3LVqgm073Do/2DB4AmhPDim+d+1T579mRnNj0/O/v7f/jVT3/y08Ojo7/+D3+LZF88/7brws3t3ePHjx89fjqdzUzlqqqeTKfCfNIe931fVdYa0/d9VVWLxc7lxUXbbZa3133fGcTpZNo0tbXunZqrbPVsQ4U47DDQ00torK2QDOZJSaz6Utxa7e8UnKshVxunkEZWu3+UuP9XNlCAN93qZsVtbxjIkBCIQNu1d2/bulsfnDwwkzoCdt63fc/APnAFUjknHJdX19enZ6tvX91FXuwujh8/in1/9vbNatNW80mDtH981LGPKM5SIAyRIzORiyAsYqx1s4n3vamNm032podXZ+fd3bpqXAzR363uuuB25rvWmcb1KDkS9geN4qXlJ/4njO6roy9awSmyWq00QlTOslrBiNjUTed7MSwcFXNiFiJar9cb2UyaibF2wCchAT56/dThj0hJfgwiGDLOOVfN54vHj5+0my4FIT8gQ+4DTWOqZ1K1ErIGHfsYarykqmLICjl5VekBMxY4Qlfzdw53syXccPi/ntPRC2XegMeB3nw/g/hSPZdaWWVjQD0sjiF0XqI+1Hvno5gFoulvIIrCC0CmnYQYhaMIFUsDUptUEY0YWmPartO7Va9YDR7IWcqsDRoyiF2+Wp9lpIu1szPlAGaO1yLYejoFYxghamBTWzcrAZNGGEZURJLMWCWN1OBHntlx/YEIERoiZ6yzBoRzLjXncsGciQ0pZCupQFkgzULMKW8gKc0MvO+Fp+OFfu9R1Pmg1AKkvIckEfcLAamPNXwkf6y8/34B2mgQpnQfSaRl+vDifR/CuGIdMTcAglxwjMlW58J2o4YojHgQRZFYAUl7hgAgxAhQEvGVXT36vucYU7Q4nwrFuhExgQ2IZO3uweGPfvzTn/83fzOdL/rO/7/+H//P/99/+l/u7pZ/9Vc/+/SHPzg9Pf317373Q4T9vb2//tu/me/u/sPff96H+Ojp073DAzSmqiwAoKlD8HVtN+2GCBc7uy9evLi6uvzss08fHB58dXv9/JuvEOG/+z/+Hx4+PPnmm+cc+P0mcwLJy9QX7z7v1oTAGEISiYMEROVf1RMhIXjJFWiSoUKRD6Ef/zaH+Hj95m1YbkzvCdFNG9fUao741fr8zann/tHHH5N1wmKcgboS6frYO3DACCwc2QA6Y7uu69q26/rQ9Y5Mv2r9uiXm6Pt20wFRtATCaLSWg9frVeh642jxYI+9r6qqns33K/PqqxfnN9eLZoos4v3q6qrr+9nJYbU7L8Hg7zZG3/OYJQB3L2RQAo4lR2D70N77oj9CecsoJ7HvvVYeKLWfvhqDVzei23R9340Nd2Nc00Df9jGyFiRGjrkiSAiJ0RibOO1TvAVFG9SjdjoytbV1zsIZFFbCtLXCJgJyKiVJTlbOb8osRKUOuPhMkL1WNeeHwG1WfIMfoiHCeygDbinP9JkMgY/zY9RAodQAKX2bAs+Urzr4lHm97q/aCAFkJdoD7cCApUESisqWHKwaeiVly0EzTmKMwgKksAALiNFmgJLyxSQBpakRqrpq5cbEGC31RhGOUQkQx9hn2aUf2uSYJ5cA7HQ+DyGU9rwgpIWmIhJi1JJZEE23SoA8sBCgaD5qNkgFQHLHSQKqnHXWWDIKdUJSWQzau3frzgov9CA+B2WocL8IxIiYqshR6c8SjSUPZzo3kAIRGEzXLfbrwuqQ1jW7XiNcQnP/MGpKrSYKsYC2RUKwxipBC7JwjPc09QglTc4qIWr5ntWa14TxYJrQ+9YrgmCMEgNbKwCitG8leMC6u0WU/ASSXieREGP03gOIEnEAYIpoRwZEWzeTncX+4QMSOjg8igyb9ebs7dumrk9OTu7u7r7++uv+8eOThw9/+pOfzueLy4vLpmk2mw0QIU6MJWtIi6Tu7m4BYHexe3Jy8tWXX7188e1nn33qe7+6u3v48MFPfvKT6WT6/PmLyGyMTQZWpkkdt9cVlTpopBDVmoyoJYcARPPGWfdnIkpLiEgoXZPzxZLtvm0O/p4hzJHI3PvtGAu59/t7v9heQNl+6R99dJv11dtz3PiJNXuHh3sPDqqm7vqu73oWXm/u4iZgROPMbDqDRbjzmtQYmGE6bYwx9aSp9nZnZKLwdLEDXTdHFIbIPNuZM0C7aq8uryc7O7aubFOhNRGk9355eztpJou9XUQhEU3BME199OT4/NWb1WozbybWGmCZT5psquZpyj5NmqnsTd0vI855lB+WZff1wId8xDLeDTO957L5OoQkLG27ub6+ur6+9j5oXipk5iYAiSGooDJkK4ecOwrosNaZic1ZTaD1e6CSh0BZfBBMYv1HT0hkLKElskTOGAdiolai5hhs0ZxJuBUrVtSbHrW2S1h3btSWKiRleEzN1UohWCYQpVLKMC4AgOoYTKk0krObEpae0D0aJ5ZiVhWqhknj2UqSd2+BxocM8b7FzgBROOS+7youmQOIWJeCmdvHb7Aw9IIEibBZEdioVbtZ5TBHAKe3IfkvEdCInrVWSYcwUxiWCGxyYQWijynNO1fbjOKkw4oxiqQl1MS5qO+21lTCqAaZMHBMTjUiiQAaA5Qlkf6lBcXDQ6Jkg4aBAbURPVhjWVhbeWi7Q82pyXeW8n4l15wNYQwZegJmH4m0rRFH4CgIGrglEYhRU27YOWeMQTAgyJFjSISXKkYJUvBADQFCLJlcIjLekepta0qbklQn2AfVRmQAcLk2C0RiCJKnHd53nlEXS+H+EXK1tQXLnzwHLOJDMMEiDSnZySyKLKlrNCKQJUtoiIw23YwxZJ3EzBhC7PuI1gYWFoxCSG4+nS0We0pBbokuL85Xy+WjR4+csd9+8zz6cPLo0bOnTxY7O5t2s1xe970/P+WdxfzhyWNgPn3z5vLy4ujoKPo4qZvPPv30d7/7zS9+8Yvl8lbzoYjM8m71+a9+s7u7//HHKSNTN7fJdjGRJqaZDD5vBZFijPk9Sc4kNawWLiIzxighpH0Fw+xJIaEoIv73OVt/djyzAHT/+EMABZwxwn1lbDNp+uijl+Xdcj6ZzeZzZvYiy9vV3Dqydrp/4JrJ3dvzm7dncLc6ODyczmaLg/2buztBU1eVmU2osk3tQh+m1h09etgGf7duAwgaV9XNzt4+GYqocXry3l9dXxMwMKNA9IEQdqaT6Xx2vdxUTTOdTC6ur5umrmY77febku9+19h6Tr/4Qybse+yHLNeQENEgrdvNq1cvX7161bbd4eHRbDZDRO8DIhlDEAUAmSMIagp08CEzn+cbLvtRlHID0KRGBclRdq5pmq7tu9YjImp7eDKkPRlSF1cGRiSjRyDRMSMBYIza6SbBY5ISa3SaPvyExUVIPmuZT6XgLV0PUm7NMPOD0VkYr4rxe+8LRQCJBltWssObi54GVzu9AYWBC5+aIESRCEykjEZqeqQwVuGU0Ewm4FJolFdw1M0zIRkxkx4za6eI4n0rkqxutHrSRmspFa5DQIQoIsCB0SEDIhojWsukTCGCEFE/SaVMLU/EkCmFieaWAWyM0dpUDZxccmZhIVJIj8dmzQjAT4JuRFCkWdgJYTbWeO+hlsmk7nsvEo1BSwQsBCUbDiCn8A1p76AocZ4RACITOcXLY4glJT5ZdQwgoLlU6SE4+t6XHSVapax3KgACZDDGbFnTltbUgnpVYLpgieID06sCrNHx8v5BhbyrfUebawRBl7cXH3hQ4fp+1sYcHA1lRCVBHBg56vk1iAKGgAGI0DBosqNYS86ltkvaOYMjRMYQJEbtyILOOgOEAIeHh/v7+y+eP3/+zTcPT06Oj4/fnL6526yfPn3ax369WdWuOn97+uWXXxweHv78r6Xt2levXqlVuF6v3r59CwBVXV9dXX7zzTd9387m088///X52cXr1292Fwdall3MVEypmmlWNX97cIIQhRAKmKO1FCIgmF1jTnMqqEiesZQQGokiUXeDjA71fcTy38rIwInEEBtb2Rk5ImTQls/OWt33tavYx67tJiBABMY4O6/bbrLZtDfLbrXZmy9mVXPW9q5qHhwdVbMdAK443lxccWRXVzd3q67rp7O5m9bVfGabKqKG6I2gXJyf15Xb3104Msvr665tjx4cVnU9nc2X7qb3ftI0d3d34fXrR/OZuGr8CH/QuowPDoyW9Y+AlL/PlwGAivjV3erLL7743e9+d3NzM5nMnjx5Op1Ovfdt2yESOAcJhDIiQAKiBPVZAWPqBFfUj6QaoULhLoxgiGixs7jhparw8Y0kPnUEUGRbaSAASXKYdJQBK3qCSDsrDBT3H3rOrfxPSGgrZLAiKVTOHhGAKtTBG8vBznwF0bjhaGXV9NZOP5oNWhJa9SIMYBLrU/4Mvyf6h5AZfwHU8jEI1uhvAQkIIWZc12ByCSGRVJfLJOEhAswSyy7KeEL6+lRoRES50Y6+p5T3MkvAWDhwFHOVVKObLS2dyiHmXxRcyhHWi1oQqOta2yEIC1fqwiIzpPKyYlCk1oMIQInUDFCy2yJRhIUTLsoIsF6vd6Y7VVV1XTeaSDCaHA6oq4kZfsJknebo9bZxJKNRplI/FDmWDC99Q4hBNMSSY+QA2lQIAWBM0mSIXGU1ExxSrTcgotoi6mApJapA2ohqAKhJEGPExJaeb+wdCx4RRGAoK9J1ynVUoBXAOLJJdBukpHpluVKqUcqBclFkqcxGWXZEdM6pggQA55gjeiFCI9qAAXKMHwywODL7ix3z7Onp6ds3L18eHh2dHD+8vL568fy5q6rz8/OHRw8+evY0BH91dfXti+ed913b7i4WfdddXJx9/fXXHPnh8YP/8POfi+//83/5z19/8aVBS6Z6/OjJkydPnXO6Z3PkX4Eoyn5wlqGYDNuy1sVASSgWJMAv6fFsW6QQCYhqX/kucfOHjX8Uyf5nHRopISJXOYwizJv1qjbTwJ6ZW3/Hnp2r1ICjzBRGzHu7i+P57Pzb16+eP1+evl1fXkMXrEMMEjZdQPHRt6tVWLW//cU/dD7u7cx3Dg4m+zv14W7vkIEF0Tl7eHhwuLs7c7UjWt0s2TUHs53ZfKd2De3AhT0FkBCiAF5cXZvT051Hx3Za682/D8n/4PhTTKh7n/0+y1pA7xjjb3/721/+4he3t7cicnx8srOz45wTEWtjCDGEgKgKwYIoY68feSpbZ5qGtkuROaproZBOVVVVXSPebd9eVm/qY6nEHl3xvU8i2UFFAhBC0aw8bTVOjPxuHheO/yc5AzprG1YE+J1JgntPuOXibt2Pj+G9s540MW6/W507GWNaaKwlYwgNgcn+r9E8aQDNoklFzwoTqlgBycCk3ilS5KixME1mGtKCEUBEoVORiChkkIxFtNkZL+WQ6ohLJtAsrCOa6qa/KaoZ8xyK6spYGBKzu22ReeJq09BbwBijxne1FoxHmV06tYVhVJ8bUkoR6Hczs3a+VP6jruuUDqboiZQTZAhQChtX1rpjXyhFSMvrhKS3rgi+tmfInidpI968X1NTj7SWiJISljmtFyChQTQikunHU88PFew5+UtEhHJBlFospW+yemYyWDbayJMAZPuEFMQlpdWl7N/86MXw08plMGXjAID2+kn9QJInD1BMOil7VyTB48KIoDpJMXltgxTRuMphzs/abDbMjEAWbW3cTtPs78weHOx//pvfvnrx/PD44cNHj5bL5Wa1mk0m7Xo9a5q/+tnPLq+urm+Wq/Um+HB9eb5e3TLzzfUVijQVrVe3R0cHu4v52dm5M+7/+n/7v5+dX/W9t8ambqcCpYhAy6twFJsnTbjXkA1w3hXJOM2BchQi1JbZKKmV1btuUCFgzJbn9xv/0tXtewcCGDKmstz3iNht1sYYJIEYQtcBA1lrEKZNPSESIWCRTW+6LnZte3O1ujhfrtop2d3JJHp/+uZNvZhD7Vrf+1VrPF+9ehtFplVto1CMFKKrDRBFYQaxTe0Ew3JzdnbRLlezaVNX5BicICMBS+WqwFxNGyQzmc0Se+/3U6ZJXb3z5j8R0nj3s/dUctG+iPjmzZtf//rXV1dXIYTJZPLgwVFdV4horZ1MTN/3XdcignOpT5IAE2FdVUlMKCScgS29JjN7nzrcKUZZVa6u63vxqeSPqqVO2cBOukAzN7V5uYRRI4H00dL3pUQgh6dNly2+K6qaLm7WcAfje47CICSyvR4MKsOSCwYqxCSHUct3xiQD9V/vcVHUj+eklkBTfQdvS0Z8iQgZdU02RnZPFZZXqZjRAUiebc4XjtGLSZpFfVFQ3A1EE3pSqyGjaBEBEEeNuJKWWeS4ZcJhBTMOMZpmpQXNGV4K0JatlWqek/sIYIMP7WZzeHhYOde2UdWJViVp7DbD6O/Z9ZhXREAipNsnBiGxxohIjFG1Tsz5XCNFO1bAmF0i0lzx5Nel7ZSCG6KdJzjHwTXZj1CtbO38oXcqIzWGmNKMJYEkgmQBYhbrlF2srecr59Da1C0EyrIBIKDylcTIIiSJ4SQEjT1j3m+IBQlPl8J8JAb9WTgrilUJHFkXiVk0X5yzDUqSKEySw55zOpgFCbIznN9MJIIGTeqJhAggfd9rjNwaO59rQKs/3D/4wcefhK5/9e23q8366OhB9GG1WUcfVnd3x8fHIfJ6ve67LoawXPZV73Qlmfny8uKbb74GgMViMWmm/5f//r//j//xv/sf/6f/+fWb08jMMQAgoPbhQJuaQ+ucDGekbNCyI1IMOJ9cTJwckne5mnRZoAhl5CctU44XDYd8ewxZnYq2vFcQv2/8Xuk/3OT3+/j30v33HiTvXfRd13WdBRARZ2y/vuv7zvuOyO0f7Isw935ONOlj161fvXh59uJbWa/msykg7DaNsRX66IxdrtY+hgqEJbZdayo7c7ZG03btxes3wlxtpvVmNXlw4HamhKTQmN+0r7/6ZnV5XaGR1bqtnHl8Ulvn79bL6+uJsZFhMps+efK4mk8joYeSM1ligVtzUjRDVlxpbf5opXvv5H7/sV6vP//88+vrazXEj46ODg72855EogSkKQyWu+2C5FR+ZtYcF0isMOqdsfc+BM/MTVPP5zt7e/u7u3t7+/uz2ezi/HK8yBp6HHF7FQVbSCtSJW8m4tCg7bgNA2kYDcAoZgkAqkuydyBJhSatnAgmEVCxN3WOcoqv2sc4sm3zzyMsOqGs2TFLjQRz3c5YzeubKCkwvSUZPejoC9L7NY831UhILhkqV9NJA0IhBMSE0kIxXRR8UIo9o4g1ZhXALNx7jlG0DEy3pYy3TVKfwomPGTJSDTk5CwttSOKF1gqurDNASZkkt7oX0n7Aq9Vqd3dX/RKRqBmmXdf2oS+TLgMwnJYi3VJxgDmKaBNCUF4qRBSRqqq0NV5m0JZhthA1Q1ZyAQmCQQQgRlLuck00T1UoCsVwSmrPaYVEfQx9DFo0jGRBoN20MUYypL5ygd31ksaYtFcHRtP720K515M2GD0qR030ligsAn3v0RoABGEOvbDPWAXmBUPE5M4PMeBcTJPtp2LgCABqOACKjSbCilnp1GlfI6DcSTkAMKpHCKxGT64P1m8jAa1K1AzwkiGJ2nhzs16x7xukCmBa15v16u3522++/oqMDTGgSGXd29NTJOp9iDEKCBloYu2c9T7EGPqOv/nm68PDw7quDNmmac7Ozn3f1XVFhgKHtuujxPV6Vbnq5OHDuq4AkqeuhrpIlg+YozLjnZaNPN2DAhAZfAgi43aZefkkoSNSuG4HPGg4yzl5MxGqvCvf/1gF/GGNn36L2y8NluJ7x+D25L811wERSMAIVK4Kdc1x4zvvmvpueRd8P5k03kdm4RCo69dvzq6/fvn621dX5+fI7BB2yLimRkIBiBArV03mUza0e7QXLdW7M+l9XG6MROkiAkL07c316zcv/a+p2Zkvjg4/+uTT6WR2dn2xubhxgmQkxhjbePbqzery+vLsAtY9z300dHh4MF/sBKKouSppNZMiuB95gZF8yObY/Tn5s0YHyn67d9nr6+ubmxtENMY0TfP48eOmaZI0Eem936w3XdczB5UhhZBO4bgxebuak2qjxxhFwBhb15Ommdb1RP8UmgdA5ZwwymyvCpFw4A5KEF9K8clOxHumCBEI0AAZQQpRvTe+52jkP4QFPUQNEmZnCVErQe5dP0ty3L7a/cECqRMRIGOipQVVGSBURLCU0GNqGyUF71NhSsjMmvRKxhCh9xyC5skCK38GgHZJzocFUX1yQkECQp9aGCChYQmIiMZo/D6m7lQ5r0uAOWZzKj8yiLAk0mfWRsUMCChIgCREDCgKKJAuGwCwJmdJMkJS23vQrj9gEbHrurZtE7UbC2grBO9D1yuzRMLIRSgRUEhGXFN1bww++hB9CH1ABg4pgMrMTV1r7FPVT07BxxLz074OIUoIXNWotBLqxWKiZlHERQRlHDkH0HwH8b33vU/eLSILn52dff3V1zuLnWY6mc1mIqkwhTXrIRE1Yl4jgSLcRSg7RMnGATCCRsV4ZGCh7DAxctu3BGo1MEuQbb4YzOJzZJOmKQPQXEUEUN45UhhFssiVnCaYrM5yGkYZHKCYv6ZcgAAIc4zR+yBEAOiIDIARMGqLhZCahFtnBdmHvl2vK4wQ+qtN23eeQjQcu/XKd621TifaY7e8vQ2RWcQY46qqbhwAty2oxy/M6/X69vYGABHM/+d/+Z8fHD+sajeVxjryG9+2603b9r5fy1oZU/q+T/2W32HkkGzz4D2kHQd5zRK974RjpjHTPGtG2RLP99Cu73Ck/kRg8w8c90Tg/Qqo++9WlEwoYWdRoveh95ZMVdd7uzsVyI33fhV99LauGKUHpsrFELjtsQ9ff/3r26sbYX786NFiZ768ua6tM4DMDITgzOX19e3dndTV3tOTB49OwJmrs/M1iyHkuxsJMXQtVe5wZxer6vTq6rcvX++ZyfT44dlvvqLWzw/27KQSAI4h+PD29PTu8uZgdw8Rd/f3Fgf7ESFwVCM6980oy/aPOL7/mr6r1CeTyWKxuDy/AICHDx8+fPjQWgsgDBAFOu/bvgNCg0pkBIi42WzI2qpyAmgk44ParpsIknSSyIEQQ4jXVze3t3cvX76yzjnnVquViFhjCW1C5pRHINmUBdwaKz3M7dIlx6g4W7NJ7wsmRQeEwgggUUSbeN+bnxzzGf0+Jb9+P0JteL9lNOb0zfiGyCiXukhKydDmuyPl4PAInNbcEdI2C4nES4SZAyALRChxQky0ezEqJZRBDPp9ScjoxVJeOUoU0QfPPffS9+HoexHVFC7OZL5PoazatlFjKeEXGT2iNRkrBgDFijlEYNZMXBBBhXYhhRCNIWO1HUXy7VBA9aXS8Gr/6RhjVVXe+9l0ql+RTBu5XweWNhgXaikw1lpjEQuBVIKRlZVSee90IjShPIQeIGKG1kVkebf8/PPPydLOYvHkyZMHDx409STlP8QIKGSQY8LP0WRHPt8PAAhHjswhRB/UWsmbSAgRBQ2iMPsQBSSxrYroq0Qp+StfD7MRvFUALdm9GyHQCr1hFIm5CSeSAcEYNIY9Uu15FyLDyJVPqeyRo2HNoU5nUxclMlNKXAKRGPsuYMS+5xCZwcRIUSCwNXZSN9qRzhgbYwRGNCQCap13fet9b4xJNhSz914ErKm++OKLs7dv0Ziu22xaiVG3clR6765tX796rQneVttzJptlhAZ8UHQWuuvUB1qnLEESEiF1Rk2o0z+ZRv3HG6icvgImSvTB9327Wd/e3q7v7jjE4wcPTh6dIEDbbXrf1w535jMw8ygMjC7Cxm82N8v11Y1D/OgHP3jy7Ol6tYy+C13HMVYTF5jXm86LUF33gAGQrLtZLn/32y/2msnj44d9jO3dCowuOx0eHT5+9uS//uf/8vI3v+1en23enj3+4WcHnz7jed0x9+v19eu31LYHR/vIECTu7O+FGM+vLtdtN5lO6snEVRUZEkNsgEe5iwCDNNR/FuOMclDqPfPzJ0PTOooFlvYX4v7+/s9+9rObq6vbm9uHDx/OZjP1KHS3OefmO3MRxozEAWDbtiH4ZlIjkgKVCAnU0cIJIkOG+tADAHM6roGlj0y9jzECGXXi7nVruV+ro1UjOQVFversqCXHFwmJjHrHUgTce4gHtqcFwGRvISt2PWjDhGGhcN+ewOw53Lse+xA0dKo66zu+Wi+d14GLe28ESYBYmfDyiS/Ibka2JUegkQVJUiMSRGutGC37AGbWakkOETgSZjjBGBjB73ptFk750hAFcgYQggGyuQFuef70F2I2BlJRkKRnk1TKWrw/AQCw1jkWiKxOGDJLIl1i6fteRMgQEAkKIBljEamqqqZpYibFBk5wojHEEWNOU2uaRmF4LfcsKhOLZSAF2EZSGwXQIAnCCFpEyNX2KCKRJQ66TXVNCD2BUEpDEyQUlhADAa1Wq2+//RYRH588Nsbq77V2CLXTA6ExlojG/T9CCL733MTe+9XdnfcBEvEsZPMwOfFRqUXQMCMIRVYTP6YDLqDpZiSAWDioJb0muUBHEJASL1hazDQ1+oZ0xgbjNB28kRYWAFJcr4gqPRGZNXuQdIiQa9uk36x3Fg9c5fxqvV534H3seuGoBh4ZMtbVVU3G1lUzmU4n08mkabp28/z5N5dXFwhAiE5JmwEQsd34zWbz+a8/F6SLy2vr3KPHj2aTKYc+hgDCoe/6NkaOztkQegEGMsU2VCd/LJHHkhcxWbywJTGhnMZyGsbhknfGP6rT9X3GH3YDRtAyhE1/e37BIj76xWz68PDw+urq2xcvQOSjJ08ODw+/vbpGaELvu+h39vYuL66w89SGm6srH+PJs6cf/fiHve+vV6t11y+vb+rKze28C36xt++apmc5v72JiLd3d5vV5vr8sj48bKbTg5Pjy7Pzu6sb2KyrprGT+pOjHzx78uTX//UXt89fTqY7bdv2HKiakaXJtF6t1nazmRtLgB6BjHnz6tXtek3WUmBedy1iYD44PqpmE29EY3VF7d1TtEUT/Lmm/kO7ovw+AaICiPDo0aO/+du//fb5i/39/Xx4MySqrcB8r8QaCXJEDD60bdc0TdIKOUOKRQhIO7FarUIFSeUghMZaY8xms5HIao3SAAdrfCtqLaWmepVSII3+5poOTCmaZMiY5Di9h4x9PJl475dYnq+8ICAK+6G8a9PeW5jk4N77uAozgHImJdNiFGzQaJIzGeestcYkLucEVqbGBpl5MNVAw5Ymi8IsTDI0awJQ+BCUuowHZuatmzdEVon3k9BASHnjIBAVU1T7XtM/idBAYgjW9w98BlkSbRtP+em35iT9bI2rJLXdsEAmAmioJka/vLvp+9Y6g4YEWRCNcWQMkamrWquWYoyevZKV+OBZw4xkyThra03Rd67KHh4oSRORQSFAKe07sMDBIihoyAIaFuXHRM54LGeajgRZCEv0IpYsKJsYJDo0JCJrrJY4t22bzjBiifllykay1pbJEQGOCuN67U7fcWqBqKdC7c6Sk06QosmSIWBJCfwAgJJrpdX0s9Zsbc6RMEjxAVAfL+2MBAho0jvzsH8hBUQzTpLDAqSzCIQk2iVE8mQgAGf0XqSqKmWk6ru2cdbG6KMnCQRskA2igAQQYgZhBnTWurqezGc7OzuPH55YQzs7889//au7u1sNaDXNpKqcMWZpVovFzmazWW3au9WmD76P/smTJ8YQROz6jplt5Zjj69enx8fHJ49PULSIO+0BGPlAmCY2G5FJRiMUXu7cijG/GT8Ag5X5ln9iBYz3A3R/sMeGAtKFqzdvV8u73d3dZjLff7BfNc3e3t5mtb44P//46ZO9/f2vuv6mv37w4AFHaH0AZ66ur/ur5brbzPZ2Dj992tV0enndAZOrnKsr5/re19PJZDYD5wjwaFJbazar9dnrN33bSZQueHSWJnV/LZO62jvYb9v+8urm+NGjb798fn33dnN7vXr+jXtytHMwFzLM3EtkQlvX08lE473Lq+vHT5/MdnZurm67tr2+uupjtIaOqhODhkmK1P6Qrt3Cjf5YZfw9feWMJCEAGkMnJ48IyRCFEEodkeZeeN/7vmPtr4aEiFVVWeu06Z9oqCkyimgCpUGV8nkPY8pyMs5NJtNJM4mB1dbXBy3ojj52fvQsyLJk0JwPZflFpCEp9x2mZb1srt0ZzkGhW8843tYUowiykKSG8PkiaT4RAUlI01AQgLJNnCYTlK8QlaxAY9ZaRGSM6gJjLBqDRMZWVVWn5JvR0DAws8Tc7shk60RSbBG1BBYgx+ogaTxF/aL3QSQSWQWtId2sPrlqeCICsippIudS5GE+NGdTkkMFhQKkzMgw4/qrXKqDJaicYsEquvP02lxJmQpVC54Sgu+6Lkoka8ga1Dc4IqK3b8+WyzvFCEQ4Rq/QMCttByJaEgDjHELqAixJL2pZrUHNKWCEFKMY3DPVcMYYIhti4BSzT/hqftJMDaZJthJNnhJ9NOeccUZj4HkZk02lWdSQ/UjcbmanmIE6wzGEGCNm2BzzkuKwiqK4HIgQkhQOrxylQcCcH5gWQlK9msknnUXTH9IHtNY7fWHSSMamdF4UhZqLu1DQWj2L2tgMgYhsrthOO03yHkIiiExECkq3m/bNmzcusgNRuAVz7nCM0cfYex88933vfTTOaVX3dH/36bOPmknzq89/dXZ25n0Q2AigcD+dzT7+5NOjo6O5D3u9770X1G6SoHfWte3ybrlp19ba3d1dSyS5ubIo3hTzWfqQs5LqfnN7tXzicZtzdOsjoqJBnYixc/zn18f54vd8i2z6y/il9308/11ui0Gur64ur66mrhLmSV1bYwTEGLPYWbxdn2oKSt/33bo9Pjw6efgQ5s3J9Ony5Pb24tJv2qqq7N78sl9f3t2G9br2MpvPMcbWtxNjI3Psfes9W5rgjq0cENbTybrvN52f7s73jIlRTJC29V3wl1dXTz56tntyHAH29/eb3V2D2N+tkJmstU29QlhuNiHE2c6cmUOMk8lk0jSrarVaB7QUY39xcVHNZ9ODXUogmkq4LQbdIoL/hFUA2Lbn3n3jvT0jOS7IrPgVkjWRWfo+cb6mlpdChK5y7FWilOxoRExxX4X8JLWTFzLEOaKXxBqqC2LI2Kppqrpeb1bDRkAEMroZaET0WJwpzCZ5jAwp4Sr5hcUdG03Ie+epmPrZpU4mPQeQoE2jS+tlABiyiTLgrBYyijKDpIvqLRICAQf2vkMUg0nqFYVnjDVk0RjJFU3qyWidKSSLgLMXnQYRkbKBhsBRsnbIz4FCObsAM6FR6PoYWaxFzpnYklSIQHHQtWFC6g3IwnmShRAJSIF4lTSqtk2hulJbCQVzK9hca4wokIpTMXfT4ORk6m0Pjd812a/re2aJjIFl5tze3l7TNEYVsLPT6VxQ2r7X5kasFFRkLSGQq1wVo3BgAOz6Xn1Q9ZB0N1DqDL+VeiNyv2m2AKhlBAG1D6OmlufGGIiYNRWLLpc1zlqCRKMB0+n04MFB5OhDaKaTo+NjV1WGSGIKKqAxgVPYu9ye/s0SAViFvO97qmtAAgnJOCjmMQACEpJ1ruwBKZQS20akIQtSmkQmdXjv3BOAPqruCU1R0q2aqGNHeUKYk54VTqfheGHOn8RxzUCOPgAiQG5GFGPsQ7hruymQqysipOAZlLZGAJGZY9/HIH3fb7p+1W4uLs7PTt8eHBw8enRyeHT8859Xn3/++fn5OTN7H2ez2cnJo6qu277TbAnrXFVXVVVxiAh4uL//xcXlb7/4ze7u7o/+4odGoN90VTOlfBoUVICR8MX8+2E2lSlMxLzTfX10FsuTv/P6HyPP/6mHZIwHAVjk+ubmbrUyM5wRNZMJIqW1D7FyjhBWq3XoPYbYrzfoObZ9C2Cbav/RwxQsNDQBmCzmN8sVIygPzGQ6cc5aa62zUeR8eVPv7xigxz/89OD46PNf/vKrl9/8ZPdn893d66vrdnNnogDL7dX1dHdx/MmzJ599vDdfOOs23t+0XaDe7Nj5zvzWms1ybQQmmSnl6uZmurMz212AM83ObBZ6MdZNaq3QScZn8uz+PAbRWOl+HxVe3jNWWukYGtf3a4nRGmOMYSABSXJDICg4lVYtG4LqNGnSoAgQaXzSEKIhBpKgUVvUKFgKhwGCAAMTGkHSeDAkAiY9vJj2xkixisBmsxGRnBqtgdbsyCICAAkQg5In5nynDDapky0aEuOkX7cnJ0qBuItXvTWlJaRWTOHsH6OIhBAxFbNKRpUJMyyd9JIgM8TIwlI0ec67utdRjQBJ8VoEQjHAkguoIHnYuW0SFP8kQeDqeLDqQxRMTQchg8S6/xKcoD6PGuwEwIPjLykR2ZiE9if/bHujyb266fcNS9agIQYBQls5H0NgDiI+BCJqmsbVlbEWkWxd7ezuCkhVu0nT6K2SaEVQBGYBDhCDBGbpfa/0HwBgjFE9oT8UZ7d4+BkREYUUCDWzgZ3ybWuJp2ZBCemb1UlFBI2Su6qytspPLdPp9OTRiXK4GGcXuwvr7DhpzRBFZNm2uHVoKhmgcAx93xtrASGKUKbLgqGYSgDAVQ6JNAqTXde8fyBXbycrVjmkB7b00Q7GgsCriVFVVV3X+WAnC1BPLEeBrI3TIxBJqkGElHYxIApJf8lAHZJ73SPtHxz86Md/0QguLy6WqxUag6p3maeuOtzdDTECUDNpAIxaKK6qLy4ubm9vPvv0s8PDg7/82V9+8/z5y5cv+76vqno+34kxtm3nqnq9XkeROc53d3cj+m9fvbh1jmM8Ojhw1p6+en325vXRyaOPPv50Np+DSczP5eiqXaiySVjy7GjgIUEmZWbSI4/s4X8NenYY71UA6fcoOwd7+7t7u/OdqnJkTAghCt/e3Gw268cPH2KUs7dv0dB8Z6fr/dXFJdUOrAkgq77zBELQNPXDowefffLpOVZ355e86Ze3d/t7uyHG2LYU7Xq9jiEg0WJ/b2d//63EejFfdZ0XXkyaumm65Sr6EHovhrquqxaz2+Xy+Vdfy6q1YOYPH8w/e2YFAxok6wWXd+tqMj04aY6OH5xfXDSz6c7e7s7uYr5HASSCMCKP2BLHTv+9uRne9IfM5B83MJn36XzVdbNY7MUQvPTKIixI1llrLIiElCSszk6mlyl2uNa7Z/dR8UEiRC0kLR6tAlWaUiqSC4KUNW8wJMZG6aDhirWaWAqVjiI9S0ndkkTiy6Uc8/50ZjEsSgVbdqMmDmV3t9xKll/3QmrpHssNIAKX4KmaBgkhl9RFImsA2fozDE7ZsFqUmlKaNKardw1y7y7yYxfgtISRt+x1FeREpLS4Wy8IpjpRKfD89hfwMLfDQmQD4HuMdEEGQDvuA0NEpQpJWxEQUeVc3dSA0jST3b0DHz1zRDIDzirFLsvQaJ5ZReiNMcwBRrB40bhjYwpHt6EGxXQyWSx2Qgir1QoERgSoEDmGEKJAiMygjFcZp8XURpfAINkocblc0sI4axGVjUcryQqvFb87bZQbV4UQcoJ2onxBxBBDiEEQrDF1VZExHCREDrpTkJItC8ltzUcmJQjk9WbIORJFamBKYUNjjJKIoUGBVPaKRMhApABZygovwqIsb5nkwQRLLDPMwkb7MiEAgasqayz7wACIWFlnybIIMjbN7Kc/+TkjOlf/8Mc/2pnPl7fL//f/8D+8PT379ONnvu++/vILlLi3v/+Dzz5z1n7zzTchhKurq9Vq7apqd7FrrAWA2hqIsa7cerX8xW9/i4i1c4DCzFXjrpZLFvnsL35UTyYl/TNjVO84uIMSzjGrPJlZDf+pave7xfefKNzfe5H3aNzys/5tzP7xkQWsjEGR1XJ5fvY2Cnddt7+7ODo8uL64OH97trt/sLezMEQcefn2igSiRXewODh54Jp6fXv34ouvf/jo6dH+frxbX683bFCcqWfTZjJhkE3vu7slAlau2bTdpvef/eTHfduu2nY/cuUq7/vQbhDw8OhoPp/u7C82ffvm9NSfXs1MNZ3OpmQYyVX1/oMHV33kzgeDkeDo4cPr5fL1q1eB43yxoKoSpZZTSQxDMZJszwMO4Vh45/U/bMLvTfLoJfiOPYOIgLSzszOZ1Kvl3fXV5Wp1Z1zlKqepQAbJWSsaxgaj3DCoIeDkbUqIMdMAoEE0ZAIOTUslmZPZBk/6sVAWDYlDmKcr39jgaDJzyZAqganxg6T+tx96zA9sbP0WyQS9ybfPy0ZZ4WZxTSX+qr4UqFpXKum0wAxIIIzAqLxs2iktw3RZRA0mdSqC0l/GCNqFsLx7tH/yhfLcqN+v+sIQGkpNeIRTQSanBGlNIJU8CdojQzlMRJAlKzootwiUGROLiaQrPN6i77h2iKjmjLIKpwW0aKyg0RxvslXdMGhBMAAIkHHNZFrVDQAbV9WTxka32WwEkgnCkUE5rwEgUWxqTWzEBI2KMYY5AmBVKSBps5mpCAwlEiuthUVEREV7QORgd885d/r2NHAMMXjfMwAaChwxhigchQlRCHKDBAYQ730IAYlCZEFQzZeiCRnBL0VBzFISIsbGL2Sqi0SLwYKCwEgEgVkLt51zVVWrfasVQJBndghGJKxHAMSg2Urz1Z2Tu9ZLQlux2BDGmMpVAMIDGysKMAJouxKRIsEFig2ifrREASf6b2QgZoh930+bSTOdAFHw/fX19S9/8Qsb+Wh/DwFn1aQih0JRAFw1OzxcrTbTncWTjz45OT4+ffVqdbd+e/r25OjoyZOT9Xr14vmL9Xr1+PGTTz/5ZDadbtp2eXf3zVffROZHJyePHj92VfX6xXJzsAo+XF6c7y4Wq7u71Wp5c3MTQnj87PHPfv7XH3300WRSswgwahCl8A3lydT4SjZqRzjQ2Mb4c40PCqN/roGAhkSQESTG9WZ9t7qr6/rRycn+fLG5WX7zuy8d0qNnT3YPDqwxYbW5u7h++/LN5HD/hz/50fz4iBzFtr98ffbV9fpwsRt8FCJsqpX49c2VXS2RKArW0wYR29W68/3EVYeHB7HzZ29ON+u1sqVPmnp3ZzHZmV/fXM0fH80P9g8fHl9fb6ALoet50+G8Mc4uDvc3dysLKMR3fTeZTprp5Obm5vTt203fL/YPaFKDo3tK909Enr9Dy47h6PdM7oeHYmxIZF212NsPMdzc3jJ4/TKOAYCcsWD0wFEUGkF6AkCinbxZiMi6ylZV1TQqslRjkSZxju8jZ3yodWCMyd4nZXhV85kIMPEkxBhSv5kPPB1mZ5EAUkUEojZQsqPMLtlKwMq2iWjTVU03TR4f3keZBjfj3hSiodIlVtsMaA5letT8v4zeizbfE4BECMzab3Y8PTL+gVNWBRYhqFIhcVsCACIZA9aCscbZdtP3fa+ky2CMNc4aq1VVRJThg8T7pA5XYaQgxJgnKpn7ouQQAILApP4d5wBvno3sS2MmANJsHAEEsLZyxlkgAiLn7HQ2W202IkKGqqquZ26+s3BVDcLWuKaZaNIdERmDAsAUhRk5AJGw5giRRIkxojAhMrImLyDCZNIsFjttux5s26zzCtZfMoCABRHulstPPv3kwfFB4Hh5dfnixbcCgkSR2eqGAmiaxiA554oVEzmGEAUYCF1VTSZTYyxmzqWiYnW/ZDwWywZKvtcICUHARGOOg0EkIs65uq7zpZQhWllOBr1QgAFrLELJNSozAAVcAgANCSlaY4ypnLPWoijPTkrd0isTqpkGIpr2nGxkPU4DKgsC2ikSBFA8R+PMZDolIgjirGVmJKzqum+7ciu6bclWDJu2D+t1e3F+eXl52XWtTstqtWqaen9//+Lisu/9k6dPHz48/vbVayKcTOq2bdt2c311AYCr1arrNm/fvl3e3jauds6GSET47NnT//h//j998oMfkTUhxLv1BtEAFDq9UV1mAaYBCUGYhhNZ/KSMuMPWrI9FQdr4GccYBMeWFPn9457Deu/Vd66QWWeyoBjfJORffdeXIQDrBSLfXl3fXF0f7R8+ODqa1PVmefflb7/o2u7h45P5wX69mBnA5eX1zXptZtMnn30yP9hlg8wSQpyYKqy605vX9XRyePyQKwrEXdSeyqY2ZgfJVdXt2aX3PRn0d2vufb/enN3cEQsx184dHx2e3VydL69Pwqd2Njn+4ce46a+fvz47O6tfHxzOa2emQFjN6p3ZfNO3b68v5mG+ONzf2d8TkJvrm4vz8wdPHuc45f1RlmBLeKXDkmDD9y5E/gjeWwLcnu3xtkDU7fCeNPXRTlCDjwzh3v7+5eWl976yruu6EKIxqB6eCAsTayqWNliTFMOKMVpjpvPp3v7B3t7+dDZ99fLbu9s75fg1qRpDQz9JYSRgIMdWoGSfIOLIVcg8rMAc8yxB3l4ymsJ38YXxg45kkQZ8NJkLQXsuCUhkFp2oQabBkE2droV5ow/CRxJjE2HiyxAgERIGNEPQmAm5NCpWIZtMDhAQVAQ7ocGQqf5xWKb8n86dZvhSYppCMs6ausa6pqqy1kbmEAOzaEvXEGPgqClbml2dorsD9iCcWzxZJESKGBkkAiMRknbliYWtM8leUq8UU2lTfqZ8syQgEQVQbGVdXdXqdCLSwf4+GWMMOuem02lTN3XTVHUjIs1k2tQT77133lpCJQAzhmOInhWSZoiGwVmnwIGuuzWGjUEgZ6u6aiaTSepDJwlv3d4dibcBRKxx69Xq5vr68MG+Vgbpw0SJkTkIqyXqXFU7Z7R/PQAgOuv2dvea6ZQB0KC1lhK5ZQKFZLTlnHNw/9jrD1R8YmZWJmcs+LmIKmBrDWVwWZgx03RDysAYYHmtAhxv90FY6P9zTh1mP89Yi0Sc6EPBkBGJKJhyeTV8gLid1KahE8MMSFqkyJIquMSHIAJVXRljBNHV9f5iXhM102nX9yGyYEKrNNNbBKJwM5kcHh1dnp9pNuZisZhOp9fXV1VVHx0eXV9ff/v8m+OTh8K+a9fOGWsmzsLq7qbrOhE5fdPf3d0F7zuJRJPppNnf/+TnP//5j370YyC72rSbrl+u1qKBQ+dEBhKGJCUpNcQoc1xKFVLsAxHGAmF73MOmt6DO8c/fq+T03ht+r84uykNNCPnw93/318ry9vbq/GI6mT48Pp5Y114tv/rlrzbL2wdPHh0/eVTNJsJydnr229/8tif47K9/evDx09DYgGIRWQQBHBkffexDM53ufXQSKwrALCxRmCNEXl8vX7/4FlkMAgKBD77btOv1/u5uU7mDvd22a199+3LjiAFsXc0fH6MgN+7y29PTV69dUx+cHKNBx9BMJ3baiLO3N7fHBweL6ZwIgeX07CJ0fVNVyR6GLWj0e9hARTF/n4EfeueHv2hLFKRzCUiItatnk+kdr0CEEJ2tMVEUBI17sZCoDU0YQgi+D75XMCtEuVutAUkRtcLOjwAIA++jZIqdQhgsiQa/IHZIaJAIUzkJIgJLzhoukRlJDW2wpIMN208fbyiZyY5FenjNSsrMPlrsKOWdZXKyjPo9C8BKmo+SSjRBREaafFiO/JMKzLQ1BBAkCofiTugzZJBSrQQUVlUNIIlglxgQNOZO1tWVmU1N3QSdWQABYcAogjFwaqWDJuv17A0PDj2jph0jM4OS+rKUEuuEk0tBEJSmKhHipmQ7NLoQaqqwICMQgBXEZtIgasiTo7B1lqyxxi4WC0S0lbOukhiNta6qiMh7X9eWRaJEEQ49SIwoStcpUXyMHEIMkckYCIgADsWIceQcOUuGBocTtx40D9JyY0OIeHlxcbO8ChKtdZPpFLQBdWTf+9D7ECIiVlU9NGYHIGums/lkOmPmACGtq0I6QKJNfAkYOFXBk/G5eYhoprsqAEREDCH0fT+ZTNIhQYHcDclZ65zT5Lr02bLHB6R0KGsDBUvGVrkMVm2RLQhgkIwWKGgVuipgaySC9k9GQmONcliOtYaIaDVh+saUHygAEmPo2hYRK1choo9sq/r42RMDENdrNCQhGO1fyoJRa7+1L5bUzaSuG2sr9a8fPTpZLGbfvni+Wd3u7e8LwsX5ee97APC+1/pwUCQDwEcfQh85QhDsIYoTQs2ea9vWM0eBXm3RwM4ZIrCWyuOoEZ3q7pmFYs5YUXey5JWk+/3eOu33jHeC6/8MowhBiHz++o0TeHxyUtXV9dn587/7fH1+/ejZk6PHjyaLHRZ5++r1y69fTGazT3/2k73Hx7EyHrQ6DcEQWjttGiHLBomwnkzW3HdtL4HXqzWj7O3tAQB7fzDdmdaNAQSJ7ao6Xd455qmxu5Ppq9dv+tVm8vCI0URjQl03zx4+2Znu7O5fv3h9+puv/MWNmU9hWtkHILWd7+6IyO3t7cTVs2ZSW6d0OrrpGVOx/B8/P/cX6J4H/K6g/66ybMR7HwcRwZhCZUqHG3wAUT0LCAQGjFVTgpSICRUgJgIUMokut+97H8J6vb65uuLI4xvXww4CMcQsG0QS3YRBUDaOFGGlRLJhIAdrRCD6wBxFTAbsshoAABGTBI9mMA0pxuNHTZlPCXrdqjpASSUnaYJGoix7z+8sSenzhyCRJUbhKJFTAaxgZiwo1xncWclk0Sk4nn1qzOBlWRj9G7WgSIHHLOjUUletWrlqOp02e3teYNP35YYl8XWXxCIcYBYArZ/Nbn6WMyAZdVWQINWScBJ3DCnHNllRSpw8PCaQ+r7jvWf7viejgWgMsV1vWgGIgYloMmm8D85Wlat76QDIWIsAtXWNq9Agc/TRdzHGJOMV7jeCeldMZICQBACNBaqtm9Z15SrI0CJlvTMOP4gIaf2wsUTQdp1f957DwcHRdDo3ZA0ExXtDCByCNcZaLdpJk0tkShwxnXBMVVn6BdsWAKZYihQHCBFJ2TGVfy6E4L0nohKH1DcSkbVW/VZRQ1QtxWSJpvrg0bdh3jf6Nsr5eYmtNd1yMkXRGLLWImAMXoR1Q0mgGHskMcaQoXcwTBFh73vdBk7hM+UxDcwhOGerqgLtf2jt4vDAIpy/2ICA73sOkRByWS6QMYDUhxgiK6FejJ6Dr42Z7u7K5vCL33358ub26ORhVdcvz87Xm030npmNQd1U3ofI0QevjnXXd8aYuutfv369t38YGGzViCGLGDnVCIzrqtIaabSMEBA4IgDE4BMJhwyr/D20Jd/HHrYn7vdf4A8fMhRWvOfFDBm+/5aKbOIYby+vP338ZOYa7/vnv/7y8vXp4e7e4eHhfD4PIqevXp29ejOZTD770V/UB7s9YQS1NAUJnXM6qcY5cSQgMYTzs7Pr84uKadNupvu79f5RK1S5yqCI9yxMEh3IzqSpkSjK21enF2/OHFUnxydV1fQRAImqeuew2sNaru5uv319fntn57P5yQN6xqa20dDOzs5t14fgmeu2a5XUXfeXxtH+3FbOuzp1NN3398i7xj9vvZr+RgBo27Zt277vvUdjrDWusmSMEVTyV8mbCwGBrHFUs4gyFZeNpYWpmHNQEDNPg7BIyGpSR6pCVC4X1vCM5sqMHk2EQwhF8chIimbJo9lMsXTCKFFc3IqAlal4T2Rge8oIBnl1/43b5k/qFaDyXfUSZC+XMnBdbnj4IeN4PECIBImoYIx0JVsBMafuMoD2/EVEQ2Sosq52dWVd8EHj84gIWLR2Tngb7kFQxBCqhcXapxZJJCSuSwRFiIlI+X1DCMwRIIL65MPNpTWXEXcTIwxYBYiNUXS3iEPsvPfROhsiZ7IRtModnJxyQqQGzSRi6EOQUAEzQ0BiAgBBco7V8UKObHKWASEZRO7D3e3t7fUN5AaNY0BJXZk8FQipSBdjEGYMXmIEV9XWWm9Sdlvk6GNQyxCNKb6g3mteS4RiZKNmDmOBl/XdMOq/oUl1MUjGQgiRmMX7QGS0lF41gdoNiUQUUcFzyZVHKa6Q6uwADWr7gYJGYtn5BdgpjIwJDlW/XWsB1XQUS4YMdhSBOTXBhJS/lvLfUgMsCUEQRYP1xAJRJHIMwSAwBw1VCBk0ViCKcN91sffRB3VLYgwiQMZFxMDc+xBDVL8VQvB368B+TvZgNv3l57/59ttv57u7667TBAIN5xgF7QEU8ijNmvSeLy4u/u7v/utq3e/uH8wWOycnJ5WjNvYQBdFChNXtMk4mzWSiZN1E2hZUIyvggydTjm7ClVAl7If5Zv/VDVEpKRCZu7aNnfd369NXL0+//GZRN7uH+/Viaiqzul22N7czVzEYH4LVIh8VNgIk2Fjnu/6q2zRkwaFtZ9EHCNzdbTgKc9zb3TXOCmFEiSxevAFGju1qBSLK2d0u29CzndbTyY41VS9ICIZheXl59vnvrt68pM0mMDYssLdHPpKAEbXD4OWrV5O6Wd4uq7ppmknx0IYo4vdTw9/jbd/1hhGuXN58334tX6SGXcpAEOnbtq4qWuyKsCElltAjJznpJxFwRE5i3VgXElUWGmOcczb1L8cMySk4J1DczKxRikIiIkSrmLHIFl5AaEFIu9iqzsqPkC+GAAjCktmyVBgNMZlMnlUmQZnvR0GykYWE42SZBM7Q/ZYaWayqYBPhqJ5rpoJOKhDeb5VmzZg81KFsFVNnw+SnSkJ9k6omjJAcYgDA4t0JKFba3S57AS+iuTWRBZQU2hoypPCtILJCw/rcZNCgGgGYCqcMICAJaV6kRt8jxBCYo6gHjEn8A4hIaQjBqaVuASFSKIpt1UwENNiIxjrjKlM5IOPqyjkXvEelw0awRAbQgHm4/+B4vrO8ur64vlh1PUQWwc5A0Lw8k7rBt22LDM5YEmCMJBC67s3r15eXZyBiyQRmpZrPuFACzhGELGksxVhDzMpdFjkikbFWE/1V2/Teo6GoxfAZxIgcxwdbg7+qpkGQRTMvcHvzYa4v1b4FgcjqKSIyzBIj972PIaKzkgEStYA4t6WURDGnTb8EdHcmMYqZdGyEPW2BQpAtMQQh7RIdvQKtQ5jBan6WxRj0iGauNhJjCZSQS7Z6hmheP8fo+y70fdf1681a/WlGBCJQlGzTBu8jh9xWOwIyGYicABXmiAgG0SGJD+x79t4wcIhXV1dg7GyxqJsmxBg5AoiGTJDIOaOMcMzsXFXXddNMlsvl2flFVU83XVddXVVVfXx8PKmbtm1j8N2mX9+tjh48qJpamRooGfPaNBp67xFBYgST2KExbXwZZve+YJX3v/LdMvv3j3c//X53dvsNvz82Ob5E5arDo6NXp2+69frqzWkMYefh7u7xEdQ2eH/+8vXt2/PKVdPF3s3Nzcbh9GBfwSfL4FiQaVo3i8Y2aHqIWooxn80unWXfTaez2e5CnGGLQNhMmr35Dkfv1+uu3VCgPvjbm6UPAclN5zvz3V2FJUSkX29efPElrNcnj09Wby82NytB9L73MRgAElQA9tWrV5O6eXB0fPTguK5qP0T98E/j3njXo/09b3jn3ZgP6LhTwZaixtxzt3IueO+977nHnEHFSfUN2gKJUKQUMiBK6ntH1hnbk8/nnrREBrOgYIQM/qRbK8q1hJSGuxc0hgCARynQIsVzKDePLCVIKflx709IcpsQyRhCwkx0k9JjcbRj1fsoHCJJn6dsHhxPsno0KiAx3V55TUbSrxyY7PcyKEMeaUUuM7AGxBE1DMvaDxC1yCUDD7g1e8jCwfv1pu29j8YwUeSoIpOTAlCQARMEm6YOEdCQNbYikyP0OhvakJAZiq5glJRunB545NInxDXdlaS8NRHRDoaAaG3lfIw6v6ZytnJkDSBWdQ2I6/WaY0TRnFsggNrYZ0cPf3zyRB60lxfnp5dvTy/O365uluxbAwFJDDKwJRNDQC2VMyhkRKIjM5tOl5uJtpIwZAgNGYtUZnArLQBLzw1CMqaqKs3NXsEdCERm732MAQQ263UYaFTvY4mUvQFIzQa1lNsaJEFT2QqzFCgMYZIbRYyvGUKIMdraMXOIQUQ632/aVouGVcmhAAEZgQhKGQopIKExG3wPOfroIKiRm9Utg8QUti/RnXRUdAshspK2UESCvb3FfD5rJk3fdxcX513X6RkQzb/KzYz7vluvV5pznsJRhM66GGLXdqnDJZeIQNLhAoDG2MqxiLG2rmofvCaKCEcQMMZOJhNjjeJMdV0fHBzUdcXMNzc3b9++2bRr5l5EjDHWuuXtHZH90Y9+dPLoSRSxVWWM2d/dbbvuH/7h7y8uzn/2s5/t7u3qFyh3aZaUYAwJS9/1Sm4gWl5HrEl2MophjbAlPRvvi1oVyCH7RzL67O9zue7rWhFJtD/jG5Dtj4zc9BHk8x7VUT5nrf30L35wdfrWRqmsiTFODvaqxRyt26w3z7/6enl59dkPfvD0o4+8o/NuVXlfNTUGXl1c31zfTr0cL/ZWF1er0EaSpnGb5WrncH9nsVjG62ZvB6ZVT8JEhBRCvFsufej8ZsMg0/m0a/towDOLkcWsme7MBdAyIEC3ah3QD3/6lzt1/fb5869+80Xrhdm3we8gcgxt17q6/suf/9XE1ZNmRtYEPWHqBuWtP0TGticchxd1ObZCaN/hwm6/7f4YnLg0zQJbCBwBQOx93/ebzWZnZyd4X9fNzc2t9yHElBZr0GgxDxJp1QZzVGxMr5P5fSMSEVljrLWO0DAnzZHTqSi5bagsiUlGUSatKx6t9udJz0AAVOh3743kGwIiGeKolPGaBYWiHevzZGMC8CBNiXLygShdWgjRe59jJVgmKn2vpBmXsjD58YtGBwGJwoY4H4zsSd9bRcwoOgtrlryufWoFAAk5RkitfUatht6/xOr3kHFWu19vfEhkWyLCAkartgFEkDn2vSRVmPg3iMgZZ4gSQUnSDBw4BPbKTq1ExkmjA6T5SyTDBQvITyuDjkZABLKmck600XECzQ0ZlbjB+5ubG5GoGDICMoMhqoQmYmo72T88+Wj34O549eb28uvzN2f9iuZNcGa1Xm3alpmBwBmygdER98EJOLTONfVkZnvvYzRVNZnOTFUJkaTodYlt5O4FulxEtnIAUFWVJasxWo4RWIL3m3XLUVLCApqSuCOplBpJALTJBSCKGM36zvn9yqqSViJN83skLzP74Cuc+sgcOTDfrVabvgMiSQQmnD0wSVYEIjOn9h05j3GwrzG/LR+be+JDUrkRFgPWWicC3nj1gFlYIGoucFVVu3uLZ8+e7e3tXV1dnp2dvXnzZrXccAROPk8AgL7zMbLemPc9sjhjprMpOGIEMoYAGYHIECrTWIphN03jqkYArXV1VUlne4CMayUkwKKWS8FkMt3ZWRwcHEyn09vb28lk8vU3X3dd37a992G5XFlbffrpx7PZrNu0QeRkbw8iv3jx7Xw+e/ny5dXV5d/+7d+u1uvb5e3+wb6rdrIZasAIoRHhwFJs1tQW7k/ypb7n+GO85bKX0v2SfP/rMCEKsgA5e/jgwV4zbW9uV8u7ajYFZwLK3WbTzKdV5arZlFFcU9vQdqvNvJ5wiDdvzs6/fr4w1dzWm7vV4f5+RPZdt7q5mS7mewf7m76j+YQtqROwWa93Z7aZTmeTZgVw03aBYffg4JMfP3hzcfHq9Jxr24We17IOnkVs4FkziHIyYAABAABJREFU7UIQkTXB/PFJ10exNmjWfd9fX103VbW/f+BXm+h7pLr3gR2BceOZ+RNRiO89tDPJyAFMOqdoF9AmRaHvlVvm5ubm4cOHalfd3t5WdVUZkkSsSyxggQBBDMQYEZAlon6NkhkEz5ERURDIEGgPbNUNRGW/cup0q3eEqpERxVqjpKMM0Q7pTdo5Hq01AMKpHqQ8iD4XAAAhGEPe90mJgl42q1vIRkAKMVL2dFnrWRkhgkThe3IJJEmqnAslI8gtObflPGJ2apKXOjjpVPQ/wMhtLqBzji6VeYFUN/Mea1XvAXMvQAQkQYNY1fV0Z6c62F8Fv7k4h5RsHCUqUZK2GRQA0D57klEEFkEBC4iMKMig9UYxxICa2y0sIoRoDRmCIKyUTqpZc1bYfS2SAos5eq1GWdTInFNmWGMRkDm2XXt7eysArrIsLCAxBkDHkBBxZJiga+Z7ezt7Hz16eiehPtql/enF5eXrN298FBvZGnCIlkxAU2tGddNU86ntOwmxqpvJfM4giDnMCaIhTcVwrDUhhBACGuOcCz4QknNW4S+IQEjex3bTIxhEa8iCEDCCoEFi1DQUNa+AEC2lCIc1hnKfURxgsA+KgQw4BF2xEEOMcbVehxCwdEN6/yfBkFEPOKX/5QhAMe5L8b7GPhIInOhxAUt5Wco1IMyEIYioiZe6Osy8Wq1iDH3fE1Fd193GRxBVpcxsiGLwKj5EpPe+3WzAoSBOdnam09mby3PId6UH2gA6awFgsbvY3V2AFihbx5XrW3LO6Y3HGNfr1d16rdURXdfFGGPkR49OZrPZJ5/+wFj7m1//6vLqsvdsjfv00yeffvqJdXXXdZ33IYSjw6Pb29tf/uIXN1dXIPDq1atXr14FZiRcLHYwM91qoItTX8tkq8H2z/9cY0CgPnwXOIrAfc+rCQKzrK9v7y4uw2Jvc3MLiGSNAPTeB4Af/83/xtx/fllyJPmBqAn3EFelziwNNNCN7ubMcLi757x/fR8/vj08nA/vkUs1Mxx0NxoFoGRWVoqrIsLdzd4Hc497s6ogZjizZDS6Kutm3BAuTPzM7Gd/zYq3t7ev3l367aSX2CrEru9W69BtD2fTJkC/XnXb9eth20zaalKH1Wpzc3t8eioXitMGmSkGj7CYTh1A2GwTKCed1s16s+mbWM2nn50eu4ODxHS3XskWhxT7rm+cX97dXV29m02nDy4uHn756/Wmv1stE2IfgoqiqEOM68315TsHxHVz228PHp7Vldsfhf1JhAxB/Yvo5Cxn7qVDm5kPmlRBhhRVZOi6ly9/2G63IlpVfrE42KzXVVU3TT0MvRnEogKKAqV3iORuBkRsiTXOO+x6ICnbnwxoMedSQDnrox0cYqgo5CipEqtiUkhIY9h4J2mQMrUTZGhzt/zHFgP30eZdBT3s+b5FSe5XpGiudfxoFoonPQor03w6Plsa6azUqtwEs8ddHMCijPFj4un7G9mELqiR8ycoaVPMRERc+gCq6h7X7l4qOWDVNIcnx9OzU729hXdXGdorNzXPBLJip501MD5GeWMdU7CAMtAm44Xy1/Zlz2h+6Ph5yQ3K9ouqojrvqhSFMJFTQvZWoYuUoizvVttNN2qOYt+oEPCkBkJrR2jLbsJVw7UEdJGOTh58eXxxs1rfrdduSG1VkyRmmVSVkR3WdVM3LQyhqpvpbD6EEGLUkS48V3cREbFjsPgfUVPVXTdsC2OD3Z0QU4gpRMdsJUDWLv7u7jaliEzOecqQBTDRwexAYhJRI7vAzMuYBz03ngRAhF3YBaDE0ouWBI0hqDU6hLJMrUoMCkSclxqOmyCXkGe9u5eFAWVKUAFFUZMxuKkIKDv7kqkcM6wdlxJARERiBRbF1WZ7+8dvvvv+h9PT47qpNpvNZtOZ0gYi2xJWDo1EzAyIcQi3t7dbSBDj4vDAO4dfe6P/QSBQQxQECbphM5tP66YisiJI1wNst9t+uzVKF1HthjCESOyIeNvHbkirdff6zZvDo8MnTx5/9qvPmkn99ddfv7u8ct73IV7d3B4cUIhxvV7fXN/MZ/OnT58SwOsXr7Zdt7y5my3mJ8dHi9k89EFCnM1mREyUiFnSDpX6cAf/yx4f3I7u/84WycePVKDOsaLyF1zd/BRAIgCN4fryXf/+ZpZw2GyG7VaTSJLl6q6dTefHRyAaEXjTtdMpOcfOyTBs16t+u314dNSK3mlMoV2tV12ni4O59MPd20tSPDw8oLZJQAJEQ9Suu7pZaT9M6mo+mxEiRdmu113fz6aT2WIeAVKMospItfMqMp/Nrm9v+xibxcJPp44Ihi5Iurm9a5um8bUOKaRO+7DZ9uA21NYN+7yvxpf9EHn+F5rWMY6zg7XHI6V0dXX18tUL71zXdcvl8s2bNzHGrts+fPi4rtu64a7vYoialZ8pEisUtKDYAABNMzk4ODg8OgbCruuHIZbmp3sFpqbg9MOwLowkj6UfbokGlXBp1po7k12KaNqT/ztMTZIAwtjSYCeVUPeqgRFLhzm0O3OuDtmFo8ablKSxjHNDrji2p/vAADY9tKfWdr8BlMIqNLrj+V9aqJmYuIRm7duFO2LMg7W8Lk2QEkhuNpftDMq4aTVp2LsQo9XH7m6IKHv82GhFLmUUyFrnWfkyGjNXsZ00l81k5kzr9LojWR5fXbKBUlQ4AFhWtVkwADCSS4B5Nuw8ISJQ3w+3t3eqQJhVINhsE/pJWx0tdL0RTzgkjCn1A4pwAoopdP3AiMw14yRqirHm2hINLo5Pf/frr+p3r2669bYPopuqaWbzxXbbyWqFiiiWLZ1tYee8pVACABE1vum2/Wq18pUnx2iRjRBTCCpS142ZB4iyXi//4eu/q5vG+/rs5Pz45NixQwBGPDo4XK/XKoLs1FTamDxQ/NCCFeveUMK+1YwlNyGl5NhZFgAW8GS3Q7Dg2SpInsk7tJ4Qo+LYhQlyOiHmyRZUAVUQ8/tsQSKh97Vjj7ChvD+BmIFIFeu6nU7blOLNzU2UqGItlWpkRrY2W8JInnkxnXnvUUFFNEmCpDE470OKCUCBURmBAIiRgyYB2fSbk6OFqxgIVIGZw7bfLlcxRHtf9u7Rk6dd14OiZaQjYhJB5OVy+fz5t48eP3z8+PF0Ovvu+febbrvthhcvXy6X66qqReT58+e3t7dPnj55+Pjxv/43/+aPf/zDweHh4yePTk5PEXG9WocQ+j4sZjN2xOQQWTRmg4ayeQhlD38CovofhTjx0//8OAj1kznYJRT04wr4I8WTq/UFakVCxiFQSg07UF2t15vNZrZYLJerEIKGiKKr65u6qquqrryboJtU9Wa14qom7x4+fbxdb25vb1MIt8tlAu1Xm+nB4fTkqD6ax6Eb3t9yH2LXVwoNkosxJBnWm9jR5nYpCGEIyI591XhPzElliLGaLmazRUKIgBEAmIURiVJKdZKJr+Nmo0OoEtxd3/pJe3x04BnFeP9/ydD/k5TxJ7/1Y/iICUEiury8fH91BQC3t7fL5RIAptMpAGy3G2LXDT0zkfNjaBYKk5SklIaUVJ1jV/mqrqezKRF750Cl6F+E3RLFksT0iceBrGb2056LBDbNB8XnHD8tOcB7+HD5tpUhwU6XZTBazQrIHYHGkNxOKVhdjzFhQS4yUUmqCYoTjPd0jgktLQhBzroq5mfmlAVVK84pJbgZdsfiiKtqbv1ujaRG0DrrfsxaLxXwW6KktBMChLkvIzEAIzKRUy3h5f31ICMQqbtSX5PHZAmtuWoOAQEkN9wbvyv2HGKuPCpISiJCCCJJJNn1f4QuF8EUsGXnmkYhtO4Q2HX9ndwRsfMO97o7EbNvqmo2gcr1FXNUHCLcrbXrOSkkBcsLoygIXhICuKgOCYHqg6Ozp48e3bx7fX11+f7G+9Q0k8XiQAE3qw0q5torwzQQmHMiMiMyoiMmoBiC965pGis/jjEOYYgxOMdW5akKSdJms0kpOR8dXzPzbDZDQFaqqiqEoCXWS4i8q6vLi+gDBVxWdcZ7bWVbfT0i+Mqbmz4CQLYkUWkXATAV5RitJBcRUAgcggJKTm+wdTaam+VqFvAYqTrrumbyhAigRGgWkiU3eF8vFgeIkNJiSMMQhmEIKiiSM+yHflCAtmkPjw6bpkYEVGBENsQecBiCxDSCPwTWvktRNcZIzhGzIoY4dOv16v0NBllMppaK6b1/9uzZer1h9mcXF03brFarv/n3/37ohy+//AIgvXr56uLi7OBg8fvf/+727u791c3t7e1qeYdEdV3XVf3u3ZvLd29+/eWXSYbptD09PX7y5Ak7Xi1X3Xa7WW9ur282x0fz2TxmD4SRGCzKMK7oEdvPA4/7fysAjIAhABTqy3t78lN7BQBKweYneiDeOws+5vmBHfhW4m4/CpXTBy51aW6VhAFDH7ruFhgmde2QQoyHR8ddjKvlnag05Lrb5c3l+5pdv+2ePXt2cnyMhydXr14uN53E0LbNyfnZbDEX1eV6vVmvwnazjLJdr+i1H/otrLspurpuG2ZPLnbD3XrZh8CT9u76etNtFYF9PVnMK+9DP7x69fLd++vzR48OT0+IcLvdBlX0jMxVVS3mc68gm3692mgfhvUGY5q4SkJMIULtbURkb8g/Vki/TPviB/O251eU8cwMrrv5yN/LC0MJiSt/fnHx7t3lixcGPssXX3zxq199jojMHsnZ2ffKYwwpVk1JFICdA8TNZtv1w9X7a0Le9tsSNTItsxf2ytAnFh907DQ/Ojy5vZLsY9SYQ8QIOTlJx3BM9utssWVvkAmtdL68726sirEKxb3WjD4CkjVINVlGe00YFAQkqYgq7SDk7IvbGZoVl0pKxmmI2WCwH1GM5HKvZuHDyYTRjFCR7JBZoyCzOzQ37S2mBhRzI+vQ7FozYu18zb4i75FYAQmBSVNCyg9vHQktJc5GGxHZOeslk50iANgrDMvzbrnQAiUqnU3vJEmL8ZMjhzgOEQpaKXDGfpxzTlWdc33fi1k6RAjQ910ahvPTs7qqDRCwpBwCXN8uw7abVlU1n0tM2g8phD4ManVSJXeNBKz9kA4DEKMIxgh9qCPMyU+Ue/KzZjqZTDddL6hCqMSKRGBcDMTsvK8sVUEByDE7jimJal3XwzAYa0NKEUiJiuFDYOi0DVffD9fX16vVSpKcHh5bCi4WeGXcCTZrUKjCVFUhd0Qo6EhBadQSlVVVq6qaTqeY9wLkPZPFQTFP91P+sol3by41z03BKxRKCXqO1yOCJWCDAiE550aakRFQMz+QmBUUkB3VyA4p9H0ABUBSopgEieu2bSeNcx4AK+dnrnYJ+24Ytptus4YkoJpUYiETVUkpphgiIvjaJ4nX1+9ffv/d+urdYj6BxmsSVHDsqqq5W3fTxfx3f/WXDx48ePf28j/+3//37c315dXl+clx7f2b16/Xq9XZ+QPPTiXVVXV9c5NSGirnjo76Ybhb3i1vr1S0qurtZp1SUE0iMQzDMAREGvpw1V+DKgBVNSPtcjp+7hi18//845MPvF8GOh5mHQz9cHt5GbrterXedsPB4aJqmsb744szmdSr0KfKJ9W46Zr5/Iz9zdt3V5fvtjd3Tx89Wcym1A/rmxsFfTd04eiYK784PGgPZqvtJoXIGKHH1A0w9NL1YdOnIbTTSbdab7vt7HD++OR4FYbNZrPZrNn5um4xwe3l1bvrayU8Oz2ZztokMYIOmyGs7prprJm0wzAAACi8efVqc3V9Mlusrm8bdrXzTdNWdTUYfliQxE8e/5xAdMmo+PgX9pfp4AcXF6vl3cuXL4jo6Ojo6dOnBweHxccFEVCVpCkhi0iyYJEVowA4xykXGkQiiSHlC+/0ko5RqtHC5kx0RUb2XiT3TknbmbgnRvbHR2G3/nfmxMjQCoq5RiO/JWZWLdMrlqJ6zwTNtgsiKqBgiimKJBAFglzurKiaQFzm1hjHT0F3qLLdMomkmEBASfZqEDDnxe5BAuXuo8OzO8wfM1JLUo0pxhhEUna4yxDj6PuUYhZHbJ52TNH6BhnDE3DCnFsKRipinRiMBxCRqqpyzinCEEJOeFYrjkqQ7QKFJCBqhJeKKJlSSwlAMGuvIUZIspuZ++8IAM48OXPpQgiiioQxxr7v6+ns+PjkzctXtnCyHgJYr9bbuxWwmzQ1MaL3k8UMUuo3GyJ2hCoarEGHIiSzgxQBYIgq23oTvpif3s6Oub+qlFqqKl+bJZetErX4OgOi894531MAsrosH7YRAJxjZk4pWUdk5qppK3IIjOTIOfbeO+9tHJfL5d3dXYqx+U1tyHZKEXFsoPSBOtxJBF9VRudkSQOqKqJo7E4pImLTNlzQZByh6o/Eq5oZiZ9qsZdnZDRfCRHA6otTUutDfO9S4Ngxs13NSvQQGaxbBBGAWk5wNhyBVAUIVSHEBGBNQTwTKehmtfrumz9PVGqibrPZDj0ZR4rZaLmgOJNfAiB7JyrL9d3tdVsB1I7X/VDah6Fjp0TgfDWdPnr27PDoaDqfD0N/eHhw8/59U/l20qyWSxX1vl4t77ZdH0IwIrckIca46br1CpzzbTP59ttvu6FfLBaEXNdt07Qxxu22M1Lu+WIOEP5nKFS74y9VDJ/SqgCl4ccohe26+JGIRQVAHIZ+s17P6mrjCQMxUbfdppubNJ9M55O2Xfj5LKb0/u07ZXr46MHTz549efrkh7//45vvfhgO5m3Fk6rabrabm2Xow+Rw4ZpqcXQEiOvlOvXdxDkja2Aiauoo6d3yNvaDZ/f47OLg9CTdvI+bpas8Ivm6Xt0tQ4qTqpoeLB59/pmfTpQ5oq6GvouxmU59U4dh6LZbVXrz6jUP4WJ+OGy318vV5PBgcbAYGIvw/kRv7H/MdPxzHqrKRA8fPvjLv/yrvu/quppOpyklVVRBkd3WLiAkxBiS5KqkTAwZ1QBM772qhhAQdipwh9aMP2T2Yhg/31sGSMRlkXx6WErH77Gpwpi8tqdmC7xXfIPyvvYnlm/fy6ggVCIATSJBVFAUEiiiCmFMyZc0YCvFMEIos0YwP5WI9SkQHcNyGe0FK/nD8RnQ4LgPYwSGeCsClALODF7nAoj9k8dYXv6iokJKablcwpu3cLfcbreW+8JEas6k6ljjW4wOwx+ImY1Z2bBwyCyJOaq7q7j6OFNQsytNFs/lABIQCbDUE2ceuBzCcDYrWkAVC0vEGJNIXTeLxRwKAJINOFPGIWGfNrdr0dS0zWQ2xfls6Dt03CxmikhhGEKAEIZNJ6VbBwlSwCNu/o/PfzvZ4ix8/T7FCbAnJusYsD+cGbBhZkfM9jsmzm4hUlXXYRjYcW1H0xSgh5q6PT258N6z97PJ7Orq3Xq9Hvo+hDgMwTmXM9+AwCLfzHtLHo3VcjqdHB+fiCgTj5QaAIBIKURNyTuaTCaxUGDu9w/QzFOcl2JKYjj/aFziaPoZD6Ui58x1UGt7aEi15pK70RhERHb2wEC5ZT0ysyqq9RRRi5KQqrVKBkQCRWMNM/bYij0BkMDQ9bfv3itSJAJQEiUBBowAlgVmc0DKIEhADr0qpJRExXnfD+Fus7LcQJFkgx9Ebjeb5XZj4Py0bf/q979/8/33z7/5Rvvu5Oys67o3b97e3t2kqFKs+K7rYoyqooAiKYTh5YvvXr9+VVVV5Zsvv/zNg4ePmNms+uVquVqvfV2Qsf9HRHZxSsZ//6KbfvB44xLfifJ7YPinv+9rf3RyOLx9HyU9enBBCnfb9fX79+80fHG8cI0TICY8Oz+rFWoF0GExmz1++GDlfOo2s6YdFLZ367CNvsYHFw+wdsv11eRwUS2mq+UWqkpD3CzvUtd552naNIvJZrsF4upwcbPZKKLzVVXXbdMSuyGkpmmQMSV59fx739SHZ6ez4+N21oh3whhFQl3FvnfI52enuu3rpp4vFq9evfruh+9Pf/crQf+/BBZRjtHXFAR21dnZed93pj1SUrBgn4zxRSQkdo5TimFIMVl+PjGKWJ4Oee99VYEVTqRUhArD6PuOphkCMuKurgHAZECWDTSy9n28zrEEifecWARTC6Ucx8BoC22Ma7Yo/KwF9zMDizuRL5iSpJgwgZBI1ukKoHttoARUrId8WdU5ZRhArDrxXq3lveMTadB6/8juDxgBT1aX5ckL5jwGeEbzRVREQozbrqPlyimgQlNVlfehL4EANXhg99YlVQ2JmHIGXBkjRVIUQVAgzS2vbKAEVAzIUch50ohAzlc1D4FUc/dkHAHQ8uoIzhGHJIYiEtgyoRAjEh4cHbTtBEpgIU+MVbFFgSCw7SSF7XqT7lbAiEmQlYiEqfJthZNh24U+SApJBTkneXuiQ25+NTtZTY5he91A7ni5tz4MdthpLIvAEjF7l1SVSJnrtgkpmqfftG1dV6qKUBHVzjXz+UHdNAJKjtvZZLqY0ApVdb1eHx0dm1+LiKrgnMscVQoKwMyHR8fL9aadTNt2EmNyzu9tU0RAEUkxElNd1xU7M3UBfiywh6pgGd1IJLl5MhRe4nJhvSesDQ8wlTbGOHbLjAl2UwqIhSQWGdAAI03j+dZcOhnDmI8hVb525EAVRRtXLZrGAwLBXbe2+A+gxNiLDMDMRCiaQiRlR866jzvvPRA5Ix8nHEEChKQaUupCcE0NoCQy8dXhdHo3mV5eXq6Xq3o+vb69vb69ETEHD5kRELz3GUFTiSkqKIS4Wi6Pjk/6vt9sNk07YcarqyskHWLohpBEDXEpuxH2fvhf4vhQaH60RHZ2G2QveHcu5FqFpm2mp6f//U/PE8DR6clmvWGNF0cHMm+REEQJgQUdkG669++uLp9/v7m5WVRNW7n3t936DpkcAHDFddNMZvNt7NfbblK76dHh/PTUsQtdL8MwIF5cPDg4PhTCF29eI2KH8urtm8V8hkSr9bobhrppyTsAjDGqKgu8fXv18vmLs4uL+dlxvZjVi7mr/EEzWad1v9nUTQ2E6nBxfnJ4ewaVjwSSJRV+EFr7H7el/gevIKrO+6ppQ5LU95IA2NoFKjoLn0oxxJBo5OsH+2dOLUYiZuLc0KVEuixzYxc5sl2vFiIiLgoYcySYCXMrlCIR7w9U0RkfJBbt6o+yFi0oixZpkANlmpnnsUBwo5A3UWM3ExEVKZfUHeKNu9CmqXwsK9aqay0obBq4+LtlqAByPtj9oiD7RXniTH1Qwu1axhIJ2YZuvH02VBUQrGl7/iIAVr46mM8XZ2c3m80Qo2NHSOh9giiliWABujOgPfpIO6tBd38WOCFbN7v1hljyexCRFIgJHLsoo12y887Hyzm7/R6JGjKTMQhOpzNf5aIUQBLFEAWQk8AQQtWLi1KpSpI0bADBEaLicLempuKmYu9K74kML7P3jhzEBKqV4jTRJKHTPer9/HSIo/VB4zQDEDlfWfJvktS2TR8GIHSVbyeTsS+vY0fk2FXsPYEqSNU2j5881iBHh4fW09A0buZrzQ2ZrTqegV07nz/74guJ0g1D0zS7TOysDjGlFOJADOxZRX3R0Gmvpqw8tJZJRWdJTGq0dlhwJYOODISiEtHIu0X30vSpAFVqzSQ4F8MZm46FJwgoESCTDBKHoJIkgQpA6aIFKilEy7oaXWq7bADpQYQp9/UqKRJEpKCaFAEdsoEpi8ODqRJIxM0aS6cWzbvA9q1mxoGY4qbbXN/BkDCkH178kDw183nlvJl87LxVfyGhaJIUy56tnKtq1/zqyy+ePHviXR1CYvabzebR44eb9frVqx82m34+bUEBNCKUADzuxg12Gk3HHXBvu+8hwGWk8f4Jo5F07/xyQfzUVX/+GEWwfqrgNf92Z6CBMk0O5lWCkBJ7Pz88nB4u+GDuyUOEGGO/2txe34blanV5efX9SwjRHc7nZ+fNbLruuxT7yIS+2Wr65ofvuhBEgX0jxErovMPBOgxUMcp2iBsJk7MTz+7qzdXNdtMs5u1kevV2dXdzc3B89PTZZ30/3F29xyQcUgpJVNIwXL96K++uqmnrq2p+cIgAuh0wCQGu1+uE8MXvf5eYwHtBUiBBBJBxpZV3vzcUP61Qi0+xM2R3VIf2yQdXKyr/o1kuIlIR2TXtZLPZSpawxMzJ2gDs3FAkxJQgRbEsDyImYoQ4KlAiijGVNORR5uq9m2KWEGWD5wQrMCcVARElpZjSJ4fC1o+K7H1CI5prES1i2gexzYmlbBeYRfDREkZSAEEQAFCVEAlHEBBzfyQtbPfGnJt5scrbZUWtKSUYS4TR7EnNuVhj46NRl466sATOU7L4ukJJwy5RXtCiwj94+J23jchMTVMdHiwOjo96VF4uM5mxigwBVQgh55GIQtIkOWVKRSVJqZUrhkd2xYGATPqOBtP4FubHEpEgkWrl6yiS2xWNgwAKmKfM7ScFMGe/25r8TCat9fgzLikRDSLgeTn0l91K0LesXhGUKCUEZVGNIawi9FynqZtOWZStuNdu7ZxvWxGBfkieiYkBDdU2t9JIsQHV+hZo9jlzaRw7hoCWrhbCMJm0s9ksiUymk8lkQs4RO4Ci5xAB2cipCHE6nVGC6Wxi5bD7uYjO5WYTqgpIIaXlanVze7tZbheLxePHj+eLefWuRoC6rs1Liyluuy0gEbsIgXOzATAqDAanxV5US5kvtcJUWkTslpnuzNBRWRSLTHHXoAmKqQpooXFm2cHaOQUBgXMJNWDfdYQACUFYU0QQlIgaMZc7Z38+IaxT9ExdGAKjcua2NdUuCsSMJoQMq1dAZjdpQ9eHEIz1ZwzwIACoqCYgBQJfuaWkm9ubTbdFAgXtth1C5Zipqoid9+2knS4Ws+ls2nX9y1cv7u7eG7QFAM5VZ2dnTVtvtqsnj483q803f/zD7d3dxfl5GIJVFwiwZnGDqukD8fSRzCoZIuO/4UORr3tW7f7X9yYC9vb9/6jH9sGho5m9ez5QACU8Oj25Ww9vX7+ufVVPJ7LtJUlcboZuGLbdzfv3oe9n0ykLVE3lpu3k5BAXEyKgoal8NfShnbTtfPLq7Zt3V+8qXzXHi4qmirjZblfvrzfrbQW42mxuh64+mjuut6G/CdvOYcf45LOnzfnJ9y9fNbOJPzmcOn83dKkf2nYqtYsxYuVBtWbevr+9HYYb/85X3vYVaOpDXJyenD262EhSx8kUbx7QvFf+SeM5TvE/4os/qtQLMszWUJAIdrE5RCyJu3l3cIIhxpgkurpumqbyPoWY0q622Vxkk6jjR1guBWiqDkc7DAEzP9RYrTPqsvyEhZaxuGmqmtLY5sjeKzNMwegzFJvAlm7x2XVveY0YZCbZ0vKtlJKkqDmjB/Z3emGnxA/sHN1JMBh1LeQX0t3pOzMZVakggFiqkExZZe2wG38rJyFMkjJivLdjSk1Y9tgAnfOuqj16EJQI1v2XINcJ5waBlkluNFcW4sySXFGiRQNEUBTJXoesJYwZI5YBrVn17i8Sg7iZ2BaQBRbtWW3oRdWZaLX1kT3CQjTinCcin9vtAaimlJKj6377p/XVFVVHVbNwvkFq1DlRFCFVEIFB+9tlXPcCwCULHRTIu2o+FcQIKq8uZVLh1jgiAQEtuM1Etr6IOcXYdb2CAgEhOuecY/P4Ukrb7bZpmhhjTKmqK1959t55X7etr2so2KxBuKqKBMxECKrCzCllBTy6wgqATCK6vFu+ePEidLGqqmEYFoeHX/7m147IMfUhBtWk2vWD5UAZfjQWLplexXFVFL9rtJUQR5e4WGslmEC2/cCQJbv2B+iMAoLLTYhLNULJLRRrQK1YoU88xBgRgKyGQAJrSqETiarifAXEgBg1RQaetpNJGzers8Xk21cvxBIdiZBQRJC9AnAefKPfo0FUJK37zs+m6BlUNQmIEiCLkgirWky767rr6+umbara36yXCspgPWFTUnBOAcF5d3x06uuqbpo/fN1f31wjQkqJiaaTdr1arVfr2XT28Pzh8uz0z99+++/+3b9rmvrXv/71wwdPtt0m6CARwWoT4SPavPsy90fCBPeOnwRFpUxfAXc+utXPXv8XHmUlZddhEFj1Q39zN/OVQ7fsgwXah9VmMZ3RetutV5VzbtoeHTw7Pjs5Pjl8f/UevTs7OKibyeu3b7mqFqfH29odPX3ivJMkbjJt6yb0/eWrd6tuqJErpul0fvTwgVvM+qHXqp6enQ7DEGftyYNzPVhsh2GoPDfN9OGFDvH89EQk3dzcrlersOk4BBGpXOXJSR9jSo5IVdbr9fTkJCAlp8i5EkMRqLQqy+P585Pzjzo+4dz91D2KCcmMlfe573qxcrOTV2Y/xrjZrpNEdjybz06OTxpfbVbrXgYgKEmXOm5/kzPFNysR2bGPHoBtn6L6EMC0MmCWLx8+KZjVbbSIALkZbbHGAdCyc7PXbuIiu5/5KWB0biAb8KY7S98GAIAYY6620eJAo4HtvMcZYt8s4G85xMJgxXUENXWXu87kMUBEJdTMsGFuSQYIRYTI6nJMLRu1AhMDZK+0iNPRhsiqCnC8ZMENUROkIDGlNLpfNpiqqpL7bejOw0ZQVWOa2qHPQKZzkQQBmNg5lJjBv2y+FBDTcseYQHaLZw/dB0SwfFokcnlAARHRMVu5C1qZlHXbAh0kRdTgsavobRpuuq4WoG0/Y3cyXxy0bYVMQ0IAEpFuQERL8U4qgBiTCAJ518ynk8M5Oae7pcmacwVIUo7prVarvt+IJMpWAjLvkoLX67Ux16xWK+e981Vd1U3TzOcHk8mMrGjPbFBrFpsUkdixSGJ2u6Wcm27CuLxCDEM/SFLv/TAMIYTDw0PHvNms26rSrlPLRgMQEe88M+9AMAUam8JjHnUrl2Jm5zPmP2KMUGgpocw5WKa/5I5boxuXsaYCLgGatqVUaKKNtcX6hHtXAaB9gawc3hBshEL+IoDAzreLxcHZaV1Xlc7rSQOObb2pSIhRiAhgCKFuGhHpul4VkmhCaBbzycGcmNzf/q2WdWwjyManEtMQ0zYMg8SToxNJyTU1MCmo9ZVSgBA0hIikCvjg4cPHTx4hpD/88eu7u1uTYd65pq7v7pYvvv+egY6Pjx4/evTi1ct//Vd/9Zuvfntxcf7u3eVdWsacI8N7KF92VMse25UL/5Ljx0++n5S+F5gfb3rvhE9++o86MtSNWFXBc8fgQYNKvxmWy+V0NtsMPTcVzyfzRVvNpvPTk+Zo3kwnIYZbSThrdTpx88UEIUqK08ns0aO6rivvN5stKaz7frlau+n05NnTtvJBUySMlZ/MZjUfTI5Ohr5fbzYwnQ7ewXSaCDrUlOJQudnBAhdzz3x8dDjbdt3danVzvb29g7yFOaYUYkLE5H10vE0Rp7VmRsasDLUE8H5u5P9Jh4E9owP5kcH0ydsxu6atiC2bkhgtRgJJDZrRpLLtuyRpMpmQY1VdrVZbpGhs/ojINNaP7mAqyGGmjGaVQNuIfEFWicCFA0sAeIeIfcJ0yDrDnOnRO83WffY2zeovC1QzqDh6nLCXIGVUi2oNs8QyLq2CR8drYvkWFCPCLmxuQJIM2CbRJBJVk6rI2BkOR3KoXWbR/j7N0eGc6izjBlLLOSiu8iemGjOGkJ9nz7AjAFIgEUwiUdQZq5IpZzMHaHwHVNQkCgCSkpn0CHmiKOvZHCgQAnIMAUU1WStgMmvEIH+LTDCS1RoZ+zSMuegI6HLMoOQFmCVlnd7NfssWkYgSJklRRRmldiq6jXG7De9v321vbw6ns//X//6/ffbgPFwv07qjpC7m1WEU/wLa9/3V28t2Nj2YL44OjrxzSYRFsiUIuXtQhk8InHe+4hiJwfoxK1kekyoy2zve3t0NQwCkpp20k2ldVQeHh5PZzMAfMGMz4xrCrqrqxoqBlMaiILNucoAB8yIQYq7rmojeX79/8/at+X/nDx40kwkigWqKSVLyVZ1BAkRVLapcy/rOewQ/aceWU4sUFwIl86Mtt1Bz+rRmKExEJaaQJFqoAQnH+JCqpBQNf/bsnfOAOvQxSaoAFayvoihq1BQlKkEzmx4/vMDKBQTnW6ycOhZARLDaW9e2iBjCUHkHACFGUUXH6nl2fNTW1Xa7UaZcXY5jSQU6cnGIwxCSqm+b+dEBITYvJglZhYgqa8NErgLH2xDC1WUf+s+ePXn69PFsPvn+++9vbm6Q6P3NNSCqpLdvXt9ev2/qtu+Hr3795f/+b/7NbHFAzMzeElVETNTc63ekuwG2CPHP6MJRsnwslz/45BfqiX3YGn6B//3BCThGzwhmR4uHX3y2nE7CcrVFTED+YBYrVx2c0Hw2Wywm85lvKqi8OoqIUahDcs5D22rTzuraKiDqSQ0AA4Kv5qnvv3/53bDePH7w4PjwqJ1Mtn335vJyjcqSmtqD966pFos5EAZEXkwmk8rSUOfT1jsO5JBIPbummh3MJw9Pw2Ybttt+vY3bAbsOEb33R7Pp7PSEmko8C2OOtyjkSuAfmZdRLv2yA2EvGxgA9osX9sbzZy5oYWRmBzCMTgtl6EqTcRypMHFTNaZ0QGUIvaomVGQkYksqGRuD5sfLsbbsyhmuCwgKmmRXBbIDyBCJSFKKIYLCPnOWfrhUcgkIWGcFwOyPISKi5FTkAgWbogI0WQeKagwcuyTYXSw2pVRqXwQyA+6uaiqvT8mFziNHAqpKVHEaQhhTi4uktQAy7nbQR9hHFnciylQIGiCmqCY00BBgVTA6WhgNuAwfAjiETL2d2TGJwPpjVMyRVMApEiMxEbPjCjkwkfFISWaLzAVmZbhHCmvjnAAAI0PMNodooavPMIA1iyNiJMZCVa27OQawJCwAsFivvXlKYuRkVJiRtQAUApBUgJm9Z2JNGrHvHd9qWq5v/2ri2s/PJ2dHm8vr7fVd6gWjahRGZCTLfAjbLvRDut0MN3cQEyRV0YKbaJlSVFAgrOuqaephCFE2QhhVyDnyXhHJMTLVkxaZQxIVqHzdTmZ1XR0dH1d1rVbjRbJbsSQKWFWVZ0dIxvIIsNsg2XQUYXYMBIWII8Z0fXPtnJtOp6v1+uD4OIdyk0hKiEA0ZpnvS3gtanjcInnNZaDikxK8GIR5gyGWjmZlcHB3NWIyRv6sZtDaddgexKZukGm1vFKRum4FNbeCwExLrSRQcTWdOs/eEXsGVXCu8GOqlfSJqkhq6kpVQwgKiM4lQVfXzbTtYxREQbToqyNLayDHfrNaDX1gx/PDRTufMlI7nSG7BDBfHMwODtG5+WI+m8++/vrvyfuq9i9e/PDg4vzo6Gg+n7979+67H76/Wy033cZWOaqiYNtOu6H/D//hP/zqy19PZ7MQIyBGkJStd1BFHOmxzHD/AHgcp+ETw39PAdvXflLX3pfvIxDyy924UofxY9+wtHkF5MofP7w4Pj8Z1pvQ9ZgEiQSRJ21VV+QcMlmuHSAAEFT+7NEjBXJtE9hCaSU6pqrWHKzy548fOYC2baDyAxK7yUn1qA8RK5+YraQNRmSfnfOZ982jB4CQYX/Tfuq4qitfHx0sRCGJxGhxFXJOiSOoGC+ERdCy+/vBFMD9XfNLBzKPZj4KLon7v/lFutykfF3VKaXtdqOaEiTvDD5WAhUREHGEyhRTSpaEjOWFzK0iIIL7USS0Nqh475myx2EgWbG3smIkRI/UxUGTdVQbY1bWvpB07B83sklm79aIevI63HE54njf3SNIkby7Bx1pq8ejrG3jzsuJTlkxZArJgq+iiGKCRJIkxRiTSu50V56wSEksUWSA7AFlJ1B2aWV53Zk+BvPgEQAt97sEzg2iza5blrTOu6qqmD2Ry1lPiMzOewcpkjpAzYzXxA4ZmLGEQomJHQGqdWtQkZyaqtmCy4+qEuKgllgDyHsWkqiVjwMiMbEKFN8+ZxoQACFlBbwbZ4VhGLD0obSnIyZmlhJOIELHTOQUNHESdslxAN2wrki5JTxb1G2FAfrbdVyu67qVPkiIk7rZbCT0w2q53V5ea0xkzVIs3ydFg+1NQjBzVVVVXSsidFsA7PvBV5Vjx76qm1YVprN53bRMPkbxVTWdzrx30/nCEQ9hMMsjFzlHUVFErKuamY1V2ziuREpfyRyARVRrJq8IYJFPAPDet2273Wzqti3LLUGKqEbXDI7JOVd2kI572dbEGOnRov9K4ZCJiXFnZOsUyhyPRKH5emCxcKel4zfmOnIznpJlJzrvvatMpw5DSCkioKVTOeeSSFAJAMI0OzpqK5ckMPMQenRsKLYipiTjlmXmrtv2wxBBfVUfnRzXbQvE1lNcciMKNGzAXvb9zc0QU1U308Xc1RUkUUbrcTyfHzz7/Ffb0J+cnDx4cPEPX//95eXbx48eTBeL91fvl8vVxcX5wcEBv3wRhj70ORUGVVVgu92+vXzz7vLyu++/f/j48ZNnz5xzSqTKKmDVyIQInMXLDl8rqQjjgv9Y+BbH42NB9fFp5p9hAVA+bU6Ns1mWw/3T9gh9P9Q8dv0ccgBVMDoA8MyHM4czBgTABCQASa0HXnZBwFiLiJrZNOdA4GhkAgAoqmliRpofHYGqogTUqIJIUPmqrgAwc7MhFuI8A4wFFLTk3+xpUMXdkwAyAhFWjfkKAcGaKuruK/t/7kzVH0uC+wWHSY/959EP0oPum8i7YNB4UGZ5ROf8bDadtJM+hPV6FUNypjtFQBPmIVBVScZrKOZ1mhgpt1BAIAXJ5AyFogF2SyOz92Eme8jCXbP5jYRkTcEJcg5S0bumDkcFtqtjLHQIOHaAAMlw7549kt3d0SNTtJ4KCJhrlvNQjPFv1DJbZVXvbRbMGabWNE+QDKtLxXkbETLMr0z5b1NLNEKFWY3nNTH+ZFy85dtZyppxzDnpTHM5RgaKgYi4cuwcsBOkpEjOM7NjJ6pAoJK0MJAoKGbKCYIESJpIiBiMaAmZMHcSltyZJ2/7lMY+PQoAmS8LtRAJ5udhK0axd8r00oAIrowdjYnBCmo8IGhZW4jIXFRFdrRSCDHFbtt33VZyUiC4psaa1AF530wr6LWLMQ6Dm7XbGDxVXHnYIgJAEi2VAE7BIVlijsW3JTecYmbv2KsH56pBUt8HAHKucs617YSIptPZdDrzvgpJmFzbTpx3k8kUEAcVwRxs0gzhK3om52wTkgLn6vTCh7IneS32Q8RWJWyf1HUdQpBhQFFS1RhVEgI6x5YqgaOHgaUuFgBKZyccVWW2elmRAYAJf9Qh3pMUAKaH0ugTIyIT13XN7BAxxnh3d2eG6NHREQgQMBGmaBsBrJSIiLtum0SCpChigWRFUvuBWVQRQAUENIkQqdH6hH5IkkSAnZ8fHBI7ARUkARIAC0EpaIgxiShgtx1UIcRE5IAYFAFpEBFA11Tnjx68fnepZGPCmmC5XCPg0fFJCP3V1ZWqbjdbSJKNIWPMCjGlhKirlV5eVlzxarv64tdfOV8DR40piqoIISDnztj3hvETQ/uxB3bfAy74YXaaVfc05Y960uMVAMdLoTHR/5gXNu6I+w83inIddSAAkFndUNLks0AqIU7NDCeAuUNsjoKoykgeiFma20WsMM32CdzXY3uDp6ZDDRmwzArMHOkZaSj3t6sBgTXkhkRQjNGCx/7IsT8InxyTf9rxYwbSpz61DYCMlfNYNY2qru5uRWQUBQIKljyEMAZhx2celToRE3KmpANzW0eXEu45mZzxYi05U5ChNQCbWjAXr7yIFmNiHPFye1AhBB7ptez2OX3a0lMUAMjIb1F2JUG47wbnF8kZI2rZwnnxmAgbl/LOfzO6QFBBC+Kad52vW/LXCE2tZQAAFFVQRwp1LerWrl3GbbRIYM9EgTJYUAacCu6OzOiripw3E4fYe1/XdQ0xWF9DKyyTTMmpaI4mopp3xEjMQIzAZgCBYgIQhZgkUQJAQnbsYxJQgtI+DpCsTYSCErKFx0AsH66wWuU3V+sRYbWwDADM7CpvWWciGmMMKeYKF8wIbbftri/fp6QxppRSioLIyNpO6rpBB+yUWQB6uHvvxHMgiI5m7RSimJYLkhKjVJwcOrYGd1isNjuc44rJO+cR2fkeJHr2RJYCXBMyEbXtdNLOnPNhGHIFfV35qkYAlBUQWPCfEJTIVRURoy1NhWhqD9Q77ytfFlKOiGOuy8pt+2ypmd8JObFDQhiSpCH2SUXB6rByebutSevPTMzEXBzWT1BR4j2pVESf5i7Y5NiSA8VqqsoOttVGTHVTO8eEqDGtV2sEdaaeIYPqI3qtIJKEkDerrSbFlM1gM+HKtgEgEgGL/WDpv+uY+75PMQGBAjJ7YhYN5gGKodUAihhSFFFHLDGCZKSBEARJkZKqAAqi8zWRB3LWy/Tg4PDLL7588/rVix9ePH78cDqd/uEPf3j37hJLN2IAsM7Qquq9A4DlcqmvXtaTaTOZnZ6eu8pXdXMwX1xdvhlCR+yJIS9206R7PsLOmP+Q0OoTCjuLaSVQGKNfu1+Xc1S1+CHZOyyStMi8X5AzjcUVsOvnpTdKVwDJmjwX6e1yc+4/F2Y9kq0izQwRe3AooBYumJz7YMvuAxfdRO7YPWL/1+Mg7o/YLqUqD7JJYMovdB+ShWKV3PdKPxiNe9f/aXX8i7X1L9Hr2cZlrutqhZBEAGAYhn4YALGqG+c4xABZlVJxDHfLZnRJ88vmtXcPKS051ppEBJUKHpZBObPpsShQ2KU4/ER0vJgAundIEfwjBqB5dlGUdGxDDqiiSQ2JJUwpWNe4nZa17xZAdRzHUY6ploxiHdFvVFUgsBi1KTOz3vJL7o1PuUg57OKwF6Pc3yNlj2VXSzQzQdtpBEaHIiFiTCTq2YlzCDkP31ma7gg62r1FKffiq4hdeUM0aSGgIJqSKiBzVVdNTAJAlrbMTFnNWjUa5eHIEXXdX/8AqM4aZRB6Qi8qzL7yjYWAY4hb3JZmFGB2GANBkm67BeQRfyCikodu4jirkZSSIHQSq9nE1w1sAzIrwaCyTP0dxK4iZAXNdevEzM7FGFXBucr7hrAClqqqHfjpdIbMMcbKV8KOiKaT2XQ6Z67CkFSw8jUxIRKDQhRAQW/NlNg3TcWuqipC8t5jFCRlBEb23hm9MCI5VxE5USw5swXDQQCAqqrGUE2IKaQUYggpAaIgAmGyxEG0NtsIAAQ4bZrZbIa5zdR9BVxmZTcxaGLR0ujNaSRLHERLvofMri4ghIQI3jvvHGYhrGNkhtlaGRXHCUFVFJUQNuu1SnLWSRitm182MyET10GUNIQwBZCUk0bCMJhZGoydz6LJRGM+gq0sScKWgm5iXyXb16SIwmxtvACBJQEoMjlJ4oiePXp0slj8X//X/+f/9//94fT0ZL3ZiKQYU9f3khKWmAgVbbdaLZeb9V/81V8fHBy+evXq+vrm8aPHv//qq+l0sd2y90yQVIZR91EGY3dSepSYux3xsQ+amyB9+rcffLfslOIe44df+YX6Ae/B0aPQy86H5RVy9lRVszTJmtQ0HmbfwX6pWSln4BKkgDOK+/IAAErKyp5Lh7BzrbMrML5htjm02Khqnj6poW1AgKBmNBRX7WMn3764++F/oWNEnoYQQJUQh2EIMTVte3B46Jzr+gEgIRDqTgGPCsCQf9NeeyX7mjdO1rOKiCoqhTIpe6xQHDz7az/1Kc/vnh66/9CSREVEQBVQSaOqCJXL5nTcXSujIjMAClxiqTOKiKXt22455O5B5b3GX4247H3VaY9f/h5tklHjgu2xe5cqqU9FbcEOi8LxQgV+skcZBWnxoEhRTDhhSjgMMAwk4ogGQjI6aCBGZu+IiRWVc+V3EkFAZyisYyUVipCJsoyYzOQ8uso75zwzY+6zTsRKCHFcOfdnB/c6YAOAtSOEIkZRiF3l2BMjAm223fL2Nsa4W4hJiMjiAZj76JktnkAVUoYxLOk7BYlBiP12CIvjoxiECFxdDUMvDq82yz9fv7mGHsnaeLGKIHKR3UgGyTsPoK6q2HM9nTC7qGL4PhHVdVM3LRF3XQ9AVIqLPLFX1RAZHCE7dG1VMxEjOuaaHWiiKBU5R1RX9aSdVHWtgk07q+q27wcRNZYLq8WyERjhaETsQwgxxBhTjNk/2IFRBEoAGVFv6qZtGkIezeQxjx531ui4eQAxB/M5ZwjsKKsyVMKkCEnUIbJjL66pK1s6BJq52FC95yjZL82GLYjZSUO3JSTjymPA3ILZphhRQAlBQYYQRMSqCUS1D8F6TJlzTgS5EwShgiZT1IqEzuqzk0Q0azpXJymzIiQrbEdQSUkkIQIzagygMmlbQvruu+evX786Pz+fzWZEzOyd47ZtN5vN9fVNGHoRtTSr+WJxcHjgnDs/O7t69+6PX3/96OLi2bPPYhyWy2Xfb0JYS+wNDYId7FWG/Welfcmbxj0p8rNHEQ07gXJPSH3q5E/fHMAKucu9lUGNhxgUUNWKaJNKhOx8I6CJAjQlqrmyMjsbimpeTpG+JXe0CDnIJA8EaFw9o3OM2bXP3HSZXSH7BAA7PZ+1yngOIv64qzae+7/uISJDDN3Qg2pdVeRc4ytf1THGGGUveRkICY2zwAAwLL3naOctmkbBEerbGVd5HFQVLGOnXNm29v5ixfEPUCNbLtfIXxaQknWLACixZFvv/jP4AQyBprwMKK8ERU0gSa31nHXoyfB3NoD3YOe9gZJM8lcKiYtGhGwOgiUV2BDkT/dUu/1l0E5eYOnejlGVTA1qVE3F9/1wBVktSxKIwiIcokAnmy2LYEHCMEcGmaDUtiqklECRFB27ytfoPTm+bz5kRFBHTJwJsveJua6pzDYAiEoqVJparA/ZwW7GLQiAmZtMLeWHmVT15vqm364NOwXYH/ECFKAgGkdWIgWJKUMkCgwwDAIKla8GEXKcQoqSRlolQVj2242EQ0JAJEeYGBCIGSHXQQmAuY3OV1VbVXVDjqpQi0Q1tNw7dj6pbvveIrYWcZrW7YMvfjNs1pfvLkNKrOQBQYCZPHPtPAgyqbFReOfbyaRtZyHEummns0WSO3NqNfO7Zp6a3X5BlLLIRISYE6iAJpXig+QJQGVUdlQhMqh1ks/yiBCZYD8mRMXvydrRggLOEXMq+yyKWrxo3MiV80yMxXfJpreqdWggU8oG4BQWW6tJAAVNqqIQhUCRiTQHZ8ACKSpMlNsgMoukgo7b4stoLt6v9XDetW3bbbcxJbSyYwVASjHGmLSwRQJCiDFGSyPHIYUk0RErZPsdAJjY+7ptp7P5/NnTZ865b7/99k9//EPfb4mU2c0n0261evX98ydPnv3Fb7/6wz/8gTQtFvOUIgCpakoxQQIspYelcn/P1v4gKKBI46/wnlAom7DIgp3gLaeUXbUnlUZlDB8dPwEhjt8urgYgIoqkfjAtC6ogQADOOe8YyDLEIa8jABJgUQlxfXsbhkEI66ap24mrvOaWndkx3a5Xse8Xi7ljEqsiB0TFoevWm3U7m7mmSiXIaVlnLLi8vpEQD44OsXJQdDCUOJVxGd3e3NaVn04miBZuLIOah2t8fbP88uoloDEVM4ti3f/Kj/Zj3nlEu1vsjzaMHpcNftnL2fj4RHgAQFU33XbbdZO2NblvvXABsO+DxUeZSh9fyMZ1ruH9kfn9AGO3zKyivDJ+azBURilyJFX3ARVbElnH7Ypl771zUYI5qVgt9+4T76l734AcJ1AAS16F4lpkehErFc2+Ae5gnnw3UFUxKNgIHa0XsVIOk4782GNCmGKBxzIGDmOMV8VaslFGDlSMIoicdzRW7ozDUaBpBZGY4rYP67UPyYXQxwR9KEWe+WzKRFiaH8iMUwUEcuwdV8iOoKTsKICB6DbZJW1HS4vJvUifjTGpatIkKoJJLdMLEIFRATGpgoK4lNK4JgxfNcsshPjm7ZvaZYfPzslkLgLZ09cMlqACCKZckKrGcNJ3gyat6iql6Bs/DKGPYeJNYjA7RkZbcq6q2LuYVAh95S0CbBSdhAiO2HFV1857YvJVNQx5JhAZiaKmOEQrUAbAFJNDfHb+4Hgyu7x88/rt69vtWrZJCEkihuQbJgSH0pJzgFVVTeYHbvpeQ5wcHi6OT9ddr0g2sQVgQEQ0DzilZCi/iBBRCAFGw/I+8GKuvAoQGscnIe1oxO2g+1ZkERQmTrLjy1YHnDuPKiEjMmQ2VvBV5Z3Tgl4hjEw6OaXTKMbMc7VOw0hkj4sGrgVkBleTV+TcLZQQUJPmaA0gkzOdq6oxhr7vJ21l+5UQmcjsRzSLgWi1XocYybkQ02bbhSG8fPnD5eXbvP0sc0d20xhj7LtuyFIMJUkcAgKixk57purudvX48ePf/vb308nkm2/+cHt7W9c1EQ59d7lZe0dN3a5Xt3/+8x9Pzo4nk5mIILIqo1YKCSlmdB8UP6UOd1P2y45xU/ysH/2xPtaiDX7+iwUkYdXY9X/4b3/XrbcAyAosoKrtbPr088/mJ0fqKREIAqhyUoxyd/X+5fc/rO9WIimpKNHR0cnTZ89mhwsGTAwJQEGHrnv+p2+ePX784MEF2XQiaoyvvv+h77rPf/NrAqPgyx4zAYZt98e//fthtfnrv/7rg4vTAXI+S2ZiAgQACeH5H/90fna++Gym2ez6YKR2auOnh+EnzvzZMfz5k/UjxXX/6IchhNi0beU9EYUhbrdD8RERlRHM/aGcLgRZeWTUwgiVC/ExlvHZ+TOGWmQF/InlZGUaSGUcMhmEGRyaYsx9u1V0L/hr72aN6IwsS62lr1mfVrCrasw1YlmlClZEVEKWqAIpJiwerwmxrH2hWDoKunfbUQaqqkqKwdJhLVvP9A9kyq491K0ApyNIYBfOVyvsS6qgUUWwsE9zYZjaNdNTBUkSPRJp4pgawSahgLoEDoHUVO5e0pnRSSYEVYnGQk+OnWOn+KG1h6CMwERM5JCscVsai1Hz1hZUJEXryJvLqDFvACz+sr3OPQWc+0khAkAIQ7ceDmazceHaciGj5tdRlJjZTaBqkcH8C4FuG3I6vqrz3COEGFzb+Mo7ZAdUWW4SKBGx88zinWvbyfpuRYTGSako3jn2jpltpTI7omgQPDMTcUqy2W6KGSIpRhMTDfLTk4sH84Pb5fLy+upqtUySZN35FhyykGvYMyARzQ4P29kBdN1kcTg/PH799q3RfhbHlxGQiAxZXa/XANj3fQghpbRNnVrYXz+s+s+Ls+hRU6WQ8U+EYivvgQq26mTE85jJ5zIoJbA6HKFCIUJIquqdyzi55E1gz2NFc0SoqlYqHqPEGEEtzw8ISUUvX79d3tw8fHRxdn7qIddRAWCKGoZoG88WRsr2GW4226v3V7NZKylst1sL2CioiBhtn6qu1+uYIgCKaN8PIcYUU2FHIS1SwVxkJEwx/vDDD6EP3XZrL2EYEQAye0S6vr7xvjo6Ovr888/n88nz5883234YhpvrawWJMWw32/fv31/fXL2/uXr8+LPHj5427YSIBfmeB2v7KP8DPyHWfy7PZ7wK3BeWNpPp5/3ae2p4/5P9D7H4vpAXCmhMm7vlxfnF0dExImqS0A/XV1fffv3H3/z+d9PTI8jiHkn15u2759980zbNb3/7m8l0mgDWm82rH1796euvf/PVV/OTIwBUECA8Pjm+effu5csXh4tFO5koAhG+v76+fXf19LPPmqqORQdi8UOu37+XPtTIV6/fHBwfkVWjFVGMigyAonHbwxA/kGF7DljRXB8O5AeDff/fH8/OvQ9KlDsXiP78XPzMgcRWyyhgNRGSUCUUQBEQCSDhCI+VXLzCRqlFJQMUtVgy0bKuNYt8xMw+9pslGcfqLllNi/IzJbpnn+z09+gZi4haSnI5z1w91nK3UUgV9Z818A5dF8r35pLeY4jbXhMIGDVx0b7milrGuDUkF0BnJ1tREu6oRRARgXBMeM4U3Oa5EyCIjYNo6SMt91GU/Bg2B2YpCjTsJr6e+kZF2JqGATIZ1y4QW0cbJipdCTVjLGN3og/ugKBI4I3BCtQKM0SiqqqmlIIIARvvlmXP7GIUuic3xsN9bBUigIqmmLpNN6lbLWUG47xmZ7oM+3hoTmg3h1jjkJhc33VI6CpSgiTivG+qegtYO9ey94AkIHn0kZ2bzqbX7xgymYTB05T7JRQrDBCByHnHxDbeYRhGrwIRjSOKABioce38sD2fH91u1rebzVwqN0jlPBBU4FgRBKbtZDGfE7vZfDGbz5yrbBbM5XXOZ7SlNNk2u9W6JG27rXnpqAhK+53B7IEMAjLVZXMtQjm2NtqxKgAZ1jYMx5LoEMEysfMVEFMIYPByElJ1RMyurivKlbgjeiNICCqAAphiGjRTdxk9CxAyImxW61cvXg7ddjKpzk6PQYXRmmCASAqhR1AVSTHa+jKXeuh7FZGU+n54/eptCokAIZlzSaawu64Dq78X1RBB4eGDh++vrvIGExFJKQbRzBXeddvtav3g9PxN25IqAs3mh+cPHgC5s7PzrutfvXhxcHBQ137ot4eHh5X3f/v3/3B9fe2cQ9Llctl3XQih77uUYuhD6Idnn3/hnA80hD4wK1vz67J6s+C7L+9+zKMa7aTRVMpZ6iONhprQ/XmH+KePD9xl23rmCFtm5tnp6dnDi8gUSTHKyfLi7//jf769vpmdHoGCceQNm80Pf/7uaH74xa9/XU/ahJoQpkeHB0cnX//d33//7fPfL+bEFQIKKDE9fvb0j3/3999/991vvvyycW233rz+7ofFfH58eoyOlRQK0TyiSt9fvX7z4OGD4/nB998+Xy9XbX3IYxo8AKiB3OgVMS9jRFDKpcx7GwR3Wegfk0SO59w3c+6f9pMe8L7g+qcdCDCZTIcwSIhN0xwdHiL4P26+STEiIpEKSA6TE0qpwNHsZxp3P4IFhnVcQ/enGEBzyHanlcckLMgNhbJvi6OEy2orJz9mT2cPXMvWEiiAphQLnWFJtSq6FndquxgJ2Z0Y1akqGLsIElMBMrQEQjJFZcmagnG8VUte9aix0aCADDND3o33U7o+NkZzVaRoEiogsO4c74/2bw5CKCo64tq7qvI+RAQEFUfsnAchFtEkjEzs2DlCQLbaGoSEKYmIMLoPF5hd34DM8gCICCCiKSUQZbLqM0maZD/tH+9jn3ZYjQyVtjoAWBpPACQRozncV7TFsAGLkdJeSEZERtdOBVJImnToe2QEb6ABMXHlvCdq2bXO1UA+pCqBtQT23k8mE/PwmB2XXiLZ+WayTkeM5Igr59kTsSokkWQmESPXvibEdj6v53PyHhRIoSJ/Ojt8dvZgUU81KAPXXLXsKSgnnfp65utZ1Uzatq4b7z2R1XtnpgtAw6IdOrbojPO+ahog6rZbIzRDwn3xOy6OElLcw3B2bwVlhVEZ53y2qKSUCiaxm/2UYka3rEkLMeYcOrJsckDSMu2GjZhGtG4fFtIWUMvZ7oceQZ48eaiSUgyU5Wee5mEI9qNlm4CC844Aal8dH58wcegHNrsCcp2EvXIMYbvdApTO1QAOqfIVs0MAVRGUBClpUEmERMASlJUeXFxM25YQVbSdTA+PTxeHhxcPHx2dHL98/frly5eHh4eLxaLvOlEl3BnLw9DHFBQEEQ4ODhaL+eXbN19//Q+Xl29ub6+vb97HGEOIKcUCAxUR8NHxwQ65//kul7XgELh/2if86U/5NB9c+ZNav6j5MVRp0QRxgEI4sEYG8Vy1jXMuDQMpsIATJMXb9zdDNzx98qxuJxFpIBwYg6Nq1j7+7OnN3e1mvdmTBTiZTJ49e7a6Xb178VbX4e3zF3EIjz57go1PpUsNAJIiC2xuVqEbHjx8eHx+Ct5dXl6iqLMOdnuvz1ZtYhROuJMuBaolAELdB4QKBPmJsdjtjJ+erH/2w/wk7z0xT9rJX/7Fv/rf/vovqsobkwMygFXakZq7lmBvRYDRa2TqCASk8oK7IsFyn/zXqJV0h+emlPZOy1ozY56F+PbDJWZbA/JGNgBMFUb3oHjv1t1bcwyh1Eyo6WoBtbykrM9G8olsAiDm5K0d7H3PmbatLihWf7Rnv95Dke5N+QdzmuGMXA5CiMiAaAlumvPMBM1Ms9zfMqzZywZHrvYVO2dsNlZ24dh7V1m3IUQsMb3iMo0pYvdyNnc7nYh4Tw6Ujb9LH8nDowoipGqoO+bcr0xLYiCn27UyRAVUY8xERHKcVGJKY+NDVZXRFjNrLAN7hIhJxNrvmIWULIYXUFKqpw04DGKpfeDQOeQp+VOqtsrzXruELgEqMPN0MrGiIM5N/oz4ySMxIaNZK8SgUEiSQUVjDDZDkqw/NSJxO50mwM5oVBVEwQEYP4skYfYT37TkXNfN0C9co04act6xc46Jrewv5yoCjsoYLVDtrKgJzS/E4qDfs3gAACCTVe3+o2IwFhhHaD/WUNSf5mr0stjN5opxAFS2xaZJNVlShIiwI4PlAZIiSErb7dZ0sNnOWmzR3JkAgR1fnJ8/efroxQ/fd13XTqeIGeVDxBBCTCKqyYg8SvS0rivn2B7s/Py8sipqVYkJiEW1i8N26D0SEI1plpQTaK1KToehF4lm94IoChBQ5fyoeIwZZkgps14zxZT+7u/+7vGjh48eP/7D118vl8vtdktECinGaC5pVVUphe+++7bv0+dVdX1Nr1+/bBtXVY+853bSlNrHLLk+SsL6UBbsC3rEfUfNhCUW0ymvfUZLR8Z92TLq0Y/dvI8gVdoTQZ9S5yIs0ERABUjp/eVVDGE6myEWdSZ6c31T19V0NlOiiBoIxXKaESeLOROv7pbT40MiyjKf6ejsrF9tX3//Ujbd9e37p198Njk8iARJxcpQSIkAOOjy9buHR6fzwwNX+Yunj777058fPn3sF1OCQk8x+jPWDEizgkDrU3J/BPLrlzEenYRdnujOp8q6654//GkdXJCJj0Z7r+x7nNQ9K2fvRNihqqV7D7JzzlXW5cVifooEknaXw5HuyrLJNF+8qJ0P9O74DrhHvlgcStNnRgmvJQPrI8tD9jG3D94Bi7NtzjmMruc9BViAcfs5p2oC5F4IKcaRpcMSogHYtIhSwYFGzPneiKua51rcZSjtzMdvQAkCjxktH5oSeQsSFApPI8CHXXHo/sva/4u2UgFA71zlPIeEQKqJiDwzK4lISGLUInYdI9vP9ZzIRCQW4kdLIMvbH6HUJWSyMCpUpMSlc5bmIjCAUhZlwm+XzJiHBx18eGSMd4TjjaGDCqJPZh+ZDWeTggBMUEhoCQAUum1IMWFEFXGeRUFAJcl2tW6BPdJU6BG0jmatm15GrBN0CqwwaSdj0tPI3+bYWWWEKlo9D2gyAY2qcQgSIqo6pAQiKioah4GyOw/1ZAKI274HEYgxhTAMAzudNO2Xj58drO/qN69v8VJiNwNXQfYpBUEkWRUWlPxDAkYg55zhwIiYkhSXPRsEZUGYnFdizKg5oiVMlh1oO83Mz10ufdG+GmM0KvOdRQkwDENK5phKiCHGZPcGIgOGprNZ21aTySSEcH19PZKhkzVPVKtbUAUgorqpD4+Pm6at26YPocbdNlTVYRhiCsWklSTJqhWRygZmqF1NTKKpEBeigG67bgjBNzUzRXPZSydk08dpCN1qY1rZMKikFtanktI55iXEZIkGzE8//+z2+upv/ubff/Hl5yGEEAKNQ888DEOMQ4zx/fXVMITZdPHo4cWDiweM8sOL77puO5+fOudGM+Knj1H7wn0dvIexlSnczWZeKVBMffiw6m/vvN33Pxb9P/rvEMIP3/9wd3uXYsSkILIdhvPz86OL80gkmONDMSVgBqZEEA0UNfPNEvGZUwiMlCwsZeRFTKcXF7ev3/3pm2+++PUXx6eniigqKTPrAQGwwvb69u7tu9///ncOiRROj45/oOdXby4fzaaOUEAFEBlRrdLS+gihEbFIQR0BYMww19yZ/Mfc2Y8Nlk87yZ/8ou4sof3flJlV2C9Y+ORhDgsCA3BK8v33b5zzfRhs4iwHMSsRa9hACEqIDtEBgOZI5W6FjN7j/dvc620Aei/k9yMICprLAbmsYHdBHZWaLa7S+10zQIUjWXT+FArN7ejgFs85a5KcoqWmgHfPUzT3aDRbaqrVH4so7YeoP7SWdvofFfBT2dla0D97IDESUAJG79AhsuZIOAAiMCOYm8wISJphOWZHRElTtAhcsQDzSKsQZlxCky12MbpvAWVCMCImHL3jvK5y/JAIkSSbWUYqh4T7+D2oYffEioQSBSOQasobwI2vmqVMBk0RkQk59wxCi6ErgtBe4i4iZrrDyqdBbAyN8rLbBsdOk2gCYlIEIooprTdr10yIuUV+WE0XrTqeDqFrldZIhFR5bx4wlUp1RCwJaVRm0TiJUEQkpmHbaxLTbEmNbUJTNwy3q9T3qlq3jasq7KukIutNGEIKIQEw6PF0djydX7SH/i7+19XXs8iNICuoJgDtus44Ea38d7PepJSYmJBN6BOxqjhn3XmRiER3Jj6OjrNjIDQTaT8H0mxARNpR7pdlPJ4wdN3I/oGIInp7exdCMLPW7mpxYmtHrwAnp+cnp0fL5d2bN2+yda+5lkBVUkwiyfJDgKBqa3Q4mU62243CnJgRQUAAxHpMWH13SrFEkpJIAEyICUDIOcy5iKqaAFA19cOQYkStiFhFAcTlsIR4JFGJfb++WzIUnlDilKTrulevXm82a1W16mfEXPENAMzctm3oJi9fvvz6D//99OTEV83p6elsPjs9Pfbef//9d8+ffzsMvUiqqvro+Oj6/dV0Mnnw4GE/bKfTWV3XCukXyu8fPT6UIp8IQv1zHR9cC4lVYbVcMRKI3N3cSky/+6u/PH54IU0VGATRyiSb6fTq9q4PA1cuI2hqJiBZJVhV1cWXz+tVQZpJe/rgfL1cnj44d5UfQJMJbQQWIFVMevny9fXlu+fVn/X1iwjKAKvl8vsYTx8/cNOWQHRPt5kzyEqUq9vNRssRkl82TjszaG9Y/nmG9yePnZTG3MMEQOHN28vNdjvEgIj7fadFQUlzs8FkabVGnCO5oFUVShrwvdvkAcZdAvPeYT7Z/ud5KkvGOSMCqKjgLu2peJLFtrAmaearoGZJD+bTYsmds2ubJ1rsb8khsA+HZnTls8ogq3/e4XbZZrD3FlMc4+vq7ob3rrZnpNxT2Ap59Y4AEpJzeRhUAcTyzJEIJI0vYliUY3aeI0gXhigxpJhI1BjLJSURhlK9Lgq5AgAgP7g4tKG2pGm7kUAOtuYouvE0C4CAJFCfTRyVTCuGiEjESgwIhSdxd7hPmlcWgr3fP290z3YofkaikZhZnFNEMfb+pKELTBxBgYnY4qYUJPZJtVbnnfduUTczwWVSSIEBGDAOw2qVK49pjzyZmSU3WsxusWVppSgxppQi7O1Sc0o0pm61wRgUtF9vELBpGiEU1bjtIURLGiIAEp37+snx2dvZ2xjRKbHZUEzOe+e8857Zieh6vRZVQHDOe1d5XyMSWOcMVzVN2zRNMdmynaiWzUucMaQ9S7/88IE4sbQG9HXVNM3IPYsFvlaRN29ee1P5SEwEZnMAAqKorjebt+/ekUORVMC/vFdSiimJsTlakXFK6W55Jylsu+5utVwcH45YFBLOZjPvfTcE0RRjiDFks9EE6H55NBjCDAIoSYZ+SNGi1wpsTirlhzTLKUk/DLZTx+WUUrq7u4tJDDEw5AUzzgNmV1dVNQxhvVpPJ9Oqbm3PV3VzdnZ6cHBQVf6bb75hdicnJ0eHh01Tf/fd88VikVJcrVaLxZz4F+VJ7fzdXbLruLp+LKtnH0L8+SM7DT9+63sfmZQiqprmq19/9eTxQ1F99+7dH//h682wPfFs1Pam3ojw8OTk1Xff3VzfnLVtRRRErfE9Jrl+dwUAi8VixAFFJdNBErjKoSNgFkQDHiTDHcCAq+Xd9c3Ng0ePeFJ3EoUQkdrptOu6q7eXF8+eEOMoX00UWgkNKTpAycIMrXXHrrz3I7W0fxT3cv+jnxyunzxymiiMHtCOimR8mA9Gf/eQhEbyavWH+8+CGfD6cHGoiqgQGCOHWhxVi2meFdi9JOgPXd6Pg7t4TyTvFqpKQpER8C8wvIikmIKUtvJFcdhrK46RyfFSBj2rimqM0SryVRQ4PzNhiSUBGhxrNC/ZWDD4TnJfrv0mi6Mmxt1jfyT+Cl6wDz4ZCRGjOQ1G5aEiEUriCWKJr+vuXmZYxpRiSjmPUS10n914Vcnx05is8czepsjPXZIURnxckhF1okNFETDyDdPgKUlKSjl3yEUSFDWWNAJMH8JnAKMH/MExSj3IS5TGWi0rYi6QLCKo3Q2gQkARAAZJOnQ9oUuaXOVd5VMmy1GRJIjttN06BkZF3YauoyEhANH1ze1//i//OfQDElopMAIQUeU4JKFdj47RccS+H5qqBsv3L3EYWw2EOd4XNl0KgZvGtXXrvLTteriLMYolLgsKQlXXla+iokdfkWdwSFQ303YyY3djQ+KqWgGGJMS+qhvHFYgFm2fT+eLo+Ozo+MTK9RhJrOOEqkiWMEbdXYroyiLLwFC2VTVjc1jX9Ww2Q6I4BFBlRFCtKhdj6rdbrcS53NqIihdjLRlCiu9vrtHjbDpVYhnr71SHYUgpg+pIqKJd1//hj19XjlWCq/xZfw4AlPtAg/eeEM2kTjHEEGznScotJ0Sk67o85hmHARANfR9TUkT2XgGSddZEAKYEIggJwX4wLmImQtTD46Pf/avfv7+5ZnJBDDShLCJUVCJoQsx+tigMKaZeBUFfvFyu1ufnp7/97W+Z+f37m4ODQyI6ODjo+365vO37frNZ13V1fn6WYcmRWvEndfEOWxvPox9Xwfe/9U/7LRShUx7P7CfLPgVy3jW1eI6oi4dn5/3mxfcveDa5ePyY2eXMDoWD48Pj09Pnz7+tfHV4ckIMAAQit9e3r354cXp21s6naU82ogIQKSI4LrNjd83ZzeYovH136aftb//V7/10ElETAoqmfnj+zZ9fv7k8fvCAmlrBzEFQAAHddtsUAgqpSlIRBPAOHWVp+P+EL/uTB+6UQT7uP5OWGJ5Fdp1n5/3+b8eFke2pkkZVPFqr3Ccds5zN4gEAxNy1B3Plve4HY3/s2LsdZkM//yL/0uRJfrJRp0p2lxR2Cjo/UH61olvtWhkcsX63aCpoZ8oXcHt/IMtOyX0DtajiXQYZfNL2LW5IoVYza2Y/AJSh1jI8SSRGTSmmmOW9jjFzyYlkZhQgisK227aS2Dtklmi5YEBIQGzZMapoQTkRSbnjkfntxVqTXSOo7IYmQVbM+TlAJbBocD0AEJH3PiWJIeWxBoWPrA0YPWDNjHGjVNr5JdlBz6sK8wiVNWAj5nzDXoJKQjFmrBCCUwoptdOWXU5GyMsOgZtGHHcoXRzey7CdQfKYIoY4vL+5ntQNWw9jUiFhZnZV0lBmJc9kVvwl5V8zHqGAmBBuu822nTaOyQKfIcQwDGvyvnICrCpSemwConPtwbxezO/6DXrPXBGzihq/tnc1mP9R14C47QcS8C5X/jC7+cHhfH54dHJyfHJmIQHOD7pzghkIrfjnAyqfYvHlhGdmZKegTNROJnVdMaB3LoTh5ub64aOLuvKO2RxwFXBEANYoUp3zbdv2obe6e2LnvCdmQCTHSBQydZ6a2SYqdVV/+eWX81k9DH3XDznjETEX1dieBHTMMYQYBkMmMvJBxMxv3ryxMLNISilxhaCSQrT14ZxDwqQSjRRHJYEKoqBmn2C3wYAcVU3jqgoRNWfUjy4CjDmapje89+1kMplNm6bp+z7EsLy7O1jMf/Obr4Y+3C2Xm8329va2aZqUUojDcrm8ubmZTidt2yLdn4IfP3TcDiIWxf5EuQze/2ksTCrSY/+3H6AeeO/rAJk7pfy2aGIrfVBiIRxUBtBEQERnjx5uhv7P333PTXt0fgacN2zV1F/85ss///ev/+Hv/+707Gy+WCBRt91cXb2fT6ZPPnsmjiJqnusM5UECAOewrqyXilopHpjnQ0PoV5vN+cMLmDY9Q685ucY3/vjBxZvLd3fL1UFdFxFBgArO/fD6zXKzAUJBEFDy7uzhg4unj5GdFN/zQx9wf2j/ueHmn7pgib8CwKgnRj8VCZ1z8/m8nc5++OGHMTPZNjllRw0K+KvWl05LHpIlK2WSK8tLoiw/jRkajTM048YZTbHttldntDu0TNAHH5vag11KF2SCj5zfNAr58c+d91s0nxr5rIioJEkJSoPRHWyjKqr2xBkStxEoKLSB51YI++Ej7v239+nu/XTv2PnKNoYllJYkhSFk6hE06pAImd5CRy9RUCMIVOx9C7WXOCjtRpyRitGEOpIDZhDS0IJxD+bNWxhfDcRXQmRm710ulgEw/JKYFMA5jiWLVUEFNdeAZrmACPppD3h/CWYILAOoOUF7nD1FAOcQLP2C7PKSUggRySmCbypiy4CzVAyIKkCkte8quozbtzx0vo3BGiYWg58ZicyO8t6T9xhDqTTbMWM4Zyi9QoYXIIkIaAD906sXq+ubh8dHx/ODWV07EQwCIQx9RCSPJChIpIxBhT3PTo+nJ0dvLjsgZMdEpMCggEiWhk2e2Tt2ruoagc7XlQIMIRBR07Sz2ezw4PDg4ABJQVFJ9wsYGZEBuZSP4T1WS6ASQbFU6mzyEDZN3bYtCjjvu277zTffhDg8efLk0cPHnqnyHEL0FWfiAwIkOD077cOwXK+IvWNf+To35wKA0uQrpSSqRuRGjg+Oj+bzdgh904eb27ugkkRz2rwAqOEpzpoMIrEarqiAgJPJFBFjjGZeiYhT0JhC3zvvLO0ZEB07JHz//ubd+/c7wE+z4y5o3UYRvSPHY8upvBcJsrY2znvOBCne+7OzsyfPntZ1/W//7b/98osvf/+739/d3a5W66pqVJa3N7cxhIODBQCsVisiGoZ4e3sXYzw4OEDkbP/vMe/su55ZeI2CWHML+T2ZAAAAO/qbnRUIoBlFyEhdvgYV42svGCj7imencMs/8w8WinI8Pz7C2kc06Y6uqR999llkXg3dArMsFYRBpV7Mv/z9b9+9fvv2zZuXb18TUTuZnD04Pzk/c20drP9gIXyBIvXrxfTB50+xcamQyqDpDQAlunj8aLFYhEypAACoCFGhOZh//tvfcFOnwpOhqkD45IvPD0+OQQQBidmoZJrpNGMQgGWEf8rz+8fq4HEuwLCwj8iMPrj8TszdCx7dM5sAgACrqnr69BE7+m//5b9uNlsq5++etERdzXYs6wqxeGkZF9gTrcVeyzOfYbwiGey/JHLfkoO93+K4jxAAMHNuZCVSkNTifo/QLBYf1xytjK/kXtJqwX9JWYtmpGTPVS0adHyiccyz0WGZIpS/m9VkfnmEYk+PCIKVSexfbfTR818qkjQlBPDEtfcECKIguWRZJaUQAATEMg4t/RUVMTElx1h5aXzqSUEJSVElATuClJgR1UR2vj2h5dORZGeELEdWtayn3J1ZidFXzgUmyv6p5t5yo+kGBvoCqhKO2DbmRDhyoiPLF4mS5LHOu1lt6TGbSasFvxjlhCqQNZEA9d4xISikJIholI2TaYOWMAbIzCwY+xBcaI4O9Gjx8ts/X0+wdyyONCGIYs4VJvZOU1RAdpWvKxw6zeY4kGMIYMlloGD9A3R/8yOtY/jz+7dvl9cns9n58dH5/GDCzltbNE0AgkTka0FNMijCbDE7OD2g69cI4piYOJXmJIjInuumcd4js6t8heqrSgBERZHqdjKZzebzg0k7HUKnigJZB2sCQmR2npxDcsSUOUbQTDnrYpVSArS8N8PPRUQIadeJCuDu7u5v//Zvv/nmTw8ePPjqq9+cn5+3bTuZN5CSJYnHNLjKLU6PquXEsUPnkViVENgoP733uaugJHJs7KtJMQBERPAOiMUQR2BVa0ooSE6A1l0fBZyvACgZfKxc1+2jR4+bpik0bACICbQLoXGciwsAiJyv63Y6rZoaiTLLtgIoikpCVUdcV+1sikxW/2YjL4U+YDQbyTl2LokMIUiSpm5OTk9E5P3VVRL913/1r1++fPlf/+vfPn/+fBiGbbdeb1bGnVLXdUqp7wfnqpTAOcZSm/ETIh73E0gAcb+MZf+0vZiC3qtpwT2G7/IRjcIsQxF7QhUzS+j9y1qEiWv/+Vdf1pULnONeouAmzWdf/koVgC11Lpd6CIM/mD2ezy4+fxaGAYms6wsgR4QEmgxOMA1oteMIXDdnjx8BQhoZbRVVIaGSd0fnZzoWamTdiaAIzEcXZ/aJgfsJFBHmJ0eHZyecQQMUAlWJOTGpvN3P4fn/8kcZ5NFiurceZO9P8N4xKygQgmVfABrXDcFeaKKwkhg4R1r4GPbTpXLdkY462O5+fzEgAkCKUbOLkc9BLLSWhTdq5Kn88N0wPxnmW5XeaMbkkGu3C41GuaMUe9oMdiyJ1rjbC0UTjA7rzmWWlDSKoAqJqIxMi2rRZjtbM044XhAEMsmVjqQhmtV1ScYWAHXMTVV79mztbcAhgEIwmH1cUvZfEulEtwDROfFemUETIUoSZERG76rT05NF1azfve8AikAYF6epF3vsHSqSuziQr+raEDgsJJopSQgxu9FZiIkooFHyj5hoQYpGD/hTFpbhI0i5Xrt0b8T7Z9k0MzGjJeNA3w02AMToa28AChJ6751oDMNqjZODeXV6dK1hBVVCMkmkRGiEb45d55MKEbJzzWTC240gWKcXIBBESz/LrXixNA9BBFX2rlnMutX6tu/u3r99tbp6cHL64Pj4sJlMXFUpIyAjoyNbet12s930m/UKLOLFzMZMn7MZuaqbqmq8r4HJVxUwVRbuQgTEqrKJaL2vYhgAMtXZCDR6Yk/siD0xQ269zcxJkhXRAKKIUG7AgDHGGKKIDv2QYrRY8tD3SeN6fXd1dfnnb/7h6dOnf/kXf3Xx4KKqm6ryVvCz2WzFMbFr2paYc6vLvCTVe19XVdd1qprzJgAEISEKIDk/WxwSO0Qs9jGANZlyVS5lYm+0zyJmO/N0Op9MZpSjXBkiXm/WVVNrQWuIqKqqg4PD8/NzJB7xMVujIoJEVV3NFgtgJM8Wwrcdb92/ikNj80HFMBcFNT6yqqpA4b/9t7+dz2enp6f/5b/8l67r2OF6vZ7P59PplJmJHHM1ny+YP517+An5BUWuYSnd/jl/bN+N3gOvTAlnWVdOKM7AKNsU8J5GvpfugAjtpAU0al1SIEiKquwZxuxiVKtVtLIwcoiurtranI9Q8EA13vkRBFYwrQmlCXxBU7Pdo6oRi9NraGapmzP2aTPjsbSrNXlqqKwpYwsgiGqhZvmfc+yjHfuf/5gRtve55n2RPbxsfJjEhozO3VtUIzmwAsmY7/SpdacldpYNzR0Nlv12fObdI2HRE7vEnjGfBDLyYsIe7hEr7iIjGXeCHYZs826vmVLKqjIJlF+W5QrlwrsouAKISExCkiPAIiKapDD6mS+J++DC3kuNRylwJeMOy76maApRJBEAE7Nzlau8qxhcKqQuqrlUOT8iYUxptd2uuu1ckjImJmHEhFmQKDFzUzePnjw+mc6+3WzeW9w3v91oTNkfiECIuVrZtrFYW3RXITtANsZ0ScZJAICgAmgdQcfM9WyIGQCA+MkkrB0KZpNTaEHssXAvP8uuh4BEjpmgtLWKQYzEqqoqZhKA3DjKJGjS7XZbeV8vZr3HLSTKUglEtK6qxWKBhF2/jSkys/fcNLWl2mJ2IAAQkMkyYvImyfNFikhMs8ODJLGLXSIdMNUSoF/fDJtK8dBPzqYHLXPdNCEEH9wQh7vbu+t3V6YbnCPHbsABERHJ+7qpJ001qXwLTFUddeidc2LMJKqV95WvHOe2Vnlos3eAIOoAPZJDcs5752hA731VVcMwUEGbRmIVK71PKYUQwZhgVSWGiNr3nWgkgn6d7q7f//D8+W9+8+uvvvpqs74jAo+skLrtNoKyd3Vdj8FOy0t0xI5d6PsPJIUlQjKR944MbNk5c2QqOYQw5ohYkQLY1CObyBbVmJPgQVX2iy6YPaJjEi79IkEL5GQ6SkQB2DvjtCdHkETHIkTb5AgAYzmxqvkhyJWviZxzfjadP//22//z//1/LuYHbds2TTOdtW3bEpElfjvm2XR6dHQkIsMw5CAT7hzWT0rhf7KuGPHk/etTeetylrlKNH5n5GRBhOI9m9AymKdcAxHEYFy0rjkWfESFDFYBKBU1UdA8wbynMwD8gTTU8YE0y3VVwFJGag0Asou7k/pjbsnofWDBJ0vmqABkyQDZGvxkwPdf6Li/1O+jEZ86CHbzs1sbZfFrTHlVljfMAp2ACxGgtYzEHJLIU5AzhNNovO5wZrW6GDDs1DDqPR2sJRGsaDq0mierIypPd+95oPhv5mxCKT8o1lFBecUqOrVYWjb3AIXhEjS3cxgXGOwpXShBnHGgtRSORxVMyXqpYcngHa3Oe4vuviwq8HOuY1Y17N5ICIQAmRiIgYiRd+t+90y2ORCJVCGBbMMQJSFoKuEeS6pTQkmQRF6+evmq6+PtMmmC3QjZlQgKfxJYE2Wg4sqjxRP3oA+03m8pRE0A1nBMylTAeE2AknwKuKeAs702Ru8tiJ1/tMU0ottU0G1RVUJXsJBcVzIMAuiAtGlrZBpJYhUBHVfs19e33XJVe8+V66H3kAQECCFBXdfn5+d3d7eZ6g/Reee9z0sWEYiRXDG1YTRYsiFGZmWDq5wyCkNSwopTzYPDYRi6u7ula2fzeVMxV6yqDskpxk2XtgMJEKhzzJ4poDW4d97XdVP52rMHx87VQ0iqZFlTNj9sDRQRSfK6MKMRVFMIVozjHDdV1TStD9ummU7ni3R7qymB7mk7ogSggEl0GAYyOhZAAAhhWK5uRaJhlER4d3f3+vWr//Sf/pNzfr3eHB4fz2czZb7dbqIkdsyOsqELufOuqsYQPxB/pZYI8/8JNZmzJuaQpZTW67XLkiTvsgIr5XinoVrArAjoKIcJESC3s1QAGIaoOkpnHEkORdPYjZx2AXKDRfMSNtXlMtuJIoIxtHjvK+eJGJlU9dWrV7c3dxcXF1XlF4uDJ08eI+KrV6/ev38/mUzadmLGTUoxxtj3XVX5qvL0i9OysEgHuK+wPwCGRh93FLVYdKmRy+yJQh0dZQDLqETIwekio8uvyMxw67thqX0Z3US1OhcE1FJokSc3+0Rjrj3sQnJQoP0PTP69qpjysvnDnfLEcvn811jgM44BmAAHRQWBXTD2nsz9F3eGf+YGH03cpz7eO1JpFmSq1SgSUC1UmKV/MXBArQiCdu9bcLqRAwBytb3uWtqZKlQUtQ59nxou2ysKmIyDPVv9Wro1IwCggqjGEFIKY26X7g4BBcsJK4knWowG832Nvzb38gGFgrOUY+cTW7Ao5VyzcvUkkslf82JD3M8h+nCaxhHfPUMec1R2iIxKaGCh4akAOVtz57ZirtfMU4iaJIgmRlUVAWHMCpQANXfHkX7oQZPxTGnuiixqKtfefH8KskWFSGzk/yqYO0ZawEZFJZpVNfYCtuiqZp7t3QDsiDigmMn7h70ec0lPz1GIMk4IpOoAONfXic1F3wtxJRKbWYOujC2j1Ro1vt6Crt9fd6t13G6lylVjtuSn09lXX3314sUPV+/f2Tj6Qs2hAEgESMTGkU1ErALjorGq2ShJUAEFGYERRAU0ISRGZE6I6tlN6pRkCEPlKiZ2QNIP0gf2qKrk2FnAjBCJnHdN09R1zZVHx9w7a1ps9TzF+EEAYMR5O2FJcehEc6mjDBEsoZp42jTHR0db6RdHR2cX55uuC5sNGI6EAGxhbQREK9hl4tzCEDGl1PcdaMqklwgAGELoug4Rq6phoqau6/mMmtrkalVX5EiN6tF2jIhI8U33bFfLLSBElxsWgRqLHGa7LsY4Zvrpbh3CmOVo+9LKloyOUSXvNsqEZlQ0687hs9PA7mNtp826K2RbJsbs+0xU1bVZYwBgOhsLU4dzrm4aRI4xbjabGCt9d5lSfPLkydOnT7338/ncObfZboZh6PsuhLBer6rKzefzyWTCvBeSGiXBnhi2NZbTQaFAfHu/3RPeYFbNeOxfDfcSSouPu1P/ZP25jXN+VH1ZEY+ar8TkZDeHqEAMqEiCigqQRHO/08KMkC2aotkRSsuscUazg7onC/eV7b0fyhWKMzSer4LFkbKn0uIoGHqLGZ3+eLQ/PorF/2OGzieOAmnovZVWoP49DVbAoWJL7ZC//ff56BDVBJLX6qedut2Tq1qLsHsu3/5vM9qMOSS8M8VyTapmIljdgRXZjcsPrCXi+YEruZPxRhKc3WgAKlyO5fHvT6lJCc1PHkWCpGRE/rmXX25OnGF3Y7EaVSyCqWERSUQkEqzoMRPlF/cUMdMD5jnCMjtjQVSxD7PzpuyJmUANLANQZcd78IQqFcs+L2ZFynYIIo7p5nY104KoWlf+4OBgnWTb9YpKTOAIYqYGM+UyulK7/0St02MMqe96yS4xEBBT1tlJcjg8P5AoJpOl5ZkUAMBJCnkpgLA1aUQ1EAuzxaL7ds3eshtXWgJgLTXX5iARshL4ximDWJtSoggQELCp0bthu333/Ptwu8SjVnVMVqTpbPbo0cV2u66qiogVM5EDmeoFzhOWkxdwlAKjBLTxz18xUM66SiAhE7Kr67ppG12FruuaWV1VPjhXOV/qA4CZiRmZrQciMfm6qpqanQMmckzMArrZbivvrGISCIMkJn58cX7StHfvr67vbpfbZUiJQ4IkDskhVVV9uFjcDdu6nZ6cnl++vbzZbMG6VWffx+i0QCSJCCGaRjKrTY2byCjPDJM3CJiIHYrEMIQGcNpOEigyuco75wCxJESqFLPOUbGqgFAsCEys1tYpM8IoCDNCEpGglGM5xk2b63qRIO1pcchtPkuzTyG1Dt5ERJjQsWN2IYayuRDNhhVFUY9UOd80jT1YNP0KmSqACL33vqosTdqSRLL6JxRQ572vaySsqmoymcwX89Xqruu6xWIBABcXFwBwe3szhKHvO2P0BFDV1lzqg4MD+GVHWXSfPkZ/Nx9UHHoFxDzJRhy+f/74s/EnazbSCfbOQsqwo/WXA/OAFBKogDKCoIwNaMytQQWUseJXVTWRIXEl/QYUjB8UxnYaBfEybmcwImjLmMn9V0wQEAEgmXVrpOX7GWp2KVYktXoPUJD4CWrOf6HDfC3cd8qLF7F/1k7E46e0OxZjQiCZS2fcilb6qZiKW6cffRXGqtB7OGL57SivsNi4AFAIAAvhAZLFo1TForRmQxddqzlCk0RRzKTYc05Vi/I20GsfgiiqF20RYAacRlVevFhJIlLy7VQ/qMEr2Qw7yLZkSwlYojEmFbUsY2PhGmcCRwh+nLCdDKEClxsv11ixrgBJJHvchh3mNNziWVsJliiSxWuBmQ0lJkAqQVJUUUmkUDs+mM9lu+0JVIUYkUEUhPYcextLyH2JVCWBRElR0xAjG+sfIAE4IiPEFBUQ2YH2O/ISAoz7Q+iGoS+TIkQ4poHtBjRDDWVJWQbbboZ2CImNrsQMuzCzq5zmDjs5eyOJove+bQkwrLfShRQ8JQGwrG6ez+dNY+m6TETIlGIcup7RQiyGseDId2Yr1Z6BSlt4+9nSc2zVpGKIMXHbtIiYVFAsl4uRyDkXQ4yYcQz2Lkd2ENg5X1XmDSvlpkxAtO0H9k5UgBlztAMnrjqfHp5XbXd8dru8vVutkiQ/RK/ggRpXHSwOZptN28wO5kdVPVO4Mtpt+x8hCaCqhmQ8VjBaJ46ZiBGEULJYz22QgBmZUFVvrq/7lKYHi2oysWLw3HPZ5sfeSBRUmYo7a5sDMHsqVAJXBnQjIWhK0WVCJIS8LEXhIzWkgECoSGAwvAIAEtZ1XVXeCMt2545giioqOKK2ridNM2knFr/R0sE0qYgkZjo6PJjPZ1VVFVvQlgE4dnVVLxYHk8kMkb2vjo9Pfv/73/+n//wfu647Pz+3euXb29tvv/12u90AqAWJ27YmomEYPun+fvJQGJOYd7Joz8e1CCJhRimQGH1VV5WnwtUOgOQyzPCBxEdCZFQonTfzXbL6JgCnyJpbs2W3ElERIkCvaZAQQ4hDgBgZEJJCSijJ+NtBQRBQIeHOaiJRjAoxUvEdTAVnPCYn5CkjeQEJQaKEGFOMZhn7umbvkUgQ0ygENPtlpMCC0oduGOqm4cparvzjjo+d4F924KhkyhT91BVGD1j3Phn/WZBWFVArT8gpgmUSR891B+QXl0BBCYDJsnTyjob7d4FSnpuvU9QTIhZzbG88RncTAABijKIKKjxGhfZOT5JiijFGhbE1nkUtJCvBXKogZdIAFAolhUhMUhqyKWTrrtAY70FA+5QqFvwQ1RIdgWz3IKIqogARkCJKxrmgBKH320XcnxyrNzFxQpmQOKUcnMpPQyXMDSApJSGqnGPnLKs0Se5HZE+hYEHTyvvptJXDxfrqXdaShECkCrkbXYqagwUICJKsuazlpikxaWbzLCvbbIESOiMiIxkmIsvHMm8NUn5HJzIylZQCst1QSm61Yd/JPR92Kyx/TXb4CAIMQ4oxMIBzTB4TwkjvpKpJEzK5plZCUNCUJKQoAoQqwJ5PT06qihyRZ1exU0fvrq7qzdaolG3RI7BY4bHZN5DtNy3UU/YmzvomYRz9EZsf732Mceg6Jp8VOBEiphRF2YbNew8WUgdUuxRnwj/rzMNMKaWMbVj2gyoiOjaXk7xvZkfVg/lhSEkBfZAJuoWrT/zkhpvKt9NmwpltMXcWIuRiCOYaACDK+CwRoAVTbTo5069kQU+IlGK8endZbdZnqMd17SpfVc47R0QWVBbVYRiQXF4HgJJSHAZsJxqTNcyhsgFQBVKySGLf9433hIhqTbPtBIUxem3iSBHNmUcGJU3Zxm/q+ujgcHW3XC9XBoWV9WO8Oeqcq+vm4ODg8PCwrmtmVszhsRweBmnb5uHDBycnx/P5vEwagaIqIrn54vDBw0fv3r13rhbAdjI9OjpS0a7rbm5uTk5ODg8P3759++7du+XyDkAPDw9PTk6aphKRyvL+9ohofuLIKJbZtUVKFjmB41zYACNR00xmB4vZbMZE3nlDmIksMAO7cvDx+mxukEMwYjkoop5YkULqlqv3l+/ubm9iTJX3JycnJ6fnblKv0nC7vluuVp1uYowQ5fLl63ev32joUQUR2fHi4ODo/IKa2uJBpIAJLl+8ev6nbzQlQlLChCAATds8/dXnh2fHxVNHEHn93Yvvnj83rziBEGHbthcPH50+eODaBhGiuXGaa7sZ0Im8efnmD//9H7763W8fPnuCDsdx++XHPrz/P3DsvK/xnx+o2/0fPvxy+VwELFWOCFN2AX4UEaHciLGgiT/iZBvaVPbyiI6gtbJIKWXvbpRtOGpDEYmI+3YN3lvJRpifF9KPHNm+2Hd+Yfw5cy0bmIhWA6401kkUJwigINxQiityoZGCjvVN5Sn2s3hH0p+dX1zMmh1SW7C74gvum2UjZkPlDVJKjCSi7JgQNSUar2coDiICIfHFyfGvv3iG+GTmqL+6vKrdhkggIZq3KRZBYEJUElRJqmDMKECOXeWc82Y1Z5x6955mKyAqWDcFNEhJ9zxaACcSy8JKqlFK2VZeB8WmMKs2D49hBYjF4jKCkozmhJBiiMjo65oIE2ZgwcbX+Jcj6CCSCGMSkQQxAQETo+J0NkGFGKJDqiuf0EBtJe8AyThMIL8tIiCTyyCRFXaPebOA7D0hIzqwBkbOaRpUtXIVA8sQ1bPExM6R45FTO+VMAzC2ZLuUAHl2ZTDZWpyiApdCi4RgqU11U7umFolJBYEceQ4UUsQhtsRfnD+CVRduN726WhmsIzRxnjYizUFVZc6GnqoVAnkAZHaqMUdTaWyKYStSACTFoe/w9uaG2fvzU2daiixYBAIQUyIgBWUmFBm67vmfv71qJykMRHR2fm5MV0FFIYlE08pDH2UCKqio5FBSVCOaLjayTUdKmvUOEgJIIX8GhcPDwz/84evb5e0ofy29w/AVZCKmqqqGGEIIxrZhNNd2L0BomvrR4wenp8fHJ8fOeec8EYtqUkkgZ+fnjx+fv3x5xI6RyHnPzqlC07Tz2fzFixcvXrx4//59jLGq6qpy5hZbN8P5fF7X9cfad9z8H0suLLK7FA1h+YbZBobXgq+qk9PT4+OjxXxeNQ0SgWKG5Pe+cO/KfB+Ug4z9sgJEvfzh7er6tq2qo6dPmWi9Xt8t77zzj+ZPm7ZFgBRTHILSAMjXby5v3149vDgj0pTituue/+nPy+X6yZdfuunEVg0DbpYrSOnzZ8+QKIIEFUFwdeXbSsq2NdT87vZWk3z11W/qtrFM17vb2++efxtDePqrL6hiNLUAigoMSKIyxJt3VxDTq+9+OD454YNJseT/5Q4CM6N/PI67f/ycZ0x76j+b96IJFVUl50khEjEwWsNVGE81WK5kZ+JuF97z6Q3F0bx1TJuV9mkAKmK06iIiCZU1t+rNGVU7Ew2zU6qqKihKIpTFt+bKmXzgHsq88zgVSpwL8m/MCwwxJxubijbu5xwGtmATlnHMkZXRICnU15STDRCssd9H+6o4/qMZkH9VSIa0nASldApKHkSWMKawyATCiBo4dt4588LLgBOioJIjbGr/+NHFg4cHVGFN/+ryz9+8/LohAkEUzWFeFckxNbI0FUzl+ZERHdHYLmlnOFnulCnM3L7AFDDuteqxw42caqa3cmILohERlg+t2A2NR8lmFEpUXQrMYPzbXTekFImwapwZNmWCABEFIJKuQn8d+42DnjQkqaIAATGKagjpxYv37y7fAahjtpQpRmdZ3wJmM1k2XBYOqiqipVO9sRGpBXGRGSMCYlVVzvkYgiOaNG2um1HddNtZO62ahrxTBMtAHn1iZmeVLz73qUUEdOyK7jdjMy9Oi9w655x30DTs3SAhDYNKjwllCG1Fn188PKzacLv95uqyAa7YgVqUFxQTkhBSMvyPdx3K2FfeVwDM5FWVLACcpf24bUdDSbttd3N9PZ1PXU4aRsxmkxXdZusRFFKMQ99LXTukruvWq1WxsTJNjQVxUwiSShL/LoETh6F//cMP/XLjhMyfhrJVtFhp3vvz87PZbDadTg8WB977EHvb5WhPpMrMqtoPw8Fi8dvf/nY2nV3fLIudnAVZVVXz2aRtqvlsbq+tgF0YBNRXVdVU06k/PT1u2lZiFFFE8r6iRE3T3t7e/s3f/I1zztzruq4fPXo0m03/8IevRWSxWFhcWeRD1WCiauc0oUkfQARR21N7LhXCWDRv+P/h4eGDi4vT05OmqdjRCNqN2Og+SLp3y93PGTcTYIH1tlsvl08/e3T24IQ9A4JEWd1tnn/7w+Wbtw8fPZ5W7dpv1sSJGBBJ4eL87Ld/8XskSJpU9N2by3/471/Pj49PswK2RQyLxeLZrz4n5wJIJBX4/9P250+SZDl6IPgBeE/VDj8jIiOvyqrq7qomZ0gZzs7+5yOU/ROGKyuzS1kuZ8nurq4rKyuvuMMPM9UHYH8Anpq6R2R1c6epGeLpbmaqpvoOHB+AD0nEYQEBkffIDF1fXX/+xReorELO9Mlnn5Ljxfc/fPnVVyiciU0EeMbb3r55M7f2P/27/+nrP3796sWLz85/7swNvW8X1tL2X+DIXRA0FR/Vv76eStDDuPX6rRAmC6QRhtU8a2u6gNMn5R2bxpc/1rrlFPBnZn3AjYxFSXUNTcuNhcvW1WEAfMlotTi1OPmE8YcD7tmVYH0P6/rbEALUTYTuyvZa5uWUwBWtaZYwG+KGCOjcy/3RHz6yE0UlVnjMXccvCdDeA52Lu+jLj/4c2VsaiIz/5PiK9EIHoqZIF4qvMJXIG7xF8DVvi7gwyVIER4srKELDULb7kYXIMdS62YzIwFfwQiuXaIFExEJRWsk9iheutGmSpXNsfMtiYXjUlPiClQVA4ICRU4fNHaXXVXhYSPM8u2dCMhObWWu2LObWWmuNiNws0EbAOusLWlNVtKaAk9tQRQRtGVuKdWDuOMzH725eH4vdVUxQbs0E0fLw/v7+N7/5zZ///OfwxVPHnoAeR4S0M3Hfw+COFbmCOQQsLCJDZVdiXF1f7fa7u3m2UutQIyQM92me2mY7nu3Gsx2Pg+lkwUZABU4JFRJqrVTEU22hZ9NwJMLFZomNMc9za60y7/a7gWw+HI+GptpMTZUcmzrsttvwAReWNkpclxYwN+S6O4gxDHWz2ZjNzKRG0S0olHCYZXBnEuES8ADM2zTrNG+HoUiJkHzYUs2UU1g4iMZx/NWvfvXsybVbu7m5eX97a5HE+nAfHucpU1p6uU4EZA6Hw+sfXrJZEZ7jUVZxoZjxzWb84ssvxnEchmEYa1aHqpIjMkwAEi5Pnz79d//u3/zrf/1X+/32+vrZN9++wNI7Bs5EUoqIlFI22034Eu4e5bzRGJKFNptaa72b5giLlHHkVoZhMx2nu7u7s7Oz1tr9/X1r85s3bz799NOf//znzLzZbMLWxMeOxx5qB70Wj2Z1MEEAZqJhGC4vLz/77LNnz55cnG/SO/EuA37aN+MHoizTZRggw/s377f74ZMvn0qRSGBj4YvnZ3+z/eX/8b//1/ev324vzwq4kMwgF5KhYlYV8QIDs+H6+Sf1t79//+79EzUv+WBSSwbc0bNGevA54sJEydMgLEedKDKBzQkkoP1u993059aaYEhpk8wIMNUfXvxwfnn22eefTfPxux++e/rZJ+VsZ0TNjRbyjn/5I+XNT7x70sFddn/kkx+AE0QpD1vmMIF7/AgOBrGIEIk7POt0uSc+8Vqz5i2u0d6T6QBkYCJQUvZIO0o9lqhTB0dVrc3z7P2GlnteV/tYVhngo6iA40FVTB+8BX82mAXVRaQNo4fCPdvzrU9MjL3DectJ3j2VRRsnr3v3XhKXfTQajhNxTKR/LWs08giN4iowQJxkqUHvisMdnEVg7uj3bwaYiFxdXTz75CpE72a3vb66FlRk2z2YBk0cm1O2EVQFEUvhgPEM2tTa7LBuO3GOuFtPy0aKqrSn0mVfVn2hfLAoZENrc9ho4VYZglfylF+3Wp7MUdmfXbaoNWtqcIylClPtWeKck7pQfPns7esX3+n94R23mYh1Mhby4o65aTse4jbMTOFsSqZiBZ3uMGbWkU2cEyvoFmusYxZhqaWOpI0E51eX5/szOx7pMImIHtXciVnVmupuN1xcX5XNML8/hP1RSomoAzkxgYUpwrEI6/K0kVSVixBRpMvd3twcygZEB0fd1C0XGsb5/ti0qVsttW7G86uL+rJaMStQMc/yO/ZV973YrO7mLrXK1fXl2zev0XMBAtUgODkzkYe5wQKDR8tLNXYqIgy4GthN1dyPx0OtGwoFwiAhqcyFXKmOhQ5wz3rcZbqdaGotWpzEk55sZ0et5Wx/FgZ3Yv9dewcZQa11ux1EYt06YIwgoAQFzQiIWc7Ozi+vLlmoZBV2IRI3tElNPfLU3L0UHmplZmKK4igAQmxmoKxlV9PQ7KUMznp2fq6K6Tg//cWzy8uL+/u7u7vb3W43z9MwDJvNBtHr8GOHd5n4l9UEEUVhRYBNtQ7nZxfX10+ePXu2326Ee8YZLejV6txHOvyhRxbUJszQprc3N88/eyZVjNEYhcAOd2z349nF+euXr7Zne+llXNEDfNaJnRwSrPrTYdas+nQ4WWc+Ot7d37x5U8dRCV7Zo39arU5hEaTrziCblZ2EBKYwt2l69+rNbrOTWhwZBoqxEMLtze39ze1Xf/03UuSTT55/++23b3588cnu5yQdOcyx+O+hgz9+dDv+v+WMnvoBkBnciVeFvdTfRzi7xIlSZyLO4txRhs8e6uCf/lamnhHSezkAyOTEcIXNtLWmquYmj1cULT9js0Y8LT8ASBqQubqDGtdXjmy6gML+kdnx1ZTlQ7CndmEIgxnMkERmE0byQI9OyjSukRXroZa6BYcEOLsVldh6f0pbCugWINZDAkO4BlwqwkW4RJupleoKPW/uJELumckVgc4F/OglREifP1rrJdWTsIO4AKJKUagLEu/AvEXfmeAdBYHcHGROERJOGD7dxXLyGjMyZ0taFhGpWTaHP4GcvcLHHy4josi2c/OhVgKkSD50GiQUVYGRJ/7tyx/evX331iahUqw5pNdXohSRIvM8NW0OaFMpaVOEixVW+Xo9nLRF/4WZ6zCUOnCbHG0zbq6ur/1wvJuUwW0+NlMQu9k8zwY/u74cNqPfBDyAcLb60ulJF8kImL5P+NxmRpa2T5ps7jbrNLd2x8xkfeeZmrFtt9vPv/xi8+c/BDWiUCTHAg5hKaWoIRogRLclLrTd7oZC93e3ET308Ah7uIhALFJKJSrururWDEMU/JVmyeXoBIcdD4f9vhEX4hPA1QGbxYANkMk9aHTMTDV6zJ0WaFQaRFl7X2vLxujrB8GyGRljLCXJXsKiBcHdjYpUWuLZDg6Om5RlaM3gC1ctiEhKIeawrNUsqrD8lP3Q9xiBS2Gql9dPpFZznO3PP/3004uLi//9f/9/tNa+/PLL77//7u7ubrvdPugAsRYtH+HOO6GXi5hYXBYiqmXYbndPnjz55Okn5/tdkVUiRcTMaN0k9fGXPvLbuMuweZ5V23Y3ovMdLXk1JLTbb19893Ka596GFeQObTZNdpgws1o7Hg7f/OlPYPrk+ScSZaZEgGtrr1+9/M//n/+EwpOpMbjWT7/48rOff0UisXm9J7TevH3727//BxRRUzU93h/M/Be//CWXOuclKes9mr385ruzYfPk4oodZ7vtJ0+fvvj+hyeff8Zlw04fN3n+ex+J67qfbKIP3l//lc6rRDBYm/YqtLwCdQ+muyIdlguvqJcgra+Jj6neLCdZ3Lz+GVVT6/hzT3oKiaeqbW6ZlQ1bdu6Du+9Q86Nl3BUjgGQDcV76IZx07gNIC+gZoo6onPQgrsjT1s3j+7idVrj7am0TA9k1LhFOpOHu/a6WUYsvIGGKZt5mHCk/rgq1ZNxjZyiSx4c6k4+qhor1ZcLyymRmb9++/dMf//zs+bUI/fDND19//fU0Td1GIyYWSgqDIPuIFq6OKOWXyGxjllprpNNmfD4a3gTtKrETh4SlLgmi6CUkS1nWXMrbTGPSKFh85FnGu6fPZ5lWvE9NNcSlsLBwqZJctn1lRwjbHSh8czz8+O7NwdvIXmElnTInoNShSAUOrTUHSNTceFGx7OCMguQrfZapp/yZWWGpQSrNRd3LMO7P9vT0aZ0dwKwtjBnM2u7ujjI4SXpwlP5QKYVzQTOxsFS4EJggoc2yOloNLIHHO/HheGjQUQjm2qZmquYKA7wZsWkdxv1+X0oRloGl1lqHWiTrnbhKa5FiyvE4YNru9rXIdrcXkTD3krCfIjNciIRlqMM4z3PkMrMzQeBMLsQF3Y5ubQ7vg5kMrrO++O4Hbo3djvNM7pJ7MxkALArJs3q8B5O61VmKAD7PU3q+C7uSu7tHJD3ynJkEFto03HhhZqgTSKQQcTbxdDBzqRXw1lov8qcwqdXcAS7R2tEAD1RfiKL4rxd/94il8G67e/rs6dnFBYnUoYrIbrdT1VevXv3pT3+6vr7a7/cfFYgnEeAPJNeDd08Y5hImlHG7vbq6fvr06dX1xWaUsjjQJ1f3RPfsj7/6sUKgBbJmaqZqmWYc3baWqxDB4ApXmJIbvBB0mr7/0zfH2zsa6rFNCh+2my9/+YuzqysTMsoONA7szs5+9be/5nGwQo0ApjpuoxtM5u8AIdyOh8OLH36kWmZtr16++OKzz379r/6H7eXZkToCCUS51HRz/+rb7y92+x+/+z6ywqdpevny1dtXby7Hz+gvA/H/cgc9HtHTi2nvrz7wCCUOsCleCKmqPSdmcew8S9V9KW45hYLpIWqY1i0//LpHbkwvqei3EC+ruXQLbzkxiKZUNVHcbJnr6wt6FK1lXOnDgeh39bCM2ZHNceXDXlLkazUMpFEZqjjxuQU2+Pic5HfA+w2v3fd8Kyq4EG3XjCjw7uxfQUC2/nWFV0LhkrXmhLT4nUxNtWkzWDq4y2irezN/9/72d7/9w6efPjvbbX/7j7/99ttv379/Z9rInNwrUYlObpFU2pdFAODRr7GWstlsWLiUykypfs1Vg1pKUm66u5MlVN3tGGcCF1oCxXmE9g4FbKbNVLGEeQOs48jJyGZtCy7g7h2qdE5Sj2hkFi5wjLSTQ7g40X2bGjnDZm0Er8JRexW+osFnbWoGKaU1HszdjXpZMdaIhK8PM8MC5iYHAgE+DDxcXfi7A45N1aQIyOFGs803N2/evb9/917S0KFSSlQxiYiBhCu4YMm27zQgniobZqbuyvTNqx90unu6Pbve7TalFAaY4BydrBr89ds333z37f39/V4vhlJ2221kBoE8EiYdTVt/GABEdRhrqaUMGSomIKiviUlK0IsEmuru0zQTM4tYKDwQgc3QWjPVu7s7VSXhUgsTm9qrl6/evvhRCOb+/PnzzrGfyJX3/xGRmy+U3vHiMA7PPvv0N7/7x0BoHBbJEgZTU2apUqMRsAib2eFw6IYDMYtZczMRqkWmXvIOINilTS0wsWiH6qDWW5mzSOa6Nw162HiDe1G4u4chvNluLq/OInE6wqnxNGdnZ999991/+A//27/9t//2q6++WnbBx4+Tr5vpEz2LIsARATGYwxY+Ozt78uT64vxssy0hvpiWi8AfS8F1Udbpyz6QR6iDsNDh/rA72xCBk3cBZLDmtzd3l9dXVGVxK80MjO1+8/mXn6EUE5bNuLs4l82oQiHLiBhuQnx5cfH8s08xDI19ihAWOOofIzMj/iPC0+fP/+f/+d/Vcbw/HH7zm3+YDgfk3l6KJPLrX714cXd3T4b7b/6kMDMjhzN9+913+2fXqLWbzb4k9/3k+H8wGf/Uu6efq3FdMNecTqLH0c8PbmPRsid/cZ7nv/D1i1u8uuBjq2q1s2kRZasF4A8+CyNC51+Dr6OiRAC0NTKQAEgzoF8jn9QeL+wPh8i8y/KH92l+QgtOa59JKBqgdjc3iXsi47d/kKOx18fs19CSWPzdRJ4dPRfBATNj5gBwqWd3n24chEjKQmepjXsmSBGRwsrkaFND50XsEiqofak5rOkf/vjnNrfLs/27Fy/n49TmaZ6m4k5kXFhK9hjyxVgI9BpJwFeHYdxsLGKUMc22rGsQM0uJfkbhXoTfQCkHnDw9YGTbO4o852ZmlE5m81UDSw9MErysXPc08wgwtXk2dmo6F6ro3GCnGV2CGU4AGTwqn+amNeuYyMzafJyOR2va5rlpEynNZtbZLNiYMp/IjTyYyVTNsq58BbcspWU5XQKIyGYc9TBTCWyUwaTH2TEf3r63aRImEGQYyjggGuohpGz0ZIw4Nkfm22IHpd4nmOBdO07vpm/fvNhKudxur3ZnZ7vdftyMQyViMro/HN6/ez+32QnDMI7jpgyFhd008sYoTFpkOrYDAd6y1OheRQQOF5nDNS9F6tn5+aeff/btt98d5ubMzjTNE25vdVYPRq2mZD4djmZOhTbjyJIAxdX11eefPr8/HuNhuLOpUrqr7B0YESnMmVgBIinl6vNP9s+uTbp5TrT0FAsYP0AS5khRCdXLCZ2TNZsBL0XMWpdzIMr0NzcP8Jo6kxcHGMWcibnBR1oklS5RlkSru3spQsx1LFfX1xCOuWMSEam1tqa//e1vnz//9Kuvfk4UmqZnIS2wUAbdiXpMrQtU4qUIO2aBudZxvz+7uDh/cn11djYOAkRgLWVdiv+HDu/JFf4Qjo7X4xNS5PLy8s3Ld5dXF1JZvL+nuHt9d/v+9vkXPwOz98J0BaiW86fXn/3yKx6GGWjkSpTVoz0CWwDL1oTMzORGASAzm6v2bBF3N4IKYyi0GVDLUHa/+PVf/+bv/+F3X//+F8PfyG4Li/xbAnA4Hn989fKv/vZXX371MysUBFiV5PXLV3//D795++bNxdNngcGc1M7DBJzH47DUAvlJBn84jH00iaijMSs3LMtBHp9+UpkffjX1A2B3MnVLhtm/EMF94INiQZBWX36a949cpEPJsQLN3Izh4qFgsaxPIopr98SCHmeNdh18GpMHRggRkcClpzN7TxqyTjnV1Z0Hv3iugCBIZGIhYWKQgMQ7oWR33PNB4y8BRcL7g8d7OErobFcdRTB40m0nJu2OCDAvMWrPPMCea0ZgcZHIq2YSZjLL3KPYqA5E8M47UAAiddLJfvzhjR1VD7MbWjNtjeJuBDwWKgxKmjGPcgAYnBjOQiIstUJnYwYxeSRBU/fjiEjAvOSku6MXVruTG1lZNby0gLCn6ThPx6EWd3OCFFqhZwDIzFjE++KxtFrIHa7kBlMP0ihfTX6IZc5FEjrLndyamxq5cxTgmsHcm8Ld5tbaNEkZ5tFqNSJ1AkukmbuTO7uxaSPiFZdRumhBP8u9HjEmT4ia+2a/pdlgRCzTfLcvQ2UqIJuPBpcikvwVMDMWByUftrtLdGEiuKu5UhfK4bw35mOhQ9NbP765Pfzp3csNly+fPvvVV7+oLEXZzG5ub2MbE5EIEzNFiJSSlx3pe1BiUKm2CriSkzAVcSnMEv2NSin14sn1F1999frdu9fv32csjkhbgxstsZ2looxpu9sJi5mpt2effXJ2dUF3dze3tzIICailK5mL3pPkJEIi6tZMo05ApAybMWhXI9IcRJUUYQ9hdZvnTGgY6gBEZpklY0zwGoioqpszgiqTiRlETZWFswWrh+FJpVRzUicjoiKpS5gsaOc40lDd1IhYRJhpt98DbAYQl1KKVHfUKq21aZq6r/ARbq8IBX3ooKWco66dQSIybur19cUnnzy9ON+OJS38h1E5AB9c57RmH4il1a4BAGJ6/tnTb/7w7Z+//v6TZ0+HTSUmM7t7d/fDn3/85NknF5eXb493ltX55IAxYaitkLE3NyU4yJKwMNHv2G3vbu9++PHHzW7bzFrEuViG7UbG6iktXEFa2KpM7mrqhOHs7PnPfvYPf/d3/Odvvvqrv6KSO9DMfnj54uj67Gef836jUJA5yEn2T682f959+913+8srGap/kIGLta792OGOn37z4wednNBsiPeRDyy/dw2Jk7pa0LQoMQiB6Y8yHtbXe3hxT5JPevyoD0C79ek9GcMDfIi+AA44xx6LQeg2ohGcekPgFBWRlRDpER8BDR6/4BloemAPuLtaU2/JW0p5+W5J/kRw5kRpGsq9Q9xhxHZejeXjkeIRVFz9LHdXd0110Z16X3ghVW1ursbuDmaAyYJNYQkChEonSSJYBgfbncU9RdcvRyFpimlSzNbJlyXbUDCVcahjZ56Pe1RbzOkSjMVVMJN1uRWDIlyYqQ5jqRsHzdZUVUPnZhZT+AP+uB0hOXRubZ4BEFERGYYaQCDS51OSSLktrbWwocJAsaihIAm+SPpgwYUot2YMdjM1BZE1tVlhJkTqbqoCtiiobmZq1qZ5nooORuxhYjkvXOphfUUek68s024hgkFuPoUCjrgvy2az4dbs9gDCrK2Zk4gw22zmzkWGYQiPLTi1Mn8soHSKCe1QbaSSMmvgBsJNEGRmbj57azodYBirK4tUEN3f3SWuxcQiXDiqfqO3TQd9nTKvjwAQg4VrHd2lCtXKUphFhnEUqSzl/PLq6snT3f6M6EcDEclmux3rUHoHQCJyS54SIhqHITrkXj15en5xGXV5JOIPNUZA7NphLBYJSyS3F5ETidTgSIkILi2E7QCAaZ5ev347zwZARCjzmeEnV8YJePvu7fffv/j0+VPVDFl5OqDcgY28IHPyXC5ehXccKxg60TlTA7tjpu1mKCyusS6lp5iyqnWm+8f+51oNcFLmP4gOhhTJBlIsRYaz/dnV9dXF+X4cgvCqh2m9uyH/pCD80AKg07nDtn72s+ff/umH3//26yoiIhGoO784v/7kaoJoi8pLQjTlGjfMNDEZ3KJLWEhGPz2DA8NuN7v+f//rfxERzfgNyzD88te/uhiuowmbA0o+7ncOaoQgSGOWi6dPvvzlL29ubqbWhlDA7ganWp9/+QVtxyNMXZtHKaNR4c9/8dWbd2+bKbx4gnG+evy0D9bYQMewHtsyj4fuI/5rVx0d8DwZo/T4pFQs3vNjHh4IWkHTeZoXrCUqJjolzumKD09cfo04TT5sr+V95C6vj1TOHdTrKp8syQ7ShHpw6srLfnChjw9ZXKQPSXicfnKAXVXT1HAKXq/TWNESVaST/ZgXyhhw/2oPeZkGJ1m0DEpwH34aqri4e0iqSLddNY9BZBC1uc3zbE3djbMuhKOrFEVyaZSG9IP713fcN4VHZDcocWtus07T1OZovg4j4TqWcRyCmC/8Xk8UOyFcJuHeG5RzZjm7xoBLqXXDtYLJlW1upM0tNgIHiy4R9UTfh4f2Qm8RqXU45Yx4uEF8dn4xDMP9/f3dHWDuxAAJ97xz4lJKphJ0244I7sGbpMwc0IoTVLW1OTQl4G7GJD0U3bOiXFWVBO4cDSZiCTIziJzBRfg04icwC2nHmaoSuRnUDULjbsumN3dHgiv5wZsNFbV6ExCYZRw3pVR3b62xCDNzNrIKUPG0XESkxnvxndzDkAYjJSIIbfd7lnq8PWzHyiCbGzKpmFmyvYObhpkVuzlp3G1JbnQpstlsgFaHYMaUWksZhlJqLZuz/eV2ez6OZ8wDQdwRuQixsQIWssi+B4gwbkauUsfx8voJ16Hp7CxcKsKQZAKRxqojmrUFhiNBrOcGaIR3iFi4urNpxGqF+JQxDnCb7fWbtywybrbjuOkx2lx1C8Frm9sf/vD12Xb/7bff3x/uwu8KSBvU41pOBCpcMkERvRGkiK+yoJf9HDavMDZjLcKOoFBAKZUpyMW4tZ7z/3ER9dEj8SVmZhLmUutmvz+7vn56dXl1thtKEDEtWCf9pS/4p7/XuxIibM82v/z1zw430/3tsc2tDnV3sR3HoqB2cAVcFWZwIpbnX/5MdVapHky6/TbWu90ITz79ZH++D0cpA0pEIKrbTXo9AXQBzz59DnMr2cjBXEnk+RefP9FWSlmgPXe6evqUAONAtGIoAsvhi+vr86trJ9IIQ//kkDwcPHpQwHP66ErtLR+n7qz1V+j08cXoW/7+6HVOb/RJcCciVTtOk7kJl1PbjIc3syiT1S2lJn48+v3aoUmX38NqC2ez62nqmyZ3T1zUPfr1RkkyMkq7IJMZ74td/7FKOqITLSRhcaOXR+6eeaTwLsaEcAbAqNc8AT1fzN0DTEp3pev4QF4ssipdydTF/SeGg+I6cW7n1QhDQE2jIiCSjRzonccpJF5AzbFgQomAnCnWojGBIuIdjQPcPGKYs8UlI1qgxC4CKVKqiISdACc3ghooI48ZC13WQMh0MBhchCQibcKFC9gBa8FemoEUx2MPOAEvWxQwSy1l6RaQvCCgzbi5vLran7W3797d3t5KqQDYmYysmarVofrC/+1pUaqbt4zXBkhoBFLVpgupcfyMhaWqRnE/6mYk4oAH+BntoKKLMBEJR6Onjtilhdi5dmPRwtTnps7EldnYC5lAK994a4XuK9rMYIE2Eim97V0ssXDtFrQmxsvcloq6MB6ZuFIJBjMiBYGYN9sd1Ntxorrb1GGsldTRPOhyKTBtzyLKSOf33vFi2clFyna7FXEpXKrUUqRUrqXwMNRNKWMpQ6m1DkMU8MSKzL4AoB7BSsOZS0EtVoQ3ozKp07DZyDCAxYicWEEeU5ZBdwtYGEyWtcIe65iC/boHNyJrgIiYhZi4loury2FTh804jGNPVA5kPUB+B3B+fv63f/urL7789P3tey7i5BZdyt04QycW60lYCGCQOCRyn0TSlo5aiBR/LCIwJ4cIO1mzpgi7LcsEiWSa5rDkku7pJ1wFnHQAiBZ4rxBLkXG72V9dXV9eXp/tt6V8EGT8y8eHMNHDd33xAik8RN5dbnaXm3UwTRWtV6rk8gdtLi6aNm3aoT3rmHgoWqNwbMa6HS8l2sdE4ABsmuLlwa2JQFbtrxwMcyGSquk4BWOSBxbWvCdGE2GpO2IAZJ5VKfYXx2qtyboO++kZWrliJ/SCe2W1Pz49JEOe1nPI1jkr+QKnqgNgbtneJxJW4OrWCcD6sCRKSydeCmJ3xoNsoZOe7lI3nArrOjhf1OxHtPYpVrfX7WkAFE0D+xMtnw8v4qOqrn8kfE7n3ENrq8K7JRDCGZ2xKyqH1yMPhKjv9bAddY8KwrQiU0Z3sDvEazcr8rcOjiXjHszIUURKB7c0VicWtUggJ2LPIemz4FBtbg1u1FuWe4+pZTkHYKou1Q2ugLIZm5uaqLJDSApFs5+sn/fuh5qpmrY2t9ZaqMtUMwm1hBAUEiEABqVOzNJz+ojwGIIO8Ccuz8wIIUongz6wgTAxyjhePb2um3E6zm1WV2tNdTqCnItEOqwroHCADBTF49ocUU1uRkBrltQfAMXuBZIGL+0L00iuPnmfDjdXsxY1LFlJ697bJ5AlYdZpy8XIqSoRqAobXNgH8f3m+zdv3t0dXtjhWAjCPjkTRxNidK5wJu42qoevoPAW/R57JZabE1OtBXPz1mJ5CPM4jto0iszGYdiUYXBhJ0HE6AW9fhDdlsPKYg27pI7D+eU5sZfC4QKzRIKSsBRKxjXvYHlYmqeMjAjjztPcVGNpRNG8mrVYDExVhFm6n0CxYJlp8Ww4TMxeHOtuFPHayKQA0BnB0KUdi2z3m1IDHeIuezJKtdC0UJH9+f7sYvNXf/3VJ588o/+Kzle3kr8rcbOMjKn17kld63dpISKAm3kpJQqiMiRGVGutdQgbJQvzPi6cUkpygLe9EDx72zCLyDDWi4vzJ0+fXF+fD8MjIo1/xvGXP/+hxlloqhZn7pQnGUQRQSGTfEVxTjdMYxdnLk9KMXIFZkTBITwEzSkoaetig2VhAKCgbu012qEFvLOOPIh6eqiybgrHyUtq9V88glDp5BA/Lozx1QCdRmUBscmXIcuA/JJ03dVkVnvnM/R2wt12JwpanuCrY07mqX4/Zub82HBblk2/jZWuJ+7wz/Iv1m0nl+1eeuojg6ddtahzRxoKnkUhgFs2TOjv5Fz7GuGnE5jS/6auG9Fh3tOCI+o1tuHrpjl/4i+LDImVjM3sKYOH5ZvZmunH9sw/pxOy8jGrIHR+TxxWd4NbGNrLmJuuE/jWi2CZNHLrxdNdNSwfRjjs5iCyiN5KqXWsdRQZrM1mokrmpOYaBS5oBO+gHBzems5z09baPLemHtlPPQ/SwE7OxMzigFJD9+ADwgj04JECTpssiPKZSIJ1KIa485c4ecQ7Qd3XERJHYL3TPAkZC5shfFQ4Mp2SyTlYqyyHhJx8Vc0GmCnqEFVl3hehp9+G1Vq29MejQ3aMbMZFDBkGYM+M3UAMELWtKYOZnEWl1Iuz27ev/u7PX7+8v6XNJlJOiCn9KotctE58EKGM4ANmOUyTBUgOV/dwzETEZ0Yvyh9LPd/uDjfHplpYhjKkp+mQXuSRNpMtGxLRiSBiIczs5GWoZ2fnLJ7dZCnbrBCJ8yJT1VUBclU3AxNFwywhIzjT1ObWWpixkaWcnmw8UZAoBIkrpawGUfhCqnNoNyahYEsigpOreadaq7UK03Q4bIchjEwQWKIwqnAREHXBG8EKEBOElFwp+0aXoVKXjKHvmjVfWeJrLyfQkWSPIeIi6s6lqjlzjThGkSIiZhrPGnTQ4ziandoyrgTfA6lwqoCmbiR20g0mYqHdbnd9fX1xdbHZSjYy/e9weOosgOAWpOWINr0LTJjvxhZ2j2WcIcRYvb7IbgplaVBQ5LnE9g6088SQTGlaeR+MVaQHOSDuWWDYGTt6FDJf97UdnAPk9FHh++h4NDWLJ/oXzugKZj0NDiS+2k2HB55o/4VX1t763Qerbp7burdQSCIP0iuKeCb3PIO4294QmpCV1yfVuyDPpyfNEvlsuhz8z+runXcx9pmD3LLDQagSMme3LANJk8hhpp56i2IZe5/miF52p7fHDx4N/8eG2NDJpSJgTFi5tb0nYp/d3hknr78KUPPKz0fEhC2TqGgZH+r5XCvDBNY02sAkNwnMGUGMaFG0xOROZDCzqaWy4mVG3Llz6hJRkcLELEWkjJtt2+8xz8NmU0SgpqbmFkE5pEjsm4zMXdtxsrmhKTQTg8FAgOHdd10m+8PBfAxBh8Yyd8BZRAhLYWUsFzMj8tYarUNzcIYLw12bt2EcuPNf99lFKCoSeLY5djNzRjC69HCFW5DvZQVMZGY/TDXsyMcChHomJmQDYARxJ3WwhCgdX4DgalZKrUPxyZzIC+/OzrY3l7fteLA2kFsnKiJQTLOyhoOVbOjA0pdgbnPC68yxvsiImAtLDqVT5VJE3h/vg5m0sGzKOJCIR7sxwDTgTwviKY/lQt5jDDlVtY67LVOu5ggPd3Qx77iUOpTaWou4NxFKrSwipfQaWgKISYrU3W53cXnJXILSUxNEXFnmHquNEI6ktujuFQng0TAQmQhFxlRKOT87e/X69XQ8mO6ia1OXByldHm1q7sw1atZUwSAhLsWFYxSimC7y3zwR+uxShZ6c5T27G4RoaZVV0iLoe09IomiMiEotl5eXu92ulPLxTM5HR9IxMtCLy5nCgtlsNleXV+cXF7ttpQzBgf/JC/6fOLpPk3ipdffXI0XHrCewRLmMgaJlY05mCDLz8OZzklO0RFggM98dwYu5FMuGEczrO4my04VV66Sd48Us7DpBlWEzIA2704t/6SDmPq5xox/qYPrwz0RlwwR/oHAf/vHBt1GypHhfrZHU0SnQzee5PTqnW835C/f8gNX9JFSBh8t/gV4joNhvzJf30s1ZXNp+h8s1fQlREZaShfWnU/C696Y83r/CukO86Nk+wojHd+aO5y7jdvrI6TFWajRL4AxuFIlbZB4usVKkSnksjH6FhLUT9+uQCfUi40R73D3lQ3BEqFqbdZpg6qqe6feLjiOwwAGGRqovgF6nGjNbRQQEQiHaSo26SBHZX15uNhsqfP70Sd2MJEwlKjLEIYnpsEVKNNzVWpvnSDACmRCxsAsKc3SBiNs399Y0I8yeMCWR8yMFHPcXdUyL+xHBESZxWpXkL0u8P3jU5M5zc/NxHKSsbasENSmKSpFxdVO1KKq1ZubZfgsgoe7hLugu0qwnRHUZEVSb2hw8F31dZFEy4O4aqibWaWijQDUTkOwTP4zjZr9DYWUypgBK2OGq8/EwCxMY23TP+kCFZZ/GHoOrlBBtTCgira8lAg21uts0HauhTbMQjeMwSumFfRlqi9a43ZE5rU90ZczMVSSyXONPX/A8ZgLU7fzq8unzZ69evpx0mrWpO0U5OXHkDDMLgUG82eyfPX3+7OlzkcEbwTlz6E+IYkhqq6AAapy81MIs7gGQsNBpjAGM43i2PxeS2DORD2jBJs/IUMqDo5OvPqDio1JKDG/YUKWUeM/MuoGc002JFnCcTSkbTmGkOMwd5FJKGFBShk+eP9+MwzAMnU/tA7bzvqdT3mSEKeVFcNGVUndnZ+dXl+cXu2Eg5o5lLxY8/gW08SOHLj2lbpSm6k0v1ZdHCHdrDRotw2M93pFC04msy1ejxX2hXNsrd8aRVlz3n9AThnp1IS2OMQNsebOZze5wyvKPD4j8f/JIibp4nI+LlBZ9fFI6XftS72P3sVF9qIZXDih17Zsm/MKxYNkHEN3i7covM42IWfgxQv74a9e6Mxe/e+QTLUq3r0mEPQ6AMvaxmAInMQekoGbmdM34ZIJEnzhPeJGTnfqEZZ68+yV5wk9rN0Rw2Nk5Dx0KSADo9MoKJFhMhsWF8EjcpN6I92STEdKRi1iuIMq2FlMk0rnMLKoVKCKPqqqmjTQDbQAbQZ0cDC51HFlh1BxQbUYuQAE2RUwJbgIvcCYewNVI3GBah+H5F58XJqmyOT/fn59xlVprRLVCMEc9laQFnkvCtJlpABdSTjqbhJ3c3JqZYYHDH5ieJyrK9Yr0KDcKHlFzAkd5JSiKV7lbW+7m2SgKMLPj8QCicbNhRouS/wXAIiJwJMO4GQXQTWBUdzfTWMpmRg8JBWNGjU7SLdxabbO2GT5kdq7FfEaxjZsmf2c+kjm5m1qVEryPxKcVMw6DSCcFc+MExqfWTK1qY2tHK2JBQwkHeQRD4QQzAhG4iAQEF4m7PfhC4zCQw2aF8TxPg4z77WYzVoOxG0yTmyW2WU8cD7wgpJewzHOrUsY6mDfz3Jdhb4aJ4u4A7fa786tLc5vnYxlKzhwzCwtxYR6yJ7xvd+PTJ9dPrp6MZXC1DpahB3ksZAMQDKjRBMaJpdSxHQ8OYhYhd0dhCR0mVII2hvuGjjZF09TSs3BeuT5pRsQsdGcAPbcxnTU1U7NACjQxuGwLzcQEDthaNSu2mDk60pgZE89tVnPA8yKEWGD7s7Ori/NhqEsZx4fa95Ho7PZVZOWgVNnst1dXT66uLrdbrp12Y6Uqf/pi//wjQYjlJvqFaYlXgJBsGqd0THJ2Z1vM6S5hnZq7RgfrQFyc4OxZxwgQmVN3ehNbyJQj5CgBJJFG2StkuIgTG5HCnUMKEplhtkhg6gGj7kWVzEaxf8o6ce+w7mlAQ7H0mXrg2qJPU+qVsP+xmpRHend9LNJg0d9ELFyKyDiO6Kmp7pEVFWmqRsQCjrBNLI2OMuRF+AR2RlwtOjqgDyYQ4C3Wt7l4pdCmqh8uy9wyUT3/4VKjdDH7cniA66b9xEtR7urgFXCagEZe/5Ep85eGcbEz0kTwk+sJ6uq263EAi8n36FLLCjN31WaWLBOmLcgCOZYczMHqpO5GZMwyjMWp8UyDaMQorQ1E5+PYJjWfyBurBSOlgqwWcTu/2Mvl+Vir1MLDWMYNCS/Pngw33QzP5RE8WYlVZE2LFAFzVPr2MKID3hmq08oJy3jtAXdzLP2xFvo1WqtGKnaPzMnCU7I+vZndHY5bKeN2IMlRzUyQjrmb+zzPBO6QLiJa6WpMEflYpAUxZRTpNCPw0A3kBpBpI6IiotSazWlCqmkkSi8TDVjT7vizZAvlWMUgxzgMtRYmWkrJ1FR1VgOgjtZ0hk7RsCgXWV86C0q8bG1KixVuTu6bYSPEc5tHGh3OjLEWCq4VBrmx59iul3nPdAsZBFN1l1JENRhUkKB431URRJdSLq+uzi/OQS4sDKnDIBKELaWKiJAQCLYZx+vL68uzixKFR7ljXFYbCCDJZsURNuAkfo3FR8xwhxVmJoqCe1oLxkjfiGKqMOc/tl2JSJij8AxAUFV5di3lBShbsJBQ6pFg5ispFvu8A9JmZnUY5mmOFRVsPyIiRaTwuBl3+60UWRO9dfEBoBf6pV+QioI5aLyEiwyb8fLq6vr66mw/1IKgEFmm8L/hWAEe/+Rh6gBYqFNOwQFrrgel5pRcRgFHOatP98e7t29v391M8zQM48Xl5dnl5bAdGwOmAIGEANX55ctXu+12u9sRQCBlJ6V2PL568eLp0+txrE7pRLNBD8eXP/74+s3reZoMfvXs6bPnnw77XWdfciK3ef7D3/3j3bv3DGeK5E2D8NXzZ5//4uc8DjjJXurr5bENlDv0pEsomtucHF9CyJIIbsbyO6nSEwjRx/dk/1HPt3pwCoDFxw4LstYhKGnj1V4auxgFvpwbwpMfLQAHwD0+DM+Yrrnrae0Fb0wIAf/glhZ5n04qLwV61pGfldmRJ3QoBswR7M/q4QcmiOOkpGOcu3MaCjFc2GXA+svU3d/UqJQChAgsEHYhCJNQJMYsA7s8VRef8W0hhNNcoszktgCskVijqrppeiQwtRY8TOGaqjfVOZrGqKMFmRIPvKlcpE2T393x3MbZ+DjP86TtaHPzpmrmUkbZlVJkv2PiUiqXwnUAi5pPc2stYp3hrHs6wUWGoYzjQMzW2gJaeAcP03KI9eSKnqgLJhj39CYq3ntDZQJOh7Faa3Anc1PvLkKKfHdEiw7vOEZEqA10ezhuLwYZSwC5fTv2feVQ1UYtg8AON59bI40MTGZmVWMHgYK4kHq2DpYl73ByYnK3aZrMbJBsNgBER95o/WdB1Rj4lTaNeAHcSylEJ6QlxrVzNiQMrqraH5GY1RTz7MwwoaXolzPl5oT7MZMuwYxIO6NxHOBoc8Nmq+5eeLw8o1LIraQp0Ldcf0jve8j79o5NICLuiRKaq3eOCCMC2A3MhUslCAsFvjCOYxmGUqQMNRi+DDCnUuq42Q11fPf2/eH+5vxse3a2R/JyEfqDLSIvy3GjVCHAbKJeNUqlFGvRe8SdoN2v5GzF5xFPn+dJmDlzODLBNWp6MsbTwbRAnKmbX0TJDgCQlMVIgCNqiqg1tagLX9DsEM6UBfrM5XicmYsUYWEuNG5qrRJMbuj5LIskTWnHhG5yLYewlFJ3+/3l5eX52b7WB17Vimsf/7KHO77/9sV+v7u42AfRajgpN29uvvnTD88+/aKAPDmTSBzHm9uv//G37168FGYucgv67us/nT+5/vmv/nrY7wgLaQeb2p9+98efffnl5f68kYNDC/lxmv/0+z/uN5vNOIYgJMd8PP7xt7+7ffv27OJ83O1u7u+++eYbdf/iF79wkvQcHdb05fffXV9cnZ+fgRHttI0w7LfOIbG7+Pcl2eYUL36M0KZiwSprCVj5dL44vw/NKc+Adn//g5j/A4XU/yTKhUnMtdY6DKXWMARVrTug6FVWywT5h1fzrEvKhdylhZmRmUa3MScy1ZN14CcXvxuBgasnEN0f1E+SJ7UahU8WOaS95A/LjdJipJxuOUvuH3i4y0ZYBC8tt6VJuRjcrim/shiI0XlZSZjFdZWu9vjIO+omw8noX9B17wsAPbcr8ttAAJlzJPyrKe7aPE1HuEM9OjCMw1CqbHa7WuT+7Xt7d3N4++7+7Y0eJtVm3oLZX6QMpQzDIKWGzCapkOLM5mjzfJxmNYcH6pZZdPHMUqQUMbiqOaAwdiJzVWXAV/EPckQVKzOYWMHBDQegaJ/pQE7VItiHHpagXg3UAS9QSEbEZzJUm3jFpCqbQUY2etCIq2efxQrOnGd398hzcnMhFg6e56Rn5BVGvJq2tefdWpvneSxjX7ZwR1RlxT4BKHgyQfGWwk0KUzSvtSgzpUByhmEjtcY3xibpS9xMG0k1GEgJHExp4Q+2U7XUKZYRueKhgcZhhLvNShvM2m6m+/0nT7afXL9qR5TqIsaEyNGngO8icYQBRg8AhJaa3Ymj3s+ZZCkz4eCdcQaJkYDJCkAwpTLUcTsSkZTCRZjZDaouhUmESjlMxx++/+Hlj/7Vz3/2fHySyXuLIWaZrBGgJaXKlCqFids8ff/9dz/+8ENAijrPLdjNAIW3oKFDZq+R23Q8kLvkvncUyTJuZnfPajTKGHCE7VdmSRdOmWuoTnFLRETzPKs6Mxcpic33QhR3IOxxknHcJvmJYLMdh3HEyYc+iX6kqAqQfPE/4hCRst3sLy+uLs4vtptBKCb9oS/7z/eD/9l62t3fvn3LRBcX+/WJpHr/5q08+bwias5AgB7b1//4u5t37/7m17+6fnJNxGb27ub977/5+r/85//863/1r7bnezCMiWBw+Nz2MmzreIRNMLhFTq3NLfjYlsKy25vblz+8+B/+9m+fPntClY/abo6HFhq750bACOrC8rMvvvj8yy+OPjeKmJQ5R/VMN5H+GU+9VjOLhZOCO2vk+rV6DtXyAXDUmeS5CxO00WPViwfLDBRERyKb3XaoVYqE27CwFP9UvOKhUkwd0xdPX5HhWvYaGdWmOgMrmyOlI5a4eqQudYD4tFaXu+j2YQeQA6xhRg8RLjeIcJxg7kaPHsOxvoO+qJ0yOS9THRMrlHVhc6ilzAAPNFEtGfa6W4EHqharbULL/P4Th7mbq3kzb7PNbjqb3U33bZ7ZwUaFeCh1u9luxu1+t7+7uT3cH6dpnmbVqcGMCeNQh1qFWKSOu20dRqawOs0FzjkRbWrT1LRlBexiIAC+5BFHCDtmyszVQJrPHJlOoUADsmqRh0S2PPiHdcCpaBPEi3Sy1ZHGB4g9yk8yMWGpAB93I1c2eiCDokx/qVbupcToGXROlC3nmjuJJKllX7/ceXAerRZVPR6OF7sLPu2skO2uwbjYOR+4l4p2aYqEm/0k34kQX3Ty+PMZQhMbnDNVyh2+sHz8pYNZxmGIijEiUtN3d7dX+4tnn3/24s9fJ61ct3z9FKIPmpE0oQy+7OAYQ2a23J8gJ3AppRKxRcacVBI3VzLUOgx1JEKttUhBgLGht5hB9Mmz59dnZ69fvbi/uzO9XBY5xSKI5DUgensRbYY6NjMHtdZ+/PHH77//XpsyMZNH/4x4jMh+LCIMZmModFJX0MpFjJ2Yfk8DsoIgQO/Ab1iYCVy4xCN3loMFU3Ezy4rt8Pw6VBhHa9qaiZTo5Cg1CIwcjlqrSPEFe4tOBA98IFpKhPuLkWs/7s/OLi4u92fbUlfoRUIGf3lFfHD8M09xQMEKj5aO2r/VUJxlNlFjAwHsUNUfvv321euXv/r1r84+eTJF+yPjcbz8WZX//J/+0zdff/03f/trKhHSZmESoBBVlkYkFB3gUEgYHmUuIVUE0HnWad6N200dZ2gpdV+LAQbWPpqZBNHziiirRMRt2VP9saKcEWvqztW77hRVUh9stP7Ww1cfad/Hv58+He5bmiuhqfmk2nuVOpVSdrvdZjPWWuP8aJDnhAc6rStdrHCUQGJ6bhMAZG1frtyw0vOTq1qP00060MyC2yybu3/koR7uqc7DumTkGYGE0zaKK5gbZTdf89DE3kXc6qF61m3PinfvEcEQng8HPuwMolP6dxjVj5PGERa0AZLXXCwo/yBnMeMMpyFFm3WeGzKuZAabtR2PbW7zsNlenZ9fnV9cXVxuN9taBmaZ7g+TNlMXLlWYSonihc1mU0RYKtVK4CzCoX4f8HTeEoH3JbrmBAaTk6nN80xUQglS6GY3VScYSJ3YAi02JyJzz0xoB7kzE4Ai1MGFjlHEHAWcaB8kca5CBDk+eWvkDjL2cVPpVJx4QqixmFS9hAZhVQFuUYMqDlZFRB7zwbsKZhKAPDqorLZd1MXXUpvMy0pY5r+HK8jdTd1aCx7rZQ/wspOB1prUxVhG3liOSgoCmDkxrUgQNRCXheIkmBq7ESokTDJNrTWNqT3e3WgdL87OinA0rwgrxBJjTeDA1MyTIqCZZQwBkY5uyREZxksmC5PDD8fj/eGwPz8jIiFSmNSx1DFygFlEpJhp6PCAjkqpA231bPfu/RvKur60UU+GSA+rCvMwjAIn5ve3Nz/++OL5J59ao0Jl9qatzcdJVXNbMUmtIkJZEk0MiaHuGzqr/5jE1KyZAJVpW0d24gU8pk6kbrFQsvqIk17ERBgwyRO4/yMiio7cQy0iYqppeSFDDS2aSLo7URiSS7gjcLe1kGEWlsJVhs3m7OLi/OJ8HEqQrzJywa+F4j/3+Gd/khyFhFvXvkgFTM1LwwAK4gAy13n+7ts/X19fXz65aoKJdHYz8splPN89vX7y6sWLr37xC+HR4AKwmTBpm8m9M6dRNJrOsH8sBiLAt9ttKeW3v/nHX/zi57uzPY9DEdKoRTS2voBCqN/f3d3d3Cj7RDYDBtRhlJrkJ+v0avRUnfUjP1Jsi3uA0ybtG3M9UD/h2lIXoifLG9Rx0JOZ1W1dLqXsdtuLy7Ohch1K3IxQQESBxZ08XWZurYWOXhRGegMnLRK/fAQg8cemRAwLIjuuWyaPnjT3Qo95pcJdHGjvEaIP1lE6nA6kte1RIZNTsbioec3cDqc4QFIHfHjHOE3QIs0e3YD7ygZbPc5fwow6UYm7m2GamjYwVZaqaubKLJs6Xl8//fLTz55ePhm4CqSw1BD3XHyg6OTGzOMwbDfbYRikFJKi4FlNW+uP5321J6ROMVSJSGTAD0bByUXW1HXBFbJKWinh8ejYoxYKOCSYnwIhnYoy/uRl0fhHlnUfJgeRdt6D9fCpubkPY6WPFtmHzuuIdimFQAkQpzscxll4REsyHsGJPXjAaV2lGt9tZiKCiklEsuQ8WgJ7dyjzocxsnqaAUAMhjyUaFTUMOtzdmxOyd172DDhtmgTYOlk8uLBYqcpzaIkHD9qXapHCLG1qs2rg4O3+/sX7b7777gduTQB3c4uGEQvinV09YwuBkBp3JaqjMtbMFqA/H3Ce371/X4dx5JFZQD7UoQ5VtVGnuDKHufHC++p+nI6vX78isfT/u2OQQqN73uY+DMP15SX5+TiO0zQTycXF1Ysf3kS43cwOx2M3lcG9BeE0Te5ea621EnpxYSIMARVza9rmiD9yldrlYw5nr/AGuqfSjYMwJH1Rl2vt2+UpRFgokndyYatq2E2egNKDdbo4R7F+mCX7sLHUOpxfnD95cr3fb0ohAmT54H+79/tPH10Q5l/mb398OUqhwh5EA7O9+vPL4/1hSWOBo03z8fbuq0+ei5QGi4iAwQk+MF9eXv7w3XeH+7v9UNUMDGsKNVcNEALam7tEnrtFSnzuu+1u99e/+ps//MNv/uP/6/8ppV5eXT379JOLJ1dls40mKX0z+zwd/+Ef/v7rr/9gghnehPdnZ3/z61/vyvnJ1ViFJD2jfus//4kRXXTq6ayPj+CyHBZvNMMjlLl7RKmLmSlDYLXU3XZ7th8dKiKRJLhST6GDTnKSOqXJ+hHc062Jw7pNTyvRFHr8lO60fpwQjo9Y/YC0YAJFZEavBjd3WZNj9OguTg2TQpEk/P14vDx/nExv99UD9SythKMfDTJ3j8CdsmIwESk/fWqZCWABXJd/Dy63/jOiP/Frm206zqUMl1dPp+NxOM67S5Tt5slnn11fPRnqNrq5l1pLrXXY1GETEWtK6vZSSyEWKoVLNSfzSZOjb8n4cId+aMGkQD757CFSsh4pE+49jNaQb6e6sniPnTQMZwfWEHSOy4r5wd3N1OGn4rMc3zVwEJOb9dcgKpXoIT6z/go1o9PWOtmPMSdCQauUMxdcVMLCfYmt52QBWZgZ5YGBHNrIFw5V7wQFqtFOwDwL5pyy8zMjbEHrMaTTDllGg3pGQ1idpVQ3HOn+lBYRO8pSHwBUSpVa727uJ1dld4Id2/Fmevfdi3I2SjdhujvYMZn4Lo+ySWNCFj2F8XiqNYwvSuFBoKEOQnx/dwCoFgZQhyG83rRmAETOEYX972/fvPr6d/+o8/EXv/xZwAXSYQ5hNjgLk4i6hx16fnGxHepmu72/P0ipFMu6VLaTHxk2VpBqqOof/vj1D9+/oqwZTIgPnVAgvA1V+/67H3/84eb2/d3bN28jpsDMEaGP+QkJ2EGNMA5ogR+Cq3S9W/q6oogXCLGwRP10+NLMgqiZwcksX5Zj/4nuJDGLnJ2dXT+5Pj8/G2qUVH9wrGTNv+wRguzNq9c+Kwlnw2un169fz5o5PFF1hiVUB5BTAZlBDRy1/8ymna+DTM21tbnNrTXVpu5GGgT4lhmYCYqYuxGVKk8/++Ti/Oz927dv37w93N3/49/9/ZNPnv38b/667ncxu05k5MT0+WdffPL0mQk18kbOpWzGTfAWefer+2h5glbLw8ZwnqDmMAlXIx1yPpWgEzF6IGw1aI8JMSiUNrsvXduoAx9RQctgoVKkDmW324wDz82IOIhZo/t8v3gqOe9EeWvtgq5gkk4vhtKCqS+LaFdpXNTjygF0ZUfORaQ9lEarb0B09gyh5A5kT/ju7FtnKA2QFFC4MyTCk9lUPVV5Btsooy/U82dOARiKpnALRhYrLPdif7GDlAl4w2mxSig1dGZykAFGzh17y3ro3HMOIGkJADiROlS9zaZGw3b//LORwbMpRHgcdhfntY6hOobNWMeBRDabXR02Gdbs92EcudseLdF71wLE4BGcQQuIsJINlu4rC8IayOJYAtxgTCB2FiERJ7i5qoWJF5MV16GQ6wSAyorkJQsBfTHJiNTN4FwEHE2Jo9FrKDkDIQpZ+0K3UmQc2bPeO5qALjnyuSLd3c3CREACXCTE4aZQJwCJ7vShX4mXSPdq6WWRmBNRWap4F98I4d1GbVhULYZzQyCKYB8zz3A1LaUkViHJU5lyu9sZ5JEG55YOlEXKcR2iebmUMrQ2J2S97BNCqaVUOc7H2RoKUS1zM2omIGHqycKeJGbkDAZFiaYyu1lkw1lHQrpk8uDOjwcJuh8FaRE+2+6daLo/WCnjODAnmXhuBtXeWEGEi5sf7u+qyOeffnl5cbls2uX+gxNbpIpEI916fnG+qXXcbN/c3oTEKbUO4+Ai42ZThyoRnwZF/111n+d5mmakYiaQlFIYIYRYRGIm37x59913L+5vbw+H+2AVAEjVHV7H6u7TNIEAdjCMzcicEH0ZiZIntvOT5MLwNCkdgNQCeNQ4qSoIzLGisWz7R9Kta3sW4iJlHMezs7Ozs7NxkNIbkT06wf8p7o0FvPn/4yjj8NVf//KTz5+TLFuf3n3/7jf/5R+V4Ra4PVepUur725tr/SQCFWRgJ4artpu721KL1NrcjMEZp+Sb29vpeGwFTpEzwqYtrC7vzpoBjYkK8/nm6X7zyeefFtDLH1/8H//lv+yuLj47+7kTMluHwMLXT558+uXnRmjw6DSsmcmTkv6fc6y9kOX3ZX4Xvyo+8MgCW/98pBoXnxfhMUQ0ITpuMg/DcH5+dn6+J4aDhEWJ3QP8c2QzYOtuCdAZ9B5O7+LM5IejxVuH1VIyBabZEzmp9/LLbkvk4KWbwCO45oOD0xZH733oDsoQMtLdp0zhDrO/1zcsfmikZVCX2qma46s/mLClQryP+AldJTigMIWT+9K+NUy7+AdasvmZ1nXbp8uDgWbm7nWo2/3ZQCR1Ax5226FKgZO6cy2yGeo41jrUupFay1CplJBfFCxSJ2UeWXkwh5nCyU6tncM0cIKpwcygHuB82g1+kglEwVCZTL1kkHLiqCUig0e7VJe0wpA+YY8oP6wDXq2aZY6JogVdWEsODnJPVaWoMu4Hc2Zoc7AjcKRoIRWnp2OZWlE1ELMIBDEgzFVKkWx0UySKSyNqmdlOHWBLudhNSgBB9Iol1sLCvRYrYxuB4xYWKcU5JyOjOR4gp2x3u7IZLVtIn+pekMhLl9EOM9emcBrq2DcSo8/OevUMQxGheT6aNwhxLfeqbs3H6pFDuCCexMQQSOZ4mUX/AO8knViU72p2qCMBseDv7+5u3r9/8vTp4XC8v7+vtSabXVqyCB43JhQuhZkcT588vd7vauVSZZEfYStG1HMYN0MdWApImMtmux2ljMNI4OPxqGrjMF5cXh5VL59cl3EEizsRSwiBUssXX37xxZfPb9/fhAwKkI9ZYEY9a4MIX375+b/61z+fj+1nP/sZM7v5NE2RxMGrZpPEvSiCMh9tWbZhVC3al5kXjFFEkNLN3TQIsVnYQ54uQuiB9D6h2cxcat3tdmdnZ/vttkpEkv97OLo/eThghbxW2ZeTP2SgQWbGTG5MYW/XOjx59smLH3949tkn2/OzcOyCVunm3c233313+eSah6KU0pnIt9vx5csXX959IduR4M7k4Ddv3kCYa21mxpnPPptCW2HSQCmIL66ua62H4xSsAeZUnJkoeLYdUHcNRvWT8mTPEPAJA/r4U/9TQDT1LK1HKvbRZx7+3V85raXEOGLCSymbzWa/349jEUZhhHFOxG726OJryPDRL7S4b/1/XSdi8XPWVwqPFt1lDGZ2Su0BAE7W8xDj5+NB69mlPUzjeGQnUnpkJ32OtWuzuqt0fNfqlZehW6wO4Kc2weL1P7rLtWtJ62HyfjsfXAmom/GTL79oV1ej1Iurq/HqjGshgqs5cRnquN+M280wbpmruoHImD1ZI9LmWO60qxKPEl4zzehBXypmznai3ksDJZKZkZELN8+pMSKQlFoKSQk4ImUuI7FJSmHeZzkVsJeP2lPL4ohAyJJ0wKCglCJE/pwhtHqfwOjisH7IPnPZu9fMydHmOdnzF5ABWbgWnxfpnAdcovVmrggQJ6bx4IAzIB6WFpF3i2bZ3L2LAkqttIrXMguczJRJdrstlSH0GREtq3ZJyEnPngjw1mZ3K0WWWQ3gpXfljDQTK6W643A8uBsMRHLX5pu7m3c+calWC0SQmjQpXpkLkYBYpGS32tAo61KXAAmco5SBLfs1He/vvv7j71XncdyYwZta8C2kqs9kKoALRTEm1VId7j6rW/D4BJkUsUipUuu42ckwOHNQkpNEK16qw6BqrbXtdvvk2bP71q6ur4ftmbE4iXCNWRvqUKqMo4BhFFXMyNIq8zB947mHcaiFTaUOBY7s4gyKrsa0bM61gUyIIFPUnoWiXfCbUkpIgdChbiaSzk5vVFFm1SVNb7VguaMNOfVSShnqdr8/uzjfjLUQ5Ce0Ly0+7oMN8BPq5b/pCAYrAQQrhwbGUHJlmJGRNzNnev7Zp69f/Pj7f/zdz3/5i/3ZGQNqfnv7/uvf/87hzz59jkQ4ncyZ6Prpk9+9/M0f//iHz778QsbB3F+/e//NN3+6ur6u40a91y0C79+8/f7bPz+7vrq6uIQUc7x6+QrEZ2dn6HmEDosc0ffv3+/evPPCzqRqgDfyuttRYQiMsiPNh0LcV/nP3cR/4PvmLw/zkj7U1gsAt3ql5wH2kzoulpYWEdVa9/vt2fl2GJaktBDmUaAFdmZnfFBVjBPIF71KuPNcnd7FxxTngysgQmTGIWct3ZeTjUGpBBcet+Xd5akTx/Ulup0q03sl68roof5vdZNplj4aPD/5Qb58GOjQKjr43K/u3Y1ZXZq630TLFRMfMDfKBguLKQMHmGWzP/vky5+J2mbc7s7PrLLDAseXOrCIVOFBQGJOoZngEem2YJunCCH0wV+maW0tnax5AkXgT6R7d754P45EOwzZB4eynQMLQ7OJJKwXCmGdTvTwOHnAi11iHdoFIEQlGtQ5YE4cNUgI6NnbHECiU7YW1Z4Cz4yMWVp3LRhEbE7kmCdtc2+l4BmqJmYSsZSe0UGgupuIMGVRc7Q3irqI5Z5bs804hr+CXiUaiHowKYTP3TSjhEhyCCKR8FxtTu1IHKnx3VDNqnDPVrR9ORGRmRJjsx2X9CuPkhhmBhlO6s3MjtMxOFml1lb4j69/fD/dX1xUrcWFnSMW0lGJ9D1ZshwrkqSzmQQcILc0YT1qhcPBieG4ubt78fLl+dn5bndGGSQV745CBGw8TIToL11IVe/ubyY9Pnl65YB3Kg4SIWEZKvciSGaqtUakZrvZEsmr12+Gujm/uCxNN7vzcDTDFXVzYmaRTtHKUiiiCU2X+nNEiTNYvJfUNTMwu8be4XJi+35oIPuiIynCwyJZQ+49AcTdm5owC1FUdoVjq/O8ol1JROQkjVLx5r5k5lLLbrc7vzg/2+9qjXWW2MjiL+Cn4nQf8+9+0qf7qc8RopAfmVGe0Il4mskeQylsBCPanp199ctf/PF3v/1P//H/fXl5UWs9zseb2/dG+Plf/fLs6szIEhEguNDV0yeff/Xlt3/+9rsff6jjaO739/eXl1df/OwrF9bgNHKIYzdsBpLf/v1vhlIj9KNzu7q+vri+diYK3h0iCJdx/N0f//jtjz/2tHhnkbOry6/+5q8HKWbk9GBGf9rT/UA9J2QMD4qDj53bX6H1n6llidaJUcuL4f9KkXEcNptxM1Y5hRN6/HZ9wyHiabk+IWpXtIWh5Gv144lW0+pL452ep/N4+sk9OJC7IxG4LQL4NToBkCffLr7Ouas4Qvc1l+W9vJE34/2VRxBCFHSkERM92Pp5K/iCuyZd/TtNW3eQ8NFj2WNIuNJlNd7xphnAMm7322EQwna7L+PQXN19HEp0VVB3hxm7O6lB4/ncYlcykRTxGMyV1DAzEXZn5bT1iTu/GBzwODHKtzNc4QSDUQbQF1sfyQMbtk103VWHK6JpVdRpek/L6DaQo8QAnLAC91BY7iwcGp1WiEcupKjucDcgnTB4ECibZXZFmmorcy+yM5jcVF2zJHQxtcAiIGragsYFhFIkmKHTS3LS4LQ7tSOKJM3OW0SrZZhPlFsg2D+yiDaWaEDQRNFaMvZgX48nE/t0KV92GogR0eVhqEs4Jx4l84EBDvOlFDWdWzOCwlDFhvLy/ua2HQegMaNUoMUXECd9CZ2KztJ+jTnum20RJTmpSPXvlNX6UT+CpZrixGqSdkrf+W6vX7958f039/dvr55eXl1fdHs5anUkiGcoqR8j9axAFcRUyvnFxYsXP5yfX42bjc5NagFHZTNYigOl1vPLCxmqM5Vax802lLCBmoU9BIt90o3K0CtxC8xUJDYAcXQ7TsEU1ECdrurU5oGo85Z0V57cXJgI2WYqZnaaJpATe2TOe+6fpJbv0iFsAylD3Wy2F5eXF+fn41CY4ZmsscL2/gU83H/qIFxcnpdauaut+PJxNzz//FMuQlNUq7mRW5HrTz7Z7revX7549fLl/e2NCD///LPLJ1fDZmMchb4ggzoA4lo//dnPzi4vX796dTweWeT5p59eXj+p42g93Z4Adx/H8a/++q+fPn16+/69tialnJ2f7S8ueBy0Kyh3k83w63/7P9o0gRCtwCmU22bkUsMxMO5rt7OYuZ+kTQqQBe2KAgSOxF/05+9ww8r3XWuRZDCiE0GkuyucI7S7fJIzL4qZx3E8O9tfXOw3o3DfDKk/P4YXLt4zkK3bzNxMiABXrKVRXOefM9URNgNS9WWFer6ZqUqBV59ybBZt2HVivNq1frcOKRR55490DryQlnBwurJRkWKZntLpgwkgITBDuHcTXeZqGeH4xdyzXmY1LwmSO5MzuSDSgYzNwEbOmW1PvcxSVQVUx42ginDZbkiEtJGDajGGujmyNJOCk9sjrsKAMVMpUmpprYWDKCwhJMdxEOLW9HA8etwHwllN5RrrgUXStMogsKPz/AQWGOld1rFf70/uHPieeM/Ui8dfr4BycsN9QTgpvdjIQyas1o43beRQbe7uaiRRimki7MRTa82s66RcAcsyjc0SwEm+5XAmBcLZcsKsLfKumYW5AI27UAzP6lExu3eoA33TehLWdHJOSwtxIW32HlNxAMze3JqZepgL6yNNsyXRIH5xj5Q0B4L+YoEXvBcvew9HF5Y2NzMIF2IxMIRnwkzZeUlK9K7Oth8gOIyZyVMzORzMPcGxD2a4AJRbDO7azNVrGQsVcgrqCQDuxtIb+jkxSJjMNQAXA46tHabp8vr66bMnpQgFbEAMILLiogzJ3Rl9r5UShHAXV5fb3bY1e3+cYSUqnTbj9oZvidiNNpv9s2fPx2ELkJRhGDZSB5JKzLM1A1jI4SwMmLmGMKAqECAbE8d9enObW4MjIFcYV6ljHcNribL1ZaFJKRI5bEi96pnoHIFJNJ0NYBJmXeNma4eJkjOL6zDs9rvLi4v9JqO/pyNsz5Nt2n92+dklIx6e9PClvyCTV24LEV0/vUAmh57eHDb1859/en+0m/kYrdrUzMio0nC+f77fPvn8c7WWQIrbvNQjphPvGvm/VbZPLndX5wHVBTVeSyTPgynIgCbORS6ePzn/5JpOLk+MI8xhZEZkoPHqnNJZp5PT5ATirE+w8GXD2eqb6AMYuW+mZYoevB9SAYtttRraRZf/hQFe7/fIvdrvdmf7/W4zrL9kEWnr7h39pfwzHFlmABI4ZVBoIMJW5J028CP4R3z/Is5CxWqmrCmgjpUB2s1ED+6gNAFSEEWtgRN5r91kc3aSQNIXxDUN2XAVInUnRzDd/F5XcnKBsl5rpc1XyzQ8xPVq7kllAVcskcr+85QItvjpgRHEULqTQ3U6HovZdrsJGniUYgxzMtUs3oRFfX9iGOw2ZxvH+PIsx0B4TV6IiYjhhUhYACM1MQBGjqazqemsBAh1Wl4Kli1AncNIYeJSJeJxcO7BNQctrIjRHS1yDz9Ygalsi0Yls2ko94UKKv2TwLy6o20R442hUm2tCQiqES0AXE1be5CkkKhE38Yx5GqdZTAEAHUR2Z1O5kBhOcwQkVxM4eb3tKTcmAGc59f1bWC9xWZEIrW3Zc49ucKfuqZL43FdRbre6LnNiTmJRRjOwjWaPsYHV2ATAAjxOAzREz48v9CmRmRMLkwRAA5iCDcLYpAESbqQ9aTxOun11ehSN49UFU5RskW9pPt4PAaQQr0dITFLrWYKcSpkhKtnTy4utoWUOG1VEQ5hJCKtaSTaxOgBYOHoLeoAlbLdjNNxxqu3YFJ3VdtuN0ky5RiHzZOnn5RhdAYVkTpuNts6HrhWjXZVTCBnznRlQ0AHHKGVmBjqCjg6HqXEcWfiIrGbqNtbRCt78QTrnXJOEBy/qppc4yk6Vjsjyf7SsI1vqeM4bMY6lp6NAATOt3hFXWv01e4htbnDa49nri9XpKuNnzoelFsS4aEb5kFbP7DNbgQLMAxqMIepNTOAoRGMBDk42k1ZYu8ecifComGmxf8s13Hv4BsahWDokOJi/wE4waS5i5eE2b6KPYXZokby2T0WMWW88ifH4XT1VQi364K+VTsQhWWnd5xiMYP64C27OyAtjsKLsQ777f5sty2FV3Urp/M+lKVEzr0YtNf7CQBk7Na990w5cU7BMhZ6uiatr0iJIgblezTycnNdiAm7eFpGPR52Za8th5lb9J0KsH6dRXaayaVEEJ5Oc9BuniApyoGOyY1UICO3jnef9sIyXe6Z4/2xCKjHGIVdvGCN5nHpTKnpVwETi0DYOXNxNFBnAXqTNGYukTeT5EbpWLY2q6lmm7xkUjLosSkMOus8HeGYj9PhcHec7+EQ4qEOQx3C1Xw04Z7pz8GYJpT7KCqRiACWFOpGS8Xv6amdFi/YyzTPqtpaC7IFEYmyrWSCztSlHM2Ogwbbh83TREEQ6IYgt/IPFmgfh4yfdGs2ccKephdLKlxQ6zlQ3UdNkyst414dFtH7KGeMhhWe+f0WkGJcs+sni5Bk8AyjK8kUtWZki8ihNUy7coVPFng3gaM5vMTd0Drdo5/LIu5oZs4ceruUGqZlxFMj89KhS85E8DE6LW1R+oYkEPFiasDNTWERJQKzOJwoi5WjihpmlpohN0+tdRxHJoF304pAge6Sm7PUsY4bkvtxu63bsZmRsBRprbFwbAM3d3JNNi5SsJM4aG6oRvvd+Ti+ZSkKSK3jdl/qCIYISS278/ML9bOzy4wb8AN/Moauj2T+cyCTzOFOVIZahyGNSn6wq2kFR4c2tpR9uccjvVS4znOQAUtGTE7D3IGa8CmIRepQx91+v9luhnKiPAXWdG8hNfDh03yIy8WxUEl223/RZ4+PR7sXDxN/HDBGQMQafk/S9riatR6vsZ4jkFkNoXVXVSXeg3DWCRZS051uu8sM+PqeiKLx8OKHpkRPK/nktvUcwnjM9VN9TOkuRv/yu3d4Ov5aC/TFVu4Wtq98X+qbcX3PfAoFgiJ3hEkK181ms9/vNpuSnP/dPhYe3FumwT6aHw9SH4s2R0sZ2uLKr241sNLTmFCH83yRbn56fBDgtuRsLJ9dD1Mf8ORh6Up5tUjTrex3cvIS4ARnMkSJcCTl9XKdMKt6J29wltR0lz8bOnvnxOqbLtRpf4QFXn28ihPY7u8YIHDvLeBT5xqcyaUyzWZu0XYlIHJzPe2XXCRhJQbq4KpKyAqi1hpSNRncZ52USFXn43R/e+cGERlKOdzc/vjD9/N8IKLddvfk6RM3snkOv7lrnhT2YQGYQ1i4CIlQQ7f8OBwhz9huC78L+EjhRHjA5qvcq2WhAzH05HDt707TQbVVL0iyC4c/CCGH17HWRF2ZEBEFH2OyhJ/mI6OVcXo3XFzNlixHWmqnAs95EG7RKFZJEyo4OIICm9ObMbcwJFmybTUJGbkU9uYiUkvRpiiC9KxKzCcRRWstJnYwE5k5LNvXA8mB5Z4VdoEyBbIT7EmLUeqqcAx1KCJ+DL6RKDTA4mKs/vXYV+RVCzsi4yq+TFU1mnSpmSlK0blNoQcYNJRag/rKXYKQ3cHEwzgM4yAicLg6MXKkyJlYnbgM293ZsDnszs43231YI1GVmyUazNbmhW62qao7keR2cOz3Z7WO5tD46lAJjujlcP30qZTx6smT+/kYLqICKEJC2rVSKYOUGiYLcdFEYnKMpFRiVjft9tyHxnVY9CISJK1BVWCO8OtBaHNrJ74C+uDsGHcWLsOw2e7Pd2cXdahcsKiYXLkPT009SifhkOGOsPY/0DTLru774AMu3Ieia/XBB79bxqSDsTXQJlNXI3N4pE0g5OMD3zTYsUJhg5wWQsK4W8p9l1Ak0UPFmTo1iPiygBXIEA/1r8snfTBHHwzcTzzjunYkVVRcPR2zxXT/cAZPgPQDXZ98ESfEmjLGwkVKHYbtZrffjrU3fFzGn6mYt+jN+gAYi0hsxrmoB1QRtKYWp5JkXLbDBGt0LfwnOymqxdDpiXZA96FTji7iNQIr68db1CoyXBQ4KC3TwPlkFLw4gdE6MUSIZT2UXXBR9z5okVKcxtkyMwElE7JVYmC81Bn9ADCgqfRzWINEhXopq1JSLSzXTUeLWZyamrIzwaWWoI0hBICcCtlMtbX4OlVtbXJzIep7PHiYtM3zNE3H43E6HA73h8PdvTA/uX5yfXE539/fvHx1f/9euPjV1bPr61Kzh/ODhQV3mCaLbtRsUxclCX51XK2bEmk8sZP2i6Q1WrKFXFef8zy31mqtMRFR0+NAuPAgmucperOjkx4sExA3mqhFrv2cNzrdugIuhaRQGkmxYIQl0qaTEDyRmuQaSkoPIw+vufN4mEul1mYbRiIvRfrk+vqh3IwX6kVOZDAq1btlFBV+7i0KY8I9h7uzcJ7bB5UXhCiNFWYWjoAr0gYOazHUlbYsiErO7k7Yw5Rt/oJHI2z1xcFdMFX3yEaKuGxwV0dP6qnPHWkYWm2apkN8C1MmKAlLKRHpN3cvpWzGUVt0VQ2M18NOBYGcahn2+/Oz/XG/O9tt98TFncwjXBrlL9wz1WmRsCeMgmm725ZS4iaJxYhmdw1TRXi736vRdr+/eX1wchc2B4uUoWrs4N5JLdwX5qKeeeEha5IvRf0kIx7I3w460mMjDwgKbybmORL4WSJYIYXRHyStnu4AD8N4fn62226GIRPeT7kSJ7mXGbnui3hc3ZLDO8/uv+CR6rzfCXXHN6y3rPjKLWDLXnuk+5LidNkpOO3elH99H2E118uAd8nrHRUOCWDMec3MiO4fZhAimJyahghLrZGhG9fUJZY5Za0PhTpMvimgvxq0cI6Tmlith65i/aSSqbdsAkBhtGQqDTHVWrbbzVB5zVXdzSZPIfCB5eAPYD/q1r855IOPppNIp7ytLqwSTnEni6a2amQWDUKSrumx+dMNO+QS6LjNYnrEJu0JM+jlnR7JlFyLFOEiSWUTaCoja28KkQSzWnfGPPPHV+bHgpP0JBlwUgqdsqhi4bllJaW7Z9uTdCbzQgt34xpg8ZwgUjcxK0MZNqMfoe7kXtwBgZvOrbVmWXpFoYzbPFubY1HPOh+Px+P98XA8zMdpmqbD8aitkWMzDJdnZ67z8e7m/t3rd2/fsJTNOMBtGGotkYiccJclEZozgrG4RJqyAWre1IKGQLXFY0QLEw4/1h2dX9kzQ9tL0vv1ddDaHHB0zJ/0zjRplng3JzrRQcRaTc0Cjk/y8J84CO5G5KEbYlkwjLwxWuFINygpJMJKyvznZSe7q1Im6CDaxUczL3cTif3V7cyT/R1egAXJxmmvJBmwq5kID1VaIZD3ICABS9V7TwmEJGQVezOZvsBJkGUCdiZXCGeaZTQIivE0tSJcS4HDtEVFrRLikzALYzrIgrxLxJ5ekaCxmS7/Yi5DdpnZNE1gGBRsas2a11qKFCbXNpnOzDSOm1s9GjOIk0UvmMMNICp1OL+4uD1MZ5eX42ZnhqZIJJHEwWaUCWtZ3JbbvmtNlFLDX48UzUyIA0optZQ6DGWYo64JBBYh4qGOVxeXzIIOKjNJkUoQYWFHJS7EIZ9LkVLK0j0mBE4MViTSwn2x+CwdfCQG26WSqYNATKrmbsw1OpYs10w24Fr3Z2fnFxf7/UZW0d9Hy7rDb1iE3+nwVMz++I3TB9aa+fHm+UgFx2Mx7F0H9EXe1WKAesFSuwi2la7I03uOCH14N3kSnUYVJ6a8x45s3AHFl6Z6BBEeP4E//JlZ6Dl9mVvYVWaI5tOXJZyQqmvBGtajc1K6OJ3X31oxP3iPP4GAKBYY6rAZxyhOpCUAnOCuhx31wfj1FWjR3tfApzFeBpZWI0Vd+C36FwAxOeXorW0gM0/ga51ntlxqdcn15UG9aIJCty3DwiIEEHOtMgTUJCJ1IXaLmlnmXl8gzEIg+kD3d1fVu1WxLK6udbvKoO5qmQXkZkSSH/u4TeoJSveHjOJFd4gUKZXmFstADcUJah7txFXjGdo8393d3t68Px4O2pq7zW26vzu2eVZv1mya5rm1VPjb3XE6mDVr0+Hu5nBz48T3l+duHrWwPT8/4dDgmmAiIapFRApAZtTU2GcBQD7Pk6qBmIpwkd4Au7fWWIaPcCLi6DO0VNAuAZhViKX/zM8wmxmZmTd3BUAsqzF9vFYCrQE6EJT3ALhH7lDm2UT1FREz1yKFa1yJnV1ba404qhqWO3TThlVYKIznSCwOxGme56juIibPJJ78f0wlSImdyRwtHr8UMVsUTaD0DXCRStHHK1I+ObSmRSi9FBawhktABNBxmmIXBmg7DmlXuVrUByUZYpqH5JHm8siDWsu6TCckABZ9C8OWANyiQtiO0yHw7VEHhkcVLAPCMmzG+1kXlbu4PcGoQqWcXVyeH9rl5fV2v59skVhJj4ewRVIWo2d+W2wUKYPUASxhm0SKoJrCPQq7Y1sX6Y12WIhltzu/vHwyDGNsKqa63Z4Nm00g/wu7CtSYUlZGFyz04EWEHUhKfI6iLz2TelDDeFKREkLWZMsmtY+KgFhapZRxHHfb7TAMwwP6llUaxSqMmnLvI47KTx6OzmD/Fz7wkZfo4Tcj23Kre+9PapkIkUymvqaD6EHID3VJvHjyrSN9ORXeonuClmzpMZvOzBIhy+vTIn87K1MXPVgpkrh6PoRFxSQWj8qTamNNf7YEOeN7PTYzpXb+KX2MRaPjgSgDMzGjFK5DGXfjMApLoLYfjo3RSgFT13T5FO7ojdLX3m0EZ7Mghch6fC8pzPvNJIYUd5TAf8yjL/B1fPNyY4vB9NNH+qJxOpgdEuPFkeEfBRIdGaCYinTPA/9cfUOmci/mRMIES9ZqKCmDLw2YFxHdr0KmRpJ9pdy9J2E5LR/JRbN27TsoBQSdO/VEMnPX1tCat4Z5btPU2jwdp5vbm3fv3t3d3mib0Uu35znYIKbWTJPsHG441tbUQ0qotakd3fx4PJhplFh5MDcvmHs3z7JsrRfmExgUFT7hgGvXYwGqUtgH7lnuFTK0YDHT0qONQKxxrUvx6LrHqpuZa1+CMQrmmcnizCTR4XC1MOhk7WRxFBFJSQPHF1ouYjPX1tzBUliEu1+17N6mOk1HqTXHAUBflY9sw/AU4y0H1DQZkYSc0AO4JMwaHClmbtrZyLKTj8jSQrujrRZEJOnNowek0btemJqweLi/zO4wzaplczBLLSUtTH50y6clGwuOe7/hDtSn3CEmcpEwyLW5t46LdPfBPQwOEFmbh1pP+zyYMeLrepanB3dJR5GGzXbc7sbNrpSxzS0SnmPJIUcmgKzTHHvQazOVWqXW6CaZ0OLJyEc4nSwspQzjQEWGYWDiWup+f0aQUIj7/f76+nrcbiP/YNmCCwS9VDbHRIRDnKZbxAJif7EQdb6JsBqj5TDTPM9qrpFeEUNn3gef4ewOEdluN9vtdrsZkpsEP3ksDpM/Snp8KCFX6ffIdbPS4D99+b/wxaDEIRyuZBacqIFVuVEMi1tIcEdPs3rkpeXdrJySlTZ7qNisM9enc+rx9NkCfH3ZRQkt0iPSwh89bL/8slr6bZz2R762uLJOyEsFgEOQB8O3chjSfUnUjR68m7PGJIU3m3G32wyj/IWZ9tVw0YPP+UM39CdODmOanMWJvWNn0QkmeLyFSSi7bzGLBDmTJwtkVpZK13mm5tn9EF12fPjFi3aXVJaOJdng4w51nNU9fyKEodL1KZ9SOB/AxSkl3b0T9af+XD7VxUF2DjK4ZBGcIAODTt2072MGAoUasNZIndQoePLUJzjaPB3ub9+/e/f23XE6Hu7uD9Nxak2tRU5WWkfqrbVjmyKQX+ogtTDzsNnUzZa4OJMB5qpmar20hykl0MqIdHcCwwUh3jUoGF3QK2iQ2AUCKhZ2psckkREJOA1+qlteoo997k4K2LPQaEkMc8/pOU31ujHfaWIzaZmNyIyIeBw2xEyWxjKQjDZm5pZmMNy1Ne7MEwbS1o7HqRKWbhV9QqN0mJfbcEA7SunuRGzwWrL/0Glzh8MY09msUYD2kNCQbhncIyMonB1w02WlZ1p7Cfnu2sxrDkMYk6o6zVPQ9UTjc6YCz+KWlkXuD9Z+FB11uzJu1tyUyFnggHCw8hEAN8/CEAB9uXlkZ7kTs8OCAYqleLTgqjUWSSmFyCObLFZW2MYRJaVEoyJ5eZjbHKNaavVWu/u4hIPT9+NSmKSbEJLwSYxFzHbY2cLjdmOEoQ7CwkRFinefarPdfvbllyC6ub93d5ZqhlKHUFdcTpSlCBOaYdFDD04iDlI3c68dOwJTKeVwOBhcVUVYW3Nzjr5AiTBE3kxoa2biUuu43Wx2m6GKAH0dIqqw/eEyzwHESSLFYy8U73isbpezqNuo/7QC9g8h11w1wT0XiEOiEu7s/TcsorGT2Kzsp5PWWzz4MJh8Uaje02Mf2xPrvpGUp64L7ekEWTMRIJ5+3qNhWHbjupSmO8G51LHsrCWiTouyOJ31Uef1p478KBOP4ziOIwdPxFrfL4qzG8IAvLf/A3xp2r5csZd1LZaEL19lZh/oyTAuI8KaG4+ZWLLgDrHlOdRzOq2cvnLOWmfV+MiRpirTCVnnVY3OyfPswAgWlzkwvnTiI2GGAtV+8GiLS5cJ1wEFWOTmWsZlaRESnvBymvXp8Ge15GOTIEbbo/+NzYeDTkdSZW3T/WFS8zYf79/f393c395O99M0T3NrZubkrm2eW9OmauTJKgCWOtZhs9nuzjb7XR3qOAxnl1dSizNnLYBn8pwTIZgwHjp38YM8+TPJ001ghggRSWGeHb3tSDf48uwHq7rY8pRE9LCTDPWMQfTtGsiIqqo1j9rCJbZlDoYQE3fSmT6xXbxQmBhOALGUyrlWwSAzj952y+4h8qbzNOkm+FiLGHNrTU25KUQsMp7cg+CKe/fZiGs215Y4eYaFDV63I9dORZngcmAG5IY26wyYWYn2763rx9ztgRRz0KxkOyonP9HSeNARLyPuhOPxOE1TWFKxrZgkzFhidlfvqY65J3uthpm11roLYe6a4W1yWiABNzdBRt4cRCzR+ygnOFOZPCiyGOBSh3HcRFxDonHoAmX1qSZy96bWKGU6FRHNAkfUWtshF0zvnxjyAkRUZIj2I5HB5H1eEJSlsRbchGUzbiadOYMRSEUIuKPW+vz581lx/8P3BvPCLrzZ7bgUBkotZSg96OlNFSKTtaPZwCnAF5ssJXNXIshaMyLiWmQct7EAYuEt3ikRSi3DOIybzWY7ijyAn4GuaIAPhd66EmmRbo+83ge7uX9n/s8/fPeReP/I4fHNdMpODbvfwno7hWz7byvtu4oBk5+E8APWhMVK/uA4ubZAeOGp8fPl5fGIOpX941IMX6eH9515EhwnPK07ZIBTpMhTV9JLQRitIGtarrIaxFBU1OeOQv+VWsZxjH3xcROHyNzUu47xk7W1ThpKb+RRQLQ/mWc+RL7brbLVSKAbrBQgLy1DEfKYkxmndMaCExf9WgF3Bz9H5DSnp9HoLRIXn9VhME4Nut4LFDkpK2+/P1cgZ0sPQe9JVYQIhHtfD9z5lB8+MD0wUkLtPwBIKEpcxKxpU23zIcrYzaf5eHN7e3uj8zTd32ibXA3etE3zcTKYus/WmrbkORSSUrf7fd2Mu/1+d36+3Z9vdjtikKkQqbo6OssFwv8ymLo1hB9mQQjmRpBunEZ/++ScyJJZouTPAEAW1G0ctttSEkP9KKZBfhjVM+5d9JspZdUGLyDwYrj10qUu66KXVtp2H1vAAQLklMetZr+5NJydGAJweHCx/u7v77TNRc4pSKhdmioRq6cJqu4KM17cliRFUtXsOpCgbLqBJLJAxr1rAggkJLLapzGGbq5t9qbgYLGIIQiLD3Bvc/APF/TcCQSjXm4aBzC3pmosHHST7rlv0P2OtAsfLHCECztNUy+jWpjMfNlcLAElM8DaUZVx2PScRgIgQQzjGaAlcK3DIAM5kxODCYgeC0lKlDvEVed5PvQ9EvGXPlgdF8+KqAB1iYU62UY05QqMLXCPTPzOJ+BoflVKsKphJbZCODEj0p5rHSadnFwqn52fBXg+1DIMtZRCTApvbklV3ZsxUC/+jt3wCAQW5iIFPQYxqc2qThIkux5OHnGpdRjHzWZTez1XFxofqtTV8dhYTnnzYQHS/8nj9C09ySuLqsO9XLRgykpDikk7icCAuU56KaOM6a+cpGX39YM7y/uHc8YWn7xf2Bmp5UIB0WnEMp2+t4vOVJ+TkjzlWzncnNmdLLKbPcgj+1dRD8sQRc6Eu8UnMvOxf2cmghNRAB6cGy7/46xWK+O4GYZhqNEfdhEbmVFKHZdCQHToAxi+CqLeJjf4I/m3/LnC02nJ4ViuBCyQOEt2D6Buamd4u/snnJQ6dGpITb1iavHUF0Yh0xbxTqNM+qA0ns1c445XW8QBd4vcCQWcRECle7eLQ5oP6441FhRTwuAT82eulS5k+PFQ9I/0ie0SJ/PQ3U2Vm97f3N6+fU3w2+1WRNo8375+8/bdm+lwB29Ertrm1g7T8f548F4JBmbZbDa78+3+bHt2fnZ+OWy2QzRNGgYAbT626Tibw1ozb7OSMwcZc2uk5qrdcl3SYRYd1zOrFnkerDiRtEYEMjM/2T/qbOvyLcC9MKLCkrjT+JraPE1zawCWDkXpMlMacWZqqq3NXai6w4lYmIciuYX6xgohkNw85k5O2eKmR1PRwxeZZegi4ubHaYponnlEyZN5LGStubdkd05gZb02IvOGFzY1yeBJyAmhVOEDCwXbERchnhGynyOnmdytqQd+44WDA6o7QdabO8QqWVZWhAJDCESXnghJNlXzKIg6Wau5i8gW89QsEgWp93z05bkQKzssZ3ISYZBkRJ+llGEz1h68786ee9TRRvSVS4iljsgkuBQCIcSJu85tDrpvNVO4M2ukoCDyvNy0mTYPnnF0R92dgCplkGLuMGZnMkSuUzfssuvGI0vtkV+oqk5Ua5UaEd96tr8Yh9Ed86zM5cnTp8N2S8LOFPY1ubMj1D8A6cJr8fZicMkhwhb5ft0BOEUAiIhJJJsP7na7YSz8oJzkgY59UCSYpv+DJ3qkjz+STPqTynx91dXnV6r3NFxNf/jhxR+//vOPP7yYjodEvvtWCNg5U2NWJQLx3ZQSMOo2jQycL0VU1zWtotzpODm4AHqn2eVeU0SjO+/enVQnwI3uD7evXr2+ubkRkefPnz95cg0+odvUEeruXuY3xm6iYDhaETv3z/U9QmRwcQrm6XS81zf4cKw9lKvIMAylJNKR2YlR//joYbvG7aZASIKQN9a8KYy4OIsFzgsG7LTSyeCRidMlAIE6kWr3cSl1JwIDTr/SV7TLiysMUCz7ZRTCL3IzcoQ5YoakFSKLQMp68TxUvekVnNZOjP6pG8qDoewZSgxjGCHqXTskSkSWRQin669SXGj5XBYQf2SSCBaZUcf7m5v3L1+S2XEYdtst3N/++OOrVy8PhztQU5i6ztqO86xu2+1OpGzPzq8//fTs6RMaNnXc1GFkGUDiXKyOVEtr8xT2lIPNdW5Qo5hfVZtnmJJ1ismwLv0UPKe8weAk6VwafT8RQR3N2mDRGigniZdKB3eAygPItG/Y4+E4jpOfg4S7zcKRJxcaT5u21lS1lAGLBQcUEWLyUAnu7tTJUH2JJQUAWMqAjlbEx5gFWVTjwgJgnuZgrzVVEjZkXIGSyDbc2tO3L7bJYotGZUsYxSIyDAN1oYMTYw0VlkFqVMhFw0SoT8cjtB0PB9YyyoZqmugdOQozANH09JHdu2B+TS04F50i5ceKSC0l1q4Qi0gU/DGzO5stCW60TM0Su6GVrRqxHHY4s4dx0k9ECp7cJZmuGPItrPioW+r7IsIA7sGx6e6+5Au0IBwtJbwTdrD3QN3iChICfIzty0RFZA4APwZZ072+u7tXUwq0lDu1D0VjANKeYunhZ5CVUqnUWgYiLlLMyBwkdX92UWvdbndB6SWlJBxA4O48rHy1kwAwd7W5VNHWYJBgzuoLJgn6QcQ8DMN2ux03YxRonHT0B1HcRy+YRR+w1bf+tII9jeJHBOJH//7IYWb/8T/+5//1f/2/ffPn79TsydXVz3/+5ZMn173E3br1Bg87+ZQ+gdiNpmkb3d7ctuNxv9uZ2f393ZMn1+Nmy8TW9+9yyk/d3snc+RgSdnt786c/ffv9d9+9fftWVT/77LP/y//yv5ydnyEj0+lBuYeutaVnovdeAJwud9D/Jsa5xA57sc5iFT4Y/O5NpuNOQGjJjhhF4kjWI2T3CQAM156W7MmTD4ep9yhsYkSWDLsPjvU4UHZp6ynOSYvxaL5TDYeeFJGonM60g5Q1hGy48sF66ajoyUtdNigMZAhCXO97vi9C7zmY69ViHsSjWYBzilj17/Ll5AXLPKnwaCFPDnbochr3/O8Plwe63OtCDObeWqNpOt7e3L55Q9rmOmyePgXT4fb9+9ev7g43k82NtMEMIJHNuBmGcbvZPfv0i7/+N//j1Zefv3h78/rtu6M6a9hl6ta4waJ0h4mJWczVwv4TBiMKn2xFYZ3TA2Ro3FSbTu5krt3uNIf0RN40zOY2S48uf/jQJw8Y0YfeSU1ba02bthb8mhHS644uh4tmZg9SOru88SWUl1MYE0zdSoXDWUSk9ISLVPuBSLXm5sbEUoo7gqo6pjFsjGUpw9zbLCCokXfAkYj8VPUrq+LNzN2NC0jea7wnTFUKOVKTmbZ5Ph4O0/F+u99s9juZWxlPQjWeNEy76HnHvODKpy1HBIMq1CW0WnNXER7rQAZ2CIMyiTHVbOANZiAuUoZInyhcGbzEudYbYDXuWB68+82LlZ3bzNzn1oLF1AjOBHUQ0saj3FBBjtaCBNXczTfjeHdX4pvqUA83ztlgqCPzQBcHiW+4uUI7dheXtdvbO1Nv5u4odfD7O2cyyiRqI1LAmrm7iKhzKcJMQkIgA01q5iDizXY7juMwjABgOpRSa1VNtLFGwyU3AkzVdPHRPMKilthFz871k9oIYcJEtdZhGCJ6vYKM0Bdyf+EDjwqA2uoPelAY/zgLulsyi0/3SII/ws8fvRjC+IfvX//7f//v/8P/9n+f1EqpP3733e3tu3/7b//N+dneEzEK6MiXVeNra0ztm2+++f3vf9/mNk1HUxtrBTBN09Nnz/7Nv/k35+cXQZlF6Ro/kEePRMrKeTmNU6gbbfrn77773R9+f7i7m6ZJzX788cd379/tz/aAEyzUo6+zpleRLw8NC7ZsXO2UxX5pSXvvjx7RETptgPUEPchjo86BDLhqu7uPYnZQNs4CQCI8T3Y4NAdr5hVpCqTuAneNQt5920dx7gcmekTqoxkjyNyTTenk7VKGeEGe1EmUQHEPEkRssGNP6EJ3ZZ+fVsviesYOjRz4dDYWFbqo7PVZOY/eSyQ+NqgfpCl8uB4SD0YGsGhhkI1gwEnfPnJjAIOTOZvZ8fju5avD7bvdZnOx3252W3ib5rvb+5t7PTaGFpZatsO4Oz/fbve1DHXcXF5fP/nk01vnl+9vVTV4XciMVJMSkVm8VGZRoyyAlr5jfUEF8o++3MPQaKrzNBuTmeZ89yhv2P0EuEPVJBMGPKK6rrqMVwnk9mR0BI+DatRZllKLFPcTZpI0j5Yt/HIB9pHzTMkL5DhIUToHPJALNNMIKlH2dQsrPW4gGkHVWoQLs0zTwczSBbFwv0JXkKmDfd2r+NSAYXHrF7yDhUp1kSDx8mg9YORmribEQgx1kMGtmRp8Vj1OU1N1oKmpGXPgB+SAWWTaOjO31mI52aPhIDTTRr4UScJRWDZ1iJr78Llz/MPyzdI6A1EdhlCBItx3xodmIzGzmiVNJC3yK1YKe6RKdcd3mqbw8MLhc2iUOSSKgphVgVMgVxFQL6WUUhxwRLddInAiXSRGkcCpSaclzLXCZnMn4cJi5tPkNzf3Nzc3HZxkiQumhI0onZlimlS1lVLERU3zbXd26NS0uSvGOtRSixS4os27oW5K0Wmy2YT4bLdnlnBUpuN0f3esVRza67ZZ3ds0vbs5HLOYeCU+esQqewk3bXPWrveizZUzuOy4/lKHdfsfH85Ymjq0/OXdRlo87A+U7kkhmbtmdXvcrRPRf/2v//B3f/d30zyBuLX5dp7+9PXXT64uf/Wrv6bIgzb0GqQ0yxcx2o7Tjz/++Pf/9e++++5bIjZVNwsi8ePx+O7du5//4he7/T7Nu1Xsrj/n6vEoMhZP9S10cvGJiO/u7169eX2cjtGbUsxEpE1zQmsxLOlJuPcGhXFyrE0ieMazKXhK+3N0gb5EEZd/HaI4xRmDMZ9ABGYXAaDzfHj/zm4zRgM3gykTixSR2ma/vz8ACGZm722NvAu8uC5nUxHkz0Sf9CTKe8v3B1lRcTcr1emLlAcWQzHut3usuUq645VZHpa6kpYlaZ0Sv+fTEiXkmx649wTS7jT1u6C8qx4CWC57cnaRrm9ghp527Mrz9pPyWvxt4NQtMZLy8lzqG2L1L9MDBcSO6f7+3avXON+Ta2Eynds8WZvVWiOGFNRK0nNjndy8lFrHTamDOk1qhRAESepuqkRQc3YyQxRWEZijMN8San1gbUYcIFOAzD3LGulUXNcfeAXjwz3cwBaZ4bFMu51zgqBFJFLkw7cLtSciwYrsObxRLmte6CES9cD0WgmgsFqzGjwzZQqRcKklvKe8shn3brxrv846SILQ5N1ANnc1bZop8d7b0XtGQCxKPLFk4YPMvZnOqm5WqfBsza0ozCwzsMyJIu3eY8+3lqaKqra5wR1qUsnT4F0ttVDrrQWzGPVUz+M0qbtSB+Xh4izMyyil7nkoc+NcjoZ63fAFMkIUNGaLxZSUKBHMls5hQ+yu8bGIB8aOysZPzIZoCgKOXr9pQFPPni4l7EI1NeVsYpF9eEOhN1NTFZBLFIAFozsne87Jr2dVff9+ev3m3fF4ZObCXqLUKlBtIwILy/3t3du3B2vHwzS5mTYnB1c2na21sYge59cvDu/fvj3f71S1MDEXVxvquN1u3769HevOzPfnF2UYYB7I6qtX77bb8XiYIm2PHdb05ubmzeu394f7xf5JPyjkiFkkwd3e3rWW9DqLErRTzi2so325BiiVwsm2WDZlnpGk60yFkj1tXnR2tFld/HJ0q3YlBFytLX8Qww1ff/0NnHfbXXio4ecf7u9gjcOWiqhNdBonV49cHGhrr168/MPvf//yxY8cuW/m6m0chvOz8zdv1E1N5wz0gEw79flqla6OGMs+ONTVSLcvtelms/nFL39JRMEZy6DdbishgQJdzmh0aCbOfU3ORG5KxCfSS88cyxihHjZlQvTNDC1BSOpLh4NotTcdAJMzVNt8vHnfwjWLmElCQsQihSDudHN7M+x2oeF7Nc6SpZwPyA9KMn/iCOx5NV6rmrrFZehvOqznqKehQTHCkdpj/VZXcxCJj0RObICsg65JO7qSPSlCHlgxHT/L66k29yBIkPhs5Bn1DEsyj1kJqPmDJIfFk45CndP4eOSDhWiMSHaPsK6VnsM9AhHaWhANVeFSg+20UfYlCjTf2dymecLBq5OhcGEpDm5m8zRJHUiEEH4X3F1bI1BziDZCBKCSiw9OqZWXp0qzk/uAEjP15rsgojRCLPseurtzJuwn0US4bbm5HYQS+UZh1oUBkPNvQSVEwZSSXib1xgsO986j1K14EBYXcFlglKotoiqh3EAUmTWnxZq5OUSAmypqSTcuyGrpAXdAXrM1BVqmC3lfv1D4ZG02bW7ChYXBmLXd3N6AvOkE8wIqyqPR3BBcTdK3gbs7k0iJmvCsXDebpwkmziwgqYWpenJCnZ5CTdUaEaRQuK2zqkY1cWcjiyQ24ogfKXr6ZITrwsznqMEPGeSdiB2RiRHiiyJWlfV2yV7NlC2yczEQojvSUlLcSfmzM4Fwbh13tyDqBXGRQbgUKsLFzU2NmGopQdzZWpvmCUDT6KLsPeRqTiRSRWqWpHWP5Ob25o9//NP3333fWmMqBCVQT22i7NfkePXi1R//8Cfmdnd/V0vdDNUZdVPO99vdpj67vrDj/Y/f/GlH9n/9H/+Vukkts9luHCvLpg4/fv9iPs4i8vz5s91uG/m17v6nP/15HId5cqACZZ6szfb29dvf/+73t+9vTuIQWEz41vT29sYJNzc3RWonY+mTbBlBd0e2GjptTuCB/DpthvwKSnbM2MPuaNoWiUNEwhIJELHG4b1ks8fVkoMtkgzhcGpH/fzTzwtXQwMR1IVxcXEm3OVk2F4Wdr9GI0IzPx4O1tqT6+uXL3548+YOzVubtM1BbTu3Y61DaD5TA7HCT3WqvWQRmauFhA48ZXn4DmEqsrtDhXmslaSUUoWYidpxMjNrjSIJ0QIfo8DY4unC5miqw1C3283Sk9q6yuj1h2GDMsDGfuIty0p0qFluEZHl3DZPxMSHdEKYMyS3TJ1wAfh4mA73d2UzxPh3Ldl31YP/nyy1FCcnXQVPti/rOzISCDn81ky86nAsul5frSKn9IUS/Q6pHU/LC+EhMtXOu5dAzHB0wiNe7nxxomh1nMRginlbshXx6Oiq/FEq4unh7eRMPZSUOSYPx+onD6JsbR5oQPbhFWJAyCsHmS5V52IkBszKrhJyPirT1NzMm0EcAkbGZdSMzGbzwZUE0VqQMlGCosorjAN3hkXIcPFyo2aJ0GvCov8RenpQqLAoCYkRMDVD0rS6g4mKdU+xk65EkKczJy9roaMU8Tv6b8vIp+8ZK3Q1JQH19OlJV4+Zh82wkGH1USbquANHlmzviggi7/mUgWojEhpcD8f77bRtrY3DGGlclr0KmlpwOpI7pun48uXLw3QwncmcDYPS9fZc71ttXDwUNWVYOKLypQQyEMBRLGUzd1UnKqUs5V+ebwQG3oJqghiu3kwz6YIzsoNutp9qrFc/c3Fyhk5FpGdIsrAoZoRWZgoLCeRgyjZCvT0GYR0DplV+uEZ6OMDhxUaqcPjZruGUBgLJkQBvaS6EZ83uIY3VmWZVa7NKG5hB5jBmWqL7+Y+ImQ+Hw+9+97vXL1+WUtyS6UVKTWJLjRVJN7d3v//dHwTz4eb22dX5ZruVIqXwWCu3+clmc//qxffz3VDoq0+uy1DAdDe13X4reHZ/++7Ni+/fvnpZSnn27NmszawBlZnfvH6rGoXi7JDWrDU93B+/+/a7w5P4UxMAAQAASURBVOEQ9n4PXjBAZjbP8+vXb97f3tZaIwK9Wqe5L8Lo7J3lOxjY90Ks5zUe1Ff5g7/SkO07hAja9O72dp5muBMh+H5iMS+QC/WYqJRycX5JhJ9/9fNhGF+8+P44TSK4vLy4vLgQ4SQ+aB6hhDD/1BRdBkR7Srgf7u9VVdvU2nx3dz+OI4DLy+t5nu5ub+e5AeRMWeXPScaEBw4TkS2tShzB0OcUlIdEgOsgPLWpqb58+/71y5embaxVmJoeVbPINvrBOFBLOb+4qCX56v/2X/36q6++WiKF0rPtjEiZmaM3TpbGEqXRDyLppPmRf2WdSIeSWNJV3QEhKbyG2QGigA+maTJt1I2uPrkpE5cTFn7y1fz2ECvICAUn8Dm4rsKnDDESco6W1ITcQGuKDV+OyKjykyj+CU3WXbbTgoPBF9f9tHaJ0pt+rBR7eeeHytJD/sADM+1fGFrIDDapmj8ek48eixv5kfsPsUTuUMBYiEtJBQGKGikFASwQdmIDE9yczNkhxCIUqGjDzEpDlVqru+uxuaqamoNgwgGX+gNhDCKAfRGxWMwbdzNroBoDQaDuyjiYSDjDq25qTcM19o41Ip4IZanxfXQkkxhRxggfat/1+CyDlhZZRAiXCc38K3dQpEQ54IRSi7BYtHNe1iVlTKCb2N2HWs15l0EASNXu7+/3u7lIDe0eEsIz8ftkHLSmNze3TWdtswCkvnU54/H4/m6PYRh3mXyRCF5QoAgR94zF6KAdUivy+6yZqWqI7LwtM1fTubm11hSqoGgc7dQs3FNX46Sw+AsGYKzpk6ETwO7KIl4GIyGx/o8JAhdGtWhBhGwuQURmaC0SG9nMw+Yx07u7O8Xk7g4exq0wS9BqMMFhpuJJIhKY2MkkDvYMMe5GdMe4mLiAOEyx+/v7r7/+Pcw2m83hfg5+lFqHcAH7iuWb97c/fPvN08vt5cXl2a7WAW4zHe1w816PR0yTm91Nt9hteDv4LCxSufjx/sl+/J/+9m9ub2/f3NxtqhBGP4Yg881mNNObm5vtdhdk1MfjfDgcm1oEpCnFXybMx/jS/4+yP2uWJUnSAzFVNTP32M52t7w396rKylq6q6u60E0s00OwRwiQfOAmMsIn/gk+8JG/gCL8CRQh+MblgcIXDJYZAugBp4FmVaNR+5KVe96821kjwt3NVJUPqubuce7NakzIyZPnnhPhbm6LLp+qfqoMLKUTKYW80+Js71cU1H6kMT7jB3G+pl+hgD2CgCP0MeKQ15dX//pf/+vfffABM4+SV0SE2chZSym2sTPz5ujoz//hn7/79runx5v1sr1/56Tb71mKUXJwYauP5MKci4iwSM6ZPQcES8kiBYQRIA9DKUVKEWVR5iIUaLXKfT+o6mLREpEosgqR08RhfR6v50UMSOoNqtWwMhbrhaiKEEiXiwh7QaJ+v/3is0+Zeei7/X5byqCqx8ebs7Ozx48f73b7EOJyuTw9PbVeXkS0XC2Wy2Xl6fN5zlaMUTvKCDtm0LbtarVSFlUdSiaKIRq2rylFrJ02AFSVzRPwoDC6weoXtCcBUKCze3cRzAdDJFRStlQX1+UyOjPudehtgem/8bQb49yyUyzVq/EYSN0ph8LW8HgAsAofmXjovkKMmESluZR++R3Vl5/QapiBLlQbBb9STdza05Mp4IrGkh1n75rd9vCfX5EajaBgF3bThGKMqUlNCwCkmKwZOVKxfBc8dOcRa+Sz9H0HKVHEKKluGDVMyI/tq57PmTFxvO5kcRKoFAbjjnTk1KAVDCGEaHyWIKrFtJFprglLA1CN44zX7+4BN6lZrzaBovlC9hiIYN0/LdcdaMyt9iZUYtRcrnZNlgFWeBVrAYR5RhiQarkAjvsYVESIiGIAy0gFcqVdxVmVmFRKGXJu28XR0VHO2WdMgYvxc9pDAgCAaL/vJA+l9CBCRZQaXhyV/a6QhHbVxoAqUOusKoSlqurMbb6hQEEQlFSHPAzDIFKLka3ei2Xoe1Ld7/YgzCoqSiKBUY/ZYvAhhBGZ95w2s1GMvgK0xgsYYhwnTQiEUJ1DuoaNzZaqzi6FgBgQQqBGuUe1Rr6BKKiSAjKLqCJFttYEvuBECuykBUpGIk4mxr2+yTAAEVFGKUBCJDQMWUhTbAii1QiDN7wIZt2biCi5/PxnP1stl2++8abFHewcAaDRyCAAEZZcnjz+ckF375ycoErpd8Is+yF3HTBHUQQVoBKkYEZOggghSskQY0vUrJtFIlS5uNlfbykRSsmLtsllGHJ/dudk0S5DiEOWkpUwnJ9fkRIB7Hb75bJBAmdyRCffRSAZHDwgIvOTLRwOAIU551xK5lK83lE8AcarAdUNyvEwV8vNeiYAAJRchpy59vcEgK7rP/7og2fPvhyNUe/YLfLaaw/eevOtX/7ylzc3WzvFfb//1a9+9vzZl8MwHG2OYkoi+uTJlxcX56vVsmmSqpcLAmDbNmYhpZR2+/3F5VXfdW3TNDFdXr7IQ18yY8WsiwiqXt/c/OxnPz0+PokxImIpZbfbGwCdYgohmNaxhDURaZo2pSSi19dXXbdXVWbJubhxKpLzsFxu7t1/7fj4+Fvf+ubl5eXjz7/Y3lza46eU7t+//+LFi5wzUVDVYeiHoTfh/9d//eMf//hH1r1q9LntZ7MtsEZJT0/P3n77bUR88uTJbrfrh0HEbfqYQtsuYkyuNKyONVioFCqOXa0rRGYthdfrzd/9+38f8V2o2U8iGoOx4ZmMZTNqcfRZXeDKuP4jQQ86+uVvAo8sOETt9IYqqgwaFLTW7pqgdoVY83RdnLp8fFkT1yQxHAuiawUW1OmqqsXSVkzVfbVG97Fo5ZyAuWim6qFUKU0HHsNXXdLccpOqI0BQ1Yea2FFGVQrYtIu2WQQkLRwAwHrwBbQ8m9HidTQfEQCFGUBEGUNDAUFFWYDAKzlsnTzR1RJJD8YAYBwmZiAQAFmXKFDgwkZdhRYM9DdAgOqxsFjUSWZ9zuYTEqc0L4vZOEOKg88iQojJwCw1GGx64Uh1WzW/jsrExj0qLhizCTyyU4MdNiBFwBijTZuqkOf12HYm356GzdfNCBUFsJJNb4+jCiKcC1iedq1JV1XOGQsIZ6PXYMSosB9ySTEQLpo2UDAwV1XNzB8TCGCebK+WjCp5GIbcl5KZs1s1Cpzz9vKKEK4uLpgLS6EYaBDseX9yD0ApYIoRrRqvutPjvquGptVPzzD9urSzJfS3ektB8CR3BQwxppRyyba5EYEoWFfiXNj60ht5FiButzcff/xR4U5JVfDeg0dHm9MYogXFreA7LlrbT24sCiDD9ur6/MUTIDg5k9XxmekJ6/jhjQnNpwAKhH03PH385apdHB+dkPdwJETkklVZlRcNLRr42tuvnx0vEyp3W5XMwzBs92XoUXhQDghrXC02cRkbhDL0hTkPiKlpm+Uypea4aY/ffO1q251fXWWF612/WS6eP3t6eX11cnq0WqwjURlKDO16dfzl46dcWBX/6q/+irmnYB2PwYcOCEboOJ4PRAA1AV1KyXnI2eDS7DFLlfrDaPr5+YX6P0OAvdpbVRWYWSfPyWAGjQZAUGAWZVEV5hwIVqvW6FYt4lxK/9Of/QQBN+v1u+++u1yuNptNSuHp0ydffvmYOZstu1gsQgilFES8f//B5ui4H4ZhGM7PzyXnEMLu5rIMGYCW63VIqc95s1mHEC8vL375y1+M/thoPZsVcnR0tFqtROTm5ub6+jrnHGNqmnR8fAwAXdcBQAgxxpBz7rpOVe/evbtYtItF89Zbb927d+/zzz/vu/2z50/Qo0+Qc8YJdSs594ZMhBByzvv9vsyMlRE5UA+i+TtXq/bJk8fn5+f7/Z6IwDr/KKGQQogxiJRhGMC6bPluNCVFgSJ5w25TWmYAlVIy1kV1XTPCTqMA1UkYju+dkKHxxLqvdQh1jtXZytXJMe4Unb3FdamgNbarzvJcJuvB10svVHWiFXg5BOumg/uOWg3GenMEM/Zr2pQnBgXPg3XaEhU1RVqD1FAznLGK/Wr4v2KITkMCaGCyE4uwgGhADEhNiDFGQgpAEQmQhIAIIhHQmIJa3V9EC6JZXx1v7QMgwgSoomT8+QA6EpyBQw11aBUznqbIjyoA5JJr8EnHD7pAn3qW6HjebUehFZ8hAEKE6T42W9aYoFxcXjx+/MVrD17T0eDESkZckQmEMZsXVS3NUpmNAgyJ65ocLr+CYm1zBL4TgEuJMVJABRWEpm1tyvxkGE/gtAOnnWNlr03TmPRnZlCQwsriLedUAYG5PHv6tNtvS98lIM15Q+1bZ/cvzs/jkSXrO3JqxgchxhTN8IcRenQ5oSBSeADQ9Wp1dXFp9qqNp+u6T64ud9eXP//5T/qhaxfx7OQsMvAu311u6Nt/GJBcaoiyV10ZZDDCPKoKRKjqLiVbh8Lao971th+/8Rhbzjylpl1tjlbr9b7bg1u47iNbVXvmgkRDNzALEF5eXPzkb/7mZnuuAQjDe+9/53vf+zvj/HohvAknRAEtVVt8+fjxL37xEyB495vvv3d0wiJiGVUkKWLfG5apKcRISIK7m2233x8dHTv8SkgBRQpLBuX1crFZtmnTtBFQSu77Muxzt+t3g5ZCyCilSZGWzSLEhoIwExtHCysgxBRCikkBynHCxekxhGabC+fy2w8/urm8ODs5WS8WAeFit8VAJ6dnlxcX69VRSvDi2dMvn3xh29wCl2Nwz1o3WVWxkW4iBeu56ciHr5eysCiD+cCOYY7Qm0DF5Go0pibnj4cPK2Lk8XYS1RCcdQ5AiGC/3z57/mTIe4WiAIjEosPQn5ycvv3OW2dnJ6UwgL722oO2TX/91/rFF58j4nq9fvfdd1NKl5eXq9Xq7OysbZeX19fb7XaxWFy+eLG9ujbzt22Xb7719tHJyfXN9uj4CAB/97vfnp+/cEpCQsKISKbU79+///DhwxBC1/XMst3uYwQALKUwy/HxcdsuY4hHR0dt2xLR5dVF13Wvvfbg9PTO8elpbNJuvxfV9dG6bZtSCoB0XffZZ58ZBboZ60QYYyCitm0RV01KuWR0Mhw9SLY3WDlgkxIRXl1dqvJy2YZg8cLKzYuQArWLxdHRxqJOekBKo8qVa0hVVEsuCkW0KDCAEljXXmPwwJHkwCSqH8XKZGW6BDxnbfSqb+kbIKqlHOonzlwxsN48UDm9qjXn5pCpfi/jd6ofQLBPGYsZjpLdsRh3ejzy8pJrii9pZeP0wINh2y6utN7VEZo8fp3qjSzRyWLvNTsUqqNP1QSZ62AcQ5cIllgizGKtU2weYoxGN0tecEEWeLMsHSJKIcaR8ZJIFTw3j4is0sbwGEVRBs9yFISQiCJAsGpsVmVX/Cpi3SWcC66OWWZhb1YWkJr4KcyFuYgwIIKK8ccA1JIrj1UpIkThOSKtzGppTmZvjjvGq7/ALS+/FqBt3zGKyyIDexddqGvtd9SR/xksv9/jXqa2S6mLI4B6cnb84tkiUAhITWqzR8q9QB1cqaCqclGiYIEidRNDrZbXKBgEFIhK4YvLy6ePvyjdDkXydn/arP/e9394eXm5xEZE7EFFhAsbatukpmma6twTKHkYwiw/0eVy/ejR608eP+UCAAGJgAJz2e12+253cXm+293ECPub65PFJt90V+cvlAuocWehiAB5VRCiNTFH8P87EGGWjXnkFosSlz4yUrla/lCtIGo3m7tHxyfL9SpcnIsUTyCsBhYX5lKAlXOxlJlFszg9OR2Gm1w6AC39YAiEHURrAgyqIQQgYpUimZVZOaa0WCwFBUNAChCCAIoUAKaaZVgyx5ACKCgqa8mCSJV+CINRRjEHwKPletEkkB44D1y67U3Z33A/cGGy8DtpFBrycH5x0aQ2hoCIMYXlYtksVrFpKERUlZxlGIChacLx0XH7Du2uLvubq5aoDRgD9n3Pqm3bHB2fLCItl7HrbpxDhKwOey4RVEEQNRBaLFgBANmEFfl7FQAogLEVIAScGfa1Cg4mc7jCRlUCO2znEtA4uhEVSi6DOTwAGGPc7fYff/RxKcWMV7Plm6ZZLpo8DM+ePlXRru/zkJs2LRateaLr9fr+/fuLxYKImLnrujzkMgwgEghTSm3TWgvH9WrTto2qxBCGISPgyclJCGCN5s07tAz8EMLx8XHTNADYtnjnzr0YGjXkI1BKMaWWC4cQU1qmpm2bpl0su32HSLt9P/D5s/MrBFCVpl1+7RvvlTyoMACIyNHREQCwMKgEKxpRSTEBQDJeFEIbQ6ydPes8KhKJ8MXFBXOJMRwfn4QQEEAFhmFwW000DwN4DRhzyVwJKcjzFUGNHotIVD3TXQREqaJhFm4UsAw6Z80yFModaiREJQrqQWPEW7q3KjPPjNGKYxnMbdrMznbdLq/yGN0OR/DaV1M6OE+JftXLdaoVvSBVP53GGptxtNVwtI8dKsvxjMxeL+lqMG3vJu3LgVZUC3wgmZJ2mn4dL23RbmYLbho+wQb4oXUQMHkshBoIYwiegUTkbWGtHy+iZYkCujq0yCwhoABaq0c3gkCFRYrpUeYiKqQHFovnYBIpYXWFxC7gKcyunp0UHxAVnIiyek0IALF2Layqa1bUWEqJIZCngE8UdrfmH90WCgBkYNmM7xLn47XNNKZo2+kCT3UXw7VUFQKe3T368rMVIoUYz07PLq6vh30HAIKqY0str7SBzWZtH5yBV2JbYcR8KvIgI8jCzIGIQJUZVA0AF/YcV0SMKYZAMVIMQYBsg9iqGwzQNE27aBFRvFMQElGgGJtmIcvFYpFzh8j7/T7f7BsIXkYL0MYUAB0zRggKgCSeK02VMoFAwR+yHkKDGdRSr/30jW4WiBBRuzk6Wa+Ol4sFYMi5X0KLiIjBAU9hKawApbDnuwVaLhYnx8dZWlVcLJaAEIKB2TJmjlqcz4jJMpdc8nqz+cZ774U2LI9PMDhcZyThFvqyKGkIMbi1BHnICMaYrzHESCEwRIFVu6L1kDCXkoe83+9u8vYGSlYpPGRSDEa7QdSVQbvtUoUwqEogXC2XFGK7WMQQzJbTMgz7rnQdgdxZH33/u9/arBbPnp+zakWbOMQQY0yb9WaV1ut1jMm27K0yRHNvDqVnLRo351sEJ8PeViVY3c2YFzo6LSaMPFMDp37YUFG/g+OF1QpCqcmAAAgpJW/4SAZT09B3X3z+qVvFMjkjKSYFzTn/8pe/HMUkAAR0ejgEJIXVaikSC+ech8ePv2BVYwxuUlosF0fHxzYzCEgUjafClN8wDIikiqvVerXauGviwa9oDbURMYZgQnxzBAjIKoogCsyMFFaruFqsAIz9X4wCqLBlionN8ozYruZ/VbCRvAauOqIAFmkeck9EIWKTvJQOrHpCUUTKwGPEpEgxkasAAT2VOhCJMtaE8+rm8Sz8fPul05sdxRhpZX/Pa1RzthyBxmKq+d6BV+it/7TXgcYzPTFGuAG8btHGMSPzGvek1XBUj1ZhroKmR5hBOTNPHZwDF8dZ8CtXVfzyc1WStaq47TMixvuozIa7ip+tuQay8DhFjx0GQAohgAIhKfsGRu9BgONgfCyqMFbreA9BATRnI4saBHnw+GhcQimJFfJakJfMszIYRVTRUnuJghCBiJASsVrNLQACxrqfTGEhmiOPyKIiHAMhgrCQkqWYGKdVLeKi0QNwCQJQuPrU4uB41TvVxgcAQAW1wQkAm3ccg7W7YeFmEdbrFRLGEO7evzeo3nSd4kHCkdtwVg0tbGg3zgHaaXXIa5nIysHRGLSJICECl4CYUgQyh96ycinEiEgxphijKBmfDSoRBgUSkLZp2iZ6eo5yLoOINCksF4tEslgshj6BhkjUYGghLtoGAIiwoUACwIJUwzgiFlMhRMegEEYrw5529KVsCn2jC6Ci9VgX1hia5fq4Wa7ToskMu12/2WxCQMCAyIhoZSfdsO373mSJwX1t2zYYRSCmSLXSzhAwLllL0RhQVVhUpHApzC/OX3z+xaeC/PCNN45OzxCVechlGEoBRFbJZcgBQqBIaDVdlg1kWAoiBiIECUgJw6AkpeTdfthddLubvN8hKHORXAJSAogpQiBre8uqzFkKq8h+t9/vO2FerJYhEgKg5N31Zbfvu93uwZtvPTg9gbfeAtFnF1eFS4oBESgEAY1EIaSU2hgb0QKqdIiJma1jYr4eVq+nRDTyNS+s95i+f4hH4+h2FUb1or0/vFqOm2noClCBY0VIrnWxon1mtlohm/3FDGWYeCG8khAQg3lpIp7GVf0TMRsAlTAQkOfOazDjScwnQFMEFgu1JPxgrLQw24pgTLohmmqsrnkIIaKHmQKh++u238izGpCYQRhEAK0wytNcQyBVqj7nrGzHTPZQb406Igd2ALHOhn03I1uTTarU/CZEVEsQsYM3Zq6qfZ/gWTxMoLM8Vq6Q8/R7nKSo/WbUCXNB7ztDoXKGuLilkQabjJTYofLR77VMVKwo+i0VePs39W71CRGgJtsffGy84FSjfPAetyc8pUpGVa6jhz9uTRviy4j2fDxet1LjwrdeMP7WvUMrriQAFEX2odkONweTqZb4iCJBjWhRxGhUNs4qoQo4sRYKixUXzCozK8ptxSxg0Kq4TlWpTh0Ylq5e92uueIypZYuyOlObH+/CFpELVLk3yQx5CogBkKF6uXEscEI3ghRRlRlESCEEMssPrTC5xhNGdU0EXDs9WETlwEJUf7eB3mol4QiW2zWhHGABelIEQcjMQNosk6U4nJwcd3l4dv7ceK9sY6jzg4NIefr0aUppGAacBTDGmrxxPCIqYO3NoKgUKSoMypIHRKWACKLMDvE6tYU5tQFrTROrxy/c7ycMgSwWKCLMBVKgQG3TxBBjaIT7iNSEpsUUQzTQI1iAlwWTFfZoxVo85diaJpHBtdUmnZChavQcnEJFFUXCEBOFiEgsvN/3CogeTQcEXa+Wx0dHL15ccubqWJtQQKiYsK0OEVlI//LyQkCP0x0F4cKGLShhz2W73RYsJ13npbEqecgWenfvQmM0PnHT55U+QtFSZlBYCnPX73e7axiu+t1V6W5Ktx/2nXHVkJl5KhhIAfq+FxFcYhsbIB1yAdUylG7fDXkIAVfLpQH6lxcX3TDERXPn/sOj9fJovXp2eaU1DOYl9BQoJKQkFtlz+Gf88m1GajRRdfyVFcbcMvaGngJgLNwyfhxgBC8mwVd/X89dXUmzEUG9aGBi34EZsbHWEdpBxKCjsLfxAiMYpzhZesRytXzjzbc+/fSzbt+9/sabAPr48ePNev3gtQePv3i83e0RoWkWj15/JCJffvll3/XWMqtpYtM0NcUXiSIiqekJCiFEcDc9gWteV7SIAV3pWt6M9zSFkVEJEUXRSXlYjLJfaZoT001e8X9wilGdaMYw6loIO8n1sSYQACwj5OCFZvLo5Msatjfzp6qj6O4ggOVyOyNYBalfoXCqNWBSatpGdVP5YaMYai1ArQHxIpzDkRr6IfMHqJFc4+0wJh70XVJ3y8zRVJUKOtYLyGGCgo55NaPYHmcPq2ny0mPWQOrtCajbu8p2hVFVE+Joedz+0OyrWqDj3wQsi5Yq8XUt+rJ2ZqJW6eTxbQ/fYPVxyQWa9n0e+h7Nq2zalBpTq8ws/ZBEZexc52YLWboV1kcAq0RFGDmtRAUJCeiWcSOiyvUIV2dJx+V2UikD2iGqn+T55Gu9Lw19FgsLIxp0bNrSy7YI0VoJeC6iWaw6re+IdriAgKZpIgVAoYAhBrMk6h5ARRQEVlUEbEhjQA5N05yd3UnpEy7DfNHNRi0sFxcXbbsAAMvRPdwL0+639hUigiJcOKuWftChKLDmHBECKLKgKILaCZke2fWiCiKyn8whD0+ePt9ub1TNfjL/QcEaO4PZC6BqrrvmnHPO0UodFLgwUnRuFQvVVwkyzhwiWaFOFWEVV5iZ5gBQc/NEgQkVkNGKxywPGa04mFV1kZpl26AqiLlNTokF1okZUJGYpfKLMAB3u+3du2dtDOb7inp7bRVp2naR2hAjVLOdiJqUvGTLRZsKKgQShGLZIgBIFFMkhC4Pu24HMex2F7y9KMOWtKSQtBWpFFGEiIQlD1yGUgoiaSmLO/dOTo65sLCkFErur19chUBw//5qtTo+Otpud5ml32/77jquT9rV0pjAD+eOBAMD7ofMnEmZRtxG59xr0DRNjLGUwsJSU+UBfNVGsTOeoldhhq/0EOBQQRshAyooCo71JwA1cVbVZK67v87O6GJ+Cs2girKqINDR5ug//7N/+G//v3/56Wef/8//F//rUsr/7f/x//z2t779w7/zd/7iL/6i2+1K4aHv/+zP/ovtdvujH/1ouVwsFqvz8/O+27/xxqPFoun63cXFZdu0q/U6pWh6PaW02+32+65tVs1i0efcdZ2IEBJRpBjJuiijudmkVpEiAgQEwWo2lKrRAWZiBhfvpn1REQhQ+36/2+3G1JsQQ9O2RDTTgSZwLYHUTSJVZZNd4+SqgqKQqiJPanrUkVXlTN6tEo0I1HTcyEOnlXjACoPQKzBdXmNtqTCFgN1lIQoVRA+IBBiqAr69Z17ScWoOoM+NW3paNxEieIsFc/VUFGf5oVXZ4y0JMnsyMJx/fmu9PSpj7Va3pHVUsgeid1RaPliHJlGnUzI+EFoicqAaWlcL89nzKYqSAT9IMOZSlSylWJWjojvvCuj5SQ4HaQgmxpUL8yCFS6+9KgQkiomQUJSHLBg0uI70yi5Qsj6Prh8RiEQKgBKqKLCyx+QFvI+R17AiWP2ViIMLzuxRT/KMNgwRo2NWYhS0WIQASEERg7A+ffps0S5nE+rOV/Vs3TOzzQJgGeO3PGBw905BQVKbNosVsgBa0aqNw62/aEkWCAAamxiblHMGCGd37q7Xm657MVoH9eWgE7PU+oSJbMtCceJGlRKQsJScqbCWIgX32+2w33cDai4kEkQt9wmdDJJUJedsCa9srAJkDQABEW5urn79618/efZlpKBq+V6syggipYCOZwEKlahetZzIK3xkbAIKaKEpFbUiUVP3fT+sVlzYJovAMrNM/tbHt2RCi0qLljYtYqMxChKHgDEQOT2QnXCC6turCigrQFy2J/fvb7eLXPoQ08np3VKplyz5Mwa8e3qS2qYIIEBEUhaCsGgW3/72txRkN2SXIoAppeVyaWlmqgAYFBkQvTuqKhAxWEgdQwjD0Oe+L5D6/a7sd93uBrhvAqWUgKjkgUsJIaSUJGciakNcLBcphJJ7wNXx8ZGICOf9drvf7YhoubhZLRZH6xU+etQPgxJJ5iamzWZzdno6aJxICt1ZgVLK0A+FM6kEZKzuFMy8AftuLm+RUj1+rWfTMerqM81F58xkdH67qXLmZYdATYxATc0bSxdAQa0nn7i0c95Glzg4WdjmViMGQsBhyIT4g+//8PTs3te//k2K6fs//OT7f/T91x89/F/ef3jn9AxVf/7zX7z//jevr6/ffPOdH/zg+0dHi88///KTjz7+3h/9Qds2zOUnP/np6enJ17/+rvGYD5lDCJ999tmvf/3bd7/23vHJ2W6///Ff//VHH36ojCHEMlLTgAqX6qJjQFAQdO4dsRoShRBAQLU4jideia0aiASAmff7vSlgJExNohja0Jj+A0BwrFpFeEbzqca3MBbpqQMMrwBBXfiaPMeDHWKLZfRwwgXYuKYNIYPgmbgVlwQMteOCLzoRYgDyWH4IgTlQxedNbXnTLT/OOG48Zxpn0YDGpSW16Dmom3lYExdq7FKBRYsysSaxlB/rMQDqCayj9rRJBituNhl9QE7ogvSg+PQVfi8AqvfFmGGQeLC53Sev83R7z8/Piz/ZrMYVAa0xroI6KZ9ZHmrpZ+5SKoqiVY0oUSJv1AOFZRiyMIsiIxYkCQkoBDQcS8U0Lc6GjJ7UNhoZhEGsoYsqAElFWtDILKa6LyUki/6gRzbGoDJUmq1JJcfRHhoV2zgVInJ1dXV5eSk8pZOMBhG6JefEVa7p9BWwj1a9bRWozaId9h1SjCF52Q+qqlqPWGWRUlQ1xYhEhbUAnqw3p6dnL16cj4PAum4Atn/U6oAnQIYQQLmwZZAYHZQwc2YtRa3RfObc91d9j8JRISKid2BWAiQgVRiG7H0dpn4sarUBu93NixfPch6oacAPvxgOYG3zxmdn8ZY0UlgCJ4+vWn7/vBzFnw8BVGXIXdfvmIuqhBBSCurLCRVAmwn3AIi83qT16YOUouSyXETjsyqlCItxFfV9HoZMBCEgD6IQ3v/2t/74j78PIOdX56cnd0pf/v1f/dX55aWxQ6BKIFy0LSBmEQJrDkgU4p07d9/9+ptfPP68f/IMFAIFA5ry0JUyzGokvRIZRlIxo4XLGcBbamSE3W7fXV5vr19I3qcUTo9PmiaKSO47iTFu1oumiTGk1DRNssB833egWnIueUAFy4kFgJJzaNtF04QQhlxIAQVPjjZ3Tk+eX/VUI3PjVItoLoWloIoi48z3NY0L1o2jFEQUlSJZp4iAcY2pgeV2riqK5cfIrjbzjwVf0rsHL5diOsriqrkt9UI8PDdJOqMQ8eQkRG+HG0IkDBRot9//Z3/2P/zm02+zQIjpH/3j/+lrD14TLl//5umnH3/68MGD1x49yFnO7t79zh9855OPP/m3//YXDx8+/LM/+weXVxd/+Zd/+cMf/uAf/NnfQwQV+c1vP/rBD/7w88+f3Gxvvve9P3z7na9vjo+ePr985723T++d/ru//PHQFQRid0aLMVkKS86ZRY21SgojYkAUKUXNL45AYE3kAZC0gCoqCSEgtovF8emJed5EGFNomgiozqJsRw7UbHFLxZj5tujrbF8OEtSc03HKf++KmHFMVrlQw+0eISLzgmncElPRUc2UqSmUUKFSgwxNs5D32Tocj+/AWpxkpgwAqGWouG0zwoxYyXmdyAdGPHfy880MNgukOmWjfTGD5Sff95U2It7+RXVC8dbfbI/WPpgOWr8MaNct/xUvQgjE4hxlFfvHCvAfjqS6gb7KlngLWrhYc1UzargwgFLNRMQqVOejsaRpix+DioW4kUjUEi9V1Qj43aupAxhrHaotLvJSbOHgFSmEcZEEDEwxSFNEeBiG3W7LwDXtol4Mx60srmlc3UAprCMREFaACSqYTpSalPuBYkCiaTtYfjkAiOiQQ5EIyEWGnEW1adv1el3XW2ar6IQwo4CzI+pTbd+tzbKJNAtwqsXkfYavri645BAjGV4jgtZEFTyQWWNCFoO38D4o6DB0zDlUJEOEi3LmQmrAF47OlioKCyiQArCEWGupxLPUedRWtvYiSJJzPwydOC8mxRTRGG5VYU7WPWV8SLsIDx7eiwi57082zZdBYqL9ds8lAyCCXF6e//a3v2bRQEgEOef15uj7f/BdCtAN/c31br/tjk9/88lnn4oIKRBgAA2oJWcSQOXqUYe33n339TceffHl49S0iBQICXR3c/XrX/3i6uoFKKCy9WbzyK/FlREQsAy573uiAKi5lIRWPq4hEkEIRMwlUEqLFqUEIhXJQ4/YtG2DCMx5GBRASx5urm+EOYWoqiHGwmUYhhgjBULFqpILRlq0bQxl3Ce2jU1aGnCJIIwFD13e0aP1n9F8DU9ECt4xvp7A6pf48bP366SDvQ/QK90IP1jou7QSs9fBjNzG5gCjyf9AwZJ3jEA0WWVOjBSIKCIiYXz+/PnD1+6//sYbf/Hf/nero+M//R/8yfX19tnTq3t3Xt/v7hYuu+vtyclxu1ieHC9/9PTJb3/7Qdf1f+/v/vHV1dVvf/vbt95+89Ebr+Vcvnjy7MMPP/rud99/9uzFdrt97713T06OF6tY9CQEbJrl2Z37kpkoYAzW4JY55zIIa993Nze7ruu6ru92+6Hr89BhIZCcVSgAqEYALgblAolAUFUhgcVi0bSNKWB3jWdrVIs6RIS32+1+v/f2TQqjQ/nKIOs05e7+wMvqBT0lBIRFRIOHA7y01dqiAQUlUhUMASy4N8GybufNLunFDubQA6ASKaJAzYeaAbm2zeoZdwi9Prhr4EkJ40HFxMtKDWtstMrP2dh0FgSub64K73CvVu7C6Z2ze83fq/PB4Pzt45e/7ffYPkSEgbjAWIxsQs+c4FGtQP2rojJIhEQxUZOUsBgqogoKIoIQR9RBZnTFB2P0IHBNlpsKwXyqBEZDC712yZZiXB1V1bqxFMhwH6dZPJjPiESOhZo1UPErVUCCXPKu37HxOlamT0Wp4BhgoJon4KGnMhQpntdQn2dcO0LAEBOEgCFYdosbiiwqHCmgKIh6bnQR69pIhBSDTt01LOApVcZV579WIhklETOrigIjyhiKASMOVxF1AXdxcTHkPqYEiKqai/W1tfC/VCcHxHajhX8oKJch94ASgw1MxMFww6q9ahkr0Q6LlpwBQJgdLTSmRlFwpMMz0QxX0pqdZ04BEUY/sfaYBqRJNXbJVDKqJJAGIJK0AQJIStQhiLBtp/1u99vf/ubOnTuGWDCXm+3Nft/1Q785Wj1/+rSwxsp/SYiJiAC73X7f59i2AqrArByb9P63vr1cJBFYrdYhRCyFCLtu1/V9t9s2bStcABhBWdg0CsZgU8rCVVAiM2Ob7tw5o6O271ZX589K3+e+uy65SamJMYYgzCyoUQLSernqur7bd1xEStne3KhoSgkRcxlEitXLL9cbY/0FVIBCkiJQjBRTohiAEJ2AEEFBCotwQPeloFbUw+Qrj//0xB/mapWi0li5aULfABOdZFL1YtXc39/vb81W+fbb/NAThEgxRSJKMQaqGciBAlEIZNwnlksFQM+ePXn+4su333n3449/t1xv/vwf/sknHz7+zS9//fabD0vfdV335MsnyzapKGn+z/7+n3zja2+B0na7/c533js+Xt1/8ODFi4umaSzKY46miLDo9fU+NhsV7nq5vNqGQG1IIcbQNBQtDMGAggAs2u97Lrzf9zc32931dre93t7cdN1+321z7j0WiIIhAAsQAY+5NVC9DdDKF6tq3RgdUbYZ6/u+6zrP1qjegt5WLfOfPYPmpamWKnBN6IMIAgTQoEJEirWnENUfVIECKRi/kEGOo36c3DVfXD+znuo72m+3dwKMVXDVtsNRj4qiZinWYs7krTmdCqDzxoA4/Tc+5AjHGDMTjlIUZaaIxqmrpUrwio07lS0f+NsunrDu5prJgHiog7G+pqE5Tu1hcqjpLVbEAhWhfGkg/nEBEEIIASgoIY8PUcG3AN5lbtw2teWee58OTaAlXImIoGXZEBhvA1o3v0CTa2/52KieIU6oXoypEY1MVABNlx+MPM5XG1TBGCesJpKiiPbDgKCKIs5G5TAvVeAlBOPURKQAxqwnEMZYwrjxAACQRWJqUtMAYgwJagqZMKNqY/a7UcGJgmigEEO0xvXsLRbcABR1/iqtkLQLpwruaKUD0zGXxQ+AgSFWtI7n5+eXV1f3NkeIqGIhFwFEReB6coz5esQHxh2GNahiV7bWGohaTzV55pRNhOt/U9YKCFyKB22kiFF21x7UoyfETjNc8+YdoLZggI7LppbwMZTu4ppJtGTMQ4P64OwECl9fXwIAYFDhPPTD0GMMjoWqAmgpg+ri9dcf9UOGn1R8tTYrvbq83O67o9PT0DaWnLVar97/1jd/8dOfcCkpBhwhg1xK12thDrl4u3vbLRWY8/Jsub6+2u/3iqhKMbXN0YYGgHKTYsIiQy7dsJMc2+MjIkQVBTR2w83m6M233rq6ur548cLYtVJKOYuCQsZSBi5lv9+fqR4fn8aUhDA1FCguFi1t+6llMqGdCAbNRltDYLaagmdDObkejDvHA0/jBvBFsfbeiPXsSF0dg4JwRPuqJH2Fch1vMV587gePbpOFDmOKVqeeUgo0xhQxRKofoUDWF4h2u5t/9a/+1cPXfv34808pxH/2T//lZ59++umnnxGU73z32zzkzz/98Pr8qajsd7vv/dEfnJwcffH5l3/94796/1vfPD05+tUvfv2r3/766PgEkfb7/S9+8ZuLi4vdbvfzn/3q4uLq4euvv/bwbi68316nRIliiMmSsEyQUs1F0PXGCHVy5n7fd7tut73Zbq8vr84vL89vbrb9ftd3Xc6dI60oznIEU9FI1TTkAoVAVY2vaHQTbboqu8ZBVLLOfI0Tf/X8wxQMRu8TbAgEIXoNR/3rhL3RlG/8sgfqLwEQ73xr2LN4S1CAl6k2xk5EUMvKsVLjqKgUzlzbQYLLDhQXbej+tEBNFTC88RUwcJWpCrMUIX8GcyHIUQStLYbnZSxf9VKroIVqRxEgmrdmzDrOukEINAZHsXZ/MHTXPD1wsAFVgRmEaUy8qFqcnMOSAAMgUYgYEiAZjYx5wAZJYi2iEw8Z1mo3tDY2Pj9IZFV85jKZmPU5RWdksnxJOUxVr/LBVkyYC1LUUL1Nxyr8zXHaiI7UA5In/RICABttZu1UDZYRHQI2TRNiAC8iMNUPADVZuq6frTuosdyHXDjEuFqvY0pNSrOwtCJafxIPKgQg8rAiqbmh6rAPCmiwerRgGHjNpK9nprZSKDwIF38+cXpPBVIFay5JGJ6fv/j8y8ePvvGNpm1BFQE967uwWlKb9Tm21spQsY7R1quVnOjcOsJctBRCOjgqNQah1WkdE3k8t1aYrVeoqqoY7iqlADirg061dL5cqqNRgUaC0O/2Hzz5IkK5c3JUuv0ihYf37+y7HTwuqgRGzBxAVAIEQ8nLUB5//vl2d/3s6eOzs7PLq+325gYQgdAIYcqQd9vdMOSh6xdW0QTKPFycv/j800/6/a5pWhCOoFEVhHPuhbMKMWcr3AlIKaXoiWQGzWO33w99r+qGLquWoSeC05OTIe13hFpSMDyaMMQYKShoKeXLJ18CUdu2m/UGRA3pLFxYOJfSdbDdbmNMA5cY09HJaT/0YeibtkkhNJU7Ca1QBggAFm37R9/57n5789lnH5eS1XKgEEa5ZhP+6NGj119//ac/++nN7nqug71JFaB6sorMgnAwWhyHh3OEKCehb3tjzN+ZSzdECjFFMvqQkNoUY7LE+GhsyzZLVBvtIBBFQrP1SVV/+h//5uc//amqAtJ/8y/+KSIyy7/77/7NT/7jjxAwF04x2R775JMPjdhVRb/44osYo4hghOcvXoQQYoj/8Sc/aVIKIf7mN7+JMXVd9/nnn1s6SBNiDBojxARef0QxBCLEQAEJVcyyQWBEpWHo991ut72+ur58fv78+vLy4sWLq6vz7fZ63wkMmStCOR5tZqYx080Lw8CQJLGkRqBAQRhAxSL3UAlr5ypzPLy3DKxXvg5UEqKAslHBmIyrPvS4tewHGyrogVr1G3qK7HQHHWGteh+jZqrvssHfdpHHD5hzStYamGpCVBX1ON1m8oNnQtrvB4de8uzZq6Q5/OtXzZcCWup4tZnUrdBpzGMAG5ydAaZ8sPHNhiy652/OFoBIEWbF8aTY2CJgRKhfGIEixQYgSPVizSAeA/RapWoRBgpQS5esNi7EGGIAskR3BLAKWfeV1XBaLSpZRqAFRjktOEEdwBaQHUOxVvyNYn59nCZlLjIAiGi5WqaURITogLXHvlOgEIIoINbOtIhWpVcXU6wTJeJoexIrsmpsmxgThZHKT401JIYEgsakREABo8zYwOt6mSNv66JftQ1s/XnouWTHzquedh5zFVRAouvd7snFC2pTs1wIi5SCoiZQWHWkIh8HMN3YbZd5pQOoSO4H4Ow1Bk7OxMhaSsk5B0kWruNcEBcAYIWTCiQonrRtfD2ZSy5kEWPVmk03HkKsvHHBIJEYAjMPw4BBmIuKpBBiwIAKUECDSkGILOxYBxGC3lxf/eRvnu77myEPIppZ9l0/qoFSCgJur28Ky/Joo6oBEZVvLi//2T/9p48/+2Tou2g596rAEiMExJq8KaDAuRAZNGocDo6li2UzAChoYd7vexyGNsSTo6NLgG6/LSoAAQBDCE3Tbjabtm1DCIqw7bYKsFouTmNIKYjwbn/z/MWLi6srK1JFoKGUEFIR3Xb78vzy6OheB9EGY2eDQiAFRDw5Ov7z//J/c/XixT/5J//n55f7KlEV0HNgTC9uNptHjx797sMPrm4uYQZRkDf0HCNRWPNlwLMjjXzOBQnM5fX8TI0/uL9bOZ7QyvkNq0RgVSzCnCkoERWSEARACZUIamG6HeEAMOl+z9oYU3YxmDgyBqva7woUwBQwAAQKVhiJhBRD0zRt27ZtG2M0qNsWNoaY3CMPRAEpxBBDcCYsZg4hNE2KKQWMzaIloqHjvstdv99ur/t+Pwzd9c319fX19ubm6ur85uaq67alWGPgSuJTmxsq66hWC5dRRIiYusW2XZizzszX19f2jBXAmJ+gv+U1quZxgUzE3k6APFDeFnUmqA3iXtbrOgZHq/4cg7J24Yk1Baia3Qd0bKN8H1FT+/crUZW63HZDJ/iZOxKjyzcDhw9gHnUYcabw6w6fPf5IgTR+avr7K4Y1fVXf2KR0nRnzt6wdCgp4Ho8fOs7CzAyiIBZixdqnKBBFpBBSYz3fggDwPPfdc3sqDqkwFt168hCg10apm+lY2/GJBQutVazYXmBlMO+uLq8fcJhNJ7nzfZsVQCsEfcvwMbv+3r17i+XyZrudd0SF6qGCGlmOopkJtpNowkwcI6rboi4SMkD00nwnK1GgwjJir0ZW4qCKt1cinI8RZ5vjcF113NCiwIKhZngRkedE1TCEilFIDlweP/ly13cUA9fegjU7ZwK+iILUIq6xTnaOOIGpRM/K8cguQEBHlXUYhr7v20CkEgilWkUpBhK15B6BIqwsJnZDbVJLDqTXDE5wKqVQkSCvj9wcr0+PF6s2gAo1iUG3u23Og+c+Aapq33eDlBYTEQQALvnq8vxme5lL3/VZRDdHJwEheE0FqUi32wEigCCBtZwULRcXL64uL0DV0svNJqQQU9Ngt3fyNlSRwoLmtQhztSgtoI7qlYvSDQOVEqP0Q98P3b7rJA8am6ZRVchFr2/665uBpRyfHMdIinh0dLxIqT/ZlJxvtquciygeH5+IyJMnTy8vr1ar89PTO4S0u9kN3TNJK6FmDkgamHZ1ff1/+b/+E+6665vtuHGtnk+9dgJF5Be/+MWvf/1rfgW/YM0yAyWndKgBLEKwtMQynf6RYWOmEuZbyBbXQU7CAMi9ZvunNbI0GoEQEhLmfihcRM0eLypCRMwiLHboRfnW9dG7yVpcoG4wAAElQhENgarXoCUzc1HUin8HV+DumNo5rUmj1eqv3dQQiUTEnPQYY4ixbdtA4erqerfbq5NvFQRgLoBQmei4XgksEjfGzgkJ5HbC8PhoMcblcnnnzp1hGD7//HMRyTmnlJh5jjeMuvTli8wlyaQ79dbLTWEinFWI1D1VobivcqkJbWU8iet2G9zqPwK8QnPpbOg2GwQHLCLjEs/+fXClgz/duvitovTDv1UP2UvEXvWer5rS27/EsaAZJhNnGuDMRTdN59rf+PhBWXgouRgvSlUUNioEoBhis2gXS0dXVaq5o3PmFq3PpFB1O1REG1RBkCiEGGNCMO/XtAGwNWcQEZZccqkZUFg3qCqEEMSYQhCAFC2dFdySsikhBUSYk6jNjGVEEX12/uKoHI3177fmcx5eGVXszMKuwwqADESgDGoFOeJLEFPEqmlZRBVSagAxiwgoBqQQqtvrslI979mBCrdELYSCAqSAjveLSGFONdiORnHngAUSBYCiCkioBE+eP7u+viZEI19UQgjeN4O8VN5wbhRUQKcbqQr4YPeoKiEWBWarSzJeS1KWvu/7bpcTCWsAzSoWhhiyUdMyawE1rlwgpDa1TdOKqvUBUANvKaAPVOvMkCWuMUJcLO6cHrWRpPRpteqH8sUXj6+vrslMikAskr1gWgAhYLg8v/j//Mt/+fjLzxQYA92999of//BPPKYral4YajHOeLJ6B1AVvry8+OC3v0WAe/fuPXz9TcRIlgVq+AILAiChCBcBJORc+m4vUgxEME3m3r/ikEvDoIT73T4O5X4uuc+h55XioigFjolV8cX5i6cffb7ZrOjkKO/707tnx4FyyXHfQYhv3Ll7enpHRH59fnlzc3MU02azYdCrbd91nRaUxUQyYD3lEalw+dUHv0MpIUItWDaVooggg0tYI3S0P9VjqrPvYMz8pn2tI2SIURVU91Jq8QjUQoVanvSyAkYnvUFCYsOW1L3w2sKE2nZBGEVAIbIIoBKGolIKMw+1u9fk8+koQavjjmPydpW6CpOw9u3tnqfVWRYDBQjUGBXAvbP68QrBVyPGD3dVF1VY6Mz3snYA4JauTzsgTb8AS4/XOkBRNlvSLzqqKr+mIOJisbA2KiJixWmW8lqn+iu1zCThZmVmWsEuN/iN7WKaTxiRjMqNdljMYzEsK5W11FoyhsLa2aEm1FbGD39WVRjpxLXagjW4Mamol1Xeq/zNSUnrLCFm/obRq371p9VR41cUEk1WCriN6btNFWrY76UrohcWISohRaCRw6pWeGEAtTLmscIJDQhmleLteURBarBIECzZJKS2jW1rgUiqyTsgqlTTzGvkoaK1zt1cs+ZNRyACEgar9FUFMlbl6m6qceMfYAH14RAJSUms54yJkkNDyF8RRlvvNkkJ7Pf7pmnWy9Wt36tlfE0n7WAtzAmoLCC1DE9BFVjGF7A1MCBQ9oVDAGOoKFyyCCVq2saALhcZo3lSIxm2GcgTxuvEAlvOmrHlj4cBPe3GMtGMkk4wBKJws93u9/tIFnYHIDRnTgG4CBFKYRevXgsCYyM6AB1ZbHwybE3YOcsQIaYInHMe8jDsy74JCUHJ++I4pmVAnxSzrAMirlfr1XK9H3pEZGFhAcCxBRupiopbXixqfSQpabOUgBASpKUgleIMcLVzF5hnjTVtcr/bffzxx89ePCHjbU4LEyUqaqZUIHJCAwBEiDHEmABgv9++OH/GWUIIzIwxjXRodaVG2MN6ixTnchMBZTuY9jCBCAqTACkBhm8+/vIf/4v/yrRVRYx9fUVEjKAVMXjo03/PFbpAxD8tRVV+/r+91wMoC6rkgZUJ27WOuAUCASFAxECWxKCq5N6HdT84OT7e77vtdjdCHWR5APXIaQUjVa0+DtH76IQYE4XIIgqk6F6PwSHm5Y6fvfUaZbdVw42agJxHnhBCoCallSJSLIA7Ir1z5zj33ZOnTyEPNkTCg+GNqk/chzNBqUaLUc9OHYAqKATz1MDqWzzRAsZ0Jwsu6ZgiGzznZ4QrXVNYVqXtBiPqciNt/sy2xDoTZ6bYaExYRTMaRjWKdU/6tYks5Op125NQwgN3tMbs/5ZX1egKxnIn1nnMuaDJpmJ6iCqQDhd0pvZG38ZSmXD2kVHwIjqhN0ItjbD/xjwDl+1IhDFgQghUqVhmxgWOA7LfTTvNqy2mp5y9q96iIrLguK6/zclu3QBXrZlN4xPreCjAWiQaUdUYQoBqlxFiGAug1VJ8au2Wk7E7D4bnCNlTg0lfHTdJTSke87AtyErULhZN21KoTBw4e9qav+fgliVUi5d1+uRjTa0yOmjvn+zl+GIm6LgLD/yvaa1NB1MgoGCkOqCKxp05KzuOWu0L++jM7rttm2t9QU0mtNje/N7o/TsrNlVllVRtAW4TCwuj+yCAI8GYARzMwmy8HFXCqunSaehjyoIqmn8/bSHwBHBvocFmm5oMQ9sRgizAqiFGU7cOUqFnYNmyif0DScF46lHIs0imMy3q5czqZcT2zGwM4CIUaLlasXYAyCy7bhvXx1YNipa5HQIFNBbogMm2kqpSoHax7HK2c+PQAU5BaBNZZjUqgAhst8NQLmMMiXTXaVpsXn/z7c+/+PTZxQUGM3jQaJkRDEn3xUmWsgNgTIc1bQGUEEMES0YDUdAQ4mKxBETCEEODUJBQ1VIvMcZYclbLClBQBQpRoRQu1m9DEUSKsnXidLswUgBRZFUGIowln2y3OcZdu6iYihX82Bh9H5pYxKlWkgA0hBhjbPc77Lo1hb3qft/lPnMRgEIzEgxVNTTBQ/KSoSIk9vcQ4t179/e7/W73Cda0HVefLlCsXunWERyP+ZiCpwIGpfqahUDWYb5wqaH9V/sf4y+r+2ttj9vYLNNiBQiU+9z3quXunXvL5eLq+qbvM5jAgMlHGuWyqhmL6BsVSXFMTpwUht/U9riFydCFkDH0TY9qh9G2s78fJxFvliUCGNG8Oc+GF45vqXrURzH5WJP8GW39A1/WwvQuu2k8D1o5sHCkxcDRW/q9tAiHL52h0MOQc85INRxYhQ+ANbmpHuJMn82n0sWF909Hoy8UwxNIPeIIgICeK6r2LqrbYxLSiOaWBZdmUNHNGggGN5puT+PklKs6U0hNJrffjoO1L5fh4qpVbOtYd1sdhdDBGtl8BwoEAQWlyEzX2KUJkYBQ0Li9EBBoFGYuU6vsOMjIrhCSvarr4U5PTSQ3cZAWbVy0lJIoCIz+m+J8HgGq7vBSMNtGQF79LCI5l5JLxV/M9TV2adP0ASqG4SLBN4DThQOamLQGrIoioONqgiIQQJQZ8/Ct160AJ1TJB9WHqEHc6e/j78TLZsHqhgAFvZMNAoBxJtd8tBGnque6nq+a3l3tLHVudpwtxGgWjFbkOBpmrn4GVJfAM8TQKcIEo/HKKQZYrBYhVXPeFLnNA4IiFhGL+hsoAaKIqCLGPkNkjQUKNM1ovtgwETGlhKmoauGyu9qu2iUgWOGprYRlnamo1OIpRGIU78fGPEbyxof1IKGxn9pJUrq62l9eX8QYj9aL/R7b5vj0zoMX1xfeZMVZcvweBgGAkWiguhHgctR4NQWJQgw5q6JYdwS0jFYlh0BlmrCYwttvvf3s+XMuJaTGaV2JlEEAYtOEGNT0uGlLSyAHIGvMILLfd6q57wcA+ODR2//vf/CPj49Ojo9PF8t1u1gfbY5jDKkhBeaSu912t7sC4cVi2TQtAITUvPPu1+/cvZP+D/87/K//K6LQ97nrBqQQIxVE62tbZQFarQ7WPMGRXsfcGzNOlstl27bWG3vcZpPXhQQ1pUVRdJIjWHed2g7yWUICwJSa5bK9vr4uZSIGmblck6cD1fU3qLNpFovV5uT07OjkbLFY5jxcnL/Ybvc8FBY8ObnTNCvVmyoQ7FIBVGvzeBhtrjHT1IruoNoXVO1vrHkQjpubh1HV6XTqXVZb8lkVcqO4t6uKiTVb8kMhU4+yTglLRq17GOitrkFNeTP7KcQYkbAwkzmlSF7a6vq41gsduBK3Xi52xpkfD9o482gZXsxJ3Z8AHYc+PqiK2IFwFLZ2NppuIzrLwsF6cCaCLW9lXz0NryTGWrpWe/DhZL3UKVe3slygzhbH6g7Mh6tPdhjC1Vq07jp13n+zJiKTZZvOZb3CbCSe8mD4UH1IP+m3X46KgSCEW0bnLDW88hIBAIwMXTYB7hv74xkDpSqAKXVqmma5aFfL2DQUAkwl+XVNq6k3Rpfdo/X6f382Zi4l534ouVT7ys1vdJ4GmW+Y+UKbVKuGKKJDd1wZ/wGrXRzhv89rLiPMb5gHzCcveFSDY6x0gudQHSqAWkmGCCgsXjVr+8Fwi/psL7sY0w8KwnNCk+nYMjOAWqaVLZ6zd3rxsBRmDASohQsSHR8dxRDBKv0QKzLu4Wa7JiHpKHCw7rM6CwfUKiNMgWBKyzbz1fXV/XuvEZFqsTq0UVfbGbayMkIc8tD3PTMbo8ho0EP1GMz0dx0OaFV6LEgaVBJAq5ByQWECqCTbhydCVEN0MqU2JQEN0RrJJaKg7udG9TPjzVVcDNfHV3CUe7PZ/PDv/MEXX37+5OnT1Aq5WwSqnlSPSM6fLnUdwN0a2/hDLixDLhkAcow3p3fC8R06Os3tildH63sPNnfubI6Wi0UTU+Scry8vhn6vqjHGxXJ5cufs6M5JiASbIwDoB95t+1Lk+OTOfuAX1/uxSbmqWkJTrVjzl6PottVVU0rrzebZs2dd140biypL85hQDS/5jgfXrDFCrD4KETqz8aGfBDNN7ICz+i/tj8vl8s7de2f37p+cncWmGfqBhc/Pz42+WxRS06LnVQl4ZXzVrS5kbr+0PvJcOxwOiNz+nwPaOtkx4whvyWhwFN29W3c+qhvnOk9ZxHlSdYz2zaycqginvTvOOREuFwtF5N3ON5kfW5BK0lO9UgSven/1Gr0sRud/HE2E2zPnxTGemjmCGTB7v6sa+2M9hXSoA+c3n91FZ/aJvY9oLID2OXr5QSYTcDQD/ZiNR/ZVszBClrcMEQBjJkMA70Nlwqrev9oolhkxuqQWeLg9YwC39ztM96KKEFQkZSzfkhlHph3MOmvT5dC6zljvtZTaxTKmhBQUdcreQJzdD24Nr2r82cro6HirSe9qXVp2sJCFsBFrWMGubnt+sgJrIPZguu38xJnON+hVtWILCApivYgn89CZQRDNvpu8z1GcmCmhoOKdHJlFmFEJzSsWAULJwzjTaHFZgpAiEgiIWnYGju7KbI+qTzuO2VeqpHVPz6AtUVFr6w0OEXF1cBRBLA8ZEQhF+ObmemAFEVINAhQggrUvwEAkIYxk0DpuJAQFKCoIwcgtZ5NsHRwKiIAQIoYYTPlf77aFS0gRSvFcOh7TZItfRkFVcj+UPIi13QAIRDGGGQ07CFRPVhBIBYBCUAwKpBiU4s0+/+aDD6+vr4zq0pwPyze33VZLKsBBEsuyBggxIkWnqyBCa+bK4PUA1q3TGX8ArLQshhDD2dny3Xff+Jv/+BNUCbZpQUSYQtgcHYUmmIttaVpYUyBCoNVqpTedYhCpYVgMoVmGZoGpVUpFcdsPqyGvYUOppRRjWixXR1DDB4tF264TJgB2+7kfOGdQTAqBVQprcvZgsEcGQgiIKSmS1opNrxoHAMIhD20pTdOYvhQRMF62GeXC5OpVHw5rEdF0rqt7gSiIVEre7bSUYnvZ8tOrz1GzMcbUE9//hIhc8pB7JF2sF03ThhiPjo+tzaWdyhBiCNEPSbX7ES1KIVAJueb+5SjDRodv/tLb/lJ9jZUIM4lqdqFXaNhHoT4Uklp2laex2CwScdE8zB7z0Ei0+OGYbjnTTyZvmqYtzvcOqFA7osutGDARqQZlhdmN6qUmp+JQ5fuLCIVVHbZ1EeMxKh3FidZFMsmrCDXldnQiBGoEXWdfZohYC5xbWKBb5OPDEgFZ2BQriF9nuNoot5a0Pt/vsS5eXtiDd5sRUGEu8qomAoBDMH90NnwlEJBQdXKCHSP2v1j40QxSJXCyD/MYnd0MVbXGew0VrEi1qlv84FrLS3CQMKZoHaitKE5UUIMIjFiqX0vnS3PwIof+LfeWiGg6KFXnm5Wh4Oe8jt9j3MYL4ykTJpd9I+MsgR8BFBWiI0Oj6aqjMgaDmq1nwNwQmzzgGuVGv7thm67eBRxEcSXt9i0YQ9nQ9VA9V0S7FccUIKA39asE89OtpyEgHkJGBzaLTzILM6AH4MZrCCIAMkIkCCkRK4XAKl3XKSWwpHEBUA8NWqFnNSd1MmUQRJVVGHRE3G+5v1YNIsH6S3t15m636/s+xKAFxhKrItZgklW1pg1oKVktem7AbwyjZDfxxcw5CxcWw8PBI0N+aBRy4fPL633XCdDo24mM3PWqVdyYRqlgFDZNG1NjtafGnyHMWPEMG94oKSaEBwAQHj26v1y1BtmJ03gpES2WixBwdkck6zYNGmK6d+f+Nve7rmOtpFFETbNuF5sYWqKkAF0/7Pbduh+aRSOqBNjEhsALUhVC3ykWJICogACL5apZrAqXfdff7PquH4I635h5iM7BQ6pkCMMsdonKoM+eP7+8vLJSFuv2AZ6NHCZLNDhDzSgEJ/dj5nGM21iUOZdh6GU6inVbA+mkBvzTliNRA0sw5L5Iuf/g/nK5Oj8/391cV1dnrH6p5fXGhjceGCQgRUBCEhGy2NghIoz1FI9PoRWT1/kpm1bbn3HUivbJiuiAuf2unaqYqvwx5gxaozdBF6i2t+b9Kg5uOw5ttA5DsEtP6tMbI3oOl+EBczNojknMJn/2s46QgKdcuCJ0plKdxqXu39qGxno4Zs6TXdwI/AUqI8Q4nRUJmIGIWrG6ekqn1JyqbxWpEtge7KDbr2m0dfbq1I+rOs7wCG9qXVJHTBDHtXoJsZnv9sPboheb+DO6SQue9mOR+en94NxSNb9Yxhx8mwMQQdHgym90GseP+x1jTCEEpMqRoqjWaM6n2BS8VDrCcRGnO9VC5EmY25G2/QaKJJhqjfvh09v/fNVtzSqvE4wG8a0JjCO+4sPAg9PhXF3m3dvnLUMNACwDZcydr/aci5X5HvO1VxFR1KJlAQEUYgx2HFWFpRTlmKKxdFpvWtfoNe44uw24wVTnbsrLQFBQ9rIeRpoGYQfWZ1wEQAORxYBFlVnaNoUQDMAyCgiAOdBfC7YtJuQHxB1hxKpabJLUOLGEWai4HmIRBWDmPmfTo6PqAhyXwXIeEECZi5UkuUA044UFEFAUg4mPADC23iWT2hgIEGtrtlC9VqysmNPhFu9v6MaorxxCbFKMySncvC6RRjhCnXzN0xuF2a5LIE2Ahw/O1qvV5dW1CXdREWYAiCHGZHYrqipRiJFyKUMplzc3l4+fhZuBs7LCqDVCakNahtSG0FAkDFi0ZMmAigqFixarT6UAwEVBiZRigKCKAGd37n/arEu3WywXWePldtBaKYK1fQ0Q5JylVi3PUSkR6bouUwZLEqzpP8wyP3YEqIo1NDmKrgPo0E9xPQfeX8sWHkL903Sax/1cM2oQAEWkcM7D0O33OfftYtF33Xa3nXt7L79ctLsMQOdPRvCcgwM/3lW33X0MdoCa2zrj15wMi5egW62oFox+KQKAJxmhi3MFYPCW6uaPoGowZXTg6bpEG/1IlRGjds9zpC6300cU9vtulrVuYkQcvflPdgYP1bOFD0YYA7TSZUC1CQDAZH14Oc1LoTrxVbL/3lHY0aq4tlr2E46rWDeSV2VWcQsVhXTyI6rBztm9EIHMqq4C3kSBjx5GCLnqYPVooJc2zOcPp/9bZKfmLKjBAEqARFL9OQWwlm1av2zAgCighOIgUD0kKjo6TqgqnIElQCAYCbDM4zETCBE0ALYpUYow7gf15XGHFwkAWYSYg5ijqpMCBJfxVL0/AGCviaWA0YzghmgRm4jBai8qU6ELaJ8tMyncTqwF8i+ZLooQoZbCTNbnDAABi6TeDg6hmw9VABEAixrRtXrWgF0ItYIuqk7KL5ZjhGRM+9Ab5WJh5hijIysMaNUpI83jiB2N2AGioDJIARFUQVESIB8zIjJLpKCWKgmWcuh+6uS+ISgCM+dhWK0pICk7QuvsOURqrKoGuVdgb9T39ct2qYmxakqCMgsRcxEZsjogzzlnWxoBy3vy2oPpXKL5osJcJmzCtHgpIlrTsxHQc7YAQAkEvDDZbLwiRZEUo0A0IYo4Gs4KAKJiUaWqDcjc+jZFj3aPLyu/UQWtfN8K1T/w01qGLJlXTXPUtld8HkDMtimSVbnvO6R2ppl8m4jqzc3Nxx99cr8NbQLCABQAoGna4+M7bbtITdM0C0srHkq/73ZSjhbHq67rc59DUIVgno7bQBZ1AGjbJYVmsaA3333n4urm/OonzHZmACyUa2mAs1AuAKmYcBAQ1eBYgsfRkAx6mbaPnSwvTTLEkFAki6AwhsBgTDl2zo222GoOPR2sriSNenp8mWgMIYTYjORiOZeLi6u//uu/CTENXX9zdZWH3o4t1RZrLlJHFuWqhrUq2nrSYQYkqlW3IADRYQImQg3HvSqGPButkbhPaPwkSgBrggve+pBoFVcgXkdyS1Dh6Oxgzd2bpIGTk6BLWEVVGIYsUuoZrNNcpwErSF7fANM6vmROjO9xyqn6QVBDNUdnabQb3F4YpYwtMVcEGmttDdWvr3rV+VMFq9QM5hdOhl69OXhh6gFOeVDscvgs46xOkms22XMXU4VvJVLVMIXFSFy5kIPJWAV1nc5K8SyHFB+jufnSlrepHVVi1UcWTOQZFlsNGvSkUQoYIsW2ac1zKCxB6vz5hIhzEFtnunoVmLzP6TfjM4wIkJ2NGMLxcr2MqQwFEc21YBGxziGIRKiiBAzoEqaaQWqeZk1b9eM9S8KqPiciEJF68y9D5G9NUq0DHodc3W4Y4a/Dk6o60QIgIHPp+x4AmtTgGHgWoGhr5pvJK0D1wHyoy1JhojERotrmNuwYI9QwGoAbXkBSrzHOvL9fCscQYgilClZrsDMeyrrBHJFAL1+ZsQniwVNrlXclc9f1wTMjkVULF4+kqqtDrK7ObKNPCNP8lyY+avKtuUpIhEgEIDTFqRzHYQC2FFgdj9XoopgBqPNT4Pwevm9Ua167EUk6JiHVWKsmtAgy883N9pc//6wMnfZDYLXEWysKEZaLi8t1Xj54cN+Cxzz6zYFSSk3TWL1cwjZSBICUmsVyEWKkGGMKi2XbNCkP3Xa7vdpen52dhhCu++smphATBQwQyAVAfSAMRE3XDZ9//nTf58JAoqXwWKOCo1Y92OHThreippxLzpld4k7rNErtad2xOomjY/HSC+uJAMu68LWYTMzxyvZGotC2bWHuczZT9ebmpssDUgQAKYUrYFu1+K1bTTvHPIvxnURo6Zno5TqUQkwpikjX7dEzNG49xm1pMOpZezMq3jr7dSg48tcf/sVUNc2MhJc/jONJeFlT2sidkar6qV+NCpgOvKVUbl9zNDunj43HxIkcDj47vmf8YVTwcztkYkXA2edvwbpfgYq/+mF+rzutzl/sAnl0XdCnuT7Q7GqjiEMFj3/hQSjbxjUzzKt9Pnq09ZHGfW6NbOxf4JJu9FoO9xaCwlRTOm4VRaDwlZPh0VY0AudIgYrIkAcqWUS8Tr3KUp3jKa+aUNfNiIogKiUXqayTgBoCtikFIAEPvnrSO4ISRKIQCRQZVBBjCJbv4kY7CFei+JEfNI4ydJ6LgWgoPfnuUR3L4VWFWSwbxadEzZD1yKKNnwjLYZ5fhQwU0GprBLxdq9uzqmL8scIi3lSoOh7BwwmiavQCFUqGlwy0iUTXXJm68hVax9nxqhFVVSilBIUIxFqxA0Ixss/KOkKThKh50JXF1LxXkXH5xukUFt3tbtrQKCiFoIiF2awh9LAKgAJpbcrhehLGMzw6NyGE1WqlNY2AWbxfoZIiASKFCIgIpO62V23kcFQ9jOCl5sosNcvUp0wViUCRVTCBoW1zwahW9Th6SGYjqSJik9Jr90+efNZJNwQGEBQBUTBjruv6lJIAUEAFESnofUkwxrjZrGV3PfSlbVMbF7aFKARRtRBsiHG1XDYnGxEpuTw/f2Ed3W9u9iklouMUg1FUVm8EFGhzfJoWi8vrmyfPXnT9sNis8pC5FKi7mhS0sHWk8VJAi9ooAWCT0mq12e13pZTZVpqcG6h+6q1zPJ7ncWOOmYpEGNEqzKZNC4eO3eGOhrZtte8xZ2ZGLpAzhIABo8eiwEKetR66ytcpQARV5FnrCLACe1Xv3m0ur4o2TXN6fFyYj45y3/fb7c101up4bg9wgscU6lbBScVN4n2y2F96QqrS/5WvuWMHkxmqAFBKCVPnNaheoe3rV87nq68/NyNefoM48iTGAzZqDsQA+rKf+YrXqHNe+r3WnDUhPbh3fepXP0K1R17x59lEKjgxivOuQN1pVcbATOKMVzw0Rm85ASZfJkXruVQjSGk/emoSzI+AeiL4TG3fejp1s3RaBXMb9aWaroMByYjOqPEOYkABKVJYS7RKn1uP9arLuHXih8ZTMbkUG4eoGkFrKRmUpDAmnOlNBVTLwg6IQcVyjQHRz3kNLVtU2FSHIkaDfYyjo55YAIAYa5x5UgduPrMFNWdZD+qWGKoiq5NzEQJDRaHdIwPVKVu/moNqEjxzWYQQxkik7R7xgp+qfx0TCY68G7X2rUQS89LEN33dj9OSzzbKJClFUCAoBERQRw9ETYEoKqGKGQtSAxO+4GO94K0YyaiboPR5J0OPhM1ioRSGUizxxLqXOB41ogcA1v0LiFgskbxGygwYICICESAi9OZJWMkHHdmqjhUCehtmWyRE8Pi5oGYp/WAJYvU8er2ooZ2ACGTZ0zKG9lEUBDgXNOtv7JSsioQnJ2vId2OwyHQtN63lm2brGA+XiISASEgKCXDZLspuC0qLdrNabQAAgES022+HPFxeXnz5GEBLIGzaZpHao836tdcexBCfPXnGzCml5XK9OT5qlouQ0oO+2wAQ0dHJ8f3Fg+bZi08+/3IovDBjkbnkTAAJMQACGy0XqTKMggW8CIAQA1KkUJP6J6BlXPrRtAEY/6Wg6uzt46+oxoQAPEow27evVAAIgGYklAJO9oQhhCam2LZN0waEYXtTyHB0qgOwCx4cCqJASDGElFKMcbFYxJRUyazMPPSl5IB0fHzCwqB8fnG+33dBQC2ER7NMi1tCy//hVkk1T8wbBfdx4bbfOQ5s5t++yv191Uf83gAWzfHD7rKq6pi5319lO5ircgupms38XG3PflaWKaPN5vYlQi0EuK0jqk5CdIV0Swvo9OVmY43yGlog9otXk4fIS9Nle7Hahxal9i93gV7+gAH0CGNV4O9bAy8no8p0PsVNap7qqIadv5EOkPCq+PUlvX8wKyZy/dIECkb/a9dWp9QYnziIklj5NbBKSKFpGq9oNV/Fc7Bu6XUvFQJztWtWto3Ls7SpJot5XEXLMOy2u5YiilWMTHBPjVNVf7w2XQJwL9FRx5r7Zao7olKdDtPNU/7hNNyZSWLOYbWLX7b8teSa8n24QXX0u1QhIEbEQEYFi2NOU7XTx72C9WHIk7MYCbTG2PlWKch8zA4bVMDdcGaLvVUfV3Es4/HoNJmEA+uCR6IKXp3iC+hepaJ3p6lzoHUTCwhAUVSMNedTdOi7nLVIDikqAoOESFafU9MRquSHCivZ846SXMz4KldXV1WT2QpobGKgCJBUdGKZwOkkmRWC7vlaC1VWUBbu+36ZYrX9zF7wEyu1g5vNrXjDGbEArTm1VcgBGl0fQGE1WQMiolwTAjxzOIRAlohhSfEUQJhEWsFFTOn0bkTYnGyW55/7yBWGYbi8vNjuthcvXuxurkDFXOGHrz14//1vvvHGG6WUDz/88IvHj7t+WG3WD9989Prrb2z2uw1AiGG93gDA5dXl1fWlSFF0CMh1iSJhiKmxEg8vFUC2ZVXArutyzgBQnEDbhmXlht7k7mUBMnmdWHfeKC0A1SlKxy2l6FGE294AAFj6DVsFYqD10dHm6GS53MS2xdSkmFD58tnT6XTCLTmrAEhEMaZFs2hSE2OseCEtmmVKCzPhSh5KyYiQmgbzMAzc9xmBYkhICMqFC/r13a+aP9X4E1deH62xlcqFQd7JbXQv/DEtHKAAivSqpK7Z0UZERRWs0TFr5NxbDuI0htm6KFkp5pijfltrTnf5KnfZlmlsnaOi1YLHUWbVqX5pSv72F6KSKIgLbKrq1o13FfUaTaiMu/NxIkAAYa9hqkptPJcwmYOvvPesxMxKh2Z5KIA1aIBIatxVOCmnus0d2gT/C0plLgLrj65sxTf+RvUDqDDBpXX6qmMDhBAAQ7UIEJz1QlVFC2s1bWuhsXFaerpGCNaVa8w1m4FV5oarijVi88M5FbCa+PUpNAubjDp0BAxhGAaKEJEQGiLyQCmgpXdaVqGwiFFI4IEhPLmd9ecIwv4bk9PVzZp/V1BwTBwq07hTS5K3IcUKrlMRYYDaRWE6UFi1o6UQsUhhVmBb8ECULGpLqKqFZZHMZ6Jb3rbtLvWclIkfatw07gGrMUEHW2nz83MeShlIFUCQoAjbOEspeRh0olZQRAyBAICFFQAIrZkEjGuDompEZ0SeqUXVjHQR7VWBgMwcLHfJ3FVSr3hWFvXUE7f6ZjtS3L0GQqyzrn3f+8N6+Fiwx0AppqUDTdWYnVlmIswSMJI9V1AVUIYKZQMQ6IyVDByxmEUlSKR4BUU1kyvgCQBYm4eBkFqfSl+v0U5AbNs2poSI1g3RtryqWl/CdtGenpxqKWmRmnZp1w0BF23bNYlL8/qjR218c7VYPnv+7PGXX56fX+733Xp9fHpy73q7v7i66fqLYchQjSeoiP2LFy9+98EHuWQk8IyGmYsz92IPkkxBFYBVylBmErwiMyOh7CsEt6rTvapaXq5xwTucr8y5cKnMKv+JMKkCQNO0m83Ryclp0y5D02ogRAIuVBtw4aGLZWchpcaaujSpTSG5G606DLlpynod88CiGilixFzyfr8vJXMpCBRjQ0SAaqsvUIkIDp/WtB6ogUQ+g7btzJGwurPxQW69Zh7wq5780He5NV2llALMIuFV8eXqNRGgEoVx/95WloeTBjN5Yn9ES4txleGiUtEzOHBGJkpGBO6MGa94+SfdDMFaSeC3OoRSJhQaD65Grpy0OgCobuuT2SbmypnPTSGomX1YORd1tpH/ttfBPPj/CBUPCGxq/vT0T5sUPjBQbi3NOJ3Ty9esuskwz4mvbzDtazoUwJidvFW9iLAqYSRLJgIy19ml6njq6whqUo87VIgTMRfWIfhuECVSq2QpClmAYrIYmQYcUx2Zix3ywkXBOQdktCuqxVOPjBJQrOMZjTmtlt6UZz9t+nHsqqoTF44iukFR9f1cks0/aopEREsplYigemaoIQYkKkPmUhBv70WdX2iW8XRL+46W37iuo3EswsxFlZEZfAmRiFg0l6IigUhVmHkyJvXWYXVTmsBZ10gJFQJSqGTKqjA5fERSyyLNhRWVEMPZndPPnj/JBgk4MQWMZefjovhDkafbVd9X3TcWEFUULKqlYOFSaWTnL/efNSASUKDFYjGRgFpOupJR3SCCByMqXDrZ/tO6mr0w8UChF8RDLlmULT16OjB12LXvrIM/thURiVUKSVwmQAIm5rFqXgHg9OT49HgjzIu2vXt6tlltnj5/9pOf/STnfHp67+zszqJdfuc7dOfOvb7vj0+P7t6/uz7abNZrAGiapjTNF18+/vizTymG0CR1yTdlMqsqTEiazlwwAgBEAqpolT2O2Fl0z15QXxmbmgTKwfZXYwlRnbKg/1PkoDoXNFnDXbKSU5t8MLDL/QtQPzaIgIjL5Wq9XqtqyVaVnVW1aZomRnB5gaqacxYphTmXnEvHpSBACHHURwX8pNwK9/jo1G+OQAHjYrE4PT09PjpaLle73fbi4uLq6rqUjIiixn09zpjAocp5eRqhDgIP6E3q51VfnutXvZA0GNbnoZnRu/hqA2gUJrObqPMa1YSjSX7CNEgHaGdq86WHqh6kjoGI+SnD0ZGA2Xa153DTHq3mEAORAOEIR9X6aJwu4mfdir8Ugplq4+mswGtVdgCmYm2bQt1L9Xkn3al1/rBuxVfMYTVnD39dVemrl80f95aDbN8Rav6KCrnzWj15V2KEgXRSBKozMO/AIFAXtepFMqSIQkoIGAKGQGE6oaoqwqSiimz0DmQcIlSDD1VZW8QQkUHGpiOIiOLtSifBDpWK0tStMQJa14sxtqqjqHV/2gxtr9My6Uv+K7W2ryaxXp5eW/Y6UgDRYNF6BVAQ0Ridb0hEZkp/OoQH9Qd1iseeR/YXIgrkxLDzk1ndaMUikrMUGcsAkLDkLKVEIGCWwuMA/NTZJ+tqEOKibZfNYr1cddhzKaiiyiMHQqjkHQFJzH8Vz/oBBGZOqTEqJlWw1s8vi5BqyqIJ3FH66HgqApKishaRXPKY+en7Ch1UscMUkEC1ifHNN17vur4MOSFBKaFplm0TquIJqqFwEAgUjPW67wdm9hwfRAhk3RnBcDGF4O2Klbl0+wyFVUSYLXSNAEQYQ/Qp8cxtNVIkUe1y/uLpl93F1Run907alWE5YDuCc1y0d+8+WC3abrcngJyH1WL55utv9X334O6DVbtOKd27cxdEn5+/IKRAYdUuAwUAIMJc8gcfffTl86evPXwIcAAeWtDBGhQrqqeKK5iRUzcZhUBm8AjzzImxI2Mzq68UPQeiw+Al+59M+3lcJrNCX/1x2zwjOSWoqnIubP6WqnfQAU+UcCMCMca4Wq4CxVyGwqzCYyJIm9JisbCGEDkPu92uFKOzLP3QAYj5i1gdffJ+GPKSt2qyCa19FFF88ODhd7/73fe/+f69u3dXq9WQy7Onzz7//PNPP/nk88dfvHjxbMidsFMbigfHDGI7MH/GU/7Kf046qaYZAEwaZf5+ImssejtiOvqacw/0q15V9oz/Hn+e326yGg88gcOXS0epnQtFanU+uiabFaPPLQD/jWGOYUa0VtV8VfkUDIQNt42VaQQ4PcLsoQ5uNL3ZdlNNUsGaWjLm7n/Vvv2KaRwnzSTvNIGmg2a42mwUMCK302Eab6yATCgAETBiICKiqOpM+iqoAIECJZg1oQcUML6X5Wq1gLFbPKgCBAJUCmATOplYnmYsAF4UjzVHTNHaB1MIAVSooIgCc52k8clVEIz4XwEYNY6xPXHKJ2+QjmqMVNaV3WLRM2dZRYQrCKLOgF41tM2X4NxUBP8REESDAg/DIsXNcvVUq0cgrulNUAfCEIKpHiAkaz1XzwvWPac6GQr2kLaE40NrNV/NSSUBFEVRzaz2mCoawiBFWZMSSlFWMOPSiDzqyQbEmCLFePfs7r17907WR0dHxyAy9N3zF09RZchDIFJFZqVAJvEXTRtiA0VStIaH2PX9zW6nMDb8Qt/nMskXsLpMAMtHd5I20H3fqWogQtUQKCCxNZ6rHZEsBxxphMh8FmwvgWoTSIlYuCE8Xi1Oj1fffv+91SJeXp5vd9sgSizEquZkKeRhEBEjPjPrWgmVEOxMokWTc8JIwMSqrKQQFElIsmDA4K0QUQrjLGlOAZDo2bOnv/vlL1988fgPvv7et772tdPj42HYA4CKlpx325vVYnH/7tnDBw8IsO/6588uUowxrI82G1MLbdPcv3//9M6ZKMcUsJ5hRXxxcf6zX/68G/oiHHH2J9vBYG2zTOYJglIt1bRTTwQhBFVl5brTFMDa0tX8NyUYUZZDo10ro4+oaI2bTv6OQw7/CSg0AhFx4W6/Q6KYOqAIRkFijYotjlw5GACAkNp2QRRVASEQxqIlpRRDKCy5SNu2pfDFxUXOZegHcCc7pJSGoS+lQPXnACClBhH6vivlgO7YEgrMGFsu1m+98eb3/vB777///tnpWcllf70FoLvHd++e3PvB9/746fNn/+7f/+Uvf/Xzq+urqmvHA+3Q4986G/O/ugulkzU+19D2g5NfTo47WVrPyM4FpoNfrSy1fkRMOk1vRNVpPaVWuspo4dl3UxeOFoeZ+BzRlwNXomLSLz3F4Qw4IZWxxFb3y99v5GBIAWt7KCLEQII48rypCYaZPTF7XveFp4eotuGkPeffRkByPkIAqAoSCUFQAAnFLzd5VqAwN15HuwWdF1rNcgILwWpl8ZxN2HTikJAAEoUmNggoAsAiLIqwXCy/8Y1vnBwfqbCInF+c7/Z7FQ0hnJyd3Dk5XQDy9dWLi3OPHmDNsLMkWQRT1zC5eX6oQQXUuIEtnSSmFIl56G1kI/jg0rsSz9Twk0ocd8Q4fdUBrWXg6gm3plfHd1rMG2Z1ZhWk+WqDsioFBBQuXAoBoGtPz6d1PU3jliITmhZpRiKYEynUQ3wLq6nJS4CzGle07sqAoEiKwpL7oUmtgAKhAAhzICK2CD+GEBWth7EiIRAtF6u79+++8/a77779zr17d5sYQ6DYIKEKDy9eXHz4wUcff/zR8ydfXnXbtm3u3bt79/T0/Xe/drrZ8H64uz5tm6ZZrvrMUxscAiSxCP/cHhwli6qmGKVwIOLCz589ExZEiETHm6NF25TCAgEhWJYAiogW1Wi0saIaIpHpThAQRuHNoklaHj147e7pydnJyZ//5/9gv9t98uknP/7xjz/6+LNlajkXVYwUUFVYUBRCtYzcBbStOZ5YIYAg0CgGoFXRRoAEApAZdiIyDL2CeVWkooECghLRk88//+DXv7o5v7h4+sVPfvr/e/Tg4f0jjwELS8/97uZme33TUGibBVHs+64MuWkaKWyGuQIEJIoRqYGAavFKgMvrq//6X/03f/0fftws2tM7p0fpuMLnzv2i1ZZUUEWZk6zMZR8RKcn4TlIV4Zq18uoM1dsb3/peIECN8xsY5hoEPSgIUI+tK1IzUQg8K05uLi/3XbdYrUNsQ4wBgwrnIUPFhqxtLRHGGBftwuq5YwhN04qwirTNAhCHgXe7/dB3pQyqSkgxpUXTxJRKSfs95Zxtfpg5pRAipbRsmphS7Lo+5+w5mK4D43Kx+to77/7hd/7g0f2Hy5Agsw4lCKmgKlMITZP+8A++99Zbb/+Lf/nP/s1f/EWfB1A1TuPRvvVLTvY6aBVhaGX3E+U9qD+yzAND08nxpGtVBQzo6RVkMXfzZ6ejhtXgetVrVBejkJw8kQO9g/7NwjtSk2XmymnMO5riY/WP1X54tfFRFfroEPtIZp7x2EjYFDiO768gOwKAqBrQ4JahN1wdB39rDskY21AsB94reicVgLdnwK0CBXPjcLyzC+Ma1oJRFZsJPeHqSAHN47PIClmWEY4Q3dix24+OapgmQpEoxDguJTOnNr311ltvvPHo9ORYhFnk8uLqw48+/uzzTwH0m++///abb8K+e/rRR5effSbBWoSLz6ajy2rbUhkJDnSlCYrRrB8N1mkdVRTJ8qXMYB+T1hCA4LAbkk7A/oFdU2Oi4OfDXG9RZjF9pgoqaHj3XCTNlAmoujIDta5wOPSDHmprrPm0XAQQKkUA4lhV5qPCGv8I1uhCBREIlECBC0PrDyEsKuJsvQjeZh3A0n8KF20XKopGUMWFAobiWFiMERBLKQwQYjhab956863vfOv9t15/6+hovVhQShADUIAQgXDx5hvH33rv0dPH3/rNrz/4m7/5mwcP7r7z5hvvf+O9h3fvS9cN2x31Wgq+8+7Xrp48RyHCMNZYuk6rsyEiNosmHJftYs/7iPTNr3/j0YMH2+vtdnvTDV2kEALmUvaZmc1ilCZFAIoIMRAhRKLQtLsQHbdhkaF/7dHD9eLBowevHW1Wi9QulsuU0g9/8P1/9I/+i5/99Fe//s0Hv/zth1xyQFARQi0gFIiiBbk9xYkqbOMYA7P2+dlvP9/0vL7Jq6yiTFhb9BH2/QCIIkoYOLMw89AP/fD4k0/zfkco2/3N1c2L88vz51972y7LpZBC3/XXV9dNTCq43/XPn73gws26qTY+qZRu34kyhYBEGGClCgD//J//8//7s5uPP/v06PgYQ3j73XdPH/S2G2fyEQwnP3wZnGPMFRpjDEjdvrN1UScf01fCZdOWd/fptlA17MYE3OR9TJ/DSWwBWkMQE9QhhKZpYtNEQkQBZgYRZuGC1R61oB0Rrdebtl0Yym4wrTGjNqlZrY4A+uvrq/1+V0rfNE1qFzEGBS0li0jbtovFouu6UkopuRRWgJTiarVaLJbb7c3YSNGs800If3Dn7L27d+/2/enFixPQ1DSqCgwlq4qEtglDl0r++p3Tf/zDP/3008e//PUvFYQwiLo3OqV84Gwu6o+Ehi3WznNVPbvlTWS6u0KTiqhIqN7Rb6Y0K3RmO6cu1e3X6MKO36VmxDgnr3eS9cuZx0DWLXhUk6BTuvBcr036ey4g648603DVpwFzRcByG1W1dpat9I0OCIMndpmOMrGpYybkeH2cnN2DzTpzvu2O/sHq1ohRHd5yeG9N3QwEIkTCKGCxG7Xxj0Y8TvFyz0/xEdi5NsOANBCkSrxcp0qUangUIBimIQzMXAaQUsldVZlTCA/fefvBw3uBCBAohqOzM6GQRQDl7a+9e/fsznB1ef18TU0DMYhlfUBt33nQkMpzMR20NHpOj7i7Wa+qwuzHWMf6ZTS0xNC2yZKCl9sRzuEllz8GEQu4+RZASQVLZmXxBECZJg4qmjSuE067z/42xj/4lrds2tbsbgSHoGOIBJgq4ysginFIIRIFLqJO9wCgKqx5yLBSGFmUva7auKMnAM1yhchok5VLGUQ4xZAGtE1neB0ShRjO7tx57733vv7u1x7df7BZLlODyyU0EZhBiiJiChAR2lXavPPgtXtnb7z+8Pz88s7p6SItlIGlICaK0sZ47/6D7fMrUAIKhhdMTQk9ComiQjpmXigBAkubmv/Z/+TPHz7YcJY+c5YyZO72w77v+8KPvzj/0Y9/TgBnxycpxkAaYggUTk5OEPTi2VNSSUh3Nkd3jjav3buzWS0XDUVQBCl5oIBt295b3/mTP/nhycnZbj988uUT4Uxeti01qx/Nu4pxDHO75YACWvjjX32Qnl3p1T4xZLPVqHa858IlKEu3213s9peXz/e73c3VxZPHnypnQmFSxNCXYdt7813mMmQGgKZpIsVu1794frHf7dfr9Wa9aZvWurMI63a767p9alO7aGObzAP+q7/69x/iUlSvr68++YRDjHdfe4CpoTEkMzN6pr2vVn1JgKDCSrRYLJxoCcCJPxGIpksc6lisdux0BGY7/GDHq84huFvvRKgtsBaLJqXm6Ojo7p2766MjJBLBvh/OLy4vLi/VvMDZZZqmXS5XiKhStFiOf85lYJFhyKlpKKhKpgBHy/Vms2nbJQCVUoZhQMSUEgAUjzJjzkMeBlWJMcQY23a53e4BRq5H+LNh/3/6zY/DB/+REGsKutu5rm5M4CAg0ts/+Hs//sGffvTJJ/tu546fIs74dA5TRxzZmqVf4YGQAlcQZjQTuO+sFtenKVYlIn4775uHL1/q9yxExdVsBIfT7YrLdKG1xgEwVnxLxkXDRwEMgTRzG0fJqiCCDrMK1oPvyIABj0ijEDUkVlUrVFiTC8CZQGfW32gfG0yIVoQJI+CAo78197vqxHq+jitLH44oko5rNHfKjS4gqPfnnbodmKhGt5FMBwTLchSFAB54mEUQoEIUJoqd4K+q/hE2qPRpaEIcVKUMkjN5yIEIdblc3L13tlimnAsShAY2Tfvgtftdv18uF3fv30UABhAiIYJRhYmpvMMhzaZ2HBA5rZWVIXEppQzZeyiQYV5AREAgMjnH9nDyCgV8uPfsndaSdvytwd851yYDKk4XXvfLuElvHZRR7GGtmnoZcVEAEen7DgBDcDpPRLAE2oqNz1feWzPCFJtmq2xRFUCqqXxgR/uWy01IWHnVUTVYcogKWhA+hNVqdf/+/ffef/+dt97arDcAosAxhRBABLp9EWbglBYEwQ/i0VF6//23nz7b7W62yiqiA4uyoEIT43K1srOvtVTFGbB8+Pa/GpdURYRgZoISFIQcFiG2DfQKqTFhAgLw8fHJz372277rOx6QcL1sETSm8PD111IIH/3ml6Ry9/T0+3/w3W9/873NahGJYq3nUxEpmWMgDk1Ib7/11h//YJd/9KNsZZQqANYxXK14NcRINayLVZIRYorx/hsPe6Dhk48YAANS8KJ9JBRRc3qfPXv2+ScfX128GPpOZRAeBKzrpYfNrq6vbXlYZRiG3A+ExKUA4PmzC2HOech5SCk1KbVtm3Pu9vvn58+btllvNsvN0vRlP/TSNhCwSL7ZXn/66cfLo/Xr774bA5It+kvMEnMfTBWYRZHbtpXiWejmeZnIUFUdc2JvS/JbpmX98ivfAn48mbH+Sc03iDGulsu7d+6cnJwsl8vlcnlyfLI+2hBZiSS99uDBr37zm6vLC63trcwoapoGLDbMwsxaSskDcynC19fXTduuNxskXa8Xd+7caZu2FOn7wdo9pZRSavJQVBAUY0iEtNtzHngI2RzRKkMAAAOFsxDuX3ZDiLt2YbKevNLY/DBP38NuH66ebYb+T//07/7oP/zoV7/+BcAoTb0NxMsK0UTEfMZwnh5cvYRbXuPsdy4nKhhTJ/z3vkbpMnpsZt67QqobdbZZXv657oIalUdEAY+Djs6fw+H+jCIK4bZM1ukN46/cMUXCMEe4X34qNGbH6hI5SdLMjzaTxVJ2wniPaeLQKmb1cJJn179ty07v8X85v5/ZSM7ugyosiIwUyNXHKywh+6B7+Orh/OnW033QmjkKcOGh73eSNxQjEFCgpkmLNiFCKRkCoErTtEfHy0ePXjs9Pdps2r7LhUvhopVwECtoXg01O9/mtqtR1xJQtLykSn6kgMyCmq2vgQIgCNZ6SCRg6wEjzq7iHvC4z6qhNH9+ByrUgFw1JnSbVCil5JxLzhZbMnNJQYTZPjXaqwCO0RMigAoLRAsocA2+WBokBiJV5VKYCwUIofaRqG57Xd266mBVM1KkhEDGGSdShAs42WGrWNmiWHUEsGarKJmzCquoTapNt0IM8d79e2+//fa3vvn+G2+82aYmgKJCSqFtIBCUXoduIASmKeXJzmhMdHKylJJ31zdcuGgGVKUghKltLC1YCVlZTXdpDW+rVQIBsIAoiCJhm9o9hrzvn3/+AnZ67+5xxzyQnt1vRIFECTAMSoMEIpK874cURIAxUWqb1aINCE1MX3vnna+9/fZqsWhijASEaJF1BRIhLkpoCeD85uuPbrbf+uUHv0UVFS7CpFxUWj+XqKqhOsWqrJxVRQlfe+/N5u1HP/nwN/3zjkjZzo05czEQ0ZMnT54/e3Z1eTX0vQrbdtTqddqRu76+hip5GbTkQS4v9t0eQbfX24vzi91u17btm2++/bV3371z5+56vQohNCnlfshtjkMQL4kWCGbFKAtfXl38+te/vBn6k7M71vbD6ygAEQgEATwqaewDI6JTSpGJ2tDNv1fKo5ngqbWhZpXp2Gq8bryxTffhiRNhIoohLNr29PT06Ojo+Ph40bZIJMzdvkPQ8/OL3W5/cnr2ta9//Tvf/Ob+5ubpk6dGTWApe6paSkFQ4eJso+R0CMxlu9237RKBQGHoh77rc+ZSCrMiYoqxSW2gphQZ+p5LJoptXOTSSwGViRxKFQhxs1rfaSM8/fhXD974f/29//Hd07PX7r92dHS8aBcxJMS4Ojo6OrmDTdv+t//84f/xfx9jfOftr733jfd+85tfsUwxCrO0J/fsv89rgvTMLQRPoEE/XQG86QhjvcfLev2VF64ONY24LgF6a1qcSzgAj4sdjqe+YVRfY44VIlJla4Rqjc9VXdVnvjUmjT9LgxuxHPBOfoKHJUOIZIX3qALCit7c3R9cFSaItd7UYr2iICqHlNoTPO5z6LYI1vxt9fSbGapkwCyhqV5/KLeByLEE88HVASE92AGGoAm4e3qw5D5zlZNMVfu+391cHx8fN3EdkWKg5XLRLqKAKBQzBpDSahUUl8dHbQjAhUIMtiCGoagqegb0uF/A8lxJKKAPhQAjhrFaxuBlAG9Ta3ZHgBBDjCEwAmJxZohZAsLv94BhdLvNHKh+mmnJ4qrectXM6TcOQ1HvFThNpIzxGFsgZRFmGqUsAyqklCiQcFHVmKhp3NWCEaUYN70Jr/pNx5DGWL5cp84tBhEEa6pYwyHeWR0VNJecS2aVEMjaXaloSu3Xv/b173z72w8fPFikFBBDoCbFJlEMAAJ9X0ouTQxgDQzVjgQgABIsFmG9WQ3d0Pd7QaKYQIOEAKAxYLtIIaKKIjkzRlAUsa7ppJVe38yaFENABBYeclLUPV9dXtOm6ddNKQzbcrJs91/ucV9ee+P+a++88duPPr7cXlMkQWDUPg+Zhzt3H7zz5httkxA0IAQKIQQKkWIEJEViBah9HEMIb77+xtPzc/jgN6VYHwJlYfHcGz+7ZiizWOUWZ2WOGFbLYUE9lkal67vMIqVw4SYlZn727Fm33yNhk5IIDLmwHVpPkkQV3fcdmFEWAANQJAHOZQiIq9WSEGMMpRREiYmOj9fHJ8eISCHcbLcpRq1c/IhoDcEtXjSU7uLihRABwHqzDiGKOFzmavJlXJECIuScdewr7u+a/K3pnNxWHa8S7qPkcmUBlrcCXgIFRJRSOtocnZ2eHR8fp5RU1LOUAbFoENleXj7+8km/3z988Nr9u3e/9tbb/XZPVc8TiHIRLqBqFXpIIUYUYxFVKcy5FCTKw3B5eW34FqLlr6RcsoIeHR0R0cXFxdB3pZQQ4yKEECilBCBEsbZ6hcVisV4vAKDEdLk+gmaR2lVpV3fv3KNmKYrdahmO76T1Oh7fAYBAYbVav/7ojdS00uVJ64BpJiUhC2ZOeOq4HGAhqjqXlRvSRJJpHS+1UOOWGbtTYHWFFVDMIR8/Pi35geJ0+WKqgb2Pr0UALZUQAQig1v69tNSjfhod6bphjGixArQvjwDxpZGMj1w/5PJVRoAZZrtx/gkjPxBl9LDxTMdXWigS5630pAEhFiZBEa4q9vAJpu2sokLTBWum8vilFe6e7357kevu8WLoDEJuqVjmXKBgnIRVoIMKEoQQIiKB8eEaHiXMpfT7rtvummZBLaYQlsu2bcOuLyIMBKQaQGNLIS1Tsjwv2zKEEBS8eQI6sdJsYKaig6VFewtCQ/VKKSoK1rgshCDs28bbnkI1iGzDjHXwiIi/TwFrlbFaIQnwRiLq+TxawDW6qLIiI4ZSCrNAonHnI/rHMKAqGD7MXEQkWnRdnDmSKDSpVYCcOQZqmtaSH6Eq4HGfVrYnW3FjIwafksocUXeIfQDMmKr2MSBQoLBoFyk1pd/3uWfOyRw2AEBcLpff+PrX33rzjVXbRIAYqIkxxmjszWXQ3BUQQKvHrgbBCJNEgvWq2S1Xuz2DcddoUAFG7MrQD32kEKRECikFAh2KqFIB7+493/JkYpXLxYvzu0cnKHp6sl6cpK6Hm/MBzvuA3H9yFbf50d2zv/8P3nv3vQf/5t/++PGTz5SEmtgPPQS8f/fusmlVGFUJKYQYUgwhYggYIlDwTA4mxJAirpbr1XIlqiylWr1eBefnSrweTsXhhcwZglIiDKgiues/+uB3H3z4IQEOXddvtyIsoqXvIyikgIgqOdsWmgNXiABQmPshA2FoU6IQkJC1CWm1XD169Ojk5OTBgwdnZ2ebzYaISimLRWvW/fnVxdHV1ZlfDcfoFqrm3L949iUQLDere/dfUwVUJZExufZWJarVXIYQMGLTNgqgakwaOhdIiGNu823Xpx48cLqclzC86qiBBfU2m81rD1979PBRDHG73fb7LoQAISJACHHVNC3RKjYN0uWz548//uToveXr9+7v3rjGkCreCLVKQqVwapqjzZGCbve73X4fc0GyxsYoAkPfiyoRxUhWPopIqtI0CXGTc95vb5i5DW1KabFo22UqPDRNW0oxqGCz3iyXCQAAVZRL4a7rNqtNAIohAFCkICVz36vx2gOmEB8+fH21WOz3V7WaS9UFVQ33Wj7Z2JwGCMHKni0B1ItTXkIRzE91TMnTgDwahqooUhNkKrro+uQldWcshJO88bThufeCdQnRhJhdwzSjNVCvAVQ/zoY5Gx5spUv2nacqKZgd/bEOpbIcKlcP055dLOBmxcRCpACsHJyhHgBARHLJuQyqrMpetTRajRZCrISBIipSWCgoiZTCyiBub+A4G3P1qwoi6ASJXhKqwmC0gcqWHEs47nL0zp0wo/+rXotfvYJhpvfGcB243aIKTjZIVCM/qiqkAszAhUveb3dNu2hX60DYNLFd4H4QVSVQEogITcQUkkXvkISluNsvtbkwEmqdf18rw3hJgEQhIIkIM7PRZBKY9XzAFYNgGcpcVGpvUF/26s2/WgFX9MOsjkq8qSC3iNxGw0fEkpcxYM59KQwQ3Ypx077G82Wi9RdRLzECd50DhbZdEkZgCBRiSIhkFLETbALgR6imMXg+vaK1WbLdChOuVU0JAiEVhIAkQCAgWSJSDEEBsrCAxhDGJT06Olq0d5vUeL2JpVArkAJaJyIu1Z3X0ZDDei5DgLbFRZsWy6XaIUNSVkHKitfbThhIAgDV4jDH4cfYmTsGCKpSShbSew/v3Hv9BBibRdMu8PKJPPvocrOHjUi6zClzQ5gSvv3OyXevvv6r3/2SUlgfrXfXJaS4WC2zlJwHgFX1BhApGBaGlj/MGghjUAQJsSCgqhRhcTXmFBIOhIBH3+vPwrnwflisVseQ2gIXF5cfP3/y+cefmHoLRkoIqpwJtWlSSoFQ+35PSEjBOvViZYvJOe/7rokpEMWUSGG3317vr5aL5fHxsU379uZme7M1FCGERBRKKR988MHi2bN3rJ8SAUt1MxAVpJT84sWzjz78oGkXp8enlkfqOTweibXNKoqABNaVMqV09+7dy6ur3e7G9jvYlqrBkXrkRnRxQgfd0K9RFIBJkFV56NSni8Xqj773R9/+zneHvvvd7z78+pPH93fbtmlDCADapuauDJvVstP+aYTn55fLX/7knf315dFJ9+h1RmhiCIEChbZpA1EuGYnefvPtt958CwjOL86fPX++23V936u1R02kBVEZFSM0DTVNbJqm2SzXx+sNEKnI9cUFKMQY27ZtF4vj43WR/OWTL40CMIRwdnZnoXmUB4VLt9/nnIc8IGLTLgBUuJQyqBTb1gR07+699fro+cWTSZ3Zn5AIrHcIIFrIdHbw66FGJ3gyUA4qkzZN+tuuh+hsdDWoOV5k9LrBHbVaXeom71hEGwCAgDzveBbrn6hJqqy1KcBZpOFlHxbdg7d1n5y/SZB6f+pRAbsMNu0rBjDK6Ge6FkBVIizKIMwiUdlAv5HwozAHtxFBcJKnLr90PMTC/qVAFsL0MIFLwVfEubX65fUXh5799JAwrbbO33lotI5naDy2FYqYrAezZzw4DGJnG7zBq/Aw7Le7zXEmpMWiiQFSMho+9th7AAhQMbzR0wSwwSgBe/Y4AgagomRlPgAKQUcvXlUs1cUPOIztYBVYjAoTEIuKKLJ11jPnsO6jvxWC/gocTUc6e1M/1axByH0/9AWP2unjavVmbg96TyQFYUWo2lUsLA9g8BEQKhAQenQdWHViFkV/SHKty0bVNOpOrZEGF3JYI3EIghoIjV3ZYnsm8Jk5l7IgRADzy1NMIQRmHoaMGEJg5QyqhBADpIh2HCCA56OPu81PDRBBCBhiTE2zv9mxckDSECCgEQPaaEvhIrlwFmEWA9BwFo2AXLKgrjerN959cPJwodnMQB263bPHXwbcdDrokIPaVCkRnpwsUxMoUrtaDP0+NSmmyMKsbHQruUi3vcrM+74LMZ6d3TvabBapCTEGoFyYuoG93bQKYjagUkfhUimUVSrIgDzkz3/6wdHR3bMXw+p6+Czf9NttE8g8CEARFRARLpU9JSyXS8sxLlnVGi15JAX2XXd+eXG02rQpEWIAKjmfP3/x0cWliK7Xm3t37p6endy7f+/evXsnZ6ftgmKA88sXP/npT970KLKhPu602MaHgCLl8uLFJx/9brNcr19/Q2GUdHNxAapqlT8G5KaUjo+P+74rpVRDa/yav6Z/ujkVAwVSfgWiCPUoE1GMzcOHr//RH30/xvTRhx9dX9/8l59++D96+sXvOZsAAD/99//ha9/61T/6X53dvRtSatu2adJyuRRQZn790es/+KMfvPn6G0C43W/Pz8/7fb66vr66utp33TCU3A+lsBX5xJRWq0XTNqvl8v79+30eut1+uVhKYQJyGswQQ6TFYhEC5Tz0u75JDRUGACtpR4TtbvfxJx8P/XByfIIUAXG5Wp/cub/JGQCUpXR9orRerWEWA0VXwK5BLT8RiYQt7dzs3lfBsr5YrwBgCXGxWASEvuv8N0QhhNoR4PYVTPHbWqSYiELTtJl5wCwAg7eEnq3dK4WjHgAkAC8NTCtCJ3PPREEVvDfNaCgAVK2pqt4yRotIEGEVtqQac4y02vHMRYz4TMwxNJxQEC2PZ8zbGsF8rMMaQR2cWyxV481m3yF0h4R9+m7jR3Zp9EQRBcv7pRkmXb35g+CP63n0En+sN7fWOM6ciP7LqYoZEI2YT0FL6Xe7oe8RIMWIZP03UdQNMsMUTVxSCIQRMbg3DhZMHx0+jxD5AqF735WygwAIzFzzKUGohrVHCSbX8RWdK3+vAlaFOqMyWys0pM/bHNa5rD/nUowTf3YZHZ9IFdRKgWXWqghAVbkydtoM2JkMsTZidpRPxw94u+jRxa0G3biuBkZXDBCRUBXQSjaIFLEfhiEPBBgEUJWZWQMQUowxBFEppZQSAjrbZfz/U/YfvbZlSZogZmZrrS2PuvLd+7SL8IjIiMxIEVlZ1dVV1chsdlcWmv+BAwpw0hOCIMgRCbLBAUGC7B4Q4IjTAgmwqrvRpbuqGNkpIkN5KHcPd39aXH2P3GIJMw7W2ufe5xFEkQcP7k/cc84Way8z++yz7zM60yrTQAhGoyblUCAqwnvMf9PF1FoTOaUValov14U2vrfg2YMlGbTRU3tcYjgTgciukGRMlDjem2b1V3/1A4UC7AjR9u7V8/OL11d7ZrKnS+f9Mmw8eQF4+vTkH/2X//T1m9eAUJQ5uyqPhQhzWZREanE9v7q6al0/mc3q8fjtydsnXzy5f3z/W9/8pilMVJMP3nVtiwLiA5ME67zznGDnNOYRBkAMgYSBlVyenHz59GR1ftlcLyz0KGxMJqkxDywc3+X73rne2r6qyslkIiwNM4j4IGFgJvfWXlxeBBeqouDAdVHWVWUODu/uH49Hk7Io6qqc7s7Gk/FoPCatgkDv3MXV5fnFeUpubjp/Q+4ceRaIfd+fn51V5bODvb2bPQRub3/vqMlGhnBRFMaYEHi7LQIgDrn07V12u/9GuutAVrn1sAw/F39YKT0eT95///3JZHpxcXF9fV2WZZ7nAPDk4GgxGhGiVmpUVlHKm31gkeL6aufJr2L8Vorqur5zeDi/vjbaeO/GRfm7Bwe/PZtOSAmIFLXfzwlV4LBarZqu9S6SMDB4b50DkKIokLDru1JR56E0alEU3odYxSuty7LywYLAhx9+mGXmk59/ykG8S31QJCSlgfHq6qpru/c/+NDo7PLyqqpqlRXOWgBwzr5982a1WpZlFQG9CBHHC4qISXANUESc8yE2p0RRCsBD4TtcwYgtv4NDDNsOEc6mE0RYSGxNsQhorZHjYNJthlHEZ9CYrK7r3d3d3d3dLM+yvGi7drXajMeTwMF5j6hg24YB2OLMQ0iBLUosQ9jc/krliiTAT4hT7i5pvuPXTiEhfLEvAIMgSxxZYvFxrAghMc5wyEIkLmlmAHULQIgBZdB92caJW2s2XoqbA9hGXrlppqTpdUlROBrFDEHz17H8GEVx22jZ9rPkK8/CzR2VOIwa41VCNH792YlwQorUUcYj2ushAAdnfd9vVUiGn4dhhHLoGWJE5jTiDQccASCSMQEx2rlGKQ6ANBY8zD4IIhARKUmwjYBIJGxKUsmO6B4JDbdFblHnfi0Ay83tiH8QTIrKX5EMT7Vv2rr41t9wCLZ3MIRuEdiakHBg8WG7EJ2zwJzSDeDAjAOfLU0oYRwCJsG0kcU6Rg2HEY9oG6HhVp9YBjehiJemjS5m2XFxIjj21nv2gURQgEWssORGFznSML8UQaHE6MA4KMQBbc95lmXaAIOg/w3JsAAAKE2kCAh0ZrQxzvqr62sXfN82YAxmhpAQNCEDCoNXyIFDAEaJFnixx8qIEFiurueuXxXEpaE8zz54b/Y7v/1wNt7NdPHy5emrn//SEq8a91ff/9GTJ08ePHh4cj3PtO6JmMN6s7lEXF9doXfIfOfo+Gsffrh7eEBKlXn+sx99/OKLLx/dOS7vHCokMWit7bo2elsn1ZLeRQwiLS9CkcRCB0RmBq3vvP9wx8LnT768Ws2XrrU5scIkBBN7KSzeB+89AFvbZ5nZ3d3JTPbWuc6GJGiACgC8d1fzuVYmNizzLC/yQlXkG6uRJvV4NKpzU6g0zAwg4rxrbU/GaK0hPUCQ6m9AAkIkUAoQObCz9ury8vnTp5vNSsU7EJ+ytIQEQJzzSllMCWccJNVKhfhQKKXyPNdaM7O1lhOVaXjzsOtg0idHjHVOKhYi2TrJLBuT7+8f3D0+9s6dn59fnJ/fu3cv2ir/m2//3i8//Eae6YOdnQfHd/d393JtbG+t9cc//uud/+J/DwBI6H1Awgf3752/Pdk0GwJ8v67/p//6Hx/8i//nbe71ttiJOyHeNBRSnbMFkAAghPB/+eD3/1Ux8cFHqJCUQlZEZme2953f/Y7veT1f9rZP25aQ95KpqGDq8izb3dlbr9aLxeLy8uK4awEAhJ1tizw/PDwsi9p5ss4DotYGEJUmkxkiFXc6RAXiJE6+3XhmpP14yNcBEIatQxBBKRJB71kp2tmZam26rkuNHsaiKOK48/Z6YAqjVBbl1z766PHjx9PpNM9zFrHeAWDX9d5xCNxbS0QhpCwfERP4GKl8AgIQgoQQCDGKEUuU/1UqevikgefYNh06OPHQB2LWOy9OfrZJoHDrpc4cOODQHBRKJKIoQRtu10XbuIrbLAG2vKBb1zNtognGjwM52/H3X3/FwmErGXTz9/JOCInaWzAsMNkKdQwhbRvOh7cnVDVepSFXEJGwPfch7EAqWggAUIAIVJpeQPQhOGdxGFGigdCnlFIqkU5SqiIC76DgIIjJIQeShC/iAC+nMh4geiNEusLAFRCRECKEn+bNIolEGxUQ0FH68NhsBBRAfft5+40vjHvrTcBLT2+IVkYpFRqGzyM1K7yrsCFD+sLMEmIe59n3fZ8pVERDeS9D+ZtuDnPAoT/PwkP+NfTx0xLaQrV4+6H6jfjQzeaKwCI+OvfERYEQEJCQRyUVhQCE4BUqSL4gwt53mzY461xRFblSqJXKcup6R1rnuY6Y8+2ORzwYJMUASuvpdOp7O96dYWbatiFNpDIkrUgJaGQBFEYgFB95TSEIC3PwLEEooL774L0PHh3vlGK4efv66abvPvjW+5P9A6F89Ojg49NnAdx60375xRck8o2vfQRPn7Fz4L3tu8vLy/XZ2fL8rM6yv/1Hf/Nb3/jmdG+HFSHC3YM7V4d35pdX7Cw7T5lhluVq2fd9XVWKtGeOrDeVuh3ALMZkkZDPAIHFCwPi7oPjjx7cffbLz7725nTatp/axUtuRZCIIvbhQwjeMzNL4OCvLi+jMcBoNOJ1CF3QWqkBnG+6Zr5cxKc6N3pUlJOdGY1BkwKFoLCoitF4TIpcCECYFXlR1dPdnawoACDpFDJE7gdFewlQiISK6rI2iJ99+gsCrus8cB+8l9R8lKFmZud8XDSxL6hIawVCYox6+Ojht37rW7PZTtt0n3766WeffdLbLpbZ2w0Oh/2MCQeK6LB4JSXQWuu6Kvd2pgqlWa9Wi2sJPngba4AiM+O6mo7HB7s7eZaBMBEVeYHS58bEA8uM0Yo4hL29/b39/c3zDSKMymLabIpm3Y1nsTIYElXYiufdeswh4YXDI0SLa+zasabcGECIAqKEalSP7997oFV2sH/n61//5s9/8lPlmvhmUoq0yrIcEL3zGlVdjY6Pjjtr9w8OitNnAKAUHR4cnM4vv/Od39nZmz57/sX3//ovJ5Pxt3/728ZoRVgWOQIyB22M1pl1IYCcnZ7+6tPPetttAdGtbrwACAIpVEmOCvM8A4C+t0RqOt3J82I+XwJQpPju7O2tliu8uNx2i5kFAfO8uH///uPHj4no9evXBwcHeZ4v5vOyquuq8i5sNg0AlWUZMR+IXK+kEcECQojDTEhgF4J3hMBhIBdzNEjdQokRPxZhjiXmNqAAbOs2+cr+LLcCFQ+Su7H+lqEiJwGEpLIQYwDSkK8QAgpFHpsI3lqQ22W5hYC23dbbPzJg1ENDJ+2oW1EwQBQSIeHUoUWOLTpGRmA1tBS3mo7CktyM0zYeA3YgudlNOYFuN6d5I82MGAMMIgnFcU4kpeLsrGwVywA4iJCgRiFIs48CUb1RKdEmqUhGmc6QFBajrU4SlrmJKIO8Gt4CLRPqOjRnZcsOSW0ViR5TQCix6gQkRD3I9sAt3Ri8SYSHevTWd2y/6BZCl0CReKVvIPltK3ZYRiLCgb1AQARvXZYbUMAkQBBABAEVCsb2NTAyaUKNsaUBICpC/smKWQYy9E1XJb5IKYE0o/+VXebWAoIgiVUWd+kAorSq6pJM5pkhAChUwMQ+2LYPVrwNVoO3xHVdV6RBGyxQM/CW+RFLxO1dUhrKwvS96V2PinSW5aMqGNUjGJFs2+3gQTwOEAjwljZifEwCqI2D86W7680ERFz7+tWTZy9fHdw/Gu8fMBJkZAoFJIVB4HB28oaD3ZmM7abhznFv2TltzGwyvXd48PD+faO06y3HRkaQ2WQyKcoyz4Gla+3pxcXJ27eTyXi33Xnx5k1ne0lKpsAQQvQQUppJOSDLZAUUoSPoMMBI46w4PNy/X0+z/lpfnVyeXzVd55mdD1ERYtBXQdv3r169yrJMKZVluQ/snYuXMuOw3ze5cBlsWZTGtVr8aO9gZzqbjseT8aTMCkVIdtPbnvteK1Xm2Z5rHxqqU7YaN+rtRkKIikApZfI8H1XTPDPNeokoZVkB4Hq9avsuBVAAEGKBEBiHGQ+jDSlCL2VZPHz06Gsffvid3/nd9x6+P6pHr9++/kf/+B/91V/9RW97AiJSSDqyMxghiu2JAKFQ1AccTMYQsarKOwf7u9Ox7RrbNNL34yIriKK7fZmX47JSAsv59fXlRWGyg/3D/d39tmvn82sA0EpNRuNyXHsWo/XR0VHfdW3bTkZjJOpG03/7P/pfdtP9zJiiKIwymdHaGKONpDIOUJFSFNXNSFFmDGq197/7n5ff++daqaIsyWtByLI80/lsOimyIs9zDgCIzKJ1FpeqEiizHERs3xsydTmqqiqEUPtQZkWmDQwYQmbyR48ev//he3/9g/Ljn/10NJkd7B+xczu707tHR+1qc3l5kVf5ZLaLSnkBpczL56+ccwghKstG1u0AkQKAECIzE1GWFc4xB9Q6K4qqKMq6HiFi3/chuN29/aQNcjMRgnmeP3zw4L333hfm12/fXpyf53l+//79qiw5BGBRhB4ghFBVNSllnfPeRYwsHUWUSQBRBAjsvWfnkAPFtlfaNLdYgwyJGSKgcJAk7UdbhR6WwZVTbpDRGDIYBvXUd6J1EmcWBBEGCcIBRCdRlGELjCDgrU1RZNiik6LhsGenqHO7PZu+5RaHeTiw4bNkkNAdasp4sIO++bbcla3okAz5zACxJv0DkSTcnSq7wUYFQtQc3Uo64HCYggikVZarPCdyQBTr1xAPlFAZDcMgqkBKtIkoy4zRWqmtuNDwVUMVla4GEQ59XESJXWAG4RDp/RCSaMlAkYuso0hhT+gXRdB3kBAFfetgftPrJu7+xn8esqRIogqBA4uK2p4whG9IcAMIgBDCkBCKD545miwNCFi01yFh4KjySUZFGbmoJAUpDYmuI+FmZSUZkFtIwm8+n5TJpLVOqIwmo0WTQ7HIeZ5lZYU69eEhsG1bxwEBiWg0GlNVWPG9pjwzcbsyGTn/7iUZWgnxopmMyiLzzgMgoiKtgyLLggKGABQIhxCJZHEMFwYJt/hiRCZE7UVvnFlZvY+otBlPJra37IAgQ9RGKU1qfnX15Mnz6Xj8W9/45jc++pp69hxCIJG6qAgQA09G48ODO1mWe+eBFGaEooLzILK3tzeqa+fci1dv3p6f51k2GY3btt2ZTdrz863CrACwBEHQWVGPp6CzrMiZNCjFIJY9EIAmEajK8oPj2eT48PztWWPtRofnJ6+e/upXIXgRYAnArJSaTqdd17VtozQZpRRgxgIAf7C8/C9+9v/GISkkxK3FctLW2gKqKXcGQDwM4XetLZp1XCUCA06DJABIRCbLirKqx1lRIWFdzwBCnudaqcVy+erNy3WzjkpOgCgSDUAFAIiwKMu+t13X7uzu3L9/bzabFabQZOpq/Ftf/1b7J+2LZ89fvHwebRwxWfghoZBSgAQYZenwZupExBgzm04P9nY1UbtaI0CmMNfEzobgAUApMkWOzKiQgz89PwsCuzt7i8XCn5/Fj7F9xwhkjAp6dzpzd44uLi5GdY0ATLSsx5tqrEhVRZllWWGyPM/zPFdKxSqBNGmtg9ZxHomKHBC5KABAKz2qahs8gxRlUZRlkZdZVty7dzdZHrWN9T0AhMBt01Z5Kc73bYs59LZ31vZtm2VZXRaRRRqhJxQpszwf5VVV5XlGhFeXF5enpwf7u7XRwOz7Js+U763Ksq2WCwAMI0YwsKCjQBLwVs9VRGtDqESEUGmlCSnPCu+dUsRMpHReVqCUUFzUQkSHh4f37t+zzm6aTXBOGxP1TEajkbUeEZVSgFopJQKj0dh6JyKbzbrZbKx1cSv21jZtI8BE0Gw2vrdVWRdZjqS2eD8mPSwcdqlUggOAGhCKFKUR4rYDsQSKMOF2WncoaYb9OXWWBWWodyVqLA7bYyS3DlvU9rmBG5aQSIp+gAypQgNBxpv9KNWqMuxyMjRtUp18K4wQ0YCbxlmyJN4QN/CETLMACTMrSY4ZkX0HsN0EcYtxboNQHO4fIne8tEBIsUWSF2VR1eidyrKh3gJFVGS5MgSCHIa5p2GPjb6/27QMh979bUQABzoZpE4NJIYJiDDHyByYMbm9pbsQJ/PAWiGKWn/wDoPhpgcsv/abuBi+Gse2ZbXcpGbpfZHlAMOiudX6j/Vxqql9EM8sAC54ET1wZAOz14p0dH+LFSGpQWY9UaNTsYrbAxXAG1IDDE6rcSELDyDgUJSqdFWSrj0SoSaTGZVlgdAqlHEJVc7ACKJEwNmu88E7o3We577rJDdkNLJ46xSi7RQiwq/hebdWISgFWa5NZ7rOalJZkSutAzBHbRhIHGwfgVnmwXHqRslERLTSAMSgW4u9V6UeHR09mEyeud6HzkNmkFEjkvCorv77/8k/QKK9w4PTi0sBYoU6M6SUczZopTMjpPoQJAQVkIiRYLazm2fFpu1evnp9fr0Y78xyZl4sP/roa+9/471/8i//29enZ5AKpvhI0O7u/h/+4R9lRd47Nx6PbNuQl7BsYBMqJrBefFBEk72d2Xj2+PF7D7/zwX/9L//5//XLLxyQyQyLtl3bW9u03XQ6qUK1XM4BJMsNC501+dBRiF8bKXcSO524hbuGflVq7MTnValNUVnbd9FbExL3ikhpY/K8KMqR1jkAEqmqHnHwxmgkOji4YzLz6s2L5XKZ2jiQwEYkylQ2ne7GcdCqGkWNxuGZIKXM4eGd3dney5evIuoUH4R0ErEUvvUEDdAhFXm5v3dQFpXrnbd9CL7vWwF2PvlG5EWule5d0246F2wAIcS8zE1mYouOCNu2tZtNVpaTelKX1WQ8aZqmLquIHwYfAjMIxN6nJqW8jzaLEnV9t2LkHLxncgQIkaOU5/lsMrHBew4mywptZuNJXdV/+N0/1Ln6b1br1XK1gT7eJOvdarUW169XK9+7Vy+fB/HRDyDLiqmkK2qbzU9/9IMew/vf+MDZDgEUolaIEE7eviK2O5Px3u7OncN90LkTsBxDB8Owe0X8bbiWKJDc22Jg0lrH4TFEJFJKKa21UgZRAXgkBUoBpdl8ADDG7O7ueu/W67VWmVJqf28vml0qY1KfVzAErsqaiJAoz3MiyrOMkJbLJQDM5/PPP/98fn0dggeUzXLRte2dO3fu3bs/2903ebbdGRBhkO4PzEESp4V5GHNN2+aN7GkcNNBx9aTtcIvBSmwBptI3IfJAABR1V3D4q/j1OMBBkbYbEdDYSEYB4SCieGD8bHPc4ZBugq4MYkjv1Ga/Fi8S7knbgjidKlHibAsPshYDiDlg+rFTmopSeWdmC+DWVUogBsUKFXWWj6azXDgrK0REBvEgQZJClUvGPfEDwnAdAwcRjj1fuGFCiKTs5+bcbiCBBMS+4+mxTU0AooogK6U53DDe+XZ4ANBD7hOrZhyAnRTaIS2Nbd2dvGAFQSCwhK2aTNwN40W85b6Z/jFNhaMwiAvBBY4KkQASLTBibzhmmpFoD4iAamBPYXQBwKQkklaDJBxEts/eICd2Ux0hJsc/QiiyPMEViFrHyTDUWZYXpclyMMZnGjWF4Ijlm//i/3X45SeRyrUtv0xm4htJDfs+IBBEVRYAwN09/s/+t7K3l/rjCFqD1qR1UjvKs7yua600IAYGzxE8SNZvklrhIoAhpaPixUfemLXdarWZF6ao+fp69fbt2dMvvjx+8GGVVZnWRpFWam82OzzeVxoCi0bxzEHCYr0ISHtlaUNgQsq0EDJInHpWisqqmF8uXrx4MV+uJru71lsX2Bj16MHxvQ+PLxeL//qf/UsIIaaesfo0xhwfHpkiR62CdxfNRvuwfH5y0Rvzdpk3TosQQtBoe/vyzaun16/+4r/7Xt91SpmdvYOyyDer1Xq16Lv+er6uqrIsR13XKdI/Hu38j+/cy7PcKJ2ZzCijkBRprU1dj6qqMsbkeZFnZVWUk8lkb3dvtjOrqsozXM/nT188/eSzT370ox/+8vItYLK4VIqMycqiLMpaaTPAvwSkCci6YG2DAON69MHDxxfX103fMUrcvAEky7LxaLy/d1Bk+f7hUZ5lzNA0XdN2fW+dC4gekXZ39/K8EBRNOvbLiQgItTF9T+xla+YW1yeRmowndTUSAdv1tm+t7bu+VYhGqfioG6Wm47GeTEHYe7taLTOTIcB4PFajEQBkWZbneb9pgg/O2kzrsihG9SjP89iliu1HVDiQw8WHQM5t91QEZAocTQsQrPNAaV/NsywzBiDSR3VdVPcO7kwnU9+5tm0U0bpZXNtNOiXC3vah75CwruuiLIqiqOv66uJ6vVonRo+IbZovP/3s519+svPDvdFkhMwoUmR5keeUmxB806zfe/zo6M6RzrJl251eXQdv4y2LAQeG+JT2xZv4so3NcHvvQkRFCtOYKCIoAGQRFNFK7e3tGWMWiwUiOOlEsKqrPMvyLDNZppQXAUItYuuqruqaCLMsQ0TMcwBo2/by8uKXv/jFl59/icJlWWRG265fr9dlUVbVlQ8ikWUWeYvCIfjo6H1TTybllDSpty1xh/9A3OMGwPhmX42dOEhDE9udcNAGTp8CwgKKBwg6BfYk7LBt30kaVtkGlJjkboPcb3pFEaIUOG+Q7psh5m1kvV08R05JlCx8J1gMYXr4IymIHrJh0I/YntIQeQccHCMfhQFVliNiUdWolQh6B9aydV4JORdS9i4gAkHEuuCdB4EoafKVyAUAiVRFsc+czjqqT8UjvQXQYAqj6cxjqCEkEhyi4DuRC7S8ozB1+9zfrYVv/ekrZfFwgROYwSJu67g+VHJbeoEAsLAL3kvgmHGkm5KUzqOPffQDJ60AERiQB1rLO+X7TRq47VJj4pgN+NQW6klpMwQQhRD1HrVSadBcqzzLjNaakCGQMLI/+Pznj370F/D/50vu3/f/i/8VTDHdJUg9F1EgUYdD6dF4orOcmaMFASc1qdvXOxpeRR0gDOx9CDmwsO/6ZtPka7TLZXN9PT87PeXgIQq+mDxKTxAIITrmrm11XnrhzlkQzhVm2qzb1sugF6QJFXEIl5eXv/j5J+vlajLdcdYFANJqVNW7uzNN6v69u1WeeWcl+HhxWSSEsN6spdl4DqPRKNpT8rJ7sXh2dbHwWgeDFPzy1fmvPvvs6ZOnr5bnq34TghT5yGRmPN0pq3oynbZNs9k0tu+0UkRGhHoyF0WtlVakM2MKUxid5VleVdXu/fuj6VRrbbSpqnFelPl4pCZTm2ee9GbTPru8Os+K16C/6FwLFF2zkJQxRZ4XWuukxjrwV1CRIFhnN10fghMoRmXx8MFjU+ZZnptMo1EAQEQcvAYNnmezXQTouk3b9peXV1UxIdKTyXg0Gv3xH//xxdXl85cvTK5BqRiFWUBYtDHeO2QZ6mMWEVKktTKZFpAg3FvbNo3SdPf+A6NM9sVnACDMmtTebMco3bZrb3tDKjh/eHBYP3wIAHmeG2NYkiaDtS6EoJTaPimRrQoQgtax5kJh650ARCU6JNoKjwNg4PRhADAajRDQO0daK8FxXior3bq9UJez3dl77z1GJRdX5/FRwyi2YgEUkdFt111dX0fG1LpprLUAwByur68m0zECfvLJL6u6AJC22bx4+aLMs8ePHu/OxiRMWlnb7+3vkzH/3ff/6le/+tR5mxQpovaQJDBM8YAmSgBISnYyTDTJtrcak6E0tCPIQAKAmGXZ3t5emjPUylrLLLbv+r6PtZBWiKQQVAimKPLpZOJDMEbHzltVVYrUy+cvfvHzX6yXy7osc6WQMFOKGHxvvXWr+bW1fVWWeVltAePbr6F4GAKtJKgZt5zh25utvBMjh9Abm6LvItQR3Ug8r5iPDJ3qQUnwpvbcdlRliIESPVjejQPDgQwyn0NYSXv7zZkNEj032qGxeo9EJdz2dOUrJzjQsDgdS6ylOEEB8Rcn1BtxuKFpSpUlWNt3fa/zPOIEXri33LWd7TvyiCBc5FppAGCAzoW+d03X984mq16R25TjW03O+C/MhMxR13X7UwI3LZHEmo6hTzBJtqVYsFVuG35Ex+nYIQj+5lRne8sl6fYJAUp6Bga9rmi9K5y0MW+RyeMBYlImIiQKzM5aSWIUkupURETQhgAgeIfRKSJs+UgpPYMYkRHCoOM7EPTTeUlcZ7zVK0oXMgICESdHFg5BfMDIQ43TQlFrAAUA2qb1zgHAT//Of3T+6GsxE1TaVHVVj0dZlmlj8jwrssJ76yUAQdU14//DfyYM7UawjaNLA3WdQBAZRZiV0ZPpLCurZtPwAHCmDmZUfUvLaSjiiV1w3jsFwaAL/bJrsg0ElRf7h0egKIAggbAQoVaklQaKkp/crjbcdK9ev/HOekUuBC/hYn59tZirLNNFboxBkMvz65/85OOL04u93V1hdtZpQi/++MHdyU4dgjx8eP/rH33tV18+c7YP3qOAMM/nV9///l/1fWfy/Btf/3qdZ0qrnQd3HxwcX14v1u3y/OL0y6fnP3vz5OzifL1ZBQkYR29YtDKISqtcV6bIqqJou7Zdr5YiLjB3bV9VlSIjSAwKyIzG04ODg+Ojo7t3j5RSzjlmyfMyy/O8LHRuhLBzbrHZ9NatV+2rl2+aTTus32gpqUDA+0DkjTJRZ4eZKcLULD6EEIJjYDQhoF12XMr03uHkYJ+0vrq8/Ks//wux/ujgznuPHmeZEVY6y1hwuVoDnLHIbDZ9/N4H+wd3rpYrk5kQmQoEwXPwHMW3AQSHVtxQBGOWGQnBhWCd73o3K8YPHz5WSpU/LgFAAPuu7/teyLfr9fzyuu+sMDx8+GiWZwDAzIvFYrVYjicTyQrm4L0DEaVV7O4MJFxRSkUhaBAJiMysFUWTwWErFEJikEHfDPb39susXK5W41F5Z+/g4eExBX779u0UD2ywp6cnves46iEjAlHUiggcLq+vqnoURCKqUdcT5x0AOOeeP3+mtTo+PjqdnzTNJsuztuvenpwcHx0xwmgyqfK8Lsu8yEMIfdd//KMfvXj1sigrGfYiDkEYhEPCNyKaMfQDcVCPv2kB4A2YSjhgU0KIOBmNq6pqmiYCWwCiFHnv2rbp+15pEhGjM2NK59binVFKhEkAFYSAxmgifPbs6fX1NQn4tqnY7o3Hs52dUFXeOZzPBQHtpN00eV6AvBNyZJjyv73TSpyY39b4v2FHvpk6GSrYbeBguSk3ATHeyxRIJNkRooiKxCCiZFUzgLm3vxK3W/dX4sGvB4h3KrchWIgMb4+qcpDmTbZnuv2WX/s0iNjvV6pAwEH9cJD2iCV8bLcCBw4+WGv7Tohc8IGDdVZa2TTrttkQYXBOQjBZFhHuznPT2U3bdn0Xe7xxAGPrWIQIEGWhh7yIo/omDUnLwDqLI8mShpNIRIIwMQuLAgFBBRiAkjvyUMDrNL2NIAABBG8JlG+vEQ95lYgwR5hUmJNFXUJIQOKz5xl9VP8bLApu6vPbc77Rs0i2FPyk9aGVRhEIQQFoJEr+RQwYnWpiahdFlW5WTGSNb2/8TQZ4c5tTn0shEQQR9j70zglSXY99no0mUyoK1soAMrvF9VXbtABw+uiD59/5Loggksny6Ww6250VRZHl+Wg0cnnurLXBoiZZLUb/+f9JRPremwAiwCGmRNHZCFkkBFYIo+mkqMpV02zN6iPRjG5ag5EozyxekIN4561R/nDHPH64v1PpUnk8LMd7U2t7F6wPloWVgihzEaE+FMqNeXty+vyLz73tucg9B8/cdN2b8zNd5FmRG6vPT05+9P0fnL89Odw7IED2HsgySTUdHR7tMfO6sYeHO3/6D/7jnb/+8fOXr/uuFWGR4Gy/WFyvl8uyKl33gHKNWUb74+KDO/bH9affP3l9df6mub7u104CGQ0BgEPE4ozOgBFREyGiyUUj5gBaQHVds25apc3+fpmbwuisHo2Pju++9+jRvXt38zwHkM1m3bYds1dYmmi6yRy8F4EsK1arzcnJWWBGldZxzAqJIv3XECYPrW0DnkNAQRSlMEcyTdeuVqvcqJ3dnf3dg3IyRmO8526xWel1s9+53gPgzs7hwcFxUZTrTbN+9nxvb+/y6vL8apkV47KuHDsfYsbXxzbI7Y4JDAE4NhqiV41n6HvXW5/lxc5sJwpxIIO3fj6fa6JmtQKhsqyd58Vq3fcOALz3y/m83azzTK+FuR6RUnmeK1IAELx//uzpcrSs6/F0OhVgQqAsV0p55yUAYhYz40TJRSDEaGYBAOPxeKTqsLv7+7/7e4+O75ENm8360q7fnr350U9++L3v/ev58ioGvBB87zrFKCBFWSpUo/Ho6Ph4b29vs2n73gUOAGCtXa1WJjN7+zvaKBs8AhCRADZt9+btaabV3aOjvCw9g/VhuVr1XYdRQz5uHxJAAjN75lgA3DSDIt5xq8sO2/A8RGW4FY8V4ngyidewLGuttYgQKRHwIWw2G22SEH2Rl5nRXdeul4sszxiEQCOiUpTnZij6ZNJ1/+fzN0cQtNaRxhV3zV8++ugff/h1CCEKWsq7YXgAz1MYGwJiPLtfm8SN0feWeRIIxy0xHsMg2THUoSEMHcZYg0bQTyWzrwQpiPBN2IZ3jwxv6Cg6Aqy3fwHEsBGbl7+5Xr71YoAkGp0CMtzoMv47X1v+xJAhxtiVjoUHPSVvPWqnl/PsPJOXtSMMzUpFSCMzPs8oy4mUAGAAbFq1XGWnJ6rrfv1AMCphDYEmdeQHJBol5dPp3sXkZkjwWETYCwcAg0Ox/pXXV8WbvoJ4QOoB3GJtpuzk3TwuxpkY/KJc2lc/JsEluJ0qjnoWIppoK/rJcdI0zn4MkpAEwzoDuL2FbUPrrZUWEu77zr+IpAFMQQKtNQTxIoGjTRvNZrNxke3t7+dVIQjAYnt7dnrSdS0AKAKVqONx6hrYB2edUgpEurbj4K3vFWtrXTyk3vZaGICc4+A5ipYJMwCGEJjF5NloOjm7vAqD57NSBNHm7IZgNqT78aQ5aALg/vz01bVYcV0InbXt+cXZDz/+ZDTby8v6zdu39x4+ctbZPuRGkYAm9fzZs/VqjUQA0jsbhLM8b9vu7ZsT11sI/q//6vtXp2eP7t5XgLa3msEoQyLT6bQaFVdXK0+gfX50dPSnf/8/fPr05WdfPum7RoQVIkVUiz0HJ8J91/3iJz/76V/+8Ad//v3P3j7b9J1lz7FLQkZYCEEIJ5NpkRfDdB0RkTZRSICINJHerOfL1Wq2szeZTOuy2t3Z3dvb39vbH43GWisACME75zkkmFErLQAIwShDSC9fvlgul6mvIQEjIAAQm/dxrURJFQZEZGt7Zztmr5Qxxohw39u2ada+Ozud/Zbg4e4eep5klZdNs968fPFyd3f3m9/85re//a2joztVXS0Wi/Pzi+v59duTE0AqqrqoKt82hIEwfo+GwbBy+4gRqfF4rJSy1gXrvA+bTbNcb1ab5cc//el3fvt3Iq4TtxrvAyjJq+p4PJlNdybTGSCEz34BEKGdIN6xcy0zC4xGY2NM3E4D8/nZ2dnVWkSqUX18dOfe8d2jw+Miy/quXzYrRGmaKssKY3SeZzozShtSaWfQRON6tDedPTg6NoKL+WK+WWw26x/8/Mc//PEPrhfXxmgVAgA4Z9ebZalzxaCNIiTruyA+L/N6MlmtNvgibVURBVea4twLACCSCPkA8+Xy7CIXgcVqNapG0+nOZrPBpM8KccseAg2n9B0AhVEQUsKVNppf2+tu9iIaejxR4zqaQcVWOiEyi3cOgNuuLW3uvROR8WhkDHVt3/VNlGU2RZlXlSIajUfj8dgYI85lqA7YHUhoy5JMphRR36vF9V7064k+OYy39zBJ0tYSb6SEIHGVxqefORKNYTvJk4DntC1+ZU8eNsOhIREIEgX65mrBQO6VhCAO1dUtZhemjp+gMEq0m44RCLejm7fq5e2GBb8ewm/CRNwfvzodmjqlQ1L0lbf/u2Iz3roqiU8GwXvw4bv/8h9+9OyXA7qTQhKmMaMbKlXsmHMIzXf/9FPEm/MaIOP4wCpEQfVuqZ86jPLuWotnGQlmEFCiTQci8A1OHS8DwOCGtE0Y0ymgbIXDAJLFQYLHhz5MgnkB5PbFi+fCSZEYEYbmg8Cw7oEHthEzBs6ANIuXqDMVV4vyXhhEaQ0JWoLgPIdoKJ1un6RKNz69yTI+3s0b4HkLhsS+PyERJXOpiDQIFGUt4yqfjCk3ENCHsFqvIiM3/hAAkEIEjA2zwD5DoxAQxHknIXjrUIBdiBeobVpqLGWq7V3wDKBIaQ6iSBFQ8I50Np5OdWYirTM2blkA0gcMpP6YpCsVbwEC/uhHP/7ev/nXq/klQWDxSFLXo7Kqq3pcT6aP3nt899F7vXXOBwJSAN71F1cXTd9GKMEzN027Wq5t0z9rO2Hfrpfe+vt3jjVqZ71opXNDOgOk/cP9IHJ+fVWOq+7SC9JkMvnWtz58/N7dRw/ufvrLz58/fYloIJrGiDjnTi4unz15ulyu7bq1zEERS+TjR+oLAbDWZjQakaLEE8GoQ0xKKTCGsMqMEQ7zxfVm0905PHr48OHu7u64GpVVnedVkWcijAjMYjtnUGnSGWUiKJpadk8/f/LFZ5976xAk+XICaq2MMVqruCZloCUhBuf9er0JIRAqU2SaSEJwfcfsCWk2nY6y8nC2/+D47r++d/fq7CIvit/7vd/7+3//P/rd3/vmzk6tDJEiAO670DT9ixdv946OfvbLz3Sey9VFZ/vRqFotFl1vBZSAAvDbJ66qykePHk0mU+tss1pdX16tlivrrPf9D3/8o5cvX/6d09MPbx5zAADn/Wq5bhrbdm46HcXMOITgrW2aTVmWWVHGvEIrPRQ9WBRFrnJr7ds3r85O3zar1Wwync1mq9Xyyy8/Pz8/JaKqqvb396azaZGXo/F4b+8grnxF9OD4OFcGOqcIiizrr93l5eVnn312dnqqSOWFodYDwNfmV//pj/9cE8WigYjKL6r6J/8mzwoiYpbs8gwAnLet3ei8mC8vu65VRm8dpMbT6cP7hzuT8WQ8LsuyLAogmkx2/ui7f/Tnf/Hn680KaJBEjMXuOxt0TLO/smVjCMKDvdj2ym+7wpPppCxKRVDkmTFmVI8UUtM0iFhkpirzEHzTbBAhsDVZcXm5zEsFUIXAnkOWZ2j0qKqPjo6yLHOBJ3WtFqrJqn/+P/hPxx98dHh4uP+DP7/7f/xfa62KooidaIieQbdeqSLEuGI9sLqRd4BkXSjBC0elhaHQFYkDxPGPCCBCAuGm58CBA6FAdM4AEVEDZ0kgpp8RGoVET9sGQkzmE7GkS9SobcweGHpDUfjVoPhOOIoqY+8Erlsdge3gUWBQDIMF4FfktbZvFLmlnJVA82iasJ1OjjA1s+SbVXl55qc7nBdyOzsQgBjgoh4Ws1kulO1U3wGb3/y9qcGiAHA7RBgnK3gQOYk63rEsv7k622Rvy3175zr8JvnimAZtB6WY2XsXXdjgdmo5JGLbz0r34tcUuZMInKQpAhQJ3gMLB48+FEppwBDA+yAAREoQfWAGVDpLpo+SfK5EOKYKfEtTZpu4bRObmxD9zuoYLIcAIuIWAgcOQpAVRZbl8ef73p6enjVNm8DyEJyzSmulNIAQggLJjS6KPPZcPQf23gvIQPLs+171vRJjre06B0xEGtVwnwDyzJRFWeR5b7sUTlIFz8wBAIFiix9YOAbguGTv3Dn6gz/8o5M3r5FAaySlZrNpluVISmdZWRWBoWv7vu+RhV1YzucgYr1FRCAFQE3bvnj53G661WLB3h3s773/6HGeZQCgtSmq2hRl6/p6PJnsTS6v5+u2CQSttfVonGWZUtVkWn/3D39rZ7Yzv16ORtOut/u7Mwh8+vbtfLWyLjgfkICJQFAoGt0PzqxIeZEbo9hbEUp3IimuAxGSMcaY/YOj5Wpzfb0EoEePHt85PCzzIldZVdSZ0T5YROQAVvVG5YUpclMIk+vkxZPnf/Fnf35xfi4SgOKKjSBhphQxe9uz8845ByLGZETKed93HTPnWaFQEMA73/cdhxCdJ9umff7kWTWuSCkgvHvv+O/+3b9zdHTnyy+fLRZrF1hnpqrKvb3Z8fHBt7/z4c6dQ/6vRqvNStXVfDkv85wZ5vOlkAagd0A6xGh+sdlsVutV0zbMQWtFlHnvT09O27aNyxsRtdFENJ/Pn375rO99WZQffe3DPecBYLlcvD1564Mfj/usKLMsy4wxJjexBSJird1waLuut733brVahuC1xjw3AHJ9fS0iBwcHiORd6KRTyrixjQdqtLl7dLy8mp+9ebtbjcrZNK/q8WScaaUQi6LUxmDXAcCB7Q7OXv/G/ev2q+u7zraq49dvXnvvdGaiyDOhFJm+d/d+XZUAorMsK8oyL3Odfff3f198+OnPfzqfX7toUQtpXOIrtdTtP8aBiO2us92rZKCdIuKorrXGuipFWCtdFkXw/urqKlbGo7p2rs8y0/Wdc70xurfrk5P26Og4BN5sNkrrLMu01o8fPzo+OmLrv7a/Y04+8SKfXS0hPzny8Lue7wIAYFmWFFN4YBHxQZRXEfTmobCQJFuR9HpFGDgKb0RdSyeQJYmOofyIb8fhN2naBqL+VgDREDuvwgJMg2Ylx4kTnWttTFZkeWGyPMsLYwwpBYLMiIyIhigLjEICpBiJBrIQS7JViKEPB1Ahhhphjm5at5Y7D7FBBgWaGEHiuHFMoIY7eushAYTID99moqlKiZJPEQQYuuHxCpAipVIIf/0//J9d/82/Z7uOgxcEUkpnWmtNWgFQb33TdN/4z/83hz/4M+AQNyyCqCYmNKjJ3ywpQkKKnJ04/5jQX0xXAgWICEMSPA6DlmXSxYKBkzzo+X41AEc9F76FJLAE7y0LRyGB2ISJ4fZdPZVIxWMCGgYGUs4ROwQDjo0+MEGUSmUR1kjECIIuBBjcF3wIIpJlmpLqDSSPFGZEQUKCLZx1q917iwuNiNs2yLbQj360BOnTAgeIjeXoKMyAAtb2JycnfdulphFzt2lMluVlSQhlnhVZrkmp6NSltTATkfdu0EMRZ/u2bUqqvPfrTUuitQ4RHhNhJAgu5EWe57l1/fDghIHid5PWD+5VwsxxSvhv/s2/+Sd/8ieffPLJxcWFSOj7bm9/v64qQCyr6u3btyFI03Tr5cYa2zXNxeXlbDpddY21NgmiMnMAQ3T38HBUjyaTcVXVWV4XZaVNDlm2tr1l9/79u5TrN8/Oemtt8JumMca0nQEFpSpGtf7WN+/Z/u+cn1389Gc/B7GX55fW9h17DiBBRDgklbeYXDMjgRCwIJBzNtb2gqSiKyekbm2UWMnz7Pj4eH599fLlCxCYTSajelyZUpMiAusyBuAA4FGRQdFGFYv15ic/+tk/+2f/4umTJ5x8Z4UIUJEi8s5Za0MIXdtb5yCOt05ndT3SKlMo1jqjlVJICG3fBe9AglL6/Pz0xdMnhvSXn/+qb9rRqN7ZmX3++Wf/8l/8i82myfNKmTKIMlk+mUwPDva/87vfmt3ZLcqpE1UUNuuCUji4biMAChIMsvld35+cnsaOPRJoowlBWPvgoBP2g9N7WtqMiNPJ9MMPv+a9ZJm5c3RHzU8BYLVab5pGa7XZNHlZOWc3m81sJ1daAwARjcfjrPXOulFVk8a9/d3d3Z2oUXV0dHRxcXF1dbXZNJeXlyAwmU7qui6KInVJAYrMmP0d32zOLi4Oq/LwztH+g8M3F2/OT98IitL647z+n9T74v3udLq/u2tIqzgGrfVkOt2Z7U2m00wbY4wm/MGbE+9ss3ZX11dIEGMSKVJKedcvlqsiL/OiUEpFQIsljCajv/3v/3tHRwc/+/hni+Xy1eVp6DhIQFTbp3zYIQd8DyPUMcCIqUKAoVEFIpJlph5VIkyEIhSC77o2hOCcU0p1XbdarYxRewf7ztnFYnF1db1aLZ1zxpi6rp31y/nVdDxBgKOjO//gT/90b7Zz0G2y7/9T9qEsy6W1y+trCQEAmqZZbzYjreOTDhhtzJOTmKSln4Ibg7AEjl0y4AG8S8PDUVL95qzTmO8AQ3Li3g7KhjCQsnjov5KAUiovitF0Z6ceTerRWGtjspy0RiQfuGs7FtFaH965K0wnJyfMPQMQo6CAhMhoiTrXkZyDcc5VtufAIBK8F2YQYOHk3puS7RvQdOBopfPgdJKMQiniIcbdFSVafPkocpYq9dSjjhM1ECQEZtJJ1g0AunLUTA5s3gUJAmIybYx2WpFWANj3rss7znIAEA4gBBAQmYUlBLoZZwWIFQIispBEUr0iTYwINIz4b/usafmlVUdInPTIoogMDncbdSxD4tQzJAZQwnbieSGoGCEABLfz1DeBN1GoQggSL4XJ3vn3oYkZL3NAkBAIVex3Bg5aSEXLXxZE0BpRAUNqjgqAj7qHKjWLmRkFmCK77x2y1ZaHxbea1vgOHSNNOcepUlLKh6DzTFdVQBRm8Hz65vXzJ1+OcqWJAEAReWsXi4XJsiIvvHOj0aiqyulsNpqMRSSqM90wsQW4bcN6DVqjD7ZpkbIQApAQEQmBIAsqrfOy2LRrSIlIasMkDe8hSU+NHB9EhIiKIq+qwjnXtq33jpmtdaOxqeq6GtXZ1TUH7lrbbjqsIDJvP/rwa+Wo/vTTTzE6VxIpkaIoRnU1rsej0Wg0muR5pfISlLpeN2/PTr/2zY/Gs9nZ5fLk/HI6Hs+v572zs50d6612xjjNXmcGf+e3H8//4z85P794+eJJsL0TDpEIEWJBH22NhvOKLDngrm/m84vxeJplRYQ3RFI7ZDs+hwiz2VQrevH8+b/9t/861+p3vv2dujJGowjErZkd9hT61q6uL371yYuPf/qLn3z8k2cvvmy7hsEJBsQ08i4izabprPXeSyKdQnyMA/N0MtnZ3Yk3TilDSqIvJBKVVc1Bnj15zh462ynUx4fHeZadvTmDgL/10bf+1r/3t5jKn3/65OT83IXw/O3Fq9M/12VhOZjMSMhzM1HkAZXEOoEkySAIMrPt+/nV9f5st65r8SKerbXMHDoOLFsd/Gy9Mhdn2ugsz4AlD2J0tjMd1SRZswaIu2nOHKxzvbXUtCy4s5MeVK31e48eKYfz+Vwk6Ey9//i9w8PDuqoVqYcPHq2Wa2Hp2q7bdGpPPbz/6Pj4eMvhYu8lcF1WB4eHKo6wIORZdv/+/bqqARiITsm+ykZCfi8fP5odVFmhySBSXtWHh0fHx3dns508zyejUd93nyy+x83m+vpqvV5Ht10kVEqTMovl5peffHZ1Nb9/9+7hwQHmOhlOE+Z59rUPPtypxm3X/dM/+zdfvnqOiYI0BN2hkxrDzXZomAdxnqGyEh98XGN1XeW5BpGubTm9naMcTp7nbdu+ffu2qkqTGyJarzd937dt2zadyJs7d45RQNHau17nxWw62d/Zq8vSnLxRpJSh7/7Bd7vZ7tXl5ZTbmEyH4JPiSSJQYQTv0vAvpsCVpjnlhjkFtyC9oVfLMAhjJRpU+rFhvxUBkVhZRrw7XpbAAErV1Xg83q2rcVaUeVmbrBAkzyK9i8LJPqCIIGU7s0lZjnRWvHn1UsQJ3JShmCCsm40eWZCSKQWIgABhmnmCf8crjmeLAonzeJQQU4zrjRN/CXGoHOGm7oo7OyAO84UhgIiiNEbvOuvaVjjE0jEEjyBKOHAAEPDBSIizKiQxAN862qQZMYhgDSVsmiQkxJizkxq6n4LMsWuewABEHAAXGCjIN1nh/w9+wBJLyVsh7Tf8TEp7tm3Y2y/eAt9xbYgPwZNnAA/C0UIq1qbCgBjZH1HKgzRJFCsBjLEzCQkAbPlJEWlJi/UmORg62FEIiZljYZaa16IQTZaRUpRn9c5OPp0CkojYtvn8s0/evHr64XuP0wUiKjJzeXnx6uULERy/HJVVOZ1OH7/3+P7DhxcXFwBQVVVZlt52aUlY59abDkmyzFrrvS2KIlpm5SpXGHFIyfM8SrfE5RNjlsRRJAAWiSxKRHTOaaW0MUphWWaj0agoiqZhAGSWru8dByD0IUjgzaYdjydFXgKz82Gs1eOHD58/eco+cAgwKNsxs9a6rkd5WQagZd8B4uuTtz6Ee48fMdHzly8ZwBR523W9tU3TgFbGZMAlM7CA1mpUGbuas+0lGkdAItgwBx+Nw+WdFwAHFzbrlbN9nhdVNSrKkVbZcOMScMLCCFBWRd+2P/rRD85PTr/8G0++/Y1vP370YDoZBY/LZXdxOX/58tWXnz95+ezN4nqzWK03zcqF4MEF7IECqIhtJR91rbXWhgi9984559x6ve66brmc7+zszGazqqq1MRI8EZjMCBBRHpja3r5881obDYTVaKSUyvKsKkaE5s2bs9bDxfX5erMCbUApz9I1vSD2NhBRUYy0CpkpCTWCQlEAlIZtAQjAdX27aiblOCacOsusd30IvfcQMxaAv/Vv/6n/83/1lTl4UooQyfYA4Nm33uZ5no9qk+f7e3smL7z3DtOTM51M7+Wjw/0Db3tt9MO7D2f1zJgsV0V2lCvRO5OdbrMZj8cPHjy4e+9eWZbM0akQIoE8BJ9l2XRn5kTevn37yZNPfvLpTxFhb2+vs7ZvW1ABSSNS9DchpQFJKw1pzF2MMWVdn56eNm2LhOvNJoSgMhP3NETSygCpTds9ffr0+vLi3t17jx8/3t+dKcok1tOKsiwbjSdHR0dPXr9IOySi3Hru8WbodNtZfveFIuIBWWkoqyIzWXC2bzsAQEXGmL5vRbgsS2NMCOHy8lKQx+NxKtwEEGC9XJf5oqpGfdd3bTstyzzLggur5dKfnfvgEaBrO1vay4vLq+vL92PdEDzEuldYIrOYJQ6oIfB2zkViDcexCkzWdUOvOMggWCQiwFtP7mFrk9ss1VSfhOA9+yBMQECmHE1mewe7OwdGFwFQSDHqzGQAYJ0LQQSQSPkA63WrlKpHo3sPHvoQrq9O2TexWZz6YxhgkEmPm/a21IlRmohS+/RWqPjKTUlHncq/7RIf/jzs5pGHRwiKCAGD8wPf+ysficF739uQWZEAAM52XdckTBQCBAiESqdyToS97UPEzFLf+ze8cMiPJJ59EudETjZs6QYgbZ9SiOdCsTNMihKlDW53QwBAb1m3kSWW9IFumaUQRcIo068pLt4cHgAkyFQBD6NoKSMbvI4gRb/AgZmCBC9BAJRRSCjCgT1IUhCNLK0Isiejx5Q2Rk5ZZALEb9r+GibC0/XA6DTCICjEW3YYISgk1IbIIzhF0/09qMo4SnH69uTTX/y8Xa/EOxkIaHmW7c52gvOddQDAgbMsu3Pnzt7e3hdffPHDH/7QGHN4ePhRXb3nHBFJ8Ov59dX11ezgTrNaLVftzu5uXuaAiJoU6QhCKZ1BEle+5aIYBsQIAbVGBCKM46og0nVuuWy01qPRyHtvbd80TVbkk6rMsowUeQ5lVcx2pgcH488+PX318uXnT77QmQkclKKoydrbXoSm43FZllmWK1QC+Gd/9r2z8/ODo6Nvffvbk+lkvVmfnJzcu3c3yzLvnHP24vJyyjyqR0ixHQXe+6dPfxUuL4LvIBqsSfJXiwzzIV+OKyCR+Fi4621vm/WaFovrsqjzvDQmz/NCGxNxjmH9cpZpYXn18uU/fPryn1T//PjOvb29A0W6abr1erVYrFzHwMaogjBHbCPKpHScEImlT+o9l9pkWQYg1rmm2fS9DcGHEFarVdu219fXeVGMJ9P93f3ZbFrXlfMiQtax6NB73wcX+dq9DUoZALy4vLi4novOglIm05QZVJogC6BCNGv1XisyWlVlXWQFgkJBGWRoMVl/w+XFJQJmWb6/d4Ca3p68hTTAEFYmW02mqVCKOapEMggJgONgneMsPxfufL935/Do+MiQmUynSOri/GpEwMxKKUTI8zzTJhhT5MV0PM1MRqgQpDDF/eN7s/Gk2awIcTabZajA+pCeTkBBYsEgo9EIAP2mIcRnz5//+Mc/Ho/r3b09HzyCXJ1f+d5GAgeRIkwu5a53tusJaTyeAOCT588tBzLqerFgFo0US0GlVFmVOs998N2mvbqeN5t2Pp9/+1vffPzwfopWgK3zptZ3798vf/HTpm2j9D3zwPRATBsIxEAbw9yW95JYMCF4RCjLfDSqlEIIFBtezrrReBybsnVdE1FZlptm3XddVdfeea11pg0RGZNba6sSiJRzjgMrpAA+MqsAUJivrq6CUnmuBRgAvHch9IkVyAyIASQEYtYxlKatK5UpQUJgH9SNB942BjMj37irb4vLrRwGhvSbbRiLIBQIaVOPRrPZbDQaZ3mOqCIvOu740TwocIikK+9d33cCARDzPD8+Pt5sFn2I13yA92VwFYokMrrZjREGBs72ACNNB+F2QTxwkrc/8/+lVh7YuRFI9d4HSUIxw7uGvZ85OGfbVpGKTRzbd+vlcr3ZOGcFOMuzoszzvMi0YQlt116enz9czI+jkjO9mx4klY+ItEQm7K0O9S02lCTdY0IMNxUoIiIhKVRqyxiUIYeOLy0DSh1v4TC7doMAb/+YPuDXssqB+iSxwPfOdY31IUkusA9ROEM4CoVFPgCLcBAOJKIwxMkuF4QlfmlK/hFIUxzjSV+Upp+TMUBsyG+HgAlUvONxajihFRyAojcnhBAAEZQCFGU0E62d1XXlFXGQzfX1pz/5eHlxgSEEa9MQSAh5lj28f+/Bvfur9YaBWXhvf//r3/jGzv5u13Vv3749OTmdzxfz4FmEEDXI+vLyyfMXpq5bUY0NGB7vHhyoLPPgQVPcGoIwKnWTusb0hJFF4l1k3noxR9ZGuL6+btv28vJyuVyGEKy1nl2W71dVURSGFCiguw+Oj+/tKIKLi6u2bSk3PgQBCImHDFrp2Wiyv38wnU6j/cfOdKYQ16tVPR1V43K+umpta4yazSYi4n1w3q9W66KsEFCiHVKgxfXmyy+/vObWKyTPwhBCTN8HMQZmkXCzTaAIx1ZX3DLF9iFKARNprU2Wl1U1Kssqy3KtNSEqoxCJlTTr/nJ+fX5xpTFTlClljDaZKTNda1NqrSHYIM55S4Z0lqNKSrMECkB0pjKTxel7rYPSWdb1zlsRts7avt+07aZp5stl23THR3dHo7E2aC0751mYAye2mCJSuQthMikOj3Z3d+9k1cQBBQAb2DMbUwLq+J75fL5eLYX78WhSlWNFGgBBkAaigiFtSIvzdt3ce+/o69/8Zh9ccJY5tOvG9u7/8a3vXPyNP9KAwuyCP7s422zWWpnDg8P9vf3T05NPP/vscnH1erl0PpR1rTJjLbe9864/v7iaFFl8fm3nuGDvvLOuKmutdUzIrbfOBQIalaUS9taJ575pEZCHDY4EcmWM0kqbxvabtinLoiwLBBAEnZlCFUbrTGXXl1cRX1NaG62Myauq3pnNZtPpZDLVJv/lZ58+ef4iK7PO+03bojaAOuX9pBSprus2mw2HoJAC9+7tKSJqpR7cvZ8b07Tdum337tz58L0P7h3fffL8WQTnABLEisn1Fbdb5K3om/bD+DQZo3Z2duqqivJtAEhErutXq9Xh4WGWZXlujMnu3LkznU66rlWkilGBSLbvALAocmMyY4wxGYs45wkpz/LxaKL6Rikym+Uf/sP/GxQ5EU2bNQCkMSt2DF6isypjxCohPi4DyWgLJwIHBpThn2KNNES7+N+QyNJp84ijuBL5VtFSj8iHEAAZEcuqms1mk/FUgrRNJ4JBAJXqrSWdEUWlGh/lzKzrY+S21iKh1kZr03DsAgJQJPcOVvFI2zmmG2Y3pyf/tupXDMJDoE2VMyJK7GVizNrV7UAsSYAptbolsLM2evXSFvAYfp4AgrW23RhCCB4A2s16fnl+8vZt26wBJKur6e7OaDRSqJi5d/386so7DxFVEoWpfEvHl6LMrZnr+H0EoCKlTqLeBcRVBAjCPilkCFA0Icdk7hJ3wdhhjOFMb62pkg9v9FLcBl2RKOOOcDtPGdKe1NiNgTq9yznXtr33QoAhBNnKd7AIi0bkOGlktCAwMBIwMMstJQ2WyFwwhhSplIYAAiettiihtg3/wfvgvUgaaRtudGQyMHMQD+CZgBQmxR8WDoHbptnYjrI8J7W+vv7k+z/48qcfo7dFYQQSWYMEqjx/+PDheDS+Wixevn7Tdm2ZF0Zpo/QH73/wH/7Jf+/NyUme53dR8v97DiC1yS9fPf/sZz+/XK3WvZvs7udEVVWWaurEiWDyvUIkRUSaB8wgJsexek/JRZJrZxBUhHt7OwcHu+v1aj6/FmHv3e5s5/j4uChLrTUCKI3TnTozdPp28erVayA4Pj5uunaxWAAAEXZ9D609mO2MJ3WeZexlvVzW4+m9u8dvT0/2dneVpvnietWuIqOxazsXgvXBc0eajFHOuTw3wPLJzz//+c8/ab1nluA5Rp3kRy03GwQMMmfhZqXhkD4PTpfB9bbZNMvF8jIqjFVVXZZVXY+1zrTR43GGrEIQ8ApYiVBu8iyrjaoATADf+W6+XgTw2miTG60xMdxJxZmYWF4DAJIHojwviFBA1uvVarnsrRURH/ji4pJZ7tw5KooahBCFACGI1kbSTItYaxWOxbnriwsqu8tV27vQe7bel2W9s7eHiuq6vnN05+Bgd71aXF/incPj1y+eLEPS+UMRBTSt6lwVEAJaP8ny4939ztuXZfXg7j12/vWrV0tt5vUo0zpuqpJn1HdlNZoc32sDf/785QuG097PXdgnnefFetNcXs4vr5eK9OtXb/JxHQkTy/mqx4KDOGsJaX49H4/GxmQcIM5AayJkhAChd531cT+LATgqxnrB5WK5XG9WbXM1v766mivQ9+/ef/DgISnFge8cHp2fnC3m89yo0WR8Z3f/YO/OeDzTxpRlVVX19XL+i88+2fSdqfKLy0vrnTZGa02KEJVz9uzsrHUW42C31iTMwq9ev7XWdZ396IOvOevOz8/vHB3tzHa++Y1vvj5523UdAABRYvcokCBJK/4G5xtg6YSrpA7xZDJRSL63zlqtDZHqur6ztq7r2WyGiKvVipn39g+ePX3SXFw2bdf3/fHR8c5sd3d3XyutlBYJlIZZgDlY227OT713pXOPf/Wz21VKVmhStm2TILaIxH152ElD8u7CBE4jYrQj8HFoOrFXGGKPlANzAFBD2LvZm+MZxhmkEBySBHbMngiqqijz3HVOhPuee9uTNibPkYio1zrLjOHggxdEEmHS2mS5ygxpDRyMMQIkIJRYQmmqNOrIEYAAgZgYYmL8haHAlQEaZ9n6ajACUDKwFUrKItsKD0EIEJP1zhC+cYsXiiALAXCynohhCEkg9H2/XhlMC9i2Ldv+zs7YVZoBoShMngfnrldXJsuqsnz44MF0MgEAkxlyJAxR6hEA+d0E7isvShVqbAhH4g6hIpQgLlKwZGuOMZCg09rclrI3c8CJEIEoAwFqIBIPwptpyChdjBACpznLiFpEOgGHEJx3MsTjEAKLBE5t82j4hGnQO11rUsTDmSKgCDjngSXq75MQBiBQEqLaa0LjcbDIkMTxEUFhSaS8kNwXk9SLMCOZ5CdPiKg4BCccguRAemNf/+zT1z/+Kc4XFVJZV4jgvQMACQEdlyrLUK2u5tfn53lZFiZTgGy9+PDg3r1Hjx+JgD47ISJg7prm9fOX529OXp+eds5fn53vTyfTcb2nUGcVhzT+X9e1IiUiIUSKEqe+jwhSarJAYOEg3ittlKa6LnZ369GoougvgQGRy6xQqIkJGZU2VZFvmvDLT7988fI1osb0FSFi+4gIBPW43t/fBy+brlkul0V9rQiLPD8+OvLOvj158/LNy9V6dXV9NZnO2r5dr9dlWWR5prXuun48qjbr/uOf/Ozk7ZntvQ8hhPhwDVONInFOLDIpUleDiYFi/jz0DlLbQxKFXrzrne2azXK5VGVZTaZ7dTku8qrIKqW1ZiVKBU8gye4PEASDF3u1OG36JRlUWmlN2hCJARjg0IgFIYJAVJxWRCZTHFgYOaAxvffehxCXX9t2wpCZMnbgQmDAQESBebFYiu+mVYktd/3cm/L19aJzrPNMkJReLdYrATBa13U9GU8e3L9nkJrFajbdO339SgEpQM1okKamHOe1661v+/mr0/nRm+n+3m5Wt3nIPvxwfnXtvbfW5sYIsCCPx6PDO4eT8aysxp988snrk9OL6/n1fMlEJq9VVvVdt2ybi+Wia/vVYvMwz2PZtGm7tmglsLP9qlk17frs7CzPi4P9w6IobdcSCDGBZ9/1IIyEMawCwLJtXp+fgjI2+LZru7692qw3rVWqePDg/Q8++AgRQvD9ptnb2VvM533XTOrq6M7x0cHdLCuJSJsMNV1cX/XOHt+7a709u74EpTKttNJRA67ve+9ZkKIpUtw6bAjeuovLq1988hmg0iIX19dnl5fjnZ333/tg9uMfnfanFC3mUcVUL9KZkhAcxHycB/AZiHBgA2hE9CFY2/edHY9zIrVcLnvnqqqqqoqZF4vlYrEyeUHa+N5V1WixWAFglCfLi9zavu1666akJIQA4EMIc/L/7D/504kKfbtC4Onu7ODOYT0eOW0OW39+3jODCA9hKYjo7RxHqoBv4unQtkuUzChBEkOr14O58rYmY5FoLM7MQhGDCkqI2YtINKDse9d3HaAmbRig0KppNiyIiFU1KrJcK92slt576xySKcu6LKoYNPK8ICQOgYYKVlLRExOKNPYw1MGpXEx83qHyA+Cb6Zkb6DidbqqcIL0nBfpbfxORNKVUcnO4efOAbTMH55y13tp4lGzt/mz3+N4xQPDMoLUXfv78RdecaaXu3T3enUxHoxEA6CwjBhw4vLGbi8P/YukeL0VE9pzzHBgQBnI0sqBKBK1EAR1o4QFQDWfwDs6ezBi24TAB/NtrIsn/WdIbZRuBQ4iM8CSwvF06cX0kkFGEEwMwDB1CltgCZubA3gcxABTrcxJGpQ0COh+CCEZhJEQJ4YZWlQy21MAoi1r3HBlwg9ZQ4BAGZD6dESJoUlpro3VOVBsz3dvNi9woNb88e/bpZ/3VPA+clfl4PFGoIi7hnXfWrpbLzWr9+uXL4MN4NDo42C/yQjyvFoumbY/v3SVUXcxWAr98+ep6MUeCyahy1/NuOX/22acH+/vVeFTNSAJzMHlRjkYjpfWQqyb7u+FRBEg9dWbnQnBaK5Hwy08++enPfvqLX/zy888/D+yc6xHwi8+/HNXTvb2DEMJ7H76nlW5b++Tp0/Ora0EajcfjyejZs2cxZbt792ia5Y+O7+/Mdq7Or50L682m3myuLi92d3f293ZZAiFcXlzE3K3ZbNqu9c6ayXhU11HlO7C8evX2459+bK1nYR98VAFLSqNDm2d4mgasgogwGtDytkLBQXFkcOKMvFUM7NebVdO2Spkir6uiHteTuhprygUViyAUJq+L3IDSvWPQQWWMSvIiy0utlOIEJGECgRBD5E8AZihx40bkPBdE8t6H4H3wRpssy5Si4LyQQ6VFIIgDRgEipuB918ni6tqM9zI0nsVaGxgM5gLinDW+UFpZ6xbzN2d0Oq7qKssno+ndo/tPPvuUOBSUFYQ56gpUjRozbZnccvPmi6fc2lpU27dlWdZ1vV6vz87Odr72PjAum0aQ6npEZE7Pzn/281+eX83nq03Xh2w8Gu/d3Tn4mrN8voS+bcFbVbSOE3XIhtBZiyAM3Pddd9o452az3czkxmQCsFqtFJNRmkSAg1JKGR0p+RfLxafPnrBgluekqOnal29P5qsNKvPZr744v7iII6e5Nns7O3t7ezt7u7lWaHTvHJKuqjrLTQBx3t5/eL8ej/76hz/YtI3ODGAEutMojqItg0VS5yKwMKiGLi4vf/bzn1d53jl7dnlxeHy0u7v39Y++vlwurQ9ISkhEvHOOcavyPxRew5aekj1KqizM7KwNPgBAtDbqe7varF+8eJFlJk5Fr9brpmk++ODDy8tLEfHeN2375Zdfjkaj9957bzweMYf5/Go6nWZ5JiJGwf79gxP1bVuJwdA0y3C4r4+PN2WBqA9/+uz5sycMBUv0E0sVjdyMb0Q4d/s3iQa8ZfXE3mtyhLrpXA2dXgkkNAQ2GZQb0tOXZVkIvm2Wfc+KMlOW4/HYO7dpOxbUxhhtvLMxDsZGeBCQ4Pu20VoXRb67t7/ZrJbzK5awnWPldH2HqaRbwWW7lcV6GABQmAbXY3gn0MT7xMIsxMMHRyeDlHTEAxsYq2k8SdKHDNBamkVmiDRREQAIPihSOzu7eWGccy6E1WajANi70Ns6L2aTiVYKAHwIHDABv3GQa4gchDQowAxtAmH2nr0TRdvLLCLBAw/GTe++tl35Le0KAN6dA44lwq3a9N148I7kVtxh08XattpFJLD33iWinwy0nJiLcvDWcmCPPvgQOPgQREVXEowsXa20Usp7DixKK+Bovh2/JX3jTW6YRGW2LAYAia6WzMERxqHq7ckhIIxG4zuHh+OqnBTFdDITJBv8ZrNRghUpBpWPRru7Bx37bT7Rtu2LZ8+bppkvFvV0UpdVVZSRLhSse/PyVV3X48kkeAci3ru356eMuLe/rxia61Xr/eL8/NWTL3cOD7OiMjkECU6psizqqopYUxzagaHjnqjrA9keQDiws/6v//pH/+pf/qs3b9/Y3kZZzbKoPvv0RV3Vk+n08eNH73/4PgJkWTLCy/MCAbquExEE1EZXZTmu6jwvMmOsdc45571zvuva4+O7SpPSpIjKvHjw6MG9u/fmi6V318KSm2w8Gq83m7qsF/P1X/7l91+/ejXsE+LZM/MwHpfm5eLiHfpbAICktKAIh2QriXLLE2XYTmJbRUhEQnA+eGvtZrNeLpdVWZZlbXSe6+LBh8d/92//8fuP3yNtPn/y9J//t6vlL97mhd7d3xEAzwF5YDKkBxMIEIgQSQ2D7AKolVFFWjIi6e85hEEeN+EnBBFfCZGlulyupsWkKnP2XkKItkd910XitVIqsLfWqrxw1ppRnWX58dHd6WTm1ryXZyPwygfsfN8vCSk4r3JuFoszZiuhYbdkmxmTZ9liPiekosq7vgdS49EIEJ8+fX5ydu58CIKoi6zc3d1/PJ4+tE5N70y1zwRQid2zF/DxfxmtUAN7RclbihCyurz76OG9o7u5yVbzxfL62nmPBk3M9JlhUJO9nF+/PnmbF+Xe/n5u8s7ay+vrsirv3r+HJE3bAQghAmPTdXq13jHTsiy74ObNonLOM+euX2yW88VV17cvX794+uwpKoWKQrjVoUgOpxDxqXQLQghB+p6yLLu6vl4pvbe727bterN5sHv/D//wb5ydnz1/8YoBhBJ66z1vH/eb/6bFl7yj4/5mrY1aaMYYJOTA0UL44uIyBN/3/XQ6qavi9OTtRx99NJvNTk9Pd/b2Vstl07bYYGf7EYyKorDWXl9fTmc7IjaoUNZ4fHfHNqeTUXH/0aOgCJT3vlVZlRnddK3WGjUJMgMNT/dNacEytHFT4StpDjjaAkXAb1DhSKNN/OtM4JtAGP8fIbeu6zebHliTYsoypRQEyLVxzBK8s/16vYy5alEWo1EdBJFws9kAgFI7k8nk3v37HPxqNd/aIqYYjBJE1ICBKqHbT7QMagagIz/r3SAdnzMUTERwuAk4KbLKtpc/lJDbZrngkJ/QEI1jU5aGK2JtH2xnXedCn8BsZyF42zQaoFmvm9EoagNE+vnttC3GtajogqAkuj0BCCJLappCLN3T/iAi3gc/eOamGp5FgGNJH/9atrFcQ5KJF4A0JCxDl04S3iHDUNOgSSosaXo9dvzCre2ThYP3jkVUvFjMSf0hMPtEiR+6CAiEFI29WJy1YSB/RQFxrSgNWSdeDwcGASGSwCzsQ3Cxth7gTByyJxZmVLfcgyPRQylEYA5d3xNLUVTaZESKvd8fT6CqoGsms93dnb2r9XK7RLqmXVzN+7YFRc1yffbm7f7uXnu0qqoqOP/29Zuqru8/eOA7KyKB2QWe7uwcTHZyJ9cnZ2wttv3q9Gx5fr57cCczeUAMwSudTadTrTSzhHCDyMYvHZr0acCMWULgUT39+ke/fXznodY6y/LMZDuzw6qqAruLizP20m163/Z5mc/qUU6alFycnV8vrwGACIt8IDdpRVrFnFMp9eTZF723WZmtm3U1ri8uL4oif/jwsTE5BxbvxYcyLwBgvV7X1ej582c/+fhHm83Gc9gqDTFzCFGClHKTRwGvEBJOhRhFdARQpx5wUr0ZKEmICCAoIrRNsxgZUQlDCL4N686uaXmlVJbpYjKbUObf++jQ5MXbyzfOt8rAbHe6s7OzWK29S7t5vPOw7dRECRYYdnsSFB2TV6W2OVwIIeCt8mFgWALEzBypafre2WoMOrq6o6D4rm+LslYKCUVnme/txrrVcnX36E5ZFpPx5HD/YMV2onzpWojzIeIcS4jaW8xd1+nMsKZ2s8hRTUbjvutAoKrGXWdNVkwnO1fXq/OzC+eCtQ4Ei3Iy27mb5/uLtXMSpNh3vQ7B5eSYmzgZwAkoAkSqqmJ3NpuMp+O6yrMMOLCwEAYOQVgDWRImJpSAAACL1WK+nI9RKtsyiQ19UWUH5oAI41i5RkJEhQDInsPF1dVqvc4UTavRxvRms2EOlt3F9eWLVy/PLy/LstRFvmrWgQOpiH0lTzMcLnbcR6OtmlI6Gqwx8/Vibl23d3Z25+jO8fHd99/78O3bM88hIAgoxIAIN7YSw+0c/hdDGHgfyeSp0gSkru8Rsayr1WYFYKyzr1+/evUKvPek1OnpRx988OFyve66bnd3dzqZtG0bQliv15nRWuvNZtP23Xp9DdI/erg/mRoUbf2GwRTFWJAEFYjq+957rw0qldCFLRyJQ63yTtRiJqIIO29t2mOSjhIh55BiQby3t+wFb8dipVSW5QDYdZ13QWtSmvIsi0wUbUxnnbWWOfRtCwJZnutKGZMJKgGMsvzOeq3VeDSazmab9XL4ikSGTse2pVZtmWQwJLDCAjRwm2A4QSGVAtgQdobC793G601NuP0HBIyjuBGbAuABIY6fvb3/3tt2s14vVy44o01Z5ME78UFCCM62681qvfJRQ4mFQ9iS+LZ3I0mqRWf51MaWW/cqdtwFAORGFgZvLbyUVkjMk26xoAVAJ/rZzQn/hjPHdz8v1grO2RBi9sJDlpN+H0KIlzSkvqZIYAkBOKAIB2/Zub4DYZMZk+eoDIgKnkOIOtJD6pH0qjgoTvz9KKOfBtoT405CEAlxHjDmtwwcJMpgx84QBw5apCqqdr2+PL/SAA+Pjw2jX67zvCgRUVw9KpCnx3fu7h7d55M3FKszZu962zWutybL0Yhtu26zuTo9p8N9FOg7+/L5K0Qz2rTxyS6m+/s7+9j3Zy/fZMHXEhRjuJ6vXr3u9o8IM5pMUEB8yPPizt3jq/lF0zchkglgC97KzThXhBYCT0fj3/nW72Ymy/OyrkbT6a5RxatXr548+zw4HI+qxdn1T/7i5x+8/1B1LvfsfWhXm27dELMizI0ihCAcBfdjVmYyc/L09OHX3pvuzgJKa7ur6/ne/v5sZ2e5XDrrYothPB61bWsys1wu/vr733/x9EXwKfoq1EZnW/pzPOD4eEfrwDhUhoikooSZigGYB/VNScBO7FDLIPcDRKmVASgx8w/Aztm2W//lD7/36uTFn3//737ta9948vTp2cUbk+n9/f0izy/nC+Y4HDpgU/HhgW1dnvYJJIVp54ojDVHfJZHwTZTYQwIghkTSDMKBJQR/en42mk2IjCHwwH3b9F07Hk/jFyiiqqraTXN9fU1EdVVdCe/v7qr1vOhX2sfNNnErommaC16Ex9NJbkYlaJOp48PDpuuUMlU5WtA6BLyeLz/++Odv3pwGL8FLXtS7xx8ePP7bMvnw1dqsGrdoOXAAVJzr1iXUjlECgHNuuVy8fd1ejOv79+6Htgfrsiy7Xiyts4oQgRlQRaMKSFSsru/7rg8yX23WpMgFF9iTJtLRXjECxxAHgBCBhTtrRZv5pgHsIlM0y4zK8+neripya23TtkpT4BB8cMkyChw7REy6EpF+wywiitAHFzXzneudtyfnZ+81GzLKJ8kzilsmgarL0c50BwcDGwF2wUUOMKZhYk2kEcjorGs723VBuLP9bDY7ONjz3q5WS+ftYrmwvev7XhA+//zzr3/9G977i4uLqqomk4m1tu/7zBjvXD2qBGG1Wn72q0+9X9fj36lHM5Vlrm+vr89K58azO0VRtx2dvL101teVyk1lXQ8CHIRVyj5vtt+BDCzAEbK5vSEPUGdCWbeSFyK8/dF3wq+IUmY0mnRdZ20gpU1eVPVIab3ZbACxKMuiKABRAWlSKKhAERABCZAAklJExAzWOiIajSZZXrq+GZAtRoWRdC2g4mM7VLLxeaPYH94GrNgEZhAGZhm0qW/HqZsgO2Qot85eUhTDiBHGAWFkBNg6AzNuNaUBhNl2zcX5mQu+rqq2KXrbA6g8K0HEdv1muYp8W9zOVQ0pIDMPRSzcHFLaO0QkAd23d2lJuHU88Zu7kKZCQEiSYEi8zxpvf8FNVzymWCzClJoyg5EWvqMBeWv1pMAvA8da4Pa4msAwriccJFl5ijZ6d/9Av3gNLgBD1AVFxBBYWEwyNA0iiigisgEgnjYyRwvXIMFzmqEecsC4g0cm+BYhQIwWbOx40zbnDKbpR1k+OtiTzWp5dSZdMyrLw529vZ07y6ZLpFlABLG97bqOma33o8m4rurgfbNpWKDvbNteTCZ7mh0IAFA12x8/fO/61at2uS69yxUAgmrb9vy8vbzS5USZHEgjMRIdH989Oz+9XlxLmtMeqAgCIBi2ktcIiGCtnV+tyrzKjEVRDx9M7hzs16W6unj9vGn0qOTO/uLPvv/0337f9v3I8oadqQtFJMKKVFnmCoGZlTFe2HNAgt71053Z3/t7/4FlfnP69mpxjYiHh4dFUaxWKyLsrR2PRru7u03T1KP6i8+++OLzLzfrzSDHjYSYGcPCEeuON6UqK0SM5C/vfaRpxRKHKFkIIqLzwTobpb4EkBAVioAgJiZnXF48eKUJILP33jH712+f/Ff/zflsupdnFUaSZ1H21nNImzISDYEdEAmTnVVcmBzxHCJC0dumDUIaVxMA530c3EYU4EBKE5Dn0HurUE4vL47uHhfVNFbWm6bJtM4zA8wM0FqntSrKYrNaNKt5lelucW36fiRgfIDALMN8OkBsP2ujnXPeuUI4V1oY9nf3dJ5VVT0eT8/PL68Xq1998fTjj3+2bnoXQpGX09290dF7+fS9FvbaICvfOwgq06SoY7toXGBRFDkj7J2bLxbX5+fXmbk+v4xeQ6PxyGRFURSzesxKS0IqOLCPuZS3VoJnB8wekASZVHzAOTXHYpaTPEVIAUZ1SSZFikgZZrFBdFHuHRyNJp1z/Xq96rqut9b2vuv6vnPW9oTKey8QbrayWFsMGhYhBOcACRfLxaZpxpNxRL5i6ze6vIzH453dHedc9B4C5uD9IGEEiCrPckTsO+udbzZNu9l0tsuLYnd3lufle++/RwSnp6dPnjxx3ka0s+tapVU0Tbq6upqOx9ba169f379/vygK50OWGa3HJtNt1wOioFJZLcDebdo+wMb1vn97snr1+hLBACijMx8Cs4+PQ9xEbyKNbKfnB+zodgQYIvQQjdKOzSCECbPFrdssAiASkdbGu40wmFwpTQiw2axDYJNnJs81qTzLtDIKkAQFyFkrAEJaKY0qsThD51LNrDPXNcEHStjsABTfKhhiVTvAeDj0jJmBaQjPwMPWzInffLs2lFu/3zZk09MLW9W84RWpbbFsE4EQhisEEIK3/Wo+98K+60Eb5z0LT2ezrm2apinqKl5hQlSEgomtEkWoIYrY3YqvxOnPWzgZRQC2DJ5hbGyIxSzsvWeQ4AOHqMRyc/C/SQkrUdaihmeIThzJBhgAMUBkXg3I87BU0tdDqlgwynrKUNMNcTgeIIMmQeydXW/WHD3EvRNhJEWkQuAQxGiNgxVI5C+H4JAYIXXloq9wZHpF0Z7APgTPHsUHMSk1RkSFZJRShLbr5pdX8/PzM1Kno8njg329OFqdnPj1UvWdKnWuTJlVk9leXlQAYHuLXjQpbRSLrBbz8+ury6urO8dHj997b/fwsCgK64IP3HZJQPX46G5XjZ+fXXXn84IJMWdQEtCu2m61KZsWR9YbwwjeO010fOfo7euX3rYyyKHcrKuEboiggIKd3cnR4Z293T2tsizLD/ans1l29+6jvYOqHpnl9ZVR1F4tF0/eHlbj4niyUVZPRut2RYhlVWZZzsDWWZMZz955K8SBw4cffXjvwf3Ty8v+lVuuVvVotH94ELtlJsuD8M7ursny9XLurP3sV59eXZz3XROC37prIaJWOj4gIbBzTpJIp4qIoHOOmQd1BCJURKi0MhnmITjnUpAGuKEeikQ9+C0EhYQQ2Q/BheCcZed5vrzOsrauq91qR4RWq41wLFsxJbLpQ2DINTHmogjRVRUBhTCKjg1tKEAW6a1lgIwyjVlklmHslYj3znbNerlcVPWszLPr9WazXu8c3skzE1Nn23ev37wCDoc7O9w149m4vzyzpyd526J1gRmYCQLG0TN2Il5rQpSubbQxmdZt10Jvf+vbv62you29Dzifr589fckBDw/vdNYC4s7B3Y7MfLFq58CkyRRZliGSgPeutbaNpJ6tmul4MpnkxaQo6npUldVstqO1RqXaruv61iBBnocg1ruAELGoLMvyPENSgQSAGDjWksyMhAK0LXqivj+ZPMsLUjH8RvcpjM0mACmrcfAuL8Zd13nvrXWbdbPZNM2m6fpe+g7FQzSOhluNrYhwcky/wHl++erNYrk+ObloW+dCH8QpBADorbK2U0oRxH091hlpA83z3Gjq2r5t1icnJ+v1erNZbZp1WRaHh/uTySSE8N57H945OrqaX683DQsjojam6/sIIC/mC3fXVVW1XC7Xq/VoNCryUilDpO7ffbSZTcaTGeqcyBgqtXFBYL7yl89ePPniZL3hLB9Fod2UCw6mTBJXo/DWvW2748NXAnCSl4nDSltiT9w5klBo8gVKBkHCIAGCY2ddb7IsBO+DhWhBpjWAsARSihQhUCxqbOOg64rRONfkvffeF1xopRTF5iCywLbN+5XoEcsful0X4hCahkFlGKJF0tTavnV71pDGYbZfkSJ5pK5FPaVttBo+DuJ5MyAzbS8miHBwwbrYTI1jOcJFmed5xiJN2/oQACCEEFKBikRKAKPT6rY1MMwPwfbGpaNNfk8icfpjoNTFM2IJ1lkBCOwFAt3WhvkNAXh70qmUTGnWzWMQxbpSnIchAqfaMzADMDvHgUkr+MpNukFVBSK7IHqlu5BkJbwHgDiZIMKYRpY4EveJiENADkjA3kO0xYpa9iwgwXvvnZPUaw7DaKwiQGDerBZvX79++eL5/OLcd11PWm+acHFx/fmX2vbBWRLuu65ZbYjUbHc/y3MA6JouuFDlZV7kjFBPpjrL7hzd2b9zuLe3l5flg8ePzs8vffCr9VqYlTH7u/tvApy8edNumt2gClJB6U6Ua+3q8rra3yhnxaogYG1n26bUam823azmzrGwCNysLhkYgPGy3Dk6mI1nk/G4bbrrq4XArKhGeYkPHx788R//nR/81fe57XIv0roDYGzw3IBVOqpeZ5mhyFVBzvLMe+eD6/oOFd69e6/ru8ury4vLSxf8bG9ntrPjQyAiQijK/ODOgfXWe//y5cuL0/PFctHZ3rlAFM0EEQE1kSayLMxsrbXWRvgxjnzE9Q0DMSoEBlDEqIwyxmRZFt8VIzGzaK2UVokwEyfZaWA+Dup3IUjXWe9DlmXf+d3f/vf/9t+bXy2/970/ExBEdWvbwnj1JE3hwqCSlvAbvLUNSCS+QZScD+J60qSUvunQDFluYG7blhAyo5fza2As8lxHtW8ArdV6vRrl+Xv37z86OvTL+erkLayX5Kywj9ujEhZhL16Cj4gpCjjr1vOFKUsCXl5eNcuV6P70cvH27cnT5y+B9O9/9w8fPHz47Pnzs4srVUyaJiztqg3BmKqc5HEP8KG1/Wq1vGBmUBAxCRBRRMqY4MJmtfK9y01+cHBQT8ZN1zabZm07I8xIQRgIgwgABOau7xhEFImAYNCGlFKkFESLqziDEYMDBwqBvNegRYBEYlkcBROICIGUzsuSlMqY2TmX5XVeNnm5Wa/X1GjnOu8tBxdhsPgzAFEAHBAosGw2zaeffnZ9fX11ddX3PYP37AOy0RoRtVZyo9ITUUQEQaVUXdfB27Ztl8tl1/VNs9lsmt72IfjXr1/v7Ox0XfeX3/8r59xqtQIUrZXWprf9kydPzi8vmr6H0J+cXBDh/Hq1WbdN23/9618vu6CVyrJR07Yf/+STslJlUZZFAexXm83ZxcWm8cx5lo8VlVs5+jSuMsSbof4TSPBsUvyLXEwEAGC8KesGbjTGhcqKboDMr0DQIsE5Z51l4RB86MXofDLdMVnugQVj9ZxS3pjw2xCsd+Vkkhd5u1yu1+vAYVSPFGkBCIFvfG1TW/BGbGP7zVtqdzROincDIKpp6m1pliIxDCMTwrBlWuGtz/uNL5ToZhhRaR6G7N8BrhFQKyAgTOgvEgqDybJkvxIt6uPgu8SCPgT2MugZxBLjnQ8ccN80pnvrcg8N8RhmE9tFJHAs+Yfkavtxmlgo7YvRNSmG22hglKQxb13ceOWZBaM1Y2JAIyCgikuHxcep3JQrpLSBRaJDDgDgzQQ65HlujFbMHEJ82GIhJQLJnQ0JRCj1AZnBA4AESnIcwpgq4cAhBPYgDOxBPIoWDj5I33Uvri5fv359enLSbtbB9iJcaMNeua5b9LZgropMRWPXviWi3Z3DIlbAPtgQ6qoelXk9Hk9mO0Vdj0aj8WxS1bUT0UYL8nK9nDgbWWWZyULb2E0DEpRRijQorUhZ79frxaxZZ/1GVHCebd/bvlktFwpREXVxdGpIdWCQlYm7GDNfXJxdnp0brax13vNkVt27PyNFSuNsd7S3P71+1Y4EtFC16kK2pKrt25EIVGVZlpUxBpkRKC+z1fWq927dbKrRaDKdvHnz5u3bt57DaDze2dnJ8uzq6spkxjmvlTk4OAwhbNabJ198uV6v2qZxzvkQiKPdtYoEU6NNMoZCdM5lWRaHi6J9AqHn4ImSAXiqdxURkdYKAIk0KUvWpthPOloFpxgcG0qDmzMhmQyNYeesMfob3/jG7//+73/2yeepyMVoV5cezndBvNSkiRBOxFxv2cWmAJu0M1nYe9EBMADFST4kRC8UEFvv+uBAgrCfTHZRBEFIGQHJc3z48OHf+u7v39/bffPki89/+NerN2+U60hCgCBRGQ4EALQAAROhAhEOAOh7661jo65Ozl4/e6aq0XVjn796Za3/g9//7rd+61vW+cvL6+WqaR0yY5CAAIooOEexE+udbxbt4kwkIJqiLLs8886Ld0IoiHlVjUZjULTpO+g0EqHW1vuub1nSnhUr4N71q2bNIkKolGIMmskYYyDLCBkxgW+IEL3/AgTnE0eGiFQixuNAjIoSQSkRIzSZLiQjAlIIyNaSc8r7yE1BEba2Zw5ZZhC1ImLmzWazWCxWq1UIQWsdRDz7uFOVZbm/v395fo6wtSKI4smiiMo8bzkAUVnWAFdt2zlnRSSEcH5+/vLly69//esM8uLFC2Ypi1oppU22Wje/+OQzz6EeTUCwtRYZtMkXq+XlLz558/ZsNtt5/70PlFYnJxevXn15dXWOiJPJuCpzk2mT5XU9LaqpUjmAEhZUiphCSqxvBji/8krg8i3POUl/GScyGYCH6RhJsTxuyGkkBLelZqxtFCmO6nogSmltdNc0TdPWo7HRudJaWIRC3MkNiCJiFlIUZbG7rmOtaeAZJ994UDIcaXx6OGpSRsnK6DKXxO/ekVnG2KNPhxibCZxC9RYyBYhDtxJiQxy2aNi2Fk1LLybWGCEPEZHgkySkIGpjqrJ0gYEUkg7C3gMh5VmmSW1XJhEpUpp01ESJQiKQxAZEgEmEUg4/QOssfEPzTRsNihDgkA3cROttxhEjV6RE6zhDmwZ5iWJCNNy57W61rcMiXTEgEkd3A5B4CYgAEAiGkH/rsCI0kBg7RBClnQEAwPb9YrGIhxlZ0vGxZM8hMJFSSiukqPUlzMyewRMhSwDhEHzkrfEg/8DBswRCZGd7DpvF4vLy4uzs9Pr6qmtbCQGYQTyAsBdBg0LCnjl4JwXkRiFxyIwuZjt5UQDA8fnr8vNsVNdVVU1m09nOTlHXWZaTIkBsuq54ezK9vsrLeodAOwdZDgR914auL8kYhQYp1gyIHPrWuna9nrPLrHPeB+v6i4vz07PT9Xrtg1fRqT6Ofg8d9HhnRfji4vzpl0/G9ejo6AgRr68v2/ZeXWqjELxt1ot2PTcQVKH6nvu+UQXbzQrZjcqiznMF4H1QWitSTdt0fWdd/+FH39jd3/vl55959kWRTXd3xuOxD6Hv+/FoZK2t63o0Gl1cXLx6+XLTbATEeS8SqfXMjFvH4ljrRDU29p6DE60S+YUIQRECc0AUIoqjz9Z7RjRkiFBlxhACETjHgWWg1A1PXFr0cbBIkAhJmxh64MWL19/73vcW1ytmjuXysHTTGo4o39BIAq2Vjz7OIpjYK3FbSy6ZAslOWJL+S+ytRDFbYgGVFYGUFy4Kc/f4sGO12axRq8nOntaGhR8/fvSNjz76/Mff/8E/+yfLF8+Nbw16BI4sLwQkVIAMQAUSKsqjzyiiUkqIHMjy8vLk1et67+D15XyxWPzB7333D37ndwno9PVpu9ywZ2u541wCGK1JKWYmESJCEd+uDPeEmAX/7Zefr0zOwUsICrHM8okfj3mtWy1XKemw1lrnUtWCwADl9RkA9N72vmcQYVCikAC80pFLEeT/w9qf9Vq2JOmBmA3ua+3pDHFiuEPem5k3M2vImpKsJpukGkKr2WgJ0IMk6E2QnqQ/oH+id+lJ0IugBhqCBkpNUiJZZBWTxZqysnLOO8d85j2twd3M9GDua++Im1mkgN4IxHDinL3X8uVuw2effUY0RmZEKgVhIIPs4ZQLWimolcmpb4wnmoIdUCWwGGg+ayTPiYAZU6KUBizTzyQlRYQQCGtvGBE2Tey6PJ/PKczv13eStQZqNH3cRIgmxMAsOeWcCeHm5vry8rLrOlHxtjFV/eyzz7quOz0/W8yXpqVHSNT2+97gbrFcMkdEFIOmaR+9+/7Dd95NOano3Xr7Nz/+SWxi1+0Wi0dte36/vltvOuTQLk5PTs7m80WIcyRWBTEhJlYWzcVteAVxUtSYjDkcYc0+TLCMupnKeiXLoZKj1cmABXWvq2029L2p+jASYs4prdf3CzkZhmG72TCH5XIZmBUtE6oqM8R2PqaxX2cgiE0EAElpyGnWtsRUG5bJtEyMwDeSRHAFq0kfwsrxc8cDUFyvg1EGFSk3ECjdPmpmClqw0Drr3ctQjEiBg7fsFDdnaorgOhAoXqAyBQAmjrENcZ5lRHY5WEBkYkaKGBjAK1YAxEjsxobZx50UbUV4Ezd2L6oOkhmVaHKquTuGVMMLKijb4bkd169DIbvX2oMDfFolRsGVLNzQOtexEOIdKtFDjccL5IejVfHxN15YI5/yuJj44cXFs8tr68eckooiIhCNuU9ZiWmawkRVx9VARVBUqQzrUziCKkSzSTaB2+urbru9vrm5vbvzOUUV6/DOVc2gmRKEJkamJJJzBpq1iwDQNnFxsvRt948//xF8/iP4Na9TgHff/EperoxBVJYaVnG10mGhOhsxEd0h9GMahr11u5T2/TgaQtcPz149//L5067rZrPZrG3rjqVp72JVT82S7u5uwPSJPUbDFy+eP3/27qKZL+dxc3f39PPPXj37coa6+LC9yZBB1wHHfhcY2qZpGUFzGsflfAHMm+2mG/rH777z3d/9nWS67ztmbmazBxcXi+VyHEcEiCGKyvJktVgsdrvdJ598EmIQzT75GAHMQMSYgbnYjNL+oZplkNxAo1CLuFZ8dGns9pw+V4wxxsjMnjQ7dxqsSMxMSXDZa8U++ZZDQk7j+PLZq0cPLu7vNjDpgRS/awA0VT2mLjskjhEAwPtbJjCwwssiJl7lMVNR4WIU1BQVjAIHarlpFOH0ZDWq6np3f7/pt+uG9eLiwlRnBieY5f66v3oZ044tYRFmU1RoYoiEINpSsJzErCHXhcUYG26iIOxzevnls3C/ux7G3/rd3/veH3yPkn76i49//vOPX2/uE2jCmGEZMCBTTpmagISAkNK4XV8/wUwIi373P/uX/82v28D/wZfoOGhXtx9yiKpBpfBrSuZqhEghhMANhmgQiRkUyIiMoWQFh6xmQtQKCd51ZFWbGMEETRmxwxBCzHmcjLgbMdFMgojIgYgpxLBaLbu+63JStc1m8+L5c0LH6HAxn61WixDYZ4DlnIjw6uryiy++GMehFKfMAocQgqo9f/789ub+5PRsNpsDYBYFROYmNDOkkNXniTGQOlOHZ/MIMGbdbXey3Q7DEB4//sY3v/OepJubGwQ7PTuZzX0wMyJCGtPkjZgJXSm4UJmh2nAo5QhDKjP18DBr4fCrGEY0RKApMkUq2Pt0TMpcPAMVyQaMTChdv1e1rHkY+t12u5jPmxCJmAOpaUpJujHtO2SOLpmiiqIBLZCZSr1kxyAFjdALurUj1IEil3+Y0GWoM4WmHeBlU1AxrM2H5QiqklOk1U+gTtpaBoQUiAMBFrHIw4iEitjCFOf9o5/8hVy/CE2TpTA6rW6nki8APPz0Z+DYjFdUXJIZidAVUY/IZVABbyvFXJ28WVEIYQqq2bD42cP/mk3t05M2GAQ74jOrqusCggGgughJxe5V1RCAoRAgywMuVBciZEQ2ywDe1XcI6CbDWZYYCySNBnkcx74HLfod4zioASFmyWP2gWjeje7G1H8cD0yvQoXHsqBI3oe8We829/f73WboBzOjCsa4E/drz2qdpNQ0wAFVMEvOOY2JkB8/ebx88qT//b//xdBrTmPXjV1PhMRe0WnbAAEAAElEQVREwbtrggHkLGNOi9XJ6uwUkGLTNG0rZw+gmWs3zDPGMFuIziAFImGiBi3QZrvllvcg45jGnG7v7z/97NPtfmc+v9qkbWfT/I2aARfLte+6V5eXr169SmP+8MMP54vlfr8fxzwLvN1s1pv11WaDpKexDbOgCMZEkSJhSuN2u1ksFsS0OjkR1ev7u+XpyX/+X/7jd9792p/95V+OImIam2a+mDdNlJzb2YwDZ5Gzs7P5fN513TAM5w/Ov/jiMxEhQiWQpGagqm4p0MFoAPUmTsk55xAZsQy5MjOP6IouJpE3IqWU3L+GENwTE6KKeSfYhDm9wekhRkQTUzPLaqLBO3LrN0OdMWWFUw6IYAo+hQwRSz+mquciRxg05JzFJMbg9aFxHJkaik19HABIHALFKGCKenK6TJJ3t7Jo4nsk70bLJP3uZv/FL8PuTvd3lPeIAkQK5IhRG8MsRFJAsJZ5vdsiIBKrQSSctw2HuFJ7tdncbPsP/uB7f+8f/Pdiu/jpjz/++U9+sb7b9pAhgrAAIVEQUQHhGROxVeQ8Ef3lux89YUPCivXZZEoc442xqYvst+V1HumHfr/fZ5GXTRTNIuIGS7ICjJwdWyZmYmPPfdVUzHx4eIMt1EkijAfxo7KbS3eienHacYg6AI0dHQkxBo6MnIsyLhGRewIRYeYYY4yp/IVjj2Rmu93u2fPn77/7rj/QJ0+eLBYLj98cu76+vr6+vjK1tplRQMfOprjOu5cQCTEgYBsJmThEji0yg5KBEZGCgYqCeazgPQVGaIR3m/V7KvP5/OTs/Obq+vr67uHDi9VJZKIsut1uY4yz+Ryw9ORVjhUi8OF319AtSTDVamtFoGsI83bOWX0AVAnHuqXRzIZhUFUVWyxWzVnTNoHZhmEATf1+fXOlOY1NE2NsDKzrtnkUbuYmnFLvfKiGmQPt99uURqw8DEePD2HBm2livfDDa3LFXuR26gACciUSlTSL1FzX+1izFg4L4FuifunIMyIQMAFYFofiP7h8BpfP3l6oX/XyXYnAoEUs+aiwfnjVgMK5Ze7oD+gLFtFK8bUBd+c++qw8uzLZzyOGMMWk9SqKmXRVRx8eW8C3WiSfSGgE3jRwSE9c4agMFagB3QG0N1NxUMr73DTnvNvsJCXwdoIsWhMXVQuRmKlwz1xpUs0mzRQtI2hhSo5FZBy393eb7X3qepEEdUbhBCEW7ANB0PY67iWeYGwQEVHNxnGkEJ+8/875N558/r/53/78P/8f7++vu5vbL3/5cR4HYACEOGtn8wUA9WPSwL/9d//wvW98U5DOH148eHghKffPXg+392FMLRMbG0pP3BEMM97YsL19Pe7vbvc7Yh5Turm73W02ogKASbQnRz8aM8OqrcpcasCz+ey3v/vbm7t127ZFs55JsvR96oeURVQl9b3QSDEoGhIag5hmybFp3m2aNswWi4Uh3K3vv/3t3/jmd779+vLm5etXhhiahphjiIFDN3az2cwxyZOTE2ISkcVigYDb9VZVJzdnZiIWwiQzdCAHOn+SQ+MVQADIolomn6CjxMHIyTbugwt6CMCu/vaV8i1UJMpjac1Zcgawtm1n7YyIqzT/G7t6CmImCkyRPWRmJlFTVe9SUpPKRFHm0gWXkjDHEFvwHQAI5sRpFZX90M9iOF80p+9ffPDw4Te/9s5qNgPR1A33X3z86Z//e+x2aMneYJSUARVMSIiLdjZ0vRoRhWxKqgFs0URUGhpdnK7+wT/4RxePHn/6s09+9Dc/uVxvlVACIGEmo5qjAwdEMkACNDQC2DWz/8Nv/g+ePHoYQwATyLVh7yDYYE0zY+b5fD6fz735Qixv95tffPyL19dXkkYA77MvcuLEwbFOEw0hmIFrZRBhESUiLIB/celAXpdGNAVC9Fg+pyT1VfqdcjYzAg7YZDTmhjkSM1UTDKAAhUDglq5p2hhjjPHs7ExVhrErgpSqjqO8++67y9USEffd/vWr169fv97v9wB4enpBjAaSJYtkVSFCIvbCxG7fq1LbzojBFEDUMFEd6go+utvpxk6TYnJtS0l5v9v3+y4gRo6LxcnV1eVm9+z9r73z5NHF2PV36/uT1WqpKw4MJm4YPYNF9Apg7ZlyVNUIzPxTCY5S5V/3KpMNoNRrPK0Co0BZ8n63F9GmaWJgH/pqllTGbr+9vblaXF81bdu2bQjBTBB5iYiEWdwVWLtamcF+3+c0WJHJ1CqtAW+d06kHabqwqiWsxy5TVbFqfx2RpwpZ1wqnqDgkLUXh0gVcZMCnhiR1g0keIJvpH3/0vb/62m+0q+U3vvURtc3V3Z0CrFan89mi7/txHNu2jaHZ7Xa77TYP/ZfxVJ+9ElOH8bAIW8IUVVjNZ2ssVMO3t/20Tb+75SEX9y+FsCM4A99kQXvu6yMWQHV62L6QBr7gOikuE3OlEsDb7wOHgvnx1xE8LqjZieiYRhMFsJTGMY2+zf1YEoVS8auRlKoT7BGq/gfUhn1V7ft+s16v1/dp7NGFdd7cFVZJ5A4H9Dlv+u68DQ0zGrKAGuZsFEN7yh/ER0r5y8/gycMH5+enN5eXTROXi/n1zc1uvzfAeQgX73/t3Q8+bBZLIVqcLClgv0/79ba739KY2dTY9oAbHa4tXe7S6/txF6gz3Pcjx6AIKWWTMu7CkMZxoI6apsHpIUHRSTaz09PT9/7e+wR48eAixnh3d/vy1auANAvx5YuXwzj6Jh006Ziza6VhGU8U2tnDnNuIxJRSGlO6eHgxjOnLL7/c7nYcA4dIgZsYXQl2Pp/f3t7GGBeLBRiISNu26/t1t+vcYhIRkYlIzinGwEzBS/Z1oKRkSeMYm9Y7qomIMWgGpxe6HC4REbGIO+uMiDFErHl/DXJ/xcsBg5xyShkRZ/OZ135EpbT/To+8vptH3g5O1o+mEIKofzQYonotwoyR2hDBIJtJlnHoZ83CGjYMviFVpd/tu/3OcqA23r++PGvCxenXzhdxyRQChbbZPvvi9vmXmPvQhiOyhhooIpSGB0Di0IQ4msUQSMRELQsBEcWHy9NH3/2t9955/Prly09+/ovhbg1EmSyDCHA2FUxmI0Ak8gHZPqWdGCm2jRqIKptSnemmBfsyA0NCBQ0UZvP5bD43sCR534/PL68u7297GcCUTKHgTL52wkxm5nvSw26PuSrHqkjxMJMqoaKSUQX+1Zs9XEQx56n3rKZHdWK9IXFo2nYYgmjyXMhxF0DwUACLSIWlJIv5qu/6nJMaeAOJK0g0s5aYur57+erl86fPiWg2m/n9I6LnPDln0QwAgZljGMa02+4553ZewHMVNRCFHChW7ljwOgoCiMOtTCGEbrsb+17GNNKAyGen501sn798/sXnz0IMktN6u/HDxSGIJPDyIxJj8AFEFd85/lV8V02UK0ZdTfJXX8c2zyHDEAMxuYpMltz12+1+N/Td3e3N3d3d/f163/VNM5svF0+ePFmuTsBkMVs6GyIbonNxTpdMNqZkkmHy8/imiT1AHb8yVKiJ4/GXip8pftRczdsLoQAu+KgOZdc3PmD1xZthCa/9x2pi8OWDJyPT/OED/sP/ZPXw4sXl66z24MHj5fJks9nc3tyuVqsHJxfXV9fPnz/rNvd6tzZ8rY6s1g82cL6Z4+SmAGQo5hnudB/HZYJyxp2grmhWCm9WAjhfAyREQ8BQgpgjYKqo/ahgSTu1pMWi7gE1CxKoSAUE4XCEoJT33hJwKTTzOhPJNUPLT4mCGiOCSk6JQq0ni4RJ3L9qVzr3ihTA6iguMwJCAEnp9ub6/u5uHIZSuj489cPlHV+WgG1yusKBZ4s5AIMpct/tttc3j+wb81P62jceD+MuAn707Y/ur28aYkz5pz/9SR5zMludn33wnY+Wj84FeN62y5MmZ+12/f3N3ebqVlLqTAxkLf2zYft03N5C2poORGKmBiHPkNlMGajUJQhUYeiHvukWyzkAeHNleciI/ZCfvfjk7OyM2rhYzm42t198/MlfIp/MFyml/dCXrFchS04+WszETLJIiwQAxAQGXdcx88np6avXr754+iUFbtp2lBxDG2cztzsxxomBZWAppdvb2/X9fdcVB4wIzJiz5JxSwhgpRGoid4yQ1ARUc5agWTCaFwzNkNkrx9lMGQkJufSLesehImpgZg6Tds7bwXV9lKlacGZum1YrKidqPgCpJr6HoNtzdymt4saMxMRMLpmpYCmnYRhUdDZrfWIoCICCpJxTirGtI8YQ1Xbr9c9uXl88PP273/3us/XV7Xr9/un8yUmznK/QsB/T55/9su/WppkpIoYsQuCq6G4vbCLrMlFUa5iT6ZhdZtCMOTCfnJznffrsZ5+8+PIlJOsjiFpGFTG1BNAF7IBnGNBAVQQIYmzmJ6fMg3FQa1WJCubnY/4MwZAsBJ7Nlov5YnGyQuSUUj+m27u715ev+r4XyX6xVjv+HZwA0MMUNQAmR/4nbFTVMhqqugMGyd4jTIRUWBvV6ULBWqHgfYAgAAFEszLP23aMTZZBJAGYSDYDIjIArTprACBZIBxseu3gtJTzx598nCVvNpuXr15CDbmIWErFlQCNkTCjmhFx4Bm20RRDiGYO45mBIRgjqXJZB1Nw5MFTWABAYOZxHIe+2++3JhmMlxgIw3vvvX93f3t9eSeavD0v5zwnpDJUodDuiMhMCI2wEG8L/fZIprCky0auzlZakSbZjWpqS4tx/REAY+b5YiZ53O+77fZ+s7k3g/X67tWrF7v9PqekWuaLvXz2+Xw+j0388IOvL9qm64eu70NsFquThh8+vHhwpeM4utYxooFPa38zbCh+RH0z6ARQl21iZSsegdUVfq6FkvI/xV9o1UCcCASlsnT0Kh2GXmlCKgQ0FZE0jAY0my9Pzy6y6myxCDEYQMopp9Q0YbVazubt2LH4cnnqXebW+3Uc+VpzH1zLglYsNxxFFQaHGun04ArMgVQLuE6DxqB1ot/BvpWcv/j0ogct6teVHYyyqSbnkHgpJE/KV8d+bzKah3+ATSOfXM0LAVUt5xyRwAzVTNX7QAvyQOimE1HUUCT78wEwIlPJ67vbl0+fdtttuTVVv7j6QVbpQZXvgGQEvdlL2aVRnsQlxGCAV1cvv////een33ly8RsfLM/iR7/x9c3dLhK3i9nls1fDbm8A3IR+SKuHF2fvPsY2oMDqdDab0+Z2HIfh9urq8uoy5YHycKe7T3dXl9Lv0AaGhJDLsUUA9WhNQAtV2JEnkd12F0Jo2rbkM1BKlq5e//Dx4wcPH6jl5dkqWX754nkIgZnGYchoiKRoCuI72LONMY8UopmFwKq63W7n8/npycnNzc3d3W0zawEs5zSnZYiNKAAREHfjeP7ggpnHMe33+6urq+12M8ooDv578YYhpTwMXYw0m81iE2PkcTRwbyBpHIemaQgJjRgIKDqBUKUQ9ZEIGWJ0wVWb+q6QXGBaj33w0UYtmwqJGMnMR7iUydIGEpumnp0jKKxKpUqW1PdmqWljjE2gonkyDn0ee3BqtR8eA1PLmnMaQYWYiwNGVs277W61CDc3l+Ow3+/WP/rxjx6tFmcffCOGeH11+eLpU8sZaglfsyhgKdGoqHJJ4tRANTC3IbhYjgApcibaU9Nfbn6+/dHzV5ebhBIbZUrSpzQgIzGAGjVNy43ZMusAYgCIxKcPP4i5xcbHYFNgH7QOdTSNKmg7m88Wq6Zp1HAYx932/tXrl89ffLnZ3WsaQMXHhEwQgjgy750ixgCKphgQURFIxbyfgwCYJsE8MBe2M0UAN9YuBhkoxGXwqn+MAZEki2YZ+3G93VxeXw1AkSMbqTIqGpq6yBmxKZgiBgYjYmQ6MEPdm3v49emnn67X65RS3/dN08QYPdat5RMCBCRCRnJVRECOzawyo1S9MwYYkcElBxBUDcR/0kspgOhlcNXU9/v1+h5OQRWb+RKIOPCji4ebzXq/TwzU9/1ms5kv504qs8oGImKRBGjFUhXNf8ek2QXCKq2mwjkOgHvZp4yAMq+xYNF18pBJDYwDhybafvf61eVmswGDlLrtdu1hDXgmKbgb97sthBAYhch2u33f9czh4uHDdx+dfPD+47PTxW63FjVmqMXBgkNjzdYVXGHRr0hLMxLW+u0BzS2zT6DM90Mz7+HW2gV7EGSeTr0CGIIiSMHBfbGoUr9LuzkAiMgoicZRqYG4nJ+EbNLOWkTKZrvtnpTsPWsWzXw1k3EuN7eouQy7wCIU6yYKsPC8pls+eGCoq1fANu+/IkT10SxIh8mJCD4+whnkQFCU6N+sm3u1ysqyap2wri7mjK6MJcQMUOK06uQrbuqpSI0OCoCvJZkuKbAVXMXqGMmc0zAMgDR9XCkdmfeJM7FHoKpK6hV682Ni+91mv9vttmszeQMVeaMWePiyr6cSqqmY3qTOACwsGuS+u+/+5F/Zg9l/9b/4nz/61gfL0zYb5FGJw/1+uHx5uUs2QAgniyff/FZcniQFA2iagNnSut9fXT//+Jeby9c09prHl+P2lQx7gIEoI2qpH7l6W4E3vlrmVJH9vgvO0/WvlEYsa5v27PRstVql3O938eLBg+3d3TiOw5hTSiAa6MByt8NSY6m3iW42m1evXp2cnJydnT57/szDtZxGSZmJEDlJ5hB8ZU9XqxDCOAzr9Xq9Xg/DIOLzNcBUHJsgRJWcU7KmIUJmlwVU0UzCYxrHcWBmM0UMBBA5oI98roGZc0q9kDaxbBARkZhxIgkePcr6F0QiVLPrm5uLm+shjQZmomPOABhjU097SQU82nRcBRGGcUh5WC4XITCAiYzj0InktvW6NSKiKiK4XkyWnJkDEdcgHAPHtpl965vf3F6+xr6/vr3965/89L3zx+1q9fSzL+6uriOy5mxi3DAi5pS9id5RJgMFH0hggEiBYyQD0AxxwCZhvOPYXd93skkKio0RG8ogsNt1TEJhH5t9Q9I2zGGZUbOklCkpzdtHq/Oztu2bmE5mYTkTpi7GaYK6qAkQAzegtu/6u/v7q6sXr1492+3vx3EvmosQUZ1UXQgmoAqMBkBmSkAGihAAArkGr1MTvCPczZPWFwAE4sCxibFpmsVsdrpazheL1XK1WCwAIaeMQJu7+xevXsYQNOVut0MDJvKyQg30S8kppxxj9PQ5cDhKLyCE4Cnpzc3NdAH+KarqsGBJm4wQgg9DNyQibtrgjUkpJZXsYxHt0IrptBgyUENUUA6MhCmnLCIq4zg6HSXlkUPUJJGZKcQQmUIe026z7U5WbRsms4Slw4bqlPuJSTUpMJJhKayU+gmyy7d5NagonZfIFZGAiSIzMakKWk5Dd331+vmz59dXl2nMCIggClqVNLxGWeRrVNPLl89ub6+dKoeIr14thv7+/u765OQcwBnvhGhcBHLebkSyA3Q+VX9+5eutr1dGsKtt1XTw+LsL6cvbZeFtJN5F9sAQCN2L9H0vJhyblmMwDQERQMW6bddiJKRmFueLtl+XSdQevBghkLk0LgAUEtXBkrwF89e0vF4FgocDduCr1MtEBCzCuohmYUqy61sD1LlKhqWo5lmUqUBB8BTJUhrrpx0wUiitAjKBD+pNHe7GdUISbLqrSSM4ZxnHMYQGEcU0q6tf+bsWMNqfjYqJiJMhHFRcr9d3tzfJ7a9V4mBdnree+VvkOQPoQDD3jDijOLINd1f/7f/zn9xtN/+T//X/8v3f/OZs2fSkpIAxvri8uluv73fb7/7B984fvwvcaM6EhEC7Tdrc3L36/OnLTz/P3Q50XEt/nboeIREIOaOgKoGV8RpT6eAQCPnjzOOYx0SrZYlskMAsEJ0slqvFIjIzNvO2RYT79brb7/3nmxgJWaDWuh2wMvMK7Gw2a9t2HMfr6+tvfOMbIYTtdoPoWqWGpjGUvnCXplJVZ3vt9/u7u7u+7/u+JyIgyJpTzlDtMgCklLquyzk739JUyNAN0TD0TdMQhhKfIgGwCqU8mAkgMgUkIjSgQ0UfKwvRabFvbTYAIKLA4J3pXz79cr3dzOcL865Ew3FIhOwKiNPa1n4uIMIY45hwHIfdTmezGTO6OphoVuXKVAQkMPBS91h1T9mpMkhETE0Tv/GNb3z84x/hOHRmP/34lx9ePP7eNz96/vmnOo5NCGMaTISwiTGklMWEfIdLzgaN235AVCPANrQcmtycbHnRA22U+lEzEFAwCgZFrSClvN7fB8Z21qzyyJCJYDZ/LHCS+6ApZFjmdi48a07j+Vl7PpdIXdNajDEENpAsqRvH2/Xu7vZmu9nc3d9eXr7ebjdZRpGs5kPG1FRUih91y89Q8ra/0+//V1cv3eq76XcBysJYLqaxwIWl88K7d5i986eJIcQYQwgcSm4JkMbU9/2+71706X93+rWbEqJ6n6xTp71xs0Rm7mvL3iijywttwp2oHygPQJk5i4pBwIDkjTMKdXZIeaZI0xsagJqiuRK9mrdZT8OLoGgaMPN+uzdVER2GweUORTOaZ/8UmkgpnJ6e3txcXV1fzZbtwwfnbus8VbFK4MeaVbvOmFkpM8OU5GDlahHh1IFZ6nTFCRERcZExub+//eKLz7744ouXL192+72LMpXkyAUgKmkRzMy1VQCz5LwfqZIWh6H7wV/dffyLj09W51/78MOPvvnRxcOL2WxOYOQEX3Q5G/Ak3h95JTP9x79qvlS0kFFVS2/qm99T/vZ2PbmABJ45i2sTj0O326oIxwbBmEFzymno99ueWHKarRbLk9Xu5h7JiwKAPizcAAkokOssIrzpRzwvf4vCXJ4EHl+R1Y4xUy2refRWob5VeSMErEVfdfpFdcGVewU+KB69QP0V92YAIJI1izA7l8oXUUVBFNWowPiGRIY8aBY0JBdOTyknIxCw7IUWRAVwYg9iOQaCqkYEnlirmvX7/dh35fEBKHi4+Gspg1NIgoCApmC9ya0MC8kZFADvb+++/0f/5tE3P/yffuNrsW3UOCg8evIwLOab168ohg++8fXFcjmAIFHgQIibu93Vy9e/+NnPrl+/JpW99q+0u0PpGUaa9E1qJQC5ZGQ10p2gev9DRPquEzmZvg0Qv/71r5+fn5+enpyczolstZzf3d7+xV/95curS58KToEVSUHUSyFABYA1jByb0IQQc8opJWK636x3+72qopVdPpvNnKET42wYBsSSzm632/v7+4rHYIwRAHyWsJuEydINQ9/3vapgrUxYIWONzJGAXeUcGS2CSFbLqgoibrIRkeu28UjTW4CZ+RiIhuqJCxppZmZjSoAdcnBYcUg55xSagHS0C2rdURGIKISY0jAMvaq0bfAZD6p5TBBTiE10mU0wE9FxHHJOjcHEtnBnMua873sNlAnPHj+8vHr9/e9/v3/1+urydQhM1g5d58JAIQRiTENiZnEBLclCzEBEBAYgGmjenqy28+Udxx5xVBMjIAbgMlfRshkGbkBs3+27bt3tb+bb25OL/uxs3y7eOYmnhBERJS93XVifzN9fPV6uaMV9O88x+lxIyTLSZvfi8u725lrUuQLZC2ceAEnOpk6rRahOFwAsm+MTj4b+H99e/ZoT9t/B64P56tH7v/XpthnHAaBAR4ggKpazjw5zn0pFvNYv/FCzmDLvyQF7MJdEkYmBK9mTDMrAYMcyFVXR9ahVxYLLcJqKaAjg6qplhqiCiSJiGkcTRYBx9AjGVJISqMFoulzMKITAeHFxsdls7u7u5m1zdtaUJa372gwQHEEtzDZPiGso4yew+M5KAbbaBqsIRuSaSLbdrjfr9fMXL168eHp1dbXbbb1fAEs45GlQBePKp7+RqnqfCRaZSTCz9fpuvd5cX199/tmnX//ww4++9a133/8gNk01Ym5PFQ6J71fAvV/zOoIBa7iMNSawknda2QS/2qj7w5csAqQAgsBKKpLS6CERtWgGSKimaRh3my1nSH1/cnYSm2YSDyim1gpjmbwNB312C1llFx2AOKxc45JRmauYA1S6tP8GMJUQyi0bwFfbkAyKU3PkYUJvjr9NVQsDuzyZKTQDqBGvurKYTpBRkR8EVXQ5A0NDEoRes6Cao3wiKWdwnoUaMiJjltxw8JIGuLS4AkAd+msqKWfvl0B7s+B3eH71nmuQOH1TITOSGHQ5u6jtzKgx3O/2n3z8yTCOy1kTG8gdYIzzs5Plg7MPvv7ht37j2zxrh6EzxPmyRYP1/ebzzz//4rNP+35nOrzOu1c27BgzFyEIq2gTARdxetO3tigWIoKZmdeusggjIXGW/PDxw9/+ze8A6Dj2BjKbNd/7O9979vz5mBIzMzEgqmgVqRKqbeVMXBIFxCyZYwgxXl5ebrZbdU6UGjMHZj/pMcbdbudlMzO7ubm9u7ur3CtkYgAome5kl0sIXznmhR2gKqpZhqEPIUYMiOpNliGEEELKWnFFK7qFgFD1MQzAXEQMcKKCT0eViKYijGv/g69wuR5JaQwNvxUkI5p6ox8BhtJamtIAlr1oLZJUs2stYdPUfkVJafR5IW88L+bNbteNPTVxn4YnTx6fXTx4+YvPf9z3LQETccCWQiciOXMMMQQRkTogNaskzoTRcwcRlSba8mTg0CEOgFKHfDGq9+uaoBmFOJsvloiSUz/sun7f73fd7vbq5MHXzh59cxnPASUCaGqu7uzLRTNvHjar02Vj84USas59v+nX693r11evLy/V8pjGLLnG26VaYUejT6FyScAo51yVPuFvlif/l3c/5BBjiLFpOQQOofYAl04FpyvF0Mxn8+VyeXZ6cnpyulwuVvNFDByYvasbANKY+6GXq8vv/J//9wT45MmTk93tzc2Vev+DmYFAFkUvphsaNjFIG4ownwHUZP34ZdU5A4D3nce2YaJsecprAYMnkYpgYAJVzr/uKPfWXhBz8SLNnlhgCGHWNKenZ123lzK92ETEp9L4wPW2bffb9dmDi9XpiVqeDv6U/9R8AbHSmqatPp2sg+0v1VNzX+VkEc/ezNTFRp5++eXd7e0wdJVO5H73TVmJr7wOvuDt9E7NwEC7QfrX+9v768vr17/1W9/9zd/63bOLR4jNUUvQhK3DW/b4qwmx1cKqp3fFYxlNP3mw6Va+d7r18nFYMuasktI4iDQhGJsoqeVh0G6/FxFyJSAkyTIO4367i4KpHwGAmJHIUQRnBeqkFD8pdpf2m+owjhFUqCBEWTeFUuXRSRrzTed7eAUxF7KoYlqGVYFYJwdsdf9C+aIUdW0vw04hgAMyCBNc41iAaqk5eZOlfxARAlE2G0VcoE9ySimFxocMW5bsivzlOHkLgio4hCKqbAYmKe3322EctIx09WQTC85ilSFQCuBvhC9EpFCK7oomCtkAENkYBaTPt69v93fb0werPMLten/f74VxdX76m7/9m+cPV72CDgqBZouYk97dr59+8fnrF58l3W5kdw39jjUBGjIZ+HFWDzmRpqkAVFXVYdpkYFUvl3b73e36/vz8ARqPksY8jln7ob+6enl9fd31Xb/vzPAbX/+mqO73O0Lc36+DBUDsJTOSGjo7JnKIMSKCms3nc0S8vLz0rCIlUTAXGQEAn2MzjuOsbRFdmiWr5pSTiDAFNApMkePeUFQKf9WAXPyFwEVSRY2QCVS9NJlyDGoMxSiBERMpZckApQ+40EEBQoCaB4NvUDyyC0eGCSqUIJ7E+CUYqvr4L4nEpWGm7IHaRIfmVR4AMDeX00YFtZxpTD2SgaH5KGLJ49hnycQCiGjUIESmYdhf3dwul6skOl8sfue3f+dHr+7QWTDIZBqIIGfNToNCM/AZyYqUzZJIjBGZEcmAMmGONAKKoXpZ1cr9ewc1GCEGxmY2W4HJaJYsZ+m77fXY3e/3t313284u5sszXl7w/FEazp9f5qbBpn1yQjOOQjD2Ce422y+fvXz+4vV6vSWUIQ19P2TJopJVso+OMRC1yukAA0BRrLbehQ5eN+2/fPgkxrZtZ+18gcEHBQczmyp1iEBM83Zxdnr24MGDxw8fPjh/cH52Np6czmdNE4Kn1AbQ9+Nut5MvP9f/5v9IKZ2drx48ON9u1/04FFsEbpoMiFWVAFUzgTEV3os7WldVmzylv7yQkfOYUkJb+LZREzQq+TE6rOEu0Ed6CAeXmXFW9BSI+PeJl71i5Hfffadtmpubq2EYck4AqJKLRyRQs9i2uuchydnJiZkwkR1lvgfsS83UfOCUOYsHJgSa7G3MESf1BURC4JR1fX37k5/86MXLF13XeanuqMj1FczyUKDESV3rV3poNaf3eohC4yBPv/xst10j0t/9e/9gPl8gTSh4SfsQ0AdL0DSjCUwNUJXI6ZJerXB9B9AyH/lwceR6pEV42syMHSnOglameSo5qQkMTVRMNZsGasSSSs5im7tbyWlOlFQBKCXphyH1Y2/90I9IPFssFyerMJtxiEQEAmZqooXfVtTF6kVVNegaE5UFRCredrLiUK9ZVOVA5H4DQn8jAzYAf8JeriWmgh77D6v4Rsk5EwVXzzj8rNlRlDLBqWpeTlcDUZ8EbAaueg2AopbrnEPJngGnnJJTP0LgpmmghoJOvrWafyOaqg5DPwy9iJQikwdFWKECxAPL6c1dRUhAGDiIWk7ZRIwsi6lZYI5APOrts1dPf/75Ox++o4LZjNoYZu2T99794Btfmy1w7AwQZ20MAbv78enTL7/45JPN/XUv/f3QdZAFEYEIDBAUCVCxzOOYDgQWJt8klFKGe4ABilrXwd3t7cnpKammlEWymoZADx8+evz40XLRNiGY6TCm/X64u7t/9uzpv/h//9N8u/3ORx8tz09ubm9++jc/3oybPFHqVFXVVRd2ux0SlR6zIuhNasoUHFJ2LhKA9X0/jMM4jiqKgT0YCiEy8zQ4FhGziMcStVynxgLIACaqOWVphCIUoWbAEFiVVTWLZhDE4oNDCKroaKGZT/g6dAL49xznN1qkUivkgwZoRCCSUhpbnk1mBY8QkgnRIyInKIhmVQEQA1Mv+2avNYo39qWcsoxsDRhy2YRgBldX1+erk8ViQcjf+94fbn75+f7lc0ZiIGZqmobyqKLgY48Rcs7JlbLNRrUGACggszRNCnFkGhQEUWxSOqgAn5thIiOCEOfzJSNQr5QhpSGncXd/2e/umGfNbLY6fRDaJ6ePvwXy/heyz7sb++bjefuAMG12u9eXV18+e76+34wpNy6f5QXfEoa4EgB4L07drehJwsSOnB6HR05lR5tYLtdJzC6DFmJs26aJoQnMxIjAVIgdFUAxAyRGDmRMnnycna0ePrx4+fLFerf1Oc+AaoYhUPkrSBp60GUTwpRbOaocK4fxLQDP2zqgFkj8C6UHnYiDQ740pTVMVI0sTPxO98CGmEUMQCS+evni/v5+GIfT01NEzCnnnMgsxAbERKSZte18sdtul8vlcrECzVASq5oP1G06mWjHhrCIhPtylblZPreHmdHDThegMdjv+k8+/ezp0y9TGsGAqTTjVfDasBwCOE4ha1ZZSqjHtZ5fkbNaMV85y+3tzc9/+bMPv/nN+WLeMHvYcvjJN9xNNXol23etK57+XffXESRQltrvtpSWCdFEBYtvAzQX37Cav6KoZRMfN6ZqamO3R5U2BgAQ0zFnT2U66dIwxhBbhna24NhwiCXNU9f2JDQqgCWSgKghV1hjWiU7ACVYiyBgpdmjbC8pBCA9yIqiImBAhZJwV1oywIQ5VUTAuXCFGo5W9Ae90l72ZdG/957lUp9QO+hn2QQHGziRoWxqOfooFZGUJAuoadaGqG0bZmYKTOSjFuGIp62qo5MsJtbgRFGrq8NUjbV5llPCPSIMMYYmqMGAQ06jSVY0UTWTBsPMMF/f/eDffP+3/s53w9kpBW5ns4dPnpyenJxeLI1BURF03gYa9fKzp5/85V/tnj2llMc0jimLGXC5lCp/Sc5qhF/xsrc2ekHMUu62u3675xVZzipiknfb7Xa7vry83O12IvLw4uHv/t7vnJ0/jNS0yH8ZZ09ffX76tY9+74Nv7Z+8Dy/v7sP96+4eY4tqeUyOhjFzzpmJfNRFVvETbmqImFVyzov53LPL/X7vw2esjF9AAAghzNo2y5hyYUmoCVjFn2vtTVURBTSPY08hcBMZuWg2BQ4aRARUVCFnBUghaIhhApxdt8GjuYoQADgDK4RijIB8tdQNJwIScuQseUxjiJELf75s8uOXx+zFxBbyYTlNbpfBefamiOwyHUGkDKJHdAxovV5//f13Hz16nHNe396jSgBiIy/lzefz/TAMWQRSRgNEBUsAidCl3LMhMmtodL4c29kecQRIAGKgamTA6KUKQyQmNM/4IGBo2pkSCg6ASEhJ8ujqJGnYdes7o1ebzXYY82u+/fgX42efnN/+J7/5+PHy9vrql5989urmpht7kCQFpC3+19NBLAjWwXtNRGhzAS6czI53coOqsPqASoPSwO/NMMgO5KmYiGl2ITww9ZL2ZG8RLDC5eCUiLhfzBw8eLBaLV1eXWYWImCFwiDESYkbQrOM4ap2IV5BhVa0S9/7COnLOzLIrmSOWMkdJ7wuy4tO6qqs2U0AflKuVhnZwDODJWLfvGua7u5vddve19965uHgoIqnvJeWcJYRGTcZxmM1nMQQA2Gy3s/kshohoJYyRinZPXXPeGf0mo6dGBqWpB4EIpmEnCIDjmF69evX8+fOcMyKVYhCgmWtbGAIaci2jGFS/Xk10/fpxyfkNShHWJ45I5PSk169e/fmf/XmMsw+//tGbFuytouevfP2Kb5hqcDVEMJtmGxSHZFSvyswnEjICVSo2oprlXMS01Ia+7/Y7M0PmPIwp5b7v0zA0wYauN7UQnDxYPOjU/OQ3z0RkZFbJcs4u874nqxcLBoWy54E+gJX4hoym9S2G0XMhRAAMptkqO9cHFTkzvSS9HvuXISSKYGikUo21b+4SJlsJSnyN4M1SGU5ZB1ht1HO+T5IsZkg0Eab9PXISRGxiE0IgQuZAgKA1vQaD2tB5jMYjGLn0M5VDTVXf0HPhnJOfRH/2zByZEbAvdE+nz2mINAcOo336Nz/6/Gcff+c//bsUmAM/efedx4/PQ0uO3IJKELt69uqv/uiPtz/86Tu70TLeq0ktyQNi0XDxNBewVmOOzhRO61Mf8HRHqv12f/nylT0BMBj7AQAuLi4ePDibzWZ/+qd/+u//9N+fnZ49ePj4yZMnr19fNqIniU7W+epf//WPf/4yPjqdrVOLC6W8b+ZkZGqE5LgCETVNk1VFTMFcebGUHkS9RotEXde9ePFit9vllMyAKVa5N2QfSVJ7P1TRc2yrtWErBTlFkpQHHKiZz0Ib6wFmDhokODRDRGaas6kKh2IXptMOFT1+K0AGpw0aqKiIHDoxEAHBBcZnsxaA/LBAVUkkqq1GJTA/vPP0+2S1HUc18xG2wuTQESKigW3W6/lsfn5+1q833/+TPxlubhd1Ro2ZhRBWi4X2+95UwbBSoAUBALJqyrnhaERCYWTuDTIQKJCCqokJoCEBAnt3IjOBBaVoCmKCzRyJQowxp5xTSiM49pQlS9fvtoFbbOZfvLh6+vnnIabv/cG3vvjs41/88uN916OmhpMqSc6FMSki3vtbNHMmESKASY77K69Cd1IBAcZgdSIVlh4bBDCTPA79MHQpDR4oiGQVVCb/HvcBCEpVWyIGbpu2bVs3KohISCEQoQH4lCqQykRwI8UGrlw9XZX/039PKaVxRE8qzWejged83kx/OIaHBhg0lwcX8Wje6pU0bGg29N3OLBA/vHgwn8/3u633mec8GLJqNsAsKaXERDHGru/3+/3JyYoLzcpPupkBsMGk8HckLmGTs/HrOXC0EAwNC+yz2e5evXrRdx2is7jdj3JxCVB45qWla2qCLOfMg9si01bFUrG658mQH84OEyGSiD19+uz1q9fvf+3rzIfOSXs7k/7bX8ffOcVNVsFcLUk8FpCej4SeCiltOo1gAIKmBIZEIDYMvQuUKsI4jn3f7Xa7NI4CNHRdTmOYz7mJFPhQZi86HrXprfjZ+i+YQBF7+wbqo0IELN1uvriHR2nV6gNgUJuEAjxYwiJvrzlnKu7Qz5gpKpCZqHOgq5LGW1X9apiOFhXf+MOvgL2bRXoZva/LTEVyMCUiUem7AQyCc4vMG9/gELUBmmkak5bGZ5ye98FwH81BQ0RnopK69qyZWU6JAzcU2lkLpr0qSPL4HBiD4VJBXt/+/M9/+NEf/B6JMdL5xcnJ+cwYNAOILonHq/UP/9m/ff0nP3jvbuAcbgGL0k+1VDiFgog1AjroAtes/de+cs63NzftbKYiYz/c39598MEHCpJSUrU8JO2TRsZZAzEY5JXFD2H5bhfO9/Sd3/neF/u7H/zNXye0AAiiRujCzrvdLoTQzmZBdRiyAsQYYyjnRyQ3TdM0jUr6/IvPf/GLX+z3exExQxHJmMFcU9D3klUXVmyEVmgEwAzVUB0tEMlpHELTNLGkTcAhBHU6aT3sJfH1EN6/WC3SoRr29vH19nRRZAns2GeR9c85qcZiN4++H5x8UQep1rChno5y9SXa80dnBmlMOWeiqKiuqQpqm+2eY1ydnO2u7u+ub3SzBTN15QuAQNg0zYIgj13SjAhqOqZxYeoIUK+Z2hmGkCnsiToAQTKDqgcrpko+vhAAAIjQCAAIMPBsBkKNtiLzlIaUR59xa6qSMiuG2JycnK8uvrXd8f6++dZ3fncYNl988fTu7k5EAgFFCchSHoLKpLxRA/GvZinTc/JnnVVQCAAYgLHUfcwA2VTVNTlKuFy0l92QSV11qQkzFI2kap8IMQRmZsSDE3I/XX4CCZC18mTMDIBSymZGRCmlVGcs+u9OiI0x4mFyjhkgl9jC2c4lh57u18xyzirq7UnOQfNR2JJzzuM2pzym/X6X8ogIT548CRx3+x2H2M5aoACZhmGYzWYxxjym/W4/a1ueNb5Qpbrs8KADfIeCmdVKR9mKasDVdEyZugAmydfX1/d39zGEwFxxUg8cCxfIieT1rSaz5LbaCUYezFitdUwNvlZLCkylTBSa0BAxIM0Wi6KO8RW8+iuvN0CEX/nV41fJRupaUJ0CMSV0Tk8+jhF9F3lFkhERbbfd7bbbNI6JaRz629ub3WY9Dl1GHMdeRQP7isEkKlJkK1ScZ2cuFQI+8fD4bkqaji6FjvUJHaIlxxGQDEjhqxTuoKUUUb0soOPWJQTwJuBSITN0PlSB8kooNDX4lgABjBCcKIulXxxryltNLEKMEZlEDUzMO60KY6tsjn4YtS50Fk0ppzGhmVRDP/ZD33dHOaOHbFDAOjN/YIFDhVIIQIyCF48dzxj60RCXy+V8sTS1MauCDaYZXVNKadN98YOfXH35av74USCcL1pj8Gl8zDh2w0//3V/+7I/++MHL29WQ92DqyiXsD5KmNaksq6rac1RCw0OcazCV+KEwmxRtyGm73++6Lon0Xdc0mDMF5tOTk3nbLmIDYGLazJvNbmM5nwk94dn5g4vv/P0/fGdJP7t92X182yKKGqgh49APwzAsFovz+VINRAbiuFqdxjgTVIfmvIR2d3f/g7/8608//jwNZaCziiqqmeWUHaH1PVBPTMldDMCJWDiNsQIRzTknyVk5BDYAYCILIZtqKuQa72GsrAfv+8zuF7k8RDiK8I6Rg6ry4soeSIHZQ2MRYXaQHCasz5e6CDKUi37j5HilBZwNgwiAqpLSKGnE0ACRmmcfcLde/9lf/uU3nryzXW+29+tmHIecs9Zis2k7m1kgYoYyQBPY552BAaIYDmDAYU8wgEktsngpWiXFGLmQT9wzZlUBVCQgCMCIIhQiN/NGc8qjc8/TmBoD4bAbhm88/lo3NpyffOtbv/On/+6f3N7dY+BCa1RTA81Z8qCaTcRTQxOZqoS+Q1HNwOdJHQp1qjqOo7ExM4pEbUiUSZiDkgUOoaDUHBibJs5ns/msjYHDFBm/CWkg1ocMiEDE7G0/x2bLSn7FxNQ0bWDHVHiSTXF4G0rVqQhZD8PgkQliAwDqdE81A8u5CP64ggFUwX2n4ahqTgkNEBoVBYPMGQFTSpJFsqiqpNx1fd/vYxMAIATe7fdNO0s5Exsyp3H00REpxpRy149N06jjMaXj3ao01MGk+c5EtDfu/uiFBZ+U9Xp9f7+ezxZt01hVCXRmiR2VArXs9XJWrR4iLGRimyq3/tigZsf1ITIRs6uphDaEiBxijLvtfrvdPWhaRKMqxfUrAOZSW6geAY4P3GQ53BHW/N9DwApcFWjiDVemUDoEDragpFtIAW3cbu9ePB+/9jWazbqb6/vXL3b31yDDMEDOoiKWpcxdUEM0Q/W0u5RJvGo5ef237geLcBraUUhU77zsNyVXVCF4ExkGCFMYUj6Gqi1zzM4sV2wKqjSJ9986pPMGiaK+KTEf6kNEgAQ+6AO8flG+XmEiv1ryCFaz+HLnnBQAicQUTcWQKBh4vV3Hflzf36ZhqCmLU3vwQK0phD0sQtJlCA8gGZQqj4HPX+/7GONyscTl0saswzDmPEjOFFF1mSx9/vKLH/7sW/9g4XdRoAcDQLi/vP7yz/56+OLLuB/R8t7GfhySqgYtIESJmwCttACDkYM/lQI00bTBTMB7+7EqZZkpqiHdb9ZXNzcAuDo9aefB+jyfLx5eXFw8eNAYrV9fjfvh8vWrzdXlfuwXiwhn57MP36EHJ7OH8+UH7+4+/UnDPkZNwaBLPSAws4gSN4iJiWftgojFtFCfSPt++OTjT3/4gx/d3axNEJTA0zJSRBQV0Vxj9QORnqpRMHNM2M+aIqGpSk6SknIA5DoWu1RJUkqmmlVyFmeEudTPZHz95AEAM0zUeNNKtAcrY8EcjgJiYsFMBGDiSJ36zLyikW4ARhwAWXU0nxGhdd8ilvJg2SlQaCM2jn2/aGeEBMYA1LSzfdf9yb/9058ulnEYg+h8vjxhbpAJDEzHvhvHcRwHZbCcNWUyi8QByNCUKDNZ5HHeboONqOqtD6XRT0Xzg9X5anWy3e41l1U1UEJkLkF3kaUzM9OojahKzhyy5NSr7Lt7ATt7cPHBo/c4xOubOwNq2hmomXSkyVRURpVBZVTNHgvDASgrptEtANQSXTF3quM4KqkXLEQyh8hOuWdwcQZmapq4nM3Pz04fXjw4OzlZLZez2SzwoQBpBQ51/cVSFzAzJvIRPaPkCYQ0MyRjZoTQNA1zcCKYWQLLCOLjTZ0A4/VgF4QhQqnDnVLOksVHTKQs8/m8aLzVQhcaIhOQpTRmTYEiIqqIk1E0Std14zCqqKWkIm3TEGOWLGqG1PUdhZDTyBaQWJhTim07CzG4inkWi7H4CyR03qhVcozHkQZGZOSA9NHL6gsRvcx5d3OjSUIIpmiqBqYg6A6gjv/SKaSqj7QidYiutoAGeFTqtqkO/UZnFyOZgYiaZRQVkVevXj1/9my5WrWzFg0IKBBbTYwmtteRy6g5bCkIFn9fy5MuRlWULbGymjxv0YppOGBQPSEAYgET6gb1DrC82bz6xc8+evL45NHj/fPnt599sbu8RMtqYbfb5nGULESkR4l0SYYnfcvDr0NgUL6vphzTAbQyYMKfn6kokRbvdhQ9ebLlJKxDdW2SwQJVoMKClpzRyTUFkTeclHHgsC2qF4FpDb233ZAA/Pa8a+nw8P2iYaobq7IjbHU+qO9IV4BFJkXHsjSNff5qU+bhKqabfCO4LhIxzJ66+Y2nlLuuiyE0Mc7nsyHlnGTMSZsWVRsD3HavPvv8ne9+h09OVCELkIJm6zfd8198cv/LT0+6YaYKJn0eU0plpqcjS36L+FYvwPEJeDusMjM1papF7MlzGsdnT7/cbdeL+eL2bnd3v3796nK73jZt0213f/Kv/ygjd/su5Ezd/tEiwKPzYR7ud5vVB6cP3nmMMXAIWRXBsuT9fh/bZhzHruvmi0jEouaqvOZd5Iiqend395Of/vT168siR6oGAP5c6kK/cfF4iGDrdlQfYsOqioBGmnNyZUoiDkwAFgJ7aBJCk1Ma05ALhKgckRkdSAAwIj5sGR8dU4JsLe7Ku1fpEKtaYYHhFI6XoqApkRFRjIGZDUBqyaeGRkf7E0ppySe4dcN+kRYtBVQkDsStwbDbD3evbs5ic0HUmDEQBoxICNg0MwQKIsB0OifKdptTI8AZMmBGiLPWFquOcEBUj6cJCelr733w5MnFp5/+wpFYLErO4Kk+spu2o4QRAAzZyCQbQEAC0yD59vKzl68+/tq7v/3O40fj0A/DMJ/P0cRM85BsgJRSLv2/uTzAiSj7lc1pRY6+dlyApTxkTJAQEZumaZo2xiYwYdDA1MZ4slqtlsvVYnnuzncxXy0Xs1ms/UelqloiHXjjwM7m89PT09lsnsuwIPO+FaiiWk3DBgpIbduYpUrDMlVxDM5Nsoj0fed97U5wF+/iFVEFgUnK42g/Izp8Mo4jHMocImV0Me33+5QSOLFLrZ3NGmy7vgsxGhS21zgOMI6rEDxSibFpmsbMfFq2KasTaN7wTZ7iyZTdTKYN39ibRVYzp7zd7vp952Cpu7Ojsu0BzkYsQyCO7pGKqXH/RsWVHZ9pPMj2FZ9UiL4IiNltat/1H//i548ePXrnvfc8MoaaCr1lIv5DMLXH8J52OwPa3vjPaSeWL2AtTU1qIFCvzZM1zSr3N1cvnn3Rp+H25cvtzXUeOrERlXa77X7XtWepsvHs8EYGCMDoeYCrktkUSNBxcVfEOcda8vPJz5Y6TqHKv7G5yisUV10BihI5HAoQpqWluMA1WEoIZavWhHNaFjArDS2+nahM0q7QwfTGR3n6G4uqxsQIIFlq6g+KhWOGiASU8zD0vVWVFqiud/q9Ji0VfqmXiIg+DsxHoUGFK1JKwzAyUtO0GkcdelV1ZVYjNR1vrl6h6XzWApEIGIKOcnd188VPf2qXlw/AAugAedAk5mPkBQtprgZHjgXV8SblWJWVQHzzVLgJoUqWcMT3/ub6B3/+52GUdbe/W6932+393e3dzU2WEZpmtlih4jIETF2OtoLt1xt9r8FF5OZkxe0stDMn8o85dX3vzTDdfs+hJeQxV2iU0BDEIIs8ffrl1eUVEx/Xw6aVxNqbfRQeTme+RmMwibqQs7FUU059DswuYgUhILhMpohp0zaprYPqsm9aVfNrMxPy3VRiUIcTsSoGqiFmGTmwD4F3X+veAh1No6maqw75eJvQMJCz6SdYrjRklCTD78rAVMxypn7oODaIjmsQYlitzh6//42zpt2+eJl3u96UUkqgDNYitLMmUnD9swfNbLM4HSQB8R4ghyCzeU84AIhPdbcShL737nv/w//RP3718g/+3b/7/ueffynlGGJtFELiN0aG+/Y3s0AERDomYgxou+3V01/8xUdPPli079yv77v9foLODCkbjuOQ8pBzUW2sFSVDg2Oyi//hV4B1SoqqDuMwXYCr4FkrDMiAi7YNDPMmnCzm5+enpyer1XJ5crJsm9jEEEKxa77UVGYqHPYPM89jaJpmNp+NkswMIFdUFoiAGQFNNQPgfD7LeUDIx8KlITQp7d0cea+RmZm5sKjHhymLAcVj1WKvmXgJ3z2uv5vvKC8kY+lDK0iFdx5zIBVpmlZVidj1Tc28qKAqWXJu2tZlTHLOIlGZoYrnThBAvX74219uWsc09N1ONblOUa2FeyBlePjr9NXjg3z8l8nDTKf4yBnX9m/PlCZoGAmZggHeXF/+7Kc/mS8WT+ZzPFAuqkepwAnh33prb471cyXZQ5JWm8qsBn9UvUbNDa3iN+pN6KpKBnm7v3/6HLtxvL0Lm02TxtEyWOp226HvRHUcEyIGZlQ81AuLlvMkFlaL83QYlIE1P4eC2pSi+8T9weIGvpJoAYA7YKtsT790rBMaHQb3gBLgiItRD0yNvfGNt4SiVAdVwboonJij6SWE0ArjT3CEVlFBN+0F767FRaPiUNXMxylX4uDxZjr4gK/GGuWJEsUYffq3ltYVnFxyS9w0LXOez9pmHkGhpxEUz9EW83a5mneEYmAAY5+efvLp648/PR2GVjOCJkuDJZehqrudahgAR/6pmPhfswPNA96SZHhlyMDMun33o7/+Ib5ab2XcW96PwzAM7APKRIACGBEYkL3u7vqu+d3f/69OP3gnA60uLs4fP57NZoisIDk70QZTkv2+V9usTk5DCI52EBNFAtH7u/tnz54x02KxOGrYAKgxTRGc/fUje+uG8iQ4qClikTuTnHIYrT4oZHOxgwokmohkycOwU3O1SxLH/WqdeOosokryV1OTNI7GHLjxq3NatTprpuQQHqKV/ByIbDZrxzQbU3+cAFUrA2ZGhz2PAKKWUxpUxCiIZOJZiO3jR4+/9zu/u6Lwwz/+t7suifTJnEhQZrYbGJoR4jLGk/OHajAy3qjkyBvizTjCLJapWE4UAPj0s8/+1R/90btPLr797e+8enW5Xm/VfM6jE0PIDEpjaA2JJj4BMSNistFMo6T7V5/fX34Wf+ebfbdPWRgMydUR2HVji9zs0ase7JppTH8USUgCKbXVNI71+wkjsiIZtZzbZTxZLk+Xq/Ozs4sH56enpyer1Xw+b5umaaLn9HVpy74q++GNTYTeZYzeZOmpgFhLZIgKkEVyTpOWs6m5o3VSVYyUc6PTFBkROGhEg2TJOY2jxJaZyQspxR76oHgw3/yeJnrq7HE8EaVx3Pe9iLRNi95SEQIC5iRI2DQtAJhpCLFcHoFpNoueTY5pGHNomNSKQf8VKRBUezCVAyrVsWx70W7f7fd7zYLqFCEflq4Fs6xllePD+7ec2P8I9nL1qGBmhgrGxhxU8heffTqfL87PzpZN67HgG0jn4VmXNP8tA/6rP2z67StfQCzdcFbzfO+7MDUgr+OKmAAQptxd3TYCebcPQ2bRAAgGuR/GfshpTGn0LMkFvtXLwd6WebhomgKByQaCSK2auAFzK2WuLwUARMBECiY1mLCqTWEA4Y2AwtQECq57bIwmftu06mYmztUqIIVfoP8EUSBXcDBzDQ6oDe9Ync+bOIevpO88b55CMIhOy/fABwEISHXfd2kcdNKJ83dz4s7RJis/ZvDVzeTtN+50vXZoaimlzCFGatvZ/JRDjEnlXmWrAGRfX8xOH5xQS5DAHcH25u7Vj3+Or+9a0QCAiAlsUE0qaofcngAqQmj4xj0fbv0tX3y0KYt+PXg9WfT++maEZbtoMJISZO+05lBmxahmAGG6BSEY5dEJnsxUoWma2XyO4HPgChrr5ZKc026/JaaHjx4RQc6pDTESd93wwx/89Scff9yGZrfflapwFdzQqt73Hzqonr+XHlOk4ExMj3WiGrCJqFpGNkTxMS7uKYmIjUOIImAmTBy4AVPRrOL+wpiJnXSL6LMTVC2l0bX+Y0MxMpGPGc6lq2qSRtLaD2YWQjw5OVFNOY1akyf3z/jWE/E/Vc0ETNA/UTSEJjQtt7PT0/PzR4/vXrwA02DAiIZGoJKMkRpEZ7C2EBiJzJrYjjO467qdyLydEzl0Vip2t9dX/+pfPl0tZh9++AGYmoyOrYqMZhmxRUI4mMt6sEucgU3TmLViOUbc7e6efvEzxH/Iwc05Ozxx7G+PAuwJsnzjWQKgI5wqZlicmdOt/VQHZkQKFE4Wy3eevPu19967uLhYLRYPHz5crVaL+XyxWLRNE0JkYgJkYkAyLoa5QsA0YZcIBEghRFd7TjmrJsdqiCiEFlVzzrvdrqDEAFbpCL5FObYh8G6385jbM1c/+2bFoYpYS0gALllk5fwWOESyQBFggjIUS9XDjt12hwSL+YwQq0qBmdl6vTk5OZm186EfkHA2g5xSEyMBe0nPh7FJzuOQBmJQrZEhYun+qeDSm/bBL+343A3juNvt+r43lZr51sZRK5XTtw3Or/x7yRQQportVw+zmRPFp3S6fkXBFBD6bv/Jx7/88MMPv7M8cU3qok/5Jg5dke4yN/BXvw7mHZ0Yc3wdh7+ja1DVBLnELjqJQfm3BLS02+y97DCOKuJj5PM47Lfr1PcmycosQjUsBOjC1Z/CnRr7lAp0eToAYAfpzNL2yrmMry4gsZUxKnoIXgAAMNREdErOQAu0YAAKKgDBXV1pyjreDFYyuVofgHr2Ky5YgWewOogcAI55TMf7wLEfdJfog5jMBbO8ON+E0IDmsVNJaAKotaZQiveVYlb8OSJNEPSxbYKj9iQC9BZ7gZw5Z+SWMDZhyOl2GCinGGK7WNF8Dgg+W7Pfd+vL60/+4gfP/+rHqy4HQgQyNQtxj7KNoojsGsxFv7vcZXmGpclgyv6prI0BlOcFUBQ0sW5VBAAy4JSvb64+gie0mBGxjrohsUBz5kCYJIPqOKYhjbd3d//2+3/6zY++dXF+stlsoQY2SMQhtG0b2c2epXHo+t2DB98KzSylITTISi+ePv3y888Dh81mc7u+lVq/sCOtg7dC9WNfdTjYCLUjqNCNHXdIKbciEMh8vHNpNy/yGpOPL2L0ZlMzHgASkhammBbBxwKflcsZx8GTc383H4EuIiEEZwQBgGkQzS4wzGxMcT5f7nf7lJOKFbEDkwokwYRkYBH9ENUE0CKoaUYOKeV9P4Qns5OHj0YiVYoEhBrNsqmgMpKRlzTcP+hdg2vWuzzejfusGFWiiZELlisiA8LQ96nv9rstB8x5KPQLk5Iql4uabA24glj1wUTcECszoaXd5gYxxQZDiFDHIYqk0icGZkgKaCbVahazBjp5Z1QpCrcANBVxQA0QA3GM8WSxfPjg4fvvvf/BBx88efz4ZLWcL+bL5XKxWETmNjYxMDMWwKLulhoZopedELjkexjQgCkyU0p9ysllQgwxpdS2EriMQvJtJuW5lN2AkhUghLBYLBBxu92aOXTcOGptRWoK2iYiGIiCFIDKnYpInvJ7sywiOY193+/3O1HjGJerVWB2rww5NU1gZssiSZqm3dxvtt12sZh3XXdxcXF+/jAg5ZzdWorZMCYmigyIaKVedxgrSIA0xe/25kkDcFudcu77MYuVmmQlAJFjsVgOfs0JDpBVxYO4Fv3rTgI4KjwUlLcaeJ8SBpMFm0wBETdEprrfbZ89/fKjjz5q28b58tMFl7BqYsMcjq2pKdVocPIuvsFd4sqfctmEXvqelgWnnWpGBMQm2ScRGJeQjEmj5rjtFIBTRlMBQ7Bx7O4uLy/ee6zjaCmpJAQBzaZZJalkX3A89PEWfVWplRrv1C3XORn4UkiuDxAnUWyscZU3vXkNGKagD9xp+BTBorAx1f8m24rVOSB6FO2eVc1UDYF8SKzz+6263gOG6SYGEQAUjJxyAKDg8Wd5UjmrGKI3ERGqCitwlpwGUKmeF8BlpbFIxx0nwVhzZH+uiIen6Me10kwNQEW023cyJowtmEmfY5bWrOH2bD7+9Z/+xXf+zvd+/x/+/UYJtt2zn/zyl3/2g+Hy5oEIEfk+EYOkIt4OV2HBI+T5eBWn3Q0+FNZM3jhYpeQNDhf5HkPDOYaV4rLTgCINmPJOR2zUAQcyI5EmW5Oh3+6//8d/8nd+/3v/8B/9vXEc+r5btA0YZBEzm81mIQYkJMZA2Pf7/X77zsmq63vJoRf56U9/mlJ6/PjxTy6vhmF449LqkavHAabuXNA37m0C7kpcP2HFqjmnlHJsy+hAP8s+rOatrHoKm5wLo5oZwENeAMtZASCEuhkMACBn6boOEWezWRObgYc66TLE0DBXF4XoWVHOQhOhyUHWumeOntdkl6eDJ6pCFFwArO9TP4zZoF2tjGMCkAJkuXgblEjXJKsNpEa0Y9yadGnIksVYRaaIxkwRuWkb2pGkcd8lAEUUJPBlDCHWiON4tcryTqvNFGJsYzAOutmsh6FfLBZNbFIavZdMD/YOoJInDk8UcTmfr+bLxXzexMjEPpLSJ1+t7q5hW5o3ONCsbS/OL9558s57T955//33H148vHjwYLVchhDaWduEWB6kWrbsspcpJW9NM8AYY4wNESpYzLmYolohVrMsSSTbER1BRCxOER7FGNU058FLByLeKw9N0zgQAqWjrDxDqDM/EIiRQOuIWYOaqcg4jlO5F8DGcRiGIaUREVarZTufx9g6ScHMfJwbqFEgFYuhWS5Pr2/vzPYpDSmlJs5mijkbRQ4xgsKYEiJAw1AkqXxzHhkI+ApqZuVcAICq7jfrvt+byuQ137Y4b89fPRwr99f/Ea+3r2DaZmqGgIykJkQYIw9jevbs6XpzP18t3YC99bn/MZ93/APeDI7MEwhbku83Lu6ApgKSEamRAIiZgpH31PkQDwWPNst0oDRs7276+/usOQ2DTEN9QE3l4PLeSuFrI56qVn9Y7o4Q80EjzuvH3t9VHLDrMVF93AGr3oiHElZyVlVVtIMZemtVDMxxEgOxYxsFZYzq4Zc5OOrNxN4qhdV/AyLqhBqolEiiYERFEwtN0DBt1unqCrs9muIR+/yrT9XMTMQ/GM1MxfWY4Qi7sJJvUgjBrJDak4nkLP2wN23FTjCcU2SR4X599fHn//S//r8+fvT40fsfXH325U//9M9f/PSX52MGRkFIYgSQxEbVw1LgG1f3q5axFBRExbXUwUXJAQDIyS4AVlvxvJsHGg6SUuzoQmMWNhlhHsDETEyFFL/x/ge/8Z3v/MWPf9g27XKxQAMmBBET5zIpAMTGRZ4BAEIIwzBcX1+/8847kpNkubm5uXz9erlc7Lbbq8vXKaVDm2/dHkd3NEU8xQUe75mJy11OeymruORor7JgDpFD2XACpV5MiAQ+ZRnAZzhhCKFt25zHcRiwgEFea/FRhkQEAVBLhG5d17mIaRPbfe7GIccgoRg6iJEQObA47qSWs8qkLYOHrVtvsPg6/59S5xFVRDFlAMs5p5RD05w+vJifnGy3a1E1VkUrrVpmwREEFEHOkXeBtuOQRMxAVIZhiLMlEU7xrbewS2m/USRDBS9nEpFaxiJshIf1L0YKC221rFvTNoqIw5iwiaGd9WlnZMYTWeEA94EdrPOsad9/771vff2bX//gw0cPHy4WC6fydl3X9f173/9j+BdPiagJYTafn52dvfPk0Yfvv/fh+++fnz84PT07X53MZzP3jqzlyFe9/9ImLlnHIY1jyilN5NHF/d13RdVst9tvTHa7XZXv8A5vNEJVHYbOq7k+Cmz0Ml7V4w0hzOfzvh89GpvCJkTIOfvOdPy5iaHwv9y2VOaFu3CoYY1q8ccA0LbtarUMTat1ArGZ+UMUEYCMM1itVmcPHgxpGMeOGWII49AzRVZjbJEYERAoJWGEcBCZsOm8lJTpK3ZjMl855/VmnXOCyvKsNYmC+QFOue9/lOfDAmQehWWHzlU8uMAiTFF4KuqxsOlysTLbrzf3r169ePL+u8di0P//et7jA1hGSBObTZhvfdfaKn2ojCMiEkIAYDUzQpUiL5lVqmcAdSm1nPZ3d9ubawNL+92U8kIJghG/sm5Yvav7KdCCflcvbW7G37qXX/cKJU01N/1ecKxQQG16gMNalN8LBIBfiQ6c3nuYfVF+yNSyiE9ecnlwJ7KXWwIAl9xEwFJcpCxiakwYGbrd/ec//KvNL3656PsAmj1K8bHJhEQ0JQJW8UEEqM3Mgmboor9HbnCiXIYQ1VjrGLYupwSWkGIk4MAZoRuGq5vP/vqv/+0/++ff/oPf/8Ff/NUXf/M3tN7YMNybgI0rig3SfRr2Jr6IBChvJ74VyISaFqMB+NQJAW+ugJrsHuAGJw+jgSpCynkMmiTPM0eiWRbSnLyfcYCh71sMT9599J//F/8FBLy8fN2YrV9f2b6nLBTUdb9jCCFwieBEOERmvr6+vr29dZW6Lz77rOGwWCx+/NmPN5u1ara6N9AFyzSL5F9Bv6red6oWhxDkSFh1WgcDyXlMqY9NEziaoQAoWpY8JiOiEIhiaeCuw9KdLN3aYmGiXdellOo5pRBCYB/CUC7A1b5Wy5O2nY2jDy/OIkrkkw0LtceUqvrEVEfyfFWN3rjByTL6zXg2pmg+wERErq+vR8mnDy9OLx7ev3iuZmKCqArmYr9KmE0VJbNapA6tU025eMBhHBZl3UBFDIJv2DLQhorKupl4pbwsMsBU5qtoAda7QEQggqZt2lab2AzjuF1vYjtPa0I2Q6XKDqqqGH6iDRHbpnn08OH5yVlA6na7vp2t5ovT0zNAHMdhv+/ef+89AHhwfv7d3/gtETk9PX388NE7jx6dLU+W7WzRtG1sGi6EI3bBQDk+9UQUmCWGRrKNmvq+33XdOKb29jJLVoWXL1+9kPHq+nq/33sKi2VWUVE5HcfRzAiZiEUkxIOqparGGBHZxbC8DKHq4Vr1qSKg6NMQCcFJLYBo5uPJ89R0V0xk3QPMHGPkEMYxa9H6UFGRMYO5rmcWhbPVg0cPv3a/viJMgUmySB6Jg58SxCICY0YK+tVyKJZo8I3zBXVuCiL2fb/b7cDHKf539zoKPX+d23TNY52uSi133X65XJ4/OOuHvNlsK+gKcJRwf9Wf/dpPtykrPNSZqLo9qyVFNVQgIEetAJCMAIhQCSAAkZICqPM1RDAbCIIQKAGCSR42N5fXn33atK3t96TqaRDVBLZe9mS5saaWAFXr1MkiaHSUgaj5qNlfkXeVFxp4cbqWgMttFYRjWgyY2INlIqfDODrmJKpF8Kyucolyv7qm9boAARWtCj6bgbtdRQCfs22uhYSSsxkwEPV5+/MvPvsX32/ud6ezlruNAHgBn4iRoHCKjqRfTTxLqGFgTcSLppGXVImwKsQCgAUzs5yTpAF9aBMTMLEiC8A4yM3tH//f/h9/84O/pBBDt6fc73d33bDnyHx6IYwbzR0dhJ//1pCz1Nhqv4c6Bo6VTUDktRYEJY9MPK3rbFwP/WmgBjAQzY0aw1GT5ERmeRwgDpiHJ6fLv/udb//Tj3/5/Mc/sZu720+/lG2XKUjMQBYbF3Ce0OAcY7y/v//0008fP3qyXCz3+/35g/P7+/u721sReWsLYW3GCIHYxRYPmNfbu6165ALbYhVTNNMsaRz7tp1biEhEhsykimKqItmQEJgJCxiork+DiIGYOBBRP4wiopLBiNClGAKQy6lZzrnv+74fFovFbDZzY+pNycdXqKqS1TU1iZiI1QQmMkQhjMCUdlToSXLOWRISowaXwb6/v7u+vv7mhx9++K1vXj1/mve32bENLewIU8gASpAJkSibJYNsoAIAJinlcZjPhVGzWRICRARPfBVEFMRQgYyAAKKIuKecMLDSFUG1sxPQDBCRiVarRdsswfDy8ja2J0gRYABAChxjFAkio78HEQFYQDpdnTw8e7BoZyi6u1t395tXT5+fP3gwX8wRcRzHi90eAM7Pzr73e7+/3+1jDKvF8myxamNkRDIjU5igUSUzFZOcVUS8XOVRUc6aUspZwCwQa7BA0Qthu932ttvd3d3tdnurvL8yu5AoxqZp2hACuuQeEZIB6ORiRaRp2qZp+r6vZ608QgefRSRw4zMSVISI1FBUsmrOmlOaDP20+f3lYowcKGVIuWDjbn4RwSu4KSURWy3Pri4vCSUsuO/7YRja2bjEB4DIAQMxEsXYoCZQmXzekceCr1iSYu6yynq9TuN4fDYBoPRrvt3vW4z0V43RVyz24Qv2xuGf3mHK+cEhOrfnXb+/u799/PCd+XyRRk1JTI7f0aZ3ofpruqHidQzIgCbRKzcgVbvrKJ2eLg8NGEJEImBXa2VGBCmt4SFG9NMFKIiG1qsOZFLJQmA67jb3L18+eHDegkRyONgHnhwRm1ANBECJ7KiIXUwofKUSdOx3bcpVi7aH1GeKABjU67gVhD5ecESYOoX8/8hTA1EAySmJ+rC5IpwJULALAnaJvvqR1buLoAIaiIp763LlBGY25pQkN2g+KMJFninr9Sdffvmv/yw/e80ymmQ2YURiBiJALA2boKXfRdShbGefHbJ5/x5H6b2YVyF7rHVKAIhESiTjCDkBGJWSjFrOzah0fa+xOX30kEQoGwC2EBpsT6EdETJiCsHJEARAEKwQEAwcSAWfcvfGqSAE0RLtAU7hgk+/QXM+X5mTAT3AddqzqaIFDTGEd3X+at0ZDboANOA+2e367pMvP5ifzAf55ff//O7dd3d36+Hu3hpq5pE5cGBDMJWiX5+8AS5cXl6F2Dx8592vf/Stl8+ff/HZZ/v1Fjylr7z46WhO64rAhKxmdfxGee5eyXIqIKKJONcGp3hI1XyCnjYzomBoyBwAg2kBf8wlBaUAuGpgxEgGYEQcwox5HGToe0MTA0KMsWma6POAwCiwuhpwCCE2wUxEc/BQy8reGdM4DIOZqBpjYGTz2zmCfI5sWfG+PkFxHHvmwCETABqncfjii88fX1x8/Tvfvnz18tkvf9IP0AqyJVNhRUFP9AFiSIRbkb1Z9r1rBirdfr08OWmaGYmOKREFIgsBJYuoU0KEAiAHlVEFERjtMM0eEACrsBFWDioAgAbGtuWGCbQJcDaL85z2akTAxm0Is5QHIiQGVUIzpniyOHlwdn5xcrpsZ21sEECydNvd2PchBDBIwwAABHi2XJ3MF4jYxEiIpqZZ0jDsDWQ2m89a5oBkziLO/bDbd/t+Pw7jdrffrO9VrZ0vTs/OZvNFWM7mZmEYSrJEJGJZZDzyhf4ygFrKN3K/W6ASNhVvWxqHAYCapnGOtEdUtSBQQCYzn+KRfZKHas4uz5FFc56QfX/6iAQERByalkIIITKLqqSUtCYvROg6q13XbbfbhxdP1uu7u7tXCByb4O2joLlMqssphDhrYh5FK0o/bTwzMCMwF3Z2c0VIZXLPdrt9/vy5TkrRWNBHqEUrKyHaMXhZzM50DD3xP/yXOytAoNKGd6jATN8AULhGaoCm6n7BTLXb7/aL/WIZJI8yjqbZOUjVwFLtVGZDMJAyG9EOr+nmXbTAC2eFaV9J5tNOMASmGBYLY+5NBYk4EAGIiI6qOGsX57P5br/vs+6ZgHRnulcRUDIjVDUZhm6zvlst2hAIXRNZnQxctMneevkDpDqOqK4n1MiJCRkpH0c1Lu+BQMee2QAMIaiJuflxcKOssIMd1e+Wh1TKYAY2MUvKt0IZmgQABBQgsJGqok6t21Pv3TQ5p5o2BEBQhAwmRECAJoAmZoQ4XN388J/9f65+8KNG+i513dALohFx1S8s9wdeai5iW3C49sP9WuFFW92MNdKsmxIAgAMjMZiQJRNhQg5zQMp5JrAADvcbyxb6oUl5FeeB5gzUYHOpY686EAr4/F+XkTtk5+VKaeL3TjseS8pnQGXUqiuGTjxULIG9mSBc4ZDGFJnObBaU3mtP8ibdb8cOAdXIFLfDq08+b2ez/Xaf5v347HXY97zt8sOlmXBoicgraQxghuMofZ9m89X9/f3LV1e/+wezf/if/cN/+k/+29vrm6HrVACLkPvbiLrVTrg6N62yQzzIJSLCJGLF8HnqKUTsWJ4K4NiNY9fOFwHbStJ2U1O0f7OWorUnta7ODGYhBAzMyCFgDux1GANDoqZpvTeUOYQQnbq1WMxjDOM4qmSRbEYHa04EiKqggDHEzMEsm3juaNUOvnEOrYz4TAMSEc+JkQENU0qvXr7c7XYfvvd+u1x2CmioxA0JmcsroABhnFmc7U33osnK8AE1JbCh74d+N2tnMXK33ZsB1pOFpipJIbMZgRqTZESIoGB8NKSoWASomYaC+uxAJcho2oRZYJ3F0I/ex+sNK4hoSDplekRESIzEhPNZu2hnhAjeQpaylcfhbUiGaoxukUhF+r73JcspqQiZ0WweGgYkMkVTy0mGIQ0jqp4sVsjczGaL5XK5PAGKKQvNN94udnZ6uhSZz+YhRMpFeLP4UULvLvOn6bzhGAMhjGM55SoyjmOMsW3b8ZApluTDpv3qI5VKXT+LqvcWe9h4ZDCB2EcAIYcw4daI4CVqdpkXKjIGfm6a2L733gddvxnGPQeczVoKERHARI0sax4HwhUzWS4nyw2aVn2kshGraG1xxIi31zevX78qYQHUmHLyBK6SY2LT8EEApwUde45DYvpGHoz2VXbWURtMpQ1YabzV8obeEjabLURySgOCUk0joXig6l/K70dWpTiTknj4zVjh8dpEvnPgFGsoT0xtO7cmhDwiUAixEaOcxzErUGhm83mTkLq7dWOAAHvS3lTRjS0KgihoiVT8EllAijOpjXrHl1miv68uGhaCAhdglrwr2wcZIjqe6UlLjZYAQiUEmqcdUMGF8n2HtbHKr6LiFir9teYGWDy4IRVayCHYrJVzm8CLOpQIKk2+xj71L1myqm6fPV1//HOSfc79ut/djt04B2SyI5vv7iGLeJcXFF56QT6xAs7T+x+v2jG4VDc3MbXIYFkyGoQwA5phYLNWMu612+4DUsTQUhOAGJkMxpz2OeVQAreyUb76qn2ox//tSRWgqU4ESPILPQ4MDUBAMwJAvk7do7xoABqaPWgWowzDKAqaIWPO3W7b7Ts1oMUCMYoN5bAgMhO4rmFRf2yRmv2+E4GTs0f32+1+yE/eeThbrp6/fD0ktdK/pqbmWS1WtN8O6JzV+KwYJKIQAoXA2vcqMi2zvcmgFpUxDTmNITSEFAhzNquiKKoamoiMbip9UKuoahYAaJgBgZlCCFmSv3E/9FrlWRAxhJAzO+M6hDCJAE8X7wTatm1TQlGXTAiq5BWRY8Mw7dBDd51KldVssCUiNtV+3w3DkCRd3d12IrO27RHzqM1Q1OEhRJrN+xhTTt4DhR6xuYCCaL/fnaxO2nYZyfphZDRGX//szcfmztQEVEwImNz8lc66r4RK02ZH1JT6yDBved7Gfp1AR5Axp10at2ns8jiWataBspSdfyciBoAmmrNKAW8lJQBQlTwOwITEZfoa5NHHACMOw8BETBxDJCYOIYY4n83UNMbGQTRADDEu28XJYgkUxqwwX7pK18XFgwuEm7ub5XKllsuDBjHnS6mKiJOcm2ZGRBo4huDP3W3JNC9kwqWnfVjAQwercxYRcIVzM8miIljDyToTCInY3QljLZofhklMIT6YQcqjbDeB2osHT7wI8uL2cru9n82a07PzECMWIgyMw7Db7drmaJDfG4QbgMKu8mmxpU9JVS9fvx6H4YB9+Nk0N87ePAtTnDFZucnQvWUA33r9+trlMS59AIr8T1UdhmEYh0LjRcIiNu0ryLWvqkhMl1cBMwnUSyBM5k17JAXOKcLMYEYGDjgbEyCJ15ByQX/MwAwRA1Cw2Ehscmiwaamd5+zDwinO5hxoNJGcMXusyc59puOiu9uU6oEP1esDFnZwZ46VA/HUDgzVx2LJgA8UrePFDLWa7s18AER+B5NEN7xRSjhefQ/0PBShgid7GnMo9VUkBd940gg+SpZswjSmezZDwohIu3737Oby44+h22Ia+jTcQbrmnJEi2vEPmpmq5VSmRvgjVzUzYWY3u2bm2knTR5WOW3yjzx0BCQE5AFogYWQiDEgBLSI0yM61QDMKXFSLELLKIHn05ig/A4QIXwkh/QGgossiFcZ0raEbALoCJfpz8wgFC1BftqyYJICNjDdDx1kvMDyYLZMQpXwVVAGkH3e7fSb6jT/83m//5nef/c1P1/t9Imq8OQwJAEIIxORr8s4776nBz3/xiVLgxerV/e7TF3dPX97cbrukZmIpJZUEgMQUOHjF1h9xPX/1OXuLqCEinp2dEdEwjgzGdNjWWiNZ/0pK4zgMTTunQDVkUjN1pdyScChQqf+BctnApopMSECMIGqmIklVhrGPsfFKECKGwDmnnHPTxqZpvHt5iiE8cYwxEuGYVDJzCJQZBStCU56a61Z6qlEPhXO3xcwH+ZmRichuu727u7u+vU4mq9VqNlvl7TrbXiGBQmyiNXFAyAh1gMobh2uz2YZ48/hJO2vblLLlxATG6IifiiIYgZIpmpgSEpm93bvlj4UImbgoBKAiyjjuCIgxNSQoWYchjbu+2w7dfhwHzWrkmCSYWTqSa62QJTjK6wcqNg0AMHPbtMBoJW1GRFSzlHMMQVDTmFPMKecZR+9lik3TNG1KOWd1EXoims1ms6ZVYg6Ai7nneWcnpw8pXF5fP3z4eEjjuFtr1ekjRDDUnDMAM3c+f121CSdEgSjUjWKTkCQeRbRlxapD8meJ0yQ5MwNApslz1282LMDA4VZ9GhxYcQ6GbAagkC2vN/cvXz178uTd2Wxxcnq22973w4jbTTuftW2DiGCYNL169er0ZLVatG6JyYc5Vv8E9a+E7iCYkFVlvbmvLeUKviJqis4aI6hoX+239Lugo/ztV5mnggfCMYXqra1ldTw8HIHbvmBmNo5D3/eFpFaiBXfEAL8yJ6kf+Oa/K4ZTlpmQSjszWAmniBkJVWW/3ewJUxMttgllEDCj2cn56tGDxeNHuQnNfLEbkdZ7MMggs5PTk4cP+5SvLl+DDqBoCOJ8WLOiBn10RVbSTir9iTghAPbWLWDxYMWwl9T5K1Iox/8IWAYMla1XCyM1BDIffzBFiwWxr9GWl5YPlAEAT7N48m6+aAX5YNYJusCS8R8TxQzMQEOgpUH/5fN//n/6ry//4q9wt9t228thf0OpjwjePex4jEHlNzoVsfIUrUh1Qs19/cvug/2jAX/F9qpZPxIHNggcAoYmhpaRESygU88QSQnFQX3ErLrX1IMooL4NWP4HXp5z+9Ny0WsAYK7LX15qeMAjFHFv8jLtO9TWZhfUPojzNOyuRMaor/Jw/expl/Nv/KO//zv//f/san3/4vrFBvUCwMUpmIh4otGiJ6yffPHFwOGD3/iNz+7ufvh//399/9/86y65FjwTBgUtirgREZEbJGZCmrzp4UpN0ZCZz87ORTPfkaFRwUS+espQ1VIezUSV1BTAkCAQG4SizXf4iGncKKqpmjIYwEF3Qk2cXBECo9spMte2FBFVYubKaii7QkvLLDEzq4PWUXN0X14KAfXc2yTqZTr98gGLbds6QJclP3v6dL/bdmOPkRdnp9/+9tdvrl7ePns5rLc6ZljMIYYRUYz8pFnZ9n6eMee82ezmi+1yeRpjyOOIaMyoAoqlw4QqL8zrEuyUi2k7Hc6iEXEgQCeJoeW03+92lpVgzwSWxzzsxnGX0k7GwdUQkImJzHDo+6HrcyqzbwMRWjk+vqonJycA0MT44OxMEEQtq6AVPFzNkgghieg4pMBjDA1ziLEJIc6aWc6SUuG5+dkMMRoGBYTY+PmczWYP2tnDh4+evnyWkqQkgOBTNIhC47IXzCFEAyQitwZQysNERAzgVC//97Rj/UHWlXxja5qBT6f2OBW8TjQle+inxykdlfU5JR9YYlwv8hDRMHbD0BGG+Xw1mzXj2KvlYRjGcQgRXIFE1IahX80bN0DFbNcyLcDkEib3CKoqKjUGdnOsVpRS3NZV0bcjkW046EwfDuG0IG+dzbe+cvgvACS3dHUlq5AQqAGkcRgRMNSJK1nIVUVKQ1oZaOgjvyaZUf8GouBUSGYODESztg/spBJAFABxg+8RsRmo5GHIBMoIHDPYqMQU5xePzt59pzk/Gxui0OhmTJ0JZgXC2UlcnUtKsN7q4APSIIsPsiyMKStF7YMigJWn4ZH4RMysQCAUutPUu/t2k89huT2UJUNDgGB15plvTQrgXNzj1T+ys9V/1iwbAADIrYhvcQDg4E3TBhVoICIEJgxeSSgAfHXQzswyUVQjhZitFdm/fvnxz36+2mwl5dfj/tW4u2stIbGriyMiksOAaUxS5vsW8Nn9sIsGOzOOiTzfoCntrgPX4AiHMfXRTS4xit4rqsQ5WxqHYd8LGRNDoJEhlQ4C60F2mgZG4aMY2gOlX+Hmp9DJH5xP+a4XUEpcLrOOYObTxHQyuwgKtgORvL+zYYY4m7XzZnYeZ6+7vZKsh/F+vd2O4zc45iY+/s63uh/9YK35FNDYQcBI6HKPAZCurq432+7py9fh7PQJouT04vL1/XY7m81ZNQCDWU59llznwICIEulUg/fonJyhJ+q5UTtfrNf3hsVVfxXSmvyEz2lnDgBWejwRYyAzyDn5RGmAMgjb8QA1lQxIBORJMKmpZlVTBFQVNQrEzAEAY5RxHFPKxQzooQwxtUsRIRHHEAhmDKpW5sZP3Ip6EHwKMKiKEqGhCIlkM+cySs7y9PnnV3evLdjsdKGRoAmzk9Xq8cOkkLvBZjON0QwM1YhKc1yZ/eA5BKU0brfb+WLZtrHvAETxoP3uV8uIaKaoQoCMqUzphIBAAYzJEJQZm4iRiqtghrG7Sd1d3495uCPr0QbJfc59zoOaq6OXFhdfnCx5GIa+79vYRGYfD1kN05HJRmTX9VEoKCISgknOSqSl7TsNw0BEkSEwQ2yaiNJOb1JxUQ6KjF4lRWw4nLXzdx8/+eL0bNa2m41XBtHlKg2AQ5jNZhxCmUBgtTqJVOaTgh0/aE+qVMSVSrjw19RDCpxk11S9RwqZAQzLeGYooF1tcwLwWXyHDJuK1wQk8OpJyqOaLleLu/vrGMNsNt/tNn0/xNjNF9Q0xBROTlYzT4hL9z9PZ+T4xIAjsZWl2LatmQGogT8vAHRYxlxXwGdgw3RMv2KJ3jqPbzzTIx9cg0TDt930AdJ3mydmBphzcgNrphxilMyBOVAIgTgEjkQcQ+QQJ+lZDoFjCLGNsW3aWYhNEyMrpd1258NtixHQlNN+37XzReksMDNUd1pACBiUA81XuFjmpqUYjRlny3B+QdaAGoDgLGZgY2wXp90uiYyGJAYkUKBjK8tdqFA1Jz2+66N/ai3O1v/TwuByB1MHrFhpzEVHhL1jG8IEy5RyqZrx9P7l95LaWt0ENUhAF/ioCbczdhGtaQIRSn2K6NgnOjk/gBkTEQZAZASp8J7kBKoN0jhkXG/t+eu2GzDn+2F8of06QiKvgwIRV0oC+jTs4nO9ZbY0GxwkYQ3MgBnBIDOzT76sub7hMTAF4FJjiCYACbA3vR33vB9DSqoZA8cGETSajBQAIZPdm9xrGpkyghZaX10uqADzoViiJZ1yHn4dQZ+z+iqbHdkLRJyGsLIBMAJ6apZB9pJ/tr0WgN84e7SKs3et3SbSbux3O1i0Jw8ftg/Ov/uf/uGf/fCvPnv1fC+iIWCMFBsFH5MKYxq77vZuvRv6kU4LxzIwRw5huYxibIwGuFiIppwd5h992oSaqGWEDGBURMwREA3t5PT80aN3bu/u1YgIkBjKpI2pSIFMgcm3n6aUYmwKVlEGj1qwJqU0DiMHqkhByXfNQFQsJR/3TkwqzgYRMJCMTGjAoWmYuTZ8JhfNF7Daj1TcjKr41HcmDIsZ42q3mz97/jSlOrOyUBnclBsAiCQ/FITsI58B1GsF3Zj2eTvklHJaP1u/ev0lgdioDcV5bClE4EAKSOL1MCz9cjgdN1Xp+27f7ZeLk7Ztcx4MoGTpehALU5edVCM1CMCBCSITt5HaNgTiJsYYGqrNxAD66unPum7oul7SoONeZafaiyQ1URBRASYuCTOKZkOkEAbJQx4jc2hiIDaVIgVaQDMQUxBAIkYS0rILAMpKY86ZxzER9VamBrXMgTlOmDXYAeZlcrlQRIDAPJ/N3n3y5A9+7/e7fj/03Wa3OYAtqinnBXMIwbtavI4LiGXONiKCeQbvT5w9UFQlnLANZOambTw/Nh987rqQPsDD0SdEI0Y0eRN6nEifhMAUQohInBXEFFSy5H4Yur5/75131/f3V9ev54tGxIaxFx8tBwQBlst52zZmWQ2opjMVaS0dZdW2S5YUrWliWK1WSCRZvLYm1WSrgYoaCDE7pqjVnOOv98JoB4s/uZJiP6dRAPUjprwc8dgHe74gaRy7br/dbIZhcHUlVYUMhIaIAmrGfpkhkFXdE1VNOTGHLAKUkZEgkmvRuIf3AVwGjBxCYGZCVlKfTQSEwNwuTs5OH5yfPVycnvJijsvFPM5mK1icPtR+JFdcIjPTbhj6JMO2T2YphhwC+1PXIubMxMUBe9bqIa5npy4H/ZVG5/KIVMRES+XQid7l+kvuV/F4BAhwhB5CzW19khPVCIAnSPTwcECLrBHiIfjz7eo7B4gL1usezdBJhIzquwsJwYikOiY0YDDKWTdrur49vd8Zynoc7lK3YR3qtflnMRIBJDVXjoYpr6x+rrR7Vuc33aCqNy0cSFBVCMt1diCDOfFkMBMc7wweZbuAZokYicAsqWgGMGvmkZCN7GbsblO3IxN1BdavBIp/2wuZeTab7XbZjmjvJZwvbc5m5Di0upNTMzJQ0F1Oz+6vzyl+NA8Pm6Va3qWEzBnwputTpNlsuXh08f8j7c+6ZVmS8zDQBvcYMnNPZz53rEJV3RpQKAAEQIKEJJKSuNRL+mf9H/q9n3pcUq+W2FxqEtIiCUGoAgqoCbfqzvfMe8rMiHB3M+sH84jMc+4FiLU6UTh3n332zozwcLfhs88+G1UmSe7BUklmCqIl624/3mx3N7f7IkLMxBGQQ4ht18NUmmIgFUwhwkAhNmrQiiTXI3TBATAlJCAzcSYyffDBB++99/5Pf/rXdohCcMZZl+IEQoWCLaXUtl2MoaquI4AqAXOIKSfNUncYYQg+GRcBQEXADIh8OzpKbqKBSFmN1TObpmlUZRizW2cidKDS5thLVNlqabxtuG9D1zU3t9evXk1mh6LO8qdqUQVVC0EJw5y8gt+bgqpYSdN2u81pAiwomQTvXdw7udgQkwIBgBHrPDF7Tj7mxw5QStnvdn3X9303jjvJabGNHpzNXDalUGv7jBwoxiasOm7bGIhCCExEaKpimkpOV69uU0rDfhApueRSJpViWidtOMxmYGpKQGIqpqFtY9d6FN+03artEMxAc8pt2y6HC7xKhjVVqTQbAy2SNKlYKSqipQgz51xCiDG2McbqYJYyNhKQ8RyANrFpY1yvVg/u3//GN77xyWcfX99eAdiMrZpIlR1t2963GhOhr1K9KY0xesEl52zwenSLLhgefWS1matpCCLyrDINcKjVq4sK6tfPCwohNE3DIQ5TzlnMhAwRmmnMROHBg8fPX7zYbscQmu12ux93UgrepVW3XsyFzf/7ewQi/bKZw/nZGTNryQaK1YF6dcZ19DmEmcpz8J1HZKJ/+AuXDsOv+V2sFcuDxcs5v3z18vLqCsAZJKqzvBKAKAGR8/isFHFXyiwgxEHBiMUoJRo5QoBpyjkTmHl2gEhEbdt2bdc0HWIAVEVQQkNer0/O7z98/Pa7Z2d3IISCiG3TtWviAGBkEIAAYSpTSlObJuha4iDb62iKXYsiVmPiGSr30AsObrPi+GBzUFt3v9WwZHmAB0SzIlt4yKyPlzHUnzuc/jc8x8y7ozp97I21h0W4riLNZGYhBEJgRmKCee4FzpKt6DTUmlSYG2MCbUCbMsHTF/2vPtk8v0pSvoDh6XjzdNrvepHj7t35mkWKy5DIUeemX1nTREDK+TBJey74gapzAg7tv4dFmMMYj7D3UszgrOu465uCsUjJeUxJBTS267aEEATttkw7zZm5IIBDYUiHyzm6ruWbWDlrhgR938d4Ukoex/3rv4JOaPQLql53eRMDMtdkMMnZKG2a1S2onK771fqT65s//8mPf/+P//Dh3fPr7e2YppyLL1fRrEU0Fyk2DPvnz59d3+4Ei5gU0ABGFALH4hmdEwtcHVUBQIvkKU3TNHlXj88PwbqYQISPHj7+7R/84OXLy2G/99X1nUpgs5TNEs4roiGqp9YhWB1DCWAAzNy0bU5TKdlXRRXGcSLKMcYQAjpPrY5xE1WRUiSXWhchRq6Vv6ZpqxCSmBt9IpKq1kKmJqIzFw4QsWnb+/fvb7fX05SW3Hd+dup3VL0OOVcpMwkRL2EdE1erXYsIHENo2y60XVG2bEQRiGs7xVFnSP0NFRFJOXVta2a73c6VE2dy8lH6PreMk1EI1DaxaUKMITLXYwcqmiU7ljxM0zhNo2u/gAmiMIEQCqLNA8w8ESSm/TBst9vzs5Pgxdu2Wa1XjJhLjhzcAftiVjc8P1zfwugTt7TILDWmqiEEVXNO+nIjVdkWkYiBWF0LuoZETbHSNM356elbb7316urVMO0W92lm0ziZmhRt23Zh1ck87HdKyb9TqQAih6sFMFOihjmoCCJVZMXMe3ndGsyPB9Cbj9WsTqdWMNdlmPvViEKMIQQ1NEtmyByIKOW8G8bN5vTBg8cff/xh3PTn5xdX16/2+6Frd5GbcRz7ruMjMSus5UZa6sFH+7Du0s3JSde2eRrRKnI2h+2V6h9DbGIwO/iDYxP0hvX7h7xcuv/4O3gwcgazpS2lPHv27NNPPz05OXG90VmxwSUnfP3BzEQFBNENhymabwlRQlRSUC2lvi+hEXm3mauuMDMyqZFWxbwYYtefbE4vLlabk2xmakiBkNhxlhmPRDIiakI8o7han6CmqAV32/TyhcZQxZ2dODbf6+tRy2x/vYHHH4oPvbAF+zxiX+EbHpxmD23wNRmw2cztrb9X/Sp6FFS35Ez39R+go3pw3QMIEBhj5JyJmUJgjiHGmKs8s6AaijnJyz8vmNiry7RN7fZy3XGy5mp389zGS0ipqPoie9OP74ZKma0mqV48IpiFELt+VUSyVyPqlS3Rutms+rtk1R4cADiS4UdBgyEbcNtj21OSFslEdtttRii9rro+hGYyuU1jAlXkYxJbDRrtqDzwd2TGMcbT09P9fpfSWOuOc6iEHn0vPcVzrg9QMV1F4DpXAVqDRuyajSPgmn7565/9X/+f/5cffPCdL58/iTH6/TolWEo20TTlm+ub/W57fX2jTZNKQsVgHApQNsuuuGJqIlJyTiIllzSMu2Hae4kUTMB0RtF99fA73/n2t7/1nV/84n/IOSGQZ1aOkFGd2HJcf609FbWT0nU26plD7+D0HNSrdF5KzDnHGANHYmY6BJEiKipeOaaZrOqDCPt+5Tq9bqYdk/SXB+nE6ML9d+5chEDn56c3N1dffPHFcjTmxzfjRwBmlnO+vb0tRVa9xNi0fUeBwLnBbataAIwRAoQQO+BAoWkwComAcQ6CjBgQyzwyxWoECGompRSLTdd1ueRSEkLN6VVltpwICMjUtF3Tdl3XtW3TNBSDs8CNEKyoSC6SpnEYh92URimTufQpQmBsYnSkimaqR2VRqY7j8OTJE9Py/jvvbvoVElMIfdf1ZlKk6zqYBdWX0YTwFU1Ez9TnyEGIKOfim3G5h4W4GELkGEGKzbYrhNBh99Zbj+8+uPO9H37/3//Hf/8//L/++/2wYybmuTVAdBgGM2vatmPuuk5Up2ly/sfSfubvuJg7nMlfRKRm6Giampkxh2XUpZkV1WXCzUzAgVIKMjvEYzMw64wwNRLHSGIgDlllt981bXv/4aOb65ubm6uLu6dN043TaEbeO7tZrzFWspTvNzWhWgyu577CHrXqh6cnZ5vN6e31FcwV9OONal7KtqrPNYOBAP9gn7u84eJcD6n00c/4FwtzxUOH25ubL7/8nPldMDWTOlMbTM0C1F5lrXwkJfBKFfqgITOjqoNgBMhEIUZkViYLAWMjajlrKWaOXxBFIuamCSEieaKAaqwQiIPNvEVCQyxakmoxFQRum6ZrY8CgUq6vyn6UEMX5ZURAsHCsDos2Sx26k11os2/uefcEr3U1QS1a1/1S3yXY0QvnWu+hUWveDXMJH2vLog+0VFhKwku9AAAMHTqtgm1eX+/7lqacrjmnxE4QBEBVH4eFAI3Y+OVT2I56fTNI/si2X4R027HGliSpFKfYEIflkXv7/Bz2VdeKSLFtu35VSk4pafFJy7xc/QEMRZoH/80Rx/IXAAA2xGJ2W8qaco+0YgLGnYxbTZlhpSeoeRS5lmkiF1p+c38foeJvPKLDc2EObducnZ1tt7fTNNVfWur1NvdyzT7YUYC5uQSwjdhGiUSIEW3SwTjg3XaDza+++Nvn+2fj/kbZS2KQUiqSVdSKjOM4DMOwG3JKGBo0C4A90C4Llmzi7bhpnMZxGtztmYmhQm0T1SqhBs7gQ0RYrzc/+t3fu3f//vMXr4oYcy22L/2RcxFhObeGWIew1jrA0b8iYoyN6z4iEXOIoSHkcRzTlIWBWBsADlQ1GV0QTUvO4J7JcRdEDCEiohNi5y4G9rAcC4qKmsUQEWgckzvOu3fvv3z5arcbcI6Jjm1cTdfMck6qNg4ZkDabzepk07QNAJ6fn99/cN95dATUhFY4ZEIgioiikkMQDpmCACkgEJkPdnDtVy0iWU1PTk6atnl5+SIGahtPrVVrQoaGoAjM2Laxa2PXhMjeIlZMrUjJecplUhUO2PexiVAKFclFchVlY0LgAJFUD2YUyTVM9rh9+lQCEhsGDkzERKuuDxxCqFFdCBEAHc+wQ2hQl4mQCNmFS/3PuTelxmFmJsUbcZWIKIb25sY5Tlc3169Uk+T9tE9lAsJHjx6fnJyN4+gJM3NgDoFDTQkAQgjrzWYYBicNqkrOdQZwjMHBhfk4HfqD5zABDIyQAoa5XgeLhfGpi4YIYjNsgXMygohLZzmRQoymasgYIyNhFhFTIj49u7i93d7e7B88urs5PS05u/VyLAPoWEN/SY3Ava/zq5mrxkbTtKv1GqrRqz4YVE1EpSCSlKKERGwG1VXgMrKsHrJ/SBK8YI44w6cHnGO+zPlqFQnMZEz7p0+/ODlde+j/pt8++quDpwbmrGhEVDUyNXPZLB/qHJvV+uTO3bxe9zFS0425FMBmtWFVJepijOvTk5PTdYicUrm5QeKGYhu66N1kkRFRAAaFMWctGQEYMBCxIgERN8DBiJ1drT6k6KvetT4Pp8jiG6b9YChsOQEEQN5XBgoV1JwrcAgQluimrunMpcaZqeXv/HWBky2xwIFPDGAIoupJOxERhxBi3/ek1gNPN7eap9i0/abnhsfsILsiGKpsry51Nw3b2893l5/wfmzIWiaKXQ5TmlLKvjhOPFMtItnM8FCuNnASEQdA46ahwFIMVI3ANycgLnipqNC8cZcY/ChGRAUTgL3ILqep6ZQYQhObFSfAEPdarIyj2QBWwORoburSeL2Iz85uWOeFq6UZQmRi5rBardfrTUpJVa2qJikhGXjUYGACyjP2YEgEQAY4at6hZFQFXYdwt2niew/1fpAWVHlIOA08sSqoSAEDM1GRlPJ2tx3G/c3NrQIxgA+itjzdXL4oaau4n9JuGqZxnJz83DQhhBYJREvKqZSiQrWL3FRFDe3eowcf/Pb3C9HNMBRANKAZdl666zwx9cflTb8q434fQghNy3MKWF8cQ4TOIKmqAYYQT0/bpmmHYchZzLQgIEREYKDIDCqeIErBady5wnbbeq4WmP1qj9uRzUDceQCE+w8e/u6PfterNbfbmxC7P//f/sybs2fvK6rqPGRE326ROTJVFus0TpKlX6+/+73v37t3L8YABtfX1198/uUwjExIgYNa2zY55ZJShuUwIs50cVXLJU/T2Mam7/uz8/Mvvvwsp9KEznxIOIAZMGIwDVYiloZKpBI5MJrP4iulpGkc01DECzEWYmgCifCYUCfJOWWvnOuBx1u3ppmK5pQnZNPhs88/H3fDi+cvuqY73WxWq1XbNN/54svf9nL1fpgHZjjKVhlliD7xktnPpAfjHJjDcRBmZrnklPI4pilNU0rh5Ysf5aJgP/npT56fngDBlCZX5kk5n51dPH36HAo4uhhDE2NTdYSAVpvT3/nd3/vTP/3Tcn0j4ll3mfFtxEpJMQAgxBCbGJuqVgQIdVoMgIGKen1tDgztuBmkkrQMACwwMrlfslIKIoFa3zYiWhT6rjNszYCJ26599OhRLunZs6fjkNabFTAy4cP7D5oYDJaZpL44CAjOsYfaB6UAFiKHSMwoIimNNbb1TlkzAxMTAwPTIjkou4SAl7BgYdSiLb9y7IPneeQLVrd41gUJ9ifLDn1VUvaCElX2nRSxV5cvX7w4e/jwgePnWkXvzeygpYyLacAjH1S9nKkBqpkBU1ifnT/+BpnUyVFmtjo9b1YbMyumSmzEDQTejUN65pzKtlvZetSuD6sVN9En5DYi/Zg4Z7ejHAJFb4wiNiBFM0RFrVOO6hhzPxROmkNTNCQgHwFxBGrWZ1QRYEebPTRz6MAUqryV14oR3qgBL1/XvnJTMFAAb1Q9bD5AA3D5U1r2izOcIZvVZhWbM57ALCFQ36tRbGLsmof3713vrrmJsq+S3KzQAmIupjaB3eRxm6dBIDRt27Zt3/s1FBE4MFBq2ArVJQEAMFETYowxcKhbzFF/MzOp+8b39zGc+3okc8jAAA0gEU2gGUAUA/LF6iSGqMwpF6OSEU1do8psFt+1A6rmTeszFuEKlXWDLattqto0zenp6TAM+/3O97g/0dnuu9PWuQhQicFmMKTpatwN1nRK3MXHd+7xWw+fdNsMyYpZscKqEWMTEMBcBr9oyWW3203jOE0TNa3W2AT329snX36+H7ZgiYL1q9h2QcQQkIhFSkpJ5wEcWNt7SVXQsFv3P/zRj956792nT19lLdUEVzapb9CK9s8FOQUDT1P2+33btm23MnBFttkiUGgaAqBSstdDYoxuzPf7YZrGUgoShkDMHGMDYCm5uRRTFsnDsDeDGKPnrK71VIoMwy6E0HYxhDAMe//Mt95694/+8I85kJjmnJDpww9/c3N9GQI5SzbnafZQIGKEKqDM0HXtarUiDilrSunk9ORP/uQ/e/e9dx2e/bf/9t9++JvfZMkdQBUYD9A3saSQmF1DyqXWAaQmY6I5l91+R0Snp6ebzebm6tJ3QkVTARgJQdGUwGKgGMg7glWylJzHYbu92U/7JAnQmLhhIgTzaVZFU8opFZ/G5LU1px3VwgeAIRQ1Uh3H9OTZ85cvXwXigEREMUa4fvGvAPb7/RdffHF+fr5arULwydQzpOTUbWQmDux9oe6mlwlXOCt7ACHnLKK6HfZwc6kmReSLJ1/Cpm+aLiK6aF/bat+t3GWakgjMEsrsydPde/f++b/4F7/45S+fPnsOR6O0AFSqI1PEqkXqLb0wC9NhJfrAkp3jUZOIzgiB/yRD5RjX6bAAJWdHwhG573sAEZW26Zr+LGcx4tXp6Tvvvvfo7bf/+qc/vX71ApWa2J1sVicnZ2kafBo6Lq3JtQ6sqoe6AyL4DZnZy5cvr66u0DVO52vGSnt1wjRKEMJgXzFxNauuo7yWpO21+aFf6xqqZCzMOgwzTWTO0Q0pAIKpjuPw7NnTs9OT1Wp9cOSqy3hiXPAweOOFMHcIOBaEzJvTs369FiklZ8klAJ0/6IkwjePN7W3KWUQt5TKkZIJgMXKKvGu60HZdvwl19ngtTXVVekq4Ddy1HAPm1BiwKiqousrqUmOthdVKZzb09sEDPnE08qea9jnKwLrMr41zBbM5qZodMBzhGHb8xm9CB0dPog4WsrlJsVLUTCDnImJuW0spaUrDbj9ud3k33G63eZpeXr4ahsHJxo64tDF2oSHneRFQF0NJVtK+DMMwrfq+bWLXrVKeiqijiFg76xdUEGu+z6H+K1JgTjMVZs57cHkdR3/H2+CwFGCGmE2TlWwiCqFIC4yhnRAmUSnFu1DNRBHEHXDdzzXSdEksA7TaMG1ghqZQu/fM9XWJab1eX1xclJKdO1ZP4fwc/S5wHiKCCxyCuBv2lwOGVprcxDFFZEwGohU5AymkIbJqsSoaJdvb25cvX4xDKjnH2IqomhXR68vLV69e7nd7mbZcytzXiGBklpY8IISAhB7UohZXm/ytb/zWH//jf9q1fWTu2o6IOXCgRZilLrib6dr25gQTtJzzdrttu1XTtaaLGcKZJyxmKGoxIgAxY9etXMQg54SA3rzEHFQlBHGbFUIITWSe1bPmGZT+QsRxHJs2uFh/SmNKSRUAGInJtOn49//RH/y3/+0X/59//T9O0943TM7Rr7zmKOask3x7eztNqev6LBZjfPzo0eO3Hp+engLA06dPP/nkk88//5yQROD0hLqupxCg75y77Ug/GDliYgsFwOssIiHy6enpbnvj1lJnKU2lsOCJHtaQSzaDgUjJaRr3w7DLkswUXFSFCNTEtArHiQgYInFg13SsdlwNDJgIcR6EjoaEIQRENICi6m1aojqltBsGDgGIeHYFVLu0nS4TjqQbPVw/sK4IuW1RzWLfdyfrZr2SMhERqsUmtpuTbtW7aJaZ7ra7nAuT+3JG8BnSUpUxDO/eefitb3770cN3/vInf4MQiC0wA9g0DaVkp3+qaowxxshVz3kBIfyWAREXsvRiEuc++Ipd++n0xM6fSMYC40jEXeeBiIjosB9OTh6EhodSLm9vP/r00/PTs83Z+TDsicNq3V/cuXNydvbyxQQCLnE04yvoIYXOkm1+JTNxQT788G+vLi8DV2GDgwWzqo1lalJKDO3fZcOPkNKv+ddqAJfs9vUXYm0e80kCMEcMoOJDz0z0+vJyt9uuXNes5oI2T4T02HFuF5tNOM7dhkREiuJhUAz9aq1gRco4TpxL33Yn6w0B5GnqNyfjMEjOWrTknHJKeXIrJ+M+TWnaDYiMaFSHSLLTxFU1Nk2zXoW+BTOU7KpyQIzINk82rXntTL1ylTKoPkXraVUfjmx2AA+Wlx5ByG+uZFjW+rDwM9pgRgAEdWAFGuJxr9h8GplycZb/ImEyjVOaCofgXCdQQ4QYY+ig6zoocufO3cvrK2ZGIgM1AGJWkSlL3o+3w01BaWPbMEkpKaXbnGW16vueKagUmCVeF9frTg0Rmeuh8gCCnZhaYT11nU2zOY1c+E7VSR/Ke8dfJ9ERbMKcFViMFRsjQBIp6sR1cO4GGoJqVaLxXegJxWLxa6ztf53Tb1fyck70arU5ORkvL1+ZKVQLDzqXDdzrzlCNeUXBkCm2qlxUQxZIBYowQCiSEcD7lcCAMUsBUVPLOW9vb/fb3TjllHODNdkvKb168XJ7sx32e0kDlWLFRaRCG7vYNDFGXzsn3xYfs5vMLeAH3/7gnbffRYU75xfvvfPO559+GoBiICL22odBLRHmnEV8qiTNaIQNw/Dq1avzOxdN07I/HQREXBh23vcZYwjITNx1PSKO434JmJgAILoegBNfCavyDswWapYAgxhjznm32/lPus0dhmG328XSGCoHPju9+Gd/8l/8+Md/8flnHyMC15bT6vDcdtf8QQkARBUAY4ybzabvewBIKf3m1x99/PGnV5c3IrLbjQ8f2MOHD5vQxBhXfe/zmqZxKMUATHwiLdWNqaopJcC4Xm/6fgUqTh6HOQZiIvclc0iKVOfsSJpGKaXklMokWsysKhgbGICbM1H1BokYw8nJSdM01QyIlpwRCQTIwL17aGLTdA2xn5sYw2wxpKQpjzESQzQkqgJS7olnitAhanntrBEEdiwlmAWzRkXWayQClLOL8/d/8P3Veu2e7/b6+ic//ssnT57MVeTq8N1FhRAZ+ez0TuDuncffIGhKmbLkhBMipjSWUryD0HVjHDVxmQJYeEyes7iO/mJBVZ0QsVz2LL4xW2HTUooTS3LOfb9StZRySglgP6W8Od+MJU+57KepXF0HxHa1YrSmaU/Pzvq+d8RgaekkYs/pax6sumxRd8CvXr36+OOPREsgRpd0cVjTEJFclqRqIlhlp9sbjKqaCxzZ9KMerdkB/6dfCzDghkHUiMFlWUTLy5cvLi7OQ4iHvM4bZOuwiRmbrTNsPJI4SKvOoY9OOSNRUSMOgIQxWmD1Im7XdquViJgU7x0Y0iiluI6zmklxVFYJTNVCchDQnaWoKU1jILJcAoWuW5lq16+Jghq+1u97lOzWv1jtdvMU2RfbDutmX13C6kUdgcAjEhYcvSsAyPwAFhqQRyo+lUMQiLgNASlkFSAMRKkmNHr16tXz5y/uXFx4RxoitE0bgKRQiE2MiRZ5yDmaUJFCJSWV6+vd7ia1SKu263oGSGlK0zQMwzRNTdM4jbsUKTlDjTgAABwW6/sVUwDDkg1Jc86+Bx1ZmTViYNmEs1EAW6QA3nyZmWWzVEpBBgOfJdQgZeBCZLVxFWpRc5bIcdq4v7+oASIfbSq/lPnDD8vOTOv1+ubmuhSdd2pFlrQqC7At+AOQIRZE7NqmOaPCQJQnkRc31OXGBMAUEFIJagimoi7rsdvtdrsdMYlILslMHb2chuHl82fbm+s0TShCqgBGhBw4NDHU5h+cw38sITKlSU2K9V33wQff67peDc/OLv7gD/7wV7/6293VbWBEn0XnGwprRc1JeIRsB3zPhv0OzU7PTruuiyEC8JLhhRBUc05pIqS2Y59Hs+qbJuSc65vYPN3ZFBENMBcpmlYUAodqiFw9CzUEjTGmPKkqM8XYiMhvPv7olx/+bdd1WYqCMMEv/uavnz97UUrx3bL0hopICCHGGJgPw8uQYohN01xeXj579uzBg4fb291HH3067CdVmKZcyi3h06YJ5+fnnrD2fZ/SNLX9oJaziaAhMKL37QCimKZckCKFtqSxiCE5MxNdnLvvV03TOmKGvjdUTYvNhaQlgaukfK3GQlVFVKu4LN25uHN+ce4RholO47jdbqf9REAMSAiBORAHjkxICJFnPfAilktJuXAKAByYEImAXmexLC4YZ4CXiNQAFYhBZzlDItK5I2dzcvLNb3yzX6/8dHz2yWdffP7lsB8JA7rwBXqnPLqxbWK/WZ/uttO7776/WZ8+ffqFaQEogOZhme9eIuLArldMROgkODMggtenAOncdV3JYnMGvHiIOXczT7kQS86plKxiOWmeFCy7MlTbtMwcYnN7u22YGbCI7IZhHKcYahiHAE5z80zG5y8c2ShT1ZwzM19dXe33O6/tLL4Uqu53dbc1ITu0w3xNsltzg9eR4K9Ned94LU56dsCzPnE1VTXUHoYhpRSjB3aLYzJQRWL3XmSLSr8H3fNPAQAAGUz7oYyjk96r6XDNf0KnWqJ3JgEHNdY2au+j00xV1KdN1+AJAclESyliakAcAckZLEDUn2we8jtgFpoQuzVQ0CO/YMcqw7OqyWsAvdPG5uzI/eXfv4yHNiQ/D55J+mP0R7oQI+UIx/b/ERNziBIQQRiJAUgNys3Nzd/89d98+zvfvrg4W3oAxnHM45CHcdgPn3/+2bNnz1LOAEqeOqqiSARaNf2YRsxjGRNyGyJR08QYVaTkXErNts3mORUzh5Aj37l3d9Vvbm93vifSOE3TdLhkVAT0fgNcJujNpIR5V8GcPR/QJwZsYmhD7ELbBQQVICgAImUiyAjoUylq/KO1fdYUAPToMCDOZZO629QMCGIIgQ65+Pyk63fmAKUWn6jiUYAIrreOYvZy3G+wKbFvCW+++PwWb+M3Hq03XUAbSDUXUBUvihqUUqZxzDkzshkUKYjAHMBwd3W5e/VSpz1JIUMChsBESBTmZjSq00iZmJi4QYpEkUITAl3d3E6jSqLQ9x98+4cP77/z8fZDl8qCGoWTL3ngKs3hgOpsYtQ0D/tblWmz2ZycnHHoFHFBXBEh5SJ7MdW+6zE2xBSalkIUKc4bNqQwR/5iIAKlDCaAJxxDXBIdIgIMMbZmVko2xKZpNej2disiF3fujHkygKdffvY//uv/9/MXz5tYZ0GVXDwO8mfqjVJt0yOyaye1Xdv3/X4YPv30067rXr68evHiBSJ2XefqJbv99sXL52qy2Wya2HnUKEURaZqGlLGUpKIOEnjT1LwCPIuNE4VAIca22aw2m37dhjYgMzKYqZRSppIn8w4xBAIURTScpavsGFZTMxDd74eXLy8RqV+t+q5ru77rVl232t3edqFdd31kCkAoFipsaKumd/vIxQCL8JSJ6zMGQvQ6C/r0EcTFldSmZz+kZIgGDCxmRRwRn1EFxNVq3XZtCDHnnFK6uryahvHO2Z39fp+mTMZkQApYQFWzldOT/v6DhynL3fv3v/+DHz5//qKUBAAGZfb/TIirfnV2dt51HVEkDIjEc7KhWrurl3zx+LXUgG2eWAPgUuduklW1FEnetle8BgQQYxNCk8GYm1W7nnajioJJKWmchi+ffjlMGwRDdlZltTxEhIzEpFWH8VB6uL6+/uijj3LOALUMUnXmwBaov3oEsLmQ+fU13Zq+2JIIL2Bp/ftShVwWZHkrb986JG+zifNl89CkadrA0WtxtWVdzdw9z8X4Q6RQ78FnbEI1M4SmRYqK60+BAcA07HbMLn3YtN2qX3EMoGJgxIyBEHxuFUSDpmkPtQNAVHEdKaeUiFlOSUWV6d5bDzWLqYYYVn1PqDBzGuptOop5vEjL7dcGpZlSNj8bWIrkXxfWvNkHDEudbP42ETVN0zRNztPiEggMQVMakbSUUlRKyrlMKU3TNOWcfvrTn07T+E//2R8ToSGknPb7fd7tp3EspaRc9vu9+TxCVELoEE8o3O36e/35yXqVdq+STt557DRTRATV/X4/F49rzwrM/uz09PQb3/jGfj/udoNfZ8rJ5+xWBwzOS/M79s6EikWbHVUhYNlhCDOPjWOIbTQOViAYArMAkBBIAf+hBVN5k9dlSyFXTeZ0d3khEbZtizhniBUUmgsNCI5FV7Gj+WVmM5oI2ew2TR/r1as4NDFe77fDzZP703jv/bfDSYfBIIupiZmYgsKw3++225yLgRfphUPg2CjA1asXu+tL1EyowdNTdK4pM/oFEFFgij5qJzAQN8wNxziN+3/zb/7np8+v/+W/+Fc/+tGdGPrTkzsnJy+H/RXMDBeozs/PZ21mck0M32kmoirDMIgIIp2cxlk4q7a0OQQ3DKOp9T3EJlJgYjYAAwkVW2xMpRQJiGomIimV25vbk5OTpmlx1iBE4MARW/TOVFULoRGRnNPJyWaNGwp8ff1qt9+LFAgNGNkMtITAZibiLTRQSBDqA8m53NzeQKDNZnNxcbHdDvv9npn7vt/tdimlcRyvr6+rMP0mxtiuVisEbGIcxzbnlPI0DEMuGQwW4BEAmqYhgtOz1Xqzdk7Zxcnpg/O7bYgAUAXPLUlOJe81jSYFvc+1lIrzQeVWIaCZC3eaCz6M4/jll19eXV35YW/btmmadb/uYhtDs+pWm74PSC3GYAAp55RajgBABjELGRhyxvHIchs2jc9EVpf/O/43m9sIUym694ahMaXdfrfd7e2LT0ouBhaYHGHOOT979ny32/+TP/rjD775wYcffvjLX/5qHIdSBAKGGF1J497d+48ePxaz0/O7/+Sf/cnf/OznX372GwSDAwsGzCDGpu/7vu/NiCiYgbrUqsis8X/wN8fh+PHBVlVBIHDVjpBSMjO1YkIqaUqjw9TM1K/71Xr98uaaAVbr9c31dSlTSqOBEOr29pqorNd9M/cXAcDc3OTdCap1JnuVaHn27NmXX35ptnQtmZnRbMGoyrV+vVwXHtXXDrfjBTA0AH79l15zDcdvUr/4ennLGUtGPD097bq+vsXxx87FTnzzIw5f1njJW66hIhBSZ/eiqvmM567rxtW6bVvC2pXna8jMDFU6fFlQpCqID0jAhBgUsWmaIgUBycBEVI2ZYwx52lvVnagmvI5rmN9ruaeaIcw2H/ErIL4ta+BZWiW5hKN/PawCvl4waLz4BzNvFxSAES3nJFambGIqOau52AL7rLrnz5+/fPny7Ox0HMbtdntzczNdb6ecVDXlNAyDzvxFYjzV5iF159Bkgj2DdS0bMhm4cjNVSQWPAd3HLwqnZkbMjx49unv3zjQ9We5GRJxy4TelLtK8eNrFk1UU5xDfLSHkvJksSXm5uxXd9lWmlozY5hq2L+usSfTG67VVhderAk5+cdBv/o7NFwZmDjaC79PlP/5xooJE1Zgi3Ei+1QKTpTKVSW9+85udpHvvPKaLdSjqYKCJqep+GG5ubqZpCqHJuZhZ17YxBgS7uXy1223NdIaBDqOT6vgSYkSGubEBEc2pDRClNC9evvx3/+5PmdtHjx8VyecXZ9/Ab3zy0YfDfjffOzh/xTcXkePPckjyZmeccx6Goe1TaDoAcFU4otbp0znnKaeiurZNA86yZiYEE79WRBzH0cdPAOA4jtvb7W7nTKVDYyJz8LsLofhuGcd0fX1jBhgYmGPXrE42++1N20QiEp8+Wop3rMYQQ4xt24cQVHy6K5pBTqnrukePHnVdv9vvSylt23Zd17ZtzllV9/u91yz7ftO2FGOkNXVtu16vVQURt7vby8uXiLX+b2JoeLI5vXv33luPH6/XG2YS0UDYsYJmL1OVnE2yltHyJHlcmgWWUkvNd/wP59eCusS+qKhMjqt7rNN1XV7n881Fi81AI2ZriQRDb9QqNEWaAgDABl1x6k1RxOzcBjGISoAQfLaAw0QkC2MYUFRzKSnLMOVhHG9vtrtx2O73u92enn4hIoYwjJMqlQlevNz+/OcfRQ4X9x6enlzcf/Donbff/ezTzz777OPtdqvFKIS333rvj/7oj9ebsykpAH/wwff/6I/++H96+WzY3wKyn1EEcXc+DAMRhdDknEvRkqWYEhGR86cAHD4/MoOOXfv+nKMiUzBw7T+inAvN/dOI0QxCiLGJgcN6vb68vdnvt02MZlpkSmmIDKENpeTddhfImk3vFRmoFom8mosGoFabZLEWvKs/+MoL54B14TP/w19fddmHIvHf9ZpT7TnpW+r9leK+Wq38zC7LOJ++JW8+8j7zBj28PQCAa8mpmZWScsr+AyJScjED179LbRtCMKqDETyFRDWsG9ykFDWLIXRte7LZcIwUQwgNUvDo38fGmAoJEDMgFiAxWNS/52IsvH4vy1odOdg5uPjqzyyNqUYIPoxhXrKvrxCAB9G4tKsfFg4BmDgEYAAMAU2ylCY2U5wI6eXLl3/+53/+rW/9VhrH7c12d3Mrw2RjtpyHcUDEEEIq6Hul7/oeWsn68e3Vp+PtLUgKKIhTyWUqAOACnVhb3b2Pq24aorBZb+7ff4QYXDfczEopUsQ7qY7ogXPNAyspaGlImJ96Rb98TcBMgdSsDKOojUitIpsFDqHrY9d13CxMQp8UUHlnrzNNjhbzNQdsZmaac+bAFV95feVNYUa6YE6RK45ngKiACMRkRFVVSEEMRWG83X3+yecY+M76Xe83N5dqzLLbbq+vr7uu5xBSSmjGhAFBJU/7rXNJZglZJHYImjxloXoinUXh5G9BMGJq21bNUsl//bO//Ntf/+7JySZEePjwfmT4+c9+VilXx9IHFVITgDqZeLl9t3EppZJzbPtZmKzmwWbmvOWc8zCOgBgjMLO3LQAAc4gxqpqIF25hteoBLKWUUup7l4+gBUgz5hhjKXXO/PX19ZOnT7AJFJiJ33nn3e3lZSBqmqhq0zSVkt3iqAECE3ITG2xY1VxI1tDef//9vu9fvHzx85/9LKXUNM16vR7HEQA8Vdrv9y9evEAku2tt2xISVx8viHiyOUGEYdj7cDlVa5r2/Ozi3r1Hm/WFmeWk3phTIKO56oLmnFEmSaPmESQhUOUDMrctl1nuxEtgWOvZ3sjp24yOrYGHQQDAIcTYBgpsyAKkxmpYzNEJBuyNxeUfRGQanTGP2pKBBtEo0QAAhKiYqbBXNKxuMeQQ2q5VQwiNIBlxky6IKEt5dXX58vJGIX74my9+88nT7373u6vzUyhyeiHfeP87kfjDX/38f/1f/5df/+bXq5P1/+G/+e/+8I//WSmWy4gY2rb/k//sv/j1r37xNz/9idaRNeTH0/XUmHm1Cg4CiegsAFwV8VTNZuVknIvWvv2OfLCRi9zXXe3yl5TShBAMQxubGMhUm8BtoJuX1wlRy1TSCFqKKVhmZiadBt70PUYGF4NDVAMUU7HFbCGSiALA+fl533fjtPdmmGVwsc2k3eWa8eAgZ6D8Tc9BZvI1vuJ1w/W13gFqXrAEKrWER4QA5HTuUIU4wAc0OSXYZpUphENbx9e/avjhjVVWimou/mEqYiIGIGpJDUSsaSDUtt0qPZuKSe2MdWEhUe2a9u7du6v1imMkCsjRFRapwi3AIbRMxSzPcw/dYnhM5pVl0wpx/30L97U3ZABwuOcDC3p5VEsqvHxxnBAbeoirIJJz5sjoQ7BE1UpKk9vNNA7b29sPf/W3q9XqZN23bdPfv8tit6+uGqTvfveD5y+ef/H0i1//5m8dHLiZhqdmYvppun2BKatmwVHU5q5ZMUsiSwyINRtDMIox3r17/2RzumDzbr7FR6PU0oLNLKCjfQW1R/f49l9bK6v/CWoN4Cq2hCCi2UpEBbQGK03InRQxITjYVlM6Z0gu7//VV855v99zYJ8dZoChabvVekqlpj/V9S6JOVWJX2RzCBvJJYUJ0NBQ1EQRMO2mq1dXq7cfuBKbgYlIGsfb29tpmr797Q82m5Pf/OZjVdUiICIylZTN510jkNrSQemhg6qqCEJA9EFsCgDz/HYMIfR9H1REytXV5WrVdV07F0hsBn4PDtjmMaWqVj2rmlYIDLW+z5Uh96tVLYsourhgYI4h5FJyKs5JOUYIETHGWDeA1QJY3x/y1Nk21afMdQpUHZynqlfX19QEBWOyrl/VdAQ4Rg4ckYA5ukJeExvmiBWPQgAsYkjYr1Y3NzdPnj792w//drvdOq57586d9WqVcxKRYb+/vb394vNPb68vNycnXduFwAAkWuvlqsYhmhohhqaJMW42JyLl5cuXvhFijJEMmkwoAFYki2TKY5mmkrPViogLLFUWW0VQ1DwpEZViqt6cY2a1lRPnzBnMbJjGlAsRd81qBSFkaLK0VhAoGgNAQF41nTGkyBODImjOgj6AxVjnPg1TQtCSiZgpEDFQsIWkgkBNpGxIwVkzZqaqNzfbV5c3+0l3u/zNb3zw/re+d+/hg66NLz97evPk2Z2Li/f++buPHz7+v/0//u/n9+/8sz/5z9enF8+vb/fDtN1djsMkpfzgB7/zxaefvrp6CkBgtCC0IjJNU4xN162aBouou45KobGDt65YEJF7X+chLixonNPNEELxRmb1PsmJGQAEwUqeht2NScrjdiwlp4Sa2oaISKWAFi2qmg1Nq1TIojEMiOhON0aaDxGenJzcuXPn8uqVqiIgH7S8/hOvxX4e/TDCLP/yd9mor3mTpTFyboetxgkJKkWQXAo7hOiUncWJuBg0oqkZLQVCmLPfJe02m+FpEKiNLqpaSsZaEZcKCqIaiGguxczVuc1KKSoiY5KccNZRKSWL6D5NJnm1WnX9imODFIADB6YQcs5pKpvTs7ttCwBFpIgAAuA8l8FRVKugPczwEszW5+9nXeHSXgm1UfW1GvACbhw/S99PTlG0ml0rInlCBZSygIpqEZHkAkneViE5n6xXP/je9x49fhSZIzGK/Oyvfqpj+v73v3v+5LxfrQAQDFTxxbDbTjfCMJJMDAbBANgYEdjH3ZiVnF14YbGhRNx1q6Zp15sNcZ0e5XTEnJOqeB/srNKxvA5BxlyUxTnQeS1yRERvoKRALYfNatMZ5mHc7QcbppYjUwNIZrWzgRAVKmh8FJDO1d26cRcx7rq8wziEyP2qR0QDZA6bzUmaUk6T1WqULoQ7JELy2cC20Fpw5naiQQyN6G2RrMW217vr55erzdpZrKWU3Xb76uXLaUq/9VvfOjk9/Z//v/9OVcdpkv0emn5zftE+e26TBTRmwCq9buBi4/Ba/FI/ERFAwQxMmNkQJKVf/PznJSUQvbp89dFvfp3SVKmdczvj8hg8lEJ0SWFyUQ5GQyVTy2na3l4zYtO2Hv2Buo4FMfu0dBbVUgQWURAwm4WjERGwii8gYuAos+NfLsFvBeYUAREv7ly89dZb2AQi2u9v0zipmIKJqLe0mllOJQFEURXrOgohmNVjXlRNNE0TIrx88eLVixfb7c4/HRAqPquZGEKkUtLl1curq5exaULgEBoA7rpV3/d+j0bokHDbNhybIiIlgTON2ZjAVADEQECSaSqSi4gYFoBiHvabjxyvJrKGnbMckQGqq3uZIahoKaLVGSmTbvcjwbVmnfrp7urshDoWygXJyMfQhhhXm00BDZECw6SapaiYQQHAgEChTiLCKotRlAUhABXkIIaqWrRMqQxT2o3ji+tX/OqFqCJABL6zOjvrw/2zB7FfrS/OVpv1yZple7Z9+nJ7swuwevutd//L/+q/fuv9d+49eGQc78auH6au3wz7oZT8R3/8T3fD7b/+n/77aSxVg40oMBOiiOSU1+sYm26eJ+4zPcQMiYnNtGLMpj780duWEBHQdeWQyFtGmJiJvAE3l4KUAVhKHobt8+dfqpZU8jjcpmlCgEAAgKYKJqo6ltI2PPdu1VkDWIU9HXVarDciUgzt48dvf/75F9Mw4HyUXrfzFW1yC3aE8NVw4TULb4AHNPuQwwD4uafFvcyGEZe3WqSgPOCbQejDJzmjTU0QiECdXQUOR0ANc6p5NADVue/hteIpwDxxyVRN+bhz2BldpiYlm1pxc2+mllOWlCBn9/yeP/isu2G/S9PY7Pdtt2q63pC4iV3f3d5ut9sBiM8vLpqmCSHMhKrX0t03AON/SOiz/CjirAaKAO6A5yaQWYFonlTkd1zbz/XwGf7ZUkqaRjJNRXMuUoqqeNqhqsMwTtN4snn73t27q/VqKWXGECVoKRIjE5IYKBhkuU1TkawiZkqBQtMRV1FbqIEren1lISICACDFrqMYHan33VYxIlByXcLZA5KPgQMFRDi6n9dLF7g4iWWREbFTOGm683Z1YjQJwW6KxhuhVikFrAJYRECVHXvw8YjIbIt792T0SLtVQbNMuwEpYIy1Zb7v+zsXFy6/nEuaxr3Vsr25vAHC3KFQJUdqlQ8IDYG7iEM2tHFMl09fBMSGGA1dJc3MYgyOP/sM8qJWxgkxfuPdb12+vLz8Yqo7ABV99K8aERIsOr4Eszc1M1VAVFNQFQIwLT/9yY8//OUvLs7Pbq6vvvjisxC4bVuiSEwIgHNANIdABFVclZBYJasouz4+YowMKGZ51gs0FUKo7KQYopaiaj7njmZb4R0m0zSqkxZrMlFHlNuMeLum43z86yNar9fndy4MgZmncbfb7SiEEBtEUm/SRaLAIjJOeZpyzqVpfDgaeZ+ImqY0rNddzpOUPOy3OZUK8zLOWlcQAiGGcZxSzuM0Nk1zchJOTk6amg2jb1IfoOkWuUhRlwC0omAGBGAIYlYAMlgWzWJWgARJwRsQShGRQ3cNqqhobTMUURNXvXWdGjIEREUsAGyai7LqdhrTvh9TMT25c6ddUYiohk0LABQC9WssCcmYLHAwL5aLFRMKPCsV1vFtdWghACKjoQJVhSEyI8umRUXS5FhbuMn7X362vvfw4t23+XRN61YAdqOOKanq9Xb36uWzpqUf/M4Pf/B736YmXt1oltR0XWi62PUiZXO2/q0Pvhf/9N8M460BMlJgiN53YOZTvWLbxya6JSRTLDVHVFUHtGrLBRGqM2CI0Dz5g4oczHm+Y0WmuWRRELWQiqrt9zehacY0TMMIKn3fdW2jJmhSciklXSM8zNI0TBQ4RC8qz2bawFtuqnViRH786O3z8w+f7j+3WaljcQNLyX9BxZmdqnKQ4H4NMfJaXJVTd22ZBf5ErGbzUPvEOoO2Nr67TZ4v1udxVCFtZi6Si2b35AooVtAI0WbIrlblao0VYOHAz59mNs9Qc7UPH8qL1YiLik+5rLfsmuMuHqo5WylWsvNnSilmKiBOQtFccslTSk1KFJpW2qZpEKGUNI6jlxt87PCctVJFWB2HQLe7c1EO5huwhe/lzVjg/Ay3G87EgeU+YK4BV69mpiLGjtQvTQpLvjijRcYAOI7p9vaWm2bKJeXifXg+mi3nPAx7EXnrrbeIORfxKaxWspjlXHIWVUBCdYqfajETABETExItOsVgFKq8gNtslwsQKQunHGbgwmEHZkZirdNeX0vXDi4b0blLM4Dg25F8lY82bv1hFzPOKMYckVfAq6brzu6IYQQKAPnIbeORcN28ap4eHz7ujegJKsaQU0o+otz9x8nJydnZGSJmSV989uk0TehEI0QinJvu5o+eBeU9MPSN2rdd3KzvXdw93ZzYmH1ibojxvffe2w3DT3/6V9M0mWnXtpLLlCQIdjH+zo9+/zexffrFpyaJloq1uQ925k5BA1CCeXe6PqwiIajHM6WU25ub7c31fr8tJRsoByYmUmKqMCBUeO0gMlDRF21t7s83sxCD1WJkzZMBCtS/MDJGij5JyXu8feUdgn5tqX39F1lcByM8q59hEG8/GcZxGkdD5EA+PKrtur7tfa6EmccJIUvhImjmo2RTToSspmrZwKZxvL6+fvnyhQdbpWRntDpCSURt2/pwJxH1fH2z2azX67brAbDkpGYqniGAU6IqFqXiPatFMFJQMQFRLWpFTUWKMzV1tl/+W1kWEQk6RiDNQBVnLlZdB2+aVLUStJj6EHhF6DYnZwH4zslps+6Jz4YnAECx6c/ucpomTUgKJpYLgCfioLN2/3ym/IzAkeQEBwJjNAy5cGz2gME7HgPzWb8p1/vNg+Z8fdKcr3LEvdj19fazz58MN1vI5fL6Vbdq3vr2ty9v8z7tsmBRC5FSyi9fvUppLGm4vLqeplyKemlyLoIgAKjobrePTdd1DquwuwadB6ypigGo6VfNCVZUqtbZidlZ9OppswGAqyAMgFJk4MDjNA37nRXdN/H0bON1JzOVoleX48sXL07Pz0NsmqZdmPbe6YSzRJRLrSDiyenp40ePnz/5UlTwSD3DK02qYrPWnpiQOXfyGNh7I6Hy/68oPdSaFxylfbPTQSQkqAUXczQKqsQCmAmgdwBpjOHhg0erfqWznMARzLzkyfXNX1tZ7yYBUKvuhzkgmKHgkVRn3dtSRNx7GBGvN2si9tZXMxEpWgrM8Z+ZKSqAMRO4zmXJOY0BTIQApInBs16b5TXgsFaz+wPzHhBww4vzSTv6CaylXrV5hqMLRsIbL5xZ0NVOzYXeekQRbO5KgvkSbMbdpzQ9ffoEA7tKuh+1GqSrjdNAiLe3t//xP/7Z2++/+/DBwwCAqinlknLO5cWLl7c3N5XOoEqAhmREpmSGoKBqIFUkawmOiKhpmlJExVQVeX4efhjUvFQpUrzHAeoO9ptFrE2KcGCSVxv9mk7evCTklSytekyKYg0AKnBoVEBUUQ/qqcu2mDGVg7TnYYURwUnAWHENnRv8c85dp0t5HolCjF3fMdPu9uby8lJElsRxeZKHp47zaEWDQIQGm/X68fvv33/rQZK0T8VV4EIIJ2dn3WqVcxYt5xfn05QNrAnNw3v37p6sf/C9H/zB9773kx//+V/8+X8Yh51ntwBukoqolVHRR4Q1LXZdDMEAfExCCFFE0Myn0kspKRUzUNFpmuouCj48B5h9lE3trXwj7oGqwlhc+WdGEygE37IGoMzeasAhtN4168dWtagW0bLoSdWSL6LB8nwPH+QhkxezipQvvvjio1//puk7Bb26eplydnGAEAMjqUEpwkyGIYmUIqaKSGp5NknWto2I/uJnv/zy8y8JrAlhrB9rasbMrTM2zRDJO33btl2tVjFGACulTClp1eAhAGQmNyilZOe+EoIKQSBVQahzOaWaXjUEQ7YqbnFIQFUPmF+NuZF9T6JnGkCL9ck5iWgxLVJy0wDCkxfPJImB0YN34+oU+hYAQtueP3okUnbjbixjyplLmabJprFIKVJKqULQy9nCOXrkEJijGIGIqrYNh6YxjtytEKlpu+/93j96/KMfPX7v/SHEYa852G4Y/vc/+/PPP/zo/uZ80/fru/dOLk5vk+w+eWEA3arntkHA/Ti+ePWy5IRazKyUnHKOwc1mAOCKviBO07TbbQGwaRoOAfBg8dzdwAyTIKJW6V0wRHDAuQ4ZIqLQdYgI4zSpqbPZRRTAdvs0TTeImHOWomCoyqsVm9AuJTBr2n673V1eXn6HQ991Xde1TeM7hAMroquFuwv2i4khvv32u7/+9a+vr146UQ/JTbeIZjFRUPPBduAlbfbMlvA1e+UvQq4+ZL5TRJ4bxav7mQM0WhpHEImZm7Ybp2QqFTZFM9OmCe+//40f/vBH29ubIe3nqq260oSTugmglo1s7r1act6jFxNjYDPxVNlxNy81FRGRolp8nlqMNaQzg1LEzEdDyaHIamagZsQcfMu7mAUhe2stE7dNy8T+u0tyvlQAYXYfilCXF/FodebNXQMXRfMQbXHkiICzDwU71oL2354TuDkY1pk+d+TGqgcwY2aKDUekEJoY+65b9SviEEJQkcurVx9/8slnX34Zu+bOnTtqgAa5SBEdx+nly1c3t9tSxOcwI2IIgYHMQo21XBDVC/F+tQYAyMRMUcgCh9h2tfG8JlWGgE5rdMdGgMtMxhroVcjzaLnqfw8Z8OwcawTIgEyMgCIZzEgsGHhb7dKwf9jK8+mlWfXP1N585wX1nn/3kLXPL6tRKAVuVquT2+2+yOjD+wC8O26RsMXlV2bKd22xvXdx9/GDR0+ffzka+EMkJr/1EELTNE1struhKJxd3Hvn3Xf6gHncP378qI1/VMr0l3/54/32Zv4YKKUAalEDFEL1ucJNU6egu4GlCk6RU2JLkXGciDEIu+aiN+Q0sVtMXJi7yzxkcRTGT1SNoo6enZTCxOYkC3OhVFiYq6ZiZrnkV5ev6i8eRdfHtYbDOh8tupmpSErp9PR0c3pyfXvzF//7X1xfXbHPDyAmJFADkFIECJumLYBaSkrZi4hNE9er1WazXvWrzz777OrqCsG1TCrwHphCDEw0A3fEtQ8qYA3JJecspQC6cGlV6SulxBCXdN3MTMWUtQ5Hqs0WClZURa2yMmaiHM2CPu525wUHcIYeVYEzBAYE14hWNVFRzKqmAmisBfKU9tN4dX3z9r0HZ1cvAUDMRlRuedWdNmWVci4i4zjxfrfdbqWMecwMBGYWgwoJY+CGAntG4Fp/VfseEUK8+/jtdx4+6P7qXzdN+/0//L373/vW9U158eJyItiV6fMnX/z6b35exvTe47ffee+9swcXOofSAACECpRVt/u9M71D4Af37v3wt3/447/4jzmNzug46n1AVU0px5iqleBZ0nfeM8dx+XHaaHVU9/wNACTquh4Ax2mQuScNwHIWyXnebMgcYhNyzkPJdbJ100iZmHGzWZ+cnMXYMgfXMGduiAiZDNHqlTvbAB8/fvtb3/rOX/z4OpeMakSVcuJb6yiXcONQQ45ZNPuNpNODOJ2rQgfrWA9mrdKYFBNEZm6aBucvQgg5a7VaaiHQe+9987/8l//Vg/sPfvwXfwHqIrFv5NzgG/sNGvFrV+3gJiIBlixlnLQUptpxz4LELrpVs6AQAhE7bCvq4xYMDlbW7wUVTAXI+ascODbMgYm1KDi5pHYHuA9epMLfDFx0cc6EoAcHCYvz8B1eyWQzlvh6uh+W07hcIsAsrGKgsqjnL9pmfmytbdt33n335OwCiCnGGKKLqGHlLIiKvHp56d60lFLEGCBLkVKmabrZbsdx1CJmwnMLBBECBjOaRUUqL9xhSzNzwcgQoyM+/Xrt/ai1VWaeXrJ4XARjF1t/jeh3ODiLU5yT0mOXjOCzawxaxVWMPTL6YENyfR9SrFKwx8qxx0Q2g7r8y9se7XlAAKYqbUjzCObDTjUwg2nKw1hKATOeL9sAwKhWJGCZu1EVo/0+WUT3u/3li+syCaoXXQjE4SBy4LftWmIqaqbjZ598+PknH334y5/fv/dwtepOzk7O79zJ0yh5snluiYdwDsqB6n5f+ym9b73ruhACAojYsB/3+10uBQBLFhVFtJzSNE3jOK76ddf1zNGxh2OTZyZaiTB2DB4gMqICSJEcMQCiSFFwCuehTcLrINttqV756+fDg4gsNWyAOXXwNn+1Bw/un52fXV9fffLxJxxiGypJHQGYqOs6j4IJkfveVNOYipqZtW2zWncnJ5umab/48oucS9d1McbtdleKONGMiVRFamiLS9Rb9bGNSs5pmuoYHyACCwiWcyFCESScl19FSkEhU0AhqrbGV89LQXagfHuEioimIkjkKk1a/4WImYkR2Jw6Q55Om4qqSbEy4WRqompbyFL2afjG9UsAEARpQt93627FiF5s2+33N7c3gV5cvnqWhqRFuHGBRQKEEJvYrrjJjQqGKMZFLBUZU8YYvvNbP/jR6ab5P7fMdPet81dD/vzLZ0MWiTyVxAg/+P53u7Z9eP/Bg0cPsY1JpVKlvOUfSURdMg9MEezs/OJf/Tf/3bvvvv/zv/mrZ19+YpYReO4o8U6ySoB3/v+REXjz9Xo8h1RzsaoA6ZBsjKEUdkxiOcjq08eBEJFDJKJxGp01DQA555PT04vzixhj1/UhNEtbERzSKUIgmwfLK1jbte+9//6vf/2rp8+eALgQR+28wHk4hyNuywXDPOIJjwxgtUSI+DoBfkGOAMDMx2hpLtnM5vmeBIBNg5vN5ubmOhdBpM3m9Jvf+K1//i/+5QcffHe73Xl535eJyC1YrWfV7o7Dsh6BuVCbbfyMoIHkPI2jmTJDbJo6Fd6UAMp8ikPTGKCIplLEVFQQEThUOqwPqBAVsZSyGcQYfX4BMQNAysk9JoWwUGhhlr173YbUcYU418PxAHbO4ZodNov7nBrZ6WsxTvjarbbIOfnTWhgBi5EzAwrEISCSIimAKKiaolYJRtWUSi7SMDMFyapFpGiesqTy4uXLV5evshRAlFwAgP2eEV2Nrc7pIMCqwlazO8+BOSAH6Ps+Nk0qWVUdV0EiUY0xrlar/aCq6oMS7Lj3Ct88XbZw6N3lO/Nr1iQlgGh42rZ316cnGNtUSFSdTAO6N1GUDJI9M30jvAFAQEY+UBjmMYWIAnVP1ObmGCMudX4A8KGwueSUb25upnEiBiJ2CkQdjTH/IBj5OKBacQYmbkqx66vbrj1BAlTH2QGZbO4UVFXXwuXaIzeVrLvt9avnXwCiAoLBydnJfgsyZTOAWWVfFUxr1YcImDEEijEiETKDAQXo+rXN5bacRvdt6l0aUlLK3Th2XR9ju0BPUNXNlJnMKjLq2tcwh7G17qM+d6dOGfczDuBoj0vyKh7r286R/DF8f3Dw5vqNeZqmlKbb7c1udzsO+z/7D//++dOn/WYVY0Qx01rX8QHO5qQYL/5R0CNrpQo3N9vb2916vf7ed75zc3Pz5MkT89HizEQgxRbH7+ZSljmbCsUDFlNvjwrkPeJeF3YSqUfHfuKE3K7ON+j2wmU2DLzpkhRRwOdMCxKCIWvtcrMKATIgI7GbPWb3FupSE6re2SQihcAFh/TFzSUAtJfPH/z5n/ZN28UmhGBqInKSpvNxuLvb7fe3w7AHsDpezncEB+KAIVAIGIICqmEWSUXulEJ//Wft3Ts0jbhaJbEnt5e3006JRTVLvrhz9u0PvpFTDiFaAGQMIWQR80FGpgAmk4zTIFYYDUwp8L0Hj/7k7t3f/73f+/CXP/vxj//3Z8++LGUyE591opK1EAQSmWWklvNriiqo4lwsz7bMzNA8vDOx4padwIzQlJibpqkgzdyWW8Fjl64rOZeM81SrlGC9pm9/57vvvPve9nY3DFMIrWsfIaJI8Ra4ahiJFFGJCkBEPDs/f/Dw4fMXz0oparnmi+TCRTyzrqh25QKolFQmrMa7hpVH6f7BNR9lW7BUusyy1F5QL9T6Z4XTk7Bar9t+9ejR4/ff/+Y3v/mt+3fvU2hUtzZDji5dYGBSGTvm/SmLobQDT/A4TUQnuEzTdHN9DWD9quu6tgksCdAgMhFyEUGqAUc5xO5IISKxeU2+FBOZee4y+zUOISCRAaSciypixGXohaqT2xHdb5BC8RjXagWnOt4ladM5gCNEgaoGreq9GK/RtfxXwhuU9JquQR1NXV0IVzT44NCxTh3wA4pmRQTMBH0oh6lKLqrFJKhknYappFSGaRymtB8++fjTZ0+fazYELEUMLTIbLgBxjcwr1e61qztArjE0hARfr4UGNLcE2GJz7XDjX/HBOu+Sunx18CMAGDBR27Vd36OxApmHV2jFIJsVMDEtqAqLta8fsRjZo/ho+cjFARiAs2z643Ky1WH1ZgcJeF5q1fCae5l/YV4hIoohlFLG3b7jNsSmpMHpyzhDCTBnRbzM8BOVRlWdgKOmkIsG6jerdtqPNzdbEfXYkIjMwNTRy9oUxBQQCYEoUGia1Xp9qmdpHHPa73Y3Nzc3w7ATLWCzK5aS0tR2nWNZKj4o0ZYI3ZnMMRCRO/tagzAFKYasFAKgOHgGSnAooCjxm0Grr9DrC+ZvB2CgRaZhHMZhv999+cXnz54+AYBf/fIXKhI5tLHJJfnA5BrH6symxxpBLW5dRG9ubm5321zKu+++81vf+tbHH33kwUvTxBDIrDgZW9VKzmqHAQ+IaKbe0Vu3MaAgNW1s2mCqlckyFyg8iTW0pS9zeSIHO+o/7T9a96hjO14E86KV17LI7HDoENnUSLOogpkWSaIpZyvmjRO7YQcA9z7+1b3/0//xqwfw//9X6VavbtMOMUeitgltG0e8d//ircfnOcmUNGXRuqvNzICJAEE15zROo4gQgYKiWcqKCJuz8x/9/h/e7vZPnz/z+NJMEEmkiJBZEwJ/nS05fjloAUtvChBgsaxeJ571hWN0HPi4ZQMRZ+soqoaGZqgK9+7f/f1/9Ifvvf+NrlsxRySOseWZIuGtRyFEnx3HIXCMFFz5m4lZ6ugXgHlQQZgP6bITsI5U8tMhrmQ7gyK+62CRKzg2K8duWFVDCGpVxwZmN8PIGMK7jx//43/6xw8ePGxiz7Exs6Jm6moNrpjpx4RmXwVLpjHvXlz+mN2+MgITZZFht7+9vpaSp31ztu679XpfboftjQuOA0BsCT3TRm/RYGAEA2NDMBBjYCQNQUUEAZAIjDhEDkGtClCLqKGIaEpJVQGBXBfFA15cSMkHZg/ONsQNPh7dlHcdutlSmFs559WtfcCvdfUc1t2XqNLqcC4DOyTvRmcYhhcvnnO3Ag6gwhgCsYGSWzQ1KZJSVrTbm9vrrp92w7jb7W52bKZiDAGBTKyIALPMXqUu/1yLwNnuvmFP0SuaRMxkh3lRNdXx3V8hYFN4LVj5GlWXZdvNVZC5ADcfuOth96zAaNCkBDmrCDARkQYuTTy+uK9HFJYYEwDnQOD4RUQh8Fd/1zf9er32NkrHYw7nig4zvJaKMEAdPj8Mw267kyJtG907wqw1q/OMl8XlzwxbVeVF6KdzcqMhnJw1bff82QudR5MSAYa58mo+68GYmCh4zMrM3DRt06Ct1+t1jN2LF8+mae8uc4kqREspsWlaqIEfLqmD14ZVJDB7Pcxr614iruCh2zR3M0ZmKs6KBCTEIjPqV7svvgJQ1KejDtj6yLnr6+uXL1/mnK+urlZ9H4kZSZnSlASRvOChdgiG+AjcmwH1nFIu+fr65sO//fDZs6eI6EMPQyBRRRWsQb8aIFHEw/gar95WB6wGArjd3vZ9y8w+33K58DnhNj76Ni6s+BnP+4oEDVSv62EcBcKAyIAERpWnNsfuCAZaYO6tNJN92YuUjclnRP/m7KFHe6uuX7V92zSRQqiyBaagTgTLTpt0rVZENS2iRUQBUs6uSSlqIUaOzZRKzvnug/t3v/uDL/fD2FF/cRr7vl2v9rvd5nwNjLEL2ACMNuVq/MSzVyJDGMcppaQqtbIMpmYEUAAICZm90VKlqBRJknNummac0iqV09PTOd/4mhfOLf4EdUOpuChQ7VZa4mBvG/Naz/K8AMH5aKWYiDLx/fsPfvg7v/3bv/3D1WoDSMzRA9gYo+tLI+F6vfI6a9M2HGJo2xijS7s83+2ffPmklBwCP370WLVcXl5ija/oyOLVCyCipmkDhTg3mCyOxMffLttm2SbL9SNiCGzg87DBp3i4DQaAcRincUJANdNSwCgwvM5+IQIkJEYmYK+tLkJjNBtOQ3DtYQQgqM0pplJSSvt9mvYyhunhPb44M8m3N1fiLZJMNI1xmtq25RBsvjic9X+MwAKDU7hkVrQIATj4UCZiQrBc1BUDc84VOjge3Fe3QB0bOdtyOpJ2MASpq+pp3HHM/3V7Knw1UYAjhwQAVSuBiAjNyFkbzJxLvr29PRsHCBE5RDZwLXz1ir4I2DCONtlHH38sKWsuMibNpY8Nc9N1K+amFDFE58MtrGvEmgO5+MdraYvnpGghUIyMiJGDQ80zwxVVVVRqydu30+tNzPXN58hu/ob/e1XJxmrSUQ0zQJ4GlO2N4RqQRAJTQC97M5qRGiEIgH7FxL+O5MCCrnils54NRCSyrzwf94Jt2965c2e/31fw55ivXc1LvSEAfzh4stl43V1ETKVt1j4KzX+FK2eK5/Xw1iCHCuYMkggAqqot0Pnp2cOHD3e7/fXN7ayo4LhI7W0wAwAOs/CcuUwHMiMRYdeuzs8REa+uXk1pr1pqKIJgIjmZqbKn0RyJ4kLLqk66FOcozQ+LEOt4J5LcNJGI1cQMvQMTTKxQNsOiQIUQY6gTcw8q10evmQNReQCqOo7jkydP9vv9Mv4hBJZC0zR5huGmeDFwSxxjWrXGzXQYho9+85snn3824+rR7NBzr6opl4qSzXUmR5n8QKuolzbnd9uv1+s5SzAAEwWpFYzjbKIGYnWHAxw6+lUPtXwDMy9YxhBbpojozbvwRkB+fDbAyyKaZMxq5T+sNj95/L02tpv1+vGd++8+evzo7sMH64sT5D4ERsyWC+ZJpu1utx/3U54o8mqzAaYhl+0wjVK2abrd7588fXZ5fXtydv7WN37riyevrm5v//F//id/8I//SZ7KnSae37+bShYQCBBbAoYsYAzcIqgjMS74YE1gMPSKipnVcd1UQ3pRy5KzikNLHAIzQUkist/vDYbdfsg5nZ+fx9jUipe9thT+kHFuLfdo/7UfQDTz/hlQz8VmH6ZqzNb3/Wp10jQdE6/Xm3ffe/vhw4c5p5ubK+bIsXGRN5EGEW9vblVK369CiDHGftVziOzOuGmaGJ8+fXp9c+Nn2XmLB4kef+pUH7eTeMDMORM55wNMArBw4183PzORZQaWFLwdetbkqgUvYuZpSr/42S/bdnXvwaMQCWEmqVWDtWB2vjs9s6+iOtUGzglEddfVzZEhZq99mFrJSo5lWSlp2O1yyQWsIBhziM3JyenKZwMbBAouZeOnQMnAjJCI6+cJUyEDFQcC1RTBCFElS8nGBKVU860uNOGwMKIhUQDAWVjB7OtYPjNOghUrs6/JdQ9KWPOyH47cYteJKHBY3pqQmRgMciklZVAABmA0AWRGBAUgtaZt337nHWQ8XZ+YQQjBuMhkIoJMIcQQgrc5ovdCVKLBkT90FYVj4tJ8nz6wxSuLxwYCEbxv8avJzrKZFrv5upXR+UsH3p3pDmaoiD4oILRNwwFLBjUhYHLxG599WU0cfMXAH3+QLS1eS4M1GAEUkVxyE99Mgn0p2raNMc7hwvKgPFhAWpQN5o9movVms91uUypZUtN2tOfZ5jIxhxCZqGkaouijcH24OhKCaZGSUhqGYbvd7ra73X7/7PmzPJUpTy60ggguMke0EDqQCMxYRPxdqBCTAhIQATdNR+ccQ9NdvnxR8uQTLFSlQDLVnLIQNQ3G2Ma2JWStnEwzVSnFx57UOoxHx0S5IGYUbUIIfrBNFUDBRy0TIBIoqGFSA4AYIyyj149Mq79n17WuyI1EwzA8efJEREII2+1WVZsQmxgXULFpWqawbKSjNMJBovogdvvdOGATYwjeX5TVVKykkqV4hSnO76HgQaQZmXElurleBhLRdrsNITD388N3ijgYBqr179f2c603o6GJTwCGqqdrDpTN/OvYhIa5QSIzkwI5l4WVOT9ZOjo7AGhiMoxSRDYisAIa8MWrlwRIRo0FbntGaGOMMSIzalQgINaBDG3Vr0/Oz5qu56YbSr5N4+04ffn06SeffzGm/N57759dPNru93fv3bu8ulp3681mg4hqOo5TjMyB3L0BAhFEQlf5cuyYCUFBSsFa/ySqcHolAjktH4g4hC7GGKpEq5Pq1eD29hYALi4uanA5Q4VL7W6pDy/+4o0DC57/hhBUVAUASL1OYgj04MHDd9/9ZhPbEELbtkgwDPuck9tD5oa4aZo2xFCK3NzcIMJqteq6vmkaJx9xE0MIMQQzG4eBiZgiAuz3g2qZaXczHWi+wuo4AU01WwGd54PWvbp0Xr0BlNCxXyYgt/6OxHlZWgyYGCk8e/b8Jz/+yR/98er8/G4I6IteM6t5PDAgATKSs8mOJI+WDwQEQFo6kqmyscBvSivzVH10ZSlpmibJg+RCFNvOAGOMdfq7CRghkLhTMDUwn9rtCvcKtk1jYO66PgaKwOrwmZQ87FFKQLI6hfeQAs92COu3v/KqrfVH7pbmkPerCFw4zggPbzGfNwCHWslPPAD4uiAAKEgqpmqiZuJtyUyoUBvEzi4uLs4ukCm4mn4RjCCU52StBjoRAwETMDpByRTRPF5DfC0r9LzeEDnG0DTIVDsg5yaWGtOhD9/TeT8dkOfj+3ptyQ4hyHx17qedroIYEdsQV227ogCAwzgUMY6xBVSzAqogNYNfkIPqXl/Dvb1wfrS/a5xaStntdrhmR4+PU/OllgPH+Laz87y0X7shvDseBKAgxK5tunY/jrtxtznbPHn+5fWrrR8cmjnn5+fnV5fXaZr6vh+G4fnzp9M0cUCXqk8pqWsYzjkTc/T5Rc7pmrH6JVbjORX2AoEfFxKfW8whULOhlrDd3l5PaQQwZsplGIZbVRWDIlakYMbAwQC11hEUCWaOcsGjdmFmYiaVgxa0qvrCM4eOMXIkJjPKAtM0lVJibEOINKeMdT8AGBIxdyEwY875o48+fvbs2TRNXdc1TTOO483tLZj6XCgA8AHjRMSBYWbTmFPBEFSLlEygaO5mjTmoFJGSihQtIo5AegRHhx1iAHXbz8kWGAAwAoKOw57R4skKXdKLgIiR2KhWFXxENCAAKhJzaGLDSAaZAAuIAqopqigSUYjkkIOPmvS9FpGIqpC9+lOe8dWaPpMamoGAWBl3IxCRaC45G1jg0DRdbJgxUGj6dd+QRVOOTYgtchyHfd5POUyrZr2O3dnJ2XkuA9ijB4/ffuv9jz757Oc//9us9N3vfa9vV3lK2K2appEiOeWUpr47ASRXezEFMGACMtOibLUxKBeVXAISzsCYubYAASIjkBaDKqSKTYxd1xa1aZy87SqVUkrZ7/dt23lxBUXJwNRojl0WnR3n66vOiu1HL1oUC9T1vzxUx2GYVDT0AQDGaUQ0Ii9ApRACh8SxmdI02wc4PTtZr05ibF07woOrSIwGaZxO1uu2a6WkGAJiBd2XhMwTYsQjS4Iz69ZqbrN4viN60DEQjUuU6e4a0Ai9xnEopuEMtv/yl7/s1qd/9I//yTo2dT0O9cxKs1kKhvWq2DUEeR63uhDH3NuQERtRCNEZoKamRQgwIKEpaAEVlZIVjXCaBi0pNBFnrEgdW6xFFXLJVQqkiGqaRSADEG02m4aDjEnKpGAJEVSoiT7HFchQKl3cwBSrGT9uflmcCcIsNgmvkZYQnWxhS/yPb/QB4xseb15ZOvR/v/YipK5tIDRGIVADbtkDGfjEZST2ko+oGpkRYvDmiiN/76y9Wq82U1gGBh75m8NEenPv7s4Yid6oEIuoSKlG1QN4PDQFvdFwdXQNywfVb/oNEBJ6WA2IhC3zhhtSBJsmzY1Cx3GAIiCiWYV05m8f3muWc58Dptcq0Ijoh1dVp2li2odwuvzAcmwQUeaJuccPZ/mvgc2KcWAIRYoiKMKQxuvttYAa4s31LQC47yREJjKzp0+ej+P+wf0Hl5eXP/3pT0spTRs9M6oYMjVzXQrBEEgRGIkRA+FM8KgPsSbDlaZdn+f8HBAQKMTm5OwCOaSXL1JKxHG9igDmY39K0WlKYIYNeFBvqrlMS8Vaq6p79f3ee+ZkTpgTUH8xF0ICoBChbfvYNi4JUUo2g6Z27JupzjqVdas3TbPf7//qL//q1eUrMJ3G6fT0ZNWvurZNKZWUVTXnHGMxBRFhZe9LBgADc6ZEziWnCUxj4JxEpahkVSslJy1qlZXJMXiDn29PT3cJDrIk/l1EDMFlNi2laRhws9lwYA4YfeDpXKLyvFbMkBktREBibZUa6YpIKnlKqVRVyjrVwgk7S9AInhxQFHE3DACoii6fSej8FgfZQbQM015V+7aTLut1QQ7UthSJ28Ch79qGAFA1ApuxKGrS3dXtuMs56bjPq5PTDngTY6QWVuf60D578nI3jqKWUkYpt7e3iNqv2putmGrTRMSjZMS8NcdABFUhK3UNmE7TpFmCebHuCBgAULVxGn29RC0XCSJt2wWOrvyK8wDmUjKClzTVnC5VD5i6A55JDI5na1Vim+uv/okhoArNHaQGSAB0fX2dUvbSKRH6BqY6ZzoAVmCTiJrYxNikSTg2C6WKidu2RaL9brfZnKz79TCauXBKEZyt2ZxswFzjqoXLJSt2v8yzzuKSAb9uIWFJYGZTowfG4VxQg5ph8zhOf/PXf/3u++9/c7UmIptrKBXenG3hsQ2z2RdhLalVH3QoqTAhB2rCXNFCImIEU/EOPUGPUMF7CUuaGmaniApIlgIurc0ESByw7bq2a8FgGIa0G9OYCPDuxZ0Qgqnu93sz6VM6ObWGEUyqP/JlqAkuSM18DjmtzaWe+XatggA1ZVV0OpWLd8wClW8IccBx+DO7yeoIjmG72bdhiAE5FO+HNUNGmAeKABASx1jbsEhNKdNYpdR8lanyqAKia/2YmiIehmcdbYUlpnstKX7t0s2maUwpw5KZ+bSe+VfoiPL9hjM+fhO/RTNQdbkbU/9owFaxg9g0KzFliiyYDAMxluTJ9nKp81vha297hLYsG8wPMyKO49i2Xdu2X3ko868ivoERvYGAAYCBTTmVuW76/Pmzn/70r6YplVKcG+iX4OxZQx1Tarr20aNHH33069vtbWAOMVSmFVAg9oIN+QYAJILZdNTRaeAbCqyORj68wEXY+UjiipjX63VK6cWLFymXk5OTto3XfLXd7ly4A8FCiH3XhBBES85tyrnkPKdlDqPWoT3ulY+FLJa4aj/spykjhb5fb07O26aLzA71T6pt2xC8Zp7dvpmBiO73Y+SGEVRte73NY26ahpmbVYsA0zSlKTUNquowDCklv4AYY9vFEFCk5DyZGTMLodcjnf/mZWCaR985ZKPqk4vMqvIGeD6BdXoBHRNqpmkKgU+aE2+59m+K6ywCEjGZEhsbIyIzgiIrKFoHkHJOU5Zan5kTEY+Q7LVwfhEHJXKEto6vcdGPxWGXkp0frVCKlvIKJi2jjBOUx2Qjwkm/6TjGEHruVv3pxend66tXwzRlpdvdNIyXJxiQKSGMImU/PLp7/9Hj/uHd+9AEQQC1cb+/uNgQYYyhaw8O2I91XVQVM825rKTzVoHGGSIuV4TmkubEXErZbrdqAGLZBExECwD2/TqEkEpOOS8BHwJQbaydlT3dzWrVmDdPwwEAjAjMasPIHAWy958wurCDIXG/PgHk6+tbEWEmIo7RZcJotVqt12sil1MFI8yKJigychQfXB1jxIbMbNzvP/v00/1uu95sxmk/jPucRyYKfByj10N3ZCq+Yi5mv3uEAs8IHME8a2EhL8PccWMAaN7vNiufNk37wx/+zvndey6YStTY1xi8N19VWcwJNABoxq9bdSKOsenbnpCsTk4jD0mDN8GSBTKuXlKllJITGBlaEcmgnvISkxJZYNAQqQcAAZtyzik1ISAAExPg9vZ63O+6vmeyVRsrywAUUGuuV62cAxvejXUUEh7fWjXaBEBIAHKYVLs8ka864K+8vlLoWOC+m9vbZ0+fh6bD0IbQtk3rIxXJi84usRMCEaoURFXM6rrlVnUrCShSCC5EYihSvNhPRyZ72RCqSnwgDuCse6zzLI9Sym63nzXf/WcAqxA2wvFePHLGb3hfe/3xg4GpKSpGRMAg1iluYu8t9Ulha9JiiMDFmyqPmwhrYRtfe7v6/cPnLJfhLFxvy3ntEo4Q6SWxr/+EtcuZYR5MjEgEamW17s8vTvf7/S9++YvNZt22LXGsRZ+jrAARQ4h3Ly7W6/Vut4OFxF+j0sqiAACnclThsZoLcZmVqrBGXT541fXQheZ09XBHZiGEi4uLnPPNzfU4TefnJ+cciJqbm2uzUoqlqRAOq9WKQ0AmDiGHkFPKOatzMKpV1OULnTVY/K8iwqyCBlamcdrvp4uLu13fey+Hx6r+ZFzzGOfqshmu1xvmQIhNqNxinmEbt4BeIPfvhBBc4cuz89hwCI2ZTmkk8r6Yqsbc9z0gCgAqBo4xNjFGAJAKG+rxPiFC4rqRiICDd1GqAYrIMIxt2zZNJJekFJUiHmzaDCW6Ta+KibN2OnNoO1Yx8bRczcXDHDFbOi6OoBcishmCrtmMB+IzuGGqOZvsBpe9Zbl8biZFJKumqeQzuNhs2m617ldtbELgB2+/PZa8S+N+GEtKKWVS825GIjrbnDSbs1XTFsaEZmbXlzebk1O/+CX18sUKBiiGKeXra2/OpyKGxmgcqnDBkk25nJmZlVz8BKhqKaAqN3IzTbntOgrcNM0BgQeX1T6ElapL1Oeyt5XSD3OWyXMC7XGhN+EwBmfXUQh9t1mt1mZbGQavsaiqF57UtO9X3o4YY3TiFbP3HVXSMjqdHumLLz7/8Ncf5mlsYvvW47cvL1+9evXcTI4tRz2TqvCa/OSC+BybxK+pyv1djnO+X//SpwaAI5J37tz50e/+bmw756wshJ5qHI4z8Pm79euj/kpYcHD0ahc2bdu0LTItdjCE4IaIiAIQG6AJGqOK5qJUiBjQRGCySi40MEEsCN16tR0GNbVcdMxs2LUtMbtlLGnc3VxNw+3Jurfz87ng6h/M3p7rcg6zoMJrrte8FZjIu+znE1MFGY9Svnrc33TA5tWBZe3nxXrN8wGYKoUmxJizcqSmaZvQNk1DkRcNRgAsRbdlABNwXknJTlCWUtyL+ADspmkooJmYqciCNh9LStXrZQ4wW/p6MURQJYxkmqb9fu+Fq1r5gCpN8MZtLn79dSrgwQHPY0F9h1U0BhG1WkoEAyRkxMa4JY5AE3jJmOAIxvnqa96Rsyb+McWjKlF8/QV/7ddf+/6+mc/Pz9q2+fTTT/f7napcXFx4mjjXVxCAiCmE0LZN13WzqCTVfTB7X58kaFYxeZ9Wjug1Xp2XDebTSKICQmAAgYCMjyJr/w8ANE1z//59M90Pt6u87lebpu1j115fXU37/WApF0lFu65p2kCeStQ/liLKkmovSIYfac9FbPYZqKpTuk1J1pt1v15tNifmV17Nnz/autmqgJRZbJqmQsQ0q/cBIniy2zRtKa7/Tuv1ZrfbO4qOiCFQiCSSnLyoWjWcEbuu6yg2qsocYmzMIOcJDjvcmVe2KLf7si9Cm77UvnP2+13XtaFpvRPGjwyqT1itD48ZjUgKAKIPjnFlCBFVd8CiqqK1n9+QKByNA8HKnq0if6rFfPDVXNJyjNHnBuWcd7aVogR2s2V25ocSKOg0lm61je2qXXX9KnQtNk0fm9XpOQYmQstJiwwpnaV0OkwZmdt2myefexUC73b7aZrWm3Wlmy2tdwayHb/8m5//1X/831YnZ9//g9+n87NCoMWT9plEUvWbmQg5RA6NjwE0FDBThVIkpbLd75u28b1mM/bKy8mEmgOIFl1k2RdgEg8exX0qzC45hBi4Ddw4IXG1Xn3rg+/cv3+vjRENRSrb/9mzZ6+uLr3BF1wGhwg5UAiOruAMbscYtcgvf/7zn/31X3Zte3Fx/s3f+ubDh/c//ig8efrEpLxmDQBM1XA+IziPl6/s16+xM0s88TW6Qp7SVM89r8wc9JqZB6Pder18/Fc+YDmny0/MRV/Dub0YwNSIDCotyDnd9WcRoE5Ewqq/BN6zBGSG4sMyMwUzDqBWcholZy1iqgBCtJ/G7XbLTAjYCG26TdP15kxbA8kF/CGnCcxmAIo804MFJF/ckB1JXs37pN6bOU3tK6Dl0Q99TQb8RhZ9eB4wpw2GhLxZb7797Q/O7twvqkbBG+zmuKji8VaJl4XU1IRmaXgRQUKfq86EHBgIwIyIrbhNVQLUuet0dgY0F7EdDTqAdR6bpjSlPAFUJkr9yTd22ewFENF7OOaNdWidPCyU1THIbJqlZJVskIE9ByAToIAGDXEkZlD1gO2QXiMeWpTnsAYXzMqv/KhmU7tr9Mjg1hO+QJD4Whl4gSfmuzAgBcnlxbPnTdf6vTitZn6IOMcPAIhMHKM39nktio4CWzQkUzVylRKzg+QegqE6+vQaHoFeWTVV4mhmISCH6IEQ1vlaHg5C27Z37pzry1Ry0qaJIdy9uNs2zeWrV/vdbpymKaX9PvR923UdotUxM5Vb5w6GcdbjNfDeYIPXBLqxht2EU9qPL/bhJt67//D09BQRShEXaocq2yKiOg7DNI4xhhhC0/TMNFNplyKCqwkSEAOia+QzBwAoJYNBLoKI8zxXm7k5agZEoQsBEJmDGeScFmizbj2Eee6OLRf2eqZSfz5nSSk3oTGfDlaE0HixU/MxXByVlzlKFhErIq4T7ZKNy2wxJGQqbnBcyNDJf4jOd2MzJSMTrJzzIxzbJTxHUZOS0ziOQ8553I6vnr+8WJ+cxHbTrjbd6cnp+fp0052s46prVn3sIsYQuo6Ze7WTIpuUd1MSgDDsr6ehSHEiugCenq4JUKVWE1Fgup2++NmvfvW//PnLX3043r0r3/mAshiBD2XT2j9Z97upMlPf9/fv3//s4w+zKqD6bBwAFBMsqCUvftT/DK4HOr+qoTnijixJiR+uupCvTb82ZmzaiMiG1Peru/cfnN+92zddDAEMVW2axuvbfdjuS1HEREylaolmZgohcmiIuYmRQ0DE25ubX/7i57vdtemqlPHm+tV7773Lwe3Sm0QTqGMT56i6GiOYjfubu2v52u3XwrSyI48959ZVZ3fWbDcVGcdpJYLoqijzpy0Tx6oL9vJ8zRaqn0WEubXdHEubIxoQ0yIqCoQGUEpJKalZpboDBEJWFwYAAgEpbiAAbO45l1KrpCCCKqXt2kChAAmBhaCIPkAADKoaohoR2vHjn2/haGXBqXFqx9jnDLDVmrH5Ms7m339K3QqHxb9+Dfp6eF51j7np84AvcOz7tdeGzT8E5+cJ7oBrGBicviZKguoN/4ShiaFrMJKhKnt9F5XIAXX0EGjeTMy12UcBAYzNdBaLWGwNEeVcUhoNpFKS6qzpw6p53us3Y2Bgx+nvwZMc70UzAzM0HMWuczrlJvrbmqKqm3tGaohZXdquMvtr3KDe9+L76QBYfO3LZkHghdbrwaE7YDgChXB5QU2WFgSa1CyXqZRhGPz6eVafV1WfcDdHsOQJov8kc3BTheZwrqpasRLQZe3qTRGRGXmA5eHD/CDUzGOP+lmmAoahaZw5BTOSga6IQ3CyWZV0MgzDNAzQtjHGs5Pz9Wp9c3s7DmPKqeSckojsw5z61HCTEACleP7lEHFVjDPzSQRavaBrTQOpgYimVF6+eGamZ2fnRFShKT/kiCWlaRhAJVDj7AT0yHceiXoUwczpjoghENHJyck0jUhYclEB5mgmJnYwXgBI3K5OYmhzTvv9rhQ1A/HeNadYLhv16OjZ0VHyr901qloRLSlLKaZKaKY+Y9fj3uqL3RhUYZzstFzRolabkmewwDFZVOe1WW0H5xCc+ldpGQzB1xahmONtRxWipEmLpDH509tf71+tXp20q7O2X4du3W4uzu8+fPz49O5Ff7JpUqY8dX0f25ZjjVZjjGukUkpYr0MTb4Zbb0Jbn14EDJor2Z8U8vX46z/7yad/+Tf9KP/4m99/MtxiFsgllzxOU1FxqlhVDISq+cUhnJycwMEs2tGwozrhhGpzajWUdkRvrmd4LqyA4wCek+HcOUGIWLmBqoqVh2yAQIxN04xjSlkC++R4NKSp2M3tdsypiQGLkSEzAjAUU0PRQjkzRxee1yIf//rXT59+CSopDdOk11Lu37+IMczqthW3mFMXQiRnA6FW/Vp8XSLia19YmUSH+BDga3i/qpWdU4qklFws+gDJzrvL/yAnoqECcVWGMpohXThUUABgPhBmruYy98QDSNFpykUM0JU9MBCZITISMxmQGqrOMPGCc/kTVFWyUtKE2BAQFARsI0TWYqUkwiUts9rITESIClTT8TmodRdylM0eFxW1rh3YkivXnVRDoVrC+ftqwAuwWB/GvPR+btFHQrrLrMJ8FCjEyIFpmUHkl0FqqAVFrofJPEN1l0rVPviPz5yeYyLPPFmhbok30/mji7Xt9rbkDEemDBe88utuzaMEd2RL8HGUE9fPRQRUK4iDyQjWgZmIWSFkQDbGQNSFJhQrZEfypsu7zLHQ1790SSNsnkvoqhFfc8Hzj/r/aM46Ycl15p9s29YfvJcYi8hut3MmN3kWB7VgmFK6vr6+vb0102V1bR4jYpVmDjMChkQIRqJFa9B76Kqa9aocRXfYduSJESxwWOpxaC64KKUkVUupjOOYc+77vm3bEJo7d+6LlJTSNI1pGkpJKmJSEG02Z55qGzHMZxsWXAiq4A3GEJmdWco+/KCUsr3dDfvdatUFZteFhTnRdIYzoiv/hTr3dB7ZUone84BbxJm3bDWsiLFhNrUSQmyaNudJxUstICI5l6ajGNrV6tRMimgu3hhEC/8JweeO2NKzv/z6chiX7WpmOac0TVKSicTILmmrc5efL4UIFFERK+JieoSIHOu2bBZyuahUPUUwQFE3hSZS8z3vQDMzZnfPHosTVE6KupsrKkUhq5VUpt0wrHbp5Ly0JylmFaWA/W0bWobAhQHSOO6G0HXUBNeKDiEgIagxwCpEbfs0TarWta0UAwA0RANWePH5l7/+y79ZJf3Gg8droO2ztLu5NTPJYsXrTn4+6pQenUf8MnPORUUCVzMLNcoxL3iHwEc2481zexT0v5a6+LYnb5tYGrfcATJziIiBAocYU8oiKqqC6vMfkVDBxmnSkjMBsc9cCHVEBgfmEGOby0jE0zj95V/+eL+7YYJApoqAICV3bTMXeV5LIRZEgzwnXQDk41z4DaLGm68jDwyLqcT59KsPV3DbNY3Tksp9vfLc/J5HmTYc8h8DVSM2NeMZ2FfzPkFarlVFRbwbnBk4UCBTP/YBjJ2nuWT99df8sipnqpSCkNsmoBdYidQqJMaGRBicXO0zLQ6486HXdL67mYpkniGpwZuCGx7K1N+eV8cb1P8TJKwlQn5z8UyJqI1N4KhVN5q8q8G7M8lzQ4cmVEDUSvGeeTUdp3HY72ti5JeICM5DI5onuADUTu9FIqMmIV6e8PYLNG+LITPb7faqBvR3bSOPYI7XBRYff/D0uFjYw1ZDJAOdTCeEycxMSbSYIpsSE4YYI0rCCqos+wRmRctFj3oJPPHobC8B40GIcYE9bSYZVWbynBnj0eUe37CCMeKDBw8ePHwYQhCRzz///OOPP97e3IJBG3pgBCDm4N2viCiil5eXwzC6lKnHeWavxciL0THAGYezY7xryZnmjKG2JA7DTk3apg0UiIgAVGQcx+3t7W63TWmc0oRgqWlySqv1ul+vOARi7PuuaWLp+yJFSs55UhUCWEAGM9AZc7PF4qrmguaZB8cQmxgbrjKffuVFRPbbG0QsRX0t2qYhZpGspm1sFwbTMosaEd+wFEQMYMUzDqsYjM/oALKm7QAsafFFKEVENaUkRUOIbbvpun673b26fHF1c2mmhgouq76kv3aoty0ssznVKES03W4JUUvBKmpvFAMALiN4DMFARaAUETOgEGLQYOQKnnUHKRiAYVXoLqKzKKnV2BCruSZ2dgObGatImYN73wN1c/j/qahJMSmihRibELoYJg27tL3ZXW1OTzgPMhkCp3HCaYpdG4kJsXj87v2MZphlKHsOjRYdx2QgIVAfIwqM19tNaN45P73Xn9xcvroabvTVyynnyQQIAwREJFxkGKt4lZmdbDZd21ztX3m4s6g/I84dLngQ9nQb5cGZK7/69tZZqa1OGall41q5Pz6dMbZtM5yd4fnFXZ/DraWEGcMgAkTlQOd3Lr588vm4mxANoBABI/tQQgd4YowxNgbw6aefffrJbxAtupIXiFX65xF6fLBmLhw7X08tPh39Mf/8csZf/6b5VHmbrQJYhdarVzBQ1ZxLjI2qjtPkTcwLWvOV+OXwlQM1s/37qjGsPhqB0Oo+XK4MAMiAfCAEUqDQIMihx/WowwXBZpI3eoPNfGsqoiWTKVf0w1WLFdC8BdPVAmsZ5pDnIM4WeH7/Sv2Z7xDxgBss6dVX6+EAX9MHvARH6MW0GnofhUw1WgSY6+CGR4VMLUVKNpNiVZNZHHIwKZoTqVbhp8pjwFmcCJmZidM4iM3ps9XkwgUQnN3u6MxyDpa1mHUTEY3k+Cm+8bLjL18rluhRMrd43/mBgSFmtWxYjNiqsJ9aNgIIEIg9dfnKKv89MeBXLm0WKjrsnjknPj4hxyecvvoOqmZ2586d73znO/6T6/X6iy++2O/30zB2j5qu62vfDsBms2a2tmlfvHhxfX3lnyyiUDOtN6/fDFRknlZZHTC8eXSXzEBd6likiLBJ7QjOYx7GoUypCw1XmRhBxOK86DyuN+u27YiIAxE1QYOEGJvohUYwWMg4PlIK50fmcRLnqMXbppk4EgfvGEZEM2qaZpqmnFMp4rsuIPhOL1J8rJPP68a5TrVsijeeV0Vr62gUJKLYcBYF5K7vRZKN3nrAAGCKhAEAVQ0xnJ6e3Ln76M69B0+effni1Yth2AKKiVfVXDzg/8fZf3VLciRpgqAQVTOnlwZDBBDgQPIu2jXzMDO95+z82u3dpz69Z3f7Zbp6p7sri2RmJa9kAAJAkBuXOjEzVRHZB1E19xsRyKxeO4E4gUvc3dRUhXzyySdFvwoqiD0+XPfHKSU/PEzgI8UzAnGog6HMDFRB1K0DhlArVogEVMF5QQAoww0NmpIpOq8bUMcaNAAQIlgwMzZmZnWVlmJvyAoM6zR4UJMhq2g2E4As0vdpnmRAoqaZHjPPADhGIg4xmKh5v76IAigI+FTXfhhUuSHJkvusqc+WlUm3na5WdxbzfHPz9OJik9PxvdP50TKn3A19VkWvR/i9AmAVnWCid95+59HDhxfnT4dhYIKRNzy2RbzWADlWfi2JOnVAx2ucvePnxfSVw+Ae/eZmk83uxYdJtgCMhprNUEWSJFHLd+6cHhwe/P43v039FkFj5Kapvb9EMUTXVk8pPf3yyXazdvu7Q+hEc0qmtT3VR7BSYbI7caQob5RT7Qkb+iSPMYZ+gw+2okbo9+hfVy24nYGZWs5J1Zyr2Hed5IztLtV75bJivLFWRxwj3YHVbsZKlulxNRCqua572XDk+k5oZmjYNvF4Ms+EURGyolY5UCt1v1fP8IgPpWwiuOtuLVJnnhYTkS+TIkih6u+QyDea9iJWUjLFAsvZbZ2r/evVDNjzBI8FFFStkHU95VA1Y/cT6oQ39nmhaiigQhbZWcCgpi4JqeoKyaSVbAmoiiLOp2BA15LE2LQc4mp9Y5mgRq6xie8+fvf73//+wfLg/OXZz372i2fPXxhAQ9wQJ9Xs6n2IwzDklIioIgB/2vNhvfYdHtSNWKI3QgVgA1UbAG7SMKGGABrvXSPvDjMGCkAMkvF/xOXeunwIlYqOATUYgK8zVkUOP5B//I48ibm+vvZmgKZpHj9+vF6vj4+P79+9P5/NYgghRmaezWYh8Pn5+d/+7d92Xed9f4SGRQYda6Zu9cV9PAMgEnFQbyktIVpJExHAqUSOHZGJSc4DilgWJUBNggCzyXQ+m4bI2257fX2dJDFzkpT6fgugWSaTSQwNAgORE7aZxLQUZvxuDXdhU3mUYE2cmEgaBqsZgBen3V6E2AwpI8Bk0k6m08CkZmkYuq7r+i6GGJsYAo+tR38URiNEAAJTDYHv3LnTtPGrZ0+IMMYGTDfrtRmKAiJmMWQStexydxaadn48nc2Ojw9evnz69Kvr60sZepSEKmiCplTyj4IMV7S4GgiX5EQITG0TsxmpMSIYjVan6rkhoclYDgTEIjgNAGpjbZ0QDJgDx8Ax5pxNBSqe5oeCY7Fd7K5HlSmU1sK9GhgUyycZpBM8u9Su608Xx4jUTmc3m9Vse9C0beDoM7SIGZnB5cPZE00zkYAcwRgAc5KV5O0aUr9erX/z058uKbbZnn/2eWza029//P3vf5uOD6SdwvlgZpKF2PPCAkQ5LhWYDw8PH77zzs9+/pPUbxMIwq2wmwhDiONXlAkFVJLCLjb2jqy660v1Y9dHZl4mrhE8EKJ12+2LZy8eP/7w3Q8/OTg8zRpSJ+7fVNPl1fn15aUImOFm3YMKEXIYfAMSUxPD8fExM11cXF9dvsySEMErDqBGgE0IgdinHQACM2Pgnb1gQvBEuA4j2gFxr2e9u79tRKjNEcgC5tvuXsEMVCXn5DThoeuGfphNnWpZKh2lGL33XrRXNdvLUXcfxFHDmmOC+QiW8qF8ZAMRgCoAQeQQ5wsNQbY95G5U2yJERiYTrAGW3w+CBx4eXQEUBnbBQGDMXx0j2jlPsz0o87UL953OqKDppNM3yEADwjgPeM8DAezR060KvoyMPgUVUATLKpIH00KnhJKqGzERmo8FLUulBclhZv9tQmpj04QGgBAF0QyFY7x778HN6nrdbwkUUYn5o48++nf/27/73ve+N23b9c3qw7ff+9k///P5y/NHjx7dOT39ze9/97svvtgOuTaHKo1rZ7ecx+6mK+ahCGBGlfludY2wRoHF0pUuQDCFZLrGYR65ISAjMPSRHmYWDFqg3nRHbrC6QUsRdfdE/YTuorH9nwWtNtcp1eAT7EdEqz6a+us6PjSzalPNbLvdXlxchBAsS9d1p0fHJ4dHxLRe3XTbzSi+41a1bdumaaC4K6gsBNwTERvXEKHw/oCYAtGQk5oROWRr9ch4FdNRQNCsQ+7TIGIQQ7h7evrBBx98/MGHDx7cF5HPP//87374w9/94XciMm2nHAKA5T6vupvJZOpEPyrUPRYR8KFOVpZjl4YXnoAaGXBMYgCGThkDMCMANLTYTBfcmFmMsWlbJur67Xq7uby+SjkfHx9TYAyMHHCPyWIGDqrXin45y8UjIp6env7gBz8QlbPzs223JQLiJoSJRTTrVcR7RtVkyLk1S6pZFDnMl4fz5dHx6f3z8/O02fTb1fnzLzerS1RBT/Jh917qiDECEfkAeUYiRCUWChkZgLBUrUfSSnGuDs+QlTqWH0kF25HGC9uPEZHRAMk0uwbW6GERgVkBzKABgywmkoFKcADgmBQgajXOoKBJ86brZrHbDp2ouFCAo4eT2QzbJrYNNV7pjHVMiIHYTPUUkEOEIXf9tltdr64vr9bXWxuwHy43nU7jnY8+ePTX/2b64F6nshlyl7KoGpiIjNJUCKWLORBRCIfHp4rUZ4nkGh26h+ZY32938GEFBV9JDcedMJrN+pM2djD67/uIb1CYTqaPH3/w6affAYpYy0mMkGV4cfbi+np1uDz8q7/6t0PXDX0aIT4AVZGu3xBYSumLzz8/e3lmpk3ThsCSM4AR0enpad/3IUSFBAgcI4fgFREqsybLqmv5bKWdREBvB/Te0lvjDCcdVUgenbh32xL5WXAyvpltt916vT46OoaKvPoftyxvWkgDMKw2DM0KM730djn0q4oqkMF5s37wvaRNAnXaQo0QiiEzAAXLoFJJWAqlMkylP80RZ9O676l+2cxUc0XIrWrlQA0nSq5U/Sbupyh79nkcUTsW3NGAzJQQAckQA76WHRvccsDjRtyPYKx+15Paav/905FTdZgVvYpmJjmTKYkMeRMY5/O5mIUQ6noVOzaZzZCpZJ5g8/n84cOHZvbixYs7RyfTdvqtTz+9szj46quv7t69++jRo/ffffeHP/nJL379KwELVOfowR+7ii0DEygcO4d0vb0E1aigC6Z7mExxxWYJNIFmDExYYBREImwAGp8scdtAu1M1c7rZrsD8RkTCHZ7vcA4cQzTR1Kc8ZFfGJKAaKX7jXXqn4OXF5c1q5RCZ5lKpAgAfg9E0cRSdXi4P3n//vYdvP/rdH36fU/JuFpf7sKq3YGoj2F36tRCZeDJpL2+uXOkJwW7FeaO8qPqIQJOs09n8ww8//N/+l//1r/7iL+/euRObCGar1eq9d9//9/+Pf//rf/lVztI0TdtOAGCz2dxcXaHBdL5AJPU5CeXNx8CaaT+wJif82jAMQMjEyGSAQKhjTa5tGkeriVLKV5cX2+1ms91su87VrMboBHeMPxy9YEXGbLQ7ZnJwsHzvg/cXB8uU0sOHj379619vh34YekSKIZppdu6Qqqmpkz1Ukwhr6BNMJtO7947fevh+IBzWN3//3//27OwF6EAgCDKebQBQUGfU7GJiRCByH+KT7StojuAormiRSS6trEW3xwNjB7retBWRiRRDgcSdaaSKZkTBzGJozIwIM4mJGKkPHHTQDnY1Ox9rw8jUp7Tabs6vLheLw6PUTzRFzUjeDOyoqCZJqlISfEJy4X8fODA7aOaxWU7mcufuw0fperNe3TQU3vnOdyYP7g0xIARQn6u2l4iUg1ZMc8rZzJYHh7P5crW6NDIyqWBTPVqvuYj6CrfWB0ppAPd+ZXfqx51qAGgWY3z/3Q/eefw+UpRaz/NmrxCpbScxNNN2evfu3SbGsSXSU9aU+idffPb5Z7/78snnL89f+owQrw3nNPgqe5WqaaJiEQlnDkRUDNCYatalGKPqV28Wy2c20EKAftVSlV8osT56aGje0mZqw5C3206ycUMAZDv+EiKU9tDbaXd1Wa5e7JMIEV3HGJxUYAqoWsYbjmUvz6OMHJbL2dBUhUCLMS9+HcYn5C/8ytMdYeWyRoUcjIgMxKPWWc3qCFCrSyg74U0bpuL9sH8ca44wXm/sA969xh4c4dd45Iv/JmDChjgimhoxcQyhaSftJHIYAZskojmTZutTxg4BirJ+znvvBQYQY8uxMSBRZeajkzuT6fz587Prq5vto7dPj0+moTk6Pv788y+ePXt2587dR4/ePjq9886jt3/2q1+er66GrocRI/imm9pvSYNa1CSy2n/AXrFDoMKRNn8qhgVTEVVBUwBTMyt8CkaqRhr3120XV+8KKLeusfEdqr93WcrT09PJZLK52V68uLBsAWPRNhh7R6DEKfv3WyBoz1n8dYm4IYZoqqairrFAdcq0mYER8/HJyXyxuLq4xMp82GXlNZgoPqCIyyMzTmeTOGmeP3/ukoRF36zsu4JFiyQVFrHZdPa9737vf/tf/91f/flfHB4eIjpVCpfLg//pf/qfs+R//3//v332+efDMLgjnEzaYejPzs6m2+1iuWzadmzJLVGRApmTk0tsXVoZVEyMkAMHH77ma+uCZj6gjZnFLPU9gDYxmDSWE4cYi/flCqd79WtUHCzhUVXgM7Xcts1HH39078F9iqGN4fs/+MHh0eGvf/XrL774PIuZoWEANHVUHxEMvMogYqCoSlkxIsV2MmkjIXDTZgNTR3B7L2CXgKAWqHCvJOGBVIzx+Oj4YHkUOO4AMKKU5KtnT2/Wayj0c3+NQihVd/AjroKKWiZbIQCW4UyoMI66VSh9z9iAZSSk7Bop5BvMG/mxALaIGDhGbprQAGLS3OX+antztr7MLQ1kShjb1mpVmoiYCIiEgWIzmUza2axtpk0bqQmTw9nMTjzW2642m6E7mM7axcHAkQJzwGA2XcyJWW/rUUA5YeASksfHJ4/efufly6eIGQD3FmB3iKp9sFf+8Y2GxUb/DfuG1mPZw8Ojb33ru9PpPIsgc82sCvwZQ8TSGITqmLVTGg3JUERvbtZnZy/Pzs6GfvAYMcbgJgPAVOHq6gpHJikiYuGL7aDmMTArscg33E79kZ2FeeX7+GYTWyZKqapIt+2zyIT4tq+5/S67THEsHo3QYR0P7EgNVFFkz5cAoM6SgzJ+xLJKt95o5KKTW285AAQo5TR91QGbqvIe4Q48MhBvkiLmyKExVSAusdYtW2vfsCUQd4Qsh6us5ghWmuFqOvtmBzxuINwL9m+/wZhxAwcCYDVzweCRu+uqiiklHzBiOZMIDslECCkNKQ2DY1b1MRgAhBAODo+fPXuRskAMJ6f3pvPlxdnL35394eLi8sP33r97eHz5/Owf/uGfum6LyH/+Z39+vDj+5L2PLs4vr2+uWYHs1jiEcZ1wb/OVmNBdooF3MgKIKQJaGeAOBAAByeEXz5W5TNmEEEMw6NO277u5K/v7nImhtB7vVuy2yOrrz2p006MPdgrMdDqdzWbTOJ2389lkdnh4OJ/OAfDy8uIPf/js+fNn235b+sadnUe7U+WzciM2XtUrQQB5f/lOJcA/pBOyjo+PDw4Ori8vsYrN7NbPnZcpkQ/HHSUOEME+/PCDEMJXX39ZShWmPFZRzMQgZzPREJp3333vL/7iLz784IOmaVJKpaPJs9Im/tVf/lXXb//Df/gPv/v971JKi8ViOp0uFovVan12dnZzc7M4OFgsFtGZKegr64W9MoW5dMqZmWkI1FCoMZCZqWf0qpJTIed72wkThSbmNDRNjLEJIYYQieiPVH5L2OIOBuCtt9765NNPJtOpZ6Uxxrt37374wYe//e1vf/nLX3z+xefXN9cZEQGTWnQQjVgMDIkoELMBCJRRSpttt95sAMAUTF1jHYuOPVZ8qV4jSYeZm6Y5PDqaT5d1jl6BP1abPqWs3hGvKqZFudYMcXc6RkcOYLYb8YnFrAMiohYMDgG8VBYBEaX2SqiLHpCZiomImKor8wlqVgkxGIMES8G2pC0KQSLp2sFAAUQJiQOHEKhtZBIs8jBpuG1p0lgbMYbYhpaImJUonhxMQWIGC2EQYABBEMKmbaaTyXq9oqKPVB5aLT4ZAMzn84cPH/3i5z/WlCtmia88YsSdDdynZ75+hMd/IO6gUxjLn4axad997/3j49NhGDRnQ/IkgIiaEAnNuYpAZJVlXS80AxG7vLo+P79cbzoAMlMHaFIaoLrg8/Pz2WyWVUYfIQZkpRewGj+1vU/7jft7fx1e7fv9pt8yl3sTkSwypGGn7vLKcgHALfu8R/tC8OGDVewRAUpIp+WLZlqiAslZtWTXBGCqfZckcQxNSwxmqjlgy+h0opJTjP67xgTVFZaAqBQWnawYQgxNRFHmUPluZWOM4dXrK1kDh/0ie90PhKA7O28IjPSNGfBrftdfbHf4/X2zSBKfWmrgRyPr0A+gllJS8eYKNVPMgimzKrOJoshQmoBrKEGITRPbdoLEWRWRJpMpIa1Xm2fPnl9dXl2cXbYhvnjy5ZMvngDAT37687cfv/fBwVETm2nbtrGJIfQDyt5Osb3bKbdQM9FRYQgAeUwNPNAiEuRATEyKu3lGPqMycJjPZjO1vNnkoYM8DYqIOA1N7Ltint/ka79pqcfPCBXf7LvuyRdPTk9Ovvut73330+/OprMYm2k7m7QzZHry5Ml/+2//9Uc/+qcXL5+LZkMtWm2ICoZmZABgBSO6peSiUFQDHNHm0Y5PJhMHpT0oMVBTq5WUW5eHVgCgqpvNhog/+ujjrtu+ePG1iEbmOyengfni4rLreh9oSMSnp6effPLJw4ePiLnvBwBgZi3rhAISJvHP//IvOPB//H/+x9/85jeb7WYymUwmk5OT4/Pzi/VmPQzDer2ezWZesQ4hhBAMa+uqFbAKwESkaWPTNOvVygdT+lN1hzQMQ9/3rlrVtk0bo6oMQ0+EMcYydujV57IHadbyi2+qEOO9e/eWi2Vsoo2uieDevXt37975zne/8y//8i+//8Pvv3761Wq1YYreVYujtJabXATkgBwU6Or6atttYgg5p7rznKJi4HBuae/3mIqJHC9ozPD66mazHogCIiCoqfZ92vaDiGPPaqJgXuGy0fRYxfmcMuvBit8mAY3ArAEQoSmCE7cccyBGNUQBEGQEjK6UaUqGqCBglrOKpJw1S86m1oe5pc7SQBrZOhIknbhwBRIThxBpMsnzdrtobdJSG7WJoQkYOQUUpkCkhJlBMRADAhCB+UDQGCbz2fxwsU1bLvK79bm5MIcoARDCyckpc8i9szjesM93T7y2Kb7+Lbe2r3zLoKjPQ3Uk9+48+Mu//Ov58qDv+6yWxAdz5X7oDazfbq6vrnLqDw+WIQZvxXQYzE9nSvL8+dnF5bWIN2GX3rZCSgIAgH5IIaaqYrSXXd4Cn/c/OsC4k/GVb+Ctn/pTV7lRl1qTrCpd1/VdZ4tlTTD2XwxdeQNMAXj8HPvJ5QgP1Ex8JyxTzt0+G3F8aQUxDaXVtKgJMTAbRkNFy2ZImHE0/L5x3mCXfQmYiIlVxvXc7SRweAzH1Hr82AWgGr1aKfzgfix4a0He7IC/CW/xYNHK3QU1HAYZ1MQzDCLzqQCIPkKlLCCqmYC6IzYFA3StIjOTSqcBRJxMJrPJbNK0mxV23XB+fknG5xdX3EySwu+ffDlsO+uTxtY0X6xW22HAwE3bcAwcOIRYRD20JIQG5jRHdNknAy9EFDkYA0VDg0aAtLANGkJWshAEqUdLWMEDAAPLhoLEGGZMODsIqhOMjUBGnQFNiYMMyiBYhweNetyvgVr7j8H/8+8Qoomk9fb40eLjd99/eP+tnKTfJo6hDZPQNJ98+K3jg5M7J3f+y3/5z0+++nzICqRmgIVAUyKs2g99O8asDkTVwm4YNPosNm9MVDMDMQN0RR4D74dxpQ41GYbOO0dXq9Xl5cUnn37r029/+2Z1s93cLObL73zy6fLw8L/+1/+6Xq+TqBlOJ82DBw/u3rsLZv0wbGiLCLFtxkOmICLSziZ/9ld/8eDtt/6P//x//P3f/32SPA3t8clRjOH8Im67fui6brvhEJqmiTHMZjPn7BQte6fjg4mImoikfugcDBixFjMTyarJTJsmNm3ULJvN2iEHIgwxEDE4Tl2Ar8pSKpEVGppLVwBC08T5fL7ZbGxjqgqEHJioKtsyfvLppx999NGm2744e/mjf/rJ2csL02yqXGjmRgTMvs6aUnr+8kXOw3Qy6XLKxiq7pkav7gGQKYKDxIaMHLkpGMl8wWFCHIkITYc0iN1gUlWUbOrVLm9KKDrJZLWfU1Wpku8IqaKyRiPgZQ4Ggrnl0dLiY8yeQvgaeWBHEERiYBFXEDUT1T7lQW6SKbcTaBoJtLU8gAxNFmwaK7MjYttiAJ2FPlCMRBEhoAVCRmE0wkgAWOTGNw2w4+MCJoCRwqyZnx6db64kJwKINQ2FEnYHIlCRQkIEMmMoeZe80eh907Wf+9bCBDiU7u6ekFCtic0PfvBn3/nev7m43m5Xa1FLaej7fr1er1ari6uLy4uLbtsdHiwfP34c24njM1KkDVHUuq5brzYpC4K3KZOqNk1jKjnnUQ5ZbBfBgykjEShUFvgIv+43HQHAPm+j3lFpKS6JBO7Fn1j9T90TWEtNACwiw9ADWBqGvu8RvZWWEbkaNtprSo6UegAAblFJREFUHd/FtXv6UjAayYKBl2SmdAU5raTo947NzTrONdtrNyIixAmHo6Ztt93KrDcTQCCS/ZTbgLT+MSAwrqxrUCnx5q0gAmtN3UUUCMBP6J54BezWzMAUFEHttrvH+jNvdsDflK7dWiKFnJVc96+KOqoagBc5Sq+olulHQuq9Z5ZFQh0lBzuGQFF+vnfv3t07d1Y3191m/ctf/erh/UeLxUFoJiklRZ4dHEbknFLfbXkyAWaOITSlNyLEYIhagw0yq1YCyjwyA640KwRgsBmHKfA0hAjYclzEyaKZBOLrLF9sb57jQIRWgF4wwIS4znktchyb4+nBPESRQVTArEU7YD4XHYD2fOwOzPuGuGZ/qev3xdDsztHxvZPTSYhPX1xu18O8WYCoZUGi46Pjf/vXf4MA/5//9P968vUTFaHIBN7zhyXsqYgi1MOGe29iNVjzqkHXddvttv6g3fq5GpAWhM2F19XMdL1eP/3q63cfv/vg3oN7d+9/+WS4c3Ln4w8+mi5nf/fD/55zyqIGdHB4+PjxO9NJ66F/SqnrSRGQfPwzKpYdHJp4/+GDH/zFnz07f/Hb3/72ZrNaTufzxYJC3Gy26816221FpO+7rrP1Zs3EIYbZbObj/RDBnJdvxoEAQLR0bQIUkcW2bdq2ZaYQAgKubq4923Ag1z266ShV75ZCzLCIUxVU30RybOL9+/cXy8W230Cxuqa9L7ULACB7bYb54PAoxpaw5KyEQcRyBp4EDo0PTh9Sf3NzDQBN09h0wpSHfnC8v/4plUKs/f+BY4ztcnlwcnw6mc4Ag0EgQgPbXl50/bDZdMMw1EXwAcP+QMGl+ZH2zLHH7+bMWK+37JckStrs80IB6qAkRFA2MGJj5hADh1h6ZUXMrO/7m9V1GTHU9efn56q4Wt0c3b17cnyynCxuqJliCMDM3M4XsQ2BAGLIbRxahpYtEnGZG44GVNt+MgEEMAMjwACWAYkXd474xderixUBBDG0euQ9Xolkql98/sX6ZsPEVTLpj4HMIyI9LtTIBd5fOoDaRFr+bUx0dHT6ne/94N69t/r8fD0kNmvaZj6bHx4ciul6s7k4v+j6fjqZTGfLITlQIAAYAhNht9n8/Ge/fPbsGRMRchqAiWIMh4eH60DD0EslJahbtqqeW++I3NjbbUMzfvg3M1NeXYI/+s1qZ1Q156SqOaV+uzWnu9f2GXjDR7h1jYtIe0pG6O1DZXYnMyGAVdYngIEiEDiUmsAQaxu6l4kJYR4bFmNNm2xdlkyoew54/EBa25zqZzF3Yc7GfPWjvuE+bFypCpSXnM2qvPvodutvK8A3Q9DfuEhW/xubPUv6SuRSR0S+iErO2PCgiQq8bwYIITQxhPHGPHbx6Hu5XN6//+D85ZmkPqfsPiJnIYp37h4dLpcM0G23/Xbz8OGD07unAnZ2efHsxQvvBC/iSH5KwULBI8AMyGCqNDOaUIgcWoCZwUkzmVFomqbB0EKYYmAiU/sa+peqiOJ5eskTEARhq3mreZMTJJEhqSSfuMwEEYFUjawAiwC1MrkDVv70GpuJCCE+uP9gPp21FId1lzaDT98lNQOLFA4PDr/7ne9+/vkfvnz6pO+6oIEiElBAHxglCIqAsmcgrNZ0rHJo/VLV7Xbb9z0R7dWkoVCNxprUHhLrlqfv+7MXL54+ffbJJx9/+vHHue8Plocxxq++/PLi4mwYegUACLPZ9Pj4uJ1M1GxIiYiB0Ur46HKo7lYMwCjyO+8+/r/+7//7w0ePfvWzX1ydX0hOiLBYLuaL2ZBT3/d91w0pSc5que9TSn0IwXFpcEljrD2wFYYz54eHGJs43oqartebscuLiFNKMU7eEIDWggxaIcgD4eJg+fjdd6fTaZYEAGVmi6HVlUbw+oCJaE5ZTQ0hxti2bWiiArmGErgVc/EoUUAIMTBMM2lgyzmVNfdNvW83kCjEpp0Qx9V6u9kmLUGmbDbr7Xbdrddp6DV1IOKdOFYBw/IUvS5Vt8HobOoOGQ0HAIyA574/Vh8soaigSoRHx0ez2TzE4JrTpgKm1zfX2+4m5wygOdlmDQiSpBtMhr5bt/PlZDlvJgHCYj4Py2XTtta0OJvgYiLTJsUIxITIBmSUDT1yQAaxUjI0b8BiUIB2Pj28c3K9vk591286zKM4GDBw00RmPDk5efvtd148/zIP2QB2e+WWsSs3ub8fdizIPVDU1xJxTGxK4kQhzJbLy+v187NzIJq0E1VDIkJ2aPLo6O7Dtx6X/BQyoopk140OIRLC+Yuz3/72N/3QB5Yh9eI7DXm1WqWUiVgqIelPXvthxP/fV73HV7/oWFFKOSc/o9vapTN+sn0fbN8Ms77h2tkqAAAVSaq5yLWaA5o7x7n3Dw8AlZgmwAFiA9iZqoG+egc29L1sB28V2X31lf1e78MM1LOsUr94JYly3/gGshqWbGiUMdH/AQdcwYjqwBGJKcYIKgoILioUQoyRidiZGWruTlCF1CDkYbMBgOl0mjU5Wef2W0DbTh4+fNh3WyZEUw4hZyHig4ODk5OTpolgEiItDiYfffLhnQf3zi5f/vOvfvb12dOCgZuNY40IYGq4sKYJMRI1AAsNR9zOQxuJG7OJqncis5WWi8bMUARgYhgNMIsBaWXD+uomhA3kG+ktS8wZRIDJJ9cTKKtRVmSQkisQONHuj263fZjH/382nS2WC0LsNt3NxXXbTAMyWlFfFQNAPDw8+vDDj//2v/6X/uoi50yCFIgyMQcZ5SkA4LUKViku1WhJVbuuG0np9ZR464qqKaqqy4ub6zMG/7eqrVc3v/vNrw8W83fffmfeTtLQdyn95Kf//PL8PGc1JEI6PDw8ODho2xYRUhoA0NDA8V52yMLzzUKmms3n3/r2tz/66KNnf/1vf/jf/+5nP/3Z06+fMoXJtD2aT5FoGPqKmatV8c4xHbl///7jx49//etfX1xceMXDEwMxGXRQUe/ANzM1yXWa7zAM22232XRHh6fHR3eaprEi81sHFvriaJHYWSwX77///t07d3wtx8WGXQOOkznJFBQLeyTn1PVdymk65xCaDNBvO1MLHANFU3AYadZOmukUbJr6eddtu75LQxrlh2p/dumtyllublZ1pkNQwJTTZrMehg5VTLNIGrGoigeC96hQ0cMyNRv7wgsM4rxxAqjL5WnHnu8pZsB52ipKaDE2MTYAEAIzA5oaaL54kfIgmswUSPvBAEBdAUAlt33f9MN8sZjN5zznJvKkpSYmQgqEgYURCQMgKiTvG0ElQAby5AJrVMIIjKBI88PDg9OT7eVlt00+Vb6GODIMMJm2P/izPz89PflP/+//+NkffqsmUEdJQU0NEXQU7BxpOgCAKD6MXYt0tsFeK20FTrR8HGRR+O9//w8vrzYff/qt2LaIpOraKORACdMYVBGiEkUEJwGFvlv/5jf/8vLlWdtEzy9NMyL46NUY42w2v8k3Ph3RoQjbuwC84u9SRfCvSUN32xgQd6IJf/Sna++zYydO0txuu5qnvjlb3EPaSgJbd5ZDoahY6VDuXJBwl9nXNNdp0ogKJn54io5COYICtpWsmsmACANTa+AT1XQ/tDLIjkeVj1dWSU2BGImgLp/TMryFQEoGevvW9qDGfbpZjc8A/KyBKSLZa1rQY7qz/yjKUcSxMRq8c3kyaSaThrLmGkSHECZNO5m0hOY1OVVLQ69D0mFwlwxgbvKyjyrF8a3B1R2Wy4MPPvggBrq5umKMOefDw8OTk5OmaYah77rN0K1Pj4/u3L93dXP99z/6x3/48T9JzsvFIjaRmOrEOmyBHkwOHtC0bZoZhwYpIgUgM0uSk6qRANJcMGRjMDYkEEDMAU3UZQ7BqDRkVLg8g63y0AVaNCESaacKDv5QRArEZkMdYrDLff81kafvGlXlJr718OHx8TEAXl1fr27WywdHDUdQyJIVXH4QifjunXvL+cHTF09BVBOaZEgQYqsGYGW01O2N75HXCJEiAAzDcH19nd1hlg6WEUIZKYIF7iBk5jIDAQGy6osXL5588fm909NHjx5Kzl2//frps74fXJQHA7AHaggi2veDqBlaiAEDATCaFpAfjYiqOB02k8l77793//79v/zrv/rHv/vHH//4x2dnL7bdejKZhhBms9m4dXJK4sDXMKjq22+//eGHH56fn5+fn7uqcIWRzMwkF4qva5emlBHRxR9UdX2z2q66q/Prt956uFguoU5oHsNOF8kLzG+99dajR4+QMOdcEYYRf/SEZjQ0/nCRmUXzerMK1xfT2Wy5WEw49llTztu+B0NJQ+DQNnEyiW1gtEbbljgaMFJKQy+qFTcAQ1S1lARxkGwGoKIuY5DykNKQZQDJoNmHRrjJk2oOCEeM/ZYVGX2wW4H9yKaakj13VCxLAb3M7OzsAuDc8RIzQ1CRfLO6cnDSZ347zAIbVLTAxIpkOJtPqGVoSNiy5aBZRcDU0HyujQEoIRpEn4lmpmpSDCMyIBVFbDCGMGmny6VlGS5XlqscByFFBMSccwj84MFb3/3O954/f7rdrgFgbFjwU1FnItVCY10iVQZIey2Uu7QSx94wRABiDIeHx9/97p/de/hOO1uWZIoYwdVT0MpwotKcAkZFGdKpk2iff/7Zr371c6AcAqdeCFXrU1NVZo4xrNfr8oxq36uf8vIHKtRRxGRgV999xfgUV1SjR9fbGWnH/7rLeY4e0Pd9v9fDWPkeoHXrWUX+C3jrFqXg5+VXnONcBlyUIBZHOS8okTSAOGsUwKACSsVfWjbJqg0W8d2pkmke0ICQKRSZ2Dfdi/86ooMvpXo+7nytGloeOZWMtlj6ChK8xuzbq1Gj31+oS1OiHtgt1vhcEADGdys08RF19qekogZESAADmEoSExWRlF39HrJYytZnzZkIXd3JdNy73u6vBuYG/ujouNusu+1WM8QYZvNZbOKQ0nq9vrm56rbrEOOXXz/76c9+8ZMf/ePl5cXJyQkyx8mEOFpWAGODkzh9GBeHGliIRAfIV6y9yWA65CR5mKA9bpcNN04MQ0RQIkMAG0y3oOIdoQWYLbcvBtep2yD3xIimgcA0giHAhMI8the9Z2SgsJumgzU72u3+cjSKdRvjEDNj4Lsnd5eLQ1U7OzsTteXBAXPw4eFu8JJISjnE+NaDh2cXL1fbm5Q7NzKMjH6Y99DCuqnQn3JVmgQA6Pv+q6++2suAFYoGPVBt3vC9RYTMHCg4ckqMgbTvh9//4bPZfPnd7373YLlcX11v+mFIwqQxNE0Tm7YJTXHABsnAiCFLEyyAmebSxx8jxxiR0JzWraCAs9nsk08+efz247/867/8yU9+/H/+n3/79ddPCXE6mU6n07ZpmAM3jYp2qr2qiXz43nvf/fa3ry8vnj996v3bSFhqw3UkDlQRq7t37sYYVbTbbru+12yisNlunj79+j5C2zaumQa7LkRDgqOj48ePH08mExcfGK0W3vJkUH/PrT+FGAnJTLebzfnFRZjM58t2MolGRVpZco7MMUSf14jQIFBoLQhk6EjJ0lChLQRAU3SJrQxl9IXre+ScsvQqGVXMXAgI67HXvXgSYYcYwdjhOe7JWwCi0yAc99uzRFZ5NUjkBCIT70bJKaWch5yTaHJZeICSqyFCzj10tm04EqFpWBE1bBGtbdpIE1accpg2FJlK7YoAAiMoApVKCkBRqC6mS9yWMFmMOYShVrJVpGx9dIacmWIT23cev3d8fNJtN0zMThLSMtRov0YDewP+3EG4IltZUTDcXczkg784cHvn3sNPPv7W8b23MpACKBCYAaqX5iqmVvbjuL8QkZjWq6t/+Ie/e/b8y7ZlU5XcgQkCNE0zm802m9V2u22a6EswpmhOu63JYm1fM9y9YTXrfxICRkTAW/14ZUfgzqCBVdtY9gx0fTfPIpI3281kOvFdWfaXp/zO36s7bC/fG8Mdt1Fl3oGamjMYiJCQnYo11oQIDcSsyl2pQRHS8FCeFMkIgILPVZ4M2bKFUrLgPM7tLt3GfjYKJgSIBkjM6Fy2MWuv82r2bHhZ3j3NgFJ5Rdy5D9x9r/wjmKHfWg30YIx//bX3GgR3DwLARPNqtTJgJ5W6oZEQ00BEJCYpDSZqLg6uRmIk4hGLquZcMFp/QURTFZFMSKogOa3W65yzKYQ2hrYxwu3Qb7abbru9uVldXlz8y6//JYug5rZtKAQjDE3EwKaAhiwwh3iAYaYooltLT7rL55zWDSiTgTHmoxAeuwg4gQEKQgY05BXrpegKTJmL4BmYWRmLYWoZ7Gropw33BkI28ZRONAK1QN5F70kmGEiNyTw0LhZsZ553IaI/PzRbzhdv3X9r2k77fnj24tlkOp8vZ0qWRYGKDMLQd30aEPHTb33n8O7JxfX5y/PnV9dXk0nbNM1kMkUjP3l7T+52Hl78AplZ3/clDrPdp0XwZMvPDKBPxUBUDkiWRU3Nh+Rc3tz85Gc/a2bz733v+81s+dY77z356knKedo0B4eHFGjQIdJEVCQrqGRIRmZkzSSK5iQ5Sw6ZD+KCgLMMmjRwaEIENdUcGv7okw/f//Dxd7//6Q9/+MMf/+jHT7/6+ubmKhI3TVsk5wnRLBB8/tnv33n74dHB8vhwuek6vxUzSyLOw2qaxonTd07vfP973z88PNxutp9/9tkvf/nLr796drNa9f0gkl4+f7ZYzqfzWQiB0MDQZ1C3bfPuu4/v3bmLhAMAhhqPIhYJi9uEnWJ5gABRzfX8ZLvdrlZrjpPpbN7Ehii4mNAwdCJZlcSIOBBGChJiE0XNxwuZYMmwEQxzymClmRsRRSXnlPIgkkAFVSqHfWdW3XKguawaERTp3FL+vZ0NA1I15rDr1dmzBtVWFM9NRFoHN8GrFdPR1IoISh6QoNtuUDEPoqCbPKz7bQI8IJhGbGdtYCbNPJ82bUOhYTaGSr8pS4tVN7VsXrfuEkKObcccYiOiWs2lq1QGYjARw+OT07cePHz29deESgSBQMzMYN//7o4Dop8XIhk5WL656k8TEzMFMCLidjp/8OCd6eJAABWcCAtqqiAouczVJKgSefWNDAAJVX75y5/96lc/BRtMsOuHPPQASsyBXb4QhmEYhi5Lhkpc8vwRSxM+mRlBAbr3cPo3X1Y4EyXEKhH3Toy2Gv59mtluT+GYvA0p5ZxEtev7dtJWpzWCKO7qivhz3VE4xjB1LYqOGBEYaEpD13UANjKfVcv0TNffwUILcKSq7mH0TnPKrs2ACExCCIAsRevPf9AxfEQstF2oGl5Ehmi18UihTpCAcbENK4/VqgE30zqnzesKAKZqCGqoNa5Fdd/3jTVgrGy6+iSQeb9pyjUNUkpDVstqCoYOQnGgOlvRRAGKioUDDFwBKyjzzfbfkMHcecnNzU0paxlwYCIaUtp2267vum673ay2223OwszzabvT5HNdDbAJ0N1m+vZkeY9ajNBp3uRhjbIiTTEOCCYwMTRFLWcDxOWnA/YMLzW/kG5Lplwekc8U8dNhBGK2Tula+gVFR44CWAAISIVxX6V41ZyzU87wDmoYE0vcbXe/iOjenbuP33msop/97g8X55ff+dajyXSSNIlBYHbVAz8rWXR+sHhn9s4jfiSasyYA+OrJl2dnL62SM/ZDp2Ksajru1N9xADsDIoy7h95wXCtbGNXUUFWq6JttttvPPv/83oOHDx48+P6/+fNuu3369Msm8HK57PphtdnEycwEyNAUNMt6s1aQBcw5kIGaSUoyDEPTRGZar1cqcnRw2MZ2BGUo4Le+/cnjd9/+m7/+tz/8ux/+5Ec/ev702Xa79j5mohACIdJPf/rPm836k08+OThYInPTNPfv35/N55vttuu6pmnu3LkzmUyY+dGjR++/+/5sNmPmv/jzP//ss89++Ytf/dM//ejLL79cr9fD0F1eppTT4eGho+5qhhzuP3zrw48/ms5mokLW4J5SVTVSewYVStdv3yVfZzRDIhG5vr5GDsgcJ1Pw2TXMkqXvezQEM2owhjCZTMorGYiKCYQQ2smEmSdNG0ZdFaKUkg0gIoP3X6viq8+/hvgAaloyCd8Luyd864HXnenGpfyvmsGbLr9pDmwgZhRCMIuIkMXUiih3AeZNRzmUnPOA/dC3ELrNerVdr9vlNg5D2Pa06aGJFgIgEQTSjBzQ9Y/cf1HxvuPHRkcEGUMM7WQynS8lxCTJmXEoClZEtxCgadt333v/57/4aRo6BHTX6+2pe0ntDhWoUdbY04n7EDSA46UMiAhhNp3fufcgNI3LSYiBmBlgmaRWV4wIoHI7PIGbNOHy/Pl/++//3/X6KkYzES/9moFn6n3XQ4FwpDJdtRSecVcu+SPXGyHo/UTLbkuRjF6gCEObgReM3MAbjOw+ybnvhzQ4taLCRnvbY/8tfQF2idjrO8oAzERyzs6QIlAAFclZTR0JKVOVYJf3A+zqfoqgYINJzmZmHeREXsMQUjMih7zrr/kT9Ew7IIfdJ/N/7fIm3AcYby3t+AueR6s3a5l5Mnr7+hMkrNcPmtUygTlogigm6hqH5PVpcwI36JjbAVYubdlwxaMHJlarxDMHkdSGpFdX1/2Q1CAgM5KJDtK5DNN2u+66jXN5iIgDemey1zzUlADuTmbvT45PqG00GhkDiFKPkHy+oy85UgbWEERZnCxAkFReDP3X/epp3/VsBW2BXQxczw30IBtJkZlByXQwbYgZsKEQgQYToyKLsYuXcHyCu7TU9tG8nWWSzz7/7Le//e1nv/vD/btvHZ+eiEnOCshQVDABGCiGbe6zqRICUYhNpEZNIbBUyV+tXXPjQdqP6d1tjPlTTXTKTe7/FoARGJbGEiBmBvRGUsdV+pyfP3/+5VdfHh0d33/46H/5d/+Xf/jh312dn01n89VmfXb+sp3MIsc2NEhoICkNsNXQ4Gw+QwRmUpXNdm0wmc1m7aQ5e/Gi22zu3rk7m07Bd5CqorXT9sNPPnzw8MGn3/rkn/7+H37zm3+JMb733nt379796quvfvvb356dnX359ZMPP/7w029/ShTu3r378OGjxXJxfXPzs5/97Nmz5++8887Dhw+9kNy2LQAw89HR0fHx8ccff/rBBx/+p//0n371q19dX1+nnFarFQAcHB4QkZnOp5N3Hj9uJq34xDTT0S1ZvXaG2//t4t5MMUZVLTqzxGkYVjc3sWkXiyWHyIGxaZoYc0qd+FgqCsy7grcBIIScZ7PZYrmMTTOdtA0SEzGzql5eXuacQRVUTNQs70PI5SSOsUGJ4b2i5NjnyCYt6htYJ5qUG4Rbd1q3iu2DnYhmoBzYwCIy82RIhEkNIiI4Qwd8woAQlR5FBZCUU8hZcpKc0IANUAyzQLagRMaaNYFaNOAS5TKRSUl4xriWEAQAEZhw0jaTts1mri+Lle9Te7lQc3r/ww/v3rv/5IvP1FDAeM+njvt/7zy85kX8XRG8B78QSw0RYbE4OD29C+TCk6Vb1Mcy0UgDBBVRMFARVXHO4GaV/vlH//jll5+HwAFRVepuAkT0kEV2YxCBqCg1lZccsVD7E7q8t28NRwtQ9sAooXX7J8d/vfqtChWmlERyGoZxjure9SqSOrqUvbcoWpQIpTZYf9Q9rHp5E8zeKKNx+yMBAAhaBjBN2TSBiU9FKoMfKKhkM/XpY34P6AOCiZA9LAbXp64tz27breqevHKD7s6LWXTgB8xdAdqrwWsYV39Xf9jbdiO0Vroq9mglPm9w1CjZFcZ3XqY8FR/GAa4VD6gGXOZrWDEre08ki3R9f3l9M6Rx4rcO/VZMTfOQhs1mPQy9qDBzE5nZ231L5R4NGuRFnKDZVe6NNCANJmuUgUwBR2gfgBLSBWBm57bpdugv++5F321MtsTCoWySAvGX+dJmZqoJbCMpZJwAIWgwEDBSnYQwD02fusFXpvaAFWBohBZ2oeX+oyvXv/z2txcvLyZN++D+W//2b/7ndtpuh04UAkexYiyVoc/Dtu8ymHiRx+NoAxVQ8TVx+cB9q1GQJfe7TIyIKaXtdjtm45XDaTUKthJfI4hKt+2IyzRDIlATdqAGse/7J0++vH//reXyPQXG0CyPTt57/K4gpmEYhiFMQsndS7GfEMFMiZmIzUBU+r4jwul0slwuvvziieR079696XRKxsigJoTIFBfLxQ/+zQ/efeftn/70p0+ePLl79+53vvOdH/ybH/z4xz/+zW9+w8wc6NGjR3fu3Ds+Op5MJk3TzOazzWY9m8/vP3hweueURrq814eRYoxHR0d//dd/3bZtzvkXv/gFDtT13c3NzWQyadrYxvb+/fvHR0e+XFA0acfCzSuFtrL/fcEJSYs+HyKU0XCac05DzkPTToh8IJoZqEjutuq6qJ6sT6fTGGM7nYjIbDFfLpYhhsiBwRjQzDabTd/3Xb/tC4gtBrpTYjRFBEAePyDWe6/bo+YR33y9HmeM6zcGlqOLCiFYaW0CRFETN0ajhpGWPmEx1tJCKqKqfd9vuy52HaWBUw4phaxNUjI0Qs3iiCICmWv+YEWLCcgQGVVhLPSo5JT6XAWNSAEAuForUzk4OHz09jtfPvncXLbiTWuwd3z2Q9I3/ZiXBwgB8ej45ODoCInAkBACEVkhWY17z6WJSmIM2SHMF08vP//st0O3mc8aADApdAwiQsaskiU5E7tGSv72xfKPj+uV/OmVYGLfCu19/VZU5YTkN70I7mHRxVYYFIqAmYlIPwxpGKzoUY+viWPwVj9qkUque9W16IvfsjJL1zdeKe6JKIhSjaLcK48fv7wRIpWZR6ZoPvVLQAXMZyabGYIiGQZupi030ZIpeou7/00uzRaZXZ5slymb+dDbmmbvLxC4tsmeAIoHu0BvknoJVWmWbscmuxfbrbt71nLQoDYZGfkkgz09Ny8rloZgJPbyko+/zGaqsWnbGJnYQ7YCzfpcyZS26/Ww7RwKNYKc0np9owZDSqvVTd/3IgJozMSBsYDPCGCSBdWaEAe0i2G7VnuJCIhZ5Dp3HZohiYGiMaKCDaZf5U0Qk5zF8k3fXenQm4EruLg6kYmZIZEW8f0SFGTGdVYUyaSGwAADKJI1gCdxshn6YV/Cf+/E3s4e6nn2b9Vcs+u3Z2dn77///g/+7Af3H9wb8jAMmShKqJNUEEXsen2z7rusYoDklEdzIozV9gxHHPfesca4NdAiQhqGYbPZuK10+cBaQxk/Pnk9WNW6YUDMrWlsWiRiI++Ub5CSyOrm5ssnT+7dvfvy/OL8/Ork8ODe/QftdOaqk6qSswWKIVKMsZ20dewQIBb1EhUdtkPAcLg8vFleffXVV33fv/Xw4Ww6xQyGRgTGLmlEJ6en77///vX1ddM0Jycnd+7cefz48dOnT589f7ZarUIIbdvW5N5ijB988MGjd94JsdlVFIDULIkoopgGUSL+/ve/f3Z29vTp05fnL7OEvu+3223TxqOjo0ePHiHiMCQ/8+omYo/IdEuFBQCVwHU3UUXFQVMiJERmQsL1avX06dPTu3Z6dGKmOXWg2TSbUd91nkNPJpMQQgxN007VNDZN004AQKwI5OacLi8vLy8vu36bc1LNagIgXne8HX6Vr4yYOWJRM9oTMXyFv4kuUQkVoRmz/DFZ3Nvpe+/IDGZIHGObc3YuNmK2Wg8WyUPfB6RAJBpSHlLKq9UqP3u2ETnlcMhhzmxAkLSZzyCympILHEPpoDZXPaJCPlalbJr6wfXoteuGrkuvOeBRhIA5vvfuB7/42T9vVpeVKGz7ZvUNKe/u+ECt5pTl9X46Mgocj49P2sm0OHREBGD0+uIO0CazQsQlI47MmIZ0dvb84vyM0Rh9/GTBC/3xlZlv6NiKjSxl/JMB1Gu3MBol3LugjkK/jZYBvBJ2eA/F+IZUfkVFsvf29YOqVa2ocYdgHY6AoxwwFDzX0BSBq8vxFwRAQg5IAfYa5KDwxs3hVSvLYJVW7WHfHhm8OnXd8bo9HhRECjGGGIHS7q720Syikmfu7r7CDGN/al0UGD98AYore7MkxSWXG6XZXoeg93ceQO1DCCEUK1PiGcQ6+Y5IUcUAAnFgHhku3gdG4GtrJlmHIa87FF3M5m3bTNpJVfAq7kFEUt9tVtcmiVTVhIwk5zwMYrrt+9X1jbMqGxdtZy4iuYiMROBKY3aTuk4Mcsbq4pTAOAipmpGWteslf7m61jRIyq4FPRAIMwBEMAIj4qIrnxOVcVk+7BgUqEdhESdINogDKINGgSMML5hZEjMmLLTb0ULbLkpFAOAyvaOaDzBA48ind07/5m/+5tNPvsXMXd/3fQqsKaWigwgwpPzy+nzbba2k544lUt2JBasYi1n1/NyyrGaWRbq+k5wd4AX0EXgIpS2D/MAgMiIDQxbN0iMBB4rcABV5Oa9EakrPvv7q7O1HTQzEfL1a/ctvf/Pxxx8vlgeEBqDoTrHh2DQxBuYxUAUEREUysqxDN8zn04cPH3Z9/+LFi9jEBw/ecoEnZgLMgdCVWq9Xq9VqNZvNRERUJrPJW4/eCk347LPPVqvVbH49X8wCBlEFRI5hEkI9Ks4A9sZANUlqpKaEHEPzF3/1F//ym9/85//8n2OMfd+v1qvTuyePHj08PDjKWcfT6LP8ShtzXVO4vcQAUGb3MlMR7UYkFFHVIXX99Xq93nSWpQ3og8191qi6RKfI0dHRZDJhQiQ2o65PN6sXw9CbmI+hSanv+3XXd6CqkkSzWR4FGD3rNmAE1NLKWCDZvYz9teR9z83UoHGX/o6WYSxwjLlRdcwmIsTs6tfMkdlHeoylOh88MvQITEDMQ2pC13Fo82qlTQMvng8iOaWh7+fLBDnHSRObhmP0w+J8R88ajQlLxmtZNPcJhqRpSGnImrOWyaxuLxwzA4AQGJkevPXWycnp+vrSxh5T86B6f1mwmBlgBKp54QgO1kbwGovN5/MH9+8zkwD5glnNzsaYu2JOfhETMtrZ1eWvf/nz9XoVCENgAEBlVTRzOpsvO4TARNT33ubuEork0/nKvvPhX2xVFVerlXklD74lAXbrSbpDNRiDFTel+9ajWm8rsZ2Z5JSGTvKQh44A/BbqAgIAumUFIwB2oS4XOCPvjvS819NfMkQObRvbNvjMm2rE/FcAAUH3PkCxeyUKVGMkNkIjxwpqXLHbz1BJx4jofMMaU5mpaBYlLjqjHm1VFBpcXP3WYS+fgZCkfoaCS5THX2dMWCncEb7eB/waZD8iFWPlyL9KRLPpdD6f5yxZxQAYKTC3TTOZTDiEon5npllkSCYoUhsSVT3NqqmvP2lT1e12vdmuARRQwVRNdMhuW1er1Xa7SSkBQIxxuVx66QtKhMiTSRs4ptytLZEBkQXE4IMCiRQhmLMgy3Ix4YxDRrrWjWBRhyBneJRSPCKSSBLJhBpCRfBc3tpwC0YiATBDUEQxY9GAMG8mq21WgrTbebssF8ZthGg6UifKdDlVQYR33nnn7bffVtPrm5t+yD6iEYkMLIuklLddv+o2hkDs2JvD0CiaYeyLAYBRbB92h9+TmPK32PpmnfoBVIxAvfnOeX0K5hUmLDqup3fubrfbr796kmKOkomIOCIiVA9kIuvV+ulXX7/3/nvvvP32s6dPz16+fP+Dd4mU2YixbRsMzDHEJrKTqsfhY4bEmFVUcxqGnqmZxHfffXcYhs+/+CLl/OjRoxhD3/ddZ9PJdD5bgOr19fXZy5eTyaTruq7r/ETdu3ev74ef//wXq/UaEAG9+552AJKbD9dDwAJ7KUIGJTPNNlvM3//g/f/yX/5WUhbNlvTo+OTBw0fMYRC1cUX9sNZjAljQsNGGudQuEalZCLHCDuztryJKSIPky8tLSfloOU8pEZHWgMnM+r6/vr42s0k7pbjXG2OWc9Ihi2TTrCIEkFWqQAQgWM1k3+Bc/+RVsqFqcIjQhzpYEZGFPctA4/WKfS/3rv4T7H9cowcM1AxVc87brgMjCpNEQ9/3MU6GbddvttysiUNSlSypH9q2mU6n7XQSXPUsMiGR833VIBuZghiLhm6IXYpdEhRCIywmFvdAVkRUUzM8PDy8/+CtJ5//QVWZzFw2u4wLGzGDilbtL8/rl+dEiJPJ5ODgAADFXFOiFMgMXCLAdG+FDYARySgP3R9+/9svv/xC80ATDqE0TfmwP09RVIUZvaoiIsOgPsqxJHv7n8V2YgQABjuV3v0fsZ0HKl/aOyBvSv7H333D3QNYmQCehyENw1BcUq187Q3BceOKzsX39aHailRSPMTYNC0s9HJCRdgFHUs2U/RJhKWvSYF85ECJdkwN1IiBiEgJsNYCCQvTvT5FEUnDIDmHERYiBDDJOacB0DnSDm0hjF17HmXeWpfRsyCOpqaWWJ3sAE7D8pEEiGj/4ySsMRoMxE3bMrEx2G5OqqloTjmLpJxzSj73w4ZEpjAkE7Es15cX29Rv1hsbSy7mkJR01cW69REVyYNXFDabm5wH1cxMsYmz2ayIrdRz3raTEMKQIJlxgVOozIhAJMBYNNsBwNhsxuFumK2h3zZZUdUPBaCVXHfsqQdVNRD/HlOZ/GdgWa03C0AD4YAYzAJAADqMk1XXqeSOdiHLnut9bd9aiW5KAIZwdHQICC9fvlQBQKLQcAiACGZJpdt23ZCSSZl9UWAuYMLtZthue0BqJq2JDH0nKeueyg8RA6GM9UjkyWQWQ+yLkgECeGfa3v6qWc6d0zt375yq5MvLi5SEUBgohJ3ZNQDJ+ezFiw8+eP+9x+92m41qms/nsdSM0cMsH5/HTIjqT6kIE4ASqWd+XdchQdM077333s3NzWeffRZjvH//PhFtNpu+64c+NTG2k3axXLaTyXQ6tZKWARHduXPnBz/4ftO0k8nE6zcqBgDEXNElqNALIjNirYQoiAgQLJfLJjbbbquqRyfH77z7HscoPi5+RGv92daO9hELg9vQpZmBaggcQkTiEDjG2EzmRihKWVUBm9ioqZqFEEQVdy+CKaXNZgNA0xAX80VsIjjFxlRT6vv+8vxstYLNZiW5QzLUGg58c11pdJ9+BovOvRX/VE3v7l7MHGwqCYen5vUTkvd3uQ9m5sLqsrpGiEBUswgmFCyMCDXNKiCIPfRIK0LC0PBkbimnrs/90K03FCJhQFHMLeUMw2CTFictWVPGtqs50ImqmBWS4GYD3Ra7npmQyT80IjJw7eh0AooBQGya+w8exKaRbjdFuDZTjXne3gPF8VffeBECL5dH88WBl3iLOq6bfVMrvVLFarlnNwMVffHi7Jc//+lqdQGQY2iZy+KOrVzjU2vbdjqd3tzclA9E5UX+yGW2B33e+vorEpW7N3rTdWtj3L60bB1ENcsiw9CrKwrs3vTWu+8t7FgbK1/HMmsINttt3yczLONLzVTUVMmMKtbipdtiOvduAgAZiBHZ0KeWvopPqaaUhmHIOQdzdA3KwVE1K5zTvc9vgOqOwVeh4neKTpGzEvfa+DH2PtOe8Ei5XnHAtr9A/sBeXfoSDpKZpb5fr1ZDzgomqqAWmJsQEVHB+jRoFldCIFWUhCljyiSmxQG8KoDeD/16syn9CaZmmnIWHVJON6vrIQ2AigTMNJk0sGdE/EEyc8FOrZb5vQJRPrgD+v4MLZgdUDyhBiBFg0xkTKguawtwW+AGzNQ0Z+Pgc7BLa5WAbNUQYRKsQY2AU8OAODc64laSrVFfmwz+ylXgd4LRj5qYvDh/8fTF00mcgVKIbWjNhl4dtJOsZuKtU0WJ3NA1NYn6vl+t110/zOfzQaRPg0jS+tSJKMaGYjBDogCAzOHO6Z2jo5Nt17k+INTkeLdb66GbzWeP3n549vLF5Y8uSq5mAIAxRhh3rtlqtbq+ur537/7BwcF8MVkuD5omEhFiUNWcs6p5vgS194O5YPxgBW3zqIsCL5fLb33rW7/4xS9+//vfN01z9+6dyWS2Xq8uLi5CCPPl8sOPPzpcHnCMqmUGCKgi08nJ6W7vInBk144LzDoqCNTEFQHKwE4kQxORxXxxdHR0fnF+ujz99FvfOr1zB7A0iozmyUpU/SfyS7dxgUOM0bemmREix0YUI1GM7XTS9JsbQohN5B1WsUtHPCPxR8NEBSTLebtd9/0250E016P/qn2s7hMAitz3/vbGoilQcsQ33kLN3WkMtfdsdImZfJ6KM3fKi1r174hEqEbMrMI2wl6qpqX5crNZq5phaGcHkFPuurTdtm1rQ8p9lxkTIhsSACMKkRF4rzsbRheuE5EhDamXTad9Z6nXrFlNSt2aoM6yGNcFTALzweKwiZNtt3n9rkssgzvPMNrBV9bY/awCItHy4Ci002wgUhxwPVDmz94/AZsfBFKTPuff/fY3n332hzQMkbydE1XUM5NKXisWPKU0mexEy9/waTy19Qbob9if9QFZfWqusVgq/W9O8W8vz2uu2l0TSs6acxqSFU1cKxnjLqLbpbpu/wgZyx/y1BgA15vt0ydf6svzIOJMqIIHe9BR35mIiTJWZoK/slmZzRoU1RXiTMcgxT+2mnZd5wp6tZjiRdrif6H0396+bUTCESB5dc6gvzlapTTX+33jwQqm6NPNdiX5sq/L6tjYbYy8I5sA5JyHYaA4DFLY8wSgBhmRidWsDDxSRRRR8xEzZOMj9vxrZypM7eb6uu86ERlZAKqSc+r7LuXkBSREnkwnbTuBGhu+uicUAFEJCUxBsxmXtmo0VCgAA04Vj6g5oNgLtooDQ3FiFadyQr9r5DKzU/9LQ4kL6pqYmaJ1mjoNg7FRFEJCaA0PwmSbM0LGUmj5o9HprYeDZvbkyy8X8+Wjt96Zz5YZLHfbnHNWncxnbj90bCPfgc2mZsAETN3QD5KNEENgxFilfZq2nU6n8/lyNp1NJhNAnM/mkyYcHx8/e/7UN4xPVgdzgE7NxipOWfCu78TMckbEEErg6q7F0+fcDxfnF3fv3JvP54v5dDKZhMBmJmIi2XJuh9j3TdtGREMqNcYCfTOYxZyTqqlo3/U84zt37n7/e+Hnv/jFs2fPFouFjz+6ub7pui6l5PH/er2eTCYcGRBdkQnAZ50DE3EIaOACCuoambV93swIzNUo3WghkebMzN/57rc//e6nzWQyXy5DExV3KUvlIlk5XQVm0xqi1qNXzyQSxRAILFuZcJiGgWNsYqNIqrJerdbXl6DQUAONs4TMkJCImQ8ODiaTWTOZhhhDCERoqptVf35+vl5dp2E7DL3k7ImDvTk/++M78E3b8VZAfssH375BKyBR6QhkVRohgf11oNpHDqYixaOogQ2D+aARM+Iwny9l0lKaSEogiqakSuppP4KRKIgYZAUkNaNslo1yhqGXobdha+stbLequZM05OywvJmXamCHqwMExDa2i/lyMV9sb85f92WvPEq4jW3s/Xv0JkQxhuk0qUESnwDthTYn4+zDu1LTTSIUSZeXl8MwEDERsPcF+Dwv55EQibjX1L7v3SipqpOxGP4V14g4lv/z82Gj93UEx+22n4hvuMZcc0RZiqMF52GlnHPWgpHsfmnP8VaIp6xbvchTVvLRVy/Pzr7++usjVZLkTbUVzUZAVPBiPhkwgGtM8PjAzIwMWgXxMWAASSFXgH28DTDLpV1qB0MWFFtKWfKVDWAjwLQHrRu+2pGEVtjc7tHfGNCUNiRC0lqVxN0iGdR6IVJhy/ivORjgzVhYzqkROkbv+I7P8i5956hm6kGcukgFIkZiAlLvjSMysNXqGh0EdHkMMxVJ/ZCHAdQ0CyCEGL3GXMZwU+G6KVgScadP9bGqFqAR0QgoICmYmJHZMcS7Yba0MECcK69BylgNKo3lWYYSMqMRlVjYJ3EwKeD4IM0ANmloiWfMjDgzawQOOMBk3g70lWxvUITARyXdtoI4Eg0rRQ4BGAnOry5//utf9iJvP3wHkVKf1usNxfho8T5Fym5QsFScDApnUA0ODw8fPHhwc3OT1e6e3vnoo48mk4kjwCEEZmYOzAEdiwZqQmyYHj9+7/e//4NIT0SG3kPoR9GKGoypK/5cX1+fnZ25rfHuEW92J1TyPgkzUHn57Pnm4duL2UJ0UNUhmeRsAE1smnbCCGnoVAYAzTlToCY2bdNOJhMO6GOfQMAAUpKuG6ZTPjw8/vijjz//4vPnz58/evTIBflUZbtdi6Ztt3n+/FnTttPZvJ1MPegppHU/U5TADEvcCiP3etTb9yVykqoaZFNBe/v990u/L6LX0NCQofZgACABVqY/ALg+8QjMAAAZj28XY0Qik5wl55yJZMi55cYAhj6DZElCSggBsITAyGFxsDw4OJjNFi7fT8QhMKBdX1/d3FyLCjHH2OQ8AAyggLAr4lvN8F+tYO6ZkuJNizRFsW1Q72/fWu3zrbzTYfwulm3spagiyVOzdkSv9xaUgciZ0L7DoJi6lPpgRmjd5ub87CkhHLdTUmODoEgCkAF82hmSGqgCiRG7jI5Cys12SN1mGPq8WfWbteZESRJlKeU3J4yPOb4BIDIjEwDEJsQYERhMAKl2XVaNMLcmtrt3T8XqXfsX2QmVpfXQYBgSBHONBKi22krT2pjEsRFrCU3g3cePf/zj+c0wcDAzlgySdZw1grVZ1ABSHq5v0pB6VS190TuPUC1JKSu4PcYx493tBAMwUDEiMDNRL6ySs+jRgIp2pr/1vunaf+671M0pRyI69MN2s91utxw9o9014IzvvvdJ9zan9ydijBxzypcvX15fXywnLVoCzWYCiBQ4NDH3Q5EMIiRgViYFz5IJiQCIMAASBAQjywlklIuuywLoiFAWFCMF1DJQHGmnKu1AMFnJeQrQAQA+UBChTl4vnrA2Fu2Wyurfu2yzJqBhPHH78fEYoYxnDHfhAZSdU+q+vpsAAdWUFGwnE1N+y6wUqE2Lt/Yd6TEOApgrGAcSSUSoIGbFW0vOaRjS4GKFmUOIMTZN48NBvQ3fANQs5bxar4Zh2LtH0IK0UmvEouAEAYQW6GEzP8WmUb1GCuAoWVUR8eVylRkgQiOiEDglHeFZxFIyRAIw3OZM2E0aisBzIzabQGRuDmMAsy9MVpjdVo/ncW+FbkWJiAwIArbuty+vLpBDjA0ZbLfb2M6RAANDLg0IiJWYVy3gbD5//PgxIsUYT05OjpcHbjBKXcMUkAzYrTsiA4QQ4/vvffjrX//6q6+fEKiRMrvabT1XHqURiebNOq/XG4dEwMBMVE3UcnapzsDAArzdbrfb7cHRgVEAYkQITUOIs9k0hghgaeg7TV3XbdYbYj45ORmavu/7xWLhilEA4oWvvu8BYDqdnpycEtPXX3/17Nmzu3fvIqJ3GTHzYrGIMaxXq8urVdO00+msbWOIBFAW3G3PqGE0mk4/PM7nb5omciQKhpBEBsmT2cwIiiIuIhKajbKtUNeViEN1TcXV1KeLVXoezezLL7/sui6JAnVEASmyaGwapChi22039KkQNdQZCRYQA4cmNjmLWUb/DMpqevnyfL1eG2iM0Qi6Dsco3g0L7I68xxm3sDKr7Jvbu3H/H29IYf1/eayj79VHsWDmO37W7rfK8iEiATJbYHEXrNV6m6qmNACAGcHNFSGFdjaZzofptCNCg0CROUpWzQogSgJMqhLUqFfKQn3CPtuQpBPNBWBrgJmw4M/jx6lXIA6BmWkynUxnUyxiF3sLUtevHtW6ber6jBEc+dgcb3RiCk1UExMrU/kQwYBKxR215CmornYIiATMeP/+vQ/e//DnP/9pIDGkMf1V1RCCJ6b1ORSIcXwQ+09uz2SDt1aUuc57jwzGb6oVth6aAQSfa+Ed2/RGHtbeMlaPpLXw4cFNznlIqeu6KU9uNxLv9sYYy912yQiAgTkQd8P2+upq6DeZvOfMEEFBiZkCQ3LX4R7cYtOoCZVPj2RIwAE5MiKQCqxy8lZ0hNrB5ulL9WyOkRYyMJi6lhx6gclcFFsQ1IBqRagouzh8ZyPpodydW+gdAc+juFraczSkDmOw186e3dpzUCwuWmXYqqpkKXIcqqpFTMqKCTETURFQLTGDeZWaKxd777wDIGLkQAAmWvu6fMKz95T5LDZgZu+JfPWEm6WULi4u+r43Vd9sbilZ4IT5PrYI2vVdHvKibe/Ml/ea+RSMENlPwGhWPVx0jraBK4kToaMFZag4lmdWghs0VehEtioJyQhZXAscY4inML0eetHcvVos+NPXMAzXN6vFYtk2jQASmBqwIQJhUYEbgY/iSNp2ogrvvPu+e4dBLPW9FQjEOyYjM6AiE3gbrgEfn5w8ePDgxfNnphmcqmK2X6Qv5lW067uUhh066S5HMecBkYw0NLOmie2kRaL5YjGdt00TY4xFdQvRzHL2yKrbbLar1QoRl4uDwPHl2fl6tbl3754XlaXOThiGwd3t8fExgD179uz8/Pzo4BAR+74PIZycHC/mi2HIm3XfD4OK9L2KEiKWR+a3X1G1vTNfTGpKKefcxpY5Uggpp5QyOzOgDIxzA7Hj+jL6RiFE8pEpzMTh9mOW8voi4sJAzCHGyBzatp00bQwBKbSxWWUZhsFqxVdy9th1u92EEERN1RCBGAOHnNL19XVKg7n8Sh5SGor4kZ/7wv7zaGAMpG650l3Zby8CtIqEjU9+72Z2pnPMgMef9PPja4XjchXeDALWeQY+tkiLRpjoXm1MTXImREmwXfH1+dnhwXGazQcf5spT4AmSAQoGsJw1GROCACqglmEbJk6H8XXCKZMzm6HYxFvuiomJKQSezWaHhwdIJNlJpX/6qv4DxpwYK7RNRNPZxJMPl0sYF6TmsD7dBglQAf15mWKI4aOPPv7qyy9XN5cKATSpOu1gd5Ef2YrA7XnlP3GZ489/nKkFNq5Vzb/+6M8XUz1ms2Vt1NS1dybTBva6yq3+KVo/Xkcbo5ziTUuz5+XlxeXVhanP+NiDvN1F1iwREDFQjAEQyMCQQIrnMjOyUheRlGBv3cYLsXBAb+/0As47jEzjBy/irv7d8gKlLOVPttC8K/TsQPDe6+69A9g4Dem2Xvtubf0GaxK8xz3xecNmkkVUVBXUaf1WoRKTLLrjSQJ4yotGVORGmKvuG0Dk6A5YTKyWyt0BO9XZ3V6McZ968MqnrcpnO4QeABhgTuG0mbRI3Ey5T1PmJTaNIhBk0+KWyrO5tVTj+wIQEQMAItc2DLMiwuBvY4qQTBKFTGgKBAiAEekwtocpbqXv6RtPdj3Mr0mF1tONRIAoouv1ZslcHsdYDtk7VsyxaSBlKHtVs+OuUgTw0O8qcIlc1TDltLq+vLq6Mh/ShYgIaioyKnIAEYJZSun66kpy6XSiAnOB02cDc9u0bz146+69h5PZYjFfLBbLZsJajSMiArGzB1JKfb/dbDbb7RYAbm5uTk5OROTly5eqev/+/dg0gAJFKt1SSojoahgA8PLsZdd1iOC/nl1CGamdRCLMOQkoYmEhjKAF74gRFeCpYJGnGjlJ27QNTPq+VxHYrW2Fcvcm9KE7YDBAc3oRIGi6LXcjCAAhBN/GIUbiwMxcVLhZUgJUzVlzJgBkBuKs5BqLTQyoOnRb5AAARBw4mOp6vUqutyuDSracch68UFDtvH/IW+lr9ZJvSH/djO5/179emaev2SzaebXdYSuLVZd35/kNkThEM1ETRMd0QLOaZaNdRuIS8z5IbHV9cXN1fnh0HIkaCpSNM9CgRMJKZFmyIBgbiIGYsiTTUnT0hhcOITIrvZr6jx6LibxiSwSHJycY+BWxonGrwN4hLSyhkrrtus6wTATQJobFYu5dYZU25Za0BO1YHKF5NgUAJqCipnTnzulHH3/0y1/8rNtu2cTUkSoAgMKMrH5x1PWsy/6mywso4zM1hj1e9y2XU6+xsVNHLeF/7bW3C1RFpYLnnkCWFxsh3IoC4+7PHqgwdN3XX3+1Wa+bEjm5Rn35hDJO+DADxBBYmqhglqVgAFZKn+AScAoohvzqLY97uAZPJRaogLFVAdE3rKz/jVhC8r0EfvcD+Noa13NXHkG49Tmg1o3VQG9Ps3slQMBisHZjHwp9aazqKBO5XBE4Ii7GBjDkoHCwXFLk6XRqNado2zawS9WDZ8pm5pbaOdEAEEJo2yaECpu/viTm6UiZnVACbpffRFhQPArTqYmoBOdMkgmVmNwhCBjLZgij8R75up5BmRFRoUWMa+JQZlZL5KRHCOCi7LjE5iRMrvL2+nXaN5Tbd4v2ytf9HsepLO6EXr582Uzawo/wkYOF/e/lAODALQVxBRTTPPRg2USwWkY0YxMGODk6bOLk5uJidbP6p3/6h88++1xFmR2sRCIyJdvr3zez3Pdpu160UY2ZwdUoAYEZQ4ghtE2cGkA/9EZhtVp12z4285xlGBIixBgTwNAPnm6KpJRS13U554uLi3fefvvOnTsi+eLigogevPXAO1vGLDalBKBN2xwfH/uLAFhKaTqdur8kIqLAgYmL/j5Weke1WVLjrep+bn9XsyIQUtis12bmxauCNtVN5o+d0OcWl4zGBZ/VdFTOKw/XGBGdwZdzbgIjRy8W5pS2m/VGV+JkEh0CI8VIYIRZZJjNZovFwt0+VdVuU91sNkPfg6kjhTlnLRDR/5C5fP302H7MjTi6nxGHxNFzjzsW9zDEeuOEhaSGRTjVjDCACiCpkolgQMAJoCKCKgBIxa5VNIMwEadhe372fDJfTKaz5fIoBAJUZ5iRKWRDMVVNIiYqKmKDmjlvNVJUNCAQQrltKrwpeTxifviYwnK5hAJ14B9fSXw1tKnGupry6WQym04JDQkYcYdqY+2YRwhK4NKxI6kcTE0mbfvtb3+7aZrf/e6352fPRcXbbUQkJUlZSVTZf9h32pu86O3neisKu41bvPbDZSwE4p9YhFfXZD918fZRyVo1CXbpXXE+uz9agVuHyaHy569vVufnFyrK1TtCBXBLSF2FgZ3olLN0JiTmrVvVkhdAiFxe+vWP7fFUATAqULHrm1LziX5/ZCVs57dHsBiLpunrR7ImNPUVw62XedPCUsXfqkevsDvzbD6fzWbZVKSMeIwhzufz5cGSiU3VZa5FRFLSLDbk/mZlXTo5OZ7MZwcHB2ZqCDGEGOPeRywhW0opV81xV96YzedEbKPWZtn5xTs23p6vUiOq4lB7ld5ERYMoAzF5e0dBQgJQsDL3cgxEyjaq7P+Sr9eve9yplUEI3gUG0IFcohySNYGiaISMiBH5fjvvdZB+fYmyrftvfxO84SHBiCbWZ4MIgCkl9yLlJ2t7pQc/rrbkD7cKtZSIfYeYgIEqKqKKybBeXf/sn3/84x/9Y991bWi8ZEmAjATEZTRqnYy0urnK/fbuyRExxkhAJAau2mQGIpZFNl2P69WSuB/6fujmMEM1EadMh+zYbs7O+w/EbYjdZnP58vzZ068//vjjt+7fS3338uz5tG2OTk45BOCyEGaWs5j1iARmZ2dnbdsAQAih77s0UIzNdBqaplVVJEw5V+ZEzXdv93QWK1N3kqpkNVUZhn6z3fisJ8BbXNDdIak1GQcIoLoiuE13RPVqjKSUUk5N04YwCbHlEJhU8zZLTjlLVpMMLBANA4Q2htjyNPI0gpKJipmpbvuuW2/TMJhmEzFTjx5lNBE4fkYDxzxugVh/7PL1caC75nYeaYwSgCV9HP8ubrgmhnVJ/Tx6DawcK1MDYgNl90siAQFxQoQi6iGtVcUDMFQ1ySlLf31zdXlzPT3chINljKBBM2YSBQE0QBXNKiKkubdBwDQ0ClA0zqyWzvZPF+7u1z+ydzpMJlNGkld1efFNf+C2Mb+V5xCG5eJgPluEGBD29BZ3H8B3nS83egXPTAG88A9HR0ff//73Hzy4/4ff/e7zz/9wcX4mQydZUzI/uOKE8+KvAJEBecwsoWTce7nYzk/b3h8vGSDedpE15No9U9spj75W3X81AjAwNRAzlJxycp5UdbAGalbpAwqwYxJ4PEHmwg2Yc3r58myzWYtmYEQgwCJ4D4amKNmrF+yMvNznTT9sTZvAbeBCnUM0Qghkok4THf3YvkBJYaeGQFmqbMSoWgZFqXrseNoPxX3SETt7CPd9EQACvDFvLltnPCYhhOB5P1QPi7cuIgpEbJK01BzBhYdjOzm+c3pwfJJyEhEVIKIYwuHB4eHRIXPQLDkP3dD12y4hCiQTy8SJstuq8Wl7AFIfLQKgKgzD0Pd9Tsk35cHBwePHjzebbS8CRGpYJgDUYgIhL+bLi/hSktWRzQqmYtipDGoZLauoO+AS3hGiReaAQGo+q6KMq3BTJHXq5F7aVI2RjQhn2V2EGwXVPCOZIjMAm7EJok0CvX1wRDeGq+scIPt8XQB3mQCwl/7uH5tSJxj/EOLyYEkAJuJoRTnhRccDi7PULDn7mB4VdRpIEUEnRB+eZNh1m77vfvHzH/3kxz/s1tcECGgYCqvCNTLQCyTg3p22mw0iLpYHTcMh8HYYtn2SGsRmyYiWzZJpmMQwCRhITUBV1ZhBJGfNvp4+YCAsl/nwcLlcXl1dPfnii+Pjo4cPHz58+PDXv/71s2fPsujJ6WmMUVH9BkChHwYfYfDs2bOmibPZtIg/ACKCqg1JEMF8qCfonv/Y/V2ajswQxHY2UdWKLI/mJJoRSFWtDIAHIBpDVyMyP3l1M5mpGu1r9aHrMWYxM5+0eHR0HEIkDqaaNRsYBJ0fx2ZCMUIzbbBtmrYZreWwGtJaUi+pTyml1dOboe9AS/+F+ZS78uZeatUCiloRlBk/yW0LUI3IiEXvdCvF5wY602pPCt/MQESdlTE2xjAzETUhsIuZII7hrxW9ZguAVdLPwT1vqiAiIKKcxfU0FbNqLjYd0RDnB8v7jx4e3b8rDdOs5XnLsUEgy6ZJIEvIBqoo7upRLAv0zgR37ospVjnl8XwVNJQQncACaoFQFRG4cl/8RxEAiXgX79Y/Ix6IRdbYLRp6Nr1YHEymixgnaKI1LHEWGJUPQ14XIUYIDvYKgIhmMUXEeRPny/cfPnr40bNPvvj882dfffn8+deb9TrlXDQpVXKW0rfBgSiYg2B064Pt+wsAKJJTVmdRenhiUkua6MISNaOrbTR7dIFqAGtSYLdCWzPD4nFFNafUeyhcQUXwON1HViGREiGSqpB38hSmlWzX2+fPvuq7mwDCRggKaj4byWF8ETUp6YXrXwWHW3lH11CwDCbBNmSbPmUQxSo0CoAIakamIYTpdNrEiL33O4Calrc0QOIQGuLgn8JJZ0U3aAeiIyKWSoPVkkx18WYw6rWW6ohJIeO/LkX5r7hQVQEphDKOJUtmZqpZtQOLrstRBhjUIlw985pypjRkzcRsYMyMVZBy/JnigHP27pfT09P5fL7ZbAOHor5OCMyqQhWlbZomhqiSSw8lIAApQgc2ILoKrWPJwKSGGjxwIUNUVcVb88NHPud+0IcFFnb9Oy1rWrBlBHOdyJQ5GpJCbZJSaxFPZ4uLrrvSAQAy7tRwXoeD9sIf9M50MEDAGJuTk2P0geZYJpciugMmQmKMaMQU2hizas4ZoJBeCkimvgNFs16db548efLb3/xSczdtCMynbAkY7X+MEAIHIkA0RTBX4g0xeH1cFAw4xIYYiaWdzo6OTg6Pju/cvbs8OIxNYyKA6s8re6ymBght0x4cHBwulzHwdru9uLg4Pz/77LPPFovF4eHh/fv3v/7q6+fPXxjA6emp+0s3PDknb//tuu7s7MWnn35ycHBwdHDgg8pFIYuKquXdE4S9yfBQq1wV0hgfbtmAqhojDcPQ9x0AJS1oLHg0bLtKAYEh+Wbwx++50a3dgog5ZRMYhj51fRMDhwiAAgYGIcZ7d+8+eHRyfHe2OG2bBTfzeTNp/QSnIa+eX24v9Pp88+LZ2bOvnrlYMUdGZFAVyyBqKg5hV9gNCq2l1JB2swXGCPebrooQuObwDqrdOwul3D6K0HkslYIzy8o1HklXfvZRMkakCqpmJWMXqlbLrVMGcHVrT5UGkUHl8M7J3UdvxeVBuziIs3nkGMwoKW4GSVlytqyoSmYQFLxyiYV7pgZrSYaAPgWwnjWXo/U8V4AMRYmAiEtXO5bCHnlnBOKOool1dWumU30zQhlgxxRms3kTIxhKBm9jc0wKwTn0OxlhRnTHjBQMvPl1t3+aNjx6+723Hr4zdN16dbVZr6+vr7vtdrvZbLtuu92u16vNdoUmZtlyfzsXx1cetdWEzt4UmVktyPpVlfK8xvrNO+b1LQQG7tpMRVwhlT0XAIfg3ZsSiGSHjJRY0YL7NLOc5Pr66uryQvMQQF1mk6ymIcVVwBhLeTZGgIGZED0QUTRUHUyu0rYD2+TtxnJnoIVd5e21JSrykfMcmHjMA4s2MqKrfBAUAzGi01BPhJmNwx7KEtQIdL+Ku/vmWAeGN84DfgVkgBowjjjn+Ca3aiUICK5AlrddZwXjdf5NvtWcgCimWTLVkJmZHa0aOQUp5b4fchYzI8KR/DxfLMTAw24klH4Y+qDgc1Tj8fHJi2fPttstesaC/rBMADKiMEmwjlHZFFGQk+GgsgJdk6QsAlBnbNayxC4geHVNdj6SWbWEuQYooF1OCUWAMiJ7IgaIYBPkg8ksbmWAPQqdo8g7SGQHPvj/EhKC678gM8cYYYT+iMH7i6CU25lDzgJITdOwKjNrjKY5J1FVFQHLAdG2/fXl5Vdff/3s668ag/tHxy4mnJMkMVUzKuiHX0wMlfjnX+2GRAjT+eLwZHZ0crJYLpgopaFpprP5sp3MOARiRqSxoDL6P89K27aZTCZEmHNGxIODg6aN6/Xq/Px8Op0+ePAgpfT0+fPzc2qauFweuL/MKYnm65srNxCXl1er1arrur6d9NYDACArmH/3lfhv3/v6riOiV0k0oCmltm1FZBgGUcs+H6M+HNtzwIzgDrg8LHvVAQOAqKioZdhs1iLifslFdpj54GB5fHw0m7cUAIJCwARdTj0imFrf9UPOlSprfeo4Es/a+XQ6m836vr+4OCdG1dT3eY9gsPO+WsPifXjxj1x75lirGmZ1Wj46sNAsyMwKDcJbAySlPHhCHJg5hCY2njv6ZYCAxISqKkpqYoZIwuDesVRVHMFUVVETyX3O19v1MeT5Yt4s5mE6CyGwQciKFMgArWfSqIaaFbMiCoAoqKmYKlh26bJCxy6JiRXwwwxIUA0QGBbzg9lsvr46v70USMijFAMCEVJVsqe62v5v9j9E3DStp0Qub2xWhKDUFMwHehe4WM1o3IEIPvC7WgMENCCNsZnPFqenpwDm7KOcc9d1OeUhD6vN9dX5y5//848uXnxNsON5FoXlV0u5O9nnEhTswUJYPsSeVrzq7ea1f91VXlNyziIZd4IeBkUAnEzBSFVVUTWLoZXuF7WUhouXL/tuC6CuBlCxHPd+CJ7pmqER+ggKNVHJZiGwEiqWCrmAXXebjeTOG3LExGCPNF5BaTM1a5iJqGi9gBbk0PyA1+1TH/ltz2pmamPvQMmJSMcwYf/ao2GbU4VMq0R0+eL+E7MRq3WfsP+tGlKhaBUAAp/Oks0MtCAY9QSW3wHvsS3kenRxiP0X9e2VUh5lNJqmmU6nIYTlYumt9GaAgZr5AhEm0/nB4YGZzWazL7/4/OzsuSOoiKDgAjEogD3RRZCMqc8poWXFIclW81nefGVdVyq7pSts1G/D11oXRmPtLspUPb4fbzObjCmJd3B7eDQhPmxns9x3MuSdD75lrxGLQtPrl1mZoQsGzOx0WSR3JBy4QaTtdrvebJrYYODYNBEAzFK3HTbd+flF168JldS6m4vNzc1mtW4QZstFJGDEwKQK2z6tu3S92WyHHs2QgL0oQiQG/ZCyyMHh4cHh8Z07p8uDQ55MZ/OZU7e8zkcYDUh9Ul8B8WCM/LBSeCbTaQhhu+36bjsMwzAMOQ/90N/c3KjqgwcPTk9Pu6Hfdt3ZyxeAcHh4UEb/5Hxzc+MvoiqXl5ez2QxUY4iIhMhQxgyXeG4k0t/auGY1S9ul+8wGgK6S4fvQyYhAZLZ/AGtC6UfcBKFIyGLJAMp3VSVJ1qyatOu2qgoGKWURMbDAQURubm4EN+0KwjljtLhoqMoVp2HoLvu0km6dN+suEB0fHUKSGOKkbVwMLaVeJPkMH9tvJ/wfvHBvY+8v1Lhc4xHwqSeIZWG9fpnFXByGiQJzjNHUQojukl0G3F2RD1cVJXfnBIqoZFp1fkgxZc2mQMwhRgUbVBLqhIkCYwwcOCiG0ECM0HYw5Ciat2vLQxZJTmhXUQTx+IOowADj1AErkwnUTKUwNyeTtmnKfEwOzIHRO/U4jDyzUYLDFKtsnrm6FxZiNIUQmxjLapVftHKoEYp2E2DBQcss83E2wwhcVsiaAACyjI+IiTHyhJo5IpnJHUjbuw+efPbFxYtn++WP28/2T1yj+fKf1dr+92ra8fpL7wlQ7/sV3y1+WkOcqCG5WA2WPjXEXYJeQsRScrOu684vXqbcA4j54AXQoilsaj56EpGYnZvi0bGaDcOgGpsm1tmIoCJgkCULgIChGdou2hg/tmT/pGNDFxbb5RO6qUxsK5LvVp3YLWTAdpGN/2/J4nbRe30WVsItREQMYOjeHhxZrSrvJeEHcwkkZip4lO4K1CoVWB4BZNdMUkXErOIodE459wlUhs0mdR2qXl5fwQq3mw0iOHK1554tpb7vtyKZCRGYGRaLxWKxmM8XCDRCIhTYg8oQGsdkXLHSgcoiJGQgYgD5K7tatT3klHKfVTpJkhQBFLFn6xjGXmAiUo+IUJGQ7Jbiz97OA6qT1WwvjTKAbNabDmwNogqAAQNGBTRYcnPSzIZOxZIgACKPiqZUQc69N9l/XzcfVabfu83RzNzAHSwXbTt98uRL/1HXhQZEUD2/vvnRP/7Ts+dPcx5ChDaGSQBWYcBJG2PDLXHkEGMwg1nWpWC4urxaXWcRDiHGQE1D3CA10+ns4PDg/r37R4dHh4dLQxxUDShnYyYAyEmQCEu3HCKaqjEV7+RhmU/aYw6z+ZznM8mLru+Hvl+v13J9eX1z/bvf/e7y8vLuvXuz+QIQN5vNxcU5M4UQzBQR+m6bRRAspbTdbPttl+YpuBJCSUnB6gwZqTw+eK2g8IopGYkIOWdHbgAIKYCTKEtW4g5bAYD83zWv3wuLi1BlSrkf+jQMOcl2s+mGISXJYiJiACy5G4aXL88xKAcNRBwZW8RQeEMI0KDLyzUB8XCxtJl1N2vJ2UBLG1IasgyuaLHrvoV94Hn/uvXFW77WbUM1xKMl8ZsbAUm/iMibEXzFPF+tBsjULKXkIsTM7Kk5M/t0YHOGDDMhOvvKsQrEAMCGgzGTJMvazKaLw4N2NqUmQgixbWLbxhga5qDQYGAKiizbwYZBJGSFnEUEVU2gtK0olmS33OTeNsSabFJh2nj3BIeAHNzokg8GH4mo9X7VrLTSjEARImFl4XsHDjUBEclAPdsk9NywhFeVRVrRM/dI9YlUefEyQgBGu01q6MASFCCTQ5iE2Ly+pUeX8KbLYJSnHpPmUSdE1cwIeQzovvl13vimAEWZW3NSm9bou0hsEjMTlw7sQFzZA4BoYLJer1Y3KxMxFbVsFvZeXCvOhOhVd1edqAVmETFwlYiRhVd/3NBwbM/bXSKScsqSY2FNIQIZuHbp7kS/8U7rCuO+nAiUUlBN4aysdX31W8hxqC/1BuntXURTX6ssriuOqa7WK4whiyTJklUNmF1CnACg67qCQaSkWQIgiUQ1BuxT36fh8vLSkUBPHMuLq2geTBKDEBcAjZk3m80wDD5TeBj6ruu6oU95kJxMQQvPFrabFVh2qMdlKzRrr7LJw7PttZmYKpoK2khvQ2BEJmRAYGZEBjNDMxC3PUV1y2f1IoJSGcm7dybHNBkAVqa/7661nXO7bJlMMfhcToQFwqNm2qW+T8NAkBDQgCufrD4C81YsJ3uX518DVMlCiKLKCGDWbbunT5+a2d2794+PTtD9rmOwiEyUhuHLJ1999dVXXb8BtCwmCanlQIjAwsgWEpIQitd5kTHSAg6b+YyYYjtt2gnHhkIMoZnOZpPpdD5fItFq8F5jZF8sUSRUdXPmaDWokt1C6tBMJYsZpJRi07ShMdC5mWecOachDdvNZttthySTabtYLIahv7w8J8Kjo0NmDoSShm67RVNJqR+GbdelnCetv2cZYuXIs0tbhBAQcfTEWMPEEML+rkdElzf1DNghBzcM5RzfRkEqZuQ5zu77WjjL2vd93/V913V9n1LOycmbmlVNbRgGcSYlKAVgJgrALSKbmqhqAGoCMxFTbDhOJrOGI0yazTqLapIkllPuRbOBjnTnarphpHruG+I9q7F3I3uXuoEnwz0X7izl8df9ZzzyQ0TvHaolhuIssmYSdvAHFZlRS0Wt6juWCjGZUxOIQkAwy55MsDaT6WyxCG0bmxhibGLThBA5NEhsxqCBGGOT1XrNEALFFlxDUJOzPQTcuxfj504WBCq7EUrQ7YK5WWDU3EUmCkSBis4GQiW6i0jKg6EgKnrZl0bwy5FbADP0EbQl/UHniexnmdWRUEnI6ZVMycNy8xDfY2qvAhaSesFkGBEohNF+7j9ofVWy21e2ZLl+T3sakaVIbArmfLRdrPnqi/hHBJ+HXXIQGw3V/k+m1KMLnhAEDkhQfG4IHDjESOgVWHSnDAhdt+36TnwOSbGqBjVlVLAsWU0DBwxcXauCKVqNVnZhDAcKTIEQDHFQE/RH7wEmGVEqSbZ5nOReHcENvhT0G0vHocusYwlddqieB+d+shz8Hh/myNXycK2M4gXztqhXa8D7z+/21/dTMUBEFd1sNsbsbdEqLvuIjpIAQN/1krNHFaZqamzKe83QDk/BnoMHb+DWbCbkosKKZnZ1dfWLX/xcVcFQBBysYuLQEDOGJkSOng4O/UZVwBfJPzebmCYR1HGHIAAa1ZAViTC4ktHItvB1BO8iB3BZ4TCiJ1hTDSsrth+sCMDW5CJ1izhZBDYrE4YZYUJ0x9pLbm6GjailOg6zfirf1uSwPUAZWDQ+Zqesi0oWTb0p2ND1iOhCjMPQMxOHIKJ9GsCDG7XT09O3Hj58/vxpSh2RBUIzVoAEZMpoAUPLbYttE2MMHHOW1fAiKSwPlpPJvJ1MmUNo23Y6i00DhMnUxCCbawRQFjduTAEZDUd9JVOALMKITH6OUESHIZvZzWqds7SNUxUAEZu2nc1nZXepiohoBrDj48Pz8/OrqyszPT4+QgQD3XYbb13zgkW/7aZNyw2Pj3h8EX8opYScy2wqVc05h1AIrvsPUas2feHgEAGiwvhjxSq9kmGOeUT55CJ936ch5ZRzlpxES8hHY12GKBCYmohIpNjOWmBTSwBGbEzKgJJTn7cIfQwRALExBCSilNKQUs7JimDq3id51d3eOt2lJPym3Gi016OTGIGy0RLW39JR7bZsVMYxNbQanKoJCiKgoCTE4IwjYiIUQ2ICJTUDNSYjJMGySwCwQW1C0zRtaCKHiIBuCzArgVFSELWkqBYoQIyk0wykmDgL9J3l3un3YKa1Om6OVVXw0/tiyAgR3GYAADqpAtlH9BICc5HdAABEUnAaoRd2MRAHim6kDUBVU05iGmMwIqvTx3flvf0tAztHC1C7gepnrctLFZMGhCopanXzISI7gSi87mvhTdcIBSmoB8Ruv3d7ZsfFGeFl23/ir7/krc/tMag3kYgMqScE162tAEPDTQgxcAyFMUAMQDGGGMKQ83q96vteVah496INAUjmatta9DdGEBg9BkZj74kzMDRXMBrTa6e9Yxmz6rkpevtTaca+dUdaQYrds4CKUFawvFz+FgUg23vI5bxYDW3AxqUaH+CfZEHX47R7uqOnUckyUjNGxNsLYaaAZdplAamJjRRIMCD5kEhP8LHgHrB7Wdl7WbCmaQ4OFk3TIAEiI/AY3JuJh95ZkiZV1ZyLGBZWOjwgIBNRCYFHrRPacYyImOvwX/QgxQBu72f/bIXVOeIKr4rpAozRxUblKg/HPDEi74VBgKAwo3gcpy/W11vLxEWh05BqfwLu7XvwzubR+06n08Ch10HMwCzEMD+eFXFsYhcgiSEYZFY2AGZumvDWWw8ODpbX11dD2ppldLk+KDyFwIGYkAMGChwcMzw8uZMkN5M2hJY5AIACKTDWappVpXLgIDVIBSvasSXL9HDER2qKlXZMb8w329ysNjfr+aQI8JopIOWUDdRJziLCgWIMrkdxdnZ2eXkpIm1smqZ10W9E0JTSthv6XnNWjoDK4+6+HRg5cDqiqTln1TgSheriAzp1oKR3DEyGZUgDAlQzRKOmYDlpBj4IRLL3vQ/dthuGvuDEOWXxjV342D5tDREDh8lkslwuF4cLDJgtA1loGExA8np1dbO6ZOQmNBwo5URaKpEhxpFe9vqhLXfyr7jMHFcfc2WrHh0BIASezxcxRhsH56kQYdd1z58/99Hd/sOIZVDS+Hl8aKmqlhHoBhwCqCFGQrZRSkp8JhABkZVSNgEoG0kSywbZIEnuBuMGI6ARiWFSEmMxsv9fZVe6G8lxpOPIrOpukhrOwZFHWiwMY9fAYt//CQw/hLUajNfyDFaHRXLYXVdmRvhHRGZVk5SlLQhCT7OqqyqPOL6I+AJCiB3GmaLyTFKACRMuy6Q5a62G3+zPc0WnBt1hSmnJCVoClJ1MVfsiAaCoLstsHY48EIZIxOYkgtorg4jkUs5KCx0X2BzV4MYauVjpH7azg56Zgqv+06aS7SgiSCxeq4jPSqX15Te2pm7VSONBK0Wh1DHYthY4Nzn/9aGqqqXktMy5pEN/gagxBg4xhkghxshkCpiYiIk4xp4pzItxrM5aktM10AYiR8+U1modboazfd6owO2r+wUNIVrpmUE302M6UivY8PTNgASEPcXE25kjk2Yb4OcG4/H3WEdIf0UBr4528/zWlhC6sft9jaxKDJGJCamWSgmBAggrBuKioEmKlHouNVMjl2LtkasVpqoyTdOyLI6oS9ONRgXgVpIJEqMmMKsJwJatxRYMdEb0NF5uT7qtmlgFcY1ktHVZ/2rATc1i9HHdAvjWTqfMqg+STpJeEzcQggVKxC+6/Yuw+7wcZwVWsMLYOlNt5BWRLg4XV1dXrcIyxrCkLAqx63b7w+FwsJwsVbX9ae9BFm0hDswhhNDF/W735s1rQIHaFAYRza8vJedSjB1eREVKSjn2e1XD6jx1AtV4N7VSjalJZKwkYVApB0w4oYhaq0GLIIISxcDUh4gK0zSWlD7f/Xyx73e7nQ80SJGSJQ/DcHt7+/DwMM1jSvNu179+/frm5ubdu3cmC7969/Wnj5+OxxMCSpFlmYfTaTpcEIUudA1ZwRrNyjmbu9ykj9Vx2fLebmMyFJ1wt9sxHxFJibQV9KwesJPCrj6fxRVzMQrccRzneZ7n2Tm/xJqIi2lGy8ayBUWIXdddX19/8fIaA4oKBuVIxJjG45JOOCAxFs0Pp8+SBFPFXVow8vmdiy0F9rEh+Yub/dmfwr7vLy8vTb+GwERYSrm/v2dmI3+1LWxkL9hq5dX3gqLnvhTJUBSYUZgDIXNNyzJuOxARSrNoQVHQDEXmYZpPwzKMkAoW0VI4doTEKMTMKoFAS3Hitn6XCSEnBVUCUSAVKNlNJQO2VlnYxDCACiKO45DTDI52GvJMSOy4hQtNzw9QrybdAGK+gbFI+cs331x+cf3H//pv4tgQZxviJ3Z9RW5NE1d3q52yus71dk+mzV1p/52NKQlPoY5zVdDU2VYHG/E+EQGoaCFAVamRDT33d588y9mGUlVIOaeUmDnGEGPg0DEzMgViYzdiDkyBKQbuCGle8jhOOScoCUACIGCRnLMT9JsIRmswyoGB7bFQBSpY38yMZkW4BdQklf9fgdTg66KGu/vFLFA7uLSuCJvD1DZW7/RZtYvqdFzmcGysYdVNdXZoM+d/lEY/jZWy1Oe3Odq+EgVQCYWMSq4+JokiAYL1ZvAguHHDFyQCMUJwEFUrqmky0TxXK/9t1MFMRqiYofLeOdEd+i1hk/ztYrw5JXWsyZJUPRXI04YrpoSmrVqzRa3atO6ER1tsCwZA9YMJNmU2ACCICfGh5J/LdB1iCHzhroKi6CHE6/3lT2UeRDyP59EqruJ+vz+8fn2DNWa53190cccxdH0fYkcWsQRLl7MRQUGhQB0FUK/jlLqJfbHZRvZrVYlFrTDRARlAQopmGEq7BC1vxhvwte2K1W5v44SbbDJEIEYG7gLHGELgWiuK4zjc393u9/319auu66CW+RLR5eVl3/fX19fjNNzf397e3n777fvvvvvu1atXNzdvuhD7vn/79u2Hv35ARCnWmziPw8AcQr/HSu1pCti8NLsrVK38aL7anjSfR1StsC1nU5nktAW++hERUcXaA9tQeL1dzqZ9x3E0O9Jpv1RNAUMdujoNEJC7rnvz5s2bt2+FtEgGUgzIDJ/vVL6XXFLOSbIs0wxJMaNFE3PJqkqE1u7k6VEn6Ld6Lc+eWUq6u7u9u7t1Ohd0LjYbc7uKnErd96FpX4MJgYzW1CWHiBgtWyDu+h0RV4EJAJpzEs2O5qLmnHOa0zTmcYZcfL8jAhMwY1EkASyUUUVQKQD0ni0HVrqSUadxVpUGI2KVxVUjAiGN4+njTz/89dtvp3kEeJRKjivtsxKweqZehT+fKGBVKT9+//3//OWbd1//+9X1S8sbQkcVjC6/PY7dwdzgM/Nn/UelZrRDpJzPku85dF3jq/MXPeBzwfjoe6gBFNFNMAJgUxkFTxTwr6yunPI8zzHG/X7nhFOWhRWDaVBGIgpMXQixFE1LWuYl50wqCFpUhLJKcY6Mektm7mLHIZyDBvUEm+XqJp+NVg2UAsAjuuX1R+zqOoj6nM2xWSWItWeDYYCARv5jWX4N4Nhc6mAnAUBgRsKoml0Dnr3DeqVNBjm6boVExZ3gTW2Ze6Ge9+AsnbadUJREVaRgKSCn4SRFYug8u8H4kkpZplmyJWJbZjFTIKsohXNB30bLC67U7iva8h7crFTXEh7Vw+bv2u+7f7kiPEiIomLtcq2cD9ELBKw6wLxwVVUHNAxfRTCWElBAEsBJ9S7NP3d9iL0q7DKwMCPsEF/uDpfT8ajjYhY3uMvgN6qveXFx8fXX/0ZkgId2cRf7XfLOylqKULOIbG6r+cxsoQ5Q1fIIk6n2qfVWVNcNbrYIIlCwdiuoSrWWFsntT7QwnrXOrEkmAMaYZeOMWElzLN1x18XDrgvurUs8BAJNaZnn+ad/3FLoXry4ZmZ1txliCH3sri4vFeHdu6/meTydhtPxYZrnu7vPOeVxHLJACB0SFilFcpG8pDnnxZ63zbLpCYNGWxOtpjKbnGrixht8E4UQd7v9OC4t61LX3hNVFMkZhGelw/M8m+pt9XiqYJ3jipFH2vir2o32u8Pvf/+H//jPP+72O7VGkUGBUTUNw603g5rncZiGhxMU4MLmAXusThGUPOscQH3XNNH5vHw8NytbsAvrLlbwHGHLf561llPDefsdXLnhbGlgczVbN0RzR9DZSqzyFZCw73umaPmLdekRjEcFrCajlGUpKec5ScooYqlHEkiQgyJF5aJUrBke5aIxTRQRGZWggLIuXdlRmXMpLRSxlW+M1MXww+f7P//5Tz/+3yctS2RvfLLieR4MtvdQa2CVRQi9SQKiUoPC1LuY3N/dnk7Hq+trkwsWZFPXwxsLoJGJN3n2SFyLttW23cTtLQiCCikwKFtGFzRJ4m98ruzPozMG5Vnxi+XrmDAgX/O2qnCD0Z4/oTv2q9dbvzaBJrnklFKM8XA4hBiIzRJnCkQhsoEM6CQkIvk0nMZ5sBYD5mJaPLCBiIbehRhDDABqOKhW2mXXi9Z0FQFUjUOP0CqejZzJ36CCyCAIpZb/IJhbrCSKlhXB5HnWNaMeAdA7utYC8Wq+o4Il5q1BIBMsPiyOfYGzkUGwAjYRAjyLQ2OtyqjNsNzUskGwH1DRmj5UvQf0oikgEbUchNK8Y7QcNi1Gvj/PMxGroqiL/5zzPE0lFxWp5pz/aP2BM/WLqx8s6glcvqqwGoUtsvJE+zo4t0J1PkZoRLKE3IpEdbtBANAaWFtFUHNmDJytTWIUIAGcSrpfpqt97Jh6QQQMiCJwoPii339e8sJghUjPQoVE1Pc9IgFiScWsQJeGoGiiis4uVM/yBVxxBahLZN0kquDxWdPmUPMHiSwNHFx5FwI1aNvWN6Ezm5vNQeaweJ4FAPnJ5IiFmkhW1Xmel2mMIVweDrHrDofD8Xg8DcPxNOz2h77vXXiIMhKTRyV2/f7icPnyWnLJOWULr/740w/v378fp7lkQZaUJqv/RHLjD2vFp9RjMwgOQdPKftrWdzOIseu6w+ECMVq8T4Ecg2l4n5Sl5FYLa8xcwzBM09QgHBG1iLMUl0SltMwEDiHEuL+4uASFT58+ERNHhh4paNY8Tce/f/fh4fiQc344Hm//cTscT6wcNdiS58AgpaX5ADRNplrjtc/6QfjkMKSK1vARbldO07tOb/IE99YNGNseo2pj74jiGxFdZCLbTmxsUyAipMQULEJkszaP43w8pXGexinnRaTYbisEmZBDEAUSIAVVDAqUSBZAIghBA2fIB0opUS5FzoPBtmMYadd1r169DAHneeq4omJAm2Ba8y8BEUMIIQRZEnqrDwUUVCt1R38Roi+/fHtzc9OFWFSlQGlcj1tvGUCfgBfSsisAnh3t7UFIDOJJtWDBIfMCZKMUt1NlKt83CFRdLarcRIfZVRtPbauz9TEgq08fr3qO7hykZX74fB9j7EoXohbRXCgUhiJEhEohFACOMSYp4zwtafEWUqoWFytFxKCjuiCttbxlnZtG9Los2jisZpuC/x3qxquxSLDcK/uvWIUMOFslqqAWFFFbwiiAgtZOTrSCOtbwRs93ja0Wr4S0pWQtRMQtCA9m2Lb7rVSUK+b6+HvTCfUcV2iGQjUveJ0YQlDVZVlOp1ObQDcCRYoLqaIrZdozbYLareFXkw5gRZ7hjHLZvqGVxui3Hxsn9dF9cQMW2TGD3Of5dTlcIQMRVltyR/yy3/+cx89wphseDbCCppzNZhMRDphFikpp3JfgHBBeGlL3PwC02qj1x5/oeDcytXZWQYByjqWDcHVrLc7e9rCNhC/uhqE1e93YtAC0lOF4vJ/GeZ605NcvX/YxEnPXdTF2WcTUVQ3cailZVCOReBWQuU5oiBUAiO4B9cOHD9M05VLQCUSdN6DkTY+2BqmdN0xt/py449/MzXUG7Ztd33tBSQ0DQ2Xk0FJI8jAMWiuOxnGcpmkcx6aobDrtRt7uMJea4bfWf3z8+PFvf/8bMe0udmEfIciS52F4GE+3pWQAIKKrq6sXV18EZSoMzuajyzQ+3N83Yf1onepZ5GldovDkQMSqC/HJ965Umpcva7ccePRhe7l/9s1XO666Ca/zOORlMYq3UirPJWouhm97VCTPS5qXNE/LOE3TtE+5cpxpARACUhTf2owCzBYimRQJiRGEJ13S1Ei7Hh+qMYQ3r9+8vfnyf9+/Nzkt8IstgXWVV+0Eq2xuql0BNIT4u9999eKLF4qMUoCxmP9lVIi1MKr5SNshN9t+84D66MP56VUrYRvzX5aE9Xf+3xLvXx5PxGAVEFX2lJxTSsMwDMNAISARMoYYMAQkyysIRNz3u2VOk7UBXf28VfmLSOVbQLFeh02l0br2zjzxc3+9Yg3YDHR9drhMh0upJK9uDKCJActrqj1a8DGe7yZKDWhq64jUPHDYPNM/AYc77DZaFYL7AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=640x360 at 0x7F9174732978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "d = display.display(frames_tracked[0], display_id=True)\n",
+    "i = 1\n",
+    "try:\n",
+    "    while True:\n",
+    "        d.update(frames_tracked[i % len(frames_tracked)])\n",
+    "        i += 1\n",
+    "except KeyboardInterrupt:\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Save tracked video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim = frames_tracked[0].size\n",
+    "fourcc = cv2.VideoWriter_fourcc(*'FMP4')    \n",
+    "video_tracked = cv2.VideoWriter('video_tracked.mp4', fourcc, 25.0, dim)\n",
+    "for frame in frames_tracked:\n",
+    "    video_tracked.write(cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))\n",
+    "video_tracked.release()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Tooling/model/facenet_pytorch/examples/finetune.ipynb b/Tooling/model/facenet_pytorch/examples/finetune.ipynb
new file mode 100644
index 0000000..4556684
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/examples/finetune.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Face detection and recognition training pipeline\n",
+    "\n",
+    "The following example illustrates how to fine-tune an InceptionResnetV1 model on your own dataset. This will mostly follow standard pytorch training patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
+    "from torch import optim\n",
+    "from torch.optim.lr_scheduler import MultiStepLR\n",
+    "from torch.utils.tensorboard import SummaryWriter\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define run parameters\n",
+    "\n",
+    "The dataset should follow the VGGFace2/ImageNet-style directory layout. Modify `data_dir` to the location of the dataset on wish to finetune on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = '../data/test_images'\n",
+    "\n",
+    "batch_size = 32\n",
+    "epochs = 8\n",
+    "workers = 0 if os.name == 'nt' else 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Determine if an nvidia GPU is available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
+    "print('Running on device: {}'.format(device))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define MTCNN module\n",
+    "\n",
+    "See `help(MTCNN)` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mtcnn = MTCNN(\n",
+    "    image_size=160, margin=0, min_face_size=20,\n",
+    "    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,\n",
+    "    device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Perfom MTCNN facial detection\n",
+    "\n",
+    "Iterate through the DataLoader object and obtain cropped faces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "dataset = datasets.ImageFolder(data_dir, transform=transforms.Resize((512, 512)))\n",
+    "dataset.samples = [\n",
+    "    (p, p.replace(data_dir, data_dir + '_cropped'))\n",
+    "        for p, _ in dataset.samples\n",
+    "]\n",
+    "        \n",
+    "loader = DataLoader(\n",
+    "    dataset,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    collate_fn=training.collate_pil\n",
+    ")\n",
+    "\n",
+    "for i, (x, y) in enumerate(loader):\n",
+    "    mtcnn(x, save_path=y)\n",
+    "    print('\\rBatch {} of {}'.format(i + 1, len(loader)), end='')\n",
+    "    \n",
+    "# Remove mtcnn to reduce GPU memory usage\n",
+    "del mtcnn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define Inception Resnet V1 module\n",
+    "\n",
+    "See `help(InceptionResnetV1)` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resnet = InceptionResnetV1(\n",
+    "    classify=True,\n",
+    "    pretrained='vggface2',\n",
+    "    num_classes=len(dataset.class_to_idx)\n",
+    ").to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define optimizer, scheduler, dataset, and dataloader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = optim.Adam(resnet.parameters(), lr=0.001)\n",
+    "scheduler = MultiStepLR(optimizer, [5, 10])\n",
+    "\n",
+    "trans = transforms.Compose([\n",
+    "    np.float32,\n",
+    "    transforms.ToTensor(),\n",
+    "    fixed_image_standardization\n",
+    "])\n",
+    "dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)\n",
+    "img_inds = np.arange(len(dataset))\n",
+    "np.random.shuffle(img_inds)\n",
+    "train_inds = img_inds[:int(0.8 * len(img_inds))]\n",
+    "val_inds = img_inds[int(0.8 * len(img_inds)):]\n",
+    "\n",
+    "train_loader = DataLoader(\n",
+    "    dataset,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    sampler=SubsetRandomSampler(train_inds)\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    dataset,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    sampler=SubsetRandomSampler(val_inds)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define loss and evaluation functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_fn = torch.nn.CrossEntropyLoss()\n",
+    "metrics = {\n",
+    "    'fps': training.BatchTimer(),\n",
+    "    'acc': training.accuracy\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "writer = SummaryWriter()\n",
+    "writer.iteration, writer.interval = 0, 10\n",
+    "\n",
+    "print('\\n\\nInitial')\n",
+    "print('-' * 10)\n",
+    "resnet.eval()\n",
+    "training.pass_epoch(\n",
+    "    resnet, loss_fn, val_loader,\n",
+    "    batch_metrics=metrics, show_running=True, device=device,\n",
+    "    writer=writer\n",
+    ")\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    print('\\nEpoch {}/{}'.format(epoch + 1, epochs))\n",
+    "    print('-' * 10)\n",
+    "\n",
+    "    resnet.train()\n",
+    "    training.pass_epoch(\n",
+    "        resnet, loss_fn, train_loader, optimizer, scheduler,\n",
+    "        batch_metrics=metrics, show_running=True, device=device,\n",
+    "        writer=writer\n",
+    "    )\n",
+    "\n",
+    "    resnet.eval()\n",
+    "    training.pass_epoch(\n",
+    "        resnet, loss_fn, val_loader,\n",
+    "        batch_metrics=metrics, show_running=True, device=device,\n",
+    "        writer=writer\n",
+    "    )\n",
+    "\n",
+    "writer.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Tooling/model/facenet_pytorch/examples/infer.ipynb b/Tooling/model/facenet_pytorch/examples/infer.ipynb
new file mode 100644
index 0000000..d36a056
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/examples/infer.ipynb
@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Face detection and recognition inference pipeline\n",
+    "\n",
+    "The following example illustrates how to use the `facenet_pytorch` python package to perform face detection and recogition on an image dataset using an Inception Resnet V1 pretrained on the VGGFace2 dataset.\n",
+    "\n",
+    "The following Pytorch methods are included:\n",
+    "* Datasets\n",
+    "* Dataloaders\n",
+    "* GPU/CPU processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "workers = 0 if os.name == 'nt' else 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Determine if an nvidia GPU is available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on device: cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
+    "print('Running on device: {}'.format(device))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define MTCNN module\n",
+    "\n",
+    "Default params shown for illustration, but not needed. Note that, since MTCNN is a collection of neural nets and other code, the device must be passed in the following way to enable copying of objects when needed internally.\n",
+    "\n",
+    "See `help(MTCNN)` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mtcnn = MTCNN(\n",
+    "    image_size=160, margin=0, min_face_size=20,\n",
+    "    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,\n",
+    "    device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define Inception Resnet V1 module\n",
+    "\n",
+    "Set classify=True for pretrained classifier. For this example, we will use the model to output embeddings/CNN features. Note that for inference, it is important to set the model to `eval` mode.\n",
+    "\n",
+    "See `help(InceptionResnetV1)` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define a dataset and data loader\n",
+    "\n",
+    "We add the `idx_to_class` attribute to the dataset to enable easy recoding of label indices to identity names later one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def collate_fn(x):\n",
+    "    return x[0]\n",
+    "\n",
+    "dataset = datasets.ImageFolder('../data/test_images')\n",
+    "dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}\n",
+    "loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Perfom MTCNN facial detection\n",
+    "\n",
+    "Iterate through the DataLoader object and detect faces and associated detection probabilities for each. The `MTCNN` forward method returns images cropped to the detected face, if a face was detected. By default only a single detected face is returned - to have `MTCNN` return all detected faces, set `keep_all=True` when creating the MTCNN object above.\n",
+    "\n",
+    "To obtain bounding boxes rather than cropped face images, you can instead call the lower-level `mtcnn.detect()` function. See `help(mtcnn.detect)` for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Face detected with probability: 0.999957\n",
+      "Face detected with probability: 0.999927\n",
+      "Face detected with probability: 0.999662\n",
+      "Face detected with probability: 0.999873\n",
+      "Face detected with probability: 0.999991\n"
+     ]
+    }
+   ],
+   "source": [
+    "aligned = []\n",
+    "names = []\n",
+    "for x, y in loader:\n",
+    "    x_aligned, prob = mtcnn(x, return_prob=True)\n",
+    "    if x_aligned is not None:\n",
+    "        print('Face detected with probability: {:8f}'.format(prob))\n",
+    "        aligned.append(x_aligned)\n",
+    "        names.append(dataset.idx_to_class[y])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Calculate image embeddings\n",
+    "\n",
+    "MTCNN will return images of faces all the same size, enabling easy batch processing with the Resnet recognition module. Here, since we only have a few images, we build a single batch and perform inference on it. \n",
+    "\n",
+    "For real datasets, code should be modified to control batch sizes being passed to the Resnet, particularly if being processed on a GPU. For repeated testing, it is best to separate face detection (using MTCNN) from embedding or classification (using InceptionResnetV1), as calculation of cropped faces or bounding boxes can then be performed a single time and detected faces saved for future use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aligned = torch.stack(aligned).to(device)\n",
+    "embeddings = resnet(aligned).detach().cpu()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Print distance matrix for classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                angelina_jolie  bradley_cooper  kate_siegel  paul_rudd  \\\n",
+      "angelina_jolie        0.000000        1.344806     0.781201   1.425579   \n",
+      "bradley_cooper        1.344806        0.000000     1.256238   0.922126   \n",
+      "kate_siegel           0.781201        1.256238     0.000000   1.366423   \n",
+      "paul_rudd             1.425579        0.922126     1.366423   0.000000   \n",
+      "shea_whigham          1.448495        0.891145     1.416447   0.985438   \n",
+      "\n",
+      "                shea_whigham  \n",
+      "angelina_jolie      1.448495  \n",
+      "bradley_cooper      0.891145  \n",
+      "kate_siegel         1.416447  \n",
+      "paul_rudd           0.985438  \n",
+      "shea_whigham        0.000000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "dists = [[(e1 - e2).norm().item() for e2 in embeddings] for e1 in embeddings]\n",
+    "print(pd.DataFrame(dists, columns=names, index=names))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/Tooling/model/facenet_pytorch/examples/lfw_evaluate.ipynb b/Tooling/model/facenet_pytorch/examples/lfw_evaluate.ipynb
new file mode 100644
index 0000000..7cde66e
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/examples/lfw_evaluate.ipynb
@@ -0,0 +1,522 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### facenet-pytorch LFW evaluation\n",
+    "This notebook demonstrates how to evaluate performance against the LFW dataset."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training, extract_face\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader, SubsetRandomSampler, SequentialSampler\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import os"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "data_dir = 'data/lfw/lfw'\n",
+    "pairs_path = 'data/lfw/pairs.txt'\n",
+    "\n",
+    "batch_size = 16\n",
+    "epochs = 15\n",
+    "workers = 0 if os.name == 'nt' else 8"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on device: cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
+    "print('Running on device: {}'.format(device))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "mtcnn = MTCNN(\n",
+    "    image_size=160,\n",
+    "    margin=14,\n",
+    "    device=device,\n",
+    "    selection_method='center_weighted_size'\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "# Define the data loader for the input set of images\n",
+    "orig_img_ds = datasets.ImageFolder(data_dir, transform=None)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
+   "source": [
+    "\n",
+    "# overwrites class labels in dataset with path so path can be used for saving output in mtcnn batches\n",
+    "orig_img_ds.samples = [\n",
+    "    (p, p)\n",
+    "    for p, _ in orig_img_ds.samples\n",
+    "]\n",
+    "\n",
+    "loader = DataLoader(\n",
+    "    orig_img_ds,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    collate_fn=training.collate_pil\n",
+    ")\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crop_paths = []\n",
+    "box_probs = []\n",
+    "\n",
+    "for i, (x, b_paths) in enumerate(loader):\n",
+    "    crops = [p.replace(data_dir, data_dir + '_cropped') for p in b_paths]\n",
+    "    mtcnn(x, save_path=crops)\n",
+    "    crop_paths.extend(crops)\n",
+    "    print('\\rBatch {} of {}'.format(i + 1, len(loader)), end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [],
+   "source": [
+    "# Remove mtcnn to reduce GPU memory usage\n",
+    "del mtcnn\n",
+    "torch.cuda.empty_cache()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [],
+   "source": [
+    "# create dataset and data loaders from cropped images output from MTCNN\n",
+    "\n",
+    "trans = transforms.Compose([\n",
+    "    np.float32,\n",
+    "    transforms.ToTensor(),\n",
+    "    fixed_image_standardization\n",
+    "])\n",
+    "\n",
+    "dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)\n",
+    "\n",
+    "embed_loader = DataLoader(\n",
+    "    dataset,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    sampler=SequentialSampler(dataset)\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [],
+   "source": [
+    "# Load pretrained resnet model\n",
+    "resnet = InceptionResnetV1(\n",
+    "    classify=False,\n",
+    "    pretrained='vggface2'\n",
+    ").to(device)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "classes = []\n",
+    "embeddings = []\n",
+    "resnet.eval()\n",
+    "with torch.no_grad():\n",
+    "    for xb, yb in embed_loader:\n",
+    "        xb = xb.to(device)\n",
+    "        b_embeddings = resnet(xb)\n",
+    "        b_embeddings = b_embeddings.to('cpu').numpy()\n",
+    "        classes.extend(yb.numpy())\n",
+    "        embeddings.extend(b_embeddings)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [],
+   "source": [
+    "embeddings_dict = dict(zip(crop_paths,embeddings))\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Evaluate embeddings by using distance metrics to perform verification on the official LFW test set.\n",
+    "\n",
+    "The functions in the next block are copy pasted from `facenet.src.lfw`. Unfortunately that module has an absolute import from `facenet`, so can't be imported from the submodule\n",
+    "\n",
+    "added functionality to return false positive and false negatives"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from scipy import interpolate\n",
+    "\n",
+    "# LFW functions taken from David Sandberg's FaceNet implementation\n",
+    "def distance(embeddings1, embeddings2, distance_metric=0):\n",
+    "    if distance_metric==0:\n",
+    "        # Euclidian distance\n",
+    "        diff = np.subtract(embeddings1, embeddings2)\n",
+    "        dist = np.sum(np.square(diff),1)\n",
+    "    elif distance_metric==1:\n",
+    "        # Distance based on cosine similarity\n",
+    "        dot = np.sum(np.multiply(embeddings1, embeddings2), axis=1)\n",
+    "        norm = np.linalg.norm(embeddings1, axis=1) * np.linalg.norm(embeddings2, axis=1)\n",
+    "        similarity = dot / norm\n",
+    "        dist = np.arccos(similarity) / math.pi\n",
+    "    else:\n",
+    "        raise 'Undefined distance metric %d' % distance_metric\n",
+    "\n",
+    "    return dist\n",
+    "\n",
+    "def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, distance_metric=0, subtract_mean=False):\n",
+    "    assert(embeddings1.shape[0] == embeddings2.shape[0])\n",
+    "    assert(embeddings1.shape[1] == embeddings2.shape[1])\n",
+    "    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n",
+    "    nrof_thresholds = len(thresholds)\n",
+    "    k_fold = KFold(n_splits=nrof_folds, shuffle=False)\n",
+    "\n",
+    "    tprs = np.zeros((nrof_folds,nrof_thresholds))\n",
+    "    fprs = np.zeros((nrof_folds,nrof_thresholds))\n",
+    "    accuracy = np.zeros((nrof_folds))\n",
+    "\n",
+    "    is_false_positive = []\n",
+    "    is_false_negative = []\n",
+    "\n",
+    "    indices = np.arange(nrof_pairs)\n",
+    "\n",
+    "    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n",
+    "        if subtract_mean:\n",
+    "            mean = np.mean(np.concatenate([embeddings1[train_set], embeddings2[train_set]]), axis=0)\n",
+    "        else:\n",
+    "          mean = 0.0\n",
+    "        dist = distance(embeddings1-mean, embeddings2-mean, distance_metric)\n",
+    "\n",
+    "        # Find the best threshold for the fold\n",
+    "        acc_train = np.zeros((nrof_thresholds))\n",
+    "        for threshold_idx, threshold in enumerate(thresholds):\n",
+    "            _, _, acc_train[threshold_idx], _ ,_ = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])\n",
+    "        best_threshold_index = np.argmax(acc_train)\n",
+    "        for threshold_idx, threshold in enumerate(thresholds):\n",
+    "            tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _, _, _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])\n",
+    "        _, _, accuracy[fold_idx], is_fp, is_fn = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])\n",
+    "\n",
+    "        tpr = np.mean(tprs,0)\n",
+    "        fpr = np.mean(fprs,0)\n",
+    "        is_false_positive.extend(is_fp)\n",
+    "        is_false_negative.extend(is_fn)\n",
+    "\n",
+    "    return tpr, fpr, accuracy, is_false_positive, is_false_negative\n",
+    "\n",
+    "def calculate_accuracy(threshold, dist, actual_issame):\n",
+    "    predict_issame = np.less(dist, threshold)\n",
+    "    tp = np.sum(np.logical_and(predict_issame, actual_issame))\n",
+    "    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n",
+    "    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))\n",
+    "    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))\n",
+    "\n",
+    "    is_fp = np.logical_and(predict_issame, np.logical_not(actual_issame))\n",
+    "    is_fn = np.logical_and(np.logical_not(predict_issame), actual_issame)\n",
+    "\n",
+    "    tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)\n",
+    "    fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)\n",
+    "    acc = float(tp+tn)/dist.size\n",
+    "    return tpr, fpr, acc, is_fp, is_fn\n",
+    "\n",
+    "def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10, distance_metric=0, subtract_mean=False):\n",
+    "    assert(embeddings1.shape[0] == embeddings2.shape[0])\n",
+    "    assert(embeddings1.shape[1] == embeddings2.shape[1])\n",
+    "    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n",
+    "    nrof_thresholds = len(thresholds)\n",
+    "    k_fold = KFold(n_splits=nrof_folds, shuffle=False)\n",
+    "\n",
+    "    val = np.zeros(nrof_folds)\n",
+    "    far = np.zeros(nrof_folds)\n",
+    "\n",
+    "    indices = np.arange(nrof_pairs)\n",
+    "\n",
+    "    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n",
+    "        if subtract_mean:\n",
+    "            mean = np.mean(np.concatenate([embeddings1[train_set], embeddings2[train_set]]), axis=0)\n",
+    "        else:\n",
+    "          mean = 0.0\n",
+    "        dist = distance(embeddings1-mean, embeddings2-mean, distance_metric)\n",
+    "\n",
+    "        # Find the threshold that gives FAR = far_target\n",
+    "        far_train = np.zeros(nrof_thresholds)\n",
+    "        for threshold_idx, threshold in enumerate(thresholds):\n",
+    "            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])\n",
+    "        if np.max(far_train)>=far_target:\n",
+    "            f = interpolate.interp1d(far_train, thresholds, kind='slinear')\n",
+    "            threshold = f(far_target)\n",
+    "        else:\n",
+    "            threshold = 0.0\n",
+    "\n",
+    "        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])\n",
+    "\n",
+    "    val_mean = np.mean(val)\n",
+    "    far_mean = np.mean(far)\n",
+    "    val_std = np.std(val)\n",
+    "    return val_mean, val_std, far_mean\n",
+    "\n",
+    "def calculate_val_far(threshold, dist, actual_issame):\n",
+    "    predict_issame = np.less(dist, threshold)\n",
+    "    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))\n",
+    "    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n",
+    "    n_same = np.sum(actual_issame)\n",
+    "    n_diff = np.sum(np.logical_not(actual_issame))\n",
+    "    val = float(true_accept) / float(n_same)\n",
+    "    far = float(false_accept) / float(n_diff)\n",
+    "    return val, far\n",
+    "\n",
+    "\n",
+    "\n",
+    "def evaluate(embeddings, actual_issame, nrof_folds=10, distance_metric=0, subtract_mean=False):\n",
+    "    # Calculate evaluation metrics\n",
+    "    thresholds = np.arange(0, 4, 0.01)\n",
+    "    embeddings1 = embeddings[0::2]\n",
+    "    embeddings2 = embeddings[1::2]\n",
+    "    tpr, fpr, accuracy, fp, fn  = calculate_roc(thresholds, embeddings1, embeddings2,\n",
+    "        np.asarray(actual_issame), nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)\n",
+    "    thresholds = np.arange(0, 4, 0.001)\n",
+    "    val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,\n",
+    "        np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)\n",
+    "    return tpr, fpr, accuracy, val, val_std, far, fp, fn\n",
+    "\n",
+    "def add_extension(path):\n",
+    "    if os.path.exists(path+'.jpg'):\n",
+    "        return path+'.jpg'\n",
+    "    elif os.path.exists(path+'.png'):\n",
+    "        return path+'.png'\n",
+    "    else:\n",
+    "        raise RuntimeError('No file \"%s\" with extension png or jpg.' % path)\n",
+    "\n",
+    "def get_paths(lfw_dir, pairs):\n",
+    "    nrof_skipped_pairs = 0\n",
+    "    path_list = []\n",
+    "    issame_list = []\n",
+    "    for pair in pairs:\n",
+    "        if len(pair) == 3:\n",
+    "            path0 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])))\n",
+    "            path1 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2])))\n",
+    "            issame = True\n",
+    "        elif len(pair) == 4:\n",
+    "            path0 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])))\n",
+    "            path1 = add_extension(os.path.join(lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3])))\n",
+    "            issame = False\n",
+    "        if os.path.exists(path0) and os.path.exists(path1):    # Only add the pair if both paths exist\n",
+    "            path_list += (path0,path1)\n",
+    "            issame_list.append(issame)\n",
+    "        else:\n",
+    "            nrof_skipped_pairs += 1\n",
+    "    if nrof_skipped_pairs>0:\n",
+    "        print('Skipped %d image pairs' % nrof_skipped_pairs)\n",
+    "\n",
+    "    return path_list, issame_list\n",
+    "\n",
+    "def read_pairs(pairs_filename):\n",
+    "    pairs = []\n",
+    "    with open(pairs_filename, 'r') as f:\n",
+    "        for line in f.readlines()[1:]:\n",
+    "            pair = line.strip().split()\n",
+    "            pairs.append(pair)\n",
+    "    return np.array(pairs, dtype=object)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [],
+   "source": [
+    "pairs = read_pairs(pairs_path)\n",
+    "path_list, issame_list = get_paths(data_dir+'_cropped', pairs)\n",
+    "embeddings = np.array([embeddings_dict[path] for path in path_list])\n",
+    "\n",
+    "tpr, fpr, accuracy, val, val_std, far, fp, fn = evaluate(embeddings, issame_list)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.995      0.995      0.99166667 0.99       0.99       0.99666667\n",
+      " 0.99       0.995      0.99666667 0.99666667]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "0.9936666666666666"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(accuracy)\n",
+    "np.mean(accuracy)\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
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diff --git a/Tooling/model/facenet_pytorch/models/inception_resnet_v1.py b/Tooling/model/facenet_pytorch/models/inception_resnet_v1.py
new file mode 100644
index 0000000..3b7de0d
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/models/inception_resnet_v1.py
@@ -0,0 +1,340 @@
+import os
+import requests
+from requests.adapters import HTTPAdapter
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .utils.download import download_url_to_file
+
+
+class BasicConv2d(nn.Module):
+
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_planes, out_planes,
+            kernel_size=kernel_size, stride=stride,
+            padding=padding, bias=False
+        ) # verify bias false
+        self.bn = nn.BatchNorm2d(
+            out_planes,
+            eps=0.001, # value found in tensorflow
+            momentum=0.1, # default pytorch value
+            affine=True
+        )
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Block35(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super().__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(256, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(256, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Block17(nn.Module):
+
+    def __init__(self, scale=1.0):
+        super().__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(896, 128, kernel_size=1, stride=1),
+            BasicConv2d(128, 128, kernel_size=(1,7), stride=1, padding=(0,3)),
+            BasicConv2d(128, 128, kernel_size=(7,1), stride=1, padding=(3,0))
+        )
+
+        self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Block8(nn.Module):
+
+    def __init__(self, scale=1.0, noReLU=False):
+        super().__init__()
+
+        self.scale = scale
+        self.noReLU = noReLU
+
+        self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1792, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=(1,3), stride=1, padding=(0,1)),
+            BasicConv2d(192, 192, kernel_size=(3,1), stride=1, padding=(1,0))
+        )
+
+        self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1)
+        if not self.noReLU:
+            self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        if not self.noReLU:
+            out = self.relu(out)
+        return out
+
+
+class Mixed_6a(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(256, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(192, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Mixed_7a(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(896, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=2)
+        )
+
+        self.branch3 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class InceptionResnetV1(nn.Module):
+    """Inception Resnet V1 model with optional loading of pretrained weights.
+
+    Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface
+    datasets. Pretrained state_dicts are automatically downloaded on model instantiation if
+    requested and cached in the torch cache. Subsequent instantiations use the cache rather than
+    redownloading.
+
+    Keyword Arguments:
+        pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'.
+            (default: {None})
+        classify {bool} -- Whether the model should output classification probabilities or feature
+            embeddings. (default: {False})
+        num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not
+            equal to that used for the pretrained model, the final linear layer will be randomly
+            initialized. (default: {None})
+        dropout_prob {float} -- Dropout probability. (default: {0.6})
+    """
+    def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_prob=0.6, device=None):
+        super().__init__()
+
+        # Set simple attributes
+        self.pretrained = pretrained
+        self.classify = classify
+        self.num_classes = num_classes
+
+        if pretrained == 'vggface2':
+            tmp_classes = 8631
+        elif pretrained == 'casia-webface':
+            tmp_classes = 10575
+        elif pretrained is None and self.classify and self.num_classes is None:
+            raise Exception('If "pretrained" is not specified and "classify" is True, "num_classes" must be specified')
+
+
+        # Define layers
+        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
+        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
+        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
+        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
+        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
+        self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2)
+        self.repeat_1 = nn.Sequential(
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+        )
+        self.mixed_6a = Mixed_6a()
+        self.repeat_2 = nn.Sequential(
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+        )
+        self.mixed_7a = Mixed_7a()
+        self.repeat_3 = nn.Sequential(
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+        )
+        self.block8 = Block8(noReLU=True)
+        self.avgpool_1a = nn.AdaptiveAvgPool2d(1)
+        self.dropout = nn.Dropout(dropout_prob)
+        self.last_linear = nn.Linear(1792, 512, bias=False)
+        self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True)
+
+        if pretrained is not None:
+            self.logits = nn.Linear(512, tmp_classes)
+            load_weights(self, pretrained)
+
+        if self.classify and self.num_classes is not None:
+            self.logits = nn.Linear(512, self.num_classes)
+
+        self.device = torch.device('cpu')
+        if device is not None:
+            self.device = device
+            self.to(device)
+
+    def forward(self, x):
+        """Calculate embeddings or logits given a batch of input image tensors.
+
+        Arguments:
+            x {torch.tensor} -- Batch of image tensors representing faces.
+
+        Returns:
+            torch.tensor -- Batch of embedding vectors or multinomial logits.
+        """
+        x = self.conv2d_1a(x)
+        x = self.conv2d_2a(x)
+        x = self.conv2d_2b(x)
+        x = self.maxpool_3a(x)
+        x = self.conv2d_3b(x)
+        x = self.conv2d_4a(x)
+        x = self.conv2d_4b(x)
+        x = self.repeat_1(x)
+        x = self.mixed_6a(x)
+        x = self.repeat_2(x)
+        x = self.mixed_7a(x)
+        x = self.repeat_3(x)
+        x = self.block8(x)
+        x = self.avgpool_1a(x)
+        x = self.dropout(x)
+        x = self.last_linear(x.view(x.shape[0], -1))
+        x = self.last_bn(x)
+        if self.classify:
+            x = self.logits(x)
+        else:
+            x = F.normalize(x, p=2, dim=1)
+        return x
+
+
+def load_weights(mdl, name):
+    """Download pretrained state_dict and load into model.
+
+    Arguments:
+        mdl {torch.nn.Module} -- Pytorch model.
+        name {str} -- Name of dataset that was used to generate pretrained state_dict.
+
+    Raises:
+        ValueError: If 'pretrained' not equal to 'vggface2' or 'casia-webface'.
+    """
+    if name == 'vggface2':
+        path = 'https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt'
+    elif name == 'casia-webface':
+        path = 'https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt'
+    else:
+        raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"')
+
+    model_dir = os.path.join(get_torch_home(), 'checkpoints')
+    os.makedirs(model_dir, exist_ok=True)
+
+    cached_file = os.path.join(model_dir, os.path.basename(path))
+    if not os.path.exists(cached_file):
+        download_url_to_file(path, cached_file)
+
+    state_dict = torch.load(cached_file)
+    mdl.load_state_dict(state_dict)
+
+
+def get_torch_home():
+    torch_home = os.path.expanduser(
+        os.getenv(
+            'TORCH_HOME',
+            os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')
+        )
+    )
+    return torch_home
diff --git a/Tooling/model/facenet_pytorch/models/mtcnn.py b/Tooling/model/facenet_pytorch/models/mtcnn.py
new file mode 100644
index 0000000..e2a7c83
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/models/mtcnn.py
@@ -0,0 +1,519 @@
+import torch
+from torch import nn
+import numpy as np
+import os
+
+from .utils.detect_face import detect_face, extract_face
+
+
+class PNet(nn.Module):
+    """MTCNN PNet.
+    
+    Keyword Arguments:
+        pretrained {bool} -- Whether or not to load saved pretrained weights (default: {True})
+    """
+
+    def __init__(self, pretrained=True):
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(3, 10, kernel_size=3)
+        self.prelu1 = nn.PReLU(10)
+        self.pool1 = nn.MaxPool2d(2, 2, ceil_mode=True)
+        self.conv2 = nn.Conv2d(10, 16, kernel_size=3)
+        self.prelu2 = nn.PReLU(16)
+        self.conv3 = nn.Conv2d(16, 32, kernel_size=3)
+        self.prelu3 = nn.PReLU(32)
+        self.conv4_1 = nn.Conv2d(32, 2, kernel_size=1)
+        self.softmax4_1 = nn.Softmax(dim=1)
+        self.conv4_2 = nn.Conv2d(32, 4, kernel_size=1)
+
+        self.training = False
+
+        if pretrained:
+            state_dict_path = os.path.join(os.path.dirname(__file__), '../data/pnet.pt')
+            state_dict = torch.load(state_dict_path)
+            self.load_state_dict(state_dict)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.prelu1(x)
+        x = self.pool1(x)
+        x = self.conv2(x)
+        x = self.prelu2(x)
+        x = self.conv3(x)
+        x = self.prelu3(x)
+        a = self.conv4_1(x)
+        a = self.softmax4_1(a)
+        b = self.conv4_2(x)
+        return b, a
+
+
+class RNet(nn.Module):
+    """MTCNN RNet.
+    
+    Keyword Arguments:
+        pretrained {bool} -- Whether or not to load saved pretrained weights (default: {True})
+    """
+
+    def __init__(self, pretrained=True):
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(3, 28, kernel_size=3)
+        self.prelu1 = nn.PReLU(28)
+        self.pool1 = nn.MaxPool2d(3, 2, ceil_mode=True)
+        self.conv2 = nn.Conv2d(28, 48, kernel_size=3)
+        self.prelu2 = nn.PReLU(48)
+        self.pool2 = nn.MaxPool2d(3, 2, ceil_mode=True)
+        self.conv3 = nn.Conv2d(48, 64, kernel_size=2)
+        self.prelu3 = nn.PReLU(64)
+        self.dense4 = nn.Linear(576, 128)
+        self.prelu4 = nn.PReLU(128)
+        self.dense5_1 = nn.Linear(128, 2)
+        self.softmax5_1 = nn.Softmax(dim=1)
+        self.dense5_2 = nn.Linear(128, 4)
+
+        self.training = False
+
+        if pretrained:
+            state_dict_path = os.path.join(os.path.dirname(__file__), '../data/rnet.pt')
+            state_dict = torch.load(state_dict_path)
+            self.load_state_dict(state_dict)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.prelu1(x)
+        x = self.pool1(x)
+        x = self.conv2(x)
+        x = self.prelu2(x)
+        x = self.pool2(x)
+        x = self.conv3(x)
+        x = self.prelu3(x)
+        x = x.permute(0, 3, 2, 1).contiguous()
+        x = self.dense4(x.view(x.shape[0], -1))
+        x = self.prelu4(x)
+        a = self.dense5_1(x)
+        a = self.softmax5_1(a)
+        b = self.dense5_2(x)
+        return b, a
+
+
+class ONet(nn.Module):
+    """MTCNN ONet.
+    
+    Keyword Arguments:
+        pretrained {bool} -- Whether or not to load saved pretrained weights (default: {True})
+    """
+
+    def __init__(self, pretrained=True):
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3)
+        self.prelu1 = nn.PReLU(32)
+        self.pool1 = nn.MaxPool2d(3, 2, ceil_mode=True)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
+        self.prelu2 = nn.PReLU(64)
+        self.pool2 = nn.MaxPool2d(3, 2, ceil_mode=True)
+        self.conv3 = nn.Conv2d(64, 64, kernel_size=3)
+        self.prelu3 = nn.PReLU(64)
+        self.pool3 = nn.MaxPool2d(2, 2, ceil_mode=True)
+        self.conv4 = nn.Conv2d(64, 128, kernel_size=2)
+        self.prelu4 = nn.PReLU(128)
+        self.dense5 = nn.Linear(1152, 256)
+        self.prelu5 = nn.PReLU(256)
+        self.dense6_1 = nn.Linear(256, 2)
+        self.softmax6_1 = nn.Softmax(dim=1)
+        self.dense6_2 = nn.Linear(256, 4)
+        self.dense6_3 = nn.Linear(256, 10)
+
+        self.training = False
+
+        if pretrained:
+            state_dict_path = os.path.join(os.path.dirname(__file__), '../data/onet.pt')
+            state_dict = torch.load(state_dict_path)
+            self.load_state_dict(state_dict)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.prelu1(x)
+        x = self.pool1(x)
+        x = self.conv2(x)
+        x = self.prelu2(x)
+        x = self.pool2(x)
+        x = self.conv3(x)
+        x = self.prelu3(x)
+        x = self.pool3(x)
+        x = self.conv4(x)
+        x = self.prelu4(x)
+        x = x.permute(0, 3, 2, 1).contiguous()
+        x = self.dense5(x.view(x.shape[0], -1))
+        x = self.prelu5(x)
+        a = self.dense6_1(x)
+        a = self.softmax6_1(a)
+        b = self.dense6_2(x)
+        c = self.dense6_3(x)
+        return b, c, a
+
+
+class MTCNN(nn.Module):
+    """MTCNN face detection module.
+
+    This class loads pretrained P-, R-, and O-nets and returns images cropped to include the face
+    only, given raw input images of one of the following types:
+        - PIL image or list of PIL images
+        - numpy.ndarray (uint8) representing either a single image (3D) or a batch of images (4D).
+    Cropped faces can optionally be saved to file
+    also.
+    
+    Keyword Arguments:
+        image_size {int} -- Output image size in pixels. The image will be square. (default: {160})
+        margin {int} -- Margin to add to bounding box, in terms of pixels in the final image. 
+            Note that the application of the margin differs slightly from the davidsandberg/facenet
+            repo, which applies the margin to the original image before resizing, making the margin
+            dependent on the original image size (this is a bug in davidsandberg/facenet).
+            (default: {0})
+        min_face_size {int} -- Minimum face size to search for. (default: {20})
+        thresholds {list} -- MTCNN face detection thresholds (default: {[0.6, 0.7, 0.7]})
+        factor {float} -- Factor used to create a scaling pyramid of face sizes. (default: {0.709})
+        post_process {bool} -- Whether or not to post process images tensors before returning.
+            (default: {True})
+        select_largest {bool} -- If True, if multiple faces are detected, the largest is returned.
+            If False, the face with the highest detection probability is returned.
+            (default: {True})
+        selection_method {string} -- Which heuristic to use for selection. Default None. If
+            specified, will override select_largest:
+                    "probability": highest probability selected
+                    "largest": largest box selected
+                    "largest_over_threshold": largest box over a certain probability selected
+                    "center_weighted_size": box size minus weighted squared offset from image center
+                (default: {None})
+        keep_all {bool} -- If True, all detected faces are returned, in the order dictated by the
+            select_largest parameter. If a save_path is specified, the first face is saved to that
+            path and the remaining faces are saved to <save_path>1, <save_path>2 etc.
+            (default: {False})
+        device {torch.device} -- The device on which to run neural net passes. Image tensors and
+            models are copied to this device before running forward passes. (default: {None})
+    """
+
+    def __init__(
+        self, image_size=160, margin=0, min_face_size=20,
+        thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,
+        select_largest=True, selection_method=None, keep_all=False, device=None
+    ):
+        super().__init__()
+
+        self.image_size = image_size
+        self.margin = margin
+        self.min_face_size = min_face_size
+        self.thresholds = thresholds
+        self.factor = factor
+        self.post_process = post_process
+        self.select_largest = select_largest
+        self.keep_all = keep_all
+        self.selection_method = selection_method
+
+        self.pnet = PNet()
+        self.rnet = RNet()
+        self.onet = ONet()
+
+        self.device = torch.device('cpu')
+        if device is not None:
+            self.device = device
+            self.to(device)
+
+        if not self.selection_method:
+            self.selection_method = 'largest' if self.select_largest else 'probability'
+
+    def forward(self, img, save_path=None, return_prob=False):
+        """Run MTCNN face detection on a PIL image or numpy array. This method performs both
+        detection and extraction of faces, returning tensors representing detected faces rather
+        than the bounding boxes. To access bounding boxes, see the MTCNN.detect() method below.
+        
+        Arguments:
+            img {PIL.Image, np.ndarray, or list} -- A PIL image, np.ndarray, torch.Tensor, or list.
+        
+        Keyword Arguments:
+            save_path {str} -- An optional save path for the cropped image. Note that when
+                self.post_process=True, although the returned tensor is post processed, the saved
+                face image is not, so it is a true representation of the face in the input image.
+                If `img` is a list of images, `save_path` should be a list of equal length.
+                (default: {None})
+            return_prob {bool} -- Whether or not to return the detection probability.
+                (default: {False})
+        
+        Returns:
+            Union[torch.Tensor, tuple(torch.tensor, float)] -- If detected, cropped image of a face
+                with dimensions 3 x image_size x image_size. Optionally, the probability that a
+                face was detected. If self.keep_all is True, n detected faces are returned in an
+                n x 3 x image_size x image_size tensor with an optional list of detection
+                probabilities. If `img` is a list of images, the item(s) returned have an extra 
+                dimension (batch) as the first dimension.
+
+        Example:
+        >>> from facenet_pytorch import MTCNN
+        >>> mtcnn = MTCNN()
+        >>> face_tensor, prob = mtcnn(img, save_path='face.png', return_prob=True)
+        """
+
+        # Detect faces
+        batch_boxes, batch_probs, batch_points = self.detect(img, landmarks=True)
+        # Select faces
+        if not self.keep_all:
+            batch_boxes, batch_probs, batch_points = self.select_boxes(
+                batch_boxes, batch_probs, batch_points, img, method=self.selection_method
+            )
+        # Extract faces
+        faces = self.extract(img, batch_boxes, save_path)
+
+        if return_prob:
+            return faces, batch_probs
+        else:
+            return faces
+
+    def detect(self, img, landmarks=False):
+        """Detect all faces in PIL image and return bounding boxes and optional facial landmarks.
+
+        This method is used by the forward method and is also useful for face detection tasks
+        that require lower-level handling of bounding boxes and facial landmarks (e.g., face
+        tracking). The functionality of the forward function can be emulated by using this method
+        followed by the extract_face() function.
+        
+        Arguments:
+            img {PIL.Image, np.ndarray, or list} -- A PIL image, np.ndarray, torch.Tensor, or list.
+
+        Keyword Arguments:
+            landmarks {bool} -- Whether to return facial landmarks in addition to bounding boxes.
+                (default: {False})
+        
+        Returns:
+            tuple(numpy.ndarray, list) -- For N detected faces, a tuple containing an
+                Nx4 array of bounding boxes and a length N list of detection probabilities.
+                Returned boxes will be sorted in descending order by detection probability if
+                self.select_largest=False, otherwise the largest face will be returned first.
+                If `img` is a list of images, the items returned have an extra dimension
+                (batch) as the first dimension. Optionally, a third item, the facial landmarks,
+                are returned if `landmarks=True`.
+
+        Example:
+        >>> from PIL import Image, ImageDraw
+        >>> from facenet_pytorch import MTCNN, extract_face
+        >>> mtcnn = MTCNN(keep_all=True)
+        >>> boxes, probs, points = mtcnn.detect(img, landmarks=True)
+        >>> # Draw boxes and save faces
+        >>> img_draw = img.copy()
+        >>> draw = ImageDraw.Draw(img_draw)
+        >>> for i, (box, point) in enumerate(zip(boxes, points)):
+        ...     draw.rectangle(box.tolist(), width=5)
+        ...     for p in point:
+        ...         draw.rectangle((p - 10).tolist() + (p + 10).tolist(), width=10)
+        ...     extract_face(img, box, save_path='detected_face_{}.png'.format(i))
+        >>> img_draw.save('annotated_faces.png')
+        """
+
+        with torch.no_grad():
+            batch_boxes, batch_points = detect_face(
+                img, self.min_face_size,
+                self.pnet, self.rnet, self.onet,
+                self.thresholds, self.factor,
+                self.device
+            )
+
+        boxes, probs, points = [], [], []
+        for box, point in zip(batch_boxes, batch_points):
+            box = np.array(box)
+            point = np.array(point)
+            if len(box) == 0:
+                boxes.append(None)
+                probs.append([None])
+                points.append(None)
+            elif self.select_largest:
+                box_order = np.argsort((box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1]))[::-1]
+                box = box[box_order]
+                point = point[box_order]
+                boxes.append(box[:, :4])
+                probs.append(box[:, 4])
+                points.append(point)
+            else:
+                boxes.append(box[:, :4])
+                probs.append(box[:, 4])
+                points.append(point)
+        boxes = np.array(boxes)
+        probs = np.array(probs)
+        points = np.array(points)
+
+        if (
+            not isinstance(img, (list, tuple)) and 
+            not (isinstance(img, np.ndarray) and len(img.shape) == 4) and
+            not (isinstance(img, torch.Tensor) and len(img.shape) == 4)
+        ):
+            boxes = boxes[0]
+            probs = probs[0]
+            points = points[0]
+
+        if landmarks:
+            return boxes, probs, points
+
+        return boxes, probs
+
+    def select_boxes(
+        self, all_boxes, all_probs, all_points, imgs, method='probability', threshold=0.9,
+        center_weight=2.0
+    ):
+        """Selects a single box from multiple for a given image using one of multiple heuristics.
+
+        Arguments:
+                all_boxes {np.ndarray} -- Ix0 ndarray where each element is a Nx4 ndarry of
+                    bounding boxes for N detected faces in I images (output from self.detect).
+                all_probs {np.ndarray} -- Ix0 ndarray where each element is a Nx0 ndarry of
+                    probabilities for N detected faces in I images (output from self.detect).
+                all_points {np.ndarray} -- Ix0 ndarray where each element is a Nx5x2 array of
+                    points for N detected faces. (output from self.detect).
+                imgs {PIL.Image, np.ndarray, or list} -- A PIL image, np.ndarray, torch.Tensor, or list.
+
+        Keyword Arguments:
+                method {str} -- Which heuristic to use for selection:
+                    "probability": highest probability selected
+                    "largest": largest box selected
+                    "largest_over_theshold": largest box over a certain probability selected
+                    "center_weighted_size": box size minus weighted squared offset from image center
+                    (default: {'probability'})
+                threshold {float} -- theshold for "largest_over_threshold" method. (default: {0.9})
+                center_weight {float} -- weight for squared offset in center weighted size method.
+                    (default: {2.0})
+
+        Returns:
+                tuple(numpy.ndarray, numpy.ndarray, numpy.ndarray) -- nx4 ndarray of bounding boxes
+                    for n images. Ix0 array of probabilities for each box, array of landmark points.
+        """
+
+        #copying batch detection from extract, but would be easier to ensure detect creates consistent output.
+        batch_mode = True
+        if (
+                not isinstance(imgs, (list, tuple)) and
+                not (isinstance(imgs, np.ndarray) and len(imgs.shape) == 4) and
+                not (isinstance(imgs, torch.Tensor) and len(imgs.shape) == 4)
+        ):
+            imgs = [imgs]
+            all_boxes = [all_boxes]
+            all_probs = [all_probs]
+            all_points = [all_points]
+            batch_mode = False
+
+        selected_boxes, selected_probs, selected_points = [], [], []
+        for boxes, points, probs, img in zip(all_boxes, all_points, all_probs, imgs):
+            
+            if boxes is None:
+                selected_boxes.append(None)
+                selected_probs.append([None])
+                selected_points.append(None)
+                continue
+            
+            # If at least 1 box found
+            boxes = np.array(boxes)
+            probs = np.array(probs)
+            points = np.array(points)
+                
+            if method == 'largest':
+                box_order = np.argsort((boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]))[::-1]
+            elif method == 'probability':
+                box_order = np.argsort(probs)[::-1]
+            elif method == 'center_weighted_size':
+                box_sizes = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+                img_center = (img.width / 2, img.height/2)
+                box_centers = np.array(list(zip((boxes[:, 0] + boxes[:, 2]) / 2, (boxes[:, 1] + boxes[:, 3]) / 2)))
+                offsets = box_centers - img_center
+                offset_dist_squared = np.sum(np.power(offsets, 2.0), 1)
+                box_order = np.argsort(box_sizes - offset_dist_squared * center_weight)[::-1]
+            elif method == 'largest_over_threshold':
+                box_mask = probs > threshold
+                boxes = boxes[box_mask]
+                box_order = np.argsort((boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]))[::-1]
+                if sum(box_mask) == 0:
+                    selected_boxes.append(None)
+                    selected_probs.append([None])
+                    selected_points.append(None)
+                    continue
+
+            box = boxes[box_order][[0]]
+            prob = probs[box_order][[0]]
+            point = points[box_order][[0]]
+            selected_boxes.append(box)
+            selected_probs.append(prob)
+            selected_points.append(point)
+
+        if batch_mode:
+            selected_boxes = np.array(selected_boxes)
+            selected_probs = np.array(selected_probs)
+            selected_points = np.array(selected_points)
+        else:
+            selected_boxes = selected_boxes[0]
+            selected_probs = selected_probs[0][0]
+            selected_points = selected_points[0]
+
+        return selected_boxes, selected_probs, selected_points
+
+    def extract(self, img, batch_boxes, save_path):
+        # Determine if a batch or single image was passed
+        batch_mode = True
+        if (
+                not isinstance(img, (list, tuple)) and
+                not (isinstance(img, np.ndarray) and len(img.shape) == 4) and
+                not (isinstance(img, torch.Tensor) and len(img.shape) == 4)
+        ):
+            img = [img]
+            batch_boxes = [batch_boxes]
+            batch_mode = False
+
+        # Parse save path(s)
+        if save_path is not None:
+            if isinstance(save_path, str):
+                save_path = [save_path]
+        else:
+            save_path = [None for _ in range(len(img))]
+
+        # Process all bounding boxes
+        faces = []
+        for im, box_im, path_im in zip(img, batch_boxes, save_path):
+            if box_im is None:
+                faces.append(None)
+                continue
+
+            if not self.keep_all:
+                box_im = box_im[[0]]
+
+            faces_im = []
+            for i, box in enumerate(box_im):
+                face_path = path_im
+                if path_im is not None and i > 0:
+                    save_name, ext = os.path.splitext(path_im)
+                    face_path = save_name + '_' + str(i + 1) + ext
+
+                face = extract_face(im, box, self.image_size, self.margin, face_path)
+                if self.post_process:
+                    face = fixed_image_standardization(face)
+                faces_im.append(face)
+
+            if self.keep_all:
+                faces_im = torch.stack(faces_im)
+            else:
+                faces_im = faces_im[0]
+
+            faces.append(faces_im)
+
+        if not batch_mode:
+            faces = faces[0]
+
+        return faces
+
+
+def fixed_image_standardization(image_tensor):
+    processed_tensor = (image_tensor - 127.5) / 128.0
+    return processed_tensor
+
+
+def prewhiten(x):
+    mean = x.mean()
+    std = x.std()
+    std_adj = std.clamp(min=1.0/(float(x.numel())**0.5))
+    y = (x - mean) / std_adj
+    return y
+
diff --git a/Tooling/model/facenet_pytorch/models/utils/detect_face.py b/Tooling/model/facenet_pytorch/models/utils/detect_face.py
new file mode 100644
index 0000000..6c4a76d
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/models/utils/detect_face.py
@@ -0,0 +1,378 @@
+import torch
+from torch.nn.functional import interpolate
+from torchvision.transforms import functional as F
+from torchvision.ops.boxes import batched_nms
+from PIL import Image
+import numpy as np
+import os
+import math
+
+# OpenCV is optional, but required if using numpy arrays instead of PIL
+try:
+    import cv2
+except:
+    pass
+
+def fixed_batch_process(im_data, model):
+    batch_size = 512
+    out = []
+    for i in range(0, len(im_data), batch_size):
+        batch = im_data[i:(i+batch_size)]
+        out.append(model(batch))
+
+    return tuple(torch.cat(v, dim=0) for v in zip(*out))
+
+def detect_face(imgs, minsize, pnet, rnet, onet, threshold, factor, device):
+    if isinstance(imgs, (np.ndarray, torch.Tensor)):
+        if isinstance(imgs,np.ndarray):
+            imgs = torch.as_tensor(imgs.copy(), device=device)
+
+        if isinstance(imgs,torch.Tensor):
+            imgs = torch.as_tensor(imgs, device=device)
+
+        if len(imgs.shape) == 3:
+            imgs = imgs.unsqueeze(0)
+    else:
+        if not isinstance(imgs, (list, tuple)):
+            imgs = [imgs]
+        if any(img.size != imgs[0].size for img in imgs):
+            raise Exception("MTCNN batch processing only compatible with equal-dimension images.")
+        imgs = np.stack([np.uint8(img) for img in imgs])
+        imgs = torch.as_tensor(imgs.copy(), device=device)
+
+    
+
+    model_dtype = next(pnet.parameters()).dtype
+    imgs = imgs.permute(0, 3, 1, 2).type(model_dtype)
+
+    batch_size = len(imgs)
+    h, w = imgs.shape[2:4]
+    m = 12.0 / minsize
+    minl = min(h, w)
+    minl = minl * m
+
+    # Create scale pyramid
+    scale_i = m
+    scales = []
+    while minl >= 12:
+        scales.append(scale_i)
+        scale_i = scale_i * factor
+        minl = minl * factor
+
+    # First stage
+    boxes = []
+    image_inds = []
+
+    scale_picks = []
+
+    all_i = 0
+    offset = 0
+    for scale in scales:
+        im_data = imresample(imgs, (int(h * scale + 1), int(w * scale + 1)))
+        im_data = (im_data - 127.5) * 0.0078125
+        reg, probs = pnet(im_data)
+    
+        boxes_scale, image_inds_scale = generateBoundingBox(reg, probs[:, 1], scale, threshold[0])
+        boxes.append(boxes_scale)
+        image_inds.append(image_inds_scale)
+
+        pick = batched_nms(boxes_scale[:, :4], boxes_scale[:, 4], image_inds_scale, 0.5)
+        scale_picks.append(pick + offset)
+        offset += boxes_scale.shape[0]
+
+    boxes = torch.cat(boxes, dim=0)
+    image_inds = torch.cat(image_inds, dim=0)
+
+    scale_picks = torch.cat(scale_picks, dim=0)
+
+    # NMS within each scale + image
+    boxes, image_inds = boxes[scale_picks], image_inds[scale_picks]
+
+
+    # NMS within each image
+    pick = batched_nms(boxes[:, :4], boxes[:, 4], image_inds, 0.7)
+    boxes, image_inds = boxes[pick], image_inds[pick]
+
+    regw = boxes[:, 2] - boxes[:, 0]
+    regh = boxes[:, 3] - boxes[:, 1]
+    qq1 = boxes[:, 0] + boxes[:, 5] * regw
+    qq2 = boxes[:, 1] + boxes[:, 6] * regh
+    qq3 = boxes[:, 2] + boxes[:, 7] * regw
+    qq4 = boxes[:, 3] + boxes[:, 8] * regh
+    boxes = torch.stack([qq1, qq2, qq3, qq4, boxes[:, 4]]).permute(1, 0)
+    boxes = rerec(boxes)
+    y, ey, x, ex = pad(boxes, w, h)
+    
+    # Second stage
+    if len(boxes) > 0:
+        im_data = []
+        for k in range(len(y)):
+            if ey[k] > (y[k] - 1) and ex[k] > (x[k] - 1):
+                img_k = imgs[image_inds[k], :, (y[k] - 1):ey[k], (x[k] - 1):ex[k]].unsqueeze(0)
+                im_data.append(imresample(img_k, (24, 24)))
+        im_data = torch.cat(im_data, dim=0)
+        im_data = (im_data - 127.5) * 0.0078125
+
+        # This is equivalent to out = rnet(im_data) to avoid GPU out of memory.
+        out = fixed_batch_process(im_data, rnet)
+
+        out0 = out[0].permute(1, 0)
+        out1 = out[1].permute(1, 0)
+        score = out1[1, :]
+        ipass = score > threshold[1]
+        boxes = torch.cat((boxes[ipass, :4], score[ipass].unsqueeze(1)), dim=1)
+        image_inds = image_inds[ipass]
+        mv = out0[:, ipass].permute(1, 0)
+
+        # NMS within each image
+        pick = batched_nms(boxes[:, :4], boxes[:, 4], image_inds, 0.7)
+        boxes, image_inds, mv = boxes[pick], image_inds[pick], mv[pick]
+        boxes = bbreg(boxes, mv)
+        boxes = rerec(boxes)
+
+    # Third stage
+    points = torch.zeros(0, 5, 2, device=device)
+    if len(boxes) > 0:
+        y, ey, x, ex = pad(boxes, w, h)
+        im_data = []
+        for k in range(len(y)):
+            if ey[k] > (y[k] - 1) and ex[k] > (x[k] - 1):
+                img_k = imgs[image_inds[k], :, (y[k] - 1):ey[k], (x[k] - 1):ex[k]].unsqueeze(0)
+                im_data.append(imresample(img_k, (48, 48)))
+        im_data = torch.cat(im_data, dim=0)
+        im_data = (im_data - 127.5) * 0.0078125
+        
+        # This is equivalent to out = onet(im_data) to avoid GPU out of memory.
+        out = fixed_batch_process(im_data, onet)
+
+        out0 = out[0].permute(1, 0)
+        out1 = out[1].permute(1, 0)
+        out2 = out[2].permute(1, 0)
+        score = out2[1, :]
+        points = out1
+        ipass = score > threshold[2]
+        points = points[:, ipass]
+        boxes = torch.cat((boxes[ipass, :4], score[ipass].unsqueeze(1)), dim=1)
+        image_inds = image_inds[ipass]
+        mv = out0[:, ipass].permute(1, 0)
+
+        w_i = boxes[:, 2] - boxes[:, 0] + 1
+        h_i = boxes[:, 3] - boxes[:, 1] + 1
+        points_x = w_i.repeat(5, 1) * points[:5, :] + boxes[:, 0].repeat(5, 1) - 1
+        points_y = h_i.repeat(5, 1) * points[5:10, :] + boxes[:, 1].repeat(5, 1) - 1
+        points = torch.stack((points_x, points_y)).permute(2, 1, 0)
+        boxes = bbreg(boxes, mv)
+
+        # NMS within each image using "Min" strategy
+        # pick = batched_nms(boxes[:, :4], boxes[:, 4], image_inds, 0.7)
+        pick = batched_nms_numpy(boxes[:, :4], boxes[:, 4], image_inds, 0.7, 'Min')
+        boxes, image_inds, points = boxes[pick], image_inds[pick], points[pick]
+
+    boxes = boxes.cpu().numpy()
+    points = points.cpu().numpy()
+
+    image_inds = image_inds.cpu()
+
+    batch_boxes = []
+    batch_points = []
+    for b_i in range(batch_size):
+        b_i_inds = np.where(image_inds == b_i)
+        batch_boxes.append(boxes[b_i_inds].copy())
+        batch_points.append(points[b_i_inds].copy())
+
+    batch_boxes, batch_points = np.array(batch_boxes), np.array(batch_points)
+
+    return batch_boxes, batch_points
+
+
+def bbreg(boundingbox, reg):
+    if reg.shape[1] == 1:
+        reg = torch.reshape(reg, (reg.shape[2], reg.shape[3]))
+
+    w = boundingbox[:, 2] - boundingbox[:, 0] + 1
+    h = boundingbox[:, 3] - boundingbox[:, 1] + 1
+    b1 = boundingbox[:, 0] + reg[:, 0] * w
+    b2 = boundingbox[:, 1] + reg[:, 1] * h
+    b3 = boundingbox[:, 2] + reg[:, 2] * w
+    b4 = boundingbox[:, 3] + reg[:, 3] * h
+    boundingbox[:, :4] = torch.stack([b1, b2, b3, b4]).permute(1, 0)
+
+    return boundingbox
+
+
+def generateBoundingBox(reg, probs, scale, thresh):
+    stride = 2
+    cellsize = 12
+
+    reg = reg.permute(1, 0, 2, 3)
+
+    mask = probs >= thresh
+    mask_inds = mask.nonzero()
+    image_inds = mask_inds[:, 0]
+    score = probs[mask]
+    reg = reg[:, mask].permute(1, 0)
+    bb = mask_inds[:, 1:].type(reg.dtype).flip(1)
+    q1 = ((stride * bb + 1) / scale).floor()
+    q2 = ((stride * bb + cellsize - 1 + 1) / scale).floor()
+    boundingbox = torch.cat([q1, q2, score.unsqueeze(1), reg], dim=1)
+    return boundingbox, image_inds
+
+
+def nms_numpy(boxes, scores, threshold, method):
+    if boxes.size == 0:
+        return np.empty((0, 3))
+
+    x1 = boxes[:, 0].copy()
+    y1 = boxes[:, 1].copy()
+    x2 = boxes[:, 2].copy()
+    y2 = boxes[:, 3].copy()
+    s = scores
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+
+    I = np.argsort(s)
+    pick = np.zeros_like(s, dtype=np.int16)
+    counter = 0
+    while I.size > 0:
+        i = I[-1]
+        pick[counter] = i
+        counter += 1
+        idx = I[0:-1]
+
+        xx1 = np.maximum(x1[i], x1[idx]).copy()
+        yy1 = np.maximum(y1[i], y1[idx]).copy()
+        xx2 = np.minimum(x2[i], x2[idx]).copy()
+        yy2 = np.minimum(y2[i], y2[idx]).copy()
+
+        w = np.maximum(0.0, xx2 - xx1 + 1).copy()
+        h = np.maximum(0.0, yy2 - yy1 + 1).copy()
+
+        inter = w * h
+        if method == 'Min':
+            o = inter / np.minimum(area[i], area[idx])
+        else:
+            o = inter / (area[i] + area[idx] - inter)
+        I = I[np.where(o <= threshold)]
+
+    pick = pick[:counter].copy()
+    return pick
+
+
+def batched_nms_numpy(boxes, scores, idxs, threshold, method):
+    device = boxes.device
+    if boxes.numel() == 0:
+        return torch.empty((0,), dtype=torch.int64, device=device)
+    # strategy: in order to perform NMS independently per class.
+    # we add an offset to all the boxes. The offset is dependent
+    # only on the class idx, and is large enough so that boxes
+    # from different classes do not overlap
+    max_coordinate = boxes.max()
+    offsets = idxs.to(boxes) * (max_coordinate + 1)
+    boxes_for_nms = boxes + offsets[:, None]
+    boxes_for_nms = boxes_for_nms.cpu().numpy()
+    scores = scores.cpu().numpy()
+    keep = nms_numpy(boxes_for_nms, scores, threshold, method)
+    return torch.as_tensor(keep, dtype=torch.long, device=device)
+
+
+def pad(boxes, w, h):
+    boxes = boxes.trunc().int().cpu().numpy()
+    x = boxes[:, 0]
+    y = boxes[:, 1]
+    ex = boxes[:, 2]
+    ey = boxes[:, 3]
+
+    x[x < 1] = 1
+    y[y < 1] = 1
+    ex[ex > w] = w
+    ey[ey > h] = h
+
+    return y, ey, x, ex
+
+
+def rerec(bboxA):
+    h = bboxA[:, 3] - bboxA[:, 1]
+    w = bboxA[:, 2] - bboxA[:, 0]
+    
+    l = torch.max(w, h)
+    bboxA[:, 0] = bboxA[:, 0] + w * 0.5 - l * 0.5
+    bboxA[:, 1] = bboxA[:, 1] + h * 0.5 - l * 0.5
+    bboxA[:, 2:4] = bboxA[:, :2] + l.repeat(2, 1).permute(1, 0)
+
+    return bboxA
+
+
+def imresample(img, sz):
+    im_data = interpolate(img, size=sz, mode="area")
+    return im_data
+
+
+def crop_resize(img, box, image_size):
+    if isinstance(img, np.ndarray):
+        img = img[box[1]:box[3], box[0]:box[2]]
+        out = cv2.resize(
+            img,
+            (image_size, image_size),
+            interpolation=cv2.INTER_AREA
+        ).copy()
+    elif isinstance(img, torch.Tensor):
+        img = img[box[1]:box[3], box[0]:box[2]]
+        out = imresample(
+            img.permute(2, 0, 1).unsqueeze(0).float(),
+            (image_size, image_size)
+        ).byte().squeeze(0).permute(1, 2, 0)
+    else:
+        out = img.crop(box).copy().resize((image_size, image_size), Image.BILINEAR)
+    return out
+
+
+def save_img(img, path):
+    if isinstance(img, np.ndarray):
+        cv2.imwrite(path, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
+    else:
+        img.save(path)
+
+
+def get_size(img):
+    if isinstance(img, (np.ndarray, torch.Tensor)):
+        return img.shape[1::-1]
+    else:
+        return img.size
+
+
+def extract_face(img, box, image_size=160, margin=0, save_path=None):
+    """Extract face + margin from PIL Image given bounding box.
+    
+    Arguments:
+        img {PIL.Image} -- A PIL Image.
+        box {numpy.ndarray} -- Four-element bounding box.
+        image_size {int} -- Output image size in pixels. The image will be square.
+        margin {int} -- Margin to add to bounding box, in terms of pixels in the final image. 
+            Note that the application of the margin differs slightly from the davidsandberg/facenet
+            repo, which applies the margin to the original image before resizing, making the margin
+            dependent on the original image size.
+        save_path {str} -- Save path for extracted face image. (default: {None})
+    
+    Returns:
+        torch.tensor -- tensor representing the extracted face.
+    """
+    margin = [
+        margin * (box[2] - box[0]) / (image_size - margin),
+        margin * (box[3] - box[1]) / (image_size - margin),
+    ]
+    raw_image_size = get_size(img)
+    box = [
+        int(max(box[0] - margin[0] / 2, 0)),
+        int(max(box[1] - margin[1] / 2, 0)),
+        int(min(box[2] + margin[0] / 2, raw_image_size[0])),
+        int(min(box[3] + margin[1] / 2, raw_image_size[1])),
+    ]
+
+    face = crop_resize(img, box, image_size)
+
+    if save_path is not None:
+        os.makedirs(os.path.dirname(save_path) + "/", exist_ok=True)
+        save_img(face, save_path)
+
+    face = F.to_tensor(np.float32(face))
+
+    return face
diff --git a/Tooling/model/facenet_pytorch/models/utils/download.py b/Tooling/model/facenet_pytorch/models/utils/download.py
new file mode 100644
index 0000000..4943ffe
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/models/utils/download.py
@@ -0,0 +1,102 @@
+import hashlib
+import os
+import shutil
+import sys
+import tempfile
+
+from urllib.request import urlopen, Request
+
+try:
+    from tqdm.auto import tqdm  # automatically select proper tqdm submodule if available
+except ImportError:
+    try:
+        from tqdm import tqdm
+    except ImportError:
+        # fake tqdm if it's not installed
+        class tqdm(object):  # type: ignore
+
+            def __init__(self, total=None, disable=False,
+                         unit=None, unit_scale=None, unit_divisor=None):
+                self.total = total
+                self.disable = disable
+                self.n = 0
+                # ignore unit, unit_scale, unit_divisor; they're just for real tqdm
+
+            def update(self, n):
+                if self.disable:
+                    return
+
+                self.n += n
+                if self.total is None:
+                    sys.stderr.write("\r{0:.1f} bytes".format(self.n))
+                else:
+                    sys.stderr.write("\r{0:.1f}%".format(100 * self.n / float(self.total)))
+                sys.stderr.flush()
+
+            def __enter__(self):
+                return self
+
+            def __exit__(self, exc_type, exc_val, exc_tb):
+                if self.disable:
+                    return
+
+                sys.stderr.write('\n')
+
+
+def download_url_to_file(url, dst, hash_prefix=None, progress=True):
+    r"""Download object at the given URL to a local path.
+    Args:
+        url (string): URL of the object to download
+        dst (string): Full path where object will be saved, e.g. `/tmp/temporary_file`
+        hash_prefix (string, optional): If not None, the SHA256 downloaded file should start with `hash_prefix`.
+            Default: None
+        progress (bool, optional): whether or not to display a progress bar to stderr
+            Default: True
+    Example:
+        >>> torch.hub.download_url_to_file('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth', '/tmp/temporary_file')
+    """
+    file_size = None
+    # We use a different API for python2 since urllib(2) doesn't recognize the CA
+    # certificates in older Python
+    req = Request(url, headers={"User-Agent": "torch.hub"})
+    u = urlopen(req)
+    meta = u.info()
+    if hasattr(meta, 'getheaders'):
+        content_length = meta.getheaders("Content-Length")
+    else:
+        content_length = meta.get_all("Content-Length")
+    if content_length is not None and len(content_length) > 0:
+        file_size = int(content_length[0])
+
+    # We deliberately save it in a temp file and move it after
+    # download is complete. This prevents a local working checkpoint
+    # being overridden by a broken download.
+    dst = os.path.expanduser(dst)
+    dst_dir = os.path.dirname(dst)
+    f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir)
+
+    try:
+        if hash_prefix is not None:
+            sha256 = hashlib.sha256()
+        with tqdm(total=file_size, disable=not progress,
+                  unit='B', unit_scale=True, unit_divisor=1024) as pbar:
+            while True:
+                buffer = u.read(8192)
+                if len(buffer) == 0:
+                    break
+                f.write(buffer)
+                if hash_prefix is not None:
+                    sha256.update(buffer)
+                pbar.update(len(buffer))
+
+        f.close()
+        if hash_prefix is not None:
+            digest = sha256.hexdigest()
+            if digest[:len(hash_prefix)] != hash_prefix:
+                raise RuntimeError('invalid hash value (expected "{}", got "{}")'
+                                   .format(hash_prefix, digest))
+        shutil.move(f.name, dst)
+    finally:
+        f.close()
+        if os.path.exists(f.name):
+            os.remove(f.name)
diff --git a/Tooling/model/facenet_pytorch/models/utils/tensorflow2pytorch.py b/Tooling/model/facenet_pytorch/models/utils/tensorflow2pytorch.py
new file mode 100644
index 0000000..7e3644c
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/models/utils/tensorflow2pytorch.py
@@ -0,0 +1,416 @@
+import tensorflow as tf
+import torch
+import json
+import os, sys
+
+from dependencies.facenet.src import facenet
+from dependencies.facenet.src.models import inception_resnet_v1 as tf_mdl
+from dependencies.facenet.src.align import detect_face
+
+from models.inception_resnet_v1 import InceptionResnetV1
+from models.mtcnn import PNet, RNet, ONet
+
+
+def import_tf_params(tf_mdl_dir, sess):
+    """Import tensorflow model from save directory.
+    
+    Arguments:
+        tf_mdl_dir {str} -- Location of protobuf, checkpoint, meta files.
+        sess {tensorflow.Session} -- Tensorflow session object.
+    
+    Returns:
+        (list, list, list) -- Tuple of lists containing the layer names,
+            parameter arrays as numpy ndarrays, parameter shapes.
+    """
+    print('\nLoading tensorflow model\n')
+    if callable(tf_mdl_dir):
+        tf_mdl_dir(sess)
+    else:
+        facenet.load_model(tf_mdl_dir)
+
+    print('\nGetting model weights\n')
+    tf_layers = tf.trainable_variables()
+    tf_params = sess.run(tf_layers)
+
+    tf_shapes = [p.shape for p in tf_params]
+    tf_layers = [l.name for l in tf_layers]
+
+    if not callable(tf_mdl_dir):
+        path = os.path.join(tf_mdl_dir, 'layer_description.json')
+    else:
+        path = 'data/layer_description.json'
+    with open(path, 'w') as f:
+        json.dump({l: s for l, s in zip(tf_layers, tf_shapes)}, f)
+
+    return tf_layers, tf_params, tf_shapes
+
+
+def get_layer_indices(layer_lookup, tf_layers):
+    """Giving a lookup of model layer attribute names and tensorflow variable names,
+    find matching parameters.
+    
+    Arguments:
+        layer_lookup {dict} -- Dictionary mapping pytorch attribute names to (partial)
+            tensorflow variable names. Expects dict of the form {'attr': ['tf_name', ...]}
+            where the '...'s are ignored.
+        tf_layers {list} -- List of tensorflow variable names.
+    
+    Returns:
+        list -- The input dictionary with the list of matching inds appended to each item.
+    """
+    layer_inds = {}
+    for name, value in layer_lookup.items():
+        layer_inds[name] = value + [[i for i, n in enumerate(tf_layers) if value[0] in n]]
+    return layer_inds
+
+
+def load_tf_batchNorm(weights, layer):
+    """Load tensorflow weights into nn.BatchNorm object.
+    
+    Arguments:
+        weights {list} -- Tensorflow parameters.
+        layer {torch.nn.Module} -- nn.BatchNorm.
+    """
+    layer.bias.data = torch.tensor(weights[0]).view(layer.bias.data.shape)
+    layer.weight.data = torch.ones_like(layer.weight.data)
+    layer.running_mean = torch.tensor(weights[1]).view(layer.running_mean.shape)
+    layer.running_var = torch.tensor(weights[2]).view(layer.running_var.shape)
+
+
+def load_tf_conv2d(weights, layer, transpose=False):
+    """Load tensorflow weights into nn.Conv2d object.
+    
+    Arguments:
+        weights {list} -- Tensorflow parameters.
+        layer {torch.nn.Module} -- nn.Conv2d.
+    """
+    if isinstance(weights, list):
+        if len(weights) == 2:
+            layer.bias.data = (
+                torch.tensor(weights[1])
+                    .view(layer.bias.data.shape)
+            )
+        weights = weights[0]
+    
+    if transpose:
+        dim_order = (3, 2, 1, 0)
+    else:
+        dim_order = (3, 2, 0, 1)
+
+    layer.weight.data = (
+        torch.tensor(weights)
+            .permute(dim_order)
+            .view(layer.weight.data.shape)
+    )
+
+
+def load_tf_conv2d_trans(weights, layer):
+    return load_tf_conv2d(weights, layer, transpose=True)
+
+
+def load_tf_basicConv2d(weights, layer):
+    """Load tensorflow weights into grouped Conv2d+BatchNorm object.
+    
+    Arguments:
+        weights {list} -- Tensorflow parameters.
+        layer {torch.nn.Module} -- Object containing Conv2d+BatchNorm.
+    """
+    load_tf_conv2d(weights[0], layer.conv)
+    load_tf_batchNorm(weights[1:], layer.bn)
+
+
+def load_tf_linear(weights, layer):
+    """Load tensorflow weights into nn.Linear object.
+    
+    Arguments:
+        weights {list} -- Tensorflow parameters.
+        layer {torch.nn.Module} -- nn.Linear.
+    """
+    if isinstance(weights, list):
+        if len(weights) == 2:
+            layer.bias.data = (
+                torch.tensor(weights[1])
+                    .view(layer.bias.data.shape)
+            )
+        weights = weights[0]
+    layer.weight.data = (
+        torch.tensor(weights)
+            .transpose(-1, 0)
+            .view(layer.weight.data.shape)
+    )
+
+
+# High-level parameter-loading functions:
+
+def load_tf_block35(weights, layer):
+    load_tf_basicConv2d(weights[:4], layer.branch0)
+    load_tf_basicConv2d(weights[4:8], layer.branch1[0])
+    load_tf_basicConv2d(weights[8:12], layer.branch1[1])
+    load_tf_basicConv2d(weights[12:16], layer.branch2[0])
+    load_tf_basicConv2d(weights[16:20], layer.branch2[1])
+    load_tf_basicConv2d(weights[20:24], layer.branch2[2])
+    load_tf_conv2d(weights[24:26], layer.conv2d)
+
+
+def load_tf_block17_8(weights, layer):
+    load_tf_basicConv2d(weights[:4], layer.branch0)
+    load_tf_basicConv2d(weights[4:8], layer.branch1[0])
+    load_tf_basicConv2d(weights[8:12], layer.branch1[1])
+    load_tf_basicConv2d(weights[12:16], layer.branch1[2])
+    load_tf_conv2d(weights[16:18], layer.conv2d)
+
+
+def load_tf_mixed6a(weights, layer):
+    if len(weights) != 16:
+        raise ValueError(f'Number of weight arrays ({len(weights)}) not equal to 16')
+    load_tf_basicConv2d(weights[:4], layer.branch0)
+    load_tf_basicConv2d(weights[4:8], layer.branch1[0])
+    load_tf_basicConv2d(weights[8:12], layer.branch1[1])
+    load_tf_basicConv2d(weights[12:16], layer.branch1[2])
+
+
+def load_tf_mixed7a(weights, layer):
+    if len(weights) != 28:
+        raise ValueError(f'Number of weight arrays ({len(weights)}) not equal to 28')
+    load_tf_basicConv2d(weights[:4], layer.branch0[0])
+    load_tf_basicConv2d(weights[4:8], layer.branch0[1])
+    load_tf_basicConv2d(weights[8:12], layer.branch1[0])
+    load_tf_basicConv2d(weights[12:16], layer.branch1[1])
+    load_tf_basicConv2d(weights[16:20], layer.branch2[0])
+    load_tf_basicConv2d(weights[20:24], layer.branch2[1])
+    load_tf_basicConv2d(weights[24:28], layer.branch2[2])
+
+
+def load_tf_repeats(weights, layer, rptlen, subfun):
+    if len(weights) % rptlen != 0:
+        raise ValueError(f'Number of weight arrays ({len(weights)}) not divisible by {rptlen}')
+    weights_split = [weights[i:i+rptlen] for i in range(0, len(weights), rptlen)]
+    for i, w in enumerate(weights_split):
+        subfun(w, getattr(layer, str(i)))
+
+
+def load_tf_repeat_1(weights, layer):
+    load_tf_repeats(weights, layer, 26, load_tf_block35)
+
+
+def load_tf_repeat_2(weights, layer):
+    load_tf_repeats(weights, layer, 18, load_tf_block17_8)
+
+
+def load_tf_repeat_3(weights, layer):
+    load_tf_repeats(weights, layer, 18, load_tf_block17_8)
+
+
+def test_loaded_params(mdl, tf_params, tf_layers):
+    """Check each parameter in a pytorch model for an equivalent parameter
+    in a list of tensorflow variables.
+    
+    Arguments:
+        mdl {torch.nn.Module} -- Pytorch model.
+        tf_params {list} -- List of ndarrays representing tensorflow variables.
+        tf_layers {list} -- Corresponding list of tensorflow variable names.
+    """
+    tf_means = torch.stack([torch.tensor(p).mean() for p in tf_params])
+    for name, param in mdl.named_parameters():
+        pt_mean = param.data.mean()
+        matching_inds = ((tf_means - pt_mean).abs() < 1e-8).nonzero()
+        print(f'{name} equivalent to {[tf_layers[i] for i in matching_inds]}')
+
+
+def compare_model_outputs(pt_mdl, sess, test_data):
+    """Given some testing data, compare the output of pytorch and tensorflow models.
+    
+    Arguments:
+        pt_mdl {torch.nn.Module} -- Pytorch model.
+        sess {tensorflow.Session} -- Tensorflow session object.
+        test_data {torch.Tensor} -- Pytorch tensor.
+    """
+    print('\nPassing test data through TF model\n')
+    if isinstance(sess, tf.Session):
+        images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
+        phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
+        embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
+        feed_dict = {images_placeholder: test_data.numpy(), phase_train_placeholder: False}
+        tf_output = torch.tensor(sess.run(embeddings, feed_dict=feed_dict))
+    else:
+        tf_output = sess(test_data)
+
+    print(tf_output)
+
+    print('\nPassing test data through PT model\n')
+    pt_output = pt_mdl(test_data.permute(0, 3, 1, 2))
+    print(pt_output)
+
+    distance = (tf_output - pt_output).norm()
+    print(f'\nDistance {distance}\n')
+
+
+def compare_mtcnn(pt_mdl, tf_fun, sess, ind, test_data):
+    tf_mdls = tf_fun(sess)
+    tf_mdl = tf_mdls[ind]
+
+    print('\nPassing test data through TF model\n')
+    tf_output = tf_mdl(test_data.numpy())
+    tf_output = [torch.tensor(out) for out in tf_output]
+    print('\n'.join([str(o.view(-1)[:10]) for o in tf_output]))
+
+    print('\nPassing test data through PT model\n')
+    with torch.no_grad():
+        pt_output = pt_mdl(test_data.permute(0, 3, 2, 1))
+    pt_output = [torch.tensor(out) for out in pt_output]
+    for i in range(len(pt_output)):
+        if len(pt_output[i].shape) == 4:
+            pt_output[i] = pt_output[i].permute(0, 3, 2, 1).contiguous()
+    print('\n'.join([str(o.view(-1)[:10]) for o in pt_output]))
+
+    distance = [(tf_o - pt_o).norm() for tf_o, pt_o in zip(tf_output, pt_output)]
+    print(f'\nDistance {distance}\n')
+
+
+def load_tf_model_weights(mdl, layer_lookup, tf_mdl_dir, is_resnet=True, arg_num=None):
+    """Load tensorflow parameters into a pytorch model.
+    
+    Arguments:
+        mdl {torch.nn.Module} -- Pytorch model.
+        layer_lookup {[type]} -- Dictionary mapping pytorch attribute names to (partial)
+            tensorflow variable names, and a function suitable for loading weights.
+            Expects dict of the form {'attr': ['tf_name', function]}. 
+        tf_mdl_dir {str} -- Location of protobuf, checkpoint, meta files.
+    """
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        tf_layers, tf_params, tf_shapes = import_tf_params(tf_mdl_dir, sess)
+        layer_info = get_layer_indices(layer_lookup, tf_layers)
+
+        for layer_name, info in layer_info.items():
+            print(f'Loading {info[0]}/* into {layer_name}')
+            weights = [tf_params[i] for i in info[2]]
+            layer = getattr(mdl, layer_name)
+            info[1](weights, layer)
+
+        test_loaded_params(mdl, tf_params, tf_layers)
+
+        if is_resnet:
+            compare_model_outputs(mdl, sess, torch.randn(5, 160, 160, 3).detach())
+
+
+def tensorflow2pytorch():
+    lookup_inception_resnet_v1 = {
+        'conv2d_1a': ['InceptionResnetV1/Conv2d_1a_3x3', load_tf_basicConv2d],
+        'conv2d_2a': ['InceptionResnetV1/Conv2d_2a_3x3', load_tf_basicConv2d],
+        'conv2d_2b': ['InceptionResnetV1/Conv2d_2b_3x3', load_tf_basicConv2d],
+        'conv2d_3b': ['InceptionResnetV1/Conv2d_3b_1x1', load_tf_basicConv2d],
+        'conv2d_4a': ['InceptionResnetV1/Conv2d_4a_3x3', load_tf_basicConv2d],
+        'conv2d_4b': ['InceptionResnetV1/Conv2d_4b_3x3', load_tf_basicConv2d],
+        'repeat_1': ['InceptionResnetV1/Repeat/block35', load_tf_repeat_1],
+        'mixed_6a': ['InceptionResnetV1/Mixed_6a', load_tf_mixed6a],
+        'repeat_2': ['InceptionResnetV1/Repeat_1/block17', load_tf_repeat_2],
+        'mixed_7a': ['InceptionResnetV1/Mixed_7a', load_tf_mixed7a],
+        'repeat_3': ['InceptionResnetV1/Repeat_2/block8', load_tf_repeat_3],
+        'block8': ['InceptionResnetV1/Block8', load_tf_block17_8],
+        'last_linear': ['InceptionResnetV1/Bottleneck/weights', load_tf_linear],
+        'last_bn': ['InceptionResnetV1/Bottleneck/BatchNorm', load_tf_batchNorm],
+        'logits': ['Logits', load_tf_linear],
+    }
+
+    print('\nLoad VGGFace2-trained weights and save\n')
+    mdl = InceptionResnetV1(num_classes=8631).eval()
+    tf_mdl_dir = 'data/20180402-114759'
+    data_name = 'vggface2'
+    load_tf_model_weights(mdl, lookup_inception_resnet_v1, tf_mdl_dir)
+    state_dict = mdl.state_dict()
+    torch.save(state_dict, f'{tf_mdl_dir}-{data_name}.pt')    
+    torch.save(
+        {
+            'logits.weight': state_dict['logits.weight'],
+            'logits.bias': state_dict['logits.bias'],
+        },
+        f'{tf_mdl_dir}-{data_name}-logits.pt'
+    )
+    state_dict.pop('logits.weight')
+    state_dict.pop('logits.bias')
+    torch.save(state_dict, f'{tf_mdl_dir}-{data_name}-features.pt')
+    
+    print('\nLoad CASIA-Webface-trained weights and save\n')
+    mdl = InceptionResnetV1(num_classes=10575).eval()
+    tf_mdl_dir = 'data/20180408-102900'
+    data_name = 'casia-webface'
+    load_tf_model_weights(mdl, lookup_inception_resnet_v1, tf_mdl_dir)
+    state_dict = mdl.state_dict()
+    torch.save(state_dict, f'{tf_mdl_dir}-{data_name}.pt')    
+    torch.save(
+        {
+            'logits.weight': state_dict['logits.weight'],
+            'logits.bias': state_dict['logits.bias'],
+        },
+        f'{tf_mdl_dir}-{data_name}-logits.pt'
+    )
+    state_dict.pop('logits.weight')
+    state_dict.pop('logits.bias')
+    torch.save(state_dict, f'{tf_mdl_dir}-{data_name}-features.pt')
+    
+    lookup_pnet = {
+        'conv1': ['pnet/conv1', load_tf_conv2d_trans],
+        'prelu1': ['pnet/PReLU1', load_tf_linear],
+        'conv2': ['pnet/conv2', load_tf_conv2d_trans],
+        'prelu2': ['pnet/PReLU2', load_tf_linear],
+        'conv3': ['pnet/conv3', load_tf_conv2d_trans],
+        'prelu3': ['pnet/PReLU3', load_tf_linear],
+        'conv4_1': ['pnet/conv4-1', load_tf_conv2d_trans],
+        'conv4_2': ['pnet/conv4-2', load_tf_conv2d_trans],
+    }
+    lookup_rnet = {
+        'conv1': ['rnet/conv1', load_tf_conv2d_trans],
+        'prelu1': ['rnet/prelu1', load_tf_linear],
+        'conv2': ['rnet/conv2', load_tf_conv2d_trans],
+        'prelu2': ['rnet/prelu2', load_tf_linear],
+        'conv3': ['rnet/conv3', load_tf_conv2d_trans],
+        'prelu3': ['rnet/prelu3', load_tf_linear],
+        'dense4': ['rnet/conv4', load_tf_linear],
+        'prelu4': ['rnet/prelu4', load_tf_linear],
+        'dense5_1': ['rnet/conv5-1', load_tf_linear],
+        'dense5_2': ['rnet/conv5-2', load_tf_linear],
+    }
+    lookup_onet = {
+        'conv1': ['onet/conv1', load_tf_conv2d_trans],
+        'prelu1': ['onet/prelu1', load_tf_linear],
+        'conv2': ['onet/conv2', load_tf_conv2d_trans],
+        'prelu2': ['onet/prelu2', load_tf_linear],
+        'conv3': ['onet/conv3', load_tf_conv2d_trans],
+        'prelu3': ['onet/prelu3', load_tf_linear],
+        'conv4': ['onet/conv4', load_tf_conv2d_trans],
+        'prelu4': ['onet/prelu4', load_tf_linear],
+        'dense5': ['onet/conv5', load_tf_linear],
+        'prelu5': ['onet/prelu5', load_tf_linear],
+        'dense6_1': ['onet/conv6-1', load_tf_linear],
+        'dense6_2': ['onet/conv6-2', load_tf_linear],
+        'dense6_3': ['onet/conv6-3', load_tf_linear],
+    }
+
+    print('\nLoad PNet weights and save\n')
+    tf_mdl_dir = lambda sess: detect_face.create_mtcnn(sess, None)
+    mdl = PNet()
+    data_name = 'pnet'
+    load_tf_model_weights(mdl, lookup_pnet, tf_mdl_dir, is_resnet=False, arg_num=0)
+    torch.save(mdl.state_dict(), f'data/{data_name}.pt')
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        compare_mtcnn(mdl, tf_mdl_dir, sess, 0, torch.randn(1, 256, 256, 3).detach())
+
+    print('\nLoad RNet weights and save\n')
+    mdl = RNet()
+    data_name = 'rnet'
+    load_tf_model_weights(mdl, lookup_rnet, tf_mdl_dir, is_resnet=False, arg_num=1)
+    torch.save(mdl.state_dict(), f'data/{data_name}.pt')
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        compare_mtcnn(mdl, tf_mdl_dir, sess, 1, torch.randn(1, 24, 24, 3).detach())
+
+    print('\nLoad ONet weights and save\n')
+    mdl = ONet()
+    data_name = 'onet'
+    load_tf_model_weights(mdl, lookup_onet, tf_mdl_dir, is_resnet=False, arg_num=2)
+    torch.save(mdl.state_dict(), f'data/{data_name}.pt')
+    tf.reset_default_graph()
+    with tf.Session() as sess:
+        compare_mtcnn(mdl, tf_mdl_dir, sess, 2, torch.randn(1, 48, 48, 3).detach())
diff --git a/Tooling/model/facenet_pytorch/models/utils/training.py b/Tooling/model/facenet_pytorch/models/utils/training.py
new file mode 100644
index 0000000..20f81df
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/models/utils/training.py
@@ -0,0 +1,144 @@
+import torch
+import numpy as np
+import time
+
+
+class Logger(object):
+
+    def __init__(self, mode, length, calculate_mean=False):
+        self.mode = mode
+        self.length = length
+        self.calculate_mean = calculate_mean
+        if self.calculate_mean:
+            self.fn = lambda x, i: x / (i + 1)
+        else:
+            self.fn = lambda x, i: x
+
+    def __call__(self, loss, metrics, i):
+        track_str = '\r{} | {:5d}/{:<5d}| '.format(self.mode, i + 1, self.length)
+        loss_str = 'loss: {:9.4f} | '.format(self.fn(loss, i))
+        metric_str = ' | '.join('{}: {:9.4f}'.format(k, self.fn(v, i)) for k, v in metrics.items())
+        print(track_str + loss_str + metric_str + '   ', end='')
+        if i + 1 == self.length:
+            print('')
+
+
+class BatchTimer(object):
+    """Batch timing class.
+    Use this class for tracking training and testing time/rate per batch or per sample.
+    
+    Keyword Arguments:
+        rate {bool} -- Whether to report a rate (batches or samples per second) or a time (seconds
+            per batch or sample). (default: {True})
+        per_sample {bool} -- Whether to report times or rates per sample or per batch.
+            (default: {True})
+    """
+
+    def __init__(self, rate=True, per_sample=True):
+        self.start = time.time()
+        self.end = None
+        self.rate = rate
+        self.per_sample = per_sample
+
+    def __call__(self, y_pred, y):
+        self.end = time.time()
+        elapsed = self.end - self.start
+        self.start = self.end
+        self.end = None
+
+        if self.per_sample:
+            elapsed /= len(y_pred)
+        if self.rate:
+            elapsed = 1 / elapsed
+
+        return torch.tensor(elapsed)
+
+
+def accuracy(logits, y):
+    _, preds = torch.max(logits, 1)
+    return (preds == y).float().mean()
+
+
+def pass_epoch(
+    model, loss_fn, loader, optimizer=None, scheduler=None,
+    batch_metrics={'time': BatchTimer()}, show_running=True,
+    device='cpu', writer=None
+):
+    """Train or evaluate over a data epoch.
+    
+    Arguments:
+        model {torch.nn.Module} -- Pytorch model.
+        loss_fn {callable} -- A function to compute (scalar) loss.
+        loader {torch.utils.data.DataLoader} -- A pytorch data loader.
+    
+    Keyword Arguments:
+        optimizer {torch.optim.Optimizer} -- A pytorch optimizer.
+        scheduler {torch.optim.lr_scheduler._LRScheduler} -- LR scheduler (default: {None})
+        batch_metrics {dict} -- Dictionary of metric functions to call on each batch. The default
+            is a simple timer. A progressive average of these metrics, along with the average
+            loss, is printed every batch. (default: {{'time': iter_timer()}})
+        show_running {bool} -- Whether or not to print losses and metrics for the current batch
+            or rolling averages. (default: {False})
+        device {str or torch.device} -- Device for pytorch to use. (default: {'cpu'})
+        writer {torch.utils.tensorboard.SummaryWriter} -- Tensorboard SummaryWriter. (default: {None})
+    
+    Returns:
+        tuple(torch.Tensor, dict) -- A tuple of the average loss and a dictionary of average
+            metric values across the epoch.
+    """
+    
+    mode = 'Train' if model.training else 'Valid'
+    logger = Logger(mode, length=len(loader), calculate_mean=show_running)
+    loss = 0
+    metrics = {}
+
+    for i_batch, (x, y) in enumerate(loader):
+        x = x.to(device)
+        y = y.to(device)
+        y_pred = model(x)
+        loss_batch = loss_fn(y_pred, y)
+
+        if model.training:
+            loss_batch.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+
+        metrics_batch = {}
+        for metric_name, metric_fn in batch_metrics.items():
+            metrics_batch[metric_name] = metric_fn(y_pred, y).detach().cpu()
+            metrics[metric_name] = metrics.get(metric_name, 0) + metrics_batch[metric_name]
+            
+        if writer is not None and model.training:
+            if writer.iteration % writer.interval == 0:
+                writer.add_scalars('loss', {mode: loss_batch.detach().cpu()}, writer.iteration)
+                for metric_name, metric_batch in metrics_batch.items():
+                    writer.add_scalars(metric_name, {mode: metric_batch}, writer.iteration)
+            writer.iteration += 1
+        
+        loss_batch = loss_batch.detach().cpu()
+        loss += loss_batch
+        if show_running:
+            logger(loss, metrics, i_batch)
+        else:
+            logger(loss_batch, metrics_batch, i_batch)
+    
+    if model.training and scheduler is not None:
+        scheduler.step()
+
+    loss = loss / (i_batch + 1)
+    metrics = {k: v / (i_batch + 1) for k, v in metrics.items()}
+            
+    if writer is not None and not model.training:
+        writer.add_scalars('loss', {mode: loss.detach()}, writer.iteration)
+        for metric_name, metric in metrics.items():
+            writer.add_scalars(metric_name, {mode: metric})
+
+    return loss, metrics
+
+
+def collate_pil(x): 
+    out_x, out_y = [], [] 
+    for xx, yy in x: 
+        out_x.append(xx) 
+        out_y.append(yy) 
+    return out_x, out_y 
diff --git a/Tooling/model/facenet_pytorch/setup.py b/Tooling/model/facenet_pytorch/setup.py
new file mode 100644
index 0000000..a884c1f
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/setup.py
@@ -0,0 +1,44 @@
+import setuptools, os
+
+PACKAGE_NAME = 'facenet-pytorch'
+VERSION = '2.5.2'
+AUTHOR = 'Tim Esler'
+EMAIL = 'tim.esler@gmail.com'
+DESCRIPTION = 'Pretrained Pytorch face detection and recognition models'
+GITHUB_URL = 'https://github.com/timesler/facenet-pytorch'
+
+parent_dir = os.path.dirname(os.path.realpath(__file__))
+import_name = os.path.basename(parent_dir)
+
+with open('{}/README.md'.format(parent_dir), 'r') as f:
+    long_description = f.read()
+
+setuptools.setup(
+    name=PACKAGE_NAME,
+    version=VERSION,
+    author=AUTHOR,
+    author_email=EMAIL,
+    description=DESCRIPTION,
+    long_description=long_description,
+    long_description_content_type='text/markdown',
+    url=GITHUB_URL,
+    packages=[
+        'facenet_pytorch',
+        'facenet_pytorch.models',
+        'facenet_pytorch.models.utils',
+        'facenet_pytorch.data',
+    ],
+    package_dir={'facenet_pytorch':'.'},
+    package_data={'': ['*net.pt']},
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+    install_requires=[
+        'numpy',
+        'requests',
+        'torchvision',
+        'pillow',
+    ],
+)
diff --git a/Tooling/model/facenet_pytorch/tests/perf_test.py b/Tooling/model/facenet_pytorch/tests/perf_test.py
new file mode 100644
index 0000000..363c569
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/tests/perf_test.py
@@ -0,0 +1,39 @@
+from facenet_pytorch import MTCNN, training
+import torch
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader, RandomSampler
+from tqdm import tqdm
+import time
+
+
+def main():
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print(f'Running on device "{device}"')
+
+    mtcnn = MTCNN(device=device)
+
+    batch_size = 32
+
+    # Generate data loader
+    ds = datasets.ImageFolder(
+        root='data/test_images/',
+        transform=transforms.Resize((512, 512))
+    )
+    dl = DataLoader(
+        dataset=ds,
+        num_workers=4,
+        collate_fn=training.collate_pil,
+        batch_size=batch_size,
+        sampler=RandomSampler(ds, replacement=True, num_samples=960),
+    )
+
+    start = time.time()
+    faces = []
+    for x, _ in tqdm(dl):
+        faces.extend(mtcnn(x))
+    elapsed = time.time() - start
+    print(f'Elapsed: {elapsed} | EPS: {len(dl) * batch_size / elapsed}')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/Tooling/model/facenet_pytorch/tests/travis_requirements.txt b/Tooling/model/facenet_pytorch/tests/travis_requirements.txt
new file mode 100644
index 0000000..b10c3c4
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/tests/travis_requirements.txt
@@ -0,0 +1,14 @@
+numpy==1.16.2
+requests==2.21.0
+torch==1.3.1
+torchvision==0.4.2
+Pillow==6.1.0
+opencv-python>=4.1.0
+scipy==1.3.0
+pandas==0.24.2
+coverage==4.5.3
+codecov==2.0.15
+jupyter==1.0.0
+tensorboard==1.14.0
+future==0.17.1
+./
diff --git a/Tooling/model/facenet_pytorch/tests/travis_test.py b/Tooling/model/facenet_pytorch/tests/travis_test.py
new file mode 100644
index 0000000..f75059f
--- /dev/null
+++ b/Tooling/model/facenet_pytorch/tests/travis_test.py
@@ -0,0 +1,230 @@
+"""
+The following code is intended to be run only by travis for continuius intengration and testing
+purposes. For implementation examples see notebooks in the examples folder.
+"""
+
+from PIL import Image, ImageDraw
+import torch
+from torch.utils.data import DataLoader
+from torchvision import transforms, datasets
+import numpy as np
+import pandas as pd
+from time import time
+import sys, os
+import glob
+
+from models.mtcnn import MTCNN, fixed_image_standardization
+from models.inception_resnet_v1 import InceptionResnetV1, get_torch_home
+
+
+#### CLEAR ALL OUTPUT FILES ####
+
+checkpoints = glob.glob(os.path.join(get_torch_home(), 'checkpoints/*'))
+for c in checkpoints:
+    print('Removing {}'.format(c))
+    os.remove(c)
+
+crop_files = glob.glob('data/test_images_aligned/**/*.png')
+for c in crop_files:
+    print('Removing {}'.format(c))
+    os.remove(c)
+
+
+#### TEST EXAMPLE IPYNB'S ####
+
+os.system('jupyter nbconvert --to script --stdout examples/infer.ipynb examples/finetune.ipynb > examples/tmptest.py')
+os.chdir('examples')
+try:
+    import examples.tmptest
+except:
+    import tmptest
+os.chdir('..')
+
+
+#### TEST MTCNN ####
+
+def get_image(path, trans):
+    img = Image.open(path)
+    img = trans(img)
+    return img
+
+trans = transforms.Compose([
+    transforms.Resize(512)
+])
+
+trans_cropped = transforms.Compose([
+    np.float32,
+    transforms.ToTensor(),
+    fixed_image_standardization
+])
+
+dataset = datasets.ImageFolder('data/test_images', transform=trans)
+dataset.idx_to_class = {k: v for v, k in dataset.class_to_idx.items()}
+
+mtcnn_pt = MTCNN(device=torch.device('cpu'))
+
+names = []
+aligned = []
+aligned_fromfile = []
+for img, idx in dataset:
+    name = dataset.idx_to_class[idx]
+    start = time()
+    img_align = mtcnn_pt(img, save_path='data/test_images_aligned/{}/1.png'.format(name))
+    print('MTCNN time: {:6f} seconds'.format(time() - start))
+    
+    # Comparison between types
+    img_box = mtcnn_pt.detect(img)[0]
+    assert (img_box - mtcnn_pt.detect(np.array(img))[0]).sum() < 1e-2
+    assert (img_box - mtcnn_pt.detect(torch.as_tensor(np.array(img)))[0]).sum() < 1e-2
+
+    # Batching test
+    assert (img_box - mtcnn_pt.detect([img, img])[0]).sum() < 1e-2
+    assert (img_box - mtcnn_pt.detect(np.array([np.array(img), np.array(img)]))[0]).sum() < 1e-2
+    assert (img_box - mtcnn_pt.detect(torch.as_tensor([np.array(img), np.array(img)]))[0]).sum() < 1e-2
+
+    # Box selection
+    mtcnn_pt.selection_method = 'probability'
+    print('\nprobability - ', mtcnn_pt.detect(img))
+    mtcnn_pt.selection_method = 'largest'
+    print('largest - ', mtcnn_pt.detect(img))
+    mtcnn_pt.selection_method = 'largest_over_theshold'
+    print('largest_over_theshold - ', mtcnn_pt.detect(img))
+    mtcnn_pt.selection_method = 'center_weighted_size'
+    print('center_weighted_size - ', mtcnn_pt.detect(img))
+
+    if img_align is not None:
+        names.append(name)
+        aligned.append(img_align)
+        aligned_fromfile.append(get_image('data/test_images_aligned/{}/1.png'.format(name), trans_cropped))
+
+aligned = torch.stack(aligned)
+aligned_fromfile = torch.stack(aligned_fromfile)
+
+
+#### TEST EMBEDDINGS ####
+
+expected = [
+    [
+        [0.000000, 1.482895, 0.886342, 1.438450, 1.437583],
+        [1.482895, 0.000000, 1.345686, 1.029880, 1.061939],
+        [0.886342, 1.345686, 0.000000, 1.363125, 1.338803],
+        [1.438450, 1.029880, 1.363125, 0.000000, 1.066040],
+        [1.437583, 1.061939, 1.338803, 1.066040, 0.000000]
+    ],
+    [
+        [0.000000, 1.430769, 0.992931, 1.414197, 1.329544],
+        [1.430769, 0.000000, 1.253911, 1.144899, 1.079755],
+        [0.992931, 1.253911, 0.000000, 1.358875, 1.337322],
+        [1.414197, 1.144899, 1.358875, 0.000000, 1.204118],
+        [1.329544, 1.079755, 1.337322, 1.204118, 0.000000]
+    ]
+]
+
+for i, ds in enumerate(['vggface2', 'casia-webface']):
+    resnet_pt = InceptionResnetV1(pretrained=ds).eval()
+
+    start = time()
+    embs = resnet_pt(aligned)
+    print('\nResnet time: {:6f} seconds\n'.format(time() - start))
+
+    embs_fromfile = resnet_pt(aligned_fromfile)
+
+    dists = [[(emb - e).norm().item() for e in embs] for emb in embs]
+    dists_fromfile = [[(emb - e).norm().item() for e in embs_fromfile] for emb in embs_fromfile]
+
+    print('\nOutput:')
+    print(pd.DataFrame(dists, columns=names, index=names))
+    print('\nOutput (from file):')
+    print(pd.DataFrame(dists_fromfile, columns=names, index=names))
+    print('\nExpected:')
+    print(pd.DataFrame(expected[i], columns=names, index=names))
+
+    total_error = (torch.tensor(dists) - torch.tensor(expected[i])).norm()
+    total_error_fromfile = (torch.tensor(dists_fromfile) - torch.tensor(expected[i])).norm()
+
+    print('\nTotal error: {}, {}'.format(total_error, total_error_fromfile))
+
+    if sys.platform != 'win32':
+        assert total_error < 1e-4
+        assert total_error_fromfile < 1e-4
+
+
+#### TEST CLASSIFICATION ####
+
+resnet_pt = InceptionResnetV1(pretrained=ds, classify=True).eval()
+prob = resnet_pt(aligned)
+
+
+#### MULTI-FACE TEST ####
+
+mtcnn = MTCNN(keep_all=True)
+img = Image.open('data/multiface.jpg')
+boxes, probs = mtcnn.detect(img)
+
+draw = ImageDraw.Draw(img)
+for i, box in enumerate(boxes):
+    draw.rectangle(box.tolist())
+
+mtcnn(img, save_path='data/tmp.png')
+
+
+#### MTCNN TYPES TEST ####
+
+img = Image.open('data/multiface.jpg')
+
+mtcnn = MTCNN(keep_all=True)
+boxes_ref, _ = mtcnn.detect(img)
+_ = mtcnn(img)
+
+mtcnn = MTCNN(keep_all=True).double()
+boxes_test, _ = mtcnn.detect(img)
+_ = mtcnn(img)
+
+box_diff = boxes_ref[np.argsort(boxes_ref[:,1])] - boxes_test[np.argsort(boxes_test[:,1])]
+total_error = np.sum(np.abs(box_diff))
+print('\nfp64 Total box error: {}'.format(total_error))
+
+assert total_error < 1e-2
+
+
+# half is not supported on CPUs, only GPUs
+if torch.cuda.is_available():
+
+    mtcnn = MTCNN(keep_all=True, device='cuda').half()
+    boxes_test, _ = mtcnn.detect(img)
+    _ = mtcnn(img)
+
+    box_diff = boxes_ref[np.argsort(boxes_ref[:,1])] - boxes_test[np.argsort(boxes_test[:,1])]
+    print('fp16 Total box error: {}'.format(np.sum(np.abs(box_diff))))
+
+    # test new automatic multi precision to compare
+    if hasattr(torch.cuda, 'amp'):
+        with torch.cuda.amp.autocast():
+            mtcnn = MTCNN(keep_all=True, device='cuda')
+            boxes_test, _ = mtcnn.detect(img)
+            _ = mtcnn(img)
+
+        box_diff = boxes_ref[np.argsort(boxes_ref[:,1])] - boxes_test[np.argsort(boxes_test[:,1])]
+        print('AMP total box error: {}'.format(np.sum(np.abs(box_diff))))
+
+            
+#### MULTI-IMAGE TEST ####
+
+mtcnn = MTCNN(keep_all=True)
+img = [
+    Image.open('data/multiface.jpg'),
+    Image.open('data/multiface.jpg')
+]
+batch_boxes, batch_probs = mtcnn.detect(img)
+
+mtcnn(img, save_path=['data/tmp1.png', 'data/tmp1.png'])
+tmp_files = glob.glob('data/tmp*')
+for f in tmp_files:
+    os.remove(f)
+
+
+#### NO-FACE TEST ####
+
+img = Image.new('RGB', (512, 512))
+mtcnn(img)
+mtcnn(img, return_prob=True)
diff --git a/Tooling/scripts/README.md b/Tooling/scripts/README.md
new file mode 100644
index 0000000..261e794
--- /dev/null
+++ b/Tooling/scripts/README.md
@@ -0,0 +1,217 @@
+### Grading Scripts
+
+
+- [Project-1 Part-1 Test Case 1](https://github.com/visa-lab/CSE546-Cloud-Computing/blob/main/scripts/project1_grader.py)
+  - Test Objective: 
+
+    | Test # | Test Case            | Test Criteria                                                                                                                | Poor                                     | Good                                                | Excellent                                                | Total Points |
+    |--------|----------------------|------------------------------------------------------------------------------------------------------------------------------|------------------------------------------|-----------------------------------------------------|----------------------------------------------------------|--------------|
+    | 1      | Validate EC2 Instance | "To check if 1) If there exists a EC2 instance with name ""web-instance"" 2) if exists then check if the state of the web-instance in ""running""" | There is no EC2 intance with name "web-instance" (0) | The EC2 instance with the name "web-instance" exists; but is not in "running" state (5) | The EC2 instance with the name "web-instance" exists; and is in "running" state (10) | 10        |
+  
+  - How to run the script: 
+    ```
+    usage: project1_grader.py [-h] [--access_keyId ACCESS_KEYID] [--access_key ACCESS_KEY]
+    Grading Script
+    options:
+    -h, --help                    show this help message and exit
+    --access_keyId ACCESS_KEYID   ACCCESS KEY ID of the grading IAM user
+    --access_key ACCESS_KEY       SECRET ACCCESS KEY of the grading IAM user
+    ```
+
+- [Project-1 Part-2 Grading Script](https://github.com/visa-lab/CSE546-Cloud-Computing/blob/main/scripts/p2_grader.py):
+
+  - How to use the script:
+    ```
+    usage: p2_grader.py [-h] [--access_keyId ACCESS_KEYID] [--access_key ACCESS_KEY] [--req_sqs REQ_SQS] [--resp_sqs RESP_SQS] [--in_bucket IN_BUCKET] [--out_bucket OUT_BUCKET]
+    Grading Script  
+    options:
+      -h, --help                    show this help message and exit
+      --access_keyId ACCESS_KEYID   ACCCESS KEY ID of the grading IAM user
+      --access_key ACCESS_KEY       SECRET ACCCESS KEY of the grading IAM user
+      --req_sqs REQ_SQS         Name of the Request SQS Queue
+      --resp_sqs RESP_SQS       Name of the Response SQS Queue
+      --in_bucket IN_BUCKET     Name of the S3 Input Bucket
+      --out_bucket OUT_BUCKET   Name of the S3 Output Bucket
+    ```
+    **Note**: We will follow the naming conventions for S3 Bucket and SQS Queue names as described in the project document to grade your submission
+    
+    ```
+      =============================================================================
+      ======== Welcome to CSE546 Cloud Computing AWS Console ======================
+      =============================================================================
+      IAM ACESS KEY ID: XXXXXXXXXXXXX
+      IAM SECRET ACCESS KEY: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
+      =============================================================================
+      1 - Validate EC2 Instances
+      2 - Validate S3 Buckets
+      3 - Validate SQS Queues
+      4 - Validate autoscaling
+      0 - Exit
+      Enter a choice:
+    ```
+    
+  
+  - **Validate EC2 Instance**: 
+    ```
+    =============================================================================
+    ======== Welcome to CSE546 Cloud Computing AWS Console ======================
+    =============================================================================
+    IAM ACESS KEY ID: XXXXXXXXXXXXX
+    IAM SECRET ACCESS KEY: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
+    =============================================================================
+    1 - Validate EC2 Instances
+    2 - Validate S3 Buckets
+    3 - Validate SQS Queues
+    4 - Validate autoscaling
+    0 - Exit
+    Enter a choice:
+    1
+    Found 1 web-tier instances in running state.
+    Found 0 app-tier instanes in running state
+    ```
+      
+  - **Validate S3 Buckets**
+    ```
+    =============================================================================
+    ======== Welcome to CSE546 Cloud Computing AWS Console ======================
+    =============================================================================
+    IAM ACESS KEY ID: XXXXXXXXXXXXX
+    IAM SECRET ACCESS KEY: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX  
+    =============================================================================
+    1 - Validate EC2 Instances
+    2 - Validate S3 Buckets
+    3 - Validate SQS Queues
+    4 - Validate autoscaling
+    0 - Exit
+    Enter a choice:
+    2
+     - WARN: If there are objects in the S3 buckets; they will be deleted
+     ---------------------------------------------------------
+    S3 Input Bucket:12345678910-in-bucket has 0 object(s)
+    S3 Output Bucket:12345678910-out-bucket has 0 object(s)
+    ```
+      
+  - **Validate SQS Queue**
+    ```
+    =============================================================================
+    ======== Welcome to CSE546 Cloud Computing AWS Console ======================
+    =============================================================================
+    IAM ACESS KEY ID: XXXXXXXXXXXXX
+    IAM SECRET ACCESS KEY: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
+    =============================================================================
+    1 - Validate EC2 Instances
+    2 - Validate S3 Buckets
+    3 - Validate SQS Queues
+    4 - Validate autoscaling
+    0 - Exit
+    Enter a choice:
+    3
+     - The expectation is the both the Request and Response SQS should exist and be EMPTY
+     - WARN: This will purge any messages available in the SQS
+     ---------------------------------------------------------
+    SQS Request Queue:12345678910-req-queue has 0 pending messages.
+    SQS Response Queue:12345678910-resp-queue has 0 pending messages.
+    ```
+      
+  - **Validate autoscaling**
+    ```
+    =============================================================================
+    ======== Welcome to CSE546 Cloud Computing AWS Console ======================
+    =============================================================================
+    IAM ACESS KEY ID: XXXXXXXXXXXXX
+    IAM SECRET ACCESS KEY: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
+    =============================================================================
+    1 - Validate EC2 Instances
+    2 - Validate S3 Buckets
+    3 - Validate SQS Queues
+    4 - Validate autoscaling
+    0 - Exit
+    Enter a choice:
+    4
+     - Run this BEFORE the workload generator client starts. Press Ctrl^C to exit.
+     - The expectation is as follows:
+     -- # of app tier instances should gradually scale and eventually reduce back to 0
+     -- # of SQS messages should gradually increase and eventually reduce back to 0
+    ------------------------------------------------------------------------------------------------------------------
+    |   # of messages in   |   # of messages in   |  # of EC2 instances  |  # of objects in S3  |  # of objects in S3  |
+    |  SQS Request Queue   |  SQS Response Queue  |   in running state   |     Input Bucket     |     Output Bucket    |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          0           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          0           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          0           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          0           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          0           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          0           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          1           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          2           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          4           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          8           |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          11          |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          16          |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          17          |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          19          |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          50          |          0           |          20          |          0           |          0           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          48          |          0           |          20          |          2           |          2           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          35          |          0           |          20          |          2           |          2           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          35          |          0           |          20          |          9           |          9           |
+    ------------------------------------------------------------------------------------------------------------------
+    |          35          |          0           |          20          |          12          |          12          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          32          |          0           |          20          |          21          |          21          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          42          |          4           |          20          |          28          |          29          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          20          |          40          |          40          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          20          |          47          |          47          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          4           |          0           |          20          |          49          |          49          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          41          |          4           |          20          |          49          |          49          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          3           |          20          |          49          |          49          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          12          |          20          |          49          |          49          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          20          |          49          |          49          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          20          |          49          |          49          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          8           |          20          |          49          |          49          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          20          |          50          |          50          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          3           |          20          |          50          |          50          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          20          |          50          |          50          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          15          |          50          |          50          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          6           |          50          |          50          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          0           |          50          |          50          |
+    ------------------------------------------------------------------------------------------------------------------
+    |          0           |          0           |          0           |          50          |          50          |
+    ------------------------------------------------------------------------------------------------------------------
+    ```
+    The above example output is for a 50-requests workload.
+
+
+**Note**: This does not show all the test cases for the project. Please refer to the project document for more details.
diff --git a/Tooling/scripts/p2_grader.py b/Tooling/scripts/p2_grader.py
new file mode 100644
index 0000000..ec1bf00
--- /dev/null
+++ b/Tooling/scripts/p2_grader.py
@@ -0,0 +1,202 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import os
+import pdb
+import time
+import json
+import boto3
+import httpx
+import sys
+import argparse
+import textwrap
+
+class aws_grader():
+    def __init__(self, access_keyId, access_key, req_sqs, resp_sqs, in_bucket, out_bucket):
+
+        self.iam_access_keyId       = access_keyId
+        self.iam_secret_access_key  = access_key
+        self.iam_session            = boto3.Session(aws_access_key_id = self.iam_access_keyId,
+                                                    aws_secret_access_key = self.iam_secret_access_key)
+        self.ec2_resources          = self.iam_session.resource('ec2', 'us-east-1')
+        self.s3_resources           = self.iam_session.resource('s3', 'us-east-1')
+        self.sqs_resources          = self.iam_session.resource('sqs', 'us-east-1')
+        self.sqs_client             = self.iam_session.client('sqs', 'us-east-1')
+        self.req_sqs_name           = req_sqs
+        self.resp_sqs_name          = resp_sqs
+        self.in_bucket_name         = in_bucket
+        self.out_bucket_name        = out_bucket
+        self.app_tier_tag           = "app-tier-instance"
+        self.web_tier_tag           = "web-instance"
+
+    def get_instance_details(self, tag, state):
+        instances = self.ec2_resources.instances.filter(
+            Filters=[
+                {'Name': 'tag:Name', 'Values': [tag+"*"]},
+                {'Name': 'instance-state-name', 'Values': [state]}
+            ]
+        )
+        return len(list(instances))
+
+    def validate_ec2_instance(self):
+        web_instances = self.get_instance_details(self.web_tier_tag, 'running')
+        app_instances = self.get_instance_details(self.app_tier_tag, 'running')
+        print(f"Found {web_instances} web-tier instances in running state.")
+        print(f"Found {app_instances} app-tier instanes in running state")
+
+    def empty_s3_bucket(self, bucket_name):
+        bucket = self.s3_resources.Bucket(bucket_name)
+        bucket.objects.all().delete()
+        print(f"{bucket_name} S3 Bucket is now EMPTY !!")
+
+    def count_bucket_objects(self, bucket_name):
+        bucket = self.s3_resources.Bucket(bucket_name)
+        count  = 0
+        for index in bucket.objects.all():
+            count += 1
+        #print(f"{bucket_name} S3 Bucket has {count} objects !!")
+        return count
+
+    def validate_s3_buckets(self):
+        print(" - WARN: If there are objects in the S3 buckets; they will be deleted")
+        print(" ---------------------------------------------------------")
+        ip_obj_count = self.count_bucket_objects(self.in_bucket_name)
+        op_obj_count = self.count_bucket_objects(self.out_bucket_name)
+
+        print(f"S3 Input Bucket:{self.in_bucket_name} has {ip_obj_count} object(s)")
+        print(f"S3 Output Bucket:{self.out_bucket_name} has {op_obj_count} object(s)")
+
+        if ip_obj_count:
+            self.empty_s3_bucket(self.in_bucket_name)
+        if op_obj_count:
+            self.empty_s3_bucket(self.out_bucket_name)
+
+    def get_sqs_queue_length(self, sqs_queue_name):
+        num_requests = self.sqs_client.get_queue_attributes(
+                QueueUrl=sqs_queue_name,
+                AttributeNames=['ApproximateNumberOfMessages'])
+        return int(num_requests['Attributes']['ApproximateNumberOfMessages'])
+
+    def validate_sqs_queues(self):
+        print(" - The expectation is the both the Request and Response SQS should exist and be EMPTY")
+        print(" - WARN: This will purge any messages available in the SQS")
+        print(" ---------------------------------------------------------")
+
+        try:
+            req_sqs  = self.sqs_resources.get_queue_by_name(QueueName=self.req_sqs_name)
+            resp_sqs = self.sqs_resources.get_queue_by_name(QueueName=self.resp_sqs_name)
+
+            ip_queue_requests = self.get_sqs_queue_length(self.req_sqs_name)
+            op_queue_response = self.get_sqs_queue_length(self.resp_sqs_name)
+
+            print(f"SQS Request Queue:{self.req_sqs_name} has {ip_queue_requests} pending messages.")
+            print(f"SQS Response Queue:{self.resp_sqs_name} has {op_queue_response} pending messages.")
+
+            if ip_queue_requests:
+                print(" - WARN: Purging the Requeust SQS. Waiting 60 seconds ..")
+                self.sqs_client.purge_queue(QueueUrl=self.req_sqs_name)
+                time.sleep(60)
+
+            if op_queue_response:
+                print(" - WARN: Purging the SQS. Waiitng 60 seconds ..")
+                self.sqs_client.purge_queue(QueueUrl=self.resp_sqs_name)
+                time.sleep(60)
+
+        except Exception as ex:
+            print(f"SQS Queues Error: {ex}. Please Check your AWS Account")
+
+    def beautify_headers(self):
+
+        column1 = " # of messages in SQS Request Queue "
+        column2 = " # of messages in SQS Response Queue "
+        column3 = " # of EC2 instances in running state "
+        column4 = " # of objects in S3 Input Bucket "
+        column5 = " # of objects in S3 Output Buket "
+
+        column_width = 20
+        wrapped_column1 = textwrap.fill(column1, column_width)
+        wrapped_column2 = textwrap.fill(column2, column_width)
+        wrapped_column3 = textwrap.fill(column3, column_width)
+        wrapped_column4 = textwrap.fill(column4, column_width)
+        wrapped_column5 = textwrap.fill(column5, column_width)
+        lines1 = wrapped_column1.split('\n')
+        lines2 = wrapped_column2.split('\n')
+        lines3 = wrapped_column3.split('\n')
+        lines4 = wrapped_column4.split('\n')
+        lines5 = wrapped_column5.split('\n')
+
+        print("-" * 114)
+        for line1, line2, line3, line4, line5 in zip(lines1, lines2, lines3, lines4, lines5):
+                print(f"| {line1.center(column_width)} | {line2.center(column_width)} | {line3.center(column_width)} | {line4.center(column_width)} | {line5.center(column_width)} |")
+
+        print("-" *114)
+
+    def validate_autoscaling(self):
+        print(" - Run this BEFORE the workload generator client starts. Press Ctrl^C to exit.")
+        print(" - The expectation is as follows:")
+        print(" -- # of app tier instances should gradually scale and eventually reduce back to 0")
+        print(" -- # of SQS messages should gradually increase and eventually reduce back to 0")
+        self.beautify_headers()
+        format_string = "| {:^20} | {:^20} | {:^20} | {:^20} | {:^20} |"
+
+        while True:
+            req_queue_count  = self.get_sqs_queue_length(self.req_sqs_name)
+            resp_queue_count = self.get_sqs_queue_length(self.resp_sqs_name)
+            num_instances    = self.get_instance_details(self.app_tier_tag, 'running')
+            ip_obj_count     = self.count_bucket_objects(self.in_bucket_name)
+            op_obj_count     = self.count_bucket_objects(self.out_bucket_name)
+            print(format_string.format(req_queue_count, resp_queue_count, num_instances, ip_obj_count, op_obj_count))
+            print("-" * 114)
+            time.sleep(2)
+
+    def display_menu(self):
+        print("\n")
+        print("=============================================================================")
+        print("======== Welcome to CSE546 Cloud Computing AWS Console ======================")
+        print("=============================================================================")
+        print(f"IAM ACESS KEY ID: {self.iam_access_keyId}")
+        print(f"IAM SECRET ACCESS KEY: {self.iam_secret_access_key}")
+        print("=============================================================================")
+        print("1 - Validate EC2 Instances")
+        print("2 - Validate S3 Buckets")
+        print("3 - Validate SQS Queues")
+        print("4 - Validate autoscaling")
+        print("0 - Exit")
+        print("Enter a choice:")
+        choice = input()
+        return choice
+
+    def main(self):
+        while(1):
+            choice = self.display_menu()
+            if int(choice) == 1:
+                self.validate_ec2_instance()
+            elif int(choice) == 2:
+                self.validate_s3_buckets()
+            elif int(choice) == 3:
+                self.validate_sqs_queues()
+            elif int(choice) == 4:
+                self.validate_autoscaling()
+            elif int(choice) == 0:
+                break
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Grading Script')
+    parser.add_argument('--access_keyId', type=str, help='ACCCESS KEY ID of the grading IAM user')
+    parser.add_argument('--access_key', type=str, help='SECRET ACCCESS KEY of the grading IAM user')
+    parser.add_argument('--req_sqs', type=str, help="Name of the Request SQS Queue")
+    parser.add_argument('--resp_sqs', type=str, help="Name of the Response SQS Queue")
+    parser.add_argument('--in_bucket', type=str, help='Name of the S3 Input Bucket')
+    parser.add_argument('--out_bucket', type=str, help='Name of the S3 Output Bucket')
+
+    args = parser.parse_args()
+
+    access_keyId = args.access_keyId
+    access_key   = args.access_key
+    req_sqs      = args.req_sqs
+    resp_sqs     = args.resp_sqs
+    in_bucket    = args.in_bucket
+    out_bucket   = args.out_bucket
+
+    aws_obj = aws_grader(access_keyId, access_key, req_sqs, resp_sqs, in_bucket, out_bucket)
+    aws_obj.main()
diff --git a/Tooling/scripts/project1_grader.py b/Tooling/scripts/project1_grader.py
new file mode 100644
index 0000000..1689620
--- /dev/null
+++ b/Tooling/scripts/project1_grader.py
@@ -0,0 +1,78 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import os
+import boto3
+import dotenv
+import httpx
+import pdb
+import json
+import argparse
+
+class aws_grader():
+    def __init__(self, access_keyId, access_key):
+
+        self.iam_access_keyId       = access_keyId
+        self.iam_secret_access_key  = access_key
+        self.iam_session            = boto3.Session(aws_access_key_id = self.iam_access_keyId,
+                                                    aws_secret_access_key = self.iam_secret_access_key)
+        self.ec2_resources          = self.iam_session.resource('ec2', 'us-east-1')
+
+    def get_tag(self, tags, key='Name'):
+
+        if not tags:
+            return 'None'
+        for tag in tags:
+            if tag['Key'] == key:
+                return tag['Value']
+        return 'None'
+
+
+    '''
+        Test Case:1
+            1) Checks if there is a web EC2 instance with the name "web-instance"
+            2) Checks if the state of "web-instance" is "running".
+    '''
+
+    def test_case_1(self):
+        message = ""
+        for instance in self.ec2_resources.instances.all():
+            name = self.get_tag(instance.tags)
+            if name == "web-instance":
+                message += "web-tier instance found."
+                state = instance.state['Name']
+                message += f" With state: {state}"
+                if state == "running":
+                    self.web_tier_instanceId = instance.id
+                    print(f"Test Case:1 Passed. {message}")
+                    return
+                else:
+                    print(f"Test Case:1 Failed. {message}")
+                    return
+
+        message += "web-tier Instance Not Found."
+        print("Test Case:1 Failed. {message}")
+
+
+    def main(self):
+        # Use the session to access resources via the role
+
+        print("======== Welcome to CSE546 Cloud Computing Grading Console ==================")
+        print(f"IAM ACESS KEY ID: {self.iam_access_keyId}")
+        print(f"IAM SECRET ACCESS KEY: {self.iam_secret_access_key}")
+
+        self.test_case_1()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Upload images')
+    parser.add_argument('--access_keyId', type=str, help='ACCCESS KEY ID of the grading IAM user')
+    parser.add_argument('--access_key', type=str, help='SECRET ACCCESS KEY of the grading IAM user')
+
+    args = parser.parse_args()
+
+    access_keyId = args.access_keyId
+    access_key   = args.access_key
+
+    aws_obj = aws_grader(access_keyId, access_key)
+    aws_obj.main()
diff --git a/Tooling/workload_generator/README.md b/Tooling/workload_generator/README.md
new file mode 100644
index 0000000..d04ecd5
--- /dev/null
+++ b/Tooling/workload_generator/README.md
@@ -0,0 +1,54 @@
+# CSE546 workload_generator
+
+This repository contains code examples for you to use our workload generator.
+
+Usage:
+```
+usage: workload_generator.py [-h] [--num_request NUM_REQUEST] [--url URL] [--image_folder IMAGE_FOLDER] [--prediction_file PREDICTION_FILE]
+Upload images
+options:
+  -h, --help            show this help message and exit
+  --num_request NUM_REQUEST
+                        one image per request
+  --url URL             URL to the backend server, e.g. http://3.86.108.221:8000/
+  --image_folder IMAGE_FOLDER
+                        the path of the folder where images are saved
+  --prediction_file PREDICTION_FILE
+                        the path of the classification results file
+```
+
+Examples:
+
+The following command sends three requests to a php backend.
+```
+python3 workload_generator.py \
+ --num_request 3 \
+ --url 'http://your_host_ip/php_server.php' \
+ --image_folder "your_local_image_folder" \
+ --prediction_file "ground_truth_prediction_file"
+```
+
+The following command sends three requests to a node.js/Python backend.
+```
+python3 workload_generator.py \
+ --num_request 3 \
+ --url 'http://<server_ip>:<server_port>' \
+ --image_folder "your_local_image_folder" \
+ --prediction_file "ground_truth_prediction_file"
+```
+**Note**: The workload generator accepts the absolute path in the command line arguments. 
+
+Sample output:
+```
+test_411.jpg uploaded!
+Classification result: test_411:Emily
+Waiting ...
+.
++++++ Test Result Statistics +++++
+Total number of requests: 1000
+Total number of requests completed successfully: 1000
+Total number of failed requests: 0
+Total number of correct predictions: 1000
+Total number of wrong predictions: 0
+Total Test Duration: 2.104762077331543 (seconds)
+```
diff --git a/Tooling/workload_generator/workload_generator.py b/Tooling/workload_generator/workload_generator.py
new file mode 100644
index 0000000..e4742b2
--- /dev/null
+++ b/Tooling/workload_generator/workload_generator.py
@@ -0,0 +1,95 @@
+__copyright__   = "Copyright 2024, VISA Lab"
+__license__     = "MIT"
+
+import sys
+import os
+import time
+import _thread
+import argparse
+import requests
+import subprocess
+import numpy as np
+import pandas as pd
+import threading
+from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import as_completed
+
+parser = argparse.ArgumentParser(description='Upload images')
+parser.add_argument('--num_request', type=int, help='one image per request')
+parser.add_argument('--url', type=str, help='URL to the backend server, e.g. http://3.86.108.221:8000/')
+parser.add_argument('--image_folder', type=str, help='the path of the folder where images are saved')
+parser.add_argument('--prediction_file', type=str, help='the path of the classification results file')
+args = parser.parse_args()
+
+num_request     = args.num_request
+url             = args.url
+image_folder    = args.image_folder
+prediction_file = args.prediction_file
+prediction_df   = pd.read_csv(prediction_file)
+responses       = 0
+err_responses   = 0
+correct_predictions = 0
+wrong_predictions   = 0
+ex_requests         = []
+
+def send_one_request(image_path):
+    global prediction_df, responses, err_responses, correct_predictions, wrong_predictions, ex_requests
+    # Define http payload, "myfile" is the key of the http payload
+    file = {"inputFile": open(image_path,'rb')}
+    try:
+        response = requests.post(url, files=file)
+        # Print error message if failed
+        if response.status_code != 200:
+            print('sendErr: '+response.url)
+            err_responses +=1
+        else :
+            filename    = os.path.basename(image_path)
+            image_msg   = filename + ' uploaded!'
+            msg         = image_msg + '\n' + 'Classification result: ' + response.text
+            print(msg)
+            responses   +=1
+            correct_result = prediction_df.loc[prediction_df['Image'] == filename.split('.')[0], 'Results'].iloc[0]
+            if correct_result.strip() == response.text.split(':')[1].strip():
+                correct_predictions +=1
+            else:
+                wrong_predictions +=1
+    except requests.exceptions.RequestException as errex:
+        print("Exception:", errex)
+        ex_requests.append(image_path)
+
+num_max_workers = 100
+image_path_list = []
+test_start_time = time.time()
+
+for i, name in enumerate(os.listdir(image_folder)):
+    if i == num_request:
+        break
+    image_path_list.append(os.path.join(image_folder,name))
+
+with ThreadPoolExecutor(max_workers = num_max_workers) as executor:
+    executor.map(send_one_request, image_path_list)
+
+print(f"Attempt-1 {responses}/{num_request} requests successful.")
+
+# Retry requests until all requests are successful or ex_requests is empty
+retry_attempt=2
+while ex_requests:
+    retry_requests = ex_requests.copy()
+    ex_requests.clear()
+    with ThreadPoolExecutor(max_workers=num_max_workers) as executor:
+        executor.map(send_one_request, retry_requests)
+    print(f"Attempt-{retry_attempt} {responses}/{num_request} requests successful.")
+    retry_attempt += 1
+
+test_duration = time.time() - test_start_time
+print("All requests have been processed or retried.")
+
+if num_request == (responses + err_responses):
+   print (f"+++++ Test Result Statistics +++++")
+   print (f"Total number of requests: {num_request}")
+   print (f"Total number of requests completed successfully: {responses}")
+   print (f"Total number of failed requests: {err_responses}")
+   print (f"Total number of correct predictions : {correct_predictions}")
+   print (f"Total number of wrong predictions: {wrong_predictions}")
+   print (f"Total Test Duration: {test_duration} (seconds)")
+   print ("++++++++++++++++++++++++++++++++++++")