code: def test_default_monitor_create_stop_and_start_monitoring_schedule_with_customizations(
      sagemaker_session, output_kms_key, volume_kms_key, predictor
  ):
      my_default_monitor = DefaultModelMonitor(
          role=ROLE,
          instance_count=INSTANCE_COUNT,
          instance_type=INSTANCE_TYPE,
          volume_size_in_gb=VOLUME_SIZE_IN_GB,
          volume_kms_key=volume_kms_key,
          output_kms_key=output_kms_key,
          max_runtime_in_seconds=MAX_RUNTIME_IN_SECONDS,
          sagemaker_session=sagemaker_session,
          env=ENVIRONMENT,
          tags=TAGS,
          network_config=NETWORK_CONFIG,
      )
  
      output_s3_uri = os.path.join(
          "s3://",
          sagemaker_session.default_bucket(),
          INTEG_TEST_MONITORING_OUTPUT_BUCKET,
          str(uuid.uuid4()),
      )
  
      statistics = Statistics.from_file_path(
          statistics_file_path=os.path.join(
              tests.integ.DATA_DIR, "monitor/statistics.json"
          ),
          sagemaker_session=sagemaker_session,
      )
  
      constraints = Constraints.from_file_path(
          constraints_file_path=os.path.join(
              tests.integ.DATA_DIR, "monitor/constraints.json"
          ),
          sagemaker_session=sagemaker_session,
      )
  
      my_default_monitor.create_monitoring_schedule(
          endpoint_input=predictor.endpoint_name,
          output_s3_uri=output_s3_uri,
          statistics=statistics,
          constraints=constraints,
          schedule_cron_expression=CronExpressionGenerator.daily(),
          enable_cloudwatch_metrics=ENABLE_CLOUDWATCH_METRICS,
      )
  
      schedule_description = my_default_monitor.describe_schedule()
      _verify_default_monitoring_schedule(
          sagemaker_session=sagemaker_session,
          schedule_description=schedule_description,
          statistics=statistics,
          constraints=constraints,
          output_kms_key=output_kms_key,
          volume_kms_key=volume_kms_key,
          network_config=NETWORK_CONFIG,
      )
  
      _wait_for_schedule_changes_to_apply(monitor=my_default_monitor)
  
      my_default_monitor.stop_monitoring_schedule()
  
      _wait_for_schedule_changes_to_apply(monitor=my_default_monitor)
  
      stopped_schedule_description = (
          my_default_monitor.describe_schedule()
      )
      assert (
          stopped_schedule_description["MonitoringScheduleStatus"]
          == "Stopped"
      )
  
      my_default_monitor.start_monitoring_schedule()
  
      _wait_for_schedule_changes_to_apply(monitor=my_default_monitor)
  
      started_schedule_description = (
          my_default_monitor.describe_schedule()
      )
      assert (
          started_schedule_description["MonitoringScheduleStatus"]
          == "Scheduled"
      )
  
      my_default_monitor.stop_monitoring_schedule()
  
      _wait_for_schedule_changes_to_apply(monitor=my_default_monitor)
  
imports: from __future__ import absolute_import
  import json
  import os
  import uuid
  import time
  import pytest
  import tests.integ
  import tests.integ.timeout
  from sagemaker.s3 import S3Uploader
  from datetime import datetime, timedelta
  from tests.integ import DATA_DIR
  from sagemaker.model_monitor import DatasetFormat
  from sagemaker.model_monitor import MonitoringDatasetFormat
  from sagemaker.model_monitor import NetworkConfig, Statistics, Constraints
  from sagemaker.model_monitor import ModelMonitor
  from sagemaker.model_monitor import DefaultModelMonitor
  from sagemaker.model_monitor import MonitoringOutput
  from sagemaker.model_monitor import DataCaptureConfig
  from sagemaker.model_monitor import BatchTransformInput
  from sagemaker.model_monitor import DataQualityMonitoringConfig
  from sagemaker.model_monitor import DataQualityDistributionConstraints
  from sagemaker.model_monitor.data_capture_config import _MODEL_MONITOR_S3_PATH
  from sagemaker.model_monitor.data_capture_config import _DATA_CAPTURE_S3_PATH
  from sagemaker.model_monitor import CronExpressionGenerator
  from sagemaker.model_monitor.monitoring_alert import MonitoringAlertSummary
  from sagemaker.processing import ProcessingInput
  from sagemaker.processing import ProcessingOutput
  from sagemaker.tensorflow.model import TensorFlowModel
  from sagemaker.utils import unique_name_from_base
  from tests.integ.kms_utils import get_or_create_kms_key
  from tests.integ.retry import retries
label_desc: Use image node_id 15 for calling a global function with example usage: test_default_monitor_create_stop_and_start_monitoring_schedule_with_customizations(sagemaker_session, output_kms_key, volume_kms_key, predictor) without return types