code: def test_default_monitor_monitoring_alerts(
      sagemaker_session, predictor
  ):
      my_default_monitor = DefaultModelMonitor(
          role=ROLE,
          instance_count=INSTANCE_COUNT,
          instance_type=INSTANCE_TYPE,
          volume_size_in_gb=VOLUME_SIZE_IN_GB,
          max_runtime_in_seconds=MAX_RUNTIME_IN_SECONDS,
          sagemaker_session=sagemaker_session,
      )
  
      output_s3_uri = os.path.join(
          "s3://",
          sagemaker_session.default_bucket(),
          "integ-test-monitoring-output-bucket",
          str(uuid.uuid4()),
      )
  
      my_default_monitor.create_monitoring_schedule(
          endpoint_input=predictor.endpoint_name,
          output_s3_uri=output_s3_uri,
          schedule_cron_expression=HOURLY_CRON_EXPRESSION,
          enable_cloudwatch_metrics=ENABLE_CLOUDWATCH_METRICS,
      )
      # No need to wait for execution
      description = my_default_monitor.describe_schedule()
      assert (
          description["MonitoringScheduleName"]
          == my_default_monitor.monitoring_schedule_name
      )
      # list alerts
      alerts, _ = my_default_monitor.list_monitoring_alerts(
          max_results=10
      )
      # we know each monitoring schedule will have a default alert
      assert len(alerts) > 0
      alert: MonitoringAlertSummary = alerts[0]
      alert_name = alert.alert_name
      alert_status = alert.alert_status
  
      my_default_monitor.update_monitoring_alert(
          alert_name, data_points_to_alert=3, evaluation_period=5
      )
      alerts, _ = my_default_monitor.list_monitoring_alerts(
          max_results=10
      )
      alert: MonitoringAlertSummary = alerts[0]
      assert len(alerts) > 0
      assert alert.alert_name == alert_name
      assert alert.alert_status == alert_status
      assert alert.data_points_to_alert == 3
      assert alert.evaluation_period == 5
  
      my_default_monitor.list_monitoring_alert_history(
          monitoring_alert_name=alert.alert_name
      )
  
      _wait_for_schedule_changes_to_apply(monitor=my_default_monitor)
  
      my_default_monitor.stop_monitoring_schedule()
      my_default_monitor.delete_monitoring_schedule()
  
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 26 for calling a global function with example usage: test_default_monitor_monitoring_alerts(sagemaker_session, predictor) without return types