code: def test_default_monitor_suggest_baseline_and_create_monitoring_schedule_without_customizations(
      sagemaker_session, predictor
  ):
      baseline_dataset = os.path.join(
          DATA_DIR, "monitor/baseline_dataset.csv"
      )
  
      my_default_monitor = DefaultModelMonitor(
          role=ROLE, sagemaker_session=sagemaker_session
      )
  
      my_default_monitor.suggest_baseline(
          baseline_dataset=baseline_dataset,
          dataset_format=DatasetFormat.csv(header=False),
          logs=False,
      )
  
      baselining_job_description = (
          my_default_monitor.latest_baselining_job.describe()
      )
  
      assert (
          baselining_job_description["ProcessingResources"][
              "ClusterConfig"
          ]["InstanceType"]
          == INSTANCE_TYPE
      )
      assert (
          baselining_job_description["ProcessingResources"][
              "ClusterConfig"
          ]["InstanceCount"]
          == INSTANCE_COUNT
      )
      assert (
          baselining_job_description["ProcessingResources"][
              "ClusterConfig"
          ]["VolumeSizeInGB"]
          == VOLUME_SIZE_IN_GB
      )
      assert (
          baselining_job_description["ProcessingResources"][
              "ClusterConfig"
          ].get("VolumeKmsKeyId")
          is None
      )
      assert (
          DEFAULT_IMAGE_SUFFIX
          in baselining_job_description["AppSpecification"]["ImageUri"]
      )
      assert ROLE in baselining_job_description["RoleArn"]
      assert (
          baselining_job_description["ProcessingInputs"][0]["InputName"]
          == "baseline_dataset_input"
      )
      assert len(baselining_job_description["ProcessingInputs"]) == 1
      assert (
          baselining_job_description["ProcessingOutputConfig"][
              "Outputs"
          ][0]["OutputName"]
          == "monitoring_output"
      )
      assert (
          baselining_job_description["ProcessingOutputConfig"].get(
              "KmsKeyId"
          )
          is None
      )
      assert (
          baselining_job_description["Environment"].get(ENV_KEY_1)
          is None
      )
      assert (
          baselining_job_description["Environment"]["output_path"]
          == "/opt/ml/processing/output"
      )
      assert (
          baselining_job_description["Environment"].get(
              "record_preprocessor_script"
          )
          is None
      )
      assert (
          baselining_job_description["Environment"].get(
              "post_analytics_processor_script"
          )
          is None
      )
      assert (
          baselining_job_description["Environment"]["dataset_source"]
          == "/opt/ml/processing/input/baseline_dataset_input"
      )
      assert (
          baselining_job_description["StoppingCondition"][
              "MaxRuntimeInSeconds"
          ]
          == DEFAULT_BASELINING_MAX_RUNTIME_IN_SECONDS
      )
      assert baselining_job_description.get("NetworkConfig") is None
  
      statistics = my_default_monitor.baseline_statistics()
      assert statistics.body_dict["dataset"]["item_count"] == 418
  
      constraints = my_default_monitor.suggested_constraints()
      assert (
          constraints.body_dict["monitoring_config"][
              "evaluate_constraints"
          ]
          == "Enabled"
      )
  
      constraints.set_monitoring(enable_monitoring=False)
  
      assert (
          constraints.body_dict["monitoring_config"][
              "evaluate_constraints"
          ]
          == "Disabled"
      )
  
      constraints.save()
  
      my_default_monitor.create_monitoring_schedule(
          endpoint_input=predictor.endpoint_name,
          schedule_cron_expression=CronExpressionGenerator.daily(),
      )
      schedule_description = my_default_monitor.describe_schedule()
      _verify_default_monitoring_schedule(
          sagemaker_session=sagemaker_session,
          schedule_description=schedule_description,
          statistics=statistics,
          constraints=constraints,
      )
  
      summary = sagemaker_session.list_monitoring_schedules()
      assert len(summary["MonitoringScheduleSummaries"]) > 0
  
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 14 for calling a global function with example usage: test_default_monitor_suggest_baseline_and_create_monitoring_schedule_without_customizations(sagemaker_session, predictor) without return types