code: def test_run_model_quality_monitor_baseline(
      sagemaker_session,
      endpoint_name,
      data_path,
      ground_truth_input,
      upload_actual_data,
  ):
      monitor = ModelQualityMonitor(
          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,
          env=TEST_ENV,
          tags=TEST_TAGS,
      )
  
      baselining_job_name = utils.unique_name_from_base(
          "model-quality-baselining-job"
      )
      print("Creating baselining job: {}".format(baselining_job_name))
      monitor.suggest_baseline(
          baseline_dataset=data_path,
          dataset_format=DatasetFormat.csv(),
          problem_type=PROBLEM_TYPE,
          job_name=baselining_job_name,
          ground_truth_attribute=HEADER_OF_LABEL,
          inference_attribute=HEADER_OF_PREDICTED_LABEL,
      )
  
      monitoring_schedule_name = utils.unique_name_from_base(
          "model-quality-suggest-baseline"
      )
      s3_uri_monitoring_output = os.path.join(
          "s3://",
          sagemaker_session.default_bucket(),
          endpoint_name,
          monitoring_schedule_name,
          "monitor_output",
      )
      monitor.create_monitoring_schedule(
          endpoint_input=EndpointInput(
              endpoint_name=endpoint_name,
              destination=ENDPOINT_INPUT_LOCAL_PATH,
              start_time_offset=START_TIME_OFFSET,
              end_time_offset=END_TIME_OFFSET,
              inference_attribute=INFERENCE_ATTRIBUTE,
          ),
          ground_truth_input=ground_truth_input,
          problem_type=PROBLEM_TYPE,
          output_s3_uri=s3_uri_monitoring_output,
          monitor_schedule_name=monitoring_schedule_name,
          schedule_cron_expression=CRON,
      )
      _verify_execution_status(monitor)
  
      _verify_model_quality_job_description(
          sagemaker_session=sagemaker_session,
          monitor=monitor,
          endpoint_name=endpoint_name,
          ground_truth_input=ground_truth_input,
      )
  
      monitor.delete_monitoring_schedule()
  
imports: from __future__ import print_function, absolute_import
  import tempfile
  from datetime import datetime, timedelta
  import json
  import os
  import numpy
  import pytest
  import pandas
  import tests.integ
  import tests.integ.timeout
  from sagemaker import image_uris, utils
  from sagemaker.model_monitor import CronExpressionGenerator, DataCaptureConfig, EndpointInput, ModelQualityMonitor, DatasetFormat
  from sagemaker.s3 import S3Uploader
  from sagemaker.utils import unique_name_from_base
  from sagemaker.model import Model
  from tests.integ import DATA_DIR
  from tests.integ.retry import retries
label_desc: Use image node_id 9 for calling a global function with example usage: test_run_model_quality_monitor_baseline(sagemaker_session, endpoint_name, data_path, ground_truth_input, upload_actual_data) without return types