code: def test_model_bias_monitor_suggest_baseline(
      model_bias_monitor,
      sagemaker_session,
      data_config,
      bias_config,
      model_config,
      model_predicted_label_config,
  ):
      # suggest baseline
      model_bias_monitor.suggest_baseline(
          data_config=data_config,
          bias_config=bias_config,
          model_config=model_config,
          model_predicted_label_config=model_predicted_label_config,
          job_name=BASELINING_JOB_NAME,
      )
      assert isinstance(
          model_bias_monitor.latest_baselining_job, ClarifyBaseliningJob
      )
      assert (
          BIAS_ANALYSIS_CONFIG
          == model_bias_monitor.latest_baselining_job_config.analysis_config._to_dict()
      )
      clarify_baselining_job = model_bias_monitor.latest_baselining_job
      assert (
          data_config.s3_data_input_path
          == clarify_baselining_job.inputs[0].source
      )
      assert (
          data_config.s3_output_path
          == clarify_baselining_job.outputs[0].destination
      )
  
      # create schedule
      _test_model_bias_monitor_create_schedule(
          model_bias_monitor=model_bias_monitor,
          sagemaker_session=sagemaker_session,
          analysis_config=None,  # will pick up config from baselining job
          baseline_job_name=BASELINING_JOB_NAME,
          endpoint_input=EndpointInput(
              endpoint_name=ENDPOINT_NAME,
              destination=ENDPOINT_INPUT_LOCAL_PATH,
              start_time_offset=START_TIME_OFFSET,
              end_time_offset=END_TIME_OFFSET,
              #  will pick up attributes from baselining job
          ),
      )
  
      # update schedule
      _test_model_bias_monitor_update_schedule(
          model_bias_monitor=model_bias_monitor,
          sagemaker_session=sagemaker_session,
      )
  
      # delete schedule
      _test_model_bias_monitor_delete_schedule(
          model_bias_monitor=model_bias_monitor,
          sagemaker_session=sagemaker_session,
      )
  
imports: from __future__ import print_function, absolute_import
  import copy
  import json
  import time
  from mock import patch, Mock, MagicMock
  import pytest
  from sagemaker.clarify import BiasConfig, DataConfig, ModelConfig, SHAPConfig, ModelPredictedLabelConfig
  from sagemaker.model_monitor import BiasAnalysisConfig, Constraints, CronExpressionGenerator, EndpointInput, BatchTransformInput, ExplainabilityAnalysisConfig, ModelBiasMonitor, ModelExplainabilityMonitor, MonitoringExecution, NetworkConfig
  from sagemaker.model_monitor.clarify_model_monitoring import ClarifyModelMonitor, ClarifyBaseliningJob, ClarifyMonitoringExecution
  from sagemaker.model_monitor.dataset_format import MonitoringDatasetFormat
label_desc: Use image node_id 20 for calling a global function with example usage: test_model_bias_monitor_suggest_baseline(model_bias_monitor, sagemaker_session, data_config, bias_config, model_config, model_predicted_label_config) without return types