code: def test_model_explainability_monitor_suggest_baseline(
      model_explainability_monitor,
      sagemaker_session,
      data_config,
      shap_config,
      model_config,
      model_scores,
      explainability_analysis_config,
  ):
      clarify_model_monitor = model_explainability_monitor
      # suggest baseline
      clarify_model_monitor.suggest_baseline(
          data_config=data_config,
          explainability_config=shap_config,
          model_config=model_config,
          model_scores=model_scores,
          job_name=BASELINING_JOB_NAME,
      )
      assert isinstance(
          clarify_model_monitor.latest_baselining_job,
          ClarifyBaseliningJob,
      )
      assert (
          explainability_analysis_config
          == clarify_model_monitor.latest_baselining_job_config.analysis_config._to_dict()
      )
      clarify_baselining_job = (
          clarify_model_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
      # noinspection PyTypeChecker
      _test_model_explainability_monitor_create_schedule(
          model_explainability_monitor=model_explainability_monitor,
          sagemaker_session=sagemaker_session,
          analysis_config=None,  # will pick up config from baselining job
          baseline_job_name=BASELINING_JOB_NAME,
          endpoint_input=ENDPOINT_NAME,
          explainability_analysis_config=explainability_analysis_config,
          #  will pick up attributes from baselining job
      )
  
      # update schedule
      _test_model_explainability_monitor_update_schedule(
          model_explainability_monitor=model_explainability_monitor,
          sagemaker_session=sagemaker_session,
      )
  
      # delete schedule
      _test_model_explainability_monitor_delete_schedule(
          model_explainability_monitor=model_explainability_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 33 for calling a global function with example usage: test_model_explainability_monitor_suggest_baseline(model_explainability_monitor, sagemaker_session, data_config, shap_config, model_config, model_scores, explainability_analysis_config) without return types