code: def check_job_config(sagemaker_session):
      return CheckJobConfig(
          role=_ROLE,
          instance_type="ml.m5.xlarge",
          instance_count=1,
          sagemaker_session=sagemaker_session,
          output_kms_key="output_kms_key",
          base_job_name=_CHECK_JOB_PREFIX,
      )
  
imports: from __future__ import absolute_import
  import json
  import re
  import pytest
  from sagemaker.clarify import DataConfig, BiasConfig, ModelConfig, ModelPredictedLabelConfig, SHAPConfig
  from sagemaker.workflow.clarify_check_step import DataBiasCheckConfig, ClarifyCheckStep, ModelBiasCheckConfig, ModelExplainabilityCheckConfig, ClarifyCheckConfig, _EXPLAINABILITY_MONITORING_CFG_BASE_NAME, _BIAS_MONITORING_CFG_BASE_NAME
  from sagemaker.model_monitor.model_monitoring import _MODEL_MONITOR_S3_PATH
  from sagemaker.workflow.parameters import ParameterString
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.pipeline import PipelineDefinitionConfig
  from sagemaker.workflow.steps import CacheConfig
  from sagemaker.workflow.check_job_config import CheckJobConfig
label_desc: Use image node_id 2 for calling a global function with example usage: check_job_config(sagemaker_session) and returns: CheckJobConfig