code: def model_quality_check_config(model_quality_baseline_dataset):
      return ModelQualityCheckConfig(
          baseline_dataset=model_quality_baseline_dataset,
          dataset_format=DatasetFormat.csv(),
          problem_type=_PROBLEM_TYPE,
          inference_attribute=_HEADER_OF_LABEL,
          ground_truth_attribute=_HEADER_OF_PREDICTED_LABEL,
      )
  
imports: from __future__ import absolute_import
  import logging
  import os
  import pytest
  from tests.integ.sagemaker.workflow.helpers import wait_pipeline_execution
  from sagemaker.workflow.parameters import ParameterString
  from tests.integ import DATA_DIR
  from sagemaker import get_execution_role, utils
  from sagemaker.model_monitor import DatasetFormat, Statistics, Constraints
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.quality_check_step import QualityCheckStep, DataQualityCheckConfig, ModelQualityCheckConfig
  from sagemaker.workflow.check_job_config import CheckJobConfig
  from tests.integ.retry import retries
label_desc: Use image node_id 10 for calling a global function with example usage: model_quality_check_config(model_quality_baseline_dataset) and returns: ModelQualityCheckConfig