code: def data_config(pipeline_session, dataset):
      output_path = "s3://{}/{}/{}/{}".format(
          pipeline_session.default_bucket(),
          "clarify_check_step",
          "analysis_result",
          utils.unique_name_from_base("result"),
      )
      analysis_cfg_output_path = "s3://{}/{}/{}/{}".format(
          pipeline_session.default_bucket(),
          "clarify_check_step",
          "analysis_cfg",
          utils.unique_name_from_base("analysis_cfg"),
      )
      return DataConfig(
          s3_data_input_path=dataset,
          s3_output_path=output_path,
          s3_analysis_config_output_path=analysis_cfg_output_path,
          label="Label",
          headers=_HEADERS,
          dataset_type="text/csv",
      )
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import pytest
  from tests.integ.sagemaker.workflow.helpers import wait_pipeline_execution
  from sagemaker.clarify import BiasConfig, DataConfig
  from sagemaker.s3 import S3Uploader, S3Downloader
  from sagemaker.workflow.clarify_check_step import DataBiasCheckConfig
  from sagemaker.workflow.monitor_batch_transform_step import MonitorBatchTransformStep
  from sagemaker.workflow.model_step import ModelStep
  from sagemaker.workflow.parameters import ParameterString
  from tests.integ import DATA_DIR
  from sagemaker.model_monitor import DatasetFormat, Statistics, Constraints
  from sagemaker.xgboost import XGBoostModel
  from sagemaker.transformer import Transformer
  from sagemaker import get_execution_role, utils
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.check_job_config import CheckJobConfig
  from tests.integ.retry import retries
  from sagemaker.workflow.quality_check_step import DataQualityCheckConfig, ModelQualityCheckConfig
label_desc: Use image node_id 8 for calling a global function with example usage: data_config(pipeline_session, dataset) and returns: DataConfig