code: def dataset(sagemaker_session):
      dataset_local_path = os.path.join(
          DATA_DIR, "pipeline/clarify_check_step/dataset.csv"
      )
      dataset_s3_uri = "s3://{}/{}/{}/{}/{}".format(
          sagemaker_session.default_bucket(),
          "clarify_check_step",
          "input",
          "dataset",
          utils.unique_name_from_base("dataset"),
      )
      return S3Uploader.upload(
          dataset_local_path,
          dataset_s3_uri,
          sagemaker_session=sagemaker_session,
      )
  
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, ClarifyCheckStep
  from sagemaker.workflow.parameters import ParameterString
  from tests.integ import DATA_DIR
  from sagemaker import get_execution_role, utils
  from sagemaker.model_monitor import Constraints
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.check_job_config import CheckJobConfig
  from tests.integ.retry import retries
label_desc: Use image node_id 5 for calling a global function with example usage: dataset(sagemaker_session) and returns: S3Uploader