code: def data_config(sagemaker_session, data_path, endpoint_name):
      output_path = os.path.join(
          "s3://",
          sagemaker_session.default_bucket(),
          endpoint_name,
          "output",
      )
      return DataConfig(
          s3_data_input_path=data_path,
          s3_output_path=output_path,
          label=HEADER_OF_LABEL,
          headers=ALL_HEADERS,
          dataset_type=DATASET_TYPE,
      )
  
imports: from __future__ import print_function, absolute_import
  from datetime import datetime, timedelta
  import json
  import numpy
  import os
  import pandas
  import pytest
  import tempfile
  import uuid
  import time
  import tests.integ
  import tests.integ.timeout
  from sagemaker import image_uris, utils
  from sagemaker.model_monitor import BiasAnalysisConfig, CronExpressionGenerator, DataCaptureConfig, EndpointInput, ExplainabilityAnalysisConfig, ModelBiasMonitor, ModelExplainabilityMonitor, BatchTransformInput, MonitoringDatasetFormat
  from sagemaker.s3 import S3Uploader
  from sagemaker.utils import unique_name_from_base
  from sagemaker.clarify import BiasConfig, DataConfig, ModelConfig, ModelPredictedLabelConfig, SHAPConfig
  from sagemaker.model import Model
  from tests.integ import DATA_DIR
  from tests.integ.retry import retries
label_desc: Use image node_id 8 for calling a global function with example usage: data_config(sagemaker_session, data_path, endpoint_name) and returns: DataConfig