code: def _upload_dataset(
      dataset_local_path, s3_dataset_path, sagemaker_session
  ):
      """Upload dataset (intended for facet or predicted labels dataset, not training dataset) to S3
  
      Args:
          dataset_local_path (str): File path to the local analysis config file.
          s3_dataset_path (str): S3 prefix to store the analysis config file.
          sagemaker_session (:class:`~sagemaker.session.Session`):
              Session object which manages interactions with Amazon SageMaker and
              any other AWS services needed. If not specified, the processor creates
              one using the default AWS configuration chain.
  
      Returns:
          The S3 uri of the uploaded dataset.
      """
      return s3.S3Uploader.upload(
          local_path=dataset_local_path,
          desired_s3_uri=s3_dataset_path,
          sagemaker_session=sagemaker_session,
      )
  
imports: from __future__ import print_function, absolute_import
  import json
  import math
  import numpy
  import os
  import pandas
  import pytest
  import statistics
  import tempfile
  from sagemaker import s3
  from sagemaker.clarify import SageMakerClarifyProcessor, BiasConfig, DataConfig, ModelConfig, ModelPredictedLabelConfig, SHAPConfig
  from sagemaker.amazon.linear_learner import LinearLearner, LinearLearnerPredictor
  from sagemaker import utils
  from tests import integ
  from tests.integ import timeout
label_desc: Use image node_id 48 for calling a global function with example usage: _upload_dataset(dataset_local_path, s3_dataset_path, sagemaker_session) and returns: s3