code: def _create_training_inputs(sagemaker_session):
      training_data_path = os.path.join(DATA_DIR, "dummy_tensor")
  
      prefix = "multi-algo"
      key = "recordio-pb-data"
  
      s3_train_data = sagemaker_session.upload_data(
          path=training_data_path,
          key_prefix=os.path.join(prefix, "train", key),
      )
  
      return {"train": s3_train_data, "test": s3_train_data}
  
imports: from __future__ import absolute_import
  import json
  import os
  import pytest
  from sagemaker import image_uris, utils
  from sagemaker.analytics import HyperparameterTuningJobAnalytics
  from sagemaker.deserializers import JSONDeserializer
  from sagemaker.estimator import Estimator
  from sagemaker.serializers import SimpleBaseSerializer
  from sagemaker.tuner import ContinuousParameter, IntegerParameter, HyperparameterTuner
  from tests.integ import datasets, DATA_DIR, TUNING_DEFAULT_TIMEOUT_MINUTES
  from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
label_desc: Use image node_id 12 for calling a global function with example usage: _create_training_inputs(sagemaker_session) and returns: dict