code: def test_huggingface_training_tf(
      sagemaker_session,
      gpu_instance_type,
      huggingface_training_latest_version,
      huggingface_training_tensorflow_latest_version,
      huggingface_tensorflow_latest_training_py_version,
  ):
      with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
          data_path = os.path.join(DATA_DIR, "huggingface")
  
          hf = HuggingFace(
              py_version=huggingface_tensorflow_latest_training_py_version,
              entry_point=os.path.join(data_path, "run_tf.py"),
              role=ROLE,
              transformers_version=huggingface_training_latest_version,
              tensorflow_version=huggingface_training_tensorflow_latest_version,
              instance_count=1,
              instance_type=gpu_instance_type,
              hyperparameters={
                  "model_name_or_path": "distilbert-base-cased",
                  "per_device_train_batch_size": 128,
                  "per_device_eval_batch_size": 128,
                  "output_dir": "/opt/ml/model",
                  "overwrite_output_dir": True,
                  "save_steps": 5500,
              },
              sagemaker_session=sagemaker_session,
              disable_profiler=True,
          )
  
          train_input = hf.sagemaker_session.upload_data(
              path=os.path.join(data_path, "train"),
              key_prefix="integ-test-data/huggingface/train",
          )
  
          hf.fit(train_input)
  
file_path: sagemaker-python-sdk/tests/integ/test_huggingface.py
imports: from __future__ import absolute_import
  import os
  import pytest
  import tests.integ
  from sagemaker.huggingface import HuggingFace, HuggingFaceProcessor
  from sagemaker.huggingface.model import HuggingFaceModel, HuggingFacePredictor
  from sagemaker.utils import unique_name_from_base
  from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
  from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
  from sagemaker.enums import EndpointType
  from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
label_desc: Use image node_id 3 for calling a global function with example usage: test_huggingface_training_tf(sagemaker_session, gpu_instance_type, huggingface_training_latest_version, huggingface_training_tensorflow_latest_version, huggingface_tensorflow_latest_training_py_version) without return types