code: def test_huggingface_torch_distributed_g5_glue(
      sagemaker_session,
      huggingface_training_latest_version,
      huggingface_training_pytorch_latest_version,
      huggingface_pytorch_latest_training_py_version,
  ):
      with timeout.timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
          estimator = HuggingFace(
              py_version=huggingface_pytorch_latest_training_py_version,
              source_dir=os.path.join(DATA_DIR, "huggingface"),
              entry_point="run_glue.py",
              role="SageMakerRole",
              transformers_version=huggingface_training_latest_version,
              pytorch_version=huggingface_training_pytorch_latest_version,
              instance_count=1,
              instance_type="ml.g5.4xlarge",
              hyperparameters={
                  "model_name_or_path": "distilbert-base-cased",
                  "task_name": "wnli",
                  "do_train": True,
                  "do_eval": True,
                  "max_seq_length": 128,
                  "fp16": True,
                  "per_device_train_batch_size": 32,
                  "output_dir": "/opt/ml/model",
              },
              distribution={"torch_distributed": {"enabled": True}},
              sagemaker_session=sagemaker_session,
              disable_profiler=True,
          )
          estimator.fit()
  
imports: from __future__ import absolute_import
  import os
  from sagemaker.huggingface import HuggingFace
  from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, timeout
label_desc: Use image node_id 1 for calling a global function with example usage: test_huggingface_torch_distributed_g5_glue(sagemaker_session, huggingface_training_latest_version, huggingface_training_pytorch_latest_version, huggingface_pytorch_latest_training_py_version) without return types