code: def test_trainer(self):
      distributed_args = f"""
              -m torch.distributed.launch
              --nproc_per_node={torch.cuda.device_count()}
              --master_port={get_torch_dist_unique_port()}
              {self.test_file_dir}/test_trainer_distributed.py
          """.split()
      output_dir = self.get_auto_remove_tmp_dir()
      args = f"--output_dir {output_dir}".split()
      cmd = [sys.executable] + distributed_args + args
      execute_subprocess_async(cmd, env=self.get_env())
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/trainer/test_trainer_distributed.py
imports: import sys
  from typing import Dict
  from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_torch_dist_unique_port, require_torch_multi_gpu
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling the TestTrainerDistributed obj's underlying member method code with example usage: obj.test_trainer() without return types