code: def test_save_csv_files(self):
      MODEL_ID = "sshleifer/tiny-gpt2"
      with tempfile.TemporaryDirectory() as tmp_dir:
          benchmark_args = PyTorchBenchmarkArguments(
              models=[MODEL_ID],
              training=True,
              inference=True,
              save_to_csv=True,
              sequence_lengths=[8],
              batch_sizes=[1],
              inference_time_csv_file=os.path.join(
                  tmp_dir, "inf_time.csv"
              ),
              train_memory_csv_file=os.path.join(
                  tmp_dir, "train_mem.csv"
              ),
              inference_memory_csv_file=os.path.join(
                  tmp_dir, "inf_mem.csv"
              ),
              train_time_csv_file=os.path.join(
                  tmp_dir, "train_time.csv"
              ),
              env_info_csv_file=os.path.join(tmp_dir, "env.csv"),
              multi_process=False,
          )
          benchmark = PyTorchBenchmark(benchmark_args)
          benchmark.run()
          self.assertTrue(
              Path(os.path.join(tmp_dir, "inf_time.csv")).exists()
          )
          self.assertTrue(
              Path(os.path.join(tmp_dir, "train_time.csv")).exists()
          )
          self.assertTrue(
              Path(os.path.join(tmp_dir, "inf_mem.csv")).exists()
          )
          self.assertTrue(
              Path(os.path.join(tmp_dir, "train_mem.csv")).exists()
          )
          self.assertTrue(
              Path(os.path.join(tmp_dir, "env.csv")).exists()
          )
  
imports: import os
  import tempfile
  import unittest
  from pathlib import Path
  from transformers import AutoConfig, is_torch_available
  from transformers.testing_utils import require_torch, torch_device
label_desc: Use image node_id 13 for calling the BenchmarkTest obj's underlying member method code with example usage: obj.test_save_csv_files() without return types