code: def _test_distiller_cli_fork(self, updates, check_contents=True):
      default_updates = dict(
          label_smoothing=0.0,
          early_stopping_patience=-1,
          train_batch_size=1,
          eval_batch_size=2,
          max_epochs=2,
          alpha_mlm=0.2,
          alpha_ce=0.8,
          do_predict=True,
          model_name_or_path="sshleifer/tinier_bart",
          teacher=CHEAP_ARGS["model_name_or_path"],
          val_check_interval=0.5,
      )
      default_updates.update(updates)
      args_d: dict = CHEAP_ARGS.copy()
      tmp_dir = make_test_data_dir(
          tmp_dir=self.get_auto_remove_tmp_dir()
      )
      output_dir = self.get_auto_remove_tmp_dir()
      args_d.update(
          data_dir=tmp_dir, output_dir=output_dir, **default_updates
      )
  
      def convert(k, v):
          if k in [
              "tgt_suffix",
              "server_ip",
              "server_port",
              "out",
              "n_tpu_cores",
          ]:
              return ""
          if v is False or v is None:
              return ""
          if v is True:  # or len(str(v))==0:
              return f"--{k}"
          return f"--{k}={v}"
  
      cli_args = [
          x
          for x in (convert(k, v) for k, v in args_d.items())
          if len(x)
      ]
      cmd = [
          sys.executable,
          f"{self.test_file_dir}/distillation.py",
      ] + cli_args
      execute_subprocess_async(cmd, env=self.get_env())
  
      contents = os.listdir(output_dir)
      contents = {os.path.basename(p) for p in contents}
      ckpt_files = [p for p in contents if p.endswith("ckpt")]
      assert len(ckpt_files) > 0
  
      self.assertIn("test_generations.txt", contents)
      self.assertIn("test_results.txt", contents)
  
      # get the following from the module, (we don't have access to `model` here)
      metrics_save_path = os.path.join(output_dir, "metrics.json")
      val_metric = "rouge2"
  
      metrics = load_json(metrics_save_path)
      # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
      print(metrics)
      last_step_stats = metrics["val"][-1]
      self.assertGreaterEqual(last_step_stats["val_avg_gen_time"], 0.01)
      self.assertIsInstance(
          last_step_stats[f"val_avg_{val_metric}"], float
      )
      self.assertEqual(len(metrics["test"]), 1)
      desired_n_evals = int(
          args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2
          + 1
      )
      self.assertEqual(len(metrics["val"]), desired_n_evals)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/seq2seq-distillation/_test_seq2seq_examples_multi_gpu.py
imports: import os
  import sys
  from pathlib import Path
  import torch
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
label_desc: Use image node_id 3 for calling the TestSummarizationDistillerMultiGPU obj's underlying member method code with example usage: obj._test_distiller_cli_fork(updates, check_contents) and returns: str, k, str, v, str, str, str, k