code: def _test_distiller_cli(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
      )
      model = distill_main(argparse.Namespace(**args_d))
      if not check_contents:
          return model
      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)
  
      metrics = load_json(model.metrics_save_path)
      last_step_stats = metrics["val"][-1]
      self.assertGreaterEqual(last_step_stats["val_avg_gen_time"], 0.01)
      self.assertGreaterEqual(1.0, last_step_stats["val_avg_gen_time"])
      self.assertIsInstance(
          last_step_stats[f"val_avg_{model.val_metric}"], float
      )
      desired_n_evals = int(
          args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1
      )
      self.assertEqual(len(metrics["val"]), desired_n_evals)
      self.assertEqual(len(metrics["test"]), 1)
      return model
  
imports: import argparse
  import logging
  import os
  import sys
  import tempfile
  from pathlib import Path
  import pytest
  import pytorch_lightning
  import torch
  from torch import nn
  import lightning_base
  from convert_pl_checkpoint_to_hf import convert_pl_to_hf
  from distillation import distill_main
  from finetune import SummarizationModule, main
  from huggingface_hub import list_models
  from parameterized import parameterized
  from run_eval import generate_summaries_or_translations
  from transformers import AutoConfig, AutoModelForSeq2SeqLM
  from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
  from utils import label_smoothed_nll_loss, lmap, load_json
label_desc: Use image node_id 9 for calling the TestSummarizationDistiller obj's underlying member method code with example usage: obj._test_distiller_cli(updates, check_contents) and returns: model, model