code: def run_seq2seq_quick(
      self,
      distributed=False,
      extra_args_str=None,
      predict_with_generate=True,
      do_train=True,
      do_eval=True,
      do_predict=True,
  ):
      output_dir = self.run_trainer(
          eval_steps=1,
          max_len=12,
          model_name=MBART_TINY,
          num_train_epochs=1,
          distributed=distributed,
          extra_args_str=extra_args_str,
          predict_with_generate=predict_with_generate,
          do_train=do_train,
          do_eval=do_eval,
          do_predict=do_predict,
      )
      logs = TrainerState.load_from_json(
          os.path.join(output_dir, "trainer_state.json")
      ).log_history
  
      if not do_eval:
          return
  
      eval_metrics = [log for log in logs if "eval_loss" in log.keys()]
  
      first_step_stats = eval_metrics[0]
      if predict_with_generate:
          assert "eval_bleu" in first_step_stats
  
          last_step_stats = eval_metrics[-1]
          assert isinstance(last_step_stats["eval_bleu"], float)
          assert not math.isnan(
              float(last_step_stats["eval_loss"])
          ), "eval_loss must not be `nan`"
  
imports: import math
  import os
  import re
  import sys
  import unittest
  from typing import Tuple
  from unittest.mock import patch
  from parameterized import parameterized
  from transformers import AutoModel
  from transformers.testing_utils import CaptureStderr, ExtendSysPath, TestCasePlus, execute_subprocess_async, get_gpu_count, get_torch_dist_unique_port, require_apex, require_bitsandbytes, require_fairscale, require_torch, require_torch_gpu, require_torch_multi_gpu, require_torch_non_multi_gpu, slow
  from transformers.trainer_callback import TrainerState
  from transformers.trainer_utils import set_seed
label_desc: Use image node_id 1 for calling the TestTrainerExt obj's underlying member method code with example usage: obj.run_seq2seq_quick(distributed, extra_args_str, predict_with_generate, do_train, do_eval, do_predict) without return types