code: def test_trainer_log_level_replica(self, experiment_id):
      # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
      experiments = dict(
          # test with the default log_level - should be info and thus log info once
          base=dict(extra_args_str="", n_matches=1),
          # test with low log_level and log_level_replica - should be noisy on all processes
          # now the info string should appear twice on 2 processes
          low=dict(
              extra_args_str="--log_level debug --log_level_replica debug",
              n_matches=2,
          ),
          # test with high log_level and low log_level_replica
          # now the info string should appear once only on the replica
          high=dict(
              extra_args_str="--log_level error --log_level_replica debug",
              n_matches=1,
          ),
          # test with high log_level and log_level_replica - should be quiet on all processes
          mixed=dict(
              extra_args_str="--log_level error --log_level_replica error",
              n_matches=0,
          ),
      )
  
      data = experiments[experiment_id]
      kwargs = dict(
          distributed=True,
          predict_with_generate=False,
          do_eval=False,
          do_predict=False,
      )
      log_info_string = "Running training"
      with CaptureStderr() as cl:
          self.run_seq2seq_quick(
              **kwargs, extra_args_str=data["extra_args_str"]
          )
      n_matches = len(re.findall(log_info_string, cl.err))
      self.assertEqual(n_matches, data["n_matches"])
  
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 10 for calling the TestTrainerExt obj's underlying member method code with example usage: obj.test_trainer_log_level_replica(experiment_id) without return types