code: def _test_loggers_pickle(tmp_path, monkeypatch, logger_class):
      """Verify that pickling trainer with logger works."""
      _patch_comet_atexit(monkeypatch)
  
      logger_args = _get_logger_args(logger_class, tmp_path)
      logger = logger_class(**logger_args)
  
      # this can cause pickle error if the experiment object is not picklable
      # the logger needs to remove it from the state before pickle
      _ = logger.experiment
  
      # logger also has to avoid adding un-picklable attributes to self in .save
      logger.log_metrics({"a": 1})
      logger.save()
  
      # test pickling loggers
      pickle.dumps(logger)
  
      trainer = Trainer(max_epochs=1, logger=logger)
      pkl_bytes = pickle.dumps(trainer)
  
      trainer2 = pickle.loads(pkl_bytes)
      trainer2.logger.log_metrics({"acc": 1.0})
  
      # make sure we restored properly
      assert trainer2.logger.name == logger.name
      assert trainer2.logger.save_dir == logger.save_dir
  
imports: import inspect
  import os
  import pickle
  from unittest import mock
  from unittest.mock import ANY, Mock
  import pytest
  import torch
  from lightning.pytorch import Callback, Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.loggers import CometLogger, CSVLogger, MLFlowLogger, NeptuneLogger, TensorBoardLogger, WandbLogger
  from lightning.pytorch.loggers.logger import DummyExperiment, Logger
  from lightning.pytorch.loggers.tensorboard import _TENSORBOARD_AVAILABLE
  from lightning.pytorch.tuner.tuning import Tuner
  from tests_pytorch.helpers.runif import RunIf
  from tests_pytorch.loggers.test_comet import _patch_comet_atexit
  from tests_pytorch.loggers.test_mlflow import mock_mlflow_run_creation
label_desc: Use image node_id 5 for calling a global function with example usage: _test_loggers_pickle(tmp_path, monkeypatch, logger_class) without return types