code: def test_batch_size_finder_callback(tmpdir, trainer_fn):
      """Test batch size finder callback with different trainer methods."""
      before_batch_size = 2
      max_trials = 4
      max_epochs = 2
      expected_scaled_batch_size = before_batch_size ** (max_trials + 1)
  
      model = BatchSizeModel(batch_size=before_batch_size)
      batch_size_finder = BatchSizeFinder(
          max_trials=max_trials, batch_arg_name="batch_size"
      )
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=max_epochs,
          callbacks=[batch_size_finder],
      )
      fn = getattr(trainer, trainer_fn)
  
      fn(model)
      after_batch_size = model.batch_size
      loop = getattr(trainer, f"{trainer_fn}_loop")
  
      if trainer_fn == "fit":
          expected_steps = (
              trainer.train_dataloader.dataset.len // after_batch_size
          )
          assert trainer.global_step == expected_steps * max_epochs
          assert trainer.current_epoch == max_epochs
          assert (
              loop.epoch_loop.batch_progress.total.completed
              == expected_steps * max_epochs
          )
      else:
          if trainer_fn == "validate":
              dl = trainer.val_dataloaders
          elif trainer_fn == "test":
              dl = trainer.test_dataloaders
          elif trainer_fn == "predict":
              dl = trainer.predict_dataloaders
  
          expected_steps = dl.dataset.len // after_batch_size
          assert trainer.global_step == 0
          assert trainer.current_epoch == 0
          assert loop.batch_progress.current.completed == expected_steps
  
      assert expected_scaled_batch_size == after_batch_size
      assert not any(
          f
          for f in os.listdir(tmpdir)
          if f.startswith(".scale_batch_size_temp_model")
      )
  
imports: import logging
  import os
  from copy import deepcopy
  from unittest.mock import patch
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.callbacks.batch_size_finder import BatchSizeFinder
  from lightning.pytorch.demos.boring_classes import BoringDataModule, BoringModel, RandomDataset
  from lightning.pytorch.tuner.tuning import Tuner
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning_utilities.test.warning import no_warning_call
  from torch.utils.data import DataLoader
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 14 for calling a global function with example usage: test_batch_size_finder_callback(tmpdir, trainer_fn) without return types