code: def test_call_to_trainer_method(tmpdir, scale_method):
      """Test that calling the trainer method itself works."""
      before_batch_size = 2
      model = BatchSizeModel(batch_size=before_batch_size)
  
      # logger file to get meta
      trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
      tuner = Tuner(trainer)
  
      after_batch_size = tuner.scale_batch_size(
          model, mode=scale_method, max_trials=5
      )
      model.batch_size = after_batch_size
      trainer.fit(model)
  
      assert (
          before_batch_size != after_batch_size
      ), "Batch size was not altered after running auto scaling of batch size"
  
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 7 for calling a global function with example usage: test_call_to_trainer_method(tmpdir, scale_method) without return types