code: def test_batch_size_finder_with_multiple_eval_dataloaders(tmpdir):
      """Test that an error is raised with batch size finder is called with multiple eval dataloaders."""
  
      class CustomModel(BoringModel):
          def val_dataloader(self):
              return [
                  super().val_dataloader(),
                  super().val_dataloader(),
              ]
  
      trainer = Trainer()
      tuner = Tuner(trainer)
      model = CustomModel()
  
      with pytest.raises(
          MisconfigurationException,
          match="Batch size finder cannot be used with multiple .* dataloaders",
      ):
          tuner.scale_batch_size(model, method="validate")
  
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 19 for calling a global function with example usage: test_batch_size_finder_with_multiple_eval_dataloaders(tmpdir) and returns: list