code: def test_scale_batch_size_fails_with_unavailable_mode(tmpdir):
      """Check the tuning raises error when called with mode that does not exist."""
  
      class TestModel(BoringModel):
          def __init__(self):
              super().__init__()
              self.batch_size = 2
  
      model = TestModel()
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=1,
          limit_val_batches=1,
          limit_train_batches=1,
      )
      tuner = Tuner(trainer)
  
      with pytest.raises(ValueError, match="should be either of"):
          tuner.scale_batch_size(model, mode="ThisModeDoesNotExist")
  
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 11 for calling a global function with example usage: test_scale_batch_size_fails_with_unavailable_mode(tmpdir) without return types