code: def test_combined_loader_flattened_setter():
      iterables = [[0], [[1], [[2]]]]
      combined_loader = CombinedLoader(iterables)
      with pytest.raises(
          ValueError,
          match=r"Mismatch in flattened length \(1\) and existing length \(3\)",
      ):
          combined_loader.flattened = [2]
      assert combined_loader.flattened == [[0], [1], [2]]
      combined_loader.flattened = [[3], [2], [1]]
      assert combined_loader.iterables == [[3], [[2], [[1]]]]
  
imports: import math
  import pickle
  from typing import Any, NamedTuple, Sequence, get_args
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.utilities.combined_loader import _LITERAL_SUPPORTED_MODES, _SUPPORTED_MODES, CombinedLoader, _MaxSize, _MaxSizeCycle, _MinSize, _Sequential
  from torch import Tensor
  from torch.utils._pytree import tree_flatten
  from torch.utils.data import DataLoader, TensorDataset
  from torch.utils.data.dataset import Dataset, IterableDataset
  from torch.utils.data.distributed import DistributedSampler
  from torch.utils.data.sampler import RandomSampler, SequentialSampler
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 12 for calling a global function with example usage: test_combined_loader_flattened_setter() without return types