code: def test_combined_loader_can_be_pickled():
      dataloader = DataLoader([0, 1, 2, 3])
  
      # sanity check that and error would be raised. if this ever changes, `_ModeIterator.__getstate__` should be updated
      iterator = iter(dataloader)
      with pytest.raises(
          NotImplementedError, match="cannot be pickled"
      ):
          pickle.dumps(iterator)
  
      numbers = list(range(10))
      cl = CombinedLoader([dataloader, numbers])
      iter(cl)
  
      iterator = cl._iterator
      assert iterator.__getstate__() == {
          "iterables": [dataloader, numbers],
          "iterators": [None, iterator.iterators[1]],
          "limits": None,
          "_idx": 0,
      }
  
      # no error
      pickle.dumps(cl)
  
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 18 for calling a global function with example usage: test_combined_loader_can_be_pickled() without return types