code: def test_combined_loader_simultaneous_workers(mode):
      """Test `CombinedLoader` to check how it initializes dataloader workers."""
  
      class TestDataLoader(DataLoader):
          def __init__(self, *args, **kwargs):
              super().__init__(*args, **kwargs)
              self.workers_active = False
  
          def _get_iterator(self):
              self.workers_active = True
              return super()._get_iterator()
  
          def _shutdown_workers(self):
              self.workers_active = False
              super()._shutdown_workers()
  
      loaders = [
          TestDataLoader(range(10), batch_size=2, num_workers=0),
          TestDataLoader(range(20), batch_size=2, num_workers=0),
      ]
      combined_loader = CombinedLoader(loaders, mode)
      # Start the dataloader
      _ = iter(combined_loader)
  
      workers_active = []
      for loader in loaders:
          workers_active.append(loader.workers_active)
  
      # Sequential only starts the first dataloader, other modes start both
      expected = [True, False] if mode == "sequential" else [True, True]
      assert workers_active == expected
  
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 9 for calling a global function with example usage: test_combined_loader_simultaneous_workers(mode) and returns: super