code: def test_combined_data_loader_validation_test(
      use_distributed_sampler,
  ):
      """This test makes sure distributed sampler has been properly injected in dataloaders when using CombinedLoader."""
  
      class CustomDataset(Dataset):
          def __init__(self, data):
              self.data = data
  
          def __len__(self):
              return len(self.data)
  
          def __getitem__(self, index):
              return self.data[index]
  
      class CustomSampler(RandomSampler):
          def __init__(self, data_source, name) -> None:
              super().__init__(data_source)
              self.name = name
  
      dataset = CustomDataset(range(10))
      combined_loader = CombinedLoader(
          {
              "a": DataLoader(CustomDataset(range(10))),
              "b": DataLoader(
                  dataset,
                  sampler=CustomSampler(dataset, "custom_sampler"),
              ),
              "c": {
                  "c": DataLoader(CustomDataset(range(10))),
                  "d": DataLoader(CustomDataset(range(10))),
              },
              "d": [
                  DataLoader(CustomDataset(range(10))),
                  DataLoader(CustomDataset(range(10))),
              ],
          }
      )
      model = BoringModel()
      trainer = Trainer(
          use_distributed_sampler=use_distributed_sampler,
          strategy="ddp",
          accelerator="cpu",
          devices=2,
      )
      trainer.strategy.connect(model)
      trainer._data_connector.attach_data(
          model, train_dataloaders=combined_loader
      )
      trainer.fit_loop.setup_data()
  
      samplers_flattened = tree_flatten(combined_loader.sampler)[0]
      assert len(samplers_flattened) == 6
      if use_distributed_sampler:
          assert all(
              isinstance(s, DistributedSampler)
              for s in samplers_flattened
          )
      else:
          assert all(
              isinstance(s, (SequentialSampler, CustomSampler))
              for s in samplers_flattened
          )
  
      datasets_flattened = [
          dl.dataset for dl in combined_loader.flattened
      ]
      assert len(datasets_flattened) == 6
      assert all(
          isinstance(ds, CustomDataset) for ds in datasets_flattened
      )
  
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 14 for calling a global function with example usage: test_combined_data_loader_validation_test(use_distributed_sampler) and returns: len, self