code: def test_combined_dataloader_for_training_with_ddp(
      use_distributed_sampler, mode, mps_count_0
  ):
      """When providing a CombinedLoader as the training data, it should be correctly receive the distributed
      samplers."""
      dim = 3
      n1 = 8
      n2 = 6
      dataloader = {
          "a": DataLoader(RandomDataset(dim, n1), batch_size=1),
          "b": DataLoader(RandomDataset(dim, n2), batch_size=1),
      }
      if mode != "max_size_cycle":
          dataloader = CombinedLoader(dataloader, mode=mode)
      model = BoringModel()
      trainer = Trainer(
          strategy="ddp",
          accelerator="auto",
          devices="auto",
          use_distributed_sampler=use_distributed_sampler,
      )
      trainer.strategy.connect(model)
      trainer._data_connector.attach_data(
          model=model, train_dataloaders=dataloader
      )
      fn = _SUPPORTED_MODES[mode]["fn"]
      expected_length_before_ddp = fn([n1, n2])
      expected_length_after_ddp = (
          math.ceil(expected_length_before_ddp / trainer.num_devices)
          if use_distributed_sampler
          else expected_length_before_ddp
      )
      trainer.fit_loop.setup_data()
      assert trainer.train_dataloader is not None
      assert isinstance(
          trainer.fit_loop._combined_loader, CombinedLoader
      )
      assert trainer.fit_loop._combined_loader._mode == mode
      assert trainer.num_training_batches == expected_length_after_ddp
  
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 16 for calling a global function with example usage: test_combined_dataloader_for_training_with_ddp(use_distributed_sampler, mode, mps_count_0) without return types