code: def test_combined_data_loader_with_max_size_cycle_and_ddp(
      monkeypatch, accelerator, use_distributed_sampler
  ):
      """This test makes sure distributed sampler has been properly injected in dataloaders when using CombinedLoader
      with ddp and `max_size_cycle` mode."""
      trainer = Trainer(
          strategy="ddp",
          accelerator=accelerator,
          devices=2,
          use_distributed_sampler=use_distributed_sampler,
      )
      model = BoringModel()
  
      combined_loader = CombinedLoader(
          {
              "a": DataLoader(RandomDataset(32, 8), batch_size=1),
              "b": DataLoader(RandomDataset(32, 8), batch_size=1),
          },
      )
      trainer.strategy.connect(model)
      trainer._data_connector.attach_data(
          model, train_dataloaders=combined_loader
      )
      trainer.fit_loop.setup_data()
  
      assert len(combined_loader) == 4 if use_distributed_sampler else 8
  
      for a_length in [6, 8, 10]:
          combined_loader = CombinedLoader(
              {
                  "a": DataLoader(range(a_length), batch_size=1),
                  "b": DataLoader(range(8), batch_size=1),
              },
              mode="max_size_cycle",
          )
          iter(combined_loader)
          length = max(a_length, 8)
          assert len(combined_loader) == length
  
          trainer._data_connector.attach_data(
              model, train_dataloaders=combined_loader
          )
  
          original_process_dataloader = (
              trainer._data_connector._prepare_dataloader
          )
  
          def non_shuffle_process_dataloader(dl, shuffle, mode):
              # avoid shuffling
              return original_process_dataloader(dl, False, mode)
  
          monkeypatch.setattr(
              trainer._data_connector,
              "_prepare_dataloader",
              non_shuffle_process_dataloader,
          )
          trainer.fit_loop.setup_data()
          monkeypatch.undo()
  
          assert (
              len(combined_loader) == length // 2
              if use_distributed_sampler
              else length
          )
          if use_distributed_sampler:
              last_batch = list(combined_loader)[-1][0]
              if a_length == 6:
                  assert last_batch == {
                      "a": torch.tensor([0]),
                      "b": torch.tensor([6]),
                  }
              elif a_length == 8:
                  assert last_batch == {
                      "a": torch.tensor([6]),
                      "b": torch.tensor([6]),
                  }
              elif a_length == 10:
                  assert last_batch == {
                      "a": torch.tensor([8]),
                      "b": torch.tensor([0]),
                  }
  
      class InfiniteDataset(IterableDataset):
          def __iter__(self):
              while True:
                  yield 1
  
      combined_loader = CombinedLoader(
          {
              "a": DataLoader(InfiniteDataset(), batch_size=1),
              "b": DataLoader(range(8), batch_size=1),
          },
          mode="max_size_cycle",
      )
      assert len(combined_loader.iterables["b"]) == 8
  
      trainer._data_connector.attach_data(
          model, train_dataloaders=combined_loader
      )
      trainer.fit_loop.setup_data()
  
      assert (
          len(combined_loader.iterables["b"]) == 4
          if use_distributed_sampler
          else 8
      )
  
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 15 for calling a global function with example usage: test_combined_data_loader_with_max_size_cycle_and_ddp(monkeypatch, accelerator, use_distributed_sampler) and returns: original_process_dataloader