code: def test_replace_distributed_sampler(tmp_path, mode):
      class IndexedRandomDataset(RandomDataset):
          def __getitem__(self, index):
              return self.data[index]
  
      class CustomDataLoader(DataLoader):
          def __init__(self, num_features, dataset, *args, **kwargs):
              # argument `num_features` unused on purpose
              # it gets automatically captured by _replace_dataloader_init_method()
              super().__init__(dataset, *args, **kwargs)
  
      class CustomBatchSampler(BatchSampler):
          pass
  
      class TestModel(BoringModel):
          def __init__(self, numbers_test_dataloaders, mode):
              super().__init__()
              self._numbers_test_dataloaders = numbers_test_dataloaders
              self._mode = mode
  
          def test_step(self, batch, batch_idx, dataloader_idx=0):
              return super().test_step(batch, batch_idx)
  
          def on_test_start(self) -> None:
              dataloader = self.trainer.test_dataloaders[0]
              assert isinstance(dataloader, CustomDataLoader)
              batch_sampler = dataloader.batch_sampler
              if self._mode == 1:
                  assert isinstance(batch_sampler, CustomBatchSampler)
                  # the batch_size is set on the batch sampler
                  assert dataloader.batch_size is None
              elif self._mode == 2:
                  assert type(batch_sampler) is BatchSampler
                  assert dataloader.batch_size == self._mode
              assert batch_sampler.batch_size == self._mode
              assert batch_sampler.drop_last
              # the sampler has been replaced
              assert isinstance(
                  batch_sampler.sampler, DistributedSampler
              )
  
          def create_dataset(self):
              dataset = IndexedRandomDataset(32, 64)
              if self._mode == 1:
                  # with a custom batch sampler
                  batch_sampler = CustomBatchSampler(
                      SequentialSampler(dataset),
                      batch_size=1,
                      drop_last=True,
                  )
                  return CustomDataLoader(
                      32, dataset, batch_sampler=batch_sampler
                  )
              if self._mode == 2:
                  # with no batch sampler provided
                  return CustomDataLoader(
                      32, dataset, batch_size=2, drop_last=True
                  )
              return None
  
          def test_dataloader(self):
              return [
                  self.create_dataset()
              ] * self._numbers_test_dataloaders
  
      model = TestModel(2, mode)
  
      trainer = Trainer(
          default_root_dir=tmp_path,
          limit_test_batches=2,
          accelerator="cpu",
          devices=1,
          strategy="ddp_find_unused_parameters_false",
      )
      trainer.test(model)
  
imports: from re import escape
  from typing import Sized
  from unittest import mock
  from unittest.mock import Mock
  import lightning.fabric
  import pytest
  from lightning.fabric.utilities.distributed import DistributedSamplerWrapper
  from lightning.fabric.utilities.warnings import PossibleUserWarning
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringDataModule, BoringModel, RandomDataset
  from lightning.pytorch.trainer.connectors.data_connector import _check_dataloader_iterable, _DataHookSelector, _DataLoaderSource, _worker_check, warning_cache
  from lightning.pytorch.trainer.states import RunningStage, TrainerFn
  from lightning.pytorch.utilities.combined_loader import CombinedLoader
  from lightning.pytorch.utilities.data import _update_dataloader
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning_utilities.test.warning import no_warning_call
  from torch import Tensor
  from torch.utils.data import BatchSampler, DataLoader, DistributedSampler, Sampler, SequentialSampler
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 1 for calling a global function with example usage: test_replace_distributed_sampler(tmp_path, mode) and returns: self, super, None, unknown, CustomDataLoader, CustomDataLoader