code: def test_dataloader_kwargs_replacement_with_iterable_dataset(mode):
      """Test that DataLoader kwargs are not replaced when using Iterable Dataset."""
      dataset = RandomIterableDataset(7, 100)
      dataloader = DataLoader(dataset, batch_size=32)
      _, dl_kwargs = _get_dataloader_init_args_and_kwargs(
          dataloader, dataloader.sampler, mode=mode
      )
      assert dl_kwargs["sampler"] is None
      assert dl_kwargs["batch_sampler"] is None
      assert dl_kwargs["batch_size"] is dataloader.batch_size
      assert dl_kwargs["dataset"] is dataloader.dataset
      assert dl_kwargs["collate_fn"] is dataloader.collate_fn
  
imports: from dataclasses import dataclass
  from unittest.mock import Mock
  import numpy
  import pytest
  import torch
  from lightning.fabric.utilities.data import _replace_dunder_methods
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import RandomDataset, RandomIterableDataset
  from lightning.pytorch.overrides.distributed import _IndexBatchSamplerWrapper
  from lightning.pytorch.trainer.states import RunningStage
  from lightning.pytorch.utilities.data import _get_dataloader_init_args_and_kwargs, _update_dataloader, extract_batch_size, has_len_all_ranks, warning_cache
  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, RandomSampler
label_desc: Use image node_id 9 for calling a global function with example usage: test_dataloader_kwargs_replacement_with_iterable_dataset(mode) without return types