code: def test_iterable_dataloader():
      loader = load_dataset(
          object_type="DataLoader",
          use_iterable_dataset=True,
          n_samples=100,
          batch_size=50,
      )
      batch = next(iter(loader))
      assert_that(batch[0].shape, equal_to((50, 1, 28, 28)))
      assert_that(
          calling(len).with_args(loader),
          raises(
              TypeError,
              r"object of type \'IterableTorchMnistDataset\' has no len()",
          ),
      )
  
file_path: deepchecks/tests/vision/datasets/test_mnist_torch.py
imports: import time
  from hamcrest import assert_that, calling, equal_to, instance_of, is_not, raises
  from torch.utils.data import DataLoader
  from deepchecks.vision import VisionData
  from deepchecks.vision.datasets.classification.mnist_torch import MNIST_DIR, MODEL_PATH, MnistModel, load_dataset, load_model
label_desc: Use image node_id 5 for calling a global function with example usage: test_iterable_dataloader() without return types