code: def test_iterable_visiondata():
      vision_data = load_dataset(
          object_type="VisionData",
          use_iterable_dataset=True,
          n_samples=100,
          batch_size=50,
      )
      batch = next(iter(vision_data))
      assert_that(batch["images"].shape, equal_to((50, 28, 28, 1)))
  
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 6 for calling a global function with example usage: test_iterable_visiondata() without return types