code: def test_regular_visiondata_with_shuffle():
      np.random.seed(42)
      vision_data = load_dataset(n_samples=100, shuffle=False)
      batch = next(iter(vision_data))
      vision_data_again = load_dataset(n_samples=100, shuffle=False)
      batch_again = next(iter(vision_data_again))
      vision_data_shuffled = load_dataset(n_samples=100, shuffle=True)
      batch_shuffled = next(iter(vision_data_shuffled))
      vision_data_shuffled_again = load_dataset(
          n_samples=100, shuffle=True
      )
      batch_shuffled_again = next(iter(vision_data_shuffled_again))
  
      assert_that(
          batch["labels"][0],
          is_not(equal_to(batch_shuffled["labels"][0])),
      )
      assert_that(
          batch["labels"][0], equal_to(batch_again["labels"][0])
      )
      assert_that(
          batch_shuffled_again["labels"][0],
          is_not(equal_to(batch_shuffled["labels"][0])),
      )
  
      assert_that(
          batch["predictions"][0][0],
          is_not(equal_to(batch_shuffled["predictions"][0][0])),
      )
      assert_that(
          batch["predictions"][0][0],
          equal_to(batch_again["predictions"][0][0]),
      )
      assert_that(
          batch_shuffled_again["predictions"][0][0],
          is_not(equal_to(batch_shuffled["predictions"][0][0])),
      )
  
imports: import numpy
  from hamcrest import assert_that, equal_to, instance_of, is_not
  from deepchecks.vision import VisionData
  from deepchecks.vision.datasets.classification.mnist_tensorflow import load_dataset, load_model
label_desc: Use image node_id 2 for calling a global function with example usage: test_regular_visiondata_with_shuffle() without return types