code: def test_shuffling_regular_dataloader(mnist_dataloader_train):
      # Arrange
      mnist_loader_deepchecks_format = replace_collate_fn_dataloader(
          mnist_dataloader_train, mnist_collate_labels
      )
      original_batch = next(iter(mnist_loader_deepchecks_format))
      vision_data_shuffled = VisionData(
          mnist_loader_deepchecks_format,
          TaskType.CLASSIFICATION.value,
          reshuffle_data=True,
      )
      shuffled_batch = next(iter(vision_data_shuffled))
      vision_data_unshuffled = VisionData(
          mnist_loader_deepchecks_format,
          TaskType.CLASSIFICATION.value,
          reshuffle_data=False,
      )
      unshuffled_batch = next(iter(vision_data_unshuffled))
  
      # Assert
      assert_that(
          original_batch["labels"], equal_to(unshuffled_batch["labels"])
      )
      assert_that(
          original_batch["labels"],
          is_not(equal_to(shuffled_batch["labels"])),
      )
  
imports: import numpy
  import pytest
  import torch
  from hamcrest import assert_that, calling, equal_to, has_length, is_not, raises
  from torch.utils.data import DataLoader
  from deepchecks.core.errors import DatasetValidationError, ValidationError
  from deepchecks.vision.datasets.classification.mnist_torch import IterableTorchMnistDataset, collate_without_model
  from deepchecks.vision.datasets.detection import coco_torch
  from deepchecks.vision.datasets.segmentation import segmentation_coco
  from deepchecks.vision.utils.test_utils import replace_collate_fn_dataloader
  from deepchecks.vision.vision_data import TaskType, VisionData
  from deepchecks.vision.vision_data.utils import validate_vision_data_compatibility
  from tests.vision.conftest import run_update_loop
label_desc: Use image node_id 17 for calling a global function with example usage: test_shuffling_regular_dataloader(mnist_dataloader_train) without return types