code: def test_shuffling_tf_dataset(tf_coco_visiondata_train, caplog):
      # Arrange
      loader_deepchecks_format = tf_coco_visiondata_train.batch_loader
      original_batch = next(iter(loader_deepchecks_format))
      vision_data = VisionData(
          loader_deepchecks_format,
          TaskType.OBJECT_DETECTION.value,
          reshuffle_data=True,
      )
      vision_data_batch = next(iter(vision_data))
  
      # Assert
      assert_that(
          list(original_batch["labels"][0][0]),
          equal_to(list(vision_data_batch["labels"][0][0])),
      )  # no shuffling happened
      assert_that(caplog.records, has_length(1))
      assert_that(
          caplog.records[0].message,
          equal_to(
              "Shuffling for tensorflow datasets is not supported. "
              "Make sure that the data used to create the Dataset was shuffled "
              "beforehand and set shuffle_batch_loader=False"
          ),
      )
  
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 19 for calling a global function with example usage: test_shuffling_tf_dataset(tf_coco_visiondata_train, caplog) without return types