code: def test_vision_data_label_comparison_with_different_datasets(
      coco_visiondata_train, mnist_visiondata_train
  ):
      # Act/Assert
      assert_that(
          calling(validate_vision_data_compatibility).with_args(
              mnist_visiondata_train, coco_visiondata_train
          ),
          raises(
              DatasetValidationError,
              "Cannot compare datasets with different task types: classification and object_detection",
          ),
      )
  
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 9 for calling a global function with example usage: test_vision_data_label_comparison_with_different_datasets(coco_visiondata_train, mnist_visiondata_train) without return types