code: def test_call_with_segmentation_maps(self):
      def common(is_instance_map=False, segmentation_type=None):
          inputs = self.comm_get_image_processing_inputs(
              with_segmentation_maps=True,
              is_instance_map=is_instance_map,
              segmentation_type=segmentation_type,
          )
  
          mask_labels = inputs["mask_labels"]
          class_labels = inputs["class_labels"]
          pixel_values = inputs["pixel_values"]
  
          # check the batch_size
          for mask_label, class_label in zip(mask_labels, class_labels):
              self.assertEqual(
                  mask_label.shape[0], class_label.shape[0]
              )
              # this ensure padding has happened
              self.assertEqual(
                  mask_label.shape[1:], pixel_values.shape[2:]
              )
  
      common()
      common(is_instance_map=True)
      common(is_instance_map=False, segmentation_type="pil")
      common(is_instance_map=True, segmentation_type="pil")
  
imports: import unittest
  import numpy
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
label_desc: Use image node_id 7 for calling the MaskFormerImageProcessingTest obj's underlying member method code with example usage: obj.test_call_with_segmentation_maps() without return types