code: def test_integration_semantic_segmentation(self):
      # load 2 images and corresponding semantic annotations from the hub
      repo_id = "nielsr/image-segmentation-toy-data"
      image1 = Image.open(
          hf_hub_download(
              repo_id=repo_id,
              filename="semantic_segmentation_image_1.png",
              repo_type="dataset",
          )
      )
      image2 = Image.open(
          hf_hub_download(
              repo_id=repo_id,
              filename="semantic_segmentation_image_2.png",
              repo_type="dataset",
          )
      )
      annotation1 = Image.open(
          hf_hub_download(
              repo_id=repo_id,
              filename="semantic_segmentation_annotation_1.png",
              repo_type="dataset",
          )
      )
      annotation2 = Image.open(
          hf_hub_download(
              repo_id=repo_id,
              filename="semantic_segmentation_annotation_2.png",
              repo_type="dataset",
          )
      )
  
      # create a image processor
      image_processing = Mask2FormerImageProcessor(
          reduce_labels=True, ignore_index=255, size=(512, 512)
      )
  
      # prepare the images and annotations
      inputs = image_processing(
          [image1, image2],
          [annotation1, annotation2],
          return_tensors="pt",
      )
  
      # verify the pixel values and pixel mask
      self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 512))
      self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 512))
  
      # verify the class labels
      self.assertEqual(len(inputs["class_labels"]), 2)
      self.assertTrue(
          torch.allclose(
              inputs["class_labels"][0], torch.tensor([2, 4, 60])
          )
      )
      self.assertTrue(
          torch.allclose(
              inputs["class_labels"][1],
              torch.tensor([0, 3, 7, 8, 15, 28, 30, 143]),
          )
      )
  
      # verify the mask labels
      self.assertEqual(len(inputs["mask_labels"]), 2)
      self.assertEqual(inputs["mask_labels"][0].shape, (3, 512, 512))
      self.assertEqual(inputs["mask_labels"][1].shape, (8, 512, 512))
      self.assertEquals(inputs["mask_labels"][0].sum().item(), 170200.0)
      self.assertEquals(inputs["mask_labels"][1].sum().item(), 257036.0)
  
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 9 for calling the Mask2FormerImageProcessingTest obj's underlying member method code with example usage: obj.test_integration_semantic_segmentation() without return types