code: def test_call_pytorch_with_coco_panoptic_annotations(self):
      # prepare image, target and masks_path
      image = Image.open(
          "./tests/fixtures/tests_samples/COCO/000000039769.png"
      )
      with open(
          "./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt",
          "r",
      ) as f:
          target = json.loads(f.read())
  
      target = {
          "file_name": "000000039769.png",
          "image_id": 39769,
          "segments_info": target,
      }
  
      masks_path = pathlib.Path(
          "./tests/fixtures/tests_samples/COCO/coco_panoptic"
      )
  
      # encode them
      image_processing = DeformableDetrImageProcessor(
          format="coco_panoptic"
      )
      encoding = image_processing(
          images=image,
          annotations=target,
          masks_path=masks_path,
          return_tensors="pt",
      )
  
      # verify pixel values
      expected_shape = torch.Size([1, 3, 800, 1066])
      self.assertEqual(encoding["pixel_values"].shape, expected_shape)
  
      expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
      self.assertTrue(
          torch.allclose(
              encoding["pixel_values"][0, 0, 0, :3],
              expected_slice,
              atol=1e-4,
          )
      )
  
      # verify area
      expected_area = torch.tensor(
          [
              147979.6875,
              165527.0469,
              484638.5938,
              11292.9375,
              5879.6562,
              7634.1147,
          ]
      )
      self.assertTrue(
          torch.allclose(encoding["labels"][0]["area"], expected_area)
      )
      # verify boxes
      expected_boxes_shape = torch.Size([6, 4])
      self.assertEqual(
          encoding["labels"][0]["boxes"].shape, expected_boxes_shape
      )
      expected_boxes_slice = torch.tensor(
          [0.2625, 0.5437, 0.4688, 0.8625]
      )
      self.assertTrue(
          torch.allclose(
              encoding["labels"][0]["boxes"][0],
              expected_boxes_slice,
              atol=1e-3,
          )
      )
      # verify image_id
      expected_image_id = torch.tensor([39769])
      self.assertTrue(
          torch.allclose(
              encoding["labels"][0]["image_id"], expected_image_id
          )
      )
      # verify is_crowd
      expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
      self.assertTrue(
          torch.allclose(
              encoding["labels"][0]["iscrowd"], expected_is_crowd
          )
      )
      # verify class_labels
      expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
      self.assertTrue(
          torch.allclose(
              encoding["labels"][0]["class_labels"],
              expected_class_labels,
          )
      )
      # verify masks
      expected_masks_sum = 822873
      self.assertEqual(
          encoding["labels"][0]["masks"].sum().item(),
          expected_masks_sum,
      )
      # verify orig_size
      expected_orig_size = torch.tensor([480, 640])
      self.assertTrue(
          torch.allclose(
              encoding["labels"][0]["orig_size"], expected_orig_size
          )
      )
      # verify size
      expected_size = torch.tensor([800, 1066])
      self.assertTrue(
          torch.allclose(encoding["labels"][0]["size"], expected_size)
      )
  
imports: import json
  import pathlib
  import unittest
  from transformers.testing_utils import require_torch, require_vision, slow
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs
label_desc: Use image node_id 6 for calling the DeformableDetrImageProcessingTest obj's underlying member method code with example usage: obj.test_call_pytorch_with_coco_panoptic_annotations() without return types