code: def test_maskformer(self):
      threshold = 0.8
      model_id = "facebook/maskformer-swin-base-ade"
  
      model = AutoModelForInstanceSegmentation.from_pretrained(model_id)
      image_processor = AutoImageProcessor.from_pretrained(model_id)
  
      image_segmenter = pipeline(
          "image-segmentation",
          model=model,
          image_processor=image_processor,
      )
  
      image = load_dataset(
          "hf-internal-testing/fixtures_ade20k", split="test"
      )
      file = image[0]["file"]
      outputs = image_segmenter(file, threshold=threshold)
  
      # Shortening by hashing
      for o in outputs:
          o["mask"] = mask_to_test_readable(o["mask"])
  
      self.assertEqual(
          nested_simplify(outputs, decimals=4),
          [
              {
                  "score": 0.9974,
                  "label": "wall",
                  "mask": {
                      "hash": "a547b7c062",
                      "shape": (512, 683),
                      "white_pixels": 14252,
                  },
              },
              {
                  "score": 0.949,
                  "label": "house",
                  "mask": {
                      "hash": "0da9b7b38f",
                      "shape": (512, 683),
                      "white_pixels": 132177,
                  },
              },
              {
                  "score": 0.9995,
                  "label": "grass",
                  "mask": {
                      "hash": "1d07ea0a26",
                      "shape": (512, 683),
                      "white_pixels": 53444,
                  },
              },
              {
                  "score": 0.9976,
                  "label": "tree",
                  "mask": {
                      "hash": "6cdc97c7da",
                      "shape": (512, 683),
                      "white_pixels": 7944,
                  },
              },
              {
                  "score": 0.8239,
                  "label": "plant",
                  "mask": {
                      "hash": "1ab4ce378f",
                      "shape": (512, 683),
                      "white_pixels": 4136,
                  },
              },
              {
                  "score": 0.9942,
                  "label": "road, route",
                  "mask": {
                      "hash": "39c5d17be5",
                      "shape": (512, 683),
                      "white_pixels": 1941,
                  },
              },
              {
                  "score": 1.0,
                  "label": "sky",
                  "mask": {
                      "hash": "a3756324a6",
                      "shape": (512, 683),
                      "white_pixels": 135802,
                  },
              },
          ],
      )
  
imports: import tempfile
  import unittest
  from typing import Dict
  import datasets
  import numpy
  import requests
  from datasets import load_dataset
  from huggingface_hub.utils import insecure_hashlib
  from transformers import MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, AutoImageProcessor, AutoModelForImageSegmentation, AutoModelForInstanceSegmentation, DetrForSegmentation, ImageSegmentationPipeline, MaskFormerForInstanceSegmentation, is_vision_available, pipeline
  from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
  from .test_pipelines_common import ANY
label_desc: Use image node_id 9 for calling the ImageSegmentationPipelineTests obj's underlying member method code with example usage: obj.test_maskformer() without return types