code: def test_oneformer(self):
      image_segmenter = pipeline(
          model="shi-labs/oneformer_ade20k_swin_tiny"
      )
  
      image = load_dataset(
          "hf-internal-testing/fixtures_ade20k", split="test"
      )
      file = image[0]["file"]
      outputs = image_segmenter(file, threshold=0.99)
      # Shortening by hashing
      for o in outputs:
          o["mask"] = mask_to_test_readable(o["mask"])
  
      self.assertEqual(
          nested_simplify(outputs, decimals=4),
          [
              {
                  "score": 0.9981,
                  "label": "grass",
                  "mask": {
                      "hash": "3a92904d4c",
                      "white_pixels": 118131,
                      "shape": (512, 683),
                  },
              },
              {
                  "score": 0.9992,
                  "label": "sky",
                  "mask": {
                      "hash": "fa2300cc9a",
                      "white_pixels": 231565,
                      "shape": (512, 683),
                  },
              },
          ],
      )
  
      # Different task
      outputs = image_segmenter(
          file, threshold=0.99, subtask="instance"
      )
      # Shortening by hashing
      for o in outputs:
          o["mask"] = mask_to_test_readable(o["mask"])
  
      self.assertEqual(
          nested_simplify(outputs, decimals=4),
          [
              {
                  "score": 0.9991,
                  "label": "sky",
                  "mask": {
                      "hash": "8b1ffad016",
                      "white_pixels": 230566,
                      "shape": (512, 683),
                  },
              },
              {
                  "score": 0.9981,
                  "label": "grass",
                  "mask": {
                      "hash": "9bbdf83d3d",
                      "white_pixels": 119130,
                      "shape": (512, 683),
                  },
              },
          ],
      )
  
      # Different task
      outputs = image_segmenter(file, subtask="semantic")
      # Shortening by hashing
      for o in outputs:
          o["mask"] = mask_to_test_readable(o["mask"])
  
      self.assertEqual(
          nested_simplify(outputs, decimals=4),
          [
              {
                  "score": None,
                  "label": "wall",
                  "mask": {
                      "hash": "897fb20b7f",
                      "white_pixels": 14506,
                      "shape": (512, 683),
                  },
              },
              {
                  "score": None,
                  "label": "building",
                  "mask": {
                      "hash": "f2a68c63e4",
                      "white_pixels": 125019,
                      "shape": (512, 683),
                  },
              },
              {
                  "score": None,
                  "label": "sky",
                  "mask": {
                      "hash": "e0ca3a548e",
                      "white_pixels": 135330,
                      "shape": (512, 683),
                  },
              },
              {
                  "score": None,
                  "label": "tree",
                  "mask": {
                      "hash": "7c9544bcac",
                      "white_pixels": 16263,
                      "shape": (512, 683),
                  },
              },
              {
                  "score": None,
                  "label": "road, route",
                  "mask": {
                      "hash": "2c7704e491",
                      "white_pixels": 2143,
                      "shape": (512, 683),
                  },
              },
              {
                  "score": None,
                  "label": "grass",
                  "mask": {
                      "hash": "bf6c2867e0",
                      "white_pixels": 53040,
                      "shape": (512, 683),
                  },
              },
              {
                  "score": None,
                  "label": "plant",
                  "mask": {
                      "hash": "93c4b7199e",
                      "white_pixels": 3335,
                      "shape": (512, 683),
                  },
              },
              {
                  "score": None,
                  "label": "house",
                  "mask": {
                      "hash": "93ec419ad5",
                      "white_pixels": 60,
                      "shape": (512, 683),
                  },
              },
          ],
      )
  
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 10 for calling the ImageSegmentationPipelineTests obj's underlying member method code with example usage: obj.test_oneformer() without return types