code: def run_pipeline_test(self, image_segmenter, examples):
      outputs = image_segmenter(
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          threshold=0.0,
          mask_threshold=0,
          overlap_mask_area_threshold=0,
      )
      self.assertIsInstance(outputs, list)
      n = len(outputs)
      if isinstance(
          image_segmenter.model,
          (MaskFormerForInstanceSegmentation, DetrForSegmentation),
      ):
          # Instance segmentation (maskformer, and detr) have a slot for null class
          # and can output nothing even with a low threshold
          self.assertGreaterEqual(n, 0)
      else:
          self.assertGreaterEqual(n, 1)
      # XXX: PIL.Image implements __eq__ which bypasses ANY, so we inverse the comparison
      # to make it work
      self.assertEqual(
          [
              {
                  "score": ANY(float, type(None)),
                  "label": ANY(str),
                  "mask": ANY(Image.Image),
              }
          ]
          * n,
          outputs,
      )
  
      dataset = datasets.load_dataset(
          "hf-internal-testing/fixtures_image_utils",
          "image",
          split="test",
      )
  
      # RGBA
      outputs = image_segmenter(
          dataset[0]["file"],
          threshold=0.0,
          mask_threshold=0,
          overlap_mask_area_threshold=0,
      )
      m = len(outputs)
      self.assertEqual(
          [
              {
                  "score": ANY(float, type(None)),
                  "label": ANY(str),
                  "mask": ANY(Image.Image),
              }
          ]
          * m,
          outputs,
      )
      # LA
      outputs = image_segmenter(
          dataset[1]["file"],
          threshold=0.0,
          mask_threshold=0,
          overlap_mask_area_threshold=0,
      )
      m = len(outputs)
      self.assertEqual(
          [
              {
                  "score": ANY(float, type(None)),
                  "label": ANY(str),
                  "mask": ANY(Image.Image),
              }
          ]
          * m,
          outputs,
      )
      # L
      outputs = image_segmenter(
          dataset[2]["file"],
          threshold=0.0,
          mask_threshold=0,
          overlap_mask_area_threshold=0,
      )
      m = len(outputs)
      self.assertEqual(
          [
              {
                  "score": ANY(float, type(None)),
                  "label": ANY(str),
                  "mask": ANY(Image.Image),
              }
          ]
          * m,
          outputs,
      )
  
      if isinstance(image_segmenter.model, DetrForSegmentation):
          # We need to test batch_size with images with the same size.
          # Detr doesn't normalize the size of the images, meaning we can have
          # 800x800 or 800x1200, meaning we cannot batch simply.
          # We simply bail on this
          batch_size = 1
      else:
          batch_size = 2
  
      # 5 times the same image so the output shape is predictable
      batch = [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]
      outputs = image_segmenter(
          batch,
          threshold=0.0,
          mask_threshold=0,
          overlap_mask_area_threshold=0,
          batch_size=batch_size,
      )
      self.assertEqual(len(batch), len(outputs))
      self.assertEqual(len(outputs[0]), n)
      self.assertEqual(
          [
              [
                  {
                      "score": ANY(float, type(None)),
                      "label": ANY(str),
                      "mask": ANY(Image.Image),
                  }
              ]
              * n,
              [
                  {
                      "score": ANY(float, type(None)),
                      "label": ANY(str),
                      "mask": ANY(Image.Image),
                  }
              ]
              * n,
              [
                  {
                      "score": ANY(float, type(None)),
                      "label": ANY(str),
                      "mask": ANY(Image.Image),
                  }
              ]
              * n,
              [
                  {
                      "score": ANY(float, type(None)),
                      "label": ANY(str),
                      "mask": ANY(Image.Image),
                  }
              ]
              * n,
              [
                  {
                      "score": ANY(float, type(None)),
                      "label": ANY(str),
                      "mask": ANY(Image.Image),
                  }
              ]
              * n,
          ],
          outputs,
          f"Expected [{n}, {n}, {n}, {n}, {n}], got {[len(item) for item in outputs]}",
      )
  
imports: import hashlib
  import unittest
  from typing import Dict
  import datasets
  import numpy
  from datasets import load_dataset
  from transformers import MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, AutoFeatureExtractor, AutoModelForImageSegmentation, AutoModelForInstanceSegmentation, DetrForSegmentation, ImageSegmentationPipeline, MaskFormerForInstanceSegmentation, is_vision_available, pipeline
  from transformers.testing_utils import nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 2 for calling the ImageSegmentationPipelineTests obj's underlying member method code with example usage: obj.run_pipeline_test(image_segmenter, examples) without return types