code: def test_post_process_instance_segmentation(self):
      image_processor = OneFormerImageProcessor(
          reduce_labels=True,
          ignore_index=0,
          size=(512, 512),
          class_info_file="ade20k_panoptic.json",
          num_text=self.processing_tester.num_text,
      )
      tokenizer = CLIPTokenizer.from_pretrained(
          "shi-labs/oneformer_ade20k_swin_tiny"
      )
      processor = OneFormerProcessor(
          image_processor=image_processor,
          tokenizer=tokenizer,
          max_seq_length=77,
          task_seq_length=77,
      )
  
      outputs = self.processing_tester.get_fake_oneformer_outputs()
      segmentation = processor.post_process_instance_segmentation(
          outputs, threshold=0
      )
  
      self.assertTrue(
          len(segmentation) == self.processing_tester.batch_size
      )
      for el in segmentation:
          self.assertTrue("segmentation" in el)
          self.assertTrue("segments_info" in el)
          self.assertEqual(type(el["segments_info"]), list)
          self.assertEqual(
              el["segmentation"].shape,
              (
                  self.processing_tester.height,
                  self.processing_tester.width,
              ),
          )
  
imports: import json
  import os
  import tempfile
  import unittest
  import numpy
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_image_processing_common import prepare_image_inputs
label_desc: Use image node_id 17 for calling the OneFormerProcessingTest obj's underlying member method code with example usage: obj.test_post_process_instance_segmentation() without return types