code: def test_post_processing_semantic_segmentation(self):
      model = BeitForSemanticSegmentation.from_pretrained(
          "microsoft/beit-base-finetuned-ade-640-640"
      )
      model = model.to(torch_device)
  
      feature_extractor = BeitFeatureExtractor(
          do_resize=True, size=640, do_center_crop=False
      )
  
      ds = load_dataset(
          "hf-internal-testing/fixtures_ade20k", split="test"
      )
      image = Image.open(ds[0]["file"])
      inputs = feature_extractor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      outputs.logits = outputs.logits.detach().cpu()
  
      segmentation = (
          feature_extractor.post_process_semantic_segmentation(
              outputs=outputs, target_sizes=[(500, 300)]
          )
      )
      expected_shape = torch.Size((500, 300))
      self.assertEqual(segmentation[0].shape, expected_shape)
  
      segmentation = (
          feature_extractor.post_process_semantic_segmentation(
              outputs=outputs
          )
      )
      expected_shape = torch.Size((160, 160))
      self.assertEqual(segmentation[0].shape, expected_shape)
  
imports: import inspect
  import unittest
  from datasets import load_dataset
  from packaging import version
  from transformers import BeitConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
label_desc: Use image node_id 6 for calling the BeitModelIntegrationTest obj's underlying member method code with example usage: obj.test_post_processing_semantic_segmentation() without return types