code: def test_inference_convnext_backbone(self):
      processor = AutoImageProcessor.from_pretrained(
          "openmmlab/upernet-convnext-tiny"
      )
      model = UperNetForSemanticSegmentation.from_pretrained(
          "openmmlab/upernet-convnext-tiny"
      ).to(torch_device)
  
      image = prepare_img()
      inputs = processor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      with torch.no_grad():
          outputs = model(**inputs)
  
      expected_shape = torch.Size(
          (1, model.config.num_labels, 512, 512)
      )
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [-8.8110, -8.8110, -8.6521],
              [-8.8110, -8.8110, -8.6521],
              [-8.7746, -8.7746, -8.6130],
          ]
      ).to(torch_device)
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import unittest
  from huggingface_hub import hf_hub_download
  from transformers import ConvNextConfig, UperNetConfig
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import 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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the UperNetModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_convnext_backbone() without return types