code: def test_inference_interpolate_pos_encoding(self):
      # ViT models have an `interpolate_pos_encoding` argument in their forward method,
      # allowing to interpolate the pre-trained position embeddings in order to use
      # the model on higher resolutions. The DINO model by Facebook AI leverages this
      # to visualize self-attention on higher resolution images.
      model = ViTModel.from_pretrained("facebook/dino-vits8").to(
          torch_device
      )
  
      feature_extractor = ViTFeatureExtractor.from_pretrained(
          "facebook/dino-vits8", size=480
      )
      image = prepare_img()
      inputs = feature_extractor(images=image, return_tensors="pt")
      pixel_values = inputs.pixel_values.to(torch_device)
  
      # forward pass
      with torch.no_grad():
          outputs = model(pixel_values, interpolate_pos_encoding=True)
  
      # verify the logits
      expected_shape = torch.Size((1, 3601, 384))
      self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [4.2340, 4.3906, -6.6692],
              [4.5463, 1.8928, -6.7257],
              [4.4429, 0.8496, -5.8585],
          ]
      ).to(torch_device)
  
      self.assertTrue(
          torch.allclose(
              outputs.last_hidden_state[0, :3, :3],
              expected_slice,
              atol=1e-4,
          )
      )
  
imports: import inspect
  import unittest
  from transformers import ViTConfig
  from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
label_desc: Use image node_id 3 for calling the ViTModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_interpolate_pos_encoding() without return types