code: def test_inference_no_head(self):
      model = Dinov2Model.from_pretrained("facebook/dinov2-base").to(
          torch_device
      )
  
      image_processor = self.default_image_processor
      image = prepare_img()
      inputs = image_processor(image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the last hidden states
      expected_shape = torch.Size((1, 257, 768))
      self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [-2.1747, -0.4729, 1.0936],
              [-3.2780, -0.8269, -0.9210],
              [-2.9129, 1.1284, -0.7306],
          ],
          device=torch_device,
      )
      self.assertTrue(
          torch.allclose(
              outputs.last_hidden_state[0, :3, :3],
              expected_slice,
              atol=1e-4,
          )
      )
  
imports: import unittest
  from transformers import Dinov2Config
  from transformers.testing_utils import is_flaky, require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_backbone_common import BackboneTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the Dinov2ModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head() without return types