code: def test_inference_image_classification_head(self):
      model = ViTHybridForImageClassification.from_pretrained(
          VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]
      ).to(torch_device)
  
      image_processor = self.default_image_processor
      image = prepare_img()
      inputs = image_processor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the logits
      expected_shape = torch.Size((1, 1000))
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = torch.tensor([-1.9090, -0.4993, -0.2389]).to(
          torch_device
      )
  
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import unittest
  from transformers import ViTHybridConfig
  from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the ViTModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head() without return types