code: def test_inference_image_classification_head(self):
      model = DinatForImageClassification.from_pretrained(
          "shi-labs/dinat-mini-in1k-224"
      ).to(torch_device)
      image_processor = self.default_image_processor
  
      image = Image.open(
          "./tests/fixtures/tests_samples/COCO/000000039769.png"
      )
      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([-0.1545, -0.7667, 0.4642]).to(
          torch_device
      )
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import collections
  import unittest
  from transformers import DinatConfig
  from transformers.testing_utils import require_natten, 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, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the DinatModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head() without return types