code: def test_inference_image_classification_head(self):
      model = ConvNextForImageClassification.from_pretrained(
          "facebook/convnext-tiny-224"
      ).to(torch_device)
  
      feature_extractor = self.default_feature_extractor
      image = prepare_img()
      inputs = feature_extractor(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([-0.0260, -0.4739, 0.1911]).to(
          torch_device
      )
  
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import inspect
  import unittest
  from transformers import ConvNextConfig
  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 2 for calling the ConvNextModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head() without return types