code: def test_inference_image_classification_head_imagenet_1k(self):
      model = Data2VecVisionForImageClassification.from_pretrained(
          "facebook/data2vec-vision-base-ft1k"
      ).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)
      logits = outputs.logits
  
      # verify the logits
      expected_shape = torch.Size((1, 1000))
      self.assertEqual(logits.shape, expected_shape)
  
      expected_slice = torch.tensor([0.3277, -0.1395, 0.0911]).to(
          torch_device
      )
  
      self.assertTrue(
          torch.allclose(logits[0, :3], expected_slice, atol=1e-4)
      )
  
      expected_top2 = [
          model.config.label2id[i]
          for i in ["remote control, remote", "tabby, tabby cat"]
      ]
      self.assertEqual(
          logits[0].topk(2).indices.cpu().tolist(), expected_top2
      )
  
imports: import inspect
  import unittest
  from transformers import Data2VecVisionConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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
label_desc: Use image node_id 2 for calling the Data2VecVisionModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head_imagenet_1k() without return types