code: def test_inference_image_classification_head_imagenet_22k(self):
      model = BeitForImageClassification.from_pretrained(
          "microsoft/beit-large-patch16-224-pt22k-ft22k"
      ).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, 21841))
      self.assertEqual(logits.shape, expected_shape)
  
      expected_slice = torch.tensor([1.6881, -0.2787, 0.5901]).to(
          torch_device
      )
  
      self.assertTrue(
          torch.allclose(logits[0, :3], expected_slice, atol=1e-4)
      )
  
      expected_class_idx = 2396
      self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
  
imports: import inspect
  import unittest
  from datasets import load_dataset
  from packaging import version
  from transformers import BeitConfig
  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 4 for calling the BeitModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_image_classification_head_imagenet_22k() without return types