code: def test_inference_masked_image_modeling_head(self):
      model = BeitForMaskedImageModeling.from_pretrained(
          "microsoft/beit-base-patch16-224-pt22k"
      ).to(torch_device)
  
      feature_extractor = self.default_feature_extractor
      image = prepare_img()
      pixel_values = feature_extractor(
          images=image, return_tensors="pt"
      ).pixel_values.to(torch_device)
  
      # prepare bool_masked_pos
      bool_masked_pos = torch.ones((1, 196), dtype=torch.bool).to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(
              pixel_values=pixel_values, bool_masked_pos=bool_masked_pos
          )
      logits = outputs.logits
  
      # verify the logits
      expected_shape = torch.Size((1, 196, 8192))
      self.assertEqual(logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [-3.2437, 0.5072, -13.9174],
              [-3.2456, 0.4948, -13.9401],
              [-3.2033, 0.5121, -13.8550],
          ]
      ).to(torch_device)
  
      self.assertTrue(
          torch.allclose(
              logits[bool_masked_pos][:3, :3], expected_slice, atol=1e-2
          )
      )
  
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 2 for calling the BeitModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_masked_image_modeling_head() without return types