code: def test_inference_masked_lm(self):
      model = ViltForMaskedLM.from_pretrained(
          "dandelin/vilt-b32-mlm"
      ).to(torch_device)
  
      processor = self.default_processor
      image = prepare_img()
      text = "a bunch of [MASK] laying on a [MASK]."
      inputs = processor(image, text, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the logits
      expected_shape = torch.Size([1, 11, 30522])
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = torch.tensor([-12.5061, -12.5123, -12.5174]).to(
          torch_device
      )
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, 0, :3], expected_slice, atol=1e-4
          )
      )
  
      # verify masked token prediction equals "cats"
      predicted_id = outputs.logits[0, 4, :].argmax(-1).item()
      assert processor.decode([predicted_id]) == "cats"
  
imports: import unittest
  from datasets import load_dataset
  from packaging import version
  from transformers import ViltConfig, is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 2 for calling the ViltModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_masked_lm() without return types