code: def test_inference_masked_lm(self):
      model = RobertaForMaskedLM.from_pretrained("roberta-base")
  
      input_ids = torch.tensor(
          [[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]
      )
      with torch.no_grad():
          output = model(input_ids)[0]
      expected_shape = torch.Size((1, 11, 50265))
      self.assertEqual(output.shape, expected_shape)
      # compare the actual values for a slice.
      expected_slice = torch.tensor(
          [
              [
                  [33.8802, -4.3103, 22.7761],
                  [4.6539, -2.8098, 13.6253],
                  [1.8228, -3.6898, 8.8600],
              ]
          ]
      )
  
      # roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
      # roberta.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
  
      self.assertTrue(
          torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)
      )
  
imports: import unittest
  from copy import deepcopy
  from transformers import RobertaConfig, is_torch_available
  from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  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 1 for calling the RobertaModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_masked_lm() without return types