code: def test_inference_masked_lm(self):
      model = Data2VecTextForMaskedLM.from_pretrained(
          "facebook/data2vec-text-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(
          [
              [
                  [0.2328, 0.0000, 1.1710],
                  [2.2525, 0.0000, 1.9937],
                  [2.1280, 0.0000, 1.8691],
              ]
          ]
      )
  
      self.assertTrue(
          torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)
      )
  
imports: import unittest
  from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
  from transformers import Data2VecTextConfig, 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
label_desc: Use image node_id 1 for calling the Data2VecTextModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_masked_lm() without return types