code: def test_inference_masked_lm(self):
      model = RoFormerForMaskedLM.from_pretrained(
          "junnyu/roformer_chinese_base"
      )
      input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
      with torch.no_grad():
          output = model(input_ids)[0]
  
      # TODO Replace vocab size
      vocab_size = 50000
  
      expected_shape = torch.Size((1, 6, vocab_size))
      self.assertEqual(output.shape, expected_shape)
  
      # TODO Replace values below with what was printed above.
      expected_slice = torch.tensor(
          [
              [
                  [-0.1205, -1.0265, 0.2922],
                  [-1.5134, 0.1974, 0.1519],
                  [-5.0135, -3.9003, -0.8404],
              ]
          ]
      )
  
      self.assertTrue(
          torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)
      )
  
imports: import unittest
  from transformers import RoFormerConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  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 RoFormerModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_masked_lm() without return types