code: def test_inference_nezha_masked_lm(self):
      model = NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base")
      input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
      attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]])
      with torch.no_grad():
          output = model(input_ids, attention_mask=attention_mask)[0]
      expected_shape = torch.Size((1, 6, 21128))
      self.assertEqual(output.shape, expected_shape)
      expected_slice = torch.tensor(
          [
              [-2.7939, -1.7902, -2.2189],
              [-2.8585, -1.8908, -2.3723],
              [-2.6499, -1.7750, -2.2558],
          ]
      )
  
      self.assertTrue(
          torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)
      )
  
imports: import os
  import tempfile
  import unittest
  from transformers import NezhaConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the NezhaModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_nezha_masked_lm() without return types