code: def test_inference_masked_lm(self):
      with torch.no_grad():
          model = EsmForMaskedLM.from_pretrained(
              "Rocketknight1/esm2_t6_8M_UR50D"
          )
          model.eval()
          input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
          output = model(input_ids)[0]
  
          vocab_size = 33
  
          expected_shape = torch.Size((1, 6, vocab_size))
          self.assertEqual(output.shape, expected_shape)
  
          expected_slice = torch.tensor(
              [
                  [
                      [15.0973, -6.6406, -1.1351],
                      [-0.2209, -9.9622, 4.2109],
                      [-1.6055, -10.0023, 1.5914],
                  ]
              ]
          )
          self.assertTrue(
              torch.allclose(
                  output[:, :3, :3], expected_slice, atol=1e-4
              )
          )
  
imports: import unittest
  from transformers import EsmConfig, is_torch_available
  from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 for calling the EsmModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_masked_lm() without return types