code: def create_and_check_for_masked_lm(
      self,
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
  ):
      model = MegaForMaskedLM(config=config)
      model.to(torch_device)
      model.eval()
      result = model(
          input_ids,
          attention_mask=input_mask,
          token_type_ids=token_type_ids,
          labels=token_labels,
      )
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, self.seq_length, self.vocab_size),
      )
  
imports: import unittest
  from transformers import MegaConfig, is_torch_available
  from transformers.testing_utils import TestCasePlus, require_torch, require_torch_fp16, 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 11 for calling the MegaModelTester obj's underlying member method code with example usage: obj.create_and_check_for_masked_lm(config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels) without return types