code: def test_conversion_masked_lm_model(self):
      if self.config_class not in MODEL_FOR_MASKED_LM_MAPPING:
          self.skipTest("No masked language modeling class.")
  
      model = MODEL_FOR_MASKED_LM_MAPPING[self.config_class](
          self.config()
      )
      adapters.init(model)
      label_dict = {}
      label_dict["labels"] = torch.zeros(
          (self.batch_size, self.seq_length),
          dtype=torch.long,
          device=torch_device,
      )
      # for encoder-decoder models such as BART, we additionally pass the decoder input ids
      if (
          "decoder_input_ids"
          in inspect.signature(model.forward).parameters
      ):
          label_dict["decoder_input_ids"] = label_dict["labels"].clone()
      self.run_test(model, label_dict=label_dict)
  
file_path: adapters/tests/test_adapter_conversion.py
imports: import inspect
  import re
  import tempfile
  import torch
  import adapters
  from adapters import AutoAdapterModel
  from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, AlbertPreTrainedModel, BertPreTrainedModel, RobertaPreTrainedModel, XLMRobertaPreTrainedModel, XmodPreTrainedModel
  from transformers.testing_utils import require_torch, torch_device
label_desc: Use image node_id 3 for calling the ModelClassConversionTestMixin obj's underlying member method code with example usage: obj.test_conversion_masked_lm_model() without return types