code: def test_conversion_multiple_choice_model(self):
      if self.config_class not in MODEL_FOR_MULTIPLE_CHOICE_MAPPING:
          self.skipTest("No token classification class.")
  
      model = MODEL_FOR_MULTIPLE_CHOICE_MAPPING[self.config_class](
          self.config()
      )
      adapters.init(model)
      label_dict = {}
      label_dict["labels"] = torch.ones(
          self.batch_size, dtype=torch.long, device=torch_device
      )
      self.run_test(
          model,
          input_shape=(self.batch_size, 2, self.seq_length),
          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 9 for calling the ModelClassConversionTestMixin obj's underlying member method code with example usage: obj.test_conversion_multiple_choice_model() without return types