code: def create_and_check_double_lm_head_model(
      self,
      config,
      input_ids,
      input_mask,
      head_mask,
      token_type_ids,
      mc_token_ids,
      *args,
  ):
      model = GPT2DoubleHeadsModel(config)
      model.to(torch_device)
      model.eval()
  
      multiple_choice_inputs_ids = (
          input_ids.unsqueeze(1)
          .expand(-1, self.num_choices, -1)
          .contiguous()
      )
      multiple_choice_input_mask = (
          input_mask.unsqueeze(1)
          .expand(-1, self.num_choices, -1)
          .contiguous()
      )
      multiple_choice_token_type_ids = (
          token_type_ids.unsqueeze(1)
          .expand(-1, self.num_choices, -1)
          .contiguous()
      )
  
      inputs = {
          "input_ids": multiple_choice_inputs_ids,
          "mc_token_ids": mc_token_ids,
          "attention_mask": multiple_choice_input_mask,
          "token_type_ids": multiple_choice_token_type_ids,
          "labels": multiple_choice_inputs_ids,
      }
  
      result = model(**inputs)
      self.parent.assertEqual(result.loss.shape, ())
      self.parent.assertEqual(
          result.logits.shape,
          (
              self.batch_size,
              self.num_choices,
              self.seq_length,
              self.vocab_size,
          ),
      )
      self.parent.assertEqual(
          result.mc_logits.shape, (self.batch_size, self.num_choices)
      )
  
imports: import datetime
  import gc
  import math
  import unittest
  from transformers import GPT2Config, is_torch_available
  from transformers.testing_utils import backend_empty_cache, require_torch, 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 13 for calling the GPT2ModelTester obj's underlying member method code with example usage: obj.create_and_check_double_lm_head_model(config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids) without return types