code: def create_and_check_for_multiple_choice(
      self,
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
  ):
      config.num_choices = self.num_choices
      model = CanineForMultipleChoice(config=config)
      model.to(torch_device)
      model.eval()
      multiple_choice_inputs_ids = (
          input_ids.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()
      )
      multiple_choice_input_mask = (
          input_mask.unsqueeze(1)
          .expand(-1, self.num_choices, -1)
          .contiguous()
      )
      result = model(
          multiple_choice_inputs_ids,
          attention_mask=multiple_choice_input_mask,
          token_type_ids=multiple_choice_token_type_ids,
          labels=choice_labels,
      )
      self.parent.assertEqual(
          result.logits.shape, (self.batch_size, self.num_choices)
      )
  
imports: import unittest
  from typing import List, Tuple
  from transformers import CanineConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, global_rng, ids_tensor, random_attention_mask
label_desc: Use image node_id 8 for calling the CanineModelTester obj's underlying member method code with example usage: obj.create_and_check_for_multiple_choice(config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels) without return types