code: def create_and_check_flaubert_for_multiple_choice(
      self,
      config,
      input_ids,
      token_type_ids,
      input_lengths,
      sequence_labels,
      token_labels,
      is_impossible_labels,
      choice_labels,
      input_mask,
  ):
      config.num_choices = self.num_choices
      model = TFFlaubertForMultipleChoice(config=config)
      multiple_choice_inputs_ids = tf.tile(
          tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)
      )
      multiple_choice_input_mask = tf.tile(
          tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)
      )
      multiple_choice_token_type_ids = tf.tile(
          tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)
      )
      inputs = {
          "input_ids": multiple_choice_inputs_ids,
          "attention_mask": multiple_choice_input_mask,
          "token_type_ids": multiple_choice_token_type_ids,
      }
      result = model(inputs)
      self.parent.assertEqual(
          result.logits.shape, (self.batch_size, self.num_choices)
      )
  
imports: import unittest
  from transformers import is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 8 for calling the TFFlaubertModelTester obj's underlying member method code with example usage: obj.create_and_check_flaubert_for_multiple_choice(config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask) without return types