code: def create_and_check_albert_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 = TFAlbertForMultipleChoice(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.assertListEqual(
          list(result["logits"].shape),
          [self.batch_size, self.num_choices],
      )
  
imports: from __future__ import annotations
  import unittest
  from transformers import AlbertConfig, is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 8 for calling the TFAlbertModelTester obj's underlying member method code with example usage: obj.create_and_check_albert_for_multiple_choice(config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels) without return types