code: def create_and_check_for_sequence_classification(
      self,
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
  ):
      config.num_labels = self.num_labels
      model = TFBertForSequenceClassification(config=config)
      inputs = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
  
      result = model(inputs)
      self.parent.assertEqual(
          result.logits.shape, (self.batch_size, self.num_labels)
      )
  
imports: from __future__ import annotations
  import unittest
  from transformers import BertConfig, 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, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
label_desc: Use image node_id 16 for calling the TFBertModelTester obj's underlying member method code with example usage: obj.create_and_check_for_sequence_classification(config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels) without return types