code: def create_and_check_for_entity_pair_classification(
      self,
      config,
      input_ids,
      attention_mask,
      token_type_ids,
      entity_ids,
      entity_attention_mask,
      entity_token_type_ids,
      entity_position_ids,
      sequence_labels,
      token_labels,
      choice_labels,
      entity_labels,
      entity_classification_labels,
      entity_pair_classification_labels,
      entity_span_classification_labels,
  ):
      config.num_labels = self.num_entity_pair_classification_labels
      model = LukeForEntityClassification(config)
      model.to(torch_device)
      model.eval()
  
      result = model(
          input_ids,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          entity_ids=entity_ids,
          entity_attention_mask=entity_attention_mask,
          entity_token_type_ids=entity_token_type_ids,
          entity_position_ids=entity_position_ids,
          labels=entity_pair_classification_labels,
      )
      self.parent.assertEqual(
          result.logits.shape,
          (self.batch_size, self.num_entity_pair_classification_labels),
      )
  
imports: import unittest
  from transformers import LukeConfig, 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, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 7 for calling the LukeModelTester obj's underlying member method code with example usage: obj.create_and_check_for_entity_pair_classification(config, input_ids, attention_mask, token_type_ids, entity_ids, entity_attention_mask, entity_token_type_ids, entity_position_ids, sequence_labels, token_labels, choice_labels, entity_labels, entity_classification_labels, entity_pair_classification_labels, entity_span_classification_labels) without return types