code: def create_and_check_for_question_answering(
      self,
      config,
      input_ids,
      bbox,
      image,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
  ):
      model = LayoutLMv2ForQuestionAnswering(config=config)
      model.to(torch_device)
      model.eval()
      result = model(
          input_ids,
          bbox=bbox,
          image=image,
          attention_mask=input_mask,
          token_type_ids=token_type_ids,
          start_positions=sequence_labels,
          end_positions=sequence_labels,
      )
      self.parent.assertEqual(
          result.start_logits.shape, (self.batch_size, self.seq_length)
      )
      self.parent.assertEqual(
          result.end_logits.shape, (self.batch_size, self.seq_length)
      )
  
imports: import os
  import random
  import tempfile
  import unittest
  from transformers.testing_utils import require_detectron2, require_torch, require_torch_multi_gpu, slow, torch_device
  from transformers.utils import is_detectron2_available, is_torch_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask
label_desc: Use image node_id 6 for calling the LayoutLMv2ModelTester obj's underlying member method code with example usage: obj.create_and_check_for_question_answering(config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels) without return types