code: def create_and_check_model(
      self,
      config,
      input_ids,
      bbox,
      image,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
  ):
      model = LayoutLMv2Model(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,
      )
      result = model(
          input_ids,
          bbox=bbox,
          image=image,
          token_type_ids=token_type_ids,
      )
      result = model(input_ids, bbox=bbox, image=image)
  
      # LayoutLMv2 has a different expected sequence length, namely also visual tokens are added
      expected_seq_len = (
          self.seq_length
          + self.image_feature_pool_shape[0]
          * self.image_feature_pool_shape[1]
      )
      self.parent.assertEqual(
          result.last_hidden_state.shape,
          (self.batch_size, expected_seq_len, self.hidden_size),
      )
      self.parent.assertEqual(
          result.pooler_output.shape,
          (self.batch_size, self.hidden_size),
      )
  
imports: 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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the LayoutLMv2ModelTester obj's underlying member method code with example usage: obj.create_and_check_model(config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels) without return types