code: def create_and_check_deta_object_detection_head_model(
      self, config, pixel_values, pixel_mask, labels
  ):
      model = DetaForObjectDetection(config=config)
      model.to(torch_device)
      model.eval()
  
      result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
      result = model(pixel_values)
  
      self.parent.assertEqual(
          result.logits.shape,
          (
              self.batch_size,
              self.two_stage_num_proposals,
              self.num_labels,
          ),
      )
      self.parent.assertEqual(
          result.pred_boxes.shape,
          (self.batch_size, self.two_stage_num_proposals, 4),
      )
  
      result = model(
          pixel_values=pixel_values,
          pixel_mask=pixel_mask,
          labels=labels,
      )
  
      self.parent.assertEqual(result.loss.shape, ())
      self.parent.assertEqual(
          result.logits.shape,
          (
              self.batch_size,
              self.two_stage_num_proposals,
              self.num_labels,
          ),
      )
      self.parent.assertEqual(
          result.pred_boxes.shape,
          (self.batch_size, self.two_stage_num_proposals, 4),
      )
  
imports: import inspect
  import math
  import unittest
  from transformers import DetaConfig, ResNetConfig, is_torch_available, is_torchvision_available, is_vision_available
  from transformers.file_utils import cached_property
  from transformers.testing_utils import require_torchvision, require_vision, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 8 for calling the DetaModelTester obj's underlying member method code with example usage: obj.create_and_check_deta_object_detection_head_model(config, pixel_values, pixel_mask, labels) without return types