code: def create_and_check_for_depth_estimation(
      self, config, pixel_values, labels
  ):
      config.num_labels = self.num_labels
      model = DPTForDepthEstimation(config)
      model.to(torch_device)
      model.eval()
      result = model(pixel_values)
      self.parent.assertEqual(
          result.predicted_depth.shape,
          (self.batch_size, self.image_size, self.image_size),
      )
  
imports: import unittest
  from transformers import Dinov2Config, DPTConfig
  from transformers.file_utils import is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 5 for calling the DPTModelTester obj's underlying member method code with example usage: obj.create_and_check_for_depth_estimation(config, pixel_values, labels) without return types