class_docstr: Here we also overwrite some of the tests of test_modeling_common.py, as DPT does not use input_ids, inputs_embeds,
  attention_mask and seq_length.
code: def test_initialization(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      configs_no_init = _config_zero_init(config)
      for model_class in self.all_model_classes:
          model = model_class(config=configs_no_init)
          # Skip the check for the backbone
          backbone_params = []
          for name, module in model.named_modules():
              if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                  backbone_params = [
                      f"{name}.{key}"
                      for key in module.state_dict().keys()
                  ]
                  break
  
          for name, param in model.named_parameters():
              if param.requires_grad:
                  if name in backbone_params:
                      continue
                  self.assertIn(
                      ((param.data.mean() * 1e9).round() / 1e9).item(),
                      [0.0, 1.0],
                      msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                  )
  
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 7 for calling the DPTModelTest obj's underlying member method code with example usage: obj.test_initialization() without return types