code: def create_and_check_model(self, config, pixel_values, labels):
      model = DonutSwinModel(config=config)
      model.to(torch_device)
      model.eval()
      result = model(pixel_values)
  
      expected_seq_len = (
          (config.image_size // config.patch_size) ** 2
      ) // (4 ** (len(config.depths) - 1))
      expected_dim = int(
          config.embed_dim * 2 ** (len(config.depths) - 1)
      )
  
      self.parent.assertEqual(
          result.last_hidden_state.shape,
          (self.batch_size, expected_seq_len, expected_dim),
      )
  
imports: import collections
  import inspect
  import unittest
  from transformers import DonutSwinConfig
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import is_torch_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
label_desc: Use image node_id 4 for calling the DonutSwinModelTester obj's underlying member method code with example usage: obj.create_and_check_model(config, pixel_values, labels) without return types