code: def test_attention_outputs(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
      config.return_dict = True
  
      for model_class in self.all_model_classes:
          inputs_dict["output_attentions"] = True
          inputs_dict["output_hidden_states"] = False
          config.return_dict = True
          model = model_class(config)
          model.to(torch_device)
          model.eval()
          with torch.no_grad():
              outputs = model(
                  **self._prepare_for_class(inputs_dict, model_class)
              )
          attentions = outputs.attentions
          expected_num_attentions = len(self.model_tester.depths)
          self.assertEqual(len(attentions), expected_num_attentions)
  
          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          config.output_attentions = True
          window_size_squared = config.window_size**2
          model = model_class(config)
          model.to(torch_device)
          model.eval()
          with torch.no_grad():
              outputs = model(
                  **self._prepare_for_class(inputs_dict, model_class)
              )
          attentions = outputs.attentions
          self.assertEqual(len(attentions), expected_num_attentions)
  
          self.assertListEqual(
              list(attentions[0].shape[-3:]),
              [
                  self.model_tester.num_heads[0],
                  window_size_squared,
                  window_size_squared,
              ],
          )
          out_len = len(outputs)
  
          # Check attention is always last and order is fine
          inputs_dict["output_attentions"] = True
          inputs_dict["output_hidden_states"] = True
          model = model_class(config)
          model.to(torch_device)
          model.eval()
          with torch.no_grad():
              outputs = model(
                  **self._prepare_for_class(inputs_dict, model_class)
              )
  
          if hasattr(self.model_tester, "num_hidden_states_types"):
              added_hidden_states = (
                  self.model_tester.num_hidden_states_types
              )
          else:
              # also another +1 for reshaped_hidden_states
              added_hidden_states = 2
          self.assertEqual(out_len + added_hidden_states, len(outputs))
  
          self_attentions = outputs.attentions
  
          self.assertEqual(
              len(self_attentions), expected_num_attentions
          )
  
          self.assertListEqual(
              list(self_attentions[0].shape[-3:]),
              [
                  self.model_tester.num_heads[0],
                  window_size_squared,
                  window_size_squared,
              ],
          )
  
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 8 for calling the DonutSwinModelTest obj's underlying member method code with example usage: obj.test_attention_outputs() without return types