code: def test_attention_outputs(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
      config.return_dict = True
  
      seq_len = getattr(self.model_tester, "seq_length", None)
  
      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
          # we add + 2 due to the 2 shallow encoders
          self.assertEqual(
              len(attentions), self.model_tester.num_hidden_layers + 2
          )
  
          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          config.output_attentions = 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
          # we add + 2 due to the 2 shallow encoders
          self.assertEqual(
              len(attentions), self.model_tester.num_hidden_layers + 2
          )
  
          self.assertListEqual(
              list(attentions[0].shape[-3:]),
              [self.model_tester.num_attention_heads, seq_len, seq_len],
          )
          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:
              added_hidden_states = 1
          self.assertEqual(out_len + added_hidden_states, len(outputs))
  
          self_attentions = outputs.attentions
  
          self.assertEqual(
              len(self_attentions),
              self.model_tester.num_hidden_layers + 2,
          )
          self.assertListEqual(
              list(self_attentions[0].shape[-3:]),
              [self.model_tester.num_attention_heads, seq_len, seq_len],
          )
  
imports: import unittest
  from typing import List, Tuple
  from transformers import CanineConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, global_rng, ids_tensor, random_attention_mask
label_desc: Use image node_id 9 for calling the CanineModelTest obj's underlying member method code with example usage: obj.test_attention_outputs() without return types