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)
      decoder_seq_length = getattr(
          self.model_tester, "decoder_seq_length", seq_len
      )
      encoder_seq_length = getattr(
          self.model_tester, "encoder_seq_length", seq_len
      )
      decoder_key_length = getattr(
          self.model_tester, "decoder_key_length", decoder_seq_length
      )
      encoder_key_length = getattr(
          self.model_tester, "key_length", encoder_seq_length
      )
      chunk_length = getattr(self.model_tester, "chunk_length", None)
      if chunk_length is not None and hasattr(
          self.model_tester, "num_hashes"
      ):
          encoder_seq_length = (
              encoder_seq_length * self.model_tester.num_hashes
          )
  
      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.encoder_attentions
              if config.is_encoder_decoder
              else outputs.attentions
          )
          self.assertEqual(
              len(attentions), self.model_tester.num_hidden_layers
          )
  
          # 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.encoder_attentions
              if config.is_encoder_decoder
              else outputs.attentions
          )
          self.assertEqual(
              len(attentions), self.model_tester.num_hidden_layers
          )
  
          if chunk_length is not None:
              self.assertListEqual(
                  list(attentions[0].shape[-4:]),
                  [
                      self.model_tester.num_attention_heads / 2,
                      encoder_seq_length,
                      chunk_length,
                      encoder_key_length,
                  ],
              )
          else:
              self.assertListEqual(
                  list(attentions[0].shape[-3:]),
                  [
                      self.model_tester.num_attention_heads / 2,
                      encoder_seq_length,
                      encoder_key_length,
                  ],
              )
          out_len = len(outputs)
  
          if self.is_encoder_decoder:
              correct_outlen = 5
  
              # loss is at first position
              if "labels" in inputs_dict:
                  correct_outlen += 1  # loss is added to beginning
              # Question Answering model returns start_logits and end_logits
              if model_class in get_values(
                  MODEL_FOR_QUESTION_ANSWERING_MAPPING
              ):
                  correct_outlen += 1  # start_logits and end_logits instead of only 1 output
              if "past_key_values" in outputs:
                  correct_outlen += (
                      1  # past_key_values have been returned
                  )
  
              self.assertEqual(out_len, correct_outlen)
  
              # decoder attentions
              decoder_attentions = outputs.decoder_attentions
              self.assertIsInstance(decoder_attentions, (list, tuple))
              self.assertEqual(
                  len(decoder_attentions),
                  self.model_tester.num_hidden_layers,
              )
              self.assertListEqual(
                  list(decoder_attentions[0].shape[-3:]),
                  [
                      self.model_tester.num_attention_heads,
                      decoder_seq_length,
                      decoder_key_length,
                  ],
              )
  
              # cross attentions
              cross_attentions = outputs.cross_attentions
              self.assertIsInstance(cross_attentions, (list, tuple))
              self.assertEqual(
                  len(cross_attentions),
                  self.model_tester.num_hidden_layers,
              )
              self.assertListEqual(
                  list(cross_attentions[0].shape[-3:]),
                  [
                      self.model_tester.num_attention_heads,
                      decoder_seq_length,
                      encoder_key_length,
                  ],
              )
  
          # 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
              )
          elif self.is_encoder_decoder:
              added_hidden_states = 2
          else:
              added_hidden_states = 1
          self.assertEqual(out_len + added_hidden_states, len(outputs))
  
          self_attentions = (
              outputs.encoder_attentions
              if config.is_encoder_decoder
              else outputs.attentions
          )
  
          self.assertEqual(
              len(self_attentions), self.model_tester.num_hidden_layers
          )
          if chunk_length is not None:
              self.assertListEqual(
                  list(self_attentions[0].shape[-4:]),
                  [
                      self.model_tester.num_attention_heads / 2,
                      encoder_seq_length,
                      chunk_length,
                      encoder_key_length,
                  ],
              )
          else:
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]),
                  [
                      self.model_tester.num_attention_heads / 2,
                      encoder_seq_length,
                      encoder_key_length,
                  ],
              )
  
imports: import os
  import tempfile
  import unittest
  from transformers import ConvBertConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 10 for calling the ConvBertModelTest obj's underlying member method code with example usage: obj.test_attention_outputs() without return types