code: def test_attention_outputs(self):
      if not self.has_attentions:
          self.skipTest(reason="Model does not output attentions")
  
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
      config.return_dict = True
  
      seq_length = getattr(self.model_tester, "seq_length", None)
      decoder_seq_length = getattr(
          self.model_tester, "decoder_seq_length", seq_length
      )
      encoder_seq_length = getattr(
          self.model_tester, "encoder_seq_length", seq_length
      )
      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
      )
  
      for model_class in self.all_model_classes:
          inputs_dict["output_attentions"] = True
          inputs_dict["output_hidden_states"] = False
          model = model_class(config)
          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)
          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
          )
  
          self.assertListEqual(
              list(attentions[0].shape[-3:]),
              [
                  self.model_tester.num_attention_heads,
                  encoder_seq_length,
                  encoder_key_length,
              ],
          )
          out_len = len(outputs)
  
          if self.is_encoder_decoder:
              correct_outlen = 5
  
              # Question Answering model returns start_logits and end_logits
              if model_class in get_values(
                  FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
              ):
                  correct_outlen += 1  # start_logits and end_logits instead of only 1 output
  
              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)
          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
          )
  
          self.assertListEqual(
              list(self_attentions[0].shape[-3:]),
              [
                  self.model_tester.num_attention_heads,
                  encoder_seq_length,
                  encoder_key_length,
              ],
          )
  
imports: import copy
  import inspect
  import json
  import random
  import tempfile
  from typing import List, Tuple
  import numpy
  import transformers
  from transformers import is_flax_available, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import CaptureLogger, is_pt_flax_cross_test, require_flax, torch_device
  from transformers.utils import CONFIG_NAME, GENERATION_CONFIG_NAME, logging
  from transformers.utils.generic import ModelOutput
label_desc: Use image node_id 17 for calling the FlaxModelTesterMixin obj's underlying member method code with example usage: obj.test_attention_outputs() without return types