code: def testTransformerWithEncoderDecoderAttentionLoss(self):
      model, features = get_model(
          transformer.transformer_supervised_attention()
      )
      expected_attention_weights = np.random.random_sample(
          size=(BATCH_SIZE, TARGET_LENGTH, INPUT_LENGTH)
      )
      features["expected_attentions"] = tf.constant(
          expected_attention_weights, dtype=tf.float32
      )
      _, extra_loss = model(features)
      with self.test_session() as session:
          session.run(tf.global_variables_initializer())
          res = session.run(extra_loss["attention_loss"])
      self.assertEqual(res.shape, ())
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.data_generators import librispeech
  from tensor2tensor.data_generators import problem_hparams
  from tensor2tensor.models import transformer
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 12 for calling the TransformerTest obj's underlying member method code with example usage: obj.testTransformerWithEncoderDecoderAttentionLoss() without return types