code: def sequence_accuracy(labels, outputs):
    
    # A sequence is correct if all of the non-padded entries are correct
    all_correct = tf.reduce_all(
        tf.logical_or(tf.equal(labels, outputs), tf.equal(labels, 0)), axis=-1
    )
    return tf.metrics.mean(all_correct)
docs: Compute the sequence-level accuracy.
  
    A sequence is only considered correct if all of its entries were predicted
    correctly.
  
    Args:
      labels: ground-truth labels, shape=(batch, packed_seq_length)
      outputs: predicted tokens, shape=(batch, seq_length)
    Returns:
      Two ops, one for getting the current average accuracy and another for
      updating the running average estimate.