docs: def padded_sequence_accuracy(logits, labels):
    """Percentage of times that predictions matches labels everywhere (non-0)."""
    with tf.variable_scope("padded_sequence_accuracy", values=[logits, labels]):
      logits, labels = _pad_tensors_to_same_length(logits, labels)
      weights = tf.to_float(tf.not_equal(labels, 0))
      outputs = tf.to_int32(tf.argmax(logits, axis=-1))
      padded_labels = tf.to_int32(labels)
      not_correct = tf.to_float(tf.not_equal(outputs, padded_labels)) * weights
      axis = list(range(1, len(outputs.get_shape())))
      correct_seq = 1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis))
      return correct_seq, tf.constant(1.0)