import tensorflow as tf

    res = (input_ - used_mean) / tf.sqrt(used_var + epsilon)
    # de-normalize
    if scale:
        res *= gamma
    res += beta

    # update variables
    if train:
        with tf.name_scope(name, "AssignMovingAvg", [mean, cur_mean, decay]):
            with ops.colocate_with(mean):
                new_mean = tf.assign_sub(
                    mean,
                    tf.check_numerics(decay * (mean - cur_mean), "NaN in moving mean."))
        with tf.name_scope(name, "AssignMovingAvg", [var, cur_var, decay]):
            with ops.colocate_with(var):
                new_var = tf.assign_sub(
