import tensorflow as tf

    brodcast_mean = tf.reshape(mean, broadcast_shape)
    std = tf.reduce_mean(tf.square(_x - brodcast_mean) + epsilon, axis=reduction_axes)
    std = tf.sqrt(std)
    brodcast_std = tf.reshape(std, broadcast_shape)
    x_normed = (_x - brodcast_mean) / (brodcast_std + epsilon)
    # x_normed = tf.layers.batch_normalization(_x, center=False, scale=False)
