import tensorflow as tf

        normed:        batch-normalized maps
    """
    with tf.variable_scope(scope) as sc:
        num_channels = inputs.get_shape()[-1].value
        beta = tf.Variable(tf.constant(0.0, shape=[num_channels]),
                           name='beta', trainable=True)
        gamma = tf.Variable(tf.constant(1.0, shape=[num_channels]),
                            name='gamma', trainable=True)
        batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name='moments')
        decay = bn_decay if bn_decay is not None else 0.9
        ema = tf.train.ExponentialMovingAverage(decay=decay)
        # Operator that maintains moving averages of variables.
