import tensorflow as tf

        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.
        ema_apply_op = tf.cond(is_training,
                               lambda: ema.apply([batch_mean, batch_var]),
                               lambda: tf.no_op())

        # Update moving average and return current batch's avg and var.
        def mean_var_with_update():
            with tf.control_dependencies([ema_apply_op]):
                return tf.identity(batch_mean), tf.identity(batch_var)

        # ema.average returns the Variable holding the average of var.
        mean, var = tf.cond(is_training,
                            mean_var_with_update,
                            lambda: (ema.average(batch_mean), ema.average(batch_var)))
        normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3)
    return normed


def batch_norm_for_fc(inputs, is_training, bn_decay, scope):
    """ Batch normalization on FC data.

    Args:
        inputs:      Tensor, 2D BxC input
