import tensorflow as tf

        return tf.nn.relu(layer)
    with tf.variable_scope('norm_layer_%s%d' % (prefix, id)) as vs:
        if norm_type == 'batch_norm':
            if is_training:
                try:
                    layer = tf.contrib.layers.batch_norm(layer, is_training=True, center=True,
                        scale=False, decay=decay, activation_fn=activation_fn, updates_collections=None, scope=vs) # updates_collections=None
                except ValueError:
                    layer = tf.contrib.layers.batch_norm(layer, is_training=True, center=True,
                        scale=False, decay=decay, activation_fn=activation_fn, updates_collections=None, scope=vs, reuse=True) # updates_collections=None
