import tensorflow as tf

                elif norm == 'B':
                    X = tf.layers.batch_normalization(X, reuse=reuse, training=is_train, name=name)
                elif norm == 'G':
                    X = tf.contrib.layers.group_norm(X, groups=16, scope=scope, reuse=reuse)
                if dropout > 0.0:
                    X = tf.layers.dropout(X, dropout, training=is_train)
                if slope < 1.0:
                    X = tf.nn.leaky_relu(X, slope) if slope > 0.0 else tf.nn.relu(X)
                return X

