import tensorflow as tf

                    init = tf.truncated_normal_initializer(stddev=init_stddev)
                X = tf.layers.conv2d(X, out_channels, kernel_size=filtersize, strides=(stride, stride), padding="valid",
                                    kernel_initializer=init)
                if norm == 'I':
                    X = tf.contrib.layers.instance_norm(X, scope=scope, reuse=reuse, epsilon=0.001)
                elif norm == 'B':
                    X = tf.layers.batch_normalization(X, reuse=reuse, training=True)
                elif norm == 'G':
