import tensorflow as tf

            with tf.variable_scope(name) as scope:
                if init_stddev <= 0.0:
                    init = tf.contrib.layers.variance_scaling_initializer(dtype=tf.float32)
                else:
                    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':
