import tensorflow as tf

                else:
                    X = self.t_conv(name + '_deconf', X, filter, f_size, 1, (not norm) and use_bias, "VALID", stddev)
                if norm == 'I':
                    X = tf.contrib.layers.instance_norm(X, scope=scope, reuse=reuse)
                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

        F = 3
        norm = self.args.norm
        # print('norm', norm)
        # print('skip cons', self.args.skip_connections)
        # print('VNET In:', I.get_shape().as_list())

        if adaption_net:
            # print('ada scope T/R', is_train, reuse_ada)
