How to use the ops.instance_norm function in ops

def encode_decode_1(self, x, reuse=False):

        with tf.variable_scope("encode_decode_1") as scope:
            if reuse == True:
                scope.reuse_variables()

            conv1 = lrelu(instance_norm(conv2d(x, output_dim=64, k_w=5, k_h=5, d_w=1, d_h=1, name='e_c1'), scope='e_in1'))
            conv2 = lrelu(instance_norm(conv2d(conv1, output_dim=128, name='e_c2'), scope='e_in2'))
            conv3 = lrelu(instance_norm(conv2d(conv2, output_dim=256, name='e_c3'), scope='e_in3'))
            # for x_{1}
            de_conv1 = lrelu(instance_norm(de_conv(conv3, output_shape=[self.batch_size, 64, 64, 128]
                                                  , name='e_d1', k_h=3, k_w=3), scope='e_in4'))
            de_conv2 = lrelu(instance_norm(de_conv(de_conv1, output_shape=[self.batch_size, 128, 128, 64]
                                                  , name='e_d2', k_w=3, k_h=3), scope='e_in5'))
            x_tilde1 = conv2d(de_conv2, output_dim=3, d_h=1, d_w=1, name='e_c4')

            return x_tilde1

conv1 = tf.nn.relu(
                instance_norm(conv2d(x, output_dim=sn, k_w=7, k_h=7, d_w=1, d_h=1, name='e_c1'), scope='e_in1'))
            conv2 = tf.nn.relu(
                instance_norm(conv2d(conv1, output_dim=sn*2, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c2'), scope='e_in2'))
            conv3 = tf.nn.relu(
                instance_norm(conv2d(conv2, output_dim=sn*4, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c3'), scope='e_in3'))

            r1 = Residual(conv3, residual_name='re_1')
            r2 = Residual(r1, residual_name='re_2')
            r3 = Residual(r2, residual_name='re_3')
            r4 = Residual(r3, residual_name='re_4')
            r5 = Residual(r4, residual_name='re_5')
            r6 = Residual(r5, residual_name='re_6')

            g_deconv1 = tf.nn.relu(instance_norm(de_conv(r6, output_shape=[self.batch_size,
                                                                           self.output_size/2, self.output_size/2, sn*2], name='gen_deconv1'), scope="gen_in"))
            # for 1
            g_deconv_1_1 = tf.nn.relu(instance_norm(de_conv(g_deconv1,
                        output_shape=[self.batch_size, self.output_size, self.output_size, sn], name='g_deconv_1_1'), scope='gen_in_1_1'))

            #Refined Residual Image learning
            g_deconv_1_1_x = tf.concat([g_deconv_1_1, x], axis=3)
            x_tilde1 = conv2d(g_deconv_1_1_x, output_dim=self.channel, k_w=7, k_h=7, d_h=1, d_w=1, name='gen_conv_1_2')

            # for 2
            g_deconv_2_1 = tf.nn.relu(instance_norm(de_conv(g_deconv1,
                        output_shape=[self.batch_size, self.output_size, self.output_size, sn]
                                                            , name='g_deconv_2_1'), scope='gen_in_2_1'))
            g_deconv_2_1_x = tf.concat([g_deconv_2_1, x], axis=3)
            x_tilde2 = conv2d(g_deconv_2_1_x, output_dim=self.channel, k_w=7, k_h=7, d_h=1, d_w=1, name='gen_conv_2_2')

def encode_decode(self, x_var, x_exemplar, img_mask, exemplar_mask, reuse=False):

        with tf.variable_scope("encode_decode") as scope:

            if reuse == True:
                scope.reuse_variables()

            x_var = tf.concat([x_var, img_mask, x_exemplar, exemplar_mask], axis=3)

            conv1 = tf.nn.relu(
                instance_norm(conv2d(x_var, output_dim=64, k_w=7, k_h=7, d_w=1, d_h=1, name='e_c1'), scope='e_in1'))
            conv2 = tf.nn.relu(
                instance_norm(conv2d(conv1, output_dim=128, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c2'), scope='e_in2'))
            conv3 = tf.nn.relu(
                instance_norm(conv2d(conv2, output_dim=256, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c3'), scope='e_in3'))

            r1 = Residual(conv3, residual_name='re_1')
            r2 = Residual(r1, residual_name='re_2')
            r3 = Residual(r2, residual_name='re_3')
            r4 = Residual(r3, residual_name='re_4')
            r5 = Residual(r4, residual_name='re_5')
            r6 = Residual(r5, residual_name='re_6')

            g_deconv1 = tf.nn.relu(instance_norm(de_conv(r6, output_shape=[self.batch_size,
                                                                           self.output_size/2, self.output_size/2, 128], name='gen_deconv1'), scope="gen_in"))
            # for 1
            g_deconv_1_1 = tf.nn.relu(instance_norm(de_conv(g_deconv1,

tf.get_variable_scope().reuse_variables()
        else:
            assert tf.get_variable_scope().reuse is False
            
        # down sampling
        x = relu(instance_norm(conv2d(images, options.nf, ks=7, s=1, name='gen_ds_conv1'), 'in1_1'))
        x = relu(instance_norm(conv2d(x, 2*options.nf, ks=4, s=2, name='gen_ds_conv2'), 'in1_2'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=4, s=2, name='gen_ds_conv3'), 'in1_3'))
        
        # bottleneck
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv1'), 'in2_1'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv2'), 'in2_2'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv3'), 'in2_3'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv4'), 'in2_4'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv5'), 'in2_5'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv6'), 'in2_6'))
        
        # up sampling
        x = relu(instance_norm(deconv2d(x, 2*options.nf, ks=4, s=2, name='gen_us_deconv1'), 'in3_1'))
        x = relu(instance_norm(deconv2d(x, options.nf, ks=4, s=2, name='gen_us_deconv2'), 'in3_2'))
        x = tanh(deconv2d(x, 3, ks=7, s=1, name='gen_us_dwconv3'))
        
        return x

def encode_decode(self, x_var, x_exemplar, img_mask, exemplar_mask, reuse=False):

        with tf.variable_scope("encode_decode") as scope:

            if reuse == True:
                scope.reuse_variables()

            x_var = tf.concat([x_var, img_mask, x_exemplar, exemplar_mask], axis=3)

            conv1 = tf.nn.relu(
                instance_norm(conv2d(x_var, output_dim=64, k_w=7, k_h=7, d_w=1, d_h=1, name='e_c1'), scope='e_in1'))
            conv2 = tf.nn.relu(
                instance_norm(conv2d(conv1, output_dim=128, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c2'), scope='e_in2'))
            conv3 = tf.nn.relu(
                instance_norm(conv2d(conv2, output_dim=256, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c3'), scope='e_in3'))

            r1 = Residual(conv3, residual_name='re_1')
            r2 = Residual(r1, residual_name='re_2')
            r3 = Residual(r2, residual_name='re_3')
            r4 = Residual(r3, residual_name='re_4')
            r5 = Residual(r4, residual_name='re_5')
            r6 = Residual(r5, residual_name='re_6')

            g_deconv1 = tf.nn.relu(instance_norm(de_conv(r6, output_shape=[self.batch_size,
                                                                           self.output_size/2, self.output_size/2, 128], name='gen_deconv1'), scope="gen_in"))
            # for 1
            g_deconv_1_1 = tf.nn.relu(instance_norm(de_conv(g_deconv1,
                        output_shape=[self.batch_size, self.output_size, self.output_size, 32], name='g_deconv_1_1'), scope='gen_in_1_1'))

            g_deconv_1_1_x = tf.concat([g_deconv_1_1, x_var], axis=3)
            x_tilde1 = conv2d(g_deconv_1_1_x, output_dim=self.channel, k_w=7, k_h=7, d_h=1, d_w=1, name='gen_conv_1_2')

def residule_block(x, dim, ks=3, s=1, name='res'):
            p = int((ks - 1) / 2)
            y = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], "CONSTANT") #CONSTANT
            y = instance_norm(conv2d(y, dim, ks, s, padding='VALID', name=name+'_c1'), name+'_in1')
            y = tf.pad(tf.nn.relu(y), [[0, 0], [p, p], [p, p], [0, 0]], "CONSTANT")
            y = instance_norm(conv2d(y, dim, ks, s, padding='VALID', name=name+'_c2'), name+'_in2')
            return y + x

def encode_decode(self, x, sn=64, reuse=False):

        print sn

        with tf.variable_scope("encode_decode") as scope:

            if reuse == True:
                scope.reuse_variables()

            conv1 = tf.nn.relu(
                instance_norm(conv2d(x, output_dim=sn, k_w=7, k_h=7, d_w=1, d_h=1, name='e_c1'), scope='e_in1'))
            conv2 = tf.nn.relu(
                instance_norm(conv2d(conv1, output_dim=sn*2, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c2'), scope='e_in2'))
            conv3 = tf.nn.relu(
                instance_norm(conv2d(conv2, output_dim=sn*4, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c3'), scope='e_in3'))

            r1 = Residual(conv3, residual_name='re_1')
            r2 = Residual(r1, residual_name='re_2')
            r3 = Residual(r2, residual_name='re_3')
            r4 = Residual(r3, residual_name='re_4')
            r5 = Residual(r4, residual_name='re_5')
            r6 = Residual(r5, residual_name='re_6')

            g_deconv1 = tf.nn.relu(instance_norm(de_conv(r6, output_shape=[self.batch_size,
                                                                           self.output_size/2, self.output_size/2, sn*2], name='gen_deconv1'), scope="gen_in"))
            # for 1
            g_deconv_1_1 = tf.nn.relu(instance_norm(de_conv(g_deconv1,
                        output_shape=[self.batch_size, self.output_size, self.output_size, sn], name='g_deconv_1_1'), scope='gen_in_1_1'))

            #Refined Residual Image learning
            g_deconv_1_1_x = tf.concat([g_deconv_1_1, x], axis=3)

def encode2(self, x, reuse=False):

        with tf.variable_scope("encode") as scope:

            if reuse == True:
                scope.reuse_variables()

            conv1 = tf.nn.relu(
                instance_norm(conv2d(x, output_dim=32, k_w=7, k_h=7, d_w=1, d_h=1, name='e_c1'), scope='e_in1'))
            conv2 = tf.nn.relu(
                instance_norm(conv2d(conv1, output_dim=64, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c2'), scope='e_in2'))
            conv3 = tf.nn.relu(
                instance_norm(conv2d(conv2, output_dim=128, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c3'), scope='e_in3'))
            conv4 = tf.nn.relu(
                instance_norm(conv2d(conv3, output_dim=128, k_w=4, k_h=4, d_w=2, d_h=2, name='e_c4'), scope='e_in4'))

            bottleneck = tf.reshape(conv4, [self.batch_size, -1])
            content = fully_connect(bottleneck, output_size=128, scope='e_ful1')
            #rotation = fully_connect(bottleneck, output_size=1, scope='e_ful2')

            return content#, rotation

def generator(images, options, reuse=False, name='gen'):
    # reuse or not
    with tf.variable_scope(name):
        if reuse:
            tf.get_variable_scope().reuse_variables()
        else:
            assert tf.get_variable_scope().reuse is False
            
        # down sampling
        x = relu(instance_norm(conv2d(images, options.nf, ks=7, s=1, name='gen_ds_conv1'), 'in1_1'))
        x = relu(instance_norm(conv2d(x, 2*options.nf, ks=4, s=2, name='gen_ds_conv2'), 'in1_2'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=4, s=2, name='gen_ds_conv3'), 'in1_3'))
        
        # bottleneck
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv1'), 'in2_1'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv2'), 'in2_2'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv3'), 'in2_3'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv4'), 'in2_4'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv5'), 'in2_5'))
        x = relu(instance_norm(conv2d(x, 4*options.nf, ks=3, s=1, name='gen_bn_conv6'), 'in2_6'))
        
        # up sampling
        x = relu(instance_norm(deconv2d(x, 2*options.nf, ks=4, s=2, name='gen_us_deconv1'), 'in3_1'))
        x = relu(instance_norm(deconv2d(x, options.nf, ks=4, s=2, name='gen_us_deconv2'), 'in3_2'))
        x = tanh(deconv2d(x, 3, ks=7, s=1, name='gen_us_dwconv3'))
        
        return x