self.input_shape = input_shape
    self.conv = tf.keras.layers.Conv2D(64,(7,7), padding='same')
    #~ down sampling
    self.down = Downsampling_Part()
    
    
    #ResBlock
    self.res1 = ResBlock(256,(3,3))
    self.res2 = ResBlock(256,(3,3))
    self.res3 = ResBlock(256,(3,3))
    self.res4 = ResBlock(256,(3,3))
    self.res5 = ResBlock(256,(3,3))
    self.res6 = ResBlock(256,(3,3))
    
    # ~Up sampling
    self.upsampleblock = UpSampleBlock()
    
def call(self, images, labels, training):
    # Down - sampling
    x = input_merge(images,labels)
    out = self.down(x);
    
    # Bottleneck
    out = self.res1(out, training=training)
    out = self.res2(out, training=training)
    out = self.res3(out, training=training)
    out = self.res4(out, training=training)
    out = self.res5(out, training=training)
    out = self.res6(out, training=training)
    
    #Up sampling
    out= self.upsampleblock(out)
    
    generated_images =  out
    
    return out