def METHOD_NAME(self):
    def d_layer(layer_input, filters, f_size=4, normalization=True):
        """Discriminator layer"""
        d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
        d = LeakyReLU(alpha=0.2)(d)
        if normalization:
            d = InstanceNormalization()(d)
        return d
    img = Input(shape=self.img_shape)
    d1 = d_layer(img, self.df, normalization=False)
    d2 = d_layer(d1, self.df*2)
    d3 = d_layer(d2, self.df*4)
    d4 = d_layer(d3, self.df*8)
    validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
    return Model(img, validity)