def METHOD_NAME(self):
    def clf_layer(layer_input, filters, f_size=4, normalization=True):
        """Classifier 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)
    c1 = clf_layer(img, self.cf, normalization=False)
    c2 = clf_layer(c1, self.cf*2)
    c3 = clf_layer(c2, self.cf*4)
    c4 = clf_layer(c3, self.cf*8)
    c5 = clf_layer(c4, self.cf*8)
    class_pred = Dense(self.num_classes, activation='softmax')(Flatten()(c5))
    return Model(img, class_pred)