implimenting DP-GAN in Keras

Hi. I'm using TFP and Keras to implement DP-GAN. I define the optimizer as follows:

optimizer = DPRMSPropGaussianOptimizer(l2_norm_clip=args.l2_norm_clip,
                                               noise_multiplier=args.noise_multiplier,
                                               num_microbatches=args.microbatches,
                                               learning_rate=args.discriminator_lr,
                                               decay=args.discriminator_decay)
loss = tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.losses.Reduction.NONE)
model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])

I also use model.train_on_batch in Keras to train both generator and discriminator. There is no need to apply noise on the gradients of discriminator when fake images are processed to the best of my knowledge. Therefore, I train the discriminator two times one for real and another for fake images. Accordingly, I change l2_clip_norm and noise_multiplier two times, while to make DP ineffective in applying discriminator gradients for fake images, I set optimizer's parameters using keras.backend as follows:

tf.keras.backend.set_value(model.optimizer.l2_norm_clip, 20.0)
tf.keras.backend.set_value(model.optimizer.noise_multiplier, 0.0)
tf.keras.backend.set_value(model.optimizer.num_microbatches, 1)

I have no problem running the code, and I know TFP has issues in Keras. However, to be sure, I want a second opinion. So, In case you notice any problems or have any suggestions, I'll be grateful to share it with me.

Start with the Keras train_on_batch flow and DPRMSPropGaussianOptimizer configuration shown in the issue. Check whether changing l2_norm_clip, noise_multiplier, and num_microbatches between real and fake batches preserves the intended privacy accounting; done requires a maintainer-confirmed, documented approach or a reproducible correction.