Sure thing!
Right now my approach is this (but I am unsure if I am correct):

Train DCGAN1 with dataset A
Train DCGAN2 with dataset B
Decide on N aka how many layers from DCGAN1 needs to be retrained by dataset B.
Create two nn.ModuleList() objects - extract from layer 1 to the penultimate layer from  trained DCGAN1. Eg: if my DCGAN has 8 layers and I want to “retrain” the last 3, I would take layers 1-5 from this GAN and store it in the first list.
In the second list, take the last N layers from the trained DCGAN2 and store them.
Combine both lists and declare a new Generator and Discriminator by iterating over this combined list.

Hope this makes sense.
In your answer, do you mean that if I have a trained DCGAN on say the MNIST dataset and I want to retrain the last N layers on the EMNIST dataset I could freeze the prev layers by setting requires_grad to False and then retrain this “frozen” GAN on the EMNIST set?
I apologize for the lengthy response, thank you for responding to my query - appreciate it!