Hi! Thanks for the response!
It does work here. Correct me if I’m wrong but what I tried to attempt was:

Define a simple neural network, instantiate it and make a copy of it
have dummy data, which in this case was just a list [1,2,3,4,5] with the targets also being [1,2,3,4,5]
define separate optimizers for the neural nets and use mse loss
With this what I attempted to find was the outputs of both networks after a single pass of the list and backprops where original was backpropped with loss.backward() and the copy was backpropped with (loss*100).backward().
My understanding is that these two networks should now produce different outputs due to different backprop values. But what I found was that both the networks produce the same output the second time.
I guess I must be going wrong somewhere