Adam, you said that:



 apaszke:

Yes, when you use the same net with the same input twice, it will construct a new graph, that will share all the leaves, but all other nodes will be exact copies of the first one, with separate state and buffers.


Is it only valid for the same net with the same input? I am interested in this specific detail because I am trying to implement a RNN unrolling the input sequence in a loop and accumulating the gradient. In my case the input is a new Tensor (taken from the sequence) at every step of the iteration. Would I be able to achieve BPTT in this case?