Hi, Thomas
Thanks,
I’ve noticed this solution, there is also a tutorial jott - Writing a Toy Backend Compiler for PyTorch 2 talks about it.
So it seems we need to create a dummy_op and a custom post pass that fuse everything in the TorchGraph into this dummy_op , and then, we could do the jit stuff during the dispatch on dummy_op? Is this right?
I also notice that there is a torch::jit::registerPrePass, is it fine to do the fusing at there too? For we also have an IR optimize system, and we might want  to get the raw graph IR. Could this fusing in PrePass
cause any issue?
Another question now is that glow also registerd a new backend torch::jit::backend<TorchGlowBackend>("glow") and a preprocess of this backend torch::jit::backend_preprocess_register("glow", preprocess);. And it seems this backend isn’t used during the transparent JIT, so when should I also implement such a backend, or what is the usecase of the  torch::jit::backend?
Sincerely