Replicating PyTorch models into environment-agnostic frameworks is error-prone and hard to maintain

Conversion of PyTorch models not that obvious sometimes

this approach of building models in one framework and then replicating them in another environment-agnostic framework introduces its own set of challenges... maintaining consistency across different frameworks also becomes an ongoing challenge. As models evolve and updates are made, ensuring that the replicated version... stays in sync with the original PyTorch model becomes a manual and error-prone process.