Method wrap

exir/tracer.py:509–518

(arg: Value, i: int)

        # Wrap all inputs as a PythonTensor subclass and insert them into the FX
        # graph as placeholder nodes
        def wrap(arg: Value, i: int) -> Value:
            placeholder = self.create_proxy("placeholder", f"ph_{i}", (), {})
            if isinstance(arg, torch.Tensor):
                return PythonTensor(arg, placeholder, is_immutable=True)
            else:
                # torch._assert(
                #     placeholder == arg,
                #     f"ph_{i} has been specialized to have value {arg}",
                # )
                return arg

        tree_args = [wrap(arg, i) for i, arg in enumerate(concrete_args)]
        if in_spec: