Aside: this is not TensorFlow, but XLA, which are two very different things. It's also possible to try this kind of thing and generate a TF graph, but TF is a much less nice compilation target.
The set of functions that some generic code calls could be considered similar to an interface or trait in other languages. If they expand the interface (by calling new functions) then you'd need to make sure the new functions they call have the appropriate implementations needed as well.
In Go, for example, the interface definition would be explicit. You'd add a method to the interface (or perhaps define a new interface) and update all the implementations you know about. If there is any outside code calling it with their own implementation, they'd get a compile error.
It does sound rather convenient if essentially every function call allows for new implementations, though.
It seems like for non-demo usage, you would want upstream maintainers to agree that their code should be TensorFlow-compatible, and have tests keeping it working.