[arinlen]

> This even starts with basic Numpy and handling tensor objects. It's not easy for a type checker to understand what operations you can do with what shape of tensor.

That doesn't sound like a Python problem.

Instead, it sounds like the natural consequence of numpy being designed in a way where their data types aren't organized into subtypes, and leave that as runtime properties. This is a natural reflection of numpy's take on vectors, matrices, and tensors, which in terms of types are just big arrays with runtime properties.

To put things in perspective, in C++, Eigen supports static dense vectors and matrices whose size is specified and known at compile-time. I'm sure Python doesn't impose addition static type constraints than C++.

[bayesian_horse]

Of course it's not a Python problem, all similar tools have the same "problem" that they can't easily fit that stuff into their type systems, so they invent some way to not care about it.