I thought of this partially as I was wondering how an implementation for ndarray itself would look like. For that, it is definitely useful to know all unique types, since if it is only ndarray, no casting whatsoever needs to be done, while if there are integers, lists, etc, an attempt has to be made to turn these into arrays
OK, so hypothetically we could invoke versions of each the numpy function that doesn't call `as[any]array`, and this would slightly speed-up subclasses that call super().__array_function__?
A longer-term goal that I had in mind here was generally for the implementations to just be able to assume their arguments are ndarray, i.e., be free to assume there is a shape, dtype, etc. That is not specifically useful for subclasses; for pure python code, it might also mean array mimics could happily use the implementation. But perhaps more importantly, the code would become substantially cleaner.
Anyway, really a longer-term goal...
All the best,
Marten