More specifically: Should we change this? It is quite trivially done, but perhaps I am missing >> a reason for omitting the non-override types. >> > > Realistically, without these other changes in NumPy, how would this > improve code using __array_function__? From a general purpose dispatching > perspective, are there cases where you'd want to return NotImplemented > based on types that don't implement __array_function__? >
I think, yes, that would be the closest analogy to the python operators. Saves you from having separate cases for types that have and do not have `__array_function__`. > I guess this might help if your alternative array class is super-explicit, > and doesn't automatically call `asmyarray()` on each argument. You could > rely on __array_function__ to return NotImplement (and thus raise > TypeError) rather than type checking in every function you write for your > alternative arrays. > Indeed. > One minor downside would speed: now __array_function__ implementations > need to check a longer list of types. > That's true. > > Another minor downside: if users follow the example of > NDArrayOperatorsMixin docstring, they would now need to explicitly list all > of the scalar types (without __array_function__) that they support, > including builtin types like int and type(None). I suppose this ties into > our recommended best practices for doing type checking in > __array_ufunc__/__array_function__ implementations, which should probably > be updated regardless: > https://github.com/numpy/numpy/issues/12258#issuecomment-432858949 > > Also true. It makes me wonder again whether passing on the types is useful at all... But I end up thinking that it is not up to an implementation to raise TypeError - it should just return NotImplemented. If we'd wanted to give more information, we might also consider passing on `overloaded_args` - then perhaps one has the best of both worlds. All the best, Marten
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