Should we require element-wise function output arguments to have the exact same shape as input arguments or should we allow broadcasting?

That's a good question. I'd prefer requiring out being of the correct shape. For functions with a single array input that implies the same shape as the input; for multiple array inputs or for other keywords that may support broadcasting, those can affect the expected shape of the output array. out itself is a special keyword in this case, the intent is for it to be filled in-place so it should not participate in broadcasting I believe.

Although some may have a use case for func(in_shape_1_by4, out=shape_10_by_4). That breaks this equivalence though:

out1 = func(inputs)
out2 = np.empty((some_shape))
func(inputs, out=out2).shape == func(inputs, out=None).shape

My mental model is that those shapes should be equal, and hence out= doesn't broadcast.

@rgommers Thanks for the feedback.

Re: broadcasting. My understanding is that, e.g., NumPy permits such broadcasting semantics.

I've updated the proposed specification to include supported data types and promotion rules, which should now roughly complete the spec for element-wise functions. Remaining concerns include:

• minimum precision requirements
• casting rules

However, these concerns can be addressed in follow-up PRs.