I always wonder how to design a vectorizable function when the sizes of the input and output arrays are different.
In other words, I am always unsure whether to shift the “core dimension” to the back

def polar_coordinates(r, theta):
    xp = array_api_compat(r, theta)
    return xp.stack([r * xp.cos(theta), r * xp.sin(theta)], axis=-1)

or to the front

def polar_coordinates(r, theta):
    xp = array_api_compat(r, theta)
    return xp.stack([r * xp.cos(theta), r * xp.sin(theta)], axis=0)

Is there any plans to add recommendations for this to array API? For reference

• Numpy's gufunc puts "core dimension" to the back https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html

• Recently added scipy.special.sph_harm_y_all puts "core dimension" to the front https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.sph_harm_y_all.html

• Since broadcasting adjusts shapes to the back, by putting "core dimension" to the back one can more easily interact with newly added feature (dimension), and vice versa.

• In terms of calculation speed, this should be related to C-style and Fortran-style indexing.