ENH: Allow arrays to be passed into the initial kwarg of reductions

[eric-wieser]

This enables something like:

bound_upper = np.full((3,), -np.inf)
bound_lower = np.full((3,), np.inf)
for vertex_array in vertex_arrays:
    data_arr.max(out=bound_upper, initial=bound_upper)
    data_arr.min(out=bound_lower, initial=bound_lower)

It also resolves #15905, as same_kind casting is now used:

In [7]: np.array([-10,20,30,-25]).min(initial=np.inf)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-966c5d8ff062> in <module>
----> 1 np.array([-10,20,30,-25]).min(initial=np.inf)

~\Repos\numeric-python\numpy\build\testenv\Lib\site-packages\numpy\core\_methods.py in _amin(a, axis, out, keepdims, initial, where)
     41 def _amin(a, axis=None, out=None, keepdims=False,
     42           initial=_NoValue, where=True):
---> 43     return umr_minimum(a, axis, None, out, keepdims, initial, where)
     44
     45 def _sum(a, axis=None, dtype=None, out=None, keepdims=False,

TypeError: Cannot cast scalar from dtype('float64') to dtype('int32') according to the rule 'same_kind'

[eric-wieser]

I don't remember if PyArray_FROM_O makes a copy, but my intent was that it not do so.

[eric-wieser]

Tests fail because initial is not normalized wrt keepdims. We could use conform_reduce_result, but that doesn't support broadcasting, which is probably useful.

[seberg]

This would probably be simpler with gh-15162, but pushing that through may be a bit more work...