dpctl does not return a zero-length array when no reduction is going to be done

[antonwolfy]

The below example behaves differently between dcptl and numpy:

import numpy, dpctl, dpctl.tensor as dpt

dpctl.__version__
# Out: '0.15.1dev0+63.g03fd73794'

a = numpy.ones((0, 2), dtype='f4')
numpy.min(a, axis=(1,))
# Out: array([], dtype=float32)

b = dpt.ones((0, 2), dtype='f4')
dpt.min(b, axis=(1,))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[15], line 1
----> 1 dpt.min(b, axis=(1,))

File ~/miniconda3/envs/dpnp_dev/lib/python3.9/site-packages/dpctl/tensor/_reduction.py:499, in min(x, axis, keepdims)
 474 def min(x, axis=None, keepdims=False):
 475 """min(x, axis=None, dtype=None, keepdims=False)
 476
 477 Calculates the minimum value of the input array `x`.
 (...)
 497 array has the same data type as `x`.
 498 """
--> 499 return _comparison_over_axis(x, axis, keepdims, ti._min_over_axis)

File ~/miniconda3/envs/dpnp_dev/lib/python3.9/site-packages/dpctl/tensor/_reduction.py:421, in _comparison_over_axis(x, axis, keepdims, _reduction_fn)
 419 res_usm_type = x.usm_type
 420 if x.size == 0:
--> 421 raise ValueError("reduction does not support zero-size arrays")
 422 if red_nd == 0:
 423 return x

ValueError: reduction does not support zero-size arrays

As I can see python Array API notes the use case as implementation-defined. Should dpctl align the behavior with numpy here?