I know, but if an element-wise operator is useful it would also be useful for libraries like NumPy that already support the @ operator for matrix multiplication.
A bit of history:
A fair amount of inspiration (or at least experience) for numpy came from MATLAB.
MATLAB has essentially two complete sets of math operators: the regular version, and the dot version.
A * B
Means matrix multiplication, and
A .* B
Means elementwise multiplication. And there is a full set of matrix and elementwise operators.
Back in the day, Numeric (numpy’s predecessor”) used the math operators for elementwise operations, and doing matrix math was unwieldy. There was a lit of discussion and a number of proosals for s full set of additional operators in python that could be used for matrix operations ( side note: there was (is) a numpy.matrix class that defines __mul__ as matrix multiplication).
Someone at some point realized that we didn’t need a full set, because multiplication was really the only compelling use case. So the @ operator was added.
Numpy, or course, is but one third party package, but it is an important one — major inconsistency with it is a bad idea.