Describe the issue:

When using numpy.typing, multiplying an array with dtype uint8 by any integer converts the typing dtype to signedinteger

Reproduce the code example:

import numpy as np
from numpy.typing import NDArray


def get_uint8() -> NDArray[np.uint8]:
    return np.random.rand(3).astype(np.uint8)


a = get_uint8()  # a is correctly NDArray[np.uint8]
b = a * 1        # b is NDArray[signedinteger]

Error message:

`b` in the code shows as `NDArray[signedinteger]`, instead of `NDArray[np.uint8]`

NumPy/Python version information:

$ python -c 'import sys, numpy; print(numpy.__version__, sys.version)'
1.23.4 3.10.7 (main, Nov 14 2022, 18:57:04) [Clang 14.0.0 (clang-1400.0.29.202)]

Context for the issue:

I'm using numpy.typing to differentiate between some arrays of type uint8 and some arrays of type float32. However, I have to use a cast every time I multiply an array by a number, which compromises the effectiveness of type checking.