torch binary operations broken for scalar inputs in `_fix_promotion`

[asford]

The dtype promotion check in _fix_promotion does not correctly identify scalar inputs, and unconditionally accesses .dtype.
This breaks binary operators with float scalar inputs.

The can be fixed by accessing dtype via getattr with a None default or validating that the input is not a scalar.
Happy to provide a PR.

Minimal repo, in version 1.4, via:

import torch
import numpy
import array_api_compat as aac

aac.__version__ ()

t = torch.arange(10)
n = numpy.arange(10)

numpy.add(n, 1.0)
torch.add(t, 1.0)

aac.get_namespace(n).add(n, 1.0)
aac.get_namespace(t).add(t, 1.0)

Raises:

      9 torch.add(t, 1.0)
     11 aac.get_namespace(n).add(n, 1.0)
---> 12 aac.get_namespace(t).add(t, 1.0)

File ~/ab/main/.conda/lib/python3.10/site-packages/array_api_compat/torch/_aliases.py:91, in _two_arg.<locals>._f(x1, x2, **kwargs)
     89 @wraps(f)
     90 def _f(x1, x2, /, **kwargs):
---> 91     x1, x2 = _fix_promotion(x1, x2)
     92     return f(x1, x2, **kwargs)

File ~/ab/main/.conda/lib/python3.10/site-packages/array_api_compat/torch/_aliases.py:104, in _fix_promotion(x1, x2, only_scalar)
    103 def _fix_promotion(x1, x2, only_scalar=True):
--> 104     if x1.dtype not in _array_api_dtypes or x2.dtype not in _array_api_dtypes:
    105         return x1, x2
    106     # If an argument is 0-D pytorch downcasts the other argument

AttributeError: 'float' object has no attribute 'dtype'

Would expect equivalent behavior to torch.add.

See:
https://gist.github.com/asford/ee688d59f0747a6507b9670a83fa7c47

[asmeurer]

Somehow missed this issue. Python scalar inputs are portable in the standard, but we should still make them work if they work with the upstream functions.

[asmeurer]

Python scalar inputs are portable in the standard

aren't portable. You shouldn't use them if you are aiming for full array API compatibility.

Fix at #101