Failing `np.clip()` on float32

[crusaderky]

e.g. https://github.com/data-apis/array-api-compat/actions/runs/13060007276/job/36440413878?pr=247

FAILED array_api_tests/test_operators_and_elementwise_functions.py::test_clip - AssertionError: out[()]=-16777216.0 but should be -16777217.0 [clip()]
  x[()]=0.0, min[()]=None, max[()]=-16777217.0
assert -16777216.0 == np.float64(-16777217.0)
Falsifying example: test_clip(
    x=array(0., dtype=float32),
    data=data(...),
)
Draw 1 (min.shape, max.shape): ((), ())
Draw 2 (min): None
Draw 3 (max): np.float64(-16777217.0)
Draw 4 (kwargs): {'max': np.float64(-16777217.0)}

The problem is somehow caused by the fact that np.float32(-16777217.0) == -16777216.0.
However I can't reproduce the error on numpy 2.0.2:

>>> np.clip(np.asarray(0, dtype=np.float32), None, -16777217.0)
np.float32(-16777216.0)  # Correctly follows rule about binops mixing pure python and arrays

>>> np.clip(np.asarray(0, dtype=np.float32), None, np.float32(-16777217.0))
np.float32(-16777216.0)

>>> np.clip(np.asarray(0, dtype=np.float32), None, np.float64(-16777217.0))
np.float64(-16777217.0)

[ev-br]

managed to catch it on numpy 2.2. A fix in gh-355

[ev-br]

Reopening: as discussed in gh-355, that fix may fix a different issue. So let's keep this open until we can repro and fix this specific issue.