In a build of NumPy against libcxx, we found the following code
regressed from almost instantaneous with NumPy 2.3 to over 40s with
NumPy 2.4.

```
import numpy as np

full_list = np.array("2015-12-01T00:00:00.000000000", 'datetime64[ns]') + np.arange(162143) * np.timedelta64(4, 'h').astype('timedelta64[ns]')
sampled_dates = full_list[10:28]
np.isin(sampled_dates, full_list)
```

Our belief is the following:
* std::unordered_set in libcxx uses power of two hash buckets.
* std::hash is the identity function.
* in this particular example, we are hashing integers (datetime64
  values) separated by multiples of a power of two.
* the net result is that all of the integers end up in the same hash
  bucket.

We can make the code more robust simply by using the same npy_fnv1a hash
used elsewhere in the same file since it will do a better job of
distributing hash bits.