BUG: Buffer dtype mismatch on Windows and NumPy 2.0

### Describe the bug

Recent [Azure CI failure for MNE-Python](https://dev.azure.com/mne-tools/mne-python/_build/results?buildId=27722&view=logs&jobId=dded70eb-633c-5c42-e995-a7f8d1f99d91&j=dded70eb-633c-5c42-e995-a7f8d1f99d91&t=02d70add-cf2e-52ae-1ea0-298f1e5f37ea) shows a NumPy 2.0 incompatibility with sklearn, both installed via 
```
	python -m pip install --only-binary ":all:" --extra-index-url "https://pypi.anaconda.org/scientific-python-nightly-wheels/simple" "numpy>=2.0.0.dev0" "scipy>=1.12.0.dev0" scikit-learn matplotlib
```

### Steps/Code to Reproduce

Not on Windows at the moment so can't make a MWE (can if it's not obvious from the traceback!) but this is what's failing on CIs:
```
pytest mne/decoding/tests/test_search_light.py -k test_search_light
```

### Expected Results

No error

### Actual Results

```
______________________________ test_search_light ______________________________
mne\decoding\search_light.py:101: in fit
    estimators = parallel(
mne\decoding\search_light.py:102: in <genexpr>
    p_func(self.base_estimator, split, y, pb.subset(pb_idx), **fit_params)
mne\decoding\search_light.py:358: in _sl_fit
    est.fit(X[..., ii], y, **fit_params)
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\sklearn\base.py:1215: in wrapper
    return fit_method(estimator, *args, **kwargs)
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\sklearn\ensemble\_bagging.py:343: in fit
    return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\sklearn\ensemble\_bagging.py:478: in _fit
    all_results = Parallel(
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\sklearn\utils\parallel.py:67: in __call__
    return super().__call__(iterable_with_config)
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\joblib\parallel.py:1900: in __call__
    return output if self.return_generator else list(output)
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\joblib\parallel.py:1829: in _get_sequential_output
    res = func(*args, **kwargs)
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\sklearn\utils\parallel.py:129: in __call__
    return self.function(*args, **kwargs)
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\sklearn\ensemble\_bagging.py:120: in _parallel_build_estimators
    features, indices = _generate_bagging_indices(
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\sklearn\ensemble\_bagging.py:65: in _generate_bagging_indices
    feature_indices = _generate_indices(
C:\hostedtoolcache\windows\Python\3.11.6\x64\Lib\site-packages\sklearn\ensemble\_bagging.py:44: in _generate_indices
    indices = sample_without_replacement(
sklearn\\utils\\_random.pyx:216: in sklearn.utils._random.sample_without_replacement
    ???
sklearn\\utils\\_random.pyx:291: in sklearn.utils._random.sample_without_replacement
    ???
sklearn\\utils\\_random.pyx:196: in sklearn.utils._random._sample_without_replacement_with_reservoir_sampling
    ???
E   ValueError: Buffer dtype mismatch, expected 'int_t' but got 'long long'
```

### Versions

```shell
Can't run as it's on CI, but these are the reported versions:

├☑ numpy                2.0.0.dev0+git20231102.439762c (OpenBLAS 0.3.23.dev with 2 threads)
├☑ sklearn              1.4.dev0
```

COMMENT:
@larsoner can you try with the nightly wheels of scikit-learn? They are also published in the same repo:

https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scikit-learn/

Maybe you need the `--pre` flag in the pip command to automatically get the pre-release (the dev build) of scikit-learn.

Labels: Bug