Failure in `check_n_features_in_after_fitting` in `tests/test_kmeans.py::test_check_estimator`

[TomAugspurger]

This is currently failing

tests/test_kmeans.py::test_check_estimator FAILED

=================================================================================================================== FAILURES ====================================================================================================================
_____________________________________________________________________________________________________________ test_check_estimator ______________________________________________________________________________________________________________

    def test_check_estimator():
        with warnings.catch_warnings(record=True):
            warnings.simplefilter("ignore", RuntimeWarning)
>           check_estimator(DKKMeans())

tests/test_kmeans.py:28:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../.venv/lib/python3.12/site-packages/sklearn/utils/_param_validation.py:216: in wrapper
    return func(*args, **kwargs)
../.venv/lib/python3.12/site-packages/sklearn/utils/estimator_checks.py:858: in check_estimator
    check(estimator)
../.venv/lib/python3.12/site-packages/sklearn/utils/_testing.py:147: in wrapper
    return fn(*args, **kwargs)
../.venv/lib/python3.12/site-packages/sklearn/utils/estimator_checks.py:4498: in check_n_features_in_after_fitting
    with raises(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <sklearn.utils._testing._Raises object at 0x1484e2ea0>, exc_type = None, exc_value = None, _ = None

    def __exit__(self, exc_type, exc_value, _):
        # see
        # https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000

        if exc_type is None:  # No exception was raised in the block
            if self.may_pass:
                return True  # CM is happy
            else:
                err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}"
>               raise AssertionError(err_msg)
E               AssertionError: `KMeans.predict()` does not check for consistency between input number
E               of features with KMeans.fit(), via the `n_features_in_` attribute.
E               You might want to use `sklearn.utils.validation.validate_data` instead
E               of `check_array` in `KMeans.fit()` and KMeans.predict()`. This can be done
E               like the following:
E               from sklearn.utils.validation import validate_data
E               ...
E               class MyEstimator(BaseEstimator):
E                   ...
E                   def fit(self, X, y):
E                       X, y = validate_data(self, X, y, ...)
E                       ...
E                       return self
E                   ...
E                   def predict(self, X):
E                       X = validate_data(self, X, ..., reset=False)
E                       ...
E                   return X

../.venv/lib/python3.12/site-packages/sklearn/utils/_testing.py:1097: AssertionError

https://github.com/dask/dask-ml/pull/1008 is adding a skip for that particular check.