array_type = 'pyarrow_array', indices_type = 'series'

    @pytest.mark.parametrize(
        "array_type", ["list", "array", "series", "polars_series", "pyarrow_array"]
    )
    @pytest.mark.parametrize("indices_type", ["list", "tuple", "array", "series"])
    def test_safe_indexing_1d_container_mask(array_type, indices_type):
        indices = [False] + [True] * 2 + [False] * 6
        array = _convert_container([1, 2, 3, 4, 5, 6, 7, 8, 9], array_type)
        indices = _convert_container(indices, indices_type)
>       subset = _safe_indexing(array, indices, axis=0)

sklearn/utils/tests/test_indexing.py:229: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
sklearn/utils/_indexing.py:323: in _safe_indexing
    return _pyarrow_indexing(X, indices, indices_dtype, axis=axis)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

X = <pyarrow.lib.Int64Array object at 0x7f08c2789c60>
[
  1,
  2,
  3,
  4,
  5,
  6,
  7,
  8,
  9
]
key = array([False,  True,  True, False, False, False, False, False, False]), key_dtype = 'bool', axis = 0

    def _pyarrow_indexing(X, key, key_dtype, axis):
        """Index a pyarrow data."""
        scalar_key = np.isscalar(key)
        if isinstance(key, slice):
            if isinstance(key.stop, str):
                start = X.column_names.index(key.start)
                stop = X.column_names.index(key.stop) + 1
            else:
                start = 0 if not key.start else key.start
                stop = key.stop
            step = 1 if not key.step else key.step
            key = list(range(start, stop, step))
    
        if axis == 1:
            # Here we are certain that X is a pyarrow Table or RecordBatch.
            if key_dtype == "int" and not isinstance(key, list):
                # pyarrow's X.select behavior is more consistent with integer lists.
                key = np.asarray(key).tolist()
            if key_dtype == "bool":
                key = np.asarray(key).nonzero()[0].tolist()
    
            if scalar_key:
                return X.column(key)
    
            return X.select(key)
    
        # axis == 0 from here on
        if scalar_key:
            if hasattr(X, "shape"):
                # X is a Table or RecordBatch
                key = [key]
            else:
                return X[key].as_py()
        elif not isinstance(key, list):
            key = np.asarray(key)
    
        if key_dtype == "bool":
>           X_indexed = X.filter(key)
E           TypeError: Argument 'mask' has incorrect type (expected pyarrow.lib.Array, got numpy.ndarray)

sklearn/utils/_indexing.py:134: TypeError