def METHOD_NAME(
    cls,
    x: NNCFTensor,
    fn: Callable[[NNCFTensor, int, NNCFTensor], Any],
    axis: int = 0,
    alpha: float = 0.01,
    keepdims: bool = False,
) -> NNCFTensor:
    if len(x.shape) == 1:
        return fn(x, axis=None, mask=None, keepdims=keepdims)
    x = x.tensor
    if axis:
        x = np.moveaxis(x, axis, 0)
    low_values, high_values = np.quantile(x, [alpha, 1 - alpha], 0)
    outliers_mask = np.logical_or(x < low_values, high_values < x)
    return fn(ONNXNNCFTensor(x), axis=0, mask=ONNXNNCFTensor(outliers_mask), keepdims=keepdims)