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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _nanpercentile_1d(values, mask, q, na_value, interpolation):
 """ Wraper for np.percentile that skips missing values, specialized to 1-dimensional case. Parameters values : array over which to find quantiles mask : ndarray[bool] locations in values that should be considered missing q : scalar or array of quantile indices to find na_value : scalar value to return for empty or all-null values interpolation : str Returns ------- quantiles : scalar or array """

    # mask is Union[ExtensionArray, ndarray]
    values = values[~mask]

    if len(values) == 0:
        if lib.is_scalar(q):
            return na_value
        else:
            return np.array([na_value] * len(q),
                            dtype=values.dtype)

    return np.percentile(values, q, interpolation=interpolation)