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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 _check_series_localize_timestamps(s, timezone):
 """ Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone. If the input series is not a timestamp series, then the same series is returned. If the input series is a timestamp series, then a converted series is returned. :param s: pandas.Series :param timezone: the timezone to convert. if None then use local timezone :return pandas.Series that have been converted to tz-naive """

    from pyspark.sql.utils import require_minimum_pandas_version
    require_minimum_pandas_version()

    from pandas.api.types import is_datetime64tz_dtype
    tz = timezone or _get_local_timezone()
    # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
    if is_datetime64tz_dtype(s.dtype):
        return s.dt.tz_convert(tz).dt.tz_localize(None)
    else:
        return s