code: def _check_series_convert_timestamps_localize(s, from_timezone, to_timezone):
      
      from pyspark.sql.utils import require_minimum_pandas_version
      require_minimum_pandas_version()
  
      import pandas as pd
      from pandas.api.types import is_datetime64tz_dtype, is_datetime64_dtype
      from_tz = from_timezone or _get_local_timezone()
      to_tz = to_timezone or _get_local_timezone()
      # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
      if is_datetime64tz_dtype(s.dtype):
          return s.dt.tz_convert(to_tz).dt.tz_localize(None)
      elif is_datetime64_dtype(s.dtype) and from_tz != to_tz:
          # `s.dt.tz_localize('tzlocal()')` doesn't work properly when including NaT.
          return s.apply(
              lambda ts: ts.tz_localize(from_tz, ambiguous=False).tz_convert(to_tz).tz_localize(None)
              if ts is not pd.NaT else pd.NaT)
      else:
          return s
docs: Convert timestamp to timezone-naive in the specified timezone or local timezone
  
      :param s: a pandas.Series
      :param from_timezone: the timezone to convert from. if None then use local timezone
      :param to_timezone: the timezone to convert to. if None then use local timezone
      :return pandas.Series where if it is a timestamp, has been converted to tz-naive