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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 _special_dates(self, calendars, ad_hoc_dates, start_date, end_date):
 """ Compute a Series of times associated with special dates. Parameters holiday_calendars : list[(datetime.time, HolidayCalendar)] Pairs of time and calendar describing when that time occurs. These are used to describe regularly-scheduled late opens or early closes. ad_hoc_dates : list[(datetime.time, list[pd.Timestamp])] Pairs of time and list of dates associated with the given times. These are used to describe late opens or early closes that occurred for unscheduled or otherwise irregular reasons. start_date : pd.Timestamp Start of the range for which we should calculate special dates. end_date : pd.Timestamp End of the range for which we should calculate special dates. Returns ------- special_dates : pd.Series Series mapping trading sessions with special opens/closes to the special open/close for that session. """

        # List of Series for regularly-scheduled times.
        regular = [
            scheduled_special_times(
                calendar,
                start_date,
                end_date,
                time_,
                self.tz,
            )
            for time_, calendar in calendars
        ]

        # List of Series for ad-hoc times.
        ad_hoc = [
            pd.Series(
                index=pd.to_datetime(datetimes, utc=True),
                data=days_at_time(datetimes, time_, self.tz),
            )
            for time_, datetimes in ad_hoc_dates
        ]

        merged = regular + ad_hoc
        if not merged:
            # Concat barfs if the input has length 0.
            return pd.Series([])

        result = pd.concat(merged).sort_index()
        return result.loc[(result >= start_date) & (result <= end_date)]