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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 _adjust_bin_edges(datetime_bins, offset, closed, index, labels):
 """This is required for determining the bin edges resampling with daily frequencies greater than one day, month end, and year end frequencies. Consider the following example. Let's say you want to downsample the time series with the following coordinates to month end frequency: CFTimeIndex([2000-01-01 12:00:00, 2000-01-31 12:00:00, 2000-02-01 12:00:00], dtype='object') Without this adjustment, _get_time_bins with month-end frequency will return the following index for the bin edges (default closed='right' and label='right' in this case):
 CFTimeIndex([1999-12-31 00:00:00, 2000-01-31 00:00:00, 2000-02-29 00:00:00], dtype='object') If 2000-01-31 is used as a bound for a bin, the value on 2000-01-31T12:00:00 (at noon on January 31st), will not be included in the month of January. To account for this, pandas adds a day minus one worth of microseconds to the bin edges generated by cftime range, so that we do bin the value at noon on January 31st in the January bin. This results in an index with bin edges like the following: CFTimeIndex([1999-12-31 23:59:59, 2000-01-31 23:59:59, 2000-02-29 23:59:59], dtype='object') The labels are still: CFTimeIndex([2000-01-31 00:00:00, 2000-02-29 00:00:00], dtype='object') This is also required for daily frequencies longer than one day and year-end frequencies. """

    is_super_daily = (isinstance(offset, (MonthEnd, QuarterEnd, YearEnd)) or
                      (isinstance(offset, Day) and offset.n > 1))
    if is_super_daily:
        if closed == 'right':
            datetime_bins = datetime_bins + datetime.timedelta(days=1,
                                                               microseconds=-1)
        if datetime_bins[-2] > index.max():
            datetime_bins = datetime_bins[:-1]
            labels = labels[:-1]

    return datetime_bins, labels