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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 first_items(self, index):
 """Meant to reproduce the results of the following first_items = pd.Series(np.arange(len(index)), index).groupby(grouper).first() with index being a CFTimeIndex instead of a DatetimeIndex. """


        datetime_bins, labels = _get_time_bins(index, self.freq, self.closed,
                                               self.label, self.base)
        if self.loffset is not None:
            if isinstance(self.loffset, datetime.timedelta):
                labels = labels + self.loffset
            else:
                labels = labels + to_offset(self.loffset)

        # check binner fits data
        if index[0] < datetime_bins[0]:
            raise ValueError("Value falls before first bin")
        if index[-1] > datetime_bins[-1]:
            raise ValueError("Value falls after last bin")

        integer_bins = np.searchsorted(
            index, datetime_bins, side=self.closed)[:-1]
        first_items = pd.Series(integer_bins, labels)

        # Mask duplicate values with NaNs, preserving the last values
        non_duplicate = ~first_items.duplicated('last')
        return first_items.where(non_duplicate)