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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 get_adjustments(self, zero_qtr_data, requested_qtr_data, last_per_qtr, dates, assets, columns, **kwargs):
 """ Creates an AdjustedArray from the given estimates data for the given dates. Parameters zero_qtr_data : pd.DataFrame The 'time zero' data for each calendar date per sid. requested_qtr_data : pd.DataFrame The requested quarter data for each calendar date per sid. last_per_qtr : pd.DataFrame A DataFrame with a column MultiIndex of [self.estimates.columns, normalized_quarters, sid] that allows easily getting the timeline of estimates for a particular sid for a particular quarter. dates : pd.DatetimeIndex The calendar dates for which estimates data is requested. assets : pd.Int64Index An index of all the assets from the raw data. columns : list of BoundColumn The columns for which adjustments need to be calculated. kwargs : Additional keyword arguments that should be forwarded to `get_adjustments_for_sid` and to be used in computing adjustments for each sid. Returns ------- col_to_all_adjustments : dict[int -> AdjustedArray] A dictionary of all adjustments that should be applied. """


        zero_qtr_data.sort_index(inplace=True)
        # Here we want to get the LAST record from each group of records
        # corresponding to a single quarter. This is to ensure that we select
        # the most up-to-date event date in case the event date changes.
        quarter_shifts = zero_qtr_data.groupby(
            level=[SID_FIELD_NAME, NORMALIZED_QUARTERS]
        ).nth(-1)

        col_to_all_adjustments = {}
        sid_to_idx = dict(zip(assets, range(len(assets))))
        quarter_shifts.groupby(level=SID_FIELD_NAME).apply(
            self.get_adjustments_for_sid,
            dates,
            requested_qtr_data,
            last_per_qtr,
            sid_to_idx,
            columns,
            col_to_all_adjustments,
            **kwargs
        )
        return col_to_all_adjustments