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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 compute_extra_rows(self, all_dates, start_date, end_date, min_extra_rows):
 """ Ensure that min_extra_rows pushes us back to a computation date. Parameters all_dates : pd.DatetimeIndex The trading sessions against which ``self`` will be computed. start_date : pd.Timestamp The first date for which final output is requested. end_date : pd.Timestamp The last date for which final output is requested. min_extra_rows : int The minimum number of extra rows required of ``self``, as determined by other terms that depend on ``self``. Returns ------- extra_rows : int The number of extra rows to compute. This will be the minimum number of rows required to make our computed start_date fall on a recomputation date. """

        try:
            current_start_pos = all_dates.get_loc(start_date) - min_extra_rows
            if current_start_pos < 0:
                raise NoFurtherDataError.from_lookback_window(
                    initial_message="Insufficient data to compute Pipeline:",
                    first_date=all_dates[0],
                    lookback_start=start_date,
                    lookback_length=min_extra_rows,
                )
        except KeyError:
            before, after = nearest_unequal_elements(all_dates, start_date)
            raise ValueError(
                "Pipeline start_date {start_date} is not in calendar.\n"
                "Latest date before start_date is {before}.\n"
                "Earliest date after start_date is {after}.".format(
                    start_date=start_date,
                    before=before,
                    after=after,
                )
            )

        # Our possible target dates are all the dates on or before the current
        # starting position.
        # TODO: Consider bounding this below by self.window_length
        candidates = all_dates[:current_start_pos + 1]

        # Choose the latest date in the candidates that is the start of a new
        # period at our frequency.
        choices = select_sampling_indices(candidates, self._frequency)

        # If we have choices, the last choice is the first date if the
        # period containing current_start_date.  Choose it.
        new_start_date = candidates[choices[-1]]

        # Add the difference between the new and old start dates to get the
        # number of rows for the new start_date.
        new_start_pos = all_dates.get_loc(new_start_date)
        assert new_start_pos <= current_start_pos, \
            "Computed negative extra rows!"

        return min_extra_rows + (current_start_pos - new_start_pos)