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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_qout(self, river_id_array=None, date_search_start=None, date_search_end=None, time_index_start=None, time_index_end=None, time_index=None, time_index_array=None, daily=False, pd_filter=None, filter_mode="mean", as_dataframe=False):
 """ This method extracts streamflow data by a single river ID or by a river ID array. It has options to extract by date or by date index. Parameters river_id_array: :obj:`numpy.array` or list or int, optional A single river ID or an array of river IDs. date_search_start: :obj:`datetime.datetime`, optional This is a datetime object with the date of the minimum date for starting. date_search_end: :obj:`datetime.datetime`, optional This is a datetime object with the date of the maximum date for ending. time_index_start: int, optional This is the index of the start of the time array subset. Useful for the old file version. time_index_end: int, optional This is the index of the end of the time array subset. Useful for the old file version. time_index: int, optional This is the index of time to return in the case that your code only wants one index. Used internally. time_index_array: list or :obj:`numpy.array`, optional This is used to extract the vales only for particular dates. This can be from the *get_time_index_range* function. daily: bool, optional If true, this will convert qout to daily average. pd_filter: str, optional This is a valid pandas resample frequency filter. filter_mode: str, optional You can get the daily average "mean" or the maximum "max". Default is "mean". as_dataframe: bool, optional Return as a pandas dataframe object. Default is False. Returns ------- qout_array: :obj:`numpy.array` This is a 1D or 2D array or a single value depending on your input search. This example demonstrates how to retrieve the streamflow associated with the reach you are interested in:: from RAPIDpy import RAPIDDataset path_to_rapid_qout = '/path/to/Qout.nc' river_id = 500 with RAPIDDataset(path_to_rapid_qout) as qout_nc: streamflow_array = qout_nc.get_qout(river_id) This example demonstrates how to retrieve the streamflow within a date range associated with the reach you are interested in:: from RAPIDpy import RAPIDDataset path_to_rapid_qout = '/path/to/Qout.nc' river_id = 500 with RAPIDDataset(path_to_rapid_qout) as qout_nc: streamflow_array = qout_nc.get_qout( river_id, date_search_start=datetime(1985,1,1), date_search_end=datetime(1985,2,4)) """

        # get indices of where the streamflow data is
        riverid_index_list_subset = None
        if river_id_array is not None:
            if not hasattr(river_id_array, "__len__"):
                river_id_array = [river_id_array]
            riverid_index_list_subset = \
                self.get_subset_riverid_index_list(river_id_array)[0]

        return self.get_qout_index(riverid_index_list_subset,
                                   date_search_start,
                                   date_search_end,
                                   time_index_start,
                                   time_index_end,
                                   time_index,
                                   time_index_array,
                                   daily,
                                   pd_filter,
                                   filter_mode,
                                   as_dataframe)