How to use the solarforecastarbiter.reference_forecasts.utils.get_next_issue_time function in solarforecastarbiter

def _issue_time_generator(observation, fx, obs_mint, obs_maxt, next_issue_time,
                          max_run_time):
    # now find all the run times that can be made based on the
    # last observation timestamp
    while next_issue_time <= max_run_time:
        data_start, data_end = utils.get_data_start_end(
            observation, fx, next_issue_time, next_issue_time)
        if data_end > obs_maxt:
            break

        if data_start > obs_mint:
            yield next_issue_time
        next_issue_time = utils.get_next_issue_time(
            fx, next_issue_time + pd.Timedelta('1ns'))

continue

        if isinstance(fx, datamodel.ProbabilisticForecast):
            fx_mint, fx_maxt = \
                session.get_probabilistic_forecast_constant_value_time_range(
                    fx.constant_values[0].forecast_id)
        else:
            fx_mint, fx_maxt = session.get_forecast_time_range(fx.forecast_id)
        # find the next issue time for the forecast based on the last value
        # in the forecast series
        if pd.isna(fx_maxt):
            # if there is no forecast yet, go back a bit from the last
            # observation. Don't use the start of observations, since it
            # could really stress the workers if we have a few years of
            # data before deciding to make a persistence fx
            next_issue_time = utils.get_next_issue_time(
                fx, obs_maxt - fx.run_length)
        else:
            next_issue_time = utils.find_next_issue_time_from_last_forecast(
                fx, fx_maxt)

        data_start, _ = utils.get_data_start_end(
            observation, fx, next_issue_time, next_issue_time)
        issue_times = tuple(_issue_time_generator(
            observation, fx, obs_mint, obs_maxt,
            next_issue_time, max_run_time))

        if len(issue_times) == 0:
            continue

        out = namedtuple(
            'PersistenceParameters',

if errors:
            logger.error(
                'Not all forecasts compatible in group with %s. '
                'The following parameters may differ: %s', run_for, errors)
            continue
        try:
            key_fx = group.loc[run_for].forecast
        except KeyError:
            logger.error('Forecast, %s,  that others are piggybacking on not '
                         'found', run_for)
            continue
        model_str = group.loc[run_for].model
        model = getattr(models, model_str)
        issue_time = group.loc[run_for].next_issue_time
        if issue_time is None:
            issue_time = utils.get_next_issue_time(key_fx, run_time)
        try:
            nwp_result = run_nwp(key_fx, model, run_time, issue_time)
        except FileNotFoundError as e:
            logger.error('Could not process group of %s, %s', run_for, str(e))
            continue
        for fx_id, fx in group['forecast'].iteritems():
            fx_vals = getattr(nwp_result, fx.variable)
            if fx_vals is None:
                logger.warning('No forecast produced for %s in group with %s',
                               fx_id, run_for)
                continue
            logger.info('Posting values %s for %s:%s issued at %s',
                        len(fx_vals), fx.name, fx_id, issue_time)
            _post_forecast_values(session, fx, fx_vals, model_str)

except json.JSONDecodeError:
            logger.warning(
                'Failed to decode extra_parameters for %s: %s as JSON',
                fx.name, fx.forecast_id)
            continue

        try:
            model = extra_parameters['model']
        except KeyError:
            logger.error(
                'Forecast, %s: %s, has no model. Cannot make forecast.',
                fx.name, fx.forecast_id)
            continue

        if run_time is not None:
            next_issue_time = utils.get_next_issue_time(fx, run_time)
        else:
            next_issue_time = None
        piggyback_on = extra_parameters.get('piggyback_on', fx.forecast_id)
        df_vals.append((fx.forecast_id, fx, piggyback_on, model,
                        next_issue_time))

    forecast_df = pd.DataFrame(
        df_vals, columns=['forecast_id', 'forecast', 'piggyback_on', 'model',
                          'next_issue_time']
        ).set_index('forecast_id')
    return forecast_df