How to use the solarforecastarbiter.pvmodel.irradiance_to_power function in solarforecastarbiter

# calls operations that do not properly broadcast Series along
            # a DataFrame time index. pvlib.irradiance.haydavies operation
            # (AI = dni_ens / dni_extra) is the known culprit, though there
            # may be more.
            ac_power = {}
            for col in forecasts[0].columns:
                member_fx = [fx.get(col) for fx in forecasts]
                member_ac_power = pvmodel.irradiance_to_power(
                    site.modeling_parameters,
                    solar_position['apparent_zenith'],
                    solar_position['azimuth'],
                    *member_fx)
                ac_power[col] = member_ac_power
            ac_power = pd.DataFrame(ac_power)
        else:
            ac_power = pvmodel.irradiance_to_power(
                site.modeling_parameters, solar_position['apparent_zenith'],
                solar_position['azimuth'], *forecasts)
    else:
        ac_power = None

    # resample data after power calculation
    resampled = list(map(resampler, (*forecasts, ac_power)))
    nwpoutput = namedtuple(
        'NWPOutput', ['ghi', 'dni', 'dhi', 'air_temperature', 'wind_speed',
                      'ac_power'])
    return nwpoutput(*resampled)

# Consider putting the code within each if/else block below into its own
    # function with standard outputs clear_ref and clear_fx. But with only two
    # cases for now, it might be more clear to leave inline.
    if (
            isinstance(site, datamodel.SolarPowerPlant) and
            observation.variable == 'ac_power'
    ):
        # No temperature input is only OK so long as temperature effects
        # do not push the system above or below AC clip point.
        # It's only a reference forecast!
        clear_ref = pvmodel.irradiance_to_power(
            site.modeling_parameters, solar_position_obs['apparent_zenith'],
            solar_position_obs['azimuth'], clearsky_obs['ghi'],
            clearsky_obs['dni'], clearsky_obs['dhi']
        )
        clear_fx = pvmodel.irradiance_to_power(
            site.modeling_parameters, solar_position_fx['apparent_zenith'],
            solar_position_fx['azimuth'], clearsky_fx['ghi'],
            clearsky_fx['dni'], clearsky_fx['dhi']
        )
    else:
        # assume we are working with ghi, dni, or dhi.
        clear_ref = clearsky_obs[observation.variable]
        clear_fx = clearsky_fx[observation.variable]

    # resample sub-interval reference clear sky to observation intervals
    clear_ref_resampled = clear_ref.resample(
        observation.interval_length, closed=closed, label=closed).mean()
    # calculate persistence index (clear sky index or ac power index)
    # avg{index_{t_start}...index_{t_end}} =
    #   avg{obs_{t_start}/clear_{t_start}...obs_{t_end}/clear_{t_end}}
    # clear_ref is calculated at high temporal resolution, so this is accurate

*forecasts, resampler, solar_position_calculator = model(
        site.latitude, site.longitude, site.elevation,
        init_time, start, end, forecast.interval_label)

    if isinstance(site, datamodel.SolarPowerPlant):
        solar_position = solar_position_calculator()
        if isinstance(forecasts[0], pd.DataFrame):
            # must iterate over columns because pvmodel.irradiance_to_power
            # calls operations that do not properly broadcast Series along
            # a DataFrame time index. pvlib.irradiance.haydavies operation
            # (AI = dni_ens / dni_extra) is the known culprit, though there
            # may be more.
            ac_power = {}
            for col in forecasts[0].columns:
                member_fx = [fx.get(col) for fx in forecasts]
                member_ac_power = pvmodel.irradiance_to_power(
                    site.modeling_parameters,
                    solar_position['apparent_zenith'],
                    solar_position['azimuth'],
                    *member_fx)
                ac_power[col] = member_ac_power
            ac_power = pd.DataFrame(ac_power)
        else:
            ac_power = pvmodel.irradiance_to_power(
                site.modeling_parameters, solar_position['apparent_zenith'],
                solar_position['azimuth'], *forecasts)
    else:
        ac_power = None

    # resample data after power calculation
    resampled = list(map(resampler, (*forecasts, ac_power)))
    nwpoutput = namedtuple(

fx_range = pd.date_range(start=forecast_start, end=forecast_end, freq=freq,
                             closed=closed_fx)
    solar_position_fx = calc_solpos(fx_range)
    clearsky_fx = calc_cs(solar_position_fx['apparent_zenith'])

    # Consider putting the code within each if/else block below into its own
    # function with standard outputs clear_ref and clear_fx. But with only two
    # cases for now, it might be more clear to leave inline.
    if (
            isinstance(site, datamodel.SolarPowerPlant) and
            observation.variable == 'ac_power'
    ):
        # No temperature input is only OK so long as temperature effects
        # do not push the system above or below AC clip point.
        # It's only a reference forecast!
        clear_ref = pvmodel.irradiance_to_power(
            site.modeling_parameters, solar_position_obs['apparent_zenith'],
            solar_position_obs['azimuth'], clearsky_obs['ghi'],
            clearsky_obs['dni'], clearsky_obs['dhi']
        )
        clear_fx = pvmodel.irradiance_to_power(
            site.modeling_parameters, solar_position_fx['apparent_zenith'],
            solar_position_fx['azimuth'], clearsky_fx['ghi'],
            clearsky_fx['dni'], clearsky_fx['dhi']
        )
    else:
        # assume we are working with ghi, dni, or dhi.
        clear_ref = clearsky_obs[observation.variable]
        clear_fx = clearsky_fx[observation.variable]

    # resample sub-interval reference clear sky to observation intervals
    clear_ref_resampled = clear_ref.resample(