code: def test_compare_arimas_xreg():
      evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])
      prediction_length = 24 * 7
  
      fourier_arima = RForecastPredictor(
          freq=freq,
          prediction_length=prediction_length,
          period=period,
          params={
              "quantiles": [0.50, 0.10, 0.90],
              "output_types": ["mean", "quantiles"],
          },
          method_name="fourier.arima",
      )
      fourier_arima_eval_metrics, _ = backtest_metrics(
          test_dataset=dataset_xreg,
          predictor=fourier_arima,
          evaluator=evaluator,
      )
  
      fourier_arima_xreg = RForecastPredictor(
          freq=freq,
          prediction_length=prediction_length,
          period=period,
          params={
              "quantiles": [0.50, 0.10, 0.90],
              "output_types": ["mean", "quantiles"],
          },
          method_name="fourier.arima.xreg",
      )
  
      fourier_arima_xreg_eval_metrics, _ = backtest_metrics(
          test_dataset=dataset_xreg,
          predictor=fourier_arima_xreg,
          evaluator=evaluator,
      )
  
      assert (
          fourier_arima_xreg_eval_metrics["mean_wQuantileLoss"]
          < fourier_arima_eval_metrics["mean_wQuantileLoss"]
      )
  
file_path: gluonts/test/ext/r_forecast/test_r_multi_seasonality.py
imports: from typing import List
  import numpy
  import pandas
  import pytest
  from gluonts.evaluation import Evaluator, backtest_metrics
  from gluonts.ext.r_forecast import R_IS_INSTALLED, RPY2_IS_INSTALLED, RForecastPredictor
  from gluonts.model.forecast import QuantileForecast
label_desc: Use image node_id 4 for calling a global function with example usage: test_compare_arimas_xreg() without return types