code: def get_old_metrics(dataset, predictor, quantile_levels):
      forecast_it, ts_it = make_evaluation_predictions(
          dataset, predictor
      )
      evaluator = Evaluator(
          quantiles=quantile_levels, calculate_owa=True
      )
      aggregated_metrics, _ = evaluator(ts_it, forecast_it)
  
      return aggregated_metrics
  
imports: from copy import deepcopy
  from typing import ChainMap, Iterator, Optional, Union
  from dataclasses import dataclass
  import numpy
  from gluonts.dataset.repository import get_dataset
  from gluonts.dataset.split import TestData, split
  from gluonts.ext.naive_2 import naive_2
  from gluonts.time_feature.seasonality import get_seasonality
  from gluonts.model.forecast import Quantile, SampleForecast
  from gluonts.model.seasonal_naive import SeasonalNaivePredictor
  from gluonts.evaluation import Evaluator
  from gluonts.evaluation.backtest import make_evaluation_predictions
  from gluonts.ev import evaluate
  from gluonts.ev.ts_stats import seasonal_error
  from gluonts.ev.metrics import MAPE, MASE, MSE, MSIS, SMAPE, Coverage, SumAbsoluteError, SumQuantileLoss, mean_absolute_label, sum_absolute_label, ND, NRMSE, RMSE, MAECoverage, MeanSumQuantileLoss, MeanWeightedSumQuantileLoss, WeightedSumQuantileLoss, OWA
label_desc: Use image node_id 3 for calling a global function with example usage: get_old_metrics(dataset, predictor, quantile_levels) and returns: aggregated_metrics