code: def _get_pred_args_from_metric(scitype, metric):
      pred_args = {
          "pred_quantiles": "alpha",
          "pred_interval": "coverage",
      }
      if scitype in pred_args.keys():
          val = getattr(metric, pred_args[scitype], None)
          if val is not None:
              return {pred_args[scitype]: val}
      return {}
  
file_path: sktime/sktime/forecasting/model_evaluation/_functions.py
imports: import time
  import warnings
  from typing import Dict, List, Optional, Union
  import numpy
  import pandas
  from sktime.datatypes import check_is_scitype, convert_to
  from sktime.exceptions import FitFailedWarning
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.utils.parallel import parallelize
  from sktime.utils.validation._dependencies import _check_soft_dependencies
  from sktime.utils.validation.forecasting import check_cv, check_scoring
label_desc: Use image node_id 6 for calling a global function with example usage: _get_pred_args_from_metric(scitype, metric) and returns: dict, dict