code: def _predict_with_local_model(
      self,
      time_series: pd.Series,
      local_model_args: dict,
  ) -> pd.DataFrame:
      # Code does conversion between confidence levels and quantiles
      levels = []
      quantile_to_key = {}
      for q in self.quantile_levels:
          level = round(abs(q - 0.5) * 200, 1)
          suffix = "lo" if q < 0.5 else "hi"
          levels.append(level)
          quantile_to_key[str(q)] = f"{suffix}-{level}"
      levels = sorted(list(set(levels)))
  
      forecast = self._get_local_model(local_model_args).forecast(
          h=self.prediction_length,
          y=time_series.values.ravel(),
          level=levels,
      )
      predictions = {"mean": forecast["mean"]}
      for q, key in quantile_to_key.items():
          predictions[q] = forecast[key]
      return pd.DataFrame(predictions)
  
imports: import logging
  from typing import Any, Dict, Type
  import numpy
  import pandas
  from .abstract_local_model import AbstractLocalModel
label_desc: Use image node_id 1 for calling the AbstractProbabilisticStatsForecastModel obj's underlying member method code with example usage: obj._predict_with_local_model(time_series, local_model_args) and returns: pd