code: def __prepare_forecast_return(
      self, target: str, pred_ts: TimeSeries, err_ts: TimeSeries
  ) -> pd.DataFrame:
      pred_df: pd.DataFrame = pred_ts.to_pd()
      if err_ts is None:
          err_df = None
      else:
          err_df = err_ts.to_pd()
  
      if err_df is None or target in list(
          err_df.columns.values
      ):  # error and predict sometimes are same
          std = pred_df[target].std()
          pred_df[f"{target}__upper"] = pred_df[
              f"{target}"
          ] + std * norm.ppf(0.975)
          pred_df[f"{target}__lower"] = pred_df[
              f"{target}"
          ] + std * norm.ppf(0.025)
      else:
          pred_df = pred_df.join(err_df, how="left")
          pred_df[f"{target}__upper"] = (
              pred_df[f"{target}"]
              + norm.ppf(0.975) * err_df[f"{target}_err"]
          )
          pred_df[f"{target}__lower"] = (
              pred_df[f"{target}"]
              + norm.ppf(0.025) * err_df[f"{target}_err"]
          )
          pred_df.drop(columns=[f"{target}_err"], inplace=True)
      return pred_df
  
imports: from merlion.models.anomaly.forecast_based.prophet import ProphetDetectorConfig, ProphetDetector
  from merlion.models.anomaly.isolation_forest import IsolationForestConfig, IsolationForest
  from merlion.models.anomaly.windstats import WindStatsConfig, WindStats
  from merlion.models.automl.autoprophet import AutoProphetConfig, AutoProphet
  from merlion.models.automl.autosarima import AutoSarimaConfig, AutoSarima
  from merlion.models.forecast.smoother import MSESConfig, MSES
  from merlion.post_process.threshold import AggregateAlarms
  from merlion.transform.moving_average import DifferenceTransform
  from scipy.stats import norm
  from merlion.models.defaults import DefaultDetectorConfig, DefaultDetector, DefaultForecaster, DefaultForecasterConfig
  from merlion.utils import TimeSeries
  from enum import Enum
  import pandas
label_desc: Use image node_id 7 for calling the BaseMerlionForecastAdapter obj's underlying member method code with example usage: obj.__prepare_forecast_return(target, pred_ts, err_ts) and returns: pred_df