code: def predict(self, df: pd.DataFrame, target: str) -> pd.DataFrame:
      forecast_step = self.max_forecast_steps
      df = df[
          df.index
          <= self.model.last_train_time
          + self.model.timedelta * forecast_step
      ]
      if len(list(df.columns.values)) == 0:
          df.loc[:, target] = 0
      predict_data = TimeSeries.from_pd(df)
      predict_pred, predict_err = self.model.forecast(
          time_stamps=predict_data.time_stamps
      )
      return self.__prepare_forecast_return(
          target=target, pred_ts=predict_pred, err_ts=predict_err
      )
  
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 6 for calling the BaseMerlionForecastAdapter obj's underlying member method code with example usage: obj.predict(df, target) and returns: self