code: def predict(self, df: pd.DataFrame, target: str) -> pd.DataFrame:
      predict_data = TimeSeries.from_pd(df[target])
      predict_label = self.model.get_anomaly_label(
          time_series=predict_data
      )
      pred_df = predict_label.to_pd()
      pred_df[f"{target}__anomaly_score"] = pred_df["anom_score"]
      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 3 for calling the BaseMerlineDetectorAdapter obj's underlying member method code with example usage: obj.predict(df, target) and returns: pred_df