code: def predict(
      self,
      n: int,
      num_samples: int = 1,
      verbose: bool = False,
      show_warnings: bool = True,
  ):
      super().predict(n, num_samples)
      forecast_dict = self.model.predict(
          h=n,
          level=(
              one_sigma_rule,
          ),  # ask one std for the confidence interval.
      )
  
      mu, std = unpack_sf_dict(forecast_dict)
      if num_samples > 1:
          samples = create_normal_samples(mu, std, num_samples, n)
      else:
          samples = mu
  
      return self._build_forecast_series(samples)
  
file_path: darts/darts/models/forecasting/sf_auto_theta.py
imports: from statsforecast.models import AutoTheta
  from darts import TimeSeries
  from darts.models.components.statsforecast_utils import create_normal_samples, one_sigma_rule, unpack_sf_dict
  from darts.models.forecasting.forecasting_model import LocalForecastingModel
label_desc: Use image node_id 3 for calling the StatsForecastAutoTheta obj's underlying member method code with example usage: obj.predict(n, num_samples, verbose, show_warnings) and returns: self