code: def predict(
      self,
      n: int,
      num_samples: int = 1,
      verbose: bool = False,
      show_warnings: bool = True,
  ) -> "TimeSeries":
      super().predict(n, num_samples)
  
      # Forecast of the SES part.
      forecast = self.model.forecast(n)
  
      # Forecast of the Linear Regression part.
      drift = self.coef * np.array(
          [
              i + (1 - (1 - self.alpha) ** self.length) / self.alpha
              for i in range(0, n)
          ]
      )
  
      # Combining the two forecasts
      forecast += drift
  
      # Re-apply the seasonal trend of the TimeSeries
      if self.is_seasonal:
          replicated_seasonality = np.tile(
              self.seasonality.pd_series()[-self.season_period :],
              math.ceil(n / self.season_period),
          )[:n]
          if self.season_mode is SeasonalityMode.MULTIPLICATIVE:
              forecast *= replicated_seasonality
          elif self.season_mode is SeasonalityMode.ADDITIVE:
              forecast += replicated_seasonality
  
      return self._build_forecast_series(forecast)
  
imports: import math
  from typing import List, Optional
  import numpy
  import statsmodels.tsa.holtwinters
  from darts.logging import get_logger, raise_if_not, raise_log
  from darts.models.forecasting.forecasting_model import LocalForecastingModel
  from darts.timeseries import TimeSeries
  from darts.utils.statistics import check_seasonality, extract_trend_and_seasonality, remove_from_series
  from darts.utils.utils import ModelMode, SeasonalityMode, TrendMode
label_desc: Use image node_id 3 for calling the Theta obj's underlying member method code with example usage: obj.predict(n, num_samples, verbose, show_warnings) and returns: self