code: def fit(self, series):
      super().fit(series)
  
      self.length = len(series)
      # normalization of data
      if self.normalization:
          self.mean = series.pd_dataframe(copy=False).mean().mean()
          raise_if_not(
              not np.isclose(self.mean, 0),
              "The mean value of the provided series is too close to zero to perform normalization",
              logger,
          )
          new_ts = series / self.mean
      else:
          new_ts = series
  
      # Check for statistical significance of user-defined season period
      # or infers season_period from the TimeSeries itself.
      if self.season_mode is SeasonalityMode.NONE:
          self.season_period = 1
      else:
          self.season_period = self.seasonality_period
      if self.season_period is None:
          max_lag = len(series) // 2
          self.is_seasonal, self.season_period = check_seasonality(
              series, self.season_period, max_lag=max_lag
          )
      else:
          # force the user-defined seasonality to be considered as a true seasonal period.
          self.is_seasonal = self.season_period > 1
  
      # Store and remove seasonality effect if there is any.
      if self.is_seasonal:
          _, self.seasonality = extract_trend_and_seasonality(
              new_ts, self.season_period, model=self.season_mode
          )
          new_ts = remove_from_series(
              new_ts, self.seasonality, model=self.season_mode
          )
  
      ts_values = new_ts.univariate_values()
      if (ts_values <= 0).any():
          self.model_mode = ModelMode.ADDITIVE
          self.trend_mode = TrendMode.LINEAR
          logger.warning(
              "Time series has negative values. Fallback to additive and linear model"
          )
  
      # Drift part of the decomposition
      if self.trend_mode is TrendMode.LINEAR:
          linreg = ts_values
      else:
          linreg = np.log(ts_values)
      self.drift = np.poly1d(
          np.polyfit(np.arange(self.length), linreg, 1)
      )
      theta0_in = self.drift(np.arange(self.length))
      if self.trend_mode is TrendMode.EXPONENTIAL:
          theta0_in = np.exp(theta0_in)
  
      if (
          theta0_in > 0
      ).all() and self.model_mode is ModelMode.MULTIPLICATIVE:
          theta_t = (ts_values**self.theta) * (
              theta0_in ** (1 - self.theta)
          )
      else:
          if self.model_mode is ModelMode.MULTIPLICATIVE:
              logger.warning(
                  "Negative Theta line. Fallback to additive model"
              )
              self.model_mode = ModelMode.ADDITIVE
          theta_t = (
              self.theta * ts_values + (1 - self.theta) * theta0_in
          )
  
      # SES part of the decomposition.
      self.model = hw.SimpleExpSmoothing(theta_t).fit()
      theta2_in = self.model.fittedvalues
  
      if (
          theta2_in > 0
      ).all() and self.model_mode is ModelMode.MULTIPLICATIVE:
          self.fitted_values = (
              theta2_in**self.wses * theta0_in**self.wdrift
          )
      else:
          if self.model_mode is ModelMode.MULTIPLICATIVE:
              self.model_mode = ModelMode.ADDITIVE
              logger.warning(
                  "Negative Theta line. Fallback to additive model"
              )
              theta_t = (
                  self.theta * ts_values + (1 - self.theta) * theta0_in
              )
              self.model = hw.SimpleExpSmoothing(theta_t).fit()
              theta2_in = self.model.fittedvalues
          self.fitted_values = (
              self.wses * theta2_in + self.wdrift * theta0_in
          )
      if self.is_seasonal:
          if self.season_mode is SeasonalityMode.ADDITIVE:
              self.fitted_values += self.seasonality.univariate_values(
                  copy=False
              )
          elif self.season_mode is SeasonalityMode.MULTIPLICATIVE:
              self.fitted_values *= self.seasonality.univariate_values(
                  copy=False
              )
      # Fitted values are the results of the fit of the model on the train series. A good fit of the model
      # will lead to fitted_values similar to ts. But one cannot see if it overfits.
      if self.normalization:
          self.fitted_values *= self.mean
  
      return self
  
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 2 for calling the FourTheta obj's underlying member method code with example usage: obj.fit(series) and returns: self