code: def fit(self, series: TimeSeries):
      super().fit(series)
      self._assert_univariate(series)
      ts = self.training_series
  
      self.length = len(ts)
  
      # 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(ts) // 2
          self.is_seasonal, self.season_period = check_seasonality(
              ts, 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
  
      new_ts = ts
  
      # Store and remove seasonality effect if there is any.
      if self.is_seasonal:
          _, self.seasonality = extract_trend_and_seasonality(
              ts, self.season_period, model=self.season_mode
          )
          new_ts = remove_from_series(
              ts, self.seasonality, model=self.season_mode
          )
  
      # SES part of the decomposition.
      self.model = hw.SimpleExpSmoothing(
          new_ts.values(copy=False)
      ).fit()
  
      # Linear Regression part of the decomposition. We select the degree one coefficient.
      b_theta = np.polyfit(
          np.array([i for i in range(0, self.length)]),
          (1.0 - self.theta) * new_ts.values(copy=False),
          1,
      )[0]
  
      # Normalization of the coefficient b_theta.
      self.coef = b_theta / (-self.theta)
  
      self.alpha = self.model.params["smoothing_level"]
      if self.alpha == 0.0:
          self.model = hw.SimpleExpSmoothing(
              new_ts.values(copy=False)
          ).fit(initial_level=ALPHA_START)
          self.alpha = self.model.params["smoothing_level"]
  
      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 Theta obj's underlying member method code with example usage: obj.fit(series) and returns: self