code: def _fit(
      self,
      series: TimeSeries,
      future_covariates: Optional[TimeSeries] = None,
  ):
      super()._fit(series, future_covariates)
      self._assert_univariate(series)
      series = self.training_series
  
      fit_df = pd.DataFrame(
          data={
              "ds": series.time_index,
              "y": series.univariate_values(),
          }
      )
      if self.is_logistic:
          fit_df = self._add_capacities_to_df(fit_df)
  
      self.model = self._model_builder(**self.prophet_kwargs)
  
      # add user defined seasonalities (from model creation and/or pre-fit self.add_seasonalities())
      interval_length = self._freq_to_days(series.freq_str)
      conditional_seasonality_covariates = (
          self._check_seasonality_conditions(
              future_covariates=future_covariates
          )
      )
      for (
          seasonality_name,
          attributes,
      ) in self._add_seasonalities.items():
          self.model.add_seasonality(
              name=seasonality_name,
              period=attributes["seasonal_periods"] * interval_length,
              fourier_order=attributes["fourier_order"],
              prior_scale=attributes["prior_scale"],
              mode=attributes["mode"],
              condition_name=attributes["condition_name"],
          )
  
      # add covariates as additional regressors
      if future_covariates is not None:
          fit_df = fit_df.merge(
              future_covariates.pd_dataframe(),
              left_on="ds",
              right_index=True,
              how="left",
          )
          for covariate in future_covariates.columns:
              if covariate not in conditional_seasonality_covariates:
                  self.model.add_regressor(covariate)
  
      # add built-in country holidays
      if self.country_holidays is not None:
          self.model.add_country_holidays(self.country_holidays)
  
      if self.suppress_stdout_stderr:
          self._execute_and_suppress_output(
              self.model.fit, logger, logging.WARNING, fit_df
          )
      else:
          self.model.fit(fit_df)
  
      return self
  
imports: import logging
  import re
  from typing import Callable, List, Optional, Sequence, Union
  import numpy
  import pandas
  import prophet
  from darts.logging import execute_and_suppress_output, get_logger, raise_if, raise_log
  from darts.models.forecasting.forecasting_model import FutureCovariatesLocalForecastingModel
  from darts.timeseries import TimeSeries
label_desc: Use image node_id 2 for calling the Prophet obj's underlying member method code with example usage: obj._fit(series, future_covariates) and returns: self