code: def update_and_transform(self, y, exogenous, **kwargs):
          
          check_is_fitted(self, "p_")
  
          self._check_endog(y)
          _, Xt = self.transform(y, exogenous, n_periods=len(y), **kwargs)
  
          # Update this *after* getting the exog features
          self.n_ += len(y)
          return y, Xt
docs: Update the params and return the transformed arrays
  
          Since no parameters really get updated in the Fourier featurizer, all
          we do is compose forecasts for ``n_periods=len(y)`` and then update
          ``n_``.
  
          Parameters
          ----------
          y : array-like or None, shape=(n_samples,)
              The endogenous (time-series) array.
  
          exogenous : array-like or None, shape=(n_samples, n_features)
              The exogenous array of additional covariates.
  
          **kwargs : keyword args
              Keyword arguments required by the transform function.