code: def fit(self, y, exogenous=None):
          
          lam1 = self.lmbda
          lam2 = self.lmbda2
  
          if lam2 < 0:
              raise ValueError("lmbda2 must be a non-negative scalar value")
  
          if lam1 is None:
              y, _ = self._check_y_exog(y, exogenous)
              _, lam1 = stats.boxcox(y, lmbda=None, alpha=None)
  
          self.lam1_ = lam1
          self.lam2_ = lam2
          return self
docs: Fit the transformer
  
          Learns the value of ``lmbda``, if not specified in the constructor.
          If defined in the constructor, is not re-learned.
  
          Parameters
          ----------
          y : array-like or None, shape=(n_samples,)
              The endogenous (time-series) array.
  
          exogenous : array-like or None, shape=(n_samples, n_features), optional
              The exogenous array of additional covariates. Not used for
              endogenous transformers. Default is None, and non-None values will
              serve as pass-through arrays.