code: def inverse_transform(self, y, exogenous=None):
          
          check_is_fitted(self, "lam1_")
          lam1 = self.lam1_
          lam2 = self.lam2_
  
          y, exog = self._check_y_exog(y, exogenous)
          if lam1 == 0:
              return np.exp(y) - lam2, exog
  
          numer = y * lam1  # remove denominator
          numer += 1.  # add 1 back to it
          de_exp = numer ** (1. / lam1)  # de-exponentiate
          return de_exp - lam2, exog
docs: Inverse transform a transformed array
  
          Inverse the Box-Cox transformation on the transformed array. Note that
          if truncation happened in the ``transform`` method, invertibility will
          not be preserved, and the transformed array may not be perfectly
          inverse-transformed.
  
          Parameters
          ----------
          y : array-like or None, shape=(n_samples,)
              The transformed 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.
  
          Returns
          -------
          y : array-like or None
              The inverse-transformed y array
  
          exogenous : array-like or None
              The inverse-transformed exogenous array