code: def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None,
                            batch_size=128):
          
  
          # check the necessary inputs parameters are here
          if check_params(Xt=Xt):
  
              transp_Xt = Xt.dot(self.A1_) + self.B1_
  
              return transp_Xt
docs: Transports target samples Xt onto target samples Xs
  
          Parameters
          ----------
          Xs : array-like, shape (n_source_samples, n_features)
              The training input samples.
          ys : array-like, shape (n_source_samples,)
              The class labels
          Xt : array-like, shape (n_target_samples, n_features)
              The training input samples.
          yt : array-like, shape (n_target_samples,)
              The class labels. If some target samples are unlabeled, fill the
              yt's elements with -1.
  
              Warning: Note that, due to this convention -1 cannot be used as a
              class label
          batch_size : int, optional (default=128)
              The batch size for out of sample inverse transform
  
          Returns
          -------
          transp_Xt : array-like, shape (n_source_samples, n_features)
              The transported target samples.