code: def get_transform(self, model_output):
          
          if model_output == "margin":
              transform = "identity"
          elif model_output == "probability":
              if self.tree_output == "log_odds":
                  transform = "logistic"
              elif self.tree_output == "probability":
                  transform = "identity"
              else:
                  raise Exception("model_output = \"probability\" is not yet supported when model.tree_output = \"" + self.tree_output + "\"!")
          elif model_output == "logloss":
  
              if self.objective == "squared_error":
                  transform = "squared_loss"
              elif self.objective == "binary_crossentropy":
                  transform = "logistic_nlogloss"
              else:
                  raise Exception("model_output = \"logloss\" is not yet supported when model.objective = \"" + self.objective + "\"!")
          return transform
docs: A consistent interface to make predictions from this model.