code: function (uid, trees, treeWeights) {
          this.logger = Logger.getLogger("ml_regression_GBTRegressionModel_js");
          var jvmObject;
          if (uid instanceof org.apache.spark.ml.regression.GBTRegressionModel) {
              jvmObject = uid;
          } else {
              var tress_uw = Utils.unwrapObject(trees);
              jvmObject = new org.apache.spark.ml.regression.GBTRegressionModel(uid, tress_uw, treeWeights);
          }
          PredictionModel.call(this, jvmObject);
  
      }
docs: @classdesc
  
  [Gradient-Boosted Trees (GBTs)]{@link http://en.wikipedia.org/wiki/Gradient_boosting}
  model for regression.
  It supports both continuous and categorical features.
  @class
  @extends module:eclairjs/ml.PredictionModel
  @memberof module:eclairjs/ml/regression
  @oaram {string} uid
  @param {DecisionTreeRegressionModel[]} trees   Decision trees in the ensemble.
  @param {float[]} treeWeights   Weights for the decision trees in the ensemble.