code: function () {
          this.logger = Logger.getLogger("SquaredL2Updater_js");
          var jvmObject;
          if (arguments[0] instanceof org.apache.spark.mllib.optimization.SquaredL2Updater) {
              jvmObject = arguments[0];
          } else {
              jvmObject = new org.apache.spark.mllib.optimization.SquaredL2Updater();
          }
  
          Updater.call(this, jvmObject);
  
      }
docs: Class used to perform steps (weight update) using Gradient Descent methods.
  For general minimization problems, or for regularized problems of the form min L(w) + regParam * R(w),
  the compute function performs the actual update step, when given some (e.g. stochastic) gradient direction
  for the loss L(w), and a desired step-size (learning rate).The updater is responsible to also perform the
  update coming from the regularization term R(w) (if any regularization is used).
  @class
  @memberof module:eclairjs/mllib/optimization
  @constructor
  @extends module:eclairjs/mllib/optimization.Updater