code: function (uid) {
          var jvmObject = new org.apache.spark.ml.feature.MinMaxScaler(uid);
          this.logger = Logger.getLogger("ml_feature_MinMaxScaler_js");
          var jvmObject;
          if (uid) {
              if (uid instanceof org.apache.spark.ml.feature.MinMaxScaler) {
                  jvmObject = uid;
              } else {
                  jvmObject = new org.apache.spark.ml.feature.MinMaxScaler(uid);
              }
          } else {
              jvmObject = new org.apache.spark.ml.feature.MinMaxScaler();
          }
          Estimator.call(this, jvmObject);
  
      }
docs: @classdesc
  Rescale each feature individually to a common range [min, max] linearly using column summary
  statistics, which is also known as min-max normalization or Rescaling. The rescaled value for
  feature E is calculated as,
  
  Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
  
  For the case E_{max} == E_{min}, Rescaled(e_i) = 0.5 * (max + min)
  Note that since zero values will probably be transformed to non-zero values, output of the
  transformer will be DenseVector even for sparse input.
  @class
  @extends module:eclairjs/ml.Estimator
  @memberof module:eclairjs/ml/feature
  @param {string} [uid]