code: function(rows,nRows,nCols) {
          this.logger = Logger.getLogger("RowMatrix_js");
          var jvmObject;
          if (arguments[0] instanceof org.apache.spark.mllib.linalg.distributed.RowMatrix) {
              jvmObject = arguments[0];
          } else if (arguments.length === 3) {
              jvmObject = new org.apache.spark.mllib.linalg.distributed.RowMatrix(Utils.unwrapObject(rows).rdd(),nRows,nCols);
          } else {
              jvmObject = new org.apache.spark.mllib.linalg.distributed.RowMatrix(Utils.unwrapObject(rows).rdd());
          }
  
  
           DistributedMatrix.call(this, jvmObject);
  
      }
docs: Represents a row-oriented distributed Matrix with no meaningful row indices.
  @memberof module:eclairjs/mllib/linalg/distributed
  @classdesc
  @param {module:eclairjs.RDD} rows stored as an RDD[Vector]
  @param {number} [nRows] number of rows. A non-positive value means unknown, and then the number of rows will
  be determined by the number of records in the RDD `rows`.
  @param {number} [nCols] number of columns. A non-positive value means unknown, and then the number of
  columns will be determined by the size of the first row.
  @class
  @extends module:eclairjs/mllib/linalg/distributed.DistributedMatrix
  @example
  var RowMatrix = require('eclairjs/mllib/linalg/distributed/RowMatrix');
  var Vectors = require("'eclairjs/mllib/linalg/Vectors");;
  var rowsList = [Vectors.dense([1.12, 2.05, 3.12]), Vectors.dense([5.56, 6.28, 8.94]), Vectors.dense([10.2, 8.0, 20.5])];
  var rows = sc.parallelize(rowsList);
  var mat = new RowMatrix(rows);