code: def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
          
          return callMLlibFunc("normalVectorRDD", sc._jsc, numRows, numCols, numPartitions, seed)
docs: Generates an RDD comprised of vectors containing i.i.d. samples drawn
          from the standard normal distribution.
  
          :param sc: SparkContext used to create the RDD.
          :param numRows: Number of Vectors in the RDD.
          :param numCols: Number of elements in each Vector.
          :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
          :param seed: Random seed (default: a random long integer).
          :return: RDD of Vector with vectors containing i.i.d. samples ~ `N(0.0, 1.0)`.
  
          >>> import numpy as np
          >>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1).collect())
          >>> mat.shape
          (100, 100)
          >>> abs(mat.mean() - 0.0) < 0.1
          True
          >>> abs(mat.std() - 1.0) < 0.1
          True