code: def normalRDD(sc, size, numPartitions=None, seed=None):
          
          return callMLlibFunc("normalRDD", sc._jsc, size, numPartitions, seed)
docs: Generates an RDD comprised of i.i.d. samples from the standard normal
          distribution.
  
          To transform the distribution in the generated RDD from standard normal
          to some other normal N(mean, sigma^2), use
          C{RandomRDDs.normal(sc, n, p, seed)\
            .map(lambda v: mean + sigma * v)}
  
          :param sc: SparkContext used to create the RDD.
          :param size: Size of the RDD.
          :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
          :param seed: Random seed (default: a random long integer).
          :return: RDD of float comprised of i.i.d. samples ~ N(0.0, 1.0).
  
          >>> x = RandomRDDs.normalRDD(sc, 1000, seed=1)
          >>> stats = x.stats()
          >>> stats.count()
          1000
          >>> abs(stats.mean() - 0.0) < 0.1
          True
          >>> abs(stats.stdev() - 1.0) < 0.1
          True