code: def exponentialRDD(sc, mean, size, numPartitions=None, seed=None):
          
          return callMLlibFunc("exponentialRDD", sc._jsc, float(mean), size, numPartitions, seed)
docs: Generates an RDD comprised of i.i.d. samples from the Exponential
          distribution with the input mean.
  
          :param sc: SparkContext used to create the RDD.
          :param mean: Mean, or 1 / lambda, for the Exponential distribution.
          :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 ~ Exp(mean).
  
          >>> mean = 2.0
          >>> x = RandomRDDs.exponentialRDD(sc, mean, 1000, seed=2)
          >>> stats = x.stats()
          >>> stats.count()
          1000
          >>> abs(stats.mean() - mean) < 0.5
          True
          >>> from math import sqrt
          >>> abs(stats.stdev() - sqrt(mean)) < 0.5
          True