code: def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
          
          return callMLlibFunc("gammaRDD", sc._jsc, float(shape),
                               float(scale), size, numPartitions, seed)
docs: Generates an RDD comprised of i.i.d. samples from the Gamma
          distribution with the input shape and scale.
  
          :param sc: SparkContext used to create the RDD.
          :param shape: shape (> 0) parameter for the Gamma distribution
          :param scale: scale (> 0) parameter for the Gamma 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 ~ Gamma(shape, scale).
  
          >>> from math import sqrt
          >>> shape = 1.0
          >>> scale = 2.0
          >>> expMean = shape * scale
          >>> expStd = sqrt(shape * scale * scale)
          >>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2)
          >>> stats = x.stats()
          >>> stats.count()
          1000
          >>> abs(stats.mean() - expMean) < 0.5
          True
          >>> abs(stats.stdev() - expStd) < 0.5
          True