code: def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
          
          return callMLlibFunc("gammaVectorRDD", sc._jsc, float(shape), float(scale),
                               numRows, numCols, numPartitions, seed)
docs: Generates an RDD comprised of vectors containing i.i.d. samples drawn
          from the Gamma distribution.
  
          :param sc: SparkContext used to create the RDD.
          :param shape: Shape (> 0) of the Gamma distribution
          :param scale: Scale (> 0) of the Gamma distribution
          :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 ~ Gamma(shape, scale).
  
          >>> import numpy as np
          >>> from math import sqrt
          >>> shape = 1.0
          >>> scale = 2.0
          >>> expMean = shape * scale
          >>> expStd = sqrt(shape * scale * scale)
          >>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, 100, 100, seed=1).collect())
          >>> mat.shape
          (100, 100)
          >>> abs(mat.mean() - expMean) < 0.1
          True
          >>> abs(mat.std() - expStd) < 0.1
          True