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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
 """ 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). (100, 100) True True """

        return callMLlibFunc("gammaVectorRDD", sc._jsc, float(shape), float(scale),
                             numRows, numCols, numPartitions, seed)