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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 gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
 """ 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). 1000 True True """

        return callMLlibFunc("gammaRDD", sc._jsc, float(shape),
                             float(scale), size, numPartitions, seed)