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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 exponentialRDD(sc, mean, size, numPartitions=None, seed=None):
 """ 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). 1000 True True """

        return callMLlibFunc("exponentialRDD", sc._jsc, float(mean), size, numPartitions, seed)