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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 train(cls, data, minSupport=0.3, numPartitions=-1):
 """ Computes an FP-Growth model that contains frequent itemsets. :param data: The input data set, each element contains a transaction. :param minSupport: The minimal support level. (default: 0.3) :param numPartitions: The number of partitions used by parallel FP-growth. A value of -1 will use the same number as input data. (default: -1) """

        model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
        return FPGrowthModel(model)