code: def train(cls, data, minSupport=0.3, numPartitions=-1):
          
          model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
          return FPGrowthModel(model)
docs: 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)