code: def train(cls, data, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000):
          
          model = callMLlibFunc("trainPrefixSpanModel",
                                data, minSupport, maxPatternLength, maxLocalProjDBSize)
          return PrefixSpanModel(model)
docs: Finds the complete set of frequent sequential patterns in the
          input sequences of itemsets.
  
          :param data:
            The input data set, each element contains a sequence of
            itemsets.
          :param minSupport:
            The minimal support level of the sequential pattern, any
            pattern that appears more than (minSupport *
            size-of-the-dataset) times will be output.
            (default: 0.1)
          :param maxPatternLength:
            The maximal length of the sequential pattern, any pattern
            that appears less than maxPatternLength will be output.
            (default: 10)
          :param maxLocalProjDBSize:
            The maximum number of items (including delimiters used in the
            internal storage format) allowed in a projected database before
            local processing. If a projected database exceeds this size,
            another iteration of distributed prefix growth is run.
            (default: 32000000)