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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=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000):
 """ 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) """

        model = callMLlibFunc("trainPrefixSpanModel",
                              data, minSupport, maxPatternLength, maxLocalProjDBSize)
        return PrefixSpanModel(model)