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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 combineByKey(self, createCombiner, mergeValue, mergeCombiners, numPartitions=None, partitionFunc=portable_hash):
 """ Generic function to combine the elements for each key using a custom set of aggregation functions. Turns an RDD[(K, V)] into a result of type RDD[(K, C)], for a "combined type" C. Users provide three functions: - C{createCombiner}, which turns a V into a C (e.g., creates a one-element list) - C{mergeValue}, to merge a V into a C (e.g., adds it to the end of a list) - C{mergeCombiners}, to combine two C's into a single one (e.g., merges the lists) To avoid memory allocation, both mergeValue and mergeCombiners are allowed to modify and return their first argument instead of creating a new C. In addition, users can control the partitioning of the output RDD. .. note:: V and C can be different -- for example, one might group an RDD of type (Int, Int) into an RDD of type (Int, List[Int]). [('a', [1, 2]), ('b', [1])] """

        if numPartitions is None:
            numPartitions = self._defaultReducePartitions()

        serializer = self.ctx.serializer
        memory = self._memory_limit()
        agg = Aggregator(createCombiner, mergeValue, mergeCombiners)

        def combineLocally(iterator):
            merger = ExternalMerger(agg, memory * 0.9, serializer)
            merger.mergeValues(iterator)
            return merger.items()

        locally_combined = self.mapPartitions(combineLocally, preservesPartitioning=True)
        shuffled = locally_combined.partitionBy(numPartitions, partitionFunc)

        def _mergeCombiners(iterator):
            merger = ExternalMerger(agg, memory, serializer)
            merger.mergeCombiners(iterator)
            return merger.items()

        return shuffled.mapPartitions(_mergeCombiners, preservesPartitioning=True)