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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 reduceByKeyAndWindow(self, func, invFunc, windowDuration, slideDuration=None, numPartitions=None, filterFunc=None):
 """ Return a new DStream by applying incremental `reduceByKey` over a sliding window. The reduced value of over a new window is calculated using the old window's reduce value : 1. reduce the new values that entered the window (e.g., adding new counts) 2. "inverse reduce" the old values that left the window (e.g., subtracting old counts) `invFunc` can be None, then it will reduce all the RDDs in window, could be slower than having `invFunc`. @param func: associative and commutative reduce function @param invFunc: inverse function of `reduceFunc` @param windowDuration: width of the window; must be a multiple of this DStream's batching interval @param slideDuration: sliding interval of the window (i.e., the interval after which the new DStream will generate RDDs); must be a multiple of this DStream's batching interval @param numPartitions: number of partitions of each RDD in the new DStream. @param filterFunc: function to filter expired key-value pairs; only pairs that satisfy the function are retained set this to null if you do not want to filter """

        self._validate_window_param(windowDuration, slideDuration)
        if numPartitions is None:
            numPartitions = self._sc.defaultParallelism

        reduced = self.reduceByKey(func, numPartitions)

        if invFunc:
            def reduceFunc(t, a, b):
                b = b.reduceByKey(func, numPartitions)
                r = a.union(b).reduceByKey(func, numPartitions) if a else b
                if filterFunc:
                    r = r.filter(filterFunc)
                return r

            def invReduceFunc(t, a, b):
                b = b.reduceByKey(func, numPartitions)
                joined = a.leftOuterJoin(b, numPartitions)
                return joined.mapValues(lambda kv: invFunc(kv[0], kv[1])
                                        if kv[1] is not None else kv[0])

            jreduceFunc = TransformFunction(self._sc, reduceFunc, reduced._jrdd_deserializer)
            jinvReduceFunc = TransformFunction(self._sc, invReduceFunc, reduced._jrdd_deserializer)
            if slideDuration is None:
                slideDuration = self._slideDuration
            dstream = self._sc._jvm.PythonReducedWindowedDStream(
                reduced._jdstream.dstream(),
                jreduceFunc, jinvReduceFunc,
                self._ssc._jduration(windowDuration),
                self._ssc._jduration(slideDuration))
            return DStream(dstream.asJavaDStream(), self._ssc, self._sc.serializer)
        else:
            return reduced.window(windowDuration, slideDuration).reduceByKey(func, numPartitions)