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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 _custom_rdd_reduce(self, reduce_func):
 """Provides a custom RDD reduce which preserves ordering if the RDD has been sorted. This is useful for us because we need this functionality as many pandas operations support sorting the results. The standard reduce in PySpark does not have this property. Note that when PySpark no longer does partition reduces locally this code will also need to be updated."""

        def accumulating_iter(iterator):
            acc = None
            for obj in iterator:
                if acc is None:
                    acc = obj
                else:
                    acc = reduce_func(acc, obj)
            if acc is not None:
                yield acc
        vals = self._rdd.mapPartitions(accumulating_iter).collect()
        return reduce(accumulating_iter, vals)