Executes the given partitionFunc on the specified set of partitions,
        returning the result as an array of elements.

        If 'partitions' is not specified, this will run over all partitions.

        >>> myRDD = sc.parallelize(range(6), 3)
        >>> sc.runJob(myRDD, lambda part: [x * x for x in part])
        [0, 1, 4, 9, 16, 25]

        >>> myRDD = sc.parallelize(range(6), 3)
        >>> sc.runJob(myRDD, lambda part: [x * x for x in part], [0, 2], True)
        [0, 1, 16, 25]

def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False):
        """
        Executes the given partitionFunc on the specified set of partitions,
        returning the result as an array of elements.

        If 'partitions' is not specified, this will run over all partitions.

        >>> myRDD = sc.parallelize(range(6), 3)
        >>> sc.runJob(myRDD, lambda part: [x * x for x in part])
        [0, 1, 4, 9, 16, 25]

        >>> myRDD = sc.parallelize(range(6), 3)
        >>> sc.runJob(myRDD, lambda part: [x * x for x in part], [0, 2], True)
        [0, 1, 16, 25]
        """
        if partitions is None:
            partitions = range(rdd._jrdd.partitions().size())

        # Implementation note: This is implemented as a mapPartitions followed
        # by runJob() in order to avoid having to pass a Python lambda into
        # SparkContext#runJob.
        mappedRDD = rdd.mapPartitions(partitionFunc)
        sock_info = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
        return list(_load_from_socket(sock_info, mappedRDD._jrdd_deserializer))