Generates an RDD comprised of vectors containing i.i.d. samples drawn
        from the Gamma distribution.

        :param sc: SparkContext used to create the RDD.
        :param shape: Shape (> 0) of the Gamma distribution
        :param scale: Scale (> 0) of the Gamma distribution
        :param numRows: Number of Vectors in the RDD.
        :param numCols: Number of elements in each Vector.
        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
        :param seed: Random seed (default: a random long integer).
        :return: RDD of Vector with vectors containing i.i.d. samples ~ Gamma(shape, scale).

        >>> import numpy as np
        >>> from math import sqrt
        >>> shape = 1.0
        >>> scale = 2.0
        >>> expMean = shape * scale
        >>> expStd = sqrt(shape * scale * scale)
        >>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, 100, 100, seed=1).collect())
        >>> mat.shape
        (100, 100)
        >>> abs(mat.mean() - expMean) < 0.1
        True
        >>> abs(mat.std() - expStd) < 0.1
        True

def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
        """
        Generates an RDD comprised of vectors containing i.i.d. samples drawn
        from the Gamma distribution.

        :param sc: SparkContext used to create the RDD.
        :param shape: Shape (> 0) of the Gamma distribution
        :param scale: Scale (> 0) of the Gamma distribution
        :param numRows: Number of Vectors in the RDD.
        :param numCols: Number of elements in each Vector.
        :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
        :param seed: Random seed (default: a random long integer).
        :return: RDD of Vector with vectors containing i.i.d. samples ~ Gamma(shape, scale).

        >>> import numpy as np
        >>> from math import sqrt
        >>> shape = 1.0
        >>> scale = 2.0
        >>> expMean = shape * scale
        >>> expStd = sqrt(shape * scale * scale)
        >>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, 100, 100, seed=1).collect())
        >>> mat.shape
        (100, 100)
        >>> abs(mat.mean() - expMean) < 0.1
        True
        >>> abs(mat.std() - expStd) < 0.1
        True
        """
        return callMLlibFunc("gammaVectorRDD", sc._jsc, float(shape), float(scale),
                             numRows, numCols, numPartitions, seed)