code: def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
          
          return callMLlibFunc("poissonVectorRDD", sc._jsc, float(mean), numRows, numCols,
                               numPartitions, seed)
docs: Generates an RDD comprised of vectors containing i.i.d. samples drawn
          from the Poisson distribution with the input mean.
  
          :param sc: SparkContext used to create the RDD.
          :param mean: Mean, or lambda, for the Poisson 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 ~ Pois(mean).
  
          >>> import numpy as np
          >>> mean = 100.0
          >>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1)
          >>> mat = np.mat(rdd.collect())
          >>> mat.shape
          (100, 100)
          >>> abs(mat.mean() - mean) < 0.5
          True
          >>> from math import sqrt
          >>> abs(mat.std() - sqrt(mean)) < 0.5
          True