code: def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
          
          return callMLlibFunc("uniformVectorRDD", sc._jsc, numRows, numCols, numPartitions, seed)
docs: Generates an RDD comprised of vectors containing i.i.d. samples drawn
          from the uniform distribution U(0.0, 1.0).
  
          :param sc: SparkContext used to create the RDD.
          :param numRows: Number of Vectors in the RDD.
          :param numCols: Number of elements in each Vector.
          :param numPartitions: Number of partitions in the RDD.
          :param seed: Seed for the RNG that generates the seed for the generator in each partition.
          :return: RDD of Vector with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.
  
          >>> import numpy as np
          >>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
          >>> mat.shape
          (10, 10)
          >>> mat.max() <= 1.0 and mat.min() >= 0.0
          True
          >>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
          4