code: def uniformRDD(sc, size, numPartitions=None, seed=None):
          
          return callMLlibFunc("uniformRDD", sc._jsc, size, numPartitions, seed)
docs: Generates an RDD comprised of i.i.d. samples from the
          uniform distribution U(0.0, 1.0).
  
          To transform the distribution in the generated RDD from U(0.0, 1.0)
          to U(a, b), use
          C{RandomRDDs.uniformRDD(sc, n, p, seed)\
            .map(lambda v: a + (b - a) * v)}
  
          :param sc: SparkContext used to create the RDD.
          :param size: Size of the RDD.
          :param numPartitions: Number of partitions in the RDD (default: `sc.defaultParallelism`).
          :param seed: Random seed (default: a random long integer).
          :return: RDD of float comprised of i.i.d. samples ~ `U(0.0, 1.0)`.
  
          >>> x = RandomRDDs.uniformRDD(sc, 100).collect()
          >>> len(x)
          100
          >>> max(x) <= 1.0 and min(x) >= 0.0
          True
          >>> RandomRDDs.uniformRDD(sc, 100, 4).getNumPartitions()
          4
          >>> parts = RandomRDDs.uniformRDD(sc, 100, seed=4).getNumPartitions()
          >>> parts == sc.defaultParallelism
          True