code: def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None):
          
          return callMLlibFunc("logNormalRDD", sc._jsc, float(mean), float(std),
                               size, numPartitions, seed)
docs: Generates an RDD comprised of i.i.d. samples from the log normal
          distribution with the input mean and standard distribution.
  
          :param sc: SparkContext used to create the RDD.
          :param mean: mean for the log Normal distribution
          :param std: std for the log Normal distribution
          :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 ~ log N(mean, std).
  
          >>> from math import sqrt, exp
          >>> mean = 0.0
          >>> std = 1.0
          >>> expMean = exp(mean + 0.5 * std * std)
          >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std))
          >>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2)
          >>> stats = x.stats()
          >>> stats.count()
          1000
          >>> abs(stats.mean() - expMean) < 0.5
          True
          >>> from math import sqrt
          >>> abs(stats.stdev() - expStd) < 0.5
          True