code: def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
          
          return callMLlibFunc("logNormalVectorRDD", sc._jsc, float(mean), float(std),
                               numRows, numCols, numPartitions, seed)
docs: Generates an RDD comprised of vectors containing i.i.d. samples drawn
          from the log normal distribution.
  
          :param sc: SparkContext used to create the RDD.
          :param mean: Mean of the log normal distribution
          :param std: Standard Deviation of the log normal 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 ~ log `N(mean, std)`.
  
          >>> import numpy as np
          >>> 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))
          >>> m = RandomRDDs.logNormalVectorRDD(sc, mean, std, 100, 100, seed=1).collect()
          >>> mat = np.matrix(m)
          >>> mat.shape
          (100, 100)
          >>> abs(mat.mean() - expMean) < 0.1
          True
          >>> abs(mat.std() - expStd) < 0.1
          True