code: def coalesce(self, numPartitions, shuffle=False):
          
          if shuffle:
              # Decrease the batch size in order to distribute evenly the elements across output
              # partitions. Otherwise, repartition will possibly produce highly skewed partitions.
              batchSize = min(10, self.ctx._batchSize or 1024)
              ser = BatchedSerializer(PickleSerializer(), batchSize)
              selfCopy = self._reserialize(ser)
              jrdd_deserializer = selfCopy._jrdd_deserializer
              jrdd = selfCopy._jrdd.coalesce(numPartitions, shuffle)
          else:
              jrdd_deserializer = self._jrdd_deserializer
              jrdd = self._jrdd.coalesce(numPartitions, shuffle)
          return RDD(jrdd, self.ctx, jrdd_deserializer)
docs: Return a new RDD that is reduced into `numPartitions` partitions.
  
          >>> sc.parallelize([1, 2, 3, 4, 5], 3).glom().collect()
          [[1], [2, 3], [4, 5]]
          >>> sc.parallelize([1, 2, 3, 4, 5], 3).coalesce(1).glom().collect()
          [[1, 2, 3, 4, 5]]