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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def runExperiment( self, e ):
 """Run the experiment across the parameter space in parallel using all the engines in the cluster. This method returns immediately. The experiments are run asynchronously, with the points in the parameter space being explored randomly so that intermediate retrievals of results are more representative of the overall result. Put another way, for a lot of experiments the results available will converge towards a final answer, so we can plot them and see the answer emerge. :param e: the experiment"""


        # create the parameter space
        space = self.parameterSpace()

        # only proceed if there's work to do
        if len(space) > 0:
            nb = self.notebook()
            
            # randomise the order of the parameter space so that we evaluate across
            # the space as we go along to try to make intermediate (incomplete) result
            # sets more representative of the overall result set
            ps = self._mixup(space)

            try:
                # connect to the cluster
                self.open()

                # submit an experiment at each point in the parameter space to the cluster
                view = self._client.load_balanced_view()
                jobs = []
                for p in ps:
                    jobs.extend((view.apply_async((lambda p: e.set(p).run()), p)).msg_ids)

                    # there seems to be a race condition in submitting jobs,
                    # whereby jobs get dropped if they're submitted too quickly
                    time.sleep(0.01)
                
                # record the mesage ids of all the jobs as submitted but not yet completed
                psjs = zip(ps, jobs)
                for (p, j) in psjs:
                    nb.addPendingResult(p, j)
            finally:
                # commit our pending results in the notebook
                nb.commit()
                self.close()