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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 mcmc_CH(self, walkerRatio, n_run, n_burn, mean_start, sigma_start, threadCount=1, init_pos=None, mpi=False):
 """ runs mcmc on the parameter space given parameter bounds with CosmoHammerSampler returns the chain """

        lowerLimit, upperLimit = self.lower_limit, self.upper_limit

        mean_start = np.maximum(lowerLimit, mean_start)
        mean_start = np.minimum(upperLimit, mean_start)

        low_start = mean_start - sigma_start
        high_start = mean_start + sigma_start
        low_start = np.maximum(lowerLimit, low_start)
        high_start = np.minimum(upperLimit, high_start)
        sigma_start = (high_start - low_start) / 2
        mean_start = (high_start + low_start) / 2
        params = np.array([mean_start, lowerLimit, upperLimit, sigma_start]).T

        chain = LikelihoodComputationChain(
            min=lowerLimit,
            max=upperLimit)

        temp_dir = tempfile.mkdtemp("Hammer")
        file_prefix = os.path.join(temp_dir, "logs")
        #file_prefix = "./lenstronomy_debug"
        # chain.addCoreModule(CambCoreModule())
        chain.addLikelihoodModule(self.chain)
        chain.setup()

        store = InMemoryStorageUtil()
        #store = None
        if mpi is True:
            sampler = MpiCosmoHammerSampler(
            params=params,
            likelihoodComputationChain=chain,
            filePrefix=file_prefix,
            walkersRatio=walkerRatio,
            burninIterations=n_burn,
            sampleIterations=n_run,
            threadCount=1,
            initPositionGenerator=init_pos,
            storageUtil=store)
        else:
            sampler = CosmoHammerSampler(
                params=params,
                likelihoodComputationChain=chain,
                filePrefix=file_prefix,
                walkersRatio=walkerRatio,
                burninIterations=n_burn,
                sampleIterations=n_run,
                threadCount=threadCount,
                initPositionGenerator=init_pos,
                storageUtil=store)
        time_start = time.time()
        if sampler.isMaster():
            print('Computing the MCMC...')
            print('Number of walkers = ', len(mean_start)*walkerRatio)
            print('Burn-in iterations: ', n_burn)
            print('Sampling iterations:', n_run)
        sampler.startSampling()
        if sampler.isMaster():
            time_end = time.time()
            print(time_end - time_start, 'time taken for MCMC sampling')
        # if sampler._sampler.pool is not None:
        #     sampler._sampler.pool.close()
        try:
            shutil.rmtree(temp_dir)
        except Exception as ex:
            print(ex, 'shutil.rmtree did not work')
            pass
        #samples = np.loadtxt(file_prefix+".out")
        #prob = np.loadtxt(file_prefix+"prob.out")
        return store.samples, store.prob