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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 hmm(args):
 """ %prog hmm workdir sample_key Run CNV segmentation caller. The workdir must contain a subfolder called `sample_key-cn` that contains CN for each chromosome. A `beta` directory that contains scaler for each bin must also be present in the current directory. """

    p = OptionParser(hmm.__doc__)
    p.add_option("--mu", default=.003, type="float",
                 help="Transition probability")
    p.add_option("--sigma", default=.1, type="float",
                 help="Standard deviation of Gaussian emission distribution")
    p.add_option("--threshold", default=1, type="float",
                 help="Standard deviation must be < this "
                      "in the baseline population")
    opts, args = p.parse_args(args)

    if len(args) != 2:
        sys.exit(not p.print_help())

    workdir, sample_key = args
    model = CopyNumberHMM(workdir=workdir, mu=opts.mu, sigma=opts.sigma,
                          threshold=opts.threshold)
    events = model.run(sample_key)
    params = ".mu-{}.sigma-{}.threshold-{}"\
             .format(opts.mu, opts.sigma, opts.threshold)
    hmmfile = op.join(workdir, sample_key + params + ".seg")
    fw = open(hmmfile, "w")
    nevents = 0
    for mean_cn, rr, event in events:
        if event is None:
            continue
        print(" ".join((event.bedline, sample_key)), file=fw)
        nevents += 1
    fw.close()
    logging.debug("A total of {} aberrant events written to `{}`"
                  .format(nevents, hmmfile))
    return hmmfile