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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 normalize_sv_coverage(*items):
 """Normalize CNV coverage, providing flexible point for multiple methods. """

    calcfns = {"cnvkit": _normalize_sv_coverage_cnvkit, "gatk-cnv": _normalize_sv_coverage_gatk}
    from bcbio.structural import cnvkit
    from bcbio.structural import shared as sshared
    items = [utils.to_single_data(x) for x in cwlutils.handle_combined_input(items)]
    if all(not cnvkit.use_general_sv_bins(x) for x in items):
        return [[d] for d in items]
    out_files = {}
    back_files = {}
    for group_id, gitems in itertools.groupby(items, lambda x: tz.get_in(["regions", "bins", "group"], x)):
        # No CNVkit calling for this particular set of samples
        if group_id is None:
            continue
        inputs, backgrounds = sshared.find_case_control(list(gitems))
        assert inputs, "Did not find inputs for sample batch: %s" % (" ".join(dd.get_sample_name(x) for x in items))
        work_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(inputs[0]), "structural",
                                                    dd.get_sample_name(inputs[0]), "bins"))
        back_files, out_files = calcfns[cnvkit.bin_approach(inputs[0])](group_id, inputs, backgrounds, work_dir,
                                                                        back_files, out_files)
    out = []
    for data in items:
        if dd.get_sample_name(data) in out_files:
            data["depth"]["bins"]["background"] = back_files[dd.get_sample_name(data)]
            data["depth"]["bins"]["normalized"] = out_files[dd.get_sample_name(data)]
        out.append([data])
    return out