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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_cnvkit(group_id, inputs, backgrounds, work_dir, back_files, out_files):
 """Normalize CNV coverage depths by GC, repeats and background using CNVkit - reference: calculates reference backgrounds from normals and pools including GC and repeat information - fix: Uses background to normalize coverage estimations http://cnvkit.readthedocs.io/en/stable/pipeline.html#fix """

    from bcbio.structural import cnvkit
    cnns = reduce(operator.add, [[tz.get_in(["depth", "bins", "target"], x),
                                    tz.get_in(["depth", "bins", "antitarget"], x)] for x in backgrounds], [])
    for d in inputs:
        if tz.get_in(["depth", "bins", "target"], d):
            target_bed = tz.get_in(["depth", "bins", "target"], d)
            antitarget_bed = tz.get_in(["depth", "bins", "antitarget"], d)
    input_backs = set(filter(lambda x: x is not None,
                                [dd.get_background_cnv_reference(d, "cnvkit") for d in inputs]))
    if input_backs:
        assert len(input_backs) == 1, "Multiple backgrounds in group: %s" % list(input_backs)
        back_file = list(input_backs)[0]
    else:
        back_file = cnvkit.cnvkit_background(cnns,
                                             os.path.join(work_dir, "background-%s-cnvkit.cnn" % (group_id)),
                                             backgrounds or inputs, target_bed, antitarget_bed)
    fix_cmd_inputs = []
    for data in inputs:
        work_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "structural",
                                                    dd.get_sample_name(data), "bins"))
        if tz.get_in(["depth", "bins", "target"], data):
            fix_file = os.path.join(work_dir, "%s-normalized.cnr" % (dd.get_sample_name(data)))
            fix_cmd_inputs.append((tz.get_in(["depth", "bins", "target"], data),
                                    tz.get_in(["depth", "bins", "antitarget"], data),
                                    back_file, fix_file, data))
            out_files[dd.get_sample_name(data)] = fix_file
            back_files[dd.get_sample_name(data)] = back_file
    parallel = {"type": "local", "cores": dd.get_cores(inputs[0]), "progs": ["cnvkit"]}
    run_multicore(cnvkit.run_fix_parallel, fix_cmd_inputs, inputs[0]["config"], parallel)
    return back_files, out_files