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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 _solve_conflict(list_c, s2p, n_cluster):
 """ Make sure sequences are counts once. Resolve by most-vote or exclussion :params list_c: dict of objects cluster :param s2p: dict of [loci].coverage = # num of seqs :param n_cluster: number of clusters return dict: new set of clusters """

    logger.debug("_solve_conflict: count once")
    if parameters.decision_cluster == "bayes":
        return decide_by_bayes(list_c, s2p)
    loci_similarity = _calculate_similarity(list_c)
    loci_similarity = sorted(loci_similarity.iteritems(), key=operator.itemgetter(1), reverse=True)
    common = sum([score for p, score in loci_similarity])
    while common > 0:
        n_cluster += 1
        logger.debug("_solve_conflict: ma %s" % loci_similarity)
        pairs = loci_similarity[0][0]
        score = loci_similarity[0][1]
        logger.debug("_solve_conflict: common %s, new %s" % (score, n_cluster))
        if parameters.decision_cluster.startswith("most-voted"):
            list_c = _split_cluster_by_most_vote(list_c, pairs)
        else:
            list_c = _split_cluster(list_c, pairs, n_cluster)
        list_c = {k: v for k, v in list_c.iteritems() if len(v.loci2seq) > 0}
        loci_similarity = _calculate_similarity(list_c)
        loci_similarity = sorted(loci_similarity.iteritems(), key=operator.itemgetter(1), reverse=True)
        #logger.note("%s %s" % (pairs, loci_similarity[0][1]))
        common = sum([score for p, score in loci_similarity])
        logger.debug("_solve_conflict: solved clusters %s" % len(list_c.keys()))
    return list_c