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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 _check_freqs(parts, indexes):
 """Ensure frequency of tumor to normal passes a reasonable threshold. Avoids calling low frequency tumors also present at low frequency in normals, which indicates a contamination or persistent error. """

    thresh_ratio = 2.7
    try:  # FreeBayes
        ao_index = parts[8].split(":").index("AO")
        ro_index = parts[8].split(":").index("RO")
    except ValueError:
        ao_index, ro_index = None, None
    try:  # VarDict
        af_index = parts[8].split(":").index("AF")
    except ValueError:
        af_index = None
    if af_index is None and ao_index is None:
        # okay to skip if a gVCF record
        if parts[4].find("<*>") == -1:
            raise NotImplementedError("Unexpected format annotations: %s" % parts[8])
    def _calc_freq(item):
        try:
            if ao_index is not None and ro_index is not None:
                ao = sum([int(x) for x in item.split(":")[ao_index].split(",")])
                ro = int(item.split(":")[ro_index])
                freq = ao / float(ao + ro)
            elif af_index is not None:
                freq = float(item.split(":")[af_index])
            else:
                freq = 0.0
        except (IndexError, ValueError, ZeroDivisionError):
            freq = 0.0
        return freq
    tumor_freq, normal_freq = _calc_freq(parts[indexes["tumor"]]), _calc_freq(parts[indexes["normal"]])
    return normal_freq <= 0.001 or normal_freq <= tumor_freq / thresh_ratio