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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 segment_snrs(filters, stilde, psd, low_frequency_cutoff):
 """ This functions calculates the snr of each bank veto template against the segment Parameters filters: list of FrequencySeries The list of bank veto templates filters. stilde: FrequencySeries The current segment of data. psd: FrequencySeries low_frequency_cutoff: float Returns ------- snr (list):
 List of snr time series. norm (list):
 List of normalizations factors for the snr time series. """

    snrs = []
    norms = []

    for bank_template in filters:
        # For every template compute the snr against the stilde segment
        snr, _, norm = matched_filter_core(
                bank_template, stilde, h_norm=bank_template.sigmasq(psd),
                psd=None, low_frequency_cutoff=low_frequency_cutoff)
        # SNR time series stored here
        snrs.append(snr)
        # Template normalization factor stored here
        norms.append(norm)

    return snrs, norms