code: def segment_snrs(filters, stilde, psd, low_frequency_cutoff):
      
      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
docs: 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.