code: def _cont_norm_regions(fluxes, ivars, cont, ranges):
      
      nstars = fluxes.shape[0]
      norm_fluxes = np.zeros(fluxes.shape)
      norm_ivars = np.zeros(ivars.shape)
      for chunk in ranges:
          start = chunk[0]
          stop = chunk[1]
          output = _cont_norm(fluxes[:,start:stop],
                             ivars[:,start:stop],
                             cont[:,start:stop])
          norm_fluxes[:,start:stop] = output[0]
          norm_ivars[:,start:stop] = output[1]
      for jj in range(nstars):
          bad = (norm_ivars[jj,:] == 0.)
          norm_fluxes[jj,:][bad] = 1.
      return norm_fluxes, norm_ivars
docs: Perform continuum normalization for spectra in chunks
  
      Useful for spectra that have gaps
  
      Parameters
      ---------
      fluxes: numpy ndarray
          pixel intensities
      ivars: numpy ndarray
          inverse variances, parallel to fluxes
      cont: numpy ndarray
          the continuum
      ranges: list or np ndarray
          the chunks that the spectrum should be split into
  
      Returns
      -------
      norm_fluxes: numpy ndarray
          normalized pixel intensities
      norm_ivars: numpy ndarray
          rescaled inverse variances