code: def volume_binned_pylal(f_dist, m_dist, bins=15):
      
      def sims_to_bin(sim):
          return (sim, 0)
  
      total = numpy.concatenate([f_dist, m_dist])
      ndbins = bin_utils.NDBins([bin_utils.LinearBins(min(total), max(total), bins),
                                 bin_utils.LinearBins(0., 1, 1)])
      vol, verr = compute_search_volume_in_bins(f_dist, total, ndbins, sims_to_bin)
      return vol.array[0], verr.array[0]
docs: Compute the sensitive volume using a distance binned efficiency estimate
  
      Parameters
      -----------
      f_dist: numpy.ndarray
          The distances of found injections
      m_dist: numpy.ndarray
          The distances of missed injections
  
      Returns
      --------
      volume: float
          Volume estimate
      volume_error: float
          The standard error in the volume