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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 volume_binned_pylal(f_dist, m_dist, bins=15):
 """ 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 """

    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]