How to use the tedana.decay.fit_decay function in tedana

mask = compute_epi_mask(first_echo_img)
        RepLGR.info("An initial mask was generated from the first echo using "
                    "nilearn's compute_epi_mask function.")
    else:
        # TODO: add affine check
        LGR.info('Using user-defined mask')
        RepLGR.info("A user-defined mask was applied to the data.")

    mask, masksum = utils.make_adaptive_mask(catd, mask=mask, getsum=True)
    LGR.debug('Retaining {}/{} samples'.format(mask.sum(), n_samp))
    io.filewrite(masksum, op.join(out_dir, 'adaptive_mask.nii'), ref_img)

    os.chdir(out_dir)

    LGR.info('Computing T2* map')
    t2s_limited, s0_limited, t2s_full, s0_full = decay.fit_decay(
        catd, tes, mask, masksum, fittype)

    # set a hard cap for the T2* map
    # anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
    cap_t2s = stats.scoreatpercentile(t2s_limited.flatten(), 99.5,
                                      interpolation_method='lower')
    LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10))
    t2s_limited[t2s_limited > cap_t2s * 10] = cap_t2s
    io.filewrite(t2s_limited, op.join(out_dir, 't2sv.nii'), ref_img)
    io.filewrite(s0_limited, op.join(out_dir, 's0v.nii'), ref_img)

    if verbose:
        io.filewrite(t2s_full, op.join(out_dir, 't2svG.nii'), ref_img)
        io.filewrite(s0_full, op.join(out_dir, 's0vG.nii'), ref_img)

    # optimally combine data

if not op.isdir(out_dir):
        LGR.info('Creating output directory: {}'.format(out_dir))
        os.mkdir(out_dir)
    else:
        LGR.info('Using output directory: {}'.format(out_dir))

    if mask is None:
        LGR.info('Computing adaptive mask')
    else:
        LGR.info('Using user-defined mask')
    mask, masksum = utils.make_adaptive_mask(catd, getsum=True)

    LGR.info('Computing adaptive T2* map')
    if fitmode == 'all':
        (t2s_limited, s0_limited,
         t2s_full, s0_full) = decay.fit_decay(catd, tes, mask, masksum,
                                              fittype)
    else:
        (t2s_limited, s0_limited,
         t2s_full, s0_full) = decay.fit_decay_ts(catd, tes, mask, masksum,
                                                 fittype)

    # set a hard cap for the T2* map/timeseries
    # anything that is 10x higher than the 99.5 %ile will be reset to 99.5 %ile
    cap_t2s = stats.scoreatpercentile(t2s_limited.flatten(), 99.5,
                                      interpolation_method='lower')
    LGR.debug('Setting cap on T2* map at {:.5f}'.format(cap_t2s * 10))
    t2s_limited[t2s_limited > cap_t2s * 10] = cap_t2s

    LGR.info('Computing optimal combination')
    # optimally combine data
    OCcatd = combine.make_optcom(catd, tes, mask, t2s=t2s_full,