How to use the pymer4.utils._return_t function in pymer4

if permute:
                perm_dat = dat.copy()
                dv_var = self.formula.split("~")[0].strip()
                grp_vars = list(self.grps.keys())
                perms = []
                for i in range(permute):
                    perm_dat[dv_var] = perm_dat.groupby(grp_vars)[dv_var].transform(
                        lambda x: x.sample(frac=1)
                    )
                    if self.family == "gaussian":
                        perm_obj = lmer.lmer(self.formula, data=perm_dat, REML=REML)
                    else:
                        perm_obj = lmer.glmer(
                            self.formula, data=perm_dat, family=_fam, REML=REML
                        )
                    perms.append(_return_t(perm_obj))
                perms = np.array(perms)
                pvals = []
                for c in range(df.shape[0]):
                    if self.family in ["gaussian", "gamma", "inverse_gaussian"]:
                        pvals.append(_perm_find(perms[:, c], df["T-stat"][c]))
                    else:
                        pvals.append(_perm_find(perms[:, c], df["Z-stat"][c]))
                df["P-val"] = pvals
                if "DF" in df.columns:
                    df["DF"] = [permute] * df.shape[0]
                    df = df.rename(columns={"DF": "Num_perm", "P-val": "Perm-P-val"})
                else:
                    df["Num_perm"] = [permute] * df.shape[0]
                    df = df.rename(columns={"P-val": "Perm-P-val"})

            if "P-val" in df.columns: