How to use the pingouin.effsize.compute_esci function in pingouin

# Remove missing values
    data = data[[x, y, subject]].dropna(axis=0)

    # Using PINGOUIN
    aov = ancova(dv=y, covar=x, between=subject, data=data)
    bw = aov.bw_  # Beta within parameter
    sign = np.sign(bw)
    dof = int(aov.at[2, 'DF'])
    n = dof + 2
    ssfactor = aov.at[1, 'SS']
    sserror = aov.at[2, 'SS']
    rm = sign * np.sqrt(ssfactor / (ssfactor + sserror))
    pval = aov.at[1, 'p-unc']
    pval = pval * 0.5 if tail == 'one-sided' else pval
    ci = compute_esci(stat=rm, nx=n, eftype='pearson').tolist()
    pwr = power_corr(r=rm, n=n, tail=tail)
    # Convert to Dataframe
    stats = pd.DataFrame({"r": round(rm, 3), "dof": int(dof),
                          "pval": pval, "CI95%": str(ci),
                          "power": round(pwr, 3)}, index=["rm_corr"])
    return stats

elif method == 'shepherd':
        r, pval, outliers = shepherd(x, y)
    elif method == 'skipped':
        r, pval, outliers = skipped(x, y, method='spearman')
    else:
        raise ValueError('Method not recognized.')

    assert not np.isnan(r), 'Correlation returned NaN. Check your data.'

    # Compute r2 and adj_r2
    r2 = r**2
    adj_r2 = 1 - (((1 - r2) * (nx - 1)) / (nx - 3))

    # Compute the parametric 95% confidence interval and power
    if r2 < 1:
        ci = compute_esci(stat=r, nx=nx, ny=nx, eftype='r')
        pr = round(power_corr(r=r, n=nx, power=None, alpha=0.05, tail=tail), 3)
    else:
        ci = [1., 1.]
        pr = np.inf

    # Create dictionnary
    stats = {'n': nx,
             'r': round(r, 3),
             'r2': round(r2, 3),
             'adj_r2': round(adj_r2, 3),
             'CI95%': [ci],
             'p-val': pval if tail == 'two-sided' else .5 * pval,
             'power': pr
             }

    if method in ['shepherd', 'skipped']: