How to use the pingouin.multicomp.multicomp function in pingouin

elif 'outliers' in cor_st_keys:
            stats.loc[i, dvs_out] = cor_st[dvs_out].values
        else:
            stats.loc[i, dvs] = cor_st[dvs].values

    # Force conversion to numeric
    stats = stats.astype({'r': float, 'r2': float, 'adj_r2': float,
                          'n': int, 'p-val': float, 'outliers': float,
                          'power': float})

    # Multiple comparisons
    stats = stats.rename(columns={'p-val': 'p-unc'})
    padjust = None if stats['p-unc'].size <= 1 else padjust
    if padjust is not None:
        if padjust.lower() != 'none':
            reject, stats['p-corr'] = multicomp(stats['p-unc'].values,
                                                method=padjust)
            stats['p-adjust'] = padjust
    else:
        stats['p-corr'] = None
        stats['p-adjust'] = None

    # Standardize correlation coefficients (Fisher z-transformation)
    stats['z'] = np.round(np.arctanh(stats['r'].values), 3)

    col_order = ['X', 'Y', 'method', 'tail', 'n', 'outliers', 'r', 'CI95%',
                 'r2', 'adj_r2', 'z', 'p-unc', 'p-corr', 'p-adjust',
                 'BF10', 'power']

    # Reorder columns and remove empty ones
    stats = stats.reindex(columns=col_order).dropna(how='all', axis=1)

assert isinstance(decimals, int), 'decimals must be an int.'
    assert method in ['pearson', 'spearman'], 'Method is not recognized.'
    assert upper in ['pval', 'n'], 'upper must be either `pval` or `n`.'
    mat = self.corr(method=method).round(decimals)
    if upper == 'n':
        mat_upper = self.corr(method=lambda x, y: len(x)).astype(int)
    else:
        if method == 'pearson':
            mat_upper = self.corr(method=lambda x, y: pearsonr(x, y)[1])
        else:
            # Method = 'spearman'
            mat_upper = self.corr(method=lambda x, y: spearmanr(x, y)[1])

        if padjust is not None:
            pvals = mat_upper.values[tif(mat, k=1)]
            mat_upper.values[tif(mat, k=1)] = multicomp(pvals, alpha=0.05,
                                                        method=padjust)[1]

    # Convert r to text
    mat = mat.astype(str)
    np.fill_diagonal(mat.values, '-')  # Inplace modification of the diagonal

    if upper == 'pval':

        def replace_pval(x):
            for key, value in pval_stars.items():
                if x < key:
                    return value
            return ''

        if stars:
            # Replace p-values by stars

stat_name = 'U-val'
                        df_ttest = mwu(x, y, tail=tail)

                # Append to stats
                if return_desc:
                    stats.at[ic, 'mean(A)'] = np.round(np.nanmean(x), 3)
                    stats.at[ic, 'mean(B)'] = np.round(np.nanmean(y), 3)
                    stats.at[ic, 'std(A)'] = np.round(np.nanstd(x), 3)
                    stats.at[ic, 'std(B)'] = np.round(np.nanstd(y), 3)
                stats.at[ic, stat_name] = df_ttest[stat_name].iat[0]
                stats.at[ic, 'p-unc'] = df_ttest['p-val'].iat[0]
                stats.at[ic, effsize] = ef

            # Multi-comparison columns
            if padjust is not None and padjust.lower() != 'none':
                _, pcor = multicomp(stats.loc[idxiter, 'p-unc'].values,
                                    alpha=alpha, method=padjust)
                stats.loc[idxiter, 'p-corr'] = pcor
                stats.loc[idxiter, 'p-adjust'] = padjust

    # ---------------------------------------------------------------------
    # Append parametric columns
    stats.loc[:, 'Parametric'] = parametric

    # Reorder and drop empty columns
    stats = stats[np.array(col_order)[np.isin(col_order, stats.columns)]]
    stats = stats.dropna(how='all', axis=1)

    # Rename Time columns
    if (contrast in ['multiple_within', 'multiple_between', 'within_between']
       and interaction):
        stats['Time'].fillna('-', inplace=True)

np.random.seed(1234)
N = 6
pvals = np.random.rand(N)
pvals[[0]] *= 0.01
pvals[[3]] *= 0.1
print('Uncorrected p-values: ', pvals)

# Multiple comparisons
# -----------------------
reject, pvals_corrected = multicomp(pvals, alpha=.05, method='bonf')
print('Bonf-corrected p-values: ', pvals_corrected)

reject, pvals_corrected = multicomp(pvals, alpha=.05, method='fdr_bh')
print('FDR-corrected (BH) p-values: ', pvals_corrected)

reject, pvals_corrected = multicomp(pvals, alpha=.05, method='fdr_by')
print('FDR-corrected (BY) p-values: ', pvals_corrected)

reject, pvals_corrected = multicomp(pvals, alpha=.05, method='holm')
print('Holm-corrected p-values: ', pvals_corrected)

assert isinstance(decimals, int), 'decimals must be an int.'
    assert method in ['pearson', 'spearman'], 'Method is not recognized.'
    assert upper in ['pval', 'n'], 'upper must be either `pval` or `n`.'
    mat = self.corr(method=method).round(decimals)
    if upper == 'n':
        mat_upper = self.corr(method=lambda x, y: len(x)).astype(int)
    else:
        if method == 'pearson':
            mat_upper = self.corr(method=lambda x, y: pearsonr(x, y)[1])
        else:
            # Method = 'spearman'
            mat_upper = self.corr(method=lambda x, y: spearmanr(x, y)[1])

        if padjust is not None:
            pvals = mat_upper.values[tif(mat, k=1)]
            mat_upper.values[tif(mat, k=1)] = multicomp(pvals, alpha=0.05,
                                                        method=padjust)[1]

    # Convert r to text
    mat = mat.astype(str)
    np.fill_diagonal(mat.values, '-')  # Inplace modification of the diagonal

    if upper == 'pval':

        def replace_pval(x):
            for key, value in pval_stars.items():
                if x < key:
                    return value
            return ''

        if stars:
            # Replace p-values by stars