How to use the causalml.metrics.classification_metrics function in causalml

_te = (p[group] * dhat_cs[group] + (1 - p[group]) * dhat_ts[group]).reshape(-1, 1)
            te[:, i] = np.ravel(_te)

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)
                treatment_filt = treatment[mask]
                X_filt = X[mask]
                y_filt = y[mask]
                w = (treatment_filt == group).astype(int)

                yhat = np.zeros_like(y_filt, dtype=float)
                yhat[w == 0] = self.models_mu_c[group].predict_proba(X_filt[w == 0])[:, 1]
                yhat[w == 1] = self.models_mu_t[group].predict_proba(X_filt[w == 1])[:, 1]

                logger.info('Error metrics for group {}'.format(group))
                classification_metrics(y_filt, yhat, w)

        if not return_components:
            return te
        else:
            return te, dhat_cs, dhat_ts

# set the treatment column to one (the treatment group)
            X_new[:, 0] = 1
            yhat_ts[group] = model.predict_proba(X_new)[:, 1]

            if y is not None and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)
                treatment_filt = treatment[mask]
                w = (treatment_filt == group).astype(int)
                y_filt = y[mask]

                yhat = np.zeros_like(y_filt, dtype=float)
                yhat[w == 0] = yhat_cs[group][mask][w == 0]
                yhat[w == 1] = yhat_ts[group][mask][w == 1]

                logger.info('Error metrics for group {}'.format(group))
                classification_metrics(y_filt, yhat, w)

        te = np.zeros((X.shape[0], self.t_groups.shape[0]))
        for i, group in enumerate(self.t_groups):
            te[:, i] = yhat_ts[group] - yhat_cs[group]

        return te

model_t = self.models_t[group]
            yhat_cs[group] = model_c.predict_proba(X)[:, 1]
            yhat_ts[group] = model_t.predict_proba(X)[:, 1]

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)
                treatment_filt = treatment[mask]
                y_filt = y[mask]
                w = (treatment_filt == group).astype(int)

                yhat = np.zeros_like(y_filt, dtype=float)
                yhat[w == 0] = yhat_cs[group][mask][w == 0]
                yhat[w == 1] = yhat_ts[group][mask][w == 1]

                logger.info('Error metrics for group {}'.format(group))
                classification_metrics(y_filt, yhat, w)

        te = np.zeros((X.shape[0], self.t_groups.shape[0]))
        for i, group in enumerate(self.t_groups):
            te[:, i] = yhat_ts[group] - yhat_cs[group]

        if not return_components:
            return te
        else:
            return te, yhat_cs, yhat_ts