How to use the alibi.confidence.TrustScore function in alibi

v_pad = np.pad(v, (0, n_pad), 'constant')
                self.d_abs_ragged.append(v_pad)
            self.d_abs_ragged = np.array(self.d_abs_ragged)

        if self.enc_model:
            enc_data = self.enc.predict(train_data)
            self.class_proto = {}  # type: dict
            self.class_enc = {}  # type: dict
            for i in range(self.classes):
                idx = np.where(preds == i)[0]
                self.class_proto[i] = np.expand_dims(np.mean(enc_data[idx], axis=0), axis=0)
                self.class_enc[i] = enc_data[idx]
        elif self.use_kdtree:
            logger.warning('No encoder specified. Using k-d trees to represent class prototypes.')
            if trustscore_kwargs is not None:
                ts = TrustScore(**trustscore_kwargs)
            else:
                ts = TrustScore()
            if self.is_cat:  # map categorical to numerical data
                train_data = ord_to_num(train_data_ord, self.d_abs)
            ts.fit(train_data, preds, classes=self.classes)
            self.kdtrees = ts.kdtrees
            self.X_by_class = ts.X_kdtree

self.d_abs_ragged = np.array(self.d_abs_ragged)

        if self.enc_model:
            enc_data = self.enc.predict(train_data)
            self.class_proto = {}  # type: dict
            self.class_enc = {}  # type: dict
            for i in range(self.classes):
                idx = np.where(preds == i)[0]
                self.class_proto[i] = np.expand_dims(np.mean(enc_data[idx], axis=0), axis=0)
                self.class_enc[i] = enc_data[idx]
        elif self.use_kdtree:
            logger.warning('No encoder specified. Using k-d trees to represent class prototypes.')
            if trustscore_kwargs is not None:
                ts = TrustScore(**trustscore_kwargs)
            else:
                ts = TrustScore()
            if self.is_cat:  # map categorical to numerical data
                train_data = ord_to_num(train_data_ord, self.d_abs)
            ts.fit(train_data, preds, classes=self.classes)
            self.kdtrees = ts.kdtrees
            self.X_by_class = ts.X_kdtree