def METHOD_NAME(self):
    for i in range(len(self.dp_ckpts)):
        self.dp_ckpt = CKPT_TRAINERS[i]
        self.dp_frz = FRZ_TRAINERS[i]
        self.valid_data = VALID_DATAS[i]
        self.stop_batch = STOP_BATCHS[i]
        valid_batch = self.valid_data.get_batch()
        natoms = valid_batch["natoms_vec"]
        tf.reset_default_graph()
        self.dp_ckpt.build(self.valid_data, self.stop_batch)
        self.dp_ckpt._init_session()
        feed_dict_ckpt = self.dp_ckpt.get_feed_dict(valid_batch, is_training=False)
        ckpt_rmse_ckpt = self.dp_ckpt.loss.eval(
            self.dp_ckpt.sess, feed_dict_ckpt, natoms
        )
        tf.reset_default_graph()
        self.dp_frz.build(self.valid_data, self.stop_batch)
        self.dp_frz._init_session()
        feed_dict_frz = self.dp_frz.get_feed_dict(valid_batch, is_training=False)
        ckpt_rmse_frz = self.dp_frz.loss.eval(
            self.dp_frz.sess, feed_dict_frz, natoms
        )
        tf.reset_default_graph()
        # check values
        np.testing.assert_almost_equal(
            ckpt_rmse_ckpt["rmse_e"], ckpt_rmse_frz["rmse_e"], default_places
        )
        np.testing.assert_almost_equal(
            ckpt_rmse_ckpt["rmse_f"], ckpt_rmse_frz["rmse_f"], default_places
        )
        np.testing.assert_almost_equal(
            ckpt_rmse_ckpt["rmse_v"], ckpt_rmse_frz["rmse_v"], default_places
        )