def METHOD_NAME(self):
    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
    )