def METHOD_NAME(self,
                                    track: np.ndarray,
                                    smooth_tau: int = 10) -> np.ndarray:
    """Gaussian-Smoothed Interpolation.
    This function is proposed in
    "StrongSORT: Make DeepSORT Great Again"
    `StrongSORT<https://arxiv.org/abs/2202.13514>`_.
    Args:
        track (ndarray): With shape (N, 7). Each row denotes
            (frame_id, track_id, x1, y1, x2, y2, score).
        smooth_tau (int, optional): smoothing parameter in GSI.
            Defaults to 10.
    Returns:
        ndarray: The interpolated tracks with shape (N, 7). Each row
            denotes (frame_id, track_id, x1, y1, x2, y2, score)
    """
    len_scale = np.clip(smooth_tau * np.log(smooth_tau**3 / len(track)),
                        smooth_tau**-1, smooth_tau**2)
    gpr = GPR(RBF(len_scale, 'fixed'))
    t = track[:, 0].reshape(-1, 1)
    x1 = track[:, 2].reshape(-1, 1)
    y1 = track[:, 3].reshape(-1, 1)
    x2 = track[:, 4].reshape(-1, 1)
    y2 = track[:, 5].reshape(-1, 1)
    gpr.fit(t, x1)
    x1_gpr = gpr.predict(t)
    gpr.fit(t, y1)
    y1_gpr = gpr.predict(t)
    gpr.fit(t, x2)
    x2_gpr = gpr.predict(t)
    gpr.fit(t, y2)
    y2_gpr = gpr.predict(t)
    gsi_track = [[
        t[i, 0], track[i, 1], x1_gpr[i], y1_gpr[i], x2_gpr[i], y2_gpr[i],
        track[i, 6]
    ] for i in range(len(t))]
    return np.array(gsi_track)