def METHOD_NAME(sax1, sax2, breakpoints, original_size):
    """Compute distance between SAX representations as defined in [1]_.
    Parameters
    ----------
    sax1 : array-like, shape=[sz, d], dtype=float64 (Linux and MacOS) or float32 (Windows)
        SAX representation of a time series.
    sax2 : array-like, shape=[sz, d], dtype=float64 (Linux and MacOS) or float32 (Windows)
        SAX representation of another time series.
    breakpoints : array-like, ndim=1, dtype=float64
    original_size : int64 (Linux and MacOS) or int32 (Windows)
    Returns
    -------
    dist_sax : float64
        SAX distance.
    References
    ----------
    .. [1] J. Lin, E. Keogh, L. Wei, et al. Experiencing SAX: a novel
       symbolic representation of time series.
       Data Mining and Knowledge Discovery, 2007. vol. 15(107)
    """
    assert sax1.shape == sax2.shape
    s = 0.0
    sz, d = sax1.shape
    for t in prange(sz):
        for di in range(d):
            if np.abs(sax1[t, di] - sax2[t, di]) > 1:
                max_symbol = max(sax1[t, di], sax2[t, di])
                min_symbol = min(sax1[t, di], sax2[t, di])
                s += (breakpoints[max_symbol - 1] - breakpoints[min_symbol]) ** 2
    dist_sax = np.sqrt(s * float(original_size) / sz)
    return dist_sax