How to use the stumpy.core.sliding_dot_product function in stumpy

def test_calculate_distance_profile(Q, T):
    m = Q.shape[0]
    left = np.linalg.norm(
        core.z_norm(core.rolling_window(T, m), 1) - core.z_norm(Q), axis=1
    )
    QT = core.sliding_dot_product(Q, T)
    μ_Q, σ_Q = core.compute_mean_std(Q, m)
    M_T, Σ_T = core.compute_mean_std(T, m)
    right = core.calculate_distance_profile(m, QT, μ_Q, σ_Q, M_T, Σ_T)
    npt.assert_almost_equal(left, right)

def test_calculate_squared_distance_profile(Q, T):
    m = Q.shape[0]
    left = np.linalg.norm(
        core.z_norm(core.rolling_window(T, m), 1) - core.z_norm(Q), axis=1
    )
    left = np.square(left)
    M_T, Σ_T = core.compute_mean_std(T, m)
    QT = core.sliding_dot_product(Q, T)
    μ_Q, σ_Q = core.compute_mean_std(Q, m)
    right = _calculate_squared_distance_profile(m, QT, μ_Q[0], σ_Q[0], M_T, Σ_T)
    npt.assert_almost_equal(left, right)

of interest

    m : int
        Window size

    Returns
    -------
    QT : ndarray
        Given `start`, return the corresponding QT

    QT_first : ndarray
         QT for the first window
    """

    QT = core.sliding_dot_product(T_B[start : start + m], T_A)
    QT_first = core.sliding_dot_product(T_A[:m], T_B)

    return QT, QT_first

core.check_nan(T_B)
    core.check_window_size(m)

    if ignore_trivial is False and core.are_arrays_equal(T_A, T_B):  # pragma: no cover
        logger.warning("Arrays T_A, T_B are equal, which implies a self-join.")
        logger.warning("Try setting `ignore_trivial = True`.")

    if ignore_trivial and core.are_arrays_equal(T_A, T_B) is False:  # pragma: no cover
        logger.warning("Arrays T_A, T_B are not equal, which implies an AB-join.")
        logger.warning("Try setting `ignore_trivial = False`.")

    n = T_B.shape[0]
    l = n - m + 1
    excl_zone = int(np.ceil(m / 4))  # See Definition 3 and Figure 3
    M_T, Σ_T = core.compute_mean_std(T_A, m)
    QT = core.sliding_dot_product(T_B[:m], T_A)
    QT_first = core.sliding_dot_product(T_A[:m], T_B)

    μ_Q, σ_Q = core.compute_mean_std(T_B, m)

    out = np.empty((l, 4), dtype=object)

    # Handle first subsequence, add exclusionary zone
    if ignore_trivial:
        P, I = stamp.mass(T_B[:m], T_A, M_T, Σ_T, 0, excl_zone)
        PR, IR = stamp.mass(T_B[:m], T_A, M_T, Σ_T, 0, excl_zone, right=True)
    else:
        P, I = stamp.mass(T_B[:m], T_A, M_T, Σ_T)
        IR = -1  # No left and right matrix profile available
    out[0] = P, I, -1, IR

    k = T_A.shape[0] - m + 1

-------
    QT : ndarray
        Given `start`, return the corresponding multi-dimensional QT

    QT_first : ndarray
        Multi-dimensional QT for the first window
    """

    d = T.shape[0]
    k = T.shape[1] - m + 1

    QT = np.empty((d, k), dtype="float64")
    QT_first = np.empty((d, k), dtype="float64")

    for i in range(d):
        QT[i] = core.sliding_dot_product(T[i, start : start + m], T[i])
        QT_first[i] = core.sliding_dot_product(T[i, :m], T[i])

    return QT, QT_first

core.check_window_size(m)

    if ignore_trivial is False and core.are_arrays_equal(T_A, T_B):  # pragma: no cover
        logger.warning("Arrays T_A, T_B are equal, which implies a self-join.")
        logger.warning("Try setting `ignore_trivial = True`.")

    if ignore_trivial and core.are_arrays_equal(T_A, T_B) is False:  # pragma: no cover
        logger.warning("Arrays T_A, T_B are not equal, which implies an AB-join.")
        logger.warning("Try setting `ignore_trivial = False`.")

    n = T_B.shape[0]
    l = n - m + 1
    excl_zone = int(np.ceil(m / 4))  # See Definition 3 and Figure 3
    M_T, Σ_T = core.compute_mean_std(T_A, m)
    QT = core.sliding_dot_product(T_B[:m], T_A)
    QT_first = core.sliding_dot_product(T_A[:m], T_B)

    μ_Q, σ_Q = core.compute_mean_std(T_B, m)

    out = np.empty((l, 4), dtype=object)

    # Handle first subsequence, add exclusionary zone
    if ignore_trivial:
        P, I = stamp.mass(T_B[:m], T_A, M_T, Σ_T, 0, excl_zone)
        PR, IR = stamp.mass(T_B[:m], T_A, M_T, Σ_T, 0, excl_zone, right=True)
    else:
        P, I = stamp.mass(T_B[:m], T_A, M_T, Σ_T)
        IR = -1  # No left and right matrix profile available
    out[0] = P, I, -1, IR

    k = T_A.shape[0] - m + 1
    for i in range(1, l):

The time series or sequence that contain your query subsequence
        of interest

    m : int
        Window size

    Returns
    -------
    QT : ndarray
        Given `start`, return the corresponding QT

    QT_first : ndarray
         QT for the first window
    """

    QT = core.sliding_dot_product(T_B[start : start + m], T_A)
    QT_first = core.sliding_dot_product(T_A[:m], T_B)

    return QT, QT_first