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

k,
                ignore_trivial,
                start + 1,
            )
        )

    results = dask_client.gather(futures)
    for i, start in enumerate(range(0, l, step)):
        stop = min(l, start + step)
        profile[start + 1 : stop], indices[start + 1 : stop, :] = results[i]

    out[:, 0] = profile
    out[:, 1:4] = indices

    threshold = 10e-6
    if core.are_distances_too_small(out[:, 0], threshold=threshold):  # pragma: no cover
        logger.warning(f"A large number of values are smaller than {threshold}.")
        logger.warning("For a self-join, try setting `ignore_trivial = True`.")

    return out

os.remove(profile_fname)
        os.remove(indices_fname)

    for i in range(1, len(device_ids)):
        # Update all matrix profiles and matrix profile indices
        # (global, left, right) and store in profile[0] and indices[0]
        for col in range(profile[0].shape[1]):  # pragma: no cover
            cond = profile[0][:, col] < profile[i][:, col]
            profile[0][:, col] = np.where(cond, profile[0][:, col], profile[i][:, col])
            indices[0][:, col] = np.where(cond, indices[0][:, col], indices[i][:, col])

    out[:, 0] = profile[0][:, 0]
    out[:, 1:4] = indices[0][:, :]

    threshold = 10e-6
    if core.are_distances_too_small(out[:, 0], threshold=threshold):  # pragma: no cover
        logger.warning(f"A large number of values are smaller than {threshold}.")
        logger.warning("For a self-join, try setting `ignore_trivial = True`.")

    return out

M_T,
        Σ_T,
        QT,
        QT_first,
        μ_Q,
        σ_Q,
        k,
        ignore_trivial,
        start + 1,
    )

    out[:, 0] = profile
    out[:, 1:4] = indices

    threshold = 10e-6
    if core.are_distances_too_small(out[:, 0], threshold=threshold):  # pragma: no cover
        logger.warning(f"A large number of values are smaller than {threshold}.")
        logger.warning("For a self-join, try setting `ignore_trivial = True`.")

    return out

if zone_start <= IL < zone_stop:  # pragma: no cover
                IL = -1
        else:
            IL = -1

        if ignore_trivial and i + 1 < D.shape[0]:
            IR = i + 1 + np.argmin(D[i + 1 :])
            if zone_start <= IR < zone_stop:  # pragma: no cover
                IR = -1
        else:
            IR = -1

        out[i] = P, I, IL, IR

    threshold = 10e-6
    if core.are_distances_too_small(out[:, 0], threshold=threshold):  # pragma: no cover
        logger.warning(f"A large number of values are smaller than {threshold}.")
        logger.warning("For a self-join, try setting `ignore_trivial = True`.")

    return out