How to use the neurokit2.signal.signal_formatpeaks._signal_formatpeaks_sanitize function in neurokit2

>>> peaks = np.sort(np.append(peaks, [1350, 11350, 18350]))  # add artifacts
    >>>
    >>> peaks_corrected = nk.signal_fixpeaks(peaks=peaks, interval_min=0.5, interval_max=1.5, method="Neurokit")
    >>> # Plot and shift original peaks to the rightto see the difference.
    >>> fig = nk.events_plot([peaks + 50, peaks_corrected], signal)
    >>> fig #doctest: +SKIP

    References
    ----------
    - Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for heart rate variability time
    series artefact correction using novel beat classification. Journal of medical engineering & technology,
    43(3), 173-181. 10.1080/03091902.2019.1640306

    """
    # Format input
    peaks = _signal_formatpeaks_sanitize(peaks)

    # If method Kubios
    if method.lower() == "kubios":

        return _signal_fixpeaks_kubios(peaks, sampling_rate=sampling_rate, iterative=iterative, show=show)

    # Else method is NeuroKit
    return _signal_fixpeaks_neurokit(
        peaks,
        sampling_rate=sampling_rate,
        interval_min=interval_min,
        interval_max=interval_max,
        relative_interval_min=relative_interval_min,
        relative_interval_max=relative_interval_max,
        robust=robust,
    )

def _rsp_fixpeaks_retrieve(peaks, troughs=None, desired_length=None):
    # Format input.
    original_input = peaks
    peaks, desired_length = _signal_formatpeaks_sanitize(original_input, desired_length, key="Peaks")
    if troughs is None:
        troughs, _ = _signal_formatpeaks_sanitize(original_input, desired_length, key="Troughs")
    return peaks, troughs, desired_length

rsp_signals = rsp_signals[0]

    if isinstance(rsp_signals, pd.DataFrame):
        rsp_cols = [col for col in rsp_signals.columns if "RSP_Phase" in col]
        if len(rsp_cols) != 2:
            edr = ecg_rsp(ecg_period, sampling_rate=sampling_rate)
            rsp_signals, _ = rsp_process(edr, sampling_rate)
            print(
                "NeuroKit warning: _hrv_rsa_formatinput():"
                "RSP signal not found. RSP signal is derived from ECG using"
                "ecg_rsp(). Please provide RSP signal."
            )

    if rpeaks is None:
        try:
            rpeaks = _signal_formatpeaks_sanitize(ecg_signals)
        except NameError:
            raise ValueError("NeuroKit error: _hrv_rsa_formatinput(): Wrong input, we couldn't extract rpeaks indices.")
    else:
        rpeaks = _signal_formatpeaks_sanitize(rpeaks)

    signals = pd.concat([ecg_signals, rsp_signals], axis=1)

    # RSP signal
    if "RSP_Clean" in signals.columns:
        rsp_signal = signals["RSP_Clean"].values
    elif "RSP_Raw" in signals.columns:
        rsp_signal = signals["RSP_Raw"].values
    elif "RSP" in signals.columns:
        rsp_signal = signals["RSP"].values
    else:
        rsp_signal = None

def _eda_fixpeaks_retrieve(peaks, onsets=None, height=None, desired_length=None):
    # Format input.
    original_input = peaks
    peaks, desired_length = _signal_formatpeaks_sanitize(original_input, desired_length, key="Peaks")
    if onsets is None:
        onsets, _ = _signal_formatpeaks_sanitize(original_input, desired_length, key="Onsets")
    if height is None:
        height, _ = _signal_formatpeaks_sanitize(original_input, desired_length, key="Height")
    return peaks, onsets, height, desired_length

def _eda_fixpeaks_retrieve(peaks, onsets=None, height=None, desired_length=None):
    # Format input.
    original_input = peaks
    peaks, desired_length = _signal_formatpeaks_sanitize(original_input, desired_length, key="Peaks")
    if onsets is None:
        onsets, _ = _signal_formatpeaks_sanitize(original_input, desired_length, key="Onsets")
    if height is None:
        height, _ = _signal_formatpeaks_sanitize(original_input, desired_length, key="Height")
    return peaks, onsets, height, desired_length

>>> rpeaks_uncorrected = nk.ecg_findpeaks(ecg)
    >>> artifacts, rpeaks_corrected = nk.ecg_fixpeaks(rpeaks_uncorrected,
    >>>                                               recursive=True,
    >>>                                               show=True)
    >>> rate_corrected = nk.ecg_rate(rpeaks_corrected,
    >>>                              desired_length=len(ecg))
    >>> rate_uncorrected = nk.ecg_rate(rpeaks_uncorrected,
                                       desired_length=len(ecg))
    >>>
    >>> fig, ax = plt.subplots()
    >>> ax.plot(rate_uncorrected, label="heart rate without artifact correction")
    >>> ax.plot(rate_corrected, label="heart rate with artifact correction")
    >>> ax.legend(loc="upper right")
    """
    # Get R-peaks indices from DataFrame or dict.
    rpeaks, desired_length = _signal_formatpeaks_sanitize(rpeaks, desired_length=desired_length)

    rr = np.ediff1d(rpeaks, to_begin=0) / sampling_rate

    # The rate corresponding to the first peak is set to the mean RR.
    rr[0] = np.mean(rr[1:])
    rate = 60 / rr

    if desired_length is not None:
        rate = signal_interpolate(rpeaks, rate, desired_length=desired_length,
                                  method=interpolation_method)

    return rate

def _rsp_fixpeaks_retrieve(peaks, troughs=None, desired_length=None):
    # Format input.
    original_input = peaks
    peaks, desired_length = _signal_formatpeaks_sanitize(original_input, desired_length, key="Peaks")
    if troughs is None:
        troughs, _ = _signal_formatpeaks_sanitize(original_input, desired_length, key="Troughs")
    return peaks, troughs, desired_length

def _eda_fixpeaks_retrieve(peaks, onsets=None, height=None, desired_length=None):
    # Format input.
    original_input = peaks
    peaks, desired_length = _signal_formatpeaks_sanitize(original_input, desired_length, key="Peaks")
    if onsets is None:
        onsets, _ = _signal_formatpeaks_sanitize(original_input, desired_length, key="Onsets")
    if height is None:
        height, _ = _signal_formatpeaks_sanitize(original_input, desired_length, key="Height")
    return peaks, onsets, height, desired_length