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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _median_window(window, window_start, multiplier, starttime, sampling_rate, debug=0):
 """ Internal function to aid parallel processing :type window: numpy.ndarry :param window: Data to look for peaks in. :type window_start: int :param window_start: Index of window start point in larger array, used \ for peak indexing. :type multiplier: float :param multiplier: Multiple of MAD to use as threshold :type starttime: obspy.core.utcdatetime.UTCDateTime :param starttime: Starttime of window, used in debug plotting. :type sampling_rate: float :param sampling_rate in Hz, used for debug plotting :type debug: int :param debug: debug level, if want plots, >= 4. :returns: peaks :rtype: list """

    MAD = np.median(np.abs(window))
    thresh = multiplier * MAD
    if debug >= 2:
        print('Threshold for window is: ' + str(thresh) +
              '\nMedian is: ' + str(MAD) +
              '\nMax is: ' + str(np.max(window)))
    peaks = find_peaks2_short(arr=window,
                              thresh=thresh, trig_int=5, debug=0)
    if debug >= 4 and peaks:
        peaks_plot(window, starttime, sampling_rate,
                   save=False, peaks=peaks)
    if peaks:
        peaks = [(peak[0], peak[1] + window_start) for peak in peaks]
    else:
        peaks = []
    return peaks