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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 flag_time_err(phase_err, time_thresh=0.02):
 """ Find large time errors in list. Scan through a list of tuples of time stamps and phase errors and return a list of time stamps with timing errors above a threshold. .. note:: This becomes important for networks cross-correlations, where if timing information is uncertain at one site, the relative arrival time (lag) will be incorrect, which will degrade the cross-correlation sum. :type phase_err: list :param phase_err: List of Tuple of float, datetime.datetime :type time_thresh: float :param time_thresh: Threshold to declare a timing error for :returns: List of :class:`datetime.datetime` when timing is questionable. """

    time_err = []
    for stamp in phase_err:
        if abs(stamp[1]) > time_thresh:
            time_err.append(stamp[0])
    return time_err