human
Complete the below
def _datetime_to_stata_elapsed_vec(dates, fmt):

    ''''Convert from datetime to SIF. http://www.stata.com/help.cgi?datetime
    Parameters
    dates : Series
    Series or array containing datetime.datetime or datetime64[ns] to
    convert to the Stata Internal Format given by fmt
    fmt : str
    The format to convert to. Can be, tc, td, tw, tm, tq, th, ty'
    '''
 What is a the Python code you requested?
assistant
#Here is the Python code you requested.
    index = dates.index
    NS_PER_DAY = ((((24 * 3600) * 1000) * 1000) * 1000)
    US_PER_DAY = (NS_PER_DAY / 1000)
    def parse_dates_safe(dates, delta=False, year=False, days=False):
        d = {}
        if is_datetime64_dtype(dates.values):
            if delta:
                delta = (dates - stata_epoch)
                d['delta'] = (delta.values.astype(np.int64) // 1000)
            if (days or year):
                dates = DatetimeIndex(dates)
                (d['year'], d['month']) = (dates.year, dates.month)
            if days:
                days = (dates.astype(np.int64) - to_datetime(d['year'], format='%Y').astype(np.int64))
                d['days'] = (days // NS_PER_DAY)
        elif (infer_dtype(dates) == 'datetime'):
            if delta:
                delta = (dates.values - stata_epoch)
                f = (lambda x: (((US_PER_DAY * x.days) + (1000000 * x.seconds)) + x.microseconds))
                v = np.vectorize(f)
                d['delta'] = v(delta)
            if year:
                year_month = dates.apply((lambda x: ((100 * x.year) + x.month)))
                d['year'] = (year_month.values // 100)
                d['month'] = (year_month.values - (d['year'] * 100))
            if days:
                f = (lambda x: (x - datetime.datetime(x.year, 1, 1)).days)
                v = np.vectorize(f)
                d['days'] = v(dates)
        else:
            raise ValueError('Columns    containing    dates    must    contain    either    datetime64,    datetime.datetime    or    null    values.')
        return DataFrame(d, index=index)
    bad_loc = isna(dates)
    index = dates.index
    if bad_loc.any():
        dates = Series(dates)
        if is_datetime64_dtype(dates):
            dates[bad_loc] = to_datetime(stata_epoch)
        else:
            dates[bad_loc] = stata_epoch
    if (fmt in ['%tc', 'tc']):
        d = parse_dates_safe(dates, delta=True)
        conv_dates = (d.delta / 1000)
    elif (fmt in ['%tC', 'tC']):
        from warnings import warn
       ...