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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 load_austres(as_series=False):
 """Quarterly residential data. Numbers (in thousands) of Australian residents measured quarterly from March 1971 to March 1994. Parameters as_series : bool, optional (default=False) Whether to return a Pandas series. If False, will return a 1d numpy array. Returns ------- rslt : array-like, shape=(n_samples,) The austres vector. Examples -------- np.array([13067.3, 13130.5, 13198.4, 13254.2, 13303.7, 13353.9, 13409.3, 13459.2, 13504.5, 13552.6, 13614.3, 13669.5, 13722.6, 13772.1, 13832.0, 13862.6, 13893.0, 13926.8, 13968.9, 14004.7, 14033.1, 14066.0, 14110.1, 14155.6, 14192.2, 14231.7, 14281.5, 14330.3, 14359.3, 14396.6, 14430.8, 14478.4, 14515.7, 14554.9, 14602.5, 14646.4, 14695.4, 14746.6, 14807.4, 14874.4, 14923.3, 14988.7, 15054.1, 15121.7, 15184.2, 15239.3, 15288.9, 15346.2, 15393.5, 15439.0, 15483.5, 15531.5, 15579.4, 15628.5, 15677.3, 15736.7, 15788.3, 15839.7, 15900.6, 15961.5, 16018.3, 16076.9, 16139.0, 16203.0, 16263.3, 16327.9, 16398.9, 16478.3, 16538.2, 16621.6, 16697.0, 16777.2, 16833.1, 16891.6, 16956.8, 17026.3, 17085.4, 17106.9, 17169.4, 17239.4, 17292.0, 17354.2, 17414.2, 17447.3, 17482.6, 17526.0, 17568.7, 17627.1, 17661.5]) 0 13067.3 1 13130.5 2 13198.4 3 13254.2 4 13303.7 dtype: float64 Notes ----- This is quarterly data, so *m* should be set to 4 when using in a seasonal context. References .. [1] P. J. Brockwell and R. A. Davis (1996) "Introduction to Time Series and Forecasting." Springer """

    rslt = np.array([13067.3, 13130.5, 13198.4, 13254.2, 13303.7, 13353.9,
                     13409.3, 13459.2, 13504.5, 13552.6, 13614.3, 13669.5,
                     13722.6, 13772.1, 13832.0, 13862.6, 13893.0, 13926.8,
                     13968.9, 14004.7, 14033.1, 14066.0, 14110.1, 14155.6,
                     14192.2, 14231.7, 14281.5, 14330.3, 14359.3, 14396.6,
                     14430.8, 14478.4, 14515.7, 14554.9, 14602.5, 14646.4,
                     14695.4, 14746.6, 14807.4, 14874.4, 14923.3, 14988.7,
                     15054.1, 15121.7, 15184.2, 15239.3, 15288.9, 15346.2,
                     15393.5, 15439.0, 15483.5, 15531.5, 15579.4, 15628.5,
                     15677.3, 15736.7, 15788.3, 15839.7, 15900.6, 15961.5,
                     16018.3, 16076.9, 16139.0, 16203.0, 16263.3, 16327.9,
                     16398.9, 16478.3, 16538.2, 16621.6, 16697.0, 16777.2,
                     16833.1, 16891.6, 16956.8, 17026.3, 17085.4, 17106.9,
                     17169.4, 17239.4, 17292.0, 17354.2, 17414.2, 17447.3,
                     17482.6, 17526.0, 17568.7, 17627.1, 17661.5])

    if as_series:
        return pd.Series(rslt)
    return rslt