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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 decode_cf_datetime(num_dates, units, calendar=None, use_cftime=None):
 """Given an array of numeric dates in netCDF format, convert it into a numpy array of date time objects. For standard (Gregorian) calendars, this function uses vectorized operations, which makes it much faster than cftime.num2date. In such a case, the returned array will be of type np.datetime64. Note that time unit in `units` must not be smaller than microseconds and not larger than days. See also -------- cftime.num2date """

    num_dates = np.asarray(num_dates)
    flat_num_dates = num_dates.ravel()
    if calendar is None:
        calendar = 'standard'

    if use_cftime is None:
        try:
            dates = _decode_datetime_with_pandas(flat_num_dates, units,
                                                 calendar)
        except (OutOfBoundsDatetime, OverflowError):
            dates = _decode_datetime_with_cftime(
                flat_num_dates.astype(np.float), units, calendar)

            if (dates[np.nanargmin(num_dates)].year < 1678 or
               dates[np.nanargmax(num_dates)].year >= 2262):
                if calendar in _STANDARD_CALENDARS:
                    warnings.warn(
                        'Unable to decode time axis into full '
                        'numpy.datetime64 objects, continuing using '
                        'cftime.datetime objects instead, reason: dates out '
                        'of range', SerializationWarning, stacklevel=3)
            else:
                if calendar in _STANDARD_CALENDARS:
                    dates = cftime_to_nptime(dates)
    elif use_cftime:
        dates = _decode_datetime_with_cftime(
            flat_num_dates.astype(np.float), units, calendar)
    else:
        dates = _decode_datetime_with_pandas(flat_num_dates, units, calendar)

    return dates.reshape(num_dates.shape)