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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 shift_dates(self,h):
 """ Auxiliary function for creating dates for forecasts Parameters h : int How many steps to forecast Returns A transformed date_index object """


        date_index = copy.deepcopy(self.index)
        date_index = date_index[self.max_lag:len(date_index)]

        if self.is_pandas is True:

            if isinstance(date_index, pd.core.indexes.datetimes.DatetimeIndex):

                if pd.infer_freq(date_index) in ['H', 'M', 'S']:

                    for t in range(h):
                        date_index += pd.DateOffset((date_index[len(date_index)-1] - date_index[len(date_index)-2]).seconds)

                else: # Assume higher frequency (configured for days)

                    for t in range(h):
                        date_index += pd.DateOffset((date_index[len(date_index)-1] - date_index[len(date_index)-2]).days)

            elif isinstance(date_index, pd.core.indexes.numeric.Int64Index):

                for i in range(h):
                    new_value = date_index.values[len(date_index.values)-1] + (date_index.values[len(date_index.values)-1] - date_index.values[len(date_index.values)-2])
                    date_index = pd.Int64Index(np.append(date_index.values,new_value))

        else:

            for t in range(h):
                date_index.append(date_index[len(date_index)-1]+1)

        return date_index