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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 _get_daily_window_data(self, assets, field, days_in_window, extra_slot=True):
 """ Internal method that gets a window of adjusted daily data for a sid and specified date range. Used to support the history API method for daily bars. Parameters asset : Asset The asset whose data is desired. start_dt: pandas.Timestamp The start of the desired window of data. bar_count: int The number of days of data to return. field: string The specific field to return. "open", "high", "close_price", etc. extra_slot: boolean Whether to allocate an extra slot in the returned numpy array. This extra slot will hold the data for the last partial day. It's much better to create it here than to create a copy of the array later just to add a slot. Returns ------- A numpy array with requested values. Any missing slots filled with nan. """

        bar_count = len(days_in_window)
        # create an np.array of size bar_count
        dtype = float64 if field != 'sid' else int64
        if extra_slot:
            return_array = np.zeros((bar_count + 1, len(assets)), dtype=dtype)
        else:
            return_array = np.zeros((bar_count, len(assets)), dtype=dtype)

        if field != "volume":
            # volumes default to 0, so we don't need to put NaNs in the array
            return_array[:] = np.NAN

        if bar_count != 0:
            data = self._history_loader.history(assets,
                                                days_in_window,
                                                field,
                                                extra_slot)
            if extra_slot:
                return_array[:len(return_array) - 1, :] = data
            else:
                return_array[:len(data)] = data
        return return_array