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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 extend_back(self, missing_dts):
 """ Resizes the buffer to hold a new window with a new cap_multiple. If cap_multiple is None, then the old cap_multiple is used. """

        delta = len(missing_dts)

        if not delta:
            raise ValueError(
                'missing_dts must be a non-empty index',
            )

        self._window += delta

        self._pos += delta

        self.date_buf = self.date_buf.copy()
        self.date_buf.resize(self.cap)
        self.date_buf = np.roll(self.date_buf, delta)

        old_vals = self.buffer.values
        shape = old_vals.shape
        nan_arr = np.empty((shape[0], delta, shape[2]))
        nan_arr.fill(np.nan)

        new_vals = np.column_stack(
            (nan_arr,
             old_vals,
             np.empty((shape[0], delta * (self.cap_multiple - 1), shape[2]))),
        )

        self.buffer = pd.Panel(
            data=new_vals,
            items=self.items,
            minor_axis=self.minor_axis,
            major_axis=np.arange(self.cap),
            dtype=self.dtype,
        )

        # Fill the delta with the dates we calculated.
        where = slice(self._start_index, self._start_index + delta)
        self.date_buf[where] = missing_dts