ut3_corewise_frame
==================

.. py:function:: t3toolbox.backend.ufv_conversions.ut3_corewise_frame(data)

   .. code-block:: python

      def ut3_corewise_frame(
              data: typ.Tuple[
                  NDArray,                          # tucker_supercore
                  NDArray,                          # tt_supercore
                  typ.Tuple[int, ...],              # shape
                  typ.Tuple[NDArray, NDArray],      # (tucker_edge_mask, tt_edge_mask)
              ],
      ) -> typ.Tuple:                               # uniform frame .data = (U, G, G, G, shape, masks)


   The uniform **corewise** frame: the twin of :py:func:`t3_corewise_frame`, carrying the
   ``(U, G, G, G)`` supercores and the matching ``(tucker, tucker, tt, tt)`` frame mask set.

   The mask half is the part worth naming -- the corewise frame's four mask slots are the plain
   tensor's two, doubled, and getting that pairing wrong produces a frame that validates but gauges
   the wrong slots.