ut3_stack
=========

.. py:function:: t3toolbox.backend.ut3_operations.ut3_stack(xx)

   .. code-block:: python

      def ut3_stack(
              xx,  # nested tuple (shaped like stack_shape) of unstacked uniform-T3 .data leaves
      ) -> typ.Tuple[
          NDArray,                          # tucker_supercore
          NDArray,                          # tt_supercore
          typ.Tuple[int, ...],              # shape
          typ.Tuple[NDArray, NDArray],      # (tucker_edge_mask, tt_edge_mask)
      ]:


   Stack an array-like tree of uniform Tucker tensor trains into one.

   Inverse of :py:func:`ut3_unstack`: stacks the supercores and rank masks onto axes
   ``1 .. num_levels`` (after the mode index), keeping the shared ``shape`` unstacked. Only the four
   ndarray components go through ``stacking.stack``; ``shape`` (a ``Sequence`` the walker would recurse
   into) is read once from the first leaf and re-attached.