ut3_make_masks
==============

.. py:function:: t3toolbox.backend.ut3_masking.ut3_make_masks(tucker_ranks, tt_ranks, n, r)

   .. code-block:: python

      def ut3_make_masks(
              tucker_ranks:   NDArray,            # HOST int, shape=(d,)   + stack_shape
              tt_ranks:       NDArray,            # HOST int, shape=(d+1,) + stack_shape
              n:              int,                # padded Tucker rank,     n >= max(tucker_ranks)
              r:              int,                # padded TT rank,         r >= max(tt_ranks)
      ) -> typ.Tuple[
          NDArray,  # tucker_edge_mask, HOST bool, static, shape=(d,)   + stack_shape + (n,)
          NDArray,  # tt_edge_mask,     HOST bool, static, shape=(d+1,) + stack_shape + (r,)
      ]:


   Build the prefix RANK edge masks for a uniform Tucker tensor train.

   Slot ``j`` of an edge is marked real iff ``j < rank`` -- the canonical prefix form
   (``docs/uniform_masks_vs_ranks.md``). The rank masks carry the stack, so each stack element may
   declare its own ranks (the variety -- see ``docs/uniform_ranks_and_varieties.md``). The physical
   ``shape`` is a separate static int tuple (not a mask), held alongside these in ``.data`` slot 2.

   Masks are STRUCTURE, so this builder emits **numpy (host)** arrays via ``np`` regardless of whether
   the supercores are jax -- the ``np.*`` here is intentional and jit-required (a jax mask becomes a
   tracer under jit and breaks the layer). See ``docs/contributor/uniform_pytree_composition.md``.