ufv_stack_axes
==============

.. py:function:: t3toolbox.backend.ufv_operations.ufv_stack_axes(xx, n_supercores, axes_start)

   .. code-block:: python

      def ufv_stack_axes(
              xx,                       # nested tuple (shaped like the stacked sub-run) of bv .data leaves
              n_supercores: int,        # 4 (frame) or 2 (variations)
              axes_start:   int,        # array axis the OUTERMOST new stack level lands on (others follow contiguously)
      ) -> typ.Tuple:                   # one stacked bv .data tuple (*supercores, shape, masks)


   Stack an array-like tree of bv ``.data`` leaves onto a CONTIGUOUS run of array axes starting at
   ``axes_start``. Inverse of :py:func:`ufv_unstack_axes`.

   Generalizes :py:func:`ufv_stack` (which stacks onto axes ``1 ..``) so the tangent layer can slot a new
   stack run at the right place -- e.g. the frame stack ``C`` *after* an existing tangent stack ``K`` (at
   ``axes_start = 1 + |K|``), keeping ``C`` inner. ``shape`` is read once (shared) and supercores/masks are
   stacked in SEPARATE :py:func:`stacking.stack` calls so the host-numpy masks are not promoted to jax.