require_unstacked_for_regularizer
=================================

.. py:function:: t3toolbox.backend.regularization.require_unstacked_for_regularizer(stack_shape, who)

   .. code-block:: python

      def require_unstacked_for_regularizer(
              stack_shape:  typ.Tuple[int, ...],   # C, from geom.stack_shape(x) / frame.stack_shape
              who:          str,                   # the calling operation, for the message
      ) -> None:


   Structural guard: a regularized fit of a STACKED point is not implemented -- raise, do not
   silently mis-weight.

   The data misfit keeps the frame stack ``C`` (``kind.sumsq`` returns one value per stack element)
   while every regularizer scalar collapses it -- ``point_norm_sq`` and ``inner`` sum every axis. So
   ``objective = misfit + rho`` would broadcast the WHOLE-STACK regularization total onto each
   element, inflating the effective ``lambda`` by about ``|C|`` and doing it unevenly (the
   smallest-norm element takes the largest relative penalty). The regularizer *gradient* is per-element
   correct, which is what makes the inconsistency easy to miss.

   Structural (a shape question), so it raises in both safety modes and is jit-safe -- shapes are
   concrete at trace time. Unstacked fits, the overwhelmingly common case, are unaffected.