tv_precompute_apply_frame_sweep_jets
====================================

.. py:function:: t3toolbox.backend.sampling_derivatives.tv_precompute_apply_frame_sweep_jets(frame, ww, pp, order)

   .. code-block:: python

      def tv_precompute_apply_frame_sweep_jets(
              frame:   typ.Tuple[
                  typ.Sequence[NDArray], typ.Sequence[NDArray],
                  typ.Sequence[NDArray], typ.Sequence[NDArray],
              ],                                      # = T3Frame.data = (U, O, P, Q)
              ww:     typ.Sequence[NDArray],          # probe vectors X,        len=d, elm_shape=W+(Ni,)
              pp:     typ.Sequence[NDArray],          # perturbation vectors P, len=d, elm_shape=W+(Ni,)
              order:  int,                            # highest derivative order
      ) -> typ.Tuple[
          typ.Sequence[NDArray],  # xi_jets. len=d, elm_shape=(2,)+W+C+(nUi,)
          typ.Sequence[NDArray],  # mu_jets. len=d, elm_shape=(order+1,)+W+C+(rLi,)
      ]:                                              # lean frame sweep -- (xi, mu) only


   The **apply**-derivative frame sweep (lean): the up-index jet ``build_input_jets(U_i x_i, U_i p_i)``
   + the left ``mu`` jets. The all-modes apply forward and its adjoint-state transpose use ONLY
   ``(xi, mu)`` (no ``nu`` / ``eta``), so this skips the right + central sweeps -- a per-step saving in
   a stochastic solve. Reused across the forward / transpose of an inner solve (the
   :py:class:`SamplingKind.precompute` hook).