tv_apply_jacobian_from_sweep
============================

.. py:function:: t3toolbox.backend.apply.tv_apply_jacobian_from_sweep(variation, ww, frame, frame_sweep)

   .. code-block:: python

      def tv_apply_jacobian_from_sweep(
              variation:  typ.Tuple[
                  typ.Sequence[NDArray],          # var_tucker_cores. len=d, elm_shape=K+C+(nOi,Ni)
                  typ.Sequence[NDArray],          # var_tt_cores.     len=d, elm_shape=K+C+(rLi,nUi,rRi)
              ],
              ww:         typ.Sequence[NDArray],  # apply vectors, len=d, elm_shape=W+(Ni,) -- for the variation's dxis
              frame:       typ.Tuple[
                  typ.Sequence[NDArray],          # up_tucker_cores  U. len=d  (unused; for a uniform call signature)
                  typ.Sequence[NDArray],          # down_tt_cores    O. len=d
                  typ.Sequence[NDArray],          # left_tt_cores    P. len=d  (unused)
                  typ.Sequence[NDArray],          # right_tt_cores   Q. len=d
              ],                                  # frame order = T3Frame.data = (up, down, left, right)
              frame_sweep: typ.Tuple[
                  typ.Sequence[NDArray],          # xis
                  typ.Sequence[NDArray],          # mus
              ],                                  # = tv_precompute_apply_frame_sweep(frame, ww)  (lean)
      ) -> NDArray:                               # the scalar apply(v, ww), one per stack element; shape = W + K + C


   Forward all-modes apply of a tangent vector reusing a precomputed frame sweep -- the bare ``𝒥`` with
   the frame edge variables injected. Equivalent to :py:func:`tv_apply`, but it takes the lean
   ``(xis, mus)`` from ``frame_sweep`` instead of recomputing them (the reuse hook for ``fitting.py``).
   Only the variation-dependent ``dxis`` is computed here. No gauge projector ``Π``.

   .. seealso:: :py:obj:`tv_precompute_apply_frame_sweep`, :py:obj:`tv_apply`