tv_apply_jacobian_derivatives_from_sweep
========================================

.. py:function:: t3toolbox.backend.sampling_derivatives.tv_apply_jacobian_derivatives_from_sweep(variation, ww, pp, frame, sweep, order)

   .. code-block:: python

      def tv_apply_jacobian_derivatives_from_sweep(
              variation:  typ.Tuple[
                  typ.Sequence[NDArray],          # var_tucker_cores dU. len=d, elm_shape=K+C+(nOi,Ni)
                  typ.Sequence[NDArray],          # var_tt_cores     dG. len=d, elm_shape=K+C+(rLi,nUi,rRi)
              ],                                  # = T3Variations.data
              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,)
              frame:       typ.Tuple[
                  typ.Sequence[NDArray], typ.Sequence[NDArray],
                  typ.Sequence[NDArray], typ.Sequence[NDArray],
              ],                                  # = T3Frame.data = (U, O, P, Q)
              sweep:      typ.Tuple[
                  typ.Sequence[NDArray],          # xi_jets
                  typ.Sequence[NDArray],          # mu_jets
              ],                                  # = tv_precompute_apply_frame_sweep_jets(frame, ww, pp, order)
              order:      int,                    # highest derivative order
      ) -> NDArray:                               # apply-derivative jets, shape=(order+1,)+W+K+C


   Variation half of :py:func:`tv_apply_derivatives` from a precomputed frame ``sweep``: the
   variation input jets + the terminal sigma carry, reusing the frame ``(xi, mu)_jets`` (apply needs no
   ``nu`` / ``eta``). The reuse hook for a fitting inner solve (frame fixed across J / J^T).