compute_dxi_tilde_jets
======================

.. py:function:: t3toolbox.backend.sampling_derivatives.compute_dxi_tilde_jets(down_tt_cores, mu_jets, nu_jets, sigma_tildes, tau_tildes, trs)

   .. code-block:: python

      def compute_dxi_tilde_jets(
              down_tt_cores:  typ.Sequence[NDArray],  # O.  len=d, elm_shape=C+(rLi,nOi,rR(i+1))
              mu_jets:        typ.Sequence[NDArray],  # len=d, elm_shape=(order+1,)+W+C+(rLi,)
              nu_jets:        typ.Sequence[NDArray],  # len=d, elm_shape=(order+1,)+W+C+(rR(i+1),)
              sigma_tildes:   typ.Sequence[NDArray],  # len=d, elm_shape=(order+1,)+W+K+C+(rR(i+1),)
              tau_tildes:     typ.Sequence[NDArray],  # len=d, elm_shape=(order+1,)+W+K+C+(rL(i+1),)
              trs:            NDArray,                # binomial tensor, shape=(order+1,order+1,order+1)
      ) -> typ.Tuple[NDArray, ...]:                   # dxi_tildes. len=d, elm_shape=(order+1,)+W+K+C+(nOi,)


   Adjoint-var-down edge-variable jets (jet-ified probing.compute_dxi_tilde): two adjoint-hooked
   combines giving delta-xi-tilde on the mode (output at the order-<=1 leg u).