compute_sigma_jets
==================

.. py:function:: t3toolbox.backend.sampling_derivatives.compute_sigma_jets(var_tt_cores, right_tt_cores, down_tt_cores, xi_jets, dxi_jets, mu_jets, trs)

   .. code-block:: python

      def compute_sigma_jets(
              var_tt_cores:   typ.Sequence[NDArray],  # dG. len=d, elm_shape=K+C+(rLi,nUi,rR(i+1))
              right_tt_cores: typ.Sequence[NDArray],  # Q.  len=d, elm_shape=C+(rRi,nUi,rR(i+1))
              down_tt_cores:  typ.Sequence[NDArray],  # O.  len=d, elm_shape=C+(rLi,nOi,rR(i+1))
              xi_jets:        typ.Sequence[NDArray],  # frame input jets, len=d, elm_shape=(2,)+W+C+(nUi,)
              dxi_jets:       typ.Sequence[NDArray],  # var  input jets, len=d, elm_shape=(2,)+W+K+C+(nOi,)
              mu_jets:        typ.Sequence[NDArray],  # frame left jets,  len=d, elm_shape=(order+1,)+W+C+(rLi,)
              trs:            NDArray,                # binomial tensor -- ONLY its shape (order) is read here
      ) -> typ.Tuple[NDArray, ...]:                   # sigma_jets. len=d, elm_shape=(order+1,)+W+K+C+(rR(i+1),)


   Variation-leftward edge-variable jets sigma (standard banded-recurrence form).

   Dense reference: :py:func:`compute_sigma_jets_trs` (equal to tolerance).

   The three-pushthrough tangent analog of :py:func:`compute_mu_jets`. Both input jets
   (``xi``, ``dxi``) are affine (size 2), so each of the three pushthroughs
   (``sigma Q(xi) + mu dG(xi) + mu O(dxi)``) is a fused two-term recurrence rather than a dense ``trs``
   contraction. The K tangent stack rides on the carried sigma / the variation core / the var input.
   Verified equal to :py:func:`compute_sigma_jets_trs` to 1e-12.