compute_tau_jets
================

.. py:function:: t3toolbox.backend.sampling_derivatives.compute_tau_jets(var_tt_cores, left_tt_cores, down_tt_cores, xi_jets, dxi_jets, nu_jets, trs)

   .. code-block:: python

      def compute_tau_jets(
              var_tt_cores:   typ.Sequence[NDArray],  # dG. len=d, elm_shape=K+C+(rLi,nUi,rR(i+1))
              left_tt_cores:  typ.Sequence[NDArray],  # P.  len=d, elm_shape=C+(rLi,nUi,rL(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,)
              nu_jets:        typ.Sequence[NDArray],  # frame right jets, len=d, elm_shape=(order+1,)+W+C+(rR(i+1),)
              trs:            NDArray,                # binomial tensor -- ONLY its shape (order) is read here
      ) -> typ.Tuple[NDArray, ...]:                   # tau_jets. len=d, elm_shape=(order+1,)+W+K+C+(rL(i+1),)


   Variation-rightward edge-variable jets tau (standard banded-recurrence form) -- sigma-banded on
   the reversed train. Dense reference: :py:func:`compute_tau_jets_trs` (equal to tolerance).