build_input_jets
================

.. py:function:: t3toolbox.backend.sampling_derivatives.build_input_jets(xis, dxis)

   .. code-block:: python

      def build_input_jets(
              xis:    typ.Sequence[NDArray],  # frame projected probes,        len=d, elm_shape=W+C+(nUi,)
              dxis:   typ.Sequence[NDArray],  # projected perturbation dirs,  len=d, elm_shape=W+C+(nUi,)
      ) -> typ.Tuple[NDArray, ...]:           # xi_jets. len=d, elm_shape=(2,)+W+C+(nUi,): order 0 = xi, 1 = dxi


   Input jets: stack each (value, direction) pair on a leading order axis.

   Since ``x + s p`` is affine in ``s``, an input vector's jet is just ``(x, p, 0, ...)`` -- value at
   order 0, direction at order 1, zero above. Stored at size 2 (orders 0,1); the pushthrough slices
   the binomial tensor to ``s in {0,1}`` accordingly.