tv_entries_transpose_derivatives_from_sweep
===========================================

.. py:function:: t3toolbox.backend.sampling_derivatives.tv_entries_transpose_derivatives_from_sweep(c, index, pp, frame, sweep, order, sum_over_probes = False)

   .. code-block:: python

      def tv_entries_transpose_derivatives_from_sweep(
              c:          NDArray,                # residual jet (scalar), shape=(order+1)+W+K+C
              index:      NDArray,                # int, shape=(d,)+W -- the grid points
              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_entries_frame_sweep_jets(frame, index, pp, order)
              order:      int,                    # highest derivative order
              sum_over_probes: bool = False,      # True: sum the sample stack W (the J^T r back-projection)
      ) -> typ.Tuple[
          typ.Tuple[NDArray, ...],  # dU_tildes
          typ.Tuple[NDArray, ...],  # dG_tildes
      ]:                                          # = T3Variations.data


   Variation gradient of :py:func:`tv_entries_derivatives_transpose` from a precomputed frame
   ``sweep``: the entries analog of :py:func:`tv_apply_transpose_derivatives_from_sweep` (ambient
   ``w_jets`` from the one-hot ``e_{index}`` + ``P``), reusing the frame ``(xi, mu)_jets``.