tv_precompute_entries_frame_sweep
=================================

.. py:function:: t3toolbox.backend.entries.tv_precompute_entries_frame_sweep(frame, index)

   .. code-block:: python

      def tv_precompute_entries_frame_sweep(
              frame:   typ.Tuple[
                  typ.Sequence[NDArray],          # up_tucker_cores  U. len=d
                  typ.Sequence[NDArray],          # down_tt_cores    O. len=d
                  typ.Sequence[NDArray],          # left_tt_cores    P. len=d
                  typ.Sequence[NDArray],          # right_tt_cores   Q. len=d
              ],                                  # frame order = T3Frame.data = (up, down, left, right)
              index:  NDArray,                    # int, shape=(d,)+W -- the grid points
      ) -> typ.Tuple[
          typ.Sequence[NDArray],  # xis. len=d, elm_shape=W+C+(nUi,) -- the FIBER-SLICED seed (not contracted)
          typ.Sequence[NDArray],  # mus. len=d, elm_shape=W+C+(rLi,)
      ]:                                          # lean frame sweep -- (xis, mus) only (entries seed)


   The all-modes **entries** frame sweep (lean): identical to :py:func:`tv_precompute_apply_frame_sweep`
   but the ``xi-hat`` seed comes from slicing the Tucker-core fibers at ``index`` (``_entry_xis``)
   instead of contracting with probe vectors. Like apply, entries uses the adjoint-state transpose, so
   only ``(xis, mus)`` are needed (no ``nu``/``eta``). Reused by the entries forward/transpose (the
   reuse hook for ``fitting.py``).

   .. seealso:: :py:obj:`tv_precompute_apply_frame_sweep`, :py:obj:`tv_entries_jacobian_from_sweep`, :py:obj:`tv_entries_transpose_from_sweep`