tv_entries_transpose_from_sweep
===============================

.. py:function:: t3toolbox.backend.entries.tv_entries_transpose_from_sweep(c, index, frame, frame_sweep, sum_over_probes = False)

   .. code-block:: python

      def tv_entries_transpose_from_sweep(
              c:          NDArray,                # residual, shape = W + C (or W + K + C)
              index:      NDArray,                # int, shape=(d,)+W -- the indices c weights (-> one-hot vectors)
              frame:       typ.Tuple[
                  typ.Sequence[NDArray], typ.Sequence[NDArray],
                  typ.Sequence[NDArray], typ.Sequence[NDArray],
              ],                                  # = T3Frame.data = (U, O, P, Q); uses U (one-hot), O, Q
              frame_sweep: typ.Tuple[
                  typ.Sequence[NDArray], typ.Sequence[NDArray],
              ],                                  # = tv_precompute_entries_frame_sweep(frame, index)  (lean: no nu/eta)
              sum_over_probes: bool = False,
      ) -> typ.Tuple[typ.Sequence[NDArray], typ.Sequence[NDArray]]:  # (dU_tildes, dG_tildes) = T3Variations.data


   Transpose of the all-modes entries reusing a precomputed frame sweep -- the bare ``𝒥ᵀ`` (entries),
   by the **adjoint-state** method (see :py:func:`_apply_transpose_adjoint`). Takes the **lean**
   ``(xis, mus)`` sweep (the reuse hook for ``fitting.py``). Identical to
   :py:func:`tv_apply_transpose_from_sweep` with the one-hot vectors ``e_{index}`` as the apply vectors.
   Full ``W + K + C``; no gauge projector ``Π``.