tv_to_dense
===========

.. py:function:: t3toolbox.backend.tv_operations.tv_to_dense(frame, variations, include_shift = False)

   .. code-block:: python

      def tv_to_dense(
              frame:      typ.Tuple[
                  typ.Sequence[NDArray],  # up_tucker_cores
                  typ.Sequence[NDArray],  # down_tt_cores
                  typ.Sequence[NDArray],  # left_tt_cores
                  typ.Sequence[NDArray],  # right_tt_cores
              ],
              variations: typ.Tuple[
                  typ.Sequence[NDArray],  # tucker_variations
                  typ.Sequence[NDArray],  # tt_variations
              ],
              include_shift:  bool = False,  # False: tangent vector v. True: base point + v.
      ) -> NDArray:  # dense tangent vector. shape=stack_shape+(N0,...,N(d-1))


   Form the dense tensor represented by a frame-variations tangent vector.

   The tangent vector is the sum of the 2d single-core-replacement terms (one per Tucker hole
   and one per TT hole). This is stack-aware: leading stack axes ride along through ``fv_to_t3``
   and ``to_dense``.