compute_orthogonal_representation_ranks
=======================================

.. py:function:: t3toolbox.backend.ranks.compute_orthogonal_representation_ranks(shape, tucker_ranks, tt_ranks, use_jax = False)

   .. code-block:: python

      def compute_orthogonal_representation_ranks(
              shape: typ.Sequence[int], # (N0, ..., N(d-1))
              tucker_ranks: typ.Union[
                  typ.Sequence[int], # (n0,...,n(d-1))
                  NDArray, # dtype=int, shape=(d,) + stack_shape
              ],
              tt_ranks: typ.Union[
                  typ.Sequence[int], # (r0,...,rd)
                  NDArray, # dtype=int, shape=(d+1,) + stack_shape
              ],
              use_jax: bool = False,
      ) -> typ.Tuple[
          typ.Union[
              typ.Tuple[int,...],  # (nU0,...,nU(d-1))
              NDArray,  # dtype=int, shape=(d,) + stack_shape
          ], # up_tucker_ranks
          typ.Union[
              typ.Tuple[int, ...],  # (nD0',...,nD(d-1)')
              NDArray,  # dtype=int, shape=(d,) + stack_shape
          ],  # down_tucker_ranks
          typ.Union[
              typ.Tuple[int,...],  # (rL0',...,rLd')
              NDArray,  # dtype=int, shape=(d+1,) + stack_shape
          ], # left_tt_ranks
          typ.Union[
              typ.Tuple[int, ...],  # (rR0',...,rRd')
              NDArray,  # dtype=int, shape=(d+1,) + stack_shape
          ],  # right_tt_ranks
      ]:


   Find ranks that would be produced by sweeping orthogonalization, except without actually doing it.