fv_to_t3
========

.. py:function:: t3toolbox.backend.fv_conversions.fv_to_t3(index, frame, variations)

   .. code-block:: python

      def fv_to_t3(
              index: typ.Tuple[
                  bool, # If True, use TT coordinate. If False, use Tucker coordinate
                  int, # index of coordinate
              ],
              frame: typ.Union[
                  typ.Tuple[
                      typ.Tuple[NDArray, ...],  # up_tucker_cores
                      typ.Tuple[NDArray, ...],  # down_tucker_cores
                      typ.Tuple[NDArray, ...],  # left_tt_cores
                      typ.Tuple[NDArray, ...],  # right_tucker_cores
                  ], # ragged
                  typ.Tuple[
                      NDArray,  # up_tucker_supercore
                      NDArray,  # down_tucker_supercore
                      NDArray,  # left_tt_supercore
                      NDArray,  # right_tucker_supercore
                  ], # uniform
              ],
              variations: typ.Union[
                  typ.Tuple[
                      typ.Tuple[NDArray, ...],  # tucker_variations
                      typ.Tuple[NDArray, ...],  # tt_variations
                  ], # ragged
                  typ.Tuple[
                      NDArray,  # tucker_variations_supercore
                      NDArray,  # tt_variations_supercore
                  ], # uniform
              ],
      ) -> typ.Union[
          typ.Tuple[
              typ.Tuple[NDArray,...], # tucker_cores
              typ.Tuple[NDArray,...], # tt_cores
          ], # ragged
          typ.Tuple[
              NDArray, # tucker_supercore
              NDArray, # tt_supercore
          ], # uniform
      ]:


   Convert ith frame-variation representation to TuckerTensorTrain.