ufv_apply_variations_masks
==========================

.. py:function:: t3toolbox.backend.ufv_masking.ufv_apply_variations_masks(data)

   .. code-block:: python

      def ufv_apply_variations_masks(
              data: typ.Tuple[
                  NDArray,             # tucker_variations_supercore, (d,)+stack_shape+(nD, N)
                  NDArray,             # tt_variations_supercore,     (d,)+stack_shape+(rL, nU, rR)
                  typ.Sequence[int],   # shape = (N0,...,N(d-1)), static int tuple
                  typ.Tuple[
                      NDArray,  # variations_up_mask,    dtype=bool, (d,)+stack_shape+(nU,)
                      NDArray,  # variations_down_mask,  dtype=bool, (d,)+stack_shape+(nD,)
                      NDArray,  # variations_left_mask,  dtype=bool, (d,)+stack_shape+(rL,)
                      NDArray,  # variations_right_mask, dtype=bool, (d,)+stack_shape+(rR,)
                  ],
              ],
      ) -> typ.Tuple[
          NDArray,  # masked_tucker_variations_supercore
          NDArray,  # masked_tt_variations_supercore
      ]:


   Zero the padded ("garbage") regions of the variation supercores via the edge masks. ``shape_mask``
   is reconstructed on the host from the static ``shape`` ints (``np``, never ``jnp``).