ufv_apply_frame_masks
=====================

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

   .. code-block:: python

      def ufv_apply_frame_masks(
              data: typ.Tuple[
                  NDArray,             # up_tucker_supercore,  (d,)+stack_shape+(nU, N)
                  NDArray,             # down_tt_supercore,    (d,)+stack_shape+(rL, nD, rR)
                  NDArray,             # left_tt_supercore,    (d,)+stack_shape+(rL, nU, rL)
                  NDArray,             # right_tt_supercore,   (d,)+stack_shape+(rR, nU, rR)
                  typ.Sequence[int],   # shape = (N0,...,N(d-1)), static int tuple
                  typ.Tuple[
                      NDArray,  # up_mask,          dtype=bool, (d,)  +stack_shape+(nU,)
                      NDArray,  # down_mask,        dtype=bool, (d,)  +stack_shape+(nD,)
                      NDArray,  # frame_left_mask,  dtype=bool, (d+1,)+stack_shape+(rL,)
                      NDArray,  # frame_right_mask, dtype=bool, (d+1,)+stack_shape+(rR,)
                  ],
              ],
      ) -> typ.Tuple[
          NDArray,  # masked_up_tucker_supercore
          NDArray,  # masked_down_tt_supercore
          NDArray,  # masked_left_tt_supercore
          NDArray,  # masked_right_tt_supercore
      ]:


   Zero the padded ("garbage") regions of the frame supercores via the edge masks. The physical
   ``shape_mask`` is reconstructed on the host from the static ``shape`` ints (``np``, never ``jnp`` --
   a traced mask breaks the layer; see ``docs/contributor/uniform_pytree_composition.md``).