pack_vectors
============

.. py:function:: t3toolbox.backend.ut3_operations.pack_vectors(unpacked_vectors, N = None)

   .. code-block:: python

      def pack_vectors(
              unpacked_vectors: typ.Sequence[NDArray],  # len=d, ith elm_shape=stack_shape+(Ni,)
              N: int = None,                            # padded length (default max(Ni))
      ) -> NDArray:                                     # packed, shape=(d,)+stack_shape+(N,)


   Zero-pad and stack a sequence of (ragged-length) vectors into one supercore-shaped tensor.

   The pad fill is zeros, and must stay FINITE: masking works by multiplication, and
   ``0 * nan = nan`` -- a ``nan``/``inf`` fill would poison masked reductions downstream
   (``docs/uniform_equivalence_contract.md``). Shape information always travels alongside the
   packed array; the fill is never used to infer shape.