t3_ones
=======

.. py:function:: t3toolbox.backend.t3_constructors.t3_ones(shape, stack_shape = (), use_jax = False)

   .. code-block:: python

      def t3_ones(
              shape:        typ.Tuple[int, ...],       # len=d, the tensor mode sizes (N0,...,N(d-1))
              stack_shape:  typ.Tuple[int, ...] = (),  # leading batch axes

              use_jax:      bool = False,  # constructor: no array inputs, so the flag chooses the output type
      ) -> typ.Tuple[
          typ.Sequence[NDArray],  # tucker_cores. len=d, rank-1, elm_shape=stack_shape+(1,Ni)
          typ.Sequence[NDArray],  # tt_cores.     len=d, rank-1, elm_shape=stack_shape+(1,1,1)
      ]:


   Construct the rank-1 Tucker tensor train representing a tensor full of ones.