t3_from_vector
==============

.. py:function:: t3toolbox.backend.t3_conversions.t3_from_vector(x_flat, shape, tucker_ranks, tt_ranks, stack_shape = ())

   .. code-block:: python

      def t3_from_vector(
              x_flat:       NDArray,                 # shape=(x_size,), all core entries flattened
              shape:        typ.Sequence[int],       # len=d, the tensor mode sizes (N0,...,N(d-1))
              tucker_ranks: typ.Sequence[int],       # len=d
              tt_ranks:     typ.Sequence[int],       # len=d+1
              stack_shape:  typ.Sequence[int] = (),  # leading batch axes
      ) -> typ.Tuple[
          typ.Sequence[NDArray],  # tucker_cores. len=d, elm_shape=stack_shape+(ni,Ni)
          typ.Sequence[NDArray],  # tt_cores.     len=d, elm_shape=stack_shape+(rLi,ni,rR(i+1))
      ]:


   Constructs a T3 from a 1D vector containing the core entries