t3_tucker_factors_shared
========================

.. py:function:: t3toolbox.backend.sharing.t3_tucker_factors_shared(x, sharing, rtol = 1e-09)

   .. code-block:: python

      def t3_tucker_factors_shared(
              x:          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+(ri, ni, r(i+1))
              ],
              sharing:    typ.Sequence,   # len=d, static; one hashable group label per mode
              rtol:       float = 1e-9,   # relative tolerance on the factor deviation
      ) -> NDArray:  # bool array, shape = stack_shape (scalar/0-d when unstacked)


   True (per stack element) where the Tucker factors are tied within every sharing group.

   The boolean form of :py:func:`t3_sharing_residual` (``residual <= rtol``) -- a
   non-enforcing checker with an explicit tolerance (the backend is check-free; frontend
   safe-mode sites pair the residual with ``safety.effective_rtol`` instead). Reduce with
   ``.all()`` for a single verdict on a stacked ``x``.

   .. rubric:: Examples

   >>> import numpy as np
   >>> import t3toolbox.tucker_tensor_train as t3
   >>> import t3toolbox.backend.sharing as sharing
   >>> np.random.seed(0)
   >>> x = t3.TuckerTensorTrain.randn((6, 6, 5), (3, 3, 2), (1, 2, 2, 1))
   >>> tk, tt = x.data
   >>> print(bool(sharing.t3_tucker_factors_shared(((tk[0], tk[0], tk[2]), tt), (0, 0, 1))))
   True
   >>> print(bool(sharing.t3_tucker_factors_shared(x.data, (0, 0, 1))))
   False