TuckerTensorTrain.allclose
==========================

.. py:method:: t3toolbox.tucker_tensor_train.TuckerTensorTrain.allclose(other, rtol = None, atol = None)

   .. code-block:: python

      def allclose(
              self,
              other: 'TuckerTensorTrain',

              rtol:  typ.Optional[float] = None,  # None: the ambient jax-aware default (safety.comparison_rtol)
              atol:  typ.Optional[float] = None,  # None: 0.0
      ) -> NDArray:  # bool, shape=stack_shape (0-d unstacked); reduce with .all()


   True where the REPRESENTED tensors are numerically equal, per stack element:
   ``||self - other|| <= atol + rtol * max(||self||, ||other||)``.

   The mathematical (tensor-level) equality check. The difference is formed as a T3 (ranks add)
   and its norm taken through orthogonalization -- numerically stable exactly when
   ``self ~= other``, the optimization-residual case. Structural mismatches (shape / d / stack)
   raise, as for subtraction. The representation-level (bitwise) check is
   :py:meth:`corewise_equal`; ``==`` is intentionally not defined -- say which you mean.