apply_model
===========

.. py:function:: t3toolbox.fitting.apply_model(geometry, x, ww, residual, regularizer = None)

   .. code-block:: python

      def apply_model(
              geometry,                            # MANIFOLD / COREWISE (or the UNIFORM_* twin for a uniform x)
              x:          typ.Union[t3.TuckerTensorTrain, ut3.UniformTuckerTensorTrain],   # the current point
              ww:         typ.Sequence[NDArray],   # sample vectors, len=d, elm_shape=W+(Ni,)
              residual:   NDArray,                 # r = apply(x) − y, shape W+C
              regularizer: typ.Any = None,         # optional regularizer, e.g. optimizers.IdentityRegularizer(λ)
      ) -> typ.Union[GaussNewtonModel, UniformGaussNewtonModel]:


   The Gauss-Newton model of an all-modes ``apply`` least-squares objective at ``x``, on ``geometry``.

   Accepts a ragged ``TuckerTensorTrain`` (-> :py:class:`GaussNewtonModel`) or a uniform
   ``UniformTuckerTensorTrain`` (-> :py:class:`UniformGaussNewtonModel`); the representation is inferred
   from ``x`` and the geometry must match. ``regularizer`` adds ``ρ(x)`` to the model (ragged only).