Optimizer is not respecting "trainable" attribute of variables.

[rubenverhack]

The current version does not respect untrainable variables. It can be fixed by placing a simple if-statement. However, I'm not sure if this is the best place. Therefore I'm not suggesting it as a PR, but report the issue here.

        eagerly_outside_functions = ops.executing_eagerly_outside_functions()
        update_ops = []
        with ops.name_scope(name or self._name, skip_on_eager=True):
            for grad, hess, var in grads_hessian_and_vars:

                # FIX UNTRAINABLE
                if var.trainable:
                    def _assume_mirrored(grad, hess):
                        if isinstance(grad, ds_values.PerReplica):
                            return ds_values.Mirrored(grad.values), ds_values.Mirrored(hess.values)
                        return grad, hess

                    grad, hess = nest.map_structure(_assume_mirrored, grad, hess)
                    # Colocate the update with variables to avoid unnecessary communication
                    # delays. See b/136304694.
                    with distribution.extended.colocate_vars_with(var):
                        with ops.name_scope("update" if eagerly_outside_functions else
                                    "update_" + var.op.name, skip_on_eager=True):
                            update_ops.extend(distribution.extended.update(
                                    var, apply_grad_to_update_var, args=(grad, hess), group=False))