5e599fb01df65d156a40f7a138ab6627a06a50db,gpflow/optimizers/natgrad.py,NaturalGradient,_natgrad_steps,#NaturalGradient#Any#Any#,113

Before Change

            self._natgrad_step(loss_fn, q_mu, q_sqrt, xi_transform)

        with tf.name_scope(f"{self._name}/natural_gradient_steps"):
            list(map(natural_gradient_step, *zip(*parameters)))

    def _natgrad_step(
        self, loss_fn: Callable, q_mu: Parameter, q_sqrt: Parameter, xi_transform: XiTransform

After Change

            tape.watch(unconstrained_variables)
            loss = loss_fn()

        q_mu_grads, q_sqrt_grads = tape.gradient(loss, [q_mus, q_sqrts])
        // NOTE that these are the gradients in *unconstrained* space

        with tf.name_scope(f"{self._name}/natural_gradient_steps"):
            for q_mu_grad, q_sqrt_grad, q_mu, q_sqrt, xi_transform in zip(

Instances