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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _build_natgrad_step_op(self, model, q_mu_param, q_sqrt_param, xi_transform):
 """ Implements equation 10 from @inproceedings{salimbeni18, title={Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models}, author={Salimbeni, Hugh and Eleftheriadis, Stefanos and Hensman, James}, booktitle={AISTATS}, year={2018} In addition, for convenience with the rest of GPflow, this code computes ∂L/∂η using the chain rule: ∂L/∂η = (∂[q_μ, q_sqrt] / ∂η)(∂L / ∂[q_μ, q_sqrt]) In total there are three derivative calculations: natgrad L w.r.t ξ = (∂ξ / ∂nat ) [ (∂[q_μ, q_sqrt] / ∂η)(∂L / ∂[q_μ, q_sqrt]) ]^T Note that if ξ = nat or [q_μ, q_sqrt] some of these calculations are the identity. """

        objective = model.objective
        q_mu, q_sqrt = q_mu_param.constrained_tensor, q_sqrt_param.constrained_tensor

        # the three parameterizations as functions of [q_mu, q_sqrt]
        etas = meanvarsqrt_to_expectation(q_mu, q_sqrt)
        nats = meanvarsqrt_to_natural(q_mu, q_sqrt)
        xis = xi_transform.meanvarsqrt_to_xi(q_mu, q_sqrt)

        # we need these to calculate the relevant gradients
        _meanvarsqrt = expectation_to_meanvarsqrt(*etas)

        ## three derivatives
        # 1) the oridinary gpflow gradient
        dL_d_mean, dL_d_varsqrt = tf.gradients(objective, [q_mu, q_sqrt])

        # 2) the chain rule to get ∂L/∂η, where eta are the expectation parameters
        dL_detas = tf.gradients(_meanvarsqrt, etas, grad_ys=[dL_d_mean, dL_d_varsqrt])

        # 3) the forward mode gradient to calculate (∂ξ / ∂nat)(∂L / ∂η)^T,
        if isinstance(xi_transform, XiNat):
            nat_dL_xis = dL_detas
        else:
            _xis = xi_transform.naturals_to_xi(*nats)
            # this line should be removed if the placeholder_with_default problem is rectified
            _xis = [tf.reshape(_xis[0], q_mu_param.shape), tf.reshape(_xis[1], q_sqrt_param.shape)]
            nat_dL_xis = self._forward_gradients(_xis, nats, dL_detas)

        # perform natural gradient descent on the ξ parameters
        xis_new = [xi - self.gamma * nat_dL_xi for xi, nat_dL_xi in zip(xis, nat_dL_xis)]

        # transform back to the model parameters [q_μ, q_sqrt]
        mean_new, varsqrt_new = xi_transform.xi_to_meanvarsqrt(*xis_new)

        # these are the tensorflow variables to assign
        q_mu_u = q_mu_param.unconstrained_tensor
        q_sqrt_u = q_sqrt_param.unconstrained_tensor

        # so the transform to work for LowerTriangular
        mean_new.set_shape(q_mu_param.shape)
        varsqrt_new.set_shape(q_sqrt_param.shape)

        q_mu_assign = tf.assign(q_mu_u, q_mu_param.transform.backward_tensor(mean_new))
        q_sqrt_assign = tf.assign(q_sqrt_u, q_sqrt_param.transform.backward_tensor(varsqrt_new))
        return q_mu_assign, q_sqrt_assign