code: def compute_target(self, N):
      self.target_auto_mus = torch.zeros(N + 1)
      self.target_auto_diag_cov = torch.zeros(N + 1)
      self.target_auto_mus[-1] = self.target_mus[N].item()
      self.target_auto_diag_cov[-1] = 1.0 / self.lambda_posts[-1].item()
      for n in range(N - 1, 0, -1):
          self.target_auto_mus[n] += self.target_mus[n].item()
          self.target_auto_mus[n] += (
              self.target_kappas[n].item() * self.target_auto_mus[n + 1]
          )
          self.target_auto_diag_cov[n] += (
              1.0 / self.lambda_posts[n].item()
          )
          self.target_auto_diag_cov[n] += (
              self.target_kappas[n].item() ** 2
          ) * self.target_auto_diag_cov[n + 1]
  
imports: import logging
  from functools import partial
  import numpy
  import pytest
  import torch
  from torch.distributions import biject_to, constraints
  import pyro
  import pyro.distributions
  import pyro.optim
  from pyro.distributions.transforms import block_autoregressive, iterated
  from pyro.infer import SVI, Trace_ELBO, TraceMeanField_ELBO
  from pyro.infer.autoguide import AutoDiagonalNormal, AutoIAFNormal, AutoLaplaceApproximation, AutoLowRankMultivariateNormal, AutoMultivariateNormal
  from pyro.infer.autoguide.guides import AutoNormalizingFlow
  from tests.common import assert_equal
  from tests.integration_tests.test_conjugate_gaussian_models import GaussianChain
label_desc: Use image node_id 1 for calling the AutoGaussianChain obj's underlying member method code with example usage: obj.compute_target(N) without return types