code: def test_natgrad(self, D=5):
      mu = torch.randn(D)
      cov = torch.randn(D, D).tril_()
      dist = MultivariateNormal(
          mu, CholLinearOperator(TriangularLinearOperator(cov))
      )
      sample = dist.sample()
  
      v_dist = NaturalVariationalDistribution(D)
      v_dist.initialize_variational_distribution(dist)
      mu = v_dist().mean.detach()
  
      v_dist().log_prob(sample).squeeze().backward()
  
      eta1 = mu.clone().requires_grad_(True)
      eta2 = (mu[:, None] * mu + cov @ cov.t()).requires_grad_(True)
      L = torch.linalg.cholesky(eta2 - eta1[:, None] * eta1)
      dist2 = MultivariateNormal(
          eta1, CholLinearOperator(TriangularLinearOperator(L))
      )
      dist2.log_prob(sample).squeeze().backward()
  
      assert torch.allclose(v_dist.natural_vec.grad, eta1.grad)
      assert torch.allclose(v_dist.natural_mat.grad, eta2.grad)
  
imports: import unittest
  import torch
  from linear_operator.operators import CholLinearOperator, TriangularLinearOperator
  import gpytorch
  from gpytorch.constraints import GreaterThan
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.variational import NaturalVariationalDistribution, TrilNaturalVariationalDistribution
label_desc: Use image node_id 3 for calling the TestNatVariational obj's underlying member method code with example usage: obj.test_natgrad(D) without return types