code: def test_one_step_optimal_high_precision(self):
      X = torch.linspace(-3, 3, 10)
      Y = torch.sin(X)
  
      class ExactGP(gpytorch.models.ExactGP):
          def __init__(self, train_x, train_y, kern, likelihood):
              super().__init__(train_x, train_y, likelihood)
              self.mean_module = gpytorch.means.ZeroMean()
              self.covar_module = kern
  
          def forward(self, x):
              mean = self.mean_module(x)
              covar = self.covar_module(x)
              return gpytorch.distributions.MultivariateNormal(
                  mean, covar
              )
  
      likelihood = gpytorch.likelihoods.GaussianLikelihood(
          noise_constraint=GreaterThan(0, initial_value=0.1)
      )
  
      kern = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
      kern.outputscale = 1
  
      model_exact_gp = ExactGP(X, Y, kern, likelihood)
      model_exact_gp.eval()
      prediction_exact = model_exact_gp(X)
  
      class NatGradsGP(gpytorch.models.ApproximateGP):
          def __init__(self, kern, inducing_points):
              variational_distribution = (
                  gpytorch.variational.NaturalVariationalDistribution(
                      inducing_points.shape[0]
                  )
              )
              variational_strategy = (
                  gpytorch.variational.VariationalStrategy(
                      self,
                      inducing_points,
                      variational_distribution,
                      learn_inducing_locations=True,
                      jitter_val=1e-24,
                  )
              )
              super().__init__(variational_strategy)
              self.mean_module = gpytorch.means.ConstantMean()
              self.covar_module = kern
  
          def forward(self, x):
              mean_x = self.mean_module(x)
              covar_x = self.covar_module(x)
              return gpytorch.distributions.MultivariateNormal(
                  mean_x, covar_x
              )
  
      model_ng = NatGradsGP(kern, X)
  
      mll = gpytorch.mlls.VariationalELBO(
          likelihood, model_ng, num_data=X.shape[0]
      )
      from torch.utils.data import DataLoader, TensorDataset
  
      data = DataLoader(TensorDataset(X, Y), batch_size=X.shape[0])
  
      variational_ngd_optimizer = gpytorch.optim.NGD(
          model_ng.variational_parameters(), num_data=X.size(0), lr=1
      )
      for _ in range(1):  # one step
          for x, y in data:
              variational_ngd_optimizer.zero_grad()
  
              loss = -mll(model_ng(x), y)
              # minibatch_iter.set_postfix(loss=loss.item())
              loss.backward()
              variational_ngd_optimizer.step()
  
      prediction_ng = model_ng(X)
  
      assert torch.allclose(
          prediction_exact.mean,
          prediction_ng.mean,
          rtol=1e-12,
          atol=1e-12,
      )
      assert torch.allclose(
          prediction_exact.variance,
          prediction_ng.variance,
          rtol=1e-12,
          atol=1e-12,
      )
  
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 1 for calling the TestNatVariational obj's underlying member method code with example usage: obj.test_one_step_optimal_high_precision() and returns: gpytorch, gpytorch