code: def _check_recovery(self, mvn, samples):
      likelihood = GaussianLikelihood()
      opt = torch.optim.Adam(likelihood.parameters(), lr=0.05)
      for _ in range(100):
          opt.zero_grad()
          loss = -likelihood.log_marginal(samples, mvn).sum()
          loss.backward()
          opt.step()
      self.assertTrue(abs(float(likelihood.noise.sqrt()) - 0.5) < 0.02)
      # Check log marginal works
      likelihood.log_marginal(samples[0], mvn)
  
imports: import pickle
  import unittest
  import torch
  from linear_operator.operators import DiagLinearOperator
  from gpytorch import settings
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.likelihoods import DirichletClassificationLikelihood, FixedNoiseGaussianLikelihood, GaussianLikelihood
  from gpytorch.likelihoods.noise_models import FixedGaussianNoise
  from gpytorch.priors import GammaPrior
  from gpytorch.test.base_likelihood_test_case import BaseLikelihoodTestCase
label_desc: Use image node_id 5 for calling the TestGaussianLikelihoodWithMissingObs obj's underlying member method code with example usage: obj._check_recovery(mvn, samples) without return types