code: def _make_data(self):
      mu = torch.zeros(2, 3)
      sigma = torch.tensor(
          [
              [
                  [1, 0.999, -0.999],
                  [0.999, 1, -0.999],
                  [-0.999, -0.999, 1],
              ]
          ]
          * 2
      ).float()
      mvn = MultivariateNormal(mu, sigma)
      samples = mvn.sample(torch.Size([10000]))  # mvn samples
      noise_sd = 0.5
      noise_dist = torch.distributions.Normal(0, noise_sd)
      samples += noise_dist.sample(samples.shape)  # noise
      return mvn, samples
  
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 4 for calling the TestGaussianLikelihoodWithMissingObs obj's underlying member method code with example usage: obj._make_data() and returns: mvn,  samples