code: def test_fixed_noise_gaussian_likelihood(self, cuda=False):
      device = torch.device("cuda") if cuda else torch.device("cpu")
      for dtype in (torch.float, torch.double):
          noise = 0.1 + torch.rand(4, device=device, dtype=dtype)
          lkhd = FixedNoiseGaussianLikelihood(noise=noise)
          # test basics
          self.assertIsInstance(lkhd.noise_covar, FixedGaussianNoise)
          self.assertTrue(torch.equal(noise, lkhd.noise))
          new_noise = 0.1 + torch.rand(4, device=device, dtype=dtype)
          lkhd.noise = new_noise
          self.assertTrue(torch.equal(lkhd.noise, new_noise))
          # test __call__
          mean = torch.zeros(4, device=device, dtype=dtype)
          covar = DiagLinearOperator(
              torch.ones(4, device=device, dtype=dtype)
          )
          mvn = MultivariateNormal(mean, covar)
          out = lkhd(mvn)
          self.assertTrue(torch.allclose(out.variance, 1 + new_noise))
          # things should break if dimensions mismatch
          mean = torch.zeros(5, device=device, dtype=dtype)
          covar = DiagLinearOperator(
              torch.ones(5, device=device, dtype=dtype)
          )
          mvn = MultivariateNormal(mean, covar)
          with self.assertWarns(UserWarning):
              lkhd(mvn)
          # test __call__ w/ observation noise
          obs_noise = 0.1 + torch.rand(5, device=device, dtype=dtype)
          out = lkhd(mvn, noise=obs_noise)
          self.assertTrue(torch.allclose(out.variance, 1 + obs_noise))
          # test noise smaller than min_fixed_noise
          expected_min_noise = settings.min_fixed_noise.value(dtype)
          noise[:2] = 0
          lkhd = FixedNoiseGaussianLikelihood(noise=noise)
          expected_noise = noise.clone()
          expected_noise[:2] = expected_min_noise
          self.assertTrue(torch.allclose(lkhd.noise, expected_noise))
  
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 2 for calling the TestFixedNoiseGaussianLikelihood obj's underlying member method code with example usage: obj.test_fixed_noise_gaussian_likelihood(cuda) without return types