code: def test_fixed_noise_likelihood(self):
      for batch_shape, m, dtype in itertools.product(
          (torch.Size(), torch.Size([2])),
          (1, 2),
          (torch.float, torch.double),
      ):
          tkwargs = {"device": self.device, "dtype": dtype}
          model, model_kwargs = self._get_model_and_data(
              batch_shape=batch_shape, m=m, **tkwargs
          )
          self.assertIsInstance(
              model.likelihood, FixedNoiseGaussianLikelihood
          )
          self.assertTrue(
              torch.equal(
                  model.likelihood.noise.contiguous().view(-1),
                  model_kwargs["train_Yvar"].contiguous().view(-1),
              )
          )
  
imports: import itertools
  import warnings
  import torch
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.gp_regression import FixedNoiseGP, HeteroskedasticSingleTaskGP, SingleTaskGP
  from botorch.models.transforms import Normalize, Standardize
  from botorch.models.transforms.input import InputStandardize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.sampling import SobolQMCNormalSampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.sampling import manual_seed
  from botorch.utils.test_helpers import get_pvar_expected
  from botorch.utils.testing import _get_random_data, BotorchTestCase
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import _GaussianLikelihoodBase, FixedNoiseGaussianLikelihood, GaussianLikelihood, HeteroskedasticNoise
  from gpytorch.means import ConstantMean, ZeroMean
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.mlls.noise_model_added_loss_term import NoiseModelAddedLossTerm
  from gpytorch.priors import GammaPrior
label_desc: Use image node_id 3 for calling the TestFixedNoiseGP obj's underlying member method code with example usage: obj.test_fixed_noise_likelihood() without return types