code: def test_load_samples(self):
      for infer_noise, dtype in itertools.product(
          [True, False], [torch.float, torch.double]
      ):
          tkwargs = {"device": self.device, "dtype": dtype}
          (
              train_X,
              train_Y,
              train_Yvar,
              model,
          ) = self._get_data_and_model(
              infer_noise=infer_noise, **tkwargs
          )
          n, d = train_X.shape
          mcmc_samples = self._get_mcmc_samples(
              num_samples=3,
              dim=train_X.shape[-1],
              infer_noise=infer_noise,
              **tkwargs,
          )
          model.load_mcmc_samples(mcmc_samples)
  
          self.assertAllClose(
              model.covar_module.base_kernel.lengthscale,
              mcmc_samples["lengthscale"],
          )
          self.assertAllClose(
              model.covar_module.outputscale,
              mcmc_samples["outputscale"],
          )
          self.assertAllClose(
              model.mean_module.raw_constant.data,
              mcmc_samples["mean"],
          )
          if infer_noise:
              self.assertAllClose(
                  model.likelihood.noise_covar.noise,
                  mcmc_samples["noise"],
              )
          else:
              self.assertAllClose(
                  model.likelihood.noise_covar.noise,
                  train_Yvar.clamp(MIN_INFERRED_NOISE_LEVEL)
                  .squeeze(-1)
                  .repeat(3, 1),
              )
  
imports: import itertools
  from unittest import mock
  from unittest.mock import patch
  import pyro
  import torch
  from botorch import fit_fully_bayesian_model_nuts, utils
  from botorch.acquisition.analytic import ExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound
  from botorch.acquisition.logei import qLogExpectedImprovement, qLogNoisyExpectedImprovement
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound
  from botorch.acquisition.multi_objective import prune_inferior_points_multi_objective, qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.multi_objective.logei import qLogExpectedHypervolumeImprovement, qLogNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.utils import prune_inferior_points
  from botorch.models import ModelList, ModelListGP
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.fully_bayesian import MCMC_DIM, MIN_INFERRED_NOISE_LEVEL, PyroModel, SaasFullyBayesianSingleTaskGP, SaasPyroModel
  from botorch.models.transforms import Normalize, Standardize
  from botorch.posteriors.fully_bayesian import batched_bisect, GaussianMixturePosterior
  from botorch.sampling.get_sampler import get_sampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.multi_objective.box_decompositions.non_dominated import NondominatedPartitioning
  from botorch.utils.safe_math import logmeanexp
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood, GaussianLikelihood
  from gpytorch.means import ConstantMean
  from linear_operator.operators import to_linear_operator
  from pyro.ops.integrator import _EXCEPTION_HANDLERS, potential_grad, register_exception_handler
label_desc: Use image node_id 9 for calling the TestFullyBayesianSingleTaskGP obj's underlying member method code with example usage: obj.test_load_samples() without return types