code: def test_load_samples(self):
      for task_rank, dtype in itertools.product(
          [1, 2], [torch.float, torch.double]
      ):
          tkwargs = {"device": self.device, "dtype": dtype}
          (
              train_X,
              train_Y,
              train_Yvar,
              model,
          ) = self._get_data_and_model(task_rank=task_rank, **tkwargs)
          d = train_X.shape[1] - 1
          mcmc_samples = self._get_mcmc_samples(
              num_samples=3, dim=d, task_rank=task_rank, **tkwargs
          )
          model.load_mcmc_samples(mcmc_samples)
  
          self.assertTrue(
              torch.allclose(
                  model.covar_module.base_kernel.lengthscale,
                  mcmc_samples["lengthscale"],
              )
          )
          self.assertTrue(
              torch.allclose(
                  model.covar_module.outputscale,
                  mcmc_samples["outputscale"],
              )
          )
          self.assertTrue(
              torch.allclose(
                  model.mean_module.raw_constant.data,
                  mcmc_samples["mean"],
              )
          )
          self.assertTrue(
              torch.allclose(
                  model.pyro_model.train_Yvar,
                  train_Yvar.clamp(MIN_INFERRED_NOISE_LEVEL),
              )
          )
          self.assertTrue(
              torch.allclose(
                  model.task_covar_module.lengthscale,
                  mcmc_samples["task_lengthscale"],
              )
          )
          self.assertTrue(
              torch.allclose(
                  model.latent_features,
                  mcmc_samples["latent_features"],
              )
          )
  
imports: import itertools
  from typing import List, Optional
  import torch
  from botorch import fit_fully_bayesian_model_nuts
  from botorch.acquisition.analytic import ExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound
  from botorch.acquisition.multi_objective import qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
  from botorch.models import ModelList, ModelListGP
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.fully_bayesian import MCMC_DIM, MIN_INFERRED_NOISE_LEVEL
  from botorch.models.fully_bayesian_multitask import MultitaskSaasPyroModel, SaasFullyBayesianMultiTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.posteriors import GaussianMixturePosterior
  from botorch.sampling.get_sampler import get_sampler
  from botorch.sampling.normal import IIDNormalSampler
  from botorch.utils.multi_objective.box_decompositions.non_dominated import NondominatedPartitioning
  from botorch.utils.test_helpers import gen_multi_task_dataset
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.means import ConstantMean
label_desc: Use image node_id 11 for calling the TestFullyBayesianMultiTaskGP obj's underlying member method code with example usage: obj.test_load_samples() without return types