code: def test_with_saas_models(self):
      # Construct a SAAS model.
      tkwargs = {"dtype": torch.double, "device": self.device}
      num_samples = 4
      model = SaasFullyBayesianSingleTaskGP(
          train_X=torch.rand(10, 4, **tkwargs),
          train_Y=torch.randn(10, 1, **tkwargs),
      )
      mcmc_samples = {
          "lengthscale": torch.rand(num_samples, 1, 4, **tkwargs),
          "outputscale": torch.rand(num_samples, **tkwargs),
          "mean": torch.randn(num_samples, **tkwargs),
          "noise": torch.rand(num_samples, 1, **tkwargs),
      }
      model.load_mcmc_samples(mcmc_samples)
      # Test proper setup & sampling support.
      gp_samples = get_gp_samples(
          model=model,
          num_outputs=1,
          n_samples=1,
      )
      self.assertTrue(is_ensemble(gp_samples))
      # Non-batch evaluation.
      samples = gp_samples(torch.rand(2, 4, **tkwargs))
      self.assertEqual(samples.shape, torch.Size([4, 2, 1]))
      # Batch evaluation.
      samples = gp_samples(torch.rand(5, 2, 4, **tkwargs))
      self.assertEqual(samples.shape, torch.Size([5, 4, 2, 1]))
  
imports: from itertools import product
  from math import pi
  from unittest import mock
  import torch
  from botorch.models.converter import batched_to_model_list
  from botorch.models.deterministic import DeterministicModel
  from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model import ModelList
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.utils.gp_sampling import get_deterministic_model, get_deterministic_model_list, get_deterministic_model_multi_samples, get_gp_samples, get_weights_posterior, GPDraw, RandomFourierFeatures
  from botorch.utils.testing import BotorchTestCase
  from botorch.utils.transforms import is_ensemble
  from gpytorch.kernels import MaternKernel, PeriodicKernel, RBFKernel, ScaleKernel
  from torch.distributions import MultivariateNormal
label_desc: Use image node_id 7 for calling the TestRandomFourierFeatures obj's underlying member method code with example usage: obj.test_with_saas_models() without return types