code: def test_with_fixed_noise(self):
      for n_samples in (1, 20):
          gp_samples = get_gp_samples(
              model=SingleTaskGP(
                  torch.rand(5, 3, dtype=torch.double),
                  torch.randn(5, 1, dtype=torch.double),
                  torch.rand(5, 1, dtype=torch.double) * 0.1,
              ),
              num_outputs=1,
              n_samples=n_samples,
          )
          samples = gp_samples(torch.rand(2, 3))
          expected_shape = (
              torch.Size([2, 1])
              if n_samples == 1
              else torch.Size([n_samples, 2, 1])
          )
          self.assertEqual(samples.shape, expected_shape)
  
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 6 for calling the TestRandomFourierFeatures obj's underlying member method code with example usage: obj.test_with_fixed_noise() without return types