code: def test_get_deterministic_model(self):
      tkwargs = {"device": self.device}
      # test is known to be non-flaky for each of these seeds
      torch.manual_seed(torch.randint(10, torch.Size([])).item())
      for dtype, m in product((torch.float, torch.double), (1, 2)):
          tkwargs["dtype"] = dtype
          use_model_list_vals = [False]
          if m == 2:
              use_model_list_vals.append(True)
          for use_model_list in use_model_list_vals:
              weights = []
              bases = []
              get_model = get_deterministic_model
              if use_model_list:
                  get_model = get_deterministic_model_list
              for i in range(m):
                  num_rff = 2 * (i + 2)
                  weights.append(torch.rand(num_rff, **tkwargs))
                  kernel = ScaleKernel(RBFKernel(ard_num_dims=2)).to(
                      **tkwargs
                  )
                  kernel.outputscale = 0.3 + torch.rand(
                      1, **tkwargs
                  ).view(kernel.outputscale.shape)
                  kernel.base_kernel.lengthscale = 0.3 + torch.rand(
                      2, **tkwargs
                  ).view(kernel.base_kernel.lengthscale.shape)
                  bases.append(
                      RandomFourierFeatures(
                          kernel=kernel,
                          input_dim=2,
                          num_rff_features=num_rff,
                      )
                  )
  
              model = get_model(weights=weights, bases=bases)
              self.assertIsInstance(
                  model,
                  DeterministicModel
                  if not use_model_list
                  else ModelList,
              )
              self.assertEqual(model.num_outputs, m)
              for batch_shape in (torch.Size([]), torch.Size([3])):
                  X = torch.rand(*batch_shape, 1, 2, **tkwargs)
                  Y = model.posterior(X).mean
                  expected_Y = torch.stack(
                      [
                          basis(X) @ w
                          for w, basis in zip(weights, bases)
                      ],
                      dim=-1,
                  )
                  self.assertAllClose(
                      Y, expected_Y, atol=1e-7, rtol=2e-5
                  )
                  self.assertEqual(
                      Y.shape, torch.Size([*batch_shape, 1, m])
                  )
  
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 2 for calling the TestRandomFourierFeatures obj's underlying member method code with example usage: obj.test_get_deterministic_model() without return types