code: def test_random_fourier_features(self):
      # test kernel that is not Scale, RBF, or Matern
      with self.assertRaises(NotImplementedError):
          RandomFourierFeatures(
              kernel=PeriodicKernel(),
              input_dim=2,
              num_rff_features=3,
          )
      tkwargs = {"device": self.device}
      for dtype in (torch.float, torch.double):
          tkwargs["dtype"] = dtype
          # test init
          # test ScaleKernel
          base_kernel = RBFKernel(ard_num_dims=2)
          kernel = ScaleKernel(base_kernel).to(**tkwargs)
          rff = RandomFourierFeatures(
              kernel=kernel,
              input_dim=2,
              num_rff_features=3,
          )
          self.assertTrue(
              torch.equal(rff.outputscale, kernel.outputscale)
          )
          # check that rff makes a copy
          self.assertFalse(rff.outputscale is kernel.outputscale)
          self.assertTrue(
              torch.equal(rff.lengthscale, base_kernel.lengthscale)
          )
          # check that rff makes a copy
          self.assertFalse(rff.lengthscale is kernel.lengthscale)
  
          for sample_shape in [torch.Size(), torch.Size([5])]:
              # test not ScaleKernel
              rff = RandomFourierFeatures(
                  kernel=base_kernel,
                  input_dim=2,
                  num_rff_features=3,
                  sample_shape=sample_shape,
              )
              self.assertTrue(
                  torch.equal(
                      rff.outputscale, torch.tensor(1, **tkwargs)
                  )
              )
              self.assertTrue(
                  torch.equal(rff.lengthscale, base_kernel.lengthscale)
              )
              # check that rff makes a copy
              self.assertFalse(rff.lengthscale is kernel.lengthscale)
              self.assertEqual(
                  rff.weights.shape, torch.Size([*sample_shape, 2, 3])
              )
              self.assertEqual(
                  rff.bias.shape, torch.Size([*sample_shape, 3])
              )
              self.assertTrue(
                  ((rff.bias <= 2 * pi) & (rff.bias >= 0.0)).all()
              )
  
          # test forward
          for sample_shape in [torch.Size(), torch.Size([7])]:
              rff = RandomFourierFeatures(
                  kernel=kernel,
                  input_dim=2,
                  num_rff_features=3,
                  sample_shape=sample_shape,
              )
              for input_batch_shape in [
                  torch.Size([]),
                  torch.Size([5]),
              ]:
                  X = torch.rand(
                      *input_batch_shape, *sample_shape, 1, 2, **tkwargs
                  )
                  Y = rff(X)
                  self.assertTrue(
                      Y.shape,
                      torch.Size(
                          [*input_batch_shape, *sample_shape, 1, 1]
                      ),
                  )
                  _constant = torch.sqrt(
                      2 * rff.outputscale / rff.weights.shape[-1]
                  )
                  _arg_to_cos = (
                      X / base_kernel.lengthscale @ rff.weights
                  )
                  _bias_expanded = rff.bias.unsqueeze(-2)
                  expected_Y = _constant * (
                      torch.cos(_arg_to_cos + _bias_expanded)
                  )
                  self.assertAllClose(Y, expected_Y)
  
          # test get_weights
          for sample_shape in [torch.Size(), torch.Size([5])]:
              with mock.patch(
                  "torch.randn", wraps=torch.randn
              ) as mock_randn:
                  rff._get_weights(
                      base_kernel=base_kernel,
                      input_dim=2,
                      num_rff_features=3,
                      sample_shape=sample_shape,
                  )
                  mock_randn.assert_called_once_with(
                      *sample_shape,
                      2,
                      3,
                      dtype=base_kernel.lengthscale.dtype,
                      device=base_kernel.lengthscale.device,
                  )
              # test get_weights with Matern kernel
              with mock.patch(
                  "torch.randn", wraps=torch.randn
              ) as mock_randn, mock.patch(
                  "torch.distributions.Gamma",
                  wraps=torch.distributions.Gamma,
              ) as mock_gamma:
                  base_kernel = MaternKernel(ard_num_dims=2).to(
                      **tkwargs
                  )
                  rff._get_weights(
                      base_kernel=base_kernel,
                      input_dim=2,
                      num_rff_features=3,
                      sample_shape=sample_shape,
                  )
                  mock_randn.assert_called_once_with(
                      *sample_shape,
                      2,
                      3,
                      dtype=base_kernel.lengthscale.dtype,
                      device=base_kernel.lengthscale.device,
                  )
                  mock_gamma.assert_called_once_with(
                      base_kernel.nu,
                      base_kernel.nu,
                  )
  
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 1 for calling the TestRandomFourierFeatures obj's underlying member method code with example usage: obj.test_random_fourier_features() without return types