code: def test_get_weights_posterior(self):
      tkwargs = {"device": self.device}
      sigma = 0.01
      input_dim = 2
      for dtype, input_batch_shape, sample_shape in (
          (torch.float, torch.Size(), torch.Size()),
          (torch.double, torch.Size(), torch.Size([5])),
          (torch.float, torch.Size([3]), torch.Size()),
          (torch.double, torch.Size([3]), torch.Size([5])),
      ):
          with self.subTest(
              dype=dtype,
              input_batch_shape=input_batch_shape,
              sample_shape=sample_shape,
          ):
              tkwargs["dtype"] = dtype
              X = torch.rand(
                  *input_batch_shape, 40, input_dim, **tkwargs
              )
              w = torch.rand(*sample_shape, input_dim, **tkwargs)
              # We have to share each sample of weights with the X.
              # Therefore, the effective size of w is
              # (sample_shape) x (input_batch_shape) x input_dim.
              for _ in range(len(input_batch_shape)):
                  w.unsqueeze_(-2)
              w = w.expand(*sample_shape, *input_batch_shape, input_dim)
              Y_true = (X @ w.unsqueeze(-1)).squeeze(-1)
              Y = Y_true + sigma * torch.randn_like(Y_true)
              posterior = get_weights_posterior(
                  X=X, y=Y, sigma_sq=sigma**2
              )
              self.assertIsInstance(posterior, MultivariateNormal)
              self.assertAllClose(w, posterior.mean, atol=1e-1)
              w_samp = posterior.sample()
              self.assertEqual(w_samp.shape, w.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 4 for calling the TestRandomFourierFeatures obj's underlying member method code with example usage: obj.test_get_weights_posterior() without return types