code: def test_get_gp_samples(self):
      # test multi-task model
      with torch.random.fork_rng():
          torch.manual_seed(0)
          X = torch.stack(
              [torch.rand(3), torch.tensor([1.0, 0.0, 1.0])], dim=-1
          )
          Y = torch.rand(3, 1)
  
      with self.assertRaises(NotImplementedError):
          gp_samples = get_gp_samples(
              model=MultiTaskGP(X, Y, task_feature=1),
              num_outputs=1,
              n_samples=20,
              num_rff_features=512,
          )
      tkwargs = {"device": self.device}
      for (
          dtype,
          m,
          use_tf,
          use_batch_model,
          batched_inputs,
          n_samples,
      ) in (
          (torch.float, 1, True, False, False, 20),
          (torch.float, 1, False, True, False, 20),
          (torch.float, 1, False, False, True, 20),
          (torch.double, 2, False, True, False, 10),
          (torch.double, 2, True, False, False, 30),
      ):
          with self.subTest(
              dtype=dtype,
              m=m,
              use_tf=use_tf,
              use_batch_model=use_batch_model,
              batched_inputs=batched_inputs,
              n_samples=n_samples,
          ):
              tkwargs["dtype"] = dtype
              model, X, Y = _get_model(
                  **tkwargs,
                  multi_output=m == 2,
                  use_transforms=use_tf,
                  batched_inputs=batched_inputs,
              )
              with torch.random.fork_rng():
                  torch.manual_seed(0)
                  gp_samples = get_gp_samples(
                      model=batched_to_model_list(model)
                      if ((not use_batch_model) and (m > 1))
                      else model,
                      num_outputs=m,
                      n_samples=n_samples,
                      num_rff_features=512,
                  )
              samples = gp_samples.posterior(X).mean
              self.assertEqual(samples.shape[0], n_samples)
              if batched_inputs:
                  self.assertEqual(samples.shape[1], 2)
              self.assertIsInstance(
                  gp_samples,
                  ModelList
                  if ((not use_batch_model) and (m > 1))
                  else DeterministicModel,
              )
              Y_hat_rff = samples.mean(dim=0)
              with torch.no_grad():
                  Y_hat = model.posterior(X).mean
              self.assertAllClose(Y_hat_rff, Y_hat, atol=5e-1)
  
              # test batched evaluation
              test_X = torch.randn(
                  13, n_samples, 3, X.shape[-1], **tkwargs
              )
              if batched_inputs:
                  test_X = test_X.unsqueeze(-3)
                  expected_shape = torch.Size([13, n_samples, 2, 3, m])
              else:
                  expected_shape = torch.Size([13, n_samples, 3, m])
              Y_batched = gp_samples.posterior(test_X).mean
              self.assertEqual(Y_batched.shape, expected_shape)
  
              if use_tf:
                  # check transforms on sample
                  if isinstance(gp_samples, DeterministicModel):
                      self.assertEqual(
                          model.outcome_transform,
                          gp_samples.outcome_transform,
                      )
                      self.assertEqual(
                          model.input_transform,
                          gp_samples.input_transform,
                      )
                  elif isinstance(gp_samples, ModelList):
                      model_list = batched_to_model_list(model)
                      for i in range(model_list.num_outputs):
                          self.assertTrue(
                              torch.equal(
                                  model_list.models[
                                      i
                                  ].outcome_transform.means,
                                  gp_samples.models[
                                      i
                                  ].outcome_transform.means,
                              )
                          )
                          self.assertTrue(
                              torch.equal(
                                  model_list.models[
                                      i
                                  ].outcome_transform.stdvs,
                                  gp_samples.models[
                                      i
                                  ].outcome_transform.stdvs,
                              )
                          )
                          self.assertEqual(
                              model_list.models[i].input_transform,
                              gp_samples.models[i].input_transform,
                          )
  
              # test incorrect batch shape check
              with self.assertRaises(ValueError):
                  gp_samples.posterior(
                      torch.randn(13, 23, 3, X.shape[-1], **tkwargs)
                  ).mean
  
              # test single sample
              means = []
              with torch.random.fork_rng():
                  torch.manual_seed(28)
                  for _ in range(10):
                      gp_samples = get_gp_samples(
                          model=batched_to_model_list(model)
                          if ((not use_batch_model) and (m > 1))
                          else model,
                          num_outputs=m,
                          n_samples=1,
                          num_rff_features=512,
                      )
                      with torch.no_grad():
                          means.append(model.posterior(X).mean)
              samples = gp_samples.posterior(X).mean
              self.assertEqual(samples.shape[:-1], X.shape[:-1])
              self.assertIsInstance(gp_samples, ModelList) if (
                  (not use_batch_model) and (m > 1)
              ) else DeterministicModel
              Y_hat_rff = torch.stack(means, dim=0).mean(dim=0)
              with torch.no_grad():
                  Y_hat = model.posterior(X).mean
              self.assertAllClose(Y_hat_rff, Y_hat, atol=5e-1)
              # test batched evaluation
              test_X = torch.randn(13, 5, 3, X.shape[-1], **tkwargs)
              if batched_inputs:
                  test_X = test_X.unsqueeze(-3)
                  expected = torch.Size([13, 5, 2, 3, m])
              else:
                  expected = torch.Size([13, 5, 3, m])
              Y_batched = gp_samples.posterior(test_X).mean
              self.assertEqual(Y_batched.shape, expected)
  
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 5 for calling the TestRandomFourierFeatures obj's underlying member method code with example usage: obj.test_get_gp_samples() without return types