code: def test_get_deterministic_model_multi_samples(self):
      tkwargs = {"device": self.device}
      n_samples = 5
      for dtype, m, batch_shape_w, batch_shape_x in (
          (torch.float, 1, torch.Size([]), torch.Size([])),
          (torch.double, 2, torch.Size([]), torch.Size([3])),
          (torch.double, 1, torch.Size([3]), torch.Size([3])),
          (torch.float, 2, torch.Size([3]), torch.Size([5, 3])),
      ):
          tkwargs["dtype"] = dtype
          with self.subTest(
              dtype=dtype,
              m=m,
              batch_shape_w=batch_shape_w,
              batch_shape_x=batch_shape_x,
          ):
              weights = []
              bases = []
              for i in range(m):
                  num_rff = 2 * (i + 2)
                  # we require weights to be of shape
                  # `n_samples x (batch_shape) x num_rff`
                  weights.append(
                      torch.rand(
                          *batch_shape_w, n_samples, 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,
                          sample_shape=torch.Size([n_samples]),
                      )
                  )
  
              model = get_deterministic_model_multi_samples(
                  weights=weights, bases=bases
              )
              self.assertIsInstance(model, DeterministicModel)
              self.assertEqual(model.num_outputs, m)
              X = torch.rand(*batch_shape_x, n_samples, 1, 2, **tkwargs)
              Y = model(X)
              for i in range(m):
                  expected_Yi = (
                      bases[i](X) @ weights[i].unsqueeze(-1)
                  ).squeeze(-1)
                  self.assertAllClose(Y[..., i], expected_Yi)
              self.assertEqual(
                  Y.shape,
                  torch.Size([*batch_shape_x, n_samples, 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 3 for calling the TestRandomFourierFeatures obj's underlying member method code with example usage: obj.test_get_deterministic_model_multi_samples() without return types