code: def setUp(self):
      super().setUp()
      torch.random.manual_seed(0)
  
      train_x = torch.rand(2, 10, 1, device=self.device)
      train_y = torch.randn(2, 10, 3, 5, device=self.device)
  
      self.model = HigherOrderGP(train_x, train_y)
  
      # check that we can assign different kernels and likelihoods
      model_2 = HigherOrderGP(
          train_X=train_x,
          train_Y=train_y,
          covar_modules=[RBFKernel(), RBFKernel(), RBFKernel()],
          likelihood=GaussianLikelihood(),
      )
  
      model_3 = HigherOrderGP(
          train_X=train_x,
          train_Y=train_y,
          covar_modules=[RBFKernel(), RBFKernel(), RBFKernel()],
          likelihood=GaussianLikelihood(),
          latent_init="gp",
      )
  
      for m in [self.model, model_2, model_3]:
          mll = ExactMarginalLogLikelihood(m.likelihood, m)
          fit_gpytorch_mll_torch(mll, step_limit=1)
  
imports: import itertools
  from unittest import mock
  import torch
  from botorch.acquisition.objective import PosteriorTransform
  from botorch.models import HigherOrderGP
  from botorch.models.higher_order_gp import FlattenedStandardize
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.optim.fit import fit_gpytorch_mll_torch
  from botorch.posteriors import GPyTorchPosterior, TransformedPosterior
  from botorch.sampling import IIDNormalSampler
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import RBFKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.mlls import ExactMarginalLogLikelihood
  from gpytorch.settings import max_cholesky_size, skip_posterior_variances
label_desc: Use image node_id 1 for calling the TestHigherOrderGP obj's underlying member method code with example usage: obj.setUp() without return types