code: def test_transforms(self):
      for dtype in [torch.float, torch.double]:
          train_x = torch.rand(10, 3, device=self.device, dtype=dtype)
          train_y = torch.randn(
              10, 4, 5, device=self.device, dtype=dtype
          )
  
          # test handling of Standardize
          with self.assertWarns(RuntimeWarning):
              model = HigherOrderGP(
                  train_X=train_x,
                  train_Y=train_y,
                  outcome_transform=Standardize(m=5),
              )
          self.assertIsInstance(
              model.outcome_transform, FlattenedStandardize
          )
          self.assertEqual(
              model.outcome_transform.output_shape, train_y.shape[1:]
          )
          self.assertEqual(
              model.outcome_transform.batch_shape, torch.Size()
          )
  
          model = HigherOrderGP(
              train_X=train_x,
              train_Y=train_y,
              input_transform=Normalize(d=3),
              outcome_transform=FlattenedStandardize(train_y.shape[1:]),
          )
          mll = ExactMarginalLogLikelihood(model.likelihood, model)
          fit_gpytorch_mll_torch(mll, step_limit=1)
  
          test_x = torch.rand(2, 5, 3, device=self.device, dtype=dtype)
          test_y = torch.randn(
              2, 5, 4, 5, device=self.device, dtype=dtype
          )
          with mock.patch.object(
              HigherOrderGP,
              "transform_inputs",
              wraps=model.transform_inputs,
          ) as mock_intf:
              posterior = model.posterior(test_x)
              mock_intf.assert_called_once()
          self.assertIsInstance(posterior, TransformedPosterior)
  
          conditioned_model = model.condition_on_observations(
              test_x, test_y
          )
          self.assertIsInstance(conditioned_model, HigherOrderGP)
  
          self.check_transform_forward(model, dtype)
          self.check_transform_untransform(model, dtype)
  
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 4 for calling the TestHigherOrderGP obj's underlying member method code with example usage: obj.test_transforms() without return types