code: def test_condition_on_observations(self):
      for dtype in [torch.float, torch.double]:
          torch.random.manual_seed(0)
          test_x = torch.rand(2, 5, 1, device=self.device, dtype=dtype)
          test_y = torch.randn(
              2, 5, 3, 5, device=self.device, dtype=dtype
          )
  
          self.model.to(dtype)
          if dtype == torch.double:
              # need to clear float caches
              self.model.train()
              self.model.eval()
          # dummy call to ensure caches have been computed
          _ = self.model.posterior(test_x)
          conditioned_model = self.model.condition_on_observations(
              test_x, test_y
          )
          self.assertIsInstance(conditioned_model, HigherOrderGP)
  
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 7 for calling the TestHigherOrderGP obj's underlying member method code with example usage: obj.test_condition_on_observations() without return types