code: def check_transform_untransform(self, model, dtype):
      output, output_var = model.outcome_transform.untransform(
          torch.randn(2, 2, 4, 5, device=self.device, dtype=dtype)
      )
      self.assertEqual(output.shape, torch.Size((2, 2, 4, 5)))
      self.assertEqual(output_var, None)
  
      output, output_var = model.outcome_transform.untransform(
          torch.randn(2, 2, 4, 5, device=self.device, dtype=dtype),
          torch.rand(2, 2, 4, 5, device=self.device, dtype=dtype),
      )
      self.assertEqual(output.shape, torch.Size((2, 2, 4, 5)))
      self.assertEqual(output_var.shape, torch.Size((2, 2, 4, 5)))
  
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 6 for calling the TestHigherOrderGP obj's underlying member method code with example usage: obj.check_transform_untransform(model, dtype) without return types