code: def check_transform_forward(self, model, dtype):
      train_y = torch.randn(
          2, 10, 4, 5, device=self.device, dtype=dtype
      )
      train_y_var = torch.rand(
          2, 10, 4, 5, device=self.device, dtype=dtype
      )
  
      output, output_var = model.outcome_transform.forward(train_y)
      self.assertEqual(output.shape, torch.Size((2, 10, 4, 5)))
      self.assertEqual(output_var, None)
  
      output, output_var = model.outcome_transform.forward(
          train_y, train_y_var
      )
      self.assertEqual(output.shape, torch.Size((2, 10, 4, 5)))
      self.assertEqual(output_var.shape, torch.Size((2, 10, 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 5 for calling the TestHigherOrderGP obj's underlying member method code with example usage: obj.check_transform_forward(model, dtype) without return types