code: def test_initialize_latents(self):
      for dtype in [torch.float, torch.double]:
          torch.random.manual_seed(0)
  
          train_x = torch.rand(10, 1, device=self.device, dtype=dtype)
          train_y = torch.randn(
              10, 3, 5, device=self.device, dtype=dtype
          )
  
          for latent_dim_sizes, latent_init in itertools.product(
              [[1, 1], [2, 3]],
              ["gp", "default"],
          ):
              self.model = HigherOrderGP(
                  train_x,
                  train_y,
                  num_latent_dims=latent_dim_sizes,
                  latent_init=latent_init,
              )
              self.assertEqual(
                  self.model.latent_parameters[0].shape,
                  torch.Size((3, latent_dim_sizes[0])),
              )
              self.assertEqual(
                  self.model.latent_parameters[1].shape,
                  torch.Size((5, latent_dim_sizes[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 9 for calling the TestHigherOrderGP obj's underlying member method code with example usage: obj.test_initialize_latents() without return types