code: def test_ModelListGP_fixed_noise(self) -> None:
      for dtype, outcome_transform in itertools.product(
          (torch.float, torch.double), ("None", "Standardize")
      ):
          model = self._base_test_ModelListGP(
              fixed_noise=True,
              dtype=dtype,
              outcome_transform=outcome_transform,
          )
          tkwargs = {"device": self.device, "dtype": dtype}
          f_x = [torch.rand(2, 1, **tkwargs) for _ in range(2)]
          f_y = torch.rand(2, 2, **tkwargs)
  
          # test condition_on_observations (incorrect noise shape error)
          with self.assertRaises(BotorchTensorDimensionError):
              model.condition_on_observations(
                  f_x, f_y, noise=torch.rand(2, 3, **tkwargs)
              )
  
imports: import itertools
  import warnings
  from copy import deepcopy
  from typing import Optional
  import torch
  from botorch.acquisition.objective import ScalarizedPosteriorTransform
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import ChainedOutcomeTransform, Log, Standardize
  from botorch.posteriors import GPyTorchPosterior, PosteriorList, TransformedPosterior
  from botorch.sampling.base import MCSampler
  from botorch.sampling.list_sampler import ListSampler
  from botorch.sampling.normal import IIDNormalSampler
  from botorch.utils.testing import _get_random_data, BotorchTestCase
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import LikelihoodList
  from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood, GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.mlls import SumMarginalLogLikelihood
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.priors import GammaPrior
  from torch import Tensor
label_desc: Use image node_id 3 for calling the TestModelListGP obj's underlying member method code with example usage: obj.test_ModelListGP_fixed_noise() without return types