code: def _get_model(
      fixed_noise=False,
      outcome_transform: str = "None",
      use_intf=False,
      **tkwargs,
  ) -> ModelListGP:
      train_x1, train_y1 = _get_random_data(
          batch_shape=torch.Size(), m=1, n=10, **tkwargs
      )
      train_y1 = torch.exp(train_y1)
      train_x2, train_y2 = _get_random_data(
          batch_shape=torch.Size(), m=1, n=11, **tkwargs
      )
      if outcome_transform == "Standardize":
          octfs = [Standardize(m=1), Standardize(m=1)]
      elif outcome_transform == "Log":
          octfs = [Log(), Standardize(m=1)]
      elif outcome_transform == "Chained":
          octfs = [
              ChainedOutcomeTransform(
                  chained=ChainedOutcomeTransform(
                      log=Log(), standardize=Standardize(m=1)
                  )
              ),
              Standardize(m=1),
          ]
      elif outcome_transform == "None":
          octfs = [None, None]
      else:
          raise KeyError(  # pragma: no cover
              "outcome_transform must be one of 'Standardize', 'Log', 'Chained', or "
              "'None'."
          )
      intfs = (
          [Normalize(d=1), Normalize(d=1)] if use_intf else [None, None]
      )
      if fixed_noise:
          train_y1_var = 0.1 + 0.1 * torch.rand_like(
              train_y1, **tkwargs
          )
          train_y2_var = 0.1 + 0.1 * torch.rand_like(
              train_y2, **tkwargs
          )
      else:
          train_y1_var = None
          train_y2_var = None
      model1 = SingleTaskGP(
          train_X=train_x1,
          train_Y=train_y1,
          train_Yvar=train_y1_var,
          outcome_transform=octfs[0],
          input_transform=intfs[0],
      )
      model2 = SingleTaskGP(
          train_X=train_x2,
          train_Y=train_y2,
          train_Yvar=train_y2_var,
          outcome_transform=octfs[1],
          input_transform=intfs[1],
      )
      model = ModelListGP(model1, model2)
      return model.to(**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 1 for calling a global function with example usage: _get_model(fixed_noise, outcome_transform, use_intf) and returns: model