code: def _get_pref_model(
      self,
      dtype: Optional[torch.dtype] = None,
      input_transform: Optional[Normalize] = None,
  ) -> PairwiseGP:
      train_X = torch.rand((2, self.x_dim), dtype=dtype)
      train_comps = torch.LongTensor([[0, 1]])
      pref_model = PairwiseGP(
          train_X, train_comps, input_transform=input_transform
      )
      return pref_model
  
imports: import itertools
  import warnings
  from typing import Optional
  import torch
  from botorch import settings
  from botorch.acquisition import LearnedObjective
  from botorch.acquisition.objective import ConstrainedMCObjective, DEFAULT_NUM_PREF_SAMPLES, ExpectationPosteriorTransform, GenericMCObjective, IdentityMCObjective, LEARNED_OBJECTIVE_PREF_MODEL_MIXED_DTYPE_WARN, LinearMCObjective, MCAcquisitionObjective, PosteriorTransform, ScalarizedPosteriorTransform
  from botorch.exceptions.errors import UnsupportedError
  from botorch.exceptions.warnings import _get_single_precision_warning, InputDataWarning
  from botorch.models.deterministic import PosteriorMeanModel
  from botorch.models.pairwise_gp import PairwiseGP
  from botorch.models.transforms.input import Normalize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.utils import apply_constraints
  from botorch.utils.testing import _get_test_posterior, BotorchTestCase
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator.operators.dense_linear_operator import to_linear_operator
  from torch import Tensor
label_desc: Use image node_id 2 for calling the TestLearnedObjective obj's underlying member method code with example usage: obj._get_pref_model(dtype, input_transform) and returns: pref_model