code: def _get_model_and_data(
      self,
      batch_shape,
      X_dim=2,
      likelihood_cls=None,
  ) -> Tuple[Model, Dict[str, Union[Tensor, PairwiseLikelihood]]]:
      train_X, train_comp = self._make_rand_mini_data(
          batch_shape=batch_shape,
          X_dim=X_dim,
      )
  
      model_kwargs = {
          "datapoints": train_X,
          "comparisons": train_comp,
          "likelihood": None
          if likelihood_cls is None
          else likelihood_cls(),
      }
      model = PairwiseGP(**model_kwargs)
      return model, model_kwargs
  
imports: import itertools
  import random
  import warnings
  from typing import Dict, Tuple, Union
  import torch
  from botorch.acquisition.objective import ScalarizedPosteriorTransform
  from botorch.exceptions import OptimizationWarning, UnsupportedError
  from botorch.exceptions.warnings import _get_single_precision_warning, InputDataWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.likelihoods.pairwise import PairwiseLikelihood, PairwiseLogitLikelihood, PairwiseProbitLikelihood
  from botorch.models.model import Model
  from botorch.models.pairwise_gp import _ensure_psd_with_jitter, PairwiseGP, PairwiseLaplaceMarginalLogLikelihood
  from botorch.models.transforms.input import Normalize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.sampling.pairwise_samplers import PairwiseSobolQMCNormalSampler
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.kernels.linear_kernel import LinearKernel
  from gpytorch.means import ConstantMean
  from gpytorch.priors import GammaPrior, SmoothedBoxPrior
  from linear_operator.utils.errors import NotPSDError
  from torch import Tensor
label_desc: Use image node_id 3 for calling the TestPairwiseGP obj's underlying member method code with example usage: obj._get_model_and_data(batch_shape, X_dim, likelihood_cls) and returns: model,  model_kwargs