code: def test_consolidation(self) -> None:
      for batch_shape, likelihood_cls in itertools.product(
          (torch.Size(), torch.Size([2])),
          (PairwiseLogitLikelihood, PairwiseProbitLikelihood),
      ):
          X_dim = 2
  
          _, model_kwargs = self._get_model_and_data(
              batch_shape=batch_shape,
              X_dim=X_dim,
              likelihood_cls=likelihood_cls,
          )
          train_X = model_kwargs["datapoints"]
          train_comp = model_kwargs["comparisons"]
  
          # Test consolidation
          i1, i2 = train_X.shape[-2], train_X.shape[-2] + 1
          dup_comp = torch.cat(
              [
                  train_comp,
                  torch.tensor(
                      [[i1, i2]],
                      dtype=train_comp.dtype,
                      device=train_comp.device,
                  ).expand(*batch_shape, 1, 2),
              ],
              dim=-2,
          )
          dup_X = torch.cat([train_X, train_X[..., :2, :]], dim=-2)
          model = PairwiseGP(datapoints=dup_X, comparisons=dup_comp)
          self.assertIs(dup_X, model.unconsolidated_datapoints)
          self.assertIs(dup_comp, model.unconsolidated_comparisons)
          if batch_shape:
              self.assertIs(dup_X, model.consolidated_datapoints)
              self.assertIs(dup_comp, model.consolidated_comparisons)
              self.assertIs(model.utility, model.unconsolidated_utility)
          else:
              self.assertFalse(
                  torch.equal(dup_X, model.consolidated_datapoints)
              )
              self.assertFalse(
                  torch.equal(dup_comp, model.consolidated_comparisons)
              )
              self.assertFalse(
                  torch.equal(
                      model.utility, model.unconsolidated_utility
                  )
              )
  
          # calling forward with duplicated datapoints should work after consolidation
          mll = PairwiseLaplaceMarginalLogLikelihood(
              model.likelihood, model
          )
          # make sure model is in training mode
          self.assertTrue(model.training)
          pred = model(dup_X)
          # posterior shape in training should match the consolidated utility
          self.assertEqual(pred.shape(), model.utility.shape)
          if batch_shape:
              # do not perform consolidation in batch mode
              # because the block structure cannot be guaranteed
              self.assertEqual(pred.shape(), dup_X.shape[:-1])
          else:
              self.assertEqual(pred.shape(), train_X.shape[:-1])
          # Pass the original comparisons through mll should work
          mll(pred, dup_comp)
  
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 5 for calling the TestPairwiseGP obj's underlying member method code with example usage: obj.test_consolidation() without return types