code: def test_condition_on_observations(self) -> None:
      for batch_shape, likelihood_cls in itertools.product(
          (torch.Size(), torch.Size([2])),
          (PairwiseLogitLikelihood, PairwiseProbitLikelihood),
      ):
          tkwargs = {"device": self.device, "dtype": self.dtype}
          X_dim = 2
  
          model, 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"]
  
          # evaluate model
          model.posterior(torch.rand(torch.Size([4, X_dim]), **tkwargs))
  
          # test condition_on_observations
  
          # test condition_on_observations with prior mode
          prior_m = PairwiseGP(None, None).to(**tkwargs)
          cond_m = prior_m.condition_on_observations(
              train_X, train_comp
          )
          self.assertIs(cond_m.datapoints, train_X)
          self.assertIs(cond_m.comparisons, train_comp)
  
          # fantasize at different input points
          fant_shape = torch.Size([2])
          X_fant, comp_fant = self._make_rand_mini_data(
              batch_shape=fant_shape + batch_shape,
              X_dim=X_dim,
          )
  
          # cannot condition on non-pairwise Ys
          with self.assertRaises(RuntimeError):
              model.condition_on_observations(X_fant, comp_fant[..., 0])
          cm = model.condition_on_observations(X_fant, comp_fant)
          # make sure it's a deep copy
          self.assertTrue(model is not cm)
  
          # fantasize at same input points (check proper broadcasting)
          cm_same_inputs = model.condition_on_observations(
              X_fant[0], comp_fant
          )
  
          test_Xs = [
              # test broadcasting single input across fantasy and model batches
              torch.rand(4, X_dim, **tkwargs),
              # separate input for each model batch and broadcast across
              # fantasy batches
              torch.rand(
                  batch_shape + torch.Size([4, X_dim]), **tkwargs
              ),
              # separate input for each model and fantasy batch
              torch.rand(
                  fant_shape + batch_shape + torch.Size([4, X_dim]),
                  **tkwargs,
              ),
          ]
          for test_X in test_Xs:
              posterior = cm.posterior(test_X)
              self.assertEqual(
                  posterior.mean.shape,
                  fant_shape + batch_shape + torch.Size([4, 1]),
              )
              posterior_same_inputs = cm_same_inputs.posterior(test_X)
              self.assertEqual(
                  posterior_same_inputs.mean.shape,
                  fant_shape + batch_shape + torch.Size([4, 1]),
              )
  
              # check that fantasies of batched model are correct
              if len(batch_shape) > 0 and test_X.dim() == 2:
                  state_dict_non_batch = {
                      key: (val[0] if val.numel() > 1 else val)
                      for key, val in model.state_dict().items()
                  }
                  model_kwargs_non_batch = {
                      "datapoints": model_kwargs["datapoints"][0],
                      "comparisons": model_kwargs["comparisons"][0],
                      "likelihood": likelihood_cls(),
                  }
                  model_non_batch = model.__class__(
                      **model_kwargs_non_batch
                  )
                  model_non_batch.load_state_dict(state_dict_non_batch)
                  model_non_batch.eval()
                  model_non_batch.posterior(
                      torch.rand(torch.Size([4, X_dim]), **tkwargs)
                  )
                  cm_non_batch = (
                      model_non_batch.condition_on_observations(
                          X_fant[0][0], comp_fant[:, 0, :]
                      )
                  )
                  non_batch_posterior = cm_non_batch.posterior(test_X)
                  self.assertAllClose(
                      posterior_same_inputs.mean[:, 0, ...],
                      non_batch_posterior.mean,
                      atol=1e-3,
                  )
                  self.assertAllClose(
                      posterior_same_inputs.distribution.covariance_matrix[
                          :, 0, :, :
                      ],
                      non_batch_posterior.distribution.covariance_matrix,
                      atol=1e-3,
                  )
  
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 6 for calling the TestPairwiseGP obj's underlying member method code with example usage: obj.test_condition_on_observations() without return types