docstr: test repeat_to_match_aug_dim to ensure it repeat the elements
  in the correct order
docstr_tok: ["test","repeat_to_match_aug_dim","to","ensure","it","repeat","the","elements","in","the","correct","order"]
code: def test_repeat_to_match_aug_dim(self):
      """test repeat_to_match_aug_dim to ensure it repeat the elements
      in the correct order
      """
      # simple working case
      target_tensor = torch.arange(3).repeat(2, 1).T
      repeated_tensor = repeat_to_match_aug_dim(
          target_tensor, torch.zeros(6)
      )
      self.assertEqual(repeated_tensor.shape, torch.Size([6, 2]))
  
      # simple invalid cases
      target_tensor = torch.rand(6, 2, 3)
      reference_tensor = torch.rand(5, 2, 3)
      with self.assertRaisesRegex(
          ValueError,
          "The first dimension of reference_tensor must be a multiple of",
      ):
          repeat_to_match_aug_dim(
              target_tensor=target_tensor,
              reference_tensor=reference_tensor,
          )
  
      # similarting real use cases
      num_outcome_samples, n, q, d = 3, 2, 4, 5
      model = SingleTaskGP(
          train_X=torch.rand(n, d), train_Y=torch.rand(n, 1)
      )
      obj = LearnedObjective(pref_model=model)
      samples = torch.rand(num_outcome_samples, q, d)
  
      # Save a reference to the original posterior method
      original_posterior_method = SingleTaskGP.posterior
  
      def nearly_zero_covar_posterior(self, *args, **kwargs):
          original_posterior = original_posterior_method(
              self, *args, **kwargs
          )
  
          # Modify the distribution
          original_posterior.distribution = MultivariateNormal(
              mean=original_posterior.distribution.mean,
              covariance_matrix=torch.diag_embed(
                  torch.full_like(
                      original_posterior.distribution.mean,
                      fill_value=1e-15,
                  )
              ),
          )
  
          # Return the modified posterior
          return original_posterior
  
      # Patch the posterior call such that sampling from the model's output will give
      # basically the same samples. This way, we are able to tell which preference
      # sample comes from which outcome sample.
      # When `samples` of shape `num_samples x ...` being passed through obj,
      # the returned augmented sample is of shape
      # `(num_pref_sample * num_samples) x ...`.
      # If num_samples = 3 and num_pref_sample = 2,
      # along the first dimension of objective, objective values should correspond to
      # index [0, 1, 2, 0, 1, 2] of `samples`.
      with patch.object(
          SingleTaskGP, "posterior", new=nearly_zero_covar_posterior
      ):
          objective = obj(samples)
  
      repeated_samples = repeat_to_match_aug_dim(
          target_tensor=samples, reference_tensor=objective
      )
  
      self.assertAllClose(
          objective,
          torch.roll(objective, shifts=num_outcome_samples, dims=0),
          rtol=1e-3,
      )
      self.assertAllClose(
          repeated_samples,
          torch.roll(
              repeated_samples, shifts=num_outcome_samples, dims=0
          ),
          rtol=1e-3,
      )
  
imports: import itertools
  from unittest import mock
  from unittest.mock import patch
  import torch
  from botorch.acquisition.objective import GenericMCObjective, LearnedObjective
  from botorch.acquisition.utils import compute_best_feasible_objective, expand_trace_observations, get_acquisition_function, get_infeasible_cost, get_optimal_samples, project_to_sample_points, project_to_target_fidelity, prune_inferior_points, repeat_to_match_aug_dim
  from botorch.exceptions.errors import BotorchTensorDimensionError, DeprecationError, UnsupportedError
  from botorch.models import SingleTaskGP
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
  from gpytorch.distributions import MultivariateNormal
label_desc: Use image node_id 1 for calling the TestPreferenceUtils obj's underlying member method code with example usage: obj.test_repeat_to_match_aug_dim() and returns: original_posterior