code: def test_initialization_errors(self):
      torch.manual_seed(1)
      tkwargs = {"device": self.device}
      standardize_model = False
      for (
          dtype,
          num_objectives,
          use_model_list,
      ) in product(
          (torch.float, torch.double),
          (1, 2, 3),
          (False, True),
      ):
          tkwargs["dtype"] = dtype
          # test batched model
          train_X = torch.rand(4, 3, 2, **tkwargs)
          train_Y = torch.rand(4, 3, num_objectives, **tkwargs)
          model = get_model(
              train_X, train_Y, use_model_list, standardize_model
          )
          num_pareto_samples = 3
          if num_objectives > 1:
              num_pareto_points = 4
          else:
              num_pareto_points = 1
  
          pareto_sets = dummy_sample_pareto_sets(
              model, num_pareto_samples, num_pareto_points
          )
  
          # test batch model error
          with self.assertRaises(NotImplementedError):
              qMultiObjectivePredictiveEntropySearch(
                  model=model,
                  pareto_sets=pareto_sets,
              )
  
          # test wrong Pareto set shape
          train_X = torch.rand(1, 2, **tkwargs)
          train_Y = torch.rand(1, num_objectives, **tkwargs)
          model = get_model(
              train_X, train_Y, use_model_list, standardize_model
          )
  
          pareto_sets = dummy_sample_pareto_sets(
              model, num_pareto_samples, num_pareto_points
          )
  
          with self.assertRaises(UnsupportedError):
              qMultiObjectivePredictiveEntropySearch(
                  model=model,
                  pareto_sets=pareto_sets.unsqueeze(0),
              )
  
          with self.assertRaises(UnsupportedError):
              qMultiObjectivePredictiveEntropySearch(
                  model=model,
                  pareto_sets=pareto_sets.unsqueeze(-1),
              )
  
file_path: botorch/test/acquisition/multi_objective/test_predictive_entropy_search.py
imports: from itertools import product
  import torch
  from botorch.acquisition.multi_objective.predictive_entropy_search import _safe_update_omega, _update_damping, qMultiObjectivePredictiveEntropySearch
  from botorch.exceptions import UnsupportedError
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.transforms.outcome import Standardize
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 1 for calling the TestQMultiObjectivePredictiveEntropySearch obj's underlying member method code with example usage: obj.test_initialization_errors() without return types