code: def dummy_sample_pareto_frontiers(model):
      m = model.models[0] if isinstance(model, ModelListGP) else model
      return torch.rand(
          3,
          4,
          model.num_outputs,
          dtype=m.train_inputs[0].dtype,
          device=m.train_inputs[0].device,
      )
  
imports: from itertools import product
  from unittest import mock
  import torch
  from botorch.acquisition.max_value_entropy_search import qMaxValueEntropy
  from botorch.acquisition.multi_objective.max_value_entropy_search import qLowerBoundMultiObjectiveMaxValueEntropySearch, qMultiObjectiveMaxValueEntropy
  from botorch.acquisition.multi_objective.utils import compute_sample_box_decomposition
  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.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 2 for calling a global function with example usage: dummy_sample_pareto_frontiers(model) and returns: torch