code: def dummy_sample_pareto_fronts(
      model, num_pareto_samples, num_pareto_points
  ):
      m = model.models[0] if isinstance(model, ModelListGP) else model
      num_objectives = model.num_outputs
      tkwargs = {
          "dtype": m.train_inputs[0].dtype,
          "device": m.train_inputs[0].device,
      }
      return torch.rand(
          num_pareto_samples,
          num_pareto_points,
          num_objectives,
          **tkwargs,
      )
  
imports: from itertools import product
  import torch
  from botorch.acquisition.multi_objective.joint_entropy_search import LowerBoundMultiObjectiveEntropySearch, qLowerBoundMultiObjectiveJointEntropySearch
  from botorch.acquisition.multi_objective.utils import compute_sample_box_decomposition
  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.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 3 for calling a global function with example usage: dummy_sample_pareto_fronts(model, num_pareto_samples, num_pareto_points) and returns: torch