code: def get_multi_objective_benchmark_problem(
      infer_noise: bool = True, num_trials: int = 4
  ) -> MultiObjectiveBenchmarkProblem:
      return (
          MultiObjectiveBenchmarkProblem.from_botorch_multi_objective(
              test_problem_class=BraninCurrin,
              test_problem_kwargs={},
              num_trials=num_trials,
              infer_noise=infer_noise,
          )
      )
  
imports: import numpy
  from ax.benchmark.benchmark_method import BenchmarkMethod
  from ax.benchmark.benchmark_problem import BenchmarkProblem, MultiObjectiveBenchmarkProblem, SingleObjectiveBenchmarkProblem
  from ax.benchmark.benchmark_result import AggregatedBenchmarkResult, BenchmarkResult
  from ax.benchmark.problems.surrogate import MOOSurrogateBenchmarkProblem, SOOSurrogateBenchmarkProblem
  from ax.core.experiment import Experiment
  from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
  from ax.modelbridge.registry import Models
  from ax.models.torch.botorch_modular.surrogate import Surrogate
  from ax.service.scheduler import SchedulerOptions
  from ax.utils.common.constants import Keys
  from ax.utils.testing.core_stubs import get_branin_multi_objective_optimization_config, get_branin_optimization_config, get_branin_search_space
  from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.test_functions.multi_objective import BraninCurrin
  from botorch.test_functions.synthetic import Branin
label_desc: Use image node_id 3 for calling a global function with example usage: get_multi_objective_benchmark_problem(infer_noise, num_trials) and returns: MultiObjectiveBenchmarkProblem