code: def get_benchmark_result() -> BenchmarkResult:
      problem = get_single_objective_benchmark_problem()
  
      return BenchmarkResult(
          name="test_benchmarking_result",
          seed=0,
          experiment=Experiment(
              name="test_benchmarking_experiment",
              search_space=problem.search_space,
              optimization_config=problem.optimization_config,
              runner=problem.runner,
              is_test=True,
          ),
          optimization_trace=np.array([3, 2, 1, 0.1]),
          score_trace=np.array([3, 2, 1, 0.1]),
          fit_time=0.1,
          gen_time=0.2,
      )
  
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 8 for calling a global function with example usage: get_benchmark_result() and returns: BenchmarkResult