code: def benchmark_one_method_problem(
      problem: BenchmarkProblemBase,
      method: BenchmarkMethod,
      seeds: Iterable[int],
  ) -> AggregatedBenchmarkResult:
      return AggregatedBenchmarkResult.from_benchmark_results(
          results=[
              benchmark_replication(
                  problem=problem, method=method, seed=seed
              )
              for seed in seeds
          ]
      )
  
file_path: Ax/ax/benchmark/benchmark.py
imports: from itertools import product
  from logging import Logger
  from time import time
  from typing import Iterable, List
  import numpy
  from ax.benchmark.benchmark_method import BenchmarkMethod
  from ax.benchmark.benchmark_problem import BenchmarkProblemBase, BenchmarkProblemWithKnownOptimum
  from ax.benchmark.benchmark_result import AggregatedBenchmarkResult, BenchmarkResult
  from ax.core.experiment import Experiment
  from ax.core.utils import get_model_times
  from ax.service.scheduler import Scheduler
  from ax.utils.common.logger import get_logger
  from botorch.utils.sampling import manual_seed
label_desc: Use image node_id 4 for calling a global function with example usage: benchmark_one_method_problem(problem, method, seeds) and returns: AggregatedBenchmarkResult