code: def run_benchmark(args: argparse.Namespace) -> None:
      sampler_list = args.sampler_list.split()
      sampler_kwargs_list = args.sampler_kwargs_list.split()
      pruner_list = args.pruner_list.split()
      pruner_kwargs_list = args.pruner_kwargs_list.split()
  
      if len(sampler_list) != len(sampler_kwargs_list):
          raise ValueError(
              "The number of samplers does not match the given keyword arguments. \n"
              f"sampler_list: {sampler_list}, sampler_kwargs_list: {sampler_kwargs_list}."
          )
  
      if len(pruner_list) != len(pruner_kwargs_list):
          raise ValueError(
              "The number of pruners does not match the given keyword arguments. \n"
              f"pruner_list: {pruner_list}, pruner_keyword_arguments: {pruner_kwargs_list}."
          )
  
      config = dict()
      for i, (sampler, sampler_kwargs) in enumerate(
          zip(sampler_list, sampler_kwargs_list)
      ):
          sampler_name = sampler
          if sampler_list.count(sampler) > 1:
              sampler_name += f"_{sampler_list[:i].count(sampler)}"
          for j, (pruner, pruner_kwargs) in enumerate(
              zip(pruner_list, pruner_kwargs_list)
          ):
              pruner_name = pruner
              if pruner_list.count(pruner) > 1:
                  pruner_name += f"_{pruner_list[:j].count(pruner)}"
              optimizer_name = (
                  f"{args.name_prefix}_{sampler_name}_{pruner_name}"
              )
              optimizer_kwargs = {
                  "sampler": sampler,
                  "sampler_kwargs": json.loads(sampler_kwargs),
                  "pruner": pruner,
                  "pruner_kwargs": json.loads(pruner_kwargs),
              }
              # We need to dynamically generate config.json sice sampler pruner combos (solvers)
              # are parametrized by user. Following sample config schema.
              # https://github.com/uber/bayesmark/blob/master/example_opt_root/config.json
              config[optimizer_name] = [
                  "optuna_optimizer.py",
                  optimizer_kwargs,
              ]
  
      with open(
          os.path.join("benchmarks", "bayesmark", "config.json"), "w"
      ) as file:
          json.dump(config, file, indent=4)
  
      samplers = " ".join(config.keys())
      metric = (
          "nll"
          if args.dataset in ["breast", "iris", "wine", "digits"]
          else "mse"
      )
      cmd = (
          f"bayesmark-launch -n {args.budget} -r {args.n_runs} "
          f"-dir runs -b {_DB} "
          f"-o {samplers} "
          f"-c {args.model} -d {args.dataset} "
          f"-m {metric} --opt-root benchmarks/bayesmark"
      )
      subprocess.run(cmd, shell=True)
  
file_path: optuna/benchmarks/run_bayesmark.py
imports: from __future__ import annotations
  import argparse
  import json
  import os
  import subprocess
  from matplotlib import cm
  from matplotlib import colors
  from matplotlib.axes import Axes
  import matplotlib.pyplot
  import numpy
  import pandas
  from xarray import Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: run_benchmark(args) without return types