code: def match_baseline_cfg(cfg_dict, cfg_dict_baseline, verbose=True):
      """Match the computational budget of a given baseline model. The current
      configuration dictionary will be modifed and returned.
  
      Args:
          cfg_dict (dict): Current experiment's configuration
          cfg_dict_baseline (dict): Baseline configuration
          verbose (str, optional): If printing matched paramter conunts
      """
      from yacs.config import CfgNode as CN
  
      stats_baseline = dict_to_stats(cfg_dict_baseline)
      set_cfg(cfg)
      cfg_new = CN(cfg_dict)
      cfg.merge_from_other_cfg(cfg_new)
      stats = match_computation(
          stats_baseline, key=["gnn", "dim_inner"]
      )
      if "gnn" in cfg_dict:
          cfg_dict["gnn"]["dim_inner"] = cfg.gnn.dim_inner
      else:
          cfg_dict["gnn"] = {"dim_inner", cfg.gnn.dim_inner}
      set_cfg(cfg)
      if verbose:
          print(
              f"Computational budget has matched - Baseline params: "
              f"{stats_baseline}, Current params: {stats}"
          )
      return cfg_dict
  
imports: import math
  from torch_geometric.graphgym.config import cfg, set_cfg
  from torch_geometric.graphgym.model_builder import create_model
label_desc: Use image node_id 5 for calling a global function with example usage: match_baseline_cfg(cfg_dict, cfg_dict_baseline, verbose) and returns: cfg_dict