code: def create_benchmark_config(model_name, config_class):
      """Return a dict with configurations required for benchmarking `model_name` model."""
  
      if model_name == "lm":
          return config_class.get_benchmark_config()
      else:
          raise RuntimeError(
              "Unrecognized args.model_mame " % args.model_name
          )
  
file_path: fairscale/benchmarks/utils.py
imports: import argparse
  from functools import reduce
  import logging
  import operator
  import datasets.wikitext2_data
  from models import transformer_lm
  import numpy
  import torch
  from torch.optim import Adam
label_desc: Use image node_id 3 for calling a global function with example usage: create_benchmark_config(model_name, config_class) and returns: config_class