code: def main():
      setup_logging()
      config_help = "\n\nConfig parameters:\n\n" + "\n".join(
          ConfigSchema.help()
      )
      parser = argparse.ArgumentParser(
          epilog=config_help,
          # Needed to preserve line wraps in epilog.
          formatter_class=argparse.RawDescriptionHelpFormatter,
      )
      parser.add_argument("config", help="Path to config file")
      parser.add_argument("-p", "--param", action="append", nargs="*")
      parser.add_argument(
          "--rank",
          type=int,
          default=SINGLE_TRAINER,
          help="For multi-machine, this machine's rank",
      )
      opt = parser.parse_args()
  
      loader = ConfigFileLoader()
      config = loader.load_config(opt.config, opt.param)
      set_logging_verbosity(config.verbose)
      subprocess_init = SubprocessInitializer()
      subprocess_init.register(setup_logging, config.verbose)
      subprocess_init.register(add_to_sys_path, loader.config_dir.name)
  
      train(config, rank=opt.rank, subprocess_init=subprocess_init)
  
file_path: PyTorch-BigGraph/torchbiggraph/train.py
imports: import argparse
  import logging
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple
  from torchbiggraph.batching import AbstractBatchProcessor
  from torchbiggraph.config import add_to_sys_path, ConfigFileLoader, ConfigSchema
  from torchbiggraph.model import MultiRelationEmbedder
  from torchbiggraph.train_cpu import TrainingCoordinator
  from torchbiggraph.train_gpu import GPUTrainingCoordinator
  from torchbiggraph.types import Rank, SINGLE_TRAINER
  from torchbiggraph.util import set_logging_verbosity, setup_logging, SubprocessInitializer
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types