code: def main():
      config_name, gpu_id = sys.argv[1], int(sys.argv[2])
      saved_suffix = sys.argv[3] if len(sys.argv) >= 4 else None
      runner = ERERunner(
          config_file="conf/train.yaml",
          config_name=config_name,
          gpu_id=gpu_id,
      )
  
      if saved_suffix is not None:
          model, start_epoch = runner.initialize_model(
              saved_suffix, continue_training=True
          )
          runner.train(model, continued=True, start_epoch=start_epoch)
      else:
          model, _ = runner.initialize_model()
          runner.train(model, continued=False)
  
imports: from deepke.triple_extraction.ASP import *
  import sys
  import logging
  import random
  import numpy
  import torch
  from torch.optim import AdamW
  import deepke.triple_extraction.ASP.util
  from deepke.triple_extraction.ASP.util.multigpu_fused_adam import FusedAdam
  from torch.optim.lr_scheduler import LambdaLR
  from torch.utils.data import DataLoader
  import time
  from os.path import join
  from datetime import datetime
  from deepke.triple_extraction.ASP.util.runner import Runner
  from metrics import EREEvaluator
  import wandb
  import hydra
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types