code: def multiprocess_train(
      rank,
      opt,
      port=61337,
      rank_offset=0,
      gpu=None,
      hostname="localhost",
  ):
      init_method = f"tcp://{hostname}:{port}"
      with distributed_utils.distributed_context(
          rank, opt, rank_offset, gpu, init_method=init_method
      ) as opt:
          # Run the actual training
          opt["multiprocessing"] = True
          loop = fsdp_utils.JoinableTrainLoop(opt)
          try:
              with fsdp_utils.fsdp_join(loop):
                  return loop.train()
          except Exception:
              import parlai.utils.logging as logging
  
              logging.critical(traceback.format_exc())
              logging.critical(
                  f"Got the above exception on worker {rank + rank_offset}. "
                  "This may cause hangs requiring manual killing of processes."
              )
              raise
  
file_path: ParlAI/parlai/scripts/multiprocessing_train.py
imports: import torch
  import os
  import signal
  import traceback
  import parlai.scripts.train_model
  import parlai.utils.distributed
  import parlai.utils.fsdp
  from parlai.core.script import ParlaiScript, register_script
label_desc: Use image node_id 1 for calling a global function with example usage: multiprocess_train(rank, opt, port, rank_offset, gpu, hostname) and returns: loop