code: def main(args=None, model=None) -> GenerativeQAModule:
      parser = argparse.ArgumentParser()
      parser = pl.Trainer.add_argparse_args(parser)
      parser = GenerativeQAModule.add_model_specific_args(
          parser, os.getcwd()
      )
      parser = GenerativeQAModule.add_retriever_specific_args(parser)
  
      args = args or parser.parse_args()
  
      Path(args.output_dir).mkdir(exist_ok=True)
  
      named_actors = []
      if args.distributed_retriever == "ray" and args.gpus > 1:
          if not is_ray_available():
              raise RuntimeError(
                  "Please install Ray to use the Ray distributed retriever."
              )
          # Connect to an existing Ray cluster.
          try:
              ray.init(address=args.ray_address, namespace="rag")
          except (ConnectionError, ValueError):
              logger.warning(
                  "Connection to Ray cluster failed. Make sure a Ray"
                  "cluster is running by either using Ray's cluster "
                  "launcher (`ray up`) or by manually starting Ray on "
                  "each node via `ray start --head` for the head node "
                  "and `ray start --address='<ip address>:6379'` for "
                  "additional nodes. See "
                  "https://docs.ray.io/en/master/cluster/index.html "
                  "for more info."
              )
              raise
  
          # Create Ray actors only for rank 0.
          if (
              "LOCAL_RANK" not in os.environ
              or int(os.environ["LOCAL_RANK"]) == 0
          ) and (
              "NODE_RANK" not in os.environ
              or int(os.environ["NODE_RANK"]) == 0
          ):
              remote_cls = ray.remote(RayRetriever)
              named_actors = [
                  remote_cls.options(
                      name="retrieval_worker_{}".format(i)
                  ).remote()
                  for i in range(args.num_retrieval_workers)
              ]
          else:
              logger.info(
                  "Getting named actors for NODE_RANK {}, LOCAL_RANK {}".format(
                      os.environ["NODE_RANK"], os.environ["LOCAL_RANK"]
                  )
              )
              named_actors = [
                  ray.get_actor("retrieval_worker_{}".format(i))
                  for i in range(args.num_retrieval_workers)
              ]
      args.actor_handles = named_actors
      assert args.actor_handles == named_actors
  
      if model is None:
          model: GenerativeQAModule = GenerativeQAModule(args)
  
      dataset = Path(args.data_dir).name
      if (
          args.logger_name == "default"
          or args.fast_dev_run
          or str(args.output_dir).startswith("/tmp")
          or str(args.output_dir).startswith("/var")
      ):
          training_logger = (
              True  # don't pollute wandb logs unnecessarily
          )
      elif args.logger_name == "wandb":
          from pytorch_lightning.loggers import WandbLogger
  
          project = os.environ.get("WANDB_PROJECT", dataset)
          training_logger = WandbLogger(
              name=model.output_dir.name, project=project
          )
  
      elif args.logger_name == "wandb_shared":
          from pytorch_lightning.loggers import WandbLogger
  
          training_logger = WandbLogger(
              name=model.output_dir.name, project=f"hf_{dataset}"
          )
  
      es_callback = (
          get_early_stopping_callback(
              model.val_metric, args.early_stopping_patience
          )
          if args.early_stopping_patience >= 0
          else False
      )
  
      trainer: pl.Trainer = generic_train(
          model,
          args,
          logging_callback=Seq2SeqLoggingCallback(),
          checkpoint_callback=get_checkpoint_callback(
              args.output_dir, model.val_metric
          ),
          early_stopping_callback=es_callback,
          logger=training_logger,
          custom_ddp_plugin=CustomDDP() if args.gpus > 1 else None,
          profiler=pl.profiler.AdvancedProfiler()
          if args.profile
          else None,
      )
      pickle_save(model.hparams, model.output_dir / "hparams.pkl")
  
      if not args.do_predict:
          return model
  
      # test() without a model tests using the best checkpoint automatically
      trainer.test()
      return model
  
imports: import argparse
  import logging
  import os
  import sys
  import time
  from collections import defaultdict
  from pathlib import Path
  from typing import Any, Dict, List, Tuple
  import numpy
  import pytorch_lightning
  import torch
  import torch.distributed
  import torch.distributed
  from pytorch_lightning.plugins.training_type import DDPPlugin
  from torch.utils.data import DataLoader
  from transformers import AutoConfig, AutoTokenizer, BartForConditionalGeneration, BatchEncoding, RagConfig, RagSequenceForGeneration, RagTokenForGeneration, RagTokenizer, T5ForConditionalGeneration
  from transformers import logging
  from transformers.integrations import is_ray_available
  from callbacks_rag import get_checkpoint_callback, get_early_stopping_callback, Seq2SeqLoggingCallback
  from distributed_pytorch_retriever import RagPyTorchDistributedRetriever
  from utils_rag import calculate_exact_match, flatten_list, get_git_info, is_rag_model, lmap, pickle_save, save_git_info, save_json, set_extra_model_params, Seq2SeqDataset
  from lightning_base import BaseTransformer, add_generic_args, generic_train
label_desc: Use image node_id 1 for calling a global function with example usage: main(args, model) and returns: model, model