code: def convert(local_rank, rank, world_size, args):
      app_state = AppState()
      app_state.data_parallel_rank = 0
      num_nodes = world_size // args.gpus_per_node
      plugins = []
      strategy = "auto"
      if args.bcp:
          plugins.append(TorchElasticEnvironment())
      if args.model_type == "gpt":
          strategy = NLPDDPStrategy()
  
      cfg = {
          "trainer": {
              "devices": args.gpus_per_node,
              "num_nodes": num_nodes,
              "accelerator": "gpu",
              "precision": args.precision,
          },
          "model": {
              "native_amp_init_scale": 2**32,
              "native_amp_growth_interval": 1000,
              "hysteresis": 2,
          },
      }
      cfg = OmegaConf.create(cfg)
  
      scaler = None
      # If FP16 create a GradScaler as the build_model_parallel_config of MegatronBaseModel expects it
      if cfg.trainer.precision == "16-mixed":
          scaler = GradScaler(
              init_scale=cfg.model.get(
                  "native_amp_init_scale", 2**32
              ),
              growth_interval=cfg.model.get(
                  "native_amp_growth_interval", 1000
              ),
              hysteresis=cfg.model.get("hysteresis", 2),
          )
      plugins.append(
          PipelineMixedPrecisionPlugin(
              precision=cfg.trainer.precision,
              device="cuda",
              scaler=scaler,
          )
      )
      trainer = Trainer(
          plugins=plugins, strategy=strategy, **cfg.trainer
      )
  
      app_state.pipeline_model_parallel_size = (
          args.pipeline_model_parallel_size
      )
      app_state.tensor_model_parallel_size = (
          args.tensor_model_parallel_size
      )
      # Auto set split rank for T5, BART, NMT if split rank is None.
      if args.pipeline_model_parallel_size > 1 and args.model_type in [
          "t5",
          "bart",
          "nmt",
      ]:
          if args.pipeline_model_parallel_split_rank is not None:
              app_state.pipeline_model_parallel_split_rank = (
                  args.pipeline_model_parallel_split_rank
              )
          else:
              if args.pipeline_model_parallel_size % 2 != 0:
                  raise ValueError(
                      f"Pipeline model parallel size {args.pipeline_model_parallel_size} must be even if split rank is not specified."
                  )
              else:
                  # If split rank is not set, then we set it to be pipeline_model_parallel_size // 2 - this is because in most cases we have the same number of enc/dec layers.
                  app_state.pipeline_model_parallel_split_rank = (
                      args.pipeline_model_parallel_size // 2
                  )
      else:
          app_state.pipeline_model_parallel_split_rank = None
  
      app_state.model_parallel_size = (
          app_state.tensor_model_parallel_size
          * app_state.pipeline_model_parallel_size
      )
  
      parallel_state.initialize_model_parallel(
          tensor_model_parallel_size=app_state.tensor_model_parallel_size,
          pipeline_model_parallel_size=app_state.pipeline_model_parallel_size,
          pipeline_model_parallel_split_rank=app_state.pipeline_model_parallel_split_rank,
      )
  
      app_state.pipeline_model_parallel_rank = (
          parallel_state.get_pipeline_model_parallel_rank()
      )
      app_state.tensor_model_parallel_rank = (
          parallel_state.get_tensor_model_parallel_rank()
      )
  
      # check for distributed checkpoint
      dist_ckpt_dir = os.path.join(
          args.checkpoint_folder, args.checkpoint_name
      )
      if os.path.isdir(dist_ckpt_dir):
          checkpoint_path = dist_ckpt_dir
      else:
          # legacy checkpoint needs model parallel injection
          checkpoint_path = inject_model_parallel_rank(
              os.path.join(args.checkpoint_folder, args.checkpoint_name)
          )
  
      logging.info(
          f"rank: {rank}, local_rank: {local_rank}, is loading checkpoint: {checkpoint_path} for tp_rank: {app_state.tensor_model_parallel_rank} and pp_rank: {app_state.pipeline_model_parallel_rank}"
      )
  
      if args.model_type == "gpt":
          model = MegatronGPTModel.load_from_checkpoint(
              checkpoint_path,
              hparams_file=args.hparams_file,
              trainer=trainer,
          )
      elif args.model_type == "sft":
          model = MegatronGPTSFTModel.load_from_checkpoint(
              checkpoint_path,
              hparams_file=args.hparams_file,
              trainer=trainer,
          )
          # we force the target for the loaded model to have the correct target
          # because the hparams.yaml sometimes contains MegatronGPTModel as the target.
          with open_dict(model.cfg):
              model.cfg.target = f"{MegatronGPTSFTModel.__module__}.{MegatronGPTSFTModel.__name__}"
  
      elif args.model_type == "bert":
          model = MegatronBertModel.load_from_checkpoint(
              checkpoint_path,
              hparams_file=args.hparams_file,
              trainer=trainer,
          )
      elif args.model_type == "t5":
          model = MegatronT5Model.load_from_checkpoint(
              checkpoint_path,
              hparams_file=args.hparams_file,
              trainer=trainer,
          )
      elif args.model_type == "bart":
          model = MegatronBARTModel.load_from_checkpoint(
              checkpoint_path,
              hparams_file=args.hparams_file,
              trainer=trainer,
          )
      elif args.model_type == "nmt":
          model = MegatronNMTModel.load_from_checkpoint(
              checkpoint_path,
              hparams_file=args.hparams_file,
              trainer=trainer,
          )
      elif args.model_type == "retro":
          model = MegatronRetrievalModel.load_from_checkpoint(
              checkpoint_path,
              hparams_file=args.hparams_file,
              trainer=trainer,
          )
      model._save_restore_connector = NLPSaveRestoreConnector()
  
      if torch.distributed.is_initialized():
          torch.distributed.barrier()
  
      model.save_to(args.nemo_file_path)
  
      logging.info(f"NeMo model saved to: {args.nemo_file_path}")
  
imports: import dis
  import os
  from argparse import ArgumentParser
  import torch
  from genericpath import isdir
  from megatron.core import parallel_state
  from omegaconf import OmegaConf, open_dict
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.models.language_modeling.megatron_bart_model import MegatronBARTModel
  from nemo.collections.nlp.models.language_modeling.megatron_bert_model import MegatronBertModel
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel
  from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
  from nemo.collections.nlp.models.machine_translation.megatron_nmt_model import MegatronNMTModel
  from nemo.collections.nlp.parts.nlp_overrides import GradScaler, NLPDDPStrategy, NLPSaveRestoreConnector, PipelineMixedPrecisionPlugin
  from nemo.utils import AppState, logging
  from nemo.utils.distributed import initialize_distributed
  from nemo.utils.model_utils import inject_model_parallel_rank
label_desc: Use image node_id 2 for calling a global function with example usage: convert(local_rank, rank, world_size, args) without return types