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
      if args.bcp:
          trainer = Trainer(
              devices=args.gpus_per_node,
              num_nodes=num_nodes,
              accelerator="gpu",
              plugins=[TorchElasticEnvironment()],
          )
      else:
          trainer = Trainer(
              devices=args.gpus_per_node,
              num_nodes=num_nodes,
              accelerator="gpu",
          )
  
      app_state.pipeline_model_parallel_size = (
          args.pipeline_model_parallel_size
      )
      app_state.tensor_model_parallel_size = (
          args.tensor_model_parallel_size
      )
  
      # no use atm, use to split ranks in encoder/decoder models.
      if (
          args.pipeline_model_parallel_size > 1
          and args.model_type in []
      ):
          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()
      )
  
      cfg = OmegaConf.load(args.hparams_file)
      model = MegatronCLIPModel(cfg.model, trainer)
  
      if args.version == "huggingface":
          hf_model = CLIPModel.from_pretrained(args.arch)
          state_dict = mapping_hf_state_dict(hf_model)
      else:
          open_model, _, _ = open_clip.create_model_and_transforms(
              args.arch, pretrained=args.version
          )
          state_dict = mapping_openclip_state_dict(open_model)
  
      model.model.load_state_dict(state_dict)
  
      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}")
  
file_path: NeMo/examples/multimodal/vision_language_foundation/clip/convert_external_clip_to_nemo.py
imports: import os
  from argparse import ArgumentParser
  import einops
  import open_clip
  import torch
  from omegaconf import OmegaConf
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.trainer.trainer import Trainer
  from transformers import CLIPModel
  from nemo.collections.multimodal.models.vision_language_foundation.clip.megatron_clip_models import MegatronCLIPModel
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
  from nemo.utils import AppState, logging
  from nemo.utils.distributed import initialize_distributed
label_desc: Use image node_id 4 for calling a global function with example usage: convert(local_rank, rank, world_size, args) without return types