code: def main(args):
      print(args)
  
      checkpoint_path = args.checkpoint_path
      save_path = args.save_path
      if isfile(checkpoint_path):
          raise FileNotFoundError(
              f"ERROR! could not find file {checkpoint_path}"
          )
  
      # Load the model.
      checkpoint = torch.load(checkpoint_path, map_location="cpu")
  
      # Load the config.
      config_megatron = checkpoint["hyper_parameters"]["cfg"]
      config_hf = GPT2Config()
      config_hf = copy_config(
          config_hf=config_hf, config_megatron=config_megatron
      )
      config_hf.architectures = ["GPT2LMHeadModel"]
  
      sd_megatron = checkpoint["state_dict"]
  
      # Convert.
      print("Converting")
      sd_hf = convert_megatron_checkpoint(sd_megatron, config_hf)
  
      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
          recursive_print(None, sd_hf)
  
      config_hf.tokenizer_class = "GPTSw3Tokenizer"
  
      # Store the config to file.
      print("Saving config")
      config_hf.save_pretrained(save_path)
  
      # Store the state_dict to file.
      output_checkpoint_file = os.path.join(
          save_path, "pytorch_model.bin"
      )
      print(f'Saving checkpoint to "{output_checkpoint_file}"')
      torch.save(sd_hf, output_checkpoint_file)
  
file_path: transformers/src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
imports: import argparse
  import os
  from os.path import isfile
  import torch
  from transformers import GPT2Config
label_desc: Use image node_id 5 for calling a global function with example usage: main(args) without return types