code: def split_and_convert(args):
      torch_device = "cuda" if torch.cuda.is_available() else "cpu"
      model = GPT2Model.from_pretrained(args.in_file).to(torch_device)
      main(
          args.saved_dir,
          model,
          args.trained_gpu_num,
          args.infer_gpu_num,
          args.processes,
          args.weight_data_type,
      )
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/faster_transformer/gpt/utils/huggingface_gpt_convert.py
imports: import argparse
  import configparser
  import os
  import sys
  from loguru import logger
  import numpy
  from transformers import GPT2Model
  from nebullvm.optional_modules.torch import torch
label_desc: Use image node_id 3 for calling a global function with example usage: split_and_convert(args) without return types