code: def convert_checkpoints(
      original_ckpt_dir: str,
      save_checkpoint_dir: str,
      tokenizer_path: str,
      model_parallel_size: int,
      max_seq_len: int = 512,
      max_batch_size: int = 4,
  ):
      dist.init_process_group("nccl")
  
      Llama.convert_checkpoints(
          original_ckpt_dir=original_ckpt_dir,
          save_checkpoint_dir=save_checkpoint_dir,
          tokenizer_path=tokenizer_path,
          max_seq_len=max_seq_len,
          max_batch_size=max_batch_size,
          model_parallel_size=model_parallel_size,
      )
  
imports: import torch
  from llama2 import Llama
  import torch.distributed
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import abc
  import fire
label_desc: Use image node_id 1 for calling a global function with example usage: convert_checkpoints(original_ckpt_dir, save_checkpoint_dir, tokenizer_path, model_parallel_size, max_seq_len, max_batch_size) without return types