code: def convert_yoso_checkpoint(
      checkpoint_path, yoso_config_file, pytorch_dump_path
  ):
      orig_state_dict = torch.load(checkpoint_path, map_location="cpu")[
          "model_state_dict"
      ]
      config = YosoConfig.from_json_file(yoso_config_file)
      model = YosoForMaskedLM(config)
  
      new_state_dict = convert_checkpoint_helper(
          config.max_position_embeddings, orig_state_dict
      )
  
      print(model.load_state_dict(new_state_dict))
      model.eval()
      model.save_pretrained(pytorch_dump_path)
  
      print(
          f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"
      )
  
imports: import argparse
  import torch
  from transformers import YosoConfig, YosoForMaskedLM
label_desc: Use image node_id 3 for calling a global function with example usage: convert_yoso_checkpoint(checkpoint_path, yoso_config_file, pytorch_dump_path) without return types