code: def main(cfg):
      cwd = get_original_cwd()
      os.chdir(cwd)
      if not os.path.exists(f"data/{cfg.model_name_or_path}.pt"):
          get_label_word(cfg)
      if not os.path.exists(cfg.data_dir):
          generate_k_shot(cfg.data_dir)
  
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
  
      data = REDataset(cfg)
      data_config = data.get_data_config()
  
      config = AutoConfig.from_pretrained(cfg.model_name_or_path)
      config.num_labels = data_config["num_labels"]
  
      model = AutoModelForMaskedLM.from_pretrained(
          cfg.model_name_or_path, config=config
      )
  
      # if torch.cuda.device_count() > 1:
      #     print("Let's use", torch.cuda.device_count(), "GPUs!")
      #     model = torch.nn.DataParallel(model, device_ids = list(range(torch.cuda.device_count())))
  
      model.to(device)
  
      lit_model = BertLitModel(
          args=cfg, model=model, device=device, tokenizer=data.tokenizer
      )
      data.setup()
  
      model.load_state_dict(
          torch.load(cfg.load_path)["checkpoint"], False
      )
      print("load trained model from {}.".format(cfg.load_path))
  
      test(cfg, model, lit_model, data)
  
file_path: DeepKE/example/re/few-shot/predict.py
imports: from logging import debug
  import hydra
  from hydra.utils import get_original_cwd
  import numpy
  import torch
  from torch.utils.data.dataloader import DataLoader
  import yaml
  import time
  from transformers import AutoConfig, AutoModelForMaskedLM
  from transformers.optimization import get_linear_schedule_with_warmup
  import os
  from tqdm import tqdm
  from deepke.relation_extraction.few_shot import *
label_desc: Use image node_id 3 for calling a global function with example usage: main(cfg) without return types