code: def test(args, model, lit_model, data):
      model.eval()
      with torch.no_grad():
          test_loss = []
          for test_index, test_batch in enumerate(
              tqdm(data.test_dataloader())
          ):
              loss = lit_model.test_step(test_batch, test_index)
              test_loss.append(loss)
          f1 = lit_model.test_epoch_end(test_loss)
          logging(args.log_dir, "| test_result: {}".format(f1))
          logging(args.log_dir, "-" * 89)
  
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 2 for calling a global function with example usage: test(args, model, lit_model, data) without return types