code: def main(cfg):
      cwd = utils.get_original_cwd()
      cfg.cwd = cwd
      print(cfg)
  
      transform = transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
              ),
          ]
      )
  
      set_seed(cfg.seed)  # set seed, default is 1
      if cfg.save_path is not None:  # make save_path dir
          if not os.path.exists(cfg.save_path):
              os.makedirs(cfg.save_path, exist_ok=True)
      print(cfg)
  
      processor = MMREProcessor(DATA_PATH, re_path, cfg)
      train_dataset = MMREDataset(
          processor, transform, IMG_PATH, AUX_PATH, mode="train"
      )
      train_dataloader = DataLoader(
          train_dataset,
          batch_size=cfg.batch_size,
          shuffle=True,
          num_workers=4,
          pin_memory=True,
      )
  
      dev_dataset = MMREDataset(
          processor, transform, IMG_PATH, AUX_PATH, mode="dev"
      )
      dev_dataloader = DataLoader(
          dev_dataset,
          batch_size=cfg.batch_size,
          shuffle=False,
          num_workers=4,
          pin_memory=True,
      )
  
      test_dataset = MMREDataset(
          processor, transform, IMG_PATH, AUX_PATH, mode="test"
      )
      test_dataloader = DataLoader(
          test_dataset,
          batch_size=cfg.batch_size,
          shuffle=False,
          num_workers=4,
          pin_memory=True,
      )
  
      re_dict = processor.get_relation_dict()
      num_labels = len(re_dict)
      tokenizer = processor.tokenizer
  
      model = IFAREModel(num_labels, tokenizer, cfg)
  
      trainer = Trainer(
          train_data=train_dataloader,
          dev_data=dev_dataloader,
          test_data=test_dataloader,
          re_dict=re_dict,
          model=model,
          args=cfg,
          logger=logger,
          writer=writer,
      )
      trainer.train()
  
file_path: DeepKE/example/re/multimodal/run.py
imports: import os
  import hydra
  import torch
  import numpy
  import random
  from torchvision import transforms
  from hydra import utils
  from torch.utils.data import DataLoader
  from deepke.relation_extraction.multimodal.models.IFA_model import IFAREModel
  from deepke.relation_extraction.multimodal.modules.dataset import MMREProcessor, MMREDataset
  from deepke.relation_extraction.multimodal.modules.train import Trainer
  import warnings
  import logging
  import wandb
label_desc: Use image node_id 2 for calling a global function with example usage: main(cfg) without return types