code: def __init__(
      self,
      train_data=None,
      dev_data=None,
      test_data=None,
      re_dict=None,
      model=None,
      args=None,
      logger=None,
      writer=None,
  ) -> None:
      self.train_data = train_data
      self.dev_data = dev_data
      self.test_data = test_data
      self.re_dict = re_dict
      self.model = model
      self.logger = logger
      self.refresh_step = 2
      self.best_dev_metric = 0
      self.best_test_metric = 0
      self.best_dev_epoch = None
      self.best_test_epoch = None
      self.optimizer = None
      self.writer = writer
  
      self.step = 0
      self.args = args
  
      if self.train_data is not None:
          self.train_num_steps = len(self.train_data) * args.num_epochs
          self.before_multimodal_train()
      self.model.to(self.args.device)
  
file_path: DeepKE/src/deepke/relation_extraction/multimodal/modules/train.py
imports: import torch
  from torch import optim
  from tqdm import tqdm
  from sklearn.metrics import classification_report
  from transformers.optimization import get_linear_schedule_with_warmup
  from .metrics import eval_result
label_desc: Use image node_id 1 to create a new Trainer object from inherited base classes: object with example: obj = Trainer(train_data, dev_data, test_data, re_dict, model, args, logger, writer)