code: def __init__(
      self,
      train_data=None,
      dev_data=None,
      test_data=None,
      model=None,
      process=None,
      label_map=None,
      args=None,
      logger=None,
      writer=None,
  ) -> None:
      self.train_data = train_data
      self.dev_data = dev_data
      self.test_data = test_data
      self.model = model
      self.process = process
      self.logger = logger
      self.label_map = label_map
      self.writer = writer
      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.step = 0
      self.args = args
      if self.train_data is not None:
          self.train_num_steps = len(self.train_data) * args.num_epochs
          self.multiModal_before_train()
  
file_path: DeepKE/src/deepke/name_entity_re/multimodal/modules/train.py
imports: import torch
  from torch import optim
  from tqdm import tqdm
  from transformers.optimization import get_linear_schedule_with_warmup
  from seqeval.metrics import classification_report
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, model, process, label_map, args, logger, writer)