code: def __init__(
      self,
      train_data=None,
      dev_data=None,
      test_data=None,
      model=None,
      process=None,
      args=None,
      logger=None,
      loss=None,
      metrics=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.metrics = metrics
      self.writer = writer
      self.loss = loss
      self.num_epochs = args.num_epochs
      self.batch_size = args.batch_size
      self.lr = args.learning_rate
      self.eval_begin_epoch = args.eval_begin_epoch
      self.device = args.device
      self.load_path = args.load_path
      self.save_path = args.save_path
      self.refresh_step = 1
      self.best_metric = 0
      self.best_dev_epoch = None
      self.optimizer = None
      if self.train_data is not None:
          self.train_num_steps = len(self.train_data) * self.num_epochs
      self.step = 0
      self.args = args
  
file_path: DeepKE/src/deepke/name_entity_re/few_shot/module/train.py
imports: import torch
  from torch import optim
  from tqdm import tqdm
  from ..utils import convert_preds_to_outputs, write_predictions
  import random
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, args, logger, loss, metrics, writer)