class_docstr: A trainer for training models. 
code: def __init__(
      self,
      args=None,
      vocab=None,
      emb_matrix=None,
      model_file=None,
      device=None,
  ):
      if model_file is not None:
          # load from file
          self.load(model_file)
      else:
          self.args = args
          self.model = (
              None
              if args["dict_only"]
              else Seq2SeqModel(args, emb_matrix=emb_matrix)
          )
          self.vocab = vocab
          self.expansion_dict = dict()
      if not self.args["dict_only"]:
          self.model = self.model.to(device)
          self.crit = loss.SequenceLoss(self.vocab.size).to(device)
          self.optimizer = utils.get_optimizer(
              self.args["optim"], self.model, self.args["lr"]
          )
  
imports: import sys
  import numpy
  from collections import Counter
  import logging
  import torch
  from torch import nn
  import torch.nn.init
  import stanza.models.common.seq2seq_constant
  from stanza.models.common.trainer import Trainer
  from stanza.models.common.seq2seq_model import Seq2SeqModel
  from stanza.models.common import utils, loss
  from stanza.models.mwt.vocab import Vocab
label_desc: Use image node_id 1 to create a new Trainer object from inherited base classes: BaseTrainer with example: obj = Trainer(args, vocab, emb_matrix, model_file, device)