class_docstr: A trainer for training models. 
code: def load(self, filename):
      try:
          checkpoint = torch.load(
              filename, lambda storage, loc: storage
          )
      except BaseException:
          logger.error("Cannot load model from {}".format(filename))
          raise
      self.args = checkpoint["config"]
      self.expansion_dict = checkpoint["dict"]
      if not self.args["dict_only"]:
          self.model = Seq2SeqModel(self.args)
          # could remove strict=False after rebuilding all models,
          # or could switch to 1.6.0 torch with the buffer in seq2seq persistent=False
          self.model.load_state_dict(checkpoint["model"], strict=False)
      else:
          self.model = None
      self.vocab = Vocab.load_state_dict(checkpoint["vocab"])
  
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 8 for calling the Trainer obj's underlying member method code with example usage: obj.load(filename) without return types