class_docstr: A complete encoder-decoder model, with optional attention.
  
  A parent class which makes use of the contextual_embedding (such as a charlm)
  can make use of unsaved_modules when saving.
class_docstr_tok: ["A","complete","encoder-decoder","model",",","with","optional","attention",".","A","parent","class","which","makes","use","of","the","contextual_embedding","(","such","as","a","charlm",")","can","make","use","of","unsaved_modules","when","saving","."]
code: def add_unsaved_module(self, name, module):
      self.unsaved_modules += [name]
      setattr(self, name, module)
  
imports: import logging
  import torch
  from torch import nn
  import torch.nn.functional
  import numpy
  import stanza.models.common.seq2seq_constant
  from stanza.models.common import utils
  from stanza.models.common.seq2seq_modules import LSTMAttention
  from stanza.models.common.beam import Beam
label_desc: Use image node_id 2 for calling the Seq2SeqModel obj's underlying member method code with example usage: obj.add_unsaved_module(name, module) without return types