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 zero_state(self, inputs):
      batch_size = inputs.size(0)
      device = self.SOS_tensor.device
      h0 = torch.zeros(
          self.encoder.num_layers * 2,
          batch_size,
          self.enc_hidden_dim,
          requires_grad=False,
          device=device,
      )
      c0 = torch.zeros(
          self.encoder.num_layers * 2,
          batch_size,
          self.enc_hidden_dim,
          requires_grad=False,
          device=device,
      )
      return h0, c0
  
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 4 for calling the Seq2SeqModel obj's underlying member method code with example usage: obj.zero_state(inputs) and returns: h0,  c0