class_docstr: An architecture combining hybrid Transformer/RNN with character-based
  inputs (token embeddings created via character-input CNN) and outputs.
  This model is very similar to https://arxiv.org/pdf/1809.02223.pdf.
class_docstr_tok: ["An","architecture","combining","hybrid","Transformer\/RNN","with","character-based","inputs","(","token","embeddings","created","via","character-input","CNN",")","and","outputs",".","This","model","is","very","similar","to","https",":","\/\/arxiv.org\/pdf\/1809.02223.pdf","."]
code: def forward(
      self,
      src_tokens,
      src_lengths,
      char_inds,
      word_lengths,
      prev_output_tokens,
      prev_output_chars,
      prev_output_word_lengths=None,
  ):
      encoder_out = self.encoder(
          src_tokens, src_lengths, char_inds, word_lengths
      )
      decoder_out = self.decoder(
          prev_output_tokens=prev_output_tokens,
          encoder_out=encoder_out,
          prev_output_chars=prev_output_chars,
      )
      return decoder_out
  
imports: import math
  from ast import literal_eval
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq.models import register_model, register_model_architecture
  from pytorch_translate import char_encoder, char_source_hybrid, char_source_model, hybrid_transformer_rnn, transformer, vocab_constants
  from pytorch_translate.data.dictionary import TAGS
  from pytorch_translate.utils import maybe_cuda
label_desc: Use image node_id 3 for calling the CharAwareHybridModel obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths, char_inds, word_lengths, prev_output_tokens, prev_output_chars, prev_output_word_lengths) and returns: decoder_out