class_docstr: A decoder that is similar to the HybridRNNDecoder but has a character
  CNN encoder to get the representation for each generated previous token.
  The decoder is similar to https://arxiv.org/pdf/1809.02223.pdf.
class_docstr_tok: ["A","decoder","that","is","similar","to","the","HybridRNNDecoder","but","has","a","character","CNN","encoder","to","get","the","representation","for","each","generated","previous","token",".","The","decoder","is","similar","to","https",":","\/\/arxiv.org\/pdf\/1809.02223.pdf","."]
docstr: From self.word_dict, extracts all character sequneces, and convert
  them to their corresponding list of characters.
docstr_tok: ["From","self.word_dict",",","extracts","all","character","sequneces",",","and","convert","them","to","their","corresponding","list","of","characters","."]
code: def _char_list_from_dict(
      self, char_dict, embed_bytes=False
  ) -> List[List[int]]:
      """
      From self.word_dict, extracts all character sequneces, and convert
      them to their corresponding list of characters.
      """
      character_list = []
      for word_index, word in enumerate(self.dictionary.symbols):
          character_list.append(
              self._char_list_for_word(
                  word_index=word_index,
                  word=word,
                  char_dict=char_dict,
                  embed_bytes=embed_bytes,
              )
          )
      return character_list
  
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 7 for calling the CharAwareHybridRNNDecoder obj's underlying member method code with example usage: obj._char_list_from_dict(char_dict, embed_bytes) and returns: character_list