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","."]
code: def _embed_prev_outputs(
      self,
      prev_output_tokens,
      incremental_state=None,
      prev_output_chars=None,
  ):
      if incremental_state is not None:
          prev_output_tokens = prev_output_tokens[:, -1:]
          if prev_output_chars is not None:
              prev_output_chars = prev_output_chars[:, -1:, :].squeeze(
                  1
              )
  
      combined_embed = self._combined_word_char_embed(
          prev_output_tokens=prev_output_tokens,
          prev_output_chars=prev_output_chars,
      )
      return combined_embed, prev_output_tokens
  
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 CharAwareHybridRNNDecoder obj's underlying member method code with example usage: obj._embed_prev_outputs(prev_output_tokens, incremental_state, prev_output_chars) and returns: combined_embed,  prev_output_tokens