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 __init__(self, task, encoder, decoder):
      super().__init__(task, encoder, decoder)
  
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 1 to create a new CharAwareHybridModel object from inherited base classes: char_source_hybrid with example: obj = CharAwareHybridModel(task, encoder, decoder)