class_docstr: WindowStreamingE2E constructor.
  
  :param E2E e2e: E2E ASR object
  :param recog_args: arguments for "recognize" method of E2E
docstr: Run the attention decoder offline.
  
  Works even if the previous layers (encoder and CTC decoder) were
  being run in the online mode.
  This method should be run after all the audio has been consumed.
  This is used mostly to compare the results between offline
  and online implementation of the previous layers.
docstr_tok: ["Run","the","attention","decoder","offline",".","Works","even","if","the","previous","layers","(","encoder","and","CTC","decoder",")","were","being","run","in","the","online","mode",".","This","method","should","be","run","after","all","the","audio","has","been","consumed",".","This","is","used","mostly","to","compare","the","results","between","offline","and","online","implementation","of","the","previous","layers","."]
code: def decode_with_attention_offline(self):
      """Run the attention decoder offline.
  
      Works even if the previous layers (encoder and CTC decoder) were
      being run in the online mode.
      This method should be run after all the audio has been consumed.
      This is used mostly to compare the results between offline
      and online implementation of the previous layers.
      """
      h, lpz = self._input_window_for_decoder(use_all=True)
  
      return self._e2e.dec.recognize_beam(
          h, lpz, self._recog_args, self._char_list, self._rnnlm
      )
  
imports: import torch
label_desc: Use image node_id 4 for calling the WindowStreamingE2E obj's underlying member method code with example usage: obj.decode_with_attention_offline() and returns: self