docstr: Refactored Decode, RAG-Style to split the FID.decoder into
  decoder_output_before_final_projection + decoder_output only for generation
  purpose Decode, RAG-Style.
  
  :param input:
      input for the decoder
  :param encoder_states:
      RAG encoder states
  :param incr_state:
      incremental decoder state
  
  :return (output, new_incr_state):
      return the output token distribution, as well as new incremental state.
docstr_tok: ["Refactored","Decode",",","RAG-Style","to","split","the","FID.decoder","into","decoder_output_before_final_projection","+","decoder_output","only","for","generation","purpose","Decode",",","RAG-Style",".",":","param","input",":","input","for","the","decoder",":","param","encoder_states",":","RAG","encoder","states",":","param","incr_state",":","incremental","decoder","state",":","return","(","output",",","new_incr_state",")",":","return","the","output","token","distribution",",","as","well","as","new","incremental","state","."]
code: def decoder(
      self,
      input: torch.LongTensor,
      encoder_states: Tuple[Any, ...],
      incr_state: Optional[Dict[str, Any]] = None,
  ) -> Tuple[torch.Tensor, Optional[Dict[str, Any]]]:
      """
      Refactored Decode, RAG-Style to split the FID.decoder into
      decoder_output_before_final_projection + decoder_output only for generation
      purpose Decode, RAG-Style.
  
      :param input:
          input for the decoder
      :param encoder_states:
          RAG encoder states
      :param incr_state:
          incremental decoder state
  
      :return (output, new_incr_state):
          return the output token distribution, as well as new incremental state.
      """
      dec_out, incr_state = self.decoder_output_before_final_projection(
          input, encoder_states[:-1]
      )
      skip_director_reranking_vec = encoder_states[-1]
      dec_out = self.decoder_output(
          dec_out,
          skip_director_reranking_vec,
          encoder_states=encoder_states,
      )
      return dec_out, incr_state
  
imports: import torch
  import torch.nn.functional
  import copy
  from typing import Optional, List, Tuple, Any, Dict, Union
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_agent import Batch
  from parlai.core.message import Message
  from parlai.agents.rag.retrievers import Document
  from parlai.agents.fid.fid import SearchQueryFiDAgent, WizIntGoldDocRetrieverFiDAgent
  import parlai.utils.logging
  from projects.seeker.agents.seeker_modules import ComboFidModel
  from projects.seeker.agents.seeker import ComboFidAgent, SeekerAgent
  from projects.fits.agents.director_bb2 import DirectorFidAgent, DirectorFidModelMixin
label_desc: Use image node_id 4 for calling the DirectorComboFidModel obj's underlying member method code with example usage: obj.decoder(input, encoder_states, incr_state) and returns: dec_out,  incr_state