class_docstr: RagModel.
  
  The RagModel operates in the following phases:
  1) retrieve:    given a tokenized query, return relevant documents
  2) expand:      given queries and documents, expand the inputs n_docs times,
                  concatenating each document with a relevant context
  3) encode:      given expanded input, encode into encoder representations
  4) decoding:    given encoder outputs, compute n_docs decoder representations for
                  each batch item.
  5) marginalize: given the decoded representations, marginalize over the documents
  appropriately.
  
  The RagModel overloads the `encoder` and `decoder` attributes of your standard
  `TorchGeneratorModel` to accomplish the five phases above.
docstr: Decode, RAG-Style.
  
  Obtain decoder representations as usual, then marginalize appropriately.
  
  :param input:
      input for the decoder
  :param encoder_state:
      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: ["Decode",",","RAG-Style",".","Obtain","decoder","representations","as","usual",",","then","marginalize","appropriately",".",":","param","input",":","input","for","the","decoder",":","param","encoder_state",":","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_state: Tuple[Any, ...],
      incr_state: Optional[Dict[str, Any]] = None,
  ) -> Tuple[torch.Tensor, Optional[Dict[str, Any]]]:
      """
      Decode, RAG-Style.
  
      Obtain decoder representations as usual, then marginalize appropriately.
  
      :param input:
          input for the decoder
      :param encoder_state:
          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.
      """
      # 1. Get decoder outputs
      (
          enc_out,
          enc_mask,
          input_turns_cnt,
          docs,
          doc_scores,
      ) = encoder_state
      dec_out, new_incr_state = self.seq2seq_decoder(
          input, (enc_out, enc_mask), incr_state
      )  # type: ignore
      dec_out = self.decoder_output(dec_out)
  
      if all([obj is not None for obj in [docs, doc_scores]]):
          # 2. Get logprobs
          n_docs = doc_scores.size(1)
          out_probs = F.log_softmax(
              dec_out, dim=-1, dtype=torch.float32  # type: ignore
          ).view(
              input.shape[0] // n_docs, n_docs, -1, dec_out.size(-1)
          )  # [bsz * beam_size, n_docs, input_len, esz]
  
          # 3. Marginalize
          marginalized = self._rag_model_interface.marginalize(
              out_probs,
              F.log_softmax(doc_scores, dim=1),
              input_turns_cnt,
          )
      else:
          # With RAG Sequence Generation, we do not marginalize over documents.
          marginalized = dec_out
  
      return marginalized, new_incr_state
  
imports: import torch
  import torch.cuda
  import torch.nn
  import torch.nn.functional
  from typing import Any, Tuple, Dict, Optional, List, Union, Type
  from parlai.agents.hugging_face.t5 import ParlaiT5Encoder, ParlaiT5Decoder, build_t5, set_device
  from parlai.agents.transformer.modules import TransformerEncoder, TransformerDecoder, get_n_positions_from_options, create_embeddings
  from parlai.core.dict import DictionaryAgent
  from parlai.core.opt import Opt
  from parlai.core.torch_generator_agent import TorchGeneratorModel
  from parlai.utils.torch import padded_tensor
  from parlai.agents.rag.retrievers import RagRetriever, retriever_factory, Document
label_desc: Use image node_id 8 for calling the RagModel obj's underlying member method code with example usage: obj.decoder(input, encoder_state, incr_state) and returns: marginalized,  new_incr_state