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: Retrieve documents and expand input via concatenation.
  
  Then, encode as usual in the seq2seq encoder.
  
  :param input:
      2D [bsz, seqlen] input to the encoder
  :param input_lengths:
      1D [bsz] lengths of each input item
  :param query_vec:
      2D [bsz*n_turns, seqlen] input for the retriever
  :param input_turns_cnt:
      1D [bsz] number of dialogue turns for each input example
  
  :return (encoder_out, encoder_mask, input_turns_cnt, top_docs, top_doc_scores):
      encoder_out: encoded representations of context/document pairs
      encoder_mask: mask for enc_out
      input_turns_cnt: pass along the input turns count for the decoder
      top_docs: List of top Documents for each batch example
      top_doc_scores: scores for each retrieved document.
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code: def encoder(
      self,
      input: torch.LongTensor,
      input_lengths: torch.LongTensor,
      query_vec: torch.LongTensor,
      input_turns_cnt: torch.LongTensor,
      positions: Optional[torch.LongTensor] = None,
      segments: Optional[torch.LongTensor] = None,
  ) -> Tuple[
      torch.Tensor,
      torch.BoolTensor,
      Optional[torch.LongTensor],
      Optional[List[List[Document]]],
      Optional[torch.Tensor],
  ]:
      """
      Retrieve documents and expand input via concatenation.
  
      Then, encode as usual in the seq2seq encoder.
  
      :param input:
          2D [bsz, seqlen] input to the encoder
      :param input_lengths:
          1D [bsz] lengths of each input item
      :param query_vec:
          2D [bsz*n_turns, seqlen] input for the retriever
      :param input_turns_cnt:
          1D [bsz] number of dialogue turns for each input example
  
      :return (encoder_out, encoder_mask, input_turns_cnt, top_docs, top_doc_scores):
          encoder_out: encoded representations of context/document pairs
          encoder_mask: mask for enc_out
          input_turns_cnt: pass along the input turns count for the decoder
          top_docs: List of top Documents for each batch example
          top_doc_scores: scores for each retrieved document.
      """
      # Retrieve, get expanded input
      if all(
          [tensor is not None for tensor in [input_lengths, query_vec]]
      ):
          (
              expanded_input,
              top_docs,
              top_doc_scores,
          ) = self.retrieve_and_concat(
              input, input_lengths, query_vec, input_turns_cnt
          )
      else:
          expanded_input = input
          top_docs = top_doc_scores = None
  
      # Run through seq2seq encoder
      tensor, mask = self.seq2seq_encoder(
          expanded_input, positions, segments
      )  # type: ignore
  
      return tensor, mask, input_turns_cnt, top_docs, top_doc_scores
  
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 7 for calling the RagModel obj's underlying member method code with example usage: obj.encoder(input, input_lengths, query_vec, input_turns_cnt, positions, segments) and returns: tensor,  mask,  input_turns_cnt,  top_docs,  top_doc_scores