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, concat with input.
  
  :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 (expanded_input, top_docs, top_doc_scores):
      expanded_input: [bsz * n_docs, seqlen+doc_len] tensor of context/document inputs
      top_docs: List of top documents for each input
      top_doc_scores: document scores for each document
docstr_tok: ["Retrieve","documents",",","concat","with","input",".",":","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","(","expanded_input",",","top_docs",",","top_doc_scores",")",":","expanded_input",":","[","bsz","*","n_docs",",","seqlen+doc_len","]","tensor","of","context\/document","inputs","top_docs",":","List","of","top","documents","for","each","input","top_doc_scores",":","document","scores","for","each","document"]
code: def retrieve_and_concat(
      self,
      input: torch.LongTensor,
      input_lengths: torch.LongTensor,
      query_vec: torch.LongTensor,
      input_turns_cnt: torch.LongTensor,
  ) -> Tuple[torch.LongTensor, List[List[Document]], torch.Tensor]:
      """
      Retrieve documents, concat with input.
  
      :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 (expanded_input, top_docs, top_doc_scores):
          expanded_input: [bsz * n_docs, seqlen+doc_len] tensor of context/document inputs
          top_docs: List of top documents for each input
          top_doc_scores: document scores for each document
      """
      # 1. Retrieve
      top_docs, top_doc_scores = self.retriever.retrieve(query_vec)
  
      # 2. Expand the input
      if input_turns_cnt is not None:
          input = input.repeat_interleave(input_turns_cnt, dim=0)  # type: ignore
          input_lengths = input_lengths.repeat_interleave(
              input_turns_cnt, dim=0
          )  # type: ignore
      expanded_input = self.concat_docs_and_input(
          input, input_lengths, top_docs, top_doc_scores.size(1)
      )
  
      return expanded_input, 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 11 for calling the RagModel obj's underlying member method code with example usage: obj.retrieve_and_concat(input, input_lengths, query_vec, input_turns_cnt) and returns: expanded_input,  top_docs,  top_doc_scores