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: Tokenize the query for the retriever.
docstr_tok: ["Tokenize","the","query","for","the","retriever","."]
code: def tokenize_query(self, query: str) -> List[int]:
      """
      Tokenize the query for the retriever.
      """
      return self.retriever.tokenize_query(query)
  
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 5 for calling the RagModel obj's underlying member method code with example usage: obj.tokenize_query(query) and returns: self