class_docstr: Poly-encoder Agent.
  
  Equivalent of bert_ranker/polyencoder and biencoder_multiple_output but does not
  rely on an external library (hugging face).
class_docstr_tok: ["Poly-encoder","Agent",".","Equivalent","of","bert_ranker\/polyencoder","and","biencoder_multiple_output","but","does","not","rely","on","an","external","library","(","hugging","face",")","."]
docstr: Encode context representation.
docstr_tok: ["Encode","context","representation","."]
code: def get_ctxt_rep(
      self, batch: Batch
  ) -> Tuple[torch.Tensor, torch.BoolTensor]:
      """
      Encode context representation.
      """
      ctxt_rep, ctxt_rep_mask, _ = self.model(
          **self._model_context_input(batch)
      )
      return ctxt_rep, ctxt_rep_mask
  
imports: from parlai.core.params import ParlaiParser
  from typing import Any, Dict, Optional, Tuple
  import torch
  from parlai.core.opt import Opt
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.core.torch_agent import Batch
  from parlai.utils.misc import recursive_getattr
  from parlai.utils.logging import logging
  from .biencoder import AddLabelFixedCandsTRA
  from .modules import BasicAttention, MultiHeadAttention, TransformerEncoder
  from .transformer import TransformerRankerAgent
label_desc: Use image node_id 10 for calling the PolyencoderAgent obj's underlying member method code with example usage: obj.get_ctxt_rep(batch) and returns: ctxt_rep,  ctxt_rep_mask