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: Score context with candidates.
docstr_tok: ["Score","context","with","candidates","."]
code: def get_scores(
      self,
      ctxt_rep: torch.Tensor,
      ctxt_rep_mask: torch.BoolTensor,
      cand_rep: torch.Tensor,
  ) -> torch.Tensor:
      """
      Score context with candidates.
      """
      return self.model(
          ctxt_rep=ctxt_rep,
          ctxt_rep_mask=ctxt_rep_mask,
          cand_rep=cand_rep,
      )
  
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 12 for calling the PolyencoderAgent obj's underlying member method code with example usage: obj.get_scores(ctxt_rep, ctxt_rep_mask, cand_rep) and returns: self