class_docstr: Actual model code for the Transresnet Agent.
docstr: Choose the best caption for each example.
  
  :param image_features:
      list of tensors of image features
  :param personalities:
      list of personalities
  :param candidates:
      list of candidates, one set per example
  :param candidates_encoded:
      optional; if specified, a fixed set of encoded candidates that is
      used for each example
  :param k:
      number of ranked candidates to return. if < 1, we return the ranks
      of all candidates in the set.
  
  :return:
      a set of ranked candidates for each example
docstr_tok: ["Choose","the","best","caption","for","each","example",".",":","param","image_features",":","list","of","tensors","of","image","features",":","param","personalities",":","list","of","personalities",":","param","candidates",":","list","of","candidates",",","one","set","per","example",":","param","candidates_encoded",":","optional",";","if","specified",",","a","fixed","set","of","encoded","candidates","that","is","used","for","each","example",":","param","k",":","number","of","ranked","candidates","to","return",".","if","<","1",",","we","return","the","ranks","of","all","candidates","in","the","set",".",":","return",":","a","set","of","ranked","candidates","for","each","example"]
code: def choose_best_caption(
      self,
      image_features,
      personalities,
      candidates,
      candidates_encoded=None,
      k=1,
  ):
      """
      Choose the best caption for each example.
  
      :param image_features:
          list of tensors of image features
      :param personalities:
          list of personalities
      :param candidates:
          list of candidates, one set per example
      :param candidates_encoded:
          optional; if specified, a fixed set of encoded candidates that is
          used for each example
      :param k:
          number of ranked candidates to return. if < 1, we return the ranks
          of all candidates in the set.
  
      :return:
          a set of ranked candidates for each example
      """
      self.eval()
      context_encoded, _ = self.forward(
          image_features, personalities, None
      )
      context_encoded = context_encoded.detach()
      one_cand_set = True
      if candidates_encoded is None:
          one_cand_set = False
          candidates_encoded = [
              self.forward(None, None, c)[1].detach()
              for c in candidates
          ]
      chosen = []
      for img_index in range(len(context_encoded)):
          context_encoding = context_encoded[
              img_index : img_index + 1, :
          ]
          scores = torch.mm(
              candidates_encoded[img_index].to(context_encoding)
              if not one_cand_set
              else candidates_encoded.to(context_encoding),
              context_encoding.transpose(0, 1),
          )
          if k >= 1:
              _, index_top = torch.topk(scores, k, dim=0)
          else:
              _, index_top = torch.topk(scores, scores.size(0), dim=0)
          chosen.append(
              [
                  candidates[img_index][idx]
                  if not one_cand_set
                  else candidates[idx]
                  for idx in index_top.unsqueeze(1)
              ]
          )
  
      return chosen
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch
  from torch import nn
  from torch import optim
  from parlai.agents.transformer.modules import TransformerEncoder
  from parlai.agents.transformer import transformer
  from parlai.utils.io import PathManager
label_desc: Use image node_id 12 for calling the TransresnetModel obj's underlying member method code with example usage: obj.choose_best_caption(image_features, personalities, candidates, candidates_encoded, k) and returns: chosen