class_docstr: Re-ranker object which uses a predictor model to rerank candidates given.
  
  This AbstractReranker can be subclassed to use any classifier or ranking model as
  the predictor.
class_docstr_tok: ["Re-ranker","object","which","uses","a","predictor","model","to","rerank","candidates","given",".","This","AbstractReranker","can","be","subclassed","to","use","any","classifier","or","ranking","model","as","the","predictor","."]
docstr: Batch operate on a list of contexts.
  
  :param contexts:
      list of augmented contexts with response candidates
  :param predictor_label_candidates:
      array of label candidates to pass to the predictor
      (currently has to be the same for all the contexts)
  :return outputs:
      return ranker batch replies
docstr_tok: ["Batch","operate","on","a","list","of","contexts",".",":","param","contexts",":","list","of","augmented","contexts","with","response","candidates",":","param","predictor_label_candidates",":","array","of","label","candidates","to","pass","to","the","predictor","(","currently","has","to","be","the","same","for","all","the","contexts",")",":","return","outputs",":","return","ranker","batch","replies"]
code: def batch_predict(
      self, contexts: List[str], predictor_label_candidates: List[str]
  ) -> List[Message]:
      """
      Batch operate on a list of contexts.
  
      :param contexts:
          list of augmented contexts with response candidates
      :param predictor_label_candidates:
          array of label candidates to pass to the predictor
          (currently has to be the same for all the contexts)
      :return outputs:
          return ranker batch replies
      """
      assert isinstance(self.predictor, TorchAgent)
      observations = [
          {"text": context, "episode_done": True}
          for context in contexts
      ]
      processed_obs = []
      for obs in observations:
          obs["label_candidates"] = predictor_label_candidates
          obs = self.predictor.observe(obs)
          self.predictor.self_observe(obs)
          processed_obs.append(obs)
      batch_replies = self.predictor.batch_act(processed_obs)
      return batch_replies
  
imports: import logging
  import torch
  from abc import ABC, abstractmethod, abstractclassmethod
  from typing import List, Optional, Tuple
  from parlai.agents.transformer.transformer import TransformerGeneratorAgent
  from parlai.core.agents import create_agent_from_model_file, Agent
  from parlai.core.build_data import modelzoo_path
  from parlai.core.message import Message
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_agent import TorchAgent
  from parlai.utils.strings import normalize_reply
  from parlai.utils.torch import argsort
  from parlai.agents.hugging_face.gpt2 import Gpt2Agent
  from projects.msc.agents.long_tga import TransformerVariantAgent
label_desc: Use image node_id 8 for calling the AbstractReranker obj's underlying member method code with example usage: obj.batch_predict(contexts, predictor_label_candidates) and returns: batch_replies