docstr: Use predictor to predict given augmented context.
  
  :param context:
      augmented context with response candidates
  :param predictor_label_candidates:
      optional array of label candidates to pass to the predictor
  :return output:
      return output from ranker act
docstr_tok: ["Use","predictor","to","predict","given","augmented","context",".",":","param","context",":","augmented","context","with","response","candidates",":","param","predictor_label_candidates",":","optional","array","of","label","candidates","to","pass","to","the","predictor",":","return","output",":","return","output","from","ranker","act"]
code: def batch_classify(
      self, contexts: List[str], predictor_label_candidates: List[str]
  ) -> Message:
      """
      Use predictor to predict given augmented context.
  
      :param context:
          augmented context with response candidates
      :param predictor_label_candidates:
          optional array of label candidates to pass to the predictor
      :return output:
          return output from ranker act
      """
      cands = []
      if self.include_label_cand_only:
          for c in predictor_label_candidates:
              cands.append(c)
      else:
          cands = [
              context + self.delimiter + response
              for context, response in zip(
                  contexts, predictor_label_candidates
              )
          ]
      predictor_outputs = self.batch_predict(
          cands, self.predictor.class_list
      )
      return predictor_outputs
  
file_path: ParlAI/parlai/agents/reranker/classifier_reranker.py
imports: from typing import Optional, List
  from parlai.core.agents import create_agent_from_model_file
  from parlai.core.message import Message
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.agents.reranker.reranker import AbstractReranker, AbstractGeneratorRerankAgent
label_desc: Use image node_id 4 for calling the ClassifierReranker obj's underlying member method code with example usage: obj.batch_classify(contexts, predictor_label_candidates) and returns: predictor_outputs