class_docstr: Ranking metric.
class_docstr_tok: ["Ranking","metric","."]
docstr: Score the given ranking object.
docstr_tok: ["Score","the","given","ranking","object","."]
code: def object_to_score_binary_ranking(
      self, ranking_object: RankingObject
  ) -> Tuple[int, float]:
      """Score the given ranking object."""
      # Input validation
      assert ranking_object.model_output is not None
      model_output = self.standardize(ranking_object.model_output)
      assert ranking_object.model_logprob is not None
      logprob = ranking_object.model_logprob
  
      # Score
      if model_output == self.standardize(self.correct_output):
          return 2, logprob
      elif model_output == self.standardize(self.wrong_output):
          return 1, -1 * logprob
      else:
          return 0, -1 * logprob
  
imports: from dataclasses import dataclass
  from typing import Callable, Dict, List, Tuple, Optional
  import pytrec_eval
  from helm.benchmark.adaptation.adapters.adapter_factory import ADAPT_RANKING_BINARY
  from helm.benchmark.adaptation.request_state import RequestState
  from helm.benchmark.adaptation.adapter_spec import AdapterSpec
  from helm.benchmark.scenarios.scenario import unpack_tag, CORRECT_TAG, Reference
  from helm.common.request import RequestResult
  from helm.common.general import binarize_dict
  from .metric import Metric
  from .metric_name import MetricName
  from .metric_service import MetricService
  from .statistic import Stat
label_desc: Use image node_id 10 for calling the RankingMetric obj's underlying member method code with example usage: obj.object_to_score_binary_ranking(ranking_object) and returns: int,  logprob, int,  unknown, int,  unknown