class_docstr: Ranking metric.
class_docstr_tok: ["Ranking","metric","."]
docstr: Return a dictionary containing the k pairs with the highest relevance values.
docstr_tok: ["Return","a","dictionary","containing","the","k","pairs","with","the","highest","relevance","values","."]
code: def limit_relevance_dict(
      relevance_dict: Dict[str, int], k: int
  ) -> Dict[str, int]:
      """Return a dictionary containing the k pairs with the highest relevance values."""
      # Sort the dictionary by the relevance values and limit the number of elements.
      relevance_dict = {
          k: v
          for k, v in sorted(
              relevance_dict.items(),
              key=lambda item: item[1],
              reverse=True,
          )
      }
      limit = min(len(relevance_dict), k)  # Limit the dictionary
      relevance_dict = {
          key: relevance_dict[key]
          for key in list(relevance_dict.keys())[:limit]
      }
      return relevance_dict
  
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 14 for calling the RankingMetric obj's underlying member method code with example usage: obj.limit_relevance_dict(relevance_dict, k) and returns: relevance_dict