code: def compute_ranking_score(
      results: Dict[str, Dict],
      qrel_dict: Dict[str, Dict],
      metrics: List[str],
      cutoffs: Optional[List[int]] = [5, 10, 20],
  ):
      """
      Compute the ranking metrics, e.g., NDCG, MAP, Recall, and Precision.
      TODO: Consider MRR.
  
      Parameters
      ----------
      results:
          The query/document ranking list by the model.
      qrel_dict:
          The groundtruth query and document relevance.
      metrics
          A list of metrics to compute.
      cutoffs:
          The cutoff values for NDCG, MAP, Recall, and Precision.
  
      Returns
      -------
      A dict of metric scores.
      """
      scores = {}
      evaluator = RankingMetrics(pred=results, target=qrel_dict)
      for k in cutoffs:
          scores.update(evaluator.compute(k=k))
  
      metric_results = dict()
      for k in cutoffs:
          for per_metric in metrics:
              if per_metric.lower() == NDCG:
                  metric_results[f"{NDCG}@{k}"] = 0.0
              elif per_metric.lower() == MAP:
                  metric_results[f"{MAP}@{k}"] = 0.0
              elif per_metric.lower() == RECALL:
                  metric_results[f"{RECALL}@{k}"] = 0.0
              elif per_metric.lower() == PRECISION:
                  metric_results[f"{PRECISION}@{k}"] = 0.0
  
      for k in cutoffs:
          for per_metric in metrics:
              if per_metric.lower() == NDCG:
                  metric_results[f"{NDCG}@{k}"] = round(
                      scores[f"{NDCG}@{k}"], 5
                  )
              elif per_metric.lower() == MAP:
                  metric_results[f"{MAP}@{k}"] = round(
                      scores[f"{MAP}@{k}"], 5
                  )
              elif per_metric.lower() == RECALL:
                  metric_results[f"{RECALL}@{k}"] = round(
                      scores[f"{RECALL}@{k}"], 5
                  )
              elif per_metric.lower() == PRECISION:
                  metric_results[f"{PRECISION}@{k}"] = round(
                      scores[f"{PRECISION}@{k}"], 5
                  )
  
      return metric_results
  
imports: import logging
  import math
  import operator
  import warnings
  from typing import Dict, List, Optional, Tuple, Union
  import evaluate
  import numpy
  from sklearn.metrics import f1_score
  from autogluon.core.metrics import Scorer, get_metric
  from ..constants import ACCURACY, AVERAGE_PRECISION, BINARY, DIRECT_LOSS, F1, FEW_SHOT_CLASSIFICATION, IOU, MAP, MATCHING_METRICS, MATCHING_METRICS_WITHOUT_PROBLEM_TYPE, MAX, METRIC_MODE_MAP, MIN, MULTICLASS, NDCG, NER, NER_TOKEN_F1, NUMERICAL, OBJECT_DETECTION, OPEN_VOCABULARY_OBJECT_DETECTION, OVERALL_ACCURACY, OVERALL_F1, PRECISION, RECALL, REGRESSION, RETRIEVAL_METRICS, RMSE, ROC_AUC, SEMANTIC_SEGMENTATION, SPEARMANR, Y_PRED, Y_PRED_PROB, Y_TRUE
  from ..problem_types import PROBLEM_TYPES_REG
label_desc: Use image node_id 5 for calling a global function with example usage: compute_ranking_score(results, qrel_dict, metrics, cutoffs) and returns: metric_results