code: def _partial_rank(self, pos_scores, neg_scores):
      pos_scores = np.array(pos_scores)
      rank_above = np.zeros(len(pos_scores))
      pos_scores_len = len(pos_scores)
  
      for score in neg_scores:
          for pos_ind in range(pos_scores_len):
              if pos_scores[pos_ind] < score:
                  rank_above[pos_ind] += 1
  
      return rank_above, len(neg_scores)
  
file_path: openrec/openrec/tf1/legacy/utils/evaluators/implicit_eval_manager.py
imports: import numpy
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling the ImplicitEvalManager obj's underlying member method code with example usage: obj._partial_rank(pos_scores, neg_scores) and returns: rank_above,  len