class_docstr: This class evaluates a SentenceTransformer model for the task of re-ranking.
  
  Given a query and a list of documents, it computes the score [query, doc_i] for all possible
  documents and sorts them in decreasing order. Then, MRR@10 and MAP is compute to measure the quality of the ranking.
  
  :param samples: Must be a list and each element is of the form: {'query': '', 'positive': [], 'negative': []}. Query is the search query,
   positive is a list of positive (relevant) documents, negative is a list of negative (irrelevant) documents.
code: def compute_metrices(self, model):
      return (
          self.compute_metrices_batched(model)
          if self.use_batched_encoding
          else self.compute_metrices_individual(model)
      )
  
file_path: sentence-transformers/sentence_transformers/evaluation/RerankingEvaluator.py
imports: from .None import SentenceEvaluator
  import logging
  import numpy
  import os
  import csv
  from ..util import cos_sim
  import torch
  from sklearn.metrics import average_precision_score
  import tqdm
label_desc: Use image node_id 3 for calling the RerankingEvaluator obj's underlying member method code with example usage: obj.compute_metrices(model) and returns: unknown