class_docstr: This class evaluates a CrossEncoder 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 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 __call__(
      self,
      model,
      output_path: str = None,
      epoch: int = -1,
      steps: int = -1,
  ) -> float:
      if epoch != -1:
          if steps == -1:
              out_txt = " after epoch {}:".format(epoch)
          else:
              out_txt = " in epoch {} after {} steps:".format(
                  epoch, steps
              )
      else:
          out_txt = ":"
  
      logger.info(
          "CERerankingEvaluator: Evaluating the model on "
          + self.name
          + " dataset"
          + out_txt
      )
  
      all_mrr_scores = []
      num_queries = 0
      num_positives = []
      num_negatives = []
      for instance in self.samples:
          query = instance["query"]
          positive = list(instance["positive"])
          negative = list(instance["negative"])
          docs = positive + negative
          is_relevant = [True] * len(positive) + [False] * len(negative)
  
          if len(positive) == 0 or len(negative) == 0:
              continue
  
          num_queries += 1
          num_positives.append(len(positive))
          num_negatives.append(len(negative))
  
          model_input = [[query, doc] for doc in docs]
          pred_scores = model.predict(
              model_input,
              convert_to_numpy=True,
              show_progress_bar=False,
          )
          pred_scores_argsort = np.argsort(
              -pred_scores
          )  # Sort in decreasing order
  
          mrr_score = 0
          for rank, index in enumerate(
              pred_scores_argsort[0 : self.mrr_at_k]
          ):
              if is_relevant[index]:
                  mrr_score = 1 / (rank + 1)
                  break
  
          all_mrr_scores.append(mrr_score)
  
      mean_mrr = np.mean(all_mrr_scores)
      logger.info(
          "Queries: {} \t Positives: Min {:.1f}, Mean {:.1f}, Max {:.1f} \t Negatives: Min {:.1f}, Mean {:.1f}, Max {:.1f}".format(
              num_queries,
              np.min(num_positives),
              np.mean(num_positives),
              np.max(num_positives),
              np.min(num_negatives),
              np.mean(num_negatives),
              np.max(num_negatives),
          )
      )
      logger.info(
          "MRR@{}: {:.2f}".format(self.mrr_at_k, mean_mrr * 100)
      )
  
      if output_path is not None and self.write_csv:
          csv_path = os.path.join(output_path, self.csv_file)
          output_file_exists = os.path.isfile(csv_path)
          with open(
              csv_path,
              mode="a" if output_file_exists else "w",
              encoding="utf-8",
          ) as f:
              writer = csv.writer(f)
              if not output_file_exists:
                  writer.writerow(self.csv_headers)
  
              writer.writerow([epoch, steps, mean_mrr])
  
      return mean_mrr
  
imports: import logging
  import numpy
  import os
  import csv
label_desc: Use image node_id 2 for calling the CERerankingEvaluator obj's underlying member method code with example usage: obj.__call__(model, output_path, epoch, steps) and returns: mean_mrr