code: def mrr_score(y_true, y_score):
      """Computing mrr score metric.
  
      Args:
          y_true (np.ndarray): Ground-truth labels.
          y_score (np.ndarray): Predicted labels.
  
      Returns:
          numpy.ndarray: mrr scores.
      """
      order = np.argsort(y_score)[::-1]
      y_true = np.take(y_true, order)
      rr_score = y_true / (np.arange(len(y_true)) + 1)
      return np.sum(rr_score) / np.sum(y_true)
  
imports: import os
  from sklearn.metrics import roc_auc_score, log_loss, mean_squared_error, accuracy_score, f1_score
  import numpy
  import yaml
  import zipfile
  import pickle
  from recommenders.datasets.download_utils import maybe_download
label_desc: Use image node_id 8 for calling a global function with example usage: mrr_score(y_true, y_score) and returns: unknown