code: def ndcg_score(y_true, y_score, k=10):
      """Computing ndcg score metric at k.
  
      Args:
          y_true (np.ndarray): Ground-truth labels.
          y_score (np.ndarray): Predicted labels.
  
      Returns:
          numpy.ndarray: ndcg scores.
      """
      best = dcg_score(y_true, y_true, k)
      actual = dcg_score(y_true, y_score, k)
      return actual / best
  
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 9 for calling a global function with example usage: ndcg_score(y_true, y_score, k) and returns: unknown