code: def dcg_score(y_true, y_score, k=10):
      """Computing dcg score metric at k.
  
      Args:
          y_true (np.ndarray): Ground-truth labels.
          y_score (np.ndarray): Predicted labels.
  
      Returns:
          np.ndarray: dcg scores.
      """
      k = min(np.shape(y_true)[-1], k)
      order = np.argsort(y_score)[::-1]
      y_true = np.take(y_true, order[:k])
      gains = 2**y_true - 1
      discounts = np.log2(np.arange(len(y_true)) + 2)
      return np.sum(gains / discounts)
  
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 11 for calling a global function with example usage: dcg_score(y_true, y_score, k) and returns: np