code: def hit_score(y_true, y_score, k=10):
      """Computing hit score metric at k.
  
      Args:
          y_true (np.ndarray): ground-truth labels.
          y_score (np.ndarray): predicted labels.
  
      Returns:
          np.ndarray: hit score.
      """
      ground_truth = np.where(y_true == 1)[0]
      argsort = np.argsort(y_score)[::-1][:k]
      for idx in argsort:
          if idx in ground_truth:
              return 1
      return 0
  
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 10 for calling a global function with example usage: hit_score(y_true, y_score, k) and returns: int, int