code: def evaluate_acc(pred, label):
      res = []
      for _value in pred:
          if _value >= 0.5:
              res.append(1)
          else:
              res.append(0)
      return accuracy_score(y_pred=res, y_true=label)
  
imports: import numpy
  import torch
  from sklearn.metrics import accuracy_score, average_precision_score, f1_score, log_loss, ndcg_score, roc_auc_score
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate_acc(pred, label) and returns: accuracy_score