code: def get_score(local_model, edge):
      """Calculate the cosine similarity score between two nodes."""
      try:
          vector1 = local_model[edge[0]]
          vector2 = local_model[edge[1]]
          return np.dot(vector1, vector2) / (
              np.linalg.norm(vector1) * np.linalg.norm(vector2)
          )
      except Exception as e:
          logging.warn("get_score warning: %s" % str(e))
          return None
          pass
  
imports: import os
  import argparse
  import time
  import numpy
  import logging
  import pickle
  import pgl
  from pgl.utils import paddle_helper
  import paddle
  import paddle.fluid
  import paddle.fluid.layers
  from utils import *
  import Dataset
  import model
  from sklearn.metrics import auc, f1_score, precision_recall_curve, roc_auc_score
label_desc: Use image node_id 5 for calling a global function with example usage: get_score(local_model, edge) and returns: unknown, None