code: def run_test(
      task,
      dataset_name,
      models,
      labels,
      save_filename,
      embedding_test_dir,
  ):
      sys.path.append(embedding_test_dir)
      from src.test import test
  
      args = {}
      if task == "classification":
          args["radio"] = [0.8]
          args["label_name"] = labels
          evalution = None
      elif task == "link_predict":
          evalution = "AUC"
          args["data_dir"] = labels
      args["sampling_mapping"] = {"Flickr": 100000, "wiki": 1000000}
  
      with cd(embedding_test_dir):
          test(
              task,
              evalution,
              dataset_name,
              models,
              save_filename=save_filename,
              **args,
          )
  
imports: import random
  import os
  import sys
  import collections
  import itertools
  import pickle
  import scipy.sparse
  import networkx
  import numpy
  from sklearn.cluster import SpectralClustering
  from sklearn import gaussian_process
  from scipy.optimize import minimize
  from bayes_opt import BayesianOptimization
label_desc: Use image node_id 9 for calling a global function with example usage: run_test(task, dataset_name, models, labels, save_filename, embedding_test_dir) without return types