code: def test_memory_warm_start():
      data = load_breast_cancer()
      X, y = data.data, data.target
  
      def objective_function(opt):
          dtc = DecisionTreeClassifier(
              max_depth=opt["max_depth"],
              min_samples_split=opt["min_samples_split"],
          )
          scores = cross_val_score(dtc, X, y, cv=5)
  
          return scores.mean()
  
      search_space = {
          "max_depth": list(np.arange(1, 10)),
          "min_samples_split": list(np.arange(2, 20)),
      }
  
      c_time1 = time.perf_counter()
      hyper0 = Hyperactive()
      hyper0.add_search(objective_function, search_space, n_iter=300)
      hyper0.run()
      diff_time1 = time.perf_counter() - c_time1
  
      c_time2 = time.perf_counter()
  
      results0 = hyper0.search_data(objective_function)
  
      hyper1 = Hyperactive()
      hyper1.add_search(
          objective_function,
          search_space,
          n_iter=300,
          memory_warm_start=results0,
      )
      hyper1.run()
  
      diff_time2 = time.perf_counter() - c_time2
  
      assert diff_time2 < diff_time1 * 0.5
  
file_path: Hyperactive/tests/_local_test_timings/_test_memory.py
imports: import time
  import numpy
  import pandas
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import cross_val_score
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.ensemble import GradientBoostingClassifier
  from hyperactive import Hyperactive
label_desc: Use image node_id 4 for calling a global function with example usage: test_memory_warm_start() and returns: scores