code: def test_memory_warm_start_manual():
      data = load_breast_cancer()
      X, y = data.data, data.target
  
      def objective_function(opt):
          dtc = GradientBoostingClassifier(
              n_estimators=opt["n_estimators"],
          )
          scores = cross_val_score(dtc, X, y, cv=5)
  
          return scores.mean()
  
      search_space = {
          "n_estimators": list(np.arange(500, 502)),
      }
  
      c_time_1 = time.perf_counter()
      hyper = Hyperactive()
      hyper.add_search(objective_function, search_space, n_iter=1)
      hyper.run()
      diff_time_1 = time.perf_counter() - c_time_1
  
      memory_warm_start = pd.DataFrame(
          [[500, 0.9], [501, 0.91]], columns=["n_estimators", "score"]
      )
  
      c_time = time.perf_counter()
      hyper0 = Hyperactive()
      hyper0.add_search(
          objective_function,
          search_space,
          n_iter=10,
          memory_warm_start=memory_warm_start,
      )
      hyper0.run()
      diff_time_2 = time.perf_counter() - c_time
  
      assert diff_time_1 * 0.5 > diff_time_2
  
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 5 for calling a global function with example usage: test_memory_warm_start_manual() and returns: scores