code: def test_trafo_1(random_state, search_space, memory_warm_start):
      search_space = search_space
      memory_warm_start = memory_warm_start
  
      c_time_0 = time.perf_counter()
      hyper = Hyperactive()
      hyper.add_search(
          objective_function_1,
          search_space,
          n_iter=10,
          random_state=random_state,
          initialize={"random": 1},
      )
      hyper.run()
      d_time_0 = time.perf_counter() - c_time_0
  
      c_time_1 = time.perf_counter()
      hyper = Hyperactive()
      hyper.add_search(
          objective_function_1,
          search_space,
          n_iter=10,
          random_state=random_state,
          initialize={"random": 1},
          memory_warm_start=memory_warm_start,
      )
      hyper.run()
      d_time_1 = time.perf_counter() - c_time_1
  
      assert d_time_1 < d_time_0 * 0.5
  
file_path: Hyperactive/tests/test_hyper_gradient_trafo.py
imports: import time
  import pytest
  import numpy
  import pandas
  from hyperactive import Hyperactive
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import cross_val_score
  from sklearn.tree import DecisionTreeClassifier
label_desc: Use image node_id 4 for calling a global function with example usage: test_trafo_1(random_state, search_space, memory_warm_start) without return types