text: import numpy as np
  from gradient_free_optimizers import RandomSearchOptimizer
  
  
  """ --- test search spaces with mixed int/float types --- """
  
  
  def test_mixed_type_search_space_0():
      def objective_function(para):
          assert isinstance(para["x1"], int)
  
          return 1
  
      search_space = {
          "x1": range(10, 20),
      }
  
      opt = RandomSearchOptimizer(search_space)
      opt.search(objective_function, n_iter=10000)
  
  
  def test_mixed_type_search_space_1():
      def objective_function(para):
          assert isinstance(para["x2"], float)
  
          return 1
  
      search_space = {
          "x2": np.arange(1, 2, 0.1),
      }
  
      opt = RandomSearchOptimizer(search_space)
      opt.search(objective_function, n_iter=10000)
  
  
  def test_mixed_type_search_space_2():
      def objective_function(para):
          assert isinstance(para["x1"], int)
          assert isinstance(para["x2"], float)
  
          return 1
  
      search_space = {
          "x1": range(10, 20),
          "x2": np.arange(1, 2, 0.1),
      }
  
      opt = RandomSearchOptimizer(search_space)
      opt.search(objective_function, n_iter=10000)
  