code: def test_hyperparameters_added(tmp_path):
      hps = hp_module.HyperParameters()
      hps.Int("a", -100, 100)
  
      oracle = bo_module.BayesianOptimizationOracle(
          objective=keras_tuner.Objective("score", direction="max"),
          max_trials=20,
          hyperparameters=hps,
          num_initial_points=2,
      )
      oracle._set_project_dir(tmp_path, "untitled")
  
      # Populate initial trials.
      for i in range(10):
          trial = trial_module.Trial(hyperparameters=hps.copy())
          trial.hyperparameters.values["a"] = 10 * i
          trial.score = i
          trial.status = "COMPLETED"
          oracle.trials[trial.trial_id] = trial
  
      # A new trial discovered a new hp and synced to oracle.hyperparameters.
      new_hps = hp_module.HyperParameters()
      new_hps.Float("b", 3.2, 6.4, step=0.2, default=3.6)
      new_hps.Boolean("c", default=True)
      oracle.update_space(new_hps)
  
      # Make a new trial, it should have b set.
      trial = oracle.create_trial("tuner0")
      assert trial.status == "RUNNING"
      assert "b" in trial.hyperparameters.values
      assert "c" in trial.hyperparameters.values
  
imports: import math
  import numpy
  import pytest
  import keras_tuner
  from keras_tuner.backend import keras
  from keras_tuner.engine import hyperparameters
  from keras_tuner.engine import trial
  from keras_tuner.tuners import bayesian
label_desc: Use image node_id 12 for calling a global function with example usage: test_hyperparameters_added(tmp_path) without return types