Implement a function that performs Bayesian optimization over a given objective function and search space. Bayesian optimization is a powerful method for hyperparameter tuning in machine learning models.
**Example:** Suppose we want to optimize the performance of a neural network on a given dataset. We can define an objective function that takes in the hyperparameters and returns the performance of the network. We can then use Bayesian optimization to find the best hyperparameters.
**Constraints:** The objective function should be a function that takes in a dictionary of hyperparameters and returns a float representing the performance of the network. The search space should be represented as a dictionary where each key is a hyperparameter name and each value is a list of possible values for that hyperparameter.
**Hint:** Use a library such as scikit-optimize to implement Bayesian optimization.
Test Cases
{'objective_function': lambda x: x[0] + x[1], 'search_space': {'learning_rate': [0.1, 0.5, 0.01, 0.05, 0.001], 'batch_size': [32, 64, 128]}[0.5, 64]{'objective_function': lambda x: x[0] + x[1], 'search_space': {'learning_rate': [0.1, 0.5, 0.01, 0.05, 0.001], 'batch_size': [32, 64, 128]}[0.01, 128]