This example demonstrates how to define a search space of integers using the Integer class, with a lower bound of 1 and an upper bound of 10. We then generate a random integer sample from this search space using the `rvs()` method. Example 2:python from skopt import gp_minimize from skopt.space import Integer # Define the objective function to be optimized def objective(x): return x[0] ** 2 # Define the search space for the optimization space = [Integer(low=-10, high=10)] # Perform Bayesian optimization on the objective function result = gp_minimize(func=objective, dimensions=space) print(result.x) # Output: [0] (the optimized value) ``` In this example, we use the Integer class to define a search space for an optimization problem using Bayesian optimization. The objective function is a simple quadratic function, and we use the `gp_minimize()` function from Scikit-optimize to perform the optimization. The `dimensions` argument is set to the search space defined using the Integer class. The output is the optimal value of the objective function found using Bayesian optimization. Overall, the skopt.space.Integer class is a powerful tool for defining search spaces of integers for optimization problems in Python using the Scikit-optimize library.