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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def selection_r(acquisition_function,
                samples_y_aggregation,
                x_bounds,
                x_types,
                regressor_gp,
                num_starting_points=100,
                minimize_constraints_fun=None):
    '''
    Selecte R value
    '''
    minimize_starting_points = [lib_data.rand(x_bounds, x_types) \
                                    for i in range(0, num_starting_points)]
    outputs = selection(acquisition_function, samples_y_aggregation,
                        x_bounds, x_types, regressor_gp,
                        minimize_starting_points,
                        minimize_constraints_fun=minimize_constraints_fun)

    return outputs