input: Write a function that creates an AI-powered Python script to optimize any given code and ensure the script prints the optimized result for a specific set of parameters. The function should follow these steps: 1) import the required libraries, 2) define a class for the parameters, 3) define a function to return the result based on the parameters, 4) define an optimization function to find the best parameters by iterating through possible values, and 5) in the main block, initialize the parameters, optimize them, and print the optimized result. The function should return the optimized result.
      
  assert optimize_code()==6
  assert optimize_code()==12
  assert optimize_code()==24
  assert optimize_code()==18
  assert optimize_code()==30
output: 
  import numpy as np
  from sklearn.model_selection import GridSearchCV
  from sklearn.ensemble import RandomForestRegressor
  
  class Parameters:
      def __init__(self, param1, param2):
          self.param1 = param1
          self.param2 = param2
  
  def get_result(params):
      return params.param1 * params.param2
  
  def optimize_code():
      param_grid = {
          'param1': np.arange(1, 6),
          'param2': np.arange(1, 6)
      }
      
      model = RandomForestRegressor()
      grid_search = GridSearchCV(model, param_grid, scoring='neg_mean_squared_error')
      
      X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
      y = np.array([2, 6, 12, 20, 30])
      
      grid_search.fit(X, y)
      
      best_params = Parameters(grid_search.best_params_['param1'], grid_search.best_params_['param2'])
      optimized_result = get_result(best_params)
      
      return optimized_result
  