text: from gym.core import Env
  from gym.spaces.discrete import Discrete
  
  class DataCenterEnv(Env):
      def __init__(self):
          # load pkl model
          # define the state, action space
          # define reward function
          self.state = None
          self.action_space = Discrete(n=100)
          self.observation_space = None
          self.step_count = 0
          pass
  
      def _step(self, action):
          # recieve one action
          # self.step_count += 1
          # new_state = self.model.run()
          # reward = self.reward(self,state, next_state, action)
          # done = bool(self.step_count > self.max_step)
          # if done is True:
          #    self.reset()
          # self.state = new_state
          pass
  
  
      def _reward(self, state, next_state, action):
          return 0
  
      def _reset(self):
          pass
  
      def _seed(self, seed=None):
          pass
  
  
  class Agent(object):
      def __init__(self):
          self.model = DQN(batch=100, lr=.001)
  
      def predict(self, obs):
          action = self.model.predict(obs)
  
          return action
  
  
  if __name__ == '__main__':
      import gym
      a = gym.make('Swimmer-v1')
      a.reset()
      for i in range(1000):
          print(a.step(action=a.action_space.sample()))
  