new_contents: import gym
  import torch.optim as optim
  import torch
  
  from ImageAtari import SimpleMonitor, wrap_dqn
  from utils import ReplayMemory
  
  use_cuda = torch.cuda.is_available()
  
  def init(game_name,momentum,lr,model,mem_size,mod):
      """Set some parameters for the training:
  
          Environment of the game and more:
          Environment
          Number of actions
          Optimizer (RMSprop for resnet and Adam for classic DeepMind model
          Replay Buffer
          Model Architecture object
      """
      env = gym.make(game_name + "NoFrameskip-v4")
      x = env.action_space
      for i in range(18,0,-1):
          if x.contains(i):
              nb_actions = (i+1)
              break
      monitored_env = SimpleMonitor(env)
      env = wrap_dqn(monitored_env,True)
      env_no_clipping = wrap_dqn(monitored_env,False)
  
      model_obj = model(nb_actions)
      if use_cuda:
          model_obj = model_obj.cuda()
  
      if mod=='resnet':
          optimizer = optim.RMSprop(model_obj.parameters(), lr = lr,
                            momentum = momentum)
      elif mod=='dqn':
          optimizer = optim.Adam(model_obj.parameters(), lr=lr)
      
  
      memory = ReplayMemory(mem_size)
  
  
  
      return env, env_no_clipping, nb_actions, optimizer, memory, model_obj
  
subject: Create init_atari.py