old_contents: import opensim as osim
  from osim.http.client import Client
  
  # Settings
  remote_base = "http://osim-rl-grader.aicrowd.com/"
  crowdai_token = "a66245c8324e2d37b92f098a57ef3f99"
  
  client = Client(remote_base)
  
  # Create environment
  observation = client.env_create(crowdai_token, env_id='L2M2019Env')
  
  # IMPLEMENTATION OF YOUR CONTROLLER
  # my_controller = ... (for example the one trained in keras_rl)
  
  while True:
      [observation, reward, done, info] = client.env_step([0.27365685, 0.3674228, 0.97836083, 0.15261972, 0.3319228, 0.03692374, 0.09905472, 0.1971763 , 0.8908676 , 0.5744208, 0.9313108 , 0.26675472, 0.54930794, 0.91221607, 0.7701997, 0.95412385, 0.43612957, 0.2880115 , 0.26009786, 0.27759373, 0.9234055 , 0.63657844], True)
      print(observation)
      if done:
          observation = client.env_reset()
          if not observation:
              break
  
  client.submit()
  
new_contents: import opensim as osim
  from osim.http.client import Client
  
  # Settings
  remote_base = "http://osim-rl-grader.aicrowd.com/"
  crowdai_token = "a66245c8324e2d37b92f098a57ef3f99"
  
  client = Client(remote_base)
  
  # Create environment
  observation = client.env_create(crowdai_token, env_id='L2M2019Env')
  
  # IMPLEMENTATION OF YOUR CONTROLLER
  # my_controller = ... (for example the one trained in keras_rl)
  
  while True:
      [observation, reward, done, info] = client.env_step([0.27365685, 0.3674228, 0.97836083, 0.15261972, 0.3319228, 0.03692374, 0.09905472, 0.1971763 , 0.8908676 , 0.5744208, 0.9313108 , 0.26675472, 0.54930794, 0.91221607, 0.7701997, 0.95412385, 0.43612957, 0.2880115 , 0.26009786, 0.27759373, 0.9234055 , 0.63657844], True)
      print(observation)
      if done:
          observation = client.env_reset()
          if not observation:
              break
  
  client.submit()
subject: test in prog