class_docstr: An earlier version of this code (#626) was originally developed as part of the following work:
  
  Rabault, J., Kuhnle, A (2019). Accelerating Deep Reinforcement Leaning strategies of Flow
  Control through a multi-environment approach. Physics of Fluids.
code: def remote(
      cls,
      port,
      environment,
      max_episode_timesteps=None,
      reward_shaping=None,
      **kwargs,
  ):
      socket = Socket()
      socket.bind(("", port))
      socket.listen(1)
      connection, address = socket.accept()
      socket.close()
      super().remote(
          connection=connection,
          environment=environment,
          max_episode_timesteps=max_episode_timesteps,
          reward_shaping=reward_shaping,
          **kwargs,
      )
  
file_path: tensorforce/tensorforce/environments/socket_environment.py
imports: from socket import SHUT_RDWR, socket
  import time
  import msgpack
  import msgpack_numpy
  from tensorforce import TensorforceError
  from tensorforce.environments import RemoteEnvironment
label_desc: Use image node_id 1 for calling the SocketEnvironment obj's underlying member method code with example usage: obj.remote(cls, port, environment, max_episode_timesteps, reward_shaping) without return types