code: def make_env(args, seed, test):
      if args.env.startswith("Roboschool"):
          # Check gym version because roboschool does not work with gym>=0.15.6
          from distutils.version import StrictVersion
  
          gym_version = StrictVersion(gym.__version__)
          if gym_version >= StrictVersion("0.15.6"):
              raise RuntimeError(
                  "roboschool does not work with gym>=0.15.6"
              )
          import roboschool  # NOQA
      env = gym.make(args.env)
      # Unwrap TimeLimit wrapper
      assert isinstance(env, gym.wrappers.TimeLimit)
      env = env.env
      # Use different random seeds for train and test envs
      env_seed = 2**32 - 1 - seed if test else seed
      env.seed(int(env_seed))
      # Cast observations to float32 because our model uses float32
      env = chainerrl.wrappers.CastObservationToFloat32(env)
      # Normalize action space to [-1, 1]^n
      env = chainerrl.wrappers.NormalizeActionSpace(env)
      if args.monitor:
          env = chainerrl.wrappers.Monitor(
              env,
              args.outdir,
              force=True,
              video_callable=lambda _: True,
          )
      if args.render:
          env = chainerrl.wrappers.Render(env, mode="human")
      return env
  
file_path: chainerrl/examples/atlas/train_soft_actor_critic_atlas.py
imports: import argparse
  import functools
  import logging
  import os
  import sys
  import chainer
  from chainer import functions
  from chainer import links
  from chainer import optimizers
  import gym
  import gym.wrappers
  import numpy
  import chainerrl
  from chainerrl import experiments
  from chainerrl import misc
  from chainerrl import replay_buffer
label_desc: Use image node_id 2 for calling a global function with example usage: make_env(args, seed, test) and returns: env