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 TimiLimit 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 = pfrl.wrappers.CastObservationToFloat32(env)
      # Normalize action space to [-1, 1]^n
      env = pfrl.wrappers.NormalizeActionSpace(env)
      if args.monitor:
          env = pfrl.wrappers.Monitor(
              env,
              args.outdir,
              force=True,
              video_callable=lambda _: True,
          )
      if args.render:
          env = pfrl.wrappers.Render(env, mode="human")
      return env
  
file_path: pfrl/examples/atlas/train_soft_actor_critic_atlas.py
imports: import argparse
  import functools
  import logging
  import sys
  import gym
  import gym.wrappers
  import numpy
  import torch
  from torch import distributions, nn
  import pfrl
  from pfrl import experiments, replay_buffers, utils
  from pfrl.nn.lmbda import Lambda
label_desc: Use image node_id 1 for calling a global function with example usage: make_env(args, seed, test) and returns: env