code: def make_gym_env(
      name,
      rl_env_max_episode_steps=-1,
      maxskip_env=False,
      rendered_env=False,
      rendered_env_resize_to=None,
      sticky_actions=False,
      output_dtype=None,
      num_actions=None,
  ):
      """Create a gym env optionally with a time limit and maxskip wrapper.
  
      NOTE: The returned env may already be wrapped with TimeLimit!
  
      Args:
        name: `str` - base name of the gym env to make.
        rl_env_max_episode_steps: `int` or None - Using any value < 0 returns the
          env as-in, otherwise we impose the requested timelimit. Setting this to
          None returns a wrapped env that doesn't have a step limit.
        maxskip_env: whether to also use MaxAndSkip wrapper before time limit.
        rendered_env: whether to force render for observations. Use this for
          environments that are not natively rendering the scene for observations.
        rendered_env_resize_to: a list of [height, width] to change the original
          resolution of the native environment render.
        sticky_actions: whether to use sticky_actions before MaxAndSkip wrapper.
        output_dtype: numpy datatype that we want the observation to be in, if None
          this defaults to the env's observation dtype. Useful for TPUs since they
          don't support uint8 which is a default observation type for a lot of envs.
        num_actions: None if we do not need discretization and the number of
          discrete actions per continuous action.
  
      Returns:
        An instance of `gym.Env` or `gym.Wrapper`.
      """
      env = gym.make(name)
      return gym_env_wrapper(
          env,
          rl_env_max_episode_steps,
          maxskip_env,
          rendered_env,
          rendered_env_resize_to,
          sticky_actions,
          output_dtype,
          num_actions,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from absl import logging
  import gym
  import gym.wrappers
  import numpy
  from PIL import Image
label_desc: Use image node_id 3 for calling a global function with example usage: make_gym_env(name, rl_env_max_episode_steps, maxskip_env, rendered_env, rendered_env_resize_to, sticky_actions, output_dtype, num_actions) and returns: gym_env_wrapper