code: def __init__(self, config):
      self._skip_env_checking = True
      if config.get("simplex_actions", False):
          self.action_space = Simplex((2,))
      else:
          self.action_space = Box(0.0, 1.0, (1,))
      self.observation_space = Box(0.0, 1.0, (1,))
      self.max_steps = config.get("max_steps", 100)
      self.state = None
      self.steps = None
  
imports: import copy
  import re
  import unittest
  import gymnasium
  import numpy
  from gymnasium.spaces import Box
  from rllib_ddpg.ddpg import DDPGConfig
  import ray
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy, fc, huber_loss, l2_loss, relu, sigmoid
  from ray.rllib.utils.replay_buffers.utils import patch_buffer_with_fake_sampling_method
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.utils.test_utils import check, check_compute_single_action, check_train_results, framework_iterator
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 1 to create a new SimpleEnv object from inherited base classes: gym with example: obj = SimpleEnv(config)