code: def step(self, action):
      self.steps += 1
      # Reward is 1.0 - (max(actions) - state).
      [rew] = 1.0 - np.abs(np.max(action) - self.state)
      terminated = False
      truncated = self.steps >= self.max_steps
      self.state = self.observation_space.sample()
      return self.state, rew, terminated, truncated, {}
  
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 3 for calling the SimpleEnv obj's underlying member method code with example usage: obj.step(action) and returns: self,  rew,  terminated,  truncated,  dict