code: def test_sac_dict_obs_order(self):
      dict_space = Dict(
          {
              "img": Box(low=0, high=1, shape=(42, 42, 3)),
              "cont": Box(low=0, high=100, shape=(3,)),
          }
      )
  
      # Dict space .sample() returns an ordered dict.
      # Make sure the keys in samples are ordered differently.
      dict_samples = [
          {k: v for k, v in reversed(dict_space.sample().items())}
          for _ in range(10)
      ]
  
      class NestedDictEnv(gym.Env):
          def __init__(self):
              self.action_space = Box(low=-1.0, high=1.0, shape=(2,))
              self.observation_space = dict_space
              self.steps = 0
  
          def reset(self, *, seed=None, options=None):
              self.steps = 0
              return dict_samples[0], {}
  
          def step(self, action):
              self.steps += 1
              terminated = False
              truncated = self.steps >= 5
              return (
                  dict_samples[self.steps],
                  1,
                  terminated,
                  truncated,
                  {},
              )
  
      tune.register_env("nested", lambda _: NestedDictEnv())
      config = (
          sac.SACConfig()
          .environment("nested")
          .training(
              replay_buffer_config={
                  "capacity": 10,
              },
              num_steps_sampled_before_learning_starts=0,
              train_batch_size=5,
          )
          .rollouts(
              num_rollout_workers=0,
              rollout_fragment_length=5,
          )
          .experimental(_disable_preprocessor_api=True)
      )
      num_iterations = 1
  
      for _ in framework_iterator(config):
          algo = config.build()
          for _ in range(num_iterations):
              results = algo.train()
              check_train_results(results)
              print(results)
          check_compute_single_action(algo)
  
imports: import gymnasium
  from gymnasium.spaces import Box, Dict, Discrete, Tuple
  import numpy
  import re
  import unittest
  import ray
  from ray.rllib.algorithms import sac
  from ray.rllib.algorithms.sac.sac_tf_policy import sac_actor_critic_loss
  from ray.rllib.algorithms.sac.sac_torch_policy import actor_critic_loss
  from ray.rllib.examples.env.random_env import RandomEnv
  from ray.rllib.examples.models.batch_norm_model import KerasBatchNormModel, TorchBatchNormModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.tf.tf_action_dist import Dirichlet
  from ray.rllib.models.torch.torch_action_dist import TorchDirichlet
  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 fc, huber_loss, relu
  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
  from ray import tune
  from ray.rllib.utils.replay_buffers.utils import patch_buffer_with_fake_sampling_method
label_desc: Use image node_id 5 for calling the TestSAC obj's underlying member method code with example usage: obj.test_sac_dict_obs_order() and returns: dict_samples,  dict, dict_samples,  int,  terminated,  truncated,  dict