code: def check_random_sampling(
      config_module: str, config: str, overrides: Dict[str, str]
  ) -> None:
      """Check if random sampling in instantiated env works."""
      env = make_env_from_hydra(config_module, config, **overrides)
  
      # estimate normalization stats if required
      if isinstance(env, ObservationNormalizationWrapper):
          normalization_statistics = obtain_normalization_statistics(
              env=env, n_samples=100
          )
          env.set_normalization_statistics(normalization_statistics)
  
      env.reset()
  
      # run interaction loop
      n_steps = 100
      for step in range(n_steps):
          # sample random action
          action = env.action_space.sample()
  
          # take env step
          state, reward, done, info = env.step(action)
          if done:
              env.reset()
  
imports: from typing import List, Dict
  import pytest
  from hydra.core.global_hydra import GlobalHydra
  from hydra import initialize_config_module, compose
  from omegaconf import DictConfig
  from torch import nn
  from maze.core.env.structured_env import StructuredEnv
  from maze.core.env.structured_env_spaces_mixin import StructuredEnvSpacesMixin
  from maze.core.utils.config_utils import make_env_from_hydra, EnvFactory
  from maze.core.utils.factory import Factory
  from maze.core.wrappers.observation_normalization.observation_normalization_utils import obtain_normalization_statistics
  from maze.core.wrappers.observation_normalization.observation_normalization_wrapper import ObservationNormalizationWrapper
  from maze.maze_cli import maze_run
  from maze.perception.models.model_composer import BaseModelComposer
label_desc: Use image node_id 5 for calling a global function with example usage: check_random_sampling(config_module, config, overrides) without return types