code: def random_env_steps(
      env: ObservationNormalizationWrapper, steps: int
  ) -> np.ndarray:
      """Randomly interact with environment"""
      observations = []
      obs = env.reset()
      observations.append(obs["observation"])
      for _ in range(steps):
          action = env.sampling_policy.compute_action(
              obs,
              maze_state=None,
              env=env,
              actor_id=ActorID(0, 0),
              deterministic=False,
          )
          obs, rew, done, info = env.step(action)
          observations.append(obs["observation"])
          if done:
              obs = env.reset()
              observations.append(obs["observation"])
      return np.vstack(observations)
  
imports: import copy
  import os
  import numpy
  import pytest
  import maze.test.core.wrappers.observation_normalization
  from maze.core.agent.random_policy import RandomPolicy
  from maze.core.env.structured_env import ActorID
  from maze.core.log_stats.log_stats import register_log_stats_writer, increment_log_step
  from maze.core.log_stats.log_stats_writer_console import LogStatsWriterConsole
  from maze.core.log_stats.log_stats_writer_tensorboard import LogStatsWriterTensorboard
  from maze.core.wrappers.log_stats_wrapper import LogStatsWrapper
  from maze.core.wrappers.maze_gym_env_wrapper import GymMazeEnv
  from maze.core.wrappers.observation_normalization.normalization_strategies.base import ObservationNormalizationStrategy
  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.test.shared_test_utils.config_testing_utils import load_env_config
label_desc: Use image node_id 1 for calling a global function with example usage: random_env_steps(env, steps) and returns: np