code: def test_steps_env_with_single_policy():
      agent_deployment = AgentDeployment(
          policy=DummyGreedyPolicy(), env=build_dummy_maze_env()
      )
  
      # Step the environment manually here and query the agent integration wrapper for maze_actions
      test_policy = DummyGreedyPolicy()
      test_env = build_dummy_maze_env()
      maze_state = test_env.reset()
      reward, done, info = None, None, None
  
      for i in range(10):
          maze_action = agent_deployment.act(
              maze_state, reward, done, info
          )
  
          # Compare with the expected maze_action on top of the env that we are stepping
          raw_expected_action = test_policy.compute_action(
              observation=test_env.observation_conversion.maze_to_space(
                  maze_state
              ),
              maze_state=maze_state,
              deterministic=True,
          )
          expected_action = test_env.action_conversion.space_to_maze(
              raw_expected_action, maze_state
          )
          assert expected_action.keys() == maze_action.keys()
          assert np.all(
              expected_action[key] == maze_action[key]
              for key in maze_action.keys()
          )
  
          maze_state, reward, done, info = test_env.step(
              expected_action
          )
  
imports: from typing import Tuple, Sequence, Optional
  import gym
  import numpy
  import pytest
  from maze.core.agent.random_policy import RandomPolicy
  from maze.core.agent_deployment.agent_deployment import AgentDeployment
  from maze.core.env.action_conversion import ActionType
  from maze.core.env.base_env import BaseEnv
  from maze.core.env.base_env_events import BaseEnvEvents
  from maze.core.env.maze_state import MazeStateType
  from maze.core.env.observation_conversion import ObservationType
  from maze.core.env.structured_env import ActorID
  from maze.core.log_events.episode_event_log import EpisodeEventLog
  from maze.core.log_events.log_events_writer import LogEventsWriter
  from maze.core.log_events.log_events_writer_registry import LogEventsWriterRegistry
  from maze.core.log_events.monitoring_events import RewardEvents
  from maze.core.log_stats.log_stats import LogStatsWriter, LogStats, LogStatsLevel
  from maze.core.log_stats.log_stats import register_log_stats_writer
  from maze.core.trajectory_recording.records.trajectory_record import StateTrajectoryRecord
  from maze.core.trajectory_recording.writers.trajectory_writer import TrajectoryWriter
  from maze.core.trajectory_recording.writers.trajectory_writer_registry import TrajectoryWriterRegistry
  from maze.core.utils.config_utils import EnvFactory, read_hydra_config
  from maze.core.wrappers.log_stats_wrapper import LogStatsWrapper
  from maze.core.wrappers.maze_gym_env_wrapper import GymMazeEnv
  from maze.core.wrappers.trajectory_recording_wrapper import TrajectoryRecordingWrapper
  from maze.test.shared_test_utils.dummy_wrappers.step_skip_in_step_wrapper import StepSkipInStepWrapper
  from maze.test.shared_test_utils.dummy_env.agents.dummy_policy import DummyGreedyPolicy
  from maze.test.shared_test_utils.helper_functions import build_dummy_maze_env, build_dummy_structured_env
label_desc: Use image node_id 1 for calling a global function with example usage: test_steps_env_with_single_policy() without return types