code: def run_maze_from_str(
      config_module: str,
      config_name: str,
      hydra_overrides: Dict[str, str],
  ) -> DictConfig:
      """Load a hydra config from a given config module + config name and additional hydra overrides and start the exp
  
      :param config_module: The config module that should be used
      :param config_name: The name of the config that should be used
      :param hydra_overrides: The hydra overrides that should be applied
      :return: A dict config of the created hydra config
      """
      cfg = load_hydra_config(
          config_module, config_name, hydra_overrides
      )
      maze_run(cfg)
  
      return cfg
  
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 7 for calling a global function with example usage: run_maze_from_str(config_module, config_name, hydra_overrides) and returns: cfg