code: def _check_image_input(observation_space: spaces.Box) -> None:
      """
      Check that the input will be compatible with Stable-Baselines
      when the observation is apparently an image.
      """
      if observation_space.dtype != np.uint8:
          warnings.warn(
              "It seems that your observation is an image but the `dtype` "
              "of your observation_space is not `np.uint8`. "
              "If your observation is not an image, we recommend you to flatten the observation "
              "to have only a 1D vector"
          )
  
      if np.any(observation_space.low != 0) or np.any(
          observation_space.high != 255
      ):
          warnings.warn(
              "It seems that your observation space is an image but the "
              "upper and lower bounds are not in [0, 255]. "
              "Because the CNN policy normalize automatically the observation "
              "you may encounter issue if the values are not in that range."
          )
  
      if (
          observation_space.shape[0] < 36
          or observation_space.shape[1] < 36
      ):
          warnings.warn(
              "The minimal resolution for an image is 36x36 for the default CnnPolicy. "
              "You might need to use a custom `cnn_extractor` "
              "cf https://stable-baselines.readthedocs.io/en/master/guide/custom_policy.html"
          )
  
file_path: stable-baselines/stable_baselines/common/env_checker.py
imports: import warnings
  from typing import Union
  import gym
  from gym import spaces
  import numpy
  from stable_baselines.common.vec_env import DummyVecEnv, VecCheckNan
label_desc: Use image node_id 2 for calling a global function with example usage: _check_image_input(observation_space) without return types