class_docstr: A DM Control Suite preprocessing wrapper for image observations.
class_docstr_tok: ["A","DM","Control","Suite","preprocessing","wrapper","for","image","observations","."]
docstr: Constructor for preprocessing wrapper.
  
  Args:
    env: The environment to wrap.
    observation_shape: The size to reshape the images to. This corresponds
      to (height, width). The output shape will be (height, width, 3), with
      3 corresponding to RGB channels.
docstr_tok: ["Constructor","for","preprocessing","wrapper",".","Args",":","env",":","The","environment","to","wrap",".","observation_shape",":","The","size","to","reshape","the","images","to",".","This","corresponds","to","(","height",",","width",")",".","The","output","shape","will","be","(","height",",","width",",","3",")",",","with","3","corresponding","to","RGB","channels","."]
code: def __init__(
      self,
      env: control.Environment,
      observation_shape: Tuple[int, int] = (84, 84),
  ):
      """Constructor for preprocessing wrapper.
  
      Args:
        env: The environment to wrap.
        observation_shape: The size to reshape the images to. This corresponds
          to (height, width). The output shape will be (height, width, 3), with
          3 corresponding to RGB channels.
      """
      super(DeepmindControlWithImagesPreprocessing, self).__init__(env)
  
      self._shape = observation_shape
  
imports: import collections
  from typing import Any, Mapping, Tuple
  from dm_control import suite
  from dm_control.rl import control
  import dm_env
  from dm_env import specs
  import gin
  import gym
  from gym import spaces
  import numpy
label_desc: Use image node_id 1 to create a new DeepmindControlWithImagesPreprocessing object from inherited base classes: DeepmindControlPreprocessing with example: obj = DeepmindControlWithImagesPreprocessing(env, observation_shape)