docstr: Creates an input placeholder tailored to a specific observation space
  
  Parameters
  ----------
  
  observation_space:
          observation space of the environment. Should be one of the gym.spaces types
  name: str
          tensorflow name of the underlying placeholder
docstr_tok: ["Creates","an","input","placeholder","tailored","to","a","specific","observation","space","Parameters","--","--","--","--","--","observation_space",":","observation","space","of","the","environment",".","Should","be","one","of","the","gym.spaces","types","name",":","str","tensorflow","name","of","the","underlying","placeholder"]
code: def __init__(self, observation_space, name=None):
      """Creates an input placeholder tailored to a specific observation space
  
      Parameters
      ----------
  
      observation_space:
              observation space of the environment. Should be one of the gym.spaces types
      name: str
              tensorflow name of the underlying placeholder
      """
      inpt, self.processed_inpt = observation_input(
          observation_space, name=name
      )
      super().__init__(inpt)
  
file_path: baselines/baselines/deepq/utils.py
imports: from baselines.common.input import observation_input
  from baselines.common.tf_util import adjust_shape
label_desc: Use image node_id 1 to create a new ObservationInput object from inherited base classes: PlaceholderTfInput with example: obj = ObservationInput(observation_space, name)