class_docstr: A wrapper that allow to use dict observation space (coming from GoalEnv) with
  the RL algorithms.
  It assumes that all the spaces of the dict space are of the same type.
  
  :param env: (gym.GoalEnv)
docstr: Inverse operation of convert_dict_to_obs
  
  :param observations: (np.ndarray)
  :return: (OrderedDict<np.ndarray>)
docstr_tok: ["Inverse","operation","of","convert_dict_to_obs",":","param","observations",":","(","np.ndarray",")",":","return",":","(","OrderedDict","<","np.ndarray",">",")"]
code: def convert_obs_to_dict(self, observations):
      """
      Inverse operation of convert_dict_to_obs
  
      :param observations: (np.ndarray)
      :return: (OrderedDict<np.ndarray>)
      """
      return OrderedDict(
          [
              ("observation", observations[: self.obs_dim]),
              (
                  "achieved_goal",
                  observations[
                      self.obs_dim : self.obs_dim + self.goal_dim
                  ],
              ),
              (
                  "desired_goal",
                  observations[self.obs_dim + self.goal_dim :],
              ),
          ]
      )
  
file_path: stable-baselines/stable_baselines/her/utils.py
imports: from collections import OrderedDict
  import numpy
  from gym import spaces
label_desc: Use image node_id 3 for calling the HERGoalEnvWrapper obj's underlying member method code with example usage: obj.convert_obs_to_dict(observations) and returns: OrderedDict