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: :param obs_dict: (dict<np.ndarray>)
  :return: (np.ndarray)
docstr_tok: [":","param","obs_dict",":","(","dict","<","np.ndarray",">",")",":","return",":","(","np.ndarray",")"]
code: def convert_dict_to_obs(self, obs_dict):
      """
      :param obs_dict: (dict<np.ndarray>)
      :return: (np.ndarray)
      """
      # Note: achieved goal is not removed from the observation
      # this is helpful to have a revertible transformation
      if isinstance(self.observation_space, spaces.MultiDiscrete):
          # Special case for multidiscrete
          return np.concatenate(
              [[int(obs_dict[key])] for key in KEY_ORDER]
          )
      return np.concatenate([obs_dict[key] for key in KEY_ORDER])
  
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 2 for calling the HERGoalEnvWrapper obj's underlying member method code with example usage: obj.convert_dict_to_obs(obs_dict) and returns: np, np