code: def init_demo_buffer(
      self, demoDataFile, update_stats=True
  ):  # function that initializes the demo buffer
      demoData = np.load(
          demoDataFile
      )  # load the demonstration data from data file
      info_keys = [
          key.replace("info_", "")
          for key in self.input_dims.keys()
          if key.startswith("info_")
      ]
      info_values = [
          np.empty(
              (self.T - 1, 1, self.input_dims["info_" + key]),
              np.float32,
          )
          for key in info_keys
      ]
  
      demo_data_obs = demoData["obs"]
      demo_data_acs = demoData["acs"]
      demo_data_info = demoData["info"]
  
      for epsd in range(
          self.num_demo
      ):  # we initialize the whole demo buffer at the start of the training
          obs, acts, goals, achieved_goals = [], [], [], []
          i = 0
          for transition in range(self.T - 1):
              obs.append(
                  [demo_data_obs[epsd][transition].get("observation")]
              )
              acts.append([demo_data_acs[epsd][transition]])
              goals.append(
                  [demo_data_obs[epsd][transition].get("desired_goal")]
              )
              achieved_goals.append(
                  [demo_data_obs[epsd][transition].get("achieved_goal")]
              )
              for idx, key in enumerate(info_keys):
                  info_values[idx][transition, i] = demo_data_info[
                      epsd
                  ][transition][key]
  
          obs.append(
              [demo_data_obs[epsd][self.T - 1].get("observation")]
          )
          achieved_goals.append(
              [demo_data_obs[epsd][self.T - 1].get("achieved_goal")]
          )
  
          episode = dict(o=obs, u=acts, g=goals, ag=achieved_goals)
          for key, value in zip(info_keys, info_values):
              episode["info_{}".format(key)] = value
  
          episode = convert_episode_to_batch_major(episode)
          global DEMO_BUFFER
          DEMO_BUFFER.store_episode(
              episode
          )  # create the observation dict and append them into the demonstration buffer
          logger.debug(
              "Demo buffer size currently ",
              DEMO_BUFFER.get_current_size(),
          )  # print out the demonstration buffer size
  
          if update_stats:
              # add transitions to normalizer to normalize the demo data as well
              episode["o_2"] = episode["o"][:, 1:, :]
              episode["ag_2"] = episode["ag"][:, 1:, :]
              num_normalizing_transitions = (
                  transitions_in_episode_batch(episode)
              )
              transitions = self.sample_transitions(
                  episode, num_normalizing_transitions
              )
  
              o, g, ag = (
                  transitions["o"],
                  transitions["g"],
                  transitions["ag"],
              )
              transitions["o"], transitions["g"] = self._preprocess_og(
                  o, ag, g
              )
              # No need to preprocess the o_2 and g_2 since this is only used for stats
  
              self.o_stats.update(transitions["o"])
              self.g_stats.update(transitions["g"])
  
              self.o_stats.recompute_stats()
              self.g_stats.recompute_stats()
          episode.clear()
  
      logger.info("Demo buffer size: ", DEMO_BUFFER.get_current_size())
  
imports: from collections import OrderedDict
  import numpy
  import tensorflow
  from tensorflow.contrib.staging import StagingArea
  from baselines import logger
  from baselines.her.util import import_function, store_args, flatten_grads, transitions_in_episode_batch, convert_episode_to_batch_major
  from baselines.her.normalizer import Normalizer
  from baselines.her.replay_buffer import ReplayBuffer
  from baselines.common.mpi_adam import MpiAdam
  from baselines.common import tf_util
label_desc: Use image node_id 6 for calling the DDPG obj's underlying member method code with example usage: obj.init_demo_buffer(demoDataFile, update_stats) without return types