class_docstr: A simple out-of-graph Replay Buffer.
  
  Stores transitions, state, action, reward, next_state, terminal (and any
  extra contents specified) in a circular buffer and provides a uniform
  transition sampling function.
  
  When the states consist of stacks of observations storing the states is
  inefficient. This class writes observations and constructs the stacked states
  at sample time.
  
  Attributes:
    add_count: int, counter of how many transitions have been added (including
      the blank ones at the beginning of an episode).
    invalid_range: np.array, an array with the indices of cursor-related invalid
      transitions
    episode_end_indices: set[int], a set of indices corresponding to the
      end of an episode.
code: def get_observation_stack(self, index):
      return self._get_element_stack(index, "observation")
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import gzip
  import math
  import os
  import pickle
  from absl import logging
  import gin.tf
  import numpy
  import tensorflow
label_desc: Use image node_id 16 for calling the OutOfGraphReplayBuffer obj's underlying member method code with example usage: obj.get_observation_stack(index) and returns: self