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.
docstr: Internal add method to add to the storage arrays.
  
  Args:
    *args: All the elements in a transition.
docstr_tok: ["Internal","add","method","to","add","to","the","storage","arrays",".","Args",":","*","args",":","All","the","elements","in","a","transition","."]
code: def _add(self, *args):
      """Internal add method to add to the storage arrays.
  
      Args:
        *args: All the elements in a transition.
      """
      self._check_args_length(*args)
      transition = {
          e.name: args[idx]
          for idx, e in enumerate(self.get_add_args_signature())
      }
      self._add_transition(transition)
  
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 8 for calling the OutOfGraphReplayBuffer obj's underlying member method code with example usage: obj._add() without return types