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: Adds a padding transition filled with zeros (Used in episode beginnings).
      
docstr_tok: ["Adds","a","padding","transition","filled","with","zeros","(","Used","in","episode","beginnings",")","."]
code: def _add_zero_transition(self):
      """Adds a padding transition filled with zeros (Used in episode beginnings)."""
      zero_transition = []
      for element_type in self.get_add_args_signature():
          zero_transition.append(
              np.zeros(element_type.shape, dtype=element_type.type)
          )
      self.episode_end_indices.discard(self.cursor())  # If present
      self._add(*zero_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 6 for calling the OutOfGraphReplayBuffer obj's underlying member method code with example usage: obj._add_zero_transition() without return types