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: Create a tuple of arrays with the type of get_transition_elements.
  
  When using the WrappedReplayBuffer with staging enabled it is important to
  create new arrays every sample because StaginArea keeps a pointer to the
  returned arrays.
  
  Args:
    batch_size: (int) number of transitions returned. If None the default
      batch_size will be used.
  
  Returns:
    Tuple of np.arrays with the shape and type of get_transition_elements.
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code: def _create_batch_arrays(self, batch_size):
      """Create a tuple of arrays with the type of get_transition_elements.
  
      When using the WrappedReplayBuffer with staging enabled it is important to
      create new arrays every sample because StaginArea keeps a pointer to the
      returned arrays.
  
      Args:
        batch_size: (int) number of transitions returned. If None the default
          batch_size will be used.
  
      Returns:
        Tuple of np.arrays with the shape and type of get_transition_elements.
      """
      transition_elements = self.get_transition_elements(batch_size)
      batch_arrays = []
      for element in transition_elements:
          batch_arrays.append(
              np.empty(element.shape, dtype=element.type)
          )
      return tuple(batch_arrays)
  
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 20 for calling the OutOfGraphReplayBuffer obj's underlying member method code with example usage: obj._create_batch_arrays(batch_size) and returns: tuple