class_docstr: Wrapper of OutOfGraphReplayBuffer with an in graph sampling mechanism.
  
  Usage:
    To add a transition:  call the add function.
  
    To sample a batch:    Construct operations that depend on any of the
                          tensors is the transition dictionary. Every sess.run
                          that requires any of these tensors will sample a new
                          transition.
docstr: Save the underlying replay buffer's contents in a file.
  
  Args:
    checkpoint_dir: str, the directory where to read the numpy checkpointed
      files from.
    iteration_number: int, the iteration_number to use as a suffix in naming
      numpy checkpoint files.
docstr_tok: ["Save","the","underlying","replay","buffer","'s","contents","in","a","file",".","Args",":","checkpoint_dir",":","str",",","the","directory","where","to","read","the","numpy","checkpointed","files","from",".","iteration_number",":","int",",","the","iteration_number","to","use","as","a","suffix","in","naming","numpy","checkpoint","files","."]
code: def save(self, checkpoint_dir, iteration_number):
      """Save the underlying replay buffer's contents in a file.
  
      Args:
        checkpoint_dir: str, the directory where to read the numpy checkpointed
          files from.
        iteration_number: int, the iteration_number to use as a suffix in naming
          numpy checkpoint files.
      """
      self.memory.save(checkpoint_dir, iteration_number)
  
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 7 for calling the WrappedReplayBuffer obj's underlying member method code with example usage: obj.save(checkpoint_dir, iteration_number) without return types