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: Loads the replay buffer's state from a saved file.
  
  Args:
    checkpoint_dir: str, the directory where to read the numpy checkpointed
      files from.
    suffix: str, the suffix to use in numpy checkpoint files.
docstr_tok: ["Loads","the","replay","buffer","'s","state","from","a","saved","file",".","Args",":","checkpoint_dir",":","str",",","the","directory","where","to","read","the","numpy","checkpointed","files","from",".","suffix",":","str",",","the","suffix","to","use","in","numpy","checkpoint","files","."]
code: def load(self, checkpoint_dir, suffix):
      """Loads the replay buffer's state from a saved file.
  
      Args:
        checkpoint_dir: str, the directory where to read the numpy checkpointed
          files from.
        suffix: str, the suffix to use in numpy checkpoint files.
      """
      self.memory.load(checkpoint_dir, suffix)
  
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 WrappedReplayBuffer obj's underlying member method code with example usage: obj.load(checkpoint_dir, suffix) without return types