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: Creates the ops necessary to sample from the replay buffer.
  
  Creates the transition dictionary containing the sampling tensors.
  
  Args:
    use_staging: bool, when True it would use a staging area to prefetch
      the next sampling batch.
docstr_tok: ["Creates","the","ops","necessary","to","sample","from","the","replay","buffer",".","Creates","the","transition","dictionary","containing","the","sampling","tensors",".","Args",":","use_staging",":","bool",",","when","True","it","would","use","a","staging","area","to","prefetch","the","next","sampling","batch","."]
code: def create_sampling_ops(self, use_staging):
      """Creates the ops necessary to sample from the replay buffer.
  
      Creates the transition dictionary containing the sampling tensors.
  
      Args:
        use_staging: bool, when True it would use a staging area to prefetch
          the next sampling batch.
      """
      if use_staging:
          logging.warning("use_staging=True is no longer supported")
      with tf.name_scope("sample_replay"):
          with tf.device("/cpu:*"):
              transition_type = self.memory.get_transition_elements()
              transition_tensors = tf.numpy_function(
                  self.memory.sample_transition_batch,
                  [],
                  [
                      return_entry.type
                      for return_entry in transition_type
                  ],
                  name="replay_sample_py_func",
              )
              self._set_transition_shape(
                  transition_tensors, transition_type
              )
              # Unpack sample transition into member variables.
              self.unpack_transition(
                  transition_tensors, transition_type
              )
  
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 3 for calling the WrappedReplayBuffer obj's underlying member method code with example usage: obj.create_sampling_ops(use_staging) without return types