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: Set shape for each element in the transition.
  
  Args:
    transition: tuple of tf.Tensors.
    transition_type: tuple of ReplayElements descriving the shapes of the
      respective tensors.
docstr_tok: ["Set","shape","for","each","element","in","the","transition",".","Args",":","transition",":","tuple","of","tf.Tensors",".","transition_type",":","tuple","of","ReplayElements","descriving","the","shapes","of","the","respective","tensors","."]
code: def _set_transition_shape(self, transition, transition_type):
      """Set shape for each element in the transition.
  
      Args:
        transition: tuple of tf.Tensors.
        transition_type: tuple of ReplayElements descriving the shapes of the
          respective tensors.
      """
      for element, element_type in zip(transition, transition_type):
          element.set_shape(element_type.shape)
  
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 4 for calling the WrappedReplayBuffer obj's underlying member method code with example usage: obj._set_transition_shape(transition, transition_type) without return types