class_docstr: An out-of-graph Replay Buffer for Prioritized Experience Replay.
  
  See circular_replay_buffer.py for details.
class_docstr_tok: ["An","out-of-graph","Replay","Buffer","for","Prioritized","Experience","Replay",".","See","circular_replay_buffer.py","for","details","."]
docstr: Internal add method to add to the underlying memory arrays.
  
  The arguments need to match add_arg_signature.
  
  If priority is none, it is set to the maximum priority ever seen.
  
  Args:
    *args: All the elements in a transition.
docstr_tok: ["Internal","add","method","to","add","to","the","underlying","memory","arrays",".","The","arguments","need","to","match","add_arg_signature",".","If","priority","is","none",",","it","is","set","to","the","maximum","priority","ever","seen",".","Args",":","*","args",":","All","the","elements","in","a","transition","."]
code: def _add(self, *args):
      """Internal add method to add to the underlying memory arrays.
  
      The arguments need to match add_arg_signature.
  
      If priority is none, it is set to the maximum priority ever seen.
  
      Args:
        *args: All the elements in a transition.
      """
      self._check_args_length(*args)
  
      # Use Schaul et al.'s (2015) scheme of setting the priority of new elements
      # to the maximum priority so far.
      # Picks out 'priority' from arguments and adds it to the sum_tree.
      transition = {}
      for i, element in enumerate(self.get_add_args_signature()):
          if element.name == "priority":
              priority = args[i]
          else:
              transition[element.name] = args[i]
  
      self.sum_tree.set(self.cursor(), priority)
      super(OutOfGraphPrioritizedReplayBuffer, self)._add_transition(
          transition
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from dopamine.replay_memory import circular_replay_buffer
  from dopamine.replay_memory import sum_tree
  from dopamine.replay_memory.circular_replay_buffer import ReplayElement
  import gin.tf
  import numpy
  import tensorflow
label_desc: Use image node_id 3 for calling the OutOfGraphPrioritizedReplayBuffer obj's underlying member method code with example usage: obj._add() without return types