class_docstr: 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:
code: def _add(self, **kwargs) -> None:
      """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:
      """
      self._check_args_length(**kwargs)
  
      # 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 element in self.get_add_args_signature():
          if element.name == "priority":
              priority = kwargs[element.name]
          else:
              transition[
                  element.name
              ] = element.metadata.input_to_storage(
                  kwargs[element.name]
              )
  
      # pyre-fixme[61]: `priority` is undefined, or not always defined.
      self.sum_tree.set(self.cursor(), priority)
      super(PrioritizedReplayBuffer, self)._add_transition(transition)
  
file_path: ReAgent/reagent/replay_memory/prioritized_replay_buffer.py
imports: import numpy
  import torch
  from reagent.replay_memory import circular_replay_buffer, sum_tree
label_desc: Use image node_id 2 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj._add() without return types