class_docstr: A sum tree data structure for storing replay priorities.
  
  A sum tree is a complete binary tree whose leaves contain values called
  priorities. Internal nodes maintain the sum of the priorities of all leaf
  nodes in their subtree.
  
  For capacity = 4, the tree may look like this:
  
               +---+
               |2.5|
               +-+-+
                 |
         +-------+--------+
         |                |
       +-+-+            +-+-+
       |1.5|            |1.0|
       +-+-+            +-+-+
         |                |
    +----+----+      +----+----+
    |         |      |         |
  +-+-+     +-+-+  +-+-+     +-+-+
  |0.5|     |1.0|  |0.5|     |0.5|
  +---+     +---+  +---+     +---+
  
  This is stored in a list of numpy arrays:
  self.nodes = [ [2.5], [1.5, 1], [0.5, 1, 0.5, 0.5] ]
  
  For conciseness, we allocate arrays as powers of two, and pad the excess
  elements with zero values.
  
  This is similar to the usual array-based representation of a complete binary
  tree, but is a little more user-friendly.
docstr: Returns the sum of all priorities stored in this sum tree.
  
  Returns:
    float, sum of priorities stored in this sum tree.
docstr_tok: ["Returns","the","sum","of","all","priorities","stored","in","this","sum","tree",".","Returns",":","float",",","sum","of","priorities","stored","in","this","sum","tree","."]
code: def _total_priority(self):
      """Returns the sum of all priorities stored in this sum tree.
  
      Returns:
        float, sum of priorities stored in this sum tree.
      """
      return self.nodes[0][0]
  
file_path: dopamine/dopamine/replay_memory/sum_tree.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import random
  import numpy
label_desc: Use image node_id 2 for calling the SumTree obj's underlying member method code with example usage: obj._total_priority() and returns: self