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: Sets the priority of the given elements according to Schaul et al.
  
  Args:
    indices: np.array with dtype int32, of indices in range
      [0, replay_capacity).
    priorities: float, the corresponding priorities.
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code: def set_priority(self, indices, priorities):
      """Sets the priority of the given elements according to Schaul et al.
  
      Args:
        indices: np.array with dtype int32, of indices in range
          [0, replay_capacity).
        priorities: float, the corresponding priorities.
      """
      assert (
          indices.dtype == np.int32
      ), "Indices must be integers, " "given: {}".format(indices.dtype)
      # Convert JAX arrays to NumPy arrays first, since it is faster to iterate
      # over the entirety of a NumPy array than a JAX array.
      priorities = np.asarray(priorities)
      for index, priority in zip(indices, priorities):
          self.sum_tree.set(index, priority)
  
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 6 for calling the OutOfGraphPrioritizedReplayBuffer obj's underlying member method code with example usage: obj.set_priority(indices, priorities) without return types