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: Fetches the priorities correspond to a batch of memory indices.
  
  For any memory location not yet used, the corresponding priority is 0.
  
  Args:
    indices: np.array with dtype int32, of indices in range
      [0, replay_capacity).
  
  Returns:
    priorities: float, the corresponding priorities.
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code: def get_priority(self, indices):
      """Fetches the priorities correspond to a batch of memory indices.
  
      For any memory location not yet used, the corresponding priority is 0.
  
      Args:
        indices: np.array with dtype int32, of indices in range
          [0, replay_capacity).
  
      Returns:
        priorities: float, the corresponding priorities.
      """
      assert indices.shape, "Indices must be an array."
      assert (
          indices.dtype == np.int32
      ), "Indices must be int32s, " "given: {}".format(indices.dtype)
      batch_size = len(indices)
      priority_batch = np.empty((batch_size), dtype=np.float32)
      for i, memory_index in enumerate(indices):
          priority_batch[i] = self.sum_tree.get(memory_index)
      return priority_batch
  
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 7 for calling the OutOfGraphPrioritizedReplayBuffer obj's underlying member method code with example usage: obj.get_priority(indices) and returns: priority_batch