class_docstr: 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.
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
  
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 6 for calling the PrioritizedReplayBuffer obj's underlying member method code with example usage: obj.get_priority(indices) and returns: priority_batch