class_docstr: Wrapper of OutOfGraphPrioritizedReplayBuffer with in-graph sampling.
  
  Usage:
  
    * To add a transition:  Call the add function.
  
    * To sample a batch:  Query any of the tensors in the transition dictionary.
                          Every sess.run that requires any of these tensors will
                          sample a new transition.
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docstr: Gets the priorities for the given indices.
  
  Args:
    indices: tf.Tensor with dtype int32 and shape [n].
  
  Returns:
    priorities: tf.Tensor with dtype float and shape [n], the priorities at
      the indices.
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code: def tf_get_priority(self, indices):
      """Gets the priorities for the given indices.
  
      Args:
        indices: tf.Tensor with dtype int32 and shape [n].
  
      Returns:
        priorities: tf.Tensor with dtype float and shape [n], the priorities at
          the indices.
      """
      return tf.numpy_function(
          self.memory.get_priority,
          [indices],
          tf.float32,
          name="prioritized_replay_get_priority_py_func",
      )
  
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 3 for calling the WrappedPrioritizedReplayBuffer obj's underlying member method code with example usage: obj.tf_get_priority(indices) and returns: tf