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: Returns a batch of transitions with extra storage and the priorities.
  
  The extra storage are defined through the extra_storage_types constructor
  argument.
  
  When the transition is terminal next_state_batch has undefined contents.
  
  Args:
    batch_size: int, number of transitions returned. If None, the default
      batch_size will be used.
    indices: None or list of ints, the indices of every transition in the
      batch. If None, sample the indices uniformly.
  
  Returns:
    transition_batch: tuple of np.arrays with the shape and type as in
      get_transition_elements().
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code: def sample_transition_batch(self, batch_size=None, indices=None):
      """Returns a batch of transitions with extra storage and the priorities.
  
      The extra storage are defined through the extra_storage_types constructor
      argument.
  
      When the transition is terminal next_state_batch has undefined contents.
  
      Args:
        batch_size: int, number of transitions returned. If None, the default
          batch_size will be used.
        indices: None or list of ints, the indices of every transition in the
          batch. If None, sample the indices uniformly.
  
      Returns:
        transition_batch: tuple of np.arrays with the shape and type as in
          get_transition_elements().
      """
      transition = super(
          OutOfGraphPrioritizedReplayBuffer, self
      ).sample_transition_batch(batch_size, indices)
      transition_elements = self.get_transition_elements(batch_size)
      transition_names = [e.name for e in transition_elements]
      probabilities_index = transition_names.index(
          "sampling_probabilities"
      )
      indices_index = transition_names.index("indices")
      indices = transition[indices_index]
      # The parent returned an empty array for the probabilities. Fill it with the
      # contents of the sum tree.
      transition[probabilities_index][:] = self.get_priority(indices)
      return transition
  
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 5 for calling the OutOfGraphPrioritizedReplayBuffer obj's underlying member method code with example usage: obj.sample_transition_batch(batch_size, indices) and returns: transition