class_docstr: An episodic ReplayBuffer with Uniform sampling.
class_docstr_tok: ["An","episodic","ReplayBuffer","with","Uniform","sampling","."]
docstr: Get a list of completed episode ids in the replay buffer.
  
  Returns:
    An int64 vector of length at most `capacity`.
code: def _completed_episodes(self):
      """Get a list of completed episode ids in the replay buffer.
  
      Returns:
        An int64 vector of length at most `capacity`.
      """
  
      def _completed_episodes():
          completed_mask = tf.equal(self._episode_completed, 1)
          return tf.boolean_mask(
              tensor=self._episodes_loc_to_id_map, mask=completed_mask
          )
  
      return self._add_episode_critical_section.execute(
          _completed_episodes
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import gin
  import tensorflow
  from tf_agents import specs
  from tf_agents.replay_buffers import episodic_table
  from tf_agents.replay_buffers import replay_buffer
  from tf_agents.replay_buffers import table
  from tf_agents.utils import common
  from tensorflow.python.data.util import nest
  from tensorflow.python.layers import utils
  from tensorflow.python.ops import list_ops
  from tensorflow.python.ops.distributions import util
label_desc: Use image node_id 15 for calling the EpisodicReplayBuffer obj's underlying member method code with example usage: obj._completed_episodes() and returns: self, tf