class_docstr: An episodic ReplayBuffer with Uniform sampling.
class_docstr_tok: ["An","episodic","ReplayBuffer","with","Uniform","sampling","."]
docstr: Returns an episode sampled uniformly from the buffer.
  
  Args:
    sample_batch_size: Not used
    num_steps: Not used
    time_stacked: Not used
  
  Returns:
    A 2-tuple containing:
  
      - An episode sampled uniformly from the buffer.
      - BufferInfo NamedTuple, containing the episode id.
code: def _get_next(
      self, sample_batch_size=None, num_steps=None, time_stacked=None
  ):
      """Returns an episode sampled uniformly from the buffer.
  
      Args:
        sample_batch_size: Not used
        num_steps: Not used
        time_stacked: Not used
  
      Returns:
        A 2-tuple containing:
  
          - An episode sampled uniformly from the buffer.
          - BufferInfo NamedTuple, containing the episode id.
      """
      with tf.device(self._device):
          with tf.name_scope("get_next"):
              episode_id = self._sample_episode_ids(
                  shape=[], seed=self._seed
              )
              row = self._get_episode_id_location(episode_id)
              data = self._data_table.get_episode_values(row)
              id_ = self._id_table.read(row)
      return data, BufferInfo(ids=id_)
  
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 10 for calling the EpisodicReplayBuffer obj's underlying member method code with example usage: obj._get_next(sample_batch_size, num_steps, time_stacked) and returns: data,  BufferInfo