code: def test_variable_length_episodes_dataset(self):
      # Add one episode of each length.
      for sequence_length in range(1, 10):
          env = test_envs.EpisodeCountingEnv(
              steps_per_episode=sequence_length
          )
          self._insert_random_data(
              env,
              num_steps=sequence_length,
              sequence_length=sequence_length,
          )
  
      replay = reverb_replay_buffer.ReverbReplayBuffer(
          self._data_spec,
          self._table_name,
          sequence_length=None,
          local_server=self._server,
      )
  
      # Make sure observations are off by 1 given we are counting transitions in
      # the env observations.
      dataset = replay.as_dataset(sample_batch_size=1)
      for sample, _ in dataset.take(5):
          episode, step = sample.observation
          self.assertIn(episode.shape[1], range(1, 10))
          self.assertIn(step.shape[1], range(1, 10))
          length = episode.shape[1]
          # All Episode id are 0.
          self.assertAllEqual([[0] * length], episode)
          # Steps id is sequential up its length.
          self.assertAllEqual([list(range(length))], step)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import reverb
  import tensorflow
  from tf_agents.drivers import py_driver
  from tf_agents.environments import test_envs
  from tf_agents.policies import random_py_policy
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.replay_buffers import reverb_utils
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 8 for calling the ReverbReplayBufferTest obj's underlying member method code with example usage: obj.test_variable_length_episodes_dataset() without return types