code: def test_sequential_ordering(self, num_steps):
      sequence_length = 10
      batch_size = 5
      env = test_envs.EpisodeCountingEnv(
          steps_per_episode=sequence_length
      )
      self._insert_random_data(
          env,
          num_steps=batch_size * sequence_length,
          sequence_length=sequence_length,
      )
  
      replay = reverb_replay_buffer.ReverbReplayBuffer(
          self._data_spec,
          self._table_name,
          sequence_length=sequence_length,
          local_server=self._server,
      )
  
      dataset = replay.as_dataset(batch_size, num_steps=num_steps)
      num_steps = num_steps or sequence_length
      for sample, _ in dataset.take(10):
          episode, step = sample.observation
          self.assertEqual((batch_size, num_steps), episode.shape)
          self.assertEqual((batch_size, num_steps), step.shape)
          for n in range(num_steps):
              # All elements in the same batch should belong to the same episode.
              self.assertAllEqual(episode[:, 0], episode[:, n])
              # All elements in the batch should have consecutive steps.
              self.assertAllEqual(step[:, 0] + n, step[:, n])
  
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 10 for calling the ReverbReplayBufferTest obj's underlying member method code with example usage: obj.test_sequential_ordering(num_steps) without return types