class_docstr: Stateful observer for writing fixed length trajectories to Reverb.
  
  This observer should be called at every environment step. It does not support
  batched trajectories.
  
  Steps are cached until `sequence_length` steps are gathered. At which point an
  item is created. From there on a new item is created every `stride_length`
  observer calls.
  
  If an episode terminates before enough steps are cached, the data is discarded
  unless `pad_end_of_episodes` is set.
docstr: Ensures that items are pushed to the service.
  
  Note: The items are not always immediately pushed. This method is often
  needed when `rate_limiter_timeout_ms` is set for the replay buffer.
  By calling this method before the `learner.run()`, we ensure that there is
  enough data to be consumed.
code: def flush(self):
      """Ensures that items are pushed to the service.
  
      Note: The items are not always immediately pushed. This method is often
      needed when `rate_limiter_timeout_ms` is set for the replay buffer.
      By calling this method before the `learner.run()`, we ensure that there is
      enough data to be consumed.
      """
      self._get_writer().flush()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Sequence, Text, Union
  from absl import logging
  import tensorflow
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
  from tf_agents.utils import lazy_loader
label_desc: Use image node_id 8 for calling the ReverbAddTrajectoryObserver obj's underlying member method code with example usage: obj.flush() without return types