code: def _create_rb_and_add_3N_episodes(
      self, drop_remainder=False, window_shift=None, repeat=1
  ):
      spec = {"a": specs.TensorSpec([], tf.int32, "spec")}
      replay_buffer = episodic_replay_buffer.EpisodicReplayBuffer(
          spec,
          capacity=10,
          dataset_drop_remainder=drop_remainder,
          dataset_window_shift=window_shift,
          begin_episode_fn=lambda _: False,
          end_episode_fn=lambda _: False,
      )
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.evaluate(tf.compat.v1.local_variables_initializer())
  
      episode_id = replay_buffer.create_episode_ids()
  
      values = np.ones(spec["a"].shape.as_list())
      values = np.stack([values, 10 * values, 100 * values])
  
      original_episode_id = self.evaluate(episode_id)
  
      for _ in range(repeat):
          # Add an episode with frames [1, 10, 100]
          replay_buffer._begin_episode_fn = lambda _: True
          episode_id = replay_buffer.add_sequence(
              {"a": values}, episode_id
          )
          # Add an episode with frames [2, 20, 200, 3, 30, 300]
          episode_id = replay_buffer.add_sequence(
              {"a": 2 * values}, episode_id
          )
          replay_buffer._begin_episode_fn = lambda _: False
          episode_id = replay_buffer.add_sequence(
              {"a": 3 * values}, episode_id
          )
          # Add an episode with frames [-1, -10, -100]
          replay_buffer._begin_episode_fn = lambda _: True
          episode_id = replay_buffer.add_sequence(
              {"a": -values}, episode_id
          )
      new_episode_id = self.evaluate(episode_id)
  
      assert (
          new_episode_id >= original_episode_id + repeat * 3
      ), "{} vs. {}".format(
          new_episode_id, original_episode_id + repeat * 3
      )
  
      return replay_buffer
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents import specs
  from tf_agents.replay_buffers import episodic_replay_buffer
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
  from tensorflow.python.ops import list_ops
label_desc: Use image node_id 30 for calling the EpisodicReplayBufferTest obj's underlying member method code with example usage: obj._create_rb_and_add_3N_episodes(drop_remainder, window_shift, repeat) and returns: replay_buffer