code: def testAddBatchGetEpisodes(self):
      num_episodes = 3
      spec = specs.TensorSpec([3], tf.int32, "lidar")
      replay_buffer = episodic_replay_buffer.EpisodicReplayBuffer(
          spec,
          capacity=num_episodes,
          begin_episode_fn=lambda _: False,
          end_episode_fn=lambda _: False,
      )
      episode_ids = replay_buffer.create_episode_ids(num_episodes)
  
      values = np.ones(spec.shape.as_list(), dtype=np.int32)
      values = np.stack([values, 10 * values, 100 * values])
      episode_ids = replay_buffer.add_batch(values, episode_ids)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.evaluate(tf.compat.v1.local_variables_initializer())
      episode_ids = self.evaluate(episode_ids)
      for episode_id in range(num_episodes):
          episode = replay_buffer._get_episode(episode_ids[episode_id])
          self.assertAllEqual(
              [values[episode_id]], self.evaluate(episode)
          )
  
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 22 for calling the EpisodicReplayBufferTest obj's underlying member method code with example usage: obj.testAddBatchGetEpisodes() without return types