code: def testGetTwoEpisodeId(self):
      spec = [
          specs.TensorSpec([3], tf.float32, "action"),
          [
              specs.TensorSpec([5], tf.float32, "lidar"),
              specs.TensorSpec([3, 2], tf.float32, "camera"),
          ],
      ]
      replay_buffer = episodic_replay_buffer.EpisodicReplayBuffer(
          spec, capacity=2
      )
      episode_id_0 = replay_buffer.create_episode_ids()
      episode_id_1 = replay_buffer.create_episode_ids()
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.evaluate(tf.compat.v1.local_variables_initializer())
      self.assertEqual(self.evaluate(episode_id_0), -1)
      self.assertEqual(self.evaluate(episode_id_1), -1)
  
      # episode_id will be updated regardless of begin_episode being False since
      # it doesn't have a valid value >= 0.
      episode_id_0 = replay_buffer._get_episode_id(
          replay_buffer.create_episode_ids()
      )
      with tf.control_dependencies([episode_id_0]):
          episode_id_1 = replay_buffer._get_episode_id(
              replay_buffer.create_episode_ids()
          )
      expected_id_0 = 0
      expected_id_1 = 1
  
      with tf.control_dependencies([episode_id_0, episode_id_1]):
          # Now episode_id should be updated only when begin_episode is True.
          episode_id_0_next = replay_buffer._get_episode_id(
              episode_id_0, begin_episode=False
          )
          with tf.control_dependencies([episode_id_0_next]):
              episode_id_1_next = replay_buffer._get_episode_id(
                  episode_id_1, begin_episode=True
              )
          expected_id_0_next = 0
          expected_id_1_next = 2
  
      self.assertEqual(
          (
              expected_id_0,
              expected_id_1,
              expected_id_0_next,
              expected_id_1_next,
          ),
          self.evaluate(
              (
                  episode_id_0,
                  episode_id_1,
                  episode_id_0_next,
                  episode_id_1_next,
              )
          ),
      )
  
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 9 for calling the EpisodicReplayBufferTest obj's underlying member method code with example usage: obj.testGetTwoEpisodeId() without return types