code: def testIterateEmpty(self):
      spec = specs.TensorSpec([3], tf.int32, "lidar")
      replay_buffer = episodic_replay_buffer.EpisodicReplayBuffer(
          spec, capacity=2
      )
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.evaluate(tf.compat.v1.local_variables_initializer())
  
      with self.assertRaisesRegex(
          tf.errors.InvalidArgumentError,
          "EpisodicReplayBuffer is empty. Make "
          "sure to add items before sampling the buffer.",
      ):
          sample_two_steps = sample_as_dataset(
              replay_buffer, num_steps=2, batch_size=1
          )[0]
          self.evaluate(sample_two_steps)
  
      with self.assertRaisesRegex(
          tf.errors.InvalidArgumentError,
          "EpisodicReplayBuffer is empty. Make "
          "sure to add items before sampling the buffer.",
      ):
          sample_episode = sample_as_dataset(
              replay_buffer, num_steps=2, batch_size=1
          )[0]
          self.evaluate(sample_episode)
  
      with self.assertRaisesRegex(
          tf.errors.InvalidArgumentError,
          "EpisodicReplayBuffer is empty. Make "
          "sure to add items before sampling the buffer.",
      ):
          sample_episode = sample_as_dataset(
              replay_buffer, num_steps=2, batch_size=1
          )[0]
          self.evaluate(sample_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 15 for calling the EpisodicReplayBufferTest obj's underlying member method code with example usage: obj.testIterateEmpty() without return types