code: def test_set_get_state(self):
      num_policies = 2
      buffer_size = 15
      num_batches = 1
  
      buffer = MultiAgentReplayBuffer(
          capacity=buffer_size,
          replay_mode="independent",
          num_shards=1,
      )
  
      self._add_multi_agent_batch_to_buffer(
          buffer, num_policies=num_policies, num_batches=num_batches
      )
  
      state = buffer.get_state()
  
      another_buffer = MultiAgentReplayBuffer(
          capacity=buffer_size,
          replay_mode="independent",
          num_steps_sampled_before_learning_starts=0,
          num_shards=1,
      )
  
      another_buffer.set_state(state)
  
      # State is equal to set of states of underlying buffers
      for _id, _buffer in buffer.replay_buffers.items():
          assert (
              _buffer.get_state()
              == another_buffer.replay_buffers[_id].get_state()
          )
  
      assert buffer._num_added == another_buffer._num_added
  
file_path: ray/rllib/utils/replay_buffers/tests/test_multi_agent_replay_buffer.py
imports: import unittest
  import numpy
  from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch, concat_samples
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.replay_buffers.multi_agent_replay_buffer import MultiAgentReplayBuffer
  from ray.rllib.utils.replay_buffers.prioritized_replay_buffer import PrioritizedReplayBuffer
  from ray.rllib.utils.replay_buffers.replay_buffer import ReplayBuffer
label_desc: Use image node_id 9 for calling the TestMultiAgentReplayBuffer obj's underlying member method code with example usage: obj.test_set_get_state() without return types