code: def _add_multi_agent_batch_to_buffer(
      self,
      buffer,
      num_policies,
      num_batches=5,
      seq_lens=False,
      **kwargs,
  ):
      def _generate_data(policy_id):
          batch = SampleBatch(
              {
                  SampleBatch.T: [0, 1],
                  SampleBatch.ACTIONS: 2 * [np.random.choice([0, 1])],
                  SampleBatch.REWARDS: 2 * [np.random.rand()],
                  SampleBatch.OBS: 2 * [np.random.random((4,))],
                  SampleBatch.NEXT_OBS: 2 * [np.random.random((4,))],
                  SampleBatch.TERMINATEDS: [False, False],
                  SampleBatch.TRUNCATEDS: [False, True],
                  SampleBatch.EPS_ID: 2 * [self.batch_id],
                  SampleBatch.AGENT_INDEX: 2 * [0],
                  SampleBatch.SEQ_LENS: [2],
                  "batch_id": 2 * [self.batch_id],
                  "policy_id": 2 * [policy_id],
              }
          )
          if not seq_lens:
              del batch[SampleBatch.SEQ_LENS]
          self.batch_id += 1
          return batch
  
      for i in range(num_batches):
          # genera a few policy batches
          policy_batches = {
              idx: _generate_data(idx)
              for idx, _ in enumerate(range(num_policies))
          }
          batch = MultiAgentBatch(policy_batches, 1)
          buffer.add(batch, **kwargs)
  
file_path: ray/python/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 2 for calling the TestMultiAgentReplayBuffer obj's underlying member method code with example usage: obj._add_multi_agent_batch_to_buffer(buffer, num_policies, num_batches, seq_lens) and returns: batch