code: def _add_multi_agent_batch_to_buffer(
      self, buffer, num_policies, num_batches=5, **kwargs
  ):
      def _generate_data(policy_id):
          batch = SampleBatch(
              {
                  SampleBatch.T: [0],
                  SampleBatch.ACTIONS: [np.random.choice([0, 1])],
                  SampleBatch.REWARDS: [np.random.rand()],
                  SampleBatch.OBS: [np.random.random((4,))],
                  SampleBatch.NEXT_OBS: [np.random.random((4,))],
                  SampleBatch.TERMINATEDS: [
                      np.random.choice([False, True])
                  ],
                  SampleBatch.TRUNCATEDS: [
                      np.random.choice([False, False])
                  ],
                  SampleBatch.EPS_ID: [self.batch_id],
                  SampleBatch.AGENT_INDEX: [self.batch_id],
                  "batch_id": [self.batch_id],
                  "policy_id": [policy_id],
              }
          )
          return batch
  
      for i in range(num_batches):
          # genera a few policy batches
          policy_batches = {
              idx: _generate_data(idx) for idx in range(num_policies)
          }
          self.batch_id += 1
          batch = MultiAgentBatch(policy_batches, 1)
          buffer.add(batch, **kwargs)
  
file_path: ray/rllib/utils/replay_buffers/tests/test_multi_agent_prioritized_replay_buffer.py
imports: import unittest
  import numpy
  from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch, DEFAULT_POLICY_ID, concat_samples
  from ray.rllib.utils.replay_buffers.multi_agent_prioritized_replay_buffer import MultiAgentPrioritizedReplayBuffer
  from ray.rllib.utils.test_utils import check
  from ray.rllib.utils.replay_buffers.replay_buffer import _ALL_POLICIES
label_desc: Use image node_id 3 for calling the TestMultiAgentPrioritizedReplayBuffer obj's underlying member method code with example usage: obj._add_multi_agent_batch_to_buffer(buffer, num_policies, num_batches) and returns: batch