code: def setup(self, config: AlgorithmConfig):
      super().setup(config)
  
      # Initialize torch process group for
      self._curr_learner_info = {}
  
      worker_ids = self.workers.healthy_worker_ids()
      assert worker_ids, "No healthy rollout workers."
  
      # Find IP and Port of the first remote worker. This is our Rank 0  worker.
      ip = self.workers.foreach_worker(
          func=lambda w: w.get_node_ip(),
          remote_worker_ids=[worker_ids[0]],
          local_worker=False,
      )[0]
      port = self.workers.foreach_worker(
          func=lambda w: w.find_free_port(),
          remote_worker_ids=[worker_ids[0]],
          local_worker=False,
      )[0]
      address = "tcp://{ip}:{port}".format(ip=ip, port=port)
      logger.info(
          "Creating torch process group with leader {}".format(address)
      )
  
      # Get setup tasks in order to throw errors on failure.
      world_size = self.workers.num_remote_workers()
      backend = self.config.torch_distributed_backend
  
      def get_setup_fn(world_rank):
          return lambda w: w.setup_torch_data_parallel(
              url=address,
              world_rank=world_rank,
              world_size=world_size,
              backend=backend,
          )
  
      funcs = [get_setup_fn(i) for i in range(world_size)]
      # Set up torch distributed on all workers. The assumption here is that
      # all workers should be healthy at this point.
      self.workers.foreach_worker(
          func=funcs, local_worker=False, healthy_only=False
      )
  
      logger.info("Torch process group init completed")
  
imports: import logging
  import time
  from typing import Optional
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.ppo import PPO, PPOConfig
  from ray.rllib.evaluation.postprocessing import Postprocessing
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics import LEARN_ON_BATCH_TIMER, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, SAMPLE_TIMER
  from ray.rllib.utils.metrics.learner_info import LearnerInfoBuilder
  from ray.rllib.utils.sgd import do_minibatch_sgd
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 2 for calling the DDPPO obj's underlying member method code with example usage: obj.setup(config) and returns: function