code: def default_resource_request(
      cls,
      config: Union[AlgorithmConfig, PartialAlgorithmConfigDict],
  ):
      if isinstance(config, AlgorithmConfig):
          cf = config
      else:
          cf = cls.get_default_config().update_from_dict(config)
  
      # Return PlacementGroupFactory containing all needed resources
      # (already properly defined as device bundles).
      return PlacementGroupFactory(
          bundles=[
              {
                  # Single CPU for the local worker. This CPU will host the
                  # main model in this example (num_workers=0).
                  "CPU": 1,
                  # Possibly add n GPUs to this.
                  "GPU": cf.num_gpus,
              },
              {
                  # Different bundle (meaning: possibly different node)
                  # for your n "remote" envs (set remote_worker_envs=True).
                  "CPU": cf.num_envs_per_worker,
              },
          ],
          strategy=cf.placement_strategy,
      )
  
file_path: ray/python/ray/rllib/examples/remote_envs_with_inference_done_on_main_node.py
imports: import argparse
  import os
  from typing import Union
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.ppo import PPO, PPOConfig
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.test_utils import check_learning_achieved
  from ray.rllib.utils.typing import PartialAlgorithmConfigDict
  from ray.tune import PlacementGroupFactory
  from ray.tune.logger import pretty_print
label_desc: Use image node_id 1 for calling the PPORemoteInference obj's underlying member method code with example usage: obj.default_resource_request(cls, config) and returns: PlacementGroupFactory