code: def get_device(config):
      """Returns a torch device edepending on a config and current worker index."""
  
      # Figure out the number of GPUs to use on the local side (index=0) or on
      # the remote workers (index > 0).
      worker_idx = config.get("worker_index", 0)
      if (
          not config["_fake_gpus"]
          and ray._private.worker._mode()
          == ray._private.worker.LOCAL_MODE
      ):
          num_gpus = 0
      elif worker_idx == 0:
          num_gpus = config["num_gpus"]
      else:
          num_gpus = config["num_gpus_per_worker"]
      # All GPU IDs, if any.
      gpu_ids = list(range(torch.cuda.device_count()))
  
      # Place on one or more CPU(s) when either:
      # - Fake GPU mode.
      # - num_gpus=0 (either set by user or we are in local_mode=True).
      # - No GPUs available.
      if config["_fake_gpus"] or num_gpus == 0 or not gpu_ids:
          return torch.device("cpu")
      # Place on one or more actual GPU(s), when:
      # - num_gpus > 0 (set by user) AND
      # - local_mode=False AND
      # - actual GPUs available AND
      # - non-fake GPU mode.
      else:
          # We are a remote worker (WORKER_MODE=1):
          # GPUs should be assigned to us by ray.
          if (
              ray._private.worker._mode()
              == ray._private.worker.WORKER_MODE
          ):
              gpu_ids = ray.get_gpu_ids()
  
          if len(gpu_ids) < num_gpus:
              raise ValueError(
                  "TorchPolicy was not able to find enough GPU IDs! Found "
                  f"{gpu_ids}, but num_gpus={num_gpus}."
              )
          return torch.device("cuda")
  
imports: import logging
  import os
  import warnings
  from typing import Dict, List, Optional, TYPE_CHECKING, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Discrete, MultiDiscrete
  from packaging import version
  import ray
  from ray.rllib.models.repeated_values import RepeatedValues
  from ray.rllib.utils.annotations import Deprecated, PublicAPI, DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import SMALL_NUMBER
  from ray.rllib.utils.typing import LocalOptimizer, SpaceStruct, TensorStructType, TensorType
label_desc: Use image node_id 10 for calling a global function with example usage: get_device(config) and returns: torch, torch