code: def gen_execute_special(special_f):
      def execute_async_special(self, f):
          """Runs f on worker 0, special_f on other workers."""
          futures = [
              self.workers[0]
              .actor._RayTrainWorker__execute.options(name=f.__name__)
              .remote(f)
          ]
          for worker in self.workers[1:]:
              futures.append(
                  worker.actor._RayTrainWorker__execute.options(
                      name=special_f.__name__
                  ).remote(special_f)
              )
          return futures
  
      return execute_async_special
  
imports: import math
  import os
  import tempfile
  import time
  from unittest.mock import patch
  import pytest
  import ray
  import ray._private.ray_constants
  from ray import train
  from ray.air._internal.util import StartTraceback
  from ray.tests.conftest import pytest_runtest_makereport
  from ray.train import DataConfig
  from ray.train._internal.backend_executor import BackendExecutor, InactiveWorkerGroupError, TrainBackendError, TrainingWorkerError
  from ray.train._internal.storage import StorageContext
  from ray.train._internal.worker_group import WorkerGroup, WorkerMetadata
  from ray.train.backend import Backend, BackendConfig
  from ray.train.constants import ENABLE_SHARE_CUDA_VISIBLE_DEVICES_ENV, ENABLE_SHARE_NEURON_CORES_ACCELERATOR_ENV, TRAIN_ENABLE_WORKER_SPREAD_ENV
  from ray.train.tensorflow import TensorflowConfig
  from ray.train.torch import TorchConfig
  from ray.util.placement_group import get_current_placement_group
  from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
label_desc: Use image node_id 3 for calling a global function with example usage: gen_execute_special(special_f) and returns: execute_async_special, futures