code: def gen_execute_single_async_special(special_f):
      def execute_single_async_special(self, i, f, *args, **kwargs):
          assert len(self.workers) == 2
          if (
              i == 0
              and hasattr(self, "should_fail")
              and self.should_fail
          ):
              kwargs["train_func"] = special_f
          return (
              self.workers[i]
              .actor._RayTrainWorker__execute.options(name=f.__name__)
              .remote(f, *args, **kwargs)
          )
  
      return execute_single_async_special
  
imports: import functools
  import time
  from unittest.mock import patch
  import pytest
  import ray
  from ray import train
  from ray.air._internal.util import StartTraceback
  from ray.train import DataConfig
  from ray.train._internal.backend_executor import BackendExecutor
  from ray.train._internal.session import get_session, init_session
  from ray.train._internal.utils import construct_train_func
  from ray.train._internal.worker_group import WorkerGroup
  from ray.train.backend import BackendConfig
  from ray.train.examples.pytorch.torch_linear_example import train_func
  from ray.train.examples.tf.tensorflow_mnist_example import train_func
  from ray.train.tests.util import mock_storage_context
  from ray.train.trainer import TrainingIterator
label_desc: Use image node_id 3 for calling a global function with example usage: gen_execute_single_async_special(special_f) and returns: execute_single_async_special, self