code: def gen_new_backend_executor(special_f):
      """Returns a BackendExecutor that runs special_f on worker 0 once."""
  
      class TestBackendExecutor(BackendExecutor):
          def __init__(self, *args, **kwargs):
              super().__init__(*args, **kwargs)
              self._has_failed = False
  
          def start_training(self, *args, **kwargs):
              special_execute = gen_execute_single_async_special(
                  special_f
              )
              if not self._has_failed:
                  self.worker_group.should_fail = True
                  self._has_failed = True
              else:
                  self.worker_group.should_fail = False
              with patch.object(
                  WorkerGroup, "execute_single_async", special_execute
              ):
                  super().start_training(*args, **kwargs)
  
      return TestBackendExecutor
  
imports: import os
  import time
  from unittest.mock import patch
  import pytest
  import ray
  from ray import train, tune
  from ray.train import RunConfig, ScalingConfig
  from ray.train._internal.backend_executor import BackendExecutor
  from ray.train._internal.worker_group import WorkerGroup
  from ray.train.backend import Backend, BackendConfig
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train.tests.util import create_dict_checkpoint, load_dict_checkpoint
  from ray.tune.callback import Callback
  from ray.tune.tune_config import TuneConfig
  from ray.tune.tuner import Tuner
label_desc: Use image node_id 4 for calling a global function with example usage: gen_new_backend_executor(special_f) and returns: TestBackendExecutor