code: def gen_new_backend_executor(special_f):
      """Returns a BackendExecutor that runs special_f on worker 0 once."""
  
      class TestBackendExecutor(BackendExecutor):
          _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 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 4 for calling a global function with example usage: gen_new_backend_executor(special_f) and returns: TestBackendExecutor