code: def test_configure_execution_options(ray_start_4_cpus):
      ds = ray.data.range(10)
      # Resource limit is too low and will trigger an error.
      options = DataConfig.default_ingest_options()
      options.resource_limits.cpu = 0
      test = TestBasic(
          1,
          True,
          {"train": 10, "test": 10},
          datasets={"train": ds, "test": ds},
          dataset_config=DataConfig(execution_options=options),
      )
      with pytest.raises(ray.train.base_trainer.TrainingFailedError):
          test.fit()
  
imports: from typing import Optional
  import random
  import pytest
  from unittest.mock import MagicMock
  import ray
  from ray import train
  from ray.train import DataConfig, ScalingConfig
  from ray.data import DataIterator
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.data._internal.execution.interfaces.execution_options import ExecutionOptions
  from ray.tests.conftest import *
label_desc: Use image node_id 4 for calling a global function with example usage: test_configure_execution_options(ray_start_4_cpus) without return types