code: def test_mismatch_report(ray_start_4_cpus):
      def train_func():
          for _ in range(2):
              train.report(dict(loss=1))
  
      def train_mismatch():
          train.report(dict(loss=1))
  
      new_backend_executor_cls = gen_new_backend_executor(
          train_mismatch
      )
  
      class DataParallelTrainerPatched(DataParallelTrainer):
          _backend_executor_cls = new_backend_executor_cls
  
      trainer = DataParallelTrainerPatched(
          train_func,
          scaling_config=scale_config,
      )
      with pytest.raises(RuntimeError):
          trainer.fit()
  
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 14 for calling a global function with example usage: test_mismatch_report(ray_start_4_cpus) without return types