code: def test_fit_train_config(ray_start_4_cpus):
      def train_func(config):
          train.report({"loss": config["x"]})
  
      trainer = DataParallelTrainer(
          train_loop_per_worker=train_func,
          scaling_config=scale_config,
          train_loop_config={"x": 100},
      )
      assert trainer.fit().metrics["loss"] == 100
  
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 7 for calling a global function with example usage: test_fit_train_config(ray_start_4_cpus) without return types