code: def test_trainer_can_restore_utility(tmp_path):
      """Make sure that `can_restore` detects an existing experiment at a
      local/remote path and only returns True if it's at the Train experiment dir root.
      """
      name = "exp_name"
      path = tmp_path / name
  
      assert not DataParallelTrainer.can_restore(path)
  
      trainer = DataParallelTrainer(
          train_loop_per_worker=lambda config: train.report(
              {"score": 1}
          ),
          scaling_config=ScalingConfig(num_workers=1),
      )
      (tmp_path / name).mkdir(exist_ok=True)
      trainer._save(tmp_path / name)
  
      assert DataParallelTrainer.can_restore(path)
  
imports: import warnings
  from pathlib import Path
  from typing import Dict, List
  import pytest
  import ray
  from ray import train
  from ray.air._internal.uri_utils import URI
  from ray.train import CheckpointConfig, RunConfig, ScalingConfig
  from ray.train.base_trainer import BaseTrainer
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train.lightgbm import LightGBMTrainer
  from ray.train.tests.util import create_dict_checkpoint, load_dict_checkpoint
  from ray.train.torch import TorchTrainer
  from ray.train.trainer import TrainingFailedError
  from ray.train.xgboost import XGBoostTrainer
  from ray.tune import Callback
  from ray.tune.experiment import Trial
label_desc: Use image node_id 10 for calling a global function with example usage: test_trainer_can_restore_utility(tmp_path) without return types