code: def test_restore_with_new_trainer(
      ray_start_4_cpus, tmpdir, propagate_logs, caplog, monkeypatch
  ):
      monkeypatch.setenv("RAY_AIR_LOCAL_CACHE_DIR", str(tmpdir))
  
      def train_func(config):
          raise RuntimeError("failing!")
  
      trainer = DataParallelTrainer(
          train_func,
          backend_config=TestConfig(),
          scaling_config=ScalingConfig(num_workers=1),
          run_config=RunConfig(name="restore_new_trainer"),
          datasets={
              "train": ray.data.from_items(
                  [{"a": i} for i in range(10)]
              )
          },
      )
      results = Tuner(trainer).fit()
      assert results.errors
  
      def train_func(config):
          dataset = train.get_dataset_shard("train")
          assert train.get_context().get_world_size() == 2
          rows = 0
          for _ in dataset.iter_rows():
              rows += 1
          assert rows == 10
  
      trainer = DataParallelTrainer(
          # Training function can be modified
          train_func,
          backend_config=TestConfig(),
          # ScalingConfig can be modified
          scaling_config=ScalingConfig(num_workers=2),
          # New RunConfig will be ignored
          run_config=RunConfig(name="ignored"),
          # Datasets and preprocessors can be re-specified
          datasets={
              "train": ray.data.from_items(
                  [{"a": i} for i in range(20)]
              )
          },
      )
      caplog.clear()
      with caplog.at_level(
          logging.WARNING, logger="ray.tune.impl.tuner_internal"
      ):
          tuner = Tuner.restore(
              str(tmpdir / "restore_new_trainer"),
              trainable=trainer,
              resume_errored=True,
          )
          assert (
              "they will be ignored in the resumed run" in caplog.text
          )
  
      results = tuner.fit()
      assert not results.errors
  
imports: import logging
  import pytest
  import ray
  from ray import train, tune
  from ray.air.constants import TRAINING_ITERATION
  from ray.train import FailureConfig, RunConfig, ScalingConfig
  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.examples.pytorch.torch_fashion_mnist_example import train_func_per_worker
  from ray.train.examples.tf.tensorflow_mnist_example import train_func
  from ray.train.tensorflow.tensorflow_trainer import TensorflowTrainer
  from ray.train.tests.util import create_dict_checkpoint, load_dict_checkpoint
  from ray.train.torch.torch_trainer import TorchTrainer
  from ray.tune.tune_config import TuneConfig
  from ray.tune.tuner import Tuner
label_desc: Use image node_id 11 for calling a global function with example usage: test_restore_with_new_trainer(ray_start_4_cpus, tmpdir, propagate_logs, caplog, monkeypatch) without return types