code: def test_trainable_error_with_trainer(
      ray_start_4_cpus, tmp_path, fail_fast
  ):
      name = f"test_trainer_errors-fail_fast={fail_fast}"
      trainer = FailingTrainer(
          run_config=RunConfig(
              storage_path=str(tmp_path),
              name=name,
              failure_config=FailureConfig(fail_fast=fail_fast),
          ),
      )
  
      if fail_fast in [False, True]:
          # There is only 1 "trial" for a Trainer,
          # so fail_fast = True/False doesn't change the behavior
          # In both cases, the error should get wrapped and raised.
          with pytest.raises(TrainingFailedError) as exc_info:
              trainer.fit()
  
          # The cause of the error should be the trainable error
          assert isinstance(
              exc_info.value.__cause__, _TestSpecificError
          )
  
          # TODO(justinvyu): Re-enable after fixing the Trainer.restore(...) error
          # message to give the correct path. Currently it recommends the local path.
          # Since the trainable failed, we should get a message about restore + setting
          # FailureConfig for retry on runtime errors for a new run.
          # assert TrainingFailedError._RESTORE_MSG.format(
          #     trainer_cls_name="FailingTrainer", path=str(tmp_path / name)
          # ) in str(exc_info.value)
          assert TrainingFailedError._FAILURE_CONFIG_MSG in str(
              exc_info.value
          )
  
      elif fail_fast == "raise":
          # The original error gets raised to the user
          with pytest.raises(_TestSpecificError):
              trainer.fit()
  
imports: import gc
  import threading
  import time
  from tempfile import TemporaryDirectory
  import pytest
  import ray
  from ray import train, tune
  from ray._private.test_utils import wait_for_condition
  from ray._raylet import GcsClient
  from ray.cluster_utils import Cluster
  from ray.core.generated import autoscaler_pb2
  from ray.train import Checkpoint, FailureConfig, RunConfig, ScalingConfig
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train.trainer import BaseTrainer, TrainingFailedError
  from ray.tune import Tuner, TuneConfig, TuneError
  from ray.tests.conftest import *
  from ray.train.tests.util import create_dict_checkpoint, load_dict_checkpoint
label_desc: Use image node_id 7 for calling a global function with example usage: test_trainable_error_with_trainer(ray_start_4_cpus, tmp_path, fail_fast) without return types