code: def _failing_train_fn(config):
      checkpoint = train.get_checkpoint()
      it = 1
      if checkpoint:
          it = load_dict_checkpoint(checkpoint)["it"] + 1
          print(
              f"\nLoading from checkpoint, which is at iteration {it}...\n"
          )
      with create_dict_checkpoint({"it": it}) as checkpoint:
          train.report({"it": it}, checkpoint=checkpoint)
      if it == 1:
          raise _TestSpecificError
  
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 3 for calling a global function with example usage: _failing_train_fn(config) without return types