code: def training_loop(self) -> None:
      scaling_config = self._validate_scaling_config(
          self.scaling_config
      )
      pgf = scaling_config.as_placement_group_factory()
      tr = train.get_context().get_trial_resources()
      # Ensure that strategy attribute didn't get dropped.
      assert pgf.strategy == "SPREAD"
      assert pgf == tr, (pgf, tr)
      return super().training_loop()
  
imports: import os
  from ray import train
  from ray.train import Checkpoint, FailureConfig, RunConfig, ScalingConfig
  from ray.air.constants import TRAIN_DATASET_KEY
  from ray.tune.tune_config import TuneConfig
  from ray.tune.tuner import Tuner
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train.xgboost import XGBoostTrainer
  from sklearn.datasets import load_breast_cancer
  import pandas
  import pytest
  import ray
  import json
  from tempfile import TemporaryDirectory
  from ray import tune
  from ray.tune.schedulers.resource_changing_scheduler import DistributeResources, ResourceChangingScheduler
  from ray.tune.schedulers.async_hyperband import ASHAScheduler
label_desc: Use image node_id 1 for calling the AssertingDataParallelTrainer obj's underlying member method code with example usage: obj.training_loop() and returns: super