code: def step(self):
      # you can also obtain current trial resources:
      current_resources = self.trial_resources
      if isinstance(current_resources, PlacementGroupFactory):
          self.nthread = current_resources.head_cpus
      else:
          self.nthread = current_resources.cpu
  
      results = {}
      config = self.config.copy()
      config["nthread"] = int(self.nthread)
      self.model = xgb.train(
          config,
          self.train_set,
          evals=[(self.test_set, "eval")],
          verbose_eval=False,
          xgb_model=self.model,
          evals_result=results,
          num_boost_round=1,
      )
      print(config, results)
      return {
          "eval-logloss": results["eval"]["logloss"][-1],
          "nthread": self.nthread,
      }
  
imports: from typing import Dict, Any, Optional, TYPE_CHECKING
  import sklearn.datasets
  import sklearn.metrics
  import os
  from sklearn.model_selection import train_test_split
  import xgboost
  from xgboost.core import Booster
  import pickle
  import ray
  from ray import train, tune
  from ray.tune.schedulers import ResourceChangingScheduler, ASHAScheduler
  from ray.tune import Trainable
  from ray.tune.execution.placement_groups import PlacementGroupFactory
  from ray.tune.experiment import Trial
  from ray.tune.integration.xgboost import TuneReportCheckpointCallback
label_desc: Use image node_id 2 for calling the BreastCancerTrainable obj's underlying member method code with example usage: obj.step() and returns: dict