code: def load_checkpoint(self, checkpoint_dir):
      path = os.path.join(checkpoint_dir, "checkpoint")
      with open(path, "rb") as inputFile:
          self.config, self.nthread, raw_model = pickle.load(inputFile)
      self.model = Booster()
      self.model.load_model(bytearray(raw_model))
      data, labels = sklearn.datasets.load_breast_cancer(
          return_X_y=True
      )
      # Split into train and test set
      train_x, test_x, train_y, test_y = train_test_split(
          data, labels, test_size=0.25
      )
      # Build input matrices for XGBoost
      self.train_set = xgb.DMatrix(train_x, label=train_y)
      self.test_set = xgb.DMatrix(test_x, label=test_y)
  
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 4 for calling the BreastCancerTrainable obj's underlying member method code with example usage: obj.load_checkpoint(checkpoint_dir) without return types