code: def get_estimator_and_data():
      if args.problem == "classification":
          X, y = make_classification(
              args.n_samples_max * 2,
              n_features=args.n_features,
              n_classes=args.n_classes,
              n_clusters_per_class=1,
              n_informative=args.n_classes,
              random_state=0,
          )
          return X, y, HistGradientBoostingClassifier
      elif args.problem == "regression":
          X, y = make_regression(
              args.n_samples_max * 2,
              n_features=args.n_features,
              random_state=0,
          )
          return X, y, HistGradientBoostingRegressor
  
file_path: scikit-learn/benchmarks/bench_hist_gradient_boosting.py
imports: import argparse
  from time import time
  import matplotlib.pyplot
  import numpy
  from sklearn.datasets import make_classification, make_regression
  from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
  from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 1 for calling a global function with example usage: get_estimator_and_data() and returns: X,  y,  HistGradientBoostingClassifier, X,  y,  HistGradientBoostingRegressor