code: def _run_GB_trees_classifier_converter(
      self, num_classes, extra_config={}, labels_shift=0
  ):
      warnings.filterwarnings("ignore")
      for max_depth in [2, 3, 8, 10, 12, None]:
          model = HistGradientBoostingClassifier(
              max_iter=10, max_depth=max_depth
          )
          np.random.seed(0)
          X = np.random.rand(100, 200)
          X = np.array(X, dtype=np.float32)
          y = np.random.randint(num_classes, size=100) + labels_shift
  
          model.fit(X, y)
          torch_model = hummingbird.ml.convert(
              model, "torch", extra_config=extra_config
          )
          self.assertTrue(torch_model is not None)
          np.testing.assert_allclose(
              model.predict_proba(X),
              torch_model.predict_proba(X),
              rtol=1e-06,
              atol=1e-06,
          )
  
imports: import unittest
  import warnings
  import numpy
  import torch
  from sklearn.ensemble._hist_gradient_boosting.gradient_boosting import HistGradientBoostingClassifier, HistGradientBoostingRegressor
  import hummingbird.ml
  from tree_utils import gbdt_implementation_map
label_desc: Use image node_id 1 for calling the TestSklearnHistGradientBoostingClassifier obj's underlying member method code with example usage: obj._run_GB_trees_classifier_converter(num_classes, extra_config, labels_shift) without return types