code: def test_torchscript_regression_batch(self):
      warnings.filterwarnings("ignore")
      max_depth = 10
      num_classes = 2
      model = GradientBoostingRegressor(
          n_estimators=10, max_depth=max_depth
      )
      np.random.seed(0)
      X = np.random.rand(103, 200)
      X = np.array(X, dtype=np.float32)
      y = np.random.randint(num_classes, size=103)
  
      model.fit(X, y)
  
      batch_size = 10
      remainder_size = X.shape[0] % batch_size
      hb_model = hummingbird.ml.convert_batch(
          model,
          "torch.jit",
          X[:batch_size, :],
          remainder_size=remainder_size,
      )
  
      self.assertIsNotNone(hb_model)
      np.testing.assert_allclose(
          model.predict(X), hb_model.predict(X), rtol=1e-06, atol=1e-06
      )
  
imports: from packaging.version import Version, parse
  import unittest
  import warnings
  import sys
  import numpy
  import psutil
  from onnxconverter_common.data_types import FloatTensorType, DoubleTensorType
  from sklearn import datasets
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, IsolationForest
  from sklearn.preprocessing import StandardScaler, LabelEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.pipeline import Pipeline
  import torch
  import hummingbird.ml
  from hummingbird.ml._utils import onnx_ml_tools_installed, onnx_runtime_installed, pandas_installed, lightgbm_installed, tvm_installed
  from hummingbird.ml import constants
label_desc: Use image node_id 11 for calling the TestExtraConf obj's underlying member method code with example usage: obj.test_torchscript_regression_batch() without return types