code: def test_lgbm_classifier_converter_torchscript(self):
      warnings.filterwarnings("ignore")
  
      for max_depth in [1, 3, 8, 10, 12]:
          model = lgb.LGBMClassifier(
              n_estimators=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(2, size=100)
  
          model.fit(X, y)
  
          torch_model = hummingbird.ml.convert(
              model, "torchscript", X, extra_config={}
          )
          self.assertIsNotNone(torch_model)
          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 hummingbird.ml
  from hummingbird.ml import constants
  from hummingbird.ml._utils import lightgbm_installed, onnx_runtime_installed, tvm_installed
  from tree_utils import gbdt_implementation_map
label_desc: Use image node_id 41 for calling the TestLGBMConverter obj's underlying member method code with example usage: obj.test_lgbm_classifier_converter_torchscript() without return types