code: def test_lightgbm_onnx_pytorch(self):
      warnings.filterwarnings("ignore")
      X = [[0, 1], [1, 1], [2, 0]]
      X = np.array(X, dtype=np.float32)
      y = np.array([100, -10, 50], dtype=np.float32)
      model = lgb.LGBMRegressor(n_estimators=3, min_child_samples=1)
      model.fit(X, y)
  
      # Create ONNX-ML model
      onnx_ml_model = convert_lightgbm(
          model,
          initial_types=[
              ("input", FloatTensorType([None, X.shape[1]]))
          ],
          target_opset=9,
      )
  
      pt_model = convert(onnx_ml_model, "torch", X)
      assert pt_model
  
      # Get the predictions for the ONNX-ML model
      session = ort.InferenceSession(onnx_ml_model.SerializeToString())
      output_names = [
          session.get_outputs()[i].name
          for i in range(len(session.get_outputs()))
      ]
      onnx_ml_pred = [[] for i in range(len(output_names))]
      inputs = {session.get_inputs()[0].name: X}
      onnx_ml_pred = session.run(output_names, inputs)
  
      np.testing.assert_allclose(
          onnx_ml_pred[0].flatten(), pt_model.predict(X)
      )
  
imports: import unittest
  import warnings
  import numpy
  from onnxconverter_common.data_types import FloatTensorType
  from hummingbird.ml import convert
  from hummingbird.ml import constants
  from hummingbird.ml._utils import onnx_ml_tools_installed, onnx_runtime_installed, lightgbm_installed
label_desc: Use image node_id 5 for calling the TestONNXLightGBMConverter obj's underlying member method code with example usage: obj.test_lightgbm_onnx_pytorch() without return types