code: def test_model_label_encoder_int_onnxml(self):
      model = LabelEncoder()
      X = np.array([1, 4, 5, 2, 0, 2], dtype=np.int64)
      model.fit(X)
  
      # Create ONNX-ML model
      onnx_ml_model = convert_sklearn(
          model, initial_types=[("input", LongTensorType_onnx(X.shape))]
      )
  
      # Create ONNX model by calling converter
      onnx_model = convert(onnx_ml_model, "onnx", X)
  
      # 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()))
      ]
      inputs = {session.get_inputs()[0].name: X}
      onnx_ml_pred = np.array(session.run(output_names, inputs)).ravel()
  
      # Get the predictions for the ONNX model
      onnx_pred = onnx_model.transform(X).ravel()
  
      # Check that predicted values match
      np.testing.assert_allclose(
          onnx_ml_pred, onnx_pred, rtol=1e-06, atol=1e-06
      )
  
file_path: hummingbird/tests/test_onnxml_label_encoder_converter.py
imports: from packaging.version import Version, parse
  import unittest
  import warnings
  import numpy
  from sklearn.preprocessing import LabelEncoder
  import torch
  from hummingbird.ml._utils import onnx_ml_tools_installed, onnx_runtime_installed, lightgbm_installed
  from hummingbird.ml import convert
label_desc: Use image node_id 1 for calling the TestONNXLabelEncoder obj's underlying member method code with example usage: obj.test_model_label_encoder_int_onnxml() without return types