code: def test_onnx_label_encoder_converter_raises_rt(self):
      warnings.filterwarnings("ignore")
      model = LabelEncoder()
      X = np.array([1, 4, 5, 2, 0, 2], dtype=np.int64)
      model.fit(X)
  
      # generate test input
      onnx_ml_model = convert_sklearn(
          model,
          initial_types=[
              ("float_input", FloatTensorType_onnx(X.shape))
          ],
      )
      onnx_ml_model.graph.node[0].attribute[0].name = "".encode()
  
      self.assertRaises(RuntimeError, convert, onnx_ml_model, "onnx", X)
  
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 4 for calling the TestONNXLabelEncoder obj's underlying member method code with example usage: obj.test_onnx_label_encoder_converter_raises_rt() without return types