code: def test_onnx_normalizer_converter_raises_rt(self):
      warnings.filterwarnings("ignore")
      X = np.array(
          [[1, 2, 3], [4, 3, 0], [0, 1, 4], [0, 5, 6]], dtype=np.float32
      )
      model = Normalizer(norm="l1")
      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].s = "".encode()
  
      self.assertRaises(RuntimeError, convert, onnx_ml_model, "onnx", X)
  
file_path: hummingbird/tests/test_onnxml_normalizer_converter.py
imports: import unittest
  import warnings
  import numpy
  import torch
  from sklearn.preprocessing import Normalizer
  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 5 for calling the TestONNXNormalizer obj's underlying member method code with example usage: obj.test_onnx_normalizer_converter_raises_rt() without return types