code: def test_scaler_converter_raises_rt_onnx(self):
      warnings.filterwarnings("ignore")
      X = np.array(
          [
              [0.0, 0.0, 3.0],
              [1.0, -1.0, 0.0],
              [0.0, 2.0, 1.0],
              [1.0, 0.0, -2.0],
          ],
          dtype=np.float32,
      )
  
      # Create SKL model for testing
      model = StandardScaler()
      model.fit(X)
  
      # Generate test input
      onnx_ml_model = convert_sklearn(
          model,
          initial_types=[("float_input", FloatTensorType(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_scaler_converter.py
imports: import unittest
  import warnings
  import numpy
  from sklearn.preprocessing import MaxAbsScaler, MinMaxScaler, StandardScaler, RobustScaler
  import torch
  from hummingbird.ml._utils import onnx_ml_tools_installed, onnx_runtime_installed
  from hummingbird.ml import convert
label_desc: Use image node_id 9 for calling the TestONNXScaler obj's underlying member method code with example usage: obj.test_scaler_converter_raises_rt_onnx() without return types