code: def test_ohe_string_raises_type_error_onnx(self):
      warnings.filterwarnings("ignore")
      model = OneHotEncoder()
      data = [
          ["a", "r", "x"],
          ["a", "r", "x"],
          ["aaaa", "r", "x"],
          ["a", "r", "xx"],
      ]
      model.fit(data)
  
      # Create ONNX-ML model
      onnx_ml_model = convert_sklearn(
          model,
          initial_types=[("input", StringTensorType_onnx([4, 3]))],
      )
  
      # Create ONNX model by calling converter, should raise error for strings
      self.assertRaises(RuntimeError, convert, onnx_ml_model, "onnx")
  
file_path: hummingbird/tests/test_onnxml_one_hot_encoder_converter.py
imports: from packaging.version import Version, parse
  import unittest
  import warnings
  import numpy
  from sklearn.preprocessing import OneHotEncoder
  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 5 for calling the TestONNXOneHotEncoder obj's underlying member method code with example usage: obj.test_ohe_string_raises_type_error_onnx() without return types