code: def test_model_one_hot_encoder_string(self):
      model = OneHotEncoder()
      data = [
          ["a", "r", "x"],
          ["a", "r", "x"],
          ["aaaa", "r", "x"],
          ["a", "r", "xx"],
      ]
      model.fit(data)
  
      onnx_ml_model = convert_sklearn(
          model,
          initial_types=[("input", StringTensorType_onnx([4, 3]))],
      )
  
      # Create ONNX model by calling converter
      onnx_model = convert(onnx_ml_model, "onnx", data)
  
      # 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: data}
      onnx_ml_pred = session.run(output_names, inputs)
  
      # Get the predictions for the ONNX model
      onnx_pred = onnx_model.transform(data)
  
      return onnx_ml_pred, onnx_pred
  
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 4 for calling the TestONNXOneHotEncoder obj's underlying member method code with example usage: obj.test_model_one_hot_encoder_string() and returns: onnx_ml_pred,  onnx_pred