code: def _test_imputer_converter(self, model, mode="onnx"):
      warnings.filterwarnings("ignore")
      X = np.array([[1, 2], [np.nan, 3], [7, 6]], dtype=np.float32)
      model.fit(X)
  
      # Create ONNX-ML model
      onnx_ml_model = convert_sklearn(
          model,
          initial_types=[
              ("float_input", FloatTensorType_onnx(X.shape))
          ],
      )
  
      # 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: X}
      onnx_ml_pred = session.run(output_names, inputs)[0]
  
      # Create test model by calling converter
      model = convert(onnx_ml_model, mode, X)
  
      # Get the predictions for the test model
      pred = model.transform(X)
  
      return onnx_ml_pred, pred
  
file_path: hummingbird/tests/test_onnxml_imputer_converter.py
imports: import unittest
  import warnings
  import numpy
  import torch
  from sklearn.impute import SimpleImputer
  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 1 for calling the TestONNXImputer obj's underlying member method code with example usage: obj._test_imputer_converter(model, mode) and returns: onnx_ml_pred,  pred