code: def test_scaler_converter_float_64(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.float64,
      )
  
      # Create SKL model for testing
      model = StandardScaler()
      model.fit(X)
  
      # Generate test input
      onnx_ml_model = convert_sklearn(
          model,
          initial_types=[
              ("double_input", DoubleTensorType([None, X.shape[1]]))
          ],
      )
  
      # Create ONNX model by calling converter
      onnx_model = convert(onnx_ml_model, "onnx", X)
      # 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]
  
      # Get the predictions for the ONNX model
      onnx_pred = onnx_model.transform(X)
  
      # Check that predicted values match
      np.testing.assert_allclose(
          onnx_ml_pred, onnx_pred, rtol=1e-06, atol=1e-06
      )
  
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 10 for calling the TestONNXScaler obj's underlying member method code with example usage: obj.test_scaler_converter_float_64() without return types