docstr: This helper function tests conversion of `ai.onnx.ml.SVMClassifier`
  which is created from a scikit-learn SVC.
  
  This then calls either "_to_onnx" or "_to_torch"
code: def _test_sv(self, classes, mode="torch"):
      """
      This helper function tests conversion of `ai.onnx.ml.SVMClassifier`
      which is created from a scikit-learn SVC.
  
      This then calls either "_to_onnx" or "_to_torch"
      """
      n_features = 20
      n_total = 100
      np.random.seed(0)
      warnings.filterwarnings("ignore")
      X = np.random.rand(n_total, n_features)
      X = np.array(X, dtype=np.float32)
      y = np.random.randint(classes, size=n_total)
  
      # Create SKL model for testing
      model = SVC()
      model.fit(X, y)
  
      # 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()))
      ]
      onnx_ml_pred = [[] for i in range(len(output_names))]
      inputs = {session.get_inputs()[0].name: X}
      pred = session.run(output_names, inputs)
      for i in range(len(output_names)):
          if "label" in output_names[i]:
              onnx_ml_pred[1] = pred[i]
          else:
              onnx_ml_pred[0] = pred[i]
  
      model = convert(onnx_ml_model, mode, X)
  
      pred = model.predict(X)
  
      return onnx_ml_pred, pred
  
file_path: hummingbird/tests/test_onnxml_sv_converter.py
imports: import unittest
  import warnings
  import numpy
  import torch
  from sklearn.svm import LinearSVC, SVC, NuSVC
  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 TestONNXSVC obj's underlying member method code with example usage: obj._test_sv(classes, mode) and returns: onnx_ml_pred,  pred