code: def _test_svc(
      self,
      num_classes,
      kernel="rbf",
      gamma=None,
      backend="torch",
      labels_shift=0,
      extra_config={},
  ):
      if gamma:
          model = SVC(kernel=kernel, gamma=gamma)
      else:
          model = SVC(kernel=kernel)
      np.random.seed(0)
      X = np.random.rand(100, 200)
      X = np.array(X, dtype=np.float32)
      y = np.random.randint(num_classes, size=100) + labels_shift
  
      model.fit(X, y)
      torch_model = hummingbird.ml.convert(
          model, backend, X, extra_config=extra_config
      )
  
      self.assertTrue(torch_model is not None)
      np.testing.assert_allclose(
          model.predict(X), torch_model.predict(X), rtol=1e-6, atol=1e-6
      )
  
imports: import unittest
  import numpy
  import torch
  from sklearn.svm import LinearSVC, LinearSVR, SVC, NuSVC, SVR
  import hummingbird.ml
  from hummingbird.ml import constants
  from hummingbird.ml._utils import tvm_installed
label_desc: Use image node_id 8 for calling the TestSklearnSVC obj's underlying member method code with example usage: obj._test_svc(num_classes, kernel, gamma, backend, labels_shift, extra_config) without return types