input: Write a python function to train a Support Vector Classifier (SVC) and predict the class of given test data.
  assert predict_svc([[0.36, 0.22, 0.91, 0.81, 0.12]], [[0.25, 0.33, 0.46, 0.83, 0.14]], [0, 0, 1, 1, 0]) == [1]
  assert predict_svc([[0.50, 0.40, 0.70, 0.80, 0.20]], [[0.25, 0.33, 0.46, 0.83, 0.14]], [0, 0, 1, 1, 0]) == [1]
  assert predict_svc([[0.10, 0.20, 0.30, 0.40, 0.50]], [[0.25, 0.33, 0.46, 0.83, 0.14]], [0, 0, 1, 1, 0]) == [0]
output: def predict_svc(test_data, train_data, train_labels):
      from sklearn.svm import SVC
      clf = SVC()
      clf.fit(train_data, train_labels)
      return clf.predict(test_data).tolist()