input: Write a function to evaluate the accuracy of a machine-learning model using a Support Vector Classifier (SVC) with the given parameters. The function should take training and testing data as inputs, fit the model, predict the test data, and then calculate and return the accuracy. The accuracy should be printed as a percentage.
  assert evaluate_model([[1, 2], [3, 4]], [0, 1], [[1, 2]], [0])==0.0
  assert evaluate_model([[1, 2], [3, 4]], [1, 0], [[1, 2]], [1])==1.0
  assert evaluate_model([[1, 2], [3, 4], [5, 6]], [0, 1, 0], [[1, 2], [5, 6]], [0, 0])==0.5
  assert evaluate_model([], [], [], [])==None
  assert evaluate_model([[1, 2]], [1], [[1, 2]], [1])==1.0
output: def evaluate_model(X_train, y_train, X_test, y_test):
      if not X_train or not X_test:
          return None
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      model = SVC()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      print(f"{accuracy * 100:.2f}%")
      return accuracy