input: Write a function to evaluate the accuracy of a machine-learning model trained using a Support Vector Classifier (SVC) with a polynomial kernel. The function should take the trained classifier, training data (X_train, y_train), and test data (X_test, y_test) as input, and return the accuracy of the model.
  
  assert evaluate_accuracy(SVC(kernel='poly', degree=2, gamma=0.05).fit(X_train, y_train), X_test, y_test) == 0.9
  assert evaluate_accuracy(SVC(kernel='poly', degree=3, gamma=0.1).fit(X_train, y_train), X_test, y_test) == 0.85
  assert evaluate_accuracy(SVC(kernel='rbf', gamma=0.1).fit(X_train, y_train), X_test, y_test) == 0.88
  assert evaluate_accuracy(SVC(kernel='linear').fit(X_train, y_train), X_test, y_test) == 0.92
  assert evaluate_accuracy(None, X_test, y_test) == None
output: def evaluate_accuracy(classifier, X_test, y_test):
      if classifier is None:
          return None
      y_pred = classifier.predict(X_test)
      return accuracy_score(y_test, y_pred)