input: from typing import List
  
  def evaluate_model_accuracy(X_train: List[List[float]], y_train: List[float], X_test: List[List[float]], y_test: List[float]) -> float:
      """ Evaluate the accuracy of a machine-learning model using a Support Vector Classifier (SVC) with polynomial kernel.
      >>> evaluate_model_accuracy([[0, 0], [1, 1]], [0, 1], [[2, 2]], [1])
      1.0
      """
output:     from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
  
      model = SVC(kernel='poly')
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)