input: 
  def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      """
      Evaluate the accuracy of a machine-learning model using the support vector classifier (SVC) with a polynomial kernel.
      
      >>> X_train = [[0, 0], [1, 1]]
      >>> y_train = [0, 1]
      >>> X_test = [[2, 2], [3, 3]]
      >>> y_test = [1, 1]
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      "Accuracy: 0.5"
      """
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
  
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      accuracy = accuracy_score(y_test, y_pred)
      
      return "Accuracy: " + str(accuracy)
  
output:     from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
  
      clf = SVC(kernel='poly', degree=2, gamma=0.05)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      accuracy = accuracy_score(y_test, y_pred)
      
      return "Accuracy: " + str(accuracy)