input: 
  def evaluate_model_accuracy(X_train: np.ndarray, y_train: np.ndarray, X_test: np.ndarray, y_test: np.ndarray) -> float:
      """
      Evaluate the accuracy of a machine-learning model using Support Vector Classifier with a polynomial kernel.
      >>> X_train = np.array([[1, 2], [2, 3], [3, 4]])
      >>> y_train = np.array([0, 1, 1])
      >>> X_test = np.array([[2, 1], [3, 2]])
      >>> y_test = np.array([0, 1])
      >>> evaluate_model_accuracy(X_train, y_train, X_test, y_test)
      1.0
      """
      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
  
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