input: from sklearn.svm import SVC
  
  def evaluate_model_accuracy(clf: SVC, X_train: np.ndarray, y_train: np.ndarray, X_test: np.ndarray, y_test: np.ndarray) -> float:
      """ Evaluate the accuracy of a given machine-learning model on a test dataset.
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> clf.fit(np.array([[1, 2], [3, 4]]), np.array([1, 0]))
      >>> evaluate_model_accuracy(clf, np.array([[1, 2], [3, 4]]), np.array([1, 0]), np.array([[5, 6], [7, 8]]), np.array([1, 0]))
      1.0
      """
      from sklearn.metrics import accuracy_score
      
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      print("Accuracy: " + str(accuracy))
      return accuracy
output:     from sklearn.metrics import accuracy_score
      
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      
      # Calculating the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      print("Accuracy: " + str(accuracy))
      return accuracy