input: 
  from sklearn.svm import SVC
  import numpy as np
  from sklearn.metrics import accuracy_score
  
  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 using train and test data.
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> X_train = np.array([[0, 0], [1, 1]])
      >>> y_train = np.array([0, 1])
      >>> X_test = np.array([[2., 2.], [3., 3.]])
      >>> y_test = np.array([1, 0])
      >>> evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test)
      0.5
      """
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      return accuracy_score(y_test, y_pred)
  
output:     clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      return accuracy_score(y_test, y_pred)