input: from sklearn.svm import SVC
  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 machine learning model using a training and test dataset.
      This function fits the model to the training data, predicts the test data, and calculates
      the accuracy score.
  
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> X_train = np.array([[1, 2], [3, 4], [5, 6]])
      >>> y_train = np.array([1, 0, 1])
      >>> X_test = np.array([[7, 8], [9, 10]])
      >>> y_test = np.array([0, 1])
      >>> 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)
      accuracy = accuracy_score(y_test, y_pred)
      print("Accuracy: " + str(accuracy))
      return accuracy
output:     clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      print("Accuracy: " + str(accuracy))
      return accuracy