input: def evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) -> float:
      """Evaluates the accuracy of a machine-learning model.
      >>> from sklearn.svm import SVC
      >>> X_train = np.array([[0, 0], [1, 1]])
      >>> y_train = np.array([0, 1])
      >>> X_test = np.array([[2, 2]])
      >>> y_test = np.array([1])
      >>> clf = SVC(kernel='poly', degree=2, gamma=0.05)
      >>> clf.fit(X_train, y_train)
      SVC(degree=2, gamma=0.05, kernel='poly')
      >>> evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test)
      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)
      
      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)
      
      return accuracy