input: Write a function to evaluate the accuracy of a machine-learning model using the sklearn library. The function should take a trained classifier, training features (X_train), training labels (y_train), test features (X_test), and test labels (y_test) as inputs, fit the model, make predictions, and return the accuracy score. If the input classifier is not fitted, return "Model not fitted" as a string.
  assert evaluate_model_accuracy(clf, [[0,0], [1,1]], [0, 1], [[2,2], [3,3]], [0, 1]) == 0.5
  assert evaluate_model_accuracy(clf, [], [], [[2,2], [3,3]], [0, 1]) == "Model not fitted"
  assert evaluate_model_accuracy(clf, [[0,0], [1,1]], [0, 1], [[2,2], [3,3]], [0, 1]) == 1.0
  assert evaluate_model_accuracy(clf, [[0,0], [1,1]], [0, 1], [[2,2], [3,3]], [0, 0]) == 0.0
  assert evaluate_model_accuracy(clf, [], [], [], []) == "Model not fitted"
output: def evaluate_model_accuracy(classifier, X_train, y_train, X_test, y_test):
      if hasattr(classifier, 'n_features_in_'):
          classifier.fit(X_train, y_train)
          y_pred = classifier.predict(X_test)
          return accuracy_score(y_test, y_pred)
      else:
          return "Model not fitted"