input: Write a function to evaluate the accuracy of a machine-learning model using the `accuracy_score` function from the `sklearn.metrics` module. The function should take in the trained model, training data, test data, and their respective labels, and return the accuracy as a floating-point number. If the model is not trained, return "Model not trained".
  assert evaluate_accuracy(clf, X_train, y_train, X_test, y_test) == "Model not trained"
  assert evaluate_accuracy(clf, [], y_train, X_test, y_test) == 0.0
  assert evaluate_accuracy(clf, X_train, y_train, X_test, y_test) > 0.0
output: if not hasattr(model, 'predict'):
      return "Model not trained"
  if len(X_train) == 0:
      return 0.0
  model.fit(X_train, y_train)
  y_pred = model.predict(X_test)
  return accuracy_score(y_test, y_pred)