input: def predict_annual_income(model, age, education_level):
      """ Predict the annual income of a customer based on their age and education level using a pre-trained neural network model.
      >>> model = Sequential()
      >>> model.add(Dense(64, input_dim = 2, activation = 'relu'))
      >>> model.add(Dense(1))
      >>> model.compile(loss = 'mean_squared_error', optimizer = 'adam')
      >>> x_train = np.array([[25, 0], [30, 0], [35, 0], [40, 0], [45, 1], [45, 2], [50, 1], [50, 2], [55, 1], [55, 2], [50, 3], [60, 3]])
      >>> y_train = [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000]
      >>> model.fit(x_train, y_train, epochs = 150, batch_size = 2)
      >>> predict_annual_income(model, 50, 3)
      [[80000]]
      """
      input_data = np.array([[age, education_level]])
      return model.predict(input_data)
output:     input_data = np.array([[age, education_level]])
      return model.predict(input_data)