input: 
  def predict_annual_income(x_train: np.ndarray, y_train: List[int], age: int, education: int) -> np.ndarray:
      """
      Predicts the annual income of a customer based on their age and education level using a neural network model.
      The model is built using Keras with two input neurons (one for age and one for education), a hidden layer with 64 neurons,
      and an output layer with 1 neuron.
      >>> 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]
      >>> age = 50
      >>> education = 3
      >>> predict_annual_income(x_train, y_train, age, education)
      array([[80000]])
      """
      model = Sequential()
      model.add(Dense(64, input_dim=2, activation='relu'))
      model.add(Dense(1))
      model.compile(loss='mean_squared_error', optimizer='adam')
      model.fit(x_train, y_train, epochs=150, batch_size=2, verbose=0)
      return model.predict(np.array([[age, education]]))
  
output:     import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
      from typing import List