input: 
  def predict_income(ages: List[int], education_levels: List[int], incomes: List[int], age: int, education: int) -> float:
      """ Predicts the annual income of a customer based on their age and education level using a neural network model.
      The model is trained on the given data.
  
      >>> predict_income([25, 30, 35, 40, 45, 45, 50, 50, 55, 55, 50, 60],
                        [0, 0, 0, 0, 1, 2, 1, 2, 1, 2, 3, 3],
                        [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000],
                        50, 3)
      80000.0
      """
      import numpy as np
      import keras 
      from keras.layers import Dense
      from keras.models import Sequential
  
      # Build model
      model = Sequential()
      model.add(Dense(64, input_dim=2, activation='relu'))
      model.add(Dense(1))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Prepare data
      x_train = np.array([ages, education_levels]).T
      y_train = np.array(incomes)
  
      # Fit the model
      model.fit(x_train, y_train, epochs=150, batch_size=2, verbose=0)
  
      # Predict income
      predicted_income = model.predict(np.array([[age, education]]))[0][0]
      return predicted_income
  
output:     return predicted_income