input: from typing import List
  
  def predict_income_by_attributes(age: int, education_level: int) -> float:
      """This function builds a neural network model using Keras to predict a customer's annual income 
      based on their age and education level. The education levels are represented as follows:
      high school -> 0, college -> 1, university -> 2, higher education -> 3.
  
      >>> predict_income_by_attributes(50, 3)
      80000.0
      """
output:     import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      # Create a simple neural network model
      model = Sequential()
      model.add(Dense(10, input_dim=2, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Dummy training data
      X_train = np.array([[25, 0], [30, 1], [35, 2], [40, 3], [45, 0], [50, 1], [55, 2], [60, 3]])
      y_train = np.array([30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000])
  
      # Train the model
      model.fit(X_train, y_train, epochs=100, verbose=0)
  
      # Predict income
      input_data = np.array([[age, education_level]])
      predicted_income = model.predict(input_data)
  
      return float(predicted_income[0][0])