input: Write a function to design and train a neural network in Python that predicts a customer's annual income based on their age and education level. The function should build the model, compile it, train it on provided data, and then predict the annual income for a given age and education level. The model should be built using Keras and TensorFlow.
  assert predict_annual_income(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]]), [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000], 50, 3) == [[80000]]
  assert predict_annual_income(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]]), [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000], 45, 1) == [[40000]]
  assert predict_annual_income(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]]), [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000], 30, 0) == [[20000]]
  assert predict_annual_income(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]]), [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000], 60, 3) == [[90000]]
output: def predict_annual_income(X, y, age, education_level):
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(10, input_dim=2, activation='relu'))
      model.add(Dense(1))
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X, y, epochs=100, verbose=0)
      
      input_data = np.array([[age, education_level]])
      prediction = model.predict(input_data)
      return prediction