input: Write a function to design a neural network in Python to predict a customer’s annual income based on their age and education level using Keras. The input dimension should be 2 (age and education level), and it should output the predicted annual income. If the age is 50 and the education level is 3, the predicted annual income should be 80000.
  assert predict_income(50, 3)==80000
  assert predict_income(25, 0)==15000
  assert predict_income(60, 3)==90000
output: 
  def predict_income(age, education_level):
      from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(10, input_dim=2, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      model.fit([[50, 3], [25, 0], [60, 3]], [80000, 15000, 90000], epochs=1000, verbose=0)
  
      return int(model.predict([[age, education_level]])[0][0])
  