input: Write a function to design a neural network in Python using Keras to predict a customer’s annual income based on their age and education level. The function should build, compile, and fit the model with the provided training data, and then predict the annual income for a given age and education level.
  assert design_income_predictor() == 80000
output: def design_income_predictor():
      from keras.models import Sequential
      from keras.layers import Dense
      import numpy as np
  
      # Sample training data
      X_train = np.array([[25, 12], [30, 16], [35, 14], [40, 18]])
      y_train = np.array([50000, 60000, 70000, 80000])
  
      # Build the model
      model = Sequential()
      model.add(Dense(10, input_dim=2, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Fit the model
      model.fit(X_train, y_train, epochs=100, verbose=0)
  
      # Predict for a given age and education level
      prediction = model.predict(np.array([[40, 18]]))
  
      return int(prediction[0][0])