input: Write a function to design a neural network in Python that predicts a customer's annual income based on their age and education level. The function should include the model architecture, compiling, training data, and a method to predict the annual income.
  
  assert predict_annual_income() == [[80000]]
output: def predict_annual_income():
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Define the model architecture
      model = Sequential([
          Dense(10, input_shape=(2,), activation='relu'),
          Dense(1, activation='linear')
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Training data
      X_train = [[25, 12], [30, 16], [35, 14], [40, 18]]
      y_train = [50000, 70000, 60000, 80000]
  
      # Train the model
      model.fit(X_train, y_train, epochs=100, verbose=0)
  
      # Predict the annual income
      prediction = model.predict([[40, 18]])
      return prediction