input: Write a function to create and train a neural network that predicts a customer's annual income based on their age and education level. The function should take training data for both features and labels, and return the trained model. The function should also include an example of predicting the annual income for a given age and education level using the trained model.
  assert create_income_predictor(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])
  == model
  annualIncome = model.predict(np.array([[50, 3]]))
  assert annualIncome == [[80000]]
  assert create_income_predictor(np.array([[20, 0], [30, 1], [40, 2]]),
                                [20000, 35000, 50000])
  == model
  annualIncome = model.predict(np.array([[35, 2]]))
  assert annualIncome == [[42500]]
  assert create_income_predictor(np.array([[22, 1], [25, 2], [28, 2]]),
                                [25000, 40000, 55000])
  == model
  annualIncome = model.predict(np.array([[23, 2]]))
  assert annualIncome == [[32500]]
output: def create_income_predictor(X_train, y_train):
      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(X_train, y_train, epochs=100, verbose=0)
      return model