input: 
  def predict_annual_income(x_train: np.array, y_train: np.array, age: int, education: int) -> np.array:
      """
      Design a neural network to predict a customer's annual income based on their age and education level.
      
      >>> 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
      )
      array([[80000]], dtype=int32)
      """
      import numpy as np
      import keras 
      from keras.layers import Dense
      from keras.models import Sequential
      
      # Build model
      model = Sequential()
      model.add(Dense(64, input_dim=2, activation='relu'))
      model.add(Dense(1))
      
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
      
      # Fit the model
      model.fit(x_train, y_train, epochs=150, batch_size=2, verbose=0)
      
      # Calculate the predicted annual income
      annualIncome = model.predict(np.array([[age, education]]))
      return annualIncome
  
output:     annualIncome = np.round(annualIncome).astype(int)
      return annualIncome