input: 
  def model_annual_income_prediction(age, education_level):
      """Design a neural network to predict a customer’s annual income based on their age and education level.
      The model is trained with the following data:
      Ages: [25, 30, 35, 40, 45, 45, 50, 50, 55, 55, 50, 60]
      Education Levels: [0, 0, 0, 0, 1, 2, 1, 2, 1, 2, 3, 3] (0: high school, 1: college, 2: university, 3: postgrad)
      Annual Incomes: [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000]
  
      The model is trained for 150 epochs with a batch size of 2.
      
      >>> model_annual_income_prediction(50, 3)
      [[80000]]
      """
      import numpy as np
      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')
  
      # Training data
      x_train = 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]])
      y_train = np.array([15000, 20000, 25000, 30000,
                          40000, 45000, 50000, 60000,
                          65000, 70000, 80000, 90000])
  
      # Fit the model
      model.fit(x_train, y_train, epochs=150, batch_size=2, verbose=0)
  
      # Predict annual income
      annualIncome = model.predict(np.array([[age, education_level]]))
      return annualIncome
  
output:     return annualIncome