input: Write a function to design a neural network model in Python using Keras to predict a customer’s annual income based on their age and education level. The model should be trained on the provided data and able to make predictions for new input values.
  import numpy as np
  import keras 
  from keras.layers import Dense
  from keras.models import Sequential
  
  def predict_annual_income(age, education):
      # 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')
      
      # Obtain some data - age (years), education level (high school, college, university)
      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]])
      
      # Labels - annual income
      y_train = [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)
      
      # Predict the annual income
      annualIncome = model.predict(np.array([[age, education]]))
      return annualIncome[0][0]
  
  # Test cases
  assert predict_annual_income(50, 3) == 80000
  assert predict_annual_income(45, 0) == 40000
  assert predict_annual_income(35, 0) == 25000
output: def predict_annual_income(age, education):
      # 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')
      
      # Obtain some data - age (years), education level (high school, college, university)
      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]])
      
      # Labels - annual income
      y_train = [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 the annual income
      annualIncome = model.predict(np.array([[age, education]]))
      return annualIncome[0][0]