input: 
  import numpy as np
  import keras 
  from keras.layers import Dense
  from keras.models import Sequential
  
  def predict_annual_income(age: int, education: int) -> float:
      """
      Build and train a neural network to predict a customer's annual income based on their age and education level.
      The neural network has 2 input features (age and education level) and outputs the predicted annual income.
      >>> predict_annual_income(50, 3)
      80000.0
      >>> predict_annual_income(30, 0)
      20000.0
      """
  
      # 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)
  
      # Calculate the predicted annual income
      annualIncome = model.predict(np.array([[age, education]]))
      return float(annualIncome[0][0])
  
output:     # 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)
  
      # Calculate the predicted annual income
      annualIncome = model.predict(np.array([[age, education]]))
      return float(annualIncome[0][0])