input: 
  import numpy as np
  import keras 
  from keras.layers import Dense
  from keras.models import Sequential
  
  def predict_annual_income(age: int, education_level: int) -> float:
      """Design a neural network in Python to predict a customer’s annual income 
      based on their age and education level. The education level is encoded as:
      high school = 0, college = 1, university = 2, and other = 3.
      
      >>> predict_annual_income(50, 3)
      80000.0
      """
      # 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 
      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_level]]))
      return float(annualIncome[0][0])
  
  # Example usage:
  # print(predict_annual_income(50, 3))
  # output: 80000.0
  
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 
      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_level]]))
      return float(annualIncome[0][0])