input: def predict_annual_income(age: int, education: int) -> float:
      """ Uses a pre-trained neural network model to predict a customer's annual income based on their age and education level.
      The education levels are encoded as follows:
      0 - High School
      1 - College
      2 - Undergraduate Degree
      3 - Higher Education Degree
      >>> predict_annual_income(50, 3)
      80000.0
      >>> predict_annual_income(35, 0)
      25000.0
      """
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(64, input_dim=2, activation='relu'))
      model.add(Dense(1))
      model.compile(loss='mean_squared_error', optimizer='adam')
      
      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 = [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000]
      
      model.fit(x_train, y_train, epochs=150, batch_size=2, verbose=0)
      
      return model.predict(np.array([[age, education]]))[0][0]
output:     import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(64, input_dim=2, activation='relu'))
      model.add(Dense(1))
      model.compile(loss='mean_squared_error', optimizer='adam')
      
      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 = [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000]
      
      model.fit(x_train, y_train, epochs=150, batch_size=2, verbose=0)
      
      return model.predict(np.array([[age, education]]))[0][0]