input: 
  def predict_income(age: int, education: int) -> np.ndarray:
      """Build and train a neural network model to predict annual income based on age and education level.
      The model is built using Keras with TensorFlow backend, and it is trained on a small dataset of ages, 
      education levels and corresponding annual incomes. The function returns the predicted annual income 
      for the given input of age and education.
      >>> predict_income(50, 3)
      array([[80000]], dtype=int32)
      """
      import numpy as np
      import keras
      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')
  
      # 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 = 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)
  
      # Calculate the predicted annual income
      annualIncome = model.predict(np.array([[age, education]]))
      return annualIncome
  
  # Example usage:
  result = predict_income(50, 3)
  print(result)  # Output: [[80000]]
  
output:     annualIncome = annualIncome.astype(np.int32)
      return annualIncome