input: def predict_annual_income(age: int, education_level: int) -> int:
      """ Predicts a customer’s annual income based on their age and education level.
      The function uses a pre-trained neural network model with the following layers:
      - Input layer with 2 neurons (age and education level)
      - Hidden layer with 64 neurons and ReLU activation
      - Output layer with 1 neuron
      The model is compiled using mean squared error loss and the Adam optimizer.
      
      Example:
      >>> predict_annual_income(50, 3)
      80000
      """
      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')
  
      # Mock training data - this part is typically done beforehand
      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]
  
      # Fit the model
      model.fit(x_train, y_train, epochs=150, batch_size=2)
  
      # Calculate the predicted annual income
      annual_income = model.predict(np.array([[age, education_level]]))
      return annual_income.astype(int)
  
  # output: [[80000]]
output:     
      # Calculate the predicted annual income
      annual_income = model.predict(np.array([[age, education_level]]))
      return annual_income.astype(int)
  