input: 
  def predict_annual_income(age: int, education_level: int, x_train, y_train) -> int:
      """ Predict a customer’s annual income based on their age and education level using a neural network.
      >>> predict_annual_income(50, 3, x_train, y_train)
      80000
      """
  
output:     from sklearn.neural_network import MLPRegressor
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
  
      # Prepare the data
      X = x_train
      y = y_train
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Scale the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Train the neural network
      model = MLPRegressor(hidden_layer_sizes=(10, 10), max_iter=1000, random_state=42)
      model.fit(X_train, y_train)
  
      # Predict the annual income
      input_data = scaler.transform([[age, education_level]])
      predicted_income = model.predict(input_data)
  
      return int(predicted_income[0])