input: 
  from typing import List
  
  def predict_annual_income(age: int, education_level: int, x_train: List[List[int]], y_train: List[int]) -> int:
      """ Predict the annual income of a customer based on their age and education level using a trained neural network model.
      The model is already trained on the given x_train and y_train data.
      The education level is represented as follows: 0 for high school, 1 for college, 2 for university, and 3 for additional higher education.
  
      >>> predict_annual_income(50, 3, [[25, 0], [30, 0], [35, 0], [40, 0], [45, 1], [45, 2], [50, 1], [50, 2], [55, 1], [55, 2], [50, 3], [60, 3]], [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000])
      80000
      """
      # Initialize the neural network 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')
  
      # Fit the model on the provided training data
      model.fit(x_train, y_train, epochs = 150, batch_size = 2, verbose = 0)
  
      # Predict the annual income for the given age and education level
      prediction = model.predict(np.array([[age, education_level]]))
      return int(prediction[0][0])
  
output:     
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  