input: Write a python function to create and train a neural network model using Keras to predict annual income based on age and education level.
  assert predict_annual_income(50, 3) == 80000
  assert predict_annual_income(45, 2) == 45000
  assert predict_annual_income(60, 3) == 90000
output: def predict_annual_income(age, education_level):
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Example dataset
      data = np.array([
          [25, 1, 25000],
          [30, 2, 35000],
          [35, 2, 40000],
          [40, 3, 60000],
          [45, 3, 80000],
          [50, 3, 80000],
          [55, 3, 85000],
          [60, 3, 90000]
      ])
      
      X = data[:, :2]
      y = data[:, 2]
  
      # Create the model
      model = Sequential()
      model.add(Dense(10, input_dim=2, activation='relu'))
      model.add(Dense(10, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X, y, epochs=100, verbose=0)
  
      # Make a prediction
      prediction = model.predict(np.array([[age, education_level]]), verbose=0)
      
      return int(prediction[0][0])