input: Write a function to design a neural network in Python which predicts a customer’s annual income based on their age and education level. The function should compile the model, fit it with some training data, and then predict the annual income for a given age and education level. If the prediction matches the expected output, the function should return True, otherwise False.
  assert predict_income([[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], [[50, 3]])=True
  assert predict_income([[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], [[25, 0]])=True
  assert predict_income([[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], [[60, 3]])=True
output: def predict_income(X_train, y_train, X_test):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      model = Sequential()
      model.add(Dense(10, input_dim=2, activation='relu'))
      model.add(Dense(1))
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=100, verbose=0)
      prediction = model.predict(X_test)
      return abs(prediction[0][0] - y_train[X_train.index(X_test[0])]) < 1000