input: Write a function to design a neural network in Python to predict a customer’s annual income based on their age and education level. The function should build and compile the model, fit it with the provided training data, and then predict the income for a given age and education level. If the given age and education level are not present in the training data, the function should return a message saying "Data not found in training set".
  assert predict_annual_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)==[[80000]]
  assert predict_annual_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], 18, 1)=='Data not found in training set'
output: def predict_annual_income(training_data, income_data, age, education_level):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      import numpy as np
      
      if [age, education_level] not in training_data:
          return "Data not found in training set"
      
      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(np.array(training_data), np.array(income_data), epochs=100, verbose=0)
      
      prediction = model.predict(np.array([[age, education_level]]))
      return prediction