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 return the model.predict() output for a given age and education level.
  
  import numpy as np
  import keras 
  from keras.layers import Dense
  from keras.models import Sequential
  
  def predict_annual_income(age, education):
      # Build 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')
  
      # Obtain some data - age (years), education level (high school, college, university)
      x_train = np.array([[25, 0], [30, 0], [35, 0], 
                          [40, 0], [45, 1], [45, 2],
                          [50, 1], [50, 2], [55, 1],
                          [55, 2], [50, 3], [60, 3]])
  
      # Labels - annual income
      y_train = [15000, 20000, 25000, 30000,
                 40000, 45000, 50000, 60000, 
                 65000, 70000, 80000, 90000]
  
      # Fit the model
      model.fit(x_train, y_train, epochs = 150, batch_size = 2)
  
      # Calculate the predicted annual income
      annual_income = model.predict(np.array([[age, education]]))
      
      return annual_income
  
  assert np.array_equal(predict_annual_income(50, 3), [[80000]])
  assert np.array_equal(predict_annual_income(45, 2), [[45000]])
  assert np.array_equal(predict_annual_income(55, 1), [[65000]])
  assert np.array_equal(predict_annual_income(60, 3), [[90000]])
output: def predict_annual_income(age, education):
      model = Sequential()
      model.add(Dense(64, input_dim=2, activation='relu'))
      model.add(Dense(1))
      model.compile(loss='mean_squared_error', optimizer='adam')
      x_train = np.array([[25, 0], [30, 0], [35, 0], [40, 0], [45, 1], [45, 2], [50, 1], [50, 2], [55, 1], [55, 2], [50, 3], [60, 3]])
      y_train = [15000, 20000, 25000, 30000, 40000, 45000, 50000, 60000, 65000, 70000, 80000, 90000]
      model.fit(x_train, y_train, epochs=150, batch_size=2)
      annual_income = model.predict(np.array([[age, education]]))
      return annual_income