input: Write a function to design a neural network model in Python that predicts a customer’s annual income based on their age and education level. Implement the neural network using Keras and train the model with the provided data. The function should return the predicted annual income based on the 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_level):
      # 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
      annualIncome = model.predict(np.array([[age, education_level]]))
      return annualIncome[0][0]
  
  assert predict_annual_income(50, 3)==80000.0
  assert predict_annual_income(55, 2)==70000.0
  assert predict_annual_income(30, 0)==20000.0
output: def predict_annual_income(age, education_level):
      # 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
      annualIncome = model.predict(np.array([[age, education_level]]))
      return annualIncome[0][0]