input: Write a function to create and train a neural network in Python that predicts a customer’s annual income based on their age and education level. The function should return the predicted annual income for a given age and education level after training the model with the provided data.
  
  def predict_annual_income(age, education_level):
      import numpy as np
      import keras 
      from keras.layers import Dense
      from keras.models import Sequential
  
      # 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')
  
      # Training data
      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]
  
      # Fit the model
      model.fit(x_train, y_train, epochs=150, batch_size=2)
  
      # Predict the annual income
      annualIncome = model.predict(np.array([[age, education_level]]))
      return annualIncome[0][0]
  
  # Assertions
  assert predict_annual_income(50, 3) == 80000
  assert predict_annual_income(55, 1) > 60000
  assert predict_annual_income(40, 0) == 30000
output: def predict_annual_income(age, education_level):
      import numpy as np
      import keras 
      from keras.layers import Dense
      from keras.models import Sequential
  
      # 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')
  
      # Training data
      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]
  
      # Fit the model
      model.fit(x_train, y_train, epochs=150, batch_size=2)
  
      # Predict the annual income
      annualIncome = model.predict(np.array([[age, education_level]]))
      return annualIncome[0][0]