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 include building, compiling, and training a keras model, as well as calculating and printing the predicted annual income for a given age and education level. Use a Sequential model with two Dense layers, the first with 64 neurons and ReLU activation, and the second with 1 neuron. The model should be compiled using mean_squared_error loss and the adam optimizer. The training data should include age (years) and education level (e.g., high school, college, university) as input features, and annual income as the label.
  
  from keras.models import Sequential
  from keras.layers import Dense
  import numpy as np
  
  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]])
      y_train = np.array([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_level]]))
      return annual_income
  
  assert predict_annual_income(50, 3) == [[80000]]
output: def predict_annual_income(age, education_level):
      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 = np.array([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_level]]))
      return annual_income