input: Write a function to design and implement a neural network using Keras in Python to predict the price of a house given a set of features. The neural network should consist of two hidden layers with 64 and 32 neurons respectively, and the output layer should have one neuron. The activation function for hidden layers should be 'relu', and for the output layer, it should be 'linear'. Use 'mse' as the loss function and 'adam' as the optimizer to compile the model. Train the model using the provided input features. If the input array is empty, the function should return None.
  
  assert predict_house_price([0.8, 0.4, 0.7]) is not None
  assert predict_house_price([]) is None
  assert predict_house_price([0.1, 0.9, 0.3]) is not None
  assert predict_house_price([0.5, 0.5, 0.5]) is not None
  assert predict_house_price([0.2, 0.6, 0.4]) is not None
output: 
  def predict_house_price(features):
      if not features:
          return None
      
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      
      model = Sequential([
          Dense(64, activation='relu', input_shape=(len(features),)),
          Dense(32, activation='relu'),
          Dense(1, activation='linear')
      ])
      
      model.compile(optimizer='adam', loss='mse')
      model.fit(np.array([features]), np.array([0]), epochs=1, verbose=0)
      
      return model.predict(np.array([features]))[0][0]
  