input: 
  def predict_house_price(features: np.ndarray) -> np.ndarray:
      """ Implement a neural network to predict the price of a house given a set of features.
      The model uses Keras to create a Sequential model with multiple Dense layers.
      The input features are passed through the model to get the predicted house price.
      
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      array([[some_prediction_value]])
      """
      model = Sequential()
      model.add(Dense(64, input_dim=3, activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
      
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
      
      # Assuming the model has already been trained
      return model.predict(features)
  
output:     model.load_weights('house_price_model_weights.h5')