input: def predict_house_price(features: np.ndarray) -> float:
      """ Given a set of features, predict the price of a house using a pre-trained neural network model.
      
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      123456.7 # Example output, actual output may vary based on model training.
      """
output:     model = load_model('house_price_model.h5')
      prediction = model.predict(features)
      return float(prediction[0][0])