input: from typing import List
  
  def predict_house_price(features: List[float]) -> float:
      """ Given a set of house features, predict the price of the house using a neural 
      network model. The model is trained to minimize the mean squared error (MSE) loss.
      >>> predict_house_price([0.8, 0.4, 0.7])
      12345.67
      """
output:     # 假设我们有一个预训练的神经网络模型
      model = [0.5, 1.0, 1.5]  # 这是模型的权重，实际应用中应从文件或数据库加载
      bias = 2.0  # 这是模型的偏置，实际应用中应从文件或数据库加载
  
      # 计算预测值
      predicted_price = sum([features[i] * model[i] for i in range(len(features))]) + bias
  
      return round(predicted_price, 2)